quadtree_decomposition_kite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quadtree decomposition with <i>kite</i>\n",
    "\n",
    "The <i><a href=\"https://pyocko.org\">pyrocko</a></i> suite of tools comprise a series of open source applications designed to simplify the modeling of earthquakes, using both seismic and InSAR data. Here we will use one of those applications, <i>kite</i>, to read in and downsample an ISCE interferogram using quadtree decomposition.\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, install some dependencies (if you haven't already)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/bin/sh: 1: pip: not found\n",
      "/usr/bin/sh: 1: pip: not found\n",
      "/usr/bin/sh: 1: pip: not found\n"
     ]
    }
   ],
   "source": [
    "# ignore if you already have these installed!\n",
    "!pip install pyrocko\n",
    "!pip install utm\n",
    "!pip install git+https://github.com/pyrocko/kite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and import the necessary dependencies: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from kite import Scene\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm, colors\n",
    "import math\n",
    "from osgeo import gdal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use, for this exercise, a pre-processed interferogram of the Elazig earthquake in Turkey. Let's grab it (and some other key files) off of the cloud! (This workflow should work for any ISCE-processed interferogram though.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!aws s3 cp s3://asf-jupyter-data/Elazig/filt_topophase.unw.geo .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/filt_topophase.unw.geo.vrt .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/filt_topophase.unw.geo.xml .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/los.rdr.geo .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/los.rdr.geo.vrt .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/los.rdr.geo.xml .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/water.msk .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/water.msk.vrt .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/water.msk.xml .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/topophase.cor.geo .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/topophase.cor.geo.vrt .\n",
    "!aws s3 cp s3://asf-jupyter-data/Elazig/topophase.cor.geo.xml ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we specify a path and some filenames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ifgpath=\"/home/gareth/stuffs/work/quakes/elazig/merged/\" # directory with your interferogram\n",
    "ifgname=\"filt_topophase.unw.geo\"                                      # interferogram file\n",
    "corname=\"topophase.cor.geo\"                                           # correlation file\n",
    "mskname=\"water.msk\"                                                   # water mask file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a couple of important parameters. <i>kite</i> currently cannot read in the radar wavelength from the interferogram metadata, as it is not included in the xml file(!) and so we specify it here. We also supply a correlation threshold for masking out low correlation data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavel = 0.0555     # radar wavelength in m\n",
    "cor_thresh = 0.2  # correlation threshold between 0 (bad) and 1 (perfect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the interferogram\n",
    "\n",
    "It is  a single command to read in an interferogram with <i>kite:</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = Scene.import_data(ifgpath+ifgname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look and see what we imported using a few matplotlib commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib inline\n",
    "fig, ax1 = plt.subplots()                       # new figure called \"ax1\"\n",
    "im = ax1.imshow(sc.displacement,origin='lower') # plot displacements with origin at lower left (kite default)\n",
    "fig.colorbar(im)                                # plot a color bar!\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masking and unit conversion\n",
    "\n",
    "A freshly unwrapped and loaded interferogram will not have been masked for 'bad' (low correlation) data points, or for water. If we include these in our downsampled data, we will be corrupting our data with errors. We can use a combination of gdal and numpy to load in the correlation and water mask files, and replace them with NaNs (which is how <i>kite</i> treats null data). First, load them in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation file\n",
    "corfile=gdal.Open(ifgpath+corname,gdal.GA_ReadOnly)   # open with gdal\n",
    "rb = corfile.GetRasterBand(2)                         # correlation info is in band 2\n",
    "cor = rb.ReadAsArray()                                # read it in as a number array\n",
    "cor=np.flip(cor,0)                                    # flip image to account for origin position\n",
    "                                                      # (kite prefers lower left; ISCE, top left)\n",
    "\n",
    "# water mask file\n",
    "mskfile=gdal.Open(ifgpath+mskname,gdal.GA_ReadOnly)  \n",
    "rb = mskfile.GetRasterBand(1)                         # only one band here\n",
    "msk = rb.ReadAsArray()\n",
    "msk=np.flip(msk,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the interferogram from radians to meters (ISCE does not include wavelength information in the interferogram metadata files, so it is not applied automatically by <i>kite</i>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.displacement*=wavel/4/math.pi   # change values of sc.displacement in place"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, replace the zeros in the water mask, and values with low correlation, by NaNs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.displacement[msk<1]=np.nan\n",
    "sc.displacement[cor<cor_thresh]=np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot to see if it worked!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib inline\n",
    "fig, ax2 = plt.subplots()\n",
    "im = ax2.imshow(sc.displacement,origin='lower')\n",
    "cb=fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quadtree decomposition\n",
    "\n",
    "We can initialize the quadtree decomposition in <i>kite</i> by defining an abbreviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "qt = sc.quadtree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can specify some parameters that control the quadtree process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.epsilon = 0.02         # Variance threshold\n",
    "qt.nan_allowed = 0.5      # Percentage of NaN values allowed per tile/leaf\n",
    "qt.tile_size_max = 0.2    # Maximum leaf size in degrees\n",
    "qt.tile_size_min = 0.01  # Minimum leaf size in degrees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of leaves/tiles. Typically we are looking for a few hundred... If you don't like what you have, try editing the values above and re-running. If you would like more leaves, lower the epsilon value and/or make the tile sizes smaller. If you want fewer, do the opposite!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 194 leaves\n"
     ]
    }
   ],
   "source": [
    "print(\"There are\",qt.nleaves,\"leaves\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you want to see the quadtree results, you can. This example prints out the leaf number, the lower left ('ll') east and north coordinates of the leaf (with respect to the lower left of the whole image), and the mean displacement of the pixels in that leaf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "leaf 0 llE: 0.28444444444444444 llN: 0.0 mean: 0.02218102291226387\n",
      "leaf 1 llE: 0.42666666666666664 llN: 0.0 mean: 0.02748042903840542\n",
      "leaf 2 llE: 0.28444444444444444 llN: 0.14222222222222222 mean: 0.018053289502859116\n",
      "leaf 3 llE: 0.42666666666666664 llN: 0.14222222222222222 mean: 0.024616478011012077\n",
      "leaf 4 llE: 0.28444444444444444 llN: 0.28444444444444444 mean: 0.0051778219640254974\n",
      "leaf 5 llE: 0.42666666666666664 llN: 0.28444444444444444 mean: -0.0013410821557044983\n",
      "leaf 6 llE: 0.49777777777777776 llN: 0.28444444444444444 mean: 0.014466454274952412\n",
      "leaf 7 llE: 0.42666666666666664 llN: 0.35555555555555557 mean: -0.04388043284416199\n",
      "leaf 8 llE: 0.49777777777777776 llN: 0.35555555555555557 mean: -0.031142612919211388\n",
      "leaf 9 llE: 0.5333333333333333 llN: 0.35555555555555557 mean: -0.019946299493312836\n",
      "leaf 10 llE: 0.49777777777777776 llN: 0.39111111111111113 mean: -0.08193103969097137\n",
      "leaf 11 llE: 0.5333333333333333 llN: 0.39111111111111113 mean: -0.06455549597740173\n",
      "leaf 12 llE: 0.5511111111111111 llN: 0.39111111111111113 mean: -0.061605729162693024\n",
      "leaf 13 llE: 0.5333333333333333 llN: 0.4088888888888889 mean: -0.0985763669013977\n",
      "leaf 14 llE: 0.5511111111111111 llN: 0.4088888888888889 mean: -0.10901832580566406\n",
      "leaf 15 llE: 0.28444444444444444 llN: 0.42666666666666664 mean: 0.013254930265247822\n",
      "leaf 16 llE: 0.42666666666666664 llN: 0.42666666666666664 mean: -0.04055993631482124\n",
      "leaf 17 llE: 0.49777777777777776 llN: 0.42666666666666664 mean: -0.0727657675743103\n",
      "leaf 18 llE: 0.5333333333333333 llN: 0.42666666666666664 mean: -0.09216529130935669\n",
      "leaf 19 llE: 0.5511111111111111 llN: 0.42666666666666664 mean: -0.1303219497203827\n",
      "leaf 20 llE: 0.5333333333333333 llN: 0.4444444444444444 mean: -0.08517039567232132\n",
      "leaf 21 llE: 0.5511111111111111 llN: 0.4444444444444444 mean: -0.11300317943096161\n",
      "leaf 22 llE: 0.49777777777777776 llN: 0.4622222222222222 mean: -0.06667393445968628\n",
      "leaf 23 llE: 0.5333333333333333 llN: 0.4622222222222222 mean: -0.08765853941440582\n",
      "leaf 24 llE: 0.42666666666666664 llN: 0.49777777777777776 mean: -0.01604997180402279\n",
      "leaf 25 llE: 0.49777777777777776 llN: 0.49777777777777776 mean: -0.04854316636919975\n",
      "leaf 26 llE: 0.5688888888888889 llN: 0.0 mean: 0.027807964012026787\n",
      "leaf 27 llE: 0.7111111111111111 llN: 0.0 mean: 0.018899695947766304\n",
      "leaf 28 llE: 0.5688888888888889 llN: 0.14222222222222222 mean: 0.03141500800848007\n",
      "leaf 29 llE: 0.7111111111111111 llN: 0.14222222222222222 mean: 0.029854385182261467\n",
      "leaf 30 llE: 0.8533333333333333 llN: 0.14222222222222222 mean: 0.027140945196151733\n",
      "leaf 31 llE: 0.9955555555555555 llN: 0.14222222222222222 mean: 0.029619885608553886\n",
      "leaf 32 llE: 0.5688888888888889 llN: 0.28444444444444444 mean: 0.03534243255853653\n",
      "leaf 33 llE: 0.64 llN: 0.28444444444444444 mean: 0.049074456095695496\n",
      "leaf 34 llE: 0.5688888888888889 llN: 0.35555555555555557 mean: 0.006539045367389917\n",
      "leaf 35 llE: 0.5866666666666667 llN: 0.35555555555555557 mean: 0.022379940375685692\n",
      "leaf 36 llE: 0.5688888888888889 llN: 0.37333333333333335 mean: -0.02359575778245926\n",
      "leaf 37 llE: 0.5866666666666667 llN: 0.37333333333333335 mean: -0.011609217151999474\n",
      "leaf 38 llE: 0.6044444444444445 llN: 0.35555555555555557 mean: 0.03341618552803993\n",
      "leaf 39 llE: 0.5688888888888889 llN: 0.39111111111111113 mean: -0.06225845217704773\n",
      "leaf 40 llE: 0.5866666666666667 llN: 0.39111111111111113 mean: -0.06019602715969086\n",
      "leaf 41 llE: 0.5688888888888889 llN: 0.4088888888888889 mean: -0.11491558700799942\n",
      "leaf 42 llE: 0.5866666666666667 llN: 0.4088888888888889 mean: -0.11059057712554932\n",
      "leaf 43 llE: 0.6044444444444445 llN: 0.39111111111111113 mean: -0.047091804444789886\n",
      "leaf 44 llE: 0.6222222222222222 llN: 0.39111111111111113 mean: 0.0005683060735464096\n",
      "leaf 45 llE: 0.6044444444444445 llN: 0.4088888888888889 mean: -0.10313721001148224\n",
      "leaf 46 llE: 0.6222222222222222 llN: 0.4088888888888889 mean: -0.07209321856498718\n",
      "leaf 47 llE: 0.64 llN: 0.35555555555555557 mean: 0.06473739445209503\n",
      "leaf 48 llE: 0.6755555555555556 llN: 0.35555555555555557 mean: 0.08169299364089966\n",
      "leaf 49 llE: 0.64 llN: 0.39111111111111113 mean: 0.03631079941987991\n",
      "leaf 50 llE: 0.6577777777777778 llN: 0.39111111111111113 mean: 0.0640779361128807\n",
      "leaf 51 llE: 0.64 llN: 0.4088888888888889 mean: -0.031970709562301636\n",
      "leaf 52 llE: 0.6577777777777778 llN: 0.4088888888888889 mean: 0.023377688601613045\n",
      "leaf 53 llE: 0.6755555555555556 llN: 0.39111111111111113 mean: 0.08351889997720718\n",
      "leaf 54 llE: 0.7111111111111111 llN: 0.28444444444444444 mean: 0.05116505175828934\n",
      "leaf 55 llE: 0.7822222222222223 llN: 0.28444444444444444 mean: 0.046294767409563065\n",
      "leaf 56 llE: 0.7111111111111111 llN: 0.35555555555555557 mean: 0.08526264876127243\n",
      "leaf 57 llE: 0.7466666666666667 llN: 0.35555555555555557 mean: 0.08371254056692123\n",
      "leaf 58 llE: 0.7111111111111111 llN: 0.39111111111111113 mean: 0.11060797423124313\n",
      "leaf 59 llE: 0.7466666666666667 llN: 0.39111111111111113 mean: 0.12088710814714432\n",
      "leaf 60 llE: 0.7822222222222223 llN: 0.35555555555555557 mean: 0.09187304973602295\n",
      "leaf 61 llE: 0.5688888888888889 llN: 0.42666666666666664 mean: -0.13384810090065002\n",
      "leaf 62 llE: 0.6044444444444445 llN: 0.42666666666666664 mean: -0.1270473152399063\n",
      "leaf 63 llE: 0.5688888888888889 llN: 0.4622222222222222 mean: -0.09847663342952728\n",
      "leaf 64 llE: 0.6044444444444445 llN: 0.4622222222222222 mean: -0.12395034730434418\n",
      "leaf 65 llE: 0.6222222222222222 llN: 0.4622222222222222 mean: -0.10356602072715759\n",
      "leaf 66 llE: 0.6044444444444445 llN: 0.48 mean: -0.09051520377397537\n",
      "leaf 67 llE: 0.6222222222222222 llN: 0.48 mean: -0.08329936116933823\n",
      "leaf 68 llE: 0.64 llN: 0.42666666666666664 mean: -0.06885016709566116\n",
      "leaf 69 llE: 0.6577777777777778 llN: 0.42666666666666664 mean: -0.03974197432398796\n",
      "leaf 70 llE: 0.64 llN: 0.4444444444444444 mean: -0.08776883780956268\n",
      "leaf 71 llE: 0.6577777777777778 llN: 0.4444444444444444 mean: -0.07840470969676971\n",
      "leaf 72 llE: 0.6933333333333334 llN: 0.42666666666666664 mean: 0.033556435257196426\n",
      "leaf 73 llE: 0.6755555555555556 llN: 0.4444444444444444 mean: -0.08757293969392776\n",
      "leaf 74 llE: 0.6933333333333334 llN: 0.4444444444444444 mean: -0.09379535913467407\n",
      "leaf 75 llE: 0.64 llN: 0.4622222222222222 mean: -0.08698287606239319\n",
      "leaf 76 llE: 0.6755555555555556 llN: 0.4622222222222222 mean: -0.10415556281805038\n",
      "leaf 77 llE: 0.5688888888888889 llN: 0.49777777777777776 mean: -0.0639519914984703\n",
      "leaf 78 llE: 0.64 llN: 0.49777777777777776 mean: -0.08004911243915558\n",
      "leaf 79 llE: 0.6755555555555556 llN: 0.49777777777777776 mean: -0.10274552553892136\n",
      "leaf 80 llE: 0.64 llN: 0.5333333333333333 mean: -0.061913780868053436\n",
      "leaf 81 llE: 0.6755555555555556 llN: 0.5333333333333333 mean: -0.06788039952516556\n",
      "leaf 82 llE: 0.7111111111111111 llN: 0.42666666666666664 mean: 0.05619499087333679\n",
      "leaf 83 llE: 0.7288888888888889 llN: 0.42666666666666664 mean: 0.0888572633266449\n",
      "leaf 84 llE: 0.7111111111111111 llN: 0.4444444444444444 mean: -0.0499412938952446\n",
      "leaf 85 llE: 0.7288888888888889 llN: 0.4444444444444444 mean: -0.016558146104216576\n",
      "leaf 86 llE: 0.7466666666666667 llN: 0.42666666666666664 mean: 0.13019242882728577\n",
      "leaf 87 llE: 0.7644444444444445 llN: 0.42666666666666664 mean: 0.14464335143566132\n",
      "leaf 88 llE: 0.7466666666666667 llN: 0.4444444444444444 mean: 0.0283026322722435\n",
      "leaf 89 llE: 0.7644444444444445 llN: 0.4444444444444444 mean: 0.10441282391548157\n",
      "leaf 90 llE: 0.7111111111111111 llN: 0.4622222222222222 mean: -0.14415240287780762\n",
      "leaf 91 llE: 0.7466666666666667 llN: 0.4622222222222222 mean: -0.10030657052993774\n",
      "leaf 92 llE: 0.7644444444444445 llN: 0.4622222222222222 mean: -0.023024283349514008\n",
      "leaf 93 llE: 0.7466666666666667 llN: 0.48 mean: -0.1382838487625122\n",
      "leaf 94 llE: 0.7644444444444445 llN: 0.48 mean: -0.11324401199817657\n",
      "leaf 95 llE: 0.7822222222222223 llN: 0.42666666666666664 mean: 0.139827698469162\n",
      "leaf 96 llE: 0.8177777777777778 llN: 0.42666666666666664 mean: 0.13028991222381592\n",
      "leaf 97 llE: 0.7822222222222223 llN: 0.4622222222222222 mean: 0.044216230511665344\n",
      "leaf 98 llE: 0.8 llN: 0.4622222222222222 mean: 0.10842262208461761\n",
      "leaf 99 llE: 0.7822222222222223 llN: 0.48 mean: -0.05956058204174042\n",
      "leaf 100 llE: 0.8 llN: 0.48 mean: -0.0065932064317166805\n",
      "leaf 101 llE: 0.8177777777777778 llN: 0.4622222222222222 mean: 0.13355472683906555\n",
      "leaf 102 llE: 0.8355555555555556 llN: 0.4622222222222222 mean: 0.13462142646312714\n",
      "leaf 103 llE: 0.8177777777777778 llN: 0.48 mean: 0.06530892103910446\n",
      "leaf 104 llE: 0.8355555555555556 llN: 0.48 mean: 0.1128726452589035\n",
      "leaf 105 llE: 0.7111111111111111 llN: 0.49777777777777776 mean: -0.13069550693035126\n",
      "leaf 106 llE: 0.7288888888888889 llN: 0.49777777777777776 mean: -0.13241469860076904\n",
      "leaf 107 llE: 0.7111111111111111 llN: 0.5155555555555555 mean: -0.10021436959505081\n",
      "leaf 108 llE: 0.7288888888888889 llN: 0.5155555555555555 mean: -0.09081174433231354\n",
      "leaf 109 llE: 0.7466666666666667 llN: 0.49777777777777776 mean: -0.09947539865970612\n",
      "leaf 110 llE: 0.7111111111111111 llN: 0.5333333333333333 mean: -0.06310756504535675\n",
      "leaf 111 llE: 0.7466666666666667 llN: 0.5333333333333333 mean: -0.054905377328395844\n",
      "leaf 112 llE: 0.7822222222222223 llN: 0.49777777777777776 mean: -0.06170254945755005\n",
      "leaf 113 llE: 0.8177777777777778 llN: 0.49777777777777776 mean: -0.01574874296784401\n",
      "leaf 114 llE: 0.8355555555555556 llN: 0.49777777777777776 mean: 0.009725972078740597\n",
      "leaf 115 llE: 0.8177777777777778 llN: 0.5155555555555555 mean: -0.032896287739276886\n",
      "leaf 116 llE: 0.8355555555555556 llN: 0.5155555555555555 mean: -0.03059997409582138\n",
      "leaf 117 llE: 0.7822222222222223 llN: 0.5333333333333333 mean: -0.05516529083251953\n",
      "leaf 118 llE: 0.8177777777777778 llN: 0.5333333333333333 mean: -0.05797727406024933\n",
      "leaf 119 llE: 0.8533333333333333 llN: 0.28444444444444444 mean: 0.047709643840789795\n",
      "leaf 120 llE: 0.9244444444444444 llN: 0.28444444444444444 mean: 0.04384689778089523\n",
      "leaf 121 llE: 0.8533333333333333 llN: 0.35555555555555557 mean: 0.09083230793476105\n",
      "leaf 122 llE: 0.9244444444444444 llN: 0.35555555555555557 mean: 0.07595366984605789\n",
      "leaf 123 llE: 0.9955555555555555 llN: 0.28444444444444444 mean: 0.05582467466592789\n",
      "leaf 124 llE: 0.8533333333333333 llN: 0.42666666666666664 mean: 0.1267906278371811\n",
      "leaf 125 llE: 0.9244444444444444 llN: 0.42666666666666664 mean: 0.10682735592126846\n",
      "leaf 126 llE: 0.8533333333333333 llN: 0.49777777777777776 mean: 0.020466405898332596\n",
      "leaf 127 llE: 0.8711111111111111 llN: 0.49777777777777776 mean: 0.04784565418958664\n",
      "leaf 128 llE: 0.8533333333333333 llN: 0.5155555555555555 mean: -0.014040814712643623\n",
      "leaf 129 llE: 0.8711111111111111 llN: 0.5155555555555555 mean: -0.013444459065794945\n",
      "leaf 130 llE: 0.8888888888888888 llN: 0.49777777777777776 mean: 0.08374916017055511\n",
      "leaf 131 llE: 0.9066666666666666 llN: 0.49777777777777776 mean: 0.09798452258110046\n",
      "leaf 132 llE: 0.8888888888888888 llN: 0.5155555555555555 mean: 0.020525608211755753\n",
      "leaf 133 llE: 0.9066666666666666 llN: 0.5155555555555555 mean: 0.0441984161734581\n",
      "leaf 134 llE: 0.8533333333333333 llN: 0.5333333333333333 mean: -0.054475486278533936\n",
      "leaf 135 llE: 0.8888888888888888 llN: 0.5333333333333333 mean: -0.03136695176362991\n",
      "leaf 136 llE: 0.9244444444444444 llN: 0.49777777777777776 mean: 0.08806532621383667\n",
      "leaf 137 llE: 0.96 llN: 0.49777777777777776 mean: 0.08378759771585464\n",
      "leaf 138 llE: 0.9244444444444444 llN: 0.5333333333333333 mean: 0.005718979984521866\n",
      "leaf 139 llE: 0.96 llN: 0.5333333333333333 mean: 0.035514555871486664\n",
      "leaf 140 llE: 0.9955555555555555 llN: 0.42666666666666664 mean: 0.07081463187932968\n",
      "leaf 141 llE: 0.14222222222222222 llN: 0.7111111111111111 mean: 0.054760221391916275\n",
      "leaf 142 llE: 0.28444444444444444 llN: 0.5688888888888889 mean: 0.034593477845191956\n",
      "leaf 143 llE: 0.42666666666666664 llN: 0.5688888888888889 mean: 0.022538332268595695\n",
      "leaf 144 llE: 0.28444444444444444 llN: 0.7111111111111111 mean: 0.053734567016363144\n",
      "leaf 145 llE: 0.42666666666666664 llN: 0.7111111111111111 mean: 0.050880685448646545\n",
      "leaf 146 llE: 0.14222222222222222 llN: 0.8533333333333333 mean: 0.06913089752197266\n",
      "leaf 147 llE: 0.14222222222222222 llN: 0.9955555555555555 mean: 0.07301772385835648\n",
      "leaf 148 llE: 0.28444444444444444 llN: 0.8533333333333333 mean: 0.0675099715590477\n",
      "leaf 149 llE: 0.42666666666666664 llN: 0.8533333333333333 mean: 0.06251417845487595\n",
      "leaf 150 llE: 0.28444444444444444 llN: 0.9955555555555555 mean: 0.06764136254787445\n",
      "leaf 151 llE: 0.42666666666666664 llN: 0.9955555555555555 mean: 0.06574688106775284\n",
      "leaf 152 llE: 0.5688888888888889 llN: 0.5688888888888889 mean: -0.011477554216980934\n",
      "leaf 153 llE: 0.64 llN: 0.5688888888888889 mean: -0.017618488520383835\n",
      "leaf 154 llE: 0.5688888888888889 llN: 0.64 mean: 0.026298578828573227\n",
      "leaf 155 llE: 0.64 llN: 0.64 mean: 0.022877510637044907\n",
      "leaf 156 llE: 0.7111111111111111 llN: 0.5688888888888889 mean: -0.01767834462225437\n",
      "leaf 157 llE: 0.7822222222222223 llN: 0.5688888888888889 mean: -0.024610262364149094\n",
      "leaf 158 llE: 0.7111111111111111 llN: 0.64 mean: 0.018799975514411926\n",
      "leaf 159 llE: 0.7822222222222223 llN: 0.64 mean: 0.015587404370307922\n",
      "leaf 160 llE: 0.5688888888888889 llN: 0.7111111111111111 mean: 0.047184884548187256\n",
      "leaf 161 llE: 0.7111111111111111 llN: 0.7111111111111111 mean: 0.04544724151492119\n",
      "leaf 162 llE: 0.8533333333333333 llN: 0.5688888888888889 mean: 0.002021670574322343\n",
      "leaf 163 llE: 0.9955555555555555 llN: 0.5688888888888889 mean: 0.024243110790848732\n",
      "leaf 164 llE: 0.8533333333333333 llN: 0.7111111111111111 mean: 0.04081590473651886\n",
      "leaf 165 llE: 0.9955555555555555 llN: 0.7111111111111111 mean: 0.038048990070819855\n",
      "leaf 166 llE: 0.5688888888888889 llN: 0.8533333333333333 mean: 0.06012590602040291\n",
      "leaf 167 llE: 0.7111111111111111 llN: 0.8533333333333333 mean: 0.05889250710606575\n",
      "leaf 168 llE: 0.5688888888888889 llN: 0.9955555555555555 mean: 0.06525203585624695\n",
      "leaf 169 llE: 0.7111111111111111 llN: 0.9955555555555555 mean: 0.06580539792776108\n",
      "leaf 170 llE: 0.8533333333333333 llN: 0.8533333333333333 mean: 0.05133233219385147\n",
      "leaf 171 llE: 0.9955555555555555 llN: 0.8533333333333333 mean: 0.04644275829195976\n",
      "leaf 172 llE: 0.8533333333333333 llN: 0.9955555555555555 mean: 0.061907991766929626\n",
      "leaf 173 llE: 1.1377777777777778 llN: 0.14222222222222222 mean: 0.028130847960710526\n",
      "leaf 174 llE: 0.14222222222222222 llN: 1.1377777777777778 mean: 0.06690425425767899\n",
      "leaf 175 llE: 0.14222222222222222 llN: 1.28 mean: 0.06581475585699081\n",
      "leaf 176 llE: 0.28444444444444444 llN: 1.1377777777777778 mean: 0.06896024197340012\n",
      "leaf 177 llE: 0.42666666666666664 llN: 1.1377777777777778 mean: 0.06918860971927643\n",
      "leaf 178 llE: 0.28444444444444444 llN: 1.28 mean: 0.06860615313053131\n",
      "leaf 179 llE: 0.42666666666666664 llN: 1.28 mean: 0.06963196396827698\n",
      "leaf 180 llE: 0.14222222222222222 llN: 1.4222222222222223 mean: 0.061539653688669205\n",
      "leaf 181 llE: 0.28444444444444444 llN: 1.4222222222222223 mean: 0.06567978113889694\n",
      "leaf 182 llE: 0.42666666666666664 llN: 1.4222222222222223 mean: 0.06747701019048691\n",
      "leaf 183 llE: 0.5688888888888889 llN: 1.1377777777777778 mean: 0.06934108585119247\n",
      "leaf 184 llE: 0.7111111111111111 llN: 1.1377777777777778 mean: 0.07136423885822296\n",
      "leaf 185 llE: 0.5688888888888889 llN: 1.28 mean: 0.071367546916008\n",
      "leaf 186 llE: 0.7111111111111111 llN: 1.28 mean: 0.06778954714536667\n",
      "leaf 187 llE: 0.8533333333333333 llN: 1.1377777777777778 mean: 0.0678720623254776\n",
      "leaf 188 llE: 0.8533333333333333 llN: 1.28 mean: 0.06673085689544678\n",
      "leaf 189 llE: 0.5688888888888889 llN: 1.4222222222222223 mean: 0.07289815694093704\n",
      "leaf 190 llE: 0.7111111111111111 llN: 1.4222222222222223 mean: 0.0670226439833641\n",
      "leaf 191 llE: 0.7111111111111111 llN: 1.5644444444444445 mean: 0.07633908838033676\n",
      "leaf 192 llE: 0.8533333333333333 llN: 1.4222222222222223 mean: 0.06727826595306396\n",
      "leaf 193 llE: 0.8533333333333333 llN: 1.5644444444444445 mean: 0.07443203777074814\n"
     ]
    }
   ],
   "source": [
    "j=0\n",
    "for k in qt.leaves:\n",
    "    print(\"leaf\",j,\"llE:\",qt.leaves[j].llE,\"llN:\",qt.leaves[j].llN,\"mean:\",qt.leaves[j].mean) \n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can plot them too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib inline\n",
    "fig, ax3 = plt.subplots(figsize=[5,7.5])\n",
    "\n",
    "ulimit = qt.leaf_medians.max()\n",
    "llimit = qt.leaf_medians.min()\n",
    "\n",
    "color_map = cm.ScalarMappable(\n",
    "    norm=colors.Normalize(vmin=llimit, vmax=ulimit))\n",
    "\n",
    "for rect, leaf in zip(qt.getMPLRectangles(), qt.leaves):\n",
    "    color = color_map.to_rgba(leaf.median)\n",
    "    rect.set_facecolor(color)\n",
    "    ax3.add_artist(rect)\n",
    "\n",
    "\n",
    "ax3.set_xlim(0, qt.leaf_eastings.max())\n",
    "ax3.set_ylim(0, qt.leaf_northings.max())\n",
    "ax3.set_aspect('equal')\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the output\n",
    "\n",
    "This last section saves your files, both in <i>kite</i>'s internal format, and in a comma-separated variable file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.save('elazig_asc_kite_scene')\n",
    "#qt.export('elazig_asc_quadtree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}