sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e7793154605fe6b7034a1afef3f497b019dd5e5dd7be298d8f9d1b673a401573 | Python | 10,599 | 285 | """Validation replay hands each op arguments with the captured ``requires_grad`` and grad mode.
ATen can pick a different kernel when an input requires grad, or when grad mode
differs: on macOS arm64 a Cin=1 3x3 conv runs ``Slow2d`` for a grad-requiring
weight and ``Winograd3x3Depthwise`` otherwise, and the two differ... |
ff25b648cb88c720590e240f5aa6d4710dcad10d2388d4a7e3c1397b22cb02c1 | Python | 10,601 | 259 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... |
8bee03a9314786b274eb38cd2734f7d5750c1b6c1bb3ce024a614a24cd572420 | Python | 10,606 | 279 | """Pinned tests for the mechanical belt (protocol-invisible coverage).
The belt membership is DERIVED per build (never hand-listed): every wrapped
entry outside torch's override registries whose probe call fires zero
``TorchFunctionMode`` callbacks and touches tensors. On this build that is
exactly ``{torch.from_numpy... |
a7b6d5ffb9d1597d08a40a3a869744caebc2a8379b517fdc6b63835f4956c166 | Python | 10,612 | 294 | # This module defines an image scraper for sphinx-gallery
# https://sphinx-gallery.github.io/
# which can be used by projects using plotly in their documentation.
import ast
import functools
import logging
import os
import textwrap
import warnings
import plotly
from plotly.basedatatypes import BaseFigure
from plotly.i... |
64d32107a0ee31dce2a5248d39f144003c9e46431e7f48fef18fffc73697b956 | Python | 10,615 | 304 | from contextlib import contextmanager
from unittest.mock import Mock, patch
import pytest
from amqp import ChannelError
from kombu import Connection, Exchange, Producer, Queue
from kombu.transport.virtual import QoS
from kombu.utils.encoding import ensure_bytes
from celery.contrib.migrate import (State, StopFiltering... |
cb3af48dba779ad14f9a6674833658c221646c228e775d17ae57b0b68c2466a8 | Python | 10,615 | 291 | import glob
import numpy as np
import torch
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3, 4, 5, 6, 7'
import sys
#os.environ['EPICS_CA_ADDR_LIST'] = '164.54.128.27'
import shutil
import custom_logger
import helper_small_model as helper
from agx_update import agx_update
from slack_update import post_m... |
0a79b2b44ebac74137ec2b0c310bf0080f90e05aac78762e91bf3cd2f2729de0 | Python | 10,618 | 295 | """Artifact-v2 layout contract tests (extract memo D1/D16; lane C04).
Unit coverage of the substrate kernel: the commit protocol's ledger append,
trusted-prefix reading (torn tail dropped, mid-break typed, missing member
typed), tail repair before appends resume, the write-once stimulus-id
sidecar, field-by-field sign... |
647088e8748d328f431bc7025e5a6141f17df042a921fb5a26ae014b56380281 | Python | 10,618 | 271 | """
Job runner plugin for executing jobs on the local system via the command line.
"""
import datetime
import logging
import os
import subprocess
import tempfile
import threading
from time import sleep
from typing import (
TYPE_CHECKING,
)
from galaxy import model
from galaxy.job_execution.output_collect import d... |
8567afcd2a45cd8ae365509cfe297e2ae347bfea9eacea98594fcdf0fbf14675 | Python | 10,620 | 279 | import os
import numpy as np
import pandas as pd
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
def test_quick_start():
# Quick-start example 1 (extract features)
from mirp import extract_features
feature_data = extract_features(
image=os.path.join(CURRENT_DIR, "data", "sts_images"),
... |
d8033c42d934c14ffe363ee933bbc47e23b5ab2ba309346ef911c1967bf31cd9 | Python | 10,627 | 280 | #!/usr/bin/env python
"""Convolutional components/networks."""
import keras_cortex.layers
import tensorflow as tf
def _residual_stack(x, filters, blocks, stride1=2, name=None):
"""A set of stacked residual blocks.
Args:
x: input tensor.
filters: integer, filters of the bottleneck layer in a block.... |
00c154f45715923f169de797abb3315642707e70ddd3ae5d6a1dd5aaca6e233c | Python | 10,633 | 284 | """Bound-method root contract (lane F41; foldA MEMO s5 item 9 + D11).
The ruled root contract, pinned on toy models: ``tl.trace`` accepts an
``nn.Module`` OR a bound method of one; the owner resolves via
``method.__self__`` and registers as a submodule of the TL-authored
wrapper root; ``stepped_module`` defaults to th... |
662b1ad7f827ffa42c6275b69ad05404a930b08e34359942b2ba8ee29b6ffa37 | Python | 10,634 | 302 | from collections import deque, Counter
import math
BIOLOGICAL_PROCESS = "GO:0008150"
MOLECULAR_FUNCTION = "GO:0003674"
CELLULAR_COMPONENT = "GO:0005575"
FUNC_DICT = {
"cc": CELLULAR_COMPONENT,
"mf": MOLECULAR_FUNCTION,
"bp": BIOLOGICAL_PROCESS,
}
NAMESPACES = {
"cc": "cellular_component",
"mf": "m... |
4a8fd131501b4f4bc92b4129cacfad09f7eefd1363cd70e187fd4633e2774a92 | Python | 10,635 | 274 | import math
import torch
from einops import rearrange, repeat
from flash_attn.bert_padding import pad_input, unpad_input
def generate_random_padding_mask(max_seqlen, batch_size, device, mode="random", zero_lengths=False):
assert mode in ["full", "random", "third"]
if mode == "full":
lengths = torch.f... |
50fd89eed9501d1366e7063dcabc919db3973553db78bc388bc3c473a065905d | Python | 10,636 | 255 | """DSE regularizer."""
import math
from typing import Tuple
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Module
class DSERegularizer(Module):
"""Dense Structure Estimator regularizer for dense representations.
This experimental regularizer is based on the train... |
9cfa979541dcdb9baebf10e716fbe4a060142b2d18be1132d5d1661d80b005a2 | Python | 10,640 | 285 | """
This is the script that is used for the wave propagation using the angular spectrum method (ASM). Refer to
Goodman, Joseph W. Introduction to Fourier optics. Roberts and Company Publishers, 2005, for principle details.
This code and data is released under the Creative Commons Attribution-NonCommercial 4.0 Int... |
b157cbc33f75f1132df530f04361824bc39a30d4051ce6df1cd5c388e49b3117 | Python | 10,640 | 219 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from typing import Any
import numpy as np
from .basetrack import TrackState
from .byte_tracker import BYTETracker, STrack
from .utils import matching
from .utils.gmc import GMC
from .utils.kalman_filter import Kal... |
e08f3c63701c9aefc4b1656aca046031f73a89057234ba976a8aff69c37d2443 | Python | 10,643 | 314 | """Phase 5 capture-unification predicate intervention tests."""
from __future__ import annotations
from typing import Any, cast
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens._trace_state import TraceState
from torchlens.fastlog import RecordContext, Recording
from torchlens.o... |
0057a506d3e9360fd0968fc631f9e69b4520261b08cdd9ee8c94ee197fb7039f | Python | 10,646 | 283 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test objects and function in the module ``tfep.io.dataset.traj``.
"""
# ======================================... |
5542d02b14067e1d862d5ec974ac09f5a407193d9e3b8cbf4d8c7638fa08da68 | Python | 10,647 | 287 | """
Unit tests for larvaworld.lib.plot.grid module.
Tests composite grid-structured figure creation using synthetic data
and mock objects. Only tests calibration_plot which doesn't require datasets.
"""
from unittest.mock import MagicMock, patch
import os
import pytest
from larvaworld.lib.plot.grid import calibrati... |
8a3c9a834aadc992d061dfa20f39ec5f728f20b8acf7a61706f64976842d1ff1 | Python | 10,652 | 325 | import argparse
import math
import time
import brainpy as bp
import brainpy.math as bm
import numba
import numpy as np
@numba.njit(fastmath=True, nogil=True)
def _adding_problem_generator(X_num, X_mask, Y, N, seq_len=6, number_of_ones=2):
for i in numba.prange(N):
positions1 = np.random.choice(np.arange(math.f... |
921b64a000def3ccbfd5ce6171ba5295b9fc41de6000ac56a93f15a0556bdb0c | Python | 10,654 | 271 | import pandas as pd
from tqdm import tqdm
import os
import torch
import torch.nn as nn
from torchmetrics import MeanSquaredError, R2Score, SpearmanCorrCoef, PearsonCorrCoef
from omegaconf import OmegaConf
import sys
script_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.dirname(script_dir))
f... |
041d8be541b11d9acd8b1d564efd46105bf91edeb7a235a40cbc83a9da6142ae | Python | 10,656 | 213 | import torch
import torch.nn as nn
import torch.nn.functional as F
from HD_BET.utils import softmax_helper
class EncodingModule(nn.Module):
def __init__(self, in_channels, out_channels, filter_size=3, dropout_p=0.3, leakiness=1e-2, conv_bias=True,
inst_norm_affine=True, lrelu_inplace=True):
... |
7d536d7e218065d205c9a78cadc0d89630fe4365d1abe65dec796b0b2ce01b08 | Python | 10,657 | 303 | # bcinetwork.py -
# Copyright (C) 2007-2009 Bastian Venthur
#
# This program 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 2 of the License, or
# (at your option) any later version.
#
# This ... |
87098e44767c232cbe4d702ae8a30a4022f2b7c8e7c0fa2e89b5db20ac5e4661 | Python | 10,659 | 322 | #!/usr/bin/python
import sys
import os
from shutil import copyfile
import re
import pandas as pd
import numpy as np
import math
import ConfigParser
import tabix
##### Return index of a1 which exists in a2 #####
def ArrayIn(a1, a2):
results = np.where(np.in1d(a1, a2))[0]
return results
def ArrayNotIn(a1, a2):
tm... |
25a0393d3229cec0ec4199e41359f0ce33ae58cbbe2aed46584cd0ad11dd6bc5 | Python | 10,660 | 241 | from distutils.command.build import build
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Code suppressing TF warnings and messages.
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import sys
import argparse
import numpy as np
import random
sys.path.append("../")
import time
import gc
from tensorflow.python.framework.... |
60896a53dc6dc04a00385e8113ecf93dd2064a536ff87c4dc11dbd057f39d5c8 | Python | 10,662 | 392 | """Callbacks available from R's C-API.
The callbacks make available in R's C-API can be specified as
Python functions, with the module providing the adapter code
that makes it possible."""
from contextlib import contextmanager
import logging
import sys
import typing
import warnings
import os
from rpy2.rinterface_lib ... |
1f1735e7fdf1ee0ca031264870e7936f428ce8be639493dc345f1c567004ce5b | Python | 10,664 | 313 | # 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... |
811b17df063cd0802691669a77f85d5b8f550ffd724234280631e7ed35b46cfb | Python | 10,666 | 283 | import torch
import torch.nn as nn
from bioseq2seq.utils.misc import aeq
from bioseq2seq.utils.loss import CommonLossCompute
def collapse_copy_scores(scores, batch, tgt_vocab, src_vocabs=None,
batch_dim=1, batch_offset=None):
"""
Given scores from an expanded dictionary
correspon... |
b82462e201cfcd12f8d20ff44689fa3b229ded9d5c8133183b088d90534a96d0 | Python | 10,668 | 283 | #!/usr/bin/env python3
"""
5D Eval — Diagnostic B: sub-type clustering within disorders.
Does k-means on AD / FTD / PD subjects in 5D space reveal stable sub-types
that a sub-type-aware classifier could exploit?
Measurements per disorder:
- k-means for k ∈ {2, 3, 4} fit on concatenated disorder subjects
- Silhoue... |
f8dfc836125ee8498689abd7a0bc2013fb9a0926b8590c3322cbeb97cde50a6c | Python | 10,668 | 269 | from inspect import getfullargspec as fargs
from typing import List, Union, Callable
import numpy
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import TimeDistributed
from angorapy.tasks.wrappers import TaskWrapper
from angorapy.utilities.error import IncompatibleModelException
from angorapy... |
96d384728a3600772cc58036b9a34c9ae1d9f9d17e1b60256bde55aa0c6bbd1b | Python | 10,669 | 265 | import os,sys,math,pickle
import random as lrand
import rpy2.robjects as robjects
import argparse
import numpy
#import svmutil
def init():
lrand.seed(1982)
robjects.r('library(splines)')
robjects.r('library(stats4)')
robjects.r('library(survival)')
robjects.r('library(mvtnorm)')
robjects.r('lib... |
3d39d673867984cd30ea8920c279ff2298d7f3f018ad8ddcc7168d783042b24b | Python | 10,672 | 326 | #!/usr/bin/env python
"""
TODO:
- define __all__ or limit available functions in some other way.
"""
import numpy as np
import pandas
from argparse import ArgumentParser
try:
# python < 3.3
from collections import Iterable
except ImportError:
# python >= 3.3
from collections.abc import Iterable
from ... |
be7b44020a897f2f98bf376d332ce55471632c68571bbfd168b7b03ecb08d652 | Python | 10,673 | 260 | """Parses an individual line in a BED file."""
from collections import namedtuple
import logging
# Initialize a global named tuple to make handling BED lines less awkward
BED_HEADER = ('chrom', 'chromStart', 'chromEnd', 'name',
'score', 'strand', 'thickStart', 'thickEnd',
'itemRgb', 'blockC... |
b100bba0452e321877d8d24180d89200fd0c485faad8087f7f357281df6c140e | Python | 10,675 | 185 | """1. Distributed training of deep video models
================================================
Training deep neural networks on videos is very time consuming. For example, training a state-of-the-art SlowFast network [Feichtenhofer18]_
on Kinetics400 dataset (with 240K 10-seconds short videos) using a server with 8 ... |
b86accab47274e612ee427b6ecd9416dcf21a7ee0b7b0e5b188c36218b0d7106 | Python | 10,675 | 301 | #!/usr/bin/env python3
"""Diagnostic O — Tier 3.3: richer feature expansions + selection.
Dx N showed Quad_LDA (linear + squared → 30 features) crosses both AD
bars (0.863/0.786). This diagnostic tests whether we can push further:
1. cubic_expansion: linear + sq + cubed (45 features)
2. pairwise_top10: l... |
6ec8a4ff4706fd2aeeadd50a9fa01bccf1f7123f1e758723ff5c04f130ef4b23 | Python | 10,676 | 275 | from itertools import accumulate
from pybedtools import BedTool, Interval
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
__all__ = ['WholeGenomeDataset', 'FastWholeGenomeDataset',
'process_genome_preds', 'post_merge_blocks']
class WholeGenomeDataset(Dataset):
'''Dataset f... |
4057c111893a55aaed2703c7c47ae79bb089f872f3a5d534df29235b370b26ed | Python | 10,679 | 237 | # 均匀采样效果较差
import json
import numpy as np
import os, sys
import random
from collections import Counter
from sklearn import preprocessing
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import torch.nn.functional ... |
1cd42781231725e68f57d3bf1adfde5bd120874ef6421d63cc74b3e2272669f5 | Python | 10,680 | 251 | '''
Load K-folding models from groups and evaluate performance
author : obanmarcos
'''
import os
import os, sys
from config import *
sys.path.append(where_am_i())
import lightning as pl
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utilities import dataloading_utilities... |
bac7dea15d212d220df4a8e5eaef3126741db0e9fb7e01b294ddd287e78755bb | Python | 10,681 | 233 | #!/usr/bin/env python3
"""
File: pic_replication_plot.py
Created: 2021/12/13
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.
"... |
c351c71372bd75f3127590dceca50463ac1178816a8b2536344745d91f920ef5 | Python | 10,681 | 283 | #!/usr/bin/env python3
import numpy as np
import seaborn as sns
import re
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import glob, struct
import sys, argparse, os
def parse_args(argv=None):
parser=argparse.ArgumentParser(description='Script for generating the jobs t... |
072e609b4df5e9ae6b3671173269e5cd1f3d57bf7dc192fc7f8cf04ee10dc2b1 | Python | 10,683 | 289 | import torch
import time
import torch.nn as nn
from sklearn.neighbors import kneighbors_graph
from .Modules import *
from .Utils import *
from .Loss import *
class PAST(nn.Module):
"""
PAST Model: latent feature extraction and spatial domain deciphering with a prior-based self-attention framework for spatial t... |
86bc107853e8852c5bfe3ee0df19c72e79cfc1c8779681f970d1eb3d84c1670f | Python | 10,683 | 290 | import numpy as np
import numpy.typing as npt
import pandas as pd
import scipy
import statsmodels.api as sm
from anndata import AnnData
from numba.typed import List
from statsmodels.stats.multitest import multipletests
from tqdm import tqdm
from .perm_utils import get_lr_bg, get_lr_features
def perform_spot_testing(... |
6bb57e433cc50382dc0649e1d2e934f42cbbf579a88d2b692ccf6d31ae7002ad | Python | 10,684 | 314 | from collections import defaultdict
from typing import (
TYPE_CHECKING,
)
from pydantic import ValidationError
from typing_extensions import Self
from galaxy.model import CollectionStateSummary
from galaxy.tool_util_models.parameters import (
AdaptedDataCollectionPromoteCollectionElementToCollectionRequestInt... |
5969f64e64166b8dc6c12af07bae34288fbd4f4cc7f7956c836445aa3953e908 | Python | 10,686 | 274 | import os
from datastructure.wsi import WholeSlideImage as WholeSlideImage
import numpy as np
from scipy.stats import rankdata
class Patient(object):
"""
A class to represent one patient with diagnosis and histological slides.
Attributes
----------
identifier : string
(Anonymized) identi... |
72c47f1320c22546fca96312343b8df4adb95c4472eccdf1ae9751b6f6aeaf56 | Python | 10,688 | 230 | import numpy as np
import prob2020.python.utils as utils
import prob2020.python.mutation_context
class SequenceContext(object):
"""The SequenceContext class allows for deciphering sequence context
and for randomly permuting mutation positions while respecting sequence context.
"""
def __init__(self, ... |
8773db8141ee07c3c6cec7d2bd2e4789147f8970e70faebfcfaa26561fb497cd | Python | 10,689 | 307 | #!/usr/bin/env python
# encoding: utf-8
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import os, sys, re
import logging
import TGraph
import TNode
import Node_alignment
import Compact_graph_whole
from Topological_sort import Topological_sort
import TGLOB... |
bb7e1d904b6561d44d2174fc1a47413f533087628c386f178d8924530c8cfe3b | Python | 10,689 | 275 | from typing import Type
from dataclasses import dataclass
import cutlass
import cutlass.cute as cute
from cutlass.cute.nvgpu import cpasync
from cutlass import Int32, const_expr
from flash_attn.cute import utils
from quack.cute_dsl_utils import ParamsBase
from cutlass.cute import FastDivmodDivisorV2
import math
@c... |
c0d52988aafea07e09dbd6b9c25570df9501ed5591caf725b4901de6093fe920 | Python | 10,690 | 316 | '''
load the RBNS data
'''
import csv
import re
import h5py
import urllib3
import urllib
import gzip
from io import StringIO
import requests
import numpy as np
import time
from utils_rbns import create_datapoints
rbns_data = 'metadata_rbns.csv'
encode_dir = 'encode/'
# PROTEIN_NAME = []
# SEQ = []
# CONCENTRATION =... |
59fc4fcdb6728d6901c63118c26e4247864865833ead6da3355baf17d70f0e32 | Python | 10,692 | 286 | import contextlib
import numpy as np
from keras.src.backend import config
from keras.src.utils.module_utils import jax
def get_test_data(
train_samples, test_samples, input_shape, num_classes, random_seed=None
):
"""Generates balanced, stratified synthetic test data to train a model on.
Args:
t... |
46d906a3f3b25844b90e3e01b74f4d81196872b63fcfea786b80bda6c2964753 | Python | 10,695 | 297 | import numpy as np
from math import sqrt
import mxnet as mx
import mxnet.ndarray as nd
import mxnet.gluon.nn as nn
from numpy import prod
# pylint: disable-all
def get_weight_key(module):
for k in module.params.keys():
if 'weight' in k:
weight_key = k
return weight_key
def compute_weigh... |
7971a560ebcbaf3d4891a10a84d8400f2cee21ff7afc93c3e4ddb739177365b2 | Python | 10,701 | 249 | """Registered buffer write-side node keeps its backend_address (r87, free FINDING-1 residual).
r85's Option-a ``backend_address`` cleanup established the rule "a REGISTERED
buffer keeps its registered address; a plain-attribute / list-element buffer is
decoupled to ``None``" and restored op / activation / input / outp... |
4a6c84bef445d9f60db72b52aed44a419777a89a10e17522e6d2f8611bc0a350 | Python | 10,702 | 262 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
PyTorch-accelerated bootstrap analysis.
"""
# ================================================================... |
9ec94071b86a7e3cccbe6dd0f2ea8f6e9576b6cbd2f60d43bfb698cf23851f4d | Python | 10,706 | 272 | #!/usr/bin/env python3
"""
Diagnostic U — 6-cohort expansion with figshare 5450293 as 4th AD cohort.
Adds the Vicchietti/Pineda figshare 5450293 cohort to the Dx P 5-cohort
pool. With this addition:
- AD-containing cohorts: 3 → 4 (ds004504 + BrainLat + ds006036 +
figshare_5450293)
- HC-only cohorts: 2 → 3 (ds... |
f4a5bde8ae9ef1511f153431673116b2f0aa1c09b86093cf30ecaba4bbab5f00 | Python | 10,706 | 236 | # 均匀采样效果较差
import json
import numpy as np
import os, sys
import random
from collections import Counter
from sklearn import preprocessing
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import torch.nn.functional ... |
64969005cc7244d7cccf4ce4b7066f57c768ed2fd1ff00013a290ec0d79ddbe9 | Python | 10,707 | 229 | #######################################################
### This code is the main routine to combine ###
### simulated SV tracks on an axon from ###
### different synapse starting locations. ###
#######################################################
### Written By: M.W. Gramlich ... |
a36ef7b3cf2fe42f624b72030c6ea20c2403ddc1fe07859184041d97d93666c8 | Python | 10,707 | 277 | """Entry/tamper matrix for scoped meta admission (memo sec 4.2 / 8.1 item 7).
Admission is SCOPED (D2): meta state/inputs are admitted if and only if
structure-only is in force. An unscoped carve-out silently captures an
unbannered value-free trace — measured to be worse than the refusal it
replaces — so every one of ... |
c6d2079b8ec0cfd24694a1d02b5db72b9a13fb4da1e1f7a59ce58d890999fecf | Python | 10,707 | 226 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
b6ebfd4baae1cd742e74ff8cb9b9d145c678225c020c1705b19b4d5074edbfcc | Python | 10,708 | 250 | import pickle
import torch
import torch.nn.functional as F
from trainingUtils import NBLoss,_convert_mean_disp_to_counts_logits #,MultipleOptimizer, MultipleScheduler, compute_kernel, compute_mmd
from models import Encoder,Decoder,GaussianDecoder,VAE,CellStateEncoder,\
CellStateDecoder, CellStateVAE,... |
0dc0e23ba1f444324e230a605cdf529f753c05d45ab620d12abd0f0f4600097c | Python | 10,709 | 254 | '''
Load K-folding models from groups and evaluate performance
author : obanmarcos
'''
import os
import os, sys
from config import *
sys.path.append(where_am_i())
import pytorch_lightning as pl
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utilities import dataloading_u... |
f77c042136c4288f1ec3a8dcdc89255d86233d7b867fc9de56b915a8392736a1 | Python | 10,709 | 292 | """Reusable Panel button helpers for the portal.
No heavy imports -- only `panel`, consistent with the "keep startup
lightweight" portal rule in CLAUDE.md.
"""
from __future__ import annotations
from typing import Any, Callable
import panel as pn
__all__: list[str] = [
"build_load_file_button",
"build_expo... |
23243f88edad12f03020148a80bbadbe9fe460568eeadf8872afff7f27cb4886 | Python | 10,714 | 256 | #!/usr/bin/env python3
"""
Dx AR — 7-cohort eval with ds005385 added (HC-density binding test)
====================================================================
Adds ds005385 (Wascher et al. 2025, Dortmund Vital — 109 HC age 60-70)
as a 7th HC-only cohort to the existing 6-cohort pool used by Dx AA.
Runs the canoni... |
af2f5f9ceec99d11c7662364cd5653f7102bb6c8e2c3a9c066b7351b6a42356c | Python | 10,716 | 262 | """Fresh/foreign ``nn.Parameter`` provenance-launder closure (r77 F1).
Round-76 (hon1 + free-roam, cross-lab) confirmed the LAST layout-class vehicle: the first
rung of ``_tensor_has_known_provenance`` treated ANY ``torch.nn.Parameter`` -- including a
fresh in-forward ``nn.Parameter(y.detach())`` with no prep stamp, n... |
15c998cad07d25b5471607767a52655e3bcb95f76dfff3f059f342b1e695fb09 | Python | 10,718 | 262 | #!/usr/bin/env python3
"""
Dx AV — Classifier-only bootstrap CIs (deployment-relevant CI)
================================================================
Convention: pre-fit rank-norm and whitening ONCE on the full pool;
bootstrap-resample only training subjects per iteration and re-fit
only the LDA classifier; held-... |
679334256510fbd0dfb1100f48c1a9f75818f6ba23c64dbd1c46f48aa6102e5e | Python | 10,718 | 304 | from functools import partial
from typing import Any, Callable, Optional, cast
import torch
import torch.nn as nn
from timm.models.vision_transformer import Block
from torch import Tensor
from torch.nn import LayerNorm, Module, Parameter, Sequential
from lightly.models import utils
from lightly.models.modules.masked_... |
9012e89dde1f0633aade2dbda76ffbe3ce96b291c71b54e5661551c975cc9860 | Python | 10,720 | 291 | import inspect
import os
import traceback
import types
from functools import wraps
from keras.src import backend
from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
from keras.src.backend.common import standardize_dtype
_EXCLUDED_PATHS = (
os.... |
55240331b7758b6c21f8a1f9b5c99bf817f39b5b40b74aeb61bbc6769a9a9965 | Python | 10,722 | 297 | """Pascal VOC Detection evaluation."""
from __future__ import division
from collections import defaultdict
import numpy as np
import mxnet as mx
from ..bbox import bbox_iou
try:
from mxnet.metric import EvalMetric
except ImportError:
from mxnet.gluon.metric import EvalMetric
class VOCMApMetric(EvalMetric):
... |
a0a0991164ee504b7f4671412d1a683f5fe65671d4bef4bb5f100d53f0b51c28 | Python | 10,723 | 174 | #!/usr/bin/env python3
from __future__ import print_function
import pandas as pd
import numpy as np
from datetime import datetime
import sys
import os
import argparse
sys.path.insert(1, os.path.dirname(__file__))
from core import *
from post import *
import genotypeio
import cis
import trans
def main():
parser =... |
b69e7c636f1d04c5c289b1c591105b26dfaaee9f784cbdb7f5ac01f6ad2c8098 | Python | 10,725 | 276 | """Round-12 security regression: ungated intervention ``import_ref`` resolver RCE.
r3/r5/r6/r7/r9/r10/r11 hardened the container and function-key resolvers, but a THIRD
ungated sink survived in ``torchlens/intervention/save.py``: ``_resolve_import_ref`` did a
bare ``importlib.import_module(module_name)`` + attribute w... |
3cd58ed1ba409be37102d19fe5ad103ca22ccd4b3643dd5138ac15f1bc9e5789 | Python | 10,726 | 295 | # from google.colab.patches import cv2_imshow
from detectron2.config import configurable
from detectron2.engine.defaults import DefaultTrainer
from detectron2.data.build import build_detection_train_loader
from detectron2.data import detection_utils
from detectron2.config import CfgNode as CN
from detectron2.stru... |
8512f752b0c7f24b4136573a9ade07792b4efdded6bb2de9fb21019e40571bc9 | Python | 10,727 | 261 | #!/usr/bin/env python3
"""
Diagnostic R — HC vs combined neurodegeneration (AD + FTD).
Motivation (from Dx Q): AD and FTD share a single pathology axis in
the 15-feature space. Centroid distances: HC-AD = 2.52, HC-FTD = 2.09,
AD-FTD = 1.24. FTD sits between HC and AD. The AD-FTD sub-axis is
weaker (~0.48 pts in PC1-PC... |
5cc6afdc5ee11bb270ce166dd6d34861873e9fe9753ac8cc0395e296f27c3a41 | Python | 10,730 | 333 | from __future__ import annotations
import json
from pathlib import Path
import pytest
from larvaworld.portal.datasets.models import (
WorkspaceDatasetRecord,
WorkspaceReplayDatasetRecord,
)
from larvaworld.portal.datasets import workspace_index
from larvaworld.portal.workspace import initialize_workspace
@... |
b86835963fae3a418c952b250da7c1c4d02c591e33ca31df1acc094c7f7fff56 | Python | 10,738 | 238 | # -*- coding: utf-8 -*-
"""
Created on Mon Feb 6 21:01:02 2023
@author: Caterina Brighi
"""
#%% Import functions
import matplotlib.pyplot as plt
import matplotlib as mpl
import SimpleITK as sitk
import numpy as np
import pandas as pd
import datetime
import os
import glob
import gzip
import shutil... |
32c3d47cfe52851ce03b35be2a9fd18753d3e74f567ec8a1bf11bdde24848ff4 | Python | 10,739 | 319 | """
The predict function using the finetuned model to make the prediction. .
"""
from argparse import Namespace
from typing import List
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from BatmanNet.data import MolCollator
from BatmanNet.data import Mo... |
ae9483c6fb90d01ad487b00068fda6de264a87fb7d6ac9fa7457d6a9de8f8e8d | Python | 10,739 | 304 | import ast
import html
import io
import uuid as _uuid
import zipfile
import yaml
from galaxy.datatypes.binary import CompressedZipArchive
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.protocols import (
DatasetProtocol,
HasMetadata,
)
from galaxy.datatypes.sniff import (
buil... |
3751d0e0555e2560c6e8581a491b56c1d44d18115ada39aeab88a7e1533e2131 | Python | 10,742 | 287 | from __future__ import annotations
import json
import matplotlib.pyplot as plt
import numpy as np
from tqdm.auto import tqdm
from ..utils import build_gt_id2annotations
from ..utils import build_pr_id2annotations
from ..utils import COLORS
from ..utils import transform_gt_into_pr
from ..utils import update_scores
fr... |
71a9c0d569ac43d1190ac26eccb769871f05cc8d86d8c5ace8caf894cd65384f | Python | 10,745 | 236 | # 均匀采样效果较差
import json
import numpy as np
import os, sys
import random
from collections import Counter
from sklearn import preprocessing
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import torch.nn.functional ... |
33f9658052d13d773bf940944e340ba76caeef608d83cdaab77a3fec4a6fd69c | Python | 10,752 | 255 | """Shard-local capture marker substrate (L8/F6, merge-ranks C2; lands DARK).
Pins the uncontended F6 substrate slice: the ``distributed_scope`` marker
field (S3-registered ``FieldPolicy.DROP``), the ONE sharded predicate over the
placements limb (census plan 3.3(c): setter and future load validator share
it), the 3.2b... |
15e9d51a328d6e2d4b78f927f7dba25aa9b405372479f36400b6224455a67185 | Python | 10,756 | 244 | """
preprocessing_02.py — re-referencing and artifact rejection (healthy + stroke)
Filtering, resampling, channel harmonization, epoching and label extraction all
happen in data_loader_01.py, where the dataset-specific details live. What
remains here is re-referencing and trial-level artifact rejection.
Re-referencin... |
f1e018ac508dab355767d23ac322a789281bd43fa7d6fa7a287590531547652b | Python | 10,756 | 189 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
a199aa57c9f42d36fd853a32637e9116f547cb3ae3be87b6fa963675a3fa0f97 | Python | 10,760 | 330 | import json
import os
import shutil
import time
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import nway.diagnostics as nwdi
import PIL.Image
import PyPDF2
import pytest
import pandas as pd
matplotlib.use('Agg')
TEST_FILE_DIR = os.path.join(
os... |
3d8c9c0269c0bb3174fc8d04ef85e6bf198efe9f96c3aa31f1753509910168f6 | Python | 10,763 | 365 | import argparse
import json
import warnings
from ast import literal_eval
import six
from galaxy_ml.model_persist import dump_model_to_h5
from galaxy_ml.utils import SafeEval, try_get_attr
from tensorflow import keras
from tensorflow.keras.models import Model, Sequential
safe_eval = SafeEval()
def _handle_shape(lite... |
1da31bd8c9c55f26350d5a7da50d742cccca73181d81e02cc4454cb82959dc60 | Python | 10,765 | 278 | from typing import Dict, List, Optional, Tuple, Union
from PIL.Image import Image
from torch import Tensor
from lightly.transforms.gaussian_blur import GaussianBlur
from lightly.transforms.multi_view_transform import MultiViewTransform
from lightly.transforms.rotation import random_rotation_transform
from lightly.tra... |
d3db0c0316a892ea5c95ad6b4ab7bfb8fcfc54c8b2b08ac3f6d12a07c7187b04 | Python | 10,765 | 291 | # 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... |
13711566f83c8071636167d0157559ccb0aa8dad1a7066c3b848d4ee7c84e6ce | Python | 10,766 | 296 | #!/usb/bin/env python
# gtec.py
# Copyright (C) 2013 Bastian Venthur
#
# This program 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 2
# of the License, or (at your option) any later version.... |
30ec8acf2741db9a4b8a473a7acf852f3dd653d3f90204003020bc4479151486 | Python | 10,771 | 398 | """Tangent space for SPD/HPD matrices."""
import numpy as np
from .base import sqrtm, invsqrtm, logm, expm
from .mean import mean_covariance
def _check_dimensions(X, Cref):
n_1, n_2 = X.shape[-2:]
n_3, n_4 = Cref.shape
if not (n_1 == n_2 == n_3 == n_4):
raise ValueError("Inputs have incompatible... |
c1633536dfd8852278f2ec8cc6450e55e0783fbd47f1fe5f3325cec2b5ded78e | Python | 10,774 | 274 | """Transforms described in https://arxiv.org/abs/1904.07850."""
# pylint: disable=too-many-function-args,not-callable
from __future__ import absolute_import
import numpy as np
import mxnet as mx
from .. import bbox as tbbox
from .. import image as timage
from .. import experimental
from ....utils.filesystem import try_... |
a4afaf6dad679f9fc29928ef74381480e665f15c9859eade014c94b34ba65bfb | Python | 10,775 | 320 | # SOURCE: vendored from quic/aimet-model-zoo @ develop
# https://raw.githubusercontent.com/quic/aimet-model-zoo/develop/aimet_zoo_torch/quicksrnet/model/models.py
# https://raw.githubusercontent.com/quic/aimet-model-zoo/develop/aimet_zoo_torch/quicksrnet/model/blocks.py
#
# QuickSRNet (Qualcomm AI Research, arXiv:2303.... |
eaa989cce2021e28d60b9f4cae7a53367c63b46d35b0392ef8de996ffb006972 | Python | 10,780 | 294 | """Functions to convert preprocessed UK Biobank BOLD data to BIDS derivatives format."""
import glob
import json
import os
from nipype import logging
from nipype.interfaces.fsl.preprocess import ApplyWarp
from xcp_d.data import load as load_data
from xcp_d.ingression.utils import (
BIDS_VERSION,
TEMPLATE_SPA... |
141ab8202dd127df1abe9b7f2b716c95b66e528e476cf9918dd4f15c64bfc98b | Python | 10,781 | 284 | """C02 FactCore substrate: identity partition, grains, joins, health, scopes.
sumfam item 7 (FactCore + IdentityIndex + grain menu + capture fingerprint),
item 8 (serialized HealthFacts + three-state verdict + alias exclusion),
item 9 (at_capture/retained_now), and the costreport item-2 aggregation face
with its D7 bo... |
40a4a9b76ab0b07116cc1434c0b5ca3b7794768aa05631a842313d9d1146cb0e | Python | 10,784 | 172 | from __future__ import division
from __future__ import print_function
from os import makedirs, path
import numpy as np
import warnings
def default_metric(curriculum_params, input_data, correct_output, output_mask, output, epoch, losses, verbosity):
""" Default metric to use to evaluate performance when using Curric... |
11ed9308a4650bcd6b4426e439e001d6fbadd5e758e3ddef28e967e1e9f32e16 | Python | 10,785 | 304 | from __future__ import print_function, division, absolute_import
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision... |
3f4d1fbc5bbe9fc814f89ab488370c5b14a26683974bcfb709e2b8df9c930961 | Python | 10,788 | 325 | # 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... |
e8c5eb28ba664fa2a3227d5b08513def7d0321c9ee2b0a3ad45d39a2f5204b7d | Python | 10,788 | 294 | """Real-model Tier-1 rows (memo sec 8.1 items 3-4, 8; sec 8.2 expectations).
Toy-only validation is disqualifying — the shipped structure-only suite was
green while five defect mechanisms lived on real models. Structure-only
capture is CPU-cheap, so genuine HF rows run every PR (offline: config-built
twins, cached che... |
beb116833e75cbd0e6ca67b43ed849263f0ffc9418079fe6d19a79d37e8b69dc | Python | 10,791 | 262 | """Per-layer result cache for the out-of-core engine.
With ``engine="out_of_core"`` each feature read (``get_buildings``, ``get_landuse``, ...) is
assembled by the bounded per-call path and then its **result** is written once to a persistent
GeoParquet under a temp directory, keyed by the source file + the read's para... |
7065ae7d6fbbe39d32cd91dfb0d71fee725d2a1281dbe99f86db65079f9634fb | Python | 10,793 | 282 | """Opaque / non-finite non-tensor model-input honesty for sparse runnable execution.
Round-4 hardening. The round-3 fix witnessed *grammar-encodable* non-tensor Python
inputs (bool/int/finite-float/str/None) so a changed control input diverges instead of
false-verifying. But a non-tensor leaf *outside* the frozen lite... |
05274e8325e9db47a61f9be877f9bfd50c90091b8c500b98c3c8e528e375d87f | Python | 10,796 | 254 | """P1-8 · 回归校准位移的区间：把 β̂ 自身的不确定度算进去。
为什么这个脚本必须存在
----------------------
稿件 3.7(b) 把肺时钟的位移换算回年龄尺度：
Δage ≈ ΔY / β̂ = (−2.11) / 0.136 ≈ −16 年
95% 区间：bootstrap 重抽 220 人校准样本 → [−20, −13]
两处问题，都是 R2 指出的、必须由脚本固化而不是手改的：
1. **方差放大因子写错了量。** 稿件写 "variance amplification ≈ 1/r² ≈ 3.4"。
但 age = Y/β̂ 是**除法**，一阶传播给出
... |
d52edd8d70d76f8ca51196b87aa891d8b50570e41841731be9101907a5daa096 | Python | 10,796 | 281 | import os as os
import sys
import json
import jsonschema
import pywt
# Local dependencies
import train
import basics
import eval
#################################################################################
def make_config(model_name):
return {
'cwd': '',
'nadh_data': '',
'fad_data': ... |
5391b3984aaffba93db648d6ecba587f6481acab24922f285bd27fe0262b3758 | Python | 10,798 | 281 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize, LogNorm
import matplotlib.animation as manimation
from mpl_toolkits.mplot3d import Axes3D
import mpl_toolkits.mplot3d as a3
from spitfire.chemistry.mechanism import ChemicalMechanismSpec
from spitfire.chemistry.flamelet2d import... |
0e1bd2729b97efcf34aa46822f5bf3190aa514dee1bfc7d963498c002abf6658 | Python | 10,802 | 329 | import attr
import numpy as np
from working.synthetic_data.mutation.codon_mutation import CodonMutation
from working.synthetic_data.splicing_mechanism.place_splice_sites import (
add_splice_sites,
)
def codon_realization(codon_table, slpd, rng):
"""
Realize codons from a splicing length pattern distribu... |
8c4c575f094fd84d3a0a8ec87ff8d36377113a7192b39ae8a593d59dbda71aaa | Python | 10,802 | 282 | """
This module contains controllers for adaptive time stepping based on embedded temporal error estimation.
"""
# Spitfire - a Python-C++ library for building tabulated chemistry models and solving differential equations
# Copyright 2019 National Technology & Engineering Solutions of Sandia, LLC (... |
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