diff --git a/dev/dev.ipynb b/dev/dev.ipynb
deleted file mode 100644
index d0618d1..0000000
--- a/dev/dev.ipynb
+++ /dev/null
@@ -1,13473 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import json\n",
-    "import pprint\n",
-    "\n",
-    "import pathlib\n",
-    "import logging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use start time from info file (instead of recording.meta_info.start_time_synced_ns)\n",
-    "# to have a more precise value and avoid having a negative first timestamp when rewriting\n",
-    "# start_time_synced_ns = int(info_json[\"start_time\"])\n",
-    "# start_time_synced_ns = 1706217550686000000\n",
-    "\n",
-    "\n",
-    "def conversion(timestamps):\n",
-    "    # Subtract start_time from all times in the recording, so timestamps\n",
-    "    # start at 0. This is to increase precision when converting\n",
-    "    # timestamps to float32, e.g. for OpenGL!\n",
-    "    SECONDS_PER_NANOSECOND = 1e-9\n",
-    "    # return (timestamps - start_time_synced_ns) * SECONDS_PER_NANOSECOND\n",
-    "    return timestamps * SECONDS_PER_NANOSECOND"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _rewrite_times(path, dtype=\"<u8\"):\n",
-    "    \"\"\"Load raw times (assuming dtype), apply conversion and save as _timestamps.npy.\"\"\"\n",
-    "\n",
-    "    timestamps = np.fromfile(str(path), dtype=dtype)\n",
-    "    new_name = f\"{path.stem}_timestamps_unix.npy\"\n",
-    "    timestamp_loc = path.parent / new_name\n",
-    "    np.save(str(timestamp_loc), timestamps)\n",
-    "\n",
-    "    timestamps = conversion(timestamps)\n",
-    "\n",
-    "    new_name = f\"{path.stem}_timestamps.npy\"\n",
-    "    # logger.info(f\"Creating {new_name}\")\n",
-    "    timestamp_loc = path.parent / new_name\n",
-    "    np.save(str(timestamp_loc), timestamps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _load_timestamps_data(path):\n",
-    "    timestamps = np.load(str(path))\n",
-    "    return timestamps.astype(np.float64)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _neon_raw_time_load(path):\n",
-    "    return np.fromfile(str(path), dtype=\"<u8\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _load_raw_data(path):\n",
-    "    raw_data = np.fromfile(str(path), \"<f4\")\n",
-    "    raw_data_dtype = raw_data.dtype\n",
-    "    raw_data.shape = (-1, 2)\n",
-    "    return np.asarray(raw_data, dtype=raw_data_dtype)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _load_worn_data(path: pathlib.Path):\n",
-    "    if not (path and path.exists()):\n",
-    "        return None\n",
-    "\n",
-    "    confidences = np.fromfile(str(path), \"<u1\") / 255.0\n",
-    "    return np.clip(confidences, 0.0, 1.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# obtained here:\n",
-    "# https://github.com/pupil-labs/neon-player/blob/master/pupil_src/shared_modules/imu_timeline/imu_timeline.py#L72\n",
-    "from scipy.spatial.transform import Rotation\n",
-    "\n",
-    "import imu_pb2\n",
-    "\n",
-    "logger = logging.getLogger(__name__)\n",
-    "\n",
-    "\n",
-    "def parse_neon_imu_raw_packets(buffer):\n",
-    "    index = 0\n",
-    "    packet_sizes = []\n",
-    "    while True:\n",
-    "        nums = np.frombuffer(buffer[index : index + 2], np.uint16)\n",
-    "        if not nums:\n",
-    "            break\n",
-    "        index += 2\n",
-    "        packet_size = nums[0]\n",
-    "        packet_sizes.append(packet_size)\n",
-    "        packet_bytes = buffer[index : index + packet_size]\n",
-    "        index += packet_size\n",
-    "        packet = imu_pb2.ImuPacket()\n",
-    "        packet.ParseFromString(packet_bytes)\n",
-    "        yield packet\n",
-    "\n",
-    "\n",
-    "class IMURecording:\n",
-    "    DTYPE_RAW = np.dtype(\n",
-    "        [\n",
-    "            (\"gyro_x\", \"<f4\"),\n",
-    "            (\"gyro_y\", \"<f4\"),\n",
-    "            (\"gyro_z\", \"<f4\"),\n",
-    "            (\"accel_x\", \"<f4\"),\n",
-    "            (\"accel_y\", \"<f4\"),\n",
-    "            (\"accel_z\", \"<f4\"),\n",
-    "            (\"pitch\", \"<f4\"),\n",
-    "            (\"yaw\", \"<f4\"),\n",
-    "            (\"roll\", \"<f4\"),\n",
-    "            (\"quaternion_w\", \"<f4\"),\n",
-    "            (\"quaternion_x\", \"<f4\"),\n",
-    "            (\"quaternion_y\", \"<f4\"),\n",
-    "            (\"quaternion_z\", \"<f4\"),\n",
-    "            (\"tsNs\", \"uint64\"),\n",
-    "            (\"ts\", \"<f8\"),\n",
-    "            (\"ts_rel\", \"<f8\"),\n",
-    "        ]\n",
-    "    )\n",
-    "\n",
-    "    def __init__(self, path_to_imu_raw: pathlib.Path, start_ts):\n",
-    "        stem = path_to_imu_raw.stem\n",
-    "        self.path_raw = path_to_imu_raw\n",
-    "        self.path_ts = path_to_imu_raw.with_name(stem + \"_timestamps.npy\")\n",
-    "        self.load(start_ts)\n",
-    "\n",
-    "    def load(self, start_ts):\n",
-    "        if not self.path_raw.exists() and self.path_ts.exists():\n",
-    "            self.ts = np.empty(0, dtype=np.float64)\n",
-    "            self.raw = []\n",
-    "            return\n",
-    "\n",
-    "        self.ts = np.load(str(self.path_ts))\n",
-    "        with self.path_raw.open(\"rb\") as raw_file:\n",
-    "            raw_data = raw_file.read()\n",
-    "            imu_packets = parse_neon_imu_raw_packets(raw_data)\n",
-    "            imu_data = []\n",
-    "            for packet in imu_packets:\n",
-    "                rotation = Rotation.from_quat(\n",
-    "                    [\n",
-    "                        packet.rotVecData.x,\n",
-    "                        packet.rotVecData.y,\n",
-    "                        packet.rotVecData.z,\n",
-    "                        packet.rotVecData.w,\n",
-    "                    ]\n",
-    "                )\n",
-    "                euler = rotation.as_euler(seq=\"XZY\", degrees=True)\n",
-    "\n",
-    "                ts = conversion(float(packet.tsNs))\n",
-    "                ts_rel = ts - start_ts\n",
-    "\n",
-    "                imu_data.append(\n",
-    "                    (\n",
-    "                        packet.gyroData.x,\n",
-    "                        packet.gyroData.y,\n",
-    "                        packet.gyroData.z,\n",
-    "                        packet.accelData.x,\n",
-    "                        packet.accelData.y,\n",
-    "                        packet.accelData.z,\n",
-    "                        *euler,\n",
-    "                        packet.rotVecData.w,\n",
-    "                        packet.rotVecData.x,\n",
-    "                        packet.rotVecData.y,\n",
-    "                        packet.rotVecData.z,\n",
-    "                        packet.tsNs,\n",
-    "                        ts,\n",
-    "                        ts_rel,\n",
-    "                    )\n",
-    "                )\n",
-    "\n",
-    "            self.raw = np.array(imu_data, dtype=IMURecording.DTYPE_RAW).view(\n",
-    "                np.recarray\n",
-    "            )\n",
-    "\n",
-    "        num_ts_during_init = self.ts.size - len(self.raw)\n",
-    "        if num_ts_during_init > 0:\n",
-    "            self.ts = self.ts[num_ts_during_init:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/by/f4g6q8690z50zbfvy6kq71cc0000gn/T/ipykernel_25601/647576048.py:14: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
-      "  if not nums:\n"
-     ]
-    }
-   ],
-   "source": [
-    "start_ts = conversion(1706217550686000000)\n",
-    "\n",
-    "with pathlib.Path(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/extimu ps1.raw\"\n",
-    ").open(\"rb\") as raw_file:\n",
-    "    raw_data = raw_file.read()\n",
-    "    imu_packets = parse_neon_imu_raw_packets(raw_data)\n",
-    "    imu_data = []\n",
-    "    for packet in imu_packets:\n",
-    "        rotation = Rotation.from_quat(\n",
-    "            [\n",
-    "                packet.rotVecData.x,\n",
-    "                packet.rotVecData.y,\n",
-    "                packet.rotVecData.z,\n",
-    "                packet.rotVecData.w,\n",
-    "            ]\n",
-    "        )\n",
-    "        euler = rotation.as_euler(seq=\"XZY\", degrees=True)\n",
-    "\n",
-    "        ts = conversion(packet.tsNs)\n",
-    "        ts_rel = ts - start_ts\n",
-    "\n",
-    "        imu_data.append(\n",
-    "            (\n",
-    "                packet.gyroData.x,\n",
-    "                packet.gyroData.y,\n",
-    "                packet.gyroData.z,\n",
-    "                packet.accelData.x,\n",
-    "                packet.accelData.y,\n",
-    "                packet.accelData.z,\n",
-    "                *euler,\n",
-    "                packet.rotVecData.w,\n",
-    "                packet.rotVecData.x,\n",
-    "                packet.rotVecData.y,\n",
-    "                packet.rotVecData.z,\n",
-    "                packet.tsNs,\n",
-    "                ts,\n",
-    "                ts_rel,\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    raw = np.array(imu_data, dtype=IMURecording.DTYPE_RAW).view(np.recarray)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([( 0.10108948, -0.53596497, 0.70762634, 0.04443359, -0.6230469 , 0.7832031 , -118.563385, -44.58207 , 124.69439 , 0.50820255, -0.1975484 , 0.2673142 , -0.7945111 , 1706217553148771064, 1.70621755e+09,  2.46277094),\n",
-       "           ( 0.2822876 , -0.47683716, 0.5893707 , 0.04882812, -0.6269531 , 0.78271484, -118.54282 , -44.59728 , 124.66329 , 0.5083921 , -0.19759709, 0.26724702, -0.7944003 , 1706217553159015064, 1.70621755e+09,  2.47301483),\n",
-       "           ( 0.2822876 , -0.11062622, 0.5245209 , 0.04150391, -0.62841797, 0.77539057, -118.5168  , -44.61387 , 124.62134 , 0.5086237 , -0.19768989, 0.26716286, -0.7942572 , 1706217553169259064, 1.70621755e+09,  2.48325872),\n",
-       "           ...,\n",
-       "           (-0.78201294, -2.6168823 , 2.653122  , 0.11816406, -0.8154297 , 0.5932617 , -107.888054, -33.64299 , 110.22006 , 0.51416355, -0.30293173, 0.32829908, -0.7321802 , 1706217591021952064, 1.70621759e+09, 40.33595181),\n",
-       "           (-1.2111664 , -2.2525787 , 2.7179718 , 0.12597656, -0.80615234, 0.61083984, -107.860855, -33.643444, 110.17224 , 0.5143746 , -0.30311283, 0.32825592, -0.73197633, 1706217591032251064, 1.70621759e+09, 40.34625101),\n",
-       "           (-1.0871887 , -1.5830994 , 2.3479462 , 0.13330078, -0.7998047 , 0.5942383 , -107.83384 , -33.64339 , 110.125694, 0.5145792 , -0.3032891 , 0.3282198 , -0.7317757 , 1706217591042550064, 1.70621759e+09, 40.35654974)],\n",
-       "          dtype=[('gyro_x', '<f4'), ('gyro_y', '<f4'), ('gyro_z', '<f4'), ('accel_x', '<f4'), ('accel_y', '<f4'), ('accel_z', '<f4'), ('pitch', '<f4'), ('yaw', '<f4'), ('roll', '<f4'), ('quaternion_w', '<f4'), ('quaternion_x', '<f4'), ('quaternion_y', '<f4'), ('quaternion_z', '<f4'), ('tsNs', '<u8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/by/f4g6q8690z50zbfvy6kq71cc0000gn/T/ipykernel_25601/647576048.py:14: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
-      "  if not nums:\n"
-     ]
-    }
-   ],
-   "source": [
-    "_rewrite_times(\n",
-    "    pathlib.Path(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/extimu ps1.time\")\n",
-    ")\n",
-    "imu_rec = IMURecording(\n",
-    "    pathlib.Path(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/extimu ps1.raw\"),\n",
-    "    conversion(1706217550686000000),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1706217553.1487713, 1706217553.1590152]"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[imu_rec.raw.ts[0], imu_rec.raw.ts[1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gaze_ps1_raw = _load_raw_data(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze ps1.raw\"\n",
-    ")\n",
-    "gaze_ps1_time = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze ps1.time\"\n",
-    ")\n",
-    "\n",
-    "gaze_200hz_raw = _load_raw_data(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze_200hz.raw\"\n",
-    ")\n",
-    "gaze_200hz_time = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze_200hz.time\"\n",
-    ")\n",
-    "\n",
-    "gaze_right_ps1_raw = _load_raw_data(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze_right ps1.raw\"\n",
-    ")\n",
-    "gaze_right_ps1_time = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/gaze_right ps1.time\"\n",
-    ")\n",
-    "\n",
-    "# '../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.time'\n",
-    "# '../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.time_aux'\n",
-    "\n",
-    "# '../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module Camera v1 ps1.time'\n",
-    "# '../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module Camera v1 ps1.time_aux'\n",
-    "\n",
-    "# imu_ps1_raw = _load_raw_data('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/extimu ps1.raw')\n",
-    "# imu_ps1_time = _neon_raw_time_load('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/extimu ps1.time')\n",
-    "\n",
-    "worn_ps1_raw = _load_worn_data(\n",
-    "    pathlib.Path(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/worn ps1.raw\")\n",
-    ")\n",
-    "# worn_200hz_raw = _neon_raw_time_load('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/worn_200hz.raw')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1., 1., 1., ..., 1., 1., 1.])"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "worn_ps1_raw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Stream:\n",
-    "    def __init__(self, name):\n",
-    "        self.name = name\n",
-    "        self.data = []\n",
-    "        self.ts = []\n",
-    "        self.ts_rel = []\n",
-    "\n",
-    "    def load(self, data):\n",
-    "        self.data = data[:]\n",
-    "        self.ts = data[:].ts\n",
-    "        self.ts_rel = data[:].ts_rel\n",
-    "\n",
-    "    def sample(self, tstamps):\n",
-    "        if len(tstamps) == 1:\n",
-    "            if tstamps < self.ts[0]:\n",
-    "                self.closest_idxs = [None]\n",
-    "\n",
-    "            if tstamps > self.ts[-1]:\n",
-    "                self.closest_idxs = [None]\n",
-    "\n",
-    "            self.closest_idxs = np.searchsorted(self.ts, tstamps)\n",
-    "\n",
-    "        sorted_tses = np.sort(tstamps)\n",
-    "\n",
-    "        self.closest_idxs = []\n",
-    "        for tc in range(1, len(sorted_tses)):\n",
-    "            prev_ts = sorted_tses[tc - 1]\n",
-    "            curr_ts = sorted_tses[tc]\n",
-    "\n",
-    "            bounded_tstamps = self.ts[(self.ts >= prev_ts) & (self.ts <= curr_ts)]\n",
-    "\n",
-    "            if len(bounded_tstamps) == 0:\n",
-    "                self.closest_idxs.append(None)\n",
-    "                continue\n",
-    "\n",
-    "            # just always take the one that is closest to the current timestamp\n",
-    "            closest_ts = bounded_tstamps[-1]\n",
-    "\n",
-    "            # this has the feeling of suboptimal\n",
-    "            self.closest_idxs.append(np.where(self.ts == closest_ts)[0][0])\n",
-    "\n",
-    "    def __iter__(self):\n",
-    "        self.idx = 0\n",
-    "        return self\n",
-    "\n",
-    "    def __next__(self):\n",
-    "        if self.idx < len(self.closest_idxs):\n",
-    "            idx = self.closest_idxs[self.idx]\n",
-    "\n",
-    "            if idx is None:\n",
-    "                self.idx += 1\n",
-    "                return None\n",
-    "            else:\n",
-    "                self.idx += 1\n",
-    "                return self.data[idx : idx + 1]\n",
-    "        else:\n",
-    "            raise StopIteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def sample(self, tstamps):\n",
-    "#     if len(tstamps) == 1:\n",
-    "#         if tstamps < self.ts[0]:\n",
-    "#             self.closest_idxs = [None]\n",
-    "\n",
-    "#         if tstamps > self.ts[-1]:\n",
-    "#             self.closest_idxs = [None]\n",
-    "\n",
-    "#         self.closest_idxs = np.searchsorted(self.ts, tstamps)\n",
-    "\n",
-    "#     sorted_tses = np.sort(tstamps)\n",
-    "\n",
-    "#     self.closest_idxs = []\n",
-    "#     for tc in range(1, len(sorted_tses)):\n",
-    "#         prev_ts = sorted_tses[tc - 1]\n",
-    "#         curr_ts = sorted_tses[tc]\n",
-    "\n",
-    "#         bounded_tstamps = self.ts[(self.ts >= prev_ts) & (self.ts <= curr_ts)]\n",
-    "\n",
-    "#         if len(bounded_tstamps) == 0:\n",
-    "#             self.closest_idxs.append(None)\n",
-    "#             continue\n",
-    "\n",
-    "#         # just always take the one that is closest to the current timestamp\n",
-    "#         closest_ts = bounded_tstamps[-1]\n",
-    "\n",
-    "#         # this has the feeling of suboptimal\n",
-    "#         self.closest_idxs.append(np.where(self.ts == closest_ts)[0][0])\n",
-    "\n",
-    "# np.recarray.sample = sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convert_gaze_to_recarray(gaze_data, ts, ts_rel):\n",
-    "    out = np.recarray(\n",
-    "        gaze_data.shape[0],\n",
-    "        dtype=[(\"x\", \"<f4\"), (\"y\", \"<f4\"), (\"ts\", \"<f8\"), (\"ts_rel\", \"<f8\")],\n",
-    "    )\n",
-    "    out.x = gaze_data[:, 0]\n",
-    "    out.y = gaze_data[:, 1]\n",
-    "    out.ts = ts.astype(np.float64)\n",
-    "    out.ts_rel = ts_rel.astype(np.float64)\n",
-    "\n",
-    "    return out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1706217591.04255"
-      ]
-     },
-     "execution_count": 296,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NeonRecording:\n",
-    "    def __init__(self):\n",
-    "        self.info = []\n",
-    "        self._calib = []\n",
-    "\n",
-    "        self.start_ts = 0\n",
-    "\n",
-    "        self.scene_camera = []\n",
-    "        self.eye1_camera = []\n",
-    "        self.eye2_camera = []\n",
-    "        self.streams = {\n",
-    "            \"gaze\": Stream(\"gaze\"),\n",
-    "            \"imu\": Stream(\"imu\"),\n",
-    "            \"scene\": Stream(\"scene\"),\n",
-    "        }\n",
-    "\n",
-    "        self._gaze_ps1_raw_time = []\n",
-    "        self._gaze_200hz_raw_time = []\n",
-    "        self._gaze_right_ps1_raw_time = []\n",
-    "        self._gaze_ps1_ts = []\n",
-    "        self._gaze_ps1_raw = []\n",
-    "        self._gaze_right_ps1_ts = []\n",
-    "        self._gaze_right_ps1_raw = []\n",
-    "        self._worn_ps1_raw = []\n",
-    "\n",
-    "    # TODO: save for the end\n",
-    "    def check(self):\n",
-    "        pass\n",
-    "\n",
-    "    @property\n",
-    "    def gaze(self):\n",
-    "        return self.streams[\"gaze\"]\n",
-    "\n",
-    "    @property\n",
-    "    def imu(self):\n",
-    "        return self.streams[\"imu\"]\n",
-    "\n",
-    "    @property\n",
-    "    def scene(self):\n",
-    "        return self.streams[\"scene\"]\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def load(rec_dir):\n",
-    "        rec = NeonRecording()\n",
-    "\n",
-    "        with open(rec_dir + \"/info.json\") as f:\n",
-    "            rec.info = json.load(f)\n",
-    "\n",
-    "        rec._start_ts_ns = rec.info[\"start_time\"]\n",
-    "        rec.start_ts = conversion(rec.info[\"start_time\"])\n",
-    "\n",
-    "        with open(rec_dir + \"calibration.bin\", \"rb\") as f:\n",
-    "            raw_data = f.read()\n",
-    "\n",
-    "        # obtained here:\n",
-    "        # https://github.com/pupil-labs/realtime-python-api/blob/main/src/pupil_labs/realtime_api/device.py#L178\n",
-    "        rec._calib = np.frombuffer(\n",
-    "            raw_data,\n",
-    "            np.dtype(\n",
-    "                [\n",
-    "                    (\"version\", \"u1\"),\n",
-    "                    (\"serial\", \"6a\"),\n",
-    "                    (\"scene_camera_matrix\", \"(3,3)d\"),\n",
-    "                    (\"scene_distortion_coefficients\", \"8d\"),\n",
-    "                    (\"scene_extrinsics_affine_matrix\", \"(4,4)d\"),\n",
-    "                    (\"right_camera_matrix\", \"(3,3)d\"),\n",
-    "                    (\"right_distortion_coefficients\", \"8d\"),\n",
-    "                    (\"right_extrinsics_affine_matrix\", \"(4,4)d\"),\n",
-    "                    (\"left_camera_matrix\", \"(3,3)d\"),\n",
-    "                    (\"left_distortion_coefficients\", \"8d\"),\n",
-    "                    (\"left_extrinsics_affine_matrix\", \"(4,4)d\"),\n",
-    "                    (\"crc\", \"u4\"),\n",
-    "                ]\n",
-    "            ),\n",
-    "        )\n",
-    "\n",
-    "        # rec.version = rec._calib['version']\n",
-    "        rec.serial = rec._calib[\"serial\"]\n",
-    "        rec.scene_camera = {\n",
-    "            \"matrix\": rec._calib[\"scene_camera_matrix\"],\n",
-    "            \"distortion\": rec._calib[\"scene_distortion_coefficients\"],\n",
-    "            \"extrinsics\": rec._calib[\"scene_extrinsics_affine_matrix\"],\n",
-    "        }\n",
-    "        rec.eye1_camera = {\n",
-    "            \"matrix\": rec._calib[\"right_camera_matrix\"],\n",
-    "            \"distortion\": rec._calib[\"right_distortion_coefficients\"],\n",
-    "            \"extrinsics\": rec._calib[\"right_extrinsics_affine_matrix\"],\n",
-    "        }\n",
-    "        rec.eye2_camera = {\n",
-    "            \"matrix\": rec._calib[\"left_camera_matrix\"],\n",
-    "            \"distortion\": rec._calib[\"left_distortion_coefficients\"],\n",
-    "            \"extrinsics\": rec._calib[\"left_extrinsics_affine_matrix\"],\n",
-    "        }\n",
-    "\n",
-    "        rec._gaze_ps1_raw_time = _neon_raw_time_load(rec_dir + \"/gaze ps1.time\")\n",
-    "        rec._gaze_200hz_raw_time = _neon_raw_time_load(rec_dir + \"/gaze_200hz.time\")\n",
-    "        rec._gaze_right_ps1_raw_time = _neon_raw_time_load(\n",
-    "            rec_dir + \"/gaze_right ps1.time\"\n",
-    "        )\n",
-    "\n",
-    "        _rewrite_times(pathlib.Path(rec_dir + \"/gaze ps1.time\"))\n",
-    "        rec._gaze_ps1_ts = _load_timestamps_data(\n",
-    "            pathlib.Path(rec_dir + \"/gaze ps1_timestamps.npy\")\n",
-    "        )\n",
-    "        rec._gaze_ps1_raw = _load_raw_data(rec_dir + \"/gaze ps1.raw\")\n",
-    "\n",
-    "        _rewrite_times(pathlib.Path(rec_dir + \"/gaze_200hz.time\"))\n",
-    "        gaze_200hz_ts = _load_timestamps_data(\n",
-    "            pathlib.Path(rec_dir + \"/gaze_200hz_timestamps.npy\")\n",
-    "        )\n",
-    "        gaze_200hz_raw = _load_raw_data(rec_dir + \"/gaze_200hz.raw\")\n",
-    "\n",
-    "        gaze_200hz_ts_rel = gaze_200hz_ts - rec.start_ts\n",
-    "        rec.streams[\"gaze\"].load(\n",
-    "            convert_gaze_to_recarray(gaze_200hz_raw, gaze_200hz_ts, gaze_200hz_ts_rel)\n",
-    "        )\n",
-    "\n",
-    "        _rewrite_times(pathlib.Path(rec_dir + \"/gaze_right ps1.time\"))\n",
-    "        rec._gaze_right_ps1_ts = _load_timestamps_data(\n",
-    "            pathlib.Path(rec_dir + \"/gaze_right ps1_timestamps.npy\")\n",
-    "        )\n",
-    "        rec._gaze_right_ps1_raw = _load_raw_data(rec_dir + \"/gaze_right ps1.raw\")\n",
-    "\n",
-    "        _rewrite_times(pathlib.Path(rec_dir + \"/extimu ps1.time\"))\n",
-    "        imu_rec = IMURecording(pathlib.Path(rec_dir + \"/extimu ps1.raw\"), rec.start_ts)\n",
-    "        rec.streams[\"imu\"].load(imu_rec.raw)\n",
-    "\n",
-    "        rec._worn_ps1_raw = _load_worn_data(pathlib.Path(rec_dir + \"/worn ps1.raw\"))\n",
-    "        # worn_ps1_raw = _load_worn_data(pathlib.Path(rec_dir + '/worn_200hz.raw'))\n",
-    "\n",
-    "        return rec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/by/f4g6q8690z50zbfvy6kq71cc0000gn/T/ipykernel_25601/647576048.py:14: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
-      "  if not nums:\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec = NeonRecording.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(804.34216, 806.01044, 1.70621755e+09, 2.608845)]\n",
-      "[(802.19934, 807.4595, 1.70621755e+09, 2.61384869)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "gs = rec.streams[\"gaze\"]\n",
-    "ts = rec.streams[\"gaze\"].ts\n",
-    "gs.sample([ts[0], ts[1], ts[2]])\n",
-    "\n",
-    "for g in gs:\n",
-    "    print(g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'android_device_id': '6296df393d96a031',\n",
-      " 'android_device_model': 'rtwo_ge',\n",
-      " 'android_device_name': 'Neon Companion',\n",
-      " 'app_version': '2.7.10-prod',\n",
-      " 'calib_version': 1,\n",
-      " 'data_format_version': '2.2',\n",
-      " 'duration': 40252000000,\n",
-      " 'firmware_version': [29, 8],\n",
-      " 'frame_id': '1',\n",
-      " 'frame_name': 'Just act natural',\n",
-      " 'gaze_frequency': 200,\n",
-      " 'gaze_offset': [0.0, 0.0],\n",
-      " 'module_serial_number': '619453',\n",
-      " 'os_version': '13',\n",
-      " 'pipeline_version': '2.6.0',\n",
-      " 'recording_id': 'f96b6e36-6c98-409c-be7c-46c3e408acb2',\n",
-      " 'start_time': 1706217550686000000,\n",
-      " 'template_data': {'data': {'c700c50c-cee1-45fb-b459-050e5ceeaeae': ['']},\n",
-      "                   'id': '94733ede-ce4c-46be-aa72-7bb80e21bfe6',\n",
-      "                   'recording_name': '2024-01-25_22:19:10',\n",
-      "                   'version': '2024-01-25T09:33:35.681129Z'},\n",
-      " 'wearer_id': '65aee704-fa44-4fec-a811-6b4b126afdb0',\n",
-      " 'wearer_ied': 63.0,\n",
-      " 'workspace_id': 'e8984f4b-fe9f-48f5-bf3e-46e1ae0669b8'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec.info\n",
-    "pprint.pprint(rec.info)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([(1, b'619453', [[885.15825992,   0.        , 824.12887524], [  0.        , 885.14923875, 612.28065252], [  0.        ,   0.        ,   1.        ]], [-0.13053092,  0.10917948, -0.0001823 , -0.00043524,  0.00068462,  0.17075814,  0.05227603,  0.02661005], [[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]], [[139.37418637,   0.        ,  99.23202584], [  0.        , 139.35188665,  96.19039145], [  0.        ,   0.        ,   1.        ]], [ 3.68394045e-02, -1.23725784e-01,  1.05156082e-03, -6.51159021e-04, -6.49958070e-01, -5.22825142e-02,  9.73858508e-03, -6.94081020e-01], [[-0.82693827,  0.18204613,  0.53200775, 16.86289978], [ 0.07135105,  0.9724648 , -0.22185867, 19.95415497], [-0.55774736, -0.14550412, -0.81715757, -7.03173828], [ 0.        ,  0.        ,  0.        ,  1.        ]], [[139.02147562,   0.        ,  93.94689674], [  0.        , 139.25351257,  96.6396657 ], [  0.        ,   0.        ,   1.        ]], [ 0.05170779, -0.11790007, -0.00186199,  0.00106443, -0.60876161, -0.04746648,  0.02421155, -0.65088475], [[ -0.84299362,  -0.15711884,  -0.51446623, -16.83016968], [ -0.06646162,   0.97948653,  -0.1902342 ,  19.75001526], [  0.53380209,  -0.12617396,  -0.8361432 ,  -7.02308655], [  0.        ,   0.        ,   0.        ,   1.        ]], 1738849524)],\n",
-       "      dtype=[('version', 'u1'), ('serial', 'S6'), ('scene_camera_matrix', '<f8', (3, 3)), ('scene_distortion_coefficients', '<f8', (8,)), ('scene_extrinsics_affine_matrix', '<f8', (4, 4)), ('right_camera_matrix', '<f8', (3, 3)), ('right_distortion_coefficients', '<f8', (8,)), ('right_extrinsics_affine_matrix', '<f8', (4, 4)), ('left_camera_matrix', '<f8', (3, 3)), ('left_distortion_coefficients', '<f8', (8,)), ('left_extrinsics_affine_matrix', '<f8', (4, 4)), ('crc', '<u4')])"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec._calib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'gaze': <__main__.Stream at 0x291d70150>,\n",
-       " 'imu': <__main__.Stream at 0x293c14650>,\n",
-       " 'scene': <__main__.Stream at 0x2a3649110>}"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.streams"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.6711 ,  806.5423 , 1.70621755e+09,  2.60384369),\n",
-       "           (804.34216,  806.01044, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934,  807.4595 , 1.70621755e+09,  2.61384869), ...,\n",
-       "           (883.96313, 1085.5363 , 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.2845 , 1084.7004 , 1.70621759e+09, 40.24368477),\n",
-       "           (884.99097, 1087.0084 , 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f4'), ('y', '<f4'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.streams[\"gaze\"].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1706217550.6860003"
-      ]
-     },
-     "execution_count": 304,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.start_ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6038436889648438"
-      ]
-     },
-     "execution_count": 305,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.streams[\"gaze\"].data.ts[0] - rec.start_ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.60384369,  2.608845  ,  2.61384869, ..., 40.2386837 ,\n",
-       "       40.24368477, 40.24882483])"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.streams[\"gaze\"].data.ts_rel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.70621755e+09, 1.70621755e+09, 1.70621755e+09, ...,\n",
-       "       1.70621759e+09, 1.70621759e+09, 1.70621759e+09])"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.streams[\"imu\"].data.ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1706217553.1487713, 1706217553.1590152]"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[rec.streams[\"imu\"].data.ts[0], rec.streams[\"imu\"].data.ts[1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gaze\n",
-      "198.69846583315015\n",
-      "2.6038436889648438\n",
-      "\n",
-      "imu\n",
-      "104.15957776951906\n",
-      "2.462770938873291\n",
-      "\n",
-      "scene\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'ts'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[309], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m name \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(name)\n\u001b[0;32m----> 5\u001b[0m avg_spf \u001b[38;5;241m=\u001b[39m (\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mts\u001b[49m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m s\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mts[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(s\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mts)\n\u001b[1;32m      6\u001b[0m avg_fps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m avg_spf\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(avg_fps)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'ts'"
-     ]
-    }
-   ],
-   "source": [
-    "for stream in rec.streams:\n",
-    "    s = rec.streams[stream]\n",
-    "    name = s.name\n",
-    "    print(name)\n",
-    "    avg_spf = (s.data.ts[-1] - s.data.ts[0]) / len(s.data.ts)\n",
-    "    avg_fps = 1 / avg_spf\n",
-    "    print(avg_fps)\n",
-    "    start_offset = s.data.ts[0] - rec.start_ts\n",
-    "    print(start_offset)\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array(True,\n",
-       "          dtype=bool)"
-      ]
-     },
-     "execution_count": 310,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all((rec.gaze.data == rec.streams[\"gaze\"].data).flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array(True,\n",
-       "          dtype=bool)"
-      ]
-     },
-     "execution_count": 311,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all((rec.imu.data == rec.streams[\"imu\"].data).flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.70621755e+09, 1.70621755e+09, 1.70621755e+09, ...,\n",
-       "       1.70621759e+09, 1.70621759e+09, 1.70621759e+09])"
-      ]
-     },
-     "execution_count": 312,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.gaze.ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 313,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all((rec.gaze.ts == rec.streams[\"gaze\"].ts).flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 314,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all((rec.imu.ts == rec.streams[\"imu\"].ts).flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 315,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(rec.imu.ts_rel == rec.streams[\"imu\"].ts_rel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 316,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(rec.gaze.ts_rel == rec.streams[\"gaze\"].ts_rel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# adapted from here:\n",
-    "# https://github.com/pupil-labs/neon-player/blob/master/pupil_src/shared_modules/pupil_recording/update/neon.py#L181\n",
-    "\n",
-    "rec_dir = \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\"\n",
-    "rec_path = pathlib.Path(rec_dir)\n",
-    "\n",
-    "labels = (rec_path / \"event.txt\").read_text().strip().split(\"\\n\")\n",
-    "\n",
-    "_rewrite_times(pathlib.Path(rec_dir + \"/event.time\"))\n",
-    "timestamps = np.load(rec_path / \"event_timestamps.npy\")\n",
-    "timestamps_unix = np.load(rec_path / \"event_timestamps_unix.npy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['recording.begin', 'recording.end']"
-      ]
-     },
-     "execution_count": 318,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.70621755e+09, 1.70621759e+09])"
-      ]
-     },
-     "execution_count": 319,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "timestamps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1706217550686000000, 1706217590938000000], dtype=uint64)"
-      ]
-     },
-     "execution_count": 320,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "timestamps_unix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('recording.begin', 1706217550.6860003), ('recording.end', 1706217590.938)]"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "events = [evt for evt in zip(labels, timestamps)]\n",
-    "events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'recording.begin': 1706217550.6860003, 'recording.end': 1706217590.938}"
-      ]
-     },
-     "execution_count": 322,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unique_events = dict(events)\n",
-    "unique_events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1706217550.6860003"
-      ]
-     },
-     "execution_count": 323,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unique_events[\"recording.begin\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('rec.b', 1706217550.6860003),\n",
-       " ('rec.b', 1706217888.6860003),\n",
-       " ('rec.c', 1706218888.938),\n",
-       " ('rec.c', 1706217590.938)]"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = [\n",
-    "    (\"rec.b\", 1706217550.6860003),\n",
-    "    (\"rec.b\", 1706217888.6860003),\n",
-    "    (\"rec.c\", 1706218888.938),\n",
-    "    (\"rec.c\", 1706217590.938),\n",
-    "]\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('rec.c', 1706217590.938),\n",
-       " ('rec.c', 1706218888.938),\n",
-       " ('rec.b', 1706217888.6860003),\n",
-       " ('rec.b', 1706217550.6860003)]"
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.reverse()\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rec.c': 1706218888.938, 'rec.b': 1706217550.6860003}"
-      ]
-     },
-     "execution_count": 326,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NeonRecording: Loading recording from: ../tests/test_data/2024-01-25_22-19-10_test-f96b6e36\n",
-      "NeonRecording: Loading recording info and calibration data\n",
-      "NeonRecording: Loading raw time files\n",
-      "NeonRecording: Loading 'gaze ps1' data\n",
-      "NeonRecording: Loading 'gaze_200hz' data\n",
-      "NeonRecording: Loading 'gaze_right_ps1' data\n",
-      "NeonRecording: Loading IMU data\n",
-      "NeonRecording: Loading events\n",
-      "NeonRecording: Parsing unique events\n",
-      "NeonRecording: Finished loading recording.\n",
-      "\n",
-      "[804.34216309] [806.01043701] [1.70621755e+09]\n",
-      "[802.19934082] [807.45947266] [1.70621755e+09]\n",
-      "\n",
-      "gz [804.34216309] [806.01043701] [1.70621755e+09]\n",
-      "gz [802.19934082] [807.45947266] [1.70621755e+09]\n",
-      "imu [-118.25288] [-44.7666] [124.22863] [1.70621755e+09]\n",
-      "gz [800.52740479] [803.5536499] [1.70621755e+09]\n",
-      "gz [804.71148682] [802.50836182] [1.70621755e+09]\n",
-      "imu [-118.21066] [-44.766228] [124.16915] [1.70621755e+09]\n",
-      "gz [804.94714355] [802.89910889] [1.70621755e+09]\n",
-      "gz [803.04333496] [808.11279297] [1.70621755e+09]\n",
-      "gz [805.105896] [805.82208252] [1.70621755e+09]\n",
-      "imu [-118.186386] [-44.75581] [124.145706] [1.70621755e+09]\n",
-      "gz [802.5491333] [805.89813232] [1.70621755e+09]\n",
-      "gz [801.65289307] [802.92651367] [1.70621755e+09]\n",
-      "imu [-117.08882] [-44.6636] [122.95194] [1.70621755e+09]\n",
-      "gz [803.89904785] [805.28161621] [1.70621755e+09]\n",
-      "imu [-117.05724] [-44.655304] [122.91567] [1.70621755e+09]\n",
-      "gz [803.19116211] [806.07861328] [1.70621755e+09]\n",
-      "gz [825.07128906] [787.12390137] [1.70621755e+09]\n",
-      "imu [-117.032364] [-44.647003] [122.87821] [1.70621755e+09]\n",
-      "gz [858.9944458] [785.83795166] [1.70621755e+09]\n",
-      "gz [887.66778564] [786.25775146] [1.70621755e+09]\n",
-      "imu [-117.03654] [-44.59934] [122.867004] [1.70621755e+09]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "gaze_data = rec.streams[\"gaze\"].data\n",
-    "gaze_ts = rec.streams[\"gaze\"].ts\n",
-    "gaze_start_offset = rec.streams[\"gaze\"].data.ts[0] - rec.start_ts\n",
-    "# there is also rec.streams['gaze'].ts_rel\n",
-    "\n",
-    "gaze = rec.streams[\"gaze\"]\n",
-    "gaze.sample([gaze_ts[0], gaze_ts[1], gaze_ts[2]])\n",
-    "\n",
-    "for g in gaze:\n",
-    "    if g:\n",
-    "        print(g.x, g.y, g.ts)\n",
-    "\n",
-    "print()\n",
-    "sample_ts = gaze_ts[:15]\n",
-    "for gaze, imu in zip(rec.gaze.sample(sample_ts), rec.imu.sample(sample_ts)):\n",
-    "    if gaze:\n",
-    "        x = gaze.x\n",
-    "        y = gaze.y\n",
-    "        ts = gaze.ts\n",
-    "\n",
-    "        print(\"gz\", x, y, ts)\n",
-    "\n",
-    "    if imu:\n",
-    "        pitch = imu.pitch\n",
-    "        yaw = imu.yaw\n",
-    "        roll = imu.roll\n",
-    "        ts = imu.ts\n",
-    "\n",
-    "        print(\"imu\", pitch, yaw, roll, ts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Stream' object has no attribute 'try_generator'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[328], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m rec\u001b[38;5;241m.\u001b[39mgaze\u001b[38;5;241m.\u001b[39msample([gaze_ts[\u001b[38;5;241m0\u001b[39m], gaze_ts[\u001b[38;5;241m1\u001b[39m], gaze_ts[\u001b[38;5;241m2\u001b[39m]])\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m \u001b[43mrec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtry_generator\u001b[49m():\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;28mprint\u001b[39m(g)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Stream' object has no attribute 'try_generator'"
-     ]
-    }
-   ],
-   "source": [
-    "rec.gaze.sample([gaze_ts[0], gaze_ts[1], gaze_ts[2]])\n",
-    "for g in rec.gaze.try_generator():\n",
-    "    print(g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.81443787, -1.1463165, -0.45204163, 0.04101563, -0.6098633, 0.7675781, -118.2757, -44.769444, 124.262, 0.51070815, -0.19833069, 0.26649785, -0.79298234, 1706217553288873064, 1.70621755e+09, 2.60287261)"
-      ]
-     },
-     "execution_count": 329,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.imu.sample_one(gaze_ts[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[8.04342163e+02, 8.06010437e+02, 1.70621755e+09, 2.60884500e+00],\n",
-       "       [8.02199341e+02, 8.07459473e+02, 1.70621755e+09, 2.61384869e+00]])"
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.gaze.to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LD_LIBRARY_PATH\"] = \"/opt/homebrew/lib\"\n",
-    "\n",
-    "import pupil_labs.video as pv\n",
-    "import tqdm\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "mfname = (\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4\"\n",
-    ")\n",
-    "# for frame in tqdm.tqdm(pv.open(mfname).decode(video=0)):\n",
-    "# frame.to_ndarray(format='bgr24')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eye_container = pv.open(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module v1 ps1.mp4\"\n",
-    ")\n",
-    "scene_container = pv.open(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 333,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<InputContainer('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4', writeable=False, streams=StreamsContainer((<AudioStream('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4' index=0 aac <av.AudioFormat fltp>)>, <VideoStream('../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4' index=1 h264 <av.VideoFormat yuv420p, 1600x1200>)>)))>"
-      ]
-     },
-     "execution_count": 333,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scene_container"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scene_container.seek_to_time(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1162, 1162)"
-      ]
-     },
-     "execution_count": 337,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(scene_container.streams.video[0].frames), len(scene_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1706217550.6860003, 1706217590.8706603, 40.18465995788574)"
-      ]
-     },
-     "execution_count": 348,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rec.start_ts, st * 1e-9, st * 1e-9 - rec.start_ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eye_container.streams.video[0].seek_to_index(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 366,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "VideoStream.decode() got an unexpected keyword argument 'video'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[366], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43meye_container\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstreams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvideo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: VideoStream.decode() got an unexpected keyword argument 'video'"
-     ]
-    }
-   ],
-   "source": [
-    "eye_container.streams.video[0]."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "36.1227 36.12428586196899\n",
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-      "36.14282222222222 36.144410774230955\n",
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-      "36.15282222222222 36.15442387771606\n",
-      "36.15782222222222 36.15941183280945\n",
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-      "36.543077777777775 36.54467265319824\n",
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-      "36.5582 36.55979292106628\n",
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-      "36.5682 36.56979267311096\n",
-      "36.5732 36.57479302597046\n",
-      "36.5782 36.579792901992796\n",
-      "36.5832 36.584792778015135\n",
-      "36.5882 36.589792654037474\n",
-      "36.5932 36.594794675827025\n",
-      "36.5982 36.59979288291931\n",
-      "36.6032 36.60479275894165\n",
-      "36.6082 36.60979382705688\n",
-      "36.6132 36.6147939414978\n",
-      "36.6182 36.61979381752014\n",
-      "36.6232 36.62479369354248\n",
-      "36.6282 36.6297938079834\n",
-      "36.6332 36.634793922424315\n",
-      "36.6382 36.639793798446654\n",
-      "36.6432 36.64479391288757\n",
-      "36.6482 36.64979378890991\n",
-      "36.6532 36.65479390335083\n",
-      "36.6582 36.65979377937317\n",
-      "36.6632 36.664793893814085\n",
-      "36.6682 36.669793769836424\n",
-      "36.6732 36.6747988910675\n",
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-      "36.693333333333335 36.6949199886322\n",
-      "36.69833333333333 36.699923917770384\n",
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-      "36.71333333333333 36.714919969558714\n",
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-      "36.803333333333335 36.80492179107666\n",
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-      "36.85845555555556 36.860047742843626\n",
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-      "36.893455555555555 36.895047828674315\n",
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-      "36.93845555555556 36.94004885864258\n",
-      "36.94345555555556 36.945048734664915\n",
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-      "36.99358888888889 36.99517862510681\n",
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-      "37.018588888888885 37.020174905776976\n",
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-      "37.05358888888889 37.055174753189085\n",
-      "37.05858888888889 37.06017486763\n",
-      "37.06358888888889 37.06517569732666\n",
-      "37.06858888888889 37.070176050186156\n",
-      "37.07358888888889 37.075175926208495\n",
-      "37.07858888888889 37.08017580223083\n",
-      "37.08358888888889 37.08517567825317\n",
-      "37.088588888888886 37.09017579269409\n",
-      "37.09358888888889 37.09517590713501\n",
-      "37.09871111111111 37.100300714492796\n",
-      "37.10371111111111 37.105300828933714\n",
-      "37.10871111111111 37.11030094337463\n",
-      "37.11371111111111 37.11530081939697\n",
-      "37.11871111111111 37.12030069541931\n",
-      "37.12371111111111 37.125301763534544\n",
-      "37.12871111111111 37.13030187797546\n",
-      "37.13371111111111 37.1353017539978\n",
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-      "37.14871111111111 37.15031091880798\n",
-      "37.153711111111114 37.155301734924315\n",
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-      "37.16871111111111 37.17030684661865\n",
-      "37.17371111111111 37.17530195426941\n",
-      "37.17871111111111 37.18030183029175\n",
-      "37.18371111111111 37.185301706314085\n",
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-      "37.20371111111111 37.205302640914915\n",
-      "37.208711111111114 37.21030299377441\n",
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-      "37.21871111111111 37.22030274581909\n",
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-      "37.24384444444444 37.24542801094055\n",
-      "37.248844444444444 37.250428840637205\n",
-      "37.25384444444445 37.25542776298523\n",
-      "37.25884444444444 37.26042883110046\n",
-      "37.263844444444445 37.26542894554138\n",
-      "37.26884444444445 37.27043382835388\n",
-      "37.27384444444444 37.27542869758606\n",
-      "37.278844444444445 37.280428812026976\n",
-      "37.28384444444445 37.285428926467894\n",
-      "37.28884444444444 37.29042880249023\n",
-      "37.293844444444446 37.29542891693115\n",
-      "37.29884444444444 37.300429746627806\n",
-      "37.303844444444444 37.30542890739441\n",
-      "37.308844444444446 37.31042878341675\n",
-      "37.31384444444444 37.3154398651123\n",
-      "37.318844444444444 37.32042972755432\n",
-      "37.32384444444445 37.32542960357666\n",
-      "37.32884444444444 37.330429956436156\n",
-      "37.333844444444445 37.33543460083008\n",
-      "37.33884444444445 37.34042970848083\n",
-      "37.34384444444444 37.34542982292175\n",
-      "37.348844444444445 37.35042993736267\n",
-      "37.35384444444445 37.35542981338501\n",
-      "37.35884444444444 37.36042968940735\n",
-      "37.363844444444446 37.365429803848265\n",
-      "37.36884444444444 37.37042991828918\n",
-      "37.373844444444444 37.375436708450316\n",
-      "37.37896666666666 37.38055460166931\n",
-      "37.383966666666666 37.38555590820312\n",
-      "37.38896666666667 37.39055578422546\n",
-      "37.393966666666664 37.39556090545654\n",
-      "37.39896666666667 37.40055577468872\n",
-      "37.40396666666667 37.40555588912964\n",
-      "37.408966666666664 37.410555765151976\n",
-      "37.41396666666667 37.415555879592894\n",
-      "37.41896666666667 37.42055575561523\n",
-      "37.423966666666665 37.425557062149046\n",
-      "37.42896666666667 37.43055574607849\n",
-      "37.43396666666667 37.43555586051941\n",
-      "37.438966666666666 37.44055573654175\n",
-      "37.44396666666667 37.44555704307556\n",
-      "37.448966666666664 37.45057074737549\n",
-      "37.453966666666666 37.45555679512024\n",
-      "37.45896666666667 37.46056382369995\n",
-      "37.463966666666664 37.46556083869934\n",
-      "37.46896666666667 37.47055690002441\n",
-      "37.47396666666667 37.47555677604675\n",
-      "37.478966666666665 37.48055665206909\n",
-      "37.48396666666667 37.48556177330017\n",
-      "37.48896666666667 37.490556880950926\n",
-      "37.493966666666665 37.495556756973265\n",
-      "37.49896666666667 37.500556632995604\n",
-      "37.50396666666666 37.50556866836548\n",
-      "37.508966666666666 37.51056163024902\n",
-      "37.51408888888889 37.51568286132812\n",
-      "37.51908888888889 37.52068273735046\n",
-      "37.52408888888889 37.525695011138914\n",
-      "37.529088888888886 37.53068272781372\n",
-      "37.53408888888889 37.53568284225464\n",
-      "37.53908888888889 37.540682718276976\n",
-      "37.544088888888886 37.545687839508055\n",
-      "37.54908888888889 37.55068294715881\n",
-      "37.55408888888889 37.55568282318115\n",
-      "37.55908888888889 37.56068269920349\n",
-      "37.56408888888889 37.56568281364441\n",
-      "37.56908888888889 37.57068388175964\n",
-      "37.57408888888889 37.575682804107664\n",
-      "37.57908888888889 37.58068363380432\n",
-      "37.584088888888886 37.58568398666382\n",
-      "37.58908888888889 37.590683862686156\n",
-      "37.59408888888889 37.59568779182434\n",
-      "37.599088888888886 37.60068361473083\n",
-      "37.60408888888889 37.60569565010071\n",
-      "37.60908888888889 37.61068384361267\n",
-      "37.61408888888889 37.61568371963501\n",
-      "37.61908888888889 37.620683834075926\n",
-      "37.62408888888889 37.625683948516844\n",
-      "37.62908888888889 37.63068382453918\n",
-      "37.63408888888889 37.63568370056152\n",
-      "37.63908888888889 37.640684768676756\n",
-      "37.644222222222226 37.64582483482361\n",
-      "37.649211111111114 37.65080969047546\n",
-      "37.6542 37.65580980491638\n"
-     ]
-    }
-   ],
-   "source": [
-    "eye_container = pv.open(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module v1 ps1.mp4\"\n",
-    ")\n",
-    "for f, st in zip(eye_container.streams.video[0].frames, eye_time):\n",
-    "    print(f.time, (st * 1e-9 - rec.start_ts - 2.603))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "0.08008888888888889 0.08010669326782227\n",
-      "0.11343333333333333 0.11344976997375489\n",
-      "0.14677777777777778 0.1467918930053711\n",
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-      "0.44687777777777776 0.4468738613128662\n",
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-      "0.7469777777777777 0.7469558296203613\n",
-      "0.7803222222222223 0.7802977142333984\n",
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-      "35.356411111111115 35.35641570663452\n",
-      "35.38975555555555 35.38975782966614\n",
-      "35.4231 35.423099952697754\n",
-      "35.45644444444444 35.45644279098511\n",
-      "35.48978888888889 35.489784914016724\n",
-      "35.523133333333334 35.52312775230408\n",
-      "35.55647777777778 35.556469875335694\n",
-      "35.589822222222224 35.58981271362305\n",
-      "35.62316666666667 35.623154598236084\n",
-      "35.65651111111111 35.656497913360596\n",
-      "35.68985555555555 35.68983979797363\n",
-      "35.7232 35.72318192100525\n",
-      "35.756544444444444 35.7565247592926\n",
-      "35.78988888888889 35.78986688232422\n",
-      "35.823233333333334 35.82320972061157\n",
-      "35.85657777777778 35.85655184364319\n",
-      "35.889922222222225 35.88989492034912\n",
-      "35.92326666666666 35.92323680496216\n",
-      "35.95661111111111 35.95657988166809\n",
-      "35.989955555555554 35.98992176628113\n",
-      "36.0233 36.023263650894165\n",
-      "36.056644444444444 36.0566067276001\n",
-      "36.08998888888889 36.089948850631714\n",
-      "36.123333333333335 36.12329192733765\n",
-      "36.15667777777778 36.156633811950684\n",
-      "36.190022222222225 36.189976888656616\n",
-      "36.223366666666664 36.22331877326965\n",
-      "36.25671111111111 36.256661849975586\n",
-      "36.290055555555554 36.29000373458862\n",
-      "36.3234 36.32334585762024\n",
-      "36.356744444444445 36.35668893432617\n",
-      "36.39008888888889 36.39003081893921\n",
-      "36.423433333333335 36.42337389564514\n",
-      "36.45677777777778 36.45671578025818\n",
-      "36.49012222222222 36.49005885696411\n",
-      "36.523466666666664 36.52340074157715\n",
-      "36.55681111111111 36.5567435798645\n",
-      "36.590155555555555 36.5900859413147\n",
-      "36.6235 36.62342877960205\n",
-      "36.656844444444445 36.65677090263367\n",
-      "36.69018888888889 36.690112787246704\n",
-      "36.723533333333336 36.72345586395264\n",
-      "36.75687777777778 36.756797748565674\n",
-      "36.79022222222222 36.79014082527161\n",
-      "36.823566666666665 36.82348294830322\n",
-      "36.856811111111114 36.856825786590576\n",
-      "36.890055555555556 36.89016790962219\n",
-      "36.9235 36.923510747909546\n",
-      "36.95684444444444 36.95685263252258\n",
-      "36.99018888888889 36.9901947555542\n",
-      "37.02353333333333 37.02353783226013\n",
-      "37.05687777777778 37.05687995529175\n",
-      "37.09022222222222 37.0902227935791\n",
-      "37.12356666666667 37.12356491661072\n",
-      "37.156911111111114 37.15690775489807\n",
-      "37.19025555555555 37.19024963951111\n",
-      "37.2236 37.22359271621704\n",
-      "37.25694444444444 37.25693483924866\n",
-      "37.29028888888889 37.290276962280274\n",
-      "37.32363333333333 37.32361980056763\n",
-      "37.35697777777778 37.35696192359924\n",
-      "37.390322222222224 37.3903047618866\n",
-      "37.42366666666667 37.423646646499634\n",
-      "37.457011111111115 37.45698972320557\n",
-      "37.49035555555555 37.49033184623718\n",
-      "37.5237 37.523674922943115\n",
-      "37.55704444444444 37.55701680755615\n",
-      "37.59038888888889 37.59035869216919\n",
-      "37.623733333333334 37.62370176887512\n",
-      "37.65707777777778 37.65704365348816\n",
-      "37.690422222222224 37.69038673019409\n",
-      "37.72376666666667 37.72372885322571\n",
-      "37.75711111111111 37.75707192993164\n",
-      "37.79045555555555 37.79041381454468\n",
-      "37.8238 37.82375689125061\n",
-      "37.857144444444444 37.85709877586365\n",
-      "37.89048888888889 37.890440660476685\n",
-      "37.923833333333334 37.92378373718262\n",
-      "37.95717777777778 37.957125860214234\n",
-      "37.990522222222225 37.990468936920166\n",
-      "38.02386666666666 38.0238108215332\n",
-      "38.05721111111111 38.057153898239136\n",
-      "38.090555555555554 38.09049578285217\n",
-      "38.1239 38.12383862113953\n",
-      "38.157244444444444 38.15718098258972\n",
-      "38.19058888888889 38.19052286720276\n",
-      "38.223933333333335 38.22386594390869\n",
-      "38.25727777777778 38.25720782852173\n",
-      "38.290622222222225 38.29055090522766\n",
-      "38.323966666666664 38.3238927898407\n",
-      "38.35731111111111 38.35723562812805\n",
-      "38.390655555555554 38.39057798957825\n",
-      "38.424 38.4239208278656\n",
-      "38.457344444444445 38.45726295089722\n",
-      "38.49068888888889 38.49060578918457\n",
-      "38.52393333333333 38.52394767379761\n",
-      "38.55717777777778 38.557289796829224\n",
-      "38.59062222222222 38.59063263511658\n",
-      "38.62396666666667 38.62397499656677\n",
-      "38.65731111111111 38.657317834854126\n",
-      "38.69065555555556 38.69065995788574\n"
-     ]
-    }
-   ],
-   "source": [
-    "scene_container = pv.open(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.mp4\"\n",
-    ")\n",
-    "for f, st in zip(scene_container.streams.video[0].frames, scene_time):\n",
-    "    print(f.time, (st * 1e-9 - rec.start_ts - 1.494))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<av.Packet of #1, dts=22213, pts=22213; 22846 bytes at 0x177149710>"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "*x = next(d)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<av.VideoStream #1 h264, yuv420p 1600x1200 at 0x17714ad40>"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scene_time = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.time\"\n",
-    ")\n",
-    "scene_time_aux = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Scene Camera v1 ps1.time_aux\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[13401000 33343000 33342000 ... 33342000 33343000 33342000]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(33000000.0, 35000000.0)"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(scene_time))\n",
-    "plt.hist(np.diff(scene_time), bins=1000)\n",
-    "plt.xlim(33000000, 35000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[13401000 33343000 33342000 ... 33342000 33343000 33342000]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(33000000.0, 35000000.0)"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(scene_time))\n",
-    "plt.hist(np.diff(scene_time), bins=1000)\n",
-    "plt.xlim(33000000, 35000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eye_time = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module v1 ps1.time\"\n",
-    ")\n",
-    "eye_time_aux = _neon_raw_time_load(\n",
-    "    \"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/Neon Sensor Module v1 ps1.time_aux\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[5001000 5004000 4996000 ... 5140000 4985000 5000000]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 6000000.0)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(eye_time))\n",
-    "plt.hist(np.diff(eye_time), bins=1000)\n",
-    "plt.xlim(0, 6000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3139000 6236000 6253000 ... 6257000 6294000 3112000]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 7000000.0)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(eye_time_aux))\n",
-    "plt.hist(np.diff(eye_time_aux), bins=1000)\n",
-    "plt.xlim(0, 7000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23412"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frame.pts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dts = [frame.dts for frame in eye_container]\n",
-    "pts = [frame.pts for frame in eye_container]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0,\n",
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-       " 112153,\n",
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-       " 113053,\n",
-       " 113503,\n",
-       " 113953,\n",
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-       " 465265,\n",
-       " 465715,\n",
-       " 466165,\n",
-       " 466615,\n",
-       " 467065,\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[450 450 450 ... 462 449 449]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 600.0)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(pts))\n",
-    "plt.hist(np.diff(pts), bins=1000)\n",
-    "plt.xlim(0, 600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[450 450 450 ... 462 449 449]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 600.0)"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(np.diff(dts))\n",
-    "plt.hist(np.diff(dts), bins=1000)\n",
-    "plt.xlim(0, 600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "deprecated pixel format used, make sure you did set range correctly\n",
-      "No accelerated colorspace conversion found from yuv420p to rgb24.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23412\n"
-     ]
-    },
-    {
-     "data": {
-      "image/jpeg": 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",
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twBawaat0N9IzGi27hbVHtmYrLNCyJ4zkaXbel6iEMNpKe9cBto8OAx0gvKnT1S4zUlgwUEzdNweyq95jZEoJ4IRQVAWpACUVjpmdsBh5k5r9XxxRYTDKqADFSOeyWCW6gsvoalB0WrVacDt8S+GKAYu1LEj07noiNGWPHCE7KnY65tYHsm8ALSxjt4Yi0blRAmQFEmwROcRvQemkWi0ZsJQUY/uEpUVkIZBi1URpiSmyREsJryWW8UhtSlAVrhLgpgpAaEXUWSUrr7+bhg1G9t5Ky4fKtHbEJtWjAFQEvxasL/00eX3DzIu8LB04U7RVos6qCm+8qn78aAm/QHlh2Os0hcW0KDppWYzPZMwvFVEvjf+jLFtDS21ht4wWwVbyNpqmm7Y+IVtqFlMCw5JCJzgxJ3bc1NkwjbBzIsQUsylxtJwTJ81BopTOvcXWKQUAdipYAmNATneuZA8WJwyFscJi+8JGJHe5kyCXWs3ql9H1cbAZWZSq366AEHZfj7XGTiga8VlGIHIteFmEC/EtlPYjhKR4cy2gEJ+JUOGsBgyyG2V8/B4QCjgfq5iA1NRNa8rF4tVslYvk6k62lP0wuP1jgaGjoqhGrYqwK4ymyBGqHsDUKAUXCySFV7l46pOuYm8fQajbAPBcqvKRCjnd0pjCwKOFNwVD5fFCLpAOzEXnNYXkysuIn7FiACpMhUpFK8uaYoFfFxjoVuauYBuMnZfgZ6Sw3KVKcDLGzPI/VloEYyvQsmS0esQyNm2t7ivGTrbCdg2YsIwNg63kYqQbMcAIhOFy/JxVJ8ExiAEGwDSL89CRMBXVSfO1PDZId9LXSLRVC8xYAZTYsqz4U/dVQGPgNxkFvkTOGC3AlDtYAQS5OnXqyXBNujXah1T2n/3Zn1G0D0bRKRc2ZUMCuCBIvACOOhhLLwaMqZA+N1AgjQSGUI7b9yDSo6DLYY0QdT8VFA6ABe+p9/rkZgQ2KkgI6ipmtA3tlv2j2A88FACvGAUPtixiMSjRU4pN0TIqIGYM+gQoxPsC5i0TS8AAIu99aVO9p5AYZMFv1JrsQpCrUBZeek9hjZiykxRGEqGRcBkRAkgXoEUz8hq5MFsBXmtCGCdcd4upkgRqBJ4S5zN9xikI7+DZ/w9T6vG1nVoBzVpAct+vO3jLDtmK8ZJit1Z2JOPsONMp184/niRI+8Xo2BCXgoSx4yzApgSywJ4kFmeYLDuLFDBLunreuoItcMrIGeeyOKWgkFPclTpdAUuX7oYSxq4t2Pve9z7teCX2sLpK7K6eETnCUjvGenQ9/b6xzwR0IWDVA8MCI8p1lu6sp2/tLBzNSlnNfjpTADucFXef3WEB9OgAEAHIrRpTRWNgVJBMkLXtIRAOJrDNk4tLInaBAD6PEPzFCldiLUlBAaMo1S8liVIVi5BcvUp68ygIxKNaXqvgtVKSvozvec97vOVcYsGUoXJ/doiZRYUsCJMwCjblqlO6QC65uMIY2fFzqR+YYKZ3+FRLn/AKSTI+zR6fwmZEO9f/TMVSvMkicAF0jreew3MxGi240e7MRSEBCjSlbLSbdtCxscv2ut03FWJqW4vCLLspAKQRxkXFw8jrEMKbgvGKchi4CCM29pLei4n8c44FDhZDtBm1oAaSS1PsDrmSGC2IkaXzKbAKWTrbfsearsIeEX8Ti/7xj38czK8Hukp+xRGJLC6gFKJ8bRbikprqUSKKKIlkN4Vx8VkqRtLzMzYmKyVAproSxn7c1zvCVTOUXiJc6mYEAEPnAZLG1ENj6i3QALAQ39n1PHXnNQBm9Ad4Rj0QNQk0Sl1eKYjeTH0Xykv8XtLv/M7v+MjjdwqQ49EwcpX0CQgVO5JKEqsqgSxOBjBhlEgx7JbMuxZALBcJY4pEAYqMc1TyWh/eOpKdAi9Qv0bpjIRdFJeRcXYuloxlfOa9ol9diauux3sUPsD/a+MbJb139FaKeSP8a/lfGllI69aKdWbau3sBwe4W+hX9WeEsd4xp+2jTZbHRiRMIJqN0xNRGE0fU1GFwJim229c8J825ckGcKy5UYk1RCTm7fh2JlqLxWRn3kqa/FsMYQ+WZkkLU42S6Hapi0ZdqlaEeIYNNd55rFl4sWW06cgWEAPhZj2b7viwSPeIX7gEBYNSg+6j9Tj6L1K1DvSsstvNdjMUCNc8EgS5dvCWDFs+uIBalKNGbhdG0BihEIDwA0aqnDblADEYuAq9KYGN3WN12NHIjpOYVgAFMFiQwPq0og50uUK5Koi+kVcsiF3Cpo+KVVPHVDKZxpTKSMGiJvDCQ7VZbIrsQFm+WjBR2FvhGO8HOqGBeqblESdHixGyUjqVFlohlwkg3psz+JcUKbK1spaVmaU93gFs0sJCNW7rLfFb7Wt0zRBIgYxi0zo99NNqs9t2ewrvSbiAFEqwoh8dnczsuxI47IcUOg4csNfuqWvaUN3HdQ4LJnrEDxphFwcp2IJXE6ASCdeq8Jix68YgY4YG7JjWlctOuA69bzM7iTdGaaX9izhjSgWfHWQ2yiLJE7qmff7Gj8inE+gCYClQYHoHnk0hhlsybkk9BYlQMLbFHHZRRGxTGbqCs1hSXyvxCoHxCevaA/TM6vMLpkLYqEgoYTim8KT2u8ciOXAOU+qeYwusEEidCpUerf4BWHG2NjGQWBaCVAkAlPnkhV4zASuIFNkVrNBVCUG1xSsSLIYyq4oSsWgqjqkShike1kTMGQGWVpKYYwRKxX3RiKf631mxlxr9clA4JxRq2evQ2ayHZGyPJNcAz5hjC28TEBbF9pNPukJexI53XxbGhNtfbpAY8mI2iGCEZi7LdXFKYBqPflZe1hYch8z6L6izhpICV3SWl6EIN8M6qe8rrZCrJsTS6U+6vOh3gdCQuiEB4irEvn8B0SCSuLcWD0rvGZTtYZJELIcUPQADUQOcV65sJqds46bxQluWEtaYQuSVQ9Gn3+g0ipYjsyZephQNgJ94RFgw6R03shEB43yJyycGCnJGiYrFEBWrNiJadYDs1XR9SWlOBTX1E8sZ5gDSvQiXps1arSjEICQZNYUbI2JZYBRajUil1YQ8ibJodm6RGgXFallqICjg8L8JyURgBxGbHoBKcFJj6jYERBt7IIpbQ4U0JPQzlS/JyBSxXq220tlbYHoFZNMJ4reirlyt8C94IFt6aU1p/xpA2cYK/LM4hnd0GQUpKgrE7n7aY0vahdWYcNt+wABOH1nGlADj/DipwedegRAAV9sx1KC7XvVpZgtWU6TBuvpLkAnARrujTHUVeyyUERv2KJOza0QILF6TPLIy6iETBFF1wSYSzMWZehbVEwr1leFwu70A/gREL772GLHvfG547Y12gpVcZCkRAPe3YAbxk6sDLCyZeESQvvK8AXgReYFNIGGwVjVxjuoWHEagUPBSYamoJxHIxUgg2GOEqVK4fKr3rXe/ye99WQW84ZUmUh5DIKIsRRjiFS3hZrBq8XLzsyGXxUPosSmlaj5I6JVlWtsIITg2qk5ewmAovUFJTxrOrT3+DVy4kpRBIP0TXK5kiliIQYSMF/kvycgXOol9iIzotO6uB89K3kixzWfxE7MhnoYhykLoRUbWzNihFOnYYZxKsWF/MEDpgHVrTsgM4cr1NAERS3h2JwlfJSp3lpSIEjxHYCCBXzCn0fj+u7F4iX9o9CpIS66YXJWmhBwUMie5SdNHlAtCOEFl40TrSbjqv1F29XlXflHEBM6rB3aHAyOXCshA6Zni0CC0jqvMAdfQ9EBQ54CRg7/NkV9cUhU5M1QoAqTJLSVcckZXFH8L1cQkAoSKIfMIlDtmSWRTFsWDwuUZxkJZAlBqEVDFwb4HU/nVb/FZN6fUPIxHdKAVwFTZVNmFUWDCBsngxCVcnRlJG4WCkgpWhhuxcEiUWmsIlKXCJwGTBg7bwmq0k+MqAYWlZcglHdReYL8mbr4AVtqSWLrGexBq2WRtTrDMFEuAp4tV/uYh0Aeio7N227z61v44rDCq0pkaB55BdX+HZGYXAROi0OCfVBuPAqJyl+tlfimIqaYsAkz4ljOyEjjlFUrFEIkbXUzFcrli30lXyeQLA7Tb2KUk9LqMQyAK9VmhrNmPvS8sC47nAr1k3CBKb2iDrWlLpcFIANMvlasiupC6O79EUcP4yqhjuHiomH5mUqGII9UFjcat7j9TkCYhFYuFlBZCJtw1QqKlCYaTPLhGdpUWBUbQUkMTTINbHHLAWFIwIkU5UPWiMUZH+tE8/7C2KOrFJRITDyF5rFgs/AAWYHdJUrPdYC1Jo3JTLQvOCIWHEE6EpAEEFbwpmSkd1pT1H01TSCmM3LR0jfISUdFHWORcqRvrJcR1B0/QvrrGVrPja/G+v30mwqmjxW2R71GaVeiMFklAsLKEbTUnhq5CF147YeuewDXWq+4rNYk8lagwmFptKxHZ9xPrquBSq4mV0xqRzGIR0cjonpi3Oypgye3UalZfxPp5Obr3UIBI1qNlI70jXnZdFU+wO3g45V1NUWsPf8qbAI+nF4eUSztWtrEeAbhwG31Iwyg6DufslRMsqAWi/hJxFtn/qYDUXACQAtakXh5fRoos/n5eu71EVBMAiXCbh7ISOR6FC9CPcCCMrHooQ2wmJnOBhNzJ67LLEhke4vK2RioVridLvIHzyk5/8rd/6LQAbL5wdubHUuqCcHJcow7oDsCuSTV5SwZACZZSCrjUFe2TpQhQALxDGSBgRMgrBgIdRUyvDlNc+lQstCzYipBEJvUBKayJFyoU9YDzp/78d6+hlea8t/o3AL8Pf3NKyGC2grwcWra2M35SS0CcsQghy46YUFiOkzXIgicNmQynGzl4n08ZdZ+d84m6j2WHcOqfRV1NnA6DdV1Vftt0dVEstlpxSrjKq5E1aBlNbdQ7GOD0FhoIZ0lmiqJyuDM+iSpTRZwtGNZ8inp7jYhWPAYzXCrgIyhbew4GtvpD3vrgsQpAoxkZURoVVA6Ofgknt44KFwmbRej1EuWWPP/DmEyyNOoCso4utaB80jFigt6a+XWrq1cClXNe7PpFI1nuvXFM/SOeyNxWkOIpRD7x6U5+fLguxbarERrqZXUh4y1GUvKiAfbqT/dOf/nSnAY/KeSksRngFsPSglBehQEgYzfKy1zs8e1l4FWDMgsea0BVMF2JNCGYrQ6TDjEfvGCLBQCwaHoGMJSopElHRXhHPBwBRrxXQ19r/W4zyvla+AHI8Ra3g1zJnfCv89zWK3BpaYessvCzGe96FtH1c1p+ssCxGSNvhdHWAbaipLZPCZaMAdHiEO/C+0JYXsxocDEhnktHZqCq0feVD5ZxgAGas2nTG9Z5r02CNd9gAd2MNqk3libIReiCU5DrTXXiXqzMslqVRtcB0LSD3SFkHhLpGyIjQlPAa2cWiMq1xU0YweAsI45XIqHE/sXVZLIVEhB2nqlge/78RLon0lomVbgURqckYWoBMlauNcujNwy+TB6Iq1S0TJEC7xRKJWu2Z9FmkENL3Ux4U1cuL0Lsg3JTw9hKf1q9XQzHAfguRC96Pvj71qU+162JZ4K2CXEqVVzuSEjqjBvFbEWDZialHNq8UMHTCVfvSMRJedoF0tAKJKZ0FmFCUXQEXzeN7SbHskdB1QdcmDF2phIWkBGjM/kU3ri/9kjX4+TaCZ1IsNis8ozXMvqXLxcgyqYa58jZtFwDsoMPpkBAHyWiPOoF4gI2mRsfPSe5NceqIY4DHsekcmhJZuBhLdH+DgAl7xU8xDZy9sex3C52xkZLetHAWqV3GjD4HSKcAS6dTXSw7vOfJLaDktQiQ6oexAtd6PP4Oto8mVolXU3icahi5OvNGFiuASqCR7onwVHG58sS6GYVTjOcfPUMhh0nxeIn0HhGlaMMFdg8B/GjdbRepKy5EkPSaQULXm1EsoyjF+UejLYRnS3FiWUpHQWLkLVatFLuLWa6SAkitk3Kdoq//g7Tf/d3fxczYO2WsZxYhXge01ytxfqbDxcilX9LboRityVXjW0pILkbl1UhfNwBkF0tpe8oFZlfKLraXSHZggMSaUIyKEW6kAxiFEBZCCUOnfLGLli2jLmrnC2iqBRlDhMatFYD13EIt0T3XlpdxdkqBKrR37aAjSuikU+FAUooySu0kO40dA+fE7jsMomThVQmj0wgM5vxwOWMdjzgBpCY1UvErkpGeEUn2NfgmCiRZoMqJl0Uv6lEnl3p8HqGrx9XmMla/0WcftSnAQ6Nm2eGdZAo8AK8QlUuUEYDLSV47COm6FuiuaRmY4mVgzAJPXKvz6bGrojiMjeoj9Ja1FecVIMzSG/F6IIQri503UTpFlWLpYOpzz2Esh8o0pkPVmMLQAUyFoPWpzGtNiY0RQ3hgIdbIg608duLN8o2Yz0HVZsvFWgI8XJ5LFv1LYVStAni1oGwpgNl1SugITffE2DY6izIAKAQMSYJBbTXLBROnFGB0Fkh6UxZTIaULf7E+amAxBYvN9ItXXrbw0vJ5dVe40QJaxpYXA2UyQhY6MAljC7ZrwcI4G+2g3Wc37WQanfBOoLPH5fzwyi7QkfbZ32GmuEEdPCmcKzqjXE6pwA5P50QxLCsJz6nv9uWHXm338YK8xn7HTMdZm666ethZlOQkO5Z0BbDrhQtSR3RGulsDaRRbp0LYa5PS0VUPgE7ZkcRAJ4yW0cWBBOMSZQU8BYwSVYD7ToE5r5r/WK+uNHTPSjjsLrxyM7rSvrGCaRHdeV0JF1tvniohKpDYSLj82bnENol4O7iIKGW1ENgUJJEGkPutHH8WBoAchgWneipVLFqjSqwCDFr/9rjjAqNPK6gwHfGyAHukCXLfshl51SM1BUaiUlgvYBnFlhpMYewqFFgZ4SFN6fIKoZjiYRerBlGMRjUUmEL/P150rUcj0XVS11n+u1YgZpylM7KMvPW/G59lB4Y5x/E6VCFZOt5GAAdbFEC60eY6POwExo7bd1FOAhgd3rmisLADO7pOYMhgjADYqlYIWeUpMJRO2jPX55zKqDYnWQrncyNOd9mVYSFOOH4KS/crF1h32VSulsJdgMdJaUEsBYs73jXRAir4shvxGNktiOWyGvA1riqB54/hgbhxIfVKcUC0lBC8datQz4d4oqDKcuF9HcDrOQDgEigfXWLVCJHYVfesEllsgHKFGGGkQCWvHaIwImEH0LzHSC4uJbEYMTAq0gL5u6neBS+XO+/fhRQul1gMYlWOFr9wUT5YeafURsdp9OkUuGqRq9YI33684x3vANMIWhapIY2lYAfWLHI1m/KCUQg7L6RYinoiASB0gsrIm1E4YTFNOaAvTqmFq6HHpaKv8f/FnsYzBeGWbuQWtgLuLiHsNh2Mbn/tlANJKITdsSHpkGA2lKXALLsvFEfxijh/HCzKtkJ2ThxOF4TuhPBWCS9CxmQFP1PkJYU8c33OqajdYq25HVXILrWaMSgDv9EpdRf05ZSqs2voFgBrB49vIyguuHYoXTdgVEZsGPDAm2qKjs1IF97lpUuKQS5IqyHQs/DgZWplcXkm3Ft1o3BF3XO6ZFxYKIJbAjXpjd3d9tCoHjsjr0JDCpeG16cnKWStFCkAVmW7WBk4/XGYnYNBJaoOLSIABoos/uzMH8n/xm/8hp8EVZLsOvSkUkgFYGbUiGerXx1SJ9EmO2YwU5tRzZpVodEeoDWOqmYhVaUSI3IAgRSr74sDO5iaxbKjMpoSeLEEJiWdPYmT8cnwRfDfqq3yexfT227T/67ulqvU+FkyNsrlXJHL/HjTV0/K6rE7dtAlCW+0j3awnT0sFw+8Uydjh1AUckiKU+T8KMPUUbHRRucKEokQdue/jKI69ryQAOw1QiFrIeOmeT/nKGqBKncXfD6owS6Ry4iTC4yihrpQT1deeVXuorkjddoIz2UkYAKR6F1V9Hqh8LJYkCrBQxG4leRVjDWX/dwcbreFsPrggMIKSqkg0voKlgmXxCLdZ4wE2DPBy9L/d7sQuVmw+0EyhUgJ7KVgLAqmOvDXj9Gz4o1wjWVH61/w6f/KQ5TC1NlBMbWjXp8/+IM/8M2a54/Uv884AtWAzbd+ll6UWLQwcpkiV7ZetMmCqg+lWuMiXFajXKjUadsYRaGi6wiMndeyANMxE5x0VAqQ3VFmoQvROBedAsmC0JRkj8SYsIM9zV7/32Ei+Zz417P891krAx8lXUkpS2L6RnW+uWsMdwXV2JbI2iYsMy6KhdceGe3gtefn66Upl02xZTgpXCwd2nbcAXBaWOy7w2OKAb69dgjBxIpyKvpinBGmFMqAoVPATvUvlogro5JW9uelqFD9apCCuNpGFoRdeYeQrhLT/jDHgZdUIKND3nF1TzXYJYV0C9hRGRmBXTdRQogeFUnh6lLIy9soBBuvsURW8iyWOuRAymdZWaT3+H3v936vmlgsFq/RKlNQe2skk6ZFAe7/ghkerB3ydohl70WIxE9hfvM3f/MDH/iAqepJncgorwXyHIjSJ5i6K0NSn3f0DyaFKDzK+OAHP3goro9Iv/d7v+dJUhISin/GrcpRedF8/vJECtcjAB2zxmH0TkGuF1UBSKEYUf1YCieJxJ8gCORVkhCtwQiRpROGnIsFJ0Kj5TYytlZiSbqR3n7MmGJMgbnHFjLLeFJmX+wdMO/dmK6Gl8YvzCILUUArsEoYv4BcwhdYeISM1VyuGWFYCIUxu5FlOpfa7LttCmm0mwA4iXPi9DokjoctdkQdRWcDwBVw3trWXW8uB4DL7jOi8rlDlKPiPDtO8EZe5LJD+sokhJFl1W7FKrUWjAEob0XEEiHdI21KZCo7u9oUpmAW6Vi8oabKMGqNRZYARTnY7Pqqa/gIeS1OsUJ6fcDwS63B8ACM3eWtOQyvkPNHcbZBfGVZL4XSpfG9H5AYuEbVWzt0Egi0fJTY1UTcTxhlsStaEQKrWBYfSby1/lE1pXuDtIofzGvielPU6vGSgv2P/uiPPvaxjzkKvnUS6DV01eWyqcAAEuH8gR/4gR4IK6hglcvuuYEBZuTFzOU84SeMvC2BXPCtjnqEKBiAVztSGNWPRJFKcrBMYYhvEn2JEwUDoNQ6lUJhKqxBCkK6RTZOMbUORgAjycWYkvHlWNQze8Y3D3wWcp+O8wtgWNkj1BFC69Ai3wF3fpj7dOGvVYAJ150ty0v8ndbitxeDibKzjqiRUZFOuK13GJyEUvRzxpPySmrEY9PtNXy3a7GOEGO/btcXLQfMeXPwREUOoKoqYZTalNFRyV7epTM9ua/s9DeSe6cwpsgJ3Zk0jYFFaj2yJOym+jWqQZHw9CpUHgsSm2j0LGDQVK8YoxVwVZ1/PUIWxY6BS8gp/SpeCLFuVgkso6VmAXubF8FFwlVMheL1uIDaBgl8NeDNonoYwULQ0f2UR7kU3+N84hOfML7zne/019bdeQVB1sl1Z78aMzuYmnyMslt1bqp/JIqRV9KKdu3hueTSAIHh1SRd3h41n7PK5RXQG7syfIZSpGPh1VD2Cmi5ZYFkbDUdBRblKcAbJ4u8bZWT1AcoednJWbjr06LRo9NzhgGVcCOqkBTF6EVsxjiNpppC1a40peNhfOsiELgR7RsFBhjyGWyBYNOfYd5kWjurhGJl9JI9whVQDbnGGWau2V+rAG+h7gVb83sWsZDPjCUCc1Vst+1zArszzgPazh4YO90RddKAbaum7KapYybEThlhBIIBuM9Oi5PDTnf4yw7ZLYCRojaNshtxqofQSUqwLG9lVLDASpI0Hha6pIo01rsx4ZWFUFgARJlqtnA1a4FFO2QPkPOsZi+4KN5OOF1ULopKiOzWgQLWXTNtESwm8HmArKa3wM0RJgCdn920dmKU7g6rybp7gyjo6PKdDNef6rG78F4Bqy+BZEaE/poFBSGLZlTcw+QnNfj9iMdPcCg91QBqUoAoKwKsUJwSad5WeQ0hwVQlL69ikPvuzBNjdWTkYnQghPjlIBhvhxSYfV/GS5SEE4/zpxGVA0MK99LHiYEiENIIzCKpLOxqa9EtDpeqLKUeSZuqC4tJTDGDUbIYcaZnLKTYdOGUSAI3hi+WZdOU0QK8tPBKUexrAXN9vso911LUjt7ltThVy9hqSFGPlHvZr00dQJaUTVmwZWekTMrOSErBFTmld8QOOgy2XiXELbCtAm23jeYSywhvx03lEohEYAeMSyAGLw4jmEB7jaGWMcAYwZw6sW4KnbGlcHJMfQmPuQpX5336OfVyod0JpxD2srQXKpR9B5JXqYwKqH66kogbp5E6bU0YBUJ6bdUjF6m7trVY/Hm5FKBxgRSJwGRBQkp3Lrwr2jb4NMEKxGiUWzI5fCJQDV7Xzwr6WFE+1EiBRXlKbIPfTvZMCGEkdsXTRrEElSX8B3/wB+lg/nVnDwc21bjGwBrzKJy2rr+dL1AizQiHhKkNSAqvdUEFLIqC6r3vfa9SvaoKE+jphHGw5O1vnKFStoyeOVF0tJXq9fHymqLCX4/CLY6VxVazloWoAcm+JtAt91X42RKxhW87GTFgbqSwkLuF6y5cJBhmSt4UFt47np1RL4y5BiikKZ2iPDA6fCF3qreixxlyiZpyWasOj5HROjAq71qY861ZlmeJxnMnf4Yx5a0F5BRUzzASkYzAhJ5RAc6JLzxOhVgjl122786YfQejY6bb5Y6u8wMmFtLIjkFebbI4Rb6MORVcwikOoSMh1pdkWRRgBKPI2EFC6OLQpRNiCmN863IaezoVxVaAEUkju0bAZGdUnuLpUtt3qYk6tcMIrCmVMzYVYqodFiSWTqlL6moIMUUF30FiIfhLyigjXQo6fjWgFQvzNlfOulsmCD530icFuH2gAPW4eIOslBy+JqgArK54sbBTBIrymcX3TagSeAtBh9eGUjwEXVd5ebmUi9xIRwUjhYYp6Yr0uqmBHcAUg1ww8joBzke/LuANxakd5AqTFEC1korVl0AWL51zgNwLpTynzXvUp2hGLsgOohRqwykvsQG8QgAI/oqnwNMJL51Xogt1qNKNk8BNp6cYs9uU9BT6LDijZSHhpVvsEj1LYbpY+EgoI3kW+EbTe2Ewwlce3VbitAX4LW9ZApiyA9iLYMUiCVDGZ/UMw4s5DB5sSDoJjGDWP6FPchnbX5soUDobyijc5tpoBwknPQaHSuXqdHSdn6qVnQ7ADun49TNHX2uRoyWien1YZMEvIyNy6WpTbVWFBydjLmMCkPHJ8Oq/ix1gCpDank2lVlWrwaUqMHkVpjXZ9Q7DVVMsMPp1j/SoFzCL4xHB437RhVQQxSLQMZQCiU7ZXRx2vSMMYIQpF/7Hbx7CueF+r889FObF0QN3yye3rLYKpvdeQaaKQASGUQ4PEKTN8AB5LGTyZUHFXDbM9fZwIAdA6+MGKlm4KCrmMlaZx4XOhRO/6h0Fi2WBGGXZRipMClOVKyMezwGLqSi5PEAw1lE9GChIeE01XiPCtaNmeFXBAFAIKiO7KDoqj3c1yM4IrGy6UakVbMTWuqMiLX0KPVEPwc8uRbouWtUTdgW2LEIAnkLPfpuGgadXwABT2ElZlEQxpcRmJCWiiEpf+Jso4U/8U2G13BSPZbmcrz5zYbOMmoW0dPa9kJI2shCBRhYFj9+UmGqBgp/gRAVMKR0FwEjA6ATSaZSazg6pEqNNdKStYcebQjqBzi0AcldReCQURicHs0CnyIFnAXbYwkM6xk6LdHKpjcsoxanpqSpnFQn+dp+e93OOspA3wrPzlsWoGNV6OKqcSyWMelQ/gBUmojIWyOsrtNPupxN+eALQrlWzKZ6WnT3F8sLTLaD2YawJthqHJ9fqnk8kjOdRtC5tg1J8fvHNkdV0Ib0RuDDCiBFJF6YsbwGwo6NcdvtHcZ/Z1Qpglb1WwCrg0jaX/llMBUYoECdXFpweL+ko1qtADALtMSNm1SpvjwWMDvUvhYZ13guoqk6DLmTxXsiiHWshtRRgilQPQkhloJILgAhpHSEZeRmNFkoNcilArHQAwoNJak3xwxjr/b5QF/djEBLeyGRE3uJkYYTZyEiKuo8ymhoha4HeNNqMGwMvBCYY8vS3Pi4kpVFq5HFSCLvsxnb/qu7xCNo4q1QI5JSOB4tAIzYK72rDRhwbi4bWSM8bD/B2M3D72CbSM9pKSEbnzY63d3iw9dXUSXCkbXS3t485jMLlhRTlQHqe/K6ZM9YJcbQqg4WueJ0CuyN06cTKawTD0KGq4PqtlzcaZecKmT7k3ZURTJvGVnsAFldGdpa+wCveVOPwtQygZd9AuCbWRJ3qVyexVvgpLA45xbRGWj0NWihJtWmJrLAQ/PJKBC/vedGtEYdVBiWuuo9CvSBWyu3CoiDUAuhqhRclASNFejpqXp9ZkHjF8LDgV00FYfMFQR1yG03xFBuVyhB6BI0eCHWDScqLwU92VMViLQQqyRQJryxWSkaHQKyFULY3VCWmdl0W5MAWF6HlYLcKqLxQrUXZMQuHwUnAjB0XGbnAeDHLm87O0upnpOOJShZG21ksnQg0nZgCN8pIlzRORvjqaY95c8UDSeGanTckfroC7IvRtLyQLUgAmFIEiHzgZ0bTCbbAFGWQXPiHwUxngVGVLbB0puy5FogqNmvlUFVqlrExisVzYR8Hg5GwsFMip4jKTpHFCjg8tnJ1VoBRRptIuKwVKiTEaXFUWOiMzpj6YdA6ydI5PDgpCnbMfNG1fXgEwtD9eYu3yZWRRSykXMTVcKQjsRcsftGkggWuwjX+UgEjqmocwDT97o2QxSKoShmk10eDbooQFbpQ3lmArpKd8s3Q933f93kcMOiRWAqNGOFlZ9csHRi/prjAUPX9h+4AeFkAkMP4+m3U9fljReslxtzeoJZMjAo8Aab9UNkeEGFWXNZFSawT1QvvoKNSU61isCUAUkimt3ggGaUzrSZRyKWzMUQKIRZIJzUJbyH6HlA6KbwdFOGqFaI9oh7iTAhnlFTDRka5rAgRgkes7QcjMABqIBQAQqlTzSpYgy2rSmTnNcXMRY9EJQIhCaVlwYknZsiEi7A3ppys15FqASUiF/AcDjq7KSowCiPFWD0As1xtPb5f0LilloK3MuhtAQtRbVneaISvsNcCeIkaSDCVVCE8V1EWuQ2yO4wKgGE0qiEGdVptRVpJ0vYJRxhPtEaSRfHwJdI1qnaTZZgYOntSxMYLbLSPknYUVehUaMThAXMCfdmDYRQI6UHpJwntQi+azwh+VUUsvPuCjaIMx8ypxqYA7fjKKosQAEdd5S6/mr1Q0rnq1kRTtVNULRhr0DiBJLka767ZYdhNKTjdQSVJqjtJVSipYiyF7pStKsuuWkX2+vQ9RAyowLQmimgZnsKLTXe8hMWKYZCICyEkRbga2NsmJZ0NIyKVxS3S02ONWmWkSvGOWBQb4M6rWDyBxCVWJ50bRjCZ8AoHZhfbMYLkVbpFB1CNLB479opjUboRj56tRcuhKjw6KQsGvZkSCozwloyOTWES4ak8eMhWRI86wq9mtNZdRrFGgUIIPK9lpTAqFRLAOrBgNqWwEHiYFEZRpslF9njvg7GnYEiQV0mKcNPVn9EYFaWOKIWz5zUNsxSmurPCQkrBQieqtcWUCNsUXiRjvusvXWUfJkBGtNKxoFVMhGNgtP5GPdoFG0pZzcBcRmUTuq+cLmfH416bEOlsKwbbZ914EWrZLqihYgqRnRLYyFXvjBTbyuL8dyocTiUlaBE6VN4UZfctAkV2a9hhoDuEXP6fi1HxqrlzKNw6Y4iN0d0B9u2Mc6hBxbBYrs6PWO2YVl4tGKt/02eKLlreN7IP0CIYlQSsHh/ZVOuqMmqcxcilHuO73/3uvvnSiGbVXCVGXQthV3BGNXePcDJK0XpC2j5eS9RO6Rob0ekpiQ8FLg66gsR7O0xFKsjU9lhKMSyQYMSyonBKAKym+iimSpe+KLBOEiMwOwtMPSD0Ex8uuZSLAafmZaeDmeJRJRGYwuLbK1FKxaBD4YwUtMJF0VlIqxNeiCy2mSimXGJbX63JmEBiRkXkjRaMXaB1pDA22jaxpkIysifKoDBWW14MdxhdCmVQjHoMbKxfdlUVkgWeYDaNvyiwLOUNLFB5aqskU43oq3YYuQg7/MbAn3MMP+amePRI0HLVyKhgpDayE3uk6zFoQW2mDoAj6wK4Hr6G2etqxlmPGCIRgqelE0ghjC1RGCGMSto0r0AuNTgMDrnXwXHty60zY2dFCfEIQgpxMpFYT0eo66QXFqVyeVn8yBaYS/1oRblBOPEzoiIUeOI0muorpHT61UJ1tiZvZbxYX21f63MPZCGKwazOtt6PWXyaY7G2OlInO2FRvG/KPEDKJoxirTxdrqZCOm+WhRFAClS8YrWsAHYYbGLRCjcF4Aov5PyJmoXg8zoIEBmUbl18EvE9i2RWB4u1M1YNFvmA7RxeLqWYipLSzgHQVWOJRZ1k1xunAmAkAuEpArmUAa8MSBmVBMAbs3oQclF8qakZq4YBPwZJxdaeEHZT4Zi5nBhFVjAG9UASANNqm0U4vU7DSCE7YcQMb1RedgxC1BxmhGAsUkhN6Flmh2Q0prRovKZl0VctGCO/M0SevcXnlUhVz8ouqixXLWc4WZ8+qlRGVMbwTe+xA0zBkN5KGqtE/fZoZQfjAmbXKYs6AWy6rYcnpgpTfB9MehHaPnvXRwxLDSYcj+2gCMRMsQUs9t3WtBTY5Cpj5AoAltEIw9uJFQLm/OP35VZtdJwI5S1Wor6YOU4s6nQCEZqKVadnxfdZ7DHDtHpy8SpGFz5PuU1S+KFPp10WJEoywlQYnQhHAkAhGZ+N7EIYQzZmuYJOVLEK050nUnavj7uvI+Q+iGmzpfCv0BDvvk6tp3VgVyeAcIJKqbVm6bpZSCDRtmUytrkVI6oN1R2k2gjlJEWHgrTikgmGq2g4FVtcbzOuhCVGsTZJCJFYlenqo+gNoHxGhASDUd046fhhpLP9nQNlDCxR4UIokKIA7B88i7JFgeGB4U0pykgqVavSmYKpjYKHCwMdMx4KFyQSiqrqN7spOx0DEaJBDAJnhyGMvDbPiAFhUWBCAo+ZK2HhMpriN5oSSrRX2pM3vClvxpSWi5dS0sJxKimkEeCenUVVLJTIh0yBf2bZlEuIKWW6KTY12476XTr1OBuOCheAIoFhkBDeFlO1vL0mFEZ498Gmc9k411g6RgwCYYwsFISdB2C5WCguf1l4CaSxI9q5RQXcMjrqGFwYAKnlZfexSC6CxzkX1ed0PFJ4hrqrJdWvKVhFupbKMzXCq99fKnTwBErqazw298hXel4Y4cYEA4X9jQQAXlUACJVKZyR0sbz0vB4a/4CE75Jk16Oyjdr0wU2b5P++xDNaOuW1mBS09LYGYVmsT2tifbxr2veBg7G8UivJWO/lkg6JBZfCKp2rCGFRqhhIsOXgZqGDGsUQlkqhA4BJKR8jJSopD+/1ZUcgWKtTFovCxcJFJ2KNYvs6Q+HV1fJiSIQoD79+rCAeohLh8FogLGByoc1CwUkYSQoSnKKa0nNVVatTVSfydtaFiM0otalAKZyhGm8B9UJBi1A7o4KBr52NsZnykqZGUSSYqHqflz3mamZXTPvCTiwFTIG8XIQCz24k8TOuKnpTY6kbgZ9Z2FceHQMAS4pRiJVZGbKflJfwQjo5xtEWWPEIBXJ5a3y9MbUpRIjXxFWxUPYXpxQaJ/EgyYUZUoiTSaqQkSLK+VEIryheJOx0RtcPmxvVGUNCANzeHho/mvUB4WI9hx+njMIdSzD3EEzZjoRA35fh6X6eBq6L19nw7zeo1nvn50cUDGgpOJXRpTXFb1p3MC9FdoQAFsqHGhn7kxZRGnEU9cvlo5lK/PvFFha5KFN4JbH4NZ8f+qEf8le71WxhEYrCyaUGJGA6UozXCi3FaE2Q+G6OWBC92D4VIteFQN42CI962AkAZgznlEoDCgTqaRSDCKjcwrwmppJxMUKKRKEHippglAuAgd6W1DwYNiMRy4UKTzBdVRNLWWDQNsXGqzxjolyJ2MHwWBEvIBJ268tOpFMhrxOgPCJWbUKMKYwUdopcwBFWZDq7KWZZ2gw1MxYSUqCtYgGTVwvAsamBUQpT4PDloue6zOchwJwOkIuxEKMUSNTAiD8SoygSEoxFUmWoh9G6iRpYU+zCuRIhIesXmAQTRQASePan2av/jicTNgpkYF6Eyq5OYwCFuY2QOlWPkeSVlNfW8OrF5jpdOnKf7a/PEWCOEOly4heLBF4iU7GWi7BgY4RsL0wVhh++FBRIURQfZ5THi1DlkkrtdtlTujvpcwpOh80dAROIxH0TWzG+8gMoVXe+uxGIByfRiJPpqreqpt4IIws2+Io3/siP/Ih0fpufYMCG32urcWz473Kt9LkIKsGMyl+EilNhoqwYoy6UTVezpN4mC4jcRyEfxPB79fy450Mf+pBfoAFAKx0AUUxrJa+pxWkBTRGi5fVjXH9QaImsADwMb522AkJMIU0JAFE2u+n5hQUi0sRiSaNtbdgApcCpxtKc0KcFVbRgKWGcDOwYBdrpCqUw6lMIxYiKgkpsRklZsrMQJGhl1JhKWAIovSmLcLQVDEbUxiIjIwYh6ke7EBXymjIqj0hBJ1NEsbOsJIp0gSmENwWVkkx5LYLGW0peBdhge9NywwAviym9FJDCk7IXXiJInCQXWO+ycDoj2gSPEOkALI6pWGsCA1ydjMACuQh807KzxLnphTrGwAFMBWbcyJIxL9r1UlT9Mtr6dh9GXwpzH2oke0XyIjQSl9aHEWfMye6kWWqE7AIjufI/8I5B7cNQOmm2fhWKqjUKwWOhZEfrwNvKUrdu7P28trcGjyvtfrquouDtspPG67dDbLpYTQF0L7wgXJh9ICoptp7RSvLuKIA4w60nTsw+DWHz/7tpqguNo/Vg4W9JjXRyzsf1dyB0armkwyadF0GRKsGgAEYMotTZuyYjmA8yQqT78Ic/7KdRytay3gm70ZtglZRkKh0dCSqKUVIt+BCnKWfPp0JlS9fK60ibyksRLlYg2oqhM/KevwuWlYnopEh1ixcGRPAaecXLV4jiWs3KBdZ2ryB2SCEUQkGuPpjuSV5GroqjpzSuVuFciQLYrYjtVIYTYERFsRyKXIoyorKsiqSwEGxICB1ndsrlfFikyE6ZXqzupMgo3QLpLbeRV0Yp6GoLDEkPg5wOIIpuGXnVqTZTRl4ioynhWsHVz5jSNYMsHL/FQUjoaCFrLaVAsWT8clUk7+Qelf5yVJUUUdEpdQSZMQU5mJIYLaCXgkiqVKM6SbG60BGB76uLe16Io2Wv60gUfNkrnh0JWgVY/HhMKWrgdZ6VISMLjC9XFEgutPbUdWXRfkshlhc5QDviAYLEBuNrPjxO14/SF3+PRX25U8RHD1OKUvvBMwVSFlGaUr9R2Z4D9aD1a7S8jHqX2u8WWTRZLIL2uWRXXjUwghHFC9GaUmXkxcmrMLpAidwXXbgmUnjdfCOmGN9z/cRP/IRvu4TDEy2IlVFebJjBqq0jjVMBknrm6KXWo5WRRfYWUCwSsbogUhNTXjAMRhjj+ZkrahKOIreU3BpmhKYQwfoph0hTtSJluQgef5Dh9FSEQAoRhY3Q1dFBzM5FIIlc9GBGlhqYhWKVCWRPj6p8Npbx13/91y0KgEVUG7mIXz03LNcinFVIaUXYWRpPrZewFG5MNxJOqSliubTPYkqXupAACjPlrYv0wHJR7lIlLEiwNWpKVMs+15Bc9IQuyn6rhyhA0uppXA1NsVWPMQHIaEpJctXdA/fZ/+GC1M7MgbMbCW9FQlZY28coyrHprVEYAHwwyO2IDWV3vn2B9VW6CwbPiAS+7lj0Tq8eU7qThgqmCuEZXf4sUls0FiVZ54oJHzk8HrHup89imJuyu8/I3cB+sOKLn/JgPFIUKdRJEStkdVJQeYMUoFr3Vl6xalCMMnzHBONsK8Y/qmXUOLvvlZD7lNG5ctTVCaYGYok8dgKtD3zLBSCXLGqQToinR8GeQuLh+NEf/dGf+qmf8plItWC9Fypx93kpjFL0yYjuIZOIBUA6bIz67Um1OKoVRRSsawUASI1ZbVYMLQwkjH6Najs/A0prV1SvSWtnShAJYAQFoxAWAkY3IkVHiRRh6y685pGoSREsYIf3EuEABHMVZ6ez1AYdIaEQzcdjIay1x9v3rj5M2kWuvJYJJ5gRoSz0aqY3VQZjNRt5wxgJjNjGdYpqpVIIQIFcalOksumMq5/OFYkQ5PSqKpwRILw9450AkLyNJUUSJwWVvE6h0QYbYQgAF6EQFvrleaU0laKyTenBKHk3LnxRFKWGLItxgRReGIEZHQ9HJUtsLPVSOCOlYu7pGB0G4g7YdPfK9bZraIEJWhZiAYHHw0UHS7JbIkkJhV09bohS8ZuGZwQoqqOCtmeFC23rrP56xMCeVJ5SXV12GDX7g20vSCdTkcglEtunKsw+UNS7N8jd9kMc33axq0EB0jnb9B4vpdpxzOf5+bd/Q8KC0wpAwlPY8SvA7YjTt0tgipdIPe6Oa64YSM+T4ilGOhhM6+Ot0ZFcikFlSjTC2IMrREY1VKcosagIRZsKI6bqYbFZpvIqEu25e6xGVhQcSqS0mtsDRCymAMCC4TMC07lMpQeAZCm90bR1aT9MW6AIjTCkpcHWcrQKjEoXCMDuji01F4uX9f3vf/9HPvIRT5ICWKzLthM50afyriRnRTKysE9qv8eUrkeSsdgskUgdD2OLpkFGgjwMhdfIiKGkCEkuRrF4NFhgYOHDsMdsVVtYBYNFCEkXHgPAUlMwN65l4AnjSOiE6z6u5oW8VPDrEdK4XKYEWPFK0osxGKP6OzBnIa51FphcVZyaixWexcjoPGjTPXT0XWnXGycXo3So6AIZ6SybljoSYF7MhN1ZankZKbJQlMcez548SZGjdQI7h+rRi0rYNUiE+0LI6LgyUrDlQkicLt9tEVG+2QHD73MNS2zAPqSoRBY/ye4wu9vYVFX7QgCUBwwmoyzI6USbdIFCuHyr6N3xcHgyPBy69oTpxTd3agBjEYKt9dGCLGIpkWATyKtChJEoKQawXsZ23BShwoyKMSpbtQjxWBAKu5HLDlqQcwGkh0NhjIKSwPEKBitHehRcAEKkpDOGKaUQFmMKwnKbRjv8BXk1dBpwWgttE/io9GBaSRTfef3CL/yCBfUZVdIDvY4F2FLgUSH2jZB58cgF3KtMV+RZlGvUKb1AlhSB8ZiufjwaF26UjlCEU9STly6QnbCkt1BGwoIQP2Zj5KIsmrF0jAiNpkgoFWNMYZ8SzDiRmj4qyFyzANCDNS52SuBgRsJV75uKrYxGpZIwjnULnsUYw/gttfAWfEYrsIvh3nYwXCGLjA3eimUUkjFmSU2NbaWxklpztPDCSTzywhPh6ewVI9BL4QZKVKBHhBHYo+AQymKzgHHSaxDAlimPBZWvl+wUD8G2Urjfv4HxUUUsfgAXu3fKV9aaQqVsX18JAJiyZQxgKoXCwNiRs6vWN1zKVpWXDo8C/NSi34GUAmHrL1AjAD7pGBXJhYddVXYNoRRKwqB4BYDZC2VXrRRCTAsPsM3NrjaizipEfhaLj1XK0BSlFN+IGlQydpiKk4ydLpaRIMVW0ZR0ABaxdouCUDqBlFwCK6AFlUggS4K8fbr4DnlRvOn+WUV7X+ceZnYhXFLETOGldya46Cs7/lLHDCYEJ6E4HBYdTAExx4CEVJVR1A73UrPTKykGIWtw4VzVn0JfCpbCT7Lbk1feOOl5TSm6yIuE5N2Y662MpXuJjDPCMHpPuRI+PnoEM+YyajwjRanEtDW/15yFK2/bYZTRUvASekffxmWx+A6nEBcSc+BS032xtYm8W3yHqksl/GS6FspmSVR5ohixoWWns4tSA4t0jocjTe9CygXmVDswBMCUEQaVOundXpiQXD6Y9NA4ugCyC9QaXc3+3MrPicSGdzDUYErUY8ou1745KiPObhlmn6eMOKVGhdPr4/mQTi8S6QIVkdGCUDTYagu0kj71QNavb9ng8eD3urWGtSMcG7xwYoqqHVGzKeGlE3Xywp8WsKCQoCIoBEhX7Y3RNoApC4a0N4Jl4gXGTmGftJTAvAgBVKMCFi5tEEZ4RgtqxLAS2WOQwtoh4QqvesIbwDL5ggCG0AqiYrfQ8IRdnQqQ1xQDRaIUXsIuHbt1YDcFQCWL3RWud+SmMITXuE5TGFPkwmNxSWApkGAmMMaSXoYzVOcp5UKaEsgpMWcxlr3wXPTZs7zRdMiUwAjvU0bTlz0udikoLTXFCmwR2i8jEl3oayEwNih+41yQXFXSOE4M7Xh4XgyMHUsKi2UnzhhCPJXKVUkAtiB+VBQuYClQDUNRc7uAASGdgC2L29u7w9vdswICwTrS+IHxROsescRGkS42h4pRVSwBFOmGY6txZ89UCmwlAiayyKX3mjIliukWYNOXK2P0Evn44xwCeCUBkBsFuiZOODYKI4CkrpJqVY4NpsfLVMssREn4uXAqAL9A4S0ORbja1jskWiNjO2JNKPCEi34+cPpPDjhFyKFhYa0gaESyqpiRJTqLiCUiGIG8rbLRKjAGrkqrAEPnlZeuVksgL4kHni6QSAfAHjOl3pRnHZEYuSrGk6xCy8RidfCjgs87HlPMdUExhacgr9SitIYfT1TYMLMYLboQeBJ54dMxsACrn5jqSAihgLEkdMJIWIymaCtMkSlc2VlOwCVlp8KQpgCUJOPT7FWpj/gnkoHhY9u42DdR7GZt6u7UcYlVQmLNCa++xsBfOyGzA1OMFi2elqI1obMDbKrNddqq2npb5lLl6utQ5HRnAwOJXEkKk24kXA5Vm8WoflMjBiFGgG41I4BKgDFQnAeHhLHsRq2JUhJvTWGjSApGKgws/rGJwi+Rlt12eE35tKIFbLyVzeunnN2gAKIYldQVkIKdrrYeMlfPH3jVo2dCwXSYfvwEQ2c04hFIkUJqr48f92hfkcpQQC+XdjCQXKbCAcBgWjTdqaRFaAQjZeGiCz/3hNtyaIOORTwLXIuVBa/nSdt4ecO01nTIFhShFTSysDNOWNgz6rP69IANeQzAylpJ0p2Sr80zwhjVacRjFG4UJamvGEo1FYW/IteUdJEbpauLYlmA6YTdyIKBQJ5lvn7cqHg7yosfoGLgSeEUXnqdRmtjArAf6BOm3YK/ixDTMBQhyewXwatG2FkaJU03FZUxqmFmR/4MUF7IZN4nw+v/G4/U1opuU4iphbJcVo/0dVVqOi+iZTe1tlGLMgVDxUIBa2qtsJmSEimvsxShUXg1d755TQXijxCAYGY00mGMMUhE2E2FGPMaiR0nhVeG0ZQROaFvlNc0GAXtBTnPKMXZloXUglE6YDDPBEVqo5+8GL3vplzFNjpR8nbhWfTrkEuEWUgfQxhNhfedV2UIcYW9L1ySMloKrzMFjy2j2COx7B4sDxAGIbxc7D71qFw6BfBK1/HWBa/x6ux8UFA2i8q9bjJuEawAb+Ppy/+UspNxTE9/tQIXCrXySm9qVJk0ifQEXkFGIisXRTN0+Cfsed3U0etOb1lxyog/ckaxOAXKy5Xw4olN6XT2KgEzFaJULkhSahhKxUd7yr02WNQKYIGMk07BUF6jcLTw0inbyGJXakFJmOEFnsRPqRXDQrARsHgojGCNjMowDcMr4zD0Q/F05dIXy1ug8a40jQRtLrHZ38pY1JuHhMHm66EWEiGqarmcS2egU97Hfi5Gge0gl3BGsdaQXc10mLayFABWqZGF0joYs7MIJDHgHECilRQGfwymkB0DCsEGrJJEeQSeK3C0S5QLA4GRPQGjGENmDFB2BTQNCVYiSFfdtALcFBYtABNRxAslnTV3/x1FuqNouSxabK2wRMKtv8uPHJL4szA82nQNZYGnwPtWSyLMpQOg2zLMYhkhwegKwEzoqKqWAoOQl96aS0cvpE9MaBlhABI65vPxx11q9ZlkMoVgyVgFYOWWiYVQ0tlrXqDSJTbVf9VDsmC2TJ5M/DCmvJ2z6oiQUVcKxUAYbYYsdDBTY2wpqqrIjFdFZyESsUKUZzoYqsrmZY+Z9xFzLZAohMYUmAEoyiZ2lzgHvMEKMaVIGr+VLB0jKWNey6VfLbDDVAAXNlNGVIRdUjxNs6RzHdJLVkNTgFkopoMVxbjwOzjYWx+RqBZDTamz7eu0WQRUFsrWGzUL6QyIMlUDPJ103FFZEyGtm3C9gxGBYGhZ6E2v0LOJQiojBZ5CKDD2i9eCZ1SqKQlw2J8+T6nK0ZU3YS+FpPAX8HHZKizCRgDM8OqBrCqjXqq5dCfxJVzs+tV7FkkFgqmBV9n0io/H0knRzafz+iGxWFFgXKWIxEisGHz7IkRG0vJSvD4AuhbIC68YP+Qy0hkJr6WrLynY68jI2zHOogbTHjjkQtTmE1n3lJdFOKN2NCv81bd2fEpBZEWM0C0QXVYxWATTm7IQYJJiBLB2qBiVYkqXhtIT2xdD3tnV0bQq2VvrlGAsBEkiETs85sgbFZYSrCjgUpjyCmxqgbK0H9rkMpqS8YQxxZNXFopV7iOoXOrHCVlJlcfIghOAhU6ZBZ4RLQWGcLEElmJgxWwKH0NjURmNCRdl4U1ZUoxcpmJTBqZ8voKhJ7jVw6kFjRMuQsGpBXfAqFm9CKEDc9WyqgqkwPi6LZZO4oeElwVMLMlrHIkQ+sl6a5AeRl4iHGFKKy+EqNNxddOqHIxRusIP4/VtOzu29Cvus4bsvQtgW5OuVYECDtfT129NqUdeFlX1EqmkH8pIx+WJAXPYjH4uo3deLokYLayraipdLum4NALvOyZtSk13YiGrigWzqyqpkmTEQMfcgvch66r0XGFK1xPMtKeEwmiUC2GtycJCVwOYQLoaKi9wtaHiPY8Q4WiZmITpRACLaqBZjMql4FKofHLT2YkGiFh2Ajm9Do3ywZf1JL4uniqlJmrgAktMt20Iq5ASrdoKNyXCuQqkZzRlzK4dRiRGlhIpG48pqtaxNQoTZyFGUpHwFOugPEbMwusXCQbkMJQr6HHB6Gi5oo2qMoTz2m/TijTFcBeWaCli0yN8LTijcVLGTafcM844ZV4W9cyewktiNgJYE8vemal+9t0WXku0WAdGIwAtPm8kHbauIpdpp5FXrBAp5KVXUvZcV0VnnQPclQI7JNXAQkraGTaNAfNSyC4KLOTypkCy042qtY8aYXQATsDlMk3uea/k50a4sfitRgeSomVUFIuAh+InwepRibXlfd/73udXmf0LGH48LMWv/uqv9gtEXP0AyAgvhbwUWVIUUCIuVOz4icdILhgpjK2wRpQE45Iygsm1EFMkCMEC0OViR+VZBBalJCFc7LyU9EaJzk+w/GcJgCpCgHsVyx4UhcoKb7RGqKtVlKxI2SsaIWGvSqOGhZTL1EJIIXvIwGJlB+MijDiRkOn1w07Kjg1YbAUXrnIArgLpUxiBASBjNgKYcuEkAGpTDFgiPIYUIX1zqy8WBbAIB35tLkaYvOmi7iH0iUrUYApvpLcapW6Ui5SrLQPmYsk4sCmSuS7/saQEbvoMM8BrlcDujx2pErAqqX5lw1hG5L4UM0Iawey+z/ns8KV2ZCNsJV2hpsbWjRJyxQRoSeViVwaMUYgpvWl6a2gEIIydJSMYHrXNe4WeQZZc98MAZkpcv2hhKBgYK2wMS5eSXcGmlQ1fai7Hydkjudw43zR4gGBIP77w+0F+q1A6dkgfkRxFI6OpHYH0JGGoNp0SLvxGedkVbBdUa5oisJI0ZU22LPGIRQuvVCS+rkiqPLEsXLWPAbObSGGsHSPj9tSD1SphPg8Q6N4a1I6CZHxARrpgOpd8ppoR0mNUlaZZai/A6haiSkZZ8EDSFbcspln0QAGQUbjGTMGAs9NlBOCiE8jA8DDENHJeyHQj4QJgv4DnGoSvbAzKUwOB5O14yVVgSvwFwhOEQuAJr6lcZakqG6Ds2OSCERUyJbxqRZH1QoFHXsGB6fUSeLmEc1U8YzBjdumIKXkWGDjXsxHyTbxcpVMniZmF0FVux3N1wNQfmyMYppAqpKswfUkVIKpALvaKBw5jKpGV34W5Gn1s9JiB2Y0sRFUCkYgipWaJ05SAtWJKpRgdAJvoyiQsBFJUbEXRxRrLck9HTxRMKZCCZ7p26sii+YPd97znPXUEIIrLW+N9sSbq8WdY/mUfRt+OAZN+V9trIgqzkupLMZ4AdkLXtWPJS8EGWbO56qt2uBhZ2gVlECsQpsbLFSFdPXlVpWZ/78wPmz71qU9Vc/dIGZDnM5j/aCOirNUth8817AAePEXQCS+pUJm8RH38wcsOUN2qXAhdn3LlNSolWBY6jN0dgDKdC7MxBoqSAqSUVwHR3pkjYcmYYhwnXRemxorEqRL2FrRVsxqysENGksJr0bg02wqwS2pkqTx421AlLEKwGVv8YaI1FkhBSyoPXnYjPAlcrpaCzn7vtzIGHj/lDptdrhFS2BvD3y338LLo5Sr2nPhEXieeqB/GaBEYCQALGT8Lu6k11L6NINUjEDm5Ql81HowXDBWAcTyMlYEzMaVkjBmh9WRBVTFGRmMK/PLaMu9mW9AuwAgUTok2pXoalceVnpKO1vQe3uMSQ7rj9973vlcN/m6qjxs+71CUQdxn/6SG0T+pYcXoCP3NL58eyuIP0b1T6pRCRiSYXVWxjLrQi1sPnMBAytsHC8bWXxQXqTCLoypszjwSxj06AWLjFU7v04zCHANRLPglAvA2qeR8c0jw8i0NndGINN4LdSxhsstRxZ7qzg1q0hZSihXVKRErJeOdPKoy5s1ihIdMAeiEoS2c5dlZYQE2niIuGMAqZ0fIblQtZK5C6IF5WcDgSd6zUrcfYJlywRsF2lQwosK2p/DKs0QA08HSGcWWgqVismSsVGMKDEVqxYdvBGY3yn4vHhVLhKKApTY1xp9y1wdIEc5LVlXTLDDk7oUvC/u9EkawipmLUl9Ga2sKpk4KcLCRKEDXdTGeGMTyMvIKLCQS0wRghXHlzQjAJSoAY94IJSVumgtDWQhFCKFgKOTwPl3XdiQMY7SmgXkv7MOeVwGSEooL5SD5ONMSSe0GCdFjGPXAMPra71Xy+4Q9PS1C/A6ejDFUp6nvfI3OHrwHKEIZASAl8jBJpE4L67HggpcIW2CxLIWUDlg42VLQq7OPuh5Ka6hgAOGRY8B5/gimeCM6kdgP2fV5r064KEauRImUYBgIC0yS1zgJGYORCJkxpXEhJYqZnj0M4yyouC7Kx3uUywhsFbjspRBLwNhZ1ym9MkoBCY/KmKsVhDHVVykA4AuELDZyRgsKCT9O6Qq3tWLpJGThsgCnF0UHqCk6gRFFMSKUgg6DkwUSgB6gQF4SjFe/BH/2KqEHnn0hBT6jMs0+2DNAdkaCs5qblisdCaEriR2srWGpna12SGPMOElGTTG2AnnjNLbF5TKGh6HnCm9MAUBLB7hsR1Gby9O7484QRhKMUqlN6Qtsx5/Z71HAYSiyyM47Nn2xWAG/QOivjKpBbX7044doThdh8WnC29GHi1/5lV/xTgnvQ4CVxAaGpFXlYolW2Z1PgBZQX2qwjE3liiEkr/C2g+IZosPQ8ZPw2mHHwC6QkSVCdnWGt5hctYyq1OeHgkAComulTFvipi5w1IwsCR1MJ55JI7qrpMcAA0BkEpswqkBlQPSUdCOXUX0UgbVaCKMUxaK6iM8AY2ShSFRvQuIJxsslF6HzZqeoHyeB5yIw7KRpVZniZzGlxyMXHRWjkY7HYlpux6UunAlRjABqKBySYkl1xO48RRVzbMYURgyFGJNqM8aARHnwvEb26snCaJpOSTLGdi3A44sQL2TF5zWykJQZn5heEeaCRJhuoQZjoddLSktND08J09mLoXF4IQm7RIWoNkDTBVaJkXARzMRaIQGjq0d20wCBS+EeujDEFKZVBbuXVNRcTfHfYXS0oiiQvInzgDZXZZRFOkajJ8avDtpiXopqFUMndOEI9e4TEIwjxyvQcRLrdNHByssiioXoSyCLdACKQZIwwtO5AhiBGYsNL3Vf0euIl+BJEUsg8exNkJ3Rd5FgdHZTiqsB+fgSLb7cEXFDK1d7LCqQr1tEScKUG2NVbmQBk0N4yHiGvCt3vVxoKb3HvNZCeRiWmqKemHUB3IXsZeVl7BvaVjNM5PSMpnFKF3M7RMes+OEpQgTachguIa2Y8pCMsKoUEye72BpsBYxEig6KRLwIIUleY/1W4YwUeCIcv1xtUMXzVswUU5yo8CxwbLykFEay6ZSBXyrw5TXmFTWdl/GQXgq97iqDEbJGjLxRWVskhKWV5DWNBEbXpgXGH4+tiSQjTPbIjQPQT02XwAyvPFEKcI2NO0jlggxsStrcu5HOPkvFFELPJWeFdTntC4DCsAHwqoHFj2+MavBHYP5lMle3b8fAgCH9AMXi9GkIOU7/pLx/lL50yEsHz0tqlgJAeLmIQDpOSueENyOSAk0Ld0Q7bJBE1DaotSopsMoBake4KZdReLQZMZzOpSyB8T7NrgjBRi6jlBSWlMotQSQwhG7kHZ6evcuw27XUKWClo5SoVusHW8w60cNp4BLf2VodvYlqvYzSYSAUIjAjBcyohiymSamBKdNzKWAW2emRIxlPnI6Fhw8Ju+rArA+SCjulPH0SpOgIFWRGDBLddVFiWQgFgNJo6gkLbyTs64hCGGWnFC6EHK4nkrtymR/k9KIyGhc4V5aKYaRk2ZiCh4tERcnCS9E+i9GUWDFTC2JMYSk778Cli3BegOzFIi/EmBQ+TErrVi6nyHIlhcsulg5MqtPYuXoiPv9lDEOnZAFbL1Lgka7thmHphPSVMnIYL44nRjF9oOaNpBDM/lBIoHNIB+5piAonGBKpVwkLDFdi2sFTDL2SuOhGUQQbkqb0wgP0aYsFANK9U3lIFmxS86o5u1PqW8hcfecIDMB+eFHAEWEyneTXhzQWUylnrFbBqocxWhpeYs+EJ1yxpRgT4YSeoqBqSkHLjiEAo6kUgbMbwaTDn2IPfCRRhlGTPrhWnnBgsZErT0ic1iWxUsCMMvISIWXkiiGAKW/IxtUDj80ebD/kUlVTBUSOBwOe2mlkrFkKC06NoEJYYLH3kLsuqo/cFgTSlKQYSTUbReU1Vn8WrlbpTpurcPjFsjyD5drYKp3EF/LOXGHZq0TUBXysfEWy8FYVNqJaIzvjwitjPBSW0oVkSSkq8uoMbMRcDbHJYsusfCNjsXgwEwphzG4snOuyPaaYz314ejQ7CTgLN5auKa9LaIQvhVFhRhfVl1Xgji6LCpGrkO4BAvOdF4sfCYH5QS8qLsUwlqUiIcs7BiSBg8ETMKOQ8PEIjMRIHE4koigYjFfoY7WBxQIQYLCeBQ+WIrVpmpfiBxQ+6J33pXxKp3T0r1wnnrELRrFGKOS460KKys4ldjolquwIlWtUxPQaaEovpKmoAlV1tvT6xtIoI0Adenf8KYCd8GHVNvsX//0bKL4mWB2VRw7pYnew8FMUcBV+BhhjNQvRIwv+LC1lPCWFgQ+AmQsywhQu60sP2QoEKKqkAKRc7EnHq3SRZx+s1DNiEGJsodjpeSmSXsv2+FvpIwGIRwowMto7pgUHvhsjN4pqTKlayISrwkxbAZYapxDGaj57fH2pgFewpRMC8CB62guLyQhwgp8kEgUoFQmFcDbyUoxZMlbP9LIoTN7drrxPSU6psjMmdzYWB4YoHkYZS0chqnL28Ku8voRTonJKeYWPuSh2T0xegb7MOOcUMInqlFc6j1SPkX9JGi2qZZFI3oSxRVZkn19KxK7xOpqFUSJTdmJKTDFgozOaqsfUSEzZTQukR8sSkkVexZuGcQ3JeVPaeBOOfFfes4vQbS1YbTdWlrEKKmhRAsHEJof0kgCM9VA4hoQxGICSiGl/dKd0hPoUok4ASmBPj/9vow9+8IP9roRn6KMf/Wj/j2tgmEV1sATibGlYLBDvKsFmb1oNW8teeCNX6xND2Ss4gPIsVBa0wCwU41KIIqbs6bwUqZs21le0WYRMAhtZQsIgJE27A/MWiG2VZAGLyhQDQEoZY6OXKNd9zCUqkpRIwJ6xNYWsX4BVy0jPQrEpLPDxR26sWoqNGACmkngxi6IgYSflSo8nvZB0Y6nldcaKDdyIEw9aykjoTVmIkmC6IAjDM2ImLMHuU0YAh9D3WZ4PCkJGZRBPUuGtTMh0X1klgsTG7jQK9AvQPvv7UzMfmsTyEhYdsTQ1KkNtrgMGcnp4Ejwdb4YtIHzTdRHVAKbYxLKkwNc+NjojTOEpaqbUC4DfbLK2j++5KIKhy0qvcwHEG8yivVbTmH1jaYx3hS4Zwsv8WV/WsrwcEXbJW6N0lWiJzhshpWqt+w//8A/75XQWIT/5kz+pkV/+5V/26U5ISyOLWC0YiY2xPfYGoAJQEe+Orydk5JT6dSzSpWitszNioAunc7V6jNIZfeFSPGMrFjg9BhaxAbLgMSXYWADK0raZUlgmwttEljGnc42hCtnjWXjTOCsvV4megeO/A0qh2mIpCwEm7LnoLUULCFlJdgEhjHGnkZfYLJb0i+zxFZEFXiJUq/ykuU5vbFyUyqsM00goJLxwEk/hYYyyKBiDccbsXBQkRgAnx8ioWkYKQXvSPE1jMC3EIfSvo4pyTnzVBJ7iiy5Cdy2J3FIoT53xOJAUbA6k8mCwIYEpr+8D/H6zKdpVIkRhwEbkwr1oRndB5S0CF4tAKYyly4LHVM1cwdgpJCQAEhmzQ5rSKa1MsHpRubt2/ipGZbXT0ASjGHZVwsFokn0F3ctizH6FnoE3nZ1y+Y+S5M2o4gmLvCo2QiqAolaAtWEqPFoYSv/CdrqjrCX/b0f+0vDHPvYx09j6+Nq7w2i5iWlRjXJ1zihSa7ykvDW7wlgyVqeSmoKxrC9GlUvHjhkni2lZ6JClsPJI6DGIovAaSUhj5HlnpMAwBqDjIXeAwGEoVYI/DHA1U0IWPnvIEgmnTGLrbMG3ZbFtBGbvdDn6QhwnBctFwKqHHSZ+VBTgSDLioRTVIhgJTKUCmNo4o/WEHKFARmOy+hnFks6VSrDBlMXJZyEVzygwWiPkpgDBGlc2/ghxRlIBzgBxLN1kJIymPg3BuL1aoPtjLwAuAM+ET/qrJBI1UBpTIKuKgsRnK2+Z31HsRYiKi4DVMhivMhQAUMHKoICxRFhsvchoa1o3MC6FRWgkxVIwAEtEgSGYo2UEcxIeny3hFsDRYlGURYduM6ojinTjhJ3EUxGtjsSUvBvBXgrv2IrS592IFoCRl24hlIfnTsv44z/+44y/9mu/BuPH0vr03PiC4K/J6MV2YliFkNEGxt/RN9U1r+LJMGJhZGTkTZTB2LJyAaswixGSMSRXbKaVDZlg5kJC4YWkhCk2ktjQMhrB2KdkXBR7wiIwPYYwwmXMWxbFgLGYhnnGmQsGj3vCi8Tbys5YFEvC6BR1kCwsQN1lLBaGsTNGcR/AOoRc8bDQ4VvkjOWqL/hCSlHGWmOHb5F5iwrGLl0wFlRgjRiymI5ZDTOmw6ewk7KwaMSalMs0C69HgV1GnKb97QpGSOtjPd/1rnf5FUTfnvhkpDbi/6JHnbxgngyB5Vph8TdyUaoE3pdkX24H8Nw4/wKVhzO7Tk0VwF54tQEEUy271K6SetAKBAYQRSgAogrhZTRWxgU5H6naFF2HPw+QCSJS4gridhpYrBSXeBUY6eG57lKmYo3C7zrkAEh4STW9HKXgFdIr0BbWWAV0VmJQpGc+fl5K1fqpkK8e/g8z/Ozdd1v+7yvxeIN6erSti+qMrfJw+sbNiltoAiMqpJGX8NbamqKUGobSRtIpwq04fFGtXnkLGYkGVVLxXCQeGUvHsryLSgnJa9o23QEC0c6iMDpLbMYAHUHhvJIWwtW07MaUVRIPTLRVu9jIcfLaTV/VbUpfby2vlktqX2CAjXoZCQUzKYup7JYUbGXkReUkYJMlqqa8CAsvhCV+CiQdIeFVpBqMYoUQtEbGa/YYBNbXmE1J5QEFENuayIKfziULAVCqaQ+KBbEadH97K/nABz7Ai9NBVQwG59bXUXoPCraKXzGUQq5azoAT3oL76OQWwPu2wPG2C5CmMDFLLVGFYY7hqvTcemCd4jdymXKxs5Aq4QoPwHivhL3epZCI8FoQCpfAx1MqzAritTStDp9IxgO6RIDgk/aSqrlbmBkrpf1godwLGiDYfSxE9piNZReuBmKaUWEVY9wpqapqcKYJl22zwUT/HmDGYOVqxElQcRl9ufBByRciWygRF1htDkOpKVUBCFx51ZZdIAWYRBLYVAiGSOiiEjXbBVGM9/IEJmKf1Md/R8KFpNR8pgRPCku0lDHnajpwIQ/2q7ssdyrgeBzuDozUGdVD54W35ozySlG4w0evTi7N2h0WArDLf07h09+oBuOCTCDLBWC56KJSWgp4FgJfJSy5xEKqTRlGgFyNVV5I9cubALAIh8FA0RpXlfBKtzYZuSCLBR5/Gb0j1aMFZ9Jt9KcoP/MzP8OYyEV4TaXzarR3qAgjL3Jj08o7MU+SV1WK8WMK4b5US+QiyC6p+uMUCyxXRSKkqBMgHQMxFVs9ksDwtpsx1Gz6LJXH1YMovKZYXDRZHn9lw/yp8scbpjid1ypFJ7iwsK//Ut5jJa642EzrIXuuu5F+F2AiF6SGueQiOpfXVMWlY6Sw+PUH74UlNlUnmP8rSH/x37e+3nskPgFZJoEOerErY0XiyWUEFsjls3H7V1VG5ACBTdVAh2S861FVIb1wihqA2VkoJKrC2W2z7QGTCCYeMEqJCpl+KC4JED6vkdwt6ZgLCbAspuxqCFDsxnsITOCM8Mquqewb2zXtABhtE5goIaaR09Wga+XZPpfEKJBRFEzfL5i2LBgACB6jDWJJgO0dQtcMT4lyVSodZ/YsOAl72S/i86KtBTCuCXuVsE+pDBaBdGCwaCmEBVhTvDCmRgDfHDmf/j/gfav10z/906aKxyPESCCFULj8UBm4FiIJUPH4S8S48DHA0P1xjSvgS3JPHrYKZvQkGU2LrTwhCaNq1YaHriTrTJeUboTnJfCmxjIaA2QRZWod5OpsHADq0mDRJ1BodOUwRapcFul7HbiIBSqWkpjCENOUjSzzMqYXPgxFPSSwkigSSaow9pAUgewK89D4Q3cr6Ngp0m9D/NIv/ZLdctw9PXVhdI6BhWC411YBLGTFWB144mKg5SojHim6LXhaEFHFjpwyXQvpNRXVdCTCI7yCzg0pBDIvwJRVOONclMrQJr1pIwuXvTelizWlEJamFXm30EtXJY0sdS2wWMVDtj5ZALBBMmYxhbF0jLVpSvLaXF/Y6AKFEDfEFsgYD4VIBNn6mFZb9QjMEqzv9Xw044UfmCJLI0WiprJYHEhjKUbOwtuOZKxOpzEGdbKL8s2OCiHRssgitpAKKF2JxFL8HYumPvv4RglYLrDwMNhiwKwdh5wLRrphGO8h7AIroFhji6lma06Htxd4TLdZpooxZaRLLQskS4KnEM8HF5hds1MFwsd/z64YsjK4UAnMIoRIepaJAxTFFXIGPbPLoQiKe8grGZ1XJIBpgprCxQhPCmeRL2+VgfGuCADCNeGypsvFDl958sZGEYVKFi+6n/VYVo34NOSzj78R4+kR5TNeiWJAZVosHQOdkuXU8VQJmAKQOFW2SrNgRBl0MEZRpgoAVknMMBfNYU5hl11tXC2RKBaApmAsdJZEiim8GIhwRtOliJ8x74V6fPEJ0wiAXAqyEAo7QJjsRlM8lGW8IKeplAEoMWcPX0dVkiWY3eFaX4xWoEVgzG7BnWZr3lLIiJ84eBaZkSyQl0SewuusuhjYMPvioYAYLuwrvChSlLHsF/1jEBWJeXoK3RrqxZFzABTcyK5CdRItwPj+vYNxz6Ue6URRAH7u535OFl8vffn0izxqXjpRYF3M6uxr4d4gYLngFUORS1JlJEIoBRqJZUHodhhFOdXIfcz3OdFdQ0tn2Y7c2aJCIlBhpkIouiCmuuDtAlLEZoFBSL8Xw0JYiIyNjx9cx25M+HAROCwlE1zYIbiaTLEEchNbUgK6ikWtPqvAqFAhUhR+Yp6EUQh715uOCoOD5VzyBgzGbjVtDJcXp7cG3gci4fFAgtUFnWBgSamMp+TP/5tXtVqru3qPod3SESpI6djplCzl1bsiWxB20koCLztFhVxCCAU5oXMR4ZFzUZLw6VfcI5aFZhQIcxE8zmL2Qox5N53yLMvsU3DSx0xXf8ZijZWUwmWq8Ran9TR1c+KhE8faRrcUrec9BSrbajusZ2xlhFx4377xDsNlWuUYqsRIT4SrR1KYeNjplQpJaYwW0jUmimHhilxGrr6zwKMRFlT1GGdgUWA+nvuYJtbT4x/rEoJQlL0GltQBgKcbUTnkojKKevZpC5sPUJ5FYwVXlVE6JFxuClGkkvrXFCnILam8vvXBD1/j6nnGU2rhXLXTEQWzgIpHxdWaRILNBamAez1IYovq/EkERqJP1fRMCCsGLwXU024kgbuNI0LSPVEKHgsBKZPe4DG0EymViznyEo0Wj/UVbkVOvmtvjOwIC6HLqE9r6rut9tIiesuRgxGEwMaWIP6mZXyWPaMRpnC6WLlkz94YbQXwxiOKbkEoLBhMhw/GpWYSPwUgTBbpOriMvPBSR2gMs3RX6Kv3NJ7Ad71q2ZGnF3hHskSb6+W4wEXdMYxNUzY9rd6+CFU/L8XqEd0BsOgaQ1m2bmOD3ybCFFXBRjy23vvVNkE6w20EhviRS8GOSgjhIi2vkaUC1sgp/el1C6Ywp8uXOlR55SJ4RDU6kK637OqpDMg4w5ysV7+/+Iu/6Ldnfe3EJsRLhEoiKYRguLgfP17xCcXdVDx7XuQEJ5hYJ9+nKiMAO4yOuFxbBQt0m+hyEQCJes0VIwTAMsYmihFga0V3kYUDYGaniMLAVZ0460teC8ULI5cRpgVvkaut8fD0HJijgKCwIg2B1LQzEQtLFVAIb1NeWbUhkCUGlmAYeoMUzQIGU0EYwtRJRjD1sFRMhCwUQmFXOUWTwK2yDWiVYcTWOaU6hZC6KMsKYB9GzZjRCtRLta0pU1EFykIB011gI0t2PeY1AmBQT65tGxcR1S5ITddLhPQKLnDFMKYPMFgW0ywV37QGG19aBKqE/SVgVAGK3bgtPp1c50dt0ymQyCemUlgHSuMKM00H5m37ojJaTxZ2ejCK7M5A62PLKKYErNNPSQqhU1o94bkoMT+g138YyQB0hE6Xm1x5dzBOckWcf36f4g/Uq+olc236JbWPf/zjTqzXUxc/+7M/6+suZnohCNXpbPvA5QGqmFJgYHdIvHe++voEJASVkMgDY3OWhKsZlQtoZXqJTImMOoLpurGcNq6vVfGkY7vviFwsXNgO+lLCcKmqk89uKpCiKUr8RkvHZaRznTciUwh6aAlcDIwAEqiJK67iwchcdLl1pR+BfSS2HywCLZARQBYLZyRiCf6kqXCwysBZRi6LZVqTpuxGU2sKLxGvUSC7antE6BIxTlikM22kzEIhpdACQhIsEtN7YF4A3gO9xBTJ0lG2nRRlE6ta3tiyaIS9REjISK66HpyScN2bCsm+vFlMR3LXo+VKqiHjGCxCiVYD8DDTa00UUVKETYcRhWqLEEnVhjEKyY7EatApamjqKNIxdCB5TW06pFgHIxhjt8LWt/uQkccPFrMRGzBYRhYlYaj4jCsMTGqJRI2TN0BR6caKfLZ6y+s6SOH/VMeoBfdfF/4g7P3vfz9yCiTBKakfKfhTXSOkanN1SACApWM0ikXYOiteqZ4e33y5TR4pn8hU5YDRBfKyAzO6jBLVQvzY4omcTgBMew2CSWfKpTDZeWGkpiCsuzDVVlQ8YFzk3GEOY2H0ShFD6Cxc8Vop+dhNjVzGDkEMujIVpYICvbshW77sjEoHKHtIWUhGIfSyZCzQaGozwGCkK6+ppPaywGilUAwwHqLyqICTxZrSCZgoIwsqQikwgNH00F0rCGCqNVN7yRszo1LDRC512blEcc1CJ+zORw3mYhFLp2Au3FgWRlGlKwULWUi6aYqoAscQc/hc9HGyTA+z8RBd9WCY8Oorl9E0AUhZyLy2TAoAFrrNoqTnogPQKbpotVk6Y8aMxRpNraHRwZC08PhNp8RjSrIHHkZg5NltSrfXyEIErnLgLIxSm1K6mRf2DIwkhYuuTt86qdP994vOjL4R80fy/kTFN1PEzzH8Ai0XQQjp+yAM3i//d2AYOnVcjI0ppYbvQIoCDqA8X6HpOiJ9U9KyuzvwALyN2PRotFxGy7vz2Y7wChEeIYUAMwLw0o2MLBSiNiP7VuN8KYYGwkIu2LkMEEpkpwjQLa86TAmlKl3vK+48fnRGAmDqlRVF16dRonT4nQ88hTc2NcpIFMOubWUwqseIhMvIRUFF57LQUjDCJOzKYO8NUljeO4YORuQSBc9CF1LxjLKwG6/mXt3wXEbhkaeExENYUow4TeFXZ1N49WMA4LVbhbATFsLS1lQncLGbFsJYJUICsF8Er4ZCmtMDvITNHk/49LvljQLhwXgD0K3hwlNkT1kxrQA7Bb4QDNbEUrB3nUQRnbI4G0ZIADDnwZSrowuGfDVQuCApxEaUumoFUlrAK8N5hoLZIORcEUKypzcWWBTaNpQRzEgG453uaujIG8SI3D+9Svd5x+cUK+Dp8evLqnUF/M0MY4+OqGXn1bupkuhIovKgbCkY6byqIi4mPJGr70tavVVVtabBjHQklHIB0Fmqh8ISieyM1dB6yqgX2ZEQRniKEPq52Fkrt22D1mQpcaEuhrJIFvHqwKAlLrF9vITxnS3CvHauLDCMHbJ42BPIxLS8AFI31QNFuMLA6LzBipIaM7upqkZO14jPn32HLASGAIxnFvx0B0IWo15a39jCG4uNqqnylMqOgX2ugSkB6t0UJq9c4aXmlTELLzb1m5KFdCWAuUbCC1Oda4eRvuJNp4e5IxcbbLEpz/CBVcLuVuRt9cZJaSNGKIqlKb3uWCBNs5taARZT3cVZm8EgE/2y2Nm2vhMSCQYYOoGZZIGcvXoAWOhNS8cSyVn9a48Kjy1XgeO/KwPHgxktQCk6DCz9Gxp+jdYNksWfiHXkfOtkYXuYMDjbvDolGHxW8vlIeGzLW9JOo++/2C0go1jnWVKuPeXCe4lSUMHHoDu6MWV2CskoCliU6SpBrv7dd96ERSUFGgm7KGzn2yVzmtOvxBJgUTRdjrw6b8qSYnS3jQRGrCZrAKflczi4WFAZu3t4Kp0lZsrFcYZqyoJQGXjALNwC4wzJKxcBBtNVJCzxN5oKb10Elvfku2QFLAS+Z8vIKDaMsfBVzkLi4VJAFnr2RtOk6QoIbGUo9wXPXkfZl7E9krGtpXDllaJKhLNsj/OOM8x9BOCdJX0WCkCYlJCl671mafEp9/A2nUUX7UIwsRQWeGNGGPyFuBtunQMQob2DScbDy+IMxAYp3JaxszgzxoqpeDrLKqQkaypYY5wLtOxjW9SdbUgl0WFKZzQlNVts5H60TPTL5SXysx52xfvhqeJbMYvQIb9nb4n6ZgpzRwJeIH5rRdhZ8KgHiS/AskCS7PFXjw9cAJCOuhCxdIC8RnaB2bcv7Lpjx0xZUrFcjSk2BQwgPJ7smjp/iCAlh/cCiM7qi3+ls7fNAqSpLNW0HPA+6bSCLIk0qLq6wgUWa5RYbIVe5mPBSSiEcSHZJUWbXaxFISy8MZRUrEBgEs+hu0R5NsaqAZDwYjFkiZwdPE6XSv0EGwx73uGXmkU9lgjSapDA7NEWYowKWD0lqkGjqdbgHTWAyBnrVOosjU15CUtdGNUwfK689/Cq4l156cPfAYyEJQUbpXGKpqyVFCMEoN8rURuAkJZUnfSOWTwLbyl6UFqKijcilAuD1cNQFqvdme5AsttoYHYjARMoS4noLOwqJHRloKWQMBf21foADFxIsIz0u7GpLrAFMEY4JFeFjUFVQnzHpGxGDfrgI6/pwAB+3owEmN1h1njrAEO4jK1GNVcGYzUYRUnd4sCgYrR93npvmXWwjDiRiLX+Vc5IURKqMHnpwrFVUl0AZ68kRhkJJAvyvCymeODPJzr7esHO3/fXPwucR1HFLHKogEVlvAEYa4ldAx4sj45APEauOmQ31cz9mNZhJfJOZKFXHC+pbiNRjFwVDKO3FigYC1odVqppXoE4+5LCSASqjSIQ5qS5CYsUyta4QB5J83MVZaRjbmXScZqGBGh9TMFMjbnKHid+uZTNRe88WStUOK0zhXCZFk7HlqCaCwCnEd5IwkPClH0jJT2M8T4t9uI4JMKNI0wpr3GNhAmsd4rUYxZVCkZls9cCGOGyCJUKCUBMO2ksVmlnV8ZIIjQCI7HFRvdkFZZlNWwqXBTJUgGiKMbsGynaEcIl9VXaqT9AuYyE5W4UAj8LgNimwk0jOZFPzBaBXne84VnotrXTxUtnaR1Gonh6ZwkA7FRzLY4xzs4k2k2Rm4rFZpFNkwB4ouUSWztW2L7YDrQyUiSNBMyjwQ5MkDzxPU4jPCkdFx1Y1NldATF6Kfgk800mnV0+FhcywEVyBlkT9n7XAEYIwvAyxYwNT/mAVUZ4wQhlehaYYB4dKejsaCnbiZA4KTG3QOlGkkVqVVkasQiNOBUQxohBAcBnLa6/D+kxVbl2THmrQQgdjFJJrQwGKWB4cxlT0E5fIpb6pXTmsHl3ABD2rYe8vEZI5HReqQukPyspGJ4xw4hSEiMxTaLKwstoTHmmNy0qwAV8XFT6RXz+CDyM8tTPKEUuGPXwshDNGmsnmBAYvcBkuadY6jC+Kvi4jcGHhY4Bu6mkNgu4zWW0boxS86KNpxTGdipjScMAl93YMiovlylC56HiTe+04y+8prCpiqW8q4TCwt7RKoSFfUfIVIpeEyTF8mrQydS7VyC7NeG9k9CjAlMkKZyCliDRCGV2Lmz4uxpGmLJj49W19ulgurPjjFXbUtsXChjakJSmFL2QZcSTRQHk/P1jc2ES81W3z3uadG9lUq7E7EaAREjIilOx7yH7KBQYrZokgKfgqQJRYqueUgOyU0yx6XxeU+GmV6mPJwOSEZukp4Er0LoIZ2fhIp2D7LITTypCV50OMKRAomydEk+VqKS8daoMxmLpGKRbRgzpQsIYpzBe2DMwKqNwURTrrCqiBswEhsuy8BJrwhJJFl4WbKbsRnpGujoZG4PdY2M4pTxJ4WYpQmbJWAh+U6PpstsFxtJZqALv4ZA6CgOg9/irlt7UCLbwUmhBoNPYl0Pr4E+I/ICWNwGwZagcVxYrhsSRUA+lFOxXkrNc7EQUC4ULxpRSkaZ0AkyHwQZstDsdOVnYwwe+jwKV5GmArxLeKsHDG3khTdm1Rm9aSR1+iaRTYetmAZ0WU9lFmQIXhZClpiiiukospmAEnsTWoSpEX6aQDiEAKjo85FXUGbKz8Bo9hdo0Vlg8RoUNYwocA52CEzMxtTim51sARBaLWySHTLbTFLUEKAiXMEgKV4siSjgdV0ixomC0RLENRo8RWISmvWumRAWTLI3akFRG01PvdQ6QsNMXSAFTM2PFF2XaQlSt8uKpME9Mv/UAHIMadKEdDdbjaFHxGpHgLJcoVIxJPNUGYEqGhKETRjq2pkgmkippt6iDbkkrDN66iZUCCaPsJ8eTsI9q5JyQxlKHBaueXKZTQt4tdEZjyl3PYlw4HfMwlWGqNpiQq6RpHa1gsHR7QZwTJ8cRYvS1zR/9GJ0BnNq3OHaEy8q4NqgYMWS3klEBUxolTW8FGCmMeYc3JVwOc16jXPaoXeACyEWJMwujqbL9Wy5CFj4wGIFBkpi2Ppfn0Jp2uugyGtvu4YUH6zzQxRrZnRyfR0znYhfo84FRC1y+X2EE0CCxpIQil0RcCoCkpJuKNVpVsH77EYARSb2gNa0MJKQiWSjGBKFpwmJ6HiGaYLuLtP2ThkVBvtp0Ldkp5bOyrYt4dkiBQkzZwRDWEnyFdowYYRSHgYsu+1Xtq28uEKoEjyrxAITEbwosimvdmia8SYF0mFydZpzYqqSXPhL4YBUjsCyKtJ2VqnLg8MCKgYEUW/i85WVkQRgmnbEFFMtlWpsIZWnKRSIpC2RLCkwwsCAUEqwUwinPBHM1ULhgKOmm6Y0F3gF3WN5ZmjbGb0x5hhTCXgp9hWkKqZdWRoO8Xhw7xUhvQSy7v2egaxvRAWsrbaIQsfZxLhZeMmS7VgFaI5UnsKlKSkfJld2YpRAMSnKSYRTjR7a8jHgGoxBGBWjEMTaN04gH+D6NGdsV93hB0uXiJdiKLTCjlsG8Mh0POrwRBhu7t0Z2U+G6EyW1kliE6MI7YtFMjdhSlE0RCCDEdKskykoaEfqNAc+rG1qnMFJwESEKYK9yI0JeAhYhXakwxstz/W5BdBLrhIhUlt/FVJ8pnJZUACBSia2aVimMToNM9BoQjtAYFUVxwIqQVR2okGwXcRL2XGBZgBNTRjz4szR98j/+yxsgxSgEUhnVhocOzVU9LY0pWKKGFMYWSPFZsHXnsaECSCrMiLkQ+CfnKdh0mLkoXOz4RcGboq0kilwwpvRcaoOXXQhjGfOWLk7gFUZ/NoWxCPfySp0l8LOx4u/GhVMqY+FLx5LeU0KHlJ1dAcomlLPr1yfoitevBh0/YEr3hIsezCuDCsApInGKkiWAs9eitbDlrbyNohIWCn7FUAaYwoiBXPWegiWVXQoY9gDsFKIGHxC6I64GQFR5FzJjDPWedyFNucTKHjKezoBKelZiM1ocKyav8iA7QgL9w0P+kWkA//SHf6+Gi+5St1Dw3pRS0LUgKQAvxXpK18OEisLucRAuROOMBKcpvJLiN1aGkQRGOwE+W8bhG+xqZTIl6vBAZBcsgd7YVcPuxXGBbQMLCgDhEoOpj7HHS93tlpT+5ota0fotckaHo3+Lm+JXIdQKjKEUAELQNqqKAlOftccymQWAMVi6WAqRHTkvWjqlegBYjCmMpggbwUQVqF9FpjeW15hgACiwjMYSaRMmIwu9dNaNiDK1klwUI6QUD97rP4wsdoFifWRx1IJhoKQbuWAIPQVBU0q9m0o0QLDGjPcGB5tyRzoJplxloVPUQO6WMI1Sc00sbFEsXMS0kaKSdkqKlshyEfxcjNY2jHAf2OkOEm8M9ZsOcE8klnBlnyslL72psSzONsUtcBfCyJVid1yZHqA42SmEgqHxMF6rxE7JeO/38p90LwNtuivGLqlKrINAOiW7JRLI4sVhhCf+hoc1ofD6XUdjuvt4HcDzpY7RVmJ2ujAQS2drSOvvBxda1iOwNfcI1B0LjOyMSPwkoR9x6MKUUTHtSLR1J5HULOdnN+pgVS4rKD1Ljx8WNclafFNpUANDcgmRRqG83Qd2TbKIqu6eTH+9RZTSvco/9mM/hsRfCxYLI3abIaNiVhWlbimTwKeH68OCMqQzjiQFuZBgSiq7vCzwBEzvpjISeCTxcLE0RVIilsAs9DCikDSyM8LkgklM5ZXRFNgSaRNnFbb+ooQn1QbclKtYeBKn2iqeC9XCedMBhKczqiE2SnrGuwtgrmd6sRvBMAdeCrUBIJTaSK8GSkgj4VoXwRq5QiLUUYswfKsUIIaOHAuXkbATgfKWOs7sxpD4swSjL7Z0pisml1E6N9/eOc+OkxawycXuTnp9XOOdZPhECvyjTY+8CssO3JTCOwAvkUXNboobDkZxeSkyVo+pb5G8CArzBb5wL45/sgbYd15uHxJU8ErFBsNyMl0/BzAyOpYOqtZMPan6apHzqk0KJKaeHk8BBjuFUxaj2J4nANetLcOG1lQIOyMdyQkzx2jEckzXDxeyaEluuq8tYoA1jIiuSS2pBpFREUZrIRNMtBTSemH2Rxg2D5LRT8JM3/3ud3/4wx+WQhtE8/07lcBS6IcCbCT4G5VKqWAhAEpqHdkFGqtHCBgAKgrwAiNRbRh2Apm0+kgY2wxtmtZysQLLXqBR6opcLtNSoB1seivJHp6dUuUZ7UXryV4UxXKhVVUWyFIwwgNQEnotmz7TnyCv+S/Chc99p70bgZNqoK9OsE3D3AMRQhIK4VIq0T7RSIpAq0qiGkwI8BX66ocgTVUinBc5vFEspRpOystuGiZ83sFQBVAGI2FpRO5auhqqsoOMLEZn3q22OwgLb03SJV1JWRplEdtIAUtPEYKkgundSoGQsht5/XjeL1L73qLsjBPH1QPkr5j1NzNcfpXjd0+RYBPeM6pNimuiIzAf8Xh72vRFGLVJAJw9xciiMC6KroW7IxS5sOFXYYDOeSsJoypRec95jZRJNdzG2NXKJUwpvLjowpQCYFSHUfoAqDwiKkACDKMB3aaz+1YTORJR/qIdKl9GeP3jKRh++7d/21JKZBW8WUb8LWVKVCwSseDBqW0MLAQhfjAuU3ZgUzo8l3boBXLBx5nCAkAvsER0tcXJJbzFycuegJX6bqezkxSxJH6JLAKXvWE0FX75z/dQFCFcxnopqly8Gufq6IThwiYWnpiyR3jXGU3v9izGJDC9NpvWQlU9AQ+PdKYUgAVmUcOQAudNCS9FlbQvmrL7jKbCa7mz25owhscMDFYKIWghC0fOIlxUGMqzAgpkrJJG02c9BsvIe29KLm9QIdJBGgkwWMxjw68eGPY4IWuHiwUyF4WE5CqjZgkG0y4XAN2XcIeTok0feRjDGB14x8M/+eoByuhTic9HymaHt1yWRazRT/rxyOvSdaFMYQSautQ65TX6ICKEt5qxucKYHWZ4LaAFMzWqebeYLoSX4h3AoEKLcL5QqKB3p97QCUMnZSVKKUaAgtgFsxAWpN4zJXqtiKzs0ojtucFAV5nEArngGeX1yjJaFHm9QYwWCydwL7TmZWSvW6Mou4Iw4WrKzgJsVABkYFOYOCu4QMwUiYydACNhSWGPxBR5m8Gi4Hs99JBFGWWJGRVRklHgYKYwAklRvCz0Ak2lM1a2jFyieFXCXpSQXKaM8edNL2/7SG86kmHmEkvYSeCmdJbL+ZqBt3ryLVbjd3SVzxs/oy5UaExYuKyMDXJUOmCMYYQTtEZJAbgcKrkgXYYdaxcAzPpwkSopqVG4EUM81Q+mBoTxFwLAWEmBjQEowLyyKE8B15aev2rDSAcgwPgxRGIcfwAjY8iU7EPGrzwpiFzalBqsZXFZfGNRonrpr9FjgHcrIV0xIVa1drjE+vGrC0tEQdY7HhiXF8DjYpFNy86FgctotXt3fOxyo+mQdYoQ0gowSpSRRRlKxaakns7TgjlfD5h8PWlye4PKjQKj4voWyavRJyiB4q1OPWP0jioOu8S9aHhgcCJZ3ZYPufp0ZQlMecV++tOf1phAVWLQmEAf55QBr0IhkCqREbMRHj/RApeSGFtK0/apqIqUsXRNt+IwdKNYI0IAnPHHgJDduFhs9CyNkKJa0kpiZ7RKgTFIRG86jLwVD1+DdxddYAJgSsCsj1EsIy/dyIU8wMFdSPbKzmIczyzPlDjvyIWUJdcz2hYkquFNOy2UxVIUbB8JHdhGWytCNzISgVYPMmauKbzsooyOqNUo1oIQGw1pB60GAGmFK7sRGwlQkUZS3vhN0aLCAMyYVBgvuxYAsgcr3UV/NpRiDHASXILWfxmNAcIHM0ohCjm7Nl0KfUmkU2vCqEcwLi+O77N832Dq2wg6oxvkYxGjOyuFWBlbLro71V2Qxd10qcViZgRO4bKwrl7HVcZKhYFnrCRgbErCACAFi5UXyKjgovQisLWiAyA8n/yl4YCG8yIIKAaIqwB1SCYGRhoKF3ZGolVcOpdbt54q7wgAF7tYq+B10ycAF6NRD9j8hMznOpxGU12BwSuMbqWMalCMqurQaIqkYlLA1FO1wdotdpWQlh4tfOERguGp5cZokcRjCoOBt/CysKMi7CnGxUISLiQU9lws2BgpyigFAMUUMxI1m2qHomwW2VlIKWKDr6rlzX4fy7LwU9OVK0yE93B6U4CXCqOQXHlNlSHLCXs6oMG4EtM6NQ0zHoF6NLUmnUilmhotReEwd8GAjYCxmzru8BYKHpuTzEtxK1rqqCIRdZdKak2EmIrFSeiQjLymRVEApAvGxZ7OyFt2utTlZTQlY4iZncyoPDoLBSBxBpAT9wWbYvBglgWAHZjRl3+L4GfMYdygvh1pZWDQglEEYkDrVuJhtOxGbL7wy2IZTXuGqt8rxgLswvZ5SghmvWMzKgMnjGvLRYRLIaSMaIfH6X0AILyPP4GrlK6oEalqILD0SUR8j4gfd0mmXCwyAfjpl7Jw0ZXIq1suFjUxWlYfcChC/IxZbBgprIJPj6I8Q5q5qvqqt7/97QidHkmVSxD6/YVWnE5p21JYCJipLEbZjQogVge4ZQrJqF8AIVwAs4+WEcAUBhtAhBRGOuFqiRnxswczSmFqnIVSJZY3HcMd0K4z5m2RTUW1/bmyMIqtSMjxSEpgUlBxKbLyYmDkbXxpyY45ZcgIY+ZCuyxc8aQshDGZnTLBQK/ycnUqGOuuhYohC5gpWHstlr2oLiRLErMjRAEWSLZHotgJNiOXKF7CAj8Xe4DBgGHsCIvUdBgrTIoKYGThcqTROpaSMpKSGu9TseWiIMccmyhIJN4UShVaGQC3w9dsI7xYt6mrSndi4V37vocCkKv1RCvWvSM+bSA0Fet2V6FYK4AWjxACY2RRAMHmxyaYo8Vc4AC9lbVjC/oUghZedmzqtF8yoj2/v8OqAnXQu8AY9axErr69VJMFBdAYRrp30cNB95HHZx+WMN4ObNLLh1OhrT4ASx8UedWNXDiMQBkF+vfftKFoHX7oQx9il+IjH/nIRz/6UbAasATAtYeBtASMXPrk0iSjEEYKYWEv1rTAjPRgURnB0iGtEb1loVStSirASNcaheCBSRif1Md/SweGVgrMxolYLlEUWWAilJouizHhFYUUIKWRN6V8dJZgUlOegYM1xrzwpgsZcnaWhVCGVDwShZGMccJQnoUDDCmQmMKo1khngQlmbPHjsSBXxBlaWPYUlmiXsbydTzoqrsP7JOUSLrbUdJiWmiV8sUZipwIsO2NRWBG6QUaHGWcWbBQY48XxOIe2WDuEkS6K3n2GlIX4jOMrvSlCPA4PBR7M1Nfsd7zjHX6Q6vz7wbNOuVw995yuEnbPjbvsbnLhd5HpwpXnn1uUl0iEVoWipID0WIhlYVdY9bjyotBWJ3uJwIhXgpcFoVfGQtFhGHlxeje5fCiR5Xxq4Ou54ePo3WFUn6mPLewS4wWmY+FSkI8zmpcDoxzK0g9F51rSIfGJiRcsKl4fdlARvcmrLAtHFCrQG9SfGvpDMVkEWlB9gpWaQtiVoUiFmRoJWB1CcpEqVzZvRgCVaDywKW/rgMcUUuChezqm8GJ7GoAFEhhg0tQoPB6jWCF27qL5rC/pYKeya807xNXPgq2ksiS5NBvhFfdoikXS8EYiI0kXOB1VxjDTU8DQzngobld0OgVP08CjrdRNeQNnuTPkuueSOsC1lo+bSVd/tFscR6U7aVVbbS4rA4bEShqtfJslBRIu5IEpMMYEoPLCVBgXklaVQiRt8e81s0hNwhuzoCKiRk5RRjD4AKYUWYiC2SmMHR7hh/oCjweAxU10a1wK9v6o24MiSpF+7bDvLbpQjHj6dkSIQBldRiTWx4jBXWPZERVoqhGL7OkBk1RgI299YWMxhfTpQYgs7R1LX1MrPvJSq4cXTN54kLQIXOfjlnzo6D2rGsOSBalgm2cUb6UEg3lK5KCDebO8Jj62qMb7ojfs8iH0ucZj5PVViixyqcBTRYdkFK4rSCl4/X8DeLN8NwvjMQLw9ltugDYSs9qIYuj1U1V0EsCYEYxkZySmLRkdz2jZ84Y3El6bJByyFUjReCQn5dM7SIEBoERlJIx4YjPVO51SU1wEW8rJer2qUWEzDSnp5Xw1CDkJbk9DlkrK9Qp9aYz+m6uMxjCb3gnTuVQyZHiWjNLlqgsh7HeSmMtrDFyR0ylC8tK1b9pKat+ht25Oo0VgdFQY6ZBthMAlBSBcRnbjlrcUIdMBwgRzDrOXHZIXFckeZ1MWohKjQFkusrO8ShXb1gPnhRSuEWfeZQEWUheBWShg5T3s12IKcePo7guX65biJrogph4mKfyVA7Tuo4xuTa+VS+RG+zfwWzojZBh3DZtiMOCPmYIWxpS4v70sRkYbgdD6a43F5w/3vSepZoXogu7ixKkMSBi9Y5COSy+mb+PzHyb9KJ0VDsjjogcudBnrP6N4FajD8vUBB1JKDMF8vtKDv3sCr1WEvEJQyUjAhMBLx64Ai063Uj5q0uF99lE3Sx/54LWKqg1DRbbfEm3zIAmLbUtvUcKztBOyCDHlBdYLPUxsLIofmFE9otRQGcsukUA1ozJCEhbCRayGHhGyaJN3eyApO0tslcS4qZAlokPOm6tY+qYsyUgoLMOkGBlzPUPep+mStoxNHwmuYtTPyFth0Y55Rhgu492VHickQMvIYspr9eyCZe9AM9LlktR22J3wLGJ525piS2cMz4VWeHvEnquSqkQgAagAOiUBgKeHVAAYgDICXHyPJeW9gwEgGcUqw+gYAFCcfy7d0RHCrClT1To5HgjrQO/dEQLW7/h4aJwuX+PdWX8cphgfBbwLYulgErlxAuUFxiMRCxILqwafEliManBzhUikJPfODRVI1N8aUhRDF0KE4+kwC0QIIBG9lulVi5NR9jo1no4QcWu4OtxwpHy7MMXg1YwABaksvFrloAsBAxDIC2bJuPrFwp6hnhidwAB7VhQkSy+R7PDEpx4LB4xEn5hltFgKU0PvGr2kMgJoAdhIcLJUHja0LDJe3OfcKM/ITpRRiIzsMAG4iqWwwwDkpSNnJMKD8QYocHZVAdh+ihpg8BvF2hWjabTxCK+A2SkkOwUskYJRMWVExb4pZDUw5grGbgoWCWP2xozGMm76TAlcMcbkED3Vw1JIivFeA1gpGnPdSc5SXgtlnZXqcHZwu0uQvNZTYBaEjJAEp8bjrIZT1lWYKfu8GS/nGVZMFkicM5pml4I9PX6ELEpVZNttSmCqJ5jx1Pd0aQHoGZETel+WMgpH6LJ4Rxz+ngmfRNwIYLHyujjsXW+Evlr7ym1xPEOExSWCtErdfx8UIjd2+xTM693psmN2swi8cKKv9qInTEZ5vXRqUyHd3USrKnXWBTsLWgdebZAYcGIT3iPALrB9fHxV0aRICDiOghVKotaqskQS7OxVQxdIAMRqRleEhUs+PeiWBaesSunRsTQS4dGbKsGEi9IVO29rzeubWyH4kXvavUEe5rNO1zOkDJ0DIAFuraXGUBm8JcKPXMZT7nXmuBghGY3ElJimgCnMNIuRBQmkWCKpUVJiZYqVnVFSogYYUWoz0mHUDHOxPm57qQFioJRRrvQy0km1hWSP59kYMiM9tmKHjOeOHJ6rqIGnlNcU4J5dyxmjpWfJSI+z8JFkrDtI65PeMbB6pjCWy/KaOgYsvAhziZqwkzpaeaUwpogitkYu42KLMg1pLAXClIEDIBFCv/jOkP0q4fHlQUhSFgAKpNRkZ77F4RLrnHh33MdOu2+RtOwe9Rs9XhYY33k4/L7zsiCQAk1dCh+IXDHfajmNrptEUYGxcDl47JjheV1tbH7kqhiceCxyK+xCmbq5xnubdBXaDn3R1SMWRgsYCM6ySCcpDABdOhiVsNApprIfn/YwutgC+FjkEAYhTY8RqOK8bapnV5neRGnbqFxGAGuqAe0xmvbgcbFbKdK6yAIjnUTwRmCV2SFUupIIpqdUrUqSjnARfaqWeKTw8NZzZQiUV+XyitUIHbNpK9VBAZORkWQxbrkpCQyhGyGtg150B3x5Hr+7hY09TuA4lxpY8QDsYin6je2R5uk/Y2CoKjBGY5Kel4VSeRHUAh0sXe+BTSELH2xRd5KMrx3LOJeoUVkZ9iyDlbRxUVOqxzhlZTsSVW5nGYVYzB1LC8gCIKlclPWFqnSM9HLxxsZF71guaZgAhYBhjjNCGEYSmCJvYAokSysQIO8JuCQkV2C5tOMkCCEOsKmDrTDXwVdZB9tFc5kpwATMUhhRUZwfxo43C1pfp7t3bocpl6pcMYR48PO6MlwU4S6j++W1wmY94WXEz0XoijGF902JJoBddmC3DDMMI0VTjOkOuaYUZqfYZQfDwAJphDQq5lqVs4bnb8P7z8l5PR8iTbEkwkAV5wfGkYJZKfVp28J5UBRU3cBi2YWgrZ9uHRdhJNVnqkNgotbyUuRFi8E6ysVrahvgkVe9PuHtk7xgFpRQSB+OkGhS2UmJcF7ZHh8H5ErQPqmP+7OpkLwFNp5OnophkUhVLKoSKISldWDsqoBxmVYDhbS7cZaRF1VJjTMiAWMhAAS/cFM80sVvGoxxsRT2pnc95N0S5g6O9tn63Knu4C0U491+10sXLZ2SXsHGpnaNomsHQDi7lhl1yugQi2WhS8pLnmU5vLfXR6ADYxTFTicBCoxnJKbE1HiInrZvvdcIr9T0Nigv8Oop0E5xSR3SKNCJXYOKcVe9CD7vOKs9QEhY2IXTHWl6t9LIIgrY92XdalF+V06IcyiXW2nFpPBwdF98hnJzfd4RLruRBYl7ikGgwvyEyKhsls6V2weJB5vLKy+2Lj47pF4Uxk7xGqiWkQWtGvr4wmIxjS2UURYWDOcTbz7uZYKw9xhdaYz6USikmmAYASjKVZPSIdUna1PfJQpBS4DZvQu6FWIJRLGgEgIgVreyC2lUnFwKkAXM66MTXuFCuJTkI5goGBvQ62MKQMfvzVaDqFoQokjjVdE50BIZiVzYwBpbfSMBNnIB04GzXM7zm0GmqpqwQxpRjc26pQOP58p8asiSEq2Wy5KLUQHVOQAXPQDwKmRRTCF0IpAXhkRiulgAdtPLfxSWps90gFzzUp7JABQy5mCzUFjWUVNgFrrRwlIsGqPdN7YClK4NgDZNwSwvNovWugnniqrG6U0RYgsvkD57/NWJdgpOektaGQLnpTMm2Y1xwuRtLKR6jE6pqK6A+9Lr4Dz7MNLUqabAYNOyVwZGlPOcUUl4tEDovD7jGE1dFn8k7y6I9Uy4blp2HTxtFOEw6sHPZQRjtKSFoJUdwJSOwbpZB6n10tp2y86KXz8hMlUhKoF0/K48sBuHGQkpS8wZWWRRw9lgaA8VCoqUCpXJGMjbIQGXeGAikgWG6Fz1JWhNJVYEHkWrnhHM+qrDGxQPcsKiB4RSKwAMsxX3loNpgwWGzt5TWqAotKqHAThtvO38cR6MYrBZbg8QKjUQeONV++O64kRVRgyEVxdErqRO6QUatZMIJ5JWD8U0vDFCyHgiN7ZhtlZeIVEVFRKJAsaTsQIauU7iC4YE4ci5VnwYIZRIKPdEGQPMHrgUAZCPgT2XcZzjWdmziE1/FsXIErMWiKmlMFoTPHXU4kTS8mqWUT3PwKbLdSevZbQkNrnwG7koBB45wNpEBczOQpeULqQUi2J8KUJIbClFtU3pjU6s98JnBPfCcUVLd4AdJ/V4RBySLoW74Bj74Y507hSY2FoAlss573rKSFG8K+D8uws9H6jY/cBIriw+E7k+rjMRVb+1Iyq8monUAKXbCmBjweBOFW7kxeY+KgkDSyM7MHJsFSwdDNE12OMfhWDVcCzQfAIwitGPh+1Ar2vmEZHbFEYpBICYIhXlMx42z5YpQgsN3JpWikRc9FJQwKy+iqUDtlhSWAtGxVivxqKqRG2ywItlgVekKREI6ZORGqwRsS6EvYVoG0yrGZjQxRphCAtasmmLIymjpdByYweokPuIQYPwRrGExZglF8v4Kfd6uOaVsfD4gzG2nnPx0sdDqX48KSyTi/6zepwrpeyDNY0/AM5opyzwXhIjwNjo6icUMELv/FBM7axxeS1yJ8T+MqaXsTW0noyEko6nSiiEq2lbJpZlZZSo8qIFblrlxQ5GAWOs8k2LNbIoA0B5pvTqVIm7QNwFz4opxYPi1Dm9tUlRG5eT3+F3vxB6gLwpvEJ0cU759ZNgV4ZXiEOIwZ+CuSwsYeC9IF0HlZSCt0fNOnu8XGfpINUJUz3VjxaPcPVLJFwxouAx+ANueG2aEl4lAUAyunTeuy6pkR2GEca0Eb81fPwo3kRlBKl80BQjOlPxKmMRqVVl9SQB6NCHICnFuuQwrreQwoE1Iw0Y3Ysgtm7VIcTUiAGgPQOj+9Aolz5Rbac9TKJ8wrQoClOV7ZQOIOFtlU1bEeEEHqfy2NVDEc4Iz6IAUvYagWHRgmkWU83SjZKqzYLQa7OlHNWhexJGGLNi20V6u2sZK0klg8VWXpXIIryxFEYCD8kO2ZRCysybftkeN6oysgRovGPo2IYBIAXemQMMtmnIqmVcxnhYUowLyQJpEUrRmWF3PFCph6VYujV0hCh2tlI3Ft50iawSnQuVjctOJ1z3Iuk4jdKBARhNEbbU4QvJhdY0S0WyUIgoDKp1YChG59yzsk89XUslBUAoxLlyON0Cdng/NvaRxzOBlhEPZki06RbNanD5iovZLyIKhMGz9TEFcNisp1IRiuI1ZTQVIrWrRLpWvHRetbkp3Vn3txVQQISYFQbscQFTEipTiltGZ4SUyI0uO365rIMrie3xq0ESIFLWlrI+IcQrUcrWxQgDKQejUW6jcHa5JZbDAslt6s0Go0jfluCUDo8UeKyyZxumKQuAKTYlokXY2+mBx2kVlCQKDEaiFN1WgHEunBYRRsMUZSDEJjuhsMATtSGHZyGmBJjU5jithvoRZqnUijcWdUWf85fEgEol5aKHKSQqlpDG8qbQRTWm5EUuEIYxWXZT3rtr4Vx3u+lc0WZZ6js4PVhRwYwpd7YSsVPuOouuWSq+dQhGv7CPr6taYGmJAthl0k5VAAwFDCDCkEYuUmorn4UyIzy9dsDKDhahaSkKBGgaPj3MYJD4E+QOqgvsOjgwjo0phUUL9BaBHV4ZRn057b7u4nEIHVFvihSku8AIL7ym2F0rUU6yEGXgxCC1RMa+hIt1reKEAeYVBYChvmR3p5SBvMIo/oqZZ8gV5vWCECEwqFi6R8KJwoxc8MgxAMil2mB0uegAEsluHbCdzv2HcLuisdNZBGtP6bjcN3WoSSbp2SG5UuAluCo5f78UHka4OthNBVJU1rVvY1AxKgjMGtWn90UZkKT98EUAc183gP2MGRsLGKNEwtHCy8ioKsxK4lJkCy1ERxWpQiFgomonkmIZuSATiXjFoqIbCTYjewLJJWPgFEZRWcAYJSV0/BSpUyAv1sfP3WO+BwIILwt7Cp7sWTJuhCcrOzbeAKbPBNvd0jSw8e49FJeosxAz+sP6dAxMi7qPdzwdxjoUGKyysTlXLAEYC3SQ2IXYU8YKSKHD69eYCJkCI7Bj+eR/fMAxFdtOgVGM8todJyFLRno1VwxMVEvdOXc2ugJ9lCgvoxvUsWGhGPE4tHQHzyjKtNS8znaH06Xl6jLKyKgSiqpENVqQ/r3nLgJC3165ZV4QVX3yk5/sVUICIAXhdfw6gbWpHgoAThl9w+GTAWZ2YCUJNxXrGjJSlMHSqDAYXXNhMJW6dB17jUgXiU65TM8DJAxa0YhMoY0tB73ELAR1PVOAiUexDyPC5cbjsx99UUrXlRGVUVZRSq9PnOwAysJJt08VqjiKhaMoBl7RcslocT3tMauHeL8AZNc58ozY5FrZAK0mHkijpGUXQpFI5XLRAdTDnuu0egl+9iRmxcOwBMjIMq8QO2HKiF9TyjBlFCtQOi6CwZSx2HFSeI0kjGmxjbkaSwRWqQMUOOQIs5R9Rsr0ZcwyO2auhH4CnoocJyUj2FJTmhZ1B7MrO2Gv+FLoi7TLLHTrFj5C+pR7iKOI0Han5GIZpnSFV5KRUQpHosOgiwBGdl7bR+honXzSqWNhl85JDhDYuVIeLxgvhUjkMDjJRAr4+L0+ouC7C315ZhTr8lsEVXEJIRSBjhbCLiASzIoH9nXd3/T2e9ISMbqeSoU0+lou1hUTziWKzoXKlEuiLqBi4NXDTkEFUxcsQlwlFnZJwZQRQ72rVgouI4yRRbiMR2MyghJh3JZDGmJxhamDzkunwGsMRrxReqvQRyQu6SERKoKieT1Aqs8e6F+sFwRSLoHwceLRM6Skwom8AH78xiJQCksGz4WcEb8tYacAMyKpf6MOWVRIRytLylID4FePSsprihAJjFhGIS2lkYuFnTExJdNbipqaEYBFFFEk5gkLWqIjMOHE1PhI8PSubUrBRiiiKMCLzW5KQi6QK+MzewBecgcPxi7w7p2LcnfRFW8k4e9RjAucnVEIe3L6v+QezsAbshWLavZNKTM6EsRhZmFPWCgslDI2bWTJriR7bUpxbh179cCwODMOkrOHGQaei9Exo7MjF2XKGJ6ls2pKEZ7LYfZ53/F2trmcDVHuCLw2XWnMjkq1+TDC4v9gB4bXz3pID5P75YdEkkKqAQ8lXfGYhUjqtGikcFQqUUbviy/qLhGwdyqAFH1RF6U8ZaBFBSOw3jvGdHUSGVnCCGfBX1IY9XOJ1R2XNpXK/vhJDau5UR1Y6DIJRiqsb46ANakO8aoEkCA6JbKIYgHjUgEk/Cc+8Ql4DfvwwgjQD969NewEv4zp8Eos3JLBmIpVhj9c0wPxJwJyacD29+ELRmo8YOrpxaTjQctbmzXiuSmdKaSRCK8A5aEVqHdRXOyqMjUGw8x4xT0ycjWNhA5DFFM9wol0VoDC1dKppK0qED8BGCHkci1FysuxjGILGUn1GEf1jJY9ywAjz9IIcycJU+wz/Z46QAyvpZ2RIgUBJpFkMdZXU7qFbYMKZ2/1BNIZKVayKcWeOi1CwBZSxo0pwsHwU4xibZyMxbI4IbYPT0hjZ2znEAAMWyH1Eoad2HRRRhj33LdIvk4rr62HdPgVTMQSSC+Fi9APQMXK7kei7gKXdFFJx+VQsQBgoxfoe7H+mQoMinc3jfAKgBHF3tUWhY1RnQCyq1BJV+Hnd4kh3T6BV2nnk4cUGADYgWu8ywWPDad31uVlLCmdos7zp2DcGkbBZMRrudVXV2JUw4IXHb2CABh5hSid99BdP/OneDsUhNmF52raU1JvwnnlapWBpROuEngjZngASCmM/Rsd/097d5Al2W0ka3jydqCBBjpHA+1/adIe3of7RxrRkcUSyafux8pqH4AOc3NzBy5wMyKrSOrBC1EUEo2g7TaVTkcz9ov55VH6BBFqrwVCGJGK2jht0KEAB0okJcs0QVMc6ZQ5haIZbYWQFbVRR/05f0ZgOFmJ1BRKSv9C2kAzylWFT9CYfUYUEro5NfCR8brGN+3uCq1QfLlMFeOQOENuHCi90HRu5h2KHM3I7uiWNjBEOdsC7IEC6z9avZHiCOUc6Q/xuqUQgYhH5nm1/x0wUvhLT9mUYbKiU9AAkA6CUJqygPyT8OGsOrKzil9WhFPgMcegw2Dmu1W+DpH9eZYf0m61DyZW4WDT1IDzHKKua+88+zUNWUz3C61Nay2aBHZ0tSHdwr07XF4nDccrgAiakLq9mIiIurO6EtKM0o4rZeVar8vSkuFAhJiicOkI1MgKmbrF9JXDB66iEvTPG0dnPE771ZSWfCt025Wh6NcuOvNhh4r94mtLFqalQtCQOQiaUKClGm0TUIoS+ujVIFfTDNKG0uHjCHF0RZNJlG6qujbUtSqaakkXbft0bqnatssUTImojoAsV0hXLROzKAW7Y5SiCqcXn2aQpXQCOKI4pHCM2mB0gAzZVqSAEB8BwufoFtmUlUVECLndbuvKhbOUOVLgheY8lNebqFCIrHKbPgVfn9EK3eTUQkQXmnNXuf0IbVGyRT/7n5Fbh58IQUy+zQHy2ba0FGtp00zbEwQHw/Q8jOdzK4VCKZgiMAfJCEyKIxTnRugzIaalKjY14ovSUZdvNI3DRzDlSOTXpDEQ32nUpGlXw0p9EYM7cpDac8L7Ga/hTrLbhNkrSS36cPpwZ9j5xGzThOi4X6RougKYmnG/tOF9pLpRA8y9UBpi7FJgkmL4LoVrRcRUlTqpVaNyGjA6tKZ8pjR+uyEF4t1KhCwT6rWrSe2pK/f1myGQBD1B1aOrOR3zqSggZDRVg2NsGTi0dC9dLpry3VglFUCg5vIrTwqIAGTU4iBoQDqDEKEJVKj12HqfNqn5pKou3Dcym4vMJ8WpTw5Z6RyFpMCJ8yUyU+1BOAoFWg4CKbhROmulcFI1U0WawGSF2LOac2GAm0rxCjMFti6O3pDt1dpGS1AzCKJC8ZM1nhqP5d/4kLv0CEWlDslXImehz85S5owTsiWMMCf9CECJ88cBMlOdx2/5gfGNtsIWDYxpr0KMngUOvCfVWBUgm8+ZTomVhqPxs87A/JjAimLmlyKK6bEa4Y6NsafcHYGngE/ByEKcPSfB6uBeJUaJzFV3JDqTlqY9J7Yq+Ey6EUiqg9o5l9JxIkINx25wvEogSjBqOGT12cZSAzIlkB1yyu6dKPP6ALrg7mAO5mniMQQNUFAC7sBD6OjEiOLa6sHLBAeyrUNTRdtnW3nML4ZJaLFMayPnRYhkR4TUoNUlVADB1EoQjBBN81WC4ytmi4nAmUS9et+no4rSbYTdQUNuQxG0zteVv3BpDawlmfpXZvyqrIZ94FJOz6b96RhfIuXaVq66dlBLqsC1wa+0NjgZkBrffuETYbWkOiNlaRzbB8dnEFOyOa1dFIKJoAQCZXX5EOsVDbRLcI9ZyCqMmEQYB7lC/KaQOULz8RO/nQjjyM0gn8GP4O/+J6k1eSfrBP45FGK0vXJbqWn9t7f5oqYIZBGySkQIL0X0BuUyuaKeKWZXDq3HKuQB2XP7LwTHoSArPxrE8zIGipriR9sJgfA7WpSZW2PsG4MG+Dg7A9SAznz62tgdgTi3+BznUAhTUQtRwotAn8gOMw6djmUHG4HpTa5yjaaimBxWVsfYtNV14/A5zr/GXMP+sAihXVKISaRgRFBCiLIUJagZIVLac2o04XpmwAgt3+vlfM+iFRtVYdOEqPOtRz3ml2TPP8+gAA5CirbGzioGDCFr76h5I6C1JFG7port00edSdSxHVdIQyrSJ2hsC7x9+P2e3/op+IUWAiZ9iCethHR8hYgL0acMFxWKkHI9ADl6Rq4TnSPYGgj93mWmmNZrRaKMA0GWbkXIRvzSo0E41qUNJYyiLUqIpQBUGq2GEfQsKlfdnE2B+Zw3v2m94cwh8pl5kn/Fbv03yjdDCilhCVVcyshbRaFw/KaWz6lJztrmx5lO/AhCcFlsUmPiDKTPbHXp9plvk432Odp0OIkki8MkmnIapRR1nDiiHGfDWKgOjR6o547Dj+ygOlFOgip6YHw018p3LlGIbqk5V5QJVrclyKKzijWGTNMJl0uHiNdTLbl9fpeE5iWiDTfLZRHVFUGlTdNUrraRmXQEKYyPCeTbPaOubJ1y9KtLUw86sRaJogR1Xtvq+j6IT8rdFIWoi2yEnF/60lVDwIjKqq0tKjqWzDgQaZrIvN2B+BLh7Ml+fTrQhHq1hWbaOrVuVZRNy+XbPoRKE5dleb5n4fR4rF8PXjF6s2Bt40gxxbESshz/SUo+XDN05NogJtEeVVp1BM0bPYb2F1/69Pk2riOLwKgZzyIfJhGF8jWmc6tgELS2lB9oRJASzROCEFSFYy16a3tPpasKf1LU2BP/9puo6DjpxL/HCGRvZoRvgsu9+XX1zdBAjgXGvJVbu2iC9iFOZNN1CFnRgXfpRQNNmU2+RSAekHKeNZxP1pTPejTS+Ubm0Hoi0eR2xuA1/FDOkYNLQTOeqpfh6MEzFcJUAo1JwXIMSHUvqoWpIpB1ezEzpwLoHBq7TQ6/qYNKKtzNRSPYYVZICT+wu9GN0utHS/qpen0SgSBQFuWI9mO7+6IQZufTcu71rihxF8oHeSK+pqRGFt/VdgE5Guv8Wwgfh+zrnUoIQ1hM9xaPIdM0FRIQCbrBVA+n/0iQhhT2bcj6haTYTVLuJ9/y7KlcfBwhr2S+LA7cBsnysYVRKNFqfaUy/cc//uE7Vz0Y1bUpWteGpUrE1BVEOY5yZI1Km4oqxCdrXRClkU0Z3Icpe83QqkJHXVUQpOswKaUhOEbV8+0bzfZESAktSUHmmAq12OoGkjWVqBOcyNLHSdDI0PSWj8/4OW/+wO871f0+55vRX0u8+/mcWHScROyDReVbOF8iTiuFI5gyjtBK52xauabxIQT5pDhOAmePzPPydPBZJfAR2nm+aD3U1cjOAI50HInjc0xXQpZaaVZlXcntzOCjee6MrHurCrBadU6BEw3uQHangG6TqKkovCx+t8yUox8/vPmYDpsD5rJowC3WVa8VIaYlNOkcJ5+jJelAUx1CmG00mlKw5HyFqCWiMSllUaDmTmHqUy7TKrNM/XCE5J7HQUtz8r1TvXFathj1cFXJdf3gTH+qYpKTpZ5Efk0YpcQxyjUWVZhPue0jYi/kegtAZPmvuvmPKnmD+Eam6b///e/eWd5QQhTksqpoQ7pFWrPe+BAho4pGYKMFczTpjSbkcymaVfdgGjWgIhH7ooStV45yB04D1DClw7NqNVLjILdwPk4gZ71BEBhHS2iscvbBbnd09MAkRpCeCAeykTODE2yaE3OEN6fqb6Dp97M+80u5q3+TM/Cua7tMQygkspXCy8op+hl58l5r52+3ZXl8ELsNVMsOO2Nw1tmIbP85+Sl4Cp0ZfA/Cw+pxePodNrRAUqVrDMdpdIqAaM6bEW2HU4kSO0tPzfMiq09MUnKVDqHJpyML7u5AOG6ct4Bb00cbP/sRhGRpWIolk3V9fBIhqytHS1QWsAtLlpTRzgB9UeBYOB2NWSkjkqCGmalcZCWiaZhRlitLXTTNcBCawnGqGy7d1HKMkPOhqFcdR5oAR9+a1jF1PhAVQg4HQRmjh+oFUZb+lEdWD1/I1CKtASgdTtAigXTwfUq0BT74iHpyBO2mF1B1/dUp7wVZkdsjUkrTF+IrbWr9jci9SujwPST6ZFXUvL+4hel5qGgXWnVfxNomKUzn1LQki0lBtg98UUx141saRNRUVDNxEBS1QFMhOBoQkyxxCBwhQT5+a9miUotcyPhmi8Jv/432efq7yJ/T/zCibhtFYc7UPndlf9A+4yH2KhFTxp95XnYVIdwx6BGoBfRMezp7rGipeRCMjgMTs/aAyHyypBwS0R6fx9qPKF8IRCU6n6eh5w+ecRhBU44sUSNDrmHipuk7VE/q+UDhcOK4MrtNavUeISXkhDvGTntniQ6Qr08/xXVF35HDcU+dfA6kc+imYBL3IqgBUxuFoBk9cKiJwnWln6pzVCcL53db+b1QlO7SUaBDvzNvbPf0rBNL04x1nY8PSBS1iKQqIYgwkN/1Jgc0VbINUlIIqHtyyPqTLopGVtNMMYS6kauEqb8d4G2qPyKyRNsdu6Yuwt/+9jf96M/y7ILEaNbmVUUWx3oUMmak2jsnQJanwqEA15sUTQKdEjSCFGiqgqArHP2opQSyTvicGuajSeH0ePArFyJK05iIFX309XorybVdyLKMohTwlbOH4UJtlOkU5iRo+oas0P8XRzO1fVeHrNs3/K35pm/gnTI/TSNrfzh39fmiPU17vk12VDxNKQSdMTvv55DxyH3oCHl8Rg/Fa8Io5CR7sunERPAoIcy57aF3nDobozk8RKTrjWBMuQhEpMNFcThyOcD4OJi9ejp4OumVVD+OjXMusbY7qEoQKbFzZTfwnXz6nXP3gtWqbQHqTV18Re0SgyNYHX0Kvj2Q1QCDwO2eW2wnZRm97DTTtkjUks4pi1JuQ0xlSUcGunpu2fnB7l3oKlokRm0pw8dL0QI0AUTmGBWwVIheOch0tShKWlRPot4ycIaAxiHlFeglwqEDV7qfCRztitL0XYyadaaJphO9GSGY1NQiqFbtGYF2Yc/MiooS5MvF6dWrdH36TTNESG40TCHNa4a+cqZF9bkNnYJc/dSJcsjMVHumdDgtMI5pJURbXXxRDrC2OfEb23PgD23WsoW8ravpvTpMYLidMbV1bCD/jY+DaQM9JiFPjTkPzj2yqIfFAfIjmAI9JtNC3b2eaWfJwRBN3CGkXwqOabgrCnTAnFU9dGz4TlfnMyZyfK8APwvV6oTQl4LDZIlSA4oqRwEu0bogluNqoIXXsObxd4Rqxkoxa166hq3FqBYdmnxMIVN3lqazatWMFGXVgUY6dp4jl+Oiqc4xIrhHidQVXKtashA6lAl6YbUoTCHj2RwN6UOjeLohxxFrO+QDVZUvByhkavR6sjCVGF0EZaQn4vXWelJuj6gheytZLR+BFALlNpQIpm9baEK1S3M0repBCeYxWCREup4rZGRtbvsupBYm0zafrPT2kQNUDk0hKXbQ2PJ1KKs3lI3CYaL6UVcVjnQgTSKZfjKdIFeOY8pnLadHFW5kpFi+kSxBUiFCpov+oM7bEt6mWxR867UP2xa7kV906TdIpBQP1Nd8j+95+Ocvmgl5jnBGyrPo8JuS8lwkQjwaByAfzYNGkM7QEneMFXVcPVZ8ocSJ4CTuPNPhsw6D3HpGEzWV6yqRctg6Y8o5lqKYLjZZIqaYdEx9nIE4yW5NCkb6RgTKjEMQYvlWpE+bYCRulKsERGmI5k1TQOYrrSUhRZkQs13AqkvsSwyQmq4U8sFCXbkasGPAcoH4FEzhaP0VAXXPBES3GMfU8uhq1Aql2ZdD/fggSquHpIBKyEp6I+JDNI3ZAvqOCiTb5yxNo/noJZGyXCXaIDT7Bdkmtn5TTBwEUdtaJ+W2VEwVLWHT9tQW10k9iOpEFU7PwNKstCyNMVMPQDlVTFWXa8yBtI/Wy5BrrOajaYOjkBBlU6ZbiWgQUT2owtEef01y0MKRw4387PY/sC/4T7vX2nPspF3it9TAz8tuw9tq2+jSdu49KY+AIFCu0VMw9fg8ZVMOfAYMN6rbI3bwOntoSlOgz0mtE9iJ9WT1ILHz0yPzTAN1IlRRh5AhKMHRRjoSA5Xgu9s0VQ9sgQm6UDm6IkuBoTFnW5PeF35UU6CjWzgaBSEpRp1Ypi3a6nSonE6IR2sHgJj6JOh+USOFJpFDmRQmB03nOvF6QWP+Np9fwnoh+N+UqqsrZHY+sMjRHFSCNJ+U6synSkLk4O2LpWoOH66zyiNbj5EhI6AhmNa3Mnwl6WhF66oI1TcRfCG5+iPFcOgoAYmPMOX0t180rUIVTVoIPk2JKtrWfgIoJ4rDwTESMZXLUdqrygMTIqXuTYCIRrYQfFFTtHKJqAikw/itXVTP+mmv0OAIHCbKIKJGgoX4EYykxl90yGg/kPMbm783gW9vmYNhQ9rMtu5e+PanfUN2zBwAt6hD7kw6A6VI5+x4I5jiE3cMPAvlIBxMIqbOAANSPq+N54+WekBS/ByVrgdMahLdUlHnVv/OlcQ61APNCEB8XxdyWiAa05sQpFy+3hJpOXRySMEx2zRFIfzWiNO6jK6MJTiQ3gKmaF4QRlJCzqpuTZNVDtj14dczR2/K4cRUFwL3Yc2oemqqCKmCybcc97FN62pLp3O+v1kbCWxhoyasObaowqZw0kY9KSO5XiEej58znoG1AdEQGBBZDeVxdEBKN14NKmK2RxzVNd0PK74TQ1ZRIv5tWMqtlpREWRBSnZ70Me1UgnAl6KhuBPZIaMrSDDWHQxWj6vBCFsWkK83BbHOUYPUJrwqELKZV64qCWtowqnKPysEtEI5ZYiLwI/3xTTZHKMfIv6eQP4+1t7X31lWhN/C3T2lSsL1SOMazC48lYidzYDkbZQGNnqOdZ54jxAPyCDgEpSMwWRBPAeLJGj1leOdBVMjRlcIRgog6NggcJaol0fGj72e2U+d8unjpc6R77qJKSJSiK1E0oRogRYS+bqUgd9QRHFR4Ua85Ph0KDh4aHVO9GXPcYpx6FtUYBVU68460WqLeFxqgACFLAad1iRI3ejNqxtop2AeyfDpG/RAXlcvppgC1rTcEVwPflA5Cy/TVRwo/NVHNnM8yRq1I4EDxpBGqjE3RmYUJMTTdK2ABfHLK4+Qb5Sog147waRqrory1SdETAqcGKHjl0TQlpRnrlGVfGN86W1K7jG93SFVOe9TaNTRVHIVKwGtMIllqSliaKc0OBGZNrluIEqY6pMBaQoSmZHMKYXIGmjJTy7FpLdMmQJQWQoZTgJienX0+WGFqMqkj8TCbYs7h/5ntt/dpgRYyPudZ9NmfHFGPg0G284FvWUApPWiO8+AhltvRQvAIPH1TNH6CyLa9/TeiZR4EAqQnIssvKJ0idfGr1eiIuqVOlEQ/SnHgTpefhXJ3UJUulII7VXrHgCZEiCkq5HVmtIqObpradvXIwtVSVBYFJ02iq6euVVMwAslqgCPdEnTogw9BOwDH6UDqrQ2BeG0RN61zUkRM4Uorobr1tgkETeuq7SKuE+VIhVtIZATIWeHzYjrvXXsXRHoNcahoAsiw1fCO0ApyrzEFSrQFOtMHhNUuZYkU5Mafglas2TYJ4ZtylOOriyyLg9+bwhTHDtq+GpBrSZg1LKq6UQrja1WuRBy7IEuJHgYROI4RqJM61zCD1FhZOGdJjwlF4ORXC6gWihRSQEgPGDiy6qmhwTF1hdnjt5P8jo4QG63pzznaEwv3ZO0YvynErm7czvQI7KfNdzBsqT2UYuQz2yvaEQLmI3ASFPL4+BxFcTokEOIhlYtAU7rD77z5d6TleheozkpxS4lIEZKuKyF8iQjOsDeIg6GxOGiaYchK1ABNiQimUlxYzeA4dXLdPkwcoFeD480ICuG3ak0iq8hEXQopyG6oQqKm0nH0Q0ou3JTB9YwjsRJ1Ygrn+7yibV1pibJEu2EE0lHRVDpy55+m97IVUUM7l59XK1Aq+oZw6j6CFiE+d5CDkMOhqwanSna27ejVIyqkRWTpylM2ZeryERgnAnKOaGR7YRk6lkLBmvHrhAPUCYJEOw6v/5h2hLIl6IogELlyp4PrWKNZGhFRfGRqnrQp0Fp0JZeCEIcJGUXzORLpSORj0ueQhfAtwRZp1RR4yj8E45tafYaPsynnp7J22BbNIHbA9N4HUzizt56CrbbhnpFnJ2RLHSch1qMR8iyMHij+BMNxgBI9U2bqqTkD0oHM1CgEpEDcJ+6+X7iBQOmaQdCGD03VVTG+G0QBwRnzFvBbV0xT1RGMNQxxhjGJ8FWMwEGwRrI03RE6EsnSAaoOl4KmPeDudZ0YiVi1RGQ9a4O4FAaXhcPoqE4ZIpRgl52CXOJuZRtitBXGioqW5Z3Ar4QsoLr1f37fQbTnBBVWUr2+4NAytQtqGy1ScxrVWdsEsbZeqDg12i7wqQlZADJrU7RChI8GNEI0ANS95oi3kqrAlahPzkQk4lPQPxPClAJnfFIagGtSb0DT08fz5iJo2pL76WHaftka/ZCqK7lCRolCeuNAVE9T9fa6ukI1qURtIEg0ChlJETcyicZCfAQma7bpnIW+vNOSn316fZDckoHnQT6Psj2E2FV7btrZQ+4+bHs5Ho0nyIibUvDQe+74QAZ0ZuhwyHLwHRL6ERqlSxH13DsM+EBTgsiiHS0ghRbCkcIQfNwgxX9aeP1C1pSCn5rdEQQ3SD9K4Pv25E4BO12dNLgoDpBUHNWVULR1EdFMDbsUFHTCKHh7wltsN4KIHqTAOXQol0UQE6KcXI7VqZgU0BRHloW4QbKMohxRF4GUByQKPJ8LUGPrKYstrQJGK5Rv5f1XIKnQQvNu8/62+BZghUJ66tOdxfCF2kEpWmeKKtTWmGpFCbS2nhpQCgIaBaMpsCkahcg42hCyBAtD1lL8dSWaJnKGqTc+JkvQFM2UuP45Naw34pTbK44pkCwz1YD2RCkYgdZCqlrEOVKaiuK02MbTwcdHKg4Qc47pT2iWb98s3HbZjZz2pN3YFjVFZh4E6yHCIba9ZySX387zHWY/UD270o1AtmeEzEyTNYoaGbLH3XOn4ySQZQ6VAyNFRbQ61EwlEueTdbN2nin4l7bcL3e+FOlWQVaKaIlONdDoDqI93R2duuqIGouqTsSUJgK/A6/JFiXk2ppyihq7EQiqmKpSlEOWH9+K1EWwFoVkmeq/3tD0bHV07IYp/vZZFrKvpdaCIHq+/vXW0Gi2d7aQAuTkUBR1Lb1fIL7CWZK2hKgY4XUZWa9MB8q3C+rpmCaHVE9FZ6JGyBbGEYW0AMocy0OzFxkdiVaCyTGd6VmuNiAathxToPSboz0IZfr5GlMasyVEtnAV+W1lPkGGDyfCsSc6oUYKhw+0y3prLaXcPgRzuFDR6m4aGDm/0Nce2xlr5LD2xCa0Az3iou0MDtBu23YP9Ek6g0fgiZTFQcYxepqerGfHJCJILGoqkZ85DKX0ZBcy1ZVEI9BIisMqp7TjlEgIjiyHRGk31uFUtJDz44WITKSsjj1EdZrd2DidT0yfO4zWAqfjVtJEhlSXlHtanwguER33VA9KU7ZYHLVoAmWZ0oSTqrRQizKlzEdDZv0e2loglkZTLhpHUSAd6VbnIw+E9W+A+ouIimKeq5s6R4t1L62QnbIqICPqbQenqA+VFJAFV5tRNLahaPyecWvGNJXIEUXjsFJUJEiKpi3TsW2CSFHaVAoOgpHBpy8lmkJPp+cBaKb123TvICkqGhEkItg+soEQ+75c03pTjpE15ciqNKZEI5yIWtJ1yBRtaVXhQ9DmyIoDCTQdkt8Y4Scf22qbsH0bsp2BMKfF6HF4Uj1u285hkO1wUkAO8El9veA8MiAdUXjHaUxkgp00D7r01BRl+MgVJdUFo+PYGN1S5G6QqZY6Zo19QiELR+t8knLXkiLOHDNIo4PNSRYfzevAqa6caVVkkTWVSNm0/dGqXCXKrVtrpInj9knBb0VJSdzdR/PWo+AlyO/yWp0GaKoI4fRphiw1U0yOdxBZo/TD86pTUg1NKCCG1GhKK9DWU0ery75nUkQQ6vHIMuVzgMob9U1ZIevhi9JRopS2QxarKE0hY09LFnLvfiAOtZopZIpAUxTSnhoZENMCfaOmaa+lAxWtn9owldiWUZMIZ5Hr05RjJAgPVLEVySVuaidJicLbK2qmDF6okVQEYw7OnDEhJ+26gU2/zNiqt5x2YKtedM6YOfHtv5231Z7CCKY9aM7AnJ6sXE9BCnFIDzECH+hMpg/E9Hy9KeBSEKTAq8KvStM+d/C3EO8L96gSjpCrQZBUOvpErodCpghZRXvHydUGjt7kInC0Iaorv7olVWOtrjMJqSUpDEeiTyXuApyC3I4uX1SWTx4RMKUDdVvDvXdURBNqycpRQ24/vcLqqnS/iXcNpZu611bh/3Fmev4KtjR/r8cN5yjvrgKJEmpTSJPrAbufOoOnbiMgorVCV8i/dRGoRfXIilqnlZtSgwPbVqHUjEVbKo4HhtxUSyqaes0bMZkUDVdU1F6oC+HLynDwjaqrZc1kEdomOFAKjvSzI88DkCsEiamWEAPykZnpaeL5QYHG8cA8m4f4euVHgzwZrxKBUmLmGCs3EJKVbvwAvto/v780e96C7Run/TFuu9pbo0cAH62sW7wUJ8Hx4+MTZ+H4ckUbSxeKQIcDlOvIOSFuhCgfPwUhBDTKdEoRdaj4HTmOaUcRp0NuKlFIoqPe1aCpCjDNTilxWTiich14ZL47yMFXDgdZe646Mh9Ch89hVdcPBL9yNaaWq2ekTMF1c8cdbCUgpfjXaIVaF9wf/xlrPn00mhI1IEtIe9qoAYjOLeFcPUKa9hspmTXdq1oT2Bikbaso0TgIQFMdKNCa1RDFlFWXythNfVgMDnFRrznpnpkOlGgNohA05eBAU41RUIhvBCqBU6GmcLnAUjgqEskRqgd1gUIaI0uQTweOCTRqPhDO8unnkIpgaiFScBSFU25bEYDETTltAj6ccVRhNI0Iz+x15kzJCnFmW3LMLz9abzvTwpv+2qrvqA1ndrjDYBttuG2UizYfR8joudttuGdUFIjML9f4VnfPsSzROKZMrQQdhn48u12unFA0+qsF7JDTBOqQSawr51OKUN0iw01x+K3I2NG13l46CFu7E9gRJcXcMlEOglA05bQXQrmzSra6LikC0BjYH8lhSpHY3rao/vKBishqeSFYHYKrVy5NIWMcWRw0HF2du2TBJhjU27JuEURIgsKc/gaQGsj2VzLzNmkL7Ai8bzp8UkwI6CXq+x5HH0xWu4kA1CWDKBfee8dLN1BpFgcixbgUIYlH4rnVldBAuaYWaME4lsbaF6A2mmLaDptOCiIFGcGoFqc+OUJ2A86qy5F+dD8+bNNXbk3qKsLT4PFbi6mURuQZQsyfebQb9sFoE76zIdHazzZwT6GNbbc9o22mR+NsOJZCnpQnK52PI5ffUenM71lzKCiBI8WIxjgVmgiHrBIuodOikNFJ61Tgd3J6R5TrXOHzO2kIclWUUl03jiwpHyi6fTWmARzippRxarI3C7USCeq5mwiUhZaCqUWJnsU8H/zdcQ5ZoM75mG5Hl4WPr3kjEY6tY3RUR9Y5nwFTQGNCEfzu2b3WGClVzj74yiBtH2pU9RIRsCQmmZYR21QrMkXbETVsihVaXgqife5ShtNKlOh/cIiP/AgfKVloxGP2FgjXomhtaK9ERWtGCmtnhSLoHEicY7Rm22S0HXTaQQiTAtebUM30w8pUKI5C6Zvi8NM35ZimLKQriRAppunbB/0DRUvnCOGw+mwUhVBTmq/Ph/Izvoa2Pxybk+W3aR/YL/+E21i7DbLbLKadhCzLFC2yre6heI723GMqpBAaBY8AYuQ7JLIcDyVEmZBE5PimTC1SziHHVEgix8GAOH4Qb5kebon4NHFoKqQN08rJIlW6lGiyXA1TV0NLQB8CaNYMh4haVgd5Oj07IKt75G/61F6XVwlSyCr6yLZ9MO0dJFpjnW0inBYFJ5u4Ua7GdEXfRabp9zma4WDKQtCwEBG+cqLaqLH/4xOU7q1WSZCFtSSZ8hVAldAHSyEIa1MqI1QZInBZuqG2/dKZR9iv6HWApmJPxecONOn4uuTT3M4qpyUvKVGJlkS/Bkxl8WUpJ2RTgKbE9dAqIpu2ZhwEDbQofKFKCzE0Ke0djiiw1U2KAxfNKYomK44S/NYIQUhW86fGc9BzjMg4TRPUHpANjGD8eWw7850lb6Nwtlf2zdMpK9AYQchzkeW5eyJo+aJC/D1Bzw7ovnnKnT38ysElUuAYRZGlO6iJdPy6q8CmaCWaOg8dD6ed05UpF404RKjmTYGq64SaKdzZ1gxBUm6Q8w8hhdO6hMpClgLUDwK/NvqQIV1df5ok1OcmUfe0tn2kiI/TJaWGSY2TTsr2AUeWl5dfD8mi3OZgRsb0EpRuqk8jfVnnUxOqkZ3589coSbRgou2X/hDktHi+94Kd6hFSh9ChSyTQmvk+/kghSMrUi5Bvy9pBvtz4Kto15ciWomgPCUeuEb/E1tB6ZHlnwT1diVJUQSbVNsnlM8o67P1IoY2TohApZKMpjkKyqrXqqRVtpUJMlrHlr2JrUTRZgkvhZxBRZqpczgP8MpRlPvIvsS/n3Wvcwr+/SvvW1qF5XrI8i1KmBvfomSfF96Q8F9G2nSMEz/IRnJAekyMhsUKQFYKYdorkqut1YOykyUJwDPCll2WEqOj6ANWqOpGqO8M4TfkuUSkxiTNFZVVFxUoDcVoXx1QDOSuKieAmCilBykjB1McW5bSEYOHdUHyFpFdLM1L4LjsROGargHPcO6+23gyUmR2gwAjiUNCSREbh3G5yJqjC+jCWYJuARisB4mB7AcnXVu8Freugl1EbQRS/Mm41Zktq0yHeFNSQId6Ixn3jo7NmetjezQi6Mmqg3jSgQ8x2k6OoEKc+VeFoQ0pryW+ZEC3pXzop4kYNa6l3KBoppgccUgi2QmKC1CaYs11uihlfIcqmSZ205+hAGLVZ0wd+4bcvi8LIX9tppW/705KBot8khCM8e3y2i03Ec3SEnBPgjs000TxiIYgo89Ad7I6TKEs2H6HcSjgqokBHyPF2itCAyuUImSphqg2noovGp6CK0yKrEnzROjFKgdSbF0TXjZpL3s9ydwSBFGaFjJ3Y0lMwyqKAqWK9SfT1xV0I8Y7QGzApIwWlCepN4haCX5Sg6yaFiF+zADH1D9cDvj0ZSAqYlFGWNs4nItJy1BZG6s4AbYpRE9gWTNR/qlmXdgENv9z6thH+9N17VIiaRA7zzpK49xQ1iQRV6UAgo3X566Hm9GdtSgBxONozxq8rUw5xaqJMFrNyeySEkJl6AJRNi0LUxQmXxYEgPBrnMzYfUpZpjrFQ0/hGFseoH23LVUV7Gisq9E2rrlE0Zr4SN/jN3B8IbC3fbHj7wxkt/0buXLhHCbFLdlgWh73x4QyTcURZx8Mzkv7Ez5C/U4Q2PlmnoioxkR+lV7fEnczGjrHn7oerQoFyXYodVIe/zo0sGmWvMCFT4g6kUbrqhVwlVURdDaDrTRCIqSiclKmxJnGYKBFGDUdKshAhBI2RLVHPeii9KIS4N4ByOHxf00w5WrUuNPfXG8D/FlRpU+8Ejm8/otSqS1N1hsCAR00TqO2O2NPweYRiErQlCtzm6lVVU8uwxdKNKknx6yQtmkrxtKRLRDDqFYGjUFe9ivWBaT2yGJoR0ivswV5HpKhEOjdzU2pwqyiLVIeGQ5DVAwfBGLOK86lBqpKffin54SXCTUOUaz/tD32rZrYLXrrxtlpaPwSzcSJs+pM4NuFe6b1LN87fduX0IOJMRLqneW9yDysavEeWk+953ZwJ0kSrFs5SHB7HVRUhOEeKR+9A9jYBdmsQvDtEnRAOBQ5mWaIaoMbRQHWNfExONy4y0E93oyo+DdW5KnKR9QAnXm+imL0XlANCcHrfYZoyHC8XhJamnPdAHz6K6iERPbAuOz4p7yDvhL7K8X0m8kqCSNSSihApWkW2XXxqcs+vgXhIRjyjXyZJw/BC0QqHKYzTkmTx9Z1jGUT1igNpsyiQ4iumlT4xiuJApFttRY2QehDt10ZAzaibmmj8psqpDmQKmeIj0yclhMxaDkcUTYgvSsRCysKpVQ4aQTizuaYh+BD6TQdy6qdRdelFFSoLWAPLihyTPxzCkOuHf0ejPZSfYmiXrHq7Ydmm273bgdu39mX4yJB8Tk85BN9jNZqqwjci7EFXUS7jVyIR0xozHY0z5cBOKSbH2CcXocwZ63EbVXcmvUGqouLK0YzmquK4aKYOsz5Zd4e+2+qVZJosB9iNsCgi0vFTNrprEF0JucLtjM87bmtvSYIl6o2Ot4mpzk3RSCknlwLTuTHZFhJfD0BZ+mF8bzQKShxZ+XjU5fCJ9opS6ZE9L0USliSZip5wOEYcErajwq1WqMRaRKCgGJCCLI0aTXsTtUKl61InatFsJXIhonSMXqL6FCqLiHJrVQNMCoKRSRGNj2nKCNLRDKm6bV/kiio0cYnLUss0BJjhc4AlqvhQXlntmMXShNcPZlaWdNNEplkovLqFfsKxTbgXvs0R4heytz3o8QuZhiwLU0qPpr3tFBkdDGMHe8oc1rOrVoL5+ELUOGiJcNbVaDGRo3VoJebQdBpxVOdzqmLEB0r0g9ko5KC6IA5VtF4rmneRCbo+Quq6aMCOnyqsE04TQTlqxkpI5Ljdroab63MQZP+hIldVLXyyRIzeEhrDqQe1IN5cQtE0owoczXsKjZmqqGHNYOrtXGCKcrC1i5GEfC8qhbUl04hdFEGX9UFICE4BzeLRmKq+kQmphw8nyLFxUvo0pKIUzGgUvKdYUkTw16tO+sCplj5buVxkRp9Rfoqf6owP4ahSIdNMusaMVqElDaAJcXRVrlEDkKxF8aNxRCOkD+EY9dNm8rWKA+FPcHwge2Teh8jpx3m43ya/J//4823LnM9raouMt920dqyHAkfzIDjtv5G/XeV4WIyDFrPo9CcumlpOBOQOjykf2VHp2KxQx88U09m7p6Qqx2E4OuRQQGZ8yg5t4g6wqYaJ1AAfx4gs1yV1Zbo4yMoZe3N1YV0uTGrupizKLpFpn4DU9bFIes0jy6pDFXEQTKdfz8iJxG+BaHANKCSqkKlmTl3/kKlRX7vGa9c0R10TlkfOkhC8ICRDWjYOcstGbguAXl6YusSUxYQwcUgB1UXgtKFG+sCQNguiVunGGsAR5Stq5FPO4dtfUwvTQ7kEOflSTBmnV6GXvSwtkVUCbl2QNres+FXRf1MhTqPEnBDNLJSDwKlnzAjjQNQ1ZvAJTuoj+HP9s33Ybrwt3qa1sRz2FjUVtYGM0+MDmrbD23+5D+scNqCpp9/5MWW3MikWnr9pKXLxu2MlIps6VPHVgvONTp1bLRFnjZmm6SjGFNI/ZWRjakIILhFElIK7rXnpxmphikp32oF66JwDTWsSyCFVM0bXwQ2CeC3QSbC2S1SaIFvb3Tg60n28oImDzPceVKuXFz7QiAY86QqQhspR0hpkGuNBSkblC7UwPnMbtYjJkW4X6CLUn7ePXIapPNDC0PBxfNiDSDRFmCzCaev5vobAkUuWwdGQrZOU9fMVRYvZhhI0VVduzxLNFFhvRkyIh2fEWVGOrayl2kifz5FYD+kQNDXSzxmCQJmOtoGZfmaK8qeT8ybykffLm26Ehb6qY6XbnM9rFLJ7w201e9ucbTUavifuCeJs2xOJ5nFkCJ6m0K0W01jFpvzlQqQb5XaWlg5kTgKEOQ86wdEwHGLaeatP4F2lbsl2kOKXiCYXbl3UNEBHyJ0CYvro4JsUB8hXtFqcPlV4NUhp4VLgDJ/varhfCRq9ECKL4ndxiPee0h6CXz9Xrk2oPQSO22oh+pTbU+BoRonz00C9tpJTGTEM3zmx5WtLCCKhVnzAIX0e+/NRQjqaEGY9WSFQ66yQqK3xRqRTi4Uom7Zr7T6cTqXrRxVr1pVu4WgQsqQYnwJQiGOqdFJx+GitkTIdNO81zGhkMdMRQsbRRuKiaEaEmKaZrJA5N47PPrivf9aGkfhypb9ZIWPOW/Tnmdq1FjvH1J6YtjNzon3errbag/DolyLrtu2nh+6gIseUuxB+pXumDpvoLcKXVeKymnbUI0j36CN0yB25TnXlplDFmK4JB8Ed5BjRJOIkTtY9R6uKa8jpAKN1d9xKvkQp1TJOB78q7loifZbhS3dD1eUr5FbazBZCCqgfDoSgEhRM7aQPKKbwpkT4TKs0tff6AkmiL00yOS6nHZGjOTzFECT3YUQfGkVQVT04ISFjy1O4PlRCboXdfK1bFYTh6APernGA+O1asjhtpalOtKHus4TXJ5qWLasm5UoxxTQyBIgUiTh6btSqqJBuhWKiqaIfWRT004r4K4ppys4aHoMw7sYHeB+k1FsB/BorEfiovu7VW/I4b/ifc2ohv7exb6a8gabbB4/D1IPzxDuoQkWN9z7XSXy4qMQR+AwHwbNOKgQYmTMktZNzrbFp1fWTE5PPlMMx1q2R1VI6Rd2OXf7SjaU7txbLfMqgw0aoukPrJJO1BGTH2HIyCsiVWBaaWghJGfUjq8bgsmjCOUKuoRDfKJoah0ile6fATY0KdbNMtc2HtGRO4vCjrgZRtfna6oMPx3bAvTWlqQTxFnB7+f7tdskQIatlqZPi11+d0TRtJY0EKcuVQi0FHTPMeuArHcHITDFZKdH4HJ0gGHHaLw4FJepE9Kg/72k0nEJGBiGCk1o+Dr6oKUeUozqHQTaNHy0cQbqxUFMhxr8t2mf85uT/Fs7nrB8IsVe31TnkXoLt6hHbjUI9Jpw3Zk8KyDFm+XLbdlntaqPoCG/VR1t1yO2vjTrpOiSyKnqorkPFMSXCwp09oL+XDIm29BRoYvpwwKnPRPLdLNFGBL2VxYH7GNKtcR18UBAK907pM5FELwhMhoPgbfB0d5YpF6HPCqKmvWtcHGupYc34YOE3qknBiXSzVFTFtFwOwyei3Plipg/lZcYwlc9U8rrFxsOGeAFZMLI3iMJy9WfULpo3jt9kY+JQY0J8slXhiG4LFDorfjaODuPjq7vtkKjX9kIUznSi3GnxeZb4OAwZp295RlOJmEy0hnXFh8hF1gwcyA83fYRPFE0/iQCP0IeJpi8aQYTCRCKGGKNtvJEKfQj/8k94oen/Evty3nYmp/W18IXakKbtTKdrm7HcEoe/OROE97w4HTyyzmFPp9F06Tj1MOSuWEv93EVYh+MQdP5dFmepcyXkUhg7ukCJpkvnp1MWcXewHmpMinsEdIa7QQTdEXceThbBf42QIAfNK0y6TkyJ66dr63eyphpLloPjXaaukS8XyBTyRiPOcX0gFGi6hgwihQORoi6CKsRVFCKFwyCi5wubMJRJ0HExU60Y2xrUfm1MDiI5k66Ybjj4or2b3H+EU+B52e/7na3B1JxuhJgpJkeIsrEpAosMaV/0Zg2FkHWuhBAaBThCTwJuiixXOWPKsoQgclklKECEqpK+lCnwZbGcBI2KQowLhWx6ch4LR85MM9MPykt805z4/DlvhC853WLv/bFSe57BbSC/h8W596GsRBo9WXycEhsD+fB0jHHK6hlBHDAhU3iHBPhNQ3OQ0jTyu3U6lC4XwjFNBx/NtI82/M488S2K44Vi7IaWTqcPO/TdzS4mTg1Ip++vwoi6oUayECNmCphCNF2Z0hGEkLXhviBA+p/BqkVcUYiLQ0dWH23cbkw6sqRvCS4jpk4I0pduSoHvjfG66SZMjgAq3RYQG9Kae3FSrL8USViAPnKk0KWWlGk4cX+jiVQl27UqGq2HqYvP0KTDWa82/ShhZJjScTIIsqiukE1V9+4zJdiLGYEJScRBECJeuZZjpPzUPC+dHCVK5ARKGZODZsQxtqgIhWRVwli3jZuOlkPtps2P/2vR0b6Ms83/5oq2D3PaZGSOx7TtTcdjtb1G5tDyWbmYcRrhVUwkcD1ELrcSRkxnqRNVAwgQhyGOqePBZ3x8JxBTJ0YlgBy98d2OTmYNSFknHARjB/LRO5fLyYfLouPLh1vWB39VKufXKSX6WIDm/pvWcE26sEL1QJY+mj+/dutddmRSPu/w4+DrlgImBwhBk4vpCxM1XdW816KoTkwtWcWyjO3DWZFlEEKikoNN12uPkEp8bD4J3/Eqjw+XrzMdA410GcG6kVvflGvLlAi8v32DKSRFLikiNHGk49RSF9sUAbm+OaasHurQmJR0xtczUBVZ6uJz4NIR+GRN+ZyqC6nLRI0xEZ5qpwHgplL4SgD5UpIylitLCkJWriiQ1f9Cpql90M8/I3PwP0dv5tfwLbOFzPm1dbUb955IubPsNoM0cpAzD93zsuF3CCEQhyOktJEPyYGYZuWGE+Q4aU4vx3mWwu8YmzrGcIdQLlC0c+jMqOu9oBl3Hg0ZUm/KYTJI+6CEH6s+d3RNIojiuOqMQ6QUo1o4UpTr7SDRvZCiGaCQWil4X6iuDYmy+Mhq6VyfdahVUwQm1/sOrq5/FZ5aOwP0eaUqogp100W7IxREhYict7JAokpydMMhgW1ao96vcoBCcF1qUWINoZkiUKyPVmhaf7KQbUevW6vq2Yi2R42UidRuhLam50f51fSzI9Uy6qFmkPlE0Ly2q2i0BBU5aDiUGb9VU6AvSwgHyKTAGVrOfOLl1owsfAsEssDI8KLwnDck0Pik/upQA78a/loBu7ENmfN5iYXsdht+cj4M+cM9G8vvAXlG7b/95DhOyfawTN03z52JFkpngpjOifPplDpCVY9JXCJCp9SYT4o5V+H1EMjvHJ7j+PwIVK7l0JQ+fU4cuJOsT3eeoGY41Crd1N2kzJcyRxa+NiDIHBdEOYJolkNTFVNqHARXydjrjFp1iUAqV5NeQ2ikKAsBI+BARC2EppEppGFSQq3ofHewoUrWmTCSeiFAaahGisp4g/DDczCl6Ky/nW0l0jGZbpj+REM0BClk1BkyHQTjQz9brxYdhEposn7ilIKsLtNem6WQqdEKt63bWVlCKqrFkY5TV2t4IbQ49RCtcX1SmB8NEoeTmX6455+mlOM0mrJCN3nKc9K5OSFfZmxDfuNyti2c7+xJexvHAx1zjnOVlCgnvzEOAmeGxp6Hdp5jvhHN4dE8x1TI6ao6kM9ctG4ZfQS4c47c1EHF5+ME1huEs5+yOC4pgptIRBaCTzcJGjvMysGNEnvXCAVK7/56Vbkvtee3RWhC9H2VI9t7Vod0aHK0Icqnw1fUJxJ//5BC1SkQ7NMJZm8i0TZEUVHprAtux85vpPfbZaIFMPgKk2ibah3CUsyHH5Xn8yeyT1+KmeKoZ9QZAkH8dkeUk280LRpZA3SQNcD0BrEkTMp8Tj3QZzhGbcAtz655+6DZdyEb2rc/UaaEcpkpHfyqA0NsiIZP7ecHgpQ6P/lPt6IQJkWUTTDOxqJGSP6YgQnehGjElxLyY436/70Nt2nfyXrTbH++uVFvUp3+nm+jKuOkg0PKQSq0NioauZFCJ7xEB485eE4Fx8GTK8vhcZDSiUlc1Hk2xRQyddRN8WkiMLhcCMtB5jvJmAohlCJ9uK7kYtJhHAimP9eXwu8wa5JfA13tmoQzJZDdNe8R/KoQUUXU6Ka4XGg4RiGFmKmLTxmI5k4ZSVFgft+ilkIaA7ZGUjQh540LVc/EKJwP53Q5jXyISnZQJjnSNSeaA/TubOX4LRWNJp8+WuVNp18rpkJGzeSn6fVBillnuQR7ctqg40UDEU0QYrWYUozEhTTGEDCNlBOhw5HSXtShlNqTLsrqpDGRcLScOMYQzFqtUNHqhgxfIoThRAsPvPH84X9mp7V8Hn9vz1N4SwwHjsB542wb9wjGuUM9NaHR6PCzpdzgR/D8cHVsHCRRjucevxNrxCyRH6cpsova94mVcPaiGYFsjpBpF5hDBJKga89X2t30DUNFrwMhuXC9uQJ81qtBabhV4wgZKbQJblMXHNjPZp+MfNJBo987yxgNwtDqigLZ7i+/PcF09ewSg3ghGnWoClyV89oWk+ndxgExC6Dbhpq2YKJw5eXLamdbPFxhX8GQ0SxVitp1BpQlRYiZKpQ+Zq8GK9GZUSKDV1djHNZ6RohTGzRFpTzy5/dbTHvI6sLROMBkjUKmcGtMn2C1ROWmCcQcgTMEh4/WPvDLilAh/FoqWnpqRvxScFRpijnCl3RaoMW2us/Od1a9zdleTS1kuU2N9nZgzi0CuRM9NVEGXNbtD/zsyHWW9txl0bn1mzpyOLpycbpKOJ0xF6Faomh1LrQllGhEg7tERkWRu+SmXhZkneQUOtL4TKIUuIrui/OfglBXVYclwsmqa+ozR4uVoop0lx3OJBLZ31sW0gARBFmcNoQOGmvJXpFeu/mnbWl4tGrOtJeWND6qECMNEVLYSoTaOKAFe5sYmTJPb+f92ttdbovB0ZN2ESqKTASIY9STKj0GjlC4ELAm+UKMSPvCsTWab809nirqASiEqZapRAo6oSwLIh1oyjBNGd9aktKqabXkshozIhghykUw0oSQitmICR9nghx4I2YV0xz5SzptS0vbbvyulVK4RcoNaUvb1XzRz1Ug7JF5DT3QdSX6uaUn6ZdhBBLOmOcu5kRxHKG7bmodp44HmkOYgtIUwjnIRiH9c1iIaVmmDmpn2O3o0BpFZRlVx3H+VfRxgz7H/RXVZyeWrCkRTg27gxxV/HIH7goTJwLpvSPqz8fREOjg0JROXAm+URv4or7K8SF9uBktp4W8vtqZqCTN2Kc4TZPQn/JWonA1vGLrHqI8aVn9NR80OmhGZqo2DoeUkQ/htAuYRCAaIgI3SoQgVAViylpVU3yJ+AzT1DuFY4qZGh0ca9EhKQ7OaevjN3z4fCk0l8JnRKQkZZwTTaLOKwdhI3DgKeSIqtLCH+45SYyfQrRS4KaFOF/YWum/XeB3toLC9jkdZCAz5YtmgeFjrjS8Kqslaylleaw5jcu9HU/Z3ekkdPkdkhzKok4jZceYCAcTQkHIYXN1Oe6ag1rIVBQzAo5LhCadshMlHZN1xZBx/IAXLcWotK5aGgdSortQRR3SMaXDbwnuuCqaoeldI11Wnw/omyK7Vka5aGQx1YUY5ZbFISXEcKzFEhD0KQRU9/w2R5p9EVMGjwOxETnqSWjLJGOWgsNpv7zqxmm1OpOCrEwjnAO3ToWII9SE3LajtcUHcjQqBVkIv74b5TK4row6NwqZ6koVDvPmFvIuJyiqENk2qBQ4ncppTIp96cHwLVNdytrgV7EG4BT4cITZaes5OkJxiONQKxQzH0EowUbRQl9vtECra5mt7l7471pvu/SW3sbaUiZUdIWaGke7K4YbWXi0HvpnwTuXj9Dx7iA5LR1pU8/dSJbgI3/0HVFXF+ISddGcPT/1keWWgpaDxkHIcTjpyFKiq1Gi0xhfJ5gM00hHbk0agQjIwLUnkZF1+HEyU7Szm89HEz7c//lLlhukc2RSaHZJ226ZN1T/WWjMSgCFWOR1QgTnvCb8gwRHB1DvNk1bGOnuhrHatQhHs4zwFpkvUcjIaDKJXgFq1017rWJ2Fv0sG7+VwLXIx5ROlqVmJAI8+/E8bxzlIJYHMdVMCB18uOVEUAjIb0RjGkucMgWjKNOANoRwTPUgkUFMMZ++DsiBDM9HgDBRPhAzBwJPysiKvjk48P+137UD7bBx9mzw64Vy+8luz+8qPR1I/MZzgZ4zdjNvX9Z+MKsu5LxJcaKEnKJa4mfePt3/Tkjntq88CIoa6yGHAsFonVI+hwjcyWfVBXKARMpS4hYk64XVz2A0/A55ZD0jiJbeZScoKuT94tOGO9XPdcq9QNFEmW7933H8vUSN4SADawxZLVNSxIE45zc19qJ2FY4qrAM+4zPJVoimkpUrk0OCaF0C0YhwgMjR+rRGBMjc+a49QXw45rZDCNiOSBeC0ASySuSTMm1f0GoeSJDP0RumJVizDpHhmAgtgY8DVwK/qBEiRLlCjaWLRsgxUhO6oxFqQ66Q8eaXhRBnm8yJFr6Ur+S0tLPyx/7Y0t52e5tG0h7OXjU+CvlnTI9j/jhCt78ph2B3pvTlNjXqxxkji3mDTtFqCTljzmEnzcET7eCVFU48Z2vkBHbX+JoBMrXS77aaerMYKUTo7pSuHJqQURZaN5rvDUBTG7KM+JYjiuP9omd/vlQ5ItpAFuJXxW2qLjLxvsGRlaUcPgVTykwicSZX9PyjblRFFeYY1WCiRlMmx3qM+Diq0tUlB8KvGx2kXtVC+ms9PnnqW2GCHLlMiCmkv/RlmaIpFA4pKweuHLIqpCptpEOQgxyIKcXSjPRFrcI7sYZ1W4l0+BCJlLU3ZY6QMUP4cP/LP8MVilwDxqZGtlx+yXM2Hee/qP+wk7cF3uv4Tuim/Zr/lm6anZP+8bMTIn3jDgZCskL5c9p/YwbfM01HotDdVafLuFAEfI6zVIlAY8fP2UZgppW4FZISLWuhpivU2e7k+zzhe5nPHV0uuTUpF6FT7TJ6NaDRcReMehO1M30u66NcbwN4Il0ZuXqmRt9f8Okq7cbRcX2shcMod4uN2khHrkLEMdl5m9DSWWFtkWsBftHt38DYA6PSe671y6p1ChzTFBSG8IFyK2xhEKa5HFFNICDjlFK7FOAtQ2OYCByg7ZBVoopMSszaKNfI4OXyMdVFZj4ZWjmELKka4KwNTIlwFXPiIJhmOKafrejwdTU+Jx8zB/nBXsObwqT+hM69hO+0d9Na8hb+nazvh2hGaNf4HKBnzTFCbk7TOJEbIZzPPsSzmw4pU4fn4Z4hcWPWSa76NCMjdJCGk+IzoUmZ8mPS4TiTEeApd5xc+94RvVCc83AKUkThWl2uKAVXm8gIwC6O+86R4uXFwRRyQfrsc7r8+LfVOH6/470jitaflAE53UTXU5YqhSiL6oQgJ99UFS+Eap0XkEaNTGYBLyBgrxvSTH+sW2prMnzW7kivhkQmRSWGyUeTyxHCJ2WcJfLknY8wTTkWQJZCTATvDppCkKRoqotGH5MDzzdNyiPhUEuKr4HeiRAbAak9ahRMjXBR4gRNVYTXCQfIaiN/I3GJRaUjmy43JHLiKS+9Egs1/RqjRf2nFvLN/Un/rtJTUDRno8fBmuLfNnKtdlbH/LX+nRAiosYplwXs5HecOgyRhaac00njzzBL5zilUrxK0BC6LI63S+HmI1QC/nbFZAGlSCzqjrvgEOfcWAlZfIJdBz5NoJHh0HdxEIQUNUL0Q5ODSd/ryRcdo5S//vWvELUQbAvZmIqK6uq8CEoDpastMeo1DXd1e32q3RZoRZTpQJSjvDHRdke9w3iMXwq+aK1wmJQWL8SEjL0syjKi4cRsAcqVC8RXZOvnQ4oWotDOWpQlALP2aMsHypLeKj5YB+QnmxNBRQ5xzsj1HKGQqE2LPLz2aGbpVP0D+2r/bO1WNec/vsK20cg+i/eMFnpYr1dGvue1rGhSCvXcm47DGc7H3GPl3GreDphOmmNwnwSgaeekE969IyU0q4eOR5cU2aFCqApHA6RKAa4TSI2tbTSduGLMFUgKgkbTVy0IM03cG4BZAhOlXDmtaoaIl4NEPiPeH/ChtV4fGvx1ajQNIMAlTvn8glox6DZCT+2mUZRKnJyYrao+JFIUNeUb8SNEFrWY9IX4lBUtBZNvAfCy8DnMstsFiBSItw/Ntr4QECIqdHI+vgyKSleiRCWUtlMIEK/kdHRVOpDhbAkhJVJrShO/qfEp+DqjpaffGI2mUOREQvhqwRfKb7y7KuvHHVtR/fP/uxeixL3/yu0R1IlonJox7ZnuydZhWZ6489YZjg/34EKWWEp3pMQerlwgWodcrfBG1xKiRHjH1ShKJJCjbkyIQqSM/ApF6G67/JiON0dROmic2jOS0hIEQV3vFKNcoFD3iwOXaI1+YCsHkatQtErD1fJ+IYXv90r//Oc/q44J9CbRia9jHB9rVGGkGAUjzfNL3BZgAjVqjkNRefn+kqG2sL38qCO3d2gIpm2fEBqfAlyjSfFZKRXueUhHNqJBmCmCVfFJwesHDhEiAlRIlim8LWgxcE6JddXuY8ribyqRX2mjFItFk86QpRuFTMNNWdMETdsKNA4TLUW31cJBtvweYYnG2+JAKEh/C9UPzo9rVlTzc/7f10LqfjTtmw1s997028PnGJ4fbD0pTnZnQdYqKSlG4sx5Nl0uBxM/ZNOeO1yiYwBPwcXjpwBhDmG5dOjXXtWJBFa382+UFRNB7jT9WOV3jJV226mZbjmmco2QcD5BvnUBqcnC4RBXZW8KCJy1HHzlnGfpmjRiGr1uEITqTUgKH9Po7eM1hMYo0M9R+vW7ZyQ9CXCocBgJrzeO5vTBKZkvU9SIz6qHoImWVHl4yxbimJYrhbMuORqF1G7KpNDaqUq0bDg1pY17IQIhbS6dZ6XngVGGV9cUvp7TB3r7UIZXdx0qqkQK/KJJpcmvGQ4DMul1GyKrpQlBTA/1WxahyO1/i/vDYK33O6v+Yytpf+4xnZXj5Id7jj3Wpp6ahwK8OUKQTimfuOeI2Xm+FcKrTlYILamyQiJ0tIjQ5HM6MwrJhZgOWQ8Oaj9oETCNQkyVftASr1W1/PIY7qoS7AqoAgdC1liHX5b+pSOkTDBOtfgcBI5Rb6buCIesTyR8L5R//etfHJoqcnw6oUbK56DR+nU10AukJdDMkHPOJbcwlWjpCS9EDjwtbCErtFQIMmvr4RzMCngRqqcnIhXAjMOBYCqUMlk+i4zA72EgACXWjBBEISl8jnIKMSlCpoyvPVka4EBKR1CassRK0PFKnlS7HM3IiBhlpSAx8gSLHurHZyVOG4Up0QjJ3zSdk/OEjBC1EB7sVMwkssD/Hf/tDtg0O/mxeceXMsROvj249hZ/ypCmExF6y0qzIxrZszOdGscZUJezXKe0KpWQEm06UpzVbjJm5yGFzjNmXaUDbIHAriRnB5sOg+AkxZdI0KiQ6S5OIdfWdRCqMaN3SkuTgqxD38WU4KcfQQm4XDRNiuqtP+M3xSQiq35wlEuWg3CyxLqrtJhKUB20nnLgPmjdTUNItMUcfH6KEr28IHHUS8QYH87vMx6k1RKRqPpkiRBMJxFTUX46vV8QgATh0ptaUQuxOo4QnH5q7QsfXpMtFpIywTRz4PQb8cepaDgmNQbUZw6fScFJ0PRWe+Kvlw6arCo2Fo2f/wONrfce/7PNp0xz+xNiG9lq8cPReiimIY1jio5cKMTZMO2ZdqiWMp16aJpvzJAdGOmO60Yn0PcMI04EUU766i739oFKyNoBM93x1rx3gVDX2ajunV57qrjIbh+C0ZSpbpnIOMzLBYLGT7/2asDFhMsyEpGoEMdUiC9RJwwiaqXIaumtWolLIfj6hMlL0TIECGmCViAh/yEPin3HM9Vrm8XHMUXm2x1Tvhrq1X0Lo6xKI4RvRNYunyUF4Ycj4BsZqT0w0ZDIyqHFLFe0fqyFYxrzlHnEU2iqrlbzi9bJojaR3/ZJZNFy+OOHqHhIHzayDjFN2d2tKa5RdPaRfR5z0ZCmi/5bJ/E32u8VeUv/t1P6LWfjUv5Tpd/WlezEN/Xo23YN1MycTgXmyAvl4DPppgyfbRoY3l0YMkc6cVmQqjgb/WR9xF6fDlwBskQ6tBzkciH17/ADXUDnkLnPCPFNcfjVdVw5CHUlhOA+9grwYafS9YOJ5k+pjujzSw9IzSBg1ky+EhB+Dr9XiUROzdSnWt3rPijR1PkaptnrhdTrnURLVWNanDLpwuUf6vOD3RRIgtwsgqzKtwA0JsQgyLVuWqIpXBYnwUKmHDiLadq2cmIK8TN+izfFXwiTlB4miGC6jY4vhVMtOnxdZU0RUjAyTGNZyCzkDYTj0Annq8unOT4OhK3EEM5tcW7kT+5rWIeN/02t7hGoclvlIGvAU+CPnx8hcsyQ0Xp2e4KL7kTF9DRvDtAt8Kw5UoyObkfOgcR0MynA8418CB2EPhdwnBPGEfWz3y9ANLnrnRQEx4jD6rBxIEKhlL3+ONJ9b8Lka09LfCHNSGQa5gup6FUQ2FQKEEK2t4TXDTIQEjMckw6EMn1FWyaa6r2hfvlFb8lqcKRJYEhnG55PNITarGqjNe15tFPSORJFZSGwpj0nuU0TAeqS4TNkiFCGSQRSPwhNRUcDqiixUHjL1gwFIR0aZeXgsFPv46PHfCJSRO9C9Bk1CpSR6xONg1+Ik2DNG00lNi16Cj/vL6BcthSEbHxT0RHC/3f8vAPtW/vJ3wa+OZ8J9rbcHISlTKfn1dTTPM/j45rd/hLx0ZwfauWa+h1tHwGcQJ9BkF1XhC4hMsTU6Sqrd4EpvtPFoWBEc7eBShOvk3prrC5mBxjIOvb5FAh2LCHaQOasYQ6+9x0QWRWvPzcUmAIknIhuU+OTMuLo0L8ORtaLCVOitadg6pdWcDSJpzo5u9BGkMOQj81XGAPVOo18IIe19epxWq0ygRbAhBKpV6H65gixWjflCBVFZpFVWTk+3IhWoYd4dieOlBLDJ1tighbMgUwhnEj4RkwcqwjR7a0D37Su9FD18Pxy6yeCQuWiiQJtUTvMX0iUQfDTR07nD4yk/kDWnz9le2Kj2JbJ8bz03/4XbTntKr9jw4FkqTVC4jemE7hnJARfCA5xEXpkcHX5ibvMfuB3TfTWPZKC4JjFaZQID+S7qE4Iy4nvcKoFJMhKkd6qpTNTHMxoBGvYyERJCRnLwuQnwjH1p13eGqLeDN59/UGbcjg10BqrHhOHuHTlLFnP/hTMOwQHbkNICRExtQlkkY9itemeBT2baA2oEFEIR75uTGeyWkw4RZUyHFmtLXFMRnNgTKBGn+Drd3VtnxGIrC6mxBxqHFEjQjTRCOoC8eu8Ejh9ICyE050XhQykoGeycoFo/DUfGAFzeA0kcrSexJRrJtBYM0C5gSEtZzTOSt/gj+K3aqPV5f/PdN6mrTqnfeawmunM1E/IokCIFBzgnq9ptlU4k5hsh1OIXxWJfKPrjQOU7rw5gWhwf4DtTuI4UT4KIThUnSh8d8Et7dXgaPERuiA4FCBGKaeDj7/1hgM05Rj5Hct8IFMabhTSj1dGrwO1Ui5aM/jdIG1rlXW7cSzH+EieotZCE1j1okoAldC5XGRRgsSNyjFgtLP4GJL5RAugao6Zymc6bhmVN7YeHLmJGE1VTYQP0YdReSCkilIYWVNOa0sECIFz1k8cCsSJwOtWD2iMg5OCEcGIbxQysqbjB8qCaK+UCkFEq24rOD0bTktIM4KWON+xmqxcrYYYZcErFyEdCOM3fkf8pw21Xfem5bdjOwz2p0e8/RxBqBM1QiE6Qo2QQCOkcSHO/KQcSzeFT9O99ZbxfcpthDsn3YWyOkJwZOWcKycNB9+YSGdPtLodQuk7UfVZDyqaMoIQIhC+XD6nK8PHoSwKSQpi2i9rIPElWkIdag8uizI8ZSPEv6HaqwpTCPKXv/xFiO+7GIdIn4mqlX7tnQ+NvajwSOsSQqgl+ViFZy9Mw9F2/2tFvbTUk5jPSVAWmhRR22csSyFrFm2/kPmiIaYMhwHDc/BTMOWwEI5cWelwIEZRHQIZpCyy1Y0jZCqUFOehn34wcSwqhKClASGilVtW+qazNI2JNOUjBKYfUla+kcUpa5q/0ZG+Kp9T/pjmZ50bqWLI7d+c/7iv0Ju1NGC7pyK/x8dZA0WNrJOJw0cwlp4D6SAhwPlwNimOENzT7B3R8UDujePM4LgFbrjRgURzLdE4Qkbp3lNABGo+XEhHWBUVqT2VXye/ZiJIYUSAOPw+MQSG7NCWgtyFJcvplSfdKspC04xRNEfnNYDAtO0Ni69bH+56XXgZ1UNvhgR94Wor6kQKRwPHmhhbbQVMmdpqMFqMUz5R5WNqjh/T2CuTbKvFaQHUasX4lD1vCiBNHUPQQipRYiXeRjQGJMUkjlzIqKv6F2qKplZ8yJP6GuJMJ2fKRY2dDMoIFCAcnS/dtCzOZ0NjN175G8m/abf/mfkzI52iewfsp+3qEdx7G2j01JiUmMs1pVZiG27MplM5UwpC4XHSgQMhmJ090xxnBo7gBBqZM893R3CkQzqxphzTct0mPhoy4xsRROunMQW1qmKKkCZkmg4wDtxdxvFGMCIA6fC7ifUvi3lx+AVWl7o3gOrnXfB8mepGGIn4tVHi9ON7N3mp8SkQLyq9ijX2fwGwwWMc5vbrlAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=384x192>"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frame = next(eye_container)\n",
-    "print(frame.dts)\n",
-    "frame.pil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "38.723978"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scene_container.duration_secs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scene_container.start_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37.659189"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eye_container.duration_secs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eye_container.start_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "116837\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'AudioFrame' object has no attribute 'pil'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[105], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(scene_container)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(frame\u001b[38;5;241m.\u001b[39mdts)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpil\u001b[49m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'AudioFrame' object has no attribute 'pil'"
-     ]
-    }
-   ],
-   "source": [
-    "frame = next(scene_container)\n",
-    "print(frame.dts)\n",
-    "frame.pil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No accelerated colorspace conversion found from yuv420p to bgr24.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NeonRecording: Loading recording from: ../tests/test_data/2024-01-25_22-19-10_test-f96b6e36\n",
-      "NeonRecording: Loading recording info and calibration data\n",
-      "NeonRecording: Loading raw time files\n",
-      "NeonRecording: Loading 'gaze ps1' data\n",
-      "NeonRecording: Loading 'gaze_200hz' data\n",
-      "NeonRecording: Loading 'gaze_right_ps1' data\n",
-      "NeonRecording: Loading IMU data\n",
-      "NeonRecording: Loading scene camera video\n",
-      "NeonRecording: Loading eye camera video\n",
-      "NeonRecording: Loading events\n",
-      "NeonRecording: Parsing unique events\n",
-      "NeonRecording: Finished loading recording.\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<pupil_labs.neon_recording.stream.VideoStream at 0x1547a3010>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "scene_data = rec.streams[\"eye\"].data\n",
-    "scene_ts = rec.streams[\"eye\"].ts\n",
-    "scene_start_offset = rec.streams[\"eye\"].ts[0] - rec.start_ts\n",
-    "# there is also rec.streams['gaze'].ts_rel\n",
-    "\n",
-    "scene = rec.streams[\"eye\"]\n",
-    "scene.sample([scene_ts[0], scene_ts[1], scene_ts[2]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26126"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = scene[20]\n",
-    "t.pts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loaded pts till 0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(192, 384)\n",
-      "(192, 384)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for s in scene:\n",
-    "    print(s.gray.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Slice 1 Indices: [2 3]\n",
-      "Slice 2 Indices: [4 5 6 7]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def find_slice_indices(data, bounds):\n",
-    "    slice_indices = []\n",
-    "    for i in range(len(bounds) - 1):\n",
-    "        a, b = bounds[i], bounds[i + 1]\n",
-    "        # Use np.where to find indices of data elements that fall between a and b\n",
-    "        indices = np.where((data > a) & (data < b))[\n",
-    "            0\n",
-    "        ]  # np.where returns a tuple, [0] gets the array of indices\n",
-    "        slice_indices.append(indices)\n",
-    "    return slice_indices\n",
-    "\n",
-    "\n",
-    "# Example numpy array of data\n",
-    "data = np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])\n",
-    "\n",
-    "# Example list of bounds (must be sorted)\n",
-    "bounds = [3, 7, 15]\n",
-    "\n",
-    "# Find the slice indices\n",
-    "slice_indices = find_slice_indices(data, bounds)\n",
-    "\n",
-    "# Display the results\n",
-    "for i, indices in enumerate(slice_indices, 1):\n",
-    "    print(f\"Slice {i} Indices: {indices}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Slice 1 Indices: [1 2 3 4 5 6 7 8 9]\n",
-      "Slice 2 Indices: [10]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def find_slice_indices(data, bounds):\n",
-    "    # Use np.searchsorted to find the insertion points for all bounds at once\n",
-    "    start_indices = np.searchsorted(data, bounds, side=\"left\")\n",
-    "    end_indices = np.searchsorted(data, bounds, side=\"right\")[\n",
-    "        1:\n",
-    "    ]  # Shift to align with start_indices for slicing\n",
-    "\n",
-    "    # Initialize a list to hold the slice indices\n",
-    "    slice_indices = []\n",
-    "    for start, end in zip(start_indices, np.append(end_indices, len(data))):\n",
-    "        # Generate the range of indices for this slice, if start is not equal to end\n",
-    "        if start != end:\n",
-    "            indices = np.arange(start, end)\n",
-    "            slice_indices.append(indices)\n",
-    "\n",
-    "    return slice_indices[\n",
-    "        :-1\n",
-    "    ]  # the last one is not needed because we only want the bounded matches\n",
-    "\n",
-    "\n",
-    "# Example numpy array of data\n",
-    "data = np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])\n",
-    "\n",
-    "# Example list of bounds (must be sorted)\n",
-    "bounds = [1, 18.1, 20]\n",
-    "\n",
-    "# Find the slice indices\n",
-    "slice_indices = find_slice_indices(data, bounds)\n",
-    "\n",
-    "# Display the results\n",
-    "for i, indices in enumerate(slice_indices, 1):\n",
-    "    print(f\"Slice {i} Indices: {indices}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = []\n",
-    "s = [1, 2, 3]\n",
-    "c = [3, 4, 5]\n",
-    "\n",
-    "t.extend(s)\n",
-    "t.extend(c)\n",
-    "\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "scene_data = rec.streams[\"eye\"].data\n",
-    "scene_ts = rec.streams[\"eye\"].ts\n",
-    "scene_start_offset = rec.streams[\"eye\"].ts[0] - rec.start_ts\n",
-    "# there is also rec.streams['gaze'].ts_rel\n",
-    "\n",
-    "scene = rec.streams[\"eye\"]\n",
-    "scene.sample([scene_ts[0], scene_ts[1], scene_ts[2]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.array([1, 2, 3.1, 4.0, 4.2, 5, 6, 7, 8, 9, 10])\n",
-    "b = np.array([2.6, 3.9, 4.1, 4.16])\n",
-    "\n",
-    "left = np.searchsorted(a, b, side=\"left\")\n",
-    "right = np.searchsorted(a, b, side=\"left\")[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 3, 3, 4])"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c = a - b[:, np.newaxis]\n",
-    "idxs = np.argmin(np.abs(c), axis=1)\n",
-    "idxs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1.6 , -0.6 ,  0.5 ,  1.4 ,  1.6 ,  2.4 ,  3.4 ,  4.4 ,  5.4 ,\n",
-       "         6.4 ,  7.4 ],\n",
-       "       [-2.9 , -1.9 , -0.8 ,  0.1 ,  0.3 ,  1.1 ,  2.1 ,  3.1 ,  4.1 ,\n",
-       "         5.1 ,  6.1 ],\n",
-       "       [-3.1 , -2.1 , -1.  , -0.1 ,  0.1 ,  0.9 ,  1.9 ,  2.9 ,  3.9 ,\n",
-       "         4.9 ,  5.9 ],\n",
-       "       [-3.16, -2.16, -1.06, -0.16,  0.04,  0.84,  1.84,  2.84,  3.84,\n",
-       "         4.84,  5.84]])"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.44999999999999996"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a[idxs[2] - 1] - a[idxs[2]]) / 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a[idxs[2]] - a[idxs[2]] + 1) / 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.6, -0.5, -1.4])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[0] - [a[idxs[0] - 1], a[idxs[0]], a[idxs[0] + 1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.0, 2.6, 3.1, 4.0)"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[idxs[0] - 1], b[0], a[idxs[0]], a[idxs[0] + 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1. ,  0.1, -0.1])"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[2] - [a[idxs[2] - 1], a[idxs[2]], a[idxs[2] + 1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.1, 4.0, 4.1, 4.2)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[idxs[2] - 1], a[idxs[2]], b[2], a[idxs[2] + 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.9999999999999996, -0.09999999999999964, 0.10000000000000053)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c[2, idxs[2] - 1], c[2, idxs[2]], c[2, idxs[2] + 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.20000000000000018"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c[2, idxs[2]] - c[2, idxs[2] + 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2.0, 2.6, 3.1]\n",
-      "[3.1, 3.9, 4.0]\n",
-      "[4.0, 4.1, 4.2]\n",
-      "[4.0, 4.16, 4.2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.array([1, 2, 3.1, 4.0, 4.2, 5, 6, 7, 8, 9, 10])\n",
-    "b = np.array([2.6, 3.9, 4.1, 4.16])\n",
-    "\n",
-    "c = a - b[:, np.newaxis]\n",
-    "idxs = np.argmin(np.abs(c), axis=1)\n",
-    "\n",
-    "for ic in range(len(idxs)):\n",
-    "    d = b[ic] - a[idxs[ic]]\n",
-    "    if np.sign(d) == +1:\n",
-    "        print([a[idxs[ic]], b[ic], a[idxs[ic] + 1]])\n",
-    "    else:\n",
-    "        print([a[idxs[ic] - 1], b[ic], a[idxs[ic]]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2.0, 2.6, 3.1] 0.5454545454545455\n",
-      "[3.1, 3.9, 4.0] 0.8888888888888887\n",
-      "[4.0, 4.1, 4.2] 0.4999999999999978\n",
-      "[4.0, 4.16, 4.2] 0.8\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.array([1, 2, 3.1, 4.0, 4.2, 5, 6, 7, 8, 9, 10])\n",
-    "b = np.array([2.6, 3.9, 4.1, 4.16])\n",
-    "\n",
-    "c = a - b[:, np.newaxis]\n",
-    "idxs = np.argmin(np.abs(c), axis=1)\n",
-    "\n",
-    "for ic, ix in enumerate(idxs):\n",
-    "    m = b[ic]\n",
-    "    d = m - a[ix]\n",
-    "\n",
-    "    if np.sign(d) == +1:\n",
-    "        l = a[ix]\n",
-    "        r = a[ix + 1]\n",
-    "    else:\n",
-    "        l = a[ix - 1]\n",
-    "        r = a[ix]\n",
-    "\n",
-    "    A = (m - l) / (r - l)\n",
-    "    print([l, m, r], A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.54545455, 3.88888889, 4.5       , 4.8       ])"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat = np.array([1, 2, 3, 4.0, 5, 6, 7, 8, 9, 10, 11])\n",
-    "t = np.interp(b, a, dat)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "list indices must be integers or slices, not NoneType",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[101], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m t \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not NoneType"
-     ]
-    }
-   ],
-   "source": [
-    "t = [1, 2, 3]\n",
-    "t[None]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.6, -0.6,  0.5,  1.4,  1.6,  2.4,  3.4,  4.4,  5.4,  6.4,  7.4])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a - b[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.70621755e+09, 1.70621755e+09, 1.70621755e+09, ...,\n",
-       "       1.70621759e+09, 1.70621759e+09, 1.70621759e+09])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gaze_ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2m2024-03-08T14:44:32.388657Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording from: ../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.389562Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording info\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.390338Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading calibration data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.392124Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading raw time (ns) files\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.393826Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading gaze data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.395661Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading IMU data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.446091Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading scene camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.479049Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading eye camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.484812Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading events \u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.486060Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Parsing unique events\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-08T14:44:32.486533Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Finished loading recording.\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "index -1 is out of bounds for axis 0 with size 0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 14\u001b[0m\n\u001b[1;32m     11\u001b[0m samp_ts \u001b[38;5;241m=\u001b[39m gaze_ts[:\u001b[38;5;241m15\u001b[39m] \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39muniform(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.0001\u001b[39m, \u001b[38;5;241m0.0001\u001b[39m, \u001b[38;5;241m13\u001b[39m), np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m])])\n\u001b[1;32m     13\u001b[0m gaze \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mstreams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgaze\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m d0 \u001b[38;5;241m=\u001b[39m \u001b[43mgaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_rob_orig\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamp_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m d1 \u001b[38;5;241m=\u001b[39m gaze\u001b[38;5;241m.\u001b[39msample_rob(samp_ts)\n\u001b[1;32m     16\u001b[0m d2 \u001b[38;5;241m=\u001b[39m gaze\u001b[38;5;241m.\u001b[39msample(samp_ts)\n",
-      "File \u001b[0;32m~/Documents/pupil/neon-recording/src/pupil_labs/neon_recording/stream/stream.py:259\u001b[0m, in \u001b[0;36mStream.sample_rob_orig\u001b[0;34m(self, tstamps)\u001b[0m\n\u001b[1;32m    252\u001b[0m     bounded_tstamps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mts[(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mts \u001b[38;5;241m>\u001b[39m prior_ts) \u001b[38;5;241m&\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mts \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m curr_ts)]\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# if len(bounded_tstamps) == 0:\u001b[39;00m\n\u001b[1;32m    255\u001b[0m     \u001b[38;5;66;03m# self.closest_idxs.append(None)\u001b[39;00m\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;66;03m# continue\u001b[39;00m\n\u001b[1;32m    257\u001b[0m \n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# just always take the one that is closest to the current timestamp\u001b[39;00m\n\u001b[0;32m--> 259\u001b[0m closest_ts \u001b[38;5;241m=\u001b[39m \u001b[43mbounded_tstamps\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# this has the feeling of suboptimal\u001b[39;00m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclosest_idxs\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mint\u001b[39m(np\u001b[38;5;241m.\u001b[39mwhere(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mts \u001b[38;5;241m==\u001b[39m closest_ts)[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]))\n",
-      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "gaze_data = rec.streams[\"gaze\"].data\n",
-    "gaze_ts = rec.streams[\"gaze\"].ts\n",
-    "gaze_start_offset = rec.streams[\"gaze\"].ts[0] - rec.start_ts\n",
-    "# there is also rec.streams['gaze'].ts_rel\n",
-    "\n",
-    "samp_ts = gaze_ts[:15] + np.concatenate(\n",
-    "    [np.array([0]), np.random.uniform(-0.0001, 0.0001, 13), np.array([0])]\n",
-    ")\n",
-    "\n",
-    "gaze = rec.streams[\"gaze\"]\n",
-    "d0 = gaze.sample_rob_orig(samp_ts)\n",
-    "d1 = gaze.sample_rob(samp_ts)\n",
-    "d2 = gaze.sample(samp_ts)\n",
-    "d3 = gaze.sample(samp_ts, method=\"linear\")\n",
-    "d4 = gaze.sample_rob_interp(samp_ts)\n",
-    "d5 = gaze.sample_mpk(samp_ts)\n",
-    "d6 = gaze.sample_rob_broadcast(samp_ts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "806.6710815429688"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d1[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d8 = []\n",
-    "for st in samp_ts:\n",
-    "    d8.append(gaze.sample_one(st))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369),\n",
-       "           (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  ),\n",
-       "           (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369),\n",
-       " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845),\n",
-       " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d8[0] == d1[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d0 [(804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d1 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d2 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d3 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d4 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d5 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n",
-      "d6 [(806.67108154, 806.54229736, 1.70621755e+09, 2.60384369)\n",
-      " (804.34216309, 806.01043701, 1.70621755e+09, 2.608845  )\n",
-      " (802.19934082, 807.45947266, 1.70621755e+09, 2.61384869)]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"d0\", d0)\n",
-    "print()\n",
-    "print(\"d1\", d1)\n",
-    "print()\n",
-    "print(\"d2\", d2)\n",
-    "print()\n",
-    "print(\"d3\", d3)\n",
-    "print()\n",
-    "print(\"d4\", d4)\n",
-    "print()\n",
-    "print(\"d5\", d5)\n",
-    "print()\n",
-    "print(\"d6\", d6)\n",
-    "print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0.])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.zeros(\n",
-    "    len(samp_ts), dtype=[(\"x\", \"<f8\"), (\"y\", \"<f8\"), (\"ts\", \"<f8\"), (\"ts_rel\", \"<f8\")]\n",
-    ").view(np.recarray)\n",
-    "a.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7600"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "38 * 200"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0018549060821533203, 0.0017579221725463868, 0.2238907051086426)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "import numpy as np\n",
-    "\n",
-    "np_near_times = []\n",
-    "np_times = []\n",
-    "rob_times = []\n",
-    "for i in range(100):\n",
-    "    t1 = time.time()\n",
-    "    d1 = gaze.sample(gaze_ts, method=\"nearest\")\n",
-    "    t2 = time.time()\n",
-    "    np_near_times.append(t2 - t1)\n",
-    "\n",
-    "    t1 = time.time()\n",
-    "    d2 = gaze.sample(gaze_ts, method=\"linear\")\n",
-    "    t2 = time.time()\n",
-    "    np_times.append(t2 - t1)\n",
-    "\n",
-    "    t1 = time.time()\n",
-    "    d3 = gaze.sample_rob_interp(gaze_ts)\n",
-    "    t2 = time.time()\n",
-    "    rob_times.append(t2 - t1)\n",
-    "\n",
-    "np.mean(np_near_times), np.mean(np_times), np.mean(rob_times)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(True, True)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(d1.x == d2.x), np.all(d1.y == d2.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(True, True)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(d2.x == d3.x), np.all(d2.y == d3.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.5"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "0.15 / 0.06"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.007461693286895752,\n",
-       " 0.05921526670455932,\n",
-       " 0.10385221004486084,\n",
-       " 0.14919528484344483)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "import numpy as np\n",
-    "\n",
-    "mpk_times = []\n",
-    "rob_times = []\n",
-    "rob_orig_times = []\n",
-    "rob_broadcast_times = []\n",
-    "for i in range(100):\n",
-    "    t1 = time.time()\n",
-    "    d4 = gaze.sample_mpk(gaze_ts)\n",
-    "    t2 = time.time()\n",
-    "    mpk_times.append(t2 - t1)\n",
-    "\n",
-    "    t1 = time.time()\n",
-    "    d5 = gaze.sample_rob(gaze_ts)\n",
-    "    t2 = time.time()\n",
-    "    rob_times.append(t2 - t1)\n",
-    "\n",
-    "    t1 = time.time()\n",
-    "    d6 = gaze.sample_rob_orig(gaze_ts)\n",
-    "    t2 = time.time()\n",
-    "    rob_orig_times.append(t2 - t1)\n",
-    "\n",
-    "    t1 = time.time()\n",
-    "    d7 = gaze.sample_rob_broadcast(gaze_ts)\n",
-    "    t2 = time.time()\n",
-    "    rob_broadcast_times.append(t2 - t1)\n",
-    "\n",
-    "(\n",
-    "    np.mean(mpk_times),\n",
-    "    np.mean(rob_times),\n",
-    "    np.mean(rob_orig_times),\n",
-    "    np.mean(rob_broadcast_times),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.10297333717346191"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "import numpy as np\n",
-    "\n",
-    "samp_one_times = []\n",
-    "for i in range(100):\n",
-    "    t1 = time.time()\n",
-    "    d8 = []\n",
-    "    for st in gaze_ts:\n",
-    "        d8.append(gaze.sample_one(st))\n",
-    "\n",
-    "    t2 = time.time()\n",
-    "    samp_one_times.append(t2 - t1)\n",
-    "\n",
-    "np.mean(samp_one_times)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "match = []\n",
-    "for i in range(len(d1)):\n",
-    "    match.append(d1[i] == d8[i])\n",
-    "\n",
-    "np.all(match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d6 = gaze.sample_rob_orig(gaze_ts)\n",
-    "gaze.closest_idxs[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d5 = gaze.sample_rob(gaze_ts)\n",
-    "gaze.closest_idxs[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.67108154,  806.54229736, 1.70621755e+09,  2.60384369),\n",
-       "           (804.34216309,  806.01043701, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934082,  807.45947266, 1.70621755e+09,  2.61384869),\n",
-       "           ...,\n",
-       "           (883.96313477, 1085.53625488, 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.28448486, 1084.70043945, 1.70621759e+09, 40.24368477),\n",
-       "           (884.9909668 , 1087.00842285, 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(True, True)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(d4.x == d5.x), np.all(d4.y == d5.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.67108154,  806.54229736, 1.70621755e+09,  2.60384369),\n",
-       "           (804.34216309,  806.01043701, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934082,  807.45947266, 1.70621755e+09,  2.61384869),\n",
-       "           ...,\n",
-       "           (883.96313477, 1085.53625488, 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.28448486, 1084.70043945, 1.70621759e+09, 40.24368477),\n",
-       "           (884.9909668 , 1087.00842285, 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(804.34216309,  806.01043701, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934082,  807.45947266, 1.70621755e+09,  2.61384869),\n",
-       "           (800.52740479,  803.5536499 , 1.70621755e+09,  2.61884475),\n",
-       "           ...,\n",
-       "           (883.96313477, 1085.53625488, 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.28448486, 1084.70043945, 1.70621759e+09, 40.24368477),\n",
-       "           (884.9909668 , 1087.00842285, 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(True, True)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(d5.x[1:] == d6.x), np.all(d5.y[1:] == d6.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.67108154,  806.54229736, 1.70621755e+09,  2.60384369),\n",
-       "           (804.34216309,  806.01043701, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934082,  807.45947266, 1.70621755e+09,  2.61384869),\n",
-       "           ...,\n",
-       "           (883.96313477, 1085.53625488, 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.28448486, 1084.70043945, 1.70621759e+09, 40.24368477),\n",
-       "           (884.9909668 , 1087.00842285, 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d7"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(True, True)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(d5.x == d7.x), np.all(d5.y == d7.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(806.67108154,  806.54229736, 1.70621755e+09,  2.60384369),\n",
-       "           (804.34216309,  806.01043701, 1.70621755e+09,  2.608845  ),\n",
-       "           (802.19934082,  807.45947266, 1.70621755e+09,  2.61384869),\n",
-       "           ...,\n",
-       "           (883.96313477, 1085.53625488, 1.70621759e+09, 40.2386837 ),\n",
-       "           (887.28448486, 1084.70043945, 1.70621759e+09, 40.24368477),\n",
-       "           (884.9909668 , 1087.00842285, 1.70621759e+09, 40.24882483)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d3 = gaze.sample_mpk(gaze_ts)\n",
-    "d3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'matplotlib'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# make a plot with 4 rows, where each row shows data sampled at a fixed rate over 1 second\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# the first row is sampled at 100 Hz, the second at 200 Hz, the third at 500 Hz, and the fourth at 1000 Hz\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# the data is a sine wave with a period of 1 second\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# the first row is sampled at 100 Hz\u001b[39;00m\n\u001b[1;32m      8\u001b[0m ts_100 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m100\u001b[39m)\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# make a plot with 4 rows, where each row shows data sampled at a fixed rate over 1 second\n",
-    "# the first row is sampled at 100 Hz, the second at 200 Hz, the third at 500 Hz, and the fourth at 1000 Hz\n",
-    "# the data is a sine wave with a period of 1 second\n",
-    "\n",
-    "# the first row is sampled at 100 Hz\n",
-    "ts_100 = np.linspace(0, 1, 100)\n",
-    "data_100 = np.sin(2 * np.pi * ts_100)\n",
-    "\n",
-    "# the second row is sampled at 200 Hz\n",
-    "ts_200 = np.linspace(0, 1, 200)\n",
-    "data_200 = np.sin(2 * np.pi * ts_200)\n",
-    "\n",
-    "# the third row is sampled at 500 Hz\n",
-    "ts_500 = np.linspace(0, 1, 500)\n",
-    "data_500 = np.sin(2 * np.pi * ts_500)\n",
-    "\n",
-    "# the fourth row is sampled at 1000 Hz\n",
-    "ts_1000 = np.linspace(0, 1, 1000)\n",
-    "data_1000 = np.sin(2 * np.pi * ts_1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.70621755e+09, 1.70621755e+09, 1.70621755e+09, 1.70621757e+09,\n",
-       "       1.70621759e+09, 1.70621759e+09, 1.70621759e+09])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_tstamps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.2143950399286514"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.uniform(0, 0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2m2024-03-12T15:28:48.767775Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording from: ../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.769442Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording info\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.771087Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading calibration data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.773302Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading raw time (ns) files\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.775611Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading gaze data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.777397Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading IMU data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.875818Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading scene camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.906987Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading eye camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.913535Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading events \u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.914691Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Parsing unique events\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-12T15:28:48.915374Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Finished loading recording.\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "{'gaze': array([[ True,  True,  True,  True],\n",
-      "       [ True,  True,  True,  True],\n",
-      "       [ True,  True,  True,  True],\n",
-      "       [ True,  True,  True,  True]])}\n",
-      "all equal gaze shifted (linear interp): True\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "import random\n",
-    "\n",
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "gaze = rec.gaze\n",
-    "imus = rec.imu\n",
-    "eye = rec.eye\n",
-    "scene = rec.scene\n",
-    "\n",
-    "tstamps = gaze.ts\n",
-    "test_tstamps = [\n",
-    "    tstamps[100],\n",
-    "    tstamps[150],\n",
-    "    tstamps[200],\n",
-    "    tstamps[len(tstamps) // 2],\n",
-    "    tstamps[-400],\n",
-    "    tstamps[-203],\n",
-    "    tstamps[-200],\n",
-    "]\n",
-    "for ic in range(len(test_tstamps)):\n",
-    "    test_tstamps[ic] = test_tstamps[ic] + np.random.uniform(0, 0.5)\n",
-    "\n",
-    "shuffled_test_tstamps = random.sample(test_tstamps, len(test_tstamps))\n",
-    "\n",
-    "gaze_samps_shifted = {\n",
-    "    \"np.interp\": [],\n",
-    "    \"np.interp_shuffled\": [],\n",
-    "    \"rob.interp\": [],\n",
-    "    \"rob.interp_shuffled\": [],\n",
-    "}\n",
-    "\n",
-    "# np.interp approach\n",
-    "gaze_samps_shifted[\"np.interp\"] = gaze.sample(test_tstamps, method=\"linear\")\n",
-    "gaze_samps_shifted[\"np.interp_shuffled\"] = gaze.sample(\n",
-    "    shuffled_test_tstamps, method=\"linear\"\n",
-    ")\n",
-    "\n",
-    "# rob approach\n",
-    "gaze_samps_shifted[\"rob.interp\"] = gaze.sample_rob_interp(test_tstamps)\n",
-    "gaze_samps_shifted[\"rob.interp_shuffled\"] = gaze.sample_rob_interp(\n",
-    "    shuffled_test_tstamps\n",
-    ")\n",
-    "\n",
-    "all_equal_interp_shifted = {\n",
-    "    \"gaze\": np.zeros((4, 4), dtype=bool),\n",
-    "}\n",
-    "kc = 0\n",
-    "for k in gaze_samps_shifted:\n",
-    "    jc = 0\n",
-    "    for j in gaze_samps_shifted:\n",
-    "        all_equal_interp_shifted[\"gaze\"][kc, jc] = np.all(\n",
-    "            gaze_samps_shifted[k] == gaze_samps_shifted[j]\n",
-    "        )\n",
-    "\n",
-    "        jc += 1\n",
-    "\n",
-    "    kc += 1\n",
-    "\n",
-    "print(all_equal_interp_shifted)\n",
-    "print(\n",
-    "    \"all equal gaze shifted (linear interp):\", np.all(all_equal_interp_shifted[\"gaze\"])\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(805.25998137,  756.99365861, 1.70621755e+09,  3.55813646),\n",
-       "           (794.2766782 ,  765.21001451, 1.70621755e+09,  3.73488307),\n",
-       "           (801.59493555,  710.08808104, 1.70621755e+09,  3.91472697),\n",
-       "           (930.27649807,  823.59277847, 1.70621757e+09, 21.6195159 ),\n",
-       "           (755.91415717,  858.2909549 , 1.70621759e+09, 38.59534764),\n",
-       "           (894.48095481, 1122.28195571, 1.70621759e+09, 39.44808292),\n",
-       "           (884.4642695 , 1098.74650958, 1.70621759e+09, 39.58002234)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gaze_samps_shifted[\"np.interp\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rec.array([(805.25998137,  756.99365861, 1.70621755e+09,  3.55813646),\n",
-       "           (794.2766782 ,  765.21001451, 1.70621755e+09,  3.73488307),\n",
-       "           (801.59493555,  710.08808104, 1.70621755e+09,  3.91472697),\n",
-       "           (930.27649807,  823.59277847, 1.70621757e+09, 21.6195159 ),\n",
-       "           (755.91415717,  858.2909549 , 1.70621759e+09, 38.59534764),\n",
-       "           (894.48095481, 1122.28195571, 1.70621759e+09, 39.44808292),\n",
-       "           (884.4642695 , 1098.74650958, 1.70621759e+09, 39.58002234)],\n",
-       "          dtype=[('x', '<f8'), ('y', '<f8'), ('ts', '<f8'), ('ts_rel', '<f8')])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gaze_samps_shifted[\"rob.interp\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2m2024-03-22T00:21:53.399818Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording from: ../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.401592Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading recording info\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.402945Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading wearer \u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.404335Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading calibration data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.405276Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading raw time (ns) files\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.406974Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading gaze data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.409662Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading IMU data\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.466368Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading scene camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.500787Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading eye camera video\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.506505Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Loading events \u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.511457Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Parsing unique events\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n",
-      "\u001b[2m2024-03-22T00:21:53.513062Z\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNeonRecording: Finished loading recording.\u001b[0m \u001b[36mfunc_name\u001b[0m=\u001b[35mload\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loaded pts till 9\n",
-      "No accelerated colorspace conversion found from yuv420p to rgb24.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(5, 1200, 1600, 3)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pupil_labs.neon_recording as nr\n",
-    "\n",
-    "rec = nr.load(\"../tests/test_data/2024-01-25_22-19-10_test-f96b6e36/\")\n",
-    "\n",
-    "ts = rec.gaze.ts\n",
-    "\n",
-    "list(rec.gaze.sample([ts[0], ts[1], ts[2]]))\n",
-    "nr.subsampled_to_numpy(rec.gaze.sample([ts[0], ts[1], ts[2]]))\n",
-    "nr.subsampled_to_numpy(rec.scene.sample(rec.scene.ts[10:15])).shape"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "robenv_latest",
-   "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": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/dev/imu_pb2.py b/dev/imu_pb2.py
deleted file mode 100644
index d13b9e9..0000000
--- a/dev/imu_pb2.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# obtained here:
-# https://github.com/pupil-labs/neon-player/blob/master/pupil_src/shared_modules/imu_timeline/imu_pb2.py
-
-# -*- coding: utf-8 -*-
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: imu.proto
-"""Generated protocol buffer code."""
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import descriptor_pool as _descriptor_pool
-from google.protobuf import symbol_database as _symbol_database
-from google.protobuf.internal import builder as _builder
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-
-
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\timu.proto\x12\x08imuproto\">\n\tAccelData\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\x10\n\x08reserved\x18\x04 \x01(\x05\"=\n\x08GyroData\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\x10\n\x08reserved\x18\x04 \x01(\x05\"J\n\nRotVecData\x12\t\n\x01w\x18\x01 \x01(\x02\x12\t\n\x01x\x18\x02 \x01(\x02\x12\t\n\x01y\x18\x03 \x01(\x02\x12\t\n\x01z\x18\x04 \x01(\x02\x12\x10\n\x08reserved\x18\x05 \x01(\x02\"\x91\x01\n\tImuPacket\x12\x0c\n\x04tsNs\x18\x01 \x01(\x04\x12&\n\taccelData\x18\x02 \x01(\x0b\x32\x13.imuproto.AccelData\x12$\n\x08gyroData\x18\x03 \x01(\x0b\x32\x12.imuproto.GyroData\x12(\n\nrotVecData\x18\x04 \x01(\x0b\x32\x14.imuproto.RotVecDatab\x06proto3')
-
-_globals = globals()
-_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
-_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'imu_pb2', _globals)
-if _descriptor._USE_C_DESCRIPTORS == False:
-  DESCRIPTOR._options = None
-  _globals['_ACCELDATA']._serialized_start=23
-  _globals['_ACCELDATA']._serialized_end=85
-  _globals['_GYRODATA']._serialized_start=87
-  _globals['_GYRODATA']._serialized_end=148
-  _globals['_ROTVECDATA']._serialized_start=150
-  _globals['_ROTVECDATA']._serialized_end=224
-  _globals['_IMUPACKET']._serialized_start=227
-  _globals['_IMUPACKET']._serialized_end=372
-# @@protoc_insertion_point(module_scope)
\ No newline at end of file