Source code for sherpa.plot.utils

#  Copyright (C) 2021, 2023
#  Smithsonian Astrophysical Observatory
#  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 3 of the License, or
#  (at your option) any later version.
#  This program is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  GNU General Public License for more details.
#  You should have received a copy of the GNU General Public License along
#  with this program; if not, write to the Free Software Foundation, Inc.,
#  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
'''Helper functions for plotting

import numpy as np

__all__ = ('histogram_line', )

[docs] def histogram_line(xlo, xhi, y): '''Manually create x, y arrays to replicate a histogram Draw the data as a histogram, manually creating the lines from the low to high edge of each bin. In the case on non-consequtive bins, the line will have some nan values, so that disjoint lines are drawn. In matplotlib, an alternative would be to create RectanglePatches, one for each bin, but I don't want each bin to go down to 0. I do not find the existing drawstyle options to be sufficient. Parameters ---------- xlo, xhi : array Lower and upper bin boundaries. Typically, ``xlo`` will contain the lower boundary and ``xhi`` the upper boundary, but this function can deal with situations where that is reversed. Both arrays have to be monotonically increasing or decreasing. y : array Dependent values for each histrogram bin_hi Returns ------- x, y2 : array x and y arrays for plotting the histogram line. ''' if (len(xlo) != len(xhi)) or (len(y) != len(xlo)): raise ValueError('All input arrays have to have the same length.') # Deal with reversed order. Can happen when converting from energy # to wavelength, or if input PHA is not ordered in increasing energy. # But if both are happening at the same time, need to switch twice, which # is a no-op. So, we get to use the elusive Python XOR operator. if (xlo[0] > xhi[0]) ^ (xhi[0] > xhi[-1]): xlo, xhi = xhi, xlo # Combine the edges and replicate the y array. This ensures that x # has the "correct" type (e.g. if xlo is int but xhi is float the # result will be float), which was the cause of issue #1838. # x = np.vstack((xlo, xhi)).T.flatten() y2 = np.vstack((y, y)).T.flatten() # Where are the edges? idxs, = np.where(xhi[:-1] != xlo[1:]) nedges = idxs.size if nedges == 0: return x, y2 # nan values need to be added where the edges are in the # duplicated array nanidxs = 2 * (idxs + 1) nans = [np.nan] * nedges # ensure the arrays are floats so we can add nan values # x = np.insert(x.astype(np.float64), nanidxs, nans) y2 = np.insert(y2.astype(np.float64), nanidxs, nans) return x, y2