{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append(\"..\")\n",
    "import rasterizer\n",
    "import raster_assistant as ra\n",
    "\n",
    "sys.path.append('../triskele/python/')\n",
    "import triskele\n",
    "\n",
    "figsize = np.array((16, 3)) * 1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Raster Combination\n",
    "\n",
    "## DSM - DTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C12 = ra.bulk_load(['../Data/lidar/C1', '../Data/lidar/C2'], 'C12', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsm = ra.rasterize_cache('z', C12, .5, 'nearest', False, '../Res/enrichment_rasters/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtm = ra.rasterize_cache('z', C12, .5, 'nearest', True, '../Res/enrichment_rasters/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Hello world')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ici nous avons lancé un print en Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(dsm)\n",
    "plt.colorbar()\n",
    "plt.title('DSM')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(dtm)\n",
    "plt.colorbar()\n",
    "plt.title('DTM')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(dsm - dtm)\n",
    "plt.colorbar()\n",
    "plt.title('DSM - DTM')\n",
    "plt.show()\n",
    "\n",
    "plt.imsave('../Res/dsm-dtm.png', dsm - dtm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NDVI\n",
    "\n",
    "With \n",
    "\n",
    "$NDVI = \\frac{NIR - Red}{NIR + Red}$\n",
    "\n",
    "and the wavelenth of the Titan\n",
    "\n",
    "| Lazer | Wavelenght | Color |\n",
    "| --- | ---: | --- |\n",
    "| C1 | 1550 nm |  IR? |\n",
    "| C2 | 1064 nm | NIR |\n",
    "| C3 | 532 nm | Green |\n",
    "\n",
    "we can compute a NDVI like intensity raster with\n",
    "\n",
    "$NDVI_{like} = \\frac{C1 - C2}{C1 + C2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
    "C2 = ra.bulk_load('../Data/lidar/C2', 'C2', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C1_raster = ra.rasterize_cache('intensity', C1, 1., 'linear', False, '../Res/enrichment_rasters/')\n",
    "C2_raster = ra.rasterize_cache('intensity', C2, 1., 'linear', False, '../Res/enrichment_rasters/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndvi = (C1_raster - C2_raster) / (C1_raster + C2_raster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(C1_raster)\n",
    "plt.title('C1')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(C2_raster)\n",
    "plt.title('C2')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(ndvi)\n",
    "plt.title('NDVI')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.imsave('../Res/ndvi_linear.png', ndvi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized NDVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C1_raster /= C1_raster.max()\n",
    "C2_raster /= C2_raster.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndvi = (C1_raster - C2_raster) / (C1_raster + C2_raster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(C1_raster)\n",
    "plt.title('C1')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(C2_raster)\n",
    "plt.title('C2')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plt.imshow(ndvi)\n",
    "plt.title('NDVI')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.imsave('../Res/ndvi_normalized_linear.png', ndvi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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