Remove unused cell.

Author: jeffcarlin

06_Comparing_Object_and_Truth_Tables.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "02961155",
+   "id": "762aaefe",
    "metadata": {},
    "source": [
     "<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5b3f505",
+   "id": "da1f6693",
    "metadata": {},
    "source": [
     "### Learning Objectives\n",
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "059227f5",
+   "id": "8ee0dfea",
    "metadata": {},
    "outputs": [
     {
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b562fc9a",
+   "id": "6e7736bf",
    "metadata": {},
    "source": [
     "### 1. Import Common Python Libraries\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "571db9b6",
+   "id": "10073880",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5804cfca",
+   "id": "bd6f5847",
    "metadata": {},
    "source": [
     "To access tables, we will use the TAP service in a similar manner to what we showed in the [Intro to DP0 notebook](https://github.com/rubin-dp0/tutorial-notebooks/blob/main/01_Intro_to_DP0_Notebooks.ipynb), and explored further in the [TAP tutorial notebook](https://github.com/rubin-dp0/tutorial-notebooks/blob/main/02_Intermediate_TAP_Query.ipynb). See those notebooks for more details."
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "9cd8365c",
+   "id": "d92075a0",
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0692f75b",
+   "id": "2c360ee6",
    "metadata": {},
    "source": [
     "### 2. Loading tables with TAP\n",
@@ -120,7 +120,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "951e7454",
+   "id": "542205dc",
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34671835",
+   "id": "fed3c407",
    "metadata": {},
    "source": [
     "For our analysis, let's choose the Object table, `dp01_dc2_catalogs.object`, and then we will compare the measurements from this table to the \"truth\" values from `dp01_dc2_catalogs.truth_match`.\n",
@@ -186,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8d8582e7",
+   "id": "f8c99854",
    "metadata": {},
    "outputs": [
     {
@@ -1480,7 +1480,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d11c682b",
+   "id": "cbf6cd38",
    "metadata": {},
    "outputs": [
     {
@@ -1558,7 +1558,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "706be8e0",
+   "id": "548c4deb",
    "metadata": {},
    "source": [
     "For this exploration, we will select a small region of sky around a random RA, Dec position. The following two cells read data centered on (RA, Dec) = (62.0, -37.0) degrees, within a radius of 0.1 degrees, for first the Object table, then the Truth-Match table. Note that we are selecting only a subset of the columns seen in the schema above. You can add or remove columns as you wish.\n",
@@ -1569,7 +1569,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "01f5a6a1",
+   "id": "31cb6776",
    "metadata": {},
    "outputs": [
     {
@@ -1600,7 +1600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92bddbf7",
+   "id": "c47251a0",
    "metadata": {},
    "source": [
     "Note: the reason for including the timing of the cells' execution will become clear later in this notebook."
@@ -1609,7 +1609,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "9a9e1d71",
+   "id": "f301e7bf",
    "metadata": {},
    "outputs": [
     {
@@ -1641,7 +1641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28f32658",
+   "id": "0d31c06b",
    "metadata": {},
    "source": [
     "These tables will be much easier to work with as `pandas` \"dataframes\". The query results have convenient methods that we can use to convert them."
@@ -1650,7 +1650,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "e00c223b",
+   "id": "cad26f3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1660,7 +1660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7959876d",
+   "id": "92a57677",
    "metadata": {},
    "source": [
     "### 3. Merge the two tables and compare measurements to truth values\n",
@@ -1671,7 +1671,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "968c4de8",
+   "id": "45e26c53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1692,30 +1692,9 @@
     "#                                suffixes=['_obj', '_truth'])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "e0501732",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14428"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tmatch_pd.index)"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "b64cb2f7",
+   "id": "efd48a2b",
    "metadata": {},
    "source": [
     "### 4. Do the same table join, but as a single query within ADQL\n",
@@ -1728,7 +1707,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "d789c1b8",
+   "id": "37da07ec",
    "metadata": {},
    "outputs": [
     {
@@ -1763,7 +1742,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "18c5f0e2",
+   "id": "1aaec194",
    "metadata": {},
    "outputs": [
     {
@@ -1783,7 +1762,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "656708df",
+   "id": "947b57c4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1817,7 +1796,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ac5ce1f",
+   "id": "70725f50",
    "metadata": {},
    "source": [
     "#### Compare the measurements from the Object table to the \"true\" values for some objects.\n",
@@ -1830,7 +1809,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "fc1537e0",
+   "id": "63794774",
    "metadata": {},
    "outputs": [
     {
@@ -1849,7 +1828,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e48f05a",
+   "id": "0b6d075a",
    "metadata": {},
    "source": [
     "Just to confirm that things look like we expect, let's plot a color-magnitude (g vs. g-i) and color-color (r-i vs. g-r) diagram."
@@ -1858,7 +1837,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "1a10bfe0",
+   "id": "c800041b",
    "metadata": {},
    "outputs": [
     {
@@ -1909,7 +1888,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59bc08df",
+   "id": "f10bef95",
    "metadata": {},
    "source": [
     "Looks pretty normal - the stellar locus in color-color space is right where one expects it to be, and the galaxies dominate at the faint end of the CMD. \n",
@@ -1920,7 +1899,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "aa255174",
+   "id": "d7d71847",
    "metadata": {},
    "outputs": [
     {
@@ -1954,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca567c55",
+   "id": "65b4c108",
    "metadata": {},
    "source": [
     "Well, that looks good -- the ratio of measured to true fluxes is centered on 1.0. It seems like the fluxes are recovered pretty well, on average.\n",
@@ -1965,7 +1944,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4780cc71",
+   "id": "ecffbcbe",
    "metadata": {},
    "outputs": [],
    "source": []