pyyeti.cla.DR_Event

class pyyeti.cla.DR_Event[source]

Setup data recovery for a specific event or set of modes.

Info

Contains data recovery information for each category. The category names are the keys. This is a copy of information in one or more DR_Def instances created during data recovery setup; eg, in a “prepare_4_cla.py” script. See DR_Def and the example below. The copy is made during the call to DR_Event.add() and new values for the uf_reds option can be set during that call.

Type:

collections.OrderedDict

UF_reds

List of all unique 4-element uncertainty factor tuples. See DR_Def.add() for more information.

Type:

list

Vars

Contains the data recovery matrices and possibly other data needed for data recovery. This is derived from the DR_Def['_vars'] dict and the current system modes. See the notes section below for an example showing what is in this dict.

Type:

dict

Notes

Here are some views of example data via pyyeti.pp.PP(). This would be after, for example:

DR = cla.DR_Event()
DR.add(nas, drdefs_for_se100)   # PAF data recovery
DR.add(nas, drdefs_for_se500)   # SC data recovery
DR.add(nas, drdefs_for_se0)     # recover "Node4"

PP(DR, 2):

<class 'pyyeti.cla.DR_Event'>[n=3]
    .Info   : <class 'dict'>[n=6]
        'PAF_ifatm': <class 'types.SimpleNamespace'>[n=20]
        'PAF_ifltm': <class 'types.SimpleNamespace'>[n=20]
        'SC_atm'   : <class 'types.SimpleNamespace'>[n=20]
        'SC_dtm'   : <class 'types.SimpleNamespace'>[n=20]
        'SC_ltm'   : <class 'types.SimpleNamespace'>[n=20]
        'Node4'    : <class 'types.SimpleNamespace'>[n=20]
    .UF_reds: [n=2]: [[n=4]: (1, 1, 1.25, 1),
                      [n=4]: (0, 1, 1.25, 1)]
    .Vars   : <class 'dict'>[n=3]
        0  : <class 'dict'>[n=1]
        100: <class 'dict'>[n=3]
        500: <class 'dict'>[n=4]

PP(DR.Vars, 2):

<class 'dict'>[n=3]
    0  : <class 'dict'>[n=1]
        'Tnode4': float64 ndarray 732 elems: (3, 977)
    100: <class 'dict'>[n=3]
        'ifatm' : float64 ndarray 46896 elems: (48, 977)
        'ifltma': float64 ndarray 17586 elems: (18, 977)
        'ifltmd': float64 ndarray 17586 elems: (18, 977)
    500: <class 'dict'>[n=4]
        'scatm' : float64 ndarray 261836 elems: (268, 977)
        'scdtmd': float64 ndarray 84999 elems: (87, 977)
        'scltma': float64 ndarray 142642 elems: (146, 977)
        'scltmd': float64 ndarray 142642 elems: (146, 977)

In that example, all the DR.Vars variables except ‘Tnode4’ have been multiplied by the appropriate ULVS matrix in the call to add(). That is, the SE 100 and 500 matrices all came from the DR_Def['_vars'].drms entry and none came from the DR_Def['_vars'].nondrms entry. The SE 0 matrix ‘Tnode4’ could come from either the .drms or .nondrms entry.

__init__()[source]

Initializes the attributes Info, UF_reds, and Vars to empty values.

The attributes are filled by calls to add().

Methods

__init__()

Initializes the attributes Info, UF_reds, and Vars to empty values.

add(nas, drdefs[, uf_reds, method])

Add data recovery definitions for an event or set of modes.

apply_uf(sol, m, b, k, nrb, rfmodes)

Applies the uncertainty factors to the modal ODE solution

frf_apply_uf(sol, nrb)

Applies the uncertainty factors to the frequency response functions (FRFs).

get_Qs()

Get list of all unique Q's used for SRS in all categories

prepare_results(mission, event)

Returns an instance of the DR_Results class.

set_dr_order(cats, where)

Set a new data recovery order