def test_copy():
"""Test that the copy method works for all classes in `declarative.py`."""
# Copies of plot objects
objects = [ImagePlot(), ContourPlot(), FilledContourPlot(), BarbPlot(), PlotObs(),
PlotGeometry()]
for obj in objects:
obj.time = datetime.now()
copied_obj = obj.copy()
assert obj is not copied_obj
assert obj.time == copied_obj.time
# Copies of MapPanel and PanelContainer
obj = MapPanel()
obj.title = 'Sample Text'
copied_obj = obj.copy()
assert obj is not copied_obj
assert obj.title == copied_obj.title
obj = PanelContainer()
obj.size = (10, 10)
copied_obj = obj.copy()
assert obj is not copied_obj
assert obj.size == copied_obj.size
# Copies of plots in MapPanels should not point to same location in memory
obj = MapPanel()
obj.plots = [PlotObs(), PlotGeometry(), BarbPlot(), FilledContourPlot(), ContourPlot(),
ImagePlot()]
copied_obj = obj.copy()
for i in range(len(obj.plots)):
assert obj.plots[i] is not copied_obj.plots[i]
def test_declarative_plot_geometry_points(ccrs):
"""Test that `PlotGeometry` correctly plots Point and MultiPoint objects."""
from shapely.geometry import MultiPoint, Point
# Points and MultiPoints to plot
irma_track = [Point(-74.7, 21.8), Point(-76.0, 22.0), Point(-77.2, 22.1)]
irma_track_shadow = MultiPoint([
Point(-64.7, 18.25),
Point(-66.0, 18.85),
Point(-67.7, 19.45),
Point(-69.0, 19.85),
Point(-70.4, 20.45),
Point(-71.8, 20.85),
Point(-73.2, 21.25),
Point(-74.7, 21.55),
Point(-76.0, 21.75),
Point(-77.2, 21.85),
Point(-78.3, 22.05),
Point(-79.3, 22.45),
Point(-80.2, 22.85),
Point(-80.9, 23.15),
Point(-81.3, 23.45),
Point(-81.5, 24.25),
Point(-81.7, 25.35),
Point(-81.7, 26.55),
Point(-82.2, 27.95),
Point(-82.7, 29.35),
Point(-83.5, 30.65),
Point(-84.4, 31.65)
])
# Plot geometry, set colors and labels
geo = PlotGeometry()
geo.geometry = irma_track + [irma_track_shadow]
geo.fill = 'blue'
geo.stroke = None
geo.marker = '^'
geo.labels = ['Point', 'Point', 'Point', 'Irma Track']
geo.label_edgecolor = None
geo.label_facecolor = None
# Place plot in a panel and container
panel = MapPanel()
panel.area = [-85, -65, 17, 30]
panel.projection = ccrs.PlateCarree()
panel.layers = ['states', 'coastline', 'borders']
panel.plots = [geo]
pc = PanelContainer()
pc.size = (12, 12)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_plot_geometry_polygons():
"""Test that `PlotGeometry` correctly plots MultiPolygon and Polygon objects."""
from shapely.geometry import MultiPolygon, Polygon
# MultiPolygons and Polygons to plot
slgt_risk_polygon = MultiPolygon([Polygon(
[(-87.43, 41.86), (-91.13, 41.39), (-95.24, 40.99), (-97.47, 40.4), (-98.39, 41.38),
(-96.54, 42.44), (-94.02, 44.48), (-92.62, 45.48), (-89.49, 45.91), (-86.38, 44.92),
(-86.26, 43.37), (-86.62, 42.45), (-87.43, 41.86), ]), Polygon(
[(-74.02, 42.8), (-72.01, 43.08), (-71.42, 42.77), (-71.76, 42.29), (-72.73, 41.89),
(-73.89, 41.93), (-74.4, 42.28), (-74.02, 42.8), ])])
enh_risk_polygon = Polygon(
[(-87.42, 43.67), (-88.44, 42.65), (-90.87, 41.92), (-94.63, 41.84), (-95.13, 42.22),
(-95.23, 42.54), (-94.79, 43.3), (-92.81, 43.99), (-90.62, 44.55), (-88.51, 44.61),
(-87.42, 43.67)])
# Plot geometry, set colors and labels
geo = PlotGeometry()
geo.geometry = [slgt_risk_polygon, enh_risk_polygon]
geo.stroke = ['#DDAA00', '#FF6600']
geo.fill = None
geo.labels = ['SLGT', 'ENH']
geo.label_facecolor = ['#FFE066', '#FFA366']
geo.label_edgecolor = ['#DDAA00', '#FF6600']
geo.label_fontsize = 'large'
# Place plot in a panel and container
panel = MapPanel()
panel.area = [-125, -70, 20, 55]
panel.projection = 'lcc'
panel.title = ' '
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [geo]
pc = PanelContainer()
pc.size = (12, 12)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_plot_geometry_lines(ccrs):
"""Test that `PlotGeometry` correctly plots MultiLineString and LineString objects."""
from shapely.geometry import LineString, MultiLineString
# LineString and MultiLineString to plot
irma_fcst = LineString([(-52.3, 16.9), (-53.9, 16.7), (-56.2, 16.6),
(-58.6, 17.0), (-61.2, 17.8), (-63.9, 18.7),
(-66.8, 19.6), (-72.0, 21.0), (-76.5, 22.0)])
irma_fcst_shadow = MultiLineString([
LineString([(-52.3, 17.15), (-53.9, 16.95), (-56.2, 16.85),
(-58.6, 17.25), (-61.2, 18.05), (-63.9, 18.95),
(-66.8, 19.85), (-72.0, 21.25), (-76.5, 22.25)]),
LineString([(-52.3, 16.65), (-53.9, 16.45), (-56.2, 16.35),
(-58.6, 16.75), (-61.2, 17.55), (-63.9, 18.45),
(-66.8, 19.35), (-72.0, 20.75), (-76.5, 21.75)])
])
# Plot geometry, set colors and labels
geo = PlotGeometry()
geo.geometry = [irma_fcst, irma_fcst_shadow]
geo.fill = None
geo.stroke = 'green'
geo.labels = ['Irma', '+/- 0.25 deg latitude']
geo.label_facecolor = None
# Place plot in a panel and container
panel = MapPanel()
panel.area = [-85, -45, 12, 25]
panel.projection = ccrs.PlateCarree()
panel.layers = ['coastline', 'borders', 'usstates']
panel.title = 'Hurricane Irma Forecast'
panel.plots = [geo]
pc = PanelContainer()
pc.size = (12, 12)
pc.panels = [panel]
pc.draw()
return pc.figure
# Make sure that both GeoDataFrames have the same coordinate reference system (CRS).
cities = cities.to_crs(wind_data.crs)
###########################
# We want to find out what the probability of tropical-storm-force winds is for each of the
# cities we selected above. Geopandas provides a spatial join method, which merges the two
# GeoDataFrames and can tell us which wind speed probability polygon each of our city points
# lies within. That information is stored in the 'PERCENTAGE' column below.
cities = geopandas.sjoin(cities, wind_data, how='left', op='within')
cities
###########################
# Plot the wind speed probability polygons from the 'geometry' column. Use the 'fill' column
# we created above as the fill colors for the polygons, and set the stroke color to 'none' for
# all of the polygons.
wind_geo = PlotGeometry()
wind_geo.geometry = wind_data['geometry']
wind_geo.fill = wind_data['fill']
wind_geo.stroke = 'none'
###########################
# Plot the cities from the 'geometry' column, marked with diamonds ('D'). Label each point
# with the name of the city, and it's probability of tropical-storm-force winds on the line
# below. Points are set to plot in white and the font color is set to black.
city_geo = PlotGeometry()
city_geo.geometry = cities['geometry']
city_geo.marker = 'D'
city_geo.labels = cities['NAME'] + '\n(' + cities['PERCENTAGE'] + ')'
city_geo.fill = 'white'
city_geo.label_facecolor = 'black'
from metpy.plots import MapPanel, PanelContainer, PlotGeometry
###########################
# Read in the geoJSON file containing the convective outlook.
day1_outlook = geopandas.read_file(
get_test_data('spc_day1otlk_20210317_1200_lyr.geojson'))
###########################
# Preview the data.
day1_outlook
###########################
# Plot the shapes from the 'geometry' column. Give the shapes their fill and stroke color by
# providing the 'fill' and 'stroke' columns. Use text from the 'LABEL' column as labels for the
# shapes.
geo = PlotGeometry()
geo.geometry = day1_outlook['geometry']
geo.fill = day1_outlook['fill']
geo.stroke = day1_outlook['stroke']
geo.labels = day1_outlook['LABEL']
geo.label_fontsize = 'large'
###########################
# Add the geometry plot to a panel and container.
panel = MapPanel()
panel.title = 'SPC Day 1 Convective Outlook (Valid 12z Mar 17 2021)'
panel.plots = [geo]
panel.area = [-120, -75, 25, 50]
panel.projection = 'lcc'
panel.layers = ['lakes', 'land', 'ocean', 'states', 'coastline', 'borders']