Forwardport PR 261 to main (ALOHA 2024: Clean up photometry contents)

09-Photutils/01-aperture_basics.ipynb

@@ -76,7 +76,7 @@
     }
    },
    "source": [
-    "We'll start by reading science data and error arrays from FITS files located in the [**data/**](data) subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
+    "We'll start by reading science data and error arrays from FITS files located in the **data/** subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
    ]
   },
   {
@@ -219,7 +219,7 @@
     "\n",
     "Further, there are two types of aperture classes, defined either with pixel or sky (celestial) coordinates.\n",
     "\n",
-    "These are the names of the [aperture classes](https://photutils.readthedocs.io/en/latest/aperture.html#classes) that are defined in pixel coordinates:\n",
+    "These are the names of the [aperture classes](https://photutils.readthedocs.io/en/stable/user_guide/aperture.html#apertures) that are defined in pixel coordinates:\n",
     "\n",
     "* `CircularAperture`\n",
     "* `CircularAnnulus`\n",
@@ -298,7 +298,7 @@
    "source": [
     "First, let's define a circular aperture at a given (x, y) pixel position and radius (in pixels).\n",
     "\n",
-    "Photutils has many tools for detecting sources (e.g., see [photutils.detection](https://photutils.readthedocs.io/en/latest/detection.html) and [photutils.segmentation](https://photutils.readthedocs.io/en/latest/segmentation.html)) and measuring source positions (e.g., [photutils.centroids](https://photutils.readthedocs.io/en/latest/centroids.html)). For this example, we'll assume we've already measured the source centroid."
+    "Photutils has many tools for detecting sources (e.g., see [photutils.detection](https://photutils.readthedocs.io/en/stable/user_guide/detection.html) and [photutils.segmentation](https://photutils.readthedocs.io/en/stable/user_guide/segmentation.html)) and measuring source positions (e.g., [photutils.centroids](https://photutils.readthedocs.io/en/stable/user_guide/centroids.html)). For this example, we'll assume we've already measured the source centroid."
    ]
   },
   {
@@ -322,7 +322,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our `aperture` variable is now an instance of a [CircularAperture](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.CircularAperture.html#photutils.aperture.CircularAperture) object."
+    "Our `aperture` variable is now an instance of a [CircularAperture](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.CircularAperture.html) object."
    ]
   },
   {
@@ -365,11 +365,11 @@
    "source": [
     "Now let's perform photometry on the XDF data using this circular aperture.\n",
     "\n",
-    "After the aperture object is created, we can  perform the photometry using the [aperture_photometry](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.aperture_photometry.html#photutils.aperture.aperture_photometry) function or the [ApertureStats](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.ApertureStats.html#photutils.aperture.ApertureStats) class.  We'll start by using the `aperture_photometry` function.  The `ApertureStats` class will be introduced later in this notebook.\n",
+    "After the aperture object is created, we can  perform the photometry using the [aperture_photometry](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.aperture_photometry.html) function or the [ApertureStats](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.ApertureStats.html) class.  We'll start by using the `aperture_photometry` function.  The `ApertureStats` class will be introduced later in this notebook.\n",
     "\n",
     "In the most basic case, we simply need to input the data array and the aperture object. The default aperture overlap method is `'exact'`.\n",
     "\n",
-    "Note that the input data array is assumed to be background subtracted.  If that is not the case, please see the documentation for the [photutils.background](https://photutils.readthedocs.io/en/latest/background.html) subpackage for tools to help subtract the background.\n",
+    "Note that the input data array is assumed to be background subtracted.  If that is not the case, please see the documentation for the [photutils.background](https://photutils.readthedocs.io/en/stable/user_guide/background.html) subpackage for tools to help subtract the background.\n",
     "\n",
     "The background has already been subtracted from our XDF example data, so we skip that step in this example.\n",
     "\n",
@@ -769,7 +769,7 @@
    },
    "outputs": [],
    "source": [
-    "f160w_zpt = 25.9463  # HST/WFC3 F160W ABmag zero point for flux in e-/s\n",
+    "f160w_zpt = 25.936  # HST/WFC3 F160W ABmag zero point for flux in e-/s\n",
     "\n",
     "# NOTE that the log10 function can be applied only to dimensionless quantities,\n",
     "# so we use the value attribute for Quantity objects to remove the units of the aperture sum\n",
@@ -801,7 +801,7 @@
     "sky_coord = wcs.pixel_to_world(x, y)  # a SkyCoord object\n",
     "\n",
     "# we can add the astropy SkyCoord object directly to the table\n",
-    "phot['sky_coord'] = sky_coord\n",
+    "phot['sky_center'] = sky_coord\n",
     "phot"
    ]
   },
@@ -811,7 +811,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "phot['sky_coord']  # a SkyCoord object"
+    "phot['sky_center']  # a SkyCoord object"
    ]
   },
   {
@@ -884,7 +884,7 @@
    "outputs": [],
    "source": [
     "# and the SkyCoord array\n",
-    "tbl['sky_coord']"
+    "tbl['sky_center']"
    ]
   },
   {
@@ -978,7 +978,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [ApertureStats](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.ApertureStats.html#photutils.aperture.ApertureStats) class can be used to create a catalog of statistics and properties for pixels within an aperture, including aperture photometry. \n",
+    "The [ApertureStats](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.ApertureStats.html) class can be used to create a catalog of statistics and properties for pixels within an aperture, including aperture photometry. \n",
     "\n",
     "It can calculate many properties, including statistics like `min`, `max`, `mean`, `median`, `std`, `sum_aper_area`, and `sum`. It also can be used to calculate morphological properties like `centroid`, `fwhm`, `semimajor_sigma`, `semiminor_sigma`, `orientation`, and `eccentricity`. Please see `ApertureStats` for the the complete list of properties that can be calculated. The properties can be accessed using `ApertureStats` attributes or output to an Astropy `QTable` using the `to_table` method.\n",
     "\n",
@@ -1122,7 +1122,7 @@
     "\n",
     "Hints:\n",
     "\n",
-    "* The HST/WFC3 F160W ABmag zero point for data in units of e-/s is `25.9463`."
+    "* The HST/WFC3 F160W ABmag zero point for data in units of e-/s is `25.936`."
    ]
   },
   {
@@ -1195,7 +1195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

09-Photutils/01-aperture_exercise_solution.py

@@ -1,8 +1,6 @@
 import astropy.units as u
-import matplotlib.pyplot as plt
 import numpy as np
 from astropy.io import fits
-from astropy.visualization import simple_norm
 from photutils.aperture import CircularAperture, aperture_photometry
 
 sci_fn = 'data/xdf_hst_wfc3ir_60mas_f160w_sci.fits'
@@ -20,7 +18,7 @@
 snr = phot['aperture_sum'] / phot['aperture_sum_err']
 phot['snr'] = snr
 
-f160w_zpt = 25.9463
+f160w_zpt = 25.936
 abmag = -2.5 * np.log10(phot['aperture_sum'].value) + f160w_zpt
 phot['f160w_abmag'] = abmag
 

09-Photutils/01-aperture_exercise_solution_plot.py

@@ -1,3 +1,6 @@
+import matplotlib.pyplot as plt
+from astropy.visualization import simple_norm
+
 plt.figure(figsize=(5, 5))
 norm = simple_norm(data, 'sqrt', percent=99.0)
 plt.imshow(data, norm=norm)

09-Photutils/02-aperture_extended.ipynb

@@ -87,7 +87,7 @@
     }
    },
    "source": [
-    "We'll start by reading science data and error arrays from FITS files located in the [**data/**](data) subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
+    "We'll start by reading science data and error arrays from FITS files located in the **data/** subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
    ]
   },
   {
@@ -502,7 +502,7 @@
     "\n",
     "Instead of generating a big table with many columns (one for each radius), we'll simply loop over the apertures and extract the fluxes from individual tables.\n",
     "\n",
-    "In this example, we manually compute the curve of growth using aperture photometry.  However, note that Photutils has a [Radial Profiles subpackage](https://photutils.readthedocs.io/en/latest/profiles.html) that makes it easy to compute a radial profile or a curve of growth."
+    "In this example, we manually compute the curve of growth using aperture photometry.  However, note that Photutils has a [Radial Profiles subpackage](https://photutils.readthedocs.io/en/stable/user_guide/profiles.html) that makes it easy to compute a radial profile or a curve of growth."
    ]
   },
   {
@@ -584,9 +584,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All `PixelAperture` objects have a `to_mask` method that returns an [ApertureMask](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.ApertureMask.html#photutils.aperture.ApertureMask) object (for a single aperture position) or a list of `ApertureMask` objects, one for each aperture position.\n",
+    "All `PixelAperture` objects have a `to_mask` method that returns an [ApertureMask](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.ApertureMask.html) object (for a single aperture position) or a list of `ApertureMask` objects, one for each aperture position.\n",
     "\n",
-    "The `ApertureMask` object contains a cutout of the aperture-mask weights and a [BoundingBox](https://photutils.readthedocs.io/en/latest/api/photutils.aperture.BoundingBox.html#photutils.aperture.BoundingBox) object that provides the bounding box where the mask is to be applied.  The `ApertureMask` object is useful for extracting the data values within an aperture, either as a 1D or 2D array."
+    "The `ApertureMask` object contains a cutout of the aperture-mask weights and a [BoundingBox](https://photutils.readthedocs.io/en/stable/api/photutils.aperture.BoundingBox.html) object that provides the bounding box where the mask is to be applied.  The `ApertureMask` object is useful for extracting the data values within an aperture, either as a 1D or 2D array."
    ]
   },
   {
@@ -802,7 +802,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

09-Photutils/03-aperture_local_bkgsub.ipynb

@@ -74,7 +74,7 @@
     }
    },
    "source": [
-    "We'll start by reading science data and error arrays from FITS files located in the [**data/**](data) subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
+    "We'll start by reading science data and error arrays from FITS files located in the **data/** subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
    ]
   },
   {
@@ -460,7 +460,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

09-Photutils/04-segmentation.ipynb

@@ -75,7 +75,7 @@
     }
    },
    "source": [
-    "We'll start by reading science data and error arrays from FITS files located in the [**data/**](data) subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
+    "We'll start by reading science data and error arrays from FITS files located in the **data/** subdirectory.  The FITS files contain 2D cutout images from the [Hubble Extreme-Deep Field (XDF)](https://archive.stsci.edu/prepds/xdf/) taken with the [Wide Field Camera 3 (WFC3)](https://www.stsci.edu/hst/instrumentation/wfc3) IR channel in the F160W filter (centered at ~1.6 $\\mu m$)."
    ]
   },
   {
@@ -243,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a segmentation image ([SegmentationImage](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SegmentationImage.html#photutils.segmentation.SegmentationImage) object).  The segmentation image is an array with the same size as the science image in which each detected source is labeled with a unique integer value (>= 1).  Background pixels have a value of 0.  As a simple example, a segmentation map containing two distinct sources (labeled 1 and 2) might look like this:\n",
+    "The result is a segmentation image ([SegmentationImage](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SegmentationImage.html) object).  The segmentation image is an array with the same size as the science image in which each detected source is labeled with a unique integer value (>= 1).  Background pixels have a value of 0.  As a simple example, a segmentation map containing two distinct sources (labeled 1 and 2) might look like this:\n",
     "\n",
     "```\n",
     "0 0 0 0 0 0 0 0 0 0\n",
@@ -294,7 +294,7 @@
     }
    },
    "source": [
-    "Comparing the data array with the segmentation image, we see that several detected sources were blended together.  We can deblend them using the [deblend_sources](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.deblend_sources.html#photutils.segmentation.deblend_sources) function, which uses a combination of multi-thresholding and watershed segmentation.\n",
+    "Comparing the data array with the segmentation image, we see that several detected sources were blended together.  We can deblend them using the [deblend_sources](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.deblend_sources.html) function, which uses a combination of multi-thresholding and watershed segmentation.\n",
     "\n",
     "The amount of deblending can be controlled with the two `deblend_sources` keywords `nlevels` and `contrast`:\n",
     "\n",
@@ -399,7 +399,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [SourceCatalog](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SourceCatalog.html#photutils.segmentation.SourceCatalog) class is the primary tool for measuring the photometry, centroids, and shape/morphological properties of sources defined in a segmentation image. In its most basic form, it takes as input the (background-subtracted) image and the segmentation image. Usually the convolved image is also input, from which the source centroids and shape/morphological properties are measured (if not input, the unconvolved image is used instead)."
+    "The [SourceCatalog](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SourceCatalog.html) class is the primary tool for measuring the photometry, centroids, and shape/morphological properties of sources defined in a segmentation image. In its most basic form, it takes as input the (background-subtracted) image and the segmentation image. Usually the convolved image is also input, from which the source centroids and shape/morphological properties are measured (if not input, the unconvolved image is used instead)."
    ]
   },
   {
@@ -432,7 +432,7 @@
     "\n",
     "Here we’ll use the `to_table` method to generate a `QTable` of source photometry and properties. Each row in the table represents a source. The columns represent the calculated source properties. The label column corresponds to the label value in the input segmentation image.\n",
     "\n",
-    "Please see the [SourceCatalog documentation](https://photutils.readthedocs.io/en/latest/api/photutils.segmentation.SourceCatalog.html#photutils.segmentation.SourceCatalog) for a complete list of the available source properties."
+    "Please see the [SourceCatalog documentation](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SourceCatalog.html) for a complete list of the available source properties."
    ]
   },
   {
@@ -665,7 +665,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [**data/**](data) subdirectory also contains a WFC3/IR F105W image of the same field used for the preceding examples. The F105W and F160W images are pixel aligned, so sources in the F105W image are located at the same pixel positions as the F160W image (if they are visible in the F105W image)."
+    "The **data/** subdirectory also contains a WFC3/IR F105W image of the same field used for the preceding examples. The F105W and F160W images are pixel aligned, so sources in the F105W image are located at the same pixel positions as the F160W image (if they are visible in the F105W image)."
    ]
   },
   {
@@ -766,7 +766,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

09-Photutils/04-segmentation_exercise2_solution.py

@@ -10,8 +10,8 @@
 f105w_cat = SourceCatalog(f105w_data, f160w_segm)
 
 # calculate the AB magnitudes using the filter zero points
-f105w_abmag = -2.5 * np.log10(f105w_cat.segment_flux.value) + 26.2687
-f160w_abmag = -2.5 * np.log10(f160w_tbl['segment_flux'].value) + 26.9463
+f105w_abmag = -2.5 * np.log10(f105w_cat.segment_flux.value) + 26.264
+f160w_abmag = -2.5 * np.log10(f160w_tbl['segment_flux'].value) + 25.936
 
 # calculate the Y-H colors
 yh_colors = f105w_abmag - f160w_abmag

09-Photutils/05-psf_photometry.ipynb

@@ -217,7 +217,7 @@
    "source": [
     "fit_shape = (5, 5)\n",
     "fit_shape = 5\n",
-    "finder= DAOStarFinder(6.0, 2.0)\n",
+    "finder = DAOStarFinder(6.0, 2.0)\n",
     "psfphot = PSFPhotometry(psf_model, fit_shape, finder=finder, aperture_radius=5, progress_bar=True)"
    ]
   },
@@ -430,7 +430,7 @@
    "id": "245f9dbb-95ea-4421-a331-e08b81bb05ca",
    "metadata": {},
    "source": [
-    "Please consult the [PSF Photometry documentation](https://photutils.readthedocs.io/en/1.10.0/psf.html) for additional features including:\n",
+    "Please consult the [PSF Photometry documentation](https://photutils.readthedocs.io/en/stable/user_guide/psf.html) for additional features including:\n",
     "\n",
     "- Fitting a single (or few) sources in an image\n",
     "- Forced Photometry\n",