#!/usr/bin/env python
# -*- coding: utf-8 -*-

#...for the MATH.
import numpy as np

#...for the plotting.
import pylab as plt

#...for the colours.
from matplotlib import colorbar, colors

from matplotlib.ticker import MultipleLocator, FormatStrFormatter

def addRadiusCircle(figax, x, y, r):
    """ Draws a circle representing the cluster radius. """

    # Adjust the centre coordinates to account foe the pixel size.
    x_c = x + 0.5; y_c = y + 0.5

    # Set the size of the "cross-hairs".
    rl = 1.5

    # Add the "cross-hairs" to the plot.
    figax.plot([x_c-rl,x_c+rl], [y_c-rl,y_c+rl], 'k-', lw=1)
    figax.plot([x_c-rl,x_c+rl], [y_c+rl,y_c-rl], 'k-', lw=1)

    # Add the circle representing the cluster radius to the plot.
    figax.add_patch(plt.Circle((x_c,y_c),r,fill=False,lw=3.0))
    figax.add_patch(plt.Circle((x_c,y_c),r,fc='k',alpha=0.1,lw=3.0))
    figax.add_patch(plt.Circle((x_c,y_c),r,fill=False,lw=1.0,ec='g'))

def addLineOfBestFit(figax, m, c):
    """ Adds a line of best fit to the cluster image. """

    ## The x values.
    xs = np.arange(0.0,256.0,0.1)

    ## The y values.
    ys = m*(xs - 0.5) + c + 0.5

    figax.plot(xs, ys, 'k-', lw=3)
    figax.plot(xs, ys, 'g-', lw=1)


def makeKlusterImage(klusterid, kl, outputpath):
    """ Create the kluster image. """

    pixels = kl.getPixelMap()

    x_min = kl.getXMin()

    x_max = kl.getXMax()

    y_min = kl.getYMin()

    y_max = kl.getYMax()

    w = kl.getWidth()

    h = kl.getHeight()

    ## The maximum count value.
    C_max = kl.getMaxCountValue()

    x_bar = kl.getXUW()

    y_bar = kl.getYUW()

    radius = kl.getRadiusUW()

    m, c, sumR = kl.getLineOfBestFitValues()

    # Create the figure.
    plt.close('all')

    figsize = 5.0 #max(radius*0.8, 3.0)

    blobfig = plt.figure(1, figsize=(figsize*1.27, figsize), dpi=150, facecolor='w', edgecolor='w')

    #blobfigax = blobfig.add_subplot(111, axisbg='#82bcff')
    blobfigax = blobfig.add_subplot(111, axisbg='#222222')

    # Add the frame background (blue).
    blobfigax.add_patch(plt.Rectangle((0,0),256,256,facecolor='#82bcff'))

    # Add a grid.
    plt.grid(1)

    # Select the "hot" colour map for the pixel counts.

    cmap = plt.cm.hot

    colax, _ = colorbar.make_axes(plt.gca())

    col_max = 10*(np.floor(C_max/10.)+1)

    colorbar.ColorbarBase(colax,cmap=cmap,norm=colors.Normalize(vmin=0,vmax=col_max))

    # Add the line of best fit.
    addLineOfBestFit(blobfigax, m, c)

    # Add the radius circle.
    addRadiusCircle(blobfigax, x_bar, y_bar, radius)

    # Loop over the pixels and plot them.
    for X, C in pixels.iteritems():
        x = X % 256; y = X / 256
        scaled_C = float(C)/float(col_max)
        blobfigax.add_patch(plt.Rectangle((x,y),1,1,facecolor=cmap(scaled_C)))

    # Set the axis limits based on the cluster radius.
    b = 3 # border

    #xlim_min = max(  0, x_bar - (np.floor(radius)+b))
    #xlim_max = min(256, x_bar + (np.floor(radius)+b))
    #ylim_min = max(  0, y_bar - (np.floor(radius)+b))
    #ylim_max = min(256, y_bar + (np.floor(radius)+b))
    xlim_min = x_bar - (np.floor(radius)+b)
    xlim_max = x_bar + (np.floor(radius)+b)
    ylim_min = y_bar - (np.floor(radius)+b)
    ylim_max = y_bar + (np.floor(radius)+b)

    blobfigax.set_xlim([xlim_min, xlim_max])
    blobfigax.set_ylim([ylim_min, ylim_max])

    # Set the axis tick mark spacing.
    blobfigax.xaxis.set_major_locator(MultipleLocator(10))
    blobfigax.yaxis.set_major_locator(MultipleLocator(10))

    # Save the figure.
    blobfig.savefig(outputpath + "/%s.png" % (klusterid))