{ "cells": [ { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "# Setup\n", "\n", "Here I will go over setting up our interfaces and emulators from a raw spectral library to prepare us for fitting some data in further examples." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false", "colab_type": "text", "id": "spgydwe4FY9j" }, "source": [ "### Getting the Grid\n", "\n", "To begin, we need a spectral model library that we will use for our fitting. One common example are the PHOENIX models, most recently computed by T.O. Husser. We provide many interfaces directly with different libraries, which can be viewed in [Raw Grid Interfaces](../api/grid_tools.rst#raw-grid-interfaces).\n", "\n", "As a convenience, we provide a helper to download PHOENIX models from the Goettingen servers. Note this will skip any files already on disk." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "Collapsed": "false", "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "colab_type": "code", "id": "BuJfe4XyERQo", "outputId": "75d34e98-cff9-4c24-8551-3a8be4c66a0c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "lte08600-6.00+0.5.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits: 100%|██████████| 330/330 [00:00<00:00, 1285.27it/s]\n" ] } ], "source": [ "import numpy as np\n", "\n", "from Starfish.grid_tools import download_PHOENIX_models\n", "\n", "ranges = [[5700, 8600], [4.0, 6.0], [-0.5, 0.5]] # T, logg, Z\n", "\n", "download_PHOENIX_models(path=\"PHOENIX\", ranges=ranges)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false", "colab_type": "text", "id": "O76RgrCVGILs" }, "source": [ "Now that we have the files downloaded, let's set up a grid interface" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "Collapsed": "false", "colab": {}, "colab_type": "code", "id": "TJtk9ecYEMBe" }, "outputs": [], "source": [ "from Starfish.grid_tools import PHOENIXGridInterfaceNoAlpha\n", "\n", "grid = PHOENIXGridInterfaceNoAlpha(path=\"PHOENIX\")" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false", "colab_type": "text", "id": "zOywmJdgGRU2" }, "source": [ "From here, we will want to set up our HDF5 interface that will allow us to go on to using the spectral emulator, but first we need to determine our model subset and instrument.\n", "\n", "### Setting up the HDF5 Interface\n", "\n", "We set up an HDF5 interface in order to allow much quicker reading and writing than compared to loading FITS files over and over again. In addition, when considering the application to our likelihood methods, we know that for a given dataset, any effects characteristic of the instrument can be pre-applied to our models, saving on computation time during the maximum likelihood estimation.\n", "\n", "Looking towards our fitting examples, we know we will try fitting some data from TRES Spectrograph. This instrument is available in our grid tools, but if yours isn't, you can always supply the FWHM in km/s. The FWHM ($\\Gamma$) can be found using the resolving power, $R$\n", "\n", "$$ \\Gamma = \\frac{c}{R} $$\n", "\n", "with $c$ in km/s. Let’s also say that, for a given dataset, we want to only use a reasonable subset of our original model grid. The data provided in future examples is a ~F3V star, so we will limit our model parameter ranges appropriately." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "Collapsed": "false", "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "colab_type": "code", "id": "DiUf8DK8E46W", "outputId": "3c61ea76-561a-4b9d-c7a9-762e6da56fe5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Processing [8.6e+03 6.0e+00 5.0e-01]: 100%|██████████| 330/330 [06:33<00:00, 1.19s/it] \n" ] } ], "source": [ "from Starfish.grid_tools.instruments import SPEX\n", "from Starfish.grid_tools import HDF5Creator\n", "\n", "creator = HDF5Creator(\n", " grid, \"F_SPEX_grid.hdf5\", instrument=SPEX(), wl_range=(0.9e4, np.inf), ranges=ranges\n", ")\n", "creator.process_grid()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false", "colab_type": "text", "id": "t02wYIl-kGMA" }, "source": [ "### Setting up the Spectral Emulator\n", "\n", "Once we have our pre-processed grid, we can make our spectral emulator and train its Gaussian process hyperparameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "Collapsed": "false", "colab": { "base_uri": "https://localhost:8080/", "height": 382 }, "colab_type": "code", "id": "5QUlomkCkNuj", "outputId": "3540ebf9-0c43-4ab4-daf6-71faf5c76491" }, "outputs": [ { "data": { "text/plain": [ "Emulator\n", "--------\n", "Trained: False\n", "lambda_xi: 1.000\n", "Variances:\n", "\t10000.00\n", "\t10000.00\n", "\t10000.00\n", "\t10000.00\n", "Lengthscales:\n", "\t[ 600.00 1.50 1.50 ]\n", "\t[ 600.00 1.50 1.50 ]\n", "\t[ 600.00 1.50 1.50 ]\n", "\t[ 600.00 1.50 1.50 ]\n", "Log Likelihood: -1272.34" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from Starfish.emulator import Emulator\n", "\n", "# can load from string or HDF5Interface\n", "emu = Emulator.from_grid(\"F_SPEX_grid.hdf5\")\n", "emu" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "Collapsed": "false", "colab": {}, "colab_type": "code", "id": "Udo3krt7kV85" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 17min 39s, sys: 1min 58s, total: 19min 38s\n", "Wall time: 4min 55s\n" ] }, { "data": { "text/plain": [ "Emulator\n", "--------\n", "Trained: True\n", "lambda_xi: 1.010\n", "Variances:\n", "\t176330.09\n", "\t1681.55\n", "\t1364.83\n", "\t433.88\n", "Lengthscales:\n", "\t[ 2039.21 16.11 3.21 ]\n", "\t[ 1313.27 1.49 1.86 ]\n", "\t[ 2122.93 2.58 2.21 ]\n", "\t[ 1009.99 1.20 3.26 ]\n", "Log Likelihood: -778.44" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time emu.train(options=dict(maxiter=1e5))\n", "emu" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false", "colab": {}, "colab_type": "code", "id": "2JDpJSR_n2DG" }, "source": [ "
\n", " \n", "**Note:** If the emulator does not optimize the first time you use ``train``, just run it again. You can also tweak the arguments passed to ``scipy.optimize.minimize`` by passing them as keyword arguments to the call.\n", " \n", "
\n", "\n", "
\n", "\n", "**Warning:** Training the emulator will take on the order of minutes to complete. The more eigenspectra that are used as well as the resolution of the spectrograph will mainly dominate this runtime.\n", "\n", "
\n", "\n", "We can do a sanity check on the optimization by looking at slice of the emulator's parameter space and the corresponding Gaussian process fit. We should see a smooth line connecting all the parameter values with some uncertainty that grows with large gaps or turbulent weights." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from Starfish.emulator.plotting import plot_emulator\n", "\n", "plot_emulator(emu)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "If we are satisfied, let's save this emulator and move on to fitting some data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "emu.save(\"F_SPEX_emu.hdf5\")" ] } ], "metadata": { "colab": { "name": "setup.ipynb", "provenance": [], "version": "0.3.2" }, "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }