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张壹
2025-12-17 10:53:43 +08:00
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30
test/old_tests/inversion_one_src/README.md Normal file
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# Inversion test
This is a test setup for inversion calculation.
1. Run all cells of `make_test_model.ipynb` or `make_test_model.py` for creating
- source, receiver file (src_rec_test.dat)
- true model (test_model_true.h5)
- initial model (test_model_init.h5)
2. then run TOMOATT forward with `input_params_pre.yml` for calculating the true arrival times at the stations
-> this will output src_rec_test_out.dat file which includes the true arrival times
```bash
mpirun --oversubscribe -np 8 ../../build/TOMOATT ./input_params_pre.yml
```
3. run TOMOATT in inversion mode with `input_params.yml`.
-> this will output src_rec_test_out.dat file which includes the true arrival times
```bash
mpirun --oversubscribe -np 8 ../../build/TOMOATT ./input_params_pre.yml
```
4. for visualizing the result files
```bash
paraview out_data_sim_0.xmf
```
0 is the source id. The kernel and model fields are output in the file of 0th source.

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test/old_tests/inversion_one_src/input_params.yml Normal file
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version : 2
domain :
min_max_dep : [-2.863,17.137] # depth in km
min_max_lat : [37.7,42.3] # latitude in degree
min_max_lon : [22.7,27.3] # longitude in degree
n_rtp : [20,50,50] # number of nodes
source :
#src_dep_lat_lon : [5.0,40.0,24.0] # source depth in km, latitude, longitude in degree
#src_dep_lat_lon : [5750.6370,46.0,36.0] # source depth in km, latitude, longitude in degree
src_rec_file : 'src_rec_test_out.dat' # source receiver file (if found, src_dep_lat_lon is ignored)
swap_src_rec : 0 # swap source and receiver (1: yes, 0: no)
model :
init_model_type : '' # 'fd' (input file) or '1d_ak135'
init_model_path : './test_model_init.h5' # path to initial model file (ignored if init_model_type is '1d_*')
inversion :
run_mode : 1 # 0 for forward simulation only, 1 for inversion
n_inversion_grid : 4 # number of inversion grid sets
n_inv_dep_lat_lon : [5,10,10] # number of the base inversion grid points
optim_method : 0 # optimization method. 0 : "grad_descent" or 1 : "lbfgs"
max_iterations_inv : 2 # maximum number of inversion iterations
step_size : 0.01 # initial step size for inversion
# parameters for laplacian iterative smoothing
l_smooth_rtp : [0.9,0.9,0.9] # smoothing coefficients for each direction
# parameters for lbfgs
regularization_weight : 20.0 # regularization weight
max_sub_iterations : 1 # maximum number of sub-iterations
parallel :
n_sims : 1 # number of simultaneous run
ndiv_rtp : [1,2,2] # number of subdomains
nproc_sub : 2 # number of subprocess used for each subdomain
calculation :
convergence_tolerance : 1e-10
max_iterations : 200
stencil_order : 3 # 1 or 3
sweep_type : 2 # 0: legacy, 1: cuthill-mckee with shm parallelization

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test/old_tests/inversion_one_src/input_params_pre.yml Normal file
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version : 2
domain :
min_max_dep : [-2.863,17.137] # depth in km
min_max_lat : [37.7,42.3] # latitude in degree
min_max_lon : [22.7,27.3] # longitude in degree
n_rtp : [20,50,50] # number of nodes
source :
#src_dep_lat_lon : [5.0,40.0,24.0] # source depth in km, latitude, longitude in degree
#src_dep_lat_lon : [5750.6370,46.0,36.0] # source depth in km, latitude, longitude in degree
src_rec_file : 'src_rec_test.dat' # source receiver file (if found, src_dep_lat_lon is ignored)
swap_src_rec : 0 # swap source and receiver (1: yes, 0: no)
model :
init_model_type : '' # 'fd' (input file) or '1d_ak135'
init_model_path : './test_model_true.h5' # path to initial model file (ignored if init_model_type is '1d_*')
inversion :
run_mode : 0 # 0 for forward simulation only, 1 for inversion
n_inversion_grid : 1
parallel :
n_sims : 1 # number of simultaneous run
ndiv_rtp : [1,2,2] # number of subdomains
nproc_sub : 2 # number of subprocess used for each subdomain
calculation :
convergence_tolerance : 1e-10
max_iterations : 200
stencil_order : 3 # 1 or 3
sweep_type : 2 # 0: legacy, 1: cuthill-mckee with shm parallelization

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test/old_tests/inversion_one_src/make_test_model.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# notebook for create init and true test model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"\n",
"# grid\n",
"R_earth = 6378.1370\n",
"\n",
"rr1=6361 \n",
"rr2=6381\n",
"tt1=(38.0-0.3)/180*math.pi\n",
"tt2=(42.0+0.3)/180*math.pi\n",
"pp1=(23.0-0.3)/180*math.pi\n",
"pp2=(27.0+0.3)/180*math.pi\n",
"\n",
"n_rtp = [20,50,50]\n",
"n_rtp.reverse()\n",
"dr = (rr2-rr1)/n_rtp[2]\n",
"dt = (tt2-tt1)/n_rtp[1]\n",
"dp = (pp2-pp1)/n_rtp[0]\n",
"rr = np.array([rr1 + x*dr for x in range(n_rtp[2])])\n",
"tt = np.array([tt1 + x*dt for x in range(n_rtp[1])])\n",
"pp = np.array([pp1 + x*dp for x in range(n_rtp[0])])\n",
"\n",
"# initial model\n",
"gamma = 0.0\n",
"s0 = 1.0/6.0\n",
"slow_p=0.06\n",
"ani_p=0.04\n",
"\n",
"eta_init = np.zeros(n_rtp)\n",
"xi_init = np.zeros(n_rtp)\n",
"zeta_init = np.zeros(n_rtp)\n",
"fun_init = np.zeros(n_rtp)\n",
"vel_init = np.zeros(n_rtp)\n",
"a_init = np.zeros(n_rtp)\n",
"b_init = np.zeros(n_rtp)\n",
"c_init = np.zeros(n_rtp)\n",
"f_init = np.zeros(n_rtp)\n",
"\n",
"# true model\n",
"eta_true = np.zeros(n_rtp)\n",
"xi_true = np.zeros(n_rtp)\n",
"zeta_true = np.zeros(n_rtp)\n",
"fun_true = np.zeros(n_rtp)\n",
"vel_true = np.zeros(n_rtp)\n",
"a_true = np.zeros(n_rtp)\n",
"b_true = np.zeros(n_rtp)\n",
"c_true = np.zeros(n_rtp)\n",
"f_true = np.zeros(n_rtp)\n",
"\n",
"c=0\n",
"for ir in range(n_rtp[2]):\n",
" for it in range(n_rtp[1]):\n",
" for ip in range(n_rtp[0]):\n",
" #eta_init[ip,it,ir] = 0.0\n",
" #xi_init[ip,it,ir] = 0.0\n",
" zeta_init[ip,it,ir] = gamma*math.sqrt(eta_init[ip,it,ir]**2 + xi_init[ip,it,ir]**2)\n",
" fun_init[ip,it,ir] = s0\n",
" vel_init[ip,it,ir] = 1.0/s0\n",
" a_init[ip,it,ir] = 1.0 + 2.0*zeta_init[ip,it,ir]\n",
" b_init[ip,it,ir] = 1.0 - 2.0*xi_init[ip,it,ir]\n",
" c_init[ip,it,ir] = 1.0 + 2.0*xi_init[ip,it,ir]\n",
" f_init[ip,it,ir] = -2.0 * eta_init[ip,it,ir]\n",
"\n",
" # true model\n",
" if (tt[it] >= 38.0/180.0*math.pi and tt[it] <= 42.0/180.0*math.pi \\\n",
" and pp[ip] >= 23.0/180.0*math.pi and pp[ip] <= 27.0/180.0*math.pi):\n",
" c+=1\n",
" 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))\n",
" else:\n",
" sigma = 0.0\n",
"\n",
" if sigma < 0:\n",
" psi = 60.0/180.0*math.pi\n",
" elif sigma > 0:\n",
" psi = 120.0/180.0*math.pi\n",
" else:\n",
" psi = 0.0\n",
"\n",
" eta_true[ip,it,ir] = ani_p*abs(sigma)*math.sin(2.0*psi)\n",
" xi_true[ip,it,ir] = ani_p*abs(sigma)*math.cos(2.0*psi)\n",
" zeta_true[ip,it,ir] = gamma*math.sqrt(eta_true[ip,it,ir]**2 + xi_true[ip,it,ir]**2)\n",
" fun_true[ip,it,ir] = s0/(1.0+sigma*slow_p)\n",
" vel_true[ip,it,ir] = 1.0/fun_true[ip,it,ir] \n",
" a_true[ip,it,ir] = 1.0 + 2.0*zeta_true[ip,it,ir]\n",
" b_true[ip,it,ir] = 1.0 - 2.0*xi_true[ip,it,ir]\n",
" c_true[ip,it,ir] = 1.0 + 2.0*xi_true[ip,it,ir]\n",
" f_true[ip,it,ir] = -2.0 * eta_true[ip,it,ir]\n",
"\n",
"\n",
"\n",
"r_earth = 6378.1370\n",
"print(\"depminmax {} {}\".format(r_earth-rr1,r_earth-rr2))\n",
"print(c)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# write out\n",
"import h5py\n",
"\n",
"fout_init = h5py.File('test_model_init.h5', 'w')\n",
"fout_true = h5py.File('test_model_true.h5', 'w')\n",
"\n",
"# write out the arrays eta_init, xi_init, zeta_init, fun_init, a_init, b_init, c_init, f_init\n",
"fout_init.create_dataset('eta', data=eta_init.T)\n",
"fout_init.create_dataset('xi', data=xi_init.T)\n",
"fout_init.create_dataset('zeta', data=zeta_init.T)\n",
"fout_init.create_dataset('fun', data=fun_init.T)\n",
"fout_init.create_dataset('fac_a', data=a_init.T)\n",
"fout_init.create_dataset('fac_b', data=b_init.T)\n",
"fout_init.create_dataset('fac_c', data=c_init.T)\n",
"fout_init.create_dataset('fac_f', data=f_init.T)\n",
"fout_init.create_dataset('vel', data=vel_init.T)\n",
"\n",
"# writeout the arrays eta_true, xi_true, zeta_true, fun_true, a_true, b_true, c_true, f_true\n",
"fout_true.create_dataset('eta', data=eta_true.T)\n",
"fout_true.create_dataset('xi', data=xi_true.T)\n",
"fout_true.create_dataset('zeta', data=zeta_true.T)\n",
"fout_true.create_dataset('fun', data=fun_true.T)\n",
"fout_true.create_dataset('fac_a', data=a_true.T)\n",
"fout_true.create_dataset('fac_b', data=b_true.T)\n",
"fout_true.create_dataset('fac_c', data=c_true.T)\n",
"fout_true.create_dataset('fac_f', data=f_true.T)\n",
"fout_true.create_dataset('vel', data=vel_true.T)\n",
"\n",
"fout_init.close()\n",
"fout_true.close()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# prepare src station file\n",
"\n",
"```\n",
" 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\n",
" 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\n",
" 26 2 MRPI 1.6125 99.3172 1100.0000 P 50.84 19.400\n",
" 26 3 HUTI 2.3153 98.9711 1600.0000 P 57.84 19.200\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"random.seed(1145141919810)\n",
"\n",
"# dummys\n",
"year_dummy = 1998\n",
"month_dummy = 1\n",
"day_dummy = 1\n",
"hour_dummy = 0\n",
"minute_dummy = 0\n",
"second_dummy = 0\n",
"mag_dummy = 3.0\n",
"id_dummy = 1000\n",
"st_name_dummy = 'AAAA'\n",
"phase_dummy = 'P'\n",
"dist_dummy = 100.0\n",
"arriv_t_dummy = 0.0\n",
"\n",
"tt1deg = tt1 * 180.0/math.pi\n",
"tt2deg = tt2 * 180.0/math.pi\n",
"pp1deg = pp1 * 180.0/math.pi\n",
"pp2deg = pp2 * 180.0/math.pi\n",
"\n",
"\n",
"n_src = 1\n",
"n_rec = [1 for x in range(n_src)]\n",
"\n",
"\n",
"lines = []\n",
"\n",
"nij_src = math.sqrt(n_src)\n",
"nij_rec = math.sqrt(n_rec[0])\n",
"\n",
"pos_src=[]\n",
"pos_rec=[]\n",
"\n",
"# create dummy src\n",
"for i_src in range(n_src):\n",
" # define one point in the domain (rr1 bottom, rr2 top)\n",
" # random\n",
" #dep = random.uniform((R_earth-rr1)*0.95,(R_earth-rr1)*0.98)\n",
" #lon = random.uniform(pp1deg,pp2deg)\n",
" #lat = random.uniform(tt1deg,tt2deg)\n",
" # regularl\n",
" dep = (R_earth-rr1)*0.9\n",
" tmp_ilon = i_src%nij_src\n",
" tmp_ilat = int(i_src/nij_src)\n",
" lon = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_src\n",
" lat = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_src\n",
"\n",
" src = [i_src, year_dummy, month_dummy, day_dummy, hour_dummy, minute_dummy, second_dummy, lat, lon, dep, mag_dummy, n_rec[i_src], id_dummy]\n",
" lines.append(src)\n",
"\n",
" pos_src.append([lon,lat,dep])\n",
"\n",
"\n",
" # create dummy station\n",
" for i_rec in range(n_rec[i_src]):\n",
" elev_rec = random.uniform(0.0,-10.0) # elevation in m\n",
" lon_rec = random.uniform(pp1deg,pp2deg)\n",
" lat_rec = random.uniform(tt1deg,tt2deg)\n",
" # regularly\n",
" #elev_rec = -10.0\n",
" #tmp_ilon = i_rec%nij_rec\n",
" #tmp_ilat = int(i_rec/nij_rec)\n",
" #lon_rec = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_rec\n",
" #lat_rec = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_rec\n",
"\n",
" rec = [i_src, i_rec, st_name_dummy+str(i_rec), lat_rec, lon_rec, elev_rec, phase_dummy, dist_dummy, arriv_t_dummy]\n",
" lines.append(rec)\n",
"\n",
" pos_rec.append([lon_rec,lat_rec,elev_rec])\n",
"\n",
"\n",
"# write out ev_arrivals file\n",
"fname = 'src_rec_test.dat'\n",
"\n",
"with open(fname, 'w') as f:\n",
" for line in lines:\n",
" for elem in line:\n",
" f.write('{} '.format(elem))\n",
" f.write('\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# draw src and rec positions\n",
"import matplotlib.pyplot as plt\n",
"\n",
"for i_src in range(n_src):\n",
" plt.scatter(pos_src[i_src][1],pos_src[i_src][0],c='r',marker='o')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot receivers\n",
"for i_rec in range(n_rec[0]):\n",
" plt.scatter(pos_rec[i_rec][1],pos_rec[i_rec][0],c='b',marker='o')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.1 64-bit ('3.9.1')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "fbd0b2a7df497f398d93ab2f589d8a5daa3108cfb7ff2b90736653cca3aeadc0"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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test/old_tests/inversion_one_src/make_test_model.py Normal file
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# %% [markdown]
# # notebook for create init and true test model
# %%
import numpy as np
import math
# grid
R_earth = 6378.1370
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 = [20,50,50]
n_rtp.reverse()
dr = (rr2-rr1)/n_rtp[2]
dt = (tt2-tt1)/n_rtp[1]
dp = (pp2-pp1)/n_rtp[0]
rr = np.array([rr1 + x*dr for x in range(n_rtp[2])])
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[0])])
# 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)
a_init = np.zeros(n_rtp)
b_init = np.zeros(n_rtp)
c_init = np.zeros(n_rtp)
f_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)
a_true = np.zeros(n_rtp)
b_true = np.zeros(n_rtp)
c_true = np.zeros(n_rtp)
f_true = np.zeros(n_rtp)
c=0
for ir in range(n_rtp[2]):
for it in range(n_rtp[1]):
for ip in range(n_rtp[0]):
#eta_init[ip,it,ir] = 0.0
#xi_init[ip,it,ir] = 0.0
zeta_init[ip,it,ir] = gamma*math.sqrt(eta_init[ip,it,ir]**2 + xi_init[ip,it,ir]**2)
fun_init[ip,it,ir] = s0
vel_init[ip,it,ir] = 1.0/s0
a_init[ip,it,ir] = 1.0 + 2.0*zeta_init[ip,it,ir]
b_init[ip,it,ir] = 1.0 - 2.0*xi_init[ip,it,ir]
c_init[ip,it,ir] = 1.0 + 2.0*xi_init[ip,it,ir]
f_init[ip,it,ir] = -2.0 * eta_init[ip,it,ir]
# 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[ip,it,ir] = ani_p*abs(sigma)*math.sin(2.0*psi)
xi_true[ip,it,ir] = ani_p*abs(sigma)*math.cos(2.0*psi)
zeta_true[ip,it,ir] = gamma*math.sqrt(eta_true[ip,it,ir]**2 + xi_true[ip,it,ir]**2)
fun_true[ip,it,ir] = s0/(1.0+sigma*slow_p)
vel_true[ip,it,ir] = 1.0/fun_true[ip,it,ir]
a_true[ip,it,ir] = 1.0 + 2.0*zeta_true[ip,it,ir]
b_true[ip,it,ir] = 1.0 - 2.0*xi_true[ip,it,ir]
c_true[ip,it,ir] = 1.0 + 2.0*xi_true[ip,it,ir]
f_true[ip,it,ir] = -2.0 * eta_true[ip,it,ir]
r_earth = 6378.1370
print("depminmax {} {}".format(r_earth-rr1,r_earth-rr2))
print(c)
# %%
# %%
# write out
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.T)
fout_init.create_dataset('xi', data=xi_init.T)
fout_init.create_dataset('zeta', data=zeta_init.T)
fout_init.create_dataset('fun', data=fun_init.T)
fout_init.create_dataset('fac_a', data=a_init.T)
fout_init.create_dataset('fac_b', data=b_init.T)
fout_init.create_dataset('fac_c', data=c_init.T)
fout_init.create_dataset('fac_f', data=f_init.T)
fout_init.create_dataset('vel', data=vel_init.T)
# 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.T)
fout_true.create_dataset('xi', data=xi_true.T)
fout_true.create_dataset('zeta', data=zeta_true.T)
fout_true.create_dataset('fun', data=fun_true.T)
fout_true.create_dataset('fac_a', data=a_true.T)
fout_true.create_dataset('fac_b', data=b_true.T)
fout_true.create_dataset('fac_c', data=c_true.T)
fout_true.create_dataset('fac_f', data=f_true.T)
fout_true.create_dataset('vel', data=vel_true.T)
fout_init.close()
fout_true.close()
# %% [markdown]
# # prepare src station file
#
# ```
# 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'
dist_dummy = 100.0
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_src = 1
n_rec = [60 for x in range(n_src)]
lines = []
nij_src = math.sqrt(n_src)
nij_rec = math.sqrt(n_rec[0])
pos_src=[]
pos_rec=[]
# create dummy src
for i_src in range(n_src):
# define one point in the domain (rr1 bottom, rr2 top)
# random
#dep = random.uniform((R_earth-rr1)*0.95,(R_earth-rr1)*0.98)
#lon = random.uniform(pp1deg,pp2deg)
#lat = random.uniform(tt1deg,tt2deg)
# regularl
dep = (R_earth-rr1)*0.9
tmp_ilon = i_src%nij_src
tmp_ilat = int(i_src/nij_src)
lon = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_src
lat = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_src
src = [i_src, year_dummy, month_dummy, day_dummy, hour_dummy, minute_dummy, second_dummy, lat, lon, dep, mag_dummy, n_rec[i_src], id_dummy]
lines.append(src)
pos_src.append([lon,lat,dep])
# create dummy station
for i_rec in range(n_rec[i_src]):
#elev_rec = random.uniform(0.0,-10.0) # elevation in m
#lon_rec = random.uniform(pp1deg,pp2deg)
#lat_rec = random.uniform(tt1deg,tt2deg)
# regularly
elev_rec = -10.0
tmp_ilon = i_rec%nij_rec
tmp_ilat = int(i_rec/nij_rec)
lon_rec = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_rec
lat_rec = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_rec
rec = [i_src, i_rec, st_name_dummy, lat_rec, lon_rec, elev_rec, phase_dummy, dist_dummy, arriv_t_dummy]
lines.append(rec)
pos_rec.append([lon_rec,lat_rec,elev_rec])
# write out ev_arrivals file
fname = 'src_rec_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_src):
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')
# %%