{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example I: Tracking data stored in files\n",
    "\n",
    "In this example notebook we are going to track rain-rates that are estimated from radar based reflectivities. The rain-rates is an example dataset from the CPOL radar archive that used to operate north of Darwin, Australia. This example dataset has been provided by Valentin Louf from the Australian Bureau of Meteorology.\n",
    "\n",
    "A more detailed example of the application of the tracking algorithm can be found in [doi: 10.1002/qj.4360](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4360)\n",
    "\n",
    "Before we get started we import all modules that are needed to apply the tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "from tintx import RunDirectory, config\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as crs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we want to apply the tracking algorithm only for a limited time period. Such time periods can be passed as string or `datetime` object. If defined as a string make sure they follow the [ISO 8061](https://en.wikipedia.org/wiki/ISO_8601) convention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "first = '2006-11-16 03:00' #Start-date\n",
    "last = '2006-11-16 11:00' #End-date\n",
    "data_files = Path(os.environ[\"DATA_FILES\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply the tracking we create an instance of the `RunDirectory` class. Normally we would have to create this instance with a `xarray.DataArray`. The `from_files` method is a convenience method that allows us to create this instance without opening a dataset. The method will open the data files and create an object. We will have to provide a filename or a list of files, the name of the variable and if not according to cf conventions the names of the *longitude*, *latitude* and *time* dimensions and the used Coordinate Reference System (CRS) of the source data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RD = RunDirectory.from_files( data_files / \"CPOL_radar.nc\", \"radar_estimated_rain_rate\",\n",
    "                             start=first, end=last, use_cftime=True, x_coord=\"longitude\", y_coord=\"latitude\", crs=\"aeqd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional keyword arguments can be added to the `xarray.open_mfdataset` method. In this example we used the `use_cftime` keyword to convert the time variable to cftime vectors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The tuning parameters\n",
    "To apply the actual tracking algorithm we can use the `get_tracks` method. The algorithm can be tuned by passing the values for the tuning parameters. See also the [tuning parameter section](api.html#tracking-parameter-guide) of the docs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tracking tuning parameters\n",
      "--------------------------\n",
      "\n",
      "The following parameter can be set to tune the cell tracking algorithm\n",
      "\n",
      "* field_thresh: units of 'field' attribute, default: 32\n",
      "    The threshold used for object detection. Detected objects are connected\n",
      "    pixels above this threshold.\n",
      "* iso_thresh: units of 'field' attribute, default: 4\n",
      "    Used in isolated cell classification. Isolated cells must not be connected\n",
      "    to any other cell by contiguous pixels above this threshold.\n",
      "* iso_smooth: pixels, default: 4\n",
      "    Gaussian smoothing parameter in peak detection preprocessing. See\n",
      "    single_max in tint.objects.\n",
      "* min_size: square kilometers, default: 8\n",
      "    The minimum size threshold in pixels for an object to be detected.\n",
      "* search_margin: meters, default: 250\n",
      "    The radius of the search box around the predicted object center.\n",
      "* flow_margin: meters, default: 750\n",
      "    The margin size around the object extent on which to perform phase\n",
      "    correlation.\n",
      "* max_disparity: float, default: 999\n",
      "    Maximum allowable disparity value. Larger disparity values are sent to a\n",
      "    large number.\n",
      "* max_flow_mag: meters per second, default: 50\n",
      "    Maximum allowable global shift magnitude. See get_global_shift in\n",
      "    tint.phase_correlation.\n",
      "* max_shift_disp: meters per second, default: 15\n",
      "    Maximum magnitude of difference in meters per second for two shifts to be\n",
      "    considered in agreement. See correct_shift in tint.matching.\n",
      "* gs_alt: meters, default: 1500\n",
      "    Altitude in meters at which to perform phase correlation for global shift\n",
      "    calculation. See correct_shift in tint.matching.\n",
      "\n",
      "\n",
      "Setting and getting the tuning parameters\n",
      "-----------------------------------------\n",
      "The parameters can the set using the :py:class:`tintx.config.set` class.\n",
      "Getting the values of the currently set parameters can be done by the\n",
      ":py:func:`tintx.config.get` method.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(config.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the tracking with a rain-rate threshold of 0.1 and a minimum size of 4. The return value of the function will be the total number of individual storms identified by the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2b02b6c1fb348cdacdfe4352bc72dcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tracking:   0%|          | 0/48 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of storm cells found: 125\n"
     ]
    }
   ],
   "source": [
    "num_cells = RD.get_tracks(field_thresh=0.1, min_size=4)\n",
    "print(f\"Number of storm cells found: {num_cells}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the track data\n",
    "The actual tracking data is stored in a multi-index [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html) and can be accessed via the `tracks` property, it will be converted to [geopandas.GeoDataFrame](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html) on the fly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "                         time   grid_x  grid_y       lon      lat  area  \\\n",
       "scan uid                                                                  \n",
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       "     3    2006-11-16 03:00:00   75.000  49.750  131.4358 -12.4288     4   \n",
       "     4    2006-11-16 03:00:00   90.684  52.842  131.8040 -12.3605    19   \n",
       "...                       ...      ...     ...       ...      ...   ...   \n",
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       "\n",
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       "     121  14.703703  3.166035     False   \n",
       "48   121  14.791470  3.332414      True   \n",
       "\n",
       "                                                   geometry  \n",
       "scan uid                                                     \n",
       "0    0    POLYGON ((-130000.000 -40000.000, -125000.000 ...  \n",
       "     1    POLYGON ((-140000.000 -27500.000, -135000.000 ...  \n",
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       "\n",
       "[299 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RD.tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After each tracking the `RunDirectory` class creates a so called hash of the resulting `DataFrame` and saves the \n",
    "tuning parameters for this specific tracking. The next time you call the tracking method the previous tuning parameters are noted and set automatically. This means that in the above example the `filed_thresh` and `min_size` parameters are automatically set for the next tracking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying the results\n",
    "Let's do some visualisation and create an animation of the tracks. This can be done by calling the `animate` method.\n",
    "\n",
    "**Note:** To convert possible UTC time strings to local time we can give a time shift (`dt`) in hours. The plot styling can be fine tuned by the `plot_style` key word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "anim = RD.animate(dt=9.5, fps=2, vmin=0.01, plot_style=dict(resolution=\"10m\", title=\"Rain-rate\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return value is a [matplotlib FuncAnimation Object](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.FuncAnimation.html). The animation can be save or displayed in a notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to save the animation to a file:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "anim.save(\"cpol_tracking.gif\", fps=3)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The traces of the tracks can also be plotted using the `plot_trajectories` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = RD.plot_trajectories(mintrace=4, plot_style=dict(lw=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For recent tintX versions the cell polygons are available. We can create a simple overview-plot like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "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": [
    "RD.tracks.plot(aspect=1, cmap=\"turbo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizing cartopy the creation of plots on a map is straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f40b80af670>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "ax = fig.add_subplot(121, projection=crs.PlateCarree())\n",
    "\n",
    "gdf = RD.tracks\n",
    "\n",
    "# plot everything\n",
    "gdf.plot(ax=ax, cmap=\"viridis\", facecolor=\"None\", transform=gdf.crs, aspect=1)\n",
    "ax.coastlines(resolution=\"10m\")\n",
    "\n",
    "# plot single track\n",
    "ax = fig.add_subplot(122, projection=crs.PlateCarree())\n",
    "uid = \"22\"\n",
    "gdf[\"geometry\"][:, uid].plot(ax=ax, \n",
    "                              cmap=\"turbo\", \n",
    "                              facecolor=\"None\", \n",
    "                              transform=gdf.crs, \n",
    "                              aspect=1)\n",
    "RD.plot_trajectories(ax=ax, mintrace=4, plot_style=dict(lw=2), uids=[uid], label=True)\n",
    "ax.coastlines(resolution=\"10m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the tracks for later analysis:\n",
    "For a later analysis the results can be saved using the `save_tracks` method. This will save the tracks `DataFrame` to a hdf5 table format. hdf5 is the format of choice as it can save additional meta data like the tracking parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "RD.save_tracks(\"/tmp/tint_tracks.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the tracked files\n",
    "\n",
    "If you have saved the tracked object with the `save_tracks` method you can create an instance of the `RunDirectory` object using the `from_dataframe` method. This method will load the tracks `DataFrame` and also try to re-open the data files that were used to track the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                    (time: 49, x: 117, y: 117)\n",
       "Coordinates:\n",
       "  * time                       (time) datetime64[ns] 2006-11-16T03:00:00 ... ...\n",
       "  * x                          (x) int32 -145000 -142500 ... 142500 145000\n",
       "  * y                          (y) int32 -145000 -142500 ... 142500 145000\n",
       "Data variables:\n",
       "    radar_estimated_rain_rate  (time, x, y) float64 dask.array&lt;chunksize=(49, 117, 117), meta=np.ndarray&gt;\n",
       "    latitude                   (x, y) float64 dask.array&lt;chunksize=(117, 117), meta=np.ndarray&gt;\n",
       "    longitude                  (x, y) float64 dask.array&lt;chunksize=(117, 117), meta=np.ndarray&gt;\n",
       "    isfile                     (time) int32 dask.array&lt;chunksize=(49,), meta=np.ndarray&gt;\n",
       "Attributes: (12/25)\n",
       "    Conventions:         CF/Radial instrument_parameters\n",
       "    version:             2017-10\n",
       "    title:               CPOL product level 2\n",
       "    institution:         Australia Bureau of Meteorology\n",
       "    references:          If you use this dataset, please cite: &#x27;An integrated...\n",
       "    source:              UF raw files\n",
       "    ...                  ...\n",
       "    state:               NT\n",
       "    history:             October 2017 recalibration: May 2017 recalibration\n",
       "    volume_number:       0\n",
       "    platform_type:       fixed\n",
       "    instrument_type:     radar\n",
       "    primary_axis:        axis_z</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-46d5cc8a-3f57-42da-9131-4bf8254d97e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46d5cc8a-3f57-42da-9131-4bf8254d97e1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 49</li><li><span class='xr-has-index'>x</span>: 117</li><li><span class='xr-has-index'>y</span>: 117</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-70558cdc-24e6-4591-91ef-75c2dbbf5de7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70558cdc-24e6-4591-91ef-75c2dbbf5de7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2006-11-16T03:00:00 ... 2006-11-...</div><input id='attrs-9b486977-6ca0-486f-b50e-500646f52b1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b486977-6ca0-486f-b50e-500646f52b1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b05017-84fb-49e9-8947-2f69140908aa' class='xr-var-data-in' type='checkbox'><label for='data-77b05017-84fb-49e9-8947-2f69140908aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2006-11-16T03:00:00.000000000&#x27;, &#x27;2006-11-16T03:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T03:20:00.000000000&#x27;, &#x27;2006-11-16T03:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T03:40:00.000000000&#x27;, &#x27;2006-11-16T03:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T04:00:00.000000000&#x27;, &#x27;2006-11-16T04:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T04:20:00.000000000&#x27;, &#x27;2006-11-16T04:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T04:40:00.000000000&#x27;, &#x27;2006-11-16T04:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T05:00:00.000000000&#x27;, &#x27;2006-11-16T05:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T05:20:00.000000000&#x27;, &#x27;2006-11-16T05:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T05:40:00.000000000&#x27;, &#x27;2006-11-16T05:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T06:00:00.000000000&#x27;, &#x27;2006-11-16T06:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T06:20:00.000000000&#x27;, &#x27;2006-11-16T06:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T06:40:00.000000000&#x27;, &#x27;2006-11-16T06:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T07:00:00.000000000&#x27;, &#x27;2006-11-16T07:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T07:20:00.000000000&#x27;, &#x27;2006-11-16T07:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T07:40:00.000000000&#x27;, &#x27;2006-11-16T07:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T08:00:00.000000000&#x27;, &#x27;2006-11-16T08:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T08:20:00.000000000&#x27;, &#x27;2006-11-16T08:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T08:40:00.000000000&#x27;, &#x27;2006-11-16T08:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T09:00:00.000000000&#x27;, &#x27;2006-11-16T09:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T09:20:00.000000000&#x27;, &#x27;2006-11-16T09:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T09:40:00.000000000&#x27;, &#x27;2006-11-16T09:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T10:00:00.000000000&#x27;, &#x27;2006-11-16T10:10:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T10:20:00.000000000&#x27;, &#x27;2006-11-16T10:30:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T10:40:00.000000000&#x27;, &#x27;2006-11-16T10:50:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T11:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-145000 -142500 ... 142500 145000</div><input id='attrs-f93cdd72-722a-41b8-9d09-911872b2c871' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f93cdd72-722a-41b8-9d09-911872b2c871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102c85da-bace-4d8d-908d-14a03153e0bb' class='xr-var-data-in' type='checkbox'><label for='data-102c85da-bace-4d8d-908d-14a03153e0bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([-145000, -142500, -140000, -137500, -135000, -132500, -130000, -127500,\n",
       "       -125000, -122500, -120000, -117500, -115000, -112500, -110000, -107500,\n",
       "       -105000, -102500, -100000,  -97500,  -95000,  -92500,  -90000,  -87500,\n",
       "        -85000,  -82500,  -80000,  -77500,  -75000,  -72500,  -70000,  -67500,\n",
       "        -65000,  -62500,  -60000,  -57500,  -55000,  -52500,  -50000,  -47500,\n",
       "        -45000,  -42500,  -40000,  -37500,  -35000,  -32500,  -30000,  -27500,\n",
       "        -25000,  -22500,  -20000,  -17500,  -15000,  -12500,  -10000,   -7500,\n",
       "         -5000,   -2500,       0,    2500,    5000,    7500,   10000,   12500,\n",
       "         15000,   17500,   20000,   22500,   25000,   27500,   30000,   32500,\n",
       "         35000,   37500,   40000,   42500,   45000,   47500,   50000,   52500,\n",
       "         55000,   57500,   60000,   62500,   65000,   67500,   70000,   72500,\n",
       "         75000,   77500,   80000,   82500,   85000,   87500,   90000,   92500,\n",
       "         95000,   97500,  100000,  102500,  105000,  107500,  110000,  112500,\n",
       "        115000,  117500,  120000,  122500,  125000,  127500,  130000,  132500,\n",
       "        135000,  137500,  140000,  142500,  145000], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-145000 -142500 ... 142500 145000</div><input id='attrs-6f434b10-a01c-45d7-8d1a-161b2c37e2fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f434b10-a01c-45d7-8d1a-161b2c37e2fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffc1abd9-64ab-4814-9c16-51ce967e82d2' class='xr-var-data-in' type='checkbox'><label for='data-ffc1abd9-64ab-4814-9c16-51ce967e82d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([-145000, -142500, -140000, -137500, -135000, -132500, -130000, -127500,\n",
       "       -125000, -122500, -120000, -117500, -115000, -112500, -110000, -107500,\n",
       "       -105000, -102500, -100000,  -97500,  -95000,  -92500,  -90000,  -87500,\n",
       "        -85000,  -82500,  -80000,  -77500,  -75000,  -72500,  -70000,  -67500,\n",
       "        -65000,  -62500,  -60000,  -57500,  -55000,  -52500,  -50000,  -47500,\n",
       "        -45000,  -42500,  -40000,  -37500,  -35000,  -32500,  -30000,  -27500,\n",
       "        -25000,  -22500,  -20000,  -17500,  -15000,  -12500,  -10000,   -7500,\n",
       "         -5000,   -2500,       0,    2500,    5000,    7500,   10000,   12500,\n",
       "         15000,   17500,   20000,   22500,   25000,   27500,   30000,   32500,\n",
       "         35000,   37500,   40000,   42500,   45000,   47500,   50000,   52500,\n",
       "         55000,   57500,   60000,   62500,   65000,   67500,   70000,   72500,\n",
       "         75000,   77500,   80000,   82500,   85000,   87500,   90000,   92500,\n",
       "         95000,   97500,  100000,  102500,  105000,  107500,  110000,  112500,\n",
       "        115000,  117500,  120000,  122500,  125000,  127500,  130000,  132500,\n",
       "        135000,  137500,  140000,  142500,  145000], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a4d2af1-c60f-419a-931f-0a58878d832c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a4d2af1-c60f-419a-931f-0a58878d832c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>radar_estimated_rain_rate</span></div><div class='xr-var-dims'>(time, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(49, 117, 117), meta=np.ndarray&gt;</div><input id='attrs-b475a357-e863-41a4-bafd-b0a9be2b5f54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b475a357-e863-41a4-bafd-b0a9be2b5f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d23caf3-6582-4aaf-951b-381b352040c7' class='xr-var-data-in' type='checkbox'><label for='data-7d23caf3-6582-4aaf-951b-381b352040c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm h-1</dd><dt><span>long_name :</span></dt><dd>Blended Rainfall Rate</dd><dt><span>standard_name :</span></dt><dd>Rainfall Rate</dd><dt><span>description :</span></dt><dd>Rainfall rate algorithm based on Thompson et al. 2016.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.12 MiB </td>\n",
       "                        <td> 5.12 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (49, 117, 117) </td>\n",
       "                        <td> (49, 117, 117) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"14.781297\" y=\"154.781297\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,14.781297,154.781297)\">49</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(117, 117), meta=np.ndarray&gt;</div><input id='attrs-b7166c1c-9864-4b03-b2eb-a879b1964b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7166c1c-9864-4b03-b2eb-a879b1964b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1363a31c-7da0-4181-8c65-8228935beb7a' class='xr-var-data-in' type='checkbox'><label for='data-1363a31c-7da0-4181-8c65-8228935beb7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 106.95 kiB </td>\n",
       "                        <td> 106.95 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (117, 117) </td>\n",
       "                        <td> (117, 117) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">117</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(117, 117), meta=np.ndarray&gt;</div><input id='attrs-157bfe1a-4f2d-4658-93e3-9703424804b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-157bfe1a-4f2d-4658-93e3-9703424804b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-418273ae-0da5-4c5d-a98f-7972a2c9b9d9' class='xr-var-data-in' type='checkbox'><label for='data-418273ae-0da5-4c5d-a98f-7972a2c9b9d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees West</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 106.95 kiB </td>\n",
       "                        <td> 106.95 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (117, 117) </td>\n",
       "                        <td> (117, 117) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isfile</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(49,), meta=np.ndarray&gt;</div><input id='attrs-bfc3c1bf-7410-40d2-a21e-40b8d9e3bd17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfc3c1bf-7410-40d2-a21e-40b8d9e3bd17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d854b199-af98-4326-881d-ac091e343596' class='xr-var-data-in' type='checkbox'><label for='data-d854b199-af98-4326-881d-ac091e343596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>0: no data for time step, 1: data exists.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 196 B </td>\n",
       "                        <td> 196 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (49,) </td>\n",
       "                        <td> (49,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8a636c7-e23a-4d66-a84c-51c4f705b741' class='xr-section-summary-in' type='checkbox'  ><label for='section-d8a636c7-e23a-4d66-a84c-51c4f705b741' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-df4a0772-a461-44fa-a944-5d9155783105' class='xr-index-data-in' type='checkbox'/><label for='index-df4a0772-a461-44fa-a944-5d9155783105' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2006-11-16 03:00:00&#x27;, &#x27;2006-11-16 03:10:00&#x27;,\n",
       "               &#x27;2006-11-16 03:20:00&#x27;, &#x27;2006-11-16 03:30:00&#x27;,\n",
       "               &#x27;2006-11-16 03:40:00&#x27;, &#x27;2006-11-16 03:50:00&#x27;,\n",
       "               &#x27;2006-11-16 04:00:00&#x27;, &#x27;2006-11-16 04:10:00&#x27;,\n",
       "               &#x27;2006-11-16 04:20:00&#x27;, &#x27;2006-11-16 04:30:00&#x27;,\n",
       "               &#x27;2006-11-16 04:40:00&#x27;, &#x27;2006-11-16 04:50:00&#x27;,\n",
       "               &#x27;2006-11-16 05:00:00&#x27;, &#x27;2006-11-16 05:10:00&#x27;,\n",
       "               &#x27;2006-11-16 05:20:00&#x27;, &#x27;2006-11-16 05:30:00&#x27;,\n",
       "               &#x27;2006-11-16 05:40:00&#x27;, &#x27;2006-11-16 05:50:00&#x27;,\n",
       "               &#x27;2006-11-16 06:00:00&#x27;, &#x27;2006-11-16 06:10:00&#x27;,\n",
       "               &#x27;2006-11-16 06:20:00&#x27;, &#x27;2006-11-16 06:30:00&#x27;,\n",
       "               &#x27;2006-11-16 06:40:00&#x27;, &#x27;2006-11-16 06:50:00&#x27;,\n",
       "               &#x27;2006-11-16 07:00:00&#x27;, &#x27;2006-11-16 07:10:00&#x27;,\n",
       "               &#x27;2006-11-16 07:20:00&#x27;, &#x27;2006-11-16 07:30:00&#x27;,\n",
       "               &#x27;2006-11-16 07:40:00&#x27;, &#x27;2006-11-16 07:50:00&#x27;,\n",
       "               &#x27;2006-11-16 08:00:00&#x27;, &#x27;2006-11-16 08:10:00&#x27;,\n",
       "               &#x27;2006-11-16 08:20:00&#x27;, &#x27;2006-11-16 08:30:00&#x27;,\n",
       "               &#x27;2006-11-16 08:40:00&#x27;, &#x27;2006-11-16 08:50:00&#x27;,\n",
       "               &#x27;2006-11-16 09:00:00&#x27;, &#x27;2006-11-16 09:10:00&#x27;,\n",
       "               &#x27;2006-11-16 09:20:00&#x27;, &#x27;2006-11-16 09:30:00&#x27;,\n",
       "               &#x27;2006-11-16 09:40:00&#x27;, &#x27;2006-11-16 09:50:00&#x27;,\n",
       "               &#x27;2006-11-16 10:00:00&#x27;, &#x27;2006-11-16 10:10:00&#x27;,\n",
       "               &#x27;2006-11-16 10:20:00&#x27;, &#x27;2006-11-16 10:30:00&#x27;,\n",
       "               &#x27;2006-11-16 10:40:00&#x27;, &#x27;2006-11-16 10:50:00&#x27;,\n",
       "               &#x27;2006-11-16 11:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0787290-7149-4061-9d70-a29f1558d045' class='xr-index-data-in' type='checkbox'/><label for='index-b0787290-7149-4061-9d70-a29f1558d045' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-145000, -142500, -140000, -137500, -135000, -132500, -130000,\n",
       "            -127500, -125000, -122500,\n",
       "            ...\n",
       "             122500,  125000,  127500,  130000,  132500,  135000,  137500,\n",
       "             140000,  142500,  145000],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;, length=117))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b8e87e8-b0fe-42ba-bb34-81ce7281082b' class='xr-index-data-in' type='checkbox'/><label for='index-3b8e87e8-b0fe-42ba-bb34-81ce7281082b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-145000, -142500, -140000, -137500, -135000, -132500, -130000,\n",
       "            -127500, -125000, -122500,\n",
       "            ...\n",
       "             122500,  125000,  127500,  130000,  132500,  135000,  137500,\n",
       "             140000,  142500,  145000],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;, length=117))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc443904-b41b-41dd-aab7-ec2e83c07d39' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc443904-b41b-41dd-aab7-ec2e83c07d39' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF/Radial instrument_parameters</dd><dt><span>version :</span></dt><dd>2017-10</dd><dt><span>title :</span></dt><dd>CPOL product level 2</dd><dt><span>institution :</span></dt><dd>Australia Bureau of Meteorology</dd><dt><span>references :</span></dt><dd>If you use this dataset, please cite: &#x27;An integrated approach to weather radar calibration and monitoring using ground clutter and satellite comparisons&#x27; by Louf et al. 2018 Journal of Atmospheric and Oceanic Technology.</dd><dt><span>source :</span></dt><dd>UF raw files</dd><dt><span>comment :</span></dt><dd>CPOL product level 2 at 2.5 km altitude.</dd><dt><span>instrument_name :</span></dt><dd>CPOL</dd><dt><span>original_container :</span></dt><dd>UF</dd><dt><span>site_name :</span></dt><dd>Gunn_Pt</dd><dt><span>radar_name :</span></dt><dd>CPOL</dd><dt><span>author :</span></dt><dd>Valentin Louf</dd><dt><span>field_names :</span></dt><dd>corrected_reflectivity DBZ VEL WIDTH ZDR PHIDP RHOHV</dd><dt><span>author_email :</span></dt><dd>valentin.louf@bom.gov.au</dd><dt><span>calibration :</span></dt><dd>Calibration offsets applied: ZH = -2.4 dB, and ZDR = -0.33 dB.</dd><dt><span>country :</span></dt><dd>Australia</dd><dt><span>created :</span></dt><dd>2017-10-26T13:29:03.517412</dd><dt><span>project :</span></dt><dd>CPOL</dd><dt><span>description :</span></dt><dd>This dataset was processed using the ARM PyART toolbox, the trmm_rsl library, the NCO toolbox, and RadX from NCAR.</dd><dt><span>state :</span></dt><dd>NT</dd><dt><span>history :</span></dt><dd>October 2017 recalibration: May 2017 recalibration</dd><dt><span>volume_number :</span></dt><dd>0</dd><dt><span>platform_type :</span></dt><dd>fixed</dd><dt><span>instrument_type :</span></dt><dd>radar</dd><dt><span>primary_axis :</span></dt><dd>axis_z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                    (time: 49, x: 117, y: 117)\n",
       "Coordinates:\n",
       "  * time                       (time) datetime64[ns] 2006-11-16T03:00:00 ... ...\n",
       "  * x                          (x) int32 -145000 -142500 ... 142500 145000\n",
       "  * y                          (y) int32 -145000 -142500 ... 142500 145000\n",
       "Data variables:\n",
       "    radar_estimated_rain_rate  (time, x, y) float64 dask.array<chunksize=(49, 117, 117), meta=np.ndarray>\n",
       "    latitude                   (x, y) float64 dask.array<chunksize=(117, 117), meta=np.ndarray>\n",
       "    longitude                  (x, y) float64 dask.array<chunksize=(117, 117), meta=np.ndarray>\n",
       "    isfile                     (time) int32 dask.array<chunksize=(49,), meta=np.ndarray>\n",
       "Attributes: (12/25)\n",
       "    Conventions:         CF/Radial instrument_parameters\n",
       "    version:             2017-10\n",
       "    title:               CPOL product level 2\n",
       "    institution:         Australia Bureau of Meteorology\n",
       "    references:          If you use this dataset, please cite: 'An integrated...\n",
       "    source:              UF raw files\n",
       "    ...                  ...\n",
       "    state:               NT\n",
       "    history:             October 2017 recalibration: May 2017 recalibration\n",
       "    volume_number:       0\n",
       "    platform_type:       fixed\n",
       "    instrument_type:     radar\n",
       "    primary_axis:        axis_z"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RD = RunDirectory.from_dataframe(\"/tmp/tint_tracks.h5\")\n",
    "RD.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>grid_x</th>\n",
       "      <th>grid_y</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>area</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>isolated</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scan</th>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>2006-11-16 03:00:00</td>\n",
       "      <td>6.308</td>\n",
       "      <td>45.731</td>\n",
       "      <td>129.8469</td>\n",
       "      <td>-12.5164</td>\n",
       "      <td>26</td>\n",
       "      <td>1.451491</td>\n",
       "      <td>0.980084</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((-130000.000 -40000.000, -125000.000 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-11-16 03:00:00</td>\n",
       "      <td>2.034</td>\n",
       "      <td>51.655</td>\n",
       "      <td>129.7554</td>\n",
       "      <td>-12.3810</td>\n",
       "      <td>29</td>\n",
       "      <td>1.398994</td>\n",
       "      <td>0.771455</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((-140000.000 -27500.000, -135000.000 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-11-16 03:00:00</td>\n",
       "      <td>103.341</td>\n",
       "      <td>50.902</td>\n",
       "      <td>132.0804</td>\n",
       "      <td>-12.4046</td>\n",
       "      <td>41</td>\n",
       "      <td>5.279258</td>\n",
       "      <td>0.977871</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((107500.000 -25000.000, 110000.000 -2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-11-16 03:00:00</td>\n",
       "      <td>75.000</td>\n",
       "      <td>49.750</td>\n",
       "      <td>131.4358</td>\n",
       "      <td>-12.4288</td>\n",
       "      <td>4</td>\n",
       "      <td>1.249542</td>\n",
       "      <td>0.653577</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((42500.000 -22500.000, 45000.000 -225...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-11-16 03:00:00</td>\n",
       "      <td>90.684</td>\n",
       "      <td>52.842</td>\n",
       "      <td>131.8040</td>\n",
       "      <td>-12.3605</td>\n",
       "      <td>19</td>\n",
       "      <td>3.560659</td>\n",
       "      <td>1.146169</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((82500.000 -17500.000, 87500.000 -175...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46</th>\n",
       "      <th>121</th>\n",
       "      <td>2006-11-16 10:40:00</td>\n",
       "      <td>37.000</td>\n",
       "      <td>78.630</td>\n",
       "      <td>130.5622</td>\n",
       "      <td>-11.7766</td>\n",
       "      <td>27</td>\n",
       "      <td>31.208767</td>\n",
       "      <td>7.085050</td>\n",
       "      <td>True</td>\n",
       "      <td>POLYGON ((-62500.000 47500.000, -62500.000 525...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2006-11-16 10:40:00</td>\n",
       "      <td>30.800</td>\n",
       "      <td>79.600</td>\n",
       "      <td>130.4244</td>\n",
       "      <td>-11.7539</td>\n",
       "      <td>5</td>\n",
       "      <td>9.346570</td>\n",
       "      <td>2.719892</td>\n",
       "      <td>True</td>\n",
       "      <td>POLYGON ((-70000.000 52500.000, -70000.000 575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">47</th>\n",
       "      <th>123</th>\n",
       "      <td>2006-11-16 10:50:00</td>\n",
       "      <td>89.444</td>\n",
       "      <td>15.333</td>\n",
       "      <td>131.7603</td>\n",
       "      <td>-13.2150</td>\n",
       "      <td>9</td>\n",
       "      <td>0.227352</td>\n",
       "      <td>0.168942</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((77500.000 -110000.000, 82500.000 -11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2006-11-16 10:50:00</td>\n",
       "      <td>37.400</td>\n",
       "      <td>78.640</td>\n",
       "      <td>130.5622</td>\n",
       "      <td>-11.7766</td>\n",
       "      <td>25</td>\n",
       "      <td>14.703703</td>\n",
       "      <td>3.166035</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((-60000.000 47500.000, -60000.000 525...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <th>121</th>\n",
       "      <td>2006-11-16 11:00:00</td>\n",
       "      <td>36.905</td>\n",
       "      <td>79.095</td>\n",
       "      <td>130.5622</td>\n",
       "      <td>-11.7766</td>\n",
       "      <td>21</td>\n",
       "      <td>14.791470</td>\n",
       "      <td>3.332414</td>\n",
       "      <td>True</td>\n",
       "      <td>POLYGON ((-62500.000 47500.000, -62500.000 500...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time   grid_x  grid_y       lon      lat  area  \\\n",
       "scan uid                                                                  \n",
       "0    0    2006-11-16 03:00:00    6.308  45.731  129.8469 -12.5164    26   \n",
       "     1    2006-11-16 03:00:00    2.034  51.655  129.7554 -12.3810    29   \n",
       "     2    2006-11-16 03:00:00  103.341  50.902  132.0804 -12.4046    41   \n",
       "     3    2006-11-16 03:00:00   75.000  49.750  131.4358 -12.4288     4   \n",
       "     4    2006-11-16 03:00:00   90.684  52.842  131.8040 -12.3605    19   \n",
       "...                       ...      ...     ...       ...      ...   ...   \n",
       "46   121  2006-11-16 10:40:00   37.000  78.630  130.5622 -11.7766    27   \n",
       "     124  2006-11-16 10:40:00   30.800  79.600  130.4244 -11.7539     5   \n",
       "47   123  2006-11-16 10:50:00   89.444  15.333  131.7603 -13.2150     9   \n",
       "     121  2006-11-16 10:50:00   37.400  78.640  130.5622 -11.7766    25   \n",
       "48   121  2006-11-16 11:00:00   36.905  79.095  130.5622 -11.7766    21   \n",
       "\n",
       "                max      mean  isolated  \\\n",
       "scan uid                                  \n",
       "0    0     1.451491  0.980084     False   \n",
       "     1     1.398994  0.771455     False   \n",
       "     2     5.279258  0.977871     False   \n",
       "     3     1.249542  0.653577     False   \n",
       "     4     3.560659  1.146169     False   \n",
       "...             ...       ...       ...   \n",
       "46   121  31.208767  7.085050      True   \n",
       "     124   9.346570  2.719892      True   \n",
       "47   123   0.227352  0.168942     False   \n",
       "     121  14.703703  3.166035     False   \n",
       "48   121  14.791470  3.332414      True   \n",
       "\n",
       "                                                   geometry  \n",
       "scan uid                                                     \n",
       "0    0    POLYGON ((-130000.000 -40000.000, -125000.000 ...  \n",
       "     1    POLYGON ((-140000.000 -27500.000, -135000.000 ...  \n",
       "     2    POLYGON ((107500.000 -25000.000, 110000.000 -2...  \n",
       "     3    POLYGON ((42500.000 -22500.000, 45000.000 -225...  \n",
       "     4    POLYGON ((82500.000 -17500.000, 87500.000 -175...  \n",
       "...                                                     ...  \n",
       "46   121  POLYGON ((-62500.000 47500.000, -62500.000 525...  \n",
       "     124  POLYGON ((-70000.000 52500.000, -70000.000 575...  \n",
       "47   123  POLYGON ((77500.000 -110000.000, 82500.000 -11...  \n",
       "     121  POLYGON ((-60000.000 47500.000, -60000.000 525...  \n",
       "48   121  POLYGON ((-62500.000 47500.000, -62500.000 500...  \n",
       "\n",
       "[299 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RD.tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining tuning parameters from tracked objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it might be useful to retrieve the tuning parameters that resulted in certain object tracks. This is especially important when using the `from_dataframe` method. As previously mentioned the `RunDirectory` class keeps track of all tuning parameters that resulted in object tracks. To retrieve such tuning parameters you can use the `get_parameters` method. By default the method will look for latest track objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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