/
photometry.py
955 lines (836 loc) · 43.6 KB
/
photometry.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module provides classes to perform PSF-fitting photometry.
"""
import warnings
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.nddata.utils import overlap_slices
from astropy.stats import SigmaClip, gaussian_sigma_to_fwhm
from astropy.table import Column, Table, hstack, vstack
from astropy.utils.exceptions import AstropyUserWarning
import numpy as np
from .groupstars import DAOGroup
from .utils import (_extract_psf_fitting_names, get_grouped_psf_model,
subtract_psf)
from ..aperture import CircularAperture, aperture_photometry
from ..background import MMMBackground
from ..detection import DAOStarFinder
from ..utils.exceptions import NoDetectionsWarning
__all__ = ['BasicPSFPhotometry', 'IterativelySubtractedPSFPhotometry',
'DAOPhotPSFPhotometry']
class BasicPSFPhotometry:
"""
This class implements a PSF photometry algorithm that can find
sources in an image, group overlapping sources into a single model,
fit the model to the sources, and subtracting the models from the
image. This is roughly equivalent to the DAOPHOT routines FIND,
GROUP, NSTAR, and SUBTRACT. This implementation allows a flexible
and customizable interface to perform photometry. For instance, one
is able to use different implementations for grouping and finding
sources by using ``group_maker`` and ``finder`` respectivelly. In
addition, sky background estimation is performed by
``bkg_estimator``.
Parameters
----------
group_maker : callable or `~photutils.psf.GroupStarsBase`
``group_maker`` should be able to decide whether a given star
overlaps with any other and label them as beloging to the same
group. ``group_maker`` receives as input an
`~astropy.table.Table` object with columns named as ``id``,
``x_0``, ``y_0``, in which ``x_0`` and ``y_0`` have the same
meaning of ``xcentroid`` and ``ycentroid``. This callable must
return an `~astropy.table.Table` with columns ``id``, ``x_0``,
``y_0``, and ``group_id``. The column ``group_id`` should cotain
integers starting from ``1`` that indicate which group a given
source belongs to. See, e.g., `~photutils.psf.DAOGroup`.
bkg_estimator : callable, instance of any `~photutils.background.BackgroundBase` subclass, or None
``bkg_estimator`` should be able to compute either a scalar
background or a 2D background of a given 2D image. See, e.g.,
`~photutils.background.MedianBackground`. If None, no
background subtraction is performed.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
finder : callable or instance of any `~photutils.detection.StarFinderBase` subclasses or None
``finder`` should be able to identify stars, i.e. compute a
rough estimate of the centroids, in a given 2D image.
``finder`` receives as input a 2D image and returns an
`~astropy.table.Table` object which contains columns with names:
``id``, ``xcentroid``, ``ycentroid``, and ``flux``. In which
``id`` is an integer-valued column starting from ``1``,
``xcentroid`` and ``ycentroid`` are center position estimates of
the sources and ``flux`` contains flux estimates of the sources.
See, e.g., `~photutils.detection.DAOStarFinder`. If ``finder``
is ``None``, initial guesses for positions of objects must be
provided.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
aperture_radius : `None` or float
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. ``aperture_radius`` must
be set if initial flux guesses are not input to the photometry
class via the ``init_guesses`` keyword. For tabular PSF models
(e.g. an `EPSFModel`), you must input the ``aperture_radius``
keyword. For analytical PSF models, alternatively you may
define a FWHM attribute on your input psf_model.
extra_output_cols : list of str, optional
List of additional columns for parameters derived by any of the
intermediate fitting steps (e.g., ``finder``), such as roundness or
sharpness.
Notes
-----
Note that an ambiguity arises whenever ``finder`` and
``init_guesses`` (keyword argument for ``do_photometry``) are both
not ``None``. In this case, ``finder`` is ignored and initial
guesses are taken from ``init_guesses``. In addition, an warning is
raised to remaind the user about this behavior.
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
finder=None, fitter=LevMarLSQFitter(), aperture_radius=None,
extra_output_cols=None):
self.group_maker = group_maker
self.bkg_estimator = bkg_estimator
self.psf_model = psf_model
self.fitter = fitter
self.fitshape = fitshape
self.finder = finder
self.aperture_radius = aperture_radius
self._pars_to_set = None
self._pars_to_output = None
self._residual_image = None
self._extra_output_cols = extra_output_cols
@property
def fitshape(self):
return self._fitshape
@fitshape.setter
def fitshape(self, value):
value = np.asarray(value)
# assume a lone value should mean both axes
if value.shape == ():
value = np.array((value, value))
if value.size == 2:
if np.all(value) > 0:
if np.all(value % 2) == 1:
self._fitshape = tuple(value)
else:
raise ValueError('fitshape must be odd integer-valued, '
'received fitshape = {}'.format(value))
else:
raise ValueError('fitshape must have positive elements, '
'received fitshape = {}'.format(value))
else:
raise ValueError('fitshape must have two dimensions, '
'received fitshape = {}'.format(value))
@property
def aperture_radius(self):
return self._aperture_radius
@aperture_radius.setter
def aperture_radius(self, value):
if isinstance(value, (int, float)) and value > 0:
self._aperture_radius = value
elif value is None:
self._aperture_radius = value
else:
raise ValueError('aperture_radius must be a positive number')
def get_residual_image(self):
"""
Returns an image that is the result of the subtraction between
the original image and the fitted sources.
Returns
-------
residual_image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
"""
return self._residual_image
def __call__(self, image, init_guesses=None):
"""
Performs PSF photometry. See `do_photometry` for more details
including the `__call__` signature.
"""
return self.do_photometry(image, init_guesses)
def do_photometry(self, image, init_guesses=None):
"""
Perform PSF photometry in ``image``.
This method assumes that ``psf_model`` has centroids and flux
parameters which will be fitted to the data provided in
``image``. A compound model, in fact a sum of ``psf_model``,
will be fitted to groups of stars automatically identified by
``group_maker``. Also, ``image`` is not assumed to be background
subtracted. If ``init_guesses`` are not ``None`` then this
method uses ``init_guesses`` as initial guesses for the
centroids. If the centroid positions are set as ``fixed`` in the
PSF model ``psf_model``, then the optimizer will only consider
the flux as a variable.
Parameters
----------
image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
Image to perform photometry.
init_guesses: `~astropy.table.Table`
Table which contains the initial guesses (estimates) for the
set of parameters. Columns 'x_0' and 'y_0' which represent
the positions (in pixel coordinates) for each object must be
present. 'flux_0' can also be provided to set initial
fluxes. If 'flux_0' is not provided, aperture photometry is
used to estimate initial values for the fluxes. Additional
columns of the form '<parametername>_0' will be used to set
the initial guess for any parameters of the ``psf_model``
model that are not fixed. If ``init_guesses`` supplied with
``extra_output_cols`` the initial values are used; if the columns
specified in ``extra_output_cols`` are not given in
``init_guesses`` then NaNs will be returned.
Returns
-------
output_tab : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process. Uncertainties on the fitted parameters
are reported as columns called ``<paramname>_unc`` provided
that the fitter object contains a dictionary called
``fit_info`` with the key ``param_cov``, which contains the
covariance matrix. If ``param_cov`` is not present,
uncertanties are not reported.
"""
if self.bkg_estimator is not None:
image = image - self.bkg_estimator(image)
if self.aperture_radius is None:
if hasattr(self.psf_model, 'fwhm'):
self.aperture_radius = self.psf_model.fwhm.value
elif hasattr(self.psf_model, 'sigma'):
self.aperture_radius = (self.psf_model.sigma.value *
gaussian_sigma_to_fwhm)
if self.aperture_radius is None:
if init_guesses is None:
raise ValueError('aperture_radius was not input and could '
'not be determined by the input psf_model '
'(e.g. an EPSFModel). For tabular PSF '
'models, you must input the aperture_radius '
'keyword. For analytical PSF models, you '
'must either input the aperture_radius '
'keyword or define a fwhm or sigma '
'attribute on your input psf_model.')
if (init_guesses is not None and
'flux_0' not in init_guesses.colnames):
raise ValueError('init_guesses were input, but the "flux_0" '
'column was not present. Initial fluxes '
'cannot be calculated because '
'aperture_radius must was not input and '
'could not be determined by the input '
'psf_model (e.g. an EPSFModel). For '
'analytical PSF models, you must either '
'input the aperture_radius keyword or '
'define a fwhm or sigma attribute on your '
'input psf_model.')
if init_guesses is not None:
# make sure the code does not modify user's input
init_guesses = init_guesses.copy()
if self.finder is not None:
warnings.warn('Both init_guesses and finder are different '
'than None, which is ambiguous. finder is '
'going to be ignored.', AstropyUserWarning)
if 'flux_0' not in init_guesses.colnames:
positions = np.transpose((init_guesses['x_0'],
init_guesses['y_0']))
apertures = CircularAperture(positions,
r=self.aperture_radius)
init_guesses['flux_0'] = aperture_photometry(
image, apertures)['aperture_sum']
# if extra_output_cols have been given, check whether init_guesses
# was supplied with extra_output_cols pre-attached and populate
# columns not given with NaNs
if self._extra_output_cols is not None:
for col_name in self._extra_output_cols:
if col_name not in init_guesses.colnames:
init_guesses[col_name] = np.full(len(init_guesses),
np.nan)
else:
if self.finder is None:
raise ValueError('Finder cannot be None if init_guesses are '
'not given.')
sources = self.finder(image)
if len(sources) > 0:
positions = np.transpose((sources['xcentroid'],
sources['ycentroid']))
apertures = CircularAperture(positions,
r=self.aperture_radius)
sources['aperture_flux'] = aperture_photometry(
image, apertures)['aperture_sum']
# init_guesses should be the initial 3 required parameters --
# x, y, flux -- and then concatentated with any additional
# sources, if there are any
init_guesses = Table(names=['x_0', 'y_0', 'flux_0'],
data=[sources['xcentroid'],
sources['ycentroid'],
sources['aperture_flux']])
# Currently only needed for the finder, as group_maker and
# nstar return the original Table with new columns, unlike
# finder
self._get_additional_columns(sources, init_guesses)
self._define_fit_param_names()
for p0, param in self._pars_to_set.items():
if p0 not in init_guesses.colnames:
init_guesses[p0] = (len(init_guesses) *
[getattr(self.psf_model, param).value])
star_groups = self.group_maker(init_guesses)
output_tab, self._residual_image = self.nstar(image, star_groups)
star_groups = star_groups.group_by('group_id')
output_tab = hstack([star_groups, output_tab])
return output_tab
def nstar(self, image, star_groups):
"""
Fit, as appropriate, a compound or single model to the given
``star_groups``. Groups are fitted sequentially from the
smallest to the biggest. In each iteration, ``image`` is
subtracted by the previous fitted group.
Parameters
----------
image : numpy.ndarray
Background-subtracted image.
star_groups : `~astropy.table.Table`
This table must contain the following columns: ``id``,
``group_id``, ``x_0``, ``y_0``, ``flux_0``. ``x_0`` and
``y_0`` are initial estimates of the centroids and
``flux_0`` is an initial estimate of the flux. Additionally,
columns named as ``<param_name>_0`` are required if any
other parameter in the psf model is free (i.e., the
``fixed`` attribute of that parameter is ``False``).
Returns
-------
result_tab : `~astropy.table.Table`
Astropy table that contains photometry results.
image : numpy.ndarray
Residual image.
"""
result_tab = Table()
for param_tab_name in self._pars_to_output.keys():
result_tab.add_column(Column(name=param_tab_name))
unc_tab = Table()
for param, isfixed in self.psf_model.fixed.items():
if not isfixed:
unc_tab.add_column(Column(name=param + "_unc"))
y, x = np.indices(image.shape)
star_groups = star_groups.group_by('group_id')
for n in range(len(star_groups.groups)):
group_psf = get_grouped_psf_model(self.psf_model,
star_groups.groups[n],
self._pars_to_set)
usepixel = np.zeros_like(image, dtype=bool)
for row in star_groups.groups[n]:
usepixel[overlap_slices(large_array_shape=image.shape,
small_array_shape=self.fitshape,
position=(row['y_0'], row['x_0']),
mode='trim')[0]] = True
fit_model = self.fitter(group_psf, x[usepixel], y[usepixel],
image[usepixel])
param_table = self._model_params2table(fit_model,
len(star_groups.groups[n]))
result_tab = vstack([result_tab, param_table])
if 'param_cov' in self.fitter.fit_info.keys():
unc_tab = vstack([unc_tab,
self._get_uncertainties(
len(star_groups.groups[n]))])
try:
from astropy.nddata.utils import NoOverlapError
except ImportError:
raise ImportError("astropy 1.1 or greater is required in "
"order to use this class.")
# do not subtract if the fitting did not go well
try:
image = subtract_psf(image, self.psf_model, param_table,
subshape=self.fitshape)
except NoOverlapError:
pass
if 'param_cov' in self.fitter.fit_info.keys():
result_tab = hstack([result_tab, unc_tab])
return result_tab, image
def _get_additional_columns(self, in_table, out_table):
"""
Function to parse additional columns from ``in_table`` and add them to
``out_table``.
"""
if self._extra_output_cols is not None:
for col_name in self._extra_output_cols:
if col_name in in_table.colnames:
out_table[col_name] = in_table[col_name]
def _define_fit_param_names(self):
"""
Convenience function to define mappings between the names of the
columns in the initial guess table (and the name of the fitted
parameters) and the actual name of the parameters in the model.
This method sets the following parameters on the ``self`` object:
* ``pars_to_set`` : Dict which maps the names of the parameters
initial guesses to the actual name of the parameter in the
model.
* ``pars_to_output`` : Dict which maps the names of the fitted
parameters to the actual name of the parameter in the model.
"""
xname, yname, fluxname = _extract_psf_fitting_names(self.psf_model)
self._pars_to_set = {'x_0': xname, 'y_0': yname, 'flux_0': fluxname}
self._pars_to_output = {'x_fit': xname, 'y_fit': yname,
'flux_fit': fluxname}
for p, isfixed in self.psf_model.fixed.items():
p0 = p + '_0'
pfit = p + '_fit'
if p not in (xname, yname, fluxname) and not isfixed:
self._pars_to_set[p0] = p
self._pars_to_output[pfit] = p
def _get_uncertainties(self, star_group_size):
"""
Retrieve uncertainties on fitted parameters from the fitter
object.
Parameters
----------
star_group_size : int
Number of stars in the given group.
Returns
-------
unc_tab : `~astropy.table.Table`
Table which contains uncertainties on the fitted parameters.
The uncertainties are reported as one standard deviation.
"""
unc_tab = Table()
for param_name in self.psf_model.param_names:
if not self.psf_model.fixed[param_name]:
unc_tab.add_column(Column(name=param_name + "_unc",
data=np.empty(star_group_size)))
if 'param_cov' in self.fitter.fit_info.keys():
if self.fitter.fit_info['param_cov'] is not None:
k = 0
n_fit_params = len(unc_tab.colnames)
for i in range(star_group_size):
unc_tab[i] = np.sqrt(np.diag(
self.fitter.fit_info['param_cov'])
)[k: k + n_fit_params]
k = k + n_fit_params
return unc_tab
def _model_params2table(self, fit_model, star_group_size):
"""
Place fitted parameters into an astropy table.
Parameters
----------
fit_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models
in this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other
suitable 2D model.
star_group_size : int
Number of stars in the given group.
Returns
-------
param_tab : `~astropy.table.Table`
Table that contains the fitted parameters.
"""
param_tab = Table()
for param_tab_name in self._pars_to_output.keys():
param_tab.add_column(Column(name=param_tab_name,
data=np.empty(star_group_size)))
if star_group_size > 1:
for i in range(star_group_size):
for param_tab_name, param_name in self._pars_to_output.items():
param_tab[param_tab_name][i] = getattr(fit_model,
param_name +
'_' + str(i)).value
else:
for param_tab_name, param_name in self._pars_to_output.items():
param_tab[param_tab_name] = getattr(fit_model,
param_name).value
return param_tab
class IterativelySubtractedPSFPhotometry(BasicPSFPhotometry):
"""
This class implements an iterative algorithm to perform point spread
function photometry in crowded fields. This consists of applying a
loop of find sources, make groups, fit groups, subtract groups, and
then repeat until no more stars are detected or a given number of
iterations is reached.
Parameters
----------
group_maker : callable or `~photutils.psf.GroupStarsBase`
``group_maker`` should be able to decide whether a given star
overlaps with any other and label them as beloging to the same
group. ``group_maker`` receives as input an
`~astropy.table.Table` object with columns named as ``id``,
``x_0``, ``y_0``, in which ``x_0`` and ``y_0`` have the same
meaning of ``xcentroid`` and ``ycentroid``. This callable must
return an `~astropy.table.Table` with columns ``id``, ``x_0``,
``y_0``, and ``group_id``. The column ``group_id`` should cotain
integers starting from ``1`` that indicate which group a given
source belongs to. See, e.g., `~photutils.psf.DAOGroup`.
bkg_estimator : callable, instance of any `~photutils.background.BackgroundBase` subclass, or None
``bkg_estimator`` should be able to compute either a scalar
background or a 2D background of a given 2D image. See, e.g.,
`~photutils.background.MedianBackground`. If None, no
background subtraction is performed.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
finder : callable or instance of any `~photutils.detection.StarFinderBase` subclasses
``finder`` should be able to identify stars, i.e. compute a
rough estimate of the centroids, in a given 2D image.
``finder`` receives as input a 2D image and returns an
`~astropy.table.Table` object which contains columns with names:
``id``, ``xcentroid``, ``ycentroid``, and ``flux``. In which
``id`` is an integer-valued column starting from ``1``,
``xcentroid`` and ``ycentroid`` are center position estimates of
the sources and ``flux`` contains flux estimates of the sources.
See, e.g., `~photutils.detection.DAOStarFinder` or
`~photutils.detection.IRAFStarFinder`.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
aperture_radius : float
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. If ``None``, one FWHM will
be used if it can be determined from the ```psf_model``.
niters : int or None
Number of iterations to perform of the loop FIND, GROUP,
SUBTRACT, NSTAR. If None, iterations will proceed until no more
stars remain. Note that in this case it is *possible* that the
loop will never end if the PSF has structure that causes
subtraction to create new sources infinitely.
extra_output_cols : list of str, optional
List of additional columns for parameters derived by any of the
intermediate fitting steps (e.g., ``finder``), such as roundness or
sharpness.
Notes
-----
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
finder, fitter=LevMarLSQFitter(), niters=3,
aperture_radius=None, extra_output_cols=None):
super().__init__(group_maker, bkg_estimator, psf_model, fitshape,
finder, fitter, aperture_radius, extra_output_cols)
self.niters = niters
@property
def niters(self):
return self._niters
@niters.setter
def niters(self, value):
if value is None:
self._niters = None
else:
try:
if value <= 0:
raise ValueError('niters must be positive.')
else:
self._niters = int(value)
except ValueError:
raise ValueError('niters must be None or an integer or '
'convertable into an integer.')
@property
def finder(self):
return self._finder
@finder.setter
def finder(self, value):
if value is None:
raise ValueError("finder cannot be None for "
"IterativelySubtractedPSFPhotometry - you may "
"want to use BasicPSFPhotometry. Please see the "
"Detection section on photutils documentation.")
else:
self._finder = value
def do_photometry(self, image, init_guesses=None):
"""
Perform PSF photometry in ``image``.
This method assumes that ``psf_model`` has centroids and flux
parameters which will be fitted to the data provided in
``image``. A compound model, in fact a sum of ``psf_model``,
will be fitted to groups of stars automatically identified by
``group_maker``. Also, ``image`` is not assumed to be background
subtracted. If ``init_guesses`` are not ``None`` then this
method uses ``init_guesses`` as initial guesses for the
centroids. If the centroid positions are set as ``fixed`` in the
PSF model ``psf_model``, then the optimizer will only consider
the flux as a variable.
Parameters
----------
image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
Image to perform photometry.
init_guesses: `~astropy.table.Table`
Table which contains the initial guesses (estimates) for the
set of parameters. Columns 'x_0' and 'y_0' which represent
the positions (in pixel coordinates) for each object must be
present. 'flux_0' can also be provided to set initial
fluxes. If 'flux_0' is not provided, aperture photometry is
used to estimate initial values for the fluxes. Additional
columns of the form '<parametername>_0' will be used to set
the initial guess for any parameters of the ``psf_model``
model that are not fixed. If ``init_guesses`` supplied with
``extra_output_cols`` the initial values are used; if the columns
specified in ``extra_output_cols`` are not given in
``init_guesses`` then NaNs will be returned.
Returns
-------
output_table : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process. Uncertainties on the fitted parameters
are reported as columns called ``<paramname>_unc`` provided
that the fitter object contains a dictionary called
``fit_info`` with the key ``param_cov``, which contains the
covariance matrix.
"""
if init_guesses is not None:
table = super().do_photometry(image, init_guesses)
table['iter_detected'] = np.ones(table['x_fit'].shape,
dtype=np.int32)
# n_start = 2 because it starts in the second iteration
# since the first iteration is above
output_table = self._do_photometry(init_guesses.colnames,
n_start=2)
output_table = vstack([table, output_table])
else:
if self.bkg_estimator is not None:
self._residual_image = image - self.bkg_estimator(image)
else:
self._residual_image = image
if self.aperture_radius is None:
if hasattr(self.psf_model, 'fwhm'):
self.aperture_radius = self.psf_model.fwhm.value
elif hasattr(self.psf_model, 'sigma'):
self.aperture_radius = (self.psf_model.sigma.value *
gaussian_sigma_to_fwhm)
output_table = self._do_photometry(['x_0', 'y_0', 'flux_0'])
return output_table
def _do_photometry(self, param_tab, n_start=1):
"""
Helper function which performs the iterations of the photometry
process.
Parameters
----------
param_names : list
Names of the columns which represent the initial guesses.
For example, ['x_0', 'y_0', 'flux_0'], for intial guesses on
the center positions and the flux.
n_start : int
Integer representing the start index of the iteration. It
is 1 if init_guesses are None, and 2 otherwise.
Returns
-------
output_table : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process.
"""
output_table = Table()
self._define_fit_param_names()
for (init_parname, fit_parname) in zip(self._pars_to_set.keys(),
self._pars_to_output.keys()):
output_table.add_column(Column(name=init_parname))
output_table.add_column(Column(name=fit_parname))
sources = self.finder(self._residual_image)
n = n_start
while((sources is not None and len(sources) > 0) and
(self.niters is None or n <= self.niters)):
positions = np.transpose((sources['xcentroid'],
sources['ycentroid']))
apertures = CircularAperture(positions,
r=self.aperture_radius)
sources['aperture_flux'] = aperture_photometry(
self._residual_image, apertures)['aperture_sum']
init_guess_tab = Table(names=['id', 'x_0', 'y_0', 'flux_0'],
data=[sources['id'], sources['xcentroid'],
sources['ycentroid'],
sources['aperture_flux']])
self._get_additional_columns(sources, init_guess_tab)
for param_tab_name, param_name in self._pars_to_set.items():
if param_tab_name not in (['x_0', 'y_0', 'flux_0']):
init_guess_tab.add_column(
Column(name=param_tab_name,
data=(getattr(self.psf_model,
param_name) *
np.ones(len(sources)))))
star_groups = self.group_maker(init_guess_tab)
table, self._residual_image = super().nstar(
self._residual_image, star_groups)
star_groups = star_groups.group_by('group_id')
table = hstack([star_groups, table])
table['iter_detected'] = n*np.ones(table['x_fit'].shape,
dtype=np.int32)
output_table = vstack([output_table, table])
# do not warn if no sources are found beyond the first iteration
with warnings.catch_warnings():
warnings.simplefilter('ignore', NoDetectionsWarning)
sources = self.finder(self._residual_image)
n += 1
return output_table
class DAOPhotPSFPhotometry(IterativelySubtractedPSFPhotometry):
"""
This class implements an iterative algorithm based on the DAOPHOT
algorithm presented by Stetson (1987) to perform point spread
function photometry in crowded fields. This consists of applying a
loop of find sources, make groups, fit groups, subtract groups, and
then repeat until no more stars are detected or a given number of
iterations is reached.
Basically, this classes uses
`~photutils.psf.IterativelySubtractedPSFPhotometry`, but with
grouping, finding, and background estimation routines defined a
priori. More precisely, this class uses `~photutils.psf.DAOGroup`
for grouping, `~photutils.detection.DAOStarFinder` for finding
sources, and `~photutils.background.MMMBackground` for background
estimation. Those classes are based on GROUP, FIND, and SKY routines
used in DAOPHOT, respectively.
The parameter ``crit_separation`` is associated with
`~photutils.psf.DAOGroup`. ``sigma_clip`` is associated with
`~photutils.background.MMMBackground`. ``threshold`` and ``fwhm``
are associated with `~photutils.detection.DAOStarFinder`.
Parameters from ``ratio`` to ``roundhi`` are also associated with
`~photutils.detection.DAOStarFinder`.
Parameters
----------
crit_separation : float or int
Distance, in units of pixels, such that any two stars separated
by less than this distance will be placed in the same group.
threshold : float
The absolute image value above which to select sources.
fwhm : float
The full-width half-maximum (FWHM) of the major axis of the
Gaussian kernel in units of pixels.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
sigma : float, optional
Number of standard deviations used to perform sigma clip with a
`astropy.stats.SigmaClip` object.
ratio : float, optional
The ratio of the minor to major axis standard deviations of the
Gaussian kernel. ``ratio`` must be strictly positive and less
than or equal to 1.0. The default is 1.0 (i.e., a circular
Gaussian kernel).
theta : float, optional
The position angle (in degrees) of the major axis of the
Gaussian kernel measured counter-clockwise from the positive x
axis.
sigma_radius : float, optional
The truncation radius of the Gaussian kernel in units of sigma
(standard deviation) [``1 sigma = FWHM /
(2.0*sqrt(2.0*log(2.0)))``].
sharplo : float, optional
The lower bound on sharpness for object detection.
sharphi : float, optional
The upper bound on sharpness for object detection.
roundlo : float, optional
The lower bound on roundess for object detection.
roundhi : float, optional
The upper bound on roundess for object detection.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
niters : int or None
Number of iterations to perform of the loop FIND, GROUP,
SUBTRACT, NSTAR. If None, iterations will proceed until no more
stars remain. Note that in this case it is *possible* that the
loop will never end if the PSF has structure that causes
subtraction to create new sources infinitely.
aperture_radius : float
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. If ``None``, one FWHM will
be used if it can be determined from the ```psf_model``.
extra_output_cols : list of str, optional
List of additional columns for parameters derived by any of the
intermediate fitting steps (e.g., ``finder``), such as roundness or
sharpness.
Notes
-----
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, crit_separation, threshold, fwhm, psf_model, fitshape,
sigma=3., ratio=1.0, theta=0.0, sigma_radius=1.5,
sharplo=0.2, sharphi=1.0, roundlo=-1.0, roundhi=1.0,
fitter=LevMarLSQFitter(),
niters=3, aperture_radius=None, extra_output_cols=None):
self.crit_separation = crit_separation
self.threshold = threshold
self.fwhm = fwhm
self.sigma = sigma
self.ratio = ratio
self.theta = theta
self.sigma_radius = sigma_radius
self.sharplo = sharplo
self.sharphi = sharphi
self.roundlo = roundlo
self.roundhi = roundhi
group_maker = DAOGroup(crit_separation=self.crit_separation)
bkg_estimator = MMMBackground(sigma_clip=SigmaClip(sigma=self.sigma))
finder = DAOStarFinder(threshold=self.threshold, fwhm=self.fwhm,
ratio=self.ratio, theta=self.theta,
sigma_radius=self.sigma_radius,
sharplo=self.sharplo, sharphi=self.sharphi,
roundlo=self.roundlo, roundhi=self.roundhi)
super().__init__(group_maker=group_maker, bkg_estimator=bkg_estimator,
psf_model=psf_model, fitshape=fitshape,
finder=finder, fitter=fitter, niters=niters,
aperture_radius=aperture_radius,
extra_output_cols=extra_output_cols)