{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers_merging import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paths = np.array([\"data/waist_ratio_1_highres.npz\",\n",
    "                  \"data/waist_ratio_1025.npz\",\n",
    "                  \"data/waist_ratio_105.npz\",\n",
    "                  \"data/waist_ratio_1075.npz\"])\n",
    "\n",
    "for path in paths:\n",
    "    data = np.load(path)\n",
    "\n",
    "    for key in data.keys():\n",
    "        globals()[key] = data[key]\n",
    "\n",
    "    # Create the figure and two subplots (2 rows, 1 column)\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "    # First plot (delta_power)\n",
    "    cax1 = ax1.imshow(np.where(delta_power!=0,delta_power,np.nan) / initial_powers[:, np.newaxis], \n",
    "                    extent=[np.min(initial_waists) / si.uW, np.max(initial_waists) / si.uW, \n",
    "                            np.min(initial_powers) / si.um, np.max(initial_powers) / si.um], \n",
    "                    origin=\"lower\", aspect=\"auto\",vmin=0.,vmax=0.21)\n",
    "    ax1.set_ylabel(\"Power (uW)\")\n",
    "    ax1.set_xlabel(\"Waist (um)\")\n",
    "    ax1.set_title(f\"Ratio of Tweezer Waists: {factor_waist2:.3f}\")\n",
    "    fig.colorbar(cax1, ax=ax1, label=\"Relative Power Sensitivity\")\n",
    "\n",
    "    # Second plot (delta_E_max)\n",
    "    cax2 = ax2.imshow(np.where(delta_E_max!=0,delta_E_max,np.nan), \n",
    "                    extent=[np.min(initial_waists) / si.uW, np.max(initial_waists) / si.uW, \n",
    "                            np.min(initial_powers) / si.um, np.max(initial_powers) / si.um], \n",
    "                    origin=\"lower\", aspect=\"auto\",vmin=0,vmax=2e-30)\n",
    "    ax2.set_ylabel(\"Power (uW)\")\n",
    "    ax2.set_xlabel(\"Waist (um)\")\n",
    "    ax2.set_title(f\"Ratio of Tweezer Waists: {factor_waist2:.3f}\")\n",
    "    fig.colorbar(cax2, ax=ax2, label=\"Energy Gap [a.u.]\")\n",
    "\n",
    "    # Adjust layout to prevent overlap\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Show the combined figure\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make high res plot for waist ratio of 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers_merging import *\n",
    "\n",
    "initial_power = 50* si.uW\n",
    "initial_waist = 1.1*si.uW\n",
    "initial_distance = 3*si.um\n",
    "\n",
    "trap: DoubleTweezer = DoubleTweezer(\n",
    "    power=0,  # Set pancake laser power to 0, no 2D trap\n",
    "    grad_z= 0*si.G/si.cm,\n",
    "    grad_r=0,\n",
    "    power_tweezer1 = initial_power,     #stationary\n",
    "    power_tweezer2 = initial_power,     #transfer tweezer\n",
    "    waist_tweezer1 = initial_waist,     #stationary\n",
    "    waist_tweezer2 = initial_waist*1.02,     #transfer tweezer\n",
    "    distance_tweezers = initial_distance,\n",
    "\n",
    "    a=180*(4 * np.pi * const.epsilon_0 * const.value(\"Bohr radius\")**3)/(2 * const.epsilon_0 * const.c),\n",
    "    wvl = 532 * si.nm,\n",
    "\n",
    "    g = 0,\n",
    ")\n",
    "\n",
    "x, y, z = trap.x, trap.y, trap.z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_deltaE(power_tweezer1, power_tweezer2, waist_tweezer1, waist_tweezer2,\n",
    "               n_levels=10,n_pot_steps=2000, initial_distance=4*si.um, plot=True):\n",
    "    \"\"\"\n",
    "    Returns the minimal energy gap and occupation numbers of the groundstate when the two tweezers merge.\n",
    "    \"\"\"\n",
    "    trap[trap.power_tweezer1] = power_tweezer1\n",
    "    trap[trap.power_tweezer2] = power_tweezer2\n",
    "    trap[trap.waist_tweezer1] = waist_tweezer1\n",
    "    trap[trap.waist_tweezer2] = waist_tweezer2\n",
    "\n",
    "    distances = np.linspace(initial_distance,0*si.um,500) #always has to have smallest distance at last index\n",
    "    energies, states, potentials = loop_distances(trap, distances,n_levels=n_levels,n_pot_steps=n_pot_steps)\n",
    "\n",
    "    new_energies, new_states, new_potentials, index_top, index, swap_index = swapped_loop_distance(distances, energies, states, potentials)\n",
    "    energies_left, energies_right, states_left, states_right = find_ass_tweezer(new_energies,new_states, return_deltaE=False)\n",
    "\n",
    "    if len(index)!=0:\n",
    "        deltaE_min = 0\n",
    "        print(\"crossover\")\n",
    "    else:\n",
    "        deltaE_min = np.inf\n",
    "        for i in range(energies_left.shape[1]):\n",
    "            for j in range(energies_right.shape[1]):\n",
    "                row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n",
    "                if row_diff<deltaE_min:\n",
    "                    deltaE_min = row_diff\n",
    "                    i_min, j_min = i,j\n",
    "        print(f\"closest approach between states: left:{i_min}, right:{j_min}\")\n",
    "\n",
    "    if plot:\n",
    "        # Create the figure with two subplots (1 row, 2 columns)\n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "        # Plot the full range on ax1\n",
    "        \"\"\"\n",
    "        ax1.plot(distances/si.um, energies_left[:, 0], color=\"red\", label=\"GS left tweezer\")\n",
    "        ax1.plot(distances/si.um, energies_right[:, 0], color=\"blue\", label=\"GS right tweezer\")\n",
    "        ax1.plot(distances/si.um, energies_left[:, 1:], color=\"red\", linestyle=\"dotted\", label=\"left tweezer\")\n",
    "        ax1.plot(distances/si.um, energies_right[:, 1:], color=\"blue\", linestyle=\"dotted\", label=\"right tweezer\")\"\"\"\n",
    "        ax1.plot(distances/si.um, energies_left[:], color=\"red\", label=\"left tweezer\")\n",
    "        ax1.plot(distances/si.um, energies_right[:], color=\"blue\", label=\"right tweezer\")\n",
    "\n",
    "        for i, ind in enumerate(index):\n",
    "            ax1.axvline(distances[ind]/si.um, color=\"k\", alpha=0.7, label=\"energy crossings\")\n",
    "\n",
    "        ax1.set_xlabel(\"Tweezer distance [um]\")\n",
    "        ax1.set_ylabel(\"Eigenenergy [a.u.]\")\n",
    "        ax1.set_title(fr\"$p_l={float(trap.subs(trap.power_tweezer1))/si.uW:.1f}$ uW, $p_r={float(trap.subs(trap.power_tweezer2))/si.uW:.1f}$ uW, $w_l={float(trap.subs(trap.waist_tweezer1))/si.um:.3f}$ um, $w_r={float(trap.subs(trap.waist_tweezer2))/si.um:.3f}$ um\")\n",
    "        ax1.grid()\n",
    "\n",
    "        # Plot the zoomed-in range (1 to 1.5 µm) on ax2\n",
    "        \"\"\"\n",
    "        ax2.plot(distances/si.um, energies_left[:, 0], color=\"red\", label=\"GS left tweezer\")\n",
    "        ax2.plot(distances/si.um, energies_right[:, 0], color=\"blue\", label=\"GS right tweezer\")\n",
    "        ax2.plot(distances/si.um, energies_left[:, 1:], color=\"red\", linestyle=\"dotted\", label=\"left tweezer\")\n",
    "        ax2.plot(distances/si.um, energies_right[:, 1:], color=\"blue\", linestyle=\"dotted\", label=\"right tweezer\")\"\"\"\n",
    "        ax2.plot(distances/si.um, energies_left[:], color=\"red\", label=\"left tweezer\")\n",
    "        ax2.plot(distances/si.um, energies_right[:], color=\"blue\", label=\"right tweezer\")\n",
    "\n",
    "        for i, ind in enumerate(index):\n",
    "            ax2.axvline(distances[ind]/si.um, color=\"k\", alpha=0.7, label=\"energy crossings\")\n",
    "\n",
    "        ax2.set_xlabel(\"Tweezer distance [um]\")\n",
    "        ax2.set_ylabel(\"Eigenenergy [a.u.]\")\n",
    "        ax2.set_title(\"Zoomed-in Region\")\n",
    "        ax2.set_xlim(0.5*waist_tweezer1, 1.5*waist_tweezer1)  # Zooming in on the region between 1.0 and 1.5 µm\n",
    "        #ax2.set_ylim(-7e-28,-5e-28)\n",
    "        ax2.grid()\n",
    "\n",
    "        # Remove duplicates in the legend (labels that appear in both subplots)\n",
    "        handles, labels = ax1.get_legend_handles_labels()\n",
    "        unique_labels = dict(zip(labels, handles))  # Remove duplicates\n",
    "        ax1.legend(unique_labels.values(), unique_labels.keys())\n",
    "\n",
    "        # Show the plots\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    return deltaE_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_power_range(initial_power, initial_waist,factor_waist2, n_spacing=20,n_levels=10):\n",
    "    \"\"\"Calculates the range of powers where there occurs no level crossing.\"\"\"\n",
    "    level_spacings = np.linspace(0,1,n_spacing)\n",
    "    delta_E = np.full(len(level_spacings),np.nan)\n",
    "\n",
    "    trap[trap.power_tweezer1] = initial_power\n",
    "    trap[trap.waist_tweezer1] = initial_waist\n",
    "    trap[trap.waist_tweezer2] = initial_waist*factor_waist2\n",
    "\n",
    "    omega_r1 = sp.sqrt(2*trap.power_tweezer1*trap.a/sp.pi/trap.m)*2/trap.waist_tweezer1**2\n",
    "\n",
    "    #offset required for 1 level spacing of the left tweezer\n",
    "    power2_offset = sp.pi*trap.waist_tweezer2**2*const.hbar*omega_r1/2/trap.a\n",
    "\n",
    "    #factor to match trap depth and offset by half(or another fraction of) the level spacing\n",
    "    power2_factor = factor_waist2**2 - level_spacings*float(trap.subs(power2_offset))/initial_power\n",
    "\n",
    "    power2s = initial_power*power2_factor\n",
    "\n",
    "    for i, power2 in enumerate(power2s):\n",
    "        delta_E[i] = get_deltaE(initial_power,power2,initial_waist,initial_waist*factor_waist2,n_levels=n_levels,plot=False)\n",
    "\n",
    "    mask = delta_E != 0\n",
    "    if np.sum(mask) == 0:\n",
    "        power_range = 0\n",
    "    else:\n",
    "        power_range = np.max(power2s[mask]) - np.min(power2s[mask])\n",
    "    return power_range, np.nanmax(delta_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crossover\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naeve\\AppData\\Local\\Temp\\ipykernel_11984\\382801629.py:24: RuntimeWarning: All-NaN slice encountered\n",
      "  row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "closest approach between states: left:2, right:1\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:1\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:2, right:2\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "closest approach between states: left:2, right:1\n",
      "crossover\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:3, right:3\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:4\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
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      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
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      "closest approach between states: left:1, right:0\n",
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      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:0, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "closest approach between states: left:1, right:0\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "crossover\n",
      "closest approach between states: left:4, right:3\n"
     ]
    }
   ],
   "source": [
    "factor_waist2 = 1.0\n",
    "\n",
    "initial_powers = np.linspace(10,500,15)*si.uW\n",
    "initial_waists = np.linspace(0.7,2.2,6)*si.um\n",
    "delta_power = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
    "delta_E_max = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
    "\n",
    "for i, pow in enumerate(initial_powers):\n",
    "    for j, wai in enumerate(initial_waists):\n",
    "\n",
    "        delta_power[i,j], delta_E_max[i,j] = get_power_range(pow,wai,factor_waist2,n_spacing=25)\n",
    "\n",
    "np.savez('data/waist_ratio_1_highres.npz', initial_powers=initial_powers, initial_waists=initial_waists, delta_power=delta_power, delta_E_max=delta_E_max, factor_waist2=factor_waist2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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