_scienceFluxSigma` is the standard deviation in the forced PSF fluxes measured on the science (direct) images.\n",
+ "It has the same definition as in Section 3.5, and also uses the unweighted mean flux in its formula.\n",
+ "\n",
+ "Demonstrate how to derive these statistics for the `scienceFlux`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee9ee4ea-3d01-4c7b-b3ee-31401b07236a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for f, filt in enumerate(use_filters):\n",
+ " diao_sci_mean = float(star_diaObject[filt + '_scienceFluxMean'])\n",
+ " diao_sci_meane = float(star_diaObject[filt + '_scienceFluxMeanErr'])\n",
+ " diao_sci_sigma = float(star_diaObject[filt + '_scienceFluxSigma'])\n",
+ " tx = np.where(star_diaSources['band'] == filt)[0]\n",
+ " dias_sci_w = 1.0/(star_diaSources['scienceFluxErr'][tx]**2)\n",
+ " dias_sci_wmean = np.sum(star_diaSources['scienceFlux'][tx] * dias_sci_w) / np.sum(dias_sci_w)\n",
+ " dias_sci_meane = 1.0 / np.sqrt(np.sum(dias_sci_w))\n",
+ " dias_sci_mean = np.sum(star_diaSources['scienceFlux'][tx])/len(tx)\n",
+ " dias_sci_sigma = np.sqrt(np.sum((star_diaSources['scienceFlux'][tx]\n",
+ " - dias_sci_mean)**2)/(len(tx)-1))\n",
+ " print(filt + '-band diaObject diaSource')\n",
+ " print('Mean %10.0f %10.0f ' % (diao_sci_mean, dias_sci_wmean))\n",
+ " print('MeanErr %10.2f %10.2f ' % (diao_sci_meane, dias_sci_meane))\n",
+ " print('Sigma %10.0f %10.0f ' % (diao_sci_sigma, dias_sci_sigma))\n",
+ " if f == 0:\n",
+ " print(' ')\n",
+ " del diao_sci_mean, diao_sci_meane, diao_sci_sigma\n",
+ " del tx, dias_sci_w, dias_sci_wmean, dias_sci_meane, dias_sci_mean, dias_sci_sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3920ec28-8976-443e-a6dd-f8622c283c59",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "LSST",
+ "language": "python",
+ "name": "lsst"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ },
+ "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/DP1/200_Data_Products/207_Timeseries_features/207_2_Timeseries_distributions.ipynb b/DP1/200_Data_Products/207_Timeseries_features/207_2_Timeseries_distributions.ipynb
new file mode 100644
index 0000000..f221451
--- /dev/null
+++ b/DP1/200_Data_Products/207_Timeseries_features/207_2_Timeseries_distributions.ipynb
@@ -0,0 +1,1155 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "2bf47867-c587-448a-860b-9833ea5d5586.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "325aa8a5-92fd-4913-a471-ad1617343be6",
+ "metadata": {},
+ "source": [
+ "# 207.2. Timeseries distributions\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "For the Rubin Science Platform at data.lsst.cloud.\\\n",
+ "Data Release: [Data Preview 1](https://dp1.lsst.io)\\\n",
+ "Container Size: Large\\\n",
+ "LSST Science Pipelines version: r29.2.0\\\n",
+ "Last verified to run: 2025-12-30\\\n",
+ "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
+ "DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9da1a210-d858-42fe-8591-570965b8be1a",
+ "metadata": {},
+ "source": [
+ "**Learning objective:** Understand the distributions of timeseries features values in the `DiaObject` table.\n",
+ "\n",
+ "**LSST data products:** `DiaObject`, `DiaSource`\n",
+ "\n",
+ "**Packages:** \n",
+ "\n",
+ "**Credit:**\n",
+ "Originally developed by the Rubin Community Science team.\n",
+ "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
+ "\n",
+ "**Get Support:**\n",
+ "Everyone is encouraged to ask questions or raise issues in the [Support Category](https://community.lsst.org/c/support) of the Rubin Community Forum.\n",
+ "Rubin staff will respond to all questions posted there."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfc73be0",
+ "metadata": {},
+ "source": [
+ "## 1. Introduction\n",
+ "\n",
+ "This tutorial illustrates the distributions of the timeseries features values in the `DiaObject` table, in the context of the limited time baseline and cadence of Data Preview 1.\n",
+ "\n",
+ "This tutorial serves as a guide for interpreting the timerseries features values, and an illustration of \"inlier\" and \"outlier\" values.\n",
+ "For each of the timeseries features this tutorial plots their distributions and correlations with other features, and marks the values of a known variable star and a known extragalactic transient as examples.\n",
+ "\n",
+ "The values and distributions of these timeseries features - and thus what constitutes \"inliers\" and \"outliers\" - will be different in future data releases, for a few reasons.\n",
+ "One reason is that future values would be derived from observations obtained with different cadences and seasons (the data for DP1 was obtained over only a seven week period).\n",
+ "Another reason is that the DP1 template images were made with the third of the images from that seven week period which had the best seeing.\n",
+ "Astrophysical transients are thus present in the template image, leading to over-subtractions and negative difference-image fluxes.\n",
+ "In the future, templates will be made from data obtained the year before.\n",
+ "\n",
+ "For future data releases, additional timeseries features are likely to be included, as discussed in Data Management Tech Note 118 [Review of Timeseries Features (DMTN-118)](https://dmtn-118.lsst.io/).\n",
+ "\n",
+ "**Related tutorials:** The 200-level tutorials on the `DiaObject` and `DiaSource` tables, and the other tutorial in the 207 series on timeseries features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc36f107",
+ "metadata": {},
+ "source": [
+ "### 1.1. Import packages\n",
+ "\n",
+ "Import `numpy`, a fundamental package for scientific computing with arrays in Python ([numpy.org](https://numpy.org)), and `matplotlib`, a comprehensive library for data visualization ([matplotlib.org](https://matplotlib.org/); [matplotlib gallery](https://matplotlib.org/stable/gallery/index.html)).\n",
+ "\n",
+ "From the `lsst` package, import modules for accessing the Table Access Protocol (TAP) service."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cddc1458",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from lsst.rsp import get_tap_service"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c217adff-25ed-4fce-95e7-8aa04630f6cc",
+ "metadata": {},
+ "source": [
+ "### 1.2. Define parameters and functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3383f6e-8c34-4cb7-aa2f-12e9b7f8efc0",
+ "metadata": {},
+ "source": [
+ "Get an instance of the TAP service, and assert that it exists."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e8184089-8a3e-4666-a194-5362a8faa541",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "service = get_tap_service(\"tap\")\n",
+ "assert service is not None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c794f7c-a7e7-474f-a659-71959ee4a2a7",
+ "metadata": {},
+ "source": [
+ "Define two functions: one to plot a standard histogram for a given timeseries feature with vertical lines to mark the two example objects, and one to plot a standard scatter plot to compare two features with colored edges for the points representing the two example objects.\n",
+ "This function will only work in this notebook, while the datasets retrieved in Section 2 are in memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d18d528-6e00-4cbf-ab41-52f4e6f448da",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def feature_histogram(feature, nbins, bounds=None):\n",
+ " \"\"\"\n",
+ " Plot the histogram for a given feature, and mark the values\n",
+ " of the known variable star and transient.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " feature: str\n",
+ " Column name of the timeseries feature.\n",
+ " nbins: int\n",
+ " Number of histogram bins.\n",
+ " bounds: float[4]\n",
+ " A four-element array of x1, x2, y1, y2.\n",
+ " \"\"\"\n",
+ "\n",
+ " plt.figure(figsize=(6, 3))\n",
+ " temp = np.concatenate((np.asarray(star_sample_diaObject[feature]),\n",
+ " np.asarray(at_sample_diaObject[feature])))\n",
+ " use_bins = np.linspace(np.min(temp), np.max(temp), nbins)\n",
+ " plt.hist(star_sample_diaObject[feature], bins=use_bins, histtype='step', log=True,\n",
+ " lw=3, alpha=0.4, color='black', label='Low-b field')\n",
+ " plt.hist(at_sample_diaObject[feature], bins=use_bins, histtype='step', log=True,\n",
+ " lw=1, color='black', label='ECDFS')\n",
+ " plt.axvline(star_diaObject[feature], lw=3, alpha=0.4, color='black', label='star')\n",
+ " plt.axvline(at_diaObject[feature], lw=1, color='black', label='transient')\n",
+ " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+ " plt.xlabel(feature)\n",
+ " plt.ylabel('N(diaObjects)')\n",
+ " if bounds is not None:\n",
+ " plt.xlim([bounds[0], bounds[1]])\n",
+ " plt.ylim([bounds[2], bounds[3]])\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c106555-a67b-4203-b4a1-74a8f1eb477e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def feature_scatter(feature1, feature2, bounds=None):\n",
+ " \"\"\"\n",
+ " Create a scatter plot of the relation between two features,\n",
+ " and mark the points for the known variable star and transient.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " feature: str\n",
+ " Column name of the timeseries feature.\n",
+ " nbins: int\n",
+ " Number of histogram bins.\n",
+ " bounds: float[4]\n",
+ " A four-element array of x1, x2, y1, y2.\n",
+ " \"\"\"\n",
+ "\n",
+ " plt.figure(figsize=(6, 3))\n",
+ " plt.plot(star_sample_diaObject[feature1], star_sample_diaObject[feature2],\n",
+ " 'o', ms=3, alpha=0.2, mew=0, color='black', label='Low-b field')\n",
+ " plt.plot(at_sample_diaObject[feature1], at_sample_diaObject[feature2],\n",
+ " 'o', ms=1, alpha=0.8, mew=0, color='black', label='ECDFS')\n",
+ " plt.plot(star_diaObject[feature1], star_diaObject[feature2],\n",
+ " '*', ms=10, mew=1, mec='cyan', color='black', label='star')\n",
+ " plt.plot(at_diaObject[feature1], at_diaObject[feature2],\n",
+ " 's', ms=6, mew=1, mec='cyan', color='black', label='transient')\n",
+ " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+ " plt.xlabel(feature1)\n",
+ " plt.ylabel(feature2)\n",
+ " if bounds is not None:\n",
+ " plt.xlim([bounds[0], bounds[1]])\n",
+ " plt.ylim([bounds[2], bounds[3]])\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77b2baae-846f-4108-97a0-1b8ea5e2adbd",
+ "metadata": {},
+ "source": [
+ "## 2. Retrieve data\n",
+ "\n",
+ "Retrieve the column names for all of the $r$-band timeseries features from the `DiaObject` table.\n",
+ "Create a comma-separated list of columns to retrieve from the `DiaObject` table, and add the `nDiaSources` column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e1dcf59-ce46-40e1-bdaf-fd370650cb71",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"SELECT column_name \" \\\n",
+ " \"FROM tap_schema.columns \" \\\n",
+ " \"WHERE table_name = 'dp1.DiaObject'\"\n",
+ "results = service.search(query).to_table()\n",
+ "\n",
+ "columns_list = ''\n",
+ "for name in results['column_name']:\n",
+ " if name.find('r_') == 0:\n",
+ " columns_list += name + ', '\n",
+ "columns_list += 'nDiaSources'\n",
+ "\n",
+ "del query, results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7457db91-6068-4f4d-abb0-65fbf7dce322",
+ "metadata": {},
+ "source": [
+ "### 2.1. Variable star\n",
+ "\n",
+ "This tutorial uses a known SX Phoenicis-type pulsating variable star captured in the Low Galactic Latitude (\"Low-$b$\") Field of DP1: Gaia DR3 2912281258855051520 (see [Carlin et al. 2025](https://ui.adsabs.harvard.edu/abs/2025RNAAS...9..161C/abstract))."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13b8703b-600c-4569-b553-98884df02587",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "star_diaObjectId = 614435753623027782"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26bc4050-fb25-4c9e-9e4f-5cc842e38c05",
+ "metadata": {},
+ "source": [
+ "Retrieve all columns in the list from the `DiaObject` table for the row corresponding to the variable star."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19419c31-61db-4388-96cc-df9e06a5503b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT ra, dec, {} FROM dp1.DiaObject WHERE diaObjectId = {}\n",
+ " \"\"\".format(columns_list, star_diaObjectId)\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db43ec20-81fc-40e1-bbd6-be4eb7587eb7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "star_diaObject = job.fetch_result().to_table()\n",
+ "assert len(star_diaObject) == 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5eedc5e-f190-4abb-b08e-6ffd574588b6",
+ "metadata": {},
+ "source": [
+ "Option to view `star_diaObject`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "680497e9-be7d-48b2-abf3-f0be5d6eb4d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# star_diaObject"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16eeb034-6728-406e-bfc8-7b875c3a9416",
+ "metadata": {},
+ "source": [
+ "Retrieve the $r$-band difference-image (`psfFlux`) and direct-image (`scienceFlux`) measurements from the `DiaSource` table, their errors, and the midpoint of the exposure time as a Modified Julian Date (MJD)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5049addc-4d40-4694-b767-8a5d1b4cf5c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT psfFlux, psfFluxErr, scienceFlux,\n",
+ " scienceFluxErr, band, midpointMjdTai\n",
+ " FROM dp1.DiaSource\n",
+ " WHERE band = 'r' AND diaObjectId = {}\n",
+ " \"\"\".format(str(star_diaObjectId))\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0eeb7633-c004-44b0-a146-a48f028ae9f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "star_diaSources = job.fetch_result().to_table()\n",
+ "assert len(star_diaSources) == 68"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9838cf6b-d617-4033-b760-2c417ff11be5",
+ "metadata": {},
+ "source": [
+ "Visualize the measured fluxes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b62a9527-75fc-4323-9456-b9f78d4b02f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 4), sharex=True)\n",
+ "ax1.errorbar(star_diaSources['midpointMjdTai'], star_diaSources['psfFlux'],\n",
+ " yerr=star_diaSources['psfFluxErr'],\n",
+ " fmt='o', ms=5, alpha=0.5, mew=0, color='black')\n",
+ "ax2.errorbar(star_diaSources['midpointMjdTai'], star_diaSources['scienceFlux'],\n",
+ " yerr=star_diaSources['scienceFluxErr'],\n",
+ " fmt='s', ms=5, alpha=0.5, mew=0, color='black')\n",
+ "ax1.set_ylabel('psfFlux [nJy]')\n",
+ "ax2.set_ylabel('scienceFlux [nJy]')\n",
+ "ax2.set_xlabel('MJD [d]')\n",
+ "plt.suptitle('r-band DiaSource fluxes vs. time for a known variable star')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba0b355c-01a1-4666-87b2-0345e3f464b3",
+ "metadata": {},
+ "source": [
+ "> **Figure 1**: The `psfFlux` (difference image flux; top) and `scienceFlux` (direct image flux; bottom) for all visits in which the star was detected with SNR>5 in the difference image, versus the MJD of the visit, for the $r$-band. The errors bars on the flux are, for most data points, relatively too small to be seen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dffd38a2-e2a2-4731-bd50-8b54f4ff4d0d",
+ "metadata": {},
+ "source": [
+ "### 2.2. Extragalactic transient\n",
+ "\n",
+ "This tutorial uses an extragalactic astrophysical transient (AT) that was captured in the Extended Chandra Deep Field South (ECDFS) for DP1: [AT 2024aigg](https://www.wis-tns.org/object/2024aigg)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b7e54c0-d680-4136-8595-2b7a4f7d09d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "at_diaObjectId = 611255759837069401"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "166f91d8-e4d9-4288-b68e-be7f21be631e",
+ "metadata": {},
+ "source": [
+ "Retrieve the `DiaObject` record and the associated `DiaSource` records, and plot the lightcurve."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd8b43e4-d8b5-4419-b8e6-989c43db0a33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT ra, dec, {} FROM dp1.DiaObject WHERE diaObjectId = {}\n",
+ " \"\"\".format(columns_list, at_diaObjectId)\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7186a221-53ae-4e5b-98b8-c67921146a2b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "at_diaObject = job.fetch_result().to_table()\n",
+ "assert len(at_diaObject) == 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7503223-1456-4d5a-8f80-c6f7699cb5fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# at_diaObject"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba3744e6-c85c-4663-ae6c-d3e3f70dada6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT psfFlux, psfFluxErr, scienceFlux,\n",
+ " scienceFluxErr, band, midpointMjdTai\n",
+ " FROM dp1.DiaSource\n",
+ " WHERE band = 'r' AND diaObjectId = {}\n",
+ " \"\"\".format(str(at_diaObjectId))\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c41d5546-7375-4952-acc0-ffef6c78ab8c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "at_diaSources = job.fetch_result().to_table()\n",
+ "assert len(at_diaSources) == 91"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c3af3f4-591a-4faa-994e-6d29cfb17707",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 4), sharex=True)\n",
+ "ax1.errorbar(at_diaSources['midpointMjdTai'], at_diaSources['psfFlux'],\n",
+ " yerr=at_diaSources['psfFluxErr'],\n",
+ " fmt='o', ms=5, alpha=0.5, mew=0, color='black')\n",
+ "ax2.errorbar(at_diaSources['midpointMjdTai'], at_diaSources['scienceFlux'],\n",
+ " yerr=at_diaSources['scienceFluxErr'],\n",
+ " fmt='s', ms=5, alpha=0.5, mew=0, color='black')\n",
+ "ax1.set_ylabel('psfFlux [nJy]')\n",
+ "ax2.set_ylabel('scienceFlux [nJy]')\n",
+ "ax2.set_xlabel('MJD [d]')\n",
+ "plt.suptitle('r-band DiaSource fluxes vs. time for an extragalactic transient')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04aab84a-3f2d-4d03-8175-6a4948ac1fc7",
+ "metadata": {},
+ "source": [
+ "> **Figure 2**: The `psfFlux` (difference image flux; top) and `scienceFlux` (direct image flux; bottom) for all visits in which the star was detected with SNR>5 in the difference image, versus the MJD of the visit, for the $r$-band. The errors bars on the flux are, for most data points, relatively too small to be seen.\n",
+ "\n",
+ "Notice that visits for which this object's brightness was similar to that of its brightness in the template image are missing from the lightcurve above (i.e., visits around MJD $\\sim 606404$ days, when `psfFlux` was near 0 nJy).\n",
+ "\n",
+ "Notice also that because this object is an astrophysical transient embedded in its host galaxy, the `scienceFlux` measurements are contaminated by host galaxy light.\n",
+ "This contamination is not simply a \"pedestal\" (i.e., are not a constant amount added to the `psfFlux`) because the forced photometry uses the PSF of the image.\n",
+ "For images with worse seeing, the PSF FWHM is larger and the forced flux measurement includes more host galaxy light.\n",
+ "The timeseries features based on `scienceFlux` will reflect this contamination."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f90f0794-c146-4ad2-8212-7a26c06fcc49",
+ "metadata": {},
+ "source": [
+ "### 2.3. Samples of diaObjects\n",
+ "\n",
+ "Retrieve two samples of `diaObjects`: one from the same DP1 field as the star (the \"Low-$b$\" field), and one from the same DP1 field as the transient (the ECDFS field).\n",
+ "\n",
+ "To define the cone search, use the coordinates of the star and transient, and a 2.0 deg radius, which will encompass the relevant DP1 field.\n",
+ "Print the coordinates of the star and transient."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "763fb7a1-3ccc-4d19-9f15-1c0bb795b0a2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('star coordinates (Low-b field): %9.6f %10.6f' %\n",
+ " (float(star_diaObject['ra']), float(star_diaObject['dec'])))\n",
+ "print('transient coordinates (ECDFS field): %9.6f %10.6f' %\n",
+ " (float(at_diaObject['ra']), float(at_diaObject['dec'])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c4e79cd-aa58-462d-b908-9a731e09ba17",
+ "metadata": {},
+ "source": [
+ "Set maximum and minimum values to apply to the `psfFluxMean` and `scienceFluxMean` values of `diaObjects`.\n",
+ "Use a magnitude of 17.5 mag, and convert it into positive and negative difference-image fluxes.\n",
+ "These flux constraints will help to omit sources that are anywhere near saturation from the samples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1181a817-c6e4-443f-8c46-c8f7d0d2ca2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flux_max = np.power(10, (17.5-31.4)/(-2.5))\n",
+ "flux_min = -1.0 * flux_max\n",
+ "print('difference flux max and min: %9.0f %9.0f' % (flux_max, flux_min))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da4b45d9-ee5b-4ba5-a63f-8f9e25c029c5",
+ "metadata": {},
+ "source": [
+ "Apply the constraints in the ADQL query statement, and add a requirement that only `diaObjects` which were detected at least 10 times in the $r$-band be retrieved.\n",
+ "This will constraint will mean that the timeseries features used in this tutorial are all measured from at least 10 detections.\n",
+ "\n",
+ "Create the query for the star's field (the \"Low-$b$\" field) and submit the query job."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1b02fa9-ca90-4eae-ba72-5aaaeec2bd88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT ra, dec, {} FROM dp1.DiaObject\n",
+ " WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', {}, {}, 2.0)) = 1\n",
+ " AND r_psfFluxMean < {} AND r_psfFluxMean > {}\n",
+ " AND r_psfFluxMax < {} AND r_psfFluxMin > {}\n",
+ " AND r_scienceFluxMean < {} AND r_psfFluxNdata > 10\n",
+ " \"\"\".format(columns_list, float(star_diaObject['ra']), float(star_diaObject['dec']),\n",
+ " flux_max, flux_min, flux_max, flux_min, flux_max)\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e571c49f-ddb9-4928-99bf-048de9e061c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "star_sample_diaObject = job.fetch_result().to_table()\n",
+ "print(len(star_sample_diaObject))\n",
+ "assert len(star_sample_diaObject) == 2769"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfc0a85d-3327-4ce8-8492-097381e30e25",
+ "metadata": {},
+ "source": [
+ "Create the query for the transients's field (the ECDFS) and submit the query job."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "039b4ee3-1f00-4762-b917-f656946a931d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT ra, dec, {} FROM dp1.DiaObject\n",
+ " WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', {}, {}, 2.0)) = 1\n",
+ " AND r_psfFluxMean < {} AND r_psfFluxMean > {}\n",
+ " AND r_psfFluxMax < {} AND r_psfFluxMin > {}\n",
+ " AND r_scienceFluxMean < {} AND r_psfFluxNdata > 10\n",
+ " \"\"\".format(columns_list, float(at_diaObject['ra']), float(at_diaObject['dec']),\n",
+ " flux_max, flux_min, flux_max, flux_min, flux_max)\n",
+ "job = service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "print('Job phase is', job.phase)\n",
+ "if job.phase == 'ERROR':\n",
+ " job.raise_if_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fae80c08-3680-4a97-80c1-99722d0016ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert job.phase == 'COMPLETED'\n",
+ "at_sample_diaObject = job.fetch_result().to_table()\n",
+ "print(len(at_sample_diaObject))\n",
+ "assert len(at_sample_diaObject) == 919"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4143d03-a199-4ca2-b43c-50136f4d7cbe",
+ "metadata": {},
+ "source": [
+ "## 3. Timeseries features\n",
+ "\n",
+ "For each of the timeseries features in turn, generate histograms and scatter plots to illustrate the distributions and correlations between the values.\n",
+ "On each plot, mark the values of the known variable star and transient.\n",
+ "\n",
+ "Recall that, as explained in the introduction, the timeseries features for future data releases will have different values, because they will be derived from datasets with very different cadences and observing windows than Data Preview 1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88dfdcad-cfbc-4aa9-ac88-9928f92860bf",
+ "metadata": {},
+ "source": [
+ "### 3.1. Ndata"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ba891fd-b07c-4c91-a6b4-645ea93a6cf5",
+ "metadata": {},
+ "source": [
+ "Create the histogram for the `psfFluxNdata` feature."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c52d33ce-a44f-4965-bc40-fd97a6ac1cf4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxNdata', 30)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c66e3e1-2aec-464a-9e7d-36a8782cc36e",
+ "metadata": {},
+ "source": [
+ "> **Figure 3:** The number of `diaObjects` with a given number of `diaSources` (number of difference image detections) in the $r$-band. While the star is in the tail of its population's distribution, the transient is not."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "41e2dfe1-296b-4879-8a4f-6932f286ddaa",
+ "metadata": {},
+ "source": [
+ "### 3.2. Min, Max, Mean\n",
+ "\n",
+ "Option to display any or all of the standard histograms for these features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b8dd34ca-a576-42f1-adbc-8dad10b5a614",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# feature_histogram('r_psfFluxMin', 50)\n",
+ "# feature_histogram('r_psfFluxMax', 50)\n",
+ "# feature_histogram('r_psfFluxMean', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cca6e59-df9f-4229-a209-165049c1eac0",
+ "metadata": {},
+ "source": [
+ "Instead of considering the minimum and maximum, derive the difference-flux amplitude as the maximum, minus the minimum: $A = f_{max}-f_{min}$.\n",
+ "The flux amplitude will always be positive, and so can be logged."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dfcee131-d60a-4426-8d01-b439ba2da4ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "star_diaObject['r_psfFluxAmp'] = star_diaObject['r_psfFluxMax'] - star_diaObject['r_psfFluxMin']\n",
+ "at_diaObject['r_psfFluxAmp'] = at_diaObject['r_psfFluxMax'] - at_diaObject['r_psfFluxMin']\n",
+ "star_sample_diaObject['r_psfFluxAmp'] = star_sample_diaObject['r_psfFluxMax'] \\\n",
+ " - star_sample_diaObject['r_psfFluxMin']\n",
+ "at_sample_diaObject['r_psfFluxAmp'] = at_sample_diaObject['r_psfFluxMax'] \\\n",
+ " - at_sample_diaObject['r_psfFluxMin']\n",
+ "\n",
+ "star_diaObject['r_psfFluxAmpLog'] = np.log10(star_diaObject['r_psfFluxAmp'])\n",
+ "at_diaObject['r_psfFluxAmpLog'] = np.log10(at_diaObject['r_psfFluxAmp'])\n",
+ "star_sample_diaObject['r_psfFluxAmpLog'] = np.log10(star_sample_diaObject['r_psfFluxAmp'])\n",
+ "at_sample_diaObject['r_psfFluxAmpLog'] = np.log10(at_sample_diaObject['r_psfFluxAmp'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "053556ef-e69f-4eb6-82c0-3e1043f9cb56",
+ "metadata": {},
+ "source": [
+ "Display the histogram for the newly-derived difference-flux amplitude feature."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b052b4d4-8e6a-4226-8d45-4fabb72325f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxAmpLog', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26895271-4247-49d0-925c-860a7a641ab8",
+ "metadata": {},
+ "source": [
+ "> **Figure 4:** The distribution of the log of the amplitude values. The star's amplitude is in the tail of the \"Low-$b$\" field sample, and the transient is not. However, as shown in Figure 2, only part of the transient's lightcurve was observed by DP1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d572d65-c52b-4744-a6f5-5b48bd6e1cc4",
+ "metadata": {},
+ "source": [
+ "### 3.3. Sigma, MAD, Chi2\n",
+ "\n",
+ "Add the logarithm for each of the `Sigma`, `MAD`, and `Chi2` features to the tables."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9eaf99d-68d5-4f2d-b7cc-61b4ceebf4b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "star_sample_diaObject['r_psfFluxSigmaLog'] = np.log10(star_sample_diaObject['r_psfFluxSigma'])\n",
+ "at_sample_diaObject['r_psfFluxSigmaLog'] = np.log10(at_sample_diaObject['r_psfFluxSigma'])\n",
+ "star_diaObject['r_psfFluxSigmaLog'] = np.log10(star_diaObject['r_psfFluxSigma'])\n",
+ "at_diaObject['r_psfFluxSigmaLog'] = np.log10(at_diaObject['r_psfFluxSigma'])\n",
+ "\n",
+ "star_sample_diaObject['r_psfFluxMADLog'] = np.log10(star_sample_diaObject['r_psfFluxMAD'])\n",
+ "at_sample_diaObject['r_psfFluxMADLog'] = np.log10(at_sample_diaObject['r_psfFluxMAD'])\n",
+ "star_diaObject['r_psfFluxMADLog'] = np.log10(star_diaObject['r_psfFluxMAD'])\n",
+ "at_diaObject['r_psfFluxMADLog'] = np.log10(at_diaObject['r_psfFluxMAD'])\n",
+ "\n",
+ "star_sample_diaObject['r_psfFluxChi2Log'] = np.log10(star_sample_diaObject['r_psfFluxChi2'])\n",
+ "at_sample_diaObject['r_psfFluxChi2Log'] = np.log10(at_sample_diaObject['r_psfFluxChi2'])\n",
+ "star_diaObject['r_psfFluxChi2Log'] = np.log10(star_diaObject['r_psfFluxChi2'])\n",
+ "at_diaObject['r_psfFluxChi2Log'] = np.log10(at_diaObject['r_psfFluxChi2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "daed908b-e24b-4fa6-bb63-ea6790f3b7cc",
+ "metadata": {},
+ "source": [
+ "Plot the histogram for the log of each of these features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3718556c-67d9-4090-b8e4-4b4f41f4f8cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxSigmaLog', 50)\n",
+ "feature_histogram('r_psfFluxMADLog', 50)\n",
+ "feature_histogram('r_psfFluxChi2Log', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e8012de-17b3-4329-bfef-8327fc4bcf43",
+ "metadata": {},
+ "source": [
+ "> **Figure 5:** The distribution of `psfFluxSigma` (top), `MAD` (middle), and `Chi2` (bottom). Vertical lines mark the star and transient. Both are in the tails of their `Chi2` distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2cf688e-ecfe-4c81-b78d-4fe03c6db4db",
+ "metadata": {},
+ "source": [
+ "Create a scatter plot to show the correlation between the difference-image flux amplitude and the log of `Chi2`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f843e09f-f124-4411-abb6-d10769147123",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_scatter('r_psfFluxAmpLog', 'r_psfFluxChi2Log')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67f079f0-2861-403f-8788-87e6ecbda490",
+ "metadata": {},
+ "source": [
+ "> **Figure 6:** A scatter plot of the log `Chi2` versus `psfFlux` amplitude, in which the known objects are outlier values."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a31a8436-f60c-4903-9de2-714ab0fc2f8c",
+ "metadata": {},
+ "source": [
+ "### 3.4. Skew\n",
+ "\n",
+ "The skewness of a distribution describes its asymetry about its peak, as in the diagram below."
+ ]
+ },
+ {
+ "attachments": {
+ "e82c4536-3537-40c5-aecc-21c5e8c02e68.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "29cdb6cb-ff1c-4e4d-a549-9e5a704ccd3a",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a25b48ff-a154-4054-9331-aeb661860661",
+ "metadata": {},
+ "source": [
+ "> **Figure 7**: An illustration of negatively and positively skewed distributions. By Rodolfo Hermans (Godot) at en.wikipedia ([CC BY-SA 3.0](https://commons.wikimedia.org/w/index.php?curid=4567445))."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d354e75-09dd-4cc4-ad41-1b1d1e8bc7d2",
+ "metadata": {},
+ "source": [
+ "Display the histogram of skew values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79056dfb-560e-426a-88bc-df5c7adb0de7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxSkew', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "802656ea-0924-4d8e-896b-d24bae7046c3",
+ "metadata": {},
+ "source": [
+ ">**Figure 8:** The histograms of skew values, with the known variable star and transient values plotted as vertical lines."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "697eb1de-0ca2-4d6d-9f20-c8165a8aed0d",
+ "metadata": {},
+ "source": [
+ "As with all timeseries features, the skew is affected by detection bias: i.e., brighter difference-image sources are more likely to be detected, which can affect the shape of the flux distribution and thus the skew."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "beafe550-4c6c-42d0-98d6-7a5f934e2358",
+ "metadata": {},
+ "source": [
+ "### 3.5. Percentiles\n",
+ "\n",
+ "The `psfFluxPercentiles` are 5, 25, 50, 75, and 95th percentiles of the cumulative distribution of difference-image fluxes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a08a07ce-25bf-4df3-aa7e-b566a61b54e0",
+ "metadata": {},
+ "source": [
+ "Option to display any of the histograms for the flux percentiles."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6df609e6-3389-437c-a1ab-9908c3bbafd8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# feature_histogram('r_psfFluxPercentile05', 50)\n",
+ "# feature_histogram('r_psfFluxPercentile25', 50)\n",
+ "# feature_histogram('r_psfFluxPercentile50', 50)\n",
+ "# feature_histogram('r_psfFluxPercentile75', 50)\n",
+ "# feature_histogram('r_psfFluxPercentile95', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "430a9bbe-2606-48cb-9479-c399bcbefe05",
+ "metadata": {},
+ "source": [
+ "Create a scatter plot to show how the 50th percentile correlates with the mean of the `psfFlux` values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2585f4a3-6d13-4e79-b3f1-3e74d0a5b4f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_scatter('r_psfFluxMean', 'r_psfFluxPercentile50',\n",
+ " bounds=[-15000, 15000, -20000, 20000])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3802c7ff-06e8-4e39-af2d-5f9c0349ebbb",
+ "metadata": {},
+ "source": [
+ "> **Figure 9:** The correlation between the 50th percentile and the mean of the `psfFlux`, with the star's value seen as a clear outlier."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6d4a069-35cc-4bdc-b548-3a2702ea96a4",
+ "metadata": {},
+ "source": [
+ "### 3.6. StetsonJ\n",
+ "\n",
+ "A variability index developed for Cepheids (defined in Stetson 1996). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d80b1fad-29f8-4b4a-85a7-4038b0a33e76",
+ "metadata": {},
+ "source": [
+ "Plot the distributions of the StetsonJ parameters for the sample of `diaObjects`, and mark the values of the known variable star."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff7d5f49-b5bf-47fd-a98c-16ec036cffa8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxStetsonJ', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "925a360a-014e-455b-957e-834a8e397b8f",
+ "metadata": {},
+ "source": [
+ "> **Figure 10:** The distributions of StetsonJ parameter for the $g$- and $r$-band (top and bottom), with the StetsonJ parameter for the known variable star marked as a vertical line.\n",
+ "\n",
+ "Since the known variable star is pulsating, and the StetsonJ parameter is designed to identify and characterize pulsating stars (Cepheids, specifically), it is not surprising that the known variable star is an outlier on this distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2f4fd9c-f718-4f04-93d7-b83f030373c8",
+ "metadata": {},
+ "source": [
+ "### 3.7. Linear fit\n",
+ "\n",
+ "The `psfFluxLinearSlope` and `psfFluxLinearIntercept` are the result of fitting a linear regression using the `scipy.optimize.lsq_linear` function.\n",
+ "The `psfFluxMaxSlope` is the maximum \"instantaneous\" slope, or the maximum ration of the series of time-ordered values of $\\Delta f / \\Delta t$.\n",
+ "\n",
+ "Plot the histogram of the best fit linear slopes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3328f9d5-5f3d-486a-a9c9-d7a5a3d0fbf8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feature_histogram('r_psfFluxLinearSlope', 100, bounds=[-1000, 1000, 0.5, 1400])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbd8da72-7179-496f-a254-896d93f7154f",
+ "metadata": {},
+ "source": [
+ "> **Figure 11:** The distribution of the slope of linear fits to all difference-image flux light curves, with axis boundaries set to display only the main distribution and not the few outliers.\n",
+ "\n",
+ "As shown in Figure 2, the transient was only observed during its decline, and thus has a negative slope, as also seen above in Figure 11.\n",
+ "\n",
+ "Option to also plot histograms for the other two linear fit parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd1c1976-ee62-4cf0-8cfb-2c0ac85d8e4f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# feature_histogram('r_psfFluxLinearIntercept', 50)\n",
+ "# feature_histogram('r_psfFluxMaxSlope', 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e493207-f280-46a5-b12a-7797edb3231e",
+ "metadata": {},
+ "source": [
+ "### 3.8. Science flux features\n",
+ "\n",
+ "As explained in Section 1, the \"science flux\" is derived from forced photometry on the science image (the direct image, not the difference image).\n",
+ "As further demonstrated in Section 2.2., and Figure 2 especially, for transient objects the science flux has host galaxy contamination.\n",
+ "The science flux and its related timeseries features should really only be used for known variable stars, and so the general distributions of the features are not very useful.\n",
+ "\n",
+ "Option to display the histograms for the timeseries features derived from the science flux."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee9ee4ea-3d01-4c7b-b3ee-31401b07236a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# feature_histogram('r_scienceFluxMean', 50)\n",
+ "# feature_histogram('r_scienceFluxMeanErr', 50)\n",
+ "# feature_histogram('r_scienceFluxSigma', 50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62c6008f-cddf-4f6c-9a74-960355bbd7c9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "LSST",
+ "language": "python",
+ "name": "lsst"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ },
+ "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}