예제 #1
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def test_barb_unit_conversion_exception(u, v):
    """Test that errors are raise if unit conversion is requested on un-united data."""
    x_pos = np.array([0])
    y_pos = np.array([0])

    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    stnplot = StationPlot(ax, x_pos, y_pos)
    with pytest.raises(ValueError):
        stnplot.plot_barb(u, v, plot_units='knots')
예제 #2
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def test_barb_projection():
    """Test that barbs are properly projected (#598)."""
    # Test data of all southerly winds
    v = np.full((5, 5), 10, dtype=np.float64)
    u = np.zeros_like(v)
    x, y = np.meshgrid(np.linspace(-120, -60, 5), np.linspace(25, 50, 5))

    # Plot and check barbs (they should align with grid lines)
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal())
    ax.gridlines(xlocs=[-135, -120, -105, -90, -75, -60, -45])
    sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree())
    sp.plot_barb(u, v)

    return fig
예제 #3
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def test_barb_no_default_unit_conversion():
    """Test that barbs units are left alone by default (#737)."""
    x_pos = np.array([0])
    y_pos = np.array([0])
    u_wind = np.array([3.63767155210412]) * units('m/s')
    v_wind = np.array([3.63767155210412]) * units('m/s')

    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    stnplot = StationPlot(ax, x_pos, y_pos)
    stnplot.plot_barb(u_wind, v_wind)
    ax.set_xlim(-5, 5)
    ax.set_ylim(-5, 5)

    return fig
예제 #4
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def test_barb_unit_conversion():
    """Test that barbs units can be converted at plot time (#737)."""
    x_pos = np.array([0])
    y_pos = np.array([0])
    u_wind = np.array([3.63767155210412]) * units('m/s')
    v_wind = np.array([3.63767155210412]) * units('m/s')

    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    stnplot = StationPlot(ax, x_pos, y_pos)
    stnplot.plot_barb(u_wind, v_wind, plot_units='knots')
    ax.set_xlim(-5, 5)
    ax.set_ylim(-5, 5)

    return fig
예제 #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
예제 #6
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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
예제 #7
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def test_stationplot_unit_conversion():
    """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, plot_units='degF', 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
예제 #8
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def test_station_plot_replace():
    """Test that locations are properly replaced."""
    fig = plt.figure(figsize=(3, 3))

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

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
    sp.plot_barb([20], [0])
    sp.plot_barb([5], [0])
    sp.plot_parameter('NW', [10.5], color='red')
    sp.plot_parameter('NW', [20], color='blue')

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

    return fig
예제 #9
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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
예제 #10
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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()
예제 #11
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                          clip_on=True,
                          transform=ccrs.PlateCarree(),
                          fontsize=10)

# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point.
stationplot.plot_parameter('NW', df['temperature'], color='black')
stationplot.plot_parameter('SW', df['dewpoint'], color='black')

# A more complex example uses a custom formatter to control how the geopotential height
# values are plotted. This is set in an earlier if-statement to work appropriate for
# different levels.
stationplot.plot_parameter('NE', df['height'], formatter=hght_format)

# Add wind barbs
stationplot.plot_barb(df['u_wind'], df['v_wind'], length=7, pivot='tip')

# Plot Solid Contours of Geopotential Height
cs = ax.contour(hght.lon,
                hght.lat,
                smooth_hght,
                range(0, 20000, cint),
                colors='black',
                transform=ccrs.PlateCarree())
clabels = plt.clabel(cs,
                     fmt='%d',
                     colors='white',
                     inline_spacing=5,
                     use_clabeltext=True)

# Contour labels with black boxes and white text
예제 #12
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def plot_map_temperature(proj,
                         point_locs,
                         df_t,
                         area='EU',
                         west=-5.5,
                         east=32,
                         south=42,
                         north=62,
                         fonts=14,
                         cm='gist_ncar',
                         path=None,
                         SLP=False):
    if path is None:
        # set up the paths and test for existence
        path = expanduser('~') + '/Documents/Metar_plots'
        try:
            os.listdir(path)
        except FileNotFoundError:
            os.mkdir(path)
    else:
        path = path
    df = df_t
    plt.rcParams['savefig.dpi'] = 300
    # =========================================================================
    # Create the figure and an axes set to the projection.
    fig = plt.figure(figsize=(20, 16))
    ax = fig.add_subplot(1, 1, 1, projection=proj)
    if area == 'Antarctica':
        df = df.loc[df['latitude'] < north]
        ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())
        theta = np.linspace(0, 2 * np.pi, 100)
        center, radius = [0.5, 0.5], 0.5
        verts = np.vstack([np.sin(theta), np.cos(theta)]).T
        circle = mpath.Path(verts * radius + center)
        ax.set_boundary(circle, transform=ax.transAxes)
    elif area == 'Arctic':
        df = df.loc[df['latitude'] > south]
        ax.set_extent([-180, 180, 60, 90], ccrs.PlateCarree())
        theta = np.linspace(0, 2 * np.pi, 100)
        center, radius = [0.5, 0.5], 0.5
        verts = np.vstack([np.sin(theta), np.cos(theta)]).T
        circle = mpath.Path(verts * radius + center)
        ax.set_boundary(circle, transform=ax.transAxes)

    else:
        ax.set_extent((west, east, south, north))
    # Set up a cartopy feature for state borders.
    state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                                name='admin_0_countries',
                                                scale='10m',
                                                facecolor='#d8dcd6',
                                                alpha=0.5)
    ax.coastlines(resolution='10m', zorder=1, color='black')
    ax.add_feature(state_boundaries, zorder=1, edgecolor='black')
    # ax.add_feature(cartopy.feature.OCEAN, zorder=0)
    # Set plot bounds
    # reset index for easier loop
    df = df.dropna(how='any', subset=['TT'])
    df = df.reset_index()
    cmap = matplotlib.cm.get_cmap(cm)
    norm = matplotlib.colors.Normalize(vmin=-30.0, vmax=30.0)
    # Start the station plot by specifying the axes to draw on, as well as the
    # lon/lat of the stations (with transform). We also the fontsize to 12 pt.
    index = 0
    a = np.arange(-30, 30, 1)
    for x in a:
        if index == 0:
            df_min = df.loc[df['TT'] < min(a)]
            df_max = df.loc[df['TT'] > max(a)]
            j = 0
            list_ex = [min(a) - 5, max(a) + 5]
            for arr in [df_min, df_max]:
                stationplot = StationPlot(ax,
                                          arr['longitude'],
                                          arr['latitude'],
                                          clip_on=True,
                                          transform=ccrs.PlateCarree(),
                                          fontsize=fonts)
                Temp = stationplot.plot_parameter('NW',
                                                  arr['TT'],
                                                  color=cmap(norm(list_ex[j])))
                try:
                    Temp.set_path_effects([
                        path_effects.Stroke(linewidth=1.5, foreground='black'),
                        path_effects.Normal()
                    ])
                except AttributeError:
                    pass
                j += 1
        # slice out values between x and x+1
        df_cur = df.loc[(df['TT'] < x + 1) & (df['TT'] >= x)]
        stationplot = StationPlot(ax,
                                  df_cur['longitude'],
                                  df_cur['latitude'],
                                  clip_on=True,
                                  transform=ccrs.PlateCarree(),
                                  fontsize=fonts)
        # plot the sliced values with a different color for each loop
        Temp = stationplot.plot_parameter('NW',
                                          df_cur['TT'],
                                          color=cmap(norm(x + 0.5)))
        try:
            Temp.set_path_effects([
                path_effects.Stroke(linewidth=1.5, foreground='black'),
                path_effects.Normal()
            ])
        except AttributeError:
            pass
        print('x={} done correctly '.format(x))
        index += 1
    # fontweight = 'bold'
    # More complex ex. uses custom formatter to control how sea-level pressure
    # values are plotted. This uses the standard trailing 3-digits of


# the pressure value in tenths of millibars.
    stationplot = StationPlot(ax,
                              df['longitude'].values,
                              df['latitude'].values,
                              clip_on=True,
                              transform=ccrs.PlateCarree(),
                              fontsize=fonts)
    try:
        u, v = wind_components(((df['ff'].values) * units('knots')),
                               (df['dd'].values * units.degree))
        cloud_frac = df['cloud_cover']
        if area != 'Arctic':
            stationplot.plot_barb(u, v, zorder=1000, linewidth=2)
            stationplot.plot_symbol('C', cloud_frac, sky_cover)
            # stationplot.plot_text((2, 0), df['Station'])

        for val in range(0, 2):
            wx = df[['ww', 'StationType']]
            if val == 0:
                # mask all the unmanned stations
                wx['ww'].loc[wx['StationType'] > 3] = np.nan
                wx2 = wx['ww'].fillna(00).astype(int).values.tolist()
                stationplot.plot_symbol('W', wx2, current_weather, zorder=2000)
            else:
                # mask all the manned stations
                wx['ww'].loc[(wx['StationType'] <= 3)] = np.nan
                # mask all reports smaller than 9
                # =7 is an empty symbol!
                wx['ww'].loc[wx['ww'] <= 9] = 7
                wx2 = wx['ww'].fillna(7).astype(int).values.tolist()
                stationplot.plot_symbol('W',
                                        wx2,
                                        current_weather_auto,
                                        zorder=2000)
        # print(u, v)
    except (ValueError, TypeError) as error:
        pass

    if SLP is True:
        lon = df['longitude'].loc[(df.PressureDefId == 'mean sea level')
                                  & (df.Hp <= 750)].values
        lat = df['latitude'].loc[(df.PressureDefId == 'mean sea level')
                                 & (df.Hp <= 750)].values
        xp, yp, _ = proj.transform_points(ccrs.PlateCarree(), lon, lat).T
        sea_levelp = df['SLP'].loc[(df.PressureDefId == 'mean sea level')
                                   & (df.Hp <= 750)]
        x_masked, y_masked, pres = remove_nan_observations(
            xp, yp, sea_levelp.values)
        slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
                                                      y_masked,
                                                      pres,
                                                      interp_type='cressman',
                                                      search_radius=400000,
                                                      rbf_func='quintic',
                                                      minimum_neighbors=1,
                                                      hres=100000,
                                                      rbf_smooth=100000)
        Splot_main = ax.contour(slpgridx,
                                slpgridy,
                                slp,
                                colors='k',
                                linewidths=2,
                                extent=(west, east, south, north),
                                levels=list(range(950, 1050, 10)))
        plt.clabel(Splot_main, inline=1, fontsize=12, fmt='%i')

        Splot = ax.contour(slpgridx,
                           slpgridy,
                           slp,
                           colors='k',
                           linewidths=1,
                           linestyles='--',
                           extent=(west, east, south, north),
                           levels=[
                               x for x in range(950, 1050, 1)
                               if x not in list(range(950, 1050, 10))
                           ])
        plt.clabel(Splot, inline=1, fontsize=10, fmt='%i')

    # stationplot.plot_text((2, 0), df['Station'])
    # 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), df['station'])

    if (area == 'Antarctica' or area == 'Arctic'):
        plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
                    bbox_inches='tight',
                    pad_inches=0)
    else:
        plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
                    bbox_inches='tight',
                    transparent="True",
                    pad_inches=0)
예제 #13
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                          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()
예제 #14
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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.")
예제 #15
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()
예제 #16
0
def plot_map_standard(proj,
                      point_locs,
                      df_t,
                      area='EU',
                      west=-9.5,
                      east=28,
                      south=35,
                      north=62,
                      fonts=14,
                      path=None,
                      SLP=False,
                      gust=False):
    if path == None:
        # set up the paths and test for existence
        path = expanduser('~') + '/Documents/Metar_plots'
        try:
            os.listdir(path)
        except FileNotFoundError:
            os.mkdir(path)
    else:
        path = path
    df = df_t.loc[(df_t['longitude'] >= west - 4)
                  & (df_t['longitude'] <= east + 4)
                  & (df_t['latitude'] <= north + 4) &
                  (df_t['latitude'] >= south - 4)]
    plt.rcParams['savefig.dpi'] = 300
    # =========================================================================
    # Create the figure and an axes set to the projection.
    fig = plt.figure(figsize=(20, 16))
    ax = fig.add_subplot(1, 1, 1, projection=proj)
    if area == 'Antarctica':
        df = df.loc[df['latitude'] < north]
        ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())
        theta = np.linspace(0, 2 * np.pi, 100)
        center, radius = [0.5, 0.5], 0.5
        verts = np.vstack([np.sin(theta), np.cos(theta)]).T
        circle = mpath.Path(verts * radius + center)
        ax.set_boundary(circle, transform=ax.transAxes)
    elif area == 'Arctic':
        df = df.loc[df['latitude'] > south]
        ax.set_extent([-180, 180, 60, 90], ccrs.PlateCarree())
        theta = np.linspace(0, 2 * np.pi, 100)
        center, radius = [0.5, 0.5], 0.5
        verts = np.vstack([np.sin(theta), np.cos(theta)]).T
        circle = mpath.Path(verts * radius + center)
        ax.set_boundary(circle, transform=ax.transAxes)

    else:
        ax.set_extent((west, east, south, north))

    # Get the wind components, converting from m/s to knots as will
    # be appropriate for the station plot.
    df['dd'][df['dd'] > 360] = np.nan
    u, v = wind_components(df['ff'].values * units('knots'),
                           df['dd'].values * units('deg'))
    cloud_frac = df['cloud_cover']
    # Change the DPI of the resulting figure. Higher DPI drastically improves
    # look of the text rendering.

    # Set up a cartopy feature for state borders.
    # state_boundaries = feat.NaturalEarthFeature(category='cultural',
    #                                             name='admin_0_countries',
    #                                             scale='10m',
    #                                             facecolor='#d8dcd6',
    #                                             alpha=0.5)
    # ax.coastlines(resolution='10m', zorder=0, color='black')
    # ax.add_feature(feat.LAND)
    ax.add_feature(feat.COASTLINE.with_scale('10m'),
                   zorder=2,
                   edgecolor='black')
    ax.add_feature(feat.OCEAN.with_scale('50m'), zorder=0)
    ax.add_feature(feat.STATES.with_scale('10m'),
                   zorder=1,
                   facecolor='white',
                   edgecolor='#5e819d')
    # ax.add_feature(cartopy.feature.OCEAN, zorder=0)
    # Set plot bounds

    # Start the station plot by specifying the axes to draw on, as well as the
    # lon/lat of the stations (with transform). We also the fontsize to 12 pt.
    stationplot = StationPlot(ax,
                              df['longitude'].values,
                              df['latitude'].values,
                              clip_on=True,
                              transform=ccrs.PlateCarree(),
                              fontsize=fonts)
    # Plot the temperature and dew point to the upper and lower left,
    # respectively, of the center point. Each one uses a different color.
    Temp = stationplot.plot_parameter('NW',
                                      df['TT'],
                                      color='#fd3c06',
                                      fontweight='bold',
                                      zorder=3)
    Td = stationplot.plot_parameter('SW', df['TD'], color='#01ff07')

    if gust == True:
        maxff = stationplot.plot_parameter('SE',
                                           df['max_gust'],
                                           color='#cb416b',
                                           fontweight='bold',
                                           zorder=3)
        maxff.set_path_effects([
            path_effects.Stroke(linewidth=1.5, foreground='black'),
            path_effects.Normal()
        ])
    # fontweight = 'bold'
    # More complex ex. uses custom formatter to control how sea-level pressure
    # values are plotted. This uses the standard trailing 3-digits of
    # the pressure value in tenths of millibars.

    if (area != 'Antarctica' and area != 'Arctic'):
        p = stationplot.plot_parameter(
            'NE',
            df['SLP'],
            formatter=lambda v: format(10 * v, '.0f')[-3:],
            color="#a2cffe")
        for x in [Temp, Td, p]:
            x.set_path_effects([
                path_effects.Stroke(linewidth=1.5, foreground='black'),
                path_effects.Normal()
            ])
    else:
        for x in [Temp, Td]:
            x.set_path_effects([
                path_effects.Stroke(linewidth=1.5, foreground='black'),
                path_effects.Normal()
            ])

    # Add wind barbs
    stationplot.plot_barb(u, v, zorder=3, linewidth=2)
    # 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.
    for val in range(0, 2):
        wx = df[['ww', 'StationType']]
        if val == 0:
            # mask all the unmanned stations
            wx['ww'].loc[wx['StationType'] > 3] = np.nan
            wx2 = wx['ww'].fillna(00).astype(int).values.tolist()
            stationplot.plot_symbol('W', wx2, current_weather, zorder=4)
        else:
            # mask all the manned stations
            wx['ww'].loc[(wx['StationType'] <= 3)] = np.nan
            # mask all reports smaller than 9
            # =7 is an empty symbol!
            wx['ww'].loc[wx['ww'] <= 9] = 7
            wx2 = wx['ww'].fillna(7).astype(int).values.tolist()
            stationplot.plot_symbol('W', wx2, current_weather_auto, zorder=4)
    if SLP == True:
        lon = df['longitude'].loc[(df.PressureDefId == 'mean sea level')
                                  & (df.Hp <= 750)].values
        lat = df['latitude'].loc[(df.PressureDefId == 'mean sea level')
                                 & (df.Hp <= 750)].values
        xp, yp, _ = proj.transform_points(ccrs.PlateCarree(), lon, lat).T
        sea_levelp = df['SLP'].loc[(df.PressureDefId == 'mean sea level')
                                   & (df.Hp <= 750)]
        x_masked, y_masked, pres = remove_nan_observations(
            xp, yp, sea_levelp.values)
        slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
                                                      y_masked,
                                                      pres,
                                                      interp_type='cressman',
                                                      search_radius=400000,
                                                      rbf_func='quintic',
                                                      minimum_neighbors=1,
                                                      hres=100000,
                                                      rbf_smooth=100000)
        Splot_main = ax.contour(slpgridx,
                                slpgridy,
                                slp,
                                colors='k',
                                linewidths=2,
                                extent=(west, east, south, north),
                                levels=list(range(950, 1050, 10)))
        plt.clabel(Splot_main, inline=1, fontsize=12, fmt='%i')

        Splot = ax.contour(slpgridx,
                           slpgridy,
                           slp,
                           colors='k',
                           linewidths=1,
                           linestyles='--',
                           extent=(west, east, south, north),
                           levels=[
                               x for x in range(950, 1050, 1)
                               if x not in list(range(950, 1050, 10))
                           ])
        plt.clabel(Splot, inline=1, fontsize=10, fmt='%i')

    # stationplot.plot_text((2, 0), df['Station'])
    # 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), df['station'])

    if (area == 'Antarctica' or area == 'Arctic'):
        plt.savefig(path + '/CURR_SYNOP_' + area + '.png',
                    bbox_inches='tight',
                    pad_inches=0)
    else:
        plt.savefig(path + '/CURR_SYNOP_' + area + '.png',
                    bbox_inches='tight',
                    transparent="True",
                    pad_inches=0)
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')