{
 "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": 779,
   "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 = 29.89\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": 780,
   "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": 781,
   "id": "5494409f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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": 781,
     "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": 782,
   "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": 783,
   "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": 784,
   "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": 785,
   "id": "1d4c0ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.081650\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.2\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 / np.sqrt(6)\n",
    "\n",
    "# Massimo tra campionario e strumentale, punto per punto\n",
    "ueste_strum  = u_strum_Dx\n",
    "umasse_strum = u_strum_m\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 )\n",
    "uDx_strum = ueste_strum\n",
    "\n",
    "# uK_strum  = np.average(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": "code",
   "execution_count": 786,
   "id": "5be377eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"x\": este,\n",
    "    \"ux\": np.sqrt(ueste**2 + uDx_strum**2),\n",
    "    \"y\": - F,\n",
    "    \"uy\": np.sqrt(uF**2 + uF_strum**2)\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": 787,
   "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:                Tue, 07 Apr 2026   Prob (F-statistic):           5.50e-08\n",
      "Time:                        17:02:50   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": 788,
   "id": "8d795186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J8py779nIkSPx888/uzSv1atXu7y0uCOMRqPdx4taXVIucD2Ut5i/55tvvmlzjYxZ4V+UC37aUVBR++XO/hZ1DO0ZNWoUDh06hCFDhqBx48YoV64cTCYThg4d6pNjC8DhJeZdXXHUZDKhcuXK+OCDD+w+X/gToIKfYBVW3HOOCAwMdOqavMK2bt2KcePGoWvXrhgyZAgqV64MrVaLjz76yO4nfe7Ol6g0YcFFRMJUqlQJ5cuXh8lksvuJTkG1a9fG6dOnIcuy1S9FFy5csNrO3Ap14cIFS2sgkH+B+KVLl6yKgXr16uHUqVNo06aN0/fyMX+f8+fPW/21Wq/X4+LFi2jUqJFT47nCPIfk5GSrT9Xy8vJw8eJFyzE1b3f27Fmbv6yfPXvWqn0MyP+l84MPPsDLL7+M1157DQkJCZZCx5n3zJ6xY8e6/EmJI8f0/PnzNo+dO3fO6tPN0NBQu78Qmj8hcIWoe0EVnr8syzh//rwlt+b3uUKFCi4df1EcPYZFHZe0tDQkJSVh5MiRVq1+5k/wHBnDUfXq1cOBAwdw+/btIj/lql27NkwmE86fP48GDRpYHr9x4wbS09NtPh13Zy5JSUm49957hRcgzv6cF+fatWvIysqyKt7N7435WOzatQt169bFggULrN6jefPmuboLRPQ/vIaLiITRarXo3r07du3aZbN0N2Ddmta+fXtcvXoVe/bssTyWm5uL9evXW70mOjoaYWFhWL9+PQwGg+Xxr776yqbdJy4uDlevXrUZAwBycnKKvS9NdHQ0KlWqhM8//9xq9cDNmzd7pNXMnrZt20Kn0+GTTz6x+kRg48aNyMjIwAMPPGCZa+XKlW3mum/fPpw5cwYPPvigzdiBgYFYsGABYmJiMHz4cMsy6M68Z/ZER0ejbdu2Lv3PkVXNvvnmG6slsI8cOYLDhw+jY8eOlsfq1q2L5ORkq7meOnUKv//+e4njFyUoKAiA+213W7ZssSzvDeRf83P9+nXL/KOjo1GvXj2sWLHCskR/QSUdf1EcPYZFHZeiPiFatWqVzWPmMVxtM+zWrRtkWcaCBQtsnjP/3Jh/Vgp//48//tjqeXfFxcXBaDTaXQHVYDC4lR9Xfs6LYjAYrFqG8/LysG7dOlSqVAlNmjQB8O97WPDcc/jwYfzxxx8u7wMR5eMnXETktC+++ALff/+9zeODBw/Gf/7zHxw8eBD9+vXDE088gYYNGyItLQ3Hjx9HUlKSpf2sf//+WLNmDf7zn/9g8ODBqFq1Kr766itLu5T5L6yBgYEYOXIk3n33XTzzzDOIi4vDpUuXsGnTJpvrM3r16oUdO3bg7bffxsGDB3HvvffCaDQiOTkZO3fuxLJlyyxLRhem0+kwatQoTJ48Gc888wzi4+Nx8eJFbNq0ye1ruBxVqVIlvPjii1iwYAGGDh2Kzp074+zZs/j0008RExODRx991DLXMWPGYPz48Rg4cCB69OhhWS66du3aRS7jXrZsWXz00UcYPHgwhg0bhk8++QSRkZEOv2e+UK9ePTz55JN48sknkZeXh9WrVyMsLAxDhw61bNO3b1+sXLkSQ4YMQd++fXHz5k18/vnnaNiwod0ixhHmX0KnTZuG9u3bQ6vVokePHk6PExoaiqeeegp9+vSxLAt/1113WRaH0Wg0mDZtGoYNG4aePXuiT58+qF69Oq5evYqDBw+iQoUKWLJkiUv74AxHj2HZsmXRsGFD7NixA/Xr10dYWBjuvvtuREZGokWLFli2bBn0ej2qV6+OH374wXJ/rYLMx3b27NmIj4+HTqdDp06dbFoni9K6dWv06tULn3zyCc6fP48OHTrAZDLht99+Q6tWrTBw4EA0atQIvXv3xrp165Ceno4WLVrg6NGj2Lx5M7p27Wr1abk7WrZsif79++Ojjz7CyZMn0a5dO+h0Opw7dw47d+7EW2+9hYcfftilsV39ObenWrVqSEhIwKVLl1C/fn0kJibi5MmTePfddy3L1j/44IPYvXs3XnnlFTz44IO4ePGiJQNqu8E2kbex4CIipxW+YaZZnz59UKNGDWzYsAELFy7E119/jc8++wxhYWFo2LCh1Y04y5cvj1WrVmHatGlYvXo1ypUrh8ceewyxsbEYOXKk1XUqAwcOhCzL+Pjjj/Hee++hUaNGWLx4MaZNm2a1nUajwcKFC7Fy5Ups3boVX3/9NYKCglCnTh0MGjTI7sXnBfXv3x9GoxHLly/HzJkzERkZicWLF2Pu3LluHjHHjRw5EpUqVcKaNWswffp0hIaGol+/fnj99det7ufTp08flC1bFgkJCfjggw9Qrlw5dO3aFW+88UaR9+AC8lvXli9fjoEDB+L555/H2rVrcddddzn0nvnCY489Bo1Gg1WrVuHmzZto2rQpJk2ahGrVqlm2adCgAd577z3MmzcP06dPR8OGDTFz5kxs27bN5WKxW7duGDRoELZv344vv/wSsiy7VHANHz4cf/75J5YuXYrMzEy0adMGb7/9tuVTHgBo1aoV1q1bh0WLFmHNmjXIyspC1apV0bRpU6uV5TzJmWM4bdo0vPvuu5g+fTr0ej1GjBiByMhIzJo1C++++y4+/fRTyLKMdu3aISEhwWZVvqZNm+K1117D559/ju+//x4mkwl79uxxuOACgOnTpyMqKgobN27EzJkzERwcjOjoaKtVGqdNm4Y6depg8+bN+Oabb1ClShW8+OKLdlc3dMfUqVMRHR2Nzz//HLNnz4ZWq0Xt2rXx6KOP4t5773VrbFd/zgsLDQ3FjBkzMG3aNKxfvx5VqlTB5MmTrVaF7dOnD27cuIF169bhwIEDaNiwId5//33s3LnTp390IVIDSfbVlaxERHasXLkS06dPx/79+62WAy/MZDKhTZs2eOihhzBt2jQvzpC84eLFi+jSpQvefPNNDBkyxNfTcdrBgwcxePBgzJ071+VPOIiISB14DRcR+UxOTo7V17m5uVi3bh3q169vVWzl5ubarHK2ZcsW3L59Gy1btvTKXImIiIhcwZZCIvKZESNGoFatWmjUqBHu3LmDL7/8EsnJyTZLLP/xxx+YPn06Hn74YYSFheHEiRPYuHEjIiMj+ekBERERKRoLLiLymfbt22Pjxo346quvYDQa0bBhQ8uF9AXVrl0bNWrUwCeffIK0tDSEhoaiV69eGDNmTIk3ziUiIiLyJV7DRURERERE5CG8houIiIiIiMhDWHARERERERF5CK/hcsKhQ4cgy7LVvXCIiIiIiKj00ev1kCTJ6h6A9rDgcoIsyzZLUxMRERERUenjaF3AgssJOp0OsiwjOjoakiT5ejrkp2RZhl6vh06nY47IZcwRicIskQjMEYngbzk6evSoQ9vxGi4iIiIiIiIPYcFFRERERETkISy4iIiIiIiIPIQFFxERERERkYew4HKSP1zAR8rHWwuQCMwRicIskQjMEYmgxhxxlUIXlFR0GY1G6PV6L82GyD6dTgetVuvraZCH8I8/JAqzRCIwRySCWnOkyIJr8+bNWLVqFc6cOYNy5cohJiYGCxYsQNmyZQEA3377LebMmYOzZ8+iVq1aeOGFF/D4449bjZGXl4fZs2fjyy+/RGZmJmJjYzFp0iRERES4PT9Zlu0GQpZlXLlyBbdv33b7e5C6FZUh0cLCwlCjRg3VnsBKM1mWYTQaodVq+f6SW5glEoE5IhHUmiPFFVyLFy9GQkIChg8fjubNm+PWrVtISkqC0WgEAPz6668YMWIE+vbtiwkTJuCnn37CW2+9hfLly+Phhx+2jDNt2jQkJiZi3LhxqF69OpYsWYJnn30W27dvR3BwsMvzK+4GZ+Ziq1q1aihXrpyqgkLimG+gLUmSxzIiyzKysrJw7do1AEDNmjU98n3It0wmEz/FJCGYJRKBOSIR1JgjRRVcycnJWLBgARYtWoQHHnjA8nj37t0t/7148WI0bdoUU6dOBQC0bt0aKSkpmDdvnqXgunLlCjZu3Ii3334bffv2BQDExMSgU6dO+PzzzzFs2DDhczcajZZiq3LlysLHJ/XwRsEFAEFBQQCAa9euoVq1aqo7eRERERH5A0UtmrFp0ybUqVPHqtgqKC8vDwcPHrT6JAsA4uPjcebMGVy8eBEAcODAAZhMJqvtwsLC0K5dO+zfv98jczdfs1WuXDmPjE/kCnMeeU0hERERkW8oquA6fPgwIiMjsWjRIrRp0wbR0dEYMGAADh8+DAC4cOEC9Hq9zXVYDRo0AJD/CZn5/ytXrozQ0FCb7czbeArbCElJmEciIiIi31JUS+H169dx7Ngx/PXXX3j77bcRFBSEJUuW4Pnnn8fu3buRlpYGAAgJCbF6nflr8/Pp6el2r9MKCQmxbOMqSZKKvY7L3C5W+DXm55wd093XclzPjuuJ14rcV/N/23vMnXFFzleJc/KHcWVZhkajsfra13PiuP4xJ3/LkhLn5G/jemNO5hyZv1bbMfTn98afxi2cIyXMydnX2qOogst8of/cuXPRqFEjAECzZs3QuXNnrFmzBu3bt/fxDPMZDAarrwv/Q1VUwWV+3tnn7I3pzriFxy5swYIFWLhwoc3jDRs2xLZt2xAVFYU33ngDzz//PID8VSV1Oh169uxZYiHRuXNnPPDAA5g0aRIAYPz48Th27Bi2bdvm8nzdOQ7Fje3u8d2/fz9WrFiBY8eOQa/XIzw8HL1798ZTTz0FnU5neV83b96MCRMm4Mcff0TFihXtzufWrVtYsmQJ9u3bh3/++QcVKlRA/fr10a1bNzzzzDNFzsn8PQpm1mAw2Mw5ICAAkiTBZDJZFqgx02g0CAjIP1XYa03U6XSQJAlGoxEmk8nuuIXnUHBcWZaLHBeA3XG1Wi20Wq3dcSVJsrzW3r6an7O3r+6M684xDAwMLHJfSxrXfHx9cQxFj+vqMfTke1NcvovbV0fGBbx7DM3j2puTJP17TSnPEeo6R3j7GJpMJp4jHNhXfzxHePMYmv9f6ecIWXZs1WlFFVwhISEICwuzFFtA/rVX99xzD06fPo0ePXoAADIyMqxel56eDgCWFsKQkBDcuXPHZvz09HSbNkNXmN/8gsxvVsF/tOxx5TlH3kjR37Ns2bJYuXKl1WPmRRjWrVuHmjVrWl67efNmlCtXDo888ojDczY///LLLyM7O9vt+br62oKPiTyGK1aswMyZM/HQQw/hvffeQ7ly5bB//368//77OHjwIObPn2/5ZbngOPbmYzAY8NxzzyEjIwMvvPACIiIicP36dfz+++/47rvv8OyzzxY5J/OY5hMdAKv/Lkyj0Vj9AaEwezcjNH+vgidqe9sUdSPD4p5zZ1zA9X311LhA8Td0LG5fC49rLqbNJ3tfHENfvDdA8cdQafkualwzJRzDwllS2jHkOeJfzpwjCvLGMSyYIZHj2sNzhGPj+uMxNH+6Ze93IiWeIxz5HR1QWMHVsGFDXLhwwe5zubm5qFevHnQ6HZKTk9GhQwfLc+brsszXdkVERODGjRtIS0uzKrCSk5Pdvg+XvRAUVNJzRRFdSLgzriRJ0Gg0iI2Ntfua5s2b22xfVLFQ1Pc0P3/XXXe5PV93nyv8fF5eHgICAiwnDGfndOLECcyaNQu9e/fGjBkzLI+3adMGDRs2xIQJE7B27VoMGjSo2ONm/u9ffvkFf/75J9asWYMWLVpYnu/ZsydMJpPN97c3hqcKS1+/trSPazAYHMqpGvbVX8dV4pz8LUtKnJO/jeutORkMBsunFSLHFfWcEsdV4px8PW7hHClhTq68tiBFLZrRqVMn3L59GydPnrQ8duvWLRw/fhxNmjRBYGAgWrVqhV27dlm9LjExEQ0aNECdOnUAAO3bt4dGo8Hu3bst26SlpeHAgQPo2LGjd3ZGxaKiorB8+XIAwKBBg/Dzzz/ju+++Q1RUFKKiojB//nyHxxo3bhx69uxp+XrTpk2IiorCiRMnMHToUDRv3hzdunXDli1bbF773Xff4YknnkDTpk3RunVrvP3228jKyrI8n5WVhalTp6J79+6W1tTJkyfbfELauXNnTJ06FQkJCejUqROaNm1quXn1pk2b8MgjjyAmJgYdOnTA7NmzbT4qL+yTTz6BJEkYOXKkzXO9e/dG/fr1sXr1aoePkfm6w6pVq9o8V9xfkYiIiIjI9xT1CVfXrl0RExODV199FaNHj0aZMmWwdOlSBAYG4qmnngIAvPTSSxg8eDDeeecdxMXF4eDBg9i2bRtmz55tGadGjRro27cvZs6cCY1Gg+rVq+Ojjz5CcHAwBgwY4Kvd8zuFe1rt3fX77bffxhtvvIGyZcti7NixAPKPv7vGjBmDfv364bnnnsP69esxbtw4xMTEWFak3LlzJ0aPHo0+ffpg5MiRuH79OmbNmoX09HRLFnJycmA0GjF69GhUqlQJ//zzD5YsWYKXX34Zn3zyidX32717N+666y689dZb0Gg0KFeuHD7++GO8//77eOaZZzBu3DicOXPGUnCNGTOmyLn/8ssviIqKQu3atW2e02g0ePDBB7Fy5UpcvXrVoWPVuHFjaDQaTJw4Ea+88gruu+8+Sz8/ERERESmbogoujUaDpUuXYvr06Zg8eTL0ej3uv/9+rF271vLX/fvvvx/z58/HnDlzsHHjRtSqVQvTpk1DXFyc1VgTJ05E+fLlMWvWLGRmZuLee+/Fxx9/bHf1QrKVlZWFJk2aWD02c+ZM9OrVy+qxhg0bokKFCihXrpxNq6E7nn76aTz99NMAgNjYWOzbtw+7du3Cyy+/DFmWMXPmTMTHx+O///2v5TVVq1bFCy+8gJdffhl33303KlWqhClTplieNxgMqFOnDp566imcPXsW4eHhluf0ej0SEhIs9626c+cO5s2bh6FDh+L1118HALRr1w46nQ4zZszAkCFDULFiRbtzv3r1KqKioorct1q1agHIv0G3IwVX/fr1MW7cOLz//vt49tlnodPp0LRpU8TFxeHJJ58str+YiIiISOmys7Nx+vRpNGjQoMjfa8zbNGzY0LKugL9Q3G9qlSpVwvvvv1/sNl26dEGXLl2K3SYwMBBjx461fOoiijP9mgXpz12GKS2j5A0F04QGQ1e/ltOvK1u2LNasWWP1WN26dUVNq0QFV6QsV64catWqhStXrgAAzp49i0uXLmHChAlWn8K1bNkSGo0Gx44dw9133w0A2LJlC1auXInz589btRueO3fOquBq1aqV1U2rDx06hKysLDz88MNW36Nt27bIycnB33//jZYtW4rf8SI888wziI+Px7fffouff/4ZSUlJmDZtGnbv3o1Vq1axtbCUcvV8RFQYs0QiMEfkquzsbBw7dgy1atWyuf1T4W1q167Ngqs0cPaEYrx5GxdaPQkUWpLSK7Ra1D++BdrKYU69TKPRICYmxjNzckDhTyJ1Oh3y8vIA5F/XBwCvvPKK3df+888/AICvv/4aY8eORf/+/TF69GiEhYXh+vXreOWVV5Cbm2v1msqVK1t9bf4evXv3LvZ72FO9evVinzc/V7NmzSK3sadq1aro378/+vfvD71ej8mTJ2PTpk3Yu3dviX+AIPUpaWUlIkcxSyQCc0QimHOUeugvXL9wHTW73I+QCmV8PS23seDyAm3lMNQ7+JnPPuFytthSurCwMADA5MmT0bRpU5vnq1WrBiD/Oq/GjRtj6tSplud+/vlnu2MWLqLNq1suWLDAbtufeYEWe1q0aIGvvvoK//zzj01RJcsy9u3bh7p166J69epFjlESnU6HZ599Fps2bcKZM2dYcBEREZFfy87Oxp+//4EK6/ei7JZ9OFOnDj5J7oOe7eqjXNlAyLJc4sJlSsWCywWO3uSsIFfa+vyFTqez+cTIkyIiIlCjRg2kpKRYrvOyJycnx+avbV999ZVD3yM2NhZBQUG4cuUKHnroIafmN2jQIGzduhXz5s3D9OnTrZ7bunUrkpOTMXHiRIdzdPv2bVSoUMGmp/ncuXMA7K9eSOpnvlmjvfsCEjmDWSIRmCNyhyzLCNr/Byp89ykCc/X4o1UDnGh+F7RpJ/D1rpOQJAlBQUGoUKGCr6fqEhZcTip8t2nKL4C2bNmCb7/9FlWrVkW1atXc+vSmJJIkYdy4cRgzZgyysrLw4IMPIigoCJcvX8a+ffswevRohIeHo23btpg6dSoWLlxoWXgjKSnJoe8REhKCV199Fe+//z6uXLmCli1bQqvVIiUlBXv27MH8+fOL7B++5557MGbMGLz33nu4c+cO+vTpg7Jly+LAgQNYtWoVunTpgieffNLmdXv37kX58uWtHrv77rvx999/44MPPkDv3r3RtGlTBAQE4OTJk/joo49Qq1YtpwtCUg+ej0gUZolEYI7IFbknk3FnzPto+fMx5LSNwa3nH8Ph5HSk/JOBujWDEVfgE66jR4/6erouYcFFbhs2bBguXLiAsWPHIj09HSNGjLB7DyqR4uLiEBISgiVLllg+tapduzY6dOiAKlWqAAAGDBiAixcvYs2aNVi+fDnat2+PWbNmoV+/fg59j+effx7Vq1fHxx9/jDVr1iAgIAD16tXDgw8+WGKf+vPPP48GDRrg448/xpgxY6DX6xEeHo4333wTTz75pN1FLiZMmGDz2GuvvYbevXuje/fu2LNnD1atWoXc3FzUqFEDjzzyCF544QW//WsPERERlV6mjEykzlyBtIQvoKlbHadefgStXx2Cu4OD0TjXhKup2ahRuZzlGq7U1FScOHHCx7N2jSTzzxEOO3r0KGRZRkxMjM3H5Tk5OZalxsuWLeujGZI/kGXZ0k7o6bYL5lK9ZFmGXq+HTqdj+w65hVkiEZgjcpQsy7jzxde4+fZCmO5koeLrz8DU/yHs3vstunXrhuDgYLs5Sk1Nxa5du9C9e3dUqlTJR7O3Zv7EraSF5vgJFxEREREReVzuyWTcGPshcpIOo/wjD6LKuyMQULs6srOzER0dXexy70FBQSVuo1QsuJzEv9qQCMwRicAlmEkUZolEYI6oKAXbB3XhtVFzw4co92ALy/NBQUGIiYkp9jpA8zb+iAWXC/jLMrmD+SERmCMShVkiEZgjsqdw+2ClCcMQNrwfpED7xblac8SCywWuLAtPZFbwrzfMEblKlmWYTCZoNBrmiNzCLJEIzBEVZtU++GgnVJn6CgJqF7+KtVpzxILLSVxjhERg0U4iGI1GuyteEjmLWSIRmCMCSm4fLIkac8SCSzAWZKQkzCMRERF5g7Ptg6UJCy5BzBeKZmVl+eXqKaROWVlZAHghMxEREXmOK+2DpQkLLkG0Wi3CwsJw7do1AEC5cuXYMkZ2eeM+XLIsIysrC9euXUNYWBi0Wq1Hvg8RERGVXjbtgxtno9wD9/t6WorDgstJxf2CXKNGDQCwFF1ERfHWNVxhYWGWXJL6sJAmUZglEoE5Kj2s2wezUemtYQh7UUz7oBpzxILLBUX9oixJEmrWrIlq1apBr9d7eVZE1nQ6nSpPWpRPkiS+vyQEs0QiMEelR+6JM7gxbrZH2gfVmiMWXC4o6dMJrVaryrCQGN5oKST1Y45IFGaJRGCO1M+Yfge3Zq5A2rJNHmsfVGuOWHA5iau+kQgGg4ELWZDbmCMShVkiEZgjdZJlGXc27sbNdxYJbx+0R405YsFFREREREQ2ck+cwY2xs5Hz02GU79U5v32wVjVfT8vvsOAiIiIiIiILq/bBiDpcfdBNLLiIiIiIiMjr7YOlBQsuJ6npAj7yHeaIRGCOSBRmiURgjvybUtoH1ZgjFlwuUGMQyHskSVLdxaDkfcwRicIskQjMkf9SUvugWnPEgouIiIiIqJSxtA++vQimTLYPehILLheUdB8uouLIsgyDwYCAgADmiFzGHJEozBKJwBz5F6W0Dxam1hyx4HIS78NFIjBHJAJzRKIwSyQCc6R8Nu2DX8xGuY7KWn1QjTliwUVEREREpGI27YMTX0DYC0+wfdBLWHAREREREalU7vHTuDFujuLaB0sTFlxERERERCpjTL+DW++tQNpy5bYPlhYsuJykpgv4yHcCAvijR+5jjkgUZolEYI6UQZZl3NmwCzffWeyX7YNqzJH69sgLWHSROyRJYobIbcwRicIskQjMkTLkHj+dv/rgwSOo8FhnVJ7iX+2Das0RCy4XcFl4cocsyzCZTNBoNMwRuYw5IlGYJRKBOfItq/bBBv7bPqjWHLHgcpIal6ok7zMajdBoNL6eBvk55ohEYZZIBObI+/y9fdAeNeaIBRcRERERkZ/x9/bB0oQFFxERERGRnzCm38GtGcuRtmKzX7cPliYsuIiIiIiIFE6N7YOlBQsuJ6npAj7yHbX1JpNvMEckCrNEIjBHnlOa2gfVmCMWXC5g0UXukCRJlfeYIO9ijkgUZolEYI48w6Z9cNMclOtwn6+n5TFqzZH69sgLuCw8uaPgSpfMEbmKOSJRmCUSgTkSS5Zl3Fm/Czen5LcPVp70IkKH9VV9+6Bac8SCy0lcFp5E0Ov10OnUfdIkz2OOSBRmiURgjsTIPXYaN8aVjvZBe9SYIxZcREREREQ+ZkzL+PfmxQ3rqr59sDRhwUVERERE5CP/tg8ugikzB5UnDy8V7YOlCQsuIiIiIiIfsGof7N0lv32wZlVfT4sEY8FFRERERORFhdsHa22ei6D29/p6WuQhLLicJEmSqlZNIe+TJAmBgYG+ngb5OeaIRGGWSATmyDFsHyyeWnPEgouIiIiIyMNyj53GjbEfIufno2wfLGVYcLmA9+Eid8iyDKPRCK1WyxyRy5gjEoVZIhGYo6IZ0zL+vXkx2weLpdYcseByEu/DRSKYTCZotVpfT4P8HHNEojBLJAJzZM2qfTArB5Xffim/fVDHX7+Lo8Yc8R0nIiIiIhLIqn2wT1dUfudltg+WYiy4iIiIiIgEYPsg2cOCi4iIiIjIDbLJhIz1u5A6dTHbB8kGU+AkNV3AR74TEMAfPXIfc0SiMEskQmnNEdsHxVJjjtS3R17AoovcwXu5kQjMEYnCLJEIpTFHVu2Dd9dj+6AAas0RCy4XcFl4cocsyzCZTNBoNMwRuYw5IlGYJRKhNOWI7YOeo9YcMRlO4rLwJILRaIRGo/H1NMjPMUckCrNEIpSGHNm0D055BQE1qvh6Wqqixhyx4CIiIiIiKoZN++CWeQhqF+vraZGfYMFFRERERGQH2wdJBKaFiIiIiKiQ3KN/57cP/nKM7YPkFhZcTlLTBXzkO2rrTSbfYI5IFGaJRFBLjoxpGUidvgzpH29h+6APqCVHBbHgcgGLLnKHJEmqvMcEeRdzRKIwSySCGnIkm0zIWLcTN6cuhpydi8rvvITQoWwf9CY15Mge9e0RkR/grQVIBOaIRGGWSAR/zpFV++DjD+XfvJjtgz7hzzkqiqI+s9u0aROioqJs/vfBBx9YbbdhwwZ0794dMTExePTRR7F3716bsTIyMjBhwgS0bNkSsbGxePXVV3Ht2jW35yjLMpeGJ7fIsgy9Xs8ckVuYIxKFWSIR/DVHxrQMXB83Gxe7DoUx/Q5qbZmH6ksms9jyEX/NUUkU+QnXsmXLEBwcbPm6evXqlv/evn07Jk2ahOHDh6N169ZITEzEiBEjsHbtWjRv3tyy3ahRo3D69Gm88847KFOmDObMmYNhw4bhiy++UOVHlURERETkGLYPkjcpMlVNmjRBpUqV7D43b9489OjRA6NGjQIAtG7dGn/99RcWLlyIhIQEAMChQ4dw4MABLF++HO3btwcAhIeHIz4+Hrt370Z8fLxX9oOIiIiIlCX3yF+4Pm42ctk+SF6iqJbCkqSkpODcuXOIi4uzejw+Ph5JSUnIy8sDAOzfvx8hISFo166dZZuIiAg0btwY+/fv9+qciYiIiMj3LO2DDw2DKSOT7YPkNYr8hKtnz564desWatWqhX79+mHo0KHQarVITk4GkP9pVUENGjSAXq9HSkoKGjRogOTkZISHh9tccBcREWEZg4iIiIjUz6p9MCeP7YPkdYpKWtWqVTFy5Eg0a9YMkiTh22+/xZw5c3D16lVMnjwZaWlpAICQkBCr15m/Nj+fnp5udQ2YWWhoKI4dO+bWHCVJsnshX1GPm58DUOzznnotx1XeeyPLMnQ6nd/tq5KOIcfN37bg9ahKmBPH9Y85+VuWlDgnfxvXG3My58j8tVKOYe7Rv3Bj7Gzk/nocFR5/CJXefsnyiVbBbdX83vjTuIVzpIQ5OftaexRVcHXo0AEdOnSwfN2+fXuUKVMGq1atwvDhw304M2sGg8Hqa41GY/nHSq/X22wfGBgIADAajTCZTFbPBQQEQJIkmEwmGI1Gu+OaV2wpTKfTFTmuVquFVquFLMs285UkyfJag8FgExhPjevIvgK2x9BT45r3VZIkp/fVkXGBoo+hRqOx+766s6/m+dqbk6+OoXnc4o6hr/Jd3DH0Zr4BniOc2VdAOfn25DnCE8eQ5wjH9pXnCPf3VQnnCNPtDKS9/zHurP7K6ubFer3e5ljwHGE9rr058RxhO67JZIIsO7aEvaIKLnvi4uKwYsUKnDx5EqGhoQDyl3yvWrWqZZv09HQAsDwfEhKCK1eu2IyVlpZm2cYd5jffHvObYE/BIBem0WiKvLN2wTfe2XFLem1xKzZ6atzi9hUo/hiKHtf8Prqzr86+N+Yfbo1Gw2NYwnPujAu4vq++yrcz5whZlmE0Gi0ne54jPDOuL84Rjo4ral8LZ0lpx5DniH8p+fcIc44KjuOL90Y2mZCz8WvcfHcJ5Jw8VHr7JYQOfRyaQJ1b4wKl9xzh6XELHkONRmPJUcHftZV6jnCk2AL8oOAqKCIiAgCQnJxs+W/z1zqdDnXr1rVsl5SUZFN1nj17FpGRkW7NwTymvQNc0kEv7nlPvZbjenZcV19rMplsTiai5lRajiHHtW2HUcKcOK7y5+RvWVLinPxtXG/NqfDvSN6eryOrD5bW98afxrX3u7av5+TKawtS/CqFiYmJ0Gq1uOeee1C3bl3Ur18fO3futNmmTZs2lo/cO3bsiLS0NCQlJVm2OXv2LE6cOIGOHTt6df5ERERE5DnG2xm4PparD5JyKeoTriFDhqBVq1aIiooCAOzZswfr16/H4MGDLS2EI0eOxJgxY1CvXj20atUKiYmJOHLkCNasWWMZJzY2Fu3bt8eECRMwduxYlClTBrNnz0ZUVBS6devmk30jIiIiInFkkwkZn++wtA9WnvIyQoc8ztUHSXEUlcjw8HB88cUXuHLlCkwmE+rXr48JEyZg0KBBlm169uyJ7OxsJCQkYOnSpQgPD8eCBQsQGxtrNdacOXMwffp0TJ48GQaDAe3bt8fEiROL7cUkIiIiIuWzah/s+xAqv82bF5NySbIzaxqWckePHgUAREdHO9W3SVSQLMswmUxOXWxJVBhzRKIwSySCt3JkvJ2B1OnLkL5yC3SRd6HqjNEIahdb8gvJL/jb+chcG8TExBS7HT/ucYE/BICUS5KkIlfLIXIUc0SiMEskgqdzxPbB0kGt5yOm1AWOrrlPZI/5xn5FrXZJ5AjmiERhlkgET+Yo9/Cf+e2Dvx5n+6DKqfV8xILLSezAJBEMBkOx930gcgRzRKIwSySC6BwVbB8MjKpvuXkxqZsaz0csuIiIiIhIMdg+SGrD5BIRERGRIrB9kNSIBRcRERER+ZTxdgZS/y8B6au2sn2QVIcFl5PUdAEf+Q5zRCIwRyQKs0QiuJIj2WRCxmc7cPPdxZBz9ag85RWEDunD9sFSTI3nI6bZBWoMAnmPJEmquxiUvI85IlGYJRLBlRxZtQ8+0Q2VJ7/E9sFSTq3nIxZcREREROQ1lvbBlVsQ2CgctbbOR1Db5r6eFpHHsOByAe/DRe6QZRkGgwEBAQHMEbmMOSJRmCUSwZEc2bQPTh3B9kGyotbzERPuJN6Hi0RgjkgE5ohEYZZIhOJyxPZBcpQaz0csuIiIiIjII9g+SMSCi4iIiIgEY/sg0b+YeiIiIiISJvfwn7g+9kPk/naC7YNEYMHlNDVdwEe+o8YlT8n7mCMShVkiETSZObgxfT7SV25FYGO2D5Jr1Hg+YsHlAhZd5A7mh0RgjkgUZoncZdM++O5IhA7pDSmAv2aSc9R6PuJPggu4LDy5Q5ZlGI1GaLVa5ohcxhyRKMwSuaNg+2D5xx9C5Xdegq5GVV9Pi/yUWs9HLLicpMalKsn7TCYTtFqtr6dBfo45IlGYJXKW8VY6UqcnWNoHa26dh4D7myBAhe1g5F1qPB+x4CIiIiIih8gmEzI+TcTNaUuAPIOlfRBaLfR6va+nR6RILLiIiIiIqERWqw/2656/+mD1ygDYAURUHBZcRERERFSkwu2DXH2QyDksuJykpgv4yHfU1ptMvsEckSjMEtlTVPtgUasPMkckghpzxILLBSy6yB2SJKnyZELexRyRKMwS2VNc+6A9zBGJoNYcseByAZeFJ3fIsmzJEHNErmKOSBRmiQoy3kpH6v8tRfqqL/PbB79cgKA2zUp8HXNEIqg1Ryy4nMSLQkkEg8Ggyjupk3cxRyQKs0SyyYSMtdtxc9pHgL7k9kF7mCMSQY05YsFFREREVIo52z5IRM5hwUVERERUCrnaPkhEzmHBRURERFSKiGgfJCLH8SfLSWq6gI98hzkiEZgjEoVZKj082T7IHJEIaswRCy4XqDEI5D2SJKnuYlDyPuaIRGGWSgdPtw8yRySCWnPEgouIiIhIpWzaB6e9itDnH2P7IJEX8afNBbwPF7lDlmUYDAYEBAQwR+Qy5ohEYZbUK/fwn7j+5izk/n4SFfo9jMqTh3ts9UHmiERQa45YcDmJ9+EiEZgjEoE5IlGYJXWxah+8JwK1vlqIoNZNPf59mSMSQY05YsFFREREpAJsHyRSJv4EEhEREfm5nD9O4cbYD73SPkhEzmHBRUREROSnjKlpSP2/BKSv9m77IBE5jgWXk9R0AR/5TgDbO0gA5ohEYZb8T+H2wSr/fRUhz/m2fZA5IhHUmCP17ZEXsOgid0iSxAyR25gjEoVZ8j827YNvv4SAapV8OifmiERQa45YcLmAy8KTO2RZhslkgkajYY7IZcwRicIs+Q8ltw8yRySCWnPEgstJalyqkrzPaDRCo9H4ehrk55gjEoVZUrb89sFtuDltqWLaB+1hjkgENeZIWT+pRERERGRRsH0wuP/DqDTZ9+2DROQcFlxERERECmPdPtgAtbYtRFArZbQPEpFzWHARERERKYSlffDdjwCjSbHtg0TkOP70OklNF/CR76itN5l8gzkiUZglZfD39kHmiERQY45YcLmARRe5Q5IkVd5jgryLOSJRmCXfU0P7IHNEIqg1R+rbIy/gsvDkjoIrXTJH5CrmiERhlnxHTe2DzBGJoNYc+d9PtI9xWXgSQa/XQ6fT+Xoa5OeYIxKFWfK+nEMncWPsbOQe8s/2QXuYIxJBjTliwUVERETkJcbUNKT+dynSP/nKb9sHicg5LLiIiIiIPEw2mZCxZhtuTvtf++D/vYaQZ3v5ZfsgETmHP+VEREREHmTVPjggDpUmDff79kEichwLLiep6QI+8h3miERgjkgUZskzSlv7IHNEIqgxRyy4XKDGIJD3SJKkuotByfuYIxKFWRJPNhqRsXZ7qWofZI5IBLXmSL0/+URERERelnPoJG68+SFy/zjF9kEiAsCCyyW8Dxe5Q5ZlGAwGBAQEMEfkMuaIRGGWxDDevI2b/12KjDXbENikIWpvX4SyLWN8PS2vYY5IBLXmiAWXk3gfLhKBOSIRmCMShVlyXWlsHywKc0QiqDFHpe9sQERERCQA2weJyBEsuIiIiIicUNrbB4nIOSy4iIiIiBwgG41IX7MNqf9dWurbB4nIcTxDOElNF/CR7wTwH2cSgDkiUZilkuX8fiL/5sXm9sHJLyGgakVfT0tRmCMSQY05Ut8eeQGLLnKHJEnMELmNOSJRmKXisX3QMcwRiaDWHLHgcgGXhSd3yLIMk8kEjUbDHJHLmCMShVmyz6Z9cPqo/PZBrdbXU1Mk5ohEUGuOWHA5SY1LVZL3GY1GaDQaX0+D/BxzRKIwS9bYPuga5ohEUGOOFLs3mZmZ6NixI6KionD06FGr5zZs2IDu3bsjJiYGjz76KPbu3Wvz+oyMDEyYMAEtW7ZEbGwsXn31VVy7ds1b0yciIiI/Y7x5G9den4lLDw+HbDCi9vZFqDZ/AostInKLYguuRYsWwWg02jy+fft2TJo0CXFxcUhISEDz5s0xYsQI/PHHH1bbjRo1Cj/88APeeecdfPDBBzh79iyGDRsGg8HgpT0gIiIifyAbjUhbtRUX2jyNzK17UWX6KNT5JoHXahGREIpsKTxz5gw+/fRTjB07Fm+//bbVc/PmzUOPHj0watQoAEDr1q3x119/YeHChUhISAAAHDp0CAcOHMDy5cvRvn17AEB4eDji4+Oxe/duxMfHe3V/iIiISJlyfj+Rf/Piw38i+Mn4/JsX8xMtIhJIkZ9wTZs2DQMGDEB4eLjV4ykpKTh37hzi4uKsHo+Pj0dSUhLy8vIAAPv370dISAjatWtn2SYiIgKNGzfG/v373Zqbmi7gI99RW28y+QZzRKKUxixZtQ8aTaiduBjV5o1nseWG0pgjEk+NOVLcHu3cuRN//fUXXnnlFZvnkpOTAcCmEGvQoAH0ej1SUlIs24WHh9sURxEREZYx3MGii9whSRICAgKYI3ILc0SilLYsWbUPflmgfbBFtK+n5tdKW47IM9SaI0W1FGZnZ2PGjBkYPXo0KlSoYPN8WloaACAkJMTqcfPX5ufT09MRHBxs8/rQ0FAcO3bM7XmaTCabIEiSVOQKhuZti3veU6/luMp7b8y3FSjq9gJK3VclHUOO+++25nuWKGFOHNc/5uRvWXLntYWfM68+mGduH5z4IrT/+0Sr4LZKeW9EjeuNORU+fmo7hv783vjTuIVzpIQ5OftaexRVcC1evBiVK1fG448/7uupFEmWZej1eqtflDUajeWu2Hq93uY1gYGBAPKXuTSZTFbPmat4k8lks0iIeVzz9yxMp9MVOa5Wq4VWq4UsyzYLhUiSZHmtwWCwCYynxnVkXwHbY+ipcc37KkmS0/vqyLiA/WOo0WhgMpmg1WqFHkPzfO3NyVfH0DxuccfQV/ku7hh6M9+Aa+cIWZZhNBoRFBTEc4TKzhGeOoZFnSPM36dcuXIujesP5whjahrSpi9D5mc7oLunAWonLkbZFtHQ6/U2Y6vlHFFwXG+cI8zbBQQEFDtfniMcHxdQxjnC3XGdOYZGo9GSI0mSFP97hCw7dm9exRRcly5dwooVK7Bw4UJkZGQAALKysiz/n5mZidDQUAD5S75XrVrV8tr09HQAsDwfEhKCK1eu2HyPtLQ0yzbuMAejqOeKUjDIhWk0miJ7Vgu+8c6OW9JrzT8A3hy3uH0Fij+Gosc1v4/u7Kuz740sy5ZPSXkMfZdvd37mPPXeOHOOKPxXQJ4jPDOuL84Rjo4ral/t/eIkYlwzX54jYDIhe20iUv9vKSDLqDx9FEKeeRSa/+2jms8RBXnjHGHOUcFtlZBvT49bGs4Rnh634DE0v7bw79pK/T3CkWILUFDBdfHiRej1erzwwgs2zw0ePBjNmjXDrFmzAORfoxUREWF5Pjk5GTqdDnXr1gWQf61WUlKSTdV59uxZREZGuj1X80fl9h4v6XWuPOfOazmuZ8d1d06e2J/SdgxL87iF/zFSwpw4rvLn5G9ZcvW1Ob8dz7958eE/EfxUD1Sa+KLNghhK21elHUNnxzX/u2Z+TOnzVcK4SpyTr8ctnCMlzMmV1xakmIKrcePGWL16tdVjJ0+exPTp0zFlyhTExMSgbt26qF+/Pnbu3ImuXbtatktMTESbNm0sH2F37NgRixYtQlJSEtq2bQsgv9g6ceIEhg4d6r2dIiIiIrdkZ2fj9OnTaNiwIYKCgkp83njzNm5O+wgZa7YhMPpuS/sgEZGvKKbgCgkJQatWrew+16RJEzRp0gQAMHLkSIwZMwb16tVDq1atkJiYiCNHjmDNmjWW7WNjY9G+fXtMmDABY8eORZkyZTB79mxERUWhW7duXtkfIiIicl92djaOHTuG2rVrWwqugkWW+flaNWogb90upP43v30wdNoI/NOqEapENfDxHhBRaaeYgstRPXv2RHZ2NhISErB06VKEh4djwYIFiI2Ntdpuzpw5mD59OiZPngyDwYD27dtj4sSJxfZiOqKkNjCikpj7hZkjcgdzRKL4W5bS7+Tir7NXcOKPw6hduzYAoPy5q8h44g0Yj5+xtA+ma2Uc27ULtevVtfvJGInlbzkiZVJrjiTZmTUNS7mjR48CAGJiYnw8EyIiotIhNTUVW7ZsQaNGjaAJCMS2H87hnyupqBOcgU4xjVF53Tco9/UvQKNwGN4cjpqd7kNIhTJITU3Frl270L17d1SqVMnXu0FEKuRobeB3n3ApgaNLQBLZY17OW6vVMkfkMuaIRPGHLN24cQN79uwBAGiNMuqWMeHuPy6h2pJtAIBfO9+DY/d3wIXv/0Td05fRs119mAx5yM7O9uW0SxV/yBEpn1pzxILLSfxAkEQw34eLyB3MEYmi9CxVqVLF8gnXT+t/wv3bvkbNm6lIblIHv7WOQG5QIDSZZ1C/nASkAV/vOgkAKFOmjI9nXrooPUfkH9SYIxZcREREpGhBQUGIrFYTpgXrUHXtdhga3oU/BrRHw97d0DEzE3/88QcuZ4ch5Wou6tYMRtz/PuE6deqUr6dORMSCi4iIiJRLNhpR9cAxpE9aDQlAlfdeh+GR9sj75hvL/TfPnz+Pno92Qo6xDGpULme5huv8+fO+nTwREVhwERERkULl/HYcmWM+QPix0wjq3x3V3nkF2ioVkZqaatkmKCgI0dHRqFo5lKsREpEiseBykpou4CPfUVtvMvkGc0SiKC1Lxhu38m9evHY7AmNsb15sLrKCgoIQFBRkd4WwgtuQdygtR+Sf1JgjFlwuYNFF7pAkSZUnE/Iu5ohEUVKWZKMR6au/ROr/JQCyjCozX0fI4EchFZpfUUWWs9uQOErKEfkvteaIBZcLuCw8uUOWZUuGmCNyFXNEoiglSzm/Hsf1sR8i78hfCH66BypPfBHaKhV9Nh9yjlJyRP5NrTliweUkLgtPIhgMBuh0Ol9Pg/wcc0Si+DJLNu2DO5ag7P1NfDIXcg/PSSSCGnPEgouIiIi8ztH2QSIif8eCi4iIiLyK7YNEVJo4XXBdvHgRe/bswe+//44zZ87g1q1bkCQJFStWREREBO6991507tzZcm8MIiIiIuB/7YPvfoSMT7cjsGkk2weJqFRwuODau3cvVqxYgd9++w2yLKNevXqoU6cOIiMjIcsy0tPTcerUKezevRszZszAfffdhyFDhqBTp06enL/XqekCPvIdjUbj6ymQCjBHJIqnsyQbjUhf9SVS/28pIElsH1QpnpNIBDXmyKGCq1+/fjh16hS6dOmCOXPmoG3btqhQoYLdbe/cuYMffvgBu3btwqhRo9CoUSOsW7dO6KR9jUUXuUOSJAQEsJuX3MMckSiezhLbB0sHnpNIBLXmyKE9atWqFRYtWoQqVaqUuG2FChXQvXt3dO/eHdevX8fq1avdniQRERH5F7YPEhHlk2Suc+6wo0ePQpZlxMTE8FMucpksy9Dr9dDpdMwRuYw5IlFEZ6lw+2ClCcPYPlgK8JxEIvhbjo4ePQoAJd5kXX2f2REREZFPsH2QiMiWwwXX8ePHnR68SRO2DhAREakd2weJiIrmcMH1+OOPO/XRniRJOHHihEuTIiIiIuWzWX3w/f8gZNAjbB8kIirA4YJr+vTpJW6Tk5OD9evX4+TJk25NioiIiJQt55dj+e2DR/9G8MCe+e2DlcN8PS0iIsVxuODq3bt3kc/l5eXh888/R0JCAq5fv44WLVpg5MiRQiaoNP5wAR8pn06n8/UUSAWYIxLFmSyxfZCKwnMSiaDGHLm1aEZeXh4+++wzLFu2DNevX0fLli3x4YcfokWLFqLmp0gsusgdzA+JwByRKI5mie2DVByek0gEtebIpYIrLy8Pn376KZYvX47r16+jVatWpaLQMpNlWbWBIM+TZRlGoxFarZY5IpcxRySKI1li+yCVhOckEkGtOXKq4MrNzbV8onXjxg20atUKs2fPxv333++p+SkOb1tGIphMJmj5V2FyE3NEohSVJcP1W0h9dwkyPktEmWZRqL1zCcrex/ZBso/nJBJBjTlyuOBauXIlli1bhps3b6J169aYO3cu7rvvPk/OjYiIiHxANhqRvnIrUqcn5LcPfjAGIQN7sn2QiMgFDhdcM2bMgCRJaNy4MRo0aIAdO3Zgx44dxb5m4sSJbk+QiIiIvIftg0REYjnVUijLMk6cOOHQ/bUkSWLBRURE5CeM128hddpHbB8kIhLM4YLr1KlTnpyH31DTBXzkO2rrTSbfYI5IBNloROaqL3H7veVsHyS38JxEIqgxR24tC19asegid0iSpMqTCXkXc0QiWNoHj53Obx986wW2D5JLeE4iEdSaIxZcLuCy8OQOWZYtGWKOyFXMEbnDavXB5o1QK3Exyt53D7NELuM5iURQa45cLri2bt2KL774AhcvXkRaWprNcumSJOG3335ze4JKw2XhSQSDwaDKO6mTdzFH5Cx7qw8GP90DBpPJ11MjFeA5iURQY45cKrjef/99rFixAtWrV0d0dDSCg4NFz4uIiIgEyvn5KK6PnY2849btg7IsAyy4iIg8xqWCa8OGDXjwwQexcOFCaDQa0XMiIiIiQQq3D9beuQRl773H19MiIio1XG4pfOCBB1hsERERKZRV+6BGw9UHiYh8xKWK6cEHH1Tl9VmOUNMFfOQ7zBGJwBxRUXJ+PoqLXYfhxvg5KP9oJ9RLWovQZ3oVWWwxSyQCc0QiqDFHkuzCKhAZGRkYPnw4oqKi8Pjjj6NmzZp2P+0KCwsTMUfFOHr0KAAgJibGxzMhIiKyZbh+C6lTFyPj8x0o07wRqrw3mu2DREQe4mht4FJLYVBQEGJjY7F8+XJ89tlnRW538uRJV4YnIiIiJ8gGw//aB5cBWg2qznoDwU/3YPsgEZECuFRwTZ06FRs2bECzZs3QrFmzUrdKIe/DRe6QZRkGgwEBAQHMEbmMOSKzolYfdBSzRCIwRySCWnPkUsG1Y8cO9OrVCzNmzBA9H8XjfbhIBOaIRGCOSrfC7YPurD7ILJEIzBGJoMYcuVRwBQQEoFmzZqLnQkRERCVg+yARkX9xaZXCHj16YO/evaLnQkRERMXIPngkf/XBCXNR4bHOqPfTpwgZ/CiLLSIiBXPpE664uDhMmzYNL7zwgmWVQq2dk32TJk3cniAREVFpZ7iWmt8+uG5nfvvgro9QNraxr6dFREQOcKngevrppwHkr0L4/fff2zxvXlRCjasUqukCPvKdgACX7zlOZMEcqZ+32geZJRKBOSIR1Jgjl/Zo+vTpoufhV1h0kTskSWKGyG3MkfplHzyCG2NnI+/EGYQMegSV3noB2kqhwr8Ps0QiMEckglpz5FLB1bt3b9Hz8CtcFp7cIcsyTCYTNBoNc0QuY47Uy9vtg8wSicAckQhqzZH6PrPzMDUuVUneZzQaodG4tGYNkQVzpC6ywYD0j7cgdcZyr68+yCyRCMwRiaDGHDm0N5MnT0ZKSorTg1+4cAGTJ092+nVERESliWX1wbfmcfVBIiKVcegTrn/++QdxcXFo3bo14uPj0aZNG9SsWdPuthcvXkRSUhJ27NiBgwcPol27dkInTEREpBZcfZCISP0cKrgSEhLw22+/YcWKFZg8eTKMRiPCwsJQu3ZthIaGQpZlpKWl4eLFi0hPT4dWq0XHjh2xatUq3H///Z7eByIiIr/iy/ZBIiLyLoev4brvvvtw3333ITU1FXv37sUff/yB5ORkXLlyBQAQFhaGbt26oXnz5njwwQdRuXJlj03al9R0AR/5jr371hE5iznyT95afdAZzBKJwByRCGrMkSRzFQiHHT16FAAQExPj45kQEZG/sWofjG2MKu+NZvsgEZEfc7Q24CqFLuCy8OSOgn/jYI7IVcyR/7BpH/zwDQQ/3ROSQlbhYpZIBOaIRFBrjlhwOYkfCJIIer0eOp3O19MgP8ccKV/2T0dwY9yHyDuRjJDBj6LShGE+bx+0h1kiEZgjEkGNOWLBRUREJJjhWipuTlmMO+vz2wdr716Kss0b+XpaRETkAyy4iIiIBLG0D05fBgRoFdc+SERE3seCi4iISAB/aR8kIiLvYsHlJDVdwEe+wxyRCMyRMqihfZBZIhGYIxJBjTlyuMfhww8/xKlTpzw5F7+hxiCQ90iSBJ1OxxyRW5gj35MNBtxeuhEprZ9C1jdJqPrhm6i9c4lfFlvMErmLOSIR1JojhwuupUuX4u+//7Z8fevWLTRu3BhJSUkemRgREZFSZf90BBe7DsXNifNQ4fGuqJe0FiGDHuG1WkREZMOtlsLSukQ678NF7pBlGQaDAQEBAcwRuYw58g01tA8WxiyRCMwRiaDWHPEaLieV1iKTxGKOSATmyHtkgwFpK7bg1oxlgC4AVT98E8FP91DNJ1rMEonAHJEIaswRCy4iIqJiWK0++MyjqDSeqw8SEZHjnCq4Ll26hOPHjwMAMjIyAADnz59HSEiI3e2bNGni1GT27duHhIQEnD59Gnfu3EH16tXRtWtXjBgxAsHBwZbtvv32W8yZMwdnz55FrVq18MILL+Dxxx+3GisvLw+zZ8/Gl19+iczMTMTGxmLSpEmIiIhwak5ERFQ6WbUP3quO9kEiIvI+SXbwc7tGjRrZ9FIWdS2T+fGTJ086NZmtW7fizz//RLNmzRAWFoa///4b8+fPR5MmTbBixQoAwK+//orBgwejb9++iI+Px08//YQlS5Zgzpw5ePjhhy1jTZ48GYmJiRg3bhyqV6+OJUuWICUlBdu3b7cq3pxx9OhRyLKMmJgYVfWVknfJsgy9Xq/KVXjIe5gjzyncPlh54ouqah8sjFkiEZgjEsHfcnT06FEAQExMTLHbOfwJ1/Tp092bkQN69epl9XWrVq0QGBiISZMm4erVq6hevToWL16Mpk2bYurUqQCA1q1bIyUlBfPmzbMUXFeuXMHGjRvx9ttvo2/fvgDyD0SnTp3w+eefY9iwYS7P0R/efFK+gAB285L7mCPxspMO48b42f+2D054AdqK9rs41IRZIhGYIxJBjTlyeI969+7tyXkUKSwsDACg1+uRl5eHgwcPYsyYMVbbxMfHY9u2bbh48SLq1KmDAwcOwGQyWX3iFRYWhnbt2mH//v1uFVwAiy5yjyRJzBC5jTkSy3D1Jm5OXYw763eVuvZBZolEYI5IBLXmSJH9EUajEbm5uTh+/DgWLlyIzp07o06dOrhw4QL0er3NdVgNGjQAACQnJ1v+v3LlyggNDbXZzryNO9S4egp5jyzLMBqNzBG5hTkSw3Lz4jZPI+ubn/JvXrzD/25e7A5miURgjkgEteZIkZ/ZderUCVevXgUAdOjQAbNmzQIApKWlAYDNIh3mr83Pp6en271OKyQkxLKNq2RZthsCSZKKDIe5Ui/ueU+9luMq770xn0yK+guOUvdVSceQ4/57r5LAwEDFzMnfxs3+6TBujpuDvJPJCB78KCpNGGZpHzRvWxryrfQsKXFO/jauN+ZkzpFOpxM6rqfmq5RxlTgnX45bOEdKmJOzr7VHkQXX0qVLkZ2djdOnT2Px4sUYPnw4Pv74Y19Py0Kv11v9sqzRaCz9pnq93mZ78z9iRqMRJpPJ6jnzjd1MJhOMRqPVc+ZxzRcQFmYOo71xtVottFqtJbgFSZJkea3BYLAJjKfGdWRfAdtj6KlxzfsqSZLT++rIuID9Y6j534X3oo+heb725uSrY2get7hj6Kt8F3cMvZlvwLVzhLlwN/83zxGOj2u8lorb05Yi64uvERjbCLV3L0WZZlHQ6/UwFRrbF+cITx3Dos4RhQsuniPUcY4oOK43jmHB7Yqbrz+cIxwdFygd5wh3x3XmGBqNRss+SZKk+HOELNtfQLAwRRZcjRrlt3LExsYiJiYGvXr1wtdff42GDRsC+HdJerP09HQAsLQQhoSE4M6dOzbjpqen27QZuqK4lVMKVuSFFQxyYRqNxvKLeGEF33hnxy3ptcVdmOipcYvbV6D4Yyh6XPP76M6+OvveyLIMk8nEY+jAc+6MC7i+r756b5w5RxT8B4DnCMfGlUwmZH28BanvrYCkC0CV2W8i+Ml4aP73j65SzhGOjivqGNr7xUnEuGY8Rzg+LuC/v0eYc1RwWyXk29PjloZzhKfHLXgMza8t/Lu2Us8RjhRbgEILroKioqKg0+lw4cIFdO7cGTqdDsnJyejQoYNlG/N1WeZruyIiInDjxg2kpaVZFVjJyclC7sMlSfYv6CvpoBf3vKdey3E9O667c/LE/pS2Y1iaxy38j5ES5qTUcbOTDuffvPjkWYQ82yv/5sUFVh8s7flWcpaUOCd/G9dbczL/u2Z+TOnzVcK4SpyTr8ctnCMlzMmV1xakyEUzCjp8+DD0ej3q1KmDwMBAtGrVCrt27bLaJjExEQ0aNECdOnUAAO3bt4dGo8Hu3bst26SlpeHAgQPo2LGjW/Nx5uASFaW4v/4QOYo5Kpnh6k1cfWUaLj86AlLZMqi9eymqzvxPqVjq3RnMEonAHJEIasyRW59w6fV6JCcnIyMjw+6FYy1atHBqvBEjRiA6OhpRUVEoW7YsTp06heXLlyMqKgpdu3YFALz00ksYPHgw3nnnHcTFxeHgwYPYtm0bZs+ebRmnRo0a6Nu3L2bOnAmNRoPq1avjo48+QnBwMAYMGODOLgNg0UXukSRJlfeYIO9ijoonGwxIW74Zt95bDugCUHX2WAQ/Fa/amxe7g1kiEZgjEkGtOZJkZ5bY+B+TyYRZs2bh008/RU5OTpHbnTx50qlxly5disTERFy4cAGyLKN27dp46KGHMGTIEFSoUMGy3Z49ezBnzhycPXsWtWrVwgsvvGC5wbFZXl4eZs+eja1btyIzMxP33nsvJk6caFlC3hXmu0lHR0ez6CKXFb72hsgVzFHRSmofJGvMEonAHJEI/pYjc20QExNT7HYuFVyLFi3CvHnz0L9/f9x333148803MWbMGISEhODTTz+FJEl444030LZtW9dmr1BHjx6FLMuIiYnxixCQMplX0ylu8RWikjBHtgxXb+LmlEW4s2E3ytx3D6q+9zrKNIvy9bQUj1kiEZgjEsHfcuRoweVSb8XmzZsRFxeHKVOmWBavaNKkCfr164f169dDkiT89NNPrgxNRETkFNlgwO0l6/NvXvztQVSdPRa1Exez2CIiIkVwqeC6cuUKWrduDeDfey3k5eVZvn700UexdetWQVMkIiKyL/vHP3CxyxDcnLwAFfo+hHpJnyJkYE9eq0VERIrh0lVpYWFhyMrKAgCUL18eFSpUQEpKitU25ntjERERiVa4fbDO1wn8RIuIiBTJpYLrnnvusfQsAkCrVq2watUqNG7cGLIsY/Xq1YiK4j98REQklmwwIG3ZJtyauQII5OqDRESkfC79C9WvXz/k5eVZ2ghHjx6N9PR0DBw4EAMHDkRmZibGjRsndKJKUdLNaolKIkmS31wMSspVGnPE9kHPKI1ZIvGYIxJBrTlyaZVCezIyMnDw4EFotVrExsYiLCxMxLCK4uhKJEREJI7hyg3cnLqYqw8SEZGiOFobCLuzWHBwsOXmxGony7LqKm/yHlmWYTQaodVqmSNyWWnIkbl9MPW95ZDK6FB1zjgEPxnHT7QEKw1ZIs9jjkgEtebIpX+1unTpgv79+yM5Odnu89988w26dOni1sSUStAHglTKmUwmX0+BVEDNOcr+8Q9c7JzfPhj8RPf89sGne7DY8hA1Z4m8hzkiEdSYI5f+5bp06RKOHz+OJ554At98843N81lZWbh8+bLbkyMiotLFcOUGrr78Li73GgmpXFnU+ToBVWe+Dm3FEF9PjYiIyCUu/6lw/PjxaNGiBUaOHIk5c+YInBIREZU25psXXzDfvHjOON68mIiIVMHla7hCQkKwZMkSLFiwAIsWLcKJEycwa9YsBAcHi5wfERGpXPaPf+DGuNnIO3UWIc8+hkrjh/ITLSIiUg23m+FHjBiBJUuW4PDhw+jbty/+/vtvEfNSLDVdwEe+ExAgbL0aKsX8PUeGKzdw9aWp+e2D5YPYPuhD/p4lUgbmiERQY46EXH3csWNHbNy4EUFBQejXrx/27NkjYljFYtFF7pAkCRqNhjkit/hzjmS9AbeXrMtvH9z7c3774PZFbB/0EX/OEikHc0QiqDVHwpZ7qlu3LtatW4du3bph165dooZVJK5USO6QZRkmk4k5Irf4a47+vXnxQq4+qBD+miVSFuaIRFBrjlz6zG716tVo2LChzeNlypTBe++9h/j4eKSmpro9OSVSWwDINwwGA3Q6na+nQX7On3JkuHIDN6cswp2NX6PM/U1Q5+sEfqKlIP6UJVIu5ohEUGOOnC64srOzMWPGDDzxxBN48skn7W7zwAMPuD0xIiLyf7LegLTlXyD1vRX5Ny+eOw7BA3jzYiIiKj2cLriCgoJw8eJF1fVWEhGRWJbVB/889+/qg2FcyZaIiEoXl/7E2KFDBxw4cED0XIiISAWsVh+sUC5/9cH3RrPYIiKiUsmla7hefvllvPbaa3jjjTfQv39/1K1bF2XKlLHZLiwszN35KQ4/2SMRNGynIgGUliO2D/ovpWWJ/BNzRCKoMUcuFVw9evQAAJw+fRrbtm0rcruTJ0+6NiuFY9FF7pAkSZX3mCDvUlqOsn84hOvjZkP/13m2D/oZpWWJ/BNzRCKoNUcu7dErr7zCooPIDbIs82eI3KaEHFmtPtgiOn/1waaRPp0TOU8JWSL/xxyRCGrMkUsF18iRI0XPw2/IsqzKIJD3yLIMvV4PnU7HHJHLfJ0jq/bBsoFsH/Rjvs4SqQNzRCKoNUdCPrPLyckBAJQtW1bEcEREpGBsHyQiInKcywXX5cuXMX/+fOzbtw+3bt0CAFSsWBEPPPAARowYgdq1awubJBER+Z7hyg3cfGcR7nzB9kEiIiJHuVRwnTlzBk899RQyMjLQtm1bNGjQAACQnJyMrVu3Yu/evfj0008REREhdLJEROR9st6AtGUb89sHg8qg6rzxCO7/MNsHiYiIHOBSwTVr1ixoNBps3rwZUVFRVs/99ddfePbZZzFr1iwsXLhQyCSJiMg32D5IRETkHpcKrl9++QXPPfecTbEFAJGRkXj66aexcuVKd+emSGq6gI98R6fT+XoKpAKezBHbB0sXnpNIBOaIRFBjjlwquAwGQ7ELZAQFBcFgMLg8KaVj0UXuYH5IBE/liO2DpQ/PSSQCc0QiqDVHLv0L2rhxY2zYsAEZGRk2z925cwcbN27EPffc4/bklEqWZV9PgfyYLMswGAzMEbnFEznK/uEQUjo/j5vvLEbwgDjUS/oUIU/Gs9hSOZ6TSATmiERQa45cvg/XsGHDEBcXhz59+qB+/foAgLNnz2Lz5s24ffs2Jk+eLHKeiqG2AJBvmEwmaLVaX0+D/JyoHBmu3MDNtxfizqZv8tsHv1mGMjF3C5gh+Quek0gE5ohEUGOOXCq42rRpg6VLl2LmzJlYunSp1XONGzfG+++/j9atWwuZIBEReYasNyAtYSNSZ7J9kIiIyFNcvg9X27ZtsWXLFly/fh2XL18GANSqVQtVq1YVNjkiIvKM7B8O4frYD6H/+wJCnnsMlcZx9UEiIiJPcLjg+vDDDxEfH49GjRpZPV61alUWWUREfqJg+2DZFtGozvZBIiIij3K4b2Tp0qX4+++/LV/funULjRs3RlJSkkcmplRqXT2FvEttvcnkG87kSNYbcHvR57jQ+ilk7f8VVeeNR61tC1lsEQCek0gM5ohEUGOOXG4pBErvAhIsusgdkiSp8mRC3uVMjmzaB8cPhTaU7YOUj+ckEoE5IhHUmiO3Cq7SSpZlFl3kMlmWLRlijshVjuSI7YPkCJ6TSATmiERQa45YcDmptH6qR2IZDAZV3kmdvKuoHHH1QXIWz0kkAnNEIqgxR04VXJcuXcLx48cBwHLT4/PnzyMkJMTu9k2aNHFzekRE5Ay2DxIRESmLJDv4kU2jRo1sPtorqrXO/PjJkyfFzFIhjh49ClmWERMTo6qPOcm7ZFmGXq+HTqdjjshlhXNUuH2wynuvs32QHMJzEonAHJEI/pajo0ePAgBiYmKK3c7hT7imT5/u3oyIiEg4WW9A2rIv/m0fnD8Bwf26s32QiIhIIRwuuHr37u3JefgNf6i2SfmYIxIh98c/cGXifOj/voDQ53uj4rghbB8kl/CcRCIwRySCGnPERTNcoMYgkPdIkqS6i0HJuwz/XM9vH9y8h6sPktt4TiIRmCMSQa05YsFFROQnbFYfZPsgERGR4rHgcgHvw0XukGUZBoMBAQEBzBE5LPvA77g+bralfTBs7POQywcBzBC5ieckEoE5IhHUmiMWXE7ifbhIBOaIHGXVPtgyxtI+aF7JiUgEnpNIBOaIRFBjjlhwEREpkKw3IG3pBqS+/zE05cqi2oK3UKFfd1X9xY+IiKg0YMFFRKQwhdsHufogERGR/2LBRUSkEEW1DxIREZH/YsHlJLbzkAhqXPKUXOdq+yBzRKIwSyQCc0QiqDFHLLhcwKKL3MH8UEFW7YND+qDi2Ocdah9kjkgUZolEYI5IBLXmiAWXC7gsPLlDlmWYTCZoNBrmqBSzaR/csxxlohs6/HrmiERhlkgE5ohEUGuOWHA5SY1LVZL3GY1GaHiz2lJJ5OqDzBGJwiyRCMwRiaDGHLHgIiLykqzvf8ONcbOhP53iVPsgERER+S8WXEREHmb45zpuTl6AO1u+dal9kIiIiPwXCy4iIg+R8/S4vXQDbr2/EpryvHkxERFRacSCy0n8RYlE0Gq1vp4CeZg32geZIxKFWSIRmCMSQY05YsHlAhZd5A5JklR5MqF8Vu2DrZqi+p53PNI+yByRKMwSicAckQhqzRELLhdwWXhyhyzLlgwxR+ph3T4YhGoL30KFJzzXPsgckSjMEonAHJEIas0RCy4ncVl4EsFgMKjyTuqlVdb+X3Fj3Bzoz6QgdOjj+e2DIRU8/n2ZIxKFWSIRmCMSQY05YsFFROQiw+VruDF5ITK3/q99cKln2geJiIjIf7HgIiJykrfbB4mIiMh/seAiInKCr9oHiYiIyD9pfD2Bgnbs2IGXXnoJHTt2RPPmzdGrVy9s3LjR5rqpDRs2oHv37oiJicGjjz6KvXv32oyVkZGBCRMmoGXLloiNjcWrr76Ka9euuT1H/gWbRGCO/I/h8jVcGfo2/nl8NLSVQlHn2+Wo8t9XfVpsMUckCrNEIjBHJIIac6SogmvlypUICgrCuHHjsHjxYnTs2BGTJk3CwoULLdts374dkyZNQlxcHBISEtC8eXOMGDECf/zxh9VYo0aNwg8//IB33nkHH3zwAc6ePYthw4bBYDC4PU81BoG8R5Ik6HQ65shPyHl63Jq/FhfaDETOj3+g2sK3UOurBSjTxLfXajFHJAqzRCIwRySCWnMkyQpadi81NRWVKlWyemzSpElITEzEL7/8Ao1Gg+7duyM6OhqzZs2ybDNgwAAEBwcjISEBAHDo0CEMGDAAy5cvR/v27QEAycnJiI+Px4cffoj4+HiX5nf06FEAQExMjEuvJyL/wvZBIiIiKoqjtYGiPuEqXGwBQOPGjXHnzh1kZWUhJSUF586dQ1xcnNU28fHxSEpKQl5eHgBg//79CAkJQbt27SzbREREoHHjxti/f7/b81RQjUp+SJZl6PV65sgHsrOzcfToUWRnZxf7uBLbBwtjjkgUZolEYI5IBLXmSFEFlz2//fYbqlevjgoVKiA5ORkAEB4ebrVNgwYNoNfrkZKSAiD/06zw8HCbjyMjIiIsY7hKbQEg32COfCM7OxvHjh2zFFbmQuvWrVs4duwYstLSi2wfLKpY8yXmiERhlkgE5ohEUGOOFL1K4a+//orExESMHTsWAJCWlgYACAkJsdrO/LX5+fT0dAQHB9uMFxoaimPHjrk9L3tBkCSpyICYC7/invfUazmu8t4b82P+tq9KOoaujmu+gz0ApGXk4O9zV3Dij8No364tgk+lIH3OKJjO/4OQIb1R6c3nofnfJ1qyLCMrKwvHjh1D7dq1UbZsWa/Mt7jXFtwXZ1/rqTlxXP+Yk79lSYlz8rdxvTEnc47MX6vtGPrze+NP4xbOkRLm5Oxr7VFswXXlyhWMHj0arVq1wuDBg309HSt6vd7q0zONRoOAgADLc4UFBgYCAIxGI0wmk9VzAQEBkCQJJpMJRqPR6jnzuOaPVwsz34Xb3rharRZarRayLNssFGK+IBHIv5t34cB4alxH9hWwPYaeGte8r5IkOb2vjowL2D+GGk3+B8uij6F5vvbm5KtjaB63uGMoOt85OTk4f/48GjZsaBnfzNye/N3+7/H7WRNS/rmNezSXkbH+BzT+6Rjk5lGo+NViXKtaDRpZg+AC8yp4XEQfQ1fOEbIsW77mOUJd5whPHcOizhHm72POodrPEY7mu7hj6M18A/7xe0TB7YqbL88Rjo8LKOMc4e64zhxDo9Fo2SdJkhR/jpBl2aomKIoiC6709HQMGzYMYWFhmD9/vuUX1NDQUAD5S75XrVrVavuCz4eEhODKlSs246alpVm2cUdxq6eY3wR7Cga5MI1GY9nPwgq+8c6OW9JrzT8A3hy3uH0Fij+Gosc1v4/u7Kuz740syzCZTDyGDjznyrgZGRmWT6IqVqxo9dy5c+dw7do1XL58Gdo8GfGnzqPZL8kwBGqR1D0G0sPtcfLAcaT88xPq1gxGj7b1Ua6sDhUqVLD8YwB47r1x5hxR+K9/PEd4ZlxfnCMcHVfUvtr7xUnEuGZKO0cU5Oq++irfSv49wpyjgtsqId+eHrc0nCM8PW7BY2h+beHftZV6jnCk2AIUWHDl5OTgxRdfREZGBtatW2fVGhgREQEg/xot83+bv9bpdKhbt65lu6SkJJuq8+zZs4iMjHRrfpIkWf5n77mSXuvKc+68luN6dlxXX2v+i40n9qe0HMOinit8gk6/k4t/bmaiZuXylmNe+3Iamn97AiG3M/Fn07o40jIC+jIBwJm/oZUl1C8HIA34ZvdJSJKEChUqoGLFisjJyRE+X3deW/AfCaXMieMqf07+liUlzsnfxvXWnMy/JJsfU/p8lTCuEufk63EL50gJc3LltQUpquAyGAwYNWoUkpOTsXbtWlSvXt3q+bp166J+/frYuXMnunbtank8MTERbdq0sXyE3bFjRyxatAhJSUlo27YtgPxi68SJExg6dKjb83TmABMVVlKhRe7Lzs7GqVOnoAkIxLYfziHlnwzUrRmMB2to0H7nEdQ6cRHGmLtx/Z0n8E/mZbQMvwunTp3C3ZGN8P3x25bt49rVR7mygZZPuH744Qdf75oFc0SiMEskAnNEIqg1R4oquKZMmYK9e/di3LhxuHPnjtXNjO+55x4EBgZi5MiRGDNmDOrVq4dWrVohMTERR44cwZo1ayzbxsbGon379pgwYQLGjh2LMmXKYPbs2YiKikK3bt3cnqej/ZpE9phbCp35KJqcc+PGDezZswcAoDXKiChjQuO9F1Dn12QYdFocjGuG7rOnoIok4fyuXWjQoAGuX7+OFvffizZty+PKzSzUqFwOIRXKWMZMTU0tsiXBF5gjEoVZIhGYIxJBrTlSVMFl/uvxjBkzbJ7bs2cP6tSpg549eyI7OxsJCQlYunQpwsPDsWDBAsTGxlptP2fOHEyfPh2TJ0+GwWBA+/btMXHixGJ7MR3hzIokREUxGo3F9jiTe6pUqYJGjRpBExCI39bsx32J36JyejqyerZDcucYZGZnWp3Iy5Qpg+joaAQFBSEoqIxVoaVkzBGJwiyRCMwRiaDGHEkyKwiHHT16FLIsIyYmRlVVN3mXeTWd4hZfIdelpqZi165deKjZfTDMWoPML/dCjr0Hlf5vFCrd39jyfPfu3REUFITTp0+jYcOGCAoKKnbc7Oxsh7f1BuaIRGGWSATmiETwtxwdPXoUABATE1Psdor6hIuIyF1ynh41v/4daeNWQFu+HKotmogKfbvZPXEHBQWVeJJ0ZVsiIiIiMxZcRKQaWft+xZ2xs1Dn7GWUf64Xqk54Adr/3bzYLCgoyNI+SERERORpLLic5A8fb5Lyqa032dcMl6/hxqQFyPxyL8q2boYaK6ahzD0N7G6rpk+qmCMShVkiEZgjEkGNOWLB5QIWXeQOSZLcXryF8sl5etxesh63Zq2CpkIQqi2ehAqPP1QqfkaZIxKFWSIRmCMSQa05Ut8eeQGXhSd3FFynhjlyXda+X3Fj3Gzoz15C6LDHUenN56EJLu/raXkNc0SiMEskAnNEIqg1Ryy4nMRFHUkE8wo85DzDpau4MXmhpX2w+vKpRbYPqh1zRKIwSyQCc0QiqDFHLLiIyC/82z64EpoK5UpV+yARERH5LxZcRKR4Wd/9ghvj55Ta9kEiIiLyXyy4iEixDJeu5q8++NV3KNumdLcPEhERkX9iwUVEisP2QSIiIlILFlxOkiSJv/SRWyRJQmBgoK+noVhsH3QMc0SiMEskAnNEIqg1Ryy4iEgRbNoHV7yLMo0jfD0tIiIiIrew4HIB78NF7pBlGUajEVqtljnC/9oHF6/DrQ9XsX3QCcwRicIskQjMEYmg1hyx4HIS78NFIphMJmi1Wl9Pw+fYPuge5ohEYZZIBOaIRFBjjlhwEZHXsX2QiIiISgsWXETkNTbtg0smo0KfrqpqGyAiIiIqiAUXEXlF1t6f89sHz11G6At9UemN59g+SERERKrHgstJ/Es8iRAQUHp+9PQXr+LmpPnI3LYPZds2R/WPp7F9UJDSlCPyLGaJRGCOSAQ15kh9e+QFLLrIHaXlXm5ybl5+++Ds1Wwf9IDSkiPyPGaJRGCOSAS15ogFlwu4LDy5Q5ZlmEwmaDQa1eaI7YOeVxpyRN7BLJEIzBGJoNYcseByEpeFJxGMRiM0Go2vpyEc2we9S605Iu9jlkgE5ohEUGOOWHARkdus2geDy7N9kIiIiOh/WHARkVvYPkhERERUNBZcROQStg8SERERlYwFl5PYIkUi+HNvMtsHlcOfc0TKwiyRCMwRiaDGHLHgcgF/sSR3SJLkt/eYyNr7M26Mmw39+X/YPuhj/pwjUhZmiURgjkgEteZIfXtE5Af87dYCNu2DK//L9kEF8LcckXIxSyQCc0QiqDFHLLicJMuyKoNA3iPLMvR6PXQ6neJzZNM++NHbqNC7i+LnXRr4U45I2ZglEoE5IhHUmiMWXERkV9a3B/NXHzz/D0JffAKVxjzL9kEiIiIiJ7HgIiIr+otXcXPifGRuZ/sgERERkbtYcBERgP+1Dy76PL99MKQC2weJiIiIBGDBRURsHyQiIiLyEBZcTuJf+0kEnU7n6ykAKNQ+2C4WNVb9HwIbhft6WuQgpeSI/B+zRCIwRySCGnPEgssFLLrIHUrIj0374NK3UeExtg/6E75XJAqzRCIwRySCWnPEgssFXBae3CHLMoxGI7RarU9yZGkfvPAPQl94Iv/mxRXKeX0e5B5f54jUg1kiEZgjEkGtOWLB5SRZln09BVIBk8kErVbr1e/J9kH18UWOSJ2YJRKBOSIR1JgjFlxEKsf2QSIiIiLfYcFFpGJW7YMvPoFKY9g+SERERORNLLiIVMiqfbD9vaix+v8QGMX2QSIiIiJvY8HlJLZhkQie6k1m+2DporYed/IdZolEYI5IBDXmiAWXC/jLK7lDkiSPnEyy9hzEjQlsHywtPJUjKn2YJRKBOSIR1JojFlwu4LLw5A5Zli0ZEpEjfcoV3Jw0H5nb97N9sBQRnSMqvZglEoE5IhHUmiMWXE7isvAkgsFgcPtO6nJuHm4v/By35qyGJjSY7YOlkIgcEQHMEonBHJEIaswRCy4iP8T2QSIiIiL/wIKLyI+wfZCIiIjIv7DgIvIDhdsHqy99B+Uf68z2QSIiIiKFY8HlJP6CSyI4k6OsPf+7eXEK2wfJGs9HJAqzRCIwRySCGnPEgssFagwCeY8kSQ5dDKq/8E9++2Di9/ntg5+wfZD+5WiOiErCLJEIzBGJoNYcseAiUhi2DxIRERGpBwsuF/A+XOQOWZZhMBgQEBBgk6OC7YNhw/uh4n+eZfsg2VVcjoicwSyRCMwRiaDWHLHgchLvw0UiFM5RwfbBoA5sHyTH8HxEojBLJAJzRCKoMUcsuIh8yJSTi7SFn+PW3E/YPkhERESkQiy4iHwka89PuDlhHtsHiYiIiFSMBReRl+kv/IMbE+che+cPbB8kIiIiUjkWXE5iqxe5ytI+OGc1NGEhqLb0bVR4rAszRS5T49K55BvMEonAHJEIaswRCy4X8BdkclbmNz/h5oS5bB8kYXgeIlGYJRKBOSIR1JojFlwu4LLw5Cib1QfXTIfu7rtgNBohMUfkBlmWYTQaodVqmSNyC7NEIjBHJIJac8SCy0lqXKqSxCvcPlhw9UFZlmEymaDVan09TfJzzBGJwiyRCMwRiaDGHLHgIhIs85ufcGP8HBguXmH7IBEREVEpx4KLSBDz6oNZOw4gqMO9qLl2BgIj6/t6WkRERETkQyy4iNxk1T5YMRTVE6agfK9Oquo9JiIiIiLXsOByEn+JpoKs2gdf6o+Krz/jUPug2nqTyTeYIxKFWSIRmCMSQY050vh6AgWdP38ekydPRq9evXDPPfegZ8+edrfbsGEDunfvjpiYGDz66KPYu3evzTYZGRmYMGECWrZsidjYWLz66qu4du2akHmy6CL9hX/wz+DxuPLkG9DVrY66+1ai8uSXHCq2JElS3eo75H3MEYnCLJEIzBGJoNYcKarg+vvvv7Fv3z7cddddaNCggd1ttm/fjkmTJiEuLg4JCQlo3rw5RowYgT/++MNqu1GjRuGHH37AO++8gw8++ABnz57FsGHDYDAY3J4nVyosvUw5uUidtRIp7QYi948/UT1hCmp+Mcepa7XMqxQyR+QO5ohEYZZIBOaIRFBrjhTVUti5c2d07doVADBu3DgcO3bMZpt58+ahR48eGDVqFACgdevW+Ouvv7Bw4UIkJCQAAA4dOoQDBw5g+fLlaN++PQAgPDwc8fHx2L17N+Lj412eo9oCQI7L/DoJNybMdbp90B6DwaDKO6mTdzFHJAqzRCIwRySCGnOkqE+4NJrip5OSkoJz584hLi7O6vH4+HgkJSUhLy8PALB//36EhISgXbt2lm0iIiLQuHFj7N+/X/zESdUs7YNPvQldvRpOtQ8SERERUemmqE+4SpKcnAwg/9Oqgho0aAC9Xo+UlBQ0aNAAycnJCA8Pt+n/jIiIsIzhDnufcplvaGuPeR7FPe+p13Jc18c15eQibdHnuD3nE2gqhqLasiko/8iDlvFcnZP5MSXtqy/HVeKc/GFcWZatHlPCnDiuf8zJ37KkxDn527jemJM5R+av1XYM/fm98adxC+dICXNy9rX2+FXBlZaWBgAICQmxetz8tfn59PR0BAcH27w+NDTUbpuis/R6vVUxp9FoEBAQYHmusMDAQACA0WiEyWSyei4gIACSJMFkMsFoNFo9Zx5XlmW745o/brU3rlarhVarhSzLNtetSZJkea3BYLAJjKfGdWRfAdtj6KlxzfsqSZLNvmbvOYjbkxfAcPEqQof3R4VXn4KmfJBlnx0ZF7B/DM2f5Io+hub3xt6cfHEMC45rb199ne/ijqE38w24do6QZdnyNc8Rvsl3cfvqzjnCU8ewqHOE+fuYc8hzhDrOEQXH9cYxLLhdcfPlOcLxcQFlnCPcHdeZY2g0Gi37JEmS4s8RsvzvH+CL41cFl1KYg1HUc0UpGOTCNBpNkS2VBd94Z8ct6bXmHwBvjlvcvgLFH0PR45rfR/O+6i/8g5sT5yNr5wEEdbgPNde+B93ddxU5ZknztXcMzf8wqfUY2lPcvvoq3+78zHnqvXHmHFHwJM9zhOfGdTffRY1rpoRjWPgXBqUdQ54j/qXk3yPMOSq470rIt6fHLQ3nCE+PW/AYajQaq0Kr4DZKPEc4UmwBflZwhYaGAshf8r1q1aqWx9PT062eDwkJwZUrV2xen5aWZtnGVZIkFXtCK+m1rjznzms5rmPjyrl5uL3wM0v7YPVlU1H+0Qcd+kFy9vsWlyF3xnXkOSWOq8Q5+cO4kiRZ/oqslDlxXP+Yk79lSYlz8rdxvTGnwjkSNa7I55Q4rhLn5Mtx7eXI13Ny9bUFKWrRjJJEREQAgM11WMnJydDpdKhbt65lu7Nnz9p8BHj27FnLGERmmbt/REqHZ3Drg5UIHfYE6v24BhV6dXLqB4mIiIiIyB6/Krjq1q2L+vXrY+fOnVaPJyYmok2bNpaKuGPHjkhLS0NSUpJlm7Nnz+LEiRPo2LGj2/Nw5iI5Ui79+cv4Z9B4XHl6LHR31UTd/atQefJwj68+aO41Zo7IHcwRicIskQjMEYmg1hwpqqUwOzsb+/btAwBcunQJd+7csRRXLVu2RKVKlTBy5EiMGTMG9erVQ6tWrZCYmIgjR45gzZo1lnFiY2PRvn17TJgwAWPHjkWZMmUwe/ZsREVFoVu3bm7NUW0BKI1MObn/tg9WCnOqfVAU5ohEYI5IFGaJRGCOSAQ15kiSFbRXFy9eRJcuXew+t3r1arRq1QoAsGHDBiQkJODy5csIDw/H66+/jk6dOlltn5GRgenTp+Prr7+GwWBA+/btMXHiRFSvXt3l+R09ehSyLCMmJobtZn4qc/ePuPHWvP/dvHgAKr4+2Ov30zL/9aa4xVeISsIckSjMEonAHJEI/pajo0ePAgBiYmKK3U5RBZfSseDyX/rzl3HDvPrgA/ejyvRRCCxh9UFP8beTCSkTc0SiMEskAnNEIvhbjhwtuBTVUkgkmiknF7cXfIrbc9f4rH2QiIiIiEovFlxO4i/q/iO/fXAuDJeuIWx4f5+0DxaluPs6EDmKOSJRmCUSgTkiEdSYI/XtkRew6FK2wu2DNT+d6bP2QXvM95kgcgdzRKIwSyQCc0QiqDVHLLhcYL6bOimLTfvg8qko/4jy2gdlWYbJZHLqDuVEhTFHJAqzRCIwRySCWnPEgstJXGNEmazaB1/qj4qjldM+aI/RaIRG41e3wSMFYo5IFGaJRGCOSAQ15ogFF/k1/fnLuPHWPGTt+kGR7YNEREREVLqx4CK/ZMr+X/vgPGW3DxIRERFR6caCi/xO5u4f8m9e7Cftg0RERERUerHgchI/QfEdq/bBB1ug5mfvI7BhPV9PyyVq600m32COSBRmiURgjkgENeaIBZcLWHR5l9raByVJUuU9Jsi7mCMShVkiEZgjEkGtOVLfHnkBl4X3Hpv2wdefgaZ8kK+n5ZaCK10yR+Qq5ohEYZZIBOaIRFBrjlhwOYnLwnuH/txl3JiojvZBe/R6PXQ6na+nQX6OOSJRmCUSgTkiEdSYIxZcpCiW9sG5a6Cp7P/tg0RERERUurHgIsXI3P0DbkyYC8Pl66ppHyQiIiKi0o0FF/mcTfvg5x+oqn2QiIiIiEovFlxOYmubODbtgyveRfmeD5SKY1wa9pE8jzkiUZglEoE5IhHUmCMWXC5QYxC8rTS3D0qSpLqLQcn7mCMShVkiEZgjEkGtOWLBRV6lP3cZN96ai6zdP7J9kIiIiIhUjwWXC3gfLueZsnNxe/5a3J63Ftoqpat9sDBZlmEwGBAQEFAq95/EYI5IFGaJRGCOSAS15ogFl5N4Hy7nWbUPvjwAFUcPLjXtg0VhjkgE5ohEYZZIBOaIRFBjjlhwkcewfZCIiIiISjsWXCQc2weJiIiIiPKx4CKhMnf9gBtvsX2QiIiIiAhgweU0fkpjH9sHnRMQwB89ch9zRKIwSyQCc0QiqDFH6tsjL2DR9S+b9sGPp6F8j448RsWQJInHh9zGHJEozBKJwByRCGrNEQsuF3BZ+HxW7YOvPImKowaxfdABsizDZDJBo9EwR+Qy5ohEYZZIBOaIRFBrjlhwOUmNS1U6y6Z9cN0HCGzA9kFnGI1GaDQaX0+D/BxzRKIwSyQCc0QiqDFHLLjIYWwfJCIiIiJyDgsucgjbB4mIiIiInMeCi4qlP3spv33w6yS2DxIREREROYkFl5NKS/ucKTsXt+etwe35n0JbtSLbBwVTW28y+QZzRKIwSyQCc0QiqDFHLLhcoPaiI3PXD7gxYS4MV27k37yY7YNCSZKkyntMkHcxRyQKs0QiMEckglpzpL498gK1Lgtv0z64nu2DnlBwpUs15oi8gzkiUZglEoE5IhHUmiMWXE5S47LwbB/0Pr1eD51O5+tpkJ9jjkgUZolEYI5IBDXmiAVXKSbLMrJ2/YAbb81j+yARERERkQew4Cql9Gcv4caEOcj65icEdWrJ9kEiIiIiIg9gwVXK2LQPrvwvysd3YPsgEREREZEHsOAqJWzaB803Ly5X1tdTIyIiIiJSLRZcTpIkye8+DbJtH5yFwAZ1fT2tUkuSJOh0Or/LESkLc0SiMEskAnNEIqg1Ryy4VMyUlZPfPrjgM7YPKgzfAxKBOSJRmCUSgTkiEdSYIxZcLlD6fbhkWUbWzgO4MXE+2wcVSJZlGI1GaLVaReeIlI05IlGYJRKBOSIR1JojFlxOUvp9uPTJF/NvXsz2QUUzmUzQarW+ngb5OeaIRGGWSATmiERQY45YcKmEuX3w1vxPEVCtEtsHiYiIiIgUgAWXn2P7IBERERGRcrHg8mNsHyQiIiIiUjYWXE5SQose2wf9n9p6k8k3mCMShVkiEZgjEkGNOWLB5QJfFTaW9sG35sFw9SbbB/2UJEmqPJmQdzFHJAqzRCIwRySCWnPEgssFvlgWXp98ETcmzEXWnp8Q1LkVam74kO2DfkqWZUuG+KkkuYo5IlGYJRKBOSIR1JojFlxO8vay8IXbB2us+i/KxbF90N8ZDAbodDpfT4P8HHNEojBLJAJzRCKoMUcsuBSqcPtgxRFPIey1gWwfJCIiIiLyIyy4FIjtg0RERERE6sCCS0FMWTm4PXcNbi1g+yARERERkRqw4HKSJ4ofWZaRteP7/JsXs32wVNBoNL6eAqkAc0SiMEskAnNEIqgxRyy4XCCy6CrYPliuS2u2D5YCkiQhIIA/euQe5ohEYZZIBOaIRFBrjtS3R37Cqn2wemXUWP1/KPdwe7YPEhERERGpCAsuJxW8P4Crr2f7YOkmyzL0ej10Oh0LbHIZc0SiMEskAnNEIqg1Ryy4vIjtg0REREREpQsLLi9g+yARERERUenEgsuDbNoHRz6FsFfZPkhEREREVFqw4PKQvDMpuDFhLrK/PYhyXVqj1sbZ0EXU8fW0iIiIiIjIi1hwOamkNkBTVg5uzfkEtxd+xvZBKpJOp/P1FEgFmCMShVkiEZgjEkGNOVJ1wXXmzBlMmzYNhw4dQvny5dGrVy+MGjUKgYGBbo1rr3hi+yA5isU3icAckSjMEonAHJEIas2RaguutLQ0PPPMM6hfvz7mz5+Pq1evYsaMGcjJycHkyZPdGrvwsvBsHyRnyLIMo9EIrVar2hMLeR5zRKIwSyQCc0QiqDVHqi24Pv/8c2RmZmLBggUICwsDABiNRkyZMgUvvvgiqlev7tK4sixb/pvtg+Qqk8kErVbr62mQn2OOSBRmiURgjkgENeZI4+sJeMr+/fvRpk0bS7EFAHFxcTCZTPjhhx/cGluWZdzZvh8p7QYibdHnqDjyKdQ98AnKx3VgsUVERERERBaq/YQrOTkZjz/+uNVjISEhqFq1KpKTk10eV7p4DVf++2Z++2DX1qjyxRy2DxIRERERkV2qLbjS09MREhJi83hoaCjS0tJcGlOv1yNk6DRkVgqF/t3hyGobgxuZt4Cjt9ydLpUyha8DJHIFc0SiMEskAnNEIvhTjvLy8hyaq2oLLk+QJAnp2z5U5XKV5F3+ciIhZWOOSBRmiURgjkgEf8qRJEmlu+AKCQlBRkaGzeNpaWkIDQ11aczY2Fh3p0VERERERKWIahfNiIiIsLlWKyMjA9evX0dERISPZkVERERERKWJaguujh074scff0R6errlsZ07d0Kj0aBdu3Y+nBkREREREZUWklzwxlIqkpaWhh49eiA8PBwvvvii5cbHjzzyiNs3PiYiIiIiInKEagsuADhz5gzeffddHDp0COXLl0evXr0wevRoBAYG+npqRERERERUCqi64CIiIiIiIvIl1V7DRURERERE5GssuIiIiIiIiDyEBRcREREREZGHsOAiIiIiIiLyEBZcREREREREHsKCi4iIiIiIyENYcBEREREREXlIgK8n4A/OnDmDadOmWd1AedSoUbyBMjls06ZNGD9+vM3jw4YNw5gxY3wwI/IH58+fx/Lly3H48GH8/fffiIiIwLZt22y227BhA5YtW4bLly8jPDwco0ePRqdOnXwwY1IqR7I0aNAg/PzzzzavTUxMRIMGDbw1VVKoHTt24Msvv8Tx48eRnp6Ou+66C4MGDcLjjz8OSZIs2/F8RCVxJEtqOx+x4CpBWloannnmGdSvXx/z58/H1atXMWPGDOTk5GDy5Mm+nh75mWXLliE4ONjydfXq1X04G1K6v//+G/v27UOzZs1gMplg7z7127dvx6RJkzB8+HC0bt0aiYmJGDFiBNauXYvmzZt7f9KkSI5kCQDuvfdejB071uqxOnXqeGOKpHArV65E7dq1MW7cOFSsWBE//vgjJk2ahCtXrmDEiBEAeD4ixziSJUBd5yNJLuqsSwCAjz76CEuWLMHevXsRFhYGAFi3bh2mTJmCvXv38hdmcoj5E66kpCRUqlTJ19MhP2EymaDR5Hd+jxs3DseOHbP5VKJ79+6Ijo7GrFmzLI8NGDAAwcHBSEhI8Op8SbkcydKgQYNQrlw5fPTRR76YIilcamqqzb9fkyZNQmJiIn755RdoNBqej8ghjmRJbecjXsNVgv3796NNmzaWYgsA4uLiYDKZ8MMPP/huYkSkeuZfkIuSkpKCc+fOIS4uzurx+Ph4JCUlIS8vz5PTIz9SUpaISmLvj4WNGzfGnTt3kJWVxfMROaykLKkRz8AlSE5ORkREhNVjISEhqFq1KpKTk300K/JXPXv2ROPGjdGlSxd89NFHMBqNvp4S+THzOSg8PNzq8QYNGkCv1yMlJcUX0yI/9vPPP6N58+aIiYnBwIED8csvv/h6SqRgv/32G6pXr44KFSrwfERuKZglMzWdj3gNVwnS09MREhJi83hoaCjS0tJ8MCPyR1WrVsXIkSPRrFkzSJKEb7/9FnPmzMHVq1d5LSC5zHwOKnyOMn/NcxQ5o0WLFujVqxfq16+Pa9euYfny5XjuuefwySefIDY21tfTI4X59ddfkZiYaLnGhucjclXhLAHqOx+x4CLygg4dOqBDhw6Wr9u3b48yZcpg1apVGD58OKpVq+bD2RERAa+++qrV1w8++CB69uyJRYsW8fobsnLlyhWMHj0arVq1wuDBg309HfJjRWVJbecjthSWICQkBBkZGTaPp6WlITQ01AczIrWIi4uD0WjEyZMnfT0V8lPmc1Dhc1R6errV80SuKFeuHB544AEcP37c11MhBUlPT8ewYcMQFhaG+fPnW64P5PmInFVUluzx9/MRC64SRERE2FyrlZGRgevXr9tc20VE5E3mc1Dhc1RycjJ0Oh3q1q3ri2kRkUrl5OTgxRdfREZGhs1tTng+ImcUlyU1YsFVgo4dO+LHH3+0/IUGAHbu3AmNRoN27dr5cGbk7xITE6HVanHPPff4eirkp+rWrYv69etj586dVo8nJiaiTZs2vDk7uSUrKwvfffcdYmJifD0VUgCDwYBRo0YhOTkZy5Yts7ktDs9H5KiSsmSPv5+PeA1XCQYMGIBPPvkEr7zyCl588UVcvXoVM2fOxIABA3gPLnLYkCFD0KpVK0RFRQEA9uzZg/Xr12Pw4MGoWrWqj2dHSpWdnY19+/YBAC5duoQ7d+5Yfplp2bIlKlWqhJEjR2LMmDGoV68eWrVqhcTERBw5cgRr1qzx5dRJYUrKkvkXn4ceegi1a9fGtWvX8PHHH+P69euYO3euL6dOCmG+/+i4ceNw584d/PHHH5bn7rnnHgQGBvJ8RA4pKUtHjhxR3fmINz52wJkzZ/Duu+/i0KFDKF++PHr16oXRo0fzrzXksGnTpuH777/HlStXYDKZUL9+fTzxxBMYNGgQJEny9fRIoS5evIguXbrYfW716tVo1aoVAGDDhg1ISEjA5cuXER4ejtdffx2dOnXy5lRJ4UrKUo0aNTB16lT8+eefuH37NoKCghAbG4sRI0agadOmXp4tKVHnzp1x6dIlu8/t2bMHderUAcDzEZWspCwZjUbVnY9YcBEREREREXkIr+EiIiIiIiLyEBZcREREREREHsKCi4iIiIiIyENYcBEREREREXkICy4iIiIiIiIPYcFFRERERETkISy4iIiIiIiIPIQFFxERkR2ZmZlo06YNvvzyS19PxeLWrVto3rw59u3b5+upEBGRg1hwERGR31m7di2ioqLwxBNPeOx7rF69GuXLl0ePHj089j2cVbFiRfTt2xdz58719VSIiMhBLLiIiMjvfPXVV9DpdDhy5AjOnz8vfHy9Xo/Vq1fjiSeegFarFT6+O5588kkcP34cSUlJvp4KERE5gAUXERH5lZSUFBw6dAgvvfQSdDodvvrqK+Hf47vvvkNqairi4uKEj+2uBg0aIDIyEps3b/b1VIiIyAEsuIiIyK989dVX0Gq16N+/P9q2bWu34Jo3bx4aNWpk8ynQpEmTEB0djVOnThX7Pb755hvUrl0b9erVs3p83LhxiI2NxeXLl/Hiiy8iNjYWHTp0wNq1awEAf/75JwYPHozmzZujU6dONnPbtGkToqKi8Ouvv2LatGlo3bo17r//fkyePBl5eXlIT0/Hm2++iRYtWqBFixaYOXMmZFm2mV/btm2xd+9eu88REZGysOAiIiK/8tVXX+H+++9HlSpVEBcXh3PnzuHIkSNW27z00kto3Lgx3nrrLdy5cwcA8P3332P9+vV4+eWX0ahRo2K/x6FDh9CkSRO7zxmNRgwbNgw1atTAmDFjULt2bUydOhWbNm3C0KFDER0djTFjxqB8+fIYO3YsUlJSbMaYNm0azp07h5EjR6Jz585Yt24d5s6di+HDh8NoNGL06NG47777sHz5cmzdutXm9U2aNEF6ejr+/vtvRw8bERH5CAsuIiLyG8eOHUNycjLi4+MBAF27drXbVqjT6fDee+/h2rVrmDFjBtLT0/HWW28hOjoaL7zwQrHfw2Aw4MKFC6hTp47d53Nzc/Hoo49iypQpePrpp7F06VKULVsWEyZMwPjx4/Hmm29i4MCBmDdvHoxGI7Zs2WIzRuXKlZGQkICnn34aM2fORGxsLJYvX467774bs2bNwlNPPYWFCxeiRo0a+OKLL2xeX7duXQDA6dOnHTlsRETkQyy4iIjIb3z11VcICAhAt27dAADBwcHo0KEDEhMTYTQarbaNjIzEq6++ig0bNmDIkCG4desW3nvvPQQEBBT7PdLS0iDLMkJCQorcpuDqiCEhIQgPD0dQUJDVNV8REREICQmx+wlX3759IUmS5eumTZtClmX07dvX8phWq0V0dLTd15vnduvWrWL3hYiIfI8FFxER+QWj0Yjt27ejdevWqFSpkuXx+Ph43Lhxw+6qfUOGDEGjRo1w5MgRjBgxAg0bNnT4+xV1fVSZMmWsvj+QX/jVqFHDqogyP56enm4zRq1atWy2A4CaNWvaPJ6WllbkHAt/PyIiUh4WXERE5Bd++uknXL9+3WblwM6dO6Ns2bJ2F89ISUmxLBv/119/OfR9QkNDIUmS3UIJQJHLxBf1uL3CTaOx/89vUY8XZi7CKlas6ND2RETkOyy4iIjIL5jvvfXQQw9ZPV6+fHk88MAD+Prrr5GTk2N53GQyYdy4cahQoQKGDx+Obdu2Yffu3SV+n4CAANSrVw8XL14Uvg+imOfWoEEDH8+EiIhKwoKLiIgULycnB7t370bbtm0RGhpq8/zDDz+MzMxMfPvtt5bHPv74Yxw6dAhTp07Fa6+9htjYWLzzzjtITU0t8fs1b94cx44dE7oPIh0/fhzBwcG4++67fT0VIiIqQfFXDhMRESnAt99+i8zMTADA0qVLbZ7Pzs4GAHz55ZeIj4/HmTNnMHfuXPTp0wedO3cGAMyYMQOPPfYYpkyZgrlz5xb7/bp06YKtW7fi7NmzCA8PF7w37vvxxx/RqVMnXsNFROQHWHAREZHiffnllwCAffv2Yd++fUVud+DAAdy6dQtjx45FxYoVMWHCBMtz9evXx+uvv47//ve/SExMtCwtb0+nTp1QsWJF7NixAy+//LK4HRHgzJkz+Ouvv6z2jYiIlEuSeZt6IiIiGwsXLsSmTZuwe/fuIhfE8IX//ve/+PXXX7Fp0yZ+wkVE5Ad4DRcREZEdzz77LLKysrB9+3ZfT8Xi1q1b2LhxI0aNGsVii4jIT/ATLiIiIiIiIg/hJ1xEREREREQewoKLiIiIiIjIQ1hwEREREREReQgLLiIiIiIiIg9hwUVEREREROQhLLiIiIiIiIg8hAUXERERERGRh7DgIiIiIiIi8hAWXERERERERB7CgouIiIiIiMhDWHARERERERF5yP8DqS7uIY6XFpoAAAAASUVORK5CYII=",
      "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": 789,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 3.91693\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.97923\n",
      "P(0, chi²)= 0.58263\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    1.250317\n",
      "1    0.360714\n",
      "2    0.037755\n",
      "3    0.653693\n",
      "4    1.339556\n",
      "5    0.274893\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": 790,
   "id": "6a910226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.4106 +- 0.2224\n",
      "BC      = 1.4233 +- 3.1272\n",
      "cov_ABC = -0.632350\n",
      "P(0, chi²)= 0.5436\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": 791,
   "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": 792,
   "id": "538ddb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 3.66772\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.91693\n",
      "P(0, chi²)= 0.54716\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    1.735332\n",
      "1    0.503968\n",
      "2    0.029958\n",
      "3    0.281908\n",
      "4    1.086900\n",
      "5    0.029650\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": 793,
   "id": "c7f54cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 23.4182722 ± 0.2217067\n",
      "BY        = 1.3248937 ± 3.1178836\n",
      "cov_ABY   = -0.628605644189924\n",
      "chi²      = 3.66651\n",
      "chi² rid  = 0.91663\n",
      "P(0, chi²)= 0.54698\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": 794,
   "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": 795,
   "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)= 0.58263\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 23.41058 ± 0.22237\n",
      "Bc  = 1.42328 ± 3.12716\n",
      "P(0, chi²)= 0.54716\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 23.41827 ± 0.22171\n",
      "By  = 1.32489 ± 3.11788\n",
      "P(0, chi²)= 0.54698\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": "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": 796,
   "id": "95dde99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_T    = 0.000100\n",
      "0    0.194561\n",
      "1    0.226829\n",
      "2    0.258983\n",
      "3    0.291654\n",
      "Name: w, dtype: float64\n",
      "0    0.000101\n",
      "1    0.000114\n",
      "2    0.000105\n",
      "3    0.000108\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Meq = df.m + ( m_mol * 0.38 )\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",
    "res_b = 0.01 / np.sqrt(6) # riso bilancia\n",
    "res_T = 0.0001             # riso T\n",
    "\n",
    "T = 2 * np.pi / df.w\n",
    "res_T2 = 2 * T * res_T\n",
    "\n",
    "res_uAd = np.sqrt(  (1 / Meq)**2 * res_T2**2 + (T2 / Meq**2)**2 * res_b**2)\n",
    "\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",
    "\n",
    "uT2 = np.sqrt(uT2**2 + res_T2**2)\n",
    "uMeq = np.sqrt(uMeq**2 + res_b**2)\n",
    "\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": 797,
   "id": "8c9f5b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    24.342754\n",
      "1    24.365364\n",
      "2    24.349862\n",
      "3    24.349744\n",
      "dtype: float64\n",
      "0    0.012642\n",
      "1    0.012325\n",
      "2    0.009869\n",
      "3    0.009059\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": 798,
   "id": "0d899f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 24.351452510052987\n",
      "sigmaC:  0.0053229729724284785\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": 799,
   "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": 800,
   "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:                Tue, 07 Apr 2026   Prob (F-statistic):           2.12e-06\n",
      "Time:                        17:02:51   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      -4.426e-06      0.000     -0.012      0.991      -0.002       0.002\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.                     1.03e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.03e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Adols  = 0.00162 ± 0.00000\n",
      "Bdols  = -0.00000 ± 0.00036\n",
      "R²     = 1.00000\n",
      "Kdols  = 24.35158 ± 0.03547\n",
      "KBdols = -8919.17465 ± 721171.83239\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": 801,
   "id": "a0ba5c8d",
   "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=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": 802,
   "id": "3c7c05e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 1.82343\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.91171\n",
      "P(0, chi²)= 0.59817\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.549870\n",
      "1    1.167744\n",
      "2    0.046649\n",
      "3    0.059166\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": 803,
   "id": "698e3c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AdC      = 0.00162 ± 0.00000\n",
      "BdC      = 0.00003 ± 0.00036\n",
      "cov_ABdC = -0.000000\n",
      "P(0,chi²)= 0.59375\n",
      "KdC      = 24.35469 ± 0.03539\n",
      "KBdC     = 1201.54723 ± 13001.21079\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": 804,
   "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": 805,
   "id": "12bb0479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 1.80159\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.90079\n",
      "P(0, chi²)= 0.59375\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.382791\n",
      "1    1.328500\n",
      "2    0.031079\n",
      "3    0.059218\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": 806,
   "id": "385db415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdY       = 0.0016210 ± 0.0000024\n",
      "BdY       = 0.0000328 ± 0.0003555\n",
      "cov_ABdY  = -8.279596207327886e-10\n",
      "chi²      = 1.80159\n",
      "chi² rid  = 0.90079\n",
      "P(0, chi²)= 0.59375\n",
      "KdY       = 24.35469 ± 0.03539\n",
      "KBdY      = 1201.80652 ± 13006.82292\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": 807,
   "id": "698bc57c",
   "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 = 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": 808,
   "id": "fc32f9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 24.35145 ± 0.00532\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Adols    = 0.0016212 ± 0.00000\n",
      "Bdols    = -0.0000044 ± 0.00036\n",
      "P(0,chi²)= 0.5981652\n",
      "Kdols    = 24.3515777 ± 0.03547\n",
      "KBdols   = -8919.1746464 ± 721171.83239\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AdC      = 0.0016210 ± 0.00000\n",
      "BdC      = 0.0000329 ± 0.00036\n",
      "P(0,chi²)= 0.5937530\n",
      "KdC      = 24.3546921 ± 0.03539\n",
      "KBdC     = 1201.54723 ± 13001.21079\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ady      = 0.00162 ± 0.00000\n",
      "Bdy      = 0.00003 ± 0.00036\n",
      "P(0,chi²)= 0.59375\n",
      "KdY      = 24.35469 ± 0.03539\n",
      "KBdY     = 1201.80652 ± 13006.82292\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:.7f} ± {uAdols:.5f}\")\n",
    "print(f\"Bdols    = {Bdols:.7f} ± {uBdols:.5f}\")\n",
    "print(f\"P(0,chi²)= {Pdols:.7f}\")\n",
    "print(f\"Kdols    = {Kdols:.7f} ± {uKdols:.5f}\")\n",
    "print(f\"KBdols   = {KBdols:.7f} ± {uKBdols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AdC      = {AdC:.7f} ± {uAdC:.5f}\")\n",
    "print(f\"BdC      = {BdC:.7f} ± {uBdC:.5f}\")\n",
    "print(f\"P(0,chi²)= {PdC:.7f}\")\n",
    "print(f\"KdC      = {KdC:.7f} ± {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": "6f4edfa1",
   "metadata": {},
   "source": [
    "# Dinamica Cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "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": 810,
   "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": 811,
   "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_473293/2995330097.py:10: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  media = num / den\n",
      "/tmp/ipykernel_473293/2995330097.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": 812,
   "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": 813,
   "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:                Tue, 07 Apr 2026   Prob (F-statistic):                nan\n",
      "Time:                        17:02:52   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.                     1.03e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.03e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\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": 814,
   "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": 815,
   "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": 816,
   "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": 817,
   "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": 818,
   "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": 819,
   "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_473293/788188722.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
      "/tmp/ipykernel_473293/788188722.py:17: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
      "/tmp/ipykernel_473293/788188722.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_473293/788188722.py:39: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  sA = np.sqrt(1 / S)\n",
      "/tmp/ipykernel_473293/788188722.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": 820,
   "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": 821,
   "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": 822,
   "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": 823,
   "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": 824,
   "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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