Example single fit

Fitting method

  • Read data

  • Select and create model

  • Fit the data with model

  • Save plots and log

Quick fit

import src.binary_sed_fitting as bsf
################################################################################
name        = 'WOCS2002'
file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
data        = bsf.load_data(file_name, mode='csv')
distance    = 831. # pc
e_distance  = 11.  # pc
################################################################################
model_name = 'kurucz'
limits = {'Te'   : [3500, 9000],
          'logg' : [   3,    5],
          'MH'   : [ 0.0,  0.0],
          'alpha': [ 0.0,  0.0]}
model = bsf.Model(model_name, limits=limits)
################################################################################
star = bsf.Star(name=name,
                distance=distance,
                e_distance=e_distance,
                data=data,
                model=model)
################################################################################
star.fit_chi2()
star.plot()

22:39:52 ----- WARNING  ----- estimate_runtime
Calculating chi2: ETA ~ 0 s
_images/output_3_1.png

Selecting model

  • Supported models: Kurucz, Koester and Kurucz_UVBLUE

import src.binary_sed_fitting as bsf
################################################################################
model_name = 'kurucz'
limits = {'Te'   : [3500, 9000],
          'logg' : [   3,    5],
          'MH'   : [ 0.0,  0.0],
          'alpha': [ 0.0,  0.0]}
model = bsf.Model(model_name, limits=limits)
print(model_name, '\n\n\n' ,model.da)
################################################################################
model_name = 'kurucz_uvblue'
limits = {'Te'   : [3500, 9000],
          'logg' : [   3,    5],
          'MH'   : [ 0.0,  0.0]}
model = bsf.Model(model_name, limits=limits)
print(model_name, '\n\n\n' ,model.da)
################################################################################
model_name = 'koester'
limits = {'Te'   : [5000, 80000],
          'logg' : [ 6.5,   9.5]}
model = bsf.Model(model_name, limits=limits)
print(model_name, '\n\n\n' ,model.da)

kurucz


 <xarray.DataArray (FilterID: 8162, Te: 23, logg: 5, MH: 1, alpha: 1)> Size: 8MB
[938630 values with dtype=float64]
Coordinates:
  * FilterID  (FilterID) <U38 1MB 'Swift/UVOT.UVM2_fil' ... 'QUIJOTE/MFI.11GH...
  * Te        (Te) int32 92B 3500 3750 4000 4250 4500 ... 8250 8500 8750 9000
  * logg      (logg) float64 40B 3.0 3.5 4.0 4.5 5.0
  * MH        (MH) float64 8B 0.0
  * alpha     (alpha) float64 8B 0.0
Attributes:
    Wavelengths:  [1.19128077e+00 1.24254209e+00 1.48134125e+02 ... 3.8683349...
    unit:         erg/s/cm2/A
    long_name:    Flux
kurucz_uvblue


 <xarray.DataArray (FilterID: 8162, Te: 12, logg: 5, MH: 1)> Size: 4MB
[489720 values with dtype=float64]
Coordinates:
  * FilterID  (FilterID) <U38 1MB 'Swift/UVOT.UVM2_fil' ... 'QUIJOTE/MFI.11GH...
  * Te        (Te) int32 48B 3500 4000 4500 5000 5500 ... 7500 8000 8500 9000
  * logg      (logg) float64 40B 3.0 3.5 4.0 4.5 5.0
  * MH        (MH) float64 8B 0.0
    alpha     float64 8B ...
Attributes:
    Wavelengths:  [1.19128077e+00 1.24254209e+00 1.48134125e+02 ... 3.8683349...
    unit:         erg/s/cm2/A
    long_name:    Flux
koester


 <xarray.DataArray (FilterID: 8162, Te: 82, logg: 13)> Size: 70MB
[8700692 values with dtype=float64]
Coordinates:
  * FilterID  (FilterID) <U38 1MB 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'
  * Te        (Te) int32 328B 5000 5250 5500 5750 ... 50000 60000 70000 80000
  * logg      (logg) float64 104B 6.5 6.75 7.0 7.25 7.5 ... 8.75 9.0 9.25 9.5
Attributes:
    Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...
    unit:         erg/s/cm2/A
    long_name:    Flux

Advance options

  • Console logging options can be changes based on requirements

    • For minimal logs: bsf.console.setLevel(bsf.logging.ERROR)

    • For typical logs: bsf.console.setLevel(bsf.logging.WARNING)

    • For detailed logs: bsf.console.setLevel(bsf.logging.INFO)

    • For debug logs: bsf.console.setLevel(bsf.logging.DEBUG)

  • Editing the bsf file and re-importing the file python     import importlib     importlib.reload(bsf)

  • Star

    • component : Name of the component. Useful for binary fits.

  • fit_blackbody

    • Fits a blackbody to given flux

    • p0 : Initial guess for the temperature and log(scaling factor). Default is [5000., -20]

  • fit_chi2

    • _trim : Trimming the number of chi2 fits to save memory

  • fit_noisy_chi2

    • total_iterations : Number of iterations for noisy fits

  • plot

    • show_plot : Showing/hiding the plots in the notebook

  • plot_public

    • Duplicate and modify the funcion to suit your need

  • create_sf_list

    • LOG_SF_STEPSIZE (=0.01) and LOG_SF_FLEXIBILITY (=2) are used to determine the stepsizes in scaling factor

  • data

    • Dataframe containing the observational data. It is cropped to the fitted filters based on filters_to_drop and wavelength_range.

  • data_all

    • A copy of the original data with an added ‘fitted’ column indicating whether the filter is used. Mmodel flux, residuals, and statistical measures are added in Fitter.get_parameters_from_chi2_minimization and Fitter.get_parameters_from_noisy_chi2_minimization.

  • data_not_fitted

    • DataFrame containing only removed filters based on filters_to_drop and wavelength_range

  • save_fig

    • Edit the function to change the plot format (e.g. PDF, PNG or multiple formats)

  • Star.df_chi2 and Star.df_chi2_noisy

    • The chi2 dataframes are saved in outputs\.

  • log files

    • The raw log files (with logging level DEBUG) are saved in data\log_<today's date>.txt

    • The summary logs are chi2 dataframe is saved in outputs\log_single_fitting.csv and outputs\log_starsystem_fitting.csv.

import src.binary_sed_fitting as bsf
import warnings
import importlib
warnings.filterwarnings("ignore")
importlib.reload(bsf)
bsf.console.setLevel(bsf.logging.INFO)
################################################################################
name        = 'WOCS2002'
file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
data        = bsf.load_data(file_name, mode='csv')
distance    = 831. # pc
e_distance  = 11.  # pc

refit = False

################################################################################
model_name = 'kurucz'
limits = {'Te'   : [3500, 9000],
          'logg' : [   3,    5],
          'MH'   : [ 0.0,  0.0],
          'alpha': [ 0.0,  0.0]}
model = bsf.Model(model_name, limits=limits)
################################################################################
star = bsf.Star(name=name,
                distance=distance,
                e_distance=e_distance,
                filters_to_drop=['KPNO/Mosaic.I'],
                wavelength_range=[3000, 1_000_000_000],
                data=data,
                model=model,
                r_limits='blackbody',
                run_name='kur',
                component='A')
################################################################################
star.fit_blackbody(p0=[5000., -20],
                   plot=True,
                   show_plot=True,
                   folder=None)
################################################################################
star.fit_chi2(refit=refit,
              _trim=1000)
star.fit_noisy_chi2(refit=refit,
                    total_iterations=100)
################################################################################
star.plot(add_noisy_seds=False,
          show_plot=True,
          folder=None,
          FR_cutoff=0.5)
star.plot_public(add_noisy_seds=False,
                 show_plot=True,
                 folder=None,
                 FR_cutoff=0.5)

22:39:59 ----- INFO     ----- __init__
==========================================================
----------------------------------------------------------
------------            WOCS2002 A            ------------
----------------------------------------------------------
==========================================================
22:39:59 ----- INFO     ----- drop_filters
                        Fitted           Not fitted
wavelength
1481.000000                     Astrosat/UVIT.F148W
1541.000000                     Astrosat/UVIT.F154W
1608.000000                     Astrosat/UVIT.F169M
2303.366368                         GALEX/GALEX.NUV
4357.276538      KPNO/Mosaic.B
5035.750275     GAIA/GAIA3.Gbp
5366.240786      KPNO/Mosaic.V
5822.388714       GAIA/GAIA3.G
7619.959993     GAIA/GAIA3.Grp
8101.609574                           KPNO/Mosaic.I
8578.159519    GAIA/GAIA3.Grvs
12350.000000     2MASS/2MASS.J
16620.000000     2MASS/2MASS.H
21590.000000    2MASS/2MASS.Ks
33526.000000      WISE/WISE.W1
46028.000000      WISE/WISE.W2
115608.000000     WISE/WISE.W3
22:39:59 ----- INFO     ----- drop_filters
Filters: used/all = 12/17
22:39:59 ----- INFO     ----- blackbody
Fit parameters: T=5141 K, log_sf=-19.68
22:39:59 ----- INFO     ----- blackbody
Fit parameters: T=5141 K, log_sf=-19.68
22:39:59 ----- WARNING  ----- calculate_chi2
Give "refit=True" if you want to rerun the fitting process.
22:39:59 ----- INFO     ----- calculate_chi2
     Te  logg   MH  alpha            sf       chi2         R          L
0  5250   4.0  0.0    0.0  2.105389e-20  42.148650  5.348053  19.575928
1  5250   4.5  0.0    0.0  2.105389e-20  43.750774  5.348053  19.575928
2  5250   3.5  0.0    0.0  2.105389e-20  46.564792  5.348053  19.575928
3  5250   5.0  0.0    0.0  2.105389e-20  51.654370  5.348053  19.575928
4  5250   3.0  0.0    0.0  2.105389e-20  60.201435  5.348053  19.575928
22:39:59 ----- INFO     ----- get_parameters_from_chi2_minimization
Te  5250
logg        4.0
MH  0.0
alpha       0.0
sf  2.1053889442869984e-20
chi2        42.148649603337915
R   5.348053395835723
L   19.57592810070829
vgf2        11.474651537318044
vgfb2       0.8009328529123378
22:39:59 ----- WARNING  ----- calculate_noisy_chi2
Give "refit=True" if you want to rerun the fitting process.
22:39:59 ----- INFO     ----- calculate_noisy_chi2
     Te  logg   MH  alpha            sf       chi2         R          L
0  5250   4.0  0.0    0.0  2.105389e-20  42.148650  5.348053  19.575928
1  5250   4.0  0.0    0.0  2.105389e-20  48.786377  5.348053  19.575928
2  5250   4.5  0.0    0.0  2.105389e-20  58.784976  5.348053  19.575928
3  5250   4.0  0.0    0.0  2.105389e-20  83.279210  5.348053  19.575928
4  5250   4.0  0.0    0.0  2.105389e-20  57.314788  5.348053  19.575928
22:39:59 ----- INFO     ----- get_parameters_from_noisy_chi2_minimization
Te  5250(-250,+250)
logg        4.0(-0.5,+0.5)
MH  0.0(-nan,+nan)
alpha       0.0(-nan,+nan)
sf  2.1053889442869984e-20(-4.792450334089126e-22,+4.904080842726991e-22)
R   5.348053395835723(-0.09359116353916891,+0.0940563746344943)
L   19.57592810070829(-0.518255617587945,+0.518255617587945)
_images/output_16_1.png _images/output_16_2.png _images/output_16_3.png 
print(star.data.index)
print(star.data_all.index)
print(star.data_not_fitted.index)
star.data.head()

Index(['KPNO/Mosaic.B', 'GAIA/GAIA3.Gbp', 'KPNO/Mosaic.V', 'GAIA/GAIA3.G',
       'GAIA/GAIA3.Grp', 'GAIA/GAIA3.Grvs', '2MASS/2MASS.J', '2MASS/2MASS.H',
       '2MASS/2MASS.Ks', 'WISE/WISE.W1', 'WISE/WISE.W2', 'WISE/WISE.W3'],
      dtype='object', name='FilterID')
Index(['Astrosat/UVIT.F148W', 'Astrosat/UVIT.F154W', 'Astrosat/UVIT.F169M',
       'GALEX/GALEX.NUV', 'KPNO/Mosaic.B', 'GAIA/GAIA3.Gbp', 'KPNO/Mosaic.V',
       'GAIA/GAIA3.G', 'GAIA/GAIA3.Grp', 'KPNO/Mosaic.I', 'GAIA/GAIA3.Grvs',
       '2MASS/2MASS.J', '2MASS/2MASS.H', '2MASS/2MASS.Ks', 'WISE/WISE.W1',
       'WISE/WISE.W2', 'WISE/WISE.W3'],
      dtype='object', name='FilterID')
Index(['Astrosat/UVIT.F148W', 'Astrosat/UVIT.F154W', 'Astrosat/UVIT.F169M',
       'GALEX/GALEX.NUV', 'KPNO/Mosaic.I'],
      dtype='object', name='FilterID')
wavelength flux error error_fraction error_2percent error_10percent log_wavelength log_flux e_log_flux fitted ... chi2_i vgf2_i vgfb2_i model_flux_median residual_flux_median fractional_residual_median ewr_median chi2_i_median vgf2_i_median vgfb2_i_median
FilterID
KPNO/Mosaic.B 4357.276538 9.309407e-14 6.130611e-15 0.065854 6.130611e-15 9.309407e-15 3.639215 -13.031078 0.028600 1 ... 0.545712 0.545712 2.366611e-01 8.856525e-14 4.528824e-15 0.048648 0.738723 0.545712 0.545712 2.366611e-01
GAIA/GAIA3.Gbp 5035.750275 1.028575e-13 3.881303e-16 0.003773 2.057150e-15 1.028575e-14 3.702064 -12.987764 0.001639 1 ... 0.000081 0.000003 1.148069e-07 1.028540e-13 3.485137e-18 0.000034 0.008979 0.000081 0.000003 1.148069e-07
KPNO/Mosaic.V 5366.240786 1.079375e-13 7.108112e-15 0.065854 7.108112e-15 1.079375e-14 3.729670 -12.966827 0.028600 1 ... 0.298089 0.298089 1.292734e-01 1.118184e-13 -3.880853e-15 -0.035955 -0.545975 0.298089 0.298089 1.292734e-01
GAIA/GAIA3.G 5822.388714 9.174216e-14 2.539155e-16 0.002768 1.834843e-15 9.174216e-15 3.765101 -13.037431 0.001202 1 ... 4.897209 0.093784 3.751362e-03 9.230407e-14 -5.619057e-16 -0.006125 -2.212964 4.897209 0.093784 3.751362e-03
GAIA/GAIA3.Grp 7619.959993 7.878734e-14 3.154436e-16 0.004004 1.575747e-15 7.878734e-15 3.881953 -13.103544 0.001739 1 ... 13.712759 0.549535 2.198141e-02 7.995545e-14 -1.168111e-15 -0.014826 -3.703074 13.712759 0.549535 2.198141e-02

5 rows × 24 columns