sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b3b376c10f4cf4029a1a58386d7432a276fe6919020f4114ae46a1410da2e761 | Python | 6,230 | 177 | from pathlib import Path
from typing import List, Tuple, Union
from natsort import natsorted
import pandas as pd
from napari.utils.notifications import show_info
def construct_path(input_path: Path, *sub_paths: str) -> Path:
"""
Construct a path by joining the base path with sub-paths.
Parameters:
... |
2cc86d503261bf11ab4058b784b20750676240ce09287a6df1cbd930692e0515 | Python | 6,231 | 158 | import requests
import pathlib
import numpy as np
from nems.tools.arrays import apply_to_dict
#remote_uri = 'https://s3-us-west-2.amazonaws.com/nemspublic/'
remote_uri = 'https://nemspublic.s3.us-west-2.amazonaws.com'
# E.g. "/path/to/NEMS/nems/tools/demo_data/saved_data/"
datapath = pathlib.Path(__file__).parent /... |
60b91efe5ac2253103ac10b734a9d019f1f142a5b95f9de00b8e3b9179984c82 | Python | 6,231 | 183 | import numpy as np
import obspy
import pytest
import torch
import seisbench.models as sbm
import seisbench.util as sbu
@pytest.mark.parametrize("variant", ["s", "m", "l"])
def test_seist_forward_shape(variant):
model = sbm.SeisT(variant=variant, in_samples=1200)
x = torch.randn(2, 3, model.in_samples)
mo... |
8b68515ffe294503e5116892fe151a793b152d1b38f4bf643cfa8a9c06585102 | Python | 6,231 | 178 | import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import Normalize
from typing import Literal
from argclz import AbstractParser, argument
from argclz.dispatch import Dispatch, dispatch
from neuralib.facemap import FaceMapResult
from neuralib.facemap.plot import plot_cmap_time_series
from n... |
a6afe53ad07a0e2859c9f6e3a487283636c3f67a068d45b6ec2ba18672c459cd | Python | 6,231 | 83 | """A module that contains :py:class:`~corelay.pipeline.base.Pipeline` implementations for spectral embeddings,
:py:class:`~corelay.pipeline.spectral.SpectralEmbedding`, and spectral clustering, :py:class:`~corelay.pipeline.spectral.SpectralClustering`. These
are specific to `Spectral Relevance Analysis (SprAy) <https:/... |
ba4dde449275afae5c46b8267bc4fccc0612e7e12b8f522338572156f0b9399b | Python | 6,232 | 129 | import numpy as np
from scipy.spatial import KDTree
from impyute.ops import matrix
from impyute.ops import wrapper
from impyute.ops import inverse_distance_weighting as idw
from . import mean
# pylint: disable=too-many-arguments
@wrapper.wrappers
@wrapper.checks
def fast_knn(data, k=3, eps=0, p=2, distance_upper_boun... |
e8030733c8fa08b611b9f3799766764b0375e4acb7d0eb6770c600077b28c5ca | Python | 6,233 | 155 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
from ..modeling import ... |
765777eca76d1716823eadc60db24bee5f3f9c9621c3d1c9d692508227af3ac5 | Python | 6,234 | 167 | import os
import pytest
import torch
import numpy as np
from fireants.registration.distributed import parallel_state
from fireants.registration.distributed.parallel_state import (
initialize_parallel_state,
cleanup_parallel_state,
all_reduce_across_dp_ranks,
all_reduce_across_gp_ranks,
get_parallel_... |
785f5bfb6c1ba44710124f9d77e3c9a8040950fbd045a528781216fab15add29 | Python | 6,235 | 186 | """Analyze the univariate and multivariate NSD-synthetic fMRI responses.
Parameters
----------
subjects : list
List of the used NSD subjects.
rois : list
List with all used NSD ROIs.
zscore : int
Whether to z-score [1] or not [0] the fMRI responses of each vertex across
the trials of each session.
ncsnr_threshold ... |
56746c7cd49800af74bf63148e8a2d0c5ae28f1c39283428ce795a5a2143e401 | Python | 6,238 | 121 | import pytest
import teneto
import numpy as np
from comet import connectivity, utils
import pydfc
from pydfc.data_loader import TIME_SERIES
from pydfc.dfc_methods import WINDOWLESS
np.random.seed(0)
# Fixture to load data once for all tests
@pytest.fixture(scope="module")
def ts():
ts = utils.load_example()
... |
8f7f243a78a30beaa45521d111a2146b33aad710fd20ef53a6d2548813b5a6cf | Python | 6,238 | 196 | # -*- coding: utf-8 -*-
import logging
import numpy as np
from tempfile import mkstemp
from dipy.data import get_sphere
from fury import actor, window
from scilpy.viz.color import generate_n_colors
from scilpy.viz.backends.fury import snapshot_scenes
from scilpy.viz.screenshot import compose_image
def plot_each_sh... |
b0b9bd34fafc47137d29d8942700df0e7792a2073970bec3b0c06aca6a83ec51 | Python | 6,238 | 146 | """YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
"""
from pathlib import Path
import torch
from models.yolo import Model, attempt_load
from utils.general import check_requirements, set_logging
from utils.goog... |
665eabe6e46d690905d7240b7b91c858b151175f251841c9c6fcd045ee85c342 | Python | 6,239 | 164 | import torch
import math
import argparse
import os
import torch.nn.functional as F
from glob import glob
from torch.utils.data import Dataset, DataLoader
from torch.optim import AdamW
from tqdm import tqdm
from IEFFEUM import model
class SimpleBatchDataset(Dataset):
def __init__(self, data_dir):
self.file... |
b8d951032258580fba6f1f8a7c1112728c06fe1d44f60e649788da4e30e3f460 | Python | 6,240 | 175 | from qtpy.QtWidgets import (
QApplication,
QGroupBox,
QHBoxLayout, QVBoxLayout,
QLabel,
QPushButton, QSpinBox,
QWidget,
)
from qtpy.QtCore import QThread, Signal, Qt
import napari
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import napari
from ._input_data_widget import InputDataGro... |
be39b95770769b7cb08007d1cb8ed539ab808bb32b725ed5e7a589b06b55b199 | Python | 6,241 | 204 | # -*- coding: utf-8 -*-
from copy import deepcopy
from glob import glob
import os
import nibabel as nib
import numpy as np
from numpy.testing import assert_equal
import pytest
from scilpy.image.labels import (combine_labels, dilate_labels,
get_data_as_labels, get_labels_from_mask,
... |
c87d2050c007bb36c919e43554d36d6bcd7762e2ac288632843cd03d4b693dc1 | Python | 6,242 | 159 | import torch
import os
import argparse
import datetime
import numpy as np
from brainCT.networks.gen_model import GUNet
from monai.transforms import (
Compose,
ToTensord,
RandAffined,
ScaleIntensityd,
RandGaussianSmoothd
)
from brainCT.train_utils.data_loader import MultiModalDataset
from brainCT.... |
0a53ed602814a9181cf8fe5b707a6a18451f0369fe4249389ed15e2400423865 | Python | 6,244 | 169 | #!/usr/bin/env python
"""
The HAL testing modules.
The TestingRandomPause module
Hazen 04/17
"""
import random
import time
import storm_control.sc_library.tcpClient as tcpClient
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.hal4000.halLib.halModule as halModule
import storm_con... |
26bf4dd5fd5986d12928997270a44a201f802f6dea1bb2d4c2fcdba06fc7814a | Python | 6,244 | 163 | """
plot mouse brains using brainglobe
"""
import numpy as np
import pandas as pd
import brainglobe_heatmap as bgh
from ast import literal_eval
from palettable.colorbrewer.sequential import PuBuGn_9
from matplotlib.colors import LinearSegmentedColormap
path = "/home/jhansen/projects/proj_synaptome/"
# min and max of... |
9b3bd48398a17c1d08028a764ccf9fb0e8c8853c03a4062e7531b303e22530ab | Python | 6,244 | 190 | #!/usr/bin/env python3
# baseline_tables.py
# 需要的第三方包：pandas, numpy, matplotlib, openpyxl (写 Excel 用)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
############################
# 1. 读取数据
############################
DATA_FILE = "Data_M1D5.csv" # ← 这里改成你的完整数据文件名... |
56dc731f1219a2cba2ff5af9daef3a3f244d3d0a45288b1504895142247b5b68 | Python | 6,245 | 114 | from pathlib import Path
import pytest
from pytest import approx
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import load_benchmark
from brainscore_vision.benchmark_helpers import PrecomputedFeatures
from brainscore_core.supported_data_standards.brain... |
5de31fa90f0521754cae33b9f167427cd34c127f9bc1f4e0017816d2c16419a6 | Python | 6,246 | 159 | import os
import os.path as osp
import numpy as np
import torch
from torch.utils.data import Dataset
import cv2
import matplotlib.pyplot as plt
import albumentations as albu
from .transform import *
import matplotlib.patches as mpatches
from PIL import Image
import random
CLASSES = ('ImSurf', 'Building', 'LowVeg', 'T... |
010e4cab5f44c0d437f31148f88fd6c63afc5be63470c4bc6cd6fa14bd8cbdc1 | Python | 6,247 | 176 | '''
Peak detection
-------------------
Functions for finding characterising cells within ground truth retinae. These
functions are designed to detect high contrast peaks that all have a similar
amplitude, such as output by the reaction-diffusion model. It has not been
tested for use on synthetic or real AOSLO images... |
925ca3b21229e43839241cf28c2ba8a0d767012737de0342c2d08da8d3f765a9 | Python | 6,248 | 127 | from distances.distances_between_graphs import *
import pygsp
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
from utils.graph_tools import *
def compute_evolution_heat_diff(node1, mode, list_heat_df1, list_heat_df2,
type_distance="auc", no... |
2a6ae8a8068eaf29b5299b1421defbc4a306b0619739b5d9db1afa62b61b7575 | Python | 6,251 | 120 | import os
from nipype import Node, Workflow
from nipype.interfaces.utility import IdentityInterface
from cvdproc.pipelines.common.register import SynthmorphNonlinear
from ...bids_data.rename_bids_file import rename_bids_file
# Template definitions: space -> {resolution: (filename, register_between_stripped)}
# For M... |
3a3d913fb9523f9f026b5b063260ac292befb9464f6a138e5e0b8d108492d2a3 | Python | 6,252 | 193 | # Copyright 2017 Calico LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
f3cc82318ea33798650fe3a9219140c98484fd239a5f0175667a2b17eb1a205b | Python | 6,254 | 163 | import mayavi
import abagen
import neuromaps
import fcn_megfmri
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn.metrics
import scipy.stats as stats
import matplotlib.pyplot as plt
from statsmodels.stats import multitest
from neuromaps.parcellate import Parcellater
from sklearn.preprocessing ... |
b84d0e5ea89ae113c41304005401f13bfd007b3da51d410e86b865d5072471ab | Python | 6,255 | 152 | import math
import torch
from torch import nn
from torch.nn import functional as F
from transformers import BeitModel, BeitConfig
from torch.nn import init
class eca_layer(nn.Module):
def __init__(self, channel, k_size=3):
super(eca_layer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
... |
3e214d6ac8a6d5dfdf9fb08b744660311c9517ab63f7d17e98ef5a573e68f22f | Python | 6,258 | 185 | """
A set of helper functions for downloading and preprocessing hippocampal data.
@author: Gautam Agarwal
@author: Ben Toker
"""
from scipy.signal import decimate
import numpy as np
import math
import os
from scipy import io
from scipy.stats import mode
import h5py
def get_behav(mat_file, fs=25):
"""
Organiz... |
5b0943f0f068320f2902e3ba103289737389e6103de4bc24afe3baa6e486268f | Python | 6,258 | 174 | # coding=utf-8
# Copyright 2018 The Google AI Language Team 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 ... |
9d5e1727a705c1f27d08d2c6a434084bd59a256d8bac9650783d1875df6326fd | Python | 6,258 | 235 | #!/usr/bin/env python3
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from optparse import OptionParser
import sys
###############################################################################
def windowToKey(a):
k = a[0] + ':' + str(a[1]) + '-' + str(a[2])
retu... |
a5a224ecb34adfa529b198ec34ec84dd7635b9d2fadea2faab3215897ccd24e9 | Python | 6,258 | 157 | # -*- coding: utf-8 -*-
"""
Created on Sat Apr 6 03:27:25 2024
@author: aka2333
"""
#%%
import numpy as np
# turn off warning messages
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# system paths
import torch
import torch.nn as nn
import f_simulation
import f_eval... |
b713df0069fa33c55c9136d90eaf8537720fceadc3dc38860b7f8c0efa87ede9 | Python | 6,260 | 191 | from typing import Generator, Tuple, Any
from zigzag.classes.stages.Stage import Stage
from zigzag.classes.cost_model.cost_model import CostModelEvaluation
import logging
import os
logger = logging.getLogger(__name__)
class RemoveExtraInfoStage(Stage):
"""
Strips extra info for subcallables to save memory
... |
190c910e86f93a8002c394ddc7b5d9135f17995fd6dec2d9870b45b4a0607d47 | Python | 6,264 | 139 | """This script shows how to call the training function. It re-uses the last example (6-SR_synthesis_synthetic), where
we use T1 and T2 synthetic as input channels (at HR but simulating data acquired at LR) and synthetic HR T1 scans as
regression target.
If you use this code, please the SynthSR paper in:
https://githu... |
943fd277f8296367c438d4a3e8313a82af488f536d9eb2abffbdba74c8237e72 | Python | 6,265 | 167 | from __future__ import annotations
from collections.abc import Callable
from PySide6 import QtCore
from PySide6 import QtGui
from PySide6 import QtWidgets
from .._ai_models import AI_TEXT_MODEL_OPTIONS
from ._info_button import InfoButton
from ._model_picker import ModelPicker
class AiTextToAnnotationWidget(QtWidg... |
e8cd781961bac04e6c2419c3ff9d70291c24785b1a3f0348c1032bee08e7d30e | Python | 6,265 | 165 | #!/usr/bin/env python3
"""Gene / CpG feature-selection (public repo version).
This script keeps a runnable example for GitHub using the bundled toy dataset,
while preserving the key paper-style step of selecting features by effect size:
selected_* = log2fc[abs(log2fc) >= 0.25]
Behavior
- Loads a CSV with a binar... |
640b50441ce08f74de75b9f0474e046aa67d03f2e09031b632b477a4f8a7d9ac | Python | 6,266 | 196 | from unittest.mock import patch
import ants
import numpy as np
from pyment.preprocessing import rescale_intensity, skullstrip_with_antspynet
SHAPE = (10, 12, 14)
RAMP_SHAPE = (7, 11, 13)
def make_ants_image(
shape: tuple = SHAPE,
spacing: tuple = (1.0, 1.0, 1.0),
value: float = 1.0,
) -> ants.ANTsImage... |
eed98a6643eeaad01728e7cd6ef6108f29b3967bb1a47614d7009228b05a6349 | Python | 6,266 | 175 | #!/usr/bin/env python
"""
HAL module for controlling a Zaber XY stage. Based on the Marzhauser class.
Hazen 04/17
Jeff 04/21
"""
from PyQt5 import QtCore
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.stageModule as stageModule
import storm_control.sc_hard... |
c0b377d84a9002605de47c643383a608cedcaa5fdcf8ea540a8d3cc427783448 | Python | 6,268 | 141 | import numpy as np
import OptimalBattery.util as ut
import os
import pandas as pd
import OptimalBattery.evaluate as ev
import Functional_Fusion.atlas_map as am
from Functional_Fusion.dataset import DataSetMDTB
import nibabel as nb
import OptimalBattery.estimate as es
import OptimalBattery.construct as ct
import Optimal... |
170dc269383ae65dbd537ba639ff915dbc9d2bddbcc7909dce166ade05508cb1 | Python | 6,272 | 163 | import multiprocessing_on_dill as multiprocessing
# from mpi4py import MPI
import mpi4py
# mpiexec -n 3 -f machinefile_mp python3 main_mpi.py
import pickle
from sympy.ntheory import factorint
from copy import deepcopy
import logging
from zigzag.classes.hardware.architecture.accelerator import Accelerator
from zigzag... |
9f9abe424f05a15a3da94d038854f4f66b01333a820f7997d0b7e3d53b3e3e26 | Python | 6,273 | 229 | import numpy as np
import datetime
import pandas as pd
try:
from visdom import Visdom
except:
print("could not find visdom package. try 'pip install visdom'. continue without...")
Visdom=None
pass
class Printer():
def __init__(self, batchsize = None, N = None):
self.batchsize = batchsize
... |
f95d195b339b8df10453814089d682d4a9690ac7e0df9086ddab5d8296ec2114 | Python | 6,273 | 146 | """
This module contains functions to get task variables from behavior data.
"""
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
import numpy as np
from pyTdr.tdrUpdateBehavior import add_block_length, add_switch_block_length
from utils.get_session_info import get_unit_spike_dirs, get_behavior_df
def ad... |
8889e0dc1a92bb4eb38cbefae8206f76b7f358321f40123b3e25dd8c3e9b83cf | Python | 6,274 | 152 | # wfield - tools to analyse widefield data - NCAAS related
# 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 the Licens... |
dafb8fe45901c408bf63dd7e90182a7c2bdc3cba223fe3d5d504caa0778fc9ec | Python | 6,274 | 142 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Functions for estimating the best YOLO batch size to use a fraction of the available GPU memory in PyTorch."""
from __future__ import annotations
from copy import deepcopy
import numpy as np
import torch
from ultralytics.utils import DEFAULT_CFG... |
c2e9ed58a9c76ccf3815af575f7c177a01ccb1f0d9d563fb2815486533b66620 | Python | 6,275 | 165 | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Estimates the per-voxel volumetric density of a set of fibertubes. In other
words, how much space is occupied by fibertubes and how much is emptiness.
1. Segments voxels that contain at least a single fibertube.
2. Valid voxels are finely sampled and we count the num... |
24e58679f8619ebab38de5a19e131937154ef289077ea9244a29eee6e440eba2 | Python | 6,276 | 186 | import argparse
import mmcv
import numpy as np
import warnings
import torch
import os
from mmcv import Config, DictAction, mkdir_or_exist
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from os import path as osp
from pathlib import Path
from depth.dataset... |
1b08cc125845cdee808eb9eff4989d71e6209669a1b8f9a59a3a2e9693fdb8d5 | Python | 6,278 | 211 | """Shared configuration helpers for CLI and UI."""
from __future__ import annotations
from typing import Any, Dict, Optional
from chemgraph.models.endpoints import (
normalize_openai_base_url as _normalize_openai_base_url,
)
from chemgraph.models.endpoints.configuration import (
has_config_section,
resol... |
8f3a069bbec7bcd48e8e010698b311a974861d77083e8eec5129a43006214535 | Python | 6,278 | 261 | # Copyright (C) 2018-2024 Intel Corporation
#
# SPDX-License-Identifier: MIT
from django.contrib import admin
from .models import (
AnnotationGuide,
Asset,
AttributeSpec,
CloudStorage,
Data,
Job,
Label,
Project,
Segment,
Storage,
Task,
)
class JobInline(admin.TabularInlin... |
d6cdf4d43f107731e97f2ef812a4c17104168a0a2311b5508be7c93fd5544c31 | Python | 6,278 | 206 | #!/usr/bin/env python
# coding: utf-8
import sys
import os
import argparse
import pandas as pd
import numpy as np
import yaml
import logging
import scipy.io
import scipy.stats
from sklearn.cluster import KMeans
from dna3bit import DNA3Bit
import warnings
logger = logging.getLogger("demux_kmeans")
logging.basicConfi... |
0c60cb0710effe32423a5f997cdf2b8269b2198918a3a41bfb8dd013a52c6f19 | Python | 6,279 | 201 | import os
import anndata
import pandas as pd
import scanpy as sc
import squidpy as sq
import numpy as np
from nest.utility import spatial_smoothing
SQUIDPY_DATASETS = ["seqfish", "slideseq", "merfish"]
VISIUM_DATASETS = [
"V1_Breast_Cancer_Block_A_Section_1",
"V1_Mouse_Brain_Sagittal_Anterior",
"V1_Mous... |
a6545efddf955a6f83c048adc7ce8c9026233ccdf09a05c88ba9188dfa80bc02 | Python | 6,281 | 179 | """ PyTorch FX Based Feature Extraction Helpers
Using https://pytorch.org/vision/stable/feature_extraction.html
"""
from typing import Callable, Dict, List, Optional, Union, Tuple, Type
import torch
from torch import nn
from ._features import _get_feature_info, _get_return_layers
try:
# NOTE we wrap torchvision ... |
d79fba32f28b05e297310e4a540eecabe68076bef58611e9a4d7abde60689469 | Python | 6,281 | 164 | """
Access to YASA tutorial data
The YASA sample dataset is stored on Zenodo
and can be accessed using the Pooch library.
"""
import time
from pathlib import Path
import pooch
__all__ = [
"fetch_sample",
]
REGISTRY = {
"sample": {
"v1": {
"base_url": "https://zenodo.org/records/1456428... |
6700849d75f95dafe168a306902783139f9a613d2762edba7357e16b800c80ef | Python | 6,283 | 117 | from brainvoice.configuration.configuration import Configuration
from brainvoice.configuration.utils import extract_modify_dict
from brainvoice.configuration.configuration_linker import ConfigurationLinker
from brainvoice.configuration.static_configuration_parser import StaticConfigurationParser
import argparse
... |
9dc338dbef0221615b6931732fe3b657af7e4ad32a6284fc1541261b01eca489 | Python | 6,283 | 163 | """
Metrics to assess performance of propensity score 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.
Names are kept as `y_true` and `y_pred` even though these regard the
treatment assignment `a` (`a... |
884034eb563e9587fa37c4ed0755478ab95c7390bc58832e5728415e2fa691ed | Python | 6,285 | 179 |
from __future__ import division
import numpy.ma as ma
import numpy as np
import scipy as s
import scipy.special as special
# Import manually defined functions
from .variational_nodes import Gamma_Unobserved_Variational_Node
class AlphaW_Node(Gamma_Unobserved_Variational_Node):
def __init__(self, dim, pa, pb, qa,... |
9a7017df8a3b463d28e3a87ac27f2f3ca4c6f3cde5a3c7700ef46132f8106bac | Python | 6,287 | 192 |
import importlib
import subprocess
import sys
import os
from tkinter import Tk, Label, Button, StringVar, ttk, Scale, HORIZONTAL, messagebox
# Auto-install required packages
required = ["tifffile", "matplotlib"]
for package in required:
try:
importlib.import_module(package)
except Import... |
a2171cb3149ee58bfba9f0c84b0437c9d675cb7651cc31ea3fc1bc81e1c4986a | Python | 6,289 | 159 |
""" Classes and functions for fitting ivim model """
import numpy as np
from scipy.optimize import curve_fit
from dipy.reconst.base import ReconstModel
from dipy.reconst.multi_voxel import multi_voxel_fit
from dipy.utils.optpkg import optional_package
class IvimModelSegmented2Step(ReconstModel):
def ... |
32e20a60035540626dc55cf832eb09b8a1b5ddab2e27906707b2b2640c983aa5 | Python | 6,292 | 156 | import glob, os, struct
import numpy as np
import json
from pathlib import Path
#calculates the error based on distance of the triangles. The triangle distance is the sum of distances between
#each point of the triangle and its nearest point of the other triangle.
#deltaY and deltaZ is the value for how far the triang... |
3000512010d34fa69448566685ff22718f5fd64a0496959d088a15a231d74d95 | Python | 6,293 | 189 | import math
import os
import numpy as np
import pandas as pd
import statsmodels.stats.multitest as multi
from scipy.stats import gamma
from tqdm.auto import tqdm
from output_paths import artifact_dir, artifact_path
def _resolve_distribution_file(path):
if path is None:
return artifact_path("distributions... |
fbb6a3b794240e457d12e337672873b3b2a61a4833e7ce21deb74227081316a4 | Python | 6,294 | 146 | import os
import os.path as op
import sys
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.gridspec import GridSpec
import seaborn as sns
sys.path.append('/imaging/hauk/rl05/fake_diamond/scripts/preprocessing')
sys.path... |
50b1c15833eec74f318285ffc858e8334c84194e5c6738e905380a75f2c261f4 | Python | 6,296 | 185 | """Spiking linear layers, including E-I variants."""
import numpy as np
import torch
from torch import nn
__all__ = [
'SpikingLinear', 'SpikingEiLinear'
]
class SpikingLinear(nn.Module):
"""Linear layer wrapper."""
def __init__(self, in_features: int, out_features: int, bias: bool = True):
"""I... |
7e9f9fd9e716ad9406c852ddcafdb81a050620d6cefc1c17c770abe194e5304d | Python | 6,296 | 207 | #Lucas Sancéré -
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import yaml
from attrdictionary import AttrDict as attributedict
#############################################################
## Load configs parameter
############################################... |
dd9cd4cbd1dc4833b5f58de3706cdf347127863540ec6d65e1ffd507b886f00e | Python | 6,296 | 205 | import os
import sys
import unittest
try:
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
except Exception:
sys.path.insert(0, os.path.dirname(os.path.abspath(".")))
from bx.intervals.intersection import (
Interval,
IntervalNode,
IntervalTree,
)
class NeighborTestCase(unittest.Tes... |
d941a82d3f8dfd9edb6672a40e33ea1eb4a4901a4798f2cf74c994f31de7534d | Python | 6,298 | 205 | """Pure-Python cheminformatics helpers (no LangChain / MCP decorators).
Provides a single implementation for PubChem lookups and RDKit
SMILES-to-3D conversion, used by both the LangChain ``@tool`` wrappers
in :mod:`cheminformatics_tools` and the MCP wrappers in
:mod:`chemgraph.mcp.mcp_tools`.
"""
from __future__ impo... |
2107cc75e96650ec7e38d476473fd2733b131aeb0ce73dbd61f728d9467879ae | Python | 6,299 | 128 | import importlib
import logging
from collections import defaultdict
import functools
import re
import torch
from torch.nn import Module
from brainscore_core.supported_data_standards.brainio.assemblies import merge_data_arrays, NeuroidAssembly, walk_coords
from brainscore_core.supported_data_standards.brainio.s3 import ... |
0619c880327b1cc8473e768193cd96aaf1ebf1143ec7b3c3b5d3157d75b8693a | Python | 6,304 | 153 | from PIL import Image
import numpy as np
import os
from skimage.filters import threshold_otsu, threshold_local
from myfunctions import augStack
def import_fun(joinpath,sigma_chosen):
## this creates one aray of 3 columns and fills it will all the images ##
all_image_array=np.zeros((256,256))[None, :, :... |
063adac4717c99f9c69de61a147e0e968c6414cb4c0a9aec65352bb3e5065d2c | Python | 6,306 | 148 | import argparse
import joblib
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from xgboost_ensemble_classifier import XGBoostEnsembleClassifier
from xgboost import XGBClassifier
from sklearn.neural_network import MLPClassifier
from sklearn... |
98c9b6f8633bfc318b776889f98e08a3aaac99c8e6e9d9fbfe1aa4df20bb57db | Python | 6,306 | 205 | import math
import numpy as np
from numba import cuda, njit, prange
from scipy.sparse import csr_matrix
from .util import fp_type
import subprocess
import warnings
from numba.core.errors import NumbaPerformanceWarning
warnings.filterwarnings("ignore", category=NumbaPerformanceWarning)
def is_cuda_available():
"""... |
5e37fee8422bafffbfe67f8c11958bcd30ada48bbfd64f7821a46c3a13cf6ce7 | Python | 6,307 | 135 | import os
import torch
from torch.utils.data import Dataset
import numpy as np
import pandas as pd
from datetime import datetime
from pathlib import Path
# import random
# from matplotlib import pyplot as plt
class RotorBladeDatasetClean(Dataset):
""" This class creates a dataset instance from a csv-file or DataF... |
93ddfdb302f4611af38413ba58c5e4075daf860ba9e7d6e8a91a4e0fa440b04a | Python | 6,308 | 166 | from benchmark import CentroidDistScore, calculate_ncc
from deepreg.predict import unwrapped_predict
from tqdm import tqdm
from network_utils import write_to_json
import deepreg.model.layer as layer
import h5py
import numpy as np
import tensorflow as tf
import yaml
def set_GPU(device):
gpus = tf.config.list_physi... |
9e21cf286f2d3f47018f3c7a1c333483d99c607fc43c3ffbedb5fb3aaddaf259 | Python | 6,308 | 151 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from typing import Any
from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
from ultralytics.utils import LOGGER
from ultralytics.utils.plotting import colors
class SecurityAlarm(BaseSolution):
"""A class... |
a6d0abd4ef732650afaa39f73d298738e35182fc8a7f830ca5cbb146e12b6c19 | Python | 6,308 | 157 | import qt
class ComboDelegate(qt.QItemDelegate):
def __init__(self, parent):
qt.QItemDelegate.__init__(self, parent)
def createEditor(self, parent, option, index):
pass
def setEditorData(self, editor, index):
editor.blockSignals(True)
editor.setCurrentText(index.model().data(index))
editor.... |
9ad49cd7d8977ed5cf7819753ab008dd7d35737f2e9226965f09bd6b163ebd8e | Python | 6,309 | 167 | import argparse
import os
import os.path as osp
import time
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score
import pandas as pd
import json
import torch_geometric.transforms as T
from tqdm import tqdm
import random
import spacy
from torch_geometric.data import Data
i... |
13daf33b366332f85d171389543f719d2104b1e1af242956dda0dc0cd848a983 | Python | 6,310 | 154 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compute the statistics (mean, std) of scalar maps, which can represent
diffusion metrics, in ROIs. Prints the results.
The ROIs can either be binary masks, or weighting masks. If the ROIs are
weighting masks, they should either contain floats between 0 and 1 or shoul... |
facb718031cc5fd92c2fa9a6feeb03363321f72fd4fc1a4dc3291e3b659762bd | Python | 6,310 | 205 | # Copyright 2024 DeepMind Technologies Limited
#
# 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 agree... |
1010a24cbc27c7c80fdc6e593ab10d74a19a98f34f540f618d41318b4ec5efcf | Python | 6,314 | 195 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Customized profling"""
import resource
import time
import os
import logging
from contextlib import ContextDecorator
import pandas as pd
logger_profiling = logging.getLogger(__name__... |
722580d16cbdfac329547b31acbf7a6d137665a17e0fa38ca6dc889db565be65 | Python | 6,314 | 210 | # Author: Lucas Berg
#
# Program that calculates the APD of every cell in the grid by passing a certain percentage.
# e.g: APD_90 --> percentage = 90
# APD_80 --> percentage = 80
# APD_70 --> percentage = 70
#
# This program also works with multiples AP's !
import sys
import subprocess
import time
imp... |
fffe81718bd87137aaaaa5b7f67f8a53b8ceb708ed40476e07bf4fadf74c4ce5 | Python | 6,314 | 187 | import time
import serial
import numpy as np
import logging
import yaml
import pyvisa as visa
import pyvisascope
from grid import get_grid
import os
if not os.path.exists("data"):
os.makedirs("data")
if os.path.exists('experiment.log'):
os.remove('experiment.log')
visa.log_to_screen('INFO')... |
149e46fad79a322f165dde6236760d238043214e7ce590943a1e327a6ea0f6ba | Python | 6,315 | 126 | import functools
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from PIL import Image
import numpy as np
import timm
from timm.data import resolve_data_config
from timm.data.transforms_fa... |
43d429bb6990255212e02b7c98e34c843d3cbaa58dbf0ed8351ea7f3aa79bd3f | Python | 6,315 | 186 | import torch
import torch.nn as nn
from typing import Any
from ._builder import build_model_with_cfg
from .registry import register_model
__all__ = ["AlexNet", "alexnet"]
class AlexNet(nn.Module):
def __init__(self, num_classes=1000, dropout=0.5, **kwargs) -> None:
super().__init__()
self.num_cl... |
31a0a630c736a8fd2578b18810c81ecadd606023bbb3ee83de76655d2ea63f19 | Python | 6,316 | 194 | """
This script is used to make plot for the cross-validation results of the
actor and observer trial's projectin on the switch evidence direction.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import os
import numpy as np
import json
import matplotlib as mp... |
e373a27d307fd98f191505c4cc751f20b5b37c56c8914201b7ec13ff50734725 | Python | 6,317 | 269 | CompScalerMeans = [
21.194441759304013,
58.20212663122281,
37.0076848719188,
36.52738520455582,
13.350626389725019,
29.468922184630255,
28.71735137747704,
78.8868535524408,
50.16950217496375,
59.56764743604155,
19.020429484306277,
61.335572740454325,
47.14515893344343... |
404b9098354ff3a7969a1942ee968393c48caae6cdb1a0c19908af962aa591fc | Python | 6,319 | 170 | """
Tests for the logging functionality.
"""
import logging
import tempfile
from pathlib import Path
from io import StringIO
import pytest
from stack_corr.logger import setup_logger, get_logger, set_log_level, ColoredFormatter
class TestLogger:
"""Test the logging functionality."""
def test_get_logger(sel... |
ebf42db5af7d6cd51c1b1066d71e5206632f156bef6b7a17a8dc0a32b1803d24 | Python | 6,319 | 137 | import numpy as np
from GEASO.utils.utils import voxel_data
from GEASO.utils.utils import normalize_coords
from GEASO.utils.utils import cal_distance
def _to_numpy_matrix(x):
if hasattr(x, "A"):
return x.A
if hasattr(x, "toarray"):
return x.toarray()
return np.asarray(x)
def inlier_from_... |
473c472a50408ad0912016e3d82a3b307e87085c21973543dad8cd802744f437 | Python | 6,320 | 167 | import warnings
from typing import List, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import Parameter
import torch_geometric.typing
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import uniform, zeros
from to... |
79b6dfbe2c0779ead1e90b833edc5fd0baef91858cd544ba5b417d622cd8cf40 | Python | 6,321 | 218 | # Generate a specific input image for SHAP explanations from "Test dataset"
import glob
from PIL import Image
import torch
import numpy as np
import sys
import pickle
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.models as models
import torch.nn.functional as F
import torchvis... |
83947e3dd0bdc777b246a9e39282e7ec33ce034a816490414ce27d70eb482e4d | Python | 6,321 | 222 | import os
import pytest
from deepreg.vis import main, string_to_list
def test_string_to_list():
string = "abc, 123, def, 456"
got = string_to_list(string)
expected = ["abc", "123", "def", "456"]
assert got == expected
class TestMain:
img_paths = "./data/test/nifti/unit_test/moving_image.nii.gz... |
b5506a34166dcdad0a7e80802f9d72b1d26435711be52f8bdf3ed909c054a047 | Python | 6,322 | 179 | # Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
f54620f6f8d6f69ae940de92849df5e50d9dd6b0e824966173ad7ebe2de2ea19 | Python | 6,322 | 149 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Performs a statistical comparison between connectivity matrices for populations
g1 and g2, using a t-test.
The inputs are any connectivity matrix, that can be obtained with
scil_connectivity_compute_matrices, used separately on the two populations.
All input matrices ... |
03206c024baea8f4b565fb9528d6f83acd1bada7ca33fc9c5c56a8789460cfc0 | Python | 6,323 | 187 | import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import matplotlib.patches as patches
import fig_constants
def label_panels_mosaic(fig, axes, xloc=0, yloc=1.0, size=fig_constants.BIGGER_SIZE):
"""
Labels the panels in a mosaic plot.
Parameters:
- fig: The figure object.
... |
6cfcb9234432f27412f960444d208fee93f9ea9b24495e9cb4f73e4ce61fc39f | Python | 6,323 | 142 | import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from tqdm import tqdm
from .nbfr import generate_grid, Model
def get_single_target_responses(neuron_locations, stim_design, spks, stim_times, um=1/1.17, valid_radius=30, resp... |
e4f0386a9efd1cac67af4f770b0e599ad6a87bfbeb59c6ab3b80997f7c1c943b | Python | 6,324 | 160 | import matplotlib.pyplot as plt
import numpy as np
import nems
from nems import Model
from nems.layers import WeightChannels, FiniteImpulseResponse, RectifiedLinear, STRF
from nems import visualization
from nems.metrics import correlation
from nems.models.LN import LN_plot_strf
import tensorflow as tf
# Check if eage... |
438474e0d5bc3ed7803f25049febfe02eed8105d39e6638a1a020961f791af3b | Python | 6,326 | 155 | """Functions for simulations related to IVIM."""
import numpy as np
import numpy.typing as npt
from ivim.models import sIVIM, diffusive, ballistic, intermediate, check_regime, DIFFUSIVE_REGIME, BALLISTIC_REGIME, INTERMEDIATE_REGIME
from ivim.io.base import write_im, read_im, read_bval, read_cval, write_bval, write_cva... |
53ff0e13a5b4b9c087066d7574f4dc5fd2ff309572603d0ff42f77c1c3087104 | Python | 6,326 | 233 | import os
import numpy as np
import torch
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from umap.umap_ import UMAP
from params import load_embeddings
def filter_nan(labels, embeddings):
labels_np = np.array(labels).ravel()
val... |
c38ea2c6c287a2b50d5b1ccae29afa3551eff4eeb1ff4dc9a824f33908339aa9 | Python | 6,328 | 180 | import numpy as np
import matplotlib.pyplot as plt
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from src.solvers.vrfb_optimizer import VRFBOptimizer
print("Generating enhanced VRFB Pareto front...")
optimizer = VRFBOptimizer()
electrode_thicknesses = [3, 5, 7, 10]
flow_rate... |
aee39cc7eaa241a0299a0b97f4b3d82a060a9e57336661c885632b54ce032fc0 | Python | 6,329 | 184 | from collections.abc import Generator, Sequence
from dataclasses import dataclass
from typing import (
TYPE_CHECKING,
Any,
overload,
)
import torch
from torch.utils.data import DistributedSampler, WeightedRandomSampler
from eir.data_load.data_loading_funcs import get_weighted_random_sampler
from eir.setup... |
3bd6ba38d040cccae7241a08a232512b62b63453d426ef86fe6d9940a8e72172 | Python | 6,330 | 175 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
from numpy.linalg import norm
from scipy.stats import sem
from nibabel.freesurfer.io import read_geometry, write_morph_data
# local inputs
from ..io.surf import read_patch
def calculate_distortion(file_patch, fi... |
a21719ae5aaca29543bbf6fc7b6ff984e403b10770fa6e50b7b52bc8abdf37a0 | Python | 6,332 | 207 |
dict_to_average = {
"sub-01": {
"AF_L": {'length': [1.00, 2.00]},
"AF_R": {'length': [8.00, 2.00]},
},
"sub-02": {
"AF_L": {'length': [9.00, 2.00]},
"AF_R": {'length': [13.00, 2.00]},
}
}
expected_dict_averaged = {
"sub-01": {
"AF_L": {'length': {'mean': 1.5... |
49b0a8c730dc145cdf147da2c957c5460253ba7b481490a85f16aae307866eac | Python | 6,334 | 185 | import inspect
import logging
from importlib import import_module
import immlib
from dipy.io.stateful_tractogram import set_sft_logger_level
from pcollections._lazy import LazyError
from AFQ.utils.docstring_parser import parse_numpy_docstring
__all__ = ["methods_descriptors", "kwargs_descriptors", "AFQclass_doc"]
... |
f993cd1ad86c5a3b34151330abd40150cff45a31cb41e4cd516fae2b34cf0f5f | Python | 6,334 | 52 | from brainscore_vision import benchmark_registry
from . import benchmark
# behavioral benchmarks
benchmark_registry['Scialom2024_rgbBehavioralAccuracyDistance'] = lambda: benchmark._Scialom2024BehavioralAccuracyDistance('rgb')
benchmark_registry['Scialom2024_contoursBehavioralAccuracyDistance'] = lambda: benchmark._Sc... |
dc6cda4b84437faa38546a693ea8e8bc8cafe6b6d097a01b8999b1cdf900b0c2 | Python | 6,337 | 167 | #!/usr/bin/env python
"""
The galvo control UI. Actual control of the hardware is
provided by a waveform functionality from the DAQ.
Jeff Moffitt 11/15
Hazen Babcock 05/17
"""
import fractions
import numpy
from PyQt5 import QtCore, QtGui, QtWidgets
import storm_control.hal4000.halLib.halDialog as halDialog
import st... |
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