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 plot_all_WLH(wlh, y=0, axlims=(0,50,50,180), cmap='jet', clines=True):
onset = wlh['onset']
Rsq = wlh['Rsq']
lat = wlh['lat']
lon = wlh['lon']
kmax = wlh['smoothing_kmax']
years=wlh['years']
if wlh['climatology']:
titlestr = 'CMAP %d-%d Climatology' % (years.min(), years.max())
else:
onset = onset[y]
Rsq = Rsq[y]
titlestr = 'CMAP %d' % years[y]
# Note: add 1 to pentad indices to index from 1-73 for comparison
# with Wang & LinHo
onset = onset + 1
# Calculate onset dates from pentads
nlat, nlon = onset.shape
onset_date = np.nan * np.ones((nlat, nlon))
for i in range(nlat):
for j in range(nlon):
jday = atm.pentad_to_jday(onset[i, j], pmin=1)
mon, day = atm.jday_to_mmdd(jday)
onset_date[i, j] = 100*mon + day
# -- Smooth with cubic spline
# lat_i = np.arange(-89.5, 90, 0.5)
# lon_i = np.arange(0, 360, 0.5)
lat_i = np.arange(-90, 90, 4.)
lon_i = np.arange(0, 360, 4.)
onset_date = atm.interp_latlon(onset_date, lat_i, lon_i, lat, lon, order=3)
clev_date = [501, 511, 521, 601, 611, 621, 701, 711]
clev_label = {}
for d in clev_date:
clev_label[d] = '%02d-%02d' % (d//100, d%100)
# Plot maps
manual = False
plt.figure(figsize=(14,10))
cmin, cmax = 20, 55
plt.subplot(211)
_, pc = atm.pcolor_latlon(onset, lat, lon, cmap=cmap, axlims=axlims)
pc.set_clim(cmin, cmax)
if clines:
_, cs = atm.contour_latlon(onset_date, lat_i, lon_i, clev=clev_date,
axlims=axlims)
plt.clabel(cs, clev_date, fmt=clev_label, manual=manual)
plt.title(titlestr + ' Onset Pentad & Day')
else:
plt.title(titlestr + ' Onset Pentad')
plt.subplot(212)
clev = np.arange(0, 1.1, 0.025)
_, pc = atm.pcolor_latlon(Rsq, lat, lon, cmap=cmap, axlims=axlims)
pc.set_clim(0., 1.)
plt.title('$R^2$ for kmax = %d' % kmax)
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)
# ======================================================================
# PLOTS
# ======================================================================
# ----------------------------------------------------------------------
# Map showing averaging regions
axlims = (-45, 45, 0, 150)
mask = {}
for n in [50, 100]:
mask1 = ds_howi[n]['mask']
mask[n] = xray.DataArray(mask1.astype(float), dims=mask1.dims,
coords=mask1.coords)
plt.figure(figsize=(10, 7))
m, cs = atm.contour_latlon(mask[100], clev=[0.99], colors='red', axlims=axlims)
_, cs2 = atm.contour_latlon(mask[50], m=m, clev=[0.99], colors='red',
linestyles='dashed')
latlon = index['TT'].attrs['north']
atm.geobox(latlon[0], latlon[1], latlon[2], latlon[3], m=m, color='green',
label='TT (T200-600)')
latlon = index['TT'].attrs['south']
atm.geobox(latlon[0], latlon[1], latlon[2], latlon[3], m=m, color='green')
latlon = index['OCI'].attrs['latlon']
atm.geobox(latlon[0], latlon[1], latlon[2], latlon[3], m=m, color='blue',
label='OCI (U850)')
