Add WFC3 notebook 'calwf3_with_v1.0_PCTE.ipynb'

_config.yml

@@ -45,7 +45,6 @@ exclude_patterns: [notebooks/DrizzlePac/align_mosaics/align_mosaics.ipynb,
                    notebooks/DrizzlePac/sky_matching/sky_matching.ipynb,
                    notebooks/DrizzlePac/use_ds9_regions_in_tweakreg/use_ds9_regions_in_tweakreg.ipynb,
                    notebooks/WFC3/calwf3_recalibration/calwf3_recal_tvb.ipynb,
-                   notebooks/WFC3/calwf3_v1.0_cte/calwf3_with_v1.0_PCTE.ipynb,
                    notebooks/WFC3/dash/dash.ipynb,
                    notebooks/WFC3/exception_report/wfc3_exception_report.ipynb,
                    notebooks/WFC3/filter_transformations/filter_transformations.ipynb,

_toc.yml

@@ -60,7 +60,7 @@ parts:
     chapters:
       - file: notebooks/WFC3/README.md
 #      - file: notebooks/WFC3/calwf3_recalibration/calwf3_recal_tvb.ipynb
-#      - file: notebooks/WFC3/calwf3_v1.0_cte/calwf3_with_v1.0_PCTE.ipynb
+      - file: notebooks/WFC3/calwf3_v1.0_cte/calwf3_with_v1.0_PCTE.ipynb
 #      - file: notebooks/WFC3/dash/dash.ipynb
 #      - file: notebooks/WFC3/exception_report/wfc3_exception_report.ipynb
 #      - file: notebooks/WFC3/filter_transformations/filter_transformations.ipynb

notebooks/WFC3/calwf3_v1.0_cte/README.md

@@ -1,19 +1,20 @@
 This directory, once cloned from the repository, should contain this
 `README.md`, the Jupyter Notebook `calwf3_with_v1.0_PCTE.ipynb`, a file
-called `archived_drkcfiles.txt` and a subdirectory `example/`.
+called `archived_drkcfiles.txt`, a `requirements.txt` file, and a subdirectory `example/`.
 
-**In order to run this Jupyter Notebook you must have created a virtual
+**To run this Jupyter Notebook you must have created a virtual
 conda environment that includes `calwf3` v3.5.2.** Version 3.5.2 of `calwf3` 
-is available in HSTCAL release 2.5.0. To create an environment with
+is available in `hstcal` release 2.5.0. To create an environment with
 `calwf3` v3.5.2 try this from the terminal:
 
 ```
 $ conda config --add channels http://ssb.stsci.edu/astroconda
-
-$ conda create -n v1_PCTE hstcal==2.5.0 python=3.7 ginga stsci-hst notebook
+$ conda create -n v1_PCTE hstcal==2.5.0 python=3.11
+$ conda activate v1_PCTE
+$ pip install -r requirements.txt
 ```
 
-In general, users wanting to use the v1.0 pixel-based CTE correction
+In most cases, users wanting to use the v1.0 pixel-based CTE correction
 within `calwf3` should use `calwf3` v3.5.2. This version will provide the
 most up-to-date calibration procedures such as time-dependent photometric
 corrections and zeropoints, while also including the v1.0 correction.

notebooks/WFC3/calwf3_v1.0_cte/example/background_median.py

@@ -16,13 +16,13 @@
     See the docstring of aperture_stats_tbl for more info.
 """
 import numpy as np
-
 # WAY faster than astropy.stats.sigma_clipped_stats
 from scipy.stats import sigmaclip
 from astropy.table import Table
 
-def aperture_stats_tbl(data, apertures,
-                       method='exact', sigma_clip=True):
+
+def aperture_stats_tbl(data, apertures, method='exact', sigma_clip=True):
+
     """Computes mean/median/mode/std in Photutils apertures.
     Compute statistics for custom local background methods.
     This is primarily intended for estimating backgrounds
@@ -63,23 +63,24 @@ def aperture_stats_tbl(data, apertures,
 
     aperture_stats = np.array(aperture_stats)
 
-
     # Place the array of the x y positions alongside the stats
     stacked = np.hstack([apertures.positions, aperture_stats])
     # Name the columns
-    names = ['X','Y','aperture_mean','aperture_median','aperture_mode',
-            'aperture_std', 'aperture_area']
+    names = ['X', 'Y', 'aperture_mean', 'aperture_median', 
+             'aperture_mode', 'aperture_std', 'aperture_area']
     # Make the table
     stats_tbl = Table(data=stacked, names=names)
 
-
     return stats_tbl
 
+
 def calc_aperture_mmm(data, mask, sigma_clip):
+
     """Helper function to actually calculate the stats for pixels
         falling within some Photutils aperture mask on some array
         of data.
     """
+
     cutout = mask.cutout(data, fill_value=np.nan)
     if cutout is None:
         return (np.nan, np.nan, np.nan, np.nan, np.nan)
@@ -95,4 +96,5 @@ def calc_aperture_mmm(data, mask, sigma_clip):
 
         mode = 3 * median - 2 * mean
         actual_area = (~np.isnan(values)).sum()
+
         return (mean, median, mode, std, actual_area)

notebooks/WFC3/calwf3_v1.0_cte/pre-requirements.sh

@@ -0,0 +1 @@
+conda install --yes -c http://ssb.stsci.edu/astroconda hstcal==2.5.0

notebooks/WFC3/calwf3_v1.0_cte/requirements.txt

@@ -0,0 +1,9 @@
+astropy
+astroquery
+crds
+jupyter
+matplotlib
+numpy
+photutils
+scipy
+wfc3tools