#!/usr/bin/env python3
from datetime import datetime
from phabricator import Phabricator
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.dates as mdates
from scipy.ndimage.filters import gaussian_filter

start = datetime(year=2020, month=7, day=1)


def format_stamp(stamp):
    dt = datetime.utcfromtimestamp(stamp)
    date = mdates.date2num(dt)
    time = dt.hour + dt.minute / 60.0
    return [date, time]


phab = Phabricator()  # This will use your ~/.arcrc file
diff = phab.differential.query()

dates = []
for d in diff:
    created = format_stamp(int(d["dateCreated"]))
    modified = format_stamp(int(d["dateModified"]))
    dates += [created, modified]



def myplot(x, y, s, bins=1000):
    heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins)
    heatmap = gaussian_filter(heatmap, sigma=s)

    extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
    return heatmap.T, extent


fig, axs = plt.subplots(2, 2)


sigmas = [0, 16, 32, 48]
x = [i for i, j in dates]
y = [j for i, j in dates]
daymonthFmt = mdates.DateFormatter('%d %B')

for ax, s in zip(axs.flatten(), sigmas):
    ax.xaxis.set_major_formatter(daymonthFmt)
    _ = plt.xticks(rotation=90)
    if s == 0:
        ax.plot(x, y, 'k.', markersize=5)
        ax.set_title("Scatter plot")
    else:
        img, extent = myplot(x, y, s)
        ax.imshow(img, extent=extent, origin='lower', cmap=cm.jet)
        ax.set_title("Smoothing with  $\sigma$ = %d" % s)

plt.show()