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Fast exoplanet transmission spectrum calculator

License: GNU General Public License v3.0

Python 99.48% Shell 0.52%

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jaymedina exowanderer soubkiou jegugih cmcgrud1 y-chachan arjunsavel spiderine vatsalpanwar icweaver michaelcho8 soumitrahazra raquelsalmeron josephyeung-32 xueqiao jiayindong kparagas giovbruno

platon's Issues

Add these values to Mie Qext unit tests

https://arxiv.org/pdf/1710.04946.pdf

Explore varying g as we go up the atmosphere

Handle binning of broadband data better

It isn't enough to just average the transit depths at all the wavelength grid points within a wavelength interval. If the stellar spectrum is sharply rising or falling, the transit depths needed to be weighted according to the amount of stellar flux at a wavelength.

Unable to reproduce best_fit spectrum from the best fit parameters

I have been following your retrieve_multinest.py example script but on some different data.

Once the best fit parameters have been determined, the plot_best=True kwarg creates a windowed plot of the spectrum. I am trying to plot the full spectrum, and when I use the best fit parameters which are returned by PLATON to generate the forward model, the two spectra visibly do not match - the majority of the data points which I have are away from the spectrum.

Are the parameters produced by the retrieval code the correct ones to put into the TransmissionSpectrumCalculator, or is there something more which must be done before this?

Decide how to include haze

Options:

a Rayleigh scattering factor plus a variable slope
In addition to the fixed Rayleigh scattering, something of the form absorption_coeff = a* n * lambda^-b, where n is the number density from the ideal gas law, and a and b are free parameters

Investigate nested sampling instead of emcee

Polish eclipse depth calculator

Make it faster and more readable
Make it easily callable
Compare to published results
Write unit tests

Allow user to plot high-resolution spectrum in best fit

Unit tests for _get_mie_absorption

Introduce documentation framework to generate public documentation

Make hydrostatic solving faster

Right now it takes 30-70% of execution time. That's OK, but is it possible to do better, i.e. by decreasing the tolerance?

Reorganize data files in more logical manner

Test code with absurd inputs to make sure it fails in an easily diagnosable way

I.e. negative masses/radii/temperatures, inputs in Earth masses instead of kg, lower limits > higher limits, etc

Plotting the results

Hi,
I've been using PLATON with different sets of free parameters. For some of these runs, despite the posteriors being almost Gaussian, the unbinned best fit solution and confidence bands are way out from the binned best fit. Do you have any idea why?

Write best fit parameters to file

Make retrieve more user-friendly

Should add functionality for FitInfo to read a file for initialization. Maybe add_fit_param can also be done through a file, though this is much less important.

Similarly, maybe it should be possible to read wavelength bins, depths, and errors from a file instead of providing them on the command line

Add C/O ratio as dimension for retrieval

Compare results with published retrieval results

In particular, we can compare the retrieval results for GJ 436b and GJ 1214b WFC3 data

PLATON fails with scipy 1.2.0rc1 and Python 2.7

This is due to a known bug in scipy 1.2.0rc1 that has since been fixed (see scipy/scipy#9540). If you do "python setup.py install" and it installs scipy 1.2.0rc1, remove it and manually install scipy 1.1.

Decide what parameters to support for retrieval

Disentangle reference pressure for radius from max_P_profile

Retrieve an ExoTransmit forward model + noise

Run a forward model for known planetary parameters with ExoTransmit, add some noise to the transmission spectrum, and retrieve the parameters. This will allow a good comparison.

Nested sampling retrieval crashes when wavelength bins for multiple instruments are passed

Hi,

I am trying to run retrieval on some simulated composite spectra for 3 different instruments. When I run retrieval using just one of each of the instruments, there is no problem, but when I combine them in any way, I get the following error message. I assume that this issue is due to the wavelength bins being passed to the retriever.

/data/jhayes/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/data/jhayes/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
Exception while calling loglikelihood function:
  params: [ 2.85511517e+27  2.39638339e+03 -7.60211364e-01  1.72730302e+00
 -1.43587464e+00]
  args: []
  kwargs: {}
  exception:
Traceback (most recent call last):
  File "/data/jhayes/anaconda3/lib/python3.7/site-packages/dynesty-1.0.0-py3.7.egg/dynesty/dynesty.py", line 860, in __call__
    return self.func(x, *self.args, **self.kwargs)
  File "/data/jhayes/EGP_classes/scripts/ML/platon-4.0-py3.7.egg/platon/combined_retriever.py", line 356, in multinest_ln_like
    eclipse_depths, eclipse_errors)
  File "/data/jhayes/EGP_classes/scripts/ML/platon-4.0-py3.7.egg/platon/combined_retriever.py", line 132, in _ln_like
    part_size=part_size, ri=ri, full_output=True)
  File "/data/jhayes/EGP_classes/scripts/ML/platon-4.0-py3.7.egg/platon/transit_depth_calculator.py", line 531, in compute_depths
    binned_wavelengths, binned_depths, stellar_spectrum = self._get_binned_corrected_depths(transit_depths, T_star, T_spot, spot_cov_frac)
  File "/data/jhayes/EGP_classes/scripts/ML/platon-4.0-py3.7.egg/platon/transit_depth_calculator.py", line 302, in _get_binned_corrected_depths
    binned_depth = np.average(depths[cond] * correction_factors[cond], weights=stellar_spectrum[cond])
  File "/data/jhayes/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py", line 422, in average
    "Weights sum to zero, can't be normalized")
ZeroDivisionError: Weights sum to zero, can't be normalized

The instruments I am using have overlapping wavelength ranges and consequently some of the bins overlap. Is this a known issue where wavelength bins can't overlap? Alternatively, I have supplied the bins grouped by instrument - should they be arranged in ascending wavelength order?

Thanks in advance

Add argument validation, especially in user-facing methods

some test (nosetests -v) failed

I executed nosetests -v after install, I got this message.
Some tests seems didn't work well.
Do you have any solutions to solve this?

test_isothermal (tests.test_TP_profiles.TestTPProfile) ... ok
test_parametric (tests.test_TP_profiles.TestTPProfile) ... ok
test_radiative_solution (tests.test_TP_profiles.TestTPProfile) ... ok
test_set_opacity (tests.test_TP_profiles.TestTPProfile) ... ok
test_isothermal (tests.test_eclipse_depth_calculator.TestEclipseDepthCalculator) ... ok
test_hd209458b (tests.test_eclipse_retrieval.TestEclipseRetrieval) ... /home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: divide by zero encountered in double_scalars
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: divide by zero encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: invalid value encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: divide by zero encountered in log
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: divide by zero encountered in log
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
iter: 1|eff(%):  0.990|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 2|eff(%):  1.961|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 3|eff(%):  2.913|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 4|eff(%):  3.846|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 5|eff(%):  4.762|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 6|eff(%):  5.660|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 7|eff(%):  6.542|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 8|eff(%):  7.407|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 9|eff(%):  8.257|logl*:   -inf<  -inf<   inf|logz:   -inf+/-niter: 10|eff(%):  9.091|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 11|eff(%):  9.910|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 12|eff(%): 10.714|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 13|eff(%): 11.404|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 14|eff(%): 12.174|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 15|eff(%): 12.931|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 16|eff(%): 13.675|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 17|eff(%): 14.407|logl*:   -inf<  -inf<   inf|logz:   -inf+/-iter: 18|eff(%): 15.126|logl*:   -inf<-214643.7<   inf|logz: -21464iter: 19|eff(%): 15.833|logl*:   -inf<-165148.1<   inf|logz: -16515iter: 20|eff(%): 16.529|logl*:   -inf<-127108.4<   inf|logz: -12711iter: 21|eff(%): 17.213|logl*:   -inf<-64553.4<   inf|logz: -64558.iter: 22|eff(%): 17.886|logl*:   -inf<-61554.0<   inf|logz: -61559.iter: 23|eff(%): 18.548|logl*:   -inf<-58508.3<   inf|logz: -58513.iter: 24|eff(%): 19.200|logl*:   -inf<-51470.6<   inf|logz: -51476.iter: 25|eff(%): 19.841|logl*:   -inf<-48870.5<   inf|logz: -48876.iter: 26|eff(%): 20.472|logl*:   -inf<-42051.5<   inf|logz: -42057.iter: 27|eff(%): 21.094|logl*:   -inf<-41209.2<   inf|logz: -41214.iter: 28|eff(%): 21.705|logl*:   -inf<-37628.0<   inf|logz: -37633.iter: 29|eff(%): 22.308|logl*:   -inf<-36570.1<   inf|logz: -36575.iter: 30|eff(%): 22.556|logl*:   -inf<-36453.4<   inf|logz: -36459.iter: 31|eff(%): 23.134|logl*:   -inf<-36365.3<   inf|logz: -36370.iter: 32|eff(%): 23.704|logl*:   -inf<-32365.1<   inf|logz: -32370.iter: 33|eff(%): 24.265|logl*:   -inf<-31297.2<   inf|logz: -31302.iter: 34|eff(%): 24.818|logl*:   -inf<-30594.9<   inf|logz: -30600.iter: 35|eff(%): 25.362|logl*:   -inf<-27808.3<   inf|logz: -27814.iter: 36|eff(%): 25.532|logl*:   -inf<-27775.1<   inf|logz: -27780.iter: 37|eff(%): 26.056|logl*:   -inf<-25358.1<   inf|logz: -25363.8+/-nan/home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: overflow encountered in double_scalars
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: overflow encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
iter: 38|eff(%): 26.389|logl*:   -inf<-23745.1<   inf|logz: -23750.iter: 39|eff(%): 26.531|logl*:   -inf<-19559.0<   inf|logz: -19564.iter: 40|eff(%): 26.846|logl*:   -inf<-18033.2<   inf|logz: -18038.iter: 41|eff(%): 27.333|logl*:   -inf<-17687.9<   inf|logz: -17693.iter: 42|eff(%): 27.815|logl*:   -inf<-17170.0<   inf|logz: -17175.iter: 43|eff(%): 28.289|logl*:   -inf<-16817.9<   inf|logz: -16823.iter: 44|eff(%): 28.758|logl*:   -inf<-16783.1<   inf|logz: -16788.iter: 45|eff(%): 29.221|logl*:   -inf<-16766.9<   inf|logz: -16772.iter: 46|eff(%): 29.677|logl*:   -inf<-15774.1<   inf|logz: -15779.iter: 47|eff(%): 30.128|logl*:   -inf<-14224.3<   inf|logz: -14230.iter: 48|eff(%): 30.573|logl*:   -inf<-11875.8<   inf|logz: -11881.iter: 49|eff(%): 31.013|logl*:   -inf<-11821.1<   inf|logz: -11826.iter: 50|eff(%): 31.447|logl*:   -inf<-11489.9<   inf|logz: -11495.iter: 51|eff(%): 31.677|logl*:   -inf<-11343.8<   inf|logz: -11349.iter: 52|eff(%): 32.099|logl*:   -inf<-11055.1<   inf|logz: 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inf|logz:  243iter: 116|+100|eff(%): 71.053|logl*:   -inf< 250.1<   inf|logz:  244.3+/-nan/home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: divide by zero encountered in log
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: divide by zero encountered in log
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: invalid value encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: overflow encountered in double_scalars
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: overflow encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:47: RuntimeWarning: divide by zero encountered in double_scalars
  T2 = T3 - np.log(P3/P2)**2/alpha2**2
/home/nishiumi/platon/platon/TP_profile.py:54: RuntimeWarning: divide by zero encountered in double_scalars
  self.temperatures[i] = T2 + np.log(P/P2)**2 / alpha2**2
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test_get_data (tests.test_get_data.TestGetData) ... ok
test_complex_refractive_index (tests.test_mie.TestMie) ... ok
test_lx_mie_values (tests.test_mie.TestMie) ... ok
test_real_refractive_index (tests.test_mie.TestMie) ... ok
test_complex (tests.test_mie_absorption.TestMieAbsorption) ... ok
test_real (tests.test_mie_absorption.TestMieAbsorption) ... SKIP: For some reason integration stalls on OS X VMs; not sure why
test_gaussian_param (tests.test_params.TestParams) ... ok
test_uniform_param (tests.test_params.TestParams) ... ok
test_bounds_check (tests.test_retrieve.TestRetriever) ... ok
test_emcee (tests.test_retrieve.TestRetriever) ... /home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/emcee-3.0rc2-py3.6.egg/emcee/moves/red_blue.py:97: RuntimeWarning: invalid value encountered in double_scalars
  lnpdiff = f + nlp - state.log_prob[j]
xkbcommon: ERROR: failed to add default include path 
Qt: Failed to create XKB context!
Use QT_XKB_CONFIG_ROOT environmental variable to provide an additional search path, add ':' as separator to provide several search paths and/or make sure that XKB configuration data directory contains recent enough contents, to update please see http://cgit.freedesktop.org/xkeyboard-config/ .
ok
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-7753.4+iter: 90|eff(%): 35.433|logl*:   -inf<-7746.1<   inf|logz: -7751.8+iter: 91|eff(%): 35.547|logl*:   -inf<-7694.4<   inf|logz: -7700.6+iter: 92|eff(%): 35.798|logl*:   -inf<-7429.3<   inf|logz: -7435.5+iter: 93|eff(%): 35.907|logl*:   -inf<-7269.7<   inf|logz: -7275.9+iter: 94|eff(%): 36.154|logl*:   -inf<-7213.6<   inf|logz: -7219.9+iter: 95|eff(%): 36.398|logl*:   -inf<-7142.7<   inf|logz: -7148.9+iter: 96|eff(%): 36.502|logl*:   -inf<-6275.9<   inf|logz: -6282.2+iter: 97|eff(%): 36.604|logl*:   -inf<-5964.0<   inf|logz: -5970.2+iter: 98|eff(%): 36.704|logl*:   -inf<-5932.5<   inf|logz: -5938.7+iter: 99|eff(%): 36.940|logl*:   -inf<-5781.1<   inf|logz: -5787.4+iter: 100|eff(%): 37.037|logl*:   -inf<-5461.5<   inf|logz: -5467.8iter: 101|eff(%): 37.269|logl*:   -inf<-5443.0<   inf|logz: -5449.3iter: 102|eff(%): 37.091|logl*:   -inf<-4949.1<   inf|logz: -4955.4iter: 103|eff(%): 36.655|logl*:   -inf<-4840.8<   inf|logz: -4847.1iter: 104|eff(%): 36.879|logl*:   -inf<-4496.4<   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-inf<-2616.0<   inf|logz: -26iter: 108|+12|eff(%): 39.867|logl*:   -inf<-2437.8<   inf|logz: -24iter: 108|+13|eff(%): 40.199|logl*:   -inf<-2176.8<   inf|logz: -21iter: 108|+14|eff(%): 40.532|logl*:   -inf<-2127.5<   inf|logz: -21iter: 108|+15|eff(%): 40.864|logl*:   -inf<-2091.5<   inf|logz: -20iter: 108|+16|eff(%): 41.196|logl*:   -inf<-2079.2<   inf|logz: -20iter: 108|+17|eff(%): 41.528|logl*:   -inf<-1963.3<   inf|logz: -19iter: 108|+18|eff(%): 41.860|logl*:   -inf<-1714.1<   inf|logz: -17iter: 108|+19|eff(%): 42.193|logl*:   -inf<-1710.1<   inf|logz: -17iter: 108|+20|eff(%): 42.525|logl*:   -inf<-1697.2<   inf|logz: -17iter: 108|+21|eff(%): 42.857|logl*:   -inf<-1534.4<   inf|logz: -15iter: 108|+22|eff(%): 43.189|logl*:   -inf<-1522.5<   inf|logz: -15iter: 108|+23|eff(%): 43.522|logl*:   -inf<-1502.0<   inf|logz: -15iter: 108|+24|eff(%): 43.854|logl*:   -inf<-1482.3<   inf|logz: -14iter: 108|+25|eff(%): 44.186|logl*:   -inf<-1356.2<   inf|logz: -13iter: 108|+26|eff(%): 44.518|logl*:   -inf<-1354.3<   inf|logz: -13iter: 108|+27|eff(%): 44.850|logl*:   -inf<-1168.4<   inf|logz: -11iter: 108|+28|eff(%): 45.183|logl*:   -inf<-1026.2<   inf|logz: -10iter: 108|+29|eff(%): 45.515|logl*:   -inf<-985.0<   inf|logz: -991iter: 108|+30|eff(%): 45.847|logl*:   -inf<-969.4<   inf|logz: -975iter: 108|+31|eff(%): 46.179|logl*:   -inf<-961.8<   inf|logz: -968iter: 108|+32|eff(%): 46.512|logl*:   -inf<-957.0<   inf|logz: -963iter: 108|+33|eff(%): 46.844|logl*:   -inf<-957.0<   inf|logz: -962iter: 108|+34|eff(%): 47.176|logl*:   -inf<-910.9<   inf|logz: -917iter: 108|+35|eff(%): 47.508|logl*:   -inf<-872.5<   inf|logz: -878iter: 108|+36|eff(%): 47.841|logl*:   -inf<-868.8<   inf|logz: -875iter: 108|+37|eff(%): 48.173|logl*:   -inf<-864.3<   inf|logz: -870iter: 108|+38|eff(%): 48.505|logl*:   -inf<-857.9<   inf|logz: -864iter: 108|+39|eff(%): 48.837|logl*:   -inf<-838.4<   inf|logz: -844iter: 108|+40|eff(%): 49.169|logl*:   -inf<-836.2<   inf|logz: -842iter: 108|+41|eff(%): 49.502|logl*:   -inf<-831.5<   inf|logz: -837iter: 108|+42|eff(%): 49.834|logl*:   -inf<-800.7<   inf|logz: -807iter: 108|+43|eff(%): 50.166|logl*:   -inf<-776.1<   inf|logz: -782iter: 108|+44|eff(%): 50.498|logl*:   -inf<-640.2<   inf|logz: -646iter: 108|+45|eff(%): 50.831|logl*:   -inf<-634.5<   inf|logz: -640iter: 108|+46|eff(%): 51.163|logl*:   -inf<-625.9<   inf|logz: -632iter: 108|+47|eff(%): 51.495|logl*:   -inf<-602.2<   inf|logz: -608iter: 108|+48|eff(%): 51.827|logl*:   -inf<-570.0<   inf|logz: -576iter: 108|+49|eff(%): 52.159|logl*:   -inf<-558.4<   inf|logz: -564iter: 108|+50|eff(%): 52.492|logl*:   -inf<-537.1<   inf|logz: -543iter: 108|+51|eff(%): 52.824|logl*:   -inf<-475.1<   inf|logz: -481iter: 108|+52|eff(%): 53.156|logl*:   -inf<-473.7<   inf|logz: -479iter: 108|+53|eff(%): 53.488|logl*:   -inf<-468.6<   inf|logz: -475iter: 108|+54|eff(%): 53.821|logl*:   -inf<-464.9<   inf|logz: -471iter: 108|+55|eff(%): 54.153|logl*:   -inf<-438.5<   inf|logz: -444iter: 108|+56|eff(%): 54.485|logl*:   -inf<-399.5<   inf|logz: -405iter: 108|+57|eff(%): 54.817|logl*:   -inf<-287.0<   inf|logz: -293iter: 108|+58|eff(%): 55.150|logl*:   -inf<-253.1<   inf|logz: -259iter: 108|+59|eff(%): 55.482|logl*:   -inf<-238.9<   inf|logz: -245iter: 108|+60|eff(%): 55.814|logl*:   -inf<-227.0<   inf|logz: -233iter: 108|+61|eff(%): 56.146|logl*:   -inf<-200.2<   inf|logz: -206iter: 108|+62|eff(%): 56.478|logl*:   -inf<-133.2<   inf|logz: -139iter: 108|+63|eff(%): 56.811|logl*:   -inf<-129.8<   inf|logz: -136iter: 108|+64|eff(%): 57.143|logl*:   -inf<-125.1<   inf|logz: -131iter: 108|+65|eff(%): 57.475|logl*:   -inf< -96.5<   inf|logz: -102iter: 108|+66|eff(%): 57.807|logl*:   -inf< -79.1<   inf|logz:  -85iter: 108|+67|eff(%): 58.140|logl*:   -inf< -34.7<   inf|logz:  -41iter: 108|+68|eff(%): 58.472|logl*:   -inf<  23.0<   inf|logz:   16iter: 108|+69|eff(%): 58.804|logl*:   -inf<  25.8<   inf|logz:   19iter: 108|+70|eff(%): 59.136|logl*:   -inf<  30.9<   inf|logz:   24iter: 108|+71|eff(%): 59.468|logl*:   -inf<  47.3<   inf|logz:   40iter: 108|+72|eff(%): 59.801|logl*:   -inf<  62.5<   inf|logz:   56iter: 108|+73|eff(%): 60.133|logl*:   -inf<  81.9<   inf|logz:   75iter: 108|+74|eff(%): 60.465|logl*:   -inf< 106.5<   inf|logz:  100iter: 108|+75|eff(%): 60.797|logl*:   -inf< 109.2<   inf|logz:  102iter: 108|+76|eff(%): 61.130|logl*:   -inf< 145.9<   inf|logz:  139iter: 108|+77|eff(%): 61.462|logl*:   -inf< 156.4<   inf|logz:  150iter: 108|+78|eff(%): 61.794|logl*:   -inf< 187.0<   inf|logz:  180iter: 108|+79|eff(%): 62.126|logl*:   -inf< 192.0<   inf|logz:  185iter: 108|+80|eff(%): 62.458|logl*:   -inf< 205.6<   inf|logz:  199iter: 108|+81|eff(%): 62.791|logl*:   -inf< 210.4<   inf|logz:  204iter: 108|+82|eff(%): 63.123|logl*:   -inf< 224.7<   inf|logz:  218iter: 108|+83|eff(%): 63.455|logl*:   -inf< 230.7<   inf|logz:  224iter: 108|+84|eff(%): 63.787|logl*:   -inf< 237.6<   inf|logz:  231iter: 108|+85|eff(%): 64.120|logl*:   -inf< 252.1<   inf|logz:  245iter: 108|+86|eff(%): 64.452|logl*:   -inf< 256.3<   inf|logz:  250iter: 108|+87|eff(%): 64.784|logl*:   -inf< 263.4<   inf|logz:  257iter: 108|+88|eff(%): 65.116|logl*:   -inf< 266.7<   inf|logz:  260iter: 108|+89|eff(%): 65.449|logl*:   -inf< 278.6<   inf|logz:  272iter: 108|+90|eff(%): 65.781|logl*:   -inf< 281.8<   inf|logz:  275iter: 108|+91|eff(%): 66.113|logl*:   -inf< 284.5<   inf|logz:  278iter: 108|+92|eff(%): 66.445|logl*:   -inf< 286.1<   inf|logz:  280iter: 108|+93|eff(%): 66.777|logl*:   -inf< 287.2<   inf|logz:  281iter: 108|+94|eff(%): 67.110|logl*:   -inf< 288.7<   inf|logz:  282iter: 108|+95|eff(%): 67.442|logl*:   -inf< 294.6<   inf|logz:  288iter: 108|+96|eff(%): 67.774|logl*:   -inf< 295.9<   inf|logz:  289iter: 108|+97|eff(%): 68.106|logl*:   -inf< 297.2<   inf|logz:  291iter: 108|+98|eff(%): 68.439|logl*:   -inf< 310.1<   inf|logz:  303iter: 108|+99|eff(%): 68.771|logl*:   -inf< 311.8<   inf|logz:  305iter: 108|+100|eff(%): 69.103|logl*:   -inf< 320.8<   inf|logz:  314.4+/-1.0ERROR
test_analytic_exponential (tests.test_tau_calculator.TestTauLOS) ... ok
test_analytic_simple (tests.test_tau_calculator.TestTauLOS) ... ok
test_dl (tests.test_tau_calculator.TestTauLOS) ... /home/nishiumi/platon/tests/test_tau_calculator.py:29: RuntimeWarning: invalid value encountered in sqrt
  dist_lower = 2*np.sqrt(r_prime**2 - r**2)
/home/nishiumi/platon/tests/test_tau_calculator.py:28: RuntimeWarning: invalid value encountered in sqrt
  dist_higher = 2*np.sqrt(r_prime_higher**2 - r**2)
ok
test_realistic (tests.test_tau_calculator.TestTauLOS) ... SKIP: Algorithm no longer identical to ExoTransmit
test_bin_wavelengths (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok
test_bounds_checking (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok
test_custom_file (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok
test_ggchem (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok
test_power_law_haze (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok
test_unbound_atmosphere (tests.test_transit_depth_calculator.TestTransitDepthCalculator) ... ok

======================================================================
ERROR: test_multinest (tests.test_retrieve.TestRetriever)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/nishiumi/platon/tests/test_retrieve.py", line 72, in test_multinest
    result = retriever.run_multinest(self.wavelength_bins, self.depths, self.errors, self.fit_info, maxcall=200, include_condensation=False, plot_best=True)
  File "/home/nishiumi/platon/platon/retriever.py", line 110, in run_multinest
    include_condensation, plot_best, maxiter, maxcall, **dynesty_kwargs)
  File "/home/nishiumi/platon/platon/combined_retriever.py", line 384, in run_multinest
    dyplot.runplot(result)
  File "/home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/dynesty-0.9.7-py3.6.egg/dynesty/plotting.py", line 182, in runplot
    wt_kde = gaussian_kde(resample_equal(-logvol, data[2]))  # KDE
  File "/home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/scipy/stats/kde.py", line 172, in __init__
    self.set_bandwidth(bw_method=bw_method)
  File "/home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/scipy/stats/kde.py", line 499, in set_bandwidth
    self._compute_covariance()
  File "/home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/scipy/stats/kde.py", line 510, in _compute_covariance
    self._data_inv_cov = linalg.inv(self._data_covariance)
  File "/home/nishiumi/.conda/envs/py35/lib/python3.6/site-packages/scipy/linalg/basic.py", line 975, in inv
    raise LinAlgError("singular matrix")
numpy.linalg.linalg.LinAlgError: singular matrix
-------------------- >> begin captured stdout << ---------------------

Evaluated params: ln_prob=-4.21e+06	Rs=1.20 R_sun	Mp=0.81 M_jup	Rp=1.61 R_jup	T=1661 K	logZ=-0.76 	CO_ratio=1.45 	log_cloudtop_P=-0.98 	log_scatt_factor=0.61 	scatt_slope=1.40 	error_multiple=0.76 	

Evaluated params: ln_prob=-2.33e+04	Rs=1.17 R_sun	Mp=0.74 M_jup	Rp=1.53 R_jup	T=1519 K	logZ=-0.92 	CO_ratio=1.28 	log_cloudtop_P=2.52 	log_scatt_factor=0.34 	scatt_slope=3.06 	error_multiple=5.66 	

Evaluated params: ln_prob=-2.51e+04	Rs=1.18 R_sun	Mp=0.68 M_jup	Rp=1.62 R_jup	T=1352 K	logZ=2.81 	CO_ratio=1.02 	log_cloudtop_P=-0.70 	log_scatt_factor=0.47 	scatt_slope=2.05 	error_multiple=6.90 	

Evaluated params: ln_prob=1.09e+02	Rs=1.19 R_sun	Mp=0.72 M_jup	Rp=1.39 R_jup	T=1367 K	logZ=2.35 	CO_ratio=0.73 	log_cloudtop_P=-0.33 	log_scatt_factor=0.36 	scatt_slope=2.16 	error_multiple=6.55 	

Evaluated params: ln_prob=-5.46e+04	Rs=1.17 R_sun	Mp=0.67 M_jup	Rp=1.57 R_jup	T=1151 K	logZ=2.41 	CO_ratio=1.41 	log_cloudtop_P=3.81 	log_scatt_factor=0.29 	scatt_slope=2.24 	error_multiple=3.83 	

Evaluated params: ln_prob=-2.08e+04	Rs=1.20 R_sun	Mp=0.71 M_jup	Rp=1.59 R_jup	T=1697 K	logZ=-0.36 	CO_ratio=0.76 	log_cloudtop_P=3.34 	log_scatt_factor=0.87 	scatt_slope=4.58 	error_multiple=8.01 	
#Parameter Lower_error Median Upper_error Best_fit
Max_lnprob 241.8159559663476
Rs 0.0 808423275.5052384 1.1920928955078125e-07 808423275.5052384
Mp 0.0 1.4270349011682383e+27 0.0 1.4270349011682383e+27
Rp 0.0 94053226.85372977 0.0 94053226.85372977
T 0.0 960.9236061213496 0.0 960.9236061213496
logZ 0.0 0.6136363753118568 0.0 0.6136363753118568
CO_ratio 0.0 0.9396814431460054 0.0 0.9396814431460054
log_cloudtop_P 0.0 0.2023394068016462 0.0 0.2023394068016462
log_scatt_factor 0.0 0.015893141904262054 0.0 0.015893141904262054
scatt_slope 0.0 1.734910969176494 0.0 1.734910969176494
error_multiple 0.0 3.6504520647536234 0.0 3.6504520647536234


--------------------- >> end captured stdout << ----------------------
-------------------- >> begin captured logging << --------------------
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs [0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 2000.0, 3000.0, 4000.0, 5000.0, 6000.0, 7000.0, 8000.0, 9000.0]
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs [0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 2000.0, 3000.0, 4000.0, 5000.0, 6000.0, 7000.0, 8000.0, 9000.0]
matplotlib.axes._base: DEBUG: update_title_pos
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs [0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 2000.0, 3000.0, 4000.0, 5000.0, 6000.0, 7000.0, 8000.0, 9000.0]
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
matplotlib.ticker: DEBUG: vmin 0.22226568888495934 vmax 10.72942200607209
matplotlib.ticker: DEBUG: ticklocs [0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 2000.0, 3000.0, 4000.0, 5000.0, 6000.0, 7000.0, 8000.0, 9000.0]
--------------------- >> end captured logging << ---------------------

----------------------------------------------------------------------
Ran 27 tests in 185.489s

FAILED (SKIP=2, errors=1)

Typo in eclipse_depths.rst

The citation to Madhusudhan & Seager 2018, actually should be Madhusudhan & Seager 2009.

Consider allowing wavelength-dependent refractive indices

The Qext code can easily be adapted to use different refractive indices. However, the current interpolation and caching schemes will no longer work, because Qext will depend on 2 real numbers instead of 1: lambda and radius. That's probably fine for the forward model, because the Mie calculation will take ~1 second. However, retrieval will take forever unless we use a 2D interpolation or caching scheme. Not impossible, but it will take some experimenting.

Write more unit tests

Use mass instead of planetary g

Pretty much every paper and exoplanet catalog species planetary mass instead of g. We already make the user specify R, and g = GM/R^2

Compute T-P profile

UltraNest support

Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ?
The interface should be very similar to pymultinest.

UltraNest is a very reliable tuning-parameter-free algorithm. I have published some examples where the UltraNest algorithm is unbiased while pymultinest gives a different answer. UltraNest is a pure-python package and very easy to install with pip or conda.

UltraNest also supports resuming from disk and MPI parallelisation, if that is useful to you.

Decide on a name

Ideas:

PyExoTransmit
ExoRetrieve
PyRetrieve
GoldenRetriever
GaseousRetriever
?

Get Travis CI working for OS X

Improve change_wavelength_bin

In TransitDepthCalculator, change_wavelength_bins is implemented in a crude way that doesn't allow the same function to be called again. We should store the unbinned data somewhere, and allow the user to change wavelength bins as often as they want.

Make PyExoTransmit compatible with both Python 2.7 and 3

md5sum missing and abundances

When running the example emcee file:

File "platon_p.py", line 56, in
result = retriever.run_emcee(bins, depths, errors,None, None, None,fit_info)
File "/home/user/anaconda3/lib/python3.7/site-packages/platon/combined_retriever.py", line 248, in run_emcee
include_condensation=include_condensation, method=rad_method)
File "/home/user/anaconda3/lib/python3.7/site-packages/platon/transit_depth_calculator.py", line 44, in init
ref_pressure, method)
File "/home/user/anaconda3/lib/python3.7/site-packages/platon/_atmosphere_solver.py", line 30, in init
get_data_if_needed()
File "/home/user/anaconda3/lib/python3.7/site-packages/platon/_get_data.py", line 16, in get_data_if_needed
with open(resource_filename(name, "md5sum")) as f:
FileNotFoundError: [Errno 2] No such file or directory: '/home/user/anaconda3/lib/python3.7/site-packages/platon/md5sum'

Thank you for the nice code!

Add error_factor to fit parameters

...so that all user-supplied errors can be multiplied by error_factor

add plotting scripts that produce publishable plots

Remove "abundances" from TransitDepthCalculator

Right now the transit depth calculator takes "abundances", which has to be a dictionary of 13 x 30 arrays spanning pressures from 10^-9 bar to 1000 bar, and temperatures from 100 to 3000 K. However, sometimes we don't have abundance information for this whole range because ggchem is unstable at very low temperatures. Is there a way to make the format of "abundances" more flexible? Can the user just specify abundances at points along the atmospheric profile? Can abundances be replaced with an AbundanceGetter object?

Haze Model - Sensitivity to the reference wavelength

Characterise the outcome of the retrieval for different reference wavelengths

Make this into a pip-installable package

Monatomic hydrogen

At low pressures (< 1 Pa), monatomic hydrogen is the dominant species. This makes perfect sense from an equilibrium chemistry perspective because hydrogen atoms can dissociate at the normal rate, but can never find another hydrogen atom to combine with. However, does this make physical sense in the context of exoplanet atmospheres?

Enable non-flat priors

Especially for the stellar radius and planetary g, we need to allow the user to include the uncertainties and take them into account in the prior. Fixing them to one value makes the retrieval biased and unrealistic.

Retrievals consistently failing 'assert' in get_line_of_sight_tau

Hi,

I've been trying to run retrievals on some simulated data, and for certain input parameters, the nested sampling retrieval fails within the first couple of likelihood calls with the following error:

Traceback (most recent call last):
File "/home/radica/.anaconda3/envs/astroconda/lib/python3.6/site-packages/dynesty/dynesty.py", line 939, in call
return self.func(x, *self.args, **self.kwargs)
File "/home/radica/.anaconda3/envs/astroconda/lib/python3.6/site-packages/platon/combined_retriever.py", line 379, in multinest_ln_like
eclipse_depths, eclipse_errors)
File "/home/radica/.anaconda3/envs/astroconda/lib/python3.6/site-packages/platon/combined_retriever.py", line 149, in _ln_like
part_size=part_size, ri=ri, P_quench=P_quench, full_output=True)
File "/home/radica/.anaconda3/envs/astroconda/lib/python3.6/site-packages/platon/transit_depth_calculator.py", line 286, in compute_depths
radii)
File "/home/radica/.anaconda3/envs/astroconda/lib/python3.6/site-packages/platon/_tau_calculator.py", line 23, in get_line_of_sight_tau
assert(np.allclose(radii, np.sort(radii)[::-1]))
AssertionError

It seems to only occur when I add the planet radius or mass as a fit parameter. If I remove radius and mass as fittable parameters, but keep everything else the same, the retrieval will run to completion.

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