#!/usr/bin/env python

__author__      = "Roman Krivonos"
__copyright__   = "Space Research Institute (IKI)"

from astropy.table import Table, Column

import matplotlib.pyplot as plt
import sys, os
from scipy.stats import norm

from ridge.utils import *
from ridge.config import *


scale = 1e-3
nbins=30

fn="detcnts.A01.crabmodel.fits"
dat = Table.read(proddir+fn)
df1 = dat.to_pandas().sort_values(by=['REV'])

fn="detcnts.A02.crabmodel.fits"
dat = Table.read(proddir+fn)
df2 = dat.to_pandas().sort_values(by=['REV'])

fn="detcnts.A03.crabmodel.fits"
dat = Table.read(proddir+fn)
df3 = dat.to_pandas().sort_values(by=['REV'])


fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, figsize=(9, 7), dpi=100)
#fig.suptitle('Vertically stacked subplots')
#plt.figure(figsize=(8, 6), dpi=80)

for axis in ['top','bottom','left','right']:
    ax1.spines[axis].set_linewidth(1)
    ax2.spines[axis].set_linewidth(1)
    ax3.spines[axis].set_linewidth(1)
ax1.tick_params(axis="both", width=1, labelsize=14)
ax2.tick_params(axis="both", width=1, labelsize=14)
ax3.tick_params(axis="both", width=1, labelsize=14)

ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
ax2.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
ax3.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

data=(df1['B_EST']-df1['B_POLY'])/df1['B_POLY']*1000
(mu, sg) = norm.fit(data)
#print(mu, sg)
ax1.set_title("25-60 keV, $\\sigma=${:.0f} mCrab".format(sg))
n, bins, patches = ax1.hist(data, nbins, density=True, facecolor='green', alpha=0.75)
y = norm.pdf(bins, mu, sg)
l = ax1.plot(bins, y, 'r--', linewidth=2)
#plot
ax1.axvline(mu, color="black", linewidth=2)
ax1.axvline(mu+sg, color="blue", linestyle="dashed", linewidth=2)
ax1.axvline(mu-sg, color="blue", linestyle="dashed", linewidth=2)
ax1.grid(visible=True)

data=(df2['B_EST']-df2['B_POLY'])/df2['B_POLY']*1000
(mu, sg) = norm.fit(data)
#print(mu, sg)
ax2.set_title("60-80 keV, $\\sigma=${:.0f} mCrab".format(sg))
n, bins, patches = ax2.hist(data, nbins, density=True, facecolor='green', alpha=0.75)
y = norm.pdf(bins, mu, sg)
l = ax2.plot(bins, y, 'r--', linewidth=2)
#plot
ax2.axvline(mu, color="black", linewidth=2)
ax2.axvline(mu+sg, color="blue", linestyle="dashed", linewidth=2)
ax2.axvline(mu-sg, color="blue", linestyle="dashed", linewidth=2)
ax2.grid(visible=True)

data=(df3['B_EST']-df3['B_POLY'])/df3['B_POLY']*1000
(mu, sg) = norm.fit(data)
#print(mu, sg)
ax3.set_title("80-200 keV, $\\sigma=${:.0f} mCrab".format(sg))
n, bins, patches = ax3.hist(data, nbins, density=True, facecolor='green', alpha=0.75)
# add a 'best fit' line
y = norm.pdf(bins, mu, sg)
l = ax3.plot(bins, y, 'r--', linewidth=2)
#plot
ax3.axvline(mu, color="black", linewidth=2)
ax3.axvline(mu+sg, color="blue", linestyle="dashed", linewidth=2)
ax3.axvline(mu-sg, color="blue", linestyle="dashed", linewidth=2)
ax3.grid(visible=True)

#x = np.arange(-10, 10, 0.001)
#plot normal distribution with mean 0 and standard deviation 1
#plt.plot(x, norm.pdf(x, 0, 1), color='red', linewidth=2)
	    
plt.xlabel('Flux, mCrab',fontsize=14, fontweight='normal')
#ax2.set_ylabel('No, x10$^{-3}$ cts s$^{-1}$ pix$^{-1}$',fontsize=14, fontweight='normal')

#plt.xscale('linear')
#plt.yscale('linear')

if not os.path.exists(figdir):
    os.makedirs(figdir)
filename=figdir+'crabmodel_sys.png'
plt.savefig(filename, bbox_inches='tight')
plt.close(fig)
print("Result is saved as {}".format(filename))