def test_latlon():
"""Test our handling of lat/lon information."""
data = xr.open_dataset(get_test_data('irma_gfs_example.nc', as_file_obj=False))
img = ImagePlot()
img.data = data
img.field = 'Temperature_isobaric'
img.level = 500 * units.hPa
img.time = datetime(2017, 9, 5, 15, 0, 0)
contour = ContourPlot()
contour.data = data
contour.field = 'Geopotential_height_isobaric'
contour.level = img.level
contour.time = img.time
panel = MapPanel()
panel.projection = 'lcc'
panel.area = 'us'
panel.plots = [img, contour]
pc = PanelContainer()
pc.panel = panel
pc.draw()
return pc.figure
def test_global():
"""Test that we can set global extent."""
data = xr.open_dataset(GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini')))
img = ImagePlot()
img.data = data
img.field = 'IR'
panel = MapPanel()
panel.area = 'global'
panel.plots = [img]
pc = PanelContainer()
pc.panel = panel
pc.draw()
return pc.figure
def test_declarative_image():
"""Test making an image plot."""
data = xr.open_dataset(GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini')))
img = ImagePlot()
img.data = data.metpy.parse_cf('IR')
img.colormap = 'Greys_r'
panel = MapPanel()
panel.title = 'Test'
panel.plots = [img]
pc = PanelContainer()
pc.panel = panel
pc.draw()
assert panel.ax.get_title() == 'Test'
return pc.figure
def test_projection_object():
"""Test that we can pass a custom map projection."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.level = 700 * units.hPa
contour.field = 'Temperature'
panel = MapPanel()
panel.area = (-110, -60, 25, 55)
panel.projection = ccrs.Mercator()
panel.layers = [cfeature.LAKES]
panel.plots = [contour]
pc = PanelContainer()
pc.panel = panel
pc.draw()
return pc.figure
def test_declarative_contour_options():
"""Test making a contour plot."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = 700 * units.hPa
contour.contours = 30
contour.linewidth = 1
contour.linecolor = 'red'
contour.linestyle = 'dashed'
contour.clabels = True
panel = MapPanel()
panel.area = 'us'
panel.proj = 'lcc'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [contour]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_barb_options():
"""Test making a contour plot."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
barb = BarbPlot()
barb.data = data
barb.level = 300 * units.hPa
barb.field = ['u_wind', 'v_wind']
barb.skip = (10, 10)
barb.color = 'blue'
barb.pivot = 'tip'
barb.barblength = 6.5
panel = MapPanel()
panel.area = 'us'
panel.projection = 'data'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [barb]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_barb_gfs_knots():
"""Test making a contour plot."""
data = xr.open_dataset(get_test_data('GFS_test.nc', as_file_obj=False))
barb = BarbPlot()
barb.data = data
barb.level = 300 * units.hPa
barb.field = [
'u-component_of_wind_isobaric', 'v-component_of_wind_isobaric'
]
barb.skip = (3, 3)
barb.earth_relative = False
barb.plot_units = 'knot'
panel = MapPanel()
panel.area = 'us'
panel.projection = 'data'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [barb]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_colorfill_no_colorbar(cfeature):
"""Test that we can use ContourFillPlot."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = FilledContourPlot()
contour.data = data
contour.level = 700 * units.hPa
contour.field = 'Temperature'
contour.colormap = 'coolwarm'
contour.colorbar = None
panel = MapPanel()
panel.area = (-110, -60, 25, 55)
panel.layers = [cfeature.STATES]
panel.plots = [contour]
pc = PanelContainer()
pc.panel = panel
pc.size = (8, 8)
pc.draw()
return pc.figure
def test_declarative_events():
"""Test that resetting traitlets properly propagates."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = 850 * units.hPa
contour.contours = 30
contour.linewidth = 1
contour.linecolor = 'red'
img = ImagePlot()
img.data = data
img.field = 'v_wind'
img.level = 700 * units.hPa
img.colormap = 'hot'
img.image_range = (3000, 5000)
panel = MapPanel()
panel.area = 'us'
panel.proj = 'lcc'
panel.layers = ['coastline', 'borders', 'states']
panel.plots = [contour, img]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
# Update some properties to make sure it regenerates the figure
contour.linewidth = 2
contour.linecolor = 'green'
contour.level = 700 * units.hPa
contour.field = 'Specific_humidity'
img.field = 'Geopotential_height'
img.colormap = 'plasma'
return pc.figure
def test_declarative_contour():
"""Test making a contour plot."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = 700 * units.hPa
contour.contours = 30
contour.linewidth = 1
contour.linecolor = 'red'
panel = MapPanel()
panel.area = 'us'
panel.proj = 'lcc'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [contour]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
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_upa_obs_convert_barb_units():
"""Test making a full upperair observation plot."""
data = pd.read_csv(get_test_data('UPA_obs.csv', as_file_obj=False))
data.units = ''
data.units = {
'pressure': 'hPa',
'height': 'meters',
'temperature': 'degC',
'dewpoint': 'degC',
'direction': 'degrees',
'speed': 'knots',
'station': None,
'time': None,
'u_wind': 'knots',
'v_wind': 'knots',
'latitude': 'degrees',
'longitude': 'degrees'
}
obs = PlotObs()
obs.data = data
obs.time = datetime(1993, 3, 14, 0)
obs.level = 500 * units.hPa
obs.fields = ['temperature', 'dewpoint', 'height']
obs.locations = ['NW', 'SW', 'NE']
obs.formats = [None, None, lambda v: format(v, '.0f')[:3]]
obs.vector_field = ('u_wind', 'v_wind')
obs.vector_field_length = 7
obs.vector_plot_units = 'm/s'
obs.reduce_points = 0
# Panel for plot with Map features
panel = MapPanel()
panel.layout = (1, 1, 1)
panel.area = (-124, -72, 20, 53)
panel.projection = 'lcc'
panel.layers = ['coastline', 'borders', 'states', 'land']
panel.plots = [obs]
# Bringing it all together
pc = PanelContainer()
pc.size = (15, 10)
pc.panels = [panel]
pc.draw()
obs.level = 300 * units.hPa
return pc.figure
def test_declarative_sfc_obs_full(ccrs):
"""Test making a full surface observation plot."""
data = pd.read_csv(get_test_data('SFC_obs.csv', as_file_obj=False),
infer_datetime_format=True,
parse_dates=['valid'])
obs = PlotObs()
obs.data = data
obs.time = datetime(1993, 3, 12, 13)
obs.time_window = timedelta(minutes=15)
obs.level = None
obs.fields = ['tmpf', 'dwpf', 'emsl', 'cloud_cover', 'wxsym']
obs.locations = ['NW', 'SW', 'NE', 'C', 'W']
obs.colors = ['red', 'green', 'black', 'black', 'blue']
obs.formats = [
None, None, lambda v: format(10 * v, '.0f')[-3:], 'sky_cover',
'current_weather'
]
obs.vector_field = ('uwind', 'vwind')
obs.reduce_points = 1
# Panel for plot with Map features
panel = MapPanel()
panel.layout = (1, 1, 1)
panel.area = (-124, -72, 20, 53)
panel.area = 'il'
panel.projection = ccrs.PlateCarree()
panel.layers = ['coastline', 'borders', 'states']
panel.plots = [obs]
# Bringing it all together
pc = PanelContainer()
pc.size = (10, 10)
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
def test_ndim_error_scalar(cfeature):
"""Make sure we get a useful error when the field is not set."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = None
panel = MapPanel()
panel.area = (-110, -60, 25, 55)
panel.projection = 'lcc'
panel.layers = [cfeature.LAKES]
panel.plots = [contour]
pc = PanelContainer()
pc.panel = panel
with pytest.raises(ValueError):
pc.draw()
def test_projection_object(ccrs, cfeature):
"""Test that we can pass a custom map projection."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.level = 700 * units.hPa
contour.field = 'Temperature'
panel = MapPanel()
panel.area = (-110, -60, 25, 55)
panel.projection = ccrs.Mercator()
panel.layers = [cfeature.LAKES]
panel.plots = [contour]
pc = PanelContainer()
pc.panel = panel
pc.draw()
return pc.figure
def test_declarative_multiple_sfc_obs_change_units(ccrs):
"""Test making a surface observation plot."""
data = parse_metar_file(get_test_data('metar_20190701_1200.txt',
as_file_obj=False),
year=2019,
month=7)
obs = PlotObs()
obs.data = data
obs.time = datetime(2019, 7, 1, 12)
obs.time_window = timedelta(minutes=15)
obs.level = None
obs.fields = [
'air_temperature', 'dew_point_temperature', 'air_pressure_at_sea_level'
]
obs.locations = ['NW', 'W', 'NE']
obs.colors = ['red', 'green', 'black']
obs.reduce_points = 0.75
obs.plot_units = ['degF', 'degF', None]
# Panel for plot with Map features
panel = MapPanel()
panel.layout = (1, 1, 1)
panel.projection = ccrs.PlateCarree()
panel.area = 'in'
panel.layers = ['states']
panel.plots = [obs]
# Bringing it all together
pc = PanelContainer()
pc.size = (12, 12)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_events():
"""Test that resetting traitlets properly propagates."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = 850 * units.hPa
contour.contours = 30
contour.linewidth = 1
contour.linecolor = 'red'
img = ImagePlot()
img.data = data
img.field = 'v_wind'
img.level = 700 * units.hPa
img.colormap = 'hot'
img.image_range = (3000, 5000)
panel = MapPanel()
panel.area = 'us'
panel.projection = 'lcc'
panel.layers = ['coastline', 'borders', 'states']
panel.plots = [contour, img]
pc = PanelContainer()
pc.size = (8, 8.0)
pc.panels = [panel]
pc.draw()
# Update some properties to make sure it regenerates the figure
contour.linewidth = 2
contour.linecolor = 'green'
contour.level = 700 * units.hPa
contour.field = 'Specific_humidity'
img.field = 'Geopotential_height'
img.colormap = 'plasma'
img.colorbar = 'horizontal'
return pc.figure
def test_declarative_barb_earth_relative():
"""Test making a contour plot."""
import numpy as np
data = xr.open_dataset(get_test_data('NAM_test.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Geopotential_height_isobaric'
contour.level = 300 * units.hPa
contour.linecolor = 'red'
contour.linestyle = '-'
contour.linewidth = 2
contour.contours = np.arange(0, 20000, 120).tolist()
barb = BarbPlot()
barb.data = data
barb.level = 300 * units.hPa
barb.time = datetime(2016, 10, 31, 12)
barb.field = [
'u-component_of_wind_isobaric', 'v-component_of_wind_isobaric'
]
barb.skip = (5, 5)
barb.color = 'black'
barb.barblength = 6.5
barb.earth_relative = False
panel = MapPanel()
panel.area = (-124, -72, 20, 53)
panel.projection = 'lcc'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [contour, barb]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_contour_cam():
"""Test making a contour plot with CAM data."""
data = xr.open_dataset(get_test_data('CAM_test.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'PN'
contour.time = datetime.strptime('2020-11-29 00:00', '%Y-%m-%d %H:%M')
contour.level = 1000 * units.hPa
contour.linecolor = 'black'
contour.contours = list(range(0, 1200, 4))
panel = MapPanel()
panel.plots = [contour]
panel.layout = (1, 1, 1)
panel.layers = ['coastline', 'borders', 'states', 'land']
panel.plots = [contour]
pc = PanelContainer()
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_station_plot_fontsize():
"""Test adjusting the font size for station plots in PlotObs."""
data = parse_metar_file(get_test_data('metar_20190701_1200.txt',
as_file_obj=False),
year=2019,
month=7)
obs = PlotObs()
obs.data = data
obs.time = datetime(2019, 7, 1, 12)
obs.time_window = timedelta(minutes=15)
obs.level = None
obs.fields = [
'cloud_coverage', 'air_temperature', 'dew_point_temperature',
'air_pressure_at_sea_level', 'current_wx1_symbol'
]
obs.plot_units = [None, 'degF', 'degF', None, None]
obs.locations = ['C', 'NW', 'SW', 'NE', 'W']
obs.formats = [
'sky_cover', None, None, lambda v: format(v * 10, '.0f')[-3:],
'current_weather'
]
obs.reduce_points = 3
obs.vector_field = ['eastward_wind', 'northward_wind']
obs.fontsize = 8
panel = MapPanel()
panel.area = 'centus'
panel.projection = 'lcc'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [obs]
pc = PanelContainer()
pc.size = (8, 8)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_declarative_figsize():
"""Test having an all float figsize."""
data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
contour = ContourPlot()
contour.data = data
contour.field = 'Temperature'
contour.level = 700 * units.hPa
contour.contours = 30
contour.linewidth = 1
contour.linecolor = 'red'
panel = MapPanel()
panel.area = 'us'
panel.projection = 'lcc'
panel.layers = ['coastline', 'borders', 'usstates']
panel.plots = [contour]
pc = PanelContainer()
pc.size = (10.5, 10.5)
pc.panels = [panel]
pc.draw()
return pc.figure
def test_attribute_error_no_suggest():
"""Test that a mistyped attribute name raises an exception w/o a fix."""
with pytest.raises(AttributeError) as excinfo:
panel = MapPanel()
panel.galaxy = 'Andromeda'
assert 'Perhaps you meant' not in str(excinfo.value)
def test_attribute_error_suggest():
"""Test that a mistyped attribute name raises an exception with fix."""
with pytest.raises(AttributeError) as excinfo:
panel = MapPanel()
panel.pots = []
assert "Perhaps you meant 'plots'?" in str(excinfo.value)
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]
narr = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
###########################
# Create a contour plot of temperature
contour = ContourPlot()
contour.data = narr
contour.field = 'Temperature'
contour.level = 850 * units.hPa
contour.linecolor = 'red'
contour.contours = 15
###########################
# Create an image plot of Geopotential height
img = ImagePlot()
img.data = narr
img.field = 'Geopotential_height'
img.level = 850 * units.hPa
###########################
# Plot the data on a map
panel = MapPanel()
panel.area = 'us'
panel.layers = ['coastline', 'borders', 'states', 'rivers', 'ocean', 'land']
panel.title = 'NARR Example'
panel.plots = [contour, img]
pc = PanelContainer()
pc.size = (10, 8)
pc.panels = [panel]
pc.show()
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
Simple Plotting
===============
Demonstrate the use of MetPy's simplified plotting interface.
Plots a sample satellite image file.
"""
import xarray as xr
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import ImagePlot, MapPanel, PanelContainer
data = xr.open_dataset(GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini')))
img = ImagePlot()
img.data = data
img.field = 'IR'
img.colormap = 'Greys_r'
panel = MapPanel()
panel.plots = [img]
pc = PanelContainer()
pc.panels = [panel]
pc.show()
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'
###########################
# Add the geometry plots to a panel and container. Finally, we are left with a complete plot of
# wind speed probabilities, along with some select cities and their specific probabilities.
panel = MapPanel()
panel.title = 'NHC 5-Day Tropical-Storm-Force Wind Probabilities (Valid 12z Aug 20 2021)'
panel.plots = [wind_geo, city_geo]
panel.area = [-90, -52, 27, 48]
panel.projection = 'mer'
panel.layers = ['lakes', 'land', 'ocean', 'states', 'coastline', 'borders']
pc = PanelContainer()
pc.size = (12, 10)
pc.panels = [panel]
pc.show()
# SPDX-License-Identifier: BSD-3-Clause
"""
Simple Plotting
===============
Demonstrate the use of MetPy's simplified plotting interface.
Plots a sample satellite image file.
"""
import xarray as xr
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import ImagePlot, MapPanel, PanelContainer
data = xr.open_dataset(
GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini')))
img = ImagePlot()
img.data = data
img.field = 'IR'
img.colormap = 'Greys_r'
panel = MapPanel()
panel.plots = [img]
pc = PanelContainer()
pc.panels = [panel]
pc.show()
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']
pc = PanelContainer()
pc.size = (12, 8)
pc.panels = [panel]
pc.show()
########################### # Create a contour plot of temperature contour = ContourPlot() contour.data = narr contour.field = 'Temperature' contour.level = 850 * units.hPa contour.linecolor = 'red' contour.contours = 15 ########################### # Create an image plot of Geopotential height img = ImagePlot() img.data = narr img.field = 'Geopotential_height' img.level = 850 * units.hPa ########################### # Plot the data on a map panel = MapPanel() panel.area = 'us' panel.layers = ['coastline', 'borders', 'states', 'rivers', 'ocean', 'land'] panel.title = 'NARR Example' panel.plots = [contour, img] pc = PanelContainer() pc.size = (10, 8) pc.panels = [panel] pc.show()
