#
# Copyright (C) 2010, 2015, 2017, 2019 - 2024
# 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.
#
import io
import logging
from collections import ChainMap
from functools import partialmethod
import numpy
from matplotlib import pyplot as plt
from sherpa.utils.err import ArgumentErr, NotImplementedErr
from sherpa.utils import formatting
from sherpa.plot.utils import histogram_line
from sherpa.plot.backends import BasicBackend
from sherpa.plot.backends import kwargs_doc as orig_kwargs_doc
from sherpa.plot.backend_utils import (translate_args,
add_kwargs_to_doc)
__all__ = ['PylabBackend']
logger = logging.getLogger(__name__)
# Most of the kwargs docstrings are still true, but some can be updated
# because matplotlib does support a wider range than just the
# set of Sherpa backend-independent options.
updated_kwarg_docs = {
'color': ['str or tuple', 'Any matplotlib color'],
'linecolor': ['str or tuple', 'Any matplotlib color'],
'marker': ['str',
'''"None" (as a string, no marker shown), "" (empty string, no marker shown),
or any matplotlib marker, e.g. any of `os+v` or others
(see matplotlib documentation).'''],
'linestyle': ['str',
'''``'noline'``,
``'None'`` (as string, same as ``'noline'``),
``'solid'``, ``'dot'``, ``'dash'``, ``'dashdot'``, ``'-'`` (solid
line), ``':'`` (dotted), ``'--'`` (dashed), ``'-.'`` (dashdot),
``''`` (empty string, no line shown), `None` (default - usually
solid line) or any other matplotlib linestyle.'''],
'drawstyle': ['str', 'matplolib drawstyle'],
}
kwargs_doc = ChainMap(updated_kwarg_docs, orig_kwargs_doc)
[docs]
class PylabBackend(BasicBackend):
"""sherpa plotting backend using matplotlib"""
name = 'pylab'
'''Alternative string names for plotting backend.
This differs from the class name, because external code such
as ciao_contrib scripts might depend on this name being present.
'''
translate_dict = {'linestyle': {
'noline': ' ',
'solid': '-',
'dot': ':',
'dash': '--',
'dashdot': '-.',
'None': ' ', },
'label': {None: '_nolegend_'},
}
'''Dict of keyword arguments that need to be translated for this backend.
The keys in this dict are keyword arguments(e.g. ``'markerfacecolor'``)
and the values are one of the following:
- A dict where the keys are the backend-independent values and the values
are the values expressed for this backend. For values not listed in the
dict, no translation is done.
- A callable. The callable translation function is called with any argument
given and allows the backend arbitrary translations. It should, at the
very least, accept all backend independent values for this parameter
without error.
Example:
>>> translate_dict = {'markerfacecolor': {'k': (0., 0., 0.)},
... 'alpha': lambda a: 256 * a}
This translates the color 'k' to tuple of RGB values and alpha values
to a number between 0 and 256.
'''
[docs]
def colorlist(self, n):
'''Generate the list of n colors for use in multi-line plots.
Generally, the color will be ordered in some way and do not repeat or
do so only after a large number of colors.
Different backends might generate different lists of colors.
Parameters
----------
n : int
Number of colors requested
Returns
-------
colors : list
list of color specifiers
'''
return plt.cm.inferno(numpy.linspace(0, 1, n))
[docs]
def clear_window(self):
"""Clear default pyplot figure."""
plt.clf()
def __exit__(self, exec_type, value, traceback):
plt.draw()
return False
[docs]
def setup_axes(self, overplot, clearwindow):
"""Return the axes object, creating it if necessary.
Parameters
----------
overplot : bool
clearwindow : bool
Returns
-------
axis
The matplotlib axes object.
"""
# Do we need to clear the window?
if not overplot and clearwindow:
self.clear_window()
return plt.gca()
[docs]
@add_kwargs_to_doc(kwargs_doc)
def setup_plot(self, axes, title=None, xlabel=None, ylabel=None,
xlog=False, ylog=False):
"""Basic plot setup.
Parameters
----------
axes
The plot axes (output of setup_axes).
{kwargs}
"""
axes.set_xscale('log' if xlog else 'linear')
axes.set_yscale('log' if ylog else 'linear')
axes.set_title(title)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
[docs]
def find_zorder(self, axes):
"""Try to come up with a good zorder value
Parameters
----------
axes
The plot axes
Returns
-------
zorder : float or None
The estimated zorder value.
Notes
-----
The following is from https://github.com/sherpa/sherpa/issues/662
which is circa matplotlib 3. The issue is how to ensure that
a plot with multiple data values (e.g. a fit plot with
data+error bars and model, and one with multiple fits),
are drawn "sensibly" so that the user can make out the model.
For this we really want the zorder of plot items to be determined
by the order they are added. However, this appears to be complicated
by the fact that the errorbar command creates multiple objects
(for the point, error bars, and I believe caps), and not just
a Line2D object. Once all the plot items are concatenated and
sorted to ensure a consistent ordering - as discussed in
https://github.com/matplotlib/matplotlib/issues/1622#issuecomment-142469866 -
we lose a "nice" order in Sherpa (for our cases it does not
seem to help that the error bar adds in a zorder Line2D of 2.1
as compared to 2, which means that the point for the error bar
is drawn on top of the error bar, but also above other lines
added to the plot.
One option would be to evaluate the current plot to find out
what the minimum zorder is, and explicitly set larger
values. Note that the default zindex values appear to be 2 and
2.1 from plot/errorbar. This should work for the case of
plot something
overplot something
but may fall apart as soon as users add their own features
to the visualization, but that may be acceptable.
"""
# Is this a sufficient set of objects?
#
objects = axes.lines + axes.collections
# could do a list comprehension, but want to catch
# AttributeErrors
zs = []
for o in objects:
try:
zo = o.get_zorder()
except AttributeError:
continue
zs.append(zo)
if len(zs) > 0:
# Add a small offset for this set of data
return max(zs) + 0.1
return None
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def histo(self, xlo, xhi, y, *,
yerr=None, title=None,
xlabel=None, ylabel=None,
overplot=False, clearwindow=True,
xerrorbars=False,
yerrorbars=False,
ecolor=None,
capsize=None,
barsabove=False,
xlog=False,
ylog=False,
linestyle='solid',
drawstyle='default',
color=None,
alpha=None,
marker='None',
markerfacecolor=None,
markersize=None,
label=None,
linewidth=None,
linecolor=None,
):
"""Draw histogram data.
The histogram is drawn as horizontal lines connecting the
start and end points of each bin, with vertical lines connecting
consecutive bins. Non-consecutive bins are drawn with a
(NaN, NaN) between them so no line is drawn connecting them.
Points are drawn at the middle of the bin, along with any
error values.
Note that the linecolor is not used, and is only included
to support old code that may have set this option (use `color` instead).
Parameters
----------
x0 : array-like or scalar number
lower bin boundary values
x1 : array-like or scalar number
upper bin boundary values
y : array-like or scalar number
y values, same dimension as `x0`.
{kwargs}
"""
if linecolor is not None:
logger.warning(
"The linecolor attribute ({}) is unused.".format(linecolor))
x, y2 = histogram_line(xlo, xhi, y)
# Note: this handles clearing the plot if needed.
#
objs = self.plot(x, y2, yerr=None, xerr=None,
title=title, xlabel=xlabel, ylabel=ylabel,
overplot=overplot, clearwindow=clearwindow,
xerrorbars=False, yerrorbars=False,
label=label,
ecolor=ecolor, capsize=capsize, barsabove=barsabove,
xlog=xlog, ylog=ylog,
linestyle=linestyle, linewidth=linewidth,
drawstyle=drawstyle,
color=color, marker=None, alpha=alpha,
)
# Draw points and error bars at the mid-point of the bins.
# Use the color used for the data plot: should it
# also be used for marker[face]color and ecolor?
#
# NOTE: this ignores any xerr parameter.
#
xmid = 0.5 * (xlo + xhi)
xerr = (xhi - xlo) / 2 if xerrorbars else None
yerr = yerr if yerrorbars else None
try:
color = objs[0].get_color()
except AttributeError:
pass
axes = plt.gca()
zorder = self.find_zorder(axes)
# Do not draw a line connecting the points
#
# Unlike plot, using errorbar for both cases.
#
axes.errorbar(xmid, y, yerr, xerr,
color=color,
alpha=alpha,
linestyle='',
marker=marker,
markersize=markersize,
markerfacecolor=markerfacecolor,
ecolor=ecolor,
capsize=capsize,
barsabove=barsabove,
linewidth=linewidth,
# We do not want two labels for the same dataset
# TODO: Do we want errorbars in the label?
# Currently, we gives a line with no marker
# label=label,
zorder=zorder)
handles, _ = axes.get_legend_handles_labels()
if len(handles) > 0:
axes.legend()
# Note that this is an internal method that is not wrapped in
# @ translate_args
# This is called from methods like plot, histo, etc.
# so the arguments passed to this method are already translated and we do
# not want to apply the translation a second time.
def _set_line(self, line, linecolor=None, linestyle=None, linewidth=None):
"""Apply the line attributes, if set.
Parameters
----------
line : matplotlib.lines.Line2D
The line to change
linecolor, linestyle, linewidth : optional
The attribute value or None.
"""
def setf(label, newval):
if newval is None:
return
func = getattr(line, f'set_{label}')
func(newval)
setf('color', linecolor)
setf('linestyle', linestyle)
setf('linewidth', linewidth)
# There is no support for alpha in the Plot.vline class
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def vline(self, x, *,
ymin=0, ymax=1,
linecolor=None,
linestyle=None,
linewidth=None,
overplot=False, clearwindow=True):
"""Draw a vertical line
Parameters
----------
x : float
x position of the vertical line in data units
{kwargs}
"""
axes = self.setup_axes(overplot, clearwindow)
line = axes.axvline(x, ymin, ymax)
self._set_line(line, linecolor=linecolor, linestyle=linestyle,
linewidth=linewidth)
# There is no support for alpha in the Plot.hline class
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def hline(self, y, *,
xmin=0, xmax=1,
linecolor=None,
linestyle=None,
linewidth=None,
overplot=False, clearwindow=True):
"""Draw a horizontal line
Parameters
----------
y : float
x position of the vertical line in data units
{kwargs}
"""
axes = self.setup_axes(overplot, clearwindow)
line = axes.axhline(y, xmin, xmax)
self._set_line(line, linecolor=linecolor, linestyle=linestyle,
linewidth=linewidth)
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def plot(self, x, y, *,
yerr=None, xerr=None, title=None,
xlabel=None, ylabel=None,
overplot=False, clearwindow=True,
xerrorbars=False,
yerrorbars=False,
ecolor=None,
capsize=None,
barsabove=False,
xlog=False,
ylog=False,
linestyle='solid',
drawstyle='default',
color=None,
marker='None',
markerfacecolor=None,
markersize=None,
alpha=None,
label=None,
linewidth=None,
linecolor=None,
xaxis=None, ratioline=None,
):
"""Draw x, y data.
This method combines a number of different ways to draw x/y data:
- a line connecting the points
- scatter plot of symbols
- errorbars
All three of them can be used together (symbols with errorbars connected
by a line), but it is also possible to use only one or two of them. By
default, a line is shown (``linestyle='solid'``), but marker and error
bars are not (``marker='None'`` and ``xerrorbars=False`` as well as
``yerrorbars=False``).
Note that the linecolor is not used, and is only included
to support old code that may have set this option (use `color` instead).
Parameters
----------
x : array-like or scalar number
x values
y : array-like or scalar number
y values, same dimension as `x`.
{kwargs}
ratioline, xaxis : None
These parameters are deprecated and not used any longer.
"""
if xaxis is not None:
logger.warning('Keyword "xaxis" is deprecated and has no effect. xaxis are always drawn for delta_xxx plots.')
if ratioline is not None:
logger.warning('Keyword "ratioline" is deprecated and has no effect. Ratio lines are always drawn for ratio plots.')
axes = self.setup_axes(overplot, clearwindow)
if linecolor is not None:
logger.warning(
f"The linecolor attribute ({linecolor}) is unused.")
# Set up the axes
if not overplot:
self.setup_plot(axes, title, xlabel, ylabel, xlog=xlog, ylog=ylog)
# See the discussion in find_zorder
zorder = None
if overplot:
zorder = self.find_zorder(axes)
# Rely on color-cycling to work for both the "no errorbar" and
# "errorbar" case.
#
# TODO: do we really need this, or can we just use errorbar
# for both cases?
#
if xerrorbars or yerrorbars:
if markerfacecolor is None:
markerfacecolor = color
xerr = xerr if xerrorbars else None
yerr = yerr if yerrorbars else None
obj = axes.errorbar(x, y, yerr, xerr,
label=label,
color=color,
linestyle=linestyle,
linewidth=linewidth,
marker=marker,
markersize=markersize,
markerfacecolor=markerfacecolor,
alpha=alpha,
ecolor=ecolor,
capsize=capsize,
barsabove=barsabove,
zorder=zorder)
else:
obj = axes.plot(x, y,
color=color,
alpha=alpha,
linestyle=linestyle,
linewidth=linewidth,
label=label,
drawstyle=drawstyle,
marker=marker,
markersize=markersize,
markerfacecolor=markerfacecolor,
zorder=zorder)
handles, _ = axes.get_legend_handles_labels()
if len(handles) > 0:
axes.legend()
return obj
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def contour(self, x0, x1, y, *,
levels=None, title=None,
xlabel=None, ylabel=None,
overcontour=False, clearwindow=True,
xlog=False,
ylog=False,
alpha=None,
linewidths=None,
linestyles='solid',
colors=None,
label=None,
):
"""Draw 2D contour data.
Parameters
----------
x0 : array-like
independent axis in the first dimenation (on regular grid, flattened)
x1 : array-like
independent axis in the second dimenation (on regular grid, flattened)
y : array-like
dependent axis (i.e. image values) (on regular grid, flattened)
{kwargs}
"""
x0 = numpy.unique(x0)
x1 = numpy.unique(x1)
y = numpy.asarray(y)
if x0.size * x1.size != y.size:
raise NotImplementedErr('contourgrids')
y = y.reshape(x1.size, x0.size)
axes = self.setup_axes(overcontour, clearwindow)
# Set up the axes
if not overcontour:
self.setup_plot(axes, title, xlabel, ylabel, xlog=xlog, ylog=ylog)
if levels is None:
axes.contour(x0, x1, y, alpha=alpha,
colors=colors,
linewidths=linewidths,
linestyles=linestyles)
else:
axes.contour(x0, x1, y, levels, alpha=alpha,
colors=colors,
linewidths=linewidths,
linestyles=linestyles)
handles, _ = axes.get_legend_handles_labels()
if len(handles) > 0:
axes.legend()
[docs]
@add_kwargs_to_doc(kwargs_doc)
@translate_args
def image(self, x0, x1, y, *,
aspect='auto',
title=None, xlabel=None, ylabel=None,
clearwindow=True,
overplot=False,
**kwargs):
"""Draw 2D image data.
.. warning::
This function is a non-functional dummy. The documentation is provided
as a template only.
Parameters
----------
x0 : array-like
independent axis in the first dimension
x1 : array-like
independent axis in the second dimension
y : array-like, with shape (len(x0), len(x1))
dependent axis (i.e. image values) in 2D
with shape (len(x0), len(x1))
{kwargs}
"""
axes = self.setup_axes(overplot, clearwindow)
# Set up the axes
if not overplot:
self.setup_plot(axes, title, xlabel, ylabel)
extent = (x0[0], x0[-1], x1[0], x1[-1])
im = axes.imshow(y, origin='lower', extent=extent, aspect=aspect)
# TODO: This should be optional and have parameters.
# but, for now, it's only used in _repr_html_ for DataIMG,
# so can hardcode.
plt.colorbar(im, ax=axes)
[docs]
def set_subplot(self, row, col, nrows, ncols, clearaxes=True,
left=None,
right=None,
bottom=None,
top=None,
wspace=0.3,
hspace=0.4):
"""Select a plot space in a grid of plots or create new grid
This method adds a new subplot in a grid of plots.
Parameters
----------
row, col : int
index (starting at 0) of a subplot in a grid of plots
nrows, ncols : int
Number of rows and column in the plot grid
clearaxes : bool
If True, clear entire plotting area before adding the new
subplot.
"""
plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top,
wspace=wspace, hspace=hspace)
# historically there have been issues with numpy integers here
nrows = int(nrows)
ncols = int(ncols)
num = int(row) * ncols + int(col) + 1
plt.subplot(nrows, ncols, num)
if clearaxes:
plt.cla()
[docs]
def set_jointplot(self, row, col, nrows, ncols, create=True,
top=0, ratio=2):
"""Move to the plot, creating them if necessary.
Parameters
----------
row : int
The row number, starting from 0.
col : int
The column number, starting from 0.
nrows : int
The number of rows.
ncols : int
The number of columns.
create : bool, optional
If True then create the plots
top : int
The row that is set to the ratio height, numbered from 0.
ratio : float
The ratio of the height of row number top to the other
rows.
"""
if (row < 0) or (row >= nrows):
emsg = f'Invalid row number {row} (nrows={nrows})'
raise ArgumentErr(emsg)
if (col < 0) or (col >= ncols):
emsg = f'Invalid column number {col} (ncols={ncols})'
raise ArgumentErr(emsg)
plotnum = row * ncols + col
if create:
# We only change the vertical spacing
ratios = [1] * nrows
ratios[top] = ratio
gs = {'height_ratios': ratios}
fig, axes = plt.subplots(nrows, ncols, sharex=True, num=1,
gridspec_kw=gs)
fig.subplots_adjust(hspace=0.05)
# Change all but the bottom row. By setting sharex
# this is probably unneeded as get_xticklabels is empty.
#
if axes.ndim == 2:
axes = axes[:-1, :].flatten()
else:
axes = axes[:-1]
plt.setp([a.get_xticklabels() for a in axes[:-1]],
visible=False)
try:
ax = plt.gcf().axes[plotnum]
except IndexError:
emsg = f"Unable to find a plot with row={row} col={col}"
raise ArgumentErr(emsg) from None
plt.sca(ax)
[docs]
def set_title(self, title: str) -> None:
"""Change the display title.
Parameters
----------
title : str
The title text to use.
"""
plt.title(title)
# HTML representation as SVG plots
[docs]
def as_svg(self, func):
"""Create HTML representation of a plot
The output is a SVG representation of the data, as a HTML
svg element, or a single div pointing out that the
plot object has not been prepared.
Parameters
----------
func : function
The function, which takes no arguments, which will create the
plot. It creates and returns the Figure.
Returns
-------
plot : str or None
The HTML, or None if there was an error (e.g. prepare not
called).
"""
svg = io.StringIO()
try:
fig = func()
fig.savefig(svg, format='svg')
plt.close(fig)
except Exception as e:
logger.debug("Unable to create SVG plot: %s", str(e))
return None
# strip out the leading text so this can be used in-line
svg = svg.getvalue()
idx = svg.find('<svg ')
if idx == -1:
logger.debug("SVG output does not contain '<svg ': %s", svg)
return None
return svg[idx:]
[docs]
def as_html_plot_or_contour(self, data, summary=None, func="plot"):
"""Create HTML representation of a contour
The output is a SVG representation of the data, as a HTML
svg element.
Parameters
----------
data : Contour instance
The contour object to display. It has already had its prepare
method called.
summary : str or None, optional
The summary of the detail. If not set then the data type
name is used.
func : str, optional
The function to call on the data object to make the plot.
Returns
-------
plot : str or None
The HTML, or None if there was an error (e.g. prepare not
called).
"""
def plotfunc():
fig = plt.figure()
getattr(data, func)()
return fig
svg = self.as_svg(plotfunc)
if svg is None:
return None
if summary is None:
summary = type(data).__name__
ls = [formatting.html_svg(svg, summary)]
return formatting.html_from_sections(data, ls)
as_html_plot = partialmethod(as_html_plot_or_contour, func="plot")
as_html_contour = partialmethod(as_html_plot_or_contour, func="contour")
as_html_image = as_html_plot
as_html_histogram = as_html_plot
as_html_pdf = as_html_plot
as_html_cdf = as_html_plot
as_html_lr = as_html_plot
as_html_data = as_html_plot
as_html_datacontour = as_html_contour
as_html_model = as_html_plot
as_html_modelcontour = as_html_contour
as_html_fit = as_html_plot
as_html_fitcontour = as_html_contour
as_html_contour1d = as_html_plot
as_html_contour2d = as_html_contour
# Needed for datastack plotting wrapper
[docs]
def initialize_plot(self, dataset, ids):
"""Create the plot window or figure for the given dataset.
Parameters
----------
dataset : str or int
The dataset.
ids : array_like
The identifier array from the DataStack object.
See Also
--------
select_plot
"""
plt.figure(ids.index(dataset['id']) + 1)
[docs]
def select_plot(self, dataset, ids):
"""Select the plot window or figure for the given dataset.
The plot for this dataset is assumed to have been created.
Parameters
----------
dataset : str or int
The dataset.
ids : array_like
The identifier array from the DataStack object.
See Also
--------
initialize_plot
"""
plt.figure(ids.index(dataset['id']) + 1)