{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Visualization for Radial 1D Stellar Wind Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerics\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "# # for now using CPU as of outdated NVIDIA Driver\n",
    "# jax.config.update('jax_platform_name', 'cpu')\n",
    "# # jax.config.update('jax_disable_jit', True)\n",
    "# # 64-bit precision\n",
    "# jax.config.update(\"jax_enable_x64\", True)\n",
    "\n",
    "# debug nans\n",
    "# jax.config.update(\"jax_debug_nans\", True)\n",
    "\n",
    "# timing\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "# fluids\n",
    "from jf1uids import WindParams\n",
    "from jf1uids import SimulationConfig\n",
    "from jf1uids import get_helper_data\n",
    "from jf1uids import SimulationParams\n",
    "from jf1uids import time_integration\n",
    "from jf1uids.fluid_equations.fluid import construct_primitive_state\n",
    "\n",
    "\n",
    "from jf1uids import get_registered_variables\n",
    "from jf1uids.option_classes import WindConfig\n",
    "\n",
    "\n",
    "# units\n",
    "from jf1uids import CodeUnits\n",
    "from astropy import units as u\n",
    "import astropy.constants as c\n",
    "from astropy.constants import m_p\n",
    "\n",
    "# wind-specific\n",
    "from jf1uids._physics_modules._stellar_wind.weaver import Weaver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiating the stellar wind simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "👷 Setting up simulation...\n"
     ]
    }
   ],
   "source": [
    "from jf1uids.option_classes.simulation_config import OPEN_BOUNDARY, REFLECTIVE_BOUNDARY, SPHERICAL\n",
    "\n",
    "\n",
    "print(\"👷 Setting up simulation...\")\n",
    "\n",
    "# simulation settings\n",
    "gamma = 5/3\n",
    "\n",
    "# spatial domain\n",
    "geometry = SPHERICAL\n",
    "box_size = 1.0\n",
    "num_cells = 401\n",
    "\n",
    "left_boundary = REFLECTIVE_BOUNDARY\n",
    "right_boundary = OPEN_BOUNDARY\n",
    "\n",
    "# activate stellar wind\n",
    "stellar_wind = True\n",
    "\n",
    "fixed_timestep = True\n",
    "num_timesteps = 10000\n",
    "\n",
    "# setup simulation config\n",
    "config = SimulationConfig(\n",
    "    runtime_debugging = True,\n",
    "    geometry = geometry,\n",
    "    box_size = box_size, \n",
    "    num_cells = num_cells,\n",
    "    wind_config = WindConfig(\n",
    "        stellar_wind = stellar_wind,\n",
    "        num_injection_cells = 10,\n",
    "        trace_wind_density = False,\n",
    "    ),\n",
    "    # fixed_timestep = fixed_timestep,\n",
    "    # num_timesteps = num_timesteps,\n",
    "    # first_order_fallback = True,\n",
    ")\n",
    "\n",
    "helper_data = get_helper_data(config)\n",
    "\n",
    "registered_variables = get_registered_variables(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_high_res = SimulationConfig(\n",
    "    geometry = geometry,\n",
    "    box_size = box_size, \n",
    "    num_cells = 2001,\n",
    "    wind_config = WindConfig(\n",
    "        stellar_wind = stellar_wind,\n",
    "        num_injection_cells = 10,\n",
    "    ),\n",
    "    # fixed_timestep = fixed_timestep,\n",
    "    # num_timesteps = num_timesteps,\n",
    "    # first_order_fallback = True,\n",
    ")\n",
    "\n",
    "helper_data_high_res = get_helper_data(config_high_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the simulation parameters and initial state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For spherical geometry, only HLL is currently supported.\n",
      "Automatically setting reflective left and open right boundary for spherical geometry.\n",
      "For stellar wind simulations, we need source term aware timesteps, turning on.\n",
      "For spherical geometry, only HLL is currently supported.\n",
      "Automatically setting reflective left and open right boundary for spherical geometry.\n",
      "For stellar wind simulations, we need source term aware timesteps, turning on.\n"
     ]
    }
   ],
   "source": [
    "# code units\n",
    "from jf1uids.option_classes.simulation_config import finalize_config\n",
    "\n",
    "\n",
    "code_length = 3 * u.parsec\n",
    "code_mass = 1e-3 * u.M_sun\n",
    "code_velocity = 1 * u.km / u.s\n",
    "code_units = CodeUnits(code_length, code_mass, code_velocity)\n",
    "\n",
    "# time domain\n",
    "C_CFL = 0.8\n",
    "t_final = 2.5 * 1e4 * u.yr\n",
    "t_end = t_final.to(code_units.code_time).value\n",
    "dt_max = 0.1 * t_end\n",
    "\n",
    "# wind parameters\n",
    "M_star = 40 * u.M_sun\n",
    "wind_final_velocity = 2000 * u.km / u.s\n",
    "wind_mass_loss_rate = 2.965e-3 / (1e6 * u.yr) * M_star\n",
    "\n",
    "wind_params = WindParams(\n",
    "    wind_mass_loss_rate = wind_mass_loss_rate.to(code_units.code_mass / code_units.code_time).value,\n",
    "    wind_final_velocity = wind_final_velocity.to(code_units.code_velocity).value\n",
    ")\n",
    "\n",
    "params = SimulationParams(\n",
    "    C_cfl = C_CFL,\n",
    "    dt_max = dt_max,\n",
    "    gamma = gamma,\n",
    "    t_end = t_end,\n",
    "    wind_params=wind_params\n",
    ")\n",
    "\n",
    "params_high_res = SimulationParams(\n",
    "    C_cfl = C_CFL,\n",
    "    dt_max = dt_max,\n",
    "    gamma = gamma,\n",
    "    t_end = t_end,\n",
    "    wind_params=wind_params\n",
    ")\n",
    "\n",
    "# homogeneous initial state\n",
    "rho_0 = 2 * c.m_p / u.cm**3\n",
    "p_0 = 3e4 * u.K / u.cm**3 * c.k_B\n",
    "\n",
    "rho_init = jnp.ones(num_cells) * rho_0.to(code_units.code_density).value\n",
    "u_init = jnp.zeros(num_cells)\n",
    "p_init = jnp.ones(num_cells) * p_0.to(code_units.code_pressure).value\n",
    "\n",
    "# get initial state\n",
    "initial_state = construct_primitive_state(\n",
    "    config = config,\n",
    "    registered_variables = registered_variables,\n",
    "    density = rho_init,\n",
    "    velocity_x = u_init,\n",
    "    gas_pressure = p_init\n",
    ")\n",
    "\n",
    "config = finalize_config(config, initial_state.shape)\n",
    "\n",
    "# initial state high res\n",
    "rho_init_high_res = jnp.ones(config_high_res.num_cells) * rho_0.to(code_units.code_density).value\n",
    "u_init_high_res = jnp.zeros(config_high_res.num_cells)\n",
    "p_init_high_res = jnp.ones(config_high_res.num_cells) * p_0.to(code_units.code_pressure).value\n",
    "\n",
    "initial_state_high_res = construct_primitive_state(\n",
    "    config = config_high_res,\n",
    "    registered_variables = registered_variables,\n",
    "    density = rho_init_high_res,\n",
    "    velocity_x = u_init_high_res,\n",
    "    gas_pressure = p_init_high_res\n",
    ")\n",
    "\n",
    "config_high_res = finalize_config(config_high_res, initial_state_high_res.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation and Gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dv = 0.1 km / s\n"
     ]
    }
   ],
   "source": [
    "final_state = time_integration(initial_state, config, params, helper_data, registered_variables)\n",
    "\n",
    "# high res final state\n",
    "final_state_high_res = time_integration(initial_state_high_res, config_high_res, params_high_res, helper_data_high_res, registered_variables)\n",
    "\n",
    "def integrator(velocity):\n",
    "    return time_integration(initial_state, config, SimulationParams(C_cfl=params.C_cfl, dt_max=params.dt_max, gamma=params.gamma, t_end=params.t_end, wind_params=WindParams(wind_mass_loss_rate=params.wind_params.wind_mass_loss_rate, wind_final_velocity=velocity)), helper_data, registered_variables)\n",
    "\n",
    "vel_sens = jax.jacfwd(integrator)(params.wind_params.wind_final_velocity)\n",
    "\n",
    "# calculate the finite difference derivative\n",
    "dv = 0.1\n",
    "# print dv in km/s\n",
    "print(f\"dv = {(dv * code_units.code_velocity).to(u.km/u.s)}\")\n",
    "vel_sens_fd = (integrator(params.wind_params.wind_final_velocity + dv) - integrator(params.wind_params.wind_final_velocity - dv)) / (2 * dv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "👷 generating plots\n",
      "0.00852260137538079 code_length / code_velocity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2013104/3031789449.py:149: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x450 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_weaver_comparison(axs, final_state, params, helper_data, code_units, rho_0, p_0):\n",
    "    print(\"👷 generating plots\")\n",
    "\n",
    "    rho = final_state[registered_variables.density_index]\n",
    "    vel = final_state[registered_variables.velocity_index]\n",
    "    p = final_state[registered_variables.pressure_index]\n",
    "\n",
    "    rho = rho * code_units.code_density\n",
    "    vel = vel * code_units.code_velocity\n",
    "    p = p * code_units.code_pressure\n",
    "\n",
    "    r_high_res = helper_data_high_res.geometric_centers * code_units.code_length\n",
    "\n",
    "    rho_high_res = final_state_high_res[registered_variables.density_index]\n",
    "    vel_high_res = final_state_high_res[registered_variables.velocity_index]\n",
    "    p_high_res = final_state_high_res[registered_variables.pressure_index]\n",
    "\n",
    "    rho_high_res = rho_high_res * code_units.code_density\n",
    "    vel_high_res = vel_high_res * code_units.code_velocity\n",
    "    p_high_res = p_high_res * code_units.code_pressure\n",
    "\n",
    "    r = helper_data.geometric_centers * code_units.code_length\n",
    "\n",
    "    # get weaver solution\n",
    "    weaver = Weaver(\n",
    "        params.wind_params.wind_final_velocity * code_units.code_velocity,\n",
    "        params.wind_params.wind_mass_loss_rate * code_units.code_mass / code_units.code_time,\n",
    "        rho_0,\n",
    "        p_0\n",
    "    )\n",
    "    current_time = params.t_end * code_units.code_time# + 12e-4 * code_units.code_time\n",
    "    print(current_time)\n",
    "    \n",
    "    # density\n",
    "    r_density_weaver, density_weaver = weaver.get_density_profile(0.01 * u.parsec, 3.5 * u.parsec, current_time)\n",
    "    r_density_weaver = r_density_weaver.to(u.parsec)\n",
    "    density_weaver = (density_weaver / m_p).to(u.cm**-3)\n",
    "\n",
    "    # velocity\n",
    "    r_velocity_weaver, velocity_weaver = weaver.get_velocity_profile(0.01 * u.parsec, 3.5 * u.parsec, current_time)\n",
    "    r_velocity_weaver = r_velocity_weaver.to(u.parsec)\n",
    "    velocity_weaver = velocity_weaver.to(u.km / u.s)\n",
    "\n",
    "    # pressure\n",
    "    r_pressure_weaver, pressure_weaver = weaver.get_pressure_profile(0.01 * u.parsec, 3.5 * u.parsec, current_time)\n",
    "    r_pressure_weaver = r_pressure_weaver.to(u.parsec)\n",
    "    pressure_weaver = (pressure_weaver / c.k_B).to(u.cm**-3 * u.K)\n",
    "\n",
    "    axs[0].set_yscale(\"log\")\n",
    "    axs[0].plot(r.to(u.parsec), (rho / m_p).to(u.cm**-3), label=\"jf1uids\")\n",
    "\n",
    "    axs[0].plot(r_density_weaver, density_weaver, \"--\", label=\"Weaver solution\")\n",
    "\n",
    "    axs[0].plot(r_high_res.to(u.parsec), (rho_high_res / m_p).to(u.cm**-3), \"-.\", label=\"jf1uids, N = {}\".format(config_high_res.num_cells))\n",
    "\n",
    "    axs[0].set_title(\"density\")\n",
    "    axs[0].set_ylabel(r\"$\\rho$ in m$_p$ cm$^{-3}$\")\n",
    "    axs[0].set_xlim(0, 3)\n",
    "\n",
    "    axs[0].legend(loc=\"upper left\")\n",
    "\n",
    "    # turn off x ticks\n",
    "    axs[0].set_xticks([])\n",
    "    axs[1].set_xticks([])\n",
    "    axs[2].set_xticks([])\n",
    "\n",
    "    axs[1].set_yscale(\"log\")\n",
    "    axs[1].plot(r.to(u.parsec), (p / c.k_B).to(u.K / u.cm**3), label=\"jf1uids\")\n",
    "    axs[1].plot(r_pressure_weaver, pressure_weaver, \"--\", label=\"Weaver solution\")\n",
    "    axs[1].plot(r_high_res.to(u.parsec), (p_high_res / c.k_B).to(u.K / u.cm**3), \"-.\", label=\"jf1uids, N = {}\".format(config_high_res.num_cells))\n",
    "\n",
    "    axs[1].set_title(\"pressure\")\n",
    "    axs[1].set_ylabel(r\"$p$/k$_b$ in K cm$^{-3}$\")\n",
    "    axs[1].set_xlim(0, 3)\n",
    "\n",
    "    axs[1].legend(loc=\"upper left\")\n",
    "\n",
    "\n",
    "    axs[2].set_yscale(\"log\")\n",
    "    axs[2].plot(r.to(u.parsec), vel.to(u.km / u.s), label=\"jf1uids\")\n",
    "    axs[2].plot(r_velocity_weaver, velocity_weaver, \"--\", label=\"Weaver solution\")\n",
    "    axs[2].plot(r_high_res.to(u.parsec), vel_high_res.to(u.km / u.s), \"-.\", label=\"jf1uids, N = {}\".format(config_high_res.num_cells))\n",
    "    axs[2].set_title(\"velocity\")\n",
    "    # ylim 1 to 1e4 km/s\n",
    "    axs[2].set_ylim(1, 1e4)\n",
    "    axs[2].set_xlim(0, 3)\n",
    "    axs[2].set_ylabel(\"v in km/s\")\n",
    "    # xlabel\n",
    "    # show legend upper left\n",
    "    axs[2].legend(loc=\"upper right\")\n",
    "\n",
    "def sensitivity_plot(axs, vel_sens, vel_sens_fd):\n",
    "\n",
    "    rho_sens_vel = vel_sens[registered_variables.density_index]\n",
    "    vel_sens_vel = vel_sens[registered_variables.velocity_index]\n",
    "    p_sens_vel = vel_sens[registered_variables.pressure_index]\n",
    "\n",
    "    rho_sens_vel_fd = vel_sens_fd[registered_variables.density_index]\n",
    "    vel_sens_vel_fd = vel_sens_fd[registered_variables.velocity_index]\n",
    "    p_sens_vel_fd = vel_sens_fd[registered_variables.pressure_index]\n",
    "\n",
    "    r = helper_data.geometric_centers * code_units.code_length\n",
    "\n",
    "    axs[0].plot(r.to(u.parsec), rho_sens_vel, label=r\"d$\\rho$/dv$_\\infty$ autodiff\")\n",
    "    axs[0].plot(r.to(u.parsec), rho_sens_vel_fd, \"--\", label=r\"d$\\rho$/dv$_\\infty$ finite diff.\")\n",
    "    axs[0].set_ylabel(r\"d$\\rho$/dv$_\\infty$\")\n",
    "    axs[0].legend(loc = \"upper left\")\n",
    "    axs[0].tick_params(axis='y')\n",
    "    axs[0].set_yscale('symlog')\n",
    "    axs[0].set_xlim(0, 3)\n",
    "    axs[0].set_xlabel(\"r in pc\")\n",
    "    axs[0].yaxis.set_label_coords(-0.15, 0.5)\n",
    "\n",
    "    axs[1].plot(r.to(u.parsec), p_sens_vel, label=r\"dp/dv$_\\infty$ autodiff\")\n",
    "    axs[1].plot(r.to(u.parsec), p_sens_vel_fd, \"--\", label=r\"dp/dv$_\\infty$ finite diff.\")\n",
    "    axs[1].set_ylabel(r\"dp/dv$_\\infty$\")\n",
    "    axs[1].legend(loc = \"lower right\")\n",
    "    axs[1].tick_params(axis='y')\n",
    "    axs[1].set_yscale('symlog')\n",
    "    axs[1].set_xlim(0, 3)\n",
    "    axs[1].set_xlabel(\"r in pc\")\n",
    "    axs[1].yaxis.set_label_coords(-0.15, 0.5)\n",
    "\n",
    "    axs[2].plot(r.to(u.parsec), vel_sens_vel, label=r\"dv/dv$_\\infty$ autodiff\")\n",
    "    axs[2].plot(r.to(u.parsec), vel_sens_vel_fd, \"--\", label=r\"dv/dv$_\\infty$ finite diff.\")\n",
    "    axs[2].set_ylabel(r\"dv/dv$_\\infty$\")\n",
    "    axs[2].legend(loc = \"upper right\")\n",
    "    axs[2].tick_params(axis='y')\n",
    "    axs[2].set_yscale('symlog')\n",
    "    axs[2].set_xlim(0, 3)\n",
    "    axs[2].set_xlabel(\"r in pc\")\n",
    "    axs[2].yaxis.set_label_coords(-0.15, 0.5)\n",
    "\n",
    "    axs[0].yaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "    axs[1].yaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "    axs[2].yaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(14, 4.5))\n",
    "\n",
    "gs = GridSpec(2, 3, height_ratios=[3, 2], figure=fig, hspace=0.1, wspace=0.3)\n",
    "\n",
    "axs_upper = [fig.add_subplot(gs[0, i]) for i in range(3)]\n",
    "axs_lower = [fig.add_subplot(gs[1, i]) for i in range(3)]\n",
    "\n",
    "plot_weaver_comparison(axs_upper, final_state, params, helper_data, code_units, rho_0, p_0)\n",
    "sensitivity_plot(axs_lower, vel_sens, vel_sens_fd)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# TODO: add finite difference here\n",
    "\n",
    "plt.savefig(\"../figures/gradients_through_stellar_wind.pdf\", bbox_inches=\"tight\")"
   ]
  }
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