Whale_Clicks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**1. Simple example - compute coordinates of the source; three hydrophones**"
      ],
      "metadata": {
        "id": "IL0gGCBMcH_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "\n",
        "def equations(vars):\n",
        "  x, y = vars\n",
        "  c = 1500\n",
        "  x1 = 0\n",
        "  y1 = 0\n",
        "  x2 = 0\n",
        "  y2 = 1\n",
        "  x3 = 1\n",
        "  y3 = 1\n",
        "  dt12 = 0.00005\n",
        "  dt13 = 2*dt12\n",
        "  eq1 = c*dt12+((x1-x)**2+(y1-y)**2)**0.5-((x2-x)**2+(y2-y)**2)**0.5\n",
        "  eq2 = c*dt13+((x1-x)**2+(y1-y)**2)**0.5-((x3-x)**2+(y3-y)**2)**0.5\n",
        "  return [eq1, eq2]\n",
        "\n",
        "def error(vars):\n",
        "\teqs = equations(vars)\n",
        "\treturn eqs[0]**2+eqs[1]**2\n",
        "\n",
        "initial_guess = [0, 0]\n",
        "\n",
        "result = minimize(error, initial_guess, method='SLSQP')\n",
        "\n",
        "print(result.x)\n",
        "print(result.fun)\n",
        "print(result.nit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dONaqb2oAXTw",
        "outputId": "b26cb381-6a1c-4d2b-e447-235bbb7b347c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.44429149 0.44977348]\n",
            "3.517227286836972e-08\n",
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Compute coordinates for different speed of sound; three hydrophones**"
      ],
      "metadata": {
        "id": "oxl3Q6X5ie_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "list_c = []\n",
        "list_loss = []\n",
        "\n",
        "for c in range(1450, 1550):\n",
        "  def equations(vars):\n",
        "    x, y = vars\n",
        "    x1 = 0\n",
        "    y1 = 0\n",
        "    x2 = 1\n",
        "    y2 = 0\n",
        "    x3 = 1\n",
        "    y3 = 1\n",
        "    dt12 = 0.00005\n",
        "    dt13 = 2*dt12\n",
        "    eq1 = c*dt12+((x1-x)**2+(y1-y)**2)**0.5-((x2-x)**2+(y2-y)**2)**0.5\n",
        "    eq2 = c*dt13+((x1-x)**2+(y1-y)**2)**0.5-((x3-x)**2+(y3-y)**2)**0.5\n",
        "    return [eq1, eq2]\n",
        "\n",
        "  def error(vars):\n",
        "    eqs = equations(vars)\n",
        "    return eqs[0]**2 + eqs[1]**2\n",
        "\n",
        "  initial_quess = [0, 0]\n",
        "\n",
        "  result = minimize(error, initial_quess, method='SLSQP')\n",
        "  x, y = result.x\n",
        "  loss = result.fun\n",
        "  #print(\"Optimised values:\")\n",
        "  #print(\"x = \", x)\n",
        "  #print(\"y = \", y)\n",
        "  list_c.append(c)\n",
        "  list_loss.append(loss)\n",
        "\n",
        "plt.plot(list_c, list_loss)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1h9-ECgoikXI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "de5cc1c7-92ef-40b7-a950-359649798555"
      },
      "execution_count": null,
      "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",
      "source": [
        "**3. Compute coordinates of the source and speed of sound; four hydrophones**"
      ],
      "metadata": {
        "id": "RXAPHOVq_2g-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShhGjoJHMG5A"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "from scipy.spatial import distance\n",
        "\n",
        "def equations(vars):\n",
        "  x, y, c = vars\n",
        "  x1 = 0\n",
        "  y1 = 0\n",
        "  x2 = 1\n",
        "  y2 = 0\n",
        "  x3 = 1\n",
        "  y3 = 1\n",
        "  x4 = 0\n",
        "  y4 = 1\n",
        "  dt12 = 0.00005\n",
        "  dt13 = 2*dt12\n",
        "  dt14 = 2*dt12 / 3\n",
        "  eq1 = c*dt12+((x1-x)**2+(y1-y)**2)**0.5-((x2-x)**2+(y2-y)**2)**0.5\n",
        "  eq2 = c*dt13+((x1-x)**2+(y1-y)**2)**0.5-((x3-x)**2+(y3-y)**2)**0.5\n",
        "  eq3 = c*dt14+((x1-x)**2+(y1-y)**2)**0.5-((x4-x)**2+(y4-y)**2)**0.5\n",
        "  return [eq1, eq2, eq3]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def error(vars):\n",
        "\teqs = equations(vars)\n",
        "\treturn eqs[0]**2+eqs[1]**2+eqs[2]**2"
      ],
      "metadata": {
        "id": "qQjNFftCRXoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "initial_guess = [0, 0, 1480]\n",
        "\n",
        "result = minimize(error, initial_guess, method='SLSQP')\n",
        "\n",
        "print(result.x)\n",
        "print(result.fun)\n",
        "print(result.nit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7-4J3ABRZc3",
        "outputId": "294123dc-5977-45b7-c0bf-a8215769dd73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4.42767754e-01 4.60707274e-01 1.47999994e+03]\n",
            "0.00032283361712857206\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Hydrophones distances**"
      ],
      "metadata": {
        "id": "Z9A7pguAkWli"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![hydrophones 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)"
      ],
      "metadata": {
        "id": "JfwIaHv1OrCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "from scipy.spatial import distance\n",
        "\n",
        "def equations_hydrophones_coordinates(vars):\n",
        "  x1, y1, z1, x2, y2, x3, y3, x4 ,y4, z5 = vars\n",
        "\n",
        "  H1 = np.array([x1, y1, z1])\n",
        "  H2 = np.array([x2, y2, z1])\n",
        "  H3 = np.array([x3, y3, 0])\n",
        "  H4 = np.array([x4, y4, 0])\n",
        "  H5 = np.array([0, 0, z5])\n",
        "\n",
        "  # Distances are given in centimeters\n",
        "  H1H2 = 108.5\n",
        "  H1H3 = 103.5\n",
        "  H1H4 = 102.3\n",
        "  H1H5 = 191.2\n",
        "\n",
        "  H2H3 = 103.2\n",
        "  H2H4 = 105.6\n",
        "  H2H5 = 192.5\n",
        "\n",
        "  H3H4 = 107.5\n",
        "  H3H5 = 125.5\n",
        "\n",
        "  H4H5 = 125\n",
        "\n",
        "  eq1 = distance.euclidean(H1, H2) - H1H2\n",
        "  eq2 = distance.euclidean(H1, H3) - H1H3\n",
        "  eq3 = distance.euclidean(H1, H4) - H1H4\n",
        "  eq4 = distance.euclidean(H1, H5) - H1H5\n",
        "  eq5 = distance.euclidean(H2, H3) - H2H3\n",
        "  eq6 = distance.euclidean(H2, H4) - H2H4\n",
        "  eq7 = distance.euclidean(H2, H5) - H2H5\n",
        "  eq8 = distance.euclidean(H3, H4) - H3H4\n",
        "  eq9 = distance.euclidean(H3, H5) - H3H5\n",
        "  eq10 = distance.euclidean(H4, H5) - H4H5\n",
        "\n",
        "\n",
        "  return [eq1, eq2, eq3, eq4, eq5, eq6, eq7, eq8, eq9, eq10]\n",
        "\n",
        "def error(vars):\n",
        "\teqs = equations_hydrophones_coordinates(vars)\n",
        "\treturn eqs[0]**2+eqs[1]**2+eqs[2]**2+eqs[3]**2+eqs[4]**2+eqs[5]**2+eqs[2]**2+eqs[7]**2+eqs[8]**2+eqs[9]**2\n",
        "\n",
        "\n",
        "initial_guess = [50, 0, -50, -50, 0, 0, 50, 0, -50, 100]\n",
        "result = minimize(error, initial_guess, method='SLSQP')\n",
        "\n",
        "print(\"Loss: \", result.fun)\n",
        "print(\"Iterations: \", result.nit)\n",
        "print('H1: ' + \"{:.2f}\".format(result.x[0]) + ', ' + \"{:.2f}\".format(result.x[1]) + ', ' + \"{:.2f}\".format(result.x[2]))\n",
        "print('H2: ' + \"{:.2f}\".format(result.x[3]) + ', ' + \"{:.2f}\".format(result.x[4]) + ', ' + \"{:.2f}\".format(result.x[2]))\n",
        "print('H3: ' + \"{:.2f}\".format(result.x[5]) + ', ' + \"{:.2f}\".format(result.x[6]) + ', ' + \"{:.2f}\".format(0))\n",
        "print('H4: ' + \"{:.2f}\".format(result.x[7]) + ', ' + \"{:.2f}\".format(result.x[8]) + ', ' + \"{:.2f}\".format(0))\n",
        "print('H5: ' + \"{:.2f}\".format(0) + ', ' + \"{:.2f}\".format(0) + ', ' + \"{:.2f}\".format(result.x[9]))"
      ],
      "metadata": {
        "id": "oL02eTFXkdZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f644e0b-3b8c-4350-d6b9-1678ed181e06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss:  9.924976106672042e-09\n",
            "Iterations:  20\n",
            "H1: 54.78, 0.31, -70.07\n",
            "H2: -53.71, 2.06, -70.07\n",
            "H3: 1.10, 54.36, 0.00\n",
            "H4: 2.82, -53.13, 0.00\n",
            "H5: 0.00, 0.00, 113.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e_9CPcYNpi7e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Compute Cartesian coordinates from real recordings**"
      ],
      "metadata": {
        "id": "SEMyl1c1Rh5t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "from scipy.spatial import distance\n",
        "import math\n",
        "\n",
        "def equations_recordings_cartesian(vars):\n",
        "  x, y, z = vars\n",
        "  c = 1490\n",
        "\n",
        "  source = np.array([x, y, z])\n",
        "\n",
        "\n",
        "  H1 = np.array([54.78, 0.31, -70.07]) / 100\n",
        "  H2 = np.array([-53.71, 2.06, -70.07]) / 100\n",
        "  H3 = np.array([1.10, 54.36, 0]) / 100\n",
        "  H4 = np.array([2.82, -53.13, 0]) / 100\n",
        "  H5 = np.array([0, 0, 113.11]) / 100\n",
        "\n",
        "  dt12_s = 98 #samples\n",
        "  dt13_s = -26\n",
        "  dt14_s = -72\n",
        "  dt15_s = -203\n",
        "\n",
        "  sampling_rate = 256000 # samples/s, kHz, magnitude resolution 32 bits\n",
        "\n",
        "  dt12 = dt12_s / sampling_rate\n",
        "  dt13 = dt13_s / sampling_rate\n",
        "  dt14 = dt14_s / sampling_rate\n",
        "  dt15 = dt15_s / sampling_rate\n",
        "\n",
        "  eq1 = c*dt12 + distance.euclidean(source, H1) - distance.euclidean(source, H2)\n",
        "  eq2 = c*dt13 + distance.euclidean(source, H1) - distance.euclidean(source, H3)\n",
        "  eq3 = c*dt14 + distance.euclidean(source, H1) - distance.euclidean(source, H4)\n",
        "  eq4 = c*dt15 + distance.euclidean(source, H1) - distance.euclidean(source, H5)\n",
        "\n",
        "  return [eq1, eq2, eq3, eq4]"
      ],
      "metadata": {
        "id": "3YcwAj3lVE8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def error_recordings(vars):\n",
        "\teqs = equations_recordings_cartesian(vars)\n",
        "\treturn eqs[0]**2+eqs[1]**2+eqs[2]**2+eqs[3]**2"
      ],
      "metadata": {
        "id": "IoyPgbEZTD_k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "initial_guess = [0, 0, 100]\n",
        "\n",
        "result = minimize(error_recordings, initial_guess, method='SLSQP')\n",
        "\n",
        "print(result.x)\n",
        "print(result.fun)\n",
        "\n",
        "\n",
        "print('theta: ', math.atan(((result.x[0]**2 + result.x[1]**2)**0.5) / result.x[2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glFrnpF0TH_b",
        "outputId": "addb07b4-6cf5-4061-b93e-cb8f4973a909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 66.14759354 -30.56575871  99.94014098]\n",
            "0.0002130260372098169\n",
            "theta:  0.6300022081177122\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Compute spherical coordinates from real recordings**"
      ],
      "metadata": {
        "id": "2mZCZaaRcHlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import pandas as pd\n",
        "from scipy.optimize import minimize\n",
        "from scipy.spatial import distance\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "df = pd.read_csv(\"Clicks.csv\")\n",
        "index = 0\n",
        "\n",
        "# Convert cantimeters to meters\n",
        "H1 = np.array([54.78, -0.31, -70.07]) / 100\n",
        "H2 = np.array([-53.71, -2.06, -70.07]) / 100\n",
        "H3 = np.array([1.10, 54.36, 0]) / 100\n",
        "H4 = np.array([2.82, -53.13, 0]) / 100\n",
        "H5 = np.array([0, 0, 113.11]) / 100\n",
        "\n",
        "sampling_rate = 256000 # samples/s, kHz, magnitude resolution 32 bits\n",
        "\n",
        "# Functions for fixed speed of sound\n",
        "def spherical_fixed_sound(vars):\n",
        "  # pfi - azimuth angle, theta - elevation angle\n",
        "  pfi, theta = vars\n",
        "  c = 1490 # Meters per second\n",
        "\n",
        "  dt12 = int(df.loc[index]['dt12']) / sampling_rate\n",
        "  dt34 = int(df.loc[index]['dt34']) / sampling_rate\n",
        "  dt51 = int(df.loc[index]['dt51']) / sampling_rate\n",
        "  dt52 = int(df.loc[index]['dt52']) / sampling_rate\n",
        "  dt53 = int(df.loc[index]['dt53']) / sampling_rate\n",
        "  dt54 = int(df.loc[index]['dt54']) / sampling_rate\n",
        "\n",
        "  # Equation of movement of the wave in H1H2 plane\n",
        "  eq1 = c*dt12*math.cos(theta) - (H1[0] - H2[0])*math.cos(pfi)\n",
        "  # Equation of movement of the wave in H3H4 plane\n",
        "  eq2 = c*dt34*math.cos(theta) - (H3[1] - H4[1])*math.sin(pfi)\n",
        "  # Equation of movement of the wave in vertical plane for H1 and H5\n",
        "  eq3 = c*dt51 - ((H5[2] - H1[2])*math.sin(theta) + H1[0]/math.tan(theta))*math.cos(pfi)\n",
        "\n",
        "  return [eq1, eq2, eq3]\n",
        "\n",
        "\n",
        "# Functions for estimated speed of sound\n",
        "def spherical_variable_sound(vars):\n",
        "  # pfi - azimuth angle, theta - elevation angle, c - speed of sound\n",
        "  pfi, theta, c = vars\n",
        "\n",
        "  dt12 = df.loc[index]['dt12'] / sampling_rate\n",
        "  dt34 = df.loc[index]['dt34'] / sampling_rate\n",
        "  dt51 = df.loc[index]['dt51'] / sampling_rate\n",
        "  dt52 = df.loc[index]['dt52'] / sampling_rate\n",
        "  dt53 = df.loc[index]['dt53'] / sampling_rate\n",
        "  dt54 = df.loc[index]['dt54'] / sampling_rate\n",
        "\n",
        "  eq1 = c*dt12*math.cos(theta) - (H1[0] - H2[0])*math.cos(pfi)\n",
        "  eq2 = c*dt34*math.cos(theta) - (H3[1] - H4[1])*math.sin(pfi)\n",
        "  eq3 = c*dt51 - ((H5[2] - H1[2])*math.sin(theta) + H1[0]/math.tan(theta))*math.cos(pfi)\n",
        "\n",
        "\n",
        "  return [eq1, eq2, eq3]\n",
        "\n",
        "def loss_fun(vars,choice:int):\n",
        "  if choice == 1:\n",
        "    eqs = spherical_fixed_sound(vars)\n",
        "  else:\n",
        "    eqs = spherical_variable_sound(vars)\n",
        "\n",
        "  return abs(eqs[0])+abs(eqs[1])+abs(eqs[2])\n",
        "\n",
        "initial_guess = [math.radians(math.pi/3), math.radians(math.pi/3)]\n",
        "# Bounds for angles\n",
        "bnds = ((0, 2*math.pi), (0, (math.pi)/2))\n",
        "\n",
        "phi = []\n",
        "theta = []\n",
        "loss = []\n",
        "speed = []\n",
        "\n",
        "# Calculating angles for fixed speed\n",
        "for i in range(len(df)):\n",
        "  index = i;\n",
        "  result = minimize(loss_fun, initial_guess, bounds=bnds, method='SLSQP',args=(1,))\n",
        "  df.at[index, 'phi'] = math.degrees(result.x[0])\n",
        "  df.at[index, 'theta'] = math.degrees(result.x[1])\n",
        "  df.at[index, 'loss'] = result.fun\n",
        "\n",
        "# Printing plots for fixed speed\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df['time'], df['phi'], label=\"phi\")\n",
        "ax.scatter(df['time'], df['theta'], label=\"theta\")\n",
        "ax.legend()\n",
        "ax.set_xlabel(\"time, s\")\n",
        "ax.set_ylabel(\"angles, degrees\")\n",
        "ax.grid()\n",
        "plt.show()\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df['time'], df['loss'])\n",
        "ax.set_xlabel(\"time, s\")\n",
        "ax.set_ylabel(\"loss\")\n",
        "ax.grid()\n",
        "plt.show()\n",
        "\n",
        "df_fixed_speed = df.round(3)\n",
        "\n",
        "# Calculating angles and speed\n",
        "phi = []\n",
        "theta = []\n",
        "loss = []\n",
        "speed = []\n",
        "bnds = ((0, 2*math.pi), (0, (math.pi)/2), (1450, 1670))\n",
        "initial_guess = [math.radians(math.pi/3), math.radians(math.pi/3), 1500]\n",
        "for i in range(len(df)):\n",
        "  index = i;\n",
        "  result = minimize(loss_fun, initial_guess, bounds=bnds, method='SLSQP',args=(2,))\n",
        "  df.at[index, 'phi'] = math.degrees(result.x[0])\n",
        "  df.at[index, 'theta'] = math.degrees(result.x[1])\n",
        "  df.at[index, 'loss'] = result.fun\n",
        "  df.at[index, 'sound speed'] = result.x[2]\n",
        "\n",
        "\n",
        "# Printing plots for estimated speed\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df['time'], df['phi'], label=\"phi\")\n",
        "ax.scatter(df['time'], df['theta'], label=\"theta\")\n",
        "ax.legend()\n",
        "ax.set_xlabel(\"time, s\")\n",
        "ax.set_ylabel(\"angles, degrees\")\n",
        "ax.grid()\n",
        "plt.show()\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df['time'], df['loss'])\n",
        "ax.set_xlabel(\"time, s\")\n",
        "ax.set_ylabel(\"loss\")\n",
        "ax.grid()\n",
        "plt.show()\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df['time'], df['sound speed'])\n",
        "ax.set_xlabel(\"time, s\")\n",
        "ax.set_ylabel(\"Speed of Sound, m/s\")\n",
        "ax.grid()\n",
        "plt.show()\n",
        "\n",
        "df_variable_speed = df.round(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3n-RgjMTdYyv",
        "outputId": "74311d90-b1ae-462b-8c6e-f6f430377f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aNVPLli1dfesrwg4AACZwOBxKSEhQbGysSktLrS6nUSstLdXatWs1bNiwWh8s6inCDgAAJgoODq7XKRdU9GFZWZlp/cgAZQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuWhp25c+eqf//+atGihWJjY3XRRRdp+/btbvMcO3ZMaWlpatWqlSIiIjRx4kTt37/fbZ7c3FyNGzdO4eHhio2N1Z133qmysrKG3BQAABCgLA07mZmZSktL07p165SRkaHS0lKNHDlSR44ccc1z2223admyZXrrrbeUmZmpffv26ZJLLnFNLy8v17hx43T8+HF9+eWXevnll7VkyRLdd999VmwSAAAIME2sXHl6errb6yVLlig2NlabNm3SOeeco0OHDumll17S0qVLdcEFF0iSFi9erDPPPFPr1q3ToEGDtHLlSmVnZ+vTTz9VXFycevXqpQcffFB33XWXHnjgATVt2tSKTQMAAAHC0rBzskOHDkmSYmJiJEmbNm1SaWmphg8f7pqna9euatu2rbKysjRo0CBlZWWpe/fuiouLc80zatQoTZs2Td9995169+5dZT0lJSUqKSlxvS4sLJQklZaWqrS0tNraKttrmg7/ov+tQ99bh763Dn1vLU/739PfT8CEHafTqVtvvVVDhgxRt27dJEn5+flq2rSpoqOj3eaNi4tTfn6+a54Tg07l9Mpp1Zk7d65mz55dpX3lypUKDw+vtc6MjAyPtgf+Qf9bh763Dn1vHfreWnX1f3FxsUfLCZiwk5aWpm3btunzzz/3+7pmzZqlGTNmuF4XFhYqKSlJI0eOVGRkZLXvKS0tVUZGhkaMGKGQkBC/1wh39L916Hvr0PfWoe+t5Wn/V56ZqUtAhJ3p06dr+fLlWrt2rU4//XRXe3x8vI4fP66DBw+6Hd3Zv3+/4uPjXfNs2LDBbXmVV2tVznOy0NBQhYaGVmkPCQmp80PtyTzwH/rfOvS9deh769D31qqr/z393Vh6NZZhGJo+fbree+89rV69WsnJyW7T+/btq5CQEK1atcrVtn37duXm5io1NVWSlJqaqq1bt6qgoMA1T0ZGhiIjI5WSktIwGwIAAAKWpUd20tLStHTpUn3wwQdq0aKFa4xNVFSUmjVrpqioKF133XWaMWOGYmJiFBkZqZtuukmpqakaNGiQJGnkyJFKSUnRn/70J82bN0/5+fm65557lJaWVu3RGwAAcGqxNOwsXLhQknTeeee5tS9evFhTpkyRJD311FMKCgrSxIkTVVJSolGjRukf//iHa97g4GAtX75c06ZNU2pqqpo3b67Jkydrzpw5DbUZAAAggFkadgzDqHOesLAwPfvss3r22WdrnKddu3ZasWKFmaUBAACb4NlYAADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1rwOO19//bW2bt3qev3BBx/ooosu0t13363jx4+bWhwAAEB9eR12brjhBu3YsUOS9OOPP+qKK65QeHi43nrrLf3lL38xvUAAAID68Drs7NixQ7169ZIkvfXWWzrnnHO0dOlSLVmyRO+8847Z9QEAANSL12HHMAw5nU5J0qeffqqxY8dKkpKSkvTLL7+YWx0AAEA9eR12+vXrp4ceekivvvqqMjMzNW7cOElSTk6O4uLiTC8QAACgPrwOO/Pnz9fXX3+t6dOn669//as6deokSXr77bc1ePBg0wsEAACojybevqFHjx5uV2NV+tvf/qbg4GBTigIAADCLT/fZOXjwoF588UXNmjVLBw4ckCRlZ2eroKDA1OIAAADqy+sjO99++62GDRum6Oho7dmzR1OnTlVMTIzeffdd5ebm6pVXXvFHnQAAAD7x+sjOjBkzdM0112jnzp0KCwtztY8dO1Zr1641tTgAAID68jrsbNy4UTfccEOV9jZt2ig/P9+UogAAAMziddgJDQ1VYWFhlfYdO3aodevWphQFAABgFq/DzoQJEzRnzhyVlpZKkhwOh3Jzc3XXXXdp4sSJphcIAABQH16HnSeeeEJFRUWKjY3V0aNHde6556pTp05q0aKFHn74YX/UCAAA4DOvr8aKiopSRkaGPv/8c3377bcqKipSnz59NHz4cH/UBwAAUC9eh51KQ4cOVb9+/RQaGiqHw2FmTQAAAKbx+jSW0+nUgw8+qDZt2igiIkI5OTmSpHvvvVcvvfSSV8tau3atxo8fr8TERDkcDr3//vtu06dMmSKHw+H2M3r0aLd5Dhw4oKuuukqRkZGKjo7Wddddp6KiIm83CwAA2JTXYeehhx7SkiVLNG/ePDVt2tTV3q1bN7344oteLevIkSPq2bOnnn322RrnGT16tPLy8lw/r7/+utv0q666St99950yMjK0fPlyrV27Vtdff713GwUAAGzL69NYr7zyip5//nkNGzZMN954o6u9Z8+e+uGHH7xa1pgxYzRmzJha5wkNDVV8fHy1077//nulp6dr48aN6tevnyTpmWee0dixY/X4448rMTHRq3oAAID9eB12fv75Z9eTzk/kdDpdl6Obac2aNYqNjVXLli11wQUX6KGHHlKrVq0kSVlZWYqOjnYFHUkaPny4goKCtH79el188cXVLrOkpEQlJSWu15X3DSotLa1xGyrb/bGNqBv9bx363jr0vXXoe2t52v+e/n68DjspKSn697//rXbt2rm1v/322+rdu7e3i6vV6NGjdckllyg5OVm7d+/W3XffrTFjxigrK0vBwcHKz89XbGys23uaNGmimJiYWu/mPHfuXM2ePbtK+8qVKxUeHl5rTRkZGb5tDExB/1uHvrcOfW8d+t5adfV/cXGxR8vxOuzcd999mjx5sn7++Wc5nU69++672r59u1555RUtX77c28XV6oorrnD9vXv37urRo4c6duyoNWvWaNiwYT4vd9asWZoxY4brdWFhoZKSkjRy5EhFRkZW+57S0lJlZGRoxIgRCgkJ8Xnd8A39bx363jr0vXXoe2t52v/VPdGhOl6HnQsvvFDLli3TnDlz1Lx5c913333q06ePli1bphEjRni7OK906NBBp512mnbt2qVhw4YpPj5eBQUFbvOUlZXpwIEDNY7zkSrGAYWGhlZpDwkJqfND7ck88B/63zr0vXXoe+vQ99aqq/89/d14FXbKysr0yCOP6Nprr7Xk0N7//d//6ddff1VCQoIkKTU1VQcPHtSmTZvUt29fSdLq1avldDo1cODABq8PAAAEHq8uPW/SpInmzZunsrIyU1ZeVFSkLVu2aMuWLZKknJwcbdmyRbm5uSoqKtKdd96pdevWac+ePVq1apUuvPBCderUSaNGjZIknXnmmRo9erSmTp2qDRs26IsvvtD06dN1xRVXcCUWAACQ5MN9doYNG6bMzExTVv7VV1+pd+/eroHNM2bMUO/evXXfffcpODhY3377rSZMmKAuXbrouuuuU9++ffXvf//b7RTUP//5T3Xt2lXDhg3T2LFjNXToUD3//POm1AcAABo/r8fsjBkzRjNnztTWrVvVt29fNW/e3G36hAkTPF7WeeedJ8Mwapz+ySef1LmMmJgYLV261ON1AgCAU4vXYefPf/6zJOnJJ5+sMs3hcKi8vLz+VQEAAJjE67DjdDr9UQcAAIBfeD1mBwAAoDHx+sjO008/XW27w+FQWFiYOnXqpHPOOUfBwcH1Lg4AAKC+vA47Tz31lP7zn/+ouLhYLVu2lCT997//VXh4uCIiIlRQUKAOHTros88+U1JSkukFAwAAeMPr01iPPPKI+vfvr507d+rXX3/Vr7/+qh07dmjgwIFasGCBcnNzFR8fr9tuu80f9QIAAHjF6yM799xzj9555x117NjR1dapUyc9/vjjmjhxon788UfNmzdPEydONLVQAAAAX3h9ZCcvL6/aOyiXlZW5njSemJiow4cP1786AACAevI67Jx//vm64YYbtHnzZlfb5s2bNW3aNF1wwQWSpK1btyo5Odm8KgEAAHzkddh56aWXFBMTo759+7qeHt6vXz/FxMTopZdekiRFREToiSeeML1YAAAAb3k9Zic+Pl4ZGRn64YcftGPHDknSGWecoTPOOMM1z/nnn29ehQAAAPXgddip1KFDBzkcDnXs2FFNmvi8GAAAAL/y+jRWcXGxrrvuOoWHh+uss85Sbm6uJOmmm27So48+anqBAAAA9eF12Jk1a5a++eYbrVmzRmFhYa724cOH68033zS1OAAAgPry+vzT+++/rzfffFODBg2Sw+FwtZ911lnavXu3qcUBAADUl9dHdv7zn/8oNja2SvuRI0fcwg8AAEAg8Drs9OvXTx999JHrdWXAefHFF5WammpeZQAAACbw+jTWI488ojFjxig7O1tlZWVasGCBsrOz9eWXXyozM9MfNQIAAPjM6yM7Q4cO1ZYtW1RWVqbu3btr5cqVio2NVVZWlvr27euPGgEAAHzm0w1yOnbsqBdeeMHsWgAAAEznUdgpLCz0eIGRkZE+FwMAAGA2j8JOdHS0x1dalZeX16sgAAAAM3kUdj777DPX3/fs2aOZM2dqypQprquvsrKy9PLLL2vu3Ln+qRIAAMBHHoWdc8891/X3OXPm6Mknn9Qf//hHV9uECRPUvXt3Pf/885o8ebL5VQIAAPjI66uxsrKy1K9fvyrt/fr104YNG0wpCgAAwCxeh52kpKRqr8R68cUXlZSUZEpRAAAAZvH60vOnnnpKEydO1Mcff6yBAwdKkjZs2KCdO3fqnXfeMb1AAACA+vD6yM7YsWO1c+dOTZgwQQcOHNCBAwc0fvx47dixQ2PHjvVHjQAAAD7z6aaCp59+uh5++GGzawEAADCd10d2AAAAGhPCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDVTw84FF1ygBx98UMXFxWYuFgAAwGemhp22bdtq1apV6tq1q5mLBQAA8JlP99mpyZIlSyRJhYWFZi4WAADAZ6Yc2Tl48KDb68jISDMWCwAAUG9eh53HHntMb775puv1ZZddplatWqlNmzb65ptvTC0OAACgvrwOO4sWLXI93TwjI0MZGRn6+OOPNWbMGN15552mFwgAAFAfXo/Zyc/Pd4Wd5cuX67LLLtPIkSPVvn1711PQAQAAAoXXR3Zatmypn376SZKUnp6u4cOHS5IMw1B5ebm51QEAANST10d2LrnkEl155ZXq3Lmzfv31V40ZM0aStHnzZnXq1Mn0AgEAAOrD67Dz1FNPqX379vrpp580b948RURESJLy8vL05z//2fQCAQAA6sPrsBMSEqI77rijSvttt91mSkEAAABm8uk+O6+++qqGDh2qxMRE7d27V5I0f/58ffDBB6YWBwAAUF9eh52FCxdqxowZGjNmjA4ePOgalBwdHa358+ebXR8AAEC9eB12nnnmGb3wwgv661//quDgYFd7v379tHXrVlOLAwAAqC+vw05OTo569+5dpT00NFRHjhwxpSgAAACzeB12kpOTtWXLlirt6enpOvPMM82oCQAAwDReX401Y8YMpaWl6dixYzIMQxs2bNDrr7+uuXPn6sUXX/RHjQAAAD7zOuz8v//3/9SsWTPdc889Ki4u1pVXXqnExEQtWLBAV1xxhT9qBAAA8JnXYUeSrrrqKl111VUqLi5WUVGRYmNjza4LAADAFD6FnUrh4eEKDw83qxYAAADTeRR2evfuLYfD4dECv/7663oVBAAAYCaPws5FF13k5zIAAAD8w6Owc//99/tl5WvXrtXf/vY3bdq0SXl5eXrvvffcgpVhGLr//vv1wgsv6ODBgxoyZIgWLlyozp07u+Y5cOCAbrrpJi1btkxBQUGaOHGiFixY4HpAKQAAOLX59Gwssxw5ckQ9e/bUs88+W+30efPm6emnn9aiRYu0fv16NW/eXKNGjdKxY8dc81x11VX67rvvlJGRoeXLl2vt2rW6/vrrG2oTAABAgPN6gHLLli2rHb/jcDgUFhamTp06acqUKbrmmmvqXNaYMWM0ZsyYaqcZhqH58+frnnvu0YUXXihJeuWVVxQXF6f3339fV1xxhb7//nulp6dr48aN6tevn6SKx1mMHTtWjz/+uBITE73dPAAAYDNeh5377rtPDz/8sMaMGaMBAwZIkjZs2KD09HSlpaUpJydH06ZNU1lZmaZOnepzYTk5OcrPz9fw4cNdbVFRURo4cKCysrJ0xRVXKCsrS9HR0a6gI0nDhw9XUFCQ1q9fr4svvrjaZZeUlKikpMT1urCwUJJUWlqq0tLSat9T2V7TdPgX/W8d+t469L116Htredr/nv5+vA47n3/+uR566CHdeOONbu3PPfecVq5cqXfeeUc9evTQ008/Xa+wk5+fL0mKi4tza4+Li3NNy8/Pr3KPnyZNmigmJsY1T3Xmzp2r2bNnV2lfuXJlnZfSZ2RkeFQ//IP+tw59bx363jr0vbXq6v/i4mKPluN12Pnkk0/02GOPVWkfNmyYbr/9dknS2LFjNXPmTG8X3WBmzZqlGTNmuF4XFhYqKSlJI0eOVGRkZLXvKS0tVUZGhkaMGKGQkJCGKhW/of+tQ99bh763Dn1vLU/7v/LMTF28DjsxMTFatmyZbrvtNrf2ZcuWKSYmRlLFwOMWLVp4u2g38fHxkqT9+/crISHB1b5//3716tXLNU9BQYHb+8rKynTgwAHX+6sTGhqq0NDQKu0hISF1fqg9mQf+Q/9bh763Dn1vHfreWnX1v6e/G6/Dzr333qtp06bps88+c43Z2bhxo1asWKFFixZJqjjsdO6553q7aDfJycmKj4/XqlWrXOGmsLBQ69ev17Rp0yRJqampOnjwoDZt2qS+fftKklavXi2n06mBAwfWa/0AAMAevA47U6dOVUpKiv7+97/r3XfflSSdccYZyszM1ODBgyXJdTqrLkVFRdq1a5frdU5OjrZs2aKYmBi1bdtWt956qx566CF17txZycnJuvfee5WYmOi6F8+ZZ56p0aNHa+rUqVq0aJFKS0s1ffp0XXHFFVyJBQAAJPn4bKwhQ4ZoyJAh9V75V199pfPPP9/1unIczeTJk7VkyRL95S9/0ZEjR3T99dfr4MGDGjp0qNLT0xUWFuZ6zz//+U9Nnz5dw4YNc91U8Omnn653bQAAwB58CjtOp1O7du1SQUGBnE6n27RzzjnH4+Wcd955MgyjxukOh0Nz5szRnDlzapwnJiZGS5cu9XidAADg1OJ12Fm3bp2uvPJK7d27t0pQcTgcKi8vN604BJ5yp6ENOQdUcPiYYluEaUByjIKDPHtILAAAVvA67Nx4443q16+fPvroIyUkJHj8NHQ0funb8jR7WbbyDv3+uI6EqDDdPz5Fo7sl1PJOAACs43XY2blzp95++2116tTJH/UgQKVvy9O0177WyScd8w8d07TXvtbCq/sQeAAAAcnrB4EOHDjQ7Qoq2F+509DsZdlVgo4kV9vsZdkqd9Y8/goAAKt4fWTnpptu0u233678/Hx17969yg19evToYVpxMEd9x9lsyDngdurqZIakvEPHtCHngFI7tjKhYneV9VfWMqhTLOOEAAAe8zrsTJw4UZJ07bXXutocDocMw2CAcgAyY5xNweGag44v83mjsv4DRUc1b4B07csbFRPRrM76GUgNAKjkddjJycnxRx3wA7PG2cS2CKtzHm/m89SJ9YcG/95eV/0MpAYAnMjrsNOuXTt/1AGT1TXOxqGKcTYjUuLrPOIxIDlGCVFhyj90rNrlOSTFR1UcPTGLr/UzkBoAcDKfbiooSdnZ2crNzdXx48fd2idMmFDvolB/Zo6zCQ5y6P7xKZr22tdySG5BojJm3D8+xdTTRL7Ub2bAAwDYh9dh58cff9TFF1+srVu3usbqSHLdb4cxO4HB7HE2o7slaOHVfaqcHor30+khX+q3eiA1ACAweR12brnlFiUnJ2vVqlVKTk7Whg0b9Ouvv+r222/X448/7o8a4QN/jLMZ3S1BI1LiG2Tgry/1WzmQGgAQuLwOO1lZWVq9erVOO+00BQUFKSgoSEOHDtXcuXN18803a/Pmzf6oE17y1zib4CBHgxwV8aV+qwZSAwACm9c3FSwvL1eLFi0kSaeddpr27dsnqWLg8vbt282tDj6rHGcj/T6uppK/xtmYyZf6KwNSTVvkUMVVWWYOpAYABD6vw063bt30zTffSKq4m/K8efP0xRdfaM6cOerQoYPpBcJ3leNs4qPcj2TER4U1iquSvK2/sQc8AIB/eH0a65577tGRI0ckSXPmzNEf/vAHnX322WrVqpXefPNN0wtE/TTkOBt/qKx/3a4C/fL9Ov3v5P613kG5oQdSAwACn9dhZ9SoUa6/d+rUST/88IMOHDigli1b8gT0ANVQ42z8JTjIoQHJMVrxvTwKao094AEAzOXzfXZOFBPDGAgElsYe8CQeeQEAZjEl7AAwF4+8AADzeD1AGYB/VT7y4uQbJFY+8iJ9W55FlQFA40TYAQJIXY+8kCoeeVHurG4OAEB1CDtAAPHmkRcAAM8QdoAAwiMvAMB8hB0ggPDICwAwH2EHCCA88gIAzEfYAQIIj7wAAPMRdoAA09ifaQYAgYabCgIBiEdeAIB5CDtAgLLDIy8AIBBwGgsAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaz8YCALtzlkt7v5SK9ksRcVK7wVJQsNVVAQ2GsAMAdpb9oZR+l1S47/e2yERp9GNSygTr6gIaEKexAMCusj+U/jXJPehIUmFeRXv2h9bUBTQwwg4A2JGzvOKIjoxqJv7Wlj6zYj7A5gg7AGBHe7+sekTHjSEV/lwxH2BzhB0AsKOi/ebOBzRihB0AsKOIOHPnAxoxwg4A2FG7wRVXXclRwwwOKbJNxXyAzRF2AMCOgoIrLi+XVDXw/PZ69KPcbwenBMIOANhVygTpslekyAT39sjEinbus4NTBDcVBAA7S5kgdR3HHZRxSiPsAIDdBQVLyWdbXQVgGU5jAQAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWwvosPPAAw/I4XC4/XTt2tU1/dixY0pLS1OrVq0UERGhiRMnav9+nvMCAAB+F9BhR5LOOuss5eXluX4+//xz17TbbrtNy5Yt01tvvaXMzEzt27dPl1xyiYXVAgCAQBPw99lp0qSJ4uPjq7QfOnRIL730kpYuXaoLLrhAkrR48WKdeeaZWrdunQYNGtTQpQIAgAAU8GFn586dSkxMVFhYmFJTUzV37ly1bdtWmzZtUmlpqYYPH+6at2vXrmrbtq2ysrJqDTslJSUqKSlxvS4sLJQklZaWqrS0tNr3VLbXNB3+Rf9bh763Dn1vHfreWp72v6e/H4dhGEa9q/KTjz/+WEVFRTrjjDOUl5en2bNn6+eff9a2bdu0bNkyXXPNNW6hRZIGDBig888/X4899lgNS60YCzR79uwq7UuXLlV4eLjp2wEAAMxXXFysK6+8UocOHVJkZGSN8wV02DnZwYMH1a5dOz355JNq1qyZz2GnuiM7SUlJ+uWXX2rsrNLSUmVkZGjEiBEKCQkxZ4PgMfrfOvS9deh769D31vK0/wsLC3XaaafVGXYC/jTWiaKjo9WlSxft2rVLI0aM0PHjx3Xw4EFFR0e75tm/f3+1Y3xOFBoaqtDQ0CrtISEhdX6oPZkH/kP/W4e+tw59bx363lp19b+nv5uAvxrrREVFRdq9e7cSEhLUt29fhYSEaNWqVa7p27dvV25urlJTUy2sEj5xlks5/5a2vl3xp7Pc6ooAADYR0Ed27rjjDo0fP17t2rXTvn37dP/99ys4OFh//OMfFRUVpeuuu04zZsxQTEyMIiMjddNNNyk1NZUrsRqb7A+l9Lukwn2/t0UmSqMfk1ImWFcXAMAWAjrs/N///Z/++Mc/6tdff1Xr1q01dOhQrVu3Tq1bt5YkPfXUUwoKCtLEiRNVUlKiUaNG6R//+IfFVcMr2R9K/5ok6aShY4V5Fe2XvULgAQDUS0CHnTfeeKPW6WFhYXr22Wf17LPPNlBFMJWzvOKIzslBR/qtzSGlz5S6jmvgwhoRZ7m090upaL8UESe1GywFBVtdFQAElIAOO7C5vV+6n7qqwpAKf66Y73ROTVbB6T8A8EijGqAMmyny8Dlmns53Kqk8/XdyWKw8/Zf9oTV1AUAAIuzAOhFx5s53qqjz9J8qTv9xRRsASCLswErtBlecdpGjhhkcUmSbivnwO29O/wE4tXAbj2oxZgfWCQquGF/yr0mqCDwnHqn4LQCNfrRivnKnBQUGKE7/AaeOmi5CqK79h48Yx1cDwo6/+HKVTEO9J5CkTKi4vPzjv0iH835vb5EgjfntH6izXNqbVdG+N0vqMKRxbaPZOP0HmO/k79LE/jVPa6jv8+JfpU9mVQ0v3S6Vtr3t3t6spXT0v1WXWTmO79IlUvNWntdTXf2S58GrctkBso8i7PiDL1fJ1PaeruOq/7DY6Wocx0mnsipfVm5j0QGp5/PS0v+RImIa5zaapfL0X2Geqh+346iYzuk/wDPVfZdGJUsdZks/rJAyZpr3fe7Ne6pTuE/68umq7dUFHUmu74h3rpGME46Q11ZPdbU0aynJIR094L6M6oJX5bKlgNlHEXbM5stN8mp9z5+kZjHVf8C+fMa79QSiura9UlDYSdMa0TaazZvTf2YLkP+lAaap6TvocH7Fn+9dLzmPuU/z+fvcy/eY6cSgU1s9NdVS7VGjGoLXyd/fnqzXzxigbCZfrpLx5D0nBh3phA9YI78ax5Ntr1Yj2kZ/qTz9F5ng3h6Z6L8vkR9WSPO7SS//QXrnuoo/53ezz2XuDOw89Zj9HWT6PsCfqqnHtFoC7/ubIztm8uYqmeSzPXyPL6pZTyCq17Y3km30p5QJNZ/i9If3bpCcR93bqvtfWkMf/TFjfXY6JQzPmf0dFDD7AE+dVE+D1dLw39+EHTP5cpWMP6+YCfSrccyoL9C30d+Cgv3/ZeH635cHj/Vo6KtBzAgpPJ/t1GX2d1Cg7QM8VVlDQ9fSgOvjNJaZfLlKxp9XzAT61Thm1Bfo22gHP22oY4bf/pe29vGGvauzGXeR5gaNpzazv4MCbR/gqcoaGrqWBlwfYcdMvtwkr873+KKR3IyvXtveSLbRDo4UeDbf+oVqsNBgVkjhBo2nNrO/gyzdB/z2/mYxJzXXtps/qR6/7I88WG8DIOyYqfIqGUlVPyw1XCVT63s84eF6ApEn/VXbtMawjXbQPNaz+Wq89FUyPTSYFVK4QeOpzezvoIbYB0S2kQbf/FsoObE9UbrsVenOXdLk5dLElyr+vHTxb8v1oJ56749OWna1y7Hm+5uwYzZfrpKp6T2uhF7dh8Xx2we+Aa/G8Yda++vVip/Gvo2NXdKA3/5Sy/9Wm7X0bFlmhQazQgo3aERN30Etfnt98fPmfJ/79J420qUvu4eXW7dKIx+Ubt1WtT1lwu/j+LpfWvHnWRd5V09t+6OTjxrVFbwC6PubAcr+4MtVMjW9p8YBn49WvGf4A43/vid19VfXcdKPX0jf/Ve68i3uoNzQ3Pq6hvv6DJwmrXmk7mWZFRrMCincoBFS9d9Bif2l9E+krmOls/5gzve5me/x5uIEb5dd0/xS9cuobT/UkFeM1oKw4y++XCVT3Xvq+pA2xNU4DaG27QgKltqlSt+tqPiToGONi5+r4U6yj1Z8Rr9e0nChwayQYuUNGhFYTv4OKi2teZovy/PXe/y17Jrmr66tru/vANhHEXYagwD5sOAUV9f/cBsyNJgZUioP29d2BBVAo0bYAeC52oJ3Q4cGM9fX0DdoBNCgCDsAzNPQocHM9XEEFbAtwg4AczV0aCCkAKgDl54DAABbI+wAAABb4zQWEKga+unhAGBThB0gEJnxNG8AgCROYwGBx4yneQMAXAg7QCAx62neAAAXwg4QSMx6mjcAwIWwAwQSs57mDQBwIewAgcSsp3kDAFwIO0AgqXyad+XDLKtwSJFtzHt6OACcAgg7QCCpfJq3pKqBxw9PDweAUwBhBwg0lU/zjkxwb49MrGjnPjsA4BVuKggEooZ+ejgA2BhhBwhUPM0bAEzBaSwAAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrPC7iVOAsN+8ZS2Yuy6t1ZlX8fW+W1GFI3eu0ok4AQEAi7Nhd9odS+l1S4b7f2yITpdGPef/0bDOX5e06iw5IPZ+Xlv6PFBFT+zqtqBMAELA4jWVn2R9K/5rkvtOXpMK8ivbsD61Zlj/XaUWdAICARtixK2d5xdENGdVM/K0tfWbFfA25LE/5sk4r6gQABDzCjl3t/bLq0Q03hlT4c8V8DbksT/myTivqBAAEPMbs2FXRfvPmM3NZnvJlnVbUCcAWyp2GNuQcUMHhY4ptEabep7ewuiSYiLBjVxFx5s1n5rI85cs6ragTQKOXvi1Ps5dlK+/QMVdbu5ahmtHVwqJgKk5j2VW7wRVXIMlRwwwOKbJNxXwNuSxP+bJOK+oE0Kilb8vTtNe+dgs6krS/sOL1p99zJNgOCDt2FRRccam1pKo7/99ej37Us3vPmLksT/myTivqBNBolTsNzV6WXdslDXr04x9U7qxuDjQmhB07S5kgXfaKFJng3h6ZWNHuzT1nzFyWP9dpRZ0AGqUNOQeqHNE5WX7hMW3IOdBAFcFfGLPjJycPdhuQHKPgoJpOr9T+ntqWVed6UiZIXceZczdhM5d1kuNlTr2atUd7DxSrXUy4/pTaXk2bBEkpE1TeZax+yEqXDpTrh/NfUkrqaAU3qeWj68c6A40vnzN/ry8Qa2rI5aDxKDhce9CplH/oqLJ2/2rK97m375Hk1X7Bl/2Iv2rxtR/8wTZh59lnn9Xf/vY35efnq2fPnnrmmWc0YMAAS2qpbrBbQlSY7h+fotHdErx6z4SeCfrwm7xqlyXJs/UEBUvJZ5uzcWYu6zdzV2TrhX/n6MQjxQ+v+F5Tz05W77YtNXtZtg4USfMGSJemSzGfZ9bal/6qM9D48jmrj0+/3685H22vdX0NXZNZ62vouhEYYluEeTTfgx99rwNHjrte+/p97u17osNDJEkHi0vdllPTfsHb9prqMaMWr/ZRDcBhGEajPxn55ptvatKkSVq0aJEGDhyo+fPn66233tL27dsVGxtb5/sLCwsVFRWlQ4cOKTIystp5SktLtWLFCo0dO1YhISE1LqtysNvJnVqZYxde3afKL7mm99TEoepvm1fXegLR3BXZem5tTp3zhQYbmjegXH/ZEKzj5RVb2Vi20R98+Zz5qvKzf9eGYB0rd/8f2Ynrk9RgNUnm9UFD9qW3PP3egW/KnYaGPrZa+YeOVfn9n/idU1LL597T73Mz9wFmqKkeM2oxYx/l6Wffk/23ZJMxO08++aSmTp2qa665RikpKVq0aJHCw8P1v//7vw1ahyeD3WYvy3Yb7Fbbe2pS27w1rScQHS9z6oV/1x10TtaYttEffPmc1Xd9Jy67uvU98OF3euDDhq3JjD5o6L5EYAkOcriOQHhzYsWX73Mz9wFmqK4es2oJxH1Uoz+Ndfz4cW3atEmzZs1ytQUFBWn48OHKysqq9j0lJSUqKSlxvS4sLJRUkSRLS0urfU9le03TpYpzmQeKjiq0lqEhB4qOat2uArfzn3W9xxcnrycQvZa1RyFBnn3YQ3+bL/SE+RvDNvqDL5+z+tj4438kuff9yf57pOIwdUPVZFYfNHRfesuT7x3Uz7AzTtM/ruypRz/+QfmFv59uiYtoIqm81s+9L9/nDbUP8NSJ9TRkLXX9u/L0s+/pv41Gfxpr3759atOmjb788kulpqa62v/yl78oMzNT69evr/KeBx54QLNnz67SvnTpUoWHh/u1XgAAYI7i4mJdeeWVdZ7GavRHdnwxa9YszZgxw/W6sLBQSUlJGjlyZK1jdjIyMjRixIgazx9uyDmga1/eWOf6/3dyf7dU78l7fHHiegLRq1l79Ngn2z2aNzTI0IP9nLr3qyCVOH8/4Bzo2+gPvnzO6mPdrgId2PFVlb73hVk1mdUHDd2X3vLkewf+4enn3pfv84baB3iqsp6GrqW2f1eefvYrz8zUpdGHndNOO03BwcHav9/9Lpf79+9XfHx8te8JDQ1VaGholfaQkJA6v1Bqm2dQp1jFRDSrdrCbVHFOOD4qTIM6xbouvavrPb6obj2B6OrBHfXQxzvkzWnbEqdDJeWORrON/uDL56w++ndorU92SMd/6/vq1hcXGSrJof2FDVOTWX3Q0H3pK0++m2AuTz733n6fN9Q+wFMn19NQtXjz76quz76n/y4a/QDlpk2bqm/fvlq1apWrzel0atWqVW6ntRpCbYPdKl/fPz7F7ZfrywA5Rw1/r209gahpkyBNPTu5zvka8zb6gy+fs/qu7+Tln/z6gQln6YEJDVuTGX3Q0H2JxsOTz7033+dm7gPMUF09ZtUSiPuoRh92JGnGjBl64YUX9PLLL+v777/XtGnTdOTIEV1zzTUNXsvobglaeHUfxUe5378hPiqsxkvtanpPQlSYbjgnWQnVLGvR1X20yMv1BKJZY1N0wznJOvkzH+SQbjgn2Rbb6A++fM7q66nLe9W6voauyaz1WdGXaDzq+tyfzMx9QHR4iOv+NpVq2i94215TPWbUEoj7qEY/QLnS3//+d9dNBXv16qWnn35aAwcO9Oi9Zt5np1LA3EG5kajxDsqq2MZ1uwr0y/frdNqZgyw/pRBIGuL3f+JnPyi4CXdQbkDcZ8c63n7uT8YdlOu3jzL7Pju2CTv14Y+wA3PR/9ah761D31uHvrcWNxUEAADwAmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYWqN/6rkZKm8iXduj4ktLS1VcXKzCwkLupmkB+t869L116Hvr0PfW8rT/K/fbdT0MgrAj6fDhw5KkpKQkiysBAADeOnz4sKKiomqczrOxJDmdTu3bt08tWrSQw1H9A8oKCwuVlJSkn376qdbnb8A/6H/r0PfWoe+tQ99by9P+NwxDhw8fVmJiooKCah6Zw5EdSUFBQTr99NM9mjcyMpIPvoXof+vQ99ah761D31vLk/6v7YhOJQYoAwAAWyPsAAAAWyPseCg0NFT333+/QkNDrS7llET/W4e+tw59bx363lpm9z8DlAEAgK1xZAcAANgaYQcAANgaYQcAANgaYQcAANgaYecka9eu1fjx45WYmCiHw6H333/fbbphGLrvvvuUkJCgZs2aafjw4dq5c6c1xdrM3Llz1b9/f7Vo0UKxsbG66KKLtH37drd5jh07prS0NLVq1UoRERGaOHGi9u/fb1HF9rFw4UL16NHDdQOv1NRUffzxx67p9HvDefTRR+VwOHTrrbe62uh//3nggQfkcDjcfrp27eqaTt/7188//6yrr75arVq1UrNmzdS9e3d99dVXrulm7XMJOyc5cuSIevbsqWeffbba6fPmzdPTTz+tRYsWaf369WrevLlGjRqlY8eONXCl9pOZmam0tDStW7dOGRkZKi0t1ciRI3XkyBHXPLfddpuWLVumt956S5mZmdq3b58uueQSC6u2h9NPP12PPvqoNm3apK+++koXXHCBLrzwQn333XeS6PeGsnHjRj333HPq0aOHWzv9719nnXWW8vLyXD+ff/65axp97z///e9/NWTIEIWEhOjjjz9Wdna2nnjiCbVs2dI1j2n7XAM1kmS89957rtdOp9OIj483/va3v7naDh48aISGhhqvv/66BRXaW0FBgSHJyMzMNAyjoq9DQkKMt956yzXP999/b0gysrKyrCrTtlq2bGm8+OKL9HsDOXz4sNG5c2cjIyPDOPfcc41bbrnFMAw+9/52//33Gz179qx2Gn3vX3fddZcxdOjQGqebuc/lyI4XcnJylJ+fr+HDh7vaoqKiNHDgQGVlZVlYmT0dOnRIkhQTEyNJ2rRpk0pLS936v2vXrmrbti39b6Ly8nK98cYbOnLkiFJTU+n3BpKWlqZx48a59bPE574h7Ny5U4mJierQoYOuuuoq5ebmSqLv/e3DDz9Uv3799D//8z+KjY1V79699cILL7imm7nPJex4IT8/X5IUFxfn1h4XF+eaBnM4nU7deuutGjJkiLp16yapov+bNm2q6Ohot3npf3Ns3bpVERERCg0N1Y033qj33ntPKSkp9HsDeOONN/T1119r7ty5VabR//41cOBALVmyROnp6Vq4cKFycnJ09tln6/Dhw/S9n/34449auHChOnfurE8++UTTpk3TzTffrJdfflmSuftcnnqOgJSWlqZt27a5nTuHf51xxhnasmWLDh06pLfffluTJ09WZmam1WXZ3k8//aRbbrlFGRkZCgsLs7qcU86YMWNcf+/Ro4cGDhyodu3a6V//+peaNWtmYWX253Q61a9fPz3yyCOSpN69e2vbtm1atGiRJk+ebOq6OLLjhfj4eEmqMhJ///79rmmov+nTp2v58uX67LPPdPrpp7va4+Pjdfz4cR08eNBtfvrfHE2bNlWnTp3Ut29fzZ07Vz179tSCBQvodz/btGmTCgoK1KdPHzVp0kRNmjRRZmamnn76aTVp0kRxcXH0fwOKjo5Wly5dtGvXLj77fpaQkKCUlBS3tjPPPNN1GtHMfS5hxwvJycmKj4/XqlWrXG2FhYVav369UlNTLazMHgzD0PTp0/Xee+9p9erVSk5Odpvet29fhYSEuPX/9u3blZubS//7gdPpVElJCf3uZ8OGDdPWrVu1ZcsW10+/fv101VVXuf5O/zecoqIi7d69WwkJCXz2/WzIkCFVbi+yY8cOtWvXTpLJ+1xfR1Hb1eHDh43NmzcbmzdvNiQZTz75pLF582Zj7969hmEYxqOPPmpER0cbH3zwgfHtt98aF154oZGcnGwcPXrU4sobv2nTphlRUVHGmjVrjLy8PNdPcXGxa54bb7zRaNu2rbF69Wrjq6++MlJTU43U1FQLq7aHmTNnGpmZmUZOTo7x7bffGjNnzjQcDoexcuVKwzDo94Z24tVYhkH/+9Ptt99urFmzxsjJyTG++OILY/jw4cZpp51mFBQUGIZB3/vThg0bjCZNmhgPP/ywsXPnTuOf//ynER4ebrz22muuecza5xJ2TvLZZ58Zkqr8TJ482TCMikvh7r33XiMuLs4IDQ01hg0bZmzfvt3aom2iun6XZCxevNg1z9GjR40///nPRsuWLY3w8HDj4osvNvLy8qwr2iauvfZao127dkbTpk2N1q1bG8OGDXMFHcOg3xvayWGH/vefyy+/3EhISDCaNm1qtGnTxrj88suNXbt2uabT9/61bNkyo1u3bkZoaKjRtWtX4/nnn3ebbtY+12EYhuHT8ScAAIBGgDE7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AALWmjVr5HA4qjyIEQC8wR2UAQSE8847T7169dL8+fNdbcePH9eBAwcUFxcnh8NhXXEAGrUmVhcAADVp2rSp4uPjrS4DQCPHaSwAlpsyZYoyMzO1YMECORwOORwO7dmzp8pprCVLlig6OlrLly/XGWecofDwcF166aUqLi7Wyy+/rPbt26tly5a6+eabVV5e7lp+SUmJ7rjjDrVp00bNmzfXwIEDtWbNGq9qXLNmjQYMGKDmzZsrOjpaQ4YM0d69e03sBQD+wpEdAJZbsGCBduzYoW7dumnOnDmSpNatW2vPnj1V5i0uLtbTTz+tN954Q4cPH9Yll1yiiy++WNHR0VqxYoV+/PFHTZw4UUOGDNHll18uSZo+fbqys7P1xhtvKDExUe+9955Gjx6trVu3qnPnznXWV1ZWposuukhTp07V66+/ruPHj2vDhg2cWgMaCcIOAMtFRUWpadOmCg8Pr/O0VWlpqRYuXKiOHTtKki699FK9+uqr2r9/vyIiIpSSkqLzzz9fn332mS6//HLl5uZq8eLFys3NVWJioiTpjjvuUHp6uhYvXqxHHnmkzvoKCwt16NAh/eEPf3Ct98wzz6znVgNoKIQdAI1KeHi4K3BIUlxcnNq3b6+IiAi3toKCAknS1q1bVV5eri5durgtp6SkRK1atfJonTExMZoyZYpGjRqlESNGaPjw4brsssuUkJBgwhYB8DfCDoBGJSQkxO21w+Gots3pdEqSioqKFBwcrE2bNik4ONhtvhMDUl0WL16sm2++Wenp6XrzzTd1zz33KCMjQ4MGDfJxSwA0FMIOgIDQtGlTt0HFZundu7fKy8tVUFCgs88+u97L6t27t2bNmqXU1FQtXbqUsAM0AlyNBSAgtG/fXuvXr9eePXv0yy+/uI7M1FeXLl101VVXadKkSXr33XeVk5OjDRs2aO7cufroo488WkZOTo5mzZqlrKws7d27VytXrtTOnTsZtwM0EoQdAAHhjjvuUHBwsFJSUtS6dWvl5uaatuzFixdr0qRJuv3223XGGWfooosu0saNG9W2bVvXPA6HQ0uWLKn2/eHh4frhhx80ceJEdenSRddff73S0tJ0ww03mFYjAP/hDsoATnk5OTnq0qWLsrOzPboUHUDjwpEdAKe8FStW6PrrryfoADbFkR0AAGBrHNkBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC29v8BaFeia2gD2nMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "df_fixed_speed.to_csv('Results_fixed.csv')"
      ],
      "metadata": {
        "id": "hCqZ9b0Rby8t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_variable_speed.to_csv('Results_variable.csv')"
      ],
      "metadata": {
        "id": "bRgtSQGqf5mZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "guess = {\n",
        "    1: [math.radians(30), math.radians(20), 1500],\n",
        "    2: [math.radians(15), math.radians(45), 1525],\n",
        "    3: [math.radians(310), math.radians(80), 1550],\n",
        "    4: [math.radians(90), math.radians(25), 1575]\n",
        "}\n",
        "\n",
        "  # Creating figure with subplots\n",
        "fig, axs = plt.subplots(2,2,figsize=(12,10))\n",
        "\n",
        "# Row and column of axis\n",
        "i,j = [0,0]\n",
        "\n",
        "for idx in guess:\n",
        "  initial_guess = guess[idx]\n",
        "\n",
        "  for k in range(30):\n",
        "    index = k;\n",
        "    result = minimize(loss_fun, initial_guess, bounds=bnds, method='SLSQP',args=(2,))\n",
        "    df.at[index, 'phi'] = math.degrees(result.x[0])\n",
        "    df.at[index, 'theta'] = math.degrees(result.x[1])\n",
        "    df.at[index, 'loss'] = result.fun\n",
        "\n",
        "  # Plot the data with x and y labels\n",
        "  axs[i,j].scatter(df['time'], df['phi'], label=\"phi\")\n",
        "  axs[i,j].scatter(df['time'], df['theta'], label=\"theta\")\n",
        "  # axs[i,j].scatter(df['time'], df['loss'],label = \"loss\")\n",
        "  axs[i,j].set_xlabel('Time')\n",
        "  axs[i,j].set_ylabel('Value')\n",
        "  axs[i,j].set_title('theta = {theta},phi = {phi}, speed of sound = {speed}'.format(theta = round(math.degrees(initial_guess[1])),phi = round(math.degrees(initial_guess[0])),speed = initial_guess[2]))\n",
        "  axs[i,j].grid()\n",
        "  axs[i,j].legend(loc='upper right')\n",
        "  # Condition for incriment of row and column index\n",
        "  if j < 1:\n",
        "      j+=1\n",
        "  else:\n",
        "      j = 0\n",
        "      i+=1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "PADFs58ShPzQ",
        "outputId": "16167b8f-cdba-4e0f-db73-3453ee2b7cde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-95-4936aefd4e2e>:58: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  eq3 = c*dt51 - ((H5[2] - H1[2])*math.sin(theta) + H1[0]/math.tan(theta))*math.cos(pfi)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "guess = {\n",
        "    1: [math.radians(math.pi/6), math.radians(math.pi/6), 1500],\n",
        "    2: [math.radians(math.pi/4), math.radians(math.pi/4), 1550],\n",
        "    3: [math.radians(math.pi/3), math.radians(math.pi/3), 1600],\n",
        "    4: [math.radians(math.pi/2), math.radians(math.pi/2), 1650]\n",
        "}\n",
        "\n",
        "for i in guess:\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfX8OBrq4Jnn",
        "outputId": "ab1166a8-c494-4541-e069-17d98d930521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "math.degrees()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuCtOuPZ49Ri",
        "outputId": "9fe79835-00c7-4ade-a9c3-a7db427ac626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "310.0"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "math.radians(310)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8aMW4iZ-fl2",
        "outputId": "177206e8-df42-4547-e2da-45b5b84afe37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.410520681182422"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bfxK_gFk-rTH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}