dem_unwrap#

demregpy.dem_unwrap(
dn,
ed,
rmatrix,
logt,
dlogt,
glc,
reg_tweak=1.0,
max_iter=10,
rgt_fact=1.5,
dem_norm0=None,
nmu=42,
warn=False,
l_emd=False,
)[source]#

Run dem_pix() over a stack of observations in serial.

Parameters:

  • dn (ndarray) – Input counts with shape (ndem, nf).

  • ed (ndarray) – Uncertainties on dn with the same shape.

  • rmatrix (ndarray) – Response matrix with shape (nt, nf).

  • logt (array_like) – Log temperature bins.

  • dlogt (array_like) – Size of temperature bins.

  • glc (array_like) – Length-nf 0/1 mask selecting the filters used for EM loci weighting.

  • reg_tweak (float, optional) – Initial Chisq target, by default 1.0

  • max_iter (int, optional) – Max number of iterations to reach target chisq before giving up, by default 10

  • rgt_fact (float, optional) – Factor to increase chisq by each iteration, by default 1.5

  • dem_norm0 (array_like, optional) – Initial guess at the dem shape, by default 0

  • nmu (int, optional) – number of reg param samples to use, by default 42

  • warn (bool, optional) – Print warnings, by default False

  • l_emd (bool, optional) – Remove sqrt from constraint matrix, by default False

Returns:

  • dem (ndarray) – DEM values with shape (ndem, nt).

  • edem (ndarray) – Vertical uncertainties on dem.

  • elogt (ndarray) – Horizontal temperature resolution estimates in log10(T).

  • chisq (array_like) – Reduced chi-squared values.

  • dn_reg (ndarray) – Reconstructed counts with shape (ndem, nf).

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