diff --git a/Basics/Calexp_guided_tour.ipynb b/Basics/Calexp_guided_tour.ipynb
new file mode 100644
index 00000000..dc7f37d5
--- /dev/null
+++ b/Basics/Calexp_guided_tour.ipynb
@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Guided Tour of LSST Calexps\n",
+    "<br>Owner(s): **David Shupe** ([@stargaser](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@stargaser))\n",
+    "<br>Last Verified to Run: **2018-08-07**\n",
+    "<br>Verified Stack Release: **v16.0** (also lsst_w_2018_31, with `getName` modification)\n",
+    "\n",
+    "We'll inspect a visit image ``calexp`` object, and then show how a coadd image differs.\n",
+    "\n",
+    "### Learning Objectives:\n",
+    "\n",
+    "After working through this tutorial you should be able to follow some best practices when working with LSST ``calexp`` (image) objects.\n",
+    "\n",
+    "### Logistics\n",
+    "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n",
+    "\n",
+    "## Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lsst.daf.persistence import Butler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lsst.afw.display as afw_display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieving and inspecting a calexp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the first part of this tutorial we will use simulated LSST data from Twinkles, see https://github.com/LSSTDESC/Twinkles/blob/master/README.md"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a data directory and create a Butler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datadir = '/project/shared/data/Twinkles_subset/output_data_v2'\n",
+    "butler = Butler(datadir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other notebooks show how to view what data are available in a Butler object. Here we get a specific one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235}\n",
+    "calexp = butler.get('calexp', **dataId)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In terms of pixel data, a calexp contains an image, a mask, and a variance.\n",
+    "\n",
+    "Let's see how to access the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To show the pixel data, we will make use of the matplotlib backend to `lsst.afw.display`.\n",
+    "\n",
+    "Due to current limitations of this backend, the display must be defined and used in the same code cell, much as matplotlib commands in a notebook must all be in one cell to produce a plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the entire calexp is displayed, masks will be overlaid. Here we will eschew the mask display by showing only the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(calexp.image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To access the pixel values as an array, use the `.array` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = calexp.image.array\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.__class__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's list all the methods for our calexp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp_methods = [m for m in dir(calexp) if not m.startswith('_')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp_methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access the masked Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.maskedImage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access the variance object and the underlying Numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.variance.array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access the mask and its underlying array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.mask.array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the dimensions of the image, mask and variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.getDimensions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image, maskedImage and Exposure objects in `lsst.afw.display` include information on **LSST pixels**, which are 0-based with an optional offset.\n",
+    "\n",
+    "For a calexp these are usually zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.getXY0()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.getX0(), calexp.getY0()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access the wcs object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wcs = calexp.getWcs()\n",
+    "wcs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The WCS object can be used e.g. to convert pixel coordinates into sky coordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wcs.pixelToSky(100.0, 100.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try accessing the metadata, and see what (header) keywords we have."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata = calexp.getMetadata()\n",
+    "# help(metadata)\n",
+    "metadata.getOrderedNames()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata.get('CCDTEMP')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Post release 16.0, the `getName` method will be available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check if our calexp has a PSF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.hasPsf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psf = calexp.getPsf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The PSF object can be used to get a realization of a PSF at a specific point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lsst.geom.coordinates import Point2D\n",
+    "psfimage = psf.computeImage(Point2D(100.,100.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access the calibration object which can be used to convert instrumental magnitudes to AB magnitudes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calib = calexp.getCalib()\n",
+    "calib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Image cutouts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make a cutout from the calexp in our session."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lsst.afw.geom as afwGeom\n",
+    "import lsst.afw.image as afwImage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = afwGeom.Box2I()\n",
+    "bbox.include(afwGeom.Point2I(2200,3200))\n",
+    "bbox.include(afwGeom.Point2I(2800,3800))\n",
+    "cutout = calexp.Factory(calexp, bbox, afwImage.LOCAL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that when the image is displayed, the pixel values relate to the parent image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(cutout.image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The coordinate of the lower-left-hand pixel is XY0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutout.getXY0()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If a cutout was all that was desired from the start, we could have used our BoundingBox together with our Butler to have read in only the cutout."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutout_calexp = butler.get('calexp_sub', bbox=bbox, immediate=True, dataId=dataId)\n",
+    "cutout_calexp.getDimensions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(cutout_calexp.image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `clone` method makes a deep copy. The result can be sliced with a BoundingBox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clone_cutout = calexp.clone()[bbox]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(clone_cutout.image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeat for a coadd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this last section, we will use Hyper Suprime-Cam (HSC) data that has been modified for tutorial purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd_butler = Butler('/project/shared/data/with-globular/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd_butler.getKeys('deepCoadd_calexp')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We cannot use queryMetadata to look up what is available for coadds. This will be fixed in Butler Gen3."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the time being, open a terminal and list files in `/project/shared/data/with-globular/` to see what's available. Or just carry on using the following example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataId = {'filter':'HSC-I', 'tract':9813, 'patch':'4,4'}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve a coadd `calexp`, and see what methods it provides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd = coadd_butler.get('deepCoadd_calexp', dataId)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd_methods = [m for m in dir(coadd) if not m.startswith('_')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd_methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "set(coadd_methods).symmetric_difference(set(calexp_methods))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result of the `set` command above shows that a calexp and a coadd have the same methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `deepCoadd_calexp` and a visit `calexp` differ mainly in the masks and the xy0 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calexp.mask.getMaskPlaneDict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd.mask.getMaskPlaneDict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coadd.getXY0()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the coadd with all masks visible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(coadd)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the image data only with a zoom and pan to some nice-looking galaxies, to show off our hyperbolic arcsine stretch:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display1 = afw_display.Display(frame=1, backend='matplotlib')\n",
+    "display1.scale(\"asinh\", \"zscale\")\n",
+    "display1.mtv(coadd.image)\n",
+    "display1.zoom(16)\n",
+    "display1.pan(18700, 17000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "LSST",
+   "language": "python",
+   "name": "lsst"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Basics/README.rst b/Basics/README.rst
index e16e174c..7ab4161c 100644
--- a/Basics/README.rst
+++ b/Basics/README.rst
@@ -14,6 +14,17 @@ This folder contains a set of tutorial notebooks exploring the basic properties
      - Owner
 
 
+   * - **Calexp_guided_tour.ipynb**
+     - Shows how to read an exposure object from a data repository, and how to access and display various parts.
+     - `ipynb <Calexp_guided_tour.ipynb>`_,
+       `rendered <https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/Basics/Calexp_guided_tour.nbconvert.ipynb>`_
+
+       .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Basics/log/Calexp_guided_tour.svg
+          :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Basics/log/Calexp_guided_tour.log
+
+     - `David Shupe <https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@stargaser>`_
+
+
    * - **Data Inventory**
      - Explore the available datasets in the LSST Science Platform shared folders.
      - `ipynb <DataInventory.ipynb>`_,