# todo:
# keywords for show_both need to be arrays. ugh.
#
# allow levels to be percent?
#
# Look into seaborn https://seaborn.pydata.org
# Also https://docs.bokeh.org/en
# especially for coloring and style
import warnings
from copy import deepcopy
import astropy.units as u
import astropy.wcs as wcs
import numpy as np
import scipy.stats as stats
from astropy import log
from astropy.units import UnitsWarning
from matplotlib.lines import Line2D
from .. import pdrutils as utils
from .modelplot import ModelPlot
from .plotbase import PlotBase
log.setLevel("WARNING") # see issue 163
[docs]
class LineRatioPlot(PlotBase):
"""LineRatioPlot plots the results of :class:`~pdrtpy.tools.lineratiofit.LineRatioFit`. It can plot maps of fit results, observations with errors on top of models, chi-square and confidence intervals and more.
:Keyword Arguments:
The methods of this class can take a variety of optional keywords. See the general `Plot Keywords`_ documentation
"""
def __init__(self, tool):
"""Init method
:param tool: The line ratio fitting tool that is to be plotted.
:type tool: `~pdrtpy.tool.LineRatioFit`
"""
super().__init__(tool)
self._figure = None
self._axis = None
self._modelplot = ModelPlot(self._tool._modelset, self._figure, self._axis)
self._ratiocolor = []
[docs]
def modelintensity(self, id, **kwargs):
"""Plot one of the model intensities
:param id: the intensity identifier, such as `CO_32``.
:type id: str
:param \**kwargs: see class documentation above
:raises KeyError: if is id not in existing model intensities
"""
ms = self._tool.modelset
if id not in ms.supported_intensities["intensity label"]:
raise KeyError(f"{id} is not in the ModelSet of your LineRatioFit")
model = ms.get_models([id], model_type="intensity")
kwargs_opts = {"title": self._tool._modelset.table.loc[id]["title"], "colorbar": True}
kwargs_opts.update(kwargs)
self._modelplot._plot_no_wcs(model[id], **kwargs_opts)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def modelratio(self, id, **kwargs):
"""Plot one of the model ratios
:param id: the ratio identifier, such as ``CII_158/CO_32``.
:type id: str
:param \**kwargs: see class documentation above
:raises KeyError: if is id not in existing model ratios
"""
if self._tool._modelratios[id].shape == (1,): # does this ever occur??
return self._tool._modelratios[id]
kwargs_opts = {
"title": self._tool._modelset.table.loc[id]["title"],
"units": u.dimensionless_unscaled,
"colorbar": True,
}
kwargs_opts.update(kwargs)
self._modelplot._plot_no_wcs(self._tool._modelratios[id], **kwargs_opts)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def observedratio(self, id, **kwargs):
"""Plot one of the observed ratios
:param id: the ratio identifier, such as ``CII_158/CO_32``.
:type id: - str
:raises KeyError: if id is not in existing observed ratios
"""
if self._tool._observedratios[id].shape == (1, 0) or self._tool.has_vectors:
return self._tool._observedratios[id]
kwargs_opts = {
"title": self._tool._modelset.table.loc[id]["title"],
"units": u.dimensionless_unscaled,
"colorbar": False,
}
kwargs_opts.update(kwargs)
self._plot(data=self._tool._observedratios[id], **kwargs_opts)
[docs]
def density(self, **kwargs):
"""Plot the hydrogen nucleus volume density map that was computed by :class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool. Default units: cm :math:`^{-3}`"""
kwargs_opts = {
"units": "cm^-3",
"aspect": "equal",
"image": True,
"contours": False,
"label": False,
"linewidths": 1.0,
"levels": None,
"norm": None,
"title": None,
}
kwargs_opts.update(kwargs)
# handle single pixel or multi-pixel non-map cases.
if self._tool._density.shape == (1,) or self._tool.has_vectors:
return utils.to(kwargs_opts["units"], self._tool._density)
tunit = u.Unit(kwargs_opts["units"])
if kwargs_opts["title"] is None:
kwargs_opts["title"] = r"n [{0:latex_inline}]".format(tunit)
self._plot(self._tool._density, **kwargs_opts)
[docs]
def radiation_field(self, **kwargs):
"""Plot the radiation field map that was computed by :class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool. Default units: Habing."""
kwargs_opts = {
"units": "Habing",
"aspect": "equal",
"image": True,
"contours": False,
"label": False,
"linewidths": 1.0,
"levels": None,
"norm": None,
"title": None,
}
kwargs_opts.update(kwargs)
# handle single pixel or multi-pixel non-map cases.
if self._tool.radiation_field.shape == (1,) or self._tool.has_vectors:
return utils.to(kwargs_opts["units"], self._tool.radiation_field)
if kwargs_opts["title"] is None:
rad_title = utils.get_rad(kwargs_opts["units"])
tunit = u.Unit(kwargs_opts["units"])
kwargs_opts["title"] = r"{0} [{1:latex_inline}]".format(rad_title, tunit)
self._plot(self._tool.radiation_field, **kwargs_opts)
# @TODO refactor this method with reduced_chisq()
[docs]
def chisq(self, **kwargs):
"""Plot the :math:`\chi^2` map that was computed by the
:class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool.
"""
kwargs_opts = {
"units": None,
"aspect": "equal",
"image": True,
"contours": True,
"label": False,
"colors": ["white"],
"linewidths": 1.0,
"norm": "simple",
"stretch": "linear",
"xaxis_unit": None,
"yaxis_unit": None,
"legend": None,
"bbox_to_anchor": None,
"loc": "upper center",
"title": None,
}
kwargs_opts.update(kwargs)
# make a sensible choice about contours if image is not shown
if not kwargs_opts["image"] and kwargs_opts["colors"][0] == "white":
kwargs_opts["colors"][0] = "black"
if self._tool.has_vectors:
raise NotImplementedError("Plotting of chi-square is not yet implemented for vector Measurements.")
if self._tool.has_maps:
data = self._tool.chisq(min=True)
if "title" not in kwargs:
kwargs_opts["title"] = r"$\chi^2$ (dof=%d)" % self._tool._dof
self._plot(data, **kwargs_opts)
else:
data = self._tool.chisq(min=False)
self._modelplot._plot_no_wcs(data, header=None, **kwargs_opts)
# Put a crosshair where the chisq minimum is.
if False:
# To do this we first get the array index of the minimum
# then use WCS to translate to world coordinates.
[row, col] = np.where(self._tool._chisq == self._tool._chisq_min.value)
mywcs = wcs.WCS(data.header)
# Suppress WCS warning about 1/cm3 not being FITS
warnings.simplefilter("ignore", category=UnitsWarning)
logn, logrf = mywcs.array_index_to_world(row, col)
warnings.resetwarnings()
n = (10 ** logn.value[0]) * u.Unit(logn.unit.to_string())
rf = (10 ** logrf.value[0]) * u.Unit(logrf.unit.to_string())
# since the minimum can now be between pixels, we use the value
# of radiation field and density directly. (Why didn't we do this before?)
if kwargs_opts["xaxis_unit"] is not None:
x = utils.to(self._tool._density, kwargs_opts["xaxis_unit"]).value
else:
x = self._tool._density.value
if kwargs_opts["yaxis_unit"] is not None:
y = utils.to(kwargs_opts["yaxis_unit"], self._tool._radiation_field).value
else:
y = self._tool._radiation_field.value
# print("Plot x,y %s,%s"%(x,y))
if kwargs_opts["title"] is None:
kwargs_opts["title"] = r"$\chi^2$ (dof=%d)" % self._tool._dof
label = r"$\chi_{min}^2$ = %.2g @ (n,FUV) = (%.2g,%.2g)" % (self._tool._chisq_min.value[0], x[0], y[0])
self._modelplot._axis[0].scatter(x, y, c="r", marker="+", s=200, linewidth=2, label=label)
# handle legend locally
if kwargs_opts["legend"]:
legend = self._modelplot._axis[0].legend(
title=kwargs_opts["title"], bbox_to_anchor=kwargs_opts["bbox_to_anchor"], loc=kwargs_opts["loc"]
)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def reduced_chisq(self, **kwargs):
"""Plot the reduced :math:`\chi^2` map that was computed by the
:class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool.
"""
kwargs_opts = {
"units": None,
"aspect": "equal",
"image": True,
"contours": True,
"label": False,
"colors": ["white"],
"linewidths": 1.0,
"norm": "simple",
"stretch": "linear",
"xaxis_unit": None,
"yaxis_unit": None,
"legend": None,
"bbox_to_anchor": None,
"loc": "upper center",
"title": None,
}
kwargs_opts.update(kwargs)
if self._tool.has_vectors:
raise NotImplementedError("Plotting of chi-square is not yet implemented for vector Measurements.")
if self._tool.has_maps:
if "title" not in kwargs:
kwargs_opts["title"] = r"$\chi_\nu^2$ (dof=%d)" % self._tool._dof
data = self._tool.reduced_chisq(min=True)
self._plot(data, **kwargs_opts)
# doesn't make sense to point out minimum chisq on a spatial-spatial map,
# so no legend
else:
data = self._tool.reduced_chisq(min=False)
self._modelplot._plot_no_wcs(data, header=None, **kwargs_opts)
# Put a crosshair where the chisq minimum is.
if False:
# To do this we first get the array index of the minimum
# then use WCS to translate to world coordinates.
[row, col] = np.where(self._tool._reduced_chisq == self._tool._reduced_chisq_min.value)
mywcs = wcs.WCS(data.header)
# Suppress WCS warning about 1/cm3 not being FITS
warnings.simplefilter("ignore", category=UnitsWarning)
logn, logrf = mywcs.array_index_to_world(row, col)
warnings.resetwarnings()
# logn, logrf are Quantities of the log(density) and log(radiation field),
# respectively. The model default units are cm^-2 and erg/s/cm^-2.
# These must be converted to plot units based on user input
# xaxis_unit and yaxis_unit.
# Note: multiplying by n.unit causes the ValueError:
# "The unit '1/cm3' is unrecognized, so all arithmetic operations with it are invalid."
# Yet by all other measures this appears to be a valid unit.
# The workaround is to us to_string() method.
n = (10 ** logn.value[0]) * u.Unit(logn.unit.to_string())
rf = (10 ** logrf.value[0]) * u.Unit(logrf.unit.to_string())
# since the minimum can now be between pixels, we use the value
# of radiation field and density directly. (Why didn't we do this before?)
if kwargs_opts["xaxis_unit"] is not None:
x = utils.to(self._tool._density, kwargs_opts["xaxis_unit"]).value
else:
x = self._tool._density.value
if kwargs_opts["yaxis_unit"] is not None:
y = utils.to(kwargs_opts["yaxis_unit"], self._tool._radiation_field).value
else:
y = self._tool._radiation_field.value
# print("Plot x,y %s,%s"%(x,y))
if kwargs_opts["title"] is None:
kwargs_opts["title"] = r"$\chi_\nu^2$ (dof=%d)" % self._tool._dof
label = r"$\chi_{\nu,min}^2$ = %.2g @ (n,FUV) = (%.2g,%.2g)" % (
self._tool._reduced_chisq_min.value[0],
x[0],
y[0],
)
self._modelplot.axis[0].scatter(x, y, c="r", marker="+", s=200, linewidth=2, label=label)
# handle legend locally
if kwargs_opts["legend"]:
legend = self._modelplot.axis[0].legend(
title=kwargs_opts["title"], bbox_to_anchor=kwargs_opts["bbox_to_anchor"], loc=kwargs_opts["loc"]
)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def show_both(self, units=["Habing", "cm^-3"], **kwargs):
"""Plot both radiation field and volume density maps computed by the
:class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool in a 1x2 panel subplot. Default units: ['Habing','cm^-3']
"""
_index = [1, 2]
_reset = [True, False]
kwargs_opts = {
"image": True,
"aspect": "equal",
"contours": False,
"label": False,
"levels": None,
"norm": None,
"title": None,
"nrows": 1,
"ncols": 2,
"index": _index[0],
"reset": _reset[0],
}
kwargs_opts.update(kwargs)
rf = self.radiation_field(units=units[0], **kwargs_opts)
kwargs_opts["index"] = _index[1]
kwargs_opts["reset"] = _reset[1]
d = self.density(units=units[1], **kwargs_opts)
# @todo don't return for plots. print for non-plots
return (rf, d)
[docs]
def confidence_intervals(self, **kwargs):
"""Plot the confidence intervals from the :math:`\chi^2` map computed by the
:class:`~pdrtpy.tool.lineratiofit.LineRatioFit` tool. Default levels: [50., 68., 80., 95., 99.]
**Currently only works for single-pixel Measurements**
"""
if self._tool.has_maps or self._tool.has_vectors:
raise NotImplementedError("Plotting of confidence intervals is not yet implemented for maps or vectors.")
kwargs_opts = {
"units": None,
"aspect": "auto",
"image": False,
"contours": True,
"label": True,
"levels": [50.0, 68.0, 80.0, 95.0, 99.0],
"colors": ["black"],
"linewidths": 1.0,
"norm": "simple",
"stretch": "linear",
"xaxis_unit": None,
"yaxis_unit": None,
"title": "Confidence Intervals",
}
# ensure levels are in ascending order
kwargs_opts["levels"] = sorted(kwargs_opts["levels"])
kwargs_opts.update(kwargs)
confidence = deepcopy(self._tool._chisq)
confidence.data = 100 * stats.distributions.chi2.cdf(confidence.data, self._tool._dof)
self._tool.confidence = confidence
self._modelplot._plot_no_wcs(data=confidence, header=None, **kwargs_opts)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def overlay_all_ratios(self, **kwargs):
"""Overlay all the measured ratios and their errors on the :math:`(n,F_{FUV})` space.
This only works for single-valued Measurements; an overlay for multi-pixel doesn't make sense.
"""
# NB: could have position and area though.
if self._tool.has_maps or self._tool.has_vectors:
raise NotImplementedError("Plotting of ratio overlays is not yet implemented for maps or vectors.")
kwargs_opts = {
"units": None,
"image": False,
"contours": False,
"meas_color": self._CB_color_cycle,
"levels": None,
"label": False,
"linewidths": 1.0,
"ncols": 1,
"norm": None,
"title": None,
"reset": True,
"legend": True,
"bbox_to_anchor": None,
"loc": "upper center",
}
kwargs_opts.update(kwargs)
# force this as ncols !=1 makes no sense.
kwargs_opts["ncols"] = 1
i = 0
_measurements = list()
_models = list()
# get_model will correctly raise exception if m.id not in ModelSet
meas_passed = False
if kwargs_opts.get("measurements", None) is not None:
# avoid modifying a passed parameter
_measurements = deepcopy(kwargs_opts["measurements"])
meas_passed = True
for m in _measurements:
if i > 0:
kwargs_opts["reset"] = False
val = self._tool.modelset.get_model(m.id)
_models.append(val)
kwargs_opts["measurements"] = [utils.convert_if_necessary(m)]
self._modelplot._plot_no_wcs(_models[i], header=None, colorcounter=i, **kwargs_opts)
i = i + 1
km = kwargs_opts.pop("measurements")
# for m in _measurements:
# print("A ",type(m))
for key, val in self._tool._modelratios.items():
# kwargs_opts['measurements'] = [self._tool._observedratios[key]]
# print(" K ",type(self._tool._observedratios[key]))
# _measurements.append(self._tool._observedratios[key])
if i > 0:
kwargs_opts["reset"] = False
# pass the index of the contour color to use via the "secret" colorcounter keyword.
self._modelplot._plot_no_wcs(
val, header=None, colorcounter=i, **kwargs_opts, measurements=[self._tool._observedratios[key]]
)
i = i + 1
# for m in _measurements:
# print("B ",type(m))
if kwargs_opts["legend"]:
lines = [Line2D([0], [0], color=c, linewidth=3, linestyle="-") for c in kwargs_opts["meas_color"][0:i]]
labels = list()
if meas_passed:
# THIS WILL EXCEPT IF m.id NOT IN MODELSET WHICH IS VERY POSSIBLE!
for m in _measurements:
try:
tt = self._tool.modelset.get_model(m.id).title
except Exception:
tt = m.id
labels.append(tt)
# labels = [m.id for m in _measurements]
title = "Observed Ratios and Intensities"
else:
title = "Observed Ratios"
labels.extend([self._tool._modelratios[k].title for k in self._tool._modelratios])
# print("LABELS ",labels)
_title = kwargs.get("title", None)
if _title is not None:
title += " " + _title
self._plt.legend(
lines, labels, loc=kwargs_opts["loc"], bbox_to_anchor=kwargs_opts["bbox_to_anchor"], title=title
)
self._figure = self._modelplot.figure
self._axis = self._modelplot.axis
[docs]
def ratios_on_models(self, **kwargs):
"""Overlay all the measured ratios and their errors on the individual models for those ratios. Plots are displayed in multi-column format, controlled the `ncols` keyword. Default: ncols=2
**Currently only works for single-pixel Measurements**
"""
if self._tool.has_maps or self._tool.has_vectors:
raise NotImplementedError("Plotting of ratio overlays is not yet implemented for maps or vectors.")
kwargs_opts = {
"units": None,
"image": True,
"colorbar": True,
"contours": True,
"colors": ["white"],
"levels": None,
"label": False,
"linewidths": 1.0,
"meas_color": ["#4daf4a"],
"ncols": 2,
"norm": "simple",
"stretch": "linear",
"index": 1,
"reset": True,
"legend": True,
"bbox_to_anchor": None,
"loc": "upper center",
}
kwargs_opts.update(kwargs)
kwargs_opts["ncols"] = min(kwargs_opts["ncols"], self._tool.ratiocount)
kwargs_opts["nrows"] = int(round(self._tool.ratiocount / kwargs_opts["ncols"] + 0.49, 0))
# defend against meas_color not being a list
if isinstance(kwargs_opts["meas_color"], str):
# warnings.warn("meas_color should be a list")
kwargs_opts["meas_color"] = [kwargs_opts["meas_color"]]
for key, val in self._tool._modelratios.items():
axidx = kwargs_opts["index"] - 1
if kwargs_opts["index"] > 1:
kwargs_opts["reset"] = False
m = self._tool._model_files_used[key]
kwargs_opts["measurements"] = [self._tool._observedratios[key]]
self._modelplot._plot_no_wcs(val, header=None, **kwargs_opts)
self._axis = self._modelplot._axis
self._figure = self._modelplot._figure
kwargs_opts["index"] = kwargs_opts["index"] + 1
if kwargs_opts["legend"]:
if "title" not in kwargs: # then it was None, and we customize it
_title = self._tool._modelratios[key].title
else:
_title = kwargs["title"]
lines = list()
labels = list()
if kwargs_opts["contours"]:
lines.append(Line2D([0], [0], color=kwargs_opts["colors"][0], linewidth=3, linestyle="-"))
labels.append("model")
lines.append(Line2D([0], [0], color=kwargs_opts["meas_color"][0], linewidth=3, linestyle="-"))
labels.append("observed")
# maybe loc should be 'best' but then it bounces around
self._axis[axidx].legend(
lines, labels, bbox_to_anchor=kwargs_opts["bbox_to_anchor"], loc=kwargs_opts["loc"], title=_title
)
# Turn off subplots greater than the number of
# available ratios
for i in range(self._tool.ratiocount, len(self._axis)):
self._axis[i].axis("off")
