{
 "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": [
    "# ==== GPU selection ====\n",
    "from autocvd import autocvd\n",
    "autocvd(num_gpus = 1)\n",
    "# =======================\n",
    "\n",
    "# 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    "# # for now using CPU as of outdated NVIDIA Driver\n",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 astronomix import WindParams\n",
    "from astronomix import SimulationConfig\n",
    "from astronomix import get_helper_data\n",
    "from astronomix import SimulationParams\n",
    "from astronomix import time_integration\n",
    "from astronomix import construct_primitive_state\n",
    "\n",
    "\n",
    "from astronomix import get_registered_variables\n",
    "from astronomix.option_classes import WindConfig\n",
    "\n",
    "\n",
    "# units\n",
    "from astronomix 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 astronomix._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 astronomix.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": [
    "from astronomix.option_classes.simulation_config import HLL\n",
    "\n",
    "\n",
    "config_high_res = SimulationConfig(\n",
    "    riemann_solver = HLL,\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. Also, only the unsplit mode has been tested.\n",
      "Setting unsplit mode for spherical geometry\n",
      "Setting MUSCL time integrator for spherical geometry\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. Also, only the unsplit mode has been tested.\n",
      "Setting unsplit mode for spherical geometry\n",
      "Setting MUSCL time integrator for spherical geometry\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 astronomix.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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dv = 0.1 km / s\n"
     ]
    }
   ],
   "source": [
    "final_state = time_integration(initial_state, config, params, 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, 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)), 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_2542770/3031789449.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=\"astronomix\")\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=\"astronomix, 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=\"astronomix\")\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=\"astronomix, 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=\"astronomix\")\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=\"astronomix, 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\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "f1uids",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}