Note

Download full notebook here

Using Full Output for Diagnostics

Sometimes it helps to have a bigger picture of what the full output is doing.

These steps will guide you through how to get out gain more intuition from your runs.

#picaso
from picaso import justdoit as jdi
from picaso import justplotit as jpi
jpi.output_notebook()

Loading BokehJS ...

We will use a cloudy Jupiter again to guide us through the exercise.

opa = jdi.opannection(wave_range=[0.3,1])

case1 = jdi.inputs()

case1.phase_angle(0)

case1.gravity(gravity=25, gravity_unit=jdi.u.Unit('m/(s**2)'))

case1.star(opa, 6000,0.0122,4.437)

case1.atmosphere(filename = jdi.jupiter_pt(), delim_whitespace=True)
case1.clouds(filename = jdi.jupiter_cld(), delim_whitespace=True)

/home/nbatalh1/codes/picaso/picaso/justdoit.py:1744: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
  df = pd.read_csv(filename, **pd_kwargs)
/home/nbatalh1/codes/picaso/picaso/justdoit.py:3151: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
  df = pd.read_csv(filename, **pd_kwargs)

Return PICASO Full Ouput

df = case1.spectrum(opa, full_output=True) #note the new last key
wno, alb, full_output = df['wavenumber'] , df['albedo'] , df['full_output']

Visualizing Full Output

Mixing Ratios

jpi.show(jpi.mixing_ratio(full_output))
#can also input any key word argument acceptable for bokeh.figure:
#show(jpi.mixing_ratio(full_output, plot_width=500, y_axis_type='linear',y_range=[10,1e-3]))

Cloud Profile

Depending on your wavelength grid, you might exceed Jupyter Notebook's data rage limit. You can fix this by initiating jupyter notebook with a higher data rate limit.

jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000

jpi.show(jpi.cloud(full_output))

Pressure-Tempertaure Profile

jpi.show(jpi.pt(full_output))

Heatmap of Molecular, Cloud, and Rayleigh Scattering Opacity

This will show you where your molecular, cloud and rayleigh scattering opacity become optically thick (\(\tau\)~1). Blue regions of the plot are optically thin, while red are optically thick.

jpi.heatmap_taus(df)

Sometimes, we just want to compare the \(\tau\sim 1\) surface for each component. This is effectively the plot below, the “photon attenuation”.

Photon Attenuation Depth

This is a useful plot to see the interplay between scattering and absorbing sources. It should explain why you are getting bright versus dark reflectivity.

To make this plot, we take data that is in previous plots (taugas, taucld, tauray), integrate it starting at the top of the atmosphere, then determine where a certain opacity hits the at_tau value.

jpi.show(jpi.photon_attenuation(full_output, at_tau=0.1, plot_width=500))

Compare it to the spectrum and you can see right away what is driving the overall shape of your spectrum

jpi.show(jpi.spectrum(wno,alb,plot_width=500))

Disecting Full Output

full_output.keys()

dict_keys(['weights', 'layer', 'wavenumber', 'wavenumber_unit', 'taugas', 'tauray', 'taucld', 'level', 'latitude', 'longitude', 'star', 'albedo_3d', 'reflected_unit'])

full_output['layer'].keys()

dict_keys(['pressure_unit', 'mixingratio_unit', 'temperature_unit', 'pressure', 'mixingratios', 'temperature', 'column_density', 'mmw', 'cloud'])

taugas = full_output['taugas'] #matrix that is nlevel x nwvno
taucld = full_output['taucld'] #matrix that is nlevel x nwvno
taugas = full_output['taugas'] #matrix that is nlevel x nwvno