sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fa5d760c7a46ce030db897cce66f1f1dc23b132edba6e12c72652c63458fbe7e | Python | 3,588 | 90 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
07c97559859545b0bf1e28d3768765bd4d94c5880436eb01c9b8409d1aa65f9a | Python | 3,589 | 108 | """
Entry point for the larvaworld command-line interface.
Dispatches to either the ``rerun`` subcommand, which replays a simulation from a
stored ``run_manifest.json``, or to the standard :class:`SimModeParser` flow that
configures and launches one of the supported simulation modes.
"""
from __future__ import annota... |
23dc5d6c9009070a7df50b70fdfd0079f7e40414c14a9dd7f57fe8982abcac9e | Python | 3,589 | 113 | """Optional smoke coverage for the technical-preview MLX backend."""
from __future__ import annotations
import pytest
pytestmark = pytest.mark.backend_mlx
mlx = pytest.importorskip("mlx")
import mlx.core as mx # noqa: E402
import mlx.nn as nn # noqa: E402
import torchlens as tl # noqa: E402
from torchlens.backe... |
d63d8555b1a36633973f473066befa84c5f4243b7705ea708185b8300c16a33a | Python | 3,589 | 108 | import torch
from pytorch_lightning import LightningModule
from torch import nn
class Block(nn.Module):
def __init__(self, embed_dim, heads):
super().__init__()
self.ln_1 = nn.LayerNorm(embed_dim)
self.ln_2 = nn.LayerNorm(embed_dim)
self.attn = nn.MultiheadAttention(embed_dim, head... |
07ad3e9566104b4c916f4694d18ac9cc56818790f5b1a25f708e7e846c690845 | Python | 3,590 | 111 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: This example requires torchvision >= 0.17 for tv_tensors support.
# To run with unsupervised segmentation masks instead of a grid:
# pip install lightly scikit-image
# Note: The model and training settings do not follow t... |
19a7578635d764e5a51b0222fef0f39e8c7a45e297b0dcc62ac5827ca34372eb | Python | 3,590 | 90 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
e070839617b846d57e13984d6773f6f2cfaf6697be6d8b2db5dc43552e406222 | Python | 3,590 | 95 | from collections import defaultdict
import numpy as np
def _strongly_connected_components(list_of_nodes, node_successors):
"""
Generate nodes in strongly connected components of graph.
Source: https://networkx.org/documentation/stable/reference/algorithms/component.html
Uses Tarjan's algorithm [1] ... |
463ea449f06e7d83fb18b08be66676f7653f7c5337bef95ed7bca74926ba5bb8 | Python | 3,593 | 92 | # SOURCE: vendored from aaberdam/AdaLISTA @ master (models.py)
#
# Ada-LISTA ("Ada-LISTA: Learned Solvers Adaptive to Varying Models", Aberdam, Golts &
# Elad, ICLR 2021). The `Adaptive_ISTA` class below is copied verbatim from the official
# repo's models.py (only the `scipy.linalg.eigvalsh` import path and stray unus... |
6b0a218b5720784c7f61b163ca5601206ce26c9c12756f58ce04ff7aa315cb0a | Python | 3,593 | 92 | #!/usr/bin/env python
"""Per-package coverage floors for the nightly smoke-tier coverage gate.
r7 R72 (sol b9 MED): the only coverage gate was a single 55% aggregate, so a
meaningful capture/postprocess/validation coverage loss could ship while the
total stayed above the bar. These floors bind the verdict-critical pac... |
def4d044e737a7ec8d43c637a8c6590b5a0c2c3f5f9f98d1a0a50ecdcc101a02 | Python | 3,593 | 115 | import argparse
import ast
import json
import sys
import warnings
import mlxtend.classifier
import mlxtend.regressor
from galaxy_ml import __version__ as galaxy_ml_version
from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
from galaxy_ml.utils import get_cv, get_estimator
from packaging.version i... |
e0719032f1fa109484569dd82212b5081d2d45e16bd4133b39bfec96e8773b6e | Python | 3,593 | 106 | """F10 lovely items 10-11: glance + seam adapters + echo purity.
One record, many densities: the cell formatter and the HTML fixture read
the SAME frozen record the line renders (a number may never exist only in
HTML); the echo observer never contaminates the capture it observes.
"""
from __future__ import annotation... |
009f671f68c73103b60de273841f0ea3ebfdd055926834779c1a9f555cb58102 | Python | 3,594 | 41 | from .base_options import BaseOptions
class TrainOptions(BaseOptions):
"""This class includes training options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
# visdom and HTML visualization para... |
fecfb3364ef69881bd13bd45431f5833c530ec705af7af8735bb9bf1ea1f706a | Python | 3,594 | 121 | #!/usr/bin/env python
import argparse
import inspect
import sys
from rdkit import Chem
from rdkit.Chem import Descriptors
def get_supplier(infile, format="smiles"):
"""
Returns a generator over a SMILES or InChI file. Every element is of RDKit
molecule and has its original string as _Name property.
... |
5c0723320ec26a41f3b455fb6cf22e58f140dcc504a857f27705f740a68e76ac | Python | 3,595 | 89 | import os
import json
import torch
import datetime
import multiprocessing
from itertools import repeat
from model.grud_model import GRUD
from data.dataset_inference import RegionInferenceDataset
from torch.utils.data import DataLoader
from utils.argument_parser import get_argument
from utils.imputation import inference... |
9698b3b0333e02c6bc9de28d7f0f76d15a9ba95a10091ccb23f6527ec2e6454a | Python | 3,595 | 105 | """
Helpers backing the dataset analysis app.
Resolves which plots apply to a selection of datasets and renders them,
keeping the plotting logic out of the UI layer.
"""
from __future__ import annotations
import os
from functools import lru_cache
from hashlib import sha256
from typing import Any, Callable
from larv... |
449275ad1f1da8a6b81351a04ac789cf51621419bcc7804e5461b185b49b63a9 | Python | 3,596 | 95 | import setuptools
from DePTH import __version__
with open("DESCRIPTION", "r", encoding = "utf-8") as fh:
long_description = fh.read()
seeds_list = []
with open("DePTH/data/ensemble_seeds_20.txt") as fp:
Lines = fp.readlines()
for line in Lines:
line_split = line.split("\t")
seed_1 = l... |
cc19862a893345fa3b8e22d824802e5d6762302179eeb3d953c09b2ce7ad31cd | Python | 3,596 | 99 | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... |
56db508972c4c3bd865600b85a5b8c96436186b58765bd9a601ededf58f8b446 | Python | 3,598 | 99 | #!/usr/bin/env python
# Retrieves data from external data source applications and stores in a dataset file.
# Data source application parameters are temporarily stored in the dataset file.
import os
import socket
import sys
import galaxy.model # noqa F401 # need to import model before sniff to resolve a circular imp... |
1f05a9903ba96a7bc58b8c2033c8543c505101bd4559fef9275a165e0c22ad0d | Python | 3,599 | 112 | from modular_splicing.models.modules.sparsity.drop_motifs_in import (
DropMotifsIn,
)
from modular_splicing.models.modules.sparsity.discretize_motifs_in import (
DiscretizeMotifsIn,
)
from modular_splicing.utils.io import load_model
from .entire_model_types import entire_model_types
from modular_splicing.util... |
6d7e8576c5aa069d580f557f5b35468225cbcf514fa73a4655d99610e7b9bd3f | Python | 3,599 | 77 | import torch
def get_freq_single_point(data : torch.Tensor, weights : torch.Tensor, pseudo_count : float) -> torch.Tensor:
"""Computes the single point frequences of the input MSA.
Args:
data (torch.Tensor): Input one-hot encoded matrix (M, L, q).
weights (torch.Tensor): Weights to assign to ... |
951bc5cff2e8b3f8bac8ef454a91b95cef5e95d0b33f5f9f4a3ff9cb714fc5c4 | Python | 3,599 | 96 | import torch
import torch.nn as nn
import torchaudio
from torchaudio.compliance import kaldi
from config.default_hparas import WINDOW_TYPE
class CMVN(torch.jit.ScriptModule):
'''
Utterence-wised zero mean unit variance from
https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/master/src/... |
5e5b8dcfbb4edd7a2fae0eec3bc2c87830ae604a6723739bcfca896db38fa5b3 | Python | 3,600 | 104 | from ._RL_agent import *
from numba import njit
@njit
def mb_forget_diffa_session_value(choices, second_steps, outcomes, n_trials, transition_type, params):
# Unpack parameters.
alpha_pos, alpha_neg, weight_mb, forget = params[:4]
if transition_type == 1:
trans_prob = [0.8, 0.2]
else:
trans_prob = [0... |
f70e947bb00ef6f5bdd621c55bc27d9a4cd071facfc80720ffb8cf3c0c2970a3 | Python | 3,600 | 93 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
f89a4c4002b8dea92c46085408165454d8f14ecb32029219443de51b6151b17f | Python | 3,600 | 127 | # --- 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 Connector(_BaseTraceHierarchyType):
_parent_path_str = "waterfall"
_path_... |
c9582c39549d40977e87622b37240ea0eedc56b3c0384b41cc17916ba26b36f8 | Python | 3,601 | 123 | 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
from .utils import get_opt
from .... |
bb1281b457504b7116f9cb4cdcefa0c0d56b6ca76ac2c70bbadfe18143ada93b | Python | 3,602 | 54 | import argparse
import os
import json
from src.utils.Config import Config
from src.train import train
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Arguments for running any datasets
parser.add_argument('-d', "--data_dir", default=None, help="Data directory containing train/val/test j... |
612707d75c3a818ec5ca69e13d10df3795815ba6b44c946dd3c780aae8bcf5e9 | Python | 3,603 | 97 | class PlotlyError(Exception):
pass
class PlotlyEmptyDataError(PlotlyError):
pass
class PlotlyGraphObjectError(PlotlyError):
def __init__(self, message="", path=(), notes=()):
"""
General graph object error for validation failures.
:param (str|unicode) message: The error message.... |
faf5002a694d5e294ba493128017c7148c3e394a753aa31b0068df3ba76f9cbd | Python | 3,603 | 110 | """Tests for specparam.plts.utils."""
import os
from specparam.modutils.dependencies import safe_import
from specparam.tests.tutils import plot_test
from specparam.tests.tsettings import TEST_PLOTS_PATH
from specparam.plts.utils import *
mpl = safe_import('matplotlib')
plt = safe_import('.pyplot', 'matplotlib')
#... |
2aa90052f467a410cfd6f88c859579956ff253c4f8ef329497659f7fc70fccb2 | Python | 3,604 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
a2d6df1d93cf6c2a9fbc262f133f5ee8237292fc1afe317251d94c160094f160 | Python | 3,604 | 113 | #!/usr/bin/env python3
# LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this sof... |
ad1b604375bb6acebbf0fead98b629559369f198a71093e9a92a39deb909ad03 | Python | 3,604 | 101 | from pathlib import Path
from typing import Callable, Union, List, Optional
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from DeepDenoise.src.dataset import CESTDataset
def get_dataset(
dir: Union[str, Path],
mode: str,
distribution: Optional[List[float]] = None,
noise_std:... |
c8578e8b63dbc2d89f8c37b3c78f8e3b7fdb9701bd709190cdb3c03935c6116a | Python | 3,604 | 81 | # FAITHFUL PORT of aritraghsh09/GaMorNet @ master (original framework: Keras/TF)
# File: gamornet/keras_module.py (gamornet_build_model_keras, LocalResponseNormalization)
#
# GaMorNet (Ghosh et al. 2020, ApJ) -- an AlexNet-style CNN for automated galaxy
# morphology classification (disk / indeterminate / spheroid) from... |
05edd060d01807f3d9ebd5ca8ea3eee48a975cb7b034e8241c0177ad65c98f58 | Python | 3,605 | 99 | import argparse
import glob
import json
import math
import multiprocessing
import os
import re
import sys
from importlib import import_module
from multiprocessing import Lock, Pool
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.utils.data as data
import tqdm
from run_utils.utils import c... |
9a7befc236658b11b5adaebff6c4f45cdbb075aa79736fa1e7a8961cb0fd6166 | Python | 3,605 | 101 | import math
import pytest
import torch
from torch import Tensor
from lightly.loss import pmsn_loss
from lightly.loss.pmsn_loss import PMSNCustomLoss, PMSNLoss
class TestPMSNLoss:
def test_regularization_loss(self) -> None:
criterion = PMSNLoss()
mean_anchor_probs = torch.Tensor([0.1, 0.3, 0.6])
... |
c55040d62f0da61cbac974acf0bc7b784601072a2c459117ac6156232bc227d7 | Python | 3,605 | 106 | """
Generate validation data from the transformed and reference point inside a .csv file
"""
from os.path import join, dirname, abspath
import plotly.express as px
import pandas as pd
from math import sqrt
import json
import csv
import sys
def main(argv):
"""Get the euclidean distance between the points of the re... |
5bcb5b38929cc8109fde6593a6c8f4460a49451c21ec34b9a1e63f840ccd6bd4 | Python | 3,606 | 99 | from typing import List
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Module
class FroSSLLoss(Module):
"""Implementation of the FroSSL loss [0].
FroSSL is a self-supervised objective that combines a variance/redundancy
regularization term with an invariance t... |
a8bbb839d91a62dc758c2eca242dc74233c8068574f6516fdff7d92e39687be5 | Python | 3,606 | 122 | from collections.abc import Callable
from typing import Any, Literal
import numpy as np
from numpy.typing import NDArray
from .._modifiers import jitter, k_shuffle, reverse_complement
from .._numba import gufunc_tokenize
from ..alphabets import DNA
class Sequential:
def __init__(self, *transforms: Callable[...,... |
16ce578155714ec6b6487368ee7196378eb048edeae3518528564b82297bfe3a | Python | 3,607 | 113 | import torch
import torch.nn as nn
from modular_splicing.models.lssi import AsymmetricConv
from modular_splicing.models.modules.lssi_in_model import (
LSSI_MODEL_THRESH,
load_individual_lssi_model,
)
from modular_splicing.utils.construct import construct
class MultipleSplicepointModels(nn.Module):
"""
... |
1f11336d8707b3a3e61d01dfd9639a99edfdb61569eb7297e729b06009a866be | Python | 3,607 | 76 | import os
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
import random
class UnalignedDataset(BaseDataset):
"""
This dataset class can load unaligned/unpaired datasets.
It requires two directories to host training images from doma... |
95fbf62be64b9e64b23d772fe50e0114a9e00894e7843fc585d140a5dcaa1d57 | Python | 3,607 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
07c089d6e4dd29d66940e9294b540ab2db3abf4e805301030ef12ab333570677 | Python | 3,608 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
151cdce5670a23572433095ffeef48f4821b9225a613ddccf5794ee4441f704d | Python | 3,609 | 94 | # miscellaneous tools
import os
import shlex
import subprocess
import sys
from tqdm import tqdm
import HTSeq
import pysam
PATH = './'
def trim_filter_read(input_fastq_read1, input_fastq_read2,
trimmed_read2_output,
tagged_read1_output, **kwargs):
"""
trims the read to UMI_BC1_BC2_BC3, and fi... |
1aa1a8d76d5b060962d9f7356ef13a852ae2432d8a1042fdac747bfbe48e633d | Python | 3,609 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
7d7a07455c070987d1215bb00f3f5f4878f493eb96e6ab95d0f60cd84916fd69 | Python | 3,609 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
9bd196b7fd5e5aeb6893b8cc659058305d5f5f4d41a04b7e1572dfafe58bb590 | Python | 3,609 | 101 | '''Code implementing the two-step task environment.'''
# © Thomas Akam, 2023, released under the GPLv3 licence.
import numpy as np
def withprob(p):
return np.random.rand() < p
# State and action IDs
initiate = 0
sec_step_A = 1
sec_step_B = 2
#Action IDs
choose_A = 3
choose_B = 4
#State IDs
choice = 3
reward_A ... |
a7a4e4f131c0d3e2ba8a8c10deaee84eb17ec4f1d5f76fea4b85dac4644f7cde | Python | 3,609 | 102 | import numpy as np
def compute_weights(labels, classes, count, verbose=False):
if verbose: print("-------------")
sum_weights = 0
for c in range(len(classes)):
if classes[c] / count > 0:
sum_weights += count / classes[c]
sum_weight_norm = 0
weights = list()
for c in range(... |
28506f055baf3d45da74ad7a7b6c4d135acec0b4952aceaac3b2688987682b20 | Python | 3,610 | 90 | import os
import sys
import json
import click
import shutil
from io import BytesIO
import numpy as np
from zipfile import ZipFile
from glob import glob
@click.command()
@click.option('-s', '--stylegan-path', default='stylegan2-ada-pytorch', show_default=True,
help="Location of the SyleGAN repository.")
... |
4a78c6e1ee771b2fc9842dfee0c665dcf350a49b1f4e475dca3b8fe70ec0bf88 | Python | 3,610 | 118 | from pathlib import Path
from typing import Dict
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from torch.nn import Module
from torch.utils.data import DataLoader
from lightly.data import Li... |
9c68637f8eb388e0eb0591c28d3df425c3ab147bf88c9312c675632c875cfe5d | Python | 3,610 | 63 | """Canonical composite authority for the redesigned data layer (Phase 1a/1b, 2026-06-16).
THE single implementation of the framework composites. `assemble_composites` holds the
arithmetic (lifted verbatim from `phi.py`'s composite block) so there is exactly ONE place
it lives — no mirrored second copy kept bit-identic... |
d25c788955fff89ae1d0929b6fefc3e3c8628710b54327f3705bbed34377c8fe | Python | 3,610 | 106 | import math
import cv2
import matplotlib.cm as cm
import numpy as np
from scipy import ndimage
from scipy.ndimage import measurements
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import affine_transform, map_coordinates
from skimage import morphology as morph
from misc.utils import... |
90923a0df7e945b5c62e46607835d87d8d6ead88e544981a0c1d27eebddb318e | Python | 3,612 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
d6c29c7933b2da193742c53ff04d8477115dee926cdcee0bc48050f685be7b74 | Python | 3,612 | 113 | import os
from random import sample
import cv2
import matplotlib
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from skimage import io
from skimage.transform import resize
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.manifold import TSNE
from model imp... |
23dccd4cbc974295f8499c73d4a4b216f04335654837e47c6cae6e9561892560 | Python | 3,613 | 115 | """Test cases for to_bitmasks.py."""
import os
import shutil
import unittest
from typing import Callable, List
import numpy as np
from PIL import Image
from scalabel.common.typing import NDArrayU8
from scalabel.label.io import load
from scalabel.label.typing import Config, Frame, Label
from bdd100k.common.utils impor... |
0811975fad60e36010b90cb3892f24b51b8ad9bc34553aa53974d0aa68d1bba3 | Python | 3,614 | 92 | import sys
from robustness.datasets import jsinV3
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import JSIN_PATH, MODEL_BASE_PATH
import os
# Make a custom build script for audio_rep_training_cochleagram_1/l2_p1_robust_training
def build_net(include_rep_in_model=... |
5c45300328ee9821cefac53264acca343ae100ccc7e73441fee63587ecee599e | Python | 3,615 | 130 | #!/usr/bin/env python
# Greg Von Kuster
import sys
# from rpy import *
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
r = robjects.r
grdevices = importr("grDevices")
assert sys.version_info[:2] >= (2, 4)
def stop_err(msg):
sys.stderr.write(msg)
sys.exit()
def main():
#... |
5067718c34460adc5c7ff86a7b4728f956cc816444ceb66f7a570c486899b4c8 | Python | 3,616 | 107 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
851c6ee1d6ac6599b874e87135ad669d4dbfdac00df23eb4a7593484aeac186a | Python | 3,616 | 92 | # -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 19:43:47 2020
@author: JUN MA
"""
import numpy as np
import nibabel as nb
import os
from collections import OrderedDict
import pandas as pd
from SurfaceDice import compute_surface_distances, compute_surface_dice_at_tolerance, compute_dice_coefficient
join... |
8bf6f07dd02f529ea1b687772bc276a02c5fcf37b1f13f9ae6f9d8edfa4f26a5 | Python | 3,616 | 61 | # Copyright 2019 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
2ed07de7e6969699ca0c0f2bdaa5aaa5cae0ccb2297d3eace80a78733ba0bb4b | Python | 3,617 | 115 | import sys
import string
import json
import itertools as it
from operator import itemgetter
import collections
from argparse import ArgumentParser
def main():
args = get_args()
traces = collections.defaultdict(list)
chroms = collections.OrderedDict()
chroms["total"] = True
for f in args.input:
... |
913913402a813af8cdc4dd680d627336cdd896e6f6fba55272b60760b301c02e | Python | 3,617 | 37 | from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self, parser):
BaseOptions.initialize(self, parser)
parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen')
parser.add_argument('--display_ncol... |
59c122d56a06c913eea8bc4663f318bc37ce425bce199b839101721bbfdd14f8 | Python | 3,618 | 43 | from .base_options import BaseOptions
class TrainOptions(BaseOptions):
"""This class includes training options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
# visdom and HTML visualization para... |
4b57b54f05cbdb52d45365d6827e134dfad8005a257a8f03a59e80fd9be0dd8e | Python | 3,620 | 101 | import os
import logging
import pandas as pd
import torch
import nibabel as nib
import numpy as np
def create_logger(folder):
"""Create a logger to save logs."""
compt = 0
while os.path.exists(os.path.join(folder,f"logs_{compt}.txt")):
compt+=1
logname = os.path.join(folder,f"logs_{compt}.tx... |
61bbb92c6d3fb7e1e52ff381a4d7d7805e4677c33e85c2175bc68a794499738b | Python | 3,620 | 120 | #!/bin/usr/env python
from adpred import ADpred
import sys
import numpy as np
HELP = '''
using adpred version {}
list of arguments
-----------------
-h | --help
-id | --uniprot-id
-s | --sequence
-l | --local-psipred <path_to_"run_psipred">
-... |
8036071242ab74fdd2ed85daae40baf8d832313995cc98a7995cbd331632dd4d | Python | 3,620 | 104 | """
===============================================================================
Display ERP
===============================================================================
Different ways to display a multichannel event-related potential (ERP).
"""
# Authors: Quentin Barthélemy
#
# License: BSD (3-clause)
import ... |
a4a9312daf7c773d9035fcf0c4e3cb7b044b575227cab8f56fb1312516b79907 | Python | 3,620 | 127 | import argparse
import numpy as np
from rdkit import Chem
from rdkit.Chem import rdShapeHelpers
def get_mol_from_file(fname, ftype):
if ftype in ["mol", "sdf"]:
mol = Chem.MolFromMolFile(options.ligand_path, strictParsing=False, sanitize=False)
elif ftype == "pdb":
mol = Chem.MolFromPDBFile(o... |
e1ab0f7b6db64d6261f2670be7739be98ed744e3e0aafdb79c247e1141c9a01a | Python | 3,620 | 113 | """Deprecation utilities."""
import warnings
from vine.utils import wraps
from celery.exceptions import CDeprecationWarning, CPendingDeprecationWarning
__all__ = ('Callable', 'Property', 'warn')
PENDING_DEPRECATION_FMT = """
{description} is scheduled for deprecation in \
version {deprecation} and removal ... |
87149af4fbf1eef0743e5eea5d3e021c8bf165973733ce8727032a4e7405690e | Python | 3,621 | 115 | """Regression tests for Torch backend per-output field handling (M7)."""
from __future__ import annotations
import copy
import pytest
import torch
import torchlens as tl
class _SingleOutputModel(torch.nn.Module):
"""Model with one ordinary tensor output from a wrapped torch call."""
def forward(self, x: ... |
ad7681518927ccf85f201bdf9ca8665d38538f4bbd60f5401bb0da4df1c22a9a | Python | 3,621 | 96 | #!/usr/bin/env python3
import os
import sys
import re
import json
import ast
import argparse
parser = argparse.ArgumentParser(description="Get final table for assembly pipeline",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-s", "--stats", nargs="+", ... |
411678b0363b87d8b54c9de4c32d5dda5b7d16f4e4aeff8ce48da7dfad844e3e | Python | 3,622 | 104 | import unittest
import requests_mock
from pydantic import ValidationError
from codecarbon.core.telemetry_client import TelemetryClient
from codecarbon.core.telemetry_schemas import TelemetryCreate
class TestTelemetryClient(unittest.TestCase):
def test_init_sets_up_client_without_calling_api(self):
with ... |
be420e601a043676828eb96482e1da16106517f0d250dc50d590da5cf78fd237 | Python | 3,623 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data load #
df1 = pd.read_csv('./data/GSE75688_GEO_processed_Breast_Cancer_raw_TPM_matrix_gene_filtering_log_nontumor198tumor317_deg.csv', index_col=0) # dimension = (genes x cells)
df2 = pd.read_csv('./data/GSE75688_GEO_processed_Breast_Cancer_raw... |
37649a8a50e56d55684aace5e93b4c6c977ebbb6b6c5f9f43baebbf8f3b40a9c | Python | 3,624 | 136 | import sys
import numpy as np
import random
from random import randint
import scipy as sc
import scipy.stats
from scipy.stats import pareto
def GetPauseLength(muPause, pattern='alternate', pausedLast='false'):
if (muPause < 1):
return 0
if (pattern == 'alternate'):
if (pausedLast is True):
... |
7449d8080f6352d41c7b063bc34ebeccce37877a05d5031ffd3a711ce83226c6 | Python | 3,625 | 118 | from pathlib import Path
from typing import Dict
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from torch.nn import Module
from torch.utils.data import DataLoader
from lightly.data import Li... |
93a414944d12b9511ea4c962ea126ed4761ac01b9a8be88b162413e7840d81e8 | Python | 3,625 | 113 | # -*- coding: UTF-8 -*-
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import math
import random
import operator
import csv
from sklearn.metrics import f1_score
'''
@author:hm
@time:2022.01.03
'''
class RF(object):
def __init__(self):
pass
def loadDataset(self,filename, split,... |
0c66cdaa825038f3aba2c161297e44ac4207d47514262a5c8b3ad8772d530030 | Python | 3,626 | 99 | """Download files with progress bar."""
import os
import re
import hashlib
import requests
from tqdm import tqdm
def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
----------
filename : str
Path to the file.
sha1_has... |
0fc0a1f61015c8a4065ef428d418d85bf4d7ede492c999df293ba6393ab3e71a | Python | 3,626 | 110 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in complian... |
226cd7ca5c25a5a74b700de53391c821660b712514a75a686cdefce6539e0bdd | Python | 3,627 | 96 | import re
import pathlib
import attr
from pyasjp.api import ASJP
from clldutils.misc import slug
from pycldf.sources import Source
import pylexibank
from pyasjp.models import MEANINGS_ALL
@attr.s
class Doculect(pylexibank.Language):
classification_wals = attr.ib(default=None)
classification_ethnologue = att... |
6aabcfe69d1b0881c4d217fe0fd3cb5da4970b4ecfd04b56bf1d85fd5d181e9e | Python | 3,627 | 106 | # coding=utf-8
# Copyright 2023 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... |
da75f8796f86377b7fa9683b52ac979c96ebf39b62af41b386ba2bd7396fdda3 | Python | 3,627 | 87 | from tensorflow.keras.layers import Conv1D, BatchNormalization, Activation, Dense, Input, Cropping1D, add, Flatten
from tensorflow.keras.models import Model
from Configurations.fd1t_cfg import sim
from Configurations import config as cfg
from Configurations import CNN
from Utilities.common_packages import np, tf
def ... |
18bd73fe360e87ade2b235911678edb8c6986e1731a2ac6c099b9f73094f38e6 | Python | 3,628 | 87 | """Pre-flight detection of model wrappers TorchLens cannot instrument.
Some third-party wrappers redirect attribute ASSIGNMENT on the model object
to their wrapped components. TorchLens prepares a model for capture by
assigning instrumented forwards onto its modules; when the wrapper forwards
those assignments elsewhe... |
b6c6bddeac72780ad4552f128dd4250ca97d07a165ecf4591808d9ab6a4c5b7e | Python | 3,628 | 109 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
6621376e8afa85c37170cc040dda9c0b5ac15eca4e705dffcaec6f002185d272 | Python | 3,629 | 115 | # --- 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 Axis(_BaseTraceHierarchyType):
_parent_path_str = "splom.dimension"
_path... |
139c04ff45bb5f6db1be65516ac4819aceb611ad535ef27585b73317a8faa5a8 | Python | 3,630 | 121 | """torch.compile rejection tests for train-mode capture APIs."""
from __future__ import annotations
import warnings
from collections.abc import Callable
import pytest
import torch
import torchlens as tl
from torchlens._capture_state_helpers import reset_compiled_model_unwrap_warning_state
from .conftest import Two... |
19bee9cc2c987e4c3c4a1cb0c923da9cf6222a93e976f310cf2aa506ab005d0c | Python | 3,631 | 117 | #!/usr/bin/env python3
import numpy as np
import pandas as pd
import logging
import sys
import os
import logging
import utility
import click
# get logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(module)s: %(message)s',
datefmt='%Y-%m-%d %H:%M',
)
@click.command()
@cl... |
c31b6b548e38407f268239f70a78f069907a5a535a812d8c341057707aa4b1df | Python | 3,631 | 105 | """
===============================================================================
Display ERP
===============================================================================
Different ways to display a multichannel event-related potential (ERP).
"""
# Authors: Quentin Barthélemy
#
# License: BSD (3-clause)
from ma... |
2673be8dfad19238d6ebacf8eac82f4b0b1355709cf22c8fe4d21fa70dcc4026 | Python | 3,632 | 124 | import os
import logging
import joblib
import yaml
import lifelines
from sklearn.base import BaseEstimator
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline
import pandas as pd
try:
import torch
import torchtuples as tt
from pycox.models import CoxPH
except:
print("... |
b4a609fe8058f869370088d359de80df787a54d3db4e4dadfc493e6eb9f161ca | Python | 3,632 | 105 | import os
import mne
import pandas
from mne.datasets import fetch_fsaverage
from mp_dip.research.show_example_dipole import show_example
from mp_dip.utils.interpolate_head_features import get_positions, \
INTERP_SIZE
from mp_dip.utils.multivariate_atom_analysis import example_to_dipole, \
get_kernels
import n... |
99815c06ca2c078eb38762270278f5003e9e79883187d47e25edc0dded328a35 | Python | 3,633 | 108 | from collections.abc import Sequence
from typing import Literal
import scanpy
from anndata import AnnData
from leidenalg.VertexPartition import MutableVertexPartition
from numpy.random.mtrand import RandomState
from scipy.sparse import spmatrix
def leiden(
adata: AnnData,
resolution: float | None = None,
... |
0fb16ffe1964b12bd80735825f00e3e9b087f11a534eb553e0e9d4efa6c63149 | Python | 3,634 | 114 | # --- 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 Line(_BaseTraceHierarchyType):
_parent_path_str = "waterfall.totals.marker"
... |
2f05bdbe610d06f073c79118abf6ba55ed2d76e9943dc0e0254bcbb77345c4cd | Python | 3,634 | 95 | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow.python.keras.layers import embeddings
import sys
print(sys.path)
import random
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from drug_interactions.features.get_features import get_features
from drug_interactions.reader.... |
4b016d087726682c40cd90c2d1c65a6f8ccd2984791ae4146e570160b449762c | Python | 3,634 | 107 | # 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... |
b0724f4b136385309251ac08ac87e90e1ac3a6b85dd7d81cec27ef8d46194381 | Python | 3,634 | 102 | # -*- coding: utf-8 -*-
# @Author : Yue Liu
# @Email : yueliu19990731@163.com
# @Time : 2021/11/25 11:11
import os
import sys
import torch
import logging
import numpy as np
def load_graph_data(root_path=".", dataset_name="dblp", show_details=False):
"""
load graph data
:param root_path: the root pa... |
ef71d8f9f50e8b83a2c1a7f7689fccd3b67996d3184cf7e9392bafee8f701e8b | Python | 3,634 | 107 | from .bulletin import Bulletin
from bs4 import BeautifulSoup
import json
class Punjab(Bulletin):
def __init__(self, basedir):
statename = 'PB'
super().__init__(basedir, statename)
self.base_html = 'http://covaprod.punjab.gov.in/covid-response.html?language=e'
self.root_url = 'htt... |
f7433f2633951acda9e13868b555b51fe33726fd462503c839efb071fdc4d4de | Python | 3,634 | 113 | # -*- coding: utf-8 -*-
"""
@File : cluster_lasso.py
@Time : 2022/06/29 10:43:55
@Author : LuluZuo XueWang
@Version : 1.0
@Desc : spatial cluster lasso
"""
from typing import Optional
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
from ipywidgets import VBox
class... |
53c0c0d284ce7b68d619c486fdb0503f6214927d8631c8e4d83b59cc8377e002 | Python | 3,635 | 131 | # --- 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 Title(_BaseTraceHierarchyType):
_parent_path_str = "funnelarea"
_path_str... |
72d2e501e32c62a2254033fcf260bb93131cd2b1c86603e8978ad7ca049d06c0 | Python | 3,635 | 82 | import argparse
import torch
from helpers.flop_count import count_macs, count_macs_transformer
from helpers.peak_memory import peak_memory_mnv3, peak_memory_cnn
from models.mn.model import get_model
from helpers.utils import NAME_TO_WIDTH
from models.preprocess import AugmentMelSTFT
def calc_complexity(args):
# ... |
7d669f1e1d987fea128bfc6c403afa9ce758f869317ed3ca65c486cf0e2b74e6 | Python | 3,637 | 114 | # --- 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 Line(_BaseTraceHierarchyType):
_parent_path_str = "sankey.node"
_path_str... |
83044a025eb7d9e04876d026aea51646624963c7dd7562a77f225bb6276727c7 | Python | 3,637 | 114 | # --- 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 Line(_BaseTraceHierarchyType):
_parent_path_str = "sankey.link"
_path_str... |
a5d38971a799d26a6d1b571e2cc65e9fef0e77402fcb9666a9c729dcb795bdcb | Python | 3,637 | 89 | # %% [markdown]
# # Image-labels metadata
#
# To ease the interpretation of label images and make them identifiable as label images,
# it is helpful to add the [`image-label` metadata](https://ngff.openmicroscopy.org/specifications/0.5/index.html#labels-metadata) to the written label image data.
# The {py:class}`ome_za... |
cea71e8ef40f66aa8245face16d5bd1f752d79f18e1f8235b0ea95a1ea803432 | Python | 3,637 | 91 | """
Target generator for Simple Baselines for Human Pose Estimation and Tracking
(https://arxiv.org/abs/1804.06208)
---------------------------------------------
Copyright (c) Microsoft
Licensed under the MIT License.
Written by Bin Xiao (Bin.Xiao@microsoft.com)
---------------------------------------------
"""
import... |
d0b0af30fa366feb67d1992e4146eb146f001661fb4457b167dec63db5f45ecf | Python | 3,637 | 122 | import numpy as np
import pytest
from pytest import approx
from pyriemann.utils.geodesic import (
geodesic,
geodesic_euclid,
geodesic_logeuclid,
geodesic_riemann,
)
from pyriemann.utils.mean import (
mean_euclid,
mean_logeuclid,
mean_riemann,
)
def get_geod_func():
geod_func = [geodes... |
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