Return fit parameters as dictionary about pyimfit HOT 6 CLOSED

That's a good suggestion. It might be better to add a new function (since it's useful to have the existing function return a pure numpy array in certain circumstances); let me look into what might be the best solution.

from pyimfit.

Sorry for the delay in working on this. I'm currently thinking it would make sense to have two new "parameter return" methods (in addition to getRawParameters):

1. A method which returns a list of OrderedDict entries, one for each function set; internally, each element of the list would resemble your suggestion (the dicts for each component would also be OrderedDicts):
   [ {component_1: {par_name1: val1, par_name2: val2}, component_2: {par_name1: val1, par_name2: val2}, ...}, {component_1: {par_name1: val1, par_name2: val2}, ... ]

2. A method which returns ModelDescription object.

How does that sound?

from pyimfit.

Sorry for the delay in working on this. I'm currently thinking it would make sense to have two new "parameter return" methods (in addition to getRawParameters):

1. A method which returns a list of OrderedDict entries, one for each function set; internally, each element of the list would resemble your suggestion (the dicts for each component would also be OrderedDicts):
   [ {component_1: {par_name1: val1, par_name2: val2}, component_2: {par_name1: val1, par_name2: val2}, ...}, {component_1: {par_name1: val1, par_name2: val2}, ... ]
2. A method which returns ModelDescription object.

How does that sound?

@perwin Sounds great! Thanks for working on this.

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Here's my current idea for the dict output. The idea is that this is a format which can also be used to define a new ModelDescription object; it also needs to handle the possibility of multiple function sets, which makes it more complicated than your initial suggestion.

{ 'options': optionsDict or None,  'function_sets': list of function-set dicts }
where
    optionsDict = {'GAIN':float, 'ORIGINAL_SKY': float, etc.}
and individual function-set dicts look like this (one per function set in the model):
    { 'name': str or None   [default], 'X0': x0-value, 'Y0': y0-value, 'function-list': list of function dicts }

Function dicts look like this:
   {  'name': str    ["Exponential", "Sersic", etc.], 'label' : str or None [default],  'parameters' : parameters-dict }

Finally, parameter dicts are OrderedDicts with str: float pairs, e.g.
   { 'PA': 45.0, 'ell': 0.23, 'I_0': 103.2, 'h': 22.1}

A very simple example, for a model with a single Gaussian component (ignoring any image-description parameters like GAIN, etc.):

bestfit_model = { 'function_sets': [{'X0': 1023.2, 'Y0': 985.4, 
                    'function_list': [{'name': 'Gaussian", 
                        'parameters': {'PA': 0.0, 'ell': 0.2, 'I_0': 1.0, 'sigma': 12.3}}] }] }

The parameter dicts might be modified to contain optional limits/'fixed' specifications, or perhaps this would be better handled by an optional 'parameter_limits' dict as part of the function dict.

How does that look to you?

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There is (finally) a new release (0.11.1) incorporating the return-fitted-model-as-dict functionality (including the ability to define new ModelDescription objects using dicts). You should be able to install it via pip.

from pyimfit.

I'm going to close this issue, but feel free to re-open it if there are problems with the dict return functionality.

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