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_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)
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)
# 1-d timeseries
var = data['U200'].sel(dayrel=day)
ts = atm.mean_over_geobox(var, 10, 30, 60, 100)
ts_reg = atm.Linreg(onset, ts)
ts_reg2 = atm.regress_field(ts, onset)
print(ts_reg.r, ts_reg2.r.values)
print(ts_reg.slope, ts_reg2.m.values)
print(ts_reg.p, ts_reg2.p.values)
# x-y data
regdays = [-60, -30, 0, 30, 60]
plotdays = [-60, -30]
clev_r = np.arange(-1.0, 1.01, 0.05)
for nm in varnms:
print(nm)
var = data[nm].sel(dayrel=regdays)
reg_daily = atm.regress_field(var, onset, axis=0)
for day in plotdays:
reg = reg_daily.sel(dayrel=day)
title = '%s day %d vs. Onset ' % (var.name, day)
cint_m = atm.cinterval(reg.m)
clev_m = atm.clevels(reg.m, cint_m, symmetric=True)
plt.figure(figsize=(11, 8))
plt.subplot(1, 2, 1)
atm.contourf_latlon(reg['r'], clev=clev_r, cmap='RdBu_r')
plt.title(title + ' - Corr Coeff')
plt.subplot(1, 2, 2)
atm.contourf_latlon(reg['m'], clev=clev_m, cmap='RdBu_r')
plt.title(title + ' - Reg Coeff')
# 1-d timeseries
var = data['U200'].sel(dayrel=day)
ts = atm.mean_over_geobox(var, 10, 30, 60, 100)
ts_reg = atm.Linreg(onset, ts)
ts_reg2 = atm.regress_field(ts, onset)
print(ts_reg.r, ts_reg2.r.values)
print(ts_reg.slope, ts_reg2.m.values)
print(ts_reg.p, ts_reg2.p.values)
# x-y data
regdays = [-60, -30, 0, 30, 60]
plotdays = [-60, -30]
clev_r = np.arange(-1.0, 1.01, 0.05)
for nm in varnms:
print(nm)
var = data[nm].sel(dayrel=regdays)
reg_daily = atm.regress_field(var, onset, axis=0)
for day in plotdays:
reg = reg_daily.sel(dayrel=day)
title = '%s day %d vs. Onset ' % (var.name, day)
cint_m = atm.cinterval(reg.m)
clev_m = atm.clevels(reg.m, cint_m, symmetric=True)
plt.figure(figsize=(11, 8))
plt.subplot(1, 2, 1)
atm.contourf_latlon(reg['r'], clev=clev_r, cmap='RdBu_r')
plt.title(title + ' - Corr Coeff')
plt.subplot(1, 2, 2)
atm.contourf_latlon(reg['m'], clev=clev_m, cmap='RdBu_r')
plt.title(title + ' - Reg Coeff')
