Merge pull request #8 from rubin-dp0/tickets/PREOPS-532

fixed schema instructions

Author: MelissaGraham

04_Intro_to_Butler.ipynb

@@ -569,8 +569,7 @@
     "\n",
     "The TAP service is the recommended way to retrieve DP0.1 catalog data for a notebook, and there are several other tutuorials that demonstrate how to use the TAP service.\n",
     "\n",
-    "But if Butler access to catalog data is needed, start by retrieving only the schema of a Butler catalog.\n",
-    "Unlike with TAP schema (as in the first tutorial notebook), the Butler schema do not come with embedded column descriptions. Refer to the DP0.1 Data Products Definitions Document (DPDD) at [dp0-1.lsst.io](http://dp0-1.lsst.io) to find out more about the columns."
+    "But if Butler access to catalog data is needed, an easy way to start is by retrieving only the schema data set for a Butler catalog, which can be done without specifying the ``dataId``. "
    ]
   },
   {
@@ -583,6 +582,77 @@
     "schema_coadd_src.asAstropy()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each of the following lines will print the schema to the screen in different ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# schema_coadd_src.schema\n",
+    "# schema_coadd_src.schema.getNames()\n",
+    "# schema_coadd_src.schema.getOrderedNames()\n",
+    "print('Number of columns in this table = ', len(schema_coadd_src.schema.getNames()) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perhaps you want to search for all schema elements that contain the term 'psf'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define an array that is all of the column names\n",
+    "all_names = schema_coadd_src.schema.getOrderedNames()\n",
+    "\n",
+    "# Loop over the names and look for the term 'psf'\n",
+    "for i,name in enumerate(all_names):\n",
+    "    if name.find('psf') >= 0:\n",
+    "        print( i, name )\n",
+    "del all_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Probably you will want to know more about the values in these columns. You can do that by printing the documentation string in the schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Turn the schema into a python dictionary, to be able to call a column by name\n",
+    "schema_dict = schema_coadd_src.schema.extract('*')\n",
+    "\n",
+    "# Print the associated docstring for each of the named columns of interest\n",
+    "for name in ['base_SdssShape_psf_xx','base_SdssShape_psf_yy','base_SdssShape_psf_xy']:\n",
+    "    doc = schema_dict[name].getField().getDoc()\n",
+    "    print(name, ' = ', doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Refer to the DP0.1 Data Products Definitions Document (DPDD) at [dp0-1.lsst.io](http://dp0-1.lsst.io) to find out more about the columns."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},