def confront(self, m):
obs, mod = self.stageData(m)
obs_area = obs.integrateInSpace().convert("1e6 km2")
mod_area = mod.integrateInSpace().convert("1e6 km2")
# Interpolate to a composed grid
lat, lon, lat_bnds, lon_bnds = il._composeGrids(obs, mod)
OBS = obs.interpolate(lat=lat,
lon=lon,
lat_bnds=lat_bnds,
lon_bnds=lon_bnds)
MOD = mod.interpolate(lat=lat,
lon=lon,
lat_bnds=lat_bnds,
lon_bnds=lon_bnds)
# Compute the different extent areas
o_mask = OBS.data.mask
o_land = (OBS.area > 1e-12) * (o_mask == 0)
o_data = np.copy(OBS.data.data)
o_data[np.where(OBS.data.mask)] = 0.
m_mask = MOD.data.mask
m_land = (MOD.area > 1e-12)
m_data = np.copy(MOD.data.data)
m_data[np.where(MOD.data.mask)] = 0.
o_and_m = (np.abs(o_data - 1) < 1e-12) * (np.abs(m_data - 1) < 1e-12)
o_not_m_land = (np.abs(o_data - 1) < 1e-12) * (np.abs(m_data) <
1e-12) * (m_land == 0)
o_not_m_miss = (np.abs(o_data - 1) < 1e-12) * (np.abs(m_data) <
1e-12) * (m_land == 1)
o_zones = 1. * o_and_m
o_zones += 2. * o_not_m_land
o_zones += 4. * o_not_m_miss
o_zones = np.ma.masked_array(o_zones, mask=o_mask)
m_and_o = (np.abs(m_data - 1) < 1e-12) * (np.abs(o_data - 1) < 1e-12)
m_not_o_land = (np.abs(m_data - 1) < 1e-12) * (np.abs(o_data) <
1e-12) * (o_land == 0)
m_not_o_miss = (np.abs(m_data - 1) < 1e-12) * (np.abs(o_data) <
1e-12) * (o_land == 1)
m_zones = 1. * m_and_o
m_zones += 2. * m_not_o_land
m_zones += 4. * m_not_o_miss
m_zones = np.ma.masked_array(m_zones, mask=m_mask)
zones = 1. * o_and_m
zones += 2. * o_not_m_miss
zones += 4. * m_not_o_miss
zones += 8. * m_not_o_land
zones = np.ma.masked_less(zones, 1)
for i, u in enumerate(np.unique(zones.compressed())):
zones[np.where(zones == u)] = i
# compute the intersection of obs and mod
obs_and_mod = Variable(name="obs_and_mod",
unit="1",
data=np.ma.masked_values(zones == 0,
0).astype(float),
lat=lat,
lon=lon,
area=MOD.area)
# compute the obs that is not the mod
data = (OBS.data.mask == 0) * (MOD.data.mask == 1)
obs_not_mod = Variable(name="obs_not_mod",
unit="1",
data=np.ma.masked_values(zones == 1,
0).astype(float),
lat=lat,
lon=lon,
area=OBS.area)
# compute the mod that is not the obs
data = (OBS.data.mask == 1) * (MOD.data.mask == 0)
mod_not_obs = Variable(name="mod_not_obs",
unit="1",
data=np.ma.masked_values(zones == 2,
0).astype(float),
lat=lat,
lon=lon,
area=MOD.area)
# compute the mod that is not the obs but because of land representation
data = (OBS.data.mask == 1) * (MOD.data.mask == 0)
mod_not_obs_land = Variable(name="mod_not_obs_land",
unit="1",
data=np.ma.masked_values(zones == 3,
0).astype(float),
lat=lat,
lon=lon,
area=MOD.area)
# compute areas
obs_and_mod_area = obs_and_mod.integrateInSpace().convert("1e6 km2")
obs_not_mod_area = obs_not_mod.integrateInSpace().convert("1e6 km2")
mod_not_obs_area = mod_not_obs.integrateInSpace().convert("1e6 km2")
mod_not_obs_land_area = mod_not_obs_land.integrateInSpace().convert(
"1e6 km2")
# determine score
obs_score = Variable(name="Missed Score global",
unit="1",
data=obs_and_mod_area.data /
(obs_and_mod_area.data + obs_not_mod_area.data))
mod_score = Variable(name="Excess Score global",
unit="1",
data=obs_and_mod_area.data /
(obs_and_mod_area.data + mod_not_obs_area.data))
# Write to datafiles --------------------------------------
obs_area.name = "Total Area"
mod_area.name = "Total Area"
obs_and_mod_area.name = "Overlap Area"
obs_not_mod_area.name = "Missed Area"
mod_not_obs_area.name = "Excess Area"
mod_not_obs_land_area.name = "Excess Area (Land Representation)"
results = Dataset("%s/%s_%s.nc" %
(self.output_path, self.name, m.name),
mode="w")
results.setncatts({"name": m.name, "color": m.color})
mod.toNetCDF4(results, group="MeanState")
obs_and_mod.toNetCDF4(results, group="MeanState")
obs_not_mod.toNetCDF4(results, group="MeanState")
mod_not_obs.toNetCDF4(results, group="MeanState")
mod_not_obs_land.toNetCDF4(results, group="MeanState")
mod_area.toNetCDF4(results, group="MeanState")
obs_and_mod_area.toNetCDF4(results, group="MeanState")
obs_not_mod_area.toNetCDF4(results, group="MeanState")
mod_not_obs_area.toNetCDF4(results, group="MeanState")
mod_not_obs_land_area.toNetCDF4(results, group="MeanState")
obs_score.toNetCDF4(results, group="MeanState")
mod_score.toNetCDF4(results, group="MeanState")
results.close()
if self.master:
results = Dataset("%s/%s_Benchmark.nc" %
(self.output_path, self.name),
mode="w")
results.setncatts({
"name": "Benchmark",
"color": np.asarray([0.5, 0.5, 0.5])
})
obs_area.toNetCDF4(results, group="MeanState")
results.close()
def confront(self, m):
obs, mod = self.stageData(m)
obs_area = obs.integrateInSpace().convert("1e6 km2")
mod_area = mod.integrateInSpace().convert("1e6 km2")
# interpolate to a composed grid
lat, lon = il.ComposeSpatialGrids(obs, mod)
iobs = obs.interpolate(lat=lat, lon=lon)
imod = mod.interpolate(lat=lat, lon=lon)
# compute the intersection of obs and mod
data = (iobs.data.mask == 0) * (imod.data.mask == 0)
obs_and_mod = Variable(name="obs_and_mod",
unit="1",
data=np.ma.masked_values(data, 0).astype(float),
lat=lat,
lon=lon)
# compute the obs that is not the mod
data = (iobs.data.mask == 0) * (imod.data.mask == 1)
obs_not_mod = Variable(name="obs_not_mod",
unit="1",
data=np.ma.masked_values(data, 0).astype(float),
lat=lat,
lon=lon)
# compute the mod that is not the obs
data = (iobs.data.mask == 1) * (imod.data.mask == 0)
mod_not_obs = Variable(name="mod_not_obs",
unit="1",
data=np.ma.masked_values(data, 0).astype(float),
lat=lat,
lon=lon)
# compute areas
obs_and_mod_area = obs_and_mod.integrateInSpace().convert("1e6 km2")
obs_not_mod_area = obs_not_mod.integrateInSpace().convert("1e6 km2")
mod_not_obs_area = mod_not_obs.integrateInSpace().convert("1e6 km2")
# determine score
obs_score = Variable(name="Obs Score global",
unit="1",
data=obs_and_mod_area.data / obs_area.data)
mod_score = Variable(name="Mod Score global",
unit="1",
data=obs_and_mod_area.data / mod_area.data)
# Write to datafiles --------------------------------------
obs_area.name = "Total Area"
mod_area.name = "Total Area"
obs_and_mod_area.name = "Overlap Area"
obs_not_mod_area.name = "Missed Area"
mod_not_obs_area.name = "Excess Area"
results = Dataset("%s/%s_%s.nc" %
(self.output_path, self.name, m.name),
mode="w")
results.setncatts({"name": m.name, "color": m.color})
mod.toNetCDF4(results, group="MeanState")
obs_and_mod.toNetCDF4(results, group="MeanState")
obs_not_mod.toNetCDF4(results, group="MeanState")
mod_not_obs.toNetCDF4(results, group="MeanState")
mod_area.toNetCDF4(results, group="MeanState")
obs_and_mod_area.toNetCDF4(results, group="MeanState")
obs_not_mod_area.toNetCDF4(results, group="MeanState")
mod_not_obs_area.toNetCDF4(results, group="MeanState")
obs_score.toNetCDF4(results, group="MeanState")
mod_score.toNetCDF4(results, group="MeanState")
results.close()
if self.master:
results = Dataset("%s/%s_Benchmark.nc" %
(self.output_path, self.name),
mode="w")
results.setncatts({
"name": "Benchmark",
"color": np.asarray([0.5, 0.5, 0.5])
})
obs_area.toNetCDF4(results, group="MeanState")
results.close()
