model fitting should prohibit selecting subset not in bounds of data

[Jdaviz-Triage-Bot]

Reporter: Kyle Conroy

Selecting a data/subset combo which does not result in any data points in the mask should raise an error from the API and prevent clicking compute with a message in the plugin UI.

To reproduce:

import numpy as np
from jdaviz import Specviz


import tempfile
from astroquery.mast import Observations
data_dir = tempfile.gettempdir()
fn = "jw02732-o004_t004_miri_ch1-shortmediumlong_x1d.fits"
result = Observations.download_file(f"mast:JWST/product/\{fn}", local_path=f'\{data_dir}/\{fn}')
specviz = Specviz()
specviz.load_spectrum(f'\{data_dir}/\{fn}', "right_spectrum")
sp = specviz.app.get_data_from_viewer('spectrum-viewer', 'right_spectrum')

from specutils import Spectrum1D

sp2 = Spectrum1D(spectral_axis=sp.spectral_axis - 2*sp.spectral_axis.unit,
                 flux=sp.flux * 1.25)

specviz.load_spectrum(sp2, data_label="left_spectrum")


specviz.show()

from glue.core.roi import XRangeROI

specviz.app.get_viewer('spectrum-viewer').apply_roi(XRangeROI(4, 4.4))  # on left (second) only

mf = specviz.plugins['Model Fitting']
mf.create_model_component('Linear1D')
mf.dataset = 'left_spectrum'  # second to be added
mf.spectral_subset = 'Subset 1'


mf.calculate_fit()

mf.dataset = 'right_spectrum'  # first to be added
mf.calculate_fit()

Note that an additional call to mf.calculate_fit() (see #1910) results in a Traceback:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 mf.calculate_fit()

File ~/repos/jdaviz/jdaviz/configs/default/plugins/model_fitting/model_fitting.py:648, in ModelFitting.calculate_fit(self, add_data)
    646     return self._fit_model_to_cube(add_data=add_data)
    647 else:
--> 648     return self._fit_model_to_spectrum(add_data=add_data)

File ~/repos/jdaviz/jdaviz/configs/default/plugins/model_fitting/model_fitting.py:675, in ModelFitting._fit_model_to_spectrum(self, add_data)
    672         self._spectrum1d.mask += subset_mask
    674 try:
--> 675     fitted_model, fitted_spectrum = fit_model_to_spectrum(
    676         self._spectrum1d,
    677         models_to_fit,
    678         self.model_equation,
    679         run_fitter=True)
    680 except AttributeError:
    681     msg = SnackbarMessage("Unable to fit: model equation may be invalid",
    682                           color="error", sender=self)

File ~/repos/jdaviz/jdaviz/configs/default/plugins/model_fitting/fitting_backend.py:75, in fit_model_to_spectrum(spectrum, component_list, expression, run_fitter, window, n_cpu)
     73     return _fit_3D(initial_model, spectrum, window=window, n_cpu=n_cpu)
     74 else:
---> 75     return _fit_1D(initial_model, spectrum, run_fitter, window=window)

File ~/repos/jdaviz/jdaviz/configs/default/plugins/model_fitting/fitting_backend.py:104, in _fit_1D(initial_model, spectrum, run_fitter, window)
    102 if run_fitter:
    103     weights = 'unc' if spectrum.uncertainty else None
--> 104     output_model = fit_lines(spectrum, initial_model, weights=weights, window=window)
    105     output_values = output_model(spectrum.spectral_axis)
    106 else:
    107     # Return without fitting.

File /opt/miniconda3/envs/jdaviz-dev/lib/python3.8/site-packages/specutils/fitting/fitmodels.py:355, in fit_lines(spectrum, model, fitter, exclude_regions, weights, window, get_fit_info, **kwargs)
    348 #
    349 # Check to see if the model has units. If it does not
    350 # have units then we are going to ignore them.
    351 #
    353 ignore_units = getattr(model_guess, model_guess.param_names[0]).unit is None
--> 355 fit_model = _fit_lines(spectrum, model_guess, fitter,
    356                        exclude_regions, weights, model_window,
    357                        ignore_units, get_fit_info, **kwargs)
    358 if model_guess.name is not None:
    359     fit_model.name = model_guess.name

File /opt/miniconda3/envs/jdaviz-dev/lib/python3.8/site-packages/specutils/fitting/fitmodels.py:497, in _fit_lines(spectrum, model, fitter, exclude_regions, weights, window, get_fit_info, ignore_units, **kwargs)
    494     if weights is not None:
    495         weights = weights[nmask]
--> 497 fit_model = fitter(model, dispersion,
    498                    flux, weights=weights, **kwargs)
    500 if hasattr(fitter, 'fit_info') and get_fit_info:
    501     fit_model.meta['fit_info'] = fitter.fit_info

File ~/repos/astropy/astropy/modeling/fitting.py:282, in fitter_unit_support.<locals>.wrapper(self, model, x, y, z, **kwargs)
    280 # We run the fitting
    281 if z is None:
--> 282     model_new = func(self, model, xdata, ydata, **kwargs)
    283 else:
    284     model_new = func(self, model, xdata, ydata, zdata, **kwargs)

File ~/repos/astropy/astropy/modeling/fitting.py:1351, in _NonLinearLSQFitter.__call__(self, model, x, y, z, weights, maxiter, acc, epsilon, estimate_jacobian, filter_non_finite)
   1345     x, y, z = self._filter_non_finite(x, y, z)
   1346 farg = (
   1347     model_copy,
   1348     weights,
   1349 ) + _convert_input(x, y, z)
-> 1351 init_values, fitparams, cov_x = self._run_fitter(
   1352     model_copy, farg, maxiter, acc, epsilon, estimate_jacobian
   1353 )
   1355 self._compute_param_cov(model_copy, y, init_values, cov_x, fitparams, farg)
   1357 model.sync_constraints = True

File ~/repos/astropy/astropy/modeling/fitting.py:1415, in LevMarLSQFitter._run_fitter(self, model, farg, maxiter, acc, epsilon, estimate_jacobian)
   1413     dfunc = self._wrap_deriv
   1414 init_values, _, _ = model_to_fit_params(model)
-> 1415 fitparams, cov_x, dinfo, mess, ierr = optimize.leastsq(
   1416     self.objective_function,
   1417     init_values,
   1418     args=farg,
   1419     Dfun=dfunc,
   1420     col_deriv=model.col_fit_deriv,
   1421     maxfev=maxiter,
   1422     epsfcn=epsilon,
   1423     xtol=acc,
   1424     full_output=True,
   1425 )
   1426 fitter_to_model_params(model, fitparams)
   1427 self.fit_info.update(dinfo)

File /opt/miniconda3/envs/jdaviz-dev/lib/python3.8/site-packages/scipy/optimize/_minpack_py.py:414, in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
    411 m = shape[0]
    413 if n > m:
--> 414     raise TypeError(f"Improper input: func input vector length N={n} must"
    415                     f" not exceed func output vector length M={m}")
    417 if epsfcn is None:
    418     epsfcn = finfo(dtype).eps

TypeError: Improper input: func input vector length N=2 must not exceed func output vector length M=0

🐱

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DISCLAIMER: This issue was autocreated by the Jdaviz Issue Creation Bot on behalf of the reporter. If any information is incorrect, please contact Duy Nguyen