def pts_clim(index_pts, nm, clev_bar=10, clev_std=np.arange(0, 21, 1),
axlims=(5, 32, 60, 100), cmap='spectral', res='c',
label_locs=None, inline_spacing=2):
"""Plot climatological mean and standard deviation of grid point indices."""
varbar = index_pts[nm].mean(dim='year')
varstd = index_pts[nm].std(dim='year')
lat1, lat2, lon1, lon2 = axlims
m = atm.init_latlon(lat1, lat2, lon1, lon2, resolution=res)
m = atm.contourf_latlon(varstd, m=m, clev=clev_std, axlims=axlims, cmap=cmap,
symmetric=False, colorbar=False, extend='max')
m.colorbar(ticks=np.arange(0, 21, 2))
_, cs = atm.contour_latlon(varbar, clev=clev_bar, axlims=axlims, colors='k',
linewidths=2)
cs_opts = {'fmt' : '%.0f', 'fontsize' : 9,
'inline_spacing' : inline_spacing}
if label_locs is not None:
cs_opts['manual'] = label_locs
plt.clabel(cs, **cs_opts)
fix_axes(axlims)
def plot_maps(var, days, grp, cmin=0, cmax=20, cint=1, axlims=(5, 35, 60, 100),
cmap='PuBuGn', res='c', extend='max', cticks=None,
daypos=(0.05, 0.85)):
"""Lat-lon maps of precip on selected days."""
clev = np.arange(cmin, cmax + cint/2.0, cint)
if cticks is None:
cticks = np.arange(cmin, clev.max() + 1, 2)
lat1, lat2, lon1, lon2 = axlims
for day in days:
grp.next()
pcp = var.sel(dayrel=day)
m = atm.init_latlon(lat1, lat2, lon1, lon2, resolution=res)
m = atm.contourf_latlon(pcp, m=m, clev=clev, axlims=axlims, cmap=cmap,
colorbar=False, extend=extend)
atm.text(day, daypos, fontsize=12, fontweight='bold')
# plt.colorbar(ax=grp.axes.ravel().tolist(), orientation='vertical',
# shrink=0.8, ticks=cticks)
atm.colorbar_multiplot(orientation='vertical', shrink=0.8, ticks=cticks)
fix_axes(axlims)
exts = ['png', 'eps'] isave = True howi, ds = onset_HOWI(uq_int, vq_int, npts) # ---------------------------------------------------------------------- # MAPS # ---------------------------------------------------------------------- # Plot climatological VIMT composites lat = atm.get_coord(ds, 'lat') lon = atm.get_coord(ds, 'lon') x, y = np.meshgrid(lon, lat) axlims = (lat1, lat2, lon1, lon2) plt.figure(figsize=(12,10)) plt.subplot(221) m = atm.init_latlon(lat1, lat2, lon1, lon2) m.quiver(x, y, ds['uq_bar_pre'], ds['vq_bar_pre']) plt.title(pre_days + ' VIMT Climatology') plt.subplot(223) m = atm.init_latlon(lat1, lat2, lon1, lon2) m.quiver(x, y, ds['uq_bar_post'], ds['vq_bar_post']) plt.title(post_days + ' VIMT Climatology') # Plot difference between pre- and post- composites plt.subplot(222) m = atm.init_latlon(lat1, lat2, lon1, lon2) #m, _ = atm.pcolor_latlon(ds['vimt_bar_diff'], axlims=axlims, cmap='hot_r') m.quiver(x, y, ds['uq_bar_diff'], ds['vq_bar_diff']) plt.title(post_days + ' minus ' + pre_days + ' VIMT Climatology') # Top N difference vectors