{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to deal with boundary conditions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/miniconda3.7/envs/hazel/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 88 from C header, got 96 from PyObject\n", " return f(*args, **kwds)\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as pl\n", "import hazel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All profiles in Hazel are assumed to be normalized to the quiet Sun at disk center. Let's do a few experiments to get a grasp at how profiles depend on the observing conditions. To facilitate things, let us wrap the experiments in a function that we can call with different parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def synthesize(theta, boundaryI=1.0):\n", "\n", " thetaB = 0.0\n", " phiB = 0.0\n", "\n", " # Test a photosphere\n", " mod = hazel.Model(working_mode='synthesis')\n", " mod.add_spectral({'Name': 'spec1', 'Wavelength': [10826, 10835, 150], 'topology': 'ph1', \n", " 'LOS': [theta,0.0,90.0], 'Boundary condition': [boundaryI,0.0,0.0,0.0]})\n", " mod.add_photosphere({'Name': 'ph1', 'Spectral region': 'spec1', 'Spectral lines': [300], \n", " 'Wavelength': [10826, 10835], 'Reference atmospheric model': 'photospheres/init_spot.1d'})\n", " mod.setup()\n", " mod.synthesize()\n", "\n", " f, ax = pl.subplots()\n", " ax.plot(mod.spectrum['spec1'].wavelength_axis, mod.spectrum['spec1'].stokes[0,:])\n", "\n", " # Test a chromosphere\n", " mod = hazel.Model(working_mode='synthesis')\n", " mod.add_spectral({'Name': 'spec1', 'Wavelength': [10826, 10835, 150], 'topology': 'ch1', \n", " 'LOS': [theta,0.0,90.0], 'Boundary condition': [boundaryI,0.0,0.0,0.0]})\n", " mod.add_chromosphere({'Name': 'ch1', 'Spectral region': 'spec1', 'Height': 3.0, 'Line': '10830', \n", " 'Wavelength': [10826, 10835], 'Reference frame': 'line-of-sight'})\n", " mod.setup()\n", "\n", "\n", " mod.atmospheres['ch1'].set_parameters([0.0, 0.0, 0.0,1.0,0.0,8.0,1.0,0.0],1.0)\n", " mod.synthesize()\n", "\n", " ax.plot(mod.spectrum['spec1'].wavelength_axis, mod.spectrum['spec1'].stokes[0,:])\n", "\n", " # Test a photosphere+chromosphere\n", " mod = hazel.Model(working_mode='synthesis')\n", " mod.add_spectral({'Name': 'spec1', 'Wavelength': [10826, 10835, 150], 'topology': 'ph1->ch1', \n", " 'LOS': [theta,0.0,90.0], 'Boundary condition': [boundaryI,0.0,0.0,0.0]})\n", " mod.add_photosphere({'Name': 'ph1', 'Spectral region': 'spec1', 'Spectral lines': [300], \n", " 'Wavelength': [10826, 10835], 'Reference atmospheric model': 'photospheres/init_spot.1d'})\n", " mod.add_chromosphere({'Name': 'ch1', 'Spectral region': 'spec1', 'Height': 3.0, 'Line': '10830', \n", " 'Wavelength': [10826, 10835], 'Reference frame': 'line-of-sight'})\n", " mod.setup()\n", "\n", " mod.atmospheres['ch1'].set_parameters([0.0, 0.0, 0.0,1.0,0.0,8.0,1.0,0.0],1.0)\n", " mod.synthesize()\n", "\n", "\n", " ax.plot(mod.spectrum['spec1'].wavelength_axis, mod.spectrum['spec1'].stokes[0,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Disk center\n", "Let us assume that we are at disk center and synthesize the profile emerging from a photosphere, a chromosphere and the combination of the two:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "synthesize(theta=0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the emergent profiles are normalized to one, as one would expect for a disk center observation. For such an observation, in inversion mode, the observed profiles should be then normalized to unit continuum because we are placed at disk center.\n", "\n", "When a photosphere is present, the continuum value will be automatically inferred in inversion mode by adapting the temperature. Any other subsequent chromosphere above the photosphere will function with the correct boundary condition. However, if only a chromosphere is present it is mandatory to define the appropriate boundary condition. As you can see in the following, the effect of the boundary condition only affects the case of a single chromosphere:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "synthesize(theta=0.0, boundaryI=0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reason for that is that a chromosphere is modeled in Hazel as a slab and, therefore, one needs to give the Stokes profiles that are entering the slab from below and there is no general way of inferring it from the observations. For this reason, Hazel just gives you the option to enter the boundary conditions as a parameter. Note that this is important in case you are inverting sunspots because you have to modify the boundary condition appropriately to define the darker regions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Away from disk center\n", "Let us consider now what happens when we are not at disk center." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "synthesize(theta=45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see, the continuum is now smaller than one, and compatible with the center-to-limb variation in the quiet Sun:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CLV = 0.9167817591620029\n" ] } ], "source": [ "mu = np.cos(45*np.pi/180)\n", "clv = hazel.util.i0_allen(10830,mu) / hazel.util.i0_allen(10830,1.0)\n", "print(\"CLV = {0}\".format(clv))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For such an observation, in inversion mode, the observed profiles should be then normalized to the quiet Sun continuum at disk center. Equivalently, one can normalize to the quiet Sun in the observed map at the given heliocentric angle and then correct the profiles by the estimated CLV given above.\n", "\n", "As a summary:\n", "\n", "1. If you have a photosphere, just normalize the profiles to the continuum of the quiet Sun at disk center and the photosphere will adapt the temperature to produce the correct continuum.\n", "\n", "2. If you only have chromospheres, then you need to define the appropriate boundary condition (in units of the quiet Sun continuum at disk center). Then, if you are dealing with sunspots or darker regions, you need to modify the boundary conditions appropriately to consider the darker continuum." ] } ], "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }