sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
53d768ac862ee145dfd1336ce2e344de5287463dc7cd140414bb979c50938a94 | Python | 5,253 | 129 | """Test-naming vestigial governance (r7 R80): round tokens stop growing.
402 test functions carry a round/sprint token (``_rNN``, ``_pN``, ``_certN``,
``_fwN``, ...) and 10 test-file stems collide once the token is stripped — a
PURPOSE-named file coexisting with a ROUND-named file of the same purpose, so
a contributor... |
6018d8b1ac39af81eb77dbec9de2e14b30e167d19d81d09b2ae55a144c7371f1 | Python | 5,253 | 136 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
An ``AuxReader`` for MDAnalysis capable of reading PLUMED-generated files.
"""
# =============================... |
0c7e1de1aa1fe09bbabcf4109cbcfb22500dd46cb197b6822d09c86bcf9266f6 | Python | 5,254 | 130 | from typing import Optional
from galaxy import exceptions
from galaxy.util import bunch
from .structure import (
get_collection,
get_structure,
leaf,
)
CANNOT_MATCH_ERROR_MESSAGE = "Cannot match collection types."
class CollectionsToMatch:
"""Structure representing a set of collections that need to ... |
6c2a3ff8aaba3a44b99e37f790e61bdfb7a2431b2ff7d3eba1d3a7536aa4a5d5 | Python | 5,254 | 159 | import pytest
import numpy as np
from BM3D.src.step1 import *
from BM3D.src.support_function import compute_distance_of_two_blocks
def test_step1_basic_estimation():
# Create a test image
noisy_img = np.random.rand(10, 10)
# Set the parameters for the function
param = (2, 1, 1, 3, 1, 1, 1, 1, "cos")... |
3d37deee3e93a294c8895fbdd9b28f09849b2b2bb8be5b6c9e50e0b38831dfe6 | Python | 5,255 | 134 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.utils import LOGGER, TESTS_RUNNING, colorstr, torch_utils
from ultralytics.utils.torch_utils import smart_inference_mode
try:
assert not TESTS_RUNNING # do not log pytest
WRITER = None # TensorBoard SummaryWriter instance
... |
bdc210ed3da6e294cec604a161c180ad0abf7df407e8cd91f17c8cce4111dcb2 | Python | 5,255 | 127 | import os
import torch
import pandas as pd
import scanpy as sc
from sklearn import metrics
import multiprocessing as mp
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import (
v_measure_score, homogeneity_score, completeness_score)
import glob2
import matplotlib.pyplot as plt
i... |
7fbdf19e76793ec84bd86f7f0a67acb686d42363efe929649a004b091e54563d | Python | 5,256 | 177 | """Gevent execution pool."""
import functools
import types
from time import monotonic
from kombu.asynchronous import timer as _timer
from . import base
try:
from gevent import Timeout
except ImportError:
Timeout = None
__all__ = ('TaskPool',)
# pylint: disable=redefined-outer-name
# We cache globals and at... |
8f543743492289d148831ddd969ae0b9d62b1d7e1aef0ffb894132a910549fe9 | Python | 5,256 | 83 | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), e... |
02960eef941c2a95a771abaab50a65c1cbbae1e5b1611b074d120ec0b2f69866 | Python | 5,257 | 158 | # Very simple functions to extract useful data from Leica Microscope
# XML files
import os
import glob
import re
def get_prop(node, prop):
return node.xpathEval('@%s'%prop)[0].content
def get_xmljob(name, patt = "*[0-9].xml"):
if name[-4:] == '.xml':
return name
folder,filepattern = os.path.sp... |
4be7071dc099808bccf7797f34d6aa5130e9f5f8bf29f75ab9b9ad5a7725a7e7 | Python | 5,257 | 168 | import numpy as np
from numpy.testing import assert_array_equal
import pytest
from pytest import approx
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from pyriemann.classification import MDM
from pyriemann.tangentspace import TangentSpace, FGDA
pytestmark = pytest.mark.numpy_only
metrics ... |
ad148b31909bb3b4259d5933c66e70057f63f6dfef3bf922cd2790e2bb8e32dc | Python | 5,257 | 109 | import sys
from collections import defaultdict
import csv
import utils
def CompileData(filename, interval):
uncleaneddata = utils.ReadDataFromFile(filename)
cleaneddata = utils.Clean(uncleaneddata, interval)
datawithvel = utils.CalculateVelocity(cleaneddata)
completedata = utils.CalculateAcceleration(... |
68e30d7bc12b89563cdf60a8586927c238cf07e5083f3269ea8f27665a9ba36b | Python | 5,259 | 164 | """Regression tests for r19a attribution honesty/robustness fixes.
Covers:
- W2A3-05: attribution restores exact per-module train/eval state (not the root flag).
- W2A3-06: attribution computes correctly under an ambient ``torch.no_grad()``.
Owner-reserved wrong-math findings (W2A3-01/02/03/13/17) are NOT fixed here;... |
832e92e42e46e7470c92ad8b2ccdd98ab15ecebf077860358800bdfadce578d9 | Python | 5,259 | 116 | """
process_recording plugin - analysis algorithms
The module can be imported independently of the base UI system.
Post-processing of electrophysiology recording, including baseline correction,
filtering, and trace-averaging
SimplyFire - Customizable analysis of electrophysiology data
Copyright (C) 2022 Megumi ... |
65d4dac4ec461f743ec118f7089514b170567162d0c4c33a96d5518a76e9edec | Python | 5,260 | 147 | """Composition-tree conftest: census prewarm + product/model galleries (row 0.6).
Fixture economics are measured law (compo memo D12): a save+load hop is
~4.7x a capture, so cells CONSUME the session gallery and may not construct
products -- read-only cells share, mutating cells fork (~75 ms even on a
567-op trace), p... |
9d53edc0a07d0625f4b14f57b3a36851c65a96f44bbc592076190d416661193d | Python | 5,260 | 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 Box(_BaseTraceHierarchyType):
_parent_path_str = "violin"
_path_str = "vi... |
1699dd7f040e88c1b0c6da5adfef97771ccfbef29033c8b9696cd151499c3d51 | Python | 5,261 | 134 | import os, json
import numpy as np
from tqdm.notebook import tqdm
from PIL import Image, ImageDraw
from cv2 import findContours, RETR_TREE,CHAIN_APPROX_SIMPLE
from sklearn.metrics import jaccard_score
import torch
from torch.utils.data import Dataset
from torchvision.transforms import ToPILImage
class leafDataset(Da... |
5bc7a31acd2d5838b2acbbe83ce1cef3ade503db0e22943705804416290e712f | Python | 5,261 | 163 | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals, absolute_import, division
from six.moves import range, zip, map, reduce, filter
from six import string_types
import numpy as np
try:
from tifffile import imwrite as imsave
except ImportError:
from tifffile import imsave
import war... |
a7a086e9bb8c781b559dd6f723979748dad2afa5fcd98ef3bc766b014d3784ce | Python | 5,261 | 158 | """Stimuli classes"""
"""
Copyright (c) 2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHA... |
87abddbfa3677868d84d5b2bfd7817d20b7a6fec4825674d8f7b358a1fc054b0 | Python | 5,262 | 162 | #!/usr/bin/env python
"""Run simple cell optimization"""
"""
Copyright (c) 2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PUR... |
6a22fdc9ee356a256d45228d627485f798184d0316a37bcd850ea492bcb2bb84 | Python | 5,263 | 44 | etas = {
'generic20_learning': {
'alpha_ATLAS_LUMI_2012': { 'muT__hat': 1.49401930723, 'muT__muT__down': 0.0338444171345, 'muT__muT__up': 0.0309159829066, 'muT__muW__down': -0.00153824251909, 'muT__muW__up': 0.00148147282231, 'muT__phi__down': 0.00734038944363, 'muT__phi__up': 0.00543738632322, 'muW__hat': 0.7620640... |
99a76ec70d75679fea9318a7cac9fbf1118bfb57ba80a8b85f3e07d7fad80fd2 | Python | 5,264 | 150 | import torch
from torch.cuda.amp import GradScaler
from torch.nn.utils import clip_grad_norm_
from torchmetrics import Accuracy, MeanMetric, Recall, Specificity, AUROC, ROC, ConfusionMatrix
from torchmetrics.functional import accuracy
from tqdm import tqdm
class Fit:
def __init__(self, amp, device):
self.... |
363ca20b95d8b4e08a65e5521f671e57c5378b8ed03b2942f55248c07380bba2 | Python | 5,266 | 159 | """Image Dataset"""
# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved
import os
from typing import Any, Callable, List, Optional, Set, Tuple, Union
import torch
import torchvision.datasets as datasets
from typing_extensions import Protocol
from lightly.data._image_loaders import default_loa... |
ab7b3b85bb4cc4211befef7aa6eb7e15dd841095adc96036e2eb758dad8ebcf5 | Python | 5,266 | 158 | #################################################################################################################################################################
####################### Testing MultiRBP #####################################################################################################################... |
bd79579f9b72dcdf0b474c73f181cb120b8a29193a5c4a0342567b7fefa45255 | Python | 5,266 | 154 | import numpy as np
from . import smoothing
from . import utils
from . import extract_signals
@utils.copy_td
def add_firing_rates(trial_data, method, std=None, hw=None, win=None, backend='convolve1d'):
"""
Add firing rate fields calculated from spikes fields
Parameters
----------
trial_data : pd.D... |
c4be47b7ee8d60dee2f26f2c5db97f201d3ad7a3514e10973c40601a21bba837 | Python | 5,266 | 149 | import os
import json
from collections import OrderedDict
import glob
#
# python3 gather_if_info.py /home/hjorth/HBP/BasalGangliaData/data/neurons/striatum/dspn /home/hjorth/HBP/bgmod/models/optim/HBP-2021Q4
#
# python3 gather_if_info.py /home/hjorth/HBP/BasalGangliaData/data/neurons/striatum/ispn /home/hjorth/HBP/bgm... |
007e43bc8df1e5793d9615987eb71f80cfb4a4d90267b9c83ac81364530a2682 | Python | 5,267 | 140 | """Round-11 F6 regression: parameterized ``if/else`` models must save runnable.
A conditional arm records a private ``_trace`` back-reference to its owning ``Trace``.
``ConditionalAccessor`` has no portable-state spec, so the bundle scrub returns it
verbatim and the arm's back-reference still points at the LIVE trace ... |
709f69bebdf8c49555c8ec3738f0397b89d2abf1112ee6f3844d124e81069108 | Python | 5,267 | 114 | """
Workflow report agent for generating markdown reports from Galaxy workflows.
"""
import logging
from pathlib import Path
from typing import Any
from galaxy.model import Workflow
from .base import (
AgentResponse,
SimpleGalaxyAgent,
)
log = logging.getLogger(__name__)
class WorkflowReportAgent(SimpleGal... |
8c3056b42f0ef7c2d9eb2e1a7dee1dc6e308ed76fedc6740b9c6b37d4793f9ec | Python | 5,270 | 205 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
1da7fc52009546f2d5937ab7842cd62d4a10611b28ec69e6b7ba41c86ce7e606 | Python | 5,271 | 135 | import torch.nn as nn
from torch.nn import functional as F
import torch
class CNN_Block(nn.Module):
def __init__(self, input_channels, num_channels, kernel_size, pooling_size):
super().__init__()
self.conv1d = nn.Conv1d(input_channels, num_channels, kernel_size=kernel_size)
self.pooling = n... |
a65f92232f387dfcbb9cb2fcf3dcd03a1185a7954b1c9837f91b03b264f5862b | Python | 5,271 | 154 | #!/usr/bin/env python
"""
Cluster a set of molecules based on their 3D overlays as determined by the SuCOS score.
This will generate a set of SD files, one for each cluster of molecules (presumably corresponding to a
binding pocket in the protein target).
SuCOS is the work of Susan Leung.
GitHub: https://github.com/... |
b6ec1b783c890aa9a4ec88c8f2e1b58c26c92a099795a135aaf8822b31cca602 | Python | 5,271 | 188 | """Round-22 runnable attestation and non-finite literal regressions."""
from __future__ import annotations
import dataclasses
import math
import struct
from pathlib import Path
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens._io.runnable import _encode_literal
from torchlens._r... |
1d986abc463238dee6ee2d2420ae985daa459136c1b0b3b668ee6fb6e116341b | Python | 5,272 | 152 | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
ed1d0c3d873dc36578352c744f5c57c0374b57198e62eb108276a41dcc3809fd | Python | 5,272 | 96 | import sys
sys.path.insert(1, '../')
import numpy as np
import pandas as pd
import os
import shutil
dataset_dict_paths = {
'ABCD': "/media/sda/Anna/TM2_segmentation/data/t1_mris/registered/z/",
'ABIDE': "/media/sda/Anna/TM2_segmentation/data/t1_mris/abide_ench_reg/z/",
'AOMIC':"/media/sda/Anna/TM2_segment... |
dec288b60b70be58de3616e8c556e9a2e1aab574c1d230657bbf074d11f91de3 | Python | 5,273 | 145 | import unittest
import torch
import torch.nn as nn
from bdpy.dl.torch import models
def _removeprefix(text: str, prefix: str) -> str:
"""Remove prefix from text. (Workaround for Python 3.8)"""
if text.startswith(prefix):
return text[len(prefix):]
return text
class MockModule(nn.Module):
de... |
3acb8fcce7b1f3075b7ef05544f383d086b2ea67848033edd0d90513d22d0026 | Python | 5,276 | 150 | """Focused coverage for glossary-conformance phase B2c structural fields."""
import re
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
import torchlens.receptive_field as receptive_field
from torchlens.options import CaptureOptions
class _B2cModel(nn.Module):
"""Model docstrin... |
cedc27d1c35ffe3293a267f4d39c510a4e560511b2a57e18fab90af8d1235977 | Python | 5,276 | 153 | from abc import ABC, abstractmethod
import os
import numpy as np
class AdditionalData(ABC):
"""
Handles additional data to be loaded.
Can be called with arguments
- path: path to the dataset
- i: index of the chunk
- j: index of the sequence in the chunk
- cl_max: context length of the u... |
da697f1176a8f066cebb94a17d0c6c7666867efac81d5a1ff83e64d5fe0a8a9d | Python | 5,278 | 144 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from role_prediction.graphormer.collator import collator
from role_prediction.graphormer.wrapper import MyRolePredictionDataset
from pytorch_lightning import LightningDataModule
import torch
from torch.nn import functional as F
from torch.utils.d... |
19b0936ac877d8fc00096df251418c085a37adcf27781158863c6e5d125e3a3a | Python | 5,280 | 133 | """``set()``-door and target-spec record lowering (W051-REPLAY out-of-fence 1-2).
Lane W051-REPLAY lowered ``Op``/``Layer`` records at HOOK-PLAN normalization,
so ``do(record, helper)`` worked, but the ``set()`` door (``do(record,
tensor)``), ``resolve_sites(record)`` and the mutator-site validation still
refused a re... |
8261126b3031596db93d0822fa5b6adc6a3a9957a953e7e07131b2f488da2c65 | Python | 5,282 | 135 | """
BASIL GUI for Oxford ASL - Preview of the structure of the data set
Copyright (C) 2020 University of Oxford
"""
import wx
class DataStructurePreview(wx.Panel):
"""
Visual preview of the structure of an ASL data set
"""
def __init__(self, parent, ntis, repeats, ntc, order, tagfirst):
wx.Pa... |
d606fb8eecf7867e49f02fc4d0fa3de16cc8a693fa953c3278bb2a0825654c58 | Python | 5,284 | 156 | #!/usr/bin/env python3
"""
File: vcf_to_dosage.py
Created: 2024/01/19
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.
"""
# S... |
583485a369a106d1b0f1f0c7a0bf7375a3aca9821f8c4847488c8b2e9472e953 | Python | 5,286 | 144 | #!/usr/bin/env python
"""
Input: set of molecules with pre-calculated physico-chemical properties
Output: set of molecules that pass all the filters
Copyright 2012, Bjoern Gruening and Xavier Lucas
TODO: AND/OR conditions?
"""
import argparse
import json
import shlex
import subprocess
import sys
impor... |
64b54ffb3c04f6f54e793234bda80c315d1ad2048ffeaafa7d591a750d46b137 | Python | 5,286 | 162 | import sys
sys.path.append("../src")
import numpy as np
import pandas as pd
import pre_process
from Bio.Seq import Seq
import opt_analysis
import entropy_opt
import seaborn as sns
import data_prep
import matplotlib.pylab as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib i... |
ad42aa716259f72a563cc9695bcd4c19834588f64a2b51dc4028e75377951ab5 | Python | 5,286 | 141 | from __future__ import annotations
import math
from typing import Optional, Sequence
import torch
import torch.nn.functional as F
def zero_like_reference(reference: torch.Tensor) -> torch.Tensor:
return reference.new_tensor(0.0)
def reconstruction_loss(prediction, target: torch.Tensor, mode: str = "mse", mask... |
2a25cd10a1dd44a6b6e0f3a1769bf574fe9eb00f90ffbd7d1c9eb5a92a09b9a3 | Python | 5,287 | 182 | """Convert BDD100K bitmasks to COCO panoptic segmentation format."""
import argparse
import json
import os
from multiprocessing import Pool
from typing import Dict, List
import numpy as np
from PIL import Image
from scalabel.common.parallel import NPROC
from scalabel.common.typing import NDArrayI32, NDArrayU8
from sc... |
4bbdee3049d176317913156c70363ff091cb38e2481c836d0921f3e80aa327a6 | Python | 5,288 | 144 | """Replacement-object identity honesty (round-3 T-RUNNABLE fix lane).
One live tensor object injected at two or more matched sites lets the LAST
fire steal the site label: every downstream consumer records the last site as
parent, the earlier site's children vanish (byte-identical payloads make the
misattributed graph... |
5dce851b289b45c1a2ac38885518c0c753d2cf17a3ebd7c454e7b234b4786281 | Python | 5,290 | 159 | from sklearn.metrics import pairwise_distances
from typing import Optional, Union
from anndata import AnnData
import numpy as np
from ..._compat import Literal
from tqdm import tqdm
_PLATFORM = Literal["Visium", "Old_ST"]
_WEIGHTING_MATRIX = Literal[
"weights_matrix_all",
"weights_matrix_pd_gd",
"weights_m... |
9710f9ef05443658d1b63d1aaf1fc798676a36415e3d61a707a1375463c36f6e | Python | 5,290 | 153 | """Tests for the agent-facing machine-readable dump ``Trace.to_agent_json``."""
from __future__ import annotations
import json
import pytest
import torch
from torch import nn
import torchlens as tl
class TinyRecurrentModel(nn.Module):
"""Model that reuses one module twice to exercise pass-qualified labels."""... |
9bf5dd0f95e0d755473f4c41de4ccffdb40af81779d43e5d41aca5fb32becc49 | Python | 5,290 | 129 | """A-GATE real-model leg: two real Qwen2.5-0.5B-Instruct turns (foldB s5 row 5).
Toy-only validation is disqualifying for chain territory: two successive
turns of one real chat model are exactly the Path-1 shape (one live model
object, different inputs, ``same_object`` relationship), and the topology
refusal is the ex... |
3ca568d794191dcbc1ea10ee5c7c83dcc8f9df270f1992786b119d309bdefb11 | Python | 5,291 | 148 | """Microbench: flat-buffer ragged reverse-complement vs gvl's awkward idiom.
Run: uv run --with attrs python scratch_bench_rc.py
Reproduces the exact awkward path gvl uses today in
``_dataset/_query.py:reverse_complement_ragged`` -> ``_ragged.py:reverse_complement``:
Ragged(ak.to_packed(ak.where(to_rc, rc_awkwa... |
42d1993bb86ac37b69433ab0fce0b1f37f415f63248762d91b0d39312c96a458 | Python | 5,291 | 120 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
adb83ca4ce9741076efa870e0fb8edcceda22bdffc9fe6126b2677801db9cc71 | Python | 5,291 | 150 | import copy
import numpy as np
import pandas as pd
from mirp._features.texture_matrix import DirectionalMatrix
class MatrixCM(DirectionalMatrix):
def __init__(self, distance: int, **kwargs):
super().__init__(**kwargs)
# Set distance
self.distance = int(distance)
# Placeholders... |
fcd980b46cbf6d8002c6d9e17bba57ff626620dd3c37a37ace7a9027b7cc5d6e | Python | 5,291 | 162 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Stripped out routines for Sentry."""
import os
import re
import sentry_sdk
from niworkflows.utils.misc import read_crashfile
from xcp_d import config
CHUNK_SIZE = 16384
# Group common events with pre... |
2e7234412f6e9ce8718e22db04340df53b16f1f02811d6e4052f6ec4f9c25801 | Python | 5,292 | 149 | from __future__ import annotations
import logging
import threading
import time
from collections.abc import Generator
from itertools import chain
from typing import Callable, Optional
import numpy as np
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
from grpc import RpcError
from minknow_... |
77c7a1d46c1a51d97b1617193758d02a03acf64f926891b8bc8fda6b865cbaf1 | Python | 5,293 | 174 | """GraphSAINT: subgraph-sampling GCN training architecture.
Paper: "GraphSAINT: Graph Sampling Based Inductive Learning Method", Zeng et
al., ICLR 2020.
This compact classic fixes one pre-sampled induced subgraph and applies the
paper's core idea: run a complete GCN on the sampled subgraph with explicit edge
and node... |
dbdf892628a27568943d9e050b2907a3f52d3626fa87d4a1032cc869c568fe37 | Python | 5,293 | 113 | """
This module define MetricsManager
"""
from metrics.metrics_factory import MetricsFactory
from converter.converters_factory import ConvertersFactory
from utils.utils import move_tensor_to_device
class MetricsManager:
"""
This class manage which metric is for each task and save the values durring training
... |
57abd5d1b215f42499991e7c4cea0f49a683cb424b1ae245f8fa80057b72af38 | Python | 5,294 | 153 | import math
from typing import Literal
import numpy as np
from anndata import AnnData
from sklearn.linear_model import LinearRegression # type: ignore
from sklearn.metrics import pairwise_distances
from tqdm import tqdm
_PLATFORM = Literal["Visium", "Old_ST"]
_WEIGHTING_MATRIX = Literal[
"weights_matrix_all",
... |
b2e753ebb0ff0433219e0114945adfa6f0f9f0ae12118abf67766e12814c4842 | Python | 5,295 | 172 | """Pool Autoscaling.
This module implements the internal thread responsible
for growing and shrinking the pool according to the
current autoscale settings.
The autoscale thread is only enabled if
the :option:`celery worker --autoscale` option is used.
"""
import os
import threading
from time import monotonic, sleep
... |
e3035628d153f376aced55cf88f9937126058d7743e2dd4c0061f2f28bce86a6 | Python | 5,295 | 115 | import json
import geopandas as gpd
import pandas as pd
import pytest
from shapely.geometry import box
from pyrosm import cli, get_data
def test_find_lists_the_extracts_of_an_area(tmp_path, monkeypatch, capsys):
"""``pyrosm find`` takes a bounding box (also one that starts with a minus sign), a
vector file ... |
f7b64d6271fad88bb5204ddeafa31836524bb9e8c7ebf00fbdc57683e8f4edfe | Python | 5,295 | 110 | """8. Extracting video features from pre-trained models
=======================================================
Feature extraction is a very useful tool when you don't have large annotated dataset or don't have the
computing resources to train a model from scratch for your use case. It's also useful to visualize what ... |
fe5fe8bac156a99e5d8021a10c8a931438a69aec28afaacf5442f866d9f23ecc | Python | 5,296 | 136 | """Autoroute kwarg-forwarding pins: no public trace option silently vanishes.
The autoroute detector branch of :func:`torchlens.trace` forwards the public
keyword bundle into detectors (which re-enter ``tl.trace`` through bridge
helpers such as ``bridge.hf.trace_text``). Any ``trace()`` parameter missing
from that bun... |
6d3576505eb597ea4800df1235811a8223e60322507ba6f669a7f8142d58ca20 | Python | 5,297 | 175 | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Iterable
import torch
import torch.nn as nn
from bdpy.dl.torch import FeatureExtractor
from bdpy.dl.torch.domain import Domain, InternalDomain
class BaseEncoder(ABC):
"""Encoder network module."""
@abstractmethod
... |
8c03d8aeff3511d53ba30496d18b72f11fd3301adb782144f7446c0f7fa16895 | Python | 5,297 | 160 | import os
import posixpath
import platform
import re
import shutil
import sys
from distutils import sysconfig
import setuptools
from setuptools.command import build_ext
HERE = os.path.dirname(os.path.abspath(__file__))
IS_WINDOWS = sys.platform.startswith("win")
with open("README.md", "r", encoding="utf-8") as f... |
9e49270e62bfedda44417d606c9476019347cfd13a4abbddef05c79dda4bea3f | Python | 5,297 | 139 | """Fork copy-on-write / structural-sharing invariants.
``Trace._fork_trace`` builds the fork as ONE structural copy of the parent graph
under a single shared ``copy.deepcopy`` memo instead of one independent copy per
field. These tests pin the two properties that make that safe:
1. ISOLATION -- no fork-owned object m... |
06e50782905fff86a0850ede0804a77d589a1fd203bfd577ba7042984d786df9 | Python | 5,298 | 44 | etas = {
'generic20_learning': {
'alpha_ATLAS_LUMI_2012': { 'muT__hat': 0.816226673571, 'muT__muT__down': 0.0407591584793, 'muT__muT__up': 0.0368081554649, 'muT__muW__down': -0.000479700448688, 'muT__muW__up': 0.00109448525299, 'muT__phi__down': -0.00363077690366, 'muT__phi__up': -0.00236353831815, 'muW__hat': 1.586... |
018ae80e11647ac09839c174703e5b1c729675e62eb0b560b30cd972b3adc8e5 | Python | 5,300 | 164 | import os
import sys
import traceback
from typing import Dict, Tuple
from pathlib import Path
import pickle
import numpy as np
from numba.core.errors import NumbaDeprecationWarning
from numba.core.errors import NumbaPendingDeprecationWarning
import warnings
warnings.simplefilter('ignore', category=NumbaDeprecationWa... |
79ded869d58d2dabe52a97bec59da923af4081c6c8445f0353380a98a69119ff | Python | 5,300 | 110 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import cv2
import numpy as np
import torch
from PIL import Image
from ultralytics.data.augment import classify_transforms
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results impor... |
7c71ac83d979e5a2bf613253ad2c5a1fc73217dab21e9e96541489d58f414774 | Python | 5,300 | 152 | """Tests for xcp_d.interfaces.utils module."""
import json
import os
import nibabel as nb
import numpy as np
import pandas as pd
from xcp_d.interfaces.utils import LINCQC, ConvertTo32
def test_conversion_to_32bit_nifti(ds001419_data, tmp_path_factory):
"""Convert nifti files to 32-bit."""
tmpdir = tmp_path... |
df11f730756f58bc4413cf98389fd1de9328ea642ae7f2afa1a22b4e9178d7cf | Python | 5,300 | 142 | """F03 item 7b: the head-ablation sugar + THE SAME-FILE ORACLE (heavy tier).
The sugar's v-facet lowering is checked candidate-by-candidate against a
hand-written torch forward_pre_hook zeroing head i's slice of the c_proj
input (no TorchLens in the oracle path) on a config-built GPT-2 running
through the REAL HF ``fr... |
e6dcda8b1c6e0fd6b27acff32f9a8d5b29f8a62d1c22bf723490fe95a0bff789 | Python | 5,301 | 128 | import time
from jinja2 import meta, TemplateError
import pytest
import promptsource.templates
from promptsource.utils import get_dataset_builder
from uuid import UUID
# Sets up Jinja environment
env = promptsource.templates.env
# Loads templates and iterates over each data (sub)set
template_collection = promptsource... |
86c4d8136105a740e7ea1f357ab6c119b668f58a681c0d8e1d4bba9861608f1c | Python | 5,302 | 139 | """In-place functionals (``inplace=True``) are captured as mutating calls."""
from __future__ import annotations
from collections.abc import Callable
from pathlib import Path
from typing import Any
import pytest
import torch
import torch.nn.functional as F
from torch import nn
import torchlens as tl
from torchlens.... |
ec2a30e8ec12ebdc4d41957f76b979db70d60e1e579d13f27cc14ef2a03043d6 | Python | 5,302 | 99 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
cohort_labels.py — 队列/对照的**图用英文标签**单点映射
------------------------------------------------------------------
为什么需要这个模块：`out/external_paired_delta.csv` 的 `contrast` 列是**中文**
（内部分析稿的语言），而图要排进英文稿件。matplotlib 默认字体 DejaVu Sans
**没有汉字字形**，于是中文会渲染成一串"豆腐块"（tofu，▯）——
out/fig7... |
3478af4755f56fd30f8c20af35b25901824f96d1c182234524c31635bcaad3ef | Python | 5,303 | 135 | """Utils for auto classification estimator"""
# pylint: disable=bad-whitespace,missing-function-docstring
import os
import math
import mxnet as mx
from mxnet import nd
from mxnet import gluon
from mxnet.gluon.data.vision import transforms
from ....data import imagenet
def rec_batch_fn(batch, ctx):
data = gluon.ut... |
52263192ffe23476f16c1306f1ab52d17593f02273334cf720f1c1bf50408365 | Python | 5,303 | 119 | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Implementation of a socket server wrapped in a ROS publisher. In this base version, the ROS node publishes the data as a string message without any processing. For more complex behaviors, create a child class, and modify the callback.
'''
import math
#import copy
impo... |
ea9118fd9d185cecfdd214f0ab069f45563a18715a663d9a48c75f3741b0e4e6 | Python | 5,303 | 157 | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from .custom_modules import FakeReLUM
from .layers import conv2d_same, pool2d_same
__all__ = ['kell2018']
class AuditoryCNN(nn.Module):
def __init__(self, num_classes=1000):
super(AuditoryCNN, self).__init__()
# Include initial batc... |
924dd3ca3dea0d71221eb1b013eceb1dce2006de630c00efca3d194c168d8eda | Python | 5,304 | 139 | import logging
import os
from pathlib import Path
import random
import shutil
import pandas as pd
from Bio import SeqIO
from review_comparison.utils import generate_fasta_file
class DeleteToSimilarSeq:
def __init__(
self,
name_train_dataset,
name_diff_dataset,
min_seq_identity,
... |
b03e130dc51ad6cb8a08a819364108d706b1df70e7b70d942c9203f7cef38f05 | Python | 5,304 | 178 | """Define parameter converters."""
from copy import deepcopy
from specparam.params.converter import AperiodicParamConverter, PeriodicParamConverter
from specparam.params.periodic import compute_peak_height
###################################################################################################
## DEFINE D... |
f189958f3820d8ac8d0577227a94754e7c3410cba56ab00972172a38afbb25b5 | Python | 5,304 | 175 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... |
3d4b6472fdb3d64d04464de3d5327778bcc76a9c7390b062069af3e73e316368 | Python | 5,305 | 129 | try:
import unittest
from copy import copy
from os.path import abspath, join
from numpy.testing import assert_allclose
import numpy as np
try:
from scipy.integrate import simpson
except ImportError:
from scipy.integrate import simps as simpson
from spitfire.chemistry.me... |
8d4a3100fdde0cc988a639bc55c7d4b181a3acfd742dfcff1f91533c8aae65ef | Python | 5,305 | 175 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test objects and function in the module ``tfep.utils.cli.tool``.
"""
# =======================================... |
d8cd42ea2fc096509c9b5d6e87d108013cba8b6eae762a1beb3d960356e242c7 | Python | 5,305 | 158 | from __future__ import annotations
from numbers import Number
from pathlib import Path
import pytest
from larvaworld.lib import reg, util
from larvaworld.lib.reg.generators import EnvConf
from larvaworld.portal.canvas_widgets.environment_mapping import (
env_params_to_canvas_state,
)
from larvaworld.portal.simul... |
1ccdd57437ed2c7bcefb843393574e8a1cb5f2e76475a27ab0389417ecb7a182 | Python | 5,306 | 188 | from abc import ABC, abstractmethod
import collections
from functools import lru_cache
import json
import os
import pickle
import numpy as np
from modular_splicing.dataset.data_rewriter import DataRewriter
from modular_splicing.dataset.h5_dataset import H5Dataset
from modular_splicing.utils.construct import construct... |
cc2241ff2c55347faf55b9f2acb737b23318cd28f466544cb26f74b944739032 | Python | 5,306 | 163 | """A generic memory recall task."""
from collections import OrderedDict
import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
from neurogym.utils.logging import logger
class MemoryRecall(TrialEnv):
"""Memory Recall.
Args:
stim_dim: int, stimulus dimension
... |
080d005c2e4e05f3c97b58a4cf9c2fe4bdb514bf077021f4a19743dd13b8dfd1 | Python | 5,307 | 131 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''Find closest positive pixel in segmentation given refined particle coordinates
Search for segmentation (MRC format) in specified directory for each tomogram in STAR file.
Find closest positive pixel in segmentation for each refined particle coordinate
Output CSV tabul... |
d79530fcb5115bdb2ce0ab3cbef882c7d55fac9245c3320726bfdc8e821b3cb2 | Python | 5,307 | 122 | ###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
"""Cityscapes Dataloader"""
import os
import numpy as np
from PIL import Image
import mx... |
802a7dfaa078d7bbeda3385a279ba4a3659b24673ed547310b27379c59b16661 | Python | 5,308 | 129 | """This file contains all the tigramite functionality"""
# License: GNU General Public License v3.0
import numpy as np
from matplotlib import pyplot as plt
from tigramite import data_processing as pp
from tigramite import plotting as tp
from tigramite.independence_tests.parcorr import ParCorr
from tigramite.independenc... |
a6c003e8c563cf8aee4acd8d6dbdb4d33526f65c114c8eb517545ef316f56dd5 | Python | 5,310 | 150 | import logging
import re
from galaxy.datatypes.data import (
get_file_peek,
Text,
)
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.protocols import DatasetProtocol
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
get_headers,
)
from galaxy.datat... |
f0989e9c6c1a7ab1d1555bf95dc9105bce6bd6ec7b5a044375412ab17836a903 | Python | 5,311 | 148 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 29 14:04:11 2023
@author: fcheng
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from plot.makeDataFrame4plot import makeDataFrame_diff_orientation,makeDataFrame_diff_orientation_1stStag... |
e77923b12d92109cb2435e5198f59c531f226b7f78c18cc1fbe5aacbbf795a1f | Python | 5,312 | 156 | """Monodepth
Digging Into Self-Supervised Monocular Depth Estimation, ICCV 2019
https://arxiv.org/abs/1806.01260
"""
from mxnet.gluon import nn
from mxnet.context import cpu
from .resnet_encoder import ResnetEncoder
from .depth_decoder import DepthDecoder
class MonoDepth2(nn.HybridBlock):
r"""Monodepth2
Par... |
5c28b7746363091767cfda374a5ddd2a864c1f06469b65af775af60b27a0ab37 | Python | 5,313 | 157 | """
Splits data into test data (one block) and training data (remaining five block)
for cross validation.
"""
import pandas as pd
import numpy as np
df1 = pd.read_csv("Source Data 1.csv", sep="\t")
df1 = df1.dropna(axis=0, how="any")
for i in range(2, 3): # repetitive
df2 = df1.query(f"subject == {i}")
df3 ... |
b033d82657e57e7d1a5f124c481bff75afd1bb8086e82355439800e269803b2d | Python | 5,313 | 125 | import os
import torch
from torch_geometric.data import InMemoryDataset, DataLoader, Batch
from torch_geometric import data as DATA
from torch.utils.data import TensorDataset
# initialize the dataset
class DTADataset(InMemoryDataset):
def __init__(self, root='/tmp', dataset='davis',
xd=None, y=N... |
2e2b16b53302c0df3c933de0d0a52ffd5b76cfaba37887293831a615ad889b0c | Python | 5,314 | 140 | #!/usr/bin/env python
import argparse
import json
from cp_common_functions import (build_header, concat_conditional,
get_json_value, get_pipeline_lines,
get_total_number_of_modules,
INDENTATION, update_module_count,
... |
ae7f916d3b6d5843d3706fa45f5ea5e0c3559943cd67a4870b3a6210b3f9a632 | Python | 5,315 | 141 | """Check the completion spec against the option tables in src/cli.cc.
The spec is hand-maintained and cli.cc is the authority, so the two drift
apart whenever an option is added, removed or reassigned. A stale spec
does not merely under-suggest: it offers options the binary rejects.
Checked here:
1. every option i... |
4991244d9990ab6557c75e84133d5a8473fa6eab26f849223d7e30cdac9bd478 | Python | 5,316 | 177 | __credits__ = ["Rushiv Arora"]
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": 3.0,
"lookat": np.array((0.0, 0.0, 1.15)),
"elevation": -20.0,
}
class HopperEnv(MuJocoPyEnv, utils.EzPi... |
620c9d6d70aa02565421426a0acbcaeda05ab53931596aa3af2a6fcad48ea945 | Python | 5,316 | 145 | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# Transformer implementation: https://github.com/lucidrains/vit-pytorch
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
class PreNorm... |
a778d81d89065012b7b4ab6f7fc10c37672a587b1588435eb1f2f9e3a3725590 | Python | 5,316 | 175 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to docume... |
9b11bc28de15581807311b15b641bc105dee70782e452a5678050265b9bd43ad | Python | 5,317 | 147 | """Construction-order factorial (W1-ORD / D8 / defect L4), memo 8.1 item 2.
Honest twins refute each other purely from the ORDER in which the two model
objects were constructed relative to TorchLens's first wrap: whichever model
was built pre-wrap holds pre-wrap function references (``self.act =
F.gelu``) and records ... |
cf4af5c832b444116c0c2c41a2a947c0d9703eb6ca48bd2bc0448af6a567febc | Python | 5,317 | 135 | """User-authored relation evidence never mints TRUST (W051-HONESTY M8).
``RELATION_CLAIM_GRADES`` reserves ``verified`` / ``consistent`` / ``divergent``
for TorchLens MEASUREMENTS, yet every evidence item that reaches a row today is
user-authored (no producer writes envelopes). A user ``"verified"`` used to be
stored ... |
81eaa79d43b6537009c6316dddca67bd031c7ee3485533c1057230a15a5fcc08 | Python | 5,318 | 149 | import itertools
import numpy as np
from digest import conds
from util import isiterable
def pairlabel(cfg, ipair):
return f'{cfg.pairings[ipair][0]}/{cfg.pairings[ipair][1]}'
def getindex(cfg, **kwargs):
if 'isi' in kwargs:
if isiterable(kwargs['isi']):
iISI = [cfg.ISIs.index(isi) for ... |
83688cf26bc5cbbfabdf630cb08f73be653b26ee59538847d82fb338ccb26b22 | Python | 5,318 | 166 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from itertools import product
import os
from eelbrain.testing import TempDir
from eelbrain._experiment import TreeModel, FileTree
class Tree(TreeModel):
_templates = dict(apath="/{afield}/",
afield=('a1', 'a2', 'a3'),
... |
62fb3aa452bf325183f22a482ef54c3ebde1044d0961fd6dc1dd3a4de78990bb | Python | 5,320 | 192 | """
Append experiment.
Split whole dataset into three part, where split one patient into three part, from start to end.
split pad dataset path: /workspace/data/split_pad_dataset_512
three part split pad dataset path: /workspace/data/three_part_split_pad_dataset
"""
# %%
import os
import sys
import shutil
# there sh... |
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