import os
import warnings
from datetime import datetime
import bqplot
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.table import QTable
from astropy.time import Time
from ipywidgets import widget_serialization
from photutils.aperture import (ApertureStats, CircularAperture, EllipticalAperture,
RectangularAperture)
from regions import (CircleAnnulusPixelRegion, CirclePixelRegion, EllipsePixelRegion,
RectanglePixelRegion)
from traitlets import Any, Bool, List, Unicode, observe
from jdaviz.core.custom_traitlets import FloatHandleEmpty
from jdaviz.core.events import SnackbarMessage
from jdaviz.core.region_translators import regions2aperture
from jdaviz.core.registries import tray_registry
from jdaviz.core.template_mixin import TemplateMixin, DatasetSelectMixin, SubsetSelect
from jdaviz.utils import bqplot_clear_figure, PRIHDR_KEY
__all__ = ['SimpleAperturePhotometry']
[docs]@tray_registry('imviz-aper-phot-simple', label="Imviz Simple Aperture Photometry")
class SimpleAperturePhotometry(TemplateMixin, DatasetSelectMixin):
template_file = __file__, "aper_phot_simple.vue"
subset_items = List([]).tag(sync=True)
subset_selected = Unicode("").tag(sync=True)
bg_subset_items = List().tag(sync=True)
bg_subset_selected = Unicode("").tag(sync=True)
background_value = Any(0).tag(sync=True)
bg_annulus_inner_r = FloatHandleEmpty(0).tag(sync=True)
bg_annulus_width = FloatHandleEmpty(10).tag(sync=True)
pixel_area = Any(0).tag(sync=True)
counts_factor = Any(0).tag(sync=True)
flux_scaling = Any(0).tag(sync=True)
result_available = Bool(False).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)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.subset = SubsetSelect(self,
'subset_items',
'subset_selected',
default_text=None,
allowed_type='spatial')
self.bg_subset = SubsetSelect(self,
'bg_subset_items',
'bg_subset_selected',
default_text='Manual',
manual_options=['Manual', 'Annulus'],
allowed_type='spatial')
self._selected_data = None
self._selected_subset = None
self._fig = bqplot.Figure()
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'
[docs] def reset_results(self):
self.result_available = False
self.results = []
self.plot_available = False
self.radial_plot = ''
bqplot_clear_figure(self._fig)
@observe('dataset_selected')
def _dataset_selected_changed(self, event={}):
try:
self._selected_data = self.dataset.selected_dc_item
if self._selected_data is None:
self.reset_results()
return
self.counts_factor = 0
self.pixel_area = 0
self.flux_scaling = 0
# Extract telescope specific unit conversion factors, if applicable.
meta = self._selected_data.meta.copy()
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':
if 'photometry' in meta and 'pixelarea_arcsecsq' in meta['photometry']:
self.pixel_area = meta['photometry']['pixelarea_arcsecsq']
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':
self.pixel_area = 0.05 * 0.05
elif detector == 'hrc': # pragma: no cover
self.pixel_area = 0.028 * 0.025
elif detector == 'sbc': # pragma: no cover
self.pixel_area = 0.034 * 0.03
elif instrument == 'wfc3' and detector == 'uvis': # pragma: no cover
self.pixel_area = 0.04 * 0.04
except Exception as e:
self.reset_results()
self._selected_data = None
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {self.dataset_selected}: {repr(e)}",
color='error', sender=self))
# Update self._selected_subset with the new self._selected_data
# and auto-populate background, if applicable.
self._subset_selected_changed()
self._bg_subset_selected_changed()
def _get_region_from_subset(self, subset):
for subset_grp in self.app.data_collection.subset_groups:
if subset_grp.label == subset:
for sbst in subset_grp.subsets:
if sbst.data.label == self.dataset_selected:
# TODO: https://github.com/glue-viz/glue-astronomy/issues/52
return sbst.data.get_selection_definition(
subset_id=subset, format='astropy-regions')
else:
raise ValueError(f'Subset "{subset}" not found')
@observe('subset_selected')
def _subset_selected_changed(self, event={}):
subset_selected = event.get('new', self.subset_selected)
if self._selected_data is None or subset_selected == '':
self.reset_results()
return
try:
self._selected_subset = self._get_region_from_subset(subset_selected)
self._selected_subset.meta['label'] = subset_selected
if isinstance(self._selected_subset, CirclePixelRegion):
self.bg_annulus_inner_r = self._selected_subset.radius
elif isinstance(self._selected_subset, EllipsePixelRegion):
self.bg_annulus_inner_r = max(self._selected_subset.width,
self._selected_subset.height) * 0.5
elif isinstance(self._selected_subset, RectanglePixelRegion):
self.bg_annulus_inner_r = np.sqrt(self._selected_subset.width ** 2 +
self._selected_subset.height ** 2) * 0.5
else: # pragma: no cover
raise TypeError(f'Unsupported region shape: {self._selected_subset.__class__}')
except Exception as e:
self._selected_subset = None
self.reset_results()
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {subset_selected}: {repr(e)}", color='error', sender=self))
def _calc_bg_subset_median(self, reg):
# Basically same way image stats are calculated in vue_do_aper_phot()
# except here we only care about one stat for the background.
data = self._selected_data
comp = data.get_component(data.main_components[0])
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('bg_annulus_inner_r', 'bg_annulus_width')
def _bg_annulus_updated(self, *args):
if self.bg_subset_selected != 'Annulus':
return
try:
inner_r = float(self.bg_annulus_inner_r)
reg = CircleAnnulusPixelRegion(
self._selected_subset.center, inner_radius=inner_r,
outer_radius=inner_r + float(self.bg_annulus_width))
self.background_value = self._calc_bg_subset_median(reg)
except Exception: # Error snackbar suppressed to prevent excessive queue.
self.background_value = 0
@observe('bg_subset_selected')
def _bg_subset_selected_changed(self, event={}):
bg_subset_selected = event.get('new', self.bg_subset_selected)
if bg_subset_selected == 'Manual':
# we'll later access the user's self.background_value directly
return
if bg_subset_selected == 'Annulus':
self._bg_annulus_updated()
return
try:
reg = self._get_region_from_subset(bg_subset_selected)
self.background_value = self._calc_bg_subset_median(reg)
except Exception as e:
self.background_value = 0
self.hub.broadcast(SnackbarMessage(
f"Failed to extract {bg_subset_selected}: {repr(e)}", color='error', sender=self))
[docs] def vue_do_aper_phot(self, *args, **kwargs):
if self._selected_data is None or self._selected_subset is None:
self.reset_results()
self.hub.broadcast(SnackbarMessage(
"No data for aperture photometry", color='error', sender=self))
return
data = self._selected_data
reg = self._selected_subset
# Reset last fitted model
fit_model = None
if self._fitted_model_name in self.app.fitted_models:
del self.app.fitted_models[self._fitted_model_name]
try:
comp = data.get_component(data.main_components[0])
try:
bg = float(self.background_value)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid background value')
aperture = regions2aperture(reg)
include_pixarea_fac = False
include_counts_fac = False
include_flux_scale = False
comp_data = comp.data
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(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(self.counts_factor)
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(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', 'xcentroid', 'ycentroid', 'sky_centroid', '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
phot_table['xcentroid'].unit = u.pix # photutils only assumes, we make it real
phot_table['ycentroid'].unit = u.pix
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 * rawsum.unit
sum_mag = -2.5 * np.log10(rawsum / flux_scale) * u.mag
else:
flux_scale = None
sum_mag = None
# Extra info beyond photutils.
phot_table.add_columns(
[bg, pixarea_fac, sum_ct, sum_ct_err, ctfac, sum_mag, flux_scale, data.label,
reg.meta.get('label', ''), Time(datetime.utcnow())],
names=['background', 'pixarea_tot', 'aperture_sum_counts',
'aperture_sum_counts_err', 'counts_fac', 'aperture_sum_mag', 'flux_scaling',
'data_label', 'subset_label', 'timestamp'],
indexes=[4, 6, 6, 6, 6, 6, 6, 21, 21, 21])
# Attach to app for Python extraction.
if (not hasattr(self.app, '_aper_phot_results') or
not isinstance(self.app._aper_phot_results, QTable)):
self.app._aper_phot_results = phot_table
else:
try:
phot_table['id'][0] = self.app._aper_phot_results['id'].max() + 1
self.app._aper_phot_results.add_row(phot_table[0])
except Exception: # Discard incompatible QTable
phot_table['id'][0] = 1
self.app._aper_phot_results = phot_table
# Plots.
# TODO: Jenn wants title at bottom.
bqplot_clear_figure(self._fig)
self._fig.title_style = {'font-size': '12px'}
# NOTE: default margin in bqplot is 60 in all directions
self._fig.fig_margin = {'top': 60, 'bottom': 60, 'left': 40, 'right': 10}
line_x_sc = bqplot.LinearScale()
line_y_sc = bqplot.LinearScale()
if self.current_plot_type == "Curve of Growth":
self._fig.title = 'Curve of growth from source centroid'
x_arr, sum_arr, x_label, y_label = _curve_of_growth(
comp_data, phot_aperstats.centroid, aperture, phot_table['sum'][0],
wcs=data.coords, background=bg, pixarea_fac=pixarea_fac)
self._fig.axes = [bqplot.Axis(scale=line_x_sc, label=x_label),
bqplot.Axis(scale=line_y_sc, orientation='vertical',
label=y_label)]
bqplot_line = bqplot.Lines(x=x_arr, y=sum_arr, marker='circle',
scales={'x': line_x_sc, 'y': line_y_sc},
marker_size=32, colors='gray')
bqplot_marks = [bqplot_line]
else: # Radial profile
self._fig.axes = [bqplot.Axis(scale=line_x_sc, label='pix'),
bqplot.Axis(scale=line_y_sc, orientation='vertical',
label=comp.units or 'Value')]
if self.current_plot_type == "Radial Profile":
self._fig.title = 'Radial profile from source centroid'
x_data, y_data = _radial_profile(
phot_aperstats.data_cutout, phot_aperstats.bbox, phot_aperstats.centroid,
raw=False)
bqplot_line = bqplot.Lines(x=x_data, y=y_data, marker='circle',
scales={'x': line_x_sc, 'y': line_y_sc},
marker_size=32, colors='gray')
else: # Radial Profile (Raw)
self._fig.title = 'Raw radial profile from source centroid'
x_data, y_data = _radial_profile(
phot_aperstats.data_cutout, phot_aperstats.bbox, phot_aperstats.centroid,
raw=True)
bqplot_line = bqplot.Scatter(x=x_data, y=y_data, marker='circle',
scales={'x': line_x_sc, 'y': line_y_sc},
default_size=1, colors='gray')
# Fit Gaussian1D to radial profile data.
# mean is fixed at 0 because we recentered to centroid.
if self.fit_radial_profile:
fitter = LevMarLSQFitter()
y_max = y_data.max()
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=0, stddev=std,
fixed={'mean': True, 'amplitude': True},
bounds={'amplitude': (y_max * 0.5, y_max)})
with warnings.catch_warnings(record=True) as warns:
fit_model = fitter(gs, x_data, y_data)
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
bqplot_fit = bqplot.Lines(x=x_data, y=y_fit, marker=None,
scales={'x': line_x_sc, 'y': line_y_sc},
colors='magenta', line_style='dashed')
bqplot_marks = [bqplot_line, bqplot_fit]
else:
bqplot_marks = [bqplot_line]
self._fig.marks = bqplot_marks
except Exception as e: # pragma: no cover
self.reset_results()
self.hub.broadcast(SnackbarMessage(
f"Aperture photometry failed: {repr(e)}", color='error', sender=self))
else:
# 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 ('xcentroid', 'ycentroid', 'sky_centroid', 'sum_aper_area',
'aperture_sum_counts')):
tmp.append({'function': key, 'result': f'{x:.4e}'})
elif key == 'sky_centroid' and x is not None:
tmp.append({'function': 'RA centroid', 'result': f'{x.ra.deg:.4f} deg'})
tmp.append({'function': 'Dec centroid', 'result': f'{x.dec.deg:.4f} deg'})
elif key in ('xcentroid', 'ycentroid', '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})'})
else:
tmp.append({'function': key, 'result': str(x)})
# Also display fit results
fit_tmp = []
if fit_model is not None and isinstance(fit_model, Gaussian1D):
for param in ('fwhm', 'amplitude'): # mean is fixed at 0
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.fit_results = fit_tmp
self.result_available = True
self.radial_plot = self._fig
self.bqplot_figs_resize = [self._fig]
self.plot_available = True
# 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)[~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 = list(radial_r)
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 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