Source code for sherpa.plot.pylab_backend

#
#  Copyright (C) 2010, 2015, 2017, 2019, 2020, 2021, 2022, 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
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#  This program is distributed in the hope that it will be useful,
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#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.
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#  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.
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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? # 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 if xerr is not None: xerr = xerr / 2. 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)
# 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)