sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9c55f17e3ff80ad90ec10528845d4324f372ca57532f2418bc731d1a30514385 | Python | 4,903 | 132 | #Autor: Carlos Alberto Hernández Nava
#ASVspoof2017 spectrograms MFCC 2D
import cv2, sys, os, gc
import librosa
import numpy as np
import pandas as pd
import seaborn as sns
import openpyxl as pxl
import tensorflow as tf
from tensorflow import keras
import IPython.display as ipd
from numpy.random import randn
from pand... |
a8b898e135840f7924c5523b6b83249662a5d8fe7be18d56349944c1e7d966a4 | Python | 4,903 | 178 | from abc import ABC, abstractproperty
from torch import nn
import torch
from torch.nn import functional as F
from modular_splicing.utils.construct import construct
class GenericLinearSRMP(nn.Module, ABC):
"""
Short range motif reprocessor that uses a linear function to
compute the influence of each motif... |
1bbd127119d3097dbe3025e3322492d29f453fd76c70b0c376121dfd8a20e588 | Python | 4,904 | 102 | #!/usr/bin/env python3
"""
File: create_PICALO_jobs.py
Created: 2021/11/23
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 tree.
"""... |
a9b0290bc235a2ccea363de2f248806be0a460d8c4949210762198741cda3fc2 | Python | 4,904 | 173 | """
Widget for editing an environment's thermal landscape.
"""
from __future__ import annotations
from typing import Any, Callable
import panel as pn
import param
from .widget_base import classattr_section, parameterized_editor, widget_block
__all__ = ["build_thermoscape_widget"]
_DEFAULT_THERMO_SOURCES = {
... |
74c22467c0b5d006000dd0aefc1bd70e30f2efac944b1256c86f7bb1589a9fd7 | Python | 4,905 | 177 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from math import floor
import numpy as np
from numpy import newaxis
FULL_SLICE = slice(None)
FULL_AXIS_SLICE = (FULL_SLICE,)
INT_TYPES = (int, np.integer)
def digitize_index(index: float, values: np.ndarray, tol: float=None):
"""Locate a scalar ``index``... |
d24c6f0ca02329278a98b876c8d1e1546ff83283e3b95d0593f408cbd0bae7a6 | Python | 4,908 | 129 | '''
This module is used to process the data to make it the standardized csv file,
ready for loading into the GNN module. Specifically, this module makes the
txt files for the Glasgow Subgraph Solver (GSS).
Commands for running the GSS along with the sed commands to clean up the
GSS output are included in the comments ... |
1d2fdaea7852d2d36238126e52476467e77b057d553fbf7e22b13114746f7f51 | Python | 4,909 | 148 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Line(_BaseLayoutHierarchyType):
_parent_path_str = "layout.newshape"
_p... |
2955b9d180be5fa174c50f4089a11f491c72561cbc225ace25e35f64d7368a61 | Python | 4,909 | 138 | """DDP unwrap smoke tests for fastlog."""
from __future__ import annotations
from collections.abc import Iterator
from pathlib import Path
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.distributed import has_vetted_snapshot
class DdpModel(nn.Module):
"""Simple model for... |
7cea0d2d793af3d627d7a951fc6d0080783cac1dfa52eb23d2817c08627137aa | Python | 4,909 | 137 | # --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --... |
25a95714c1c39c03da3f2fbe789f3184d44d8f41e454a5e305d261ea6a0e61dd | Python | 4,911 | 118 | import collections.abc as container_abcs
import re
from queue import Queue
from threading import Thread
from typing import Any, Optional, Union
import torch
from torch import Tensor
from torch._six import string_classes
from torch.utils.data import DataLoader, Dataset
class AsynchronousLoader:
"""Class for async... |
9e1874949cbc4432758c927d3801a174395416c0c15ee25fe922789189ebf823 | Python | 4,911 | 171 | import os
import cv2
import matplotlib
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
matplotlib.use("QtAgg")
import numpy as np
from skimage import io
from skimage.transform import resize
from sklearn.model_selection import KFold
from model import... |
e3454b77cb744557ce337469b08f5765526c314134fc0b166c6b1ddb8366c35d | Python | 4,911 | 141 | from biss.backend import BissClassifier, save_prediction, get_volume_share
from glob import glob
from os import path
import threading
import time
from IPython.display import clear_output
from IPython.display import display
#from google.colab import files
from zipfile import ZipFile as zip
from ipywidgets import Layout,... |
a7c104f4e496f445039254b6060ddc3a4b7a316ca225019ad0e0964707e701d1 | Python | 4,912 | 162 | #! /usr/bin/env python
import fnmatch
import os
import re
import warnings
import numpy as np
import tifffile as tf
from scipy.ndimage.filters import gaussian_filter
def main(infile, nx, nz, sig=1, pad=12):
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
indat... |
e12c4b67ffe996193e823ea0fab211405bf5640de67a367a600cad23ae5627be | Python | 4,912 | 131 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in November 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
@author: Thomas Bonald <tbonald@enst.fr>
"""
from typing import Optional, Union
import numpy as np
from scipy import sparse
from sknetwork.classification.base import BaseClassifier
fro... |
ffcf51592d86c7a64c14b874e475fd6d313111d0d29954fae0b9070b7a685d1c | Python | 4,912 | 136 | """
Plot impact of single-trial history bias as well as of signed stimulus strength
on amplitude and slope of M1 beta lateralization
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from mne.stats import permutation_cluster_1samp_test
import seaborn as sns
from mpl_axes... |
668721f7e13d36fa55439dce605de8ce641b87a6fdcc1da4973217c5616680ae | Python | 4,913 | 146 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
import pandas as pd
import argparse
import os
import sys
import pwd
import pdb
import csv
import re
import math
from collection... |
38f3ac6ceb22febe8cea0b670aaacc1e6cf2ed1118504433fe3bf3ea142cf234 | Python | 4,914 | 151 | # %%
'''
Code to take individual subject parameter estimates from SPM in
regions of interest.
We use the mean within rois / spheres.
Also an 'eroded' version where ROI size has been controlled for
'''
import sys
import nibabel as nib
import numpy as np
import pandas as pd
from nilearn.masking import apply_mask
from ... |
33ac1dc066862a9d7b930eb0a9e9fb42b69b00a5a094150b1ce1ff27d49e121a | Python | 4,915 | 155 | from unittest import mock
import pytest
from api.mocks import FakeAuthContext
from carbonserver.api.errors import UserException
from carbonserver.api.infra.repositories.repository_emissions import (
SqlAlchemyRepository,
)
from carbonserver.api.schemas import Emission, EmissionCreate
from carbonserver.api.service... |
4335f8c640ab87f6adf78adac9e1b58195f9c7725575e9e00ea9b3de99ddd485 | Python | 4,916 | 141 | import numpy as np
from scipy import interpolate
from mitochondria import Mito, to_dpsi
from utils import Recorder
def get_steady_state():
try:
kk = np.load('./steady_state.npz')
baselines = kk['baselines']
all_nads = kk['nad_ss']
all_atps = kk['atp_ss']
all_psis = kk['psi... |
85cc4961bf7b85212059585773c3bc36504abf70d7b93980cf8872bb2b693f06 | Python | 4,916 | 161 | import json
import os
import warnings
import SimpleITK as itk
import torch
import torchio as tio
itk.ProcessObject.SetGlobalWarningDisplay(False)
warnings.filterwarnings('ignore')
def set_transform(size, depth, resample, mode):
preprocess = tio.Compose([
tio.ToCanonical(),
tio.Resample(resample)... |
c34b4742332594d8f8a3c2d0e4d086bd929005738787b973d34f23d2d443d864 | Python | 4,916 | 170 | __author__ = 'quentin'
from git import Repo
from git import GitCommandError
import logging
import traceback
import os
class DeviceUpdater(object):
def __init__(self, git_working_dir):
self._git_working_dir = git_working_dir
self._working_repo = Repo(git_working_dir)
self._origin = self.... |
72e9f15b27f54809d4ebff577d80c533b2de570ae25e9458374de7333e522c09 | Python | 4,918 | 114 | import torch
from pytorch_lightning import Trainer
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
from lightly.transforms.torchvision_v2_compatibility import torchvision_transforms as T
from lightly.utils.benchmarking import FinetuneClassifier, LinearClassifier
... |
fd20bcf18fc87408d4296b571f98fecbb8b3952bb1220bd8bff4faf98dea840a | Python | 4,920 | 121 | """
This module define MetricAccuracy
"""
import logging
from metrics.generic_metric import GenericMetric
import numpy as np
import pickle as pkl
import torch
class MetricFMax(GenericMetric):
"""
This class define the accuracy metric
"""
def __init__(self, data_for_metric):
self.number_of_thr... |
1a437b614d7de8f9f580cf955e800c43161a1f535bf0f21f1f0679c1a459f1d0 | Python | 4,921 | 161 | import json
import pytest
from database import CategoryModel
category1_id = 0
category2_id = 0
category3_id = 0
class TestCategory:
@classmethod
def setup_class(cls):
CategoryModel.objects.delete()
@pytest.mark.run(before='test_post_categories')
def test_get_empty(self, client):
re... |
effc092d7c0119706afa68277766a310fe2a1141334c74d6737ba5eaa558a69d | Python | 4,921 | 168 | # --- 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 Domain(_BaseTraceHierarchyType):
_parent_path_str = "pie"
_path_str = "pi... |
aa13bec452b0dbd4d853b248266778de96a5b989e045925c05246c6528c5d961 | Python | 4,922 | 147 | from __future__ import annotations
import pytest
import torch
import torchvision
from lightly.models import utils
from lightly.utils import dependency
if dependency.torchvision_vit_available():
from torchvision.models.vision_transformer import VisionTransformer
from lightly.models.modules import MAEBackbone... |
da707aeaeb26cd8ce78b453c761909c43f98f671a99d45d3068fef7085a090ce | Python | 4,923 | 142 | # ----------------------------------------------------------------------------
# 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.
# --------------------------------------------------... |
2ae11c9ee297aa826a8cb04a0963083517286f5a77e9951afc3c26e0eeb1411e | Python | 4,924 | 131 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import io
import os
from pathlib import Path
from setuptools import find_packages
from setuptools import setup
from Cython.Build import cythonize
def read(*names, **kwargs):
with io.open(
... |
5ca4c320fc34cc08c800b3bc446312b9acc8944e0ad5ef4e6625a3e5ae792c68 | Python | 4,927 | 134 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... |
9efd762d2f3523cc103f651addb15648d0ec448a05abe4eab7c7eec32448f676 | Python | 4,927 | 131 | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... |
0c74a0f3fe5fa889f64bcdbec4ed2bd65cba7948b62fcc318eb58a7ba641af26 | Python | 4,928 | 155 | import warnings
import json
import random
from .base import Renderer
from ..exporter import Exporter
class VegaRenderer(Renderer):
def open_figure(self, fig, props):
self.props = props
self.figwidth = int(props["figwidth"] * props["dpi"])
self.figheight = int(props["figheight"] * props["dp... |
7e36d0cd393d5ae316c15f6d7f69e8d8dbd903d8ca313fbff007f5ca97782032 | Python | 4,929 | 148 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Line(_BaseLayoutHierarchyType):
_parent_path_str = "layout.newselection"
... |
ddb824627674b896f9d5b953828e7e6b110e46d185337e2b5ae272cf2f44de62 | Python | 4,929 | 150 | import numpy as np
import Code_General_utility_spikes_pickling as util
from copy import deepcopy as dcp
import matplotlib.pyplot as plt
import Code_General_Nassi_functions as nf
__RES_DPI = 48
_CND = 600
_THRESHOLD = -20
_DISP = 0
_N_NEURONS = 10
_N_RUNS = 10
_N_STIMULI = 18
_T_STIM = 2 # s
_T_STIM_PRESENT = 500 # m... |
c3e88b63fc794772097ea4327eea764deea515f686a0fb8c47df594ea7b0e3e7 | Python | 4,931 | 135 | # SOURCE: vendored from
# https://github.com/dspanah/Sensor-Based-Human-Activity-Recognition-DeepConvLSTM-Pytorch
# @ master (notebooks/1.0-dsp-DeepConvLSTM.ipynb, cell defining `HARModel`)
#
# DeepConvLSTM (Ordonez & Roggen 2016, Sensors 16(1):115) for wearable-sensor
# human activity recognition: four stacked 1D-conv... |
e0aa2e44bd3c6254a1b0c9bb161fdd30ff0fef28ae255dffe2a193bb8b62cece | Python | 4,931 | 109 | '''Tests for bdpy.bdata.metadata.'''
import unittest
import numpy as np
from numpy.testing import assert_array_equal
from bdpy.bdata import metadata
class TestMetadata(unittest.TestCase):
'''Tests for bdpy.bdata.metadata.'''
def __init__(self, *args, **kwargs):
super(TestMetadata, self).__init__(... |
04f95788c2e68e5fa59d5c687d02103939cb9c3117f58ebe0192d33b122133c3 | Python | 4,933 | 139 | #!/usr/bin/env python
import argparse
def main(args):
if args.header:
h1 = True
h2 = True
else:
h1 = False
h2 = False
cache = list()
out = open(args.outfile, "w+")
write_buffer = list()
def _readline(header=False):
with open(args.f2) as handle2:
... |
566cfae3e538cf8bd80ce6ccf6ee630a4bb7ee5f8c8cd0722dfe9db3bc2e190e | Python | 4,933 | 142 | # 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.
import torch
import to... |
89812f49ecd8af26865773d5def1d99e6fb93045c6401b829c2a4faec077deee | Python | 4,933 | 152 | import numpy as np
import scipy
import scipy.spatial
import string
import os,re
import random
import util
to1letter = {
"ALA":'A', "ARG":'R', "ASN":'N', "ASP":'D', "CYS":'C',
"GLN":'Q', "GLU":'E', "GLY":'G', "HIS":'H', "ILE":'I',
"LEU":'L', "LYS":'K', "MET":'M', "PHE":'F', "PRO":'P',
"SER":'S', "THR":... |
fe41b6e75e2e6e18b841dea6b5f3de92b1acd4eeb73d1bd3027a016a0fefa3ad | Python | 4,933 | 151 | import numpy as np
import scipy
import scipy.spatial
import string
import os,re
import random
import util
to1letter = {
"ALA":'A', "ARG":'R', "ASN":'N', "ASP":'D', "CYS":'C',
"GLN":'Q', "GLU":'E', "GLY":'G', "HIS":'H', "ILE":'I',
"LEU":'L', "LYS":'K', "MET":'M', "PHE":'F', "PRO":'P',
"SER":'S', "THR":'... |
457c02fdc8b66b0e2bbea7918d3e5cef4f77452abdfb4cd878516c79411d942e | Python | 4,934 | 141 | """Functionality used by multiple algorithms"""
# from collections import namedtuple
import numpy as np
from tns.morphmath import bifurcation as _bif
bif_methods = {'bio_oriented': _bif.bio_oriented,
'symmetric': _bif.symmetric,
'directional': _bif.directional,
'random': ... |
71a14581ee2bee92ad046134defb2f201af78fddaa65287943578b15e94f5ab6 | Python | 4,934 | 128 | import sys
sys.path.extend(["src"])
from src.predict import evaluate_model
import matplotlib.pyplot as plt
import utils
import numpy as np
from prodict import Prodict
import matplotlib.lines as mlines
def plot_select_cases(cfg: Prodict) -> None:
"""Generates patient-level plots comparing TDNODE-generated predic... |
8426f4660922626ff109c98676b65e06e87765fa6af67c620f8a3cb5eb9b1cec | Python | 4,934 | 115 | import cv2
from PyQt5 import QtCore
from score_behavior.score_analyzer import FrameAnalyzer
from score_behavior.global_defs import DeviceState
import logging
from .dialog_controller import TrialDialogController
logger = logging.getLogger(__name__)
# noinspection PyAttributeOutsideInit
class ObjectSpaceFrameAnalyzer... |
3bfcdae3d83aa1f5ff58283de32650294146c77b62ca479983ddefe704842937 | Python | 4,936 | 112 | from typing import Tuple, Iterable, List
import torch.nn as nn
from torch import Tensor
class InvertibleModule(nn.Module):
"""
Base class for all invertible modules in FrEIA.
Given ``module``, an instance of some InvertibleModule.
This ``module`` shall be invertible in its input dimensions,
so t... |
63e2dfeaf90672519b7988f17611760a41870e42b30a2d4d904673be853b2e20 | Python | 4,937 | 124 | import argschema
import marshmallow
import platform
from typing import Union
from pathlib import Path, PureWindowsPath
from nway.onprem_utils import query_utils
from nway.schemas import (CellROISchema, OnPremExperimentSchema,
ExperimentContainerSchema,
OnPremGenerated... |
6a170e839a7af27368c3aff0cb9a6b6182b4299ab6da59b40ac267f00c833022 | Python | 4,937 | 148 | """Validation of dependencies of packages
"""
from collections import namedtuple
from pip._vendor.packaging.utils import canonicalize_name
from pip._internal.operations.prepare import make_abstract_dist
from pip._internal.utils.misc import get_installed_distributions
from pip._internal.utils.typing import MYPY_CHECK... |
740b7e24e1960ed2e09ee262b0351fd5af2350ed1f2edaf088ab9761860c7f3a | Python | 4,938 | 112 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import d... |
94de260fcc500dd3393922b4cf7ba8201d72d83a754ba062813aac91edbdf3c5 | Python | 4,938 | 110 | from datetime import datetime
import warnings
import sklearn
import experiments.config as config
from datasets import load_from_disk
import auggam.linear
import auggam.data
import pandas as pd
from copy import deepcopy
from collections import defaultdict
from sklearn.model_selection import StratifiedKFold
from sklearn.... |
089237dfdd9fc7baa7690fd05a0d688d7a9b0b7a4d81984e3a2c4d3aeb7b15ae | Python | 4,942 | 116 | # General imports
import os
import sys
import torch
import numpy as np
import pandas as pd
import seaborn as sns
import xarray as xr
import matplotlib.pyplot as plt
import pytorch_lightning
from tqdm.auto import tqdm
# EUGENe imports and settings
import eugene as eu
from eugene import settings
settings.dataset_dir = "... |
1a56e092a9077eac994cff200461f3b9f566a257e17f689d747a20dbcb25fa8c | Python | 4,942 | 141 | import numpy as np
from scipy import interpolate
from mitochondria import Mito, to_dpsi
from utils import Recorder
def get_steady_state():
try:
kk = np.load('./steady_state/steady_state.npz')
baselines = kk['baselines']
all_nads = kk['nad_ss']
all_atps = kk['atp_ss']
all_p... |
3c1b908aeb02de250a2698dba940be31625bfea055bde996ee55a650ff9d8dc4 | Python | 4,942 | 144 | """
classifier_eegnet_05.py — EEGNet (PyTorch), per-group models
Same evaluation logic as the Random Forest module: folds are split by subject
via GroupKFold, so no subject appears in both train and test.
"""
import os
from typing import Dict, Tuple
import numpy as np
from sklearn.model_selection import GroupKFold
f... |
40935c75d61e227922cad8359b4c12980ef50f3195ee56dcd8c229b5253b84ac | Python | 4,942 | 167 | import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 2,
"distance": 4.0,
"lookat": np.array((0.0, 0.0, 1.15)),
"elevation": -20.0,
}
class Walker2dEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
... |
7581d48ee5e9c880e258f8786c2122c9b206c8180d13f19b5979ecff4542abd4 | Python | 4,942 | 117 | ###################################################
### This code simulates vesicle motility and ###
### presynapse capture during axonal motion. ###
### ###
### This is the first phase of the simulation ###
### This code generates a collection of single ###
##... |
a8dd95de1912f3abea2fa64768bdc13c1c4bb9fb875611a6a516d9ea0b493b71 | Python | 4,942 | 120 | """Utils for auto Mask-RCNN estimator"""
# pylint: disable=consider-using-enumerate
import os
import logging
import mxnet as mx
from mxnet import gluon
from .... import data as gdata
from ....data import batchify
from ....data.sampler import SplitSortedBucketSampler
from ....utils.metrics.coco_instance import COCOIns... |
4ad31e14573fd93c24aa210050977b637b0b3ee9279b23870bd4df305aa0d710 | Python | 4,943 | 105 | import sys
# setting path
sys.path.append('../TM2_segmentation')
import os
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
from scripts.preprocess_utils import find_file_in_path, register_to_template
split = "HEALTHY" # select the split to perform registration on
# this is cr... |
160f01da7b373bcd85389fb61fd3b46878c3e35d85f140d7b8d32e75252d79f8 | Python | 4,944 | 106 | import sys
# setting path
sys.path.append('../TM2_segmentation')
import os
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
from scripts.preprocess_utils import find_file_in_path, register_to_template
split = "HEALTHY" # select the split to perform registration on
# this is c... |
ae9d32c02d6c8df9881e5a508ac168567c160939234ccf5db42cfdf817cf3e97 | Python | 4,944 | 131 | """F03 ledger memo items 5b + 5c.
5b: the edited-rerun divergence-detector composition row ("after 5b: no
warning, divergence_count == 0"). The panel's 3.4 defect (TorchLens's own
edit tripping the detector on resnet18, measured at f74e4a9e) does NOT
reproduce on the F03 base tip — a 4-cell mode x helper matrix on tor... |
de8e536636c85cdf7ce3adcdecebd4a10f3df89c06f1c8e2484829e57e9d2fdf | Python | 4,945 | 123 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.collections as mc
import matplotlib.colors as mpcolors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from tqdm.auto import tqdm
from . import xpu, stats, cluster
from .xpu import cupy_wrapper, iscupy, to_numpy, usin... |
78d40e92dc937972417326a77781fd8507e766a1ea44c9543bf12c74e380ebb2 | Python | 4,947 | 129 | """grind-p3 T11: validation/backward.py input/output walkers are guarded.
``_clone_inputs_with_grad`` and ``_sum_tensors`` were unguarded recursion with
no memo: a legal deep nest or a self-referential container died in a raw
``RecursionError`` from library internals, and a DAG-shaped tree that reuses
one sub-containe... |
4203cb1e89b521dfc3128d5033d1ded19c0779e07afb785715aae46ce0c9e52d | Python | 4,948 | 144 | # SOURCE: vendored from RLAgent/gated-path-planning-networks @ master
# https://github.com/RLAgent/gated-path-planning-networks
# Files: models/GPPN.py (Planner), no changes to the architecture; only the
# `args` object (originally an `argparse.Namespace` built by
# `utils/experiment.py:parse_args`) was replaced with a... |
bf701d1217aac26ea71cd8bf086b0eef698f27a9c691396709f14ed427b1b1c7 | Python | 4,948 | 141 | """grind-p3 T11: GradFnCall equality, hashing, and ordinal_index safety/cost.
The dataclass-generated ``__eq__`` compared raw field tuples, so two
like-labeled calls carrying saved multi-element gradient tensors raised an
untyped elementwise torch ``RuntimeError`` -- which also detonated inside
``ordinal_index``'s ``l... |
e3004c48cdfe818e827f89d774d7849d0e416dc0d437db9764d27fce6f7f01f0 | Python | 4,948 | 78 | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., bicycle_gan, pix2pix, test) and
different datasets (with option '--dataset_mode': e.g., aligned, single).
You need to specify the dataset ('--dataroot'), experiment name ('--name'), and ... |
2d850dfbc948c0c93e21d4ff0d691c6180e55b3cf430779459fb719187ae7add | Python | 4,950 | 135 | import os
import numpy as np
from astropy.io.fits.util import path_like
from mne.utils import _ensure_int, logger
import os.path as op
_multi = {
'str': (str,),
# 'numeric': (np.floating, float, int_like),
'path-like': path_like,
# 'int-like': (int_like,),
# 'callable': (_Callable(),),
}
def read... |
eb620b1e09f2cba874f84dddf9a924ad574b3ef2c33d1d2a751fc1e92b4d3610 | Python | 4,950 | 143 | """A/B overhead measurement for ``tl.record`` on real vision models.
Methodology follows the promoted P2 A/B harness
(``tests/test_perf_capture_ab.py``): torch intra-op threads pinned to 1,
median-of-N wall clock with one warmup call, load context recorded. This
script extends the small-capture rows (tiny CNN/MLP) wit... |
a0be84a6826586898fd8d4bd89f375803b640d3fc689c6d1776c6101bcab5c5a | Python | 4,951 | 149 | from typing import Callable, List, Tuple
import imodelsx
import numpy as np
from spacy.lang.en import English
from os.path import dirname, join
import os.path
import pickle as pkl
import inspect
def explain_ngrams(
text_str_list: List[str],
mod: Callable[[List[str]], np.ndarray[float]],
ngrams: int = 3,
... |
78daed971365b7272f2aef20b5df2aeec7ef7fb04a94a13d59580be105acf7ef | Python | 4,953 | 131 | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... |
04263b0112fe766fd67a8bf889303a7d1656e4e1ceec5a7ee786b6d8a03645e1 | Python | 4,954 | 175 | from __future__ import annotations
import dataclasses
import enum
from typing import Final
import numpy as np
import numpy.typing as npt
from PySide6.QtCore import Qt
from .._shape import Shape
from .._shape import nearest_edge_index
from .._shape import nearest_rotation_point_index
from .._shape import nearest_vert... |
7b23d9e8365b0948e62f7df951314fe3aa8c2521a2f67d2e97499e5308f00bb0 | Python | 4,954 | 122 | import pandas as pd
import re,sys
import os
import biotite.sequence.io.fasta as fasta
from utils import parse_config,setup_fonts
import seaborn as sns
import matplotlib.pyplot as plt
def needle_commands_from_blast(blast_file,test_name,train_name,parent):
test_fasta = fasta.FastaFile.read(test_name)
train_... |
c27eb791f49152e11cea7a0f7f61a2de5093edfa565a086fe167ea7cdcc5e79a | Python | 4,954 | 113 | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, pad_sequence
class RETAIN(nn.Module):
def __init__(self, num_features, dim_emb=128, dropout_emb=0.5, dim_alpha=128, dim_beta=128, dim_num=2,
dropout_context=0.5, dim_output=1, num_layers=1... |
e06c094f0dab54828768ea8ab250ca8cd89b22e23c14386782fb90dda8b4b0f0 | Python | 4,954 | 144 | """Utility functions used to adapt galaxy.util.search to Galaxy model index queries."""
from sqlalchemy import (
and_,
func,
or_,
select,
)
from sqlalchemy.orm import (
aliased,
InstrumentedAttribute,
)
from sqlalchemy.sql.expression import BinaryExpression
from galaxy import model
from galaxy... |
07e7364353a324e315596095c12c15e29790aa041b0abe5621629fa86609c0dd | Python | 4,956 | 158 | import torch
import torch.nn as nn
from modular_splicing.utils.io import load_model
LSSI_MODEL_THRESH = -10.0
class BothLSSIModels(nn.Module):
"""
Represents both an acceptor and a donor model. Core LSSI as used
in the main model.
Also performs multiple projections, to represent the splicepoint as... |
1afafab43d329a0ef9a8aea99185c7d5168d2045a599ccddb0e78f44fe12ee82 | Python | 4,956 | 114 | import itertools
import json
import os
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from angorapy.common.const import PATH_TO_EXPERIMENTS, QUALITATIVE_COLOR_PALETTE
experiment_ids = [['1674975602294059', '1674975602446141', '1671749718306373'],
[... |
4f578f7a2ddbdd54aa7c0de17e33900e07aea0f14d22b3103456953fab511368 | Python | 4,956 | 120 | """L6 stage-4a exit gate: GREP-VERIFIED independence from the attribution kit.
The design memo (sec 2.4, r3 fix for sol MAJOR-5) split stage 4 in two
merges precisely so the SHEDDABLE attribution-kit capstone (4b) is
demonstrably detachable from the non-shed 4a content: Selection resolution,
cross-run patching, and ev... |
1fbce1dbc8613a3c13673ddab366aa327867b4c63cb6ad31590a30c20a45f153 | Python | 4,957 | 196 | from functools import wraps
import torchmetrics
import torch
import numpy as np
from .utils.data import transform_to_elementwise_shape, transform_class_predictions
# https://torchmetrics.readthedocs.io/en/latest/pages/quickstart.html
# https://torchmetrics.readthedocs.io/en/latest/pages/implement.html
# Decorator... |
3e75420b0acd8919e2cedf275e8b9dd13d937a151d99d3315aaf2aac53498a05 | Python | 4,957 | 145 | from typing import Any, List, Mapping, Optional, Tuple, Callable
import numpy as np
from imodelsx.llm import get_llm
def summarize_ngrams(
llm: Callable[[str], str],
ngrams_list: List[str],
num_summaries: int = 2,
prefix_str="Here is a list of phrases:",
suffix_str="What is a common theme among th... |
7c6bb278064cffe1c8f770f29f7ae22b6f86e1b816487c0e1d742250c868039a | Python | 4,957 | 139 | """``do(<Op record>, edit)`` / ``do(<Layer record>, edit)`` (AUD-CODE 4.10).
A record is the most precise site spelling a user can hold, yet the hook
door refused it ("Unsupported site query") and the spec lowering would have
dropped an ``Op`` to its BARE ``layer_label`` (the last pass of a multi-pass
layer) had it be... |
925cc31311c3fe70aca77d26b83dd4dbd59e2d7c4690fea21f0c382a75e83e44 | Python | 4,957 | 137 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""
Module defines single :class 'DataReadProxy'.
Author:
Mateusz Kruszyński <mateusz.kruszynski@gmail.com>
"""
import os.path
import struct
import numpy as np
from . import signal_cons... |
f762bdadc33c6ef6613cc0c2b01e5dd209ba8f0dfb0f2e141fb3a837cbcc2f3d | Python | 4,957 | 143 | import pathlib
import shutil
import tempfile
import lmfit
import nanite.model
from nanite.model import residuals
import numpy as np
data_dir = here = pathlib.Path(__file__).parent / "data"
class MockModelModule:
def __init__(self, model_key, **kwargs):
super(MockModelModule, self).__init__()
# r... |
4daac470542672ebea6d1702461421b3c7818772a58e78da18aed251c034d3b2 | Python | 4,958 | 161 | """Thin views over the lazy reader (extract D14's views layer).
These functions consume ONLY the reader's public protocol — metadata,
``iter_batches``, ``rows`` — and are therefore structurally unable to reach
storage internals. They ship in the same release as the reader so the
documented access patterns (the SAE/CLT... |
9fc97dec6eb7d858a40d62fd5474db9292b0c074d602a7642a6738db13d0c9f2 | Python | 4,958 | 143 | #!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
import os
import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import trim_mean, genextreme, pearsonr, spearmanr
from preprocessing import get_zmap_dataset, features_from_df, preprocess, FeatureSelector
from inspectio... |
e613251f6dea4b5c9fff8d4ac72cc73bc4f0a799cb0d568c6c1d2a31d28e1b69 | Python | 4,958 | 128 | import sys
import numpy as np
from ast import literal_eval
from collections import defaultdict
def score_multiple(gold_list, test_list, value_for_div_by_0=0.0):
# accumulate counts for all gold/test files
pcounts = np.array([0, 0, 0, 0], dtype=np.int32)
rcounts = np.array([0, 0, 0, 0], dtype=np.int32)
... |
51b8fcfe0621c6b27db83b9eea8c06e3d68c36e8374cbe78e5c5e5d32a9f3920 | Python | 4,961 | 144 | import unittest
from unittest import mock
import pytest
import responses
from codecarbon.external.geography import CloudMetadata, GeoMetadata
from tests.testdata import (
CLOUD_METADATA_AWS,
CLOUD_METADATA_AZURE,
CLOUD_METADATA_GCP,
CLOUD_METADATA_GCP_EMPTY,
GEO_METADATA_CANADA,
GEO_METADATA_U... |
6fc4f16d63e8a9b3be9b4ad93c78edf2233e05230ab30178758b2b11cc61c1c1 | Python | 4,961 | 93 | #!/usr/bin/env python
# Created on: Dec 6, 2013
__author__ = "Sven Kreiss, Kyle Cranmer"
__version__ = "0.1"
__description__ = """
Intended to be run from main directory (not plotsForPaper).
Specify list of plots (eps files) to copy here. Give a tuple of src and dest filename
if you need to rename fiels.
The idea i... |
9e2d477dad9a3cf4ea84af6301470f0b126fdbf5e5543a4746f4086c42123009 | Python | 4,961 | 96 | import matplotlib
import matplotlib.pylab as plt
from matplotlib.ticker import AutoMinorLocator
import seaborn as sns
import glob
import pandas as pd
import os
sns.set(style='ticks', context='talk', palette='Set1')
fig1, axes1 = plt.subplots(4, 3, figsize=(12, 15), num='RMSD')
fig2, axes2 = plt.subplots(4, 3, figsize=... |
6d01437784253da0288c592e12f2afa0321adbb7fb43b80b24a9a821492b7f0d | Python | 4,963 | 152 | #!/usr/bin/env python
"""
Test runner for notification system tests.
Demonstrates the comprehensive test coverage for the enhanced notification system.
"""
import os
import subprocess
import sys
from pathlib import Path
def run_tests():
"""Run all notification system tests."""
# Change to the node director... |
a7851a6da73848d9b86149fec1ced2f50a3fe7109e197fda28ce2f17fe0b5df8 | Python | 4,963 | 184 | from typing import Optional, Union
from anndata import AnnData
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
import numpy as np
import stlearn.plotting.utils as utils
def deconvolution_plot(
adata: AnnData,
library_id: str = None,
use_label: str = "louvain",
cluste... |
2189d63617b35b4554544e639998ed125158695f84448d9aa6c07507fc3a15eb | Python | 4,965 | 124 | '''Test tns.generate.section code'''
import json
import os
import numpy as np
from nose.tools import assert_dict_equal
from numpy.testing import assert_array_almost_equal, assert_equal
from tns.generate.algorithms.common import TMDStop
from tns.morphmath import sample
from tns.generate.algorithms.tmdgrower import (TM... |
30e4fae7db3a01945f4d546f6af75cd55daff7f60c59dd06006eb2990b860df6 | Python | 4,965 | 107 | """Data parallel task for Mask R-CNN Model."""
import mxnet as mx
from mxnet import autograd
from gluoncv.utils.parallel import Parallelizable
class ForwardBackwardTask(Parallelizable):
""" Mask R-CNN training task that can be scheduled concurrently using Parallel.
Parameters
----------
net : gluon.... |
481f1091af106861cc01184b72b4e804610a87154a75c3f57596e414fbd61d60 | Python | 4,965 | 178 | import matplotlib as mpl
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
from anndata import AnnData
def deconvolution_plot(
adata: AnnData,
library_id: str | None = None,
use_label: str = "leiden",
cluster: int | str | None = None,
celltype: str | None = Non... |
07be4036fb19880277357fb3a9978351506784921dafd10d5466887a8343d1f9 | Python | 4,966 | 106 | from mirp._data_import.generic_file import MaskFile
from mirp.data_import.import_image import _import_image
from mirp._data_import.utilities import supported_mask_modalities, supported_file_types, flatten_list
def import_mask(
mask,
sample_name: None | str | list[str] = None,
mask_name: None |... |
3afc25fb54264ec7fe2d214a4cd23db55fa778a0e7dad571a2d353ee8cf5788e | Python | 4,966 | 114 | # Given sumstats file, generate a file that can be used by subsequent scripts to
# generate MsCAVIAR input files.
import argparse, math, sys
def parseargs(): # handle user arguments
parser = argparse.ArgumentParser(description="Given sumstats file, generate" +
" a formatted file used by subsequent scripts to... |
2ea08863f880ed19b97d28b555f1ad425794201ed532286e32e98dd54e1f90f5 | Python | 4,967 | 169 | # --- 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 Domain(_BaseTraceHierarchyType):
_parent_path_str = "table"
_path_str = "... |
631ba0e6c4383a813c3c9f9680e6feb1447cacfc94f971dc466292c5a9ca6a59 | Python | 4,967 | 164 | '''Figure module
This file is a part of BdPy.
Functions
---------
makefigure
Create a figure
box_off
Remove upper and right axes
draw_footnote
Draw footnote on a figure
'''
__all__ = [
'box_off',
'draw_footnote',
'make_violinplots',
'makefigure',
]
import matplotlib.pyplot as plt
impor... |
d279208f42914069320e32be8c36caf3e4a049e888916b0a979a442b2b8a575a | Python | 4,969 | 158 | import torch
import torch.nn.functional as F
import numpy as np
MAX_DIM = 1024
def cosine_pull_loss(a, b, labels=None):
a = F.normalize(a, dim=-1)
b = F.normalize(b, dim=-1)
if labels is None:
loss = - (a * b).sum(-1).mean()
else:
loss = 0
cossim_a = - torch.einsum('ij,kj->ik... |
0ef81b8706c89b0ffcd78bea4bd9d987f682ad19cbb9eba18132a0017f5bf226 | Python | 4,970 | 137 | # Copyright 2017 The TensorFlow 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 applica... |
efc92fcd73f61bf2fc39985f75532a8ad7c0a57ea433e3ac893ade186dafd557 | Python | 4,970 | 95 | import matplotlib
import matplotlib.pylab as plt
from matplotlib.ticker import AutoMinorLocator
import seaborn as sns
import glob
import pandas as pd
import os
sns.set(style='ticks', context='talk', palette='Set1')
fig1, axes1 = plt.subplots(4, 3, figsize=(12, 15), num='RMSD')
fig2, axes2 = plt.subplots(4, 3, figsize=... |
0c4a62e131a393ac330698e1b195a2ad9d999bc5aba86e5b3944a189e18a4d41 | Python | 4,971 | 117 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
cohort_counts.py — 五个队列的样本/受试者计数：**从产物推导，不手抄**
------------------------------------------------------------------
为什么需要：Table 1 的每一行、Figure 1 的抬头、以及摘要里的
「1,991 samples from 769 subjects」此前都是**手抄**的。手抄的后果已经发生：
· 受试者总数写成 **769**，而产物给出 20 + 383 + 40 + 229 + 96 = **768... |
4637080f8036df99dc58ae7b80a59e0dd6914e10624aa35562209329682597e1 | Python | 4,972 | 259 | #!/usr/bin/env python
'''Generate models for affinity predictions'''
# [SGD|Adam] * [regular|rankloss|ranklosswneg] [xaviar|radial] [0.01|0.001]
modelstart = '''layer {
name: "data"
type: "MolGridData"
top: "data"
top: "label"
top: "affinity"
include {
phase: TEST
}
molgrid_data_param {
sourc... |
861c8bc7008d339d38d6c77ea4eef15800ef02d735de49c060a9e610f9178fc8 | Python | 4,973 | 153 | import dataclasses
import importlib
import os
import unittest
from unittest.mock import patch
from codecarbon.output_methods.emissions_data import EmissionsData
from codecarbon.output_methods.metrics import prometheus
EMISSIONS_DATA = EmissionsData(
timestamp="2021-01-01T00:00:00",
project_name="test_project"... |
9012cce8e593120be69067487e26d3baac142b16ef9525dc68766776c97ac68e | Python | 4,973 | 166 | #!/usr/bin/env python
#
# stringdb documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 9 13:47:02 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# Al... |
18f65e904313218c16e478a323c157a9fd48fdfe83cd345728c3c76fee0b9408 | Python | 4,974 | 113 | """
Author: Derek van Tilborg -- TU/e -- 23-05-2022
Message Passing Neural Network for molecular property prediction using continuous kernel-based convolution
(edge-conditioned convolution) [1] and global graph pooling using a graph multiset transformer [2] instead of
the Set2Set method used in [1]. I added dropout to... |
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