{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to load and use Cotrending Basis Vectors for Kepler, K2 and TESS?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cotrending Basis Vectors (CBVs) are generated in the PDC component of the Kepler/K2/TESS pipeline and are used to remove systematic trends in light curves. They are built from the most common systematic trends observed in each PDC Unit of Work (Quarter for Kepler, Campaign for K2 and Sector for TESS). Each Kepler and K2 module output and each CCD in TESS has its own set of CBVs. You can read an introduction to the CBVs in [Demystifying Kepler Data](https://arxiv.org/pdf/1207.3093.pdf) or to greater detail in the [Kepler Data Processing Handbook](https://archive.stsci.edu/kepler/manuals/KSCI-19081-003-KDPH.pdf). The same basic method to generate CBVs is used for all three missions.\n", "\n", "This tutorial provides examples of how to load CBVs from MAST and set them up in a design matrix for use to remove systematic trends. Please consult the [DesignMatrix page](../api/lightkurve.correctors.DesignMatrix.html) in the API docs for the full details on that class. A convenient tool has been created called `CBVCorrector` which will utilize the CBVs for the custom removal of systematic trends. See the [CBVCorrector HOWTO page](https://docs.lightkurve.org/tutorials/04-how-to-use-cbvcorrector.html) for example usage." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cotrending Basis Vector Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are three basic types of CBVs: \n", "- **Single-Scale** contains all systematic trends combined in a single set of basis vectors. \n", "- **Multi-Scale** contains systematic trends in specific wavelet-based band passes. There are usually three sets of multi-scale basis vectors in three bands.\n", "- **Spike** contains only short impulsive spike systematics.\n", "\n", "There are two different correction methods in PDC: Single-Scale and Multi-Scale. Single-Scale performs the correction in a single bandpass. Multi-Scale performs the correction in three separate wavelet-based bandpasses. Both corrections are performed in PDC but we can only export a single PDC light curve for each target. So, PDC must choose which of the two on a per-target basis. Generally speaking, single-scale performs better at preserving longer period signals. But at periods close to transiting planet durations multi-scale performs better at preserving signals. PDC therefore mostly chooses multi-scale for use within the planet finding pipeline and for the archive. You can find in the light curve FITS header which PDC method was chosen (keyword “PDCMETHD”). Additionally, a seperate correction is alway performed to remove short impulsive systematic spikes.\n", "\n", "For an individual's research needs, the mission supplied PDC lightcurves might not be ideal and so the CBVs are provided to the user to perform their own correction. All three CBV types are provided at MAST for TESS, however only Single-Scale is provided at MAST for Kepler and K2. For TESS, Cotrending Basis Vectors are currently only supplied at a 2-minute cadence. For Kepler/K2 only for the 30-minute target cadence." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtaining the CBVs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two tools are available to automatically download the CBVs relevent to your target of study: `get_kepler_cbvs` and `get_tess_cbvs`. Here is an example loading in the TESS Multi-Scale Band 2 CBVs for TESS target TIC 99180739, which happens to be on camera 1 and CCD 1. Note that you do not need to have a lightcurve object already loaded. One can directly request whichever CBVs one desires." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from lightkurve import search_lightcurve, download_tess_cbvs, download_kepler_cbvs\n", "import numpy as np\n", "lc = search_lightcurve('TIC 99180739', mission='tess', sector=10).download(flux_column='sap_flux')\n", "cbvs = download_tess_cbvs(sector=lc.sector, camera=lc.camera, ccd=lc.ccd, cbv_type = 'MultiScale', band=2)\n", "cbvs.__repr__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This TessCotrendingBasisVectors object contains 8 Multi-Scale band 2 CBVs for Sector 10, Camera 1, CCD 1. A CBV object contains only one type of CBVs. To obtain all types you must request each seperately. The basis vectors themselves are containted in an astropy.timeseries.TimeSeries Table:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "TessCotrendingBasisVectors length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
timeCADENCENOGAPVECTOR_1VECTOR_2VECTOR_3VECTOR_4VECTOR_5VECTOR_6VECTOR_7VECTOR_8
objectint32boolfloat32float32float32float32float32float32float32float32
1569.43314028183246224False-0.003580346703529358-0.00253998534753918650.0003182471264153719-0.0051018320955336090.0019720413256436586-0.002591928234323859-0.008483666926622390.009498000144958496
1569.4345291701247246225False-0.003526911837980151-0.00244245002977550030.00030286889523267746-0.0048374542966485020.002021507592871785-0.0025524157099425793-0.0084635727107524870.009511380456387997
1569.4359180584206246226False-0.0034735281951725483-0.0023451193701475860.0002874816709663719-0.0045736455358564850.0020708078518509865-0.0025129010900855064-0.0084433332085609440.009524472057819366
" ], "text/plain": [ "TESS CBVs, Sector.Camera.CCD : 10.1.1, CBVType.Band: MultiScale.2, nCBVs : 8" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show the head of the CBV table\n", "cbvs[1:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All data contained in the MAST CBV FITS files is downloaded. The `time`, `CADENCENO` and `GAP` columns can be used to select which cadences you desire. Extra information is in the `cbvs.meta` dict. The CBVS are interpolated accross the gaps and so should be used in gapped cadences with extreme caution. Below we will plot the first 4 CBVs. Note: _CBVs use 1-based indexing!_" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cbvs.plot(cbv_indices=np.arange(1,5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " As further examples, below we will request both Kepler and K2 CBVs." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cbvsKepler = download_kepler_cbvs(mission='Kepler', quarter=8, module=16, output=4)\n", "cbvsK2 = download_kepler_cbvs(mission='K2', campaign=15, channel=24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Converting the CBVs to a LightKurve DesignMatrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can access individual CBVs directly by referencing the column in the table (I.e. `cbvs['VECTOR_#']`) however a better way to use the CBVs for correcting your light curves is to convert the CBVs to a LightKurve DesignMatrix:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.1.1.SingleScale DesignMatrix (18900, 8)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbv_designmatrix = cbvs.to_designmatrix(cbv_indices=np.arange(1,9), name='10.1.1.SingleScale')\n", "cbv_designmatrix" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cbv_designmatrix.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aligning the CBVs with your light curve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CBVs obtained from MAST may not have the same cadences as the your target. A method called `align` will allow you to align your CBVs to the cadence numbers in your target lightcurve object. The method will use the cadence number (i.e. `lc.cadenceno`) to perform the alignment. Only cadence numbers that exist in both the CBVs and the light curve will have values in the returned CBVs. All cadence numbers that exist in the light curve but not in the CBVs will have NaNs returned for the CBVs on those cadences and the GAP set to True." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Take a cut of the LC loaded above\n", "lc_short = lc[501:1501]\n", "# Take a different cut of the CBVs\n", "cbvs_short = cbvs[0:1000]\n", "# These cuts do not overlap\n", "np.all(lc_short.cadenceno == cbvs_short.cadenceno)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Align the cuts\n", "cbvs_aligned = cbvs_short.align(lc_short)\n", "# They now fully overlap\n", "np.all(lc_short.cadenceno == cbvs_aligned.cadenceno)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolating the CBVs to an arbitrary light curve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above `align` method will only keep cadences that line up exactly between the CBVs and the light curve based on the cadence numbers. What if you have a light curve with cadence times not exactly lining up with the CBV cadences? For example, what if you want to use the 2-minute CBVs for cotrending against the 30-minute FFIs? A more general method is `inteprolate`, which uses PCHIP interpolation to generate CBVs at the cadence times of an arbitrary light curve." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Get a light curve from a TESS FFI\n", "from lightkurve import search_tesscut\n", "search_result = search_tesscut('HAT-P-11', sector=14)\n", "tpf = search_result.download(cutout_size=20)\n", "target_mask = tpf.create_threshold_mask(threshold=15, reference_pixel='center')\n", "ffi_lc = tpf.to_lightcurve(aperture_mask=target_mask)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Get the Single-Scale CBVs for this light curve\n", "cbvs = download_tess_cbvs(sector=ffi_lc.sector, camera=ffi_lc.camera, ccd=ffi_lc.ccd, cbv_type = 'SingleScale')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Interpolate the CBVs to the FFI cadence times\n", "cbvs_interpolated = cbvs.interpolate(ffi_lc, extrapolate=False)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# All cadence times agree\n", "np.all(ffi_lc.time.value == cbvs_interpolated.time.value)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# The CBVs are now interpolated to this FFI-derived light curve\n", "import matplotlib.pyplot as plt\n", "_, ax = plt.subplots(2, sharex=True)\n", "ax[0].plot(ffi_lc.time.value, ffi_lc.flux, '.b')\n", "ax[0].set_title('HAT-P-11')\n", "cbvs_interpolated.plot(cbv_indices=np.arange(1,4), ax=ax[1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some notes about interpolating Cotrending Basis Vectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Currently, only TESS 2-minute and Kepler/K2 30-minute CBVs are archived on MAST\n", "- 20-second CBVs will be available for the TESS extended mission beginning with Sector 27. Each set of 20-second CBVs will be for the entire field of view.\n", "- FFI CBVs are also being developed and will begin to be exported soon.\n", "- The CBVs are generated to account for systematics at a specific cadence. They will not necessarily properly represent systematics at a different cadence, but in some cases can still be beneficial.\n", "- Please remain conscious of the Nyquist frequency and aliasing when interpolating CBVs." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }