sdapy.snerun.snobject._ax1¶

snobject._ax1(show_title=False, show_telluric=True, show_text=True, show_data=True, show_peaks=True, show_fits=True, text_type='date', show_fit_error=True, **kwargs)¶

Spectra plot.

Parameters

plot_specsources: `list`

which sources of spectra to show

show_stypestr

spectral data type, options: ‘original’, ‘rest’, ‘bin’, ‘continuum’, ‘flat’

show_elementstr

[full] show full range spectra; 2. [sntype] show characteristic features depends on SN type; 3. else, should be specific element, e.g. ‘H~$lpha$’, check and define elements in https://github.com/saberyoung/HAFFET/blob/master/sdapy/constants.py

v_boundslist

guessed velocity range (unit: 1e3 km/s)

continuum_methodstr

The function type for continumm fitting, valid functions are “scalar”, “linear”, “quadratic”, “cubic”, “poly”, and “exponential”

continuum_degreeint

degree of polynomial when method=”poly”, for continuum fitting

pfactorint

threshold used for peak detection

plot_mcmctfloat between 0 and 1

a threshold that select good mc samples for plotting, rangiing from 0 to 1, e.g. 0.5 means selecting samplings from the top 50 percent of all samplings relying on the likelihoods

plot_nsamplesint

how many random MC samples to be plotted

show_telluricbool

show telluric lines or not

show_titlebool

show subplot title or not

show_databool

if show data points

show_peaksbool

if show peaks and valleys of spectra

show_fit_errorbool

if False, only show best fit, otherwise, show errors or random samplings

show_fitsbool

if show model fittings

show_textbool

show spectra infos to the right of each spectrum

alphabestfloat between 0 and 1

matplotlib alpha for best fit fitting

alphasamplefloat between 0 and 1

matplotlib alpha for random samplings or errors

text_typestr

time type in the text: date or phase or jd

verbosebool

Enable progress report