sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
121b63923241eaa0c334a63092546c4940382a234b45efc1bcbf2b7d796ba2a7 | Python | 4,068 | 97 | """Tests for the OBJ/MTL/colormap writer (numpy + matplotlib; no vtk needed)."""
import os
import tempfile
import numpy as np
from surface_morphometrics import export_obj as eo
def _mesh():
pts = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [2, 0, 0]], float)
faces = np.array([[0, 1, 2], [0, 2, 3],... |
f5243c92f543868332ba0fde98b5c84daeb2df3b6e37517ed3575dc8fd524562 | Python | 4,068 | 105 | import subprocess
import os
import glob
import pandas as pd
import multiprocessing as mp
import subprocess
import argparse
def read_result(xinput):
index, path = xinput
print(f" * {index}: {path}")
df_ind = pd.read_csv(path, sep="\t")
df_ind.rename(columns={'bam': 'sample_id'}, inplace=True)
df_i... |
55ce148525db03648f0837ab5cda9be634922e47a5fcaaaf7d3b9ca65d9bdf94 | Python | 4,070 | 123 | # -*- coding: utf-8 -*-
"""
Outlier detection
This script looks for outliers in a functional time series using the Afni
function 3dToutcount. The fraction of outliers found within a volume defined by
a mask are written out for each time point. Additionally, a graphical
visualization, a regressor of no interest and a s... |
f8c2a72a011af7679edb68e84b6bf2506285e1559d405914506225c9c594df0c | Python | 4,070 | 124 | # -*- coding: utf-8 -*-
"""
Simulation study regarding the hemodynamic response function
Joram Soch, MPI Leipzig <soch@cbs.mpg.de>
2023-08-24, 19:20: first version
2023-08-28, 10:15: minor changes
2023-08-28, 15:38: corrected derivatives
"""
### Step 0: specify settings ##############################################... |
48dfae5d71adf44e910c1f20c5f5eca55ca0a3232fa826db619b40e6a349c7df | Python | 4,071 | 113 | """Contains the command-line interface for the ViRelAy application."""
import argparse
import atexit
import os
import flask
from virelay import __version__
from virelay.model import Workspace
from virelay.server import Server
def run_cli_app() -> None:
"""Runs the application with a command-line interface (CLI... |
6142808fcc1e967801a173b056874a5c07eda40b5dfca24139f91f6b1ba8c060 | Python | 4,072 | 105 | import os
from argparse import ArgumentParser
from dataset import SATARDataset
import numpy as np
import time
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from model import SATAR, BotClassifier
from utils import calc_metrics
device = torch.device('cuda' if torch.cuda.is_available() else '... |
b1f8b8d4caade843f076d90c6e8b59788e3749f433551ac5a1e4971833bce13c | Python | 4,073 | 129 | import logging
from collections import defaultdict
from torch.utils.data.sampler import Sampler
import random, torch
from flair.data import FlairDataset
log = logging.getLogger("flair")
class ImbalancedClassificationDatasetSampler(Sampler):
"""Use this to upsample rare classes and downsample common classes in ... |
3bdefe6b2d455c2cd57b318c6e371d77ba18f694f508b50540de77502ab3fc35 | Python | 4,074 | 113 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
import sys
from collections import deque
import time
import logging
import math
from deepgo.utils import FUNC_DICT, Ontology, NAMESPACES
from deepgo.metrics import compute_metrics
logging.basicConfig(format='%(levelname)s:%(message)s', le... |
8372fed77678b77ef29cf3e67f255cfe5b25a245fc106a7807abf487006a21a1 | Python | 4,074 | 101 | """Why a bundle shows no peak-detection traces.
uv run python python/diagnose_bundle_traces.py <run>.meanap
Reads the bundle's own manifest and params, so it answers the question without
the output folder, the raw data or the run's log.
"""
from __future__ import annotations
import sys
from pathlib import Path
... |
f8d0119c4f61516c3008deeee782593e148224f9eb941bbdf3fd62011105d06e | Python | 4,074 | 123 | from obspy.clients.fdsn.mass_downloader.domain import (
CircularDomain,
Domain,
RectangularDomain,
)
from obspy.geodetics.base import locations2degrees
class RectangleDomain(RectangularDomain):
"""
A rectangular domain defined by latitude and longitude bounds. Edges are included in the domain.
... |
be208f660a0db77af8c532f711b6cb1cb4cc606a8e541d819df051bc637857e7 | Python | 4,075 | 124 | # -*- coding: utf-8 -*-
"""
Simulation of imaging MTF
The purpose of the following script is to simulate the imaging modulation
transfer function from a GE-EPI from imaging of a thin slid. The slid is
parallel to the x-direction (read direction). The following steps are done:
1. generate input image
2. simulat... |
bfb3bb04c0600c0199373a8c2ba87d7ba33f9b0f357903155eb5f4bef9a6860f | Python | 4,075 | 97 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from copy import copy
import torch
from ultralytics.data.utils import get_split_fraction
from ultralytics.models.yolo.detect import DetectionTrainer
from ultralytics.nn.tasks import RTDETRDetectionModel
from ultra... |
1e24a8774390f530dadef9ada79035edbf1954ed4cae1703bb53d0f2f06dd9a7 | Python | 4,076 | 114 | from typing import Any, Optional, Sequence, Tuple, Union
import torch.nn as nn
from monai.utils import ensure_tuple_rep
from monai.networks.nets import UNet
from monai.networks.blocks import Convolution, ResidualUnit, SubpixelUpsample
from monai.networks.layers import Act, Conv, Norm
from monai.networks.layers.simplela... |
9f7900e5a63cc8651d73db630bc83a0c3eb81f60d6c61672e085a89c64889c06 | Python | 4,076 | 118 | import numpy as np
from scipy.spatial.transform import Rotation
from scipy.interpolate import RBFInterpolator
from sklearn.kernel_approximation import Nystroem # Nyström 近似
from sklearn.neighbors import NearestNeighbors
def kernel_interpolate(
Y, # (N, D) target points
y, # (M, D) source points
... |
0972c8b15594e518c15876c3993e78e481e44ac3cf1386504ab1c79cbb4fe670 | Python | 4,077 | 113 | from pathlib import Path
from typing import TYPE_CHECKING
import pandas as pd
if TYPE_CHECKING:
from eir.setup.output_setup import al_output_objects_as_dict
def check_performance_result_wrapper(
outputs: "al_output_objects_as_dict",
run_path: Path,
max_thresholds: tuple[float, float],
min_thresh... |
7f57e31777c0256808dfea7ea5e826cbbb833633cac8ccdec9be9c0eb38bfb8f | Python | 4,077 | 87 | import numpy as np
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import pandas as pd
'''
Experiment Information:
https://arxiv.org/pdf/1706.06969.pdf
- 5 subjects
- 800 trials ... |
88d136afc3b510539a57573449241c171267b18e63a1e3f6acc826dd87033ea9 | Python | 4,077 | 128 | import os
import numpy as np
import torch
from scipy.sparse import csr_matrix
import numba
import logging
import random
import spotd.simulation_st
from spotd.utils import *
import scvi
def get_scvi_latent(
st_ad,
sm_ad,
n_layers,
n_latent,
gene_likelihood='zinb... |
3cb4ae02d7c419dc3b9adccc47617e0310805c3cfa07dfee1b73816a5ff2a04a | Python | 4,078 | 118 | '''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.... |
5e7735c8edac62ee2bd5f18f9dad9106da68a4bfbcde540f19053cca3c19f095 | Python | 4,078 | 77 | import numpy as np
from collections import OrderedDict
from tqdm import tqdm
from .base import TemporalContextInferencerBase
from brainscore_vision.model_helpers.activations.temporal.utils import stack_with_nan_padding
class BlockInferencer(TemporalContextInferencerBase):
"""Inferencer that divides the original ... |
6bc1723a3e56758f49c2fffedc2e1becde6588196d01d9744783ab86d426a52d | Python | 4,078 | 125 | from .qt import *
from .observable_item_models import TreeObservableCollectionModel
from .config import ObservableDict
import os
import oslex
import shutil
import typing
import asyncio
import logging
import platform
import subprocess
from .task_controller.util import create_task_with_exc_handling
LOGGER = logging.get... |
3074c297605f854a1a415fe5c4d7cde239ec8a00cc53030282fa48ee67377f59 | Python | 4,079 | 88 | import numpy as np
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import pandas as pd
'''
Experiment Information:
https://arxiv.org/pdf/1706.06969.pdf
- 4 subjects
- 1120 images... |
6ef20c505606305c96f3ffa215fdd4f691f83473f1e8db901e4ba2c8897ee973 | Python | 4,080 | 161 | # am i optimizing prematurely? do we need this heavy machinary?
from sqlalchemy import Column, Integer, String, Float, ForeignKey, create_engine
from sqlalchemy.ext.declarative import declarative_base, declared_attr
from sqlalchemy.orm import relationship, sessionmaker
Base = declarative_base()
session = None
de... |
083f0bfcf19b7fdb4e13a7078ed03b384b76478acd17c0bcd589153f6307d35b | Python | 4,081 | 111 | from dataclasses import dataclass
from typing import Optional
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric.nn.pool.select import SelectOutput
@dataclass(init=False)
class ConnectOutput:
r"""The output of the :class:`Connect` method, which holds the coarsened
graph... |
7a309d1b635e0e37868f10c0de8807e08bf78c14f2dc5a68665b536042029d69 | Python | 4,081 | 107 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script harmonizes labels across a set of lesion files represented in
NIfTI format. It ensures that labels are consistent across multiple input
images by matching labels between images based on spatial proximity and
overlap criteria.
The script works iteratively, ... |
3723abfad5c4cf3066615aa2249b5b3073bda69de20163bb924d39bdeb56fdeb | Python | 4,084 | 120 | import os
from typing import List, Optional
import hydra
from omegaconf import DictConfig
from pytorch_lightning import (
Callback,
LightningDataModule,
LightningModule,
Trainer,
seed_everything,
)
from pytorch_lightning.loggers import LightningLoggerBase
from {{cookiecutter.project_slug}} import u... |
e56c4211cd0fffe3ad928ac9ad5c4921d894e731cd2cc04a57307da9cbc195de | Python | 4,084 | 118 | from zigzag.classes.cacti.cacti_parser import CactiParser
import logging
logger = logging.getLogger(__name__)
class MemoryInstance:
def __init__(
self,
name: str,
size: int,
r_bw: int,
technology: int = 45,
datawidth: int = 1,
w_bw: int = 0,
r_cost: f... |
d05a8196491e511b43c252507b7f02876caa659b385ba0ec0e69ea71c05eca58 | Python | 4,085 | 107 | import csv
import os
import pickle
from typing import List
import numpy as np
import pandas as pd
from rdkit.Chem import PandasTools
def save_features(path: str, features: List[np.ndarray]) -> None:
"""
Saves features to a compressed :code:`.npz` file with array name "features".
:param path: Path to a :... |
ff1dc5e0b5cedd4c78ae19b53fb20f5901f13dbdea5e96b5f41bd6929cf4b031 | Python | 4,085 | 88 | import numpy as np
import torch
import faiss
import torch.nn as nn
from typing import List, Mapping, Optional, Union, Tuple, Any
from numpy import ndarray
from sklearn.metrics.pairwise import euclidean_distances
from anndata import AnnData
def spatial_match(embes: List[torch.Tensor],
reorder: Optio... |
7482313c4a53525a2a726884e191cf9f2da150d41c4929accbaf0bbd93fd06af | Python | 4,086 | 106 | import torch
import torchvision
from torch import Tensor
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.hub import load_state_dict_from_url
from torchvision.models.efficientnet import _efficientnet_conf
from .functions import ReverseLayerF
# torchvision.__version__ == '... |
81a418b2949b48e6f7ea1091f953cc5d2f544ae8e4bc9e6bb77c85471069c504 | Python | 4,086 | 104 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Splits the DWI image at certain indices along the last dimension (b-values).
Many indices can be given at once by specifying multiple values. The splited
volumes are in the same order as in the original file. Also outputs the
corresponding .bval and .bvec files.
This ... |
8e6c5da676acd1ee23520d09c2661e721a8c57b849438b57931054dd220fb0be | Python | 4,086 | 86 | from .create_visualization import visualization
from .create_visualization import visualization_continue
from .create_visualization import visualization_continue_fastq
from .qiita_visualization import qiita_visualization
from .fastq_visualization import fastq_visualization
import os
# This function downloads the acc... |
e9d92ee5b71e2ac6e2f2ed62982ea87c7c5707a69818f8b81647e71281446a1a | Python | 4,087 | 140 | #!/usr/bin/env python
"""
Given a list of intervals in BED format (`interval_file`) and a set of scores
(`score_file`) print each interval plus the average, minimum, and maximum of
the scores that fall in that interval. Scores can either be wiggle format
data or a directory containing binned array files (named accordi... |
eeea51c88ebeee70a06b4e1184bce00196cd1e5842d24992bf11a5829fd89af5 | Python | 4,087 | 112 | # -*- coding: utf-8 -*-
import os
join = os.path.join
import random
import numpy as np
from skimage import io
import SimpleITK as sitk
def dcm2nii(dcm_path, nii_path):
"""
Convert dicom files to nii files
"""
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(dcm_path)
... |
0694e1819e9e05e30db39ef6af4bf70f6448df60cc922d68be9e7ebc196c9d71 | Python | 4,088 | 78 | from nipype.interfaces.fsl import Reorient2Std
from PUMI.engine import BidsPipeline, NestedNode as Node
from PUMI.pipelines.anat.anat_proc import anat_proc
from PUMI.pipelines.func.compcor import anat_noise_roi, compcor
from PUMI.pipelines.anat.func_to_anat import func2anat
import os
from PUMI.pipelines.func.func_proc ... |
109d04511aa4c7befbf6717261434e275ec55d98d999c06ccfa48074cf296184 | Python | 4,088 | 94 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Connectome extraction module."""
import os
import json
import numpy as np
from connectome_manipulator import log, profiler
from connectome_manipulator.connectome_manipulation.manipu... |
f5caf2e91aa983d636bfe4ead657adb3b2977d8cff5ff6376ea3011cd2bd8b10 | Python | 4,088 | 108 | import collections
import numpy as np
from abc import ABC
import torch
import torch.nn.functional as F
from torch import nn, autograd
class CM(autograd.Function):
@staticmethod
def forward(ctx, inputs, targets, features, momentum):
ctx.features = features
ctx.momentum = momentum
ctx.s... |
e8d0ee4e5833aba5b60e474f7f79ed90d96245bb3c8a4f4bd568113d6df1db00 | Python | 4,089 | 128 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Patrick Lenz
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licen... |
ff24120f55df74b36df41c680d6702ebdca1e4899daeedc10622888efee171ad | Python | 4,089 | 101 | import anndata
import pandas as pd
import numpy as np
import logging
from typing import Optional, Dict
logging.basicConfig(level=logging.INFO)
class STMetricEvaluator:
def __init__(self,
real_h5ad: str,
pred_csv: str,
index_col: int = 0):
sel... |
235c2b9b545ef56acd99fb83bf3945aea67697b994ad6ab8d0900a02fdb2e7b8 | Python | 4,090 | 137 | #
# SPDX-FileCopyrightText: Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-FileContributor: Alexandre Bittar <abittar@idiap.ch>
#
# SPDX-License-Identifier: BSD-3-Clause
#
# This file is part of the sparch package
#
"""
This is where the dataloader is defined for the SHD and SSC datasets.
"""
im... |
06b7cb4f9fb245826d34c7d75c5e0d80cefeb4d6eadf6c1244b89c09b6e71a34 | Python | 4,091 | 187 | import re
def get_user_id(user):
return user['id']
def get_username(user):
return user['username']
def get_screen_name(user):
return user['name']
def has_description(user):
des = user['description']
if des is None or des == ' ':
return False
else:
return True
def has_lo... |
7dea3f77732cbe498fa353bc4faf95c1b6d7396aa36b4aad566530f08b2b5f9a | Python | 4,091 | 78 | #!/usr/bin/env python
import common
import chromatics
import os
import pandas as pd
import sys
from glob import glob
config_fn = sys.argv[1]
cell_line = config_fn.split('/')[0]
config = common.parse_config(config_fn)
os.chdir(os.path.expanduser(config['working_dir']))
expression_cutoff = 0.3
idr_cutoff = 0.1
segme... |
1d98455aa0a45df6fbdf7a6b8af156da47294f754763b343e432b451088e55f8 | Python | 4,092 | 143 | #!/usr/bin/python
"""
This function accepts a csv file produced by aratools.utils.exportMaSIVPoints2Lasagna.m
Each row of the csv file produced by that function is in this format:
nodeId,parentID,z position,x position,y position
This module imports this file and turns it into a format suitable
for plotting in lasagna.... |
4f454f44e89fee6ca0e84caa40b2e7a38371706d0057eb073305fb888be0143b | Python | 4,093 | 90 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from typing import Any
import cv2
from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
from ultralytics.utils import LOGGER
from ultralytics.utils.plotting import colors
class ObjectBlurrer(BaseSolution):
... |
cbecad7743048a34e5b652c89ddf7523a44d877a479669f49ea2065f1453ddee | Python | 4,093 | 144 | from types import SimpleNamespace
import pytest
from langchain_core.tools import BaseTool, tool
from chemgraph.registry import (
DuplicateRegistryEntryError,
RegistryUnavailableError,
RuntimeRequirement,
ToolRegistry,
ToolSpec,
UnknownRegistryEntryError,
)
EXPECTED_TOOLS = (
"extract_out... |
2c7a29a40fd7954a96e4cbcaa31180df2dcf4f549316ec00977ced9b2d19c39e | Python | 4,094 | 120 | """Group recordings into chains: the same field of view across DIVs.
A chain is one field of view followed over days. Working out which recordings
belong together is pure string handling, but there is one trap in it: **both
naming conventions in this dataset put the imaging date in the name alongside
the DIV**, and th... |
e08b22af107bfabc4d4086c1114d1a9da424f9642637dcbf47c9e050cc9e3ec3 | Python | 4,095 | 100 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 13 15:33:01 2022
@author: Yoshi
"""
import os
import numpy as np
from PIL import Image,ImageDraw,ImageFont
os.chdir("../map_generators")
from cell_drawer import *
def modulation_index_mapper(map_output_path,canvas,width,height,cell_locations,scale,radius,color_key,ce... |
05db802cf41e40269a61942b7ba31fad838d6fa516c057ced1c20432fe0ce2e3 | Python | 4,097 | 112 | import numpy as np
import copy
import joblib
import os
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from xgboost import XGBClassifier
class XGBoostEnsembleClassifier(BaseEstimat... |
c7a6257800a8ee124d4d9862a3039d3e928ee7d702464b26b07f566b86543cd2 | Python | 4,097 | 109 | import os
import unittest
import SVMTK
tests_dir = os.path.dirname(__file__)
class Slice_Test(unittest.TestCase):
def test_unpack_mesh(self):
slc = SVMTK.Slice();
slc.add_constraint([SVMTK.Point_2(0,0),SVMTK.Point_2(0,1),SVMTK.Point_2(1,1), SVMTK.Point_2(1,0),SVMTK.Point_2(0,0) ] )
slc... |
061b8a2cc028be15d44e717a152e7bb6fa6cb455bb5ebba6e3fb5d75bd934711 | Python | 4,099 | 120 | # %% ### import modules
from pathlib import Path
import seaborn as sns
import pandas as pd
import numpy as np
import bap1b.figures as figures
# %% ### set paths
data_dir = Path(__file__).resolve().parents[1] / "data"
results_dir = Path(__file__).resolve().parents[1] / "results"
# %% ### load data
df_results = pd.rea... |
2b6790a7c59a6af7d02aa4676ee1e8750de1f6a070d5cc415c2d709f88e98d61 | Python | 4,099 | 148 | """
:mod:`metrics` --- metrics and measures
=============================================================
"""
import numpy as np
__all__ = [
"gini", # Gini coefficient of inequality
]
def gini(arr, mode="all"):
"""Calculate the Gini coefficient(s) of a matrix or vector.
Parameters
----------
a... |
49aa20ec87951fbad29bf9928ed256228a00c768225c4b652366770976de9d94 | Python | 4,099 | 123 | import nibabel as nib
import numpy as np
import scipy.io
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from tqdm.auto import tqdm
from AFQ.data.utils import BUNDLE_MAT_2_PYTHON
# This dictionary is used to convert the names of the bundles
# from old pyAFQ bundle names to the new pyAFQ bundle names... |
5e5acc12034fce7b0c6cd72a80bc8836ee4ac9e1ac8cb1011d8b478327c0ae77 | Python | 4,100 | 106 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Removal of streamlines that are out of the volume bounding box. In voxel space,
no negative coordinate and no above volume dimension coordinate are possible.
Any streamline that do not respect these two conditions is removed.
The --cut_invalid option will cut streamli... |
f3f69ba8a321923cd4db99447b595361f255551e959e45643656cf7d7c0b75ea | Python | 4,100 | 99 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(1, r'../function_scripts')
import plotting_related_functions
import os
import pandas as pd
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.formula.api import ols
import... |
6223880a2c993afc079e5ed609b3d1b72ffad73e9d17000b5d6ffb05d9c0470a | Python | 4,101 | 95 |
import numpy as np
import xarray
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import scipy.io as sio
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
DATA_DIR = '/braintree/data2/active/users/tmarques/bs_datasets/Ringach2002.mat'
ASSE... |
40e6c2c752983f4fbd39ab627a9ce6b446c36c9e58a45aff46d05d658cd568ae | Python | 4,103 | 114 | import os
import sys
import time
import matplotlib.pyplot as plt
import numpy as np
# by Shaoxuan Yuan at 2024/04/22
import torch
from PIL import Image
from scipy.ndimage import distance_transform_edt
try:
from mol_segment.unet import U_Net
except:
from unet import U_Net
# 从unet模块导入U_Net类
#... |
d5d2184547d90274b85881aea18c749bebd8942fabe1d7edc82222b51a3c65d7 | Python | 4,103 | 102 | from .base import MLPBlock
from torch import nn
import torch as th
from dgl.nn.pytorch import GINConv, GraphConv
import torch.nn.functional as F
class GMSA(nn.Module):
def __init__(self, input_dim, hidden_dim, num_heads, feat_drop, att_drop):
super(GMSA, self).__init__()
self.gcn1 = GraphConv(inpu... |
fa26295719756807efc6b9580a81a4c0e87ab4a006284587b10c6ae051f71e66 | Python | 4,103 | 117 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Extract symbol_set from trail21-50 model files and save them to concept_vectors folder.
This script reads PyTorch model files from /workspace/Results/param/ directory,
extracts the 'symbol_set' tensor from each file, and saves them individually
to /workspace/Results/c... |
10e70a305e96411db3f3a4833bee57d215f003f7d4a57fbfa12c4033f3aff1ca | Python | 4,105 | 163 | #!/usr/bin/env python
"""
Application to convert AXT file to MAF file. Reads an AXT file from standard
input and writes a MAF file to standard out; some statistics are written to
standard error.
axt_to_maf primary:lengths_file secondary:lengths_file < axt_file > maf_file
--silent: prevents stats report
Lengths ... |
e3d7e4654d7b3e5623e61b2db6c87ea488ff7de69f7c7e064e49277ff0cbe6c7 | Python | 4,107 | 112 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from http import HTTPStatus
import pytest
from shared.utils.config import get_method, patch_method
class TestGrowthData:
INITIAL_PROMPT_STATE = {
"github_prompt_shown": False,
"github_prompt_support_clicked": False,
"g... |
20ed804b4a2ea769e56a01c9ecdd13cf520c56bc6f1037843d5e1f94e52251cc | Python | 4,108 | 107 | """
Manual assignment dialog for background configurations.
"""
from .pyqt_utils import *
class ManualBackgroundAssignmentDialog(QDialog):
"""Dialog for assigning saved background configurations to images."""
def __init__(
self, image_names, configuration_names, current_assignments=None, parent=None... |
3d3ef06a9a7d7501ca468085e1ff4cb508370fe5ebae940ba650de68879ca3fb | Python | 4,108 | 132 | import pytest
import numpy as np
from impyute.imputation.cs import mean
from impyute.ops import error
from impyute.ops import wrapper
# pylint:disable=redefined-builtin
try:
raise ModuleNotFoundError
except NameError:
class ModuleNotFoundError(Exception):
"placeholder required for python2.7"
pa... |
3aa91e9d9ae0b5545501d73e80bb93e8beaf5d0d8652a0b3ca90e80b170905b1 | Python | 4,109 | 117 | # Generated by Django 3.2.8 on 2021-10-26 14:52
import django.db.models.deletion
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
ope... |
b77f233c64855941e3647a47e2558cbf2dbd12379216ea7dbc6361cbc8b81cf2 | Python | 4,109 | 98 | import os
import random
import numpy as np
from PIL import Image
from tqdm import tqdm
#-------------------------------------------------------#
# 想要增加测试集修改trainval_percent
# 修改train_percent用于改变验证集的比例 9:1
#
# 当前该库将测试集当作验证集使用，不单独划分测试集
#-------------------------------------------------------#
trainval_percent... |
bf08fbdc2ed79d2792a8a75d9e0c80f4bc5adc77c04fdc3dcafd0dc0c785347a | Python | 4,111 | 121 | #!/usr/bin/env python
import os
import sys
import tqdm
import pdb
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
class ComplexGaborLayer2D(nn.Module):
'''
Implicit representation with complex Gabor nonlinearity with 2D activation function
Inputs;
... |
12a1b19ae245b724b6fc076f9536b0693dc3d6cb668293a1565333e3591c0a04 | Python | 4,113 | 117 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 18 18:03:53 2023
@author: rl05
"""
import os
import os.path as op
from mne import read_epochs, compute_source_morph, read_source_spaces
from mne.minimum_norm import read_inverse_operator, apply_inverse
import config
subjects = [f'sub-{subject}'... |
5e9eb28b9566eb6cd6a15f2aa0900b5b8e1dc6915c44a910f9b2d7a5214f1acb | Python | 4,113 | 119 | import sys
import cebra
import os
import tempfile
import torch
import pandas as pd
import numpy as np
import pickle as pkl
import matplotlib
import cebra.models
import cebra.models
import cebra.data
from cebra.models.model import _OffsetModel, ConvolutionalModelMixin
from torch import nn
from scipy import stats
from ce... |
514740daa886e5e46ac3d32f52422bcb6699d73333426302a45a868b0865fe5d | Python | 4,114 | 81 | import os
from nipype.interfaces.base import TraitedSpec, File, Directory, CommandLineInputSpec, File, TraitedSpec, CommandLine, Directory
class DenoiseDegibbsInputSpec(CommandLineInputSpec):
dwi_img = File(desc="Path to the DWI image", mandatory=True, argstr="%s", position=0)
dwi_bvec = File(desc="Path to the... |
6bb07bc47ffa07898fe4e62f46c8a09d51620fadcdc3b7f5c0be6458d0d16612 | Python | 4,115 | 139 | import logging
from pathlib import Path
from typing import ClassVar, Iterable
from argclz import argument, str_tuple_type
from neuralib.atlas.typing import Channel, Source, HEMISPHERE_TYPE, Area, PLANE_TYPE
from rscvp.atlas.dir import AbstractCCFDir
from rscvp.util.io import get_io_config
__all__ = ['HistOptions']
... |
679965f79651862bb01895963ef8cd7f71c4ae42e6e83a68c7c08c3c9d5ff763 | Python | 4,117 | 115 | """XGBoost evaluator."""
from tabnanny import verbose
import xgboost as xgb
import numpy as np
#from base_evaluator import Evaluator
"""
XGboost Evalutor Wrapper class.
"""
class XGBoostEvaluator():
def __init__(
self,
model_type = "XGBRanker",
load_weight = True,
hpo_wrapper=Fal... |
bb7a59b5b6b3cdcfa0836eb87903fa04da55ba82cd63b898bd6efb562ab051bb | Python | 4,117 | 105 | #!/usr/bin/env python3
"""Generate the Python MEA-Stim figures into a MATLAB-mirroring output tree.
Runs the ported stim pipeline (detect -> patterns -> clean -> analysis) for each
test recording and writes every ported figure under
<out-root>/2_NeuronalActivity/2A_IndividualNeuronalAnalysis/<Grp>/<recording>/
u... |
5c33e132f3640c8d28cdd82060f257aa271b585e0985b649e6f6faba42714c48 | Python | 4,118 | 112 | 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... |
9d126812b6a91705920d82e5edd151231746fd0ad8e57b4a219a13b4f0b897ed | Python | 4,118 | 127 | # Copyright (C) 2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from enum import StrEnum
from django.conf import settings
from cvat.apps.engine.permissions import JobPermission, TaskPermission
from cvat.apps.iam.permissions import OpenPolicyAgentPermission
class LambdaPe... |
f3879ce11c527ffe221ef2628eb5bd48787f4e595403eceec403b463bce32c2c | Python | 4,118 | 126 | """
Example usage of CollapsibleGroupBox.
This file demonstrates how to use the CollapsibleGroupBox widget
to replace standard QGroupBox with collapsible sections.
"""
from PySide6.QtWidgets import (
QApplication,
QMainWindow,
QWidget,
QVBoxLayout,
QLabel,
QCheckBox,
QPushButton,
QSpin... |
0e30da066e2283bb0d66014f92d804b5da5a9492727c857431ba60f3e10d3c27 | Python | 4,119 | 111 | 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
import json
import ijson
import torch_geometric.transforms as T
from torch_geometric.nn import ChebConv, GCNConv # noqa
from tqdm import tqdm
p... |
83ef000e85a651b603c737a6444b86f4e23478e8a8d6d2b32c551e0afa08432c | Python | 4,119 | 95 | import random
import subprocess
from typing import Dict, Any, Optional
class TestsetClass:
"""
A class for handling test sets and calculating dispersion energies.
"""
def assign_random_values(self, N: int = 50, seed: int = 42) -> None:
"""
Creates a new dictionary with the same keys as... |
b6088c5adefa3fc6a07c3b26dff0971a557d7f30f5a77be5506eda5f014ecd60 | Python | 4,120 | 122 | # -*- coding: utf-8 -*-
from loguru import logger
def load_data(file_path):
data = []
labels = set()
count = 0
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip('\n')
if '-DOCSTART-' in line:
continue
terms ... |
c4a373ff99afedc8eb604f6ab4bc9f474bc22056abc28a1af9a646436a2d7691 | Python | 4,122 | 95 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import numpy as np
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['tracking.zip'])
tmp... |
aefe5bcd50a92746cd491984e4ddbee860b4d0cf9f90c29a20fd921ea4bffe8e | Python | 4,123 | 120 | from collections import OrderedDict
from torch import nn
from torch.nn.init import xavier_normal
import torch
import torch.nn.init as init
from attorch.regularizers import LaplaceL2
from torch.autograd import Variable
from attorch.module import ModuleDict
from staticnet import logger as log
class Shifter:
def _... |
c40a57bc54f209124c2b5a6fc8206ff0fb184349b139bb74516e3f24aca85bd5 | Python | 4,126 | 115 | from __future__ import absolute_import
from collections import defaultdict
import numpy as np
from sklearn.metrics import average_precision_score
from ..utils import to_numpy
def _unique_sample(ids_dict, num):
mask = np.zeros(num, dtype=np.bool)
for _, indices in ids_dict.items():
i = np.random.choi... |
f0bc839509a63a9f1e79e5148c1a856a0bfb0c3a6ee7d79c0b1d4a14715f1d96 | Python | 4,126 | 107 | import torch
from typing import Optional
from transformers.models.dinov2.modeling_dinov2 import Dinov2Embeddings, Dinov2Model, Dinov2ForImageClassification, BaseModelOutputWithPooling, ImageClassifierOutput
class ConditionedDinov2Embeddings(Dinov2Embeddings):
def forward(self, pixel_values: torch.Tensor, conditi... |
fb4f404aa86377689838d056e0a7f91ced4586c86c6892d8b0f30a50b50883d2 | Python | 4,126 | 120 | import copy
import time
import torch
import numpy as np
import torch.nn as nn
from layer import CLGNN_GCN
from layer import CLGNN_GIN
from sklearn.metrics import precision_recall_curve, roc_auc_score, roc_curve, average_precision_score, f1_score, auc
def train_model(data_o, data_a, train_loader, test_loader, args):
... |
36332d3b644deaf25ae4d3d9c1663f2536797aa25a135a9f58984120401020f0 | Python | 4,127 | 108 | """ Common functions for creating enrich2 datasets (for GB1 and Bgl3) """
import os
from os.path import join
from subprocess import call
import pandas as pd
def create_e2_config_file(inp_fn, sel_fn, e2_output_dir, config_file_save_dir):
"""
creates an enrich2 config file for a 2 timepoint selection experiment
... |
8c58488c4383ceb47f9abad4d9a677bdcde39e7380025d5fd84fab0e16ec7b9f | Python | 4,127 | 50 | import argparse
def parser():
parser = argparse.ArgumentParser(description = 'CATS-Net on ImageNet1k concept abstraction task')
parser.add_argument('--dataset', type = str, default = 'imagenet1k', choices = ['cifar10', 'cifar100', 'imagenet1k', 'imagenet100'], help = 'Which dataset to be used')
parser.add_... |
e2949892488114218db0e08e0645721580da2392199c7bcb737b93df3d5fae90 | Python | 4,128 | 90 | import torch
# define the celcomen class
class celcomen(torch.nn.Module):
# define initialization function
def __init__(self, input_dim, output_dim, n_neighbors, seed=0):
super(celcomen, self).__init__()
# define the seed
torch.manual_seed(seed)
# set up the graph convolution
... |
f5ed8393d387598c9cbc049d2bedd7f8efd461a51cabb47cdf88948e1df3e358 | Python | 4,128 | 135 | try:
import torch
import torch.nn as nn
import torch.nn.functional as F
except ModuleNotFoundError:
print('pytorch is not installed. Using without it.')
import sys
import numpy as np
from pathlib import Path
from scipy.interpolate import interp1d
from scipy.io import loadmat
import platform
import warni... |
2372703cf656263aead77cc4818bc4b7047e47007bd3e0d6c8544bd4b346f3c1 | Python | 4,129 | 130 | import argparse
import torch
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
from logger import Logger
class MLP(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout):
super(MLP, self).__init... |
18b8143d60d9df4461953c1e2b854b273fb190a14a9270c12e877f0960485023 | Python | 4,130 | 109 | from __future__ import annotations
import re
from typing import Final
from loguru import logger
from ._shape_color import migrate_shape_color
def migrate_config(*, config_from_yaml: dict) -> None:
migrate_shape_color(config=config_from_yaml)
if "instance_label_auto_increment" in config_from_yaml:
l... |
ad6f4e324e074b7980f67c95b99b9b9ac13d2f9a0eb8c1007941baa6605b4ba2 | Python | 4,130 | 76 | #!/usr/bin/env python
# export_architecture.py
import sys
from pathlib import Path
# ──────────────────────────────────────────────────────────────────────────────
# 1. 将项目根目录加入 Python 模块搜索路径
# ──────────────────────────────────────────────────────────────────────────────
ROOT = Path(__file__).parent.resolve()
sys.pa... |
af73ed3b363d3f88e6897494ac2ba4ce29f042fe968460216b437f6b0f0544dc | Python | 4,131 | 94 | #!/usr/bin/env python
from __future__ import print_function
import os
import json
import argparse
import sys
# Prints to stderr
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
## Main
parser = argparse.ArgumentParser(description="Get all samples below minimum reads (UMIs) or minimum detected... |
bb59d1ff8164c7e496e3d563bc2b77cd46f280fdb67a71ae499d731be6d8b2e0 | Python | 4,131 | 125 | import os
import re
from typing import List, Tuple, Any
from glob import glob
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from torchvision.transforms import functional as T
from torch.utils.data import DataLoader
import torch
from plio import Section
from pli_transforms.geometric import ... |
13422b2448a9a08e43b9a485754e0a3234d469c529d3f32fedbfe63f08c18937 | Python | 4,132 | 128 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from datetime import timedelta
from allauth.account.adapter import get_adapter
from django.conf import settings
from django.contrib.sites.shortcuts import get_current_site
from django.core.exceptions impor... |
48a787997ace4db891a0d8495822cd4059dd1e78f739ac26a32fc5defde0a2ba | Python | 4,132 | 81 | from dtiplayground.dmri.common.tools.base import ExternalToolWrapper
class DTIReg(ExternalToolWrapper):
def __init__(self,binary_path= None, **kwargs):
super().__init__(binary_path, **kwargs)
def execute_with_args(self,args,**kwargs):
self.setArguments(args)
return self.execute(args)
... |
bda26fb7bc8e9e1675a83aed9f3356e267e60f3bd9dd8f4bd3488fe1f6c891f5 | Python | 4,132 | 145 | from typing import Callable, List, Any
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
import pli_cyto.utils.functions as f
def adversarial_ce_loss(disc: nn.Module, image: Tensor, target: bool = True):
valid = torch.full((len(image), 1), target, dtype=image.dtype... |
2471d690e6805686121cb76c43500223631c8a1dc731b283fd4e0961790b5709 | Python | 4,133 | 130 | # -*- coding: utf-8 -*-
import logging
import numpy as np
from dipy.direction.peaks import peak_directions
def find_order_from_nb_coeff(data):
if isinstance(data, np.ndarray):
shape = data.shape
else:
shape = data
return int((-3 + np.sqrt(1 + 8 * shape[-1])) / 2)
def get_sh_order_and_f... |
fe6898f753a4dd38d662e9b8f87940098a82cc1fffbe4300823141378870e2f2 | Python | 4,133 | 128 | import torch
import numpy as np
from skimage.measure import shannon_entropy
def ssim_f(img1, img2, fused, measure):
return .5 * (measure(img1, fused) + measure(img2, fused))
def mi_f(img1, img2, fused):
return MI(img1, fused) + MI(img2, fused)
def _to_uint8(img):
if not img.dtype == np.uint8:
... |
349ba03faed84c4092b2c641e10aeed8c8e0d2cf5bf74620e46bcd30e4899448 | Python | 4,135 | 117 | """XGBoost evaluator."""
from tabnanny import verbose
import xgboost as xgb
import numpy as np
#from base_evaluator import Evaluator
"""
XGboost Evalutor Wrapper class.
"""
class XGBoostEvaluator():
def __init__(
self,
model_type = "XGBRanker",
load_weight = True,
hpo_wrapper=Fal... |
8a5d57741be400338ef3a350b79b754158b4d0bb19501c31f9d4775ec3244243 | Python | 4,135 | 110 | import os
import numpy as np
import pandas as pd
import time as tm
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import rpy2.robjects as robjects
def run_LDA_rejection(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0, Threshold = 0.7):
'''
run b... |
bba8fee7e887040483099efbe9dc9f088d4df30baf20c8de7dff24b61897dd0e | Python | 4,137 | 116 | import copy
from typing import Callable, Tuple
import torch
from torch import Tensor
from torch.nn import Module, Parameter
from torch_geometric.nn.inits import reset, uniform
EPS = 1e-15
class DeepGraphInfomax(torch.nn.Module):
r"""The Deep Graph Infomax model from the
`"Deep Graph Infomax" <https://arxiv... |
28411ef254f6d6072504aa084d564dd9555cbd529e33016b4d0ec189fc2b1a5d | Python | 4,138 | 104 | # 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, ... |
71ca2852c39aab9ac3a90d2120009b105f43adee0cbab1486fd2b3cf543b1b6b | Python | 4,138 | 90 | import gzip
import re
import argparse
import subprocess
import pandas as pd
def read_gzip_file(file_path, header="infer"):
with gzip.open(file_path, 'rt') as f:
return pd.read_csv(f, sep='\t', header=header)
def write_gzip_file(df, file_path):
with gzip.open(file_path, 'wt') as f:
df.to_csv(f,... |
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