def __init__(self, **kwargs):
TimeDiffPlotRoutines.__init__(self, **kwargs)
def mean_radiance(rad, colors, band_idx):
res = []
for snd_idx in range(colors.shape[0]):
beg_idx = band_idx > 0 and numpy.sum(colors[snd_idx,:band_idx]) or 0
end_idx = band_idx < colors.shape[0] and numpy.sum(colors[snd_idx,:band_idx+1]) or numpy.sum(colors[snd_idx,:])
res.append( numpy.mean(rad[snd_idx,beg_idx:end_idx]) )
return numpy.array(res)
for band_idx, band_name in enumerate(("abo2", "wco2", "sco2")):
def mean_data_rad_band(band_idx):
return lambda rad, colors: [ mean_radiance(r,c,band_idx) for r,c in zip(rad, colors) ]
self._create_dataset_routines("%s_rad_mean" % band_name,
"Mean /SpectralParameters/modeled_radiance %s" % band_name.upper(),
"Mean %s Modeled Radiance" % band_name.upper(),
source_datasets=["/SpectralParameters/modeled_radiance","num_colors_per_band"],
translate_func=mean_data_rad_band(band_idx))
self._create_dataset_routines("%s_rad_mean_uncert" % band_name,
"Mean /SpectralParameters/measured_radiance_uncert %s" % band_name.upper(),
"Mean %s Measured Radiance Uncertainty" % band_name.upper(),
source_datasets=["/SpectralParameters/measured_radiance_uncert","num_colors_per_band"],
translate_func=mean_data_rad_band(band_idx))
def __init__(self, **kwargs):
TimeDiffPlotRoutines.__init__(self, **kwargs)
self._create_dataset_routines(
"xco2", "RetrievalResults/xco2", "XCO2 (ppm)",
translate_func=lambda ov: [ x * 1e6 for x in ov ],
diff_limits=(-2, 2))
self._create_dataset_routines(
"psurf", "RetrievalResults/surface_pressure_fph", "Surface Pressure Retrieved (mb)",
translate_func=lambda ov: [ x * 1e-2 for x in ov ])
self._create_dataset_routines(
"psurf_ap", "RetrievalResults/surface_pressure_apriori_fph", "Surface Pressure A priori (mb)",
translate_func=lambda ov: [ x * 1e-2 for x in ov ])
self._create_dataset_routines(
"temp_offset", "RetrievalResults/temperature_offset_fph", "Temperature Offset (k)")
self._create_dataset_routines(
"h2o_scale_factor", "/RetrievalResults/h2o_scale_factor", "H2O Scale Factor")
self._create_dataset_routines(
"abo2_chi2", "reduced_chi_squared_o2_fph", "O2 reduced chi^2")
self._create_dataset_routines(
"wco2_chi2", "reduced_chi_squared_weak_co2_fph", "Weak CO2 reduced chi^2")
self._create_dataset_routines(
"sco2_chi2", "reduced_chi_squared_strong_co2_fph", "Strong CO2 reduced chi^2")
self._create_dataset_routines(
"abo2_rms", "/SpectralParameters/relative_residual_mean_square_o2", "O2 RMS")
self._create_dataset_routines(
"wco2_rms", "/SpectralParameters/relative_residual_mean_square_weak_co2", "Weak CO2 RMS")
self._create_dataset_routines(
"sco2_rms", "/SpectralParameters/relative_residual_mean_square_strong_co2", "Strong CO2 RMS")
self._create_dataset_routines(
"abo2_albedo", "albedo_o2", "O2 Albedo")
self._create_dataset_routines(
"wco2_albedo", "albedo_weak_co2", "Weak CO2 Albedo")
self._create_dataset_routines(
"sco2_albedo", "albedo_strong_co2", "Strong CO2 Albedo")
def rs_val_trans(orig_val):
return [ rs[:,0] for rs in orig_val ]
self._create_dataset_routines(
"abo2_radiance_scaling", "RetrievalResults/radiance_scaling_o2", "O2 Radiance Scaling",
translate_func=rs_val_trans)
self._create_dataset_routines(
"wco2_radiance_scaling", "RetrievalResults/radiance_scaling_weak_co2", "Weak CO2 Radiance Scaling",
translate_func=rs_val_trans)
self._create_dataset_routines(
"sco2_radiance_scaling", "RetrievalResults/radiance_scaling_strong_co2", "Strong CO2 Radiance Scaling",
translate_func=rs_val_trans)
def rs_slope_trans(orig_val):
return [ rs[:,1] for rs in orig_val ]
self._create_dataset_routines(
"abo2_radiance_scaling_slope", "RetrievalResults/radiance_scaling_o2", "O2 Radiance Scaling Slope",
translate_func=rs_slope_trans)
self._create_dataset_routines(
"wco2_radiance_scaling_slope", "RetrievalResults/radiance_scaling_weak_co2", "Weak CO2 Radiance Scaling Slope",
translate_func=rs_slope_trans)
self._create_dataset_routines(
"sco2_radiance_scaling_slope", "RetrievalResults/radiance_scaling_strong_co2", "Strong CO2 Radiance Scaling Slope",
translate_func=rs_slope_trans)
# Set any BAD values to nan so they are filtered out of the plot
def filter_bad_values(value_sets):
return [numpy.where(values == BAD_VALUE, numpy.nan, values) for values in value_sets]
self._create_dataset_routines(
"windspeed", "RetrievalResults/wind_speed", "Windspeed", translate_func=filter_bad_values)
aer_types_ds = "/Metadata/AerosolTypes"
if self.analysis_env != None and self.analysis_env.data_objs[0].get(aer_types_ds, False):
aer_types = self.analysis_env.data_objs[0].get_sounding_data(aer_types_ds)[0, :]
for aer_idx, aer_name in enumerate(aer_types):
if "retrieved_aerosol_aod_by_type" in self.analysis_env.data_objs[0]["/RetrievalResults"].keys():
# So that scoping works out right we have to have a
# function return a function so aer_idx binds to the
# value for the current index, not the last one
def aer_filter_wrap(aer_idx):
def filter_aer_type(file_aer_data):
return [ aer_data[:, aer_idx] for aer_data in file_aer_data ]
return filter_aer_type
routine_name = "aerosol_%s" % aer_name.lower()
display_name = "Aerosol %s" % aer_name
dataset_name = "RetrievalResults/retrieved_aerosol_aod_by_type"
self._create_dataset_routines(routine_name, display_name, display_name, translate_func=aer_filter_wrap(aer_idx), source_datasets=[dataset_name])
else:
routine_name = "aerosol_%s" % aer_name.lower()
display_name = "Aerosol %s" % aer_name
dataset_name = "RetrievalResults/aerosol_%s_aod" % aer_name.lower()
self._create_dataset_routines(routine_name, display_name, display_name, source_datasets=[dataset_name])
