def latlon_plot(varnm, reg, day_or_season, coeff='m', stipple_kw={},
axlims=(-60, 60, 40, 120)):
regdata = reg[varnm + '_latlon']
keys = [key for key in regdata if key.endswith('_' + coeff)]
clim = atm.climits(regdata[keys].to_array(), symmetric=True,
percentile=99.9)
xname, yname = 'lon', 'lat'
lat = atm.get_coord(regdata, 'lat')
if max(np.diff(lat)) > 1:
xsample, ysample = 1, 1
else:
xsample, ysample = 2, 2
if isinstance(day_or_season, int):
key = varnm + '_DAILY_'
var = regdata[key + coeff].sel(dayrel=day_or_season)
p = regdata[key + 'p'].sel(dayrel=day_or_season)
titlestr = varnm + ' Day %d' % day_or_season
else:
key = varnm + '_' + day_or_season + '_'
var = regdata[key + coeff]
p = regdata[key + 'p']
titlestr = varnm + ' ' + day_or_season
pts_mask = stipple_mask(p)
atm.pcolor_latlon(var, axlims=axlims, fancy=False)
plt.clim(clim)
atm.stipple_pts(pts_mask, xname, yname, xsample, ysample, **stipple_kw)
plt.title(titlestr)
def plot_reg(pts_reg, nm, clev=0.2, xsample=1, ysample=1,
axlims=(5, 32, 60, 100), cline=None, color='0.3', alpha=1.0,
markersize=2):
"""Plot regression of grid point indices onto large-scale index."""
var = pts_reg[nm]['m']
mask = pts_reg[nm]['pts_mask']
xname = atm.get_coord(mask, 'lon', 'name')
yname = atm.get_coord(mask, 'lat', 'name')
atm.contourf_latlon(var, clev=clev, axlims=axlims, extend='both')
atm.stipple_pts(mask, xname=xname, yname=yname, xsample=xsample,
ysample=ysample, color=color, alpha=alpha,
markersize=markersize)
if cline is not None:
atm.contour_latlon(var, clev=[cline], axlims=axlims, colors='b',
linewidths=2)
fix_axes(axlims)
def sector_plot(var, p, stipple_kw={}, grp=None, ylim=None,
yticks=None, clim=None):
xname, yname = 'dayrel', 'lat'
pts_mask = stipple_mask(p)
lat = atm.get_coord(var, 'lat')
days = atm.get_coord(var, 'dayrel')
xsample = 3
if max(np.diff(lat)) > 1:
ysample = 1
else:
ysample = 2
#utils.contourf_lat_time(lat, days, var)
vals = np.ma.masked_array(var.T.values, mask=np.isnan(var.T))
plt.pcolormesh(days, lat, vals, cmap='RdBu_r')
cb = plt.colorbar()
atm.stipple_pts(pts_mask, xname, yname, xsample, ysample, **stipple_kw)
plt.title(varnm)
plt.xlabel('Relative Day')
plt.ylabel('Latitude')
plt.grid(True)
xticks = np.arange(-120, 201, 30)
xlims = (-120, 200)
plt.xlim(xlims)
plt.xticks(xticks)
if grp is not None:
if grp.col > 0:
plt.ylabel('')
if grp.row < grp.nrow - 1:
plt.xlabel('')
if ylim is not None:
plt.ylim(ylim)
if yticks is not None:
plt.yticks(yticks)
if clim is not None:
plt.clim(clim)
else:
clim = atm.climits(var, symmetric=True, percentile=99.9)
plt.clim(clim)