Source code for sherpa.plot

#
#  Copyright (C) 2009, 2015, 2016, 2018 - 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.
#

"""A visualization interface to Sherpa.

Classes provide access to common plotting tasks, which is done by the
plotting backend defined in the ``options.plot_pkg`` setting of the
Sherpa configuration file. Note that plot objects can be created
and used even when only the `sherpa.plot.backends.BasicBackend` is
available.
"""

from __future__ import annotations

from configparser import ConfigParser
import contextlib
import copy
import logging
import importlib
from typing import Any, Literal, Optional, Sequence, Union

import numpy as np

from sherpa import get_config
from sherpa.data import Data1D, Data1DInt, Data2D
from sherpa.estmethods import Covariance
from sherpa.models.model import Model
from sherpa.optmethods import LevMar, NelderMead
from sherpa.plot.backends import BaseBackend, BasicBackend, PLOT_BACKENDS
from sherpa.stats import Stat, Likelihood, LeastSq, Chi2XspecVar
from sherpa.utils import NoNewAttributesAfterInit, erf, \
    bool_cast, parallel_map, dataspace1d, histogram1d, get_error_estimates
from sherpa.utils.err import ArgumentTypeErr, ConfidenceErr, \
    IdentifierErr, PlotErr, StatErr
from sherpa.utils.numeric_types import SherpaFloat

# PLOT_BACKENDS only contains backends in modules that are imported successfully
# but modules are not discovered by itself. Entrypoints would solve this problem
# but the current implementation does not have this capability.
# See docstring of sherpa.plot.backends.MetaBaseBackend for details.
#
for name in ["pylab", "pylab_area", "bokeh"]:
    try:
        importlib.import_module(f"sherpa.plot.{name}_backend")
    except ImportError:
        pass

config = ConfigParser()
config.read(get_config())

warning = logging.getLogger(__name__).warning

# TODO: why is this module globally changing the invalid mode of NumPy?
_ = np.seterr(invalid='ignore')

basicbackend = BasicBackend()
'''This backend has all the defaults that are backend-independent.
For all the plot classes in this module (e.g. FitPlot, JointPlot, ...)
the default settings are set in code like `plot_prefs = xxx.get_plot_defaults()`
where xxx is some backend.
'''

# This may be over-ridden below, depending on the options.plot_pkg setting.
#
backend: BaseBackend = basicbackend
'''Currently active backend module for plotting.'''

# In the current design, this code is executed when this
# module imported and then the values are shared between all instances of a class.
# That allows one to change those default "globally" (i.e. for all instances of the class)
# but it means that the default values are based on the backend that is active when
# this module is imported. That depends on the sherpa.rc setting and thus `plot_prefs`
# might contain e.g. matplotlib specific defaults that are not applicable when other
# backends are used.
# Thus, we currently initialize them with the BasicBackend that has only the
# backend-independent (i.e. those that works for any backend) defaults set.


plot_opt_str = config.get('options', 'plot_pkg', fallback='BasicBackend')
plot_opt = [o.strip() for o in plot_opt_str.split()]

for plottry in plot_opt:
    if plottry in PLOT_BACKENDS:
        backend = PLOT_BACKENDS[plottry]()
        break
    warning("Plotting backend '%s' not found or dependencies missing. Trying next option.", plottry)
else:
    # The backend will default to BasicBackend from above
    warning(f"Tried the following backends listed in {get_config()}: \n" +
            f"{plot_opt}\n" +
            "None of these imported correctly, so using the 'BasicBackend'.\n" +
            f"List of backends that have loaded and would be available: {[k for k in PLOT_BACKENDS]}")

__all__ = ('Plot', 'Contour', 'Point', 'Histogram',
           'MultiPlot',
           'HistogramPlot', 'DataHistogramPlot',
           'ModelHistogramPlot', 'SourceHistogramPlot',
           'PDFPlot', 'CDFPlot', 'LRHistogram',
           'SplitPlot', 'JointPlot',
           'DataPlot', 'TracePlot', 'ScatterPlot',
           'PSFKernelPlot',
           'DataContour', 'PSFKernelContour',
           'ModelPlot', 'ComponentModelPlot',
           'ComponentModelHistogramPlot', 'ComponentTemplateModelPlot',
           'SourcePlot', 'ComponentSourcePlot',
           'ComponentSourceHistogramPlot', 'ComponentTemplateSourcePlot',
           'PSFPlot',
           'ModelContour', 'PSFContour', 'SourceContour',
           'FitPlot',
           'FitContour',
           'DelchiPlot', 'DelchiHistogramPlot',
           'ChisqrPlot', 'ChisqrHistogramPlot',
           'ResidPlot', 'ResidHistogramPlot',
           'RatioPlot', 'RatioHistogramPlot',
           'ResidContour',
           'RatioContour',
           'Confidence1D', 'Confidence2D',
           'IntervalProjection', 'IntervalUncertainty',
           'RegionProjection', 'RegionUncertainty',
           'TemporaryPlottingBackend',
           'set_backend',
           )

_stats_noerr = ('cash', 'cstat', 'leastsq', 'wstat')
"""Statistics that do not make use of uncertainties."""


[docs] def set_backend(new_backend: Union[str, BaseBackend, type[BaseBackend]]) -> None: '''Set the Sherpa plotting backend. Plotting backends are registered in Sherpa with a string name. See the examples below for how to get a list of available backends. Parameters ---------- new_backend : string, class, or instance Set a sherpa plotting backend. The backend can be passed in as an object, or as a convenience, as a simple string Example ------- Set the backend to use Pylab from matplotlib for plotting. This is probably what most users need: >>> from sherpa.plot import set_backend >>> set_backend('pylab') Get a list of registered backends: >>> from sherpa.plot import PLOT_BACKENDS >>> PLOT_BACKENDS {'BaseBackend': <class 'sherpa.plot.backends.BaseBackend'>, ...} This list shows the names and the class for each backend. Details for each backend can be found in the Sherpa documentation or by inspecting the backend classes using normal Python functionality: >>> from sherpa.plot.backends import BasicBackend >>> help(BasicBackend) Help on class BasicBackend in module sherpa.plot.backends: <BLANKLINE> class BasicBackend(BaseBackend) | A dummy backend for plotting. ... ''' global backend if isinstance(new_backend, str): try: backend = PLOT_BACKENDS[new_backend]() except KeyError: raise IdentifierErr('noplotbackend', new_backend, list(PLOT_BACKENDS.keys())) from None return # It's a class and that class is a subclass of BaseBackend if isinstance(new_backend, type) and issubclass(new_backend, BaseBackend): backend = new_backend() return if isinstance(new_backend, BaseBackend): backend = new_backend return raise ArgumentTypeErr('tempplotbackend', new_backend)
[docs] class TemporaryPlottingBackend(contextlib.AbstractContextManager): '''Set the Sherpa plotting backend as a context, e.g. for a single plot This changes the logging level globally for all modules in sherpa. Parameters ---------- new_backend : string, class, or instance Set a sherpa plotting backend. The backend can be passed in as an instance of a plotting backend class. For simplicity, the user can also pass in a string naming a loaded backend class or the class itself; calling this context manager will then create an instance. Example ------- >>> from sherpa.plot import TemporaryPlottingBackend, DataPlot >>> from sherpa.data import Data1D >>> with TemporaryPlottingBackend('pylab'): ... x1 = [100, 200, 600, 1200] ... y1 = [2000, 2100, 1400, 3050] ... d1 = Data1D('oned', x1, y1) ... plot1 = DataPlot() ... plot1.prepare(d1) ... plot1.plot() ''' def __init__(self, new_backend: BaseBackend) -> None: self.backend = new_backend def __enter__(self) -> TemporaryPlottingBackend: global backend self.old = backend set_backend(self.backend) return self # As exc_type/val/tb are not used here we type them as Any, and the # return value is marked as an explicit False rather than bool since # it avoids warnings from mypy (as __exit__ is special). # def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> Literal[False]: global backend backend = self.old return False
def _make_title(title, name=''): """Return the plot title to use. Parameters ---------- title : str The main title to use name : str or None The identifier for the dataset. Returns ------- title : str If the name is empty or None then use title, otherwise use title + ' for ' + name. """ if name in [None, '']: return title return f"{title} for {name}" def _errorbar_warning(stat): """The warning message to display when error bars are being "faked". Parameters ---------- stat : sherpa.stats.Stat instance The name attribute is used in the error message. Returns ------- msg : str The warning message """ return "The displayed errorbars have been supplied with the " + \ "data or calculated using chi2xspecvar; the errors are not " + \ f"used in fits with {stat.name}" def calculate_errors(data, stat, yerrorbars=True): """Calculate errors from the statistics object.""" if stat is None: return None # Do we warn about adding in error values? The logic here isn't # quite the same as the other times _errorbar_warning is used. # This suggests that the code really should be re-worked to # go through a common code path. # # Do we require that self.yerr is always set, even if the # value is not used in a plot? I am going to assume not # (since the existing code will not change self.yerr if # the stat.name is not in _stats_noerr but an exception is # raised by get_yerr). # # This assumes that the error bars calculated by data.to_plot are # over-ridden once a statistic is given. However, how does this # work if the statistic is a Chi2 variant - e.g. Chi2DataVar - # but the user has given explicit errors (i.e. they are not to # be calculated by the "DataVar" part but used as is). Does # data.get_yerr handle this for us, or are invalid errors # used here? It appears that the correct answers are being # returned, but should we only call data.get_yerr if yerr # is None/empty/whatever is returned by to_plot? This also # holds for the Resid/RatioPlot classes. # # Note that we should probably return a value for yerr if # we can, even if 'yerrorbars' is set to False, so that # downstream users can make use of the value even if the # plot doesn't. This is similar to how labels or xerr # attributes are created even if they don't get used by the # plot. # msg = _errorbar_warning(stat) if stat.name in _stats_noerr: if yerrorbars: warning(msg) return data.get_yerr(True, Chi2XspecVar.calc_staterror) try: return data.get_yerr(True, stat.calc_staterror) except Exception: # TODO: can we report a useful error here? # # It is possible that this is actually an unrelated # error: it's unclear what error class is expected to # be thrown, but likely ValueError as this is raised # by Chi2DataVar when sent values < 0 # (note that over time the behavior has changed from # <= 0 to < 0, but this error message has not been # changed). # if yerrorbars: warning("%s\nzeros or negative values found", msg) return None
[docs] class Plot(NoNewAttributesAfterInit): "Base class for line plots" plot_prefs = basicbackend.get_plot_defaults() "The preferences for the plot." def __init__(self): """ Initialize a Plot object. All 1D line plot instances utilize Plot, which provides a generic interface to a backend. Once an instance of Plot is initialized no new attributes of the class can be made. (To eliminate the accidental creation of erroneous attributes) """ self.plot_prefs = self.plot_prefs.copy() super().__init__()
[docs] @staticmethod def vline(x, ymin=0, ymax=1, linecolor=None, linestyle=None, linewidth=None, overplot=False, clearwindow=True): "Draw a line at constant x, extending over the plot." backend.vline(x, ymin=ymin, ymax=ymax, linecolor=linecolor, linestyle=linestyle, linewidth=linewidth, overplot=overplot, clearwindow=clearwindow)
[docs] @staticmethod def hline(y, xmin=0, xmax=1, linecolor=None, linestyle=None, linewidth=None, overplot=False, clearwindow=True): "Draw a line at constant y, extending over the plot." backend.hline(y, xmin=xmin, xmax=xmax, linecolor=linecolor, linestyle=linestyle, linewidth=linewidth, overplot=overplot, clearwindow=clearwindow)
def _merge_settings(self, kwargs): """Return the plot preferences merged with user settings.""" return {**self.plot_prefs, **kwargs}
[docs] def plot(self, x, y, yerr=None, xerr=None, title=None, xlabel=None, ylabel=None, overplot=False, clearwindow=True, **kwargs): """Plot the data. Parameters ---------- x, y The data values to plot. They should have the same length. yerr, xerr : optional The symmetric errors to apply to the y or x values, or `None` for no errors along the axis. If given, they should match the length of the data. title, xlabel, ylabel : optional, string The optional plot title and axis labels. These are ignored if overplot is set to `True`. overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- overplot """ opts = self._merge_settings(kwargs) backend.plot(x, y, yerr=yerr, xerr=xerr, title=title, xlabel=xlabel, ylabel=ylabel, overplot=overplot, clearwindow=clearwindow, **opts)
[docs] def overplot(self, *args, **kwargs): """Add the data to an existing plot. This is the same as calling the plot method with overplot set to True. See Also -------- plot """ kwargs['overplot'] = True self.plot(*args, **kwargs)
[docs] class Contour(NoNewAttributesAfterInit): "Base class for contour plots" contour_prefs = basicbackend.get_contour_defaults() "The preferences for the plot." def __init__(self): """ Initialize a Contour object. All 2D contour plot instances utilize Contour, which provides a generic interface to a backend. Once an instance of Contour is initialized no new attributes of the class can be made. (To eliminate the accidental creation of erroneous attributes) """ self.contour_prefs = self.contour_prefs.copy() super().__init__() def _merge_settings(self, kwargs): """Return the plot preferences merged with user settings.""" return {**self.contour_prefs, **kwargs}
[docs] def contour(self, x0, x1, y, title=None, xlabel=None, ylabel=None, overcontour=False, clearwindow=True, **kwargs): opts = self._merge_settings(kwargs) backend.contour(x0, x1, y, title=title, xlabel=xlabel, ylabel=ylabel, overcontour=overcontour, clearwindow=clearwindow, **opts)
[docs] def overcontour(self, *args, **kwargs): kwargs['overcontour'] = True self.contour(*args, **kwargs)
[docs] class Point(NoNewAttributesAfterInit): "Base class for point plots" point_prefs = basicbackend.get_point_defaults() "The preferences for the plot." def __init__(self): """ Initialize a Point object. All 1D point plot instances utilize Point, which provides a generic interface to a backend. Once an instance of Point is initialized no new attributes of the class can be made. (To eliminate the accidental creation of erroneous attributes) """ self.point_prefs = self.point_prefs.copy() super().__init__() def _merge_settings(self, kwargs): """Return the plot preferences merged with user settings.""" return {**self.point_prefs, **kwargs}
[docs] def point(self, x, y, overplot=True, clearwindow=False, **kwargs): """Draw a point at the given location. Parameters ---------- x, y The coordinates of the plot. overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's point_prefs dictionary. """ opts = self._merge_settings(kwargs) backend.plot(x, y, overplot=overplot, clearwindow=clearwindow, **opts)
[docs] class Image(NoNewAttributesAfterInit): """Base class for image plots .. warning:: This class is experimental and subject to change in the future. Currently, is is only used within _repr_html_ methods. """ image_prefs = basicbackend.get_image_defaults() "The preferences for the plot." def __init__(self): """ Initialize a Image object. Once an instance of Image is initialized no new attributes of the class can be made. (To eliminate the accidental creation of erroneous attributes.) """ self.image_prefs = self.image_prefs.copy() super().__init__() def _merge_settings(self, kwargs): """Return the plot preferences merged with user settings.""" return {**self.image_prefs, **kwargs}
[docs] def plot(self, x0, x1, y, overplot=True, clearwindow=False, **kwargs): """Draw a point at the given location. Parameters ---------- x0, x1 : array-like Value for the image axes. y : array-like, 2D The coordinates of the plot. overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's image_prefs dictionary. """ opts = self._merge_settings(kwargs) backend.image(x0, x1, y, overplot=overplot, clearwindow=clearwindow, **opts)
[docs] class Histogram(NoNewAttributesAfterInit): "Base class for histogram plots" histo_prefs = basicbackend.get_histo_defaults() "The preferences for the plot." def __init__(self): """ Initialize a Histogram object. All 1D histogram plot instances utilize Histogram, which provides a generic interface to a backend. Once an instance of Histogram is initialized no new attributes of the class can be made. (To eliminate the accidental creation of erroneous attributes) """ self.histo_prefs = self.histo_prefs.copy() super().__init__() def _merge_settings(self, kwargs): """Return the plot preferences merged with user settings.""" return {**self.histo_prefs, **kwargs}
[docs] def plot(self, xlo, xhi, y, yerr=None, title=None, xlabel=None, ylabel=None, overplot=False, clearwindow=True, **kwargs): """Plot the data. Parameters ---------- xlo, xhi, y The data values to plot (the bin edges along the X axis and the bin values for the Y axis). They should have the same length. yerr : optional The symmetric errors to apply to the y values, or `None` for no errors along the axis. If given, they should match the length of the data. title, xlabel, ylabel : optional, string The optional plot title and axis labels. These are ignored if overplot is set to `True`. overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- overplot """ opts = self._merge_settings(kwargs) backend.histo(xlo, xhi, y, yerr=yerr, title=title, xlabel=xlabel, ylabel=ylabel, overplot=overplot, clearwindow=clearwindow, **opts)
[docs] def overplot(self, *args, **kwargs): kwargs['overplot'] = True self.plot(*args, **kwargs)
[docs] class HistogramPlot(Histogram): """Base class for histogram-style plots with a prepare method.""" def __init__(self): self.xlo = None self.xhi = None self.y = None self.xlabel = None self.ylabel = None self.title = None super().__init__() def __str__(self): xlo = self.xlo if self.xlo is not None: xlo = np.array2string(np.asarray(self.xlo), separator=',', precision=4, suppress_small=False) xhi = self.xhi if self.xhi is not None: xhi = np.array2string(np.asarray(self.xhi), separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(np.asarray(self.y), separator=',', precision=4, suppress_small=False) return f"""xlo = {xlo} xhi = {xhi} y = {y} xlabel = {self.xlabel} ylabel = {self.ylabel} title = {self.title} histo_prefs = {self.histo_prefs}""" def _repr_html_(self): """Return a HTML (string) representation of the histogram plot.""" return backend.as_html_histogram(self) @property def x(self): """Return (xlo + xhi) / 2 This is intended to make it easier to swap between plot- and histogram-style plots by providing access to an X value. """ if self.xlo is None or self.xhi is None: return None # As we do not (yet) require NumPy arrays, enforce it. xlo = np.asarray(self.xlo) xhi = np.asarray(self.xhi) return (xlo + xhi) / 2
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Histogram.plot(self, self.xlo, self.xhi, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs)
[docs] @staticmethod def vline(x, ymin=0, ymax=1, linecolor=None, linestyle=None, linewidth=None, overplot=False, clearwindow=True): "Draw a line at constant x, extending over the plot." backend.vline(x, ymin=ymin, ymax=ymax, linecolor=linecolor, linestyle=linestyle, linewidth=linewidth, overplot=overplot, clearwindow=clearwindow)
[docs] @staticmethod def hline(y, xmin=0, xmax=1, linecolor=None, linestyle=None, linewidth=None, overplot=False, clearwindow=True): "Draw a line at constant y, extending over the plot." backend.hline(y, xmin=xmin, xmax=xmax, linecolor=linecolor, linestyle=linestyle, linewidth=linewidth, overplot=overplot, clearwindow=clearwindow)
# I think we want slightly different histogram preferences # than most (mark by point rather than line). # def get_data_hist_prefs(): """Copy the data preferences to the histogram class""" hprefs = basicbackend.get_model_histo_defaults() dprefs = basicbackend.get_data_plot_defaults() for k, v in dprefs.items(): if k in hprefs: hprefs[k] = v return hprefs
[docs] class DataHistogramPlot(HistogramPlot): """Create 1D histogram plots of data values.""" histo_prefs = get_data_hist_prefs() "The preferences for the plot." def __init__(self): self.yerr = None super().__init__() @property def xerr(self): """Return abs(xhi - xlow) / 2 The plotting backends actually calculate the xerr values explicitly rather than use this field, so it is provided as a property in case users need it. .. versionchanged:: 4.16.1 This field is now a property. """ if self.xlo is None or self.xhi is None: return None # As we do not (yet) require NumPy arrays, enforce it. xlo = np.asarray(self.xlo) xhi = np.asarray(self.xhi) return np.abs(xhi - xlo) / 2
[docs] def prepare(self, data, stat=None): """Create the data to plot Parameters ---------- data The Sherpa data object to display (it is assumed to be one dimensional and represent binned data). stat : optional The Sherpa statistics object to use to add Y error bars if the data has none. See Also -------- plot """ # Need a better way of accessing the binning of the data. # Maybe to_plot should return the lo/hi edges as a pair # here. # plot = data.to_plot() (_, self.y, self.yerr, _, self.xlabel, self.ylabel) = plot self.xlo, self.xhi = data.get_indep(True) if stat is not None: yerrorbars = self.histo_prefs.get('yerrorbars', True) self.yerr = calculate_errors(data, stat, yerrorbars) self.title = data.name
# We have # # - the base class Histogram.plot can accept a yerr argument # - the superclass (HistogramPlot.plot) does not know # anything about yerr # # so the superclass is over-ridden here to basically call # the base class. #
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Histogram.plot(self, self.xlo, self.xhi, self.y, yerr=self.yerr, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs)
[docs] class ModelHistogramPlot(HistogramPlot): """Display a model as a histogram.""" # TODO: should this include # histo_prefs = shplot.basicbackend.get_model_histo_defaults() def __init__(self): super().__init__() self.title = 'Model'
[docs] def prepare(self, data: Data1DInt, model: Model, stat: Optional[Stat] = None) -> None: """Create the plot. The stat parameter is ignored. """ plot = data.to_plot(yfunc=model) (_, y, _, _, self.xlabel, self.ylabel) = plot # taken from get_x from Data1DInt indep = data.get_evaluation_indep(filter=True, model=model) self.xlo = indep[0] self.xhi = indep[1] self.y = y[1] assert self.y.size == self.xlo.size
[docs] class SourceHistogramPlot(ModelHistogramPlot): """Display a source model as a histogram.""" def __init__(self): super().__init__() self.title = 'Source'
[docs] class PDFPlot(HistogramPlot): """Display the probability density of an array. See Also -------- CDFPlot """ def __init__(self): self.points = None super().__init__() def __str__(self): points = self.points if self.points is not None: points = np.array2string(np.asarray(self.points), separator=',', precision=4, suppress_small=False) return (f'points = {points}\n' + HistogramPlot.__str__(self)) def _repr_html_(self): """Return a HTML (string) representation of the PDF plot.""" return backend.as_html_pdf(self)
[docs] def prepare(self, points, bins=12, normed=True, xlabel="x", name="x"): """Create the data to plot. Parameters ---------- points The values to display. bins : int, optional The number of bins to use. normed : bool, optional See the description of the density parameter in the numpy.histogram routine. xlabel : optional, string The label for the X axis. name : optional, string Used to create the plot title. See Also -------- plot """ self.points = points self.y, xx = np.histogram(points, bins=bins, density=normed) self.xlo = xx[:-1] self.xhi = xx[1:] self.ylabel = "probability density" self.xlabel = xlabel self.title = f"PDF: {name}"
[docs] class CDFPlot(Plot): """Display the cumulative distribution of an array. The cumulative distribution of the data is drawn along with vertical lines to indicate the median, 15.87%, and 84.13% percentiles. See Also -------- PDFPlot Examples -------- Show the cumulative distribution of 1000 randomly-distributed points from a Gumbel distribution: >>> import numpy as np >>> rng = np.random.default_rng() # requires NumPy 1.17 or later >>> pts = rng.gumbel(loc=100, scale=20, size=1000) >>> plot = CDFPlot() >>> plot.prepare(pts) >>> plot.plot() """ median_defaults = {"linestyle": 'dash', "linecolor": 'orange', "linewidth": 1.5} """The options used to draw the median line.""" lower_defaults = {"linestyle": 'dash', "linecolor": 'blue', "linewidth": 1.5} """The options used to draw the 15.87% line.""" upper_defaults = {"linestyle": 'dash', "linecolor": 'blue', "linewidth": 1.5} """The options used to draw the 84.13% line.""" plot_prefs = basicbackend.get_cdf_plot_defaults() """The plot options (the CDF and axes).""" def __init__(self): self.x = None self.y = None self.points = None self.median = None self.lower = None self.upper = None self.xlabel = None self.ylabel = None self.title = None super().__init__() def __str__(self): x = self.x if self.x is not None: x = np.array2string(self.x, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) points = self.points if self.points is not None: points = np.array2string(self.points, separator=',', precision=4, suppress_small=False) return f"""points = {points} x = {x} y = {y} median = {self.median} lower = {self.lower} upper = {self.upper} xlabel = {self.xlabel} ylabel = {self.ylabel} title = {self.title} plot_prefs = {self.plot_prefs}""" def _repr_html_(self): """Return a HTML (string) representation of the CDF plot.""" return backend.as_html_cdf(self)
[docs] def prepare(self, points, xlabel="x", name="x"): """Create the data to plot. Parameters ---------- points The values to display (they do not have to be sorted). xlabel : optional, string The label for the X axis. name : optional, string Used to create the Y-axis label and plot title. See Also -------- plot """ self.points = np.asarray(points) self.x = np.sort(points) (self.median, self.lower, self.upper) = get_error_estimates(self.x, True) xsize = len(self.x) self.y = (np.arange(xsize) + 1.0) / xsize self.xlabel = xlabel self.ylabel = f"p(<={xlabel})" self.title = f"CDF: {name}"
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Plot.plot(self, self.x, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs) # Note: the user arguments are not applied to the vertical lines # Plot.vline(self.median, overplot=True, clearwindow=False, **self.median_defaults) Plot.vline(self.lower, overplot=True, clearwindow=False, **self.lower_defaults) Plot.vline(self.upper, overplot=True, clearwindow=False, **self.upper_defaults)
[docs] class LRHistogram(HistogramPlot): "Derived class for creating 1D likelihood ratio distribution plots" def __init__(self): self.ratios = None self.lr = None self.ppp = None super().__init__() def __str__(self): ratios = self.ratios if self.ratios is not None: ratios = np.array2string(np.asarray(self.ratios), separator=',', precision=4, suppress_small=False) return '\n'.join([f'ratios = {ratios}', f'lr = {self.lr}', HistogramPlot.__str__(self)]) def _repr_html_(self): """Return a HTML (string) representation of the LRHistogram plot.""" return backend.as_html_lr(self)
[docs] def prepare(self, ratios, bins, niter, lr, ppp): "Create the data to plot" self.ppp = float(ppp) self.lr = float(lr) y = np.asarray(ratios) self.ratios = y self.xlo, self.xhi = dataspace1d(y.min(), y.max(), numbins=bins + 1)[:2] y = histogram1d(y, self.xlo, self.xhi) self.y = y / float(niter) self.title = "Likelihood Ratio Distribution" self.xlabel = "Likelihood Ratio" self.ylabel = "Frequency"
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Histogram.plot(self, self.xlo, self.xhi, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs) if self.lr is None: return # Note: the user arguments are not applied to the vertical line # if self.lr <= self.xhi.max() and self.lr >= self.xlo.min(): HistogramPlot.vline(self.lr, linecolor="orange", linestyle="solid", linewidth=1.5, overplot=True, clearwindow=False)
[docs] class SplitPlot(Plot, Contour): """Create multiple plots. Attributes ---------- rows : int Number of rows of plots. The default is 2. cols : int Number of columns of plots. The default is 1. """ plot_prefs = basicbackend.get_split_plot_defaults() "The preferences for the plot." def __init__(self, rows=2, cols=1): self.reset(rows, cols) super().__init__() def __str__(self): return (('rows = %s\n' + 'cols = %s\n' + 'plot_prefs = %s') % (self.rows, self.cols, self.plot_prefs))
[docs] def reset(self, rows=2, cols=1): "Prepare for a new set of plots or contours." self.rows = rows self.cols = cols self._reset_used() self._current = (0, 0) self._cleared_window = False
def _clear_window(self): if not self._cleared_window: backend.clear_window() self._cleared_window = True def _reset_used(self): self._used = np.zeros((self.rows, self.cols), np.bool_) def _next_subplot(self): row, col = np.where(~self._used) if row.size != 0: row, col = row[0], col[0] else: self._reset_used() row, col = 0, 0 return row, col
[docs] def addplot(self, plot, *args, **kwargs): "Add the plot to the next space." row, col = self._next_subplot() self.plot(row, col, plot, *args, **kwargs)
[docs] def addcontour(self, plot, *args, **kwargs): "Add the contour plot to the next space." row, col = self._next_subplot() self.contour(row, col, plot, *args, **kwargs)
[docs] def plot(self, row, col, plot, *args, **kwargs): "Add the plot in the given space." self._clear_window() clearaxes = ((not kwargs.get('overplot', False)) and kwargs.get('clearwindow', True)) backend.set_subplot(row, col, self.rows, self.cols, clearaxes, **self.plot_prefs) kwargs['clearwindow'] = False plot.plot(*args, **kwargs) self._used[row, col] = True self._current = (row, col)
[docs] def contour(self, row, col, plot, *args, **kwargs): "Add the contour in the given space." self._clear_window() clearaxes = ((not kwargs.get('overcontour', False)) and kwargs.get('clearwindow', True)) backend.set_subplot(row, col, self.rows, self.cols, clearaxes, **self.plot_prefs) kwargs['clearwindow'] = False plot.contour(*args, **kwargs) self._used[row, col] = True self._current = (row, col)
[docs] def overlayplot(self, plot, *args, **kwargs): "Add the plot to the current space without destroying the contents." self.overplot(self._current[0], self._current[1], plot, *args, **kwargs)
[docs] def overlaycontour(self, plot, *args, **kwargs): "Add the contour to the current space without destroying the contents." self.overcontour(self._current[0], self._current[1], plot, *args, **kwargs)
# FIXME: work on overplot issue
[docs] def overplot(self, row, col, plot, *args, **kwargs): "Add a plot to the given space without destroying the contents." kwargs['overplot'] = True self.plot(row, col, plot, *args, **kwargs)
[docs] def overcontour(self, row, col, plot, *args, **kwargs): "Add a contour plot to the given space without destroying the contents." kwargs['overcontour'] = True self.contour(row, col, plot, *args, **kwargs)
# TODO: move logic from sherpa.ui.utils.Session._plot_jointplot # regarding the X axis here #
[docs] class JointPlot(SplitPlot): """Multiple plots that share a common axis This supports two plots, where the top plot is twice as tall as the bottom plot. """ def __init__(self): super().__init__()
[docs] def plottop(self, plot, *args, overplot=False, clearwindow=True, **kwargs): create = clearwindow and not overplot if create: self._clear_window() backend.set_jointplot(0, 0, self.rows, self.cols, create=create) # The code used to check if the plot was an instance of # FitPlot, which has been updated to check for the presence # of attributes instead. # if hasattr(plot, 'xlabel'): plot.xlabel = '' elif hasattr(plot, 'dataplot') and hasattr(plot, 'modelplot'): dplot = plot.dataplot mplot = plot.modelplot if hasattr(dplot, 'xlabel'): dplot.xlabel = '' if hasattr(mplot, 'xlabel'): mplot.xlabel = '' kwargs['clearwindow'] = False plot.plot(*args, overplot=overplot, **kwargs)
[docs] def plotbot(self, plot, *args, overplot=False, **kwargs): backend.set_jointplot(1, 0, self.rows, self.cols, create=False) # FIXME: terrible hack to remove title from bottom plot.title = '' kwargs['clearwindow'] = False plot.plot(*args, overplot=overplot, **kwargs)
[docs] class DataPlot(Plot): """Create 1D plots of data values. Attributes ---------- x : array_like The X value for each point (the independent variable). y : array_like The Y value for each point (the dependent variable). xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value, if set. xlabel, ylabel, title : str Plot labels. Examples -------- Plot up an example dataset. The default appearance is to draw a symbol at each point, but no line connecting the points (the actual choice depends on the plot backend): >>> from sherpa.data import Data1D >>> from sherpa.plot import DataPlot >>> data = Data1D('a dataset', [10, 20, 25], [2, -7, 4]) >>> dplot = DataPlot() >>> dplot.prepare(data) >>> dplot.plot() The plot attributes can be changed to adjust the appearance of the plot, and the data re-drawn. The following also shows how the plot preferences can be over-ridden to turn off the points and draw a dotted line connecting the points (this assumes that the Matplotlib backend is in use): >>> dplot.xlabel = 'length' >>> dplot.ylabel = 'data values' >>> dplot.plot(marker=' ', linestyle='dashed') """ plot_prefs = basicbackend.get_data_plot_defaults() "The preferences for the plot." def __init__(self): self.x = None self.y = None self.yerr = None self.xerr = None self.xlabel = None self.ylabel = None self.title = None super().__init__() def __str__(self): x = self.x if self.x is not None: x = np.array2string(self.x, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) yerr = self.yerr if self.yerr is not None: yerr = np.array2string(self.yerr, separator=',', precision=4, suppress_small=False) xerr = self.xerr if self.xerr is not None: xerr = np.array2string(self.xerr, separator=',', precision=4, suppress_small=False) return (('x = %s\n' + 'y = %s\n' + 'yerr = %s\n' + 'xerr = %s\n' + 'xlabel = %s\n' + 'ylabel = %s\n' + 'title = %s\n' + 'plot_prefs = %s') % (x, y, yerr, xerr, self.xlabel, self.ylabel, self.title, self.plot_prefs)) def _repr_html_(self): """Return a HTML (string) representation of the data plot.""" return backend.as_html_data(self)
[docs] def prepare(self, data, stat=None): """Create the data to plot Parameters ---------- data The Sherpa data object to display (it is assumed to be one dimensional). stat : optional The Sherpa statistics object to use to add Y error bars if the data has none. See Also -------- plot """ (self.x, self.y, self.yerr, self.xerr, self.xlabel, self.ylabel) = data.to_plot() if stat is not None: yerrorbars = self.plot_prefs.get('yerrorbars', True) self.yerr = calculate_errors(data, stat, yerrorbars) self.title = data.name
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Plot.plot(self, self.x, self.y, yerr=self.yerr, xerr=self.xerr, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs)
[docs] class TracePlot(DataPlot): plot_prefs = basicbackend.get_model_plot_defaults()
[docs] def prepare(self, points, xlabel="x", name="x"): """The data to plot. Parameters ---------- points The trace to plot (one dimensional). The X axis is set to consecutive integers, starting at 0. xlabel : optional This value is ignored. name : optional, string Used to create the Y-axis label and plot title. See Also -------- plot """ self.x = np.arange(len(points), dtype=SherpaFloat) self.y = points self.xlabel = "iteration" self.ylabel = name self.title = f"Trace: {name}"
[docs] class ScatterPlot(DataPlot): plot_prefs = basicbackend.get_scatter_plot_defaults()
[docs] def prepare(self, x, y, xlabel="x", ylabel="y", name="(x,y)"): """The data to plot. Parameters ---------- x, y The points to plot. The number of points in the two sequences should be the same. xlabel, ylabel, name : optional, str The axis labels and name for the plot title. See Also -------- plot """ self.x = np.asarray(x, dtype=SherpaFloat) self.y = np.asarray(y, dtype=SherpaFloat) self.xlabel = xlabel self.ylabel = ylabel self.title = f"Scatter: {name}"
[docs] class PSFKernelPlot(DataPlot): "Derived class for creating 1D PSF kernel data plots"
[docs] def prepare(self, psf, data=None, stat=None): """Create the data to plot Parameters ---------- psf The Sherpa PSF to display data : optional The Sherpa data object to pass to the PSF. stat : optional The Sherpa statistics object to use to add Y error bars if the data has none. See Also -------- plot """ psfdata = psf.get_kernel(data) DataPlot.prepare(self, psfdata, stat) # self.ylabel = 'PSF value' # self.xlabel = 'PSF Kernel size' self.title = 'PSF Kernel'
[docs] class DataContour(Contour): """Create contours of 2D data. Attributes ---------- x0, x1 : array_like The coordinates of each point (the independent variables), as one-dimensional arrays. y : array_like The Y value for each point (the dependent variable), as a one-dimensional array. levels : array_like or `None` The values at which to draw contours. xlabel, ylabel, title : str Plot labels. """ contour_prefs = basicbackend.get_data_contour_defaults() "The preferences for the plot." def __init__(self): self.x0 = None self.x1 = None self.y = None self.xlabel = None self.ylabel = None self.title = None self.levels = None super().__init__() def __str__(self): x0 = self.x0 if self.x0 is not None: x0 = np.array2string(self.x0, separator=',', precision=4, suppress_small=False) x1 = self.x1 if self.x1 is not None: x1 = np.array2string(self.x1, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) return (('x0 = %s\n' + 'x1 = %s\n' + 'y = %s\n' + 'xlabel = %s\n' + 'ylabel = %s\n' + 'title = %s\n' + 'levels = %s\n' + 'contour_prefs = %s') % (x0, x1, y, self.xlabel, self.ylabel, self.title, self.levels, self.contour_prefs)) def _repr_html_(self): """Return a HTML (string) representation of the contour plot.""" return backend.as_html_datacontour(self)
[docs] def prepare(self, data, stat=None): (self.x0, self.x1, self.y, self.xlabel, self.ylabel) = data.to_contour() self.title = data.name
[docs] def contour(self, overcontour=False, clearwindow=True, **kwargs): Contour.contour(self, self.x0, self.x1, self.y, levels=self.levels, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overcontour=overcontour, clearwindow=clearwindow, **kwargs)
[docs] class PSFKernelContour(DataContour): "Derived class for creating 2D PSF Kernel contours"
[docs] def prepare(self, psf, data=None, stat=None): psfdata = psf.get_kernel(data) super().prepare(data=psfdata) # self.xlabel = 'PSF Kernel size x0' # self.ylabel = 'PSF Kernel size x1' self.title = 'PSF Kernel'
[docs] class ModelPlot(Plot): """Create 1D plots of model values. Attributes ---------- x : array_like The X value for each point (the independent variable). y : array_like The Y value for each point (the dependent variable). xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value, if set. xlabel, ylabel, title : str Plot labels. Examples -------- Plot up an example dataset. The default appearance is to draw a line between each point: >>> from sherpa.data import Data1D >>> from sherpa.models.basic import StepLo1D >>> from sherpa.plot import ModelPlot >>> data = Data1D('a dataset', [10, 20, 25], [2, -7, 4]) >>> model = StepLo1D() >>> model.xcut = 19 >>> mplot = ModelPlot() >>> mplot.prepare(data, model) >>> mplot.plot() The plot attributes can be changed to adjust the appearance of the plot, and the data re-drawn. The following also shows how the plot preferences can be over-ridden to draw the model as squares with no line connecting them (this assumes that the Matplotlib backend is in use): >>> mplot.xlabel = 'length' >>> mplot.ylabel = 'model values' >>> mplot.plot(marker='s', linestyle='none') """ plot_prefs = basicbackend.get_model_plot_defaults() "The preferences for the plot." def __init__(self): self.x = None self.y = None self.yerr = None self.xerr = None self.xlabel = None self.ylabel = None self.title = 'Model' super().__init__() def __str__(self): x = self.x if self.x is not None: x = np.array2string(self.x, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) yerr = self.yerr if self.yerr is not None: yerr = np.array2string(self.yerr, separator=',', precision=4, suppress_small=False) xerr = self.xerr if self.xerr is not None: xerr = np.array2string(self.xerr, separator=',', precision=4, suppress_small=False) return (('x = %s\n' + 'y = %s\n' + 'yerr = %s\n' + 'xerr = %s\n' + 'xlabel = %s\n' + 'ylabel = %s\n' + 'title = %s\n' + 'plot_prefs = %s') % (x, y, yerr, xerr, self.xlabel, self.ylabel, self.title, self.plot_prefs)) def _repr_html_(self): """Return a HTML (string) representation of the model plot.""" return backend.as_html_model(self)
[docs] def prepare(self, data: Data1D, model: Model, stat: Optional[Stat] = None) -> None: """Create the data to plot Parameters ---------- data The Sherpa data object to display (it is assumed to be one dimensional). This defines the grid over which the model is displayed. model The Sherpa model expression to evaluate and display. stat : optional This parameter is unused. See Also -------- plot """ (self.x, self.y, self.yerr, self.xerr, self.xlabel, self.ylabel) = data.to_plot(yfunc=model) self.y = self.y[1]
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. See Also -------- prepare, overplot """ Plot.plot(self, self.x, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs)
[docs] class ComponentModelPlot(ModelPlot): """Component model plots.""" plot_prefs = basicbackend.get_component_plot_defaults() "The preferences for the plot." # TODO: ComponentSourcePlot calls data.to_component_plot but this # just uses data.to_plot. Does it matter? #
[docs] def prepare(self, data, model, stat=None): super().prepare(data=data, model=model, stat=stat) self.title = f'Model component: {model.name}'
[docs] class ComponentModelHistogramPlot(ModelHistogramPlot): """Component model plots for histogram data. ..versionchanged:: 4.16.1 The `histo_prefs` attribute is now properly set (plot_prefs is also no-longer set). """ histo_prefs = basicbackend.get_component_histo_defaults() "The preferences for the plot." # TODO: ComponentSourceHistogramPlot calls data.to_component_plot # but this just uses data.to_plot. Does it matter? #
[docs] def prepare(self, data, model, stat=None): super().prepare(data=data, model=model, stat=stat) self.title = f'Model component: {model.name}'
[docs] class ComponentTemplateModelPlot(ComponentModelPlot):
[docs] def prepare(self, data, model, stat=None): self.x = model.get_x() self.y = model.get_y() self.xlabel = data.get_xlabel() self.ylabel = data.get_ylabel() self.title = f'Model component: {model.name}'
[docs] class SourcePlot(ModelPlot): """Create 1D plots of unconvolved model values. Attributes ---------- x : array_like The X value for each point (the independent variable). y : array_like The Y value for each point (the model value). xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value, if set. xlabel, ylabel, title : str Plot labels. """ def __init__(self): super().__init__() self.title = 'Source'
[docs] class ComponentSourcePlot(SourcePlot): """Component source plots.""" plot_prefs = basicbackend.get_component_plot_defaults()
[docs] def prepare(self, data, model, stat=None): (self.x, self.y, self.yerr, self.xerr, self.xlabel, self.ylabel) = data.to_component_plot(yfunc=model) self.y = self.y[1] self.title = f'Source model component: {model.name}'
[docs] class ComponentSourceHistogramPlot(SourceHistogramPlot): """Component source plots for histogram data. ..versionchanged:: 4.16.1 The `histo_prefs` attribute is now properly set (plot_prefs is also no-longer set). """ histo_prefs = basicbackend.get_component_histo_defaults()
[docs] def prepare(self, data, model, stat=None): plot = data.to_component_plot(yfunc=model) (_, y, _, _, self.xlabel, self.ylabel) = plot # taken from get_x from Data1DInt indep = data.get_evaluation_indep(filter=True, model=model) self.xlo = indep[0] self.xhi = indep[1] self.y = y[1] assert self.y.size == self.xlo.size self.title = f'Source model component: {model.name}'
[docs] class ComponentTemplateSourcePlot(ComponentSourcePlot):
[docs] def prepare(self, data, model, stat=None): self.x = model.get_x() self.y = model.get_y() if np.iterable(data.mask): x = data.to_plot()[0] mask = (self.x > x.min()) & (self.x <= x.max()) self.x = self.x[mask] self.y = self.y[mask] self.xlabel = data.get_xlabel() self.ylabel = data.get_ylabel() self.title = f'Source model component: {model.name}'
[docs] class PSFPlot(DataPlot): "Derived class for creating 1D PSF kernel data plots"
[docs] def prepare(self, psf, data=None, stat=None): """Create the data to plot Parameters ---------- psf The Sherpa PSF to display data : optional The Sherpa data object to pass to the PSF. stat : optional The Sherpa statistics object to use to add Y error bars if the data has none. See Also -------- plot """ psfdata = psf.get_kernel(data, False) DataPlot.prepare(self, psfdata, stat) self.title = psf.kernel.name
[docs] class ModelContour(Contour): "Derived class for creating 2D model contours" contour_prefs = basicbackend.get_model_contour_defaults() "The preferences for the plot." def __init__(self): self.x0 = None self.x1 = None self.y = None self.xlabel = None self.ylabel = None self.title = 'Model' self.levels = None super().__init__() def __str__(self): x0 = self.x0 if self.x0 is not None: x0 = np.array2string(self.x0, separator=',', precision=4, suppress_small=False) x1 = self.x1 if self.x1 is not None: x1 = np.array2string(self.x1, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) return (('x0 = %s\n' + 'x1 = %s\n' + 'y = %s\n' + 'xlabel = %s\n' + 'ylabel = %s\n' + 'title = %s\n' + 'levels = %s\n' + 'contour_prefs = %s') % (x0, x1, y, self.xlabel, self.ylabel, self.title, self.levels, self.contour_prefs)) def _repr_html_(self): """Return a HTML (string) representation of the model contour plot.""" return backend.as_html_modelcontour(self)
[docs] def prepare(self, data, model, stat=None): """Prepare the data. .. versionchanged:: 4.16.1 The stat argument is now unused and will likely be removed in a future release. """ (self.x0, self.x1, self.y, self.xlabel, self.ylabel) = data.to_contour(yfunc=model) self.y = self.y[1]
[docs] def contour(self, overcontour=False, clearwindow=True, **kwargs): super().contour(self.x0, self.x1, self.y, levels=self.levels, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overcontour=overcontour, clearwindow=clearwindow, **kwargs)
[docs] class PSFContour(DataContour): "Derived class for creating 2D PSF contours"
[docs] def prepare(self, psf, data=None, stat=None): psfdata = psf.get_kernel(data, False) super().prepare(data=psfdata) self.title = psf.kernel.name
[docs] class SourceContour(ModelContour): "Derived class for creating 2D model contours" def __init__(self): super().__init__() self.title = 'Source'
[docs] class FitPlot(Plot): """Combine data and model plots for 1D data. Attributes ---------- dataplot The Sherpa plot object used to display the data. modelplot The Sherpa plot object used to display the model. Examples -------- If dplot and mplot are data and model plots respectively, such as those created in the examples for `DataPlot` and `ModelPlot`, then a combined plot can be created with: >>> from sherpa.data import Data1D >>> from sherpa.models.basic import StepLo1D >>> from sherpa.plot import DataPlot, ModelPlot, FitPlot >>> data = Data1D('a dataset', [10, 20, 25], [2, -7, 4]) >>> dplot = DataPlot() >>> fplot = FitPlot() >>> model = StepLo1D() >>> model.xcut = 19 >>> mplot = ModelPlot() >>> dplot.prepare(data) >>> mplot.prepare(data, model) >>> fplot.prepare(dplot, mplot) >>> fplot.plot() Keyword arguments can be given in the `plot` call, and these are passed through to both the data and model plots (in the following example the Matplotlib backend is assumed to be in use): >>> fplot.plot(color='k') """ plot_prefs = basicbackend.get_fit_plot_defaults() """The preferences for the plot. Note that the display for the data and model plots are controlled by the preferences for the dataplot and modelplot objects, so this is currently unused. """ def __init__(self): self.dataplot = None self.modelplot = None super().__init__() def __str__(self): data_title = None if self.dataplot is not None: data_title = self.dataplot.title model_title = None if self.modelplot is not None: model_title = self.modelplot.title return (('dataplot = %s\n%s\n\nmodelplot = %s\n%s') % (data_title, self.dataplot, model_title, self.modelplot)) def _repr_html_(self): """Return a HTML (string) representation of the fit plot.""" return backend.as_html_fit(self)
[docs] def prepare(self, dataplot, modelplot): """Create the data to plot Parameters ---------- dataplot The Sherpa plot object representing the data (normally a DataPlot instance). modelplot The Sherpa plot object representing the model (normally a ModelPlot instance). See Also -------- plot """ self.dataplot = dataplot self.modelplot = modelplot
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): """Plot the data. This will plot the data sent to the prepare method. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. They are sent to both the dataplot and modelplot instances. See Also -------- prepare, overplot """ if self.dataplot is None or self.modelplot is None: raise PlotErr("nodataormodel") # Note: the user preferences are sent to *both* the data and # model plot. Is this a good idea? # self.dataplot.plot(overplot=overplot, clearwindow=clearwindow, **kwargs) self.modelplot.overplot(**kwargs)
[docs] class FitContour(Contour): "Derived class for creating 2D combination data and model contours" contour_prefs = basicbackend.get_fit_contour_defaults() "The preferences for the plot." def __init__(self): self.datacontour = None self.modelcontour = None super().__init__() def __str__(self): data_title = None if self.datacontour is not None: data_title = self.datacontour.title model_title = None if self.modelcontour is not None: model_title = self.modelcontour.title return (('datacontour = %s\n%s\n\nmodelcontour = %s\n%s') % (data_title, self.datacontour, model_title, self.modelcontour)) def _repr_html_(self): """Return a HTML (string) representation of the fit contour plot.""" return backend.as_html_fitcontour(self)
[docs] def prepare(self, datacontour, modelcontour): self.datacontour = datacontour self.modelcontour = modelcontour
[docs] def contour(self, overcontour=False, clearwindow=True, **kwargs): # Note: the user arguments are applied to both plots self.datacontour.contour(overcontour=overcontour, clearwindow=clearwindow, **kwargs) self.modelcontour.overcontour(**kwargs)
[docs] class BaseResidualPlot(ModelPlot): """Residuals of model + data. Subclasses need to implement `_calc_y` and `_title`. .. versionadded:: 4.16.1 Notes ----- The ylog setting is ignored, whether given as a preference or a keyword argument, so the Y axis is always drawn with a linear scale. Errors are supported on the Y axis. """ residual_axis: float = 0 """At what point on the y axis is the residual line drawn.""" residual_color: str = 'k' """The color of the residual line.""" residual_lw: float = 0.8 """The line width of the residual line.""" def _calc_y(self, data: Data1D, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: """Define the self.y and self.yerr fields""" raise NotImplementedError() def _change_ylabel(self) -> None: """Change the self.ylabel field, if needed.""" pass def _title(self, data: Data1D) -> None: """Set the self.title field""" raise NotImplementedError()
[docs] def prepare(self, # type: ignore[override] data: Data1D, model: Model, # Note that the superclass has stat as optional stat: Stat) -> None: plot = data.to_plot(yfunc=model) (self.x, y, staterr, self.xerr, self.xlabel, self.ylabel) = plot self._calc_y(data, stat, y, staterr) self._change_ylabel() self._title(data)
[docs] def plot(self, # type: ignore[override] overplot: bool = False, clearwindow: bool = True, **kwargs) -> None: x = self.x y = self.y # The y axis is only drawn with a linear scale. # self.plot_prefs['ylog'] = False kwargs.pop('ylog', True) # The superclass is not called since the y errors are # potentially included in the plot. # Plot.plot(self, x, y, yerr=self.yerr, xerr=self.xerr, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs) super().hline(y=self.residual_axis, xmin=0, xmax=1, linecolor=self.residual_color, linewidth=self.residual_lw, overplot=True)
[docs] class BaseResidualHistogramPlot(ModelHistogramPlot): """Residuals of model + data for histogram data. Subclasses need to implement `_calc_y` and `_title`. .. versionadded:: 4.16.1 Notes ----- The ylog setting is ignored, whether given as a preference or a keyword argument, so the Y axis is always drawn with a linear scale. Errors are supported on the Y axis. """ residual_axis: float = 0 """At what point on the y axis is the residual line drawn.""" residual_color: str = 'k' """The color of the residual line.""" residual_lw: float = 0.8 """The line width of the residual line.""" def __init__(self) -> None: self.yerr: Optional[np.ndarray] self.yerr = None super().__init__() def _calc_x(self, data: Data1DInt, model: Model) -> None: """Define the xlo and xhi fields""" # taken from get_x from Data1DInt indep = data.get_evaluation_indep(filter=True, model=model) self.xlo = indep[0] self.xhi = indep[1] def _calc_y(self, data: Data1DInt, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: """Define the self.y and self.yerr fields""" raise NotImplementedError() def _change_ylabel(self) -> None: """Change the self.ylabel field, if needed.""" pass def _title(self, data: Data1DInt) -> None: """Set the self.title field""" raise NotImplementedError()
[docs] def prepare(self, # type: ignore[override] data: Data1DInt, model: Model, # Note that the superclass has stat as optional stat: Stat) -> None: plot = data.to_plot(model) (_, y, staterr, _, self.xlabel, self.ylabel) = plot self._calc_x(data, model) self._calc_y(data, stat, y, staterr) self._change_ylabel() self._title(data)
[docs] def plot(self, # type: ignore[override] overplot: bool = False, clearwindow: bool = True, **kwargs) -> None: xlo = self.xlo xhi = self.xhi y = self.y # The y axis is only drawn with a linear scale. # self.histo_prefs['ylog'] = False kwargs.pop('ylog', True) # The superclass is not called since the y errors are # potentially included in the plot. # Histogram.plot(self, xlo, xhi, y, yerr=self.yerr, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs) super().hline(y=self.residual_axis, xmin=0, xmax=1, linecolor=self.residual_color, linewidth=self.residual_lw, overplot=True)
[docs] class BaseResidualContour(ModelContour): """Residuals of model + data for contour data. Subclasses need to implement `_calc_y` and `_title`. .. versionadded:: 4.16.1 """ def _calc_y(self, ylist: tuple[np.ndarray, np.ndarray]) -> None: """Define the self.y field""" raise NotImplementedError() def _title(self, data: Data2D) -> None: """Set the self.title field""" raise NotImplementedError()
[docs] def prepare(self, data: Data2D, model: Model, stat = None) -> None: (self.x0, self.x1, ys, self.xlabel, self.ylabel) = data.to_contour(yfunc=model) self._calc_y(ys) self._title(data)
[docs] def contour(self, # type: ignore[override] overcontour: bool = False, clearwindow: bool = True, **kwargs) -> None: x0 = self.x0 x1 = self.x1 y = self.y Contour.contour(self, x0, x1, y, levels=self.levels, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overcontour=overcontour, clearwindow=clearwindow, **kwargs)
[docs] class DelchiPlot(BaseResidualPlot): """Create plots of the delta-chi value per point. The value of (data-model)/error is plotted for each point. Attributes ---------- x : array_like The X value for each point. y : array_like The Y value for each point: (data-model)/error xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value (each element is `1`). xlabel, ylabel, title : str Plot labels. Notes ----- The ylog setting is ignored, whether given as a preference or a keyword argument, so the Y axis is always drawn with a linear scale. """ plot_prefs = basicbackend.get_resid_plot_defaults() "The preferences for the plot." def _calc_y(self, data: Data1D, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: """Define the self.y and self.yerr fields""" if staterr is None: if stat.name in _stats_noerr: raise StatErr('badstat', "DelchiPlot", stat.name) staterr = data.get_yerr(True, stat.calc_staterror) self.y = (ylist[0] - ylist[1]) / staterr self.yerr = np.ones_like(self.y) def _change_ylabel(self) -> None: self.ylabel = 'Sigma' def _title(self, data: Data1D) -> None: self.title = _make_title('Sigma Residuals', data.name)
[docs] class DelchiHistogramPlot(BaseResidualHistogramPlot): """Create plots of the delta-chi value per point.""" histo_prefs = basicbackend.get_resid_histo_defaults() "The preferences for the delchi plot." def _calc_y(self, data: Data1DInt, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: if staterr is None: if stat.name in _stats_noerr: raise StatErr('badstat', "DelchiHistogramPlot", stat.name) staterr = data.get_yerr(True, stat.calc_staterror) self.y = (ylist[0] - ylist[1]) / staterr self.yerr = staterr / staterr def _change_ylabel(self) -> None: self.ylabel = 'Sigma' def _title(self, data: Data1DInt) -> None: self.title = _make_title('Sigma Residuals', data.name)
[docs] class ChisqrPlot(ModelPlot): """Create plots of the chi-square value per point. The value of ((data-model)/error)^2 is plotted for each point. Attributes ---------- x : array_like The X value for each point. y : array_like The Y value for each point: ((data-model)/error)^2 xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value. Will be `None` here. xlabel, ylabel, title : str Plot labels. """ plot_prefs = basicbackend.get_model_plot_defaults() "The preferences for the plot." def _calc_chisqr(self, ylist, staterr): dy = ylist[0] - ylist[1] return dy * dy / (staterr * staterr)
[docs] def prepare(self, data, model, stat): (self.x, y, _, self.xerr, self.xlabel, self.ylabel) = data.to_plot(model) if stat.name in _stats_noerr: raise StatErr('badstat', "ChisqrPlot", stat.name) staterr = data.get_yerr(True, stat.calc_staterror) self.y = self._calc_chisqr(y, staterr) # TODO: should this # self.yerr = staterr * staterr self.ylabel = backend.get_latex_for_string(r'\chi^2') self.title = _make_title( backend.get_latex_for_string(r'\chi^2'), data.name)
# TODO: should this draw x/y errors?
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): Plot.plot(self, self.x, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs)
[docs] class ChisqrHistogramPlot(ModelHistogramPlot): """Create plot of the ChiSq residuals per point.""" histo_prefs = basicbackend.get_resid_histo_defaults() "The preferences for the residual plot." def _calc_x(self, data: Data1DInt, model: Model) -> None: """Define the xlo and xhi fields""" # taken from get_x from Data1DInt indep = data.get_evaluation_indep(filter=True, model=model) self.xlo = indep[0] self.xhi = indep[1] def _calc_chisqr(self, ylist: tuple[np.ndarray, np.ndarray], staterr: np.ndarray) -> np.ndarray: dy = ylist[0] - ylist[1] return dy * dy / (staterr * staterr)
[docs] def prepare(self, # type: ignore[override] data: Data1DInt, model: Model, stat: Stat) -> None: plot = data.to_plot(model) (_, y, _, _, self.xlabel, self.ylabel) = plot self._calc_x(data, model) # if staterr is None: if stat.name in _stats_noerr: raise StatErr('badstat', "ChisqrHistogramPlot", stat.name) staterr = data.get_yerr(True, stat.calc_staterror) self.y = self._calc_chisqr(y, staterr) self.ylabel = backend.get_latex_for_string(r'\chi^2') self.title = _make_title( backend.get_latex_for_string(r'\chi^2'), data.name)
[docs] class ResidPlot(BaseResidualPlot): """Create plots of the residuals (data - model) per point. The value of (data-model) is plotted for each point. Attributes ---------- x : array_like The X value for each point. y : array_like The Y value for each point: data - model. xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value, if set. xlabel, ylabel, title : str Plot labels. Notes ----- The ylog setting is ignored, whether given as a preference or a keyword argument, so the Y axis is always drawn with a linear scale. """ plot_prefs = basicbackend.get_resid_plot_defaults() "The preferences for the plot." def _calc_y(self, data: Data1D, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: """Define the self.y and self.yerr fields""" self.y = ylist[0] - ylist[1] if stat.name in _stats_noerr: calcfn = Chi2XspecVar.calc_staterror if self.plot_prefs.get('yerrorbars', True): warning(_errorbar_warning(stat)) else: calcfn = stat.calc_staterror self.yerr = data.get_yerr(True, calcfn) def _change_ylabel(self) -> None: self.ylabel = 'Data - Model' def _title(self, data: Data1D) -> None: self.title = _make_title('Residuals', data.name)
[docs] class ResidHistogramPlot(BaseResidualHistogramPlot): """Create plots of the residual value per point.""" histo_prefs = basicbackend.get_resid_histo_defaults() "The preferences for the residual plot." def _calc_y(self, data: Data1DInt, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: self.y = ylist[0] - ylist[1] # See the discussion in DataPlot.prepare try: yerrorbars = self.histo_prefs['yerrorbars'] except KeyError: yerrorbars = True if stat.name in _stats_noerr: calcyerr = Chi2XspecVar.calc_staterror if yerrorbars: warning(_errorbar_warning(stat)) else: calcyerr = stat.calc_staterror self.yerr = data.get_yerr(True, calcyerr) def _change_ylabel(self) -> None: self.ylabel = 'Data - Model' def _title(self, data: Data1DInt) -> None: self.title = _make_title('Residuals', data.name)
[docs] class ResidContour(BaseResidualContour): "Derived class for creating 2D residual contours (data-model)" contour_prefs = basicbackend.get_resid_contour_defaults() "The preferences for the plot." def _calc_y(self, ylist: tuple[np.ndarray, np.ndarray]) -> None: self.y = ylist[0] - ylist[1] def _title(self, data): self.title = _make_title('Residuals', data.name)
[docs] class RatioPlot(BaseResidualPlot): """Create plots of the ratio of data to model per point. The value of data / model is plotted for each point. Attributes ---------- x : array_like The X value for each point. y : array_like The Y value for each point: data / model. xerr : array_like The half-width of each X "bin", if set. yerr : array_like The error on the Y value, if set. xlabel, ylabel, title : str Plot labels. Notes ----- The ylog setting is ignored, whether given as a preference or a keyword argument, so the Y axis is always drawn with a linear scale. """ plot_prefs = basicbackend.get_ratio_plot_defaults() "The preferences for the plot." residual_axis = 1 """At what point on the y axis is the residual line drawn.""" def _calc_y(self, data: Data1D, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: """Define the self.y and self.yerr fields""" dvals = np.array(ylist[0]) mvals = np.asarray(ylist[1]) bad = np.where(mvals == 0.0) dvals[bad] = 0.0 mvals[bad] = 1.0 self.y = dvals / mvals if stat.name in _stats_noerr: calcfn = Chi2XspecVar.calc_staterror if self.plot_prefs.get('yerrorbars', True): warning(_errorbar_warning(stat)) else: calcfn = stat.calc_staterror self.yerr = data.get_yerr(True, calcfn) self.yerr = self.yerr / ylist[1] def _change_ylabel(self) -> None: self.ylabel = 'Data / Model' def _title(self, data: Data1D) -> None: self.title = _make_title('Ratio of Data to Model', data.name)
[docs] class RatioHistogramPlot(BaseResidualHistogramPlot): """Create plots of the ratio value per point.""" # Turn on yerrorbars by default. What is the best way to do this? # histo_prefs = basicbackend.get_resid_histo_defaults() | {"yerrorbars": True} "The preferences for the ratio plot." residual_axis = 1 def _calc_y(self, data: Data1DInt, stat: Stat, ylist: tuple[np.ndarray, np.ndarray], staterr: Optional[np.ndarray]) -> None: # should not need np.asarray here but leave in for safety dvals = np.asarray(ylist[0]) mvals = np.asarray(ylist[1]) bad = np.where(mvals == 0.0) dvals[bad] = 0.0 mvals[bad] = 1.0 self.y = dvals / mvals # See the discussion in DataPlot.prepare try: yerrorbars = self.histo_prefs['yerrorbars'] except KeyError: yerrorbars = True if stat.name in _stats_noerr: calcyerr = Chi2XspecVar.calc_staterror if yerrorbars: warning(_errorbar_warning(stat)) else: calcyerr = stat.calc_staterror self.yerr = data.get_yerr(True, calcyerr) / ylist[1] def _change_ylabel(self) -> None: self.ylabel = 'Data / Model' def _title(self, data: Data1DInt) -> None: self.title = _make_title('Ratio of Data to Model', data.name)
[docs] class RatioContour(BaseResidualContour): "Derived class for creating 2D ratio contours (data divided by model)" contour_prefs = basicbackend.get_ratio_contour_defaults() "The preferences for the plot." def _calc_y(self, ylist: tuple[np.ndarray, np.ndarray]) -> None: data = np.array(ylist[0]) model = np.asarray(ylist[1]) bad = np.where(model == 0.0) data[bad] = 0.0 model[bad] = 1.0 self.y = data / model def _title(self, data: Data2D) -> None: self.title = _make_title('Ratio of Data to Model', data.name)
def calc_par_range(minval: float, maxval: float, nloop: int, delv: Optional[float] = None, log: Optional[bool] = False) -> np.ndarray: """Calculate the parameter range to use. This assumes that the arguments have already been checked for validity. Parameters ---------- minval, maxval : number The requested parameter range, with maxval > minval. nloop : int The number of values to create. This is not used if delv is set. delv : float or None, optional If set this is the spacing to use (and over-rides the nloop parameter). It must be positive and, when `log` is set, the spacing is 10**delv. log : bool, optional Should the spacing be linear or logarithmic? Returns ------- pars : ndarray The parameter values. Raises ------ ConfidenceErr If either minval or maxval are not positive definite when the `log` option is set. """ if delv is not None: # This assumes that delv > eps, but this should be safe. # The float(eps) term is for mypy. # eps = np.finfo(np.float32).eps maxval = maxval + float(eps) if log: if minval <= 0.0 or maxval <= 0.0: raise ConfidenceErr('badarg', 'Log scale', 'on positive boundaries') minval = np.log10(minval) maxval = np.log10(maxval) if delv is None: x = np.linspace(minval, maxval, nloop) else: x = np.arange(minval, maxval, delv) if log: x = 10**x return x
[docs] class Confidence1D(DataPlot): """The base class for 1D confidence plots. .. versionchanged:: 4.16.1 Handling of log-scaled axes and use of the delv argument has been improved, and the string output now includes the parameter value (if available). """ plot_prefs = basicbackend.get_confid_plot_defaults() "The preferences for the plot." # This is only used for an error message conf_type = "unknown" "The type of confidence analysis." def __init__(self): self.min = None self.max = None self.nloop = 20 self.delv = None self.fac = 1 self.log = False self.parval = None self.stat = None self.numcores = None super().__init__() def __setstate__(self, state): self.__dict__.update(state) if 'stat' not in state: self.__dict__['stat'] = None if 'parval' not in state: self.__dict__['parval'] = None if 'numcores' not in state: self.__dict__['numcores'] = None def __str__(self): x = self.x if self.x is not None: x = np.array2string(self.x, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) return (f'x = {x}\n' + f'y = {y}\n' + f'min = {self.min}\n' + f'max = {self.max}\n' + f'nloop = {self.nloop}\n' + f'delv = {self.delv}\n' + f'fac = {self.fac}\n' + f'log = {self.log}\n' + f'parval = {self.parval}') def _repr_html_(self): """Return a HTML (string) representation of the confidence 1D plot.""" return backend.as_html_contour1d(self)
[docs] def prepare(self, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None): """Set the data to plot. This defines the range over which the statistic will be calculated, but does not perform the evaluation. Parameters ---------- min, max : number or None, optional The minimum and maximum parameter value to used. If either is not set then the range is calculated using the fac parameter. nloop : int, optional The number of points at which to evaluate the statistic. It must be greater than 1. This is used when delv is set to None. delv : number or None, optional The spacing of the parameter grid. This takes precedence over nloop. fac : number, optional Used when either min or max are not set. The parameter range in this case is taken to be fac times the separation of the covariance limits for the parameter (unless explicitly given). log : bool, optional Should the parameter be evaluated on a logarithmically-spaced grid rather than a linearly-spaced one? numcores : int or None, optional Should the parameter evaluation use multiple CPU cores if available? See Also -------- calc """ self.min = min self.max = max self.nloop = nloop self.delv = delv self.fac = fac self.log = log self.numcores = numcores
def _interval_init(self, fit, par): """Calculate the grid to use for the parameter. Parameters ---------- fit : sherpa.fit.Fit instance The current fit. par : sherpa.models.parameter.Parameter instance The parameter to analyze. Returns ------- x : ndarray The parameter values to use (also set to self.x) """ # Validate the values first (as much as we can). # if self.min is not None and not np.isscalar(self.min): raise ConfidenceErr('badarg', 'Parameter limits', 'scalars') if self.max is not None and not np.isscalar(self.max): raise ConfidenceErr('badarg', 'Parameter limits', 'scalars') if self.nloop <= 1: raise ConfidenceErr('badarg', 'Nloop parameter', '> 1') if self.delv is not None and self.delv <= 0: raise ConfidenceErr('badarg', 'delv parameter', '> 0') self.stat = fit.calc_stat() self.parval = par.val self.xlabel = par.fullname self.ylabel = 'Statistic Value' if self.min is None or self.max is None: oldestmethod = fit.estmethod fit.estmethod = Covariance() r = fit.est_errors() index = list(r.parnames).index(par.fullname) fit.estmethod = oldestmethod if self.min is None: self.min = par.min minval = r.parmins[index] if minval is not None and not np.isnan(minval): self.min = par.val + minval if self.max is None: self.max = par.max maxval = r.parmaxes[index] if maxval is not None and not np.isnan(maxval): self.max = par.val + maxval v = (self.max + self.min) / 2. dv = np.fabs(v - self.min) self.min = v - self.fac * dv self.max = v + self.fac * dv if self.min < par.min: self.min = par.min if self.max > par.max: self.max = par.max # check user limits for errors if self.min >= self.max: raise ConfidenceErr('badlimits') self.x = calc_par_range(self.min, self.max, self.nloop, delv=self.delv, log=self.log) return self.x
[docs] def calc(self, fit, par): """Evaluate the statistic for the parameter range. This requires prepare to have been called, and must be called before plot is called. Parameters ---------- fit The Sherpa fit instance to use (defines the statistic and optimiser to use). par The parameter to iterate over. See Also -------- plot, prepare Notes ----- This method is assumed to be over-ridden in derived classes, where it will perform the statistic calculations needed to create the visualization. This version should be called from these classes as it validates the fit and par arguments. """ if isinstance(fit.stat, LeastSq): raise ConfidenceErr('badargconf', fit.stat.name) # Check the parameter # - is thawed # - is part of the model expression # if par.frozen: raise ConfidenceErr('frozen', par.fullname, f'interval {self.conf_type}') if par not in fit.model.pars: raise ConfidenceErr('thawed', par.fullname, fit.model.name)
[docs] def plot(self, overplot=False, clearwindow=True, **kwargs): if self.log: self.plot_prefs['xlog'] = True Plot.plot(self, self.x, self.y, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overplot=overplot, clearwindow=clearwindow, **kwargs) # Note: the user arguments are not applied to the lines # if self.stat is not None: Plot.hline(self.stat, linecolor="green", linestyle="dash", linewidth=1.5, overplot=True, clearwindow=False) if self.parval is not None: Plot.vline(self.parval, linecolor="orange", linestyle="dash", linewidth=1.5, overplot=True, clearwindow=False) if self.log: self.plot_prefs['xlog'] = False
[docs] class Confidence2D(DataContour, Point): """The base class for 2D confidence contours. .. versionchanged:: 4.16.1 Handling of log-scaled axes and use of the delv argument has been improved. """ contour_prefs = basicbackend.get_confid_contour_defaults() point_prefs = basicbackend.get_confid_point_defaults() # This is only used for an error message conf_type = "unknown" "The type of confidence analysis." def __init__(self): self.min = None self.max = None self.nloop = (10, 10) self.fac = 4 self.delv = None self.log = (False, False) self.sigma = (1, 2, 3) self.parval0 = None self.parval1 = None self.stat = None self.numcores = None super().__init__() def __setstate__(self, state): self.__dict__.update(state) if 'stat' not in state: self.__dict__['stat'] = None if 'numcores' not in state: self.__dict__['numcores'] = None def __str__(self): x0 = self.x0 if self.x0 is not None: x0 = np.array2string(self.x0, separator=',', precision=4, suppress_small=False) x1 = self.x1 if self.x1 is not None: x1 = np.array2string(self.x1, separator=',', precision=4, suppress_small=False) y = self.y if self.y is not None: y = np.array2string(self.y, separator=',', precision=4, suppress_small=False) return (f'x0 = {x0}\n' + f'x1 = {x1}\n' + f'y = {y}\n' + f'min = {self.min}\n' + f'max = {self.max}\n' + f'nloop = {self.nloop}\n' + f'fac = {self.fac}\n' + f'delv = {self.delv}\n' + f'log = {self.log}\n' + f'sigma = {self.sigma}\n' + f'parval0 = {self.parval0}\n' + f'parval1 = {self.parval1}\n' + f'levels = {self.levels}') def _repr_html_(self): """Return a HTML (string) representation of the confidence 2D plot.""" return backend.as_html_contour2d(self)
[docs] def prepare(self, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None): """Set the data to plot. This defines the ranges over which the statistic will be calculated, but does not perform the evaluation. Parameters ---------- min, max : sequence of number or None, optional The minimum and maximum parameter values to used. If set then they must contain two elements, and if not then the range is calculated using the fac parameter. nloop : sequence of int, optional The number of points at which to evaluate the statistic, where each value must be greater than 1. This is used when delv is set to None. delv : sequence of number or None, optional The spacing of the parameter grids, and if set it must contain two values each greater than 0. This takes precedence over nloop. fac : number, optional Used when either min or max are not set. The parameter range in this case is taken to be fac times the separation of the covariance limits for the parameter (unless explicitly given). log : sequence of bool, optional Should each parameter be evaluated on a logarithmically-spaced grid rather than a linearly-spaced one? sigma : sequence of number, optional The sigma values at which to draw contours. This is only used if levels is set to None. levels : sequence of number or None, optional The levels at which the contours are drawn. This over-rides the sigma setting. numcores : int or None, optional Should the parameter evaluation use multiple CPU cores if available? See Also -------- calc """ self.min = min self.max = max self.nloop = nloop self.delv = delv self.fac = fac self.log = log self.sigma = sigma self.levels = levels self.parval0 = None self.parval1 = None self.numcores = numcores
def _region_init(self, fit, par0, par1): """Calculate the grid to use for the parameters. Parameters ---------- fit : sherpa.fit.Fit instance The current fit. par0, par1 : sherpa.models.parameter.Parameter instance The parameters to analyze. Returns ------- x : 2D ndarray The parameter values to use. Unlike the Confidence1D case this is not just self.x0 or self.x1, but is a 2D array where each row represent each pair of parameters, so the first column is par0 and the second column is par1. """ def check2(value, pname, opt=True): """Check value is None (when opt set) or has size of 2""" if opt and value is None: return None if np.isscalar(value): raise ConfidenceErr('badarg', pname, 'a list') if len(value) != 2: raise ConfidenceErr('badarg', pname, 'a list of size 2') # Ensure we return an ndarray return np.asarray(value) # Issue #1093 points out that if min or max is a tuple we can # have a problem, as below the code can assign to an element # of one of them. So ensure we have a list not a tuple. The # exact setting of min/max (whether a tuple, list, or ndarray) # makes the following code a bit messy. # # See also #1967 which discusses what type we should accept # for fields like min and max. # if self.min is not None: try: self.min = list(self.min) except TypeError: raise ConfidenceErr( 'badarg', 'Parameter limits', 'a list') from None if self.max is not None: try: self.max = list(self.max) except TypeError: raise ConfidenceErr( 'badarg', 'Parameter limits', 'a list') from None # We ignore the return value for min/max check2(self.min, "Parameter limits") check2(self.max, "Parameter limits") nloop = check2(self.nloop, "Nloop parameter", opt=False) delv = check2(self.delv, "delv parameter") if np.any(nloop <= 1): raise ConfidenceErr('badarg', 'Nloop parameter', 'a list with elements > 1') if delv is not None and np.any(delv <= 0): raise ConfidenceErr('badarg', 'delv parameter', 'a list with elements > 0') self.stat = fit.calc_stat() self.xlabel = par0.fullname self.ylabel = par1.fullname self.parval0 = par0.val self.parval1 = par1.val if self.levels is None: stat = self.stat if self.sigma is None or np.isscalar(self.sigma): raise ConfidenceErr('needlist', 'sigma bounds') sigma = np.asarray(self.sigma) lvls = stat - (2. * np.log(1. - erf(sigma / np.sqrt(2.)))) self.levels = np.asarray(lvls, dtype=SherpaFloat) if self.min is None or self.max is None: oldestmethod = fit.estmethod fit.estmethod = Covariance() r = fit.est_errors() fit.estmethod = oldestmethod index0 = list(r.parnames).index(par0.fullname) index1 = list(r.parnames).index(par1.fullname) if self.min is None: self.min = np.array([par0.min, par1.min]) min0 = r.parmins[index0] min1 = r.parmins[index1] if min0 is not None and not np.isnan(min0): self.min[0] = par0.val + min0 if min1 is not None and not np.isnan(min1): self.min[1] = par1.val + min1 if self.max is None: self.max = np.array([par0.max, par1.max]) max0 = r.parmaxes[index0] max1 = r.parmaxes[index1] if max0 is not None and not np.isnan(max0): self.max[0] = par0.val + max0 if max1 is not None and not np.isnan(max1): self.max[1] = par1.val + max1 # This assumes that self.min/max are ndarray v = (self.max + self.min) / 2. dv = np.fabs(v - self.min) self.min = v - self.fac * dv self.max = v + self.fac * dv hmin = np.array([par0.min, par1.min]) hmax = np.array([par0.max, par1.max]) for i in [0, 1]: # check user limits for errors if self.min[i] >= self.max[i]: raise ConfidenceErr('badlimits') if self.min[i] < hmin[i]: self.min[i] = hmin[i] if self.max[i] > hmax[i]: self.max[i] = hmax[i] if delv is None: # Just make the following code easier to write delv = [None, None] x0 = calc_par_range(self.min[0], self.max[0], self.nloop[0], delv=delv[0], log=self.log[0]) x1 = calc_par_range(self.min[1], self.max[1], self.nloop[1], delv=delv[1], log=self.log[1]) # Does it matter whether this is x0,x1 or x1,x0? Not for the # calculation of the parameter values, but the contour # plotting may care (but at least self.x0 and self.x1 should # match the ordering of self.y). # x0g, x1g = np.meshgrid(x0, x1) self.x0 = x0g.flatten() self.x1 = x1g.flatten() return np.array([self.x0, self.x1]).T
[docs] def calc(self, fit, par0, par1): """Evaluate the statistic for the parameter range. This requires prepare to have been called, and must be called before contour is called. Parameters ---------- fit The Sherpa fit instance to use (defines the statistic and optimiser to use). par0, par1 The parameters to iterate over. See Also -------- contour, prepare Notes ----- This method is assumed to be over-ridden in derived classes, where it will perform the statistic calculations needed to create the visualization. This version should be called from these classes as it validates the fit and par arguments. """ if isinstance(fit.stat, LeastSq): raise ConfidenceErr('badargconf', fit.stat.name) # Check that each parameter # - is thawed # - is part of the model expression # for par in [par0, par1]: if par.frozen: raise ConfidenceErr('frozen', par.fullname, f'region {self.conf_type}') if par not in fit.model.pars: raise ConfidenceErr('thawed', par.fullname, fit.model.name)
# TODO: should this be overcontour rather than overplot?
[docs] def contour(self, overplot=False, clearwindow=True, **kwargs): if self.log[0]: self.contour_prefs['xlog'] = True if self.log[1]: self.contour_prefs['ylog'] = True # Work around possible naming issue with overplot/overcontour. # Set overcontour if either overcontour or overplot are set. # The overcontour argument must be removed from kwargs if # sent, hence it us used first here (if second lazy evaluation # could mean the pop is never executed). # overcontour = kwargs.pop('overcontour', False) or overplot Contour.contour(self, self.x0, self.x1, self.y, levels=self.levels, title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, overcontour=overcontour, clearwindow=clearwindow, **kwargs) # Note: the user arguments are not applied to the point # point = Point() point.point_prefs = self.point_prefs point.point(self.parval0, self.parval1, overplot=True, clearwindow=False) if self.log[0]: self.contour_prefs['xlog'] = False if self.log[1]: self.contour_prefs['ylog'] = False
class IntervalProjectionWorker(): """Used to evaluate the model by IntervalProjection. .. versionchanged:: 4.16.1 The calling convention was changed. See Also -------- IntervalUncertaintyWorker """ def __init__(self, par, fit, otherpars): self.par = par self.fit = fit self.otherpars = otherpars def __call__(self, val): self.par.val = val # It there are other parameters then we need to fit to get the # best-fit statistic. # if self.otherpars: r = self.fit.fit() return r.statval # If this was the only free parameter we can just calculate # the statistic value. # return self.fit.calc_stat()
[docs] class IntervalProjection(Confidence1D): """The Interval-Projection method. Evaluate the parameter value on a grid of points, allowing the other thawed parameters to be fit. .. versionchanged:: 4.16.1 Handling of log-scaled axes has been improved and the string output now includes the parameter value (if available). See Also -------- IntervalUncertainty """ conf_type = "projection" def __init__(self): self.fast = True super().__init__()
[docs] def prepare(self, fast=True, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None): self.fast = fast Confidence1D.prepare(self, min, max, nloop, delv, fac, log, numcores)
[docs] def calc(self, fit, par, methoddict=None, cache=True): self.title = 'Interval-Projection' Confidence1D.calc(self, fit, par) # If "fast" option enabled, set fitting method to # lmdif if stat is chi-squared, # else set to neldermead. # If current method is not LM or NM, warn it is not a good # method for estimating parameter limits. # if not isinstance(fit.method, (NelderMead, LevMar)): warning("%s is inappropriate for confidence limit " "estimation", fit.method.name) oldfitmethod = fit.method if bool_cast(self.fast) is True and methoddict is not None: if isinstance(fit.stat, Likelihood): if not isinstance(fit.method, NelderMead): fit.method = methoddict['neldermead'] warning("Setting optimization to %s for interval " "projection plot", fit.method.name) elif not isinstance(fit.method, LevMar): fit.method = methoddict['levmar'] warning("Setting optimization to %s for interval " "projection plot", fit.method.name) xvals = self._interval_init(fit, par) oldpars = fit.model.thawedpars par.freeze() # We know that par is thawed, so we can check to see whether a fit # is needed by looking for other parameters. # otherpars = len(oldpars) > 1 # Store these before we enter the try block as they are used # in the finally block. # startup = fit.model.startup teardown = fit.model.teardown try: fit.model.startup(cache) # these calls are unnecessary for every fit fit.model.startup = return_none fit.model.teardown = return_none worker = IntervalProjectionWorker(par, fit, otherpars) res = parallel_map(worker, xvals, self.numcores) self.y = np.asarray(res) finally: # Set back data that we changed par.thaw() fit.model.startup = startup fit.model.teardown = teardown fit.model.teardown() fit.model.thawedpars = oldpars fit.method = oldfitmethod
class IntervalUncertaintyWorker(): """Used to evaluate the model by IntervalUncertainty. .. versionchanged:: 4.16.1 The calling convention was changed. See Also -------- IntervalProjectionWorker """ def __init__(self, par, fit): self.par = par self.fit = fit def __call__(self, val): self.par.val = val return self.fit.calc_stat()
[docs] class IntervalUncertainty(Confidence1D): """The Interval-Projection method. Evaluate the parameter value on a grid of points, where the other thawed parameters are *not* changed from their current values. .. versionchanged:: 4.16.1 Handling of log-scaled axes has been improved and the string output now includes the parameter value (if available). See Also -------- IntervalProjection """ conf_type = "uncertainty"
[docs] def calc(self, fit, par, methoddict=None, cache=True): self.title = 'Interval-Uncertainty' Confidence1D.calc(self, fit, par) thawed = [p for p in fit.model.pars if not p.frozen] oldpars = fit.model.thawedpars xvals = self._interval_init(fit, par) for p in thawed: p.freeze() try: fit.model.startup(cache) worker = IntervalUncertaintyWorker(par, fit) res = parallel_map(worker, xvals, self.numcores) self.y = np.asarray(res) finally: # Set back data that we changed for p in thawed: p.thaw() fit.model.teardown() fit.model.thawedpars = oldpars
class RegionProjectionWorker(): """Used to evaluate the model by RegionProjection. .. versionchanged:: 4.16.1 The calling convention was changed. See Also -------- RegionUncertaintyWorker """ def __init__(self, par0, par1, fit, otherpars): self.par0 = par0 self.par1 = par1 self.fit = fit self.otherpars = otherpars def __call__(self, pars): (self.par0.val, self.par1.val) = pars # It there are other parameters then we need to fit to get the # best-fit statistic. # if self.otherpars: r = self.fit.fit() return r.statval # If these were the only two free parameters we can just # calculate the statistic value. # return self.fit.calc_stat() def return_none(cache=None): """dummy implementation of callback for multiprocessing""" return None
[docs] class RegionProjection(Confidence2D): """The Region-Projection method. Evaluate the statistic on a grid of points for two parameters, where the other thawed parameters are fit for each location. .. versionchanged:: 4.16.1 Support for logarithmically-spaced grids has been improved. See Also -------- RegionUncertainty """ conf_type = "projection" def __init__(self): self.fast = True super().__init__()
[docs] def prepare(self, fast=True, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None): self.fast = fast Confidence2D.prepare(self, min, max, nloop, delv, fac, log, sigma, levels, numcores)
[docs] def calc(self, fit, par0, par1, methoddict=None, cache=True): self.title = 'Region-Projection' Confidence2D.calc(self, fit, par0, par1) # If "fast" option enabled, set fitting method to # lmdif if stat is chi-squared, # else set to neldermead # If current method is not LM or NM, warn it is not a good # method for estimating parameter limits. # if not isinstance(fit.method, (NelderMead, LevMar)): warning("%s is inappropriate for confidence limit " "estimation", fit.method.name) oldfitmethod = fit.method if bool_cast(self.fast) is True and methoddict is not None: if isinstance(fit.stat, Likelihood): if not isinstance(fit.method, NelderMead): fit.method = methoddict['neldermead'] warning("Setting optimization to %s for region " "projection plot", fit.method.name) elif not isinstance(fit.method, LevMar): fit.method = methoddict['levmar'] warning("Setting optimization to %s for region " "projection plot", fit.method.name) oldpars = fit.model.thawedpars otherpars = len(oldpars) > 2 # Store these before we enter the try block as they are used # in the finally block. # startup = fit.model.startup teardown = fit.model.teardown try: fit.model.startup(cache) # these calls are unnecessary for every fit fit.model.startup = return_none fit.model.teardown = return_none grid = self._region_init(fit, par0, par1) par0.freeze() par1.freeze() worker = RegionProjectionWorker(par0, par1, fit, otherpars) results = parallel_map(worker, grid, self.numcores) self.y = np.asarray(results) finally: # Set back data after we changed it par0.thaw() par1.thaw() fit.model.startup = startup fit.model.teardown = teardown fit.model.teardown() fit.model.thawedpars = oldpars fit.method = oldfitmethod
class RegionUncertaintyWorker(): """Used to evaluate the model by RegionUncertainty. .. versionchanged:: 4.16.1 The calling convention was changed. See Also -------- RegionProjectionWorker """ def __init__(self, par0, par1, fit): self.par0 = par0 self.par1 = par1 self.fit = fit def __call__(self, pars): (self.par0.val, self.par1.val) = pars return self.fit.calc_stat()
[docs] class RegionUncertainty(Confidence2D): """The Region-Projection method. Evaluate the statistic on a grid of points for two parameters, where the other thawed parameters are *not* changed from their current values. .. versionchanged:: 4.16.1 Support for logarithmically-spaced grids has been improved. See Also -------- RegionProjection """ conf_type = "uncertainty"
[docs] def calc(self, fit, par0, par1, methoddict=None, cache=True): self.title = 'Region-Uncertainty' Confidence2D.calc(self, fit, par0, par1) thawed = [i for i in fit.model.pars if not i.frozen] oldpars = fit.model.thawedpars try: fit.model.startup(cache) grid = self._region_init(fit, par0, par1) for p in thawed: p.freeze() worker = RegionUncertaintyWorker(par0, par1, fit) result = parallel_map(worker, grid, self.numcores) self.y = np.asarray(result) finally: # Set back data after we changed it for p in thawed: p.thaw() fit.model.teardown() fit.model.thawedpars = oldpars
[docs] class MultiPlot: """Combine multiple line-style plots. Allow multiple line-style plots - so those plot classes that use the `plot` method to display - to be drawn in the same area. Each plot is added with the add method. """ __slots__ = ("plots", "title") def __init__(self) -> None: self.plots: list[Union[Plot, HistogramPlot]] = [] self.title = "" # The typing here says Plot but we actually want the sub-classes # like DataPlot (i.e. those that use the prepare method to set up # the data to plot so that the plot method requires no data # arguments) rather than the actual Plot class. #
[docs] def add(self, plot: Union[Plot, HistogramPlot]) -> None: """Add the plot to the list of data to plot. A copy of the plot object is stored, rather than the input argument. The `title` attribute can be set or changed. Parameters ---------- plot : instance The plot or histogram object to add. It must have a `plot` method. """ # A copy is stored since often the same underlying object is # returned by the UI routines. It also allows the code to # change the self.plots array and not worry about changing the # calling code. # self.plots.append(copy.deepcopy(plot))
[docs] def plot(self, overplot: bool = False, clearwindow: bool = True, **kwargs) -> None: """Plot the data. Parameters ---------- overplot : bool, optional If `True` then add the data to an existing plot, otherwise create a new plot. clearwindow : bool, optional Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? **kwargs These values are passed on to the plot backend, and must match the names of the keys of the object's plot_prefs dictionary. Note that the same arguments are passed to each plot. """ for plot in self.plots: plot.plot(overplot=overplot, clearwindow=clearwindow, **kwargs) # To decide whether we draw a title or not we do rely on # the clearwindow setting, since it is not guaranteed to # be set (e.g. if this is being displayed within a # SplitPlot). So the overplot setting is used to decide # whether to add in the title or not. # if not overplot: backend.set_title(self.title) overplot = True clearwindow = False