Mercurial > octave
view scripts/plot/draw/private/__calc_isovalue_from_data__.m @ 31220:5b021ecc8bfe stable
pie3: Fix "Too many input" args error.
* __pie__.m (update_text_pos): Change prototype to match the expected number of
input arguments.
author | Pantxo Diribarne <pantxo.diribarne@gmail.com> |
---|---|
date | Wed, 31 Aug 2022 22:01:39 +0200 |
parents | 796f54d4ddbf |
children | 597f3ee61a48 |
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######################################################################## ## ## Copyright (C) 2016-2022 The Octave Project Developers ## ## See the file COPYRIGHT.md in the top-level directory of this ## distribution or <https://octave.org/copyright/>. ## ## This file is part of Octave. ## ## Octave is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## Octave is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with Octave; see the file COPYING. If not, see ## <https://www.gnu.org/licenses/>. ## ######################################################################## ## Undocumented internal function. ## -*- texinfo -*- ## @deftypefn {} {@var{isoval} =} __calc_isovalue_from_data__ (@var{data}) ## Calculate a @nospell{"good"} iso value from histogram of data. ## @end deftypefn ## called from isocaps, isosurface function isoval = __calc_isovalue_from_data__ (data) ## use a maximum of 10,000-20,000 samples to limit runtime of hist step = 1; ndata = numel (data); if (ndata > 20_000) step = floor (ndata / 10_000); data = data(1:step:end); ndata = numel (data); endif num_bins = 100; [bin_count, bin_centers] = hist (data(:), num_bins); ## if one of the first two bins contains more than 10 times the count as ## compared to equally distributed data, remove both (zero-padded + noise) if (any (bin_count(1:2) > 10 * (ndata / num_bins))) bin_count(1:2) = []; bin_centers(1:2) = []; endif ## if bins have low counts, remove them (but keep them if we would lose ## more than 90% of bins) bins_to_remove = find (bin_count < max (bin_count)/50); if (length (bins_to_remove) < .9 * num_bins) bin_centers(bins_to_remove) = []; endif ## select middle bar of histogram with previous conditions isoval = bin_centers(floor (numel (bin_centers) / 2)); endfunction