sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f4e872478fa658fe19e632db6ac3ac329ab3bdfb3b6ef2c1ac1b170a94a110b6 | Python | 20,162 | 619 | import json
from collections.abc import Collection
from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import ToolNode
from chemgraph.tools.ase_tools import (
run_ase,
extract_output_json,
)
from ... |
7fa9917830e9e72a53ff571acfafc6a461b4a641bd7fc0c797ac636670ec26e4 | Python | 20,166 | 418 | #!/usr/bin/env python
# coding: utf-8
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch_geometric.nn import GCNConv
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold
from skl... |
eb614e495fedb7316522b716d7f3a235b05e8814151adeb918a20097dcf7b8a1 | Python | 20,172 | 601 | from collections.abc import Callable, Iterable, Sequence
from contextlib import nullcontext
from dataclasses import dataclass
from typing import (
TYPE_CHECKING,
Any,
)
import torch
from torch import autocast, nn
from torch.nn.utils import clip_grad_norm_
from torch.optim.optimizer import Optimizer
from eir.d... |
50b1b01031702913055faa64c3fcdd629748f667c4ccfff2ddac92b3ef4ad02f | Python | 20,174 | 472 | alg_name = 'graph_saint'
import os
os.environ["OMP_NUM_THREADS"] = "2" # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = "2" # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = "2" # export MKL_NUM_THREADS=1
os.environ["VECLIB_MAXIMUM_THREADS"] = "2" # export VECLIB_MAXIMUM_THREADS=1
os.... |
ca8750723269cd56801e45c5d1fe801847ae5b070fb6d94c8eba9a25e7089ba7 | Python | 20,175 | 467 | """Test the run progress model and its display.
Run from the repo root::
uv run python python/test_progress.py
The clock is injected, so every timing assertion here is exact rather than
flaky. What matters is not that a number appears but that it is *right*: an
estimate that is confidently wrong is worse than no... |
1b061552f898f4d0ba7d58c913d3b938e58d2e9d921e5a76d2a782f2774cbdc2 | Python | 20,180 | 588 | import os
import re
import pickle
import inspect
import numpy as np
import pandas as pd
import importlib_resources
def load_timeseries(path=None):
"""
Load time series data from a file.
Supported file formats are: .pkl, .txt, .npy, .mat, and .tsv
Parameters
----------
path : string
pat... |
a5fcb788278db788b04d7ccc7c557e9a98dafa612247059ec00c16ba9f0c8714 | Python | 20,192 | 485 | import json
import itertools
from pathlib import Path
from typing import Dict, List, Tuple, Union
from magicgui.widgets import FunctionGui
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.figure import Figure
import mat... |
776386c8daf0c0e40ced4fc2eaf4e61fa557c90d6b87e220f634cc40ddf7b38c | Python | 20,208 | 506 | from typing import Any, Dict, Tuple
import numpy as np
import torch
from torch import Tensor
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.utils import (
add_self_loops,
coalesce,
... |
876160b1427c8073dc1c88e45e21ba97e8f7a4f526ddaea4e327326deec02808 | Python | 20,214 | 493 | import torch
import numpy as np
from torch import nn, einsum
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from typing import Union, List
from timm.layers import trunc_normal_
class CyclicShift3D(nn.Module):
def __init__(self, displacement):
super().__init__()
asse... |
83e9c183b810e9e86887751004929c96a9ccbed1b0111e759a11f23de846f80b | Python | 20,223 | 373 | import tempfile
import unittest
from pathlib import Path
import numpy as np
from dtiplayground.dmri.preprocessing import qc_metrics, batch_report
def _gradients(n_dirs=30, n_b0=5, bval=1000.0, seed=0):
rng = np.random.default_rng(seed)
dirs = rng.normal(size=(n_dirs, 3))
dirs /= np.linalg.norm(dirs, axi... |
49db3af674c2f74c19ff6c3b08c42741e582e18e04c0d5e7ea8ceaefa929e6b7 | Python | 20,225 | 401 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 1 09:16:38 2024
@author: rl05
Perform cluster-based permutation tests on ROI data
"""
import os
import os.path as op
import sys
import pickle
import numpy as np
import math
import matplotlib.pyplot as plt
from mne import read_source_estimate
fr... |
7bd3270b394cd0d89479e43d9e4425d91eb7ccfc3dd9ecfc044ad5e6ab144445 | Python | 20,254 | 427 | import numpy as np
from scipy.signal import butter, filtfilt, hilbert, iirnotch, resample
from sklearn.preprocessing import StandardScaler
from scipy.interpolate import interp1d
from scipy.stats import sem, zscore
from General import *
from Utilities_and_Loading import *
def perform_BP_all_data(all_data, global_param... |
97456e82fd7ec492961f540e75c771ff650b4bbdcad4ddd03067c44d8a6b8fe4 | Python | 20,254 | 437 | from __future__ import annotations
import csv
import os.path
import pickle
import datetime
from tqdm import tqdm
from metaphlan.utils.merge_metaphlan_tables import merge
from .utilities import run_cmd, ReadsData, check_conda_qiime2
import json
import shutil
CONDA_PREFIX = os.environ.get("CONDA_PREFIX", None)
def ch... |
248c36e128571180ad0d973e32ef36dfb2914b54d794cb3bbf7f2549f5121bd1 | Python | 20,262 | 461 | """Gabor-filter entropy features for arbitrary image sets.
This is a standalone Python port of the descriptor behind the
GaborFilterEntropy4x4Cosine model from the EntropyReanalysis MATLAB scripts.
The default settings match that model: 256x256 grayscale images, six octave
frequency bands [1, 2, 4, 8, 16, 32], si... |
f41fb9c98002f3d36ea17a9715a911ce186b3d432d4b4bee73e1604a103cda96 | Python | 20,265 | 621 | """Train an ABMIL model for classification.
The inputs to the model are bags of embedded histology patches.
The outputs of the model are classification predictions.
"""
from __future__ import annotations
import dataclasses
import json
import math
import random
import shutil
from pathlib import Path
from typing impo... |
3750b13cd665d68dab6a5f9733957c02c9cf3c0ab188059a9152a5d9a9ada4a3 | Python | 20,267 | 564 | from typing import List, Tuple, Iterable
import math
import torch
import torch.nn.functional as F
from torchvision.transforms import InterpolationMode
import torchvision.transforms.functional as T
import numpy as np
def array(torch_tensor, permute=True):
"""
Converts a torch.Tensor to a numpy array on the CP... |
4ef57965680edb7703072f703d1e58e0c2f0a99d2dd97dd758a7b4cfd014cbb3 | Python | 20,268 | 398 | import pandas as pd
import logging, os, sys, yaml
from collections import defaultdict
novascope_scripts = os.path.dirname(os.path.abspath(__file__))
sys.path.append(novascope_scripts)
from bricks import check_input, check_path, create_dict, get_last5_from_md5, create_symlink, create_dirs_and_get_paths
from bricks impo... |
e5b6a4743a469af6cbba77cd88aa2a40aa479ef5466069a419ae6bd67120cc1f | Python | 20,271 | 623 | #!/usr/bin/env python
"""
Interface to Point Grey's PySpin Python module.
Tested with "Spinnaker 1.19 for Python 2 and 3 - Windows (64-bit)".
Note: As currently written this is designed to work with 12 bit cameras. See
onImageEvent() in SpinImageEventHandler class.
Hazen 01/19
"""
import numpy
import os
impor... |
5790bc81690d6a821764de35d04d110e7b584890e189aee153834c0065df5b04 | Python | 20,293 | 578 | import numpy as np
from .parameter import Parameter
# TODO: add examples and tests
# (for both Phi and Parameter)
# TODO: TF compatibility functions? during fitting, values would have to exist
# as TF primitives, but need to be able to translate to/from
# TF and NEMS representations.
class Phi:
... |
07394b4c85c31282fc655b50d4f6cce044c88d78ffd96e98af3f687d3087fc2e | Python | 20,297 | 486 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Filtering of a tractogram based on any combination of conditions involving a
ROI (ex: keep streamlines whose endoints are inside the ROI, exclude
streamlines not entirely included in a ROI, etc.)
See also:
- scil_tractogram_detect_loops
- scil_tractogram_fil... |
c52ac40685294d941837cf878ed1323e21018dd4ddd5737e64fd5635d978a761 | Python | 20,316 | 360 | import pandas as pd
import numpy as np
import torch
import argparse
def convert_to_binary(value):
return 1 if value > 0 else 0
def modify_node_index(index_value,lncRNA_size,miRNA_size,drug_size):
if index_value < lncRNA_size:
return index_value
elif lncRNA_size <= index_value < (lncRNA_... |
a609dc4263197d97887d642ca44aaea1aec57e65a838924faaf14794d8ef58c3 | Python | 20,320 | 498 | #!/usr/bin/python
#
## @file
#
# An xml parser class that takes a sequence recipe xml file and converts it to
# a flat sequence file that can be read by Dave
#
# Jeffrey Moffitt
# 1/5/14
# 9/4/14: Updated to Dave4 format
# jeffmoffitt@gmail.com
#
#
# Import
#
import os
import sys
import traceback
from xml.etree impo... |
1b00fc8d8277920de96b3d1eb8cf762a690115cb60c0925ef9958875229f4253 | Python | 20,329 | 508 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn ... |
4cb601e03ab03e3000c812661ff32f8cf3ff6b5ea236f9f6a1b3978c557c265f | Python | 20,334 | 434 | #!/usr/bin/env python3
"""
BehaveAI Launcher (project-aware)
- Projects are stored in ./projects/
- Each project is a subdirectory containing:
- BehaveAI_settings.ini
- clips/ input/ output/ models/ yamls/ (created by the launcher)
- Launches scripts with the project's directory as cwd and passes the project path a... |
ba88bfa9f8fc4cf44dc962313790af7f901d2c28c76161f684349dc9133149bb | Python | 20,336 | 527 | # 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, ... |
a225e445ce002fef5131cc7f2480e7d5abcf8c6e46580c5726f6c9e23cee7641 | Python | 20,343 | 451 | import os
import torch
import math
import yaml
import warnings
import argparse
import numpy as np
import pandas as pd
import torch.nn.functional as F
import scipy.spatial as spa
from tqdm import tqdm
from torch_geometric.data import Data
from scipy.special import softmax
from Bio.PDB import PDBParser, ShrakeRupley
from... |
7999bb8f0adf5f576f702b64f75279491c5e143dd4f6e8df17675d9c85b00886 | Python | 20,349 | 544 | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 17 16:50:29 2021
@author: axthi
"""
import gc
import copy
import nibabel as nib
import numpy as np
import os
from scipy import ndimage
from scipy.ndimage.filters import uniform_filter
import scipy.ndimage.morphology as mrph
from simnibs import mesh_io, sim_struct, __ver... |
894864b5c63f8e81df0f1a6d3f89a0da32ce89d61c17772ca756f7061ff00630 | Python | 20,359 | 440 | """
Supplementary Information Figure S3: Phase-cycled bSSFP profiles - SpinWalk simulations vs. in vivo data
2x4 subplot layout comparing bSSFP magnitude and phase profiles across phase cycling angles:
- Column A: 3T SpinWalk simulation results for different fiber orientations (θ = 0°, 30°, 60°, 90°)
- Column B: 9.4T ... |
82b8a7dc912e832b0c4663940e528d8dad63823e329090a0ff2b9ec57721ccfe | Python | 20,365 | 561 | # -*- coding: utf-8 -*-
"""Full cortex reconstruction
The purpose of the following workflow is to compute the segmentation for hires MP2RAGE
(or MEMPRAGE) data and bring the resulting surface data of the cortical boundaries into
a regular grid representation. The script is divided into five parts. Single steps of of
e... |
3d7481d3767c026a969adf362c12e099799ecc13d06cf22dd11996bd9512d1f9 | Python | 20,366 | 443 | import warnings
from pprint import pprint
import datajoint as dj
import pandas as pd
from djaddon import hdf5
try:
from pupil_tracking import PupilTracker
except ImportError:
warnings.warn("Failed to import pupil_tacking library. You won't be able to populate trk.EyeFrame")
schema = dj.schema('pipeline_pupi... |
ccacdb5b77b49666394f3f68e964572b53835826dc3c924204415a22cf84ed2a | Python | 20,371 | 624 | # -*- coding: utf-8 -*-
"""Time-series manipulation."""
import os
from math import prod
import nibabel as nb
import numpy as np
from nilearn.signal import clean
from numpy.fft import fft, ifft
from scipy.interpolate import InterpolatedUnivariateSpline as Interp
from scipy.ndimage import gaussian_filter
from ..io.fil... |
a1440a88e7b25c0dbabb0845d9381388fa1ff9bacffc6a68daf17b8c58502103 | Python | 20,380 | 646 | """
Plot the probability of switching as a function of the outcome and
player identity for one session, based on trial_info.csv
"""
from scipy.stats import ttest_ind
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from utils.plots import beutify
from copy import deepcopy
... |
54319879671576fb9e39dc5575387c5cf6a88e329b121ad6e6998d90798c7145 | Python | 20,383 | 532 | import gc
import torch
import numpy as np
import cv2
from torch.nn import functional as F
from os import listdir, makedirs, getcwd
from os.path import join, exists, isfile, isdir, basename
from glob import glob
from ipywidgets import interact, widgets, FileUpload
from IPython.display import display
from matplotlib impo... |
3c0f13b52a84e271580d71e9387e1a5094880ea1260250ac4805744bf89321a6 | Python | 20,393 | 525 | #!/usr/bin/env python
"""
HAL interface to National Instruments cards.
Hazen 04/17
"""
import numpy
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.daqModule as daqModule
import storm_control.sc_hardware.nationalInstruments.nicontrol as nicontrol
import stor... |
027e1555b9e6d2770bb7eeef4bc4d93e399411e9c21124d6601a87161f3d7aa7 | Python | 20,395 | 617 | # laminar_rs/gradients.py
"""
Gradient-based analyses and laminar (dis)similarity metrics.
This module provides:
- BrainSpace-based gradient estimation from connectivity / affinity matrices
- Inter-areal and intra-areal laminar dissimilarity measures
- Clustering of eigenvectors with sign-invariant distances
- Cosine-... |
0b9b0cb492aa4fff0dd788e43298a6b385e77b4eb5e79b566082dc66df55d65d | Python | 20,418 | 504 | """Tests for mcmlnet.utils.rescaling module."""
import copy
import numpy as np
import pytest
import torch
from mcmlnet.utils.rescaling import DataRescaler
class TestDataRescaler:
"""Test cases for DataRescaler class."""
def setup_method(self) -> None:
"""Setup common variables for tests."""
... |
8a8286f7bf44394d2f98621be89c6213097375f41b5cc8140373dba5c10808ca | Python | 20,428 | 503 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import csv
import json
import logging
import math
import os
from itertools import combinations
import matplotlib.pyplot as plt
import numpy as np
class data_for_stat(object):
"""
Method 'init' in the name will initialise argument of the object
Method 'get'... |
8d30fb06b435fcb848def7c18a9d31efe6bb9b148f5e9e4ec1e8f08dbcfd2025 | Python | 20,429 | 434 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Check a model's accuracy on a test or val split of a dataset.
Usage:
$ yolo val model=yolo26n.pt data=coco8.yaml imgsz=640
Usage - formats:
$ yolo val model=yolo26n.pt # PyTorch
yolo26n.torchscript ... |
f08dab011c475cb1221d8c1711691d82faa1f4a0de6feb113a81a65bc43b41c2 | Python | 20,430 | 356 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Module for YOLO model metadata.
This module stores detailed metadata for various YOLO models including authors, organizations,
publication dates, arXiv links, GitHub repositories, documentation URLs, and performance metrics.
FLOPs values mirror ea... |
cc36dd45882a99833a85a43638c110144d278e63237463c52a172c2599e32053 | Python | 20,444 | 526 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Main module for connectome manipulations:
- Loads a SONATA connectome using SNAP
- Applies manipulation(s) to the connectome, as specified by the manipulation config dict
- Writes bac... |
2b3c56c62ccb6dc0813da2af608c54f842e624d12415b9cb2695a1d74b7141d5 | Python | 20,445 | 502 | #!/usr/bin/python
#
## @file
#
# An xml parser class that takes a sequence recipe xml file and converts it to
# a flat sequence file that can be read by Dave
#
# Jeffrey Moffitt
# 1/5/14
# 9/4/14: Updated to Dave4 format
# jeffmoffitt@gmail.com
#
#
# Import
#
import os
import sys
import traceback
from xml.etree impo... |
083bb17b9ee0f4d58fe9916317e55b1a91ef4726a329b5b44faafcdc2c85a293 | Python | 20,462 | 646 | import io
import numpy as np
import pytest
import xml.etree.ElementTree as ET
from pathlib import Path
from aicspylibczi import CziFile, remote_reads_available
from _aicspylibczi import (
PylibCZI_CDimCoordinatesOverspecifiedException,
PylibCZI_RegionSelectionException,
)
@pytest.mark.parametrize(
"as_s... |
c1237c453fd490a59f149ae49fa8c2d439b14fb5ffc19422699dc6f7302990fc | Python | 20,465 | 509 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import argparse
import time
from collections import defaultdict
from urllib.parse import urlparse
import cv2
import numpy as np
import torch
from transformers import AutoModel, AutoProcessor
from ultralytics impor... |
2e9d02cd73cfab278a1b4ae5580bacffadcf612115bad04ce3aaab105b4ddf17 | Python | 20,471 | 568 | # -*- coding: utf-8 -*-
"""
Created on Sat Feb 13 19:00:43 2016
@author: ajaver
"""
import collections
import os
import numpy as np
import pandas as pd
import tables
from scipy.ndimage.filters import median_filter, minimum_filter, maximum_filter
from tierpsy.analysis.int_ske_orient.checkFinalOrientation import check... |
6fd2d004123327f182701f03116784f94165e8ee1f217938654dcd43824461fb | Python | 20,484 | 590 | r'''
The nanonis_programming_interface class initializes a socket connection
to Nanonis, allowing the user to send commands to Nanonis through a
TCP/IP connection.
'''
import _thread as thread
import atexit
import socket
import struct
import time
# Defines data types and their sizes in bytes
datatype_dict = {'int':'... |
7cf6ae4542e6e882c410f26a280a7d31ea7af008a834f9c855924472b4751e6b | Python | 20,485 | 516 | import argparse
import os
import shutil
import sys
import time
import warnings
from random import sample
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn import metrics
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from utilities.dat... |
1efe0f318bb1de1006a16f571be8cf525d1969411c1a7aeae8f6caa35fa055a2 | Python | 20,488 | 543 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import numpy as np
import random
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
# --------------------------------------------------
# ... |
bb88827d8cab9a1a92438483ed97b5b3f4a052fa10f935750b5dffb04a2abdde | Python | 20,517 | 566 | import numpy as np
import polars as pl
import scipy
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter1d
from tqdm import trange
from typing import NamedTuple, Callable
from neuralib.locomotion import running_mask1d
from neuralib.suite2p import (
Suite2PResult,
get_neuron_signal,
... |
4fd554e1eec01c079a17bd0f2f542d0991490cb5ad10a3a6636889109324a393 | Python | 20,532 | 483 | import numpy as np
from PySide6 import QtGui, QtWidgets, QtCore
from PySide6.QtWidgets import QWidget, QMenuBar, QMainWindow, QVBoxLayout, QLabel, QHBoxLayout, QGridLayout, QPushButton, QCheckBox, QSlider, QTableWidget, QTableWidgetItem, QTabWidget, QGroupBox, QSpinBox, QFileDialog, QMessageBox, QComboBox
from PySide6.... |
e8d5eea7b8d800b529a47ada6e8955677115c4f12c2eb353b3b030a329d30c6b | Python | 20,542 | 542 | """
InceptionNeXt implementation, paper: https://arxiv.org/abs/2303.16900
Some code is borrowed from timm: https://github.com/huggingface/pytorch-image-models
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpe... |
e5562e421e21c2750b4a17fc5ebbd5d2664dcc79def5c7862f7eebd8795f0ceb | Python | 20,565 | 480 | import torch
import torch.nn.functional as F
from deepcardio.neuralop_core.models.base_model import BaseModel
from deepcardio.neuralop_core.models.fno import FNO
from deepcardio.neuralop_core.layers.embeddings import SinusoidalEmbedding2D
from deepcardio.neuralop_core.layers.spectral_convolution import SpectralConv
f... |
de3fcf04d5e949c25997ed0351e64a8a02c3c3211aec03ce99fbbe6994c28192 | Python | 20,581 | 374 | from brainvoice.data.estimates_loader_utils import load_estimate_labels, load_latent_variables_estimates
import argparse
import numpy as np
from tqdm import tqdm
import pandas as pd
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.linear_model import LogisticRegression
from sklearn.m... |
4ad9252c98bb7bc9c409ef42f06f65efebc700f00b6c2edf05cdedb11b8cddd1 | Python | 20,584 | 508 | import sys
import os
import pandas as pd
import pyreadr
import torch
import glob
import functools
import random
import yaml
import torch_geometric.transforms as T
import torch.nn.functional as F
from math import ceil
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.loader import DenseDataLoad... |
10d7d57942e519498329b15d95ab16b3244e4b3a9513b576a34018ef3065ace5 | Python | 20,588 | 471 | import torch
import numpy as np
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Subset
import copy
def dataset(config):
## Dataset
transform = transforms.Compose([
transforms.ToTensor(), #
])
if config.train_task == "... |
6691403b845a753c67bb7f9803eb8bf14b3ccb933e7d6651063e583325967c5c | Python | 20,589 | 501 | import json
import itertools
from pathlib import Path
from typing import Dict, List, Tuple, Union
from magicgui.widgets import FunctionGui
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.figure import Figure
import mat... |
47a046e8da25c8ec760aea12bc986f9b44232ceb06b5caaaa30b0e18be80b579 | Python | 20,592 | 634 | # by Shaoxuan Yuan at 2024/04/22
from __future__ import division, print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class conv_block(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_ch, out_ch):
super(conv_block... |
e5a3d7fd9342c02b4c0a1d531f2d5117bc3fc6596d1775674205d26cb6894d06 | Python | 20,622 | 645 | """
This script makes the raster and histogram plot of two simultaneously recorded
units.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import os
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
fro... |
6d662d860338b299ab6bc84289f1940bfad8158547909d5b500246fb328e2236 | Python | 20,630 | 528 | import pytest
import json
import numpy as np
from unittest.mock import Mock, patch
from stack_corr.correction import StackCorrector, correct_stack
class TestStackCorrector:
"""Test the StackCorrector class."""
@pytest.fixture
def mock_image_data(self):
"""Create mock image data for testing."""
... |
ea1f13021593b16c6e3154bf1b8a297a313ef02bbe11e8bc4df5e640e53dd709 | Python | 20,633 | 546 | #!/usr/bin/env python3
"""
Train DREAM-RNN models using the official @DREAM_paper implementation
This script implements the exact DREAM-RNN architecture and training methodology
from the @DREAM_paper tutorial notebooks for DeepSTARR data.
"""
import argparse
import os
# Suppress TensorFlow warnings since we're using ... |
55d7f57ab030ed750b77aee213216d531c0c09b1ce0ac4ed05a2944d65a34028 | Python | 20,641 | 614 | # -*- coding: utf-8 -*-
"""Flattening utilities."""
import datetime
import math
import os
import shutil as sh
import matplotlib.pyplot as plt
import nibabel as nb
import numpy as np
import shapely.geometry as geometry
from descartes import PolygonPatch
from nibabel.freesurfer.io import read_geometry, write_morph_data... |
6bf4faa583fe9d47af3e1e465d461526385a15b0686882d694204137f4f02fc0 | Python | 20,643 | 534 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
BundleSeg output visualization tool.
This script allows visualization of segmented bundles and after-the-fact
filtering to explore optimal thresholds for each bundle. It uses
FURY for 3D rendering.
It includes tools to slice images, filter streamlines, and interactive... |
93d6f8e626a8be49324625931d38e794c2d7c1c8c534ede3fdf9b11059d5a1ed | Python | 20,644 | 496 | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... |
237144e964d7425c562aa3ec1e00af5d365ee82b51c40d8f57a79557101d6b63 | Python | 20,645 | 513 | import os
import glob
import tqdm
import skimage.io as skio
from natsort import natsorted
import numpy as np
from skimage import draw
import json
import skimage.exposure as skex
import time
import skimage as ski
from pathlib import Path
from multiprocessing import Pool, cpu_count
class hipctPreprocessor(object):
... |
14779b9c7f9ac369021ffb49ed32887916cb35b0330803ccec635b33687a85ea | Python | 20,652 | 547 | """Publication-style TBSS/JHU axial mosaic without MRIcroGL.
Layers, from back to front:
1. user supplied anatomical/FA background (grayscale)
2. JHU ROI statistic values (continuous colormap)
3. user supplied TBSS skeleton (green)
The statistical CSV uses the same convention as the other cvdproc surface
... |
d1380943a4532abc27df42879bb0d04924e4cf31bc07cf6ca4cc33671e7ca395 | Python | 20,652 | 613 | import numpy as np
import nelpy as nel
from nelpy.core._eventarray import _restrict_event_series_to_intervals
from nelpy.utils import ragged_array
def _reference_restrict_events(events, intervals):
events = np.asarray(events)
pieces = []
for start, stop in intervals:
start_idx = np.searchsorted(e... |
1e0a873f5ce9dabde1058dbb946afba01ce08472b965339ea8171d97a6a02eba | Python | 20,654 | 472 | # /------------------------------------------------------------------------------+
# | 19-APR-2023 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
27404334a6c26a5cf2cfe38543c12e2902f62ad46207abb6d83a55dfe1050db9 | Python | 20,662 | 362 | import argparse
import multiprocessing
import shutil
from multiprocessing import Pool
from typing import Union, Tuple, List, Callable
import numpy as np
from acvl_utils.morphology.morphology_helper import remove_all_but_largest_component
from batchgenerators.utilities.file_and_folder_operations import load_json, subfi... |
2fc7d8cb092f5a3b1bec015c72e8429f41956448eb49a352bb7ec73c6dc055a8 | Python | 20,664 | 554 | # -*- coding: utf-8 -*-
"""
Fiducial Metrics Extraction Pipeline
A modular, open-access tool for extracting anatomical measurements
from 3D Slicer fiducial files.
Features:
- Extract distances and measurements from fiducial landmarks
- Support for T1 and T2 modalities
- Cross-platform path handling
- Configur... |
de9413f837f18c8b66f08eb509eb1fc62cb11a66cbd26ccaf21c60ee3a9c6ab2 | Python | 20,668 | 636 | import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
from tqdm import tqdm
import pickle as pkl
import os
from FORCE.utils import *
class RNN:
"""
RNN via FORCE learning.
API:
.reset(reset_activity=True, reset_weights=True)
.save_weights()
.load_weights()
.activation(x, beta... |
dab5be2452c4fcf32332e3605931e0674692980a4de239a0e983ef5ac681b210 | Python | 20,669 | 403 | # Copyright (c) 2026 Rohit Jena. All rights reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its use, reproduction, or... |
12ad31c5f8bd042070db7c479e1911ae1c15d625889034b5ce6ba81635fc1b75 | Python | 20,686 | 686 | import os
import shutil
import pytest
from torch.optim import SGD
from torch.optim.optimizer import Optimizer
from torch.optim.adam import Adam
import flair.datasets
from flair.data import Dictionary, Sentence, MultiCorpus
from flair.embeddings import (
WordEmbeddings,
TokenEmbeddings,
FlairEmbeddings,
... |
df1b650b6872e1d1a75ff54fc182577111c4f49f2db63630048fa4dd33e115c2 | Python | 20,688 | 482 | """
Module for evaluating the performance of task batteries for both simulations and real data.
Author: Bassel Arafat
"""
import torch as pt
import OptimalBattery.estimate as et
import OptimalBattery.util as ut
import numpy as np
import OptimalBattery.construct as ct
import Functional_Fusion.atlas_map as am
import cort... |
73682fc0726a7be990428198405e949ab19a1d1dd01172fcb9f4d943f078942b | Python | 20,689 | 443 | import os
import torch
import numpy as np
import scipy.signal as sig
import pandas as pd
from scipy.sparse import csr_matrix
from sklearn.model_selection import StratifiedShuffleSplit
class TrialDataset:
def __init__(
self,
root: str,
sessions,
areas,
neurons,
trial... |
83bec99efc042c396896d898991411d547f5f565ee30ebc9d7c386208f8253a5 | Python | 20,689 | 594 | # Source: https://github.com/neuraloperator/neuraloperator/blob/main/neuralop/training/trainer.py
import torch
from torch import nn
from timeit import default_timer
from pathlib import Path
from typing import Union
import sys
import json
from .losses import LpLoss
from deepcardio.train_state import load_training_state... |
21b3efa2132aa48c0635f13552cfea2eef9b15a1895b636a2ec16299143daa27 | Python | 20,699 | 311 | import os
import shutil
from nipype.interfaces.base import CommandLineInputSpec, File, TraitedSpec, CommandLine, BaseInterfaceInputSpec, BaseInterface
from traits.api import Str, Int, Directory, Bool, Either
# usage: segment_subregions [-h] [--cross CROSS] [--long-base LONG_BASE] [--sd SD] [--suffix SUFFIX]
# ... |
e3c054773511237b981c932e66c9c10ca235810ed405e070a4eb0475363a065a | Python | 20,706 | 360 | """A module that contains the base classes for pipelines, :py:class:`~corelay.pipeline.base.Pipeline`, and tasks of pipelines,
:py:class:`~corelay.pipeline.base.Task`, which are used to perform a specific set of operations on data.
"""
import collections
import typing
from collections.abc import Callable
from corelay... |
8a3da6cb97267caf6b01c72afae546a60fd91990b2486e99dc2f535c926a5d8c | Python | 20,731 | 538 | import numpy as np
import random
import pandas as pd
import sys
class NoOtherItemsError(Exception):
"""Error that shows that list has only 1 item and randomly choosing will not produce something else
"""
pass
def generate_stimulus_sequence(num_blocks, numerosity, stimulus_types):
"""Generates a sequ... |
0dceb75b63058e4110b2c1997fc756571a3551098a7b9778b88c1cb524aad78b | Python | 20,732 | 448 | """
Spatial Gene Discovery Module for HarveST
Discovers domain-specific spatial variable genes using random walk with restart (RWR)
"""
import numpy as np
import pandas as pd
import scanpy as sc
import os
import random
import scipy.sparse as sp
from tqdm import tqdm
from pathlib import Path
import warnings
from typing... |
0ae60beb12ebaeafbadf7fa3c50e038d218567902ccdcd19adad610dee24e775 | Python | 20,756 | 569 | #Lucas Sancéré
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
import lightgbm
import numpy as np
import xgboost
import yaml
from attrdictionary import AttrDict as attributedict
from sklearn.metrics import balanced_accuracy_score
from sklearn.model_selection import StratifiedKFold
from histo_mine... |
0f47cd619b40e7b5e65dc4fed1536887d4a2d8c6738d4c1e889938244e951f5e | Python | 20,762 | 462 | # This file contains a bunch of functions from Benjamin's lab2im package
# (it's just much lighter to import...)
import os,sys
import nibabel as nib
import numpy as np
import argparse
from scipy.interpolate import RegularGridInterpolator as rgi
from scipy.ndimage import gaussian_filter as gauss_filt
from scipy.ndimag... |
4d091cf6ce000a4df78d5b0c5a4f497d9d07986cf255019a4f9c1522b890d3c1 | Python | 20,765 | 655 | """octagonal maze linearization"""
import copy
import numpy as np
from ..core import _analogsignalarray, _epocharray
from ..auxiliary import _position
from .. import utils
from ..plotting.core import colorline
# TODO: linsmooth(), wrap(), unwrap(), assign_segments(), lin_to_ideal(), ideal_to_lin(), 2d_to_ideal()
d... |
6c4377478cbe48fcdf2ca8673d54283fd4fbd02b65a6262248348075a8dd09dc | Python | 20,769 | 445 | #!/usr/bin/env python
# coding: utf-8
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch_geometric.nn import GCNConv
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold
from skl... |
ed01fb855e7ccb2b283db05e2ac7aa877d8ecc15de59880dc2f1d19a4d74d808 | Python | 20,770 | 446 | #!/usr/bin/env python
# coding: utf-8
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch_geometric.nn import GCNConv
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold
from skl... |
deeb82096da921ca910adbf75512b58b743436db260e43a8708635344754419c | Python | 20,771 | 400 | import numpy as np
import pandas as pd
import numba as nb
from numba import jit
import collections
import random
from .data_downsample import downsample_cell,downsample_matrix_by_cell
from .data_augmentation import random_augment,random_augmentation_cell
import logging
# logging.basicConfig(level=print,
# ... |
e1f06e829f5c8211b603d5745407426ecad4005bfc525f276e69c02a779f3217 | Python | 20,777 | 504 | import keras
import keras.backend as K
import keras.layers as KL
import numpy as np
from keras.models import Model
import nibabel as nib
def unet(nb_features,
input_shape,
nb_levels,
conv_size,
nb_labels,
name='unet',
prefix=None,
feat_mult=1,
poo... |
e36bd5e02a575f70b49336e4102f13923a98213c332e45dbf9ddeccf49d94ed6 | Python | 20,787 | 440 | import numpy as np
import os
import tempfile
from ivim.models import NO_REGIME, DIFFUSIVE_REGIME, BALLISTIC_REGIME, INTERMEDIATE_REGIME
from ivim.sim import noise
from ivim.seq.lte import calc_c, G_from_b, MONOPOLAR, BIPOLAR
from ivim.io.base import write_im, read_im, write_bval, write_cval, write_time, write_k
from iv... |
053156468fa07e97787bdaf95540b00c10e7ee87c09b1b16bd60d50bacec9946 | Python | 20,823 | 464 | import numpy as np
import pandas as pd
import json
import os
import networkx as nx
import pickle as pkl
from collections import Counter
import matplotlib.pyplot as plt
import random
import jieba
from snownlp import SnowNLP
from wordcloud import WordCloud
import matplotlib.font_manager as fm
path1 = r'E:\毕业论文\数据集\台湾选举... |
4b2fb65dc8124073778c92a3eacc8f286a646d3a8225a43e5357228ba124fa5a | Python | 20,826 | 456 | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import sem
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib
matplotlib.use('TkAgg')
def plot_3d_line(ax, arr, set_color='lightblue', set_alpha=0.5, set_linewidth=0.5, set_label='None'):
for i in range(arr.shape[0]):
... |
b568bbc82daa791d221488a676cffecc237531571e58bd159a2fd793edb1fe2e | Python | 20,838 | 551 | """
Module to define updates for non-conjugate matrix factorisation models.
Reference: 'Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorisation models' by Seeger and Bouchard (2012)
PseudoY: general class for pseudodata
PseudoY_seeger: general class for pseudodata using seeger aproach
Poi... |
ef7d89fcc007440f233e356fb9900e43460089ec2aeadb6783be1fa32998b9da | Python | 20,847 | 563 | from typing import Optional, Union, Tuple
from anndata import AnnData
import numpy as np
import pandas as pd
from matplotlib.axes import Axes
from matplotlib import colormaps
from matplotlib.collections import LineCollection, PolyCollection
from matplotlib.colors import Normalize
from matplotlib.patches import Patch
fr... |
48ae6d1af6723ae509df85c8aeccd5a498beda47827cade4658509a0262c4274 | Python | 20,848 | 575 | """
(C) Copyright 2021 IBM Corp.
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
dis... |
03c60e7ec929d39ad4e8d474a9045911a81430bf48eeae40c41393350127849f | Python | 20,852 | 519 | import argparse
import os
import shutil
import sys
import time
import warnings
from random import sample
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn import metrics
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from cgcnn.data im... |
fb432cf8f20c00777e3d1ab780e8a80eb3494b0e856e93b2deb65a116d50f4c2 | Python | 20,863 | 489 | import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import numpy as np
import logging
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
class ModelConfig:
batch_size = 20
depths = 5
filters_root = 8
kernel_size = [7, 1]
pool_size = [4, 1]
dilation_rate = [1, 1]
class_wei... |
3362cf0fb8b4c5851ab0455e20dab0b42f3c66b02ba01c94fa4a03bc624ff7c3 | Python | 20,867 | 528 | #pylint: skip-file
#type: ignore
"""
Implementation of the simple task
"""
import time
import math
import enum
import typing
import asyncio
import logging
import datetime
import numpy as np
import os
import stl
from ..qt import *
from . import task_context
from .widgets import Form, ListAsTabsWidget
from .util impor... |
986986d7feb0105d55ddba49d66ddbe8907c17aa6f7cbee0257961a831fe77ed | Python | 20,888 | 455 | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import sem
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib
matplotlib.use('TkAgg')
def plot_3d_line(ax, arr, set_color='lightblue', set_alpha=0.5, set_linewidth=0.5, set_label='None'):
for i in range(arr.shape[0]):
... |
2c16afec5b16778e4eec1c798f855fa423bbd4873d8d5d32a2a998c9f7a1708c | Python | 20,890 | 631 | """Train an ABMIL model for single- or multi-task regression.
The inputs to the model are bags of embedded histology patches.
The outputs of the model are scalar values representing the continuous predictions.
"""
from __future__ import annotations
import dataclasses
import json
import math
import random
import shu... |
964d34b0c4efba45d58d516cee1c077fad4dd9c45c038ed56abdf37d9effdbdb | Python | 20,896 | 510 | import sys
import os
import pandas as pd
import pyreadr
import torch
import glob
import functools
import random
import yaml
import torch_geometric.transforms as T
import torch.nn.functional as F
from math import ceil
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.loader import DenseDataLoad... |
79c2271f2901f42cd4aafbc17a5f23bb17fa0b641950e7ce2b509fbac8a969d7 | Python | 20,905 | 451 | import sys
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from datetime import datetime
import torchvision.models as models
import scipy.io as io
import os
from pathlib import Path
import argparse
import scipy.stats as stats
import random
import json
from tqdm import tqdm
os.environ[... |
2bb186bb0c384e3c6ecbb2ab2083ab8029fe1fe13b296456411dfcf934b56560 | Python | 20,907 | 593 | import abc
try:
import anndata
except ImportError:
anndata = None
import batchglm.api as glm
import dask
import logging
import numpy as np
import pandas as pd
import scipy
import scipy.sparse
import sparse
from typing import Union
from .det import _DifferentialExpressionTestSingle, DifferentialExpressionTestWa... |
f4465315f06f77b80b9b5dce27e8f9729f1208131cf193bd819da40a08e42626 | Python | 20,914 | 449 | #!/usr/bin/env python
# coding: utf-8
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch_geometric.nn import GCNConv
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold
from skl... |
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