# coding: utf-8
# Distributed under the terms of the MIT License.
""" This file implements plotting routines specifically
for the PXRD objects defined in the
matador.fingerprints.pxrd module.
"""
from matador.plotting.plotting import plotting_function
from matador.utils.cell_utils import get_space_group_label_latex
from matador.crystal import Crystal
__all__ = ['plot_pxrd']
[docs]@plotting_function
def plot_pxrd(
pxrds, two_theta_range=None, rug=False, rug_height=0.05, rug_offset=0.04, offset=None,
ax=None, labels=None, figsize=None, text_offset=0.1, filename=None, **kwargs
):
""" Plot PXRD or PXRDs.
Parameters:
pxrds (list or matador.fingerprints.pxrd.PXRD): the PXRD
or list of PXRDs to plot.
Keyword arguments:
two_theta_range (tuple): plotting limits for 2theta
rug (bool): whether to provide a rug plot of all peaks.
rug_height (float): size of rug ticks.
rug_offset (float): offset of rug ticks.
offset (float): extra space added between patterns (as fraction
of max intensity). Default 0.1.
labels (list of str): list of labels to plot alongside pattern.
figsize (tuple): specify a figure size, the default
scales with the number of PXRDs to be plotted.
ax (matplotlib.Axis): optional axis object to plot on.
text_offset (float): amount by which to offset the labels.
filename (str): optional filename for saving.
"""
import matplotlib.pyplot as plt
if not isinstance(pxrds, list):
pxrds = [pxrds]
if labels is not None and not isinstance(labels, list):
labels = [labels]
if figsize is None:
_user_default_figsize = plt.rcParams.get('figure.figsize', (8, 6))
height = len(pxrds) * max(0.5, _user_default_figsize[1] / 1.5 / len(pxrds))
figsize = (_user_default_figsize[0], height)
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
if offset is None and rug:
offset = 0.2
elif offset is None:
offset = 0.1
colour_cycle = ax._get_lines.prop_cycler
for ind, pxrd in enumerate(pxrds):
if isinstance(pxrd, Crystal):
pxrd = pxrd.pxrd
elif isinstance(pxrd, dict) and 'pxrd' in pxrd:
pxrd = pxrd['pxrd']
c = next(colour_cycle).get('color')
if labels:
label = labels[ind]
else:
label = get_space_group_label_latex(pxrd.spg) + '-' + pxrd.formula
ax.plot(pxrd.two_thetas, (1 - offset) * pxrd.pattern + ind, c=c)
ax.text(0.95, ind+text_offset, label,
transform=ax.get_yaxis_transform(),
horizontalalignment='right')
if rug:
import numpy as np
peaks = np.unique(pxrd.peak_positions)
for peak in peaks:
ax.plot([peak, peak], [ind-rug_height-rug_offset, ind-rug_offset], c=c, alpha=0.5)
if len(pxrds) > 1:
ax.set_yticks([])
else:
import numpy as np
ax.set_yticks(np.linspace(0, 1, 5, endpoint=True))
ax.set_ylim(-0.2, len(pxrds)+0.1)
if two_theta_range is not None:
ax.set_xlim(*two_theta_range)
ax.set_ylabel('Relative intensity')
ax.set_xlabel('$2\\theta$ (degrees)')
if any([kwargs.get('pdf'), kwargs.get('svg'), kwargs.get('png')]):
bbox_extra_artists = None
if filename is None:
filename = '-'.join([pxrd.formula for pxrd in pxrds]) + '_pxrd'
if kwargs.get('pdf'):
plt.savefig('{}.pdf'.format(filename),
bbox_inches='tight', transparent=True, bbox_extra_artists=bbox_extra_artists)
if kwargs.get('svg'):
plt.savefig('{}.svg'.format(filename),
bbox_inches='tight', transparent=True, bbox_extra_artists=bbox_extra_artists)
if kwargs.get('png'):
plt.savefig('{}.png'.format(filename),
bbox_inches='tight', transparent=True, bbox_extra_artists=bbox_extra_artists)