src/qinfer/tomography/plotting_tools.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# plotting_tools.py: Functions for plotting tomographic data and estimates.
##
# © 2017, Chris Ferrie (csferrie@gmail.com) and
# Christopher Granade (cgranade@cgranade.com).
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
##
# TODO: unit tests!
## FEATURES ##################################################################
from __future__ import absolute_import
from __future__ import division
## IMPORTS ###################################################################
from builtins import map
import numpy as np
try:
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
except ImportError:
import warnings
warnings.warn("Could not import matplotlib.")
plt = None
Ellipse = None
# Since the rest of QInfer does not require QuTiP,
# we need to import it in a way that we don't propagate exceptions if QuTiP
# is missing or is too early a version.
from qinfer.utils import get_qutip_module
qt = get_qutip_module('3.2')
## EXPORTS ###################################################################
__all__ = [
'plot_rebit_modelparams',
'plot_decorate_rebits',
'plot_cov_ellipse',
'plot_rebit_prior',
'plot_rebit_posterior'
]
## CONSTANTS #################################################################
REBIT_AXES = [1, 2]
## FUNCTIONS #################################################################
def plot_rebit_modelparams(modelparams, rebit_axes=REBIT_AXES, **kwargs):
"""
Given model parameters representing rebits, plots the
rebit states as a scatter plot. Additional keyword arguments
are passed to :ref:`plt.scatter`.
:param np.ndarray modelparams: Model parameters representing
rebits.
:param list rebit_axes: List containing indices for the :math:`x`
and :math:`z` axes.
"""
mps = modelparams[:, rebit_axes] * np.sqrt(2)
plt.scatter(mps[:, 0], mps[:, 1], **kwargs)
def plot_decorate_rebits(basis=None, rebit_axes=REBIT_AXES):
"""
Decorates a figure with the boundary of rebit state space
and basis labels drawn from a :ref:`~qinfer.tomography.TomographyBasis`.
:param qinfer.tomography.TomographyBasis basis: Basis to use in
labeling axes.
:param list rebit_axes: List containing indices for the :math:`x`
and :math:`z` axes.
"""
ax = plt.gca()
if basis is not None:
labels = list(map(r'$\langle\!\langle {} | \rho \rangle\!\rangle$'.format,
# Pick out the x and z by default.
[basis.labels[rebit_axes[0]], basis.labels[rebit_axes[1]]]
))
plt.xlabel(labels[0])
plt.ylabel(labels[1])
ax.add_artist(plt.Circle([0, 0], 1, color='k', fill=False))
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-1.1, 1.1)
ax.set_aspect('equal')
def plot_cov_ellipse(cov, pos, nstd=2, ax=None, **kwargs):
# Copied from https://github.com/joferkington/oost_paper_code in
# accordance with its license agreement.
"""
Plots an `nstd` sigma error ellipse based on the specified covariance
matrix (`cov`). Additional keyword arguments are passed on to the
ellipse patch artist.
:param cov: The 2x2 covariance matrix to base the ellipse on.
:param pos: The location of the center of the ellipse. Expects a 2-element
sequence of ``[x0, y0]``.
:param nstd: The radius of the ellipse in numbers of standard deviations.
Defaults to 2 standard deviations.
:param ax: The axis that the ellipse will be plotted on. Defaults to the
current axis.
:return: A matplotlib ellipse artist.
"""
def eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:,order]
if ax is None:
ax = plt.gca()
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
# Width and height are "full" widths, not radius
width, height = 2 * nstd * np.sqrt(vals)
ellip = Ellipse(xy=pos, width=width, height=height, angle=theta, **kwargs)
ax.add_artist(ellip)
return ellip
def plot_rebit_prior(prior, rebit_axes=REBIT_AXES,
n_samples=2000, true_state=None, true_size=250,
force_mean=None,
legend=True,
mean_color_index=2
):
"""
Plots rebit states drawn from a given prior.
:param qinfer.tomography.DensityOperatorDistribution prior: Distribution over
rebit states to plot.
:param list rebit_axes: List containing indices for the :math:`x`
and :math:`z` axes.
:param int n_samples: Number of samples to draw from the
prior.
:param np.ndarray true_state: State to be plotted as a "true" state for
comparison.
"""
pallette = plt.rcParams['axes.color_cycle']
plot_rebit_modelparams(prior.sample(n_samples),
c=pallette[0],
label='Prior',
rebit_axes=rebit_axes
)
if true_state is not None:
plot_rebit_modelparams(true_state,
c=pallette[1],
label='True', marker='*', s=true_size,
rebit_axes=rebit_axes
)
if hasattr(prior, '_mean') or force_mean is not None:
mean = force_mean if force_mean is not None else prior._mean
plot_rebit_modelparams(
prior._basis.state_to_modelparams(mean)[None, :],
edgecolors=pallette[mean_color_index], s=250, facecolors='none', linewidth=3,
label='Mean',
rebit_axes=rebit_axes
)
plot_decorate_rebits(prior.basis,
rebit_axes=rebit_axes
)
if legend:
plt.legend(loc='lower left', ncol=3, scatterpoints=1)
def plot_rebit_posterior(updater, prior=None, true_state=None, n_std=3, rebit_axes=REBIT_AXES, true_size=250,
legend=True,
level=0.95,
region_est_method='cov'
):
"""
Plots posterior distributions over rebits, including covariance ellipsoids
:param qinfer.smc.SMCUpdater updater: Posterior distribution over rebits.
:param qinfer.tomography.DensityOperatorDistribution: Prior distribution
over rebit states.
:param np.ndarray true_state: Model parameters for "true" state to plot
as comparison.
:param float n_std: Number of standard deviations out from the mean
at which to draw the covariance ellipse. Only used if
region_est_method is ``'cov'``.
:param float level: Credibility level to use for computing
region estimators from convex hulls.
:param list rebit_axes: List containing indices for the :math:`x`
and :math:`z` axes.
:param str region_est_method: Method to use to draw region estimation.
Must be one of None, ``'cov'`` or ``'hull'``.
"""
pallette = plt.rcParams['axes.color_cycle']
plot_rebit_modelparams(updater.particle_locations,
c=pallette[0],
label='Posterior',
s=12 * np.sqrt(updater.particle_weights * len(updater.particle_weights)),
rebit_axes=rebit_axes,
zorder=-10
)
plot_rebit_modelparams(true_state,
c=pallette[1],
label='True', marker='*', s=true_size,
rebit_axes=rebit_axes
)
if prior is not None:
plot_rebit_modelparams(
prior._basis.state_to_modelparams(prior._mean)[None, :],
edgecolors=pallette[3], s=250, facecolors='none', linewidth=3,
label='Prior Mean',
rebit_axes=rebit_axes
)
plot_rebit_modelparams(
updater.est_mean()[None, :],
edgecolors=pallette[2], s=250, facecolors='none', linewidth=3,
label='Posterior Mean',
rebit_axes=rebit_axes
)
if region_est_method == 'cov':
# Multiplying by sqrt{2} to rescale to Bloch ball.
cov = 2 * updater.est_covariance_mtx()
# Use fancy indexing to cut out all but the desired submatrix.
cov = cov[rebit_axes, :][:, rebit_axes]
plot_cov_ellipse(
cov, updater.est_mean()[rebit_axes] * np.sqrt(2),
nstd=n_std,
edgecolor='k', fill=True, lw=2,
facecolor=pallette[0],
alpha=0.4,
zorder=-9,
label='Posterior Cov Ellipse ($Z = {}$)'.format(n_std)
)
elif region_est_method == 'hull':
# Find the convex hull from the updater, projected
# on the rebit axes.
faces, vertices = updater.region_est_hull(level, modelparam_slice=rebit_axes)
polygon = Polygon(vertices * np.sqrt(2),
facecolor=pallette[0], alpha=0.4, zorder=-9,
label=r'Credible Region ($\alpha = {}$)'.format(level),
edgecolor='k', lw=2, fill=True
)
# TODO: consolidate add_patch code with that above.
plt.gca().add_patch(polygon)
plot_decorate_rebits(updater.model.base_model._basis,
rebit_axes=rebit_axes
)
if legend:
plt.legend(loc='lower left', ncol=4, scatterpoints=1)