Inheritance diagram for smica.model.Model:
Public Member Functions | |
| def | add_comp |
| def | get_comp_by_number |
| def | get_comp_by_name |
| def | __init__ |
| def | get_dim |
| def | check_covmat_dim |
| def | check_bin_dim |
| def | dim |
| def | get_theta |
| def | set_theta |
| def | printp |
| def | fim |
| def | covariance |
| def | covariance4D |
| def | mixmat |
| def | powspec |
| def | autopowspec |
| def | noise |
| def | set_mixmat |
| def | set_powspec |
| def | set_noise |
| def | error_model |
| def | mismatch |
| def | close_form |
| def | joint_diag |
| def | ortho_subspace |
| def | quasi_newton |
| def | wiener |
| def | winf |
| def | mismatch_penalty |
| def | __cg_mismatch__ |
| def | cg_mismatch |
| def | cg_score |
| def | cg_fim |
| def | cg_ifim |
| def | conjugate_gradient |
| def | plot_mixmat |
| def | plot_powspec |
| def | plot_noise |
| def | plot_power |
| def | plot_em |
| def | plot_mismatch |
| def | full_fim |
| def | error_model_full_fim |
Public Attributes | |
| ncomp | |
| nbin | |
| ndet | |
Properties | |
| dim = property(dim) | |
Detailed Description
A model. A model is made of several components which share the same dimensions (number of detectors, number of bins). Each of the component contribute to its covariance, which is adjusted to the empirical one.
Constructor & Destructor Documentation
| def smica.model.Model.__init__ | ( | self, | |
complist = None, |
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ndet = 0, |
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nbin = 0, |
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ncomp = 0 |
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| ) |
Set content and/or dimension of object. Parameters ---------- ndet : number of detectors nbin : number of bins ncomp : number of components complist : list of Component object instance (dimensions have to be compatible). If None, an arbitrary list of ncomp-1 source components and one noise component is created.
Member Function Documentation
| def smica.model.Model.__cg_mismatch__ | ( | self, | |
| theta, | |||
| stats, | |||
| nmodes | |||
| ) |
- Todo:
- Find a way to avoid re-set of free parameters for mismatch, score and FIM evaluation.
| def smica.model.Model.add_comp | ( | self, | |
| component | |||
| ) |
Add a component to model. Parameters ---------- component : instance of component object.
| def smica.model.Model.autopowspec | ( | self, | |
bin = None |
|||
| ) |
Return source auto power spectra array. Parameters ---------- bin : bin number. If None, complete array is returned. Returns ------- array, shape (dim, nbin).
| def smica.model.Model.cg_fim | ( | self, | |
| theta, | |||
| stats, | |||
| nmodes | |||
| ) |
Set model with new parameters and return Fisher Information Matrix. Parameters ---------- theta : array-like, shape (N, 1), where N is the number of free parameters. stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- array-like, shape (N, N), where N is the number of free parameters.
| def smica.model.Model.cg_ifim | ( | self, | |
| theta, | |||
| stats, | |||
| nmodes | |||
| ) |
Set model with new parameters and return inverse of Fisher Information Matrix. Parameters ---------- theta : array-like, shape (N, 1), where N is the number of free parameters. stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- array-like, shape (N, N), where N is the number of free parameters.
| def smica.model.Model.cg_mismatch | ( | self, | |
| theta, | |||
| stats, | |||
| nmodes | |||
| ) |
Set model with new parameters and return integrated mismatch between model and stats. Parameters ---------- theta : array-like, shape (N, 1), where N is the number of free parameters. stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- scalar.
| def smica.model.Model.cg_score | ( | self, | |
| theta, | |||
| stats, | |||
| nmodes | |||
| ) |
Set model with new parameters and return score. Parameters ---------- theta : array-like, shape (N, 1), where N is the number of free parameters. stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- array-like, shape (N, 1), where N is the number of free parameters.
| def smica.model.Model.check_bin_dim | ( | self, | |
| bins | |||
| ) |
Check dimension compatibility between bins and model. Parameters ---------- bins : array-like, shape (1, nbin).
| def smica.model.Model.check_covmat_dim | ( | self, | |
| stats | |||
| ) |
Check dimension compatibility between stats and model. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations.
| def smica.model.Model.close_form | ( | self, | |
| stats | |||
| ) |
Update local parameters using a close form algorithm. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations.
| def smica.model.Model.conjugate_gradient | ( | self, | |
| stats, | |||
| nmodes, | |||
maxiter = 10, |
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avextol = 1e-3, |
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plot = False, |
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print_param = False |
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| ) |
Update model parameters with Kullback divergence minimum using conjugate gradient algorithm. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). maxiter : maximum number of iterations. avextol : convergence is assumed when the gradient falls below this amount. plot : boolean. If True, output a plot of the line search state and criterion convergence.
| def smica.model.Model.covariance | ( | self, | |
bin = None |
|||
| ) |
Return the covariance matrix(ces). Parameters ---------- bin : bin number. If None, return all matrices in a three dimensional array. Returns ------- array, shape (ndet, ndet, nbin).
| def smica.model.Model.covariance4D | ( | self) |
Return the covariance matrix(ces) of each component. Parameters ---------- bin : bin number. If None, return all matrices in a three dimensional array. Returns ------- array, shape (ndet, ndet, nbin, ncomp).
| def smica.model.Model.dim | ( | self) |
Return total sources dimension. This comprises uncorrelated and correlated sources.
| def smica.model.Model.error_model | ( | self, | |
| nmodes | |||
| ) |
Return a model object which parameter values are equal to error bars derived from the FIM. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- model object instance.
| def smica.model.Model.error_model_full_fim | ( | self, | |
| nmodes | |||
| ) |
Return a model object which parameter values are equal to error bars derived from the full FIM. Parameters ---------- nmodes : array-like, shape (nbin,1). Number of modes of each bin. Returns ------- model object instance.
| def smica.model.Model.fim | ( | self, | |
| nmodes | |||
| ) |
Return Fisher Information Matrix for global parameters. Only a subset of parameters is used in this FIM, ignoring those which varies from bin to bin. Parameters ---------- nmodes : array-like, shape (1, nbin). number of modes for each bin. Returns ---------- array-like, shape (N, N), where N is the number of free global parameters.
| def smica.model.Model.full_fim | ( | self, | |
| nmodes | |||
| ) |
Return full Fisher Information Matrix, ie global and local parameters with coupling Parameters ---------- nmodes : array-like, shape (1, nbin). number of modes for each bin. Returns ---------- A tuple of three element. The first on is an array-like, shape (N, N), where N is the number of parameters. This is the FIM. The second is a list of label for each line / column of the previous return. The last is a list of information describing each subbloc of the FIM. Each element of the list is a list containing - the name of the component - the class fot the component - the number of parameters - a list of two elements : the start and end index in the FIM
| def smica.model.Model.get_comp_by_name | ( | self, | |
| name | |||
| ) |
Return component object. Parameters ---------- name : component name. Returns ------- Instance of component object.
| def smica.model.Model.get_comp_by_number | ( | self, | |
| c | |||
| ) |
Return component object. Parameters ---------- c : component number Returns ------- Instance of component object.
| def smica.model.Model.get_dim | ( | self) |
Return the number of free parameters of the model. This is the sum of all components free parameters.
| def smica.model.Model.get_theta | ( | self) |
Return model parameters as a vector. Returns ---------- array-like, shape (N, 1), where N is the number of free parameters.
| def smica.model.Model.joint_diag | ( | self, | |
| stats, | |||
| nmodes | |||
| ) |
Update mixing matrix using joint diagonalisation algorithm. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin.
| def smica.model.Model.mismatch | ( | self, | |
| stats, | |||
| nmodes, | |||
bin = None, |
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exact = False |
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| ) |
Return integrated mismatch between model and stats. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. bin : bin number. If None, mismatch is integrated over bins.
| def smica.model.Model.mismatch_penalty | ( | self) |
Prior contribution to the mismatch. Default is 0. Extend get_penalty function of one component to set a prior contribution. Returns ------- scalar.
| def smica.model.Model.mixmat | ( | self) |
Return mixing matrix. Returns ------- array, shape (ndet, dim).
| def smica.model.Model.noise | ( | self, | |
bin = None |
|||
| ) |
Return noise power spectra array. Parameters ---------- bin : bin number. If None, complete array is returned. Returns ------- array, shape (ndet, nbin).
| def smica.model.Model.ortho_subspace | ( | self, | |
| stats, | |||
| nmodes, | |||
| col, | |||
qmin = None, |
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qmax = None |
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| ) |
Update mixing matrix with a matrix orthogonal to its first column. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. col : array-like, shape (ndet, 1). First column of the mixing matrix.
| def smica.model.Model.plot_em | ( | self, | |
| nmodes, | |||
freqlist = None, |
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figfile = None |
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| ) |
Plot components electro-magnetic spectra. Parameters ---------- nmodes : array-like, shape (nbin,1). Number of modes of each bin. freqlist : array-like, shape (ndet, 1), x axis values. figfile : string, filename where to save the plot.
| def smica.model.Model.plot_mismatch | ( | self, | |
| stats, | |||
| nmodes, | |||
figfile = None, |
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xaxis = None |
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| ) |
Plot spectral mismatch between model and stats. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin. figfile : string, filename where to save the plot. xaxis: array-like, shape (nbin,1). the multipole range.
| def smica.model.Model.plot_mixmat | ( | self, | |
freqlist = None, |
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compname = None, |
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figfile = None |
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| ) |
Plot mixing matrix. Parameters ---------- freqlist : array-like, shape (ndet, 1), x axis values. compname : list, length (dim), column labels. figfile : string, filename where to save the plot.
| def smica.model.Model.plot_noise | ( | self, | |
freqname = None, |
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figfile = None |
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| ) |
Plot noise power spectra. Parameters ---------- freqname : list, length (ndet), plot labels. figfile : string, filename where to save the plot.
| def smica.model.Model.plot_power | ( | self, | |
figfile = None, |
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xaxis = None |
|||
| ) |
Plot components power spectra. Parameters ---------- figfile : string, filename where to save the plot. xaxis: array-like, shape (nbin,1). the multipole range.
| def smica.model.Model.plot_powspec | ( | self, | |
compname = None, |
|||
figfile = None |
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| ) |
Plot power spectra. Parameters ---------- compname : list, length (dim), plot labels. figfile : string, filename where to save the plot.
| def smica.model.Model.powspec | ( | self, | |
bin = None |
|||
| ) |
Return source power spectra array. Parameters ---------- bin : bin number. If None, complete array is returned. Returns ------- array, shape (dim, dim, nbin).
| def smica.model.Model.printp | ( | self, | |
| theta | |||
| ) |
Print component parameters
| def smica.model.Model.quasi_newton | ( | self, | |
| stats, | |||
| nmodes | |||
| ) |
Update local parameters by direct resolution of the system. Power spectra matrices are diagonal (no correlation between sources). Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. nmodes : array-like, shape (nbin,1). Number of modes of each bin.
| def smica.model.Model.set_mixmat | ( | self, | |
| mixmat | |||
| ) |
Set mixing matrix. Parameters ---------- mixmat : array-like, shape (ndet, dim).
| def smica.model.Model.set_noise | ( | self, | |
| N, | |||
bin = None, |
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fixed = None |
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| ) |
Set noise power spectra. Parameters ---------- powspec : array-like, shape (ndet, nbin). bin : bin number. If None, all bins are set. fixed : array-like, shape (ndet, nbin) Entries which are not equal to zero point to fixed indices. It is also possible to use keywords 'all' and 'null' to fix (respectively) all parameters or none.
| def smica.model.Model.set_powspec | ( | self, | |
| powspec, | |||
bin = None, |
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diag_opt = 0 |
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| ) |
Set source power spectra. Parameters ---------- powspec : array-like, shape (dim, dim, nbin). bin : bin number. If None, all bins are set. diag_opt : if true set cross spectra of correlated component to zero (powspec is then (dim, nbin)).
| def smica.model.Model.set_theta | ( | self, | |
| theta, | |||
verbose = False |
|||
| ) |
Set new values for model parameters. Parameters ---------- theta : array-like, shape (N, 1), where N is the number of free parameters.
| def smica.model.Model.wiener | ( | self, | |
| stats, | |||
diag_opt = 0 |
|||
| ) |
Update local parameters with Wiener approximation. The Wiener filter is computed from mixing matrix and noise values. Parameters ---------- stats : array-like, shape (ndet, ndet, nbin). Second order statistics ie empirical covariance matrices of the observations. diag_opt : if true set cross spectra of correlated component to zero.
| def smica.model.Model.winf | ( | self) |
Compute multi-components wiener filter in the high snr limit. Returns ------- array-like, shape (nccomp-1,m,m,N).
The documentation for this class was generated from the following file:
- smica/model.py
