import matplotlib.pyplot as plt
import numpy as np

from .common import GraphCommand


class Command(GraphCommand):

    def handle(self, *args, **options):
        infos = {}
        for i in range(7):
            for j in range(24):
                infos[(i, j)] = 0

        def hour_shift(x):
            x = x - 4
            if x < 0:
                x += 24
            return x

        for commit in self.get_events(options):
            coords = (
                    self.get_event_datetime(commit, options).weekday(),
                    hour_shift(self.get_event_datetime(commit, options)).hour))
            infos[coords] = infos[coords] + 1

        draw_punchcard(infos,
                ax2_ticks=list(range(4, 24)) + list(range(4)))
        self.plot(options)

# https://stackoverflow.com/a/14850998
def draw_punchcard(infos,
                ax1=range(7),
                ax2=range(24),
                ax1_ticks=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],
                ax2_ticks=range(24),
                ax1_label='Day',
                ax2_label='Hour'):
    # - infos: Dictionary of quantities to display.
    #          They are indexed by key of type (val1,val2) with
    #          val1 included in ax1 and val2 included in ax2.

    # build the array which contains the values
    data = np.zeros((len(ax1),len(ax2)))
    for key in infos:
        data[key[0],key[1]] = infos[key]
    data = data/float(np.max(data))

    # shape ratio
    r = float(data.shape[1])/data.shape[0]

    # Draw the punchcard (create one circle per element)
    # Ugly normalisation allows to obtain perfect circles instead of ovals....
    for y in range(data.shape[0]):
        for x in range(data.shape[1]):
            circle = plt.Circle((x/float(data.shape[1]-1)*(data.shape[0]-1),y/r),
                                data[y][x]/float(data.shape[1])*data.shape[0]/2)
            plt.gca().add_artist(circle)

    plt.ylim(0-0.5,  data.shape[0]-0.5)
    plt.xlim(0, data.shape[0])
    plt.yticks(np.arange(0,len(ax1)/r-.1,1/r), ax1_ticks)
    xt = np.linspace(0, len(ax1)-1, len(ax2))
    plt.xticks(xt, ax2_ticks)
    plt.xlabel(ax2_label)
    plt.ylabel(ax1_label)
    plt.gca().invert_yaxis()

    # make sure the axes are equal, and resize the canvas to fit the plot
    plt.axis('equal')
    plt.axis([-1, 7, 7/r, -1])
    scale = 1 #0.5
    plt.gcf().set_size_inches(data.shape[1]*scale,data.shape[0]*scale, forward=True)