import re
import numpy as np
from copy import deepcopy
import astropy.units as u
from specutils import Spectrum1D
from specutils.utils import QuantityModel
from traitlets import Bool, List, Unicode, observe
from glue.core.data import Data
from jdaviz.core.events import SnackbarMessage
from jdaviz.core.registries import tray_registry
from jdaviz.core.template_mixin import (PluginTemplateMixin,
SelectPluginComponent,
SpectralSubsetSelectMixin,
SubsetSelect,
DatasetSelectMixin,
DatasetSpectralSubsetValidMixin,
AutoTextField,
AddResultsMixin)
from jdaviz.core.custom_traitlets import IntHandleEmpty
from jdaviz.core.user_api import PluginUserApi
from jdaviz.configs.default.plugins.model_fitting.fitting_backend import fit_model_to_spectrum
from jdaviz.configs.default.plugins.model_fitting.initializers import (MODELS,
initialize,
get_model_parameters)
__all__ = ['ModelFitting']
class _EmptyParam:
def __init__(self, value, unit=None):
self.value = value
self.unit = unit
self.quantity = u.Quantity(self.value,
self.unit if self.unit is not None else u.dimensionless_unscaled)
[docs]@tray_registry('g-model-fitting', label="Model Fitting", viewer_requirements='spectrum')
class ModelFitting(PluginTemplateMixin, DatasetSelectMixin,
SpectralSubsetSelectMixin, DatasetSpectralSubsetValidMixin,
AddResultsMixin):
"""
See the :ref:`Model Fitting Plugin Documentation <specviz-model-fitting>` for more details.
Only the following attributes and methods are available through the
:ref:`public plugin API <plugin-apis>`:
* :meth:`~jdaviz.core.template_mixin.PluginTemplateMixin.show`
* :meth:`~jdaviz.core.template_mixin.PluginTemplateMixin.open_in_tray`
* ``dataset`` (:class:`~jdaviz.core.template_mixin.DatasetSelect`):
Dataset to fit the model.
* ``spatial_subset`` (:class:`~jdaviz.core.template_mixin.SubsetSelect`):
Only exposed for Cubeviz. Spatially collapsed spectrum to use to fit the model.
* ``spectral_subset`` (:class:`~jdaviz.core.template_mixin.SubsetSelect`)
* ``model_component`` (:class:`~jdaviz.core.template_mixin.SelectPluginComponent`)
* ``poly_order``
* ``model_component_label`` (:class:`~jdaviz.core.template_mixin.AutoTextField`)
* :meth:`create_model_component`
* :meth:`remove_model_component`
* :meth:`model_components`
* :meth:`get_model_component`
* :meth:`set_model_component`
* :meth:`reestimate_model_parameters`
* ``equation`` (:class:`~jdaviz.core.template_mixin.AutoTextField`)
* ``cube_fit``
Only exposed for Cubeviz. Whether to fit the model to the cube instead of to the
collapsed spectrum.
* ``add_results`` (:class:`~jdaviz.core.template_mixin.AddResults`)
* ``residuals_calculate`` (bool)
Whether to calculate and expose the residuals (model minus data).
* ``residuals`` (:class:`~jdaviz.core.template_mixin.AutoTextField`)
Label of the residuals to apply when calling :meth:`calculate_fit` if ``residuals_calculate``
is ``True``.
* :meth:`calculate_fit`
"""
dialog = Bool(False).tag(sync=True)
template_file = __file__, "model_fitting.vue"
form_valid_model_component = Bool(False).tag(sync=True)
spatial_subset_items = List().tag(sync=True)
spatial_subset_selected = Unicode().tag(sync=True)
# model components:
model_comp_items = List().tag(sync=True)
model_comp_selected = Unicode().tag(sync=True)
poly_order = IntHandleEmpty(0).tag(sync=True)
comp_label = Unicode().tag(sync=True)
comp_label_default = Unicode().tag(sync=True)
comp_label_auto = Bool(True).tag(sync=True)
comp_label_invalid_msg = Unicode().tag(sync=True)
model_equation = Unicode().tag(sync=True)
model_equation_default = Unicode().tag(sync=True)
model_equation_auto = Bool(True).tag(sync=True)
model_equation_invalid_msg = Unicode().tag(sync=True)
eq_error = Bool(False).tag(sync=True)
component_models = List([]).tag(sync=True)
display_order = Bool(False).tag(sync=True)
cube_fit = Bool(False).tag(sync=True)
# residuals (non-cube fit only)
residuals_calculate = Bool(False).tag(sync=True)
residuals_label = Unicode().tag(sync=True)
residuals_label_default = Unicode().tag(sync=True)
residuals_label_auto = Bool(True).tag(sync=True)
residuals_label_invalid_msg = Unicode('').tag(sync=True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._default_spectrum_viewer_reference_name = kwargs.get(
"spectrum_viewer_reference_name", "spectrum-viewer"
)
self._default_flux_viewer_reference_name = kwargs.get(
"flux_viewer_reference_name", "flux-viewer"
)
self._units = {}
self._fitted_model = None
self._fitted_spectrum = None
self.component_models = []
self._initialized_models = {}
self._display_order = False
if self.config == "cubeviz":
self.spatial_subset = SubsetSelect(self,
'spatial_subset_items',
'spatial_subset_selected',
default_text='Entire Cube',
allowed_type='spatial')
else:
self.spatial_subset = None
# create the label first so that when model_component defaults to the first selection,
# the label automatically defaults as well
self.model_component_label = AutoTextField(self, 'comp_label', 'comp_label_default',
'comp_label_auto', 'comp_label_invalid_msg')
self.model_component = SelectPluginComponent(self,
items='model_comp_items',
selected='model_comp_selected',
manual_options=list(MODELS.keys()))
# when accessing the selected data, access the spectrum-viewer version
self.dataset._viewers = [self._default_spectrum_viewer_reference_name]
# require entries to be in spectrum-viewer (not other cubeviz images, etc)
self.dataset.add_filter('layer_in_spectrum_viewer')
self.equation = AutoTextField(self, 'model_equation', 'model_equation_default',
'model_equation_auto', 'model_equation_invalid_msg')
self.residuals = AutoTextField(self, 'residuals_label', 'residuals_label_default',
'residuals_label_auto', 'residuals_label_invalid_msg')
# set the filter on the viewer options
self._update_viewer_filters()
@property
def user_api(self):
expose = ['dataset']
if self.config == "cubeviz":
expose += ['spatial_subset']
expose += ['spectral_subset', 'model_component', 'poly_order', 'model_component_label',
'model_components', 'create_model_component', 'remove_model_component',
'get_model_component', 'set_model_component', 'reestimate_model_parameters',
'equation', 'add_results', 'residuals_calculate', 'residuals']
if self.config == "cubeviz":
expose += ['cube_fit']
expose += ['calculate_fit']
return PluginUserApi(self, expose=expose)
def _param_units(self, param, model_type=None):
"""Helper function to handle units that depend on x and y"""
y_params = ["amplitude", "amplitude_L", "intercept", "scale"]
if param == "slope":
return str(u.Unit(self._units["y"]) / u.Unit(self._units["x"]))
elif model_type == 'Polynomial1D':
# param names are all named cN, where N is the order
order = int(float(param[1:]))
return str(u.Unit(self._units["y"]) / u.Unit(self._units["x"])**order)
elif param == "temperature":
return str(u.K)
elif param == "scale" and model_type == "BlackBody":
return str("")
return self._units["y"] if param in y_params else self._units["x"]
def _update_parameters_from_fit(self):
"""Insert the results of the model fit into the component_models"""
for m in self.component_models:
submodel_index = None
name = m["id"]
if hasattr(self._fitted_model, "submodel_names"):
for i in range(len(self._fitted_model.submodel_names)):
if name == self._fitted_model.submodel_names[i]:
m_fit = self._fitted_model[name]
submodel_index = i
if submodel_index is None:
continue
elif self._fitted_model.name == name:
m_fit = self._fitted_model
else:
# then the component was not in the fitted model
continue
temp_params = []
for i in range(0, len(m_fit.parameters)):
temp_param = [x for x in m["parameters"] if x["name"] ==
m_fit.param_names[i]]
temp_param[0]["value"] = m_fit.parameters[i]
# The submodels don't have uncertainties attached, only the compound model
if self._fitted_model.stds is not None:
std_name = temp_param[0]["name"]
if submodel_index is not None:
std_name = f"{std_name}_{submodel_index}"
if std_name in self._fitted_model.stds.param_names:
temp_param[0]["std"] = self._fitted_model.stds[std_name]
temp_params += temp_param
m["parameters"] = temp_params
# Trick traitlets into updating the displayed values
component_models = self.component_models
self.component_models = []
self.component_models = component_models
def _update_parameters_from_QM(self):
"""
Parse out result parameters from a QuantityModel, which isn't
subscriptable with model name
"""
if hasattr(self._fitted_model, "submodel_names"):
submodel_names = self._fitted_model.submodel_names
submodels = True
else:
submodel_names = [self._fitted_model.name]
submodels = False
fit_params = self._fitted_model.parameters
param_names = self._fitted_model.param_names
for i in range(len(submodel_names)):
name = submodel_names[i]
m = [x for x in self.component_models if x["id"] == name][0]
temp_params = []
if submodels:
idxs = [j for j in range(len(param_names)) if
int(param_names[j][-1]) == i]
else:
idxs = [j for j in range(len(param_names))]
# This is complicated by needing to handle parameter names that
# have underscores in them, since QuantityModel adds an underscore
# and integer to indicate to which model a parameter belongs
for idx in idxs:
if submodels:
temp_param = [x for x in m["parameters"] if x["name"] ==
"_".join(param_names[idx].split("_")[0:-1])]
else:
temp_param = [x for x in m["parameters"] if x["name"] ==
param_names[idx]]
temp_param[0]["value"] = fit_params[idx]
temp_params += temp_param
m["parameters"] = temp_params
# Trick traitlets into updating the displayed values
component_models = self.component_models
self.component_models = []
self.component_models = component_models
def _update_initialized_parameters(self):
# If the user changes a parameter value, we need to change it in the
# initialized model
for m in self.component_models:
name = m["id"]
for param in m["parameters"]:
quant_param = u.Quantity(param["value"], param["unit"])
setattr(self._initialized_models[name], param["name"],
quant_param)
def _warn_if_no_equation(self):
if self.model_equation == "" or self.model_equation is None:
example = "+".join([m["id"] for m in self.component_models])
snackbar_message = SnackbarMessage(
f"Error: a model equation must be defined, e.g. {example}",
color='error',
sender=self)
self.hub.broadcast(snackbar_message)
return True
else:
return False
def _get_1d_spectrum(self):
# retrieves the 1d spectrum (accounting for spatial subset for cubeviz, if necessary)
if self.config == 'cubeviz' and self.spatial_subset_selected != 'Entire Cube':
# then we're acting on the auto-collapsed data in the spectrum-viewer
# of a spatial subset. In the future, we may want to expose on-the-fly
# collapse options... but right now these will follow the settings of the
# spectrum-viewer itself
return self.app.get_data_from_viewer(
self._default_spectrum_viewer_reference_name,
self.spatial_subset_selected
)
else:
return self.dataset.selected_obj
@observe("dataset_selected", "spatial_subset_selected")
def _dataset_selected_changed(self, event=None):
"""
Callback method for when the user has selected data from the drop down
in the front-end. It is here that we actually parse and create a new
data object from the selected data. From this data object, unit
information is scraped, and the selected spectrum is stored for later
use in fitting.
Parameters
----------
event : str
IPyWidget callback event object. In this case, represents the data
label of the data collection object selected by the user.
"""
if not hasattr(self, 'dataset'):
# during initial init, this can trigger before the component is initialized
return
selected_spec = self._get_1d_spectrum()
if selected_spec is None:
return
# Replace NaNs from collapsed Spectrum1D in Cubeviz
# (won't affect calculations because these locations are masked)
selected_spec.flux[np.isnan(selected_spec.flux)] = 0.0
self._units["x"] = str(
selected_spec.spectral_axis.unit)
self._units["y"] = str(
selected_spec.flux.unit)
def _default_comp_label(self, model, poly_order=None):
abbrevs = {'BlackBody': 'BB', 'PowerLaw': 'PL', 'Lorentz1D': 'Lo'}
abbrev = abbrevs.get(model, model[0].upper())
if model == "Polynomial1D":
abbrev += f'{poly_order}'
# append a number suffix to avoid any duplicates
ind = 1
while abbrev in [cm['id'] for cm in self.component_models]:
abbrev = f'{abbrev.split("_")[0]}_{ind}'
ind += 1
return abbrev
@observe('model_comp_selected', 'poly_order')
def _update_comp_label_default(self, event={}):
self.display_order = self.model_comp_selected == "Polynomial1D"
self.comp_label_default = self._default_comp_label(self.model_comp_selected,
self.poly_order)
@observe('comp_label')
def _comp_label_changed(self, event={}):
if not len(self.comp_label.strip()):
# strip will raise the same error for a label of all spaces
self.comp_label_invalid_msg = 'label must be provided'
return
if self.comp_label in [cm['id'] for cm in self.component_models]:
self.comp_label_invalid_msg = 'label already in use'
return
self.comp_label_invalid_msg = ''
def _update_model_equation_default(self):
self.model_equation_default = '+'.join(cm['id'] for cm in self.component_models)
def _reinitialize_with_fixed(self):
"""
Reinitialize all component models with current values and the
specified parameters fixed (can't easily update fixed dictionary in
an existing model)
"""
temp_models = []
for m in self.component_models:
fixed = {}
# Set the initial values as quantities to make sure model units
# are set correctly.
initial_values = {p["name"]: u.Quantity(p["value"], p["unit"]) for p in m["parameters"]}
for p in m["parameters"]:
fixed[p["name"]] = p["fixed"]
# Have to initialize with fixed dictionary
temp_model = MODELS[m["model_type"]](name=m["id"], fixed=fixed,
**initial_values, **m.get("model_kwargs", {}))
temp_models.append(temp_model)
return temp_models
[docs] def create_model_component(self, model_component=None, model_component_label=None,
poly_order=None):
"""
Add a new model component to the list of available model components
Parameters
----------
model_component : str
Type of model component to add. If not provided, will default according to
``model_component``.
model_component_label : str
Name of the model component to add. If not provided, will default according to
``model_component_label`` (if ``model_component_label.auto`` is True and
``model_component`` is passed as an argument, then the default label will be recomputed
rather than applying the current value).
poly_order : int
Order of the polynomial if ``model_component`` is (or defaults to) "Polynomial1D".
Will raise an error if provided and ``model_component`` is not "Polynomial1D".
If not provided, will default according to ``poly_order``.
"""
model_comp = model_component if model_component is not None else self.model_comp_selected
if model_comp != "Polynomial1D" and poly_order is not None:
raise ValueError("poly_order should only be passed if model_component is Polynomial1D")
poly_order = poly_order if poly_order is not None else self.poly_order
# if model_component was passed and different than the one set in the traitlet, AND
# model_component_label is not passed, AND the auto is enabled on the label, then
# recompute a temporary default model label rather than use the value set in the traitlet
if model_comp != self.model_comp_selected and model_component_label is None and self.model_component_label.auto: # noqa
comp_label = self._default_comp_label(model_comp, poly_order)
else:
comp_label = model_component_label if model_component_label is not None else self.comp_label # noqa
# validate provided label (only allow "word characters"). These should already be
# stripped by JS in the UI element, but we'll confirm here (especially if this is ever
# extended to have better API-support)
if re.search(r'\W+', comp_label) or not len(comp_label):
raise ValueError(f"invalid model component label '{comp_label}'")
if comp_label in [cm['id'] for cm in self.component_models]:
raise ValueError(f"model component label '{comp_label}' already in use")
new_model = self._initialize_model_component(model_comp, comp_label, poly_order=poly_order)
self.component_models = self.component_models + [new_model]
# update the default label (likely adding the suffix)
self._update_comp_label_default()
self._update_model_equation_default()
def _initialize_model_component(self, model_comp, comp_label, poly_order=None):
new_model = {"id": comp_label, "model_type": model_comp,
"parameters": [], "model_kwargs": {}}
model_cls = MODELS[model_comp]
if model_comp == "Polynomial1D":
# self.poly_order is the value in the widget for creating
# the new model component. We need to store that with the
# model itself as the value could change for another component.
new_model["model_kwargs"] = {"degree": poly_order}
elif model_comp == "BlackBody":
new_model["model_kwargs"] = {"output_units": self._units["y"],
"bounds": {"scale": (0.0, None)}}
initial_values = {}
for param_name in get_model_parameters(model_cls, new_model["model_kwargs"]):
# access the default value from the model class itself
default_param = getattr(model_cls, param_name, _EmptyParam(0))
default_units = self._param_units(param_name,
model_type=new_model["model_type"])
if default_param.unit is None:
# then the model parameter accepts unitless, but we want
# to pass with appropriate default units
initial_val = u.Quantity(default_param.value, default_units)
else:
# then the model parameter has default units. We want to pass
# with jdaviz default units (based on x/y units) but need to
# convert the default parameter unit to these units
initial_val = default_param.quantity.to(default_units)
initial_values[param_name] = initial_val
masked_spectrum = self._apply_subset_masks(self._get_1d_spectrum(), self.spectral_subset)
mask = masked_spectrum.mask
initialized_model = initialize(
MODELS[model_comp](name=comp_label,
**initial_values,
**new_model.get("model_kwargs", {})),
masked_spectrum.spectral_axis[~mask] if mask is not None else masked_spectrum.spectral_axis, # noqa
masked_spectrum.flux[~mask] if mask is not None else masked_spectrum.flux)
# need to loop over parameters again as the initializer may have overridden
# the original default value
for param_name in get_model_parameters(model_cls, new_model["model_kwargs"]):
param_quant = getattr(initialized_model, param_name)
new_model["parameters"].append({"name": param_name,
"value": param_quant.value,
"unit": str(param_quant.unit),
"fixed": False})
self._initialized_models[comp_label] = initialized_model
new_model["Initialized"] = True
return new_model
[docs] def remove_model_component(self, model_component_label):
"""
Remove an existing model component.
Parameters
----------
model_component_label : str
The label given to the existing model component
"""
if model_component_label not in [x["id"] for x in self.component_models]:
raise ValueError(f"model component with label '{model_component_label}' does not exist")
self.component_models = [x for x in self.component_models
if x["id"] != model_component_label]
del self._initialized_models[model_component_label]
self._update_comp_label_default()
self._update_model_equation_default()
[docs] def get_model_component(self, model_component_label, parameter=None):
"""
Get a (read-only) dictionary representation of an existing model component.
Parameters
----------
model_component_label : str
The label given to the existing model component
parameter : str
Optional. The name of a valid parameter in the model component, in which case only
the information on that parameter is returned.
"""
try:
model_component = [x for x in self.component_models
if x["id"] == model_component_label][0]
except IndexError:
raise ValueError(f"'{model_component_label}' is not a label of an existing model component") # noqa
comp = {"model_type": model_component['model_type'],
"parameters": {p['name']: {'value': p['value'],
'unit': p['unit'],
'fixed': p['fixed']} for p in model_component['parameters']}} # noqa
if parameter is not None:
return comp['parameters'].get(parameter)
return comp
[docs] def set_model_component(self, model_component_label, parameter, value=None, fixed=None):
"""
Set the value or fixed attribute of a parameter in an existing model component.
Parameters
----------
model_component_label : str
The label given to the existing model component
parameter : str
The name of a valid parameter in the model component.
value : float
Optional. The new initial value of the parameter. If not provided or None, will
remain unchanged.
fixed : bool
Optional. The new state of the fixed attribute of the parameter. If not provided
or None, will remain unchanged.
Returns
-------
updated dictionary of the parameter representation
"""
cms = self.component_models
try:
model_component = [x for x in cms if x["id"] == model_component_label][0]
except IndexError:
raise ValueError(f"'{model_component_label}' is not a label of an existing model component") # noqa
try:
parameter = [p for p in model_component['parameters'] if p['name'] == parameter][0]
except IndexError:
raise ValueError(f"'{parameter}' is not the name of a parameter in the '{model_component_label}' model component") # noqa
if value is not None:
if not isinstance(value, (int, float)):
raise TypeError("value must be a float")
parameter['value'] = value
if fixed is not None:
if not isinstance(fixed, bool):
raise TypeError("fixed must be a boolean")
parameter['fixed'] = fixed
self.component_models = []
self.component_models = cms
return parameter
[docs] def vue_reestimate_model_parameters(self, model_component_label=None, **kwargs):
self.reestimate_model_parameters(model_component_label=model_component_label)
[docs] def reestimate_model_parameters(self, model_component_label=None):
"""
Re-estimate all free parameters in a given model component given the currently selected
data and subset selections.
Parameters
----------
model_component_label : str or None.
The label given to the existing model component. If None, will iterate over all model
components.
"""
if model_component_label is None:
return [self.reestimate_model_parameters(model_comp["id"])
for model_comp in self.component_models]
try:
model_index, model_comp = [(i, x) for i, x in enumerate(self.component_models)
if x["id"] == model_component_label][0]
except IndexError:
raise ValueError(f"'{model_component_label}' is not a label of an existing model component") # noqa
# store user-fixed parameters so we can revert after re-initializing
fixed_params = {p['name']: p for p in model_comp['parameters'] if p['fixed']}
new_model = self._initialize_model_component(model_comp['model_type'],
model_component_label,
poly_order=model_comp['model_kwargs'].get('degree', None)) # noqa
# revert fixed parameters to user-value
new_model['parameters'] = [fixed_params.get(p['name'], p) for p in new_model['parameters']]
self.component_models[model_index] = new_model
# length hasn't changed, so we need to force the traitlet to update
self.send_state("component_models")
# return user-friendly info on revised model
return self.get_model_component(model_component_label)
@property
def model_components(self):
"""
List of the labels of existing model components
"""
return [x["id"] for x in self.component_models]
[docs] def vue_add_model(self, event):
self.create_model_component()
[docs] def vue_remove_model(self, event):
self.remove_model_component(event)
@observe('model_equation')
def _model_equation_changed(self, event):
# Length is a dummy check to test the infrastructure
if len(self.model_equation) == 0:
self.model_equation_invalid_msg = 'model equation is required'
return
self.model_equation_invalid_msg = ''
@observe("dataset_selected", "dataset_items", "cube_fit")
def _set_default_results_label(self, event={}):
label_comps = []
if hasattr(self, 'dataset') and (len(self.dataset.labels) > 1 or self.app.config == 'mosviz'): # noqa
label_comps += [self.dataset_selected]
if self.cube_fit:
label_comps += ["cube-fit"]
label_comps += ["model"]
self.results_label_default = " ".join(label_comps)
@observe("results_label")
def _set_residuals_label_default(self, event={}):
self.residuals_label_default = self.results_label+" residuals"
@observe("cube_fit")
def _update_viewer_filters(self, event={}):
if event.get('new', self.cube_fit):
# only want image viewers in the options
self.add_results.viewer.filters = ['is_image_viewer']
else:
# only want spectral viewers in the options
self.add_results.viewer.filters = ['is_spectrum_viewer']
[docs] def calculate_fit(self, add_data=True):
"""
Calculate the fit.
Parameters
----------
add_data : bool
Whether to add the resulting spectrum/cube to the app as a data entry according to
``add_results``.
Returns
-------
fitted model
fitted spectrum/cube
residuals (if ``residuals_calculate`` is set to ``True``)
"""
if not self.spectral_subset_valid:
valid, spec_range, subset_range = self._check_dataset_spectral_subset_valid(return_ranges=True) # noqa
raise ValueError(f"spectral subset '{self.spectral_subset.selected}' {subset_range} is outside data range of '{self.dataset.selected}' {spec_range}") # noqa
if self.cube_fit:
return self._fit_model_to_cube(add_data=add_data)
else:
return self._fit_model_to_spectrum(add_data=add_data)
[docs] def vue_apply(self, event):
self.calculate_fit()
def _fit_model_to_spectrum(self, add_data):
"""
Run fitting on the initialized models, fixing any parameters marked
as such by the user, then update the displayed parameters with fit
values
"""
if self._warn_if_no_equation():
return
models_to_fit = self._reinitialize_with_fixed()
masked_spectrum = self._apply_subset_masks(self._get_1d_spectrum(), self.spectral_subset)
try:
fitted_model, fitted_spectrum = fit_model_to_spectrum(
masked_spectrum,
models_to_fit,
self.model_equation,
run_fitter=True,
window=None
)
except AttributeError:
msg = SnackbarMessage("Unable to fit: model equation may be invalid",
color="error", sender=self)
self.hub.broadcast(msg)
return
self._fitted_model = fitted_model
self._fitted_spectrum = fitted_spectrum
if add_data:
self.app.fitted_models[self.results_label] = fitted_model
self.add_results.add_results_from_plugin(fitted_spectrum)
if self.residuals_calculate:
# NOTE: this will NOT load into the viewer since we have already called
# add_results_from_plugin above.
self.add_results.add_results_from_plugin(masked_spectrum-fitted_spectrum,
label=self.residuals.value,
replace=False)
self._set_default_results_label()
# Update component model parameters with fitted values
if isinstance(self._fitted_model, QuantityModel):
self._update_parameters_from_QM()
else:
self._update_parameters_from_fit()
# Also update the _initialized_models so we can use these values
# as the starting point for cube fitting
self._update_initialized_parameters()
if self.residuals_calculate:
return fitted_model, fitted_spectrum, masked_spectrum-fitted_spectrum
return fitted_model, fitted_spectrum
def _fit_model_to_cube(self, add_data):
if self._warn_if_no_equation():
return
if self.dataset_selected in self.app.data_collection.labels:
data = self.app.data_collection[self.dataset_selected]
else: # User selected some subset from spectrum viewer, just use original cube
data = self.app.data_collection[0]
# First, ensure that the selected data is cube-like. It is possible
# that the user has selected a pre-existing 1d data object.
if data.ndim != 3:
snackbar_message = SnackbarMessage(
f"Selected data {self.dataset_selected} is not cube-like",
color='error',
sender=self)
self.hub.broadcast(snackbar_message)
return
# Get the primary data component
spec = data.get_object(cls=Spectrum1D, statistic=None)
snackbar_message = SnackbarMessage(
"Fitting model to cube...",
loading=True, sender=self)
self.hub.broadcast(snackbar_message)
# Retrieve copy of the models with proper "fixed" dictionaries
models_to_fit = self._reinitialize_with_fixed()
# Apply masks from selected subsets
for subset in [self.spatial_subset, self.spectral_subset]:
spec = self._apply_subset_masks(spec, subset)
try:
fitted_model, fitted_spectrum = fit_model_to_spectrum(
spec,
models_to_fit,
self.model_equation,
run_fitter=True,
window=None
)
except ValueError:
snackbar_message = SnackbarMessage(
"Cube fitting failed",
color='error', loading=False, sender=self)
self.hub.broadcast(snackbar_message)
raise
# Save fitted 3D model in a way that the cubeviz
# helper can access it.
if add_data:
for m in fitted_model:
temp_label = "{} ({}, {})".format(self.results_label, m["x"], m["y"])
self.app.fitted_models[temp_label] = m["model"]
count = max(map(lambda s: int(next(iter(re.findall(r"\d$", s)), 0)),
self.data_collection.labels)) + 1
label = self.app.return_data_label(f"{self.results_label}[Cube]", ext=count)
# Create new glue data object
output_cube = Data(label=label,
coords=data.coords)
output_cube['flux'] = fitted_spectrum.flux.value
output_cube.get_component('flux').units = fitted_spectrum.flux.unit.to_string()
if add_data:
self.add_results.add_results_from_plugin(output_cube)
self._set_default_results_label()
snackbar_message = SnackbarMessage(
"Finished cube fitting",
color='success', loading=False, sender=self)
self.hub.broadcast(snackbar_message)
return fitted_model, output_cube
def _apply_subset_masks(self, spectrum, subset_component):
"""
For a spectrum/spectral cube ``spectrum``, add a mask attribute
if none exists. Mask excludes non-selected spectral and/or
spatial subsets.
"""
# only look for a mask if there is a selected subset:
if subset_component.selected == subset_component.default_text:
return spectrum
spectrum = deepcopy(spectrum)
subset_mask = subset_component.selected_subset_mask
if spectrum.mask is not None:
if subset_mask.ndim == 3:
if spectrum.mask.ndim == 1:
# if subset mask is 3D and the `spectrum` mask is 1D, which
# happens when `spectrum` has been collapsed from 3D->1D,
# then also collapse the 3D mask in the spatial
# dimensions (0, 1) so that slices in the spectral axis that
# are masked in all pixels become masked in the spectral subset:
subset_mask = np.all(subset_mask, axis=(0, 1))
spectrum.mask |= subset_mask
else:
if subset_mask.ndim < spectrum.flux.ndim:
# correct the shape of spectral/spatial axes when they're different:
subset_mask = np.broadcast_to(subset_mask, spectrum.flux.shape)
elif (subset_mask.ndim == spectrum.flux.ndim and
subset_mask.shape != spectrum.flux.shape):
# if the number of dimensions is correct but shape is
# different, rearrange the arrays for specutils:
subset_mask = np.swapaxes(subset_mask, 1, 0)
spectrum.mask = subset_mask
return spectrum