tutorials/S2_Morse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Morse grid imports\n",
    "\n",
    "Here we look at applied examples to import data for use in Dr Peter Morse 3D visualtsation software _(in prep.)_. Data must be exported as $1800 \\times 3600$ px png files. This might change in future. \n",
    "\n",
    "Generally, the code in this tutorial runs rather slow as there is a large amount of interpolation. Have patience. \n",
    "\n",
    "Folder `../local` is used to download data and export pngs. \n",
    "\n",
    "This notebook also show some real examples of how agrid can be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import warnings\n",
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "config_file = \"../agrid/agrid.py\"\n",
    "#with open(config_file) as f:\n",
    "#    code = compile(f.read(), config_file, 'exec')\n",
    "#    exec(code, globals(), locals())\n",
    "\n",
    "# ... or use you Python path or present working directory:\n",
    "import sys, os\n",
    "sys.path.append('..')\n",
    "\n",
    "from agrid.agrid.grid import Grid\n",
    "from agrid.acc import download\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import vtk\n",
    "from vtk.util.numpy_support import vtk_to_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'agrid.agrid.grid' from '../agrid/agrid/grid.py'>\n"
     ]
    }
   ],
   "source": [
    "import inspect\n",
    "print(inspect.getmodule(Grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from morse_2019 import export_png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMEAN2\n",
    "\n",
    "Download the mantel model from from Becker and Boschi, The University of Texas at Austin. See original paper [Becker and_Boschi 2002](http://www-udc.ig.utexas.edu/external/becker/becker_and_boschi_2002.pdf) for details. SMEAN2 contains additional datasets. (This was written before download functions wrere implimented in agrid, and is kept as an example of the functionlality in Jupyter.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ‘../../data/smean2/smean_grd.tgz’ already there; not retrieving.\n",
      "x smean2/\n",
      "x smean2/dvs.8.grd\n",
      "x smean2/dvs.32.grd\n",
      "x smean2/dvs.15.grd\n",
      "x smean2/dvs.29.grd\n",
      "x smean2/dvs.17.grd\n",
      "x smean2/dvs.28.grd\n",
      "x smean2/convert\n",
      "x smean2/dvs.44.grd\n",
      "x smean2/dvs.27.grd\n",
      "x smean2/dvs.55.grd\n",
      "x smean2/dvs.58.grd\n",
      "x smean2/dvs.22.grd\n",
      "x smean2/dvs.20.grd\n",
      "x smean2/dvs.6.grd\n",
      "x smean2/dvs.53.grd\n",
      "x smean2/dvs.11.grd\n",
      "x smean2/dvs.38.grd\n",
      "x smean2/dvs.33.grd\n",
      "x smean2/dvs.1.grd\n",
      "x smean2/dvs.13.grd\n",
      "x smean2/dvs.49.grd\n",
      "x smean2/depths.dat\n",
      "x smean2/dvs.50.grd\n",
      "x smean2/dvs.30.grd\n",
      "x smean2/d.31.model.ab\n",
      "x smean2/dvs.9.grd\n",
      "x smean2/dvs.42.grd\n",
      "x smean2/dvs.54.grd\n",
      "x smean2/dvs.43.grd\n",
      "x smean2/dvs.31.grd\n",
      "x smean2/dvs.47.grd\n",
      "x smean2/dvs.24.grd\n",
      "x smean2/dvs.7.grd\n",
      "x smean2/dvs.14.grd\n",
      "x smean2/dvs.4.grd\n",
      "x smean2/dvs.39.grd\n",
      "x smean2/dvs.34.grd\n",
      "x smean2/dvs.48.grd\n",
      "x smean2/dvs.23.grd\n",
      "x smean2/README\n",
      "x smean2/dvs.5.grd\n",
      "x smean2/dvs.46.grd\n",
      "x smean2/dvs.51.grd\n",
      "x smean2/dvs.19.grd\n",
      "x smean2/dvs.16.grd\n",
      "x smean2/dvs.41.grd\n",
      "x smean2/dvs.36.grd\n",
      "x smean2/dvs.25.grd\n",
      "x smean2/dvs.37.grd\n",
      "x smean2/dvs.35.grd\n",
      "x smean2/dvs.10.grd\n",
      "x smean2/dvs.21.grd\n",
      "x smean2/dvs.12.grd\n",
      "x smean2/dvs.45.grd\n",
      "x smean2/dvs.18.grd\n",
      "x smean2/dvs.57.grd\n",
      "x smean2/dvs.52.grd\n",
      "x smean2/dvs.56.grd\n",
      "x smean2/dvs.40.grd\n",
      "x smean2/dvs.26.grd\n",
      "x smean2/dvs.2.grd\n",
      "x smean2/dvs.3.grd\n",
      "\n",
      "GMT/Netcdf grid expansions of the SMEAN2 composite tomography\n",
      "\"model\". The approach follows that used by Becker and Boschi (G-Cubed,\n",
      "2002), but uses updated models:\n",
      "\n",
      "Gypsum-S (Simmons et al., 2011), S40RTS (Ritsema et al., 2011), and\n",
      "SAVANI (Auer et al., 2014).\n",
      "\n",
      "All files can be converted to ASCII files with lon-lat-value by GMT\n",
      "\n",
      "grd2xyz dvs.1.grd > data.lonlatv\n",
      "\n",
      "for example. \n",
      "\n",
      "These grid files were extracted from the original, spherical harmonics\n",
      "version, which is also included as file d.31.model.ab, and can be used\n",
      "via the \"shansyn\" tools, for example, available at\n",
      "\n",
      "http://www-udc.ig.utexas.edu/external/becker/sdata.html\n",
      "\n",
      "with the bash script \"convert\" that is part of this tar file. Shansyn\n",
      "comes also pre-installed as part of the Unified Geodynamics Earth\n",
      "Science Computing Environment at\n",
      "\n",
      "http://www-udc.ig.utexas.edu/external/becker/ugesce.html\n",
      "\n",
      "The grid file values are given are percentage v_S wave speed variation\n",
      "related to PREM, with layer averages removed.\n",
      "\n",
      "The original model was expanded on 58 levels, bottom up, at the depths\n",
      "indicated in km in the file depths.dat. Note that crustal levels\n",
      "shallower than 75 km are meaningless.\n",
      "\n",
      "For any questions or comments, please contact Thorsten Becker at\n",
      "twb@ig.utexas.edu.\n",
      "\n",
      "Thorsten Becker, Austin, June 2016"
     ]
    }
   ],
   "source": [
    "! mkdir -p ../../data/smean2\n",
    "! wget -nc http://www-udc.ig.utexas.edu/external/becker/ftp/smean2_grd.tgz \\\n",
    "    -O ../../data/smean2/smean_grd.tgz\n",
    "! tar -xvzf ../../data/smean2/smean_grd.tgz -C ../../data/\n",
    "\n",
    "! printf \"\\n\"\n",
    "! cat ../../data/smean2/README"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the data is global and we define a grid with the right rsolution from start. However, as we will see later, we could work on regional data as well. The exported png will always have a global coverage with alpha covering the excluded areas. \n",
    "\n",
    "We read depth values for smean from a provided ascii file. However we also have a regular depth dimention, defined as regular 50km depths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  20000   40000   60000   80000  100000  120000  140000  160000  180000\n",
      "  200000  220000  240000  260000  280000  300000  320000  340000  360000\n",
      "  380000  400000  420000  440000  460000  480000  500000  520000  540000\n",
      "  560000  580000  600000  620000  640000  660000  680000  700000  720000\n",
      "  740000  760000  780000  800000  820000  840000  860000  880000  900000\n",
      "  920000  940000  960000  980000 1000000 1050000 1100000 1150000 1200000\n",
      " 1250000 1300000 1350000 1400000 1450000 1500000 1550000 1600000 1650000\n",
      " 1700000 1750000 1800000 1850000 1900000 1950000 2000000 2050000 2100000\n",
      " 2150000 2200000 2250000 2300000 2350000 2400000 2450000 2500000 2550000\n",
      " 2600000 2650000 2700000 2750000 2800000 2850000]\n",
      "[2875000. 2825000. 2775000. 2725000. 2675000. 2625000. 2575000. 2525000.\n",
      " 2475000. 2425000. 2375000. 2325000. 2275000. 2225000. 2175000. 2125000.\n",
      " 2075000. 2025000. 1975000. 1925000. 1875000. 1825000. 1775000. 1725000.\n",
      " 1675000. 1625000. 1575000. 1525000. 1475000. 1425000. 1375000. 1325000.\n",
      " 1275000. 1225000. 1175000. 1125000. 1075000. 1025000.  975000.  925000.\n",
      "  875000.  825000.  775000.  725000.  675000.  625000.  575000.  525000.\n",
      "  475000.  425000.  375000.  325000.  275000.  225000.  175000.  125000.\n",
      "   75000.   25000.]\n"
     ]
    }
   ],
   "source": [
    "#columns from left to right represent radius,depth,density,Vpv,Vph,Vsv,Vsh,eta,Q-mu,Q-kappa\n",
    "km = 1000\n",
    "\n",
    "smean_depths = km * np.loadtxt('../../../data/smean2/depths.dat')\n",
    "max_depth = np.max(smean_depths)\n",
    "\n",
    "depths = km * np.array([*range(20, 1000, 20)] + [*range(1000, int(max_depth)//km, 50)])\n",
    "\n",
    "prem_d, prem_vsv, prem_vsh = km * np.loadtxt('../../../data/models/PREM_1s.csv', \n",
    "                              delimiter = ',', \n",
    "                              usecols = [1, 5, 6],\n",
    "                              unpack=True)\n",
    "\n",
    "prem_vsv = prem_vsv[prem_d < max_depth]\n",
    "prem_vsh = prem_vsh[prem_d < max_depth]\n",
    "prem_d = prem_d[prem_d < max_depth]\n",
    "\n",
    "print(depths)\n",
    "print(smean_depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:  (RGB: 3, X: 3600, X_edge: 3601, Y: 1800, Y_edge: 1801, Z: 87, Z_edge: 88)\n",
      "Coordinates:\n",
      "  * X        (X) float32 -180.0 -179.89998 -179.79994 ... 179.89998 180.0\n",
      "  * Y        (Y) float32 -90.0 -89.89995 -89.79989 ... 89.79989 89.89995 90.0\n",
      "  * Z        (Z) float32 20000.0 40000.0 60000.0 ... 2800000.0 2850000.0\n",
      "  * X_edge   (X_edge) float32 -180.04999 -179.94997 ... 179.94997 180.04999\n",
      "  * Y_edge   (Y_edge) float32 -90.049995 -89.949936 ... 89.949936 90.049995\n",
      "  * Z_edge   (Z_edge) float64 1e+04 3e+04 5e+04 ... 2.825e+06 2.875e+06\n",
      "  * RGB      (RGB) <U1 'R' 'G' 'B'\n",
      "    XV       (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    YV       (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    lat      (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    lon      (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "Data variables:\n",
      "    *empty*\n"
     ]
    }
   ],
   "source": [
    "world = Grid(crs_tgt = 4326, \n",
    "             left= -180, up=90, down= -90, right=180.0, \n",
    "             res=(0.09999,0.09999), \n",
    "             depths = depths)\n",
    "\n",
    "array = np.empty((world.ny, world.nx, world.ds.coords['Z'].size))\n",
    "array[:] = np.nan\n",
    "print(world.ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds['PREM_VSV'] = (('Z'), world.change_coord(prem_vsv, prem_d, 'Z') )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can read SMEAN files. set_center alows us to read 0-360 as -180 - 180 lon. read_grid uses default nearest neighbour interpolation of datapoints to fit the predefined Morse grid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:   (RGB: 3, SMEAN_Z: 58, X: 3600, X_edge: 3601, Y: 1800, Y_edge: 1801, Z: 87, Z_edge: 88)\n",
      "Coordinates:\n",
      "  * X         (X) float32 -180.0 -179.89998 -179.79994 ... 179.89998 180.0\n",
      "  * Y         (Y) float32 -90.0 -89.89995 -89.79989 ... 89.79989 89.89995 90.0\n",
      "  * Z         (Z) float32 20000.0 40000.0 60000.0 ... 2800000.0 2850000.0\n",
      "  * X_edge    (X_edge) float32 -180.04999 -179.94997 ... 179.94997 180.04999\n",
      "  * Y_edge    (Y_edge) float32 -90.049995 -89.949936 ... 89.949936 90.049995\n",
      "  * Z_edge    (Z_edge) float64 1e+04 3e+04 5e+04 ... 2.825e+06 2.875e+06\n",
      "  * RGB       (RGB) <U1 'R' 'G' 'B'\n",
      "    XV        (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    YV        (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    lat       (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "    lon       (Y, X) float32 dask.array<shape=(1800, 3600), chunksize=(180, 360)>\n",
      "  * SMEAN_Z   (SMEAN_Z) float64 2.875e+06 2.825e+06 ... 7.5e+04 2.5e+04\n",
      "Data variables:\n",
      "    PREM_VSV  (Z) float64 3.9e+03 4.4e+03 4.404e+03 ... 7.266e+03 7.266e+03 nan\n"
     ]
    }
   ],
   "source": [
    "world.ds.coords['SMEAN_Z'] = smean_depths\n",
    "\n",
    "print(world.ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2875.0, JHJHJHJ\n",
      "2825.0, JHJHJHJ\n",
      "2775.0, JHJHJHJ\n",
      "2725.0, JHJHJHJ\n",
      "2675.0, JHJHJHJ\n",
      "2625.0, JHJHJHJ\n",
      "2575.0, JHJHJHJ\n",
      "2525.0, JHJHJHJ\n",
      "2475.0, JHJHJHJ\n",
      "2425.0, JHJHJHJ\n",
      "2375.0, JHJHJHJ\n",
      "2325.0, JHJHJHJ\n",
      "2275.0, JHJHJHJ\n",
      "2225.0, JHJHJHJ\n",
      "2175.0, JHJHJHJ\n",
      "2125.0, JHJHJHJ\n",
      "2075.0, JHJHJHJ\n",
      "2025.0, JHJHJHJ\n",
      "1975.0, JHJHJHJ\n",
      "1925.0, JHJHJHJ\n",
      "1875.0, JHJHJHJ\n",
      "1825.0, JHJHJHJ\n",
      "1775.0, JHJHJHJ\n",
      "1725.0, JHJHJHJ\n",
      "1675.0, JHJHJHJ\n",
      "1625.0, JHJHJHJ\n",
      "1575.0, JHJHJHJ\n",
      "1525.0, JHJHJHJ\n",
      "1475.0, JHJHJHJ\n",
      "1425.0, JHJHJHJ\n",
      "1375.0, JHJHJHJ\n",
      "1325.0, JHJHJHJ\n",
      "1275.0, JHJHJHJ\n",
      "1225.0, JHJHJHJ\n",
      "1175.0, JHJHJHJ\n",
      "1125.0, JHJHJHJ\n",
      "1075.0, JHJHJHJ\n",
      "1025.0, JHJHJHJ\n",
      "975.0, JHJHJHJ\n",
      "925.0, JHJHJHJ\n",
      "875.0, JHJHJHJ\n",
      "825.0, JHJHJHJ\n",
      "775.0, JHJHJHJ\n",
      "725.0, JHJHJHJ\n",
      "675.0, JHJHJHJ\n",
      "625.0, JHJHJHJ\n",
      "575.0, JHJHJHJ\n",
      "525.0, JHJHJHJ\n",
      "475.0, JHJHJHJ\n",
      "425.0, JHJHJHJ\n",
      "375.0, JHJHJHJ\n",
      "325.0, JHJHJHJ\n",
      "275.0, JHJHJHJ\n",
      "225.0, JHJHJHJ\n",
      "175.0, JHJHJHJ\n",
      "125.0, JHJHJHJ\n",
      "75.0, JHJHJHJ\n",
      "25.0, JHJHJHJ\n",
      "[[0.00145861 0.00145861 0.00145861 0.00145861 0.00145861 0.00145861\n",
      "  0.00145861 0.00145861 0.00145861 0.00145861 0.00145861 0.00145861\n",
      "  0.00145861 0.00145861]\n",
      " [0.00140298 0.00140298 0.00140298 0.00140298 0.00140298 0.00140298\n",
      "  0.00140298 0.00140298 0.00140298 0.00140298 0.00140282 0.00140282\n",
      "  0.00140282 0.00140282]\n",
      " [0.00134735 0.00134735 0.00134735 0.00134735 0.00134735 0.00134735\n",
      "  0.00134735 0.00134735 0.00134735 0.00134719 0.00134702 0.00134702\n",
      "  0.00134702 0.00134702]\n",
      " [0.00129172 0.00129172 0.00129172 0.00129172 0.00129172 0.00129172\n",
      "  0.00129172 0.00129172 0.00129156 0.0012914  0.00129123 0.00129123\n",
      "  0.00129123 0.00129108]\n",
      " [0.00123609 0.00123609 0.00123609 0.00123609 0.00123609 0.00123609\n",
      "  0.00123609 0.00123592 0.00123576 0.0012356  0.00123544 0.00123544\n",
      "  0.00123528 0.00123512]\n",
      " [0.00118045 0.00118045 0.00118045 0.00118045 0.00118045 0.00118045\n",
      "  0.00118029 0.00118013 0.00117996 0.0011798  0.00117964 0.00117948\n",
      "  0.00117933 0.00117917]\n",
      " [0.00112178 0.00112178 0.00112178 0.00112178 0.00112178 0.00112178\n",
      "  0.0011215  0.00112134 0.00112118 0.00112102 0.00112085 0.0011207\n",
      "  0.00112054 0.00112038]\n",
      " [0.00106312 0.00106312 0.00106312 0.00106312 0.00106312 0.00106312\n",
      "  0.00106284 0.00106256 0.0010624  0.00106224 0.00106207 0.00106192\n",
      "  0.00106176 0.0010616 ]\n",
      " [0.00100445 0.00100445 0.00100445 0.00100445 0.00100445 0.00100445\n",
      "  0.00100417 0.00100389 0.00100361 0.00100345 0.00100329 0.00100313\n",
      "  0.00100297 0.0010027 ]\n",
      " [0.00094578 0.00094578 0.00094578 0.00094578 0.00094578 0.00094578\n",
      "  0.00094551 0.00094523 0.00094495 0.00094467 0.00094451 0.00094435\n",
      "  0.00094408 0.00094381]\n",
      " [0.00088711 0.00088711 0.00088711 0.00088711 0.00088711 0.00088711\n",
      "  0.00088683 0.00088655 0.00088627 0.00088599 0.00088571 0.00088544\n",
      "  0.00088517 0.00088491]\n",
      " [0.0008265  0.0008265  0.0008265  0.0008265  0.0008265  0.0008265\n",
      "  0.00082622 0.00082594 0.00082566 0.00082538 0.00082505 0.00082474\n",
      "  0.00082447 0.0008242 ]\n",
      " [0.00076589 0.00076589 0.00076589 0.00076589 0.00076589 0.00076589\n",
      "  0.00076561 0.00076533 0.00076505 0.00076472 0.00076439 0.00076407\n",
      "  0.00076375 0.00076349]\n",
      " [0.00070528 0.00070528 0.00070528 0.00070528 0.00070528 0.00070528\n",
      "  0.000705   0.00070472 0.00070439 0.00070406 0.00070373 0.00070341\n",
      "  0.00070309 0.00070278]]\n"
     ]
    }
   ],
   "source": [
    "smean_array = np.empty((world.ny, world.nx, smean_depths.size))\n",
    "\n",
    "for i, a in enumerate(smean_depths):\n",
    "    print(a//km, end=', ')\n",
    "    index_name = smean_depths//km - i # Get right file name for each depth\n",
    "    fname = '../../../data/smean2/dvs.%s.grd'%(i+1)\n",
    "    if os.path.isfile(fname):\n",
    "        smean_array[:,:,i] = world.read_grid(fname, xyz = ('lon','lat','z'), set_center = True, pad_around = True)\n",
    "        \n",
    "smean_array /= 100    #\"The grid file values are given are percentage v_S wave \n",
    "#                #speed variation related to PREM, with layer averages removed.\"\n",
    "    \n",
    "print(smean_array[:14,:14,25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[autoreload of agrid.agrid.grid failed: Traceback (most recent call last):\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 245, in check\n",
      "    superreload(m, reload, self.old_objects)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 450, in superreload\n",
      "    update_generic(old_obj, new_obj)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 387, in update_generic\n",
      "    update(a, b)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 357, in update_class\n",
      "    update_instances(old, new)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 317, in update_instances\n",
      "    update_instances(old, new, obj, visited)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 317, in update_instances\n",
      "    update_instances(old, new, obj, visited)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 317, in update_instances\n",
      "    update_instances(old, new, obj, visited)\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 300, in update_instances\n",
      "    for obj in (obj for obj in objects if id(obj) not in visited):\n",
      "  File \"/Users/tobiasstal/anaconda3/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 300, in <genexpr>\n",
      "    for obj in (obj for obj in objects if id(obj) not in visited):\n",
      "KeyboardInterrupt\n",
      "]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'SMEAN_Z' (SMEAN_Z: 58)>\n",
      "array([2875000., 2825000., 2775000., 2725000., 2675000., 2625000., 2575000.,\n",
      "       2525000., 2475000., 2425000., 2375000., 2325000., 2275000., 2225000.,\n",
      "       2175000., 2125000., 2075000., 2025000., 1975000., 1925000., 1875000.,\n",
      "       1825000., 1775000., 1725000., 1675000., 1625000., 1575000., 1525000.,\n",
      "       1475000., 1425000., 1375000., 1325000., 1275000., 1225000., 1175000.,\n",
      "       1125000., 1075000., 1025000.,  975000.,  925000.,  875000.,  825000.,\n",
      "        775000.,  725000.,  675000.,  625000.,  575000.,  525000.,  475000.,\n",
      "        425000.,  375000.,  325000.,  275000.,  225000.,  175000.,  125000.,\n",
      "         75000.,   25000.])\n",
      "Coordinates:\n",
      "  * SMEAN_Z  (SMEAN_Z) float64 2.875e+06 2.825e+06 2.775e+06 ... 7.5e+04 2.5e+04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../agrid/agrid/grid.py:1173: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  resolution=map_res,\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-2af8b21bbf50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSMEAN_Z\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSMEAN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSMEAN_Z\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m75\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/proj/grid/grid/agrid/agrid/grid.py\u001b[0m in \u001b[0;36mmap_grid\u001b[0;34m(self, im_data, vectors, v_col, v_alpha, v_lw, v_x_offset, v_y_offset, vmin, vmax, cmap, cbar, cbar_label, extent, line_c, line_grid_c, line_w, circ_map, figsize, land_only, ocean_color, no_land_fill, title, save_name, show, map_res, draw_coast, draw_grid, par, mer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0murcrnrlat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m                     \u001b[0mresolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_res\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m                     epsg=basemap_epsg, **kwargs)\n\u001b[0m\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mextent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/mpl_toolkits/basemap/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat, llcrnrx, llcrnry, urcrnrx, urcrnry, width, height, projection, resolution, area_thresh, rsphere, ellps, lat_ts, lat_1, lat_2, lat_0, lon_0, lon_1, lon_2, o_lon_p, o_lat_p, k_0, no_rot, suppress_ticks, satellite_height, boundinglat, fix_aspect, anchor, celestial, round, epsg, ax)\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;31m# replace coastsegs with line segments (instead of polygons)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoastsegs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_readboundarydata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gshhs'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mas_polygons\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# create geos Polygon structures for land areas.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0;31m# currently only used in is_land method.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/mpl_toolkits/basemap/__init__.py\u001b[0m in \u001b[0;36m_readboundarydata\u001b[0;34m(self, name, as_polygons)\u001b[0m\n\u001b[1;32m   1392\u001b[0m                         \u001b[0;32mfor\u001b[0m \u001b[0mpoly\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpolys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1393\u001b[0m                             \u001b[0;31m# try to fix \"non-noded intersection\" errors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1394\u001b[0;31m                             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_valid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpoly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1395\u001b[0m                             \u001b[0;31m# if polygon instersects map projection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1396\u001b[0m                             \u001b[0;31m# region, process it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "world.ds['SMEAN'] = (('Y', 'X', 'SMEAN_Z'), smean_array)\n",
    "\n",
    "\n",
    "print(world.ds.SMEAN_Z)\n",
    "\n",
    "world.map_grid(world.ds.SMEAN.sel(SMEAN_Z=75*km))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01785030737492207"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.ds.SMEAN.sel(SMEAN_Z=75*km).values[1000,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we export the 3D grids as png files for each depth slice. We store a set of original depth slices in ''morse/smean/z_orig/' and also the interpolated depth slices in 'morse/smean/z_even/'. This might take some time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.12535165761833922 0.07612317009959951\n"
     ]
    }
   ],
   "source": [
    "smean_min = np.nanmin(smean_array)\n",
    "smean_max = np.nanmax(smean_array)\n",
    "\n",
    "print(smean_min, smean_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-8ac33e575c5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_smean_array\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchange_coord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msmean_array\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msmean_depths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Z'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/proj/grid/grid/agrid/agrid/grid.py\u001b[0m in \u001b[0;36mchange_coord\u001b[0;34m(self, array, old, new, fill_value, interpol, axis, bounds_error, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m                                     \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m                                     \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m                                     **kwargs)(new)\n\u001b[0m\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfold_to_low_res\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlarge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmall\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/polyint.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \"\"\"\n\u001b[1;32m     78\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    659\u001b[0m         \u001b[0;31m#    The behavior is set by the bounds_error variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0mx_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 661\u001b[0;31m         \u001b[0my_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    662\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extrapolate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m             \u001b[0mbelow_bounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabove_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_call_linear\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    603\u001b[0m         \u001b[0mx_hi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0my_lo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlo\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m         \u001b[0my_hi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m         \u001b[0;31m# Note that the following two expressions rely on the specifics of the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#new_smean_array  = world.change_coord(smean_array, old=smean_depths, new='Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.shape(new_smean_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#world.ds['SMEAN_INTERPOL'] = (('Y', 'X', 'Z'), new_smean_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "#pickle.dump(world, open( \"../local/world.p\", \"wb\" ), protocol=4)\n",
    "#w = open( \"../local/world.p\", \"wb\" )\n",
    "#pickle.dump(world,w,protocol=pickle.HIGHEST_PROTOCOL)\n",
    "#world = pickle.load( open( \"../local/world.p\", \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! mkdir -p ../local/morse/smean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_min = -0.024\n",
    "clip_max = 0.024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a quick look at the interpolation for some test pixels, it looks OK. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pixels = [(12,12),(123,123),(500,500),(0,0)]\n",
    "\n",
    "for test_pixel in test_pixels:\n",
    "    plt.plot(world.ds['SMEAN'][test_pixel[0],test_pixel[1],:], -world.ds['SMEAN_Z']/km)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we convert the original depths to png rasters. We clip the values to +/- 2.4%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "planes: 3\n",
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      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/0025_smean2.png, -0.024, 0.024, 8, nearest, -0.12535165761833922, 0.0616335960167271, 0.0, 1.0, 0.0, 255.0, 0, 255\n",
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      "../local/morse/smean2/0125_smean2.png, -0.024, 0.024, 8, nearest, -0.06561713460108877, 0.07612317009959951, 0.0, 1.0, 0.0, 255.0, 0, 255\n",
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      "\n",
      "../local/morse/smean2/0175_smean2.png, -0.024, 0.024, 8, nearest, -0.04738297446398065, 0.06784743098185572, 0.0, 1.0, 0.0, 255.0, 0, 255\n",
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      "\n",
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      "\n",
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    {
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      "../local/morse/smean2/2125_smean2.png, -0.024, 0.024, 8, nearest, -0.020014183404073266, 0.013089036051533186, 0.08235294117647059, 0.7725490196078432, 21.0, 197.0, 21, 197\n",
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      "\n",
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      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2425_smean2.png, -0.024, 0.024, 8, nearest, -0.022778670821999185, 0.01513690729625523, 0.023529411764705882, 0.8156862745098039, 6.0, 208.0, 6, 208\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2475_smean2.png, -0.024, 0.024, 8, nearest, -0.02323786815664789, 0.015402440533307526, 0.01568627450980392, 0.8196078431372549, 4.0, 209.0, 4, 209\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2525_smean2.png, -0.024, 0.024, 8, nearest, -0.02391142762686286, 0.015723293076515574, 0.0, 0.8274509803921568, 0.0, 211.0, 0, 211\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2575_smean2.png, -0.024, 0.024, 8, nearest, -0.02454520921045514, 0.0163200834007489, 0.0, 0.8392156862745098, 0.0, 214.0, 0, 214\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2625_smean2.png, -0.024, 0.024, 8, nearest, -0.025481693148312844, 0.017943085625202002, 0.0, 0.8745098039215686, 0.0, 223.0, 0, 223\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2675_smean2.png, -0.024, 0.024, 8, nearest, -0.026762483336879087, 0.019617034016328035, 0.0, 0.9098039215686274, 0.0, 232.0, 0, 232\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2725_smean2.png, -0.024, 0.024, 8, nearest, -0.028057282361701255, 0.021135154848670935, 0.0, 0.9411764705882353, 0.0, 240.0, 0, 240\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2775_smean2.png, -0.024, 0.024, 8, nearest, -0.029320683573046152, 0.022602167888194344, 0.0, 0.9725490196078431, 0.0, 248.0, 0, 248\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2825_smean2.png, -0.024, 0.024, 8, nearest, -0.030573778344432867, 0.02408871099768021, 0.0, 1.0, 0.0, 255.0, 0, 255\n",
      "greyscale: False\n",
      "alpha: False\n",
      "planes: 3\n",
      "bitdepth: 8\n",
      "interlace: 0\n",
      "size: (3600, 1800)\n",
      "\n",
      "../local/morse/smean2/2875_smean2.png, -0.024, 0.024, 8, nearest, -0.031819123805844354, 0.025643014979932557, 0.0, 1.0, 0.0, 255.0, 0, 255\n"
     ]
    }
   ],
   "source": [
    "depths = world.ds['SMEAN_Z']\n",
    "\n",
    "csv_file = \"../local/morse/smean2/log_smean2_8rgb_clip_24_24.csv\"\n",
    "header = '''depth, png_file, clip_min, clip_max, bit_depth, interpolation, min in data, max in data, min normalised, max normalised, min to png, max to png, min from png, max from png \\n'''\n",
    "\n",
    "with open(csv_file, \"w\") as log_file:\n",
    "    log_file.write(header)\n",
    "\n",
    "for z in depths[::-1]:\n",
    "    layer_data = world.ds['SMEAN'].sel(SMEAN_Z=z).values\n",
    "    if np.isfinite(layer_data).any():\n",
    "        report = export_png(world,\n",
    "                        data = layer_data, \n",
    "                        png_name = '../local/morse/smean2/%04d_smean2.png'%(int(z)//km), \n",
    "                        v_min = clip_min, v_max = clip_max,\n",
    "                        clip = True, \n",
    "                        flip=True,\n",
    "                        png_format = 'RGB',\n",
    "                        set_geometry = False)\n",
    "        print(report)\n",
    "        with open(csv_file, \"a\") as log_file:\n",
    "            log_file.write('%s, %s \\n'%(z.values , report))\n",
    "    else:\n",
    "        print('only nan:',z)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'png pixel values (8-bits)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2016 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2,\n",
    "                       figsize= (12,28), \n",
    "                       gridspec_kw={'width_ratios': [3, 1]}, \n",
    "                       sharey=True)\n",
    "turbo = 1\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "def gaussian(x, mean, amplitude, standard_deviation):\n",
    "    return amplitude * np.exp( - ((x - mean) / standard_deviation) ** 2)\n",
    "\n",
    "#depths_smean = world.ds['SMEAN_Z'][::-1]\n",
    "\n",
    "\n",
    "ax[0].set_xlim(-0.06,0.06)\n",
    "ax[1].set_xlim(-0.06,0.06)\n",
    "ax[0].set_ylim(-2900,25)\n",
    "    \n",
    "    \n",
    "mean_p = np.nanmean(world.ds['SMEAN'].values, axis=(0,1))\n",
    "std_p = np.nanstd(world.ds['SMEAN'].values, axis=(0,1))\n",
    "max_p = np.nanmax(world.ds['SMEAN'].values, axis=(0,1))\n",
    "min_p = np.nanmin(world.ds['SMEAN'].values, axis=(0,1))\n",
    "\n",
    "d = -world.ds['SMEAN_Z'].values/1000\n",
    "ax[1].fill_betweenx(d,-std_p, std_p,interpolate=True, color='gray', alpha=0.5)\n",
    "ax[1].plot(mean_p, d, c='k', ls='-', alpha=0.5)\n",
    "\n",
    "ax[1].plot(min_p, d, c='k', ls=':', alpha=0.3)\n",
    "ax[1].plot(max_p, d, c='k', ls=':', alpha=0.3)\n",
    "ax[1].set_xticks(np.array([clip_min, 0, clip_max]))\n",
    "\n",
    "\n",
    "\n",
    "ax[1].axvline(0, c='brown', alpha=0.3, ls=':')    \n",
    "ax[1].axvline(clip_min, c='k', alpha=0.2)\n",
    "ax[1].axvline(clip_max, c='k', alpha=0.2)\n",
    "\n",
    "for i, z in enumerate(world.ds['SMEAN_Z']):\n",
    "    layer_data = world.ds['SMEAN'].sel(SMEAN_Z=z).values.flatten()[::turbo]\n",
    "    d = z.values // 1000\n",
    "    \n",
    "    #print(d)\n",
    "    ax[0].axhline(-d, c='green', alpha=0.2)\n",
    "    ax[1].axhline(-d, c='green', alpha=0.2)\n",
    "    ax[0].text(-0.058,5-d,'%s km'%d, c='green')\n",
    "        \n",
    "    if np.isfinite(layer_data).any():\n",
    "        clip_layer = np.clip(layer_data, clip_min, clip_max)\n",
    "        \n",
    "        counts, bins = np.histogram(clip_layer, \n",
    "                                        bins = 301,\n",
    "                                        range=(-0.05,0.04))\n",
    "        \n",
    "    \n",
    "        counts = counts//3000  \n",
    "        bin_heights, bin_borders, _ = ax[0].hist(bins[:-1], bins, weights=counts,\n",
    "                                        label='histogram', \n",
    "                                        bottom= -d, \n",
    "                                        density = True,      \n",
    "                                        color = 'r', alpha=0.6)\n",
    "        \n",
    "    \n",
    "    \n",
    "        counts, bins = np.histogram(layer_data, \n",
    "                                        bins = 301,\n",
    "                                        range=(-0.05,0.05))\n",
    "        \n",
    "        counts = counts//3000\n",
    "        bin_heights, bin_borders, _ = ax[0].hist(bins[:-1], bins, weights=counts,\n",
    "                                        label='histogram', \n",
    "                                        bottom= -d, \n",
    "                                        density = True,      \n",
    "                                        color = 'k', alpha=0.5)\n",
    "        \n",
    "            \n",
    "ax[0].axvline(0, c='brown', alpha=0.3, ls=':')\n",
    "ax[0].axvline(clip_min, c='k', alpha=0.2)\n",
    "ax[0].axvline(clip_max, c='k', alpha=0.2)\n",
    "\n",
    "\n",
    "ax_png = ax[0].twiny()\n",
    "bit_depth = 8\n",
    "ax_png.set_xlim(-0.06,0.06)\n",
    "ax_png.set_xticks(np.array([clip_min, 0, clip_max]))\n",
    "ax_png.set_xticklabels(np.array([0, 2**bit_depth//2-1, 2**bit_depth-1]))\n",
    "ax_png.set_xlabel('png pixel values (8-bits)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLAD M15\n",
    "\n",
    "Now let's import the model from [Bozdağ et al (2016)](https://academic.oup.com/gji/article/207/3/1739/2404568). Unfortunately, it appears that the data is not availible from any open repo (?!), but I belive that the group is willing to assist if contacted. \n",
    "\n",
    "The SMEAN data was easy to import, but exporting the GLAD-M15 data is more compicated as I only get access to an unstructured grid, in a normalised space. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load a vtk file to reader\n",
    "reader = vtk.vtkXMLUnstructuredGridReader()\n",
    "reader.SetFileName('../local/GLAD-M15/reg_1_dvsv.vtu')\n",
    "reader.Update()\n",
    "\n",
    "# Get the coordinates of nodes in the mesh\n",
    "nodes_vtk_array= reader.GetOutput().GetPoints().GetData()\n",
    "\n",
    "#The data field is the first scalar in the vtu file\n",
    "data_vtk_array = reader.GetOutput().GetPointData().GetArray(0)\n",
    "\n",
    "print(data_vtk_array, nodes_vtk_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get two arrays. Note that sizes are equal (Number Of Tuples: 4712064 and Number Of Tuples: 4712064) and the coordinate are 3D (NumberOfComponents: 3). Read coordinates and data as numpy arrays and have a look: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_numpy_array = vtk_to_numpy(nodes_vtk_array)\n",
    "X, Y, Z = nodes_numpy_array[:,0] , nodes_numpy_array[:,1] , nodes_numpy_array[:,2]\n",
    "V = vtk_to_numpy(data_vtk_array)\n",
    "\n",
    "print(X[:10],Y[:10],Z[:10],V[:10], sep='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coordinates are stored in the range [-1..1]. We assume a spherical Earth and compute an array with every points distance from centre. Function `sphere_to_layer()` takes a slice from the sphere and save to a 2D array. delta_r should be as small as possible, for best vertical resolution, but if too few points are used the vertical resolution gets bad. \n",
    "\n",
    "We loop through the harmonics of the sphere and read all points within a spherical shell defined by the radius +/- delta_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sphere_to_layer(d, R, V, LAT, LON, xxx, yyy, \n",
    "                    delta_r = 32*km, \n",
    "                    min_s = 5000,\n",
    "                    r_earth =  (6357*km + 6378*km) / 2, \n",
    "                    interpolation = 'nearest'):\n",
    "    \n",
    "    A = np.zeros_like(xxx).astype('float')\n",
    "    A[:] = np.nan\n",
    "    upper = 1-(d-delta_r)/r_earth\n",
    "    lower = 1-(d+delta_r)/r_earth\n",
    "    S = (R > lower) & (R < upper) # Select points in spherical shell\n",
    "    s_sum = np.count_nonzero(S) # Check how many points in shell\n",
    "\n",
    "    if s_sum >= min_s:\n",
    "        A = interpolate.griddata((LON[S], LAT[S]),\n",
    "                V[S], (xxx, yyy), method = interpolation)\n",
    "    print(d//1000, 'km \\t N=', sum(S),u'\\t \\N{BLACK RIGHT-POINTING TRIANGLE}', np.nanmean(A))\n",
    "    return np.flipud(A) #Because ulike lat, rows start from top. \n",
    "\n",
    "R = np.sqrt(X*X + Y*Y + Z*Z) # The distance from each point to centre of spherical Earth. \n",
    "\n",
    "LAT = world.shape3[0]/180 * (np.arccos( Z / R) * 180/np.pi) \n",
    "LON = world.shape3[1]/360 * (np.arctan2(Y, X) * 180/np.pi + 180)\n",
    "\n",
    "xxx, yyy = np.meshgrid(range(0, world.shape3[1]), range(world.shape3[0], 0, -1)) \n",
    "\n",
    "glad = np.zeros(world.shape3) # self.shape3 is a tuple of the models dimensions\n",
    "\n",
    "for i, d in enumerate(world.ds['Z'].values):\n",
    "    glad[:,:,i] = sphere_to_layer(d, R, V, LAT, LON, xxx, yyy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately there are not enugh data points at 1350, 1450, 2150 and 2250 km depth. We'd need to look in a broad spherical shell to get datapoints: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_i, missing_d = [], []\n",
    "\n",
    "for i in range(glad.shape[2]):\n",
    "    if (np.count_nonzero(np.isnan(glad[:,:,i]) ) > 1000):\n",
    "        missing_i.append(i)\n",
    "        missing_d.append(world.ds['Z'].values[i])\n",
    "      \n",
    "for i,d in zip(missing_i, missing_d):\n",
    "    print(i, end=' ')\n",
    "    glad[:,:,i] = sphere_to_layer(d, R, V, LAT, LON, xxx, yyy, delta_r = 45*km)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we can be more precise with the first layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "glad[:,:,0] = sphere_to_layer(50*km, R, V, LAT, LON, xxx, yyy, delta_r = 14*km)   \n",
    "glad[:,:,1] = sphere_to_layer(150*km, R, V, LAT, LON, xxx, yyy, delta_r = 20*km)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We save the array to the object and have a look. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds['GLAD'] = (('Y', 'X', 'Z'), glad)\n",
    "glad_min = np.nanmin(world.ds['GLAD'].values)\n",
    "glad_max = np.nanmax(world.ds['GLAD'].values)\n",
    "\n",
    "print(glad_min, glad_max)\n",
    "#print(smean_min, smean_max)\n",
    "\n",
    "z_map = 0\n",
    "world.map_grid(world.ds['GLAD'].isel(Z=z_map), cmap='magma_r', vmin=-0.06, vmax=0.06)\n",
    "#world.map_grid(world.ds['SMEAN'].isel(Z=z_map), cmap='magma_r', vmin=-0.06, vmax=0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds['DIFF'] = world.ds['GLAD']-world.ds['SMEAN']\n",
    "\n",
    "diff_min = np.nanmin(world.ds['DIFF'].values)\n",
    "diff_max = np.nanmax(world.ds['DIFF'].values)\n",
    "\n",
    "print(diff_min, diff_max)\n",
    "\n",
    "world.map_grid(world.ds['DIFF'].isel(Z=z_map), cmap='BrBG', vmin=-0.06, vmax=0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! mkdir -p ../local/glad/16_bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, z in enumerate(world.ds['Z']):\n",
    "    report = export_morse_png(world, world.ds['GLAD'].isel(Z=i).values, '../local/glad/16_bit/%04d_%s_glad.png'%(int(z)//km, i+1), \n",
    "                          v_min = glad_min, v_max = glad_max, \n",
    "                              set_geometry = False, \n",
    "                             png_format = 'RGB', \n",
    "                             bit_depth = 16)\n",
    "    with open(\"../local/morse/glad/log_16bit.txt\", \"a\") as log_file:\n",
    "        log_file.write(report)\n",
    "    print(report)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the grid: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.save(world.ds, '../local/smean_and_glad.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also interpolate new depth values to make a regular 3D grid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds['Z_NEW'] = range(0,2850*km, 100*km)\n",
    "\n",
    "world.ds['SMEAN_INTER'] = ( ('Y', 'X', 'Z'), \n",
    "                           world.change_coord(world.ds['SMEAN'], world.ds['Z'], world.ds['Z_NEW']) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AuSREM\n",
    "\n",
    "Australian crustal and lithospheric seismic model.\n",
    "\n",
    "Can be downloaded from http://rses.anu.edu.au/seismology/AuSREM/Downloads/ We assume a local file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = '../local/ausrem_SV_100.txt'\n",
    "data = world.read_ascii(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a mask, as the 'nearest neigbur' interpolation tested, will extrapolate values to all cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_master = np.isfinite(data)\n",
    "\n",
    "plt.imshow(alpha_master)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we test a number of different formats for the output png file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for _format in ['L', 'LA', 'RGB', 'RGBA']:\n",
    "    for _bit_depth in [8, 16]:\n",
    "        for _interpol in ['nearest', 'linear', 'cubic']:\n",
    "            data = world.read_ascii(fname, interpol =_interpol)\n",
    "            \n",
    "            if _interpol == 'nearest':\n",
    "                how_confine = 'mask'\n",
    "                mask_to_value = np.nan\n",
    "            else:\n",
    "                how_confine = 'input'\n",
    "                mask_to_value = None\n",
    "            \n",
    "            report = export_morse_png(world,\n",
    "                          data = data, \n",
    "                          png_format = _format,\n",
    "                          png_name = '../local/AuSREM/AuSREM_100km_%s_%sbit_%s.png'%( _interpol,_bit_depth,_format),\n",
    "                          interpol_method =  _interpol,\n",
    "                          confine_data = how_confine,\n",
    "                          confine_mask = alpha_master,\n",
    "                        mask_to_value = mask_to_value,\n",
    "                          v_min = 4, v_max = 5, \n",
    "                          clip=True,\n",
    "                          set_geometry = False, \n",
    "                          bit_depth=_bit_depth)\n",
    "\n",
    "            print(report)\n",
    "            with open(\"../local/AuSREM_log.txt\", \"a\") as log_file:\n",
    "                log_file.write(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!sips -g all ../local/AuSREM/AuSREM_100km_nearest_16bit_L.png\n",
    "#!open ../local/new/AuSREM_100km_nearest_16bit_L.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world.map_grid('SMEAN'.sel(Z=1000) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat, lon, persent"
   ]
  }
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