{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earth Engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/davemlz/spyndex/blob/main/docs/tutorials/ee.ipynb)\n",
    "![level7](https://raw.githubusercontent.com/davemlz/spyndex/main/docs/_static/level7.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New level: Level 7 - `spyndex + Google Earth Engine`!\n",
    "\n",
    "Remember to install `spyndex`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U spyndex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's start!\n",
    "\n",
    "First, import `spyndex` and `Earth Engine`:\n",
    "\n",
    "> We also import `eemont` and `geemap` to make things easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spyndex\n",
    "import ee, eemont, geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And remember to initialize `Earth Engine`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `ee.Number`\n",
    "\n",
    "Just as Python Built-In types, the `ee.Number` can be used with `spyndex`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "NIR = ee.Number(0.678)\n",
    "RED = ee.Number(0.123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check our data types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NIR type: <class 'ee.ee_number.Number'>, value: 0.678\n",
      "RED type: <class 'ee.ee_number.Number'>, value: 0.123\n"
     ]
    }
   ],
   "source": [
    "print(f\"NIR type: {type(NIR)}, value: {NIR.getInfo()}\")\n",
    "print(f\"RED type: {type(RED)}, value: {RED.getInfo()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's compute the `NDVI`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = spyndex.computeIndex(\"NDVI\",{\"N\": NIR, \"R\": RED})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And our result is also an `ee.Number`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx type: <class 'ee.ee_number.Number'>, value: 0.6928838951310862\n"
     ]
    }
   ],
   "source": [
    "print(f\"idx type: {type(idx)}, value: {idx.getInfo()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `ee.Image`\n",
    "\n",
    "All spectral indices from `Awesome Spectral Indices` can be computed automatically in Python for `Earth Engine` by using `eemont` for most datasets (Sentinel-2, MODIS, Landsat, etc.). However, some datasets are not included. NAIP is a good example, let's use a NAIP image!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = ee.Image(\"USDA/NAIP/DOQQ/m_3009057_ne_15_1_20170910\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NAIP images values go from 0 to 255, so, let's correct that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = img / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the image metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Image',\n",
       " 'bands': [{'id': 'R',\n",
       "   'data_type': {'type': 'PixelType',\n",
       "    'precision': 'float',\n",
       "    'min': 0,\n",
       "    'max': 1},\n",
       "   'dimensions': [6753, 7640],\n",
       "   'crs': 'EPSG:26915',\n",
       "   'crs_transform': [1, 0, 698395, 0, -1, 3334843]},\n",
       "  {'id': 'G',\n",
       "   'data_type': {'type': 'PixelType',\n",
       "    'precision': 'float',\n",
       "    'min': 0,\n",
       "    'max': 1},\n",
       "   'dimensions': [6753, 7640],\n",
       "   'crs': 'EPSG:26915',\n",
       "   'crs_transform': [1, 0, 698395, 0, -1, 3334843]},\n",
       "  {'id': 'B',\n",
       "   'data_type': {'type': 'PixelType',\n",
       "    'precision': 'float',\n",
       "    'min': 0,\n",
       "    'max': 1},\n",
       "   'dimensions': [6753, 7640],\n",
       "   'crs': 'EPSG:26915',\n",
       "   'crs_transform': [1, 0, 698395, 0, -1, 3334843]},\n",
       "  {'id': 'N',\n",
       "   'data_type': {'type': 'PixelType',\n",
       "    'precision': 'float',\n",
       "    'min': 0,\n",
       "    'max': 1},\n",
       "   'dimensions': [6753, 7640],\n",
       "   'crs': 'EPSG:26915',\n",
       "   'crs_transform': [1, 0, 698395, 0, -1, 3334843]}]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.getInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We have 4 bands (R, G, B, N) and all of them go now from 0 to 1.\n",
    "\n",
    "Let's compute some indices!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = spyndex.computeIndex(\n",
    "    index = [\"VARI\",\"ExG\",\"NDVI\"],\n",
    "    params = {\n",
    "        \"R\": img[\"R\"],\n",
    "        \"G\": img[\"G\"],\n",
    "        \"B\": img[\"B\"],\n",
    "        \"N\": img[\"N\"]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we check the indices, we'll see that the result is another `ee.Image`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx type: <class 'ee.image.Image'>\n"
     ]
    }
   ],
   "source": [
    "print(f\"idx type: {type(idx)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's add those indices as new bands in the original image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = img.addBands(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we check our bands, we'll see that the new bands (indices) were added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R', 'G', 'B', 'N', 'VARI', 'ExG', 'NDVI']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.bandNames().getInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize evrything interactively with `geemap`!\n",
    "\n",
    "First, import the colormaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geemap.colormaps as cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then, plot everything!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "878c3125b2794febbb3e9fab862c0f5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[30.09375197359924, -90.90623221580759], controls=(WidgetControl(options=['position', 'transparent_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Map = geemap.Map()\n",
    "Map.addLayer(img[[\"R\",\"G\",\"B\"]],{\"min\":0,\"max\":0.3},\"RGB\")\n",
    "Map.addLayer(img[\"VARI\"],{\"palette\":cm.palettes.ndvi},\"VARI\")\n",
    "Map.addLayer(img[\"ExG\"],{\"palette\":cm.palettes.ndvi},\"ExG\")\n",
    "Map.addLayer(img[\"NDVI\"],{\"palette\":cm.palettes.ndvi},\"NDVI\")\n",
    "Map.centerObject(img,13)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amazing!!!! :)"
   ]
  }
 ],
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