# (C) British Crown Copyright 2017, Met Office
#
# This code is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This code is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# Plot reanalysis pressure and DWR observations
import iris
import iris.analysis
import matplotlib
import matplotlib.cm
import cartopy.crs as ccrs
import numpy
import pandas
import Meteorographica as mg
import collections
[docs]def plot_contour(ax,extent,dte,field,obs_r,dwr_obs,**kwargs):
"""Make a mslp contour plot with DWR observations
Args:
ax (:`cartopy.axes`): Axes to hold the plot
extent (:obj:`list`) Plot extent (lon.min,lon.max,lat.min,lat.max)
dte (:obj:`datetime.datetime`): Time of plot
field (:obj:`iris.Cube.cube`): Reanalysis mslp ensemble
obs_r (:obj:`pandas.DataFrame`): Reanalysis observations.
dwr_obs (:obj:`pandas.DataFrame`): DWR observations.
Kwargs:
projection (:obj:`cartopy.crs`): Map projection to use, default UKV.
obs_radius (:obj:`float`): Reanalysis obs plot size, default 0.15.
contour_levels (:obj:`list`): Values to plot contours at, default numpy.arange(870,1050,10).
contour_width (:obj:`float`): Width of member contour lines, default 0.1.
contour_mask (:obj:`float`): Plot mean contours where spread < this, default 3.
n_contours (:obj:`int`): Max. number of member contour to plot, default None - all members.
label_mean_contour (:obj:`bool`): Label the mean contour lines? Default=True.
dwr_color (:obj:`str`): Either a color name, or the string 'anomaly' (default) to color by pressure anomaly.
dwr_anomaly_range (:obj:`float`): Anomaly color scale factor, default 20.
dwr_radius (:obj:`float`): DWR obs plot size, default 0.25.
stations (:obj:`list`): Names (DWR format) of stations to be included, default is stations in dwr_obs.
station_latlon (:obj:`dict`): Latitiudes and Longitudes of stations to be included, default is taken from dwr_obs.
|
"""
# Set keyword argument defaults
kwargs.setdefault('projection',ccrs.RotatedPole(pole_longitude=177.5,
pole_latitude=35.5))
kwargs.setdefault('obs_radius',0.15)
kwargs.setdefault('contour_levels',numpy.arange(870,1050,10))
kwargs.setdefault('contour_width',0.1)
kwargs.setdefault('contour_mask',3.0)
kwargs.setdefault('n_contours',None)
kwargs.setdefault('label_mean_contour',True)
if dwr_obs is not None:
kwargs.setdefault('dwr_color','anomaly')
kwargs.setdefault('dwr_anomaly_range',20.0)
kwargs.setdefault('dwr_radius',0.25)
kwargs.setdefault('stations',list(collections.OrderedDict.fromkeys(
dwr_obs.loc[:,'name']).keys()))
if 'station_latlon' not in kwargs:
latlon={}
for station in kwargs.get('stations'):
latlon[station]=DWR.get_station_location(dwr_obs,station)
kwargs['station_latlon']=latlon
ax.set_axis_off()
ax.set_extent(extent,crs=kwargs.get('projection'))
ax.background_patch.set_facecolor(ax.get_figure().get_facecolor())
mg.background.add_grid(ax)
land_img=ax.background_img(name='GreyT', resolution='low')
# Plot the DWR obs
if dwr_obs is not None:
if kwargs.get('dwr_color')=='anomaly':
cmap = matplotlib.cm.get_cmap('coolwarm')
interpolator = iris.analysis.Linear().interpolator(field,
['latitude', 'longitude'])
interpolated={}
for station in kwargs.get('stations'):
try:
interpolated[station]=DWR.at_station_and_time(
dwr_obs,station,dte)
except Exception:
interpolated[station]=None
ensemble=interpolator([kwargs.get('station_latlon')[station]['latitude'],
kwargs.get('station_latlon')[station]['longitude']])
if interpolated[station] is None:
spoof=pandas.DataFrame(data={
'latitude' : kwargs.get('station_latlon')[station]['latitude'],
'longitude': kwargs.get('station_latlon')[station]['longitude'],
'values' : 0},index=[0])
mg.observations.plot(ax,spoof,
lat_label='latitude',lon_label='longitude',
radius=kwargs.get('dwr_radius'),
facecolor='grey',edgecolor='black')
else:
stn_diff=interpolated[station]-numpy.mean(ensemble.data)
dfn=max(0.01,min(0.99,
stn_diff/kwargs.get('dwr_anomaly_range')+0.5))
stn_color=cmap(dfn)
mg.observations.plot(ax,dwr_obs[dwr_obs['name']==station],
lat_label='latitude',lon_label='longitude',
radius=kwargs.get('dwr_radius'),
facecolor=stn_color,edgecolor='black',
zorder=5)
else:
for station in kwargs.get('stations'):
mg.observations.plot(ax,dwr_obs[dwr_obs['name']==station],
lat_label='latitude',lon_label='longitude',
radius=kwargs.get('dwr_radius'),
facecolor=kwargs.get('dwr_color'),edgecolor='black',
zorder=5)
# Plot the 20CR obs
if obs_r is not None:
if 'Assimilation.indicator' in obs_r.index: # in V2 only
obs_s=obs_r.loc[(obs_r['Assimilation.indicator']==1)]
if not obs_s.empty:
mg.observations.plot(ax,obs_s,radius=kwargs.get('obs_radius'),
zorder=10)
obs_s=obs_r.loc[(obs_r['Assimilation.indicator']==0)]
if not obs_s.empty:
mg.observations.plot(ax,obs_s,radius=kwargs.get('obs_radius'),
facecolor='grey',edgecolor='yellow',
zorder=100)
else:
if not obs_r.empty:
mg.observations.plot(ax,obs_r,radius=kwargs.get('obs_radius'),
zorder=100)
# Contour spaghetti plot of ensemble members
if kwargs.get('n_contours') is not None:
field_r=field.extract(iris.Constraint(member=
field.coord('member').points[0:kwargs.get('n_contours')]))
else: field_r=field
mg.pressure.plot(ax,field_r,type='spaghetti',
resolution=0.25,
levels=kwargs.get('contour_levels'),
colors='blue',
label=False,
linewidths=kwargs.get('contour_width'),
zorder=15)
# Add the ensemble mean - with labels
field_m=field.collapsed('member', iris.analysis.MEAN)
field_s=field.collapsed('member', iris.analysis.STD_DEV)
field_m.data[numpy.where(field_s.data>kwargs.get('contour_mask'))]=numpy.nan
mg.pressure.plot(ax,field_m,
resolution=0.25,
levels=kwargs.get('contour_levels'),
colors='black',
label=kwargs.get('label_mean_contour'),
linewidths=2,
zorder=20)
