{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Modelling and fitting a spectrum with two resolved lines\n", "\n", "Based on what we have seen in the example [Modelling and fitting one emission line](./script_example_model+fit_1_line.ipynb) we will model and fit a spectrum with two resolved lines. This example will then be used in [Modelling and fitting two unresolved emission lines with a Bayesian approach](./script_example_model+fit_2_lines_bayes.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import orb.fit\n", "import pylab as pl\n", "import numpy as np\n", "from orb.core import Lines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Second step: modelling and fitting a spectrum with two resolved lines\n", "\n", "No particular difficulty here. A classical algorithm is good enough." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "incident angle theta (in degrees): 15.445939567249903\n" ] }, { "data": { "text/plain": [ "(15200, 15270)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "halpha_cm1 = Lines().get_line_cm1('Halpha')\n", "\n", "step = 2943\n", "order = 8\n", "step_nb = 840\n", "axis_corr = 1.0374712062298759\n", "theta = orb.utils.spectrum.corr2theta(axis_corr)\n", "print('incident angle theta (in degrees):', theta)\n", "zpd_index = 168\n", "\n", "# model spectrum\n", "velocity1 = 250\n", "broadening1 = 15\n", "spectrum_axis, spectrum1 = orb.fit.create_cm1_lines_model_raw([halpha_cm1], [1], step, order, step_nb, axis_corr, zpd_index=zpd_index, fmodel='sincgauss',\n", " sigma=broadening1, vel=velocity1)\n", "\n", "velocity2 = 10\n", "broadening2 = 30\n", "spectrum_axis, spectrum2 = orb.fit.create_cm1_lines_model_raw([halpha_cm1], [1], step, order, step_nb, axis_corr, zpd_index=zpd_index, fmodel='sincgauss',\n", " sigma=broadening2, vel=velocity2)\n", "\n", "spectrum = spectrum1 + spectrum2\n", "\n", "# add noise\n", "spectrum += np.random.standard_normal(spectrum.shape) * 0.02\n", "\n", "spectrum_axis = orb.utils.spectrum.create_cm1_axis(np.size(spectrum), step, order, corr=axis_corr)\n", "\n", "pl.plot(spectrum_axis, spectrum)\n", "pl.xlim((15200, 15270))\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "velocity (in km/s): [244.5(1.4) 10.51(86)]\n", "broadening (in km/s): [20.7(2.2) 31.33(98)]\n", "flux (in the unit of the spectrum amplitude / unit of the axis fwhm): [0.673(40) 1.663(51)]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nm_laser = 543.5 # wavelength of the calibration laser, in fact it can be any real positive number (e.g. 1 is ok)\n", "\n", "# pos_def must be given here because, by default all the lines are considered \n", "# to share the same velocity. i.e. sigma_def = ['1', '1']. As the two lines do not have \n", "# the same velocity we put them in two different velocity groups: sigma_def = ['1', '2']\n", "#\n", "# pos_cov is the velocity of the lines in km/s. It is a covarying parameter,\n", "# because the reference position -i.e. the initial guess- of the lines is set\n", "# \n", "# sigma_guess is the initial guess on the broadening (in km/s)\n", "\n", "fit = orb.fit.fit_lines_in_spectrum(spectrum, [halpha_cm1, halpha_cm1], step, order, nm_laser, theta, zpd_index, \n", " wavenumber=True, apodization=1, fmodel='sincgauss',\n", " pos_def=['1', '2'],\n", " pos_cov=[velocity1, velocity2], \n", " sigma_guess=[broadening1, broadening2])\n", "print('velocity (in km/s): ', fit['velocity_gvar'])\n", "print('broadening (in km/s): ', fit['broadening_gvar'])\n", "print('flux (in the unit of the spectrum amplitude / unit of the axis fwhm): ', fit['flux_gvar'])\n", "pl.plot(spectrum_axis, spectrum, label='real_spectrum')\n", "pl.plot(spectrum_axis, fit['fitted_vector'], label='fit')\n", "pl.xlim((15200, 15270))\n", "pl.legend()\n", "\n" ] } ], "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": "3.7.5" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "61px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 4 }