3_Earth_Engine_Intermediate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51a9cd6d",
   "metadata": {},
   "source": [
    "# 3. Earth Engine Intermediate\n",
    "\n",
    "Module 2 builds on the basic Earth Engine skills you have gained. This model introduces the parallel programming concepts using Map/Reduce - which is key in effectively using Earth Engine for analyzing large volumes of data. You will learn how to use the Earth Engine API for calculating various spectral indices, do cloud masking and then use map/reduce to do apply these computations to collections of imagery. You will also learn how to take long time-series of data and create charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c8b7240",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%load_ext dotenv\n",
    "%autoreload 2\n",
    "%dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02f9d0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import geemap\n",
    "from geemap import Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f45ac74",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db73d57",
   "metadata": {},
   "source": [
    "Let's init a map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7b1069b",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Map()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186c3d60",
   "metadata": {},
   "source": [
    "## 01. Earth Engine Objects\n",
    "\n",
    "This script introduces the basics of the Earth Engine API. When programming in Earth Engine, you must use the Earth Engine API so that your computations can use the Google Earth Engine servers. To learn more, visit Earth Engine Objects and Methods section of the Earth Engine User Guide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bd535a5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# Let's see how to take a list of numbers and add 1 to each element\n",
    "my_list = ee.List.sequence(1, 10)\n",
    "\n",
    "# Define a function that takes a number and adds 1 to it\n",
    "my_function = lambda number: number + 1\n",
    "print(my_function(1))\n",
    "\n",
    "# Re-Define a function using Earth Engine API\n",
    "my_function = lambda number: ee.Number(number).add(1)\n",
    "\n",
    "# Map the function of the list\n",
    "new_list = my_list.map(my_function)\n",
    "print(new_list.getInfo())\n",
    "\n",
    "# Extracting value from a list\n",
    "value = new_list.get(0)\n",
    "print(value.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f4a4c5b3",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ComputedObject' object has no attribute 'add'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-e9e1039b5239>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Let's try to do some computation on the extracted value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnew_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\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[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ComputedObject' object has no attribute 'add'"
     ]
    }
   ],
   "source": [
    "# Casting\n",
    "\n",
    "# Let's try to do some computation on the extracted value\n",
    "new_value = value.add(1)\n",
    "print(new_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46b6701a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# You get an error because Earth Engine doesn't know what is the type of 'value'\n",
    "# We need to cast it to appropriate type first\n",
    "value = ee.Number(value)\n",
    "new_value = value.add(1)\n",
    "print(new_value.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15b70195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Date', 'value': 1577836800000}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dates\n",
    "# For any date computation, you should use ee.Date module\n",
    "date = ee.Date('2019-01-01')\n",
    "future_date = date.advance(1, 'year')\n",
    "future_date.getInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7596a340",
   "metadata": {},
   "source": [
    "As a general rule, **you should always use Earth Engine API methods in your code**. \n",
    "\n",
    "There is one exception where you will need to use client-side Python method. If you want to get the current time, the server doesn’t know your time. You need to use Python method and cast it to an Earth Engine object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4bc687d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'Date', 'value': 1624116058684}\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "now = ee.Date(datetime.now())\n",
    "print(now.getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8951c937",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a51f6db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241])\n",
    "\n",
    "now = ee.Date(datetime.now())\n",
    "\n",
    "# Apply another filter to the collection below to filter images\n",
    "# collected in the last 1-month\n",
    "# Do not hard-code the dates, it should always show images\n",
    "# from the past 1-month whenever you run the script\n",
    "# Hint: Use ee.Date.advance() function\n",
    "# to compute the date 1 month before now\n",
    "filtered = s2.filter(ee.Filter.lt(\"CLOUDY_PIXEL_PERCENTAGE\", 30))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\\\n",
    "    .filterDate(now.advance(-1, \"month\"), now)\n",
    "\n",
    "print(filtered.size().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0a9bf2",
   "metadata": {},
   "source": [
    "## 2. Calculating Indices\n",
    "\n",
    "Spectral Indices are central to many aspects of remote sensing. Whether you are studying vegetation or tracking fires. You will need to compute a pixel-wise ratio of 2 or more bands. The most commonly used formula for calculating an index is the Normalized Difference between 2 bands. Earth Engine provides a helper function `normalizedDifference()` to help calculate normalized indices, such as Normalized Difference Vegetation Index (NDVI). For more complex formula, you can also use the `expression()` function to describe the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "23778aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Sentinel-2 image collection\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "\n",
    "# Get the administrative borders \n",
    "admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
    "\n",
    "# Filter for Bangalore\n",
    "bangalore = admin2.filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'))\n",
    "geometry = bangalore.geometry()\n",
    "\n",
    "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\n",
    "\n",
    "# Calculate the median of the image collection\n",
    "image = filtered.median() \n",
    "\n",
    "# Calculate  Normalized Difference Vegetation Index (NDVI)\n",
    "# based on 'NIR' (B8) & 'RED' (B4)\n",
    "ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi'])\n",
    "\n",
    "# Calculate Modified Normalized Difference Water Index (MNDWI)\n",
    "# based on 'GREEN' (B3) and 'SWIR1' (B11)\n",
    "mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']) \n",
    "\n",
    "# Calculate Soil-adjusted Vegetation Index (SAVI)\n",
    "# 1.5 * ((NIR - RED) / (NIR + RED + 0.5))\n",
    "\n",
    "# For more complex indices, you can use the expression() function\n",
    "# Note: \n",
    "# For the SAVI formula, the pixel values need to converted to reflectances\n",
    "# Multiplyng the pixel values by 'scale' gives us the reflectance value\n",
    "# The scale value is 0.0001 for Sentinel-2 dataset\n",
    "savi = image.expression(\n",
    "    \"1.5 * ((NIR - RED) / (NIR + RED + 0.5))\", {\n",
    "      'NIR': image.select('B8').multiply(0.0001),\n",
    "      'RED': image.select('B4').multiply(0.0001),\n",
    "}).rename('savi')\n",
    "\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']};\n",
    "ndviVis = {\"min\": 0, \"max\": 1, \"palette\": ['white', 'green']}\n",
    "ndwiVis = {\"min\": 0, \"max\": 0.5, \"palette\": ['white', 'blue']}\n",
    "\n",
    "m.addLayer(image.clip(geometry), rgbVis, 'Image')\n",
    "m.addLayer(mndwi.clip(geometry), ndwiVis, 'mndwi')\n",
    "m.addLayer(savi.clip(geometry), ndviVis, 'savi') \n",
    "m.addLayer(ndvi.clip(geometry), ndviVis, 'ndvi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2bc851",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97023dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
    "\n",
    "bangalore = admin2.filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'))\n",
    "geometry = bangalore.geometry()\n",
    "\n",
    "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\n",
    "\n",
    "image = filtered.median() \n",
    "\n",
    "# Exercise\n",
    "# Calculate the Normalized Difference Built-Up Index (NDBI) for the image\n",
    "# Hint: NDBI = (SWIR1 – NIR) / (SWIR1 + NIR)\n",
    "# Visualize the built-up area using a 'red' palette\n",
    "ndbi = image.expression(\n",
    "    '(SWIR1 - NIR) / (SWIR1 + NIR)', {\n",
    "      'NIR': image.select('B8').multiply(0.0001),\n",
    "      'SWIR1': image.select('B11').multiply(0.0001),\n",
    "}).rename('ndbi')\n",
    "\n",
    "ndbiVis = {\"min\": 0, \"max\": 1, \"palette\": ['white', 'red']}\n",
    "m.addLayer(ndbi.clip(geometry), ndbiVis, 'ndbi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a00ff9e7",
   "metadata": {},
   "source": [
    "## 3. Computation on ImageCollections\n",
    "\n",
    "So far we have learnt how to run computation on single images. If you want to apply some computation - such as calculating an index - to many images, you need to use `map()`. \n",
    "\n",
    "You first define a function that takes 1 image and returns the result of the computation on that image. Then you can `map()` that function over the `ImageCollection` which results in a new `ImageCollection` with the results of the computation. This is similar to a for-loop that you maybe familiar with - but using `map()` allows the computation to run in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1f5f2748",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "admin1 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level1\")\n",
    "    \n",
    "karnataka = admin1.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))\n",
    "geometry = karnataka.geometry()\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']}\n",
    "\n",
    "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\n",
    "\n",
    "composite = filtered.median().clip(karnataka)\n",
    "m.addLayer(composite, rgbVis, 'Karnataka Composite')  \n",
    "\n",
    "# Write a function that computes NDVI for an image and adds it as a band\n",
    "def add_ndvi(img):\n",
    "    ndvi = img.normalizedDifference([\"B8\", \"B4\"]).rename(\"ndvi\")\n",
    "    return img.addBands(ndvi)\n",
    "\n",
    "# Map the function over the collection\n",
    "with_ndvi = filtered.map(add_ndvi)\n",
    "\n",
    "composite = with_ndvi.median()\n",
    "\n",
    "ndvi_composite = composite.select('ndvi').clip(karnataka)\n",
    "\n",
    "palette = [\n",
    "  'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',\n",
    "  '74A901', '66A000', '529400', '3E8601', '207401', '056201',\n",
    "  '004C00', '023B01', '012E01', '011D01', '011301']\n",
    "\n",
    "ndviVis = {\"min\": 0, \"max\": 0.5, \"palette\": palette}\n",
    "m.addLayer(ndvi_composite, ndviVis, 'ndvi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82614098",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f8506d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "admin1 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level1\")\n",
    "  \n",
    "karnataka = admin1.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))\n",
    "geometry = karnataka.geometry()\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']}\n",
    "\n",
    "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\n",
    "\n",
    "composite = filtered.median().clip(karnataka)\n",
    "m.addLayer(composite, rgbVis, 'Karnataka Composite')  \n",
    "\n",
    "# This function calculates both NDVI an d NDWI indices\n",
    "# and returns an image with 2 new bands added to the original image.\n",
    "def add_indices(img):\n",
    "    ndvi = img.normalizedDifference([\"B8\", \"B4\"]).rename(\"ndvi\")\n",
    "    ndwi = img.normalizedDifference([\"B3\", \"B8\"]).rename(\"ndwi\")\n",
    "    return img.addBands(ndvi).addBands(ndwi)\n",
    "\n",
    "# Map the function over the collection\n",
    "with_indices = filtered.map(add_indices)\n",
    "\n",
    "# Composite\n",
    "composite = with_indices.median()\n",
    "\n",
    "# Extract the 'ndwi' band and display a NDWI map\n",
    "# use the palette ['white', 'blue']\n",
    "viz = {\"min\": 0, \"max\": 1, \"palette\": [\"white\", \"blue\"]}\n",
    "m.addLayer(composite.select(\"ndwi\").clip(karnataka), viz, name=\"Blue Karnataka\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d62f88",
   "metadata": {},
   "source": [
    "## 04. Cloud Masking\n",
    "\n",
    "Masking pixels in an image makes those pixels transparent and excludes them from analysis and visualization. To mask an image, we can use the `updateMask()` function and pass it an image with `0` and `1` values. All pixels where the mask image is `0` will be masked.\n",
    "\n",
    "Most remote sensing datasets come with a `QA` or Cloud Mask band that contains the information on whether pixels is cloudy or not.\n",
    "\n",
    "The script below takes the Sentinel-2 masking function and shows how to apply it on an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4e734170",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = ee.Image('COPERNICUS/S2/20190703T050701_20190703T052312_T43PGP')\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    "\n",
    "m.centerObject(image)\n",
    "m.addLayer(image, rgbVis, 'Full Image')\n",
    "\n",
    "# Write a function for Cloud masking\n",
    "def maskS2clouds(image):\n",
    "    qa = image.select('QA60')\n",
    "    cloudBitMask = 1 << 10\n",
    "    cirrusBitMask = 1 << 11\n",
    "    mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
    "    return image.updateMask(mask).select(\"B.*\").copyProperties(image, [\"system:time_start\"])\n",
    "\n",
    "maskedImage = ee.Image(maskS2clouds(image))\n",
    "m.addLayer(maskedImage, rgbVis, 'Masked Image')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8755e6b",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3fc6a110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Level-2A Surface Reflectance image\n",
    "imageSR = ee.Image('COPERNICUS/S2_SR/20190703T050701_20190703T052312_T43PGP')\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    "m.centerObject(imageSR)\n",
    "m.addLayer(imageSR, rgbVis, 'SR Image')\n",
    "\n",
    "\n",
    "# Function to remove cloud and snow pixels from Sentinel-2 SR image\n",
    "def maskCloudAndShadowsSR(image):\n",
    "    cloudProb = image.select('MSK_CLDPRB')\n",
    "    snowProb = image.select('MSK_SNWPRB')\n",
    "    cloud = cloudProb.lt(5)\n",
    "    snow = snowProb.lt(5)\n",
    "    scl = image.select('SCL')\n",
    "    shadow = scl.eq(3)\n",
    "    cirrus = scl.eq(10)\n",
    "    # Cloud probability less than 5% or cloud shadow classification\n",
    "    mask = (cloud.And(snow)).And(cirrus.neq(1)).And(shadow.neq(1))\n",
    "    return image.updateMask(mask)\n",
    "\n",
    "# Exercise\n",
    "# Apply the above cloud masking function to SR image\n",
    "# Add the masked image to the map\n",
    "masked = maskCloudAndShadowsSR(imageSR)\n",
    "m.addLayer(masked, rgbVis, name=\"masked img\")\n",
    "\n",
    "# Hint: After adding the masked image to the map, turn-off\n",
    "# the original image layer to see the result of the masking function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54be5fa0",
   "metadata": {},
   "source": [
    "## 05. Reducers\n",
    "\n",
    "When writing parallel computing code, a Reduce operation allows you to compute statistics on a large amount of inputs. In Earth Engine, you need to run reduction operation when creating composites, calculating statistics, doing regression analysis etc. \n",
    "\n",
    "The Earth Engine API comes with a large number of built-in reducer functions (such as `ee.Reducer.sum()`, `ee.Reducer.histogram()`, `ee.Reducer.linearFit()` etc.) that can perform a variety of statistical operations on input data. \n",
    "\n",
    "You can run reducers using the `reduce()` function. Earth Engine supports running reducers on all data structures that can hold multiple values, such as Images (reducers run on different bands), `ImageCollection`, `FeatureCollection`, `List`, `Dictionary` etc. \n",
    "\n",
    "The script below introduces basic concepts related to reducers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "920bb321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'ee.ee_list.List'>\n",
      "<class 'ee.computedobject.ComputedObject'>\n",
      "<class 'ee.ee_number.Number'>\n",
      "Reducer on Collection <class 'dict'>\n",
      "Image <class 'ee.image.Image'>\n",
      "Reducer on Image <class 'ee.image.Image'>\n",
      "<class 'ee.dictionary.Dictionary'>\n",
      "Average value in B4 ee.ComputedObject({\n",
      "  \"functionInvocationValue\": {\n",
      "    \"functionName\": \"Dictionary.get\",\n",
      "    \"arguments\": {\n",
      "      \"dictionary\": {\n",
      "        \"functionInvocationValue\": {\n",
      "          \"functionName\": \"Image.reduceRegion\",\n",
      "          \"arguments\": {\n",
      "            \"geometry\": {\n",
      "              \"functionInvocationValue\": {\n",
      "                \"functionName\": \"Image.geometry\",\n",
      "                \"arguments\": {\n",
      "                  \"feature\": {\n",
      "                    \"functionInvocationValue\": {\n",
      "                      \"functionName\": \"Collection.first\",\n",
      "                      \"arguments\": {\n",
      "                        \"collection\": {\n",
      "                          \"functionInvocationValue\": {\n",
      "                            \"functionName\": \"Collection.map\",\n",
      "                            \"arguments\": {\n",
      "                              \"baseAlgorithm\": {\n",
      "                                \"functionDefinitionValue\": {\n",
      "                                  \"argumentNames\": [\n",
      "                                    \"_MAPPING_VAR_0_0\"\n",
      "                                  ],\n",
      "                                  \"body\": {\n",
      "                                    \"functionInvocationValue\": {\n",
      "                                      \"functionName\": \"Image.select\",\n",
      "                                      \"arguments\": {\n",
      "                                        \"bandSelectors\": {\n",
      "                                          \"constantValue\": [\n",
      "                                            \"B.*\"\n",
      "                                          ]\n",
      "                                        },\n",
      "                                        \"input\": {\n",
      "                                          \"argumentReference\": \"_MAPPING_VAR_0_0\"\n",
      "                                        }\n",
      "                                      }\n",
      "                                    }\n",
      "                                  }\n",
      "                                }\n",
      "                              },\n",
      "                              \"collection\": {\n",
      "                                \"functionInvocationValue\": {\n",
      "                                  \"functionName\": \"Collection.filter\",\n",
      "                                  \"arguments\": {\n",
      "                                    \"collection\": {\n",
      "                                      \"functionInvocationValue\": {\n",
      "                                        \"functionName\": \"Collection.filter\",\n",
      "                                        \"arguments\": {\n",
      "                                          \"collection\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"Collection.filter\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"collection\": {\n",
      "                                                  \"functionInvocationValue\": {\n",
      "                                                    \"functionName\": \"ImageCollection.load\",\n",
      "                                                    \"arguments\": {\n",
      "                                                      \"id\": {\n",
      "                                                        \"constantValue\": \"COPERNICUS/S2\"\n",
      "                                                      }\n",
      "                                                    }\n",
      "                                                  }\n",
      "                                                },\n",
      "                                                \"filter\": {\n",
      "                                                  \"functionInvocationValue\": {\n",
      "                                                    \"functionName\": \"Filter.lessThan\",\n",
      "                                                    \"arguments\": {\n",
      "                                                      \"leftField\": {\n",
      "                                                        \"constantValue\": \"CLOUDY_PIXEL_PERCENTAGE\"\n",
      "                                                      },\n",
      "                                                      \"rightValue\": {\n",
      "                                                        \"constantValue\": 30\n",
      "                                                      }\n",
      "                                                    }\n",
      "                                                  }\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          },\n",
      "                                          \"filter\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"Filter.dateRangeContains\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"leftValue\": {\n",
      "                                                  \"functionInvocationValue\": {\n",
      "                                                    \"functionName\": \"DateRange\",\n",
      "                                                    \"arguments\": {\n",
      "                                                      \"end\": {\n",
      "                                                        \"constantValue\": \"2019-12-31\"\n",
      "                                                      },\n",
      "                                                      \"start\": {\n",
      "                                                        \"constantValue\": \"2019-01-01\"\n",
      "                                                      }\n",
      "                                                    }\n",
      "                                                  }\n",
      "                                                },\n",
      "                                                \"rightField\": {\n",
      "                                                  \"constantValue\": \"system:time_start\"\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          }\n",
      "                                        }\n",
      "                                      }\n",
      "                                    },\n",
      "                                    \"filter\": {\n",
      "                                      \"functionInvocationValue\": {\n",
      "                                        \"functionName\": \"Filter.intersects\",\n",
      "                                        \"arguments\": {\n",
      "                                          \"leftField\": {\n",
      "                                            \"constantValue\": \".all\"\n",
      "                                          },\n",
      "                                          \"rightValue\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"Feature\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"geometry\": {\n",
      "                                                  \"functionInvocationValue\": {\n",
      "                                                    \"functionName\": \"GeometryConstructors.Point\",\n",
      "                                                    \"arguments\": {\n",
      "                                                      \"coordinates\": {\n",
      "                                                        \"constantValue\": [\n",
      "                                                          77.60412933051538,\n",
      "                                                          12.952912912328241\n",
      "                                                        ]\n",
      "                                                      }\n",
      "                                                    }\n",
      "                                                  }\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          }\n",
      "                                        }\n",
      "                                      }\n",
      "                                    }\n",
      "                                  }\n",
      "                                }\n",
      "                              }\n",
      "                            }\n",
      "                          }\n",
      "                        }\n",
      "                      }\n",
      "                    }\n",
      "                  }\n",
      "                }\n",
      "              }\n",
      "            },\n",
      "            \"image\": {\n",
      "              \"functionInvocationValue\": {\n",
      "                \"functionName\": \"Collection.first\",\n",
      "                \"arguments\": {\n",
      "                  \"collection\": {\n",
      "                    \"functionInvocationValue\": {\n",
      "                      \"functionName\": \"Collection.map\",\n",
      "                      \"arguments\": {\n",
      "                        \"baseAlgorithm\": {\n",
      "                          \"functionDefinitionValue\": {\n",
      "                            \"argumentNames\": [\n",
      "                              \"_MAPPING_VAR_0_0\"\n",
      "                            ],\n",
      "                            \"body\": {\n",
      "                              \"functionInvocationValue\": {\n",
      "                                \"functionName\": \"Image.select\",\n",
      "                                \"arguments\": {\n",
      "                                  \"bandSelectors\": {\n",
      "                                    \"constantValue\": [\n",
      "                                      \"B.*\"\n",
      "                                    ]\n",
      "                                  },\n",
      "                                  \"input\": {\n",
      "                                    \"argumentReference\": \"_MAPPING_VAR_0_0\"\n",
      "                                  }\n",
      "                                }\n",
      "                              }\n",
      "                            }\n",
      "                          }\n",
      "                        },\n",
      "                        \"collection\": {\n",
      "                          \"functionInvocationValue\": {\n",
      "                            \"functionName\": \"Collection.filter\",\n",
      "                            \"arguments\": {\n",
      "                              \"collection\": {\n",
      "                                \"functionInvocationValue\": {\n",
      "                                  \"functionName\": \"Collection.filter\",\n",
      "                                  \"arguments\": {\n",
      "                                    \"collection\": {\n",
      "                                      \"functionInvocationValue\": {\n",
      "                                        \"functionName\": \"Collection.filter\",\n",
      "                                        \"arguments\": {\n",
      "                                          \"collection\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"ImageCollection.load\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"id\": {\n",
      "                                                  \"constantValue\": \"COPERNICUS/S2\"\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          },\n",
      "                                          \"filter\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"Filter.lessThan\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"leftField\": {\n",
      "                                                  \"constantValue\": \"CLOUDY_PIXEL_PERCENTAGE\"\n",
      "                                                },\n",
      "                                                \"rightValue\": {\n",
      "                                                  \"constantValue\": 30\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          }\n",
      "                                        }\n",
      "                                      }\n",
      "                                    },\n",
      "                                    \"filter\": {\n",
      "                                      \"functionInvocationValue\": {\n",
      "                                        \"functionName\": \"Filter.dateRangeContains\",\n",
      "                                        \"arguments\": {\n",
      "                                          \"leftValue\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"DateRange\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"end\": {\n",
      "                                                  \"constantValue\": \"2019-12-31\"\n",
      "                                                },\n",
      "                                                \"start\": {\n",
      "                                                  \"constantValue\": \"2019-01-01\"\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          },\n",
      "                                          \"rightField\": {\n",
      "                                            \"constantValue\": \"system:time_start\"\n",
      "                                          }\n",
      "                                        }\n",
      "                                      }\n",
      "                                    }\n",
      "                                  }\n",
      "                                }\n",
      "                              },\n",
      "                              \"filter\": {\n",
      "                                \"functionInvocationValue\": {\n",
      "                                  \"functionName\": \"Filter.intersects\",\n",
      "                                  \"arguments\": {\n",
      "                                    \"leftField\": {\n",
      "                                      \"constantValue\": \".all\"\n",
      "                                    },\n",
      "                                    \"rightValue\": {\n",
      "                                      \"functionInvocationValue\": {\n",
      "                                        \"functionName\": \"Feature\",\n",
      "                                        \"arguments\": {\n",
      "                                          \"geometry\": {\n",
      "                                            \"functionInvocationValue\": {\n",
      "                                              \"functionName\": \"GeometryConstructors.Point\",\n",
      "                                              \"arguments\": {\n",
      "                                                \"coordinates\": {\n",
      "                                                  \"constantValue\": [\n",
      "                                                    77.60412933051538,\n",
      "                                                    12.952912912328241\n",
      "                                                  ]\n",
      "                                                }\n",
      "                                              }\n",
      "                                            }\n",
      "                                          }\n",
      "                                        }\n",
      "                                      }\n",
      "                                    }\n",
      "                                  }\n",
      "                                }\n",
      "                              }\n",
      "                            }\n",
      "                          }\n",
      "                        }\n",
      "                      }\n",
      "                    }\n",
      "                  }\n",
      "                }\n",
      "              }\n",
      "            },\n",
      "            \"maxPixels\": {\n",
      "              \"constantValue\": 10000000000.0\n",
      "            },\n",
      "            \"reducer\": {\n",
      "              \"functionInvocationValue\": {\n",
      "                \"functionName\": \"Reducer.mean\",\n",
      "                \"arguments\": {}\n",
      "              }\n",
      "            },\n",
      "            \"scale\": {\n",
      "              \"constantValue\": 100\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      },\n",
      "      \"key\": {\n",
      "        \"constantValue\": \"B4\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241])\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "    \n",
    "# Computing stats on a list\n",
    "myList = ee.List.sequence(1, 10)\n",
    "print(type(myList))\n",
    "\n",
    "# Use a reducer to compute min and max in the list\n",
    "mean = myList.reduce(ee.Reducer.mean())\n",
    "print(type(mean))\n",
    "\n",
    "# Apply a reducer on a image collection\n",
    "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\\\n",
    "    .select('B.*')\n",
    "\n",
    "print(type(filtered.size()))\n",
    "collMean = filtered.reduce(ee.Reducer.mean())\n",
    "print('Reducer on Collection', type(collMean.getInfo()))\n",
    "\n",
    "# Apply a reducer on an image\n",
    "image = ee.Image(filtered.first())\n",
    "print('Image', type(image))\n",
    "\n",
    "imageMean = image.reduce(ee.Reducer.mean())\n",
    "print('Reducer on Image', type(imageMean))\n",
    "\n",
    "# If we want to compute min and max for each band, use reduceRegion instead\n",
    "stats = image.reduceRegion(**{\n",
    "  \"reducer\": ee.Reducer.mean(),\n",
    "  \"geometry\": image.geometry(),\n",
    "  \"scale\": 100,\n",
    "  \"maxPixels\": 1e10\n",
    "})\n",
    "print(type(stats))\n",
    "\n",
    "# Result of reduceRegion is a dictionary. \n",
    "# We can extract the values using .get() function\n",
    "print('Average value in B4', stats.get('B4'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5490217",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f6cab7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B1': 1232.1078293483356,\n",
       " 'B10': 7.8499765588373185,\n",
       " 'B11': 1211.213314580403,\n",
       " 'B12': 560.4913267698079,\n",
       " 'B2': 959.2437880918895,\n",
       " 'B3': 856.0890764181906,\n",
       " 'B4': 527.2372245663386,\n",
       " 'B5': 845.3614627285515,\n",
       " 'B6': 2655.7172995780593,\n",
       " 'B7': 3523.71870604782,\n",
       " 'B8': 3409.1233005157055,\n",
       " 'B8A': 3722.547116736991,\n",
       " 'B9': 1118.8030942334742,\n",
       " 'QA10': 0,\n",
       " 'QA20': 0,\n",
       " 'QA60': 0}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometry = ee.Geometry.Polygon([[\n",
    "  [82.60642647743225, 27.16350437805251],\n",
    "  [82.60984897613525, 27.1618529901377],\n",
    "  [82.61088967323303, 27.163695288375266],\n",
    "  [82.60757446289062, 27.16517483230927]\n",
    "]])\n",
    "          \n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']}\n",
    "image = ee.Image('COPERNICUS/S2/20190223T050811_20190223T051829_T44RPR')\n",
    "\n",
    "m.addLayer(image, rgbVis, 'Image')\n",
    "m.addLayer(geometry, {\"color\": 'red'}, 'Farm')\n",
    "m.centerObject(geometry)\n",
    "\n",
    "ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi')\n",
    "\n",
    "# Exercise\n",
    "# Compute the average NDVI for the farm from the given image\n",
    "# Hint: Use the reduceRegion() function\n",
    "stats = image.reduceRegion(**{\n",
    "    \"reducer\": ee.Reducer.mean(),\n",
    "    \"geometry\": geometry,\n",
    "    \"scale\": 100,\n",
    "    \"maxPixels\": 1e10\n",
    "})\n",
    "\n",
    "stats.getInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38f334f",
   "metadata": {},
   "source": [
    "## 06. Time-Series Charts\n",
    "\n",
    "Now we can put together all the skills we have learnt so far: `filter`, `map`, `reduce`, and `cloud-masking` to create a chart of average NDVI values for a given farm over 1 year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "389ceaa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing statistics ...\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/f885ac3e805e74f340f5bd46aaab2875-8007d69394cb1f65aed90ad2f1431c60:getFeatures\n",
      "Please wait ...\n",
      "Data downloaded to /Users/akramzaytar/projects/Learn/GEE/ezmap/notebooks/timeseries.csv\n"
     ]
    }
   ],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "geometry = ee.Geometry.Polygon([[\n",
    "  [82.60642647743225, 27.16350437805251],\n",
    "  [82.60984897613525, 27.1618529901377],\n",
    "  [82.61088967323303, 27.163695288375266],\n",
    "  [82.60757446289062, 27.16517483230927]\n",
    "]])\n",
    "\n",
    "m.addLayer(geometry, {\"color\": 'red'}, 'Farm')\n",
    "m.centerObject(geometry)\n",
    "\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']};\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.date('2017-01-01', '2017-12-31'))\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\n",
    "\n",
    "# Write a function for Cloud masking\n",
    "def maskS2clouds(img):\n",
    "    qa = img.select('QA60')\n",
    "    cloudBitMask = 1 << 10\n",
    "    cirrusBitMask = 1 << 11\n",
    "    mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
    "    return img.updateMask(mask).divide(10000).select(\"B.*\").copyProperties(img, [\"system:time_start\"])\n",
    "\n",
    "filtered = filtered.map(maskS2clouds)\n",
    "\n",
    "# Write a function that computes NDVI for an image and adds it as a band\n",
    "def addNDVI(img):\n",
    "    ndvi = img.normalizedDifference(['B8', 'B4']).rename('ndvi')\n",
    "    return img.addBands(ndvi)\n",
    "\n",
    "# Map the function over the collection\n",
    "withNdvi = filtered.map(addNDVI)\n",
    "\n",
    "image = withNdvi.select(\"ndvi\").toBands()\n",
    "\n",
    "geemap.zonal_statistics(image, ee.FeatureCollection(geometry), \n",
    "                        \"data/timeseries.csv\", statistics_type='MEAN', scale=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eefe851e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/timeseries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8dbd1b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe890c97e20>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "_, ax = plt.subplots()\n",
    "ax.plot([val for val in df.values.tolist()[0] if not pd.isna(val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8595ba",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Delete the farm boundary from the previous script and add another farm at a location of your choice\n",
    "\n",
    "Print the chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ebc6e8c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing statistics ...\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/4746cc160a30967d0c18c82667a97965-fd0f7a361f85f7044bfc3f96ea14d7b6:getFeatures\n",
      "Please wait ...\n",
      "Data downloaded to /Users/akramzaytar/projects/Learn/GEE/ezmap/notebooks/timeseries.csv\n"
     ]
    }
   ],
   "source": [
    "aoi = ee.FeatureCollection(m.draw_features[0])\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "\n",
    "m.addLayer(aoi, {\"color\": 'red'}, 'Farm')\n",
    "m.centerObject(aoi)\n",
    "\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']};\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.date('2017-01-01', '2017-12-31'))\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.geometry(aoi))\n",
    "\n",
    "# Write a function for Cloud masking\n",
    "def maskS2clouds(img):\n",
    "    qa = img.select('QA60')\n",
    "    cloudBitMask = 1 << 10\n",
    "    cirrusBitMask = 1 << 11\n",
    "    mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
    "    return img.updateMask(mask).divide(10000).select(\"B.*\").copyProperties(img, [\"system:time_start\"])\n",
    "\n",
    "filtered = filtered.map(maskS2clouds)\n",
    "\n",
    "# Write a function that computes NDVI for an image and adds it as a band\n",
    "def addNDVI(img):\n",
    "    ndvi = img.normalizedDifference(['B8', 'B4']).rename('ndvi')\n",
    "    return img.addBands(ndvi)\n",
    "\n",
    "# Map the function over the collection\n",
    "withNdvi = filtered.map(addNDVI)\n",
    "\n",
    "image = withNdvi.select(\"ndvi\").toBands()\n",
    "\n",
    "geemap.zonal_statistics(image, aoi, \"data/timeseries.csv\", statistics_type='MEAN', scale=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "25235e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe8805dd6a0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = pd.read_csv(\"data/timeseries.csv\")\n",
    "_, ax = plt.subplots()\n",
    "ax.plot([val for val in df.values.tolist()[0] if not pd.isna(val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06700843",
   "metadata": {},
   "source": [
    "## Assignment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b66c8725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing statistics ...\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/28b8a5cb6ab003ec06b56209ec97e6cc-0c04ffaefb23434c5f9c1b05ec7da7e5:getFeatures\n",
      "Please wait ...\n",
      "Data downloaded to /Users/akramzaytar/projects/Learn/GEE/ezmap/notebooks/timeseries.csv\n"
     ]
    }
   ],
   "source": [
    "geometry = ee.Geometry.Point([77.54849920033682, 12.91215102400037])\n",
    "    \n",
    "# Assignment\n",
    "# Use Gridded Climate dataset to chart a 40+ year time series\n",
    "# of temparature at any location\n",
    "\n",
    "\n",
    "# Workflow\n",
    "# [x] Load the TerraClimate collection\n",
    "imgs = ee.ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")\n",
    "\n",
    "# [x] Select the 'tmmx' band\n",
    "imgs = imgs.select([\"tmmx\"])\n",
    "\n",
    "# [x] Scale the band values\n",
    "imgs = imgs.map(lambda img: img.multiply(0.1).copyProperties(img, [\"system:time_start\"]))\n",
    "\n",
    "# [x] Filter the scaled collection\n",
    "imgs = imgs.filter(ee.Filter.geometry(geometry))\n",
    "img = imgs.toBands() \n",
    "\n",
    "# Export statistics\n",
    "geemap.zonal_statistics(img, ee.FeatureCollection(geometry), \n",
    "                        \"timeseries.csv\", statistics_type='MEAN', \n",
    "                        scale=img.projection().nominalScale())\n",
    "\n",
    "# # Hint1\n",
    "# # Data needed to be scaled by 0.1\n",
    "# # map() a function and multiply each image\n",
    "# # Multiplying creates a new image that doesn't have the same properties\n",
    "# # Use copyProperties() function to copy timestamp to new image\n",
    "# var tmax = terraclimate.select('tmmx')\n",
    "# var tmaxScaled = tmax.map(function(image) {\n",
    "#   return image.multiply(0.1)\n",
    "#     .copyProperties(image,['system:time_start']);\n",
    "# })\n",
    "\n",
    "# # Hint2\n",
    "# # You will need to specify a scale in meters for charting\n",
    "# # Use projection().nominalScale() to find the \n",
    "# # image resolution in meters\n",
    "# var image = ee.Image(terraclimate.first())\n",
    "# print(image.projection().nominalScale())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bff022b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe8726edca0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = pd.read_csv(\"timeseries.csv\")\n",
    "_, ax = plt.subplots()\n",
    "ax.plot([val for val in df.values.tolist()[0] if not pd.isna(val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189c5832",
   "metadata": {},
   "source": [
    "We will probably use docs: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "411eb35b",
   "metadata": {},
   "outputs": [],
   "source": [
    "geemap.ee_search()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef88d391",
   "metadata": {},
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}