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‎06_Comparing_Object_and_Truth_Tables.ipynb‎

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"cells": [
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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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"cell_type": "markdown",
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"### Learning Objectives\n",
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{
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{
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"cell_type": "markdown",
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"### 1. Import Common Python Libraries\n",
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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](https://github.com/rubin-dp0/tutorial-notebooks/blob/main/02_Intermediate_TAP_Query.ipynb). See those notebooks for more details."
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"# from rubin_jupyter_utils.lab.notebook import get_catalog, retrieve_query\n",
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"from rubin_jupyter_utils.lab.notebook import get_tap_service\n",
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"\n",
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"# Deprecated\n",
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"# service = get_catalog()\n",
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"service = get_tap_service()"
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"cell_type": "markdown",
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"### 2. Loading tables with TAP\n",
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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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"### 3. Extract a table of joined results from two tables, using a single query within ADQL\n",
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"Notice the \"%%time\" cell magic we used. This was included to highlight that selecting more than 14000 objects from two tables and joining them takes less than 2 seconds (typically). Doing two separate queries, then joining them using `pandas`, typically takes more than a full minute."
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"### 4. Compare table values by plotting"
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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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"fig, ax = plt.subplots(1, 2, figsize=(15, 8))\n",
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"\n",
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"plt.sca(ax[0]) # set the first axis as current\n",
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"# plt.rcParams.update({'figure.figsize' : (9, 11)})\n",
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"\n",
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"plt.plot(truth_plus_meas['mag_g_cModel'][gx] - truth_plus_meas['mag_i_cModel'][gx],\n",
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" truth_plus_meas['mag_g_cModel'][gx], 'k.', alpha=0.2, label='galaxies')\n",
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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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