python-awips/_sources/examples/generated/METAR_Station_Plot_with_MetPy.rst.txt

=============================
METAR Station Plot with MetPy
=============================
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/METAR_Station_Plot_with_MetPy.ipynb>`_
Python-AWIPS Tutorial Notebook

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Objectives
==========

-  Use python-awips to connect to an edex server
-  Define and filter data request for METAR surface obs
-  Extract necessary data and reformat it for plotting
-  Stylize and plot METAR station data using Cartopy, Matplotlib, and
   MetPy

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Table of Contents
-----------------

| `1
  Imports <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#imports>`__\ 
| `2 Function:
  get_cloud_cover() <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#function-get-cloud-cover>`__\ 
| `3 Initial
  Setup <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#initial-setup>`__\ 
|     `3.1 Initial EDEX
  Connection <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#initial-edex-connection>`__\ 
|     `3.2 Setting Connection Location
  Names <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#setting-connection-location-names>`__\ 
| `4 Filter by
  Time <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#filter-by-time>`__\ 
| `5 Use the
  Data! <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#use-the-data>`__\ 
|     `5.1 Get the
  Data! <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#get-the-data>`__\ 
|     `5.2 Extract all
  Parameters <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#extract-all-parameters>`__\ 
|     `5.3 Populate the Data
  Dictionary <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#populate-the-data-dictionary>`__\ 
| `6 Plot the
  Data! <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#plot-the-data>`__\ 
| `7 See
  Also <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#see-also>`__\ 
|     `7.1 Related
  Notebooks <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#related-notebooks>`__\ 
|     `7.2 Additional
  Documentation <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html#additional-documentation>`__\ 

1 Imports
---------

The imports below are used throughout the notebook. Note the first two
imports are coming directly from python-awips and allow us to connect to
an EDEX server, and define a timrange used for filtering the data. The
subsequent imports are for data manipulation and visualization.

.. code:: ipython3

    from awips.dataaccess import DataAccessLayer
    from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange
    from datetime import datetime, timedelta
    import numpy as np
    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    import matplotlib.pyplot as plt
    from metpy.calc import wind_components
    from metpy.plots import StationPlot, StationPlotLayout, sky_cover
    from metpy.units import units

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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2 Function: get_cloud_cover()
-----------------------------

Returns the cloud fraction values as integer codes (0 through 8).

.. code:: ipython3

    def get_cloud_cover(code):
        if 'OVC' in code:
            return 8
        elif 'BKN' in code:
            return 6
        elif 'SCT' in code:
            return 4
        elif 'FEW' in code:
            return 2
        else:
            return 0

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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3 Initial Setup
---------------

3.1 Initial EDEX Connection
~~~~~~~~~~~~~~~~~~~~~~~~~~~

First we establish a connection to Unidata’s public EDEX server. With
that connection made, we can create a `new data request
object <http://unidata.github.io/python-awips/api/IDataRequest.html>`__
and set the data type to **obs**.

Then, because we’re going to uses MetPy’s
`StationPlot <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlot.html>`__
and
`StationPlotLayout <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlotLayout.html>`__
we need to define several parameters, and then set them on the data
request object.

.. code:: ipython3

    # EDEX Request
    edexServer = "edex-cloud.unidata.ucar.edu"
    DataAccessLayer.changeEDEXHost(edexServer)
    request = DataAccessLayer.newDataRequest("obs")
    
    # define desired parameters
    single_value_params = ["stationName", "longitude", "latitude", 
                           "temperature", "dewpoint", "windDir",
                           "windSpeed"]
    multi_value_params = ["skyCover"]
    
    params = single_value_params + multi_value_params
    
    # set all parameters on the request
    request.setParameters(*(params))

3.2 Setting Connection Location Names
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We are also going to define specific station IDs so that our plot is not
too cluttered.

.. code:: ipython3

    # Define a list of station IDs to plot
    selected = ['KPDX', 'KOKC', 'KICT', 'KGLD', 'KMEM', 'KBOS', 'KMIA', 'KMOB', 'KABQ', 'KPHX', 'KTTF',
                'KORD', 'KBIL', 'KBIS', 'KCPR', 'KLAX', 'KATL', 'KMSP', 'KSLC', 'KDFW', 'KNYC', 'KPHL',
                'KPIT', 'KIND', 'KOLY', 'KSYR', 'KLEX', 'KCHS', 'KTLH', 'KHOU', 'KGJT', 'KLBB', 'KLSV',
                'KGRB', 'KCLT', 'KLNK', 'KDSM', 'KBOI', 'KFSD', 'KRAP', 'KRIC', 'KJAN', 'KHSV', 'KCRW',
                'KSAT', 'KBUY', 'K0CO', 'KZPC', 'KVIH', 'KBDG', 'KMLF', 'KELY', 'KWMC', 'KOTH', 'KCAR',
                'KLMT', 'KRDM', 'KPDT', 'KSEA', 'KUIL', 'KEPH', 'KPUW', 'KCOE', 'KMLP', 'KPIH', 'KIDA', 
                'KMSO', 'KACV', 'KHLN', 'KBIL', 'KOLF', 'KRUT', 'KPSM', 'KJAX', 'KTPA', 'KSHV', 'KMSY',
                'KELP', 'KRNO', 'KFAT', 'KSFO', 'KNYL', 'KBRO', 'KMRF', 'KDRT', 'KFAR', 'KBDE', 'KDLH',
                'KHOT', 'KLBF', 'KFLG', 'KCLE', 'KUNV']
    
    # set the location names to the desired station IDs 
    request.setLocationNames(*(selected))

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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4 Filter by Time
----------------

Here we decide how much data we want to pull from EDEX. By default we’ll
request 1 hour, but that value can easily be modified by `adjusting the
``timedelta(hours = 1)`` <https://docs.python.org/3/library/datetime.html#timedelta-objects>`__
in line ``2``. The more data we request, the longer this section will
take to run.

.. code:: ipython3

    # Time range
    lastHourDateTime = datetime.utcnow() - timedelta(hours = 1)
    start = lastHourDateTime.strftime('%Y-%m-%d %H')
    beginRange = datetime.strptime( start + ":00:00", "%Y-%m-%d %H:%M:%S")
    endRange = datetime.strptime( start + ":59:59", "%Y-%m-%d %H:%M:%S")
    timerange = TimeRange(beginRange, endRange)

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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5 Use the Data!
---------------

5.1 Get the Data!
~~~~~~~~~~~~~~~~~

Now that we have our ``request`` and TimeRange ``timerange`` objects
ready, we’re ready to get the data array from EDEX.

.. code:: ipython3

    # Get response
    response = DataAccessLayer.getGeometryData(request,timerange)

5.2 Extract all Parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~

In this section we start gathering all the information we’ll need to
properly display our data. First we create an empty dictionary and array
to keep track of all data and unique station IDs. We also create a
boolean to help us only grab the first entry for ``skyCover`` related to
a station id.

   **Note**: The way the data responses are returned, we recieve many
   ``skyCover`` entries for each station ID, but we only want to keep
   track of the most recent one (first one returned).

After defining these variables, we are ready to start looping through
our response data. If the response is an entry of ``skyCover``, and this
is a new station id, then set the skyCover value in the obs dictionary.
If this is not a skyCover entry, then explicitly set the ``timeObs``
variable (because we have to manipulate it slightly), and dynamically
set all the remaining parameters.

.. code:: ipython3

    # define a dictionary and array that will be populated from our for loop below
    obs = dict({params: [] for params in params})
    station_names = []
    time_title = ""
    i = 0
    
    # cycle through all the data in the response, in reverse order to get the most recent data first
    for ob in reversed(response):
        avail_params = ob.getParameters()
        #print(avail_params)
        # if it has cloud information, we want the last of the 6 entries (most recent)
        if "skyCover" in avail_params:
            if i == 5:
                # store the associated cloud cover int for the skyCover string
                obs['skyCover'].append(get_cloud_cover(ob.getString("skyCover")))
            i = i + 1
        elif "stationName" in avail_params:
            # If we already have a record for this stationName, skip
            if ob.getString('stationName') not in station_names:
                station_names.append(ob.getString('stationName'))
                i = 0
                if time_title == "":
                    time_title = str(ob.getDataTime())
                for param in single_value_params:            
                    if param in avail_params:
                        try:
                            obs[param].append(ob.getNumber(param))
                        except TypeError:
                            obs[param].append(ob.getString(param))
                    else:
                        obs[param].append(None)

5.3 Populate the Data Dictionary
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Next grab the variables out of the obs dictionary we just populated,
attach correct units, (calculate their components, in the instance of
wind) and put them into a new dictionary that we will hand the plotting
function later.

.. code:: ipython3

    data = dict()
    data['stid'] = np.array(obs['stationName'])
    data['latitude']  = np.array(obs['latitude'])
    data['longitude'] = np.array(obs['longitude'])
    data['air_temperature'] = np.array(obs['temperature'], dtype=float)* units.degC
    data['dew_point_temperature'] = np.array(obs['dewpoint'], dtype=float)* units.degC
    
    direction = np.array(obs['windDir'])
    direction[direction == -9999.0] = 'nan'
    
    u, v = wind_components(np.array(obs['windSpeed']) * units('knots'),
                               direction * units.degree)
    data['eastward_wind'], data['northward_wind'] = u, v
    data['cloud_coverage'] = np.array(obs['skyCover'])

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

--------------

6 Plot the Data!
----------------

Now we have all the data we need to create our plot! First we’ll assign
a projection and create our figure and axes.

Next, we use Cartopy to add common features (land, ocean, lakes,
borders, etc) to help give us a more contextual map of the United States
to plot the METAR stations on. We create and add a title for our figure
as well.

Additionally, we use `MetPy’s
StationPlotLayout <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlotLayout.html>`__
to instantiate a custom layout and define all the attributes we want
displayed. We need to then set the data dictionary (containing all of
our data values) on the custom layout so it knows what to draw.

Finally, we display the plot!

.. code:: ipython3

    proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
                                 standard_parallels=[35])
    # Create the figure
    fig = plt.figure(figsize=(20, 10))
    ax = fig.add_subplot(1, 1, 1, projection=proj)
    
    # Add various map elements
    ax.add_feature(cfeature.LAND)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAKES)
    ax.add_feature(cfeature.COASTLINE)
    ax.add_feature(cfeature.STATES)
    ax.add_feature(cfeature.BORDERS, linewidth=2)
    
    # Set plot bounds
    ax.set_extent((-118, -73, 23, 50))
    ax.set_title(time_title + " | METAR | " + edexServer)
    
    # Winds, temps, dewpoint, station id
    custom_layout = StationPlotLayout()
    custom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')
    custom_layout.add_value('NW', 'air_temperature', fmt='.0f', units='degF', color='darkred')
    custom_layout.add_value('SW', 'dew_point_temperature', fmt='.0f', units='degF', color='darkgreen')
    custom_layout.add_symbol('C', 'cloud_coverage', sky_cover)
    
    stationplot = StationPlot(ax, data['longitude'], data['latitude'], clip_on=True,
                              transform=ccrs.PlateCarree(), fontsize=10)
    stationplot.plot_text((2, 0), data['stid'])
    custom_layout.plot(stationplot, data)
    
    plt.show()



.. image:: METAR_Station_Plot_with_MetPy_files/METAR_Station_Plot_with_MetPy_26_0.png


`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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7 See Also
----------

-  `Aviation Weather Center Static METAR Plots
   Information <https://www.aviationweather.gov/metar/help?page=plot>`__

7.1 Related Notebooks
~~~~~~~~~~~~~~~~~~~~~

-  `Grid Levels and
   Parameters <http://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html>`__
-  `Colored Surface Temperature
   Plot <http://unidata.github.io/python-awips/examples/generated/Colored_Surface_Temperature_Plot.html>`__

7.2 Additional Documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**python-awips:**

-  `DataAccessLayer.changeEDEXHost() <http://unidata.github.io/python-awips/api/DataAccessLayer.html#awips.dataaccess.DataAccessLayer.changeEDEXHost>`__
-  `DataAccessLayer.newDataRequest() <http://unidata.github.io/python-awips/api/DataAccessLayer.html#awips.dataaccess.DataAccessLayer.newDataRequest>`__
-  `IDataRequest <http://unidata.github.io/python-awips/api/IDataRequest.html>`__
-  `DataAccessLayer.getGeometryData <http://unidata.github.io/python-awips/api/PyGeometryData.html>`__

**datetime:**

-  `datetime.datetime <https://docs.python.org/3/library/datetime.html#datetime-objects>`__
-  `datetime.utcnow() <https://docs.python.org/3/library/datetime.html?#datetime.datetime.utcnow>`__
-  `datetime.timedelta <https://docs.python.org/3/library/datetime.html#timedelta-objects>`__
-  `datetime.strftime() and
   datetime.strptime() <https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior>`__

**numpy:**

-  `np.array <https://numpy.org/doc/stable/reference/generated/numpy.array.html>`__

**cartopy:**

-  `cartopy projection
   list <https://scitools.org.uk/cartopy/docs/v0.14/crs/projections.html?#cartopy-projection-list>`__
-  `cartopy feature
   interface <https://scitools.org.uk/cartopy/docs/v0.14/matplotlib/feature_interface.html>`__

**matplotlib:**

-  `matplotlib.pyplot() <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html>`__
-  `matplotlib.pyplot.figure() <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html>`__
-  `matplotlib.pyplot.figure.add_subplot <https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.add_subplot>`__
-  `ax.set_extent <https://matplotlib.org/stable/api/image_api.html?highlight=set_extent#matplotlib.image.AxesImage.set_extent>`__
-  `ax.set_title <https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html>`__

**metpy:**

-  `metpy.calc.wind_components <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_components.html>`__
-  `metpy.plots.StationPlot() <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlot.html>`__
-  `metpy.plots.StationPlotLayout() <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlotLayout.html>`__
-  `metpy.units <https://unidata.github.io/MetPy/latest/api/generated/metpy.units.html>`__

`Top <https://unidata.github.io/python-awips/examples/generated/METAR_Station_Plot_with_MetPy.html>`__

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