cylammarco/wdphottools

WDPhotTools--A White Dwarf Photometric Toolkit in Python

wdphottools's Issues

Array handling issue with plotter.plot_atmosphere_model() with RBF interpolation

On the left, it is the correct plot of the white dwarf cooling sequence at different surface gravity using the CT interpolator. On the right, it uses the RBF interpolator. I believe there is something wrong with the list/array handling somewhere in the atmosphere model reader, and for some reason, fitter can use the RBF interpolator to fit WD atmosphere properly but in plotter it fails.

WDfitter inherits AtmosphereModelReader

Currently, WDfitter holds an instance of AtmosphereModelReader. It should be inheriting it to make the future extensions of the package easier without needing to write a wrapper function for every new method added.

Density returned is sometimes negative

This only happens when the burst duration is very short (~10^7).

Check units in the cooling interpolator

There may be inconsistent units used for different models in the interpolator.

Class name changes for the readers

Python class should start capitalised.

density not stored in object if CSV output fails

WDPhotTools/src/WDPhotTools/theoretical_lf.py

Lines 1359 to 1360 in 9de2848

	self.Mag = Mag
	self.number_density = number_density

These two lines should move above the CSV so that in case of CSV export failed, the computed density can still be retrieved from the object directly.

fitting Mbol & WD mass

Hi, I am trying to fit for the WD mass and Mbol. Broadly, I am following the approach outlined here: https://wdphottools.readthedocs.io/en/latest/tutorials/fitting.html. From what I understood, one needs to pick for the independent list one parameter from (Mbol or Teff) and the second one from (mass/Mbol) for the fitting. I am trying to run something like this:

from WDPhotTools.fitter import WDfitter
ftr = WDfitter()

ftr.fit(
    atmosphere="H",
    filters=["G3", "G3_BP", "G3_RP"],
    mags=[19.1, 18.8, 19.2],
    mag_errors=[0.006, 0.01, 0.07],
    distance=210.0,
    distance_err=10,
    extinction_convolved=True,
    kind="cubic",
    Rv=3.1,
    ebv=0.1,
    independent=["Mbol", "mass"],
    initial_guess=[13.0, 0.6],
    method="least_squares"
   )

but I keep getting this error:

ValueError                                Traceback (most recent call last)

[<ipython-input-58-2b54e9b12163>](https://localhost:8080/#) in <cell line: 4>()
      2 ftr = WDfitter()
      3 
----> 4 ftr.fit(
      5     atmosphere="H",
      6     filters=["G3", "G3_BP", "G3_RP"],

[/usr/local/lib/python3.9/dist-packages/WDPhotTools/fitter.py](https://localhost:8080/#) in fit(self, atmosphere, filters, mags, mag_errors, allow_none, distance, distance_err, extinction_convolved, kind, Rv, ebv, ra, dec, independent, initial_guess, logg, atmosphere_interpolator, reuse_interpolator, method, nwalkers, nsteps, nburns, progress, refine, refine_bounds, kwargs_for_RBF, kwargs_for_CT, kwargs_for_minimize, kwargs_for_least_squares, kwargs_for_emcee)
   2061                         # the [:2] is to separate the distance from the filters
   2062                         self.best_fit_params[j][i] = float(
-> 2063                             self.interpolator[j][i](self.results[j].x[0])
   2064                         )
   2065 

[/usr/local/lib/python3.9/dist-packages/WDPhotTools/atmosphere_model_reader.py](https://localhost:8080/#) in atmosphere_interpolator(*x)
    486                 def atmosphere_interpolator(*x):
    487 
--> 488                     x_0, x_1 = np.asarray(x, dtype="object").reshape(-1)
    489 
    490                     if independent[0] in ["Teff", "age"]:

ValueError: not enough values to unpack (expected 2, got 1)

I am probably missing something obvious. I would be glad for any help.
Thanks.

uncertainty estimation seems wrong

It is reported that the error estimations are wrong: empty when directly using minimiser, way underestimated when using MCMC.

WDLF inherits CoolingModelReader

Currently, WDLF holds an instance of CoolingModelReader. It should be inheriting it to make the future extensions of the package easier without needing to write a wrapper function for every new method added.

Running on scipy 1.8.1 ok but not scipy 1.9

Hi, thanks for letting me know. Also found that when running on scipy 1.8.1 everything runs ok but when running on scipy 1.9 I get the following error: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''.

Originally posted by @cm775 in #15 (reply in thread)

Consider using a fraction of the total extinction as a function of distance

Harris et al. (2006) used the following scheme in https://arxiv.org/pdf/astro-ph/0510820.pdf

The SDSS database gives the total interstellar absorption and reddening along the line of sight for each star, determined from the reddening maps of Schlegel et al. (1998). Because most of the WDs in this paper have distances within a few hundred pc, they are affected by only a fraction of the total absorption and reddening. This fraction is determined as part of the fitting for the distance of each star in an iterative procedure. We make the assumption that the absorption is zero for stars with distances < 100 pc, that it is the full absorption for stars with distances from the Galactic plane |z| > 250 pc, and that the absorption varies linearly along the line of sight between these distances.

wrong extinction labelling

Error message as follow

>>> ftr.show_best_fit(display=False)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "c:\xxxxxxxxxxxxxxxxxxxxxx\wdphottools\WDPhotTools\fitter.py", line 2268, in show_best_fit
    reddening = [
  File "c:\xxxxxxxxxxxxxxxxxxxxxx\wdphottools\WDPhotTools\fitter.py", line 2269, in <listcomp>
    self.best_fit_params[k]["Av_" + f]
KeyError: 'Av_g_ps1'

MS lifetime for different metallicity

include MS lifetime models at different metallicities.

README.md example photometric fitting plot is out of date.

The current plots are showing the fitted solution using 5 Pan-STARRS and 3 Gaia filters, but the code also includes JHK photometry.

Moving the cooling model interpolator

Move the cooling model interpolator from theoretical_lf.py to cooling_model_reader.py as it should be the method under the cooling model reader.

Manually rescale RBF interpolation for Teff

RBF interpolator does not natively support rescaling before interpolation. This has to be done before interpolating the Temperature because the dynamic range is very different from the mass, Mbol and logg.

Wrong IFMR relations in code

The current C18 IFMR in code is not the authors' adopted solution that is based on the MIST stellar evolution model, see

https://ui.adsabs.harvard.edu/abs/2018ApJ...866...21C/abstract