{
 "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": 232,
   "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": 233,
   "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": 234,
   "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": 234,
     "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": 235,
   "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": 236,
   "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": 237,
   "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": 238,
   "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": 239,
   "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": 240,
   "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:                Wed, 08 Apr 2026   Prob (F-statistic):           5.50e-08\n",
      "Time:                        09:57:16   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": 241,
   "id": "8d795186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = Bols + Aols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare OLS\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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": 243,
   "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": 244,
   "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": 245,
   "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": 246,
   "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": 247,
   "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": 248,
   "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": 249,
   "id": "95dde99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_T    = 0.000800\n",
      "0    0.194561\n",
      "1    0.226829\n",
      "2    0.258983\n",
      "3    0.291654\n",
      "Name: w, dtype: float64\n",
      "0    0.000707\n",
      "1    0.000765\n",
      "2    0.000815\n",
      "3    0.000864\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Meq = df.m + ( m_mol * 0.3333 )\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 = 0.0008             # 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": 250,
   "id": "8c9f5b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    24.059519\n",
      "1    24.122421\n",
      "2    24.137082\n",
      "3    24.160799\n",
      "dtype: float64\n",
      "0    0.087485\n",
      "1    0.081324\n",
      "2    0.075925\n",
      "3    0.071586\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": 251,
   "id": "0d899f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 24.125209570730643\n",
      "sigmaC:  0.0392090048263168\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": 252,
   "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": 253,
   "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:                Wed, 08 Apr 2026   Prob (F-statistic):           2.12e-06\n",
      "Time:                        09:57:17   Log-Likelihood:                 32.343\n",
      "No. Observations:                   4   AIC:                            -60.69\n",
      "Df Residuals:                       2   BIC:                            -61.91\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0023      0.000      6.369      0.024       0.001       0.004\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.                     1.01e+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, 1.01e+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  = 0.00226 ± 0.00035\n",
      "R²     = 1.00000\n",
      "Kdols  = 24.35158 ± 0.03547\n",
      "KBdols = 17.47973 ± 2.74463\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": 254,
   "id": "a0ba5c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zt4CrsvGk91D6MaKkfDztGFHZ91DrY0R1ZKP1McJdj7NXZuPOx4jyfEcHhDVhJV2blZGRgcuXL6N+/foVHu/GG2/Evn37iox/6NChYh3q6dOn0bhx40rXXfAXlbIapso0U5K/yLvDuFfmfK1mq7pqkjBuSe+JFtkU/CXL2fuGO2cjZd2CL/ql5SNtW91t3KrWxGxk1lTwXGn5SKtXYk3MRm5N7paNM2pyxbpXEnWz5o4dO+LgwYNIT08vfGz37t0wGAxFZjQsrx9++AHXXXddkfEtFgsOHTpU+Njp06fx66+/omPHjlWqvSJvujuaN28emjRpUux///3vfwEATZo0wbJlywqX37x5M3bs2FGuse+5554iE6iMHTu2cNyquFZzrIV9+/Zh4MCBuPXWW3HTTTfh4YcfxnvvvVfsZ+/NmzejSZMmSElJKXWs1NRUvP7667jvvvvQokULtGvXDo899hhWrlzp4q2oOkVRdDcLkjthPnIxG9mYj1zMRi69ZiPql7C+ffti9erVGDFiBIYPH47ExETExsaib9++Re4RNmDAAFy4cAF79+4FAHz55ZfYunUr7r77bkRHR8NiseCjjz7C/v378c477xSu17p1a3To0AHjx4/HmDFj4O3tjdmzZ6NJkya47777qlR7ec//dGc+Pj5YtWpVsccAYMOGDYiJiSl8fMuWLfDz88ODDz5Y4dd59tlnkZ2dXbViUfT6KAnZLF++HG+88QY6d+6M2NhY+Pr6Yt++fYiNjcXhw4cRHx9f5s/1V7LZbBgwYAAyMjIwbNgw1K9fH0lJSfjxxx/xxRdfYODAga7dmCoquKZRWpNM/2I+cjEb2ZiPXMxGLr1mI6oJCw4OxqpVqzBt2jSMGDEC/v7+6NWrF0aOHFlkuasv8LvuuuuQn5+Pt99+G6mpqQgNDUWTJk2wevVqtG3btsi6cXFxmDlzJiZOnAibzYYOHTpgwoQJZZ7feS0VmQnFnRkMBrRq1arE50p7vDLq1KnjtLGc1Rzn5+fDZDKVu0m62q+//oq33noLPXr0wKxZswofb9euHRo2bIjx48djzZo16N+/f7nG+/bbb/HHH3/g/fffR5s2bQof79atW7ELTaWy2WxlnnNN2mI+cjEb2ZiPXMxGLj1mI6oJA4AGDRpc83Sq1atXF1tnwYIF5Ro/MDAQr7/+Ol5//fXKlkglaNKkCV555RUMHjwY/fv3x7ffflv4OAA899xzeP7558s11tixY3H8+HF89NFHAP49NW/cuHHYsmUL3nnnHXz//feoWbMmnn32WTz88MNF1v3yyy8xf/58/PHHH/Dz88N9992HMWPGwN/fHwCQnZ2Nt956CwcOHMDFixcRHh6ODh064OWXXy5y+4J77rmn8JfVtWvX4p9//sHBgwcRFhaGzZs3Y8WKFThz5gxCQkLwyCOP4IUXXijz/harV6+Goiglvgc9evTA4sWLsWrVqnI3YQW3bIiIiCj2XGUbRSIiIiKqHvy2RhVis9mK/K+kXwEnTZqEZs2a4eabb8aGDRuwYcMG9O7du8qvPXr0aHTo0AHz589H06ZNMXbsWJw6darw+d27d+OZZ55B48aNER8fj9GjR+PTTz8tvHccAOTm5sJut2PkyJFYsmQJ/ve//+G7777Ds88+W+z19uzZgy+//BKvvvoqFixYAD8/P6xYsQITJkxAhw4dsHDhQgwdOhTvvfceZs+eXWbt3333HZo0aYJatWoVe85gMOA///kPzp49i8TExHK9F02bNoXBYMCECRNw6NAh5Ofnl2s9IiIiItKeuF/CSK7s7GzceOONRR6LjY1F9+7dizzWsGFDBAQEwM/Pz6mnKT7++ON4/PHHAfx7fd9XX32FTz75BM8++yxUVUVsbCy6du2KGTNmAPj3VMSIiAgMHz4czz77LBo1aoSwsDBMmTKlcEybzYbatWujX79+OH36NOrVq1f4nNVqxZIlS+Dn5wcAyMzMxNy5czFkyBCMGjUKANC+fXuYzWbMmjULgwcPRmhoaIm1JyYmFv4qWJLo6GgAwMWLF4tc/1iaunXrYuzYsXjzzTcxcOBAmM1mtGzZEl26dMFjjz1WpdNriYiIiNxBSkoKDh8+jNtvv73wNlXugt/UnKAy1xxZz1yAw1L8nmjVwRAcCHPdmGsveBUfHx+8//77RR67cvZJV+vQoUPh/+/n54eYmJjC+76dPn0a58+fx/jx4wtnGlRVFW3atIHBYMDx48fRqFEjAMDWrVuxcuVK/PXXX0UmADlz5kyRJuy2224rbMAA4MiRI8jOzsYDDzxQZDbDO+64A7m5uThx4kSxaxBdacCAAejatSs+//xzfPvttzh06BCmT5+OPXv2YNWqVeJPS9TTxbfuiPnIxWxkYz5yMRu5KpuNxWLBr7/+iqZNm7IJ06uKfHjsyWn4+7bHAK0mUDAaUfeXrTCGh1RoNYPBgBYtWrimpnK48pot4N+b+hWchpeamgoAGDFiRInr/vPPPwCAvXv3YsyYMejTpw9GjhyJkJAQXL58GSNGjEBeXl6RdcLDw4v8u+A1evToUeZrlCQyMrLM5wuei4qKKnWZkkRERKBPnz7o06cPrFYrJk6ciM2bN+OLL75Ap06dKjRWdVKUsm+ESNpiPnIxG9mYj1zMRq6KZpOTk4OcnBwAQOLlZFitNiReTkZwcDAAwNfXF76+vi6p1ZnYhGnAGB6COt+s0/SXsIo2YNKFhIQAACZOnIiWLVsWe75mzZoA/r1urGnTpkXuS1YwicjVrm6sC3bu+Pj4Epul2rVrl1pfmzZtsGPHDvzzzz+Fpx4WUFUVX331Fa677rpynYpYGrPZjIEDB2Lz5s04deqU6CaMiIiIqDJOnjyJ48ePw2qz49LR08jztWHX3q/x5x+/w2wyonnz5pr+aFBebMKcpKJToVfmdEB3Yjabi/2y5Er169dHVFQUzp49W3jdWEn3ncjNzS3215by3lS6devW8PX1xcWLF9G5c+cK1de/f39s27YNc+fOxcyZM4s8t23bNiQkJOC1114r93hpaWkICAgodu3XmTNnAJQ8a6IkqqrCZrPBZDLx9BCBmI9czEY25iMXs5Grotk0bNgQUaoJSZMWwWv/D9jd4xb8GFoLD7Vqj3q1gt3iVzCATZhT6OU+YRVRv359bN26FZ9//jkiIiJQs2bNKv3Kcy2KomDs2LEYPXo0srOzcffdd8PX1xfnz5/HV199hVGjRqFevXq44447MHXqVMyfP79wco9Dhw6V6zWCgoLwwgsv4M0338TFixfRtm1bGI1GnD17Fp999hnmzZtX6o7frFkzjB49Gm+88QYyMzPxyCOPwMfHB/v378eqVavQqVMn9OvXr9h6X3zxReH0+gUaNWqEEydOFN53rGXLljCZTPjtt9+waNEixMTEVLhJ1AL3G9mYj1zMRjbmIxezkau82Thy8pD+znvIfHcjvIL88eVD9+JStIrooABEhLpH81WATRi5xNChQ/H3339jzJgxSE9Pr9B9wiqrS5cuCAoKwsKFCwt/3YqJicGdd96JGjVqAAD69u2Lc+fO4f3338eyZcvQoUMHvP3223j00UfL9RqDBg1CZGQkVqxYgffffx8mkwl16tTB3Xfffc3zmQcNGoQGDRpgxYoVGD16NKxWK+rVq4dXXnkF/fr1K3EijfHjxxd77H//+x969OiB+++/H5999hlWrVqFvLw8REVF4cEHH8SwYcMQEBBQru0hIiIikk5VVWR9vA/JE+Nh/ecy/rm7JS7cfyusuTkwXkpEtH8GDnz9BQC4zemIiso/C1TJsWPHoKoqWrRoUewn1Nzc3MJpz318fDSqUL9KOh2RZHwuVVWF1WqF2WxmNgIxH7mYjWzMRy5mI9e1ssn/8wySxs9Bzlffw6/T7QiYOBzWqH9nQjx37hx2796NBx54oPDafK0n5jh27BgAXLMR5C9hREREREQkiiMjCylvroBlyQcw1Y5E1Puz4HffHcUatebNm6N27dqcol6P+BcVuZiNXJwqWDbmIxezkY35yMVs5LoyG9XhQMbGT5AydSEcWdkIe3kQgp/tA4OPd7H1wsLC0LVr1+os1WnYhDkJv+zLw0zkYjayMR+5mI1szEcuZiPXldnk/fwHLo+LQ953x+Hf/R7UmPIsTLVcN7GbltiEOUlFp6gn17vyckdmI4uqqnA4HDAYDMxGIOYjF7ORjfnIxWzkUlUV1sspSJu1DBnvfwSvG+ohZssc+Ha4WevSXIpNmBNwbhO52BzLZbfbS5wRkmRgPnIxG9mYj1zMRh7VZoNl5TakzFoKRQXCp7+A4EEPQzF5fovi+VsoAJs0koSfRyIiItJazsGfkDQ+Dvm/JsC/7wOoMeFpmGq61+QaVcEmzIUKLjLMzs52m7t3k+fLzs4GwAuUiYiIqPrZ/rmM5MkLkLn5U3jf3BQxuxfC2LwhjDr7XsImzIWMRiNCQkJw6dIlAICfnx9PjatGvE9YUaqqIjs7G5cuXUJISAiMRqPWJREREZFOqHn5SFu4EanvvAeDvw8i5oxFYN8ugKLAarVqXV61YxPmBGV9wY+KigKAwkaMqhevCSsuJCSk8HOpJTaBsjEfuZiNbMxHLmajnay9h5A8YS6sf/2D4CGPIPSVQTAGBQD497uaHrNhE+YkpX3RVxQF0dHRqFmzpi67fJLFbDaLONApiiKiDioZ85GL2cjGfORiNtqwJpxD0mvzkL3nIHzvvBlRq16H1w31iiyj12zYhDnJtX5xMRqNuvyAaYmnI8rFbGRjPnIxG9mYj1zMpno5snKQGrcaaQvWw1QzDJHLpsL/wbtLfO/1mg2bMCfgbHNy2Ww2TkAhFLORjfnIxWxkYz5yMRvXU1UVWVs/R9LkBXAkpyH0+X4IeeEJGPx8ylxPj9mwCSMiIiIioirJ+/UUksbPQe6BI/Dr0gE1pj4Pc90YrcsSi00YERERERFVij0tA6lvLINlxVaY68YgesNb8LvnNq3LEo9NGBERERERVYjqcCBjzcdInrEIam4+wiYMQ8iw3lC89HVaYWWxCXMCPV1E6G6YjVzMRjbmIxezkY35yMVsnCf3h1+QNDYOeT/9joDe9yF84jMwRdWo9Hh6zIZNmJPo8cMjnaIourvI010wG9mYj1zMRjbmIxezcQ7bpRSkTFuIjPW74NW8EWI+mg/f21pWaUy9ZsMmjIiIiIiISqVabbAs+xCpsSsAkxE13nwJQf0fhMLbL1UamzAnudZ9wqj6qaoKm80Gk8nEbIRhNrIxH7mYjWzMRy5mU3nZ+75H0vg5sJ74G0EDHkLY2CEwhgU7bXy9ZsMmzAl4nzC5mI1czEY25iMXs5GN+cjFbCrGevYikifGI+ujr+DTtgUi9y6Bd8vGLnktPWbDJoyIiIiIiAAAjpw8pM1fi7S5a2AICkDNd19DQM/OuvqVqjqwCSMiIiIi0jlVVZG9ez+SXpsH24XLCHn6UYSOGgBDgJ/WpXkkNmFERERERDqWf/JvJI2fg5wvvoXvPbchev1b8GpYR+uyPBqbMCfgz7NymUz8iEvFbGRjPnIxG9mYj1zMpjhHZjZS316JtEWbYIqJQNTqmfC7v321f7fVYzb622IXYSMmj6IozEUoZiMb85GL2cjGfORiNkWpqorMD/Ygecq7cKRnIuylgQge0RcGH+9qr0Wv2bAJcxJOUS+PqqpwOBwwGAzMRhhmIxvzkYvZyMZ85GI2/yfv6J9IGheH3G+Pwf+h/yB8ygiYa0dqVo9es2ET5gR6nFbTXdjtdhgMBq3LoBIwG9mYj1zMRjbmI5fes7GnWJAycwnSV22HuUldRG+Og9+dt2hdFgB9ZsMmjIiIiIjIQ6l2O9Lf246UmUsBmx3h055H8KAeUMxsA7TEd5+IiIiIyAPlHD6KpHFxyD9+AoH9uiFswnCYIkK1LovAJoyIiIiIyKPYLiYhecoCZH6wF96tm6LW7oXwueVGrcuiK7AJcwI9XUTobvR2frE7YTayMR+5mI1szEcuPWSj5luRtmgjUt9eBcXXGxGzxyCwX1cowrddD9lcjU2Yk7ARk0dRFF3ed8IdMBvZmI9czEY25iOXHrLJ/uwbJL06B9YzFxA8qAdCxwyCMThQ67KuSQ/ZlER/W+winKJenitnrWQ2sjAb2ZiPXMxGNuYjlydnYz1zAUmvzUP27v3wad8akcunwbtZA63LKjdPzqYsbMKcgFPUy2W1WmE2m7Uug0rAbGRjPnIxG9mYj1yelo0jOxdpc95H2vx1MISHIHLJFPh3/49bNjKelk15sAkjIiIiInITqqoia/uXSJ4UD9vlVISMeAyh/3sCBn9frUujCmATRkRERETkBvJ/P43L4+KQu/9H+N3fHjHTnoe5Xi2ty6JKYBNGRERERCSY3ZKB1NgVsCzbDPP10Yha9yb8771d67KoCtiEEREREREJpDocyFi3CykzFsGRlYuw8UMRMrw3FG8vrUujKmIT5gSKorjlRZCeTlEUeHnxICURs5GN+cjFbGRjPnK5Yza5P/6KpHFxyPvxNwT07IzwSc/AFB2hdVlO547ZOAObMCIiIiIiIWyXU5EyfREy1n4MrxsbImZ7PHzb3aR1WeRkbMKchPcJk0dVVdjtdhiNRmYjDLORjfnIxWxkYz5yuUM2qs0Gy7ItSI1dDhgU1HhjFIKefBCKh9/I2B2ycQXPTrWa8D5hcjkcDhiNRq3LoBIwG9mYj1zMRjbmI5fkbHL2/4jL4+Jg/eMMgp58CGHjhsAYHqJ1WdVGcjauwiaMiIiIiKgapKSk4PDhw7j99tsRFhYG67lEJE+aj6ztX8CnTXNE7l0C75uaaF0mVQM2YURERERELpKTk4OcnBwAwLlz53D8+HHUrhmJnAUbkbtoEwyBAag5/1UE9L5fV6fj6R2bMCIiIiIiFzl58iSOHz8OALCkWRD+699Qlk5AtiUbiXe3RPDI/qh3e1uNq6TqxibMCfhXC7lMHn4xqztjNrIxH7mYjWzMRy6tsmnYsCFq1aqFjN/O4EzcW6idcBbn69bG9cumo9mN9eDr66tJXZLocb/R3xa7CBsxeXj/NrmYjWzMRy5mIxvzkUvLbByZ2ch+5z1YV25DgI8PvujSEoeiWuLZkDAEalKRLHrdb9iEOQmnqJdHVVU4HA4YDAZmIwyzkY35yMVsZGM+cmmRjaqqyPxwLy5OmAukZ+Jcp1b4ulFt2NUs1DZn45efDuDP40Y0b94cLVq0qJaaJNLrfsMmzAk4Rb1cdrsdBoNB6zKoBMxGNuYjF7ORjfnIVZ3Z5B07gaRxccj95ij8unSA1+gnEV47Er4nE/DFZ5/iP53uROOG9QGApyNCn/sNmzAiIiIiIiewp6YjZeYSpK/aDnPD6xD9wWz43XVr4fORERaYzSZERoQjLCxMw0pJa2zCiIiIiIiqQLXbkb56B1JeXwLVakP45GcQPKQXFHPRr9rBwcFo1qwZgoODNaqUpGATRkRERERUSbnfHsPlsbORf+wEAvs8gLDXnoYpMrzEZcPCwtC1a9dqrpAkYhPmBHq6iNDd6O38YnfCbGRjPnIxG9mYj1zOzsZ2MQnJ0xYic+Mn8L6pCWrtfBc+bZo79TX0Qo/7jbgm7NSpU5g+fTqOHDkCf39/dO/eHS+++CK8vLxKXefSpUtYuXIlDhw4gL///huBgYFo06YNRo0ahVq1ahUu98033+DJJ58stn7Xrl0xe/bsKtXNRkweRVF0ed8Jd8BsZGM+cjEb2ZiPXM7MRs23wrLkA6S8tRKKtxkR77yMwH7doBiNThlfb/S634jaYovFggEDBqBu3bqYN28eEhMTMWvWLOTm5mLixImlrvfLL79g79696NmzJ2666Sakpqbi3XffRe/evfHRRx8Vu/Bx5syZqF+/fuG/Q0NDXbZNpC3eOkAuZiMb85GL2cjGfORyRjbZX3yLpPFzYE04h6CnHkbY2CEwhvBuX1Wlx/1GVBO2fv16ZGVlIT4+HiEhIQD+nbJyypQpGD58OCIjI0tc75ZbbsGuXbuKdNE333wz7r77bmzduhWDBg0qsnyjRo2cej8GVVV1+eGRTlVVWK1WmM1mZiMMs5GN+cjFbGRjPnJVNRvrXxeQPDEeWTu/hk+7mxC5dAq8b2zogkr1R6/7jagTMPft24d27doVNmAA0KVLFzgcDhw4cKDU9YKCgor9jBkVFYWwsDBcunTJVeUSERERkQdzZOci5Y1lONuhP3KP/I6aiychZts8NmBUZaJ+CUtISEDPnj2LPBYUFISIiAgkJCRUaKzTp08jOTkZDRo0KPbcsGHDkJaWhoiICHTr1g3/+9//4OPjU6XaiYiIiMgzqKqKrI++QvLEeNgupSDkmT4IfbE/DAF+WpdGHkJUE5aeno6goKBijwcHB8NisZR7HFVVMX36dNSsWRPdunUrfDwwMBBDhgxBmzZt4O3tjcOHD2P58uVISEjAokWLqlS7qqrFHlMUpcTHC54rbT1Xr6uXcQueYzbyxi14rrRstKjJ08atak0l5SN1W91t3KrWxGxk1nRlNlcuJ7VeiTVJySb/jzNIHj8HOV//AL/O7RD9wWyY69cusiyzcd64rthvqlqTs8ctiagmzFnmzZuHw4cPY+nSpfDz+7+/WDRr1gzNmjUr/He7du1Qs2ZNTJ06FUePHkXLli0r/ZpWq7XIeawGg6HwFEmr1Vps+YLZHu12OxwOR5HnTCYTFEWBw+GA3W4v8lzBuAXnz17NbDaXOq7RaITRaISqqrDZbEWeUxSlcF2bzVbsQ+SqccuzrUDx97Ai41Y0m4Jzkiu6reUZF6je97Bg3JJqclU2Bdta2ntYMG7BZ7ikbLT6fJf1Hlbn5xuQc4woyMeTjxHO/HxX5zGivNm48zGitPdQ6jHCarUWyUcPx4gC7nCMuFY2jvRMWN55D5krtsJ0XRSi1rwBn063wW63F3mv3OUY4Yz3sLqOEVcf19z5GKGq5ZsnQlQTFhQUhIyMjGKPWyyWct9ZfOPGjZg/fz5mzJiBdu3aXXP5Ll26YOrUqTh+/Hilm7CCYEp7wwuCKcmVH+6rGQyGUu+bcOWHoaLjXmvdsqYJddW4ZW0rUPZ7WNa43t7ehbWVd9yCZauyrVpk46r3sDLjluc9LPgCUVI2Wn2+q7LPuSobrY4Rvr6+hf//1TzpGOGqz7crjxGVzcaT3kPJx4iCsa/Ox9OOEe74PaK0bIwGAzI3foKUaYvgyMpG6JjBCHn6URh8vKGqqtsdI9zxOHt1Nu58jChPAwYIa8Lq169f7NqvjIwMXL58uciU8qXZu3cvJk+ejBdeeAG9evVyVZklKusgV5aynnfVuhzXteNKrMndxpVYk7uNW5V1r3XTTGnb6m7jVmVdZiO3poJfV1wxblk87T101bilPZ/70+9IGheHvO9/QUCPTgif/CxMMTU1rbcq67rjuBJrcsW4VxM1O2LHjh1x8OBBpKenFz62e/duGAwGtG/fvsx1v/nmG4waNQq9e/fGiBEjyv2aH3/8MQBUecr6ipwDStWj4OdkZiMPs5GN+cjFbGRjPnJdnY09KRWXRr6B8/cNg5qdg5itcxG5eHKRBoyqh173G1G/hPXt2xerV6/GiBEjMHz4cCQmJiI2NhZ9+/Ytco+wAQMG4MKFC9i7dy8A4NSpUxgxYgTq1q2L7t2746effipcNiwsDHXq1AEAjB49Gtdffz2aNWtWODHHypUrce+991apCdPbh8adOByOUn9uJm0xG9mYj1zMRjbmI5fD4YBBVZG+chtS3lgGAKgx4wUEPfUwlDJOMSPX0+N+I+oTFxwcjFWrVmHatGkYMWIE/P390atXL4wcObLIcldf4Pfzzz8jIyMDGRkZeOyxx4os26NHD8yaNQvAvzdp3rFjB5YvXw6r1YpatWrh6aefxrBhw1y/cURERESkmdxDP8PyWjzyfz+NwMe7IfzVYTDWCNW6LNIpReXPOFVy7NgxqKqKFi1aVOg8UHK9gplx9HYHdnfAbGRjPnIxG9mYj0y2C5eQNGk+srZ+Du9bmqHGrJHwaXWD1mXR/+dp+82xY8cAXPtSJ1G/hBEREREROYOal4+0dzcgdfZ7MPj5IuydlxHcrxsMOjvtjWRiE+YEntC1eyq9nV/sTpiNbMxHLmYjG/ORIWvPQSRPmAfr2X8QPKQnQkcPBAL8oFxjhlHShh73GzZhTsJGTB5FUXS5U7sDZiMb85GL2cjGfLRnTTiHpAlzkb33EHw73oKo1a/Dq0k9rcuiMuh1v2ET5iTlvTs2VR9VVQtzYTayMBvZmI9czEY25qMdR2Y2UuNWI+3dDTDVDEPk8mnw/+9dhTkwG7n0mg2bMCfg3CZy2Wy2Mu96TtphNrIxH7mYjWzMp3qpqorMrZ8hedICOFIsCH3hcYQ8/zgMfj7FlmU2cukxGzZhREREROR28n45iaTxc5B78Cf4d70T4VOfg/n6GK3LIioXNmFERERE5DbsaRlImbUU6Su2wly/NqI3vg2//7TVuiyiCmETRkRERETiqXY7MtZ+jOQZi6Hm5iN84tMIHtoLipe+TmMjz8AmzAn0dBGhu2E2cjEb2ZiPXMxGNubjGrnf/4KksbOR9/MfCHj0foS/9jRMUTUqNAazkUuP2bAJcxI9fnikUxRFdxd5ugtmIxvzkYvZyMZ8nM+WmIyUaQuRsWE3vFo0Qq2PF8CnbYsKj8Ns5NJrNmzCiIiIiEgU1WqDZekHSIldAcVsQo23RiPoif9C0eH9pMgzsQlzEt4nTB5VVWGz2WAymZiNMMxGNuYjF7ORjfk4R/ZX3yNpfBysJ88iaEB3hI0bAmNoUJXGZDZy6TUbNmFOwPuEycVs5GI2sjEfuZiNbMyn8qx//4PkifOR9fFX8LmtJSI/nQTvFo2cNj6zkUuP2bAJIyIiIiLNOHLykBa/Fmlz34chJAg1F05EwCP36upXEdIfNmFEREREVO1UVUXWzq+RPDEetn8uI+TpPggd9SQMAX5al0bkcmzCiIiIiKha5Z/4C0nj5yDny+/g1+l2RG98C14N6mhdFlG1YRPmBPy5XC49TnnqLpiNbMxHLmYjG/MpmyMjCylvrYRl8SaYakci6v1Z8Lvvjmr5LsVs5NJjNmzCnISNmDzMRC5mIxvzkYvZyMZ8Sqc6HMjctAfJU9+FIyMLYS8PQvCzfWDw8a6W12c2cuk1GzZhTsIp6uVRVRV2ux1Go5HZCMNsZGM+cjEb2ZhPyfJ+/gOXx8Uh77vj8O9+D2pMeRamWpHVWgOzkUuv2bAJcwI9TqvpLhwOB4y8saNIzEY25iMXs5GN+fwfe3IaUl5fgvTVO+B1Qz3EbJkD3w43a1YPs5FLj9mwCSMiIiIip1FtNqSv2o6UWUsBh4rw6S8geNDDUEz82klUgHsDERERETlFzqGfkTQuDvm/nkJgv64Ie3U4TBGhWpdFJA6bMCIiIiKqEts/l5E8eQEyN38K75ubotYni+DTuqnWZRGJxSbMCfR0EaG70dv5xe6E2cjGfORiNrLpLR81Lx9pCzci9Z33YPD3QcScsQjs2wWKwaB1acXoLRt3osds2IQ5CRsxeRRF0eVO7Q6YjWzMRy5mI5un55OSkoLDhw/j9ttvR1hYGLL2HkLyhLmw/vUPgoc8gtCXn4IxOFDrMkvk6dm4M71mwybMSThFvTyqqhbmwmxkYTayMR+5mI1snp6PxWLBr7/+ihsCw5C3cAuy9xyE7503I2rV6/C6oZ7W5ZXJ07NxZ3rNhk2YE3CKerlsNpsu78LuDpiNbMxHLmYjm6flk5OTg5ycHABA4tkLaPLFcTje/gg5EaHwn/MKgh7uBC8/P42rLB9Py8aT6DEbNmFEREREVKKTJ0/i+LFjCP7+T9TechBNcvJw7JZGyOrRHkYlE81PnUKLFi20LpPI7bAJIyIiIqISRWVa4ffel8APv+FEnVo41vF6nPS+Ho/feAuuiwpEcHCw1iUSuSU2YURERERUhN2SgdRZy2BZvgW5NYKQMLQLfvD3BazpiDLn4czJozh/xojmzZsjLCxM63KJ3A6bMCfQ00WE7obZyMVsZGM+cjEb2dw9H9XhQMaaj5E8YxHU3HwEjRmE0H4PIMbLjJonE/DFZ5/iP53uROOG9QEAvr6+Gldcfu6ejSfTYzZswpxEjx8e6RRF0d1Fnu6C2cjGfORiNrK5ez65P/6KpLFxyDvyGwJ6dUb4pGdhiqpR+HxkhAVmswmREeFu9+uXu2fjyfSaDZswIiIiIh2zXUpByvRFyFi3E17NGyFmx3z43t6y2HLBwcFo1qwZrwMjcgI2YU7C+4TJo6oqbDYbTCYTsxGG2cjGfORiNrK5Wz6q1QbLss1IjV0OmIyoETsKQU8+BKWUG+eGhYWha9eu1Vylc7hbNnqi12zYhDkB7xMmF7ORi9nIxnzkYjayuUs+2V//gKTxc2D94wyCBjyEsHFDYQzz7F+43CUbPdJjNmzCiIiIiHTCei4RyRPjkbXjS/i0bYHIT5fCu2Vjrcsi0h02YUREREQezpGbh7T565A2530YggJQc8EEBPS6T1enfxFJwiaMiIiIyEOpqors3fuR9No82C5cRsjw3gh9aSAMAX5al0aka2zCnIB/RZLLZOJHXCpmIxvzkYvZyCYpn/yTfyNp/BzkfPEtfP/TFtHr34JXwzpal6UZSdlQUXrMRn9b7CJsxORRFIW5CMVsZGM+cjEb2aTk48jMRurbK5G2aBNMMRGIeu91+D3QQURtWpGSDRWn12zYhDkJp6iXR1VVOBwOGAwGZiMMs5GN+cjFbGTTOh9VVZH5wR4kT3kXjvRMhL40ACHPPgaDr3e11yKN1tlQ6fSaDZswJ9DjtJruwm63w2AwaF0GlYDZyMZ85GI2smmVT97RP5E0Lg653x6D/4N3I3zKCJivi6r2OiTjviOXHrNhE0ZERETkpuwpFqTMXIL093bA3PA6RH84G34db9W6LCK6BjZhRERERG5GtduR/t52pMxcCtjsCJ/yLIIH94Ri5lc7InfAPZWIiIjIjeQcPoqkcXHIP34CgX27IOy1p2GqGaZ1WURUAWzCnEBPFxG6G72dX+xOmI1szEcuZiObK/OxXUxC8pQFyPxgL7xb3YBauxbC59YbXfZ6nob7jlx6zIZNmJOwEZNHURRd3nfCHTAb2ZiPXMxGNlflo+ZbkbZ4E1LfWgnFxwsR77yCwMe7QdHhF9fK4r4jl16z0d8WuwinqJfnylkrmY0szEY25iMXs5HNFflkf/4Nkl6dC+vp8wh+6mGEjhkMY0igU8bWE+47cuk1GzZhTsAp6uWyWq0wm81al0ElYDayMR+5mI1szsrHeuYCkibOQ/au/fC5oxUil02Fd7MGTqhQv7jvyKXHbNiEEREREQnhyM5F2tz3kRa/DobwEEQungz/h+/R1S8ERHrAJoyIiIhIY6qqImvHl0ieNB+2SykIebYvQl/sD4O/r9alEZELsAkjIiIi0lD+76eRND4OOV//CL/77kDMh3Ew16+tdVlE5EJswpyApwjIxWzkYjayMR+5mI1sFcnHnp6J1NjlsCzdDHOdaEStjYV/53YurE7fuO/Ipcds2IQ5iR4/PNIpiqK7izzdBbORjfnIxWxkK28+qsOBjPW7kDJ9ERxZuQgbNwQhTz8KxdurGqrUJ+47cuk1GzZhRERERNUk98hvSBoXh7wffkXAI/cifPKzMEVHaF0WEVUzNmFOwvuEyaOqKmw2G0wmE7MRhtnIxnzkYjaylZWP7XIqUmYsQsbanfBqVh8x2+bB945W2hSqQ9x35NJrNmzCnID3CZOL2cjFbGRjPnIxG9muzke12WBZvhWpbywDDApqzHwRQQMegmLiV7Dqxn1HLj1mwyMAERERkQvkHDiCpHFxyP/9NIL6P4iw8UNhDA/RuiwiEoBNGBEREZET2c4nInnyu8ja9jm82zRH7b1L4H1TE63LIiJB2IQREREROYEjNw/p8euQHr8WBn8/1Ix/FQG974NiMGhdGhEJwybMCfR0EaG7MfGce7GYjWzMRy5mI4+qqsjecxBJr86F7Xwigof2QtjLT8EQ6K91aXQF7jty6TEb/W2xi7ARk0dRFOYiFLORjfnIxWzkyT91FsmvzkX2Z4fhe3cbRK99A16N62pdFl2F+45ces1GXBN26tQpTJ8+HUeOHIG/vz+6d++OF198EV5epd/A8NKlS1i5ciUOHDiAv//+G4GBgWjTpg1GjRqFWrVqFVk2MTER06dPx/79+2E2m9G5c2eMGzcOAQEBVaqbU9TLo6oqHA4HDAYDsxGG2cjGfORiNnI4MrOROvs9pC3cCFNkOCJXzoBflw5QVZXfCQTiviOXXrMR1YRZLBYMGDAAdevWxbx585CYmIhZs2YhNzcXEydOLHW9X375BXv37kXPnj1x0003ITU1Fe+++y569+6Njz76CGFhYQAAq9WKIUOGAADefvtt5Obm4o033sBLL72ERYsWVbpuPU6r6S7sdjsMPBdfJGYjG/ORi9loS1VVZG75DMmTF8CRakHoi/0R8lw/GHy9C+93xHxk4r4jlx6zEdWErV+/HllZWYiPj0dISAiAf0OZMmUKhg8fjsjIyBLXu+WWW7Br164i55PefPPNuPvuu7F161YMGjQIAPDJJ5/gxIkT2LlzJ+rXrw8ACAoKwuDBg3H06FG0bNnStRtIREREbivv+EkkjY9D7qGf4d/tLoRPHQFznWityyIiNySq5dy3bx/atWtX2IABQJcuXeBwOHDgwIFS1wsKCip2QV9UVBTCwsJw6dKlIuM3adKksAEDgPbt2yMkJARfffWV8zaEiIiIPIY9NR2Xx8zGuU6DYU9KQ/SmdxC1cjobMCKqNFG/hCUkJKBnz55FHgsKCkJERAQSEhIqNNbp06eRnJyMBg0aFBn/ygYM+PdiwHr16lV4fCIiIvJsqt2OjDUfI3nGYqj5VoRPfgbBQ3pBMYv6+kREbkjUUSQ9PR1BQUHFHg8ODobFYin3OKqqYvr06ahZsya6detWZPzAwMAqj381RVFKvC6stMcLniuotSJjOmNdvYxb1vrMRttxy1rf07ZVq3GrWlNJy0jdVncbt6o1MZvqqSn3u+NIGheH/KN/IqDPAwh/7WkYa4aV+NoF45aUj57fQ0njMhu547oim6rW5OxxSyKqCXOWefPm4fDhw1i6dCn8/Pyq5TVtNluRfxsMhsJTJK1Wa7HlC2Z7tNvtcDgcRZ4zmUxQFAUOhwN2u73EcVVVLXFcs9lc6rhGoxFGo7HwwuErKYpSuK7NZiv2IXLVuOXZVqD4e1iecQ0GAwwGQ4WzMZvNUBSlwttannGB6n0PC8YtqSZXZVOwraW9hwXZGI3GUt9DrT7fZb2H1fn5BrQ9RgAotr2eeIxwxefb1ccIoOLZuOMxouDLjBbHiLwLl5A6fRGyP9gLc8vGqLltLgLatSqstzzHiIL/66nHCHf8HlGd2fB7xLXHLSubgm2Veoy41veIgqbyWkQ1YUFBQcjIyCj2uMViQXBwcLnG2LhxI+bPn48ZM2agXbt2xcbPzMwscfzo6Kqd113wgShJQTAlufLDfbWCJqIkV34YKjrutdYt64Z5rhq3rG0Fyn4PSxu3YCeoaDYFy1ZlW7XIxhXvYWXHvdZ7WHDwKi0brT7fVdnnXJWNFscI4P+2p6R8POUYUdlxtTxGAJXPxpPeQ1ccI9R8KyxLP0TKmyugeJlR4+2XEdivK5QrxrnWPnfl+3R1Pp50jHDH7xHVmQ2/R1Rs3JKykXiMKKneq1Vkmn1RTVj9+vWLXZuVkZGBy5cvF7uWqyR79+7F5MmT8cILL6BXr14ljv/nn38WeUxVVZw+fRrt27evdN0FX/ZL+zJZlrKed9W6ehm34C8ZBX9Ncda4rlxXL+NeKxstavK0catak9VqrdS+40nvIbORO66za8r+8jskjZ8D66mzCHrqYYSNHQJjSPHLF8ozbmn5ePp76A7jMhu54zo7G2fU5Ip1ryRqdsSOHTvi4MGDSE9PL3xs9+7dMBgM12ySvvnmG4waNQq9e/fGiBEjSh3/999/x5kzZwofO3ToENLS0nDXXXc5ZRuIiIjIPVj//gcXB76Kf3qPgrFGCGp/vgwRs0aW2IARETmTqCasb9++8Pf3x4gRI7B//358+OGHiI2NRd++fYvcI2zAgAHo3Llz4b9PnTqFESNGoG7duujevTt++umnwv/9/fffhcvdf//9aNSoEZ5//nl88cUX2LlzJ8aPH4+7776b9wgjIiLyQCkpKdi5cydSUlIKH3Pk5CEldjnOtn8CuT/+hpqLJyFm2zx439hQw0qJSE9EnY4YHByMVatWYdq0aRgxYgT8/f3Rq1cvjBw5sshyV1/g9/PPPyMjIwMZGRl47LHHiizbo0cPzJo1C8C/57IuXboU06dPx6hRo2AymdC5c2eMHz/e9RtHRERE1c5iseDXX39F06ZNERoaiqyP9yF5YjxsF5MQ8kwfhI58EoaA6pnEi4iogKJWZC5FKubYsWNQVRUtWrSo0Hmg5HoFM+OUdd0RaYPZyMZ85GI25ZOTk4OcnBwAwJ8nE/DFZ5/i3kYtUHP1LtgO/gzve9qi5usvwqvBdU59XeYjF7ORy9OyOXbsGACgRYsWZS4n6pcwd1XapBykrYLZbZiNPMxGNuYjF7Mpn5MnT+L48eOw2uz4+8QFtDx4DCFxO5ERGoizw7qidt//oraTGzCA+UjGbOTSazZswsij6W2HdifMRjbmIxezubaGDRsiJjoa51fuxE0r98Lbmo99rW9CqxnP4bZ6EfD19XXZazMfuZiNXHrMhk2Yk5T3xmxUfVRVhd1uh9FoZDbCMBvZmI9czKZ88o/+iYwJ8+D/0x9IaNoQP7aJQkZMC3SOLN89RyuL+cjFbOTSazZswpyAl9XJ5XA4yrzpKWmH2cjGfORiNqWzJ6checZipL//EXKiQvHX891xrmYg8i4lorZ/Bg58/QUAoHnz5te8XqOymI9czEYuPWbDJoyIiIjcmmqzIX3lNqTMWgoACJ78DEJ73YtaJiPOnTuH3bt344472qF27doA4NLTEYmIyoNNGBEREbmtnIM/IWl8HPJ/TUDg490Q/uowGGuEFlmmefPmqF27NsLCwjSqkoioKDZhRERE5HZsFy4hefICZG75DN63NEOtTxbBp3XTYsuFhYWha9euGlRIRFQ6NmFOoKeLCN2N3s4vdifMRjbmI5fes1Hz8pH27gakzl4Ng78PIuaOQ2CfB6AYDFqXBoD5SMZs5NJjNmzCnISNmDyKouhyp3YHzEY25iOX3rPJ2nMQyRPmwfr3Pwge2hOhLz8FY1CA1mUV0ns+kjEbufSaDZswJ+EU9fKoqlqYC7ORhdnIxnzk0ms21oRzSJowF9l7D8G34y2IWv06vJrU07qsYvSajztgNnLpNRs2YU7AKerlstlsMJvNWpdBJWA2sjEfufSUjSMrB6mz30PauxtgqhmGyOXT4P/fu0R/UdNTPu6G2cilx2zYhBEREZEoqqoic+tnSJ60AI4UC0JfeBwhzz8Og5+P1qURETkFmzAiIiISI++Xk0gaPwe5B3+Cf9c7ET71OZivj9G6LCIip2ITRkRERJqzp2UgZdZSpK/YCnO9Woje8Bb87rlN67KIiFyCTZgTSD43Xe8MQqYspuKYjWzMRy5Py0a125Gx9mMkz1gMNTcfYa8NR8iw3lC83PP6EE/Lx5MwG7n0mA2bMCdhIyaPoigwmfgRl4jZyMZ85PK0bHK//wVJY2cj7+c/END7PoRPfAamqBpal1VpnpaPJ2E2cuk1G/1tMREREWnKlpiMlGkLkbFhN7yaN0LMR/Phe1tLrcsiIqo2bMKc4Mr7G5AcqqrCarXCbDYzG2GYjWzMRy53z0a12mBZ9iFSY1cAJiNqvPkSgvo/CMVDbtTq7vl4MmYjl16zYRNGRERELpe973skjZ8D64m/ETTgIYSNHQJjWLDWZRERaYJNGBEREbmM9exFJE+MR9ZHX8HntpaI/HQpvFs00rosIiJNsQkjIiIip3Pk5CFt/lqkzV0DQ1AAar77GgJ6dtbV6UZERKVhE0ZEREROo6oqsnd9jaTX4mH75zJCnn4UoaMGwBDgp3VpRERiVKoJ+/333/HDDz/g1KlTSE1NhaIoCA0NRf369XHzzTejadOmzq5TNP5VTy6z2T3vM6MHzEY25iOX5GzyT/yFpPFzkPPld/C95zZEb3gLXg3raF1WtZKcj94xG7n0mE25m7Dk5GSsXbsWW7duxYULF6CqKsxmM4KDg6GqKtLT02G1WqEoCqKjo9GjRw889thjqFHDfe/3URFsxORhJnIxG9mYj1xSs3FkZCHl7ZWwLNoEU+1IRK2eCb/724ut11X0tr3uhNnIpddsytWEvfnmm1i7di38/f3xwAMP4I477sCNN96IyMjIIsslJibil19+wYEDB7Bx40YsX74cTzzxBF566SWXFC8Jp6iXR1VV2O12GI1GZiMMs5GN+cglLRtVVZG56RMkT3kXjowshI1+CsEj+sLg4611aZqQlg/9H2Yjl16zKVcT9v333+PNN99Ep06dynxzIiMjERkZiXvuuQcTJkzAZ599hqVLlzqtWKlUVdW6BCqFw+GA0UPuP+NpmI1szEcuKdnk/fwHksbFIfe74/B/6D8InzIC5tqR117Rw0nJh4pjNnLpMZtyNWEbNmyo8MCKouDee+/FvffeW+F1iYiISCZ7igUpry9B+nvbYW5SF9Gb4+B35y1al0VE5FY4OyIRERFdk2q3I33VdqTMXAI4VIRPex7Bg3pAMfOrBBFRRfHISURERGXKOfQzksbFIf+Xkwjs1w1hE4bDFBGqdVlERG6rQk3YyZMnsXjxYpw6dQqhoaHo1q0bHn744WLXiW3fvh1jxozBb7/95tRipdLTRYTuRm/nF7sTZiMb85GrOrOx/XMZyVPeReaHe+HduilqfbIIPjc3q7bXd0fcd+RiNnLpMZtyN2FnzpxB7969Ybfb0bBhQ5w4cQLjxo3Dpk2bMGfOHERERLiyTvHYiMmjKIoud2p3wGxkYz5yVVc2al4+0hZtQurbq6D4eSNi9hgE9usKxWBw+Wu7M+47cjEbufSaTbmbsLi4OPj7+2PNmjW4/vrrAQDbtm3DtGnT0KdPHyxduhT169d3WaHScYp6eVRVLcyF2cjCbGRjPnJVRzZZnx5G8qtzYP3rHwQP6oHQMYNgDA50yWt5Gu47cjEbufSaTbn/pPXzzz/jiSeeKGzAAKB79+7YsGEDDAYD+vXrh6NHj7qkSOk4Rb1cNptN6xKoFMxGNuYjl6uysZ4+j3+eGIuLj70MY3QEan+xHDVe/x8bsAriviMXs5FLj9mUuwlLS0tDjRo1ij3eoEEDrF+/HlFRURgwYAC+/vprpxZIREREruPIzkXKzKU4e+eTyDt2ApFLpiBmyxx4N9Xv2S1ERK5W7tMRa9WqhT/++KPE52rUqIH3338fw4cPxzPPPIOOHTs6rUAiIiJyPlVVkbX9SyRPioftcipCRjyG0P89AYO/r9alERF5vHL/Eta2bVvs3r271J8LAwICsGLFCtx55534/PPPnVYgEREROVf+76dx4ZEXkThkIryaN0Kd/asRPn4oGzAiompS7l/CevTogaSkJBw/fhytWrUqcRkvLy/Mnz8fM2fOxO+//+6sGsXT00WE7obZyMVsZGM+clUlG7slA6mxK2BZthnm66MRte5N+N97uxOrI+47cjEbufSYjaJyVokqOXbsGACgRYsWGldCRERUMtXhQMa6XUievhBqdh5CXxqAkOG9oXh7aV0aEZFHKW9vUKGbNRMREZF7yf3xVySNi0Pej78hoGdnhE96BqZofd/bk4hIa5VuwnJycuDry3PHC/A+YfKoqgqbzQaTycRshGE2sjEfuSqSje1yKlKmL0LG2o/hdWNDxGyPh2+7m6qpUn3iviMXs5FLr9mUe2KOKyUlJeHxxx93di1ui2d0ysVs5GI2sjEfua6VjWq1IW3RJpy9vR+ydu5DjTdGofZnS9mAVRPuO3IxG7n0mE2Ffwk7ffo0hgwZUuI9w4iIiEg7Oft/xOVxcbD+cQZBTz6EsHFDYAwP0bosIiK6SoWasB9++AHPPvssrr/+eixbtsxVNREREVEFWM8lInnSfGRt/wI+bZojcu8SeN/UROuyiIioFOVuwnbv3o1XXnkFjRs3xrJlyxAQEODKuoiIiOgaHLl5sMxfj9Q5q2EI9EfN+a8ioPf9urqugojIHZW7CRs5ciSaNWuGFStWIDAw0JU1uR3+x04uk4kTgErFbGRjPnKZTCaoqorsTw4g6bV5sJ1LRPDw3gh7aSAMgf5al6d73HfkYjZy6TGbcm+xj48PkpKSkJaWxiasBGzE5FEUhbkIxWxkYz5yKYoCa8JZJI2fi5zPv4Hv3W0QvTYWXo2u17o0AvcdyZiNXHrNptyzI65ZswZ2ux1PPvkkzp0758qa3JIeZ3WRTlVV2O12ZiMQs5GN+cjkyMxG0pQFOHvnAFhP/oWoVTMQvfFtNmCCcN+Ri9nIpddsyt2ENWvWDBs2bICvry/69++P8+fPu7Iut6K3D407sdvtWpdApWA2sjEfOVRVRcYHe/D37f2QvvRDBP3vcdT+ejX8u3bU5V+PpeO+IxezkUuP2VToPmG1atXC+vXrERMTg/79+7uqJiIiIgKQd+wELjz4HC49Mw0+bZqj9oH3ETzySRh8vbUujYiIqqDCN2sOCgrCihUrcNNNvOkjERGRK9hT03H5lbdx7t4hsKdaEP3BbEStmA7zdVFal0ZERE5QqalIvLy8MHv2bGfXQkREpGuq3Y701TuQ8voSwGZH+JRnETy4JxSz/mYOIyLyZDyqOwHPyZfLaDRqXQKVgtnIxnyqX843R5E0Lg75x04gsG8XhE0YDlNkeLHlmI1szEcuZiOXHrNxahN29uxZ5Ofno0GDBs4c1i2wEZNHURRd7tTugNnIxnyql+1iEpKnvovMTXvg3eoG1Nq1ED633ljissxGNuYjF7ORS6/ZVKoJe++993DkyJEipySOGzcOW7duBQA0bdoUS5YsQXh48b/geSpVVdmICXPlrJXMRhZmIxvzqR5qvhVpizch9a2VUHy8EPHOKwh8vBsUQ+mXazMb2ZiPXMxGLr1mU+GJOQBg06ZNRRqsr7/+Glu2bMGjjz6KCRMm4Ny5c4iPj3dakdJxinq5rFar1iVQKZiNbMzHtbI//wZn7xqIlGmLEPRYV9Q5vA5B/R8sswErwGxkYz5yMRu59JhNpX4Ju3DhQpFTDnft2oXatWtjypQpAICkpCRs27bNORUSERF5COtfF5D02jxk79oPnztaIXLZVHg3098p/EREelepJuzqX34OHDiATp06Ff67Vq1aSEpKqlplREREHsKRnYu0ue8jLX4dDOEhiFw8Gf4P36OrU2+IiOj/VOp0xLp16+LTTz8F8O+piJcuXULHjh0Ln7948SKCgoKcUyEREZGbUlUVmdu/wNn2TyB13loEP9MHdQ6+j4AendiAERHpWKV+CRs8eDBeeukltGnTBjk5OWjQoAE6dOhQ+Pw333yDG264wWlFSsf/kMrFbORiNrIxn6rL/+M0ksbPQc6+H+B33x2I+TAO5vq1qzwus5GN+cjFbOTSYzaVasK6deuGkJAQfPXVVwgKCkK/fv1gMv07VFpaGoKDg9G9e3enFiqdHj880imKArPZrHUZVAJmIxvzqRp7eiZSY5fDsnQzzHWiEbU2Fv6d2zllbGYjG/ORi9nIpddsFJVT+1XJsWPHAAAtWrTQuBIiItKS6nAgY/0upExfBEdWDkJHDUDI049C8fbSujQiIqom5e0NnHqzZj3jfcLkUVUVNpsNJpOJ2QjDbGRjPhWXe+Q3JI2LQ94PvyKgRyeET34WppiaTn8dZiMb85GL2cil12zKNTFH165dsXXrVuTn55d74Pz8fHz44Yfo2rVrpYtzF/wxUS5mIxezkY35lI89KRWXXpyF8/cPh5qTi5itcxG5eLJLGrACzEY25iMXs5FLj9mU65ewHj16YObMmZgxYwbuuecetGvXDjfeeCNq164NX19fAEB2djbOnTuH48eP4+DBg/jiiy9gNpsxePDgChV06tQpTJ8+HUeOHIG/vz+6d++OF198EV5eZZ/OsWbNGuzbtw8///wzUlNTMWfOHDzwwANFlvnmm2/w5JNPFlu3a9eumD17doXqJCIi/VJtNqSv2IqUN5YBAGq8/j8EDewOxcQTTIiI6NrK9V+LoUOH4rHHHsMHH3yALVu2YNu2bYU/FxqNRgCA3W4H8G8n26hRIzz//PPo1asXAgICyl2MxWLBgAEDULduXcybNw+JiYmYNWsWcnNzMXHixDLXLbg59F133YWtW7eWuezMmTNRv379wn+HhoaWu0YiItK3nANHkDQ+Dvm/nUbgE/9F+PihMNbgf0eIiKj8yv0nu4CAAAwcOBADBw7EuXPncOTIESQkJCAtLQ0AEBISgvr166NVq1a47rrrKlXM+vXrkZWVhfj4eISEhAD4t7mbMmUKhg8fjsjIyDLXNRgMOHfu3DWbsEaNGnEiDSIiqhDbhUtInjQfmVs/h/etN6LWnsXwaaWf27EQEZHzVOq8idq1a6N27arf6+Rq+/btQ7t27QobMADo0qULJk2ahAMHDuCRRx4pdV2DoVL3nXYKPV1E6G5MPDVILGYjG/P5P2pePtIWrEdq3GoY/P0QMW88Ah+9H4pG/91hNrIxH7mYjVx6zEbUFickJKBnz55FHgsKCkJERAQSEhKc9jrDhg1DWloaIiIi0K1bN/zvf/+Dj49PlcZkIyaPoijMRShmIxvz+T9Zew4g6dV5sJ27iOChvRA6eiCMQeU/zd7ZmI1szEcuZiOXXrMR1YSlp6cjKCio2OPBwcGwWCxVHj8wMBBDhgxBmzZt4O3tjcOHD2P58uVISEjAokWLqjS2w+Eo9gFSFKXU2V4Kli3reVetq5dxgX9PZzUYDMxG2LhA2dloUZOnjVuVdR0OBxwOR7F8pG6rK8a1JpxD0oS5yPn0MHw73oqo92fCq3HdIsswG/f8fLtyXFVVi+UjtV6JNTEbuTW5WzZVrcnZ45ZEVBPmas2aNUOzZs0K/92uXTvUrFkTU6dOxdGjR9GyZctKjauqKqxWa5H/IBoMhsKfVq1Wa7F1CmZ7tNvtcDgcRZ4ruE+Cw+EonPDk6nELXvNqBXccL2lco9EIo9FYeD+GKynK/92t3GazFfsQuWrc8mwrUPw9LO+4Vqu12JeVa2VjNpuhKEqFt7U84wLV+x4WjFtSTa7KpmBbS3sPC8a12WzF/vql9ee7rPewOj/fgPbHiLy8vCL3bPHUY8TV4zqycpAxdy3SF2+CKTIc4Usmw7dLh8LjScG2anmMqGg27nqMKOs9lHqMsFqtRe535MnHCHf8HlFd2Wh9jHDH7xFX3yfMnY8Rqlq+eweLasKCgoKQkZFR7HGLxYLg4GCXvGaXLl0wdepUHD9+vNJNGPB/H5bSnivNlR/uqxkMhlKvdbvyw1DRca+1blnn5bpq3LK2FSj7PbzWuBXNpmDZqmyrFtm48j2s6LgVeQ9Lykarz3dV9jlXZaPVMcJkMpWaj6cdIwwGA1RVRdaWz5A85V04Ui0I/d8TCB7xGAx+xU9V1/oYUdls3PUYUdHnqjIu4JxjREn5eNoxwl2/R1RHNlofI6QeZ6817pXZuPMxojwNGCCsCatfv36xa78yMjJw+fLlIlPKS3T1X/SvfPxa61Xmuaqsq5dxC/4SwWzkjXutbLSoydPGrWpNld133PE9zPvlJJLGxSH30M/w79YR4VOfg7lOdJljurqmsp7TUzbuVNOVXx5LykdavRJrYjZya3K3bJxRkyvWvZJ2UwqWoGPHjjh48CDS09MLH9u9ezcMBgPat2/vktf8+OOPAYBT1hMR6Yw9NR2Xx8zGuXsGw56UhuhN7yBq5YxyNWBERERV4ZJfwnJzc5GSkoKYmJgKrde3b1+sXr0aI0aMwPDhw5GYmIjY2Fj07du3yD3CBgwYgAsXLmDv3r2Fjx07dgznz59HSkoKAODnn38GAISFhaFt27YAgNGjR+P6669Hs2bNCifmWLlyJe69994qNWEV6Xqpeml56wIqG7ORzZPzUe12ZKz5GMkzFkPNtyJ80jMIHtITilfpp59I4snZeALmIxezkUuP2VSoCTt06BDi4+Nx6tQphIaGolu3bhg8eDB8fX2LLLdnzx6MGTMGv/32W4WKCQ4OxqpVqzBt2jSMGDEC/v7+6NWrF0aOHFlkuZIu8FuzZg22bNlS+O/ly5cDANq2bYvVq1cD+PcmzTt27MDy5cthtVpRq1YtPP300xg2bFiF6iwJGzF5FEXR5X0n3AGzkc2T88n97jiSxsUh7+c/EPDoAwh/bThMUTW0LqvcPDkbT8B85GI2cuk1G0Ut51yKx48fR58+fRAcHIxbb70Vly9fxk8//YQ6depgwYIFaNCgQeGy27dvr1QT5o6OHTsGAGjevDkbMWGu/GgzG1mYjWyemI8tMRnJUxcic+NueLVsjIiZL8Knrfudhu6J2XgS5iMXs5HL07Ip6A2udZZdudvOefPmoXbt2tiwYQNCQkIAAN9//z1eeuklPPbYY1iwYAFuvfXWylfsxipyTwCqXlartcwZbkg7zEY2T8lHtdpgWfIBUt5cAcXLjBpvjUbQE/+FUsqsWO7AU7LxVMxHLmYjlx6zKfcJmL/88gv69OlT2IABwK233ootW7agTp06GDx4MD799FNX1EhERFRh2V9+h7N3DUTylHcR2Pt+1Dm8FsEDurt1A0ZERJ6h3E1YdnY2AgMDiz0eFhaG1atX49Zbb8WLL76ITZs2ObVAIiKiirD+/Q8uDnwV//QeBWN4CGp/tgwRsaNgDA3SujQiIiIAFWjC6tSpg6NHj5b4nK+vLxYuXIjOnTtj4sSJWLdundMKJCIiKg9HTh5S3lyBs+2fQO4Pv6LmwomI2T4P3s0bal0aERFREeVuwu644w7s2bMHOTk5JT5vNpvxzjvvoG/fvjhy5IjTCiQiIiqLqqrI/Hgfznboj9TZ7yF4aG/UObQGgT07e8RF3kRE5HnKPTFHr169oKoqTp8+jWbNmpW4jKIomDRpEq6//nr88ccfTitSupLu8E3aUxQFZrOZ2QjEbGRzp3zyT/yFpPFzkPPld/DrdDuiN74FrwZ1tC7LZdwpGz1iPnIxG7n0mk25p6inkpV3GkoiInIeR0YWUt5aCcviTTDVjkSN6S/A7747dPcfcSIiksXpU9RT2VRV5X/8hVFVFXa7HUajkdkIw2xkk5yP6nAgc9MeJE99F47MbIS9PAjBz/aBwcdb69KqheRsiPlIxmzk0ms2lW7CkpKSUKNGDWfW4rb4Y6JcDocDRk5HLRKzkU1iPnk//4HL4+KQ991x+He/BzWmPAtTrUity6p2ErOh/8N85GI2cukxm3JPzHGlU6dO4dFHH3V2LURERMXYk9Nw+aU3ca7zUKiZ2YjZMgdRS6fosgEjIiLPUOFfwr7//nuMGDECt9xyiyvqISIiAgCoNhvSV21HyqylgENF+PQXEDzoYSgmnklPRETurUL/Jdu1axfGjh2Ldu3aYe7cua6qiYiIdC7n4E9IGh+H/F8TENivK8JeHQ5TRKjWZRERETlFuZuw5cuX480338Rdd92F+Ph4mPiXyEJ6uojQ3fBzKhezkU2rfGz/XEby5AXI3PwpvG9uilqfLIJP66aa1CIV9x3ZmI9czEYuPWZT7i2OjY1FmzZtMG/ePF2+UdfCRkwe3r9NLmYjmxb5qHn5SFu4EanvvAeDvw8i5oxFYN8uUAyVunTZY3HfkY35yMVs5NJrNuXupmrUqIHff/8dv/32G1q2bOnKmtwSp6iXR1XVwlyYjSzMRrbqzidr7yEkT5gL61//IHjIIwh9ZRCMQQEuf113xH1HNuYjF7ORS6/ZlPtPjBs3bkTNmjUxePBgHD9+3JU1uR1OUS+XzWbTugQqBbORrTrysSacwz+Pj8HFfq/AVKsmrvtyBWpMf4EN2DVw35GN+cjFbOTSYzblbsJiYmKwbt06NGnSBIMGDcIvv/ziyrqIiMhDObJykDxjMf6+80nk/3ISkcumIvrDOHjdUE/r0oiIiKpFhU62DwoKwvLly9G+fXsMGjTIVTUREZEHUlUVmVs+w993PAHLuxsQ+nw/XHdwDQIe+o+uTkEhIiKq8AwbXl5emD17NmJjY11RDxEReaC8X08hafwc5B44Ar8uHVBj6vMw143RuiwiIiJNVHqaw1deecWZdbg1/gVXLgNnVhOL2cjmrHzslgykzloGy4qtMNeNQfT6t+DX6TanjK1X3HdkYz5yMRu59JhNuZuw7777Dg0aNEBYWJgr63FbbMTkURSFt1MQitnI5ox8VIcDGWs+RvKMRVBz8xE2YRhChvWG4mV2UpX6xH1HNuYjF7ORS6/ZlLvtfPLJJ3HgwAFX1kLkdJy5Ui5mI1tV8sn94Recf+BpXB4VC797bkOdw2sR+lw/NmBOwn1HNuYjF7ORS4/ZlLvt1OObU15X3t+A5FBVFVarFWazmdkIw2xkq2w+tkspSJm+CBnrdsKreSPEfDQfvrfxvpLOxH1HNuYjF7ORS6/Z6O+3PyIicirVaoNl2Wakxi4HTEbUePMlBPV/EIrRqHVpREREIlWoCdNTd0pERNeW/fUPSBoXB+uJvxE04CGEjR0CY1iw1mURERGJVqEm7OWXX8bLL79crmUVRcGvv/5aqaKIiEg267lEJE+MR9aOL+HTtgUi9y6Bd8vGWpdFRETkFirUhN1xxx2oW7eui0ohIiLpHLl5SJu/Dmlz3ochKAA1330NAT0780wJIiKiCqhQE/bwww/jwQcfdFUtbotfPuQymzkbm1TMRrar81FVFdm79yPptXmwXbiMkKcfReioATAE+GlUoX5x35GN+cjFbOTSYzacmMNJ2IjJw0zkYjayXZ1P/sm/kTR+DnK++Ba+99yG6PVvwathHY2q0zfuO7IxH7mYjVx6zYZNmJNwinp5VFWF3W6H0WhkNsIwG9kK8lFy8pD2ziqkLdoEU0wEolbPhN/97ZmZhrjvyMZ85GI2cuk1GzZhTsB7qMnlcDhg5DTZIjEbuVRVRcamT2CZsQSO9EyEvjQAISMeg8HHW+vSCNx3pGM+cjEbufSYTbmbsN9//92VdRARkQB5R//E5XFxyPv2GPwfvBvhU0bAfF2U1mURERF5FP4SRkREsKdYkDJzCdJXbYe58fWIWP8mAu+5TVenhhAREVUXNmFERDqm2u1If287UmYuBWx2hE99DkGDesAGnmZNRETkKmzCnIB/KZZLb+cXuxNmo72cw0eRNC4O+cdPIPCxrgibMBymmmFQVRVGh0Pr8qgU3HdkYz5yMRu59JgNmzAnYSMmj6Ioutyp3QGz0ZbtYhKSpyxA5gd74d26KWrtXgifW24sfJ75yMVsZGM+cjEbufSaDZswJ+EU9fKoqlqYC7ORhdloQ823Im3RRqS+vQqKrzciZo9BYL+uUAyGossxH7GYjWzMRy5mI5des2ET5gScol4um82my7uwuwNmU72yP/sGSa/OgfXMBQQP6oHQMYNgDA4sdXnmIxezkY35yMVs5NJjNmzCiIg8mPXMBSS9Ng/Zu/fDp31rRC6fBu9mDbQui4iISNfYhBEReSBHdi7S5ryPtPnrYAgPQeSSKfDv/h9dnepBREQkFZswIiIPoqoqsrZ/ieRJ8bBdTkXIiMcQ+r8nYPD31bo0IiIi+v/YhDkB/7IsF7ORi9k4X/7vp5E0Pg45X/8Iv/vbI2ba8zDXq1WpsZiPXMxGNuYjF7ORS4/ZsAlzEj1+eKRTFEV3F3m6C2bjXHZLBlJjV8CybDPM10cjat2b8L/39kqPx3zkYjayMR+5mI1ces2GTRgRkZtSHQ5krNuFlBmL4MjKRdj4oQgZ3huKt5fWpREREVEZ2IQ5Ce8TJo+qqrDZbDCZTMxGGGZTdbk//oqkcXHI+/E3BPTsjPBJz8AUHeGUsZmPXMxGNuYjF7ORS6/ZsAlzAt4nTC5mIxezqRzb5VSkTF+EjLUfw+vGhojZHg/fdjc5/XWYj1zMRjbmIxezkUuP2bAJIyJyA6rNBsuyLUiNXQ4YFNR4YxSCnnwQiomHcSIiInfD/3oTEQmXs/9HXB4XB+sfZxDU/0GEjR8KY3iI1mURERFRJbEJIyISynouEcmT5iNr+xfwbtMctfcugfdNTbQui4iIiKqITZgT6OkiQnejxylP3QWzKZ0jNw+WBRuQGvceDAH+qBn/KgJ63wfFYKi2GpiPXMxGNuYjF7ORS4/ZsAlzEjZi8jATuZhNyVRVRfaeg0iaMBe2c4kIHtYbYaMHwhDoX611MB+5mI1szEcuZiOXXrNhE+YknKJeHlVV4XA4YDAYmI0wzKa4/FN/I/nVecj+7DB8726D6DVvwKtxXU1qYT5yMRvZmI9czEYuvWbDJswJ9Ditpruw2+0wVOMpXFR+zOZfjsxspL7zHtIWboApOgKRK2fAv+udmv+HiPnIxWxkYz5yMRu59JgNmzAiIg2oqorMzZ8iefICONLSETrySYQ81w8GX2+tSyMiIiIXYxNGRFTN8o6fRNK4OOQe/hn+3e5C+NQRMNeJ1rosIiIiqiZswoiIqok9NR0pM5cifdU2mBteh+gPZsPvrlu1LouIiIiqGZswJ9D62g0qndFo1LoEKoWeslHtdqS//xFSXl8CNd+K8MnPIHhILyhmuYdgPeXjbpiNbMxHLmYjlx6zkfsNwM2wEZNHURRd7tTuQE/Z5H57DJfHxSH/6J8I7PMAwl57GqbIcK3LKpOe8nE3zEY25iMXs5FLr9mwCXMSTlEvj6qqhbkwG1n0kI3tYhKSpy1E5sZP4H1TE9Ta+S582jTXuqxy0UM+7orZyMZ85GI2cuk1GzZhTsAp6uWy2Wy6vAu7O/DUbNR8KyxLPkDKWyuheJsR8c7LCOzXDYqb/ZXPU/PxBMxGNuYjF7ORS4/ZsAkjInKS7C++RdL4ObAmnEPQUw8jbOwQGEMCtS6LiIiIhGETRkRURda/LiB5Yjyydn4Nn3Y3IXLpFHjf2FDrsoiIiEgocbemPnXqFJ566im0atUK7du3R2xsLPLz86+53po1azB8+HDcfvvtaNKkCXbv3l3icomJiXj++efRunVrtG3bFq+++ioyMzOdvRlEpAOO7FykvLEMZzv0R+6R31Fz8STEbJvHBoyIiIjKJOqXMIvFggEDBqBu3bqYN28eEhMTMWvWLOTm5mLixIllrrtt2zYAwF133YWtW7eWuIzVasWQIUMAAG+//TZyc3Pxxhtv4KWXXsKiRYsqXbeeLiJ0N8xGLnfORlVVZH30FZInxsN2KQUhz/RB6Iv9YQjw07o0p3HnfDwds5GN+cjFbOTSYzaimrD169cjKysL8fHxCAkJAQDY7XZMmTIFw4cPR2RkZJnrGgwGnDt3rtQm7JNPPsGJEyewc+dO1K9fHwAQFBSEwYMH4+jRo2jZsmWla9fjh0c6RVF0d5Gnu3DnbPL/OI2k8XOQs+8H+HVuh+gPZsOrwXVal+VU7pyPp2M2sjEfuZiNXHrNRtTpiPv27UO7du0KGzAA6NKlCxwOBw4cOFDmugbDtTdl3759aNKkSWEDBgDt27dHSEgIvvrqq0rXTUSez5GRhaSJ8Th791Ow/n0RUWtmIXptrMc1YEREROR6opqwhISEIg0S8O8vVREREUhISHDJ+IqioF69elUen9PUy6OqKqxWK7MRyJ2yUR0OpK/fhb9v64f0VdsQ9sogXPf1Kvjf117r0lzGnfLRG2YjG/ORi9nIpddsRJ2OmJ6ejqCgoGKPBwcHw2KxOGX8wMDi00VXdfyCm8xdTVGUUj9QBacvlvW8q9bVy7gA4HA4mI3AcYGys9GippLWzfv5DySNjUPeD7/A/+F7ED7pGZhqRRYZR1K9zlpXVdUS85G6re42blVrYjYyayr4LnB1PlLrlVgTs5Fbk7tlU9WanD1uSUQ1Ye7MarUWuS7MYDDAZDIVPnc1Ly8vAP9e8+ZwOIo8ZzKZoCgKHA4H7HZ7kecKxi34q8HVCs6pLWlco9EIo9EIVVVhs9mKPHfl+bg2m63Yh8hV45ZnW4Hi72FFxq1oNmazGYqiVHhbyzMuUL3vYcG4JdXkqmwKtrW097Bg3ILPcEnZaPX5LthWe3IaLG8sR9banTDfUA8xW+fCu91NsNlsRepy5Xso5RhRkI8nHyOc+fmuzmNEebNx52NEae+h1seI0sa1Wq1F8tHDMaKAOxwjqisbKceIAu5wjLj6uObOxwhVVYt8tymNqCYsKCgIGRkZxR63WCwIDg52yvglTUdvsVgQHR1dpbELPiylPVeaKz/cVzMYDKVe63blh6Gi415r3YKdojrHLWtbgbLfw2uNW9FsCpatyrZqkY0r38OKjluR97CkbLT6fCsOB7Lf247UWcsAAOEzXkDQwO4wmM1QVVWTbLQ6RphMplLz8bRjhCs/3xUZt4CrsvGk91CrY0R597mS8vG0Y4S7fo+ojmy0Pka463H2ymzc+RhRngYMENaE1a9fv9i1WRkZGbh8+XKxa7kqO/6ff/5Z5DFVVXH69Gm0b1+16zsK/qpS0uPXWq8yz1VlXb2MW/CXCGYjb9xrZaNFTTkHjiBpfBzyfzuNwMe7IfzVYTDWCK3yuK6q19U1VXbfcafPoVbjVrUmZiOzpiu/PJaUj7R6JdbEbOTW5G7ZOKMmV6x7JVETc3Ts2BEHDx5Eenp64WO7d++GwWCocpNUMP7vv/+OM2fOFD526NAhpKWl4a677qr0uBV5w6l6lfXXCtKWlGxsFy4hcdhkXHj4BSi+Pqi1ZzFqzh5TpAHTIyn5UHHMRjbmIxezkUuP2Yja4r59+2L16tUYMWIEhg8fjsTERMTGxqJv375F7hE2YMAAXLhwAXv37i187NixYzh//jxSUlIAAD///DMAICwsDG3btgUA3H///Vi0aBGef/55jBo1Cjk5OYiNjcXdd99dpXuEAWzEJCrrVxbSloRs1Lx8pL27Aamz34PB3xcRc8chsM8DUMpxuwtPJyEfKhmzkY35yMVs5NJrNqKasODgYKxatQrTpk3DiBEj4O/vj169emHkyJFFlivpAr81a9Zgy5Ythf9evnw5AKBt27ZYvXo1gH/PNV26dCmmT5+OUaNGwWQyoXPnzhg/fnyVay/vRXhUfQpm26nI+blUPbTOJmvPQSRPmAfr2X8QPKQnQl9+CsaggGqvQyqt86HSMRvZmI9czEYuvWajqBWZS5GKOXbsGFRVRYsWLXT1wXEHBTPjlDUxB2lDq2ysCeeQNGEusvcegm/HW1Dj9f/Bq0m9ant9d8F9Ry5mIxvzkYvZyOVp2Rw7dgwA0KJFizKXE/VLGBGRKzgys5Eatxpp726AqWYYIpdPg/9/7/KIgz0RERG5HzZhROSxVFVF5tbPkDxpARwpFoS+8DhCnn8cBj8frUsjIiIiHWMTRkQeKe+Xk0gaPwe5B3+Cf9c7ET71OZivj9G6LCIiIiI2Yc7AU5rkKuvmgKQtV2VjT8tAyqylSF+xFeb6tRG98W34/aetS17Lk3HfkYvZyMZ85GI2cukxGzZhTsJGTB5FUXR53wl34IpsVLsdGWs/RvKMxVBz8xE+8WkED+0Fxav0u95TybjvyMVsZGM+cjEbufSajf622EU4Rb08V078yWxkcXY2ud//gqSxs5H38x8IePR+hL/2NExRNao8rl5x35GL2cjGfORiNnLpNRs2YU7AWf7lKpjylORxRja2xGSkTFuIjA274dWiEWI+mg/f26p243X6F/cduZiNbMxHLmYjlx6zYRNGRG5HtdpgWfoBUt9cCZiMqPHWaAQ98V8oRqPWpRERERFdE5swInIr2V99j6TxcbCePIugAd0RNm4IjKFBWpdFREREVG5swojILVjPXkTya/HI+vgr+NzWEpGfToJ3i0Zal0VERERUYWzCiEg0R04e0uLXIm3u+zCEBKHmwokIeOReXV28S0RERJ6FTZgTKIrCL4QCKYoCLy8vrcugEpQnG1VVkbXzayRPjIftn8sIeboPQkc9CUOAXzVVqV/cd+RiNrIxH7mYjVx6zYZNGBGJk3/iLySNn4OcL7+DX6fbEb3xLXg1qKN1WUREREROwSbMSXifMHlUVYXdbofRaGQ2wpSWjSMjCylvrYRl8SaYakci6v1Z8LvvDuZXzbjvyMVsZGM+cjEbufSaDZswJ+B9wuRyOBwwctpyka7MRnU4kLlpD5KnvgtHRhbCXh6E4Gf7wODjrXGV+sV9Ry5mIxvzkYvZyKXHbNiEEZGm8n7+A5fHxSHvu+Pw734Pakx5FqZakVqXRUREROQybMKISBP2FAvSYlcg4/2P4HVDPcRsmQPfDjdrXRYRERGRy7EJI6JqpdpssKzchtQ3lgEOFeHTX0DwoIehmHg4IiIiIn3gtx4n0NNFhO7GxC/2ouQc+hlJ4+KQ/8tJBPTrhvBXh8FUM0zrsqgE3HfkYjayMR+5mI1cesxGf1vsImzE5OH92+Sw/XMZyZMXIHPzp/Bu3RS1PlkEn5ubaV0WlYL7jlzMRjbmIxezkUuv2bAJcxJOUS+PqqpwOBwwGAzMRiNqXj7SFm1C6turoPh5IyJuLAIf6wIoCux2O7MRivuOXMxGNuYjF7ORS6/ZsAlzAk5RL1fBF32qflmfHkbyq3Ng/esfBA9+BKGvPAVjcCCA/7snCLORi/nIxWxkYz5yMRu59JgNmzAicirr6fNIem0esj85AJ8ONyNy5Qx4N62vdVlEREREYrAJIyKncGTlIHXO+7AsWA9jjRBELp0K/4fu1tWpBURERETlwSaMiKpEVVVkbfsCyZPnw56UhpARjyHkhcdh8PfVujQiIiIikdiEOQH/0i+X3s4vrm55vyUgafwc5O7/EX4PdECNac/DXDemXOsyG9mYj1zMRjbmIxezkUuP2bAJcxI2YvIoiqLL+05UB7slA6lvLIdl+RaY68Ygev1b8Ot0W7nXZzayMR+5mI1szEcuZiOXXrPR3xaTrvDWAc6lOhzIWLsTyTMWQc3JQ9irQxEy/FEoXuaKj8VsRGM+cjEb2ZiPXMxGLj1mwybMCVRV1eWHRzpVVWG1WmE2m5mNE+T++CuSxsYh78hvCOjVGeGTnoUpqkalxmI2sjEfuZiNbMxHLmYjl16zYRNGRGWyXUpByvRFyFi3E17NGyFmx3z43t5S67KIiIiI3BabMCIqkWq1wbJsM1JjlwMmI2rEjkLQkw9BMRq1Lo2IiIjIrbEJI6Jisr/+AUnj58D6xxkEDXgIYeOGwhgWrHVZRERERB6BTRgRFbKeS0TyxHhk7fgSPm1bIPLTpfBu2VjrsoiIiIg8CpswJ9DTRYTuxmyu+Kx9euTIzUPa/HVIm/M+DIH+qLlgAgJ63efSzzazkY35yMVsZGM+cjEbufSYDZswJ2EjJg8zuTZVVZG9ez+SXpsH2/lLCB7eG2EvDYQh0N+lr8tsZGM+cjEb2ZiPXMxGLr1mwybMSThFvTyqqsJut8NoNDKbEuSf/BtJ4+cg54tv4Xt3G0SvexNeja6vltdmNrIxH7mYjWzMRy5mI5des2ET5gSqqmpdApXC4XDAyNn8inBkZiP1nVVIW7gRpugIRK2aAb8ud1b7gY/ZyMZ85GI2sjEfuZiNXHrMhk0YkU6oqorMD/ciefICOCwZCB31JEJG9IPB11vr0oiIiIh0hU0YkQ7kHTuBpHFxyP3mKPz/exfCpz4H83VRWpdFREREpEtswog8mD3FgpRZS5G+ajvMDa9D9Iez4dfxVq3LIiIiItI1NmFOoKeLCN2N3s4vLqDa7UhfvQMpry8BbHaET3kWwYN7QjHL2eX1mo27YD5yMRvZmI9czEYuPWYj5xuZm2MjJo+iKLrcqXO+OYqkcXHIP3YCgX27IOy1p2GqGaZ1WUXoNRt3wXzkYjayMR+5mI1ces2GTZiTcIp6eVRVLcxFD9nYLiYheeq7yNy0B96tbkCtXQvhc+uNWpdVIr1l426Yj1zMRjbmIxezkUuv2bAJcwJOUS+XzWbz+Luwq/lWpC3ehNS3VkLx8ULEO68g8PFuUAwGrUsrkx6ycWfMRy5mIxvzkYvZyKXHbNiEEbmx7M+/QdKrc2E9fR7BTz2M0DGDYQwJ1LosIiIiIioDmzAiN2Q9cwFJE+che9d++NzRCpHLpsK7WQOtyyIiIiKicmATRuRGHNm5SJv7PtLi18EQHoLIxZPh//A9ujqHmoiIiMjdsQlzAn4BlstTslFVFVk7vkTypPmwXUpByLN9Efpifxj8fbUurdI8JRtPxXzkYjayMR+5mI1cesyGTZiT6PHDI52iKB5xkWf+76eRND4OOV//CL/77kDMh3Ew16+tdVlV4inZeCrmIxezkY35yMVs5NJrNmzCiISyp2ciNXY5LEs3w1wnGlFrY+HfuZ3WZRERERFRFbEJcxLeJ0weVVVhs9lgMpncKhvV4UDG+l1Imb4IjqxchI0bgpCnH4Xi7aV1aU7jrtnoBfORi9nIxnzkYjZy6TUbNmFOwPuEyeVu2eQe+Q1J4+KQ98OvCHjkXoRPegammJpal+US7paN3jAfuZiNbMxHLmYjlx6zYRNGJIA9KRXJ0xchY+1OeDWrj5ht8+B7RyutyyIiIiIiF2ATRqQh1WaDZflWpL6xDFCAGjNfRNCAh6CYuGsSEREReSp+0yPSSM6BI0gaF4f8308jqP+DCBs/FMbwEK3LIiIiIiIXYxPmBHq6iNDdSJzy1HY+EUmTFiBr2+fwbtMctfcugfdNTbQuq9pJzIb+D/ORi9nIxnzkYjZy6TEbNmFOwkZMHmmZOHLzYFmwAalzVsPg74ea8a8ioPd9UAwGrUurdtKyoaKYj1zMRjbmIxezkUuv2bAJcxJOUS+Pqqqw2+0wGo2aZ5O15wCSXp0H27mLCB7WG2GjB8IQ6K9pTVqSlA0Vx3zkYjayMR+5mI1ces2GTZgT6HFaTXfhcDhgNBo1e/38U2eRPGEusj89DN+72yB6zSx4Na6rWT2SaJ0NlY35yMVsZGM+cjEbufSYDZswIhdwZGYjdfZ7SFu4EabIcESunAH/rnfq6i88RERERFQyNmFETqSqKjK3fIbkyQvgSLUg9MX+CHmuHwy+3lqXRkRERERCsAkjcpK84yeRND4OuYd+hn+3uxA+dQTMdaK1LouIiIiIhBE3LdupU6fw1FNPoVWrVmjfvj1iY2ORn59/zfVUVcXixYtx9913o2XLlujTpw9++umnIst88803aNKkSbH/jRw5sko18xQzuarj/GJ7ajouj5mNc50Gw56UhuhN7yBq5XQ2YNegt3O/3Q3zkYvZyMZ85GI2cukxG1G/hFksFgwYMAB169bFvHnzkJiYiFmzZiE3NxcTJ04sc90lS5Zg7ty5GD16NJo0aYI1a9Zg0KBB2LZtG6677roiy86cORP169cv/HdoaGiVa2cjJo+iKC7dqVW7HRlrPkbyjMVQ860In/wMgof0gmIWtVuJ5OpsqGqYj1zMRjbmIxezkUuv2Yj6trh+/XpkZWUhPj4eISEhAAC73Y4pU6Zg+PDhiIyMLHG9vLw8LFq0CIMGDcLAgQMBALfccgseeOABLFu2DJMnTy6yfKNGjdCiRQun1s4p6uVRVbUwF2dnk/vdcVweOxv5R/9EwKMPIHzi0zBFhjv1NTyZK7OhqmM+cjEb2ZiPXMxGLr1mI+p0xH379qFdu3aFDRgAdOnSBQ6HAwcOHCh1vR9//BGZmZno0qVL4WNeXl7o3Lkz9u3b58qSAXCKeslsNptzx0tMRuKIGTjf9RkAQK2PFyBy/qtswCrB2dmQczEfuZiNbMxHLmYjlx6zEdWEJSQkFDlNEACCgoIQERGBhISEMtcDUGzdBg0a4MKFC8jNzS3y+LBhw9C0aVN07NgRb7zxRrHnia6mWm1IW7Aef9/eD9mfHkLE2y+j9p7F8Gnr3F9UiYiIiMjziTodMT09HUFBQcUeDw4OhsViKXM9Ly8veHsXnQY8KCgIqqrCYrHAx8cHgYGBGDJkCNq0aQNvb28cPnwYy5cvR0JCAhYtWlSl2kv6NUxRlFJ/JSv4ubWs5121rl7GLXiuqtlkf/kdkl+dA+upcwga2B1hY4fAEBJY4mt72nuoVTZa1ORp41a1ppLykbqt7jZuVWtiNjJrujKbK5eTWq/EmpiN3JrcLZuq1uTscUsiqglztWbNmqFZs2aF/27Xrh1q1qyJqVOn4ujRo2jZsmWlx7ZarUXOYzUYDDCZTIXPXc3LywvAv9e8ORyOIs+ZTCYoigKHwwG73V7kuYJxVVUtcVyz2VzquEajEUajEaqqFvvZV1GUwnVtNluxD5Grxi3PtgLF38OKjFvRbMxmMxRFQd6Z80iZNB85u/bD+7YWiJw/Ab4tG8NoNMLhcBTb1vKMC1Tve1gwbkk1uSqbgm1VFKXMz3fBZ7ikbLT6fJf1Hlbn5xuQc4woyMeTjxHO/HyXta3OPkaUNxt3PkaU9h5KPUZYrdYi+ejhGFHAHY4R1ZWNlGNEAXc4Rlx9XHPnY4Sqlm+eCFFNWFBQEDIyMoo9brFYEBwcXOZ6+fn5yMvLK/JrWHp6OhRFKXPdLl26YOrUqTh+/HiVmrCCD0tpz5Xmyg/31QwGAwyGks8YvfLDUNFxr7VuwU5RneOWta1A2e/hteot2FnLO66am4/UeWuQNm8NDCFBqLlwIvx7dCoyxrW2VYtsXPUeVmbcgvfqWp/v0rLR6vNdlX3OVdloeYwoLR9POka46vPt6mNEZbLxpPdQ8jFCUZQS8/HEY0RppB4jqisbCceI0kg9RlydjTsfI8rTgAHCmrD69esXu/YrIyMDly9fLna919XrAcDp06dxww03FD6ekJCAmJgY+Pj4uKbg/09RlDIPctdatzLPVWVdPY1b8JfC8qynqiqyPt6H5InxsF1MQsgzfRA68kkYAvycWpO7vYdaZKNVTZ40blXWNRgMFdp3nPGcnsatyrrMRm5NBb+ulJaPtHol1sRs5NbkbtlUtSZXjHs1URNzdOzYEQcPHkR6enrhY7t374bBYED79u1LXe/mm29GQEAAdu3aVfiY1WrFnj170LFjxzJf8+OPPwYAp09ZT+4l/88z+Kf3KCQ+NQFeTeriuq/fQ/hrT5fYgBERERERVYWoX8L69u2L1atXY8SIERg+fDgSExMRGxuLvn37FrlH2IABA3DhwgXs3bsXAODt7Y3hw4dj3rx5CAsLQ+PGjbFu3TqkpaVh8ODBheuNHj0a119/PZo1a1Y4McfKlStx7733VrkJK+/5n1R9Cs7pLet0REdGFlLeXAHLkg9gqh2FqDWz4H9f6Q0/OUd5siHtMB+5mI1szEcuZiOXXrMR1YQFBwdj1apVmDZtGkaMGAF/f3/06tULI0eOLLJcSRf4DR06FKqqYvny5UhJSUHTpk2xbNkyXHfddYXLNGrUCDt27MDy5cthtVpRq1YtPP300xg2bFiV6q7ITChUvUrLRnU4kLHxE6RMXQhHVjbCXhmE4Gf6wODjXeLy5Hzcb2RjPnIxG9mYj1zMRi49ZqOoetxqJzp27BhUVUWLFi101b27g4KZca6eNCXv5z9weVwc8r47joCH70H45GdhqhVZxkjkbKVlQzIwH7mYjWzMRy5mI5enZXPs2DEA177USdQvYUSuZE9OQ/KMxch4/yN43VAPMVvnwrd9a63LIiIiIiKdYRNGHk+12ZC+ajtSZi0FANSY8QKCnnoYShlTjBIRERERuQq/hTqBJ/x06qms3/2CxPFzkP9bAgIf74bwV4fBWCNU67IIZd9ng7THfORiNrIxH7mYjVx6zEZ/W+wibMRksV24hOTJC5C55TN439IMtT5ZBJ/WTbUui/6/gvuCkEzMRy5mIxvzkYvZyKXXbNiEOQmnqJdBzctH2rsbkDp7NQx+PgiPG4Ogvl1gKOWu6KQNVVXhcDgqdGd5qj7MRy5mIxvzkYvZyKXXbNiEOQEnmJQha89BJE+YB+vf/yB4aE+Ejh4Iu683FIOoe5LT/2e322FgNmIxH7mYjWzMRy5mI5ces2ETRm7PmnAOSa/NQ/aeg/C982ZEvfc6vG6oB1VVYbdatS6PiIiIiKgINmHkthxZOUiNW420BethqhmGyGVT4f/g3br6KZuIiIiI3A+bMHI7qqoia+vnSJq8AI7kNIQ+3w8hLzwBg5+P1qUREREREV0TmzAn4C8v1Sfv11NIGj8HuQeOwK9LB9SY+jzMdWNKXV5v5xe7E2YjG/ORi9nIxnzkYjZy6TEbNmFOwkbMtexpGUh9YxksK7bCXDcG0Rvegt89t5W5jqIourzvhDtgNrIxH7mYjWzMRy5mI5des9HfFrsIp6h3DdXhQMaaj5E8YxHU3HyETRiGkGG9oXiZr73uFbNWMhtZmI1szEcuZiMb85GL2cil12zYhDkBp6h3jdwffkHS2Djk/fQ7Anrfh/CJz8AUVaNCY1itVpjN127YqPoxG9mYj1zMRjbmIxezkUuP2bAJI3Fsl1KQMm0hMtbvglfzRoj5aD58b2updVlERERERE7BJozEUK02WJZ9iNTYFYDJiBpvvoSg/g9CMRq1Lo2IiIiIyGnYhJEI2fu+R9L4ObCe+BtBAx5C2NghMIYFa10WEREREZHTsQlzAj1dROhs1rMXkTwxHlkffQWf21oi8tOl8G7RyGnjMxu5mI1szEcuZiMb85GL2cilx2zYhDmJHj88VeHIyUPa/LVIm7sGhqAA1Hz3NQT07OzU91FRFN1d5OkumI1szEcuZiMb85GL2cil12zYhFG1UlUV2bu+RtJr8bD9cxkhTz+K0FEDYAjw07o0IiIiIqJqwSbMSXifsGvLP/EXksbPQc6X38H3ntsQveEteDWs47LXU1UVNpsNJpOJ2QjDbGRjPnIxG9mYj1zMRi69ZsMmzAl4n7CyOTKykPL2SlgWbYKpVk1ErZ4Jv/vbV8uOxmzkYjayMR+5mI1szEcuZiOXHrNhE0Yuo6oqMjd9guQp78KRkYWw0U8heERfGHy8tS6NiIiIiEgzbMLIJfKO/omksbOR+91x+D/0H4RPGQFz7UityyIiIiIi0hybMHIqe4oFKTOXIH3Vdpib1EX05jj43XmL1mUREREREYnBJswJ9HQRYWlUux3p721HysylgM2O8GnPI3hQDyhmbT9iJhM/4lIxG9mYj1zMRjbmIxezkUuP2ehvi11Ez41YzuGjSBoXh/zjJxDYrxvCJgyHKSJU67KgKIquc5GM2cjGfORiNrIxH7mYjVx6zYZNmJPocYp628UkJE9ZgMwP9sK7dVPU2r0QPrfcqHVZhVRVhcPhgMFg0F020jEb2ZiPXMxGNuYjF7ORS6/ZsAlzAr1Nq6nmW5G2aCNS314FxdcbEbPHILBfVygGg9alFWO322EQWBcxG+mYj1zMRjbmIxezkUuP2bAJowrJ/uwbJL06B9YzFxA8qAdCxwyCMThQ67KIiIiIiNwGmzAqF+uZC0h6bR6yd++HT/vWiFwxHd5N62tdFhERERGR22ETRmVyZOcibc77SJu/DobwEEQumQL/7v/R1Tm7RERERETOxCbMCTyxIVFVFVnbv0TypHjYLqciZMRjCP3fEzD4+2pdWoXo7fxid8JsZGM+cjEb2ZiPXMxGLj1mwybMSTypEcv//TQuj4tD7v4f4Xd/e8RMex7merW0LqvCFEXR5X0n3AGzkY35yMVsZGM+cjEbufSajf622EU8YYp6uyUDqbErYFm2GebroxG17k3433u71mVV2pWzVrp7Np6G2cjGfORiNrIxH7mYjVx6zYZNmBO4+xT1qsOBjHW7kDx9IdTsPISNH4qQ4b2heHtpXVqVWa1WmM1mrcugEjAb2ZiPXMxGNuYjF7ORS4/ZsAnTudwff0XSuDjk/fgbAnp2RvikZ2CKjtC6LCIiIiIij8UmTKdsl1ORMn0RMtZ+DK8bGyJmezx8292kdVlERERERB6PTZjOqDYbLMu2IDV2OWBQUOONUQh68kEoOrwgkoiIiIhIC/zmrSM5+3/E5XFxsP5xBkFPPoSwcUNgDA/RuiwiIiIiIl1hE+YEiqKIns3Fei4RyZPmI2v7F/Bp0xyRe5fA+6YmWpflcoqiwGw2i85Gr5iNbMxHLmYjG/ORi9nIpdds2IR5MEduHizz1yN1zmoYAv1Rc/6rCOh9v64+5HraVnfDbGRjPnIxG9mYj1zMRi49ZsMmzEkk3SdMVVVkf3IASa/Ng+1cIoKH90bYSwNhCPTXurRqpaoq7HY7jEajmGzoX8xGNuYjF7ORjfnIxWzk0ms2bMKcQNJ9wvJP/Y2k8XOR8/k38L27DaLXxsKr0fVal6UZh8MBo9GodRlUAmYjG/ORi9nIxnzkYjZy6TEbNmEewpGZjdR3ViFt4UaYoiMQtWoG/Lrcqau/KBARERERuQM2YW5OVVVkfrgXyVPehSMtHaGjnkTIiH4w+HprXRoREREREZWATZgbyzt2Aknj4pD7zVH4//cuhE99DubrorQui4iIiIiIysAmzAmq+5Q/e2o6UmYuQfqq7TA3vA7RH8yG3123VmsN7kJv5xe7E2YjG/ORi9nIxnzkYjZy6TEbNmFOUh2NmGq3I331DqS8vgSw2RE+5VkED+4JxcwYS6Ioii53anfAbGRjPnIxG9mYj1zMRi69ZsNv707i6inqc745iqRxccg/dgKBfbsgbMJwmCLDXfZ6nkBV1cJcOEGJLMxGNuYjF7ORjfnIxWzk0ms2bMKcwJVT1NsuJiF56rvI3LQH3q1uQK1dC+Fz640uez1PY7PZYDabtS6DSsBsZGM+cjEb2ZiPXMxGLj1mwyZMKDXfirTFm5D61kooPl6IeOcVBD7eDYrBoHVpRERERERUBWzCBMr+/BskvToX1oRzCB7UA6FjBsMYEqh1WURERERE5ARswgSx/nUByRPjkbXza/i0uwmRS6fA+8aGWpdFREREREROxCbMCap6EaEjOxdp89YgLX4tDKHBqLl4EgIe7qSrixNdxcDTN8ViNrIxH7mYjWzMRy5mI5ces2ET5iSVaZhUVUXWR18heWI8bJdSEPJMH4S+2B+GAD8XVKg/iqLAZOJHXCJmIxvzkYvZyMZ85GI2cuk1G/1tsRD5f5xG0vg5yNn3A/w6t0PMh3Ew16+tdVlERERERORibMKc4Mr7G1yLPT0TqW+ugGXphzBfF42oNW/A/747qqFK/VFVFVarFWazmad2CsNsZGM+cjEb2ZiPXMxGLr1mwyasmqgOBzI27EbKtEVwZGUjbMxghDzTB4q3l9alERERERFRNWITVg1yf/odSePikPf9Lwjo0Qnhk5+FKaam1mUREREREZEG2IS5kD0pFckzFiNjzcfwaloPMVvnwrd9a63LIiIiIiIiDbEJcwHVZkP6iq1IeWMZAKDG6/9D0MDuUHQ48wsRERERERXFrsAJrryIMOfAESSNj0P+b6cR+MR/ET5+KIw1QjWsTt/MZrPWJVApmI1szEcuZiMb85GL2cilx2zE3Rnt1KlTeOqpp9CqVSu0b98esbGxyM/Pv+Z6qqpi8eLFuPvuu9GyZUv06dMHP/30U7HlEhMT8fzzz6N169Zo27YtXn31VWRmZla5bvs/l5E4dBIuPPwCFD9f1NqzGDXfeYUNmIYURSn8H8nCbGRjPnIxG9mYj1zMRi69ZiOqCbNYLBgwYACsVivmzZuHkSNHYuPGjZg1a9Y1112yZAnmzp2LgQMHYtGiRYiIiMCgQYNw9uzZwmWsViuGDBmCM2fO4O2338bkyZOxf/9+vPTSS1Wq27R2N/5u9zhyDvyEiHnjUevjBfBpdUOVxqSqU1UVNpsNqqpqXQpdhdnIxnzkYjayMR+5mI1ces1G1OmI69evR1ZWFuLj4xESEgIAsNvtmDJlCoYPH47IyMgS18vLy8OiRYswaNAgDBw4EABwyy234IEHHsCyZcswefJkAMAnn3yCEydOYOfOnahfvz4AICgoCIMHD8bRo0fRsmXLStVtXvERgob2QujLT8EYFFCpMcg1HA4HjEaj1mVQCZiNbMxHLmYjG/ORi9nIpcdsRP0Stm/fPrRr166wAQOALl26wOFw4MCBA6Wu9+OPPyIzMxNdunQpfMzLywudO3fGvn37iozfpEmTwgYMANq3b4+QkBB89dVXla47Z/F4hE99jg0YERERERFdk6gmLCEhoUiDBPz7S1VERAQSEhLKXA9AsXUbNGiACxcuIDc3t9TxFUVBvXr1yhz/WtS60ZVel4iIiIiI9EXU6Yjp6ekICgoq9nhwcDAsFkuZ63l5ecHb27vI40FBQVBVFRaLBT4+PkhPT0dgYGCFxy+L1WqFqqo4fvx4pdYn11JVVXcXeroLZiMb85GL2cjGfORiNnJ5Ujb5+fnl2hZRTZg78pQPjKdiPnIxG9mYj1zMRjbmIxezkcuTsinvTI+imrCgoCBkZGQUe9xisSA4OLjM9fLz85GXl1fk17D09HQoilK4blBQUInT0VssFkRHV+6UwtatW1dqPSIiIiIi0idR14TVr1+/2LVZGRkZuHz5crFrua5eDwBOnz5d5PGEhATExMTAx8en1PFVVcXp06fLHJ+IiIiIiMhZRDVhHTt2xMGDB5Genl742O7du2EwGNC+fftS17v55psREBCAXbt2FT5mtVqxZ88edOzYscj4v//+O86cOVP42KFDh5CWloa77rrLuRtDRERERERUAkUVdGc0i8WCbt26oV69ehg+fDgSExMxa9YsPPjgg5g4cWLhcgMGDMCFCxewd+/ewscWL16MefPmYfTo0WjcuDHWrVuH/fv3Y9u2bbjuuusA/NuYPfLIIwCAUaNGIScnB7GxsWjSpAkWLVpUvRtLRERERES6JKoJA4BTp05h2rRpOHLkCPz9/dG9e3eMHDkSXl5ehcv0798f58+fx+eff174mKqqWLx4MdauXYuUlBQ0bdoU48aNK3bNVmJiIqZPn479+/fDZDKhc+fOGD9+PAICeI8vIiIiIiJyPXFNGBERERERkScTdU0YERERERGRp2MTRkREREREVI3YhBEREREREVUjNmFERERERETViE0YERERERFRNWITRkREREREVI3YhBEREREREVUjk9YFuKtTp05h+vTpRW4q/eKLLxa5qTS53q5du7B9+3b88ssvSE9Px/XXX4/+/fujZ8+eUBQFwL839/7222+Lrbtz5040aNCgukvWjc2bN2PcuHHFHh86dChGjx5d+O9NmzZh6dKluHDhAurVq4eRI0fiP//5T3WWqjul7RMA8M4776Bbt27cb6rJX3/9hWXLluHnn3/GiRMnUL9+fXz00UfFlivPfpKRkYGZM2fi008/hdVqxZ133okJEyagZs2a1bU5Huda+WRmZmLFihX46quvcObMGXh5eaFly5YYOXIkmjRpUrjcuXPn0KlTp2Lj33TTTdi4cWO1bIunKc++U97jGPcd57tWPqXtEwDg5eWFY8eOlbmcJ+w7bMIqwWKxYMCAAahbty7mzZuHxMREzJo1C7m5uZg4caLW5enKypUrUatWLYwdOxahoaE4ePAgXnvtNVy8eBHPPfdc4XI333wzxowZU2Td2rVrV3e5urR06VIEBgYW/jsyMrLw///444/x2muv4emnn8btt9+OnTt34rnnnsOaNWvQqlUrDarVh0mTJiEzM7PIY6tWrcKePXvQrl27wse437jeiRMn8NVXX+Gmm26Cw+GAqqrFlinvfvLiiy/i5MmTmDx5Mry9vREXF4ehQ4fiww8/hMnE/9xXxrXyuXDhAjZs2ICePXvixRdfRF5eHpYvX44+ffrgww8/LPYHi1GjRuG2224r/Le/v3+1bIcnKs++A5TvOMZ9x/mulU/NmjWxYcOGIo+pqoohQ4bg9ttvLzaeR+47KlXYwoUL1VatWqmpqamFj61fv15t2rSpevHiRe0K06Hk5ORij02YMEG9+eabVbvdrqqqqj7xxBPqsGHDqrs03fvwww/Vxo0bl5hRgfvuu08dNWpUkcf69OmjDhkyxNXl0VXuuecedejQoYX/5n5TPQqOU6qqqmPGjFG7detWbJny7Cc//vij2rhxY/Xrr78ufOzUqVNqkyZN1I8//tgFlevDtfLJyspSs7OzizyWmZmptm3bVp06dWrhY2fPnlUbN26s7tq1y7UF60h59p3yHMe477hGefK52uHDh9XGjRurO3fuLHzMk/cdXhNWCfv27UO7du0QEhJS+FiXLl3gcDhw4MAB7QrTobCwsGKPNW3aFJmZmcjOztagIiqvs2fP4syZM+jSpUuRx7t27YpDhw4hPz9fo8r058cff8S5c+fw4IMPal2K7hgMZf9nuLz7yb59+xAUFIT27dsXLlO/fn00bfr/2rv3oKjKNw7gX8AFXWCXKJIgGG61ytVLLoHI6ioFipFNF5gBmSSHRsVLo+WoMQjOyM/LEDUajHQx1GawzImNKESQRi1dJlLEKRXFQBGTloVolQV+fzjstC7GeuEsLd/PDH/se95992HPPOfMs+c975mImpqahx/4KDHU/hGLxRg3bpxRm6OjI7y9vdHW1jacoY16Q+0bczF3hsf97B+VSgUnJycolcphiGjkYRF2HxobG+Hn52fUJpFI4ObmhsbGRgtFRQNqa2sxfvx4ODk5GdpOnDiBSZMmISQkBMnJyTh58qQFIxxd4uPjMXHiRMyePRuFhYXo7e0FAEOu+Pr6GvX39/dHT08Pfv/9d8FjHa1UKhXEYrHJvHvmjeWZmyeNjY3w9fU13As7wM/Pj+clgWm1WsM9MHfKysrCxIkTERERgQ0bNkCj0Qgf4Cgz1HGMuTMy9PT04Pvvv0dMTAwcHBxMtltj7nCi633QarWQSCQm7VKpFB0dHRaIiAao1WqUlZUZzf+eNm0aEhIS4OPjg7a2Nnz00Ud4/fXXUVxcjMmTJ1swWuvm5uaGjIwMhIWFwcbGBocPH8Z7772Ha9euITMz05Ard+bSwGvmkjD0ej2+/fZbKJVKiMViQzvzZmQwN0+0Wq3RvZcDpFIp6uvrhzlK+qetW7fCxsYGSUlJhjZ7e3skJSUhKioKEokEv/zyCwoKClBfX4/9+/dDJBJZMGLrZc5xjLkzMtTU1ECj0SA+Pt6o3Zpzh0UYWY3W1lasWrUK4eHhWLhwoaF9+fLlRv1mzpyJ+Ph47Ny5E7t27RI6zFFjxowZmDFjhuF1VFQUHBwcsHv3brz55psWjIz+6ejRo2hvbzc58TFviO7dl19+iZKSEuTm5sLd3d3Q/vjjjyMrK8vwWi6X46mnnkJ6ejoqKiowd+5cC0Rr/Xgc++8oLS3FY489ZrQ4FGDducPpiPdBIpGgs7PTpL2jowNSqdQCEZFWq8XixYvh4uKCDz744F/nIovFYigUCpw5c0bACAm4fe9kb28vzp49a8iVO3NJq9UCAHNJICqVCi4uLoiKivrXfswbyzA3TyQSicmKlwDPS0I6cuQIMjMzsWTJEixYsGDI/gqFAmKxmDkloMGOY8wdy/vrr79QVVWFuLg42NnZDdnfWnKHRdh9GGyecGdnJ65fvz7oHHAaXjqdDunp6ejs7DRZDp1GroFcuTOXGhsbIRKJ4OXlZYmwRhWdTodDhw4hNjb2Pz2lw5qZmyd+fn64ePGiyTLQFy9e5HlJAHV1dVixYgVefPFFrFixwtLh0D1g7lheRUUFdDrdqFscikXYfYiOjsaxY8cMv0QCQHl5OWxtbY1W16Hhp9frsXLlSjQ2NqKoqMjoGVR3093djerqaoSEhAgQIf1TWVkZ7OzsEBgYCC8vL/j4+KC8vNykT0REBB98LoDDhw+ju7vbrBMf88YyzM2T6OhodHR04Pjx44Y+Fy9eRENDA6KjowWNebQ5f/480tPT8eyzz2Ljxo1mv6+qqgrd3d3MKQENdhxj7lieSqWCt7c3wsLCzOpvLbnDe8LuQ2JiIoqLi7F06VKkp6fj2rVr2LJlCxITE80qAujh2bhxI6qqqrB27Vp0dXWhrq7OsC0wMBCnTp1CUVERYmJi4Onpiba2NnzyySe4fv068vPzLRf4KJCWlobw8HDIZDIAQGVlJUpKSrBw4UK4ubkBADIyMrB69Wp4e3sjPDwcZWVlOHXqFPbs2WPJ0EeN0tJSeHh4YOrUqUbtarWaeSOQv//+G0eOHAEAtLS0oKury1BwyeVyuLq6mpUnkydPRlRUFNatW4d33nkHDg4OyMvLg0wmw3PPPWeR/80aDLV/+vv7kZaWBgcHB6Smphot5ODk5ISAgAAAQG5uLmxsbDBp0iRIJBKcOnUKhYWFCA4Oxpw5c4T/x6zAUPtm4MfZoY5jzJ3hYc6xDQDa29tx/PhxLF68eNBxrDl3bPrvvP5KZrlw4QJycnLw888/w9HREQkJCVi1ahV/vReYUqlES0vLoNsqKyvR29uL7Oxs/Prrr9BoNBg3bhwmT56MZcuWITQ0VOBoR5dNmzbhhx9+QGtrK/r6+uDj44NXXnkFKSkpRksB79+/H7t27cKVK1fg6+uLt956C7NmzbJg5KNDR0cHpk+fjtTUVKxZs8ZoW1NTE/NGIM3NzSaPBhjw2WefITw8HIB5edLZ2YnNmzejoqICer0eUVFR2LBhA38cfABD7R8ARgtB/ZNcLkdxcTGA2/vv888/R1NTE3Q6HcaPH485c+Zg+fLlRo9TIfMNtW/c3d3NPo4xdx4+c49te/fuRXZ2NsrKyuDv72/S15pzh0UYERERERGRgHhPGBERERERkYBYhBEREREREQmIRRgREREREZGAWIQREREREREJiEUYERERERGRgFiEERERERERCYhFGBERERERkYBYhBEREQns6tWrCAkJQW1t7T2/9/z58wgMDMRvv/02DJEREZEQWIQREZHVOHDgAGQyGWQyGdRqtcn2/v5+KBQKyGQypKenWyDC23bs2IGwsDBMnTr1nt8bEBAAhUKB999/fxgiIyIiIbAIIyIiq+Pg4ACVSmXSfuLECbS2tsLe3t4CUd3W3t6OgwcPIjEx8b7HSExMREVFBS5fvvwQIyMiIqGwCCMiIqujUChQXl4OvV5v1K5SqRAUFAQ3NzcLRQZ8/fXXsLOzw6xZs+57jMjISEilUnz11VcPMTIiIhIKizAiIrI68+bNg0ajwdGjRw1tt27dwnfffYf58+cP+p6+vj58+umnmDdvHkJCQhAZGYnMzEx0dHQY9evv78fOnTsRHR2NsLAwpKSk4Ny5c1AqlVi7du2QsR06dAihoaFwdHQ02bZ3717Mnj0boaGhePnll6FWq5GSkoKUlBSjfiKRCHK5HJWVleZ8HURENMKwCCMiIqvj6emJSZMm4ZtvvjG01dTUoLOzE3Pnzh30PZmZmdi6dSumTJmC9evX46WXXkJpaSnS0tLQ09Nj6Jefn4/8/HxMmDABb7/9Nry8vLBo0SJ0d3cPGVdPTw9Onz6NoKAgk2379u1DdnY23N3dsWbNGjzzzDNYunQpWltbBx0rKCgI586dQ1dX15CfS0REI8sYSwdAREQ0HObPn4/t27dDp9Nh7NixKC0txbRp0zB+/HiTvmq1Gvv378e2bduMrpSFh4fjjTfeQHl5OebPn4/29nYUFRVh5syZKCgogI2NDQAgLy8PBQUFQ8Z09epV6HQ6PPnkk0btt27dQn5+PkJCQrB7926MGXP79CyTybB27Vq4u7ubjOXl5YW+vj40NjYiNDT0nr4bIiKyLF4JIyIiqxQXF4ebN2+iqqoKXV1dqK6uvutUxPLycjg7O2P69Olob283/AUFBUEsFuOnn34CABw7dgw9PT1ITk42FGAAkJqaalZMGo0GACCRSIza6+vrodFo8OqrrxoKMOB2ISmVSgcda2CMP//806zPJiKikYNXwoiIyCq5uroiIiICKpUKOp0Ovb29eP755wft29TUhM7OTkRERAy6/caNGwCAK1euAAB8fHxMPutuxdJg+vv7jV4PjOvt7W3UPmbMGHh6epo1BhER/XewCCMiIqsVHx+Pd999F3/88Qeio6NNrkAN6Ovrw6OPPopt27YNut3V1fWhxOPi4gIA0Gq1DzzWwBiPPPLIA49FRETC4nREIiKyWjExMbC1tUVdXR3i4+Pv2s/b2xsajQZTpkxBZGSkyd+ECRMAAB4eHgCAS5cuGb2/vb3dZBXFwTzxxBMYO3YsmpubjdoHxr3zuV96vR4tLS2DjtXc3AxbW1v4+voO+blERDSysAgjIiKr5ejoiKysLGRkZECpVN61X1xcHHp7e7Fz506TbXq93nDVKTIyEiKRCHv27DGaDrh7926z4hGJRAgODkZ9fb1Re3BwMFxcXFBSUmL0bLPS0tK7FndnzpxBQEAAnJ2dzfpsIiIaOTgdkYiIrNqCBQuG7COXy/Haa6+hsLAQZ8+exfTp0yESiXDp0iWUl5dj/fr1iI2NhaurKxYtWoTCwkKkp6dDoVCgoaEBNTU1Zk8LnD17NvLy8tDV1QUnJycAgL29PTIyMpCTk4PU1FTExcWhpaUFBw4cMLlPDLi91P3JkyeRlJR0b18GERGNCLwSRkREBCA7Oxs5OTm4ceMG8vLysH37dvz444944YUXMGXKFEO/lStXIiMjAw0NDdiyZQsuX76Mjz/+GGKx2KzPSUhIQF9fn8mDlpOTk7FhwwZcvXoV//vf/6BWq/Hhhx/C2dkZDg4ORn2PHz8OjUZjVoFJREQjj00/l1ciIiJ6YEqlEnK5HLm5uUP2XbduHS5duoR9+/b9a7++vj5EREQgJiYGmzZtMrQvWbIENjY22LFjxwPHTUREwuOVMCIiIoEtW7YMp0+fRm1traHt5s2bJsvOHzx4EBqNBnK53NB24cIFVFdXY8WKFYLFS0REDxfvCSMiIhKYh4cHTp8+bdRWV1eHzZs3IzY2Fi4uLmhoaMAXX3yBp59+GrGxsYZ+/v7+aGhoEDpkIiJ6iFiEERERjQCenp5wd3dHcXExOjo6IJVKkZCQgNWrV8Pe3t7S4RER0UPEe8KIiIiIiIgExHvCiIiIiIiIBMQijIiIiIiISEAswoiIiIiIiATEIoyIiIiIiEhALMKIiIiIiIgExCKMiIiIiIhIQCzCiIiIiIiIBMQijIiIiIiISEAswoiIiIiIiAT0f2Cro+Q6xM4vAAAAAElFTkSuQmCC",
      "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": 255,
   "id": "3c7c05e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 0.03921\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.01961\n",
      "P(0, chi²)= 0.01942\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.011216\n",
      "1    0.026289\n",
      "2    0.000774\n",
      "3    0.000933\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": 256,
   "id": "698e3c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AdC      = 0.00162 ± 0.00002\n",
      "BdC      = 0.00230 ± 0.00258\n",
      "cov_ABdC = -0.000000\n",
      "P(0,chi²)= 0.01927\n",
      "KdC      = 24.35604 ± 0.26416\n",
      "KBdC     = 17.14417 ± 19.22366\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": 257,
   "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": 258,
   "id": "12bb0479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 0.03892\n",
      "GdL       = 2\n",
      "Chi² rid  = 0.01946\n",
      "P(0, chi²)= 0.01927\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.008684\n",
      "1    0.027599\n",
      "2    0.000978\n",
      "3    0.001658\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": 259,
   "id": "385db415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdY       = 0.0016209 ± 0.0000176\n",
      "BdY       = 0.0023027 ± 0.0025820\n",
      "cov_ABdY  = -4.486957037705209e-08\n",
      "chi²      = 0.03892\n",
      "chi² rid  = 0.01946\n",
      "P(0, chi²)= 0.01927\n",
      "KdY       = 24.35604 ± 0.26416\n",
      "KBdY      = 17.14417 ± 19.22366\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": 260,
   "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": 261,
   "id": "fc32f9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 24.12521 ± 0.03921\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Adols    = 0.0016212 ± 0.00000\n",
      "Bdols    = 0.0022585 ± 0.00035\n",
      "P(0,chi²)= 0.0194154\n",
      "Kdols    = 24.3515777 ± 0.03547\n",
      "KBdols   = 17.4797252 ± 2.74463\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AdC      = 0.0016209 ± 0.00002\n",
      "BdC      = 0.0023027 ± 0.00258\n",
      "P(0,chi²)= 0.0192717\n",
      "KdC      = 24.3560387 ± 0.26416\n",
      "KBdC     = 17.14417 ± 19.22366\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ady      = 0.00162 ± 0.00002\n",
      "Bdy      = 0.00230 ± 0.00258\n",
      "P(0,chi²)= 0.01927\n",
      "KdY      = 24.35604 ± 0.26416\n",
      "KBdY     = 17.14417 ± 19.22366\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": 262,
   "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": 263,
   "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": 264,
   "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_87397/2995330097.py:10: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  media = num / den\n",
      "/tmp/ipykernel_87397/2995330097.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": 265,
   "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": 266,
   "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:                Wed, 08 Apr 2026   Prob (F-statistic):                nan\n",
      "Time:                        09:57:18   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.                     1.01e+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, 1.01e+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": 267,
   "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": 268,
   "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": 269,
   "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": 270,
   "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": 271,
   "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": 272,
   "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_87397/788188722.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
      "/tmp/ipykernel_87397/788188722.py:17: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
      "/tmp/ipykernel_87397/788188722.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_87397/788188722.py:39: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  sA = np.sqrt(1 / S)\n",
      "/tmp/ipykernel_87397/788188722.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": 273,
   "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": 274,
   "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": 275,
   "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": 276,
   "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": 277,
   "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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