import os
import warnings
from datetime import datetime, timezone
import astropy
import numpy as np
from astropy import units as u
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.modeling import Parameter
from astropy.modeling.models import Gaussian1D
from astropy.time import Time
from glue.core.message import SubsetUpdateMessage
from ipywidgets import widget_serialization
from packaging.version import Version
from photutils.aperture import (ApertureStats, CircularAperture, EllipticalAperture,
RectangularAperture)
from traitlets import Any, Bool, Integer, List, Unicode, observe
from jdaviz.core.custom_traitlets import FloatHandleEmpty
from jdaviz.core.events import SnackbarMessage, LinkUpdatedMessage, SliceValueUpdatedMessage
from jdaviz.core.region_translators import regions2aperture, _get_region_from_spatial_subset
from jdaviz.core.registries import tray_registry
from jdaviz.core.template_mixin import (PluginTemplateMixin, DatasetMultiSelectMixin,
SubsetSelect, ApertureSubsetSelectMixin,
TableMixin, PlotMixin, MultiselectMixin, with_spinner)
from jdaviz.utils import PRIHDR_KEY
__all__ = ['SimpleAperturePhotometry']
ASTROPY_LT_5_2 = Version(astropy.__version__) < Version('5.2')
[docs]
@tray_registry('imviz-aper-phot-simple', label="Aperture Photometry")
class SimpleAperturePhotometry(PluginTemplateMixin, ApertureSubsetSelectMixin,
DatasetMultiSelectMixin, TableMixin, PlotMixin, MultiselectMixin):
"""
The Aperture Photometry plugin performs aperture photometry for drawn regions.
See the :ref:`Aperture Photometry Plugin Documentation <aper-phot-simple>` 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`
* :meth:`~jdaviz.core.template_mixin.PluginTemplateMixin.close_in_tray`
"""
template_file = __file__, "aper_phot_simple.vue"
uses_active_status = Bool(True).tag(sync=True)
aperture_area = Integer().tag(sync=True)
background_items = List().tag(sync=True)
background_selected = Unicode("").tag(sync=True)
background_value = FloatHandleEmpty(0).tag(sync=True)
pixel_area_multi_auto = Bool(True).tag(sync=True)
pixel_area = FloatHandleEmpty(0).tag(sync=True)
counts_factor = FloatHandleEmpty(0).tag(sync=True)
flux_scaling_multi_auto = Bool(True).tag(sync=True)
flux_scaling_warning = Unicode("").tag(sync=True)
flux_scaling = FloatHandleEmpty(0).tag(sync=True)
result_available = Bool(False).tag(sync=True)
result_failed_msg = Unicode("").tag(sync=True)
results = List().tag(sync=True)
plot_types = List([]).tag(sync=True)
current_plot_type = Unicode().tag(sync=True)
plot_available = Bool(False).tag(sync=True)
radial_plot = Any('').tag(sync=True, **widget_serialization)
fit_radial_profile = Bool(False).tag(sync=True)
fit_results = List().tag(sync=True)
# Cubeviz only
cube_slice = Unicode("").tag(sync=True)
is_cube = Bool(False).tag(sync=True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.background = SubsetSelect(self,
'background_items',
'background_selected',
dataset='dataset',
default_text='Manual',
manual_options=['Manual'],
filters=['is_spatial', 'is_not_composite'])
headers = ['xcenter', 'ycenter', 'sky_center',
'sum', 'sum_aper_area',
'aperture_sum_counts', 'aperture_sum_counts_err',
'aperture_sum_mag',
'min', 'max', 'mean', 'median', 'mode', 'std', 'mad_std', 'var',
'biweight_location', 'biweight_midvariance', 'fwhm',
'semimajor_sigma', 'semiminor_sigma', 'orientation', 'eccentricity',
'data_label', 'subset_label']
self.table.headers_avail = headers
self.table.headers_visible = headers
self.plot_types = ["Curve of Growth", "Radial Profile", "Radial Profile (Raw)"]
self.current_plot_type = self.plot_types[0]
self._fitted_model_name = 'phot_radial_profile'
# override default plot styling
self.plot.figure.fig_margin = {'top': 60, 'bottom': 60, 'left': 65, 'right': 15}
self.plot.viewer.axis_y.tick_format = '0.2e'
self.plot.viewer.axis_y.label_offset = '55px'
self.session.hub.subscribe(self, SubsetUpdateMessage, handler=self._on_subset_update)
self.session.hub.subscribe(self, LinkUpdatedMessage, handler=self._on_link_update)
# Custom dataset filters for Cubeviz
if self.config == "cubeviz":
def valid_cubeviz_datasets(data):
comp = data.get_component(data.main_components[0])
img_unit = u.Unit(comp.units) if comp.units else u.dimensionless_unscaled
acceptable_types = ['spectral flux density wav',
'photon flux density wav',
'spectral flux density',
'photon flux density']
return ((data.ndim in (2, 3)) and
((img_unit == (u.MJy / u.sr)) or
(img_unit.physical_type in acceptable_types)))
self.dataset.add_filter(valid_cubeviz_datasets)
self.session.hub.subscribe(self, SliceValueUpdatedMessage,
handler=self._on_slice_changed)
# TODO: expose public API once finalized
# @property
# def user_api(self):
# return PluginUserApi(self, expose=('multiselect', 'dataset', 'aperture',
# 'background', 'background_value',
# 'pixel_area', 'counts_factor', 'flux_scaling',
# 'calculate_photometry',
# 'unpack_batch_options', 'calculate_batch_photometry'))
def _on_slice_changed(self, msg):
if self.config != "cubeviz":
return
self.cube_slice = f"{msg.value:.3e} {msg.value_unit}"
self._cube_wave = u.Quantity(msg.value, msg.value_unit)
@observe("dataset_selected")
def _on_dataset_selected_changed(self, event={}):
if self.config != "cubeviz":
return
# self.dataset might not exist when app is setting itself up.
if hasattr(self, "dataset") and self.dataset.selected_dc_item.ndim > 2:
self.is_cube = True
else:
self.is_cube = False
def _get_defaults_from_metadata(self, dataset=None):
defaults = {}
if dataset is None:
meta = self.dataset.selected_dc_item.meta.copy()
else:
meta = self.dataset._get_dc_item(dataset).meta.copy()
# Extract telescope specific unit conversion factors, if applicable.
if PRIHDR_KEY in meta:
meta.update(meta[PRIHDR_KEY])
del meta[PRIHDR_KEY]
if 'telescope' in meta:
telescope = meta['telescope']
else:
telescope = meta.get('TELESCOP', '')
if telescope == 'JWST':
# Hardcode the flux conversion factor from MJy to ABmag
mjy2abmag = 0.003631
if 'photometry' in meta and 'pixelarea_arcsecsq' in meta['photometry']:
defaults['pixel_area'] = meta['photometry']['pixelarea_arcsecsq']
if 'bunit_data' in meta and meta['bunit_data'] == u.Unit("MJy/sr"):
defaults['flux_scaling'] = mjy2abmag
elif 'PIXAR_A2' in meta:
defaults['pixel_area'] = meta['PIXAR_A2']
if 'BUNIT' in meta and meta['BUNIT'] == u.Unit("MJy/sr"):
defaults['flux_scaling'] = mjy2abmag
elif telescope == 'HST':
# TODO: Add more HST support, as needed.
# HST pixel scales are from instrument handbooks.
# This is really not used because HST data does not have sr in unit.
# This is only for completeness.
# For counts conversion, PHOTFLAM is used to convert "counts" to flux manually,
# which is the opposite of JWST, so we just do not do it here.
instrument = meta.get('INSTRUME', '').lower()
detector = meta.get('DETECTOR', '').lower()
if instrument == 'acs':
if detector == 'wfc':
defaults['pixel_area'] = 0.05 * 0.05
elif detector == 'hrc': # pragma: no cover
defaults['pixel_area'] = 0.028 * 0.025
elif detector == 'sbc': # pragma: no cover
defaults['pixel_area'] = 0.034 * 0.03
elif instrument == 'wfc3' and detector == 'uvis': # pragma: no cover
defaults['pixel_area'] = 0.04 * 0.04
return defaults
@observe('flux_scaling_multi_auto')
def _multiselect_flux_scaling_warning(self, event={}):
if not self.flux_scaling_multi_auto:
self.flux_scaling_warning = ''
return
no_flux_scaling = [dataset for dataset in self.dataset.selected
if 'flux_scaling' not in self._get_defaults_from_metadata(dataset)]
if len(no_flux_scaling):
self.flux_scaling_warning = ('Could not determine flux scaling for '
f'{", ".join(no_flux_scaling)}. Those entries will '
'default to zero. Turn off auto-mode to provide '
'flux-scaling manually.')
else:
self.flux_scaling_warning = ''
@observe('flux_scaling')
def _singleselect_flux_scaling_warning(self, event={}):
if not self.multiselect:
# disable warning once user changes value
self.flux_scaling_warning = ''
@observe('dataset_selected')
def _dataset_selected_changed(self, event={}):
if not hasattr(self, 'dataset'):
# plugin not fully initialized
return
if self.dataset.selected_dc_item is None:
return
if self.multiselect:
# defaults are applied within the loop if the auto-switches are enabled,
# but we still need to update the flux-scaling warning
self._multiselect_flux_scaling_warning()
return
try:
defaults = self._get_defaults_from_metadata()
self.counts_factor = 0
self.pixel_area = defaults.get('pixel_area', 0)
self.flux_scaling = defaults.get('flux_scaling', 0)
if 'flux_scaling' in defaults:
self.flux_scaling_warning = ''
else:
self.flux_scaling_warning = ('Could not determine flux scaling for '
f'{self.dataset.selected}, defaulting to zero.')
except Exception as e:
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {self.dataset_selected}: {repr(e)}",
color='error', sender=self))
# auto-populate background, if applicable.
self._aperture_selected_changed()
def _on_subset_update(self, msg):
if not self.dataset_selected or not self.aperture_selected:
return
if self.multiselect:
self._background_selected_changed()
return
sbst = msg.subset
if sbst.label == self.aperture_selected and sbst.data.label == self.dataset_selected:
self._aperture_selected_changed()
elif sbst.label == self.background_selected and sbst.data.label == self.dataset_selected:
self._background_selected_changed()
def _on_link_update(self, msg):
if not self.dataset_selected or not self.aperture_selected:
return
# Force background auto-calculation to update when linking has changed.
self._aperture_selected_changed()
@observe('aperture_selected', 'multiselect')
def _aperture_selected_changed(self, event={}):
if not self.dataset_selected or not self.aperture_selected:
return
if self.multiselect is not isinstance(self.aperture_selected, list):
# then multiselect is in the process of changing but the traitlet for aperture_selected
# has not been updated internally yet
return
if self.multiselect:
self._background_selected_changed()
return
# NOTE: aperture_selected can be triggered here before aperture_selected_validity is updated
# so we'll still allow the snackbar to be raised as a second warning to the user and to
# avoid acting on outdated information
# NOTE: aperture area is only used to determine if a warning should be shown in the UI
# and so does not need to be calculated within user API calls that don't act on traitlets
try:
# Sky subset does not have area. Not worth it to calculate just for a warning.
if hasattr(self.aperture.selected_spatial_region, 'area'):
self.aperture_area = int(np.ceil(self.aperture.selected_spatial_region.area))
else:
self.aperture_area = 0
except Exception as e:
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {self.aperture_selected}: {repr(e)}",
color='error', sender=self))
else:
self._background_selected_changed()
@property
def _cubeviz_slice_ind(self):
fv = self.app.get_viewer(self.app._jdaviz_helper._default_flux_viewer_reference_name)
return fv.slice
def _calc_background_median(self, reg, data=None):
# Basically same way image stats are calculated in vue_do_aper_phot()
# except here we only care about one stat for the background.
if data is None:
if self.multiselect:
if len(self.dataset.selected) == 1:
data = self.dataset.selected_dc_item[0]
else:
raise ValueError("cannot calculate background median in multiselect")
else:
data = self.dataset.selected_dc_item
comp = data.get_component(data.main_components[0])
if self.config == "cubeviz" and data.ndim > 2:
comp_data = comp.data[:, :, self._cubeviz_slice_ind].T # nx, ny --> ny, nx
# Similar to coords_info logic.
if '_orig_spec' in getattr(data, 'meta', {}):
w = data.meta['_orig_spec'].wcs.celestial
else:
w = data.coords.celestial
else: # "imviz"
comp_data = comp.data # ny, nx
w = data.coords
if hasattr(reg, 'to_pixel'):
reg = reg.to_pixel(w)
aper_mask_stat = reg.to_mask(mode='center')
img_stat = aper_mask_stat.get_values(comp_data, mask=None)
# photutils/background/_utils.py --> nanmedian()
return np.nanmedian(img_stat) # Naturally in data unit
@observe('background_selected')
def _background_selected_changed(self, event={}):
background_selected = event.get('new', self.background_selected)
if background_selected == 'Manual':
# we'll later access the user's self.background_value directly
return
if self.multiselect:
# background_value will be recomputed within batch mode anyways and will
# be replaced in the UI with a message
self.background_value = -1
return
try:
reg = _get_region_from_spatial_subset(self, self.background.selected_subset_state)
self.background_value = self._calc_background_median(reg)
except Exception as e:
self.background_value = 0
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {background_selected}: {repr(e)}", color='error', sender=self))
@with_spinner()
def calculate_photometry(self, dataset=None, aperture=None, background=None,
background_value=None, pixel_area=None, counts_factor=None,
flux_scaling=None, add_to_table=True, update_plots=True):
"""
Calculate aperture photometry given the values set in the plugin or any overrides provided
as arguments here (which will temporarily override plugin values for this calculation only).
Parameters
----------
dataset : str, optional
Dataset to use for photometry.
aperture : str, optional
Subset to use as the aperture.
background : str, optional
Subset to use to calculate the background.
background_value : float, optional
Background to subtract, same unit as data. Automatically computed if ``background``
is set to a subset.
pixel_area : float, optional
Pixel area in arcsec squared, only used if sr in data unit.
counts_factor : float, optional
Factor to convert data unit to counts, in unit of flux/counts.
flux_scaling : float, optional
Same unit as data, used in -2.5 * log(flux / flux_scaling).
add_to_table : bool, optional
update_plots : bool, optional
Returns
-------
table row, fit results
"""
if self.multiselect and (dataset is None or aperture is None): # pragma: no cover
raise ValueError("for batch mode, use calculate_batch_photometry")
if dataset is not None:
if dataset not in self.dataset.choices: # pragma: no cover
raise ValueError(f"dataset must be one of {self.dataset.choices}")
data = self.dataset._get_dc_item(dataset)
else:
# we can use the pre-cached value
data = self.dataset.selected_dc_item
if aperture is not None:
if aperture not in self.aperture.choices:
raise ValueError(f"aperture must be one of {self.aperture.choices}")
if aperture is not None or dataset is not None:
reg = self.aperture._get_spatial_region(subset=aperture if aperture is not None else self.aperture.selected, # noqa
dataset=dataset if dataset is not None else self.dataset.selected) # noqa
# determine if a valid aperture (since selected_validity only applies to selected entry)
_, _, validity = self.aperture._get_mark_coords_and_validate(selected=aperture)
if not validity.get('is_aperture'):
raise ValueError(f"Selected aperture {aperture} is not valid: {validity.get('aperture_message')}") # noqa
else:
# use the pre-cached value
if not self.aperture.selected_validity.get('is_aperture'):
raise ValueError(f"Selected aperture is not valid: {self.aperture.selected_validity.get('aperture_message')}") # noqa
reg = self.aperture.selected_spatial_region
# Reset last fitted model
fit_model = None
# TODO: remove _fitted_model_name cache?
if self._fitted_model_name in self.app.fitted_models:
del self.app.fitted_models[self._fitted_model_name]
comp = data.get_component(data.main_components[0])
if background is not None and background not in self.background.choices: # pragma: no cover
raise ValueError(f"background must be one of {self.background.choices}")
if background_value is not None:
if ((background not in (None, 'Manual'))
or (background is None and self.background_selected != 'Manual')):
raise ValueError("cannot provide background_value with background!='Manual'")
elif (background == 'Manual'
or (background is None and self.background.selected == 'Manual')):
background_value = self.background_value
elif background is None and dataset is None:
# use the previously-computed value in the plugin
background_value = self.background_value
else:
bg_reg = self.aperture._get_spatial_region(subset=background if background is not None else self.background.selected, # noqa
dataset=dataset if dataset is not None else self.dataset.selected) # noqa
background_value = self._calc_background_median(bg_reg, data=data)
try:
bg = float(background_value)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid background value')
if self.config == "cubeviz" and data.ndim > 2:
comp_data = comp.data[:, :, self._cubeviz_slice_ind].T # nx, ny --> ny, nx
# Similar to coords_info logic.
if '_orig_spec' in getattr(data, 'meta', {}):
w = data.meta['_orig_spec'].wcs
else:
w = data.coords
else: # "imviz"
comp_data = comp.data # ny, nx
w = data.coords
if hasattr(reg, 'to_pixel'):
sky_center = reg.center
if self.config == "cubeviz" and data.ndim > 2:
ycenter, xcenter = w.world_to_pixel(self._cube_wave, sky_center)[1]
else: # "imviz"
xcenter, ycenter = w.world_to_pixel(sky_center)
else:
xcenter = reg.center.x
ycenter = reg.center.y
if data.coords is not None:
if self.config == "cubeviz" and data.ndim > 2:
sky_center = w.pixel_to_world(self._cubeviz_slice_ind, ycenter, xcenter)[1]
else: # "imviz"
sky_center = w.pixel_to_world(xcenter, ycenter)
else:
sky_center = None
aperture = regions2aperture(reg)
include_pixarea_fac = False
include_counts_fac = False
include_flux_scale = False
if comp.units:
img_unit = u.Unit(comp.units)
bg = bg * img_unit
comp_data = comp_data << img_unit
if u.sr in img_unit.bases: # TODO: Better way to detect surface brightness unit?
try:
pixarea = float(pixel_area if pixel_area is not None else self.pixel_area)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid pixel area')
if not np.allclose(pixarea, 0):
include_pixarea_fac = True
if img_unit != u.count:
try:
ctfac = float(counts_factor if counts_factor is not None else self.counts_factor) # noqa
except ValueError: # Clearer error message
raise ValueError('Missing or invalid counts conversion factor')
if not np.allclose(ctfac, 0):
include_counts_fac = True
try:
flux_scale = float(flux_scaling if flux_scaling is not None else self.flux_scaling)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid flux scaling')
if not np.allclose(flux_scale, 0):
include_flux_scale = True
phot_aperstats = ApertureStats(comp_data, aperture, wcs=data.coords, local_bkg=bg)
phot_table = phot_aperstats.to_table(columns=(
'id', 'sum', 'sum_aper_area',
'min', 'max', 'mean', 'median', 'mode', 'std', 'mad_std', 'var',
'biweight_location', 'biweight_midvariance', 'fwhm', 'semimajor_sigma',
'semiminor_sigma', 'orientation', 'eccentricity')) # Some cols excluded, add back as needed. # noqa
rawsum = phot_table['sum'][0]
if include_pixarea_fac:
pixarea = pixarea * (u.arcsec * u.arcsec / (u.pix * u.pix))
# NOTE: Sum already has npix value encoded, so we simply apply the npix unit here.
pixarea_fac = (u.pix * u.pix) * pixarea.to(u.sr / (u.pix * u.pix))
phot_table['sum'] = [rawsum * pixarea_fac]
else:
pixarea_fac = None
if include_counts_fac:
ctfac = ctfac * (rawsum.unit / u.count)
sum_ct = rawsum / ctfac
sum_ct_err = np.sqrt(sum_ct.value) * sum_ct.unit
else:
ctfac = None
sum_ct = None
sum_ct_err = None
if include_flux_scale:
flux_scale = flux_scale * phot_table['sum'][0].unit
sum_mag = -2.5 * np.log10(phot_table['sum'][0] / flux_scale) * u.mag
else:
flux_scale = None
sum_mag = None
# Extra info beyond photutils.
phot_table.add_columns(
[xcenter * u.pix, ycenter * u.pix, sky_center,
bg, pixarea_fac, sum_ct, sum_ct_err, ctfac, sum_mag, flux_scale, data.label,
reg.meta.get('label', ''), Time(datetime.now(tz=timezone.utc))],
names=['xcenter', 'ycenter', 'sky_center', 'background', 'pixarea_tot',
'aperture_sum_counts', 'aperture_sum_counts_err', 'counts_fac',
'aperture_sum_mag', 'flux_scaling',
'data_label', 'subset_label', 'timestamp'],
indexes=[1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 18, 18, 18])
if self.config == "cubeviz":
if data.ndim > 2:
slice_val = self._cube_wave
else:
slice_val = u.Quantity(np.nan, self._cube_wave.unit)
phot_table.add_column(slice_val, name="slice_wave", index=29)
if add_to_table:
try:
phot_table['id'][0] = self.table._qtable['id'].max() + 1
self.table.add_item(phot_table)
except Exception: # Discard incompatible QTable
self.table.clear_table()
phot_table['id'][0] = 1
self.table.add_item(phot_table)
# User wants 'sum' as scientific notation.
self.table._qtable['sum'].info.format = '.6e'
# Plots.
if update_plots:
if self.current_plot_type == "Curve of Growth":
if self.config == "cubeviz" and data.ndim > 2:
self.plot.figure.title = f'Curve of growth from aperture center at {slice_val:.4e}' # noqa: E501
else:
self.plot.figure.title = 'Curve of growth from aperture center'
x_arr, sum_arr, x_label, y_label = _curve_of_growth(
comp_data, (xcenter, ycenter), aperture, phot_table['sum'][0],
wcs=data.coords, background=bg, pixarea_fac=pixarea_fac)
self.plot._update_data('profile', x=x_arr, y=sum_arr, reset_lims=True)
self.plot.update_style('profile', line_visible=True, color='gray', size=32)
self.plot.update_style('fit', visible=False)
self.plot.figure.axes[0].label = x_label
self.plot.figure.axes[1].label = y_label
else: # Radial profile
self.plot.figure.axes[0].label = 'pix'
self.plot.figure.axes[1].label = comp.units or 'Value'
if self.current_plot_type == "Radial Profile":
if self.config == "cubeviz" and data.ndim > 2:
self.plot.figure.title = f'Radial profile from aperture center at {slice_val:.4e}' # noqa: E501
else:
self.plot.figure.title = 'Radial profile from aperture center'
x_data, y_data = _radial_profile(
phot_aperstats.data_cutout, phot_aperstats.bbox, (xcenter, ycenter),
raw=False)
self.plot._update_data('profile', x=x_data, y=y_data, reset_lims=True)
self.plot.update_style('profile', line_visible=True, color='gray', size=32)
else: # Radial Profile (Raw)
if self.config == "cubeviz" and data.ndim > 2:
self.plot.figure.title = f'Raw radial profile from aperture center at {slice_val:.4e}' # noqa: E501
else:
self.plot.figure.title = 'Raw radial profile from aperture center'
x_data, y_data = _radial_profile(
phot_aperstats.data_cutout, phot_aperstats.bbox, (xcenter, ycenter),
raw=True)
self.plot._update_data('profile', x=x_data, y=y_data, reset_lims=True)
self.plot.update_style('profile', line_visible=False, color='gray', size=10)
# Fit Gaussian1D to radial profile data.
if self.fit_radial_profile:
fitter = LevMarLSQFitter()
y_max = y_data.max()
x_mean = x_data[np.where(y_data == y_max)].mean()
std = 0.5 * (phot_table['semimajor_sigma'][0] +
phot_table['semiminor_sigma'][0])
if isinstance(std, u.Quantity):
std = std.value
gs = Gaussian1D(amplitude=y_max, mean=x_mean, stddev=std,
fixed={'amplitude': True},
bounds={'amplitude': (y_max * 0.5, y_max)})
if ASTROPY_LT_5_2:
fitter_kw = {}
else:
fitter_kw = {'filter_non_finite': True}
with warnings.catch_warnings(record=True) as warns:
fit_model = fitter(gs, x_data, y_data, **fitter_kw)
if len(warns) > 0:
msg = os.linesep.join([str(w.message) for w in warns])
self.hub.broadcast(SnackbarMessage(
f"Radial profile fitting: {msg}", color='warning', sender=self))
y_fit = fit_model(x_data)
self.app.fitted_models[self._fitted_model_name] = fit_model
self.plot._update_data('fit', x=x_data, y=y_fit, reset_lims=True)
self.plot.update_style('fit', color='magenta',
markers_visible=False, line_visible=True)
else:
self.plot.update_style('fit', visible=False)
# Parse results for GUI.
tmp = []
for key in phot_table.colnames:
if key in ('id', 'data_label', 'subset_label', 'background', 'pixarea_tot',
'counts_fac', 'aperture_sum_counts_err', 'flux_scaling', 'timestamp'):
continue
x = phot_table[key][0]
if (isinstance(x, (int, float, u.Quantity)) and
key not in ('xcenter', 'ycenter', 'sky_center', 'sum_aper_area',
'aperture_sum_counts', 'aperture_sum_mag', 'slice_wave')):
tmp.append({'function': key, 'result': f'{x:.4e}'})
elif key == 'sky_center' and x is not None:
tmp.append({'function': 'RA center', 'result': f'{x.ra.deg:.6f} deg'})
tmp.append({'function': 'Dec center', 'result': f'{x.dec.deg:.6f} deg'})
elif key in ('xcenter', 'ycenter', 'sum_aper_area'):
tmp.append({'function': key, 'result': f'{x:.1f}'})
elif key == 'aperture_sum_counts' and x is not None:
tmp.append({'function': key, 'result':
f'{x:.4e} ({phot_table["aperture_sum_counts_err"][0]:.4e})'})
elif key == 'aperture_sum_mag' and x is not None:
tmp.append({'function': key, 'result': f'{x:.3f}'})
elif key == 'slice_wave':
if data.ndim > 2:
tmp.append({'function': key, 'result': f'{slice_val:.4e}'})
else:
tmp.append({'function': key, 'result': str(x)})
if update_plots:
# Also display fit results
fit_tmp = []
if fit_model is not None and isinstance(fit_model, Gaussian1D):
for param in ('mean', 'fwhm', 'amplitude'):
p_val = getattr(fit_model, param)
if isinstance(p_val, Parameter):
p_val = p_val.value
fit_tmp.append({'function': param, 'result': f'{p_val:.4e}'})
self.results = tmp
self.result_available = True
if update_plots:
self.fit_results = fit_tmp
self.plot_available = True
return phot_table, fit_model
[docs]
def vue_do_aper_phot(self, *args, **kwargs):
if self.dataset_selected == '' or self.aperture_selected == '':
self.hub.broadcast(SnackbarMessage(
"No data for aperture photometry", color='error', sender=self))
return
try:
if self.multiselect:
# even though plots aren't show in the UI when in multiselect mode,
# we'll create the last entry so if multiselect is disabled, the last
# iteration will show and not result in confusing behavior
self.calculate_batch_photometry(add_to_table=True, update_plots=True)
else:
self.calculate_photometry(add_to_table=True, update_plots=True)
except Exception as e: # pragma: no cover
self.plot.clear_all_marks()
msg = f"Aperture photometry failed: {repr(e)}"
self.hub.broadcast(SnackbarMessage(msg, color='error', sender=self))
self.result_failed_msg = msg
else:
self.result_failed_msg = ''
[docs]
def unpack_batch_options(self, **options):
"""
Unpacks a dictionary of options for batch mode, including all combinations of any values
passed as tuples or lists. For example::
unpack_batch_options(dataset=['image1', 'image2'],
aperture=['Subset 1', 'Subset 2'],
background=['Subset 3'],
flux_scaling=3
)
would result in::
[{'aperture': 'Subset 1',
'dataset': 'image1',
'background': 'Subset 3',
'flux_scaling': 3},
{'aperture': 'Subset 2',
'dataset': 'image1',
'background': 'Subset 3',
'flux_scaling': 3},
{'aperture': 'Subset 1',
'dataset': 'image2',
'background': 'Subset 3',
'flux_scaling': 3},
{'aperture': 'Subset 2',
'dataset': 'image2',
'background': 'Subset 3',
'flux_scaling': 3}]
Parameters
----------
options : dict, optional
Dictionary of values to override from the values set in the plugin/traitlets. Each
entry can either be a single value, or a list. All combinations of those that contain
a list will be exposed. If not provided and the plugin is in multiselect mode
(``multiselect = True``), then the current values set in the plugin will be used.
Returns
-------
options : list
List of all combinations of input parameters, which can then be used as input to
`calculate_batch_photometry`.
"""
if not isinstance(options, dict):
raise TypeError("options must be a dictionary")
if not options:
if not self.multiselect: # pragma: no cover
raise ValueError("must either provide a dictionary or set plugin to multiselect mode") # noqa
options = {'dataset': self.dataset.selected, 'aperture': self.aperture.selected}
# TODO: use self.user_api once API is made public
user_api = self # .user_api
invalid_keys = [k for k in options.keys() if not hasattr(user_api, k)]
if len(invalid_keys):
raise ValueError(f"{invalid_keys} are not valid inputs for batch photometry")
def _is_single(v):
if isinstance(v, (list, tuple)):
if len(v) == 1:
return True, v[0]
return False, v
return True, v
single_values, mult_values = {}, {}
for k, v in options.items():
is_single, this_value = _is_single(v)
if is_single:
single_values[k] = this_value
else:
mult_values[k] = this_value
def _unpack_dict_list(mult_values, single_values):
if not len(mult_values):
return [single_values]
options_list = []
# loop over the first item in mult_values
# any remaining mult values will require recursion
this_attr, this_values = list(mult_values.items())[0]
remaining_mult_values = {k: v for j, (k, v) in enumerate(mult_values.items()) if j > 0}
for this_value in this_values:
if not len(remaining_mult_values):
options_list += [{this_attr: this_value, **single_values}]
continue
options_list += _unpack_dict_list(remaining_mult_values,
{this_attr: this_value, **single_values})
return options_list
return _unpack_dict_list(mult_values, single_values)
@with_spinner()
def calculate_batch_photometry(self, options=[], add_to_table=True, update_plots=True,
full_exceptions=False):
"""
Run aperture photometry over a list of options. Unprovided options will remain at their
values defined in the plugin.
To provide a list of values per-input, use `unpack_batch_options` to and pass that as input
here.
Parameters
----------
options : list
Each entry will result in one computation of aperture photometry and should be
a dictionary of values to override from the values set in the plugin/traitlets.
add_to_table : bool
Whether to add results to the plugin table.
update_plots : bool
Whether to update the plugin plots for the last iteration.
full_exceptions : bool, optional
Whether to expose the full exception message for all failed iterations.
"""
# input validation
if not isinstance(options, list):
raise TypeError("options must be a list of dictionaries")
if not np.all([isinstance(option, dict) for option in options]):
raise TypeError("options must be a list of dictionaries")
if not len(options):
if not self.multiselect: # pragma: no cover
raise ValueError("must either provide manual options or put the plugin in multiselect mode") # noqa
# unpack the batch options as provided in the app
options = self.unpack_batch_options()
failed_iters, exceptions = [], []
for i, option in enumerate(options):
# only update plots on the last iteration
this_update_plots = i == len(options) and update_plots
defaults = self._get_defaults_from_metadata(option.get('dataset',
self.dataset.selected))
if self.pixel_area_multi_auto:
option.setdefault('pixel_area', defaults.get('pixel_area', 0))
if self.flux_scaling_multi_auto:
option.setdefault('flux_scaling', defaults.get('flux_scaling', 0))
try:
self.calculate_photometry(add_to_table=add_to_table,
update_plots=this_update_plots,
**option)
except Exception as e:
failed_iters.append(i)
if full_exceptions:
exceptions.append(e)
if len(failed_iters):
err_msg = f"inputs {failed_iters} failed and were skipped."
if full_exceptions:
err_msg += f" Exception messages: {exceptions}"
else:
err_msg += " To see full exceptions, run individually or pass full_exceptions=True" # noqa
raise RuntimeError(err_msg)
# NOTE: These are hidden because the APIs are for internal use only
# but we need them as a separate functions for unit testing.
def _radial_profile(radial_cutout, reg_bb, centroid, raw=False):
"""Calculate radial profile.
Parameters
----------
radial_cutout : ndarray
Cutout image from ``ApertureStats``.
reg_bb : obj
Bounding box from ``ApertureStats``.
centroid : tuple of int
``ApertureStats`` centroid or desired center in ``(x, y)``.
raw : bool
If `True`, returns raw data points for scatter plot.
Otherwise, use ``imexam`` algorithm for a clean plot.
"""
reg_ogrid = np.ogrid[reg_bb.iymin:reg_bb.iymax, reg_bb.ixmin:reg_bb.ixmax]
radial_dx = reg_ogrid[1] - centroid[0]
radial_dy = reg_ogrid[0] - centroid[1]
radial_r = np.hypot(radial_dx, radial_dy)
# Sometimes the mask is smaller than radial_r
if radial_cutout.shape != reg_bb.shape:
radial_r = radial_r[:radial_cutout.shape[0], :radial_cutout.shape[1]]
radial_r = radial_r[~radial_cutout.mask].ravel() # pix
radial_img = radial_cutout.compressed() # data unit
if raw:
i_arr = np.argsort(radial_r)
x_arr = radial_r[i_arr]
y_arr = radial_img[i_arr]
else:
# This algorithm is from the imexam package,
# see licenses/IMEXAM_LICENSE.txt for more details
radial_r = np.rint(radial_r).astype(int)
y_arr = np.bincount(radial_r, radial_img) / np.bincount(radial_r)
x_arr = np.arange(y_arr.size)
return x_arr, y_arr
def _curve_of_growth(data, centroid, aperture, final_sum, wcs=None, background=0, n_datapoints=10,
pixarea_fac=None):
"""Calculate curve of growth for aperture photometry.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Data for the calculation.
centroid : tuple of int
``ApertureStats`` centroid or desired center in ``(x, y)``.
aperture : obj
``photutils`` aperture to use, except its center will be
changed to the given ``centroid``. This is because the aperture
might be hand-drawn and a more accurate centroid has been
recalculated separately.
final_sum : float or `~astropy.units.Quantity`
Aperture sum that is already calculated in the
main plugin above.
wcs : obj or `None`
Supported WCS objects or `None`.
background : float or `~astropy.units.Quantity`
Background to subtract, if any. Unit must match ``data``.
n_datapoints : int
Number of data points in the curve.
pixarea_fac : float or `None`
For ``flux_unit/sr`` to ``flux_unit`` conversion.
Returns
-------
x_arr : ndarray
Data for X-axis of the curve.
sum_arr : ndarray or `~astropy.units.Quantity`
Data for Y-axis of the curve.
x_label, y_label : str
X- and Y-axis labels, respectively.
Raises
------
TypeError
Unsupported aperture.
"""
n_datapoints += 1 # n + 1
if hasattr(aperture, 'to_pixel'):
aperture = aperture.to_pixel(wcs)
if isinstance(aperture, CircularAperture):
x_label = 'Radius (pix)'
x_arr = np.linspace(0, aperture.r, num=n_datapoints)[1:]
aper_list = [CircularAperture(centroid, cur_r) for cur_r in x_arr[:-1]]
elif isinstance(aperture, EllipticalAperture):
x_label = 'Semimajor axis (pix)'
x_arr = np.linspace(0, aperture.a, num=n_datapoints)[1:]
a_arr = x_arr[:-1]
b_arr = aperture.b * a_arr / aperture.a
aper_list = [EllipticalAperture(centroid, cur_a, cur_b, theta=aperture.theta)
for (cur_a, cur_b) in zip(a_arr, b_arr)]
elif isinstance(aperture, RectangularAperture):
x_label = 'Width (pix)'
x_arr = np.linspace(0, aperture.w, num=n_datapoints)[1:]
w_arr = x_arr[:-1]
h_arr = aperture.h * w_arr / aperture.w
aper_list = [RectangularAperture(centroid, cur_w, cur_h, theta=aperture.theta)
for (cur_w, cur_h) in zip(w_arr, h_arr)]
else:
raise TypeError(f'Unsupported aperture: {aperture}')
sum_arr = [ApertureStats(data, cur_aper, wcs=wcs, local_bkg=background).sum
for cur_aper in aper_list]
if isinstance(sum_arr[0], u.Quantity):
sum_arr = u.Quantity(sum_arr)
else:
sum_arr = np.array(sum_arr)
if pixarea_fac is not None:
sum_arr = sum_arr * pixarea_fac
sum_arr = np.append(sum_arr, final_sum)
if isinstance(sum_arr, u.Quantity):
y_label = sum_arr.unit.to_string()
sum_arr = sum_arr.value # bqplot does not like Quantity
else:
y_label = 'Value'
return x_arr, sum_arr, x_label, y_label
