mirror of
https://github.com/Richard-Sti/csiborgtools.git
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28e93e917f
* Add a new plot * Add a binned trend * Fix bug * Improve plot further * Add new plotting * add max overlap * edit get_overlap * Add max overlap plot * Update plot * Add max overlap key * add max dist flag * Improve plotting
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
# Copyright (C) 2023 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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import numpy
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from scipy.stats import binned_statistic
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dpi = 600
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fout = "../plots/"
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mplstyle = ["science"]
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def latex_float(*floats, n=2):
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"""
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Convert a float or a list of floats to a LaTeX string(s). Taken from [1].
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Parameters
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----------
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floats : float or list of floats
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The float(s) to be converted.
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n : int, optional
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The number of significant figures to be used in the LaTeX string.
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Returns
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-------
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latex_floats : str or list of str
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The LaTeX string(s) representing the float(s).
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References
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----------
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[1] https://stackoverflow.com/questions/13490292/format-number-using-latex-notation-in-python # noqa
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"""
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latex_floats = [None] * len(floats)
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for i, f in enumerate(floats):
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float_str = "{0:.{1}g}".format(f, n)
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if "e" in float_str:
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base, exponent = float_str.split("e")
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latex_floats[i] = r"{0} \times 10^{{{1}}}".format(base,
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int(exponent))
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else:
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latex_floats[i] = float_str
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if len(floats) == 1:
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return latex_floats[0]
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return latex_floats
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def binned_trend(x, y, weights, bins):
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"""
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Calculate the weighted mean and standard deviation of `y` in bins of `x`.
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Parameters
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----------
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x : 1-dimensional array
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The x-coordinates of the data points.
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y : 1-dimensional array
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The y-coordinates of the data points.
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weights : 1-dimensional array
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The weights of the data points.
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bins : 1-dimensional array
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The bin edges.
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Returns
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-------
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stat_x : 1-dimensional array
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The x-coordinates of the binned data points.
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stat_mu : 1-dimensional array
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The weighted mean of `y` in bins of `x`.
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stat_std : 1-dimensional array
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The weighted standard deviation of `y` in bins of `x`.
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"""
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stat_mu, __, __ = binned_statistic(x, y * weights, bins=bins,
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statistic="sum")
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stat_std, __, __ = binned_statistic(x, y * weights, bins=bins,
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statistic=numpy.var)
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stat_w, __, __ = binned_statistic(x, weights, bins=bins, statistic="sum")
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stat_x = (bins[1:] + bins[:-1]) / 2
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stat_mu /= stat_w
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stat_std /= stat_w
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stat_std = numpy.sqrt(stat_std)
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return stat_x, stat_mu, stat_std
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