Small changes in the GSAOI tutorial and added script to display the t…

…utorial images

geminidr/doc/tutorials/GSAOIImg-DRTutorial/01_introduction.rst

@@ -37,7 +37,7 @@ machine. You can test that by typing the following commands:
 Where ``geminiconda`` is the name of the conda environment where DRAGONS should
 be installed. If you have an error message, make sure:
 
-    - Conda is properly installed;
+    - AnaConda or MiniConda is properly installed;
 
     - A Conda Virtual Environment is properly created and is active;
 
@@ -51,9 +51,8 @@ be installed. If you have an error message, make sure:
 Download Sample Files
 =====================
 
-For this tutorial we selected a very sparse field. This makes the sky
-subtraction less prone to errors. The selected data set was observed for the
-GS-2017A-Q-29 program on the night starting on May 04, 2017.
+For this tutorial we selected data observed for for the GS-2017A-Q-29 program on
+the night starting on May 04, 2017.
 
 You can search and download the files on the
 `Gemini Archive <https://archive.gemini.edu/searchform>`_ using the
@@ -78,14 +77,14 @@ can be downloaded using `this link
 <https://archive.gemini.edu/download/exposure_time=150/notengineering/GSAOI/Pass/DARK/present/canonical>`_.
 or copying and pasting the following address to search form into your browser: ::
 
-..    https://archive.gemini.edu/searchform/exposure_time=150/cols=CTOWEQ/notengineering/GSAOI/Pass/DARK
+    https://archive.gemini.edu/searchform/exposure_time=150/cols=CTOWEQ/notengineering/GSAOI/Pass/DARK
 
 
 The FLAT files to build the BPM can be downloaded directly by `clicking here
 <https://archive.gemini.edu/download/20171201-20171231/object=Domeflat/filter=H/notengineering/GSAOI/Pass/present/canonical>`_
 or using the following address: ::
 
-..    https://archive.gemini.edu/searchform/object=Domeflat/cols=CTOWEQ/filter=H/notengineering/GSAOI/20171201-20171231/Pass
+    https://archive.gemini.edu/searchform/object=Domeflat/cols=CTOWEQ/filter=H/notengineering/GSAOI/20171201-20171231/Pass
 
 
 Copy all the files to the same place in your computer. Then use ``tar`` and

geminidr/doc/tutorials/GSAOIImg-DRTutorial/02_data_reduction.rst

@@ -33,7 +33,7 @@ For this tutorial, we will be also using other `Supplemental tools
 <https://dragons-recipe-system-users-manual.readthedocs.io/en/latest/supptools.html>`_,
 like dataselect_, showd_, typewalk_, and caldb_.
 
-.. warning:: Some primitives use a lot of RAM memory and they can make `reduce`
+.. warning:: Some primitives use a lot of RAM memory and they can make reduce_
     crash. Our team is aware of this problem and we are working on that. For
     now, if that happens to you, you might need to run the pipeline on a
     smaller data set.
@@ -83,8 +83,8 @@ reduction pipeline does not organize the data.
 
 That means that we first need to identify these files and create lists that will
 be used in the data-reduction process. For that, we will use the dataselect_
-command line. Please, refer to the `dataselect page <dataselect>`_ for details
-regarding its usage. Let us start with the DARK files: ::
+command line. Please, refer to the dataselect_ page for details regarding its
+usage. Let us start with the DARK files: ::
 
    $ dataselect --tags DARK raw/*.fits > list_of_darks.txt
 

geminidr/doc/tutorials/GSAOIImg-DRTutorial/scripts/show_flat_corrected_image.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+"""
+Displays a Flat Corrected and Sky Subtracted GSAOI data reduced with DRAGONS
+to be used in the GSAOIIMG-Tutorial.
+"""
+
+import astrodata
+import gemini_instruments
+import numpy as np
+
+from astropy import visualization
+from copy import copy
+from matplotlib import pyplot as plt
+from matplotlib import colors
+
+
+def main():
+
+    # filename = 'S20170505S0102_flatCorrected.fits'
+    filename = get_filename()
+
+    ad = astrodata.open(filename)
+    print(ad.info())
+
+    fig = plt.figure(num=filename, figsize=(8, 8))
+    fig.suptitle('{}'.format(filename))
+
+    palette = copy(plt.cm.viridis)
+    palette.set_bad('w', 1.0)
+
+    norm = visualization.ImageNormalize(
+        np.dstack([ad[i].data for i in range(4)]),
+        stretch=visualization.LinearStretch(),
+        interval=visualization.ZScaleInterval()
+    )
+
+    ax1 = fig.add_subplot(224)
+    ax1.imshow(
+        np.ma.masked_where(ad[0].mask > 0, ad[0].data),
+        norm=colors.Normalize(vmin=norm.vmin, vmax=norm.vmax),
+        origin='lower',
+        cmap=palette
+    )
+
+    ax1.annotate('d1', (20, 20), color='white')
+    ax1.set_xlabel('x [pixels]')
+    ax1.set_ylabel('y [pixels]')
+
+    ax2 = fig.add_subplot(223)
+    ax2.imshow(
+        np.ma.masked_where(ad[1].mask > 0, ad[1].data),
+        norm=colors.Normalize(vmin=norm.vmin, vmax=norm.vmax),
+        origin='lower',
+        cmap=palette
+    )
+
+    ax2.annotate('d2', (20, 20), color='white')
+    ax2.set_xlabel('x [pixels]')
+    ax2.set_ylabel('y [pixels]')
+
+    ax3 = fig.add_subplot(221)
+    ax3.imshow(
+        np.ma.masked_where(ad[2].mask > 0, ad[2].data),
+        norm=colors.Normalize(vmin=norm.vmin, vmax=norm.vmax),
+        origin='lower',
+        cmap=palette
+    )
+
+    ax3.annotate('d3', (20, 20), color='white')
+    ax3.set_xlabel('x [pixels]')
+    ax3.set_ylabel('y [pixels]')
+
+    ax4 = fig.add_subplot(222)
+    ax4.imshow(
+        np.ma.masked_where(ad[3].mask > 0, ad[3].data),
+        norm=colors.Normalize(vmin=norm.vmin, vmax=norm.vmax),
+        origin='lower',
+        cmap=palette
+    )
+
+    ax4.annotate('d4', (20, 20), color='white')
+    ax4.set_xlabel('x [pixels]')
+    ax4.set_ylabel('y [pixels]')
+
+    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
+
+    plt.savefig(filename.replace('.fits', '.png'))
+    plt.show()
+
+
+def get_filename():
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('filename', type=str,
+                        help='Path to the fits file')
+
+    args = parser.parse_args()
+
+    return args.filename
+
+
+if __name__ == main():
+    main()