sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
d663a33ae24d19749ddd3ed71069ccd5713034b5eddcd764e8659e7c0c07ae35 | Python | 8,948 | 243 | import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from torch_geometric.utils import degree
import math
### GIN convolution along the graph structure
class GINConv(MessagePassing):
def __init__(self, emb_dim):... |
c06e36ad5a47109682db93f1c4d2cdff9379777df0c35d69bdb807d5c07e54aa | Python | 8,952 | 237 | import torch
import torch.nn as nn
from collections import OrderedDict
import math
from .Maxout_Conv2d import (Maxout_Conv2d,Maxout_Conv2d_BN)
#eps=1e-03
#momentum=0.003
eps=1e-05
momentum=0.1
downsample=32
# branches=4
def conv_bn(inp, oup, stride):
return nn.Sequential(OrderedDict([
('1conv',nn.Conv2d(i... |
16df6cab6ee1ab09b4d3919cbe62c6c271a82a860ac9609011a394faef186925 | Python | 8,954 | 255 | # Benchmark script for LightGlue on real images
import argparse
import time
from collections import defaultdict
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch._dynamo
from lightglue import LightGlue, SuperPoint
from lightglue.utils import load_image
torch.set_gr... |
931aaf5ac05672807cb963e8d7a0704ad6ce08831f8903e8b4eb673e2e4ddfa4 | Python | 8,956 | 284 | from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base import WaveformModel
class DeepDenoiser(WaveformModel):
"""
.. document_args:: seisbench.models DeepDenoiser
"""
_annotate_args = WaveformModel._annotate_args.copy()
_annotate... |
9c76a52c2a01d17cc2a57efe18b5382c7d179aaeafa3ae47aa63687bf84218c8 | Python | 8,956 | 184 | """ Test Generating Custom Conductance Inputs
Author: Aaron Miller
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig Germany
Brain Networks Group
Generates synaptic conductance timeseries for AMPA, NMDA, GABAa, and GABAa synapses based on biexponential functions
convolved with a crosscut of the Axon... |
7e1f1128c5ea88f6ca1e06843ffb5c14c9ff91062dcf11fea76a7cc367d3db7e | Python | 8,958 | 206 | import glob
import numpy as np
import os.path
import pandas as pd
from PIL import Image
from fuse.data.ops.op_base import OpReversibleBase
from fuse.data.ops.ops_read import OpReadDataframe
from fuse.utils.ndict import NDict
from skimage.io import imread
from skimage.transform import resize
from typing import Optional,... |
a3d739e7fbe35f102842f1d70c19c41fc68dd89006d9efc5d9b0f931d10ec238 | Python | 8,960 | 303 | # -*- coding: utf-8 -*-
from dipy.io.utils import get_reference_info
import json
import nibabel.orientations as ornt
import numpy as np
from numpy.lib.index_tricks import r_ as row
import pickle
RAS_AXES_NAMES = ["sagittal", "coronal", "axial"]
RAS_AXES_COORDINATES = ["x", "y", "z"]
RAS_AXES_BASIS_VECTORS = ["i", "j... |
d55afd5eca25dc8a6ac07ddcd17d5a0338be4f424b2c9f9236c0a51d7e7da15f | Python | 8,962 | 254 | """``Isocortex3dProjector``: slab geometry and the three thickness types."""
import numpy as np
import pytest
from ccf_streamlines.projection import Isocortex3dProjector
def _projector(mini_ccf, **kwargs):
kwargs.setdefault("hemisphere", "left")
return Isocortex3dProjector(
mini_ccf.view_lookup_file... |
2c2a4cf81f443e9c982b07fd48a5014d105123a181b45bc88d856db0484f7024 | Python | 8,963 | 258 | import math
import numbers
from PIL import Image, ImageOps, ImageEnhance
import numpy as np
import random
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion
from scipy.ndimage import maximum_filter
class Compose(object):
def __init__(self, transforms):
self.transforms = transfo... |
06fc74ddc684f48b7225410315483ad2d39edbd76327942ed8031f9e42d262a3 | Python | 8,965 | 208 | """The toolbar, and what happens to it when the window is not wide.
A QToolBar that does not fit folds its trailing items into an overflow chevron.
That is fine for Tutorial. It was not fine for the Mode selector, which sat at
the far end behind an expanding spacer and a logo: adding two actions during the
GUI reorgan... |
640219a4228a9b467fb349b1078a8e2a4cf8601e57d976b8e4d64089a0e09af3 | Python | 8,965 | 242 | import copy
import pickle
import unittest
from typing import TypeVar
from cvat.apps.engine.lazy_list import LazyList
T = TypeVar("T")
class SliceGetter:
def __class_getitem__(cls, slice_: slice) -> slice:
if not isinstance(slice_, slice):
raise TypeError("Use slice_getter only for slices")
... |
81b1d1f7a99186d07266fb7f503f0af176fea506d8c877dcecefe5ebf2a376fb | Python | 8,969 | 222 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import base64
from pathlib import Path
from typing import Any
import cv2
import numpy as np
from PIL import Image
from ultralytics.utils.checks import check_requirements
class LLM:
"""OpenAI-compatible large... |
abe85ed919da850dc027b069fb6b6b6fc8c59d284379cff1a717fc246cfafedb | Python | 8,969 | 223 | # Model validation metrics
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from . import general
def fitness(x):
# Model fitness as a weighted combination of metrics
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (x[:, :4] * w).sum(... |
dbc16946a97d16dfaabeba74fa99dc20fa5a32417525e722d6e8b265a2ceb0c3 | Python | 8,971 | 198 | # (C) Copyright 2019 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... |
1b8a166e9d6e9ccde7ead215504645f93ff9b400b9f4dfa995538e9c53e3e7e1 | Python | 8,979 | 271 | # -*- coding: utf-8 -*-
"""
Pleiotropy comparison with paper of 2024
"""
#Study of genes from paper automated profiling of gene function during embryonic developmental
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.cluster.hierarchy import dendrogram
from matplotlib... |
d1f9f4a9732adfa26b56a3196476373c6340f5faeedc46d84cab774e2e6631bd | Python | 8,982 | 245 | # Copyright (c) 2020, Soohwan Kim. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... |
d2f058e0ba291d4383e9b7cf236a69f822b138a03ee6562b73638e9d630cf6ec | Python | 8,983 | 274 | # type: ignore # This file is adapted from external source and kept untyped
"""
Adapted from: https://github.com/Liuhong99/Sophia - Sophia: A Scalable
Stochastic Second-order Optimizer for Language Model Pre-training
Original Author: Liuhong99
Notes: This version may include modifications.
License: MIT License
C... |
decb522d188d423cc0bda34d6dac8d87863f2806a8f31dcd0ce2ca892803d417 | Python | 8,985 | 267 | """
Utilities for working with the local dataset cache. Copied from AllenNLP
"""
from pathlib import Path
from typing import Tuple, Optional, Sequence, cast
import os
import base64
import logging
import shutil
import tempfile
import re
from urllib.parse import urlparse
import mmap
import requests
import zipfile
import... |
11d0231456ace446977ccc9dea76349e0d8cc33afeeda32e5abb798d4225cc9e | Python | 8,990 | 221 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 4 02:30:50 2024
@author: aka2333
"""
# In[]
import numpy as np
import scipy
from scipy import stats
from scipy.stats import vonmises # circular distribution
from scipy import ndimage
import pandas as pd
# basic plot functions
import matplotlib as mpl
i... |
02e05870266cc1d2c49f9fffec85d467980d865479e941616f16bc94bf990dd2 | Python | 8,994 | 218 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Martin J. Kuehn, Wadim Koslow, Annalena Lange, Khoa Nguyen
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use... |
d74c28fc5b5011e6f6bffad89e1c235efee2faabdb0c21ec9f2cb4a3a1117906 | Python | 8,994 | 194 | '''Set of functions to construct a graph as a combination of
smaller subgraphs (of aparticular shape, defined in the shapes.py file)
'''
import graphwave
import math
import networkx as nx
import numpy as np
from shapes import *
from graphwave.utils.utils import *
def build_structure(width_basis, basis_type, list_shap... |
11de0e786792be3a813c26f83548a04f87072cb3fa28fb79a984cb3ff822fdfd | Python | 8,995 | 260 | import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from typing import Dict, Tuple, Optional
from dataclasses import dataclass
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from src.solvers.pemfc_fitter import PEMFCFitter
from src.solv... |
5a1d58fac54de5aaa53936826a0df9a2b1d497da0ec82c5af0db6b7c7ac81f73 | Python | 8,996 | 275 | # Kai Sandbrink
# 2022-03-09
# Various utility functions
import matplotlib.pyplot as plt
import numpy as np
import yaml, os, copy, math
from datetime import datetime
# %% GENERAL FUNCTIONS
def sigmoid(x):
return 1 / (1 + math.exp(-x))
def combine_tuples(tuples):
'''
Arguments
---------
tuple... |
9e71dd39207b0c2c3a736f4d054fc8812d93d4bf1c856419baf2650d19936373 | Python | 8,997 | 188 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Module for alteration of synapse property values."""
import numpy as np
from connectome_manipulator import log, profiler
from connectome_manipulator.access_functions import get_node_... |
097f3bce0bde06c5f0bf218421e843b5b20ffb54732f1729128392e96f7ed231 | Python | 8,998 | 217 | from PyNite import FEModel3D
import numpy as np
from matplotlib import pyplot as plt
import sys
from time import time
import pickle
alp = 3.14/6 # needle tip angle
E = 35*10**3 # Elastic Modulus of Needle (Nitinol, MPa)
G = E/(2*(1+.33)) # Shear Modulus of Needle (Nitinol, MPa)<
outer_diameter = 1.270 # mm
inner_diame... |
3452cd68849995526cab58c206bdf84d6529350890d7a3953f03769fbaece3e2 | Python | 9,004 | 320 | import datetime
import logging
from pathlib import Path
from typing import Tuple
import hydra
import numpy as np
import pandas as pd
from obspy.geodetics import gps2dist_azimuth
from pyrocko.modelling import eikonal
from tqdm import tqdm
logger = logging.getLogger("benchmark")
@hydra.main(
version_base=None, co... |
42dcc05ea9071b83d427f8db4398fbcf31bb2471eca8b9c2abca1d3df16fd4f8 | Python | 9,007 | 185 | import numpy as np
import sys
import pandas as pd
import glob
import os
print (os.getcwd())
from matplotlib import pyplot
import argparse as argp
from GrpPlotUtil2 import group_to_word, read_IDfile
def ArgParserSyn(commandline,do_exit):
parser = argp.ArgumentParser()
parser.add_argument('IDfile', type=str)
... |
1bc00c0ae559bbb210a9fba5dbedade87a02d1855f0ad35da3ea9750073594d5 | Python | 9,008 | 206 | #!/usr/bin/env python
# coding: utf-8
'''
Dependencies:
pip install pandas numpy matplotlib scipy requests nrrd voxcell
External dependencies: calculate_density_mm3(), get_all_filenames(), get_csv_filenames(), extract_prefix_from_filenames()
chmod +x script.py
In this code we estimate densities:
- density1: official... |
84ad1cc55a721c11a8b37e318ce25495e6b8a5cba733f30c5f02219ecebfd495 | Python | 9,013 | 220 | #!/usr/bin/env python
"""
Interface to a Logitech gamepad 310 joystick. This will also
work with some other Logitech joysticks.
The joystick needs to be in "direct input" mode.
This is done using the switch on the back of the joystick.
Hazen 9/12
Jeff 9/12
"""
import pywinusb.hid as hid
class Gamepad3... |
a7de43e944e2330bcb66d797e07e098c0ae8940b6eb94e7a65aee14ccb80f5ec | Python | 9,016 | 215 | # -*- coding: utf-8 -*-
"""TODO"""
import numpy as np
from scipy.special import expit
from scipy.stats import logistic
import core.core_global as core
from core.core_definitions import simulation_timestep
from core.connection import Connection
from core.euler_trace_2d import EulerTrace2D
class RewiringConnection(Co... |
f42d2556e4a4f221e7726dee84b98bc3c00638324b53bd150772c88f255f84e9 | Python | 9,016 | 219 | # to avoid cluttering: new functionsfile (many functions in functionsPCA are depricated)
# contains less functions, more CPU friendly
import numpy as np
import pandas as pd
import pywt
from functions.algebra import unitvector, unitvector_space, angle2vec
### IMPORT ###
def cwt_signal(signal, scales, dt=1/30,
... |
cf2fb0fa7ca5488e7c53ccc85407863b82ac4fd948be88d9fe506c9dd5be3955 | Python | 9,018 | 183 | #!/usr/bin/env python3
"""Stage 3 -- metrics, participant-level inference, and manuscript tables.
Reads the prediction files written by stage 2 and emits:
tables/main_results.csv discrimination + calibration, with bootstrap CIs
tables/operational.csv false alarms per hour and warning lead time
ta... |
e889d38bab75a7dff778523e60fa1945f623a282dbde399eb8511a4453df58cf | Python | 9,023 | 216 | import numpy as np
import skimage.io as skio
import glob
import os
from natsort import natsorted
import skimage.io as skio
import json
import shutil
from tqdm import tqdm
def seed_everything(seed):
np.random.seed(seed)
def all_zero(lb_file):
'''
Check if the label is all zero
Args:
lb_file: la... |
e8c6e3251160a2ff76f322be8c6368e23b583dc9807372ecb7dce96efe355bfc | Python | 9,023 | 163 | import chromatics
import numpy as np
import pandas as pd
import scipy.stats as stats
def get_enrichment(a, b, c):
a_with_c = chromatics.bedtools('intersect -sorted -u -f 1.0', a, c)
b_with_c = chromatics.bedtools('intersect -sorted -u -f 1.0', b, c)
ct = np.zeros([2, 2])
ct[0, 0] = len(b) - len(b_with... |
ba4e03d0291a1acad3c38f178500efd8c40e3219300fbba7278008e20ae5205c | Python | 9,025 | 258 | import torch
import torch.utils.data as Data
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, r2_score
from train_model import SpectrumDataset, CONFIG, setup_seed
from model.network_947331 import Network
import model.network_947331 as netwo... |
cf04784619fcadfdb0cb46e50eb25b07b034559a0743ed9dd9cef0a1eafd7f1c | Python | 9,025 | 272 | from __future__ import annotations
import multiprocessing
from typing import Optional, Tuple, Union
import cv2
import kornia
import numpy as np
import torch
from scipy import ndimage
from sklearn.cluster import MiniBatchKMeans
Box = Tuple[float, float, float, float]
def normalize_min_max(image: Union[np.ndarray, t... |
f8b10875e730267930fc35006ece1d83eaca78b3e81aa9c8d371db907bd70992 | Python | 9,026 | 124 | """ This script investigates the features within the components of the trained neural network.
This version's cropping tries to avoid the radar echo to be included.
"""
import os
import argparse
from utils.env_config import setup_environment
import timm
from data.dataloader import prepare_dataloaders
from model_an... |
aa3f7b479d7b57d494d59b52e90b5c1cf960b40a18ec16c39c1d62a70bc1c380 | Python | 9,028 | 148 | """Small synthetic checks; no OpenPlant images or downloaded weights are used."""
import csv
import importlib.util
import json
import tempfile
import unittest
from pathlib import Path
from openplant.train_utils import TrainConfig, config_dict, read_classes
ML_AVAILABLE = all(importlib.util.find_spec(name) for name i... |
d691628652d3ea9658b736d40c9cdc5af14fb1c21f4b881c60f340e131e95987 | Python | 9,029 | 201 | import random
import math
from typing import List, Mapping, Optional, Union
import scanpy as sc
import numpy as np
import pandas as pd
from anndata import AnnData
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from GEASO.plot_utils.color import *
def get_color(n=1, cmap: str = 'scanpy', seed: int... |
9292db23d7af765086714d4c613d908d0c6df22395aedff7d5ff70140dd5213a | Python | 9,030 | 212 | """CAT-NAP activity statistics.
Ports:
- ``calTwopActivityStats.m`` → :func:`calc_twop_activity_stats`
- ``getCellTypeMatrix.m`` → :func:`get_cell_type_matrix`
The stats are the calcium-imaging counterpart of the electrophysiology
``Ephys`` struct (see ``pipeline/step2.py``); they are returned as a dict keyed
... |
c9c123c697318227a8f7b0b1cb1b7500c7f55ea63924d191a0e76aa5d568475d | Python | 9,031 | 281 | import json
import os
import sys
import tempfile
from time import time
import numpy as np
from pyrosetta import *
from pyrosetta.rosetta.protocols.minimization_packing import MinMover
from utils_ros.arguments import *
#from utils_trX2dy.utils_data import read_fasta
from utils_ros.utils_ros import *
os.environ["OPENB... |
c8e0905d5954d285467cbfaa3887cfb6b475f20eb5f8b563253203571bb727db | Python | 9,032 | 235 | import torch
import torchvision
from torchvision import models
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import os
def upsize(x,scale_factor=2):
#x = F.interpolate(x, size=e.shape[2:], mode='nearest')
x = F.interpolate(x, scale_factor=scale_factor, mode='near... |
ed90285d873b129d4d229aaae0274f453341af3240bfa8d9245a395fe74edaf4 | Python | 9,033 | 244 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
from typing import Any, List, Tuple, Union
import numpy as np
import torch
from torch.nn import functional as F
class Keypoints:
"""
Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
containing the x,... |
1f5b0bcd4bb68800a495ca556c0c456338123875bba6f93f5174b9e47d538883 | Python | 9,034 | 219 | import nipype.interfaces.utility as utility
from PUMI.engine import FuncPipeline, NestedNode as Node, QcPipeline
from PUMI.pipelines.multimodal.image_manipulation import vol2png, pick_volume
from PUMI.plot.carpet_plot import plot_carpet
from nipype.interfaces import fsl, ants
from nipype.interfaces.c3 import C3dAffineT... |
4fdf90c1b5f731daf01e564bb627e36b309eacd992e2560864c24a34b4d4945f | Python | 9,037 | 260 | # %%
import os
import glob
import random
from os import listdir, makedirs
from os.path import join, isfile, isdir, basename
from tqdm import tqdm
import numpy as np
import torch
from torch._dynamo import OptimizedModule
from torch import multiprocessing as mp
from datetime import datetime
import cv2
from skimage import... |
0682863c550128057a4ae2a1272a99e5fd23be4231181506527cde64be5383d6 | Python | 9,038 | 219 | from functools import partial
from typing import List, Tuple, Callable, Union, OrderedDict, Optional, Dict
import os.path
import os
import torch
import torch.nn as nn
from torch import Tensor
import torch.optim.lr_scheduler
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWri... |
9a3a1eea8ee563b043a5d6ecee5360a4284d93a93c46ea537296cfb9cf9fa707 | Python | 9,039 | 263 | #Aidan Fike
#July 21, 2019
#
#Updated by Kevin Schult March-July 2020 with contribution from Tim Ling
#File to create a chimera script which creates a random pdb structure
#
#Edited and revised
#Minh N. Ho, August 2022
#
#NOTE: Errors that are produced by Chimera are sent to a log file, not to
#the console. Check every... |
d4e9f46aba0badd65c8fb4338969b7c584866c6543c7fa0ac7e92fbcbde08bce | Python | 9,039 | 226 | import numpy as np
from sklearn.model_selection import train_test_split
import torch.utils.data as Data
# from orin import GP_WAITER as gp
from model import GP_WAITER as gp
import shutil
import os
import torch
from torch import nn
import torch.optim as optim
import pandas as pd
from torch.utils.tensorboard import Summa... |
06d16715402c53e5f7737679bedbc631112a977aa964f95ae4b8567742438f98 | Python | 9,046 | 295 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ###... |
c22086845e283a3f9d249f610f5041081fb853fdd974288376a26194404abeca | Python | 9,046 | 266 | from functools import update_wrapper, wraps
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
try:
from torch.optim.optimizer import _use_grad_for_differentiable, _default_to_fused_or_foreach
has_recent_pt = True
except ImportError:
has_recent_pt = False
from typing import L... |
4b138ee05ddb48d7ae80a2ba5fd25b4422f9030144295c145771afeffd889e86 | Python | 9,047 | 288 | # coding=utf-8
"""
Module to train a network using init files and a CLI.
"""
import argparse
import os
from typing import Dict, List, Tuple, Union
import tensorflow as tf
import deepreg.config.parser as config_parser
import deepreg.model.optimizer as opt
from deepreg import log
from deepreg.callback import build_ch... |
be55a555de3b6a1287f20d69cfd3ab62afc1644cdd155fbfc04ff9a2088c8701 | Python | 9,050 | 252 | # -*- coding: utf-8 -*-
#
# test_domain_decomposition.py
import unittest
import arbor as arb
# check Arbor's configuration of mpi and gpu
gpu_enabled = arb.__config__["gpu"]
"""
all tests for non-distributed arb.domain_decomposition
"""
# Dummy recipe
class homo_recipe(arb.recipe):
def __init__(self, n=4):
... |
4b2a11f339dc26b06b1e0f9ab9de8f3a4bb278f48398409a9eeabe85cc6cdb8a | Python | 9,051 | 181 | import logging
import os
from pathlib import Path
import pytorch_lightning as pl
import torch.nn as nn
from pytorch_lightning.plugins import DDPPlugin
from project.datasets.PICP.picp_dgl_data_module import PICPDGLDataModule
from project.utils.deepinteract_constants import NODE_COUNT_LIMIT, RESIDUE_COUNT_LIMIT
from pr... |
3e22fcbbc79a2a833b382bf689303ffab7ecd67e037d6ea6245c445c08f2256e | Python | 9,056 | 292 | """Brain-Score wrapper for DeepEISNN EI-SNN CIFAR10 VGG19."""
from __future__ import annotations
import functools
import json
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Iterable
import numpy as np
if not hasattr(np, "int"):
np.int = np.int32
import torch
fro... |
ac619b6e2b3b8649168dea5aa30958a1a2c6a643f77c7bd2c74c10349294993e | Python | 9,058 | 238 | import collections
import numpy as np
from matplotlib.axes import Axes
from scipy.signal import butter, lfilter
from scipy.stats import pearsonr
from typing import NamedTuple
from argclz import AbstractParser
from neuralib.io import csv_header
from neuralib.plot import plot_figure
from neuralib.plot.tools import Ancho... |
ce9a2513116b0a7aad2f191f7fd26da064a1e0fa36a3273a63ab5d4b03b5784b | Python | 9,061 | 190 | #
# Reference : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3864968/
#
# Written by SK Park , NIRAL, UNC
# 2021-04-18
import dtiplayground.dmri.preprocessing as prep
import yaml
from pathlib import Path
import os
import markdown
import numpy as np
import time
import os
# import SLICE_Check.computations as c... |
d56361948f814819e1910936fc20fc3aacde85c5e45d95d1ccaa04c614e50fd8 | Python | 9,063 | 299 | #!/usr/bin/env python
# -*- coding : utf-8 -*-
"""Adds utilities for preparing the data for eddy and analysis"""
import os # mkdir
import os.path as op # dirname, basename, join, splitext
import re # regex substitution
import shutil # rmtree
import subprocess
from ..preprocessing import util # preprocessing
DW... |
97b47441b806f869c44f9c2aed6e3979a54be951347e1fd4e55249d6ffafe4f6 | Python | 9,065 | 239 | import typing
from typing import Optional, Tuple, Union
import torch.nn.functional as F
from torch import Tensor
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import PairTensor # ... |
1a92e2a0d57365b00574b854524973ec183e0f5d7821e74c10bf108b1a58a2fe | Python | 9,070 | 273 | from typing import Literal
import torch
from torch import Tensor, nn
from torch_geometric.nn.models import GAT, GCN
from torch_geometric.nn.models.basic_gnn import BasicGNN
from torch_geometric.nn.norm.layer_norm import LayerNorm
class GNNModelSelector:
"""Helper class to select and configure GNN models."""
... |
c732662920f3f1345d813b174fc22fabf2743d3c0372887f25da26e77deb8b9a | Python | 9,070 | 159 | #!/usr/bin/env python3
from nipype.interfaces.fsl import Reorient2Std
from PUMI.engine import BidsPipeline, NestedNode as Node, BidsApp, create_dataset_description
from PUMI.pipelines.anat.anat_proc import anat_proc
from PUMI.pipelines.func.compcor import anat_noise_roi
from PUMI.pipelines.anat.func_to_anat import fun... |
e110fd92fc5f17f2ea80e6b931d4e86d005d68274c9ea7b616c90791296bb2ab | Python | 9,070 | 277 | import os
import sys
import time
from typing import Dict, Any, Optional, List, Tuple
from dataclasses import dataclass, field
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from src.llm_orchestrator.code_generator import CodeGenerator
from src.llm_orchestrator.prompt_templates import get... |
e8eb2d8cffb1a603406aaf6fb07f6997624758904e5f5d7916c41c79b9d0a043 | Python | 9,074 | 242 | # -*- coding: utf-8 -*-
"""
Example script to set up the parameters for the image processing pipeline
"""
######################### Import modules
import os, numpy, math
import ClearMap.Settings as settings
import ClearMap.IO as io
from ClearMap.Alignment.Resampling import resampleData;
from ClearMap.Alignment.Elas... |
502d09cbf69d76e142984037017592decca0533529d8c4e36737ded158f5891d | Python | 9,077 | 243 | from typing import Any, Optional, Type
import numpy as np
import torch
import seisbench
from .das_base import (
DASAnnotateCallback,
DASModel,
DASPickingCallback,
PatchingStructure,
)
from .base import WaveformModel
class DASWaveformModelWrapper(DASModel):
"""
This class is a wrapper to allo... |
ba8a3bb4e793108c1af0286fde5065b5c7ec87fdb04e37bc6290419dec546842 | Python | 9,078 | 255 | # -*- coding: utf-8 -*-
# @Author : LG
import os
from json import dump, load
from typing import Dict, Tuple
import imgviz
import tqdm
import yaml
class ISAT:
"""
The ISAT format provides a structured approach for representing image annotations.
File Naming: Each image has a corresponding .json file na... |
92227f0e7eb7f94f7ea2510666fcdab660c1746aa839477cf03349ffa62ef216 | Python | 9,084 | 263 | from typing import Literal, Iterable
import numpy as np
import pandas as pd
import pingouin as pg
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from argclz import AbstractParser, argument
from argclz.dispatch import Dispatch, dispatch
from neuralib.plot import plot_figure... |
2e50fe1b4ec1780360d88a7d24d7997f29e89369964f6ed94e7aeea85cd3c9c6 | Python | 9,085 | 205 | #-*-coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import sys
sys.path.append("../")
import unittest
import types
import jieba
if sys.version_info[0] > 2:
from imp import reload
jieba.initialize()
test_contents = [
"这是一个伸手不见五指的黑夜。我叫孙悟空，我爱北京，我爱Python和C++。",
"我不喜欢日本和服。",
"雷猴回... |
ecd413db9a6659d855c79f5eb5abf0e3391e416a8e1dfee44e0adac0580e3115 | Python | 9,085 | 182 | import torch
import argparse
import h5py
import numpy as np
import xgboost as xgb
from sklearn.externals import joblib
from multiprocessing.pool import Pool
import sys
from chinn.common import check_chrom, chrom_to_int
from chinn.data_preparation_helper import get_one_side_data_parallel, load_pairs
from chinn.train imp... |
de4fd8c68b3c901b843e72bd6c96af68a732e3107603fcf46a1fa9dab85df916 | Python | 9,088 | 279 | """
Created on 2025.04.23
Updated on 2026.02.18
Author: Lina Jeantin
Code used to train a random forest classifier to detect sleep stages from markers of iEEG complexity.
Requirements:
results: dict containing the results of spectral and complexity analysis (see get_complexity_results.py)
Organised as... |
2e0079df3c66ab9073f36f960ebe1bb297e8c067c3922154dda9bdd50f088831 | Python | 9,089 | 268 | from collections.abc import Iterable, Sequence
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any
import eir.models.output.sequence.sequence_output_modules
from eir.models.input.sequence.transformer_models import (
BasicTransformerFeatureExtractorModelConfig,
SequenceModelConfig,
)... |
2eb5ae6cbcdcfe317e7f002159032494f39bfdded0935ebd60055207f19101d7 | Python | 9,089 | 242 | """
Copyright 1999 Illinois Institute of Technology
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publis... |
44ac10bb7bbb3e22222c55133976472efe72bdf4d133ea55cc66ac2b925cdd0d | Python | 9,089 | 239 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import time
import cv2
from ultralytics import YOLO
from ultralytics.utils import LOGGER
from ultralytics.utils.plotting import Annotator, colors
enable_gpu = False # Set True if running with CUDA
model_file = "... |
e64e025d4c61a3c73f92e58188fed115032b5cb21805dfd3c6424d5045abacec | Python | 9,089 | 197 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 4 11:42:07 2025
By Guido Meijer
"""
import numpy as np
import pandas as pd
import json
from pathlib import Path
from spikeinterface import load_sorting_analyzer
from spikeinterface.widgets import plot_sorting_summary
def manual_curation(results_p... |
7a73cc1a1289ee5ad4f4d9a307aa9f410e33d35f6de0eb6505669947bb5db864 | Python | 9,093 | 279 | """Initializers for TensorFlow layers."""
from typing import List, Optional, Tuple
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import Model, layers, initializers
from tensorflow.keras.initializers import Initializer
tfb = tfp.bijectors
class WeightInitializ... |
aa4be77327fa7a30077aae9527bcfc083f47e766f30fc53688a9410c09f26c86 | Python | 9,093 | 256 | import argparse
import torch
import numpy as np
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor
from torch_geometric.data import GraphSAINTRandomWalkSampler
from torch_geometric.nn import GCNConv
from torch_geometric.utils import to_undirected
from ogb.li... |
f074926f0d3874d203cdbc385738053e62a11409b311bd8f234726f24be9ae9e | Python | 9,093 | 285 | # -*- coding: utf-8 -*-
"""
Plotting routines for overlaying labels, tilings, and sectioning of 3d data sets
Supported functionality:
* plot volumetric data as a sequence of tiles via :func:`plotTiling`
* overlay points on images via :func:`overlayPoints` and
:func:`plotOverlayPoints`
* overlay lab... |
490b13e60b7463b95fe445944b2e1d80809bfabfbfd6275d2370c2172c79fed3 | Python | 9,100 | 374 | #!/usr/bin/env python
# coding: utf-8
# In[88]:
import torch
import numpy as np
import pandas as pd
from torch_geometric.data import Data, HeteroData
from datetime import datetime as dt
from dataset import fast_merge,df_to_mask
from tqdm import tqdm
# In[89]:
node=pd.read_json("/data2/whr/czl/TwiBot22-baselines/... |
9074bda01eb9e9f53e22dfb11eb61435db26fb3a942e6478c70e9e0b1a00498d | Python | 9,103 | 322 | import json
import numpy as np
import pytest
import tifffile
from napari_dmc_brainmap.stitching.layout import (
layout_from_stage_positions,
legacy_serpentine_index_grid,
)
from napari_dmc_brainmap.stitching.stitching import (
iter_tile_pages,
load_embedded_stage_positions,
load_tile_section,
... |
0b0456c7e50ddb034e9f98d9ac5c307f59e5382e8153601f385d31a007bbb63f | Python | 9,106 | 182 | import csv
import os
from os.path import join
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
def f_score(inputs, target, beta=1, smooth = 1e-5, threhold = 0.5):
n, c, h, w = inputs.size()
nt, ht, wt, ct = target.size()
if h != ht and ... |
dacfbe751e1a86635eff9c62b25fc48b16552d2170f162146bdfffcaa553d97d | Python | 9,107 | 238 | import numpy as np
from glob import glob
import os
import pandas as pd
import re
ctx_regions = ['AUD','MO','SS','VIS']#append if you use more!!!
runsets = {'wwspont':'runC00dMP3_brain','nwspont':'runC00dMI3_brain',\
'wwresp':'runCrespZP_brain','nwresp':'runCrespZI_brain',\
'wwIBL':'runIBLPasdMP3... |
bcba3a6b55175e62fa8a32085c622a1f02fb304dbeef490c7af4dd63f03f560d | Python | 9,109 | 297 | import numpy as np
import scipy.sparse
import time
def coarsen(A, levels, self_connections=False):
"""
Coarsen a graph, represented by its adjacency matrix A, at multiple
levels.
"""
graphs, parents = metis(A, levels)
perms = compute_perm(parents)
# [print('perms len',len(x)) for x in perms... |
132a090c6773c4e891c44d91d47ed0a1af3446b8d83bc91044ea128384dd9474 | Python | 9,110 | 268 | from collections.abc import Iterable, Sequence
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any
import eir.models.output.sequence.sequence_output_modules
from eir.models.input.sequence.transformer_models import (
BasicTransformerFeatureExtractorModelConfig,
SequenceModelConfig,
)... |
b296cfe5972c438056f36b4973f09f53a0ccb3883beb872d4f0031593b5fa39e | Python | 9,110 | 264 | """This file contains generic accessors to handle getting data from core objects"""
import numpy as np
from .. import core
__all__ = ["SliceExtractor", "ItemGetterLoc", "ItemGetterIloc"]
class SliceExtractor(object):
"""
Extracts and validates slice indices for interval, series, and event dimensions.
... |
13f42e0a1ce0e0d1e9502a9808ea3c2469bc41a3deeb34d33e1086041c6033ef | Python | 9,113 | 246 | import click as ck
import pandas as pd
import torch as th
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch import optim
import copy
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from itertools import cycle
import math
from deepgo.torch_utils import FastTe... |
5bc6cb493fe2ee059aef6b6552e42481b911c3e4e5b8cc04d56ae38b33ed00b8 | Python | 9,114 | 223 | from collections import OrderedDict # pylint: disable=g-importing-member
import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrap... |
9616221106c244b43ed436bc44aa5ec51c910f52f530bcd998d0b5d4bf891dc9 | Python | 9,114 | 224 | """The queue page of the Run tab: several saved analyses, run one after another.
The Run tab's other page configures *one* run. This is for the other case — a few
different analyses to get through, and nobody wanting to sit up for the
handovers. Each entry is a parameter file saved from the toolbar, so building a
queu... |
a4ec63dc2f510b3d653cbc51a9f6ea675f39950444da096a58718f7cb7dcde94 | Python | 9,114 | 199 | import logging
from dipy.core.gradients import (gradient_table,
unique_bvals_tolerance, get_bval_indices)
from dipy.io.gradients import read_bvals_bvecs
import nibabel as nib
import numpy as np
from scilpy.dwi.utils import extract_dwi_shell
from scilpy.gradients.bvec_bval_tools import... |
0cf09ba0d1724b1656be1beb3ebc2064545e8b677b64b1c36bbf36c7ba7f696d | Python | 9,118 | 266 | """
Methods for evaluating causal inference models.
(C) Copyright 2019 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 ... |
1756a6c59fa45f664d149c12711008821f9d697062aeb513cf721f68f7a92119 | Python | 9,119 | 270 | # commonly used functions
# updated to Python 3
import sys
import subprocess
###############################################################################
def read_fasta_file_to_list(fastaFile):
myDict = {}
inFile = open(fastaFile,'r')
line = inFile.readline()
line = line.rstrip()
if line[0] != ... |
db022685550649fca397e51accf59a808ec7789244cef755f9f7ff110dfd5c6a | Python | 9,119 | 251 | "tests for bx.align.epo"
import random
import unittest
import numpy as np
from bx.align._epo import (
bed_union,
cummulative_intervals,
)
from bx.align.epo import (
Chain,
EPOitem,
)
class TestBed(unittest.TestCase):
def setUp(self):
self.N = random.randint(1, 1000)
def test_ci(sel... |
cc44ae4c8da43892aaa201d480b7e6bb9aaa0c5da07caa852a5c9ad26fb106b2 | Python | 9,122 | 255 | import numpy as np
from collections import Counter
from functools import reduce
# Finding Traces
# Saving SHAP of all layers
def N_top_func(layer,c):
percent = 0.1
positive = 0
N_tops = 0
temp = [] # to save and find the min N top among all N tops of training images
# load SHAPs values for a layer across ... |
cd11ef4bb37d3f56d23dd397f23f2f8e9ef10fb87448cb043737631c3b27cd13 | Python | 9,122 | 284 | import os, sys, math, random, argparse, numpy as np
import bpy
import bmesh
import blender_util as bu
import json
from math import radians
from mathutils import Matrix
obj_types = [0, 1, 2, 3, 4]
textypes = [0, 1, 2, 3]
pointangle = 3.5
def angle_rad(x):
return np.dot(x, (np.pi/180))
## check if an object exist ... |
cd7b9947db2e8f51a57a2030aed4c8cfc8f703b4817a7d5aa96453f52c963819 | Python | 9,123 | 225 | # In general_utils.py
__all__ = ['get_transient_timestamps_mod', 'calculate_auROC', 'significant_modulation_proportion', 'generate_labels_from_cv', 'calculate_auROC_with_permutation']
import numpy as np
import scipy.signal
from scipy.signal import argrelextrema
from sklearn.metrics import roc_auc_score
from random imp... |
0d95b7ee57a3f8acbdebd052f5ae0163affd966fa9e8e83b829537c6f769c684 | Python | 9,124 | 225 | from collections import OrderedDict # pylint: disable=g-importing-member
import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrap... |
9628224eb15791ff5e3aa9e2a02b24e8e2f06e314cf2973f19c0daf372bb1952 | Python | 9,124 | 291 | from collections.abc import Sequence
from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
import pytest
from eir import train
from tests.conftest import should_skip_in_gha_macos
from tests.test_modelling.test_modelling_utils import check_performance_result_wrapper
if TYPE_CHECKING:
pass
... |
f6817417f92e1dc750098be0c46d6cbc4dde80d35bac48b1571a3fe16c9eb028 | Python | 9,124 | 296 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import hashlib
import hmac
import json
from copy import copy
from http import HTTPStatus
from typing import TypeVar
import requests
import rq
from crum import get_current_request
from django.db.models import Model
from django.http import HttpRequest... |
97f2a9df84a4e1124774218a56aed689f23e545b6841946f10711295b2527b81 | Python | 9,126 | 236 | """Predictions from single folds.
Predictions are generated by predictors for causal models. They contain the estimates
for single folds and are combined in the EvaluationResults objects for further analysis.
"""
from collections import namedtuple
from typing import Union
import warnings
import pandas as pd
from ..ut... |
b2080a55e58f4f1a8161af40f3168426917a86f314a32f5f740b290ec2110ce7 | Python | 9,126 | 198 | #!/usr/bin/env python
"""
BundleParc: automatic tract labelling without tractography.
This method takes as input fODF maps and outputs 71 bundle label maps. These maps can then be used to perform tractometry/tract profiling/radiomics. The bundle definitions follow TractSeg's minus the whole CC.
**IMPORTANT**: fODF i... |
2f03cdb8ec90a5fb90cee34f90d2f5f03e4a3b4ba07f39d09055952e55e363bf | Python | 9,128 | 212 | from .utils import *
from .rclone import rclone_get_data,rclone_upload_data
from .slurm import has_slurm, submit_slurm_job
from .uge import has_uge, submit_uge_job
def load_plugins():
an = dict(files = glob(pjoin(
labdata_preferences['plugins_folder'],'analysis_*.py')))
an['names'] = [os.path.basename(... |
47ede1dc595d19747e3c2aa5afa9cbcc5dfa8f5831f0cad26f219b42e8c5f607 | Python | 9,128 | 239 | import importlib
import pytest
import numpy as np
from nems import Model
from nems.models.dataset import DataSet
from nems.layers import WeightChannels, FiniteImpulseResponse, DoubleExponential
from nems.layers.base import Layer
def test_constructor():
# No required args
model = Model()
assert len(model... |
89f1c5e4016006acd7ee0276c2e513e393e3d4b915a3d976184de3d36ea27b04 | Python | 9,130 | 181 | """
Script to perform gene set enrichment analysis. Examples in run.py as thresh_loadings_and_gsea_*(.., do_gsea=True, ..)
"""
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
import gseapy as gp
import os
from end2endPLS import BasePLS, ThresholdPredictiveWeights, ThresholdPredictiveCompo... |
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