Trigger execution step to verify the updates

notebooks/DrizzlePac/align_mosaics/align_mosaics.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "## Learning Goals\n",
     "\n",
-    "By the end of this notebook tutorial, you will:\n",
+    "By the end of this notebook tutorial, you will: \n",
     "\n",
     "- Download WFC3 UVIS & IR images with `astroquery`\n",
     "- Check the active WCS (world coordinate system) solution in the FITS images\n",
@@ -1244,7 +1244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/DrizzlePac/align_sparse_fields/align_sparse_fields.ipynb

@@ -63,7 +63,7 @@
     "## Introduction <a id=\"intro\"></a>\n",
     "[Table of Contents](#toc)\n",
     "\n",
-    "This notebook demonstrates aligning long exposures which have relatively few stars and a large number of cosmic rays. It is based on the example described in the ISR linked here ([ACS ISR 2015-04: Basic Use of SExtractor Catalogs With TweakReg - I](https://ui.adsabs.harvard.edu/abs/2015acs..rept....4L/abstract)), but uses a much simpler methodology.\n",
+    "This notebook demonstrates aligning long exposures which have relatively few stars and a large number of cosmic rays. It is based on the example described in the ISR linked here ([ACS ISR 2015-04: Basic Use of SExtractor Catalogs With TweakReg - I](https://ui.adsabs.harvard.edu/abs/2015acs..rept....4L/abstract)), but uses a much simpler methodology. \n",
     "\n",
     "Rather than making use of external software (e.g. [SExtractor](http://www.astromatic.net/software/sextractor)) and going through the extra steps to create 'cosmic-ray cleaned' images for each visit, this notebook demonstrates new features in `TweakReg` designed to mitigate false detections.\n",
     "\n",
@@ -1013,7 +1013,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/DrizzlePac/align_to_catalogs/align_to_catalogs.ipynb

@@ -49,7 +49,7 @@
    "source": [
     "<a id=\"intro\"></a>\n",
     "## Introduction\n",
-    "The alignment of HST exposures is a critical step in image stacking or combination performed with software such as `AstroDrizzle`.  Generally, a *relative* alignment is performed to align one or multiple images to another image that is designated as the reference image. The reference image is generally the deepest exposure and/or that covering the largest area of all the exposures.  This process aligns the images to each other, but the pointing error of the observatory can still cause the images to have incorrect *absolute* astrometry. When absolute astrometry is desired, the images can be aligned to an external catalog with an absolute world coordinate system (WCS). In this example, we will provide a workflow to query catalogs such as SDSS and Gaia using the astroquery package, and then align the images to that catalog using TweakReg. \n",
+    "The alignment of HST exposures is a critical step in image stacking or combination performed with software such as `AstroDrizzle`.  Generally, a *relative* alignment is performed to align one or multiple images to another image that is designated as the reference image. The reference image is generally the deepest exposure and/or that covering the largest area of all the exposures.  This process aligns the images to each other, but the pointing error of the observatory can still cause the images to have incorrect *absolute* astrometry. When absolute astrometry is desired, the images can be aligned to an external catalog with an absolute world coordinate system (WCS). In this example, we will provide a workflow to query catalogs such as SDSS and Gaia using the astroquery package, and then align the images to that catalog using TweakReg.\n",
     "\n",
     "The workflow in this notebook for aligning images to [Gaia](https://www.cosmos.esa.int/web/gaia/home) is based on [WFC3 ISR 2017-19: Aligning HST Images to Gaia: a Faster Mosaicking Workflow](https://www.stsci.edu/files/live/sites/www/files/home/hst/instrumentation/wfc3/documentation/instrument-science-reports-isrs/_documents/2017/WFC3-2017-19.pdf) and contains a subset of the information and code found in [this repository](https://github.com/spacetelescope/gaia_alignment).  For more information, see the notebook in that repository titled [Gaia_alignment.ipynb](https://github.com/spacetelescope/gaia_alignment/blob/master/Gaia_alignment.ipynb).\n",
     "\n",
@@ -965,7 +965,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/DrizzlePac/drizzle_wfpc2/drizzle_wfpc2.ipynb

@@ -41,7 +41,7 @@
    "source": [
     "## Introduction <a id=\"intro\"></a>\n",
     "\n",
-    "Extra care must be taken when using `AstroDrizzle` to combine observations from detectors comprised of multiple chips of varying sensitivity. `AstroDrizzle` works with calibrated images in units of counts (electrons or Data Numbers) or count rates and not in units of flux. It assumes that all input frames can be converted to physical flux units using a single inverse-sensitivity factor, recorded in the FITS image headers as `PHOTFLAM`, and the output drizzled product simply copies the `PHOTFLAM` keyword value from the first input image. When this occurs, the inverse-sensitivity will vary across the final drizzled product, and users will need to keep track of which sources fell on which chip when doing photometry. Moreover, varying detector sensitivities will affect the cosmic-ray rejection algorithm used by `AstroDrizzle`, and this may result in the misidentification of some good pixels as cosmic rays.\n",
+    "Extra care must be taken when using `AstroDrizzle` to combine observations from detectors comprised of multiple chips of varying sensitivity. `AstroDrizzle` works with calibrated images in units of counts (electrons or Data Numbers) or count rates and not in units of flux. It assumes that all input frames can be converted to physical flux units using a single inverse-sensitivity factor, recorded in the FITS image headers as `PHOTFLAM`, and the output drizzled product simply copies the `PHOTFLAM` keyword value from the first input image. When this occurs, the inverse-sensitivity will vary across the final drizzled product, and users will need to keep track of which sources fell on which chip when doing photometry. Moreover, varying detector sensitivities will affect the cosmic-ray rejection algorithm used by `AstroDrizzle`, and this may result in the misidentification of some good pixels as cosmic rays. \n",
     "\n",
     "This is a typical situation when drizzle-combining images from HST instruments with different chip sensitivities, e.g. Wide Field and Planetary Camera 2 (WFPC2). For more detail, see the section on [Gain Variation](http://www.stsci.edu/instruments/wfpc2/Wfpc2_dhb/wfpc2_ch53.html) under 'Position-Dependent Photometric Corrections' in the WFPC2 Data Handbook. As a result, each of the four chips requires a [unique PHOTFLAM](http://www.stsci.edu/instruments/wfpc2/Wfpc2_dhb/wfpc2_ch52.html#1933986) header keyword value. A similar situation may occur when drizzle-combining observations taken over a span of several years as detector's sensitivity declines over time, see e.g. [ACS ISR 2016-03](https://doi.org/10.3847/0004-6256/152/3/60).\n",
     "\n",
@@ -530,7 +530,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.5"
   },
   "varInspector": {
    "cols": {

notebooks/DrizzlePac/mask_satellite/mask_satellite.ipynb

@@ -53,7 +53,7 @@
     "## Introduction <a id=\"intro_ID\"></a>\n",
     "[Table of Contents](#toc)\n",
     "\n",
-    "Even though Hubble has a small field of view, satellites are commonly captured in images. The cosmic ray rejection algorithm in Astrodrizzle is not well suited to eliminate satellite trails, and the affected adjacent pixels that make up their wings leave ugly blemishes in stacked images.  \n",
+    "Even though Hubble has a small field of view, satellites are commonly captured in images. The cosmic ray rejection algorithm in Astrodrizzle is not well suited to eliminate satellite trails, and the affected adjacent pixels that make up their wings leave ugly blemishes in stacked images.\n",
     "\n",
     "To fix this problem, the pixels around satellite trails need to be marked as bad in the affected images. There are several ways to accomplish this goal. The ACS Team developed multiple algorithms to automatically detect and mask satellite trails. The newest is a module called `findsat_mrt` and is decribed in [ISR ACS 2022-08](https://www.stsci.edu/files/live/sites/www/files/home/hst/instrumentation/acs/documentation/instrument-science-reports-isrs/_documents/isr2208.pdf). The 'readthedocs' page can be found here: [MRT-based Satellite Trail Detection](https://acstools.readthedocs.io/en/latest/findsat_mrt.html). The second module is called `satdet` and is described in [ISR ACS 2016-01](http://www.stsci.edu/hst/acs/documents/isrs/isr1601.pdf).  The 'readthedocs' page for the software can be found here: [Satellite Trails Detection](https://acstools.readthedocs.io/en/stable/satdet.html). `findsat_mrt` has the benefit of significantly improved sensitivity over `satdet` but it is more computationally demanding. \n",
     "\n",
@@ -1158,7 +1158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/DrizzlePac/optimize_image_sampling/optimize_image_sampling.ipynb

@@ -47,7 +47,7 @@
     "## Introduction \n",
     "[Table of Contents](#toc)\n",
     "\n",
-    "This example was written to help users better understand the subtleties in improving image sampling for dithered data. One of the powers of the *drizzling* algorithm is that, given properly dithered images, it can restore much of the information lost due to undersampled images ([Fruchter and Hook, 2002](http://iopscience.iop.org/article/10.1086/338393/pdf)). \n",
+    "This example was written to help users better understand the subtleties in improving image sampling for dithered data. One of the powers of the *drizzling* algorithm is that, given properly dithered images, it can restore much of the information lost due to undersampled images ([Fruchter and Hook, 2002](http://iopscience.iop.org/article/10.1086/338393/pdf)).\n",
     "\n",
     "This work is based on [ISR ACS 2015-01](https://www.stsci.edu/files/live/sites/www/files/home/hst/instrumentation/acs/documentation/instrument-science-reports-isrs/_documents/isr1501.pdf), which contains a more detailed discussion than presented here. \n",
     "\n",
@@ -735,7 +735,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/DrizzlePac/sky_matching/sky_matching.ipynb

@@ -66,7 +66,7 @@
    "source": [
     "## Introduction <a id=\"intro\"></a>\n",
     "\n",
-    "When creating an image mosaic, `AstroDrizzle` has the ability to compute the sky and then either subtract or equalize the background in input images. Users may select the algorithm used for the sky subtraction via the `skymethod` parameter. \n",
+    "When creating an image mosaic, `AstroDrizzle` has the ability to compute the sky and then either subtract or equalize the background in input images. Users may select the algorithm used for the sky subtraction via the `skymethod` parameter.\n",
     "\n",
     "There are four methods available in sky matching: `localmin`, `match`, `globalmin`, and `globalmin+match`.\n",
     "\n",
@@ -954,7 +954,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.5"
   },
   "varInspector": {
    "cols": {

notebooks/DrizzlePac/using_updated_astrometry_solutions/using_updated_astrometry_solutions.ipynb

@@ -68,7 +68,7 @@
     "\n",
     "***\n",
     "\n",
-    "The following Python packages are required to run the Jupyter Notebook:\n",
+    "The following Python packages are required to run the Jupyter Notebook: \n",
     " - [**os**](https://docs.python.org/3/library/os.html) - change and make directories\n",
     " - [**glob**](https://docs.python.org/3/library/glob.html) - gather lists of filenames\n",
     " - [**shutil**](https://docs.python.org/3/library/shutil.html#module-shutil) - remove directories and files\n",
@@ -981,7 +981,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/WFC3/calwf3_recalibration/calwf3_recal_tvb.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "This notebook shows two reprocessing examples for WFC3/IR observations impacted by time-variable background (TVB). \n",
     "\n",
-    "By the end of this tutorial, you will:\n",
+    "By the end of this tutorial, you will: \n",
     "- Analyze exposure statistics for each read in an IMA file using `pstat`.\n",
     "- Reprocess a single exposure and an image association using `calwf3`.\n",
     "- Combine the reprocessed exposures using `astrodrizzle`.\n",
@@ -769,7 +769,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/WFC3/calwf3_v1.0_cte/calwf3_with_v1.0_PCTE.ipynb

@@ -268,10 +268,7 @@
    },
    "outputs": [],
    "source": [
-    "bestref_input = 'crds bestrefs --update-bestrefs --sync-references=1 --files idv404axq_raw.fits'\n",
-    "run_bestref = os.system(bestref_input)\n",
-    "if run_bestref != 0:\n",
-    "    print(f\"bestref failed with exit code: {run_bestref}\")"
+    "!crds bestrefs --update-bestrefs --sync-references=1 --files idv404axq_raw.fits"
    ]
   },
   {
@@ -990,7 +987,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.5"
   },
   "varInspector": {
    "cols": {

notebooks/WFC3/ir_scattered_light_calwf3_corrections/Correcting_for_Scattered_Light_in_IR_Exposures_Using_calwf3_to_Mask_Bad_Reads.ipynb

@@ -14,7 +14,7 @@
     "(TVB) due to scattered light from observing close to the Earth's limb. This method illustrates how to mask bad reads in the RAW image and then reprocess with `calwf3`, and it may be used for rejecting anomalous reads occurring either at the beginning or at the end of an exposure.\n",
     "\n",
     "\n",
-    "By the end of this tutorial, you will:\n",
+    "By the end of this tutorial, you will: \n",
     "\n",
     "- Compute and plot the difference between IMA reads to identify the reads affected by TVB.\n",
     "- Reprocess a single exposure with `calwf3` by excluding the first few reads which are affected by scattered light.\n",
@@ -712,7 +712,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/WFC3/ir_scattered_light_manual_corrections/Correcting_for_Scattered_Light_in_IR_Exposures_by_Manually_Subtracting_Bad_Reads.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "This notebook presents one of two available methods to correct for a time variable background (TVB) due to scattered light from observing close to the Earth's limb. This method illustrates how to manually subtract any bad reads from the final exposure read of the WFC3/IR IMA data. \n",
     "\n",
-    "By the end of this tutorial, you will: \n",
+    "By the end of this tutorial, you will:\n",
     "\n",
     "- Compute and plot the difference between IMA reads to identify those affected by TVB. \n",
     "- Correct a single exposure in which the first few reads are affected by scattered light by subtracting those \"bad\" reads from the final IMA read.\n",
@@ -1050,7 +1050,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/WFC3/persistence/wfc3_ir_persistence.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "This notebook shows how to use the Hubble Space Telescope WFC3/IR persistence model to flag pixels affected by persistence in the calibrated (FLT) science images. When the images are sufficiently dithered to step over the observed persistence artifacts, AstroDrizzle may be used to exclude those flagged pixels when combining the FLT frames.  \n",
     "\n",
-    "By the end of this tutorial, you will:\n",
+    "By the end of this tutorial, you will: \n",
     "\n",
     "- Download images and persistence products from MAST.\n",
     "- Flag affected pixels in the data quality arrays of the FLT images.\n",
@@ -528,7 +528,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,