revision after memory error identified

Author: kadrlica

03_Intro_to_AFW_Display.ipynb

@@ -14,6 +14,7 @@
     "Contact author: Alex Drlica-Wagner (kadrlica@fnal.gov) <br>\n",
     "Credit: Brant Robertson and the LSST Stack Club <br>\n",
     "Target audience: All DP0 delegates, especially those new to Notebooks. <br>\n",
+    "Container Size: medium <br>\n",
     "<br>\n",
     "Questions welcome at <a href=\"https://community.lsst.org/c/support/dp0\">community.lsst.org/c/support/dp0</a> <br>\n",
     "Find DP0 documentation and resources at <a href=\"https://dp0-1.lsst.io\">dp0-1.lsst.io</a> <br>\n",
@@ -22,8 +23,7 @@
     "\n",
     "This tutorial is designed to help users get a brief feel for the `lsst.afw.display` library that enables the visual inspection of data. The [`lsst.afw` library](https://github.com/lsst/afw) provides an \"Astronomical Framework\" (afw) while the `lsst.daf.*` libraries (see, e.g., [daf_base](https://github.com/lsst/daf_base)) provides a Data Access Framework (daf). Both libraries are used in this tutorial, with the `lsst.daf.persistence` library used to access a calibrated exposure (calexp) and the `lsst.afw.display` library used to show the exposure image on the screen.\n",
     "\n",
-    "### Credit:\n",
-    "This tutorial is based on the [`AFW_Display_Demo.ipynb`](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Visualization/AFW_Display_Demo.ipynb) notebook originally written by Brant Robertson and maintained by the LSST Stack Club. More examples of the use of `lsst.afw.display` can be found in the [Stack ](https://pipelines.lsst.io/getting-started/display.html).\n",
+    "**Credit:** This tutorial is based on the [`AFW_Display_Demo.ipynb`](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Visualization/AFW_Display_Demo.ipynb) notebook originally written by Brant Robertson and maintained by the LSST Stack Club. More examples of the use of `lsst.afw.display` can be found in the [Stack ](https://pipelines.lsst.io/getting-started/display.html).\n",
     "\n",
     "### Set Up"
    ]
@@ -65,23 +65,6 @@
     "import warnings                      #imports the warnings library"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's go ahead and import from `astropy` the image stretch limits from the familiar `zscale()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#from astropy.visualization import ZScaleInterval     #This function allows use to use the `zscale()` rescaling limits function familiar from, e.g., DS9, to adjust the image stretch.\n",
-    "#zscale = ZScaleInterval()                            #create an alias to the `ZScaleInterval()` function"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -95,8 +78,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#warnings.simplefilter(\"ignore\", category=FutureWarning)   #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls\n",
-    "#warnings.simplefilter(\"ignore\", category=UserWarning)     #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls"
+    "warnings.simplefilter(\"ignore\", category=FutureWarning)   #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls\n",
+    "warnings.simplefilter(\"ignore\", category=UserWarning)     #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls"
    ]
   },
   {
@@ -219,8 +202,10 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "fig,ax = plt.subplots(1,2,figsize=(14,7))\n",
     "\n",
@@ -245,8 +230,10 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "fig = plt.figure()\n",
     "display = afwDisplay.Display()\n",
@@ -255,29 +242,11 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('hello')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Congrats!** We've plotted an image using `lsst.afw.display`!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('hello')"
+    "**Congratulations!** We've plotted an image using `lsst.afw.display`!"
    ]
   },
   {
@@ -289,33 +258,15 @@
     "The `calexp` returned by the butler contains more than just the image pixel values (see the Stack Club [calexp tutorial](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Calexp_guided_tour.ipynb) for more details). One other component is the mask plane associated with the image. `AFWDisplay` provides a nice pre-packaged interface for overplotting the mask associated with an image. A mask is composed of a set of \"mask planes\", 2D binary bit maps corresponding to pixels that are masked for various reasons (see [here](https://pipelines.lsst.io/v/DM-11392/getting-started/display.html#interpreting-displayed-mask-colors) for more details)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"hello\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We'll follow the same steps as above to display the image, but we'll add a few modifications\n",
     "\n",
-    "* We explicitly set the transparency of the overplotted mask (0 = transparent, 1 = opaque)\n",
-    "* We explicitly set the color of the 'DETECTED' mask plane to 'blue' (i.e. all pixels associated with detected objects).\n",
-    "* We pass the full `calexp` object to `mtv` instead of just the image plane."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"hello\")"
+    "1. We explicitly set the transparency of the overplotted mask (0 = transparent, 1 = opaque)\n",
+    "2. We explicitly set the color of the 'DETECTED' mask plane to 'blue' (i.e. all pixels associated with detected objects).\n",
+    "3. We pass the full `calexp` object to `mtv` instead of just the image plane."
    ]
   },
   {