{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a80529e4",
   "metadata": {},
   "source": [
    "# Analisi dei dati con il sonar\n",
    "Minimo Indice:\n",
    "- Analisi dei dati statici\n",
    "- Analisi dei dati dinamici\n",
    "  - Sonar\n",
    "  - Cronometro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32702b8f",
   "metadata": {},
   "source": [
    "## Import delle librerie e set di variabili gloabali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f34c5b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "from scipy.stats import chi2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "g = 9.807\n",
    "ug = 0.001\n",
    "\n",
    "m_mol = 15.43\n",
    "um_mol = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0b8b1d",
   "metadata": {},
   "source": [
    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
    "- Lettura del CSV\n",
    "- Creazione del data frame\n",
    "- Deviazioni standard\n",
    "\n",
    "ATTENZIONE: Linea cursed ~17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'dinamica1.csv')\n",
    "\n",
    "def calcola_stats(df, prefix, err_arbitrario):\n",
    "  cols = [col for col in df.columns if col.startswith(prefix)]\n",
    "\n",
    "  def riga_stats(row):\n",
    "    valori = row[cols].dropna()\n",
    "    n = len(valori)\n",
    "\n",
    "    if n == 0:\n",
    "      return pd.Series({prefix: np.nan, f\"u{prefix}\": np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({prefix: valori.iloc[0], f\"u{prefix}\": err_arbitrario})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)\n",
    "      return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
    "\n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df[prefix] = stats[prefix]\n",
    "  df[f\"u{prefix}\"] = stats[f\"u{prefix}\"]\n",
    "\n",
    "  return df\n",
    "\n",
    "\n",
    "df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n",
    "df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"a\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"t\", err_arbitrario=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5494409f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m1</th>\n",
       "      <th>a1</th>\n",
       "      <th>ua1</th>\n",
       "      <th>w1</th>\n",
       "      <th>uw1</th>\n",
       "      <th>c1</th>\n",
       "      <th>uc1</th>\n",
       "      <th>a2</th>\n",
       "      <th>ua2</th>\n",
       "      <th>w2</th>\n",
       "      <th>uw2</th>\n",
       "      <th>c2</th>\n",
       "      <th>uc2</th>\n",
       "      <th>w</th>\n",
       "      <th>uw</th>\n",
       "      <th>m</th>\n",
       "      <th>um</th>\n",
       "      <th>c</th>\n",
       "      <th>uc</th>\n",
       "      <th>a</th>\n",
       "      <th>ua</th>\n",
       "      <th>t</th>\n",
       "      <th>ut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.610000</td>\n",
       "      <td>9.210</td>\n",
       "      <td>0.029</td>\n",
       "      <td>14.2459</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>268.151</td>\n",
       "      <td>0.020</td>\n",
       "      <td>10.690</td>\n",
       "      <td>0.040</td>\n",
       "      <td>14.2434</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>268.326</td>\n",
       "      <td>0.026</td>\n",
       "      <td>14.24465</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>108.610000</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>268.2385</td>\n",
       "      <td>0.123744</td>\n",
       "      <td>9.9500</td>\n",
       "      <td>1.046518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.636667</td>\n",
       "      <td>10.037</td>\n",
       "      <td>0.025</td>\n",
       "      <td>13.1939</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>259.605</td>\n",
       "      <td>0.018</td>\n",
       "      <td>9.744</td>\n",
       "      <td>0.027</td>\n",
       "      <td>13.1913</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>259.745</td>\n",
       "      <td>0.020</td>\n",
       "      <td>13.19260</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>128.636667</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>259.6750</td>\n",
       "      <td>0.098995</td>\n",
       "      <td>9.8905</td>\n",
       "      <td>0.207182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>148.380000</td>\n",
       "      <td>9.930</td>\n",
       "      <td>0.030</td>\n",
       "      <td>12.3469</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>251.525</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.410</td>\n",
       "      <td>0.030</td>\n",
       "      <td>12.3461</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>251.542</td>\n",
       "      <td>0.023</td>\n",
       "      <td>12.34650</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>148.380000</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>251.5335</td>\n",
       "      <td>0.012021</td>\n",
       "      <td>10.6700</td>\n",
       "      <td>1.046518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>168.530000</td>\n",
       "      <td>11.340</td>\n",
       "      <td>0.030</td>\n",
       "      <td>11.6345</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>243.211</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.500</td>\n",
       "      <td>0.030</td>\n",
       "      <td>11.6344</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>243.130</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.63445</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>168.530000</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>243.1705</td>\n",
       "      <td>0.057276</td>\n",
       "      <td>11.4200</td>\n",
       "      <td>0.113137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           m1      a1    ua1       w1     uw1       c1    uc1      a2    ua2  \\\n",
       "0  108.610000   9.210  0.029  14.2459  0.0008  268.151  0.020  10.690  0.040   \n",
       "1  128.636667  10.037  0.025  13.1939  0.0007  259.605  0.018   9.744  0.027   \n",
       "2  148.380000   9.930  0.030  12.3469  0.0007  251.525  0.021  11.410  0.030   \n",
       "3  168.530000  11.340  0.030  11.6345  0.0005  243.211  0.021  11.500  0.030   \n",
       "\n",
       "        w2     uw2       c2    uc2         w        uw           m        um  \\\n",
       "0  14.2434  0.0009  268.326  0.026  14.24465  0.001768  108.610000  0.002887   \n",
       "1  13.1913  0.0009  259.745  0.020  13.19260  0.001838  128.636667  0.002887   \n",
       "2  12.3461  0.0006  251.542  0.023  12.34650  0.000566  148.380000  0.002887   \n",
       "3  11.6344  0.0006  243.130  0.021  11.63445  0.000071  168.530000  0.002887   \n",
       "\n",
       "          c        uc        a        ua   t  ut  \n",
       "0  268.2385  0.123744   9.9500  1.046518 NaN NaN  \n",
       "1  259.6750  0.098995   9.8905  0.207182 NaN NaN  \n",
       "2  251.5335  0.012021  10.6700  1.046518 NaN NaN  \n",
       "3  243.1705  0.057276  11.4200  0.113137 NaN NaN  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8d8cb4",
   "metadata": {},
   "source": [
    "# Analisi statica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1b1164",
   "metadata": {},
   "source": [
    "## Permutazioni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "976d5531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n",
      "6\n",
      "############################################################\n",
      "[ 8.5635 16.705  25.068   8.1415 16.5045  8.363 ]\n",
      "[0.15846924 0.12432618 0.13635615 0.09972211 0.11437001 0.0585235 ]\n",
      "############################################################\n",
      "[-20.02666667 -39.77       -59.92       -19.74333333 -39.89333333\n",
      " -20.15      ]\n",
      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248]\n"
     ]
    }
   ],
   "source": [
    "perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
    "\n",
    "este = np.array([df.c[x] - df.c[j] for x,j in perm])\n",
    "ueste = np.array([np.sqrt(df.uc[x]**2 + df.uc[j]**2) for x,j in perm])\n",
    "\n",
    "masse = np.array([df.m[x] - df.m[j] for x,j in perm])\n",
    "umasse = np.array([np.sqrt(df.um[x]**2 + df.um[j]**2) for x,j in perm])\n",
    "\n",
    "\n",
    "print(perm)\n",
    "print(len(perm))\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(este)\n",
    "print(ueste)\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(masse)\n",
    "print(umasse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37fbd7c",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo del K\n",
    "- Calcolo della media pesata (sui K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22.93472529 23.34776354 23.44165629 23.78221089 23.70468175 23.62920603]\n",
      "[0.42444376 0.17379748 0.12754214 0.29135079 0.16430027 0.16544183]\n"
     ]
    }
   ],
   "source": [
    "F = masse * g\n",
    "uF = np.sqrt((g * umasse)**2 + (masse * ug)**2)\n",
    "\n",
    "K = - F / este\n",
    "uK = np.sqrt((1/este)**2 * uF**2   +   (F / este**2)**2 * ueste**2  )\n",
    "\n",
    "\n",
    "print(K)\n",
    "print(uK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 23.52082621035826\n",
      "sigmaC:  0.07342397251895814\n"
     ]
    }
   ],
   "source": [
    "def mediaPesata(x, ux, dim = 0):\n",
    "  x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
    "  sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
    "\n",
    "  w = 1.0 / (sigma_arr ** 2)\n",
    "\n",
    "  num = np.nansum(w * x_arr, axis=dim)\n",
    "  den = np.nansum(w, axis=dim)\n",
    "\n",
    "  media = num / den\n",
    "  sigma = np.sqrt(1.0 / den)\n",
    "\n",
    "  return media, sigma\n",
    "\n",
    "media, uA = mediaPesata(K, uK)\n",
    "print(\"K-medio:\", media)\n",
    "print(\"sigmaC: \", uA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21baebb4",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici\n",
    "Ogni blocco ha:\n",
    "- Regressione\n",
    "- grafico\n",
    "- chi^2\n",
    "\n",
    "Nota: Ax + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5be377eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"x\": este,\n",
    "    \"ux\": ueste,\n",
    "    \"y\": - F,\n",
    "    \"uy\": uF\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec60a92",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aefe7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.044e+04\n",
      "Date:                lun, 06 apr 2026   Prob (F-statistic):           5.50e-08\n",
      "Time:                        10:22:59   Log-Likelihood:                -14.807\n",
      "No. Observations:                   6   AIC:                             33.61\n",
      "Df Residuals:                       4   BIC:                             33.20\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1355      3.495      0.039      0.971      -9.569       9.840\n",
      "x             23.4652      0.230    102.161      0.000      22.827      24.103\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   0.555\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.395\n",
      "Skew:                          -0.285   Prob(JB):                        0.821\n",
      "Kurtosis:                       1.880   Cond. No.                         37.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Aols  = 23.46517 ± 0.22969\n",
      "Bols  = 0.13548 ± 3.49525\n",
      "R² = 0.99962\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(data[\"x\"])\n",
    "model = sm.OLS(data[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "Bols = model.params[\"const\"]\n",
    "Aols = model.params[\"x\"]\n",
    "uBols = model.bse[\"const\"]\n",
    "uAols = model.bse[\"x\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8d795186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = Bols + Aols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare OLS\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 6.07430\n",
      "GdL       = 4\n",
      "Chi² rid  = 1.51858\n",
      "P(0, chi²)= 0.80633\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    1.582374\n",
      "1    0.516368\n",
      "2    0.051305\n",
      "3    1.091998\n",
      "4    2.022571\n",
      "5    0.809686\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = Bols + Aols * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "Pols = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f26f65e",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6a910226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.3952 +- 0.1756\n",
      "BC      = 1.8060 +- 2.2463\n",
      "cov_ABC = -0.358328\n",
      "P(0, chi²)= 0.7197\n"
     ]
    }
   ],
   "source": [
    "def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
    "  # Stima iniziale di A con sola sigma_y\n",
    "  sy2 = sigma_y**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",
    "  sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
    "  return sigma_eq\n",
    "\n",
    "\n",
    "def reg_lin(x, y, sigma_x, sigma_y):\n",
    "  x = np.asarray(x, dtype=float)\n",
    "  y = np.asarray(y, dtype=float)\n",
    "  sigma_x = np.asarray(sigma_x, dtype=float)\n",
    "  sigma_y = np.asarray(sigma_y, dtype=float)\n",
    "\n",
    "  # Propagazione errore x → y\n",
    "  sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
    "\n",
    "  # Somme pesate\n",
    "  w    = 1.0 / sigma_y_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",
    "  sigma_A = np.sqrt(Sw  / delta)\n",
    "  sigma_B = np.sqrt(Sxx / delta)\n",
    "  cov_AB  = -Sx / delta\n",
    "\n",
    "  # Chi quadro\n",
    "  x2  = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
    "  dof = len(x) - 2\n",
    "  chi = sc.stats.chi2.cdf(x2, dof)  # P(X² > x2)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
    "\n",
    "\n",
    "AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AC      = {AC:.4f} +- {uAC:.4f}\")\n",
    "print(f\"BC      = {BC:.4f} +- {uBC:.4f}\")\n",
    "print(f\"cov_ABC = {covABC:.6f}\")\n",
    "print(f\"P(0, chi²)= {chiC:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b49ec5a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "  data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = BC + AC * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare Carpi\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "538ddb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 5.10905\n",
      "GdL       = 4\n",
      "Chi² rid  = 1.27726\n",
      "P(0, chi²)= 0.72371\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    2.404581\n",
      "1    0.797907\n",
      "2    0.040448\n",
      "3    0.332099\n",
      "4    1.521888\n",
      "5    0.012127\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = BC + AC * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (AC * data[\"ux\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "PC = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec42f61",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c7f54cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 23.4018867 ± 0.1749174\n",
      "BY        = 1.7284277 ± 2.2381063\n",
      "cov_ABY   = -0.35570868522503013\n",
      "chi²      = 5.10759\n",
      "chi² rid  = 1.27690\n",
      "P(0, chi²)= 0.72357\n"
     ]
    }
   ],
   "source": [
    "def york_fit(x, y, sx, sy, max_iter=50, tol=1e-15):\n",
    "  # Pesi iniziali\n",
    "  wx = 1 / sx**2\n",
    "  wy = 1 / sy**2\n",
    "\n",
    "  # Stima iniziale della pendenza (OLS y|x)\n",
    "  A = np.cov(x, y, aweights=wy)[0,1] / np.cov(x, x, aweights=wy)[0,1]\n",
    "\n",
    "  for _ in range(max_iter):\n",
    "    A_old = A\n",
    "\n",
    "    # 1) Pesi combinati\n",
    "    Wi = (wx * wy) / (A**2 * wy + wx)\n",
    "\n",
    "    # 2) x e y pesati (eq. 10)\n",
    "    X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
    "    Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
    "\n",
    "    # 3) Variabili centrate\n",
    "    Ui = x - X_bar\n",
    "    Vi = y - Y_bar\n",
    "\n",
    "    # 4) beta\n",
    "    beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
    "\n",
    "    # 5) Aggiornamento pendenza\n",
    "    A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
    "\n",
    "    # Convergenza\n",
    "    if abs(A - A_old) < tol:\n",
    "      break\n",
    "\n",
    "  # 6) Intercetta\n",
    "  B = Y_bar - A * X_bar\n",
    "\n",
    "  # S = somma Wi (xi - x)**2\n",
    "  S = np.sum(Wi * Ui**2)\n",
    "\n",
    "  sA = np.sqrt(1 / S)\n",
    "  sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n",
    "\n",
    "  # cov(A,B)\n",
    "  cov_AB = -X_bar * sA**2\n",
    "\n",
    "  # CHI QUADRATO\n",
    "  chi2 = np.sum(Wi * (Vi - A * Ui)**2)\n",
    "  dof = len(x) - 2\n",
    "  chi2_red = chi2 / dof\n",
    "\n",
    "  return A, B, sA, sB, cov_AB, chi2, chi2_red\n",
    "\n",
    "AY, BY, uAY, uBY, cov_ABY, chi2_val, chi2_red = york_fit(data.x, data.y, data.ux, data.uy)\n",
    "PY = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"AY        = {AY:.7f} ± {uAY:.7f}\")\n",
    "print(f\"BY        = {BY:.7f} ± {uBY:.7f}\")\n",
    "print(f\"cov_ABY   = {cov_ABY}\")\n",
    "print(f\"chi²      = {chi2_val:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fc824066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = BY + AY * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare York\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a6fec5",
   "metadata": {},
   "source": [
    "## Raccolta finale dei dati caso statico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1203e1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 23.52083 ± 0.07342\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 23.46517 ± 0.22969\n",
      "Bols  = 0.13548 ± 3.49525\n",
      "P(0, chi²)= 0.80633\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 23.39521 ± 0.17556\n",
      "Bc  = 1.80604 ± 2.24633\n",
      "P(0, chi²)= 0.72371\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 23.40189 ± 0.17492\n",
      "By  = 1.72843 ± 2.23811\n",
      "P(0, chi²)= 0.72357\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"Ac  = {AC:.5f} ± {uAC:.5f}\")\n",
    "print(f\"Bc  = {BC:.5f} ± {uBC:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"Ay  = {AY:.5f} ± {uAY:.5f}\")\n",
    "print(f\"By  = {BY:.5f} ± {uBY:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcfef24",
   "metadata": {},
   "source": [
    "## Aggiunta degli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "aaa95426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.020000\n",
      "uF_strum     = 0.072065\n",
      "uK_strum     = 0.462991\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.02\n",
    "\n",
    "# Incertezza strumentale su una differenza: res/sqrt(12) * sqrt(2) = res/sqrt(6)\n",
    "u_strum_m  = res_b / np.sqrt(6)\n",
    "u_strum_Dx = res_c               #incertezza delle misure di C DISCUTERE\n",
    "\n",
    "# Massimo tra campionario e strumentale, punto per punto\n",
    "ueste_strum  = np.maximum(ueste,  u_strum_Dx)\n",
    "umasse_strum = np.maximum(umasse, u_strum_m)\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
    "uDx_strum = np.max(ueste_strum)\n",
    "\n",
    "uK_strum  = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
    "\n",
    "print(f\"u_strum_m    = {u_strum_m:.6f}\")\n",
    "print(f\"u_strum_Dx   = {u_strum_Dx:.6f}\")\n",
    "print(f\"uF_strum     = {uF_strum:.6f}\")\n",
    "print(f\"uK_strum     = {uK_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23be271e",
   "metadata": {},
   "source": [
    "### Propapazione dell'errore strumentale max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a9f2a9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Media pesata\n",
    "uA_fin   = np.sqrt(uA**2      + uK_strum**2)\n",
    "\n",
    "# OLS\n",
    "uAols_fin = np.sqrt(uAols**2  + uK_strum**2)\n",
    "uBols_fin = np.sqrt(uBols**2  + uK_strum**2)\n",
    "\n",
    "# Carpi\n",
    "uAC_fin  = np.sqrt(uAC**2     + uK_strum**2)\n",
    "uBC_fin  = np.sqrt(uBC**2     + uK_strum**2)\n",
    "\n",
    "# York\n",
    "uAY_fin  = np.sqrt(uAY**2     + uK_strum**2)\n",
    "uBY_fin  = np.sqrt(uBY**2     + uK_strum**2)\n",
    "\n",
    "\n",
    "# Nuovo uy e ux per ricalcolare i chi^2\n",
    "uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
    "ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
    "\n",
    "# OLS\n",
    "F_fit_ols = Bols + Aols * data[\"x\"]\n",
    "sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
    "chi2_ols  = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
    "GdL       = len(data) - 2\n",
    "\n",
    "# Carpi\n",
    "F_fit_c   = BC + AC * data[\"x\"]\n",
    "sigma_c   = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
    "chi2_c    = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
    "\n",
    "# York\n",
    "F_fit_y   = BY + AY * data[\"x\"]\n",
    "sigma_y_   = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
    "chi2_y     = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41baee7a",
   "metadata": {},
   "source": [
    "## Risultati della propagazione dell'errore strumentale massimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c95874fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 23.52083 ± 0.46878\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 23.46517 ± 0.51683\n",
      "Bols  = 0.13548 ± 3.52578\n",
      "Chi² OLS   = 2.10939  |  rid = 0.52735  |  P = 0.28435\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AC  = 23.39521 ± 0.49516\n",
      "BC  = 1.80604 ± 2.29355\n",
      "Chi² Carpi = 2.14027  |  rid = 0.53507  |  P = 0.29002\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "AY  = 23.40189 ± 0.49493\n",
      "BY  = 1.72843 ± 2.28549\n",
      "Chi² York  = 2.13715  |  rid = 0.53429  |  P = 0.28945\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
    "print(f\"Chi² OLS   = {chi2_ols:.5f}  |  rid = {chi2_ols/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AC  = {AC:.5f} ± {uAC_fin:.5f}\")\n",
    "print(f\"BC  = {BC:.5f} ± {uBC_fin:.5f}\")\n",
    "print(f\"Chi² Carpi = {chi2_c:.5f}  |  rid = {chi2_c/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"AY  = {AY:.5f} ± {uAY_fin:.5f}\")\n",
    "print(f\"BY  = {BY:.5f} ± {uBY_fin:.5f}\")\n",
    "print(f\"Chi² York  = {chi2_y:.5f}  |  rid = {chi2_y/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb210b61",
   "metadata": {},
   "source": [
    "# Dinamica Sonar\n",
    "Qui specifico la formula perché non è così ovvia:\n",
    "- $M_{eq} = M + \\frac m 3$\n",
    "- $\\omega = \\frac{2\\pi}{T} \\quad \\Rightarrow \\quad T = \\frac{2\\pi}{\\omega}$\n",
    "- A: coefficiente della regressione lineare $A = \\frac{T^2}{M_{eq}}$\n",
    "- $K = \\frac{4 \\pi^2}{A}$\n",
    "\n",
    "Nota: nelle formule comparirà un $/1000$, questo serve a riportare il valore in unità di misura standard: N/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "95dde99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.194561\n",
      "1    0.226829\n",
      "2    0.258983\n",
      "3    0.291654\n",
      "Name: w, dtype: float64\n",
      "0    0.000048\n",
      "1    0.000063\n",
      "2    0.000024\n",
      "3    0.000004\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Meq = df.m # ( m_mol / 3 )\n",
    "uMeq = np.sqrt( df.um**2 + um_mol**2 / 9 )\n",
    "\n",
    "T2 = ( 2 * np.pi / df.w )**2\n",
    "uT2 = np.sqrt( (8*np.pi**2 / df.w**3)**2 * df.uw**2 )\n",
    "\n",
    "print(T2)\n",
    "print(uT2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd743d4",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo della Meq (massa equivalente)\n",
    "- Calcolo della media pesata sui K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8c9f5b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    22.038061\n",
      "1    22.388529\n",
      "2    22.618463\n",
      "3    22.812293\n",
      "dtype: float64\n",
      "0    0.005543\n",
      "1    0.006287\n",
      "2    0.002179\n",
      "3    0.000658\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Ad = T2 / Meq\n",
    "uAd = np.sqrt( (1/Meq)**2 * uT2**2 + (T2/Meq**2)**2 * uMeq**2 )\n",
    "\n",
    "Kd = 4 * np.pi**2 / Ad\n",
    "uKd = np.sqrt( (4 * np.pi**2 / Ad**2)**2 * uAd**2 )\n",
    "\n",
    "Kd /= 1000\n",
    "uKd /= 1000\n",
    "\n",
    "print(Kd)\n",
    "print(uKd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0d899f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 22.78251103059837\n",
      "sigmaC:  0.000622923916689791\n"
     ]
    }
   ],
   "source": [
    "def mediaPesata(x, ux, dim = 0):\n",
    "  x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
    "  sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
    "\n",
    "  w = 1.0 / (sigma_arr ** 2)\n",
    "\n",
    "  num = np.nansum(w * x_arr, axis=dim)\n",
    "  den = np.nansum(w, axis=dim)\n",
    "\n",
    "  media = num / den\n",
    "  sigma = np.sqrt(1.0 / den)\n",
    "\n",
    "  return media, sigma\n",
    "\n",
    "KdM, uKdM = mediaPesata(Kd, uKd)\n",
    "print(\"K-medio:\", KdM)\n",
    "print(\"sigmaC: \", uKdM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6741544e",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici\n",
    "Ogni blocco ha:\n",
    "- Regressione\n",
    "- grafico\n",
    "- chi^2\n",
    "\n",
    "Nota: Ax + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4b4c2682",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "datad = pd.DataFrame({\n",
    "    \"x\": Meq,\n",
    "    \"ux\": uMeq,\n",
    "    \"y\": T2,\n",
    "    \"uy\": uT2\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad203fbd",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "90d191d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 4.713e+05\n",
      "Date:                lun, 06 apr 2026   Prob (F-statistic):           2.12e-06\n",
      "Time:                        10:23:01   Log-Likelihood:                 32.343\n",
      "No. Observations:                   4   AIC:                            -60.69\n",
      "Df Residuals:                       2   BIC:                            -61.91\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0184      0.000     55.553      0.000       0.017       0.020\n",
      "x              0.0016   2.36e-06    686.488      0.000       0.002       0.002\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.752\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.628\n",
      "Skew:                          -0.880   Prob(JB):                        0.730\n",
      "Kurtosis:                       2.180   Cond. No.                         883.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Adols  = 0.00162 ± 0.00000\n",
      "Bdols  = 0.01841 ± 0.00033\n",
      "R²     = 1.00000\n",
      "Kdols  = 24.35158 ± 0.03547\n",
      "KBdols = 2.14448 ± 0.03860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(datad[\"x\"])\n",
    "model = sm.OLS(datad[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "Bdols = model.params[\"const\"]\n",
    "Adols = model.params[\"x\"]\n",
    "uBdols = model.bse[\"const\"]\n",
    "uAdols = model.bse[\"x\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "Kdols = (2*np.pi)**2 / Adols / 1000\n",
    "uKdols = np.sqrt( (4*np.pi**2 / Adols**2)**2 * uAdols**2 ) / 1000\n",
    "KBdols = (2*np.pi)**2 / Bdols / 1000\n",
    "uKBdols = np.sqrt( (4*np.pi**2 / Bdols**2)**2 * uBdols**2 ) / 1000\n",
    "\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"Adols  = {Adols:.5f} ± {uAdols:.5f}\")\n",
    "print(f\"Bdols  = {Bdols:.5f} ± {uBdols:.5f}\")\n",
    "print(f\"R²     = {R2:.5f}\")\n",
    "print(f\"Kdols  = {Kdols:.5f} ± {uKdols:.5f}\")\n",
    "print(f\"KBdols = {KBdols:.5f} ± {uKBdols:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a0ba5c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
    "yfit = Bdols + Adols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit, yfit,\n",
    "  color=\"crimson\", linewidth=1, zorder=10, label=\"Fit lineare OLS\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T^2 (s^2)\")\n",
    "plt.title(\"Legge di ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3c7c05e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 17.93044\n",
      "GdL       = 2\n",
      "Chi² rid  = 8.96522\n",
      "P(0, chi²)= 0.99987\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0     2.355712\n",
      "1     3.797810\n",
      "2     0.836627\n",
      "3    10.940294\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = Bdols + Adols * datad[\"x\"]\n",
    "sigma = np.sqrt(datad[\"uy\"]**2 + (Adols * datad[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(datad)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "Pdols = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pdols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datad[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1296a66",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "698e3c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AdC      = 0.00162 ± 0.00000\n",
      "BdC      = 0.01841 ± 0.00011\n",
      "cov_ABdC = -0.000000\n",
      "P(0,chi²)= 0.96181\n",
      "KdC      = 24.34964 ± 0.00989\n",
      "KBdC     = 2.14403 ± 0.01267\n"
     ]
    }
   ],
   "source": [
    "def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
    "  # Stima iniziale di A con sola sigma_y\n",
    "  sy2 = sigma_y**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",
    "  sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
    "  return sigma_eq\n",
    "\n",
    "\n",
    "def reg_lin(x, y, sigma_x, sigma_y):\n",
    "  x = np.asarray(x, dtype=float)\n",
    "  y = np.asarray(y, dtype=float)\n",
    "  sigma_x = np.asarray(sigma_x, dtype=float)\n",
    "  sigma_y = np.asarray(sigma_y, dtype=float)\n",
    "\n",
    "  # Propagazione errore x → y\n",
    "  sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
    "\n",
    "  # Somme pesate\n",
    "  w    = 1.0 / sigma_y_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",
    "  sigma_A = np.sqrt(Sw  / delta)\n",
    "  sigma_B = np.sqrt(Sxx / delta)\n",
    "  cov_AB  = -Sx / delta\n",
    "\n",
    "  # Chi quadro\n",
    "  x2  = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
    "  dof = len(x) - 2\n",
    "  chi = sc.stats.chi2.cdf(x2, dof)  # P(X² > x2)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
    "\n",
    "\n",
    "AdC, BdC, uAdC, uBdC, covABdC, chidC = reg_lin(datad[\"x\"], datad[\"y\"], datad[\"ux\"], datad[\"uy\"])\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "KdC = (2*np.pi)**2 / AdC / 1000\n",
    "uKdC = np.sqrt( (4*np.pi**2 / AdC**2)**2 * uAdC**2 ) / 1000\n",
    "KBdC = (2*np.pi)**2 / BdC / 1000\n",
    "uKBdC = np.sqrt( (4*np.pi**2 / BdC**2)**2 * uBdC**2 ) / 1000\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AdC      = {AdC:.5f} ± {uAdC:.5f}\")\n",
    "print(f\"BdC      = {BdC:.5f} ± {uBdC:.5f}\")\n",
    "print(f\"cov_ABdC = {covABdC:.6f}\")\n",
    "print(f\"P(0,chi²)= {chidC:.5f}\")\n",
    "print(f\"KdC      = {KdC:.5f} ± {uKdC:.5f}\")\n",
    "print(f\"KBdC     = {KBdC:.5f} ± {uKBdC:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a0ab8534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "  data=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
    "yfit = BdC + AdC * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare Carpi\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T^2 (s^2)\")\n",
    "plt.title(\"Legge di ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "12bb0479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 6.53021\n",
      "GdL       = 2\n",
      "Chi² rid  = 3.26511\n",
      "P(0, chi²)= 0.96181\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    1.366019\n",
      "1    5.154390\n",
      "2    0.000185\n",
      "3    0.009620\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = BdC + AdC * datad[\"x\"]\n",
    "sigma = np.sqrt(datad[\"uy\"]**2 + (AdC * datad[\"ux\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(datad)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "PdC = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PdC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datad[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bf75d6",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "385db415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdY       = 0.0016213 ± 0.0000007\n",
      "BdY       = 0.0184132 ± 0.0001088\n",
      "cov_ABdY  = -7.147083208880052e-11\n",
      "chi²      = 6.53021\n",
      "chi² rid  = 3.26511\n",
      "P(0, chi²)= 0.96181\n",
      "KdY       = 24.34964 ± 0.00989\n",
      "KBdY      = 2.14403 ± 0.01267\n"
     ]
    }
   ],
   "source": [
    "def york_fit(x, y, sx, sy, max_iter=50, tol=1e-15):\n",
    "  # Pesi iniziali\n",
    "  wx = 1 / sx**2\n",
    "  wy = 1 / sy**2\n",
    "\n",
    "  # Stima iniziale della pendenza (OLS y|x)\n",
    "  A = np.cov(x, y, aweights=wy)[0,1] / np.cov(x, x, aweights=wy)[0,1]\n",
    "\n",
    "  for _ in range(max_iter):\n",
    "    A_old = A\n",
    "\n",
    "    # 1) Pesi combinati\n",
    "    Wi = (wx * wy) / (A**2 * wy + wx)\n",
    "\n",
    "    # 2) x e y pesati (eq. 10)\n",
    "    X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
    "    Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
    "\n",
    "    # 3) Variabili centrate\n",
    "    Ui = x - X_bar\n",
    "    Vi = y - Y_bar\n",
    "\n",
    "    # 4) beta\n",
    "    beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
    "\n",
    "    # 5) Aggiornamento pendenza\n",
    "    A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
    "\n",
    "    # Convergenza\n",
    "    if abs(A - A_old) < tol:\n",
    "      break\n",
    "\n",
    "  # 6) Intercetta\n",
    "  B = Y_bar - A * X_bar\n",
    "\n",
    "  # S = somma Wi (xi - x)**2\n",
    "  S = np.sum(Wi * Ui**2)\n",
    "\n",
    "  sA = np.sqrt(1 / S)\n",
    "  sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n",
    "\n",
    "  # cov(A,B)\n",
    "  cov_AB = -X_bar * sA**2\n",
    "\n",
    "  # CHI QUADRATO\n",
    "  chi2 = np.sum(Wi * (Vi - A * Ui)**2)\n",
    "  dof = len(x) - 2\n",
    "  chi2_red = chi2 / dof\n",
    "\n",
    "  return A, B, sA, sB, cov_AB, chi2, chi2_red\n",
    "\n",
    "AdY, BdY, uAdY, uBdY, cov_ABdY, chi2_val, chi2_red = york_fit(datad.x, datad.y, datad.ux, datad.uy)\n",
    "PdY = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "KdY = (2*np.pi)**2 / AdY / 1000\n",
    "uKdY = np.sqrt( (4*np.pi**2 / AdY**2)**2 * uAdY**2 ) / 1000\n",
    "KBdY = (2*np.pi)**2 / BdY / 1000\n",
    "uKBdY = np.sqrt( (4*np.pi**2 / BdY**2)**2 * uBdY**2 ) / 1000\n",
    "\n",
    "\n",
    "print(f\"AdY       = {AdY:.7f} ± {uAdY:.7f}\")\n",
    "print(f\"BdY       = {BdY:.7f} ± {uBdY:.7f}\")\n",
    "print(f\"cov_ABdY  = {cov_ABdY}\")\n",
    "print(f\"chi²      = {chi2_val:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PdY:.5f}\")\n",
    "print(f\"KdY       = {KdY:.5f} ± {uKdY:.5f}\")\n",
    "print(f\"KBdY      = {KBdY:.5f} ± {uKBdY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "698bc57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XRqqq5nyvPN2hqXjuPwZcuHABERERxX7wYmm/5q3b6mW7ufdbNtR+u572LcuaKvP35trnXb2tV49r8nS77rZa9PXWbfW83ZL2LcuaKtv3pqC2Wmy3KBWp4bV0dWKOjh074sCBA3neurd7924YDIY8ZzT01A8//IC6devm2b7NZsM333yTc9nJkydx7NgxdOzYsWyLL4D7ZdCgoCDNt01UFu598tqX24mIiIhInq5eCYuLi8OaNWswcuRIDB8+HOfPn8esWbMQFxeX5zPCBgwYgHPnzmHv3r0AgC+++ALbtm3DHXfcgZo1a8Jms2HHjh34+uuv8dZbb+XcrmXLlujQoQPGjh2LV155Bf7+/pgzZw5iYmJw1113iT0uvopDeuPeJ0tzLB4RERERlY2uhjCLxYJVq1ZhypQpGDlyJIKDg9G7d2+MGjUqz/WuPcCvbt26yM7Oxptvvonk5GREREQgJiYGa9asQZs2bfLc9u2338b06dMxfvx4OBwOdOjQAa+99lqR7+8sDocsOWwrq7D3Q1PZsa0s9pXFvnLYVhb7ymFbbSkq/xReJocPHwYANGvWLN/XMjMzcfLkSTRs2BABAQHlvTRNzZs3D/Hx8fkub9y4MXbs2IGYmBi8/PLLGDJkCADg/fffh9lsxv3331/stu+8807ccccdGD9+PABgzJgxOHLkCHbs2KHtg/CSMWPG4IsvvsDu3bvzfBA5AMycORMbN27Erl27UKNGjVJtf968eVi+fDkOHTrk8W0q0r5JREREpBdFzQa56eqVMF/m6ekofVlAQABWrVqV7zIA2LRpE2rVqpVz+datWxEUFOTREHatp556ChkZGQV+DoUvevnll/H5559j9uzZmDZtWs7lv/32G1avXo2XXnqp1ANYWZXkVKrkOZ5URhb7ymJfOWwri33lsK32OIRpoLK8mGgwGNCiRYsCv1bY5aVRr149AHl/4L0tOzsbJpOpyFOsFiYyMhJjxozBq6++il69eqFVq1ZQVRUTJ05ETEwM+vfvX6Y1lQVPzCHH4XAUeYpbKhv2lcW+cthWFvvKYVtt6ersiOS7YmJisGzZMgBA//798d133+GLL75ATEwMYmJiMG/ePI+3NWbMGNx33305/37//fcRExODY8eOYejQoWjRogXuuusubNu2Ld9tv/jiCzz88MNo3rw5brvtNkyYMAEZGRk5X8/IyMDkyZNx991346abbsKdd96J8ePH5/t8ujvvvBOTJ0/GkiVL8J///AfNmzdHSkpKznruv/9+NGvWDLfffjvmzJmT70MIr9WzZ0+0adMGEydOhMPhwHvvvYeff/4ZkydPhtFoxNmzZ/Hss8+iVatWaNGiBYYMGYLffvvN4zVdKz4+HjfddBO+/PLLItdFRERE5KuSkpKwc+dOJCUleXspJcZXwqhErn3lxGg05nulasKECXjppZcQEBCAV155BQA0ebvd6NGj0adPHwwaNAibN2/GmDFj0KxZMzRq1AjA1Y8zGDVqFHr16oVnnnkGFy9exJtvvonU1FTMmTMHwNVjoZxOJ0aNGoXIyEj8888/WLhwIZ566imsWbMmz/19/PHHqF+/PsaNGweDwYCgoCCsWLECs2fPxoABAzBmzBicOHEiZwgbPXp0keufNGkSHnjgAcyZMwdbtmxBv3790LRpU6Snp6N///4wGAyYNGkS/P39sWDBAjz22GP48MMPUbNmzSLXdC33cWaLFy/GrbfeWtbsRERERLpks9lw7NgxNGnSBJGRkd5eTolwCCOPZWRk4MYbb8xz2axZs9CjR488l1133XUICQlBUFCQpm9T7NevH/r16wfg6scNfPnll9izZw+eeuopqKqKWbNmoXv37nmOu6pWrRqGDRuGp556Co0bN0ZkZCQmTZqU83WHw4E6derg0UcfzTlRhZvdbseSJUtyBp309HTMnTsXQ4cOxQsvvAAAaN++PcxmM2bMmIEhQ4YgIiKi0PU3bNgQw4cPx7x581CjRg08//zzAK6+snbu3Dl89NFHOQPlLbfcgv/85z9YtWoVxowZU+iacnO/xXHXrl1YuXIlbrrpppImJiIiIvIZl69kw+F04fKVbG8vpcQ4hGmgNMcs2f86B5ctrfgrCjBYQmFuUKv4K14jICAAa9euzXNZ7g/DltahQ4ec/x8UFIRatWrh33//BXD1Q7fPnj2LsWPH5nm1rk2bNjAYDDhy5AgaN24MANi2bRtWrlyJv//+O89bFf/66688Q9itt96aZ9g5dOgQMjIycM899+S5j3bt2iEzMxN//PFHvo9EuNawYcMwb948PPLIIwgODgYAfP/992jcuHHOAAYA4eHhaNeuHX744Yc8t792TW6qquLll1/G/v37sXr1atxwww1FrsNND8fbVVRsK4t9ZbGvHLaVxb5y9NI2KSkJNpsNGYd+x7m5m2BsVg0bd/2AOADBgX6wWCw+8aoYhzCNlGTHdFpTcOrWRwCXS3BFRTAa0eDoNhirhJfoZgaDodjTbWrJfQYed9vQ0NA8XzebzcjOvvqXj+TkZADAyJEjC9zWP//8AwDYu3cvXnnlFfTt2xejRo1CeHg4Ll68iJEjRyIrKyvPbapUqZLn3+776NmzZ5H3URQ/P7+ctbulpqaiatWq+a5bpUoV/PHHH0Wuyc1ut+Ozzz5Du3btcP311xe7Djez2aybJ9WKRFEUHrwsiH1lsa8ctpXFvnL01Pb7jz6GedkHqHf0DCIjQ6C2joKSegI7tp+ByWhAbGwsunfv7u1lFotDmBcYq4Sj3sENXn0lrKQDmN65P39r/PjxaN68eb6vV69eHcDV48aaNGmCyZMn53ztu+++K3Cb1w4nFosFwNWTXhR0jFudOnVKtXaLxYKTJ0/mu9xqtebcZ2FrcvPz88OiRYvwxBNPYOLEiXkeHxEREZGvc6VnIHnuOjSavxEIDkD26MfxgSEQavpJIKwR7uvWKueVMF/AIUwjJT2VemneDuhLzGZzvleWSsp9inpPPgIgOjoaNWrUwOnTp3OOGytIZmZmvr/kbN++3aP1tGzZEoGBgfj333/RtWtXj27jiVatWmHPnj1ITExEdHQ0gKsHmh44cAB9+/b1eDutW7fG/PnzMXz4cPj7+2PcuHHF3sZut8Pf35+vhmlMVVU4HA6YTCa2FcC+sthXDtvKYl853myrOp1IW/8RkqYvgystHeFPxSHi2X4whASh77HfsGP7OdzXrRWaxsaU67rKikOYBirL54SVRHR0NLZt24bPPvsM1apVQ/Xq1REVFSV2f4qiYMyYMRg9ejQyMjJwxx13IDAwEOfOncOXX36JUaNGoWHDhmjXrh0mT56MhISEnJN7fPPNNx7dR1hYGJ599lnMnj0b//77L9q0aQOj0YjTp0/j008/xbx58xAYGFjitffq1QsrV67E8OHD8fzzz+ecHdFkMmHAgAEl2lbbtm0xb948jBw5EoGBgTknECkM9105bCuLfWWxrxy2lcW+crzRNuPz72CdkIDsXxMR8vBdqDJuGEy1/+/3yeBAP5iMBgQH+pX72sqKQxiJeOKJJ3Dq1Cm88sorSE1NxdNPP41nnnlG9D67deuGsLAwLFy4MOfVrdq1a+P222/POeYqLi4OZ86cwdq1a7Fs2TJ06NABb775Jvr06ePRfQwePBhRUVFYsWIF1q5dC5PJhHr16uGOO+4o9XulQ0JCsGbNGsyYMQOvv/46XC4Xbr75ZqxduzbP6ek91alTJ7z99tt47rnn4O/vX+hxckRERER6lH38JC5NSMCVzw4i4LabUHvvEgS0yH/SMYvFgtjYWJ95C2Juiso/GZTJ4cOHoaoqmjVrlu/l2czMzJzTngcEBHhphb7L/VbE3CfnIG24983atWsjNDSUfTWmqirsdjtPfCKEfWWxrxy2lcW+csqrreNCEpJnLUfqmu0w16+FyAlPIrj77T71/Tx8+DAAFHsyO74SRkREREREXuO6kgXbos1IfmctFJMRVSaNhGVwTyh++jgjowQOYRrwpenc17CtLL2cbrYiYltZ7CuLfeWwrSz2lSPRVnW5kL71UyRNXQTHeSssg3si4sWBMEaEaX5fesMhTCMcFrTHpvL4Vk8ZbCqLfWWxrxy2lcW+ciTaXvn2F1jHxyPr0K8IvrcTao0fAXN06T7uxxdxCNNISU9RT8XLfbgi28pwOp3cdwWoqgqXywWDwcC2AthXFvvKYVtZ7CtHy7b2k2dhnbwAl3d8Cf8WN6DWB/MQ2K6FNgv1IRzCNMBzm8jhgCDL6XR6ewkVltPphMFg8PYyKiz2lcW+cthWFvvKKWtbZ0oakt9aBdvS92CsFonq819DyENdoVTS7xeHsHLAIY30xr1PcsAlIiIiSWq2HbYV25D85kqo2XZEvjQIlhF9YQj09/bSvIpDmCD3AYwZGRml+hBfIikZGRkAAJOJTwFERESkPVVVkbHrK1gnLYD9r3MIe+w+RLw8GKaoKt5emi7wNzBBRqMR4eHhuHDhAgAgKCiIrzyUAD8nTHuqqiIjIwMXLlyAxWKB0Wj09pKIiIiogsn6+TdcGh+PzAM/IfA/bRC1chr8m0R7e1m6wiFMA0UNCDVq1ACAnEGMSobHhMkIDw9HVFQU3yoriAOuLPaVxb5y2FYW+8rxpK3j3AVYpy1G+uY9MN/QEDU3voGgzreWw+p8D4cwjRQ2KCiKgpo1a6J69eqw2+3lvCqi/MxmM/8jJUxRFDYWxL6y2FcO28piXznFtXWlZyBl3nqkLNgIQ0gQqr35EkIf7Q6Fhz0UimU0UtwrNkajkU8MJcS3I8piXzlsK4t9ZbGvHLaVxb5yCmurOp1IW78TSTOWwpWaDsuIvoh4th8MocFeXK1v4BCmAb6lS47D4RD5hHa6in3lsK0s9pXFvnLYVhb7yrm2bcYX/4N1QjyyjyUipHdXRI4bDnOdKC+u0LdwCCMiIiIiIo9k/3YS1gnzkfHptwi4tTlqf7wYAS2beHtZPodDGBERERERFcl5KRkpb61B2todMNWtgagVUxF8b0e+9bOUOIQREREREVGBXJlZsC3ajOS310IxGlBl4pOwDO4FxY9v+ywLDmEa4F8A5LCtLPaVw7ay2FcW+8phW1nsqx1VVZG+9VMkTVkIx7+XEDqwByJHD4KpSri3l1YhcAjTCH/otacoCg+uFcS+cthWFvvKYl85bCuLfbVz5eAvsI6PR9aPvyK4++2oueUt+DWq5+1lVSgcwoiIiIiICPaTZ2GdshCXt38B/5tiUGvbXAS2b+ntZVVIHMI0UtznhFHJqaoKh8MBk8nEtgLYVw7bymJfWewrh21lsW/pOVPSkDxnNWxLtsBYLRLVE8YhpPddUAwGAGwrgUOYBvg5YXLYVhb7ymFbWewri33lsK0s9i0Z1e5A6sptSJq9AmqWHZEvDoTlyb4wBAXkvy7baopDGBERERFRJaKqKjJ2fw3rpAWwnzyL0H73IvKVITBFVfH20ioNDmFERERERJVE1s+/4dKEBGTuP4TAO25B1PIp8I9t5O1lVTocwoiIiIiIKjjHPxeRNG0x0jbvgfn6+qixYTaCOt/KY7y8hEOYBrjzyjGZuItKYl85bCuLfWWxrxy2lcW++bnSM5ASvx4p8zdCCQ5E1dkvIqzfvVBK2IpttcWaGuEgpj1FUdhVEPvKYVtZ7CuLfeWwrSz2zUt1OpG2YReSpi+By5YOy4g+iHjuMRhCg0u8LbbVHocwjfAU9dpTVRUulwsGg4FtBbCvHLaVxb6y2FcO28pi3/+T8eX3sE6IR/bREwh5qCsixw2DuW6NUm+PbbXHIUwDPGWnHKfTCcP//4wK0h77ymFbWewri33lsK2syt43+7eTsE6cj4xPvkVAm2aovWcRAm6O1WTblb2t1jiEERERERH5MMfFZCTPXo7U1dthqhuFqOVTEHxfJ75qpWMcwoiIiIiIfJArMwu2xVuQ8vYawKCgyoQRsAzuBcXfz9tLo2JwCCMiIiIi8iGqqiJ926dImrIIjn8uwjKoJyJGD4Qx0uLtpZGHOIRpgC/1yuF7j2Wxrxy2lcW+sthXDtvKqgx9M787jEvj45H1wzEEdeuAmu++Cb9G9cTvtzK0LU8cwjTCQUx7iqLwMykEsa8ctpXFvrLYVw7byqrofe1/nYN1ykJc/vBz+DW/HrW2zUVg+5blct8Vva03sKZGeIp67eU+6yTbao995bCtLPaVxb5y2FZWRe3rtKUhec5q2Ja8B2OVcFSPH4eQh++CUo6vTFXUtt7EIUwDPEW9HLvdDrPZ7O1lVFjsK4dtZbGvLPaVw7ayKlJf1e5A6qoPkDR7BdTMbES88DjCn4yDISjAK+upSG31gEMYEREREZFOqKqKjD37YZ20APYTpxH6aHdEjhkKU42q3l4aaYhDGBERERGRDmT98jsuTUhA5tc/IrBTa0QtnQT/G6/z9rJIAIcwIiIiIiIvcvxzEUn/XYK0TbthblwPNdbPQlCX23j8VQXGIYyIiIiIyAtc6RlISdiAlPkboQQFoOrMUQjrfz8UnomwwuN3WAOKovAvFQIURYGfHz/xXQr7ymFbWewri33lsK0sX+qrOp1I27gbSdOXwJWSBsvwhxH+3GMwhoV4e2kF8qW2voJDGBERERGRxpKSkvDtt9/itttuQ2RkZM7lGfu+h3V8ArKP/omQXl0QOW4YzPVqenGl5A0cwjTCzwnTnqqqcDqdMBqNbCuAfeWwrSz2lcW+cthWlt762mw2HDt2DE2aNEFkZCSy//gb1onzkfHxAQTc0hS1dy9EQKsbvb1Mj+itbUXAIUwD/JwwOS6XC0aj0dvLqLDYVw7bymJfWewrh21l6anv5SvZcDhduPyPFRcXbEXq6g9hqlMdUcsmI/j+O3xumNFT24qAQxgRERERkQaSkpJgs9lw+Uo23t1+EE32H4dx3h7YzEYYnu6DkKEPIaRGlLeXSTrAIYyIiIiISAPffvstjh09ihrHzqDnl8cQfDkLx2+siz9uvwGugAzE/vgDunfv7u1lkg5wCCMiIiIi0kBr/3DE7DwKHDmBU9c1wMe31EZq/eaI69YKwYF+sFgs3l4i6QSHMA342nt6fYmJn5Mhin3lsK0s9pXFvnLYVpa3+tr/PgfrlEW4/MFn8GvWGFW3voMrkcFwbf8Acd1aoWlsjFfWpSXuu9piTY1wENMeP39NFvvKYVtZ7CuLfeWwrSxv9HXa0pDy9hqkLN4CY6QF1eaNRWifu6EYDAg+eRImowHBgb7/+Vrcd7XHIUwjPEW99lRVhcvlgsFgYFsB7CuHbWWxryz2lcO2ssqzr2p3IHX1h0iatRxqZhYiRvVH+JNxMAQH5lzHYrEgNja2QrwFkfuu9jiEaYCnqJfjdDphMBi8vYwKi33lsK0s9pXFvnLYVpZ0X1VVkbH3AKwT5sN+4jRCH+mOyFeHwlSjar7rRkZGVqiTcHDf1RaHMCIiIiKiYmQd/gPWCfG48tWPCOzYClFLJsG/6XXeXhb5KA5hRERERESFcPx7CUn/XYK0jbtgvq4eaqybiaCubfm2PCoT3Q1hJ06cwNSpU3Ho0CEEBwejR48eeP755+HnV/hBjRcuXMDKlSuxf/9+nDp1CqGhobjlllvwwgsvoHbt2jnXO3jwIB5//PF8t+/evTvmzJkj8niIiIiIyPe4Ll9ByvyNSIlfDyXQH1VnjEJY//uhmHX36zP5IF3tRTabDQMGDECDBg0wb948nD9/HjNmzEBmZibGjx9f6O2OHj2KvXv34qGHHsJNN92E5ORkLFiwAA8//DB27NiByMjIPNefPn06oqOjc/4dERFRpnXzLyFy+N5jWewrh21lsa8s9pXDtrK06Ks6nUjbtBtJ05fCmWRD+PA+CH/+MRjDQjRYoe/ivqstXQ1hGzduxOXLlxEfH4/w8HAAVw8CnDRpEoYPH46oqKgCb9eqVSvs2rUrz+cX3Hzzzbjjjjuwbds2DB48OM/1GzdujGbNmmm6dg5i2lMUhZ9JIYh95bCtLPaVxb5y2FaWFn0zvvoB1vEJyD7yB0J6dkbka8NhrldToxX6Lu672tPVSLtv3z60bds2ZwADgG7dusHlcmH//v2F3i4sLCzfjlGjRg1ERkbiwoULUsulcsAzT8piXzlsK4t9ZbGvHLaVVdq+2X/8jX8eG4N/ej0PJdAftXctRNTiiRzAcuG+qy1dDWGJiYl53iYIXB2wqlWrhsTExBJt6+TJk7BarWjUqFG+rw0bNgxNmjRBx44dMXPmTGRmZpZp3aqqcscUoKoq7HY72wphXzlsK4t9ZbGvHLaVVZq+TmsKLr4yB6dvH4DsXxMRtWQSan80HwGtbxRcqe/hvqs9Xb2umJqairCwsHyXWywW2Gw2j7ejqiqmTp2K6tWr49577825PDQ0FEOHDsUtt9wCf39/fPvtt1i+fDkSExOxaNGiMq29oJ1SUZRCd1b32xeL+rrUbX1lu+7LfGW9el5TUX31uF49rqkk23X/Ycb9Ie5sqO12S9JXD+v1hTUV1VeP69XjmjzZbu7/eXu9ZbmtXrdbkr5qVjZsS99Dypw1UFUVkeOGIWxoLxgC/HPui9+bvF+7tq2e16u3NRVEV0OYVubNm4dvv/0WS5cuRVBQUM7lsbGxiI2Nzfl327ZtUb16dUyePBm//PILmjdvXur7tNvteY4LMxgMOW+RtNvt+a7vPtuj0+mEy+XK8zWTyQRFUeByueB0OvN8zb1dVVUL3K7ZbC50u0ajEUajEaqqwuFw5Pmaoig5t3U4HPl2IqntFvVYc/e89rGWZbvFfW/MZjMURSnxY/Vku0D5NnRvt7A1uZVnQ/d2i2rorf27qIYl2a77uu5GpWnI54jCH6t7OwaDIedAcT5HaPcc4b4PRVFy9kM+R2jzHOFyuXKuoyhKmRryOSL/Y3U4HHn6FtRQVVVc2f4lUqYvhfPsBYQN6IHQUY/BEGmBE4Dz/1+PzxF5t+u+rXt7Zd1uRX6OyP0HrKLoaggLCwtDWlpavsttNhssFotH29i8eTMSEhIwbdo0tG3bttjrd+vWDZMnT8aRI0fKNIS5d5bCvlaY3Dv3tXL/gnGt3DtDSbdb3G2LOvBSarsFPdbcPwRabje3grbr/j6W5bF643tT0seau6/eGnpr/y7Lz1zu7bqfnN0dStPQjc8RV+V+rO6+ue+HzxFXafFY3X1zb0tvDX31OcLd9trfGfgcUfbtGgyGnNsW9DuZ2WxG5vdHYR0fj6zvjyLo7naosnE2/BrXL/LVCz5H5L1tYb/v8jniKoPB4NEABuhsCIuOjs537FdaWhouXryY71ixguzduxcTJ07Es88+i969e0sts0CKohQYvbhvRFFfl7qtL25XYk383uh3u3pcU0m3m3u/ZUPtt+tp37KsqTJ/b6593tXbevW4Jk+3626rRV9v3VbP2y2or+P0v0iashDp2z6DX9PGqPn+2wi6vZUu1qv1baV/FyvN77tsWDBdDWEdO3bEwoUL8xwbtnv3bhgMBrRv377I2x48eBAvvPACHn74YYwcOdLj+/zoo48AoEynrC9JcCqZov4SQWXHvnLYVhb7ymJfOWwrK3dfZ2o6Ut5eA9viLTCEh6LaO2MQ2vceKIW8AkJF476rLV0NYXFxcVizZg1GjhyJ4cOH4/z585g1axbi4uLyfEbYgAEDcO7cOezduxcAcOLECYwcORINGjRAjx498NNPP+VcNzIyEvXq1QMAjB49GvXr10dsbGzOiTlWrlyJLl26lPlzwziIaY9NZbGvHLaVxb6y2FcO28py91UdDthWb0fSrGVQr2Qh/Nl+CB/5CAzBgV5eoe/ivqs9XQ1hFosFq1atwpQpUzBy5EgEBwejd+/eGDVqVJ7rXXuA388//4y0tDSkpaXhkUceyXPdnj17YsaMGQCufkjz9u3bsXz5ctjtdtSuXRsjRozAsGHDyrx2Tw/CI8+pqgqn0wmj0ci2AthXDtvKYl9Z7CuHbWW5XC6kf7wfKZMXwv7naYTGdUPkq0NhqlnN20vzedx3taeoJTmXIuVz+PBhqKqKZs2acafUmPvMNkWd9IRKj33lsK0s9pXFvnLYVk7WkT9hHR+PK1/9gIDbb0bVSU/Dv1ljby+rwuC+67nDhw8DKP5QJ129EkZERERE5CnHv5eQNH0p0jbshLlRXVRdMQWh3TsWeTY+Ij3gEEZEREREPsV1+QpSFmxEyrz1UAL8UXX68wjtfz8c4OEh5Bs4hBERERGRT1BdLqRt3oOkaYvhTLIhfFhvhD/fH0ZL6NXP+yrgA3qJ9IhDmAb4Fxc5hX2QHmmDfeWwrSz2lcW+cti29K58/SMujY9H9uE/ENzjTlR5fTjM9WvluQ77ymFbbXEI0wgHMe0pisIfeEHsK4dtZbGvLPaVw7alk/3nKVgnLUDG7q/h3/pG1P5oPgLa5D/pAfvKYVvtcQjTCE9Rrz1VVXO6sq322FcO28piX1nsK4dtS8ZpTUHyGythW7kNpprVELV4IoIfvLPQduwrh221xyFMAzzLvxyHw8FPaBfEvnLYVhb7ymJfOWxbPDUrG7al7yH5rdWAqiJy7BOwPNEbhgD/Ym/LvnLYVlscwoiIiIjI61RVxeUPv4B1ygI4zlxA2OMPIPLlQTBWjfD20og0xyGMiIiIiLwq84ejsL4ej8z/HUFQ17aouX4W/K5v4O1lEYnhEEZEREREXmE/9Q+Spi5C+tZP4Xfjdaj53hwEdWzt7WURieMQpgEeoCiHbWWxrxy2lcW+sthXDtte5UxNR8rba2Fb/C4M4aGo9vYYhMbdA6WMZ+BjXzlsqy0OYRrhjqk9RVF4AKgg9pXDtrLYVxb7ymFbQHU4kLpmO5JmLYd6+QrCn3kU4SMfgSEkqMzbZl85bKs9DmFEREREJEpVVWR88i2sExNg/+MUQvveg8ixT8BUs5q3l0bkFRzCNMLPCdOeqqpwOBwwmUxsK4B95bCtLPaVxb5yKmvbrKN/wjohAVe+/B4BHW5G1ILx8G9+veb3U1n7lge21R6HMA3wc8LksK0s9pXDtrLYVxb7yqlMbR3/XkLSjKVIW78T5ug6qLFmOoLubi/6S3xl6lve2FZbHMKIiIiISDOujEykLNiIlLnroQT4oep/n0PYgB5QzPy1k8iNPw1EREREVGaqy4X0dz+GddpiOK0psDzxECJGPQ6jJdTbSyPSHQ5hRERERFQmV/YfwqXx8cj+5XcEP/AfVHl9BMwNanl7WUS6xSFMAzxAUQ5PhyqLfeWwrSz2lcW+cipa2+wTp2CdtAAZu76Gf6tY1NqRgMBbm3ttPRWtr56wrbY4hGmEg5j22FQW+8phW1nsK4t95VSkts4kG5LfWAnbiq0w1ayG6osnIOTBzl59jBWpr96wrfY4hGmEp6jXnqqqcDqdMBqNbCuAfeWwrSz2lcW+cipCWzUrG7Zl7yP5rVVQnS5EvvoELMN6wxDg7+2lVYi+esW22uMQpgGeslOOy+WC0Wj09jIqLPaVw7ay2FcW+8rx1baqquLy9i9gnbIQjtPnEfb4/Yh4aTBM1SK8vbQ8fLWvL2BbbXEIIyIiIqJCZf54DNbX45H53WEEdbkNNdfOgF9MQ28vi8incQgjIiIionzsp/9F0rTFSH9vL/xio1Hz3bcQdMct3l4WUYXAIYyIiIioEktKSsK3336L2267DZGRkXClXUby22tgW/QuDJYQVJvzCkIf6QaFb0Uj0gyHMA3wAEU5fO+xLPaVw7ay2FcW+8rRY1ubzYZjx47hhsbXw/jhPiTNXAb18hWEP/0Iwp9+FIaQIG8v0WN67FtRsK22OIRphIOY9hRF4Q+8IPaVw7ay2FcW+8rRa9vLGVmo+sc/cD46Dpf+OoeQPvegyrgnYKpV3dtLKxG99q0I2FZ7HMI0wlPUa09V1ZyubKs99pXDtrLYVxb7ytFT26SkJNhsNmQcSUTS5KW4/a8z+KdeLUQseR2GmxrDHGBCpFdXWHJ66lvRsK32OIRpgKeol+NwOPgJ7YLYVw7bymJfWewrRy9t/7f7E5iWfYj6h/9GSFgQPu/WHH83iELwbz/B9OcviI2NRffu3b29zBLTS9+KiG21xSGMiIiIqJJwZWTCtmATrpu7FjAZkf10HLYFhMF1+S8grBHu69YKwYF+sFgs3l4qUYXGIYyIiIioglNdLqRv+RjWaUvgvJgEyxO9ETHqcRjDQ9Hn2G/Ysf0f3NetFZrGxnh7qUSVAocwIiIiogrsyv5DsE5IQNbPvyH4/jtQ5fURMDesnfP14EA/mIwGBAf6eXGVRJULhzAN8ABFOWwri33lsK0s9pXFvnLKs232idNImrwAl3d+Bf+bm6DW9gQE3tY83/UsFgtiY2MrxFsQue/KYVttcQjTCHdM7SmKwgNABbGvHLaVxb6y2FdOebV1Jqci+Y2VsC1/H6YaVVF90QSEPHgnFIOhwOtHRkb65Ek4rsV9Vw7bao9DGBEREVEFoGbbYVv+PpLfWAnV6ULkmKGwDHsYhkB/by+NiK7BIUwj/Jww7amqCofDAZPJxLYC2FcO28piX1nsK0eqraqquLzjS1gnL4Tj1D8Ie/x+RLw8BKZqEZrdhy/gviuHbbXHIUwD/JwwOWwri33lsK0s9pXFvnK0bpt56FdYX49H5sFfENT5NtRcMx1+NzTU9D58CfddOWyrLQ5hRERERD7GfuY8kqYtQvqWvfCLjUbNzW8i6D9tvL0sIvIQhzAiIiIiH+FKu4zkd9bCtmgzDKEhqPbWywh9tDsUo9HbSyOiEuAQRkRERKRzqsOB1HUfIXnmMrjSMxD+1CMIf+ZRGEKCvL00IioFDmEa4AGKckwm7qKS2FcO28piX1nsK6c0bTM+PYhLExNgP34SIX3uRpWxT8BUO0pgdb6P+64cttUWa2qEg5j2FEVhV0HsK4dtZbGvLPaVU9K2WcdOwDohAVe++B8C2rVA1CdL4X9TjOAKfRv3XTlsqz0OYRrhKeq1p6oqXC4XDAYD2wpgXzlsK4t9ZbGvHE/bOs5bkTRzGdLWfQRzg1qosWoagrrdzu9HMbjvymFb7XEI0wBP2SnH6XTCYDB4exkVFvvKYVtZ7CuLfeUU1dZ1JQu2BZuQPHctFLMJVSY/DcugB6H4mct5lb6L+64cttUWhzAiIiIiL1JdLqS/txfWqYvhvJgEy5BeiHhhAIwRYd5eGhEJ4RBGRERE5CVXDvwE64QEZP10HMH3dUKV10fAHF3H28siImEcwoiIiIjKWfaJ00iashCXP9oH/5ZNUOvDeAS2vcnbyyKicsIhTAM8QFEO33ssi33lsK0s9pXFvnJUWzouvbMWqcu3wlQ9EtUXvI6QXl2gsLkmuO/KYVttcQjTCAcx7SmKws+kEMS+cthWFvvKYl8ZarYdtuVbkfzmSqh2ByJfHgzL8D4wBPp7e2kVBvddOWyrPdbUCE9Rr73cZ51kW+2xrxy2lcW+sthXW6qq4vLOr5A0aQHsf59D6GP3IeLlwTBHVfH20ioc7rty2FZ7HMI0wFPUy7Hb7TCbeWpeKewrh21lsa8s9tVG5k/HYX09Hpnf/ozAO29F1MqpUK6rCxPbiuG+K4dttcUhjIiIiEhDjrPnYZ22GOnvfgy/JtGouekNBN15K1RVhd1u9/byiEgHOIQRERERacCVnoHkuetgW7ARhtBgVHvrJYQ+0h0Kj6UhomvwWYGIiIioDFSnE2nrP0LS9GVwpaXD8mQcIp7tB0NIkLeXRkQ6xSFMAzxAUQ7bymJfOWwri31lsa/nMj7/DtYJCcj+NREhD9+FKuOGwVQ7qtDrs60s9pXDttriEKYR7pjaUxSFB4AKYl85bCuLfWWxr2eyj5/EpQkJuPLZQQTcdhNq712CgBY3FHkbtpXFvnLYVnscwoiIiIg85LiQhORZy5G6ZjvM9WshauU0BHe/nX+MJaIS4RCmEX5OmPZUVYXD4YDJZGJbAewrh21lsa8s9i2Y60oWbIs2I/mdtVBMRlSZNBKWwT2h+Hn+6gDbymJfOWyrPQ5hGuDnhMlhW1nsK4dtZbGvLPb9P6rLhfStnyJp6iI4zlthGdwTES8OhDEirHTbY1tR7CuHbbXFIYyIiIioAFe+/QXW8fHIOvQrgu/thFrjR8AcXcfbyyKiCoBDGBEREVEu9pNnYZ28AJd3fAn/Fjeg1gfzENiuhbeXRUQVCIcwIiIiIgDOlDQkv7UKtqXvwVgtEtXnv4aQh7pCMRi8vTQiqmA4hGmAByjKMZm4i0piXzlsK4t9ZVW2vmq2HbYV25D85kqo2XZEvjQIlhF9YQj01/y+Klvb8sa+cthWW7qreeLECUydOhWHDh1CcHAwevTogeeffx5+fn6F3ubChQtYuXIl9u/fj1OnTiE0NBS33HILXnjhBdSuXTvPdc+fP4+pU6fi66+/htlsRteuXfHqq68iJCSkTOvmIKY9RVHYVRD7ymFbWewrqzL1VVUVGbu+gnXSAtj/Ooewx+5DxMuDYYqqInJ/lamtN7CvHLbVnq6GMJvNhgEDBqBBgwaYN28ezp8/jxkzZiAzMxPjx48v9HZHjx7F3r178dBDD+Gmm25CcnIyFixYgIcffhg7duxAZGQkAMBut2Po0KEAgDfffBOZmZmYOXMmXnzxRSxatKhMa+cp6rWnqipcLhcMBgPbCmBfOWwri31lVZa+WT//hkvj45F54CcE/qcNolZOg3+TaNH7rCxtvYV95bCt9nQ1hG3cuBGXL19GfHw8wsPDAQBOpxOTJk3C8OHDERUVVeDtWrVqhV27duV5mfTmm2/GHXfcgW3btmHw4MEAgD179uCPP/7Azp07ER199Yk2LCwMQ4YMwS+//ILmzZuXat08Zaccp9MJA9+LL4Z95bCtLPaVVZH7Os5dgHXaYqRv3gPzDQ1Rc+MbCOp8a7ndf0VuqwfsK4dttaWrkvv27UPbtm1zBjAA6NatG1wuF/bv31/o7cLCwvK9T7VGjRqIjIzEhQsX8mw/JiYmZwADgPbt2yM8PBxffvmldg+EiIiIdMWVnoGk6Utx6rZHceXz71DtzZdQ9/Pl5TqAERG56eqVsMTERDz00EN5LgsLC0O1atWQmJhYom2dPHkSVqsVjRo1yrP93AMYcPU9rg0bNizx9omIiEj/VKcTaet3ImnGUrhS02EZ0RcRz/aDITTY20sjokpMV0NYamoqwsLyfwK9xWKBzWbzeDuqqmLq1KmoXr067r333jzbDw0NLfP2C7vPaymKUuhbFd3vpy3q61K39ZXtui/zlfXqeU1F9dXjevW4ppJsV1XVnP+xofbbLUlfPazXF9ZUVF89rtfT22Z88T8kTUhA9q+JCHmoKyJfGw5T7eoF3r48Hmvu/2m53fK+rV63W5q+3lyvL62poLZ6Xq/e1lQQXQ1hWpk3bx6+/fZbLF26FEFBQeVyn3a7Pc+BigaDIectkna7Pd/13Wd7dDqdcLlceb5mMpmgKApcLhecTmeer7m3q6pqgds1m82FbtdoNMJoNEJVVTgcjjxfUxQl57YOhyPfTiS13aIeq6IoOe89vvaxlmW7xX1vzGYzFEUp8WP1ZLtA+TZ0b7ewNbn7lmdD93aLauit/buohiXZrqpePYDZ/e/SNORzROGP1d039/EJfI7Q7jnC3dfhcOTsh772HHHl2AkkT16AzM++g1+bpqj+UQKCWt0Io9GY89hK0lCr5wiXywWXy5XzO0NZGvI5Iv9jdTd29y2qIZ8jPHusuZ8jcrct63a9/Rwh+XtE7j9gFUVXQ1hYWBjS0tLyXW6z2WCxWDzaxubNm5GQkIBp06ahbdu2+bafnp5e4PZr1qxZukXj6jemqFPou78xBcm9c1/LYDAUegBk7p2hpNst7rZFfQ6E1HYLe6zuHzKtt+tW0HbdPzhleaze+N6U5bHqraG39u+y/Mxp3dCNzxFXlaWh3vbvwrbrxob/d52SPkc4LyYjefZypK7ZAVPdGohaPgVB93bM8wsRnyOuqojPEaX9XUxv+3dh23Xz1n8DAwICNN8uUPF+j/BkAAN0NoRFR0fnOzYrLS0NFy9ezHcsV0H27t2LiRMn4tlnn0Xv3r0L3P7vv/+e5zJVVXHy5Em0b9++bIsHCoxe3DeiqK9L3dZXtpv7ZW+JNVX27410X2/dVg/bLeztGmXdbnndVu/bLUnfsqypsn5vCno7jd7We+3XXZlZsC16F8lvr4FiNKDKxCdhGdwLil/hv9xpvSZPtnvt2+S02m5531av2y1N37KsqTJ9b4q7nh7Xq8c15aarsyN27NgRBw4cQGpqas5lu3fvhsFgKHZIOnjwIF544QU8/PDDGDlyZKHbP378OP7666+cy7755hukpKSgU6dOpV53Sd7/SSVT0EvJpB32lcO2sthXlq/0VVUVae9/gtNt+yFpxlKEPXov6n23EeEj+hY4gOmBr7T1Vewrh221pashLC4uDsHBwRg5ciS+/vprvPfee5g1axbi4uLyfEbYgAED0LVr15x/nzhxAiNHjkSDBg3Qo0cP/PTTTzn/O3XqVM717r77bjRu3BjPPPMMPv/8c+zcuRNjx47FHXfcUerPCCMiIiJZSUlJ2LlzJ5KSknIuu3LwF5y9ZzguDJ8E/+bXo+7Xq1F12rMwRnp2+AIRkTfp6u2IFosFq1atwpQpUzBy5EgEBwejd+/eGDVqVJ7rXXuA388//4y0tDSkpaXhkUceyXPdnj17YsaMGQCuvud06dKlmDp1Kl544QWYTCZ07doVY8eOlX9wREREVCo2mw3Hjh1DkyZNEGq7AuuUhbi8/Qv43xSDWtvmIrB9S28vkYioRBSV76Urk8OHD0NVVTRr1qxE7wOl4rnPbOM+Qw5pi33lsK0s9pWlx75Hjv2GPe++i3svqjC//xmM1SJRZdwTCOl9F5QiDvjXGz22rUjYVw7beu7w4cMAgGbNmhV5PV29EkZEREQEXH0Los1mw+W0DPw8fS3u+eog4FJhf/w+KAPuhT2quk8NYEREuXEI04CiKPyrgAD3KULZVgb7ymFbWewrSy99v/3mGyR/+Blu/Pwo2qRcxokmtfFjm+tgiHDC9NEOxMbGonv37l5dY0nppW1Fxb5y2FZ7HMJI1/jDLot95bCtLPaV5e2+WT//hhvn74DjuyNwto7Fh82bIjkgBQhrhLhurRAc6Ofx54fqjbfbVnTsK4dttcUhTCOefjo2eU5VVTidThiNRrYVwL5y2FYW+8ryZl/HPxeRNG0x0jbvgfn6+qixYTaCOt+Ku3/9HTu2f4D7urVC09iYcl2TlrjvymJfOWyrPQ5hGuC5TeS4XK5CP9Gcyo595bCtLPaVVd59XekZSIlfj5T5G6EEB6Lq7BcR1u9eKKarv6YEB/rBZDQgONCv3NYkhfuuLPaVw7ba4hBGREREXqE6nUjbsAtJ05fAZUuHZUQfRDz3GAyhwXmuZ7FYEBsb67NvQSQiuhaHMCIiIip3GV9+D+uEeGQfPYGQh7oictwwmOvWKPC6kZGRPncSDiKionAIIyIionKT/dtJWCfOR8Yn3yKgTTPU3rMIATfHentZRETlikOYBniAohy+91gW+8phW1nsK0uir+NiMpJnL0fq6u0w1Y1C1PIpCL6vU6X7byj3XVnsK4dttcUhTCOV7T8i5UFRFP7AC2JfOWwri31lad3XlZkF2+ItSHl7DWBQUGXCCFgG94Li7/sn2Sgp7ruy2FcO22qPQ5hGeIp67amqmtOVbbXHvnLYVhb7ytKqr6qqSN/2KZKmLILjn4uwDOqJiNEDYYysvCfX4L4ri33lsK32OIRpgKeol+NwOGA2m729jAqLfeWwrSz2lVXWvpnfHcal8fHI+uEYgrp1QM1334Rfo3oartB3cd+Vxb5y2FZbHMKIiIhIE/a/zsE6ZSEuf/g5/Jpfj1rb5iKwfUtvL4uISHc4hBEREVGZOG1pSJ6zGrYl78FYJRzV48ch5OG7oBgM3l4aEZEucQgjIiKiUlHtDqSu+gBJs1dAzcxGxAuPI/zJOBiCAry9NCIiXeMQpgEeoCjHwL+iimJfOWwri31lFddXVVVk7NkP66QFsJ84jdBHuyNyzFCYalQtpxX6Lu67sthXDttqi0OYRjiIaU9RFJhM3EWlsK8ctpXFvrKK65v1y++4NCEBmV//iMBOrRG1dBL8b7yuHFfou7jvymJfOWyrPdYkIiKiYjn+uYik/y5B2qbdMDeuhxrrZyGoy238IyQRUSlwCNNA7s9OIO2oqgq73Q6z2cy2AthXDtvKYl9Z1/Z1pWcgJWEDUuZvhBIUgKozRyGs//1Q+FfxEuO+K4t95bCt9vgMSkRERPmoTifSNu1B0vQlcKWkwTL8YYQ/9xiMYSHeXhoRkc/jEEZERER5ZH71I2xTFiH76J8I6dUFkeOGwVyvpreXRURUYXAIIyIiIgBA9h9/wzpxPjI+PgD/W5qi9u6FCGh1o7eXRURU4XAIIyIiquScl5KRNGsFUld/CFPt6qiyaDzCHuzMU1ITEQkp1RB2/Phx/PDDDzhx4gSSk5OhKAoiIiIQHR2Nm2++GU2aNNF6nbrGAxTlmM1mby+hQmNfOWwri3214crMgm3JFqTMWQMoCqqMH4GwwT2h+Pt5e2kVFvddWewrh2215fEQZrVasX79emzbtg3nzp2Dqqowm82wWCxQVRWpqamw2+1QFAU1a9ZEz5498cgjj6Bq1crxwY0cxLTHprLYVw7bymLfslNVFZe3fQbr1EVwnLsAy8AHETF6IIxVwr29tAqN+64s9pXDttrzaAibPXs21q9fj+DgYNxzzz1o164dbrzxRkRFReW53vnz53H06FHs378fmzdvxvLly/HYY4/hxRdfFFm8nvAU9dpTVRVOpxNGo5FtBbCvHLaVxb5lk/m/I7g0Ph5Z3x9F0D0dUHPTG/C7rl7O19lXDtvKYl85bKs9j4aw77//HrNnz0bnzp2LDB8VFYWoqCjceeedeO211/Dpp59i6dKlmi1Wr1RV9fYSKiyXywWj0ejtZVRY7CuHbWWxb8nZ/z4H65RFuPzBZ/Br1hi1tr6DwA43F3hd9pXDtrLYVw7basujIWzTpk0l3rCiKOjSpQu6dOlS4tsSERGRNpy2NKS8vQYpi7fAGGlBtXljEdrnbig86QYRkdfw7IhEREQVkGp3IHX1h0iatRxqZhYiRvVH+JNxMAQHentpRESVHocwIiKiCkRVVWTsPQDrhPmwnziN0Ee6I/LVoTDVqBwnyiIi8gUlGsL+/PNPLF68GCdOnEBERATuvfdePPjgg/mOE/vwww/xyiuv4Ndff9V0sXrFAxTl8L3HsthXDtvKYt+CZR3+A9YJ8bjy1Y8I7NgKUUsmwb/pdSXeDvvKYVtZ7CuHbbXl8RvC//rrLzz88MPYvXs3VFXFH3/8gVdffRX9+vXDxYsXJdfoEziIaU9RFJ6FRxD7ymFbWeybn+PfS7jw7HSc6TwEjn+tqLFuJmpumVOqAYx95bCtLPaVw7ba8/iVsLfffhvBwcFYt24d6tevDwD44IMPMGXKFPTt2xdLly5FdHS02EL1jqeo156qqjld2VZ77CuHbWWx7/9xXb6ClPkbkRK/HkqgP6rOGIWw/vdDMZf+aAP2lcO2sthXDttqz+NXwn7++Wc89thjOQMYAPTo0QObNm2CwWDAo48+il9++UVkkXrHU9TLcTgc3l5Chca+cthWVmXvqzqdSF3/EU7d9iiS314Dy5CHUO+7jbAM7lmmAcytsveVxLay2FcO22rL4yEsJSUFVavmP6i3UaNG2LhxI2rUqIEBAwbgq6++0nSBRERE9H8yvvoBZ7o8gYvPzUBg25tQ75t1qDJ+BIxhId5eGhERecjjP5fVrl0bv/32W4Ffq1q1KtauXYvhw4fjySefRMeOHTVbIBEREQHZf/wN66QFyNizH/63NEXtXQsR0PpGby+LiIhKweNXwtq0aYPdu3cX+lJkSEgIVqxYgdtvvx2fffaZZgskIiKqzJzWFFx8ZQ5O3z4A2ccTEbVkEmp/NJ8DGBGRD/P4lbCePXvi0qVLOHLkCFq0aFHgdfz8/JCQkIDp06fj+PHjWq1R93iAohy2lcW+cthWVmXoq2ZlI2XJFqTMWQMAqPL6cFiGPgTF30/8vitDX29hW1nsK4dttaWoPKtEmRw+fBgA0KxZMy+vhIiIKgJVVXH5g89hnboQjjMXEDawByJfGgRjlXBvL42IiIrh6WxQ9lMoERERkSYyvz+KS+PjkfW/Iwi6uz1qbpgNv8b1i78hERH5lFIPYVeuXEFgYKCWa/Fp/Jww7amqCofDAZPJxLYC2FcO28qqiH3tp/5B0pSFSN/2GfyaNkbN999G0O2tvLKWithXL9hWFvvKYVvteXxijtwuXbqEfv36ab0Wn8V3dMphW1nsK4dtZVWUvs7UdFgnL8Dpdo/hyjc/o9rcV1HnkyVeG8DcKkpfPWJbWewrh221VeJXwk6ePImhQ4cW+JlhREREVDzV4UDq6u1ImrUM6pUshD/3GMKfioMhmO8wISKqDEo0hP3www946qmnUL9+fSxbtkxqTURERBWSqqrI2PsNrBPnw/7nKYTGdUPk2CdgqsE/bBIRVSYeD2G7d+/Gyy+/jOuvvx7Lli1DSEiI5LqIiIgqlKwjf8I6IR5X9v2AwNtvRtSiCfBv1tjbyyIiIi/weAgbNWoUYmNjsWLFCoSGhkquyefwAEU5JhNP4CmJfeWwrSxf6uv49xKSpi9F2oadMF9XDzXWzUBQ13a6/m+HL/X1NWwri33lsK22PK4ZEBCAS5cuISUlhUNYAfT8H1NfpSgKuwpiXzlsK8tX+rouX0HKgo1ImbceSqA/qk5/HmGPPwDFrO9fZHylry9iW1nsK4dttefx2RHXrVsHp9OJxx9/HGfOnJFck0/iGWO0p6oqnE4n2wphXzlsK0vvfVWXC6kbd+HUbY8iec4aWAb3RL2DG2AZ0kv3Axig/76+jG1lsa8cttWex0NYbGwsNm3ahMDAQPTv3x9nz56VXJdP4Q4px+l0ensJFRr7ymFbWXrte+XrH3Gmy1BcfOa/CLytOeodWIsqE56C0eJb7yDRa9+KgG1lsa8cttVWiT4nrHbt2ti4cSNq1aqF/v37S62JiIjIp2T/eQr/9H8V53o+B8XfD7V3LkDUkkkw16/l7aUREZEOlfjDmsPCwrBixQrcdNNNEushIiLyGU5rCi69+jZO3/44so+dQNTiiai9cwECbmnq7aUREZGOlerN6X5+fpgzZ47WayEiIvIJalY2bEvfQ/JbqwFVReS4YbAMfQiGAH9vL42IiHyA/o8Q9gE8W4wco9Ho7SVUaOwrh21leauvqqq4/OEXsE5ZAMeZCwgb0AORLw2EsWqEV9YjhfuvHLaVxb5y2FZbmg5hp0+fRnZ2Nho1aqTlZn0CBzHtKYrCH3hB7CuHbWV5q2/mD0dhfT0emf87gqC72qHm+lnwu75Bua9DGvdfOWwri33lsK32SjWErV69GocOHcrzlsRXX30V27ZtAwA0adIES5YsQZUqVTRZpC9QVZWDmMZyn3WSbbXHvnLYVlZ597Wf+gdJUxchfeun8LvxOtR8bw6COrYWv19v4f4rh21lsa8cttVeiU/MAQDvvvtungHrq6++wtatW9GnTx+89tprOHPmDOLj4zVbpN7xFPVy7Ha7t5dQobGvHLaVVR59nanpsE5eiNPtHsOVAz+h2jtjUOfTpRV6AHPj/iuHbWWxrxy21VapXgk7d+5cnrcc7tq1C3Xq1MGkSZMAAJcuXcIHH3ygzQqJiIjKkepwIHXNdiTNWg718hWEP9sP4U/FwRAS5O2lERFRBVGqIezaV37279+Pzp075/y7du3auHTpUtlWRkREVI5UVUXGJ9/COjEB9j9OIbTvPYgc+wRMNat5e2lERFTBlOrtiA0aNMAnn3wC4OpbES9cuICOHTvmfP3ff/9FWFiYNiskIiISlnX0T/zz8Av499GXYaxeBXU+WYrq88ZyACMiIhGleiVsyJAhePHFF3HLLbfgypUraNSoETp06JDz9YMHD+KGG27QbJF6xwMU5bCtLPaVw7aytOrr+PcSkmYsRdr6nTBH10GNNdMRdHf7Sv/9q+yPXxLbymJfOWyrrVINYffeey/Cw8Px5ZdfIiwsDI8++ihMpqubSklJgcViQY8ePTRdqN5xx9Seoigwm83eXkaFxb5y2FaWFn1dGZlIWbARKXPXQwnwQ9X/PoewAT2gmPnxmdx/5bCtLPaVw7baU1Se2q9MDh8+DABo1qyZl1dCRETFUV0upL/7MazTFsNpTYHliYcQMepxGC2h3l4aERFVAJ7OBqU6JkzSiRMnMGjQILRo0QLt27fHrFmzkJ2dXezt1q1bh+HDh+O2225DTEwMdu/ene86Bw8eRExMTL7/jRo1qszr5iyrPVVVYbfb2VYI+8phW1ml7Xtl/yGc6foELjw9DQG3NEW9/WtRdeJIDmDX4P4rh21lsa8cttWeR++76N69O4YNG4bu3bvDz8/Pow1nZ2dj+/btWLZsGXbu3OnRbWw2GwYMGIAGDRpg3rx5OH/+PGbMmIHMzEyMHz++yNu6T4nfqVOnnA+NLsz06dMRHR2d8++IiAiP1lcY7pBy2FYW+8phW1kl6Zt94hSskxYgY9fX8G8Vi1o7EhB4a3PB1fk+7r9y2FYW+8phW215NIT17NkT06dPx7Rp03DnnXeibdu2uPHGG1GnTh0EBgYCADIyMnDmzBkcOXIEBw4cwOeffw6z2YwhQ4Z4vJiNGzfi8uXLiI+PR3h4OADA6XRi0qRJGD58OKKiooq8rcFgwJkzZ4odwho3bsy3DxIRVXDOJBuS31gJ24qtMNWshuqLJyDkwc48hpeIiLzOoyHsiSeewCOPPIItW7Zg69at+OCDD3L+I2Y0GgFcHZaAq1Ny48aN8cwzz6B3794ICQnxeDH79u1D27ZtcwYwAOjWrRsmTJiA/fv3o1evXoXe1mDQ3TsriYjIC9SsbNiWvY/kt1ZBdboQ+eoTsAzrDUOAv7eXRkREBKAEZ0cMCQnBwIEDMXDgQJw5cwaHDh1CYmIiUlJSAADh4eGIjo5GixYtULdu3VItJjExEQ899FCey8LCwlCtWjUkJiaWapsFGTZsGFJSUlCtWjXce++9eO655xAQEKDZ9omIqPypqorL27+AdcpCOE6fR9jj9yPipcEwVSvbW86JiIi0Vqpz8dapUwd16tTRei1ITU0t8EOeLRYLbDZbmbcfGhqKoUOH4pZbboG/vz++/fZbLF++HImJiVi0aFGpt6soSoHvky3scvfXgMLfXyt5W1/ZrqqqMJlMPrNePa+pqL56XK8e11SS7aqqCqPRCFVV2VBguwX1zfzxGKzjE5D13WEEdrkNNdbMgP8NDXWxXi1vWx7bvbavHterxzV5st3cbb293rLcVq/bLU1fb67Xl9ZUUFs9r1dvaypIpfpAlNjYWMTGxub8u23btqhevTomT56MX375Bc2bl/5AbYfDkeffBoMh5xdcu92e7/ruE5w4nU64XK48XzOZTFAUBS6XK+dtntduV1XVArfr/gyHgrZrNBpzfoCuXW/uz39wOBz5diKp7Zb2sWqxXaDg743ZbIaiKCV+rJ5sFyjfhu7tFrSm3Nst6IxHUg3d2y2qobf276IalvZ7U9h2+Rzh2XaLe6zOsxeQNHUR0t//BOYmDVFtw0wEdGyd57gvPkeU/jnC/Vb/8mxY2Z4jStuQzxGeP1ag6P8G8jmi+OcIRVHKdf/25eeI3H/AKoquhrCwsDCkpaXlu9xms8FisYjcZ7du3TB58mQcOXKkTEOYe4coSFEfbpf7P4DXMhgMhR7rlntnKOl2i7ut+4eiPLdb0GNVVRVOp1Pz7eZW0Hbd38eyPFZvfG9K+ljdfXM/EWqxXaDsDb21f5flZy73dlVVzfPkXpqGbnyOuCr3Y1VVFQ5bGlLmbUDq4ndhsISg6pyXERrXDUoB9623/buw7bp5+znCvf/mvq7eGvrqc0Tutrl/Z+BzRNm3625aUF+g6IZ6278L266bN/ZvVVVzvl7Q77t6a+jN5whPBjBAZ0NYdHR0vmO/0tLScPHixTynlNcb98RbUPTivhFFfV3qtr60XYfDkfPXEK3XxO/N1b7FPWGwYem263K5cp7g2VC77aoOB9LW7EDSzGVQM64g/OlHEP70ozCEBOlyvb6ypqL2Xy23q4fbenu77ra5r8+G2m23pH3LsqbK9r0pqK0W2y1KRWp4LV0NYR07dsTChQvzHBu2e/duGAwGtG/fXuQ+P/roIwDFf6o1ERF5j6qqyPj0IKwTE2D/7S8E9e6Kqq8Nh7l24R9dQkREpFe6GsLi4uKwZs0ajBw5EsOHD8f58+cxa9YsxMXF5fmMsAEDBuDcuXPYu3dvzmWHDx/G2bNnkZSUBAD4+eefAQCRkZFo06YNAGD06NGoX78+YmNjc07MsXLlSnTp0oVDGBGRTmUdOwHrhARc+eJ/CGjfEtUTxsHQJBqmIt4uQkREpGe6GsIsFgtWrVqFKVOmYOTIkQgODkbv3r0xatSoPNcr6AC/devWYevWrTn/Xr58OQCgTZs2WLNmDYCrH9K8fft2LF++HHa7HbVr18aIESMwbNgw4UdGREQl5ThvRdKMpUhbvxPmBrVQY/V/EXRPBwAFH8hNRETkKxS1JOdS9FBmZiaSkpJQq1YtrTetO4cPHwYANG3atETvA6XiuU8cUdj7j6ls2FcO25aNKyMTtgWbkDx3HRR/MyJGD4JlYA8ofv934gj2lcO+cthWFvvKYVvPuWeD4t5lV/ipSgrwzTffoF+/frjtttvQrVs3xMfH48qVK/mu9/HHH6Nz584l2bTP4w6pPUVRijzrJJUN+8ph29JRXS6kbd6NU237IenNlQgb2AP1vtuI8GG9cwYwgH2lsa8ctpXFvnLYVnseD2FHjhzB0KFDcfLkSbRp0wbh4eFISEjAgw8+iBMnTkiu0ScIvKBY6bk/eJFtZbCvHLYtuSv7D+HsXcNwYeQ0BLSKRb39a1F10kgYw0PzXZd9ZbGvHLaVxb5y2FZ7Hh8TNm/ePNSpUwebNm1CeHg4AOD777/Hiy++iEceeQTz589H69atpdapa9wh5djt9iI/q4HKhn3lsK1nsk+cRtLkBbi88yv439wEtbYnIPC24j+zkX1lsa8ctpXFvnLYVlsevxJ29OhR9O3bN2cAA4DWrVtj69atqFevHoYMGYJPPvlEYo1ERFTBOJNTcWncXJzu0B9Zv/yO6osmoPauhR4NYERERL7O4yEsIyMDoaH53xYSGRmJNWvWoHXr1nj++efx7rvvarpAIiKqONRsO1IWbsKpW/oidf1HiBwzFHUPrENory5QDCU6TJmIiMhnefxfvHr16uGXX34p8GuBgYFYuHAhunbtivHjx2PDhg2aLZCIiHyfqqpI3/4FTrXvD+uE+Qjp2Rn1vtuIiOcegyHQ39vLIyIiKlceD2Ht2rXDxx9/XODZEAHAbDbjrbfeQlxcHA4dOqTZAomIyLdlHvoV5+5/GucHvw6/RnVR98uVqDZ7NEzVIry9NCIiIq/w+MQcvXv3hqqqOHnyJGJjYwu8jqIomDBhAurXr4/ffvtNs0XqnaIoPGWnAEVRYDab2VYI+8ph26vsZ84jadoipG/ZC7/YaNTc/CaC/tOmzNtlX1nsK4dtZbGvHLbVnsiHNVcmnn4gGxFRZeFKu4zkd9bCtmgzDKEhiHx1KEIf7Q7FaPT20oiIiER5Oht4/EoYFU1VVf51QGP8dHZZ7CunsrZVHQ6krvsIyTOXwZWegfCnHkH4M4/CEBKk7f1U0r7lhX3lsK0s9pXDttor9RB26dIlVK1aVcu1+Cy+mCjH5XLByL+ei2FfOZWtbcanB3FpYgLsx08ipM/dqDL2CZhqR4ndX2XrW97YVw7bymJfOWyrrVKdD/jEiRPo06eP1mshIiIfk3XsBM49/AL+iRsNY6QFdT5ZiqiE10QHMCIiIl9X4lfCvv/+e4wcORKtWrWSWA8REfkAx3krkmYuQ9q6j2BuUAs1Vk1DULfb+TYVIiIiD5RoCNu1axfGjBmDtm3bYu7cuVJrIiIinXJdyYJtwSYkz10LxWxClclPwzLoQSh+Zm8vjYiIyGd4PIQtX74cs2fPRqdOnRAfHw+Tief0cONffuVwP5PFvnIqWlvV5UL6e3thnboYzotJsAzphYgXBsAYEeaV9VS0vnrDvnLYVhb7ymFbbXlcc9asWbjlllswb948fhMKwEFMe/z8NVnsK6eitb1y4CdYJyQg66fjCL6vE6q8PgLm6DpeW09F66s37CuHbWWxrxy21Z7H01TVqlVx/Phx/Prrr2jevLnkmnwST1GvPVVVc7qyrfbYV05FaZt94jSSpizE5Y/2wb9lE9T6MB6BbW/y9rIqTF+9Yl85bCuLfeWwrfY8Pjvi5s2bUb16dQwZMgRHjhyRXJPP4Snq5TgcDm8voUJjXzm+3NaZnIpLr83F6dsfR9ZPx1F9weuovXuhLgYwN1/u6wvYVw7bymJfOWyrLY+HsFq1amHDhg2IiYnB4MGDcfToUcl1ERFROVOz7UhZuBmn2sQhde0ORL48GHW/WY/Q3ndBMZTqE02IiIioACX6r2pYWBiWL1+O9u3bY/DgwVJrIiKicqSqKtI/2ofTHR6HdUICQnrciXrfbUTE8/1hCPT39vKIiIgqnBKfYcPPzw9z5szBrFmzJNZDRETlKPOn47C+Ho/Mb39G4J23ImrVNPg3ifb2soiIiCq0Up/m8OWXX9ZyHT6NByjKMfAtUKLYV47e2zrOnod12mKkv/sx/JpEo+amNxB0563eXpbH9N7X17GvHLaVxb5y2FZbHg9h//vf/9CoUSNERkZKrsdncRDTnqIo/DgEQewrR89tXekZSJ67DrYFG2EIDUa1t15C6CPdoeh0vQXRc9+KgH3lsK0s9pXDttrzeKR9/PHHsX//fsm1EOXDM0/KYl85emurOhxIXf0hTrV5BLYFG2F5Mg71vtuIsP4P+NQA5qa3vhUN+8phW1nsK4dtteXxf3kZvnC5PzuBtKOqKux2O8xmM9sKYF85emub8dlBWCfOR/aviQh5+C5UGTcMptpR3l5Wqemtb0XDvnLYVhb7ymFb7fnenz+JiMgj2cdP4tKEBFz57CACbrsJtfcuQUCLG7y9LCIiokqvREMYJ18iIv1zXEhC8sxlSF27A+b6tRC1chqCu9/O53AiIiKdKNEQ9tJLL+Gll17y6LqKouDYsWOlWhQREZWc60oWbAs3IfmdtVDMJlSZNBKWwT2h+Jm9vTQiIiLKpURDWLt27dCgQQOhpRARUWmoLhfS3/8ESVMXwXEhCZbBPRHx4kAYI8K8vTQiIiIqQImGsAcffBD333+/1Fp8Ft/iI8ds5l/wJbGvnPJqe+Wbn2GdkICsQ78i+N5OqDV+BMzRdcrlvr2J+64s9pXDtrLYVw7baosn5tAIBzHtsaks9pVTHm3tiWdgnbwQlz/6Ev4tbkCtD+YhsF0L8fvVA+67sthXDtvKYl85bKs9DmEa4SnqtaeqKpxOJ4xGI9sKYF85km2dKWlIfnMlbMveh7FaJKrPfw0hD3WFYvD4Yx99HvddWewrh21lsa8cttUehzAN8DPU5LhcLhiNRm8vo8JiXzlat1Wz7bCt2IbkN1ZAtTsQ+dIgWEb0hSHQX7P78CXcd2Wxrxy2lcW+cthWWx4PYcePH5dcBxERFUBVVWTs+grWiQtg//scwh67DxEvD4Ypqoq3l0ZERESlxFfCiIh0Kuvn33Dp9XnI/OZnBP6nDaJWTYN/k2hvL4uIiIjKiEMYEZHOOM6eh/W/S5C+eQ/MNzREzY1vIKjzrd5eFhEREWmEQ5gGeICiHL73WBb7yilNW1d6BlLmrUfK/A0whAaj2psvIfTR7lBMfKq+FvddWewrh21lsa8cttUW/8uuEQ5i2lMUhT/wgthXTknbqk4n0tZ/hKTpy+BKS4dlRF9EPNsPhtBgwVX6Lu67sthXDtvKYl85bKs9DmEa4Snqtaeqak5XttUe+8opSduMz7+DdWICso8lIqR3V0SOGw5znahyWqlv4r4ri33lsK0s9pXDttrjEKYBnqJejsPh4Ce0C2JfOcW1zT5+EtaJ85Hx6bcIuLU5an+8GAEtm5TjCn0b911Z7CuHbWWxrxy21RaHMCKicuS4mIzkWcuQuno7TPVqImrFVATf25F/WSQiIqpEOIQREZUD15Us2BZtRvI7a6EYDagy6SlYBveC4se/KhIREVU2HMKIiASpLhfSt36KpKmL4Pj3EiyDeyHixQEwRlq8vTQiIiLyEg5hGuDbiOSwrSz2laMoCjIPHoZ1QjyyfvwVwd1vR80tb8GvUT1vL61C4L4ri33lsK0s9pXDttriEKYR7pjaUxSFB4AKYl85jr/OwTp5AS7v+BL+N8Wg1ra5CGzf0tvLqjC478piXzlsK4t95bCt9jiEERFpxJmShuS3VsG29D0Yq0WiesI4hPS+C4rB4O2lERERkY5wCNMIPydMe6qqwuFwwGQysa0A9tWOanfAtmIbkt9YATXLjogXByB46EPwCwthWwHcd2Wxrxy2lcW+cthWexzCNMDPCZPDtrLYt2xUVUXG7q9hnTgf9r/OIbTfvYh8ZQiM1SNht9u9vbwKjfuuLPaVw7ay2FcO22qLQxgRUSlk/fwbLo2PR+aBnxB4xy2IWjEV/rGNAPA/VERERFQ0DmFERCXgOHcB1mlLkP7uHpivr48aG2YjqPOtfHsGEREReYxDGBGRB1zpGUiJX4+U+RuhBAei6uwXEdbvXigmPo0SERFRyfC3Bw3wL+ByeDpUWexbPNXpRNqGXUiavgQuWzosI/og4rnHYAgNLvJ2bCuLfWWxrxy2lcW+cthWWxzCNMJBTHtsKot9i5fx5fewTohH9tETCHmoKyLHDYO5bo1ib8e2sthXFvvKYVtZ7CuHbbXHIUwjPEW99lRVhcvlgsFgYFsB7Fu47N9OwjpxPjI++RYBbZqh9p5FCLg51uPbs60s9pXFvnLYVhb7ymFb7XEI0wDPhCbH6XTCwA+6FcO+eTkuJiN59nKkrt4OU90oRC2fguD7OpXqPzhsK4t9ZbGvHLaVxb5y2FZbHMKIqNJzZWbBtngLUt5eAxgUVJkwApbBvaD4+3l7aURERFQBcQgjokpLVVWkb/sUSVMWwfHPRVgG9UTE6IEwRlq8vTQiIiKqwDiEEVGllPndYVwaH4+sH44hqFsH1Hz3Tfg1quftZREREVElwCFMAzxAUY7RaPT2Eiq0ytjX/tc5WKcsxOUPP4df8+tRa9tcBLZvqfn9VMa25Yl9ZbGvHLaVxb5y2FZbHMI0wkFMe4qi8AdeUGXr67SlIXnOatiWvAdjlXBUjx+HkIfvgiJwkHFla1ve2FcW+8phW1nsK4dttcchTCM8Rb32VFXN6cq22qssfVW7A6mrPkDS7BVQM7MR8cLjCH8yDoagALn7rCRtvYV9ZbGvHLaVxb5y2FZ7HMI0wFPUy3E4HPyEdkEVua+qqsjYsx/WSQtgP3EaoY92R+SYoTDVqFou91+R2+oB+8piXzlsK4t95bCttjiEEVGFk/XL77g0IQGZX/+IwE6tEbV0EvxvvM7byyIiIiICAOjuE9dOnDiBQYMGoUWLFmjfvj1mzZqF7OzsYm+3bt06DB8+HLfddhtiYmKwe/fuAq93/vx5PPPMM2jZsiXatGmDcePGIT09XeuHQURe4PjnIi4881+c6TIUzgtW1Fg/CzXffYsDGBEREemKrl4Js9lsGDBgABo0aIB58+bh/PnzmDFjBjIzMzF+/Pgib/vBBx8AADp16oRt27YVeB273Y6hQ4cCAN58801kZmZi5syZePHFF7Fo0SJNHwsRlR9XegZSEjYgZf5GKEEBqDpzFML63w/FpKunOCIiIiIAOhvCNm7ciMuXLyM+Ph7h4eEAAKfTiUmTJmH48OGIiooq8rYGgwFnzpwpdAjbs2cP/vjjD+zcuRPR0dEAgLCwMAwZMgS//PILmjdvXqp18wBFOWwry9f7qk4n0jbuRtL0JXClpMEy/GGEP/cYjGEh3l6az7fVO/aVxb5y2FYW+8phW23p6u2I+/btQ9u2bXMGMADo1q0bXC4X9u/fX+RtDR6cZnrfvn2IiYnJGcAAoH379ggPD8eXX35Z6nUD3DElKIoCs9nMtkJ8vW/Gvu9xpvNQXHx+BgLbt0TdA2tR5fURuhnAfLmt3rGvLPaVw7ay2FcO22pPV6+EJSYm4qGHHspzWVhYGKpVq4bExERNtp97AAOu7lQNGzbUZPtEJC/7j79hnTgfGR8fQMAtTVF790IEtLrR28siIiIi8piuhrDU1FSEhYXlu9xiscBms2my/dDQUM23r6oqXC5Xvr8OKIpS6Onr3dct6utSt/WV7aqqCqfTCaPRWOBfXvS2Xj2vqai+piKOm9JTQ+elFCTPXo7U1dthql0d1ZdNRvB9nfJsSy/fG1VV4XA4YDKZYDAYdNPQ09vqfbsl6auH9frCmorqq8f16nFNnmw3d1v35Wyo3XZL09eb6/WlNblcrnxt9bxeva2pILoawnyZ3W7Ps1MaDIacX27tdnu+6/v5+QG4esyby+XK8zX3Du5yueB0OvN8zb1dVVUL3K778xsK2q7RaITRaMx5ksrN/TIzcPVzIK7diaS2W9Rjzb0zX/tYy7Ld4r437pfbS/pYPdkuUL4N3dstbE1u5dnQvd2iGubev9XMbKQtfx+pc9dDURRUGT8CwQMegGo25bm9Fvt3UQ1Lsl33dd3tS9OQzxGFP1b3dhRFyRkS+Byh3XNE7vtw74d6fo64druA3P5d1ucI9y+yqnr1Q2/L0pDPEfkfq8PhyNO3qIZ8jvDssV77HOFuW9btVuTniNyNiqKrISwsLAxpaWn5LrfZbLBYLJpsv6DT0dtsNtSsWbNM2y7qfbJFfbBd7p37WgaDodBj3XLvDCXdbnG3LeqVEantFvRYc/8QaLnd3Ararvv7WJbH6o3vTUkfa+6+emvo/uXk8gefIWnqIjjOXUTYwAcR8eIAmKpGFPmXJqmGJdmue33uDqVp6MbniKtyP1Z339z3w+eIq7R4rO6+ubelt4be2r/L+hzhbnvt7wx8jij7dg0GQ85tC/qdjM8RV5XlsZpMpkJ/39VbQ28+R3gygAE6G8Kio6PzHZuVlpaGixcv5juWq7Tb//333/NcpqoqTp48ifbt25dp24qiFBi9uG9EUV+Xuq0vbldiTfze6He7mf87gkvj45H1/VEE3dMBNTe9Cb/r6nl1TSXdbu79lvuh9tv1tG9Z1lSZvzfXPu/qbb16XJOn23W31aKvt26r5+2WtG9Z1lTZvjcFtdViu0WpSA2vpauzI3bs2BEHDhxAampqzmW7d++GwWAo85Dk3v7x48fx119/5Vz2zTffICUlBZ06dSrz9omobOx/n8O/QyfgbPcnoWZlo9bWd1BzzfQ8AxgRERGRr9PVK2FxcXFYs2YNRo4cieHDh+P8+fOYNWsW4uLi8nxG2IABA3Du3Dns3bs357LDhw/j7NmzSEpKAgD8/PPPAIDIyEi0adMGAHD33Xdj0aJFeOaZZ/DCCy/gypUrmDVrFu64445Sf0YYULKpl0qmqJd8qez00tdpS0PK22uQsngLjJEWVJs3FqF97obiwUdP6JVe2lZU7CuLfeWwrSz2lcO22tJVTYvFglWrVmHKlCkYOXIkgoOD0bt3b4waNSrP9Qo6wG/dunXYunVrzr+XL18OAGjTpg3WrFkD4Op7TpcuXYqpU6fihRdegMlkQteuXTF27Ngyr52DmPaKexsilY0e+qp2B1JXf4ikWcuhZmYhYlR/hD8ZB0NwoFfXVVZ6aFuRsa8s9pXDtrLYVw7bak9RS3IuRcrn8OHDAICmTZty59SYql499X9JDnIkz3mzr6qqyNh7ANYJ82E/cRqhj3RH5KtDYapRtVzXIYX7riz2lcW+cthWFvvKYVvPuWeDZs2aFXk9Xb0S5qs4x8pxOp1FnlGHysYbfbMO/wHrhHhc+epHBHZshaglk+Df9LpyXUN54L4ri31lsa8ctpXFvnLYVlscwoioXDj+vYSk/y5B2sZdMF9XDzXWzURQ17b8ixoRERFVOhzCiEiU6/IVpMzfiJT49VAC/VF1xiiE9b8fiplPP0RERFQ58bcgIhKhOp1I27QbSdOXwplkQ/jwPgh//jEYw0K8vTQiIiIir+IQpgG+nUoO33ssS6pvxlc/wDo+AdlH/kBIz86IfG04zPVqityXXnHflcW+sthXDtvKYl85bKstDmEa4SCmPUVR+JkUgiT6Zv/xN6yTFiBjz37439IUtXctREDrGzW9D1/AfVcW+8piXzlsK4t95bCt9lhTI6qqchDTWO6zTrKt9rTs67SmIGnWCqSu+gCmOtURtWQSgnv8p9J+37jvymJfWewrh21lsa8cttUehzAN8BT1cux2O8xms7eXUWGVta+alY2UJVuQMufqB6JXeX04LEMfguLvp9USfRb3XVnsK4t95bCtLPaVw7ba4hBGRCWmqiouf/A5rFMXwnHmAsIG9kDkS4NgrBLu7aURERER6R6HMCIqkczvj+LS+Hhk/e8Igu5uj5obZsOvcX1vL4uIiIjIZ3AIIyKP2E/9g6QpC5G+7TP4NW2Mmu+/jaDbW3l7WUREREQ+h0MYERXJmZqOlLfXwLZ4Cwzhoag291WE9rkbitHo7aURERER+SQOYRpQFIVnihGgKAr8/HiCBynF9VUdDqSu3o6kWcugXslC+HOPIfypOBiCA8txlb6J+64s9pXFvnLYVhb7ymFb7XEII6I8VFVFxt5vYJ04H/Y/TyE0rhsixz4BU42q3l4aERERUYXAIUwj/Jww7amqCqfTCaPRyLYCCuqbdeRPWCfE48q+HxB4+82IWjQB/s0ae3mlvof7riz2lcW+cthWFvvKYVvtcQjTAD8nTI7L5YKRxx6Jcfd1/HsJSdOXIm3DTpivq4ca62YgqGs7PtGWAfddWewri33lsK0s9pXDttriEEZUibkyriB5yTqkxK+HEuiPqtOfR9jjD0Ax86mBiIiISAp/0yKqhFSXC2mbdiPpv4vhTEpF+LDeCH++P4yWUG8vjYiIiKjC4xBGVMlc+fpHXBofj+zDfyDwgTtQ9fUR8GtQ29vLIiIiIqo0OIRpgMfNyDGZuItqJfvPU7BOWoCM3V/Dv/WNqPXRfPi3vpH7rxDuu7LYVxb7ymFbWewrh221xZoa4S+y2uPnr2nDaU1B8hsrYVu5DaZa1RG1eCKCH7yTbQVx35XFvrLYVw7bymJfOWyrPQ5hGuEp6rWnqipcLhcMBgPbloKalQ3b0veQ/NZqQFUROW4YLEMfgiHA/+rX2VcM28piX1nsK4dtZbGvHLbVHocwDfAU9XKcTicMBoO3l+FTVFXF5Q+/gHXKAjjOXEDYgB6IfGkgjFUj8l2XfeWwrSz2lcW+cthWFvvKYVttcQgjqkAyfzgK6+vxyPzfEQTd1Q4118+C3/UNvL0sIiIiIsqFQxhRBWA/9Q+Spi5C+tZP4Xfjdaj53hwEdWzt7WURERERUQE4hBH5MGdqOlLeXgvb4ndhCA9FtXfGILTvPVD4ifZEREREusUhTAM8QFEO33tcMNXhQOqa7UiatRxqRibCn+2H8KfiYAgJKtF22FcO28piX1nsK4dtZbGvHLbVFocwjXAQ056iKPxMimuoqoqMT76FdWIC7H+cQmjfexA59gmYalYr8bbYVw7bymJfWewrh21lsa8cttUea5Ku8dT//yfr6J+wTkjAlS+/R0CHmxG1YDz8m19fpm2yrxy2lcW+sthXDtvKYl85bKstDmEaUFWVO6YAVVVht9thNpsrdVvHv5eQNGMp0tbvhDm6DmqsnYGgu9qVuQn7ymFbWewri33lsK0s9pXDttrjEEakU66MTKQs2IiUueuhBPih6n+fQ9iAHlDM/LElIiIi8mX8bY5IZ1SXC+nvfgzrtMVwWlNgeeIhRIx6HEZLqLeXRkREREQa4BBGpCNX9h/CpfHxyP7ldwQ/8B9UeX0EzA1qeXtZRERERKQhDmFEOpB94hSskxYgY9fX8G8Vi9ofzUdAm2beXhYRERERCeAQpgEeoCjHbDZ7ewminEk2JL+xErYVW2GqWQ3VF09AyIOdy22fquh9vYltZbGvLPaVw7ay2FcO22qLQ5hGOIhpryI3VbOyYVv2PpLfWgW4VES++gQsw3rDEOBfbmuoyH29jW1lsa8s9pXDtrLYVw7bao9DmEZ4inrtqaoKp9MJo9FYYdqqqorL27+AdcpCOE6fR9jjDyDy5UEwVo3wyloqWl+9YFtZ7CuLfeWwrSz2lcO22uMQpgFVVb29hArL5XLBaDR6exmayPzxGKyvxyPzu8MI6toWNdfNhN/1Dby6porUV2/YVhb7ymJfOWwri33lsK22OIQRCbOf/hdJ0xYj/b298LuxEWpumYOgTq29vSwiIiIi8hIOYURCXGmXkfz2GtgWvQuDJQTV3h6D0Lh7oPCvSERERESVGocwIo2pDgdS1+5A0sxlUC9fQfgzjyJ85CMwhAR5e2lEREREpAMcwjTAAxTl+NJ7j1VVRcanB2GdmAD7738jtM/diBz7BEy1qnt7aYXypb6+hm1lsa8s9pXDtrLYVw7baotDmEY4iGlPURSf+YHPOnYC1gkJuPLF/xDQviWiEl6D/00x3l5WkXypr69hW1nsK4t95bCtLPaVw7ba4xCmEZ6iXnuqquZ01Wtbx3krkmYsRdr6nTA3rI0aa6Yj6O72ul1vbr7Q11exrSz2lcW+cthWFvvKYVvtcQjTAE9RL8fhcOjyE9pdGZmwLdiE5LnroPibUXXqMwgb+CAUs2/9SOm1b0XAtrLYVxb7ymFbWewrh2215Vu/MRJ5mepyIX3Lx7BOWwLnxSRYnuiNiFGPwxge6u2lEREREZGP4BBG5KEr+w/BOiEBWT//huD770CV10fA3LC2t5dFRERERD6GQxhRMbJPnEbS5AW4vPMr+N/cBLW2JyDwtubeXhYRERER+SgOYRrgAYpyvNnWmZyK5DdWwrb8fZhqVEX1RRMQ8uCdUAwGr61Ja9x35bCtLPaVxb5y2FYW+8phW21xCNMId0ztKYrilQNA1Ww7bMvfR/IbK6E6XYgcMxSWYQ/DEOhf7muR5K2+lQHbymJfWewrh21lsa8cttUehzCi/09VVVze8SWskxfCceofhD1+PyJeHgJTtQhvL42IiIiIKhAOYRrh54RpT1VVOBwOmEwm8baZh36F9fV4ZB78BUGdb0PNNdPhd0ND0fv0tvLsW9mwrSz2lcW+cthWFvvKYVvtcQjTAD8nTI50W/uZ80iatgjpW/bCLzYaNTe/iaD/tBG9Tz3hviuHbWWxryz2lcO2sthXDttqi0MYVUqutMtIfmctbIs2wxAagmpvvYzQR7tDMRq9vTQiIiIiquA4hFGlojocSF33EZJnLoMrPQPhTz2C8GcehSEkyNtLIyIiIqJKgkMYVRoZnx7EpYkJsB8/iZA+d6PK2Cdgqh3l7WURERERUSXDIUwDPEBRjhanQ806dgLWCQm48sX/ENCuBaI+WQr/m2I0WJ3v4+lm5bCtLPaVxb5y2FYW+8phW21xCNMIBzHtlbWp47wVSTOXIW3dRzA3qIUaq6YhqNvt/F79f+wgh21lsa8s9pXDtrLYVw7bao9DmEZ4inrtqaoKp9MJo9FYorauK1mwLdiE5LlroZhNqDL5aVgGPQjFj3/Bya20fal4bCuLfWWxrxy2lcW+cthWexzCNMBTdspxuVwwenjGQtXlQvp7e2GduhjOi0mwDOmFiBcGwBgRJrxK31WSvlQybCuLfWWxrxy2lcW+cthWWxzCqEK4cuAnWCckIOun4wi+rxOqvD4C5ug63l4WEREREVE+HMLIp2WfOI2kKQtx+aN98G/ZBLU+jEdg25u8vSwiIiIiokJxCCOf5ExORfKbK2FbvhWm6pGovuB1hPTqAsVg8PbSiIiIiIiKpLvfWE+cOIFBgwahRYsWaN++PWbNmoXs7Oxib6eqKhYvXow77rgDzZs3R9++ffHTTz/luc7BgwcRExOT73+jRo0q05p5gKKca997rGbbkbJwM061iUPq2h2IfHkw6n6zHqG97+IAVgp8b7cctpXFvrLYVw7bymJfOWyrLV29Emaz2TBgwAA0aNAA8+bNw/nz5zFjxgxkZmZi/PjxRd52yZIlmDt3LkaPHo2YmBisW7cOgwcPxgcffIC6devmue706dMRHR2d8++IiIgyr52DmPYURcn5gVdVFZd3foWkSQtg//scwvrfj4iXB8NUPdLLq/RdufuStthWFvvKYl85bCuLfeWwrfZ0NYRt3LgRly9fRnx8PMLDwwEATqcTkyZNwvDhwxEVFVXg7bKysrBo0SIMHjwYAwcOBAC0atUK99xzD5YtW4aJEyfmuX7jxo3RrFkzTdfOU9RrT1VVqKqK7J9/g3V8AjK//RmBd96KqFXT4N8kuvgNUJHcfRVF4b6rMbaVxb6y2FcO28piXzlsqz1dvX9r3759aNu2bc4ABgDdunWDy+XC/v37C73djz/+iPT0dHTr1i3nMj8/P3Tt2hX79u2TXDIAnqJeiuPseZx/cgrO3jUMLlsaam56A7U2vcEBTEMOh8PbS6iw2FYW+8piXzlsK4t95bCttnQ1hCUmJuZ5myAAhIWFoVq1akhMTCzydgDy3bZRo0Y4d+4cMjMz81w+bNgwNGnSBB07dsTMmTPzfZ28y5WeAet/l+B0237I3Pc9qr75Eup8tgxBd97q7aUREREREZWZrt6OmJqairCw/B+sa7FYYLPZirydn58f/P3981weFhYGVVVhs9kQEBCA0NBQDB06FLfccgv8/f3x7bffYvny5UhMTMSiRYvKtPaCXg1TFKXQV8ncL+UW9XWp2+p1u6rDgbT1O5E8czlcaekIG9EXIU/2gV94GFDA9r29Xonblud2c1+mt/XqcU0l2a77bRvut26wobbbLUlfPazXF9ZUVF89rlePa/Jku7n/5+31luW2et1uafp6c72+tKaC2up5vXpbU0F0NYRJi42NRWxsbM6/27Zti+rVq2Py5Mn45Zdf0Lx581Jv226353mPrMFggMlkyvnatfz8/ABcPebN5XLl+ZrJZIKiKHC5XHA6nXm+5t6uqqoFbtdsNhe6XaPRCKPRCFVV872krChKzm0dDke+nUhqu+7HevnTb6+edOP4SQT16gLLmCEw14nKuf61j9XT7RbVsKDtuh+roiglfqyebBeQa1jQY3Vvt7A1uZVnQ/d2i2rorf27qIYl2a77uu5GpWnI54jCH6t7OwaDAYb/f1ZUPkdo9xzhvg9FUXL2Qz5HaPMc4XK5cq6jKEqZGvI5Iv9jdTgcefoW1ZDPEZ491tzPEbnblnW7Ffk5IvcfsIqiqyEsLCwMaWlp+S632WywWCxF3i47OxtZWVl5Xg1LTU2FoihF3rZbt26YPHkyjhw5UqYhzL2zFPa1wuTeua+V+xeMa+XeGUq63eJu6/6hKI/tZh8/CevE+bjy2UEE3NYc1T9eDP8WNwC4+ouA+4e2tOstqmFh23V/H8vyWL3xvSnpY83dV28NvbV/l+VnLvd23U/A7g6laehW2Z8j3HI/Vnff3PfD54irtHis7r65t6W3hr76HJG77bV/uOVzRNm/N+7fxa7tC/A5wq2sj7Wgtlps91q+/BzhyQAG6GwIi46OznfsV1paGi5evJjveK9rbwcAJ0+exA033JBzeWJiImrVqoWAgACZBf9/7r+2FPa14m5bmq+V5bZ62K7jQhKSZy5D6todMNevhaiV0xDc/fY81ymqa3mvt7xuW57ble7rrdvqYbu5X0HQcrvldVu9b7ckfcuypsr6vbm2r1bb1cttvbndgtpqsd3yvq1et1uavmVZU2X63hgMhgLblnW7lanhtXR1Yo6OHTviwIEDSE1Nzbls9+7dMBgMaN++faG3u/nmmxESEoJdu3blXGa32/Hxxx+jY8eORd7nRx99BACan7KeCua6koXkOatxqk0c0j/8HFUmjUTdr1cj5N6OJdpxiYiIiIh8la5eCYuLi8OaNWswcuRIDB8+HOfPn8esWbMQFxeX5zPCBgwYgHPnzmHv3r0AAH9/fwwfPhzz5s1DZGQkrr/+emzYsAEpKSkYMmRIzu1Gjx6N+vXrIzY2NufEHCtXrkSXLl3KPIR5+v7Pykp1uZD+/idImroIjgtJsAzuiYgXB8IYkf9ELDm3yXVcDdtqj33lsK0s9pXFvnLYVhb7ymFb7elqCLNYLFi1ahWmTJmCkSNHIjg4GL1798aoUaPyXK+gA/yeeOIJqKqK5cuXIykpCU2aNMGyZctQt27dnOs0btwY27dvx/Lly2G321G7dm2MGDECw4YNK9O6S3ImlMroyjc/wzohAVmHfkXwvZ1Qa/wImKPreHRbtpXFvnLYVhb7ymJfOWwri33lsK22FJVFy+Tw4cNQVRXNmjXjXwauYU88A+vkhbj80Zfwb3EDqkwaicB2LTy+vfvMNkWd9IRKj33lsK0s9pXFvnLYVhb7ymFbzx0+fBhA8Yc66eqVMKoYnClpSH5zJWzL3oexWiSqz38NIQ91heLBSSCIiIiIiCo6DmGkGTXbDtuKbUh+YwVUuwORLw2CZURfGAL9i78xEREREVElwSFMA5X9ZVlVVZGx6ytYJy6A/e9zCHvsPkS8PBimqCpl3nZRn8VAZce+cthWFvvKYl85bCuLfeWwrbZYUyOVdRDL+vk3XHp9HjK/+RmB/2mDqFXT4N+k8M90Kwn3Z36QDPaVw7ay2FcW+8phW1nsK4dttcchTCOV7RT1jrPnYf3vEqRv3gPzDQ1Rc+MbCOp8q6b3oaoqXC5XiT59nDzHvnLYVhb7ymJfOWwri33lsK32OIRpoDKdYNKVnoGUeeuRMn8DDKHBqPbmSwh9tDsUoZeonU4nDDyhhxj2lcO2sthXFvvKYVtZ7CuHbbXFIYw8ojqdSFv/EZKmL4MrLR2WEX0R8Ww/GEKDvb00IiIiIiKfwiGMipXx+XewTkxA9rFEhPTuishxw2GuE+XtZRERERER+SQOYVSo7OMnYZ04HxmffouAW5uj9seLEdCyibeXRURERETk0ziEaaCiHaDouJiM5FnLkLp6O0z1aiJqxVQE39vRK4+T7z2Wxb5y2FYW+8piXzlsK4t95bCttjiEaaQiDGKuK1mwLdqM5HfWQjEaUGXSU7AM7gXFz+yV9SiKws+kEMS+cthWFvvKYl85bCuLfeWwrfZYUyO+fIp61eVC+tZPkTR1ERz/XoJlcC9EvDgAxkiLd9eV66yTvtpWz9hXDtvKYl9Z7CuHbWWxrxy21R6HMA348inqrxz8Bdbx8cj68VcEd78dNbe8Bb9G9by9rBx2ux1ms3deiasM2FcO28piX1nsK4dtZbGvHLbVFoewSsp+8iyskxfg8o4v4X9TDGptm4vA9i29vSwiIiIiogqPQ1gl40xJQ/Jbq2Bb+h6M1SJRPWEcQnrfBYUHWxIRERERlQsOYZWEanfAtmIbkt9YATXLjsgXB8LyZF8YggK8vTQiIiIiokqFQ5gG9HyAoqqqyNj9NawT58P+1zmE9rsXka8MgSmqireX5hE9t60I2FcO28piX1nsK4dtZbGvHLbVFocwjehxx8z6+TdcGh+PzAM/IfCOWxC1Yir8Yxt5e1keUxSFB4AKYl85bCuLfWWxrxy2lcW+cthWexzCKiDHuQuwTluC9Hf3wHx9fdTYMBtBnW/V5aBIRERERFTZcAjTiB4+J8yVnoGU+PVImb8RSnAgqs5+EWH97oXiox+up6oqHA4HTCaT19tWROwrh21lsa8s9pXDtrLYVw7bas83fzvXGW9/TpjqdCJtwy4kTV8Cly0dlhF9EPHcYzCEBnt1XVrwdtuKjn3lsK0s9pXFvnLYVhb7ymFbbXEI83EZX/wP1okJyD56AiEPdUXkuGEw163h7WUREREREVEhOIT5qOzfTsI6cT4yPvkWAW2aofaeRQi4OdbbyyIiIiIiomJwCPMxjovJSJ69HKmrt8NUNwpRy6cg+L5OfH8uEREREZGP4BCmgfIYgFyZWbAtehfJb6+BYjSgyoQRsAzuBcXfT/y+vcnkoycV8RXsK4dtZbGvLPaVw7ay2FcO22qLNTUiNYipqor0rZ8iaeoiOP65CMugnogYPRDGSIvI/emJoih8hU8Q+8phW1nsK4t95bCtLPaVw7ba4xCmEYlT1Gd+dxiXxscj64djCOrWATXffRN+jeppeh96pqoqXC4XDAYDf/AFsK8ctpXFvrLYVw7bymJfOWyrPQ5hGtD6lJ32v87BOnkBLm//An7Nr0etbXMR2L6lpvfhK5xOJwwGg7eXUWGxrxy2lcW+sthXDtvKYl85bKstDmE64rSlIfmt1bAtfQ/GKuGoHj8OIQ/fBYU7PBERERFRhcEhTAdUuwOpK7ch6Y2VUDOzEfHC4wh/Mg6GoABvL42IiIiIiDTGIcyLVFVFxp79sE6cD3viGYQ+2h2RY4bCVKOqt5dGRERERERCOIRpoDQHKGb9/BsuTUhA5v5DCOzUGlHLJsP/xusEVufb+N5jWewrh21lsa8s9pXDtrLYVw7baotDmEY8HcQc/1xE0rTFSNu8B+bG9VBj/SwEdbmNZ5opgKIo/EwKQewrh21lsa8s9pXDtrLYVw7bao81NVLcKepd6RlISdiAlIQNUIIDUXXmKIT1vx8Kd+hC5T7rJIdU7bGvHLaVxb6y2FcO28piXzlsqz1OABoo6hT1qtOJtI27kTR9CVwpabAMfxjhzz0GY1hIOa7Qd9ntdpjNZm8vo8JiXzlsK4t9ZbGvHLaVxb5y2FZbHMIEZXz5PawT4pF99ARCenVB5LhhMNer6e1lERERERGRF3EIE5D9+1+wTpyPjL3fIOCWpqi9eyECWt3o7WUREREREZEOcAjTkPNSMpJmrUDq6g9hqlMdUcsmI/j+O/jeWSIiIiIiysEhTAvZdqTMW4+Ut9cAioIq40fAMqQXFH8/b6+MiIiIiIh0hkOYBgIHTUHSpRRYBj6IiNEDYawS7u0lVQiKosBsNvOVRCHsK4dtZbGvLPaVw7ay2FcO22qPQ5gGXNG1UX/rXPhdV8/bS6lw+MMui33lsK0s9pXFvnLYVhb7ymFbbfGjrzWQPXUEzI3qensZFY6qqnA4HEV+BACVHvvKYVtZ7CuLfeWwrSz2lcO22uMQpgHukHJcLpe3l1Chsa8ctpXFvrLYVw7bymJfOWyrLQ5hRERERERE5YhDGBERERERUTniEEZERERERFSOOIRpgGeLkWM0Gr29hAqNfeWwrSz2lcW+cthWFvvKYVtt8RT1GuEgpj1FUfgDL4h95bCtLPaVxb5y2FYW+8phW+3xlTCN8AyJ2lNVFS6Xi22FsK8ctpXFvrLYVw7bymJfOWyrPQ5hGuAOKcfhcHh7CRUa+8phW1nsK4t95bCtLPaVw7ba4hBGRERERERUjjiEERERERERlSMOYUREREREROWIQ5gGeGZEOQYDd1FJ7CuHbWWxryz2lcO2sthXDttqi6eo1wgHMe0pigKTibuoFPaVw7ay2FcW+8phW1nsK4dttceRloiIiIiIqBxxCNOAqqo8Tb0AVVWRnZ3NtkLYVw7bymJfWewrh21lsa8cttUehzAiIiIiIqJyxCGMiIiIiIioHHEIIyIiIiIiKkccwoiIiIiIiMoRhzAN8PT0csxms7eXUKGxrxy2lcW+sthXDtvKYl85bKst3Q1hJ06cwKBBg9CiRQu0b98es2bNQnZ2drG3U1UVixcvxh133IHmzZujb9+++Omnn/Jd7/z583jmmWfQsmVLtGnTBuPGjUN6enqZ181BTHuKouT8j7THvnLYVhb7ymJfOWwri33lsK32dDWE2Ww2DBgwAHa7HfPmzcOoUaOwefNmzJgxo9jbLlmyBHPnzsXAgQOxaNEiVKtWDYMHD8bp06dzrmO32zF06FD89ddfePPNNzFx4kR8/fXXePHFF8u8dp6yU3uqqsLhcLCtEPaVw7ay2FcW+8phW1nsK4dttaerj77euHEjLl++jPj4eISHhwMAnE4nJk2ahOHDhyMqKqrA22VlZWHRokUYPHgwBg4cCABo1aoV7rnnHixbtgwTJ04EAOzZswd//PEHdu7ciejoaABAWFgYhgwZgl9++QXNmzcv1bq5Q8pxuVwwGo3eXkaFxb5y2FYW+8piXzlsK4t95bCttnT1Sti+ffvQtm3bnAEMALp16waXy4X9+/cXersff/wR6enp6NatW85lfn5+6Nq1K/bt25dn+zExMTkDGAC0b98e4eHh+PLLL7V9MERERERERAXQ1RCWmJiYZ0ACrr5SVa1aNSQmJhZ5OwD5btuoUSOcO3cOmZmZhW5fURQ0bNiwyO0TERERERFpRVdvR0xNTUVYWFi+yy0WC2w2W5G38/Pzg7+/f57Lw8LCoKoqbDYbAgICkJqaitDQ0BJvvyh2ux2qquLIkSOluj0VTVVVHgQqiH3lsK0s9pXFvnLYVhb7ymFbz2RnZ3vUSVdDmC/iziiLfWWxrxy2lcW+sthXDtvKYl85bOsZT88iqashLCwsDGlpafkut9lssFgsRd4uOzsbWVlZeV4NS01NhaIoObcNCwsr8HT0NpsNNWvWLNWaW7ZsWarbERERERFR5aSrY8Kio6PzHZuVlpaGixcv5juW69rbAcDJkyfzXJ6YmIhatWohICCg0O2rqoqTJ08WuX0iIiIiIiKt6GoI69ixIw4cOIDU1NScy3bv3g2DwYD27dsXerubb74ZISEh2LVrV85ldrsdH3/8MTp27Jhn+8ePH8dff/2Vc9k333yDlJQUdOrUSdsHQ0REREREVABF1dGHXNlsNtx7771o2LAhhg8fjvPnz2PGjBm4//77MX78+JzrDRgwAOfOncPevXtzLlu8eDHmzZuH0aNH4/rrr8eGDRvw9ddf44MPPkDdunUBXB3MevXqBQB44YUXcOXKFcyaNQsxMTFYtGhR+T5YIiIiIiKqlHQ1hAHAiRMnMGXKFBw6dAjBwcHo0aMHRo0aBT8/v5zr9O/fH2fPnsVnn32Wc5mqqli8eDHWr1+PpKQkNGnSBK+++mq+Y7bOnz+PqVOn4uuvv4bJZELXrl0xduxYhISElNtjJCIiIiKiykt3QxgREREREVFFpqtjwoiIiIiIiCo6DmFERERERETliEMYERERERFROeIQRkREREREVI44hBEREREREZUjDmFERERERETliEMYERERERFROeIQVkonTpzAoEGD0KJFC7Rv3x6zZs1Cdna2t5flc3bt2oUnn3wSHTt2RIsWLdCjRw9s2bIF13583bvvvou7774bzZo1wwMPPIDPP//cSyv2XZcvX0bHjh0RExODw4cP5/ka+5be1q1b8eCDD6JZs2a49dZbMXToUGRmZuZ8/bPPPsMDDzyAZs2a4e6778Z7773nxdX6jk8//RQPP/wwWrZsiQ4dOuC5557D6dOn812P+27x/v77b4wfPx49evRAbGws7rvvvgKv50nLtLQ0jB07Fm3atEHLli3x7LPP4sKFC9IPQbeKa5ueno558+ahd+/eaN26Ndq1a4cRI0bgt99+y7ctts3P033X7ZNPPkFMTEyB12Pf/Dztm5qaiqlTp6JDhw5o1qwZunTpguXLl+e5TnZ2NmbOnIn27dujRYsWGDRoEBITE8vjYfgsDmGlYLPZMGDAANjtdsybNw+jRo3C5s2bMWPGDG8vzeesXLkSgYGBGDNmDBYsWICOHTvi9ddfR0JCQs51PvroI7z++uvo1q0blixZghYtWuDpp5/GTz/95L2F+6D58+fD6XTmu5x9S2/BggWYMmUKunfvjmXLlmHy5MmoU6dOTufvv/8eTz/9NFq0aIElS5agW7duGDduHHbv3u3llevbwYMH8fTTT+O6665DQkICxo4di+PHj2Pw4MF5Blzuu575448/8OWXX6J+/fpo1KhRgdfxtOXzzz+P/fv3Y+LEiXjjjTdw8uRJPPHEE3A4HOXwSPSnuLb/r707DWrq7OIA/gcFLEhArCMVYUQckE0pFRBhQHEpqJS6jMRWtMUFq6JiraLiTt0pUuvu2Lrbal0qMlhrVapSKu6KM0pwA8VRICSAbOa+HxjyNk2QGGkQ+v/N8IHzPPfmcObODSf3uTePHz/Gjz/+CD8/P6xduxZLly6FXC5HeHg4JBKJylzWVp02x26t8vJyLFu2DO+++67GcdZXnTb1LSsrQ0REBC5fvoy5c+di27ZtGD9+vNqH5fHx8Thw4ABiYmKwbt06VFZW4rPPPoNcLtfHn9I0CfTaNm3aJHh4eAhFRUXK2P79+wVnZ2chPz+/8RJrggoKCtRicXFxgqenp/Dy5UtBEARhwIABwowZM1TmhIeHC+PGjdNLjs1Bdna24OHhIezbt09wdHQUrl+/rhxjfXUjkUgEFxcX4cyZM3XOiYyMFMLDw1ViM2bMEEJCQv7t9Jq0+fPnC0FBQYJCoVDG0tPTBUdHR+HixYvKGI9d7dSeSwVBEGbPni0MGjRIbY42tbx8+bLg6Ogo/PHHH8qYRCIRnJychOPHj/8Lmb/96qttaWmpUFZWphIrKSkRvL29hSVLlihjrK1m2hy7tdauXSt8+umnGuexvpppU9/ExEShb9++QmlpaZ37efLkieDs7Czs379fGSsqKhI8PDyELVu2NGzSzQivhOkgLS0Nvr6+sLS0VMZCQkKgUChw/vz5xkusCbKyslKLOTs7o6SkBGVlZXj06BHu37+PkJAQlTkDBw5Eeno6l4BqKT4+HmKxGPb29ipx1ld3hw4dQseOHREYGKhxvLKyEhkZGQgODlaJDxw4EBKJBLm5ufpIs0mqrq6GmZkZDAwMlDFzc3MAUH76ymNXe4aGr36r17aWaWlpEIlE8PPzU87p3LkznJ2dkZaW1vCJNwH11dbU1BTvvPOOSszMzAx2dnYqS+FYW83qq2+thw8f4vvvv0dcXJzGcdZXM23qe/DgQQwbNgympqZ1zjl37hwUCoXK+52lpSX8/Pz+0/WtD5swHeTk5KBz584qMZFIhHbt2nH9awO4dOkS2rdvj9atWyvr+c/mwcHBAVVVVRrvESFVqampuHPnDiZPnqw2xvrq7tq1a3B0dMSGDRvg6+sLNzc3iMViXLt2DUDNPwVVVVVq54raJR88V9Rt6NChkEgk2LNnD+RyOR49eoRvvvkGLi4u8PT0BMBjtyFpW8ucnBzY29urNMdAzT+zPJ61J5PJcPfuXZVzA2v7Zr7++muEhYWha9euGsdZX93k5ubi2bNnaNOmDSZOnAg3Nzd4e3sjLi4OpaWlynk5OTlo27YtLCwsVLZ3cHBgfV+BTZgOZDIZRCKRWtzCwgLFxcWNkFHzkZmZiZSUFERGRgKAsp7/rHft76z3q7148QIrVqxATEwMWrdurTbO+uru2bNnOHfuHI4ePYqFCxdi/fr1MDAwQGRkJAoKCljbN9CjRw989913SEhIQI8ePdCvXz8UFBRg69ataNGiBQAeuw1J21rKZDLlFcm/43vf61m9ejUMDAwwcuRIZYy11d3vv/+OK1euYNq0aXXOYX118/z5cwDAypUrYWFhga1btyImJgapqamYP3++cl5d9RWJRKzvK7Rs7ASIauXn5yMmJgY+Pj4YPXp0Y6fTLGzcuBFt27bFsGHDGjuVZkcQBJSVlSEpKUn56Wv37t0RFBSE3bt3w9/fv5EzbLouX76MWbNmYcSIEejduzekUik2bNiACRMmYO/evWjVqlVjp0ikk59//ln5IC9ra+vGTqfJq6iowLJlyxAdHa3x9gZ6MwqFAkDNVfKVK1cCAHx9fdGyZUvExcUhJiYGtra2jZlik8YrYToQiUQan/ZSXFysdimWtCOTyTB+/HhYWlpi3bp1ynXKtfX8Z71lMpnKOKnLy8vD9u3bMXXqVMjlcshkMpSVlQGoedpRaWkp6/sGRCIRLC0tVZa/WFpawsXFBdnZ2aztG4iPj0fPnj0RGxuLnj17Ijg4GFu2bEFWVhaOHj0KgOeGhqRtLUUiEUpKStS253ufds6ePYsFCxZg0qRJGDJkiMoYa6ubHTt2wNDQEIMGDYJMJoNMJkNVVRUUCgVkMpnyfkbWVze1tfHx8VGJ9+zZE0DN0xWBuusrk8lY31dgE6YDTWuI5XI5nj17pnb/B9WvvLwcUVFRkMvl2LZtm8ol7dp6/rPeOTk5MDIy4icwr5Cbm4uqqipMmDABXl5e8PLywsSJEwEAo0ePxueff876voEuXbrUOVZRUQE7OzsYGRlprC0AniteQSKRqN3bYW1tjTZt2uDhw4cAeG5oSNrWsnPnzrh3757ao6nv3bvH47keV69exbRp0/Dxxx9rXDbH2uomJycHDx48gK+vr/J9Ljk5GRKJBF5eXsrvZWR9dWNrawtjY+M6xysqKgDU1Pf58+dqSw81PUOB/o9NmA4CAgJw4cIF5aeEQM3DDwwNDVWevEP1q66uxvTp05GTk4Nt27ahffv2KuO2trbo1KmT2vcqpaSkwNfX95Unh/86Z2dn7Ny5U+Vnzpw5AIDFixdj4cKFrO8b6NOnD6RSKW7fvq2MFRUV4datW3B1dYWxsTF8fHxw4sQJle1SUlLg4OCAjh076jvlJqNDhw7IyspSieXl5aGoqAg2NjYAeG5oSNrWMiAgAMXFxUhPT1fOuXfvHrKyshAQEKDXnJuS7OxsREVFoWfPnli8eLHGOaytbsaPH6/2Pufv7w8bGxvs3LkTQUFBAFhfXRkbG8PPz0+lbgBw4cIFAICrqysAwN/fH4aGhvj111+Vc4qLi3Hu3DnW9xV4T5gOxGIxdu3ahcmTJyMqKgpPnz7FqlWrIBaL1ZoIerXFixfj9OnTiI2NRUlJicoXg7q4uMDY2BjR0dGYOXMm7Ozs4OPjg5SUFFy/fh27d+9uvMSbAJFIpLaEoJarq6vy5Mn66qZfv35wd3fH1KlTERMTAxMTE2zZsgXGxsb45JNPAABffPEFRo8ejUWLFiEkJAQZGRlITk5GYmJiI2f/dhOLxVi2bBni4+MRFBQEqVSqvL/x749R57GrnRcvXuDs2bMAaprZkpISZcPl7e0NKysrrWr5/vvvw9/fH3PnzsXs2bNhYmKCxMREODk5YcCAAY3ytzW2+morCALGjh0LExMTjBkzBjdv3lRu27p1a+UVddZWs/rq6+DgoPYlw4cPH8bTp09V3v9YX820OTdMmTIFYrEYX375JYYMGYIHDx4gISEBoaGhsLOzA1CzUmH48OFYtWoVDA0N0b59e2zevBnm5uYQi8WN9ve97QyEf16bJa1IJBIsXboUV65cgZmZGcLCwhATE8NPX19TUFAQ8vLyNI6dOnVKebXgwIED2Lp1Kx4/fgx7e3vMmDEDffr00WeqzUJGRgZGjx6NgwcPwt3dXRlnfXVTWFiI5cuX4/Tp06iqqkKPHj0wZ84claWKp06dwtq1a3Hv3j106NABEyZMwPDhwxsx67efIAjYv38/9u3bh0ePHsHMzAweHh6IiYlR+4eLx279cnNz0bdvX41jO3fuVP6zqk0t5XI5li9fjpMnT6K6uhr+/v6Ii4v7z34AWV9tAdT5oClvb2/s2rVL+Ttrq07bY/fvYmNjcfPmTSQnJ6vEWV912tY3PT0da9aswZ07d2BhYYHQ0FC1/3krKyuRmJiIo0ePorS0FJ6enoiLi1M7Z9P/sQkjIiIiIiLSI94TRkREREREpEdswoiIiIiIiPSITRgREREREZEesQkjIiIiIiLSIzZhREREREREesQmjIiIiIiISI/YhBEREREREekRmzAiIiI9e/LkCdzd3XHp0qXX3jY7OxsuLi64c+fOv5AZERHpA5swIiJqNg4dOgQnJyc4OTkhMzNTbVwQBAQGBsLJyQlRUVGNkGGN9evXo3v37vjggw9ee9suXbogMDAQ33777b+QGRER6QObMCIianZMTEyQnJysFv/rr7+Qn58PY2PjRsiqRmFhIY4cOQKxWKzzPsRiMU6ePImHDx82YGZERKQvbMKIiKjZCQwMRGpqKqqrq1XiycnJcHV1Rbt27RopM+CXX35BixYt0KdPH5330atXL1hYWODw4cMNmBkREekLmzAiImp2Bg0aBKlUivPnzytjlZWVOHHiBEJDQzVuo1Ao8MMPP2DQoEFwd3dHr169sGDBAhQXF6vMEwQBGzZsQEBAALp3746IiAjcvXsXQUFBiI2NrTe33377Dd26dYOZmZna2J49e9C3b19069YNw4cPR2ZmJiIiIhAREaEyz8jICN7e3jh16pQ25SAiorcMmzAiImp2bGxs4OHhgePHjytjaWlpkMvlGDhwoMZtFixYgNWrV8PT0xPz5s3D0KFDcezYMYwdOxZVVVXKeUlJSUhKSkLXrl0xa9Ys2NraIjIyEmVlZfXmVVVVhRs3bsDV1VVtbO/evViyZAmsra3x1VdfoUePHpg8eTLy8/M17svV1RV3795FSUlJva9LRERvl5aNnQAREdG/ITQ0FAkJCSgvL0erVq1w7NgxeHl5oX379mpzMzMzceDAAaxZs0blSpmPjw/GjRuH1NRUhIaGorCwENu2bUPv3r2xadMmGBgYAAASExOxadOmenN68uQJysvL0bFjR5V4ZWUlkpKS4O7ujh07dqBly5q3ZycnJ8TGxsLa2lptX7a2tlAoFMjJyUG3bt1eqzZERNS4eCWMiIiapZCQEFRUVOD06dMoKSnBmTNn6lyKmJqaCnNzc/j5+aGwsFD54+rqClNTU2RkZAAALly4gKqqKowaNUrZgAHAmDFjtMpJKpUCAEQikUr85s2bkEqlGDFihLIBA2oaSQsLC437qt1HUVGRVq9NRERvD14JIyKiZsnKygq+vr5ITk5GeXk5Xr58iQ8//FDj3AcPHkAul8PX11fjeEFBAQDg8ePHAIBOnTqpvVZdzZImgiCo/F67Xzs7O5V4y5YtYWNjo9U+iIio6WATRkREzdbgwYMxf/58PH/+HAEBAWpXoGopFAq0bdsWa9as0ThuZWXVIPlYWloCAGQy2Rvvq3Yfbdq0eeN9ERGRfnE5IhERNVv9+/eHoaEhrl69isGDB9c5z87ODlKpFJ6enujVq5faT9euXQEAHTp0AADcv39fZfvCwkK1pyhq8t5776FVq1bIzc1Vidfu95/f+1VdXY28vDyN+8rNzYWhoSHs7e3rfV0iInq7sAkjIqJmy8zMDIsWLUJ0dDSCgoLqnBcSEoKXL19iw4YNamPV1dXKq069evWCkZERdu/erbIccMeOHVrlY2RkBDc3N9y8eVMl7ubmBktLS/z0008q32127NixOpu7W7duoUuXLjA3N9fqtYmI6O3B5YhERNSsDRkypN453t7eCA8Px+bNm3H79m34+fnByMgI9+/fR2pqKubNm4fg4GBYWVkhMjISmzdvRlRUFAIDA5GVlYW0tDStlwX27dsXiYmJKCkpQevWrQEAxsbGiI6OxtKlSzFmzBiEhIQgLy8Phw4dUrtPDKh51P3FixcxcuTI1ysGERG9FXgljIiICMCSJUuwdOlSFBQUIDExEQkJCfjzzz/x0UcfwdPTUzlv+vTpiI6ORlZWFlatWoWHDx9i+/btMDU11ep1wsLCoFAo1L5oedSoUYiLi8OTJ0+wcuVKZGZmYuPGjTA3N4eJiYnK3PT0dEilUq0aTCIievsYCHy8EhER0RsLCgqCt7c3VqxYUe/cuXPn4v79+9i7d+8r5ykUCvj6+qJ///6Ij49XxidNmgQDAwOsX7/+jfMmIiL945UwIiIiPZsyZQpu3LiBS5cuKWMVFRVqj50/cuQIpFIpvL29lTGJRIIzZ85g2rRpesuXiIgaFu8JIyIi0rMOHTrgxo0bKrGrV69i+fLlCA4OhqWlJbKysnDw4EE4OjoiODhYOc/BwQFZWVn6TpmIiBoQmzAiIqK3gI2NDaytrbFr1y4UFxfDwsICYWFhmDlzJoyNjRs7PSIiakC8J4yIiIiIiEiPeE8YERERERGRHrEJIyIiIiIi0iM2YURERERERHrEJoyIiIiIiEiP2IQRERERERHpEZswIiIiIiIiPWITRkREREREpEdswoiIiIiIiPSITRgREREREZEe/Q8RmcA39HyXSAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "  data=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
    "yfit = BdY + AdY * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare York\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T^2 (s^2)\")\n",
    "plt.title(\"Legge di ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e612bc6",
   "metadata": {},
   "source": [
    "## Raccolta finale di dati dinamici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fc32f9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 22.78251 ± 0.00062\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Adols    = 0.00162 ± 0.00000\n",
      "Bdols    = 0.01841 ± 0.00033\n",
      "P(0,chi²)= 0.99987\n",
      "Kdols    = 24.35158 ± 0.03547\n",
      "KBdols   = 2.14448 ± 0.03860\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AdC      = 0.00162 ± 0.00000\n",
      "BdC      = 0.01841 ± 0.00011\n",
      "P(0,chi²)= 0.96181\n",
      "KdC      = 24.34964 ± 0.00989\n",
      "KBdC     = 2.14403 ± 0.01267\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ady      = 0.00162 ± 0.00000\n",
      "Bdy      = 0.01841 ± 0.00011\n",
      "P(0,chi²)= 0.96181\n",
      "KdY      = 24.34964 ± 0.00989\n",
      "KBdY     = 2.14403 ± 0.01267\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {KdM:.5f} ± {uKdM:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Adols    = {Adols:.5f} ± {uAdols:.5f}\")\n",
    "print(f\"Bdols    = {Bdols:.5f} ± {uBdols:.5f}\")\n",
    "print(f\"P(0,chi²)= {Pdols:.5f}\")\n",
    "print(f\"Kdols    = {Kdols:.5f} ± {uKdols:.5f}\")\n",
    "print(f\"KBdols   = {KBdols:.5f} ± {uKBdols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AdC      = {AdC:.5f} ± {uAdC:.5f}\")\n",
    "print(f\"BdC      = {BdC:.5f} ± {uBdC:.5f}\")\n",
    "print(f\"P(0,chi²)= {PdC:.5f}\")\n",
    "print(f\"KdC      = {KdC:.5f} ± {uKdC:.5f}\")\n",
    "print(f\"KBdC     = {KBdC:.5f} ± {uKBdC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"Ady      = {AdY:.5f} ± {uAdY:.5f}\")\n",
    "print(f\"Bdy      = {BdY:.5f} ± {uBdY:.5f}\")\n",
    "print(f\"P(0,chi²)= {PdY:.5f}\")\n",
    "print(f\"KdY      = {KdY:.5f} ± {uKdY:.5f}\")\n",
    "print(f\"KBdY     = {KBdY:.5f} ± {uKBdY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caf197a",
   "metadata": {},
   "source": [
    "## Aggiunta degli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f4897de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_T    = 0.001000\n",
      "res_T2   = 0.001080\n",
      "res_b    = 0.004082\n",
      "res_uAd  = 0.000010\n",
      "uKd_strum= 0.131094\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01 / np.sqrt(6) # riso bilancia\n",
    "res_T = 0.001             # riso T\n",
    "\n",
    "T = 2 * np.pi / df.w\n",
    "res_T2 = np.max(2 * T * res_T)\n",
    "\n",
    "res_uAd = np.max(np.sqrt(  (1 / Meq)**2 * res_T2**2 + (T2 / Meq**2)**2 * res_b**2))\n",
    "\n",
    "uKd_strum = np.max(np.abs(4 * np.pi**2 / Ad**2) * res_uAd) / 1000\n",
    "\n",
    "print(f\"res_T    = {res_T:.6f}\")\n",
    "print(f\"res_T2   = {res_T2:.6f}\")\n",
    "print(f\"res_b    = {res_b:.6f}\")\n",
    "print(f\"res_uAd  = {res_uAd:.6f}\")\n",
    "print(f\"uKd_strum= {uKd_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "37d88464",
   "metadata": {},
   "outputs": [],
   "source": [
    "uKdM_fin  = np.sqrt(uKdM**2   + uKd_strum**2)\n",
    "\n",
    "uAdols_fin  = np.sqrt(uAdols**2  + res_uAd**2)\n",
    "uBdols_fin  = np.sqrt(uBdols**2  + res_uAd**2)\n",
    "uKdols_fin  = np.sqrt(uKdols**2  + uKd_strum**2)\n",
    "\n",
    "uAdC_fin    = np.sqrt(uAdC**2    + res_uAd**2)\n",
    "uBdC_fin    = np.sqrt(uBdC**2    + res_uAd**2)\n",
    "uKdC_fin    = np.sqrt(uKdC**2    + uKd_strum**2)\n",
    "\n",
    "uAdY_fin    = np.sqrt(uAdY**2    + res_uAd**2)\n",
    "uBdY_fin    = np.sqrt(uBdY**2    + res_uAd**2)\n",
    "uKdY_fin    = np.sqrt(uKdY**2    + uKd_strum**2)\n",
    "\n",
    "\n",
    "uy_fin_d = np.sqrt(datad[\"uy\"]**2 + res_T2**2)\n",
    "ux_fin_d = np.sqrt(datad[\"ux\"]**2 + res_b**2)\n",
    "\n",
    "GdLd = len(datad) - 2\n",
    "\n",
    "\n",
    "F_fit_dols = Bdols + Adols * datad[\"x\"]\n",
    "sigma_dols  = np.sqrt(uy_fin_d**2 + (Adols * ux_fin_d)**2)\n",
    "chi2_dols   = np.sum(((datad[\"y\"] - F_fit_dols) / sigma_dols)**2)\n",
    "\n",
    "F_fit_dC   = BdC + AdC * datad[\"x\"]\n",
    "sigma_dC    = np.sqrt(uy_fin_d**2 + (AdC * ux_fin_d)**2)\n",
    "chi2_dC     = np.sum(((datad[\"y\"] - F_fit_dC) / sigma_dC)**2)\n",
    "\n",
    "F_fit_dY   = BdY + AdY * datad[\"x\"]\n",
    "sigma_dY    = np.sqrt(uy_fin_d**2 + (AdY * ux_fin_d)**2)\n",
    "chi2_dY     = np.sum(((datad[\"y\"] - F_fit_dY) / sigma_dY)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c27c50",
   "metadata": {},
   "source": [
    "## Risultati sulla propagazione dell'errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9075e52d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata Kd = 22.78251 ± 0.13110\n",
      "\n",
      "RISULTATI OLS:\n",
      "Adols = 0.00162 ± 0.00001\n",
      "Bdols = 0.01841 ± 0.00033\n",
      "Kdols = 24.35158 ± 0.13581\n",
      "Chi²  = 0.01897 | rid = 0.00949 | P = 0.00944\n",
      "\n",
      "RISULTATI Carpi:\n",
      "AdC   = 0.00162 ± 0.00001\n",
      "BdC   = 0.01841 ± 0.00011\n",
      "KdC   = 24.34964 ± 0.13147\n",
      "Chi²  = 0.02061 | rid = 0.01030 | P = 0.01025\n",
      "\n",
      "RISULTATI York:\n",
      "AdY   = 0.00162 ± 0.00001\n",
      "BdY   = 0.01841 ± 0.00011\n",
      "KdY   = 24.34964 ± 0.13147\n",
      "Chi²  = 0.02061 | rid = 0.01030 | P = 0.01025\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata Kd = {KdM:.5f} ± {uKdM_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI OLS:\")\n",
    "print(f\"Adols = {Adols:.5f} ± {uAdols_fin:.5f}\")\n",
    "print(f\"Bdols = {Bdols:.5f} ± {uBdols_fin:.5f}\")\n",
    "print(f\"Kdols = {Kdols:.5f} ± {uKdols_fin:.5f}\")\n",
    "print(f\"Chi²  = {chi2_dols:.5f} | rid = {chi2_dols/GdLd:.5f} | P = {chi2.cdf(chi2_dols, GdLd):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI Carpi:\")\n",
    "print(f\"AdC   = {AdC:.5f} ± {uAdC_fin:.5f}\")\n",
    "print(f\"BdC   = {BdC:.5f} ± {uBdC_fin:.5f}\")\n",
    "print(f\"KdC   = {KdC:.5f} ± {uKdC_fin:.5f}\")\n",
    "print(f\"Chi²  = {chi2_dC:.5f} | rid = {chi2_dC/GdLd:.5f} | P = {chi2.cdf(chi2_dC, GdLd):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI York:\")\n",
    "print(f\"AdY   = {AdY:.5f} ± {uAdY_fin:.5f}\")\n",
    "print(f\"BdY   = {BdY:.5f} ± {uBdY_fin:.5f}\")\n",
    "print(f\"KdY   = {KdY:.5f} ± {uKdY_fin:.5f}\")\n",
    "print(f\"Chi²  = {chi2_dY:.5f} | rid = {chi2_dY/GdLd:.5f} | P = {chi2.cdf(chi2_dY, GdLd):.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4edfa1",
   "metadata": {},
   "source": [
    "# Dinamica Cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0f407f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "Name: t, dtype: float64\n",
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "T2t  = (df.t / 20)**2\n",
    "uT2t = 2 * (df.t / 20) * (df.ut / 20)\n",
    "\n",
    "print(T2t)\n",
    "print(uT2t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d097869",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "99bc83a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Adt  = T2t / Meq\n",
    "uAdt = np.sqrt( (1/Meq)**2 * uT2t**2 + (T2t/Meq**2)**2 * uMeq**2 )\n",
    "\n",
    "Kdt  = 4 * np.pi**2 / Adt\n",
    "uKdt = (4 * np.pi**2 / Adt**2) * uAdt\n",
    "\n",
    "Kdt\n",
    "uKdt\n",
    "\n",
    "print(Kdt)\n",
    "print(uKdt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ae55d12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio (t): nan\n",
      "sigmaC  (t): inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_386440/2995330097.py:10: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  media = num / den\n",
      "/tmp/ipykernel_386440/2995330097.py:11: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  sigma = np.sqrt(1.0 / den)\n"
     ]
    }
   ],
   "source": [
    "KdtM, uKdtM = mediaPesata(Kdt, uKdt)\n",
    "KdtM /= 1e3\n",
    "uKdtM /= 1e3\n",
    "print(\"K-medio (t):\", KdtM)\n",
    "print(\"sigmaC  (t):\", uKdtM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634a06e9",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "75e94b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "datat = pd.DataFrame({\n",
    "    \"x\":  Meq,\n",
    "    \"ux\": uMeq,\n",
    "    \"y\":  T2t,\n",
    "    \"uy\": uT2t\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7761130",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "896388e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                         nan\n",
      "Model:                            OLS   Adj. R-squared:                    nan\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                lun, 06 apr 2026   Prob (F-statistic):                nan\n",
      "Time:                        10:23:04   Log-Likelihood:                    nan\n",
      "No. Observations:                   4   AIC:                               nan\n",
      "Df Residuals:                       2   BIC:                               nan\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const             nan        nan        nan        nan         nan         nan\n",
      "x                 nan        nan        nan        nan         nan         nan\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                     nan\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                  nan\n",
      "Skew:                             nan   Prob(JB):                          nan\n",
      "Kurtosis:                         nan   Cond. No.                         883.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE OLS (t):\n",
      "Atols   = nan ± nan\n",
      "Btols   = nan ± nan\n",
      "R²      = nan\n",
      "Kdtols  = nan ± nan\n",
      "KBdtols = nan ± nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(datat[\"x\"])\n",
    "model = sm.OLS(datat[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "Btols  = model.params[\"const\"]\n",
    "Atols  = model.params[\"x\"]\n",
    "uBtols = model.bse[\"const\"]\n",
    "uAtols = model.bse[\"x\"]\n",
    "R2t    = model.rsquared\n",
    "\n",
    "Kdtols  = 4 * np.pi**2 / Atols  / 1000\n",
    "uKdtols = (4 * np.pi**2 / Atols**2)  * uAtols  / 1000\n",
    "KBdtols  = 4 * np.pi**2 / Btols  / 1000\n",
    "uKBdtols = (4 * np.pi**2 / Btols**2) * uBtols  / 1000\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS (t):\")\n",
    "print(f\"Atols   = {Atols:.5f} ± {uAtols:.5f}\")\n",
    "print(f\"Btols   = {Btols:.5f} ± {uBtols:.5f}\")\n",
    "print(f\"R²      = {R2t:.5f}\")\n",
    "print(f\"Kdtols  = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
    "print(f\"KBdtols = {KBdtols:.5f} ± {uKBdtols:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "61e40e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = Btols + Atols * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit OLS t\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di ?? — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "354687ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 0.00000\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.00000\n",
      "P(0, chi²)= 0.00000\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit  = Btols + Atols * datat[\"x\"]\n",
    "sigma  = np.sqrt(datat[\"uy\"]**2 + (Atols * datat[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
    "\n",
    "N   = len(datat)\n",
    "nut = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nut\n",
    "Ptols = chi2.cdf(chi2_val, df=nut)\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nut}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Ptols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datat[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28f2d34",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8d6cd1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B (Carpi, t):\n",
      "AtC      = nan ± nan\n",
      "BtC      = nan ± nan\n",
      "cov_ABtC = nan\n",
      "P(0,chi²)= nan\n",
      "KdtC     = nan ± nan\n",
      "KBdtC    = nan ± nan\n"
     ]
    }
   ],
   "source": [
    "AtC, BtC, uAtC, uBtC, covABtC, chitC = reg_lin(datat[\"x\"], datat[\"y\"], datat[\"ux\"], datat[\"uy\"])\n",
    "\n",
    "KdtC  = 4 * np.pi**2 / AtC  / 1000\n",
    "uKdtC = (4 * np.pi**2 / AtC**2)  * uAtC  / 1000\n",
    "KBdtC  = 4 * np.pi**2 / BtC  / 1000\n",
    "uKBdtC = (4 * np.pi**2 / BtC**2) * uBtC  / 1000\n",
    "\n",
    "print(\"Ax + B (Carpi, t):\")\n",
    "print(f\"AtC      = {AtC:.5f} ± {uAtC:.5f}\")\n",
    "print(f\"BtC      = {BtC:.5f} ± {uBtC:.5f}\")\n",
    "print(f\"cov_ABtC = {covABtC:.6f}\")\n",
    "print(f\"P(0,chi²)= {chitC:.5f}\")\n",
    "print(f\"KdtC     = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
    "print(f\"KBdtC    = {KBdtC:.5f} ± {uKBdtC:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "592c257c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = BtC + AtC * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit Carpi (t)\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "12f276cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 0.00000\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.00000\n",
      "P(0, chi²)= 0.00000\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0   NaN\n",
      "1   NaN\n",
      "2   NaN\n",
      "3   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit  = BtC + AtC * datat[\"x\"]\n",
    "sigma  = np.sqrt(datat[\"uy\"]**2 + (AtC * datat[\"ux\"])**2)\n",
    "chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
    "chi2_red = chi2_val / nut\n",
    "PtC = chi2.cdf(chi2_val, df=nut)\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nut}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PtC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datat[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0823cca1",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d04ee20e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtY       = nan ± inf\n",
      "BtY       = nan ± nan\n",
      "cov_ABtY  = nan\n",
      "chi²      = 0.00000\n",
      "chi² rid  = 0.00000\n",
      "P(0, chi²)= 0.00000\n",
      "KdtY      = nan ± nan\n",
      "KBdtY     = nan ± nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_386440/788188722.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
      "/tmp/ipykernel_386440/788188722.py:17: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
      "/tmp/ipykernel_386440/788188722.py:27: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
      "/tmp/ipykernel_386440/788188722.py:39: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  sA = np.sqrt(1 / S)\n",
      "/tmp/ipykernel_386440/788188722.py:40: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n"
     ]
    }
   ],
   "source": [
    "AtY, BtY, uAtY, uBtY, cov_ABtY, chi2_val_t, chi2_red_t = york_fit(datat.x, datat.y, datat.ux, datat.uy)\n",
    "PtY = chi2.cdf(chi2_val_t, df=nut)\n",
    "\n",
    "KdtY  = 4 * np.pi**2 / AtY  / 1000\n",
    "uKdtY = (4 * np.pi**2 / AtY**2)  * uAtY  / 1000\n",
    "KBdtY  = 4 * np.pi**2 / BtY  / 1000\n",
    "uKBdtY = (4 * np.pi**2 / BtY**2) * uBtY  / 1000\n",
    "\n",
    "print(f\"AtY       = {AtY:.7f} ± {uAtY:.7f}\")\n",
    "print(f\"BtY       = {BtY:.7f} ± {uBtY:.7f}\")\n",
    "print(f\"cov_ABtY  = {cov_ABtY}\")\n",
    "print(f\"chi²      = {chi2_val_t:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red_t:.5f}\")\n",
    "print(f\"P(0, chi²)= {PtY:.5f}\")\n",
    "print(f\"KdtY      = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
    "print(f\"KBdtY     = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e795e732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = BtY + AtY * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit York (t)\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038b2684",
   "metadata": {},
   "source": [
    "## Raccolta finale di dati dinamici col cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "46ef07bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE (cronometro):\n",
      "Media pesata K  = nan ± inf\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Atols    = nan ± nan\n",
      "Btols    = nan ± nan\n",
      "P(0,chi²)= 0.00000\n",
      "Kdtols   = nan ± nan\n",
      "KBdtols  = nan ± nan\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AtC      = nan ± nan\n",
      "BtC      = nan ± nan\n",
      "P(0,chi²)= 0.00000\n",
      "KdtC     = nan ± nan\n",
      "KBdtC    = nan ± nan\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "AtY      = nan ± inf\n",
      "BtY      = nan ± nan\n",
      "P(0,chi²)= 0.00000\n",
      "KdtY     = nan ± nan\n",
      "KBdtY    = nan ± nan\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE (cronometro):\")\n",
    "print(f\"Media pesata K  = {KdtM:.5f} ± {uKdtM:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Atols    = {Atols:.5f} ± {uAtols:.5f}\")\n",
    "print(f\"Btols    = {Btols:.5f} ± {uBtols:.5f}\")\n",
    "print(f\"P(0,chi²)= {Ptols:.5f}\")\n",
    "print(f\"Kdtols   = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
    "print(f\"KBdtols  = {KBdtols:.5f} ± {uKBdtols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AtC      = {AtC:.5f} ± {uAtC:.5f}\")\n",
    "print(f\"BtC      = {BtC:.5f} ± {uBtC:.5f}\")\n",
    "print(f\"P(0,chi²)= {PtC:.5f}\")\n",
    "print(f\"KdtC     = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
    "print(f\"KBdtC    = {KBdtC:.5f} ± {uKBdtC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"AtY      = {AtY:.5f} ± {uAtY:.5f}\")\n",
    "print(f\"BtY      = {BtY:.5f} ± {uBtY:.5f}\")\n",
    "print(f\"P(0,chi²)= {PtY:.5f}\")\n",
    "print(f\"KdtY     = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
    "print(f\"KBdtY    = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70201336",
   "metadata": {},
   "source": [
    "## Aggiungi gli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "dfcd391e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_t     = 0.000500\n",
      "res_t2   = nan\n",
      "res_uAdt      = nan\n",
      "uKdt_strum    = nan\n"
     ]
    }
   ],
   "source": [
    "res_t  = 0.01 / 20  # risoluzione cronometro\n",
    "res_t2 = np.max(2 * df.t * res_t)\n",
    "\n",
    "res_uAdt = np.max(np.sqrt( (1 / Meq)**2* res_t2**2 +(T2t / Meq**2)**2  * res_b**2))\n",
    "uKdt_strum = np.max(np.abs(4 * np.pi**2 / Adt**2) * res_uAdt) / 1000\n",
    "\n",
    "print(f\"res_t     = {res_t:.6f}\")\n",
    "print(f\"res_t2   = {res_t2:.6f}\")\n",
    "print(f\"res_uAdt      = {res_uAdt:.6f}\")\n",
    "print(f\"uKdt_strum    = {uKdt_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b52fea3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "uKdtM_fin   = np.sqrt(uKdtM**2   + uKdt_strum**2)\n",
    "\n",
    "uAtols_fin  = np.sqrt(uAtols**2  + res_uAdt**2)\n",
    "uBtols_fin  = np.sqrt(uBtols**2  + res_uAdt**2)\n",
    "uKdtols_fin = np.sqrt(uKdtols**2 + uKdt_strum**2)\n",
    "\n",
    "uAtC_fin    = np.sqrt(uAtC**2    + res_uAdt**2)\n",
    "uBtC_fin    = np.sqrt(uBtC**2    + res_uAdt**2)\n",
    "uKdtC_fin   = np.sqrt(uKdtC**2  + uKdt_strum**2)\n",
    "\n",
    "uAtY_fin    = np.sqrt(uAtY**2    + res_uAdt**2)\n",
    "uBtY_fin    = np.sqrt(uBtY**2    + res_uAdt**2)\n",
    "uKdtY_fin   = np.sqrt(uKdtY**2  + uKdt_strum**2)\n",
    "\n",
    "uy_fin_t = np.sqrt(datat[\"uy\"]**2 + res_t2**2)\n",
    "ux_fin_t = np.sqrt(datat[\"ux\"]**2 + res_b**2)\n",
    "\n",
    "GdLt = len(datat) - 2\n",
    "\n",
    "F_fit_tols = Btols + Atols * datat[\"x\"]\n",
    "sigma_tols  = np.sqrt(uy_fin_t**2 + (Atols * ux_fin_t)**2)\n",
    "chi2_tols   = np.sum(((datat[\"y\"] - F_fit_tols) / sigma_tols)**2)\n",
    "\n",
    "F_fit_tC   = BtC + AtC * datat[\"x\"]\n",
    "sigma_tC    = np.sqrt(uy_fin_t**2 + (AtC * ux_fin_t)**2)\n",
    "chi2_tC     = np.sum(((datat[\"y\"] - F_fit_tC) / sigma_tC)**2)\n",
    "\n",
    "F_fit_tY   = BtY + AtY * datat[\"x\"]\n",
    "sigma_tY    = np.sqrt(uy_fin_t**2 + (AtY * ux_fin_t)**2)\n",
    "chi2_tY     = np.sum(((datat[\"y\"] - F_fit_tY) / sigma_tY)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858bbff7",
   "metadata": {},
   "source": [
    "## Risultati della propagazione dell'errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0b1e4a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE (cronometro):\n",
      "Media pesata K  = nan ± nan\n",
      "\n",
      "RISULTATI OLS:\n",
      "Atols  = nan ± nan\n",
      "Btols  = nan ± nan\n",
      "Kdtols = nan ± nan\n",
      "Chi²   = 0.00000 | rid = 0.00000 | P = 0.00000\n",
      "\n",
      "RISULTATI Carpi:\n",
      "AtC    = nan ± nan\n",
      "BtC    = nan ± nan\n",
      "KdtC   = nan ± nan\n",
      "Chi²   = 0.00000 | rid = 0.00000 | P = 0.00000\n",
      "\n",
      "RISULTATI York:\n",
      "AtY    = nan ± nan\n",
      "BtY    = nan ± nan\n",
      "KdtY   = nan ± nan\n",
      "Chi²   = 0.00000 | rid = 0.00000 | P = 0.00000\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE (cronometro):\")\n",
    "print(f\"Media pesata K  = {KdtM:.5f} ± {uKdtM_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI OLS:\")\n",
    "print(f\"Atols  = {Atols:.5f} ± {uAtols_fin:.5f}\")\n",
    "print(f\"Btols  = {Btols:.5f} ± {uBtols_fin:.5f}\")\n",
    "print(f\"Kdtols = {Kdtols:.5f} ± {uKdtols_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tols:.5f} | rid = {chi2_tols/GdLt:.5f} | P = {chi2.cdf(chi2_tols, GdLt):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI Carpi:\")\n",
    "print(f\"AtC    = {AtC:.5f} ± {uAtC_fin:.5f}\")\n",
    "print(f\"BtC    = {BtC:.5f} ± {uBtC_fin:.5f}\")\n",
    "print(f\"KdtC   = {KdtC:.5f} ± {uKdtC_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tC:.5f} | rid = {chi2_tC/GdLt:.5f} | P = {chi2.cdf(chi2_tC, GdLt):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI York:\")\n",
    "print(f\"AtY    = {AtY:.5f} ± {uAtY_fin:.5f}\")\n",
    "print(f\"BtY    = {BtY:.5f} ± {uBtY_fin:.5f}\")\n",
    "print(f\"KdtY   = {KdtY:.5f} ± {uKdtY_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tY:.5f} | rid = {chi2_tY/GdLt:.5f} | P = {chi2.cdf(chi2_tY, GdLt):.5f}\")"
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