From 8caa4f227b45008860ab07f690a0d5b044127bcb Mon Sep 17 00:00:00 2001
From: linarphy <linarphy@linarphy.net>
Date: Wed, 9 Apr 2025 13:44:31 +0200
Subject: [PATCH] add current code

---
 bad weather.ipynb |  697 +++++++++++++++++++
 main.ipynb        |  582 ++++++++++++++++
 mise.toml         |    5 +
 pyproject.toml    |   22 +
 uv.lock           | 1670 +++++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 2976 insertions(+)
 create mode 100644 bad weather.ipynb
 create mode 100644 main.ipynb
 create mode 100644 mise.toml
 create mode 100644 pyproject.toml
 create mode 100644 uv.lock

diff --git a/bad weather.ipynb b/bad weather.ipynb
new file mode 100644
index 0000000..b22f362
--- /dev/null
+++ b/bad weather.ipynb	
@@ -0,0 +1,697 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6c9023fe-f86d-428a-8072-4264ff672025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from rich.console import Console\n",
+    "from rich.table import Table\n",
+    "\n",
+    "from numpy import loadtxt\n",
+    "\n",
+    "from numpy import float32, bool as np_bool\n",
+    "from numpy.typing import NDArray\n",
+    "from typing import Any\n",
+    "from collections.abc import Callable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2d82f94d-b69a-4ad0-b042-08d045925545",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import mean, diff, sqrt, pi, linspace\n",
+    "\n",
+    "from backscattering_analyzer.experiment import Experiment\n",
+    "from backscattering_analyzer.acquisition import AcquisitionType\n",
+    "\n",
+    "from matplotlib.pyplot import figure, show\n",
+    "\n",
+    "from science_signal import Signal\n",
+    "from science_signal.generator import sin as sin_gen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62328640-6c4f-47a1-8461-9637a50f52e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_over(signal1: Signal, signal2: Signal) -> bool:\n",
+    "    \"\"\"\n",
+    "    True if signal1 is over signal2\n",
+    "    \"\"\"\n",
+    "    return not ((signal1 - signal2).y < 0).any()\n",
+    "\n",
+    "\n",
+    "def get_speed(signal: Signal) -> NDArray[float32]:\n",
+    "    \"\"\"\n",
+    "    get speed from position signal\n",
+    "    \"\"\"\n",
+    "    return 1 / mean(diff(signal.x)) * diff(signal.y)\n",
+    "\n",
+    "\n",
+    "def compute_rms(signal: NDArray[float32]) -> float:\n",
+    "    \"\"\"\n",
+    "    compute RMS of data\n",
+    "    \"\"\"\n",
+    "    return sqrt(mean(signal**2))  # pyright: ignore[reportAny]\n",
+    "\n",
+    "\n",
+    "def accelerate_timesignal(factor: float, signal: Signal) -> Signal:\n",
+    "    \"\"\"\n",
+    "    accelerate a signal by a factor\n",
+    "    \"\"\"\n",
+    "    return Signal(\n",
+    "        signal.x[0] + (signal.x - signal.x[0]) * (1 / factor),\n",
+    "        signal.y,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def generate_movement(max_speed: float, start: int, end: int) -> Signal:\n",
+    "    \"\"\"\n",
+    "    generate a sine movement with a given maximum speed\n",
+    "    \"\"\"\n",
+    "    amplitude: float = sqrt(max_speed / (2 * pi))\n",
+    "    frequency: float = sqrt(max_speed / (2 * pi))\n",
+    "    signal = sin_gen(end - start, 1000, frequency, amplitude)\n",
+    "    return Signal(\n",
+    "        start + signal.x,\n",
+    "        signal.y,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def fit_value(\n",
+    "    start: float,\n",
+    "    end: float,\n",
+    "    nb_loop: int,\n",
+    "    experiment: Experiment,\n",
+    "    reference: Signal,\n",
+    "    generate: Callable[[float, Any], Signal],\n",
+    "    condition: Callable[[Signal, Signal], np_bool | bool],\n",
+    "    generate_args: list[Any],\n",
+    ") -> float:\n",
+    "    \"\"\"\n",
+    "    find the value that is the closest to NOT respecting the condition function\n",
+    "    Considering it start by not respecting the condition, and after some time, it will\n",
+    "    \"\"\"\n",
+    "    for _ in range(nb_loop):\n",
+    "        values = linspace(start, end, 10, dtype=float32)\n",
+    "        detected = False\n",
+    "        for j in range(len(values)):\n",
+    "            value = values[j]\n",
+    "            experiment.reference_movement = generate(value, *generate_args)\n",
+    "            experiment.projection_reference = experiment.compute_projection(\n",
+    "                AcquisitionType.REFERENCE\n",
+    "            )\n",
+    "            if (\n",
+    "                not condition(reference, experiment.projection_reference)\n",
+    "                and not detected\n",
+    "            ):\n",
+    "                detected = True\n",
+    "                end = value\n",
+    "                start = values[j - 1]\n",
+    "        if not detected:\n",
+    "            raise Exception(\"not in range\")\n",
+    "    return start + (end - start) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "587e2e3a-767b-4a6e-80df-188b6152bbb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "console = Console()\n",
+    "o5_mat = (\n",
+    "    Path(\"/home/demagny/data\")\n",
+    "    / \"simulation\"\n",
+    "    / \"optickle\"\n",
+    "    / \"transfer_function\"\n",
+    "    / \"O5.mat\"\n",
+    ")\n",
+    "sensitivities: dict[str, Signal] = dict()\n",
+    "for name in [\"high\", \"low\"]:\n",
+    "    data = loadtxt(\n",
+    "        Path(\n",
+    "            \"/home/demagny/data/sensitivity/O5/23932_O5{}SensASD.txt\".format(\n",
+    "                name.capitalize()\n",
+    "            )\n",
+    "        ),\n",
+    "        dtype=float32,\n",
+    "    )\n",
+    "    sensitivities[name] = Signal(\n",
+    "        data[:, 0],\n",
+    "        data[:, 1],\n",
+    "    )\n",
+    "sensitivities[\"high\"].y /= 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b7f534cc-0b50-4fc1-ba62-8a36747eb646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_BENCH = \"SDB1\"\n",
+    "C_COUPLING = \"../main_script_virgo/values-coupling.toml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a72635ea-8395-4783-9be1-8a89e82b7e21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "no excited signal given, cannot get factors of the excited signal\n",
+      "no excited signal given, cannot get factors of the excited signal\n"
+     ]
+    }
+   ],
+   "source": [
+    "base_experiment = Experiment(C_BENCH, \"2024_08_15\", C_COUPLING, 0.1)\n",
+    "temp_experiment = Experiment(C_BENCH, \"2024_08_15\", C_COUPLING, 0.1)\n",
+    "base_experiment.factors = {\"pre\": 2e-10 * 1e6, \"true\": 2e-10}\n",
+    "temp_experiment.factors = {\"pre\": 2e-10 * 1e6, \"true\": 2e-10}\n",
+    "base_experiment.modelisation_file = str(o5_mat)\n",
+    "temp_experiment.modelisation_file = str(o5_mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03d6c7a8-3391-49f3-af96-c476f6ff8160",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "factor = default_rng().random(1) * 10\n",
+    "# check if frequency is multiplied by the factor after going into accelerate_timesignal function\n",
+    "assert abs(factor/mean(diff(base_experiment.reference_movement.x)) - 1/mean(diff(accelerate_timesignal(factor, base_experiment.reference_movement).x))) < 1e-1, abs(factor/mean(diff(base_experiment.reference_movement.x)) - 1/mean(diff(accelerate_timesignal(factor, base_experiment.reference_movement).x)))\n",
+    "# check if amplitude is not modified after going into accelerate_timesignal function\n",
+    "assert (base_experiment.reference_movement.y == accelerate_timesignal(factor, base_experiment.reference_movement).y).all(), base_experiment.reference_movement.y - accelerate_timesignal(factor, base_experiment.reference_movement).y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8dbb68d6-fba3-4488-b951-b5bf860152d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_fitted_value = fit_value(\n",
+    "    1e-6,\n",
+    "    1e-5,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"high\"],\n",
+    "    generate_movement,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement.x[0],\n",
+    "        base_experiment.reference_movement.x[-1],\n",
+    "    ],\n",
+    ")\n",
+    "low_fitted_value = fit_value(\n",
+    "    1e-6,\n",
+    "    1e-5,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"low\"],\n",
+    "    generate_movement,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement.x[0],\n",
+    "        base_experiment.reference_movement.x[-1],\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "132a82c5-b69b-402c-b278-c565ccf6e504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_movement = generate_movement(\n",
+    "    high_fitted_value,\n",
+    "    base_experiment.reference_movement.x[0],\n",
+    "    base_experiment.reference_movement.x[-1],\n",
+    ")\n",
+    "temp_experiment.reference_movement = high_movement\n",
+    "high_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)\n",
+    "low_movement = generate_movement(\n",
+    "    low_fitted_value,\n",
+    "    base_experiment.reference_movement.x[0],\n",
+    "    base_experiment.reference_movement.x[-1],\n",
+    ")\n",
+    "temp_experiment.reference_movement = low_movement\n",
+    "low_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2966a38e-9020-4cdd-a186-70519afb85de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Figure = figure()\n",
+    "_ = Figure.gca().loglog(\n",
+    "    high_projection.x, high_projection.y, label=\"projection for high\"\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"high\"].x,\n",
+    "    sensitivities[\"high\"].y,\n",
+    "    label=\"one order below high sensitivity\",\n",
+    ")\n",
+    "_ = Figure.gca().loglog(low_projection.x, low_projection.y, label=\"projection for low\")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"low\"].x, sensitivities[\"low\"].y, label=\"low sensitivity\"\n",
+    ")\n",
+    "_ = Figure.gca().legend()\n",
+    "Figure.gca().grid(True, \"both\", \"both\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cf7aa342-1ffe-4262-80fd-e900acf590c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">  Vérification des calculs: haute   </span>\n",
+       "<span style=\"font-style: italic\">            sensibilitée            </span>\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> nom     </span>┃<span style=\"font-weight: bold\"> RMS      </span>┃<span style=\"font-weight: bold\"> vitesse max </span>┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 3.33E-06 │ 4.71E-06    │\n",
+       "│ mesuré  │ 3.04E-06 │ 4.71E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m  Vérification des calculs: haute   \u001b[0m\n",
+       "\u001b[3m            sensibilitée            \u001b[0m\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mnom    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mRMS     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvitesse max\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 3.33E-06 │ 4.71E-06    │\n",
+       "│ mesuré  │ 3.04E-06 │ 4.71E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">  Vérification des calculs: basse   </span>\n",
+       "<span style=\"font-style: italic\">            sensibilitée            </span>\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> nom     </span>┃<span style=\"font-weight: bold\"> RMS      </span>┃<span style=\"font-weight: bold\"> vitesse max </span>┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 3.66E-06 │ 5.18E-06    │\n",
+       "│ mesuré  │ 3.41E-06 │ 5.18E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m  Vérification des calculs: basse   \u001b[0m\n",
+       "\u001b[3m            sensibilitée            \u001b[0m\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mnom    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mRMS     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvitesse max\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 3.66E-06 │ 5.18E-06    │\n",
+       "│ mesuré  │ 3.41E-06 │ 5.18E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = Table(title=\"Vérification des calculs: haute sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(high_fitted_value / sqrt(2)),\n",
+    "    \"{:.2E}\".format(high_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(high_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(high_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)\n",
+    "\n",
+    "table = Table(title=\"Vérification des calculs: basse sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(low_fitted_value / sqrt(2)),\n",
+    "    \"{:.2E}\".format(low_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(low_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(low_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "245c09ba-be17-4f8e-a41a-e6e6f02696a7",
+   "metadata": {},
+   "source": [
+    "# Utilisation du mouvement réel du banc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c61da9e2-0339-4345-b601-395ef00b380c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_fitted_value = fit_value(\n",
+    "    0.5,\n",
+    "    3,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"high\"],\n",
+    "    accelerate_timesignal,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement,\n",
+    "    ],\n",
+    ")\n",
+    "low_fitted_value = fit_value(\n",
+    "    0.5,\n",
+    "    6,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"low\"],\n",
+    "    accelerate_timesignal,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement,\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13cbbcf4-4753-4cef-90a3-83477177a102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_movement = accelerate_timesignal(\n",
+    "    high_fitted_value, base_experiment.reference_movement\n",
+    ")\n",
+    "low_movement = accelerate_timesignal(\n",
+    "    low_fitted_value, base_experiment.reference_movement\n",
+    ")\n",
+    "temp_experiment.reference_movement = high_movement\n",
+    "high_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)\n",
+    "temp_experiment.reference_movement = low_movement\n",
+    "low_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5ff45ff8-8d0f-4f9a-a060-839700121e43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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F1QnLyGHj7G1cyoCazdsTHOiRb9/4dfGc0J/gpP4kQe2DaOrRtFTnVdHKO8ayaL+y9487lZe2rLKQPmK9PiL9w3r9o6T7VbU+cutKS3GUaVJTFjp37ozJVPgtyYMHD2bw4MF3bMfW1hZbW9t827VabbE+7OLWK+0+xalbUB1fNy29mniz5lgcvx6Np209n3z7eau96RPUh7XRa/nh1A982PXDUsVoLeUdY1m0X1n7R3HLS1tWWUgfsV4fkf5hvf5R0v2qSh8pSVtleku3l5cXarWauLi4PNvj4uLw9fUty0NVaw+1ttwB9tvhq2TrC74ENabJGAA2XNzAlfTqcYeYEEKI6q1MkxobGxvatGlDRERE7jaTyURERAQdOnQoy0NVax3reeJmYyY1y8DGE3EF1mno3pAOfh0wmo18f+L7Co5QCCGEqHglTmrS09OJjIzMvYMpKiqKyMhILl2yLM8fHh7OokWLWLJkCSdPnmTixIlkZGTk3g0l7p5apRBWwzK/+6f9MYXWG9t8LAArz64kVZdaEaEJIYQQVlPiOTX79++ne/fuue9vTcYdM2YMixcvZvjw4SQkJDBjxgxiY2MJCQlh3bp1+SYPi7sT6m1i/RUVO88lkpiuw8sp//yhDn4daOjekDPJZ/gz6k/ccKv4QIUQQogKUuKRmm7dumE2m/O9Fi9enFtn0qRJXLx4EZ1Ox549ewgLCyvLmAXgZQctarpgMsPaY7EF1lEUhUcaPgLA6vOruYu794UQQohKr9I9+0kUX//mlsnXfxy+WmidfnX7Yae2w3/HGa7HHauo0IQQQogKJ0nNPax/c8slvb3RScSlZRdYx8XGhYdsO/DMGhOhny3l+mf/xZSVVZFhCiGEEBVCkpp7mL+bPa0D3TCb4c8j1wqt17N+P44EKWiMZpIXLeLCgIHciIiQy1FCCCGqFElq7nEDWvoDsP54wfNqAFqH9OX78YHMHqpCX8MN/dWrXH5uEpefmUhOTOF3TwkhhBD3Eklq7nG9m1ouQe2/mExKZk6BdRRFoW9QP/Y1UvHVtGA8n3oKtFrSt23jwoCBJMybj+m2h38KIYQQ9yJJau5xAR4ONPJxxmgys/V0QqH1+gb1BWB70h5snxtP3V9X49ChPeacHBLnzePCwEGkb9tWUWELIYQQZU6SmiqgZ1NvADaeLHh1YYC6rnXxVfliMBnYeHEjtnXrEvj119T85GM03t7oY2KIefoZYiZNQn9FHqsghBDi3iNJTRXQs4nlEtS20wnkGAp/GGhLm5YArIlaA1guS7n060fdNWvwGD8eNBrSN0VwfsBAEhf+D1NOwZezhBBCiMpIkpoqILiWG15OtqTrDOyNTi60XnNtcwAOxB0gOfufemonR3xenkrdVb/g0K4d5uxsEubOJWrwA2Ts2lXu8QshhBBlQZKaKkClUri/seUS1OZT8YXW81B70NCtISazie2Xt+crt23QgMBvl+A/+0PUXl7kREdzafwTXH7x/9DHFn53lRBCCFEZSFJTRfRoYklq/jp3vch63Wp1A2Dzpc0FliuKguugQdRbuwb30aNApeLGunWc7z+A6199hVmvL9O4hRBCiLIiSU0V0aGeJ2qVQvT1TJKKuDv7VlKz6+ousgyFryysdnbG99VXqfPLSuxbtcKcmUn87I+48OCDZOzZW8bRCyGEEHdPkpoqwsVOS0iAGwCnU5RC6zVyb4Sfox/Zxmz+vvr3Hdu1a9yY2j98j9+776L28CDn3HkujRnDlSlT0ccXfqlLCCGEqGiS1FQhnet7AXAqtfCkRlEUegT2AGBLzJZitauoVLgNfZB6a9fgNuJRUBTS/viDC/36k7RkCWaD4e6DF0IIIe6SJDVVyH0NLEnNmVQFo6nw5zp1D+gOwLbL2zCZC78F/N/Urq74vfEGQT//jF3LlpgyMoh7732ihj1E5sGDdxe8EEIIcZckqalCggPccLLVkGlQOHEtrdB6rX1a46h1JCk7iVNJp0p8HPvmzQha9iO+b85C7eqK7vRpLo58jKvTpmO4XvREZSGEEKK8SFJThWjVKtrXcQdgRxF3QWlVWkJ9QwHLhOHSUFQq3B95hLrr1uL28MMApK5ezfl+/UlauhSz0ViqdoUQQojSkqSmiulU3xOAneeLHjHp6N/RUu/Kzrs6nsbdHb+33iRo+TLsmjbFlJZG3JtvEf3wI2QdPnxXbQshhBAlIUlNFdOhriWpORSTis5Q+GhJJ/9OAETGR5Khz7jr49oHBxP080/4zHgdlbMz2SdOED38Ua69/jqG5MJXORZCCCHKiiQ1VUxdLwectGZyDCaOXE4ttF6ASwABzgEYzAb2xe4rk2MrajUeI0dSb91aXIcMASDl5xVc6NuP5OU/YTYVf1KyEEIIUVKS1FQxiqJQ39ly59PeqKQi65bVJah/03h64v/+e9T+4XtsGzXCmJpK7BtvEP3oCLKOHS/TYwkhhBC3SFJTBdVzsSQ1f18o3rya0k4WvhOHNm2os3IFPq9OR+XoSPaRI0Q//DDXZs3CmFr4KJIQQghRGpLUVEG3kpoDF5MxGAu/5BPqG4paUXPpxiWupl8tl1gUjQaP0aOpu3YNLgMHgtlMyo/LON+vPym/rJJLUkIIIcqMJDVVkJ8DuNpryMwxcvxq4evVONk40cyzGQD74/aXa0xab29qfjSbwCVLsKlfD2NSEtdefZWLj48i+1TJ18oRQggh/k2SmipIpUDb2pb1avZEFX0Jqq1vW4Aymyx8J45hodRdtQrvqVNQHBzIOniQqKHDiH33XYw3blRIDEIIIaomSWqqqHZBlqTmTpOF2/pYkpr9seU7UnM7RavF84knqLfmT5z79gWTieRvv+PS4AdwPnQIs7nwRzwIIYQQhZGkpopqV/ufpMZUxHOgWnm3Qq2ouZx+mdiM2IoKDwCtry+15n5CwJdfYhMUhDExEb9ly7ky/gl0Z89WaCxCCCHufZLUVFFN/Zyx16pJyzZwPiG90HpONk408WgCVNwlqHwxdO5End9+xeP5yZi0WrL37+fCg0OJ++BDjOl3vzCgEEKI6qHSJTUxMTF069aNpk2b0rJlS37++edilYm8NGoVLWu5AnDwUtEr+t6aV3Mg7kC5x1UYlY0NHhMmEP1SOI49eoDBQNI333Chf3/S1q6VS1JCCCHuqNIlNRqNhrlz53LixAk2bNjAiy++SEZGxh3LRH6tb16COngxpch67XzbAdYbqbmdwd0dv0/nEvC/hWgDAzHEx3Pl/8KJeeIJdBeirB2eEEKISqzSJTV+fn6EhIQA4Ovri5eXF0lJSXcsE/m1DryZ1NxhpKaVdytUiopLNy4RlxFXEaHdkVPXrtT9/Te8Jk1CsbEhY9duLjzwAPEff4IpM9Pa4QkhhKiESpzUbN++nUGDBuHv74+iKKxevTpfnfnz5xMUFISdnR1hYWHs3bu3VMEdOHAAo9FIQEBAicqERatANwDOxqeTlqUvtJ6zjTON3BsBcCjhUEWEViwqW1tqTHqOun/8jmPXLqDXc/2LLzg/cCBpGzfKJSkhhBB5aEq6Q0ZGBsHBwYwfP56hQ4fmK1++fDnh4eEsXLiQsLAw5s6dS58+fTh9+jTe3t4AhISEYDAY8u27YcMG/P39AUhKSmL06NEsWrQoX72iym7R6XTodLrc92lplkXo9Ho9en3hv+BvlRVV5272KU7dO9Upqvz2MldbLQHu9sQkZ3HwYlKRbbb0asnJpJMcjD3I/TXvv+N5lIfCzkvx88P3v/8lY8tWEj/4AMPVq1yZ/DwOnTvjNX0aNoGBd9V+WcRYVvtUZP+4mzitpbxjlD5SeJn0D+v1j5LuV9X6SEnaU8x38eeuoiisWrWKITefyAwQFhZGu3btmDdvHgAmk4mAgAAmT57MtGnTitWuTqejV69eTJgwgVGjRhW77HYzZ85k1qxZ+bYvXboUBweHYsVRFXx7VsWBRBV9axnpF1D4P/XhnMP8nPkzNdU1meg8sQIjLBklJwePLVtw37YdldGISaMhuWtXkrp3w6zVWjs8IYQQZSwzM5ORI0eSmpqKi4tLkXXLNKnJycnBwcGBFStW5El0xowZQ0pKCr/++usd2zSbzYwcOZJGjRoxc+bMYpf9W0EjNQEBASQmJhb5oej1ejZu3EivXr3QFvOXZEn2KU7dO9UpqvzfZd/vucSsP07RuZ4HD3vHF9rm1fSrDPxtIBpFw/aHt2OnsSvWuZelknyOOdHRJLz7Hlm7dwOgqVmTGtOn4di1a5m0X5ZtVOb+cbfnVtHKO0bpI4WXSf+wXv8o6X5VrY+kpaXh5eVVrKSmxJefipKYmIjRaMTHxyfPdh8fH04V8/k+O3fuZPny5bRs2TJ3vs53331HixYtiiz7N1tbW2xtbfNt12q1xfqwi1uvtPsUp+6d6hRVfqusbZAXAEeupDGsRuH7BLoFUsO+BglZCZxJO0MbnzbFOo/yUKzPpkEDHL7+ihvrNxD3/vsYrlzh2qTJOHXvjs+r07EpYq5Vaf5ty6KNytg/7jZOaynvGKWPFF4m/cN6/aOk+5VrHzGbUVJTyYmJIScqmpzoaLIvnMcvKRlt//5l+vmXpK0yTWrKQufOnTEV8uTmospEwRr7OWOnVZGWbSA+q/B6iqIQXCOYTZc2cTjhsFWTmuJSFAWXvn1wuq8ziQsWcH3xEtK3bCFjxw48xo3D6+mnUDk6WjtMIYS4Z5myssg+f56cqChyoqPRRUWhuxBFvbNnib7tasgtDnZ2Vr2Jo0yTGi8vL9RqNXFxeW8LjouLw9fXtywPJYpJq1bRspYbe6OSiE5Xiqwb4h3CpkubiIyPrJjgyojK0RHvKVNwHTKEuHffI2PXLq5/8QWpq1bhPeUlXAYNQlFVutULhBCiUjCbTBiuXUMXFZ2bvGRfuECdkye58ErBc2HVACoV2po1sakThE1QEJrAQA7GxdGwqiQ1NjY2tGnThoiIiNw5NSaTiYiICCZNmlSWhxIl0CrwZlJzo+ikJrhGMACHEw5jNptRlKLrVza29esT8NWXpG/eTNz7H6CPieHqK9NIXvojPv95DU3jxtYOUQghrMZ440Zu0pJ17jx+u3dz6auv0V+6hDk7O1/9Wxd91K6u2NSpg01QEDZ16qAODODvSzHc//hj2Nw2Gq7X68las8aqf0SWOKlJT0/n3Llzue+joqKIjIzEw8ODwMBAwsPDGTNmDG3btiU0NJS5c+eSkZHBuHHjyjRwUXxtAt15SL2NS2n1i6zXxLMJWpWWpOwkLt+4TIDLvbcGkKIoON9/P4733UfS4iUkLlxI1uHDRD/8CM5DhqBu3szaIQohRLkx6/XkREfjePIkyQkJGC9eIicqCl10NMbExDx1nYGcW2+0WmwCA7GpE4RtUBDqwED2X7tG1xEjsLu5HMster2enDVrUGxsKuScSqLESc3+/fvp3r177vvw8HDAcofT4sWLGT58OAkJCcyYMYPY2FhCQkJYt25dvsnDouK0c0qkm+ZLMINhw0W0PaeDrVO+erZqW5p4NuFIwhEiEyLvyaTmFpWNDV5PTcD1gQdI+HgOqb/+xo3Vqwlau5bkzExqjB1bKf9DCiHEnZjNZoxJSXnmueTcunQUEwMGAzWB6wXsq6lRA5s6ddAEBnIuK4vg/v1wqF8fbc2aKJp/UgK9Xk/2mjWo3d0r7LzKQomTmm7dut1xEtCkSZPkclMl4u7izG51CB1MB2DfPDj1C/R+C5oPg39dYgqpEcKRhCMcTjjMoHqDrBRx2dH6eOP/wQe4jxjBtbffQXfsGNc//oQbK3/Be9orOHXrds9dZhNCVA8mnY6c6IuWy0Xnz+GzYycxP/yAPvoippsLyhZEsbcjy90drxYtsatX9+alozrY1AlC7WT5g1av17N/zRocu3Sp9HezlUSlu/tJlAP32iyrP5svj/3JHOcfcbtxFVY+Afu/gf6zwadpbtWWNVoClnk1VYl9SAi1fvienW+/Tc3NW8i5eJHLE5/FISwM7ylTsG/R3NohCiGqIbPZjCE2Nv+IS1QU+qtX4bZBBFcg934jRUHr7587z8WmThC2depgU6cOZg8P1q5bR/MyvrX6XiBJTTXRqrY7M4+04UXPbixu9Df8NQcu7oCFnSH0Keg2DezdcicLn0k+Q6Y+Ewdt1Vl9WVGpSGvblo7h4aR+/TVJS74lc88eoh9+GJf+/ajx4ovFfuSCEEKUhDEtjZyLF8k6dx7PTZuI3bIV/UXLKIw5q/D1NlTOztjUqYO2diBROTk0790b+/oNsKkdiMqu4EVSK/OjLMqbJDXVRKsAVwD2XcnE+ORU1C2Hw4bX4OTvsGcBHFsBPWfhGzwCbwdv4jPjOX79OO1821k58rKncnLC+6WXcB8xgoRPPyP1t99IW7OWtI2bcH/0UbwmPoPGw8PaYQoh7jEmnQ79pUvooi2L0VlelsTFeP2fGS6eQPrtO2o02AQEFDjqovbwQFEU9Ho9B9aswal372o3+lISktRUEw29nbBVmcnQGTkbf4PGvrVh+PdwLgLWvgLXz8Kvz8KBxQTXrM3GzHgOJxyukknNLVp/f/w/eB+PcWOJ/2gOGTt2kPzdd6T+8gueTz6B+6hRudefhRACAJMJ/eXL6C5fuS1xsbz+fbno3zTe3mhr1+aaolDvvs7Y16tvWeOlVi0USVTKhCQ11YRGrSLQyczZNIWDF1No7Hvz+Rn174eJuyyjNVs/gMt7CU47yUZPdw7H7ocWT1o38Apg17gxgV8uImPXLuI++gjdiZMkfPoZ1xcvwWPMaDxGjULt7GztMIUQFST37qJ/JS26C1HUj47motFY6L4qZ2fLiEtQEDZBtS0jLkFBaANro3ZyRK/Xc3jNGtpWw/kuFUGSmmqkjjOcTYMDF5MZGXbb3BGNDXR6AVo8DBtnEHzG8uDRIzF/Yd73FUqbsaBSWyfoCuTYsSN1Vqwgbc1aEj//nJwLF0j87L8k3UpuRo+GQq5hCyHuPcb0DHIu5r9UlBMdjenGjQL3UQGKjQ02tQNvS17+ed26XCSsQ5KaaqSOs2VYdF90UsEVXPxh2Jc0ufAYmu3Pk6RWcXn9VAIOLoH+H0FAaAVGax2KSoXrwAG49OtL2rp1JH6+gJzz50n87zySvlmMy7ChaPz8rB2mEKKYTDk56C9ftiQsN9d1ufUyJCQUvqOiWB4BcHvCElCLHRcu0GvECGzkD5xKSZKaaqSuixmNSuFSUiYXr2dQ27Pghz3a1u1OkxPNOXr9GIed3Am4dhi+6gUhj0HPmeDkXeB+VYmiVuM6YAAufftyY/16Ej7/nJxz50lZ8i11VCpiDx7Cc9xY7ENC5K8yIazMpNOhj4kh68IF3LdvJ/7AAYyXL5Nz8RL6a9egiAchq728sAmqjU2QZSXdWwmMNiAAla1tnrp6vR5DSgqKuuqPXN+rJKmpRuzU0DrQjb3RyWw/k8CoDoU/wTrYO4Sj149xpO1IBiYmQeT3EPmD5W6pri9D6NOWy1ZVnKJW49K/P859+5K+fTvXv1lM1p49pG/YQPqGDdg2aoTbsKG4DBqE5h5beVOI8pa2bj1mowHXAQPuui1TVhY5MTHoL10i5+JFci5eIufSJXIuXcRwLTZ3gm4N4N/L0qkcHdHWDsQ26Nbzi24mL7Vro3ZxuevYROUhSU01c199T/ZGJ7PtTCKjOgQVWi+4RjDfn/yewylnYMhyaDMW1k6Fq4dgw38sC/f1fQ8a9qmw2K1JUalw7tYNu06d2PTV17S4eJH0P/9Ed/o0ce++R9zsj3Du3h2X/v1xuq8zKsfCE0ZR/SQtWULqmjUELlpUbX6JmnQ6rrz4IgBOnTujdnW98z6ZmeTExNxMWi7eTGAsyYshNrbIfVVOTmgDA0nQaKjdvj12depY5r3Uri3zXKoRSWqqmfsaeDFn0zl2nEsgXWfAybbgLnBrZeEzSWfIMmRhH9AOntwMh5fCplmQdB6WPgL1e1mSG68GFXkaVpXj54vPE+Pxm/YKqX/+SerKX8g+fpwbGzZwY8MGFFtbHDt3xrlnTxw7dgQPGcGp7uLeex+A6998g/cLL1g5mophyszM873a1dVyV1FiIjkxl9Ffjrk58hJDzuXL6C9dKnqOC6ByccGmdm3Lgxdr18amdiDam9+r3d0xGAwcXbOG1nJnUbUlSU0109TPmbpejlxIzGD9sViGtalVYD0/Rz9q2NcgISuBE9dP0ManDahU0OpxaDIYtn8Ify+Ecxvh8y0Q9ozlspTdnf8aqyrUrq54jByJx8iRZJ8+Tepvv3Fjw0b0MTGkR0SQHhEBgLZuXWr4+pBuY4NT69Zo5eGu1ZY5K9vaIZQ7U2Ym+itXyDp2PHfb1WnTLbdIX75c5Oq5YPl/pQ2qjU3gzeQlyPJVGxgol3jFHUlSU80oisKQVjX5eOMZVhy4XGhSoygKLWu0JOJSBEcSjliSmlvsXKD329B6rGVV4jPrYPc8OLwM7p9hSXyqwS3gt7Nr1Ai7qVPxnjIF3enT3NiwkfStW8k+eRL9hQu4X7hA7K7dgOUpuXYtWmDXvBm2DRpgW7cuNoGBsviWqPzMZoxpaRgTEtBfvYr+6jXL12tXLV8vX8mzcu4tmXv2/PNGpULr64s2IABtQC1sAgKxCaiFNiAAm4AA1G5uFXc+osqRpKYaGtamFp9GnGX3hescvZxKi1oFj64E1wgm4lJE4Q+39KoPI5fD2U2wbpplVeLfn4f9X0HfD6B2h3I8i8pJURTsGjfGrnFjajw/GWNKCmm7dnHqp5/xvn6dnPPnMSQkkL55M+mbN/+z481l0rW1a1NDn0NyQgJ2tWqh9fND4+eHxssLRaWy3omJKs2ck4NZr0exs8MQG4td9EVurFmDKT7+ZvJyFf2Vq9SLiSFKp7tjeypnZ7S1amG4dg21pycejz+GtlaAJXnx90exqfo3GQjrkKSmGqrpZs8Dwf78cugKH288zTfjCl5/5vYndpvN5sIn2jXoCXV3w94vYOv7cO0wfNMXmj8EvWaBa8GjQdWB2s0Np169iNfradu/P2q9nuyTJ8k+doys48fJuRBFzoULlgmSN5/M6w5c37krb0MaDRp3d9Senqjc3fHNziLx+Am0NbzQeHiidndD7eKCysUFs4MDSk4O5iKWaxfVi9lsxpSaiiExEUNiIrrYWNy3byfh6FFM8Qnc2LAhT/1AIK6Adm6Nv6rd3dH6+998+aH190fj54dNrVpoa9WqNpOhReUjSU019VyP+vx+5CpbTiew8UQcvZrmn+fRzLMZGkVDYlYi1zKu4e/kX3iDai10eA5aPAKb34KD31oeknl6DXT+P+g4GbT25XhG9waVgwMObdrg0Oafy3lmsxlDXBw5Fy6Qdf48p3fuItDeHmNcHPpr1zDExYHBgCEhIXcipQuQciiy0OM0AM6/+RZqV1fULi6WhMfVBcXJmRrJSVw/dw6NiwsqR0fUTk6oHB0x2dlhe/kyOVFR4OaG2tERxcFB7hqppMxGI4b0dIzJKRhTkjEmJd3sI4nkxMfjf+I4Md9/j/H6dYwJiZj/9eTmGkBqQQ2r1ehdnHGuWw/bWjXR+FmSFpWPDzvPnOH+4cOxlaRFVFKS1FRT9Wo48eR9dVmw9Tz/WX2UNrXd8XDMOyRsp7GjkUcjjl8/zuGEw0UnNbc41YDBn0G7JywPyry0G7a8Awe/g95vQtMhIL8k81AUxTLHwNcXm3btSHRxIfS2uzfMBgOG69cxXr+O4XoSuvg4ju/cRQMfb8zJKZaylBSMaamYUtMw3rgBRiPo9RgTEzEmJuY5njuQfHN+z7/VBi79d94/G1QqVA4OqG4mPpYkyPJV5eCAYm+Pys4elYM9ip09Knt7VPZ2lu03X4qdnaUNu39t18iPH7PBgCk9HX1KCjbXrpF18CDZWVmY0jMwpadjTE21/NsmJ1u+pqRgSE6mXkIC56dNL/LhiU7Avy8UqVxd0dTwQu3pSVxODoGtWmPr728Z6XNywrZJE8wuLqxdv57+/7qDSK/Xo09JQWUvf5yIykt+qlRjk3vUZ8PxWM4nZDDl58N8NaZtvr/KW3m34vj14+y+upt+dfoVv3G/YBi3Fo7/AhtmQOol+Hks1GpnWZU4qHOZnktVpmg0aH18cu+astXrSdZo8CrkttWcnBzWr1rF/e3bo8rMxJiahvFGGqa0NHKSkzlz8CB1ff0gKwtThuWXpykjA2N6OhmJidiaTJgyMiyrsJpMlvL09LI/Ma3WkuDY2aHY2aHYaFFsbFBpbVBsbnvZ2uaWodZQ48oVEs+cRWNn90+ZRouiVoFKjaJRW77eel/kVxWYTJZLdSYzBoMeh1OnyXByQq1SWZKGmy+zyQQmM2a9PncOSu5XfQ7mHD1mvR5Ddjbe584Rv3cvZGVhvPHPZ5e0eDHpW7dizEjHlJ6R506gIOBKMT+626fhq5ycULu5ofbwQFOjBhovL1SeHpy8Fktwt27Y+fmi8fJC7eWF6uZcllsPVSzo1mf9v0Z0hLiXSFJTjTnYaPjviNYM+Xwnm0/F89WOKJ68r26eOl1qdeH7k9+z/fJ2TGYTKqUEk1UVBZoPg4b9YOdc2PVfuLwPFg+ABr3h/jfAt3nZnpRAURRMdnaW+Q4F/MK6XqMGYYX8MluzZg39+/dHo9Fgzs6+LeHJsCRAGRmYMixJjikzC1N2FuasrJvfZ2PKysSclY0p62ZZ7vab9bKy/lmyXq/HpNdjSvv3+q9FcwdSduy4m4+oSLWAa998c1dtuJF/VdtbcqKj821TbG3Ra7XYe3paLgc6O6NyckTt7ILa3d2StLi5onZ3Bycndh45SrfBg7Hz8izwrjm9Xk/qmjU49egu67WIakWSmmquqb8L/xnQhBm/HueDdadoX9eT5jX/uRuqrU9bnLROXM++ztHEowTXCC75QWwcoPur0HY8bPsQDiyGsxvg7EZo+Qh0fw3ca5fdSYm7pihK7qUiatQos3bNZrNldONmgmPKupkI6XJujnrc/KrTYcq5+X3OzRGRnBwM2VmcP3WKOrVqoRgM/5QZDGA0YjYaLV9NpvxfDYb8280mFOXmiI2igAKpaTdwdXOz3G2mUlBQ/ilXKZbRDq0WlY0NilZreeV+b4NJreb8xWgaNG2K1tnFcunOwQGzLhu1pxcqWxvLtpsvtaMjBkXJTSjvlITo9Xpy4uPRFJLQCFGhzGZIj4PEs6jiTxGUcATob7VwJKkRjGpfm7/OJrLxRBzPLzvEH5M742Bj6RpatZYutbqwJmoNv5//vXRJzS3OvjDwY8uE4s1vwfFVcGQ5HPsF2j0JHarHSqvVmaIoKLa2YGtbqvVI9Ho9e9asyTPnqCzp9XqOFjO5KKqNvWvWFDgaVsROpTqWEBUmJxOun4PrZ1HFn6Z19HbUX82BpAuQcwOwXBZtqHUHPrZamJLUCBRF4cNhLel3+S8uJGQw67cTfPBQy9zyBxs8yJqoNay5sIaX2r6EveYuJwp61oOHF0PH5yFiFlzYCnsWoDn0HY08ekJWB9BW/SeBCyFEpWIyQWoMXD+HKv40LWIiUC/9Cq6fh7TLudXUQMDt+ykqcAvE5FGfK2lqapsLfyp6eZOkRgDg7mjDx8ODeezLPSzfH0OPJt70aeYLQKhvKDWdanIl/Qorz6zk8aaPl81Ba7aG0b/C+c2waSbKtcM0jl2Ned4mCH3KMqLj6FU2xxJCCGGRnYoSe4paSTtRbY2E5POQeM7yTD+D5VEeaqAuwO03T9q7g2cDTB71OJVgoGHHgWh8GoFHXdDYYtTrOb5mDbVLMveyjElSI3J1rOfFM13r3bzN+xjt63ji6qBFpah4osUTvLn7TRYdXcSDDR7EUVuGT6Gu1wPqdMNwbBUZa97ANTsGdnwMexZa5uF0fB6c5XlJQghRXIrZaElSUqItq70nnrVcPko8CxnxaIA2ABf/taNKCx51MHnU43yKijrteqPxaQyeDcDREwCjXs/ZNWto0Lg/VLJ5XZLUiDxeuL8B64/HciEhg3fWnODDhyxzaIbUH8KS40u4mHaRTw9+yqthr5btgVUqzE0Gs/WCigH1VWh2fgzXIi3PlNr3JYQ8Bu2ftTyaQQghhGWSbub1mwnLP4mLJvEMA69HoYo0Fr6rozfXFQ/cG4SirtEIvBqAZ31wqw1qDUa9nhNr1hAUUvkSl6JIUiPysNOq+XBYSx7+325+2n+ZwcE16dzAC61Ky6uhr/L0pqf58dSPdKvVjY41O5Z9AIoKc6P+0Gyw5e6o7R9abgPf/5Xl1aAPdHgW6nSVRfyEENWDPgviz+CXsg/VzjOQfOGfJCY7JV915ebLrLFH8axnSVa8GlhGW7zqg2d9DGoHdt6cFK++h5KWO5GkRuTTNsiD0e1rs2T3Rab9coQN/9cFBxsNHWt2ZETjEfx46kf+s/M/rBi8Ag87j/IJQlGgYW9o0Aui/4Ldn1ueBn52veXl0xxCJ0CzoZanhgshxL3MZMydpMv18/9cLrp+HlJj0GImFCCqgH1dA/IkLga3Omw+conuDzyO1sa24ONV0TvuJKkRBZratzGbTsZzOTmLeZvP8XLfxgD8X5v/Y++1vZxPPc9/dvyHeffPK9mCfCWlKFCni+V1/Tz8vQAif4C4Y/D7C7BuuiWxaT0KAsJk9EYIUXmZzZCRiEf6GZTDKTdHXG4mLkkXwFj4E9DNti6kqL1wrdsGVY2G/yQxHvUsa4HdXlevJ+vUGstdSdVMpUtqYmJiGDVqFPHx8Wg0Gl5//XUefvjhPHUyMzNp0qQJDz/8MB999JGVIq3anGw1vDGoKU99d4BFf11gWJta1KvhhL3Gng+7fsjIP0fy15W/+P7E94xuNrpigvKsBwM+gh6vwaEfLA/NTDwNkd9bXp4NLCsYN3sQvBtXTExCCPFvOZmWSbrXz92cnHsu93ttdgr3AZwtYD+1jeVOIs/6eV9eDTBoXdi+di39+/dHVYUuF5W1SpfUaDQa5s6dS0hICLGxsbRp04b+/fvj6PjP3TbvvPMO7du3t2KU1UOvpj50b1SDLacTmPnbcb4dH4qiKDR0b8jUtlN5e8/bfHLwE9r4tqGZZ7OKC8zeHTpOstzyHbPXktwc/8VyjXnb+5ZXjSaW5KbJQPBuKiM4QoiyZTJCykXLKMutu4pujbrctqZLQTK1ntjVao7q1jwXz/qWP9rcAkGlLninKnq5qKxVuqTGz88PPz8/AHx9ffHy8iIpKSk3qTl79iynTp1i0KBBHDt2zJqhVnmKovDGoGbsPLedv84msu5YLP1aWP5tHmn0CLuv7SbiUgSv/vUqPw36CVt1Idduyy9ACAyzvPq+B6fXWFYpPhcBCSdh60nY+i641IT6PVHq9EBjzK7YGIUAiN4JUduhy1RQV7ofu6IwZjNkJOSOsqgSzhB6YSea/70NydFgzCl8Xzu3f+4ouu2ldwlg48atMuJSTkr8v2v79u3Mnj2bAwcOcO3aNVatWsWQIUPy1Jk/fz6zZ88mNjaW4OBg/vvf/xIaGlri4A4cOIDRaCQg4J+1C6dMmcLs2bPZtWtXidsTJRfk5cjTXevy383neOuPE3RtVAMHGw2KojCr4ywi4yO5kHqB/x3+H8+3ft56gdq5QPCjlldWMpxaAydWW36RpF2Bg0vQHFxCP1SQsBCCOkFgewjsIAv8ifK3+OazcJy8od0T1o2lIq1/zfKLv/9sa0dStMwkSIq6ecno5vyWW6MuutTcamrA7/b91LaWERbPereNuNx83VzTJR8ZcSlXJU5qMjIyCA4OZvz48QwdOjRf+fLlywkPD2fhwoWEhYUxd+5c+vTpw+nTp/H2tix9HxISgsFgyLfvhg0b8Pf3ByApKYnRo0ezaNGi3PJff/2Vhg0b0rBhwzsmNTqdDp3un0lXaTefBKzX69EX0alulRVV5272KU7dO9UpqrywstKc1y0TOtXml4OXuZKSzf+2nmNS93oAOKgcmN5uOlP+msLXx76mW81uNPFoUuL2yyLGPDRO0PwRy0ufhXJpF8r5CJSzG1GlRMG1Q5bX7nkAmD3qYvZtefMVjNmnBTgUfFdXVe0fJY3TWso7xrJov6A2bv09bkw8i+kuP/t75mdITjram//H9O2ftzz7rZwVGWNWCkrSBUi+gJJ0ASX5AiRFoSRfQMlKLrRNMwq4BmD2rIfRrQ4n4/U07NAftXcjyyhwCS8XlbaPVck+UkwlaU8xm83m0h5IUZR8IzVhYWG0a9eOefMsndlkMhEQEMDkyZOZNm1asdrV6XT06tWLCRMmMGrUqNzt06dP5/vvv0etVpOeno5er+ell15ixowZ+dqYOXMms2bNyrd96dKlODg45NsuinYwUWHJWTW2KjP/aWXExeafsmUZyzimP4avypeJzhNRK4X8J68E7HMS8Uw/g0fGGTzTT+OSfaXAeplaT27Y1STdzo8bdn6k2/qTbueHTuMi83NEiT1wyDKZ/lyNvhyvNdLK0VQMjSGDAUcnArC+2Sdk2xQyclHGx3TSxeF48+Wki8393taYXuS+2Ro30u18yLD1JcPGm3Q7X9Jt/ciw9caksilyX1G+MjMzGTlyJKmpqbi4FL2ER5kmNTk5OTg4OLBixYo8ic6YMWNISUnh119/vWObZrOZkSNH0qhRI2bOnFlovcWLF3Ps2LFC734qaKQmICCAxMTEIj8UvV7Pxo0b6dWrV7GfsFuSfYpT9051iiovrKw053U7k8nMw1/s4ciVNEaG1mLWoKa5ZdezrvPwmodJ0aUwKXgS45uNL3H7ZRFjqdrPTEKJPYwSe/Tm1yMoyQUtBGFhtnUlVeWOU0AzFPfa4BqI2S0As2sguAWAjdNdnZe1+kdJ47QWq/SRMmhD+47lEqcxbCKmnm/d1XHvmZ8hmdfRftLIsu/kw5ZRjbtlNkN2iuX/aHIUys2RFpIuoCRHoWReL3p3Jx/M7nXAo55lhNa9DmaPuuBeB2zu/OiX8uofZb3fPdNHiiktLQ0vL69iJTVlOmMtMTERo9GIj0/e5/T4+Phw6tSpYrWxc+dOli9fTsuWLVm9ejUA3333HS1atChRLLa2ttja5p+4qtVqi/VhF7deafcpTt071SmqvLCy0pzXLa8OaMqjX/zN8v1XGN+5HvW9Lb/AfbW+vNzuZV7d8SpfHvuSgfUHUtOp9D/A7ibGErfv6gOuvaFR738qZKdC3HFIPGO5oyHxjOWVfBFFl4obqXAmuuDG7d3B2d8y1O7sB86+qBy88U25gk28Lxr3WpZ5Feq7+7cvTp3S9I/iHtvaKrSPlGEbapW6yNVbq9TPEOWfv5e1KqX4S+0b9ZZF6JKjLfNckqNve13MM8elINkaV2z8mqDyrA+edS23SHvUA4+6KLZOlMU4a3n1j7Ler9L3kWIqSVuVbhp+586dMZnu/NjysWPHln8wIo/2dT3p2cSbTSfj+XDdKb4Y3Ta3bGDdgfxy9hf2x+3ng70f8FmPz6wY6V2yc4XaHS2v2+mz0cef4UDEL7Sr7436xhXLLZ0plyw/bLNTLJOUs5Ih/njubmogDCDq05tbFMvkZMca4OBp+XrzvcrWDb+USyiX3MHF17Ldzg1U1W8RLXGPM902D8J42/dms+X/SHI0JP87aYmG1MtgvsPvACcfy+iKZ72b67pYvuqdA1gf8ZfcWVSNlWlS4+XlhVqtJi4uLs/2uLg4fH3Lf5KYKH/T+jVmy+kENpyIY190Eu2CLBNqFUXhtbDXePj3h9kSs4Xtl7fTpVYXK0dbxrR24N2EONdWmNoV8LyU7FTLD+QbsTdf1+BGLKa0q6TEnMJdk42SHgcmg+U20YyEfIdQw82l0P/7z0ZFbUluHLzAwQO1nRvBCemothyw3GFh72EZIXK4+VXjhMpUthP1hCi2m6vmEnvbkhtb3wN9JqTEWP4IuMNoCxo7cA8q+OVWO98Kurkq8SR3UTHKNKmxsbGhTZs2RERE5M6pMZlMREREMGnSpLI8lLCS+t7OPNI2gB/3XuKj9adZ9lR7lJsTZ+u71+fxpo+z+Phi3t3zLqG+odhp7KwccQWyc7W8fPIuRGjU6/nr5oPjtGq15am6N65BZiJkXLckN5mJkJGIKT2e5Cvn8LAxoGQmWX74m42QHmd5ASogCGDXlgLD0AKDAPOJ528mO+6Wr/buqGzdaHI1CdXfFywJ0a2YbV1A44iN4Yblr2r5K1cURncDUhItSyWkXr7tFWP5mnYFDP9aD+rYivztOPn+k6h41MmbuDh6y+ikKJUSJzXp6emcO3cu931UVBSRkZF4eHgQGBhIeHg4Y8aMoW3btoSGhjJ37lwyMjIYN25cmQYurGdyj/qsPHCZPVFJ7Dp/nU71/1nnZWLwRNZEreFK+hW+OfYNE0MmWjHSSkilAqcallcBjHo9O24lQFotGHSWJOjWyE5WCsYbCZw5speGATVQ61L+ueSVmQRZyZizU1DMJhR9Bugz8qxuqgYaAsT9nu/YWqAfwNHnQOtoWfvnVtJze/Lz7212LpZLZLdv01TwQozi7mx+27Km0+D/otxIwD95L6q9lyAjPnfUUZN2lQEpV9AcKs4CloplXtmNa5a3nV6wrJbrVtvy8EW3wMJHW4S4CyVOavbv30/37t1z34eHhwOWO5wWL17M8OHDSUhIYMaMGcTGxhISEsK6devyTR4W9y5/N3tGhgWyeFc0H204Tcd6nrmjNQ5aB6a2ncrU7VP55vg3PNTwIWo4FPwLXBSDxhZc/C2vm0x6PWcS/Knfu4BLYIAhR8fG31fS6762aPU3IDM5N/ExpicQfSqSOj4uqHLSLZfMbr7M2SkoORmWRm4lRLd+KZWU2sZyJ5itkyURuvW9jRPYOlteueXOYON82/f/+qq1l9voS8psRm3Mtoya6FJyRwLJSITMRNQ34gmNOoF68WeWsuRoy37zQ9EA7QCi8zapcNsvDFtXS9LiFgCutW6+bvve2R80chu0qHglTmq6devGne4CnzRpklxuquKe7V6PZfsucehSCltPJ9C9sXduWZ+gPnx/8nsOJxxmXuQ8ZnXMv16QKEeKCr3G0TKR8l9Jj0mv51j6GgILmEhp0OtZ++fv9OvRGa0hI0/CQ3Yq6NLyb8t93Sy7NVfCmANZSZZXGZzPraRHo7Gja5YedcI8y1/6WgdL0qO1v+17xwK23VZPrbUkXfleWjArd56kWtbMZjAZUJt0ltE29JYHIuozQZ91M8HMsrxyMlDp0mkYexDVpt2QcyP/v0F2KprsVAaajXCk4EOquLkybtq/CzSYXWqRpLfBPbAJKtdauXfyGRxqsPXAaboOfASto3u5fiRClFalu/tJ3Bu8ne0Y3SGIL7Zf4OONZ+jWqEbuaI2iKExpO4VRa0ex6uwqRjYeSSOPRlaOWBSHWVFb5t9ove9cuSAmo2XOhe4G5KSDLt2SDN36Pic9f3nOzTq5399WD7MlydBZEiYFcAO4fKnMzvl2WuABwHxY/U+yo7EBldYyWqSoAOXm97e/V+W+1wDdb9xAc3GmZVK44bbnA+2eB0d+siR9Rr3lDiFjDlpgIMDhO8eoBpoAFDGIdmtcy6zSoDh43by77uZkc0cvjHbuHL0QS/PQbmhcfCxlzr5g44TBYMi9BHp74mvW68k4nlrgWkxCVBaS1IhSe7pLXX74+yJHr6Sy4UQcfZr9c4dbiHcIvWv3ZsPFDXxy4BMW9lpoxUhFhVGpwd7N8rpbJpNltCI30bmBITudvTu3EdqqBRpTzs3RjNtHNbJu+z4zzwgH+iwwZFmSCWPOP4mFMceSfNxGMRstdQ1ZoCskvkIogAtAYVNPMuKLbkBt868RJ4ebo1KW700aOy7FXiegQQvUDu435zG55ZnTpNc4sH7b3/QZMAStTf7LQCa9notpa2jWuL9MChdViiQ1otQ8nWwZ16kO87ac4+MNZ+jVxAeV6p+5Dy+2fpHNMZvZeXUnO6/spFPNTlaMVtxzVKqb82ycwNmyyazXk+CShLlRGf8yNpnApEefncHG9Wvo1b0LWsWcNwHCbLlUZDaTO4Jkvvn1tvcGQw579uwlrGNnNDYO/1zusnG0XI5TaSzvb31Va9GbFNZv2kyfAYPR2toXGapRr+fwmjXUvL/gOVUA6PUYVbYyF0lUO5LUiLsy4b66LNkdzem4G6w5do2BLf+Z0BrgEsCIxiP47sR3fHrwUzr4d0ClyG2aohJSqUBlC7Yq9Bpny2rQpUyazHo9iSfSMQe0z9+GW0DBO+n1GNV2lkRHCFFq8htG3BVXBy1Pdq4LwNxNZzGa8k4in9BiAo5aR04mnSTiUoQ1QhRCCFFNSFIj7tq4zkG42ms5F5/O74ev5ilzt3NnVFPLk9bnHZqH0WS0RohCCCGqAUlqxF1zsdPyVBfLaM2nEWcxGPPeEjuq6ShcbFy4kHqBNVFrrBGiEEKIakCSGlEmxnYMwsPRhqjEDFYdupKnzMXGhXHNLStKfx75OXp5LpEQQohyIEmNKBOOthqe6WoZrfls81n0/xqtGdl4JB52HlxOv8yv5361RohCCCGqOElqRJkZ1T4ILydbYpKyWHHgcp4yB60DT7Z4EoCFhxeiM5Zw8Q8hhBDiDiSpEWXG3kbNxG71AJi3+Rw6Q95JwY80egRvB2/iMuNYcaaAp/YKIYQQd0GSGlGmHgsLxMfFlispWfy0LyZPma3alqdbPg3Al0e/JNtQnKf9CiGEEMUjSY0oU3ZaNc91rw/AvC3nyNbnHa15sP6D+Dv6k5iVyPLTy60RohBCiCpKkhpR5oa3C8Df1Y64NB0/7s374EGtWsvTwZbRmq+PfU2mPtMaIQohhKiCJKkRZc5Wo2ZSjwYAzN9ynqycvKM1g+oNIsA5gKTsJH489aM1QhRCCFEFSVIjysVDbWpRy92exHQdX/51IU+ZVqXlmeBnAPjm+Dek56RbI0QhhBBVjCQ1olzYaFRM7dMIgM+3nudaalae8gF1BhDkEkSqLpXvT35vjRCFEEJUMZLUiHIzONifdkHuZOmNvLfmVJ4ytUrNsyHPAvDt8W9Jy0mzRohCCCGqEElqRLlRFIU3BjVDUeC3w1fZG5WUp7xPUB/qu9Xnhv4G3x7/1kpRCiGEqCokqRHlqnlNVx5tFwjA9F+O5LnFW6Wockdrvj/5PSm6FGuEKIQQooqQpEaUu1f6NqKGsy3nEzL4ZNOZPGX3B95PY4/GZOgz+O7kd1aKUAghRFUgSY0od24ONrz7YAsAFm2/wMFLybllKkXFs8GW0ZofT/9IuknuhBJCCFE6ktSICtGrqQ8PtqqJyQyTlx4iJTMnt6xbQDeaezYn25jNX7q/rBilEEKIe5kkNaLCzBzcjNqeDlxJyeKlnw5jMpkBy4Ti51o9B8Ae3R4SshKsGaYQQoh7lCQ1osK42mv5/LHW2GhURJyK57+bz+WWdfLvREuvlhgw8NWxr6wYpRBCiHuVJDWiQjXzd+XtB5oD8MmmM6w6dBm4OVoTbBmtWXluJWeSzxTahhBCCFEQSWpEhXukXQBPd6kLwMsrjrDrfCIA7Xza0VTbFKPZyLt73sVsNlszTCGEEPeYSpfUxMTE0K1bN5o2bUrLli35+eef85RHRUXRvXt3mjZtSosWLcjIyLBSpOJuvNK3MQNa+KE3mnn62wMcjkkBoL99f+zUdhyIO8CaqDXWDVIIIcQ9pdIlNRqNhrlz53LixAk2bNjAiy++mCdxGTt2LG+++SYnTpxg27Zt2NraWjFaUVoqlcKcR4IJq+PBDZ2BUV/t4fjVNNxUboxvNh6A2ftmk5KdYt1AhRBC3DMqXVLj5+dHSEgIAL6+vnh5eZGUZFle//jx42i1Wu677z4APDw80Gg01gpV3CU7rZqvx7ajbW130rINjF18gCsZMLrJaOq61uV69nXe3vO2tcMUQghxjyhxUrN9+3YGDRqEv78/iqKwevXqfHXmz59PUFAQdnZ2hIWFsXfv3lIFd+DAAYxGIwEBAQCcPXsWJycnBg0aROvWrXn33XdL1a6oPBxtNXwzrh0hAW6kZOmZd0LNqWtZvNv5XdSKmvXR61kXtc7aYQohhLgHlHiYIyMjg+DgYMaPH8/QoUPzlS9fvpzw8HAWLlxIWFgYc+fOpU+fPpw+fRpvb28AQkJCMBgM+fbdsGED/v7+ACQlJTF69GgWLVqUW24wGPjrr7+IjIzE29ubvn370q5dO3r16pWvLZ1Oh06ny32flmZ5CrRer0ev1xd6frfKiqpzN/sUp+6d6hRVXlhZac6rotip4atRrRi35ABHrqQx6pv9/O+xVoxvNp5Fxxbx5u43aeTWiFpOte7qOGXxGVTV/lHSOK2lvGOUPnJv/gy5par2j5LuV9X6SEnaU8x3cYuJoiisWrWKIUOG5G4LCwujXbt2zJs3DwCTyURAQACTJ09m2rRpxWpXp9PRq1cvJkyYwKhRo3K37969m5kzZ7J+/XoAZs+eDcDUqVPztTFz5kxmzZqVb/vSpUtxcHAo9jmKiqMzwqJTKs6mqdAoZsY01LNL+wWXjZfxU/vxlNNTaBWttcMUQghRgTIzMxk5ciSpqam4uLgUWbdMJ6Tk5ORw4MABpk+fnrtNpVLRs2dPdu/eXaw2zGYzY8eOpUePHnkSGoB27doRHx9PcnIyrq6ubN++naeffrrAdqZPn054eHju+7S0NAICAujdu3eRH4per2fjxo306tULrbZ4v0BLsk9x6t6pTlHlhZWV5rwqml6vR6VsZE2KN5tPJ7L4rC2vDnyLxZf+j2u6axz0OMjM9jNRFKXU7d/tZ1BV+0dpz62ilXeM0kfu/Z8hVbF/lHS/qtZHbl1pKY4yTWoSExMxGo34+Pjk2e7j48OpU6eK1cbOnTtZvnw5LVu2zJ2v891339GiRQs0Gg3vvvsuXbp0wWw207t3bwYOHFhgO7a2tgXeGaXVaov1YRe3Xmn3KU7dO9UpqrywstKcV0XSqmDeiBBe+/Ukqw5d4a3f4nis6//xR8Isfo/6nXru9XiixRN3d4wy+Ayqav8oaZzWUt4xSh+5d3+GQNXtHyXdr6r0kZK0VeluHercuTMmk6nQ8n79+tGvX78KjEhUNK1axZyHg/FwtOGrHVH8sM2WDq0e51j2t8w9OBcfRx8G1i04mRVCCFF9lekt3V5eXqjVauLi4vJsj4uLw9fXtywPJao4lUrh9YFNeX1gUxQFdh9qii99AHh95+vsurrLyhEKIYSobMo0qbGxsaFNmzZERETkbjOZTERERNChQ4eyPJSoJp7oXIf5Iy0PwTx7sitO+rYYTAZe2PwC+2L3WTs8IYQQlUiJk5r09HQiIyOJjIwELI8tiIyM5NKlSwCEh4ezaNEilixZwsmTJ5k4cSIZGRmMGzeuTAMX1Uf/Fn788GQYrva2XDs/BK2uKdnGbJ6LeI6DcQetHZ4QQohKosRJzf79+2nVqhWtWrUCLElMq1atmDFjBgDDhw/no48+YsaMGYSEhBAZGcm6devyTR4WoiTaBXmwcmJHark5kxQ1AiWrIVmGLCZumkhkfKS1wxNCCFEJlDip6datG2azOd9r8eLFuXUmTZrExYsX0el07Nmzh7CwsLKMWVRT9b2d+OXZjrTw9yLt4uOYMuuRacjk6Y1Py6UoIYQQle/ZT0IUxdvZjuVPt6dP0wAyLo3BkGFJbCZumshfl/+ydnhCCCGsSJIacc9xsNGw4LE2PN2lMVkxYzHcaIzOqOP5Lc+zIXqDtcMTQghhJZLUiHuSSqUwvV8T3n+wNfqro9GntsRgMjB121RWn1tt7fCEEEJYgSQ14p72aGggS8Z3QJv0ODkpbTFh4vWdr7P42GLu4rFmQggh7kGS1Ih7Xqf6Xqx69j68daPIud4ZgDkH5vD2329jMOV/GrwQQoiqSZIaUSXU93bi1+c609z+cbLjBmA2K/x05iee2fQMiVmJ1g5PCCFEBZCkRlQZHo42LJ3QngGBw8m+/Dhmk5Y91/Yw7LeH+Pva39YOTwghRDmTpEZUKbYaNZ8MD+H5Dg+SGT0JY7YPSdnXeWrDUyw4sgCj2WjtEIUQQpQTSWpElaMoCs/f34AfxzyAW/IUcpJDMWNm0bFFfJ3+DfGZ8dYOUQghRDmQpEZUWWF1PVn34v0M8JtM1pVHMRttuGiM5uE/h7P98nZrhyeEEKKMSVIjqjQXOy0fDw9h7sBxqK6FY8yqyQ19Ks9FPMd7e94j25Bt7RCFEEKUEUlqRLUwsKU/fz4zmJrJT5OT1BGApaeW8vDvwzmddNrK0QkhhCgLktSIasPXxY5nm6h4qfVUcq48gcngTHTaBYb/MYLFxxZjMpusHaIQQoi7IEmNqFZUCozrWJvfxj+Bf/pr6G80xWjWM+fAHMatfYLYjFhrhyiEEKKUJKkR1VIjX2f+eK4vY+vNRHdtKGaTloMJ+xm86kHWRa+zdnhCCCFKQZIaUW3ZatRM69eEZSNfxCNlGsasWmQZ05m6bSpTtr5Cqi7V2iEKIYQoAUlqRLXXOtCdDZOG8WjND8hJ7IHZrLD+4hoGrHyAvy7/Ze3whBBCFJMkNUIAdlo1Mwa15Pthb+CS/CImnRep+us8G/Esr/71Ouk56dYOUQghxB1IUiPEbdoFebDpudE86DOHnOudMJsVfr+wmv6/PMDuq7utHZ4QQogiSFIjxL842Gh4+4HWLH7gHZySJmHK8SBZF89TG59i1u530Jl11g5RCCFEASSpEaIQHep5sum58Qz0/IicpPYA/Bq1ko9T5nEg7oCVoxNCCPFvktQIUQQnWw0fDG3H14Pewyn5OUx6NzKUZCZETGDGjrfI0GdYO0QhhBA3SVIjRDF0qu/F5mcnMMx7DvrkdgCsOv8TfX4eLA/HFEKISkKSGiGKyd5Gzev9WvFsjQfwSHsOU447qfp4not4jhcjXiY5O9naIQohRLUmSY0QJVTbCdY8OY4nguZjSL4Ps1kh4vJa+qwYyJ/n/8RsNls7RCGEqJYkqRGiFGw0KsJ7teD3kR8QqHsFY7YPWcY0pu2Yxri1z8gzpIQQwgokqRHiLtT3duaPCSOZ0nwBpqTemE1qDiTsov/KwXx3fKk8+VsIISqQJDVC3CWVSmF8pwasH/sWLZSZGDNrozdn8eH+93hw1UjOppy1dohCCFEtVLqkJiYmhm7dutG0aVNatmzJzz//nKf8k08+oVmzZjRt2pTnn39e5i+ISqOWuwNLxzzAR53/h03KMMxGGy7cOM7wNSP4NX0dmfpMa4cohBBVWqVLajQaDXPnzuXEiRNs2LCBF198kYwMy1ogCQkJzJs3jwMHDnD06FEOHDjA33//beWIhfiHoij0b1GTbc/8h4Een2C40Qwwsc+wg36rhrDp4mZrhyiEEFVWpUtq/Pz8CAkJAcDX1xcvLy+SkpJyyw0GA9nZ2ej1evR6Pd7e3laKVIjCOdlqeH9IF35+8H94Z07ElOPODUMi/7f1Bcb+OZGr6VetHaIQQlQ5JU5qtm/fzqBBg/D390dRFFavXp2vzvz58wkKCsLOzo6wsDD27t1bquAOHDiA0WgkICAAgBo1ajBlyhQCAwPx9/enZ8+e1KtXr1RtC1ERmtd05c/xT9Lb9Dzm5B6YzWoOJO6g/8rBLDi0CL1Jb+0QhRCiytCUdIeMjAyCg4MZP348Q4cOzVe+fPlywsPDWbhwIWFhYcydO5c+ffpw+vTp3FGVkJAQDAZDvn03bNiAv78/AElJSYwePZpFixbllicnJ/PHH38QHR2Nvb09/fr1Y/v27XTp0iVfWzqdDp3unwcPpqWlAeSO8BTmVllRde5mn+LUvVOdosoLKyvNeVW08o6xLNovTRsmo4FuvlpeaD+TmRv+Ys+NL8Exis+PfMbPp3/lvc6v09qndbHbL4/+UdxjW1tV7SPyM6RsVNX+UdL9qlofKUl7ivkuZtoqisKqVasYMmRI7rawsDDatWvHvHnzADCZTAQEBDB58mSmTZtWrHZ1Oh29evViwoQJjBo1Knf7zz//zNatW5k/fz4As2fPxmw28/LLL+drY+bMmcyaNSvf9qVLl+Lg4FCS0xSiTJ1IgZ8TItG5r0WlscwXa6AEM9S5L84qZ+sGJ4QQlUxmZiYjR44kNTUVFxeXIuuWeKSmKDk5ORw4cIDp06fnblOpVPTs2ZPdu3cXqw2z2czYsWPp0aNHnoQGICAggF27dpGdnY1Wq2Xr1q089dRTBbYzffp0wsPDc9+npaUREBBA7969i/xQ9Ho9GzdupFevXmi12mLFXJJ9ilP3TnWKKi+srDTnVdHKO8ayaL8s+kd/4HlDXxbuGMjXpxagOO/lLIf5KO004xqPp9Y1b/r17leh/aO051bRqksfudu68jOkavWPku5X1frIrSstxVGmSU1iYiJGoxEfH5882318fDh16lSx2ti5cyfLly+nZcuWufN1vvvuO1q0aEH79u3p378/rVq1QqVScf/99zN48OAC27G1tcXW1jbfdq1WW6wPu7j1SrtPcereqU5R5YWVlea8Klp5x1gW7d9t/9Bq4aVebXgsdB7T/viTvTe+AvsYvjz1OY4mLzziPeke1O2uYihN/yhOu5VBdegjZVFXfoZU3vZL20Z17CMlaatMk5qy0LlzZ0ymwldhfeedd3jnnXcqMCIhyo+vqx2LHxvGrvNdeGX9NyTZriJDk8jz2yYTcqwT73Z9jQDnAGuHKYQQ94QyvaXby8sLtVpNXFxcnu1xcXH4+vqW5aGEqFI61qvBlqenMKn+/zAldcZsVhF5fScDf3mA93Z/QpYhy9ohCiFEpVemSY2NjQ1t2rQhIiIid5vJZCIiIoIOHTqU5aGEqHI0ahVPdmzCqwF96WL/LoaM+pjQs/TM13Rf1p/VZ/+QFbSFEKIIJU5q0tPTiYyMJDIyEoCoqCgiIyO5dOkSAOHh4SxatIglS5Zw8uRJJk6cSEZGBuPGjSvTwIWoqpy08OnQ3iwb9DU1dc9gynEjw5jI67um0//nR4iMP2ztEIUQolIq8Zya/fv3071799z3t+4wGjNmDIsXL2b48OEkJCQwY8YMYmNjCQkJYd26dfkmDwshihYc4M7aCc/y25EBvL1jAVkOm7icdYrxm8YRRAtC0kOo7V7b2mEKIUSlUeKkplu3bnccAp80aRKTJk0qdVBCCAtFUXggOIg+Td9l/vaH+ObEQnDeR7RylEG/DWFYvRG8FPosTjZO1g5VCCGsrtI9+0kIkZ+dVs1L97dj67jP6erwNoaMepgxsOL8d3Rb1oclx37EYMq/SrcQQlQnktQIcQ/xcrJl7oP9mOw2ljqGSRh1NdCZ0/jowLvcv2wwWy5tt3aIQghhNZLUCHEPqumosGLUOD7r/D3OGQ9jNjiQpI/h+S3P8dCv47lsuGLtEIUQosJJUiPEPUpRFHo29Wf70//hpaZfo7nRDbNJzYWMSBamL+CJdf/HpbRL1g5TCCEqjCQ1QtzjNGoV4zo0Y9dTc3ms1ueYb7TBbFY4lLSNAb8MJnzTDBKzEq0dphBClDtJaoSoIuxt1Ezv1ZGIx+fT5MYkjOmNQTGy8coqev7Ul3d2fUK6Pt3aYQohRLmRpEaIKsbD0YbHA3344+Evaa2djjErACM6lp39mp4rBrA5Yxc5xhxrhymEEGVOkhohqqgAdweWjBzJ8oFLCTJOxKTzIsd8g836Ndz/8yCWnfgFo8lo7TCFEKLMSFIjRBXXopYbv49/lgXdl+GZPRKT3pkMUwLv7HuDrj8OYPWZPzGZTdYOUwgh7pokNUJUE10a+LBh3Es8rA7HJfMBzEZ7Ug1XeH33NHose4AN0ZvlgZlCiHuaJDVCVCOKotDKQ8vm8TN4I+QHHDL6YTbacj0nmmm7pjA7eSHbLu+U5EYIcU+SpEaIakilUni4dQN2Pv0+Lzf9Ftv0nphNNqSprvB/2ycz4OcR/H11r7XDFEKIEpGkRohqTKNWMbp9U3Y9PYdn63wByZ0xmzTEZB1nwsYnGLxiFAdjI60dphBCFIskNUIIbDQqJnRqyYzAvowNWIDqRifMZjVRGZGMWT+KB1eO41BcpLXDFEKIIklSI4TIZaOGF7q2YeeEeYyutQAlPQyzWcW59P2MXjeKB1aMYd+1A9YOUwghCiRJjRAiHydbDS/37MDuJxfyeK35KOmhmM0qLmQcZPyGsQz6+XH+vrrP2mEKIUQektQIIQrlaKthWs/O/D3hf4wJWIAqvT1ms4rozMNM2DieAT+PZMfl3XK3lBCiUpCkRghxRw42Gqbe35G/JyxkfOD/UKV3xGxWcynzKBMjnqL/z4+yLWaHJDdCCKuSpEYIUWz2NmrCe7Tn7wmf80Tt/6FOt9wtdTnrBJM2T6TP8oeJiN4myY0QwiokqRFClJi9jZr/6x7G30/N46k6i1Df6ILZpOGa7jQvbptEjx+HsPrMWnm2lBCiQklSI4QoNTutmue7teXvpz9jcsNvsM3ojtmkJVF/gdd3v0yXpf349thP6I16a4cqhKgGJKkRQtw1O62apzuF8PfTc5nW/DscM/tiNtqRZrzG7ANv0emHnsw/8A2Z+kxrhyqEqMIkqRFClBmNWsXj7Zqx6+kPeaftcjyyh2LSO5NlTmLhsY/pvPR+3t31Gam6VGuHKoSogiSpEUKUOZVK4YGWddn61Ezm3bcCf/3jmHI80JPOj2cX0XVZT6ZvfY+EzARrhyqEqEIkqRFClBtFUejeyJ/1T77CNz1XUI+nMGb7YiSbPy4u5f6fevPsuleJSYuxdqhCiCpAkhohRIUIrVOD1WMms2LQClpowjFm1sasGPgr7nf6/zKAkb8+x+G4I9YOUwhxD5OkRghRoZr6u7L0sXGse+Qn7nN6A1NGI1DMHE3ZzuPrHqPf8hFsiNqMyWyydqhCiHtMpUtqUlJSaNu2LSEhITRv3pxFixblKf/jjz9o1KgRDRo04Msvv7RSlEKIuxXo6cDnwx7irzFLedD7Y5T0tpjNKi5nH+Ol7S/QZekAlhxdjs6os3aoQoh7RKVLapydndm+fTuRkZHs2bOHd999l+vXrwNgMBgIDw9n8+bNHDp0iNmzZ+eWCSHuTR6ONrzZrxd7JnzJs/W+xi7jfsxGW1INl/no4Nt0+uF+Pvh7vtwxJYS4o0qX1KjVahwcHADQ6XSYzebcJdf37t1Ls2bNqFmzJk5OTvTr148NGzZYM1whRBmxt1Hz7H1t+PuZT3ir9TI8dMMw6V3RmVP5/vRCuiy7n6lb3ybJmGTtUIUQlVSJk5rt27czaNAg/P39URSF1atX56szf/58goKCsLOzIywsjL1795boGCkpKQQHB1OrVi2mTp2Kl5cXAFevXqVmzZq59WrWrMmVK1dKegpCiEpMrVJ4MKQ+Wye8wRfdVlLXPAFjth8mdERc/YWP0z5hzJ8vcjj+qLVDFUJUMiVOajIyMggODmb+/PkFli9fvpzw8HDeeOMNDh48SHBwMH369CE+Pj63zq35Mv9+Xb16FQA3NzcOHz5MVFQUS5cuJS4urpSnJ4S4VymKQqf6Pvw69nlWP7CCdnbTMGY0sEwqTt3O42tH0mfZcH47t06eMSWEAEBT0h369etHv379Ci3/+OOPmTBhAuPGjQNg4cKF/Pnnn3z99ddMmzYNgMjIyGIdy8fHh+DgYP766y8eeugh/P3984zMXLlyhdDQ0AL31el06HT/TDBMS0sDQK/Xo9cX/hyaW2VF1bmbfYpT9051iiovrKw051XRyjvGsmi/qvaPksZZ0ep42vO/oY9wNak/r634mSPswuR4iKu6E7y2cyrv7KrBIw0fYVyLR3C2cS71caSPyM+Q8m6/tG1U5z5SkvYU860JK6WgKAqrVq1iyJAhAOTk5ODg4MCKFStytwGMGTOGlJQUfv311zu2GRcXh4ODA87OzqSmptKpUyd+/PFHWrRogcFgoEmTJmzduhVXV1fatGnDrl278PT0zNfOzJkzmTVrVr7tS5cuzZ2zI4S4N+UYYWfiDbZn7UXntAeVxvJMKcVkQyNVa/o6tcdL42XlKIUQZSEzM5ORI0eSmpqKi4tLkXVLPFJTlMTERIxGIz4+Pnm2+/j4cOrUqWK1cfHiRZ566qncCcKTJ0+mRYsWlmA1GubMmUP37t0xmUy8/PLLBSY0ANOnTyc8PDz3fVpaGgEBAfTu3bvID0Wv17Nx40Z69eqFVqstVswl2ac4de9Up6jywspKc14VrbxjLIv2q2r/KO25VbRbMQ7o24shWi1m8yNsOXuNz/b+RFTOetR2cZzib06l/00Dp3ZMajOWzv7tURSlRO1LH5GfIeXVfmnbqM595NaVluIo06SmLISGhhZ5eWrw4MEMHjz4ju3Y2tpia2ubb7tWqy3Wh13ceqXdpzh171SnqPLCykpzXhWtvGMsi/arav8oaZzWcnuMfZrVpk+zqZyNe4bZ2/9gZ8IqVI4nOZu+jxe27cNdE8ATLUczvMkQ7DR2JW6/LGIsj33kZ0jhqurPkJLuV1X6SEnaKtNbur28vFCr1fkm9sbFxeHr61uWhxJCiDwa+DjzxcMj2Dn2e0b4z0OTfh9mkw3Jhhg+OvgOHX/ozn+2fUhchtx4IERVVaZJjY2NDW3atCEiIiJ3m8lkIiIigg4dOpTloYQQokBuDja82qsre5+Zx39aLsUjeximHDf0pPNr9Hf0/Lk3w1c/w87Lf3MXUwqFEJVQiS8/paenc+7cudz3UVFRREZG4uHhQWBgIOHh4YwZM4a2bdsSGhrK3LlzycjIyL0bSgghKoJWreLRNo14tM1MDlycxMc7VxGZ+jsqhyhOpO7kmYiduGpqMarpSB5vPhRHraO1QxZC3KUSJzX79++ne/fuue9vTcYdM2YMixcvZvjw4SQkJDBjxgxiY2MJCQlh3bp1+SYPCyFERWlT24sfak8gMX00n+/cwarzP6G330+q4TLzjnzIgiOfcZ9fX55r9Zi1QxVC3IUSJzXdunW745DtpEmTmDRpUqmDEkKI8uDlZMuMPvfzqrE7vx+7wIJ9y7lsigDbBLZeW83Wa6vxMNbFfEHDwAZ90Kgq3b0UQogiVLpnPwkhRHnTqFU8GFyfDU++xqrBq+no+Crm9OaYzQpJ6gu8/vc0On5/P+/s/IzErERrhyuEKCZJaoQQ1VojXxf+99AIdj3xLU8EfoE6pTsmgxNZ5iSWnVtE9+U9GbF6EjtkYrEQlZ6MrQohBOBip2XSfW0ISovDscFLLDzwG6cy16O2v8ix1G1MjNiGk8qPhxoOY1zww3jYeVg7ZCHEv8hIjRBC3EalQPeG/vwyejJbRq5gqPcctBkdMRttSTddY/GpeXRb1oORv06S28KFqGRkpEYIIQrh42LHrH69mWHqxfoTF1mwfyXnszehtr/M0ZRtPBOxDWe1Hw83HMaYljJ6I4S1SVIjhBB3oFYp9G8eRP/mL3El5Vk+37mVNdGr0dsf4AbX+PrkPL45sYAWHp15quWjMnojhJVIUiOEECVQ082edwb0Y5axD2uOX2ThgZVE6yJQ21/mSPI2Jm3bhq3Rk+N7LvFEq0fwdZRHxAhRUWROjRBClIJGrWJwyzqsGTeF9Y+sZIDHh6jTLXNvdOrrLDu/iF4/9+aBFWP59ewacow51g5ZiCpPkhohhLhLAR4OvD+oH/ufWcC7bZbjlToMY2YdUMxcyDjAf3a9Qvvvu/Lixjc4kXjC2uEKUWXJ5SchhCgjGrWKfk0DMUe3IrTLFL47eIiVZ1aTrv0bvTaNiKu/EHH1F7y0dRjeZCiPNhmCm52btcMWosqQpOZfjEYjGo2G7OxsjEZjsfbR6/XF3qc4de9Up6jywspKEqO1lHeMZdF+adq4F/pHac+tolXGPqLValGr1fm2eznZ8vL99zG1R2f2X7zOgr/Xsuf6ehSHYyTqo5h/ZA6fH55LM/eOjG8+DJPZVNanI0S1I0nNTWazmdjYWJKTk/H19SUmJgZFUYq9b3H3KU7dO9UpqrywspLEaC3lHWNZtF+aNu6F/lHac6tolbWPuLm54evrW+A+iqLQLsiLdkGjyNCNYMWhM3x7dBXXjH+htr/CsZS/CN/xFxqTC7u3n+Sp1o9Q371+WZ6WENWGJDU3xcbGkpKSQo0aNTCZTDg7O6NSFW/KkclkIj09HScnpzvuU5y6d6pTVHlhZSWJ0VrKO8ayaL80bdwL/aO051bRKlsfMZvNZGZmEh8fD4Cfn1+R9R1tNYxp35Qx7ZtyPiGdL/fsYP3F39HZ7cegSWPt5R9Ze/lHvLR1GdpwMCOaPYCXvVeZnJsQ1YEkNVguOaWkpODt7Y27uztpaWnY2dmV6JdWTk5OsfYpTt071SmqvLCyksRoLeUdY1m0X5o27oX+Udpzq2iVsY/Y29sDEB8fj7e3d7GPVa+GE+8N7Ms7pj5sOR3D7E0/cFkdCQ6nSNRf4Ivjc/ni2GfUd27N482H0r9eT+w19qU5LSGqDUlqsFxHB3BwcLByJEKIe9Gtnx16vb7A+TVFUakUutT3I/1MU7re/yK/nzjPD8d+51LOX6jtYziXvp+Zf+/nrb/fpI1XF0Y3HyLzb4QohCQ1t6ms8wiEEJVbWf3scLTVMCq0OaNCm3M15UW+2buX387/QbpmL9gksTdxA3u3bkBrcmHXthM82fohGnk0KpNjC1EVSFIjhBCVkL+bPa/17sqr5i4cjklh0b7N7Li2HqNDJHp1GuuuLGPdlWW4a2ozoM4ARrccgp9T0XN6hKjqKueFc1EpdOvWjRdffLFcj7F161YURSElJaVcj2M2m3n66aepU6cOarWayMjIMms7OjoaRVGKbHPp0qV4eJTsYYdjx45lyJAhdxecuOcpikJIoDvzhw1j/8SFzAn9mZqpIzGnN8dsVpNsuMj3Zz+n98re9Fo2nAUHviM5O9naYQthFTJSIwr1yy+/oNVqy6y9bt26ERISwty5c3O3dezYkWvXruHq6lquDwFct24dS5Ys4ffff6dFixYlmtBZFh588EGGDh1aoccUVY9WraJHI3+yzzelR6//Y92pi3x/7DfOZfyFyiGKWN0JPj92gs+PfoSnqR4pJ3QMa9IfJxsna4cuRIWQkZpqKCeneM+g8fDwwNnZuVxjsbGxKXR9j7J0/vx5/Pz8CAsLw9fXF42m5Pm82WzGYDCU6vj29vYVnkiJqs1Oq+bh1o34dfRUdo1dyQsNv8Xf+DDGrJqgmLiuPstHkW/RcWkXHv7lGX49u45sQ7a1wxaiXElSUwiz2UxmjqHYr6wcY5nVLcmIRbdu3Zg0aRKTJk3C1dUVb29v3nnnnTxtBAUF8fbbb/PMM8/g5ubGU089BcDKlStp1qwZtra2BAUFMWfOnHxt3375SafTMWXKFGrWrImjoyNhYWFs3bo1zz47d+6kW7duODg44O7uTp8+fUhOTmbs2LFs27aNTz/9FEVRUBSF6OjoAi8/3SmuoKAg3n33XcaPH4+zszOBgYF88cUXhX5GY8eOZfLkyVy6dAl3d3fq1q2bez7PP/883t7e2NnZ0blzZ/bt25e7363Y1q5dS5s2bbC1tWXHjh2FHufChQt0794dBwcHgoOD2b17d25ZQZef3n77bby9vXF2dubJJ59k2rRptG7dOl+7H330EX5+fnh6evLcc8/l3q0nxC2u9lqe7NCK9eNnsPWx1YypNR/71Psx6mpgVvScurGT/+yaSvsf7mP07/9HxMXtGMylS9CFqMzk8lMhsvRGms/caJVjH5vZq0T1lyxZwhNPPMHevXvZu3cvzzzzDPXr1+fpp5/OrTNnzhymTp3KW2+9hUql4sCBAzzyyCPMnDmT4cOHs2vXLp599lk8PT0ZO3ZsgceZNGkSJ06cYNmyZfj7+7Nq1Sr69u3L0aNHadCgAZGRkdx///2MHz+eTz/9FI1Gw5YtWzAajXz66aecOXOG5s2b8+abbwJQo0YNoqOj8xwjMjKSRx999I5xzZkzh7feeotXX32VFStWMHHiRLp27UqjRvnvBPn000+pV68eX3zxBZs2bcLNzQ2Al19+mZUrV7JkyRJq167Nhx9+SJ8+fTh37lyeBGTatGl89NFH1K1bF1dX10L/HV577TU++ugjGjRowGuvvcaIESM4d+5cgeud/PDDD7zzzjt8/vnndOrUiWXLljFnzhzq1KmTp96WLVvw8/Njy5YtnDt3juHDh9OyZUuGDx9eaByievN2tuOFLh1okJ5Ms7CZ/HB4L2uj1nJDsw+0qRxK2sShnZtQmexZu+FvRrccQseaYWhU8utA3PukF1cBAQEBfPLJJyiKQoMGDThw4ACffvppnqSme/fuTJo0CRcXF1QqFY899hj3338/r7/+OgANGzbkxIkTzJ49u8Ck5tKlS3zzzTdcunQJf39/AKZMmcK6dev45ptvePfdd/nwww9p27Ytn3/+ee5+zZo1y/3exsYGBwcHfH19Cz2X+fPn06NHjzvG1b9/f5599lkAXnnlFT755BO2bNlSYFLj6uqKs7MzarUaHx8fXFxcyMjIYMGCBSxevJh+/foBsGjRIjZu3MhXX33F1KlTc/d/88036dXLkmiaTCbS0tIKjH3KlCkMGDAAgFmzZtGsWTPOnTtHw4YN89X973//yxNPPMG4ceMAmDFjBhs2bCA9PT1PPXd3d+bNm4daraZx48YMGDCAzZs3S1IjiqW2pyNv9OnNG/TmxLUUvt63hS2XN5Jtewg06exJXMeezevQ4kw77y6MbD6Qdt75RwuFuFdIUlMIe62aE2/2KVZdk8nEjbQbOLvc+dEKxalrq1a4UYJL3+3bt88zJyU0NJT58+djNBpzFwJr27Ztnn1OnjzJAw88kGdbp06dmDt3bp79bjl69ChGozHfL2idToenpydgGWV5+OGHix94Ac6cOcODDz54x7hatmyZW64oCr6+vrlL1RfH+fPn0ev1dOrUKXebVqslNDSUkydP5qn778+uMLfHdGu5/Pj4+AKTmtOnT+cmZbeEhoayefPmPNuaNWuW59/Cz8+Po0ePFiseIW7X1M+NjwY/iNk8hD3n4/hg3TIuKCcw2B1Br7nBrvg/2bX5TzQ4UsvcEJtLDvQI6oKt2tbaoQtRbJLUFEJRFBxsircyqMlkwmCjxsFGU6yk5k51TaayXy3U0dHxrvZPT09HrVZz4MCBfAmPk5Plzopby8VXhH/flaUoSrl8blD8z+72mG4lmXcbU0Wep6geFEWhTW1PxgbUoU/fZzlwOZlvD0WwJ24rerujGDTpRCuHeHnHIdQ77Gjp0YHhTftzn39Ha4cuxB1JUlMF7NmzJ8/7ffv20aBBgyKXa2/SpAk7d+7Ms23nzp00bNiwwP1atWqF0WgkPj6e++67r8A2W7ZsSUREBLNmzSqw3MbGBqPRWOS5NGzYsERxlVa9evWwsbFh586d1K5dG7Ascb9v375yX5sHoFGjRuzbt4/Ro0fnbrt9krIQFUGtUuhUz4dO9UZiNI1gX3QiSw5uYceVTZgcj4M2jUNJWzi0YwsqbPAz18dwzkTvej1xtinfOyOFKA25+6kKuHTpEuHh4Zw+fZoff/yRRYsWMXny5CL3eemll4iIiOCtt97izJkzLFmyhHnz5jFlypQC6zds2JDHHnuM0aNH88svvxAVFcXevXt57733+PPPPwGYPn06+/bt49lnn+XIkSOcOnWKBQsWkJiYCFjuWtqzZw/R0dEkJiYWOOIwadIkNm/eXOy4SsvR0ZGJEycydepU1q1bx4kTJ5gwYQKZmZk88cQTZXqsgkyePJmvvvqKJUuWcPbsWd5++22OHDkij+oQVqNWKbSvW4PPHniQN2sP4IuuK+nu8hbaGz0w5XhgIocryglm7p1Bpx/v4+FV41l+aoUs9CcqFRmpqQJGjx5NVlYWoaGhqNVqnn766dzbtgvTunVrfvrpJ2bMmMFbb72Fn58fb775ZqF3PgF88803vP3227z00ktcuXIFLy8v2rdvz8CBAwFL4rNhwwZeffVVQkNDsbe3JywsjBEjRgCWibRjxoyhadOmZGVlERUVle8YwcHBLFu2jJkzZxY7rtJ6//33MZlMjBo1ihs3btC2bVvWr1+Pu7t7mR/r3x577DEuXLjAlClTyM7O5pFHHmHs2LHs3bu33I8txJ2oFAgN8qRTgyGYTA9w8FIS3x/YxeZLGzE4HkdtG8+ptH28vWcfb+95i/rOLRnaqB996/SihkMNa4cvqrFKl9SkpKTQs2dPDAYDBoOBF154gQkTJgAQExPDqFGjiI+PR6PR8Prrr9/1xNSqQKvVMnfuXBYsWJB7d87tf/FHR0cXeNfOsGHDGDZsWKHt6nS63Pkyt44za9asQi8vAXTt2jXf5aNbGjZsmGftFrCM3txaU+fWyM2wYcOK/Hf9923gwB0fe/Diiy/y/PPP5/kM7Ozs+Oyzz/jss88K3Kdbt27FWjPo9nO4xc3NLc95jRw5kmeeeSZPnddffz33Li+AXr16Ua9evdz3ixcvznesuXPnFnkHlhBlTaVSaBvkSXDNvvzxp4maLV9nxZFDbL4cQaY2ErXdVc7diOTD/ZF8uP99ajs25cGGfelXtzf+Tv7WDl9UM5UuqXF2dmb79u04ODiQkZFB8+bNGTp0KJ6enmg0GubOnUtISAixsbG0adOG/v373/UkWJGXTqfj6NGjHD9+nOeff97a4VRJmZmZLFy4kD59+qBWq/nxxx/ZtGkT69evt3ZoQhRKpUCrADdC6/bGbO7F4cup/BQZyYboTWRqDqF2uMTFjOPMPXScuYfm4G/fgMEN+jCgbh+CXIOsHb6oBipdUqNWq3FwcAAsv1zNZnPuX7x+fn65t8r6+vri5eVFUlKSJDVlbO3atYwePZrBgwfz0EMPWTucKklRFNasWcM777xDdnY2jRo1YuXKlfTs2VNGYcQ9QVEUQgLcCAnoxjvmrhy5nMqKw8dYH7WJG+qDqB2iuZp1loVHzrLwyDy8bYMYUK83A+v3oYFbA5k/JspFiZOa7du3M3v2bA4cOMC1a9dYtWpVvicJz58/n9mzZxMbG0twcDD//e9/CQ0NLfYxUlJS6Nq1K2fPnmX27Nl4eXnlq3PgwAGMRiMBAQElPYUq5d+PKSgLQ4YMkV+s5cze3p5Nmzbl2y63a4t7kaIoBAe4ERzQmTfNnTh2JY1fjpxi3YVNpCgHUTueI14XzTcnvuCbE1/gYeNPvzq9GVS/L009m0qCI8pMiZOajIwMgoODGT9+fIFPHV6+fDnh4eEsXLiQsLAw5s6dS58+fTh9+nTuA/1CQkIKfDDghg0b8Pf3x83NjcOHDxMXF8fQoUN56KGH8PHxya2XlJTE6NGjWbRoUaFx6nQ6dDpd7vtbv6T1en2+Z+fo9XrMZjMmkyl3VOjW++IoyT7FqXunOkWVF1ZWmvOqaOUdY1m0X1X7R0njtJbK2kdu/ezQ6/W5+5XkGV236hZnn+LUvVOdosoLKytJjI19HHi1V2umm1txJi6d346dZ+2FLSSY96NxPEtSzlV+OL2YH04vxkVTg56BPRhQtzfBNYJRKaW/KbckMVqr/dK2UdX6SEmUpD3FXJKnJ/57Z0XJN1ITFhZGu3btmDdvHmD5zx4QEMDkyZOZNm1aiY/x7LPP0qNHj9zLIDqdjl69ejFhwgRGjRpV6H4zZ84scELr0qVLcy9v3aLRaPD19SUgIAAbG5sSxyiEqN5ycnKIiYkhNja21E9yrw7is2D/9RwOZZ8lzeYYGqfTKKqc3HIbsxNNtU1pZduMIE0QaqXs1qYS967MzExGjhxJamoqLi4uRdYt06QmJycHBwcHVqxYkSfRGTNmDCkpKfz66693bDMuLg4HBwecnZ1JTU2lU6dO/Pjjj7Ro0QKz2czIkSNp1KgRM2fOLLKdgkZqAgICSExMzPehZGdnExMTQ1BQELa2tty4cQNnZ+diD4mazeZi71OcuneqU1R5YWUlidFayjvGsmi/NG3cC/2jtOdW0SprH8nOziY6OpqAgADUajUbN26kV69e+VaELoxery/2PsWpe6c6RZUXVlaSGIvjYlImfx69xK9nt3I1Zx8a55Mo6n+eD2OncqZLza4MrNubUN9QbNR3/oOzrGMsj/ZL20Z17CO3pKWl4eXlVaykpkwnCicmJmI0GvNcKgLw8fHh1KlTxWrj4sWLPPXUU7kThCdPnkyLFi0Ay8qyy5cvp2XLlqxevRqA7777Lrf8dra2ttja5n9miVarzfdhG41GFEVBpVLl/iC79b44bg03F2ef4tS9U52iygsrK0mM1lLeMZZF+6Vp417oHyWN01oqax+59bNDq9Xmrnxd0M+aOynJPsWpe6c6RZUXVlaa8ypIfR9XXvBpwQs9WxCTlMmfR2P45eQ2Lmb/jcb5BNmaG2yI+YMNMX9go3Kgk/99DK7fl041O2GvKfqRLGUVY3m2X9o2qlMfub294qp0dz+FhoYWuuZI586dK+21fiGEEKUT4OHAM10b8UzXRlxJGcWao1f45cRfXMjcjcb5ODnaNLZcXs+Wy+vRKLaE+XZkcP2+dKnVBScbpzsfQFQbZZrUeHl5oVariYuLy7M9Li4OX1/fsjyUEEKIKqimmz0T7qvPhPvqE5s6grVHr7DyxG7Opu9C43wMg00yO69tYee1LajQ0MY7jMEN+tI9oDsOKoc7H0BUaWU6fmtjY0ObNm2IiIjI3WYymYiIiKBDhw5leShRhYwdOzbfsgBlITo6GkVR7rja8J1069atQh5yecuzzz7Lgw8+WGSdli1b8umnnxa7zbL6LCrSzJkzCQkJKbJOac5LUZTcy9eicvN1tWNc53r88dTj/PXkx7zSbAn1cv5DTmJ3jLoamDCwL34nr+98nS7LujJu/dPs1e3letZ1a4curKTEIzXp6emcO3cu931UVBSRkZF4eHgQGBhIeHg4Y8aMoW3btoSGhjJ37lwyMjIYN25cmQYuRHW2efPmKj/6OWXKlDwPZh07dizJycksWbIkd1tAQADXrl0rcC2rwly7di33+V7R0dHUqVOHQ4cO3TGBEtbl7WLH6I51GN2xDgk3hrD++DVWHzvI0dQdqJ2Ogd01Dl/fx2Hgt1W/09S9JYPq96Vn7Z74Olbt/yviHyVOavbv30/37t1z34eHhwOWO5wWL17M8OHDSUhIYMaMGcTGxhISEsK6devyTR4W1Y9ery+XyXtmsxmj0YhGU+mmiJUbLy+vfEsTVDVOTk55nj1WELVaXeLkrqong9VBDWdbHm8fxOPtg7iePoCNJ+JYdewwh5P+QuV0DLV9DCeSD3Ni32E+2PcB9V2bMrBeb3rX7k2AS/VesLWqK/Hlp1sP+fv36/aH702aNImLFy+i0+nYs2cPYWFhZRlzxTCbISej+C99ZtnVLcFd9jqdjueffx5vb2/s7Ozo0qULBw8ezC3funUriqIQERFB9+7dcXJyomPHjpw+fTpPO7/++iutW7fGzs6OunXrMmvWrCLX2zCZTLz55pvUqlULW1vb3OT1lluXBZYvX07Xrl2xs7Pjhx9+wGg0Eh4ejpubG56enrz88sv5HgZpMpl47733qFOnDvb29gQHB7NixYp857R27VratGmDra0tO3bsKDTWU6dO0bFjRxwcHOjQoQPbtm3LU37s2DH69euHk5MTPj4+jBo1isTExELbS0lJYcyYMbi7u+Pg4EC/fv04e/YsYEmwatSokSfe1q1b07hx49z3O3bswNbWlszMzEKPAfDRRx/h5+eHp6cnzz33XJ4FqP59+enUqVN07twZOzs7mjdvztatW1Gr1fkus1y4cIFBgwbh5OREcHBwvgeM3s5sNjNz5kwCAwOxtbXF398/z7PAdDodU6ZMoWbNmjg6OhIWFpZnhevFixfj5ubG+vXradKkCU5OTvTt25dr167l1tm6dSuhoaE4Ojri5uZGp06duHjxIpD38tPMmTNZsmQJv/32G+7u7qjVarZu3Zrn8pPJZKJWrVosWLAgz3kcOnQIlUqV2+7tl5/q1KkDQKtWrVAUhR49erBz505sbW2JjY3N086LL77IfffdV+jnJazD08mWR0MDWT5+EH9NmMkD6mdobvwQffwgDJlBmM0K51JPMPfgXPqv6k+LJS34PPJzolKjrB26KAfV50/bktJnwvu1ilVVBbgVs9li1Z12uZitwcsvv8zKlStZsmQJtWvX5oMPPmDYsGGcPXs2z5D866+/zttvv03t2rV59tlnGT9+fO7TtP/66y9Gjx7NZ599xn333cf58+d56qmnAHjjjTcKPO6nn37KnDlz+N///kerVq34+uuvGTx4MMePH6dBgwb/nMq0acyZM4dWrVphZ2fHnDlzWLx4MV9//TVNmjRhzpw5rFq1ih49euTu8/777/PDDz+wcOFCGjRowPbt23n88cepUaMGXbt2zdP2Rx99RN26dXMvJxRk6tSpzJ07l8aNG/Phhx/ywAMPEBUVhaenJykpKfTo0YMnn3ySTz75hKysLF555RUeeeQRNm/eXGB7zz77LNHR0fz222+4uLjwyiuv0L9/f06cOIFWq6VLly5s3bqVhx56iOTkZE6ePImdnR2nTp2iadOmbNu2jXbt2hU50rJ161b8/f3ZsmUL586dY/jw4YSEhOQ+sf52RqORIUOGEBgYyJ49e0hNTc0dQf23119/nZkzZxIcHMzrr7/OiBEjOHfuXIGjXCtXruSTTz5h2bJlNGvWjNjYWA4fPpxbPmnSJE6cOMGyZcvw9/dn1apV9O3bl6NHj+b2gczMTD766CO+++47VCoVjz/+OFOmTOGHH37AYDAwZMgQJkyYwI8//khOTg579+4tcH2YKVOmcPLkSVJTU/n0009xdnbGy8uLq1ev5tZRqVSMGDGCpUuXMnHixNztP/zwA506daJ27dr52t27dy+hoaFs2rSJZs2aodFo0Gg01K1bl++++46pU6cCllHGH374gQ8//LCwfzJRCbg5aGnvbebN/j3JNPRk04k4fj12ir1x21F7bkClyQBgweEFLDj8T/K7uO9i2vi0sVbYogxVzsUoRLFkZGSwYMECZs+eTb9+/WjatClffPEF9vb2fP3113nqvvXWW3Tq1ImmTZsybdo0du3aRXa2ZaGrWbNmMW3aNMaMGUPdunXp1asXb731Fv/73/8KPfZHH33EK6+8wqOPPkqjRo344IMPCAkJYe7cuXnqvfjiiwwdOpQ6derg5+fH3LlzmT59OkOHDqVJkyYsXLgQV1fX3Po6nY733nuPr7/+mj59+lC3bl3Gjh3L448/ni+eN998k169elGvXj08PDwKjXXSpEkMGzYsN4lydXXlq6++AmDevHm0atWKd999l8aNG+cmaFu2bOHMmTP52jp79ixr167liy++4L777iM4OJgffviBK1eu5P71361bt9wRi+3bt9OqVSs6d+6cu23r1q15krOCuLu7M2/ePBo3bszAgQMZMGBAngn4t9u4cSPnz5/n22+/JTg4mM6dO/Of//ynwLrh4eH06dOHhg0bMmvWLC5evJhnjtztLl26hK+vLz179iQwMJDQ0NDcpOrSpUt88803/Pzzz9x3333Uq1ePKVOm0LlzZ7755pvcNvR6PQsXLqRt27a0bt2aSZMm5Z5HWloaqampDBw4kHr16tGkSRPGjBlDYGBgvlicnJywt7fH1tYWHx8ffH19C1z9+7HHHmPnzp1cunQJsIz6LVu2jMcee6zAc6xRowYAnp6e+Pr65vaj8ePH5zmP33//nezsbB555JEC2xGVj6u9lmFtavHtmJ7se/513mm9klbMJjt2MIb0hpjN//z6G7tuLP1XPsCCwwuITo22XtDirslITWG0DvDq1TvXw/KDM+3GDVycnYu1uNod66rtIPvGHY97/vx59Ho9nTp1+idsrZbWrVtz8uTJPHVbtmyZ+/2tJ53Hx8cTGBjI4cOH2blzJ++8805uHaPRSHZ2NpmZmflGFNLS0rh69Wqe4wJ06tQpz1/yAG3bts39PjU1lWvXruW5HPn/7d17WFTV/j/w9zBcB4ERUBBBSGQQUm4iiApMiqEWioF2DAWio6aiqeCt1ARTTC3Fk5XaN9RfIp40pUzNNMEiBVIhUkDwiJIlaEfuyGVmfn9w2DnOHWcYGD+v5+F5ZvZae+3PHpfDYq2119LX14evry8zBPWf//wHTU1NmDBhglg5ra2t8Pb2llm2PI8/eaevr48RI0Ywn09hYSHOnz8vde7GzZs3wePxxI4VFxdDX19f7B6srKzg6urKlBkcHIy33noL9+/fR3Z2NoKDg8HlcpGdnY05c+bg559/xooVK+TG7O7uziziBnT8mxUVFUnNW1paCgcHB7G5Ij4+PlLzyqoHjw+PdZo+fTp27NiBwYMHY+LEiZg8eTLCwsKgr6+PoqIiCAQCic+npaUFVlZWzHsOhwNnZ2exa1ZXVwMALC0tERsbi9DQUEyYMAEhISGYMWMGE1dXeHl5wc3NDenp6Vi1ahWys7NRXV2N6dOnq1ROTEwM1q5di0uXLmHUqFHYt28fZsyYAVNT0y7HRrTHzNgA4d4DEe49EPdq+Ug9dwN5t39HteVKJk9lw3/wccHH+LjgY+bY/on74WMj/f8S6ZmoUSMLiwUYKvkFJhQCBoKO/IpWIVUmrwYWGHx8gm5n937nQoYNDQ1ISkqSukGpsbHxU11X1V8CjY0d3cPffvstBg4cKJb25ArR6vgF09DQgLCwMLz//vsSaV395Tp8+HBYWloiOzsb2dnZ2LBhA8zMzLBz507k5+ejra0No0ePllvGkxOqWSyWWhaelFcPnuTg4IDS0lKcPXsW33//PRYsWICtW7ciOzsbDQ0NYLPZuHz5sljjC4BYA1HafTw+hyotLQ2LFy/G6dOncfjwYaxZswbff/89Ro0a1eV7jIqKYho16enpmDhxolhDSxn9+/dHWFgY0tLS8Nxzz+HUqVNi84VI72VrYYyUVzwAeOBe7Ti8lXEVubfvQt/sGgzMf4V+n797aGNOxzCv90/cj+GWkqvXk56FGjW9mLOzMwwNDZGTk8PMF2hra8PVq1dVWlfFx8cHpaWlGDJkiFL5zc3NYWdnh5ycHLFhlJycHPj5+ck8z8LCAgMGDEBubi6CgoIAAO3t7bh8+TLTs+Dq6gojIyPcuXNH4RCNsi5duiR2vStXriA+Ph5Ax70fPXoUTk5OSj095ebmhvb2duTm5mLs2LEAgL/++gulpaVwd3cH0PGLOzAwEJmZmbh27RrGjh2L9vZ2tLS0YPfu3fD19VXrX/yurq6orKxEVVUV85Th1atX1VK2iYkJwsLCEBYWhoULF2Lo0KEoKiqCt7c3BAIBqqurn3ryrLe3N7y9vbF69WoEBAQgPT1daqPG0NAQAoFAYXmvvfYa1qxZg8uXL+PIkSP49NNPZebtHMKSVu4///lPzJw5E/b29nB2dpbomSS9n62FMQ7PC4BIJMJbGc74utAXLP0a9HHZLJH38QbOmJYxsDZQfhkB0n2oUdOLmZqaYv78+Vi+fDmzTtD777+PpqYmxMXFKV3OunXr8PLLL2PQoEGIjIyEnp4eCgsL8dtvv+G9996Tes7y5cvx7rvvwtnZGV5eXkhLS0NBQQEOHjwo91pvvfUWNm/eDBcXFwwdOhQffvghampqmHQzMzMkJCRg6dKlEAqFGDt2LGpra5GTkwNzc3PExMTILlyGXbt2wcXFBa6urtiyZQsePnzIfD4LFy7E3r17MXPmTKxYsQKWlpYoLy9HRkYGPvvsM4leCBcXF0yePBnz5s3D7t27YWZmhlWrVmHgwIGYOnUqk4/P5yMhIQG+vr7o06cP6urqEBgYiIMHDzKTT9Wlc15RTEwMtmzZgtraWmYo8Wk2fdy3bx8EAgH8/f3B4XDwxRdfwMTEBI6OjrCyskJUVBSio6OZieD379/HuXPn4OHhgZdeeklh+bdu3cKePXswZcoU2NnZobS0FGVlZYiOjpaa38nJCd999x3Kysrg6Ogoc3K4k5MTRo8ejTfeeAMCgQBTpkyRGUP//v1hYmKC06dPw97eHoaGhsxnFhoaCnNzc7z33ntITk5W4hMjvRWLxcLOmd7YOdMb1fWP4LeRCwDQM/oTpoMlF7l84WjHsiYssFAYXdhjN4B9FtFE4V5u8+bNiIiIwOzZs+Hj44ObN2/i6NGjcp8GelJoaChOnDiBM2fOYOTIkRg1ahS2b98u9WmRTosXL8ayZcuQkJCA4cOH4/Tp0/j666/FnnySJiEhAbNnz0ZMTAwCAgJgZmYmsXpucnIy1q5di5SUFLi5uWHixIn49ttvmcdvVbV582Zs3rwZ3t7eyM3NxfHjx5knwzp7nAQCAV588UUMHz4cS5YsAZfLlTnnadeuXfDx8cHLL7+MgICOv/JOnjwpNtQSHBwMgUAAPp/PHOPz+RLH1KHz0e2GhgaMHDkSc+fOZZ5+eprhQy6Xi71792LMmDHw8PDA2bNn8c033zBDOWlpaYiOjkZCQgJcXV0RHh6O/Px8qRN9peFwOCgpKUFERAR4PB7mzp2LhQsXYt68eVLzz5kzBzweD+PGjYONjQ3z9J40UVFRKCwsxLRp02BiInvzQ319fezcuRO7d++GnZ2dWF3U09NDbGwsBAKBzIYW0T39zYxRsfkl3EqZDN8Bz6O+eDPqizej9S/JHkkRRPA44IHh+4fjtwe/aSFaIkH0jKitrRUBENXW1kqkNTc3i65fvy5qbm4WCQQC0cOHD0UCgUDpslU5R5m8ivLIS5eV1pX76m6ajlEd5feW+nHq1CkRAFF5ebnS51IdkSw/Li5OFBYWpvC8x79DWltbRcePHxe1trYqfV1VzlEmr6I88tJlpXXlvrqbpmI8nH9H5LjyRMfPquOiYfuGyf3RRIzPch2R9/v7STT8RIgOOHbsGPr06QMXFxfcuHEDS5cuxZgxY8SePCLKq62txbVr15Ceno6vv/5a2+EQLZvh64AZvg7Yl/MfrP+mGPXFHXNu9Exuw9TpE4n8w/d3TCj+NfpXGprqZtSoIUQH1NfXY+XKlbhz5w6sra0RFBSk0oaXRNy0adOQl5eHN998U2J5AfLsivJzQN8HRcgTPIeDeZUQNjv+r4Ejgpnbaon8Hgc6llAomF0Ath5bIp2oHzVqCNEB0dHRzLwPoVCIuro6mJubazmq3uuHH35QuOYUeXatD3PDxlc84LTq2/8dYTG9N4mR1dh97UOx/F7/zwtAR88N0Sz6X0sIIYR0QcXml1CwTrwnb9uR/tjo9R12jd8lkd/jgAemfTNN4jhRH2rUEEIIIV3E5RiiYrP4EgaLD11FUZkdimKKsGnsJrG02/W3saZmDZra5G9oS7qGGjWEEELIU6rY/BL4rv2Y91u/K0Xq2TKEOYehKEZyi5OxX47Figvyt0shqqNGDSGEEKIG+173w/ZXPZn328/ewI2qjn38imKKJObUnLp1inlSiqgHNWoIIYQQNZnmbY+VE//eIPbF7ReY/c5YLBauvHYFXgZeYudQw0Z9qFFDCCGEqNF8vvj6UM+tPin2PtI0EsdePiZ2jBo26kGNGiITn89XaWPMrsjKygKLxRLb/0kTRCIR5s2bh+eeew5sNhsFBQVqK7uiogIsFkutZRJCercnJw9f+6NW7L2juSMKZheIHaOGzdOjRg2R6auvvsKGDRvUVp60RtLo0aPx559/wsLCQm3Xkeb06dPYv38/MjIycPfuXQwbNkyj1yOEkLy3xzOvX9r5k0Q6W4+NwuhCsWNzz8zVeFy6jBo1z6DW1lal8llaWsLMzEyjsRgaGsLW1lbjS4nfvHkTAwYMgL+/P2xtbaGvr/q6kyKRCO3t7RqIjhCii/qbi28oe/2POok8eiw95Mz8e3PWi39eRGNbo8Zj01XUqJFBJBKhqa1J6Z/m9ma15e2cVKYMPp+P+Ph4xMfHw8LCAv3798fGjRvFynBycsJ7772HN998E1wuF3PndvwlcPToUTz//PMwMjKCk5MTPvjgA4myH+9ZaWlpQWJiIgYOHAhTU1P4+/sjKytL7JycnBzw+XxwOBz07dsXoaGhePjwIWJjY5GdnY3U1FSwWCywWCxUVFRIHX5SFJeTkxM2bdqEuLg4mJmZYdCgQdizZ4/Mzyg2NhaLFi3CnTt30LdvXwwePJi5n8WLF6N///4wNjbG2LFjkZ+fz5zXGdupU6cwYsQIGBkZ4aefJP/akiY7Oxt+fn4wMjLCwIEDsX79eqZBdOLECXC5XAgEAgBAQUEBWCwWVq1axZw/Z84czJo1S6lrEUJ6rrKNk5jXk3f+KDWPuaE5YtxjmPej0kdpPC5dRdskyNDc3oyAjACtXPviPy6qlH///v144403kJeXx+xXM2TIEMybN4/J88EHH2D58uXYsGED9PT0cPnyZcyYMQPr16/Hq6++ip9//hkLFiyAlZUVYmNjpV4nPj4e169fR0ZGBuzs7HDs2DFMnDgRRUVFcHFxQUFBAcaPH4+4uDikpqZCX18f58+fh0AgQGpqKm7cuIFhw4YhOTkZANCvXz9UVFSIXaOgoAD/+Mc/FMb1wQcfYMOGDXj77bdx5MgRzJ8/H8HBwXB1dZWIOzU1Fc7OztizZw/Onj0LLpcLAFixYgWOHj2K/fv3w9HREVu2bEFoaCjKy8thaWnJnL9q1Sps27YNgwcPVmqY7O7du5g8eTJiY2Nx4MABXL9+HXPnzoWFhQWSkpIQGBiI+vp6XL16Fb6+vsjOzoa1tbVYA/HChQtYuXKlwmsRQno2A7ZyfQeJIxOx//p+5n12ZTaCHYI1FZbOop4aHeDg4IDt27fD1dUVUVFRmDNnjsRmhi+88ALi4+Ph7OwMZ2dnfPjhhxg/fjzWrl0LHo+H2NhYxMfHY+vWrVKvcefOHaSlpeHLL79EYGAgnJ2dkZiYiLFjxyItLQ0AsGXLFvj6+uLjjz+Gp6cnnn/+ecTHx8Pa2hoWFhYwNDQEh8OBra0tbG1twWZLbvC2a9cujBs3TmFckydPxoIFCzBkyBCsXLkS1tbWOH/+vNTYLSwsYGZmBjabDRsbG/Tr1w+NjY345JNPsHXrVkyaNAnu7u7Yu3cvTExM8H//939i5ycnJ2PChAlwdnYWa+zI8vHHH8PBwQEfffQRhg4divDwcKxatQoffvghhEIhLCws4OXlxTRisrKysHTpUly9ehUNDQ34448/UF5ejuBg+kIjRBcciPNjXu/58ZbMfFdmXWFex/8Qr9GYdBX11Mhgom+C3NdylcorFApRX18PMzMzhZvgKZPXSM8I9ahXOtZRo0aJzUnx8/PDrl27IBAImIaDr6+v2DnFxcWYOnWq2LExY8Zgx44dYud1KioqgkAgAI/HEzve0tICKysrAB29LNOnT1c6bmlu3LiBadPE90aRFpeHhweTzmKxYGtri+rqaqWvc/PmTbS1tWHMmDHMMQMDA/j5+aG4uFgs75OfnSLFxcUICAgQ+zfx9/dHQ0MDfv/9dwwaNAjBwcHIyspCQkICfvzxR6SkpODf//43fvrpJ9y9exd2dnZwcXFR6bqEkJ4piPfYSsNnypAqYxDAgG0g9r6qsQo2pjaaDE3nUKNGBhaLBY4BR6m8QqEQ7frt4BhwlGrUKMorFApVjlcRU1PTpzq/oaEBbDYbly9flmjw9OnTBwBgYmLyVNdQhYGB+H9+Foulkc8NePrPTho+n4/PP/8chYWFMDAwwNChQ8Hn85GdnY2qqioEBQWp/ZqEkJ4vLyoPfgc7enZCjoRI3WKByEbDTzogN1e8Ryk/Px8uLi5Sh3c6ubm5IScnR+xYTk4OeDye1PO8vb0hEAhQXV2NIUOGiP3Y2toC6Og9OXfunMxrGhoaMpNjZeHxeCrF1VXOzs4wNDQUu1ZbWxvy8/Ph7u7+VGW7ubnh4sWLYpO1c3NzYWZmBnt7ewBg5tVs376dGWbqbNTk5OTQ0BMhOiZpyvPMa6GcZ0FM9Lvvj0NdRI0aHXDnzh0sW7YMpaWlOHToEPbu3YtFixbJPSchIQHnzp3Dhg0bcOPGDezfvx8fffQREhMTpebn8XiIiopCdHQ0vvrqK9y6dQt5eXlISUnBt99+CwBYvXo18vPzsWDBAvz6668oKSnBJ598ggcPHgDoeGopNzcXFRUVePDggdSelfj4ePzwww9Kx9VVpqammD9/PpYvX47Tp0/j+vXrmDNnDpqamvDGG288VdkLFixAZWUlFi1ahJKSEmRmZmLz5s1YunQp0zvXt29feHh44ODBg+Dz+QCAoKAgXLlyhebTEKKDXh3pwLz+7nf5v3pHDfj76aebNTc1FpMuokaNDoiOjkZzczP8/PywaNEizJs3j3lsWxYfHx/8+9//RkZGBoYNG4Z169YhOTlZ5pNPAJCWlobo6GgkJCTA1dUV4eHhyM/Px6BBgwB0NHzOnDmDwsJC+Pn5ISAgAJmZmcyaMImJiWCz2XB3d0e/fv1w584diWt4enoiIyNDpbi6avPmzYiIiMDs2bPh4+OD8vJyfPfdd+jbt+9TlTtw4ECcPHkSeXl58PT0xIIFCzBr1iy88847YvmCg4MhEAiYRo2lpSXc3d1hY2Mj9SkuQkjvZWzwd0/z7wqWodkz4e8lKsIzwzUUkW7qcXNqampqEBISgvb2drS3t+Ott97CnDlzxPI0NTXBzc0N06dPx7Zt27QUac9hYGCAHTt24JNPPoFQKERdXZ3YJNWKigrm+OMiIiIQEREhs9yWlhZmvkzndZKSkpCUlCTznODgYInho048Hg8XL4o/ru7k5MQM03T23ERERMidcPzkY+AAFG5RsGTJEixevFjsMzA2NsbOnTuxc+dOqefw+Xyl1gx6/B46BQcHIy8vDwCYz/7JBf927NiBHTt2iB27cuWKxL8TIUQ3GLL10CoQorFd/mKjml6MVJf1uEaNmZkZLly4AA6Hg8bGRgwbNgyvvPIK84QNAGzcuBGjRtHiRJrS0tKCoqIiXLt2DYsXL9Z2OIQQohNaBR1/uN2qp0aLpvS44Sc2mw0Op+Opo5aWFohEIrG/gsvKylBSUoJJkybJKoI8pVOnTmHcuHGYMmUKIiMjtR0OIYQ8c15weIF53SpQbmsb0oVGzYULFxAWFgY7OzuwWCwcP35cIs+uXbvg5OQEY2Nj+Pv7M93wyqqpqYGnpyfs7e2xfPlyWFtbM2mJiYlISUlRNWydlZWVJTGE8bTCw8NRV1eHL774QuLRaUIIIV3zivdApfOuH72eef1Z0WcaiEY3qTz81NjYCE9PT8TFxeGVV16RSD98+DCWLVuGTz/9FP7+/tixYwdCQ0NRWlqK/v37AwC8vLykbgx45swZ2NnZgcvlorCwEFVVVXjllVcQGRkJGxsbZGZmgsfjgcfj4eeff5YbZ0tLC1paWpj3nfMU2tra0NbWJpa3ra0NIpEIQqGQ6RXqfK8MVc5RJq+iPPLSZaV15b66m6ZjVEf5ulo/VI1TW3pqHen87mhra2POe/J7Rp7OvMqco0xeRXnkpctKUyVGbdF0jE9b/vQRdvjq6l0AwF/1TbAyk70Wmhn7782EPyn8BLG8WKWvrWt1RJXyWCJVdk988mQWC8eOHUN4eDhzzN/fHyNHjsRHH30EoOM/u4ODAxYtWiS2YZ+yFixYgHHjxiEyMhKrV6/GF198ATabjYaGBrS1tSEhIQHr1q2TOG/9+vVSJ7Smp6czw1ud9PX1YWtrCwcHBxgaGqocIyHk2dba2orKykrcu3ePdnInMrULgYTcjr6EeHcBXCzk//pdU7OGef0e9z2NxtaTNTU14bXXXkNtbS3Mzc3l5lVro6a1tRUcDgdHjhwRa+jExMSgpqYGmZmZCsusqqoCh8OBmZkZamtrMWbMGBw6dAjDhw8Xy7dv3z789ttvMp9+ktZT4+DggAcPHkh8KI8ePUJlZSWcnJxgZGTEbGOg7Ax0kUik9DnK5FWUR166rDRVYtQWTceojvK7UkZvqB9dvbfu1lPryKNHj1BRUQEHBwew2Wx8//33mDBhgtLDt21tbUqfo0xeRXnkpctKUyVGbdF0jOoo32XtGQDAy8NtsH2Gp9y8Puk+zOvc6bnPbB2pq6uDtbW1Uo0atT799ODBAwgEAtjYiO9VYWNjg5KSEqXKuH37NubOnctMEF60aJFEg0YZRkZGMDIykjhuYGAg8WELBAKwWCzo6ekxX2Sd75XR2d2szDnK5FWUR166rDRVYtQWTceojvK7UkZvqB+qxqktPbWOdH53GBgYMCtfS/uuUUSVc5TJqyiPvHRZaV25r+6m6RjVUf6Joip8FKV8GQ2CBpWvrSt1RJWyetwj3X5+fgrXHAGgkcXYCCGEkJ5o/Ffjn+khKGWp9U8da2trsNlsVFVViR2vqqpi9gcihBBCiGJj7MZoO4ReR62NGkNDQ4wYMUJsU0OhUIhz584hIEDGXuvkqfD5fCxZskTbYXSLiooKsFgshT15qn4msbGxYnPACCGkJ/Dq76XtEHodlYefGhoaUF5ezry/desWCgoKYGlpiUGDBmHZsmWIiYmBr68v/Pz8sGPHDjQ2NuL1119Xa+Dk2ePg4IA///yTWbcoKysLL7zwAh4+fAgul8vk++qrr1Qag01NTRVb4JHP58PLy0vt6/8QQogqPPp5aDuEXkflRs0vv/yCF174e6XDZcuWAeh4wmnfvn149dVXcf/+faxbtw737t2Dl5cXTp8+LTF5mBBVsdlspYYxLS0tVSrXwsKiqyERQojGjLAZoe0Qeh2Vh586N/l78mffvn1Mnvj4eNy+fRstLS3Izc2Fv7+/OmPuFiKRCMKmJuV/mpvVlvcpnrLHw4cP8eabb8LKygocDgeTJk1CWVkZc082NjY4cuQIk9/LywsDBgxg3v/0008wMjJCU1OT1PKzsrLg5+cHU1NTcLlcjBkzBrdv32bSMzMz4ePjA2NjYwwePBhJSUli63awWCx89tlnmDZtGjgcDlxcXPD1118z6TU1NZg1axb69esHExMTuLi4IC0tDYD48FNFRQXTuO7bty9YLBYzefzx4ae3335bav3z9PREcnIyAPHhp9jYWGRnZyM1NRUsFgssFgu3bt3CkCFDJJYPKCgoAIvFEuu5JIQQdTFiSz7BS+TrcU8/9RSi5mbc8B2p0jlVirMoldfll3yVrvu4119/HTdu3MDx48fB5XKxcuVKTJ48Gb/99htYLBYCAwORlZWFyMhIPHz4EMXFxTAxMUFJSQmGDh2K7OxsjBw5UmKBQgBob29HeHg45syZg0OHDqG1tRV5eXnMY/A//vgjoqOjsXPnTgQGBuLmzZuYO3cuAODdd99lyklKSsKWLVuwdetW/Otf/0JUVBRu374NLpeLjRs3ori4GKdOnYK1tTXKy8vR3NwsEYuDgwOOHj2KiIgIlJaWwtzcHCYmJhL5oqKikJKSgps3b+K5554DAFy7dg2//vorjh49KpE/NTUVN27cwLBhw5hGT79+/RAXF4e0tDSmZxIA0tLSEBQUhCFDhqjyT0QIIURDeuZiFKRLysrK8M033yA1NRWBgYHw9PTEwYMHcffuXWaPLj6fj6ysLAAd+3h5e3uLHcvKykJwcLDU8uvq6lBbW4uXX34Zzs7OcHNzQ0xMDAYNGgSgo7GyatUqxMTEYPDgwZgwYQI2bNiA3bt3i5UTGxuLmTNnYsiQIdi0aRMaGhqY/cF+//13eHl5wdfXF05OTggJCUFYWJhELGw2mxlm6t+/P2xtbaUOIz3//PPw9PREeno6cyw9PR3+/v5SGyMWFhYwNDQEh8OBra0tbG1twWazERsbi9LSUibOtrY2pKenIy4uTtY/ByGEkG5GPTUysExM4HrlslJ5hUIh6urrYW5mptTiaoryioyMgPp6lWMuLi6Gvr4+fH19mWNWVlZwdXVFSUkJQkNDERQUhCVLluD+/fvIzs4Gn8+Hra0tsrKy8MYbb+Dnn3/GihUrpJZvaWmJ2NhYhIaGYsKECQgJCcGMGTOY4avCwkLk5ORg48aNzDkCgQCPHj1CU1MT0/vj4fH35DdTU1OYm5ujuroaABAXF4eYmBhcvXoVL774IsLDwzF69GiVP4vHRUVF4fPPP8c777wDkUiEjIwMsR4XZdjZ2eGll15CWloa3n//fXzzzTdoaWnB9OnTnyo2Qggh6kM9NTKwWCzocTjK/5iYqC2vJpeoHz58OCwtLZGdnc00avh8PrKzs5Gfn4+2tja5jYi0tDRcvHgRo0ePxuHDh8Hj8XDp0iUAHU/GJSUloaCggPkpKipCWVkZjI2NmTKefDKJxWIxK7lOmDABt27dwtKlS/HHH39g/PjxSExMfKp7njlzJkpLS3HlyhXk5uaisrISr776qsrl/POf/8Thw4fR3NzMTIqXNkxHCCFEO6inRoe4ubmhvb0dv/zyCyZMmAAA+Ouvv1BaWgo3NzcAYObVZGZm4tq1axg7diw4HA5aWlqwe/du+Pr6wtTUVO51vL294e3tjdWrVyMgIADp6ekYNWoUfHx8UFpa+tRzTPr164eYmBjExMQgMDAQy5cvl7rHV+fmowKBQG559vb2CA4ORnp6OmpraxESEsLsGC+NoaGh1DInT54MU1NTfP755/juu+9w4cIFFe+MEPKsCxnaD2dL7ms7DJ1FPTU6xMXFBVOmTMGSJUvw008/obCwELNmzcLAgQMxdepUJh+fz8ehQ4fg5eWFPn36QE9PD0FBQTh48KDM+TRAx5pEq1evxsWLF3H79m2cOXMGZWVlTINp3bp1OHDgAJKSknDt2jUUFxcjIyMDa9askVnmkzZt2oTMzEyUl5fj2rVrOHHiBFP+kxwdHcFisXDixAncv38fDQ0NMsuNiorC4cOHkZmZiddee01uDE5OTsjNzUVFRQUePHjA9CKx2WzExMQgOTkZLi4utKAkIURlH0wfDp6FENsildvTcNPYTTA1MEVmmOINoQk1anTO559/Dk9PT0yZMgUBAQEQiUQ4efKk2JBPcHAwBAIB+Hw+c4zP50scexKHw0FJSQkiIiLA4/Ewd+5cLFy4EPPmzQMAhIaG4sSJEzhz5gxGjhyJUaNGYfv27XB0dFQ6fkNDQ7zzzjvw8PBAUFAQ2Gw2MjIypOYdOHAgMznZxsYG8fHxMsuNjIzEX3/9hebmZoWrBycmJoLNZsPd3R39+vXDnTt3mLS4uDi0trbS3mOEkC7hGOpjobsQUz0HKM4MIMw5DJdeuwQHMwcNR6YbaPipl+t8aqlT37598emnn8Lc3FzqLt1Ax9o0T66Fs2TJEoVbC9jY2ODYsWNy84SGhiI0NFRmurQ1eGpqapgYExMTkZycLHUStZOTk8T5a9euxdq1a8WOPfmZAACXy0VTUxPq6urQp08fsbTH11gCAB6Ph4sXL0qN/+7duzAwMMDs2bOlphNCCNEeatQQooSWlhbcv38fycnJmDp1Kq2QTQghPRANPxGihEOHDsHR0RE1NTVISkrSdjiEEEKkoEYNIUqIjY2FQCBAfn4+7OzstB0OIYQQKahRQwghhBCdQI2axzzNRpKEkGcXfXcQ0jNQowZ/r3Ara2dqQgiRp/O748nVsgkh3YuefkLHompcLhfV1dUQCoUQCoV49OiRwn2cOgmFQrS2tip1jjJ5FeWRly4rTZUYtUXTMaqj/K6U0RvqR1fvrbv1tDoiEonQ1NSE6upqcLlcsNlsseUTCCHdixo1/2NrawsAuH//Ppqbm2FiYqL0HkwikUjpc5TJqyiPvHRZaarEqC2ajlEd5XeljN5QP7p6b92tp9YRLpfLfIcQQrSHGjX/w2KxMGDAAPTt2xfnzp1DUFCQ0l3JbW1tuHDhglLnKJNXUR556bLSVIlRWzQdozrK70oZvaF+dPXeultPrCMGBgZgs9lqj4UQojpq1DyBzWajvb0dxsbGSn+pqXKOMnkV5ZGXLiutK/fV3TQdozrK19X60dV76269oY4QQrSnZw6cE0IIIYSoiBo1hBBCCNEJ1KghhBBCiE54ZubUdC6OVVdXJzdfW1sbs5uzKhNBlT1HmbyK8shLl5XWlfvqbpqOUR3l62r96Oq9dTeqI/QdIo+u1g9Vz9O1OtL5e1uZRS6fmUZNfX09AMDBwUHLkRBCCCFEVfX19bCwsJCbhyV6Rtb3FgqF+OOPP2BmZqZw/YmRI0ciPz9fpfJVOUeZvIryyEuXllZXVwcHBwdUVlbC3NxcqTi1oSuffXeXr4v1A6A6os7ydbGOUP1QX/ldLeNZrSMikQj19fWws7NTuCjmM9NTo6enB3t7e6Xystlslf9BVDlHmbyK8shLl5dmbm7eo7+QuvLZd3f5ulw/AKojVEfoO0TT5Xe1jGe5jijqoelEE4WlWLhwoUbPUSavojzy0rsSf0+h6djVUT7VD+2iOkJ1RB5drR+qnves1pFnZvjpWVdXVwcLCwvU1tb26L+yiPZQHSHyUP0givSEOkI9Nc8IIyMjvPvuuzAyMtJ2KKSHojpC5KH6QRTpCXWEemoIIYQQohOop4YQQgghOoEaNYQQQgjRCdSoIYQQQohOoEYNIYQQQnQCNWoIIYQQohOoUUMwbdo09O3bF5GRkdoOhfRAlZWV4PP5cHd3h4eHB7788ktth0R6mJqaGvj6+sLLywvDhg3D3r17tR0S6YGamprg6OiIxMREjV2DHukmyMrKQn19Pfbv348jR45oOxzSw/z555+oqqqCl5cX7t27hxEjRuDGjRswNTXVdmikhxAIBGhpaQGHw0FjYyOGDRuGX375BVZWVtoOjfQg77zzDsrLy+Hg4IBt27Zp5BrUU0PA5/NhZmam7TBIDzVgwAB4eXkBAGxtbWFtbY3//ve/2g2K9ChsNhscDgcA0NLSApFIBPp7mTyurKwMJSUlmDRpkkavQ42aXu7ChQsICwuDnZ0dWCwWjh8/LpFn165dcHJygrGxMfz9/ZGXl9f9gRKtUWcduXz5MgQCARwcHDQcNelO6qgjNTU18PT0hL29PZYvXw5ra+tuip5omjrqR2JiIlJSUjQeKzVqernGxkZ4enpi165dUtMPHz6MZcuW4d1338WVK1fg6emJ0NBQVFdXd3OkRFvUVUf++9//Ijo6Gnv27OmOsEk3Ukcd4XK5KCwsxK1bt5Ceno6qqqruCp9o2NPWj8zMTPB4PPB4PM0HKyI6A4Do2LFjYsf8/PxECxcuZN4LBAKRnZ2dKCUlRSzf+fPnRREREd0RJtGirtaRR48eiQIDA0UHDhzorlCJljzN90in+fPni7788ktNhkm0pCv1Y9WqVSJ7e3uRo6OjyMrKSmRubi5KSkrSSHzUU6PDWltbcfnyZYSEhDDH9PT0EBISgosXL2oxMtJTKFNHRCIRYmNjMW7cOMyePVtboRItUaaOVFVVob6+HgBQW1uLCxcuwNXVVSvxku6lTP1ISUlBZWUlKioqsG3bNsyZMwfr1q3TSDzUqNFhDx48gEAggI2NjdhxGxsb3Lt3j3kfEhKC6dOn4+TJk7C3t6cGzzNEmTqSk5ODw4cP4/jx4/Dy8oKXlxeKioq0ES7RAmXqyO3btxEYGAhPT08EBgZi0aJFGD58uDbCJd1M2d8z3UW/269IepyzZ89qOwTSg40dOxZCoVDbYZAezM/PDwUFBdoOg/QCsbGxGi2femp0mLW1NdhstsSEvaqqKtja2mopKtKTUB0hilAdIfL0tPpBjRodZmhoiBEjRuDcuXPMMaFQiHPnziEgIECLkZGeguoIUYTqCJGnp9UPGn7q5RoaGlBeXs68v3XrFgoKCmBpaYlBgwZh2bJliImJga+vL/z8/LBjxw40Njbi9ddf12LUpDtRHSGKUB0h8vSq+qGRZ6pItzl//rwIgMRPTEwMk+df//qXaNCgQSJDQ0ORn5+f6NKlS9oLmHQ7qiNEEaojRJ7eVD9o7ydCCCGE6ASaU0MIIYQQnUCNGkIIIYToBGrUEEIIIUQnUKOGEEIIITqBGjWEEEII0QnUqCGEEEKITqBGDSGEEEJ0AjVqCCGEEKITqFFDCCGEEJ1AjRpCCCGE6ARq1BBCCCFEJ1CjhhBCCCE64f8DbY8a8MweVPMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Figure = figure()\n",
+    "_ = Figure.gca().loglog(\n",
+    "    high_projection.x, high_projection.y, label=\"projection for high\"\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"high\"].x,\n",
+    "    sensitivities[\"high\"].y,\n",
+    "    label=\"one order below high sensitivity\",\n",
+    ")\n",
+    "_ = Figure.gca().loglog(low_projection.x, low_projection.y, label=\"projection for low\")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"low\"].x, sensitivities[\"low\"].y, label=\"low sensitivity\"\n",
+    ")\n",
+    "_ = Figure.gca().legend()\n",
+    "Figure.gca().grid(True, \"both\", \"both\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "12b9b3ee-a882-4088-9705-5c8a4c38834c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">  Vérification des calculs: haute   </span>\n",
+       "<span style=\"font-style: italic\">            sensibilitée            </span>\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> nom     </span>┃<span style=\"font-weight: bold\"> RMS      </span>┃<span style=\"font-weight: bold\"> vitesse max </span>┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 1.07E-06 │ 6.04E-06    │\n",
+       "│ mesuré  │ 1.07E-06 │ 6.04E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m  Vérification des calculs: haute   \u001b[0m\n",
+       "\u001b[3m            sensibilitée            \u001b[0m\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mnom    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mRMS     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvitesse max\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 1.07E-06 │ 6.04E-06    │\n",
+       "│ mesuré  │ 1.07E-06 │ 6.04E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">  Vérification des calculs: basse   </span>\n",
+       "<span style=\"font-style: italic\">            sensibilitée            </span>\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> nom     </span>┃<span style=\"font-weight: bold\"> RMS      </span>┃<span style=\"font-weight: bold\"> vitesse max </span>┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 1.30E-06 │ 7.37E-06    │\n",
+       "│ mesuré  │ 1.30E-06 │ 7.37E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m  Vérification des calculs: basse   \u001b[0m\n",
+       "\u001b[3m            sensibilitée            \u001b[0m\n",
+       "┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mnom    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mRMS     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvitesse max\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ calculé │ 1.30E-06 │ 7.37E-06    │\n",
+       "│ mesuré  │ 1.30E-06 │ 7.37E-06    │\n",
+       "└─────────┴──────────┴─────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = Table(title=\"Vérification des calculs: haute sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(compute_rms(get_speed(base_experiment.reference_movement)) * high_fitted_value),\n",
+    "    \"{:.2E}\".format(max(get_speed(base_experiment.reference_movement)) * high_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(high_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(high_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)\n",
+    "\n",
+    "table = Table(title=\"Vérification des calculs: basse sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(compute_rms(get_speed(base_experiment.reference_movement)) * low_fitted_value),\n",
+    "    \"{:.2E}\".format(max(get_speed(base_experiment.reference_movement)) * low_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(low_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(low_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5e985e2c-ac0d-4ba7-8527-cadf16fcb22d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">base rms: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.33E-06</span>\n",
+       "base max speed: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.52E-06</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "base rms: \u001b[1;36m1.\u001b[0m\u001b[1;36m33E-\u001b[0m\u001b[1;36m06\u001b[0m\n",
+       "base max speed: \u001b[1;36m7.\u001b[0m\u001b[1;36m52E-\u001b[0m\u001b[1;36m06\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "console.print(\n",
+    "    \"base rms: [repr.number]{:.2E}[/repr.number]\\nbase max speed: [repr.number]{:.2E}[/repr.number]\".format(\n",
+    "        compute_rms(get_speed(base_experiment.reference_movement)),\n",
+    "        max(get_speed(base_experiment.reference_movement)),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5f447e42-d7a9-4c69-8721-d6c21ba0cf15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8034556</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9801055</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;36m0.8034556\u001b[0m \u001b[1;36m0.9801055\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "console.print(high_fitted_value, low_fitted_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fca5784-d5ca-4626-9e27-650b6d5c679a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..f985c10
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,582 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c9023fe-f86d-428a-8072-4264ff672025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from rich.console import Console\n",
+    "from rich.table import Table\n",
+    "\n",
+    "from numpy import loadtxt\n",
+    "\n",
+    "from numpy import float32, bool as np_bool\n",
+    "from numpy.typing import NDArray\n",
+    "from typing import Any\n",
+    "from collections.abc import Callable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d82f94d-b69a-4ad0-b042-08d045925545",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import mean, diff, sqrt, pi, linspace\n",
+    "from numpy.random import default_rng\n",
+    "\n",
+    "from backscattering_analyzer.experiment import Experiment\n",
+    "from backscattering_analyzer.acquisition import AcquisitionType\n",
+    "\n",
+    "from matplotlib.pyplot import figure, show\n",
+    "\n",
+    "from science_signal import Signal\n",
+    "from science_signal.generator import sin as sin_gen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d91d36b8-28fb-40d8-a4e6-77b659839fbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from labellines import labelLines  # from matplotlib-label-lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62328640-6c4f-47a1-8461-9637a50f52e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_over(signal1: Signal, signal2: Signal) -> bool:\n",
+    "    \"\"\"\n",
+    "    True if signal1 is over signal2\n",
+    "    \"\"\"\n",
+    "    return not ((signal1 - signal2).y < 0).any()\n",
+    "\n",
+    "\n",
+    "def get_speed(signal: Signal) -> NDArray[float32]:\n",
+    "    \"\"\"\n",
+    "    get speed from position signal\n",
+    "    \"\"\"\n",
+    "    return 1 / mean(diff(signal.x)) * diff(signal.y)\n",
+    "\n",
+    "\n",
+    "def compute_rms(signal: NDArray[float32]) -> float:\n",
+    "    \"\"\"\n",
+    "    compute RMS of data\n",
+    "    \"\"\"\n",
+    "    return sqrt(mean(signal**2))  # pyright: ignore[reportAny]\n",
+    "\n",
+    "\n",
+    "def accelerate_timesignal(factor: float, signal: Signal) -> Signal:\n",
+    "    \"\"\"\n",
+    "    accelerate a signal by a factor\n",
+    "    \"\"\"\n",
+    "    return Signal(\n",
+    "        signal.x[0] + (signal.x - signal.x[0]) * (1 / factor),\n",
+    "        signal.y,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def generate_movement(max_speed: float, start: int, end: int) -> Signal:\n",
+    "    \"\"\"\n",
+    "    generate a sine movement with a given maximum speed\n",
+    "    \"\"\"\n",
+    "    amplitude: float = sqrt(max_speed / (2 * pi))\n",
+    "    frequency: float = sqrt(max_speed / (2 * pi))\n",
+    "    signal = sin_gen(end - start, 1000, frequency, amplitude)\n",
+    "    return Signal(\n",
+    "        start + signal.x,\n",
+    "        signal.y,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def fit_value(\n",
+    "    start: float,\n",
+    "    end: float,\n",
+    "    nb_loop: int,\n",
+    "    experiment: Experiment,\n",
+    "    reference: Signal,\n",
+    "    generate: Callable[[float, Any], Signal],\n",
+    "    condition: Callable[[Signal, Signal], np_bool | bool],\n",
+    "    generate_args: list[Any],\n",
+    ") -> float:\n",
+    "    \"\"\"\n",
+    "    find the value that is the closest to NOT respecting the condition function\n",
+    "    Considering it start by not respecting the condition, and after some time, it will\n",
+    "    \"\"\"\n",
+    "    for _ in range(nb_loop):\n",
+    "        values = linspace(start, end, 10, dtype=float32)\n",
+    "        detected = False\n",
+    "        for j in range(len(values)):\n",
+    "            value = values[j]\n",
+    "            experiment.reference_movement = generate(value, *generate_args)\n",
+    "            experiment.projection_reference = experiment.compute_projection(\n",
+    "                AcquisitionType.REFERENCE\n",
+    "            )\n",
+    "            if (\n",
+    "                not condition(reference, experiment.projection_reference)\n",
+    "                and not detected\n",
+    "            ):\n",
+    "                detected = True\n",
+    "                end = value\n",
+    "                start = values[j - 1]\n",
+    "        if not detected:\n",
+    "            raise Exception(\"not in range\")\n",
+    "    return start + (end - start) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "587e2e3a-767b-4a6e-80df-188b6152bbb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "console = Console()\n",
+    "o5_mat = (\n",
+    "    Path(\"/home/demagny/data\")\n",
+    "    / \"simulation\"\n",
+    "    / \"optickle\"\n",
+    "    / \"transfer_function\"\n",
+    "    / \"O5.mat\"\n",
+    ")\n",
+    "sensitivities: dict[str, Signal] = dict()\n",
+    "for name in [\"high\", \"low\"]:\n",
+    "    data = loadtxt(\n",
+    "        Path(\n",
+    "            \"/home/demagny/data/sensitivity/O5/23932_O5{}SensASD.txt\".format(\n",
+    "                name.capitalize()\n",
+    "            )\n",
+    "        ),\n",
+    "        dtype=float32,\n",
+    "    )\n",
+    "    sensitivities[name] = Signal(\n",
+    "        data[:, 0],\n",
+    "        data[:, 1],\n",
+    "    )\n",
+    "sensitivities[\"high\"].y /= 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7f534cc-0b50-4fc1-ba62-8a36747eb646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_BENCH = \"SDB1\"\n",
+    "C_COUPLING = \"../main_script_virgo/values-coupling.toml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a72635ea-8395-4783-9be1-8a89e82b7e21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_experiment = Experiment(C_BENCH, \"2024_06_07\", C_COUPLING, 0.1)\n",
+    "temp_experiment = Experiment(C_BENCH, \"2024_06_07\", C_COUPLING, 0.1)\n",
+    "base_experiment.factors = {\"pre\": 2e-10 * 1e6, \"true\": 2e-10}\n",
+    "temp_experiment.factors = {\"pre\": 2e-10 * 1e6, \"true\": 2e-10}\n",
+    "base_experiment.modelisation_file = str(o5_mat)\n",
+    "temp_experiment.modelisation_file = str(o5_mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "850dd2af-2b7b-43b3-8758-6b36d9bced20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "factor = default_rng().random(1) * 10\n",
+    "# check if frequency is multiplied by the factor after going into accelerate_timesignal function\n",
+    "assert (\n",
+    "    abs(\n",
+    "        factor / mean(diff(base_experiment.reference_movement.x))\n",
+    "        - 1\n",
+    "        / mean(\n",
+    "            diff(accelerate_timesignal(factor, base_experiment.reference_movement).x)\n",
+    "        )\n",
+    "    )\n",
+    "    < 1e-1\n",
+    "), abs(\n",
+    "    factor / mean(diff(base_experiment.reference_movement.x))\n",
+    "    - 1\n",
+    "    / mean(diff(accelerate_timesignal(factor, base_experiment.reference_movement).x))\n",
+    ")\n",
+    "# check if amplitude is not modified after going into accelerate_timesignal function\n",
+    "assert (\n",
+    "    base_experiment.reference_movement.y\n",
+    "    == accelerate_timesignal(factor, base_experiment.reference_movement).y\n",
+    ").all(), (\n",
+    "    base_experiment.reference_movement.y\n",
+    "    - accelerate_timesignal(factor, base_experiment.reference_movement).y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dbb68d6-fba3-4488-b951-b5bf860152d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_fitted_value = fit_value(\n",
+    "    1e-6,\n",
+    "    1e-5,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"high\"],\n",
+    "    generate_movement,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement.x[0],\n",
+    "        base_experiment.reference_movement.x[-1],\n",
+    "    ],\n",
+    ")\n",
+    "low_fitted_value = fit_value(\n",
+    "    1e-6,\n",
+    "    1e-5,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"low\"],\n",
+    "    generate_movement,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement.x[0],\n",
+    "        base_experiment.reference_movement.x[-1],\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "132a82c5-b69b-402c-b278-c565ccf6e504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_movement = generate_movement(\n",
+    "    high_fitted_value,\n",
+    "    base_experiment.reference_movement.x[0],\n",
+    "    base_experiment.reference_movement.x[-1],\n",
+    ")\n",
+    "temp_experiment.reference_movement = high_movement\n",
+    "high_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)\n",
+    "low_movement = generate_movement(\n",
+    "    low_fitted_value,\n",
+    "    base_experiment.reference_movement.x[0],\n",
+    "    base_experiment.reference_movement.x[-1],\n",
+    ")\n",
+    "temp_experiment.reference_movement = low_movement\n",
+    "low_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2966a38e-9020-4cdd-a186-70519afb85de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Figure = figure()\n",
+    "_ = Figure.gca().loglog(\n",
+    "    high_projection.x, high_projection.y, label=\"projection for high\"\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"high\"].x,\n",
+    "    sensitivities[\"high\"].y,\n",
+    "    label=\"one order below high sensitivity\",\n",
+    ")\n",
+    "_ = Figure.gca().loglog(low_projection.x, low_projection.y, label=\"projection for low\")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"low\"].x, sensitivities[\"low\"].y, label=\"low sensitivity\"\n",
+    ")\n",
+    "_ = Figure.gca().legend()\n",
+    "Figure.gca().grid(True, \"both\", \"both\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf7aa342-1ffe-4262-80fd-e900acf590c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = Table(title=\"Vérification des calculs: haute sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(high_fitted_value / sqrt(2)),\n",
+    "    \"{:.2E}\".format(high_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(high_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(high_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)\n",
+    "\n",
+    "table = Table(title=\"Vérification des calculs: basse sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(low_fitted_value / sqrt(2)),\n",
+    "    \"{:.2E}\".format(low_fitted_value),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(low_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(low_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7ca8aea-80f5-4306-916d-a4cb9240bd1b",
+   "metadata": {},
+   "source": [
+    "# Utilisation du mouvement réel du banc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c61da9e2-0339-4345-b601-395ef00b380c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_fitted_value = fit_value(\n",
+    "    1,\n",
+    "    3,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"high\"],\n",
+    "    accelerate_timesignal,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement,\n",
+    "    ],\n",
+    ")\n",
+    "low_fitted_value = fit_value(\n",
+    "    1,\n",
+    "    6,\n",
+    "    5,\n",
+    "    temp_experiment,\n",
+    "    sensitivities[\"low\"],\n",
+    "    accelerate_timesignal,\n",
+    "    is_over,\n",
+    "    [\n",
+    "        base_experiment.reference_movement,\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13cbbcf4-4753-4cef-90a3-83477177a102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_movement = accelerate_timesignal(\n",
+    "    high_fitted_value, base_experiment.reference_movement\n",
+    ")\n",
+    "low_movement = accelerate_timesignal(\n",
+    "    low_fitted_value, base_experiment.reference_movement\n",
+    ")\n",
+    "temp_experiment.reference_movement = high_movement\n",
+    "high_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)\n",
+    "temp_experiment.reference_movement = low_movement\n",
+    "low_projection = temp_experiment.compute_projection(AcquisitionType.REFERENCE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12b9b3ee-a882-4088-9705-5c8a4c38834c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = Table(title=\"Vérification des calculs: haute sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(base_experiment.reference_movement)) * high_fitted_value\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(base_experiment.reference_movement)) * high_fitted_value\n",
+    "    ),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(high_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(high_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)\n",
+    "\n",
+    "table = Table(title=\"Vérification des calculs: basse sensibilité\")\n",
+    "table.add_column(\"nom\")\n",
+    "table.add_column(\"RMS\")\n",
+    "table.add_column(\"vitesse max\")\n",
+    "\n",
+    "table.add_row(\n",
+    "    \"calculé\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(base_experiment.reference_movement)) * low_fitted_value\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(base_experiment.reference_movement)) * low_fitted_value\n",
+    "    ),\n",
+    ")\n",
+    "table.add_row(\n",
+    "    \"mesuré\",\n",
+    "    \"{:.2E}\".format(\n",
+    "        compute_rms(get_speed(low_movement)),\n",
+    "    ),\n",
+    "    \"{:.2E}\".format(\n",
+    "        max(get_speed(low_movement)),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "console.print(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ff45ff8-8d0f-4f9a-a060-839700121e43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Figure = figure(figsize=(8, 5))\n",
+    "_ = Figure.gca().loglog(\n",
+    "    high_projection.x,\n",
+    "    high_projection.y,\n",
+    "    label=\"rms = {:.2E}, max = {:.2E}\".format(\n",
+    "        compute_rms(get_speed(high_movement)), max(get_speed(high_movement))\n",
+    "    ),\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    low_projection.x,\n",
+    "    low_projection.y,\n",
+    "    label=\"rms= {:.2E} max= {:.2E}\".format(\n",
+    "        compute_rms(get_speed(low_movement)), max(get_speed(low_movement))\n",
+    "    ),\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"low\"].x, sensitivities[\"low\"].y, label=\"low sensitivity\"\n",
+    ")\n",
+    "_ = Figure.gca().loglog(\n",
+    "    sensitivities[\"high\"].x,\n",
+    "    sensitivities[\"high\"].y,\n",
+    "    label=\"one order below high sensitivity\",\n",
+    ")\n",
+    "\n",
+    "labelLines(Figure.gca().get_lines(), xvals=[9, 20, 50, 50], align=False)\n",
+    "\n",
+    "# _ = Figure.gca().legend(loc=\"upper right\")\n",
+    "_ = Figure.gca().set_xlim(5, 100)\n",
+    "_ = Figure.gca().set_ylim(1e-26, 1e-18)\n",
+    "_ = Figure.gca().set_xlabel(\"Frequencies Hz\")\n",
+    "_ = Figure.gca().set_ylabel(\"Sensitivity $\\\\frac { 1 } { \\\\sqrt{ Hz } }$\")\n",
+    "_ = Figure.gca().set_title(\"projection of noise from backscatterd light on SRB\")\n",
+    "Figure.gca().grid(True, \"both\", \"both\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e985e2c-ac0d-4ba7-8527-cadf16fcb22d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "console.print(\n",
+    "    \"base rms: [repr.number]{:.2E}[/repr.number]\\nbase max speed: [repr.number]{:.2E}[/repr.number]\".format(\n",
+    "        compute_rms(get_speed(base_experiment.reference_movement)),\n",
+    "        max(get_speed(base_experiment.reference_movement)),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f447e42-d7a9-4c69-8721-d6c21ba0cf15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "console.print(high_fitted_value, low_fitted_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ef341b0-8d63-4130-81f7-82cdad1b0052",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mise.toml b/mise.toml
new file mode 100644
index 0000000..9164165
--- /dev/null
+++ b/mise.toml
@@ -0,0 +1,5 @@
+[tools]
+python = "3.13.2"
+
+[env]
+_.python.venv = { path = ".venv", create = true, uv_create_args = ["--seed"] }
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3e92e91
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,22 @@
+[project]
+name = "jupyter-speed"
+version = "0.1.0"
+description = "Jupyter work to compute and check maximum speed accepted on benches on Virgo"
+authors = [{name = "augustin.demagny", email = "augustin.demagny@ligo.org"}]
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "backscattering-analyzer",
+    "basedpyright>=1.28.4",
+    "ipympl>=0.9.7",
+    "jupyter-lsp>=2.2.5",
+    "jupyterlab>=4.4.0",
+    "matplotlib-label-lines>=0.8.0",
+    "rich>=14.0.0",
+    "ruff>=0.11.4",
+    "science-signal",
+]
+
+[tool.uv.sources]
+science-signal = { path = "../main_script_virgo/modules/science_signal" }
+backscattering-analyzer = { path = "../main_script_virgo/modules/backscattering-analyzer" }
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..8e871c9
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1670 @@
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