diff --git a/Funzioni/Statistica.ipynb b/Funzioni/Statistica.ipynb
new file mode 100644
index 0000000..2413376
--- /dev/null
+++ b/Funzioni/Statistica.ipynb
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e1f82c70",
+   "metadata": {},
+   "source": [
+    "# Import librerie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b2b806a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Heavy lifting\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Mostrare i dati\n",
+    "import ipysheet\n",
+    "from IPython.display import display\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Calcolo simbolico\n",
+    "import sympy as sp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96b0e93d",
+   "metadata": {},
+   "source": [
+    "# Compatibilità"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e50c6d18",
+   "metadata": {},
+   "source": [
+    "**compat( x1, x2, u1, u2 )**\n",
+    "\n",
+    "*input:*\n",
+    "  - x1, x2: quantità\n",
+    "  - u1, u2: incertezze associate\n",
+    "\n",
+    "*output:*\n",
+    "  - k: fattore di compatibilità (scarto normalizzato)\n",
+    "  - diff: differenza x1 - x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d07a554d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compat(x1, x2, u1,u2):\n",
+    "    \n",
+    "    diff = x1 - x2\n",
+    "    k = np.abs(diff) / np.sqrt(u1**2 + u2**2)\n",
+    "    return k, diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc3e1539",
+   "metadata": {},
+   "source": [
+    "# Classe Data\n",
+    "\n",
+    "*attributi:*\n",
+    "\n",
+    "- campione: dati grezzi\n",
+    "- analisi_stat:\n",
+    "    - valori medi\n",
+    "    - incertezze strumentali, statistiche e complessive\n",
+    "    - lunghezza campione\n",
+    "    - outlier\n",
+    "    - residui con incertezza\n",
+    "- param_reg:\n",
+    "    - A, B\n",
+    "    - uA, uB, covAB\n",
+    "    - chi², P"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2649eb4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Data:\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 campione,\n",
+    "                 analisi_stat=None, param_reg=None):\n",
+    "        \n",
+    "        self.campione     = campione\n",
+    "        self.analisi_stat = analisi_stat\n",
+    "        self.param_reg    = param_reg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21e942b",
+   "metadata": {},
+   "source": [
+    "## Analisi con criterio di Chauvenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050285f6",
+   "metadata": {},
+   "source": [
+    "**data.stat_chauv( prefissi, err_strumentale )**\n",
+    "\n",
+    "*input data.campione:*\n",
+    "  - prefix: nome/lista di nomi delle variabili da analizzare\n",
+    "  - err_strumentale: incertezza/lista di incertezze da risoluzione strumentale\n",
+    "\n",
+    "*output data.analisi_stat:*  \n",
+    "  - \\<prefix>: media del campione\n",
+    "  - u\\<prefix>_strum: incertezzaa da risoluzione strumentale\n",
+    "  - u\\<prefix>_stat: incertezza statistica (s/sqrt(N))\n",
+    "  - u\\<prefix>: incertezza complessiva\n",
+    "  - n\\<prexix>: lunghezza del campione\n",
+    "  - out\\<prefix>: lista outlier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "91931bb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Probabilità\n",
+    "def p_t_student(valori, err_strumentale):\n",
+    "\n",
+    "  n   = len(valori)\n",
+    "  GdL = n - 1\n",
+    "\n",
+    "  media = valori.mean()\n",
+    "  s     = valori.std(ddof=1)\n",
+    "\n",
+    "  s = np.sqrt(s**2 + err_strumentale**2)\n",
+    "  return 2 * (1 - stats.t.cdf(np.abs(valori - media) / s, df=GdL))\n",
+    "\n",
+    "\n",
+    "# Indice del peggiore outlier o None\n",
+    "def trova_outlier(valori, err_strumentale):\n",
+    "\n",
+    "  n       = len(valori)\n",
+    "  soglia  = 1 / (2 * n)\n",
+    "  p       = p_t_student(valori, err_strumentale)\n",
+    "  idx_min = np.argmin(p)\n",
+    "\n",
+    "  if p[idx_min] < soglia:\n",
+    "    return idx_min\n",
+    "  \n",
+    "  return None\n",
+    "\n",
+    "\n",
+    "# Rimozione outlier\n",
+    "def rimuovi_outlier(valori, err_strumentale):\n",
+    "  \n",
+    "  rimossi  = []\n",
+    "  campione = valori.copy()\n",
+    "\n",
+    "  while len(campione) > 2:\n",
+    "    idx = trova_outlier(campione, err_strumentale)\n",
+    "\n",
+    "    if idx is None: # nessun outlier: stop\n",
+    "      break\n",
+    "\n",
+    "    rimossi.append(campione[idx])\n",
+    "    campione = np.delete(campione, idx)\n",
+    "\n",
+    "  return campione, rimossi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "70c37b24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analisi statistica con criterio di Chauvenet\n",
+    "def stat_chauv(self, prefissi, err_strumentale):\n",
+    "\n",
+    "    analisi_stat = self.analisi_stat\n",
+    "    campione     = self.campione\n",
+    "    \n",
+    "    \n",
+    "    if isinstance(prefissi, str):\n",
+    "        prefissi = [prefissi]\n",
+    "    if isinstance(err_strumentale, (int, float)):\n",
+    "        err_strumentale = [err_strumentale] * len(prefissi)\n",
+    "\n",
+    "    for prefix, err_strum in zip(prefissi, err_strumentale):\n",
+    "        cols = [col for col in campione.columns if col.startswith(prefix)]\n",
+    "\n",
+    "        for i, row in campione.iterrows():\n",
+    "            valori            = row[cols].dropna().values.astype(float)\n",
+    "            campione, rimossi = rimuovi_outlier(valori, err_strum)\n",
+    "\n",
+    "            n = len(campione)\n",
+    "            s = campione.std(ddof=1)\n",
+    "            s = s / np.sqrt(n)\n",
+    "            u = np.sqrt(s**2 + err_strum**2)\n",
+    "\n",
+    "            analisi_stat.at[i, prefix]              = campione.mean()\n",
+    "            analisi_stat.at[i, f\"u{prefix}_strum\"]  = err_strum\n",
+    "            analisi_stat.at[i, f\"u{prefix}_stat\"]   = s\n",
+    "            analisi_stat.at[i, f\"u{prefix}\"]        = u\n",
+    "            analisi_stat.at[i, f\"n{prefix}\"]        = n\n",
+    "            analisi_stat.at[i, f\"out{prefix}\"]      = rimossi\n",
+    "\n",
+    "    return self\n",
+    "\n",
+    "\n",
+    "# Aggiunta a classe Data\n",
+    "Data.stat_chauv = stat_chauv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86fcfbe5",
+   "metadata": {},
+   "source": [
+    "## Regressione lineare\n",
+    "y = Ax + By"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e6a2c2e",
+   "metadata": {},
+   "source": [
+    "**data.reg_lin( stampa_param=True, plot_regressione=True, residui=True,**  \n",
+    "&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;**x_label=\"\", y_label=\"\", r_label=\"\",**  \n",
+    "&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;**titolo_reg=\"\", titolo_residui=\"\",**  \n",
+    "&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;**cd_A=4, cd_B=4, scala_barre=1 )**  \n",
+    "\n",
+    "*input data.analisi_stat:*\n",
+    "  - x, y: campioni x e y\n",
+    "  - ux, uy: relative incertezze\n",
+    "\n",
+    "*input:*\n",
+    "  - stampa_param: opzione stampa parametri regressione\n",
+    "  - plot_regressione: opzione plot regressione lineare\n",
+    "  - residui: opzione calcolo e plot residui\n",
+    "  - x_label, y_label, r_label: etichette assi plot regressione e residui\n",
+    "  - titolo_reg, titolo_residui: titolo plot regressione e residui\n",
+    "  - cd_A, cd_B: cifre decimali visualizzazione parametri\n",
+    "  - scala barre: scala ingrandimento barre di errore nella regressione\n",
+    "\n",
+    "*output param_reg:*\n",
+    "  - A, B: parametri regressione\n",
+    "  - uA, uB, covAB: relative incertezze e covarianze\n",
+    "  - chi², P: chi quadro e relativa probabilità\n",
+    "\n",
+    "*output analisi_stat:*\n",
+    "  - r, ur: residui e relativa incertezza"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ccd1e25",
+   "metadata": {},
+   "source": [
+    "### Residui"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7c815197",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot residui\n",
+    " \n",
+    "def plot_residui(x, r,\n",
+    "                 ux, ur,\n",
+    "                 x_label, r_label, titolo):\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "    # Residui con barre d'errore\n",
+    "    ax.errorbar(\n",
+    "        x, r,\n",
+    "        xerr=ux, yerr=ur,\n",
+    "        fmt='o', color=sns.color_palette()[0],\n",
+    "        ecolor='gray', elinewidth=1, capsize=3,\n",
+    "        markersize=5, label=\"Residui\"\n",
+    "    )\n",
+    "\n",
+    "    # Linea dello zero\n",
+    "    ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
+    "\n",
+    "    # Estetica\n",
+    "    sns.despine(ax=ax)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_ylabel(r_label)\n",
+    "    ax.set_title(titolo)\n",
+    "    ax.legend()\n",
+    "    ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "53ba0123",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calcolo residui\n",
+    "\n",
+    "def residui(analisi_stat,\n",
+    "            A_num, B_num,\n",
+    "            uA_num, uB_num, covAB_num):\n",
+    "\n",
+    "  # Variabili simboliche\n",
+    "  A, B, x, y = sp.symbols('A B x y', real=True)\n",
+    "  uA, uB, ux, uy = sp.symbols('uA uB ux uy', positive=True)\n",
+    "  covAB = sp.symbols('covAB', real=True)\n",
+    "\n",
+    "  # Residuo: r = y - (Ax + B)\n",
+    "  r = y - (A*x + B)\n",
+    "\n",
+    "  # Propagazione errore (con covarianza A,B)\n",
+    "  dr_dA  = sp.diff(r, A)\n",
+    "  dr_dB  = sp.diff(r, B)\n",
+    "  dr_dx  = sp.diff(r, x)\n",
+    "  dr_dy  = sp.diff(r, y)\n",
+    "\n",
+    "  u_r  = sp.sqrt(\n",
+    "    (dr_dA * uA)**2 +\n",
+    "    (dr_dB * uB)**2 +\n",
+    "    (dr_dx * ux)**2 +\n",
+    "    (dr_dy * uy)**2 +\n",
+    "    2 * dr_dA * dr_dB * covAB\n",
+    "  )\n",
+    "\n",
+    "  # Funzioni numeriche\n",
+    "  r_fn   = sp.lambdify((x , y , A , B ), r, 'numpy')\n",
+    "  u_r_fn = sp.lambdify(\n",
+    "    (x , y , ux , uy , A , B ,  uA , uB , covAB ),\n",
+    "    u_r , 'numpy'\n",
+    "  )\n",
+    "\n",
+    "  # Calcolo numerico\n",
+    "  analisi_stat[\"r\"] = r_fn(\n",
+    "    analisi_stat[\"x\"],\n",
+    "    analisi_stat[\"y\"],\n",
+    "    A_num,\n",
+    "    B_num\n",
+    "  )\n",
+    "\n",
+    "  analisi_stat[\"ur\"] = u_r_fn(\n",
+    "    analisi_stat[\"x\"], analisi_stat[\"y\"],\n",
+    "    analisi_stat[\"ux\"], analisi_stat[\"uy\"],\n",
+    "    A_num, B_num,\n",
+    "    uA_num, uB_num, covAB_num\n",
+    "  )\n",
+    "\n",
+    "  return analisi_stat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b61f26",
+   "metadata": {},
+   "source": [
+    "### Regressione"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "299faa92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot regressione\n",
+    "\n",
+    "def plot_reg(x, y, ux, uy,\n",
+    "             A, B, uA, uB, P,\n",
+    "             x_label, y_label, titolo,\n",
+    "             cd_A=4, cd_B=4, scala_barre=1):\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "    x_fit = np.linspace(x.min(), x.max(), 300)\n",
+    "    y_fit = A * x_fit + B\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        x, y,\n",
+    "        xerr = scala_barre * ux,\n",
+    "        yerr = scala_barre * uy,\n",
+    "        fmt='o', color=sns.color_palette()[0],\n",
+    "        ecolor='gray', elinewidth=1, capsize=3,\n",
+    "        markersize=5, label=f\"Dati (barre errore x{scala_barre})\"\n",
+    "    )\n",
+    "    ax.plot(\n",
+    "        x_fit, y_fit,\n",
+    "        color='red', linewidth=1.5,\n",
+    "        label = f\"$A={A:.{cd_A}f}\\\\pm{uA:.{cd_A}f}$\\n\"\n",
+    "                f\"$B={B:.{cd_B}f}\\\\pm{uB:.{cd_B}f}$\\n\"\n",
+    "                f\"$P(chi², ∞)={P:.4f}$\\n\"\n",
+    "    )\n",
+    "\n",
+    "    sns.despine(ax=ax)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_title(titolo)\n",
+    "    ax.legend(fontsize=9)\n",
+    "    ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9d3f0378",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Funzioni regressione\n",
+    "\n",
+    "def uy_equiv(x, y, ux, uy):\n",
+    "  \n",
+    "  # Stima iniziale di A con sola uy\n",
+    "  sy2   = uy**2\n",
+    "  Sw    = np.sum(1 / sy2)\n",
+    "  Sx    = np.sum(x / sy2)\n",
+    "  Sxx   = np.sum(x**2 / sy2)\n",
+    "  Sy    = np.sum(y / sy2)\n",
+    "  Sxy   = np.sum(x * y / sy2)\n",
+    "  delta = Sxx * Sw - Sx**2\n",
+    "  A_est = (Sxy * Sw - Sx * Sy) / delta\n",
+    "\n",
+    "  # Propagazione\n",
+    "  u_eq = np.sqrt(uy**2 + A_est**2 * ux**2)\n",
+    "  return u_eq\n",
+    "\n",
+    "\n",
+    "def reg_lin(self,\n",
+    "            stampa_param=True, plot_regressione=True, residui=True,\n",
+    "            x_label=\"\", y_label=\"\", r_label=\"\",\n",
+    "            titolo_reg=\"\", titolo_residui=\"\",\n",
+    "            cd_A=4, cd_B=4, scala_barre=1):\n",
+    "  \n",
+    "  x  = np.asarray(self.analisi_stat[\"x\"], dtype=float)\n",
+    "  y  = np.asarray(self.analisi_stat[\"y\"], dtype=float)\n",
+    "  ux = np.asarray(self.analisi_stat[\"ux\"], dtype=float)\n",
+    "  uy = np.asarray(self.analisi_stat[\"uy\"], dtype=float)\n",
+    "\n",
+    "  # Propagazione errore x → y\n",
+    "  uy_eq = uy_equiv(x, y, ux, uy)\n",
+    "\n",
+    "  # Somme pesate\n",
+    "  w     = 1.0 / uy_eq**2\n",
+    "  Sw    = np.sum(w)\n",
+    "  Sx    = np.sum(w * x)\n",
+    "  Sxx   = np.sum(w * x**2)\n",
+    "  Sy    = np.sum(w * y)\n",
+    "  Sxy   = np.sum(w * x * y)\n",
+    "  delta = Sxx * Sw - Sx**2\n",
+    "\n",
+    "  # Parametri\n",
+    "  A     = (Sxy * Sw - Sx * Sy)  / delta\n",
+    "  B     = (Sxx * Sy - Sxy * Sx) / delta\n",
+    "  uA    = np.sqrt(Sw  / delta)\n",
+    "  uB    = np.sqrt(Sxx / delta)\n",
+    "  covAB = -Sx / delta\n",
+    "\n",
+    "  # Chi quadro\n",
+    "  x2  = np.sum((y - A * x - B)**2 / uy_eq**2)\n",
+    "  dof = len(x) - 2\n",
+    "  P   = stats.chi2.sf(x2, dof)\n",
+    "\n",
+    "  # Raccolta parametri\n",
+    "  self.param_reg[\"A\"]     = A\n",
+    "  self.param_reg[\"B\"]     = B\n",
+    "  self.param_reg[\"uA\"]    = uA\n",
+    "  self.param_reg[\"uB\"]    = uB\n",
+    "  self.param_reg[\"covAB\"] = covAB\n",
+    "  self.param_reg[\"x2\"]    = x2\n",
+    "  self.param_reg[\"P\"]     = P\n",
+    "\n",
+    "  # Stampa\n",
+    "  if stampa_param == True:\n",
+    "    print(\"Ax + B : \")\n",
+    "    print(f\"A          = {A:.{cd_A}f} ± {uA:.{cd_A}f}\")\n",
+    "    print(f\"B          = {B:.{cd_B}f} ± {uB:.{cd_B}f}\")\n",
+    "    print(f\"covAB      = {covAB:.6f}\")\n",
+    "    print(f\"chi²       = {x2:.2f}\")\n",
+    "    print(f\"P(chi², ∞) = {P:.2f}\")\n",
+    "  \n",
+    "  # Plot\n",
+    "  if plot_regressione == True:\n",
+    "    plot_reg(x, y, ux, uy,\n",
+    "             A, B, uA, uB, P,\n",
+    "             x_label, y_label, titolo_reg,\n",
+    "             cd_A, cd_B, scala_barre)\n",
+    "  \n",
+    "  # Residui\n",
+    "  if residui == True:\n",
+    "    self = self.residui(A, B, uA, uB, covAB)\n",
+    "    plot_residui(x, self.analisi_stat[\"r\"],\n",
+    "                 ux, self.analisi_stat[\"ur\"],\n",
+    "                 x_label, r_label, titolo_residui)\n",
+    "\n",
+    "  return self\n",
+    "\n",
+    "\n",
+    "# Aggiunta a classe Data\n",
+    "Data.reg_lin = reg_lin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b311d9e2",
+   "metadata": {},
+   "source": [
+    "# Plot gaussiane"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "170849f1",
+   "metadata": {},
+   "source": [
+    "**plot_gauss( gaussiane )**\n",
+    "\n",
+    "*input gaussiane:*  \n",
+    "[ (mi, sigma, colore label),... ]\n",
+    "  - mi: valore medio (misura)\n",
+    "  - sigma: deviazione standard (incertezza)\n",
+    "  - colore: indice colore nella palette\n",
+    "  - label: etichetta per la didascalia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f515a2bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_gauss(gaussiane):\n",
+    "    \n",
+    "    # Creazione figura\n",
+    "    plt.figure(figsize=(12, 7))\n",
+    "\n",
+    "    # Creazione asse x\n",
+    "    xMin = float('inf')\n",
+    "    xMax = float('-inf')\n",
+    "\n",
+    "    for mu, sigma, _, _ in gaussiane:\n",
+    "        minimoLocale  = mu - 4 * sigma\n",
+    "        massimoLocale = mu + 4 * sigma\n",
+    "        \n",
+    "        if minimoLocale < xMin:\n",
+    "            xMin = minimoLocale\n",
+    "        \n",
+    "        if massimoLocale > xMax:\n",
+    "            xMax = massimoLocale\n",
+    "    \n",
+    "    x = np.linspace(xMin, xMax, 1000)\n",
+    "\n",
+    "    # Ciclo gaussiane\n",
+    "    for mu, sigma, colore, etichetta in gaussiane:\n",
+    "        y = stats.norm.pdf(x, mu, sigma)\n",
+    "        plt.plot(x, y, color=sns.color_palette()[colore], linewidth=1, label=etichetta)\n",
+    "        \n",
+    "        puntiLinee = [mu - sigma, mu, mu + sigma]\n",
+    "        for px in puntiLinee:\n",
+    "            py = stats.norm.pdf(px, mu, sigma) \n",
+    "            plt.vlines(x=px,\n",
+    "                       ymin=0, ymax=py,\n",
+    "                       colors=sns.color_palette()[colore],\n",
+    "                       linestyles='dashed', linewidth=1)\n",
+    "            \n",
+    "    # Dettagli estetici finali\n",
+    "    plt.ylim(bottom=0)\n",
+    "    plt.title('Confronto dati')\n",
+    "    plt.xlabel('k')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # Mostriamo il grafico\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/funzioni.ipynb b/funzioni.ipynb
index 313d3c3..7064544 100644
--- a/funzioni.ipynb
+++ b/funzioni.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e7f6f9db",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "e31f5b72",
    "metadata": {},
    "outputs": [],
@@ -48,10 +48,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a88eb843",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(np.float64(1.162476387438193), -1)\n"
+     ]
+    }
+   ],
    "source": [
     "def compatibilita(x1, x2, u1, u2):\n",
     "  diff = x1 - x2\n",
@@ -79,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "3d561eb0",
    "metadata": {},
    "outputs": [
@@ -134,10 +142,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "ffdeea69",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "media pesata: 10.885245901639344\n",
+      "sigma: 0.15364425591947517\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "def mediaPesata(x, ux, dim = 0):\n",
@@ -185,10 +202,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "2c4561c9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maschera: [False False  True False False False False False False]\n",
+      "Outlier: [50.]\n",
+      "NP: [6.26782319 7.07278344 0.37856532 6.66695415        nan 7.48227073\n",
+      " 5.87724449 5.44192616 3.56273523]\n"
+     ]
+    }
+   ],
    "source": [
     "def outlier_t(x, ux, dim=0):\n",
     "  # Conversione posizionale\n",
@@ -261,19 +289,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "78c30380",
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'pd' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 49\u001b[39m\n\u001b[32m     45\u001b[39m   chi = sc.stats.chi2.cdf(x2, dof)  \u001b[38;5;66;03m# P(X² > x2)\u001b[39;00m\n\u001b[32m     47\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m A, B, sigma_A, sigma_B, cov_AB, chi\n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m df = \u001b[43mpd\u001b[49m.DataFrame({\n\u001b[32m     50\u001b[39m   \u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m1.0\u001b[39m, \u001b[32m2.0\u001b[39m, \u001b[32m3.0\u001b[39m, \u001b[32m4.0\u001b[39m, \u001b[32m5.0\u001b[39m],\n\u001b[32m     51\u001b[39m   \u001b[33m\"\u001b[39m\u001b[33my\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m2.1\u001b[39m, \u001b[32m3.9\u001b[39m, \u001b[32m6.2\u001b[39m, \u001b[32m7.8\u001b[39m, \u001b[32m10.1\u001b[39m],\n\u001b[32m     52\u001b[39m   \u001b[33m\"\u001b[39m\u001b[33mux\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m],\n\u001b[32m     53\u001b[39m   \u001b[33m\"\u001b[39m\u001b[33muy\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m]\n\u001b[32m     54\u001b[39m   })\n\u001b[32m     56\u001b[39m A, B, sA, sB, covAB, chi = reg_lin(df[\u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33my\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33mux\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33muy\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m     57\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAx + B : \u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mNameError\u001b[39m: name 'pd' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ax + B : \n",
+      "A      = 1.9900 +- 0.0892\n",
+      "B      = 0.0500 +- 0.2959\n",
+      "cov_AB = -0.023880\n",
+      "p-value chi² = 0.2813\n"
      ]
     }
    ],
@@ -346,7 +374,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -360,7 +388,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
diff --git a/mollaDefinitiva/molla-1/molla1.ipynb b/mollaDefinitiva/molla-1/molla1.ipynb
index a2a0c3d..69439ae 100644
--- a/mollaDefinitiva/molla-1/molla1.ipynb
+++ b/mollaDefinitiva/molla-1/molla1.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 42,
    "id": "3a34cc3f",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 43,
    "id": "1e2fabea",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 44,
    "id": "5f29a38d",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 45,
    "id": "a5ff5ff5",
    "metadata": {},
    "outputs": [
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 46,
    "id": "e649eddc",
    "metadata": {},
    "outputs": [],
@@ -233,14 +233,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 47,
    "id": "42ff21d6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f8c7a38853e24240b3b315ce0a53d3ac",
+       "model_id": "a459321191844ca5a43de4699ff8c72b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 48,
    "id": "0500f913",
    "metadata": {},
    "outputs": [],
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 49,
    "id": "5d7ca0a0",
    "metadata": {},
    "outputs": [
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 50,
    "id": "cd68b201",
    "metadata": {},
    "outputs": [
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 51,
    "id": "68eb66dc",
    "metadata": {},
    "outputs": [
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 52,
    "id": "f98a3612",
    "metadata": {},
    "outputs": [],
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 53,
    "id": "831a4ac6",
    "metadata": {},
    "outputs": [
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 54,
    "id": "a345ce91",
    "metadata": {},
    "outputs": [
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 55,
    "id": "4d8e0c76",
    "metadata": {},
    "outputs": [
@@ -604,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 56,
    "id": "d75d9912",
    "metadata": {},
    "outputs": [
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 57,
    "id": "26c5dc3c",
    "metadata": {},
    "outputs": [],
@@ -681,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 58,
    "id": "278a5713",
    "metadata": {},
    "outputs": [
@@ -754,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 59,
    "id": "d6195b96",
    "metadata": {},
    "outputs": [],
@@ -782,14 +782,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 60,
    "id": "02e2d183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77f7a74ee7db42e3a71d15860575416a",
+       "model_id": "b5e85ac7e5114d4e81aa5bfa0b9d6d68",
        "version_major": 2,
        "version_minor": 0
       },
@@ -818,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 61,
    "id": "06f9ccef",
    "metadata": {},
    "outputs": [
@@ -849,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 62,
    "id": "75653d7a",
    "metadata": {},
    "outputs": [
@@ -904,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 63,
    "id": "09445aac",
    "metadata": {},
    "outputs": [],
@@ -928,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 64,
    "id": "1ecf9ab2",
    "metadata": {},
    "outputs": [
@@ -993,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 65,
    "id": "4088453c",
    "metadata": {},
    "outputs": [
@@ -1041,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 66,
    "id": "1584a5d4",
    "metadata": {},
    "outputs": [],
@@ -1068,14 +1068,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 67,
    "id": "ac4860fa",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb263fda869a4e73b2afe795f556f900",
+       "model_id": "2edff6df7c984567a1c9326fc1324973",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1104,7 +1104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 68,
    "id": "8917049c",
    "metadata": {},
    "outputs": [
@@ -1134,7 +1134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 69,
    "id": "d5646b8c",
    "metadata": {},
    "outputs": [
@@ -1189,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 70,
    "id": "e35a9456",
    "metadata": {},
    "outputs": [],
@@ -1213,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 71,
    "id": "8de2a48e",
    "metadata": {},
    "outputs": [
@@ -1268,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 72,
    "id": "af6143df",
    "metadata": {},
    "outputs": [
@@ -1295,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 73,
    "id": "a1e69bd3",
    "metadata": {},
    "outputs": [
@@ -1346,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 74,
    "id": "56679fc4",
    "metadata": {},
    "outputs": [
@@ -1391,14 +1391,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 75,
    "id": "455271a9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "efe47853aaba4e8887e03021b388d7dc",
+       "model_id": "8d526367a60e4b03992bdae29ec743d8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1414,7 +1414,7 @@
     "# Calcolo numerico Meq e w^2\n",
     "\n",
     "dfSonar['alp'] = 1/3\n",
-    "dfSonar['ualp'] = 0\n",
+    "dfSonar['ualp'] = 0.000001\n",
     "\n",
     "dfSonar['1/Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
     "dfSonar['u1/Meq']= uMeq_fn(dfSonar['m'], dfSonar['um'], dfSonar['alp'], dfSonar['ualp'], massaMolla, umassaMolla)\n",
@@ -1438,7 +1438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 76,
    "id": "02119d66",
    "metadata": {},
    "outputs": [
@@ -1470,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 77,
    "id": "de571f32",
    "metadata": {},
    "outputs": [
@@ -1529,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 78,
    "id": "96f12fe6",
    "metadata": {},
    "outputs": [],
@@ -1553,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 79,
    "id": "c87033e0",
    "metadata": {},
    "outputs": [
@@ -1607,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 80,
    "id": "e87e24b7",
    "metadata": {},
    "outputs": [
@@ -1695,13 +1695,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 81,
    "id": "dc913446",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAAJwCAYAAACOB+KMAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAg1ZJREFUeJzs3XecVOWh//HvmbqzvcKy9N6LIpaQKCoBS1SMRiVNEq9GxSR2gxU0kURFseJNbhRvbmwxwWjMT2OJGAExUbGLglRpS9m+O+2c3x9n5+zO1lnYZZeZzzuvzXlOmTPPwLjMd55mWJZlCQAAAAAAtMvV3RUAAAAAAOBQQYgGAAAAACBBhGgAAAAAABJEiAYAAAAAIEGEaAAAAAAAEkSIBgAAAAAgQYRoAAAAAAASRIgGAAAAACBBhGgAAAAAABJEiAYA4CD54osvNGPGDOXk5MgwDD377LPdXaUuNX/+fBmG0an3fP3112UYhl5//fVOvS8AAIkiRAMAUsr69ev1k5/8REOGDFFaWpqys7M1depU3Xvvvaqtre3S5z7//PP14Ycf6le/+pX+8Ic/6IgjjujS52vqk08+0fz587Vx48aD+rz746GHHtLSpUu7uxoAADTj6e4KAABwsLzwwgv6zne+I7/frx/+8IcaN26cQqGQ3nzzTV1zzTX6+OOP9dvf/rZLnru2tlarVq3SDTfcoMsuu6xLnqM9n3zyiRYsWKBp06Zp0KBB3VKHRD300EMqLCzUnDlz4o4fe+yxqq2tlc/n656KAQBSHiEaAJASNmzYoPPOO08DBw7Ua6+9pj59+jjn5s6dq3Xr1umFF17osucvLS2VJOXm5rZ7bXV1tTIyMrqsLocyl8ultLS07q4GACCF0Z0bAJAS7rjjDlVVVen3v/99XICOGTZsmH7+8587+5FIRLfddpuGDh0qv9+vQYMG6frrr1cwGIx73KBBg/Stb31Lb775po488kilpaVpyJAh+t///V/nmvnz52vgwIGSpGuuuUaGYTgtwbFxw5988om++93vKi8vT1//+tc7vQ5Lly7Vd77zHUnS8ccfL8Mwmo0tfuihhzR27Fj5/X6VlJRo7ty5KisrS+jP980339SUKVOUlpamoUOH6r//+79bvO7RRx/VCSecoF69esnv92vMmDFasmRJs9fz8ccfa/ny5U49p02bJokx0QCA7kdLNAAgJTz//PMaMmSIvva1ryV0/X/913/pscce09lnn62rrrpKq1ev1sKFC/Xpp59q2bJlcdeuW7dOZ599ti644AKdf/75euSRRzRnzhxNnjxZY8eO1be//W3l5ubqiiuu0OzZs3XKKacoMzMz7h7f+c53NHz4cN1+++2yLKvT63DsscfqZz/7me677z5df/31Gj16tCQ52/nz52vBggWaPn26LrnkEq1du1ZLlizRv//9b61YsUJer7fVP6sPP/xQM2bMUFFRkebPn69IJKJbbrlFvXv3bnbtkiVLNHbsWJ1++unyeDx6/vnndemll8o0Tc2dO1eStHjxYv30pz9VZmambrjhBklq8V4AAHQLCwCAJFdeXm5Jss4444yErl+zZo0lyfqv//qvuONXX321Jcl67bXXnGMDBw60JFlvvPGGc2zXrl2W3++3rrrqKufYhg0bLEnWnXfeGXfPW265xZJkzZ49u8vr8Kc//cmSZP3zn/+Mu+euXbssn89nzZgxw4pGo87xBx54wJJkPfLII63+WVmWZc2aNctKS0uzNm3a5Bz75JNPLLfbbTX9qFFTU9Ps8TNnzrSGDBkSd2zs2LHWcccd1+zaf/7zny2+BgAADha6cwMAkl5FRYUkKSsrK6Hr//73v0uSrrzyyrjjV111lSQ1Gzs9ZswYfeMb33D2i4qKNHLkSH355ZcJ1/Hiiy/utjq88sorCoVCuvzyy+VyNXw0uPDCC5Wdnd3mWPFoNKqXXnpJs2bN0oABA5zjo0eP1syZM5tdHwgEnHJ5ebl2796t4447Tl9++aXKy8vbrSsAAN2NEA0ASHrZ2dmSpMrKyoSu37Rpk1wul4YNGxZ3vLi4WLm5udq0aVPc8cbhMSYvL0/79u1LuI6DBw/utjrE7jVy5Mi44z6fT0OGDGn2XI2VlpaqtrZWw4cPb3au6f0kacWKFZo+fboyMjKUm5uroqIiXX/99ZJEiAYAHBIYEw0ASHrZ2dkqKSnRRx991KHHGYaR0HVut7vF41b92OZENG6h7a46dLX169frxBNP1KhRo3T33Xerf//+8vl8+vvf/6577rlHpml2dxUBAGgXLdEAgJTwrW99S+vXr9eqVavavXbgwIEyTVNffPFF3PGdO3eqrKzMmWm7K3VFHVoL5LF7rV27Nu54KBTShg0b2nyuoqIiBQKBZvVs6X7PP/+8gsGgnnvuOf3kJz/RKaecounTp7f4BUKiXx4AAHCwEaIBACnh2muvVUZGhv7rv/5LO3fubHZ+/fr1uvfeeyVJp5xyiiR7lujG7r77bknSqaee2rWV7aI6xNaebrps1fTp0+Xz+XTffffFtVz//ve/V3l5eZvP5Xa7NXPmTD377LPavHmzc/zTTz/VSy+91OxaKb51vLy8XI8++miLdU10eS0AAA4munMDAFLC0KFD9fjjj+vcc8/V6NGj9cMf/lDjxo1TKBTSypUr9ac//Ulz5syRJE2cOFHnn3++fvvb36qsrEzHHXec3n77bT322GOaNWuWjj/++C6vb1fUYdKkSXK73frNb36j8vJy+f1+Z83mefPmacGCBTrppJN0+umna+3atXrooYc0ZcoUff/732/zvgsWLNCLL76ob3zjG7r00ksViUR0//33a+zYsfrggw+c62bMmCGfz6fTTjtNP/nJT1RVVaXf/e536tWrl7Zv3x53z8mTJ2vJkiX65S9/qWHDhqlXr1464YQTOvyaAQDobIRoAEDKOP300/XBBx/ozjvv1F//+lctWbJEfr9fEyZM0KJFi3ThhRc61/7P//yPhgwZoqVLl2rZsmUqLi7WvHnzdMsttxy0+nZ2HYqLi/Xwww9r4cKFuuCCCxSNRvXPf/5TvXr10vz581VUVKQHHnhAV1xxhfLz83XRRRfp9ttvb3ONaEmaMGGCXnrpJV155ZW6+eab1a9fPy1YsEDbt2+PC9EjR47UM888oxtvvFFXX321iouLdckll6ioqEg//vGP4+558803a9OmTbrjjjtUWVmp4447jhANAOgRDKsnzTgCAAAAAEAPxphoAAAAAAASRIgGAAAAACBBhGgAAAAAABJEiAYAAAAAIEGEaAAAAAAAEkSIBgAAAAAgQT1unWjTNLVt2zZlZWXJMIzurg4AAAAAIMlZlqXKykqVlJTI5Wq7rbnHheht27apf//+3V0NAAAAAECK2bJli/r169fmNT0uRGdlZUmyK5+dnd3NtQEAAAAAJLuKigr179/fyaNt6XEhOtaFOzs7mxANAAAAADhoEhlSzMRiAAAAAAAkiBANAAAAAECCCNEAAAAAACSox42JBgAAAJBaotGowuFwd1cDSc7r9crtdh/wfQjRAAAAALpNVVWVtm7dKsuyursqSHKGYahfv37KzMw8oPsQogEAAAB0i2g0qq1btyo9PV1FRUUJzYwM7A/LslRaWqqtW7dq+PDhB9QiTYgGAAAA0C3C4bAsy1JRUZECgUB3VwdJrqioSBs3blQ4HD6gEM3EYgAAAAC6FS3QOBg6631GiAYAAAAAIEGEaAAAAAAAEkSIBgAAAICDaP78+Zo0aZKzP2fOHM2aNcvZnzZtmi6//PKDXi8khonFAAAAAKAb3XvvvSzxdQghRAMAAABAN8rJyTnge4RCIfl8vk6oDdpDd24AAAAA6CDTNHXHHXdo2LBh8vv9GjBggH71q19Jkq677jqNGDFC6enpGjJkiG666SaFw+FW79W0O7ckRSIRXXbZZcrJyVFhYaFuuummuNbqQYMG6bbbbtMPf/hDZWdn66KLLpIk/fnPf9bYsWPl9/s1aNAgLVq0qPNffIqjJRoAAABAzxGqkXZ/fvCft3CE5EtP+PJ58+bpd7/7ne655x59/etf1/bt2/XZZ59JkrKysrR06VKVlJToww8/1IUXXqisrCxde+21Cd//scce0wUXXKC3335b//nPf3TRRRdpwIABuvDCC51r7rrrLt1888265ZZbJEnvvPOOzjnnHM2fP1/nnnuuVq5cqUsvvVQFBQWaM2dOws+NthGiAQAAAPQcuz+XfnvcwX/ei5ZLJZMSurSyslL33nuvHnjgAZ1//vmSpKFDh+rrX/+6JOnGG290rh00aJCuvvpqPfnkkx0K0f3799c999wjwzA0cuRIffjhh7rnnnviQvQJJ5ygq666ytn/3ve+pxNPPFE33XSTJGnEiBH65JNPdOeddxKiOxEhGgAAAEDPUTjCDrTd8bwJ+vTTTxUMBnXiiSe2eP6pp57Sfffdp/Xr16uqqkqRSETZ2dkdqs7RRx8twzCc/WOOOUaLFi1SNBqV2+2WJB1xxBHN6nXGGWfEHZs6daoWL14c9zgcmA6F6CVLlmjJkiXauHGjJGns2LG6+eabdfLJJ0uS6urqdNVVV+nJJ59UMBjUzJkz9dBDD6l3796dXnEAAAAASciXnnCLcHcJBAKtnlu1apW+973vacGCBZo5c6ZycnL05JNPdsnY5IyMjE6/J9rXoYnF+vXrp1//+td655139J///EcnnHCCzjjjDH388ceSpCuuuELPP/+8/vSnP2n58uXatm2bvv3tb3dJxQEAAACgOwwfPlyBQECvvvpqs3MrV67UwIEDdcMNN+iII47Q8OHDtWnTpg4/x+rVq+P233rrLQ0fPrzN1uTRo0drxYoVccdWrFihESNG0ArdiTrUEn3aaafF7f/qV7/SkiVL9NZbb6lfv376/e9/r8cff1wnnHCCJOnRRx/V6NGj9dZbb+noo4/uvFoDAAAAQDdJS0vTddddp2uvvVY+n09Tp05VaWmpPv74Yw0fPlybN2/Wk08+qSlTpuiFF17QsmXLOvwcmzdv1pVXXqmf/OQnevfdd3X//fe325p91VVXacqUKbrtttt07rnnatWqVXrggQf00EMP7e9LRQv2e0x0NBrVn/70J1VXV+uYY47RO++8o3A4rOnTpzvXjBo1SgMGDNCqVataDdHBYFDBYNDZr6io2N8qAQAAAMBBcdNNN8nj8ejmm2/Wtm3b1KdPH1188cW64IILdMUVV+iyyy5TMBjUqaeeqptuuknz58/v0P1/+MMfqra2VkceeaTcbrd+/vOfO8tYtebwww/X008/rZtvvlm33Xab+vTpo1tvvZVJxTqZYTVebCwBH374oY455hjV1dUpMzNTjz/+uE455RQ9/vjj+tGPfhQXiCXpyCOP1PHHH6/f/OY3Ld5v/vz5WrBgQbPj5eXlHR58DwAAAODQUVdXpw0bNmjw4MFKS0vr7uogybX1fquoqFBOTk5CObRDY6IlaeTIkVqzZo1Wr16tSy65ROeff74++eSTjt7GMW/ePJWXlzs/W7Zs2e97AQAAdFQH2xMAACmuw925fT6fhg0bJkmaPHmy/v3vf+vee+/Vueeeq1AopLKyMuXm5jrX79y5U8XFxa3ez+/3y+/3d7zmAAAAB+ixeStkWdLZ101WZh6tYACA9nW4Jbop0zQVDAY1efJkeb3euBnq1q5dq82bN+uYY4450KcBAADodFX7gqouC6q2MtzdVQEAHCI61BI9b948nXzyyRowYIAqKyv1+OOP6/XXX9dLL72knJwcXXDBBbryyiuVn5+v7Oxs/fSnP9UxxxzDzNwAAAAAgKTQoRC9a9cu/fCHP9T27duVk5OjCRMm6KWXXtI3v/lNSdI999wjl8uls846S8FgUDNnzmQ6dQAA0OPV1dASDQBITIdC9O9///s2z6elpenBBx/Ugw8+eECVAgAAOJgq99R1dxUAAIeIAx4TDQAAcMgy7E1dNS3RAIDEEKIBAEBKsiwrlqFlRsxurQsA4NBBiAYAACnJjFiKLREdCka7tzIAgEMGIRoAAKSkcKghONeUhbqxJgCQHAYNGqTFixd3dzW6HCEaAACkpEiooQt3NEp3bgCpZ/78+Zo0aVKHH7d06VLl5uY2O/7vf/9bF1100YFXrIfr0OzcAAAAySLSqCXaJEQDOMSFQiH5fL5urUNRUVG3Pv/BQks0AABISZFw4xDdjRUBcMh55plnNH78eAUCARUUFGj69Omqrq6WJJmmqVtvvVX9+vWT3+/XpEmT9OKLLzqP3bhxowzD0F/+8hcdf/zxSk9P18SJE7Vq1Srnmj179mj27Nnq27ev0tPTNX78eD3xxBNxdZg2bZouu+wyXX755SosLNTMmTNbrOvrr7+uI488UhkZGcrNzdXUqVO1adMmLV26VAsWLND7778vwzBkGIaWLl0qSbr77rs1fvx4ZWRkqH///rr00ktVVVXl3O9HP/qRysvLncfNnz9fUvPu3GVlZfrJT36i3r17Ky0tTePGjdPf/vY35/yf//xnjR07Vn6/X4MGDdKiRYv2++/kYKIlGgAApKTG3blpiQZ6jtpIrTaUbzjozzs4Z7ACnkC7123fvl2zZ8/WHXfcoTPPPFOVlZX617/+Jat+psJ7771XixYt0n//93/rsMMO0yOPPKLTTz9dH3/8sYYPH+7c54YbbtBdd92l4cOH64YbbtDs2bO1bt06eTwe1dXVafLkybruuuuUnZ2tF154QT/4wQ80dOhQHXnkkc49HnvsMV1yySVasWJFi3WNRCKaNWuWLrzwQj3xxBMKhUJ6++23ZRiGzj33XH300Ud68cUX9corr0iScnJyJEkul0v33XefBg8erC+//FKXXnqprr32Wj300EP62te+psWLF+vmm2/W2rVrJUmZmZnNnts0TZ188smqrKzU//3f/2no0KH65JNP5Ha7JUnvvPOOzjnnHM2fP1/nnnuuVq5cqUsvvVQFBQWaM2dOAn9j3YcQDQAAUlJsYrHc3uny+tzdXBsAMRvKN+jcv5170J/3qW89pTEFY9q9bvv27YpEIvr2t7+tgQMHSpLGjx/vnL/rrrt03XXX6bzzzpMk/eY3v9E///lPLV68WA8++KBz3dVXX61TTz1VkrRgwQKNHTtW69at06hRo9S3b19dffXVzrU//elP9dJLL+npp5+OC9HDhw/XHXfc0WpdKyoqVF5erm9961saOnSoJGn06NHO+czMTHk8HhUXF8c97vLLL3fKgwYN0i9/+UtdfPHFeuihh+Tz+ZSTkyPDMJo9rrFXXnlFb7/9tj799FONGDFCkjRkyBDn/N13360TTzxRN910kyRpxIgR+uSTT3TnnXcSogEAAHqiWEu0P92jKOtEAz3G4JzBeupbT3XL8yZi4sSJOvHEEzV+/HjNnDlTM2bM0Nlnn628vDxVVFRo27Ztmjp1atxjpk6dqvfffz/u2IQJE5xynz59JEm7du3SqFGjFI1Gdfvtt+vpp5/WV199pVAopGAwqPT09Lh7TJ48uc265ufna86cOZo5c6a++c1vavr06TrnnHOc52vNK6+8ooULF+qzzz5TRUWFIpGI6urqVFNT06wOrVmzZo369evnBOimPv30U51xxhlxx6ZOnarFixcrGo06LdY9ESEaAACkpNjEYnu3Vaugb0Y31wZATMATSKhFuLu43W69/PLLWrlypf7xj3/o/vvv1w033KDVq1eroKAg4ft4vV6nbBiGJLsLtCTdeeeduvfee7V48WJnbPLll1+uUCh+Ob6MjPZ/dz366KP62c9+phdffFFPPfWUbrzxRr388ss6+uijW7x+48aN+ta3vqVLLrlEv/rVr5Sfn68333xTF1xwgUKhUMIhOhBov2v8oYqJxQAAQEqKhehwMKpImJZoAIkzDENTp07VggUL9N5778nn82nZsmXKzs5WSUlJszHKK1as0JgxiX8xsGLFCp1xxhn6/ve/r4kTJ2rIkCH6/PPP97u+hx12mObNm6eVK1dq3LhxevzxxyVJPp9P0Wj8zIrvvPOOTNPUokWLdPTRR2vEiBHatm1b3DUtPa6pCRMmaOvWra3We/To0S3+OY0YMaJHt0JLtEQDAIAUFQmZcrkNmVFLZtTq7uoAOESsXr1ar776qmbMmKFevXpp9erVKi0tdcYaX3PNNbrllls0dOhQTZo0SY8++qjWrFmjP/7xjwk/x/Dhw/XMM89o5cqVysvL0913362dO3d2KIhL0oYNG/Tb3/5Wp59+ukpKSrR27Vp98cUX+uEPfyjJHu+8YcMGp+t1VlaWhg0bpnA4rPvvv1+nnXaaVqxYoYcffjjuvoMGDVJVVZVeffVVTZw4Uenp6c1aqI877jgde+yxOuuss3T33Xdr2LBh+uyzz2QYhk466SRdddVVmjJlim677Tade+65WrVqlR544AE99NBDHXqN3YGWaAAAkJLCoajcXvujELNzA0hUdna23njjDZ1yyikaMWKEbrzxRi1atEgnn3yyJOlnP/uZrrzySl111VUaP368XnzxRT333HNxM3O358Ybb9Thhx+umTNnatq0aSouLtasWbM6XNf09HR99tlnOuusszRixAhddNFFmjt3rn7yk59Iks466yyddNJJOv7441VUVKQnnnhCEydO1N13363f/OY3GjdunP74xz9q4cKFcff92te+posvvljnnnuuioqKWp3c7M9//rOmTJmi2bNna8yYMbr22mudFuzDDz9cTz/9tJ588kmNGzdON998s2699dYeP6mYJBlWbC72HqKiokI5OTkqLy9XdnZ2d1cHAAAkqbf/tkEfvr5VdVVhZRWm6Ye//Fp3VwlIOXV1ddqwYYMGDx6stLS07q4Oklxb77eO5FBaogEAQEqKhKLypbk15LBCqUc1KQAAejJCNAAASEmRkClvmkf5fTJlmaRoAEBiCNEAACAlRcJRGYa0Y0O5M1M3AADtIUQDAICUZEYtyZK2frpPkQgt0QCAxBCiAQBASjKjlgx3rMzs3ACAxBCiAQBASjKjplyu+iWuIpZ62IIlAIAeihANAABSkhm1ZLiMhn0mFwMAJIAQDQAAUpJpWvJ4Xeo1KEuSFA3TpRsA0D5CNAAASElm1JI/3aPDZw509gEAaA8hGgAApCQzasowDIXqIpKkaISWaACpYc6cOZo1a5azP23aNF1++eXdVp/GDMPQs88+293VaJOnuysAAADQHcyopXAwqtce+0wS3bkBpK6//OUv8nq93V0NSdL27duVl5fX3dVoEyEaAACkpKYTi9ESDeBQFgqF5PP59uux+fn5nVyb/VdcXNzdVWgX3bkBAEBKMqOWXO6G/WiEMdEAEvPMM89o/PjxCgQCKigo0PTp01VdXS1JMk1Tt956q/r16ye/369JkybpxRdfdB67ceNGGYahv/zlLzr++OOVnp6uiRMnatWqVc41e/bs0ezZs9W3b1+lp6dr/PjxeuKJJ+LqMG3aNF122WW6/PLLVVhYqJkzZ7ZY12g0qiuvvFK5ubkqKCjQtdde22xJv6bduQcNGqTbb79dP/7xj5WVlaUBAwbot7/9bdxjrrvuOo0YMULp6ekaMmSIbrrpJoXDYef8/PnzNWnSJD3yyCMaMGCAMjMzdemllyoajeqOO+5QcXGxevXqpV/96ldx923anXvr1q2aPXu28vPzlZGRoSOOOEKrV692zi9ZskRDhw6Vz+fTyJEj9Yc//KHFP4fOREs0AABISWbUctaJlmiJBnqKcCiqsh01B/15c4vT5fW5271u+/btmj17tu644w6deeaZqqys1L/+9S8nmN57771atGiR/vu//1uHHXaYHnnkEZ1++un6+OOPNXz4cOc+N9xwg+666y4NHz5cN9xwg2bPnq1169bJ4/Gorq5OkydP1nXXXafs7Gy98MIL+sEPfqChQ4fqyCOPdO7x2GOP6ZJLLtGKFStare+iRYu0dOlSPfLIIxo9erQWLVqkZcuW6YQTTmjzdS5atEi33Xabrr/+ej3zzDO65JJLdNxxx2nkyJGSpKysLC1dulQlJSX68MMPdeGFFyorK0vXXnutc4/169fr//2//6cXX3xR69ev19lnn60vv/xSI0aM0PLly7Vy5Ur9+Mc/1vTp03XUUUc1q0NVVZWOO+449e3bV88995yKi4v17rvvyjTt39fLli3Tz3/+cy1evFjTp0/X3/72N/3oRz9Sv379dPzxx7f7d7m/DKvp1xDdrKKiQjk5OSovL1d2dnZ3VwcAACSpx+e/pcL+Wfri3zslSd+++nD1GZbbvZUCUkxdXZ02bNigwYMHKy0tTZJUurlST9/+74Nel3Oun6KiAVntXvfuu+9q8uTJ2rhxowYOHNjsfN++fTV37lxdf/31zrEjjzxSU6ZM0YMPPqiNGzdq8ODB+p//+R9dcMEFkqRPPvlEY8eO1aeffqpRo0a1+Lzf+ta3NGrUKN11112S7NbjiooKvfvuu23Wt6SkRFdccYWuueYaSVIkEtHgwYM1efJkp8V32rRpmjRpkhYvXizJbon+xje+4bTqWpal4uJiLViwQBdffHGLz3PXXXfpySef1H/+8x9Jdkv0nXfeqR07digry/5zPemkk7R27VqtX7/e+RJz1KhRmjNnjn7xi19Isluily1bplmzZum3v/2trr76am3cuLHFLudTp07V2LFj41rJzznnHFVXV+uFF15odn1L77eYjuRQWqIBAEBKMk1LGTk+zb7lKD2xYDUt0UAPkVucrnOun9Itz5uIiRMn6sQTT9T48eM1c+ZMzZgxQ2effbby8vJUUVGhbdu2aerUqXGPmTp1qt5///24YxMmTHDKffr0kSTt2rVLo0aNUjQa1e23366nn35aX331lUKhkILBoNLT4+s4efLkNutaXl6u7du3x7XyejweHXHEEc26dDfVuH6GYai4uFi7du1yjj311FO67777tH79elVVVSkSiTQLn4MGDXICtCT17t1bbrc7rhdQ79694+7b2Jo1a3TYYYe1Omb7008/1UUXXRR3bOrUqbr33nvbfG0HihANAABSkhm15Pa45EuzPw4xJhroGbw+d0Itwt3F7Xbr5Zdf1sqVK/WPf/xD999/v2644QatXr1aBQUFCd+n8WzYhmFPchjrpnznnXfq3nvv1eLFizV+/HhlZGTo8ssvVygUirtHRkZGJ7yi9usXq2OsfqtWrdL3vvc9LViwQDNnzlROTo6efPJJLVq0qN17tHXfpgKBwIG+jC7BxGIAACAlmVFLodqIXvrdh5IYEw0gcYZhaOrUqVqwYIHee+89+Xw+LVu2TNnZ2SopKWk2RnnFihUaM2ZMwvdfsWKFzjjjDH3/+9/XxIkTNWTIEH3++ecdrmdOTo769OkTNxFXJBLRO++80+F7NbZy5UoNHDhQN9xwg4444ggNHz5cmzZtOqB7tmTChAlas2aN9u7d2+L50aNHH/Cf9f6gJRoAAKQkM2rKsqQdX1bU79MSDaB9q1ev1quvvqoZM2aoV69eWr16tUpLSzV69GhJ0jXXXKNbbrlFQ4cO1aRJk/Too49qzZo1+uMf/5jwcwwfPlzPPPOMVq5cqby8PN19993auXPnfoXDn//85/r1r3+t4cOHa9SoUbr77rtVVlbW4fs0rd/mzZv15JNPasqUKXrhhRe0bNmyA7pnS2bPnq3bb79ds2bN0sKFC9WnTx+99957Kikp0THHHKNrrrlG55xzjg477DBNnz5dzz//vP7yl7/olVde6fS6NEaIBgAAKck0LRluo9E+LdEA2pedna033nhDixcvVkVFhQYOHKhFixbp5JNPliT97Gc/U3l5ua666irt2rVLY8aM0XPPPRc3M3d7brzxRn355ZeaOXOm0tPTddFFF2nWrFkqLy/vcH2vuuoqbd++Xeeff75cLpd+/OMf68wzz9yve8WcfvrpuuKKK3TZZZcpGAzq1FNP1U033aT58+fv9z1b4vP59I9//ENXXXWVTjnlFEUiEY0ZM0YPPvigJGnWrFm69957ddddd+nnP/+5Bg8erEcffVTTpk3r1Ho0xezcAAAgJf328uUaM7WP3n91qyRp+pzRGnl0n26uFZBa2potGehsnTU7N2OiAQBASrLXiW7cEt2j2hUAAD0UIRoAAKQkM2opkOXT8d8f5ewDANAeQjQAAEg5lmXJMi35M7wa8/USudyGLFqiAQAJIEQDAICUE+u6HQlF9cmb22QYdOcGACSG2bkBAEDKiXXdDtVG9K+nvpDH56I7N9CNethcx0hSnfU+oyUaAACknFhgdrntj0KGy6AlGugGbrdbkhQKhbq5JkgFsfdZ7H23v2iJBgAAKceM2mtCG/Wzc7tcjIkGuoPH41F6erpKS0vl9XrlctHGh65hmqZKS0uVnp4uj+fAYjAhGgAApJyGlmh733AxOzfQHQzDUJ8+fbRhwwZt2rSpu6uDJOdyuTRgwAAZhtH+xW0gRAMAgJQTC8xen0clw3NVtrOaEA10E5/Pp+HDh9OlG13O5/N1Sm8HQjQAAEg5scCcXZimM686XH+4cSVjooFu5HK5lJaW1t3VABLCoAMAAJByGo+JjoZNyZAsWqIBAAkgRAMAgJQTa4mu2F2nh3/6uiyTdaIBAIkhRAMAgJQTC8yxicVcLkI0ACAxhGgAAJByYi3RsSWuDLeL7twAgIQQogEAQMpxlriqn6XVMAxaogEACSFEAwCAlBObWMxV3xLtchGiAQCJIUQDAICUE2uJzuuTrvMXfk2+gJvu3ACAhBCiAQBAyom1Onv9bmXmpcnldtESDQBIiKe7KwAAAHCwxVqiq8tCevNPX8iMms4xAADaQks0AABIObEx0eFQVOvfLZVlSRYt0QCABBCiAQBAymmYndveNwzDCdYAALSFEA0AAFJO03WiXW4xJhoAkBBCNAAASDkNLdF2iDZcBmOiAQAJIUQDAICUE+u6nZnn19GzhsjrdzMmGgCQEEI0AABIOWbUksttKCM3TZNPGiRfmofu3ACAhLDEFQAASDmxEB2sCWvbF2WyTIvu3ACAhNASDQAAUo5pWnK5DFXsrtPfl3yocDhKd24AQEII0QAAIOVYpiXDbTj7LsOgOzcAICGEaAAAkHJiLdExzM4NAEgUIRoAAKQcy7RkGPEhmu7cAIBEEKIBAEDKsUx7YjG3x6W8PhnyeF20RAMAEkKIBgAAKcesb4nOL8nQd285SunZPsZEAwASQogGAAApxzIlo9GnIBfduQEACSJEAwCAlGOZlgyXodItlfrt5ctVWxVSlO7cAIAEEKIBAEDKMa362bktKVwXlWEYsgjRAIAEEKIBAEDKsaJ2S3SMwTrRAIAEEaIBAEDKiXXnjnG5CdEAgMQQogEAQMoxLXsysRi6cwMAEkWIBgAAKSfWEp1bnK5zrp+ijDw/LdEAgIR0KEQvXLhQU6ZMUVZWlnr16qVZs2Zp7dq1cddMmzZNhmHE/Vx88cWdWmkAAIADYZqWXC7J63OraECWvD63LNOSZRGkAQBt61CIXr58uebOnau33npLL7/8ssLhsGbMmKHq6uq46y688EJt377d+bnjjjs6tdIAAAAHItYSXbm3TsufWKtgTcQ5DgBAWzwdufjFF1+M21+6dKl69eqld955R8cee6xzPD09XcXFxZ1TQwAAgE5mmZYMw1BdVVgfLf9KR542WFJ9C7W7mysHAOjRDmhMdHl5uSQpPz8/7vgf//hHFRYWaty4cZo3b55qampavUcwGFRFRUXcDwAAQFeyTEsud6PZuesnGTOZXAwA0I4OtUQ3ZpqmLr/8ck2dOlXjxo1zjn/3u9/VwIEDVVJSog8++EDXXXed1q5dq7/85S8t3mfhwoVasGDB/lYDAACgw8wmS1zFynTnBgC0Z79D9Ny5c/XRRx/pzTffjDt+0UUXOeXx48erT58+OvHEE7V+/XoNHTq02X3mzZunK6+80tmvqKhQ//7997daAAAA7bJMe1mrmFiIZoZuAEB79itEX3bZZfrb3/6mN954Q/369Wvz2qOOOkqStG7duhZDtN/vl9/v359qAAAA7JfY7NyBLK8mnthfaRn2RyK6cwMA2tOhMdGWZemyyy7TsmXL9Nprr2nw4MHtPmbNmjWSpD59+uxXBQEAADqbZdnduTPz0vT17wxXeo79hT4hGgDQng61RM+dO1ePP/64/vrXvyorK0s7duyQJOXk5CgQCGj9+vV6/PHHdcopp6igoEAffPCBrrjiCh177LGaMGFCl7wAAACAjrKillwel0J1Ee3dVi0zatrH6c4NAGhHh1qilyxZovLyck2bNk19+vRxfp566ilJks/n0yuvvKIZM2Zo1KhRuuqqq3TWWWfp+eef75LKAwAA7I/YxGLlu2r15zveUU15yD5OSzQAoB0daom2rLb/Yenfv7+WL19+QBUCAADoarHu3DFMLAYASNQBrRMNAABwKLLMhrWhpYaZuunODQBoDyEaAACkHDNqyWj0KShWpiUaANAeQjQAAEg5se7chstQWqZXbo/9kYiWaABAewjRAAAg5Vj1E4sV9svUBXd9Q3nFGZJoiQYAtI8QDQAAUo5pWvFjous/EVlmN1UIAHDIIEQDAICUE2uJ3rOtSn+4aZUqdtc6xwEAaAshGgAApBzTlFyGITNiqaK0VlbUPk6IBgC0hxANAABSjt0S3bBvuO0tY6IBAO0hRAMAgJRjmZZcbtaJBgB0HCEaAACkHNO0nOAsNYRoWqIBAO3xdHcFAAAADrbYxGI5RQGd9tOJyipMs4+ToQEA7aAlGgAApJxYiPYFPBowtkBpGV7nOAAAbSFEAwCAlGNakstlqLo8qLef/1K1lWH7eJQQDQBoGyEaAACkHCtqz85dUx7Sv1/YqLrKkH2c/twAgHYQogEAQMqxLLs7d0ysTHduAEB7CNEAACDlmKYlV6MQHftERIgGALSHEA0AAFKO1WSJKxdLXAEAEkSIBgAAKcc0LbnchvzpHo04srfSMmOzc3dzxQAAPR7rRAMAgJRjmfY46OzCgL7547H2QYOWaABA+2iJBgAAKcfuzi1FwlGV7apRJByVyzAYEw0AaBchGgAApJRYUDZchvZtr9Efb35L+7bXyHAbLHEFAGgXIRoAAKSUWJdtl9uIO264DJlRQjQAoG2EaAAAkFKclmgjPkS7DCYWAwC0jxANAABSitMS7WreEk13bgBAewjRAAAgpcRystEkRLvcBrNzAwDaxRJXAAAgpVjRhpboogFZmvvwCZLs7t3Mzg0AaA8t0QAAIKWYzuzc8ccNFy3RAID2EaIBAEBKiY17NlyG9u2o1jO/+Y/27aiW4RIt0QCAdhGiAQBASmm8TnQkZGrnhgpFQqZcLoPZuQEA7SJEAwCAlGJG25idm5ZoAEA7CNEAACClNO7O3ZiLMdEAgAQQogEAQEqJddl2tTCxGC3RAID2EKIBAEBKcWbnNgxlFaRp+o/GKKsgjSWuAAAJYZ1oAACQUpyJxdyG0jK8GnlUsSTJ5TZEhgYAtIeWaAAAkFJiLdEul6HaypA+fH2raitDMgyWuAIAtI8QDQAAUorVqDt31b6g3njyc1XtC8pgYjEAQAII0QAAIKXEJhZraXZuWqIBAO0hRAMAgJQSW+KKdaIBAPuDEA0AAFKKGY2tEx1/nBANAEgEIRoAAKQUZ0y0y5DX71b/Mfny+t1yucSYaABAu1jiCgAApBSzUXfu7MKATv/ZJEmxluhurBgA4JBASzQAAEgpjVuiTdNSqDYi07SYnRsAkBBCNAAASCmxMdEul6E9W6v0uyve0J6tVfbs3BYhGgDQNkI0AABIKbGc3HSJKyYWAwAkghANAABSSkN37vjjhmE4rdQAALSGEA0AAFJKLEQ3XSfa5RLduQEA7SJEAwCAlGI2mlisMWbnBgAkgiWuAABASmk8O3d+3wz9+M6vy5fusScWY0w0AKAdhGgAAJBSnO7chiG326VAlk+SWOIKAJAQunMDAICUYtZ32TbchspLa/TCQx+ovLSG2bkBAAkhRAMAgJTidOc2pFBtVBs/2K1QbZSWaABAQgjRAAAgpZimJcOwl7RqzMXEYgCABBCiAQBASrFMq9nM3JK9bjRLXAEA2kOIBgAAKcWyrGZrREv1E4tFCdEAgLYRogEAQEoxow0t0Rm5fk09e5gycv1yGUwsBgBoH0tcAQCAlGKZckJ0erZPk6YPkGQfozs3AKA9tEQDAICUYlmWjPpPQHXVYa17Z5fqqsNyuZmdGwDQPkI0AABIKabZMCa6ck+dXvrdR6rcUyfDkCzGRAMA2kGIBgAAKaX12bkN0RANAGgPIRoAAKSUxi3RjRkuJhYDALSPEA0AAFKKZVoyjOYh2kWIBgAkgBANAABSimVKhtsO0W6vS4X9M+X2umiJBgAkhBANAABSSuPu3Pl9MnTuDUcqv0+GXC5m5wYAtI8QDQAAUordnbv5ccNlt1IDANAWQjQAAEgpjWfnLt1cqSWX/VOlmysZEw0ASAghGgAApBTLtORyNzRFmxE7ONtLXBGiAQBtI0QDAICUYrYyO7fhMiRLtEYDANpEiAYAACmlcXfuxmLBmtZoAEBbCNEAACClmJbkauETUOwYLdEAgLYQogEAQEpp3BKdV5yu824+UnnF6c7a0czQDQBoi6e7KwAAAHAwmdGGdaI9PrcKSjIlNXTnpiUaANAWWqIBAEBKsayGluiKPbV67Q+fqmJPrROsTUI0AKANhGgAAJBSGnfnDlZH9OmK7QpWR5xjtEQDANpCiAYAACnFMuW0OjdGSzQAIBGEaAAAkFLM1pa4cjGxGACgfR0K0QsXLtSUKVOUlZWlXr16adasWVq7dm3cNXV1dZo7d64KCgqUmZmps846Szt37uzUSgMAAOwvy7RkNM/QMuo/FZkmKRoA0LoOhejly5dr7ty5euutt/Tyyy8rHA5rxowZqq6udq654oor9Pzzz+tPf/qTli9frm3btunb3/52p1ccAABgf1hmw+zcgSyfDp85UIEsHy3RAICEdGiJqxdffDFuf+nSperVq5feeecdHXvssSovL9fvf/97Pf744zrhhBMkSY8++qhGjx6tt956S0cffXTn1RwAAGA/mKblrAmdmefXMWcOlSSV76qRxMRiAIC2HdCY6PLycklSfn6+JOmdd95ROBzW9OnTnWtGjRqlAQMGaNWqVS3eIxgMqqKiIu4HAACgq9jdue0QHaqL6Ku1+xSqizjB2rII0QCA1u13iDZNU5dffrmmTp2qcePGSZJ27Nghn8+n3NzcuGt79+6tHTt2tHifhQsXKicnx/np37///lYJAACgXWaj7tzlu2r17D3vqXwX60QDABKz3yF67ty5+uijj/Tkk08eUAXmzZun8vJy52fLli0HdD8AAIC2WGbDJGKNxVqn6c4NAGhLh8ZEx1x22WX629/+pjfeeEP9+vVzjhcXFysUCqmsrCyuNXrnzp0qLi5u8V5+v19+v39/qgEAANBhlmW1uE50LFgzsRgAoC0daom2LEuXXXaZli1bptdee02DBw+OOz958mR5vV69+uqrzrG1a9dq8+bNOuaYYzqnxgAAAAfAjLa8TrTLTXduAED7OtQSPXfuXD3++OP661//qqysLGecc05OjgKBgHJycnTBBRfoyiuvVH5+vrKzs/XTn/5UxxxzDDNzAwCAHsGyGkK0y20oI9fvBGiJ7twAgLZ1KEQvWbJEkjRt2rS4448++qjmzJkjSbrnnnvkcrl01llnKRgMaubMmXrooYc6pbIAAAAHyjIbQnRB30zN+fVUSdLe7dWSaIkGALStQyE6kSUf0tLS9OCDD+rBBx/c70oBAAB0FdOUXEYL3bldTCwGAGjfAa0TDQAAcKixTMtZE3rPV1Va+osV2vNVVaOJxQjRAIDWEaIBAEBKsdeJri9HLVWXBeMmG2N2bgBAWwjRAAAgpVim5awJ3VisO7eZwPA1AEDqIkQDAICU0nhiscaclugoIRoA0DpCNAAASCmmacUtaRUTa51mdm4AQFsI0QAAIKU07s6d0yugWVccppxeASdYJ7IaCQAgdXVoiSsAAIBDnWXKmYnbl+ZR35F5kqRgbcQ5DwBAa2iJBgAAKcWyGsZEV+0LatWy9araF1RsrjHTJEUDAFpHiAYAACnFjFrOTNy1lSG9+9Im1VaGnGO0RAMA2kKIBgAAKaXV2bljY6KZWAwA0AZCNAAASCmm1dAS3ZiL2bkBAAkgRAMAgJRiTyzWxjrRhGgAQBsI0QAAIGVYliXLbGiJ9md4NHpqH/kz7AVLDJdBiAYAtIklrgAAQMqILQEdW+IquyCgE34w2jlvuCQm5wYAtIWWaAAAkDJircyxrtuRUFR7tlUpEopKkly0RAMA2kGIBgAAKcMJ0fWTiO3bUaMnb31b+3bU2MddhiyLEA0AaB0hGgAApIzYzNsud/OJxSS7JdqMEqIBAK0jRAMAgJTRtCW6KcOgJRoA0DZCNAAASBlW/aRhLa0TLUmGmzHRAIC2EaIBAEDKMJ2JxRqOuTwNgdplMDs3AKBtLHEFAABSRqyrdmx27qIBWbrkgeOd86wTDQBoDy3RAAAgZcQmDWu1O7fLcFqrAQBoCSEaAACkjKbrRO/dXq2nfvW29m6vdo7TEg0AaAshGgAApIym3bmjYVO7t1QpGrYHQrsI0QCAdhCiAQBAyojNzr32re2qLg82P29Z2r6uvMVzAABIhGgAAJBCYmOiP1u1QzXloeYXWJZ2bqxo+RwAACJEAwCAFBLrzt0ao5UJxwAAiGGJKwAAkDJiM28fPWuIsgrSJEkzLxznlD1et/qNynP2AQBoihANAABSRmzSsAFjCpSW4ZUkDZvcyznv9rqUVZDmnAMAoCm6cwMAgJQRm1js83/vUE1FSDUVIa15ZbNqKuwx0GbU0p6tVc4+AABNEaIBAEDKiHXnXvPyFlWXBVVdFtSKZ9apusyejdsyLe3aVOnsAwDQFCEaAACkjPbWgDb4ZAQAaAf/VAAAgJTRfohmdm4AQNsI0QAAIGWY7Sxx5TII0QCAtjE7NwAASBmxluh+o/LkC7glSYMmFDpll8el9Gyfsw8AQFOEaAAAkDLMqB2ip88Zo4xcvyTp1EsnOOd9AY96DcxSTlF6t9QPANDz0Z0bAACkjFhv7mBNRNGoqWjUVG1lSNGovfaVYUjhkOnsAwDQFCEaAACkjFh37iduXa29X1Vr71fVeuSaN7X3q2pJUjgY0Vdr9zn7AAA0RYgGAAApo73ZucXEYgCAdhCiAQBAyjDbW+KKEA0AaAchGgAApIz2WqJdrBMNAGgHIRoAAKSM9kK0QYgGALSDJa4AAEDKMOsn3b7grq/Ll+6VJF14z7Hy+O11oQOZHhUOyFJBv8zuqiIAoIcjRAMAgJRh1a9x5c/wOuOffYGGj0Mut0uG6NYNAGgd3bkBAEDKiHXnfv7+91W2s0ZlO2v03H1rVLazRpIUqos6xwEAaAkt0QAAIGWYUUuGS9ryyV6Fg1FJ8WXLshQORp19AACaoiUaAACkDMuy2pw8jCWuAADtIUQDAICUYZltB2Vm5wYAtIcQDQAAUoZpWmqrsdnFJyMAQDsYEw0AAFKGZVpyuQ0de94IZeb5JSmu7At45E/3OPsAADRFiAYAACnDNC253C6Nn9bPOda47At45PW7FcjydUf1AACHADotAQCAlGHVd+deu3qH6qrDqqsOO2VJMiOmQnURZx8AgKYI0QAAIGVYpiXLkl559BNV7qlT5Z46pyxJodqoQrVRZx8AgKYI0QAAIGVYlmS08emnrXMAAEiEaAAAkEJM05KLdaIBAAeAEA0AAFKGFbVkqK11og9iZQAAhyRm5wYAACnDtCy5PIZ6D86Wx2cn5sZlt9ctGXL2AQBoihANAABShmVa8vjcOvu6I5xjjcuZuX65XIbyijO6o3oAgEMAX7MCAICUYZltj3s2XIYs0zqINQIAHGoI0QAAIGVYpqVoJKoHL35NpZsrVbq50ilLUvW+oCxL2rWpoptrCgDoqQjRAAAgZZiW1U5LtL21aIwGALSCEA0AAFKGFbVktLHElZwQTYoGALSMEA0AAFKG1d460fXLXzEuGgDQGkI0AABIGaZlqY1louVyE6IBAG1jiSsAAJAyLFPypbn1vVuPVmaeX5Liypl5aZKknF7p3VZHAEDPRogGAAApwzItudwu5TYKyY3LXp9bkuR201kPANAy/oUAAAApwzQtmRFTLz/ysSp216pid61TlqTaqpAkqbx+HwCApgjRAAAgZVimJVPS52/vVLAmomBNxClLUiQclSQFq8PdWEsAQE9GiAYAACnDMi1nBu6WxNaQNplYDADQCkI0AABIGaYpudr49BNbQ5p1ogEArSFEAwCAlGFZloy2QrQRC9EHqUIAgEMOs3MDAICUYUYtef1uTTl1kNJzfJIUV07P8kqS0tL5iAQAaBn/QgAAgJRhmZZ8aR4dedoQ51jjciDbXi86LcN30OsGADg00J0bAACkDMuyZJqWNn+8R6HaiEK1EacsNczOHaqLdGc1AQA9GCEaAACkDMu0FAmZev7+91VeWqvy0lqnLEk15UFJctaNBgCgqQ6H6DfeeEOnnXaaSkpKZBiGnn322bjzc+bMkWEYcT8nnXRSZ9UXAABgv5mmZLS+wlWjicWYWQwA0LIOh+jq6mpNnDhRDz74YKvXnHTSSdq+fbvz88QTTxxQJQEAADqDZbYzO7crtk70QaoQAOCQ0+GJxU4++WSdfPLJbV7j9/tVXFyc0P2CwaCCwaCzX1FR0dEqAQAAJMQ0LbnaaIpmiSsAQHu6ZEz066+/rl69emnkyJG65JJLtGfPnlavXbhwoXJycpyf/v37d0WVAAAAZJmWXB6XsosCcnkMuTyGU5Ykt9feutro8g0ASG2dHqJPOukk/e///q9effVV/eY3v9Hy5ct18sknKxqNtnj9vHnzVF5e7vxs2bKls6sEAAAgyQ7RgSyffnDbMSooyVRBSaZTlqS84gxJUlZ+WndWEwDQg3X6OtHnnXeeUx4/frwmTJigoUOH6vXXX9eJJ57Y7Hq/3y+/39/Z1QAAAGjGshrGPbfE5YyJpj83AKBlXb7E1ZAhQ1RYWKh169Z19VMBAAC0yYyaqqsO6fdX/0u7t1Zp99YqpyxJZTtrJEnlu2q6s5oAgB6s01uim9q6dav27NmjPn36dPVTAQAAtMkyJcOS6qrCsupbmxuXY6JRpucGALSswyG6qqoqrlV5w4YNWrNmjfLz85Wfn68FCxborLPOUnFxsdavX69rr71Ww4YN08yZMzu14gAAAB1lWZbkbnOh6PrrDlKFAACHnA6H6P/85z86/vjjnf0rr7xSknT++edryZIl+uCDD/TYY4+prKxMJSUlmjFjhm677TbGPQMAgG7X7hJX9QPdrJbnQwUAoOMhetq0afa3uK146aWXDqhCAAAAXcUyLWct6JbEJhZr67MOACC1dfmYaAAAgJ7CNC0Fsr0669rJyukVkKS4cm7vdElSINvbbXUEAPRshGgAAJAyLFPy+NwqHpLjHGtc9gXsj0Zud5cvYAIAOETxLwQAAEgZlmkpXBfVm3/6QlX76lS1r84pS1JNWVCSVFsZ6s5qAgB6MEI0AABIGZZpKRKO6v1Xt6i2MqzayrBTlqS6mogkKVi/BQCgKUI0AABIGabV9sRisVMWy0QDAFpBiAYAACnDiloyXG2FaPucyezcAIBWEKIBAEBKsCxLltXQ2tzmtSYhGgDQMmbnBgAAKSHWuOwPeDTuuL5Ky7SXsWpcTsv0ynBJbi/tDACAlhGiAQBASoi1LgeyfTpsxkDn+HGzRzrlrPw0ebxu+QN8RAIAtIyvWQEAQEqIhWgzaql0c6XCoajCoahTlqRwKCpLUiTEzGIAgJYRogEAQEow60N0bUVIT9/+b5XtqFHZjhqnLEllO2oUCUZVwzrRAIBWEKIBAEBKiLVEtzU7d9NrAQBoihANAABSQmzt57bWiW64lhANAGgZIRoAAKQE02mJTuBa1okGALSCqScBAEBKsKyG7tzeNLdU3yDduOxsydAAgFYQogEAQEowo3Yyzu2drosWH+ccb1wu6p+l7MI0BTJ9B71+AIBDA925AQBASoiNc3a1MybacBlO128AAJoiRAMAgJQQ685dua9Ojy9Yrb3bqrV3W7VTlqS926pVubdOtRUscQUAaBnduQEAQEqIzc4ty9K+7dWKRuwDjcvRiCkzYikaNVu5CwAg1dESDQAAUoLTRTuBJa5Ed24AQCsI0QAAICXExkQnsk40GRoA0BpCNAAASAkdWSfaIkUDAFrBmGgAAJASYsE4Kz9Np1wyXtmFaZIUV84uTFNu74A8fne31RMA0LMRogEAQEqITSzmT/eq74g85/jgiUVO2Z/uVSDLJ5crgXHTAICURHduAACQEmJLXNXVhPTOixtVXR5UdXnQKUtSdXlQVfuCCtdFurOqAIAejBANAABSQmxMdLAqoree/VI15SHVlIecsiTVlIdUuadOoWC0O6sKAOjBCNEAACAlWNHEZ+eWxcRiAICWEaIBAEBKMGPBOKHZubu2LgCAQxchGgAApITY7NyJTBpmEqIBAK1gdm4AAJASGs/OPfTwIvkC9segxmVfwKP0HJ8MVrgCALSCEA0AAFJCrCU6uzBNJ1003jneuJxTFFCfITkKM7EYAKAVdOcGAAApITY7t2VaqtpXp2jEVDRiOmVJzrFolP7cAICWEaIBAEBKiLVEl+2q1WPzVmrvtmrt3VbtlCVp77Zqbfxwj0KsEw0AaAUhGgAApIRYS3RCK1zREA0AaAUhGgAApATLSnx2bkI0AKA1hGgAAJASnGCcyDrRsTWlAQBoghANAABSghmNdedOpCWaEA0AaBlLXAEAgJQQa13u1T9LF98/TS63HaYblwv7ZWrM10u0c2N5t9UTANCzEaIBAEBKiLUuuzzxHfHcjcZIGy5Dbo9LshKYfQwAkJLozg0AAFKCZVpyuQyV7azRskXvqmxnTVxZksp21ujLNaUKh6LdXFsAQE9FiAYAACnBNO2W5nAwqm1flCkcjMaVJSkcjKq6LOiMnwYAoClCNAAASAmWaclI9JMPE4sBAFpBiAYAACnBNC0ZCawRLUmWCNEAgJYRogEAQEqIjYlOBN25AQCtIUQDAICUYFl2S3Rmvl/Hf3+UMvP9cWVJysz3a9CEQjE3NwCgNSxxBQAAUoJV3507kOnTmK+XOMcblwOZPvUelKWdGyu6o4oAgEMALdEAACAlmFFLLkOqrQrpkze3qbYqFFeW7HOlmytlRs1uri0AoKciRAMAgJRgWfYSV1V7g/rn/32mqr3BuLIkVe0N6ss1uxkTDQBoFSEaAACkhA7Nzs0SVwCAVhCiAQBASrAI0QCATkCIBgAAKaEjS1xZZGgAQCsI0QAAICXEunN7/W6VDM+V1++OK0uS1+9WTq8ALdEAgFYRogEAQEqwTMnlknJ7p+vMqw5Xbu/0uLJknzvsmwNoiQYAtIoQDQAAUkJsTLRlWoqGTVmmFVeOXWNZDWUAAJoiRAMAgJRgWpYMw9DurVV6+Keva/fWqriyJO3eWqXlj3/uXA8AQFOEaAAAkBIs05LLndjEYrHrAQBoihANAABSghW1W6ITvt7swsoAAA5ZhGgAAJASTMuS0YFPPiYt0QCAFhCiAQBASrBn56Y7NwDgwBCiAQBASojNzp1fkqHzF35N+SUZcWVJyi/J0LTvj3KuBwCgKU93VwAAAOBgsExLLpcht8elzLw053jjstvjUka2TxLduQEALaMlGgAApASzviW6vLRWL/72Q5WX1saVJam8tFbv/mOTJFqiAQAtI0QDAICUEOvOHaqNaP27pQrVRuLKkhSqjWj7unJJtEQDAFpGiAYAACnBNKUOrHDFElcAgBYRogEAQEqwLIvZuQEAB4wQDQAAUkKsO3fC11uEaABAc4RoAACQEmIhOj3Hp6NnDVF6ji+uLEnpOT6NPbZEkmRGCdEAgOYI0QAAICWY9UtcZeT4NfmkQcrI8ceVJSkjx6/RX7NDNC3RAICWEKIBAEBKsFuipWBNWBveL1WwJhxXluxz29eV1V/fjZUFAPRYhGgAAJASTFMyXIYqdtfp70s+VMXuuriyJFXsrtOKZ9bVX09LNACgOUI0AABICR2eWIwQDQBoASEaAACkhI4ucUVLNACgJYRoAACQEswoLdEAgANHiAYAACnBMi25DENuj0t5fTLk9rjiypLk9riUUxRwrgcAoKkOh+g33nhDp512mkpKSmQYhp599tm485Zl6eabb1afPn0UCAQ0ffp0ffHFF51VXwAAgP1iWZLhNpRfkqHv3nKU8ksy4sqSlF+SodMvn2Rfz+zcAIAWdDhEV1dXa+LEiXrwwQdbPH/HHXfovvvu08MPP6zVq1crIyNDM2fOVF1d3QFXFgAAYH+ZUTOhMdEul/3xiDHRAICWeDr6gJNPPlknn3xyi+csy9LixYt144036owzzpAk/e///q969+6tZ599Vuedd96B1RYAAGA/maYll9tQ6ZZKLVv0rs686nBJcspF/bPsc3e9K4nu3ACAlnXqmOgNGzZox44dmj59unMsJydHRx11lFatWtXiY4LBoCoqKuJ+AAAAOpsVm1jMksJ1UclSfFn1+8GoJFqiAQAt69QQvWPHDklS796944737t3bOdfUwoULlZOT4/z079+/M6sEAAAgqb4luiOzc1uEaABAc90+O/e8efNUXl7u/GzZsqW7qwQAAJJQrDt3wtdHCdEAgOY6NUQXFxdLknbu3Bl3fOfOnc65pvx+v7Kzs+N+AAAAOpvV0XWiaYkGALSgU0P04MGDVVxcrFdffdU5VlFRodWrV+uYY47pzKcCAADokFh37tzidJ1z/RTlFqfHlSUptzhd377WnnCMJa4AAC3p8OzcVVVVWrdunbO/YcMGrVmzRvn5+RowYIAuv/xy/fKXv9Tw4cM1ePBg3XTTTSopKdGsWbM6s94AAAAdEuvO7fW5VTQgyzneuOz1udVroN0rjtm5AQAt6XCI/s9//qPjjz/e2b/yyislSeeff76WLl2qa6+9VtXV1broootUVlamr3/963rxxReVlpbWebUGAADooFh37sq9dXr3pU06fOZASXLKWflp9rkXN0lidm4AQMs6HKKnTZvW5hghwzB066236tZbbz2gigEAAHQWy7RkWZLLbaiuKqyPln+lMVNLJMkpZ+Wn2efe+Mp5DAAATXX77NwAAABdzaxvAEh0iSvDIEQDAFpGiAYAAEnPql+uKtHZuQ2XITORicVq99k/AICUQYgGAABJLza+OdF1ohNqiX7/KemukdKdw6S3/+dAqwgAOEQQogEAQNIzow3duQNZXk08sb8CWd64siRn3+U22p5YbN8m6W9XSEOPl3IHSK/cIlVsPxgvBQDQzQjRAAAg6cValQ2Xocy8NH39O8OVmZcWV5bk7LvcrrZbot96SPIGpLP+R7rwNcnlkd568GC8FABANyNEAwCApNe4O3eoLqIdX5YrVBeJK0ty9mWo9dVIwrXS+09Ih31fkiHt/kIad7b0/pNSNHyQXhEAoLsQogEAQNJr3J27fFet/nzHOyrfVRtXluTsS22MiV73qlRXLh32A2nPOun335QGfk2qLpXW//OgvB4AQPchRAMAgKTndOdOeGKxNmbn/uIfUsFwqXBYw7GCIfbY6HWvHGBNAQA9HSEaAAAkvcYt0YkwXK20RFuWHZSHz2j6CGnYdGndywdYUwBAT0eIBgAASc8ZE51giHa5Wpmde896qeIraegJzc8Nmy7t/dKeuRsAkLQI0QAAIOk17s5tuAylZXpluOLLkhr2DaPllugtb0kypP5T7H2XR0ovsLf9j66/5u2D8IoAAN2FEA0AAJJe4+7chf0ydcFd31Bhv8y4siRn3+NztxyiN78l9R4rpeXY+8XjpGu/tLcZBVLBMGkrIRoAkhkhGgAAJL3GS1wlwuVupTv3lrel/ke2/sD+R0lbVu9PFQEAhwhCNAAASHpOd26XoT3bqvSHm1Zpz7aquLIkZ980TVnRJiE6VCPt/lwqObzh2K5PpXsn2VtJ6neEtOMjKVx3EF4VAKA7EKIBAEDSa9yd24xYqiitlRmx4sqSnH1DhqJNW6JLP5Vk2d25YyJBad8GeytJxRMlKyrt+uQgvCoAQHcgRAMAgKTX0J07sY8+LrfRvCV658f22ldFo1p/YK/R9jU7PtzfqgIAejhCNAAASHqxQJzomGjDMJzWa8fOj6X8oZIvvfUH+tKlwhGEaABIYoRoAACQ9Dq6TrThVvOJxXZ+HN+VuzXF4wnRAJDECNEAACDpmY0mFsspCui0n05UTlEgrizJ2feleeNboi2r5RCdP0T6/p/tbUzRKKn0M/sxAICkQ4gGAABJr3F3bl/AowFjC+QLeOLKkpx9j9clM2o23KByh1S7t3mITsuWhk23tzGFI6S6Mql6dxe/KgBAdyBEAwCApNe4O3d1eVBvP/+lqsuDcWVJzr5pWs6yWJLsVmipeYiu3CH9c6G9jSkcYW93f95VLwcA0I0I0QAAIOmZpt2qbLgN1ZSH9O8XNqqmPBRXluTsW1Ezvjv3zo8kX5aUMyD+xpU7pOW/jg/R+UPsQdW713b1ywIAdANCNAAASHpWtGMTi8llxE8stutTqdcoyZXARyePT8ofLO3+Yj9qCgDo6QjRAAAg6XV0dm6Xu8kSV3vXSwXDEn/CwhFSKS3RAJCMCNEAACDpmVFLMuzZuRPharpO9J719hrRiSocQUs0ACQpQjQAAEh6lmk5rdD+dI9GHNlb/nRPXLnxOY/P1dCdu3afPTN3wZDmNw7kSuPPsbeNFY6QyjdLoZque1EAgG5BiAYAAEnPbBSiswsD+uaPxyq7MBBXbnwuLcPXsMTVni/tbUst0XmDpLN+Z28bi83QvYfWaABINoRoAACQ9MyoJcNth+hIOKqyXTWKhKNx5cbnLKlhiau96+1tfgst0eE6u6t3uC7+eOFwe0uXbgBIOoRoAACQ9Bq3RO/bXqM/3vyW9m2viSs3PheqizSMid6zXsooktKym9+49DPp/sPtbWOBXCmjF2tFA0ASIkQDAICkZ5mWXO4El7eSPYu3E6L3dnBSsZj8wdK+TR1/HACgRyNEAwCApGdGrYRn5pYkl9GwLJb2rJcK9iNE5w2W9m3o+OMAAD0aIRoAACS9xt25E2HE1om2rPqW6BbGQ7cnb5C0b2PHHwcA6NEI0QAAIOlZ0f3ozm1aUs1eqa58P1uiB0lVO1nmCgCSjKe7KwAAANDVTLOhO3fRgCzNffgE51zjcuzcuy9tspe4cmbmbiVEl0yS5pe3fC627FXZJqnX6AN8BQCAnoKWaAAAkPQ63J3bZciKWg3dsfMHd/xJY4+hSzcAJBVCNAAASHqNu3Pv21GtZ37zH+3bUR1Xbnyurjpsd+cu3yKl5Ur+rJZvvPsL6X+mt7wedGZvyZNGiAaAJEOIBgAASa9xd+5IyNTODRWKhMy4cuNzlmnZE4uVbZFy+7d+41C1tPXf9rYpw7C7dO9lhm4ASCaEaAAAkPQ62p3b5TYaWqJzBuz/EzNDNwAkHUI0AABIevszO7csydy3te2W6PYQogEg6RCiAQBA0mvcnTsRRv0nJKt8m5RzICF6sD07t2nu/z0AAD0KS1wBAICkZzZqic4qSNP0H41RVkGaJMWVY+ci4aj9uFBQ7rZaonMHSGf+1t62JG+QFKmTqnZI2SWd9noAAN2HlmgAAJD0rEZjotMyvBp5VLHSMrxx5cbn/AF735S77Zbo9Hxp4rn2tiV5A+1t2eZOey0AgO5FiAYAAEmvcXfu2sqQPnx9q2orQ3HlxufCofqW6PZCdPVu6e3f2duW5PSzt2VbOu21AAC6FyEaAAAkPbs7t/2xp2pfUG88+bmq9gXjyo3PhWrD9uPcmVJGYes3Lt8q/f1qe9sSf5a9znQ5LdEAkCwI0QAAIOmZUbNDs3Mbhn2tmdXXXu/5QOT2pyUaAJIIIRoAACS9aMSSuyMhuv5aK6vfgT95zoDWW6oBAIccQjQAAEh6dkt04h973PXjp82MTphRO7e/VE5LNAAkC5a4AgAASa/xEldev1v9x+TL63dLUlzZOZdmf0Qys9oJ0f4saegJ9rY1OfXduS3rwLuGAwC6HSEaAAAkvcYhOrd3uk7/2STnXONy7NzOL3baj0vv0/aNC4ZKP1jW9jU5/aRwtVS7r/WlsAAAhwy6cwMAgKTXeHZu07QUqo3INK24cuNzqqoP0RnF7d1Yqquwt63JrV8ii7WiASApEKIBAEDSM6OmXB67JXrP1ir97oo3tGdrVVy58bmKrbGW6F5t33jHh9Kv+9vb1uQMsLeMiwaApECIBgAASS8a7djs3K7a3ZIk019w4E+eUSh5AixzBQBJghANAACSXkdn5zZqSyVJVmd8VDIMe1w0y1wBQFIgRAMAgKTXeGKxRBg1pc7jOkVuf6mcMdEAkAwI0QAAIOl1NES7anbZjzM7KUTn9KM7NwAkCUI0AABIembUkstlf+zJ75uhH9/5deX3zYgrNz5XEH6//nFm2zfuPVa6Zr29bUvOACYWA4AkwTrRAAAg6ZmRhtm53W6XAlk+51zjstvtUiAg1dXY45fbbYl2e+2Jw9qT21+q2SOFqiVfRsdfAACgx6AlGgAAJD2z0ezc5aU1euGhD1ReWhNXds49+K6qIvnO49q090vp8fPsbVty6teKZnIxADjkEaIBAEBSsyxLpmk5s3OHaqPa+MFuhWqjcWXn3Kc1CitNUgIhuq5C+vz/2du25NaHaMZFA8AhjxANAACSWqxLdodm55Ydqq3Omlgsq0QyXIyLBoAkQIgGAABJLdaa3KHZuQM5cY89YG6PHaQJ0QBwyCNEAwCApNYQohP/2GNk9ZbhMtqfnbsjcvvTnRsAkgCzcwMAgKRmRuwgHGuJzsj1a+rZw5SR65ekuHJGrl9TBy5XRmGW3OsNRdtric4ukWb8yt62J6c/LdEAkAQI0QAAIKk17c6dnu3TpOkDnPONy+nZPk3KeE4qOkUuj0tmpJ0QndlL+tpliVUkt7+0eVXHKg8A6HHozg0AAJJatL5Ltru+O3dddVjr3tmluupwXFmS6qqCWrejn+oCg+T2GM5jW1W7T/p4mb1tT04/qWKbFI0c0OsBAHQvQjQAAEhqTVuiK/fU6aXffaTKPXVxZUmq3LJNL+29QpXqJ5fLcLqCt2rfJulPc+xte3IGSFZUqtx+IC8HANDNCNEAACCpdWh27tLP7a3HL5fH1f6Y6I7I6WdvX18oVe7ovPsCAA4qQjQAAEhqsRm2XZ4EPvbs+9LeGh65Pa72W6I7Ihai1/yREA0AhzBCNAAASGodaomu2WNvfelyuY3OWydakvyZkj+r8+4HAOgWhGgAAJDUmoZot9elwv6ZcntdcWVJcof2qNC7Qe60NLkT6c7tDUjFE+xtIrL6SIH8xK8HAPQ4LHEFAACSmtlkdu78Phk694YjnfONy/nebTr3mG3S2Avk+tt/2u/OXTRSuvhfiVemYLiUO8B+HADgkERLNAAASGrRjnTnLtsi5fSXpPqW6E4cEy3Za0WXb+3cewIADipCNAAASGoN3bntjz2lmyu15LJ/qnRzZVxZkkp3Wlry/DdV+u479pjoSDvdube/L91WZG8TYbik0s+kbWv29+UAALoZIRoAACS1WJfsxi3RjcOxU64tkyJ1MuWVLEsut0vR9rpzW5YUDdnbRGT2trd1FYlWHwDQwxCiAQBAUkt4du7yLXG7bk8nz84tNYToKpa4AoBDVaeH6Pnz58swjLifUaNGdfbTAAAAJCThEF0WH6JdbpczKVmnySq2t1W7Ove+AICDpktm5x47dqxeeeWVhifxMAk4AADoHk1n525V+RbJcDu7bo+haHtjojsqLdfeVu7s3PsCAA6aLkm3Ho9HxcXFXXFrAACADmk6O3decbrOu/lI5RTaazU75U83K69AOu+8QuUMHiLXx1vab4kuGild+paUNyixyvQaJWX3t8dRAwAOSV0Sor/44guVlJQoLS1NxxxzjBYuXKgBAwa0eG0wGFQwGHT2KyqYaAMAAHQeM2rJcBkyXHaI9vjcKijJdM475fIt8uSXqGDcBEmS251AS7Q3IPUanXhlvAGpYIhUtb1DrwEA0HN0+pjoo446SkuXLtWLL76oJUuWaMOGDfrGN76hysrKFq9fuHChcnJynJ/+/ft3dpUAAEAKM6Nm3Hjoij21eu0Pn6piT21cWeVbVWH002u/fEQVG9bL5UlgTHTZZumvl9nbRJRtliq2SnvWH8ArAgB0p04P0SeffLK+853vaMKECZo5c6b+/ve/q6ysTE8//XSL18+bN0/l5eXOz5YtW1q8DgAAYH+YUSsuRAerI/p0xXYFqyNxZZVtUdDVS59uHaTg3n1yJdISXbNXeu8P9jYRNXvtAF3+1QG8IgBAd+ryGb9yc3M1YsQIrVu3rsXzfr9ffr+/q6sBAABSVNMQ3aJIUKreJaXnO4fcXTE7d0zdPilcJ3nTuub+AIAu0+XrRFdVVWn9+vXq06dPVz8VAABAM3Z37nY+8sSWnEovcA65umKd6MYqaI0GgENRp4foq6++WsuXL9fGjRu1cuVKnXnmmXK73Zo9e3ZnPxUAAEC7olFL7vZaoqt22NtGIdrtdika6aKWaMleUgsAcMjp9BC9detWzZ49WyNHjtQ555yjgoICvfXWWyoqKurspwIAAGiXGYnvzh3I8unwmQMVyPI1lMNbJcOtwIAROnzkZgV6FSXWEp3ZS/r6FfY2EZm9pK/9zC6XEaIB4FDU6WOin3zyyc6+JQAAwH6LRk25PQ3tBpl5fh1z5lBn/5gzh0qv/J+U3VeZg4bomCuGSJLcn29pvyU6u0SaPj/xymSXSDNukz54Wirf2pGXAQDoIbp8TDQAAEB3ioZNuRqF6FBdRF+t3adQXaShvPsrKW+gQuVl+mr5coXKy+T2GDLbm507WClt+Je9TUTs+qw+dOcGgEMUIRoAACS1aMSUx9vwkad8V62evec9le+qbSjvrJZyB6h83To9+0RU5evWyeV2yTQtWWYbQXrPeumxbyW+7nPs+kAuIRoADlGEaAAAkNSikfju3C2q3CHlDog75PLY46i7ZIbuzF6MiQaAQxQhGgAAJLVo2JLb087s3LV7pdyBcYfc9ctiRbtirejMYnuJK7MLZ/8GAHQJQjQAAEhqCbVESwe5Jbq3FA1JVTs7/94AgC5FiAYAAEnNbBKiXW5DGbl+udyGXc605FJEyh0gl9ejDM8+ubyehpbotmbodnulrBJ7m4jY9bn97f2yTfv7sgAA3aTTl7gCAADoSSJhU/6MhpBb0DdTc3491dmfc+4G6f9tk7JLVDChv+Y8MEmSVP3JHknttET3Hitd9WnilYldH6yy9/dtkgYcnfjjAQDdjpZoAACQ1KIRU25vGx95yjZLOf0klzvucEIt0fvLnyllFEn7Nnb+vQEAXYoQDQAAklo0YsrTqDv3nq+qtPQXK7Tnqyq7/Lcp2uM9zD73wRotvezP2vPBGrnc9WOi21oreufH0qLR9jYRja/PG0SIBoBDEN25AQBAUotGrLgx0WbUUnVZ0OmmXR3MkJnZzz4Xjqg6kiczHJHLk8Ds3NGwVLnN3iZUmUbXE6IB4JBESzQAAEhqdnfudpa4yipudsjdlbNzS3aIZmIxADjkEKIBAEBSi4ZNp1W5mVCNvc3q0+yUq35MtNkVY6Ile13qim1SuK5r7g8A6BKEaAAAkNTaXCc6tk5zdvMQHWuJjnRViM4bJMmSyrd0zf0BAF2CEA0AAJJa0xCd0yugWVccppxeAeV4tmtW3o3KGWSv25wzbJhmzXYrZ9gwebz2bN3RcBshumCodP7f7G0iGl+fN8g+xrhoADikMLEYAABIak1DtC/No74j8+yd6k3qm/6FVNjXPpeTq77HHSdJMqvtycLaDNH+LGnwNxKvTOPrvemSy0uIBoBDDC3RAAAgaVmWJTNiydNoneiqfUGtWrZeVfuCqtq+Q6vqLlJVuR2Yq7Zs0qp7lqpqyyZnbelIWyG6Ypv0ynx7m4jG17vcUu4AQjQAHGII0QAAIGnF1niOjW+WpNrKkN59aZNqK0Oq3b1H7+45UbWVIfvcrlK9u3aAaneVOmtLR9saE121S3rzHnubiKbX5w0kRAPAIYYQDQAAklZsUrBWZ+eu2N7qYw2XIZfHaLs794HKGyTtY5krADiUEKIBAEDSigXgFmfntiypYmubj/d4XIqEujpEb7TrAgA4JBCiAQBA0op1xXZ7W/jIU7tPCte2+Xi3z61oJNoVVbPlDZJClVLN3q57DgBApyJEAwCApOWE6EYt0f4Mj0ZP7SN/aKv8rkqNPjwgf4a9YIk/P0+j+22UP9+evdvjcbU9sVh6vnTYD+xtIppeH1vmqmxjR14WAKAbscQVAABIWi2F6OyCgE74wWjpvT9K7lKd8KNJkjdgnxs8VCfc2LDms9vbTojOHSCd8UDiFWp6fe5Ae7tvo9R3cuL3AQB0G1qiAQBA0orNzt14iatIKKo926oUKd2oSOYg7SmNKhKyu2xHqqu056MPFKmush/nc7U9sVi4Vtr1abvdwlu9PpArpeUyQzcAHEII0QAAIGnFWpFdjZa42rejRk/e+rb2bdmjfYHJdnlHjX3us8/05AO7te+zzyTZLdhthujStdJDR9vbRLR0fd4gae+GDr0uAED3IUQDAICk1VJ3bkfFV1J23zYf72mvO3dnKBgq7f2ya58DANBpCNEAACBptRmiy7+Ssvu1+Xi3192160RLUsFwafcXXfscAIBOQ4gGAABJq811osPVUm7bIfrgtEQPk6p3SXXlXfs8AIBOQYgGAABJq7V1ol1uSbKknL5x46VlGHIpLBmG87g214k2DMntc65vV0vXFw6zt7vXJXYPAEC3YokrAACQtBq6czeE1qIBWbrkwh3SsxukMSN0yQPpDecOn6xLHm54vMfrUiTURkt0n4nSTaWJV6il6wvqQ/SedVI/lrkCgJ6OlmgAAJC0omFTMlrozr1nnT2pmC+95QfWs1uiu7g7tz9Lyuoj7WFcNAAcCgjRAAAgaYWDUXl8bhmNuk/v3V6tp/7fKO1NP8ou/+pt7d1ebZ/7+CM9dcX/ae/HH0mqD9HtLXH18Dc6tsRVS9cXDGNyMQA4RBCiAQBA0oqEovL64j/uRMOmdlcVKpozzC5vqXKCcrSuTrtrSxStq5OUQHfucK204wN7m4jWri8cbreOAwB6PEI0AABIWuGQKY/PHX8wGra3eQPbfbzH61Kkq7tzS3ZL9J71knkQngsAcEAI0QAAIGlFglF5/U1CdPlWe5s3qN3Huz0HYZ1oyV4rOlIrVWzt+ucCABwQQjQAAEhakVBUnibLW2nfBnubm0BLtM+lSLiNJa46i7PMFeOiAaCnI0QDAICkFQ6ZzVqis8JfaGavh5XVr1hZBWmaeeE4ZRWk2ecGDdTMk6qVNcgO2G6vS2bEkmVaLT9B3kDpO0sT6hre5vW5AyVPQCr9rAOvDgDQHVgnGgAAJK1IKCpPkxCdVv6xhg2ukjK8kqRhk3s1nCso0rBZpzn7saWxopEWxlZLUiBPGntm4hVq7XqXWyoaKe38JPF7AQC6BS3RAAAgaYWDUXm88eG3ZvtXWlNxkmoqQqqpCGnNK5tVUxGqP7dNa/7ncdVs3ybJ7s4tSZHWxkVX7ZJWPmBvE9HW9b3HSrs+Tuw+AIBuQ4gGAABJKxKKyutv9HHHjKq6tEwrPh2n6rKgqsuCWvHMOlWXBSVJ1du2acV/ilW9rT5E1wfwSKiVcdEV26R/3GBvE9HW9b3GSLs+k8yDMAYbALDfCNEAACBpNVviat9GyQwn/PjYeOpw8CAE295j7Bm6923s+ucCAOw3QjQAAEhakWBU3sYhescHHXq8N+0ghuheY+ztTrp0A0BPRogGAABJq9nEYjs+lNILE368rz5Eh+oOQojO7C0F8qVdTC4GAD0ZIRoAACQtuzt3o4872z+Qr/cADZpQKF/ALV/A7ZQlyZedpUH5m+TLzpIkef32QibhukjLT5CWLY042d4moq3rDcOeXIyWaADo0VjiCgAAJK3m3bk/VM5h39epJ05wDp16aUM5Z/hInXr7SGe/3e7c+UOk7z6ZeIXau77XGGn9q4nfDwBw0NESDQAAklI0aso0LWdyMFXtkqp2KNprgmorQ4pGTUWjplOWpGgoqNqdOxQN2bN1e7wuGUYb3bmjYal6t71NqFLtXN97rLT3SylU3ZGXCgA4iAjRAAAgKUXqW4+d2bm325OK7dUIPXLNm9r7VbX2flXtlCVp74cf6pFbPtHeDz+UJBmGIa/frXBrIXrnx9KdQxPvgt3e9SWTJMt06goA6HkI0QAAIClFQnbrsjMmescHkj9byirp0H28aR6Fgq2Mie5svcZInjRp27sH5/kAAB1GiAYAAEkpNo7ZGRO94wOp9zjJ1bGPP74098FZ4kqS3F6pz0Tpq3cOzvMBADqMEA0AAJJSJFzfnTs2Jnr7+1KfCW08omVtdufuCiWHE6IBoAcjRAMAgKQUC75en1uqKrUn7Oo3pcP38aa5W1/iqiv0nSzt2yjV7D14zwkASBhLXAEAgKQUrLWDrz/DI21ZbR8ccLQKsjJ14T3HOi3UjcsFEyfpwl9XypOZ5dzH6/e03p27eLz0iy2SLyOxSiVyfd/D7e1X70rDpyd2XwDAQUNLNAAASErBmvoQHagP0dl9pZx+crkM+QIeuVxGXFmSXB6PfLl5cnka2hm8fnfrS1y53FJatr1NRCLX5w+R0nLp0g0APRQhGgAAJKVgTVhuj8te4mrLaqn/UZKksp01eu6+NSrbWRNXlqSytZ/puV88qrK1nzn3aXNisT3rpT+caW8Tkcj1hmG3Rn/1n8TuCQA4qAjRAAAgKQVrIvKne6RwnbTtPWnA0ZLsWbu3fLJX4WA0rixJ4aoqbSkbqHBVlXMfb1ob3bmDldL61+xtQpVK8PqBX5M2rZKiB3EsNgAgIYRoAACQlJwQvf19KRqS+h+5X/exu3Mf5DA7+DgpVCltX3NwnxcA0C5CNAAASErBmrAdore8JXkzpN7j9+s+vjS3QrUHOUSXHGbXecMbB/d5AQDtIkQDAICkZLdEe+3u0wOPkdz7tyhJINOrSMhUJHQQ14p2e+0u3YRoAOhxCNEAACApBWsi8vslbVopDfumczwzz69jzxuhzDx/XFmSMvv107FHlyqzXz/n+rRMnySptirc/Ely+kmn3GVvE9GR6wcfK21+S4oEE7s3AOCgIEQDAICkFKyJyB/ZZY+HHt4QogNZPo2f1k+BLF9cWZICvYs1fs65CvQubnS9V5JU11KIziiUjrzQ3iaiI9cPPlaK1LLUFQD0MIRoAACQlII1Yflr1kt5g+y1l+vVVYe1dvUO1VWH48qSVFe6S2v/9KzqSnc516dl2CG6tirU/Elq9krvP2VvE9GR64vHS4F86Yt/JHZvAMBBsX+DgwAAQLssy1IwGlRdpE510TrVRGrscqROtZFaexutVTgaVsSKKBwNK2yGFTEjCptNyvXXRMyILMuSJSvueZz/xc7FThuSx/DI7XLLbbjlcXnkNtxyu9zOcZfhcspel1d+t9/+8fgbyo1/Gh1P96Qr4AnIMIzu+UNuQ7AmIn/5x9JR37TXXq5XuadOrzz6ic65fookOeW0DK8qN2/WK69m65whm5VW1EuSnFbqFluiyzZLyy6SLloupee3X6mOXO9yS6NOkT55TjrxlrjXAADoPoRoAACaiJgRVYYqVRWqUlXY/qkMVTrb6nC1qkJVqgxXxm2rw9WqCdeoLtoQkhuH3bYYMuR1eeV1e+VxeeR1NWwbl90ut4zY/wx727jepbWl6pXeS363X4YMmTIVNaOKWtGGbaNyxIw4+xHLDuzBSFAhs4VW1zbqnu5NV4Ynw956M5TpzXTKGd4M53ymL1NZvixl+7Kdnxx/jrL92fK7/R3+u2qNGTUVDkblr9sqDb/4gO7l8bnk9rpaHhPd1UafIb33f9KuT6XeYw7+8wMAmiFEAwCSWm2kVuXBcpUFy5yf8rr4/bJgWdw1laHKVu/nc/mU6ctUpjfTDoTeLGX6MlWYXahMX6bTMpvmSWvYugNOOXY84AkozZ0mv8cvn8snt8t9wK/1kz2f6Ny/navFxy/WmIIDC1ymZSoUDSkYDTb8RIIKmvXb+mM14RpVR+wvD6rD1c5P7Pi2qm2qicSfC0ZbnijL7/bHB2tftrL9Dfv5afnKS8tTnj/PKef4c+Qymo9Oq6u2l6Ty+6P22OIDYBiGApnelluiu9qQ4yR/tvTpc4RoAOghCNEAgENOxIxoT+0e7a7drdLa0oZtjb3dU7tHpbWl2lu3t8XA5jbcyvHnKNefq1x/rnL8ORqWO8wpx7aNQ3Km125B9bl93fCKDz6X4XJCf2cLRUOqCFWoPFiuilCFKoIV9raFY1sqt8R9wWFaZrN65vpzlefPswN2mh2wcyuKZWiIPhjUT6W7P1BhoFCFgUJl+7L3q85pmd7uaYn2+KURM6VPn5em/eLgPz8AoBlCNACgx6iN1DpBOBaOd9fuVmlNaVxg3le3L66btCFDeWl5KgoUqTC9UENyh+jIPkfaYao+KDuBOS1HWd6sHjmG90AFPAFNKJqggCfQ3VVpk8/tc0JtR5iWqYpghfYG92pf3T6V1ZU55X11+7S3zi5vqdwi/8aNOkZDdLu5RrX/+JdzD6/Lq0HmCE3JO0O/fvd2O3j3HqN/bH1JxVaeApE65WXsUTRtQtxzBzK9qqtsoYu7L0PqN8XeJvTiO3i9JI05Q/rwT9LOT2iNBoAewLAsK7HBWgdJRUWFcnJyVF5eruzs/fu2GADQc1iWpfJgebNW49Kahhbj2LHqcHXcY70urxOMC9MKVZRepMJAoX0sUKjCdLucn5Yvj4vvhdHgw/sX682Px+j7dx+lfZFy7anb43wZs7t2t/bU7XHKu2t3a2/tXkWsSNw9srxZKggUqDBQqFHvnahAMFPpZ+2Oew8WpRcp15/bYpfyThMJSfeMlUafJn3r7q57HgBIYR3JoXziAADsl8Zdqp1gXN+luvGx3bW7FTbju8FmejOdAFKUXqTRBaPjQkksMGf7spOyxRhdLFilqi2blZE+QlnpOcpSjgZkD2jzIaZlxn3Z0/i9vbt2t6p9ZXLtytBTHz2qilBF3GM9hscJ27EvfZz3c6DI+fKnIFAgr8vb8dfj8UmT50hvPSRNny+l0cgAAN2JEA0AiNNel+pYyGipS3V+Wr7TQjwkd4iO6nOUCgIFcUGiMFDY47sbH6piE4s99a2nDnhisUPamj+qKpylzN55LZ4u3Vypp2//t7PEVaxcNCBPkY/X6x+/q9M5F/ZR0eTTnMd8pK/0xvrP9cY5/1JUkYYvib76t0pfvUWlU07Vbo9bpTWl+nj3x05rd9Mx3HneLBVW71FRn8kqzB0c99+G08MiUKh0b3p8pY/4kfSvRdL7T0pHXdS5f14AgA4hRANACrAsSxWhCpXWlMZPxFW72wnMsYBcFa6Ke2ysS3WOP0e1kVpNKJqg6VnTnQ//sZBMl2r0CJGQtPIBVafNU2ZhVqfdNqcoIMu0VLmnTrm90lWSWaKSzBIpbEmVVdKws6WSSXGPiZpR7Qvui//vbucH2rLmMX1Qu1tlVlTv7HxHpTWlzZYUy/BmxLVmF6YXqmjYFBW+84AKS0arKLMPvTUAoJvwaQcADmHBaLBZt9Om+/vbpTpWjn1Ij7Vy/ubY36R2Kyd6tvf+IJVvUZXRV0V5nTezeE6R3XuiorRWub3S27na5na5nZbl0RptH8wYok/eeEB/zdqup06wlyKLfcnV0pdbsf1P936q3dqnqgxLeu1S5zl8Ll/c/ACx52vawp2flt8py6gBAAjRANDjxFqvmoZhJyDXNew3Xc/YZbhUkGaPzcwP5Dtdqp0P1nSpRjKr3Sf983ZFxs5WxT9NJ/h2hsw8v1wuQ+WltZ12zxjDMJTjz1GOP0dDc4e2eW3ts5do97qXtPvcpSqN1jW0cNcPt3hv13stzmDvMlzKT8tXUaCh90jTL8xivx/8bn+nv0YASCaEaADoYpZlqSZSo711e50leBovx7O3bq/2Bvc6oXlv3d5m4yizfdlO+C0MFGpU/qiG/bRCZ1KjXH8urU1IXf+4SYrUac+Ya2S9ulFFAzqvO7fL7VJWYZrKd3d+iO6IwAk3q//Hz6r/mj9Lp93b6nVhM6y9tXubTfoXC93rytZp1fZV2l27WxGzyazkviy7JbuFSdIKA4XKTctVflq+cvw5+zdRGgAc4ljiCgA6KGpGVRWuUnmwvCEUB/e1GJJj+03HO0p2MM5Py7fXqfXnOeOLY8E4FpLzA/k9omUoGA1qZ/VO9c7o3SPqg+ZS+u/ow2ekP18gnXavPqr+pt548nNddO+x8nibf6kUCUdVtS+ozDz7zyhW9njditTUqGrrZmX2GyBPeny37b8v+UCh2ohmXXl4w8FwnVTxlZTdV/Im0H08XKfgvg3a6fGod86A/ft7emep9PzPpbN+L40/u+OPbyQ2K3nTkN14KbpYuTbS/AuELF+W8vx5zu+x3LRcp9z4WL4/X7lpucr0ZjKGG0CPxBJXAJCAiBlRRahC5cFylQfL48rlIXtbFixTRbAi7lhlqDKum2RMjj/HDsX+POWn5Wtc4TjlpdnlxmG5IFBwSLbg+N3+dpcJQvdK2b+jjSukZy+RJpwnHX6+Sv+4Vvl9MloM0JLk8brjxjU3LnvS05U7YlSLj+s7Ik+rlq1XJBxtuLc3TSpouwt2HG+a/L1G64D+lg4/337Nz/1Myhss9Zu837dyGS77d1NankbkjWjz2upwtfbU7tG+4D7ny8J9wX0qqytzjn1Z/qX27bSPVYYrm93D4/I4ATvHn6NsX7bz4+z7G45l+7OV48tRli+LXjYAegxCNIBDkmmZqg5XqypUpcpwpapCVaoKV6kyVNnysXCVs18Vso+19AFPkgKegPOBLsefoxxfjooziuP2Yx/2Yh8+c/25ST8z9dbKrXpgzQO6bNJl6pfVr7urgxak5N/RFy9Lz/xYGnC0dPr9siR99fk+9R2e2+pDKnbXavVzX+qo04dIklPOLgyoYv0XWv2Hf+moH3xD2UOHxz2u78hcRSOmdn5Zob4j65fP2rdReu1X0gk3SHmD2q/vvo3a+spNeiAvV5cddd3+/T0ZhvSte6SyTdL/ni7NflIa/I2O36eDMrwZyvBmaECCXwGEo2GVBRsCthO463vqlIfsLy9Ly0pVEapwvsiMWtEW75fpzYwL2bHfw5neTGX4MpTpzbTL3oy4YxneDGX5shTwBOQyXJ35RwIgRXXZJ74HH3xQd955p3bs2KGJEyfq/vvv15FHHtlVTwegBzMtU7WRWtWEa1QTqYkrN97WRmrjy02O1URq7OAcrlJ1uLrV53MbbmX67A9TWb4s+wOUN0slmSXOsSxflnL9uU4rhxOQ/Tmp1w02QRWhCr3w5Qv64ZgfdndV0IqU+jsKVUtv3iO9cZc0Yqb07d9JHp92flmu8l21Ou67I1t9aLAmos/f3qlJ0+0w2LgcLCvX5zsGaVJZebPHFZRkKpDl1fo1pQ0hurZM+vBp6Zi5UsvLUserLVPFF/9PL/Ttox+GLunoq27gz5R+sEx6Yrb0v2dI37hSOvYaydNzfn953V5n9v9ExeaQqAhWxAXrilBFi8e2V213/k2oCle12OU8xpDhfBHQNGRneDMU8ARa/En3pNtlb8vnk/0LVADNdcl/9U899ZSuvPJKPfzwwzrqqKO0ePFizZw5U2vXrlWvXr264ikBtMCyLEWtqMJm2P6JhhvK9fsRM9LsfMgMqS5Sp1A0pLponYLRoIKRoL2NBu1jbewHo0HVReqcctOllVric/kU8NofVtI96Ur32tuAN6CCQIH6Z/V3jmX5spwPQFneLGX6Mp1tpjdTAU+AMXdAsir/SvrgKent30o1e6Rpv5COvVZyuWRZlt77x2Zl5vnVb0QiibZjDJehccf21bv/2KzJMwcqI7ebA6svQ/r+n6V/LZLeuFN674/S0RdL486Wcvp2b932k2E0BN0+6tPhx0fMiP2Fa8gO1bHeR42/gG3p2M6anaqN1Ko2XGtv639aGrrTlNflbRasfW6f/G5/s23jn5bOtXbM4/LI6/LK6/LGlWP7/JsHHFxdEqLvvvtuXXjhhfrRj34kSXr44Yf1wgsv6JFHHtEvfvGLrnjKQ1LjOd1iv6RbOta0HF+0ml/b2j1amEOuvce19/jGx1urb2v1SbS+TR9jWZZMmbIsyymblilL9fuW6cxsbFpms2tj11iq37ZyP+fx9dfErm/tfrGfqBW1f8xoXDliRezzsbJpXxsxI87jImak1cc327ciCtcH4IgTghsCcSgaUtgMJ/QBoC1N/9FPc/vl96TJ7/wDn6Y8X6786Y2vSasv++T32Pv2t/kBBWLhuP7b/fT6b/Y7PD7Yav7fjCMaOYBX3cFHduTyLpjH0Wr0/12i8X+foaAMy5AVCsoMNmrt2a+n77w6x/9+7Mq5Mlu594E+ZSfW2aypkct0y6ypUTStQl323rCa/l7uxHtHw1KwXApWSsEKqWavtG+DtGeDtO1dafdayROQRp8uHXuVlDdQVsRS5Z5qffT6Vn25plQzLxwnw9U1oWLCCf310b+26bn71mjq2cNU4DEVsFwyLEvdEmPcXvuLhLFnSivulV69TXr5ZqnXGKl4gtR7rJTTT0ovkDIKpbQcye2zH+f222WX2+4ingQ8Lo8znvpAWZalYDQYF6ob/9REalQbrlVdtK7heP1+7EvkUDSkYDSoylBl3H7jcuwL6wN63YbHCddxW7dXHqPJttF5j+GR2+WW23DLZbjkNtwt7rsMlzyGRy7DZZddntavN9xyuVq+3pBhbw2joSxDhtFyudXzqr9H43Kjcy7DJRlyyi3dw6j/Lzb2BUTT/ZhWr2vt+naui+23dH1rj23YtH1Pvkw5eDp9du5QKKT09HQ988wzmjVrlnP8/PPPV1lZmf7617/GXR8MBhUMBp398vJyDRgwQFu2bOnRs3Pv+mq9/nLXxm6uRTf/h9Lt87p3/y+K7q6BIcZ2AUCMx+fSlFMHa/xxbY8z3r2lUn9Z9K6+fZU9y3asXNg/S7vfe1d/eaxS3z4/S4WHHd7i4/dur9arj32qfTuaDCsxJJdh2P84tPYPhGXJioYVNQy5u6IF0ZJkmZJM+9uNnrUICzqorb89q523Tnt/813zzujuT0boqaoztuiqX87p7mq0qaKiQv3791dZWZlycnLavLbTQ/S2bdvUt29frVy5Usccc4xz/Nprr9Xy5cu1evXquOvnz5+vBQsWdGYVAAAAAADosC1btqhfv7a/jO32mRDmzZunK6+80tk3TVN79+5VQUFBl3dJiH3b0NNbvdF9eI+gLbw/0B7eI2gL7w+0h/cI2sN7pPNYlqXKykqVlJS0e22nh+jCwkK53W7t3Lkz7vjOnTtVXFzc7Hq/3y+/P35ijtzc3M6uVpuys7N506FNvEfQFt4faA/vEbSF9wfaw3sE7eE90jna68Yd0+kDKn0+nyZPnqxXX33VOWaapl599dW47t0AAAAAABxquqQ795VXXqnzzz9fRxxxhI488kgtXrxY1dXVzmzdAAAAAAAcirokRJ977rkqLS3VzTffrB07dmjSpEl68cUX1bt37654uv3m9/t1yy23NOtODsTwHkFbeH+gPbxH0BbeH2gP7xG0h/dI9+j02bkBAAAAAEhWLDILAAAAAECCCNEAAAAAACSIEA0AAAAAQIII0QAAAAAAJChpQvTChQs1ZcoUZWVlqVevXpo1a5bWrl3rnN+7d69++tOfauTIkQoEAhowYIB+9rOfqby8vM37Wpalm2++WX369FEgEND06dP1xRdfdPXLQSfrqvfHnDlzZBhG3M9JJ53U1S8HXaC994gk/eQnP9HQoUMVCARUVFSkM844Q5999lmb9+V3SPLoqvcIv0eSQyLvjxjLsnTyySfLMAw9++yzbd6X3yHJo6veI/wOSQ6JvD+mTZvW7O/64osvbvO+/A7pGkkTopcvX665c+fqrbfe0ssvv6xwOKwZM2aourpakrRt2zZt27ZNd911lz766CMtXbpUL774oi644II273vHHXfovvvu08MPP6zVq1crIyNDM2fOVF1d3cF4WegkXfX+kKSTTjpJ27dvd36eeOKJrn456ALtvUckafLkyXr00Uf16aef6qWXXpJlWZoxY4ai0Wir9+V3SPLoqveIxO+RZJDI+yNm8eLFMgwjofvyOyR5dNV7ROJ3SDJI9P1x4YUXxv1d33HHHW3el98hXcRKUrt27bIkWcuXL2/1mqefftry+XxWOBxu8bxpmlZxcbF15513OsfKysosv99vPfHEE51eZxw8nfH+sCzLOv/8860zzjijC2qI7pbIe+T999+3JFnr1q1r8Ty/Q5JbZ7xHLIvfI8mqtffHe++9Z/Xt29favn27JclatmxZq/fgd0hy64z3iGXxOyRZtfT+OO6446yf//znCd+D3yFdJ2laopuKdcPNz89v85rs7Gx5PJ4Wz2/YsEE7duzQ9OnTnWM5OTk66qijtGrVqs6tMA6qznh/xLz++uvq1auXRo4cqUsuuUR79uzp1Lqie7T3Hqmurtajjz6qwYMHq3///i1ew++Q5NYZ75EYfo8kn5beHzU1Nfrud7+rBx98UMXFxe3eg98hya0z3iMx/A5JPq39G/PHP/5RhYWFGjdunObNm6eamppW78HvkK7Tdjo4RJmmqcsvv1xTp07VuHHjWrxm9+7duu2223TRRRe1ep8dO3ZIknr37h13vHfv3s45HHo66/0h2d2nvv3tb2vw4MFav369rr/+ep188slatWqV3G53V1QfB0Fb75GHHnpI1157raqrqzVy5Ei9/PLL8vl8Ld6H3yHJq7PeIxK/R5JRa++PK664Ql/72td0xhlnJHQffockr856j0j8DklGrb0/vvvd72rgwIEqKSnRBx98oOuuu05r167VX/7ylxbvw++QrpOUIXru3Ln66KOP9Oabb7Z4vqKiQqeeeqrGjBmj+fPnH9zKodt15vvjvPPOc8rjx4/XhAkTNHToUL3++us68cQTO7PaOIjaeo9873vf0ze/+U1t375dd911l8455xytWLFCaWlp3VBTdJfOfI/weyT5tPT+eO655/Taa6/pvffe68aaoafozPcIv0OST2v/xjRu3Bk/frz69OmjE088UevXr9fQoUMPdjVTWtJ1577sssv0t7/9Tf/85z/Vr1+/ZucrKyt10kknKSsrS8uWLZPX6231XrFuNDt37ow7vnPnzg51sUHP0Znvj5YMGTJEhYWFWrduXWdVGQdZe++RnJyc/9/O/YNGsXZxAD4XE1MmBOIfEGTVmMpCQYLNsiAEBMXCzl5QUbBQsLGxEBvRxtZUpgqGVex01yaIkEJSRUhYFMU/YBGFqCCcW3zc8HndsINmszj3eWCKkJnD+7KHs/yYnYnR0dGoVqsxPT0dCwsLMTMz07aWGVJO69kj7Zgjf7a1+qPRaMTS0lIMDQ1FX1/f6qNCJ06ciFqt1raWGVJO69kj7Zghf7ZO3zH/b3x8PCJizc/aDOme0oTozIxz587FzMxMNBqNqFQqP53z6dOnmJiYiM2bN8f9+/c73jmqVCqxbdu2ePz48Q81nj17FocOHVr3PdA93eiPdl6/fh0fP36M7du3r8ey2UBFeqTdNZkZ3759a/t/M6RcutEj7Zgjf6ZO/XH58uWYn5+P58+frx4RETdv3ozJycm2Nc2QculGj7RjhvyZfuU75p8eWeuzNkO6qEcvNFt3Z86cycHBwXzy5Em+fft29VhZWcnMzOXl5RwfH899+/bl4uLiD+d8//59tc7Y2Fjeu3dv9e/r16/n0NBQ1uv1nJ+fz+PHj2elUskvX75s+B75dd3oj8+fP+fFixfz6dOn2Wq18tGjR3ngwIEcHR3Nr1+/9mSf/LpOPbK0tJTXrl3Lubm5fPnyZc7OzuaxY8dyeHg4379/v1rHDCmvbvSIOVIenfqjnWjz5mUzpLy60SNmSHl06o/FxcW8evVqzs3NZavVynq9nrt27cpqtfpDHTNkY5QmREdE22NycjIzM5vN5prntFqtH+r8c03m/14Nf+XKldy6dWsODAzk4cOH88WLFxu7OX5bN/pjZWUlJyYmcmRkJPv7+3Pnzp156tSpfPfu3cZvkN/WqUfevHmTR44cyS1btmR/f3/u2LEjT548mQsLCz/VMUPKqRs9Yo6UR6f+WOuafwckM6S8utEjZkh5dOqPV69eZbVazeHh4RwYGMg9e/bkpUuXcnl5+ac6Zkj3/ZWZ+du3swEAAOA/oDTPRAMAAEC3CdEAAABQkBANAAAABQnRAAAAUJAQDQAAAAUJ0QAAAFCQEA0AAAAFCdEAAABQkBANACVQq9XiwoULvV4GAJSeEA0AAAAFCdEAAABQkBANACX08OHDGBwcjLt37/Z6KQBQKn29XgAAsL6mpqbi9OnTMTU1FUePHu31cgCgVNyJBoASuX37dpw9ezYePHggQANAF7gTDQAlMT09HR8+fIjZ2dk4ePBgr5cDAKXkTjQAlMT+/ftjZGQk7ty5E5nZ6+UAQCkJ0QBQErt3745msxn1ej3Onz/f6+UAQCn5OTcAlMjevXuj2WxGrVaLvr6+uHXrVq+XBAClIkQDQMmMjY1Fo9GIWq0WmzZtihs3bvR6SQBQGn+lh6YAAACgEM9EAwAAQEFCNAAAABQkRAMAAEBBQjQAAAAUJEQDAABAQUI0AAAAFCREAwAAQEFCNAAAABQkRAMAAEBBQjQAAAAUJEQDAABAQX8DzGUHTO9cy/cAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1200x700 with 1 Axes>"
       ]
@@ -1728,8 +1728,8 @@
     "xMin = float('inf')\n",
     "xMax = float('-inf')\n",
     "for mu, sigma, _, _ in gaussiane:\n",
-    "    minimoLocale = mu - 4 * sigma\n",
-    "    massimoLocale = mu + 4 * sigma\n",
+    "    minimoLocale = mu - 3 * sigma\n",
+    "    massimoLocale = mu + 3 * sigma\n",
     "    \n",
     "    if minimoLocale < xMin:\n",
     "        xMin = minimoLocale\n",
@@ -1761,7 +1761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 82,
    "id": "45a291fb",
    "metadata": {},
    "outputs": [
@@ -1771,36 +1771,93 @@
      "text": [
       "Compatibilità calibro e sonar statico:\n",
       "k = 0.87\n",
-      "\n",
-      "Compatibilità calibro e sonar dinamico:\n",
-      "k = 1.31\n",
-      "\n",
-      "Compatibilità sonar statico e sonar dinamico:\n",
-      "k = 0.73\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "# Fattori di compatibilità\n",
+    "# Fattori di compatibilità dati statici\n",
     "\n",
     "def print_k(x1, u1, x2, u2):\n",
     "    k = abs(x1-x2) / np.sqrt(u1**2 + u2**2)\n",
     "    print(f\"k = {k:.2f}\\n\")\n",
     "\n",
     "print(\"Compatibilità calibro e sonar statico:\")\n",
-    "print_k(AC, uAC, AS2, uAS2)\n",
-    "\n",
-    "print(\"Compatibilità calibro e sonar dinamico:\")\n",
-    "print_k(AC, uAC, AD, uAD)\n",
-    "\n",
-    "print(\"Compatibilità sonar statico e sonar dinamico:\")\n",
-    "print_k(AS2, uAS2, AD, uAD)"
+    "print_k(AC, uAC, AS2, uAS2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 83,
+   "id": "44d63a1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AStat = 23.939 ± 0.042\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Media pesata dati statici\n",
+    "\n",
+    "AStat = ( AC/(uAC**2) + AS2/(uAS2**2) ) / ( 1/(uAC**2) + 1/(uAS2**2) )\n",
+    "uAStat = 1 / np.sqrt( 1/(uAC**2) + 1/(uAS2**2) )\n",
+    "\n",
+    "print(f\"AStat = {AStat:.3f} ± {uAStat:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "27079e36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compatibilità k statico e sonar dinamico:\n",
+      "k = 1.22\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fattori di compatiilità dati statici e dinamici\n",
+    "\n",
+    "print(\"Compatibilità k statico e sonar dinamico:\")\n",
+    "print_k(AStat, uAStat, AD, uAD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "fd47805e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k = 23.890 ± 0.013\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Media pesata dati statici e dinamici\n",
+    "\n",
+    "kBest = ( AStat/(uAStat**2) + AD/(uAD**2) ) / ( 1/(uAStat**2) + 1/(uAD**2) )\n",
+    "ukBest = 1 / np.sqrt( 1/(uAStat**2) + 1/(uAD**2) )\n",
+    "\n",
+    "print(f\"k = {kBest:.3f} ± {ukBest:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
    "id": "d4af85a1",
    "metadata": {},
    "outputs": [],
@@ -1818,7 +1875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 87,
    "id": "57de948b",
    "metadata": {},
    "outputs": [
diff --git a/mollaDefinitiva/molla-2/molla2.ipynb b/mollaDefinitiva/molla-2/molla2.ipynb
index 26efc67..315c150 100644
--- a/mollaDefinitiva/molla-2/molla2.ipynb
+++ b/mollaDefinitiva/molla-2/molla2.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 476,
+   "execution_count": 1,
    "id": "3a34cc3f",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 477,
+   "execution_count": 2,
    "id": "1e2fabea",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 478,
+   "execution_count": 3,
    "id": "264a5b1d",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a5ff5ff5",
    "metadata": {},
    "outputs": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 480,
+   "execution_count": 5,
    "id": "e649eddc",
    "metadata": {},
    "outputs": [],
@@ -234,14 +234,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 481,
+   "execution_count": 6,
    "id": "42ff21d6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fcd29487f60f47f2999c10d2c204f606",
+       "model_id": "722fa004f851472cb356ab99ec9c396b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 482,
+   "execution_count": 7,
    "id": "dfab4331",
    "metadata": {},
    "outputs": [],
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 483,
+   "execution_count": 8,
    "id": "5d7ca0a0",
    "metadata": {},
    "outputs": [
@@ -386,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 484,
+   "execution_count": 9,
    "id": "cd68b201",
    "metadata": {},
    "outputs": [
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 533,
+   "execution_count": 10,
    "id": "68eb66dc",
    "metadata": {},
    "outputs": [
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 486,
+   "execution_count": 11,
    "id": "f98a3612",
    "metadata": {},
    "outputs": [],
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 487,
+   "execution_count": 12,
    "id": "831a4ac6",
    "metadata": {},
    "outputs": [
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 488,
+   "execution_count": 13,
    "id": "5643144a",
    "metadata": {},
    "outputs": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 489,
+   "execution_count": 14,
    "id": "d6195b96",
    "metadata": {},
    "outputs": [],
@@ -618,14 +618,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 490,
+   "execution_count": 15,
    "id": "02e2d183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6fbbe2af87b46f6bd876d7fa65a0afd",
+       "model_id": "0ee5a718b7a14194847b0a7f62c703ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 491,
+   "execution_count": 16,
    "id": "06f9ccef",
    "metadata": {},
    "outputs": [
@@ -685,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 534,
+   "execution_count": 17,
    "id": "75653d7a",
    "metadata": {},
    "outputs": [
@@ -740,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 493,
+   "execution_count": 18,
    "id": "09445aac",
    "metadata": {},
    "outputs": [],
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 494,
+   "execution_count": 19,
    "id": "1ecf9ab2",
    "metadata": {},
    "outputs": [
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 495,
+   "execution_count": 20,
    "id": "4088453c",
    "metadata": {},
    "outputs": [
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 496,
+   "execution_count": 21,
    "id": "1584a5d4",
    "metadata": {},
    "outputs": [],
@@ -896,14 +896,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 497,
+   "execution_count": 22,
    "id": "ac4860fa",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da658554d3694086a4b6377dd2a29bad",
+       "model_id": "b540b35e61894ffeb58f5f85aecfb401",
        "version_major": 2,
        "version_minor": 0
       },
@@ -932,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 498,
+   "execution_count": 23,
    "id": "8917049c",
    "metadata": {},
    "outputs": [
@@ -962,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 535,
+   "execution_count": 24,
    "id": "d5646b8c",
    "metadata": {},
    "outputs": [
@@ -1017,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 500,
+   "execution_count": 25,
    "id": "e35a9456",
    "metadata": {},
    "outputs": [],
@@ -1041,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 501,
+   "execution_count": 26,
    "id": "8de2a48e",
    "metadata": {},
    "outputs": [
@@ -1097,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 502,
+   "execution_count": 27,
    "id": "af6143df",
    "metadata": {},
    "outputs": [
@@ -1124,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 503,
+   "execution_count": 28,
    "id": "a1e69bd3",
    "metadata": {},
    "outputs": [
@@ -1175,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 504,
+   "execution_count": 29,
    "id": "56679fc4",
    "metadata": {},
    "outputs": [
@@ -1220,14 +1220,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 505,
+   "execution_count": 30,
    "id": "455271a9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "984132e247534e84a5813a2f51437173",
+       "model_id": "371558377daf421fbae4ee1e04dd9b3a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1243,7 +1243,7 @@
     "# Calcolo numerico Meq e w^2\n",
     "\n",
     "dfSonar['alp'] = 1/3\n",
-    "dfSonar['ualp'] = 0\n",
+    "dfSonar['ualp'] = 0.000000000001\n",
     "\n",
     "dfSonar['1/Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
     "dfSonar['u1/Meq']= uMeq_fn(dfSonar['m'], dfSonar['um'], dfSonar['alp'], dfSonar['ualp'], massaMolla, umassaMolla)\n",
@@ -1267,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 506,
+   "execution_count": 31,
    "id": "02119d66",
    "metadata": {},
    "outputs": [
@@ -1299,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 537,
+   "execution_count": 32,
    "id": "de571f32",
    "metadata": {},
    "outputs": [
@@ -1358,7 +1358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 508,
+   "execution_count": 33,
    "id": "96f12fe6",
    "metadata": {},
    "outputs": [],
@@ -1382,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 509,
+   "execution_count": 34,
    "id": "c87033e0",
    "metadata": {},
    "outputs": [
@@ -1436,7 +1436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 522,
+   "execution_count": 35,
    "id": "97921cfc",
    "metadata": {},
    "outputs": [
@@ -1484,14 +1484,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 523,
+   "execution_count": 36,
    "id": "0b3f8a4e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "251b3b72f66d4ebd8de95410ff7e96a6",
+       "model_id": "57acfdfd8a2242a9bb2338407fefd332",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1514,7 +1514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 524,
+   "execution_count": 37,
    "id": "0421b1cb",
    "metadata": {},
    "outputs": [
@@ -1546,7 +1546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 525,
+   "execution_count": 38,
    "id": "739c2c88",
    "metadata": {},
    "outputs": [
@@ -1603,7 +1603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 526,
+   "execution_count": 39,
    "id": "2fb7140a",
    "metadata": {},
    "outputs": [],
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 527,
+   "execution_count": 40,
    "id": "b44b497b",
    "metadata": {},
    "outputs": [
@@ -1679,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 528,
+   "execution_count": 41,
    "id": "e87e24b7",
    "metadata": {},
    "outputs": [
@@ -1781,13 +1781,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 529,
+   "execution_count": 42,
    "id": "0bc96952",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1200x700 with 1 Axes>"
       ]
@@ -1815,8 +1815,8 @@
     "xMin = float('inf')\n",
     "xMax = float('-inf')\n",
     "for mu, sigma, _, _ in gaussiane:\n",
-    "    minimoLocale = mu - 4 * sigma\n",
-    "    massimoLocale = mu + 4 * sigma\n",
+    "    minimoLocale = mu - 3 * sigma\n",
+    "    massimoLocale = mu + 3 * sigma\n",
     "    \n",
     "    if minimoLocale < xMin:\n",
     "        xMin = minimoLocale\n",
@@ -1848,7 +1848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 530,
+   "execution_count": 43,
    "id": "da18d444",
    "metadata": {},
    "outputs": [
@@ -1884,7 +1884,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 531,
+   "execution_count": 44,
+   "id": "5b70e361",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ADin = 3.14960 ± 0.00087\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Media pesata dati dinamici\n",
+    "\n",
+    "ADin = ( ACr/(uACr**2) + AD/(uAD**2) ) / ( 1/(uACr**2) + 1/(uAD**2) )\n",
+    "uADin = 1 / np.sqrt( 1/(uACr**2) + 1/(uAD**2) )\n",
+    "\n",
+    "print(f\"ADin = {ADin:.5f} ± {uADin:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
    "id": "d4af85a1",
    "metadata": {},
    "outputs": [],
@@ -1902,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 532,
+   "execution_count": 46,
    "id": "57de948b",
    "metadata": {},
    "outputs": [