sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2e9f1327f4e66b6dc8e6e9575af86496146a12f09a87dd8544adf8e3e1be32cd | Python | 4,816 | 121 | #!/usr/bin/env python3
from BeattieModel import BeattieModel
import argparse
import os
import common
import numpy as np
import logging
import matplotlib.pyplot as plt
import regex as re
import pandas as pd
import common
import myokit as mk
import cProfile
from BeattieModel import BeattieModel
def main():
mean_par... |
a2b89f656c88389946f78c4b3130520bdcfb7623d8409174dc853c2bf525e0a8 | Python | 4,817 | 135 | import logging
from dataclasses import dataclass
from typing import Optional
import jwt
from dependency_injector.wiring import Provide
from fastapi import Depends, HTTPException
from fastapi.security import APIKeyCookie, HTTPBearer
from carbonserver.api.errors import AuthenticationError
from carbonserver.api.services... |
ae6de04b915514d83dc93bebf726493db766e9c8e2d2d0fafc909a8f968c9ea5 | Python | 4,819 | 125 | import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
# TODO: are trials counted correctly in this task?
# TODO: No longer maintained, use existing task to build this task
class IBL(TrialEnv):
metadata = { # noqa: RUF012
"paper_link": "https://www.sciencedirect.com/sc... |
d74edf89f5a61c4b28e86c8d873bc706584c1dae19aeb40b55ee7c4d137d1d77 | Python | 4,820 | 119 | import yaml
import torch
import torch.nn as nn
from argparse import Namespace
from collections import defaultdict, Counter
from bioseq2seq.inputters.inputter import _load_vocab, _build_fields_vocab, get_fields, IterOnDevice
from bioseq2seq.inputters.corpus import ParallelFastaCorpus, ParallelTextCorpus
from bioseq2seq.... |
1c88430511b577456ccfecf9e9826958da2257979fc935e1172eb788120a306a | Python | 4,822 | 158 | from collections import Counter
import functools
import itertools
import tqdm
import torch
import numpy as np
from permacache import permacache, drop_if_equal
from modular_splicing.dataset.basic_dataset import basic_dataset
from modular_splicing.evaluation.run_evaluation import evaluate_model_on_data
from modular_spl... |
eee62161b3676aea386f2b758b2ac5ec9ef13802d760a53a90949f618e1b586d | Python | 4,822 | 130 | """
1D diffusion simulations with NEURON 8.2.6: 1D cuboid
Last updated: .2026
Author: Saana Seppälä
### Description
This script performs one-dimensional calcium diffusion simulations using
the NEURON reaction-diffusion (RxD) framework. A cylinder morphology
is represented as a single NEURON section discretized into... |
d7307f1b0e35e8dfc398243639922e10a7ba85bb98a01dae784eee9c2fb17d9b | Python | 4,823 | 130 | """Manifest <-> metadata integrity anchors (AUD-CODE 3.0b).
``manifest.json`` and ``metadata.pkl`` are written together by one writer, so
the facts both carry must agree at load or the artifact has been edited,
truncated, or spliced from two sources. The loader compares:
* the ``tlspec_version`` stamp -- the manifest... |
18cdfc8a96f479c56929f78e07d9db552c351455aecabfcf5fe5cb2a1d5bd3ba | Python | 4,824 | 134 | import copy
from typing import List, Tuple
import torch
from pytorch_lightning import LightningModule
from torch import Tensor
from torch.nn import Identity
from torchvision.models import resnet50
from lightly.loss.tico_loss import TiCoLoss
from lightly.models.modules.heads import TiCoProjectionHead
from lightly.mode... |
2e5d6029a7e6ec06ea43f487e13133cabffb494c7b319c7b9875f8a67ebefe80 | Python | 4,824 | 174 | import pytest
import rpy2.rinterface as rinterface
import rpy2.robjects as robjects
import array
# TODO: move to common module for testing
def almost_equal(x, y, epsilon = 0.00001):
return abs(y - x) <= epsilon
def test_init_invalid():
with pytest.raises(TypeError):
robjects.vectors.IntArray(3)
de... |
4c0985871aa8bb40b4c1a7cc04c6dee9725dc81053846272fbc6479f1bd6fc1d | Python | 4,826 | 140 | import threading
import time
import gc # garbage collection
from Utilities.common_packages import np
from Configurations import config as cfg
from Configurations import CNN
from tensorflow.keras.utils import Sequence
# define indexes
idxSimChaNum = cfg.EnumSimParams.chanum.value
class DataGeneratorFD1T(Sequence):
... |
9796f9e2268862ae833ece6467d2571e74d6ffcbee7470e8a3d5c819da469313 | Python | 4,827 | 99 | import torch
from torch import nn
from torch import Tensor
from collections import OrderedDict
import numpy as np
class GetDensity(torch.nn.Module):
def __init__(self,rs,inta,cutoff,nipsin,norbit,ocmod_list):
super(GetDensity,self).__init__()
'''
rs: tensor[ntype,nwave] float
inta:... |
82255dab12b9ab35bf3e24f1b61b4ebcd8e4b14e2e168351209eb8731f2573e4 | Python | 4,828 | 144 | """Pins for the trace inventory surfaces: ``sites_table`` and ``bill_of_materials``.
Both are read-only rollups of already-captured facts (DOCUMENTED-UNSTABLE
spellings). ``sites_table`` follows the L1 consumer matrix: a legacy keyless
artifact refuses ``site_key_unavailable``, never a silently empty table.
"""
from ... |
9bf51c2ff172b62cc1bb5d11caa0816a0e7123eafc49e1f4fc60267303054655 | Python | 4,828 | 126 | import os
import numpy as np
import math
from tensorflow.keras.utils import Sequence
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from imageio import imread
from scipy.special import expit
from skimage.transform import resize
class SegmentationSequence(Sequence):
def __init__(self, imag... |
b28d18c077d7861de7204f0d56b5ad21323773ae944b0f91cf1e1a32e84c5c29 | Python | 4,828 | 131 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from eelbrain.testing import assert_dataobj_equal
from eelbrain import Categorial, Factor, NDVar, Scalar, UTS, Var, datasets, table, combine
def test_cast_to_ndvar():
"Test table... |
1161cd535acc2e8df68258028e7690a4aba3481a638e51d372ba0ac38be19ba0 | Python | 4,829 | 144 | import glob
import numpy as np
import argparse
import yasa
import matplotlib.pyplot as plt
from mp_dip.utils.utils import read_raw
from mp_dip.obci_readmanager.signal_processing.tags import tags_file_writer as tags_writer
TAG_CONVERT_DICT = {'N1': "1",
'N2': "2",
'N3': "3",
... |
4cfacbb34b1406ec86e92cd9fcde531ffbc4dc733daa555e9883c56de24bd0c9 | Python | 4,829 | 152 | """
Unit tests for utils/img_proc.py and utils/video.py.
Tests merge_blobs contour merging and video file listing.
"""
import os
import shutil
import tempfile
import unittest
import cv2
import numpy as np
from ethoscope.utils.img_proc import merge_blobs
from ethoscope.utils.video import (
ensure_video_directory... |
d0ab4971d7d635f071807d6a291cefb75966b50674d5645e3470d454f93e945f | Python | 4,831 | 126 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import absl.app
import argparse
import numpy as np
import wave
import csv
import os
import sys
from deepspeech import Model
from deepspeech_training.util.evaluate_tools import calculate_and_print_report
from... |
8bd409b922ae68c41c4475c103212f849f22632ea07c411fa0360987012ab173 | Python | 4,833 | 151 | """Policy-level tests for MLX preview option honesty."""
from __future__ import annotations
from pathlib import Path
from typing import Any
import pytest
from torchlens.backends import BackendUnsupportedError
from torchlens.backends._options import (
MLX_EXTRA_KWARG_POLICY,
MLX_PREVIEW_TRACE_OPTION_POLICY,
... |
c29f2c9ceb45361ce4bbf4ae2a89b4557b8fa732bcd9c7642a9fded7ec6e6c19 | Python | 4,833 | 122 | import numpy as np
import pytest
import gym
from gym import envs
from gym.envs.registration import EnvSpec
from tests.envs.utils import mujoco_testing_env_specs
EPS = 1e-6
def verify_environments_match(
old_env_id: str, new_env_id: str, seed: int = 1, num_actions: int = 1000
):
"""Verifies with two environm... |
a54917620e4c3360794bec2ee1d87528445f4322f2fd65cea24535e747e52541 | Python | 4,835 | 165 | import os
import glob
import seaborn as sns
from os import PathLike
from typing import Optional, Union, Sequence, Mapping
import matplotlib.pyplot as plt
from ._utils import (
_plot_seaborn,
many_logs2pandas,
_save_fig,
)
from .. import settings
def metric_curve(
log_path,
metric,
hue: Optiona... |
a7785be5044463a497d0c9617eb7983b3eab35cc6a3ec8acfc0912af3def5bf8 | Python | 4,836 | 153 | import argparse
import json
import warnings
import pandas as pd
from mlxtend.frequent_patterns import association_rules, fpgrowth
from mlxtend.preprocessing import TransactionEncoder
def main(
inputs,
infile,
outfile,
min_support=0.5,
min_confidence=0.5,
min_lift=1.0,
min_conviction=1.0,
... |
e585610c83d861bc35c3eeba95a2a56a2c7cc97f96da56a9ff42a838f082e0dd | Python | 4,836 | 150 | """Adapted from: https://github.com/https-deeplearning-ai/GANs-Public."""
import torch
import torch.nn as nn
from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg
if _TORCHVISION_AVAILABLE:
from torchvision.models import vgg19
else: # pragma: no cover
warn_mis... |
18431315395660c49282c4f05d2c739f913fd35f1b727928995ab83950dba975 | Python | 4,838 | 181 | #pomocni fajl za evaluaciju LSTM modela
# pomocne funkcije za evaluaciju modela i za metrike
#osnovne biblioteke
import numpy as np
import pandas as pd
import math
#LSTM:
import tensorflow as tf
from tensorflow import keras
from keras import layers
#Early stopping
from keras.callbacks import EarlyS... |
0f83a2c3d944c85fb11c9ddf279fd77248002c3aec0d3625b32713d69e35e7a1 | Python | 4,839 | 173 | """The ``celery shell`` program, used to start a REPL."""
import os
import sys
from importlib import import_module
import click
from celery.bin.base import CeleryCommand, CeleryOption, handle_preload_options
def _invoke_fallback_shell(locals):
import code
try:
import readline
except ImportError... |
175fde0508accf650e2b4bfb06a8b2be6bcfe6bbba55f0efa1e8e52539419293 | Python | 4,839 | 151 | __author__ = "diana"
import cv2
try:
CV_VERSION = int(cv2.__version__.split(".")[0])
except Exception:
CV_VERSION = 2
import logging
import numpy as np
from ethoscope.core.roi import ROI
from ethoscope.roi_builders.roi_builders import BaseROIBuilder
class ArenaMaskROIBuilder(BaseROIBuilder):
def __i... |
5f32d713e7bedd09cf39f8e49a6fd2b808809f49cb9cd6d95fcf6682201cef19 | Python | 4,839 | 138 | """F29: the tiered CLI -- tier-0 torch purity, exit codes, CI gates.
Tier-0 verbs must leave torch out of ``sys.modules`` (the memo's purity
test); exit codes are a closed set CI can branch on; ``--fail-on`` gates
READ existing result records, never recompute.
"""
from __future__ import annotations
import json
impor... |
8ab28e8e241d2f268dc5495503171a85ea19c27c7e804075ffbeb95df1bb06e6 | Python | 4,839 | 161 | import dataclasses
import os
from prometheus_client import (
CollectorRegistry,
Counter,
Gauge,
delete_from_gateway,
push_to_gateway,
)
from prometheus_client.exposition import basic_auth_handler
from codecarbon.external.logger import logger
from codecarbon.output_methods.base_output import BaseOu... |
b362eaa61764eecfb2b5a7487ddfd6d95b83474de853bb496d12f63506baceb5 | Python | 4,840 | 136 | """THE summary pin-derivation script (F08; summary memo section 4, binding).
Numbers discipline: every count and total in the summary memo's tables is
RE-DERIVED at build time by this ONE committed script against pinned
versions -- the panel's tables establish structure, never byte-final
constants. The derivation basi... |
b51c535e4b41acd4679f6dffd021dbf432e636a467aee66f73beac7a965c9375 | Python | 4,840 | 64 | from argparse import Namespace,ArgumentParser
def meta_train_parse_args() -> Namespace:
parser = ArgumentParser()
parser.add_argument("--meta_batchsz",type=int,default=15,help='the meta-learning batch size in meta-training')
parser.add_argument("--meta_lr",type=float,default=1e-3,help='the meta-learning ... |
9195f793f6ef24f943cd91f03fd0fb1b97440e00d1efd9d5df08552ecf348e3b | Python | 4,841 | 129 | ## here I used dbscan_.py from sklearn as a reference
import numpy as np
from . import metrics
from .Cluster_ import Cluster
def dbscan(points, eps, min_pts, distances=None,dist_fn='euclidean',verbose=True):
""" Implementation of DBSCAN density-based clustering algorithm
Parameters:
- `points`: input ... |
12a5f298063a76f706fccdf7004d9571cc99f9f1402fe9e7dd3d051c9f8f1dac | Python | 4,842 | 119 | from typing import ClassVar
import numpy as np
import torch
import torch.nn as nn
from torchvision import models, transforms
class Model:
_WEIGHTS: ClassVar[dict] = {
("resnet50", "v1"): models.ResNet50_Weights.IMAGENET1K_V1,
("resnet50", "v2"): models.ResNet50_Weights.IMAGENET1K_V2,
("vg... |
3909eacb7c714920bdd15c949bcce0ace81d05816b6431813a723ee2d31b83cf | Python | 4,842 | 100 | from unittest import TestCase, mock
import numpy as np
import spateo.segmentation.label as label
from ..mixins import TestMixin, create_random_adata
class TestLabel(TestMixin, TestCase):
def test_watershed(self):
X = np.zeros((10, 10))
X[:3, :3] = 1
X[7:, 7:] = 1
mask = X > 0
... |
7903735173fd74228b4f7bb5de18721441dd4734e6c278c33abbb63fe053a04e | Python | 4,842 | 147 | from math import sqrt
from model import common
import torch
from torch import nn
import torch.nn.functional as F
def make_model(args, parent=False):
return DFSRCNNPS(args)
class DFSRCNNPS(nn.Module):
"""
Args:
upscale_factor (int): Image magnification factor.
"""
# def __init__(self, ups... |
839190f26da2d45afac57a1e0c569fa715a0ab6552edae0ccecc6ccbab010b2f | Python | 4,842 | 115 | """
The pyrho fused-LASSO objective function.
Classes:
RhomapObjective: The objective function for a given dataset.
"""
from __future__ import division
import numpy as np
from scipy.sparse import dok_matrix
class RhomapObjective(object):
"""
The fused-LASSO objective function for a given dataset.
A... |
6c923217aaf218b20279e5d0bea61c7b4580ce069e68f982e6184f08c683b6fa | Python | 4,843 | 99 | # pytest unit tests for sct_extract_metric
import csv
import numpy as np
import pytest
import logging
from spinalcordtoolbox.scripts import sct_extract_metric
from spinalcordtoolbox.utils.sys import sct_test_path
logger = logging.getLogger(__name__)
@pytest.mark.sct_testing
def test_sct_extract_metric_against_gro... |
187938728a3231e20c1c1a9663831e1e75d1b25e8e2fdd910f43f2b70b68daf3 | Python | 4,846 | 191 | """GCNN-P4: group-equivariant CNN over translations and 90-degree rotations.
Paper: Group Equivariant Convolutional Networks, Cohen and Welling, ICML 2016.
The defining primitive is group convolution on P4, the group generated by image
translations and rotations by 0, 90, 180, and 270 degrees. This compact model lif... |
514f19369f55f146e3df3840fdcbf8ee7860169882b38e9c8097cfe39c96dcd3 | Python | 4,846 | 123 | """DeviceRef vocabulary conformance (R17-2).
``ir/refs.py`` documents ``DeviceRef.backend`` as the HARDWARE device class
(``"cpu"``, ``"cuda"``), never the framework namespace ``DtypeRef.backend``
carries. tf and paddle used to write ``DeviceRef(backend="tf"/"paddle", ...)``
directly; every backend must now route thro... |
f673c9741a6d304840cbb7e1ef98bf36ca391fa31f6ebf554ac0893bb1053305 | Python | 4,846 | 139 | import zipfile
from pathlib import Path
import numpy as np
import pytest
import zarr
from zarr.storage import LocalStore, MemoryStore, StorePath, ZipStore
from ome_zarr import OMEZarrImage, OMEZarrMultiscale
from ome_zarr.data import create_zarr
from ome_zarr.io import ZarrLocation, parse_url
from ome_zarr.reader imp... |
64fdda931c0dfe6dfb3016d8d4b563aa05fba109c2eea9d155abbe8bf396f4a9 | Python | 4,848 | 156 | #!/usr/bin/env python
# Created on: October 8, 2013
__author__ = "Sven Kreiss, Kyle Cranmer"
__version__ = "0.1"
import ROOT
def getNll( pdf, data, minStrategy=0, enableOffset=True, globObs=None ):
""" Generic functions to use with minimize() from BatchProfileLikelihood. """
# config minimizer
ROOT.RooAbsRea... |
739971d2d281bc685a3b318a9bfe2685ac27902e2f50833ce1cc2ad5a4379007 | Python | 4,848 | 192 | """Functions to compute measures of spiking activity."""
import numpy as np
from spiketools.utils.extract import get_range
from spiketools.measures.conversions import convert_times_to_counts
###################################################################################################
##########################... |
0b4c5735b0a52aef504766470d69a58e431f70c0edeb3341670cca24db4006fb | Python | 4,849 | 170 | import itertools
from mock import Mock, patch
import numpy as np
from numpy import testing as npt
from scipy import stats
from tns.astrocyte.section import SectionSpatialGrower
from tns.generate.tree import SectionParameters
POINT_CLOUD_POINTS = np.array([
[0.0, 0.0, 0.0],
[0.1, 0.1, 0.1],
[0.2, 0.2, 0.... |
15185a8fa0afb0029f021d8b545b24fd6b58cd40191a4074a58d69dd05c953e1 | Python | 4,850 | 127 | # 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... |
7672b3bd57f9a35599e2b73352e7b94b28f7e88c7765071041f2a82e06f089c3 | Python | 4,852 | 132 | """
Author: Bruno De Santi, PhD
Affiliation: Multi-modality Medical Imaging Lab (M3I Lab), University of Twente, Enschede, The Netherlands
Date: 20/09/2023
Description: Deformation field regularization functions
Acknowledge: The code was adapted from
J. M. Wolterink, J. C. Zwienenberg, and C. Brune, “Implicit Neural... |
cdbb626ded8211e12bca26ccd5e1a5dc2ed85b51176167acc7e49982f0956b31 | Python | 4,852 | 138 | # coding=utf-8
# Copyright 2024 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... |
0083f2debc915e9d49f54d6d3e175bcab2507736663d2e8f7a9cf6bb29a260f3 | Python | 4,853 | 129 | #Autor: Carlos Alberto Hernández Nava
#ASVspoof2017 spectrograms 100 x 100 color
import cv2, sys, os, gc
import numpy as np
import pandas as pd
import seaborn as sns
import openpyxl as pxl
import tensorflow as tf
from tensorflow import keras
from numpy.random import randn
from pandas import ExcelWriter
import matplotl... |
163803314568bb5399abaf1753dbc4c2ef56fbb397b2db7f34de2b5d405a702f | Python | 4,853 | 175 | import matplotlib
from matplotlib import pyplot as plt
from typing import Optional, Union
from anndata import AnnData
def plot_mask(
adata: AnnData,
library_id: str = None,
show_spot: bool = True,
spot_alpha: float = 1.0,
cmap: str = "vega_20_scanpy",
tissue_alpha: float = 1.0,
mask_alpha... |
d09470227686de4836ff2ea6f7f14b727d3687183d68f5c0936abc9f0f4e7caf | Python | 4,854 | 138 | # coding=utf-8
# Copyright 2024 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... |
232414214d27f32310d6a8b46ef9fc047ba17ca338e9b22352627f9ddf76575c | Python | 4,855 | 117 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# In case you've sequenced the same sample multiple times, use this script to
# merge those samples together and then rmdup the pooled data.
# WARNING: This assumes you've used the pipeline up to this point, and have pre-sorted & fixmated .bams.
# Why use this script, wh... |
361993053dcd50812541bb6e518e1a1b59897f22ade003a6081eb6d1a2e9681d | Python | 4,858 | 111 | """
Author: Derek van Tilborg -- TU/e -- 09-06-2022
Script to replicate the benchmark results
"""
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
from MoleculeACE.models import RF, SVM, GBM, KNN, MLP, GCN, ... |
e38758cead2f4f4905cb86d427eaca14f3062d67a687b0790f8fea3030c70997 | Python | 4,858 | 266 | #!/usr/bin/env python
'''Generate models for affinity predictions'''
# variables:
# kernel size: 3 or 7
# width: [32,32,32] [64,32,32] [64,32,16] [32,16,16]
# pool or not
# stride 1,2,3 (>1 if no pool)
modelstart = '''layer {
name: "data"
type: "MolGridData"
top: "data"
top: "label"
top: "affinity"
incl... |
00acb0c937330600cbba5c1de903f167da5d5c5e8a9b5e315767978d462567dc | Python | 4,860 | 147 | 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 = 10
k = 2.0
tau = np.random.uniform(10.0, 20.0, size=(N,))
tau_s = 5.0
eta = 10.0
v_thr = ... |
7c6aeed289f43a188f3a0cdb58507bdd8fa7e01e722bd2e028044bb65290a827 | Python | 4,860 | 147 | """ Statistics calculation utility """
import time
import math
import sys
from bioseq2seq.utils.logging import logger
class Statistics(object):
"""
Accumulator for loss statistics.
Currently calculates:
* accuracy
* perplexity
* elapsed time
"""
def __init__(self, loss=0, n_words=0,... |
914186a69b038175777598d46861dda61bdf2728d8a600b76c43ae36428ff53d | Python | 4,860 | 147 | 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 = 10
k = 2.0
tau = np.random.uniform(10.0, 20.0, size=(N,))
tau_s = 5.0
eta = 10.0
v_thr = ... |
34fba8111fc237842821df02678b38055d35587346642b7a7c4b4976ff9554cd | Python | 4,861 | 176 | import matplotlib
from anndata import AnnData
from matplotlib import pyplot as plt
def plot_mask(
adata: AnnData,
library_id: str | None = None,
show_spot: bool = True,
spot_alpha: float = 1.0,
cmap_name: str = "vega_20_scanpy",
tissue_alpha: float = 1.0,
mask_alpha: float = 0.5,
spot_... |
90822f9eac208548b7914ef0c3f745d2c93e0b5f5a6877a5a4e1a562623bcd14 | Python | 4,861 | 157 | import tensorflow as tf
import tensorflow_datasets as tfds
from IPython.display import clear_output
import matplotlib.pyplot as plt
from time import gmtime, strftime
from unet2 import create_model_UNet2
# network model parameters
OUTPUT_CLASSES = 3
INPUT_SIZE = 128
# training parameters
EPOCHS = 20
BATCH_SIZE = 64... |
df7d5cd2cf378a33401906d60249a2d26d18b62bce248674b7f9bb2b65966569 | Python | 4,861 | 171 | import pytest
import rpy2.robjects as robjects
rinterface = robjects.rinterface
import rpy2.rlike.container as rlc
import array
import csv
import os
import tempfile
def test_init_from_taggedlist():
letters = robjects.r.letters
numbers = robjects.r('1:26')
df = robjects.DataFrame(rlc.NamedList((le... |
1650e9603a817d02c9c6547659f9f9fb55d06988935cd18cf8477162e168c596 | Python | 4,862 | 134 | from ..utils import gislib, utils, constants
from ..core.trajectorydataframe import *
import numpy as np
import inspect
def compress(tdf, spatial_radius_km=0.2):
"""Trajectory compression.
Reduce the number of points in a trajectory for each individual in a TrajDataFrame. All points within a radius of `sp... |
41389cedced3cb4a9d6f7d74c582f3a795db194c770be5d399b13de4fbb29ead | Python | 4,862 | 114 | """Intervention honesty: the ``resample_ablate`` -> ``scramble_elements``
honest rename (edits memo row 1, decision D19; alias posture: hard rename,
no alias, artifact-load compatibility KEPT).
The helper is an elementwise iid scramble -- a false friend of the field's
"resampling ablation" (coherent donor patching). S... |
eaa27de84a44ad1f1b0baa883ec7b76793ee0973362646a7f93ebb7d5e3b2abe | Python | 4,864 | 181 | import flask
def test_template_rendered(app, client):
@app.route("/")
def index():
return flask.render_template("simple_template.html", whiskey=42)
recorded = []
def record(sender, template, context):
recorded.append((template, context))
flask.template_rendered.connect(record, a... |
17afd73d13382889a20a8b1007674320411ad5f405cee0e818c93644e520926e | Python | 4,868 | 131 | from conftest import get_metrics
import numpy as np
import pytest
from pytest import approx
from pyriemann.utils.distance import distance_riemann
from pyriemann.utils.tangentspace import (
exp_map_euclid, exp_map_logeuclid, exp_map_riemann,
log_map_euclid, log_map_logeuclid, log_map_riemann,
upper, unupper... |
d38dda287489a4a98bee1a60918bb73e0bd5adb85bcd0ca785ea0385258101b8 | Python | 4,869 | 124 | """grind-p3 T5.5: ``tl.fastlog.recover`` is bounded and total over hostile indexes.
``recover.py`` parsed the JSONL index and metadata with raw ``json.loads`` /
``json.load`` plus an unbounded ``read_text``: a depth-bomb line escaped the
``JSONDecodeError`` handler as a raw ``RecursionError`` out of the public
``tl.fa... |
b661fe1a2e049675c9ef68d4036a474c974c02626872c8177b60552647069241 | Python | 4,870 | 131 | from typing import Any, Optional
import torch
from pytorch_lightning import LightningModule
from pl_bolts.models.detection.retinanet import create_retinanet_backbone
from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg
if _TORCHVISION_AVAILABLE:
from torchvision.... |
d60381ea09bfb784bfeec8cbfe71e6415577631187278bdf87f3742519875dc6 | Python | 4,870 | 143 | """Stateful live-model ``Trace.run`` diagnostics."""
from __future__ import annotations
import warnings
import pytest
import torch
from torch import nn
import torchlens as tl
def test_training_mode_stateless_model_does_not_warn() -> None:
"""Training mode alone should not trigger the live-state warning."""
... |
ed3398074ff2b6e48096d57855f7d2eec9279ae62df2ae2b4c5a37bea3929d9b | Python | 4,870 | 147 | from pyoperant import hwio, components, panels, utils
from pyoperant.interfaces import comedi_, pyaudio_
import socket
_VOGEL_MAP = {
1: ('/dev/comedi0', 2, 0, 2, 8), # box_id:(subdevice,in_dev,in_chan,out_dev,out_chan)
2: ('/dev/comedi0', 2, 4, 2, 16),
3: ('/dev/comedi0', 2, 24, 2, 32),
4: ('/dev/come... |
27c87c721ef335c60d015975f9d306f22fd44c2f75b4d7faf178ff4523ecafc6 | Python | 4,871 | 152 | from types import SimpleNamespace
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tqdm.auto as tqdm
from permacache import permacache, stable_hash, drop_if_equal
from .compute_psi import all_psi_values
def directional_strand_histogram(all_table):
plt.figure(dpi=200)
bins = np.ar... |
47d188dc8ca69702461c3e68c0e2594ab609e69269f56751587db19cd3b6402f | Python | 4,871 | 135 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for Louvain"""
import unittest
from sknetwork.clustering import Louvain
from sknetwork.data import karate_club, star_wars
from sknetwork.data.test_graphs import *
from sknetwork.utils import bipartite2undirected
class TestLouvainClustering(unittest.TestCase):
... |
a96c1708be2a78834543e1ea3a10a9a540f84f0b8656152b0adbdcc83ed60e1e | Python | 4,871 | 80 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
d1817c90ea94ce65639ff9c82ee9f597b264ceb6dfbd3f9cbbcd3ff892dd0780 | Python | 4,872 | 122 | """YOLO default config"""
# pylint: disable=unused-variable,missing-function-docstring,bad-whitespace,missing-class-docstring
from typing import Union, Tuple
from autocfg import dataclass, field
@dataclass
class YOLOv3:
# Base network name which serves as feature extraction base.
base_network : str = 'darknet... |
a0afc03d8a86c29b8a64918c69cd144938f7989b01f40fe6c1034e54b16c80e5 | Python | 4,873 | 100 | import torch
from torch import nn
from torch import Tensor
from collections import OrderedDict
import numpy as np
class GetDensity(torch.nn.Module):
def __init__(self,rs,inta,cutoff,nipsin,norbit,ocmod_list):
super(GetDensity,self).__init__()
'''
rs: tensor[ntype,nwave] float
inta:... |
db95e89e1b97cf68908c30e6dafa1f2499a1a9a1a3e46005fc595e54634db1f6 | Python | 4,873 | 152 | import logging
import time
import serial
from ethoscope.hardware.interfaces.interfaces import SimpleSerialInterface
class WrongSerialPortError(Exception):
pass
class NoValidPortError(Exception):
pass
class OptoMotor(SimpleSerialInterface):
_baud = 115200
_n_channels = 20
def __init__(self, ... |
2a852d210dd05bed01e981f303346e417e62b0724ef6227202dce6e6a98fa74b | Python | 4,875 | 176 | """
The core functionalities of the larvaworld platform
"""
__displayname__ = "Core library"
__all__ = [
# Global registry object
"funcs",
# Subpackages
"param",
"reg",
"plot",
"model",
"process",
"screen",
"sim",
# Re-exported from process.dataset
"ParamLarvaDataset",
... |
368fff22893dd1f71b7411c4563fef231f5a89b9bb0332bf10f33c945bcaf311 | Python | 4,876 | 107 | import os
import re
import pandas as pd
import appdirs
from score_behavior.score_session_manager import SessionManager
from score_behavior import appauthor, appname
from score_behavior.score_config import get_config_section
import logging
logger = logging.getLogger(__name__)
class ObjectSpaceSessionManager(Session... |
d9abae090fc5c8ba5fad73747b9315e137d5b7797fd46e1898ddf918cbbbf69b | Python | 4,876 | 148 | import os
import numpy as np
from mirp import extract_images
from mirp._images.ct_image import CTImage
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
def test_median_denoiser_method():
# 3D variant
data = extract_images(
write_images=False,
export_images=True,
image_export_... |
ec0a2b0354e103f3a143c954275746e2504ea089cbefe5e9a0b1c86996a6df46 | Python | 4,877 | 131 | """Structural non-tensor input-tree honesty: empty containers + key types (r29-C2).
The H1 non-tensor leaf-path SET witness records only LEAF paths, so it was blind to two
structural changes that steer unobserved Python control flow:
* An EXTRA/removed EMPTY container (codex-F1): ``{'flag': {}}`` adds no leaf path, s... |
c2034b297496f53e304744bbcaa72cbd156d38f91e0bc7937d19c4cc300afb31 | Python | 4,879 | 121 | #!/home/ubuntu/miniconda2/bin/python
from __future__ import print_function
import glob, os, gc, sys
import os.path
import csv
import numpy as np
np.random.seed(1337) # for reproducibility
from time import time
from subprocess import (call, Popen, PIPE)
from itertools import product
from IPython.display import display... |
66424d65e0a96007228b27558d0527a05cc75ee6580549708a54f09f5872f15b | Python | 4,880 | 97 | # Contains arguments for generating input audio representations
# These can be used either as part of the transformation or as
# part of the preprocessing (which will be included in the graph
# for gradient computation)
# Feather et al. 2022 used cochleagram representations, however parameters
# for a mel-spectrogram... |
c072ea9e548104606c6f2ffebd84eef6e70caa591d15e0e912e692a21023f230 | Python | 4,880 | 188 | """Phase 13 discoverability tests."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.intervention.resolver import SiteTable
class M(nn.Module):
"""Small model with a stable relu site.
Kept inline deliberately (NOT migrat... |
8d93a2ce41b334d333756091ed91ebe82f2d5a2877f611311207ad20ca424407 | Python | 4,881 | 160 | from __future__ import annotations
import time
from typing import Optional
# Temporary store for which channels passed/failed calibration
# TODO: Will be replaced by CORE-32
CALIB_PASS_CHANNELS: dict[int, bool] = {}
# TODO: Remove for [CORE-906] to tell how long a channel has been in each well
CHANNEL_TIME_IN_WELL: ... |
a6e8c42399cc78d1c659fc9f44ff2549444f74d6e6fb28814680eed97247cfa1 | Python | 4,881 | 112 | #!/usr/bin/env python
"""Convert plain tabular or quoted TSV to Parquet with explicit header handling."""
import argparse
import csv
import math
import re
import sys
from collections.abc import Iterable
from decimal import (
Decimal,
InvalidOperation,
)
try:
import pyarrow as pa
import pyarrow.parquet... |
d3eca0d78e6d3c08441924362600d27cd9548493f451b93df81c415555b9f5a4 | Python | 4,881 | 166 | """
FUNCTIONS for similarity network fusion, diffusion map embedding and domain adapted ML
Author: Dominik Kraft
"""
from brainspace.gradient import embedding
from adapt.instance_based import TrAdaBoostR2
from scipy.stats import pearsonr
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
fro... |
5a1dfe5bbdc0db2324ad0ae631ddcd50aa9cc657147d3b06c92d6a8d2dac3d8a | Python | 4,882 | 149 | #!/usr/bin/env python3
# Setup script for PyPI; use CMakeFile.txt to build extension modules
from __future__ import annotations
import contextlib
import os
import re
import shutil
import string
import subprocess
import sys
from collections.abc import Generator
from pathlib import Path
from tempfile import TemporaryDi... |
b41f1d549792f78c5fd55df1fd2a0f78dec282b6e59d353a4251333f54d30c18 | Python | 4,883 | 103 | """Калибровка прибора.
Прежде чем измерять неизвестное, детектор прогоняют по известному источнику.
Здесь два таких источника — политики, для которых мы ЗАРАНЕЕ знаем ответ:
threshold — поведение полностью определяется внутренним состоянием.
I(энергия; действие) обязана быть большой. Если метрика этог... |
f496b9b1987d740f25ddace6dd85d1667c448c0b20b850b3931100eaa86fd935 | Python | 4,883 | 125 | # SOURCE: vendored from https://github.com/abdulsalam-bande/KDeep @ main
# (pytorch/model.py, `CNNScore` class). Community PyTorch reimplementation of
# the KDeep 3D-CNN binding-affinity scorer (Jimenez et al., JCIM 2018), a
# SqueezeNet-style network of "fire" squeeze/expand Conv3d blocks operating
# on 16-channel vox... |
36381d80def5a3a210415c7711abffcfc635817fbf921b13816ddfbfb8174ca4 | Python | 4,884 | 111 | import os
from angorapy.agent.utils import extract_discrete_action_probabilities
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import gymnasium as gym
import numpy as np
import tensorflow as tf
from scipy.stats import norm, entropy, beta
from angorapy.common.policies import GaussianPolicyDistribution, CategoricalPolicyD... |
ab5855643dbfc92d8b8ce769d7b1ec3f1adcde2a5ceeb684ed6c5822a82521bd | Python | 4,884 | 132 | import pandas as pd
import nibabel as nib
from os.path import join as pjoin
from magicbox.io.io import CiftiReader
from cxy_visual_dev.lib.predefine import mmp_map_file, LR_count_32k,\
L_offset_32k, L_count_32k, R_offset_32k, R_count_32k,\
s1200_1096_myelin, s1200_1096_thickness, s1200_avg_myelin,\
s1200_av... |
631e816a8ad9384cecdf397d9172dd93a20bfa3699a285a4a54286b4a5838492 | Python | 4,888 | 192 | """
Main Script containing the following steps:
- perform similarity network fusion and diffusion map embedding in PNC
- split ABCD data into baseline (only T1), baseline, follow up
- train ML model on PNC embeddings with supervised Domain Adaptation
- ABCD *baseline only* data enhances prediction of embedding in unsee... |
b333bcf17d116c7afbd8f142e61b4cef19ce0b0dd6c83b847ad5ff777c957b11 | Python | 4,889 | 141 | # Copyright 2021 Google Inc. All Rights Reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the foll... |
6dd865ec3519d73edeba464b92da0ed6f8648bdb02e7bea005670581e978ebce | Python | 4,890 | 114 | """
pip._vendor is for vendoring dependencies of pip to prevent needing pip to
depend on something external.
Files inside of pip._vendor should be considered immutable and should only be
updated to versions from upstream.
"""
from __future__ import absolute_import
import glob
import os.path
import sys
# Downstream r... |
ecd83c5f1f812b063d9dd53ce403c96f0101c7a8246d3a73e2b958d568123508 | Python | 4,890 | 121 | """Deployment envelope (lane F37): bitsandbytes 4/8-bit capture on CPU.
Quantized capture is DISCLOSED-DEGRADED, never silently wrong: outputs match
the bare forward bit-for-bit at fp32 tolerance, the quantized-module warning
fires at entry, the compat row reads pass/warning, and the validation
tripwires keep their ho... |
5c57cc92a753367b14cab037de04983f9634b2cd79e4a3710b86a1c5404d14fe | Python | 4,891 | 149 | """
densratio.core
~~~~~~~~~~~~~~
Estimate Density Ratio p(x)/q(y)
"""
from numpy import array, linspace
from .KLIEP import KLIEP as _KLIEP
from .RuLSIF import RuLSIF as _RuLSIF
from .helpers import is_numeric, to_ndarray
_METHODS = ("uLSIF", "RuLSIF", "KLIEP")
def densratio(x, y, method="uLSIF", sigma="auto", la... |
7931f9fc7c390c2c26400a4d01a610186f4283bb29302d0e949c1955b2cf7971 | Python | 4,892 | 142 | #################################################################################################################################################################
####################### Testing MulitRBP #####################################################################################################################... |
67e167050a45c43caeffca953d6a2f6aa4bc68fc29075de9ff1e0aeca1ee8393 | Python | 4,893 | 86 | import torch
#import opt_einsum as oe
class Neigh_List(torch.nn.Module):
def __init__(self,cutoff:float,nlinked:int):
# nliked used for the periodic boundary condition in cell linked list
super(Neigh_List,self).__init__()
self.cutoff=cutoff
self.cell_list=self.cutoff/nlinked
... |
726a9e723026200556940be54f63f9f7ea816cae27f9c95ca5156aab1d7354e2 | Python | 4,893 | 130 | #!/usr/bin/env python3
"""Суточный автономный прогон протокола B: «Когда выгодно учиться».
Шесть условий на ОДНИХ И ТЕХ ЖЕ seed'ах (см. sim/protocol_b.py), 100 000
тиков каждое, единый предок на seed. Копится в runs/daily/protocol_b.jsonl,
по строке на прогон, с датой, хешем коммита и версией протокола.
Объявленные ч... |
8ae1d66869db50edc5736486f6e7a5dbfd5e8406a533059b13c3756e29fda5f7 | Python | 4,893 | 130 | #!/usr/bin/env python3
"""Суточный автономный прогон протокола B: «Когда выгодно учиться».
Шесть условий на ОДНИХ И ТЕХ ЖЕ seed'ах (см. sim/protocol_b.py), 100 000
тиков каждое, единый предок на seed. Копится в runs/daily/protocol_b.jsonl,
по строке на прогон, с датой, хешем коммита и версией протокола.
Объявленные ч... |
1d15dd2f495a16aa0694cb34bb971349a25e0ae2310f627e2d4eadfc9a2bf8ea | Python | 4,895 | 144 | import numpy as np
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
import brian2.only as br
from contextlib import contextmanager
from functools import partial
def starstar(mapfn, f, kwargs_list):
"""Maps `f` to `kwargs_list` using `mapfn`."""
return mapfn(partial(apply_kwar... |
c81d6013519a79a1d70b31b246b03c3f0b1398e295939d8579857fa842bde0c4 | Python | 4,898 | 123 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
fro... |
d54b132dc0052799fcd4090f15a2374f60f8bd8b35c8f321bec8f452c73bd34f | Python | 4,899 | 136 | """Armed distributed state never steers a capture that has no process group.
Arming is process-lifetime by design: it survives ``destroy_process_group`` so
the group-lifecycle ledger outlives every group (a disarm-and-rearm after
teardown would read the emptied group registry as "no history" and reuse
lifetime ordinal... |
6eba260eb21ea92161feece33994d7ca226f3c9ec5f08b65ee43f07a3d63abcc | Python | 4,901 | 156 | # 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... |
82e9a39ce7473e151e5d5c930d1aa982d3344ee9d1c9a333c660100f007cb926 | Python | 4,902 | 138 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from external_src.group_free_3D.pointnet2.pointnet2_modules import PointnetSAModuleVotes, PointnetFPModul... |
033f3e6a2ea707d9943328e7f85914285770f344dce79fe9596bfa2e8e2b285b | Python | 4,903 | 126 | import os
import numpy as np
import math
from tensorflow.keras.utils import Sequence
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from imageio import imread
from scipy.special import expit
from skimage.transform import resize
# This is a custom Keras Sequence class that is used to load and pr... |
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