Use Photutils 3+ use_future_column_names context manager

CHANGELOG.rst

@@ -27,7 +27,7 @@ number of the code change for that issue.  These PRs can be viewed at:
 - Deprecated the TEAL helper "run" functions as well as the editpars 
   parameters in base drizzlepac functions. [#2152]
 
-- Added code to handle deprecations in photutils v3.0.0. [#2089]
+- Added code to handle deprecations in photutils v3.0.0. [#2089, #2153]
 
 
 3.11.0 (28-Apr-2026)

drizzlepac/haputils/_detection_utils.py

@@ -17,6 +17,7 @@
 import numpy as np
 from photutils.detection import find_peaks
 from photutils.utils.exceptions import NoDetectionsWarning
+from photutils import use_future_column_names
 
 
 def _filter_data(data, kernel, mode='constant', fill_value=0.0,
@@ -339,8 +340,9 @@ def _find_stars(data, kernel, threshold_eff, min_separation=None,
     with warnings.catch_warnings():
         # suppress any NoDetectionsWarning from find_peaks
         warnings.filterwarnings('ignore', category=NoDetectionsWarning)
-        tbl = find_peaks(convolved_data, threshold_eff, footprint=footprint,
-                         mask=mask)
+        with use_future_column_names():
+            tbl = find_peaks(convolved_data, threshold_eff, footprint=footprint,
+                             mask=mask)
 
     if tbl is None:
         return None

drizzlepac/haputils/align_utils.py

@@ -26,6 +26,7 @@
 from photutils.background import Background2D
 from photutils.detection import DAOStarFinder
 from photutils.utils import NoDetectionsWarning
+from photutils import use_future_column_names
 
 import stwcs
 from stwcs.wcsutil import HSTWCS
@@ -60,8 +61,7 @@
 log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout,
                             format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
 
-PHOTUTILS_GE_3 = minversion(photutils, "2.3.1.dev")
-photutils.future_column_names = True
+PHOTUTILS_GE_3 = minversion(photutils, "3.0.0")
 if PHOTUTILS_GE_3:
     X_CENTROID = 'x_centroid'
     Y_CENTROID = 'y_centroid'
@@ -803,7 +803,8 @@ def find_alignment_sources(self, output=True, dqname='DQ', crclean=False,
         daofind = DAOStarFinder(fwhm=self.kernel_fwhm, threshold=1.0e-10)
 
         log.debug("Determining source properties as src_table...")
-        src_table = daofind(sciarr, mask=bkg_mask)
+        with use_future_column_names():
+            src_table = daofind(sciarr, mask=bkg_mask)
         del sci_bkgsub, bkg_mask, sci_gauss  # explicitly clean up memory
         if src_table is not None:
             log.info("Total Number of detected sources: {}".format(len(src_table)))

drizzlepac/haputils/analyze.py

@@ -42,8 +42,7 @@
 log = logutil.create_logger(__name__, level=logutil.logging.INFO, stream=sys.stdout,
                             format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
 
-PHOTUTILS_GE_3 = minversion(photutils, "2.3.1.dev")
-photutils.future_column_names = True
+PHOTUTILS_GE_3 = minversion(photutils, "3.0.0")
 
 __all__ = ['analyze_data', 'analyze_wrapper', 'mvm_analyze_wrapper']
 

drizzlepac/haputils/astrometric_utils.py

@@ -49,6 +49,7 @@
 from astropy.utils.decorators import deprecated
 
 import photutils
+from photutils import use_future_column_names
 from photutils.segmentation import (detect_sources, SourceCatalog, detect_threshold,
                                     deblend_sources, SegmentationImage)
 
@@ -86,8 +87,7 @@
 ASTROMETRIC_CAT_ENVVAR = "ASTROMETRIC_CATALOG_URL"
 DEF_CAT_URL = 'http://gsss.stsci.edu/webservices'
 
-PHOTUTILS_GE_3 = minversion(photutils, "2.3.1.dev")
-photutils.future_column_names = True
+PHOTUTILS_GE_3 = minversion(photutils, "3.0.0")
 if PHOTUTILS_GE_3:
     X_CENTROID = 'x_centroid'
     Y_CENTROID = 'y_centroid'
@@ -816,13 +816,15 @@ def build_auto_kernel(imgarr, whtarr, fwhm=3.0, threshold=None, source_box=7,
     kern_img[:, -edge:] = 0.0
     kernel_psf = False
 
-    peaks = find_peaks(kern_img, threshold=threshold * 5, box_size=isolation_size)
+    with use_future_column_names():
+        peaks = find_peaks(kern_img, threshold=threshold * 5, box_size=isolation_size)
     if peaks is None or (peaks is not None and len(peaks) == 0):
         tmean = threshold.mean() if isinstance(threshold, np.ndarray) else threshold
         if tmean > kern_img.mean():
             kern_stats = sigma_clipped_stats(kern_img)
             threshold = kern_stats[2]
-        peaks = find_peaks(kern_img, threshold=threshold, box_size=isolation_size)
+        with use_future_column_names():
+            peaks = find_peaks(kern_img, threshold=threshold, box_size=isolation_size)
 
     if peaks is not None:
         # Sort based on peak_value to identify brightest sources for use as a kernel
@@ -929,7 +931,8 @@ def find_fwhm(psf, default_fwhm, log_level=logutil.logging.INFO):
                                               sub_shape=(11, 11),
                                               maxiters=2)
 
-        phot_results = itr_phot_obj(psf)
+        with use_future_column_names():
+            phot_results = itr_phot_obj(psf)
     except Exception as x_cept:
         log.warning(f"The find_fwhm() failed due to problem with fitting. Trying again. Exception: {x_cept}")
         return None
@@ -1206,7 +1209,8 @@ def extract_sources(img, dqmask=None, fwhm=3.0, kernel=None, photmode=None,
             detection_img[segm.data[seg_slice] == 0] = 0
 
             # Detect sources in this specific segment
-            seg_table = daofind.find_stars(detection_img)
+            with use_future_column_names():
+                seg_table = daofind.find_stars(detection_img)
 
             # Pick out brightest source only
             if src_table is None and seg_table:
@@ -1229,7 +1233,8 @@ def extract_sources(img, dqmask=None, fwhm=3.0, kernel=None, photmode=None,
                     segimg = SegmentationImage(segment.data)
                     segment_properties = SourceCatalog(detection_img, segimg)
 
-                    sat_table = segment_properties.to_table()
+                    with use_future_column_names():
+                        sat_table = segment_properties.to_table()
                     seg_table['flux'][max_row] = sat_table[flux_colname][0]
                     seg_table['peak'][max_row] = sat_table['max_value'][0]
                     xcentroid = sat_table[X_CENTROID][0]
@@ -1251,7 +1256,8 @@ def extract_sources(img, dqmask=None, fwhm=3.0, kernel=None, photmode=None,
     else:
         log.debug("Determining source properties as src_table...")
         cat = SourceCatalog(img, segm)
-        src_table = cat.to_table()
+        with use_future_column_names():
+            src_table = cat.to_table()
         # Make column names consistent with IRAFStarFinder column names
         src_table.rename_column(flux_colname, 'flux')
         src_table.rename_column(FERR_COLNAME, 'flux_err')

drizzlepac/haputils/catalog_utils.py

@@ -23,6 +23,7 @@
                                   StdBackgroundRMS)
 from photutils.detection import DAOStarFinder, IRAFStarFinder
 from photutils.utils import calc_total_error
+from photutils import use_future_column_names
 
 from stsci.tools import logutil
 from stwcs.wcsutil import HSTWCS
@@ -55,8 +56,7 @@
 log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout,
                             format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
 
-PHOTUTILS_GE_3 = minversion(photutils, "2.3.1.dev")
-photutils.future_column_names = True
+PHOTUTILS_GE_3 = minversion(photutils, "3.0.0")
 if PHOTUTILS_GE_3:
     X_CENTROID = 'x_centroid'
     Y_CENTROID = 'y_centroid'
@@ -1125,12 +1125,14 @@ def identify_sources(self, **pars):
                                                                               reg_rms_median))
                     daofind = DAOStarFinder(fwhm=source_fwhm,
                                             threshold=self.param_dict['nsigma'] * reg_rms_median)
-                    reg_sources = daofind(region, mask=self.image.inv_footprint_mask)
+                    with use_future_column_names():
+                        reg_sources = daofind(region, mask=self.image.inv_footprint_mask)
                 elif self.param_dict["starfinder_algorithm"] == "iraf":
                     log.info("IRAFStarFinder(fwhm={}, threshold={}*{})".format(source_fwhm, self.param_dict['nsigma'],
                                                                                reg_rms_median))
                     isf = IRAFStarFinder(fwhm=source_fwhm, threshold=self.param_dict['nsigma'] * reg_rms_median)
-                    reg_sources = isf(region, mask=self.image.inv_footprint_mask)
+                    with use_future_column_names():
+                        reg_sources = isf(region, mask=self.image.inv_footprint_mask)
                 elif self.param_dict["starfinder_algorithm"] == "psf":
                     log.info("UserStarFinder(fwhm={}, threshold={}*{})".format(source_fwhm, self.param_dict['nsigma'],
                                                                               reg_rms_median))
@@ -1179,8 +1181,9 @@ def identify_sources(self, **pars):
                         if self.diagnostic_mode:
                             fits.PrimaryHDU(data=region).writeto(_region_name, overwrite=True)
 
-                        reg_sources = daofind(region,
-                                              mask=self.image.inv_footprint_mask)
+                        with use_future_column_names():
+                            reg_sources = daofind(region,
+                                                  mask=self.image.inv_footprint_mask)
 
                         _region_name = self.image.imgname.replace(reg_suffix, 'starfind_sources{}.ecsv'.format(masknum))
                         if self.diagnostic_mode:
@@ -1200,7 +1203,8 @@ def identify_sources(self, **pars):
                             daofind = DAOStarFinder(
                                 fwhm=source_fwhm,
                                 threshold=self.param_dict['nsigma'] * reg_rms_median)
-                        reg_sources = daofind(region, mask=self.image.inv_footprint_mask)
+                        with use_future_column_names():
+                            reg_sources = daofind(region, mask=self.image.inv_footprint_mask)
                 else:
                     err_msg = "'{}' is not a valid 'starfinder_algorithm' parameter input in the catalog_generation parameters json file. Valid options are 'dao' for photutils.detection.DAOStarFinder() or 'iraf' for photutils.detection.IRAFStarFinder().".format(self.param_dict["starfinder_algorithm"])
                     log.error(err_msg)
@@ -2008,7 +2012,8 @@ def identify_sources(self, **pars):
             enforce_icrs_compatibility(self.source_cat)
             # Convert source_cat which is a SourceCatalog to an Astropy Table - need the data in tabular
             # form to filter out bad rows and correspondingly bad segments before the filter images are processed.
-            total_measurements_table = Table(self.source_cat.to_table(columns=['label', 'xcentroid', 'ycentroid', 'sky_centroid_icrs']))
+            with use_future_column_names():
+                total_measurements_table = Table(self.source_cat.to_table(columns=['label', 'xcentroid', 'ycentroid', 'sky_centroid_icrs']))
 
             # Filter the table to eliminate nans or inf based on the coordinates, then remove these segments from
             # the segmentation image too
@@ -2424,7 +2429,8 @@ def measure_sources(self, filter_name):
 
         enforce_icrs_compatibility(self.source_cat)
 
-        filter_measurements_table = Table(self.source_cat.to_table(columns=include_filter_cols))
+        with use_future_column_names():
+            filter_measurements_table = Table(self.source_cat.to_table(columns=include_filter_cols))
 
         # Compute aperture photometry measurements and append the columns to the measurements table
         self.do_aperture_photometry(imgarr, filter_measurements_table, self.imgname, filter_name)

drizzlepac/haputils/deconvolve_utils.py

@@ -13,6 +13,7 @@
 
 from photutils.detection import StarFinderBase, find_peaks
 from photutils.utils import NoDetectionsWarning
+from photutils import use_future_column_names
 
 from stsci.tools import fileutil as fu
 
@@ -965,8 +966,9 @@ def find_point_sources(drzname, data=None, mask=None,
             fits.PrimaryHDU(data=decmask.astype(np.uint16)).writeto(drzname.replace('.fits', '_deconv_mask.fits'),
                                                                 overwrite=True)
     # find sources in deconvolved image
-    dec_peaks = find_peaks(decdrz, threshold=0.0,
-                       mask=invmask, box_size=box_size)
+    with use_future_column_names():
+        dec_peaks = find_peaks(decdrz, threshold=0.0,
+                               mask=invmask, box_size=box_size)
 
     # Use these positions as an initial guess for the final position
     peak_mask = (drz * 0.).astype(np.uint8)
@@ -983,7 +985,8 @@ def find_point_sources(drzname, data=None, mask=None,
 
     # Use this new mask to find the actual peaks in the original input
     # but only to integer pixel precision.
-    peaks = find_peaks(drz, threshold=0., box_size=box_size // 2)
+    with use_future_column_names():
+        peaks = find_peaks(drz, threshold=0., box_size=box_size // 2)
     if len(peaks) == 0:
         peaks = None
 

drizzlepac/haputils/photometry_tools.py

@@ -64,6 +64,7 @@
 from drizzlepac.haputils import constants
 from drizzlepac.haputils.background_median import aperture_stats_tbl
 from photutils.aperture import aperture_photometry
+from photutils import use_future_column_names
 
 def iraf_style_photometry(phot_apertures, bg_apertures, data, photflam, photplam, error_array=None,
                           bg_method='mode', epadu=1.0):
@@ -107,7 +108,8 @@ def iraf_style_photometry(phot_apertures, bg_apertures, data, photflam, photplam
     if bg_method not in ['mean', 'median', 'mode']:
         raise ValueError('Invalid background method, choose either \
                           mean, median, or mode')
-    phot = aperture_photometry(data, phot_apertures, error=error_array)
+    with use_future_column_names():
+        phot = aperture_photometry(data, phot_apertures, error=error_array)
     bg_phot = aperture_stats_tbl(data, bg_apertures, sigma_clip=True)
     names = ['X-Center', 'Y-Center', 'ID']
     x, y = phot_apertures[0].positions.T

drizzlepac/haputils/svm_quality_analysis.py

@@ -49,6 +49,7 @@
 from itertools import chain
 import numpy as np
 from photutils.detection import DAOStarFinder
+from photutils import use_future_column_names
 from scipy import ndimage
 from scipy.spatial import KDTree
 
@@ -1057,7 +1058,8 @@ def find_hap_point_sources(filt_obj, log_level=logutil.logging.NOTSET):
     log.info("DAOStarFinder(fwhm={}, threshold={}*{})".format(img_obj.kernel_fwhm,
                                                               nsigma, img_obj.bkg_rms_median))
     daofind = DAOStarFinder(fwhm=img_obj.kernel_fwhm, threshold=nsigma * img_obj.bkg_rms_median)
-    sources = Table(daofind(image, mask=exclusion_mask))
+    with use_future_column_names():
+        sources = Table(daofind(image, mask=exclusion_mask))
     cat_name = filt_obj.product_basename + "_point-cat-fxm.ecsv"
 
     return {"filt_obj": filt_obj, "sources": sources, "cat_name": cat_name}