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Remove show_in_notebook from NB06.
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Lines changed: 41 additions & 34 deletions

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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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"id": "f57b0a8f",
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"id": "b4c448b2",
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"metadata": {},
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"source": [
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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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"id": "de133e88",
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"id": "186d1af7",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "06717364",
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"id": "5a4bf8bf",
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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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"execution_count": null,
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"id": "4249d4a4",
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"id": "0b144186",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "e165773f",
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"id": "c4f7ec82",
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"metadata": {},
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"source": [
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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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"id": "06c690d4",
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"id": "19d846b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set up some options, and import a couple more tools we will need:\n",
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"pandas.set_option('display.max_rows', 100)\n",
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"pandas.set_option('display.max_rows', 200)\n",
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"\n",
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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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},
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{
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"cell_type": "markdown",
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"id": "b37dee2b",
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"id": "4631cbf4",
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"metadata": {},
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"source": [
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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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"execution_count": null,
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"id": "75f87bac",
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"id": "960aa123",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = service.search(\"SELECT description,\\\n",
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" table_name FROM TAP_SCHEMA.tables\")\n",
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"results.to_table().show_in_notebook()"
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"results_tab = results.to_table()\n",
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"results_tab"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e20bb66c",
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"id": "06be5674",
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"metadata": {},
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"source": [
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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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"cell_type": "code",
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"execution_count": null,
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"id": "4132e8ba",
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"id": "03151319",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = service.search(\"SELECT column_name, datatype, description,\\\n",
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" unit from TAP_SCHEMA.columns\\\n",
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" WHERE table_name = 'dp01_dc2_catalogs.object'\")\n",
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"results.to_table().show_in_notebook()"
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"# Note that we use the .to_pandas() method here so that all rows will display.\n",
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"# Astropy will truncate the table for display, whereas we set the maximum number of \n",
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"# rows for pandas to display to 200 in a cell above.\n",
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"results.to_table().to_pandas()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b1b28378",
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"id": "6154c44d",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = service.search(\"SELECT column_name, datatype, description,\\\n",
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" unit from TAP_SCHEMA.columns\\\n",
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" WHERE table_name = 'dp01_dc2_catalogs.truth_match'\")\n",
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"results.to_table().show_in_notebook()"
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"results_tab = results.to_table()\n",
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"results_tab"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cb9b1bc2",
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"id": "31611cc2",
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"metadata": {},
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"source": [
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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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"execution_count": null,
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"id": "b83f89c3",
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"id": "f52289b2",
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"metadata": {},
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"outputs": [],
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"source": [
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" FROM dp01_dc2_catalogs.object\\\n",
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" WHERE CONTAINS(POINT('ICRS', ra, dec),\\\n",
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" CIRCLE('ICRS', 62.0, -37.0, 0.1)) = 1 \")\n",
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"# results.to_table().show_in_notebook() # To print the table to the screen."
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"# results_tab = results.to_table()\n",
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"# results_tab # To print the table to the screen."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1646302",
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"id": "0c724a81",
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"metadata": {},
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"source": [
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" CIRCLE('ICRS', 62.0, -37.0, 0.1)) = 1\\\n",
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" AND match_objectId >= 0\\\n",
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" AND is_good_match = 1\")\n",
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"# results.to_table().show_in_notebook() # To print the table to the screen."
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"# results_tab = results.to_table() \n",
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"# results_tab # To print the table to the screen."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2525473b",
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"id": "8c2e0b33",
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"metadata": {},
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"source": [
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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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"cell_type": "code",
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"id": "11cc80a5",
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"id": "3fe65409",
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},
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{
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"cell_type": "markdown",
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"id": "fde56720",
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"### 3. Merge the two tables and compare measurements to truth values\n",
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{
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"id": "7c36b2b5",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "95178746",
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"cell_type": "markdown",
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"id": "39a4506a",
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"#### Compare the measurements from the Object table to the \"true\" values for some objects.\n",
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{
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},
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"cell_type": "markdown",
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"id": "3bb3c8ec",
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"metadata": {},
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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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{
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"cell_type": "markdown",
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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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"id": "21e17629",
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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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"metadata": {},
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"source": []

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