Source code for threeML.io.plotting.data_residual_plot

from __future__ import division

from builtins import object, zip

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MaxNLocator
from past.utils import old_div
from threeML.config.config import threeML_config
from threeML.exceptions.custom_exceptions import custom_warnings
from threeML.io.logging import setup_logger
from threeML.io.package_data import get_path_of_data_file
from threeML.io.plotting.step_plot import step_plot

plt.style.use(str(get_path_of_data_file("threeml.mplstyle")))


log = setup_logger(__name__)


[docs]class ResidualPlot(object): def __init__(self, **kwargs): """ A class that makes data/residual plots :param show_residuals: to show the residuals :param ratio_residuals: to use ratios instead of sigma :param model_subplot: and axis or list of axes to plot to rather than create a new one """ self._ratio_residuals = False self._show_residuals = True if "show_residuals" in kwargs: self._show_residuals = bool(kwargs.pop("show_residuals")) if "ratio_residuals" in kwargs: self._ratio_residuals = bool(kwargs.pop("ratio_residuals")) # this lets you overplot other fits if "model_subplot" in kwargs: model_subplot = kwargs.pop("model_subplot") # turn on or off residuals if self._show_residuals: assert ( type(model_subplot) == list ), "you must supply a list of axes to plot to residual" assert ( len(model_subplot) == 2 ), "you have requested to overplot a model with residuals, but only provided one axis to plot" self._data_axis, self._residual_axis = model_subplot else: try: self._data_axis = model_subplot self._fig = self._data_axis.get_figure() except (AttributeError): # the user supplied a list of axes self._data_axis = model_subplot[0] # we will use the figure associated with # the data axis self._fig = self._data_axis.get_figure() else: # turn on or off residuals if self._show_residuals: self._fig, (self._data_axis, self._residual_axis) = plt.subplots( 2, 1, sharex=True, gridspec_kw={"height_ratios": [2, 1]}, **kwargs ) else: self._fig, self._data_axis = plt.subplots(**kwargs) @property def figure(self): """ :return: the figure instance """ return self._fig @property def data_axis(self): """ :return: the top or data axis """ return self._data_axis @property def residual_axis(self): """ :return: the bottom or residual axis """ assert self._show_residuals, "this plot has no residual axis" return self._residual_axis @property def show_residuals(self): return self._show_residuals @property def ratio_residuals(self): return self._ratio_residuals
[docs] def add_model_step(self, xmin, xmax, xwidth, y, label, **kwargs): """ Add a model but use discontinuous steps for the plotting. :param xmin: the low end boundaries :param xmax: the high end boundaries :param xwidth: the width of the bins :param y: the height of the bins :param label: the label of the model :param **kwargs: any kwargs passed to plot :return: None """ step_plot( np.asarray(list(zip(xmin, xmax))), old_div(y, xwidth), self._data_axis, label=label, **kwargs )
[docs] def add_model(self, x, y, label, **kwargs): """ Add a model and interpolate it across the energy span for the plotting. :param x: the evaluation energies :param y: the model values :param label: the label of the model :param **kwargs: any kwargs passed to plot :return: None """ self._data_axis.plot(x, y, label=label, **kwargs)
[docs] def add_data( self, x, y, residuals, label, xerr=None, yerr=None, residual_yerr=None, show_data=True, **kwargs ): """ Add the data for the this model :param x: energy of the data :param y: value of the data :param residuals: the residuals for the data :param label: label of the data :param xerr: the error in energy (or bin width) :param yerr: the errorbars of the data :param **kwargs: any kwargs passed to plot :return: """ # if we want to show the data if show_data: self._data_axis.errorbar(x, y, yerr=yerr, xerr=xerr, label=label, **kwargs) # if we want to show the residuals if self._show_residuals: # normal residuals from the likelihood if not self.ratio_residuals: residual_yerr = np.ones_like(residuals) self._residual_axis.axhline(0, linestyle="--", color="k") self._residual_axis.errorbar(x, residuals, yerr=residual_yerr, **kwargs)
[docs] def finalize( self, xlabel="x", ylabel="y", xscale="log", yscale="log", show_legend=True, invert_y=False, ): """ :param xlabel: :param ylabel: :param xscale: :param yscale: :param show_legend: :return: """ if show_legend: self._data_axis.legend( fontsize=threeML_config.plotting.residual_plot.legend_font_size, loc=0 ) self._data_axis.set_ylabel(ylabel) self._data_axis.set_xscale(xscale) if yscale == "log": self._data_axis.set_yscale(yscale, nonpositive="clip") else: self._data_axis.set_yscale(yscale) if self._show_residuals: self._residual_axis.set_xscale(xscale) locator = MaxNLocator(prune="upper", nbins=5) self._residual_axis.yaxis.set_major_locator(locator) self._residual_axis.set_xlabel(xlabel) if self.ratio_residuals: log.warning( "Residuals plotted as ratios: beware that they are not statistical quantites, and can not be used to asses fit quality" ) self._residual_axis.set_ylabel("Residuals\n(fraction of model)") else: self._residual_axis.set_ylabel("Residuals\n($\sigma$)") else: self._data_axis.set_xlabel(xlabel) # This takes care of making space for all labels around the figure self._fig.tight_layout() # Now remove the space between the two subplots # NOTE: this must be placed *after* tight_layout, otherwise it will be ineffective self._fig.subplots_adjust(hspace=0) if invert_y: self._data_axis.set_ylim(self._data_axis.get_ylim()[::-1]) return self._fig