{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PostProcessing Lateral"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test dell'utilizzo delle linee in coordinate omogenee per il postprocessing delle linee laterali -- non la migliore soluzione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.signal import savgol_filter\n",
    "from scipy.signal import medfilt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opened: True, FPS: 59.94005994005994, Total Frames: 227.0\n"
     ]
    }
   ],
   "source": [
    "# Define the relative path to the video file\n",
    "notebook_dir = Path().resolve()\n",
    "project_root = notebook_dir.parent.parent\n",
    "video_path = project_root / \"data\" / \"recording_3\" / \"Recording_3_normal_speed.mp4\"\n",
    "video_path = str(video_path)\n",
    "\n",
    "# Load the video\n",
    "cap = cv2.VideoCapture(video_path)\n",
    "\n",
    "# Check\n",
    "print(\n",
    "    f\"Opened: {cap.isOpened()}, FPS: {cap.get(cv2.CAP_PROP_FPS)}, Total Frames: {cap.get(cv2.CAP_PROP_FRAME_COUNT)}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:11: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  lines.iloc[i][j] = np.fromstring(lines.iloc[i][j].strip(\"[]\"), sep=\" \")\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  lines.iloc[i][j] = np.fromstring(lines.iloc[i][j].strip(\"[]\"), sep=\" \")\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:11: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value`\n",
      "  lines.iloc[i][j] = np.fromstring(lines.iloc[i][j].strip(\"[]\"), sep=\" \")\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  lines.iloc[i][j] = lines.iloc[i][j].tolist()\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  lines.iloc[i][j] = lines.iloc[i][j].tolist()\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:12: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value`\n",
      "  lines.iloc[i][j] = lines.iloc[i][j].tolist()\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:13: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if lines.iloc[i][j][0] == 0 and lines.iloc[i][j][1] == 0 and lines.iloc[i][j][2] == 0 and lines.iloc[i][j][3] == 0:\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1685785927.py:15: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  print(type(lines.iloc[1][0]))\n"
     ]
    }
   ],
   "source": [
    "# Define the path to the CSV file\n",
    "input_data_path = (\n",
    "    project_root / \"data\" / \"auxiliary_data\" / \"lane_lines\" / \"three_lines_3.csv\"\n",
    ")\n",
    "\n",
    "# Load the CSV file into a DataFrame\n",
    "lines = pd.read_csv(input_data_path)\n",
    "\n",
    "\n",
    "# convert string to list\n",
    "for i in range(0, len(lines)):\n",
    "    for j in range(0, 3):\n",
    "        lines.iloc[i][j] = np.fromstring(lines.iloc[i][j].strip(\"[]\"), sep=\" \")\n",
    "        lines.iloc[i][j] = lines.iloc[i][j].tolist()\n",
    "        if (\n",
    "            lines.iloc[i][j][0] == 0\n",
    "            and lines.iloc[i][j][1] == 0\n",
    "            and lines.iloc[i][j][2] == 0\n",
    "            and lines.iloc[i][j][3] == 0\n",
    "        ):\n",
    "            lines.iloc[i][j] = None\n",
    "print(type(lines.iloc[1][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Disegna la linea sul frame\"\"\"\n",
    "\n",
    "\n",
    "def write_line_on_frame(frame, line):\n",
    "    # Create a copy of the original frame to draw the first line\n",
    "    modified_frame = np.copy(frame)\n",
    "\n",
    "    # Extract the first line's rho and theta\n",
    "    if line is not None:\n",
    "        x1, y1, x2, y2 = line\n",
    "\n",
    "        # Draw the first line on the frame\n",
    "        cv2.line(modified_frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)\n",
    "\n",
    "    # return the modified frame\n",
    "    return modified_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_lines_on_frame(frame, lines):\n",
    "    for i in range(len(lines)):\n",
    "        print(\"linea\", i, \":\", lines[i])\n",
    "        frame = write_line_on_frame(frame, lines[i])\n",
    "    return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide_lines(lines):\n",
    "    left_lines = []\n",
    "    right_lines = []\n",
    "    for i in range(len(lines)):\n",
    "        if lines.iloc[i][1] is not None:\n",
    "            x1_1 = lines.iloc[i][1][0]\n",
    "            x2_1 = lines.iloc[i][1][2]\n",
    "            if lines.iloc[i][2] is not None:\n",
    "                x1_2 = lines.iloc[i][2][0]\n",
    "                x2_2 = lines.iloc[i][2][2]\n",
    "                min_x_1 = min(x1_1, x2_1)\n",
    "                min_x_2 = min(x1_2, x2_2)\n",
    "                if min_x_1 < min_x_2:\n",
    "                    left_lines.append(lines.iloc[i][1])\n",
    "                    right_lines.append(lines.iloc[i][2])\n",
    "                else:\n",
    "                    left_lines.append(lines.iloc[i][2])\n",
    "                    right_lines.append(lines.iloc[i][1])\n",
    "            else:\n",
    "                right_lines.append(None)\n",
    "                left_lines.append(None)\n",
    "        else:\n",
    "            left_lines.append(None)\n",
    "            right_lines.append(None)\n",
    "    return left_lines, right_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_closest_points(lines):\n",
    "    closest_points = []\n",
    "    for line in lines:\n",
    "        if line is None:\n",
    "            closest_points.append((0, 0))  # will be removed later\n",
    "            continue\n",
    "        x1, y1, x2, y2 = line\n",
    "        # Line equation: ax + by + c = 0\n",
    "        a = y2 - y1\n",
    "        b = x1 - x2\n",
    "        c = x2 * y1 - x1 * y2\n",
    "\n",
    "        # Closest point to the origin (0, 0) on the line\n",
    "        denom = a**2 + b**2\n",
    "        x_closest = -a * c / denom\n",
    "        y_closest = -b * c / denom\n",
    "        closest_points.append((x_closest, y_closest))\n",
    "\n",
    "    # create a proper DataFrame\n",
    "    closest_points_df = pd.DataFrame(closest_points, columns=[\"X\", \"Y\"])\n",
    "    closest_points_df[\"Frame\"] = range(len(closest_points_df))\n",
    "    closest_points_df = closest_points_df[[\"Frame\", \"X\", \"Y\"]]\n",
    "\n",
    "    return closest_points_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df, threshold=1.5):\n",
    "    # Ensure 'X' and 'Y' are numeric\n",
    "    df[[\"X\", \"Y\"]] = df[[\"X\", \"Y\"]].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "    # Compute the distance between consecutive points\n",
    "    dx = df[\"X\"].diff()\n",
    "    dy = df[\"Y\"].diff()\n",
    "    distances = np.sqrt(dx**2 + dy**2)\n",
    "    # Define outlier threshold\n",
    "    median_dist = distances.median()\n",
    "    std_dist = distances.std()\n",
    "    outlier_threshold = threshold * std_dist\n",
    "\n",
    "    mask = []\n",
    "\n",
    "    for i in range(0, len(distances) - 1):\n",
    "        # Remove points that are  (0, 0)  (the ones that I set when it doesn't detect any line)\n",
    "        if df[\"X\"].iloc[i] == 0 and df[\"Y\"].iloc[i] == 0:\n",
    "            mask.append(i)\n",
    "            prev_index = i - 1\n",
    "            while prev_index in mask and prev_index >= 0:\n",
    "                prev_index -= 1\n",
    "            if prev_index < 0:\n",
    "                print(\"No valid previous index found. Breaking.\")\n",
    "                break\n",
    "            distances[i + 1] = np.sqrt(\n",
    "                (df[\"X\"].iloc[i + 1] - df[\"X\"].iloc[prev_index]) ** 2\n",
    "                + (df[\"Y\"].iloc[i + 1] - df[\"Y\"].iloc[prev_index]) ** 2\n",
    "            )\n",
    "            continue\n",
    "        if distances[i] > outlier_threshold:\n",
    "            # Create a mask to identify the outlier point\n",
    "            mask.append(i)\n",
    "            # convert the next point distance in the distance with respect to the previous point\n",
    "            if i < len(distances) - 1 and i > 0:\n",
    "                # select the previous point as the last point evaluated non present in the mask\n",
    "                prev_index = i - 1\n",
    "                while prev_index in mask and prev_index >= 0:\n",
    "                    prev_index -= 1\n",
    "                if prev_index < 0:\n",
    "                    print(\"No valid previous index found. Breaking.\")\n",
    "                    break\n",
    "                distances[i + 1] = np.sqrt(\n",
    "                    (df[\"X\"].iloc[i + 1] - df[\"X\"].iloc[prev_index]) ** 2\n",
    "                    + (df[\"Y\"].iloc[i + 1] - df[\"Y\"].iloc[prev_index]) ** 2\n",
    "                )\n",
    "    # Remove the outlier points from the DataFrame\n",
    "    df = df.drop(mask)\n",
    "\n",
    "    # compute means and standard deviations of points\n",
    "    mean_x = df[\"X\"].mean()\n",
    "    mean_y = df[\"Y\"].mean()\n",
    "    std_x = df[\"X\"].std()\n",
    "    std_y = df[\"Y\"].std()\n",
    "\n",
    "    threshold_std = 1\n",
    "\n",
    "    # CHANGE WITH RESPECT THE HORIZONTAL!!!!!!!!!!! HERE WE REMOVE THIS\n",
    "    # Remove points too far from the mean\n",
    "    # df = df[(df['X'] > mean_x - threshold_std*std_x) & (df['X'] < mean_x + threshold_std*std_x) & (df['Y'] > mean_y - threshold_std*std_y) & (df['Y'] < mean_y + threshold_std*std_y)]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [],
   "source": [
    "def median_filter(df, kernel_size=3):\n",
    "    \"\"\"\n",
    "    Smooths the X and Y coordinates using a median filter to reduce noise.\n",
    "    \"\"\"\n",
    "    # Apply a median filter to smooth the coordinates (kernel size 5 by default)\n",
    "    df[\"X\"] = medfilt(df[\"X\"], kernel_size=kernel_size)\n",
    "    df[\"Y\"] = medfilt(df[\"Y\"], kernel_size=kernel_size)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Savitzky_Golay_filter(df, window_length=20, polyorder=3):\n",
    "    \"\"\"\n",
    "    Smooths the X and Y coordinates using Savitzky-Golay filter to reduce noise.\n",
    "    \"\"\"\n",
    "    df[\"X\"] = savgol_filter(df[\"X\"], window_length=window_length, polyorder=polyorder)\n",
    "    df[\"Y\"] = savgol_filter(df[\"Y\"], window_length=window_length, polyorder=polyorder)\n",
    "\n",
    "    # # Round the smoothed coordinates to integers\n",
    "    # df['X'] = df['X'].round().astype(int)\n",
    "    # df['Y'] = df['Y'].round().astype(int)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_missing_coordinates(df, start_frame=0, end_frame=200):\n",
    "    \"\"\"\n",
    "    Interpolate the missing coordinates between the frames from start_frame to end_frame.\n",
    "    Ensures a smooth trajectory by interpolating both X and Y coordinates.\n",
    "    \"\"\"\n",
    "    # Create a DataFrame with all frames from start_frame to end_frame\n",
    "    all_frames = pd.DataFrame({\"Frame\": range(start_frame, end_frame + 1)})\n",
    "\n",
    "    # Merge with the original dataframe\n",
    "    df_full = pd.merge(all_frames, df, on=\"Frame\", how=\"left\")\n",
    "\n",
    "    # Interpolate the missing 'X' and 'Y' coordinates (linear interpolation)\n",
    "    df_full[\"X\"] = df_full[\"X\"].interpolate(method=\"linear\")\n",
    "    df_full[\"Y\"] = df_full[\"Y\"].interpolate(method=\"linear\")\n",
    "\n",
    "    # Fill any remaining NaN values using forward and backward filling\n",
    "    df_full[\"X\"] = df_full[\"X\"].bfill().ffill()\n",
    "    df_full[\"Y\"] = df_full[\"Y\"].bfill().ffill()\n",
    "\n",
    "    # Apply smoothing to ensure a smooth trajectory\n",
    "    df_full = Savitzky_Golay_filter(df_full)\n",
    "\n",
    "    return df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_trajectory(df, title):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.plot(df[\"X\"], -df[\"Y\"], \"o-\", label=\"Cleaned Trajectory\")\n",
    "    plt.xlabel(\"X Coordinate\")\n",
    "    plt.ylabel(\"Y Coordinate\")\n",
    "    plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [],
   "source": [
    "def points_to_lines(df, line_length=1):\n",
    "    lines = []\n",
    "    for i in range(0, len(df)):\n",
    "        x, y = df.iloc[i][\"X\"], df.iloc[i][\"Y\"]\n",
    "        # Compute the direction vector orthogonal to the line connecting the point to the origin\n",
    "        dx, dy = (\n",
    "            -y,\n",
    "            x,\n",
    "        )  # Rotate the vector (x, y) by 90 degrees to get orthogonal direction\n",
    "\n",
    "        # Append the line as a pair of points\n",
    "        lines.append(\n",
    "            [\n",
    "                int(x + line_length * dx),\n",
    "                int(y + line_length * dy),\n",
    "                int(x - line_length * dx),\n",
    "                int(y - line_length * dy),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    return lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cartesian_to_homogeneous(lines):\n",
    "    homogeneous_lines = []\n",
    "    for line in lines:\n",
    "        hom_line = np.cross([line[0], line[1], 1], [line[2], line[3], 1])\n",
    "        hom_line = hom_line / hom_line[0]\n",
    "        homogeneous_lines.append([hom_line[1], hom_line[2]])\n",
    "    # create a proper DataFrame\n",
    "    homogeneous_lines_df = pd.DataFrame(homogeneous_lines, columns=[\"X\", \"Y\"])\n",
    "    homogeneous_lines_df[\"Frame\"] = range(len(homogeneous_lines_df))\n",
    "    homogeneous_lines_df = homogeneous_lines_df[[\"Frame\", \"X\", \"Y\"]]\n",
    "    return homogeneous_lines_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover_lines(lines):\n",
    "    a = 1\n",
    "    recovered_lines = []\n",
    "    for i in range(len(lines)):\n",
    "        b = lines[\"X\"][i]\n",
    "        c = lines[\"Y\"][i]\n",
    "\n",
    "        if b != 0:\n",
    "            # Intersezione con l'asse y (x = 0)\n",
    "            p1 = (0, -c / b)\n",
    "        else:\n",
    "            # Se la retta è verticale, scegli un altro punto\n",
    "            p1 = (-c / a, 1200)\n",
    "\n",
    "        if a != 0:\n",
    "            # Intersezione con l'asse x (y = 0)\n",
    "            p2 = (-c / a, 0)\n",
    "        else:\n",
    "            # Se la retta è orizzontale, scegli un altro punto\n",
    "            p2 = (0, -c / b)\n",
    "        recovered_lines.append([p1[0], p1[1], p2[0], p2[1]])\n",
    "\n",
    "    return recovered_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_lines(lines):\n",
    "    # transform lines to homogeneous coordinates\n",
    "    lines_hom_df = cartesian_to_homogeneous(lines)\n",
    "    plot_trajectory(lines_hom_df, \"original points\")\n",
    "    # # fin the point of the lines closet to the origin and create the proper dataframe\n",
    "    # closest_points_df = find_closest_points(lines)\n",
    "    # plot_trajectory(closest_points_df, \"raw points\")\n",
    "\n",
    "    # Remove outliers\n",
    "    points_cleaned = remove_outliers(lines_hom_df)\n",
    "    plot_trajectory(points_cleaned, \"cleaned points\")\n",
    "    # Smooth the trajectory to reduce noise\n",
    "    points_smoothed = median_filter(points_cleaned)\n",
    "    plot_trajectory(points_smoothed, \"smoothed points\")\n",
    "\n",
    "    # Interpolate missing coordinates\n",
    "    points_interpolated = interpolate_missing_coordinates(points_smoothed)\n",
    "    plot_trajectory(points_interpolated, \"interpolated points\")\n",
    "\n",
    "    # # convert the points to the lines\n",
    "    # adjusted_lines = points_to_lines(points_interpolated)\n",
    "\n",
    "    adjusted_lines = recover_lines(points_interpolated)\n",
    "\n",
    "    return adjusted_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_lines(lines):\n",
    "    left_lines, right_lines = divide_lines(lines)\n",
    "    left_lines_processed = adjust_lines(left_lines)\n",
    "    right_lines_processed = adjust_lines(right_lines)\n",
    "    processed_lines = []\n",
    "    for i in range(len(left_lines_processed)):\n",
    "        processed_lines.append(\n",
    "            [lines.iloc[i][0], left_lines_processed[i], right_lines_processed[i]]\n",
    "        )\n",
    "    return processed_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if lines.iloc[i][1] is not None:\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  x1_1 = lines.iloc[i][1][0]\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  x2_1 = lines.iloc[i][1][2]\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if lines.iloc[i][2] is not None:\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:9: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  x1_2 = lines.iloc[i][2][0]\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:10: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  x2_2 = lines.iloc[i][2][2]\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:14: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  left_lines.append(lines.iloc[i][1])\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:15: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  right_lines.append(lines.iloc[i][2])\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:17: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  left_lines.append(lines.iloc[i][2])\n",
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\1882061068.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  right_lines.append(lines.iloc[i][1])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAIjCAYAAAAN/63DAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAArptJREFUeJztnQd4VHXaxV/SSUgPaRASOoTeRERQEAFBsO23KnZde1t1FbtgxV7W7i7qrnXdFRuKgqKogPTeIaGlQXohfb7n/GfuZGZyJ5kkM8mU83ueyzAzd+7cySQz5773vOftZDAYDEIIIYQQQoiP4NfRO0AIIYQQQkh7QgFMCCGEEEJ8CgpgQgghhBDiU1AAE0IIIYQQn4ICmBBCCCGE+BQUwIQQQgghxKegACaEEEIIIT4FBTAhhBBCCPEpKIAJIYQQQohPQQFMCCEu4L333pNOnTpJZmZmix/7888/q8fi0pXgOebNmyftDZ4Tz00IIR0FBTAhhBCPYeXKlUpAFxUVdfSuEEI8GApgQghxAZdddpmcOHFCUlNTW/zYiRMnqsfi0ht58MEH1etrrQCeP38+BTAhpE0EtO3hhBBCLCkvL5ewsDDx9/dXS2vw8/OTkJAQ8VYCAgLUQgghHQUrwIQQosPGjRvlrLPOkoiICOnSpYucccYZsnr1al2f7y+//CI33XSTxMfHS/fu3e16gOvr69Xp++TkZAkNDZVJkybJjh07JC0tTa688somPcCnn366DB48WK2Px+Hx3bp1k2eeecZqn6qrq+Xhhx+WUaNGSWRkpBLjEyZMkOXLl7fq56Dty6effir333+/JCYmqm3Onj1bDh8+3Gj9zz77TD13586dJS4uTi699FI5evRosx5gXL/lllvkiy++UK8zODhYBg0aJEuWLLF63N13363+37NnT/UYy5/x0qVL5dRTT5WoqCj1nvXv31/tMyGE2MJDcEIIsWH79u1KNEL83nPPPRIYGChvvfWWEqEQu2PHjrVaH+K3a9euSniiAmyP++67TwnWWbNmybRp02Tz5s3qsrKy0qH9KiwslOnTp8v5558vf/7zn+W///2vzJ07V4YMGaLEOigpKZF//OMfcvHFF8u1114rpaWl8s9//lM9z5o1a2T48OGt+pk88cQTSmzi+fLy8uSll16SKVOmyKZNm5TY1UT/VVddJWPGjJGnnnpKcnNz5eWXX5bff/9dHVBAmDbFb7/9Jp9//rn6eYaHh8srr7wiF1xwgRw6dEhiY2PV696zZ498/PHH8uKLLyqBDfCzx3t29tlny9ChQ+XRRx9VAnrfvn3quQkhpBEGQgghVpx77rmGoKAgw/79+823ZWVlGcLDww0TJ0403/buu+8a8DF66qmnGmpra622od2XkZGhrufk5BgCAgLUti2ZN2+eWu+KK64w37Z8+XJ1Gy41TjvtNHXbv/71L/NtVVVVhsTERMMFF1xgvg37gdstKSwsNCQkJBiuvvpqq9uxvUceeaTJn4W2L926dTOUlJSYb//Pf/6jbn/55ZfV9erqakN8fLxh8ODBhhMnTpjX++abb9R6Dz/8sPk2PKft1w+u42e+b98+822bN29Wt//973833/bss89a/Vw1XnzxRXX7sWPHmnw9hBACaIEghBAL6urq5IcffpBzzz1XevXqZb49KSlJ5syZo6qUqLJagkprc37fH3/8UWpra1V105Jbb73V4X3DaX1YCjSCgoLkpJNOkgMHDphvw37gds1yUVBQoJ539OjRsmHDBmktl19+uarKavzpT39SP5Nvv/1WXV+3bp2qDOP1WfqXZ86cKQMGDJDFixc3+xyoKPfu3dt8HdVcVOEtX589tOryl19+qV43IYQ0BQUwIYRYcOzYMamoqFD+UVsGDhyoxJWt9xV+1OY4ePCguuzTp4/V7TExMRIdHe3QvsFfbOudxWNhjbDk/fffV+IRQhTWAVgEIECLi4ultfTt29fqOvYDr0Xz32qvT+/nBgGs3d8UPXr0aHSb3uvT48ILL5Tx48fLX/7yF0lISJCLLrpI/vOf/1AME0J0oQAmhJA2onlgXY29KrPRQWDkgw8+UA11qKTC+4smMjSHTZ482e3FoCOvr6n3YMWKFbJs2TIVQbdlyxYlis8880xV1SeEEEsogAkhxAJUS5GwsHv37kb37dq1S0WUpaSktHi7Wh4wGrMsyc/Pd6jC6ShojIN1A81kEIJofoO1wNFGO3vs3bu3kSjFa0GCheXr0/u54bbW5CHr0dQEObw3SOt44YUXVFoGGvd++umnVidgEEK8FwpgQgixqUJOnTpVeUktI8yQaPDRRx+pmC34UlsKhBmyb9944w2r21999VVxRRXVsmr6xx9/yKpVq9q03X/9618qUcJSaGdnZ5vTJ+AxRgzcm2++KVVVVeb1vvvuO9m5c6fyAjsDRLAB20EY8DrboiVeWO4PIYQAxqARQogNjz/+uDlTFk1dEK6IQYOQss3ddRT4Um+//XZ5/vnnVYYu4swQgwaBiDivpiqbLQFRYKj+nnfeeUp0ZmRkKFGanp4uZWVlrd4uvMr4eSDmDAcDiEGDBxgNgABRcU8//bS6/7TTTlMxbFoMGqrEd9xxh1NeHzKGwQMPPKB8vnhexMoh+gwWCLxmVJvRkPf6668r3zT2mxBCLKEAJoQQGzCA4ddff1W5vcizhXcW2b/w19pmALcECETYK9555x3lVR03bpxKnIBAc9bkN/h/c3JylGD//vvvlfDFfmNAheVgjZaCgRLw1eLngUowKtoQmHg9ls+N6wsWLFB5wajWQojjdTeXAewoyBh+7LHHlKiHvxnvDUQ+DipQsV+4cKEcP35cHVRAiGNsMgaCEEKIJZ2QhWZ1CyGEkHYDp/KRdICqM6qa7gZEMybPQUAj+owQQrwBeoAJIaSdOHHiRKPbYCUAmDJHCCGkfaAFghBC2olPP/1UjQueMWOGGmqBoRoY64umO2TYEkIIaR8ogAkhpJ3AcAo01KGRDtPktMY42B8IIYS0H/QAE0IIIYQQn4IeYEIIIYQQ4lNQABNCCCGEEJ+CHmAHQdZkVlaWhIeHOy2wnhBCCCGEOA84e5FVnpycrMaj24MC2EEgflNSUjp6NwghhBBCSDMcPnxYTYK0BwWwg6Dyq/1AIyIiOnp3CCGEEEKIDUjYQcFS0232oAB2EM32APFLAUwIIYQQ4r40Z1dlExwhhBBCCPEpKIAJIYQQQohPQQFMCCGEEEJ8CnqACSGEEB+Jh6qtrZW6urqO3hVCWo2/v78aKd/WSFoKYEIIIcTLqa6uluzsbKmoqOjoXSGkzYSGhkpSUpIEBQW1ehsUwIQQQoiXD3LKyMhQlTMMB4Bo4EAn4qlnMXAwd+zYMfU73bdv3yaHXTQFBTAhhBDixUAwQAQjGxWVM0I8mc6dO0tgYKAcPHhQ/W6HhIS0ajtsgiOEEEJ8gNZWygjxxt9l/jUQQgghhBCfggKYEEIIIYT4FBTAhBBCCHGIunqDrNqfL19uOqoucd0dQFPfF198IZ7A6aefLn/961/b9TnnzZsnw4cPb9fndHcogAkhhBDSLEu2ZcupT/8kF7+zWm7/ZJO6xHXc7kpycnLk1ltvlV69eklwcLBq5ps1a5b8+OOP4m1AHEPM21twf2v429/+5tSf1zwvENRMgSCEEEJIk0Dk3vjBBrGt9+YUV6rb37h0pEwfnOT0583MzJTx48dLVFSUPPvsszJkyBCpqamR77//Xm6++WbZtWuXeBOff/65SjYAhw8flpNOOkmWLVsmgwYNUrfZ5t7iZ4FEhObo0qWLWtyN6urqNmX5tgVWgAkhxMNO9xLijDzViupah5bSyhp55KvtjcSv2o7pct5XO9R6jmwPz+0oN910k6p8rlmzRi644ALp16+fEoN33nmnrF692u7jIB7//Oc/K+EcExMj55xzjhLTGmvXrpUzzzxT4uLiJDIyUk477TTZsGGD1TbwvP/4xz/kvPPOU/FxyJz96quvrNbZtm2bnHXWWUpcJiQkyGWXXSbHjx83319eXi6XX365uh+DG55//vkmXy/2NTExUS1du3ZVt8XGxppvw//feOMNmT17toSFhckTTzyhJvtdc8010rNnTxUR1r9/f3n55Zebrdj+4x//kIEDB6oYsQEDBsjrr79udf+RI0fk4osvVvuE5xo9erT88ccf8t5778n8+fNl8+bN5so0bgOHDh1SP2u83oiICPUe5ObmNtoPPDf2F8/9r3/9S72uqqoqq+c/99xz1c/TVbACTAghOny7JVse/HKbFJQbqzEgKTJEHpmV7pJKFyHtyYmaOkl/+HunbAtyNqekUobM+8Gh9Xc8Ok1Cg5qXHwUFBbJkyRIl8iDAbIG41QNV0WnTpsm4cePk119/VWNzH3/8cZk+fbps2bJFVRxLS0vliiuukL///e9KkEOYzpgxQ/bu3Svh4eHmbUHoPfPMM6r6jHUvueQSlT8LUVhUVCSTJ0+Wv/zlL/Liiy/KiRMnZO7cuUr0/fTTT+rxd999t/zyyy/y5ZdfSnx8vNx///1KaLfFPgARuWDBAnnppZfUa0PGc/fu3eWzzz5TQnLlypVy3XXXKcGNfdHjww8/lIcfflheffVVGTFihGzcuFGuvfZa9XPGz6WsrEwdFHTr1k2Jfohv7Dee68ILL1TCH+8NqtMABxG4TxO/eM0Yu40qPdb/+eefzc+9b98++d///qeq3RjOggOL2267TT3P//3f/6l18vLyZPHixfLDD479TrUGCmBCCLHhicU75J1fMxrdnu3i072EELESShCnqE62hE8//VSJMVQZtYl37777rhLMEGJTp05VwtWSt99+W90P4Xb22Webb7/yyitVFRQ8+eST8sorr6hqNMS0Jh5xu8bChQuVR3nPnj1q6t4///lP+eCDD+SMM85Q97///vtKrLaFOXPmyFVXXWV1G4S6Biqrq1atkv/85z92BfAjjzyiRP/5559vfsyOHTvkrbfeUgL4o48+UtPWUCmH2Ad9+vQxPx4iF+Ibwlhj6dKlsnXrVjWhDT8DgOouKvbYzpgxY8y2B9yuVbi114T3SBPA+Jn16NGj1Z5nR6AAJoQQC55YvF3e+bXhVKletWv+1zvkzPRE8ffjOFnimXQO9FeVWEdYk1EgV767ttn13rtqjJzUM8ah53aEllglLMGpeYhny0ouqKyslP3796v/47T8gw8+qAQxqo2wEVRUVKhT+JYMHTrU/H9UR3FaH+trz7N8+XJdby2eBxVhiL2xY8eab4eYhEWhLcCKYMtrr72mxDf2X3tee1Vm2DL279+vbBOo+mqgYotKLti0aZMS95r4dYSdO3cq4auJX5Cenq4OLHCfJoBTU1OtxC/AfuD+o0ePqqozLBU4+HDlyG4KYEIIMfHtlqwmxa9lJRiiYFzv2HbZL0KcDYSFIzYEMKFvV2X/QcObniSFREmMDFHrOfOgEKfGsZ8tbXTD6ftRo0ap0/y2aMILVc78/HzllYUgQ7oELBNaA5qGbYMZ9gfVZe15kEbx9NNPN3oe2A8gwl2BrR3kk08+USkPqOjiNUD4w7IBv64e2G/wzjvvWIlzAEsCgJfYVejZWSC2hw0bpirDqNBv375dWSC8tgluxYoV6pcHpwn0MvzsxYDgjbX0CMGTg6MyHGXgiEZ7czXg+ZkwYYIyW+PIBH4eQgixBA1u8Pw6Sl5ppUv3hxB3AaIW3ndgK2+167jf2WdEUH2ElxfVTVQtbYEHV4+RI0cqLy88tzhtb7loFc7ff/9d+U7h+8Upeghgy+Y1R8DzQKilpaU1eh6IvN69eysBbSlECwsLlT3CmeC1nHLKKaphEEISz69VuvVAs15ycrIcOHCg0X7DCqFVvlEFhsbSAz5qVM0tQUMdmg+xaMBWgfcJleDmgJcalV9YIaZMmWJVSfY6AYxfaCh+/HLrkZ2dbbWgvA8BjE5QDYhf/ALCe/LNN98oUQ3zt0ZJSYk6msAR3vr165V4hoEcfh9CCNFARbegvMbh9ePDQ1y6P4S4E/C8w/uOSq8luO5KTzz0AYQW4sDQOAVhi9Pp8OKi2qkHdAHSHdCQhSY4eFJhdYDgRbKBVl3+97//rbYFgYrHtLTqiQYvCER4hOFxhehEPBv8udhnWCNQlEMjHJri0DiG0/p+fs6VXngt69atU88Ncf3QQw+p/WmK+fPny1NPPaV+jngMvLsQni+88IK6H68J/l4kMUBgQyzj5w9vMYDox88VIhkHDkhwgGhFTB1+lmiYg1caCRhoptOzbdgCHzDeH1Smr776anE1HWqBQHQIFntYmqsBuignTZqkwrABfnHRhYg3WvvhoksTR3TPPfecOsLBKRCc0oB4xhELjvTwhuFNthTKhBDfpqUV3UKLdAhCfAGIXHjfcbCIvxccBMLz60ovPL7vIaaQBHHXXXepYhhsDLA4IA5MD0SWoRiGRAY0eSHxAb5SNKLhbDFAcxo0AKq4qDSikQ02gpYAjQFxiOdBoQ0iEMU2NMhpIhdFN80qAWsCXkNxcbE4k+uvv16lOCBtAUVCiFdUg7/77rsmq62hoaFq/yDQUbGGeNUm1EEvIYEB+wtNBX8wqrhawRKFSKQ4QJOhwgvxDHEPnYahJRMnTlQ/A/wsoMscAdV5bBfWBwhvV9PJ0FqXuZPBm7Zo0SK7LxqGdXROooMSRwkAohZvDk4paOBNgtUBcSDI7sPRB6rAlvYKmNbRAYojt+joaN3nwy+yZSYdtoE/Evzian9AhBDvATm/mGzlKPBE/jZ3MhvhiNuD5i9U67TcVeJ73Hfffaoa/ttvv4k7c8YZZ6hCJSrTrf2dhl6DmG5Or3nMIAwIXxw9aZEd2nhEeHwsQSwHfEO4T1sHfhdLtOvaOnrg1AB+gNriai8KIaRjQSULotZRtEY4QghxV1DjhDUDY5C1aXLuSGFhoSqCwqoCa0l74DECGNVe+Era6+gVR0s4etAWS1M3IcR7G31aUs9lIxwhxJ2BfoF1AZYGDOFwV0aMGKEsFEjUaGtMnFfFoKFsv3v3bhVubesR1vL4LC0QsDZo/mFcWo7hA9p1W4+xJegIxUII8b1Gn/sXbXWoIY6NcIQQdwbpWLYjht2RTIsx1e2FR1SAYVaH4R2JEZagAxTma6Q7aKDTEhl9WrYd1oEZHqMRNZAYgSMMe/5fQohvi+DV902RmLAgu+ugSgy7hCOh/4QQQtyPDhXA6IxEIgMWoEVqWE5igZkZDW3oWLQFmXPoMMQEEcRtoBvzlltukYsuukh1ZwI0zKH0jygSxKWhiozg6zvvvLMdXykhxJMICvCTJ88brHufK3NPCXElbtLzTohb/C53qABGbh18H1gARCn+//DDD1tNOMEL1WZx24KYM8wJR+cgojpOPfVUq4xfNLAhygPiGlVkpEZg+4xAI4Q0Vwk+f2S3Rre7OveUEGejTTPDqF9CvIEK0++y7aQ+j4xBc3ccjdUghHgP1/97nXy/PVfmnJQiY3vFtkvuKSGuAPm5sAwiOQn5r4geJcTTgGSF+EX/F/zNGDndWr3mEU1whBDSER+06w8aR62eP7K7jE6j35d4LlrTt23jOCGeCMRvU0EGjkABTAghNtTVG+SbzVlyvKxK/P1EBibxrA/xbFDxRbUMFWDLpnBCPA3YHvz9/du8HQpgQgixYMm2bJn/9Q416ALU1YtMeeEX1fRG3y/xdCAcnCEeCPF0PCIGjRBC2kv83vjBBrP41cgprlS3435CCCGeDwUwIYSYbA+o/Op1BWu34X6sRwghxLOhACaEEBFZk1HQqPJrCWQv7sd6hBBCPBsKYEIIQXd8aaVT1yOEEOK+UAATQoiIyvh15nqEEELcFwpgQggRUQMukiKbF7eF5dXtsj+EEEJcBwUwIYQgHsqvkzw0c2Cz6z22mI1whBDi6VAAE0KIieiw4GbXYSMcIYR4PhTAhBBigo1whBDiG1AAE0KICTbCEUKIb0ABTAghNo1wnezcj9txP9YjhBDiuVAAE0KIRSPcI7PS1f9tRbB2HfdjPUIIIZ4LBTAhhFgwfXCSvHHpSEm0iUSLDguU1+aMUPcTQgjxbCiACSHEBojch2amS5B/w0dkQXmNPLZ4pyzZlt2h+0YIIaTtUAATQogNELk3f7RBquvqrW7PKa6UGz/YQBFMCCEeDgUwIYRYgCEX87/eIXqjLrTbcD+HYRBCiOdCAUwIIRZgyAWGXdgDspfDMAghxLOhACaEEAs4DIMQQrwfCmBCCLGAwzAIIcT7oQAmhBALOAyDEEK8HwpgQgixAEMuHpo5ULcJjsMwCCHEOwjo6B0ghBB3AhFnyPvVA8MxIH45DIMQQjwbCmBCCLEQv8j5tRdwhuEYFL+EEOL50AJBCCHN5P9qPPrNdub/EkKIF0ABTAghDuT/gpySKnn1p33ttk+EEEJcAwUwIYS0INf3xWV7OAqZEEI8HApgQghpYa4vRyETQohnQwFMCCEW+b+OwFHIhBDi2VAAE0KIKf8XEWeOwlHIhBDiuVAAE0KICUSc3TGlr0PrchQyIYR4LhTAhBBiwS2T+0pihH1xy1HIhBDi+VAAE0KIjRVi3ux089hjSzgKmRBCvAMKYEII0bFCvDZnhAT4NR6F/MalIzkNjhBCPBwKYEIIsQE5v48t3im19Q23xYQFcRQyIYR4CRTAhBBiI35v/GBDo6lwheXVcvNHGzgEgxBCvAAKYEIIMYHhFhhyoTfiQruNQzAIIcTzoQAmhBATGG5hW/m1BLKXQzAIIcTzoQAmhJAWDrfgEAxCCPFsKIAJIaSFwy04BIMQQjwbCmBCCDGB4RYYcmEv4ZdDMAghxDugACaEEBMYboEhF3otbhyCQQgh3gMFMCGEWICc38vHpTa6nUMwCCHEewjo6B0ghBB3o9YUczZraJJMSU9Qnl/YHlj5JYQQ74ACmBBCbNhwsFBdzhyaxIovIYR4IbRAEEKIBWVVtbInt1T9f0SP6I7eHUIIIS6AApgQQkxgwtsnaw4JHBBxYUES1yW4o3eJEEKIC6AFghBCRGTJtmw15libBHe8vFrGPLFUzhveTaakJ9IDTAghXkQng8HAofYOUFJSIpGRkVJcXCwREREdvTuEECeL3xs/2KAbf6aB/F9EoNETTAghnq/XaIEghIiv2x5Q+W2uEpBTXKlEMsQyIYQQz4YCmBDi06zJKDDbHppCE8gQyxDNhBBCPBcKYEKIT5NX2rz41YDshViGaCaEEOK5UAATQnwaDLlwpWgmhBDiflAAE0J8GqQ7oMGtJfkOmccrXLhHhBBCXA0FMCHEp0G0GdIdWsInaw/RB0wIIR4MBTAhxOdBtNkbl45UlWBHoA+YEEI8GwpgQggxieDf5k6WWyb1cWh9+oAJIcRzoQAmhBALO8T4PnEua54jhBDiHlAAE0KITlOcPdAsh/uxHiGEEM+EApgQQmyqwLOHNT3uGE1zWI8QQohn0qECeMWKFTJr1ixJTk6WTp06yRdffNFonZ07d8rs2bPVXOewsDAZM2aMHDp0yHz/6aefrh5rudxwww1W28D6M2fOlNDQUImPj5e7775bamtr2+U1EkI8C4w6fntFht37r5vYU/mFCSGEeC4dKoDLy8tl2LBh8tprr+nev3//fjn11FNlwIAB8vPPP8uWLVvkoYcekpAQ69OT1157rWRnZ5uXZ555xnxfXV2dEr/V1dWycuVKef/99+W9996Thx9+2OWvjxDiWSDaDKOO7QWcoeb71eZsRqARQoiHE9CRT37WWWepxR4PPPCAzJgxw0rQ9u7du9F6qOwmJibqbuOHH36QHTt2yLJlyyQhIUGGDx8ujz32mMydO1fmzZsnQUFBTno1hBBPB9FmiDhzZBTyuN6x7bpvhBBCfMADXF9fL4sXL5Z+/frJtGnTlHVh7NixujaJDz/8UOLi4mTw4MFy3333SUVFw5SmVatWyZAhQ5T41cD2SkpKZPv27Xafv6qqSq1juRBCvBtHo80YgUYIIZ6N2wrgvLw8KSsrkwULFsj06dNVJfe8886T888/X3755RfzenPmzJEPPvhAli9frsTvv//9b7n00kvN9+fk5FiJX6Bdx332eOqpp5TvWFtSUlJc8joJIe6Do9FmjEAjhBDPpkMtEM1VgME555wjd9xxh/o/7Avw8b755pty2mmnqduuu+4682NQ6U1KSpIzzjhD+Yf17BKOAjF95513mq+jAkwRTIhvRKDlFFfq+oDhAU5kBBohhHg8blsBhqUhICBA0tPTrW4fOHCgVQqELbBJgH379qlLeINzc3Ot1tGu2/MNg+DgYImIiLBaCCHeDaLNEHGmhxZ6xgg0QgjxfNxWAKM5DZFnu3fvtrp9z549kpqaavdxmzZtUpeoBINx48bJ1q1blaVCY+nSpUrQ2oprQghBxNkbl46U+HDrBtmEiGB1OyPQCCHE8+lQCwQ8vlqlFmRkZCgBGxMTIz169FB5vRdeeKFMnDhRJk2aJEuWLJGvv/5aRaIB2Bw++ugjlRQRGxurYtJgl8D6Q4cOVetMnTpVCd3LLrtMpUnA9/vggw/KzTffrKq8hBCih18n2/oAq76EEOItdDIYDB0WaAkhC2FryxVXXKGyesHChQtVQ9qRI0ekf//+Mn/+fOULBocPH1YNb9u2bVOZwvDoolEOAtfSsnDw4EG58cYb1fNhmAa2j+Y6WCwcBR5gNMMVFxfTDkGIlw/CuPGDDY08wJr8ZRWYEELcF0f1WocKYE+CApgQ7wcDLk59+ie7WcBaE9xvcyfTB0wIIR6s19zWA0wIIe48CIMQQojnQgFMCCEmOAiDEEJ8AwpgQggxwUEYhBDiG1AAE0KIzSAMe+5e3I77OQiDEEI8GwpgQghxYBCGBgdhEEKI50MBTAghFiDi7LqJPcVW4+I6bmcEGiGEeD4UwIQQYpMD/PaKDKm3CYhEYCRux/2EEEI8GwpgQgixyAGe//WORkMwgMG0zPtqu1qPEEKI50IBTAghDuYAg5ySKnn1p4YR7oQQQjwPCmBCCGlhvu+Ly/bQCkEIIR4MBTAhhLQi3xdWCVohCCHEM6EAJoQQmxxgR+BIZEII8VwogAkhpAU5wJZwJDIhhHgmFMCEEGIBcn7vmNLXoXU5EpkQQjwTCmBCCLHhlsl9JTHCvrjlSGRCCPFsKIAJIUTHCjFvtr4VQhsQx5HIhBDiuVAAE0KIHSvEsO6RjW5PjAyRNy4dyZHIhBDiwQR09A4QQog7Ul9vkEMFFer/j50zSCI6ByrPL2wPrPwSQohnQwFMCCE67DtWJoUVNdI50F8uOqmHBPrzhBkhhHgL/EQnhBAbMODikzWH1P97dw0Tv06s+BJCiDdBAUwIIRZgxPGpT/8kC3/PVNe3ZZWo6xx9TAgh3gMFMCGEmIDIvfGDDWrKmyU5xZXqdopgQgjxDiiACSHEZHuY//UOMejcp92G+7EeIYQQz4YCmBBCRGRNRkGjyq8lkL24H+sRQgjxbCiACSFERPJKK526HiGEEPeFApgQQkRUxq8z1yOEEOK+UAATQoiIGnCRFNm8uC0sr26X/SGEEOI6KIAJIURETXd7aObAZtd7bDEb4QghxNOhACaEEBPRYcHNrsNGOEII8XwogAkhxAQb4QghxDegACaEEBNshCOEEN+AApgQQmwa4TrZuR+3436sRwghxHOhACaEEItGuEdmpetOg4P4xe0XjUmRb7Zkyar9+WyGI4QQDyWgo3eAEELcjajQQCmqqLG6rXOQvwQF+MmLy/aab0M1GIJ5+uCkDthLQgghrYUVYEIIMbFkW7bc+MGGRuIXVFTXNbodiRA3fLBBvt2S1Y57SQghpK1QABNCiIiyM8z/eoeu/aE5bvl4o3y7JdsFe0UIIcQVUAATQoiIyvZFRbc1wAp800cbVAWZEEKI+0MBTAghTsr2RQWZjXGEECLqsxDNwl9uOuqWTcNsgiOEECdl+2pT4sb1jnXKPhFCiCeyZFu2KghYnlVzt6ZhVoAJIcSBDGBH4ZQ4Qogvs8TUTGxrKcsprlS3u4tVjAKYEEIsMoBBW0Qwp8QRQnyVuiaaiQ1uZhWjACaEEBM4NffGpSMlMdJaxKIyfO2ENPFrQhlzShwhxNf5ZXdek83EBgurWEdDDzAhhNiI4DPTE9UHNOwMqOhC1KJC/Nu+fNmZXdroMZouRgUZ6xFCiLdTXVsvu3NKZdORItl82LjszSvzGKsYBTAhhNgAEWvbyPbr3mNK/Pr7icSEBsuxsirzfYlu1txBCCHOxGAwSGZ+hRK5myB2jxTJ9qwSJYI91SpGAUwIIc0Av9qT3+5S/798XJo8ODNdt0JMCCHeQF5ppWw5XKyErhK8h4ukpLJWd11Yv0anxciYtGgZnhIl1/1rveSWVOr6gDuZCgbuYBWjACaEkGZYtPGo7MwukfCQALltcl/dCjEhhHgiZVW1svWIUexqVoYsOz7eTp1E+ieEy5i0GBmdFq2Eb7eozlbrzJudrtIeIHYNbmwVowAmhJAmqKypk+d/2K3+f/OkPhIdFtTRu0QIIa2ips7k2z1sErtHjL5dg51QhpBAP1XVheAdlRotI1OjJSIksMnnQA/FX6f0k3d/z5CiEzVuaxWjACaEkCb4528ZqmsZVY4rT0nr6N0hhBCHfbsH4du1sDHAt1vVhG83rkuQjE5tqO4OSo6QQDQ+tGEARlTnQLlqfJrcYjp75i5QABNCiI7nFx7fA8fK5O8/7VW3/W1aPwkJ9O/oXSOEEF2OlVbJFpONYRMsDYeLpNiiAqtH765h5uouLlNjQ6UTfA5tGIBhW0zGPry0bK/0Twx3m+ovoAAmhJBmKhgBfp0k2J/ilxDiHpRX1cq2o5pvt1hVeI8WnWjyMYH+nWRo9ygZnWqs7kL0xjjJ0tXcAAxIatwPe4S7VIEpgAkhpJkKRm29QW7+aIO84TfSrSoYhBDf8O3uyS1VQlfz7eJ6c8PUIjsHKrE7Ks1Y3R3SLdJlZ7F+23fM4QEY7tJATAFMCCHNVDA03K2CQQjxPt/u4YITVsMltmUVS2VN83m7PWJCzdVdeHj7dO0ifi74rKqsqVOpOKhAb1VLiezOKfGYARgaFMCEECKiKhOeVsEghHg2+WXw7RotDFoMWWFF075dgINwNKhp3l0I3/iIEJeI3R2a2D1iFLxIjUDBwFMHYGhQABNCSAsqE+5UwSCEeA4V1fDtlpia1Ixi90hh075djbAgfxVBpiU0IJosLNi5Eu5ENcRusdpHCN1tDojdtNhQOblXrJyUFiMLluxSjXjuPgBDgwKYEEJaUJlwpwoGIcQ9qVW+3TJzVRcVXkd8uxqJEZiuZqzuoso7IDFcAloQR+aIGN+RZRS6mtjdl1fW7P5pghfL2F4xkhTZMAQjNNjfIwZgaFAAE0KIiKpMYKRnUzaIJDerYBBC3MO3i0qu5XAJiEpHfLuW09VU9q6pwovc8dbGkeklRsDGAAuD5tvdf6x5sduc4LUFDcJvXDqyUYqOuw3A0KAAJoQQk6du9rAkeWtFht11cL87VTAIIe1PQXm11djgzUeK1W2OEhxgMV0tLVpG9ohWiQ3OGmusVXYtxa69SW9tEbx6QOSiURi9ErCL4YwZigbu+LlJAUwIIaYUiK82Zze5Du6/Z/pAt/wwJ4Q4H/hit2dpTWrGGLJDBRUt2kZsWJBVdXdQcqQEBbTdzlBaWaMmuzWkMRRLxvFyh8WuMwSvHvh89IRGYQpgQghxIAUCMAWCEO8+CN6bV2ry7BrF7u7c0hYnHvTCdLVUY3UXVV6IzLbaGUogdo82iF1cHjhe3uLtuELweioUwIQQ0oJ0h6U7ciiACfEC3y4mp6nhEkeMTWoQlRXVdS3aDqarYcCEyt7F0InUaIntEtymfcPo4O2a0DVVeFHZbQ0UvPahACaEkBakO3y5KUsemOle3cyEkKYpqoBv1zRJzdSodrzMcd+uRkRIgHmMMKq7Q7u3bbpacUWNGnRhmcZwML/C7nOjsa66zn5zHQWvhwjgFStWyLPPPivr16+X7OxsWbRokZx77rlW6+zcuVPmzp0rv/zyi9TW1kp6err873//kx49eqj7Kysr5a677pJPPvlEqqqqZNq0afL6669LQkKCeRuHDh2SG2+8UZYvXy5dunSRK664Qp566ikJCKD+J4QYQaNGTFigFJQ3HUKfX15NGwQhbgyGN8AbqwldXGbaEZXNkRLT2ezdheBty3Q1iHDLjF1c2vMTd4/uLD3jwpQHGY1tWLKKTjRKbqDgbT0dqgDLy8tl2LBhcvXVV8v555/f6P79+/fLqaeeKtdcc43Mnz9fIiIiZPv27RIS0lCpueOOO2Tx4sXy2WefSWRkpNxyyy1qW7///ru6v66uTmbOnCmJiYmycuVKJbQvv/xyCQwMlCeffLJdXy8hxH1BRfe84d3kn79nNrsuh2EQ4h7An4v8WrPYPVIku7JLpbYVk8qga9GgZp6ulhYtCa2crlZYXm2u6qKJDpcYcWxPZMNGkRYbJidq6qSsslZ5j3/fd7yR4E2F4O0ZKyf3jpGxPWMlOYqCt7V0MsAI4wbAIG5bAb7ooouUUP33v/+t+5ji4mLp2rWrfPTRR/KnP/1J3bZr1y4ZOHCgrFq1Sk4++WT57rvv5Oyzz5asrCxzVfjNN99UVeVjx45JUFCQQ/tXUlKiBDaeE0KcEOJ9rNqfLxe/s7rZ9T6+9mRWgAlpZyBXsoorzTYGzbdbruPbRc9Zc+oG09VG9Ig2V3dbO10NEWjmqq5pXDD8xXpAwA7uFimDkyOlZ1yo8hzvyimV1Qfy1eMpeNuOo3rNbT0A9fX1qrJ7zz33KFvDxo0bpWfPnnLfffeZRTKsEzU1NTJlyhTz4wYMGKDsEZoAxuWQIUOsLBHYHiwRqCaPGDFC9/lhp8Bi+QMlhPj2MAx3HOdJiLcCf6w5b1c1qhXL8bKG72WN0CB/6RIcoKq+NbVGj2xVbWOfbEJEsPLvjsFI4bSYVk1Xyy+rsrIwwNJgT+ymmcTuENPSIzZUTYNbfaBAvtuWTcHbwbitAM7Ly5OysjJZsGCBPP744/L000/LkiVLlL0BXt7TTjtNcnJyVAU3KirK6rEQu7gP4NJS/Gr3a/fZAx5h2C4IIb6DI8Mw3G2cJyHe4tvFtDLL4RJ6yQcBfp2kt8mHi3HDNXX1UnSiRvJKqxpVgPvFh1uNE4avtiVxZMdKq5RItYweQwVaD/h1jWI3Ql3CSoGnWpdZoATvgiW7KHjdDLeuAINzzjlH+XzB8OHDlY8XFgYIYFeCSvOdd95pVQFOSUlx6XMSQjqWJduy5e0mxO91E3u63ThPQjzRt3vgWJlpuAQEb7HszC7R9e2iipoWF6YeA7ELy8DunNJGFV5MVxumpqsZB06o6Wqhjk9Xg6/faGFoaFLLKWksdiFqIXaHmGwMSux2i5CIkECV1QvB+/PuY7LgOwped8dtBXBcXJxKaUDqgyXw9/7222/q/2hsq66ulqKiIqsqcG5urrpPW2fNmjVW28D92n32CA4OVgshxDfAFyxm2NuzDaJuxElwhLTctwshaTlcAgITqQZ6E9MgYnEJMVxdW68a3CAobYnBdDVlZTDaGQa3YLpaXkmlVewYLnNLqnTFbi9N7JpsDOnJERIeEmiexLYus1Be+2mf8vBiOxS8noPbCmBYG8aMGSO7d++2un3Pnj2Smpqq/j9q1CjVJPfjjz/KBRdcoG7D+og9GzdunLqOyyeeeEJZKuLj49VtS5cuVcZoW3FNCPFdmpsEh+81ToIjpPkhDmgE04ZLQPDa2hNA50B/GdI90ixcYWfA39e6gwW6YhRC1HKcMKqwzdkZIL6xLU3oamJXb3+wKVgrbMUuvMUamuCF2KXg9Xw6VADD47tv3z7z9YyMDNm0aZPExMSoRra7775bLrzwQpk4caJMmjRJeYC//vpr+fnnn9X66PJDRBqsCngMRO2tt96qRC8a4MDUqVOV0L3sssvkmWeeUb7fBx98UG6++WZWeAkhLY42YwQaIUaqautkZ7ZxdLCq8B4pkgPHGvt2ccakf0K4qu72ie+ihCmE8sZDRfLJ2kONpq9huhpEqObdxRLXzHQ1rdIM8a0J3a1HS3Sb5nACB/uhpTFAiKcnRTRKgIDgXb4rj4LXS+lQAbxu3TolbDU0zy0GVbz33nty3nnnKb8vGtJuu+026d+/vxqCgWxgjRdffFH8/PxUBdhyEIaGv7+/fPPNNyr1AcI4LCxMbf/RRx9t51dLCPGGSXCOrkeIN1EP3+5x+HaLZYspmQFNazV1jU1DPWJCldgd1j1SpaZgmAMGU6C6++naQ41EZDimq5mSGXCJxzY1XQ1iF9Vi6zSGYt3JbhC7fePDzQ1qELsDkyIkNKix/GGF17dwmxxgd4c5wIR4vwf41Kd/kpziSl0fsBaB9tvcyfQAE68HfwcNTWpFqrJaquPbhRcXQheiFWOBOwcGyP5jZbL+YKGszSyQI4WNI8KQxqBVd3HZN97+dDVIFMSMGS0MDQ1qmMhoC/4usS3NwoBLVHY7B+mLaQpe78Tjc4AJIaQ9wZcnIs5u/GBDo/u0r2ZGoBFvBOkFELiaZxeiV8+Hq3y73SB2I2Vo9yiVowshCrGL9IOFv2VISaW1SMafC7y0mncXlziQtCd2IZgbLAyYolaiBk3Ygr/Dfgnh5tgxTew2VTmm4CWWUAATQogJRJy9NmeE/O2zzVJR0xCzhC9siF9GoBFv8O1iVLBlk9p+Hd8uhGv/xAgZnhIpw7pHmdMZNh4uUoJ34e8ZSqjaWiAwlGJEjygldNV0tR5RVo1ktmLXMo0BS2FFjW72r1HsRsrg7sbqLsR3U2IXUPCSpqAAJoQQixzgxxbvtBK/OMX70EyKX+KZvt2M/HKLJrVi2ZlVoial2ZIS01kJXYwDhtgdlByhbBAQkPDuvr3igBzQGUwRHx6shK5W3R2Y1Hi6GsTuoYIKKwvDtqxiKdIRu2iAM4tdk5WhvwNiF1DwkpZAAUwIISbxC/uDrf+3sLxabv5og7zhN5IimLg1yLe1HC6By1IbSwKIDg00NakZBS+8u8i2hShdn1ko76w4oKq8ej5bpDmMUtPVjILXdroaxG7m8fJGDWq21ghN7A5IhIUhwkrsBgc0L3YdFbxoyDu5V4yc3CtWxvaKlW4UvMQEBTAhxOdpaggGbsPXO+4/Mz2RHmDiFkD8QfApoWsSvXo51piQZvTtGiu7w7tHqWovBOkGeHcPFsgbv+xX27CdroZ8XqxvHDYRraarRYUGWVWYM/MrGsQuIsiyinVFd5C/nwxI0tIYjAsqvY4Or9BeMwUvcRYUwIQQn4dDMIg7g4loGP+LnF3NzrDvWJnYZjjh2AyiUvPsolkN1+Ghhd8WVd23VuxXInJPXmmjx8PuY0xmQPZujKrMatVYzU7xy55jZqG7/WiJbjIERO3AxAaxO7gVYhdQ8BJXQgFMCPF5HB1usXRHDgUwcSnKQpBfYRodbKzsIgkBItgWiD2jZ9fYqAahiWEOmKq2K6dU1mYUyOs/71e2BgyJsAXT1CzHCWPaGuwMxszfcvlua465SW1HVonu+GJUmJGrC7FsKXYDbXzADgveg5rgLVBVZZydsYSClzgLCmBCiM/j6HCLhb9nykk9Y+gFJk49+Npi8utqqQx6ftnIzkbf7nBz5m6UdA03Tkcrr6pVU9Xe+fWAqphuPFQo5TbT1VAFNk5XM1Z3UenF4yEwM46XqeEWH64+pETn9qziRo/XxC4izSwb1DBRrTViF1Dwko6EApgQ4vNA1CZFhjRpgwD0ApO2gAoq7APacAksWTq/c7AKDE6OMApeU7Ma0gu0ZrPcEqMdB4MmYGvARDZb4YjpahC52oQ1bAPbxZAK7MNry/cpoYvqsu0oYhAS6Kdydc1it3uk9OnapVHCQ0ug4CXuBAUwIcTn0YZg3KAzBMMSeoGJo9TUmXy7FsMl9uY19u1C0/aLDzfaGExiF0kIWlUVdgQ87qM1h8yRZIcLGk9Xg1BU1d005O9GS6+4LpJhSmNYsi1Hnv1+t7IxnKip0x1wgdgzbaAERG/vrmFtEruAgpe4MxTAhBBiGoJxzfg0+efvmU7zDBPf8e0ehG/XwsaAyqptqgKAwNM8uxC8EJyWgyIqa+qUnUGr7mLCmt50NfhuteouqsTl1cbqMkTmog1HVFW40iLP2nJQhSZ2tTSGXl27OOWMBirc2G8KXuK1Ari2tlZ+/vln2b9/v8yZM0fCw8MlKytLzVzu0qWL8/eSEELagSnpiQ4JYEc9w8Q7OVZapTyz2nAJXBafaDzUISIkwMrGMDQlstHvTn5Zlazcd1xVSiF2t+pMV0OFdmRqlPLujkiJki4hAZJxzFjdxUS2nXbEbpgSu5qFwWhn6BnnHLELKHiJTwnggwcPyvTp0+XQoUNSVVUlZ555phLATz/9tLr+5ptvumZPCSHExcAzCW1gG7VkCe7HesQ3QIMZhJ02XAIV3qNFjS0I8Neismo5TS3NwrerVYphS1DV3cxCWXuwQA4c05+uhmQGbAfNb7X1BiVyEUH21i/7dSvLqCJbVnZxiVQHPyd61Sl4iU8L4Ntvv11Gjx4tmzdvltjYBg/ceeedJ9dee62z948QQtoNnHJuSvwC3I/16AH2Tt/untxSq+ESuG77OwFNi4Ywy+ES8O3a5twiumx7VpHZu4vfm+Nljaer9UvoooRzROdA1XyGEcEQl8t25unGn4VD7FrEjuEyLda5YhdQ8BJvpsUC+Ndff5WVK1dKUFDDNBiQlpYmR48edea+EUJIu+Kot5ceYM8H1Vg0k1kOl8BwBz0rARJCLIdLQHBidLAtsEFsOFRorO5mFqhqsd50Nfh3YY/AkImgAOOQii83ZUl1nY7YDQmQwcnGFAZN7KbGhDpd7AIKXuJLtFgA19fXS11d4y7SI0eOKCsEIYR4Ko56e+kB9jzgtd1yxGhh0GLICitqdAWnUew2NKolRIToCmhYIbTqLi535zaergZrApbgQD8JCfAXgxhkR1Zjny+AKLYUuhC+lvFnzoaCl/gyLRbAU6dOlZdeeknefvttdR1/mGVlZfLII4/IjBkzXLGPhBDSbnnAUaGB6hS0HpAhiZEhaj3ivlRUw7dbYrYxYNGLDgvyNw52gNd2qGnARE87VoI6kw8XNgaIxnV2pqtpQLNC8CLVQW+CGry9lhYGLCkxnV0mdh0VvNiHk3vGmgRvjHSPDnXZ/hDiUQL4+eefl2nTpkl6erpUVlaqFIi9e/dKXFycfPzxx67ZS0IIaQcw6tie+AWQCsgL5hAM9wFjf/fklpmruqjw6vl2AaaWGZvUjGIXvl3YEOw1v2FbWoUX0WR6QtYeqARrmbs4qLIVu92jXSt2AfYXyRIQuxC9SI2g4CWklQK4e/fuqgHu008/VZeo/l5zzTVyySWXSOfOPDVCCPFMIAww5a0pokMD1RQ40jHAdgC/rOVwCYg6Pd9uYkSIebgEmtQGd4+UCB3frkZeSaWKItPyd5HjaysWHSEmLMgkdCOUhWFwO4ldQMFLiAsF8IoVK+SUU05RgheLZTYw7ps4cWJLN0kIIR0Oprs1NwoZnlFOgWs/CsqrrcYGbz5SrG7TS0VAxq65Ua17lLKq2APT1fYdKzNWd2FnOFgohwoqWrx/sWax2zAuODkypF3ELqDgJaQdBfCkSZMkOztb4uPjrW4vLi5W9+k1yBFCiLvDBIiO5UR1nWzP0prUjDFkeqI00L+TpCdFmIUuLpvLu4UPF+JQ8+6iwqs3uKIp4rrYiN1ukSodor3ELqDgJaQDBTBOQen9wefn50tYWJiz9osQQtoVJkC0HxBte/NKTZ5do9hFgoKe5aBX1zBlYdAydwcm2fftaqBKrI0RRnUXI4L1Isbs0TU82Cx0BydHqMouLBXtKXYBBS8hbiCAzz//fHWJD4Arr7xSgoODzfeh6rtlyxZljSCEEE8EyQ6o6OUUV6pmt9YkQECcwCKBKjGEMtb19YY5LS5MDZc4YmxSQ/pARXWdrvAcbjE6GMITaQnNbT8zv8Iodk0Na/t1pqvZA1PXrBrUukfqxp61BxS8hLihAI6MjDR/2CDv17LhDUMxTj75ZE6CI4R4LBCqSHi44YMNrUqAWLItWzXRWfqIIajxmOmDk8RXKKqAb9c0Sc3UqKY3/QzZuBCcqknN1KzmSJUV09rQoKYJ3p/35Ok2wemREGEjdrtFSnwHiV1AwUuIBwjgd9991zzx7W9/+xvtDoQQYiF+b/xgQ6PKMarJuP2NS0d6pQiGtxZi1Jy3e7hIVWP1fLuYfqaydlUMWZT06trFoep4SWWNbFB2hkJZvjtPPZ8j4OCjwbMbof7f0fYVCl5C3IdOBpR0SbOUlJSoKjia/SIiIjp6dwghTgZC5NSnf7KbBKFZIH6bO9lKuLX2cZ4GXue+vDKr4RK7skulVs+3GxdmalIzVnYhfkMCm/btahinqxXI8l158sWmLIcb1Eb2iDaKXUxSS45UdoqOhoKXEPfVay1uggP//e9/5T//+Y8cOnRIqqutT21t2KB/+pAQQjw5Bg2yBffbxqC19nHuDOoiWcWVZhuD5tst1/HtxnXRfLtGsTu0W5REhjbt29WAGNyVUyI/7cyT91ZmSr5OxJkeU9MTVDVZq/DGdul4sQsoeAnxHFosgF955RV54IEHVCPcl19+KVdddZXs379f1q5dKzfffLNr9pIQQtw0Bi2n+IRTt98RFFfUNOTtqka1YjleVtVovbAgf9Ukpg2XwGVLosAwoviX3cfk3ZWZ6oDAEUanRsukAfFmsYtBE+4CBS8hPiSAX3/9dXn77bfl4osvlvfee0/uuece6dWrlzz88MNSUODYBxohhHhDDBq8v48t3unU7beHb3dHtsm3a8rczTjeODUhwK+TDEgKN2ftosrb20HfrgZGEqOy+9EfhxxaH+L20rE9ZEzPGGVjiHYjsauNR0asGsQuli1HKHgJ8RkBDNuDFneGJIjS0lL1/8suu0wlQbz66qvO30tCCHExiCyLCg2Uogr9AQm2MWgQv/YSI5p6XHsCcXbgWJlpuAQEb7HszC7R9e2mxYZaDZcYlOy4bxfkl1Upz+67v2eoccWOMHtYsvxpVHdlZ4gKdS+x66jgxZhjiF0leHvGSEoMBS8hXimAExMTVaU3NTVVevToIatXr5Zhw4ZJRkaG8o0RQognsnRHjl3xaxuDBhF07+dbHd52U/FpzgKfvzkllVbDJXBKHqfp9ZrGzGODlW+3ZdVWiF0MmvhozSH5efcxhx4DQX3lKWkyNT3RYY9we0PBS4jv0GIBPHnyZPnqq69kxIgRyv97xx13qKa4devWmYdlEEKIJwGRgwzfpogODZQz0xPV/yGOmhLLlo956vwhLolAwyhfTDjThktA8OaVNvbtdg40+na14RLDUiKlW1Rnh3278AJDSP+657i8vypTd1qbLSGBfnLlKT3lsnGp6rncFQpeQnyXFgtg+H/r642h42h6i42NlZUrV8rs2bPl+uuvd8U+EkKIS2kuyQEUVtSYkxxW7c93aLtzxvZwivitqq2TndnG0cGqwnukSA7oTDtDlbl/QrjVcIk+XbtIgL+fw416SHvYeqREFm/Nkj25ZQ497sz0BLliXJryvAY6+FwdAQUvIaTVAtjPz08tGhdddJFaSPvAUauEdHwCRL3BscljrfnLrIdv93i51XAJNK3V1DWuvKbCt6tZGbpHyqDkSOkc5JhvN6+kUlV2sWw8VCS/7HHMyoCM38vHpcrp/ePV8ztaSe4IKHgJIU7NAS4qKpI1a9ZIXl6euRqscfnll7dmk8QBOGqVkI5PgMDf4b9WHXRo/XG94ppdB9PitCa1LVgOF0upjm8XCQmWNoah3aMcjgTLhdg9YhS7qPCuOpAvFTqZvnpcdnKqnNavq4xKjXa7VAZbKHgJIS4TwF9//bVccsklUlZWpiZsWB794/8UwK7BV0etEuIOCRAA9xeWV8tNHzk27Afrn2wz+AJjfSFENc8uRG9uiR3fbjdYGIw2BoheCLfmqq1aIxyeQ1kZIHizSuSYjjdYj3G9YmViv64yOs04Va0lKRAdAQUvIaTdBPBdd90lV199tTz55JMSGsoPkvZs0NFrPcFt+ErE/WjQoR2CENdQXVMnj37TdKOcJY+dM0iJUMsmtf06vl38yfZPjDB6dk12hr7xzft2IXZxNkir6mqXx8scm6aGUcET+sbJmLQYNWwCGb9+bv75QcFLCOkwAXz06FG57bbbKH7bEUdHrb63MkPmnJTqsAeQENLwN9ZcqkNFTb1U1DjmFU4ID5a7/rNFqusae4UxKAFCV9kZTHm7oUEBDo0mtqrsHi12eHQwQNbu6NQYGZMWrewM8RHuMZijJYIXr982w5iClxDSLgJ42rRpKvIM09+IezXoPPbNTnnq210yMClCfcGNTDV+0SW3YFQpIb6Is8cU55osB4hB0ywMELwQobFdgpsVuxgkoQldLNuzSqSgBWK3S3CAjOgRZa7uDu8R1azIdgcoeAkh7UWLPxFnzpwpd999t+zYsUOGDBkigYHWgeaIQyPOpSUjVPFloX1pYgQpSIgINgriHkZRjIpTcACrxIS4akzxtPQEeWBmuqr2NnXwqYld7W92m2lB5FpLQDPs6LSG6u6AxAiPsENR8BJCOopOhhaOb7OMQGu0sU6dpK7Osc5iT6OkpEQiIyOluLhYNf+1J/C4nfr0T6rhTe/NwtdcXHiwXDQmRb7dmq3rM7QkKMBPTX7CF+UIJYqjnC4ACPEk8Dc26rGlUnSiZcLTHh9eM1bG97VOgMBH7eECG7GbVezQQA1LoKeR9auqu2nRSvi687AJW8G73sbDS8FLCOkIvdZiAeyrdKQAtkyBAJZvmFbj0VIg8HbuyimVb7Zkydebs+VQQYVD2+8REyoje0SZrRP4gnU0PJ8Qb+DlZXvkxWV727ydsCB/2fzIVHNl19KzW1LZON7MkalqsE/AvwvBi4PWyM7uOUq4NYIX4h3DRSh4CSHOgALYywRwa3KA8dbii/ebLdmyeEu2HC060aIvcXgXNUE8MiVaIkM940uXkFZXgR9f2mxFNiigk1TX2v/YDPDrpERrWVXrzobFdQkyi11Ud2FZcufpaq0RvMYKb4y6pOAlhLitAH7llVfkuuuuk5CQEPX/pkBChDfiDgK4LZPg8DZvOFSkKsOwSVhmjwb6d5LEyBBJiuys/m8viL9PfBcZ1cPoMYRtolec+8cmEdISvt2SJTd9tNEp28KfBj5cm/uE7d01zCx4YWtw9+lqllDwEkK8WgD37NlTJT/Exsaq/9vdWKdOcuDAAfFG3EUAOwOMWl2bWaAqwxDDllFKcV2CZfrgBJVLiq9gjEjdeKhQjWa1BadhYZsYaRLFqBiHBbt/pzkh9li1P18ufmd1m7cTGuQvNXX1jcYX4wATE9yQzIDqLv5uHJ3m5g5Q8BJC3B1aIJyMNwlgS2rr6mX1AYjhLFmyPcfq9G9iRIjMGJIkZw9LktSYUCWGNxwqVF+ACPevrKlvVPFCBJsmiLE4Mr2KEHfhy01H5fZPNjltezhIhNgdZaruesJ0NUsoeAkhngYFsJPxVgFsCSpWv+07Lt9szpYftudY2SDwJQchPGtosvIk4ktwZ3aJbDhYKOshjA8W6nqMUVEelWr0EmMZlOxZAoD4Fs6oAGOM8QMzByp7Uh8PmK5mCQUvIcTTcaoAvvPOOx1+4hdeeEG8EV8QwJZU1dbJij3HVWV46Y5cqahuaOhJiw2Vs4cmK0GMtAitwouYNq1CjGV7VnGjU8BB/n4yqFuE8hJrgzoSPGAiFfENHG2Ea4o3TYksngAFLyHE23CqAJ40aZLV9Q0bNkhtba30799fXd+zZ4/4+/vLqFGj5KeffhJvxNcEsCUnqutk+e48JYZ/2pVnZX1AY9zZQ5OUIMb/LamsqVPRT/iCNQrjIjle1tB8Z/kFaxzUgUpxjAxICveYrnfiXSBp5QZT3GBruGNKX7l9Sj9xVyh4CSHeTomrLBCo8P7888/y/vvvS3R0tLqtsLBQrrrqKpkwYYLcdddd4o34sgC2/QJdtjNXNdD9svuYVNc1iOEBieEya1iyskn0iG38pakNAlh/qEA2HCxSX8S7ckrE5vtXnULGyFjNNoHcU09qFCKeiTZwxjJmsCUgkvC3uZPdagIbBS8hxNcocZUA7tatm/zwww8yaNAgq9u3bdsmU6dOlaysLPFGKIAbU1JZI0u3Qwxnya97j1t9sULAojI8c2hyk1OqyqpqZcthoxhef6hQeYn1hgX0igszWyaweJq3kniv/9d2GE1HUlFdK+syKXgJIb5LiYN6LaA1Gz527Fij23FbaWlpy/eUOC3rt72JCAmUC0Z1V0thebV8vz1HVYZX7j+uvnixPPntLmVtgEVi5tCkRn7fLsEBckqfOLVoEW0HjpeZfcTILt6XV6Zi2LD8d/0RtV54SICqDBu9xFFqUlZ4CAd1kNaDv7fWkNjEMJr2ELwNFd4C2Xy4iIKXEEIcoMUV4Msvv1x+/fVXef755+Wkk05St/3xxx9y9913KwsErBHeSHtVgFs67c0dgc/3u2058s3mLFmTWWAeBIBeOURBwSZx1uBElRDhCEUV1SqCTfMSbzpcZNWUp20bDXmqSmyKYfOkgQLE8yrAE/vGyWn9uspl49IkKKB9POsUvIQQ0kEWiIqKCvnb3/4mCxculJoaY6d0QECAXHPNNfLss89KWFiYeCPtIYAhfm/8YIOaHuWup1hbSm5JpRq2gcowvrg1UNA+pXecsklMH5woUaFBLcou3pVTqgZ0aNYJeIttiQ0LMlaJTbYJ2DIYwUbsUV1bLwMe+q6RJ705XHmASsFLCCFuIIDr6urk999/lyFDhkhQUJDs379f3d67d2+vFb7tJYCba8DpZDrV6m5NNi0BOcGLt2QpMQx7hEaAXyc5tS/EcLJMHZSgrBWtOX2Nxjothm3rkWKrBj3teZBhrHmJMbAjuQl/MvEt3MEDTMFLCCFuWgEOCQmRnTt3NjkS2RtxtQB29Mv342tPlnG9Y8XTOZhfroQwFgzUsMwJntivq8waliRTBia0erQycoy3Z5kGdZiWvNIq3eodBLE2vS49KaLdTmcT75kC19oDVEcF71iT2B3XK5bTFQkhpCOa4AYPHiwHDhzwOQHsLg04+JLuGh4kveI8OwUhNTZMbp7URy37j5Wp6XNfb8lSDW+IWcMSEugnkwfEq8rwpP7x0jnIcftCcIC/ErVY/jLBGMGGCjTEhuYn3pFdoirui7dkq8X4OD8Z1j1KRmB6nWlYh6NeZeLZoOG0tUCy4ncJzatNHaBS8BJCiHvQ4grwkiVL5L777pPHHntMDb6wtT54a0SYu1SANaJCA81VSywQbS0RiO4IfhV355YqMYxotcz8CvN9oUH+qiIMz/Bp/bsqgdtWIEY2Hy5WtgnjSOdC3QlgmHynxLTpZ90vIdxjbSikaRvS+AU/Sk5J4zMFjnLVKWnyyOxBLRK8yZEhcnJvWBooeAkhxG0tEH5+DaeHLT+ksRlch0/YG2kvDzDGCdt7QyACE8KDJcNCGNr6WzFJDSJtdJpnjxjG7xMsDKgKQxCjeqsRHhwgZw5KUGkSp/aJc9rUODxnxvFyi8l1hbI3r8ycYmEZ3YbYNaN1Iko12kV2ZgSbN/Di0t3y8o/7Wv14fCLePLm3+p2h4CWEEC8SwL/88kuT95922mnijbRnCgQwNNFkk1dSKX9kFMgfGfnqlOue3DLd7eGLVYlhCLXUaBmQGOGRlUv8iiL67BuTVSGnpNKqEj59UKKySaApKMDJI5SLT9So5zZaJ4z2CQzvsATapW98F/PUOlxicAdFjefx3Pe75NXlxuZeZ0HBSwghXiCAfRV3zgHOL6uStZkFquIEYYzxwnrvaliQv1mgoULsicMjMCgDVgVkDC/emqMyhy1jz84aYhTDyBt2hdhHpX5Pbqm5SgzrhKVVQyM6NND8s4Z9YlhKpIQGta6hj7Qfz32/W15d3voKsEaQfyd59NzBMr53HAUvIYR4iwAuKiqSf/7znyoNAmAs8tVXX62e0Ftpz1HIbZ0EV1xRowQxKsQQxNuOFutmm2KT/RNhm4iS0SbrhCd9WePn9MeBfPl6S7Y6cCi08O/GhwfLjCFJyiYBm4IrXxNE+AbT1Dpcbj5SJFW11hFseP8GJoWbG+sgij3pZ+0r/LI7T654d61TtvXhX8bKeNOEQ0IIIR4ugNetWyfTpk2Tzp07myfBrV27Vk6cOCE//PCDjBw50uFtrVixQg3PWL9+vWRnZ8uiRYvk3HPPNd9/5ZVXNposh+dGI55GWlqaHDx40Gqdp556Su69917z9S1btsjNN9+s9rNr165y6623yj333OO2AtjZlFbWqIqlsk0cyFcZvLa+REvhiOowBNrotBiPiQWrqauXlfvzVWUYI5lLKmutuuoxhhkNdEO6RbpcdGKgAqLdtCEdEMV6+c74WWtNjKgWD+4W4ZTmPtJ63lmxX574dpdTtnXLpD7yt2n9nbItQgghHSyAMe64T58+8s4776gJcKC2tlb+8pe/qHg0iFpH+e6779RgDaRJnH/++boCODc3V959913zbcHBwRIdHW0lgDGF7tprrzXfFh4ebk6nwA+iX79+MmXKFJVesXXrVlWtfumll+S6667zCQFsCzrTMTRiTUa+rM4oUB5XiDY9EEU2tDsqxA2JEy2Z2tYR4LX8uveY8gwv3ZFr5dntEROqhDBsEqjItlcFNqvohLmxDpXi7UcbH4QgA3lI90hVsdasE/Ee3MjoiTz85Tb51yrrA+rWcsuk3vK3aQOcsi1CCCEdLIBR+d24caMMGGD9wb5jxw4ZPXq0GpXcGiBE9AQw7BZffPGF3cdBAP/1r39Vix5vvPGGPPDAA5KTk6Om1wFUh7HNXbt2+aQAtqWypk6JYNguYJuASKus0RfEoA8avuBvTXP/hi+8tp935ymbxE878+RETUNKSa+uYUoIzxqaJH0Twtt9v1CJN4vig4WSX17daL2UmM7muDtcDkgMd3qjH2ngnRUH5IlvjdautvLhNWNlfF9aIAghxCsEcEJCgvz73/+WqVOnWt3+/fffy+WXX64qts4UwBCqEK6o+k6ePFkef/xxiY2NtRLAlZWVUlNTIz169JA5c+bIHXfcYa5OY5/ww7AU0cuXL1fbKigosKomW1JVVaUWDWwjJSXFKwWwXgV169Eic1Pd+swCKa+2H2+Hhi9jdThG2SdgMwgJ9HfLyvePO/NUxvDy3cesqt4QllplOC2u/cd648/wYH6FWRBjQSay7V8novCQ+dxgnYhy+4q8J/HrnmNy2cI1bd4OGk63zJvmkakrhBDiybhsEtyFF16oLAfPPfecnHLKKeo22Bjuvvtuufjii8WZTJ8+XVkjMHVu//79cv/998tZZ50lq1atEn9/o8C67bbblO84JiZGVq5cqWwO8BO/8MIL6n5Ufm2n1kHEa/fZE8DwEc+fP198EXh+jXnCMXLzJJHaunqVyaua6g4UyJrMAim18Nii+WzZzjy1gED/TjK4W6SqEis/cWp0m6ZsOQukMKApDgt80Zg2h4zhFXuPya6cUrU898Me5cWFEJ45JElSYkLbZd9wAAjhjeX8kd3VbdhHDOrQvMSIYcPPfdWBfLVo9O4aZq4Q47J3V8+eEtiRHNepwreGQA/wzRNCiC/T4gpwdXW1Ertvvvmm8v6CwMBAufHGG2XBggXKo+usCrAt8Bj37t1bli1bJmeccYbuOgsXLpTrr79eysrK1L6gUg0B/NZbb1nZNZBcgcuBAwfqbseXK8COpC8gag1iWMsitkxg0APeWy2PGKK4X3y424g0pGagcQ5DN9BIh9engag4rTKcGBnS4RFw+46VmSvEqBYfOFbeaL2IkABz0oSaEpgSpYZ3kOb5568H5LHFzrFAfHztyU2ORSaEEOKBOcDw+qIqCyBKQ0PbVilzRAADpDjABgGRq8f27dtl8ODByt/bv3//VlsgfMkD7CxhhoQJNNVBGFvm8+oRHhKgkg+05joIzTA3EGnIVF6yPUdVhldn5JstCLA4j0mNkbOHJclZg5Oka3jrDvScTUF5taoMa9YJVIwtfc62cXfKOtEjRnmL3dW33ZEs2nBE7vjPZqds6+WLhss5w7s5ZVuEEEI62AKhAcGrice2il9HOXLkiOTn50tSkv5ACLBp0yY1rjk+Pl5dHzdunGqCg0cYlWqwdOlSJY4dFb+kaVDJ7ZcQrpbLxqUpP+uB4+XGproDxixi2xgwnMpfseeYWtQ2OokMTIowCuI0YyYx4svam9guwXLJ2FS1IIf5u605yjO8NrNQWT+wzPtqu5rqharw9MGJEhPWcR5cPPcZAxPUosXB7coutfISY4w0YtmwfLD6kFovrkuQuUKMZbCb+rbbm8RI5/3OuYPthxBCiJMqwPX19aoC+/zzzyubgRY7dtdddymhCfHpKHj8vn3GqUsjRoxQvt1JkyYpPy8WeHAvuOACSUxMVNVmZPeWlpaqKDPYG+AF/uOPP9RjsA+4jgY4+IS1/GAcAUDswgoxd+5c2bZtm4pBe/HFF302Bq29wa/Y4YITqqKqJU3genNgAp4m0DCoA7FlHZWAgBizb7dmqzSJzYeLzLejyQnDDmCTmDYoUSI7u99kvdySSpUyoXmJtx8tkeo665QP+LYHJUdaeYk72vLREcD+MurxpVLUjKWnKVBXx8/ut7mT2QRHCCHeYoFAkxmmwEGcjh8/Xt3222+/ybx581QW7xNPPOHwtn7++WclXm254oorVHwZ7BCIXEMUWnJyshKxjz32mLmJbcOGDXLTTTcpuwP8uvD6XnbZZXLnnXdaeZEtB2HExcWpQRgQwy2BAtj5gtLcVJdRoCrGzdE50F9ZJZQoNg3r6AjBebigQmUMozKM5kDLHN+J/SCGk2VKeoLb+m4RwbY9y9hchzzodQcLdS0rqMAjZUI7CEGFPtDLI9jaKoA1ufvGpSPtji4nhBDigQIYQhQNcLNnz7a6/csvv1Ri9OjRo+KNUAC7lrySSuOkOlOVeE+u8exCU8DC2heZxKYxzrBPpMaGtqu39cCxMlmsxHC2ii3TCA7wk0n945VnePKAeJVA4a7gI+BI4QlzYx0uYZewHRaoDUUx+oiNDY0daf9wBav258vF76xu9eNx1uKRWekUv4QQ4m0COCQkRFVUMV3Nkt27d8vw4cPVSGRvhAK4/ZvR1mYWmLOIkTrhyG+q5m1F0oTmbW2v8cJ7ckvVKGaIYcuKNirXZwyMV5Xh0/t39QivbXlVrWw+UmS2TmB6XfGJxlXRnnFhDYM6UqOkb3y4R5/2/3LTUbn9k00Orz8kOULun5mu/OLw/J7UM8ajXz8hhHg6LhPAY8eOVcsrr7xidTtsBbAYrF7d+uqJO0MB3PFRZRDEyjaRUSDbjhY3qlDayzQeikxii1HOaHRzJfiT2pFdYrZJWPqdYYuYmp6gKsOn9umq9s9Tkj4g6hsEcaHszWtcpQ8PDpDhPaLMohj/jwhxP1+0syrAL/95uJwzkkkPhBDi9QL4l19+kZkzZ6qpa0hYAGg+O3z4sHz77bcyYcIE8UYogN0LDImAEFO2iQP5aqxwrSOK2FS1bGiuc+3gCPx5Yd8ghGGVyLJIw4B/edqgBFUZPqV3rMeNOMZByYbDxjHOEMSbDhU1mhgIN0r/hHAVeaf9zNPa2abSUg/wqU//1Ci1xB4f/mWsaoIkhBDiAznAWVlZ8tprr6nmM4BhEvD/wh/srVAAuzcYc4yGrjUZxiziTYeLrEYdNwWE6MgeUTI6LUZVLtFo1znI3yVV1I2HC+XrzdmyeGu2HCttaDyDlxaRakiTGNsz1iNPo2NiIHzQsEtoleJDBRWN1sNrxc97pMlLDF+xK37erWXJtmy54YMNDq3LrF9CCPGxQRi+BgWwZ4GkA4hgLXYNYqyyxjFBHODXSdKTI6wi2JwdCYZKI/YNleHvtuWogRYaGLKBMcwQwxDk7jIxrzVA5KM6rAniLUeLGx2YaD/vBi9xtCRHhnRolfjlZXvkxWV7m12P094IIcTLBfDevXvl4YcfViOFbTeIJ8EoZOQD9+rVS7wRCmDPBqJr69Eic1Pd+syCRqfrmwKRYEoMm+LXBiQ6L5MYldNVB/LV9DlMobNsNkOqgBLDw5JlWPdIt7UOtOR90CLYNh5CBFuB5JY0jmBLjDBmQGsxbMgobk+/NA5Qxi/4SXJK7Fsh8N4w65cQQrxcAGNoRFRUlDzzzDO69yNXF0+K/F5vhALYu4DoRIavOYs4s0BNp3OUsCB/5WsdafIRO6vZCwLx933H5evNWfLDjlwpq2rYJ4wvnjkkWVWGByVHeLwYBvj4gS/amEls9BLjfYEAtdfMiJ85DkJcPY4aVogbTVYI2w9J/OSZ9UsIIT4ggDFN7YMPPpAxY8bo3r9+/XqZM2eOikPzRiiAvRsILkStQQxrWcSFLRiGoDV7aVVi2Ca6R3duk0iFjeOXPcdUmsSPO3OlwqJi3SsuTGYOTZJZw5LVCGpv83OjcdDSOqH3XvSICbUQxFEyIDHC6dXYp77dIe/8mmGVOIJnuG5iT7lvRrpTn4sQQogbCuDOnTurprfU1FTd+w8ePKia4SoqGje9eAMUwL4FGtb2HStTCRNoqoMw1puW1hSoUKI6rHmJ23Ia/0R1nfy0K095hnFZZeGj7ZfQRSVJoDLcq2sX8TbwEZWZX2Ec5WyqFO/JK22UC42qPCrxaKwbAVGcEi2RoYFtrgDrfUCyAkwIIT4igBMTE+Wjjz6SyZMn697/448/yiWXXCI5OTnijVAA+zb4M0EOrmqqO2DMInY0KstyOtwwTFLDkA5Tw1d0KyapwRaBijBsEqgQ19Q1/AmnJ0WojOFZQ5MlJSZUvJWSyhoVu6ZlEuP/pRZ2EQ3jpECjZQKVYlTOHWkqbM4DjC2gMZIeYEII8XIB/Oc//1lqampk0aJFuvefc845EhQUJJ999pl4IxTAxBL82WDAxWqTXQK2CcuBF47Su6sxkxiWCQg0XG+JbQINcz9sz1E2CXiHLbOQ0TSHyjCsEslRncWbgWDdm1eqovA0UZxhMY1PIyo0UEakGBvr8PPGAUlYcOMx1UyBIIQQz8TpAnjjxo1q8MXZZ58t99xzj/IEA9gi0Bi3ePFiWblypYwcOVK8EQpg0hxZRScamuoyCqzGITtKdGigWZxBFA/tHunw6OTC8mqVIgGbBCaaWfpWYcWARWLGkCSJj3BupJs7j9NG0sT6Q6YItiNFjaLwULwdmNQQeYdKMaYM3vihYznA14xPk4dmDXLRKyCEEOIWOcDffPONXH311ZKfn291e2xsrPzjH/+Q2bNni7dCAUxaSl5JpXFSnalKvCe38ejg5gj076S8w2YvcVq0xIeHOJS/Cw8rhm6sPVhg9suiuDy2Z4yqDJ81ONHlY6HdiZq6etmZXWL2EkMcHy1qXLWHKHZwqKDijil95ZbJfWmFIIQQbx6EceLECVmyZIns27dPnQbu16+fTJ06VUJDvddvCCiAiTMqkmszC8xZxEidaM0YGsShaZYJCGOkQDQlvnKKK9XkOVSGIfo08BiMYEZleNqgRIkKbbkf2dPJLj5hZZtAVnSdY/NSGuUWz5udzqY4QgjpYDgJzslQABNnU1xRowSxsk1kFKhT7/Yqj5iWFuDfSaU/2P7FhgcHqPQDiGJUifH/Ljq+VnCksEIWb4EYzpatR4utKs0T+nZVYvjM9AQJd0KmsSfyn3WH5J7/bm3VY5kMQQghHQ8FsJOhACauprSyRlUilW3iQL7KwrVsagOdA/0lONBP/Dt1UhVcJEJY5gPr+VqxYJKdbXNd5vFyVRlGmsSunFLz7YhqO71fVzV9bsrAeAkN0hfT3sg/fz0gjy3e2arHMhmCEEI6HgpgJ0MBTDpiIAROz6/JMGYRbzpcpCbFWRLZOVAtqBBDdJVX1arJarqjhU3xaxjUAYEcaDHKeV9eqfILwyax/1hD815IoJ+cMSBBVYYnDYh3uCHPU1m08ajc8emmNm2DyRCEEOJFAjgrK0uSk5PFV6EAJh0NJsNBBBtHN+erarFtqkFESID0iA2VAD8/JYpP1NSp6m6dTiV5WEqk2TaB9AMMjcDHAdaHEIZN4mB+hdWgCdgj0EA3oV+cBAd4nxhGesbF76xu0zZevmi4nDO8m9P2iRBCSAcK4OjoaHnttdfUuGNfpL0FMAQLkgPySitV1/9JPWN4WpVYgWowmra0prr1mQVSbmOHgBc4PTlCCV4I4uq6etl8uEhKKvWHRqA6PMokitNiQ2Xb0RKzGLZMTAgPCVCNc6gMj+8TZ1VN9mS+3ZItN33kWASaPVgBJoQQLxLAr7/+usydO1emT58ub731lsTExIgv0Z4CGPFV87/eYTVpLCkyRB6ZxS5zYp/aunrZnlXSkEWcWSClNkIXQnhEjygVfxbo10lq6g2q+U5vaERsWJA5aQKXtXUG+WFHjny7NVtyS6qssounD4YYTpaTe8V67IEaDjpPffqnFk/4swR/p/QAE0KIl3mAMzIy5JprrpEdO3bIO++8I7NmzRJfob0EMMTvjR9sENs3Rfs6ZZc5cRQIOkStQQxrWcSFFTVW66DhDZPR+sR3UVXceoNBieitR4pVtdhqXX8/GdI9Uq1vMEWIYZvHy6rN68R1CZYZQ4xiGMLZkbHD3mR/eH3OSJkxlH+fhBDilU1wr776qtxxxx0ycOBACQiw7hDfsKFtpw99WQA3V4FilzlpC/VqXHCZuakOwvh4WUMlV4tDG9o9SoncUFOU2i7T8Ij88gaha5lJjMpwtp3GO0yeO3tYktpeS0Y8dwRfbjoqt3/CBjhCCPEFvdbifKODBw/K559/rjzB55xzTiMBTFoPqmlNnX41Vt0q1Xr8kiUtBdXY/onharlsXJpqeMO45jWm2DX4iPH7pU1KAzjQGpwcIReM6i4JESHqIAwiev1B42S7wwWNJ6lp5JRUysLfM9SCGDb4hVEZHtwtwi3FsCMT9poDnn1CCCHuT4sqwLA93HXXXTJlyhTlA+7atav4Cu1RAW5pBWpqeoJcdFKKnNqnqzqVTUhbwEcBBO1qk10CtglbgQvdOjAxQsb2ijFFqXWS/XnlSjAjoQKpE82B5joIYVSG+yeEu40YdoYHmBVgQgjxMgsEmt/WrFkjL730klx++eXia7SHAHaGB3FC3zi5cEyKTOofL2F2poER4ihZRScamuoyClTF2JZ+CV1kbM9YlSCBdAhEp61DFTmzUFWBm+O2yX1k9vBuyofc0Tz17Q55a0VGix9HexIhhHipAD7zzDPl3Xffle7du4sv0p4e4JziykZNcABfqxGdA1UFbfORhjG2zYFIK4jiMwbEq+5/QlpLXkmlcVKdqUoMG4QtvbqGydieMUoUwyN8tKhSRbStP1SoYtWaAnF/D5+dLoO7RYqnVIDZoEoIIe4DJ8F5eAoEMDjwJXuiuk6W7syVT9cekt/35Tv8PAMSw+Xik3rI5AHx0j268ZhcQhwhv6xK1mYWmLOIkTph+4nSIyZUCWKIW6RI5JdVK8vEz7vzZMOhoia3/7ep/WT2sG5KSLv6d7S1Z2AYUUgIIe4DBbAP5wBjYtive4/Lp2sPy7KduQ4/LyrLF44xiuLeXcMkwEuGG5D2o7iiRgliZZvIKFAZwzZD6CQ5MkTG9opVonh0WozU1NXLT7vy5Nnvdze5beQXzxySpDKJBydHOt333hIP/nP/N1TFxnFIDSGEuBcUwE7G0yfBQRRDmHy27oh8tTnL4cd1DQ+Wi+ApHhCvmp86B3nf+FviOkora1S1V9kmDuTLliPFUmujiOPDg9XvN0TxyT0hiA3y5Lc75bd9x5vc9hiLqXVYYsKC2qUCPCAhXJbcMbFNz0UIIcQ1UAB7uABuDyCKMRZ30caj8snaww4/DuN14SlGpXhQcoREhbZNeBDfoaK6VjYcLDJnESM5AiOdLYGQPSkNgjhGesaFyY7sEnlmSdPVYc17PKpHtGmcc7T07tqlRbYJ7MeAh75rVLG25bk/DZU/jU4xX+fYckIIcR8ogJ2MNwpgPeAp3pFdLN9syVYWiorq5mOtAL7vYZ+Y1L+rDOoWqU5z01dMHDkIgwg2jm7OV9XiyhprQRwREqBEZUpMqPIP7z9WpqbVNUdUaKASxNo4Zwz4aOoMhqMV4PevHiOn9YtX/+fYckIIcS8ogJ2Mrwhg+6K4RJbvylOVYtvpYU1x7vBkZZ9ApbhnXBdWxkizVditR4vMTXVIjyi3OQjDGQjYJo6VVUlpZa3D2w7w66QOziCGUSHGZXxESIs9wLdM6iN3nNlPlu7I4dhyQghxMyiAnYwvC+CmRPGq/cfl03WHm5wIpjfAA6I4PSlCTSULCaSvmOhTW1evqr3mLOLMghaJXssqconO45CAogRxWowE+nWSez/f6tD2EiOCpbK2XooqanTvZy4wIYR0DBTAToYC2HFRjDG5n284KrtySh1+7Kl94uR02CeSIyU9OUIiOwe6dF+JZwK/LaLWIIa1LOJCOyLUktAgfxX9lxTZWcKC/WXr0RLdyDZIVWd+IHIyHCGEtC8UwF4ogD2x2UbzFG8+XCxfb8mSjc3kvlqC09Sn94OnOELSkyIlISKYvmJiRX29QfbmlZmb6iCMm7PohAcHyJmDEuT0/vHSJdhf/W7Ce7zxUGEju0VbmTE4Ua44JU2G94iS4ACe6SCEEFdDAexlAtibmm2QBLAzu0S2HilWQzxaOsADwgWeYixpsWHi5+YHAaT9wMcZxjWvMcWuwUfc1GQ3nGk4a3CinD00WQorquTWjx3LAW4pwQF+KscY0/GQbjGyRzStP4QQ4gIogL1IAGvT4by52cZSFCP/ddnOPIcfmxobaqwUm+wTfRO6sNpGFPh4gz99tckuAduEPb86jqOai0BrCZGdA2R8nzj1vMfLqq3uC/L3k2EpkXKyGggSKyNToyQ0KMB5T04IIT5KCQWwdwhg2B5Offonu1Usb2620UQxhiesyyyUxVuzHX5sXJdgk6cYleJIGZgULuEh9BUTkayiEw1NdRkFqmLsTGwPTPERu/9Yufk5cZlbUtUooWJo90irCXlIuyCEENIyKIC9RAA7nE161Rg5rb8xm9SbgSjekVUiW4/CV1wkX2xyfKodBMVp/bqqKrEmjDHpjvg2eSWV8tryffL+qoNO2V5z1iR85B7Mr5DVJosGrBpZNge4OJgd3C1STcaDZQKCOIIHcIQQ0iwUwF4igB3NJtWYOSRJHp6VLgkW+aa+JIphoViyPadFAzwaRHGkEsY9YkLZbOdjOHqg6QivzxkpM4Y6bknCR/CRwhNmQYxLXLf9PcXv6MnKQxyrJuVFhlIQE0KILRTAPlYB1gNibsH5Q2VI90jxNWxF8Yq9xxr5MJsCCRtalRiXfeK7SKC/n0v3mXQcsBqNeWKpFJQ3H6nWFM6yJB2FTeNAg2UiM7/C+nk6oSE0QtklTu4VIyf1jFUjpAkhxNcpoQD2Lg9wTnGlbj6p9oV76cmp8uz3u5s8/f/sn4bK9MGJPlvdLK+qVTnFEMTbjharahuEhqMM6Raphncglg2ieGBSBBuXvIhvt2TJTR9tbHIdxKaVVdW1e/4v/v4hhI0T8vLlwLHGvuX+CeHKLqElTcAHTwghvkYJBbD3pUAAQzMpEHg7l2zLkbv/u0XKquxPzLr3rAFyzak9fb6qaSuKNxwqbFRta4peXcPMVWKtYsxKnOdy7b/WytId9hNIrh6fJgt/z2x2Oy9fNFzOGd5NXAWywI1Rb0bLBLKQbendNcyYMtErVnmJLcc+E0KIt0IB7GQ8NQcYwu7ez7eocbL2uPikFJk7fYBEhVK42YpiWCi2HClSXfyOgvcFYhjV4nSTOMbIXV+tvHsK9uIGLTl7aJJ8syXb7SbA5ZdVmWLejIJYbwpjz7gwZZnQqsTJUZ3bbf8IIaS9oAD2MgHsjElwOI362Dc7mowTO6V3rDx+7mDp1bWLk/bau0TxFlOlGMJ4n07VramBC8o+kaxZKCKlV1yYBPh4Bd5daC5u0HasMiYcGtw4lrCwvFrWZBorxLBM4HfX9pMezZ5GQWyMXkuJCe2o3SWEEKdBAeyFAtiZ4Av87z/tldd/3m93nW5RneWZPw1VYf5EXxSjsg4x3BpRjOlgAzRRbLJPYNIdJ4S1Py1pNkUhX+9T050H0xSfqJF1EMSm2DX8rtoO/cDfO6rDxqSJGKahEEI8EgpgJ+NtAtiS+nqDfLb+sMz939Ym13vyvCFy0ZgUjh5uAviud1iI4pbaJ/Cj7d21i1UCBS4ZeeVecYOB/p2kps76ozMqNFAWnD/E7cSvHqWVNbLuYKG5Qgy7T62NIk6MCDHbJZA0AQsFBTEhxN2hAHYy3iyAbVm5/7jM/d8WuyNjwfWn9ZLbz+jLFIQWiuKtR4rUZUtEsVadsxLF3SKUQKEgca8cYJwx+fPoFPHEsxloAFVZxAcKZPORokYCH0NjNMsEmuoQDcjfP0KIu0EB7GR8SQBbknG8XB76Ypv8tu+43XWmD0qUR2anS1Ikm2paIoq3m2wTmn0CI3lb8teItAnNV6wN8kCVzttGYrubB7g5kiKCZWhKlAztHqWi87BEe1gyCCxSGyGITZaJjYeLpLq23mqduC5Bqg9Bi13rFx/Os0OEkA6HAtjJ+KoAtqS4okae+X6XfPjHIbvrwMP69AVDZVhKVLvumzfgDFHcOdBfBiaFW9kn+iV2keAA+oqdkQLRWpACMrQ7xDCEcaQM9jBbS2VNnWw6XGS2TKBaXFljLYijQwNlTBoGcxgF8cDECApiQki7QwHsZCiArampq5f3fs+UJ77d2WS3PE4JYzwzT5W2XRRrC6ryLfmrDfDrpE5XW457xv8jQjxHgLWnCL7rP5ul3MFR2nrcOrmPSv1AYoj2fumRFhsqQ1SVOEIJ48HdIiTcQ94TVIPhb9fGN6/LLJQTNdY/s4iQAKsKMc5WMPWEEOJqKICdDAWwffAr9MOOXLn7s81SUml/+Mbd0/rLtRN6SVAAvwQ7WhQDdPlbJlDgksMSjOJuyLzvpcrmlL+j2GYAI4EB79cW01juLUeL7PrrMVhlKGwT3Y2VYojGsOAAjzggxu+hViFem1HQ6CAC0yjHpEWbY9cGd4v0+UE8hBDnQwHsZCiAHQen7+9ftFVVwOzx59Hd5d6zBnJqmhO7+hHJplknlH2ihY12AONzNVGsVYxTY0J96lR2Wxri8GN69eKRMmNoUrM5vduykBJiFMV4v/TGcmupIEO6R5qFMURx5yD3trTU1tWr30eIYYhiZBKX2hwc4wzRqNRoZZlAygSq4Dw4JoS0FQpgJ0MB3DpySyrl8cU75evNWXbXQTXoifMGS5/48HbdN58TxUeMnuKWgsqd5is2iuII6Rsf7rVipaWRaLZ0amUW8PGyKvP7pA1cySlp3JSHJse+8V2MnmJTo52750ejyXBndonZMoGBPqiMWxIS6KcEsbJM9IyR4T2i6F0nhLQYCmAPFsBtnfjmzp3lry3fJ68u32d3HUR7wTc8oW8cfcMuFsWayGqNKEYObr+EcPPI50HdImVgUoQSy74eiebMaXB5JZWmcdzaWO5iJZT1fN79E8OtGu3w/rjrQQqyx3fnlqqECTWcI6NACsqrGw2KGdEjyuwhHtkj2q1FPiHEPaAA9lABjCac+V/vsIpjSooMkUdmpXtEwH5LvgD/u+GI3PPfLU2u99i5g2XOST284gDA3UWxdiq+taIYxytpsWHmKrHmK4atwpPAAeiox5dKUYV1hbKtXmBngI9rVIW190oTxrbiEQT5+6nKPby2mjDum9DFLX23eF1788qUIDZGrxU0Evp4PcNSIo0pEz1jZWRqFHPICSGNoAD2QAFsL4bJnUesOgucGsXwjYP5FXbXuXZCT7l9Sj+vqDK6OyUQxUcb7BOtFcUgISK4IX1C5RZHSkpMZ7et8DtLAL980XA5Z3g3cTX4CId/2Nhg1+AptrUYaFVVHKBofmLYJ5AQ4m4HmHhN+H3TBnPAS5xbUtWo6g1hrzXVjU6L4WcDIUQogD1MADcXxO/M06ruTubxcnn4q+2yYs8xu+ucmZ4g82YPUhPSSPuLYgitbU1EfDVHeEiAWQxrk+36dO3iFjFZzpoK54oKsKPgYx1JE0icsPQUl1bV6mZH4z1QjXamSnGvuDC3anzE68HBsdZUB2GcZfNZic9FVLsxpQ6WCQhiRv0R4nuUUAB7lgB29EsXVghUlXwlPQFVrOd/2C3/WnXQ7jr9ErrIgguGKo8g6RhRvBVCyySOWyuK4VdFM5cxgcIojDFMob0TD9raBKfZltztYBW2o8z88oZGu6PFKp5NL/M4LMhf+bqNlWII4yi3SgPB19aRwhPmpjoIY9toOewqqt2wS8A2cVJajEcNHyGEtA4KYA8TwC390o0NC1KnLrGgI7xvQrj6f3x4sNueWnZG1uj7KzNVqoQ9cIr32f8bJmcPSXKbL2tfFMUQwkb7RNtEMd5CjHe2nGyHS1eOFnZGBfhND7Er4cxTxvEyVSHW/MTbs4obTXnTqvZqtLOKZDPaJ9zJygIbyB8WlolMGzsVdnNAIgQxptXFyEk9Y32mkECIL1FCAeydFeDmwJdUX7MwDpc+CUaBnBzZ2asEIX5tl+3Mk3v+u1kKm/Bq3nVmP7l2Yi92j7uZKN56pKiRQGkJyZEh5iqx0UIRqW5zhhhrqwf4mvFp8tCsQeKpIMN3/7FyNelNa7TbkV2iBoTYgol3RtuEyT7RPcpp70NbySmuNFomMoyWCb1c7P4J4cougSox0na6hntWwyYhxEMF8IoVK+TZZ5+V9evXS3Z2tixatEjOPfdc8/1XXnmlvP/++1aPmTZtmixZssR8vaCgQG699Vb5+uuvxc/PTy644AJ5+eWXpUuXLuZ1tmzZIjfffLOsXbtWunbtqta/55573NIDjA9tvTcEXycJkSHy8oXDZfORItlwsEg2HCqUvNLGkUh6wOenVYt7W1SNU6I7u4Xvsq2gavXAom2y6XCR3XUuGNld7psxwONSCbwVNSEtyyiKNY9qW0RxVGigyVfcUCnu1bXlDV5oRr3hgw0e6f115dmXPbml5vcKwhi5vjV1Bt2zU9rgDmMCRZRqhOxoUYxYScRLahXiPblljdbp3TXMmDKB4Rw9YzgZkRAPxCME8HfffSe///67jBo1Ss4//3xdAZybmyvvvvuu+bbg4GCJjm7wep511llKPL/11ltSU1MjV111lYwZM0Y++ugj8w+iX79+MmXKFLnvvvtk69atcvXVV8tLL70k1113nVumQACDAykQWvf3hkMQxIWy8VChirOqrXf8LUW0EMavNtgpwlVUEuKs3DVD1JHs1Ce+3SlfbrI/fAMjWR8/d4jKTiXuJ4otI9naIooxXAGnvS1FMd5ze2cEmmtGbQpfalQFVbV1sienzKrRDiJZ7/MHldUGP7FRGCPjvCPJL6syCmJThXhXTmmjdWC/gWVCqxIns+mWELfHIwSwJagO6AngoqIi+eKLL3Qfs3PnTklPT1eV3dGjR6vbUB2eMWOGHDlyRJKTk+WNN96QBx54QHJyciQoyOj3uvfee9U2d+3a5XU5wJU1dUo4QBCjQgxxfMzBKrEl+AJPjQ01VophpTAJZIxldfcxrLbDN974Zb+88uNeu+vgy/mZC4bK6f27dniVitgRxRYjntsqivG7jcQJLa9YXSZFqgap1lqRfCGq0BHw+QMhCYuLVimGKNY7JsfQm4YRz0YbRWwHnp0pqqi2EsSwfdh+O/aICTUJYmP0WkpMaEftLiHE2wUwhCqEK6q+kydPlscff1xiY42nFxcuXCh33XWXFBYWmh9TW1srISEh8tlnn8l5550nl19+ufphWIro5cuXq23BPmFZTbakqqpKLRrYRkpKisdNgtO6pSGGN6JSfKhQdrSwSmwJ9GH36M7GSrGFnQLiONzNI4fQBb9o41G5539b1M/YHvNnD5I5Y3u45cAAoi+KsTSVIe0I+L2OCQuULUdKWvxY/Hkip/q+Gelt2gdvBAehO7IbrBOoFu87VtZIXALEGhq9xEZBjCUqNKjDfsfWZRoFMZrrsO+2HxvYX1SHTzZNq4NA5kE0IR2LVwjgTz75REJDQ6Vnz56yf/9+uf/++5W3d9WqVeLv7y9PPvmk8gjv3r3balvx8fEyf/58ufHGG2Xq1Knq8bBIaOzYsUMGDRqkLgcOHKi7P/PmzVPbsKU9BHB7fCGpKjEqxKpSXKQ7XtUSCPCQAD/dyCTLig7sE1Z2ivguLu3Ybws4yLj38y26zTEaV4/vKX89sy/zRD1MFGs5xW0Vxd6a/tDRlFfVKruW1mgHUWxv0ApEpWWlGPaJjvh7xMTEdQcLzR5i7LNtIQGfgZpdAkkTsFBQEBPSvniFALblwIED0rt3b1m2bJmcccYZLhXAHVkBbm8sq8SaIEaDi+2He6B/J+VxxJcP0iZQ8a2orpV9eWWNpjTpRbYpcdzV2HwHYQzrgbt8ORzML1fWk5925dld54wB8Wr4Bk97eg7FFTWyLcvaPuFKUYxGvPUPnukTHmBXZkpr1WJ77xUGdTSMeI5UKSDtPQUOIh6fmdpgDjQn2zYF4jNOs0ygqQ6fg+7ymUeIrwtgj5ob2atXL4mLi5N9+/YpAZyYmCh5edaCBRYIWBtwH8AlGuks0a5r6+iBZjssvgA+kCHqsGijW1ElRnVGNdgp+0ShHC+rNoXNNwTOo7sbAyjwwY5IJOTwHiqokL15ZUoYQ1jnl1dLvslbpxfZpjXeaXaKjohsS40Nk4VXjjFXEl9cukfeW5lptc6Pu/LUonWLY/jG6NRofqG5MfD1ju8TpxZ7ohiVPPzOOgNEp7360z65fUpfp2zPl8CBNdIzLBM04MvddrRENdppCRT4TEG1GMtXm42NrvgTRH+CpZ8Y3u7QINd9xYUFB8iEvl3Von1m4nNytckysfGwsf/imy3ZatGKAVqFGJf94sO9Kp6SEE/CoyrAaGzr0aOH8vPOnj3b3AS3bt06lSQBfvjhB5k+fXqjJjiI3sBA42kzWCk+//xzt2yCc1e00arGxjrjsjO7tJGXFqkS+OKBKB6ZGqU68PHFsO9YqezNLVPCeH9emZpIZc+GGxrkr77MlLfYomqMU6HtXVlD/NMHqw+q6rA9UBl/5k9DZfawbqz8eSiWovj7bdmy8XBxq7fFKrBrKSivNh24NFSK9VI78OPHwXXDiOdIGZgU0W6Z4GgIRCyjZpnAZ6btgJHo0EAZk9bQVIf94+8NIT5ggSgrK1PVXDBixAh54YUXZNKkSRITE6MW2BiQ64tKLTzAyO4tLS1VUWZadRYxaBC3b775pjkGDYkQWgwafgD9+/dXVoi5c+fKtm3bVAzaiy++6HYxaJ4G7A/4AjJaJ4pU9QPVXlvgi4MYhige0SNaBncz/vwwHQxVYghjdZlXqm7TyxYFiGXDqU/zkA+TraK9ItvwpwKLxNz/bVHVcHv8dUpfuX5ib49KyyBGcEA3fsGPktOEpcdXs4DdGVRaGzKKi2TzkWLd9JsAv07SLyG8YaJd90gVixcc4Pq/VQwSwVk1bXzzusxCOVFj3VMRERKgmp61CjFyrb0hp52Q9sQjBPDPP/+sBK8tV1xxharcohq8ceNGFYWGai5E7GOPPSYJCQnmdWF3uOWWW6wGYbzyyit2B2HAQoFBGBDDLYEC2LlV4kHdTFViU6U4KbKzeQrVQVgocstk/zGI41Jj1fhYme541o6MbEOaxkNfbpP1BxtSSGw5f0Q3ufesAQzU9xCcNZHx5YuGm+1EpGPILak0CmJUik1WF70DdJzBwZkqy0Y7iGRXp8Dg7BKq11qFeG1GQaMmY/iakVmuVYjhe2Y6DSFeIIA9CQpg11aJkXVsrBBHycjUaJXPalmVQYQZhn6gSqxVjTU7RWlVre5zwxeYEh1qnoCnCWNXRLYhsm7Bd7vk8w1H7a4zskeUGr4BiwhxT77cdFRu/2RTm7fDCrD7ga86WCW0KrFmn9AbeY0zSrAjWA7vgBXLldVYHPwjGUONbz5QIGsyC6S0sraRPWxUarSaVoeUiSHdojx2YBEhroIC2MlQADsH/Lqh4UgTxLhEcL5elRhWCWOF2FgpRgKF3vaQQAFhrKwUx8pknxLHpVKo88VmKbgt49o0keyMyDZ4nt9ecUBeXLbH7jpohnn6gqEyeUA8m2C8rAKMLOG1D9AD7EkJOCo6z0IY2wpPbaogpglq+cQQxa0Zte0o+ExEGo82mAOxjWjQtd0nCGJlmegZI8N7RLWLnYMQd4YC2MlQALs2TkirEqNCjOQJNLrYkhwZIiNMYhjVVHwZNVX9wKhTVIm1SrEmkvOamIwX1yXI2ICXYC2MWxvZhi+xrzdnyT3/3SLVdfoWDvDw2ely6cmprOZ0MHi/Rj/+gxRW6J9VcITX54yQGUOTnbpfpP3A2SYcpBttE0ZBjMpsmc6ZJlRkB0MUWzTaoSfBFQe12K/duaUqYUIN58goaPQ5ic8PfDZqHmJ8VrZX0x8h7gIFsJOhAG4/8CuJ/E+zl/hgkezKKWmUGoEPe3zh4ANfqxQnOOC1RRUFNop9mp3CZKmAxcIeaE4xD/ewGPbRksg2vC4E6d//+Vb1nPa48pQ0uWNKPxXhRdqfa95bIz/uOtaqx14/kdPgvBGIz4z8cuUj1irFiGezbWID4cEBqsdhaPcoc6XYFRPi8HmCzxEIYmP0WkGjgUY4kzYsJdIsiFEtdmU0HCHuAAWwk6EA7vgqMYLm1ThnNayjUNfigNGkIywEMbqoHa2q4jkwFc7sMzZlGR9sJrJNieGuxsg2rWrsSGTbofwKefSb7bJsp/3hG6f37yqPzBqkJkqR9qkAj3liqRSU27fPNAUnwfnW7wqacyGIjQkURapSXFVbr3sArQSxqdEOzWwYve1MUYyvcmQjq5QJU2Od7YAipGBAkGtNdaPTYtp9gAghroYC2MlQALsX+LXNRJXYJIZhm9itUyUO1qrEyjphFMYtTWRAnidyiy0b71oa2aZVje1FtqEq/fKyvbLw9wy7+4HtPXHeENX8wuEb7ucBxjsCn/pvcyfT/+ujoJENnxGqUny0SF0iCUfP/oQM4CHdo6wa7RAZ6ay/be1MmtZUB2GcZZOXjN9TiHFMqUOFGIKYY9+Jp0MB7GQogN0fePS2HDY21mkT7IrsVIktBTG6vVvjvUWMkZp6p3KMravGelUg7QsnLVZLpjBNwOtqHdmGvNBP1h6Sh7/cbve5oa+e+dMwOXd4MnNC3SwFggkQxBL8Pe/JLbVqtNuVXdpo1DyI6xIsQ7pFmIUxRLGzIhS1hj8thxjC2DjZ0/pzBSk1sEwgaeKktBhasYjHQQHsZCiAPQ/8aqNKq4lhVIvxRaRXJcYXjTaoA7nE8eEhbTo1erTwhNX0O6PnuEy3kcYyss0yrg3T7zByGYH5GL7RVPPebZP7yPWn9VbjWUnHpkBcPT5NHp41yGn7RLwPnFXanVOqGu22qWpxsfpssk3D0cbNI+5MNdmZGu0glJ1BVtEJJYRX7zcKYpxVs/1cQkYy7BI483RSz1iJcUJSDiGuhALYyVAAewcQoJtRJTZZJzYeLtKtEsOfp6VNoFqMKnFbA+jxp5ZTUtkox3hPXqnuPuhFtuEL8t+rD0pTf7XnDE+W+84aqBsbR5rm2y1ZctNHG9u8HXqBSWtE8Y7sEqtGO3xW6PUfIBHHaJswNtphcUaEY05xpdEyoZrq8mX/sfJG6/RPCFd2CVSJMbUOCTmEuBMUwE6GAtg70RpHjILYOKgDUUO2fxXI2xzaLUpGmEY6Y3HWBz/2AcNBzBaK3FKVZ9xcZJsjDEuJksfPGay+LIkjY5B/UgcpbYFeYOLMQUKYOKkN7UCjHT6v9L61U2I6q88oeHpRLcZlZOfANg/4Qf6w1lS3J7dxeg3OUsEugcY6eIk59ZJ0NBTAToYC2HcorayRzYdN0+tUNnFRowB67QtnRIpzq8RNRbZZ2imaimyzB74Mn/nTUJkyMIHCzIVjkDXoBSau+nxC2sRWkyjGAquXHug3sGy0w4TNtkzBRLa6EsSm4RwYYmQLEmtgmdCqxMlRxjH3hLQXFMBOhgLYtzNAVZVYG9RxsEjZFtqzSqwX2YYIJsscY1xvKrLNFnxJzT9nkGrGoyB23hhkjZcvGi7nDO/mtO0R0tSB8nZUiNXwDmMChW2Dm+bphUA1CmKjrxhRka3tHSiqqLYSxLBw2H4uIhLSKIiN0WspMaGtfZmEOAQFsJOhACaWlKgqMbzERWZhXKIzPhVVYk0MYxmQFO70KrGtjxDVoIZEilL5dmuOQ48d3wdd37GmKXhdJNVOZJu3wgow8SYKy6tlW5bJT2yqFuudOcKxL/oL0GinJVBAFGupNC0V4usyC8we4m1ZJY0a+5DCg+rwyabhHK4YEkJ8mxIKYOdCAUyarxKXmQUxFghQ27+uzoH+6lSkZYOdszq6m4tsQybo8l158sS3Ox16DELzU2NDG0ZCm7KMEdnmjeNV8UU96vGlTTYkOgI9wMRdwaQ4ZZswNdphgIee5x2/tzgINiZPGC0UOHgPDvBvcdMxBPFqk4cYz2sb/4bsY80ugaQJVKgpiElboAB2MhTApDVV4k1aBJupwa5Up0qMCogmhlWVODG8XbJ9Ua35+4975R+/2R++0WxkG0RxV2NkG8SxJ0+VcoYA1r6232AKBPEQ8koqLTKKjZe2I5VBoH8n6ZcQbhTFplg2XG/JWSJYt/B5qA3mwHRP22FCsI1plgk01eFzhYKYtAQKYCdDAUycUSWGT9eYSVxkrhLbgiqxyiU2CWKI41gXV4mrauvks3VH5MEvtrVpO1pkm2XVGEI5KjTIJywQeP2PzEqn+CUeixbXqNkmNGFcUF7daN0gfz8ZmBRuGvFsTKDA37yjNq8T1XWqMLDaZJlALCUGh1gSGxZkrhDjsl98uPjxzAppAgpgJ0MBTFxVhd1kkUuM/+tViWFF0MTwCBdXiSHUV+w9Jvd/vrXR6FTbVIkzBsSrL0sI+WNNRLbFdQmyFsam6nHXLsFuU91pbRPcDRN7ycDkCDU8BbmotD0QbwMyAf5hY4Ndg6dYLx0Hg4UwTc6y0Q62KUf+LtDDgM9ALXYNn4mVNfWNRkiPSWtoqkP6Dv/miCUUwE6GApi0BxCfyACGIEb8mr0qcWiQqUqsNdilRrtsQtPO7BKZ//V25eOzx9lDk+TG03urLzDjaGjHItsiQgKUfcJ2Ah6C/ttbGLemAowK2M7HpvMLmPgckA5ImkDihKWnuFRn2iXOaiGCzTi8w2ih6BUX1mwlF9VgZB9rKROYinmipq7RZwgOPLUKMRr4OB7etymhAHYuFMDEbarEh4p0v2SQ+anGOSvrRJSa2OTsLwIE4z///R75dN1hu+tgKtWj5wyS4SlRSsRqkW1KGGuXeaVyqKDCbmRbWJC/9NYEsUXVGBFKrhKb8ACf+vRPahqWox+K15/WS03dI4QYD+Az88sbGu2OFqt4tvJqa9EK0C8AUaw12uFzIzUmtElRjGZebFurEEMQ246Xx3bHpEWbK8SwZbgyeYe4HxTAToYCmLhjlVhrsEOlVa9KPKw7muuMucSwTjizSgxh+/6qTHlmyW6766A6s+CCoTI1PaGRGNci27RKsTbsA7fZdoproOEGlSPLqrEzI9uWbMuWGz/Y4JAAjgkNkrUPTmH1l5BmDiwzjpepCrHmJ96eVdzI2gDCQwKMo51NnmKIY4ylt3c2qLauXmUPozoMUbwms6CRhQyfg6NSo9W0OqRMoPrsS/GOvkgJBbBzoQAm7kxxRY1sPNyQNmGvSoyIoRFInDBZJ/onOmcIBr6IvtuWI/f+b4tutUfj/hkD5NKTUyU0KKDZyDZNEGtVY1SRq2waZPQi27S4ttZGtn27JVtu+XhDswNFzh2eLC9dNKJF2yaEGD8v9h8rV/YGrdEOQta2AQ5EhQYaRbFpxDOqxfYsUhDbsGxplgkM6bD1KWNgEQSxskz0jJHhPaJaHO9G3BsKYCdDAUw8CXwRoKpqTJwwVorxhaNnNRiWYhLEqVFqtHN0G6vE+EjB8z30xXb1pWaPOWN7yF+n9FXNY46+pqOFJ2QvhLGpaqwuc0vtim7byDbNTtFUZJujXmD87N6+bLSc3DuWVWBC2ggOfPfkllqNeIaYtY1J05IhjFXihka7hIgQ3bNlu3NLVcKEGs6RUdAozQLVYFjGNA8xPgu9MefclyihAHYuFMDE08HYUsQMbVSCuEj5im39cwAWA9glNOsEsj7bIvAO5VfIU9/tVBXipqbQPXR2ugxIjGh1bBOqxFZ2iryyJjN9UUWCz9iyagyh/MueYy1Kg2D0GSGui2fck1Nm1WgHkaxnkUJ+sFEQNzTa2Y6ix2cFPhcgiI3RawWNMo/R2DosJdIsiFEtbuqMFXE/KICdDAUw8TZQUUU11XJ63QGdKjEqpfhC0GwTsFC0NtcXIvzNXw7Im7/st7sOPH9PnDdEJvSJa1PeJz7a8surzU13llXjpiLbwoP9pbTKvo3DFg6/IKT9QO/ArhxUiovMnmKIYj3LEg5ONfsEhDEuLTPV8Rlx4Hi5eTAHGutyS6oa2asgqLWmutFpMR498McXKKEAdi4UwMQXKCyvNiZOmAQxvMR69oJeXcMsItiiVBW1pVVifJF9vuGo3L9oa5PrPXX+EDlvRDennpaEZ3rfsVKbqnHTkW1NwfHHhHQcGKixI9vUZGcSxegd0FM33aI6m7zExkY7iOLI0EB1H+QQ+g8ghI1JEwWNPhPw941kCUypQ4UYgjgixPh44h5QADsZCmDiq1ViVFe06XVosEPFxBZURBB7pgZ1IIYtJdr8pdKS4RsPLNrWpAi94bTecv3EXm32Kbc1AcIeH197sozrHevEvSKEtAbYu3ZklZgb7SCM9T67tHH0DZ7iSCVwNVF7uKDCVB02Rq8h+9gSHO9i8AcsE0iaOCktpkWffcT5UAA7GQpgQhqqxCpxwmSd2HxYv0rcW6sSm0Y6w1/rqKUBzS+PfbNDVu7Pt7vOjCGJMnf6ABWB5qwM4OwmJt85wssXDZdzhndr8/4QQpxPSWWNbD9aIluPNtgnUPHVA70Qmm0Cy6BukepAP6vohBLCq/cbBXGmzePReIteBtglELt2Us9Ylw0pIvpQADsZCmBC7IvH3TmmKvEh4wQ7ZPnaEo4qsWmUs6oUO1glzi2plJeW7ZWP1xyyuw6mP2H4BhpWWjNBrjVT4PRgBZgQzwJ9CduOlpgb7SCKjxQ2PguFjxXEKlo22qUnRSpRba4QH8jXTdvBUCLYJVAlxtQ62+Y84lwogJ0MBTAhjoOoIdglNOvE5iNFUqFTJUbyAsSwNqijuSoxTmt+sPqgLPhul911UKWBb3jaoESHA++/3HS0RckPttADTIh3fX4ZbRMNlWK9s0P4U0f/Q0PyRKTEdQlWn3fatLo9uWW6Z8fQVKeGc/SMkXidCDfSeiiAnQwFMCFtC75HHqca1GHKJbY9dahbJe4RLZGdA+3mhn6/PUfu+3xro+lPlsAmcenJPSS8iUaVtlSAmQJBiPeDMfDblCg2Wig2HynWTZNBagSiI7VGOzTdYRgHzoyhUowEC70BRbBMaFXi5KjO7fSqvBMKYCdDAUyIc8kvq1JfCpp1YvPhYjlR03SVGH7iPl0bV4m14RuPfLVdnc60x0VjUuT2KX0lKbKzrgc4p7iyxU1wzAEmxDeBPcuYPFEkW0yNdohetCXQv5PyBUMQo+EOCTjopVh/qFC2Z5U0SqvAOkZBbIxeS4kJbb8X5QVQADsZCmBCXF8lRnXEaJ0wCmO9BpXwEC1xwiiI8X/bKvHB/HJ55vvdsnhLtt3nQ4PKgzPTZVByhPINtzQF4vJxqXLW4CTl6aPtgRACOQWrhNE20WCf0BvIA3sWehfSYiGI66Wipk4KyqtkZ3apOiC3BFVkY1OdcTgHBHJreh18hRIKYOdCAUxI+3Pcskp8sFB9odhWifE9gKqwlkmMy94WVWJUWt759YC8/vP+JqfCPX7eYGWFeOfXDIf27aGZA+XK8T0pfgkhdoHEQlMdhLClMNazbYUE+klabJhU19ZLeXWt6psor6ptNOQjMSLEbJfAJRIrKIgboAB2MhTAhLhPlVgTxKgUHypoXCWOQJXY5COGIIavGLmeOPX4xcajcu/nTQ/fcBTaHwghLQW55/jcMtomjIIYVgi90fSOgFQJzTKBpjrYxnxZEJdQADsXCmBC3LdKrIlhCGME3+OUoiX4LkDChOX0OlRaft+fLw9/uc1uFmhzsAGOEOIsUZyRX658xFqlGP0Men0RzREbFqSsWZplol98eJvGynsaFMBOhgKYEM8A6RC7sq1zifWqxPANN3iJo2RdZqG8/OPeVj1nYkSw/H7vGbRDEEKcBrzA+4+VKUGMBAoc3KNSXFVrfYDfHFGhgWpCndZUNzApwqs/qyiAnQwFMCGeC+KKLJvr9KrEbeWOKf1UwgQhhLjSBrY3r8xYKTYN70DjXHWd459nsIid1LPBQ4xmvAB/xzLTPQEKYCdDAUyI91aJsRwuaDz9qaW8SSsEIaSdQdPcntxSq0Y7fL7V2nbPNTE8aHRatNEy0TNGBneLlEAPFsQUwE6GApgQ7w+6f2DRNlm6I7dNTXGcBkcI6WjQ8IsR9Wi026aqxcVKJNtGrOkRGuSvxsprgnho9yiHp2p6kl4LaNe9IoQQNwWNdMvaIH4BMkDXZBTIuN6xTtsvQghpKSGB/jIsJUotlqJ4R3aJVaPdvryyRjFriF/7de9xtRi35acEsbJM9IxRqTrBAf7N7gPENj4PUVyIDw9xu8x0CmBCiM+DD+r5X+9o8RQ4PfBhTwgh7iiKtSQcjYrqWtmRVWIe2oH+iAPHy62m06Ff4vd9+WoBqAaPSIkyp0xge9i2JRgshM9UFAXcNTaSApgQ4vOgSmH5Qd0WUOkghBBPIDQI/t8YtWiUVtaotAljo50xgSLjeLmV5/iPjAK1yI8iQf5+Miwl0txUV1BeLX/9ZFOjggJGzWPaprvERlIAE0J8HmdVbVHhwGk+QgjxVMJDAlV1F4tG8Yka2Y4KsRreYUyg0BqHkUCxNrNQLa8ut79dCGIYIFAZPjM9scPtEBTAhBCfx1lV29nDkjr8Q50QQpxNZOdAOaVPnFo0MGZ+W5bJT2yyUBwtajpNx+BGvRIUwIQQnwdV25iwIHXqri28vSJDRvSIdovTe4QQ4kqiw4JkQt+uatH4YPVBefCLbR7RK+E5uRaEEOIiULU9d3iyU7aF03uORA0RQoi30btrF4/plaAAJoQQEeVJayuWp/cIIcQXz6YlRYYor68endyoV4ICmBBCTB/ciRHOqUq4w+k9QgjpiLNpj8xKV/+3FcHaddzvDr0SFMCEEGL64L5oTIpTtuUOp/cIIaQjmD44SUWdJUZafw7iurtEoAE2wRFCiIna+vo2byMhPMgtTu8RQkhHMX1wkrKVcRIcIYR4BG3/cEZHtDt9yBNCSEfg79epw6POmoIWCEIIMeGMD+vQYNYVCCHE3aEAJoQQE5h8FBzQto/Fiqo6p+0PIYQQ10ABTAghJr7fliNVtW3zAf+27xhzgAkhxM2hACaEEBElWh/4Ymubt5NTUsUcYEIIcXMogAkhRESJ1sKKGqdsK6f4hFO2QwghxDVQABNCiJNFa0F5tdO2RQghxPlQABNCiJNFa0yXYKdtixBCiPOhACaEECeLVmeNVCaEEOIaKIAJIcTJonVUarTTtkUIIcT5UAATQoiIGtMZFRrolG29vnyfU7ZDCCHENVAAE0KIk3npx72yZFt2R+8GIYQQO1AAE0KIKQatyEkxaGD+1zs4EIMQQtyUDhXAK1askFmzZklycrJ06tRJvvjiC7vr3nDDDWqdl156yer2tLQ0dbvlsmDBAqt1tmzZIhMmTJCQkBBJSUmRZ555xmWviRDimeSVVjp1e9nFlRyIQQghbkqHCuDy8nIZNmyYvPbaa02ut2jRIlm9erUSyno8+uijkp2dbV5uvfVW830lJSUydepUSU1NlfXr18uzzz4r8+bNk7ffftvpr4cQ4rnEh4e4vagmhBDiHAKkAznrrLPU0hRHjx5Vgvb777+XmTNn6q4THh4uiYmJuvd9+OGHUl1dLQsXLpSgoCAZNGiQbNq0SV544QW57rrrnPI6CCHe0QSHJIickkq3FtWEEEK83ANcX18vl112mdx9991KuNoDlofY2FgZMWKEqvDW1taa71u1apVMnDhRiV+NadOmye7du6WwsNDuNquqqlT12HIhhHgv/n6d5OGz0522vU4iMjwlymnbI4QQ4iUV4OZ4+umnJSAgQG677Ta76+C+kSNHSkxMjKxcuVLuu+8+ZYNAhRfk5ORIz549rR6TkJBgvi86Wj+v86mnnpL58+c79fUQQtyD+nqDlFXXSmllrZScqDFfbs8udtpzoP1t/NM/yZPnDZbpg5Octl1CCCFeLIDh13355Zdlw4YNqrHNHnfeeaf5/0OHDlWV3uuvv14J2ODg1k92gpC23DYqwGigI4R0PJU1dVJS2SBc1aXN9dLKGinRLk9Y3F9ZI2VVtWIwtM945Rs/2CBvXDqSIpgQQtwItxXAv/76q+Tl5UmPHj3Mt9XV1cldd92lkiAyMzN1Hzd27FhlgcD9/fv3V97g3Nxcq3W06/Z8wwDiuS0CmhCiD6LBykxCVC0njCJVT8RailazqD1RK9V19U7ZlyB/P4noHCDhIYESHhKgKsPbspxvd0Ik2pnpicpmQQghpONxWwEM7++UKVOsboN3F7dfddVVdh+HBjc/Pz+Jj49X18eNGycPPPCA1NTUSGCgccrT0qVLlTi2Z38ghOhjMBjkRE2dWaSaK6wW4tR43bYa2yBiUX11Bjgx1CU4QCJM4hWXmpiNCDFddg6Q0KAAJTzV0qmTBPh3Ej9c+nWS2nqDVFTXSnlVnbosraqVPXllUl3rHIENDBaRaON6xzptu4QQQjxUAJeVlcm+fQ0jQzMyMpSAhZ8XlV80tlkCAYuqLcSr1uD2xx9/yKRJk1QSBK7fcccdcumll5rF7Zw5c5SX95prrpG5c+fKtm3blLXixRdfbOdXS0jHU1tX36jSWmK38qpflYVodAYhgX7myqsmYjsH+jeIVZNgtbpucTsEMMR4RVWdlFfXSkV1nbIcHCk8oUQ2rpdX1UqVE8VsW2AkGiGEuA8dKoDXrVunxKuG5rm94oor5L333mv28bAofPLJJyrXF6kNaHaDALb07kZGRsoPP/wgN998s4waNUri4uLk4YcfZgQa8cjqa3l1nd1Kq7EKaytYrauzEIzOAGfygwL8JMDPT/2/kWj1N176+RkrrariarpNW6+6ziAVJqGaWV6uhKyzrA164DnDgvwlLBhVYYvLoAAJDQ5Q9+3OKZWNh4tc8vyMRCOEEPehkwHfqqRZ0AQHMV1cXCwREREdvTvEA8Fp9SYrrU1WY40i1h0m60JI4mPDlfsCb25YsL+yLzS6DPI3C1b9+yFoTcI2yF/ZJHAd22yqoXbJtmy54YMNTn8teMbEyBD5be5keoAJIcRN9JrbeoAJccfYLCtPK/5fZVmN1U8c0NZ1l1Pxzmhis7UyhFmITuvKqs1lo8qr9pgG8do5yF9Vl9v7NaFRzVU8Miud4pcQQtwICmDi9aBaCfHZXGyWbdJAR8RmuRq96ikEaZitONUTsTr3aw1mng4a1NCo5ih47bBuNEdUaKAsOH8II9AIIcTNoAAmHhubZZs8oBebpYlbV3pLXQE0pVmY2qmeqvtbYA8ICfBXnlzS9ga17lGdVVpEc/z9whEyoX/XNuwZIYQQV0AB7KGCEBUrfGmjseaknjFuW4XTi82yrbTqxWY1rOO82CxXAW+ppUjVFaCm27s041fVBG5wQNN+VdKxDWqOiF+w/lAhBTAhhLghFMAeBhp14FW0PF2bFBmiPIauOM1aY4rNshar9mOz1DpVronNcga2flX7p/utq6fu5FclzgcHkfg7aokNwhHeW5Upt57R120PUAkhxFehAPYw8YuxqrZyMqe4UnfcqiOxWZaVVlfGZrWG5rv9m76/i5f6VYnzwe8FDiL1/r7aQlFFDQdgEEKIG0IB7CFoXep6X87abbd9vFF6d92rplm1Z2yWI37VJtMAdHyt9KuS9gYHjziI/NtnW5xqu+EADEIIcT8ogL2oSx2DBXbmlDa5DoYSQGRq1dGm/KpNVVjDLEQs/arEm0RwbW293PLJJqdtkwMwCCHE/aAA9hAcrSJdPi5VJvTtalfE0q9KSNNnWp74bpdD60aHBkphRU2zAzDgLyaEEOJeUAB7CI5Wkc4anES/ISHtkAfclPjV4AAMQghxT1gO9LAudXtfpbgd97PaRIjrz7TA/tMU+Ht8bc4IDsAghBA3hQLYw7rUga0I1q6z2kRI+5xpaW4KHHpP9+aVO2mvCCGEOBsKYA/sUoev0BJct41AI4S45kxLVOdAh7b14rI9KrqQEEKI+9HJgLBY0iwlJSUSGRkpxcXFEhER0aH74kmT4AjxNCBab/hgg937/zSym/x3w1GHtgUx/dvcyfz7JIQQN9NrbILzQPBlykY3QjqG/PJqh9dFQx0HYRBCiPtBCwQhhNgMnGmK5buPtWibHIRBCCHuBwUwIYS0IgbNUTgIgxBC3A9aIAghxAXVWg7CIIQQ94UVYEIIcUG1Ft3FjCYkhBD3hAKYEEIcjEFrCRiVfGZ6ohO2RAghxNlQABNCiM7AGVtaKooxKhmeYkIIIe4HBTAhhNgQGdp42EVUaKCcMaBri7bDBAhCCHFPKIAJIcRiCMaNH2yQoooa3Yru0O7RLdpe5vEKJ+4dIYQQZ0EBTAghFhnATY3GfG9lhkR19nd4m5+sPaS2SwghxL2gACaEEAczgFEFHpAY0eJJcIQQQtwLCmBCCGmBX3fL0RKXbJcQQkj7QQFMCCEtyACuqK5zyXYJIYS0HxTAhBBiygCO6tw4/UGP4ADHQtFiw4I4CY4QQtwQCmBCCDFlAF81vqdD69bXO7bNc4YncxIcIYS4IRTAhBBi4pbJfVTerz0gZbsEB0iNg8kOnARHCCHuCQUwIYSYQLV2wflDdO/T6rj1DpZ/I0MCaH8ghBA3hQKYEEIsmD44Sd68dKQkRlg3ryVGhshfp/STihrHBPCZ6Qm0PxBCiJtCAUwIIbpY2xwMBoMUn6h2+NHjese5YJ8IIYQ4gwCnbIUQQrxsHLKtyze3pEoW/p7p8HaKKhwXy4QQQtoXVoAJIcSBcci4DYYGR00NMV2Cnbx3hBBCnAUrwIQQ4uA4ZMeyH4zYeogJIYS4D6wAE0KIk8cWJ0WGMAGCEELcGApgQghx8tjiR2alMwGCEELcGApgQggxgaotqrdt4a9n9FFRaoQQQtwXCmBCCDGBqu1DM9PbtI1aB8ckE0II6TgogAkhxILosKA2bqElrXKEEEI6AgpgQghxYiPcuF4cgEEIIe4OBTAhhDipES4qNFBO7h3r1P0hhBDifCiACSHEphEuppU2iAXnD2H6AyGEeAAUwIQQYgEE7LnDk1v0mMSIYHnz0pFMfyCEEA+Bk+AIIcSGM9MTZeHvmQ6t+9DMgXLl+J6s/BJCiAfBCjAhhOjYIKI6Bzq0blx4MMUvIYR4GBTAhBBiAwTtVeN7tuv0OEIIIe0HBTAhhOhwy+Q+KtXBHqj5YmocqsWEEEI8CwpgQgixUwVGqoMemuHhkVnptD8QQogHQgFMCCF2QKoD0h1Q6bUkMTJE3mDqAyGEeCxMgSCEkCaAyEUqxJqMAjUlDp5f2B5Y+SWEEM+FApgQQpoBYnccJ7wRQojXQAsEIYQQQgjxKSiACSGEEEKIT0EBTAghhBBCfAoKYEIIIYQQ4lNQABNCCCGEEJ+CApgQQgghhPgUFMCEEEIIIcSnoAAmhBBCCCE+RYcK4BUrVsisWbMkOTlZOnXqJF988YXddW+44Qa1zksvvWR1e0FBgVxyySUSEREhUVFRcs0110hZWZnVOlu2bJEJEyZISEiIpKSkyDPPPOOy10QIIYQQQtybDhXA5eXlMmzYMHnttdeaXG/RokWyevVqJZRtgfjdvn27LF26VL755hslqq+77jrz/SUlJTJ16lRJTU2V9evXy7PPPivz5s2Tt99+2yWviRBCCCGEuDcdOgr5rLPOUktTHD16VG699Vb5/vvvZebMmVb37dy5U5YsWSJr166V0aNHq9v+/ve/y4wZM+S5555TgvnDDz+U6upqWbhwoQQFBcmgQYNk06ZN8sILL1gJZUIIIYQQ4hu4tQe4vr5eLrvsMrn77ruVcLVl1apVyvagiV8wZcoU8fPzkz/++MO8zsSJE5X41Zg2bZrs3r1bCgsL7T53VVWVqh5bLoQQQgghxPNxawH89NNPS0BAgNx222269+fk5Eh8fLzVbVg/JiZG3aetk5CQYLWOdl1bR4+nnnpKIiMjzQu8w4QQQgghxPPpUAtEU8Cv+/LLL8uGDRtU81t7c99998mdd95pvl5cXCw9evRgJZgQQgghxE3RdJrBYPBMAfzrr79KXl6eEp0adXV1ctddd6kkiMzMTElMTFTrWFJbW6uSIXAfwGVubq7VOtp1bR09goOD1WL7A2UlmBBCCCHEvSktLVVn8D1OAMP7Cz+vJfDu4varrrpKXR83bpwUFRWpavGoUaPUbT/99JPyDo8dO9a8zgMPPCA1NTUSGBiobkNiRP/+/SU6Otrh/UFD3eHDhyU8PLxDKtLuCg4McFCAnw2i6EjHw/fE/eB74n7wPXE/+J64FyUe+n6g8gvxq5cc5jYCGHm9+/btM1/PyMhQCQ3w8KLyGxsba7U+BCyqthCvYODAgTJ9+nS59tpr5c0331Qi95ZbbpGLLrrI/MLnzJkj8+fPV/nAc+fOlW3btilrxYsvvtiifUVjXffu3Z3yur0R/HF40h+IL8D3xP3ge+J+8D1xP/ieuBcRHvh+NFX5dQsBvG7dOpk0aZL5uua5veKKK+S9995zaBuIOYPoPeOMM5RIveCCC+SVV16x+iH88MMPcvPNN6sqcVxcnDz88MOMQCOEEEII8VE6VACffvrpzZqULYHv1xZUiz/66KMmHzd06FDlKSaEEEIIIcStY9CI+4NGwUceecSqYZB0LHxP3A++J+4H3xP3g++JexHs5e9HJ0NLSrCEEEIIIYR4OKwAE0IIIYQQn4ICmBBCCCGE+BQUwIQQQgghxKegACaEEEIIIT4FBTBpltdee03S0tIkJCRETdhbs2ZNk+t/9tlnMmDAALX+kCFD5Ntvv223ffUVWvKevPPOOzJhwgQ1+RALJiw29x4S1/+daHzyySdquuS5557r8n30NVr6nmCyKDLjk5KSVOd7v379+PnVwe/JSy+9pIZfde7cWU0lu+OOO6SysrLd9tebWbFihcyaNUsNDsNn0BdffNHsY37++WcZOXKk+vvo06ePwzMb3BKkQBBij08++cQQFBRkWLhwoWH79u2Ga6+91hAVFWXIzc3VXf/33383+Pv7G5555hnDjh07DA8++KAhMDDQsHXr1nbfd2+lpe/JnDlzDK+99pph48aNhp07dxquvPJKQ2RkpOHIkSPtvu/eSkvfE42MjAxDt27dDBMmTDCcc8457ba/vkBL35OqqirD6NGjDTNmzDD89ttv6r35+eefDZs2bWr3ffdWWvqefPjhh4bg4GB1iffj+++/NyQlJRnuuOOOdt93b+Tbb781PPDAA4bPP/8caWCGRYsWNbn+gQMHDKGhoYY777xTfb///e9/V9/3S5YsMXgiFMCkSU466STDzTffbL5eV1dnSE5ONjz11FO66//5z382zJw50+q2sWPHGq6//nqX76uv0NL3xJba2lpDeHi44f3333fhXvoWrXlP8D6ccsophn/84x+GK664ggK4g9+TN954w9CrVy9DdXV1O+6lb9HS9wTrTp482eo2iK/x48e7fF99DXFAAN9zzz2GQYMGWd124YUXGqZNm2bwRGiBIHaprq6W9evXq1PmGhg3jeurVq3SfQxut1wfTJs2ze76xPXviS0VFRVSU1OjpiiSjntPHn30UYmPj5drrrmmnfbUd2jNe/LVV1/JuHHjlAUiISFBBg8eLE8++aTU1dW14557L615T0455RT1GM0mceDAAWVJmTFjRrvtN/He7/cOHYVM3Jvjx4+rD398GViC67t27dJ9TE5Oju76uJ10zHtiy9y5c5Xny/aDjLTfe/Lbb7/JP//5T9m0aVM77aVv0Zr3BOLqp59+kksuuUSJrH379slNN92kDhYxDYu0/3syZ84c9bhTTz0VZ6ultrZWbrjhBrn//vvbaa+JI9/vJSUlcuLECeXT9iRYASbEh1iwYIFqulq0aJFqQiHtT2lpqVx22WWqOTEuLq6jd4eYqK+vVxX5t99+W0aNGiUXXnihPPDAA/Lmm2929K75LGi4QhX+9ddflw0bNsjnn38uixcvlscee6yjd414AawAE7vgy9nf319yc3Otbsf1xMRE3cfg9pasT1z/nmg899xzSgAvW7ZMhg4d6uI99R1a+p7s379fMjMzVfe1pfgCAQEBsnv3bundu3c77Ln30pq/EyQ/BAYGqsdpDBw4UFW9cPo+KCjI5fvtzbTmPXnooYfUweJf/vIXdR2pQuXl5XLdddepgxNYKEj7kWjn+z0iIsLjqr+Avz3ELvjARyXkxx9/tPqixnV45fTA7Zbrg6VLl9pdn7j+PQHPPPOMqposWbJERo8e3U576xu09D1BRODWrVuV/UFbZs+eLZMmTVL/R9QTaf+/k/Hjxyvbg3YwAvbs2aOEMcVvx7wn6FewFbnaAYqxb4u0J+O87fu9o7vwiPvH1iCG5r333lOxJ9ddd52KrcnJyVH3X3bZZYZ7773XKgYtICDA8Nxzz6nIrUceeYQxaB38nixYsEBFD/33v/81ZGdnm5fS0tIOfBW+/Z7YwhSIjn9PDh06pNJRbrnlFsPu3bsN33zzjSE+Pt7w+OOPd+Cr8O33BN8feE8+/vhjFcH1ww8/GHr37q3ShkjbKS0tVfGYWCAHX3jhBfX/gwcPqvvxXuA9sY1Bu/vuu9X3O+I1GYNGvBpk/fXo0UOJKMTYrF692nzfaaedpr68LfnPf/5j6Nevn1ofkSmLFy/ugL32blrynqSmpqoPN9sFXy6k4/5OLKEAdo/3ZOXKlSq2ESINkWhPPPGEiqsjHfOe1NTUGObNm6dEb0hIiCElJcVw0003GQoLCzto772L5cuX6343aO8BLvGe2D5m+PDh6v3D38i7775r8FQ64Z+OrkITQgghhBDSXtADTAghhBBCfAoKYEIIIYQQ4lNQABNCCCGEEJ+CApgQQgghhPgUFMCEEEIIIcSnoAAmhBBCCCE+BQUwIYQQQgjxKSiACSGEEEKIT0EBTAghpBFXXnmlnHvuuebrp59+uvz1r3/t0H0ihBBnQQFMCCFOoK6uTk455RQ5//zzrW4vLi6WlJQUeeCBB5p8/L59++Sqq66S7t27S3BwsPTs2VMuvvhiWbdunbgDn3/+uTz22GNO3ea8efNk+PDhTt0mIYQ4AgUwIYQ4AX9/f3nvvfdkyZIl8uGHH5pvv/XWWyUmJkYeeeQRu4+FyB01apTs2bNH3nrrLdmxY4csWrRIBgwYIHfddZdL97umpsah9fAawsPDXbovhBDSXlAAE0KIk+jXr58sWLBAid7s7Gz58ssv5ZNPPpF//etfEhQUpPsYg8Gg7AZ9+/aVX3/9VWbOnCm9e/dWlVGIZmxDY+vWrTJ58mTp3LmzxMbGynXXXSdlZWXm++vr6+XRRx81V5GxDQhyjczMTOnUqZN8+umnctppp0lISIgS66he33nnnRIVFaW2e88996j9ssTWApGWliZPPvmkXH311UoY9+jRQ95++22rx8ydO1f9TEJDQ6VXr17y0EMPmQU3Dhbmz58vmzdvVvuEBbeBoqIi+ctf/iJdu3aViIgI9ZqxHiGEOAsKYEIIcSIQv8OGDZPLLrtMCdSHH35YXbfHpk2bZPv27arS6+fX+CMZohSUl5fLtGnTJDo6WtauXSufffaZLFu2TG655Rbzui+//LI8//zz8txzz8mWLVvU+rNnz5a9e/dabfPee++V22+/XXbu3KnWwWMgPhcuXCi//fabFBQUqAp0c+Bxo0ePlo0bN8pNN90kN954o+zevdt8P4QxtouKNvbtnXfekRdffFHdd+GFF6rXPGjQIHWwgAW3gf/7v/+TvLw8+e6772T9+vUycuRIOeOMM9R+EUKIUzAQQghxKjt37kT51DBkyBBDTU1Nk+t++umnat0NGzY0ud7bb79tiI6ONpSVlZlvW7x4scHPz8+Qk5OjricnJxueeOIJq8eNGTPGcNNNN6n/Z2RkqOd66aWXrNZJSkoyPPPMM+br2Ofu3bsbzjnnHPNtp512muH22283X09NTTVceuml5uv19fWG+Ph4wxtvvGH3NTz77LOGUaNGma8/8sgjhmHDhlmt8+uvvxoiIiIMlZWVVrf37t3b8NZbbzX5MyKEEEcJcI6MJoQQooFKKk77Z2RkyJEjR5RdwB62VgN7oFqLSnJYWJj5tvHjxyvbA6qusEVkZWWp2yzBdVv7AKq2lk16qL6OHTvWfFtAQIBap7l9Gzp0qPn/sDAkJiaqyq0GrBavvPKK7N+/X1k1amtrlaWhKbCvWBdWDEtOnDihtkMIIc6AFghCCHEiK1euVKf5v/nmGznppJPkmmuuaVJIwiMLdu3a1W77aCmi20JgYKDVdYhgCHKwatUqueSSS2TGjBnqZwGbBJIwqqurm9wmxG9SUpKyhlguEPl33323U/abEEIogAkhxElUVFSohjZ4YSdNmiT//Oc/Zc2aNfLmm2/afQwa1dLT05WfVhOPlqAhDAwcOFBVR+EF1vj999+Vb7h///6qspqcnKxuswTXsX17REZGKsH5xx9/mG9DpRbe27YeCKSmpirRi2oymvwOHjxotQ4aA9GAZwn8vjk5OaoK3adPH6slLi6uTftECCEaFMCEEOIk7rvvPlXtRRIEgPUBDWlIVUACgx6omr777rsqAm3ChAny7bffyoEDB1QT2xNPPCHnnHOOWg/VVKQ2XHHFFbJt2zZZvny5arhDs11CQoJaBxXSp59+WlkPUDFFsxuqp2h4awrcj33+4osvVCUaDW2a8G4tELyHDh1SKRiwLsAKYdtYh58PbCLYx+PHj0tVVZVMmTJFxo0bp4Zw/PDDD+rnBjENIe0umciEEM+HApgQQpzAL7/8Iq+99poSs/D/alx//fVqQEZTVghYJSDuUOW89tprVbUX6Q1Ih3jppZfUOtjm999/r5IQxowZI3/6059UMsKrr75q3s5tt92m4syQrjBkyBAVgfbVV18pMdoUWB9CGuIa4hPpDeedd16bfh7Y/zvuuEOlVKDKDRGLGDRLLrjgApk+fbqqliPy7OOPP1YHBDgImDhxohoMAovIRRddpKrHmtAnhJC20gmdcG3eCiGEEEIIIR4CK8CEEEIIIcSnoAAmhBBCCCE+BQUwIYQQQgjxKSiACSGEEEKIT0EBTAghhBBCfAoKYEIIIYQQ4lNQABNCCCGEEJ+CApgQQgghhPgUFMCEEEIIIcSnoAAmhBBCCCE+BQUwIYQQQggRX+L/AXjp+8HIy6UyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\AppData\\Local\\Temp\\ipykernel_30456\\3375636903.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  processed_lines.append([lines.iloc[i][0], left_lines_processed[i], right_lines_processed[i]])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linea 0 : [-1015.88053204, 1019.54290965, 983.88053204, 1050.45709035]\n",
      "linea 1 : [0, np.float64(1653.9602350159053), np.float64(1707.700970429418), 0]\n",
      "linea 2 : [0, np.float64(4693.788614085843), np.float64(1705.4036866454373), 0]\n",
      "linea 0 : [-1015.88030089, 1018.5279644, 983.88030089, 1049.4720356]\n",
      "linea 1 : [0, np.float64(1650.2004501028518), np.float64(1704.8556824185437), 0]\n",
      "linea 2 : [0, np.float64(4680.069299402103), np.float64(1702.4270406002918), 0]\n",
      "linea 0 : [-1015.87983658, 1016.49798703, 983.87983658, 1047.50201297]\n",
      "linea 1 : [0, np.float64(1646.0590904130281), np.float64(1701.395876895738), 0]\n",
      "linea 2 : [0, np.float64(4666.729186543849), np.float64(1699.0911060943786), 0]\n",
      "linea 0 : [-1015.87960341, 1015.48295475, 983.87960341, 1046.51704525]\n",
      "linea 1 : [0, np.float64(1641.5735128984225), np.float64(1697.3874811614428), 0]\n",
      "linea 2 : [0, np.float64(4653.717817794185), np.float64(1695.4305870915537), 0]\n",
      "linea 0 : [-1015.87913502, 1013.45280257, 983.87913502, 1044.54719743]\n",
      "linea 1 : [0, np.float64(1636.7810471894757), np.float64(1692.8964225161), 0]\n",
      "linea 2 : [0, np.float64(4640.98690771192), np.float64(1691.4801875556727), 0]\n",
      "linea 0 : [-1015.8788998, 1012.43768249, 983.8788998, 1043.56231751]\n",
      "linea 1 : [0, np.float64(1631.7190845145872), np.float64(1687.9886282601512), 0]\n",
      "linea 2 : [0, np.float64(4628.4901316483), np.float64(1687.2746114505912), 0]\n",
      "linea 0 : [-1015.8784273, 1010.40735396, 983.8784273, 1041.59264604]\n",
      "linea 1 : [0, np.float64(1626.425154978774), np.float64(1682.7300256940384), 0]\n",
      "linea 2 : [0, np.float64(4616.182948760457), np.float64(1682.8485627401653), 0]\n",
      "linea 0 : [-1015.87819002, 1009.39214533, 983.87819002, 1040.60785467]\n",
      "linea 1 : [0, np.float64(1620.9369933146356), np.float64(1677.1865421182033), 0]\n",
      "linea 2 : [0, np.float64(4604.022456738979), np.float64(1678.2367453882503), 0]\n",
      "linea 0 : [-1014.89251912, 1008.33881937, 984.89251912, 1037.66118063]\n",
      "linea 1 : [0, np.float64(1615.292593130799), np.float64(1671.4241048330878), 0]\n",
      "linea 2 : [0, np.float64(4591.9672759109735), np.float64(1673.4738633587021), 0]\n",
      "linea 0 : [-1014.89230871, 1007.32447688, 984.89230871, 1036.67552312]\n",
      "linea 1 : [0, np.float64(1609.530249610257), np.float64(1665.5086411391333), 0]\n",
      "linea 2 : [0, np.float64(4579.977460733759), np.float64(1668.5946206153765), 0]\n",
      "linea 0 : [-1014.89188605, 1005.29570756, 984.89188605, 1034.70429244]\n",
      "linea 1 : [0, np.float64(1600.8844067214006), np.float64(1656.513706046936), 0]\n",
      "linea 2 : [0, np.float64(4569.914430552305), np.float64(1660.623870343261), 0]\n",
      "linea 0 : [-1014.89167378, 1004.28128056, 984.89167378, 1033.71871944]\n",
      "linea 1 : [0, np.float64(1594.4681709554322), np.float64(1650.566018622787), 0]\n",
      "linea 2 : [0, np.float64(4560.718630425319), np.float64(1655.2619040099978), 0]\n",
      "linea 0 : [-1014.89146089, 1003.26682523, 984.89146089, 1032.73317477]\n",
      "linea 1 : [0, np.float64(1591.1440375973978), np.float64(1644.6305973972198), 0]\n",
      "linea 2 : [0, np.float64(4538.041515270497), np.float64(1650.7652416790277), 0]\n",
      "linea 0 : [-1014.89103322, 1001.23782923, 984.89103322, 1030.76217077]\n",
      "linea 1 : [0, np.float64(1586.4297225233083), np.float64(1638.4559186009615), 0]\n",
      "linea 2 : [0, np.float64(4517.3647622530225), np.float64(1646.4530806880016), 0]\n",
      "linea 0 : [-1014.89081844, 1000.2232884, 984.89081844, 1029.7767116]\n",
      "linea 1 : [0, np.float64(1580.5284570504384), np.float64(1632.4909097134498), 0]\n",
      "linea 2 : [0, np.float64(4499.139767918563), np.float64(1641.8716127536582), 0]\n",
      "linea 0 : [-1014.89038696, 998.19412063, 984.89038696, 1027.80587937]\n",
      "linea 1 : [0, np.float64(1572.1112147653396), np.float64(1628.3931187804114), 0]\n",
      "linea 2 : [0, np.float64(4483.132679294391), np.float64(1637.5404658291288), 0]\n",
      "linea 0 : [-1014.88995292, 996.16483749, 984.88995292, 1025.83516251]\n",
      "linea 1 : [0, np.float64(1563.5269563778381), np.float64(1623.7125569178854), 0]\n",
      "linea 2 : [0, np.float64(4468.690087887388), np.float64(1633.2643183548669), 0]\n",
      "linea 0 : [-1014.88973493, 995.15015245, 984.88973493, 1024.84984755]\n",
      "linea 1 : [0, np.float64(1556.1048588766853), np.float64(1619.6873418744754), 0]\n",
      "linea 2 : [0, np.float64(4454.607397352664), np.float64(1628.803065381317), 0]\n",
      "linea 0 : [-1014.88929701, 993.12069498, 984.88929701, 1022.87930502]\n",
      "linea 1 : [0, np.float64(1550.3800094470284), np.float64(1615.1212909121255), 0]\n",
      "linea 2 : [0, np.float64(4443.0250072108465), np.float64(1624.5151263378962), 0]\n",
      "linea 0 : [-1014.88885648, 991.09112043, 984.88885648, 1020.90887957]\n",
      "linea 1 : [0, np.float64(1544.55848471171), np.float64(1610.688450591875), 0]\n",
      "linea 2 : [0, np.float64(4431.959063617841), np.float64(1620.2942712055851), 0]\n",
      "linea 0 : [-1014.88841331, 989.06142809, 984.88841331, 1018.93857191]\n",
      "linea 1 : [0, np.float64(1537.7051439032462), np.float64(1605.725627619118), 0]\n",
      "linea 2 : [0, np.float64(4420.597123188141), np.float64(1615.7579703349227), 0]\n",
      "linea 0 : [-1014.88819073, 988.04653753, 984.88819073, 1017.95346247]\n",
      "linea 1 : [0, np.float64(1529.28561407084), np.float64(1601.5632545147334), 0]\n",
      "linea 2 : [0, np.float64(4409.996269071863), np.float64(1611.5068901284792), 0]\n",
      "linea 0 : [-1015.87228009, 985.01802549, 983.87228009, 1016.98197451]\n",
      "linea 1 : [0, np.float64(1521.4776797803759), np.float64(1597.2874357515998), 0]\n",
      "linea 2 : [0, np.float64(4401.5321853827745), np.float64(1607.402211782367), 0]\n",
      "linea 0 : [-1015.87176828, 982.98603775, 983.87176828, 1015.01396225]\n",
      "linea 1 : [0, np.float64(1514.615998270998), np.float64(1592.9451847815717), 0]\n",
      "linea 2 : [0, np.float64(4394.170141901114), np.float64(1603.2143736252888), 0]\n",
      "linea 0 : [-1016.85495183, 980.96840262, 982.85495183, 1015.03159738]\n",
      "linea 1 : [0, np.float64(1507.9995902449828), np.float64(1587.936549330712), 0]\n",
      "linea 2 : [0, np.float64(4388.57942928084), np.float64(1599.0587726480387), 0]\n",
      "linea 0 : [-1016.85436885, 978.93421258, 982.85436885, 1013.06578742]\n",
      "linea 1 : [0, np.float64(1502.2411375171832), np.float64(1582.8913551646422), 0]\n",
      "linea 2 : [0, np.float64(4383.03630629454), np.float64(1594.9177299995424), 0]\n",
      "linea 0 : [-1016.85378234, 976.89988501, 982.85378234, 1011.10011499]\n",
      "linea 1 : [0, np.float64(1497.0833768574748), np.float64(1577.5363335571885), 0]\n",
      "linea 2 : [0, np.float64(4379.331644222883), np.float64(1591.0334386560396), 0]\n",
      "linea 0 : [-1017.8354172, 973.85782509, 981.8354172, 1010.14217491]\n",
      "linea 1 : [0, np.float64(1493.270342133147), np.float64(1572.4745533439109), 0]\n",
      "linea 2 : [0, np.float64(4375.783391689511), np.float64(1586.9367661870385), 0]\n",
      "linea 0 : [-1017.83475171, 971.82118633, 981.83475171, 1008.17881367]\n",
      "linea 1 : [0, np.float64(1490.323219330176), np.float64(1566.9082208768973), 0]\n",
      "linea 2 : [0, np.float64(4373.058954001115), np.float64(1582.8835862788137), 0]\n",
      "linea 0 : [-1018.81551357, 969.79221966, 980.81551357, 1008.20778034]\n",
      "linea 1 : [0, np.float64(1487.7072592173124), np.float64(1560.8393352968385), 0]\n",
      "linea 2 : [0, np.float64(4370.153559412141), np.float64(1578.8426471947662), 0]\n",
      "linea 0 : [-1018.81476535, 967.75331252, 980.81476535, 1006.24668748]\n",
      "linea 1 : [0, np.float64(1485.7277243435756), np.float64(1554.2378848766405), 0]\n",
      "linea 2 : [0, np.float64(4355.315659513941), np.float64(1575.191243487199), 0]\n",
      "linea 0 : [-1019.79392597, 964.69961572, 979.79392597, 1005.30038428]\n",
      "linea 1 : [0, np.float64(1484.8844241513068), np.float64(1548.0524710276784), 0]\n",
      "linea 2 : [0, np.float64(4339.55974845099), np.float64(1571.8150709890504), 0]\n",
      "linea 0 : [-1019.79350703, 963.67899376, 979.79350703, 1004.32100624]\n",
      "linea 1 : [0, np.float64(1480.8455250555182), np.float64(1542.537582632086), 0]\n",
      "linea 2 : [0, np.float64(4330.010229993225), np.float64(1568.0982087126026), 0]\n",
      "linea 0 : [-1019.79266533, 961.63762392, 979.79266533, 1002.36237608]\n",
      "linea 1 : [0, np.float64(1476.8523124037922), np.float64(1537.635710590839), 0]\n",
      "linea 2 : [0, np.float64(4317.598449801025), np.float64(1565.0452694924331), 0]\n",
      "linea 0 : [-1020.7704872, 958.5763467, 978.7704872, 1001.4236533]\n",
      "linea 1 : [0, np.float64(1472.2328278609093), np.float64(1533.5173072807463), 0]\n",
      "linea 2 : [0, np.float64(4304.969481357036), np.float64(1562.041794449883), 0]\n",
      "linea 0 : [-1020.76954786, 956.53255572, 978.76954786, 999.46744428]\n",
      "linea 1 : [0, np.float64(1470.261521331463), np.float64(1528.5170353395038), 0]\n",
      "linea 2 : [0, np.float64(4292.351126221621), np.float64(1559.0002777585462), 0]\n",
      "linea 0 : [-1021.74656819, 954.48779478, 977.74656819, 999.51220522]\n",
      "linea 1 : [0, np.float64(1468.5442162259858), np.float64(1523.8088414514027), 0]\n",
      "linea 2 : [0, np.float64(4282.436730046193), np.float64(1556.1862249279357), 0]\n",
      "linea 0 : [-1021.7455278, 952.44163937, 977.7455278, 997.55836063]\n",
      "linea 1 : [0, np.float64(1467.2799007718236), np.float64(1518.9299175889992), 0]\n",
      "linea 2 : [0, np.float64(4272.630484397498), np.float64(1553.648470841394), 0]\n",
      "linea 0 : [-1022.72073402, 949.36836908, 976.72073402, 996.63163092]\n",
      "linea 1 : [0, np.float64(1463.3722626974395), np.float64(1514.2562858584085), 0]\n",
      "linea 2 : [0, np.float64(4258.289169326811), np.float64(1550.9488854988701), 0]\n",
      "linea 0 : [-1022.71958289, 947.31972152, 976.71958289, 994.68027848]\n",
      "linea 1 : [0, np.float64(1458.7270087219702), np.float64(1510.0513408734093), 0]\n",
      "linea 2 : [0, np.float64(4241.268270911207), np.float64(1548.0312593830454), 0]\n",
      "linea 0 : [-1022.71900465, 946.29532257, 976.71900465, 993.70467743]\n",
      "linea 1 : [0, np.float64(1453.7724541535624), np.float64(1506.826836770292), 0]\n",
      "linea 2 : [0, np.float64(4227.979832148437), np.float64(1544.7473961493129), 0]\n",
      "linea 0 : [-1023.69278557, 943.21422846, 975.69278557, 992.78577154]\n",
      "linea 1 : [0, np.float64(1449.7819184293862), np.float64(1502.921564828054), 0]\n",
      "linea 2 : [0, np.float64(4218.849127039007), np.float64(1541.4639295418133), 0]\n",
      "linea 0 : [-1023.69151274, 941.16294378, 975.69151274, 990.83705622]\n",
      "linea 1 : [0, np.float64(1446.0525804289077), np.float64(1499.2287742721508), 0]\n",
      "linea 2 : [0, np.float64(4211.23426434607), np.float64(1538.232660231012), 0]\n",
      "linea 0 : [-1023.69087335, 940.1372218, 975.69087335, 989.8627782]\n",
      "linea 1 : [0, np.float64(1442.1851327017707), np.float64(1495.2994923683716), 0]\n",
      "linea 2 : [0, np.float64(4198.832104552941), np.float64(1535.1178747904205), 0]\n",
      "linea 0 : [-1024.66389346, 938.0751065, 974.66389346, 989.9248935]\n",
      "linea 1 : [0, np.float64(1438.8608415123522), np.float64(1491.5425208748309), 0]\n",
      "linea 2 : [0, np.float64(4181.700578670901), np.float64(1532.4017260862126), 0]\n",
      "linea 0 : [-1024.66249519, 936.02124493, 974.66249519, 987.97875507]\n",
      "linea 1 : [0, np.float64(1435.6595313273253), np.float64(1487.4866054767292), 0]\n",
      "linea 2 : [0, np.float64(4157.61082292974), np.float64(1530.0909061787881), 0]\n",
      "linea 0 : [-1024.66179278, 934.99423016, 974.66179278, 987.00576984]\n",
      "linea 1 : [0, np.float64(1432.4367038708776), np.float64(1483.367440508135), 0]\n",
      "linea 2 : [0, np.float64(4135.772220501774), np.float64(1527.5148862224296), 0]\n",
      "linea 0 : [-1024.66108817, 933.96715916, 974.66108817, 986.03284084]\n",
      "linea 1 : [0, np.float64(1429.0137113224625), np.float64(1479.4545976325282), 0]\n",
      "linea 2 : [0, np.float64(4118.032468889725), np.float64(1525.075339881724), 0]\n",
      "linea 0 : [-1024.66038136, 932.94003177, 974.66038136, 985.05996823]\n",
      "linea 1 : [0, np.float64(1425.453561498735), np.float64(1475.7827596166574), 0]\n",
      "linea 2 : [0, np.float64(4106.34926507977), np.float64(1522.2297710869584), 0]\n",
      "linea 0 : [-1024.65967233, 931.9128478, 974.65967233, 984.0871522]\n",
      "linea 1 : [0, np.float64(1421.7665193130113), np.float64(1472.0133861892823), 0]\n",
      "linea 2 : [0, np.float64(4094.1047349872333), np.float64(1519.3288986254927), 0]\n",
      "linea 0 : [-1023.68568593, 931.92951258, 975.68568593, 982.07048742]\n",
      "linea 1 : [0, np.float64(1418.297841621275), np.float64(1468.497613548891), 0]\n",
      "linea 2 : [0, np.float64(4082.07099158187), np.float64(1516.324655566224), 0]\n",
      "linea 0 : [-1023.68502834, 930.90330473, 975.68502834, 981.09669527]\n",
      "linea 1 : [0, np.float64(1414.615539164904), np.float64(1464.8289081939513), 0]\n",
      "linea 2 : [0, np.float64(4065.8285775575155), np.float64(1513.57467505883), 0]\n",
      "linea 0 : [-1023.68436868, 929.87704204, 975.68436868, 980.12295796]\n",
      "linea 1 : [0, np.float64(1411.5940931500238), np.float64(1461.4273266127914), 0]\n",
      "linea 2 : [0, np.float64(4050.130999556516), np.float64(1511.5895108978566), 0]\n",
      "linea 0 : [-1023.68370695, 928.85072435, 975.68370695, 979.14927565]\n",
      "linea 1 : [0, np.float64(1408.1606392591787), np.float64(1457.927220597608), 0]\n",
      "linea 2 : [0, np.float64(4032.0862161330265), np.float64(1509.4164410292544), 0]\n",
      "linea 0 : [-1023.68370695, 928.85072435, 975.68370695, 979.14927565]\n",
      "linea 1 : [0, np.float64(1405.1908550022833), np.float64(1454.516596854979), 0]\n",
      "linea 2 : [0, np.float64(4018.052972900256), np.float64(1507.2223873498283), 0]\n",
      "linea 0 : [-1022.70889464, 928.87271293, 976.70889464, 977.12728707]\n",
      "linea 1 : [0, np.float64(1401.9021995592793), np.float64(1451.403204984731), 0]\n",
      "linea 2 : [0, np.float64(4006.4932626703203), np.float64(1505.5695511376684), 0]\n",
      "linea 0 : [-1022.70889464, 928.87271293, 976.70889464, 977.12728707]\n",
      "linea 1 : [0, np.float64(1399.1641319322302), np.float64(1448.5262740333217), 0]\n",
      "linea 2 : [0, np.float64(3998.4871566850643), np.float64(1503.7762114821935), 0]\n",
      "linea 0 : [-1021.73364796, 929.92115398, 977.73364796, 976.07884602]\n",
      "linea 1 : [0, np.float64(1396.1763321365024), np.float64(1445.6170217562972), 0]\n",
      "linea 2 : [0, np.float64(3990.3107411487545), np.float64(1501.1309682864185), 0]\n",
      "linea 0 : [-1021.73364796, 929.92115398, 977.73364796, 976.07884602]\n",
      "linea 1 : [0, np.float64(1393.3769640366033), np.float64(1442.9523346436658), 0]\n",
      "linea 2 : [0, np.float64(3987.4250691580287), np.float64(1498.1292922701994), 0]\n",
      "linea 0 : [-1021.73364796, 929.92115398, 977.73364796, 976.07884602]\n",
      "linea 1 : [0, np.float64(1390.7632920949595), np.float64(1440.5735460440146), 0]\n",
      "linea 2 : [0, np.float64(3992.555846132737), np.float64(1494.9501693569412), 0]\n",
      "linea 0 : [-1020.7578112, 931.99275258, 978.7578112, 976.00724742]\n",
      "linea 1 : [0, np.float64(1388.0345023517725), np.float64(1438.2740281216145), 0]\n",
      "linea 2 : [0, np.float64(3997.830259116482), np.float64(1491.991443741853), 0]\n",
      "linea 0 : [-1020.7578112, 931.99275258, 978.7578112, 976.00724742]\n",
      "linea 1 : [0, np.float64(1385.6527413733836), np.float64(1436.1532854980924), 0]\n",
      "linea 2 : [0, np.float64(4000.6110485701097), np.float64(1489.1118001730006), 0]\n",
      "linea 0 : [-1019.78077987, 934.06218262, 979.78077987, 975.93781738]\n",
      "linea 1 : [0, np.float64(1383.6949277135277), np.float64(1434.2069276138182), 0]\n",
      "linea 2 : [0, np.float64(3998.1055625334207), np.float64(1486.4437455057912), 0]\n",
      "linea 0 : [-1019.7812381, 935.08407452, 979.7812381, 976.91592548]\n",
      "linea 1 : [0, np.float64(1381.964923682641), np.float64(1432.3713708727582), 0]\n",
      "linea 2 : [0, np.float64(3994.4176828474087), np.float64(1484.1063905290962), 0]\n",
      "linea 0 : [-1018.80297335, 937.1502022, 980.80297335, 976.8497978]\n",
      "linea 1 : [0, np.float64(1380.6688443004182), np.float64(1430.726284191892), 0]\n",
      "linea 2 : [0, np.float64(3992.32829447556), np.float64(1481.8513984661608), 0]\n",
      "linea 0 : [-1018.80379406, 939.19158281, 980.80379406, 978.80841719]\n",
      "linea 1 : [0, np.float64(1379.4442986873985), np.float64(1429.489577785264), 0]\n",
      "linea 2 : [0, np.float64(3997.149515142889), np.float64(1479.1949115032635), 0]\n",
      "linea 0 : [-1017.82426508, 941.25329503, 981.82426508, 978.74670497]\n",
      "linea 1 : [0, np.float64(1378.5977824757188), np.float64(1428.5510711564702), 0]\n",
      "linea 2 : [0, np.float64(4004.678408811458), np.float64(1476.7644699939963), 0]\n",
      "linea 0 : [-1017.82499484, 943.29225581, 981.82499484, 980.70774419]\n",
      "linea 1 : [0, np.float64(1377.86050672442), np.float64(1427.5153952494793), 0]\n",
      "linea 2 : [0, np.float64(4011.271618250934), np.float64(1474.0930052606757), 0]\n",
      "linea 0 : [-1016.84421925, 945.34958284, 982.84421925, 980.65041716]\n",
      "linea 1 : [0, np.float64(1377.2590779073619), np.float64(1426.650707332005), 0]\n",
      "linea 2 : [0, np.float64(4012.700609863981), np.float64(1471.8001538980104), 0]\n",
      "linea 0 : [-1016.84486416, 947.38615265, 982.84486416, 982.61384735]\n",
      "linea 1 : [0, np.float64(1376.5718528076895), np.float64(1426.228855577744), 0]\n",
      "linea 2 : [0, np.float64(4012.1772364748504), np.float64(1469.7215091021126), 0]\n",
      "linea 0 : [-1016.84550507, 949.42257127, 982.84550507, 984.57742873]\n",
      "linea 1 : [0, np.float64(1376.179503158906), np.float64(1426.3018292070478), 0]\n",
      "linea 2 : [0, np.float64(4009.8737345860495), np.float64(1468.0837132400484), 0]\n",
      "linea 0 : [-1015.86398779, 953.50739814, 983.86398779, 986.49260186]\n",
      "linea 1 : [0, np.float64(1376.4389168310915), np.float64(1426.7546428544588), 0]\n",
      "linea 2 : [0, np.float64(4005.5039294138905), np.float64(1467.074479921957), 0]\n",
      "linea 0 : [-1015.86454682, 955.54132433, 983.86454682, 988.45867567]\n",
      "linea 1 : [0, np.float64(1376.6300134099927), np.float64(1427.0525124104784), 0]\n",
      "linea 2 : [0, np.float64(3997.430098741053), np.float64(1466.247079811731), 0]\n",
      "linea 0 : [-1015.86537893, 958.59195276, 983.86537893, 991.40804724]\n",
      "linea 1 : [0, np.float64(1377.1062893560731), np.float64(1427.4839891609038), 0]\n",
      "linea 2 : [0, np.float64(3993.1598488813424), np.float64(1465.4478572906066), 0]\n",
      "linea 0 : [-1015.86592942, 960.62553237, 983.86592942, 993.37446763]\n",
      "linea 1 : [0, np.float64(1378.0770585955725), np.float64(1428.3312645769552), 0]\n",
      "linea 2 : [0, np.float64(3988.5518871765917), np.float64(1465.0120735923329), 0]\n",
      "linea 0 : [-1015.86674884, 963.67564492, 983.86674884, 996.32435508]\n",
      "linea 1 : [0, np.float64(1379.1650786701002), np.float64(1429.121836879093), 0]\n",
      "linea 2 : [0, np.float64(3987.2998031383154), np.float64(1464.4465052372436), 0]\n",
      "linea 0 : [-1015.86756077, 966.72545171, 983.86756077, 999.27454829]\n",
      "linea 1 : [0, np.float64(1380.413212692693), np.float64(1429.9838909016696), 0]\n",
      "linea 2 : [0, np.float64(3982.63063387593), np.float64(1463.8189520164822), 0]\n",
      "linea 0 : [-1014.88430268, 970.78877836, 984.88430268, 1001.21122164]\n",
      "linea 1 : [0, np.float64(1382.130750808058), np.float64(1431.164378461383), 0]\n",
      "linea 2 : [0, np.float64(3980.16332785748), np.float64(1463.181748726604), 0]\n",
      "linea 0 : [-1014.88500339, 973.83490895, 984.88500339, 1004.16509105]\n",
      "linea 1 : [0, np.float64(1384.2670330424992), np.float64(1432.74111181938), 0]\n",
      "linea 2 : [0, np.float64(3980.503877812363), np.float64(1462.57758948728), 0]\n",
      "linea 0 : [-1015.86995253, 975.87306528, 983.86995253, 1008.12693472]\n",
      "linea 1 : [0, np.float64(1386.672569342052), np.float64(1434.5009206609402), 0]\n",
      "linea 2 : [0, np.float64(3981.5646031698284), np.float64(1461.9451628718666), 0]\n",
      "linea 0 : [-1015.8707354, 978.92167662, 983.8707354, 1011.07832338]\n",
      "linea 1 : [0, np.float64(1389.4217319711347), np.float64(1436.5087287138426), 0]\n",
      "linea 2 : [0, np.float64(3982.9442084790085), np.float64(1461.3840121911715), 0]\n",
      "linea 0 : [-1015.87176828, 982.98603775, 983.87176828, 1015.01396225]\n",
      "linea 1 : [0, np.float64(1392.2950606736176), np.float64(1438.655839737831), 0]\n",
      "linea 2 : [0, np.float64(3984.4594995303273), np.float64(1460.9279813293588), 0]\n",
      "linea 0 : [-1015.87253484, 986.0339715, 983.87253484, 1017.9660285]\n",
      "linea 1 : [0, np.float64(1395.1495033144718), np.float64(1440.692639370084), 0]\n",
      "linea 2 : [0, np.float64(3986.9138571696344), np.float64(1460.4919981007574), 0]\n",
      "linea 0 : [-1015.87329455, 989.08161919, 983.87329455, 1020.91838081]\n",
      "linea 1 : [0, np.float64(1398.1036894449499), np.float64(1442.8581345499244), 0]\n",
      "linea 2 : [0, np.float64(3989.1588685790093), np.float64(1460.1822599202724), 0]\n",
      "linea 0 : [-1015.87404748, 992.12898337, 983.87404748, 1023.87101663]\n",
      "linea 1 : [0, np.float64(1400.817579942528), np.float64(1445.096019448949), 0]\n",
      "linea 2 : [0, np.float64(3990.1571443056255), np.float64(1459.8067005528067), 0]\n",
      "linea 0 : [-1015.87454572, 994.16040324, 983.87454572, 1025.83959676]\n",
      "linea 1 : [0, np.float64(1403.2294496830834), np.float64(1447.1804869052294), 0]\n",
      "linea 2 : [0, np.float64(3995.442488079236), np.float64(1459.2335117091343), 0]\n",
      "linea 0 : [-1015.87528754, 997.20730049, 983.87528754, 1028.79269951]\n",
      "linea 1 : [0, np.float64(1405.0819222956666), np.float64(1449.2719889468744), 0]\n",
      "linea 2 : [0, np.float64(3998.227385201992), np.float64(1458.6546298470332), 0]\n",
      "linea 0 : [-1015.87602281, 1000.2539209, 983.87602281, 1031.7460791]\n",
      "linea 1 : [0, np.float64(1406.561925776761), np.float64(1451.169931621756), 0]\n",
      "linea 2 : [0, np.float64(3996.1234073904857), np.float64(1458.249404688279), 0]\n",
      "linea 0 : [-1015.8767516, 1003.3002669, 983.8767516, 1034.6997331]\n",
      "linea 1 : [0, np.float64(1407.6251044165758), np.float64(1452.8923955018472), 0]\n",
      "linea 2 : [0, np.float64(3986.6546335053567), np.float64(1458.5601670665917), 0]\n",
      "linea 0 : [-1015.87747397, 1006.34634092, 983.87747397, 1037.65365908]\n",
      "linea 1 : [0, np.float64(1409.0361285826405), np.float64(1454.1760156060368), 0]\n",
      "linea 2 : [0, np.float64(3976.387624091579), np.float64(1458.6645341525953), 0]\n",
      "linea 0 : [-1015.87795203, 1008.376907, 983.87795203, 1039.623093]\n",
      "linea 1 : [0, np.float64(1409.9190958458898), np.float64(1455.0659062279599), 0]\n",
      "linea 2 : [0, np.float64(3968.498645543874), np.float64(1458.5804334996706), 0]\n",
      "linea 0 : [-1015.8786639, 1011.42253299, 983.8786639, 1042.57746701]\n",
      "linea 1 : [0, np.float64(1410.4887979628872), np.float64(1455.7677770605926), 0]\n",
      "linea 2 : [0, np.float64(3968.1163781702535), np.float64(1457.8588348028616), 0]\n",
      "linea 0 : [-1015.87913502, 1013.45280257, 983.87913502, 1044.54719743]\n",
      "linea 1 : [0, np.float64(1410.7217492104926), np.float64(1456.2574140993806), 0]\n",
      "linea 2 : [0, np.float64(3963.2080361216126), np.float64(1457.180988108332), 0]\n",
      "linea 0 : [-1015.87983658, 1016.49798703, 983.87983658, 1047.50201297]\n",
      "linea 1 : [0, np.float64(1410.6897353322584), np.float64(1456.7449301541526), 0]\n",
      "linea 2 : [0, np.float64(3954.217175383083), np.float64(1456.8008682085185), 0]\n",
      "linea 0 : [-1015.88030089, 1018.5279644, 983.88030089, 1049.4720356]\n",
      "linea 1 : [0, np.float64(1410.5061840171643), np.float64(1457.0046996343683), 0]\n",
      "linea 2 : [0, np.float64(3942.4687166501985), np.float64(1456.5825833755157), 0]\n",
      "linea 0 : [-1015.88076252, 1020.55782606, 983.88076252, 1051.44217394]\n",
      "linea 1 : [0, np.float64(1410.1358707176264), np.float64(1457.031890954615), 0]\n",
      "linea 2 : [0, np.float64(3928.3873023864403), np.float64(1456.43928133385), 0]\n",
      "linea 0 : [-1015.88122149, 1022.58757269, 983.88122149, 1053.41242731]\n",
      "linea 1 : [0, np.float64(1410.0251829950294), np.float64(1456.9633167917914), 0]\n",
      "linea 2 : [0, np.float64(3915.1300790620176), np.float64(1456.239233002905), 0]\n",
      "linea 0 : [-1015.88144998, 1023.60240308, 983.88144998, 1054.39759692]\n",
      "linea 1 : [0, np.float64(1409.8169380532809), np.float64(1457.0918049614465), 0]\n",
      "linea 2 : [0, np.float64(3903.189181536863), np.float64(1455.8356959526677), 0]\n",
      "linea 0 : [-1015.88190498, 1025.63197841, 983.88190498, 1056.36802159]\n",
      "linea 1 : [0, np.float64(1409.4270320001297), np.float64(1456.9801918111566), 0]\n",
      "linea 2 : [0, np.float64(3893.8144952587177), np.float64(1455.247649965732), 0]\n",
      "linea 0 : [-1015.88235737, 1027.66144035, 983.88235737, 1058.33855965]\n",
      "linea 1 : [0, np.float64(1408.897993511082), np.float64(1456.9791270686987), 0]\n",
      "linea 2 : [0, np.float64(3885.8711510124353), np.float64(1454.6349141553865), 0]\n",
      "linea 0 : [-1015.8825826, 1028.676129, 983.8825826, 1059.323871]\n",
      "linea 1 : [0, np.float64(1408.241217900295), np.float64(1456.7248364636591), 0]\n",
      "linea 2 : [0, np.float64(3878.157657924517), np.float64(1453.9869090029206), 0]\n",
      "linea 0 : [-1014.8969963, 1030.64741154, 984.8969963, 1059.35258846]\n",
      "linea 1 : [0, np.float64(1407.6013961196575), np.float64(1456.2932626434638), 0]\n",
      "linea 2 : [0, np.float64(3868.798968599525), np.float64(1453.3017401347136), 0]\n",
      "linea 0 : [-1014.89719312, 1031.66113012, 984.89719312, 1060.33886988]\n",
      "linea 1 : [0, np.float64(1407.1023088650807), np.float64(1455.739032138931), 0]\n",
      "linea 2 : [0, np.float64(3860.4091031116945), np.float64(1452.685615531975), 0]\n",
      "linea 0 : [-1014.89758508, 1033.68848876, 984.89758508, 1062.31151124]\n",
      "linea 1 : [0, np.float64(1407.590002499132), np.float64(1454.466961470212), 0]\n",
      "linea 2 : [0, np.float64(3852.5639104825686), np.float64(1452.0039720276914), 0]\n",
      "linea 0 : [-1014.89778022, 1034.70212898, 984.89778022, 1063.29787102]\n",
      "linea 1 : [0, np.float64(1407.7182528175724), np.float64(1453.4240477109493), 0]\n",
      "linea 2 : [0, np.float64(3845.9336511947545), np.float64(1451.2751737637445), 0]\n",
      "linea 0 : [-1014.89778022, 1034.70212898, 984.89778022, 1063.29787102]\n",
      "linea 1 : [0, np.float64(1406.970980621011), np.float64(1452.891039092724), 0]\n",
      "linea 2 : [0, np.float64(3834.121736471293), np.float64(1450.8255472849796), 0]\n",
      "linea 0 : [-1014.8979748, 1035.71574322, 984.8979748, 1064.28425678]\n",
      "linea 1 : [0, np.float64(1405.712345566442), np.float64(1452.7245584091695), 0]\n",
      "linea 2 : [0, np.float64(3819.3440668477147), np.float64(1450.7505221613326), 0]\n",
      "linea 0 : [-1014.89816883, 1036.72933156, 984.89816883, 1065.27066844]\n",
      "linea 1 : [0, np.float64(1402.5304441317135), np.float64(1453.989496598946), 0]\n",
      "linea 2 : [0, np.float64(3805.7741220239695), np.float64(1450.8977730500765), 0]\n",
      "linea 0 : [-1014.8983623, 1037.74289407, 984.8983623, 1066.25710593]\n",
      "linea 1 : [0, np.float64(1401.2520767256503), np.float64(1453.9314016090607), 0]\n",
      "linea 2 : [0, np.float64(3797.690331047153), np.float64(1450.2219185565211), 0]\n",
      "linea 0 : [-1015.88436131, 1036.79263329, 983.88436131, 1067.20736671]\n",
      "linea 1 : [0, np.float64(1399.1178301396567), np.float64(1454.8702542744622), 0]\n",
      "linea 2 : [0, np.float64(3793.091473223855), np.float64(1449.4907082592463), 0]\n",
      "linea 0 : [-1015.88458081, 1037.8070719, 983.88458081, 1068.1929281]\n",
      "linea 1 : [0, np.float64(1396.3360794441658), np.float64(1455.9195251080146), 0]\n",
      "linea 2 : [0, np.float64(3791.6489362558955), np.float64(1448.473651116884), 0]\n",
      "linea 0 : [-1015.88458081, 1037.8070719, 983.88458081, 1068.1929281]\n",
      "linea 1 : [0, np.float64(1393.189407814783), np.float64(1457.2155094495488), 0]\n",
      "linea 2 : [0, np.float64(3782.980540910385), np.float64(1447.8883012622064), 0]\n",
      "linea 0 : [-1015.88479968, 1038.82148312, 983.88479968, 1069.17851688]\n",
      "linea 1 : [0, np.float64(1389.6527464588464), np.float64(1458.4484912351888), 0]\n",
      "linea 2 : [0, np.float64(3775.7805045907867), np.float64(1447.0506236899946), 0]\n",
      "linea 0 : [-1015.88501793, 1039.83586703, 983.88501793, 1070.16413297]\n",
      "linea 1 : [0, np.float64(1385.3350596512298), np.float64(1460.9976857416239), 0]\n",
      "linea 2 : [0, np.float64(3769.3067591057516), np.float64(1446.160955299439), 0]\n",
      "linea 0 : [-1015.88501793, 1039.83586703, 983.88501793, 1070.16413297]\n",
      "linea 1 : [0, np.float64(1382.6304074000534), np.float64(1461.7348026493996), 0]\n",
      "linea 2 : [0, np.float64(3764.635368537708), np.float64(1445.0610684495932), 0]\n",
      "linea 0 : [-1015.88523555, 1040.8502237, 983.88523555, 1071.1497763]\n",
      "linea 1 : [0, np.float64(1379.0689583049357), np.float64(1463.4187242169085), 0]\n",
      "linea 2 : [0, np.float64(3759.4096872852647), np.float64(1443.9066856568372), 0]\n",
      "linea 0 : [-1015.88523555, 1040.8502237, 983.88523555, 1071.1497763]\n",
      "linea 1 : [0, np.float64(1376.186523358507), np.float64(1464.9542874748167), 0]\n",
      "linea 2 : [0, np.float64(3757.5672510929944), np.float64(1442.4619806161124), 0]\n",
      "linea 0 : [-1015.88545256, 1041.86455323, 983.88545256, 1072.13544677]\n",
      "linea 1 : [0, np.float64(1373.9269529620124), np.float64(1465.322729759056), 0]\n",
      "linea 2 : [0, np.float64(3757.285633778654), np.float64(1440.800126863336), 0]\n",
      "linea 0 : [-1015.88545256, 1041.86455323, 983.88545256, 1072.13544677]\n",
      "linea 1 : [0, np.float64(1372.4563477878032), np.float64(1464.6815427272743), 0]\n",
      "linea 2 : [0, np.float64(3756.7631930997145), np.float64(1439.1370120400427), 0]\n",
      "linea 0 : [-1015.88566896, 1042.87885567, 983.88566896, 1073.12114433]\n",
      "linea 1 : [0, np.float64(1371.8377577262595), np.float64(1462.829779455917), 0]\n",
      "linea 2 : [0, np.float64(3753.94293230424), np.float64(1437.4637423504041), 0]\n",
      "linea 0 : [-1016.87093383, 1041.93402091, 982.87093383, 1074.06597909]\n",
      "linea 1 : [0, np.float64(1372.1668902489585), np.float64(1460.032675461702), 0]\n",
      "linea 2 : [0, np.float64(3750.0336156888225), np.float64(1435.842291145004), 0]\n",
      "linea 0 : [-1016.87093383, 1041.93402091, 982.87093383, 1074.06597909]\n",
      "linea 1 : [0, np.float64(1373.3773609369787), np.float64(1456.3191030008827), 0]\n",
      "linea 2 : [0, np.float64(3747.5918828229346), np.float64(1434.221281347518), 0]\n",
      "linea 0 : [-1016.87117742, 1042.9491879, 982.87117742, 1075.0508121]\n",
      "linea 1 : [0, np.float64(1375.4258398992463), np.float64(1452.2398096707936), 0]\n",
      "linea 2 : [0, np.float64(3744.4449433226314), np.float64(1432.527803163398), 0]\n",
      "linea 0 : [-1016.87117742, 1042.9491879, 982.87117742, 1075.0508121]\n",
      "linea 1 : [0, np.float64(1376.6102108764474), np.float64(1448.6951316149248), 0]\n",
      "linea 2 : [0, np.float64(3739.974117300145), np.float64(1431.0930257001912), 0]\n",
      "linea 0 : [-1016.87142032, 1043.96432628, 982.87142032, 1076.03567372]\n",
      "linea 1 : [0, np.float64(1377.493379773589), np.float64(1445.372600576458), 0]\n",
      "linea 2 : [0, np.float64(3736.2523524424773), np.float64(1429.6810554507856), 0]\n",
      "linea 0 : [-1015.88609992, 1044.90737962, 983.88609992, 1075.09262038]\n",
      "linea 1 : [0, np.float64(1378.0057956796204), np.float64(1442.1323896053755), 0]\n",
      "linea 2 : [0, np.float64(3729.49048990637), np.float64(1428.5445800663404), 0]\n",
      "linea 0 : [-1015.88631448, 1045.92160129, 983.88631448, 1076.07839871]\n",
      "linea 1 : [0, np.float64(1378.897249975891), np.float64(1439.195274375134), 0]\n",
      "linea 2 : [0, np.float64(3725.0774015143943), np.float64(1427.329913201163), 0]\n",
      "linea 0 : [-1015.88631448, 1045.92160129, 983.88631448, 1076.07839871]\n",
      "linea 1 : [0, np.float64(1381.071572117691), np.float64(1434.6707547256617), 0]\n",
      "linea 2 : [0, np.float64(3721.4214215719344), np.float64(1425.94825454638), 0]\n",
      "linea 0 : [-1015.88652844, 1046.93579618, 983.88652844, 1077.06420382]\n",
      "linea 1 : [0, np.float64(1381.8562945167214), np.float64(1432.1276680885226), 0]\n",
      "linea 2 : [0, np.float64(3720.0248876838045), np.float64(1424.474373298657), 0]\n",
      "linea 0 : [-1015.88652844, 1046.93579618, 983.88652844, 1077.06420382]\n",
      "linea 1 : [0, np.float64(1382.4336308619545), np.float64(1429.946144612165), 0]\n",
      "linea 2 : [0, np.float64(3723.2462991055795), np.float64(1422.6499024413004), 0]\n",
      "linea 0 : [-1014.90045461, 1048.8903981, 984.90045461, 1077.1096019]\n",
      "linea 1 : [0, np.float64(1382.772136338841), np.float64(1428.588352058521), 0]\n",
      "linea 2 : [0, np.float64(3731.083537186567), np.float64(1421.1194321318255), 0]\n",
      "linea 0 : [-1014.90045461, 1048.8903981, 984.90045461, 1077.1096019]\n",
      "linea 1 : [0, np.float64(1383.235609709611), np.float64(1428.4298608472254), 0]\n",
      "linea 2 : [0, np.float64(3743.2474858444775), np.float64(1419.5622285196735), 0]\n",
      "linea 0 : [-1014.90064161, 1049.90365637, 984.90064161, 1078.09634363]\n",
      "linea 1 : [0, np.float64(1383.9130739763636), np.float64(1429.8312398275714), 0]\n",
      "linea 2 : [0, np.float64(3757.929759813111), np.float64(1418.5319231838434), 0]\n",
      "linea 0 : [-1014.90064161, 1049.90365637, 984.90064161, 1078.09634363]\n",
      "linea 1 : [0, np.float64(1385.9428063074213), np.float64(1430.4484435404163), 0]\n",
      "linea 2 : [0, np.float64(3777.0402678691667), np.float64(1417.887369328577), 0]\n",
      "linea 0 : [-1013.91344614, 1050.84324413, 985.91344614, 1077.15675587]\n",
      "linea 1 : [0, np.float64(1386.9058081716141), np.float64(1433.5309043437517), 0]\n",
      "linea 2 : [0, np.float64(3796.5237168060717), np.float64(1418.006192539431), 0]\n",
      "linea 0 : [-1013.91360859, 1051.85559576, 985.91360859, 1078.14440424]\n",
      "linea 1 : [0, np.float64(1389.4851950075742), np.float64(1436.4901402857329), 0]\n",
      "linea 2 : [0, np.float64(3818.514760711092), np.float64(1418.7948331137393), 0]\n",
      "linea 0 : [-1013.91360859, 1051.85559576, 985.91360859, 1078.14440424]\n",
      "linea 1 : [0, np.float64(1392.1440443443907), np.float64(1439.3314984907583), 0]\n",
      "linea 2 : [0, np.float64(3840.641781622122), np.float64(1420.056559267038), 0]\n",
      "linea 0 : [-1013.91360859, 1051.85559576, 985.91360859, 1078.14440424]\n",
      "linea 1 : [0, np.float64(1395.1383108792604), np.float64(1442.416636289676), 0]\n",
      "linea 2 : [0, np.float64(3861.107265015027), np.float64(1421.8793065170914), 0]\n",
      "linea 0 : [-1012.92564779, 1053.80578478, 986.92564779, 1078.19421522]\n",
      "linea 1 : [0, np.float64(1398.2830612707444), np.float64(1445.833396211128), 0]\n",
      "linea 2 : [0, np.float64(3880.8974106300616), np.float64(1423.9639217837293), 0]\n",
      "linea 0 : [-1012.92564779, 1053.80578478, 986.92564779, 1078.19421522]\n",
      "linea 1 : [0, np.float64(1401.6968522628365), np.float64(1449.4273082601471), 0]\n",
      "linea 2 : [0, np.float64(3900.074313360342), np.float64(1426.3418167837344), 0]\n",
      "linea 0 : [-1012.92564779, 1053.80578478, 986.92564779, 1078.19421522]\n",
      "linea 1 : [0, np.float64(1405.1970568818065), np.float64(1452.8420639582025), 0]\n",
      "linea 2 : [0, np.float64(3917.1295835093947), np.float64(1428.6379472439094), 0]\n",
      "linea 0 : [-1012.92564779, 1053.80578478, 986.92564779, 1078.19421522]\n",
      "linea 1 : [0, np.float64(1408.9021983474352), np.float64(1456.5112768354898), 0]\n",
      "linea 2 : [0, np.float64(3932.004511205623), np.float64(1431.4424887542157), 0]\n",
      "linea 0 : [-1012.92578708, 1054.81721159, 986.92578708, 1079.18278841]\n",
      "linea 1 : [0, np.float64(1413.270091207419), np.float64(1460.6358747630466), 0]\n",
      "linea 2 : [0, np.float64(3943.9310245798565), np.float64(1434.7765180590234), 0]\n",
      "linea 0 : [-1011.93676428, 1055.75422571, 987.93676428, 1078.24577429]\n",
      "linea 1 : [0, np.float64(1417.3005209442395), np.float64(1464.508490605202), 0]\n",
      "linea 2 : [0, np.float64(3956.10631216704), np.float64(1437.8196598093389), 0]\n",
      "linea 0 : [-1011.93676428, 1055.75422571, 987.93676428, 1078.24577429]\n",
      "linea 1 : [0, np.float64(1421.734784809754), np.float64(1468.901612737246), 0]\n",
      "linea 2 : [0, np.float64(3964.051899990331), np.float64(1441.0533938539672), 0]\n",
      "linea 0 : [-1011.93676428, 1055.75422571, 987.93676428, 1078.24577429]\n",
      "linea 1 : [0, np.float64(1426.6575919301235), np.float64(1473.7158104287867), 0]\n",
      "linea 2 : [0, np.float64(3974.092838116098), np.float64(1444.479947783404), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1431.0932544642485), np.float64(1478.6075221863357), 0]\n",
      "linea 2 : [0, np.float64(3981.536862091209), np.float64(1447.9463985975126), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1436.4578016409125), np.float64(1484.0012685649604), 0]\n",
      "linea 2 : [0, np.float64(3986.382606716862), np.float64(1451.23356395787), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1441.6333407734537), np.float64(1489.1962620127358), 0]\n",
      "linea 2 : [0, np.float64(3995.877731149368), np.float64(1454.7343889109), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1446.9195383745903), np.float64(1494.6137898174584), 0]\n",
      "linea 2 : [0, np.float64(4005.1855287265894), np.float64(1458.3624692221645), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1452.300515121789), np.float64(1500.5075573329016), 0]\n",
      "linea 2 : [0, np.float64(4014.4883519476307), np.float64(1461.9704057439335), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1457.6423334778985), np.float64(1506.2847203809167), 0]\n",
      "linea 2 : [0, np.float64(4024.3788686284956), np.float64(1465.7893455441251), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1462.737775623393), np.float64(1511.6916461979436), 0]\n",
      "linea 2 : [0, np.float64(4034.334886446665), np.float64(1469.5966579507813), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1467.6289546641435), np.float64(1516.6211948649297), 0]\n",
      "linea 2 : [0, np.float64(4043.2476693884882), np.float64(1473.3498990287576), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1472.524798275373), np.float64(1521.328052355022), 0]\n",
      "linea 2 : [0, np.float64(4051.9161788011643), np.float64(1476.8678286475058), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1476.2096833999722), np.float64(1525.2325521714158), 0]\n",
      "linea 2 : [0, np.float64(4058.3765483238367), np.float64(1479.9834412288224), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1479.4422361247819), np.float64(1528.6753381216058), 0]\n",
      "linea 2 : [0, np.float64(4063.0836406004555), np.float64(1482.8199007792923), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1481.98680240639), np.float64(1531.6099558252579), 0]\n",
      "linea 2 : [0, np.float64(4068.0234015892765), np.float64(1485.179551686925), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1484.270459449799), np.float64(1533.8852348557498), 0]\n",
      "linea 2 : [0, np.float64(4072.012272750764), np.float64(1486.9881113508304), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1486.229480336048), np.float64(1535.5443795939743), 0]\n",
      "linea 2 : [0, np.float64(4075.610007778037), np.float64(1488.3995352480417), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1488.1561894685763), np.float64(1537.3285796481962), 0]\n",
      "linea 2 : [0, np.float64(4079.020902204994), np.float64(1489.963486380509), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1490.2547742301679), np.float64(1539.3484654004517), 0]\n",
      "linea 2 : [0, np.float64(4082.8226421094955), np.float64(1491.5898327972395), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1492.0907724629499), np.float64(1541.291723244332), 0]\n",
      "linea 2 : [0, np.float64(4087.7616769329593), np.float64(1492.9300887851323), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1494.7036675104243), np.float64(1543.9140946659295), 0]\n",
      "linea 2 : [0, np.float64(4099.026361138155), np.float64(1494.2028269282332), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1497.2981026232717), np.float64(1546.6868350782277), 0]\n",
      "linea 2 : [0, np.float64(4107.541641432567), np.float64(1496.3440707880131), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1500.361433872177), np.float64(1549.6551535498277), 0]\n",
      "linea 2 : [0, np.float64(4103.809710307487), np.float64(1499.0560122760191), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1503.5870680167181), np.float64(1552.720300480497), 0]\n",
      "linea 2 : [0, np.float64(4103.195599074757), np.float64(1501.962069256469), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1506.8131160500395), np.float64(1556.0967947659815), 0]\n",
      "linea 2 : [0, np.float64(4108.208020104554), np.float64(1504.9634478968655), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1510.2017766619322), np.float64(1559.9839022617696), 0]\n",
      "linea 2 : [0, np.float64(4114.469051722373), np.float64(1508.0031471140312), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1513.4923664349067), np.float64(1563.8830577541562), 0]\n",
      "linea 2 : [0, np.float64(4123.813954330548), np.float64(1510.969768057298), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1516.8671644400383), np.float64(1567.314571079609), 0]\n",
      "linea 2 : [0, np.float64(4139.768926813185), np.float64(1513.237442508307), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1520.387242262381), np.float64(1570.7943021330193), 0]\n",
      "linea 2 : [0, np.float64(4151.405023963192), np.float64(1515.9343774426666), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1523.9379172936249), np.float64(1574.2529357663489), 0]\n",
      "linea 2 : [0, np.float64(4156.7390732251315), np.float64(1519.0893240613964), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1527.2661894513983), np.float64(1577.9957140773417), 0]\n",
      "linea 2 : [0, np.float64(4153.915023326758), np.float64(1522.9599670844295), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1530.0700038675943), np.float64(1581.5315495893794), 0]\n",
      "linea 2 : [0, np.float64(4156.249828401524), np.float64(1526.3068221658507), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1533.007404365877), np.float64(1584.6338473599787), 0]\n",
      "linea 2 : [0, np.float64(4151.579612428569), np.float64(1530.1609847090072), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1535.8612354366421), np.float64(1587.38793836355), 0]\n",
      "linea 2 : [0, np.float64(4147.65668906906), np.float64(1533.4819317957736), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1538.6271991019523), np.float64(1590.0508203234851), 0]\n",
      "linea 2 : [0, np.float64(4143.890567415811), np.float64(1536.6300706758484), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1540.8976147263259), np.float64(1592.3481002943126), 0]\n",
      "linea 2 : [0, np.float64(4143.652322970957), np.float64(1539.4565607977572), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1542.8378826510987), np.float64(1594.559736079771), 0]\n",
      "linea 2 : [0, np.float64(4142.4608600684705), np.float64(1542.0464402058128), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1544.8417535256838), np.float64(1596.980824797939), 0]\n",
      "linea 2 : [0, np.float64(4148.193675497676), np.float64(1544.0721498077548), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1546.6547462165133), np.float64(1599.7373375240131), 0]\n",
      "linea 2 : [0, np.float64(4159.433283973658), np.float64(1545.477955112693), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1548.4168174728604), np.float64(1602.1949022762444), 0]\n",
      "linea 2 : [0, np.float64(4171.51981387822), np.float64(1546.9320427399725), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1550.112017629932), np.float64(1604.516954303225), 0]\n",
      "linea 2 : [0, np.float64(4172.368440922529), np.float64(1549.1402417695608), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1551.826896908915), np.float64(1606.6267338614673), 0]\n",
      "linea 2 : [0, np.float64(4168.592497218436), np.float64(1551.296868508088), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1553.4692495833858), np.float64(1608.4031391296078), 0]\n",
      "linea 2 : [0, np.float64(4175.605313853568), np.float64(1552.6605282897724), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1555.1696112946584), np.float64(1610.1548325615902), 0]\n",
      "linea 2 : [0, np.float64(4182.353877148703), np.float64(1553.7050035921748), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1556.8569378182149), np.float64(1611.774313489784), 0]\n",
      "linea 2 : [0, np.float64(4192.311497090439), np.float64(1554.5336012916441), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1558.6515559508855), np.float64(1612.9939231346973), 0]\n",
      "linea 2 : [0, np.float64(4199.156769257519), np.float64(1555.1828480588797), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1560.2167458410556), np.float64(1613.9601250133587), 0]\n",
      "linea 2 : [0, np.float64(4203.163395292953), np.float64(1556.0661550237949), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1561.8716743043021), np.float64(1615.2384281378413), 0]\n",
      "linea 2 : [0, np.float64(4192.433410987015), np.float64(1558.2509971730017), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1562.795373622301), np.float64(1615.6403395231675), 0]\n",
      "linea 2 : [0, np.float64(4184.888164098581), np.float64(1559.4001793090586), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1564.5498364661585), np.float64(1616.3579814863806), 0]\n",
      "linea 2 : [0, np.float64(4168.8802124510985), np.float64(1561.718611197049), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1566.1323621793556), np.float64(1616.9852750161172), 0]\n",
      "linea 2 : [0, np.float64(4152.677179357639), np.float64(1563.991341767686), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1567.4849370417019), np.float64(1617.5516912841433), 0]\n",
      "linea 2 : [0, np.float64(4137.471301005732), np.float64(1566.1314212547736), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1568.549251894204), np.float64(1618.0867014622245), 0]\n",
      "linea 2 : [0, np.float64(4124.411383255707), np.float64(1568.0518998921166), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1569.2669240127318), np.float64(1618.6197767221265), 0]\n",
      "linea 2 : [0, np.float64(4114.614339251779), np.float64(1569.6658279135195), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1569.5797836726083), np.float64(1619.1803882356153), 0]\n",
      "linea 2 : [0, np.float64(4109.18381880463), np.float64(1570.8862555527871), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1569.4302242193708), np.float64(1619.7980071744564), 0]\n",
      "linea 2 : [0, np.float64(4109.235788296929), np.float64(1571.626233043724), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1568.761613092307), np.float64(1620.5021047104156), 0]\n",
      "linea 2 : [0, np.float64(4115.931530229748), np.float64(1571.7988106201346), 0]\n",
      "linea 0 : [-1010.94686363, 1056.69126945, 988.94686363, 1077.30873055]\n",
      "linea 1 : [0, np.float64(1567.5187596160217), np.float64(1621.3221520152583), 0]\n",
      "linea 2 : [0, np.float64(4130.519354143575), np.float64(1571.317038515824), 0]\n",
      "Adjusted video saved to C:\\Users\\miche\\OneDrive\\Documenti\\GitHub\\bowling-analysis\\data\\recording_3\\Lines_video_processed.mp4\n"
     ]
    }
   ],
   "source": [
    "# Reset the video to the beginning\n",
    "cap.set(cv2.CAP_PROP_POS_FRAMES, 0)\n",
    "\n",
    "# Define the codec and create a VideoWriter object to save the modified frames\n",
    "output_path = project_root / \"data\" / \"recording_3\" / \"Lines_video_processed.mp4\"\n",
    "fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")  # Use 'mp4v' codec for MP4 format\n",
    "fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
    "frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "out = cv2.VideoWriter(str(output_path), fourcc, fps, (frame_width, frame_height))\n",
    "\n",
    "# Process the lines\n",
    "processed_lines = process_lines(lines)\n",
    "# print('Processed lines:', processed_lines)\n",
    "\n",
    "# initializing df\n",
    "output_df = pd.DataFrame(columns=[\"frame_number\", \"horizontal\", \"left\", \"right\"])\n",
    "\n",
    "# Loop through each frame in the video\n",
    "frame_index = 0\n",
    "while frame_index < len(lines):\n",
    "    ret, video_frame = cap.read()\n",
    "    if not ret:\n",
    "        print(\"End of video or failed to read the frame at iteration\", frame_index)\n",
    "        break\n",
    "    # print(f\"Processing frame {frame_index}\")\n",
    "\n",
    "    # draw the lines on the frame\n",
    "    modified_frame = write_lines_on_frame(video_frame, processed_lines[frame_index])\n",
    "\n",
    "    # add lines to the output df\n",
    "    output_df.loc[len(output_df)] = [\n",
    "        frame_index,\n",
    "        processed_lines[frame_index][0],\n",
    "        processed_lines[frame_index][1],\n",
    "        processed_lines[frame_index][2],\n",
    "    ]\n",
    "\n",
    "    # Write the modified frame to the output video\n",
    "    out.write(modified_frame)\n",
    "\n",
    "    # Increment the frame index\n",
    "    frame_index += 1\n",
    "\n",
    "# Release the video capture and writer objects\n",
    "# cap.release()\n",
    "out.release()\n",
    "\n",
    "print(f\"Adjusted video saved to {output_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lines array saved to C:\\Users\\miche\\OneDrive\\Documenti\\GitHub\\bowling-analysis\\data\\auxiliary_data\\lane_lines\\definitive_lines_3.csv\n"
     ]
    }
   ],
   "source": [
    "# Define the output path for the lines CSV file\n",
    "output_lines_path = (\n",
    "    project_root / \"data\" / \"auxiliary_data\" / \"lane_lines\" / \"definitive_lines_3.csv\"\n",
    ")\n",
    "\n",
    "# Convert lines_array to a DataFrame\n",
    "lines_df = pd.DataFrame(output_df)\n",
    "\n",
    "# Save the DataFrame to a CSV file\n",
    "output_df.to_csv(output_lines_path, index=False)\n",
    "\n",
    "print(f\"Lines array saved to {output_lines_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read from created df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "string_lines_path = (\n",
    "    project_root / \"data\" / \"auxiliary_data\" / \"lane_lines\" / \"definitive_lines_3.csv\"\n",
    ")\n",
    "\n",
    "output_lines = pd.read_csv(string_lines_path)\n",
    "\n",
    "print(type(output_lines[\"horizontal\"][0]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}