sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
710e87563931136356e6efacf283d3f87053a06737043908b805e8ea3080595b | Python | 9,767 | 256 | """
Author: Derek van Tilborg -- TU/e -- 23-05-2022
Functions to run a ChemBerta transformer for bioactivity prediction. We used pre-trained weights of ChemBerta trained
on 10 million SMILES strings from PubChem.
- RobertaRegressionHead: regression head that is slapped on the frozen pre-trained model
- Tra... |
826bd25ad4ff48909eda4d93ba565bea1fba3a07c94747dd8cef67ad8156daeb | Python | 9,770 | 233 | """W051-IO: write/read symmetry for ``metadata.pkl`` (AUD-CODE 2.20 / 3.11g).
``tl.save`` must never write metadata bytes that a default ``tl.load`` cannot
read back. Before this lane an arbitrary user object under
``Trace.annotations`` saved silently and made the WHOLE artifact unloadable
with a misleading "corrupt p... |
9e04698e35a461b60f360c9c1ba4ec2974a241baae7c94607bc67d4aa11204a4 | Python | 9,770 | 234 | from keras.src.api_export import keras_export
from keras.src.quantizers.quantization_config import QuantizationConfig
@keras_export("keras.quantizers.GPTQConfig")
class GPTQConfig(QuantizationConfig):
"""Configuration class for the GPTQ (Accurate Post-Training
Quantization) algorithm.
GPTQ is a post-trai... |
d3525efc4eba5f3633f4a6b4a3fb171e1a5b6e1e513d82a7e54d1052ff392fad | Python | 9,771 | 238 | """Implementation of a space that represents graph information where nodes and edges can be represented with euclidean space."""
from typing import NamedTuple, Optional, Sequence, Tuple, Union
import numpy as np
from gym.logger import warn
from gym.spaces.box import Box
from gym.spaces.discrete import Discrete
from g... |
baa908a51664c2c03566c1581c278971a3e6548d80a79bc8655b0bad0b3607b6 | Python | 9,773 | 297 | """Round-25 runnable producer capability regressions."""
from __future__ import annotations
from collections.abc import Callable
from dataclasses import replace
from pathlib import Path
from typing import Any
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens._io.runnable import b... |
76631149a1b1ba7137c0f4f2366e90075c726f7cb8d58308644ad3d78d7839c4 | Python | 9,775 | 221 | # 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 ... |
ae73d0dea269a1e2976a9f55fe1d5bc03f5147aef5c60f46e71a72311f23402f | Python | 9,780 | 273 | #!/usr/bin/env python3
"""
5D Eval — Diagnostic F 7D classifier battery.
Merges 5D + extras (IAF, DFA, KOP_beta, LZc) across cohorts, then runs:
T_AD: AD vs HC (5D baseline vs 7D with +IAF +DFA)
T_FTD: FTD vs HC
T1: Cortical (AD+FTD) vs Subcortical (PD) — PD cohorts will be 5D-only
since w... |
bbe4b81b27abf3924285f68e74761d4f3c9a7f93b0ba59dffca7263a5ba49dc9 | Python | 9,783 | 253 | """Echo narration on real architectures (snoop memo section 5; R0 realism).
Toy-only validation is disqualifying: these rows exercise a config-built
GPT-2 (real architecture, zero network), the real torchvision resnet18 with
its pretrained weights, and the real distilgpt2 checkpoint. Crash rows use
REAL failure shapes... |
1ea6c0dcba3f308e5b17ce18a8e91a9af11e7bf1b69785ac812fc0a9a819b131 | Python | 9,785 | 227 | #!/usr/bin/env python3
#-*-coding:utf-8-*-
# 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/lic... |
f7e67c6b450bf8d5b79e01b984f58fdb387c81e978cd6feff94ba8d76b51f624 | Python | 9,785 | 346 | """Round-24 runnable regressions for ``__idiv__`` and buffer ``.data`` host writes."""
from __future__ import annotations
from collections.abc import Callable
from functools import partial
from pathlib import Path
from typing import Any
import pytest
import torch
from torch import nn
import torchlens as tl
from tor... |
4c642e426a0f2f206e96fb982b3f43cd7f24cf4c0db9c62bd832757c7a84ebfe | Python | 9,786 | 316 | # %%
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.make_model import MakeVideoModule, early_fusion, late_fusion, single_frame
from pytorch_lightning import LightningModule
from utils.metrics import *
from sklearn.metrics import confusion_matrix, ConfusionMatrixDi... |
df79acafb234ae051ec95916d256988bda6501eeb7f6005839991a0b0d16f50b | Python | 9,788 | 221 | #!/usr/bin/env python3
"""
Dx Z+ — verification of the brainlat-out tool with perm null + bootstrap.
Pool: ds004504 + ds006036 + figshare 5450293 (AD cohorts) +
ds003775 + LEMON ≥60 (HC-only cohorts).
n = 365 (96 AD + 269 HC).
Same canonical pipeline as Dx X (HC-cov whitening, λ = 0.001,
Quad_LDA), 500 perm + 500 boo... |
9031c8fce8a2175de22f1726d61f4216b4ebdfd6164d0befc7e43d113c01d628 | Python | 9,789 | 299 | """Queryable per-op nonfinite record: ``Trace.nonfinite_ops`` and friends.
Covers the two evidence bases (post-hoc saved-payload scan vs capture-time
``track_nonfinite`` recording), the coverage disclosure that keeps a scoped
clean answer honest, the deferred-flag drain, the zero-cost-when-off
contract, and the struct... |
e24fba9ed5c7682017045853142b51b3900dc09956b96be27dccc9f14064a833 | Python | 9,791 | 227 | import numpy as np
import pandas as pd
from library.comput_utils import acc_metrics
class PSPkernel:
def __init__(self, tau, tau_s):
"""Alpha function for post-syanptic potential.
Args:
tau (float): Decay time constant of the alpha function kernel.
tau_s (float): Rising t... |
831380ae25944368108703c8034740e16b1f6971b395e3b6ae36052c173e08bc | Python | 9,796 | 292 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in November 2019
@author: Quentin Lutz <qlutz@enst.fr>
"""
import pickle
import shutil
import tarfile
from os import environ, makedirs, remove, listdir
from os.path import abspath, commonprefix, exists, expanduser, isfile, join
from pathlib import Path
from ty... |
5311824af590cd8ef89fec6025a734129c27d17af9f934ffa400c43c12d69da1 | Python | 9,801 | 222 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 29 18:04:08 2021
This is the configuration file for Experiment 4, cue validity 0.75, AI vs learning speed analysis. It differs from the
main Experiment 4, cue validity 0.75 configuration file in just one aspect - it is set to train more (N=100) mode... |
bd2855c00f934ada591d396f48d8eb026c6d11ad8d5889544d4b1ff1fe093cd3 | Python | 9,802 | 374 | # Copyright 2023 DeepMind Technologies Limited
#
# 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 applicable law or agree... |
c8be8b1cb2ec8e794aa1949090123eae67b50bee30e8c2bdedddfa60a9249411 | Python | 9,803 | 329 | # --- 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 Header(_BaseTraceHierarchyType):
_parent_path_str = "table"
_path_str = "... |
d13059ff0d4f1cb632aef87546c6dda332e19e99ac1e18f72626d31cc48a887a | Python | 9,803 | 308 | # pylint: disable=missing-function-docstring, line-too-long, consider-using-with
"""
Utility functions, misc
"""
import os
import sys
import time
import functools
import logging
from contextlib import contextmanager
from functools import wraps
import numpy as np
import torch.nn as nn
import torch
def read_labelmap(l... |
ae570582092f6affff1deb24ed256ec763650d2fb1220cea987a4b014b717a01 | Python | 9,805 | 202 | """
Some key layers used for constructing a Capsule Network. These layers can used to construct CapsNet on other dataset,
not just on MNIST.
*NOTE*: some functions can be implemented in multiple ways, I keep all of them. You can try them for yourself just by
uncommenting them and commenting their counterparts.
Author... |
b6a4b0db3018cf70ad1a1082e075c621575a2a5724afd670a46113133e5b43ba | Python | 9,805 | 279 | """Parse buildings from one PBF with one tool and print {"seconds", "features"} as JSON.
`benchmarks_scaling.ipynb` runs this as a separate subprocess per (tool, area) so each parse
is isolated: the parent process samples the whole process tree's peak memory with psutil and
enforces a timeout, and a crash or out-of-me... |
596c04fc00a00b1557a2435c5db9c83b0831a6291bf2d81298c20d3e77d31bb0 | Python | 9,806 | 210 | '''
TNS class : Grower object that contains the grower functionality.
'''
import copy
import logging
import numpy as np
from morphio.mut import Morphology # pylint: disable=import-error
from tns.generate.soma import SomaGrower
from tns.morphmath import sample
from tns.generate.tree import TreeGrower
from tns.generat... |
0ba238db66229bb128d7c5d2dc955cf3dd6fd4f446d020c17d9e92e0033acb08 | Python | 9,814 | 304 | """S2 capture-spine golden projections across public capture surfaces."""
from __future__ import annotations
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.fastlog import RecordContex... |
83d292a46d14e01abced0d3fb8e68da9157be2ab850dba2ccc23e8b0737dbd3e | Python | 9,815 | 269 | """Two-term compute record + FMA convention (A5/A6).
Lane A07 (2026-08-27). Spec: the summary design memo 3.2 + build
item 9; costreport item 3 (MACs stop being flops//2 everywhere). True MACs =
sum(fma_macs); a ReLU has ZERO MACs; a biased Linear(8,16) has 256 (never the
272 that flops//2 fabricates); count_fma_as_tw... |
22814b350b206e6642570f6421bcce6db473b9985b6e0bd23ae208b9e650eb6b | Python | 9,818 | 238 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to... |
1a56774e0a25de1d9cecadba98dd2f035dac2f3780b905bd6968b74046b84abe | Python | 9,820 | 327 | from collections import Counter
from functools import lru_cache, reduce
import itertools
import attr
from cached_property import cached_property
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from permacache import permacache, stable_hash
import torch
import tqdm.auto as tqdm
from sklearn.... |
72ccbd506b220de4fa14663f47c819c4f2c703a50f8f34276e25a0f922041467 | Python | 9,823 | 274 | #################################################################################################################################################################
####################### Creating inputs for Multi-resBind ###################################################################################################... |
5d4138d344fe7c1538b93695510854cf58e9413a5c6b9a4714abdffe9880e9b1 | Python | 9,825 | 323 | """Phase 4b intervention replay-template and output-path tests."""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
from typing import Any
import pytest
import torch
import torchlens as tl
from torchlens.backends.torch.ops import (
_replace_output_tensors_b... |
ec23373eddda0e681afb74c7e5d1f8cc099665f80506840e86991b7cb5380abf | Python | 9,826 | 282 | """
http://incompleteideas.net/MountainCar/MountainCar1.cp
permalink: https://perma.cc/6Z2N-PFWC
"""
import math
from typing import Optional
import numpy as np
import gym
from gym import spaces
from gym.envs.classic_control import utils
from gym.error import DependencyNotInstalled
class MountainCarEnv(gym.Env):
... |
0a9d6b89a2eada3211531dd4924e91a165f625a644ace8761698e22c891b7555 | Python | 9,827 | 226 | import numpy as np
from numpy import array
import random
from gym import spaces
from gym.utils import seeding
class TunlEnv(object):
def __init__(self, delay, seed=1):
"""Trial-unique, delayed nonmatch-to-location (TUNL) task (Talpos et al., 2010)
In each trial/episode, there are 5 stages:
... |
2cdb88c59ff1a6f6b002c6237a4825ae745360cb3fc688b2c81176016c799e8f | Python | 9,833 | 237 | import tvm
from tvm.contrib import graph_runtime
from typing import List
import cv2
import numpy as np
try:
import torch
except ImportError:
torch = None
class PoseEstimationInferenceModel():
def __init__(
self,
model_prefix,
gpu_id,
image_width=1280,
... |
59b6dacd80da3acade90ed2b3b75776b300bca198c9c3b510d004bdd0d4d8a15 | Python | 9,833 | 223 | # 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 ... |
d14d6f576fb14b902db9561c561de615e343ce657ae2d067cd3b4ba0ef95e809 | Python | 9,833 | 208 | """
FitzHugh-Nagumo with the Analytical Jacobian
============================================
The `FitzHugh-Nagumo (FHN) model <http://www.scholarpedia.org/article/FitzHugh-Nagumo_model>`_ is a textbook 2D
reduction of the Hodgkin-Huxley equations. The system has a single cubic non-linearity and is a clean target for
... |
d3ee6c0b158470d33d688aa3f884445531790a7b7350adb5f825edab8ece0e76 | Python | 9,833 | 230 | import argparse
import torch
import torch.backends.cudnn as cudnn
from torchvision import models
from data_aug.contrastive_learning_dataset import ContrastiveLearningDataset
from models.resnet_simclr import ResNetSimCLR
from simclr import SimCLR
import torchvision
import sys
import numpy as np
import os
import yaml
imp... |
5031d21f727794522461bfe851da7d00713a03ba6cf4b87bf8b459f9bd0d97c2 | Python | 9,835 | 240 | import os.path as os_path
import importlib.resources
import traceback
import nanite
from PyQt6 import uic, QtCore, QtWidgets
from PyQt6.QtCore import QStandardPaths
from ..extensions import ExtensionManager, SUPPORTED_FORMATS
class ExtensionErrorWrapper:
def __init__(self, ehash):
self.ehash = ehash
... |
6475007041eb9ed2c08a9900443a8fd0ddb4f61f8c6108c8a6e3463acf7a3b73 | Python | 9,838 | 271 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import d... |
824c40716c26e52a2896fc4bfc66ccfb835f5ff404e966e60336ce10082c6e3b | Python | 9,838 | 223 | # 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 ... |
df011ea8fdf50c913a82820260cc91cddd75524dbc3af239d2557d5f08efc5c1 | Python | 9,841 | 245 | """
.. _tut-source-level:
Tutorial part 2: RSA on source-level MEG data
---------------------------------------------
In this tutorial, we will perform source-level RSA analysis on MEG data.
If you haven't done so, I recommend first completing :ref:`tut-sensor-level`.
While sensor-level RSA is useful to get a first... |
0389c0f7d59441c8942dfd71b21c11fbe27e08ab97e98194990d450857151f93 | Python | 9,843 | 227 | import os, qt, ctk, slicer, sys
current_dir = os.path.dirname(os.path.abspath(__file__))
root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
sys.path.append(root_dir)
from biomedisa_extension.SegmentEditorBiomedisaPrediction.Logic.BiomedisaPredictionParameter import BiomedisaPredictionParameter
fr... |
cc28bfca8fd790fed54c90adf088410cb461ce7c4055b8a5f21977c59c6ec24e | Python | 9,843 | 277 | # 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... |
1de6d16eb598c9546a10572b22d0d426d89436c07f555423bb8d49a07631b87e | Python | 9,844 | 250 | import numpy as np
from skimage.metrics import structural_similarity
from functools import partial
from typing import List, Union, Callable, Tuple
import nway.image_processing_utils as impu
import nway.transforms as tf
class MetaRegistration():
"""Attempts multiple types of registration and chooses the best
o... |
a85a6616a5d6c1011e6632a879efed190560465d0f0fae52891d8598869bf1ae | Python | 9,845 | 235 | #!/usr/bin/env python3
"""
Dx AM — multi-channel ablation / reduced-montage sensitivity (figshare).
Question for clinical deployment: does the tool work with reduced
electrode montages — clinical-screening 10-channel, consumer-grade
4-channel, etc.?
Method:
Reuse Dx AL's per-epoch raw-input capture for figshare. Fo... |
acf5e4f919fe61f446d53589b14dec46d8b173005f323efe11c0f7a7d73d30a2 | Python | 9,845 | 260 | import ast
from os import PathLike
from typing import Sequence
import numpy as np
import pandas as pd
from chemprop.data import MolAtomBondDatapoint
from chemprop.featurizers.molecule import MoleculeFeaturizerRegistry
from chemprop.utils import create_and_call_object, make_mol, parallel_execute
def build_MAB_data_f... |
12317c828b4d339ca279d31caf289bcbdf93507b0acda9b4ec53ac87d1e802d7 | Python | 9,846 | 298 | import abc
import logging
import os
import re
from collections.abc import Callable
from galaxy.datatypes.binary import Binary
from galaxy.datatypes.data import (
get_file_peek,
Text,
)
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.protocols import DatasetProtocol
from galaxy.datat... |
110ebadc4905a29cebb8dadbfed832fce9bed65bcda0828b01af3f0e4f480aa3 | Python | 9,849 | 203 | '''Alignment of two sets of cells (align_cells; see its docstring).
The cells are rasterized into density images at a fine pixel size with a kernel of a fixed
physical width, and LDDMM is run coarse to fine on mean-pooled copies of these images: each
level starts from the affine transform and velocity field of the pre... |
86bb3fb70ea3603610b8330134fb11efb1c6824d7233c847a022f6c03f23e593 | Python | 9,849 | 280 | """Tests for the xcp_d.interfaces.nilearn module."""
import os
import nibabel as nb
import numpy as np
import pandas as pd
from xcp_d.interfaces import nilearn
def test_nilearn_merge(ds001419_data, tmp_path_factory):
"""Test xcp_d.interfaces.nilearn.Merge."""
tmpdir = tmp_path_factory.mktemp('test_nilearn_... |
754dfe1e0790f2740e14ef3247ae50e043cfa92e0d98031ba3cafa16e43c5d58 | Python | 9,850 | 287 | import logging
import galaxy.exceptions
from galaxy import (
model,
security,
)
from galaxy.managers import users
log = logging.getLogger(__name__)
class RBACPermissionFailedException(galaxy.exceptions.InsufficientPermissionsException):
pass
class RBACPermission:
"""
Base class for wrangling/c... |
5122302cd7890670834a36e7c2946c98e17d908cacc4b83f52d81b20ff877c3d | Python | 9,853 | 252 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""
Module defines single :class 'SmartTagsManager'.
Author:
Mateusz Kruszyński <mateusz.kruszynski@gmail.com>
"""
import queue
from typing import Optional
from functools import cmp_to_ke... |
3d92af8378ba72ec13932f895c071dcba0bd22d6180d0202aa4c97160c2c2db0 | Python | 9,855 | 247 | # tests/unit/util/test_util_shapely_aux.py
"""
Unit tests for larvaworld.lib.util.shapely_aux module.
Tests shapely geometry utility functions with synthetic Point objects.
All tests use deterministic geometric data for reproducibility.
"""
import pytest
from shapely import geometry
from larvaworld.lib.util.shapely_... |
0ea98685c98c8423ed63c318b4350b98896e200449c4c49c839607403d9f9d99 | Python | 9,857 | 238 | """
check_leakage.py — quantify evaluation leakage on label-free data.
Generates synthetic EEG in which the labels carry NO information about the
signal, then runs the classification pipeline under controlled and uncontrolled
protocols. Any accuracy above 0.50 is manufactured by the protocol.
Two leakage sources are ... |
af2af3a4ee1045d24084330c2ccab61fd4934ba521ed80e1e72fde614e834b51 | Python | 9,860 | 242 | from __future__ import annotations
"""IO handler for eFEL"""
"""
Copyright (c) 2015, EPFL/Blue Brain Project
This file is part of eFEL <https://github.com/BlueBrain/eFEL>
This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License version 3.0 as... |
bbd1abdc40b58ebb840c737fb5ce60f5693de7aba0a90abfb5f9e5a41ad2b459 | Python | 9,861 | 194 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 26 15:33:40 2022
This distributer contains a special case condition, if user waits more than
1.5 seconds after successfully doing a double or triple blink, the algorithm
won't wait for the next event but sends the double blink event. Since 1.5
seconds is enough... |
f86a224f77ee1fef5d58a2b415b0a055d3b6db4fbcaf1569c14d2ae92125d7f8 | Python | 9,861 | 184 | # -*- coding: utf-8 -*-
import os
import yaml
import argparse
import numpy as np
from utils import check_folder, read_yaml, save_yaml, write, get_subject_name, get_output_name, aggregate_cv, create_maps, fetch_masker, fetch_data, get_nscans
from utils import get_splitter_information, get_compression_information, get_d... |
4ca988699c04b08e99c654b0d377b99f8a817b145c4ef33fe65072987d1d2a90 | Python | 9,864 | 275 | import numpy as np
import pandas as pd
from sklearn import preprocessing
import torch
from sklearn.metrics import confusion_matrix
import operator
import sklearn.metrics
import json
from scDGD.classes import GaussianMixture
import seaborn as sns
import matplotlib.pyplot as plt
import umap
def order_matrix_by_max_per_... |
dc66b68ee4e846f98a59ae36c7055ac3d272bd79a0f3602d746953f7cb3aa9d3 | Python | 9,864 | 277 | '''
load the RBNS data
'''
import csv
import re
import h5py
import urllib
import gzip
from io import StringIO
import requests
import numpy as np
import time
import os
from utils_rbns import create_datapoints
from constants import get_args
from map_protein_name import get_map_protein
rbns_data = 'metadata_rbns.csv'
#... |
bd267fbbf7beb150e7d9e95c394717eee2364ca684f1df71eb6d752eca4d76ad | Python | 9,866 | 314 | """Round-47 hon1_1 immunizer -- generators held on a container SUBCLASS's own attribute.
r45 hon1_1 descends model-held containers by PROTOCOL (every ``Mapping`` keys+values, every non-leaf
``Collection`` elements). But a container SUBCLASS can hold a generator as its OWN ``__dict__`` /
``__slots__`` attribute (``self... |
3bb9792a242eff65ae317904abf03271f1cfdd2c4d23c04ae06ca011eba4617f | Python | 9,867 | 267 | """Embedd Mayavi in Eelbrain
for testing:
src = datasets.get_mne_sample(src='ico', sub=[0])['src']
brain = plot.brain.brain(src.source, mask=False,hemi='lh',views='lat')
"""
from logging import getLogger
from mayavi.core.ui.api import SceneEditor, MlabSceneModel
import numpy as np
from traits.api import HasTraits, I... |
f9bb6e2edd5df7f20b3e618aa8d492c74243657b572d09253208fa7af074de31 | Python | 9,869 | 258 | """SM100 backward with a 64-row KV tile per CTA (FLASH_ATTENTION_BWD_TILE_N=64) against the
default 128-row tile and against FP64 eager.
The 64-row tile puts the accumulators in the M=64 TMEM layout and stages P / dS in SMEM (see
NOTE [M=64 accumulator layout] in flash_bwd_sm100.py); it is the layout the hd256 backwar... |
534e29e19f61a29793df05f80fdf62f20692782af72fb2dfb1ffd973f5b18686 | Python | 9,870 | 288 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Some code is from https://github.com/princeton-vl/pose-ae-train/blob/454d4ba113bbb9775d4dc259ef5e6c07c2ceed54/utils/group.py
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modif... |
c0f71db246f9bada85d670d098f79b2d06306f8e4ae1ba913b1cbf08d205fa85 | Python | 9,870 | 238 | 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,... |
53a781e46a2c72f067e85e8d5694bab45efcb7c08595ce4f6bca90ea4b93478c | Python | 9,874 | 288 | """.. _lightly-detectron-tutorial-6:
.. role:: bash(code)
:language: bash
Tutorial 6: Pre-train a Detectron2 Backbone with Lightly
==========================================================
In this tutorial we show how you can do self-supervised pre-training of a
`Detectron2 <https://github.com/facebookresearch/d... |
f7b0bc93467ff611af99ae8b7b2a0b5804080c95cfcdf517f6078b059d9cb231 | Python | 9,875 | 265 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
This file contains the definition of encoders used in https://arxiv.org/pdf/1705.02364.pdf
"""
import numpy as np
import t... |
bdea20708711a44caf6fbdf30f03415bd5887ca3ea856c24e5aec21a12f37b14 | Python | 9,876 | 234 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.sparse as sp
import seaborn as sns
import sklearn.neighbors
import torch
def Transfer_pytorch_Data(adata):
from torch_geometric.data import Data
G_df = adata.uns["Spatial_Net"].copy()
cells = np.array(adata.o... |
cd270b5a5c67240100b0acefb79e1e352989fec9d8bb949971b39765dba55aa3 | Python | 9,877 | 190 | import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import pgl
from layers import *
class GeomMPNN_2D(nn.Layer):
def __init__(self, node_in_dim, edge_in_dim, rbf_dim, hidden_dim, max_dist, \
num_conv, num_pool, dropout, dropout_pool, activation=F.relu):
super(GeomMP... |
4357d391e2b3d7994eaa97a642e9a97c545b8c941d296b8e225a52e876770802 | Python | 9,879 | 257 | """Observe item 15: track_device_memory CPU-side core (fake-provider legs).
Never-reset counters, signed deltas, high-water ADVANCE semantics, the
None+status grammar replacing every fabricated zero, and the attempted-call
OOM row. GPU cells (real allocator, OOM attribution, pass-observation parity
on device) run on t... |
5ee33bf4d38580bd0048672a3b5e4d86928f57c19818ba6b4b2272142cd90edd | Python | 9,881 | 245 | # Copyright (c) 2023, Tri Dao.
import time
import torch
import pytest
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
from flash_attn.models.gpt import GPTLMHeadModel
from flash_attn.models.btlm import btlm_config_to_gpt2_config, remap_state_dict_hf_btlm
from flash_attn.utils.pretrained impo... |
bc1d9445e9e9aaa0083ea26596fdf5a1440f5c8206c62fc88015fc41367fbc1f | Python | 9,882 | 340 | """OpenPI π0 and π0.5 compact vision-language-action models.
Paper: Black et al. 2024, "π0: A Vision-Language-Action Flow Model for
General Robot Control"; OpenPI also exposes π0-FAST, an autoregressive VLA
using FAST action tokenization.
"""
from __future__ import annotations
import torch
import torch.nn as nn
FLO... |
75955e099acf00a763e77f10738c5875a485533adde12cad50d05415213683f8 | Python | 9,883 | 338 | #!/usr/bin/python
#-*-coding:utf-8-*-
# 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 wi... |
7677188b8f9e137f23db51b5119fa764cd96463a1af1da5cdb7fe59415b2a4c7 | Python | 9,884 | 242 | """Lane A11 neuro fails-open fixes (neuro MEMO item 4 defect slice, D4-D7).
Pins:
- The *_evolution default sweep processes only stimulus-indexed sites: buffer
overwrites and leading-axis mismatches are SKIPPED with one summarized
disclosure (previously a stock resnet18 sweep fabricated 78 buffer
pseudo-RDMs in... |
14429f8587d6bdd12bccf6f4e82841f4870040c0b4b548eab12cd6c8c195e959 | Python | 9,885 | 229 | """Custom BatchNorm implementation"""
# pylint: disable=unused-argument,bad-continuation
import logging
import torch
import torch.distributed as dist
from torch import nn
from torch.autograd.function import Function
from torch.nn import functional as F
from torch.nn import BatchNorm2d
from ..utils.comm import get_worl... |
14e4870501bda47083c0e70a3fbb35431f3b66836f149ccc911ff87f9741fc4c | Python | 9,885 | 283 | """Projective descriptor goldens for the target-anchored reverse solve."""
from __future__ import annotations
from collections.abc import Iterator
from fractions import Fraction
import pytest
import torch
from support.rf_isolation import preserved_rf_registry
from torch import nn
import torchlens as tl
from torchle... |
dac6097908a36ab391c52d38e407b92b5b2585a9bfe39cae50a9f22ba6bf0dc5 | Python | 9,891 | 324 | # coding=utf-8
# Copyright 2025 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... |
1d95a513655d14b8688840814821d8b12f5e1944540d142a7819a5ad8d257723 | Python | 9,892 | 216 | import json
import numpy as np
import os, sys
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 as F
from torch.utils.dat... |
85f0bcd7041a8d1b8e79b2e093510b9d3852d8e656eab2909ae11969046f979f | Python | 9,892 | 249 | from argparse import ArgumentParser
import torch
from unires.struct import settings
from unires.run import preproc
def _preproc(pth, atlas_rigid, common_output, denoising, device, dir_out, fov, label_file,
label_channel_index, label_repeat_index, linear, plot_conv, prefix,
print_info, reg_sc... |
45028b2f0704138e3014e909d5b32bab71d76ed6ce83d344213105604e25f267 | Python | 9,893 | 254 | """
Plot Model Components
=====================
Plotting power spectrum model parameters and components.
"""
###################################################################################################
# Import the SpectralGroupModel object
from specparam import SpectralGroupModel
# Import utilities to manag... |
3ef90066bd090c0f96e9c1ce3890f848fe2eb8eade837c62aa1fb01fd3dc7521 | Python | 9,894 | 211 | import torch
from .base_model import BaseModel
from . import networks
class BiCycleGANModel(BaseModel):
@staticmethod
def modify_commandline_options(parser, is_train=True):
return parser
def __init__(self, opt):
if opt.isTrain:
assert opt.batch_size % 2 == 0 # load two images... |
695edc10e920cceaab6a3f5f0a3b2dbe5502264cf218ff9f6a8a14ec2dfe28d3 | Python | 9,895 | 236 | """
Manipulating Objects
====================
Examples with combining, sub-selecting, dropping, and averaging power spectrum models.
"""
###################################################################################################
#
# As you fit power spectrum models, you may end up with multiple model objects,... |
d79394f65c10ff28ce07b3e505f15edb970b9c806d55466cea22d0cc3ece8c8e | Python | 9,899 | 263 | # -*- coding: UTF-8 -*-
import numpy as np
import math
import random
import operator
import csv
from array import array
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest
from scipy.stats import pearsonr
from sklearn.feature_selection import SelectKBest
from sklear... |
1f4bb3a9423b2de098cbd22fe2b19022331529ea3a83bd389d9d3d96244ff0ca | Python | 9,901 | 247 | #!/usr/bin/env python3
"""
build_trained_model.py — Fit and serialize the canonical trained model.
Produces ``trained_model/trained_model.json`` containing every parameter
needed to apply the trained classifier to a new cohort *without
retraining*. ``deploy_classifier.py`` consumes this artifact.
The trained model h... |
9b815b05fbc9516fe05dc719585f0e5b102c6e1b0470a6b57d734d0a9ff2e0c0 | Python | 9,901 | 277 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in April 2020
@author: Thomas Bonald <bonald@enst.fr>
"""
from typing import Iterable, Optional
import numpy as np
from sknetwork.hierarchy.postprocess import cut_straight
from sknetwork.visualization.colors import STANDARD_COLORS
def get_index(dendrogram, ... |
c33c2ed15638f374ece9b99f144c5d0dbf30b2e726ed01865c6f9e17e70ee10e | Python | 9,902 | 264 | # -*- coding: UTF-8 -*-
import numpy as np
import math
import random
import operator
import csv
from array import array
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest
from scipy.stats import pearsonr
from sklearn.feature_selection import SelectKBest
from sklear... |
6d219d008c477c5a44fa04fb619859f78bad820155534c4a486d652820330a82 | Python | 9,906 | 295 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test MAF layer in tfep.nn.flows.maf.
"""
# ===================================================================... |
f0ecd6e4cf6a97f0f0a6135a3eabdf213cfb8daaf7fbc1d652ebc78d9784c17e | Python | 9,906 | 263 | # -*- coding: UTF-8 -*-
import numpy as np
import math
import random
import operator
import csv
from array import array
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest
from scipy.stats import pearsonr
from sklearn.feature_selection import SelectKBest
from sklear... |
80aa3206db05cadc9c47b0d18f4696096a9e3966cd74442cbaba826c0e2dc5e0 | Python | 9,907 | 239 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 6 18:03:02 2021
@author: Audrey Mazancieux
This script performes several group level "event-related" analyses using
LC time serie that have been extracted from ROI delineated in native
space and then translated in this space.
Prior to this step,... |
5f5b2d1f6616175a614482ddea8cc0617b5fd834221f6a352133f156238a187d | Python | 9,909 | 181 | #!/usr/bin/env python3
import os,sys
import matplotlib
matplotlib.use('Agg')
from pylab import *
from collections import defaultdict
from lefse.lefse import *
import argparse
colors = ['r','g','b','m','c','y','k','w']
def read_params(args):
parser = argparse.ArgumentParser(description='Plot results')
parser... |
8536ed50f3c5704a2efe2809be7f3ef96d4a336f06a004dd50bdc111820b9ebb | Python | 9,913 | 300 | # 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... |
de1a2e06e98ced3ea39693543ac0c56a9882838204362fddf2ac53f62af13514 | Python | 9,916 | 255 | #!/usr/bin/env python
"""Tests for `stlearn` package."""
import unittest
import numpy as np
from numba.typed import List
import stlearn as st
import stlearn.tl.cci.het as het
import stlearn.tl.cci.het_helpers as het_hs
from tests.utils import read_test_data
# Per line - which cells to annotate
CELL_TYPE_ANNOTATION... |
2668d15fe7cbf369fa4009b1782b0e67db58e35eb2f4d50e05eb9e573641d46b | Python | 9,917 | 276 | """Tests for seqpro.rag.to_padded (flat-buffer ragged densify-and-pad)."""
from __future__ import annotations
import awkward as ak
import numpy as np
import pytest
from seqpro.rag import Ragged, lengths_to_offsets
from seqpro.rag import zip as rag_zip
from seqpro.rag._core import Ragged as CoreRagged
from seqpro.rag... |
a50c9af601d582e082c5d61313343d4f89d0089e44d2ffaf878435e9058f93de | Python | 9,918 | 272 | """
Tunnel Utilities API
Provides utilities for tunnel configuration and URL management.
"""
import os
from typing import Any
from .base import BaseAPI, error_decorator
class TunnelUtils(BaseAPI):
"""Tunnel utilities for managing tunnel configuration and URLs."""
def __init__(self, server_instance):
... |
8e786db4e00bafaf45fe5cc1820f87c16ad979f323f743c266ce63ff0712a800 | Python | 9,919 | 318 | """Digest primitives, built once, used everywhere (extract memo item 1).
Two primitives with PINNED algorithm ids + versions (they are manifest fields
any third party can recompute from):
* :func:`merkle_digest` — the threaded Merkle CRYPTO digest (extract D6):
per-entry leaf digests hashed in a thread pool, folded... |
ca164ad81c86ab3c109c3d76c81901ab4c58fe47163f420d2c63d30b589f0406 | Python | 9,919 | 251 | from model import esrt_common
import torch
import torch.nn as nn
import torch.nn.functional as F
from thop import profile
from util.non import NONLocalBlock2D
from util.tools import extract_image_patches,\
reduce_mean, reduce_sum, same_padding, reverse_patches
from util.transformer import drop_path, DropPath, Pat... |
7cb4243b5492a8d7d8eac7e7881ab9898f39a2438599bb9f03f8cb5e79f2a07e | Python | 9,921 | 254 | """Tests for the CouchbaseBackend."""
import json
from datetime import timedelta
from unittest.mock import MagicMock, Mock, patch, sentinel
import pytest
from celery import states
from celery.app import backends
from celery.backends import couchbase as module
from celery.backends.couchbase import CouchbaseBackend
fro... |
aaa0a3aa270c44be01a0767bea904e6132d931bce9de4ba5e6f94db949ef1b2a | Python | 9,925 | 270 | """EPISODE export tests (memo D11-D14, B7) on a real tiny episode capture.
The step-membership join runs through the persisted module-call ops list
with the stack cross-check asserted equal; drivers window-assign exactly
once; boundary proxies mint reserved-prefix step-stable ids; the budget
binds per-step graphs with... |
cf03018dbee9509628c2ebaa49837baab61eadac6dd21547128d409429f65e30 | Python | 9,925 | 252 | """
Generic, data-model-agnostic canvas object placement helpers.
Extracted from the click/select/hit-test/table loop originally built for
`environment_builder_app.py`'s environment-object placement (food sources,
borders), so it can be reused for other click-to-place canvas interactions
(e.g. Single Experiment's larv... |
09eccc36d896f48eb1f51cc97449a82074fc79cea6ad5038d826700274174e05 | Python | 9,926 | 258 | # Copyright 2025 Google.
#
# 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 applicable law or agreed to in writing, soft... |
40912ce755106df8948fb7fce729299e2e53930f0e9dd72d62c6d20475465600 | Python | 9,927 | 322 | from typing import Dict
import argparse
from pathlib import Path
import numpy as np
import numpy.typing as npt
import pandas as pd
import torch
from torch_geometric.loader import DataLoader
import torch_geometric.transforms as T
from coeffnet.predata.interpolator import GenerateParametersInterpolator
from coeffne... |
a3f5db946a30d86402e566789bf0af9525dd1c8975692680a0328bf12b633531 | Python | 9,929 | 282 | import logging
from tqdm import tqdm
import numpy as np
import random
import dgl
from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling
from dgl.dataloading import GraphDataLoader
import torch
from torch.utils.data.sampler import SubsetRandomSampler
from sklearn.model_selection import StratifiedKFold, Gr... |
805d449ac749f87a50f3a3114385710f0f926ff45521275000b74d9e80721e5c | Python | 9,930 | 218 | # Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
class IndexFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, input, indices)... |
10172e203bf394ef647d0b99a322cb95a28759a4126c363ceee41caea092451c | Python | 9,931 | 259 | """
Common code across figures.
"""
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from jaratoolbox import extraplots
from jaratoolbox import extrastats
import studyutils
import scipy
import pandas as pd
import seaborn as sns
matplotlib.rcParams['font.family'] = 'Helvetica'
matplotlib... |
163f56e1b3dc52690e37106ce479e7dbae56e84ed6e3c339a4a90c160cbd54a9 | Python | 9,931 | 189 | '''
Uses the predicted scene graphs to generate a surgery report.
'''
import json
from pathlib import Path
from typing import List, Optional
from helpers.inm_helpers.configurations import TAKE_SPLIT
def augment_scene_graphs_with_roles(rels, key, role_predictions):
key = key.rsplit('_', 1)[0]
if key in role_p... |
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