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      "source": [
        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/ImageCollection/creating_monthly_imagery.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/ImageCollection/creating_monthly_imagery.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/ImageCollection/creating_monthly_imagery.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('Installing geemap ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import ee\n",
        "import geemap"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "p1 = ee.Geometry.Point([103.521, 13.028])\n",
        "p2 = ee.Geometry.Point([105.622, 13.050])\n",
        "Date_Start = ee.Date('2000-05-01')\n",
        "Date_End = ee.Date('2007-12-01')\n",
        "Date_window = ee.Number(30)\n",
        "\n",
        "# Create list of dates for time series\n",
        "n_months = Date_End.difference(Date_Start, 'month').round()\n",
        "print(\"Number of months:\", n_months.getInfo())\n",
        "dates = ee.List.sequence(0, n_months, 1)\n",
        "print(dates.getInfo())\n",
        "\n",
        "def make_datelist(n):\n",
        "    return Date_Start.advance(n, 'month')\n",
        "\n",
        "\n",
        "dates = dates.map(make_datelist)\n",
        "print(dates.getInfo())\n",
        "\n",
        "\n",
        "def fnc(d1):\n",
        "    S1 = ee.ImageCollection('LANDSAT/LT5_L1T_TOA') \\\n",
        "        .filterDate('2000-05-01', '2007-12-01') \\\n",
        "        .filter(ee.Filter.calendarRange(1, 14, 'month')) \\\n",
        "        .sort('CLOUD_COVER') \\\n",
        "        .filterBounds(p1).first()\n",
        "    S2 = ee.ImageCollection('LANDSAT/LT5_L1T_TOA') \\\n",
        "        .filterDate('2000-05-01', '2007-12-01') \\\n",
        "        .filter(ee.Filter.calendarRange(1, 14, 'month')) \\\n",
        "        .sort('CLOUD_COVER') \\\n",
        "        .filterBounds(p2).first()\n",
        "\n",
        "    mosaic = ee.ImageCollection([ee.Image(S1), ee.Image(S2)]).mosaic()\n",
        "\n",
        "    return mosaic\n",
        "\n",
        "\n",
        "list_of_images = dates.map(fnc)\n",
        "print('list_of_images', list_of_images.getInfo())\n",
        "mt = ee.ImageCollection(list_of_images)\n",
        "print(mt.getInfo())\n",
        "# Map.addLayer(mt, {}, 'mt')\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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