# %% [markdown]
# # notebook for create init and true test model

# %%
import numpy as np
import math

# grid
R_earth = 6371.0

rr1=6361
rr2=6381
tt1=(38.0-0.3)/180*math.pi
tt2=(42.0+0.3)/180*math.pi
pp1=(23.0-0.3)/180*math.pi
pp2=(27.0+0.3)/180*math.pi

n_rtp = [10,50,50]
dr = (rr2-rr1)/(n_rtp[0]-1)
dt = (tt2-tt1)/(n_rtp[1]-1)
dp = (pp2-pp1)/(n_rtp[2]-1)
rr = np.array([rr1 + x*dr for x in range(n_rtp[0])])
tt = np.array([tt1 + x*dt for x in range(n_rtp[1])])
pp = np.array([pp1 + x*dp for x in range(n_rtp[2])])

# initial model
gamma = 0.0
s0 = 1.0/6.0
slow_p=0.06
ani_p=0.04

eta_init = np.zeros(n_rtp)
xi_init  = np.zeros(n_rtp)
zeta_init = np.zeros(n_rtp)
fun_init = np.zeros(n_rtp)
vel_init = np.zeros(n_rtp)

# true model
eta_true = np.zeros(n_rtp)
xi_true  = np.zeros(n_rtp)
zeta_true = np.zeros(n_rtp)
fun_true = np.zeros(n_rtp)
vel_true = np.zeros(n_rtp)

c=0
for ir in range(n_rtp[0]):
    for it in range(n_rtp[1]):
        for ip in range(n_rtp[2]):
            # already initialized above
            #eta_init[ir,it,ip] = 0.0
            #xi_init[ir,it,ip]  = 0.0
            zeta_init[ir,it,ip] = gamma*math.sqrt(eta_init[ir,it,ip]**2 + xi_init[ir,it,ip]**2)
            fun_init[ir,it,ip] = s0
            vel_init[ir,it,ip] = 1.0/s0

            # true model
            if (tt[it] >= 38.0/180.0*math.pi and tt[it] <= 42.0/180.0*math.pi \
            and pp[ip] >= 23.0/180.0*math.pi and pp[ip] <= 27.0/180.0*math.pi):
                c+=1
                sigma = math.sin(2.0*math.pi*(tt[it]-38.0/180.0*math.pi)/(4.0/180.0*math.pi))*math.sin(2.0*math.pi*(pp[ip]-23.0/180.0*math.pi)/(4.0/180.0*math.pi))
            else:
                sigma = 0.0

            if sigma < 0:
                psi = 60.0/180.0*math.pi
            elif sigma > 0:
                psi = 120.0/180.0*math.pi
            else:
                psi = 0.0

            eta_true[ir,it,ip] = ani_p*abs(sigma)*math.sin(2.0*psi)
            xi_true[ir,it,ip]  = ani_p*abs(sigma)*math.cos(2.0*psi)
            zeta_true[ir,it,ip] = gamma*math.sqrt(eta_true[ir,it,ip]**2 + xi_true[ir,it,ip]**2)
            fun_true[ir,it,ip] = s0/(1.0+sigma*slow_p)
            vel_true[ir,it,ip] = 1.0/fun_true[ir,it,ip]


#r_earth = 6378.1370
print("depminmax {} {}".format(R_earth-rr1,R_earth-rr2))
print(c)


# %%
# write out in hdf5 format
import h5py

fout_init = h5py.File('test_model_init.h5', 'w')
fout_true = h5py.File('test_model_true.h5', 'w')

# write out the arrays eta_init, xi_init, zeta_init, fun_init, a_init, b_init, c_init, f_init
fout_init.create_dataset('eta', data=eta_init)
fout_init.create_dataset('xi', data=xi_init)
fout_init.create_dataset('zeta', data=zeta_init)
fout_init.create_dataset('vel', data=vel_init)

# writeout the arrays eta_true, xi_true, zeta_true, fun_true, a_true, b_true, c_true, f_true
fout_true.create_dataset('eta', data=eta_true)
fout_true.create_dataset('xi', data=xi_true)
fout_true.create_dataset('zeta', data=zeta_true)
fout_true.create_dataset('vel', data=vel_true)

fout_init.close()
fout_true.close()


# %% [markdown]
# # prepare src station file
#
# The following code creates a src_rec_file for the inversion, which describes the source and receiver positions and arrival times.
# Format is as follows:
#
# ```
#         26 1992  1  1  2 43  56.900    1.8000     98.9000 137.00  2.80    8    305644 <- src   　: id_src year month day hour min sec lat lon dep_km mag num_recs id_event
#      26      1      PCBI       1.8900     98.9253   1000.0000  P   10.40  18.000      <- arrival : id_src id_rec name_rec lat lon elevation_m phase epicentral_distance_km arrival_time_sec
#      26      2      MRPI       1.6125     99.3172   1100.0000  P   50.84  19.400
#      26      3      HUTI       2.3153     98.9711   1600.0000  P   57.84  19.200
#      ....
#
# ```

# %%
import random
random.seed(1145141919810)

# dummys
year_dummy = 1998
month_dummy = 1
day_dummy = 1
hour_dummy = 0
minute_dummy = 0
second_dummy = 0
mag_dummy = 3.0
id_dummy = 1000
st_name_dummy = 'AAAA'
phase_dummy = 'P'
arriv_t_dummy = 0.0

tt1deg = tt1 * 180.0/math.pi
tt2deg = tt2 * 180.0/math.pi
pp1deg = pp1 * 180.0/math.pi
pp2deg = pp2 * 180.0/math.pi


n_srcs = 8 # source will be placed around the domain
r_src = 15 # radius of the source in degree

lines = []

#nij_rec = math.sqrt(n_rec[0])

pos_src=[]
#pos_rec=[]

# center of the domain
lon_c = (pp1deg+pp2deg)/2.0
lat_c = (tt1deg+tt2deg)/2.0

# step of angle in degree = 360.0/n_srcs
d_deg = 360.0/n_srcs

for i_src in range(n_srcs):

    i_deg = i_src*d_deg
    lon = lon_c + r_src*math.cos(i_deg/180.0*math.pi)
    lat = lat_c + r_src*math.sin(i_deg/180.0*math.pi)
    # depth of the source
    dep = 10.0 + 0.5*i_src

    src = [i_src, year_dummy, month_dummy, day_dummy, hour_dummy, minute_dummy, second_dummy, lat, lon, dep, mag_dummy, 0, id_dummy]
    lines.append(src)

    pos_src.append([lon,lat,dep])


# write out ev_arrivals file
fname = 'src_only_test.dat'

with open(fname, 'w') as f:
    for line in lines:
        for elem in line:
            f.write('{} '.format(elem))
        f.write('\n')


# %%
# draw src and rec positions
import matplotlib.pyplot as plt

for i_src in range(n_srcs):
    plt.scatter(pos_src[i_src][1],pos_src[i_src][0],c='r',marker='o')

# %%
# plot receivers
#for i_rec in range(n_rec[0]):
#    plt.scatter(pos_rec[i_rec][1],pos_rec[i_rec][0],c='b',marker='o')

# %%