# 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
# 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 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
```