def plot_variance_maps(E,cbar=True,hostname="taurus"):
# given a certain experiment or dataset over a certain daterange,
# plot the MJO-related variance on a map
# load the variance map
VV,lat,lon = variance_maps(E,hostname=hostname)
# set up the map projection
map = Basemap(projection='mill',llcrnrlat=-90,urcrnrlat=90,\
#llcrnrlon=-180,urcrnrlon=180,resolution='c')
llcrnrlon=0,urcrnrlon=360,resolution='c')
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30),linewidth=0.25)
map.drawparallels(np.arange(-90,90,30),linewidth=0.25)
# compute native map projection coordinates of lat/lon grid.
X,Y = np.meshgrid(lon,lat)
x, y = map(X, Y)
# choose color map based on the variable in question
E['extras'] = 'MJO variance'
colors,cmap,cmap_type = DSS.state_space_HCL_colormap(E)
## if no color limits are specified, at least make them even on each side
#if clim is None:
# clim = np.nanmax(np.absolute(VV))
#print('------clim----------')
#print(clim)
# contour data over the map.
cs = map.contourf(x,y,VV,15,cmap=cmap)
#cs = map.contourf(x,y,M,len(colors)-1,colors=colors)
#cs = map.contourf(X,Y,M,len(colors)-1,cmap=cmap,extend="both",vmin=-clim,vmax=clim)
# apply color limits, but not if it's a log contour plot
#if log_levels is None:
# print('applying color limits')
# if cmap_type == 'divergent':
# plt.clim([-clim,clim])
# else:
# plt.clim([0,clim])
#if cbar:
# if (clim > 1000):
# CB = plt.colorbar(cs, shrink=0.6, extend='both',format='%.1e')
# if (clim < 0.001):
# CB = plt.colorbar(cs, shrink=0.6, extend='both',format='%.1e')
# else:
CB = plt.colorbar(cs, shrink=0.6, extend='both')
def plot_correlations_lag_lat_or_lon(E,climatology_option='NODA',maxlag=25,lag_versus_what='lon',nilter_order=50,cbar=True,hostname="taurus",debug=False):
"""
given a certain experiment or dataset over a certain daterange,
plot the correlation between wind or precip anomalies in one reference
region, relative to everywhere else, either
as a function of latitude and longite, and Lag.
this should produce figures like Figs. 5-6 of Waliser et al.
INPUTS:
E - a standard DART experiment dictionary, with the variable field and level range corresponding to some MJO variable
maxlag: the limit of the lag (in days) that we look at
lag_versus_what: choose 'lat' or 'lon'
cbar: set to True to have a colorbar
hostname: computer name - default is Taurus
climatology_option: choose which climatology to take the anomalies to respect with -- default is "NODA"
"""
# load the correlation field
R,S,L,x = correlations_lag_lat_or_lon(E,maxlag,lag_versus_what,filter_order,climatology_option,hostname=hostname,verbose=debug)
# choose color map based on the variable in question
E['extras'] = 'Correlation'
colors,cmap,cmap_type = DSS.state_space_HCL_colormap(E,reverse=True)
# choose axis labels
plt.ylabel('Lag (days)')
if lag_versus_what=='lat':
plt.xlabel('Latitude')
if lag_versus_what=='lon':
plt.xlabel('Longitude')
# set the contour levels - it depends on the color limits and the number of colors we have
clim = 1.0
if cmap_type == 'divergent':
clevels = np.linspace(start=-clim,stop=clim,num=11)
else:
clevels = np.linspace(start=0,stop=clim,num=11)
# contour plot of the chosen variable
cs = plt.contourf(x,L,R,levels=clevels,cmap=cmap)
plt.clim([-1.0,1.0])
if (cbar is not None):
CB = plt.colorbar(cs, shrink=0.6, extend='both', orientation=cbar)
return x,L,R,S
