Note

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import warnings
warnings.filterwarnings('ignore')
import numpy as np
#picaso
from picaso import justdoit as jdi
from picaso import justplotit as jpi
from bokeh.io import output_notebook
output_notebook()

Loading BokehJS ...

How do I load in target properties from Exo.Mast?

nexsci = jdi.all_planets()

nexsci.head()

	objectid	pl_name	pl_letter	hostid	hostname	hd_name	hip_name	tic_id	disc_pubdate	disc_year	...	cb_flag	pl_angsep	pl_angseperr1	pl_angseperr2	pl_angseplim	pl_angsepformat	pl_angsepstr	pl_angsepsymerr	pl_angsep_reflink	pl_ndispec
0	3.12508	OGLE-2016-BLG-1227L b	b	2.104385	OGLE-2016-BLG-1227L	NaN	NaN	NaN	2020-03	2020.0	...	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
1	3.11091	HD 132406 b	b	2.573350	HD 132406	HD 132406	HIP 73146	TIC 309808219	2007-10	2007.0	...	0.0	28.000	NaN	NaN	0.0	NaN	28.000	NaN	<a refstr=CALCULATED_VALUE href=/docs/pscp_cal...	0.0
2	3.12740	TOI-1260 c	c	2.572931	TOI-1260	NaN	NaN	TIC 355867695	2021-08	2021.0	...	0.0	0.893	NaN	NaN	0.0	NaN	0.893	NaN	<a refstr=CALCULATED_VALUE href=/docs/pscp_cal...	0.0
3	3.11122	HD 149143 b	b	2.579040	HD 149143	HD 149143	HIP 81022	TIC 281520119	2006-02	2005.0	...	0.0	0.723	NaN	NaN	0.0	NaN	0.723	NaN	<a refstr=CALCULATED_VALUE href=/docs/pscp_cal...	0.0
4	3.11374	HD 99492 b	b	2.564070	HD 99492	HD 99492	HIP 55848	TIC 363549734	2005-01	2004.0	...	0.0	6.590	NaN	NaN	0.0	NaN	6.590	NaN	<a refstr=CALCULATED_VALUE href=/docs/pscp_cal...	0.0

5 rows × 682 columns

nexsci.loc[nexsci['hostname']=='HAT-P-26']

	objectid	pl_name	pl_letter	hostid	hostname	hd_name	hip_name	tic_id	disc_pubdate	disc_year	...	cb_flag	pl_angsep	pl_angseperr1	pl_angseperr2	pl_angseplim	pl_angsepformat	pl_angsepstr	pl_angsepsymerr	pl_angsep_reflink	pl_ndispec
4891	3.1101	HAT-P-26 b	b	2.103496	HAT-P-26	NaN	NaN	TIC 420779000	2011-02	2010.0	...	0.0	0.338	NaN	NaN	0.0	NaN	0.338	NaN	<a refstr=CALCULATED_VALUE href=/docs/pscp_cal...	0.0

1 rows × 682 columns

#first isolate the row
hatp26_row = nexsci.loc[nexsci['hostname']=='HAT-P-26']

#add it to load planet function with opacity
opa = jdi.opannection(wave_range=[1,5])
hatp26 = jdi.load_planet(hatp26_row,opa , st_metfe=0)#hatp26 is misst st_metfe so we must add it as a keyword

Load planet function adds in: - planet properties and stellar properties - parameterized PT profile

That means you still need to add in the chemistry and cloud parameters

co=1.0#c/o ratio 1x solar
logfeh = 1.5 #10^1.5 ~ 30x solar
hatp26.chemeq_visscher(co,logfeh) #adds in template chemistry from sonora
df = hatp26.spectrum(opa, calculation='transmission+thermal')

x,y = jdi.mean_regrid(df['wavenumber'], df['fpfs_thermal'],R=150)
plot = [jpi.spectrum(x,y,plot_width=400, y_axis_type='log',y_axis_label='FpFs')]
x,y = jdi.mean_regrid(df['wavenumber'], df['transit_depth'],R=150)
plot += [jpi.spectrum(x,y,plot_width=400,y_axis_label='(Rp/Rs)^2')]

jpi.show(jpi.row(plot))

How do I access the pressure-temperature profile parameterizations?

#start by loading in some template properties
opa = jdi.opannection(wave_range=[1,5])
hatp26_row = nexsci.loc[nexsci['hostname']=='HAT-P-26']
hatp26 = jdi.load_planet(hatp26_row,opa, st_metfe=0)

How do the input parameters effect the parameterization?

fig = [jpi.figure(width=300, height=300, y_axis_type='log', y_range=[100,1e-6],x_range=[400,1700],
                  y_axis_label='Pressure(bars)',x_axis_label='Temperature (K)',
                 title='Effect of logg1')]
colors = jpi.pals.magma(10)
for i, logg1 in enumerate(np.linspace(-2,2,10)):
    hatp26.guillot_pt(576.17, T_int=100, logg1=logg1, logKir=-1.5)
    fig[0].line(hatp26.inputs['atmosphere']['profile']['temperature'],
            hatp26.inputs['atmosphere']['profile']['pressure'],color=colors[i])

fig += [jpi.figure(width=300, height=300, y_axis_type='log', y_range=[100,1e-6],x_range=[400,1700],
                 x_axis_label='Temperature (K)',title='Effect of LogKir')]
for i,logKir in enumerate(np.linspace(-2,2,10)):
    hatp26.guillot_pt(576.17, T_int=100, logg1=-2, logKir=logKir)
    fig[1].line(hatp26.inputs['atmosphere']['profile']['temperature'],
            hatp26.inputs['atmosphere']['profile']['pressure'], color=colors[i])

fig += [jpi.figure(width=300, height=300, y_axis_type='log', y_range=[100,1e-6],x_range=[400,1700],
                 x_axis_label='Temperature (K)',title='Effect of Tint')]
for i,T_int in enumerate(np.linspace(50,300,10)):
    hatp26.guillot_pt(576.17, T_int=T_int, logg1=-2, logKir=-2)
    fig[2].line(hatp26.inputs['atmosphere']['profile']['temperature'],
            hatp26.inputs['atmosphere']['profile']['pressure'], color=colors[i])

jpi.show(jpi.row(fig))

How do I return the output from photon attenuation plot?

#first isolate the row
hatp26_row = nexsci.loc[nexsci['hostname']=='HAT-P-26']

#add it to load planet function with opacity
opa = jdi.opannection(wave_range=[0.3,1])
hatp26 = jdi.load_planet(hatp26_row,opa ,st_metfe=0)
co = 1
logfeh=2
hatp26.chemeq_visscher(co,logfeh)
df = hatp26.spectrum(opa, calculation='reflected',full_output=True)

plot,wave,at_pressures_gas,at_pressures_cld,at_pressures_ray = jpi.photon_attenuation(df['full_output'],
                                                                                     return_output=True)

#now you can make your own plot!
fig = jpi.figure(y_axis_type='log',y_range=[10,1e-3],height=250, width=500)
for i,iy,ilab in zip([0,1],[at_pressures_gas,at_pressures_ray ], ['Molecular','Rayleigh']):
    x,y = jpi.mean_regrid(1e4/wave,iy, R=150)
    fig.line(1e4/x,y,legend_label=ilab, line_width=4, color=jpi.Colorblind8[i])
jpi.show(fig)

Can I use the phase angle function to specify a non-zero phase for thermal emission?

The phase angle function computes the incoming and outgoing angles. However, non-zero phase functionality is specific to reflected light observations only. This might be confusing because of course it is possible to observe thermal emission of planet at non-zero phase. However, unlike reflected light, thermal emission radiates from the planet in all directions (regardless of phase angle).

Do not force the code to run at non-zero phase angles for thermal emission.

I’m confused about what opacity file to use

Question: I’m confused about the opacity files. Version 1.0 (opacity.db) is the low res version that covers the “useful” wavelengths (0.3 to 14 micron) while version 2.0 has two files that cover the 0.6-6 micron at higher resolution and 4.8-15 micron. What should I download? And once I download it, where do I put them? If it’s just the one file that seems straightforward in terms of where to put it but what happens when there are two files (or all 3) in the same folder?

Answer:

What do I download?

The low sampled files across a large wavelength range, which is on Zenodo as V1 is great for quick calculations that don’t necessarily need to be accurate. For example: proposals, example models, any testing, retrievals on fake data.

However, when comparing to real data, it’s important to use higher sampling. This tutorial shows users estimated sampling errors. Therefore, it is important to have higher sampling files as well. The higher sampling files are located here under V2.

So depending on your use case, the answer might be: download both!

Where do I put all the files?

PICASO uses the function `justdoit.opannection <https://natashabatalha.github.io/picaso/picaso.html#picaso.justdoit.opannection>`__ to grab the opacity file located in the reference directory `opacities <https://github.com/natashabatalha/picaso/tree/master/reference/opacities>`__. In the installation instructions you will notice there is a step to place the zenodo file here. Just for completeness, internally, we specify the name of this file here.

The general recommendation is to keep one “default” file in your reference/opacities folder so that you do not need to worry about always specifying a file when running the code. Then assign one place, easy to locate, where you include the rest of the files. In order to access these will need to point to this file path using the filename_db keyword in opannection.

Has your question not been answered? Feel free to contact us!

Submit an issue on Github: https://github.com/natashabatalha/picaso/issues