Kalman-and-Bayesian-Filters-in-Python/code/book_plots.py

# -*- coding: utf-8 -*-

"""Copyright 2015 Roger R Labbe Jr.


Code supporting the book

Kalman and Bayesian Filters in Python
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python


This is licensed under an MIT license. See the LICENSE.txt file
for more information.
"""


from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import matplotlib.pyplot as plt
import numpy as np

def plot_errorbars(bars, xlims):

    i = 1.0
    for bar in bars:
        plt.errorbar([bar[0]], [i], xerr=[bar[1]], fmt='o', label=bar[2] , capthick=2, capsize=10)
        i += 0.2

    plt.ylim(0, 2)
    plt.xlim(xlims[0], xlims[1])
    show_legend()
    plt.gca().axes.yaxis.set_ticks([])
    plt.show()




def show_legend():
    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))


def bar_plot(pos, ylim=(0,1), title=None):
    plt.cla()
    ax = plt.gca()
    x = np.arange(len(pos))
    ax.bar(x, pos, color='#30a2da')
    if ylim:
        plt.ylim(ylim)
    plt.xticks(x+0.4, x)
    if title is not None:
        plt.title(title)


def set_labels(title=None, x=None, y=None):
    """ helps make code in book shorter. Optional set title, xlabel and ylabel
    """
    if x is not None:
        plt.xlabel(x)
    if y is not None:
        plt.ylabel(y)
    if title is not None:
        plt.title(title)


def set_limits(x, y):
    """ helper function to make code in book shorter. Set the limits for the x
    and y axis.
    """

    plt.gca().set_xlim(x)
    plt.gca().set_ylim(y)

def plot_predictions(p, rng=None):
    if rng is None:
        rng = range(len(p))
    plt.scatter(rng, p, marker='v', s=40, edgecolor='r',
                facecolor='None', lw=2, label='prediction')



def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
    plot_filter(filter_xs[:, 0])
    plot_track(xs[:, 0])

    if zs is not None:
        plot_measurements(zs)
    show_legend()
    set_labels(title=title, y='meters', x='time (sec)')
    if aspect_equal:
        plt.gca().set_aspect('equal')
    plt.xlim((-1, len(xs)))
    plt.show()


def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
                      lines=False, **kwargs):
    """ Helper function to give a consistant way to display
    measurements in the book.
    """

    plt.autoscale(tight=True)
    '''if ys is not None:
        plt.scatter(xs, ys, marker=marker, c=c, s=s,
                    label=label, alpha=alpha)
        if connect:
           plt.plot(xs, ys, c=c, lw=1, alpha=alpha)
    else:
        plt.scatter(range(len(xs)), xs, marker=marker, c=c, s=s,
                    label=label, alpha=alpha)
        if connect:
           plt.plot(range(len(xs)), xs, lw=1, c=c, alpha=alpha)'''

    if lines:
        if ys is not None:
            plt.plot(xs, ys, color=color, lw=lw, ls='--', label=label, **kwargs)
        else:
            plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
    else:
        if ys is not None:
            plt.scatter(xs, ys, edgecolor=color, facecolor='none',
                        lw=2, label=label, **kwargs)
        else:
            plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none',
                        lw=2, label=label, **kwargs)


def plot_residual_limits(Ps, stds=1.):
    """ plots standand deviation given in Ps as a yellow shaded region. One std
    by default, use stds for a different choice (e.g. stds=3 for 3 standard
    deviations.
    """

    std = np.sqrt(Ps) * stds

    plt.plot(-std, color='k', ls=':', lw=2)
    plt.plot(std, color='k', ls=':', lw=2)
    plt.fill_between(range(len(std)), -std, std,
                 facecolor='#ffff00', alpha=0.3)


def plot_track(xs, ys=None, label='Track', c='k', lw=2, **kwargs):
    if ys is not None:
        plt.plot(xs, ys, color=c, lw=lw, ls=':', label=label, **kwargs)
    else:
        plt.plot(xs, color=c, lw=lw, ls=':', label=label, **kwargs)


def plot_filter(xs, ys=None, c='#013afe', label='Filter', var=None, **kwargs):
#def plot_filter(xs, ys=None, c='#6d904f', label='Filter', vars=None, **kwargs):


    if ys is None:
        ys = xs
        xs = range(len(ys))

    plt.plot(xs, ys, color=c, label=label, **kwargs)

    if var is None:
        return

    var = np.asarray(var)

    std = np.sqrt(var)
    std_top = ys+std
    std_btm = ys-std

    plt.plot(xs, ys+std, linestyle=':', color='k', lw=2)
    plt.plot(xs, ys-std, linestyle=':', color='k', lw=2)
    plt.fill_between(xs, std_btm, std_top,
                     facecolor='yellow', alpha=0.2)




def _blob(x, y, area, colour):
    """
    Draws a square-shaped blob with the given area (< 1) at
    the given coordinates.
    """
    hs = np.sqrt(area) / 2
    xcorners = np.array([x - hs, x + hs, x + hs, x - hs])
    ycorners = np.array([y - hs, y - hs, y + hs, y + hs])
    plt.fill(xcorners, ycorners, colour, edgecolor=colour)

def hinton(W, maxweight=None):
    """
    Draws a Hinton diagram for visualizing a weight matrix.
    Temporarily disables matplotlib interactive mode if it is on,
    otherwise this takes forever.
    """
    reenable = False
    if plt.isinteractive():
        plt.ioff()

    plt.clf()
    height, width = W.shape
    if not maxweight:
        maxweight = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))

    plt.fill(np.array([0, width, width, 0]),
             np.array([0, 0, height, height]),
             'gray')

    plt.axis('off')
    plt.axis('equal')
    for x in range(width):
        for y in range(height):
            _x = x+1
            _y = y+1
            w = W[y, x]
            if w > 0:
                _blob(_x - 0.5,
                      height - _y + 0.5,
                      min(1, w/maxweight),
                      'white')
            elif w < 0:
                _blob(_x - 0.5,
                      height - _y + 0.5,
                      min(1, -w/maxweight),
                      'black')
    if reenable:
        plt.ion()


if __name__ == "__main__":
    p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,
     0.06288015, 0.06109133, 0.0581008,  0.09334062]*2
    bar_plot(p)
    plot_measurements(p)