Merge branch 'main' into change_readme

.github/workflows/weekly_html_accessibility_check.yml

@@ -3,11 +3,26 @@ on:
   schedule:
     - cron: '0 4 * * 0' # 0400 UTC every Sunday
   workflow_dispatch:
+    inputs:
+      total_error_limit:
+        required: false
+        description: 'The maximum total allowed number of HTML accessibility errors. To skip this testing requirement, enter the value "-1". If not explicitly specified, the default value is "0".'
+        default: 0
+        type: string
+      total_warning_limit:
+        required: false
+        description: 'The maximum total allowed number of HTML accessibility warnings. To skip this testing requirement, enter the value "-1". If not explicitly specified, the default value is "0".'
+        default: 0
+        type: string
 
 jobs:
   Scheduled:
-   uses: spacetelescope/notebook-ci-actions/.github/workflows/html_accessibility_check.yml@main
+   uses: spacetelescope/notebook-ci-actions/.github/workflows/html_accessibility_check.yml@v3
    with:
      target_url: https://spacetelescope.github.io/${{ github.event.repository.name }}/
+     python-version: ${{ vars.PYTHON_VERSION }}
+     total_error_limit: ${{ inputs.total_error_limit || 0 }}
+     total_warning_limit: ${{ inputs.total_warning_limit || 0 }}
+
    secrets:
      A11YWATCH_TOKEN: ${{ secrets.A11YWATCH_TOKEN }}

README.md

@@ -9,6 +9,3 @@
 ```
 
 The repository is currently under construction and not yet complete. As a result, it is recommended that the [older version](https://github.com/spacetelescope/notebooks) be reviewed and referenced instead. While this may cause inconvenience, it is important to ensure that the final version is of the highest quality possible. 
-
-# notebook_ci_template
-Structural template for notebook CI system

_toc.yml

@@ -63,6 +63,7 @@ parts:
     - file: notebooks/STIS/view_data/view_data.ipynb
     - file: notebooks/STIS/extraction/1D_Extraction.ipynb
     - file: notebooks/STIS/low_count_uncertainties/Low_Count_Uncertainties.ipynb
+    - file: notebooks/STIS/contrast_sensitivity/STIS_Coronagraphic_Observation_Feasibility.ipynb
   - caption: WFC3
     chapters:
     - file: notebooks/WFC3/README.md

notebooks/.gitignore

@@ -36,3 +36,5 @@ MAST/Kepler/Kepler_TPF/mastDownload/
 !**/skyfile.txt
 !**/exclusions.txt
 !**/inclusions*.txt
+!STIS/contrast_sensitivity/ETC_example.jpg
+!STIS/contrast_sensitivity/TWHya.txt

notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb

@@ -89,7 +89,6 @@
     "- *os* for setting environment variables\n",
     "- *shutil* for managing directories\n",
     "- *numpy* for math and array calculations\n",
-    "- *collections OrderedDict* for making dictionaries easily\n",
     "- *matplotlib pyplot* for plotting\n",
     "- *matplotlib.colors LogNorm* for scaling images\n",
     "- *astropy.io fits* for working with FITS files\n",
@@ -110,10 +109,10 @@
     "import os\n",
     "import shutil\n",
     "import numpy as np\n",
-    "from collections import OrderedDict\n",
     "import matplotlib.pyplot as plt\n",
     "from astropy.io import fits\n",
-    "from photutils import datasets\n",
+    "from astropy.modeling.models import Gaussian2D\n",
+    "from photutils.datasets import make_noise_image, make_model_params, make_model_image\n",
     "from astroquery.mast import Observations\n",
     "from acstools import acsccd\n",
     "from acstools import acscte\n",
@@ -298,7 +297,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. "
+    "First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. We get use the shape of one of the flc image SCI extensions for creating the (x, y) coordinates of the sources."
    ]
   },
   {
@@ -307,27 +306,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n_sources = 300\n",
-    "param_ranges = [('amplitude', [500, 30000]),\n",
-    "                ('x_mean', [0, 4095]),\n",
-    "                ('y_mean', [0, 2047]),\n",
-    "                ('x_stddev', [1.05, 1.07]),\n",
-    "                ('y_stddev', [1.05, 1.07]),\n",
-    "                ('theta', [0, np.pi])]\n",
+    "wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
     "\n",
-    "param_ranges = OrderedDict(param_ranges)\n",
+    "shape = wfc2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_sources = 300\n",
     "\n",
-    "sources = datasets.make_random_gaussians_table(n_sources, param_ranges, \n",
-    "                                               seed=12345)\n",
+    "sources = make_model_params(shape, n_sources, x_name='x_mean', y_name='y_mean',\n",
+    "                            amplitude=(500, 30000), x_stddev=(1.05, 1.07), \n",
+    "                            y_stddev=(1.05, 1.07), theta=(0, np.pi), seed=12345)\n",
     "\n",
-    "print(sources)"
+    "sources"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we get the shape of one of the `flc` image `SCI` extensions and make an image from the table of Gaussian sources. Note that this step may take a few minutes. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
+    "Next, we make an image from the table of Gaussian sources. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
    ]
   },
   {
@@ -336,11 +339,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
-    "\n",
-    "shape = wfc2.shape\n",
-    "\n",
-    "synth_stars_image = datasets.make_gaussian_sources_image(shape, sources)\n",
+    "model = Gaussian2D()\n",
+    "synth_stars_image = make_model_image(shape, model, sources, \n",
+    "                                     x_name='x_mean', y_name='y_mean', progress_bar=True)\n",
     "\n",
     "synth_stars_image = np.random.poisson(synth_stars_image)"
    ]
@@ -382,7 +383,7 @@
     "ax.imshow(flc, vmin=0, vmax=200, interpolation='nearest', cmap='Greys_r', origin='lower')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -442,7 +443,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -722,9 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "flt_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flt.fits', ext=1)\n",
@@ -737,15 +736,13 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "flc_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flc.fits', ext=1)\n",
@@ -758,7 +755,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -829,7 +826,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "noise_image = datasets.make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
+    "noise_image = make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
     "\n",
     "wfc1 += noise_image + synth_stars_image\n",
     "wfc2 += noise_image + synth_stars_image\n",
@@ -860,7 +857,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -986,14 +983,14 @@
     "rn_C = hdr['READNSEC']\n",
     "rn_D = hdr['READNSED']\n",
     "\n",
-    "img_rn_A = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_A)\n",
-    "img_rn_B = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_B)\n",
-    "img_rn_C = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_C)\n",
-    "img_rn_D = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_D)\n",
+    "img_rn_A = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_A)\n",
+    "img_rn_B = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_B)\n",
+    "img_rn_C = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_C)\n",
+    "img_rn_D = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_D)\n",
     "\n",
     "wfc1_rn = np.hstack((img_rn_A, img_rn_B))\n",
     "wfc2_rn = np.hstack((img_rn_C, img_rn_D))"
@@ -1290,7 +1287,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -1309,7 +1306,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -1365,7 +1362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

notebooks/ACS/acs_cte_forward_model/requirements.txt

@@ -3,6 +3,6 @@ astropy>=5.3.3
 astroquery>=0.4.6
 matplotlib>=3.7.0
 numpy>=1.23.4
-photutils>=1.6.0
+photutils>=2.0.2
 crds>=11.17.7
-stsci.tools>=4.1.0
+stsci.tools>=4.1.0

notebooks/ACS/acs_focus_diverse_epsfs/acs_focus_diverse_epsfs.ipynb

@@ -40,7 +40,7 @@
     "import matplotlib.colors as colors\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# import the focus_diverse_psfs functions from acstools \n",
+    "# import the focus_diverse_psfs functions from acstools\n",
     "from acstools.focus_diverse_epsfs import psf_retriever, multi_psf_retriever, interp_epsf"
    ]
   },
@@ -57,7 +57,7 @@
    "id": "c5ce3a38",
    "metadata": {},
    "source": [
-    "Let's begin with downloading the focus-diverse ePSF FITS file that matches a single observation of our choosing. For this example, we will aim to retrieve the ePSF file for the observation rootname \"jds408jsq\", from GO-15445 (PI W. Keel). \n",
+    "Let's begin with downloading the focus-diverse ePSF FITS file that matches a single observation of our choosing. For this example, we will aim to retrieve the ePSF file for the observation rootname \"jds408jsq\", from GO-15445 (PI W. Keel).\n",
     "\n",
     "Please note that only IPPPSSOOT formats will work (e.g. jds408jsq), and the tool does not support inputs in the form of association IDs or product names (e.g. jds408010 or jds408011).\n",
     "\n",
@@ -140,7 +140,7 @@
     "\n",
     "# open the file with astropy.io\n",
     "with fits.open(retrieved_filepath) as hdu:\n",
-    "    hdu.info() # Display basic information about the file"
+    "    hdu.info()  # Display basic information about the file"
    ]
   },
   {
@@ -165,11 +165,12 @@
     "\n",
     "\n",
     "def show_ePSF(grid_index):\n",
-    "    plt.imshow(ePSFs[grid_index], cmap='viridis', norm=colors.LogNorm(vmin=1e-4), origin='lower')\n",
+    "    plt.imshow(ePSFs[grid_index], cmap='viridis',\n",
+    "               norm=colors.LogNorm(vmin=1e-4), origin='lower')\n",
     "    cbar = plt.colorbar()\n",
     "    cbar.set_label('Fractional Energy')\n",
-    "    \n",
-    "    \n",
+    "\n",
+    "\n",
     "widgets.interact(show_ePSF, grid_index=(0, 89, 1))"
    ]
   },
@@ -277,7 +278,7 @@
    "outputs": [],
    "source": [
     "# # use astroquery to grab ACS/WFC observations from GO-13376 (PI K. McQuinn)\n",
-    "obsTable = Observations.query_criteria(obs_collection='HST', proposal_id=\"13376\", \n",
+    "obsTable = Observations.query_criteria(obs_collection='HST', proposal_id=\"13376\",\n",
     "                                       instrument_name=\"ACS/WFC\", provenance_name=\"CALACS\")\n",
     "\n",
     "# retrieve the data products for the above observations\n",
@@ -400,7 +401,7 @@
    "source": [
     "# # get interpolated ePSF in detector space with specified sub-pixel shifts\n",
     "P = interp_epsf(ePSFs, x, y, chip,\n",
-    "                pixel_space=True, \n",
+    "                pixel_space=True,\n",
     "                subpixel_x=0.77, subpixel_y=0.33)\n",
     "\n",
     "plt.imshow(P, cmap='viridis', norm=colors.LogNorm(vmin=1e-4), origin='lower')\n",

notebooks/ACS/acs_sbc_dark_analysis/acs_sbc_dark_analysis.ipynb

@@ -762,7 +762,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

notebooks/ACS/acs_sbc_dark_analysis/requirements.txt

@@ -3,4 +3,3 @@ astroquery>=0.4.6
 drizzlepac>=3.5.1
 matplotlib>=3.7.0
 numpy>=1.23.4
-photutils>=1.12.0  # The drizzlepac needs deprecated methods such as DAOGroup.

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_mosaics/requirements.txt

@@ -4,4 +4,3 @@ drizzlepac>=3.5.1
 ipython>=8.11.0
 matplotlib>=3.7.0
 numpy>=1.23.4
-photutils==1.12.0  # The drizzlepac needs deprecated methods such as DAOGroup.

notebooks/DrizzlePac/align_multiple_visits/requirements.txt

@@ -6,4 +6,3 @@ ipython
 matplotlib
 numpy
 jupyter
-photutils==1.12.0

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,