PierrickKoch/atlaas-dynamic1.ipynb

## atlaas-dynamic1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fekzc2My8r3r15Dc2Vz/EzsXwnrXWn+vKXf+/fzKtkIvhUvX91c+utb6/+kb1U9fb+RBh\nbIk3OJCTuVWfrP71WusXDz0eXp+TXwNuqgcPPBS284PVj57MU/031Q/PzMcPPCZ2sNb6+sk//3v1\nqa5MC+VieLl6aa316yefP9mVUL6mQ4SxJd4uPnc8Lq6fq7661vqnhx4Iu5mZPz4zbzn5/juqv1R5\ncPICWGv99Frr3rXW93bl77zPrbV+/NDjYjszc9fJb9qamTdXf7n6r4cdFdtaa71avTQz7zjZ9N7q\nq9fb/6xvvtvZWuubM/MPurLE2x3V49ss8cat4WTZvqPqrTPzYvXRb01o59Y2Mz9Y/Z3qt0/mqa7q\np9da/+GwI2NLf7L6Vycr+9xR/cJa6zMHHhO8EfyJ6lMzs7rSTT+/1vrsgcfEbj5c/fzM/NHqd6oP\nXG/Hm75cGwAA3IosPQIAAAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKCq/w9AZJiD\nZ/rg2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc140b0ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3PGZJkqZu3ADNgG3PAC5sli9sru9Z/yGAqroaOCTJEfs5TknSLKgpXyZo3AAt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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc140bbf450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import atlaas\n",
    "import numpy as np\n",
    "from atlaas.helpers.gdal2 import gdal2\n",
    "from atlaas.helpers.matplot import hist, show\n",
    "\n",
    "## clean up previous run\n",
    "import glob\n",
    "import subprocess\n",
    "subprocess.check_output(['rm'] + glob.glob('atlaas.*x*.tif'))\n",
    "####\n",
    "\n",
    "test = atlaas.Atlaas()\n",
    "test.init(120.0, 120.0, 0.1, 0, 0, 0, 31, True)\n",
    "\n",
    "transformation = np.identity(4, dtype=np.double)\n",
    "\n",
    "## cloud1 is a \"flat\" scan (all Z = 0)\n",
    "cloud1 = np.random.rand(200000, 4).astype('float32')\n",
    "cloud1[:,2] = 0 # all Z = 0\n",
    "cloud1 *= 20 # x,y = random [0..20]\n",
    "# merge it once\n",
    "test.merge(cloud1, transformation)\n",
    "####\n",
    "\n",
    "## cloud_obj is a \"dynamic\" object (all Z = 3m)\n",
    "cloud_obj = np.random.rand(20000, 4).astype('float32')\n",
    "cloud_obj[:,2] = 2\n",
    "cloud_obj *= 3\n",
    "# cloud1 + cloud_obj\n",
    "cloud2 = np.append(cloud1, cloud_obj, axis=0)\n",
    "# merge both\n",
    "test.merge(cloud2, transformation)\n",
    "####\n",
    "\n",
    "filepath = 'out2.tif'\n",
    "test.region('region.png')\n",
    "atlaas.merge('atlaas.*x*.tif', filepath)\n",
    "g = gdal2( filepath )\n",
    "band = g.dtm()\n",
    "hist(band)\n",
    "show(band, 0, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc140c424d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dOL6qTgReB5zX8ZglSZq6cQM0A7Y9A7igWb6gub53/YcBquoq4JAkR+znOCVJ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\nPnSxnWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1409c44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merge cloud1 back again\n",
    "test.merge(cloud1, transformation)\n",
    "####\n",
    "\n",
    "filepath = 'out3.tif'\n",
    "test.region('region.png')\n",
    "atlaas.merge('atlaas.*x*.tif', filepath)\n",
    "g = gdal2( filepath )\n",
    "band = g.dtm()\n",
    "hist(band)\n",
    "show(band, 0, 3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}