Changes

Changes from IDL SME to PySME:

SME structure:

• Python Class object

• all properties can be accessed as properties (sme.name) or as items ([“name”])

• all properties are case insensitive

• Can read IDL sav files, but not write, uses numpy npy format instead

• fitresult values in fitresult object (e.g. covariance matrix)

• pname, ab_free, and glob_free are now combined in fitparameters

• idlver still called that, but contains information about Python version

• cscale is the polynomial coefficients describing the continuum (of any degree)

• preferred access to spectra, through wave, spec, uncs, mask, synth

  • all of which represent the data as Iliffe vectors, i.e. a list of arrays of arbitrary length (The underlying data structure is the same, and is still accessable, wave is renamed to wob) also available are mask_good, mask_bad, mask_line, and mask_continuum, which are boolean Iliffe vectors of the masks

• obsolete properties removed (smod_orig, cmod_orig, cmod, jint, sint, psig_l, psig_r, rchisq, crms, lrms, vmac_pro, cintb, cintr, obs_type, clim)

• redundant properties are now dynamic (nmu, nseg, md5)

• property “obs_name” renamed to object

• property “wave” renamed to wob, but “wave” still exists but as an Iliffe vector (as described above)

• flags, cscale_flag and vrad_flag now use strings instead of integers

  • cscale_flag : “none”, “fix”, “constant”, “linear”, “quadratic”

  • vrad_flag : “none”, “whole”, “each”

• cscale and vrad are always the expected size (i.e. equal to the number of segments, and polynomial degrees)

Abundance:

• Python Class object

• set of three default solar abundances available (“asplund2009”, “grevesse2007”, “lodders2003”)

• can output abundances in different formats (‘sme’, ‘n/nTot’, ‘n/nH’, and ‘H=12’)

• internal format is ‘H=12’, i.e. log(X/H) + 12

• metallicity is included as part of the abundance

LineList:

• Python Class object

• uses a Pandas Dataframe under the hood

• combines species, atomic, lulande, extra, depth, lineref, term_upp, and term_low

• also provides relative error measure for each line (based on error code in lineref)

NLTE:

• works similar to IDL

• NLTE can be activated/deactivated with sme.nlte.set_nlte(element_name) / remove_nlte(element_name)

• NLTE lines are flagged by the C library in sme.nlte.flags

Synthetic Spectrum:

• uses the same C library as IDL SME

• sme_func, gives almost the same results as in IDL

• there are slight differences in the interpolation of the atmospheres

• continuum and radial velocity are now determined at the same time using a least_squares fit (with method “trf”)

Solver:

• Switched solving algorithm to use scipy.optimize.least_squares with method=”trf”

  • “trf” = Trust-region Reflective

  • robust loss function “soft_l1” resid = 2 * ((1 + z)**0.5 - 1) with z = model - obs / uncs

• also includes bounds, but only for the outer edges of the atmosphere grid

• will find different minima than IDL SME, but within a reasonable range and often with slightly better chi square

• for parameters outside the atmosphere grid, the an infinite residual is returned, which causes the least squares to avoid that area

Logging:

• log with standard logging module

• output both to console and to file

C interface:

• as mentioned above uses the same library as IDL SME

• general purpose interface is idl_call_external in cwrapper.py

  • numerics are converted to ctypes elements

  • numpy arrays are converted to appropiate ctypes (memory stays the same if possible)

  • strings (and string arrays) are copied into IDL_string structures (memory will change)

  • output is written back into original arrays if necessary

• specific functions are implemented in sme_synth.py

  • data types are converted to the necessary values

  • the size of input arrays is stored for the output functions (e.g. number of lines)

  • errors in the C code will raise Exceptions (ValueErrors)

Plotting:

• two plotting modules

• one for matplotlib.pyplot (creates a window)

• one for plot.ly (creates a html file with javascript)

• both provide functionality to move through the segments

• can also change the mask from within the plot (requires a jupyter notebook session for plot.ly)