# varname = 'fogpresent'
varname = 'SMOIS'
# varname = 'fogbase'
# varname = 'fogpct'
# varname = 'QCLOUD'
# varname = 'uvmet'
# varname = 'wa'
# varname = 'LU_INDEX'
read_data = True
if read_data:
# wildcard_pat = "*d{:02d}02_2009-06-\{0[0-9],1[01]\}".format(DOMAIN)
wildcard_pat = "*d{:02d}_2005-*".format(DOMAIN)
vd = var_diff(os.path.join(ctl_dir, wildcard_pat),
os.path.join(dry_dir, wildcard_pat),
label_A='ctl',
label_B='drysoil_CLM',
varname=varname)
vd.read_files()
vd.to_netcdf(
os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'{}_d{:02d}_CLM_drysoil.nc'.format(varname, DOMAIN)))
print('done reading files ({})'.format(datetime.datetime.now() - t0))
# vd.mask_land_or_water(mask_water=False)
else:
vd = var_diff(ncfile=os.path.join(
'/', 'global', 'cscratch1', 'sd', 'twhilton',
'{varname}_d{DOMAIN:02d}_CLM_drysoil.nc'.format(varname=varname,
DOMAIN=DOMAIN)))
# vd = is_foggy_obrien_2013(vd)
# vd.get_significance_mask(significance=0.99, adj_autocorr=False)
import os
from plot_diff import var_diff, graphics
if __name__ == "__main__":
rootdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'WRFv3.9_Sensitivity', 'FromScratch')
ctl_dir = os.path.join(rootdir, 'WRFV3_Ctl', 'run')
ctl2_dir = os.path.join(rootdir, 'WRFV3_Ctl2', 'run')
vd = var_diff(os.path.join(ctl_dir, 'summen_sensitivity_ctl',
'*d01_2009-06*'),
os.path.join(ctl2_dir, 'summen_sensitivity_ctl2',
'*d01_2009-06*'),
label_A='ctl',
label_B='ctl2',
varname='QCLOUD')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=True)
for this_t in range(0, 120): # 255
fig = graphics(vd, t_idx=this_t, layer=0, domain=1, pfx='ctl2')
if __name__ == "__main__":
rootdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'WRFv3.9_Sensitivity')
ctl_dir = os.path.join(rootdir, 'WRFv3.9_Sensitivity_Ctl', 'WRFV3', 'run')
ctl_dir_new = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'WRFv3.9_Sensitivity', 'FromScratch',
'WRFV3_Ctl', 'run')
urb_dir = os.path.join(rootdir, 'FromScratch', 'WRFV3_urban', 'run')
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,
import os
from plot_diff import var_diff, graphics
if __name__ == "__main__":
rootdir = os.path.join('/', 'global', 'cscratch1', 'sd',
'twhilton', 'WRFv3.9_Sensitivity', 'FromScratch')
ctl_dir = os.path.join(rootdir, 'WRFV3_Ctl', 'run')
dry_dir = os.path.join(rootdir, 'WRFV3_Dry', 'run')
# vd = var_diff(os.path.join(ctl_dir, 'met_em.d01.2009-06-02_00:00:00.nc'),
# os.path.join(dry_dir, 'met_em.d01.2009-06-02_00:00:00.nc'),
# label_A='ctl met_em',
# label_B='dry met_em',
# varname='SM000010')
vd = var_diff(os.path.join(ctl_dir, 'summen_sensitivity_ctl',
'wrfsees_ccs3pb1_ls2_d02_2009-06-02_00:00:00'),
os.path.join(dry_dir, 'summen_sensitivity_dry',
'wrfsees_ccs3pb1_ls2_d02_2009-06-02_00:00:00'),
label_A='ctl out',
label_B='dry out',
varname='SMOIS')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=True)
for this_t in range(0, 6): # 255
fig = graphics(vd, t_idx=this_t, layer=0, domain=2)
import os
from plot_diff import var_diff, graphics
if __name__ == "__main__":
cscratch = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton')
ctl_dir = os.path.join(cscratch, 'WRFv3.9_Sensitivity',
'WRFv3.9_Sensitivity_Ctl_short', 'WRFV3',
'run')
dry_dir = os.path.join(cscratch, 'WRFv3.9_Sensitivity',
'WRFv3.9_Sensitivity_DrySoil_newrst', 'WRFV3',
'run', 'summen_sensitivity_drysoil')
vd = var_diff(os.path.join(ctl_dir, 'wrfrst_d01_2009-06-02*'),
os.path.join(dry_dir, 'wrfsees_ccs3pb1_ls2_d01_2009-06-02*'),
label_A='short_ctl rst',
label_B='short_dry out',
varname='QCLOUD')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=True)
for this_t in range(0, 1): # 255
fig = graphics(vd, t_idx=this_t, layer=0, domain=1)
out_dir = os.path.join(cscratchdir, 'plots_temporary')
# insignificant_pixels = ma.masked_invalid(means_diff_test.main(0.95)).mask
varname = 'HFX'
runkey = 'CLM_nourban'
runkey = 'CLM_drysoil'
runkey = 'RWurban'
read_data = True
if read_data:
# wildcard_pat = "*d{:02d}02_2009-06-\{0[0-9],1[01]\}".format(DOMAIN)
wildcard_pat = "*d{:02d}_2009-06-*".format(DOMAIN)
vd = var_diff(os.path.join(ctl_dir, wildcard_pat),
os.path.join(urb_dir, wildcard_pat),
label_A='ctl',
label_B='urbanRW',
varname=varname)
vd.read_files()
vd.get_significance_mask(significance=0.95, adj_autocorr=True)
vd.to_netcdf(
os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'{}_d{:02d}_RWurban.nc'.format(varname, DOMAIN)))
print('done reading files ({})'.format(datetime.datetime.now() - t0))
# vd.mask_land_or_water(mask_water=False)
else:
vd = var_diff(ncfile=os.path.join(
cscratchdir,
# '/Users/tim/work/Data/SummenWRF/',
# '{varname}_d{DOMAIN:02d}_RWurban.nc'.format(
'{varname}_d{DOMAIN:02d}_{runkey}.nc'.format(
"""compare wrf.ll_points() to XLONG, XLAT variables
"""
nc = netCDF4.Dataset(wrffile, 'r')
xlat = nc.variables['XLAT_U'][...].squeeze()
xlong = nc.variables['XLONG_U'][...].squeeze()
ll_points_list = ll_points(xlat, xlong)
return (ll_points_list, xlat, xlong)
if __name__ == "__main__":
varname = 'fogpresent'
read_data = False
vd = var_diff(ncfile=os.path.join(
'/Users/tim/work/Data/SummenWRF/',
'{varname}_d{DOMAIN:02d}_CLM_nourban.nc'.format(varname=varname,
DOMAIN=DOMAIN)))
# for k in vd.data.keys():
# vd.data[k] = vd.data[k] * 100.0
if vd.p is None:
vd.get_significance_mask(significance=0.95, adj_autocorr=True)
pfx = 'gridtest'
# 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)
cscratchdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton')
rootdir = os.path.join(cscratchdir, 'WRFv4.0_Sensitivity')
ctl_dir = os.path.join(rootdir, 'WRFCLMv4.0_NCEPDOEp2', 'WRFV4', 'run',
'summen_2005_ctl_NCEPDOE')
nourb_dir = os.path.join(rootdir, 'WRFCLMv4.0_NCEPDOEp2_deurbanized',
'WRFV4', 'run', 'summen_2005_deurbanized_NCEPDOE')
out_dir = os.path.join(cscratchdir, 'plots_temporary')
if READ_DATA:
# wildcard_pat = "*d{:02d}02_2009-06-\{0[0-9],1[01]\}".format(DOMAIN)
wildcard_pat = "*d{:02d}_2005-*".format(DOMAIN)
re_pat = "d{:02d}_all\\.nc".format(DOMAIN)
vd = var_diff(os.path.join(ctl_dir, re_pat),
os.path.join(nourb_dir, re_pat),
label_A='ctl',
label_B='no_urban_CLM',
varname=varname)
vd.read_files()
# for k in vd.data.keys():
# z_ax = vd.var_axes.index('Lay')
# idx = [slice(None), ] * vd.data[k].ndim
# idx[z_ax] = slice(0, 10)
# vd.data[k] = np.mean(vd.data[k][idx], axis=z_ax)
# vd.var_axes.pop(vd.var_axes.index('Lay'))
vd.get_significance_mask(significance=0.95, adj_autocorr=False)
vd.to_netcdf(
os.path.join(
'/', 'global', 'cscratch1', 'sd', 'twhilton',
'{}_d{:02d}_CLM_nourban_noautocorr.nc'.format(varname,
DOMAIN)))
def main(significance=None):
if significance is None:
significance = significance_default
cscratchdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton')
rootdir = os.path.join(cscratchdir, 'WRFv3.9_Sensitivity')
ctl_dir = os.path.join(rootdir, 'WRFV3_Ctl', 'run',
'summen_sensitivity_ctl')
urb_dir = os.path.join(rootdir, 'WRFV3_redwoodsurban', 'run',
'summen_redwoodsurban')
# urb_dir = os.path.join('/', 'global', 'cscratch1', 'sd',
# 'twhilton', 'WRFv3.9_Sensitivity',
# 'FromScratch', 'WRFV3_urban', 'run',
# 'summen_sensitivity_urban')
out_dir = os.path.join(cscratchdir, 'plots_temporary')
if read_vardiff_from_netcdf:
vd = var_diff(
ncfile=os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'fog_pct_d02_RWurban.nc'))
else:
# wildcard_pat = "*d{:02d}02_2009-06-\{0[0-9],1[01]\}".format(DOMAIN)
wildcard_pat = "*d{:02d}_2009-06-*".format(DOMAIN)
vd = var_diff(
os.path.join(ctl_dir, wildcard_pat),
os.path.join(urb_dir, wildcard_pat),
label_A='ctl',
label_B='urbanRW',
# varname='HFX')
varname='fogpresent')
# varname='fogbase')
# varname='fogpct')
vd.read_files()
vd.to_netcdf(
os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'fog_pct_d02_RWurban.nc'))
print('calculating probabilities')
z = vd.diff_means_test(adj_autocorr=adj_autocorr)
vectorized_cdf = np.vectorize(lambda x: norm.cdf(x, 0.0, 1.0))
p = vectorized_cdf(z)
p = np.ma.masked_less(p, significance)
# draw map
print('drawing map')
fig = MyFig(figsize=(8, 8))
ax = fig.add_subplot(111, projection=CoastalSEES_WRF_prj())
this_cmap = matplotlib.cm.get_cmap('Dark2_r')
this_map = CoastalSEES_WRF_Mapper(ax=ax, domain=DOMAIN)
this_map.pcolormesh(vd.lon, vd.lat, p, vmin=0, vmax=1.0, cmap=this_cmap)
ax.set_title(('urbanized redwood experiment\n'
'fog differences significant '
'at {:0.0f}%'.format(significance * 100.0)))
significant_patch = mpatches.Patch(color=this_cmap.colors[-1],
label='signficant')
ax.legend(handles=(significant_patch, ))
fname = ('fogpct_RWurban_d{domain:02d}_means_diff_'
'map_2tail_adj{adj}_p{sig:0.2f}.png'.format(domain=DOMAIN,
adj=adj_autocorr,
sig=significance))
fig.savefig(fname=fname)
print('saved {}'.format(fname))
return (p)
from plot_diff import var_diff, graphics
if __name__ == "__main__":
rootdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'WRFv3.9_Sensitivity', 'FromScratch')
ctl_dir = os.path.join(rootdir, 'WRFV3_Ctl', 'run')
dry_dir = os.path.join(rootdir, 'WRFV3_Dry', 'run')
# vd = var_diff(os.path.join(ctl_dir, 'met_em.d01.2009-06-02_00:00:00.nc'),
# os.path.join(dry_dir, 'met_em.d01.2009-06-02_00:00:00.nc'),
# label_A='ctl met_em',
# label_B='dry met_em',
# varname='SM000010')
vd = var_diff(os.path.join(ctl_dir, 'summen_sensitivity_ctl_d01_3hr',
'wrfinput_ctl_3hr_d01_2009-06*'),
os.path.join(dry_dir, 'summen_sensitivity_dry_d01_3hr',
'wrfinput_dry_3hr_d01_2009-06*'),
label_A='ctl out',
label_B='dry out',
varname='QCLOUD')
read_data = True
if read_data:
vd.read_files()
# vd.mask_land_or_water(mask_water=True)
for this_t in range(0, 20): # 255
fig = graphics(vd,
t_idx=this_t,
layer=0,
domain=1,
pfx='fromscratch_nomatch_t')
import os
from plot_diff import var_diff, graphics
if __name__ == "__main__":
rootdir = os.path.join('/', 'global', 'cscratch1', 'sd', 'twhilton',
'WRFv3.9_Sensitivity', 'FromScratch')
dry0p05_dir = os.path.join(rootdir, 'WRFV3_Dry', 'run')
dry0p70_dir = os.path.join(rootdir, 'WRFV3_0.7Dry', 'run')
ctl_dir = os.path.join(rootdir, 'WRFV3_Ctl', 'run')
vd_0p05_0p70 = var_diff(os.path.join(dry0p05_dir, 'summen_sensitivity_dry',
'*d01_2009-06-0[12]*'),
os.path.join(dry0p70_dir,
'summen_sensitivity_0.7dry',
'*d01_2009-06-0[12]*'),
label_A='dry 0.05',
label_B='dry 0.70',
varname='QCLOUD')
vd_d0p7_ctl = var_diff(os.path.join(dry0p70_dir,
'summen_sensitivity_0.7dry',
'*d02_2009-06-0[12]*'),
os.path.join(ctl_dir, 'summen_sensitivity_ctl',
'*d02_2009-06-0[12]*'),
label_A='dry 0.70',
label_B='control',
varname='QCLOUD')
read_data = True
if read_data:
vd_0p05_0p70.read_files()
vd_d0p7_ctl.read_files()