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Clear outputs.
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‎06_Comparing_Object_and_Truth_Tables.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "markdown",
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"id": "bfbea2b1",
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"metadata": {},
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"source": [
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"### 1. Import Common Python Libraries\n",
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{
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"cell_type": "code",
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{
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"cell_type": "markdown",
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"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. See those notebooks for more details."
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "markdown",
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"### 2. Loading tables with TAP\n",
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{
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"cell_type": "code",
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{
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"cell_type": "markdown",
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"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",
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{
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{
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"cell_type": "code",
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{
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"cell_type": "markdown",
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"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",
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{
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"cell_type": "code",
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{
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" \"FROM dp01_dc2_catalogs.truth_match \"\\\n",
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" \"WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 62.0, -37.0, 0.1)) = 1 \"\\\n",
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" \"AND match_objectId >= 0 AND is_good_match = 1\")\n",
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"#results.to_table().show_in_notebook() # To print the table to the screen.\n"
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"#results.to_table().show_in_notebook() # To print the table to the screen."
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{
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"cell_type": "markdown",
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"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."
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{
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"### 3. Merge the two tables and compare measurements to truth values\n",
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{
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"#### Compare the measurements from the Object table to the \"true\" values for some objects.\n",
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"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."
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"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",
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"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",
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"\n",
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"Congratulations! You have now learned how to compare measurements in the DP0.1 catalogs to the \"true\" simulated properties of objects. Have fun exploring more properties!"
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