# -*- 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)

from contextlib import contextmanager
import ipywidgets
import matplotlib as mpl
import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import numpy as np


_default_size = (9, 4)

def equal_axis(sz=_default_size[0]):
    """ set size of axis in inches, using the same for each"""
    pylab.rcParams['figure.figsize'] = sz, sz
    plt.axis('equal')

def reset_figsize():
    """ reest axis size in inches to the default size for the book"""
    mpl.rcParams['figure.figsize'] = _default_size


def set_figsize(x=_default_size[0], y=_default_size[1]):
    """ set the figure size of the plot to the specified size in inches"""

    mpl.rcParams['figure.figsize'] = x, y


@contextmanager
def figsize(x=8, y=3):
    """Temporarily set the figure size using 'with figsize(a, b):'"""

    size = pylab.rcParams['figure.figsize']
    set_figsize(x, y)
    yield
    pylab.rcParams['figure.figsize'] = size


""" If the plot is inline (%matplotlib inline) we need to
do special processing for the interactive_plot context manager,
otherwise it outputs a lot of extra <matplotlib.figure.figure
type output into the notebook."""

IS_INLINE = mpl.get_backend().find('backend_inline') != -1


def plot_errorbars(bars, xlims, ylims=(-1, 1)):
    """Plots a list of error bars with optional x and y limits.
    The list `bars` is a list of tuples (or any iterable) containing
    (mean value, error plus/minus, label)
    For example (160, 3, 'A') draws an error bar from 157 to 163, with the
    legend label 'A`)
    Parameters
    ----------
    bars : list
        list of tuples in form (mean, error +/-, label)
    x-lims : tuple
        tuple containing min and max values for x axis
    y-lims : tuple, optional
        tuple containing min and max values for y axis ( the default is (-1, 1))
    Example
    -------
    >>> plot_errorbars([(160, 3, 'A'), (170, 9, 'B')], xlims=(150, 180))
    """

    with figsize(y=2):
        i = 0.0
        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(*ylims)
        plt.xlim(xlims[0], xlims[1])
        show_legend()
        plt.gca().axes.yaxis.set_ticks([])
        plt.show()


def predict_update_chart(box_bg = '#CCCCCC',
                arrow1 = '#88CCFF',
                arrow2 = '#88FF88'):
    plt.figure(figsize=(4, 4), facecolor='w')
    ax = plt.axes((0, 0, 1, 1),
                  xticks=[], yticks=[], frameon=False)

    pc = Circle((4, 5), 0.7, fc=box_bg)
    uc = Circle((6, 5), 0.7, fc=box_bg)
    ax.add_patch (pc)
    ax.add_patch (uc)

    plt.text(4, 5, "Predict\nStep",ha='center', va='center', fontsize=12)
    plt.text(6, 5, "Update\nStep",ha='center', va='center', fontsize=12)

    #btm arrow from update to predict
    ax.annotate('',
                xy=(4.1, 4.5),  xycoords='data',
                xytext=(6, 4.5), textcoords='data',
                size=20,
                arrowprops=dict(arrowstyle="simple",
                                fc="0.6", ec="none",
                                patchB=pc,
                                patchA=uc,
                                connectionstyle="arc3,rad=-0.5"))
    #top arrow from predict to update
    ax.annotate('',
                xy=(6, 5.5),  xycoords='data',
                xytext=(4.1, 5.5), textcoords='data',
                size=20,
                arrowprops=dict(arrowstyle="simple",
                                fc="0.6", ec="none",
                                patchB=uc,
                                patchA=pc,
                                connectionstyle="arc3,rad=-0.5"))

    ax.annotate('Measurement ($\mathbf{z_k}$)',
                xy=(6.3, 5.6),  xycoords='data',
                xytext=(6,6), textcoords='data',
                size=14,
                arrowprops=dict(arrowstyle="simple",
                                fc="0.6", ec="none"))

    # arrow from predict to state estimate
    ax.annotate('',
                xy=(4.0, 3.8),  xycoords='data',
                xytext=(4.0,4.3), textcoords='data',
                size=12,
                arrowprops=dict(arrowstyle="simple",
                                fc="0.6", ec="none"))

    ax.annotate('Initial\nConditions ($\mathbf{x_0}$)',
                xy=(4.05, 5.7),  xycoords='data',
                xytext=(2.5, 6.5), textcoords='data',
                size=14,
                arrowprops=dict(arrowstyle="simple",
                                fc="0.6", ec="none"))

    plt.text (4, 3.7,'State Estimate ($\mathbf{\hat{x}_k}$)',
              ha='center', va='center', fontsize=14)
    plt.axis('equal')
    plt.show()


def show_residual_chart(show_eq=True, show_H=False):
    plt.figure(figsize=(11, 3.), facecolor='w')
    est_y = ((164.2 - 158)*.8 + 158)

    ax = plt.axes(xticks=[], yticks=[], frameon=False)
    ax.annotate('', xy=[1,159], xytext=[0, 158],
                arrowprops=dict(arrowstyle='->',
                                ec='r', lw=3, shrinkA=6, shrinkB=5))

    ax.annotate('', xy=[1, 159], xytext=[1, 164.2],
                arrowprops=dict(arrowstyle='-',
                                ec='k', lw=3, shrinkA=8, shrinkB=8))

    ax.annotate('', xy=(1., est_y), xytext=(0.9, est_y),
                arrowprops=dict(arrowstyle='->', ec='#004080',
                                lw=2,
                                shrinkA=3, shrinkB=4))


    plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
    plt.scatter ([1], [164.2], c='b',s=128)
    plt.scatter ([1], [159], c='r', s=128)
    plt.text (1.05, 158.8, r"prior $(\bar{x}_t)$", ha='center',va='top',fontsize=18,color='red')
    plt.text (0.5, 159.6, "prediction", ha='center',va='top',fontsize=18,color='red')
    plt.text (1.0, 164.4, r"measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
    plt.text (0, 157.8, r"posterior ($x_{t-1}$)", ha='center', va='top',fontsize=18)
    plt.text (1.02, est_y-1.5, "residual($y$)", ha='left', va='center',fontsize=18)
    if show_eq:
        if show_H:
            plt.text (1.02, est_y-2.2, r"$y=z-H\bar x_t$", ha='left', va='center',fontsize=18)
        else:
            plt.text (1.02, est_y-2.2, r"$y=z-\bar x_t$", ha='left', va='center',fontsize=18)
    plt.text (0.9, est_y, "new estimate ($x_t$)", ha='right', va='center',fontsize=18)
    plt.text (0.8, est_y-0.5, "(posterior)", ha='right', va='center',fontsize=18)
    if show_eq:
        plt.text (0.75, est_y-1.2, r"$\bar{x}_t + Ky$", ha='right', va='center',fontsize=18)
    plt.xlabel('time')
    ax.yaxis.set_label_position("right")
    plt.ylabel('state')
    plt.xlim(-0.1, 1.5)


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


def bar_plot(pos, x=None, ylim=(0,1), title=None, c='#30a2da',
             **kwargs):
    """ plot the values in `pos` as a bar plot.
    **Parameters**
    pos : list-like
        list of values to plot as bars
    x : list-like, optional
         If provided, specifies the x value for each value in pos. If not
         provided, the first pos element is plotted at x == 0, the second
         at 1, etc.
    ylim : (lower, upper), default = (0,1)
        specifies the lower and upper limits for the y-axis
    title : str, optional
        If specified, provides a title for the plot
    c : color, default='#30a2da'
        Color for the bars
    **kwargs : keywords, optional
        extra keyword arguments passed to ax.bar()
    """

    ax = plt.gca()
    if x is None:
        x = np.arange(len(pos))
    ax.bar(x, pos, color=c, **kwargs)
    if ylim:
        plt.ylim(ylim)
    plt.xticks(np.asarray(x), x)
    if title is not None:
        plt.title(title)


def plot_belief_vs_prior(belief, prior, **kwargs):
    """ plots two discrete probability distributions side by side, with
    titles "belief" and "prior"
    """

    plt.subplot(121)
    bar_plot(belief, title='belief', **kwargs)
    plt.subplot(122)
    bar_plot(prior, title='prior', **kwargs)


def plot_prior_vs_posterior(prior, posterior, reverse=False, **kwargs):
    """ plots two discrete probability distributions side by side, with
    titles "prior" and "posterior"
    """
    if reverse:
        plt.subplot(121)
        bar_plot(posterior, title='posterior', **kwargs)
        plt.subplot(122)
        bar_plot(prior, title='prior', **kwargs)
    else:
        plt.subplot(121)
        bar_plot(prior, title='prior', **kwargs)
        plt.subplot(122)
        bar_plot(posterior, title='posterior', **kwargs)


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


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 FloatSlider(value, **kwargs):
    """
    Creates an ipwidgets FloatSlider with continuous update
    turned off
    """
    return ipywidgets.FloatSlider(value, continuous_update=False, **kwargs)


def IntSlider(value, **kwargs):
    """
    Creates an ipwidgets IntSlider with continuous update
    turned off
    """
    return ipywidgets.IntSlider(value, continuous_update=False, **kwargs)


def plot_measurements(xs, ys=None, dt=None, color='k', lw=1, label='Measurements',
                      lines=False, **kwargs):
    """ Helper function to give a consistant way to display
    measurements in the book.
    """
    if ys is None and dt is not None:
        ys = xs
        xs = np.arange(0, len(ys)*dt, dt)

    plt.autoscale(tight=False)
    if lines:
        if ys is not None:
            return plt.plot(xs, ys, color=color, lw=lw, ls='--', label=label, **kwargs)
        else:
            return plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
    else:
        if ys is not None:
            return plt.scatter(xs, ys, edgecolor=color, facecolor='none',
                        lw=2, label=label, **kwargs),
        else:
            return 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, dt=None, label='Track', c='k', lw=2, **kwargs):
    if ys is None and dt is not None:
        ys = xs
        xs = np.arange(0, len(ys)*dt, dt)
    if ys is not None:
        return plt.plot(xs, ys, color=c, lw=lw, ls=':', label=label, **kwargs)
    else:
        return plt.plot(xs, color=c, lw=lw, ls=':', label=label, **kwargs)


def plot_filter(xs, ys=None, dt=None, c='C0', label='Filter', var=None, **kwargs):
    """ plot result of KF with color `c`, optionally displaying the variance
    of `xs`. Returns the list of lines generated by plt.plot()"""

    if ys is None and dt is not None:
        ys = xs
        xs = np.arange(0, len(ys) * dt, dt)
    if ys is None:
        ys = xs
        xs = range(len(ys))

    lines = plt.plot(xs, ys, color=c, label=label, **kwargs)
    if var is None:
        return lines

    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)

    return lines


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()
