sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
96d08d36eaca0d2852b92c6cca492ff4125d66e1bae36b217217407923538b4f | Python | 8,741 | 231 | """W051 regressions for the step-join ENTRY BASIS (audit 3.3).
The join basis was "first tensor argument" with no disclosure of WHICH
argument was read: a kwargs-first masked LM (``forward(attention_mask=None,
input_ids=None)``) graded its attention MASK and reported false exogenous
positions on a true continuation; a... |
e483a968d507d5262d0b9048d070cfca6f31d887eeff89270ea0c67f05dbc7a2 | Python | 8,741 | 177 | from unittest import TestCase, mock
import numpy as np
import spateo.segmentation.icell as icell
from ..mixins import TestMixin, create_random_adata
class TestICell(TestMixin, TestCase):
def setUp(self):
self.utils_patch = mock.patch("spateo.segmentation.icell.utils")
self.em_patch = mock.patch... |
f6c73c61a602ee3b8b87bfa90284fbc96e567b77c3a6d86c0e3919c5f12b64c6 | Python | 8,741 | 346 | import datetime
import decimal
import io
import uuid
import pytest
from werkzeug.http import http_date
import flask
from flask import json
from flask.json.provider import DefaultJSONProvider
@pytest.mark.parametrize("debug", (True, False))
def test_bad_request_debug_message(app, client, debug):
app.config["DEBU... |
f8490be3cac5b5975dd804db5d5fda6a29dc432bafb9aea16a45c41aee660cb3 | Python | 8,741 | 189 | import numpy as np
from tqdm.auto import tqdm
from . import xpu, regression
from .xpu import cupy_wrapper, iscupy, get_array_module
@cupy_wrapper(['arr'])
def getdff(arr, medianfilt_size=7, minfilt_size=61, gaussfilt_sigma=61., onlydf=False, epsilon=1.):
""" Calculate deltaF / F0. This function calculates the dF... |
2b20c0831b98dc5f961e4875e60a33798cf326d8af8fc89599c2f925f0028779 | Python | 8,742 | 220 | """
Author: Bruno De Santi, PhD
Affiliation: Multi-modality Medical Imaging Lab (M3I Lab), University of Twente, Enschede, The Netherlands
Date: 20/09/2023
Description: Collection of processing techniques used in MUVINN-reg
Paper/Project Title: Automated three-dimensional image registration for longitudinal photoacou... |
de3bbe1eeabb8c5f447048727c0e8d5c3f3709ce79c7d29976afa61d71c5882e | Python | 8,742 | 249 | import copy
import os
import torch
import torch.utils.data
import torchvision
import torchvision.transforms as T
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
class FilterAndRemapCocoCategories:
def __init__(self, categories, remap=True):
self.categories = categories
... |
44414619675844b260e8128e6c2b1ce41bf2387669f92b8d7968be7481301b1c | Python | 8,743 | 287 | #!/usr/bin/env python
"""
Basic SuCOS scoring. Allows a set of molecules from a SD file to be overlayed to a reference molecule,
with the resulting scores being written as properties in the output SD file.
SuCOS is the work of Susan Leung.
GitHub: https://github.com/susanhleung/SuCOS
Publication: https://doi.org/10.26... |
b5f744f115fbc094880367ab17058a9d89dc3a1159fb19df9b2cecb8feb88185 | Python | 8,743 | 309 | from matplotlib import pyplot as plt
from PIL import Image
import pandas as pd
import matplotlib
import numpy as np
import networkx as nx
from stlearn._compat import Literal
from typing import Optional, Union
from anndata import AnnData
import warnings
from stlearn.utils import _read_graph
def pseudotime_plot(
a... |
906774e6e7927c55be1d97877d000242d046fe064b2b9d412d855d8519cd8a8d | Python | 8,745 | 251 | import os
import json
from volume import Volume
import pandas as pd
import pydicom
import numpy as np
def dcm2json(seq_overview, dcm2bids_tags, utils_dict, dcm_list, json_path, pet_doses=None, verbose=False):
"""
Converting dicom metadata to json file saved in session folder
:param seq_overview: [pandas ... |
8d53ebc99cf818a808cbd73ba53af03dc90e39a6cc0d16d560095120286ac575 | Python | 8,746 | 279 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import d... |
0a091fbc7edcee33392cb63d7191c37dce66ec7a58739d518785a242b5afe881 | Python | 8,750 | 209 | import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
from src.getdata import getdata_from_csv
from src.utils import DrugTargetDataset, collate, AminoAcid, ci, kd_loss
from src.models.DAT import DAT3
parser = argparse.ArgumentParser()
parser.add_argument('--cuda', default=True... |
b9bdd163fa2e4f579f5d25744d0a2a1168d4b9b379d4534ecd51aa4e2e457e04 | Python | 8,750 | 216 | #!/usr/bin/env python
"""Fetch every third-party input this pipeline needs.
Nothing this script downloads is redistributed in the archive. Run it to rebuild
the working tree from the original sources.
python deposit/fetch_data.py --all
python deposit/fetch_data.py --proteoclock --organaging
python deposit... |
5d5c84653a3852d723cddfddab9b93c67cf1a0b157a9d7c89bc4720fff5be02f | Python | 8,752 | 318 | import numpy as np
from . import utils
from . import extract_signals
def get_range(arr, axis=None):
"""
Difference between the highest and the lowest value
Parameters
----------
arr : np.array
typically a time-varying signal
axis : int, optional
if None, difference between th... |
536b0d0abd0e5d5c44105c694dc6debda747a0e44e16a1486d3f3c55b8832d5a | Python | 8,754 | 236 | import torch
import pandas as pd
from brainbox.physiology import neural
from src import datasets, train, util
class PVC1ModelQuery:
static_model_query = {
"vgg": {"3.1": [0.66, 1.0, 1.5]},
"bwt": {"None": [0.66, 1.0, 1.5]}
}
dynamic_model_query = {
"stack_prediction_4-6-2_True":
... |
811f2e65465a4212df7a21cb0c3ce50dee1d063d2ef5d79a2f706e7633b583e4 | Python | 8,754 | 227 | from __future__ import print_function, division, absolute_import
import os
import torch # http://pytorch.org/about/
from torch.autograd import Variable
from torch.utils import model_zoo
import torchvision # https://github.com/pytorch/vision
import torchvision.models as models
import torchvision.transforms as transfor... |
900de1a38b61d7aa0461beffb05f5c702675af361d39542c8f40c694bd7353dd | Python | 8,756 | 209 | """B4 + B15: join-guarded differential consumers and the diff projection matrix.
``changed()`` / ``top_changed()`` pair cross-trace occurrences through the
shipped guarded join (labels leave the join key), and ``diff_report`` settles
the union of both captures into the closed added / removed / unresolved /
excluded_ma... |
ea19e2d7183973ebce8994874584762967da01d43b66a6f3630a150d9b6cac9b | Python | 8,757 | 238 | from __future__ import absolute_import, division
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from trainingUtils import MultipleOptimizer, MultipleScheduler, compute_kernel, compute_mmd
from models import Decoder, SimpleEncoder,LocalDiscriminator,PriorDiscriminator,Classifier,SpeciesCovariate... |
1319a4a867c61d92c63f73db02805b6399dbd3924565ae57c0bf45e444d3d23b | Python | 8,758 | 279 | import gc
import sys
import time
import pytest
from celery.utils.dispatch import Signal
if sys.platform.startswith('java'):
def garbage_collect():
# Some JVM GCs will execute finalizers in a different thread, meaning
# we need to wait for that to complete before we go on looking for the
... |
faf6615444e053442e58920bc24f7eae6d674b255a00d971aed237556b90915f | Python | 8,759 | 264 | """Base Transform class and relate utils."""
import torch
from bioseq2seq.utils.logging import logger
from bioseq2seq.utils.misc import check_path
from bioseq2seq.inputters.fields import get_vocabs
class Transform(object):
"""A Base class that every transform method should derived from."""
def __init__(self,... |
8b0c95bce48ecf788eab3a82646dcb721f3a4db8be83caa46e42ccc9f14a889c | Python | 8,760 | 236 | """A synchronous vector environment."""
from copy import deepcopy
from typing import Any, Callable, Iterator, List, Optional, Sequence, Union
import numpy as np
from gym import Env
from gym.spaces import Space
from gym.vector.utils import concatenate, create_empty_array, iterate
from gym.vector.vector_env import Vect... |
2ec96bdf1ca0242ac696f27324e10b27f290a712c15085c446a69a3d00b95cb1 | Python | 8,766 | 303 | # -*- coding: utf-8 -*-
import numpy as np
from matplotlib import pyplot as plt
import random
import sklearn
import seaborn as sns
import scipy as sp
from matplotlib import rc
import matplotlib.ticker as mtick
import matplotlib as mpl
from scipy import signal
from sklearn.linear_model import LinearRegres... |
781010b7f408b689d24d0948ce912eab87db2cba37aabb5d2dfb32d31bf277fd | Python | 8,766 | 210 | from contextlib import AbstractContextManager
from typing import List
from dependency_injector.providers import Callable
from sqlalchemy import Text, and_, cast, func
from carbonserver.api.domain.projects import Projects
from carbonserver.api.errors import NotFoundError, NotFoundErrorEnum, UserException
from carbonse... |
a806b98e6a7f7e44019552f82d6d7837baeb30f364df2300a4d14bc5110fba25 | Python | 8,766 | 221 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in April 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
from typing import Union, Tuple, Optional
import numpy as np
from scipy import sparse
from sknetwork.linalg.sparse_lowrank import SparseLR
from sknetwork.utils.check import check_form... |
11c818853f3a8daea571c727964747ef36f78805c5eedca9a1d3808739035296 | Python | 8,770 | 251 | """Fetches gene sequence from gene fasta created by extract_genes.py"""
import prob2020.python.utils as utils
class GeneSequence(object):
def __init__(self, fasta_obj,
nuc_context=1.5):
self.fasta = fasta_obj
self.nuc_context = nuc_context
def set_gene(self, bed_line):
... |
0d25d72d51e414ac633d029671e121911ab9c2d455ab98f626a4cf894720759b | Python | 8,772 | 192 | import logging
import os
import shutil
from galaxy import (
model,
util,
)
log = logging.getLogger(__name__)
def do_split(job_wrapper):
parent_job = job_wrapper.get_job()
working_directory = os.path.abspath(job_wrapper.working_directory)
parallel_settings = job_wrapper.get_parallelism().attribu... |
a30244deffcc420a008de80c480cb9375d155fd7c9cc8a3fb77725ad174280ec | Python | 8,772 | 166 | ##########################################################################
## ##
## Copyright (c) 2019-2025 Philipp Lösel. All rights reserved. ##
## ##
## This file is par... |
45abd69a94040fc6482d731ad3eb5e8b83edf21dcf8dc377f24fb983b3ff3d0c | Python | 8,777 | 230 | import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
from src.getdata import getdata_from_csv, get_cold_data_from_csv
from src.utils import DrugTargetDataset, collate, AminoAcid, ci
from src.models.DAT import DAT3
import random
parser = argparse.ArgumentParser()
parser.add_argu... |
7c4d284210d4ace510c6b045c1a4561a1bb57a3506304e3a3e47d528f05fd5f0 | Python | 8,777 | 213 | import sys
import os
import gc
sys.path.append('../TM2_segmentation')
import SimpleITK as sitk
import numpy as np
import pandas as pd
import nibabel as nib
from skimage.transform import resize, rescale
from scipy.ndimage import rotate
from scipy import ndimage
import matplotlib.pyplot as plt
from numpy import median
i... |
b322edf661a6d6036905ee70edcdc6d7e7c4742f1e0f9ff5c9bac77a796308d1 | Python | 8,777 | 241 | # %%
'''
Splits patients into medicated and non-medicated
Reruns the behavioural and glm analyses
'''
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import pingouin as pg
from functions.data_helpers import get_phenotype, get_task_beh_data
from functions.data_helpers impor... |
3ab70392dfab4c95a64c776abbcff295634cbed6addc75047e3464eb2c0450e9 | Python | 8,779 | 295 | """Logging utilities."""
import logging
import numbers
import os
import sys
import threading
import traceback
from contextlib import contextmanager
from typing import AnyStr, Sequence # noqa
from kombu.log import LOG_LEVELS
from kombu.log import get_logger as _get_logger
from kombu.utils.encoding import safe_str
fro... |
86b31e156e5d7d9db469b74081d8aeca29e240117b84c91a83fb7d143aa4dcbc | Python | 8,779 | 206 | """C03: the ONE addressing repair (site-key-primary targets, guarded join).
Leverage B4/B5 + surgery Build 0a/0b, one substrate:
- Saved spec target manifests carry ``resolved_site_keys`` (site-key-primary;
labels are display-only disclosure -- the ME ordinal rule).
- ``check_spec_compat`` compares site keys FIRST ... |
77dec1e24150c86c4fddd070dde494afea1942e850eec144309b486d8074c63d | Python | 8,780 | 202 | from contextlib import AbstractContextManager
from typing import List
from dependency_injector.providers import Callable
from fastapi import HTTPException
from sqlalchemy import and_, func
from carbonserver.api.domain.experiments import Experiments
from carbonserver.api.infra.database.sql_models import Emission as Sq... |
439f11a30444d49e9ce93a5779dfd08d8ce1a2869b9fefe79d8b386998777ca1 | Python | 8,788 | 223 | #!/usr/bin/env python3
"""
File: prepare_metabrain_eqtl_file.py
Created: 2021/12/06
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 ... |
0f3b8c106a70a8c3af62a4bc3b8ef6f011d349941961d0828c1b8683d30c4163 | Python | 8,789 | 167 | # Written by Seonwoo Min, Seoul National University (mswzeus@gmail.com)
# PLUS
import os
import sys
import argparse
import numpy as np
import torch
import plus.config as config
from plus.data.alphabets import Protein
import plus.data.homology as homology
import plus.data.dataset as dataset
import plus.model.plus_rnn... |
6a8165f2ec4c0f869a865d3964fc88728b24ae24d583cacdc0edf87e3545a6d4 | Python | 8,791 | 237 | """Stream a layer to a GeoParquet file without materialising the whole output frame.
The chunks (the point nodes, then ways in batches, then the relations) are assembled one
at a time and written in that order by one writer, under the columns of the in-memory
reader's result in its order. Every way chunk carries the t... |
9d25c6e7359aa202aab1607c8e0b3e5b5492e8baa49d63a5a5e37b30b6479e53 | Python | 8,791 | 259 | # --- 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 Dimension(_BaseTraceHierarchyType):
_parent_path_str = "splom"
_path_str ... |
c38a7630e98c7473d6eb154df8e76a4ea1d9f14715d31034eb0b7702942e9d6e | Python | 8,792 | 277 | import os
from types import SimpleNamespace
import pytest
import typer
from codecarbon.cli import monitor as monitor_module
class FakeTracker:
def __init__(self, *args, **kwargs):
self.kwargs = kwargs
self.stopped = 0
self._conf = {"output_file": "emissions.csv"}
self._output_han... |
367a5f02722136b05ea006b82185aed77389b76d94b6dff5093921ce907647ff | Python | 8,793 | 257 | # pylint: disable=missing-function-docstring, missing-class-docstring
"""
A Closer Look at Spatiotemporal Convolutions for Action Recognition
CVPR 2018, https://arxiv.org/abs/1711.11248
Large-scale weakly-supervised pre-training for video action recognition
CVPR 2019, https://arxiv.org/abs/1905.00561
"""
import torch
i... |
dbf200b488a18d9c55459795d391a27c14c451cdcd96ccf5310dfcd39383cbb9 | Python | 8,794 | 210 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
@author: Pramod Bharadwaj Chandrashekar
@email: pchandrashe3@wisc.edu
"""
import math
import numpy as np
import pandas as pd
import DeepCORE_attention_util as dau
def match_enhancer_location(gene_info, enhancer_info):
"""
Function to extract all the locations... |
6630eb5867cb7879ea4b1f92b19bc1fb5d1e4d257d84c476df4a82f0912385a0 | Python | 8,795 | 221 | # coding: utf-8
from itertools import chain, starmap
from collections import Counter
import torch
from torchtext.data import Dataset as TorchtextDataset
from torchtext.data import Example
from torchtext.vocab import Vocab
def _join_dicts(*args):
"""
Args:
dictionaries with disjoint keys.
Return... |
e8646b5451d0e7df80114104a7b27e5af6762a90d78fd51223e9c550ff6d90a7 | Python | 8,795 | 214 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import base64
from pathlib import Path
from typing import Any
import cv2
from ultralytics.data.loaders import LoadPilAndNumpy
from ultralytics.utils.checks import check_requirements
class LLM:
"""OpenAI-comp... |
0fa5ff3a38901e6f4a34f68aed0ca8273e99827f1a358491d31a99dc8e8174b8 | Python | 8,796 | 253 | """Tests for the Raspberry Pi power interface (`vcgencmd pmic_read_adc`)."""
import time
from types import SimpleNamespace
from unittest.mock import MagicMock, patch
import pytest
from codecarbon.core import cpu
from codecarbon.core.units import Energy, Power, Time, Water
# Shape of `vcgencmd pmic_read_adc`, with r... |
49187da1358e7450a45400155f55a75b036896b63be8c48ecffb558a22b138ca | Python | 8,797 | 239 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test the continuous normalizing flow layer in tfep.nn.flows.continuous.
"""
# ================================... |
7d49f03d50c96d603dc8f48951945537761434f567ed4e7bd04b8da5e393e180 | Python | 8,800 | 200 | """MS COCO object detection dataset."""
from __future__ import absolute_import
from __future__ import division
import os
import numpy as np
from PIL import Image
import mxnet as mx
from .utils import try_import_pycocotools
from ..base import VisionDataset
__all__ = ['COCOInstance']
class COCOInstance(VisionDatase... |
f71e8daa4b378987da375c7865c04a2aedf1d5e3fc3eaab519f7c099fc390c90 | Python | 8,800 | 224 | #!/usr/bin/env python
"""This scripts checks whether mutations are specified correctly.
Specifically, this script tests mutations to check whether they are
reported as being on the positive strand or on the coding strand.
"""
# fix problems with pythons terrible import system
import sys
import os
file_dir = os.path.di... |
8458622ac1da984caab348d1a8ad28dd481cc165d8c4b3b211484fa9a25bc516 | Python | 8,801 | 265 | from typing import List, Optional, Union
import numpy as np
import pyvista as pv
from pyvista import DataSet, MultiBlock, PolyData, UnstructuredGrid
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
####################
# Integrate models #
####################
def m... |
e224c6e08e58b827dce5e6a2acd33f3278b82f7e25a81ea8bfdad111036c7d69 | Python | 8,801 | 223 | """History artifact tests: atomic append, recovery, ring policies (D19/D20)."""
from __future__ import annotations
import json
import os
from pathlib import Path
import pytest
import torch
from torchlens.observability import (
CommittedBlock,
Histogram,
HistoryArtifactError,
HistoryReader,
Histo... |
98930f41b964d8efe8c5bb35369260c47438f18bb264b314a3fad994cd42fe40 | Python | 8,803 | 214 | """Bounding box visualization functions."""
from __future__ import absolute_import, division
import random
try:
import mxnet as mx
except ImportError:
mx = None
from .image import plot_image
def plot_bbox(img, bboxes, scores=None, labels=None, thresh=0.5,
class_names=None, colors=None, ax=None,
... |
0664db0ca77127d1e4ec0c0060b82f5370abfda3381c865fc226b2ee56a5a4d7 | Python | 8,804 | 230 | """Tensor->Python-scalar escape honesty for sparse runnable execution.
Round-8 hardening (the r8 "money bug"). A tensor->Python-scalar escape
(``.item()`` / ``int()`` / ``float()`` / ``aten._local_scalar_dense``) used AS A
VALUE bakes the derived scalar into a downstream op as a literal constant. The
escape breaks the... |
d262423162b89fa1192203c8610180317e912cafbf1026465b57818bb6a002ae | Python | 8,804 | 236 | """Checks kit item 4/5: the D6 nonfinite table test (memo test plan row 3).
fp32 x {inf, nan} x {clip, no clip}; scaler x {inf, nan}; bf16 x {inf, nan}.
With a scaler S-B never fires and 0 parameters move; without one S-B fires
and raises BEFORE the write with weights bitwise clean after; the clipping
collateral count... |
9d748ad37f192b1d0a958406abd3772ae27d9a51fcf5412795afa1a6ba901b4c | Python | 8,805 | 227 | # SOURCE: vendored from oxwhirl/wqmix @ master (files: src/modules/mixers/qmix.py,
# src/modules/mixers/qmix_central_no_hyper.py, src/modules/agents/rnn_agent.py,
# src/modules/agents/central_rnn_agent.py -- fetched 2026-07-02).
#
# This vendors the REAL WQMIX (Weighted QMIX) architecture: the per-agent recurrent Q-net... |
41bf19434fd57e7b82c69d3f7846b12f6aad0593308b6868c18493e34994dc36 | Python | 8,807 | 210 | """C07X identity-fact load validators (tlspec v9 entry-dark slots).
The Trace-level root entry-point fact (``Trace.root_entry_point``, written
unconditionally at capture; foldA D10) and the two entry-dark Op facts the
C07X amendment declares (``Op.episode_step``, F-EPISODE writes;
``Op.tl_authored_root``, F41 writes),... |
d00fcf2a9fbb8533f416114bbfcf23677120f2cb9be7e85ecf2727b7bea8a903 | Python | 8,807 | 261 | """
Integration tests for memory leak issue #8882.
These tests reproduce memory leak scenarios that occur when Celery tasks
raise unhandled exceptions, causing ExceptionInfo objects to not be
properly garbage collected.
"""
import gc
import logging
import os
import tracemalloc
from celery import Celery
logger = log... |
bc57b2962a2d4274a7931e57930981eafa078414d51515dcc57452eb45881528 | Python | 8,811 | 245 | '''DNN Feature decoding - decoders training script'''
from __future__ import print_function
from itertools import product
import os
from time import time
import warnings
import argparse
import bdpy
from bdpy.dataform import Features, save_array
from bdpy.distcomp import DistComp
from bdpy.ml import ModelTraining
fr... |
97f5a6ad36ac4f35be7f30b638dbb9ed950d34b6e2fec3ba8f889d3beb8eefbe | Python | 8,812 | 224 | """Entry contract for structure-only capture (L7a wave 0, D8-default branch).
Covers the memo's sec 1.2 options threading, sec 1.3/2.2 Layer-0 entry
conflicts, the sec 1.5 entry-matrix cells reachable without D8 (E-1
degenerate form (b), E-6 flag-off zero-diff), and the S3-registrar mode-marker
prep. Every structure-o... |
82e57d5eeb56c3459ff55b51c334c6ee65386604a261eb834ddabcba7279586e | Python | 8,814 | 200 | from enum import Enum
from itertools import product
from typing import List, Any
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from drug_interactions.datasets.Datasets import TrainDataset, TestDataset, NewOldTestDataset, NewNewTestDataset, ... |
d81e2fd4a49780e7d4a828a6b156b3f91d4f01234f2f9a8c27d6fd7e1719da2a | Python | 8,815 | 273 | """
This module contain all graphics and plotting
"""
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def probplot(prob, sigma=1, xmin=-10, xmax=10, ymin=-10, ymax=10, ax=None, colorbar=True, grid=False):
"""Plot a conditional probability matrix.
Parameters
----------
prob :... |
732bcea738e9511b3638a7f41ae436ba45d069d830d5db9b0c84b73aa1add40f | Python | 8,819 | 221 | """A06 capture-options truth: the cache key covers EVERY semantic option.
WALKTHROUGH list-A row 26 (first clause): ``cache=True`` could serve captures
that never armed ``raise_on_nan`` / ``track_nonfinite`` / ``save_budget`` --
the key was a hand-enumerated include-list, so semantic knobs added after it
silently fell... |
6d29b07d468c2291557a526debe9a1057c2eb18fb691fb5f319587306edb3b83 | Python | 8,820 | 309 | # --- 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 Delta(_BaseTraceHierarchyType):
_parent_path_str = "indicator"
_path_str ... |
1c7910b0bb04b587d43ba6031232936593aeec2cb88510cb3b591092fecde57b | Python | 8,821 | 251 | """
navigation_sim.py — headless SimPy navigation model
Separate from simulation_06.py, which keeps the PyQt5 demo GUI. This module is
what the pipeline imports: no Qt dependency, no hardcoded accuracy table.
The key difference from the earlier version: measured decoding accuracy is the
probability that a decoded com... |
25e92d7b62a510ac065f4fb480c595b4cc40279a2c885f84e7e6dac22e4ab63f | Python | 8,824 | 101 | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# NO CHECKED-IN PROTOBUF GENCODE
# source: osmformat.proto
# Protobuf Python Version: 6.33.5
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _desc... |
10b008af5980d82e4d134309627f17b3f1a631e118b2490c7a4f020cbd921531 | Python | 8,825 | 200 | import logging
import os
import sys
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from torch.cuda.amp import GradScaler, autocast
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from utils import save_config_file, accuracy, save_checkpoint
import pandas as pd
impor... |
87676cbabbe6d6b568aa392bf4c8314af74da18689f93532819c91dc8906cb8e | Python | 8,826 | 250 | import math
import typing as ty
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence
class Embedding(nn.Module):
def __init__(self,
dim_numerical: int,
dim_embedding: int,
dim_categorical: int,
... |
ce29c362b601e2dd75b3ac5d962e82a93001ac66ed324da0839e4b13a4bd64cd | Python | 8,826 | 212 | """
Functions for verifying files.
Copyright (C) 2021 Tham Cheng Yong
This file is part of NanoVar.
NanoVar is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) a... |
61582bb5e904bb0007650f411a55eed21a4ed333548263560420c0beb5feab35 | Python | 8,827 | 246 | import threading
import time
import unittest
from unittest import mock
from codecarbon.external import scheduler as scheduler_module
from codecarbon.external.scheduler import PeriodicScheduler
from tests.testutils import FakeClock
# Fake seconds: time only moves when a test calls clock.advance().
INTERVAL = 1.0
# Rea... |
7dc5836275d2aa38ffcd35ad0a2f381d2dd7ed369dc1d6f2a5445ef209d1afe0 | Python | 8,828 | 313 | # -*- coding: utf-8 -*-
from docutils import nodes
from docutils.parsers.rst.directives.admonitions import BaseAdmonition
from docutils.parsers.rst.directives import unchanged
class HandbookAdmonition(BaseAdmonition):
"""RST directive
"""
node_class = nodes.admonition
# empty is no allowed
has_co... |
205f1351d17f4730d6732d853f15790b77749f4f6480637ace48106af356cc76 | Python | 8,829 | 248 | import numpy as np
import matplotlib.pyplot as plt
from scipy.special import softmax
import pandas as pd
from utils_ensemble import list_of_result_to_dico, acc_of_ensemble
import torch
from scipy.signal import savgol_filter
from sklearn.model_selection import ShuffleSplit
import json
import argparse
from utils.json_loa... |
2d248e1548ebb0a3531607617c03bf3a6858af1687667788efbb423ddf9d705b | Python | 8,829 | 301 | from typing import List, Any
import numpy as np
# auto-object related utility functions
#######################################
def get_branch_info(branch: Any) -> tuple:
"""Extract the relevant data from a solution branch object.
Parameters
----------
branch
Solution branch as returned by ... |
96deca7354e47d6de0fa44f26c5556a830936e2db35883e176d49b428b929148 | Python | 8,831 | 187 | """this script is used to prepare DET dataset for tracking,
which is Object detection in Large Scale Visual Recognition Challenge 2015 (ILSVRC2015)
Code adapted from https://github.com/STVIR/pysot"""
import argparse
import tarfile
import os
import glob
try:
import xml.etree.cElementTree as ET
except ImportError:
... |
627407b8d0676562248ea5d48b5cb37059a6c0845b2114b7b4dca1b4fdb39052 | Python | 8,834 | 219 | import copy
import torch
import torch.nn as nn
from model.vq import VQLayer
class MaskConvBlock(nn.Module):
""" Masked Convolution Blocks as described in NPC paper """
def __init__(self, input_size, hidden_size, kernel_size, mask_size):
super(MaskConvBlock, self).__init__()
assert kernel_size-m... |
4ca0b63437f3d34b4738b920ecf884c9efab48281a8e8437eea08a72178c8f17 | Python | 8,835 | 226 | '''Trains a simple convnet on a custom dataset'''
from __future__ import print_function
import tensorflow
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import os
import cv2
from numpy.linalg import inv
from skimage import *
from skima... |
10a6296420feb1cc566b8e32d5c4c6fdb6891ac850698c6e544fcfa1f5d48e3c | Python | 8,836 | 310 | """
The Folder class helps to navigate a hierarchical data tree.
"""
import json
import string
from collections import UserDict
from datetime import datetime
from pathlib import Path
from rich.console import Console # , ConsoleOptions, RenderResult
from rich.panel import Panel
from rich.tree import Tree
from .inter... |
73e38ba89a4e05765a43ad32184eed01ef65b6ffbaec96542a250eaeb22d385f | Python | 8,836 | 301 | """Phase 8 multi-trace follow-on tests."""
from __future__ import annotations
import json
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.intervention._topology.topology import compare_topology
class _ModuleModel(nn.Module):
"""Tiny module-nested model for bund... |
bebc7064896d9e949c1aab0c916c445314221a2f3b9a34ab93191371da5ae0e6 | Python | 8,836 | 253 | '''Tests for bdpy.bdata.bdata.'''
import unittest
import numpy as np
from numpy.testing import assert_array_equal
from bdpy.bdata.bdata import BData
class TestBdata(unittest.TestCase):
'''Tests of 'bdata' module'''
def __init__(self, *args, **kwargs):
super(TestBdata, self).__init__(*args, **kwar... |
53627f9359203c894d2ba3d1a8e13ddc6adc45b6ad2046ca80592499dfd9e538 | Python | 8,837 | 222 |
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from .utils import gabor_kernel
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
... |
c4c3ac98cb3789e838d63161a331768a80c742efbd350840f6d230e0f813bae7 | Python | 8,837 | 255 | from __future__ import annotations
from collections.abc import Callable
from larvaworld.gui_v2.registry_bridge import GuiNavigationModel
from PySide6.QtCore import Qt
from PySide6.QtWidgets import (
QFrame,
QGridLayout,
QHBoxLayout,
QLabel,
QPushButton,
QVBoxLayout,
QWidget,
)
def build_... |
23f6ab4a967c89e4d09fd74445865dbbf70cb3521ad8947dc31d937f6050d75a | Python | 8,838 | 210 | """
Finding 'Oscillations' With Filters
===================================
Examining the results of filtering aperiodic signals.
This example uses the
`neurodsp <https://neurodsp-tools.github.io/>`_
module for time series simulations & analyses.
"""
##################################################################... |
bb4e3890ff54bf1674697c64a6124a98bcb67aad171ebdd5c3d2cf0f7f27c0fa | Python | 8,838 | 250 | ##############################################################################
## swigprocess.py -- Script to generate swig based interface for python and R
## Revision: $Id$
##
## Copyright (C) Kshitij Kulshreshtha
##
## This file is part of ADOL-C. This software is provided as open source.
## Any use, reproduction, o... |
64afd80919c0dd34bc645ec652029b3067b93234308f1fe9256cfc84c65297f6 | Python | 8,840 | 280 | # This example requires the following dependencies to be installed:
# pip install lightly
# 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 copy
from functools im... |
5fe7305128cc0218b0f9b23023e6b822774df0a3277f8d263a16d73aaa0c4660 | Python | 8,842 | 216 | """
This module define TrainingFineTune
"""
import logging
import torch
from dataset_manager.training_dataset_manager import TrainingDatasetManager
from model_manager.model_factory import ModelFactory
from model_manager.weight_modifier import WeightModifier
from config_manager.config_manager import ConfigManager
from u... |
40f578bfb0b8c04a22c26f9334c9570805c9cb2b816066fed976623c57ec146c | Python | 8,845 | 168 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import os
import sys
import json
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import tensorflow.compat.v1.logging as tflogging
tflogging.set_verbosity(tflogging.ERROR)
import logging
loggi... |
6ab08be1030fff29baa91f6562680f0ee2eccfc23f6e8990dfe45dede28d2176 | Python | 8,846 | 238 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 28 10:17:17 2020
@author: Audrey Mazancieux
This script performes event_related analyses for the LC and translated LC in
the y axis. It includes:
- Creation of translated mask
- Time serie extraction for each mask
- Epoch and save ... |
232e9d4a9ce4eff319528f16843c2b385771b41ac93a0bfd6f0804477ce2055b | Python | 8,848 | 211 | # -*- coding: utf-8 -*-
"""
General class for estimator models: fit an array of regressors to fMRI data.
===================================================
A EstimatorModel instanciation requires:
- model: an instance of sklearn.linear_model that will be use to fit a
design-matrix to fMRI data,
- alpha: i... |
3f7943a7a4b125782d913c6932eb4e755789a45d5731386c56fb5da7d60fa4e5 | Python | 8,852 | 230 | """Observe-kit item 1: NaN label unification identity fixtures + the raw-label lint.
One op, ONE public name: every NaN surface (find_nan, bisect_nan, the
CaptureError message and structured fields, CaptureOutcome.boundary_label,
PartialTrace summaries) reads its label out of the step-8 raw-to-final
identity map produ... |
d0437207602aded20930868f6b2936534bc90e2d769316b90b2933ff182e4bd0 | Python | 8,852 | 203 | #!/usr/bin/env python
# Make the robot grasp objects from table top
# Python libs
import sys, os
import json
import numpy as np
import math
# ROS libs
import tf2_ros # probably not needed
import rospy
import moveit_commander
# ROS messages
from actionlib import SimpleActionClient
from tiago_pick_demo.msg import... |
1bfee8453557c3b52217e9bb524cdeec1b424377dc9f2882b6c2af84de07e10a | Python | 8,853 | 216 | """
06: Metrics & Model Evaluation
==============================
An overview of metrics & model evaluation to examine model fit quality.
"""
###################################################################################################
# Import the model object
from specparam import SpectralModel
# Import fun... |
1fda3664fb844073a2a34e376809f69f663c398c7074a44d6ad1898bacceac65 | Python | 8,853 | 227 | """The consumers highly-optimized inner loop."""
import errno
import socket
from celery import bootsteps
from celery.exceptions import WorkerLostError
from celery.utils.log import get_logger
from . import state
__all__ = ('asynloop', 'synloop')
# pylint: disable=redefined-outer-name
# We cache globals and attribute... |
043058aba102ac32b2c5c74ad94455c57507b35f6287955fc32f5f152cc94402 | Python | 8,854 | 254 | from biomedisa.features.biomedisa_helper import *
from tifffile import imread
import numpy as np
import time
import numba
@numba.jit(nopython=True)
def reduce_blocksize(data, value=1, buff=25):
zsh, ysh, xsh = data.shape
argmin_z, argmax_z, argmin_y, argmax_y, argmin_x, argmax_x = zsh, 0, ysh, 0, xsh, 0
fo... |
10a70957826e97299b688e5d2d730c2b498a70a9f041f9be9666adc436d5a2ff | Python | 8,854 | 256 | """
Tests for the rover/sitter behavioural phenotypes derived from the generic
(species-average) Drosophila DEB model.
"""
from __future__ import annotations
import json
import pytest
from larvaworld.lib.model.deb import deb_equations as de
from larvaworld.lib.model.deb import rover_sitter_model as rs
#: Yield coe... |
07327e2e35821de77a84aa0638269fee60ac8f0c4f990bc6ad9f1a4d4e1087b0 | Python | 8,855 | 237 | # Run test with:
# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/ops/test_fused_dense_parallel.py
import math
import pytest
import torch
import torch.nn.functional as F
from apex.transformer import parallel_state, tensor_parallel
from flash_attn.ops.fused_dense import ColumnParallelLinear, FusedDense, Fu... |
23e0f4a39e3318b1a243fe117c5059c29ffea3744362b8a11fdb24b7ee69534f | Python | 8,855 | 192 | """Deployment-envelope detection helpers (lane F37, R5).
The population TorchLens is aimed at loads large models through Hugging Face
Accelerate: ``device_map="auto"`` dispatch, CPU/disk offload, and 4/8-bit
quantization. Offloaded modules hold META parameters between forwards -- the
real values live in the hook's ``w... |
0355307561f254795ad251a0dfc25e3965db07ffbc578f3bf85b3f399ea3650f | Python | 8,857 | 237 | #!/usr/bin/env python3
"""
File: plot_double_triangle_heatmap.py
Created: 2022/05/21
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... |
91c9cf904d6922061f007bb2ca96a21acfc9019e5462e37dbbd600838eb4b351 | Python | 8,858 | 186 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Shared ReID encoder used by BoT-SORT, Deep OC-SORT, and TrackTrack.
* `.pt` YOLO checkpoints — loaded via `YOLO()`; embeddings are pulled from the second-to-last
layer through the predictor's `embed=[...]` argument (works with classification and Re... |
a33184c1fbfc38d048f516106051a3102f6cd9eff38cadaf46755ccf15a3109c | Python | 8,861 | 222 | #!/usr/bin/env python3
"""
Dx BA — Pool expansion test (§3.15) redo under proper per-fold
================================================================
Re-runs Dx AR's 7-cohort vs 6-cohort comparison under the proper
per-fold per-cohort-deployment pipeline. Tests whether adding
ds005385 (Dortmund Vital, 109 HC age ... |
d8a6a1fd85d1246a2905c45b70d6f1cfdb2a608b8f198c28511124813c3cd8cc | Python | 8,861 | 333 | def filter_map_mean(newtwork, filter_dic):
import os
# newtwork2022 = newtwork.replace('2018', '2022')
outfile = open(newtwork.replace('.', 'EdgeFmean.'), 'w')
type_ls_file = open(os.path.join(os.path.split(newtwork)[0], 'excluded_types_list.txt'), 'w')
type_ls = []
with open(newtwork)... |
76d6664ac64d926618a4504e0c4b446398aa45c32b129bb171e06ad26a51ee7c | Python | 8,866 | 282 | # SOURCE: vendored from dipjyoti92/StarGAN-Voice-Conversion-2 @ master
# https://raw.githubusercontent.com/dipjyoti92/StarGAN-Voice-Conversion-2/master/model.py
#
# StarGAN-VC2 (Interspeech 2019): Kaneko et al., "StarGAN-VC2: Rethinking
# Conditional Methods for StarGAN-Based Voice Conversion". Community PyTorch
# refe... |
8e1f940c8c658e89405aea99782d24038e6f77c612ef73c73d3bf72588f5d7a3 | Python | 8,867 | 239 | import warnings
import numpy as np
from typing import Generator, Self
from mirp._data_import.dicom_file import MaskDicomFile
from mirp._data_import.utilities import get_pydicom_meta_tag
from mirp._data_import.dicom_multi_frame import (ImageDicomMultiFrame, ImageDicomMultiFrameStack,
... |
664d70507593ce3af6c86f5308c7c00717bec657c344bde691ac690857a21fbe | Python | 8,869 | 200 | import os
import numpy as np
import matplotlib.pyplot as plt
from os.path import join
from library.script_wrappers import find_nidx_along_traj, best_worst_analysis
from library.shared_vars import sim_results_dir, plots_dir
from library.utils import load_pickle
from library.visualization import plot_popras, plot_phase_p... |
9726d161f29cd0abe96d81c3a18caa10ed3a0ccc9525ba9cfb59232286049d58 | Python | 8,870 | 221 | #!/usr/bin/env python
"""
WandB Script Wrapper
This script is intended to serve as a wrapper to execute an existing target Python script
without any modifications, while capturing its standard output, standard error, and system
metrics (CPU, memory, and GPU) with Weights & Biases (WandB). It leverages WandB's
built-... |
e67069473fa7c3aaf763230d326c11883c735b7ad878c2a0d901e88734e93fc7 | Python | 8,871 | 270 | """IO utility functions.
"""
import math
from typing import Optional, Tuple, Union
import cv2
import numpy as np
import pandas as pd
from anndata import AnnData
from scipy.sparse import csr_matrix, issparse, spmatrix
from scipy.spatial import Delaunay
from shapely.geometry import LineString, MultiPolygon, Point, Polyg... |
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