Idela_Gas_N_particles.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Idela_Gas_N_particles.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/6arlos6/Ideal_Gas/blob/main/Idela_Gas_N_particles.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MN1j_YSyhqKg"
      },
      "source": [
        "# Cargar librerias:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpWlxdgIhErH"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import matplotlib.animation as animation\n",
        "\n",
        "import scipy.stats as stats\n",
        "import scipy.io"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YkxH2rph3G_"
      },
      "source": [
        "# Definir Clase < Particle > :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFqPEfYahMIs"
      },
      "source": [
        "class Particle:\n",
        "  def __init__(self, mass, radius, position, velocity):\n",
        "    self.mass = mass\n",
        "    self.radius = radius\n",
        "    self.position = position\n",
        "    self.velocity = velocity"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MOPEZlx0mj-G"
      },
      "source": [
        "# Constantes y tiempo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b91Dj_HbmpgS"
      },
      "source": [
        "# Constantes:\n",
        "\n",
        "N = 100\n",
        "\n",
        "inferior_box = -20\n",
        "superior_box = 20\n",
        "\n",
        "Ra = -1.\n",
        "Rb = 15.\n",
        "\n",
        "r0 = 0.5\n",
        "R = 2\n",
        "mu, sigma = 0., 15.\n",
        "m = 0.01\n",
        "\n",
        "FPS = 60\n",
        "\n",
        "# Tiempo:\n",
        "\n",
        "Dt = 0.001\n",
        "time = np.arange(0,3,Dt)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBpUF_6GiBdy"
      },
      "source": [
        "# Condiciones Iniciales ( $ \\mathbf{r}_{i}(0)$ , $\\mathbf{v}_{i}(0)$):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZFYz1wqiqoU"
      },
      "source": [
        "## Funcion para def. $ \\mathbf{r}_{i}(0)$ sin solapes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yStXERQqho_O"
      },
      "source": [
        "def initial_pos(N, r0, R, a, b):\n",
        "# N := Numero de particulas.\n",
        "# r0 := radio de las particulas.\n",
        "# R := Dimension del sistema.\n",
        "# s:= Rango maximo de volumen.\n",
        "  r = np.zeros((N,R))\n",
        "  for i in range(1,N):\n",
        "    band0 = 1\n",
        "    while band0 >= 1:\n",
        "      band0 = 0\n",
        "      rt = (b-a)*np.random.rand(1,R) + a\n",
        "      for j in range(0, i):\n",
        "        rij = np.linalg.norm(rt - r[j])\n",
        "        if rij <= 2*r0:\n",
        "          band0 +=1\n",
        "    r[i] = rt\n",
        "  return r"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Chat gpt\n",
        "\n",
        "def initial_pos(N, r0, R, a, b):\n",
        "    r = np.zeros((N, R))\n",
        "    for i in range(1, N):\n",
        "        band0 = 1\n",
        "        while band0 >= 1:\n",
        "            band0 = 0\n",
        "            rt = np.random.uniform(a, b, size=(1, R))\n",
        "            rij = np.linalg.norm(rt - r[:i], axis=1)\n",
        "            if np.any(rij <= 2 * r0):\n",
        "                band0 += 1\n",
        "        r[i] = rt\n",
        "    return r"
      ],
      "metadata": {
        "id": "7oU8DaAIOo0R"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wiXr0e6kPOq"
      },
      "source": [
        "## Definir ensemble + C.I."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDD03vcvkNia"
      },
      "source": [
        "Ensemble_Particles = []\n",
        "\n",
        "R0 = initial_pos(N, r0, R, Ra, Rb)\n",
        "\n",
        "for i in range(0,N):\n",
        "  zpi = np.zeros([len(time),R])\n",
        "  zpi[0] = R0[i]\n",
        "  zvi = np.zeros([len(time),R])\n",
        "  zvi[0] = np.random.normal(mu, sigma, [R])\n",
        "  Ensemble_Particles.append(Particle(m, r0, zpi, zvi))\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUykmTLSoAmh"
      },
      "source": [
        "# Eventos - Interacciones:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tw3zbsb-o7a1"
      },
      "source": [
        "## Función de choque con paredes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1ca-7AepBeE"
      },
      "source": [
        "def isWall(p,n,m,r,i):\n",
        "\n",
        "\ttf1 = 0\n",
        "\ttf2 = 0\n",
        "\n",
        "\tif p.position[i,1] < (n + r) or p.position[i,1] >= m - r:\n",
        "\t\tp.velocity[i,1] = -p.velocity[i,1]\n",
        "\t\tp.velocity[i+1] = p.velocity[i]\n",
        "\t\ttf1 = 1\n",
        "\n",
        "\tif p.position[i,0] < (n + r) or p.position[i,0] >= m - r:\n",
        "\t\tp.velocity[i,0] = -p.velocity[i,0]\n",
        "\t\tp.velocity[i+1] = p.velocity[i]\n",
        "\t\ttf2 = 1\n",
        "\n",
        "\tif tf1 == 0 and tf2 == 0:\n",
        "\t\tp.velocity[i+1] = p.velocity[i]\n",
        "\n",
        "\treturn p"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def isWall(p, n, m, r, i):\n",
        "    tf1 = 0\n",
        "    tf2 = 0\n",
        "    pos_i = p.position[i]\n",
        "    vel_i = p.velocity[i]\n",
        "\n",
        "    if pos_i[1] < (n + r) or pos_i[1] >= m - r:\n",
        "        vel_i[1] = -vel_i[1]\n",
        "        tf1 = 1\n",
        "\n",
        "    if pos_i[0] < (n + r) or pos_i[0] >= m - r:\n",
        "        vel_i[0] = -vel_i[0]\n",
        "        tf2 = 1\n",
        "\n",
        "    if tf1 == 0 and tf2 == 0:\n",
        "        p.velocity[i+1] = vel_i\n",
        "\n",
        "    p.velocity[i+1] = vel_i\n",
        "    return p\n"
      ],
      "metadata": {
        "id": "jPJriMWiPDuv"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4zUMhxnwCBd"
      },
      "source": [
        "## Función de colición entre particulas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6op7d0NDwAcc"
      },
      "source": [
        "def isCollision(p1,p2,i):\n",
        "\n",
        "\t# i = 1 ---- j = 2\n",
        "\n",
        "\ta = (p1.position[i,0] - p2.position[i,0])**2\n",
        "\tb = (p1.position[i,1] - p2.position[i,1])**2\n",
        "\td = np.sqrt(a + b)\n",
        "\n",
        "\tif d < (p1.radius + p2.radius)**1.1:\n",
        "\t\trij = (p1.position[i] - p2.position[i])/(d)\n",
        "\t\tA = -2*((p1.mass*p2.mass)/(p1.mass + p2.mass))\n",
        "\t\tB = p1.velocity[i] - p2.velocity[i]\n",
        "\t\tC = np.dot(B, rij)\n",
        "\t\tq = (A*C)*rij\n",
        "\t\tp1.velocity[i] = p1.velocity[i] + q/p1.mass\n",
        "\t\tp1.velocity[i+1] = p1.velocity[i]\n",
        "\t\tp2.velocity[i] = p2.velocity[i] - q/p2.mass\n",
        "\t\tp2.velocity[i+1] = p2.velocity[i]\n",
        "\n",
        "\treturn p1, p2"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def isCollision(p1, p2, i):\n",
        "    a = (p1.position[i, 0] - p2.position[i, 0])**2\n",
        "    b = (p1.position[i, 1] - p2.position[i, 1])**2\n",
        "    d_squared = a + b\n",
        "\n",
        "    if d_squared < (p1.radius + p2.radius)**2.2:  # Evitar la raíz cuadrada\n",
        "        rij = (p1.position[i] - p2.position[i]) / np.sqrt(d_squared)\n",
        "        A = -2 * ((p1.mass * p2.mass) / (p1.mass + p2.mass))\n",
        "        B = p1.velocity[i] - p2.velocity[i]\n",
        "        C = np.dot(B, rij)\n",
        "        q = (A * C) * rij\n",
        "        p1.velocity[i] += q / p1.mass\n",
        "        p1.velocity[i+1] = p1.velocity[i]\n",
        "        p2.velocity[i] -= q / p2.mass\n",
        "        p2.velocity[i+1] = p2.velocity[i]\n",
        "\n",
        "    return p1, p2\n"
      ],
      "metadata": {
        "id": "PlDwSniZP5fx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def isCollision(p1, p2, i):\n",
        "    a = (p1.position[i, 0] - p2.position[i, 0])**2\n",
        "    b = (p1.position[i, 1] - p2.position[i, 1])**2\n",
        "    d_squared = a + b\n",
        "\n",
        "    if d_squared < (p1.radius + p2.radius)**2.2:  # Evitar la raíz cuadrada\n",
        "        rij = (p1.position[i] - p2.position[i]) / np.sqrt(d_squared)\n",
        "        A = -2 * ((p1.mass * p2.mass) / (p1.mass + p2.mass))\n",
        "        B = p1.velocity[i] - p2.velocity[i]\n",
        "        C = np.dot(B, rij)\n",
        "        q = (A * C) * rij\n",
        "        p1.velocity[i] += q / p1.mass\n",
        "        p1.velocity[i+1] = p1.velocity[i]\n",
        "        p2.velocity[i] -= q / p2.mass\n",
        "        p2.velocity[i+1] = p2.velocity[i]\n",
        "\n",
        "    return p1, p2\n"
      ],
      "metadata": {
        "id": "aZ0t3eq6Pngv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFd4hyyIwV9g"
      },
      "source": [
        "# Evolución temporal ( $ \\mathbf{r}_{i}(t)$ , $\\mathbf{v}_{i}(t)$):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9M0f2DdwiJY"
      },
      "source": [
        "for i in range(0,len(time)-1):\n",
        "  print('time = {} [s] \\n'.format(time[i]))\n",
        "\n",
        "  for j in range(0,N):\n",
        "    pj = Ensemble_Particles[j]\n",
        "    pj = isWall(pj, inferior_box, superior_box, pj.radius, i)\n",
        "    Ensemble_Particles[j] = pj\n",
        "\n",
        "  for ki in range(0,N):\n",
        "    for kj in range(ki+1,N):\n",
        "      pki = Ensemble_Particles[ki]\n",
        "      pkj = Ensemble_Particles[kj]\n",
        "      pki, pkj = isCollision(pki,pkj,i)\n",
        "      Ensemble_Particles[ki] = pki\n",
        "      Ensemble_Particles[kj] = pkj\n",
        "\n",
        "  for j in range(0,N):\n",
        "    pj = Ensemble_Particles[j]\n",
        "    pj.position[i+1] = pj.position[i] + pj.velocity[i]*Dt\n",
        "    Ensemble_Particles[j] = pj"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(time) - 1):\n",
        "    print('time = {} [s]\\n'.format(time[i]))\n",
        "    for j in range(N):\n",
        "        pj = Ensemble_Particles[j]\n",
        "        pj = isWall(pj, inferior_box, superior_box, pj.radius, i)\n",
        "        Ensemble_Particles[j] = pj\n",
        "\n",
        "    for ki in range(N):\n",
        "        for kj in range(ki + 1, N):\n",
        "            pki = Ensemble_Particles[ki]\n",
        "            pkj = Ensemble_Particles[kj]\n",
        "            pki, pkj = isCollision(pki, pkj, i)\n",
        "            Ensemble_Particles[ki] = pki\n",
        "            Ensemble_Particles[kj] = pkj\n",
        "\n",
        "    for j in range(N):\n",
        "        pj = Ensemble_Particles[j]\n",
        "        pj.position[i + 1] = pj.position[i] + pj.velocity[i] * Dt\n",
        "        Ensemble_Particles[j] = pj\n"
      ],
      "metadata": {
        "id": "2Ba-g0zFQZt-",
        "outputId": "5afc702d-44db-4cf9-8553-b8d53ac1b779",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mSe han truncado las últimas 5000 líneas del flujo de salida.\u001b[0m\n",
            "time = 0.499 [s]\n",
            "\n",
            "time = 0.5 [s]\n",
            "\n",
            "time = 0.501 [s]\n",
            "\n",
            "time = 0.502 [s]\n",
            "\n",
            "time = 0.503 [s]\n",
            "\n",
            "time = 0.504 [s]\n",
            "\n",
            "time = 0.505 [s]\n",
            "\n",
            "time = 0.506 [s]\n",
            "\n",
            "time = 0.507 [s]\n",
            "\n",
            "time = 0.508 [s]\n",
            "\n",
            "time = 0.509 [s]\n",
            "\n",
            "time = 0.51 [s]\n",
            "\n",
            "time = 0.511 [s]\n",
            "\n",
            "time = 0.512 [s]\n",
            "\n",
            "time = 0.513 [s]\n",
            "\n",
            "time = 0.514 [s]\n",
            "\n",
            "time = 0.515 [s]\n",
            "\n",
            "time = 0.516 [s]\n",
            "\n",
            "time = 0.517 [s]\n",
            "\n",
            "time = 0.518 [s]\n",
            "\n",
            "time = 0.519 [s]\n",
            "\n",
            "time = 0.52 [s]\n",
            "\n",
            "time = 0.521 [s]\n",
            "\n",
            "time = 0.522 [s]\n",
            "\n",
            "time = 0.523 [s]\n",
            "\n",
            "time = 0.524 [s]\n",
            "\n",
            "time = 0.525 [s]\n",
            "\n",
            "time = 0.526 [s]\n",
            "\n",
            "time = 0.527 [s]\n",
            "\n",
            "time = 0.528 [s]\n",
            "\n",
            "time = 0.529 [s]\n",
            "\n",
            "time = 0.53 [s]\n",
            "\n",
            "time = 0.531 [s]\n",
            "\n",
            "time = 0.532 [s]\n",
            "\n",
            "time = 0.533 [s]\n",
            "\n",
            "time = 0.534 [s]\n",
            "\n",
            "time = 0.535 [s]\n",
            "\n",
            "time = 0.536 [s]\n",
            "\n",
            "time = 0.537 [s]\n",
            "\n",
            "time = 0.538 [s]\n",
            "\n",
            "time = 0.539 [s]\n",
            "\n",
            "time = 0.54 [s]\n",
            "\n",
            "time = 0.541 [s]\n",
            "\n",
            "time = 0.542 [s]\n",
            "\n",
            "time = 0.543 [s]\n",
            "\n",
            "time = 0.544 [s]\n",
            "\n",
            "time = 0.545 [s]\n",
            "\n",
            "time = 0.546 [s]\n",
            "\n",
            "time = 0.547 [s]\n",
            "\n",
            "time = 0.548 [s]\n",
            "\n",
            "time = 0.549 [s]\n",
            "\n",
            "time = 0.55 [s]\n",
            "\n",
            "time = 0.551 [s]\n",
            "\n",
            "time = 0.552 [s]\n",
            "\n",
            "time = 0.553 [s]\n",
            "\n",
            "time = 0.554 [s]\n",
            "\n",
            "time = 0.555 [s]\n",
            "\n",
            "time = 0.556 [s]\n",
            "\n",
            "time = 0.557 [s]\n",
            "\n",
            "time = 0.558 [s]\n",
            "\n",
            "time = 0.559 [s]\n",
            "\n",
            "time = 0.56 [s]\n",
            "\n",
            "time = 0.561 [s]\n",
            "\n",
            "time = 0.562 [s]\n",
            "\n",
            "time = 0.5630000000000001 [s]\n",
            "\n",
            "time = 0.5640000000000001 [s]\n",
            "\n",
            "time = 0.5650000000000001 [s]\n",
            "\n",
            "time = 0.5660000000000001 [s]\n",
            "\n",
            "time = 0.5670000000000001 [s]\n",
            "\n",
            "time = 0.5680000000000001 [s]\n",
            "\n",
            "time = 0.5690000000000001 [s]\n",
            "\n",
            "time = 0.5700000000000001 [s]\n",
            "\n",
            "time = 0.5710000000000001 [s]\n",
            "\n",
            "time = 0.5720000000000001 [s]\n",
            "\n",
            "time = 0.5730000000000001 [s]\n",
            "\n",
            "time = 0.5740000000000001 [s]\n",
            "\n",
            "time = 0.5750000000000001 [s]\n",
            "\n",
            "time = 0.5760000000000001 [s]\n",
            "\n",
            "time = 0.577 [s]\n",
            "\n",
            "time = 0.578 [s]\n",
            "\n",
            "time = 0.579 [s]\n",
            "\n",
            "time = 0.58 [s]\n",
            "\n",
            "time = 0.581 [s]\n",
            "\n",
            "time = 0.582 [s]\n",
            "\n",
            "time = 0.583 [s]\n",
            "\n",
            "time = 0.584 [s]\n",
            "\n",
            "time = 0.585 [s]\n",
            "\n",
            "time = 0.586 [s]\n",
            "\n",
            "time = 0.587 [s]\n",
            "\n",
            "time = 0.588 [s]\n",
            "\n",
            "time = 0.589 [s]\n",
            "\n",
            "time = 0.59 [s]\n",
            "\n",
            "time = 0.591 [s]\n",
            "\n",
            "time = 0.592 [s]\n",
            "\n",
            "time = 0.593 [s]\n",
            "\n",
            "time = 0.594 [s]\n",
            "\n",
            "time = 0.595 [s]\n",
            "\n",
            "time = 0.596 [s]\n",
            "\n",
            "time = 0.597 [s]\n",
            "\n",
            "time = 0.598 [s]\n",
            "\n",
            "time = 0.599 [s]\n",
            "\n",
            "time = 0.6 [s]\n",
            "\n",
            "time = 0.601 [s]\n",
            "\n",
            "time = 0.602 [s]\n",
            "\n",
            "time = 0.603 [s]\n",
            "\n",
            "time = 0.604 [s]\n",
            "\n",
            "time = 0.605 [s]\n",
            "\n",
            "time = 0.606 [s]\n",
            "\n",
            "time = 0.607 [s]\n",
            "\n",
            "time = 0.608 [s]\n",
            "\n",
            "time = 0.609 [s]\n",
            "\n",
            "time = 0.61 [s]\n",
            "\n",
            "time = 0.611 [s]\n",
            "\n",
            "time = 0.612 [s]\n",
            "\n",
            "time = 0.613 [s]\n",
            "\n",
            "time = 0.614 [s]\n",
            "\n",
            "time = 0.615 [s]\n",
            "\n",
            "time = 0.616 [s]\n",
            "\n",
            "time = 0.617 [s]\n",
            "\n",
            "time = 0.618 [s]\n",
            "\n",
            "time = 0.619 [s]\n",
            "\n",
            "time = 0.62 [s]\n",
            "\n",
            "time = 0.621 [s]\n",
            "\n",
            "time = 0.622 [s]\n",
            "\n",
            "time = 0.623 [s]\n",
            "\n",
            "time = 0.624 [s]\n",
            "\n",
            "time = 0.625 [s]\n",
            "\n",
            "time = 0.626 [s]\n",
            "\n",
            "time = 0.627 [s]\n",
            "\n",
            "time = 0.628 [s]\n",
            "\n",
            "time = 0.629 [s]\n",
            "\n",
            "time = 0.63 [s]\n",
            "\n",
            "time = 0.631 [s]\n",
            "\n",
            "time = 0.632 [s]\n",
            "\n",
            "time = 0.633 [s]\n",
            "\n",
            "time = 0.634 [s]\n",
            "\n",
            "time = 0.635 [s]\n",
            "\n",
            "time = 0.636 [s]\n",
            "\n",
            "time = 0.637 [s]\n",
            "\n",
            "time = 0.638 [s]\n",
            "\n",
            "time = 0.639 [s]\n",
            "\n",
            "time = 0.64 [s]\n",
            "\n",
            "time = 0.641 [s]\n",
            "\n",
            "time = 0.642 [s]\n",
            "\n",
            "time = 0.643 [s]\n",
            "\n",
            "time = 0.644 [s]\n",
            "\n",
            "time = 0.645 [s]\n",
            "\n",
            "time = 0.646 [s]\n",
            "\n",
            "time = 0.647 [s]\n",
            "\n",
            "time = 0.648 [s]\n",
            "\n",
            "time = 0.649 [s]\n",
            "\n",
            "time = 0.65 [s]\n",
            "\n",
            "time = 0.651 [s]\n",
            "\n",
            "time = 0.652 [s]\n",
            "\n",
            "time = 0.653 [s]\n",
            "\n",
            "time = 0.654 [s]\n",
            "\n",
            "time = 0.655 [s]\n",
            "\n",
            "time = 0.656 [s]\n",
            "\n",
            "time = 0.657 [s]\n",
            "\n",
            "time = 0.658 [s]\n",
            "\n",
            "time = 0.659 [s]\n",
            "\n",
            "time = 0.66 [s]\n",
            "\n",
            "time = 0.661 [s]\n",
            "\n",
            "time = 0.662 [s]\n",
            "\n",
            "time = 0.663 [s]\n",
            "\n",
            "time = 0.664 [s]\n",
            "\n",
            "time = 0.665 [s]\n",
            "\n",
            "time = 0.666 [s]\n",
            "\n",
            "time = 0.667 [s]\n",
            "\n",
            "time = 0.668 [s]\n",
            "\n",
            "time = 0.669 [s]\n",
            "\n",
            "time = 0.67 [s]\n",
            "\n",
            "time = 0.671 [s]\n",
            "\n",
            "time = 0.672 [s]\n",
            "\n",
            "time = 0.673 [s]\n",
            "\n",
            "time = 0.674 [s]\n",
            "\n",
            "time = 0.675 [s]\n",
            "\n",
            "time = 0.676 [s]\n",
            "\n",
            "time = 0.677 [s]\n",
            "\n",
            "time = 0.678 [s]\n",
            "\n",
            "time = 0.679 [s]\n",
            "\n",
            "time = 0.68 [s]\n",
            "\n",
            "time = 0.681 [s]\n",
            "\n",
            "time = 0.682 [s]\n",
            "\n",
            "time = 0.683 [s]\n",
            "\n",
            "time = 0.684 [s]\n",
            "\n",
            "time = 0.685 [s]\n",
            "\n",
            "time = 0.686 [s]\n",
            "\n",
            "time = 0.687 [s]\n",
            "\n",
            "time = 0.6880000000000001 [s]\n",
            "\n",
            "time = 0.6890000000000001 [s]\n",
            "\n",
            "time = 0.6900000000000001 [s]\n",
            "\n",
            "time = 0.6910000000000001 [s]\n",
            "\n",
            "time = 0.6920000000000001 [s]\n",
            "\n",
            "time = 0.6930000000000001 [s]\n",
            "\n",
            "time = 0.6940000000000001 [s]\n",
            "\n",
            "time = 0.6950000000000001 [s]\n",
            "\n",
            "time = 0.6960000000000001 [s]\n",
            "\n",
            "time = 0.6970000000000001 [s]\n",
            "\n",
            "time = 0.6980000000000001 [s]\n",
            "\n",
            "time = 0.6990000000000001 [s]\n",
            "\n",
            "time = 0.7000000000000001 [s]\n",
            "\n",
            "time = 0.7010000000000001 [s]\n",
            "\n",
            "time = 0.7020000000000001 [s]\n",
            "\n",
            "time = 0.7030000000000001 [s]\n",
            "\n",
            "time = 0.704 [s]\n",
            "\n",
            "time = 0.705 [s]\n",
            "\n",
            "time = 0.706 [s]\n",
            "\n",
            "time = 0.707 [s]\n",
            "\n",
            "time = 0.708 [s]\n",
            "\n",
            "time = 0.709 [s]\n",
            "\n",
            "time = 0.71 [s]\n",
            "\n",
            "time = 0.711 [s]\n",
            "\n",
            "time = 0.712 [s]\n",
            "\n",
            "time = 0.713 [s]\n",
            "\n",
            "time = 0.714 [s]\n",
            "\n",
            "time = 0.715 [s]\n",
            "\n",
            "time = 0.716 [s]\n",
            "\n",
            "time = 0.717 [s]\n",
            "\n",
            "time = 0.718 [s]\n",
            "\n",
            "time = 0.719 [s]\n",
            "\n",
            "time = 0.72 [s]\n",
            "\n",
            "time = 0.721 [s]\n",
            "\n",
            "time = 0.722 [s]\n",
            "\n",
            "time = 0.723 [s]\n",
            "\n",
            "time = 0.724 [s]\n",
            "\n",
            "time = 0.725 [s]\n",
            "\n",
            "time = 0.726 [s]\n",
            "\n",
            "time = 0.727 [s]\n",
            "\n",
            "time = 0.728 [s]\n",
            "\n",
            "time = 0.729 [s]\n",
            "\n",
            "time = 0.73 [s]\n",
            "\n",
            "time = 0.731 [s]\n",
            "\n",
            "time = 0.732 [s]\n",
            "\n",
            "time = 0.733 [s]\n",
            "\n",
            "time = 0.734 [s]\n",
            "\n",
            "time = 0.735 [s]\n",
            "\n",
            "time = 0.736 [s]\n",
            "\n",
            "time = 0.737 [s]\n",
            "\n",
            "time = 0.738 [s]\n",
            "\n",
            "time = 0.739 [s]\n",
            "\n",
            "time = 0.74 [s]\n",
            "\n",
            "time = 0.741 [s]\n",
            "\n",
            "time = 0.742 [s]\n",
            "\n",
            "time = 0.743 [s]\n",
            "\n",
            "time = 0.744 [s]\n",
            "\n",
            "time = 0.745 [s]\n",
            "\n",
            "time = 0.746 [s]\n",
            "\n",
            "time = 0.747 [s]\n",
            "\n",
            "time = 0.748 [s]\n",
            "\n",
            "time = 0.749 [s]\n",
            "\n",
            "time = 0.75 [s]\n",
            "\n",
            "time = 0.751 [s]\n",
            "\n",
            "time = 0.752 [s]\n",
            "\n",
            "time = 0.753 [s]\n",
            "\n",
            "time = 0.754 [s]\n",
            "\n",
            "time = 0.755 [s]\n",
            "\n",
            "time = 0.756 [s]\n",
            "\n",
            "time = 0.757 [s]\n",
            "\n",
            "time = 0.758 [s]\n",
            "\n",
            "time = 0.759 [s]\n",
            "\n",
            "time = 0.76 [s]\n",
            "\n",
            "time = 0.761 [s]\n",
            "\n",
            "time = 0.762 [s]\n",
            "\n",
            "time = 0.763 [s]\n",
            "\n",
            "time = 0.764 [s]\n",
            "\n",
            "time = 0.765 [s]\n",
            "\n",
            "time = 0.766 [s]\n",
            "\n",
            "time = 0.767 [s]\n",
            "\n",
            "time = 0.768 [s]\n",
            "\n",
            "time = 0.769 [s]\n",
            "\n",
            "time = 0.77 [s]\n",
            "\n",
            "time = 0.771 [s]\n",
            "\n",
            "time = 0.772 [s]\n",
            "\n",
            "time = 0.773 [s]\n",
            "\n",
            "time = 0.774 [s]\n",
            "\n",
            "time = 0.775 [s]\n",
            "\n",
            "time = 0.776 [s]\n",
            "\n",
            "time = 0.777 [s]\n",
            "\n",
            "time = 0.778 [s]\n",
            "\n",
            "time = 0.779 [s]\n",
            "\n",
            "time = 0.78 [s]\n",
            "\n",
            "time = 0.781 [s]\n",
            "\n",
            "time = 0.782 [s]\n",
            "\n",
            "time = 0.783 [s]\n",
            "\n",
            "time = 0.784 [s]\n",
            "\n",
            "time = 0.785 [s]\n",
            "\n",
            "time = 0.786 [s]\n",
            "\n",
            "time = 0.787 [s]\n",
            "\n",
            "time = 0.788 [s]\n",
            "\n",
            "time = 0.789 [s]\n",
            "\n",
            "time = 0.79 [s]\n",
            "\n",
            "time = 0.791 [s]\n",
            "\n",
            "time = 0.792 [s]\n",
            "\n",
            "time = 0.793 [s]\n",
            "\n",
            "time = 0.794 [s]\n",
            "\n",
            "time = 0.795 [s]\n",
            "\n",
            "time = 0.796 [s]\n",
            "\n",
            "time = 0.797 [s]\n",
            "\n",
            "time = 0.798 [s]\n",
            "\n",
            "time = 0.799 [s]\n",
            "\n",
            "time = 0.8 [s]\n",
            "\n",
            "time = 0.801 [s]\n",
            "\n",
            "time = 0.802 [s]\n",
            "\n",
            "time = 0.803 [s]\n",
            "\n",
            "time = 0.804 [s]\n",
            "\n",
            "time = 0.805 [s]\n",
            "\n",
            "time = 0.806 [s]\n",
            "\n",
            "time = 0.807 [s]\n",
            "\n",
            "time = 0.808 [s]\n",
            "\n",
            "time = 0.809 [s]\n",
            "\n",
            "time = 0.81 [s]\n",
            "\n",
            "time = 0.811 [s]\n",
            "\n",
            "time = 0.812 [s]\n",
            "\n",
            "time = 0.8130000000000001 [s]\n",
            "\n",
            "time = 0.8140000000000001 [s]\n",
            "\n",
            "time = 0.8150000000000001 [s]\n",
            "\n",
            "time = 0.8160000000000001 [s]\n",
            "\n",
            "time = 0.8170000000000001 [s]\n",
            "\n",
            "time = 0.8180000000000001 [s]\n",
            "\n",
            "time = 0.8190000000000001 [s]\n",
            "\n",
            "time = 0.8200000000000001 [s]\n",
            "\n",
            "time = 0.8210000000000001 [s]\n",
            "\n",
            "time = 0.8220000000000001 [s]\n",
            "\n",
            "time = 0.8230000000000001 [s]\n",
            "\n",
            "time = 0.8240000000000001 [s]\n",
            "\n",
            "time = 0.8250000000000001 [s]\n",
            "\n",
            "time = 0.8260000000000001 [s]\n",
            "\n",
            "time = 0.8270000000000001 [s]\n",
            "\n",
            "time = 0.8280000000000001 [s]\n",
            "\n",
            "time = 0.8290000000000001 [s]\n",
            "\n",
            "time = 0.8300000000000001 [s]\n",
            "\n",
            "time = 0.8310000000000001 [s]\n",
            "\n",
            "time = 0.8320000000000001 [s]\n",
            "\n",
            "time = 0.833 [s]\n",
            "\n",
            "time = 0.834 [s]\n",
            "\n",
            "time = 0.835 [s]\n",
            "\n",
            "time = 0.836 [s]\n",
            "\n",
            "time = 0.837 [s]\n",
            "\n",
            "time = 0.838 [s]\n",
            "\n",
            "time = 0.839 [s]\n",
            "\n",
            "time = 0.84 [s]\n",
            "\n",
            "time = 0.841 [s]\n",
            "\n",
            "time = 0.842 [s]\n",
            "\n",
            "time = 0.843 [s]\n",
            "\n",
            "time = 0.844 [s]\n",
            "\n",
            "time = 0.845 [s]\n",
            "\n",
            "time = 0.846 [s]\n",
            "\n",
            "time = 0.847 [s]\n",
            "\n",
            "time = 0.848 [s]\n",
            "\n",
            "time = 0.849 [s]\n",
            "\n",
            "time = 0.85 [s]\n",
            "\n",
            "time = 0.851 [s]\n",
            "\n",
            "time = 0.852 [s]\n",
            "\n",
            "time = 0.853 [s]\n",
            "\n",
            "time = 0.854 [s]\n",
            "\n",
            "time = 0.855 [s]\n",
            "\n",
            "time = 0.856 [s]\n",
            "\n",
            "time = 0.857 [s]\n",
            "\n",
            "time = 0.858 [s]\n",
            "\n",
            "time = 0.859 [s]\n",
            "\n",
            "time = 0.86 [s]\n",
            "\n",
            "time = 0.861 [s]\n",
            "\n",
            "time = 0.862 [s]\n",
            "\n",
            "time = 0.863 [s]\n",
            "\n",
            "time = 0.864 [s]\n",
            "\n",
            "time = 0.865 [s]\n",
            "\n",
            "time = 0.866 [s]\n",
            "\n",
            "time = 0.867 [s]\n",
            "\n",
            "time = 0.868 [s]\n",
            "\n",
            "time = 0.869 [s]\n",
            "\n",
            "time = 0.87 [s]\n",
            "\n",
            "time = 0.871 [s]\n",
            "\n",
            "time = 0.872 [s]\n",
            "\n",
            "time = 0.873 [s]\n",
            "\n",
            "time = 0.874 [s]\n",
            "\n",
            "time = 0.875 [s]\n",
            "\n",
            "time = 0.876 [s]\n",
            "\n",
            "time = 0.877 [s]\n",
            "\n",
            "time = 0.878 [s]\n",
            "\n",
            "time = 0.879 [s]\n",
            "\n",
            "time = 0.88 [s]\n",
            "\n",
            "time = 0.881 [s]\n",
            "\n",
            "time = 0.882 [s]\n",
            "\n",
            "time = 0.883 [s]\n",
            "\n",
            "time = 0.884 [s]\n",
            "\n",
            "time = 0.885 [s]\n",
            "\n",
            "time = 0.886 [s]\n",
            "\n",
            "time = 0.887 [s]\n",
            "\n",
            "time = 0.888 [s]\n",
            "\n",
            "time = 0.889 [s]\n",
            "\n",
            "time = 0.89 [s]\n",
            "\n",
            "time = 0.891 [s]\n",
            "\n",
            "time = 0.892 [s]\n",
            "\n",
            "time = 0.893 [s]\n",
            "\n",
            "time = 0.894 [s]\n",
            "\n",
            "time = 0.895 [s]\n",
            "\n",
            "time = 0.896 [s]\n",
            "\n",
            "time = 0.897 [s]\n",
            "\n",
            "time = 0.898 [s]\n",
            "\n",
            "time = 0.899 [s]\n",
            "\n",
            "time = 0.9 [s]\n",
            "\n",
            "time = 0.901 [s]\n",
            "\n",
            "time = 0.902 [s]\n",
            "\n",
            "time = 0.903 [s]\n",
            "\n",
            "time = 0.904 [s]\n",
            "\n",
            "time = 0.905 [s]\n",
            "\n",
            "time = 0.906 [s]\n",
            "\n",
            "time = 0.907 [s]\n",
            "\n",
            "time = 0.908 [s]\n",
            "\n",
            "time = 0.909 [s]\n",
            "\n",
            "time = 0.91 [s]\n",
            "\n",
            "time = 0.911 [s]\n",
            "\n",
            "time = 0.912 [s]\n",
            "\n",
            "time = 0.913 [s]\n",
            "\n",
            "time = 0.914 [s]\n",
            "\n",
            "time = 0.915 [s]\n",
            "\n",
            "time = 0.916 [s]\n",
            "\n",
            "time = 0.917 [s]\n",
            "\n",
            "time = 0.918 [s]\n",
            "\n",
            "time = 0.919 [s]\n",
            "\n",
            "time = 0.92 [s]\n",
            "\n",
            "time = 0.921 [s]\n",
            "\n",
            "time = 0.922 [s]\n",
            "\n",
            "time = 0.923 [s]\n",
            "\n",
            "time = 0.924 [s]\n",
            "\n",
            "time = 0.925 [s]\n",
            "\n",
            "time = 0.926 [s]\n",
            "\n",
            "time = 0.927 [s]\n",
            "\n",
            "time = 0.928 [s]\n",
            "\n",
            "time = 0.929 [s]\n",
            "\n",
            "time = 0.93 [s]\n",
            "\n",
            "time = 0.931 [s]\n",
            "\n",
            "time = 0.932 [s]\n",
            "\n",
            "time = 0.933 [s]\n",
            "\n",
            "time = 0.934 [s]\n",
            "\n",
            "time = 0.935 [s]\n",
            "\n",
            "time = 0.936 [s]\n",
            "\n",
            "time = 0.937 [s]\n",
            "\n",
            "time = 0.9380000000000001 [s]\n",
            "\n",
            "time = 0.9390000000000001 [s]\n",
            "\n",
            "time = 0.9400000000000001 [s]\n",
            "\n",
            "time = 0.9410000000000001 [s]\n",
            "\n",
            "time = 0.9420000000000001 [s]\n",
            "\n",
            "time = 0.9430000000000001 [s]\n",
            "\n",
            "time = 0.9440000000000001 [s]\n",
            "\n",
            "time = 0.9450000000000001 [s]\n",
            "\n",
            "time = 0.9460000000000001 [s]\n",
            "\n",
            "time = 0.9470000000000001 [s]\n",
            "\n",
            "time = 0.9480000000000001 [s]\n",
            "\n",
            "time = 0.9490000000000001 [s]\n",
            "\n",
            "time = 0.9500000000000001 [s]\n",
            "\n",
            "time = 0.9510000000000001 [s]\n",
            "\n",
            "time = 0.9520000000000001 [s]\n",
            "\n",
            "time = 0.9530000000000001 [s]\n",
            "\n",
            "time = 0.9540000000000001 [s]\n",
            "\n",
            "time = 0.9550000000000001 [s]\n",
            "\n",
            "time = 0.9560000000000001 [s]\n",
            "\n",
            "time = 0.9570000000000001 [s]\n",
            "\n",
            "time = 0.9580000000000001 [s]\n",
            "\n",
            "time = 0.9590000000000001 [s]\n",
            "\n",
            "time = 0.96 [s]\n",
            "\n",
            "time = 0.961 [s]\n",
            "\n",
            "time = 0.962 [s]\n",
            "\n",
            "time = 0.963 [s]\n",
            "\n",
            "time = 0.964 [s]\n",
            "\n",
            "time = 0.965 [s]\n",
            "\n",
            "time = 0.966 [s]\n",
            "\n",
            "time = 0.967 [s]\n",
            "\n",
            "time = 0.968 [s]\n",
            "\n",
            "time = 0.969 [s]\n",
            "\n",
            "time = 0.97 [s]\n",
            "\n",
            "time = 0.971 [s]\n",
            "\n",
            "time = 0.972 [s]\n",
            "\n",
            "time = 0.973 [s]\n",
            "\n",
            "time = 0.974 [s]\n",
            "\n",
            "time = 0.975 [s]\n",
            "\n",
            "time = 0.976 [s]\n",
            "\n",
            "time = 0.977 [s]\n",
            "\n",
            "time = 0.978 [s]\n",
            "\n",
            "time = 0.979 [s]\n",
            "\n",
            "time = 0.98 [s]\n",
            "\n",
            "time = 0.981 [s]\n",
            "\n",
            "time = 0.982 [s]\n",
            "\n",
            "time = 0.983 [s]\n",
            "\n",
            "time = 0.984 [s]\n",
            "\n",
            "time = 0.985 [s]\n",
            "\n",
            "time = 0.986 [s]\n",
            "\n",
            "time = 0.987 [s]\n",
            "\n",
            "time = 0.988 [s]\n",
            "\n",
            "time = 0.989 [s]\n",
            "\n",
            "time = 0.99 [s]\n",
            "\n",
            "time = 0.991 [s]\n",
            "\n",
            "time = 0.992 [s]\n",
            "\n",
            "time = 0.993 [s]\n",
            "\n",
            "time = 0.994 [s]\n",
            "\n",
            "time = 0.995 [s]\n",
            "\n",
            "time = 0.996 [s]\n",
            "\n",
            "time = 0.997 [s]\n",
            "\n",
            "time = 0.998 [s]\n",
            "\n",
            "time = 0.999 [s]\n",
            "\n",
            "time = 1.0 [s]\n",
            "\n",
            "time = 1.0010000000000001 [s]\n",
            "\n",
            "time = 1.002 [s]\n",
            "\n",
            "time = 1.0030000000000001 [s]\n",
            "\n",
            "time = 1.004 [s]\n",
            "\n",
            "time = 1.0050000000000001 [s]\n",
            "\n",
            "time = 1.006 [s]\n",
            "\n",
            "time = 1.0070000000000001 [s]\n",
            "\n",
            "time = 1.008 [s]\n",
            "\n",
            "time = 1.0090000000000001 [s]\n",
            "\n",
            "time = 1.01 [s]\n",
            "\n",
            "time = 1.0110000000000001 [s]\n",
            "\n",
            "time = 1.012 [s]\n",
            "\n",
            "time = 1.0130000000000001 [s]\n",
            "\n",
            "time = 1.014 [s]\n",
            "\n",
            "time = 1.0150000000000001 [s]\n",
            "\n",
            "time = 1.016 [s]\n",
            "\n",
            "time = 1.0170000000000001 [s]\n",
            "\n",
            "time = 1.018 [s]\n",
            "\n",
            "time = 1.0190000000000001 [s]\n",
            "\n",
            "time = 1.02 [s]\n",
            "\n",
            "time = 1.0210000000000001 [s]\n",
            "\n",
            "time = 1.022 [s]\n",
            "\n",
            "time = 1.0230000000000001 [s]\n",
            "\n",
            "time = 1.024 [s]\n",
            "\n",
            "time = 1.025 [s]\n",
            "\n",
            "time = 1.026 [s]\n",
            "\n",
            "time = 1.027 [s]\n",
            "\n",
            "time = 1.028 [s]\n",
            "\n",
            "time = 1.029 [s]\n",
            "\n",
            "time = 1.03 [s]\n",
            "\n",
            "time = 1.031 [s]\n",
            "\n",
            "time = 1.032 [s]\n",
            "\n",
            "time = 1.033 [s]\n",
            "\n",
            "time = 1.034 [s]\n",
            "\n",
            "time = 1.035 [s]\n",
            "\n",
            "time = 1.036 [s]\n",
            "\n",
            "time = 1.037 [s]\n",
            "\n",
            "time = 1.038 [s]\n",
            "\n",
            "time = 1.039 [s]\n",
            "\n",
            "time = 1.04 [s]\n",
            "\n",
            "time = 1.041 [s]\n",
            "\n",
            "time = 1.042 [s]\n",
            "\n",
            "time = 1.043 [s]\n",
            "\n",
            "time = 1.044 [s]\n",
            "\n",
            "time = 1.045 [s]\n",
            "\n",
            "time = 1.046 [s]\n",
            "\n",
            "time = 1.047 [s]\n",
            "\n",
            "time = 1.048 [s]\n",
            "\n",
            "time = 1.049 [s]\n",
            "\n",
            "time = 1.05 [s]\n",
            "\n",
            "time = 1.051 [s]\n",
            "\n",
            "time = 1.052 [s]\n",
            "\n",
            "time = 1.053 [s]\n",
            "\n",
            "time = 1.054 [s]\n",
            "\n",
            "time = 1.055 [s]\n",
            "\n",
            "time = 1.056 [s]\n",
            "\n",
            "time = 1.057 [s]\n",
            "\n",
            "time = 1.058 [s]\n",
            "\n",
            "time = 1.059 [s]\n",
            "\n",
            "time = 1.06 [s]\n",
            "\n",
            "time = 1.061 [s]\n",
            "\n",
            "time = 1.062 [s]\n",
            "\n",
            "time = 1.063 [s]\n",
            "\n",
            "time = 1.064 [s]\n",
            "\n",
            "time = 1.065 [s]\n",
            "\n",
            "time = 1.066 [s]\n",
            "\n",
            "time = 1.067 [s]\n",
            "\n",
            "time = 1.068 [s]\n",
            "\n",
            "time = 1.069 [s]\n",
            "\n",
            "time = 1.07 [s]\n",
            "\n",
            "time = 1.071 [s]\n",
            "\n",
            "time = 1.072 [s]\n",
            "\n",
            "time = 1.073 [s]\n",
            "\n",
            "time = 1.074 [s]\n",
            "\n",
            "time = 1.075 [s]\n",
            "\n",
            "time = 1.076 [s]\n",
            "\n",
            "time = 1.077 [s]\n",
            "\n",
            "time = 1.078 [s]\n",
            "\n",
            "time = 1.079 [s]\n",
            "\n",
            "time = 1.08 [s]\n",
            "\n",
            "time = 1.081 [s]\n",
            "\n",
            "time = 1.082 [s]\n",
            "\n",
            "time = 1.083 [s]\n",
            "\n",
            "time = 1.084 [s]\n",
            "\n",
            "time = 1.085 [s]\n",
            "\n",
            "time = 1.086 [s]\n",
            "\n",
            "time = 1.087 [s]\n",
            "\n",
            "time = 1.088 [s]\n",
            "\n",
            "time = 1.089 [s]\n",
            "\n",
            "time = 1.09 [s]\n",
            "\n",
            "time = 1.091 [s]\n",
            "\n",
            "time = 1.092 [s]\n",
            "\n",
            "time = 1.093 [s]\n",
            "\n",
            "time = 1.094 [s]\n",
            "\n",
            "time = 1.095 [s]\n",
            "\n",
            "time = 1.096 [s]\n",
            "\n",
            "time = 1.097 [s]\n",
            "\n",
            "time = 1.098 [s]\n",
            "\n",
            "time = 1.099 [s]\n",
            "\n",
            "time = 1.1 [s]\n",
            "\n",
            "time = 1.101 [s]\n",
            "\n",
            "time = 1.102 [s]\n",
            "\n",
            "time = 1.103 [s]\n",
            "\n",
            "time = 1.104 [s]\n",
            "\n",
            "time = 1.105 [s]\n",
            "\n",
            "time = 1.106 [s]\n",
            "\n",
            "time = 1.107 [s]\n",
            "\n",
            "time = 1.108 [s]\n",
            "\n",
            "time = 1.109 [s]\n",
            "\n",
            "time = 1.11 [s]\n",
            "\n",
            "time = 1.111 [s]\n",
            "\n",
            "time = 1.112 [s]\n",
            "\n",
            "time = 1.113 [s]\n",
            "\n",
            "time = 1.114 [s]\n",
            "\n",
            "time = 1.115 [s]\n",
            "\n",
            "time = 1.116 [s]\n",
            "\n",
            "time = 1.117 [s]\n",
            "\n",
            "time = 1.118 [s]\n",
            "\n",
            "time = 1.119 [s]\n",
            "\n",
            "time = 1.12 [s]\n",
            "\n",
            "time = 1.121 [s]\n",
            "\n",
            "time = 1.122 [s]\n",
            "\n",
            "time = 1.123 [s]\n",
            "\n",
            "time = 1.124 [s]\n",
            "\n",
            "time = 1.125 [s]\n",
            "\n",
            "time = 1.1260000000000001 [s]\n",
            "\n",
            "time = 1.127 [s]\n",
            "\n",
            "time = 1.1280000000000001 [s]\n",
            "\n",
            "time = 1.129 [s]\n",
            "\n",
            "time = 1.1300000000000001 [s]\n",
            "\n",
            "time = 1.131 [s]\n",
            "\n",
            "time = 1.1320000000000001 [s]\n",
            "\n",
            "time = 1.133 [s]\n",
            "\n",
            "time = 1.1340000000000001 [s]\n",
            "\n",
            "time = 1.135 [s]\n",
            "\n",
            "time = 1.1360000000000001 [s]\n",
            "\n",
            "time = 1.137 [s]\n",
            "\n",
            "time = 1.1380000000000001 [s]\n",
            "\n",
            "time = 1.139 [s]\n",
            "\n",
            "time = 1.1400000000000001 [s]\n",
            "\n",
            "time = 1.141 [s]\n",
            "\n",
            "time = 1.1420000000000001 [s]\n",
            "\n",
            "time = 1.143 [s]\n",
            "\n",
            "time = 1.1440000000000001 [s]\n",
            "\n",
            "time = 1.145 [s]\n",
            "\n",
            "time = 1.1460000000000001 [s]\n",
            "\n",
            "time = 1.147 [s]\n",
            "\n",
            "time = 1.1480000000000001 [s]\n",
            "\n",
            "time = 1.149 [s]\n",
            "\n",
            "time = 1.1500000000000001 [s]\n",
            "\n",
            "time = 1.151 [s]\n",
            "\n",
            "time = 1.1520000000000001 [s]\n",
            "\n",
            "time = 1.153 [s]\n",
            "\n",
            "time = 1.154 [s]\n",
            "\n",
            "time = 1.155 [s]\n",
            "\n",
            "time = 1.156 [s]\n",
            "\n",
            "time = 1.157 [s]\n",
            "\n",
            "time = 1.158 [s]\n",
            "\n",
            "time = 1.159 [s]\n",
            "\n",
            "time = 1.16 [s]\n",
            "\n",
            "time = 1.161 [s]\n",
            "\n",
            "time = 1.162 [s]\n",
            "\n",
            "time = 1.163 [s]\n",
            "\n",
            "time = 1.164 [s]\n",
            "\n",
            "time = 1.165 [s]\n",
            "\n",
            "time = 1.166 [s]\n",
            "\n",
            "time = 1.167 [s]\n",
            "\n",
            "time = 1.168 [s]\n",
            "\n",
            "time = 1.169 [s]\n",
            "\n",
            "time = 1.17 [s]\n",
            "\n",
            "time = 1.171 [s]\n",
            "\n",
            "time = 1.172 [s]\n",
            "\n",
            "time = 1.173 [s]\n",
            "\n",
            "time = 1.174 [s]\n",
            "\n",
            "time = 1.175 [s]\n",
            "\n",
            "time = 1.176 [s]\n",
            "\n",
            "time = 1.177 [s]\n",
            "\n",
            "time = 1.178 [s]\n",
            "\n",
            "time = 1.179 [s]\n",
            "\n",
            "time = 1.18 [s]\n",
            "\n",
            "time = 1.181 [s]\n",
            "\n",
            "time = 1.182 [s]\n",
            "\n",
            "time = 1.183 [s]\n",
            "\n",
            "time = 1.184 [s]\n",
            "\n",
            "time = 1.185 [s]\n",
            "\n",
            "time = 1.186 [s]\n",
            "\n",
            "time = 1.187 [s]\n",
            "\n",
            "time = 1.188 [s]\n",
            "\n",
            "time = 1.189 [s]\n",
            "\n",
            "time = 1.19 [s]\n",
            "\n",
            "time = 1.191 [s]\n",
            "\n",
            "time = 1.192 [s]\n",
            "\n",
            "time = 1.193 [s]\n",
            "\n",
            "time = 1.194 [s]\n",
            "\n",
            "time = 1.195 [s]\n",
            "\n",
            "time = 1.196 [s]\n",
            "\n",
            "time = 1.197 [s]\n",
            "\n",
            "time = 1.198 [s]\n",
            "\n",
            "time = 1.199 [s]\n",
            "\n",
            "time = 1.2 [s]\n",
            "\n",
            "time = 1.201 [s]\n",
            "\n",
            "time = 1.202 [s]\n",
            "\n",
            "time = 1.203 [s]\n",
            "\n",
            "time = 1.204 [s]\n",
            "\n",
            "time = 1.205 [s]\n",
            "\n",
            "time = 1.206 [s]\n",
            "\n",
            "time = 1.207 [s]\n",
            "\n",
            "time = 1.208 [s]\n",
            "\n",
            "time = 1.209 [s]\n",
            "\n",
            "time = 1.21 [s]\n",
            "\n",
            "time = 1.211 [s]\n",
            "\n",
            "time = 1.212 [s]\n",
            "\n",
            "time = 1.213 [s]\n",
            "\n",
            "time = 1.214 [s]\n",
            "\n",
            "time = 1.215 [s]\n",
            "\n",
            "time = 1.216 [s]\n",
            "\n",
            "time = 1.217 [s]\n",
            "\n",
            "time = 1.218 [s]\n",
            "\n",
            "time = 1.219 [s]\n",
            "\n",
            "time = 1.22 [s]\n",
            "\n",
            "time = 1.221 [s]\n",
            "\n",
            "time = 1.222 [s]\n",
            "\n",
            "time = 1.223 [s]\n",
            "\n",
            "time = 1.224 [s]\n",
            "\n",
            "time = 1.225 [s]\n",
            "\n",
            "time = 1.226 [s]\n",
            "\n",
            "time = 1.227 [s]\n",
            "\n",
            "time = 1.228 [s]\n",
            "\n",
            "time = 1.229 [s]\n",
            "\n",
            "time = 1.23 [s]\n",
            "\n",
            "time = 1.231 [s]\n",
            "\n",
            "time = 1.232 [s]\n",
            "\n",
            "time = 1.233 [s]\n",
            "\n",
            "time = 1.234 [s]\n",
            "\n",
            "time = 1.235 [s]\n",
            "\n",
            "time = 1.236 [s]\n",
            "\n",
            "time = 1.237 [s]\n",
            "\n",
            "time = 1.238 [s]\n",
            "\n",
            "time = 1.239 [s]\n",
            "\n",
            "time = 1.24 [s]\n",
            "\n",
            "time = 1.241 [s]\n",
            "\n",
            "time = 1.242 [s]\n",
            "\n",
            "time = 1.243 [s]\n",
            "\n",
            "time = 1.244 [s]\n",
            "\n",
            "time = 1.245 [s]\n",
            "\n",
            "time = 1.246 [s]\n",
            "\n",
            "time = 1.247 [s]\n",
            "\n",
            "time = 1.248 [s]\n",
            "\n",
            "time = 1.249 [s]\n",
            "\n",
            "time = 1.25 [s]\n",
            "\n",
            "time = 1.2510000000000001 [s]\n",
            "\n",
            "time = 1.252 [s]\n",
            "\n",
            "time = 1.2530000000000001 [s]\n",
            "\n",
            "time = 1.254 [s]\n",
            "\n",
            "time = 1.2550000000000001 [s]\n",
            "\n",
            "time = 1.256 [s]\n",
            "\n",
            "time = 1.2570000000000001 [s]\n",
            "\n",
            "time = 1.258 [s]\n",
            "\n",
            "time = 1.2590000000000001 [s]\n",
            "\n",
            "time = 1.26 [s]\n",
            "\n",
            "time = 1.2610000000000001 [s]\n",
            "\n",
            "time = 1.262 [s]\n",
            "\n",
            "time = 1.2630000000000001 [s]\n",
            "\n",
            "time = 1.264 [s]\n",
            "\n",
            "time = 1.2650000000000001 [s]\n",
            "\n",
            "time = 1.266 [s]\n",
            "\n",
            "time = 1.2670000000000001 [s]\n",
            "\n",
            "time = 1.268 [s]\n",
            "\n",
            "time = 1.2690000000000001 [s]\n",
            "\n",
            "time = 1.27 [s]\n",
            "\n",
            "time = 1.2710000000000001 [s]\n",
            "\n",
            "time = 1.272 [s]\n",
            "\n",
            "time = 1.2730000000000001 [s]\n",
            "\n",
            "time = 1.274 [s]\n",
            "\n",
            "time = 1.2750000000000001 [s]\n",
            "\n",
            "time = 1.276 [s]\n",
            "\n",
            "time = 1.2770000000000001 [s]\n",
            "\n",
            "time = 1.278 [s]\n",
            "\n",
            "time = 1.2790000000000001 [s]\n",
            "\n",
            "time = 1.28 [s]\n",
            "\n",
            "time = 1.281 [s]\n",
            "\n",
            "time = 1.282 [s]\n",
            "\n",
            "time = 1.283 [s]\n",
            "\n",
            "time = 1.284 [s]\n",
            "\n",
            "time = 1.285 [s]\n",
            "\n",
            "time = 1.286 [s]\n",
            "\n",
            "time = 1.287 [s]\n",
            "\n",
            "time = 1.288 [s]\n",
            "\n",
            "time = 1.289 [s]\n",
            "\n",
            "time = 1.29 [s]\n",
            "\n",
            "time = 1.291 [s]\n",
            "\n",
            "time = 1.292 [s]\n",
            "\n",
            "time = 1.293 [s]\n",
            "\n",
            "time = 1.294 [s]\n",
            "\n",
            "time = 1.295 [s]\n",
            "\n",
            "time = 1.296 [s]\n",
            "\n",
            "time = 1.297 [s]\n",
            "\n",
            "time = 1.298 [s]\n",
            "\n",
            "time = 1.299 [s]\n",
            "\n",
            "time = 1.3 [s]\n",
            "\n",
            "time = 1.301 [s]\n",
            "\n",
            "time = 1.302 [s]\n",
            "\n",
            "time = 1.303 [s]\n",
            "\n",
            "time = 1.304 [s]\n",
            "\n",
            "time = 1.305 [s]\n",
            "\n",
            "time = 1.306 [s]\n",
            "\n",
            "time = 1.307 [s]\n",
            "\n",
            "time = 1.308 [s]\n",
            "\n",
            "time = 1.309 [s]\n",
            "\n",
            "time = 1.31 [s]\n",
            "\n",
            "time = 1.311 [s]\n",
            "\n",
            "time = 1.312 [s]\n",
            "\n",
            "time = 1.313 [s]\n",
            "\n",
            "time = 1.314 [s]\n",
            "\n",
            "time = 1.315 [s]\n",
            "\n",
            "time = 1.316 [s]\n",
            "\n",
            "time = 1.317 [s]\n",
            "\n",
            "time = 1.318 [s]\n",
            "\n",
            "time = 1.319 [s]\n",
            "\n",
            "time = 1.32 [s]\n",
            "\n",
            "time = 1.321 [s]\n",
            "\n",
            "time = 1.322 [s]\n",
            "\n",
            "time = 1.323 [s]\n",
            "\n",
            "time = 1.324 [s]\n",
            "\n",
            "time = 1.325 [s]\n",
            "\n",
            "time = 1.326 [s]\n",
            "\n",
            "time = 1.327 [s]\n",
            "\n",
            "time = 1.328 [s]\n",
            "\n",
            "time = 1.329 [s]\n",
            "\n",
            "time = 1.33 [s]\n",
            "\n",
            "time = 1.331 [s]\n",
            "\n",
            "time = 1.332 [s]\n",
            "\n",
            "time = 1.333 [s]\n",
            "\n",
            "time = 1.334 [s]\n",
            "\n",
            "time = 1.335 [s]\n",
            "\n",
            "time = 1.336 [s]\n",
            "\n",
            "time = 1.337 [s]\n",
            "\n",
            "time = 1.338 [s]\n",
            "\n",
            "time = 1.339 [s]\n",
            "\n",
            "time = 1.34 [s]\n",
            "\n",
            "time = 1.341 [s]\n",
            "\n",
            "time = 1.342 [s]\n",
            "\n",
            "time = 1.343 [s]\n",
            "\n",
            "time = 1.344 [s]\n",
            "\n",
            "time = 1.345 [s]\n",
            "\n",
            "time = 1.346 [s]\n",
            "\n",
            "time = 1.347 [s]\n",
            "\n",
            "time = 1.348 [s]\n",
            "\n",
            "time = 1.349 [s]\n",
            "\n",
            "time = 1.35 [s]\n",
            "\n",
            "time = 1.351 [s]\n",
            "\n",
            "time = 1.352 [s]\n",
            "\n",
            "time = 1.353 [s]\n",
            "\n",
            "time = 1.354 [s]\n",
            "\n",
            "time = 1.355 [s]\n",
            "\n",
            "time = 1.356 [s]\n",
            "\n",
            "time = 1.357 [s]\n",
            "\n",
            "time = 1.358 [s]\n",
            "\n",
            "time = 1.359 [s]\n",
            "\n",
            "time = 1.36 [s]\n",
            "\n",
            "time = 1.361 [s]\n",
            "\n",
            "time = 1.362 [s]\n",
            "\n",
            "time = 1.363 [s]\n",
            "\n",
            "time = 1.364 [s]\n",
            "\n",
            "time = 1.365 [s]\n",
            "\n",
            "time = 1.366 [s]\n",
            "\n",
            "time = 1.367 [s]\n",
            "\n",
            "time = 1.368 [s]\n",
            "\n",
            "time = 1.369 [s]\n",
            "\n",
            "time = 1.37 [s]\n",
            "\n",
            "time = 1.371 [s]\n",
            "\n",
            "time = 1.372 [s]\n",
            "\n",
            "time = 1.373 [s]\n",
            "\n",
            "time = 1.374 [s]\n",
            "\n",
            "time = 1.375 [s]\n",
            "\n",
            "time = 1.3760000000000001 [s]\n",
            "\n",
            "time = 1.377 [s]\n",
            "\n",
            "time = 1.3780000000000001 [s]\n",
            "\n",
            "time = 1.379 [s]\n",
            "\n",
            "time = 1.3800000000000001 [s]\n",
            "\n",
            "time = 1.381 [s]\n",
            "\n",
            "time = 1.3820000000000001 [s]\n",
            "\n",
            "time = 1.383 [s]\n",
            "\n",
            "time = 1.3840000000000001 [s]\n",
            "\n",
            "time = 1.385 [s]\n",
            "\n",
            "time = 1.3860000000000001 [s]\n",
            "\n",
            "time = 1.387 [s]\n",
            "\n",
            "time = 1.3880000000000001 [s]\n",
            "\n",
            "time = 1.389 [s]\n",
            "\n",
            "time = 1.3900000000000001 [s]\n",
            "\n",
            "time = 1.391 [s]\n",
            "\n",
            "time = 1.3920000000000001 [s]\n",
            "\n",
            "time = 1.393 [s]\n",
            "\n",
            "time = 1.3940000000000001 [s]\n",
            "\n",
            "time = 1.395 [s]\n",
            "\n",
            "time = 1.3960000000000001 [s]\n",
            "\n",
            "time = 1.397 [s]\n",
            "\n",
            "time = 1.3980000000000001 [s]\n",
            "\n",
            "time = 1.399 [s]\n",
            "\n",
            "time = 1.4000000000000001 [s]\n",
            "\n",
            "time = 1.401 [s]\n",
            "\n",
            "time = 1.4020000000000001 [s]\n",
            "\n",
            "time = 1.403 [s]\n",
            "\n",
            "time = 1.4040000000000001 [s]\n",
            "\n",
            "time = 1.405 [s]\n",
            "\n",
            "time = 1.4060000000000001 [s]\n",
            "\n",
            "time = 1.407 [s]\n",
            "\n",
            "time = 1.408 [s]\n",
            "\n",
            "time = 1.409 [s]\n",
            "\n",
            "time = 1.41 [s]\n",
            "\n",
            "time = 1.411 [s]\n",
            "\n",
            "time = 1.412 [s]\n",
            "\n",
            "time = 1.413 [s]\n",
            "\n",
            "time = 1.414 [s]\n",
            "\n",
            "time = 1.415 [s]\n",
            "\n",
            "time = 1.416 [s]\n",
            "\n",
            "time = 1.417 [s]\n",
            "\n",
            "time = 1.418 [s]\n",
            "\n",
            "time = 1.419 [s]\n",
            "\n",
            "time = 1.42 [s]\n",
            "\n",
            "time = 1.421 [s]\n",
            "\n",
            "time = 1.422 [s]\n",
            "\n",
            "time = 1.423 [s]\n",
            "\n",
            "time = 1.424 [s]\n",
            "\n",
            "time = 1.425 [s]\n",
            "\n",
            "time = 1.426 [s]\n",
            "\n",
            "time = 1.427 [s]\n",
            "\n",
            "time = 1.428 [s]\n",
            "\n",
            "time = 1.429 [s]\n",
            "\n",
            "time = 1.43 [s]\n",
            "\n",
            "time = 1.431 [s]\n",
            "\n",
            "time = 1.432 [s]\n",
            "\n",
            "time = 1.433 [s]\n",
            "\n",
            "time = 1.434 [s]\n",
            "\n",
            "time = 1.435 [s]\n",
            "\n",
            "time = 1.436 [s]\n",
            "\n",
            "time = 1.437 [s]\n",
            "\n",
            "time = 1.438 [s]\n",
            "\n",
            "time = 1.439 [s]\n",
            "\n",
            "time = 1.44 [s]\n",
            "\n",
            "time = 1.441 [s]\n",
            "\n",
            "time = 1.442 [s]\n",
            "\n",
            "time = 1.443 [s]\n",
            "\n",
            "time = 1.444 [s]\n",
            "\n",
            "time = 1.445 [s]\n",
            "\n",
            "time = 1.446 [s]\n",
            "\n",
            "time = 1.447 [s]\n",
            "\n",
            "time = 1.448 [s]\n",
            "\n",
            "time = 1.449 [s]\n",
            "\n",
            "time = 1.45 [s]\n",
            "\n",
            "time = 1.451 [s]\n",
            "\n",
            "time = 1.452 [s]\n",
            "\n",
            "time = 1.453 [s]\n",
            "\n",
            "time = 1.454 [s]\n",
            "\n",
            "time = 1.455 [s]\n",
            "\n",
            "time = 1.456 [s]\n",
            "\n",
            "time = 1.457 [s]\n",
            "\n",
            "time = 1.458 [s]\n",
            "\n",
            "time = 1.459 [s]\n",
            "\n",
            "time = 1.46 [s]\n",
            "\n",
            "time = 1.461 [s]\n",
            "\n",
            "time = 1.462 [s]\n",
            "\n",
            "time = 1.463 [s]\n",
            "\n",
            "time = 1.464 [s]\n",
            "\n",
            "time = 1.465 [s]\n",
            "\n",
            "time = 1.466 [s]\n",
            "\n",
            "time = 1.467 [s]\n",
            "\n",
            "time = 1.468 [s]\n",
            "\n",
            "time = 1.469 [s]\n",
            "\n",
            "time = 1.47 [s]\n",
            "\n",
            "time = 1.471 [s]\n",
            "\n",
            "time = 1.472 [s]\n",
            "\n",
            "time = 1.473 [s]\n",
            "\n",
            "time = 1.474 [s]\n",
            "\n",
            "time = 1.475 [s]\n",
            "\n",
            "time = 1.476 [s]\n",
            "\n",
            "time = 1.477 [s]\n",
            "\n",
            "time = 1.478 [s]\n",
            "\n",
            "time = 1.479 [s]\n",
            "\n",
            "time = 1.48 [s]\n",
            "\n",
            "time = 1.481 [s]\n",
            "\n",
            "time = 1.482 [s]\n",
            "\n",
            "time = 1.483 [s]\n",
            "\n",
            "time = 1.484 [s]\n",
            "\n",
            "time = 1.485 [s]\n",
            "\n",
            "time = 1.486 [s]\n",
            "\n",
            "time = 1.487 [s]\n",
            "\n",
            "time = 1.488 [s]\n",
            "\n",
            "time = 1.489 [s]\n",
            "\n",
            "time = 1.49 [s]\n",
            "\n",
            "time = 1.491 [s]\n",
            "\n",
            "time = 1.492 [s]\n",
            "\n",
            "time = 1.493 [s]\n",
            "\n",
            "time = 1.494 [s]\n",
            "\n",
            "time = 1.495 [s]\n",
            "\n",
            "time = 1.496 [s]\n",
            "\n",
            "time = 1.497 [s]\n",
            "\n",
            "time = 1.498 [s]\n",
            "\n",
            "time = 1.499 [s]\n",
            "\n",
            "time = 1.5 [s]\n",
            "\n",
            "time = 1.5010000000000001 [s]\n",
            "\n",
            "time = 1.502 [s]\n",
            "\n",
            "time = 1.5030000000000001 [s]\n",
            "\n",
            "time = 1.504 [s]\n",
            "\n",
            "time = 1.5050000000000001 [s]\n",
            "\n",
            "time = 1.506 [s]\n",
            "\n",
            "time = 1.5070000000000001 [s]\n",
            "\n",
            "time = 1.508 [s]\n",
            "\n",
            "time = 1.5090000000000001 [s]\n",
            "\n",
            "time = 1.51 [s]\n",
            "\n",
            "time = 1.5110000000000001 [s]\n",
            "\n",
            "time = 1.512 [s]\n",
            "\n",
            "time = 1.5130000000000001 [s]\n",
            "\n",
            "time = 1.514 [s]\n",
            "\n",
            "time = 1.5150000000000001 [s]\n",
            "\n",
            "time = 1.516 [s]\n",
            "\n",
            "time = 1.5170000000000001 [s]\n",
            "\n",
            "time = 1.518 [s]\n",
            "\n",
            "time = 1.5190000000000001 [s]\n",
            "\n",
            "time = 1.52 [s]\n",
            "\n",
            "time = 1.5210000000000001 [s]\n",
            "\n",
            "time = 1.522 [s]\n",
            "\n",
            "time = 1.5230000000000001 [s]\n",
            "\n",
            "time = 1.524 [s]\n",
            "\n",
            "time = 1.5250000000000001 [s]\n",
            "\n",
            "time = 1.526 [s]\n",
            "\n",
            "time = 1.5270000000000001 [s]\n",
            "\n",
            "time = 1.528 [s]\n",
            "\n",
            "time = 1.5290000000000001 [s]\n",
            "\n",
            "time = 1.53 [s]\n",
            "\n",
            "time = 1.5310000000000001 [s]\n",
            "\n",
            "time = 1.532 [s]\n",
            "\n",
            "time = 1.5330000000000001 [s]\n",
            "\n",
            "time = 1.534 [s]\n",
            "\n",
            "time = 1.5350000000000001 [s]\n",
            "\n",
            "time = 1.536 [s]\n",
            "\n",
            "time = 1.537 [s]\n",
            "\n",
            "time = 1.538 [s]\n",
            "\n",
            "time = 1.539 [s]\n",
            "\n",
            "time = 1.54 [s]\n",
            "\n",
            "time = 1.541 [s]\n",
            "\n",
            "time = 1.542 [s]\n",
            "\n",
            "time = 1.543 [s]\n",
            "\n",
            "time = 1.544 [s]\n",
            "\n",
            "time = 1.545 [s]\n",
            "\n",
            "time = 1.546 [s]\n",
            "\n",
            "time = 1.547 [s]\n",
            "\n",
            "time = 1.548 [s]\n",
            "\n",
            "time = 1.549 [s]\n",
            "\n",
            "time = 1.55 [s]\n",
            "\n",
            "time = 1.551 [s]\n",
            "\n",
            "time = 1.552 [s]\n",
            "\n",
            "time = 1.553 [s]\n",
            "\n",
            "time = 1.554 [s]\n",
            "\n",
            "time = 1.555 [s]\n",
            "\n",
            "time = 1.556 [s]\n",
            "\n",
            "time = 1.557 [s]\n",
            "\n",
            "time = 1.558 [s]\n",
            "\n",
            "time = 1.559 [s]\n",
            "\n",
            "time = 1.56 [s]\n",
            "\n",
            "time = 1.561 [s]\n",
            "\n",
            "time = 1.562 [s]\n",
            "\n",
            "time = 1.563 [s]\n",
            "\n",
            "time = 1.564 [s]\n",
            "\n",
            "time = 1.565 [s]\n",
            "\n",
            "time = 1.566 [s]\n",
            "\n",
            "time = 1.567 [s]\n",
            "\n",
            "time = 1.568 [s]\n",
            "\n",
            "time = 1.569 [s]\n",
            "\n",
            "time = 1.57 [s]\n",
            "\n",
            "time = 1.571 [s]\n",
            "\n",
            "time = 1.572 [s]\n",
            "\n",
            "time = 1.573 [s]\n",
            "\n",
            "time = 1.574 [s]\n",
            "\n",
            "time = 1.575 [s]\n",
            "\n",
            "time = 1.576 [s]\n",
            "\n",
            "time = 1.577 [s]\n",
            "\n",
            "time = 1.578 [s]\n",
            "\n",
            "time = 1.579 [s]\n",
            "\n",
            "time = 1.58 [s]\n",
            "\n",
            "time = 1.581 [s]\n",
            "\n",
            "time = 1.582 [s]\n",
            "\n",
            "time = 1.583 [s]\n",
            "\n",
            "time = 1.584 [s]\n",
            "\n",
            "time = 1.585 [s]\n",
            "\n",
            "time = 1.586 [s]\n",
            "\n",
            "time = 1.587 [s]\n",
            "\n",
            "time = 1.588 [s]\n",
            "\n",
            "time = 1.589 [s]\n",
            "\n",
            "time = 1.59 [s]\n",
            "\n",
            "time = 1.591 [s]\n",
            "\n",
            "time = 1.592 [s]\n",
            "\n",
            "time = 1.593 [s]\n",
            "\n",
            "time = 1.594 [s]\n",
            "\n",
            "time = 1.595 [s]\n",
            "\n",
            "time = 1.596 [s]\n",
            "\n",
            "time = 1.597 [s]\n",
            "\n",
            "time = 1.598 [s]\n",
            "\n",
            "time = 1.599 [s]\n",
            "\n",
            "time = 1.6 [s]\n",
            "\n",
            "time = 1.601 [s]\n",
            "\n",
            "time = 1.602 [s]\n",
            "\n",
            "time = 1.603 [s]\n",
            "\n",
            "time = 1.604 [s]\n",
            "\n",
            "time = 1.605 [s]\n",
            "\n",
            "time = 1.606 [s]\n",
            "\n",
            "time = 1.607 [s]\n",
            "\n",
            "time = 1.608 [s]\n",
            "\n",
            "time = 1.609 [s]\n",
            "\n",
            "time = 1.61 [s]\n",
            "\n",
            "time = 1.611 [s]\n",
            "\n",
            "time = 1.612 [s]\n",
            "\n",
            "time = 1.613 [s]\n",
            "\n",
            "time = 1.614 [s]\n",
            "\n",
            "time = 1.615 [s]\n",
            "\n",
            "time = 1.616 [s]\n",
            "\n",
            "time = 1.617 [s]\n",
            "\n",
            "time = 1.618 [s]\n",
            "\n",
            "time = 1.619 [s]\n",
            "\n",
            "time = 1.62 [s]\n",
            "\n",
            "time = 1.621 [s]\n",
            "\n",
            "time = 1.622 [s]\n",
            "\n",
            "time = 1.623 [s]\n",
            "\n",
            "time = 1.624 [s]\n",
            "\n",
            "time = 1.625 [s]\n",
            "\n",
            "time = 1.6260000000000001 [s]\n",
            "\n",
            "time = 1.627 [s]\n",
            "\n",
            "time = 1.6280000000000001 [s]\n",
            "\n",
            "time = 1.629 [s]\n",
            "\n",
            "time = 1.6300000000000001 [s]\n",
            "\n",
            "time = 1.631 [s]\n",
            "\n",
            "time = 1.6320000000000001 [s]\n",
            "\n",
            "time = 1.633 [s]\n",
            "\n",
            "time = 1.6340000000000001 [s]\n",
            "\n",
            "time = 1.635 [s]\n",
            "\n",
            "time = 1.6360000000000001 [s]\n",
            "\n",
            "time = 1.637 [s]\n",
            "\n",
            "time = 1.6380000000000001 [s]\n",
            "\n",
            "time = 1.639 [s]\n",
            "\n",
            "time = 1.6400000000000001 [s]\n",
            "\n",
            "time = 1.641 [s]\n",
            "\n",
            "time = 1.6420000000000001 [s]\n",
            "\n",
            "time = 1.643 [s]\n",
            "\n",
            "time = 1.6440000000000001 [s]\n",
            "\n",
            "time = 1.645 [s]\n",
            "\n",
            "time = 1.6460000000000001 [s]\n",
            "\n",
            "time = 1.647 [s]\n",
            "\n",
            "time = 1.6480000000000001 [s]\n",
            "\n",
            "time = 1.649 [s]\n",
            "\n",
            "time = 1.6500000000000001 [s]\n",
            "\n",
            "time = 1.651 [s]\n",
            "\n",
            "time = 1.6520000000000001 [s]\n",
            "\n",
            "time = 1.653 [s]\n",
            "\n",
            "time = 1.6540000000000001 [s]\n",
            "\n",
            "time = 1.655 [s]\n",
            "\n",
            "time = 1.6560000000000001 [s]\n",
            "\n",
            "time = 1.657 [s]\n",
            "\n",
            "time = 1.6580000000000001 [s]\n",
            "\n",
            "time = 1.659 [s]\n",
            "\n",
            "time = 1.6600000000000001 [s]\n",
            "\n",
            "time = 1.661 [s]\n",
            "\n",
            "time = 1.6620000000000001 [s]\n",
            "\n",
            "time = 1.663 [s]\n",
            "\n",
            "time = 1.6640000000000001 [s]\n",
            "\n",
            "time = 1.665 [s]\n",
            "\n",
            "time = 1.666 [s]\n",
            "\n",
            "time = 1.667 [s]\n",
            "\n",
            "time = 1.668 [s]\n",
            "\n",
            "time = 1.669 [s]\n",
            "\n",
            "time = 1.67 [s]\n",
            "\n",
            "time = 1.671 [s]\n",
            "\n",
            "time = 1.672 [s]\n",
            "\n",
            "time = 1.673 [s]\n",
            "\n",
            "time = 1.674 [s]\n",
            "\n",
            "time = 1.675 [s]\n",
            "\n",
            "time = 1.676 [s]\n",
            "\n",
            "time = 1.677 [s]\n",
            "\n",
            "time = 1.678 [s]\n",
            "\n",
            "time = 1.679 [s]\n",
            "\n",
            "time = 1.68 [s]\n",
            "\n",
            "time = 1.681 [s]\n",
            "\n",
            "time = 1.682 [s]\n",
            "\n",
            "time = 1.683 [s]\n",
            "\n",
            "time = 1.684 [s]\n",
            "\n",
            "time = 1.685 [s]\n",
            "\n",
            "time = 1.686 [s]\n",
            "\n",
            "time = 1.687 [s]\n",
            "\n",
            "time = 1.688 [s]\n",
            "\n",
            "time = 1.689 [s]\n",
            "\n",
            "time = 1.69 [s]\n",
            "\n",
            "time = 1.691 [s]\n",
            "\n",
            "time = 1.692 [s]\n",
            "\n",
            "time = 1.693 [s]\n",
            "\n",
            "time = 1.694 [s]\n",
            "\n",
            "time = 1.695 [s]\n",
            "\n",
            "time = 1.696 [s]\n",
            "\n",
            "time = 1.697 [s]\n",
            "\n",
            "time = 1.698 [s]\n",
            "\n",
            "time = 1.699 [s]\n",
            "\n",
            "time = 1.7 [s]\n",
            "\n",
            "time = 1.701 [s]\n",
            "\n",
            "time = 1.702 [s]\n",
            "\n",
            "time = 1.703 [s]\n",
            "\n",
            "time = 1.704 [s]\n",
            "\n",
            "time = 1.705 [s]\n",
            "\n",
            "time = 1.706 [s]\n",
            "\n",
            "time = 1.707 [s]\n",
            "\n",
            "time = 1.708 [s]\n",
            "\n",
            "time = 1.709 [s]\n",
            "\n",
            "time = 1.71 [s]\n",
            "\n",
            "time = 1.711 [s]\n",
            "\n",
            "time = 1.712 [s]\n",
            "\n",
            "time = 1.713 [s]\n",
            "\n",
            "time = 1.714 [s]\n",
            "\n",
            "time = 1.715 [s]\n",
            "\n",
            "time = 1.716 [s]\n",
            "\n",
            "time = 1.717 [s]\n",
            "\n",
            "time = 1.718 [s]\n",
            "\n",
            "time = 1.719 [s]\n",
            "\n",
            "time = 1.72 [s]\n",
            "\n",
            "time = 1.721 [s]\n",
            "\n",
            "time = 1.722 [s]\n",
            "\n",
            "time = 1.723 [s]\n",
            "\n",
            "time = 1.724 [s]\n",
            "\n",
            "time = 1.725 [s]\n",
            "\n",
            "time = 1.726 [s]\n",
            "\n",
            "time = 1.727 [s]\n",
            "\n",
            "time = 1.728 [s]\n",
            "\n",
            "time = 1.729 [s]\n",
            "\n",
            "time = 1.73 [s]\n",
            "\n",
            "time = 1.731 [s]\n",
            "\n",
            "time = 1.732 [s]\n",
            "\n",
            "time = 1.733 [s]\n",
            "\n",
            "time = 1.734 [s]\n",
            "\n",
            "time = 1.735 [s]\n",
            "\n",
            "time = 1.736 [s]\n",
            "\n",
            "time = 1.737 [s]\n",
            "\n",
            "time = 1.738 [s]\n",
            "\n",
            "time = 1.739 [s]\n",
            "\n",
            "time = 1.74 [s]\n",
            "\n",
            "time = 1.741 [s]\n",
            "\n",
            "time = 1.742 [s]\n",
            "\n",
            "time = 1.743 [s]\n",
            "\n",
            "time = 1.744 [s]\n",
            "\n",
            "time = 1.745 [s]\n",
            "\n",
            "time = 1.746 [s]\n",
            "\n",
            "time = 1.747 [s]\n",
            "\n",
            "time = 1.748 [s]\n",
            "\n",
            "time = 1.749 [s]\n",
            "\n",
            "time = 1.75 [s]\n",
            "\n",
            "time = 1.7510000000000001 [s]\n",
            "\n",
            "time = 1.752 [s]\n",
            "\n",
            "time = 1.7530000000000001 [s]\n",
            "\n",
            "time = 1.754 [s]\n",
            "\n",
            "time = 1.7550000000000001 [s]\n",
            "\n",
            "time = 1.756 [s]\n",
            "\n",
            "time = 1.7570000000000001 [s]\n",
            "\n",
            "time = 1.758 [s]\n",
            "\n",
            "time = 1.7590000000000001 [s]\n",
            "\n",
            "time = 1.76 [s]\n",
            "\n",
            "time = 1.7610000000000001 [s]\n",
            "\n",
            "time = 1.762 [s]\n",
            "\n",
            "time = 1.7630000000000001 [s]\n",
            "\n",
            "time = 1.764 [s]\n",
            "\n",
            "time = 1.7650000000000001 [s]\n",
            "\n",
            "time = 1.766 [s]\n",
            "\n",
            "time = 1.7670000000000001 [s]\n",
            "\n",
            "time = 1.768 [s]\n",
            "\n",
            "time = 1.7690000000000001 [s]\n",
            "\n",
            "time = 1.77 [s]\n",
            "\n",
            "time = 1.7710000000000001 [s]\n",
            "\n",
            "time = 1.772 [s]\n",
            "\n",
            "time = 1.7730000000000001 [s]\n",
            "\n",
            "time = 1.774 [s]\n",
            "\n",
            "time = 1.7750000000000001 [s]\n",
            "\n",
            "time = 1.776 [s]\n",
            "\n",
            "time = 1.7770000000000001 [s]\n",
            "\n",
            "time = 1.778 [s]\n",
            "\n",
            "time = 1.7790000000000001 [s]\n",
            "\n",
            "time = 1.78 [s]\n",
            "\n",
            "time = 1.7810000000000001 [s]\n",
            "\n",
            "time = 1.782 [s]\n",
            "\n",
            "time = 1.7830000000000001 [s]\n",
            "\n",
            "time = 1.784 [s]\n",
            "\n",
            "time = 1.7850000000000001 [s]\n",
            "\n",
            "time = 1.786 [s]\n",
            "\n",
            "time = 1.7870000000000001 [s]\n",
            "\n",
            "time = 1.788 [s]\n",
            "\n",
            "time = 1.7890000000000001 [s]\n",
            "\n",
            "time = 1.79 [s]\n",
            "\n",
            "time = 1.7910000000000001 [s]\n",
            "\n",
            "time = 1.792 [s]\n",
            "\n",
            "time = 1.793 [s]\n",
            "\n",
            "time = 1.794 [s]\n",
            "\n",
            "time = 1.795 [s]\n",
            "\n",
            "time = 1.796 [s]\n",
            "\n",
            "time = 1.797 [s]\n",
            "\n",
            "time = 1.798 [s]\n",
            "\n",
            "time = 1.799 [s]\n",
            "\n",
            "time = 1.8 [s]\n",
            "\n",
            "time = 1.801 [s]\n",
            "\n",
            "time = 1.802 [s]\n",
            "\n",
            "time = 1.803 [s]\n",
            "\n",
            "time = 1.804 [s]\n",
            "\n",
            "time = 1.805 [s]\n",
            "\n",
            "time = 1.806 [s]\n",
            "\n",
            "time = 1.807 [s]\n",
            "\n",
            "time = 1.808 [s]\n",
            "\n",
            "time = 1.809 [s]\n",
            "\n",
            "time = 1.81 [s]\n",
            "\n",
            "time = 1.811 [s]\n",
            "\n",
            "time = 1.812 [s]\n",
            "\n",
            "time = 1.813 [s]\n",
            "\n",
            "time = 1.814 [s]\n",
            "\n",
            "time = 1.815 [s]\n",
            "\n",
            "time = 1.816 [s]\n",
            "\n",
            "time = 1.817 [s]\n",
            "\n",
            "time = 1.818 [s]\n",
            "\n",
            "time = 1.819 [s]\n",
            "\n",
            "time = 1.82 [s]\n",
            "\n",
            "time = 1.821 [s]\n",
            "\n",
            "time = 1.822 [s]\n",
            "\n",
            "time = 1.823 [s]\n",
            "\n",
            "time = 1.824 [s]\n",
            "\n",
            "time = 1.825 [s]\n",
            "\n",
            "time = 1.826 [s]\n",
            "\n",
            "time = 1.827 [s]\n",
            "\n",
            "time = 1.828 [s]\n",
            "\n",
            "time = 1.829 [s]\n",
            "\n",
            "time = 1.83 [s]\n",
            "\n",
            "time = 1.831 [s]\n",
            "\n",
            "time = 1.832 [s]\n",
            "\n",
            "time = 1.833 [s]\n",
            "\n",
            "time = 1.834 [s]\n",
            "\n",
            "time = 1.835 [s]\n",
            "\n",
            "time = 1.836 [s]\n",
            "\n",
            "time = 1.837 [s]\n",
            "\n",
            "time = 1.838 [s]\n",
            "\n",
            "time = 1.839 [s]\n",
            "\n",
            "time = 1.84 [s]\n",
            "\n",
            "time = 1.841 [s]\n",
            "\n",
            "time = 1.842 [s]\n",
            "\n",
            "time = 1.843 [s]\n",
            "\n",
            "time = 1.844 [s]\n",
            "\n",
            "time = 1.845 [s]\n",
            "\n",
            "time = 1.846 [s]\n",
            "\n",
            "time = 1.847 [s]\n",
            "\n",
            "time = 1.848 [s]\n",
            "\n",
            "time = 1.849 [s]\n",
            "\n",
            "time = 1.85 [s]\n",
            "\n",
            "time = 1.851 [s]\n",
            "\n",
            "time = 1.852 [s]\n",
            "\n",
            "time = 1.853 [s]\n",
            "\n",
            "time = 1.854 [s]\n",
            "\n",
            "time = 1.855 [s]\n",
            "\n",
            "time = 1.856 [s]\n",
            "\n",
            "time = 1.857 [s]\n",
            "\n",
            "time = 1.858 [s]\n",
            "\n",
            "time = 1.859 [s]\n",
            "\n",
            "time = 1.86 [s]\n",
            "\n",
            "time = 1.861 [s]\n",
            "\n",
            "time = 1.862 [s]\n",
            "\n",
            "time = 1.863 [s]\n",
            "\n",
            "time = 1.864 [s]\n",
            "\n",
            "time = 1.865 [s]\n",
            "\n",
            "time = 1.866 [s]\n",
            "\n",
            "time = 1.867 [s]\n",
            "\n",
            "time = 1.868 [s]\n",
            "\n",
            "time = 1.869 [s]\n",
            "\n",
            "time = 1.87 [s]\n",
            "\n",
            "time = 1.871 [s]\n",
            "\n",
            "time = 1.872 [s]\n",
            "\n",
            "time = 1.873 [s]\n",
            "\n",
            "time = 1.874 [s]\n",
            "\n",
            "time = 1.875 [s]\n",
            "\n",
            "time = 1.8760000000000001 [s]\n",
            "\n",
            "time = 1.877 [s]\n",
            "\n",
            "time = 1.8780000000000001 [s]\n",
            "\n",
            "time = 1.879 [s]\n",
            "\n",
            "time = 1.8800000000000001 [s]\n",
            "\n",
            "time = 1.881 [s]\n",
            "\n",
            "time = 1.8820000000000001 [s]\n",
            "\n",
            "time = 1.883 [s]\n",
            "\n",
            "time = 1.8840000000000001 [s]\n",
            "\n",
            "time = 1.885 [s]\n",
            "\n",
            "time = 1.8860000000000001 [s]\n",
            "\n",
            "time = 1.887 [s]\n",
            "\n",
            "time = 1.8880000000000001 [s]\n",
            "\n",
            "time = 1.889 [s]\n",
            "\n",
            "time = 1.8900000000000001 [s]\n",
            "\n",
            "time = 1.891 [s]\n",
            "\n",
            "time = 1.8920000000000001 [s]\n",
            "\n",
            "time = 1.893 [s]\n",
            "\n",
            "time = 1.8940000000000001 [s]\n",
            "\n",
            "time = 1.895 [s]\n",
            "\n",
            "time = 1.8960000000000001 [s]\n",
            "\n",
            "time = 1.897 [s]\n",
            "\n",
            "time = 1.8980000000000001 [s]\n",
            "\n",
            "time = 1.899 [s]\n",
            "\n",
            "time = 1.9000000000000001 [s]\n",
            "\n",
            "time = 1.901 [s]\n",
            "\n",
            "time = 1.9020000000000001 [s]\n",
            "\n",
            "time = 1.903 [s]\n",
            "\n",
            "time = 1.9040000000000001 [s]\n",
            "\n",
            "time = 1.905 [s]\n",
            "\n",
            "time = 1.9060000000000001 [s]\n",
            "\n",
            "time = 1.907 [s]\n",
            "\n",
            "time = 1.9080000000000001 [s]\n",
            "\n",
            "time = 1.909 [s]\n",
            "\n",
            "time = 1.9100000000000001 [s]\n",
            "\n",
            "time = 1.911 [s]\n",
            "\n",
            "time = 1.9120000000000001 [s]\n",
            "\n",
            "time = 1.913 [s]\n",
            "\n",
            "time = 1.9140000000000001 [s]\n",
            "\n",
            "time = 1.915 [s]\n",
            "\n",
            "time = 1.9160000000000001 [s]\n",
            "\n",
            "time = 1.917 [s]\n",
            "\n",
            "time = 1.9180000000000001 [s]\n",
            "\n",
            "time = 1.919 [s]\n",
            "\n",
            "time = 1.92 [s]\n",
            "\n",
            "time = 1.921 [s]\n",
            "\n",
            "time = 1.922 [s]\n",
            "\n",
            "time = 1.923 [s]\n",
            "\n",
            "time = 1.924 [s]\n",
            "\n",
            "time = 1.925 [s]\n",
            "\n",
            "time = 1.926 [s]\n",
            "\n",
            "time = 1.927 [s]\n",
            "\n",
            "time = 1.928 [s]\n",
            "\n",
            "time = 1.929 [s]\n",
            "\n",
            "time = 1.93 [s]\n",
            "\n",
            "time = 1.931 [s]\n",
            "\n",
            "time = 1.932 [s]\n",
            "\n",
            "time = 1.933 [s]\n",
            "\n",
            "time = 1.934 [s]\n",
            "\n",
            "time = 1.935 [s]\n",
            "\n",
            "time = 1.936 [s]\n",
            "\n",
            "time = 1.937 [s]\n",
            "\n",
            "time = 1.938 [s]\n",
            "\n",
            "time = 1.939 [s]\n",
            "\n",
            "time = 1.94 [s]\n",
            "\n",
            "time = 1.941 [s]\n",
            "\n",
            "time = 1.942 [s]\n",
            "\n",
            "time = 1.943 [s]\n",
            "\n",
            "time = 1.944 [s]\n",
            "\n",
            "time = 1.945 [s]\n",
            "\n",
            "time = 1.946 [s]\n",
            "\n",
            "time = 1.947 [s]\n",
            "\n",
            "time = 1.948 [s]\n",
            "\n",
            "time = 1.949 [s]\n",
            "\n",
            "time = 1.95 [s]\n",
            "\n",
            "time = 1.951 [s]\n",
            "\n",
            "time = 1.952 [s]\n",
            "\n",
            "time = 1.953 [s]\n",
            "\n",
            "time = 1.954 [s]\n",
            "\n",
            "time = 1.955 [s]\n",
            "\n",
            "time = 1.956 [s]\n",
            "\n",
            "time = 1.957 [s]\n",
            "\n",
            "time = 1.958 [s]\n",
            "\n",
            "time = 1.959 [s]\n",
            "\n",
            "time = 1.96 [s]\n",
            "\n",
            "time = 1.961 [s]\n",
            "\n",
            "time = 1.962 [s]\n",
            "\n",
            "time = 1.963 [s]\n",
            "\n",
            "time = 1.964 [s]\n",
            "\n",
            "time = 1.965 [s]\n",
            "\n",
            "time = 1.966 [s]\n",
            "\n",
            "time = 1.967 [s]\n",
            "\n",
            "time = 1.968 [s]\n",
            "\n",
            "time = 1.969 [s]\n",
            "\n",
            "time = 1.97 [s]\n",
            "\n",
            "time = 1.971 [s]\n",
            "\n",
            "time = 1.972 [s]\n",
            "\n",
            "time = 1.973 [s]\n",
            "\n",
            "time = 1.974 [s]\n",
            "\n",
            "time = 1.975 [s]\n",
            "\n",
            "time = 1.976 [s]\n",
            "\n",
            "time = 1.977 [s]\n",
            "\n",
            "time = 1.978 [s]\n",
            "\n",
            "time = 1.979 [s]\n",
            "\n",
            "time = 1.98 [s]\n",
            "\n",
            "time = 1.981 [s]\n",
            "\n",
            "time = 1.982 [s]\n",
            "\n",
            "time = 1.983 [s]\n",
            "\n",
            "time = 1.984 [s]\n",
            "\n",
            "time = 1.985 [s]\n",
            "\n",
            "time = 1.986 [s]\n",
            "\n",
            "time = 1.987 [s]\n",
            "\n",
            "time = 1.988 [s]\n",
            "\n",
            "time = 1.989 [s]\n",
            "\n",
            "time = 1.99 [s]\n",
            "\n",
            "time = 1.991 [s]\n",
            "\n",
            "time = 1.992 [s]\n",
            "\n",
            "time = 1.993 [s]\n",
            "\n",
            "time = 1.994 [s]\n",
            "\n",
            "time = 1.995 [s]\n",
            "\n",
            "time = 1.996 [s]\n",
            "\n",
            "time = 1.997 [s]\n",
            "\n",
            "time = 1.998 [s]\n",
            "\n",
            "time = 1.999 [s]\n",
            "\n",
            "time = 2.0 [s]\n",
            "\n",
            "time = 2.001 [s]\n",
            "\n",
            "time = 2.0020000000000002 [s]\n",
            "\n",
            "time = 2.003 [s]\n",
            "\n",
            "time = 2.004 [s]\n",
            "\n",
            "time = 2.005 [s]\n",
            "\n",
            "time = 2.0060000000000002 [s]\n",
            "\n",
            "time = 2.007 [s]\n",
            "\n",
            "time = 2.008 [s]\n",
            "\n",
            "time = 2.009 [s]\n",
            "\n",
            "time = 2.0100000000000002 [s]\n",
            "\n",
            "time = 2.011 [s]\n",
            "\n",
            "time = 2.012 [s]\n",
            "\n",
            "time = 2.013 [s]\n",
            "\n",
            "time = 2.0140000000000002 [s]\n",
            "\n",
            "time = 2.015 [s]\n",
            "\n",
            "time = 2.016 [s]\n",
            "\n",
            "time = 2.017 [s]\n",
            "\n",
            "time = 2.0180000000000002 [s]\n",
            "\n",
            "time = 2.019 [s]\n",
            "\n",
            "time = 2.02 [s]\n",
            "\n",
            "time = 2.021 [s]\n",
            "\n",
            "time = 2.0220000000000002 [s]\n",
            "\n",
            "time = 2.023 [s]\n",
            "\n",
            "time = 2.024 [s]\n",
            "\n",
            "time = 2.025 [s]\n",
            "\n",
            "time = 2.0260000000000002 [s]\n",
            "\n",
            "time = 2.027 [s]\n",
            "\n",
            "time = 2.028 [s]\n",
            "\n",
            "time = 2.029 [s]\n",
            "\n",
            "time = 2.0300000000000002 [s]\n",
            "\n",
            "time = 2.031 [s]\n",
            "\n",
            "time = 2.032 [s]\n",
            "\n",
            "time = 2.033 [s]\n",
            "\n",
            "time = 2.0340000000000003 [s]\n",
            "\n",
            "time = 2.035 [s]\n",
            "\n",
            "time = 2.036 [s]\n",
            "\n",
            "time = 2.037 [s]\n",
            "\n",
            "time = 2.0380000000000003 [s]\n",
            "\n",
            "time = 2.039 [s]\n",
            "\n",
            "time = 2.04 [s]\n",
            "\n",
            "time = 2.041 [s]\n",
            "\n",
            "time = 2.0420000000000003 [s]\n",
            "\n",
            "time = 2.043 [s]\n",
            "\n",
            "time = 2.044 [s]\n",
            "\n",
            "time = 2.045 [s]\n",
            "\n",
            "time = 2.0460000000000003 [s]\n",
            "\n",
            "time = 2.047 [s]\n",
            "\n",
            "time = 2.048 [s]\n",
            "\n",
            "time = 2.049 [s]\n",
            "\n",
            "time = 2.05 [s]\n",
            "\n",
            "time = 2.051 [s]\n",
            "\n",
            "time = 2.052 [s]\n",
            "\n",
            "time = 2.053 [s]\n",
            "\n",
            "time = 2.054 [s]\n",
            "\n",
            "time = 2.055 [s]\n",
            "\n",
            "time = 2.056 [s]\n",
            "\n",
            "time = 2.057 [s]\n",
            "\n",
            "time = 2.058 [s]\n",
            "\n",
            "time = 2.059 [s]\n",
            "\n",
            "time = 2.06 [s]\n",
            "\n",
            "time = 2.061 [s]\n",
            "\n",
            "time = 2.062 [s]\n",
            "\n",
            "time = 2.063 [s]\n",
            "\n",
            "time = 2.064 [s]\n",
            "\n",
            "time = 2.065 [s]\n",
            "\n",
            "time = 2.066 [s]\n",
            "\n",
            "time = 2.067 [s]\n",
            "\n",
            "time = 2.068 [s]\n",
            "\n",
            "time = 2.069 [s]\n",
            "\n",
            "time = 2.07 [s]\n",
            "\n",
            "time = 2.071 [s]\n",
            "\n",
            "time = 2.072 [s]\n",
            "\n",
            "time = 2.073 [s]\n",
            "\n",
            "time = 2.074 [s]\n",
            "\n",
            "time = 2.075 [s]\n",
            "\n",
            "time = 2.076 [s]\n",
            "\n",
            "time = 2.077 [s]\n",
            "\n",
            "time = 2.078 [s]\n",
            "\n",
            "time = 2.079 [s]\n",
            "\n",
            "time = 2.08 [s]\n",
            "\n",
            "time = 2.081 [s]\n",
            "\n",
            "time = 2.082 [s]\n",
            "\n",
            "time = 2.083 [s]\n",
            "\n",
            "time = 2.084 [s]\n",
            "\n",
            "time = 2.085 [s]\n",
            "\n",
            "time = 2.086 [s]\n",
            "\n",
            "time = 2.087 [s]\n",
            "\n",
            "time = 2.088 [s]\n",
            "\n",
            "time = 2.089 [s]\n",
            "\n",
            "time = 2.09 [s]\n",
            "\n",
            "time = 2.091 [s]\n",
            "\n",
            "time = 2.092 [s]\n",
            "\n",
            "time = 2.093 [s]\n",
            "\n",
            "time = 2.094 [s]\n",
            "\n",
            "time = 2.095 [s]\n",
            "\n",
            "time = 2.096 [s]\n",
            "\n",
            "time = 2.097 [s]\n",
            "\n",
            "time = 2.098 [s]\n",
            "\n",
            "time = 2.099 [s]\n",
            "\n",
            "time = 2.1 [s]\n",
            "\n",
            "time = 2.101 [s]\n",
            "\n",
            "time = 2.102 [s]\n",
            "\n",
            "time = 2.103 [s]\n",
            "\n",
            "time = 2.104 [s]\n",
            "\n",
            "time = 2.105 [s]\n",
            "\n",
            "time = 2.106 [s]\n",
            "\n",
            "time = 2.107 [s]\n",
            "\n",
            "time = 2.108 [s]\n",
            "\n",
            "time = 2.109 [s]\n",
            "\n",
            "time = 2.11 [s]\n",
            "\n",
            "time = 2.111 [s]\n",
            "\n",
            "time = 2.112 [s]\n",
            "\n",
            "time = 2.113 [s]\n",
            "\n",
            "time = 2.114 [s]\n",
            "\n",
            "time = 2.115 [s]\n",
            "\n",
            "time = 2.116 [s]\n",
            "\n",
            "time = 2.117 [s]\n",
            "\n",
            "time = 2.118 [s]\n",
            "\n",
            "time = 2.119 [s]\n",
            "\n",
            "time = 2.12 [s]\n",
            "\n",
            "time = 2.121 [s]\n",
            "\n",
            "time = 2.122 [s]\n",
            "\n",
            "time = 2.123 [s]\n",
            "\n",
            "time = 2.124 [s]\n",
            "\n",
            "time = 2.125 [s]\n",
            "\n",
            "time = 2.126 [s]\n",
            "\n",
            "time = 2.1270000000000002 [s]\n",
            "\n",
            "time = 2.128 [s]\n",
            "\n",
            "time = 2.129 [s]\n",
            "\n",
            "time = 2.13 [s]\n",
            "\n",
            "time = 2.1310000000000002 [s]\n",
            "\n",
            "time = 2.132 [s]\n",
            "\n",
            "time = 2.133 [s]\n",
            "\n",
            "time = 2.134 [s]\n",
            "\n",
            "time = 2.1350000000000002 [s]\n",
            "\n",
            "time = 2.136 [s]\n",
            "\n",
            "time = 2.137 [s]\n",
            "\n",
            "time = 2.138 [s]\n",
            "\n",
            "time = 2.1390000000000002 [s]\n",
            "\n",
            "time = 2.14 [s]\n",
            "\n",
            "time = 2.141 [s]\n",
            "\n",
            "time = 2.142 [s]\n",
            "\n",
            "time = 2.1430000000000002 [s]\n",
            "\n",
            "time = 2.144 [s]\n",
            "\n",
            "time = 2.145 [s]\n",
            "\n",
            "time = 2.146 [s]\n",
            "\n",
            "time = 2.1470000000000002 [s]\n",
            "\n",
            "time = 2.148 [s]\n",
            "\n",
            "time = 2.149 [s]\n",
            "\n",
            "time = 2.15 [s]\n",
            "\n",
            "time = 2.1510000000000002 [s]\n",
            "\n",
            "time = 2.152 [s]\n",
            "\n",
            "time = 2.153 [s]\n",
            "\n",
            "time = 2.154 [s]\n",
            "\n",
            "time = 2.1550000000000002 [s]\n",
            "\n",
            "time = 2.156 [s]\n",
            "\n",
            "time = 2.157 [s]\n",
            "\n",
            "time = 2.158 [s]\n",
            "\n",
            "time = 2.1590000000000003 [s]\n",
            "\n",
            "time = 2.16 [s]\n",
            "\n",
            "time = 2.161 [s]\n",
            "\n",
            "time = 2.162 [s]\n",
            "\n",
            "time = 2.1630000000000003 [s]\n",
            "\n",
            "time = 2.164 [s]\n",
            "\n",
            "time = 2.165 [s]\n",
            "\n",
            "time = 2.166 [s]\n",
            "\n",
            "time = 2.1670000000000003 [s]\n",
            "\n",
            "time = 2.168 [s]\n",
            "\n",
            "time = 2.169 [s]\n",
            "\n",
            "time = 2.17 [s]\n",
            "\n",
            "time = 2.1710000000000003 [s]\n",
            "\n",
            "time = 2.172 [s]\n",
            "\n",
            "time = 2.173 [s]\n",
            "\n",
            "time = 2.174 [s]\n",
            "\n",
            "time = 2.1750000000000003 [s]\n",
            "\n",
            "time = 2.176 [s]\n",
            "\n",
            "time = 2.177 [s]\n",
            "\n",
            "time = 2.178 [s]\n",
            "\n",
            "time = 2.179 [s]\n",
            "\n",
            "time = 2.18 [s]\n",
            "\n",
            "time = 2.181 [s]\n",
            "\n",
            "time = 2.182 [s]\n",
            "\n",
            "time = 2.183 [s]\n",
            "\n",
            "time = 2.184 [s]\n",
            "\n",
            "time = 2.185 [s]\n",
            "\n",
            "time = 2.186 [s]\n",
            "\n",
            "time = 2.187 [s]\n",
            "\n",
            "time = 2.188 [s]\n",
            "\n",
            "time = 2.189 [s]\n",
            "\n",
            "time = 2.19 [s]\n",
            "\n",
            "time = 2.191 [s]\n",
            "\n",
            "time = 2.192 [s]\n",
            "\n",
            "time = 2.193 [s]\n",
            "\n",
            "time = 2.194 [s]\n",
            "\n",
            "time = 2.195 [s]\n",
            "\n",
            "time = 2.196 [s]\n",
            "\n",
            "time = 2.197 [s]\n",
            "\n",
            "time = 2.198 [s]\n",
            "\n",
            "time = 2.199 [s]\n",
            "\n",
            "time = 2.2 [s]\n",
            "\n",
            "time = 2.201 [s]\n",
            "\n",
            "time = 2.202 [s]\n",
            "\n",
            "time = 2.203 [s]\n",
            "\n",
            "time = 2.204 [s]\n",
            "\n",
            "time = 2.205 [s]\n",
            "\n",
            "time = 2.206 [s]\n",
            "\n",
            "time = 2.207 [s]\n",
            "\n",
            "time = 2.208 [s]\n",
            "\n",
            "time = 2.209 [s]\n",
            "\n",
            "time = 2.21 [s]\n",
            "\n",
            "time = 2.211 [s]\n",
            "\n",
            "time = 2.212 [s]\n",
            "\n",
            "time = 2.213 [s]\n",
            "\n",
            "time = 2.214 [s]\n",
            "\n",
            "time = 2.215 [s]\n",
            "\n",
            "time = 2.216 [s]\n",
            "\n",
            "time = 2.217 [s]\n",
            "\n",
            "time = 2.218 [s]\n",
            "\n",
            "time = 2.219 [s]\n",
            "\n",
            "time = 2.22 [s]\n",
            "\n",
            "time = 2.221 [s]\n",
            "\n",
            "time = 2.222 [s]\n",
            "\n",
            "time = 2.223 [s]\n",
            "\n",
            "time = 2.224 [s]\n",
            "\n",
            "time = 2.225 [s]\n",
            "\n",
            "time = 2.226 [s]\n",
            "\n",
            "time = 2.227 [s]\n",
            "\n",
            "time = 2.228 [s]\n",
            "\n",
            "time = 2.229 [s]\n",
            "\n",
            "time = 2.23 [s]\n",
            "\n",
            "time = 2.231 [s]\n",
            "\n",
            "time = 2.232 [s]\n",
            "\n",
            "time = 2.233 [s]\n",
            "\n",
            "time = 2.234 [s]\n",
            "\n",
            "time = 2.235 [s]\n",
            "\n",
            "time = 2.236 [s]\n",
            "\n",
            "time = 2.237 [s]\n",
            "\n",
            "time = 2.238 [s]\n",
            "\n",
            "time = 2.239 [s]\n",
            "\n",
            "time = 2.24 [s]\n",
            "\n",
            "time = 2.241 [s]\n",
            "\n",
            "time = 2.242 [s]\n",
            "\n",
            "time = 2.243 [s]\n",
            "\n",
            "time = 2.244 [s]\n",
            "\n",
            "time = 2.245 [s]\n",
            "\n",
            "time = 2.246 [s]\n",
            "\n",
            "time = 2.247 [s]\n",
            "\n",
            "time = 2.248 [s]\n",
            "\n",
            "time = 2.249 [s]\n",
            "\n",
            "time = 2.25 [s]\n",
            "\n",
            "time = 2.251 [s]\n",
            "\n",
            "time = 2.2520000000000002 [s]\n",
            "\n",
            "time = 2.253 [s]\n",
            "\n",
            "time = 2.254 [s]\n",
            "\n",
            "time = 2.255 [s]\n",
            "\n",
            "time = 2.2560000000000002 [s]\n",
            "\n",
            "time = 2.257 [s]\n",
            "\n",
            "time = 2.258 [s]\n",
            "\n",
            "time = 2.259 [s]\n",
            "\n",
            "time = 2.2600000000000002 [s]\n",
            "\n",
            "time = 2.261 [s]\n",
            "\n",
            "time = 2.262 [s]\n",
            "\n",
            "time = 2.263 [s]\n",
            "\n",
            "time = 2.2640000000000002 [s]\n",
            "\n",
            "time = 2.265 [s]\n",
            "\n",
            "time = 2.266 [s]\n",
            "\n",
            "time = 2.267 [s]\n",
            "\n",
            "time = 2.2680000000000002 [s]\n",
            "\n",
            "time = 2.269 [s]\n",
            "\n",
            "time = 2.27 [s]\n",
            "\n",
            "time = 2.271 [s]\n",
            "\n",
            "time = 2.2720000000000002 [s]\n",
            "\n",
            "time = 2.273 [s]\n",
            "\n",
            "time = 2.274 [s]\n",
            "\n",
            "time = 2.275 [s]\n",
            "\n",
            "time = 2.2760000000000002 [s]\n",
            "\n",
            "time = 2.277 [s]\n",
            "\n",
            "time = 2.278 [s]\n",
            "\n",
            "time = 2.279 [s]\n",
            "\n",
            "time = 2.2800000000000002 [s]\n",
            "\n",
            "time = 2.281 [s]\n",
            "\n",
            "time = 2.282 [s]\n",
            "\n",
            "time = 2.283 [s]\n",
            "\n",
            "time = 2.2840000000000003 [s]\n",
            "\n",
            "time = 2.285 [s]\n",
            "\n",
            "time = 2.286 [s]\n",
            "\n",
            "time = 2.287 [s]\n",
            "\n",
            "time = 2.2880000000000003 [s]\n",
            "\n",
            "time = 2.289 [s]\n",
            "\n",
            "time = 2.29 [s]\n",
            "\n",
            "time = 2.291 [s]\n",
            "\n",
            "time = 2.2920000000000003 [s]\n",
            "\n",
            "time = 2.293 [s]\n",
            "\n",
            "time = 2.294 [s]\n",
            "\n",
            "time = 2.295 [s]\n",
            "\n",
            "time = 2.2960000000000003 [s]\n",
            "\n",
            "time = 2.297 [s]\n",
            "\n",
            "time = 2.298 [s]\n",
            "\n",
            "time = 2.299 [s]\n",
            "\n",
            "time = 2.3000000000000003 [s]\n",
            "\n",
            "time = 2.301 [s]\n",
            "\n",
            "time = 2.302 [s]\n",
            "\n",
            "time = 2.303 [s]\n",
            "\n",
            "time = 2.3040000000000003 [s]\n",
            "\n",
            "time = 2.305 [s]\n",
            "\n",
            "time = 2.306 [s]\n",
            "\n",
            "time = 2.307 [s]\n",
            "\n",
            "time = 2.308 [s]\n",
            "\n",
            "time = 2.309 [s]\n",
            "\n",
            "time = 2.31 [s]\n",
            "\n",
            "time = 2.311 [s]\n",
            "\n",
            "time = 2.312 [s]\n",
            "\n",
            "time = 2.313 [s]\n",
            "\n",
            "time = 2.314 [s]\n",
            "\n",
            "time = 2.315 [s]\n",
            "\n",
            "time = 2.316 [s]\n",
            "\n",
            "time = 2.317 [s]\n",
            "\n",
            "time = 2.318 [s]\n",
            "\n",
            "time = 2.319 [s]\n",
            "\n",
            "time = 2.32 [s]\n",
            "\n",
            "time = 2.321 [s]\n",
            "\n",
            "time = 2.322 [s]\n",
            "\n",
            "time = 2.323 [s]\n",
            "\n",
            "time = 2.324 [s]\n",
            "\n",
            "time = 2.325 [s]\n",
            "\n",
            "time = 2.326 [s]\n",
            "\n",
            "time = 2.327 [s]\n",
            "\n",
            "time = 2.328 [s]\n",
            "\n",
            "time = 2.329 [s]\n",
            "\n",
            "time = 2.33 [s]\n",
            "\n",
            "time = 2.331 [s]\n",
            "\n",
            "time = 2.332 [s]\n",
            "\n",
            "time = 2.333 [s]\n",
            "\n",
            "time = 2.334 [s]\n",
            "\n",
            "time = 2.335 [s]\n",
            "\n",
            "time = 2.336 [s]\n",
            "\n",
            "time = 2.337 [s]\n",
            "\n",
            "time = 2.338 [s]\n",
            "\n",
            "time = 2.339 [s]\n",
            "\n",
            "time = 2.34 [s]\n",
            "\n",
            "time = 2.341 [s]\n",
            "\n",
            "time = 2.342 [s]\n",
            "\n",
            "time = 2.343 [s]\n",
            "\n",
            "time = 2.344 [s]\n",
            "\n",
            "time = 2.345 [s]\n",
            "\n",
            "time = 2.346 [s]\n",
            "\n",
            "time = 2.347 [s]\n",
            "\n",
            "time = 2.348 [s]\n",
            "\n",
            "time = 2.349 [s]\n",
            "\n",
            "time = 2.35 [s]\n",
            "\n",
            "time = 2.351 [s]\n",
            "\n",
            "time = 2.352 [s]\n",
            "\n",
            "time = 2.353 [s]\n",
            "\n",
            "time = 2.354 [s]\n",
            "\n",
            "time = 2.355 [s]\n",
            "\n",
            "time = 2.356 [s]\n",
            "\n",
            "time = 2.357 [s]\n",
            "\n",
            "time = 2.358 [s]\n",
            "\n",
            "time = 2.359 [s]\n",
            "\n",
            "time = 2.36 [s]\n",
            "\n",
            "time = 2.361 [s]\n",
            "\n",
            "time = 2.362 [s]\n",
            "\n",
            "time = 2.363 [s]\n",
            "\n",
            "time = 2.364 [s]\n",
            "\n",
            "time = 2.365 [s]\n",
            "\n",
            "time = 2.366 [s]\n",
            "\n",
            "time = 2.367 [s]\n",
            "\n",
            "time = 2.368 [s]\n",
            "\n",
            "time = 2.369 [s]\n",
            "\n",
            "time = 2.37 [s]\n",
            "\n",
            "time = 2.371 [s]\n",
            "\n",
            "time = 2.372 [s]\n",
            "\n",
            "time = 2.373 [s]\n",
            "\n",
            "time = 2.374 [s]\n",
            "\n",
            "time = 2.375 [s]\n",
            "\n",
            "time = 2.376 [s]\n",
            "\n",
            "time = 2.3770000000000002 [s]\n",
            "\n",
            "time = 2.378 [s]\n",
            "\n",
            "time = 2.379 [s]\n",
            "\n",
            "time = 2.38 [s]\n",
            "\n",
            "time = 2.3810000000000002 [s]\n",
            "\n",
            "time = 2.382 [s]\n",
            "\n",
            "time = 2.383 [s]\n",
            "\n",
            "time = 2.384 [s]\n",
            "\n",
            "time = 2.3850000000000002 [s]\n",
            "\n",
            "time = 2.386 [s]\n",
            "\n",
            "time = 2.387 [s]\n",
            "\n",
            "time = 2.388 [s]\n",
            "\n",
            "time = 2.3890000000000002 [s]\n",
            "\n",
            "time = 2.39 [s]\n",
            "\n",
            "time = 2.391 [s]\n",
            "\n",
            "time = 2.392 [s]\n",
            "\n",
            "time = 2.3930000000000002 [s]\n",
            "\n",
            "time = 2.394 [s]\n",
            "\n",
            "time = 2.395 [s]\n",
            "\n",
            "time = 2.396 [s]\n",
            "\n",
            "time = 2.3970000000000002 [s]\n",
            "\n",
            "time = 2.398 [s]\n",
            "\n",
            "time = 2.399 [s]\n",
            "\n",
            "time = 2.4 [s]\n",
            "\n",
            "time = 2.4010000000000002 [s]\n",
            "\n",
            "time = 2.402 [s]\n",
            "\n",
            "time = 2.403 [s]\n",
            "\n",
            "time = 2.404 [s]\n",
            "\n",
            "time = 2.4050000000000002 [s]\n",
            "\n",
            "time = 2.406 [s]\n",
            "\n",
            "time = 2.407 [s]\n",
            "\n",
            "time = 2.408 [s]\n",
            "\n",
            "time = 2.4090000000000003 [s]\n",
            "\n",
            "time = 2.41 [s]\n",
            "\n",
            "time = 2.411 [s]\n",
            "\n",
            "time = 2.412 [s]\n",
            "\n",
            "time = 2.4130000000000003 [s]\n",
            "\n",
            "time = 2.414 [s]\n",
            "\n",
            "time = 2.415 [s]\n",
            "\n",
            "time = 2.416 [s]\n",
            "\n",
            "time = 2.4170000000000003 [s]\n",
            "\n",
            "time = 2.418 [s]\n",
            "\n",
            "time = 2.419 [s]\n",
            "\n",
            "time = 2.42 [s]\n",
            "\n",
            "time = 2.4210000000000003 [s]\n",
            "\n",
            "time = 2.422 [s]\n",
            "\n",
            "time = 2.423 [s]\n",
            "\n",
            "time = 2.424 [s]\n",
            "\n",
            "time = 2.4250000000000003 [s]\n",
            "\n",
            "time = 2.426 [s]\n",
            "\n",
            "time = 2.427 [s]\n",
            "\n",
            "time = 2.428 [s]\n",
            "\n",
            "time = 2.4290000000000003 [s]\n",
            "\n",
            "time = 2.43 [s]\n",
            "\n",
            "time = 2.431 [s]\n",
            "\n",
            "time = 2.432 [s]\n",
            "\n",
            "time = 2.433 [s]\n",
            "\n",
            "time = 2.434 [s]\n",
            "\n",
            "time = 2.435 [s]\n",
            "\n",
            "time = 2.436 [s]\n",
            "\n",
            "time = 2.437 [s]\n",
            "\n",
            "time = 2.438 [s]\n",
            "\n",
            "time = 2.439 [s]\n",
            "\n",
            "time = 2.44 [s]\n",
            "\n",
            "time = 2.441 [s]\n",
            "\n",
            "time = 2.442 [s]\n",
            "\n",
            "time = 2.443 [s]\n",
            "\n",
            "time = 2.444 [s]\n",
            "\n",
            "time = 2.445 [s]\n",
            "\n",
            "time = 2.446 [s]\n",
            "\n",
            "time = 2.447 [s]\n",
            "\n",
            "time = 2.448 [s]\n",
            "\n",
            "time = 2.449 [s]\n",
            "\n",
            "time = 2.45 [s]\n",
            "\n",
            "time = 2.451 [s]\n",
            "\n",
            "time = 2.452 [s]\n",
            "\n",
            "time = 2.453 [s]\n",
            "\n",
            "time = 2.454 [s]\n",
            "\n",
            "time = 2.455 [s]\n",
            "\n",
            "time = 2.456 [s]\n",
            "\n",
            "time = 2.457 [s]\n",
            "\n",
            "time = 2.458 [s]\n",
            "\n",
            "time = 2.459 [s]\n",
            "\n",
            "time = 2.46 [s]\n",
            "\n",
            "time = 2.461 [s]\n",
            "\n",
            "time = 2.462 [s]\n",
            "\n",
            "time = 2.463 [s]\n",
            "\n",
            "time = 2.464 [s]\n",
            "\n",
            "time = 2.465 [s]\n",
            "\n",
            "time = 2.466 [s]\n",
            "\n",
            "time = 2.467 [s]\n",
            "\n",
            "time = 2.468 [s]\n",
            "\n",
            "time = 2.469 [s]\n",
            "\n",
            "time = 2.47 [s]\n",
            "\n",
            "time = 2.471 [s]\n",
            "\n",
            "time = 2.472 [s]\n",
            "\n",
            "time = 2.473 [s]\n",
            "\n",
            "time = 2.474 [s]\n",
            "\n",
            "time = 2.475 [s]\n",
            "\n",
            "time = 2.476 [s]\n",
            "\n",
            "time = 2.477 [s]\n",
            "\n",
            "time = 2.478 [s]\n",
            "\n",
            "time = 2.479 [s]\n",
            "\n",
            "time = 2.48 [s]\n",
            "\n",
            "time = 2.481 [s]\n",
            "\n",
            "time = 2.482 [s]\n",
            "\n",
            "time = 2.483 [s]\n",
            "\n",
            "time = 2.484 [s]\n",
            "\n",
            "time = 2.485 [s]\n",
            "\n",
            "time = 2.486 [s]\n",
            "\n",
            "time = 2.487 [s]\n",
            "\n",
            "time = 2.488 [s]\n",
            "\n",
            "time = 2.489 [s]\n",
            "\n",
            "time = 2.49 [s]\n",
            "\n",
            "time = 2.491 [s]\n",
            "\n",
            "time = 2.492 [s]\n",
            "\n",
            "time = 2.493 [s]\n",
            "\n",
            "time = 2.494 [s]\n",
            "\n",
            "time = 2.495 [s]\n",
            "\n",
            "time = 2.496 [s]\n",
            "\n",
            "time = 2.497 [s]\n",
            "\n",
            "time = 2.498 [s]\n",
            "\n",
            "time = 2.499 [s]\n",
            "\n",
            "time = 2.5 [s]\n",
            "\n",
            "time = 2.501 [s]\n",
            "\n",
            "time = 2.5020000000000002 [s]\n",
            "\n",
            "time = 2.503 [s]\n",
            "\n",
            "time = 2.504 [s]\n",
            "\n",
            "time = 2.505 [s]\n",
            "\n",
            "time = 2.5060000000000002 [s]\n",
            "\n",
            "time = 2.507 [s]\n",
            "\n",
            "time = 2.508 [s]\n",
            "\n",
            "time = 2.509 [s]\n",
            "\n",
            "time = 2.5100000000000002 [s]\n",
            "\n",
            "time = 2.511 [s]\n",
            "\n",
            "time = 2.512 [s]\n",
            "\n",
            "time = 2.513 [s]\n",
            "\n",
            "time = 2.5140000000000002 [s]\n",
            "\n",
            "time = 2.515 [s]\n",
            "\n",
            "time = 2.516 [s]\n",
            "\n",
            "time = 2.517 [s]\n",
            "\n",
            "time = 2.5180000000000002 [s]\n",
            "\n",
            "time = 2.519 [s]\n",
            "\n",
            "time = 2.52 [s]\n",
            "\n",
            "time = 2.521 [s]\n",
            "\n",
            "time = 2.5220000000000002 [s]\n",
            "\n",
            "time = 2.523 [s]\n",
            "\n",
            "time = 2.524 [s]\n",
            "\n",
            "time = 2.525 [s]\n",
            "\n",
            "time = 2.5260000000000002 [s]\n",
            "\n",
            "time = 2.527 [s]\n",
            "\n",
            "time = 2.528 [s]\n",
            "\n",
            "time = 2.529 [s]\n",
            "\n",
            "time = 2.5300000000000002 [s]\n",
            "\n",
            "time = 2.531 [s]\n",
            "\n",
            "time = 2.532 [s]\n",
            "\n",
            "time = 2.533 [s]\n",
            "\n",
            "time = 2.5340000000000003 [s]\n",
            "\n",
            "time = 2.535 [s]\n",
            "\n",
            "time = 2.536 [s]\n",
            "\n",
            "time = 2.537 [s]\n",
            "\n",
            "time = 2.5380000000000003 [s]\n",
            "\n",
            "time = 2.539 [s]\n",
            "\n",
            "time = 2.54 [s]\n",
            "\n",
            "time = 2.541 [s]\n",
            "\n",
            "time = 2.5420000000000003 [s]\n",
            "\n",
            "time = 2.543 [s]\n",
            "\n",
            "time = 2.544 [s]\n",
            "\n",
            "time = 2.545 [s]\n",
            "\n",
            "time = 2.5460000000000003 [s]\n",
            "\n",
            "time = 2.547 [s]\n",
            "\n",
            "time = 2.548 [s]\n",
            "\n",
            "time = 2.549 [s]\n",
            "\n",
            "time = 2.5500000000000003 [s]\n",
            "\n",
            "time = 2.551 [s]\n",
            "\n",
            "time = 2.552 [s]\n",
            "\n",
            "time = 2.553 [s]\n",
            "\n",
            "time = 2.5540000000000003 [s]\n",
            "\n",
            "time = 2.555 [s]\n",
            "\n",
            "time = 2.556 [s]\n",
            "\n",
            "time = 2.557 [s]\n",
            "\n",
            "time = 2.5580000000000003 [s]\n",
            "\n",
            "time = 2.559 [s]\n",
            "\n",
            "time = 2.56 [s]\n",
            "\n",
            "time = 2.561 [s]\n",
            "\n",
            "time = 2.562 [s]\n",
            "\n",
            "time = 2.563 [s]\n",
            "\n",
            "time = 2.564 [s]\n",
            "\n",
            "time = 2.565 [s]\n",
            "\n",
            "time = 2.566 [s]\n",
            "\n",
            "time = 2.567 [s]\n",
            "\n",
            "time = 2.568 [s]\n",
            "\n",
            "time = 2.569 [s]\n",
            "\n",
            "time = 2.57 [s]\n",
            "\n",
            "time = 2.571 [s]\n",
            "\n",
            "time = 2.572 [s]\n",
            "\n",
            "time = 2.573 [s]\n",
            "\n",
            "time = 2.574 [s]\n",
            "\n",
            "time = 2.575 [s]\n",
            "\n",
            "time = 2.576 [s]\n",
            "\n",
            "time = 2.577 [s]\n",
            "\n",
            "time = 2.578 [s]\n",
            "\n",
            "time = 2.579 [s]\n",
            "\n",
            "time = 2.58 [s]\n",
            "\n",
            "time = 2.581 [s]\n",
            "\n",
            "time = 2.582 [s]\n",
            "\n",
            "time = 2.583 [s]\n",
            "\n",
            "time = 2.584 [s]\n",
            "\n",
            "time = 2.585 [s]\n",
            "\n",
            "time = 2.586 [s]\n",
            "\n",
            "time = 2.587 [s]\n",
            "\n",
            "time = 2.588 [s]\n",
            "\n",
            "time = 2.589 [s]\n",
            "\n",
            "time = 2.59 [s]\n",
            "\n",
            "time = 2.591 [s]\n",
            "\n",
            "time = 2.592 [s]\n",
            "\n",
            "time = 2.593 [s]\n",
            "\n",
            "time = 2.594 [s]\n",
            "\n",
            "time = 2.595 [s]\n",
            "\n",
            "time = 2.596 [s]\n",
            "\n",
            "time = 2.597 [s]\n",
            "\n",
            "time = 2.598 [s]\n",
            "\n",
            "time = 2.599 [s]\n",
            "\n",
            "time = 2.6 [s]\n",
            "\n",
            "time = 2.601 [s]\n",
            "\n",
            "time = 2.602 [s]\n",
            "\n",
            "time = 2.603 [s]\n",
            "\n",
            "time = 2.604 [s]\n",
            "\n",
            "time = 2.605 [s]\n",
            "\n",
            "time = 2.606 [s]\n",
            "\n",
            "time = 2.607 [s]\n",
            "\n",
            "time = 2.608 [s]\n",
            "\n",
            "time = 2.609 [s]\n",
            "\n",
            "time = 2.61 [s]\n",
            "\n",
            "time = 2.611 [s]\n",
            "\n",
            "time = 2.612 [s]\n",
            "\n",
            "time = 2.613 [s]\n",
            "\n",
            "time = 2.614 [s]\n",
            "\n",
            "time = 2.615 [s]\n",
            "\n",
            "time = 2.616 [s]\n",
            "\n",
            "time = 2.617 [s]\n",
            "\n",
            "time = 2.618 [s]\n",
            "\n",
            "time = 2.619 [s]\n",
            "\n",
            "time = 2.62 [s]\n",
            "\n",
            "time = 2.621 [s]\n",
            "\n",
            "time = 2.622 [s]\n",
            "\n",
            "time = 2.623 [s]\n",
            "\n",
            "time = 2.624 [s]\n",
            "\n",
            "time = 2.625 [s]\n",
            "\n",
            "time = 2.626 [s]\n",
            "\n",
            "time = 2.6270000000000002 [s]\n",
            "\n",
            "time = 2.628 [s]\n",
            "\n",
            "time = 2.629 [s]\n",
            "\n",
            "time = 2.63 [s]\n",
            "\n",
            "time = 2.6310000000000002 [s]\n",
            "\n",
            "time = 2.632 [s]\n",
            "\n",
            "time = 2.633 [s]\n",
            "\n",
            "time = 2.634 [s]\n",
            "\n",
            "time = 2.6350000000000002 [s]\n",
            "\n",
            "time = 2.636 [s]\n",
            "\n",
            "time = 2.637 [s]\n",
            "\n",
            "time = 2.638 [s]\n",
            "\n",
            "time = 2.6390000000000002 [s]\n",
            "\n",
            "time = 2.64 [s]\n",
            "\n",
            "time = 2.641 [s]\n",
            "\n",
            "time = 2.642 [s]\n",
            "\n",
            "time = 2.6430000000000002 [s]\n",
            "\n",
            "time = 2.644 [s]\n",
            "\n",
            "time = 2.645 [s]\n",
            "\n",
            "time = 2.646 [s]\n",
            "\n",
            "time = 2.6470000000000002 [s]\n",
            "\n",
            "time = 2.648 [s]\n",
            "\n",
            "time = 2.649 [s]\n",
            "\n",
            "time = 2.65 [s]\n",
            "\n",
            "time = 2.6510000000000002 [s]\n",
            "\n",
            "time = 2.652 [s]\n",
            "\n",
            "time = 2.653 [s]\n",
            "\n",
            "time = 2.654 [s]\n",
            "\n",
            "time = 2.6550000000000002 [s]\n",
            "\n",
            "time = 2.656 [s]\n",
            "\n",
            "time = 2.657 [s]\n",
            "\n",
            "time = 2.658 [s]\n",
            "\n",
            "time = 2.6590000000000003 [s]\n",
            "\n",
            "time = 2.66 [s]\n",
            "\n",
            "time = 2.661 [s]\n",
            "\n",
            "time = 2.662 [s]\n",
            "\n",
            "time = 2.6630000000000003 [s]\n",
            "\n",
            "time = 2.664 [s]\n",
            "\n",
            "time = 2.665 [s]\n",
            "\n",
            "time = 2.666 [s]\n",
            "\n",
            "time = 2.6670000000000003 [s]\n",
            "\n",
            "time = 2.668 [s]\n",
            "\n",
            "time = 2.669 [s]\n",
            "\n",
            "time = 2.67 [s]\n",
            "\n",
            "time = 2.6710000000000003 [s]\n",
            "\n",
            "time = 2.672 [s]\n",
            "\n",
            "time = 2.673 [s]\n",
            "\n",
            "time = 2.674 [s]\n",
            "\n",
            "time = 2.6750000000000003 [s]\n",
            "\n",
            "time = 2.676 [s]\n",
            "\n",
            "time = 2.677 [s]\n",
            "\n",
            "time = 2.678 [s]\n",
            "\n",
            "time = 2.6790000000000003 [s]\n",
            "\n",
            "time = 2.68 [s]\n",
            "\n",
            "time = 2.681 [s]\n",
            "\n",
            "time = 2.682 [s]\n",
            "\n",
            "time = 2.6830000000000003 [s]\n",
            "\n",
            "time = 2.684 [s]\n",
            "\n",
            "time = 2.685 [s]\n",
            "\n",
            "time = 2.686 [s]\n",
            "\n",
            "time = 2.6870000000000003 [s]\n",
            "\n",
            "time = 2.688 [s]\n",
            "\n",
            "time = 2.689 [s]\n",
            "\n",
            "time = 2.69 [s]\n",
            "\n",
            "time = 2.691 [s]\n",
            "\n",
            "time = 2.692 [s]\n",
            "\n",
            "time = 2.693 [s]\n",
            "\n",
            "time = 2.694 [s]\n",
            "\n",
            "time = 2.695 [s]\n",
            "\n",
            "time = 2.696 [s]\n",
            "\n",
            "time = 2.697 [s]\n",
            "\n",
            "time = 2.698 [s]\n",
            "\n",
            "time = 2.699 [s]\n",
            "\n",
            "time = 2.7 [s]\n",
            "\n",
            "time = 2.701 [s]\n",
            "\n",
            "time = 2.702 [s]\n",
            "\n",
            "time = 2.703 [s]\n",
            "\n",
            "time = 2.704 [s]\n",
            "\n",
            "time = 2.705 [s]\n",
            "\n",
            "time = 2.706 [s]\n",
            "\n",
            "time = 2.707 [s]\n",
            "\n",
            "time = 2.708 [s]\n",
            "\n",
            "time = 2.709 [s]\n",
            "\n",
            "time = 2.71 [s]\n",
            "\n",
            "time = 2.711 [s]\n",
            "\n",
            "time = 2.712 [s]\n",
            "\n",
            "time = 2.713 [s]\n",
            "\n",
            "time = 2.714 [s]\n",
            "\n",
            "time = 2.715 [s]\n",
            "\n",
            "time = 2.716 [s]\n",
            "\n",
            "time = 2.717 [s]\n",
            "\n",
            "time = 2.718 [s]\n",
            "\n",
            "time = 2.719 [s]\n",
            "\n",
            "time = 2.72 [s]\n",
            "\n",
            "time = 2.721 [s]\n",
            "\n",
            "time = 2.722 [s]\n",
            "\n",
            "time = 2.723 [s]\n",
            "\n",
            "time = 2.724 [s]\n",
            "\n",
            "time = 2.725 [s]\n",
            "\n",
            "time = 2.726 [s]\n",
            "\n",
            "time = 2.727 [s]\n",
            "\n",
            "time = 2.728 [s]\n",
            "\n",
            "time = 2.729 [s]\n",
            "\n",
            "time = 2.73 [s]\n",
            "\n",
            "time = 2.731 [s]\n",
            "\n",
            "time = 2.732 [s]\n",
            "\n",
            "time = 2.733 [s]\n",
            "\n",
            "time = 2.734 [s]\n",
            "\n",
            "time = 2.735 [s]\n",
            "\n",
            "time = 2.736 [s]\n",
            "\n",
            "time = 2.737 [s]\n",
            "\n",
            "time = 2.738 [s]\n",
            "\n",
            "time = 2.739 [s]\n",
            "\n",
            "time = 2.74 [s]\n",
            "\n",
            "time = 2.741 [s]\n",
            "\n",
            "time = 2.742 [s]\n",
            "\n",
            "time = 2.743 [s]\n",
            "\n",
            "time = 2.744 [s]\n",
            "\n",
            "time = 2.745 [s]\n",
            "\n",
            "time = 2.746 [s]\n",
            "\n",
            "time = 2.747 [s]\n",
            "\n",
            "time = 2.748 [s]\n",
            "\n",
            "time = 2.749 [s]\n",
            "\n",
            "time = 2.75 [s]\n",
            "\n",
            "time = 2.751 [s]\n",
            "\n",
            "time = 2.7520000000000002 [s]\n",
            "\n",
            "time = 2.753 [s]\n",
            "\n",
            "time = 2.754 [s]\n",
            "\n",
            "time = 2.755 [s]\n",
            "\n",
            "time = 2.7560000000000002 [s]\n",
            "\n",
            "time = 2.757 [s]\n",
            "\n",
            "time = 2.758 [s]\n",
            "\n",
            "time = 2.759 [s]\n",
            "\n",
            "time = 2.7600000000000002 [s]\n",
            "\n",
            "time = 2.761 [s]\n",
            "\n",
            "time = 2.762 [s]\n",
            "\n",
            "time = 2.763 [s]\n",
            "\n",
            "time = 2.7640000000000002 [s]\n",
            "\n",
            "time = 2.765 [s]\n",
            "\n",
            "time = 2.766 [s]\n",
            "\n",
            "time = 2.767 [s]\n",
            "\n",
            "time = 2.7680000000000002 [s]\n",
            "\n",
            "time = 2.769 [s]\n",
            "\n",
            "time = 2.77 [s]\n",
            "\n",
            "time = 2.771 [s]\n",
            "\n",
            "time = 2.7720000000000002 [s]\n",
            "\n",
            "time = 2.773 [s]\n",
            "\n",
            "time = 2.774 [s]\n",
            "\n",
            "time = 2.775 [s]\n",
            "\n",
            "time = 2.7760000000000002 [s]\n",
            "\n",
            "time = 2.777 [s]\n",
            "\n",
            "time = 2.778 [s]\n",
            "\n",
            "time = 2.779 [s]\n",
            "\n",
            "time = 2.7800000000000002 [s]\n",
            "\n",
            "time = 2.781 [s]\n",
            "\n",
            "time = 2.782 [s]\n",
            "\n",
            "time = 2.783 [s]\n",
            "\n",
            "time = 2.7840000000000003 [s]\n",
            "\n",
            "time = 2.785 [s]\n",
            "\n",
            "time = 2.786 [s]\n",
            "\n",
            "time = 2.787 [s]\n",
            "\n",
            "time = 2.7880000000000003 [s]\n",
            "\n",
            "time = 2.789 [s]\n",
            "\n",
            "time = 2.79 [s]\n",
            "\n",
            "time = 2.791 [s]\n",
            "\n",
            "time = 2.7920000000000003 [s]\n",
            "\n",
            "time = 2.793 [s]\n",
            "\n",
            "time = 2.794 [s]\n",
            "\n",
            "time = 2.795 [s]\n",
            "\n",
            "time = 2.7960000000000003 [s]\n",
            "\n",
            "time = 2.797 [s]\n",
            "\n",
            "time = 2.798 [s]\n",
            "\n",
            "time = 2.799 [s]\n",
            "\n",
            "time = 2.8000000000000003 [s]\n",
            "\n",
            "time = 2.801 [s]\n",
            "\n",
            "time = 2.802 [s]\n",
            "\n",
            "time = 2.803 [s]\n",
            "\n",
            "time = 2.8040000000000003 [s]\n",
            "\n",
            "time = 2.805 [s]\n",
            "\n",
            "time = 2.806 [s]\n",
            "\n",
            "time = 2.807 [s]\n",
            "\n",
            "time = 2.8080000000000003 [s]\n",
            "\n",
            "time = 2.809 [s]\n",
            "\n",
            "time = 2.81 [s]\n",
            "\n",
            "time = 2.811 [s]\n",
            "\n",
            "time = 2.8120000000000003 [s]\n",
            "\n",
            "time = 2.813 [s]\n",
            "\n",
            "time = 2.814 [s]\n",
            "\n",
            "time = 2.815 [s]\n",
            "\n",
            "time = 2.816 [s]\n",
            "\n",
            "time = 2.817 [s]\n",
            "\n",
            "time = 2.818 [s]\n",
            "\n",
            "time = 2.819 [s]\n",
            "\n",
            "time = 2.82 [s]\n",
            "\n",
            "time = 2.821 [s]\n",
            "\n",
            "time = 2.822 [s]\n",
            "\n",
            "time = 2.823 [s]\n",
            "\n",
            "time = 2.824 [s]\n",
            "\n",
            "time = 2.825 [s]\n",
            "\n",
            "time = 2.826 [s]\n",
            "\n",
            "time = 2.827 [s]\n",
            "\n",
            "time = 2.828 [s]\n",
            "\n",
            "time = 2.829 [s]\n",
            "\n",
            "time = 2.83 [s]\n",
            "\n",
            "time = 2.831 [s]\n",
            "\n",
            "time = 2.832 [s]\n",
            "\n",
            "time = 2.833 [s]\n",
            "\n",
            "time = 2.834 [s]\n",
            "\n",
            "time = 2.835 [s]\n",
            "\n",
            "time = 2.836 [s]\n",
            "\n",
            "time = 2.837 [s]\n",
            "\n",
            "time = 2.838 [s]\n",
            "\n",
            "time = 2.839 [s]\n",
            "\n",
            "time = 2.84 [s]\n",
            "\n",
            "time = 2.841 [s]\n",
            "\n",
            "time = 2.842 [s]\n",
            "\n",
            "time = 2.843 [s]\n",
            "\n",
            "time = 2.844 [s]\n",
            "\n",
            "time = 2.845 [s]\n",
            "\n",
            "time = 2.846 [s]\n",
            "\n",
            "time = 2.847 [s]\n",
            "\n",
            "time = 2.848 [s]\n",
            "\n",
            "time = 2.849 [s]\n",
            "\n",
            "time = 2.85 [s]\n",
            "\n",
            "time = 2.851 [s]\n",
            "\n",
            "time = 2.852 [s]\n",
            "\n",
            "time = 2.853 [s]\n",
            "\n",
            "time = 2.854 [s]\n",
            "\n",
            "time = 2.855 [s]\n",
            "\n",
            "time = 2.856 [s]\n",
            "\n",
            "time = 2.857 [s]\n",
            "\n",
            "time = 2.858 [s]\n",
            "\n",
            "time = 2.859 [s]\n",
            "\n",
            "time = 2.86 [s]\n",
            "\n",
            "time = 2.861 [s]\n",
            "\n",
            "time = 2.862 [s]\n",
            "\n",
            "time = 2.863 [s]\n",
            "\n",
            "time = 2.864 [s]\n",
            "\n",
            "time = 2.865 [s]\n",
            "\n",
            "time = 2.866 [s]\n",
            "\n",
            "time = 2.867 [s]\n",
            "\n",
            "time = 2.868 [s]\n",
            "\n",
            "time = 2.869 [s]\n",
            "\n",
            "time = 2.87 [s]\n",
            "\n",
            "time = 2.871 [s]\n",
            "\n",
            "time = 2.872 [s]\n",
            "\n",
            "time = 2.873 [s]\n",
            "\n",
            "time = 2.874 [s]\n",
            "\n",
            "time = 2.875 [s]\n",
            "\n",
            "time = 2.876 [s]\n",
            "\n",
            "time = 2.8770000000000002 [s]\n",
            "\n",
            "time = 2.878 [s]\n",
            "\n",
            "time = 2.879 [s]\n",
            "\n",
            "time = 2.88 [s]\n",
            "\n",
            "time = 2.8810000000000002 [s]\n",
            "\n",
            "time = 2.882 [s]\n",
            "\n",
            "time = 2.883 [s]\n",
            "\n",
            "time = 2.884 [s]\n",
            "\n",
            "time = 2.8850000000000002 [s]\n",
            "\n",
            "time = 2.886 [s]\n",
            "\n",
            "time = 2.887 [s]\n",
            "\n",
            "time = 2.888 [s]\n",
            "\n",
            "time = 2.8890000000000002 [s]\n",
            "\n",
            "time = 2.89 [s]\n",
            "\n",
            "time = 2.891 [s]\n",
            "\n",
            "time = 2.892 [s]\n",
            "\n",
            "time = 2.8930000000000002 [s]\n",
            "\n",
            "time = 2.894 [s]\n",
            "\n",
            "time = 2.895 [s]\n",
            "\n",
            "time = 2.896 [s]\n",
            "\n",
            "time = 2.8970000000000002 [s]\n",
            "\n",
            "time = 2.898 [s]\n",
            "\n",
            "time = 2.899 [s]\n",
            "\n",
            "time = 2.9 [s]\n",
            "\n",
            "time = 2.9010000000000002 [s]\n",
            "\n",
            "time = 2.902 [s]\n",
            "\n",
            "time = 2.903 [s]\n",
            "\n",
            "time = 2.904 [s]\n",
            "\n",
            "time = 2.9050000000000002 [s]\n",
            "\n",
            "time = 2.906 [s]\n",
            "\n",
            "time = 2.907 [s]\n",
            "\n",
            "time = 2.908 [s]\n",
            "\n",
            "time = 2.9090000000000003 [s]\n",
            "\n",
            "time = 2.91 [s]\n",
            "\n",
            "time = 2.911 [s]\n",
            "\n",
            "time = 2.912 [s]\n",
            "\n",
            "time = 2.9130000000000003 [s]\n",
            "\n",
            "time = 2.914 [s]\n",
            "\n",
            "time = 2.915 [s]\n",
            "\n",
            "time = 2.916 [s]\n",
            "\n",
            "time = 2.9170000000000003 [s]\n",
            "\n",
            "time = 2.918 [s]\n",
            "\n",
            "time = 2.919 [s]\n",
            "\n",
            "time = 2.92 [s]\n",
            "\n",
            "time = 2.9210000000000003 [s]\n",
            "\n",
            "time = 2.922 [s]\n",
            "\n",
            "time = 2.923 [s]\n",
            "\n",
            "time = 2.924 [s]\n",
            "\n",
            "time = 2.9250000000000003 [s]\n",
            "\n",
            "time = 2.926 [s]\n",
            "\n",
            "time = 2.927 [s]\n",
            "\n",
            "time = 2.928 [s]\n",
            "\n",
            "time = 2.9290000000000003 [s]\n",
            "\n",
            "time = 2.93 [s]\n",
            "\n",
            "time = 2.931 [s]\n",
            "\n",
            "time = 2.932 [s]\n",
            "\n",
            "time = 2.9330000000000003 [s]\n",
            "\n",
            "time = 2.934 [s]\n",
            "\n",
            "time = 2.935 [s]\n",
            "\n",
            "time = 2.936 [s]\n",
            "\n",
            "time = 2.9370000000000003 [s]\n",
            "\n",
            "time = 2.938 [s]\n",
            "\n",
            "time = 2.939 [s]\n",
            "\n",
            "time = 2.94 [s]\n",
            "\n",
            "time = 2.9410000000000003 [s]\n",
            "\n",
            "time = 2.942 [s]\n",
            "\n",
            "time = 2.943 [s]\n",
            "\n",
            "time = 2.944 [s]\n",
            "\n",
            "time = 2.945 [s]\n",
            "\n",
            "time = 2.946 [s]\n",
            "\n",
            "time = 2.947 [s]\n",
            "\n",
            "time = 2.948 [s]\n",
            "\n",
            "time = 2.949 [s]\n",
            "\n",
            "time = 2.95 [s]\n",
            "\n",
            "time = 2.951 [s]\n",
            "\n",
            "time = 2.952 [s]\n",
            "\n",
            "time = 2.953 [s]\n",
            "\n",
            "time = 2.954 [s]\n",
            "\n",
            "time = 2.955 [s]\n",
            "\n",
            "time = 2.956 [s]\n",
            "\n",
            "time = 2.957 [s]\n",
            "\n",
            "time = 2.958 [s]\n",
            "\n",
            "time = 2.959 [s]\n",
            "\n",
            "time = 2.96 [s]\n",
            "\n",
            "time = 2.961 [s]\n",
            "\n",
            "time = 2.962 [s]\n",
            "\n",
            "time = 2.963 [s]\n",
            "\n",
            "time = 2.964 [s]\n",
            "\n",
            "time = 2.965 [s]\n",
            "\n",
            "time = 2.966 [s]\n",
            "\n",
            "time = 2.967 [s]\n",
            "\n",
            "time = 2.968 [s]\n",
            "\n",
            "time = 2.969 [s]\n",
            "\n",
            "time = 2.97 [s]\n",
            "\n",
            "time = 2.971 [s]\n",
            "\n",
            "time = 2.972 [s]\n",
            "\n",
            "time = 2.973 [s]\n",
            "\n",
            "time = 2.974 [s]\n",
            "\n",
            "time = 2.975 [s]\n",
            "\n",
            "time = 2.976 [s]\n",
            "\n",
            "time = 2.977 [s]\n",
            "\n",
            "time = 2.978 [s]\n",
            "\n",
            "time = 2.979 [s]\n",
            "\n",
            "time = 2.98 [s]\n",
            "\n",
            "time = 2.981 [s]\n",
            "\n",
            "time = 2.982 [s]\n",
            "\n",
            "time = 2.983 [s]\n",
            "\n",
            "time = 2.984 [s]\n",
            "\n",
            "time = 2.985 [s]\n",
            "\n",
            "time = 2.986 [s]\n",
            "\n",
            "time = 2.987 [s]\n",
            "\n",
            "time = 2.988 [s]\n",
            "\n",
            "time = 2.989 [s]\n",
            "\n",
            "time = 2.99 [s]\n",
            "\n",
            "time = 2.991 [s]\n",
            "\n",
            "time = 2.992 [s]\n",
            "\n",
            "time = 2.993 [s]\n",
            "\n",
            "time = 2.994 [s]\n",
            "\n",
            "time = 2.995 [s]\n",
            "\n",
            "time = 2.996 [s]\n",
            "\n",
            "time = 2.997 [s]\n",
            "\n",
            "time = 2.998 [s]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhH7ab-SMeGc"
      },
      "source": [
        "Speed = []\n",
        "for j in range(0,N):\n",
        "  pj = Ensemble_Particles[j]\n",
        "  v = pj.velocity\n",
        "  Speed.append(v)\n",
        "\n",
        "Speed = np.array(Speed )\n",
        "name = 'speed' + str(N) + '.mat'\n",
        "scipy.io.savemat(name, {'Speed': Speed})"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Speed[-1][:,0]**2 + Speed[-1][:,1]**2"
      ],
      "metadata": {
        "id": "RCPiJyCFRjjA",
        "outputId": "95f0d3dd-1cd6-49c3-e09e-adde69a3e64b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 96.45908389, 152.26938762, 152.26938762, ...,   0.        ,\n",
              "         0.        ,   0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(Speed[-1][:,0]**2 + Speed[-1][:,1]**2, bins=10, color='blue', edgecolor='black')"
      ],
      "metadata": {
        "id": "emvkw7i_RaJd",
        "outputId": "c65c593d-5865-4a41-f96f-a3c2dc06d173",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([28188.,   717.,    30.,     0.,     0.,     0.,     0.,   263.,\n",
              "            0.,   802.]),\n",
              " array([  0.        ,  99.14047559, 198.28095118, 297.42142677,\n",
              "        396.56190236, 495.70237795, 594.84285354, 693.98332913,\n",
              "        793.12380472, 892.26428031, 991.4047559 ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yKXzDT3DZkh"
      },
      "source": [
        "# Distribución de Boltzman < velocidades $f(v)$ >  :\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUCNiz4I5A1Q"
      },
      "source": [
        "#Fitting function\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "def func(v, A):\n",
        "  p = A*v*np.exp(-(A*v**2)/(2))\n",
        "  return p\n",
        "\n",
        "Ao = 14.\n",
        "\n",
        "\n",
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbmAYSuyDj5K",
        "outputId": "c2e3ec6f-d3e5-4d4d-8a28-e4a67c993bae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        }
      },
      "source": [
        "V = []\n",
        "\n",
        "for j in range(0,N):\n",
        "  pj = Ensemble_Particles[j]\n",
        "  v = pj.velocity[-1]\n",
        "  vm = np.sqrt(v[0]**2 + v[1]**2)\n",
        "  V.append(vm)\n",
        "\n",
        "V = np.round(V, 1)\n",
        "Vl = V.tolist()\n",
        "Vs = set(Vl)\n",
        "VL = list(Vs)\n",
        "\n",
        "Vv = []\n",
        "for i in range(0,len(VL)):\n",
        "  n = np.where(V == VL[i])\n",
        "  Vv.append(len(n[0]))\n",
        "print(VL)\n",
        "print(Vv)\n",
        "\n",
        "params = stats.maxwell.fit(V, floc=0)\n",
        "print(params)\n",
        "\n",
        "A = plt.hist(V,len(V), facecolor='g', alpha=0.3, density=True)\n",
        "print(A[1])\n",
        "\n",
        "x = np.linspace(0, 60, 100)\n",
        "plt.plot(A[0],A[1])\n",
        "#plt.plot(x, stats.maxwell.pdf(x, *params), lw=3)\n",
        "\n",
        "plt.grid()\n",
        "plt.xlabel('speed')\n",
        "plt.ylabel('N particles')\n",
        "plt.show()\n",
        "\n",
        ""
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[32.2, 1.8, 17.8]\n",
            "[1, 1, 1]\n",
            "(0, 12.278809755338653)\n",
            "[ 1.8        11.93333333 22.06666667 32.2       ]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "x and y must have same first dimension, but have shapes (3,) and (4,)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-da706328469b>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;31m#plt.plot(x, stats.maxwell.pdf(x, *params), lw=3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2810\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2811\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2812\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   2813\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2814\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1686\u001b[0m         \"\"\"\n\u001b[1;32m   1687\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1688\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1689\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1690\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m             yield from self._plot_args(\n\u001b[0m\u001b[1;32m    312\u001b[0m                 this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey)\n\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    505\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    506\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (3,) and (4,)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f387-uSevVxz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "f16af4f2-c200-4acb-c5a2-cd94c2475151"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.gca()\n",
        "\n",
        "def actualizar(i):\n",
        "  i = i*FPS\n",
        "  ax.clear()\n",
        "  V = []\n",
        "  for j in range(0,N):\n",
        "    pj = Ensemble_Particles[j]\n",
        "    v = pj.velocity[i]\n",
        "    vm = np.sqrt(v[0]**2 + v[1]**2)\n",
        "    V.append(vm)\n",
        "  V = np.round(V, 1)\n",
        "  plt.hist(V,45, facecolor='g', alpha=0.3, density=True)\n",
        "\n",
        "ani = animation.FuncAnimation(fig,\n",
        "\t\t\t\t\t\t\tfunc = actualizar,\n",
        "\t\t\t\t\t\t\tframes = int(len(time)/FPS),\n",
        "\t\t\t\t\t\t  blit=False,\n",
        "\t\t\t\t\t\t\trepeat=True)\n",
        "\n",
        "ani.save('Histogram_speed.gif', writer = \"pillow\", fps=10)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOaD5k1Z1rkX"
      },
      "source": [
        "# Animación:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIPEWi2g1wZm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "c27ff9c1-4606-4198-c4bc-3eba83934731"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.gca()\n",
        "\n",
        "def actualizar(i):\n",
        "  i = i*FPS\n",
        "  ax.clear()\n",
        "  ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
        "  ax.set_xlim(inferior_box -2, superior_box)\n",
        "  ax.set_ylim(inferior_box -2 , superior_box)\n",
        "  ax.add_patch(\n",
        "      patches.Rectangle((inferior_box, inferior_box),\n",
        "                        superior_box-inferior_box, superior_box-inferior_box,\n",
        "                        edgecolor='black', facecolor=\"none\"))\n",
        "  AB = []\n",
        "  for j in range(0,N):\n",
        "    pj = Ensemble_Particles[j]\n",
        "    aj = pj.position[i,0]\n",
        "    bj = pj.position[i,1]\n",
        "    ax.add_patch(plt.Circle((aj,bj), pj.radius))\n",
        "    plt.scatter(pj.position[i,0],pj.position[i,1],s=(pj.radius),c='b')\n",
        "    AB.append((aj,bj))\n",
        "\n",
        "ani = animation.FuncAnimation(fig,\n",
        "\t\t\t\t\t\t\tfunc = actualizar,\n",
        "\t\t\t\t\t\t\tframes = int(len(time)/FPS),\n",
        "\t\t\t\t\t\t  blit=False,\n",
        "\t\t\t\t\t\t\trepeat=True)\n",
        "\n",
        "ani.save('Ideal_gas.gif', writer = \"pillow\", fps=10)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# chat gpt\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.gca()\n",
        "ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
        "ax.set_xlim(inferior_box - 2, superior_box)\n",
        "ax.set_ylim(inferior_box - 2, superior_box)\n",
        "ax.add_patch(\n",
        "    patches.Rectangle((inferior_box, inferior_box),\n",
        "                      superior_box - inferior_box, superior_box - inferior_box,\n",
        "                      edgecolor='black', facecolor=\"none\"))\n",
        "scat = ax.scatter([], [], s=[], c='b')\n",
        "\n",
        "def actualizar(i):\n",
        "    idx = i * FPS\n",
        "    AB = np.zeros((N, 2))\n",
        "    for j in range(N):\n",
        "        pj = Ensemble_Particles[j]\n",
        "        AB[j] = pj.position[idx]\n",
        "    scat.set_offsets(AB)\n",
        "    scat.set_sizes(Ensemble_Particles[0].radius * 100)  # Ajusta el tamaño de las partículas\n",
        "    return scat,\n",
        "\n",
        "ani = animation.FuncAnimation(fig,\n",
        "                              func=actualizar,\n",
        "                              frames=int(len(time) / FPS),\n",
        "                              blit=True,  # Indica que solo los objetos que cambian necesitan ser actualizados\n",
        "                              repeat=True)\n",
        "\n",
        "ani.save('Ideal_gas.gif', writer=\"pillow\", fps=10)\n"
      ],
      "metadata": {
        "id": "thjdO5HDQtnR",
        "outputId": "307936dd-87ce-4abe-cd43-1ba22c71207a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "len() of unsized object",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-8cacf9a81507>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mscat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m ani = animation.FuncAnimation(fig,\n\u001b[0m\u001b[1;32m     25\u001b[0m                               \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mactualizar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m                               \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mFPS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, func, frames, init_func, fargs, save_count, cache_frame_data, **kwargs)\u001b[0m\n\u001b[1;32m   1692\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1694\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1696\u001b[0m         \u001b[0;31m# Need to reset the saved seq, since right now it will contain data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent_source\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1415\u001b[0m             \u001b[0mevent_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1416\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_source\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevent_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, event_source, blit)\u001b[0m\n\u001b[1;32m    878\u001b[0m                                                       self._stop)\n\u001b[1;32m    879\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_blit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__del__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_setup_blit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1208\u001b[0m         \u001b[0;31m# _post_draw needs to be called first to initialize the renderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1209\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1210\u001b[0m         \u001b[0;31m# Then we need to clear the Frame for the initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1211\u001b[0m         \u001b[0;31m# This is typically handled in _on_resize because QT and Tk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_post_draw\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1162\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;31m# The rest of the code in this class is to facilitate easy blitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3148\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3150\u001b[0;31m         \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"draw_event\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;34m\"\"\"Generate an event with name ``self.name`` on ``self.canvas``.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 )\n\u001b[1;32m   1747\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1749\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1765\u001b[0m         \u001b[0;31m# Call the func with framedata and args. If blitting is desired,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1766\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1767\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1768\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1769\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-38-8cacf9a81507>\u001b[0m in \u001b[0;36mactualizar\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mAB\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mscat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mscat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEnsemble_Particles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradius\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Ajusta el tamaño de las partículas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mscat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_sizes\u001b[0;34m(self, sizes, dpi)\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m             \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: len() of unsized object"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function _draw_all_if_interactive at 0x7f4fd3a35000> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "len() of unsized object",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_draw_all_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    969\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallow_rasterization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_sizes\u001b[0;34m(self, sizes, dpi)\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m             \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: len() of unsized object"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "len() of unsized object",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2340\u001b[0m                 )\n\u001b[1;32m   2341\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2342\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    969\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallow_rasterization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_sizes\u001b[0;34m(self, sizes, dpi)\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m             \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: len() of unsized object"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}