diff --git a/docs/getting_started/.ipynb_checkpoints/tutorial-checkpoint.ipynb b/docs/getting_started/.ipynb_checkpoints/tutorial-checkpoint.ipynb new file mode 100644 index 00000000..9644d5c0 --- /dev/null +++ b/docs/getting_started/.ipynb_checkpoints/tutorial-checkpoint.ipynb @@ -0,0 +1,702 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quickstart Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1 Fast Light Curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a simple first example, we will use $\\texttt{eleanor}$ to create a target pixel file (TPF) and light curve for a given TIC target." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import eleanor\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will load the data for TIC 38846515 (WASP-100), a relatively bright star observed in Sector 1. $\\texttt{eleanor}$ is able to extract a light curve easily and automatically for this unblended object. Calling source will assign a $\\textit{Gaia}$ ID, TESS magnitude, sector, camera, ccd, and associated postcard." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "star = eleanor.Source(tic=38846515, sector=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also pass through a Gaia DR2 ID or RA/Dec pair, either as a tuple or an Astropy SkyCoord object. For example, the following three calls all point to the same target:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.307, RA 68.959732, and Dec -64.02704\n", + "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.307, RA 68.959732, and Dec -64.02704\n", + "Created TAP+ (v1.0.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: False\n", + "\tPort: 80\n", + "\tSSL Port: 443\n", + "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.307, RA 68.95981586357303, and Dec -64.02703903114245\n" + ] + } + ], + "source": [ + "star = eleanor.Source(tic=38846515, sector=1)\n", + "\n", + "print('Found TIC {0} (Gaia {1}), with TESS magnitude {2}, RA {3}, and Dec {4}'\n", + " .format(star.tic, star.gaia, star.tess_mag, star.coords[0], star.coords[1]))\n", + "\n", + "star = eleanor.Source(coords=(68.959732, -64.02704), sector=1)\n", + "\n", + "print('Found TIC {0} (Gaia {1}), with TESS magnitude {2}, RA {3}, and Dec {4}'\n", + " .format(star.tic, star.gaia, star.tess_mag, star.coords[0], star.coords[1]))\n", + "\n", + "star = eleanor.Source(gaia=4675352109658261376, sector=1)\n", + "\n", + "print('Found TIC {0} (Gaia {1}), with TESS magnitude {2}, RA {3}, and Dec {4}'\n", + " .format(star.tic, star.gaia, star.tess_mag, star.coords[0], star.coords[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gaia has quite a bit of precision on the star's position! Now that we have our Source information, we simply call the TargetData() function, which will extract a target pixel file, perform aperture photometry with an automatically optimized aperture choice, and complete some simple systematics corrections." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ozymandias1/anaconda2/envs/python3/lib/python3.5/site-packages/scipy/signal/_savitzky_golay.py:135: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", + " coeffs, _, _, _ = lstsq(A, y)\n", + "/Users/ozymandias1/anaconda2/envs/python3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n", + " return f(*args, **kwds)\n", + "100%|██████████| 1282/1282 [00:14<00:00, 86.22it/s]\n" + ] + } + ], + "source": [ + "data = eleanor.TargetData(star, height=15, width=15, bkg_size=31, do_psf=True, do_pca=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\texttt{eleanor}$ may download a file here. Don't be afraid of this download. We have created an intermediate step between FFIs and TPFs called $\\textbf{postcards}$. In order to create a TPF, $\\texttt{eleanor}$ downloads the postcard your source falls on. We'll talk through the flags you can set, but let's look at a light curve first." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,0,'Time')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "q = data.quality == 0\n", + "\n", + "plt.plot(data.time[q], data.raw_flux[q]/np.median(data.raw_flux[q])-0.01, 'k')\n", + "plt.plot(data.time[q], data.corr_flux[q]/np.median(data.corr_flux[q]) + 0.01, 'r')\n", + "plt.plot(data.time[q], data.pca_flux[q]/np.median(data.pca_flux[q]) + 0.03, 'y')\n", + "plt.plot(data.time[q], data.psf_flux[q]/np.median(data.psf_flux[q]) + 0.05, 'b')\n", + "\n", + "plt.ylabel('Normalized Flux')\n", + "plt.xlabel('Time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's a planet there! What does our aperture look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(data.aperture)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's save these data to a FITS file so we have them later. By default, this will be saved to a `~/.eleanor` directory, but that can be changed by setting a `directory`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]\n" + ] + } + ], + "source": [ + "data.save()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2 Custom Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That was easy! But what if you're not satisfied with $\\texttt{eleanor}$'s default choice of aperture? Well, we provide you with two ways to create your own aperture.\n", + "\n", + "(1) $\\texttt{eleanor}$ can help you create your own mask. By calling custom_aperture, you can choose from a circular or rectangular aperture. You can also choose the size (radius or length x width) and the pixel the aperture is centered on. The aperture will only be created on pixels within the TPF (it won't spill over to other pixels)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "eleanor.TargetData.custom_aperture(data, shape='circle', r=1)\n", + "eleanor.TargetData.get_lightcurve(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(2) You can pass in your own mask. Create a 2D array of the same shape as your TPF and pass in the aperture using the following command" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.zeros(np.shape(data.tpf[0]))\n", + "mask[6:8,6:8] = 1\n", + "plt.imshow(mask, origin='lower')\n", + "data.get_lightcurve(aperture=mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data.time[q], data.raw_flux[q], '.')\n", + "\n", + "plt.ylabel('Flux')\n", + "plt.xlabel('Time')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.2.1 Ultra-Custom Apertures: Click the pixels you want!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hide_input": true + }, + "source": [ + "We have created a user-friendly method to create your own aperture by selecting the pixels you wish to be included in the photometry. Here's how you would go about doing so:" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "vis = eleanor.Visualize(target)\n", + "cust_lc = vis.click_aperture()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "hide_input": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(url='customApExample.gif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Currently, this feature only works when called from the terminal. We are working towards creating an option to embed this feature in a Jupyter Notebook... So stay tuned!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3 Systematics Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we called eleanor.TargetData() in 1.1, some simple systematics corrections were automatically performed on the light curve. Let's apply those explicitly to the newly created raw light curve from the custom aperture." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/Users/ozymandias1/research/tess/eleanor/eleanor']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corr_flux = eleanor.TargetData.jitter_corr(data, flux=data.raw_flux)\n", + "eleanor.__path__" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,0,'Time')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data.time[q], corr_flux[q], 'k.')\n", + "\n", + "plt.ylabel('Flux')\n", + "plt.xlabel('Time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then run pca with this flux time series to remove any shared systematics with nearby stars." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "eleanor.TargetData.pca(data, flux=corr_flux, modes=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,0,'Time')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data.time[q], data.pca_flux[q], 'k.')\n", + "plt.ylabel('Flux')\n", + "plt.xlabel('Time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PSF modeling is always an option too!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1282/1282 [00:15<00:00, 80.33it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5,0,'Time')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eleanor.TargetData.psf_lightcurve(data, model='gaussian', likelihood='poisson')\n", + "plt.plot(data.time[q], data.psf_flux[q], 'k.')\n", + "plt.ylabel('Flux')\n", + "plt.xlabel('Time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.4 Stars Observed in Multiple Sectors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\texttt{eleanor}$ has some built-in tools to make it easier to work with stars that are observed in multiple TESS sectors. Let's consider WASP-100 again, which was observed in both Sectors 1 and 2. When we previously called `eleanor.Source()`, we can instead call `eleanor.multi_sectors()`, which will make a list of Source objects for all sectors requested." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "star = eleanor.multi_sectors(tic=38846515, sectors=[1,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[, ]\n" + ] + } + ], + "source": [ + "print(star)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yep, it's a list of objects!\n", + "We can then call `eleanor.TargetData()` on each." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1282/1282 [00:14<00:00, 85.56it/s]\n", + "100%|██████████| 1245/1245 [00:14<00:00, 84.30it/s]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data0 = eleanor.TargetData(star[0], height=15, width=15, bkg_size=31, do_psf=True, do_pca=False)\n", + "data1 = eleanor.TargetData(star[1], height=15, width=15, bkg_size=31, do_psf=True, do_pca=False)\n", + "\n", + "q0 = data0.quality == 0\n", + "q1 = data1.quality == 0\n", + "\n", + "plt.plot(data0.time[q0], data0.psf_flux[q0]/np.median(data0.psf_flux[q0]) + 0.05, 'k.')\n", + "plt.plot(data1.time[q1], data1.psf_flux[q1]/np.median(data1.psf_flux[q1]) + 0.04, 'r.')\n", + "\n", + "plt.ylabel('Normalized Flux')\n", + "plt.xlabel('Time')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Presently, $\\texttt{eleanor}$ will only work with data from Sectors 1 and 2; future releases will handle additional sectors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are only interested in one Sector your star was observed in, you can call `eleanor.Source(sector=1)` and specify the Sector you are interested in. If your star was observed in multiple sectors and you call `eleanor.Source()`, it will return the light curve from the most recent Sector the star was observed in." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hide_input": true + }, + "source": [ + "#### 1.5 Good practices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're still exploring what the best practices are generally. Good practices seem to differ across the detector, but here's what we believe so far. If you discover anything that works well for you, we and other $\\texttt{eleanor}$ users would surely love to know them!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good background subtraction is very important. The size of the region to use for an \"ideal\" background changes across the detector. Generally, bigger is better, we typically recommend using a region larger than the standard TPF. Currently, the background used must be a rectangle centered on the star, with size `bkg_size`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PSF modeling (`do_psf=True`) seems to work very well for relatively bright, isolated stars. Presently the only PSF model is a Gaussian, but both Gaussian and Poisson likelihood functions are possible. Do note that this requires tensorflow, and there is not presently a tensorflow package for Python 3.7. This will thus only work for python 3.0 - 3.6. Note that PSF modeling is very much in beta and our current implementation has known issues for very faint stars (fainter than I ~ 15, generally)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you find anything that works well for your science, or uncover any issues, please let us know! Github [issues](https://github.com/afeinstein20/eleanor/issues) or [pull requests](https://github.com/afeinstein20/eleanor/pulls) are welcomed." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/getting_started/tutorial.ipynb b/docs/getting_started/tutorial.ipynb index 0f57fbbe..9644d5c0 100644 --- a/docs/getting_started/tutorial.ipynb +++ b/docs/getting_started/tutorial.ipynb @@ -128,7 +128,7 @@ } ], "source": [ - "data = eleanor.TargetData(star, height=15, width=15, bkg_size=31, do_psf=True)\n" + "data = eleanor.TargetData(star, height=15, width=15, bkg_size=31, do_psf=True, do_pca=True)\n" ] }, { @@ -694,7 +694,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.1" } }, "nbformat": 4,