sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e6cf098cef6e626d9053f69e011d15668bbfa97268762ac18bb11511032d10c3 | Python | 5,171 | 98 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
09326e7369ab787f8622bb49b88d0b87b1c8384b9533a6f46764fa50f5d867cc | Python | 5,172 | 166 | import networkx as nx
import numpy as np
from numpy import linalg as LA
from scipy.integrate import quad
from scipy.linalg import eigh
from scipy.sparse import csgraph
from scipy.spatial import distance
from sklearn.metrics.cluster import adjusted_rand_score
_eps = 10 ** (-5)
def _assert_before_distance(adj1, adj2):... |
77dec6f7f462be772a9443fd5d7ae0eea26fce54acf84113847789c5754ee23f | Python | 5,172 | 153 | import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from torch_geometric.data import DataLoader, Batch
from torch.optim.lr_scheduler import ReduceLROnPlateau
from pathlib import Path
import copy
from matplotlib... |
9bd05b2771b3832bd32fc1aea7da4670599bc89df5700d93a6636257c544f71e | Python | 5,173 | 165 | import os
import random as python_random
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateModel
from gradient_accumulator import GradientAccumulateOptimizer
# get current tf minor version
tf... |
85f21c766cfbf14cb56b2f3f05265e1c26e0b7a539bf186ae8ee0611a92473b7 | Python | 5,175 | 161 | from torch.nn.parameter import Parameter
import torch
import torch.nn.functional as F
from .dense_layers import LinearLayer, Residual_block
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import softmax
#import torch_scatter
from torch_geometric.utils import scatter
class GATLayerCus(Message... |
3e1ad1482c78a510a022c5e080a02cc51809a1f79e88c987a4c1711aa4251af1 | Python | 5,176 | 111 | #=========================================================================
#
# Copyright NumFOCUS
#
# 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/license... |
7d1f444d99d9303ff787552ab82a75f3c0dc916d6fd805cf4d86030f699950aa | Python | 5,178 | 130 | import numpy as np
from ._perturb import embed_pattern_seqs, tile_pattern_seqs
def embed_deepstarr_distance_cooperativity(
null_sequences,
motif_a,
motif_b,
step=1,
allow_overlap=False
):
"""
Generates sequences for a motif cooperativity analysis similar to that performed in the DeepSTARR p... |
40ee9ae6586b00abf13b002b7addc3d846bdaf7cc045965e6a796baeb77b3db0 | Python | 5,179 | 174 | import pandas as pd
import fiona
import json
import os
import fnmatch
from ..core.trajectorydataframe import TrajDataFrame
from ..core.flowdataframe import FlowDataFrame
from ..preprocessing import filtering, compression
from tqdm import tqdm
def write(skmob_df, file):
"""
Write a TrajDataFrame to a json file.... |
8a1dd84ccff1a96182a8a657be92b64f585ae7306ee64c14665e8a408d92d72d | Python | 5,180 | 155 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# For a full list of documentation options, see:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# ----------------------------------------------------------------------------
import os
from os.path import dirname ... |
dcefe16d700aaca1c438b05b4a2bb7757bbb3219f8a1e424b5d209f30fa04e31 | Python | 5,180 | 120 | #!/usr/bin/env python3
"""
check_dois.py — verify every DOI in the JNE reference list resolves and that the
returned title matches what the manuscript claims.
Run: venv/bin/python check_dois.py
IOP treats references to non-existent sources as strong evidence of irresponsible
AI use: rejection at submission, retracti... |
ffac5c289979a505e7b8c30dcc1efa4565be342f19d64231eda066e7cb9a63e0 | Python | 5,183 | 143 | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.backend.common import global_state
from keras.src.backend.common.remat import RematScope
from keras.src.backend.common.remat import get_current_remat_mod... |
c754ad5b2f9dfa31bf5ae5b1958219dd50eb8cc9848e3db80e47fafa21f79df9 | Python | 5,184 | 119 | # pylint: disable=unused-import,too-many-function-args
"""Anchor box generator for SSD detector."""
from __future__ import absolute_import
import numpy as np
from mxnet import gluon
class SSDAnchorGenerator(gluon.HybridBlock):
"""Bounding box anchor generator for Single-shot Object Detection.
Parameters
... |
f04d1d75495cedfc8ded0318ee55a2abfccf38103783db0742e3a2e65f7b715e | Python | 5,184 | 157 | """CenterNet training target generator."""
from __future__ import absolute_import
import numpy as np
from mxnet import nd
from mxnet import gluon
class CenterNetTargetGenerator(gluon.Block):
"""Target generator for CenterNet.
Parameters
----------
num_class : int
Number of categories.
o... |
a5b08181562d0dde48ae6df426aedae2e993d4af9c898dea9b6afa58076059ad | Python | 5,185 | 166 | """Fastlog backward gradient recording tests."""
from __future__ import annotations
import warnings
from pathlib import Path
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.fastlog import CaptureSpec, GradRecordContext, InvalidStorageError
class TinyRelu(nn.Module):
"""Ti... |
b3fa38fb3c59b4afe0d08ea644c5035ede497addda2cd35b3e96349b4227788e | Python | 5,185 | 148 | #!/usr/bin/env python3
"""
File: calc_avg_gene_expression.py
Created: 2022/02/17
Last Changed:
Author: M.Vochteloo
Copyright (C) 2020 University Medical Center Groningen.
A copy of the BSD 3-Clause "New" or "Revised" License can be found in the
LICENSE file in the root directory of this source tre... |
d69dd26422ef4d6092ba3d9fb1bd74cb4433277b3504fc7b773c286d475c738f | Python | 5,185 | 154 | """Preview backends must match torch's exact relation-label convention.
Torch (``postprocess/labeling.py``) relabels ``parents``/``children`` (and the
lineage sets ``input_ancestors``/``output_descendants``/``root_ancestors``)
through a CONDITIONAL mapping: a referenced op's bare ``layer_label`` when its
layer has a s... |
60affd771030392879f0f29c378c2648191b276153e31b88d9883f0c351abac1 | Python | 5,188 | 141 | #!/usr/bin/env python3
# import required modules
from keras import layers
from keras import models
from keras import optimizers
from keras import callbacks
import numpy as np
from argparse import ArgumentParser
def parse_cmd_args():
"""
"""
parser = ArgumentParser()
parser.add_argument('-d', '--direc... |
79d28a645c285f8379cb5797de37e079005f79a38beb29ef17dce310b66422fa | Python | 5,188 | 121 | import itertools
import json
import os
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import matplotlib
import statsmodels.stats.api as sms
from angorapy.common.const import PATH_TO_EXPERIMENTS, QUALITATIVE_COLOR_PALETTE
font = {'family' : 'Times',
'weight... |
808a349513dd72b31494f06edcdd8589b6b2175b288ccd14b787025dece5173a | Python | 5,188 | 129 | """
Author: Bruno De Santi, PhD
Affiliation: Multi-modality Medical Imaging Lab (M3I Lab), University of Twente, Enschede, The Netherlands
Date: 20/09/2023
Description: Collection of metrics to evaluate registration performance
MSE: mean square error
PSNR: peak signal-to-noise ratio
SSIM: structural similarity index
N... |
ad5a2d0de5c35a2ea870bbedce44fd3ce9a21247b33ed89556e169748aa09fd0 | Python | 5,188 | 148 | from typing import List
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from models.basic import DeepLearningBackbone
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResN... |
23f9c2ff4af24643d9db32e5618dd9214906db641ec284d9695cfc18fdaa6f0c | Python | 5,189 | 133 | # -*- coding: utf-8 -*-
import os
import numpy as np
from numba import set_num_threads
import pandas as pd
from scipy.sparse.construct import random
from scipy.sparse.sputils import matrix
import sklearn
from sklearn.preprocessing import StandardScaler
import umap
import matplotlib
matplotlib.use("Agg") #サーバーでプロットする... |
dbabfe308f59662cfc0227754128b94451349f7cd488c8780a522c7ceb5f857c | Python | 5,189 | 152 | import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from torch_geometric.data import DataLoader, Batch
from torch.optim.lr_scheduler import ReduceLROnPlateau
from pathlib import Path
import copy
from matplotlib... |
a4b96a8c6f39c4339dcdff4bb5923d38ce2375eea60e6f23e182284bee80f1e2 | Python | 5,190 | 123 | #!/usr/bin/env python3
import os
import sys
from utils import pdb_utils
import time
from argparse import ArgumentParser
import numpy as np
def parse_cmd():
"""Parse command-line arguments.
Returns
-------
Command-line arguments.
"""
parser = ArgumentParser()
parser.add_argument('-i', '--... |
2756f3fa82a97186de424c7f08d992fe3a9d78670828b5e6e35e3d23cf050d22 | Python | 5,191 | 122 | import torch
import math
import torch.nn as nn
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=True,
):
output_attentions = True
mixed_query_layer = self.query... |
57c5267605736ae7118b89c4f39b276ea098142d7dcc703151734960568df5a6 | Python | 5,192 | 162 | import torch
import torch.nn as nn
from modular_splicing.models.modules.positional_encoding import PositionalEncoding
from modular_splicing.models.modules.sparsity_propagation import (
sparsity_propagation_types,
)
from modular_splicing.utils.construct import construct
from .tanh_attention.tanh_convolved_attn im... |
7ba48b6291ba7f434d544e2d4fdc83446cd2b556a21bf1ecd514672029623a9f | Python | 5,196 | 142 | """
Dataproviders that iterate over lines from their sources.
"""
import logging
from galaxy.util import (
Element,
etree,
)
from . import line
_TODO = """
"""
log = logging.getLogger(__name__)
# ----------------------------------------------------------------------------- hierarchal/tree data providers
c... |
5964e50e70a1a82f5f738d344789bb2c85a517153148385a44b1f43cc05a42c3 | Python | 5,197 | 140 | # This example requires the following dependencies to be installed:
# pip install "lightly[timm]"
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU: fewer clusters, a shal... |
14e1a239eb3103b2b4bb676e0acf59710690f21cc3584d81ee751b36f69ab294 | Python | 5,198 | 128 | # -----------------------------------------------------------------------------
# Copyright (c) 2015, The Deblur Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# -------------------------------------------------... |
76c14f003674af59c2615d55608ce3438a99b993479c123dc8812a59a9a829d8 | Python | 5,198 | 136 | """Netron export x existing-feature composition rows (F14, netron memo s7).
Every row names a composition of this exporter with an EXISTING feature:
C2 export x interventions (marks visible in the artifact), C3 export x
halted capture (the honesty marker is the composition most likely to rot
silently -- the exporters ... |
bb9b7670dae00c56fce71c63346300758c03d4cc521bfe438fb6c46d8dabd684 | Python | 5,198 | 150 | """Tier T3: the installed netron wheel's parser executes on the artifact.
The harness (tests/test_netron_export_harness.mjs) imports netron's real
``onnx.js`` ModelFactory -- no transcription -- and reports what netron
WOULD draw: matched reader, node/function populations, painted edge
labels, sidebar strings, drill-d... |
76043814af366b1813a4bc27da0e1e4d6ad5ad553cc5994c49ac8b059fbb9a73 | Python | 5,199 | 139 | """Tests for the xcp_d.interfaces.concatenation module."""
import os
from nipype.interfaces.base import Undefined, isdefined
from xcp_d.interfaces import concatenation
def test_cleannamesource(datasets):
"""Test xcp_d.interfaces.concatenation.CleanNameSource."""
nifti_file = os.path.join(
datasets[... |
931d30cb0342975c904788d10b1121a1cca557d287eeb9a3060c6a221b93b506 | Python | 5,199 | 158 | from __future__ import annotations
from pathlib import Path
import zipfile
import numpy as np
import pandas as pd
import pytest
from larvaworld.lib.process.import_aux import (
DLCImportError,
DLCScaleValidationError,
read_deeplabcut_tracks,
)
from larvaworld.lib.process import discover_deeplabcut_source_... |
44bdaae35ee2a7fe9fdd73f93aee554f03a9a6fa4ec84453cf251337843c1f1c | Python | 5,200 | 124 | """12. Run an object detection model on NVIDIA Jetson module
==========================================================
This tutorial shows how to install MXNet v1.6 (with Jetson support) and GluonCV on a Jetson module and deploy a pre-trained GluonCV model for object detection.
What's in this tutorial?
-------------... |
fe0b3c7197084305faa719ff8fe75ff4b255b33b82ce5c3beb4f3f25b075e275 | Python | 5,200 | 128 | import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the origin... |
351cd28034d68bbc88caced694f95bbf5af1d4bc38a991b2e25ca11a5c1eb271 | Python | 5,201 | 154 | from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
dev = torch.device('cuda:0')
class MultipleOptimizer:
def __init__(self, *op):
self.optimizers = op
def zero_grad(self):
for op in self.optimizers:
op.zero_grad()
def step(self):
for op in ... |
cff918480307870c576797773228c6dd16494651ec0b082ca5434db940bc93f0 | Python | 5,201 | 255 | """
Force choice 16 category imagenet, choosing the maximum of a cateogry
that falls into the class.
"""
import csv
import json
import os
FILE_DIRECTORY = os.path.realpath(os.path.dirname(__file__))
knife = ['n03041632']
keyboard = ['n03085013', 'n04505470']
elephant = ['n02504013', 'n02504458']
bicycle = ['n... |
47de77f46556f8c03b759e930e2feaa7c8b6be006b68f9771c6687bba1023125 | Python | 5,205 | 210 | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... |
bc89a2cfa693985a8ef1719aa52c4ceded6f1771b815095e60fe6db0b9ce0434 | Python | 5,206 | 129 | import os
import nibabel as nib
import numpy as np
import imageio
import scipy.ndimage as ndimage
def resample(image, spacing, new_spacing=[1, 1], mode='constant'):
""" Resample an image to a new spacing.
"""
new_shape = np.round(image.shape * (np.array(spacing) / np.array(new_spacing)))
resize_factor... |
e871bf43ef30f5a90acd947a58e8b7c1a8195b46c7df635d51388369249dfd7c | Python | 5,208 | 287 | #!/usr/bin/env python
'''Generate models for affinity predictions'''
# variables:
# BALANCED(true)
# POSEPREDICT
# RECEPTOR(false)
# AFFMIN(0)
# AFFMAX(0)
# AFFSTEP(0)
basemodel = '''layer {
name: "data"
type: "MolGridData"
top: "data"
top: "label"
top: "affinity"
include {
phase: TEST
}
molgrid_... |
fe93524bb1c0900a2fdf6df026aade6205daec4b3b26b5af47de3256fa8ddbd4 | Python | 5,208 | 166 | # %%
'''
Runs PPI within GLM regions of interest
Note that this only runs on the cluster because it needs AFNI
'''
import platform
import h5py
import sys
import os
import numpy as np
import pandas as pd
from time import time
from nilearn.signal import clean
from ppi_afni import perform_ppi
sys.path.insert(0, '..')
fro... |
51e8dfa6227125df0bbbec2072cba8feeff4d71866bf0e9df2c4a45cc764f849 | Python | 5,209 | 184 | """Terminals and colors."""
from __future__ import annotations
import base64
import os
import platform
import sys
from functools import reduce
__all__ = ('colored',)
from typing import Any
BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8)
OP_SEQ = '\033[%dm'
RESET_SEQ = '\033[0m'
COLOR_SEQ = '\033[1;... |
e2cdf172a8f2a45b3f5c555b38794bef6e2dd897c95ac42481b9e156be32108e | Python | 5,209 | 197 | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
0431c0870e1948751ed97c4d50dfd38b621664bfa2d0b30cb1a004ed18aefbb3 | Python | 5,211 | 103 | # General imports
import os
import sys
import numpy as np
import pandas as pd
import seaborn as sns
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
# EUGENe imports and settings
import eugene as eu
from eugene import settings
settings.dataset_dir = "/cellar/users/aklie/data/eugene/revision/ray13"
settings.o... |
7d250b538907698f23bc5fe109bd94bb66c07a8b2a188cc6bdeb6de657d5c1ae | Python | 5,211 | 82 | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), e... |
3a9eec00bef1e2f76d84293fce5c5c014d7dadee847f356dd961895ba707b492 | Python | 5,212 | 155 | import h5py
import os
from modular_splicing.dataset.generic_dataset import GenericDataset
from modular_splicing.utils.multi_h5_file import MultiH5File
class H5Dataset(GenericDataset):
"""
Represents a dataset that is stored in an HDF5 file.
Data in this file must be stored under keys C# where C is a cha... |
736a7d78625b0cc6aa1f3d819220e37a060ebc0230972677bbbd44c87fd404d7 | Python | 5,214 | 86 | import pandas as pd
import logging
from utils_stats import output_summary_of_ensemble_strategy
from utils.json_loader import load_json_into_pandas_dataframe
"""
dict_all_ensembles = {
"My_network_with_label_smoothing": [
"old_before_may_2021/label_smoothing_EC_pred/fine_tune_for_EC_prediction_with_label_sm... |
acc178bc90d5d648e0304f1f258adc8e308c5a5ebd9850d0bd60573e5ae6964b | Python | 5,214 | 154 | import random
from typing import Tuple
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import ImageFilter
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset
from moco_data_set import MoCoDataset
class MoCoDataModule(LightningDataMo... |
f3e099b546ea828126a2cbe9a0b5a7bf5cb7501ac4e35150346da8d96c3ce68e | Python | 5,215 | 216 | """
Created on Thu Dec 16 23:56:35 2021
@author: apeyra4
"""
import pynapple as nap
import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
def assemblyPCA(spike_counts, epochRef, epochTest = None, method = 'marcenko', numComp = None):
'''
Parameters
----... |
04b93916bb8a1a833c891e6c19d44789940274307c48c5247c53ca18523056a5 | Python | 5,216 | 138 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
fro... |
202029199b3f86eec896ef2117112e8366373f928c93fd046006851abc8160cb | Python | 5,216 | 159 | # SOURCE: vendored from locuslab/TCN @ master
# https://raw.githubusercontent.com/locuslab/TCN/master/TCN/tcn.py
#
# "TCN-based ECG-Denoising" (queue candidate: TCN architecture applicable to
# ECG denoising/regression, e.g. the ECG-TCN paper arxiv:2103.13740, which uses
# the same causal dilated Temporal Convolutional... |
79b0fd11a93fd3e9038a8d64d347e6f5e0d6c482a8b9474dd000129d516c9329 | Python | 5,216 | 125 | import numpy as np
import sklearn.model_selection
import tqdm
from angorapy.analysis.investigators import base_investigator
import tensorflow as tf
from angorapy.analysis.sindy.autoencoder import SindyAutoencoder
from angorapy.analysis.util.sindy import compute_z_derivatives, sindy_library_tf
from angorapy.common.po... |
2373f2d27a40699ee2f6c97eeab9da8003f18fd866905febdd8b80070fda1dcd | Python | 5,217 | 179 | '''bdpy.evals.metrics'''
import warnings
import numpy as np
from scipy.spatial.distance import cdist
def profile_correlation(x, y):
'''Profile correlation.'''
sample_axis = 0
orig_shape = x.shape
n_sample = orig_shape[sample_axis]
_x = x.reshape(n_sample, -1)
_y = y.reshape(n_sample, -1)
... |
e1165108d36c76b38071fac61b056682078e4b25da77649d893c95bfb54a264a | Python | 5,219 | 147 | from __future__ import print_function
from __future__ import division
import os.path as osp
import mxnet as mx
import numpy as np
import gluoncv as gcv
from mxnet import autograd, gluon
from math import pi, cos
from gluoncv.utils import LRScheduler, LRSequential
def compare(obj, niter, expect, **kwargs):
np.test... |
10bdc2b2a86849f21ce2c9c624ad6177ebc8db5590af414d829e02910e4093df | Python | 5,220 | 142 | import os
import sys
import argparse
import mne
import numpy as np
import pandas
import pylab as pb
from mne.datasets import fetch_fsaverage
from mp_dip.utils.visualise_atoms import reconstruct, interpolate_feature_for_visualisation
from mp_dip.research.parcellation_search import get_label_from_head_position_atlasre... |
3b2cd33efe2b17d9725646d06c915ae9c2e8cf8bf4edb8f77dc4d39bcbc116f1 | Python | 5,224 | 180 | import functools
import attr
# import tqdm
import os
import numpy as np
import torch
from utils import batched
# from permacache import permacache, stable_hash
MOTIFS_DIRECTORY = os.path.join(
os.path.dirname(__file__), "data_motifs", "motifs"
)
SPLICE_MOTIFS_DIRECTORY = os.path.join(
os.path.dirname(__fil... |
2b01a2b0710b02140ce6646a051e90641bad6acf19584784689898eaba45517a | Python | 5,225 | 130 | from os import PathLike
from warnings import warn
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import AnnData
def read_10x_h5(filename: PathLike, atac_only: bool = True, *args, **kwargs) -> AnnData:
"""Read a 10x Genomics ``.h5`` file, keeping only the ATAC (peak) features by default.
... |
ca5dc07b5e5202891d5dd45ef283955bdb692ba2bfb9e8c0ed81c59b15d04c55 | Python | 5,225 | 143 | # SVHN model specification
import torch
import torch.distributions as dist
import torch.nn as nn
import torch.nn.functional as F
from numpy import sqrt
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
from torchvision.utils import save_image, make_grid
from src.utils import Constan... |
f18a9eed1f7865f7b2123a935dc9fb06f0e2030ed58ed3261214cbed0775553e | Python | 5,225 | 154 | import warnings
import torchinfo
import torch
import torch.nn as nn
from tqdm import tqdm as tqdm_dataloader # tqdm(dataloader) causes an error if tqdm.auto.tqdm is used
from .data import read_batch
def get_model_summary(model, input_size=None, verbose=0, **kwargs):
device = next(model.parameters()).device
... |
15fe16c9052f8a88da713d8bb72419a5f110f86cff4acd5d2ec390404f2b4009 | Python | 5,226 | 134 | """Agent service layer for AI agent management."""
import logging
from functools import partial
from typing import (
Any,
)
import anyio
from galaxy.agents import GalaxyAgentDependencies
from galaxy.agents.registry import AgentRegistry
from galaxy.agents.router import QueryRouterAgent
from galaxy.config import G... |
2c1a998a62b08d9241f4d5efc766be561f31b2bb9f81bf06f4845befad86e976 | Python | 5,226 | 195 | """
Test configuration and fixtures for ethoscope_node tests.
This module provides common pytest fixtures and configuration for all tests
in the ethoscope_node package.
"""
import os
import shutil
import tempfile
from pathlib import Path
from unittest.mock import Mock, patch
import pytest
# Test data directory
TEST... |
4fb052e2d6015b7171b8b9b1ac57a28ee7bfac97086a449f708c83f2fd5c5235 | Python | 5,226 | 109 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
6e5b3fd287c18a0bf93dc3e00ac27c6319f9437b66e7d2ae7b6b54b4ce96edd6 | Python | 5,226 | 157 | import sys
import unittest
from unittest.mock import patch, MagicMock
import pynvml
from carbontracker import exceptions
from carbontracker.components.gpu.nvidia import NvidiaGPU
class PynvmlStub:
@staticmethod
def nvmlInit():
pass
@staticmethod
def nvmlShutdown():
# NvidiaGPU._handle... |
24f420db901cbcfb5ff8ca6b302c92236ca0535808c9d135618c75a1597702d3 | Python | 5,227 | 159 | import matplotlib.pyplot as plt
import torch
from torch import nn
from rectipy import Network
import numpy as np
device = "cpu"
dtype = torch.float64
# model parameters
node = "neuron_model_templates.spiking_neurons.lif.lif"
N = 100
k = 2.0
tau = np.random.uniform(10.0, 15.0, size=(N,))
tau_s = 5.0
eta = 10.0
v_thr =... |
148076b9a32e86aa76927cb050f4fd05ccddbecf2217fa1985d790dba4c66f53 | Python | 5,229 | 145 | import torch.nn as nn
import torchvision.models as models
import torch
from config import *
import torch
# Supported CNN models
cnn_dict = {
"resnet18": models.resnet18,
"resnet34": models.resnet34,
"resnet50": models.resnet50,
"resnet101": models.resnet101,
"resnet152": models.resnet152,
}
# Supp... |
c0e4cc6182906fa78985f683234f9556f63ea1de69e20a6f7a60fa85006b9fae | Python | 5,229 | 135 | """F29: the determinism contract, enforced as a test (agent memo 3.9).
Same artifact bytes + torchlens version + normalized arguments ->
byte-identical output, across fresh processes, PYTHONHASHSEED values, working
directories, TZ, and locale. Golden files live in tests/agent_surface_goldens
next to the COMMITTED arti... |
1d145e1bb7ab2e3f29b36e6ad51c2b2f33a8920d31cff677dc7dfe7e2b431be2 | Python | 5,230 | 171 | import fnmatch
import os
import stat
import sys
import zipfile
from shutil import copyfile
HERE = os.path.abspath(os.path.dirname(__file__))
CONDA_PREFIX = os.environ.get("CONDA_PREFIX", False)
if sys.platform.startswith("darwin"):
PLATFORM = "darwin"
elif sys.platform.startswith("win32"):
PLATFORM = "win32"
... |
f3dbe0cfbd92519cb8d7029a2fb32b20b76cf2482bead1cef67e1d03d381ec95 | Python | 5,231 | 147 | # MNIST model specification
import torch
import torch.distributions as dist
import torch.nn as nn
import torch.nn.functional as F
from numpy import prod, sqrt
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import save_image, make_grid
from src.utils import ... |
f82687311cd57935038eb461c9f654cd8352ca461e0fc1384da6bfc4a2f41471 | Python | 5,231 | 153 | """Test Dataset for supervised learning."""
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING, Any
import numpy as np
from neurogym.core import TrialEnv
from neurogym.envs.registration import all_envs, make
from neurogym.utils import spaces
from neurogym.utils.data import Dataset
f... |
05c7ec4dd4ab49f61552399fb3b75475d96995c4358ae1b01cdd8ea1e4e66082 | Python | 5,233 | 147 | import prob2020.python.utils as utils
import prob2020.python.indel as indel
import pandas as pd
def count_frameshift_total(mut_df,
bed_path,
use_unmapped=False,
to_zero_based=False):
"""Count frameshifts for each gene.
Parameter... |
e4e3afde806cdef383cb15a45683786c1aa1448dd4b833807459d565d7422db2 | Python | 5,234 | 193 | import inspect
import os
import pathlib
import pytest
import re
from typing import TYPE_CHECKING
import pysam
import pysam.samtools
import pysam.bcftools
from TestUtils import BAM_DATADIR, CBCF_DATADIR, make_data_files
try:
import mypy.api
except ImportError:
pytest.skip('mypy API not available', allow_modul... |
50aa62fb0681403ad912fbb5fae65c3ec563a2bff768b6d31c100ba1253374ed | Python | 5,235 | 135 | from __future__ import annotations
import numpy as np
from scipy.optimize import linear_sum_assignment
from ..utils import get_overlap
def init_stats(gt: dict, categories: dict) -> dict:
"""
Initializes statistics for each category based on ground truth annotations.
Parameters:
- gt (dict): Ground ... |
f3bf3b35593f1754e07c734ad01def3e665f9294a1ab1f8092360a15afc49a8e | Python | 5,235 | 168 | """
abfinfo.exe was used to create a few text documents of header information.
These tests compare header values from pyABF vs those in the text documents.
"""
import sys
import pytest
import glob
import os
import datetime
try:
# this ensures pyABF is imported from this specific path
sys.path.insert(0, "src")... |
43db9afe3790adecc0d4d27e7148a456e06010a7f3a371f5f677a0d01e97872c | Python | 5,236 | 138 | """Profiler join against a REAL ``torch.profiler`` Chrome trace.
Each op event is assigned to exactly one layer: the k-th ``aten::conv2d``
event to the k-th conv layer, so per-layer rows sum to the profiler's own
event total with one event per row (the old name-substring join gave every
conv layer every conv event and... |
46d2f0e3f6548fabde15d96b9ff2c3ebf499fc3b3d5006777de96d03341bb1d0 | Python | 5,236 | 146 | """grind-r8 cluster 4.0.5: capture admission ordering (R54 + R57).
The typed concurrent-capture refusal used to fire only AFTER the loser had
already run process-global side effects: ``tl.trace`` orchestration reset and
overwrote the admitted winner's capture runtime context (clearing its
replay-template flag and inte... |
f15a42b244186602f6dfe4d448c34f510a0098e2ba319601bcc296c98fc1eb6d | Python | 5,236 | 152 | """grind-p3 T5.3/T5.4: a bundle that SAVES always LOADS (foreign container types).
Two save-ok/load-fail traps in the portable scrub:
* A USER tuple subclass (e.g. a namedtuple from the caller's module) in KEEP
metadata was preserved by TYPE into ``metadata.pkl``. The default-deny safe
unpickler then refused the ... |
1b9ec29a20ee5c7fedbae4e13a62bc4f0e69ec06e194c191c0570aa6e09b2697 | Python | 5,237 | 154 | import json
import rclpy
from rclpy.node import Node
from rclpy.parameter import Parameter
from std_msgs.msg import Bool, Int32, Float32MultiArray, MultiArrayDimension
import numpy as np
from saliency_code import Saliency
class SaliencyROS2Node(Node):
def __init__(self):
'''
Initialize the Salienc... |
96994f4f4750443a3ff4b1ac1549571e116bbe4086249025d7f145e52aefc325 | Python | 5,237 | 145 | from __future__ import annotations
import os
import unittest
import warnings
from unittest import mock
from coco_froc_analysis.froc import froc_point
from coco_froc_analysis.froc import generate_froc_curve
# remove tqdm logging while testing
def notqdm(iterable, *args, **kwargs):
return iterable
class TestFr... |
76b2b61b817faeca737e173257df367d599453666f1fe78a434aeb6e29c94e22 | Python | 5,238 | 90 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
d8552e56088f6fe7410967e78aa097517124bb7a9866bb267ac2fd11bc2593a7 | Python | 5,238 | 133 | '''
Code for downloading and processing KITTI data (Geiger et al. 2013, http://www.cvlibs.net/datasets/kitti/)
'''
import os,sys
import requests
from bs4 import BeautifulSoup
import urllib
import numpy as np
from imageio import imread
import hickle as hkl
import scipy
import scipy.misc
from PIL import Image
# Where KI... |
53f281de8f8df4e8c72a1b2253082277b175fe84fce05ad3bcfb4bd37f91b071 | Python | 5,240 | 186 | """An offline label visualizer for BDD100K file.
Works for 2D / 3D bounding box, segmentation masks, etc.
"""
import argparse
import concurrent.futures
from typing import Dict
import numpy as np
from scalabel.common.parallel import NPROC
from scalabel.common.typing import NDArrayF64
from scalabel.label.typing import... |
6b781f9528c4e5bc2129574ba43a9b7d75501e81bd893a26e88aaef025a5a61f | Python | 5,241 | 97 | from pyabf.abf2.section import Section
class DACSection(Section):
"""
Information about the DAC (what gets clamped).
There is 1 item per DAC.
"""
def __init__(self, fb):
Section.__init__(self, fb, 108)
self.nDACNum = [None]*self._entryCount
self.nTelegraphDACScaleFactorEn... |
ae9af0facbc8104b4decacab57eee540fabdc8f14fd40b92d16f720277f8584a | Python | 5,241 | 131 | import os
from abc import abstractmethod
from typing import Any, Callable, List, Optional, Union
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset, random_split
class VisionDataModule(LightningDataModule):
EXTRA_ARGS: dict = {}
name: str = ""
... |
a5313b1d24723f770cedd2038ef94096dc51e35d434b6db7319b5725fd4e98d4 | Python | 5,242 | 157 | import os
import gc
from tqdm import tqdm
from joblib import Parallel, delayed
from irnlm.utils import save_pickle, write, check_folder
from irnlm.data.text_tokenizer import tokenize
from irnlm.data.utils import get_possible_morphs, get_possible_pos
def extract_syntax(doc, morphs, pos):
"""Extract number of clos... |
03b5983084126920ab66c6b08afe1e2c02bb824d726c204f575af7a4e0c3cdbf | Python | 5,243 | 44 | etas = {
'generic20_learning': {
'alpha_ATLAS_LUMI_2012': { 'muT__hat': 1.45680382599, 'muT__muT__down': 0.0403344500821, 'muT__muT__up': 0.0295059948201, 'muT__muW__down': -0.00170117606185, 'muT__muW__up': 0.00615134457977, 'muT__phi__down': -0.00520694457924, 'muT__phi__up': -0.00486484949081, 'muW__hat': 1.85214... |
2037ba1c67092885652765eca689814acc30e5aaa751209cb8e3f61d38cad8ce | Python | 5,244 | 156 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Project(_BaseTraceHierarchyType):
_parent_path_str = "surface.contours.y"
... |
565c6ac6bc4e35199833352b9d6c29b995040a4baf2252a680ac269a21d679a1 | Python | 5,244 | 137 | import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.losses import binary_crossentropy
beta = 0.25
alpha = 0.25
gamma = 2
epsilon = 1e-5
smooth = 1
class Semantic_loss_functions(object):
def __init__(self):
print ("semantic loss functions initialized")
def dice_coef(sel... |
8c38bb4b4e3621c1462c75f6fc25bf4beeaefa1abf520cdada6055b3cfe664c0 | Python | 5,244 | 156 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Project(_BaseTraceHierarchyType):
_parent_path_str = "surface.contours.x"
... |
ec4052d26bf477135c8a07b05291703309318b7029e0808a6c49769656c161e7 | Python | 5,244 | 156 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Project(_BaseTraceHierarchyType):
_parent_path_str = "surface.contours.z"
... |
ac2ec03df331628faaa3a1be7fa00e8edfa1921b916bb27a2e729e5de595dcde | Python | 5,245 | 147 | """Intervention honesty: a depth-2 store key cannot reach the three depth-1
splice sites without refusing (edits memo row 0d).
The v1 splice sites substitute at ``arg_path[0]`` directly; a nested key
reaching one of them would silently replace the WHOLE top-level argument at
the wrong address -- well-formed, audited, ... |
adf4edcb0464b7d00e138a18f05f9b924c11b37caee70125879e7dcac8f44b57 | Python | 5,245 | 154 | """ Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swis... |
bf99bbb2e9ca8aa872e2cf2fad01e052e55e0c63072b406c75506a33cc277a3f | Python | 5,245 | 131 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import slowfast.datasets.utils as data_utils
import slowfast.utils.logging as logging
import slowfast.visualization.tensorboard_vis as tb
import torch
from slowfast.utils.misc import get_class_names
from s... |
f1462bcd9a86657583793881bd9adb57909a729a5f88ca1186c917573767eea4 | Python | 5,246 | 153 | """Wrappers encapsulating envs to modulate n_steps, rewards, and control state initialization."""
import abc
from typing import List, OrderedDict, Tuple, Dict, Any, SupportsFloat
import gymnasium as gym
import numpy
try:
from mpi4py import MPI
except ImportError:
MPI = None
from angorapy.common.senses import... |
c6be45d28f4159ed8b0059a793b55e689dc5d955080cf202d5d7137f393a3783 | Python | 5,247 | 153 | import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from pyriemann.geometry.mean import gmean
from pyriemann.spatialfilters import Whitening
pytestmark = pytest.mark.numpy_only
n_components = 3
expl_var = 0.9
max_cond = 10
dim_red = [
None,
{"n_components": n_components},
... |
d0be5223ac5696549ccc33e5a05f915c14c45616c27a9e49d4a01f7e4bc01469 | Python | 5,247 | 180 | import gzip
import os
import pysam
from TestUtils import TABIX_DATADIR
FN_COMPRESSED = "example.bed.gz"
FN_UNCOMPRESSED = "example.bed"
FN_LARGE_COMPRESSED = "example_large.bed.gz"
FN_LARGE_UNCOMPRESSED = "example_large.bed"
def read_python_compressed(fn):
'''iterate through with python.'''
with gzip.open(f... |
e1992d87630cdd9076aa2dd0dec657a836d13623bb1266cb4db23eddea461991 | Python | 5,248 | 137 | import SimpleITK as sitk
import numpy as np
from skimage.transform import resize
import nibabel as nib
def read_file(filename):
img = nib.load(filename)
data = img.get_data()
aff = img.affine
return data, aff
def save_img(image, affine, filename):
nifti_img = nib.Nifti1Image(image, affine)
n... |
1f69781711787164f500789635d7b4736d3b602e2507eda9bdfa80634242a480 | Python | 5,249 | 122 | # from this import d
import imp
import os
import math
from re import L
import torch
from torch import nn
import torchvision
from torch.nn.functional import relu
from torch.nn import MaxPool2d
from electric_unit import ChannelRepeat, ComplexConv2d, ConvBlock, ChannelShuffle
import torch.nn.functional as F
import torchsu... |
c7bf52b453c69c834e480fc58f3663ac9f96276eb18de21d75e8c1e62a8f6c48 | Python | 5,249 | 171 | """The bidirectional deprecation-door gate (oracles D6/D21, build item 5).
Contract: a DeprecationWarning fires at CALL TIME if and only if the registry
says the door is deprecated; a registered door's warning must NAME the
registered replacement, and the replacement must RESOLVE. Both failure
directions go red:
- a ... |
e6db23720c141e9ac7a4e4c07a17d3427865dd42ba976756033ae017fba8a9fd | Python | 5,249 | 135 | """Generate OSMnx reference fixtures for the pyrosm <-> OSMnx parity tests.
Downloads OSM data from the Overpass API at the same historical moment as the
``helsinki_region_pbf`` test extract (an Overpass "attic" query via the ``[date:...]``
setting) using OSMnx, and saves each layer to disk as a GeoParquet file. The p... |
7d879f71d4f7059aea5112be510a53da15b4f4fc0561cb2e6448dc48dbd9d1a1 | Python | 5,250 | 185 | import os
import sys
import pytest
from larvaworld.lib import reg
from larvaworld.lib.sim import ExpRun
from larvaworld.lib.process import LarvaDataset
from larvaworld.lib.reg.generators import ExpConf
RUN_WINDOWS_HEAVY = os.environ.get("LW_ENABLE_WINDOWS_HEAVY") == "1"
pytestmark = [
pytest.mark.integration,
... |
246fdf320b0de353f0bd0c67beade07411572581e69bc51f2e9903ae04671f7a | Python | 5,252 | 156 | import random
from pathlib import Path
from typing import Tuple
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import ImageFilter
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset
from logo_data_set import LoGoDataset
class LoGo... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.