def test_arrow_projection_list(wind_projection_list):
"""Test that arrows will be projected when lat/lon lists are provided."""
lat, lon, u, v = wind_projection_list
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
stnplot = StationPlot(ax, lon, lat)
stnplot.plot_arrow(u, v)
assert stnplot.arrows
def create_ornaments(n_ornaments,color='k'):
"""create ornaments and return the artist."""
ornaments_y=np.random.triangular(3,3.5,24.5,n_ornaments)
bounds=get_x_bounds(ornaments_y)
ornaments_x=np.random.rand(n_ornaments)*2*bounds-bounds
stationplot=StationPlot(ax,ornaments_x,ornaments_y,clip_on=True,fontsize=24)
symbols=np.array(list(wx_code_map.values()))
wx=symbols[np.random.randint(0,102,n_ornaments)]
return stationplot.plot_symbol('c',wx,current_weather,color=color)
def test_scalar_unit_conversion_exception():
"""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_parameter('C', 50, plot_units='degC')
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')
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')
def test_plot_symbol_fontsize():
"""Test changing fontsize in plotting of symbols."""
fig = plt.figure(figsize=(3, 3))
ax = plt.subplot(1, 1, 1)
sp = StationPlot(ax, [0], [0], fontsize=8, spacing=32)
sp.plot_symbol('E', [92], current_weather)
sp.plot_symbol('W', [96], current_weather, fontsize=100)
return fig
def test_barb_projection(wind_plot, ccrs):
"""Test that barbs are properly projected (#598)."""
u, v, x, y = wind_plot
# 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=[-120, -105, -90, -75, -60], ylocs=np.arange(24, 55, 6))
sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree())
sp.plot_barb(u, v)
return fig
def test_barb_projection_list():
"""Test that barbs will be projected when lat/lon lists are provided."""
lat = [38.22, 38.18, 38.25]
lon = [-85.76, -85.86, -85.77]
u = [1.89778964, -3.83776523, 3.64147732] * units('m/s')
v = [1.93480072, 1.31000184, 1.36075552] * units('m/s')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
stnplot = StationPlot(ax, lon, lat)
stnplot.plot_barb(u, v)
assert stnplot.barbs
def test_arrow_unit_conversion(barbs_units):
"""Test that arrow units can be converted at plot time (#737)."""
x_pos, y_pos, u_wind, v_wind = barbs_units
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
stnplot = StationPlot(ax, x_pos, y_pos)
stnplot.plot_arrow(u_wind, v_wind, plot_units='knots')
ax.set_xlim(-5, 5)
ax.set_ylim(-5, 5)
return fig
def test_arrow_projection(wind_plot):
"""Test that arrows are properly projected."""
u, v, x, y = wind_plot
# 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_arrow(u, v)
sp.plot_arrow(u, v) # plot_arrow used twice to hit removal if statement
return fig
def test_symbol_pandas_timeseries():
"""Test the usage of Pandas DatetimeIndex as a valid `x` input into StationPlot."""
pd.plotting.register_matplotlib_converters()
rng = pd.date_range('12/1/2017', periods=5, freq='D')
sc = [1, 2, 3, 4, 5]
ts = pd.Series(sc, index=rng)
fig, ax = plt.subplots()
y = np.ones(len(ts.index))
stationplot = StationPlot(ax, ts.index, y, fontsize=12)
stationplot.plot_symbol('C', ts, sky_cover)
ax.xaxis.set_major_locator(matplotlib.dates.DayLocator())
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%-d'))
return fig
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
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
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
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
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
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()
sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree())
sp.plot_barb(u, v)
return fig
def test_nws_layout():
"""Test metpy's NWS layout for station plots."""
fig = plt.figure(figsize=(3, 3))
# testing data
x = np.array([1])
y = np.array([2])
data = {'air_temperature': np.array([77]) * units.degF,
'dew_point_temperature': np.array([71]) * units.degF,
'air_pressure_at_sea_level': np.array([999.8]) * units('mbar'),
'eastward_wind': np.array([15.]) * units.knots,
'northward_wind': np.array([15.]) * units.knots, 'cloud_coverage': [7],
'present_weather': [80], 'high_cloud_type': [1], 'medium_cloud_type': [3],
'low_cloud_type': [2], 'visibility_in_air': np.array([5.]) * units.mile,
'tendency_of_air_pressure': np.array([-0.3]) * units('mbar'),
'tendency_of_air_pressure_symbol': [8]}
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12, spacing=16)
nws_layout.plot(sp, data)
sp.ax.set_xlim(0, 3)
sp.ax.set_ylim(0, 3)
return fig
def test_simple_layout():
"""Test metpy's simple layout for station plots."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
data = {
'air_temperature': np.array([32., 212.]) * units.degF,
'dew_point_temperature': np.array([28., 80.]) * units.degF,
'air_pressure_at_sea_level': np.array([29.92, 28.00]) * units.inHg,
'eastward_wind': np.array([2, 0]) * units.knots,
'northward_wind': np.array([0, 5]) * units.knots,
'cloud_coverage': [3, 8],
'present_weather': [65, 75],
'unused': [1, 2]
}
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12)
simple_layout.plot(sp, data)
sp.ax.set_xlim(0, 6)
sp.ax.set_ylim(0, 6)
return fig
def test_stationlayout_api():
"""Test the StationPlot API."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
data = {'temp': np.array([32., 212.]) * units.degF, 'u': np.array([2, 0]) * units.knots,
'v': np.array([0, 5]) * units.knots, 'stid': ['KDEN', 'KSHV'], 'cover': [3, 8]}
# Set up the layout
layout = StationPlotLayout()
layout.add_barb('u', 'v', units='knots')
layout.add_value('NW', 'temp', fmt='0.1f', units=units.degC, color='darkred')
layout.add_symbol('C', 'cover', sky_cover, color='magenta')
layout.add_text((0, 2), 'stid', color='darkgrey')
layout.add_value('NE', 'dewpt', color='green') # This should be ignored
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12)
layout.plot(sp, data)
sp.ax.set_xlim(0, 6)
sp.ax.set_ylim(0, 6)
return fig
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
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
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
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
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
def plotStations(self, obs):
self.obs = obs
print('setting station plot')
self.stnPlots = StationPlot(self.axes,
self.obs['longitude'],
self.obs['latitude'],
clip_on=True,
transform=self.PlateCarree)
print('plotting')
self.stationLayout.plot(self.stnPlots, self.obs)
self.canvas.draw()
print('Done plotting')
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
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
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
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
def test_plot_symbol_fontsize():
"""Test changing fontsize in plotting of symbols."""
fig = plt.figure(figsize=(3, 3))
ax = plt.subplot(1, 1, 1)
sp = StationPlot(ax, [0], [0], fontsize=8, spacing=32)
sp.plot_symbol('E', [92], current_weather)
sp.plot_symbol('W', [96], current_weather, fontsize=100)
return fig
def test_station_layout_odd_data():
"""Test more corner cases with data passed in."""
fig = plt.figure(figsize=(9, 9))
# Set up test layout
layout = StationPlotLayout()
layout.add_barb('u', 'v')
layout.add_value('W', 'temperature', units='degF')
# Now only use data without wind and no units
data = {'temperature': [25.]}
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), [1], [2], fontsize=12)
layout.plot(sp, data)
assert True
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
def radar_plus_obs(station, my_datetime, radar_title=None, bb=None,
station_radius=75000., station_layout=simple_layout,
field='reflectivity', vmin=None, vmax=None,
sweep=0):
if radar_title is None:
radar_title = 'Area '
radar = get_radar_from_aws(station, my_datetime)
# Lets get some geographical context
if bb is None:
lats = radar.gate_latitude
lons = radar.gate_longitude
min_lon = lons['data'].min()
min_lat = lats['data'].min()
max_lat = lats['data'].max()
max_lon = lons['data'].max()
bb = {'north' : max_lat,
'south' : min_lat,
'east' : max_lon,
'west' : min_lon}
else:
min_lon = bb['west']
min_lat = bb['south']
max_lon = bb['east']
max_lat = bb['north']
print('min_lat:', min_lat, ' min_lon:', min_lon,
' max_lat:', max_lat, ' max_lon:', max_lon)
index_at_start = radar.sweep_start_ray_index['data'][sweep]
time_at_start_of_radar = num2date(radar.time['data'][index_at_start],
radar.time['units'])
pacific = pytz.timezone('US/Central')
local_time = pacific.fromutc(time_at_start_of_radar)
fancy_date_string = local_time.strftime('%A %B %d at %I:%M %p %Z')
print(fancy_date_string)
metar_cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nws/metar/ncdecoded/catalog.xml?'
'dataset=nws/metar/ncdecoded/Metar_Station_Data_fc.cdmr')
dataset = list(metar_cat.datasets.values())[0]
ncss = NCSS(dataset.access_urls["NetcdfSubset"])
query = ncss.query().accept('csv').time(time_at_start_of_radar)
query.lonlat_box(north=max_lat, south=min_lat, east=max_lon, west=min_lon)
query.variables('air_temperature', 'dew_point_temperature', 'inches_ALTIM',
'wind_speed', 'wind_from_direction', 'cloud_area_fraction', 'weather')
data = ncss.get_data(query)
lats = data['latitude'][:]
lons = data['longitude'][:]
tair = data['air_temperature'][:]
dewp = data['dew_point_temperature'][:]
slp = (data['inches_ALTIM'][:] * units('inHg')).to('mbar')
# Convert wind to components
u, v = mpcalc.get_wind_components(data['wind_speed'] * units.knot,
data['wind_from_direction'] * units.deg)
# Need to handle missing (NaN) and convert to proper code
cloud_cover = 8 * data['cloud_area_fraction']
cloud_cover[np.isnan(cloud_cover)] = 9
cloud_cover = cloud_cover.astype(np.int)
# For some reason these come back as bytes instead of strings
stid = [s.decode() for s in data['station']]
# Convert the text weather observations to WMO codes we can map to symbols
#wx_text = [s.decode('ascii') for s in data['weather']]
#wx_codes = np.array(list(to_code(wx_text)))
sfc_data = {'latitude': lats, 'longitude': lons,
'air_temperature': tair, 'dew_point_temperature': dewp, 'eastward_wind': u,
'northward_wind': v, 'cloud_coverage': cloud_cover,
'air_pressure_at_sea_level': slp}#, 'present_weather': wx_codes}
fig = plt.figure(figsize=(10, 8))
display = pyart.graph.RadarMapDisplayCartopy(radar)
lat_0 = display.loc[0]
lon_0 = display.loc[1]
# Set our Projection
projection = cartopy.crs.Mercator(central_longitude=lon_0,
min_latitude=min_lat, max_latitude=max_lat)
# Call our function to reduce data
filter_data(sfc_data, projection, radius=station_radius, sort_key='present_weather')
print(sweep)
display.plot_ppi_map(
field, sweep, colorbar_flag=True,
title=radar_title +' area ' + field + ' \n' + fancy_date_string,
projection=projection,
min_lon=min_lon, max_lon=max_lon, min_lat=min_lat, max_lat=max_lat,
vmin=vmin, vmax=vmax)
# Mark the radar
display.plot_point(lon_0, lat_0, label_text='Radar')
# Get the current axes and plot some lat and lon lines
gl = display.ax.gridlines(draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
# Make the station plot
stationplot = StationPlot(display.ax, sfc_data['longitude'], sfc_data['latitude'],
transform=cartopy.crs.PlateCarree(),
fontsize=12)
station_layout.plot(stationplot, sfc_data)
return display, time_at_start_of_radar
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.")
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
#
# 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,
data['longitude'],
data['latitude'],
transform=ccrs.PlateCarree(),
fontsize=12)
# The layout knows where everything should go, and things are standardized using
# the names of variables. So the layout pulls arrays out of `data` and plots them
# using `stationplot`.
simple_layout.plot(stationplot, data)
plt.show()
###########################################
# or instead, a custom layout can be used:
# Just winds, temps, and dewpoint, with colors. Dewpoint and temp will be plotted
# out to Farenheit tenths. Extra data will be ignored
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
#
# 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,
data['lon'].values,
data['lat'].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', 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'],
s=30,
marker='+',
transform=ccrs.PlateCarree(),
color='lightgrey',
zorder=-1)
# Add gridlines for every 5 degree lat/lon
ax.gridlines(linestyle='solid',
ylocs=range(15, 71, 5),
xlocs=range(-150, -49, 5))
# 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 10 pt.
stationplot = StationPlot(ax,
df['longitude'].values,
df['latitude'].values,
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')
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)
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
#
# 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, data['lon'].values, data['lat'].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', 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.
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)
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 8))
add_metpy_logo(fig, 70, 30, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.STATES.with_scale('50m'))
# Set plot bounds
ax.set_extent((-104, -93, 33.4, 37.2))
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:])
