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https://github.com/Richard-Sti/csiborgtools.git
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ee222cd010
* Update nb * Update script * Update script * Rename * Update script * Update script * Remove warning * Ignore minors when extracting MAH * Fix paths bug * Move notebooks * Move files * Rename and delete things * Rename file * Move file * Rename things * Remove old print statement * Add basic MAH plot * Add random MAH path * Output snapshot numbers * Add MAH random extraction * Fix redshift bug * Edit script * Add extracting random MAH * Little updates * Add CB2 redshift * Add some caching * Add diagnostic plots * Add caching * Minor updates * Update nb * Update notebook * Update script * Add Sorce randoms * Add CB2 varysmall * Update nb * Update nb * Update nb * Use catalogue HMF * Move definition of radec2galactic * Update nb * Update import * Update import * Add galatic coords to catalogues * Update nb
132 lines
4.1 KiB
Python
132 lines
4.1 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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"""
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Various utility functions.
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"""
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import numpy
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from scipy.special import erf
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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 nan_weighted_average(arr, weights=None, axis=None):
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if weights is None:
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weights = numpy.ones_like(arr)
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valid_entries = ~numpy.isnan(arr)
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# Set NaN entries in arr to 0 for computation
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arr = numpy.where(valid_entries, arr, 0)
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# Set weights of NaN entries to 0
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weights = numpy.where(valid_entries, weights, 0)
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# Compute the weighted sum and the sum of weights along the axis
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weighted_sum = numpy.sum(arr * weights, axis=axis)
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sum_weights = numpy.sum(weights, axis=axis)
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return weighted_sum / sum_weights
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def nan_weighted_std(arr, weights=None, axis=None, ddof=0):
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if weights is None:
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weights = numpy.ones_like(arr)
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valid_entries = ~numpy.isnan(arr)
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# Set NaN entries in arr to 0 for computation
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arr = numpy.where(valid_entries, arr, 0)
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# Set weights of NaN entries to 0
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weights = numpy.where(valid_entries, weights, 0)
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# Calculate weighted mean
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weighted_mean = numpy.sum(
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arr * weights, axis=axis) / numpy.sum(weights, axis=axis)
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# Calculate the weighted variance
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variance = numpy.sum(
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weights * (arr - numpy.expand_dims(weighted_mean, axis))**2, axis=axis)
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variance /= numpy.sum(weights, axis=axis) - ddof
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return numpy.sqrt(variance)
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def compute_error_bars(x, y, xbins, sigma):
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bin_indices = numpy.digitize(x, xbins)
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y_medians = numpy.array([numpy.median(y[bin_indices == i])
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for i in range(1, len(xbins))])
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lower_pct = 100 * 0.5 * (1 - erf(sigma / numpy.sqrt(2)))
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upper_pct = 100 - lower_pct
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y_lower = numpy.full(len(y_medians), numpy.nan)
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y_upper = numpy.full(len(y_medians), numpy.nan)
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for i in range(len(y_medians)):
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if numpy.sum(bin_indices == i + 1) == 0:
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continue
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y_lower[i] = numpy.percentile(y[bin_indices == i + 1], lower_pct)
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y_upper[i] = numpy.percentile(y[bin_indices == i + 1], upper_pct)
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yerr = (y_medians - numpy.array(y_lower), numpy.array(y_upper) - y_medians)
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return y_medians, yerr
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def normalize_hexbin(hb):
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hexagon_counts = hb.get_array()
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normalized_counts = hexagon_counts / hexagon_counts.sum()
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hb.set_array(normalized_counts)
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hb.set_clim(normalized_counts.min(), normalized_counts.max())
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