{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Local background estimation\n", "\n", "`opticam` supports specification of local background estimators, which can improve the background estimation in cases where the background varies considerably across an image. In this notebook, I will demonstrate how to define a local background estimator for use with `opticam`, as well as explain `opticam`'s default local background estimator.\n", "\n", "## Generating Data\n", "\n", "First thing's first, we need to generate some dummy data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:17.632382Z", "iopub.status.busy": "2026-02-16T18:31:17.632297Z", "iopub.status.idle": "2026-02-16T18:31:22.261230Z", "shell.execute_reply": "2026-02-16T18:31:22.260883Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] variable source is at (131, 115)\n", "[OPTICAM] variability RMS: 0.02 %\n", "[OPTICAM] variability frequency: 0.135 Hz\n", "[OPTICAM] variability phase lags:\n", " [OPTICAM] g-band: 0.000 radians\n", " [OPTICAM] r-band: 1.571 radians\n", " [OPTICAM] i-band: 3.142 radians\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "Generating observations: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Creating source catalogs.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "[OPTICAM] Aligning 1:g images: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Plot saved to /tmp/tmptpynue59/out/reduced/diag/background.pdf.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reducer = opticam.Reducer(\n", " data_directory=out_dir / 'data',\n", " out_directory=out_dir / 'reduced',\n", " )\n", "\n", "reducer.create_catalogs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `DefaultLocalBackground`\n", "\n", "Now we look at the default local background estimator, `DefaultLocalBackground`. The default local background estimator uses an elliptical annulus that scales the `semimajor_sigma` and `semiminor_sigma` parameters of the catalogue's `psf_params` attribute, which is defined when the source catalogs are created (or read from file if the catalogs were created previously). Let's see what this dictionary looks like:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:34.123061Z", "iopub.status.busy": "2026-02-16T18:31:34.122981Z", "iopub.status.idle": "2026-02-16T18:31:34.124900Z", "shell.execute_reply": "2026-02-16T18:31:34.124587Z" } }, "outputs": [ { "data": { "text/plain": [ "{'1:g': {'semimajor_sigma': np.float64(1.413218686650065),\n", " 'semiminor_sigma': np.float64(1.3790703013931664),\n", " 'orientation': np.float64(-75.23359473318958)},\n", " '2:r': {'semimajor_sigma': np.float64(1.4132191514395016),\n", " 'semiminor_sigma': np.float64(1.3790698107870591),\n", " 'orientation': np.float64(-75.23520649261539)},\n", " '3:i': {'semimajor_sigma': np.float64(1.4132373104484879),\n", " 'semiminor_sigma': np.float64(1.3790481577857459),\n", " 'orientation': np.float64(-75.23624808916581)}}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reducer.psf_params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that all three cameras have the same PSF parameters, since these simulated data are the same for each camera. In practise, the PSF parameters will generally be different between cameras. \n", "\n", "We can see each camera has three PSF parameters:\n", "- `semimajor_sigma`: the standard deviation of the PSF along the semi-major axis (assuming a 2D Gaussian PSF)\n", "- `semiminor_sigma`: the standard deviation of the PSF along the semi-minor axis (assuming a 2D Gaussian PSF)\n", "- `orientation`: the orientation of the PSF in degrees\n", "\n", "When defining a custom local background estimator, it's a good idea to use these values to define your annulus (more on this later). For now, let's perform photometry using the default local background estimator:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:34.125688Z", "iopub.status.busy": "2026-02-16T18:31:34.125590Z", "iopub.status.idle": "2026-02-16T18:31:43.610828Z", "shell.execute_reply": "2026-02-16T18:31:43.610476Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] [OPTICAM] Photometry results will be saved to lcs/optimal_annulus in out/reduced.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "[OPTICAM] Performing photometry on 1:g images: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phot = opticam.OptimalPhotometer(\n", " local_background_estimator=opticam.DefaultLocalBackground(),\n", ")\n", "\n", "reducer.photometry(phot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the resulting light curve file for a random source:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:43.611897Z", "iopub.status.busy": "2026-02-16T18:31:43.611816Z", "iopub.status.idle": "2026-02-16T18:31:43.625612Z", "shell.execute_reply": "2026-02-16T18:31:43.625282Z" } }, "outputs": [ { "data": { "text/html": [ "
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160309.999814529.8228427.979934100.6141869.383994
260309.999826513.3904108.106454100.50085310.390129
360309.999837517.1831067.825227102.1831998.896059
460309.999849513.3691147.994656101.3340629.896461
560309.999861485.2714917.913833100.13050310.254058
660309.999872512.0763097.98243199.4341779.829064
760309.999884519.5166367.93373999.5445239.452757
860309.999895508.9163317.87017399.3568049.532862
960309.999907536.0172287.92855999.2401229.023487
\n", "
" ], "text/plain": [ " BMJD flux flux_err bkg bkg_err\n", "0 60309.999803 523.848788 8.282679 101.069653 10.799642\n", "1 60309.999814 529.822842 7.979934 100.614186 9.383994\n", "2 60309.999826 513.390410 8.106454 100.500853 10.390129\n", "3 60309.999837 517.183106 7.825227 102.183199 8.896059\n", "4 60309.999849 513.369114 7.994656 101.334062 9.896461\n", "5 60309.999861 485.271491 7.913833 100.130503 10.254058\n", "6 60309.999872 512.076309 7.982431 99.434177 9.829064\n", "7 60309.999884 519.516636 7.933739 99.544523 9.452757\n", "8 60309.999895 508.916331 7.870173 99.356804 9.532862\n", "9 60309.999907 536.017228 7.928559 99.240122 9.023487" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "default_g_band_source_2_df = pd.read_csv(\n", " out_dir / 'reduced' / 'lcs' / 'optimal_annulus' / '1:g_source_2.csv',\n", " )\n", "\n", "default_g_band_source_2_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the light curve file has `bkg` and `bkg_err` columns, which are only included if a local background estimator was used to compute the light curve. We can see that the average background is about 100, which is the expected value, and the average background error is about $\\sqrt{100} = 10$, which is also the expected value. The default local background estimator is therefore working well in this case.\n", "\n", "## Custom Local Background\n", "\n", "The default local background estimator uses the mean and standard deviation in the sigma-clipped annulus to estimate the background and its error, respectively. We will therefore define a custom local background estimator that works slightly differently, using the median instead of the mean, and the median absolute deviation instead of the standard deviation. \n", "\n", "To define a custom local background estimator, any function with the following input parameters can be used:\n", "- `data`: the image data as a 2D `numpy` array\n", "- `error`: the error in the image data as a 2D `numpy` array\n", "- `position`: the position of a source as a tuple (x, y)\n", "- `semimajor_axis`: the semi-major standard deviation of the PSF (assumed to be a 2D Gaussian)\n", "- `semiminor_axis`: the semi-minor standard deviation of the PSF (assumed to be a 2D Gaussian)\n", "- `orientation`: the orientation of the PSF in degrees\n", "\n", "For this example, I'll use the [`photutils.aperture`](https://photutils.readthedocs.io/en/stable/user_guide/aperture.html) package to handle the heavy lifting, and define a custom local background function that returns the median local background and its median absolute deviation:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:43.626658Z", "iopub.status.busy": "2026-02-16T18:31:43.626576Z", "iopub.status.idle": "2026-02-16T18:31:43.628576Z", "shell.execute_reply": "2026-02-16T18:31:43.628292Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing custom_local_background.py\n" ] } ], "source": [ "%%writefile custom_local_background.py\n", "from photutils.aperture import EllipticalAnnulus, ApertureStats\n", "from astropy.stats import SigmaClip\n", "\n", "def custom_local_background(\n", " data, # the image data as a 2D numpy array\n", " position, # the position of a source as a tuple (x, y)\n", " semimajor_axis, # the semimajor axis of the PSF in pixels (assuming a 2D Gaussian PSF)\n", " semiminor_axis, # the semiminor axis of the PSF in pixels (assuming a 2D Gaussian PSF)\n", " orientation, # the orientation of the PSF in degrees\n", " ):\n", " \n", " # set inner and outer radius scale of annulus\n", " r_in_scale = 5\n", " r_out_scale = 7.5\n", " \n", " # define the annulus\n", " annulus = EllipticalAnnulus(\n", " positions=position,\n", " a_in=r_in_scale * semimajor_axis,\n", " a_out=r_out_scale * semimajor_axis,\n", " b_in=r_in_scale * semiminor_axis,\n", " b_out=r_out_scale * semiminor_axis,\n", " theta=orientation,\n", " )\n", " \n", " # compute the statistics in the annulus\n", " stats = ApertureStats(\n", " data,\n", " annulus,\n", " sigma_clip=SigmaClip(sigma=3.5, maxiters=10), # clip outliers from the annulus\n", " )\n", " \n", " median_local_background = stats.median\n", " \n", " # median absolute deviation (MAD) of the local background\n", " mad_std_local_background = stats.mad_std\n", " \n", " return median_local_background, mad_std_local_background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due to compatibility issues, routines defined inside of IPython notebooks cannot be used by `multiprocessing` (used by `opticam`). To overcome this issue, we have used the `%%writefile` magic command to write our `custom_local_background()` function to a temporary module called `custom_local_background.py`, such that it can be imported by `multiprocessing`. This is required in Python $\\geq$ 3.14.\n", "\n", "There is no requirement to use `photutils` for estimating local backgrounds, but it does make things easier.\n", "\n", "Let's now perform photometry using our custom local background estimator:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:43.629438Z", "iopub.status.busy": "2026-02-16T18:31:43.629358Z", "iopub.status.idle": "2026-02-16T18:31:53.039393Z", "shell.execute_reply": "2026-02-16T18:31:53.038944Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] [OPTICAM] Photometry results will be saved to lcs/optimal_annulus in out/reduced.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "[OPTICAM] Performing photometry on 1:g images: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from custom_local_background import custom_local_background\n", "\n", "\n", "custom_phot = opticam.OptimalPhotometer(\n", " local_background_estimator=custom_local_background,\n", " )\n", "\n", "reducer.photometry(custom_phot, overwrite=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:53.040347Z", "iopub.status.busy": "2026-02-16T18:31:53.040270Z", "iopub.status.idle": "2026-02-16T18:31:53.044436Z", "shell.execute_reply": "2026-02-16T18:31:53.044175Z" } }, "outputs": [ { "data": { "text/html": [ "
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BMJDfluxflux_errbkgbkg_err
060309.999803524.4779058.436872100.76396511.418520
160309.999814528.3402247.974216101.1503689.387024
260309.999826511.3933198.095579101.26891010.380381
360309.999837515.2071667.975145103.0159179.592005
460309.999849510.3180467.891368102.5583699.505810
560309.999861486.0107337.85334799.9127419.987173
660309.999872513.1199508.09674598.98378810.299108
760309.999884520.8002177.64201699.2537318.091121
860309.999895509.1302857.86591599.2835569.509957
960309.999907534.5165058.12474699.5914379.924216
\n", "
" ], "text/plain": [ " BMJD flux flux_err bkg bkg_err\n", "0 60309.999803 524.477905 8.436872 100.763965 11.418520\n", "1 60309.999814 528.340224 7.974216 101.150368 9.387024\n", "2 60309.999826 511.393319 8.095579 101.268910 10.380381\n", "3 60309.999837 515.207166 7.975145 103.015917 9.592005\n", "4 60309.999849 510.318046 7.891368 102.558369 9.505810\n", "5 60309.999861 486.010733 7.853347 99.912741 9.987173\n", "6 60309.999872 513.119950 8.096745 98.983788 10.299108\n", "7 60309.999884 520.800217 7.642016 99.253731 8.091121\n", "8 60309.999895 509.130285 7.865915 99.283556 9.509957\n", "9 60309.999907 534.516505 8.124746 99.591437 9.924216" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "custom_g_band_source_2_df = pd.read_csv(\n", " out_dir / 'reduced' / 'lcs' / 'optimal_annulus' / '1:g_source_2.csv',\n", " )\n", "\n", "custom_g_band_source_2_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, the background and its error are both close to the expected values. Let's therefore compare the local background estimations more clearly:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:53.045365Z", "iopub.status.busy": "2026-02-16T18:31:53.045294Z", "iopub.status.idle": "2026-02-16T18:31:53.123059Z", "shell.execute_reply": "2026-02-16T18:31:53.122689Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "fig, axes = plt.subplots(\n", " nrows=2,\n", " tight_layout=True,\n", " sharex=True,\n", " )\n", "\n", "axes[0].plot(\n", " default_g_band_source_2_df['BMJD'],\n", " default_g_band_source_2_df['bkg'],\n", " label='Default',\n", ")\n", "axes[0].plot(\n", " custom_g_band_source_2_df['BMJD'],\n", " custom_g_band_source_2_df['bkg'],\n", " ls='--',\n", " label='Custom',\n", ")\n", "axes[0].axhline(\n", " 100,\n", " color='k',\n", " zorder=0,\n", ")\n", "\n", "axes[1].plot(\n", " default_g_band_source_2_df['BMJD'],\n", " default_g_band_source_2_df['bkg_err'],\n", " label='Default',\n", ")\n", "axes[1].plot(\n", " custom_g_band_source_2_df['BMJD'],\n", " custom_g_band_source_2_df['bkg_err'],\n", " ls='--',\n", " label='Custom',\n", ")\n", "axes[1].axhline(\n", " 10,\n", " color='k',\n", " zorder=0,\n", ")\n", "\n", "axes[1].set_xlabel('BMJD [days]')\n", "axes[0].set_ylabel('Background [Counts]')\n", "axes[1].set_ylabel('Background RMS [Counts]')\n", "\n", "axes[0].legend()\n", "axes[1].legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, both methods are around the expected values, and so either local background estimator would likely give pretty good results in practise.\n", "\n", "That concludes the local background tutorial for `opticam`! The default local background estimator should be \"good enough\" in most cases, but we have seen how to implement custom local background estimators if required. Once again, I refer to the excellent `photutils` documentation for further details on implementing custom local background estimators via the `photutils.aperture` subpackage: https://photutils.readthedocs.io/en/stable/user_guide/aperture.html#local-background-subtraction." ] } ], "metadata": { "kernelspec": { "display_name": "opticam6", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 2 }