{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D Stellar wind\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we consider a simple spherically symmetric stellar wind model.\n", "We use numpy and astropy to conveniently define the model parameters." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from astropy import units, constants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Model setup\n", "\n", "---\n", "\n", "### Geometry\n", "The model box is given by the radial coordinate $r \\in [r_{\\star}, r_{\\text{out}}] = [1, 10^{4}] \\ \\text{au}$. The model is discretised on a logarithmically-spaced grid consisting of 1024 elements with $r \\in [r_{\\text{in}}, r_{\\text{out}}] = [10^{-1}, 10^{4}] \\ \\text{au}$.\n", "Note that $r_{\\text{in}} < r_{\\star}$, such that several rays hit the stellar surface, since, for concenience, we use the same discretisation for the impact parameters of the rays. We impose a boundary condition at $r=r_{\\star}$, such that the part of the model inside the star ($r r_star] = v_in + (v_inf - v_in) * (1.0 - r_star / rs[rs > r_star])**beta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Density\n", "We assume the density and velocity to be related through the conservation of mass, such that,\n", "\\begin{equation*}\n", "\\rho \\left( r \\right) \\ = \\ \\frac{\\dot{M}}{4 \\pi r^{2} \\, v(r)},\n", "\\end{equation*}\n", "where, for the mass-loss rate, we take a typical value of $\\dot{M} = 5.0 \\times 10^{-6} \\ M_{\\odot} / \\text{yr}$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Mdot = (3.0e-6 * units.M_sun / units.yr).si.value\n", "\n", "rho = Mdot / (4.0 * np.pi * rs**2 * v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### CO abundance\n", "The CO abundance is assumed to be proportional to the density, such that, $n^{\\text{CO}}(r) = 3.0 \\times 10^{-4} \\, N_{A} \\, \\rho(r) / m^{\\text{H}_2}$, with $N_{A}$ Avogadro's number, and $m^{\\text{H}_2} = 2.02 \\ \\text{g}/\\text{mol}$, the molar mass of $\\text{H}_{2}$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "n_CO = (3.0e-4 * constants.N_A.si.value / 2.02e-3) * rho\n", "n_CO[rs<=r_star] = n_CO[n_CO r_star] = T_star * (r_star / rs[rs > r_star])**epsilon" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Micro-turbulence\n", "Finally, we assume a constant turbulent velocity $v_{\\text{turb}}(r) = 1 \\ \\text{km}/\\text{s}$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "v_turb = (1.0e+0 * units.km / units.s).si.value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### TensorModel\n", "With all the data in place, we can start building a pomme model.\n", "First, we store all model parameters as a TensorModel object and store this in an HDF5 file.\n", "We will use this later as the ground truth to verify our reconstructions against." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from pomme.model import TensorModel\n", "\n", "model = TensorModel(sizes=r_out, shape=n_elements)\n", "model['log_r' ] = np.log(rs)\n", "model['log_CO' ] = np.log(n_CO)\n", "model['log_turbulence'] = np.log(v_turb)\n", "model['log_v_in' ] = np.log(v_in)\n", "model['log_v_inf' ] = np.log(v_inf)\n", "model['log_beta' ] = np.log(beta)\n", "model['log_epsilon' ] = np.log(epsilon)\n", "model['log_T_star' ] = np.log(T_star)\n", "model['log_r_star' ] = np.log(r_star)\n", "model.save('1D_stellar_wind_truth.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### SphericalModel\n", "First, we define the functions that can generate the model distributions from the model parameters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import torch\n", "from pomme.utils import planck, T_CMB\n", "\n", "def get_velocity(model):\n", " \"\"\"\n", " Get the velocity from the TensorModel.\n", " \"\"\"\n", " # Extract parameters\n", " r = torch.exp(model['log_r'])\n", " v_in = torch.exp(model['log_v_in'])\n", " v_inf = torch.exp(model['log_v_inf'])\n", " beta = torch.exp(model['log_beta'])\n", " R_star = torch.exp(model['log_r_star'])\n", " # Compute velocity\n", " v = torch.empty_like(r)\n", " v[r <= r_star] = v_in\n", " v[r > r_star] = v_in + (v_inf - v_in) * (1.0 - r_star / r[r > r_star])**beta\n", " # Return\n", " return v\n", "\n", "def get_temperature(model):\n", " \"\"\"\n", " Get the temperature from the TensorModel.\n", " \"\"\"\n", " # Extract parameters\n", " r = torch.exp(model['log_r'])\n", " T_star = torch.exp(model['log_T_star'])\n", " epsilon = torch.exp(model['log_epsilon'])\n", " r_star = torch.exp(model['log_r_star'])\n", " # Compute temperature\n", " T = torch.empty_like(r) \n", " T[r <= r_star] = T_star\n", " T[r > r_star] = T_star * (r_star / r[r > r_star])**epsilon\n", " # Return\n", " return T\n", "\n", "def get_abundance(model):\n", " \"\"\"\n", " Get the abundance from the TensorModel.\n", " \"\"\"\n", " return torch.exp(model['log_CO'])\n", "\n", "def get_turbulence(model):\n", " \"\"\"\n", " Get the turbulence from the TensorModel.\n", " \"\"\"\n", " return torch.exp(model['log_turbulence']) * torch.ones_like(model['log_r'])\n", "\n", "def get_boundary_condition(model, frequency, b):\n", " \"\"\"\n", " Get the boundary condition from the TensorModel.\n", " model: TensorModel\n", " The TensorModel object containing the model.\n", " frequency: float\n", " Frequency at which to evaluate the boundary condition.\n", " b: float\n", " Impact parameter of the line-of-sight in the spherical model.\n", " \"\"\"\n", " # Extract parameters\n", " T_star = torch.exp(model['log_T_star'])\n", " r_star = torch.exp(model['log_r_star'])\n", " # Compute boundary condition\n", " if b > r_star:\n", " return planck(temperature=T_CMB, frequency=frequency)\n", " else:\n", " return planck(temperature=T_star, frequency=frequency)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these functions, we can build a SphericalModel object that can be used to generate synthetic observations or reconstruct the required parameters.\n", "The SphericalModel class is a convenience class that can make the necessary transformations, e.g. for ray tracing in a spherically symmetric geometry." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from pomme.model import SphericalModel\n", "\n", "smodel_truth = SphericalModel(rs, model, r_star=r_star)\n", "smodel_truth.get_velocity = get_velocity\n", "smodel_truth.get_abundance = get_abundance\n", "smodel_truth.get_turbulence = get_turbulence\n", "smodel_truth.get_temperature = get_temperature\n", "smodel_truth.get_boundary_condition = get_boundary_condition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Spectral lines\n", "We base our reconstructions on synthetic observations of two commonly observed rotational CO lines $J = \\{(3-2), \\, (7-6)\\}$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You have selected line:\n", " CO(J=3-2)\n", "Please check the properties that were inferred:\n", " Frequency 3.457959899e+11 Hz\n", " Einstein A coeff 2.497000000e-06 1/s\n", " Molar mass 28.0101 g/mol\n", "You have selected line:\n", " CO(J=7-6)\n", "Please check the properties that were inferred:\n", " Frequency 8.066518060e+11 Hz\n", " Einstein A coeff 3.422000000e-05 1/s\n", " Molar mass 28.0101 g/mol\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/frederikd/.local/lib/python3.9/site-packages/astroquery/lamda/core.py:145: UserWarning: The first time a LAMDA function is called, it must assemble a list of valid molecules and URLs. This list will be cached so future operations will be faster.\n", " warnings.warn(\"The first time a LAMDA function is called, it must \"\n" ] } ], "source": [ "from pomme.lines import Line\n", "\n", "lines = [Line('CO', i) for i in [2, 6]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Frequencies\n", "Next, we define the velocity/frequency range.\n", "We observe the lines in 50 frequency bins, centred around the lines, with a spacing of 500 m/s." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "vdiff = 500 # velocity increment size [m/s]\n", "nfreq = 50 # number of frequencies\n", "\n", "velocities = nfreq * vdiff * torch.linspace(-1, +1, nfreq, dtype=torch.float64)\n", "frequencies = [(1.0 + velocities / constants.c.si.value) * line.frequency for line in lines]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Synthetic observations\n", "We can now generate synthetic observations, directly from the SphericalModel object.\n", "We will use these later to derive our reconstructions." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "obss = smodel_truth.image(lines, frequencies, r_max=r_out)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the resulting synthetic spectral line observations." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for line, obs in zip(lines, obss):\n", " plt.plot(velocities, obs, label=line.description)\n", "plt.legend()\n", "plt.xlabel('Velocity [m/s]')\n", "plt.ylabel('Brightness [W / m$^2$ / Hz]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Reconstruction setup\n", "In this example, we will try to reconstruct the CO abundance, velocity, and temperature distribution.\n", "First, we define the model object for the reconstruction. Note that in the smodel defined above, all the right parameters are already stored, so we need a new one for the reconstruction.\n", "We take, $n_{\\text{CO}}^{\\text{init}}(r) = 5.0 \\times 10^{14} \\, \\text{m}^{-3} \\, (r_{\\text{in}}/r)^{2} $, as initial guess for the CO abundance distribution, and initialise both the velocity and the temperature with the correct values." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "smodel_recon = SphericalModel(\n", " rs = smodel_truth.rs,\n", " model_1D = smodel_truth.model_1D.deepcopy(),\n", " r_star = smodel_truth.r_star,\n", ")\n", "smodel_recon.get_abundance = lambda model: torch.exp(model['log_CO'])\n", "smodel_recon.get_velocity = lambda model: torch.exp(model['log_velocity'])\n", "smodel_recon.get_temperature = lambda model: torch.exp(model['log_temperature'])\n", "smodel_recon.get_turbulence = get_turbulence\n", "smodel_recon.get_boundary_condition = get_boundary_condition\n", "\n", "# Define initial guess for the CO abundance\n", "n_CO_init = 5.0e+14 * (smodel_recon.rs.min()/smodel_recon.rs)**2\n", "\n", "# Intialize model with the truth, except for the CO abundance\n", "smodel_recon.model_1D['log_CO' ] = np.log(n_CO_init).copy()\n", "smodel_recon.model_1D['log_velocity' ] = np.log(v ).copy()\n", "smodel_recon.model_1D['log_temperature'] = np.log(T ).copy()\n", "\n", "# Fix all parameters, except for the ones we want to fit\n", "smodel_recon.model_1D.fix_all()\n", "smodel_recon.model_1D.free(['log_CO', 'log_velocity', 'log_temperature'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore the model parameters with the info() function." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable key: Free/Fixed: Field: Min: Mean: Max:\n", " log_r Fixed True +2.343e+01 +2.919e+01 +3.494e+01\n", " log_CO Free True +1.082e+01 +2.233e+01 +3.385e+01\n", " log_turbulence Fixed False +6.908e+00 +6.908e+00 +6.908e+00\n", " log_v_in Fixed False +4.605e+00 +4.605e+00 +4.605e+00\n", " log_v_inf Fixed False +9.903e+00 +9.903e+00 +9.903e+00\n", " log_beta Fixed False -6.931e-01 -6.931e-01 -6.931e-01\n", " log_epsilon Fixed False -5.108e-01 -5.108e-01 -5.108e-01\n", " log_T_star Fixed False +7.824e+00 +7.824e+00 +7.824e+00\n", " log_r_star Fixed False +2.573e+01 +2.573e+01 +2.573e+01\n", " log_velocity Free True -4.605e+00 +6.928e+00 +9.903e+00\n", " log_temperature Free True +2.298e+00 +5.613e+00 +7.824e+00\n", "sizes: (1495978707000000.0,)\n", "shape: (1024,)\n" ] } ], "source": [ "smodel_recon.model_1D.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, can plot them." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "log_turbulence 6.907755278982137\n", "log_v_in 4.605170185988092\n", "log_v_inf 9.903487552536127\n", "log_beta -0.6931471805599453\n", "log_epsilon -0.5108256237659907\n", "log_T_star 7.824046010856292\n", "log_r_star 25.731216669096725\n" ] }, { "data": { "image/png": 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", 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", 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "smodel_recon.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Loss functions\n", "We first create Loss object that can conveniently store the different losses.\n", "We will use a reproduction loss (split into an averaged and relative component), a smoothness, and a continuity loss." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from pomme.loss import Loss, diff_loss\n", "\n", "losses = Loss(['avg', 'rel', 'smt', 'cnt'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "#### Reproduction loss\n", "We split the reproduction loss into an averaged and a relative component,\n", "\\begin{equation*}\n", "\\mathcal{L}_{\\text{rep}}\\big(f(\\boldsymbol{m}), \\boldsymbol{o} \\big)\n", "\\ = \\\n", "\\mathcal{L}_{\\text{rep}}\\Big( \\big\\langle f(\\boldsymbol{m}) \\big\\rangle, \\, \\left\\langle\\boldsymbol{o}\\right\\rangle \\Big)\n", "\\ + \\\n", "\\mathcal{L}_{\\text{rep}}\\left( \\frac{f(\\boldsymbol{m})}{\\big\\langle f(\\boldsymbol{m})\\big\\rangle}, \\, \\frac{\\boldsymbol{o}}{\\left\\langle \\boldsymbol{o}\\right\\rangle} \\right) ,\n", "\\end{equation*}" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Define averaging and relative function\n", "avg = lambda arr: arr.mean(axis=1)\n", "rel = lambda arr: torch.einsum(\"ij, i -> ij\", arr, 1.0/avg(arr))\n", "\n", "def avg_loss(smodel, imgs):\n", " \"\"\"\n", " Compute the average loss.\n", " \"\"\"\n", " return torch.nn.functional.mse_loss(avg(imgs), avg(obss))\n", "\n", "def rel_loss(smodel, imgs):\n", " \"\"\"\n", " Compute the relative loss.\n", " \"\"\"\n", " return torch.nn.functional.mse_loss(rel(imgs), rel(obss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "#### Smoothness loss\n", "The smoothnes loss in spherical symmetry is defined as, \n", "\\begin{equation}\n", "\\mathcal{L}[q]\n", "\\ = \\\n", "\\int_{0}^{\\infty} 4 \\pi r^{2} \\text{d} r \\ \\big\\{ \\partial_{r} q(r) \\big\\}^{2}\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def smoothness_loss(smodel):\n", " \"\"\"\n", " Smoothness loss for CO, velocity, and temperature distributions.\n", " \"\"\"\n", " # Get a mask for the elements outsife the star\n", " outside_star = torch.from_numpy(smodel.rs) > torch.exp(smodel.model_1D['log_r_star'])\n", " # Compute and return the loss\n", " return ( diff_loss(smodel.model_1D['log_CO' ][outside_star]) \\\n", " + diff_loss(smodel.model_1D['log_velocity' ][outside_star]) \\\n", " + diff_loss(smodel.model_1D['log_temperature'][outside_star]) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "#### Continuity loss\n", "In spherical symmetry, the regularisation loss that assumes a steady state and enforces the continuity equation, reads,\n", "\\begin{equation*}\n", "\\mathcal{L}[\\rho, v]\n", "\\ = \\\n", "\\int_{0}^{\\infty} 4\\pi r^{2} \\text{d}r \\left\\{ \\frac{1}{\\rho \\, r^{2}} \\, \\partial_{r} \\left( r^{2} \\rho \\, v \\right) \\right\\}^{2} .\n", "\\end{equation*}" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def steady_state_cont_loss(smodel):\n", " \"\"\"\n", " Loss assuming steady state hydrodynamics, i.e. vanishing time derivatives.\n", " \"\"\"\n", " # Get a mask for the elements outsife the star\n", " outside_star = torch.from_numpy(smodel.rs) > torch.exp(smodel.model_1D['log_r_star'])\n", " # Get the model variables\n", " rho = smodel.get_abundance(smodel.model_1D)[outside_star]\n", " v_r = smodel.get_velocity (smodel.model_1D)[outside_star]\n", " r = torch.from_numpy(smodel.rs) [outside_star]\n", " # Continuity equation (steady state): div(ρ v) = 0\n", " loss_cont = smodel.diff_r(r**2 * rho * v_r, r) / (rho*r**2)\n", " # Compute the mean squared losses\n", " loss = torch.mean(4.0*torch.pi*r**2*(loss_cont)**2)\n", " # Return losses\n", " return loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Fit function\n", "With everything in place, we can finally define the fit function." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from torch.optim import Adam\n", "from tqdm import tqdm\n", "\n", "def fit(losses, smodel, N_epochs=10, lr=1.0e-1, w_avg=1.0, w_rel=1.0, w_smt=1.0, w_cnt=1.0):\n", " # Define optimiser\n", " optimizer = Adam(smodel.model_1D.free_parameters(), lr=lr)\n", " # Iterate optimiser\n", " for _ in tqdm(range(N_epochs)):\n", " # Forward model\n", " imgs = smodel.image(lines, frequencies, r_max=r_out)\n", " # Compute the losses\n", " losses['avg'] = w_avg * avg_loss(smodel, imgs)\n", " losses['rel'] = w_rel * rel_loss(smodel, imgs)\n", " losses['smt'] = w_smt * smoothness_loss(smodel)\n", " losses['cnt'] = w_cnt * steady_state_cont_loss(smodel)\n", " # Set gradients to zero\n", " optimizer.zero_grad()\n", " # Backpropagate gradients\n", " losses.tot().backward()\n", " # Update parameters\n", " optimizer.step()\n", " # Return the images and losses\n", " return imgs, losses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Experiments" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/3 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "imgs, losses = fit(losses, smodel_recon,\n", " N_epochs = 100,\n", " lr = 1.0e-1,\n", " w_avg = 1.0e+0,\n", " w_rel = 1.0e+0,\n", " w_smt = 1.0e+0,\n", " w_cnt = 1.0e+0,\n", ")\n", "smodel_recon.model_1D.save('1D_stellar_wind_recon_100.h5')\n", "losses.plot()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "smodel_recon.model_1D = TensorModel.load('1D_stellar_wind_recon_100.h5')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(dpi=130)\n", "plt.plot(smodel_truth.rs, smodel_truth.get_abundance(smodel_truth.model_1D).data, label='Truth')\n", "plt.plot(smodel_recon.rs, smodel_recon.get_abundance(smodel_recon.model_1D).data, label='Reconstruction')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel('r [m]')\n", "plt.ylabel('CO abundance [m$^{-3}$]')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(dpi=130)\n", "plt.plot(smodel_truth.rs, smodel_truth.get_temperature(smodel_truth.model_1D).data, label='Truth')\n", "plt.plot(smodel_recon.rs, smodel_recon.get_temperature(smodel_recon.model_1D).data, label='Reconstruction')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel('r [m]')\n", "plt.ylabel('Temperature [K]')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(dpi=130)\n", "plt.plot(smodel_truth.rs, smodel_truth.get_velocity(smodel_truth.model_1D).data, label='Truth')\n", "plt.plot(smodel_recon.rs, smodel_recon.get_velocity(smodel_recon.model_1D).data, label='Reconstruction')\n", "plt.xscale('log')\n", "plt.xlabel('r [m]')\n", "plt.ylabel('velocity [m/s]')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "magritte", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 2 }