TomoATT/test/old_tests/heterogeneous_inv_test/make_test_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# notebook for create init and true test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depminmax 10.0 -10.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "\n",
    "# grid\n",
    "R_earth = 6371.0\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 = [50,50,50]\n",
    "dr = (rr2-rr1)/(n_rtp[0]-1)\n",
    "dt = (tt2-tt1)/(n_rtp[1]-1)\n",
    "dp = (pp2-pp1)/(n_rtp[2]-1)\n",
    "rr = np.array([rr1 + x*dr for x in range(n_rtp[0])])\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[2])])\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_sign_rtp = [1,2,2] # number of sign wave in each direction\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",
    "\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",
    "\n",
    "\n",
    "def make_1d_sign_wave(n_sign_rtp, rr, tt, pp):\n",
    "    \"\"\"\n",
    "    make 1d sign wave for each direction following n_sign_rtp\n",
    "    n_sign_rtp: frequency of sign wave in each direction\n",
    "    \"\"\"\n",
    "\n",
    "    n_r = rr.shape[0]\n",
    "    n_t = tt.shape[0]\n",
    "    n_p = pp.shape[0]\n",
    "\n",
    "    r_1d = np.zeros(n_r)\n",
    "    t_1d = np.zeros(n_t)\n",
    "    p_1d = np.zeros(n_p)\n",
    "\n",
    "    for ir in range(n_r):\n",
    "        r_1d[ir] = math.sin(2.*n_sign_rtp[0]*math.pi*ir/(n_r-1))\n",
    "\n",
    "    for it in range(n_t):\n",
    "        t_1d[it] = math.sin(2.*n_sign_rtp[1]*math.pi*it/(n_t-1))\n",
    "\n",
    "    for ip in range(n_p):\n",
    "        p_1d[ip] = math.sin(2.*n_sign_rtp[2]*math.pi*ip/(n_p-1))\n",
    "    \n",
    "   \n",
    "\n",
    "    # plot 1d sign wave\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure()\n",
    "    plt.plot(r_1d)\n",
    "    plt.figure()\n",
    "    plt.plot(t_1d)\n",
    "    plt.figure()\n",
    "    plt.plot(p_1d)\n",
    "    plt.show()\n",
    "\n",
    "    return r_1d, t_1d, p_1d\n",
    "\n",
    "# make 1d sign wave for each direction following n_sign_rtp\n",
    "r_1d, t_1d, p_1d = make_1d_sign_wave(n_sign_rtp, rr, tt, pp)\n",
    "\n",
    "for ir in range(n_rtp[0]):\n",
    "    for it in range(n_rtp[1]):\n",
    "        for ip in range(n_rtp[2]):\n",
    "            # already initialized above\n",
    "            #eta_init[ir,it,ip] = 0.0\n",
    "            #xi_init[ir,it,ip]  = 0.0\n",
    "            zeta_init[ir,it,ip] = gamma*math.sqrt(eta_init[ir,it,ip]**2 + xi_init[ir,it,ip]**2)\n",
    "            fun_init[ir,it,ip] = s0\n",
    "            vel_init[ir,it,ip] = 1.0/s0\n",
    "\n",
    "            # true model\n",
    "            sigma = r_1d[ir]*t_1d[it]*p_1d[ip] \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[ir,it,ip] = ani_p*abs(sigma)*math.sin(2.0*psi)\n",
    "            xi_true[ir,it,ip]  = ani_p*abs(sigma)*math.cos(2.0*psi)\n",
    "            zeta_true[ir,it,ip] = gamma*math.sqrt(eta_true[ir,it,ip]**2 + xi_true[ir,it,ip]**2)\n",
    "            fun_true[ir,it,ip] = s0/(1.0+sigma*slow_p)\n",
    "            vel_true[ir,it,ip] = 1.0/fun_true[ir,it,ip]    \n",
    "\n",
    "\n",
    "#r_earth = 6378.1370\n",
    "print(\"depminmax {} {}\".format(R_earth-rr1,R_earth-rr2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out in hdf5 format\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)\n",
    "fout_init.create_dataset('xi', data=xi_init)\n",
    "fout_init.create_dataset('zeta', data=zeta_init)\n",
    "fout_init.create_dataset('vel', data=vel_init)\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)\n",
    "fout_true.create_dataset('xi', data=xi_true)\n",
    "fout_true.create_dataset('zeta', data=zeta_true)\n",
    "fout_true.create_dataset('vel', data=vel_true)\n",
    "\n",
    "fout_init.close()\n",
    "fout_true.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prepare src station file\n",
    "\n",
    "The following code creates a src_rec_file for the inversion, which describes the source and receiver positions and arrival times.\n",
    "Format is as follows:\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",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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_srcs = [10,20,20]\n",
    "n_src = n_srcs[0]*n_srcs[1]*n_srcs[2]\n",
    "n_recs = [10,10]\n",
    "n_rec = n_recs[0]*n_recs[1]\n",
    "\n",
    "lines = []\n",
    "\n",
    "pos_src=[]\n",
    "pos_rec=[]\n",
    "\n",
    "\n",
    "# create receiver coordinates\n",
    "elev_recs=[]\n",
    "lon_recs=[]\n",
    "lat_recs=[]\n",
    "rec_names=[]\n",
    "n_rec_2 = 0\n",
    "for i in range(n_recs[0]):\n",
    "    for j in range(n_recs[1]):\n",
    "\n",
    "        if (i >= n_recs[0]/4 or j >= n_recs[1]/4) and (j % 4 != 0 or i % 4 != 0):\n",
    "            continue\n",
    "\n",
    "        n_rec_2 += 1\n",
    "\n",
    "        id_rec = i*n_recs[1]+j\n",
    "        #elev_recs.append(random.uniform(-100.0,-100.0)) # elevation in m\n",
    "        #elev_recs.append(0) # elevation in m\n",
    "        #lon_recs .append(random.uniform(pp1deg*1.1,pp2deg*0.9))\n",
    "        #lat_recs .append(random.uniform(tt1deg*1.1,tt2deg*0.9))\n",
    "        rec_names.append(id_rec)\n",
    "        # regularly\n",
    "        elev_recs.append(0.0)\n",
    "        lon_recs.append(pp1deg + i*(pp2deg-pp1deg)/n_recs[0])\n",
    "        lat_recs.append(tt1deg + j*(tt2deg-tt1deg)/n_recs[1])\n",
    "\n",
    "\n",
    "\n",
    "# create source coordinates\n",
    "c=0\n",
    "for ir in range(n_srcs[0]):\n",
    "    for it in range(n_srcs[1]):\n",
    "        for ip in range(n_srcs[2]):\n",
    "\n",
    "            # make the number of sources one-fourth if the position is right hand side\n",
    "            if (ip >= n_srcs[2]/4 or it >=n_srcs[1]/4) and (ip%4 != 0 or it%4 != 0):\n",
    "                continue\n",
    "            \n",
    "            if ir == 0:\n",
    "                c+=1\n",
    "\n",
    "            i_src = ir*n_srcs[1]*n_srcs[2] + it*n_srcs[2] + ip\n",
    "            # define one point in the domain (rr1 bottom, rr2 top)\n",
    "            # random\n",
    "            #dep = random.uniform((R_earth-rr1)*0.5,(R_earth-rr1)*0.98)\n",
    "            #lon = random.uniform(pp1deg,pp2deg)\n",
    "            #lat = random.uniform(tt1deg,tt2deg)\n",
    "\n",
    "            # regular\n",
    "            dep = (R_earth-rr1)/n_srcs[0]*ir\n",
    "            lon = pp1deg + ip*(pp2deg-pp1deg)/n_srcs[2]\n",
    "            lat = tt1deg + it*(tt2deg-tt1deg)/n_srcs[1]\n",
    "\n",
    " \n",
    "            src = [i_src, year_dummy, month_dummy, day_dummy, hour_dummy, minute_dummy, second_dummy, lat, lon, dep, mag_dummy, n_rec_2, 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_2):\n",
    "                \n",
    "                elev_rec = elev_recs[i_rec]\n",
    "                lon_rec  = lon_recs[i_rec]\n",
    "                lat_rec  = lat_recs[i_rec]\n",
    "                st_name_dummy = rec_names[i_rec]\n",
    "        \n",
    "                rec = [i_src, i_rec, st_name_dummy, 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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw src and rec positions\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n_src_2d = c#n_srcs[0]*n_srcs[1]*n_srcs[2]\n",
    "\n",
    "for i_src in range(n_src_2d):\n",
    "    plt.scatter(pos_src[i_src][1],pos_src[i_src][0],c='r',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot receivers\n",
    "for i_rec in range(n_rec_2):\n",
    "    plt.scatter(pos_rec[i_rec][1],pos_rec[i_rec][0],c='b',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rec_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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