The core of PyImfit is the Imfit class, which acts as a wrapper around the underlying C++ ModelObject instance. It holds a ModelDescription instance which describes the model to be fit to the data (or just used to generate a model image, if no actual fitting is to be done). In the (usual) case of fitting the model to an image, it also holds the data image, optional PSF images, and parameters that describe the image (A/D gain, etc.).

It also has methods for running a fit and for inspecting the results.

class pyimfit.fitting.FitResult

Represents the result of fitting an image.

params

The best-fit vector of parameter values.

Type

ndarray

success

Whether or not the optimizer exited successfully.

Type

bool

status

Termination status of the optimizer. Its value depends on the underlying solver. Refer to message for details.

Type

int

message

Description of the cause of the termination.

Type

str

nfev

Number of evaluations of the objective function.

Type

int

nit

Number of iterations performed by the optimizer.

Type

int

OTHER IDEAS

AIC,BIC fitStatistic, reduced fitStatistic

Notes

There may be additional attributes not listed above depending of the specific solver. Since this class is essentially a subclass of dict with attribute accessors, one can see which attributes are available using the keys() method.

class pyimfit.fitting.Imfit(model_descr: Union[pyimfit.descriptions.ModelDescription, dict], psf=None, psfNormalization=True, quiet=True, nproc=0, chunk_size=10, subsampling=True, zeroPoint=None)

The main class for fitting models to images using Imfit. Can also be used to create images based on models.

Due to some library limitations, this object can only fit the model specified in the constructor. If you want to fit several models, you need to create one instance of Imfit for each model. On the other hand, one instance can be used to fit the same model to any number of images, or to fit and then create the model image.

AIC
BIC
fitConverged
fitError
fitStatistic
fitTerminated
nIter
nPegged
nValidPixles
numberedParameterNames
parameterErrors
reducedFitStatistic

See also

parse_config_file

property AIC

bias-corrected Akaike Information Criterion for the fit.

Type

float

property BIC

Bayesian Information Criterion for the fit.

Type

float

computeFitStatistic(newParameters)

Returns the fit-statistic value for the specified parameter vector. (Which fit statistic will calculated is set by the loadData() or fit() methods.)

Parameters

newParameters (ndarray of float) –

Returns

fitStatistic

Return type

float

doFit(solver='LM', verbose=None, getSummary=True)

Fit the model to previously supplied data image.

Parameters
  • solver (string, optional) –

    One of the following solvers (optimization algorithms) to be used for the fit:
    • 'LM' : Levenberg-Marquardt.

    • 'NM' : Nelder-Mead Simplex.

    • 'DE' : Differential Evolution.

  • verbose (int or None, optional) – set this to an integer to specify a feedback level for the fit (this overrides the Imfit object’s internal verbosity setting)

  • getSummary (bool, optional) – if True, a summary of the fit is returned (as a string)

Returns

result

Return type

FitResult object

Examples

TODO: Examples of doFit().

fit(image, error=None, mask=None, solver='LM', verbose=None, getSummary=True, **kwargs)

Supply data image (and optionally mask and/or error images) and image info, then fit the model to the data.

Parameters
  • image (2-D numpy array (ndarray or MaskedArray)) – Image to be fitted. Can be a masked array.

  • error (2-D numpy array, optional) – error/weight image, same shape as image. If not set, errors are generated from image. See also the keyword args use_poisson_mlr, use_cash_statistics, and use_model_for_errors.

  • mask (2-D numpy array, optional) – Array containing the masked pixels; must have the same shape as image. Pixels set to True are bad by default (see the kwarg mask_format for other options). If not set and image is a masked array, then its mask is used. If both masks are present, the final mask is composed by masking any pixel that is masked in either of the input masks.

  • solver (string, optional) –

    One of the following solvers (optimization algorithms) to be used for the fit:
    • 'LM' : Levenberg-Marquardt.

    • 'NM' : Nelder-Mead Simplex.

    • 'DE' : Differential Evolution.

  • verbose (int or None, optional) – set this to an integer to specify a feedback level for the fit (this overrides the Imfit object’s internal verbosity setting)

  • getSummary (bool, optional) – if True, a summary of the fit is returned (as a string)

  • keywords. (See loadData() for list of allowed extra) –

Returns

result

Return type

FitResult object

property fitConverged

indicates whether fit converged.

Type

bool

property fitStatistic

the \(\chi^2\), Poisson MLR, or Cash statistic of the fit.

Type

float

property fitTerminated

indicates whether fit terminated for any reason.

Type

bool

getFitResult()

Returns a summary of the fitting process.

Returns

result

Return type

FitResult object

getModelAsDict()

Returns current model (including parameter values) as a dict suitable for use with ModelDescription.dict_to_ModelDescription class method.

Returns

model_dict

Return type

dict

getModelDescription()
Returns

model – A copy of the currently fitted model, or a copy of the template model if no fitting has taken place yet.

Return type

ModelDescription

getModelFluxes(newParameters=None)

Computes and returns total and individual-function fluxes for the current model and current (or user-supplied) parameter values.

Parameters

newParameters (1-D numpy array of float, optional) – vector of parameter values to use in computing model (default is to use current parameter values, e.g., from most rencent fit, instead)

Returns

(totalFlux, individualFluxes) – totalFlux = total flux (or magnitude) of model individualFluxes = numpy ndarray of fluxes/magnitudes for each image-function in the model

Return type

tuple of (float, ndarray of float)

getModelImage(shape=None, newParameters=None, includeMask=False)

Computes and returns the image described by the currently fitted model, the input model if no fit was performed, or user-supplied parameters.

Parameters
  • shape (tuple, optional) – Shape of the image in (Y, X) = (nRows, nColumns) format. Do NOT supply this if Imfit object’s image shape has already been defined via loadData() or fit() method!

  • newParameters (1-D numpy array of float, optional) – vector of parameter values to use in computing model (default is to use current parameter values, e.g., from fit)

  • includeMask (bool, optional) – Specifies whether output should be numpy masked array, if there is a mask image associated with the data image.

Returns

image – Image computed from the current model. If a mask is associated with the original data image, then the returned image is a numpy masked array

Return type

2-D numpy array

getModelMagnitudes(newParameters=None, zeroPoint=None)

Computes and returns total and individual-function magnitudes for the current model and current (or user-supplied) parameter values.

Parameters
  • newParameters (1-D numpy array of float, optional) – vector of parameter values to use in computing model (default is to use current parameter values, e.g., from most rencent fit, instead)

  • zeroPoint (float, optional) –

    If present, returned values are magnitudes, computed as

    zeroPoint - 2.5*log10(flux)

    (default is to use value of object’s zeroPoint property)

Returns

(totalMag, individualMags) – totalFlux = total flux (or magnitude) of model individualFluxes = numpy ndarray of fluxes/magnitudes for each image-function in the model

Return type

tuple of (float, ndarray of float)

getParameterErrors()

Returns current best-fit model parameter uncertainties (from L-M minimization).

Returns

errors – A 1D array containing the Levenberg-Marquardt parameter uncertainties.

Return type

ndarray of float

getParameterLimits()

Returns a list containing lower and upper limits for all parameters in the model.

Returns

parameterLimits – [(lower_limit, upper_limit)_1, (lower_limit, upper_limit)_2, …]

Return type

list of 2-element tuples of float

getRawParameters()

Returns current model parameter values.

Returns

raw_params – A 1D array containing all the model parameter values.

Return type

ndarray of float

loadData(image, error=None, mask=None, **kwargs)

Supply the underlying ModelObject instance with data image and error model, optionally including error and/or mask images.

Parameters
  • image (2-D numpy array (ndarray or MaskedArray)) – Image to be fitted. Can be a masked array.

  • error (2-D numpy array, optional) – error/weight image, same shape as image. If not set, errors are generated from image. See also the keyword args use_poisson_mlr, use_cash_statistics, and use_model_for_errors.

  • mask (2-D numpy array, optional) – Array containing the masked pixels; must have the same shape as image. Pixels set to True are bad by default (see the kwarg mask_format for other options). If not set and image is a masked array, then its mask is used. If both masks are present, the final mask is composed by masking any pixel that is masked in either of the input masks.

Keyword Arguments
  • n_combined (integer) – Number of images which were averaged to make final image (if counts are average or median). Default: 1

  • exp_time (float) – Exposure time in seconds (only if image is in ADU/sec). Default: 1.0

  • gain (float) – Image gain (e-/ADU). Default: 1.0

  • read_noise (float) – Image read noise (Gaussian sigma, in e-). Default: 0.0

  • original_sky (float) – Original sky background (ADUs) which has already been subtracted from image. Default: 0.0

  • error_type (string) –

    Values in error should be interpreted as:
    • 'sigma' (default).

    • 'weight'.

    • 'variance'.

  • mask_format (string) –

    Values in mask should be interpreted as:
    • 'zero_is_good' (default).

    • 'zero_is_bad'.

  • psf_oversampling_list (list of PsfOversampling) – List of PsfOversampling objects, describing oversampling regions, PSFs, and oversampling scales.

  • use_poisson_mlr (boolean) – Use Poisson MLR (maximum-likelihood-ratio) statistic instead of chi^2. Takes precedence over error, use_model_for_errors`, and ``use_cash_statistics. Default: False

  • use_cash_statistics (boolean) – Use Cash statistic instead of chi^2 or Poisson MLR. Takes precedence over error and use_model_for_errors. Default: False

  • use_model_for_errors (boolean) – Use model values (instead of data) to estimate errors for chi^2 computation. Takes precedence over error. Default: False

property nIter

number of solver iterations during fit.

Type

int

property nPegged

number of parameters pegged against limits at end of fit.

Type

int

property nValidPixels

number of non-masked pixels in data image.

Type

int

property numberedParameterNames

List of parameter names for the current model, annotated by function number. E.g., [“X0_1”, “Y0_1”, “PA_1”, “ell_1”, “I_0_1”, “h_1”, …]

Type

list of str

property parameterErrors

estimated parameter errors from fit (L-M solver only)

Type

ndarray of float or None

property reducedFitStatistic

the “reduced” \(\chi^2\) or Poisson MLR of the fit.

Type

float

runBootstrap(nIterations, ftol=1e-08, verboseFlag=False, seed=0, getColumnNames=False)

Do bootstrap resampling for a model.

Parameters
  • nIterations (int) – How many bootstrap samples to generate and fit

  • ftol (float, optional) – fractional tolerance in fit statistic for determining fit convergence

  • verboseFlag (bool, optional) – if True, a progress bar is printed during the boostrap iterations

  • seed (int, optional) – random number seed (default is to use system clock)

  • getColumnNames (bool, optional) – if True, then column (parameter) names are returned as well

Returns

  • bootstrapOutput (2-D ndarray of float)

  • OR

  • (columnNames, bootstrapOutput) (tuple of (list of str, 2-D ndarray of float))

saveCurrentModelToFile(filename: str, includeImageOptions=False)

Saves the current model and parameter values to a text file in Imfit-configuration-file format.

Parameters
  • filename (str) – Name for the output file

  • includeImageOptions (bool, optional) – if True, then image-description options (“GAIN”, etc.) are also written to the output file

property zeroPoint

photometric zero point (used by getModelMagnitudes method).

Type

float

pyimfit.fitting.MakePsfOversampler(psfImage, oversampleScale, regionSpec, psfNormalization=True)

Helper function to generate PsfOversampling objects (corrects input psfImage, sets up region string, etc.).

Parameters
  • psfImage (2-D numpy array) – the oversampled PSF image

  • oversampleScale (int) –

    oversampling scale of psfImage relative to data image: number of PSF-image

    pixels per data-image pixel in one dimension (must be >= 1)

  • regionSpec (sequence of int) – specifies inclusive boundaries of image region to be oversampled [x1,x2,y1,y2]

  • psfNormalization (bool, optional) – Normalize the PSF image before using. Default: True.

Returns

oversampleInfo

Return type

instance of PsfOversampling class