{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00caf7ad",
   "metadata": {},
   "source": [
    "# Stream generation\n",
    "\n",
    "This notebook demonstrates how to simulate a stellar stream from a dissolving globular cluster and verifies that the numerical integration is self-consistent.\n",
    "\n",
    "The workflow proceeds as follows:\n",
    "\n",
    "1. **Define a globular cluster** — a Plummer sphere with a chosen mass and half-mass radius, populated with star particles drawn from the ergodic distribution.\n",
    "2. **Choose orbital initial conditions** — the cluster's center of mass is placed at apoapsis using pseudo-Keplerian parameters (radius, eccentricity, orientation angles).\n",
    "3. **Integrate the cluster orbit backward in time** — the center of mass trajectory is computed from today back to an earlier epoch.\n",
    "4. **Strip the stream forward** — star particles are initialized around the cluster's past position and integrated forward to today in the time-varying host potential, producing a tidal stream.\n",
    "5. **Retrace the stream backward** — the final stream positions are integrated backward along the same host trajectory, recovering the original initial conditions.\n",
    "6. **Evaluate energy conservation** — the fractional energy error $\\left| (E_{\\rm retrace} - E_0) / E_0 \\right|$ is computed for each particle as a quantitative check.\n",
    "\n",
    "Since the equations of motion are time-reversible, a well-implemented integrator should recover the initial conditions to within the numerical truncation error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "915176d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tstrippy\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    \"font.serif\": [\"Computer Modern Roman\"],\n",
    "    \"font.size\": 15,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d3f2cd",
   "metadata": {},
   "source": [
    "## Define helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e73600b",
   "metadata": {},
   "source": [
    "A function for creating initial orbital conditions and viewing the geometry. \n",
    "\n",
    "`apoapsis_shot` - puts the particle at it's apoapsis, given the spherical coordinates and allows the user to modify the direction of the velocity vector and scale the speed down based on the circular velocity. \n",
    "\n",
    "`plot_initial_orbital_conditions_geometry` - shows the geometry as a check \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49757cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apoapsis_shot(potential, params, radius, eccen, theta, phi, polar_mix, getVecs=False):\n",
    "    \"\"\"\n",
    "    Generate initial position and velocity for an orbit launched from apogee.\n",
    "    \n",
    "    Uses pseudo-Keplerian parameters to construct phase-space coordinates\n",
    "    in the Milky Way potential.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    potential : function \n",
    "        tstrippy.potentials.pouliasis2017pii()\n",
    "    params : array-like\n",
    "        Potential parameters (from tstrippy.Parsers.pouliasis2017pii())\n",
    "    radius : float\n",
    "        Distance from galactic center (kpc)\n",
    "    eccen : float\n",
    "        Eccentricity-like speed scaling, 0 ≤ eccen < 1.\n",
    "        At eccen=0, speed = circular speed. At eccen→1, speed→0.\n",
    "    theta : float\n",
    "        Polar angle in spherical coordinates (radians), 0 ≤ theta ≤ π.\n",
    "        theta=π/2 is the equatorial plane.\n",
    "    phi : float\n",
    "        Azimuthal angle in spherical coordinates (radians), 0 ≤ phi < 2π.\n",
    "    polar_mix : float\n",
    "        Polar angle (radians), 0 ≤ polar_mix ≤ π.\n",
    "        polar_mix=0 gives pure azimuthal (planar) motion.\n",
    "        polar_mix=π/2 gives equal azimuthal and meridional mix.\n",
    "        polar_mix=π gives pure meridional motion.\n",
    "    getVecs : bool, optional\n",
    "        If True, also return basis vectors. Default False.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    rvec : ndarray, shape (3,)\n",
    "        Position vector [x, y, z]\n",
    "    vvec : ndarray, shape (3,)\n",
    "        Velocity vector [vx, vy, vz]\n",
    "    azimuth_hat : ndarray, shape (3,) (if getVecs=True)\n",
    "    meridional_hat : ndarray, shape (3,) (if getVecs=True)\n",
    "    \"\"\"\n",
    "    # Build Cartesian position from spherical coordinates\n",
    "    x = radius * np.cos(phi) * np.sin(theta)\n",
    "    y = radius * np.sin(phi) * np.sin(theta)\n",
    "    z = radius * np.cos(theta)\n",
    "    rvec = np.array([x, y, z])\n",
    "    \n",
    "    # Compute circular speed from the local force\n",
    "    fx, fy, fz, _ = potential(params, x, y, z)\n",
    "    fmag = np.sqrt(fx**2 + fy**2 + fz**2)\n",
    "    vcirc = np.sqrt(radius * fmag)\n",
    "    \n",
    "    # Define spherical basis vectors at (theta, phi)\n",
    "    azimuth_hat = np.array([-np.sin(phi), np.cos(phi), 0])\n",
    "    meridional_hat = np.array([np.cos(theta)*np.cos(phi), \n",
    "                            np.cos(theta)*np.sin(phi), \n",
    "                            -np.sin(theta)])  # Remove the negative sign\n",
    "\n",
    "    # Mix azimuthal and meridional directions using polar_mix as angle\n",
    "    vdirection = np.cos(polar_mix) * azimuth_hat + np.sin(polar_mix) * meridional_hat\n",
    "    vdirection /= np.linalg.norm(vdirection)    \n",
    "    \n",
    "    # Scale by eccentricity and circular speed\n",
    "    vvec = vcirc * (1 - eccen) * vdirection\n",
    "    \n",
    "    if getVecs:\n",
    "        return rvec, vvec, azimuth_hat, meridional_hat\n",
    "    else:\n",
    "        return rvec, vvec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56f341e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_initial_orbital_conditions_geometry(rvec, vvec, azimuthal, meridional, theta, phi, \n",
    "                          polar_mix, figsize=(8.2, 8.2)):\n",
    "    \"\"\"\n",
    "    Plot orbital initialization geometry with proper spherical coordinate visualization.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    rvec : ndarray, shape (3,)\n",
    "        Position vector [x, y, z]\n",
    "    vvec : ndarray, shape (3,)\n",
    "        Velocity vector [vx, vy, vz]\n",
    "    azimuthal : ndarray, shape (3,)\n",
    "        Azimuthal basis vector $\\hat{e}_\\phi$\n",
    "    meridional : ndarray, shape (3,)\n",
    "        Meridional basis vector $\\hat{e}_\\theta$\n",
    "    theta : float\n",
    "        Polar angle (radians)\n",
    "    phi : float\n",
    "        Azimuthal angle (radians)\n",
    "    polar_mix : float\n",
    "        Polar mix angle (radians)\n",
    "    figsize : tuple, optional\n",
    "        Figure size (default (12, 12))\n",
    "    \"\"\"\n",
    "    radius = np.linalg.norm(rvec)\n",
    "    \n",
    "    # Prepare vectors for plotting\n",
    "    vhat = vvec / np.linalg.norm(vvec)\n",
    "    scale = radius * 0.4\n",
    "    \n",
    "    # Cylindrical vector (projection onto xy plane)\n",
    "    cyl_vec = np.array([rvec[0], rvec[1], 0])\n",
    "    \n",
    "    # Arc radii\n",
    "    arc_radius_main = radius * 0.25  # for phi and theta\n",
    "    arc_radius_mix = 0.75 * scale  # for polar_mix\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(111, projection=\"3d\")\n",
    "    \n",
    "    # ===== THE TRIANGLE =====\n",
    "    # 1. Position vector from origin to rvec\n",
    "    ax.quiver(0, 0, 0, rvec[0], rvec[1], rvec[2],\n",
    "              color=\"black\", linewidth=3, arrow_length_ratio=0.08, label=\"$\\\\vec{r}$\")\n",
    "    \n",
    "    # 2. Vertical line from rvec down to xy plane\n",
    "    ax.plot([rvec[0], rvec[0]], [rvec[1], rvec[1]], [rvec[2], 0], \n",
    "            'k:', linewidth=1, alpha=1, )\n",
    "    \n",
    "    # 3. Cylindrical vector in xy plane (from origin to projection)\n",
    "    ax.plot([0, cyl_vec[0]], [0, cyl_vec[1]], [0, 0], \n",
    "            \"k:\", linewidth=1 )\n",
    "\n",
    "    \n",
    "    # ===== BASIS VECTORS =====\n",
    "    tip = rvec\n",
    "    ax.quiver(tip[0], tip[1], tip[2], scale*azimuthal[0], scale*azimuthal[1], scale*azimuthal[2],\n",
    "              color=\"tab:blue\", linewidth=2.5, arrow_length_ratio=0.2, label=\"$\\hat{e}_\\phi$\")\n",
    "    ax.quiver(tip[0], tip[1], tip[2], scale*meridional[0], scale*meridional[1], scale*meridional[2],\n",
    "              color=\"tab:red\", linewidth=2.5, arrow_length_ratio=0.2, label=\"$\\hat{e}_\\\\theta$\")\n",
    "    \n",
    "    # ===== VELOCITY VECTOR =====\n",
    "    vscale = 2 \n",
    "    ax.quiver(tip[0], tip[1], tip[2], vscale*scale*vhat[0], vscale*scale*vhat[1], vscale*scale*vhat[2],\n",
    "              color=\"tab:green\", linewidth=2.5, arrow_length_ratio=0.2, label=\"velocity\")\n",
    "\n",
    "    ax.scatter(*tip, color=\"black\", s=60, zorder=5) # the initial position \n",
    "    \n",
    "    # ===== ANGLE ARCS =====\n",
    "    \n",
    "    # φ angle in xy plane (from x-axis to projection of rvec)\n",
    "    phi_arc = np.linspace(0, phi, 50)\n",
    "    phi_x = arc_radius_main * np.cos(phi_arc)\n",
    "    phi_y = arc_radius_main * np.sin(phi_arc)\n",
    "    phi_z = np.zeros_like(phi_arc)\n",
    "    ax.plot(phi_x, phi_y, phi_z, color='orange', linewidth=2, alpha=0.8)\n",
    "    ax.text(arc_radius_main*1.4*np.cos(phi/2), arc_radius_main*1.4*np.sin(phi/2), 0.2, \n",
    "            r'$\\phi$', fontsize=15, fontweight='bold', color='orange')\n",
    "    \n",
    "    # θ angle: from +z axis down to position vector\n",
    "    # We need to sweep from +z direction to the direction of rvec\n",
    "    # This is trickier - we sweep in the meridional plane defined by phi\n",
    "    theta_arc = np.linspace(0, theta, 50)\n",
    "    theta_x = arc_radius_main * np.sin(theta_arc) * np.cos(phi)\n",
    "    theta_y = arc_radius_main * np.sin(theta_arc) * np.sin(phi)\n",
    "    theta_z = arc_radius_main * np.cos(theta_arc)\n",
    "    ax.plot(theta_x, theta_y, theta_z, color='purple', linewidth=2, linestyle='-', alpha=0.8)\n",
    "    ax.text(arc_radius_main*1.4*np.sin(theta/2)*np.cos(phi), \n",
    "            arc_radius_main*1.4*np.sin(theta/2)*np.sin(phi),\n",
    "            arc_radius_main*1.4*np.cos(theta/2),\n",
    "            r'$\\theta$', fontsize=15, fontweight='bold', color='purple')\n",
    "    \n",
    "    # polar_mix angle: between azimuthal and meridional at tip\n",
    "    pm_arc = np.linspace(0, polar_mix, 50)\n",
    "    pm_arc_x = tip[0] + arc_radius_mix * (np.cos(pm_arc) * azimuthal[0] + np.sin(pm_arc) * meridional[0])\n",
    "    pm_arc_y = tip[1] + arc_radius_mix * (np.cos(pm_arc) * azimuthal[1] + np.sin(pm_arc) * meridional[1])\n",
    "    pm_arc_z = tip[2] + arc_radius_mix * (np.cos(pm_arc) * azimuthal[2] + np.sin(pm_arc) * meridional[2])\n",
    "    ax.plot(pm_arc_x, pm_arc_y, pm_arc_z, color='brown', linewidth=2.5, alpha=0.9)\n",
    "    ax.text(tip[0] + arc_radius_mix*1.5*np.cos(polar_mix/2)*azimuthal[0] + arc_radius_mix*1.5*np.sin(polar_mix/2)*meridional[0],\n",
    "            tip[1] + arc_radius_mix*1.5*np.cos(polar_mix/2)*azimuthal[1] + arc_radius_mix*1.5*np.sin(polar_mix/2)*meridional[1],\n",
    "            tip[2] + arc_radius_mix*1.5*np.cos(polar_mix/2)*azimuthal[2] + arc_radius_mix*1.5*np.sin(polar_mix/2)*meridional[2],\n",
    "            r'$\\alpha_{\\mathrm{mix}}$', fontsize=13, fontweight='bold', color='brown')\n",
    "\n",
    "    # Supplementary arc (up to pi) in a lighter shade for visual plane guidance\n",
    "    pm_supp = np.linspace(polar_mix, np.pi/2, 80)\n",
    "    pm_supp_x = tip[0] + arc_radius_mix * (np.cos(pm_supp) * azimuthal[0] + np.sin(pm_supp) * meridional[0])\n",
    "    pm_supp_y = tip[1] + arc_radius_mix * (np.cos(pm_supp) * azimuthal[1] + np.sin(pm_supp) * meridional[1])\n",
    "    pm_supp_z = tip[2] + arc_radius_mix * (np.cos(pm_supp) * azimuthal[2] + np.sin(pm_supp) * meridional[2])\n",
    "    ax.plot(pm_supp_x, pm_supp_y, pm_supp_z, color='brown', linewidth=2.0, alpha=0.25, linestyle='--')\n",
    "    \n",
    "    # ===== XY PLANE REFERENCE =====\n",
    "    xy_plane = np.linspace(0, 2*np.pi, 100)\n",
    "    xy_r = 1.3 * radius\n",
    "    ax.plot(xy_r * np.cos(xy_plane), xy_r * np.sin(xy_plane), np.zeros_like(xy_plane), \n",
    "            'k', linewidth=0.5, alpha=1, linestyle='-')\n",
    "    \n",
    "    # ===== COORDINATE AXES =====\n",
    "    axis_len = 1.3 * radius\n",
    "    ax.plot([-axis_len, axis_len], [0, 0], [0, 0], linewidth=1.5, c='gray', alpha=1)\n",
    "    ax.plot([0, 0], [-axis_len, axis_len], [0, 0], linewidth=1.5, c='gray', alpha=1)\n",
    "    ax.plot([0, 0], [0, 0], [-0.3*axis_len, axis_len], linewidth=1.5, c='gray', alpha=1)\n",
    "    \n",
    "    # ===== FORMATTING =====\n",
    "    ax.legend(loc=\"upper left\", fontsize=12, framealpha=0.95)\n",
    "    ax.set_xlabel(\"x (kpc)\", fontsize=13, fontweight='bold')\n",
    "    ax.set_ylabel(\"y (kpc)\", fontsize=13, fontweight='bold')\n",
    "    ax.set_zlabel(\"z (kpc)\", fontsize=13, fontweight='bold')\n",
    "    ax.set_title(\"Orbital initial conditions\\nDefined at apogee\",\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    \n",
    "    maxs = 1.5 * radius\n",
    "    ax.set_xlim(-maxs, maxs)\n",
    "    ax.set_ylim(-maxs, maxs)\n",
    "    ax.set_zlim(-0.5*maxs, maxs)\n",
    "    ax.set_box_aspect((1, 1, 1))\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6e0738",
   "metadata": {},
   "source": [
    "### Integration functions\n",
    "\n",
    "`integrate_orbit` - integrates the orbit of the center of mass of the system \n",
    "\n",
    "`make_host_kinematics` - takes the result from `integrate_orbit` and allows the user to downsample the data points. We will check the numerical uncertainty through down-sampling the orbital points\n",
    "\n",
    "`integrate_particles_in_host` - creates a stream. Can go backward or forward. \n",
    "\n",
    "`plot_stream_retrace` - view the retrace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5aa1ef9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def integrate_orbit(\n",
    "    staticgalaxy,\n",
    "    initial_kinematics,\n",
    "    integration_params,\n",
    "    backward=False,\n",
    "):\n",
    "    \"\"\"\n",
    "    Integrate a single host orbit in the static galaxy.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    staticgalaxy : sequence\n",
    "        [\"potential_name\", potential_params]\n",
    "    initial_kinematics : sequence\n",
    "        [x0, y0, z0, vx0, vy0, vz0] for one particle\n",
    "    t0 : float\n",
    "        Initial integration time\n",
    "    dt : float\n",
    "        Integrator timestep\n",
    "    nstep : int\n",
    "        Number of timesteps\n",
    "    backward : bool, optional\n",
    "        If True, call setbackwardorbit() before integrating\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    orbit : dict\n",
    "        {\n",
    "            \"time\": time array,\n",
    "            \"x\": x array,\n",
    "            \"y\": y array,\n",
    "            \"z\": z array,\n",
    "            \"vx\": vx array,\n",
    "            \"vy\": vy array,\n",
    "            \"vz\": vz array,\n",
    "            \"backward\": bool,\n",
    "            \"dt\": dt,\n",
    "            \"nstep\": nstep,\n",
    "        }\n",
    "    \"\"\"\n",
    "    tstrippy.integrator.deallocate()\n",
    "    nstep = integration_params[-1]\n",
    "    try:\n",
    "        tstrippy.integrator.setstaticgalaxy(*staticgalaxy)\n",
    "        tstrippy.integrator.setintegrationparameters(*integration_params)\n",
    "        tstrippy.integrator.setinitialkinematics(*initial_kinematics)\n",
    "\n",
    "        if backward:\n",
    "            tstrippy.integrator.setbackwardorbit()\n",
    "\n",
    "        xt, yt, zt, vxt, vyt, vzt = tstrippy.integrator.leapfrogintime(nstep, 1)\n",
    "        time = tstrippy.integrator.timestamps.copy()\n",
    "\n",
    "        return {\n",
    "            \"time\": time.copy(),\n",
    "            \"x\": xt[0].copy(),\n",
    "            \"y\": yt[0].copy(),\n",
    "            \"z\": zt[0].copy(),\n",
    "            \"vx\": vxt[0].copy(),\n",
    "            \"vy\": vyt[0].copy(),\n",
    "            \"vz\": vzt[0].copy(),\n",
    "            \"backward\": bool(backward),\n",
    "            \"dt\": dt,\n",
    "            \"nstep\": nstep,\n",
    "        }\n",
    "    finally:\n",
    "        tstrippy.integrator.deallocate()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b2e6b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_host_kinematics(\n",
    "    orbit,\n",
    "    sample_stride=1,\n",
    "    target_time_order=\"increasing\",\n",
    "):\n",
    "    \"\"\"\n",
    "    Convert an orbit trajectory into host kinematics for the host perturber.\n",
    "\n",
    "    This function is the decoupling layer between:\n",
    "    1. how the host orbit was integrated\n",
    "    2. how densely the host is sampled\n",
    "    3. how that host trajectory is passed into the particle integration\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    orbit : dict\n",
    "        Output from integrate_orbit()\n",
    "    sample_stride : int, optional\n",
    "        Keep every sample_stride-th point from the orbit.\n",
    "        This is the main control knob for degrading host temporal sampling.\n",
    "    target_time_order : {\"increasing\", \"decreasing\"}, optional\n",
    "        Desired ordering of the returned host time array.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    host : dict\n",
    "        {\n",
    "            \"time\": timeH,\n",
    "            \"x\": xH,\n",
    "            \"y\": yH,\n",
    "            \"z\": zH,\n",
    "            \"vx\": vxH,\n",
    "            \"vy\": vyH,\n",
    "            \"vz\": vzH,\n",
    "        }\n",
    "    \"\"\"\n",
    "    if sample_stride < 1:\n",
    "        raise ValueError(\"sample_stride must be >= 1\")\n",
    "\n",
    "    time = np.asarray(orbit[\"time\"]).copy()\n",
    "    x = np.asarray(orbit[\"x\"]).copy()\n",
    "    y = np.asarray(orbit[\"y\"]).copy()\n",
    "    z = np.asarray(orbit[\"z\"]).copy()\n",
    "    vx = np.asarray(orbit[\"vx\"]).copy()\n",
    "    vy = np.asarray(orbit[\"vy\"]).copy()\n",
    "    vz = np.asarray(orbit[\"vz\"]).copy()\n",
    "\n",
    "    if time.size < 2:\n",
    "        raise ValueError(\"orbit must contain at least 2 time points\")\n",
    "\n",
    "    # Downsample first so the host grid can be coarser than the particle grid.\n",
    "    sample_idx = np.arange(0, time.size, sample_stride, dtype=int)\n",
    "    if sample_idx[-1] != time.size - 1:\n",
    "        sample_idx = np.append(sample_idx, time.size - 1)\n",
    "\n",
    "    time = time[sample_idx]\n",
    "    x = x[sample_idx]\n",
    "    y = y[sample_idx]\n",
    "    z = z[sample_idx]\n",
    "    vx = vx[sample_idx]\n",
    "    vy = vy[sample_idx]\n",
    "    vz = vz[sample_idx]\n",
    "\n",
    "    is_increasing = bool(time[1] > time[0])\n",
    "    is_decreasing = bool(time[1] < time[0])\n",
    "\n",
    "    if not (is_increasing or is_decreasing):\n",
    "        raise ValueError(\"host time array is not strictly monotonic\")\n",
    "\n",
    "    if target_time_order not in (\"increasing\", \"decreasing\"):\n",
    "        raise ValueError(\"target_time_order must be 'increasing' or 'decreasing'\")\n",
    "\n",
    "    # If we reverse the time ordering, we also reverse the physical direction\n",
    "    # of traversal along the orbit, so the velocities must change sign.\n",
    "    want_increasing = target_time_order == \"increasing\"\n",
    "    if want_increasing and is_decreasing:\n",
    "        time = time[::-1]\n",
    "        x = x[::-1]\n",
    "        y = y[::-1]\n",
    "        z = z[::-1]\n",
    "        vx = -vx[::-1]\n",
    "        vy = -vy[::-1]\n",
    "        vz = -vz[::-1]\n",
    "    elif (not want_increasing) and is_increasing:\n",
    "        time = time[::-1]\n",
    "        x = x[::-1]\n",
    "        y = y[::-1]\n",
    "        z = z[::-1]\n",
    "        vx = -vx[::-1]\n",
    "        vy = -vy[::-1]\n",
    "        vz = -vz[::-1]\n",
    "\n",
    "    return {\n",
    "        \"time\": time.copy(),\n",
    "        \"x\": x.copy(),\n",
    "        \"y\": y.copy(),\n",
    "        \"z\": z.copy(),\n",
    "        \"vx\": vx.copy(),\n",
    "        \"vy\": vy.copy(),\n",
    "        \"vz\": vz.copy(),\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6699581",
   "metadata": {},
   "outputs": [],
   "source": [
    "def integrate_particles_in_host(\n",
    "    staticgalaxy,\n",
    "    initial_kinematics,\n",
    "    t0,\n",
    "    dt,\n",
    "    nstep,\n",
    "    hostkinematics,\n",
    "    hostmass,\n",
    "    hostradius,\n",
    "    backward=False,\n",
    "):\n",
    "    \"\"\"\n",
    "    Integrate particles inside a supplied host trajectory.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    staticgalaxy : sequence\n",
    "        [\"potential_name\", potential_params]\n",
    "    initial_kinematics : sequence\n",
    "        [x0, y0, z0, vx0, vy0, vz0]\n",
    "        Each entry may be scalar or array-like depending on particle count.\n",
    "    t0 : float\n",
    "        Initial integration time\n",
    "    dt : float\n",
    "        Particle integrator timestep\n",
    "    nstep : int\n",
    "        Number of particle timesteps\n",
    "    hostkinematics : dict\n",
    "        Output from make_host_kinematics()\n",
    "    hostmass : sequence\n",
    "        Example: [\"constant\", cluster_mass]\n",
    "    hostradius : float\n",
    "        Example: cluster_char_radius\n",
    "    backward : bool, optional\n",
    "        If True, call setbackwardorbit() before integrating particles\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result : dict\n",
    "        {\n",
    "            \"xf\", \"yf\", \"zf\",\n",
    "            \"vxf\", \"vyf\", \"vzf\"\n",
    "        }\n",
    "    \"\"\"\n",
    "    tstrippy.integrator.deallocate()\n",
    "    try:\n",
    "        tstrippy.integrator.setstaticgalaxy(*staticgalaxy)\n",
    "        tstrippy.integrator.setintegrationparameters(t0, dt, nstep)\n",
    "        tstrippy.integrator.setinitialkinematics(*initial_kinematics)\n",
    "        tstrippy.integrator.inithostkinematics(\n",
    "            hostkinematics[\"time\"],\n",
    "            hostkinematics[\"x\"],\n",
    "            hostkinematics[\"y\"],\n",
    "            hostkinematics[\"z\"],\n",
    "            hostkinematics[\"vx\"],\n",
    "            hostkinematics[\"vy\"],\n",
    "            hostkinematics[\"vz\"],\n",
    "        )\n",
    "        tstrippy.integrator.inithostmass(*hostmass)\n",
    "        tstrippy.integrator.inithostradius(hostradius)\n",
    "\n",
    "        if backward:\n",
    "            tstrippy.integrator.setbackwardorbit()\n",
    "\n",
    "        tstrippy.integrator.leapfrogtofinalpositions()\n",
    "\n",
    "        return {\n",
    "            \"xf\": tstrippy.integrator.xf.copy(),\n",
    "            \"yf\": tstrippy.integrator.yf.copy(),\n",
    "            \"zf\": tstrippy.integrator.zf.copy(),\n",
    "            \"vxf\": tstrippy.integrator.vxf.copy(),\n",
    "            \"vyf\": tstrippy.integrator.vyf.copy(),\n",
    "            \"vzf\": tstrippy.integrator.vzf.copy(),\n",
    "        }\n",
    "    finally:\n",
    "        tstrippy.integrator.deallocate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e50ca852",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_energy_error(potential,params,initial,final):\n",
    "    x0,y0,z0,vx0,vy0,vz0=initial\n",
    "    xf,yf,zf,vxf,vyf,vzf=final \n",
    "    _,_,_,PHI0 = potential(params,x0,y0,z0)\n",
    "    _,_,_,PHIf = potential(params,xf,yf,zf)\n",
    "    T0 = (1/2) * (vx0**2 + vy0**2 + vz0**2)\n",
    "    Tf = (1/2) * (vxf**2 + vyf**2 + vzf**2)\n",
    "    E0 = PHI0 + T0\n",
    "    Ef = PHIf + Tf\n",
    "    return np.abs((Ef-E0) / E0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "677ca9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_stream_retrace(\n",
    "    orbit,\n",
    "    initial,\n",
    "    stream,\n",
    "    retrace,\n",
    "    nparticles=None,\n",
    "    nskip=100,\n",
    "    figsize=(8.2, 4.2),\n",
    "    axis_labels=None,\n",
    "    inset_bounds=(0.62, 0.62, 0.34, 0.34),\n",
    "    inset_pad_factor=5.0,\n",
    "):\n",
    "    \n",
    "    xp, yp = initial \n",
    "    xt, yt = orbit \n",
    "    xf, yf = stream \n",
    "    x0, y0 = retrace \n",
    "    if axis_labels is None:\n",
    "        axis_labels = {\"xlabel\": \"X [kpc]\", \"ylabel\": \"Y [kpc]\"}\n",
    "\n",
    "    fig, axis = plt.subplots(1, 2, figsize=figsize)\n",
    "\n",
    "    # main panel\n",
    "    axis[0].scatter(xp, yp, s=60, color=\"tab:blue\", label=\"Initial\")\n",
    "    axis[0].plot(xt[::nskip], yt[::nskip], color=\"tab:blue\", lw=1.2)\n",
    "    axis[0].scatter(xf, yf, s=40, color=\"tab:red\", label=\"Stream\")\n",
    "    axis[0].scatter(x0, y0, s=8, color=\"black\", label=\"Retrace\")\n",
    "\n",
    "    # inset near initial host position\n",
    "    axins = axis[0].inset_axes(inset_bounds)\n",
    "    axins.scatter(xp, yp, s=60, color=\"tab:blue\", label=\"Initial\")\n",
    "    axins.scatter(x0, y0, s=5, color=\"black\", label=\"Retrace\")\n",
    "\n",
    "    x_center, y_center = xt[0], yt[0]\n",
    "    pad = inset_pad_factor * max(np.std(xp), np.std(yp))\n",
    "    if pad == 0:\n",
    "        pad = 1e-3\n",
    "\n",
    "    axins.set_xlim(x_center - pad, x_center + pad)\n",
    "    axins.set_ylim(y_center - pad, y_center + pad)\n",
    "    axins.set_aspect(\"equal\", adjustable=\"box\")\n",
    "    axins.set_xticks([])\n",
    "    axins.set_yticks([])\n",
    "\n",
    "    if nparticles is not None:\n",
    "        axins.text(\n",
    "            0.01, 0.99,\n",
    "            rf\"$\\rm{{N_p}}$: {int(nparticles):d}\",\n",
    "            transform=axins.transAxes,\n",
    "            ha=\"left\",\n",
    "            va=\"top\"\n",
    "        )\n",
    "\n",
    "    axis[0].indicate_inset_zoom(axins, edgecolor=\"gray\")\n",
    "    axis[0].legend(loc=\"upper left\")\n",
    "    axis[0].set(**axis_labels)\n",
    "    return fig, axis, axins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed1fe4b",
   "metadata": {},
   "source": [
    "## Initialize system\n",
    "1. Get the parameters for the Milky Way potential\n",
    "2. Pick parameters for the Plummer sphere\n",
    "3. Pick orbital parameters\n",
    "\n",
    "All quantities use the following unit system: distances in **kpc**, velocities in **km/s**, and masses in **solar masses**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f1f7178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 820x820 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# galaxy\n",
    "MWparams                = tstrippy.Parsers.pouliasis2017pii()\n",
    "G = MWparams[0]\n",
    "# cluster\n",
    "cluster_mass            = 5e5      # solar masses\n",
    "cluster_half_mass_radius= 5e-3   # kpc\n",
    "cluster_num_partilces   = int(1e2)\n",
    "cluster_char_radius     = tstrippy.ergodic.convertHalfMassRadiusToPlummerRadius(cluster_half_mass_radius)\n",
    "# populate the sphere\n",
    "xp,yp,zp,vxp,vyp,vzp = tstrippy.ergodic.isotropicplummer(G,cluster_mass,cluster_half_mass_radius,cluster_num_partilces)\n",
    "# the orbit \n",
    "radius = 8\n",
    "eccen = 0.2\n",
    "theta = np.pi/4\n",
    "phi =-3*np.pi/4\n",
    "polar_mix = np.pi/6\n",
    "rvec,vvec,azimuthal,meridional = apoapsis_shot(tstrippy.potentials.pouliasis2017pii, MWparams, radius, eccen, theta, phi, polar_mix, getVecs=True)\n",
    "fig, ax=plot_initial_orbital_conditions_geometry(rvec,vvec,azimuthal,meridional,theta,phi,polar_mix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b281723",
   "metadata": {},
   "source": [
    "## Obtain integration parameters\n",
    "Let the total integration time be based on the cross time of the galaxy\n",
    "\n",
    "$$ T = \\sqrt{\\frac{a_{\\rm{halo}}}{\\rm{GM_{halo}}}}$$\n",
    "\n",
    "The time-step should be based on the internal dynamical time of the globular cluster\n",
    "\n",
    "$$ \\tau = \\sqrt{\\frac{\\rm{a_{cluster}}^3}{\\rm{GM_{cluster}}}}$$\n",
    "\n",
    "Of course, you are free to pick a desired time for the total integration time. If you wish to integrate for X billion years or Y million years, remember that you must convert to the integration units: s kpc / km. This easily done with astropy's unit module. Note that the timestep has to be smaller than the internal dynamical time of the cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1985c23c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integration time    :           0.55 s kpc / km\n",
      "time step           :    0.000001618 s kpc / km\n",
      "----- in years -----\n",
      "integration time    :            540 Myr \n",
      "time step           :           1582 yr \n",
      "\n",
      "Number of steps     :         341708 \n"
     ]
    }
   ],
   "source": [
    "# user sets the resolution and total integration time \n",
    "factor_dt = 1e-2\n",
    "factor_n_gal_characteristic_times = 10\n",
    "halo_mass_param = MWparams[1]\n",
    "halo_char_radius = MWparams[2]\n",
    "# compute the characteristic times \n",
    "Tcross = np.sqrt(halo_char_radius**3 / (G*halo_mass_param))\n",
    "tau = np.sqrt(cluster_char_radius**3 / (G*cluster_mass))\n",
    "# set the integration parameters \n",
    "dt = factor_dt*tau\n",
    "integration_time = factor_n_gal_characteristic_times*Tcross\n",
    "NSTEP = int(integration_time / dt)\n",
    "# update the integration time to make sure that it is a multiple of NSTEPS\n",
    "integration_time = dt*NSTEP\n",
    "print(\"{:20s}: {:14.2f} s kpc / km\".format(\"integration time\", integration_time))\n",
    "print(\"{:20s}: {:14.9f} s kpc / km\".format(\"time step\", dt))\n",
    "# convert to years \n",
    "from astropy import units as u \n",
    "unitT = u.s * u.kpc / u.km \n",
    "dt_years = (dt*unitT).to(u.yr)\n",
    "integration_time_years = (integration_time*unitT).to(u.Myr)\n",
    "print(\"----- in years -----\")\n",
    "print(\"{:20s}: {:14.0f} \".format(\"integration time\", integration_time_years))\n",
    "print(\"{:20s}: {:14.0f} \".format(\"time step\", dt_years))\n",
    "print(\"\")\n",
    "print(\"{:20s}: {:14d} \".format(\"Number of steps\", NSTEP))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2bf0dab",
   "metadata": {},
   "source": [
    "## Integrate the orbit of the globular cluster\n",
    "\n",
    "The cluster orbit is integrated **backward** from today (t = 0) to an earlier epoch. This gives us the trajectory the cluster must have followed to arrive at the chosen position today. The same trajectory, read forward in time, is then used as the moving host potential during stream generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34c61caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "staticgalaxy = [\"pouliasis2017pii\", MWparams]\n",
    "\n",
    "# You can now choose independent resolutions.\n",
    "host_dt = dt\n",
    "host_nstep = NSTEP\n",
    "particle_dt = dt\n",
    "particle_nstep = NSTEP\n",
    "\n",
    "\n",
    "# 1. Integrate the host orbit once.\n",
    "host_orbit = integrate_orbit(\n",
    "    staticgalaxy=staticgalaxy,\n",
    "    initial_kinematics=[*rvec, *vvec],\n",
    "    integration_params=[0,host_dt,host_nstep],\n",
    "    backward=True,\n",
    ")\n",
    "\n",
    "# 2. Build the host kinematics for the forward stripping run.\n",
    "# sample_stride is the knob you can vary to make the host more coarsely sampled.\n",
    "host_forward = make_host_kinematics(\n",
    "    host_orbit,\n",
    "    sample_stride=1,\n",
    "    target_time_order=\"increasing\",\n",
    ")\n",
    "\n",
    "# Populate particles around the host starting point.\n",
    "initial_stream_kinematics = [\n",
    "    xp + host_forward[\"x\"][0],\n",
    "    yp + host_forward[\"y\"][0],\n",
    "    zp + host_forward[\"z\"][0],\n",
    "    vxp + host_forward[\"vx\"][0],\n",
    "    vyp + host_forward[\"vy\"][0],\n",
    "    vzp + host_forward[\"vz\"][0],\n",
    "]\n",
    "\n",
    "hostmass = [\"constant\", cluster_mass]\n",
    "hostradius = cluster_char_radius\n",
    "\n",
    "# 3. Run the stream forward in the supplied host trajectory.\n",
    "stream = integrate_particles_in_host(\n",
    "    staticgalaxy=staticgalaxy,\n",
    "    initial_kinematics=initial_stream_kinematics,\n",
    "    t0=host_forward[\"time\"][0],\n",
    "    dt=particle_dt,\n",
    "    nstep=particle_nstep,\n",
    "    hostkinematics=host_forward,\n",
    "    hostmass=hostmass,\n",
    "    hostradius=hostradius,\n",
    "    backward=False,\n",
    ")\n",
    "\n",
    "xf = stream[\"xf\"]\n",
    "yf = stream[\"yf\"]\n",
    "zf = stream[\"zf\"]\n",
    "vxf = stream[\"vxf\"]\n",
    "vyf = stream[\"vyf\"]\n",
    "vzf = stream[\"vzf\"]\n",
    "\n",
    "# 4. Build the host kinematics for the backward retrace.\n",
    "host_backward = make_host_kinematics(\n",
    "    host_orbit,\n",
    "    sample_stride=1,\n",
    "    target_time_order=\"decreasing\",\n",
    ")\n",
    "\n",
    "# 5. Retrace the stream backward.\n",
    "retrace = integrate_particles_in_host(\n",
    "    staticgalaxy=staticgalaxy,\n",
    "    initial_kinematics=[xf, yf, zf, vxf, vyf, vzf],\n",
    "    t0=0.0,\n",
    "    dt=particle_dt,\n",
    "    nstep=particle_nstep,\n",
    "    hostkinematics=host_backward,\n",
    "    hostmass=hostmass,\n",
    "    hostradius=hostradius,\n",
    "    backward=True,\n",
    ")\n",
    "\n",
    "x0 = retrace[\"xf\"]\n",
    "y0 = retrace[\"yf\"]\n",
    "z0 = retrace[\"zf\"]\n",
    "vx0 = retrace[\"vxf\"]\n",
    "vy0 = retrace[\"vyf\"]\n",
    "vz0 = retrace[\"vzf\"]\n",
    "\n",
    "# Optional aliases so your existing plotting cell still works.\n",
    "timestamps = host_forward[\"time\"]\n",
    "xt = host_forward[\"x\"]\n",
    "yt = host_forward[\"y\"]\n",
    "zt = host_forward[\"z\"]\n",
    "vxt = host_forward[\"vx\"]\n",
    "vyt = host_forward[\"vy\"]\n",
    "vzt = host_forward[\"vz\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47848a9c",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The left panel shows the stream (red) spread along the host orbit (blue line), with the retrace (black) overlaid on the initial cluster positions. The inset zooms in on the initial position to show how closely the retrace recovers the starting conditions.\n",
    "\n",
    "The right panel shows the distribution of fractional energy error $\\left| (E_{\\rm retrace} - E_0) / E_0 \\right|$ across all particles. A well-behaved leapfrog integrator should produce errors $\\lesssim 10^{-4}$ for the timestep chosen here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d094cd61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 820x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initial = [initial_stream_kinematics[0],initial_stream_kinematics[1]]\n",
    "orbit = [xt, yt]\n",
    "stream = [xf, yf]\n",
    "retrace = [x0, y0]\n",
    "final = [x0,y0,z0,vx0,vy0,vz0]\n",
    "energyerror = compute_energy_error(tstrippy.potentials.pouliasis2017pii,MWparams,initial_stream_kinematics,final)\n",
    "nbins = int(np.sqrt(energyerror.shape[0]))\n",
    "AXIS = {\"xlabel\": r\"Energy Conservation $\\left| \\frac{ E_{\\rm{retrace}}-E_0} { E_0} \\right|$\", \"xscale\": \"log\",\"ylabel\":r\"$\\rm{N}_{\\rm{particles}}$\"}\n",
    "bins=np.logspace(np.log10(energyerror.min()), np.log10(energyerror.max()), nbins)\n",
    "\n",
    "fig, axis, axins = plot_stream_retrace(\n",
    "    orbit, initial, stream, retrace,\n",
    "    nparticles=cluster_num_partilces,\n",
    "    nskip=100,figsize=(8.2,4)\n",
    ")\n",
    "axis[1].hist(energyerror,bins=bins, histtype='step', color=\"k\")\n",
    "axis[1].set(**AXIS)\n",
    "axis[1].yaxis.set_label_position(\"right\")\n",
    "axis[1].yaxis.tick_right()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2194f7",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook confirms that the leapfrog integrator in `tstrippy` respects time-reversal symmetry. Starting from a Plummer sphere, stripping a stream forward over 10 galactic crossing times, and retracing backward recovers the initial phase-space coordinates to within numerical truncation error. This gives confidence that the stream generation workflow is correctly implemented."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tstrippy",
   "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": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}