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_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
pc.size = (15, 15)
pc.panels = [panel]
# Show the figure
pc.show()
#########################################################################
# Plot Surface Obs
# ----------------
#
# We can also plot surface (or upper-air) observations at point locations using the simplified
# syntax. Whether it is surface or upper-air data, the ``PlotObs()`` class is what you would
# want to use. Then you would add those observations to a map panel and then collect the panels
# to plot the figure; similar to what you would do for a gridded plot.
df = metar.parse_metar_file(get_test_data('metar_20190701_1200.txt', False),
year=2019,
month=7)
# Let's take a look at the variables that we could plot coming from our METAR observations.
print(df.keys())
# Set the observation time
obs_time = datetime(2019, 7, 1, 12)
#########################################################################
# Setting of our attributes for plotting observations is pretty straignforward and just needs
# to be lists for the variables, and a comparable number of items for plot characteristics that
# are specific to the individual fields. For example, the locations around a station plot, the
# plot units, and any plotting formats would all meed to have the same number of items as the
# fields attribute.
#
import matplotlib.pyplot as plt
from metpy.calc import reduce_point_density
from metpy.cbook import get_test_data
from metpy.io import metar
from metpy.plots import add_metpy_logo, current_weather, sky_cover, StationPlot
###########################################
# The setup
# ---------
#
# First read in the data. We use the metar reader because it simplifies a lot of tasks,
# like dealing with separating text and assembling a pandas dataframe
# https://thredds-test.unidata.ucar.edu/thredds/catalog/noaaport/text/metar/catalog.html
data = metar.parse_metar_file(get_test_data('metar_20190701_1200.txt', as_file_obj=False))
# Drop rows with missing winds
data = data.dropna(how='any', subset=['wind_direction', 'wind_speed'])
###########################################
# This sample data has *way* too many stations to plot all of them. The number
# of stations plotted will be reduced using `reduce_point_density`.
# Set up the map projection
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
standard_parallels=[35])
# Use the Cartopy map projection to transform station locations to the map and
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['longitude'].values,
from io import StringIO
from urllib.request import urlopen
from metpy.io import metar
from metpy.plots.declarative import *
from metpy.units import units
# Set Date
date = datetime(2020, 12, 20, 12)
# Get Data
data = StringIO(
urlopen('http://bergeron.valpo.edu/current_surface_data/'
f'{date:%Y%m%d%H}_sao.wmo').read().decode('utf-8',
'backslashreplace'))
df = metar.parse_metar_file(data, year=date.year, month=date.month)
# Plot Data
obs = PlotObs()
obs.data = df
obs.time = date
obs.time_window = timedelta(minutes=15)
obs.level = None
obs.fields = ['air_temperature']
# Panel for plot with Map features
panel = MapPanel()
panel.layout = (1, 1, 1)
panel.projection = 'lcc'
panel.area = 'in'
panel.layers = ['states']
