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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Convert to RGB format (vector data)\nThis example demonstrates convertion from wind data to uv rgb format (with HKO earth format).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Definitions\n\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import all required modules and methods:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Python package to allow system command line functions\nimport os\n# Python package to manage warning message\nimport warnings\n# Python package for time calculations\nimport pandas as pd\n# Python package for numerical calculations\nimport numpy as np\n# Python package for image generation\nfrom PIL import Image\n# Python package for image preview\nimport matplotlib.pyplot as plt\n\n# swirlspy iris parser function\nfrom swirlspy.rad.iris import read_iris_grid\n# swirlspy regrid function\nfrom swirlspy.preprocess import grid_align\n# swirlspy rgb convertion function\nfrom swirlspy.utils.conversion import to_rgb_data, to_hko_earth_format\n# directory constants\nfrom swirlspy.tests.samples import DATA_DIR\nfrom swirlspy.tests.outputs import OUTPUT_DIR\n\nwarnings.filterwarnings(\"ignore\")\n\n# Logging\nstart_time = pd.Timestamp.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Loading radar data\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Specify the basetime\nbasetime = pd.Timestamp('201902190800')\n\n# Reading the wind data\nreflec = read_iris_grid(\n    os.path.join(\n        DATA_DIR,\n        basetime.strftime(\"iris/ppi/TMS%y%m%d%H%M02.PPIMK3B\")\n    )\n)\n\ninitialising_time = pd.Timestamp.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Reproject to WGS 84, required for HKO earth format\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# calculate x, y step size\ny_step = -0.025\nx_step = 0.025\ny = np.arange(24.5, 20.5, y_step)\nx = np.arange(112, 116, x_step)\n\nreflec_wgs84 = grid_align(\n    reflec, reflec.attrs['area_def'].proj_str,\n    x, y, '+proj=longlat +datum=WGS84 +no_defs'\n)\n\npreparation_time = pd.Timestamp.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Convert image data into rgb format\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# only shape with (y, x) is allowed\ndata = reflec_wgs84.sel(time=basetime)\n\n# only allow positive values\ndata = data.where(data >= 0)\n\n# this step is not necessary, depends on any meta data and preprocess is required by your platform\nearth_data = to_hko_earth_format(data, coords_dp=3, y_step=y_step, x_step=x_step)\nrgb = to_rgb_data(earth_data)\n\nconvertion_time = pd.Timestamp.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualisation\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "path = os.path.join(OUTPUT_DIR, \"rgb_wind.png\")\nwith Image.fromarray(rgb, 'RGBA') as img:\n    img.save(path, 'png')\n\n# preview\nwith Image.open(path) as image:\n    plt.axis('off')\n    plt.imshow(image)\n    plt.plot()\n    plt.show()\n\nvisualise_time = pd.Timestamp.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Checking run time of each component\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(f\"Start time: {start_time}\")\nprint(f\"Initialising time: {initialising_time}\")\nprint(f\"Preparation time: {preparation_time}\")\nprint(f\"Convertion time: {convertion_time}\")\nprint(f\"Visualise time: {visualise_time}\")\n\nprint(f\"Time to initialise: {initialising_time - start_time}\")\nprint(f\"Time to prepare information: {preparation_time - initialising_time}\")\nprint(f\"Time to convertion: {convertion_time - preparation_time}\")\nprint(f\"Time to visualise: {visualise_time - convertion_time}\")\n\nprint(f\"Total: {visualise_time - start_time}\")"
      ]
    }
  ],
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      "display_name": "Python 3",
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
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