import sys, getopt

import numpy as np

import matplotlib as mpl

import matplotlib.pyplot as plt

import exorings

from astropy.io import ascii

# set sensible imshow defaults

mpl.rc('image', interpolation='nearest', origin='lower', cmap='gray')

mpl.rc('axes.formatter', limits=(-7, 7))

def plot_gradient_fit(t, f, fn, xt, yt, p):

p.set_ylabel('Light curve gradient [$L_\star/day$]')

################################################################################

# BEGIN main program

################################################################################

def helpme():

sys.exit()

# parse command line options

try:

opts, args = getopt.getopt(sys.argv[1:], "hd:o:", ["dfile=", "ofile="])

except getopt.GetoptError:

helpme()

sys.exit(2)

for opt, arg in opts:

if opt == '-h':

help()

elif opt in ("-d", "--dfile"):

fitsin_disk = arg

read_in_disk_parameters = True

elif opt in ("-o", "--ofile"):

plotfileout = arg

# get light curve gradients

grad = ascii.read("gradients.txt")

grad_time = grad['col1'] + 54222.

grad_mag = np.abs(grad['col2'])

# read in or create the ring system tau and radii

print 'Reading in disk parameters from %s' % fitsin_disk

(res, taun_ringsxx, rad_ringsxx, dstar) = exorings.read_ring_fits(fitsin_disk)

# make the radius and projected gradient for the measured gradient points

(ring_disk_fit, grad_disk_fit) = \

exorings.make_ring_grad_line(grad_time, res[0], res[1], res[2], res[3])

# produce fine grained gradient and ring values

samp_t = np.arange(-100, 100, 0.001) + 54222.

(samp_r, samp_g) = exorings.make_ring_grad_line(samp_t, res[0], res[1], res[2], res[3])

hjd_minr = samp_t[np.argmin(samp_g)]

exorings.print_disk_parameters(res, hjd_minr, samp_r)

# plotting fit of gradients from ellipse curve to J1407 gradients

plt.rc('font', **{'family':'sans-serif', 'sans-serif':['Helvetica']})

plt.rc('text', usetex=True)

figfit = plt.figure(figsize=(10, 6))

f2 = figfit.add_subplot(111)

f2.set_ylim([0, 1.1*np.max(grad_mag)])

f2.set_xlim([np.min(samp_t), np.max(samp_t)])

plot_gradient_fit(grad_time, grad_disk_fit * np.max(grad_mag), grad_mag, \

samp_t, samp_g*np.max(grad_mag), f2)

# make ticks thicker

for ax in figfit.axes: # go over all the subplots in the figure fig

for i in ax.spines.itervalues(): # ... and go over all the axes too...

i.set_linewidth(2)

ax.minorticks_on() # switch on the minor ticks

# set the tick lengths and tick widths

ax.tick_params('both', length=15, width=2, which='major')

ax.tick_params('both', length=6, width=1, which='minor')

# adjust text size on the axes

f2.tick_params(axis='both', which='major', labelsize=14)

print 'writing plot out to file %s' % plotfileout

plt.savefig(plotfileout)