t_end = 1
pfx = 'drysoilCLM'
time_title_str = None
else:
t_end = 1
pfx = pfx + '_timeavg'
vd.aggregate_time(time_avg=True)
if varname is "fogpresent":
print("multiplying fogpresent avg by 100 to get a percent")
for k in vd.data.keys():
vd.data[k] = vd.data[k] * 100
time_title_str = 'June 2005'
for this_t in range(0, t_end): #
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx=pfx,
savedir=out_dir,
time_title_str=time_title_str)
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig = plotter.plot(cb_orientation=cb_orientation,
vmin=1,
vmax=21,
mask=vd.insignificant_mask)
print('done driver ({})'.format(datetime.datetime.now() - t0))
vd = var_diff(
os.path.join(ctl_dir_new, 'summen_sensitivity_ctl',
'*d{:02d}_2009-06-02*.nc'.format(DOMAIN)),
os.path.join(urb_dir, 'summen_sensitivity_urban',
'*d{:02d}_2009-06-02*.nc'.format(DOMAIN)),
label_A='ctl',
label_B='urban',
# varname='LCL')
varname='QCLOUD')
# varname='uvmet')
# varname='wa')
# varname='LU_INDEX')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=False)
for this_t in range(0, 47): #
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx='QCLOUD_debug04')
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig = plotter.plot(cb_orientation=cb_orientation)
# vmin=0,
# vmax=500)
t_end = 200
else:
t_end = 1
pfx = pfx + '_timeavg'
vd.aggregate_time(time_avg=True)
if varname is "fogpresent":
print("multiplying fogpresent avg by 100 to get a percent")
for k in vd.data.keys():
vd.data[k] = vd.data[k] * 100
time_title_str = 'June 2005'
for this_t in range(10, t_end): #
if this_series == 'single':
time_title_str = vd.time[this_t].strftime('%Y-%m-%d_%H%M')
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx=pfx,
savedir=out_dir,
time_title_str=time_title_str)
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig = plotter.plot(cb_orientation=cb_orientation,
vmin=None,
vmax=None,
mask=None)
print('done driver ({})'.format(datetime.datetime.now() - t0))
data_orig = copy.copy(vd.data)
for i, this_t in enumerate(pvals_xr.t.values):
last_i = i
for i, this_t in enumerate(pvals_xr.t.values):
if (i <= last_i) and (i > 0):
vd.d = pvals_xr.mean_diff.data[i, ...]
vd.abs_max = np.nanmax(np.abs(pvals_xr.mean_diff.data))
for k in data_orig.keys():
vd.data[k] = np.mean(data_orig[k][:pvals_xr.idx.values[i],
...],
axis=0,
keepdims=True)
plotter = VarDiffPlotter(
vd,
fig_type='png',
t_idx=0,
layer=0,
domain=DOMAIN,
pfx='pvals_{}_{}'.format(varname, runkey),
# savedir='/Users/tim/work/Plots/Summen/',
savedir=os.path.join(cscratchdir, 'plots_temporary'),
time_title_str=np.datetime_as_string(this_t, unit='m'),
show_title=False)
p_insignificant95 = twotail_signif(pvals_xr.p.data[i, ...], 0.95)
p_insignificant99 = twotail_signif(pvals_xr.p.data[i, ...], 0.99)
d_LT_epsilon = np.abs(vd.d) < 1e-6
fig = plotter.plot(cb_orientation='vertical',
mask=np.logical_or(p_insignificant95,
d_LT_epsilon))
# mask=pvals_xr.p.data[i, ...] < 0.995)
# for this_series in ['all_tstamps', 'time_avg']:
t_end = 1
pfx = pfx + '_timeavg'
vd.aggregate_time(time_avg=True)
time_title_str = 'June 2005'
for k in vd.data.keys():
vd.data[k][...] = np.ones(vd.data[k].shape)
xy = get_wrf_xy((SanFrancisco, SantaCruz))
vd.data['ctl'][0, xy.data[1, 0], xy.data[0, 0]] = 50
vd.data['no_urban_CLM'][0, xy.data[1, 1], xy.data[0, 1]] = 100
for this_t in range(0, t_end): #
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx=pfx,
savedir='.',
time_title_str=time_title_str)
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig, ax = plotter.plot(cb_orientation=cb_orientation,
vmin=0,
vmax=100,
mask=None) #vd.p > 0.05)
# varname='wa')
# varname='LU_INDEX')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=False)
# vd = sum_layers(vd, time_avg=True)
vd = is_foggy_obrien_2013(vd)
pfx = 'urban'
for this_series in ['all_tstamps', 'time_avg']:
if this_series == 'all_tstamps':
t_end = 1440
pfx = '2018-02-08'
else:
t_end = 1
pfx = pfx + '2018-02-08_timeavg'
vd = sum_layers(vd, time_avg=True)
for this_t in range(0, t_end): #
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx=pfx)
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig = plotter.plot(cb_orientation=cb_orientation)
# vmin=0,
# vmax=500)
# for this_series in ['all_tstamps', 'time_avg']:
for this_series in ['time_avg']:
if this_series == 'all_tstamps':
t_end = 1
pfx = 'nourbanCLM_1day'
time_title_str = None
else:
t_end = 1
pfx = pfx + '_timeavg'
vd.aggregate_time(time_avg=True)
time_title_str = ''
for this_t in range(0, t_end): #
plotter = VarDiffPlotter(vd,
t_idx=this_t,
layer=0,
domain=DOMAIN,
pfx=pfx,
savedir='.',
time_title_str=time_title_str)
if DOMAIN == 1:
cb_orientation = 'horizontal'
else:
cb_orientation = 'vertical'
fig = plotter.plot(cb_orientation=cb_orientation,
vmin=None,
vmax=None,
mask=vd.p > PVAL,
hatch_z_score=True)
# mask=None)
# ##################################################
ax[3].set_extent(bbox_SFBay, crs=ccrs.PlateCarree())
ax[4].set_extent(bbox_LA, crs=ccrs.PlateCarree())
ax[5].set_extent(bbox_LA, crs=ccrs.PlateCarree())
cbar = fig.colorbar(cm,
ax=ax,
cmap=cmap,
extend='neither',
ticks=np.linspace(0.5, 21.5, 21))
cbar.set_ticks(np.linspace(0.5, 21.5, 21))
cbar.set_ticklabels(list(ctable['long_name']))
fig.savefig(fname=os.path.join('/', 'global', 'cscratch1', 'sd',
'twhilton', 'plots_temporary',
'land_use_dominant.png'))
for this_pft in range(3): # range(len(ctable)):
vd_LUfrac = get_LUfrac_diff(this_pft, wrfin)
lu_str = '{:02d}-{}'.format(
ctable['PFTnum'][this_pft],
ctable['long_name'][this_pft].replace('/', ''))
plotter = VarDiffPlotter(
vd_LUfrac,
t_idx=0,
layer=0,
domain=2,
pfx=lu_str,
savedir=os.path.join('/', 'global', 'cscratch1', 'sd',
'twhilton', 'plots_temporary'),
time_title_str=lu_str)
fig = plotter.plot(vmin=0.0, vmax=1.0)