import matplotlib
matplotlib.use('Agg')  # use a non-interactive backend
import CoolProp
import os.path

web_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
tar_fil = os.path.join(web_dir, '_static', 'CoolPropLogo.png')
tar_fil_long = os.path.join(web_dir, '_static', 'CoolPropLogoLong.png')
tar_fil_long_large = os.path.join(web_dir, '_static', 'CoolPropLogoLongLarge.png')

import matplotlib
import numpy as np
import CoolProp as CP
import matplotlib.pyplot as plt
import scipy.interpolate

# Prepare the constants
Water = CP.AbstractState("HEOS", "Water")
pc = Water.keyed_output(CP.iP_critical)
Tc = Water.keyed_output(CP.iT_critical)
T_min = 200
T_max = 1000
p_max = Water.keyed_output(CP.iP_max)
p_triple = 611.657
T_triple = 273.16

# Prepare the data for the melting line
steps = 2000

TT = []
PP = list(np.logspace(np.log10(p_triple), np.log10(p_max), steps))
for p in PP:
    TT.append(Water.melting_line(CP.iT, CP.iP, p))

# Zone VI
for T in np.linspace(max(TT), 355, int(steps / 10)):
    TT.append(T)
    theta = T / 273.31
    pi = 1 - 1.07476 * (1 - theta**4.6)
    p = pi * 632.4e6
    PP.append(p)

# Zone VII
for T in np.linspace(355, 715, int(steps / 10)):
    TT.append(T)
    theta = T / 355
    lnpi = 0.173683e1 * (1 - 1 / theta) - 0.544606e-1 * (1 - theta**5) + 0.806106e-7 * (1 - theta**22)
    p = np.exp(lnpi) * 2216e6
    PP.append(p)

# Changes number of points
steps = int(steps / 10.0)

p_melt = np.logspace(np.log10(np.min(PP)), np.log10(np.max(PP)), steps)
T_melt_f = scipy.interpolate.interp1d(np.log10(PP), TT)
#T_melt_f = scipy.interpolate.spline(np.log10(PP),TT,np.log10(p_melt))
T_melt = T_melt_f(np.log10(p_melt))
#T_melt = np.array(TT)
#p_melt = np.array(PP)

#
# Prepare the data for the saturation line
T_sat = np.linspace(T_triple, Tc, len(T_melt))
p_sat = CP.CoolProp.PropsSI('P', 'T', T_sat, 'Q', [0] * len(T_sat), 'Water')

#
# Prepare density data
TT, DD, PP = [], [], []
for T in np.linspace(T_min, T_max, steps):
    for p in np.logspace(np.log10(np.min(p_melt)), np.log10(np.max(p_melt)), steps):
        Tm = scipy.interpolate.interp1d(p_melt, T_melt)(p)
        if T < Tm: continue
        if p > p_max: pin = p_max
        else: pin = p
        D = CP.CoolProp.PropsSI('D', 'T', T, 'P', pin, 'Water')
        TT.append(T)
        DD.append(np.log10(D))
        PP.append(p)

#tt = np.linspace(T_min, T_max, steps)
#pp = np.logspace(np.log10(p_triple), np.log10(p_max), steps)
#tt, pp = np.meshgrid(tt, pp)
#dd = np.empty(tt.shape)
#dd[:][:] = np.NAN
#nr,nc = tt.shape
# for i in range(nr):
    # for j in range(nc):
        #Tm = T_melt_f(np.log10(pp[i][j]))
        # if tt[i][j] < Tm: continue
        #D = CP.CoolProp.PropsSI('D','T',tt[i][j],'P',pp[i][j],'Water')
        #dd[i][j] = np.log10(D)

#
# Define colours etc
lw = 3
melt_args = dict(color='orange', lw=lw, solid_capstyle='round')
sat_args = melt_args.copy()

nm = matplotlib.colors.Normalize(min(DD), max(DD))
rho_args = dict(cmap=plt.cm.get_cmap('Blues'), norm=nm)

fig = plt.figure(figsize=(1.0, 1.0))
ax = fig.add_axes((0.0, 0.0, 1.0, 1.0))

plt.plot(T_melt, p_melt, **melt_args)
plt.plot(T_sat, p_sat, **sat_args)
plt.scatter(TT, PP, c=DD, edgecolor='none', s=6, **rho_args)
#plt.contourf(tt, pp, dd, steps, **rho_args )
delta_x = np.min(T_melt) * 0.01
x_lim = [np.min(T_melt) - delta_x, np.max(T_melt) + delta_x]
y_lim = [np.min(p_melt) * 0.875, np.max(p_melt) * 1.125]
# y_lim = [np.power(np.log10(np.min(p_melt))*1.01,10),
#         np.power(np.log10(np.max(p_melt))*1.01,10)]

ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.set_yscale('log')
ax.axis('off')

plt.savefig(os.path.basename(__file__) + '.pdf')
plt.savefig(tar_fil, transparent=True, dpi=100)
plt.close()

fig = plt.figure(figsize=(9, 3))
ax = fig.add_axes((0.66, 0.01, 0.333, 0.98))
plt.plot(T_melt, p_melt, **melt_args)
plt.plot(T_sat, p_sat, **sat_args)
plt.scatter(TT, PP, c=DD, edgecolor='none', s=6, **rho_args)

#ax.text(0.4,800, "CoolProp", size = 12)
ax.annotate('CoolProp', xy=(-0.1, 0.5), xycoords='axes fraction', fontsize=88, ha='right', va='center')  # va='baseline')


ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.set_yscale('log')
ax.axis('off')

plt.savefig(tar_fil_long_large, transparent=True)
plt.close()

# # Just a hack to resize the image
# import Image
# size = 288, 288
# try:
# 	im = Image.open(tar_fil_long_large)
# 	im.thumbnail(size, Image.ANTIALIAS)
# 	im.save(tar_fil_long)
# 	#im.resize(size, Image.ANTIALIAS).save(tar_fil_long)
# except IOError:
# 	print "Error resizing '%s'" % tar_fil_long_large