Example #1
0
def test_plot_text_fontsize():
    """Test changing fontsize in plot_text."""
    fig = plt.figure(figsize=(3, 3))
    ax = plt.subplot(1, 1, 1)

    # testing data
    x = np.array([1])
    y = np.array([2])

    # Make the plot
    sp = StationPlot(ax, x, y, fontsize=36)
    sp.plot_text('NW', ['72'], fontsize=24)
    sp.plot_text('SW', ['60'], fontsize=4)

    sp.ax.set_xlim(0, 3)
    sp.ax.set_ylim(0, 3)

    return fig
Example #2
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def test_plot_text_fontsize():
    """Test changing fontsize in plot_text."""
    fig = plt.figure(figsize=(3, 3))
    ax = plt.subplot(1, 1, 1)

    # testing data
    x = np.array([1])
    y = np.array([2])

    # Make the plot
    sp = StationPlot(ax, x, y, fontsize=36)
    sp.plot_text('NW', ['72'], fontsize=24)
    sp.plot_text('SW', ['60'], fontsize=4)

    sp.ax.set_xlim(0, 3)
    sp.ax.set_ylim(0, 3)

    return fig
Example #3
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def test_station_plot_locations():
    """Test that locations are properly replaced."""
    fig = plt.figure(figsize=(3, 3))

    locations = ['C', 'N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N2', 'NNE', 'ENE', 'E2',
                 'ESE', 'SSE', 'S2', 'SSW', 'WSW', 'W2', 'WNW', 'NNW']
    x_pos = np.array([0])
    y_pos = np.array([0])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x_pos, y_pos, fontsize=8, spacing=24)
    for loc in locations:
        sp.plot_text(loc, [loc])

    sp.ax.set_xlim(-2, 2)
    sp.ax.set_ylim(-2, 2)

    return fig
Example #4
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def test_stationplot_clipping():
    """Test the that clipping can be enabled as a default parameter."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16, clip_on=True)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(1, 5)
    sp.ax.set_ylim(1.75, 4.25)

    return fig
Example #5
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def test_stationplot_api():
    """Test the StationPlot API."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(0, 6)
    sp.ax.set_ylim(0, 6)

    return fig
Example #6
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def test_stationplot_clipping():
    """Test the that clipping can be enabled as a default parameter."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16, clip_on=True)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(1, 5)
    sp.ax.set_ylim(1.75, 4.25)

    return fig
Example #7
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def test_stationplot_api():
    """Test the StationPlot API."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(0, 6)
    sp.ax.set_ylim(0, 6)

    return fig
Example #8
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def test_stationplot_api():
    """Test the StationPlot API."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text("E", ["KOKC", "ICT"], color="blue")
    sp.plot_parameter("NW", [10.5, 15], color="red")
    sp.plot_symbol("S", [5, 7], high_clouds, color="green")

    sp.ax.set_xlim(0, 6)
    sp.ax.set_ylim(0, 6)

    return fig
Example #9
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# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW',
                           data['dew_point_temperature'],
                           color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE',
                           data['slp'],
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather)

# Add wind barbs
stationplot.plot_barb(u, v)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['stid'])

plt.show()
Example #10
0
stationplot = StationPlot(ax,
                          longitude.values,
                          latitude.values,
                          clip_on=True,
                          transform=ccrs.PlateCarree(),
                          fontsize=12)

# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', temperature, color='red')
stationplot.plot_parameter('SW', dewpoint, color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE',
                           pressure.m,
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Add wind barbs
stationplot.plot_barb(u, v)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and -1 in y.
stationplot.plot_text((2, -1), station_id)

# Add title and display figure
plt.title('Oklahoma Mesonet Observations', fontsize=16, loc='left')
plt.title('Time: 2100 UTC 09 September 2019', fontsize=16, loc='right')
plt.show()
Example #11
0
                          transform=ccrs.PlateCarree(), fontsize=12)

# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW', data['dew_point_temperature'],
                           color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['slp'], formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather)

# Add wind barbs
stationplot.plot_barb(u, v)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['stid'])

plt.show()
Example #12
0
                          fontsize=12)

# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW', data['dewpoint'], color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['slp'],
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather)

# Add wind barbs
stationplot.plot_barb(u, v)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), stid)

plt.show()
Example #13
0
def main():
    ### START OF USER SETTINGS BLOCK ###

    # FILE/DATA SETTINGS
    # file path to input file
    datafile = '/home/jgodwin/python/sfc_observations/surface_observations.txt'
    timefile = '/home/jgodwin/python/sfc_observations/validtime.txt'
    # file path to county shapefile
    ctyshppath = '/home/jgodwin/python/sfc_observations/shapefiles/counties/countyl010g.shp'
    # file path to ICAO list
    icaopath = '/home/jgodwin/python/sfc_observations/icao_list.csv'
    icaodf = pd.read_csv(icaopath, index_col='STATION')

    # MAP SETTINGS
    # map names (doesn't go anywhere (yet), just for tracking purposes)
    maps = ['CONUS', 'Texas', 'Tropical Atlantic']
    # minimum radius allowed between points (in km)
    radius = [100.0, 50.0, 75.0]
    # map boundaries (longitude/latitude degrees)
    west = [-122, -108, -100]
    east = [-73, -93, -60]
    south = [23, 25, 10]
    north = [50, 38, 35]
    restart_projection = [True, False, True]
    # use county map? (True/False): warning, counties load slow!
    usecounties = [False, False, False]

    # OUTPUT SETTINGS
    # save directory for output
    savedir = '/var/www/html/images/'
    # filenames for output
    savenames = ['conus.png', 'texas.png', 'atlantic.png']

    # TEST MODE SETTINGS
    test = False  # True/False
    testnum = 5  # which map are you testing? corresponds to index in "maps" above

    ### END OF USER SETTING SECTION ###

    # if there are any missing weather codes, add them here
    wx_code_map.update({
        '-RADZ': 59,
        '-TS': 17,
        'VCTSSH': 80,
        '-SGSN': 77,
        'SHUP': 76,
        'FZBR': 48,
        'FZUP': 76
    })

    ### READ IN DATA / SETUP MAP ###
    # read in the valid time file
    vt = open(timefile).read()
    # read in the data
    for i in range(len(maps)):
        if test and i != testnum:
            continue
        with open(datafile) as f:
            data = pd.read_csv(f,header=0,names=['siteID','lat','lon','elev','slp','temp','sky','dpt','wx','wdr',\
                'wsp'],na_values=-99999)
            # drop rows with missing winds
            data = data.dropna(how='any', subset=['wdr', 'wsp'])

        # remove data not within our domain

        data = data[(data['lat'] >= south[i]-2.0) & (data['lat'] <= north[i]+2.0) \
            & (data['lon'] >= west[i]-2.0) & (data['lon'] <= east[i]+2.0)]

        # filter data (there seems to be one site always reporting a really anomalous temperature
        data = data[data['temp'] <= 50]

        print("Working on %s" % maps[i])
        # set up the map projection central longitude/latitude and the standard parallels
        cenlon = (west[i] + east[i]) / 2.0
        cenlat = (south[i] + north[i]) / 2.0
        sparallel = cenlat
        if cenlat > 0:
            cutoff = -30
            flip = False
        elif cenlat < 0:
            cutoff = 30
            flip = True
        # create the projection
        if restart_projection:
            proj = ccrs.LambertConformal(central_longitude=cenlon,
                                         central_latitude=cenlat,
                                         standard_parallels=[sparallel],
                                         cutoff=cutoff)
            point_locs = proj.transform_points(ccrs.PlateCarree(),
                                               data['lon'].values,
                                               data['lat'].values)
        data = data[reduce_point_density(point_locs, radius[i] * 1000)]
        # state borders
        state_boundaries = cfeature.NaturalEarthFeature(category='cultural',\
            name='admin_1_states_provinces_lines',scale='50m',facecolor='none')
        # county boundaries
        if usecounties[i]:
            county_reader = shpreader.Reader(ctyshppath)
            counties = list(county_reader.geometries())
            COUNTIES = cfeature.ShapelyFeature(counties, ccrs.PlateCarree())
        ### DO SOME CONVERSIONS ###
        # get the wind components
        u, v = wind_components(data['wsp'].values * units('knots'),
                               data['wdr'].values * units.degree)
        # convert temperature from Celsius to Fahrenheit
        data['temp'] = cToF(data['temp'])
        data['dpt'] = cToF(data['dpt'])
        # convert the cloud fraction value into a code of 0-8 (oktas) and compenate for NaN values
        cloud_frac = (8 * data['sky'])
        cloud_frac[np.isnan(cloud_frac)] = 10
        cloud_frac = cloud_frac.astype(int)
        # map weather strings to WMO codes (only use first symbol if multiple are present
        data['wx'] = data.wx.str.split('/').str[0] + ''
        wx = [
            wx_code_map[s.split()[0] if ' ' in s else s]
            for s in data['wx'].fillna('')
        ]

        # get the minimum and maximum temperatures in domain
        searchdata = data[(data['lat'] >= south[i]) & (data['lat'] <= north[i]) \
            & (data['lon'] >= west[i]) & (data['lon'] <= east[i])]
        min_temp = searchdata.loc[searchdata['temp'].idxmin()]
        max_temp = searchdata.loc[searchdata['temp'].idxmax()]
        max_dewp = searchdata.loc[searchdata['dpt'].idxmax()]

        # look up the site names for the min/max temp locations
        min_temp_loc = icaoLookup(min_temp['siteID'], icaodf)
        max_temp_loc = icaoLookup(max_temp['siteID'], icaodf)
        max_dewp_loc = icaoLookup(max_dewp['siteID'], icaodf)
        text_str = "Min temp: %.0f F at %s (%s)\nMax temp: %.0f F at %s (%s)\nMax dewpoint: %.0f F at %s (%s)"\
             % (min_temp['temp'],min_temp['siteID'],min_temp_loc,\
                max_temp['temp'],max_temp['siteID'],max_temp_loc,\
                max_dewp['dpt'],max_dewp['siteID'],max_dewp_loc)

        ### PLOTTING SECTION ###
        # change the DPI to increase the resolution
        plt.rcParams['savefig.dpi'] = 255
        # create the figure and an axes set to the projection
        fig = plt.figure(figsize=(20, 10))
        ax = fig.add_subplot(1, 1, 1, projection=proj)
        # add various map elements
        ax.add_feature(cfeature.LAND, zorder=-1)
        ax.add_feature(cfeature.OCEAN, zorder=-1)
        ax.add_feature(cfeature.LAKES, zorder=-1)
        ax.add_feature(cfeature.COASTLINE, zorder=2, edgecolor='black')
        ax.add_feature(state_boundaries, edgecolor='black')
        if usecounties[i]:
            ax.add_feature(COUNTIES,
                           facecolor='none',
                           edgecolor='gray',
                           zorder=-1)
        ax.add_feature(cfeature.BORDERS, linewidth=2, edgecolor='black')
        # set plot bounds
        ax.set_extent((west[i], east[i], south[i], north[i]))

        ### CREATE STATION PLOTS ###
        # lat/lon of the station plots
        stationplot = StationPlot(ax,data['lon'].values,data['lat'].values,clip_on=True,\
            transform=ccrs.PlateCarree(),fontsize=6)
        # plot the temperature and dewpoint
        stationplot.plot_parameter('NW', data['temp'], color='red')
        stationplot.plot_parameter('SW', data['dpt'], color='darkgreen')
        # plot the SLP using the standard trailing three digits
        stationplot.plot_parameter(
            'NE', data['slp'], formatter=lambda v: format(10 * v, '.0f')[-3:])
        # plot the sky condition
        stationplot.plot_symbol('C', cloud_frac, sky_cover)
        # plot the present weather
        stationplot.plot_symbol('W', wx, current_weather)
        # plot the wind barbs
        stationplot.plot_barb(u, v, flip_barb=flip)
        # plot the text of the station ID
        stationplot.plot_text((2, 0), data['siteID'])
        # plot the valid time
        plt.title('Surface Observations valid %s' % vt)
        # plot the min/max temperature info and draw circle around warmest and coldest obs
        props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
        plt.text(west[i],
                 south[i],
                 text_str,
                 fontsize=12,
                 verticalalignment='top',
                 bbox=props,
                 transform=ccrs.Geodetic())
        projx1, projy1 = proj.transform_point(min_temp['lon'], min_temp['lat'],
                                              ccrs.Geodetic())
        ax.add_patch(
            matplotlib.patches.Circle(xy=[projx1, projy1],
                                      radius=50000,
                                      facecolor="None",
                                      edgecolor='blue',
                                      linewidth=3,
                                      transform=proj))
        projx2, projy2 = proj.transform_point(max_temp['lon'], max_temp['lat'],
                                              ccrs.Geodetic())
        ax.add_patch(
            matplotlib.patches.Circle(xy=[projx2, projy2],
                                      radius=50000,
                                      facecolor="None",
                                      edgecolor='red',
                                      linewidth=3,
                                      transform=proj))
        projx3, projy3 = proj.transform_point(max_dewp['lon'], max_dewp['lat'],
                                              ccrs.Geodetic())
        ax.add_patch(
            matplotlib.patches.Circle(xy=[projx3, projy3],
                                      radius=30000,
                                      facecolor="None",
                                      edgecolor='green',
                                      linewidth=3,
                                      transform=proj))
        # save the figure
        outfile_name = savedir + savenames[i]
        plt.savefig(outfile_name, bbox_inches='tight')

        # clear and close everything
        fig.clear()
        ax.clear()
        plt.close(fig)
        f.close()

    print("Script finished.")
Example #14
0
# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'].values, color='red')
stationplot.plot_parameter('SW', data['dew_point_temperature'].values,
                           color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['air_pressure_at_sea_level'].values,
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', data['cloud_coverage'].values, sky_cover)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', data['present_weather'].values, current_weather)

# Add wind barbs
stationplot.plot_barb(data['eastward_wind'].values, data['northward_wind'].values)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['station_id'].values)

plt.show()
Example #15
0
stationplot.plot_parameter('SW',
                           data['dew_point_temperature'],
                           color='darkgreen',
                           clip_on=True)

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE',
                           data['slp'],
                           clip_on=True,
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover, clip_on=True)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather, clip_on=True)

# Add wind barbs
stationplot.plot_barb(u, v)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), stid, clip_on=True)

plt.show()
def makeStationPlot(plotTitle, plotFileName, maxDataAge, maxLat, minLat,
                    maxLon, minLon, stationDensity, textSize, figX, figY, dpi,
                    showCountryBorders, showStateBorders, showCountyBorders):

    #
    # Data Polling

    # Get data from AWC TDS
    dataURL = "https://www.aviationweather.gov/adds/dataserver_current/httpparam?dataSource=metars&requestType=retrieve&format=csv&minLat=" + str(
        minLat) + "&minLon=" + str(minLon) + "&maxLat=" + str(
            maxLat) + "&maxLon=" + str(maxLon) + "&hoursBeforeNow=" + str(
                maxDataAge)

    # First read in the data. We use pandas because it simplifies a lot of tasks, like dealing
    # with strings
    data = pd.read_csv(dataURL,
                       header=5,
                       usecols=(1, 3, 4, 5, 6, 7, 8, 12, 21, 22),
                       names=[
                           'stid', 'lat', 'lon', 'air_temperature',
                           'dew_point_temperature', 'wind_dir', 'wind_speed',
                           'slp', 'weather', 'cloud_fraction'
                       ],
                       na_values=-99999)

    #
    # Data Handling

    # convert T and Td from °C to °F
    data['air_temperature'] = (data['air_temperature'] * (9 / 5.0)) + 32
    data['dew_point_temperature'] = (data['dew_point_temperature'] *
                                     (9 / 5.0)) + 32

    # change sky category to %
    data['cloud_fraction'] = data['cloud_fraction'].replace(
        'SKC', 0.0).replace('CLR', 0.0).replace('CAVOK', 0.0).replace(
            'FEW', 0.1875).replace('SCT',
                                   0.4375).replace('BKN', 0.750).replace(
                                       'OVC', 1.000).replace('OVX', 1.000)

    # Drop rows with missing winds
    data = data.dropna(how='any', subset=['wind_dir', 'wind_speed'])

    # Set up the map projection
    proj = ccrs.LambertConformal(
        central_longitude=(minLon + (maxLon - minLon) / 2),
        central_latitude=(minLat + (maxLat - minLat) / 2))

    # Set station density, in x meter radius
    point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values,
                                       data['lat'].values)
    data = data[reduce_point_density(point_locs, stationDensity * 1000)]

    # Get the wind components, converting from m/s to knots as will be appropriate
    u, v = wind_components(
        (data['wind_speed'].values * units('m/s')).to('knots'),
        data['wind_dir'].values * units.degree)

    # Convert the fraction value into a code of 0-8 and compensate for NaN values
    cloud_frac = (8 * data['cloud_fraction'])
    cloud_frac[np.isnan(cloud_frac)] = 10
    cloud_frac = cloud_frac.astype(int)

    # Map weather strings to WMO codes. Only use the first symbol if there are multiple
    wx = [
        wx_code_map[s.split()[0] if ' ' in s else s]
        for s in data['weather'].fillna('')
    ]

    #
    # Plot Setup

    # Set DPI of the resulting figure
    plt.rcParams['savefig.dpi'] = dpi

    # Create the figure and an axes set to the projection.
    fig = plt.figure(figsize=(figX, figY))
    ax = fig.add_subplot(1, 1, 1, projection=proj)

    # Set plot bounds
    ax.set_extent((minLon, maxLon, minLat, maxLat))

    # Add geographic features
    if showCountyBorders:
        ax.add_feature(USCOUNTIES.with_scale('500k'),
                       edgecolor='gray',
                       linewidth=0.25)

    if showStateBorders:
        state_borders = cfeature.NaturalEarthFeature(
            category='cultural',
            name='admin_1_states_provinces_lakes',
            scale='50m',
            facecolor='none')
        ax.add_feature(state_borders, edgecolor='gray', linewidth=0.5)

    if showCountryBorders:
        country_borders = cfeature.NaturalEarthFeature(
            category='cultural',
            name='admin_0_countries',
            scale='50m',
            facecolor='none')
        ax.add_feature(country_borders, edgecolor='black', linewidth=0.7)

    #
    # Create Station Plots

    # Set station location, setup plot
    stationplot = StationPlot(ax,
                              data['lon'].values,
                              data['lat'].values,
                              clip_on=True,
                              transform=ccrs.PlateCarree(),
                              fontsize=textSize)

    # Plot the temperature and dew point
    stationplot.plot_parameter('NW', data['air_temperature'], color='red')
    stationplot.plot_parameter('SW',
                               data['dew_point_temperature'],
                               color='darkgreen')

    # Plot pressure data
    stationplot.plot_parameter('NE',
                               data['slp'],
                               formatter=lambda v: format(10 * v, '.0f')[-3:])

    # Plot cloud cover
    stationplot.plot_symbol('C', cloud_frac, sky_cover)

    # Plot current weather
    stationplot.plot_symbol('W', wx, current_weather)

    # Add wind barbs
    stationplot.plot_barb(u, v)

    # Plot station id
    stationplot.plot_text((2, 0), data['stid'])

    # Set a title and show the plot
    ax.set_title(plotTitle)

    # Export fig
    fig.savefig('/home/CarterHumphreys/bin/send2web/' +
                datetime.utcnow().strftime("%Y%m%d-%H00") + '_' +
                plotFileName + '.png',
                bbox_inches='tight')
    fig.savefig('/home/CarterHumphreys/bin/send2web/' + plotFileName + '.png',
                bbox_inches='tight')