diff --git a/molla/compatibilita/comp1.ipynb b/molla/compatibilita/comp1.ipynb
index 2127e4c..659d9ad 100644
--- a/molla/compatibilita/comp1.ipynb
+++ b/molla/compatibilita/comp1.ipynb
@@ -5,10 +5,10 @@
    "id": "ba4e56bc",
    "metadata": {},
    "source": [
-    "## Valori dinamici\n",
+    "## Valori statici (calibro)\n",
     "N = 10\n",
     "\n",
-    "A      = 23.96 +- 0.16 "
+    "A      = 23.97 +- 0.16 "
    ]
   },
   {
@@ -16,15 +16,26 @@
    "id": "aaf30c1f",
    "metadata": {},
    "source": [
-    "## Valori statici\n",
+    "## Valori statici (sonar)\n",
     "N = 6\n",
     "\n",
     "A      = 23.46 +- 0.23\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7a851edb",
+   "metadata": {},
+   "source": [
+    "## Valori dinamici (sonar)\n",
+    "N = 4\n",
+    "\n",
+    "K nostro:  24.35 +- 0.25\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "id": "b349ba73",
    "metadata": {},
    "outputs": [],
@@ -35,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "a68eb302",
    "metadata": {},
    "outputs": [
@@ -43,18 +54,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t = 1.717\n",
-      "p-value (two-tailed) = 10.8084 %\n"
+      "t = 1.751\n",
+      "p-value (two-tailed) = 10.1827 %\n"
      ]
     }
    ],
    "source": [
-    "# Valori dimanici (sonar)\n",
+    "# Valori statici (calibro)\n",
     "Nd = 10\n",
-    "Ad = 23.96\n",
+    "Ad = 23.97\n",
     "uAd = 0.16 * 3\n",
     "\n",
-    "#Valori statici (calibro)\n",
+    "\n",
+    "#Valori statici (sonar)\n",
     "Ns = 6\n",
     "As = 23.46\n",
     "uAs = 0.23 * 3\n",
@@ -73,6 +85,88 @@
     "print(f\"t = {t:.3f}\")\n",
     "print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2746d086",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t = -1.147\n",
+      "p-value (two-tailed) = 27.3612 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Valori statici (calibro)\n",
+    "Nd = 10\n",
+    "Ad = 23.97\n",
+    "uAd = 0.16 * 3\n",
+    "\n",
+    "#Valori dinamici (sonar)\n",
+    "Ns = 4\n",
+    "As = 24.35\n",
+    "uAs = 0.25 * 3\n",
+    "\n",
+    "#Nomi coerenti con Cannelli\n",
+    "GdL = Nd + Ns - 2\n",
+    "\n",
+    "s2 = ( (Nd - 1) * uAd**2  +  (Ns - 1) * uAs**2 ) / GdL\n",
+    "\n",
+    "sigma2 = ( s2 / Nd )  + ( s2 / Ns )\n",
+    "\n",
+    "t = ( Ad - As ) / np.sqrt( sigma2 )\n",
+    "\n",
+    "\n",
+    "p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
+    "print(f\"t = {t:.3f}\")\n",
+    "print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "be0c6cc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t = -1.934\n",
+      "p-value (two-tailed) = 8.9234 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Valori statici (sonar)\n",
+    "Nd = 6\n",
+    "Ad = 23.46\n",
+    "uAd = 0.23 * 3\n",
+    "\n",
+    "#Valori dinamici (sonar)\n",
+    "Ns = 4\n",
+    "As = 24.35\n",
+    "uAs = 0.25 * 3\n",
+    "\n",
+    "#Nomi coerenti con Cannelli\n",
+    "GdL = Nd + Ns - 2\n",
+    "\n",
+    "s2 = ( (Nd - 1) * uAd**2  +  (Ns - 1) * uAs**2 ) / GdL\n",
+    "\n",
+    "sigma2 = ( s2 / Nd )  + ( s2 / Ns )\n",
+    "\n",
+    "t = ( Ad - As ) / np.sqrt( sigma2 )\n",
+    "\n",
+    "\n",
+    "p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
+    "print(f\"t = {t:.3f}\")\n",
+    "print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/molla/dinamica/mollaDinamica1.ipynb b/molla/dinamica/mollaDinamica1.ipynb
index e42ebba..7738b75 100644
--- a/molla/dinamica/mollaDinamica1.ipynb
+++ b/molla/dinamica/mollaDinamica1.ipynb
@@ -2,15 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 305,
    "id": "f34c5b88",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_4545/438762488.py:5: DeprecationWarning: `scipy.odr` is deprecated as of version 1.17.0 and will be removed in SciPy 1.19.0. Please use `https://pypi.org/project/odrpack/` instead.\n",
+      "  from scipy.odr import ODR, Model, RealData\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import scipy as sc\n",
     "from scipy.stats import chi2\n",
+    "from scipy.odr import ODR, Model, RealData\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
@@ -26,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 306,
    "id": "08efb2be",
    "metadata": {},
    "outputs": [],
@@ -57,7 +67,7 @@
     "  return df\n",
     "\n",
     "\n",
-    "df = calcola_stats(df, \"w\", err_arbitrario=0.025)\n",
+    "df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
     "df = calcola_stats(df, \"m\", err_arbitrario=0.01)\n",
     "df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
     "df = calcola_stats(df, \"a\", err_arbitrario=0.01)"
@@ -65,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 307,
    "id": "5494409f",
    "metadata": {},
    "outputs": [
@@ -130,7 +140,7 @@
        "      <td>268.326</td>\n",
        "      <td>0.026</td>\n",
        "      <td>14.24465</td>\n",
-       "      <td>0.025</td>\n",
+       "      <td>0.00125</td>\n",
        "      <td>108.610000</td>\n",
        "      <td>0.01</td>\n",
        "      <td>268.2385</td>\n",
@@ -154,7 +164,7 @@
        "      <td>259.745</td>\n",
        "      <td>0.020</td>\n",
        "      <td>13.19260</td>\n",
-       "      <td>0.025</td>\n",
+       "      <td>0.00130</td>\n",
        "      <td>128.636667</td>\n",
        "      <td>0.01</td>\n",
        "      <td>259.6750</td>\n",
@@ -178,7 +188,7 @@
        "      <td>251.542</td>\n",
        "      <td>0.023</td>\n",
        "      <td>12.34650</td>\n",
-       "      <td>0.025</td>\n",
+       "      <td>0.00040</td>\n",
        "      <td>148.380000</td>\n",
        "      <td>0.01</td>\n",
        "      <td>251.5335</td>\n",
@@ -202,7 +212,7 @@
        "      <td>243.130</td>\n",
        "      <td>0.021</td>\n",
        "      <td>11.63445</td>\n",
-       "      <td>0.025</td>\n",
+       "      <td>0.00020</td>\n",
        "      <td>168.530000</td>\n",
        "      <td>0.01</td>\n",
        "      <td>243.1705</td>\n",
@@ -221,11 +231,11 @@
        "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  0.025  108.610000  0.01   \n",
-       "1  13.1913  0.0009  259.745  0.020  13.19260  0.025  128.636667  0.01   \n",
-       "2  12.3461  0.0006  251.542  0.023  12.34650  0.025  148.380000  0.01   \n",
-       "3  11.6344  0.0006  243.130  0.021  11.63445  0.025  168.530000  0.01   \n",
+       "        w2     uw2       c2    uc2         w       uw           m    um  \\\n",
+       "0  14.2434  0.0009  268.326  0.026  14.24465  0.00125  108.610000  0.01   \n",
+       "1  13.1913  0.0009  259.745  0.020  13.19260  0.00130  128.636667  0.01   \n",
+       "2  12.3461  0.0006  251.542  0.023  12.34650  0.00040  148.380000  0.01   \n",
+       "3  11.6344  0.0006  243.130  0.021  11.63445  0.00020  168.530000  0.01   \n",
        "\n",
        "          c      uc        a      ua  \n",
        "0  268.2385  0.0875   9.9500  0.7400  \n",
@@ -234,7 +244,7 @@
        "3  243.1705  0.0405  11.4200  0.0800  "
       ]
      },
-     "execution_count": 183,
+     "execution_count": 307,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 308,
    "id": "976d5531",
    "metadata": {},
    "outputs": [
@@ -292,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 309,
    "id": "2ad19283",
    "metadata": {},
    "outputs": [
@@ -319,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 310,
    "id": "5f59d6c9",
    "metadata": {},
    "outputs": [
@@ -354,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 311,
    "id": "aefe7756",
    "metadata": {},
    "outputs": [
@@ -368,7 +378,7 @@
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 1.044e+04\n",
       "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           5.50e-08\n",
-      "Time:                        17:16:40   Log-Likelihood:                -14.806\n",
+      "Time:                        19:05:38   Log-Likelihood:                -14.806\n",
       "No. Observations:                   6   AIC:                             33.61\n",
       "Df Residuals:                       4   BIC:                             33.20\n",
       "Df Model:                           1                                         \n",
@@ -432,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 312,
    "id": "8d795186",
    "metadata": {},
    "outputs": [
@@ -470,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 313,
    "id": "1d42b009",
    "metadata": {},
    "outputs": [
@@ -542,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 314,
    "id": "986ff4a6",
    "metadata": {},
    "outputs": [
@@ -578,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 315,
    "id": "ef0817f4",
    "metadata": {},
    "outputs": [
@@ -594,7 +604,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 315,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -605,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 316,
    "id": "cebe6742",
    "metadata": {},
    "outputs": [
@@ -693,7 +703,7 @@
        "5   8.3630  0.041382  197.590900  0.140134"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 316,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 317,
    "id": "2d4b7144",
    "metadata": {},
    "outputs": [
@@ -782,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 318,
    "id": "32e9948f",
    "metadata": {},
    "outputs": [
@@ -819,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 319,
    "id": "e2407a04",
    "metadata": {},
    "outputs": [
@@ -891,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 320,
    "id": "95dde99f",
    "metadata": {},
    "outputs": [
@@ -904,10 +914,10 @@
       "2    0.258983\n",
       "3    0.291654\n",
       "Name: w, dtype: float64\n",
-      "0    0.000683\n",
-      "1    0.000860\n",
-      "2    0.001049\n",
-      "3    0.001253\n",
+      "0    0.000034\n",
+      "1    0.000045\n",
+      "2    0.000017\n",
+      "3    0.000010\n",
       "dtype: float64\n"
      ]
     }
@@ -926,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 321,
    "id": "1bb433f0",
    "metadata": {},
    "outputs": [
@@ -940,7 +950,7 @@
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 4.713e+05\n",
       "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           2.12e-06\n",
-      "Time:                        17:16:41   Log-Likelihood:                 32.343\n",
+      "Time:                        19:05:39   Log-Likelihood:                 32.343\n",
       "No. Observations:                   4   AIC:                            -60.69\n",
       "Df Residuals:                       2   BIC:                            -61.91\n",
       "Df Model:                           1                                         \n",
@@ -1006,7 +1016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 322,
    "id": "67d16b84",
    "metadata": {},
    "outputs": [
@@ -1014,34 +1024,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "24351.577653480002\n",
-      "0.0016211852129718464\n",
-      "Forse K   24.351577653480003\n",
-      "Forse uK  16717.076316471204\n"
+      "K :  24.351577653480003\n",
+      "uK : 1.1291301601259716e-05\n"
      ]
     }
    ],
    "source": [
-    "Kstrano = (4 * np.pi**2) / pente\n",
-    "uKstrano = (4 * np.pi**2) / u_pente\n",
-    "print(Kstrano)\n",
-    "print(pente)\n",
     "\n",
-    "K = Kstrano / 1000\n",
-    "uK = uKstrano / 1000\n",
-    "print(\"Forse K  \", K)\n",
-    "print(\"Forse uK \", uK)"
+    "K = (2*np.pi)**2 / pente / 1000\n",
+    "uK = np.sqrt( (4*np.pi / pente**2)**2 * u_pente**2 ) / 1e6\n",
+    "\n",
+    "print(\"K : \", K)\n",
+    "print(\"uK :\", uK)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 323,
    "id": "a0ba5c8d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1106,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 324,
    "id": "859f2337",
    "metadata": {},
    "outputs": [
@@ -1115,10 +1120,10 @@
      "output_type": "stream",
      "text": [
       "Ax + B : \n",
-      "A      = 0.0016202 +- 0.0000212\n",
-      "B      = 0.0023963 +- 0.0029727\n",
+      "A      = 0.0016212 +- 0.0000007\n",
+      "B      = 0.0022701 +- 0.0001084\n",
       "cov_AB = -0.000000\n",
-      "p-value chi² = 0.0156\n"
+      "p-value chi² = 0.9964\n"
      ]
     }
    ],
@@ -1184,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 325,
    "id": "fef04a85",
    "metadata": {},
    "outputs": [
@@ -1192,19 +1197,120 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "K nostro:  24.36602987010158\n",
-      "uK nostro: 0.2494340628966531\n"
+      "K nostro:  24.351020780404994\n",
+      "uK nostro: 0.24912686265195058\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "K = (2*np.pi)**2 / A / 1000\n",
     "uK = np.sqrt( (4*np.pi / A**2)**2 * uA**2 ) / 1e6\n",
     "\n",
     "print(\"K nostro: \", K)\n",
     "print(\"uK nostro:\", uK)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "id": "5b191469",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 = 11.23106\n",
+      "Gradi di libertà = 2\n",
+      "Chi^2 ridotto = 5.61553\n",
+      "Probabilità P(0 → chi^2) = 0.99636\n"
+     ]
+    }
+   ],
+   "source": [
+    "F_fit = B + A * dfDin[\"masse\"]\n",
+    "sigma = np.sqrt(dfDin[\"utmp2\"]**2 + (A * dfDin[\"umasse\"])**2)\n",
+    "\n",
+    "\n",
+    "chi2_val = np.sum( ((dfDin[\"tmp2\"] - F_fit) / sigma)**2 )\n",
+    "\n",
+    "N = len(dfDin)\n",
+    "nu = N - 2\n",
+    "\n",
+    "chi2_red = chi2_val / nu\n",
+    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "\n",
+    "\n",
+    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
+    "print(f\"Gradi di libertà = {nu}\")\n",
+    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
+    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "id": "9ac14621",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== REGRESSIONE T^2 vs Meq (ODR/York) ===\n",
+      "A = 0.0016212 ± 0.0000016\n",
+      "B = 0.0022701 ± 0.0002570\n",
+      "cov(A,B) = -7.179292197278527e-11\n",
+      "chi² = 11.23106\n",
+      "chi² ridotto = 5.61553\n",
+      "\n",
+      "=== COSTANTE ELASTICA DINAMICA DA ODR ===\n",
+      "k = 24.351014 ± 0.023754 N/m\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# --- DATI DELLA TUA ANALISI ---\n",
+    "x = Meq.to_numpy()      # masse equivalenti\n",
+    "sx = uMeq.to_numpy()    # errori masse equivalenti\n",
+    "y = T2.to_numpy()       # T^2\n",
+    "sy = uT2.to_numpy()     # errori di T^2\n",
+    "\n",
+    "# --- MODELLO LINEARE ---\n",
+    "def f(B, x):\n",
+    "    return B[0] * x + B[1]   # A*x + B\n",
+    "\n",
+    "# Pacchetto \"RealData\" con errori anche su x\n",
+    "dati = RealData(x, y, sx=sx, sy=sy)\n",
+    "\n",
+    "# Modello lineare\n",
+    "modello = Model(f)\n",
+    "\n",
+    "# Esecuzione ODR (metodo di York)\n",
+    "odr = ODR(dati, modello, beta0=[0.01, 0])   # stima iniziale: A≈0.01, B≈0\n",
+    "out = odr.run()\n",
+    "\n",
+    "A, B = out.beta\n",
+    "sA, sB = out.sd_beta\n",
+    "cov_AB = out.cov_beta[0, 1]\n",
+    "chi2 = out.sum_square\n",
+    "chi2_red = out.res_var\n",
+    "\n",
+    "print(\"=== REGRESSIONE T^2 vs Meq (ODR/York) ===\")\n",
+    "print(f\"A = {A:.7f} ± {sA:.7f}\")\n",
+    "print(f\"B = {B:.7f} ± {sB:.7f}\")\n",
+    "print(f\"cov(A,B) = {cov_AB}\")\n",
+    "print(f\"chi² = {chi2:.5f}\")\n",
+    "print(f\"chi² ridotto = {chi2_red:.5f}\")\n",
+    "\n",
+    "# --- COSTANTE ELASTICA ---\n",
+    "K = (2*np.pi)**2 / A / 1000     # k in N/m → convertito a N/mm?\n",
+    "uK = (2*np.pi)**2 / (A**2) * sA / 1000\n",
+    "\n",
+    "print(\"\\n=== COSTANTE ELASTICA DINAMICA DA ODR ===\")\n",
+    "print(f\"k = {K:.6f} ± {uK:.6f} N/m\")\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/molla/dinamica/mollaDinamica2.ipynb b/molla/dinamica/mollaDinamica2.ipynb
index 92c5068..012c66a 100644
--- a/molla/dinamica/mollaDinamica2.ipynb
+++ b/molla/dinamica/mollaDinamica2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 26,
    "id": "f34c5b88",
    "metadata": {},
    "outputs": [],
@@ -11,6 +11,7 @@
     "import pandas as pd\n",
     "import scipy as sc\n",
     "from scipy.stats import chi2\n",
+    "from scipy.odr import ODR, Model, RealData\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
@@ -26,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 27,
    "id": "08efb2be",
    "metadata": {},
    "outputs": [],
@@ -47,6 +48,7 @@
     "    else:\n",
     "      media = valori.mean()\n",
     "      sigma = valori.std(ddof=1)\n",
+    "      sigma = sigma * np.sqrt(24)  if prefix == \"w\" else sigma  # Line cursed per non duplicare il codice\n",
     "      return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
     "\n",
     "  stats = df.apply(riga_stats, axis=1)\n",
@@ -56,16 +58,15 @@
     "  return df\n",
     "\n",
     "\n",
-    "df = calcola_stats(df, \"w\", err_arbitrario=0.01)\n",
+    "df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
     "df = calcola_stats(df, \"m\", err_arbitrario=0.01)\n",
-    "df = calcola_stats(df, \"t\", err_arbitrario=0.01)\n",
     "df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
     "df = calcola_stats(df, \"a\", err_arbitrario=0.01)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 28,
    "id": "5494409f",
    "metadata": {},
    "outputs": [
@@ -123,8 +124,6 @@
        "      <th>uw</th>\n",
        "      <th>m</th>\n",
        "      <th>um</th>\n",
-       "      <th>t</th>\n",
-       "      <th>ut</th>\n",
        "      <th>c</th>\n",
        "      <th>uc</th>\n",
        "      <th>a</th>\n",
@@ -164,11 +163,9 @@
        "      <td>0.011</td>\n",
        "      <td>15.87</td>\n",
        "      <td>7.657975</td>\n",
-       "      <td>0.001711</td>\n",
+       "      <td>0.008385</td>\n",
        "      <td>49.25</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>15.7075</td>\n",
-       "      <td>0.133010</td>\n",
        "      <td>484.70125</td>\n",
        "      <td>0.284314</td>\n",
        "      <td>9.362775</td>\n",
@@ -206,11 +203,9 @@
        "      <td>0.014</td>\n",
        "      <td>18.16</td>\n",
        "      <td>6.559652</td>\n",
-       "      <td>0.000519</td>\n",
+       "      <td>0.002542</td>\n",
        "      <td>69.28</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>18.2700</td>\n",
-       "      <td>0.078740</td>\n",
        "      <td>423.41700</td>\n",
        "      <td>0.226641</td>\n",
        "      <td>10.427250</td>\n",
@@ -248,11 +243,9 @@
        "      <td>0.011</td>\n",
        "      <td>20.49</td>\n",
        "      <td>5.845038</td>\n",
-       "      <td>0.000689</td>\n",
+       "      <td>0.003374</td>\n",
        "      <td>88.97</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>20.4350</td>\n",
-       "      <td>0.117331</td>\n",
        "      <td>363.24975</td>\n",
        "      <td>0.073109</td>\n",
        "      <td>11.257075</td>\n",
@@ -290,11 +283,9 @@
        "      <td>0.010</td>\n",
        "      <td>22.15</td>\n",
        "      <td>5.328455</td>\n",
-       "      <td>0.000787</td>\n",
+       "      <td>0.003858</td>\n",
        "      <td>108.61</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>22.3650</td>\n",
-       "      <td>0.187172</td>\n",
        "      <td>303.60455</td>\n",
        "      <td>0.168669</td>\n",
        "      <td>10.106500</td>\n",
@@ -332,11 +323,9 @@
        "      <td>0.015</td>\n",
        "      <td>24.26</td>\n",
        "      <td>4.925600</td>\n",
-       "      <td>0.001009</td>\n",
+       "      <td>0.004943</td>\n",
        "      <td>128.64</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>24.5150</td>\n",
-       "      <td>0.386480</td>\n",
        "      <td>242.93550</td>\n",
        "      <td>0.138954</td>\n",
        "      <td>10.637000</td>\n",
@@ -368,22 +357,22 @@
        "3  0.00030  303.842  0.016  22.55   9.959  0.014  5.32822  0.00020  303.445   \n",
        "4  0.00030  242.789  0.017  25.09   9.118  0.021  4.92610  0.00030  243.115   \n",
        "\n",
-       "     uc4     t4         w        uw       m    um        t        ut  \\\n",
-       "0  0.011  15.87  7.657975  0.001711   49.25  0.01  15.7075  0.133010   \n",
-       "1  0.014  18.16  6.559652  0.000519   69.28  0.01  18.2700  0.078740   \n",
-       "2  0.011  20.49  5.845038  0.000689   88.97  0.01  20.4350  0.117331   \n",
-       "3  0.010  22.15  5.328455  0.000787  108.61  0.01  22.3650  0.187172   \n",
-       "4  0.015  24.26  4.925600  0.001009  128.64  0.01  24.5150  0.386480   \n",
+       "     uc4     t4         w        uw       m    um          c        uc  \\\n",
+       "0  0.011  15.87  7.657975  0.008385   49.25  0.01  484.70125  0.284314   \n",
+       "1  0.014  18.16  6.559652  0.002542   69.28  0.01  423.41700  0.226641   \n",
+       "2  0.011  20.49  5.845038  0.003374   88.97  0.01  363.24975  0.073109   \n",
+       "3  0.010  22.15  5.328455  0.003858  108.61  0.01  303.60455  0.168669   \n",
+       "4  0.015  24.26  4.925600  0.004943  128.64  0.01  242.93550  0.138954   \n",
        "\n",
-       "           c        uc          a        ua  \n",
-       "0  484.70125  0.284314   9.362775  0.908254  \n",
-       "1  423.41700  0.226641  10.427250  0.454472  \n",
-       "2  363.24975  0.073109  11.257075  0.794837  \n",
-       "3  303.60455  0.168669  10.106500  1.299853  \n",
-       "4  242.93550  0.138954  10.637000  1.188344  "
+       "           a        ua  \n",
+       "0   9.362775  0.908254  \n",
+       "1  10.427250  0.454472  \n",
+       "2  11.257075  0.794837  \n",
+       "3  10.106500  1.299853  \n",
+       "4  10.637000  1.188344  "
       ]
      },
-     "execution_count": 36,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -397,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 29,
    "id": "976d5531",
    "metadata": {},
    "outputs": [
@@ -443,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 30,
    "id": "2ad19283",
    "metadata": {},
    "outputs": [
@@ -472,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 31,
    "id": "5f59d6c9",
    "metadata": {},
    "outputs": [
@@ -507,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 32,
    "id": "aefe7756",
    "metadata": {},
    "outputs": [
@@ -520,8 +509,8 @@
       "Dep. Variable:                      F   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 3.114e+05\n",
-      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           1.19e-19\n",
-      "Time:                        14:02:46   Log-Likelihood:                -14.035\n",
+      "Date:                Fri, 03 Apr 2026   Prob (F-statistic):           1.19e-19\n",
+      "Time:                        11:31:18   Log-Likelihood:                -14.035\n",
       "No. Observations:                  10   AIC:                             32.07\n",
       "Df Residuals:                       8   BIC:                             32.68\n",
       "Df Model:                           1                                         \n",
@@ -572,13 +561,50 @@
     "print(\"\\nRISULTATI REGRESSIONE:\")\n",
     "print(f\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
     "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
-    "print(f\"R² = {R2:.5f}\")\n",
-    "\n"
+    "print(f\"R² = {R2:.5f}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 33,
+   "id": "8d795186",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 = 18.49884\n",
+      "Gradi di libertà = 8\n",
+      "Chi^2 ridotto = 2.31235\n",
+      "Probabilità P(0 → chi^2) = 0.98222\n"
+     ]
+    }
+   ],
+   "source": [
+    "## X^2 sui dai fatti così\n",
+    "F_fit = intercetta + pente * data[\"este\"]\n",
+    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
+    "\n",
+    "\n",
+    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
+    "\n",
+    "N = len(data)\n",
+    "nu = N - 2\n",
+    "\n",
+    "chi2_red = chi2_val / nu\n",
+    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "\n",
+    "\n",
+    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
+    "print(f\"Gradi di libertà = {nu}\")\n",
+    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
+    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "id": "1d42b009",
    "metadata": {},
    "outputs": [
@@ -650,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 35,
    "id": "986ff4a6",
    "metadata": {},
    "outputs": [
@@ -686,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 36,
    "id": "ef0817f4",
    "metadata": {},
    "outputs": [
@@ -706,7 +732,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -717,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 37,
    "id": "cebe6742",
    "metadata": {},
    "outputs": [
@@ -837,7 +863,7 @@
        "9   60.66905  0.218535  196.41418  0.140117"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -848,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 38,
    "id": "2d4b7144",
    "metadata": {},
    "outputs": [
@@ -926,7 +952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 39,
    "id": "32e9948f",
    "metadata": {},
    "outputs": [
@@ -943,7 +969,7 @@
    ],
    "source": [
     "F_fit = B + A * data[\"este\"]\n",
-    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
+    "sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
     "\n",
     "\n",
     "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
@@ -963,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 40,
    "id": "e2407a04",
    "metadata": {},
    "outputs": [
@@ -1032,6 +1058,542 @@
     "\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "95dde99f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    0.673181\n",
+      "1    0.917483\n",
+      "2    1.155540\n",
+      "3    1.390456\n",
+      "4    1.627202\n",
+      "Name: w, dtype: float64\n",
+      "0    0.001474\n",
+      "1    0.000711\n",
+      "2    0.001334\n",
+      "3    0.002013\n",
+      "4    0.003266\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Dinamica davvero\n",
+    "\n",
+    "Meq = df.m + ( m_mol / 3 )\n",
+    "uMeq = np.sqrt( df.um**2 + um_mol**2 / 9 )\n",
+    "\n",
+    "T2 = ( 2 * np.pi / df.w )**2\n",
+    "uT2 = np.sqrt( (8*np.pi**2 / df.w**3)**2 * df.uw**2 ) \n",
+    "print(T2)\n",
+    "print(uT2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1bb433f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   tmp2   R-squared:                       1.000\n",
+      "Model:                            OLS   Adj. R-squared:                  1.000\n",
+      "Method:                 Least Squares   F-statistic:                 7.624e+04\n",
+      "Date:                Fri, 03 Apr 2026   Prob (F-statistic):           1.05e-07\n",
+      "Time:                        11:31:19   Log-Likelihood:                 23.705\n",
+      "No. Observations:                   5   AIC:                            -43.41\n",
+      "Df Residuals:                       3   BIC:                            -44.19\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const          0.0219      0.004      5.124      0.014       0.008       0.035\n",
+      "masse          0.0120   4.35e-05    276.124      0.000       0.012       0.012\n",
+      "==============================================================================\n",
+      "Omnibus:                          nan   Durbin-Watson:                   1.430\n",
+      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.670\n",
+      "Skew:                          -0.251   Prob(JB):                        0.716\n",
+      "Kurtosis:                       1.279   Cond. No.                         344.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "RISULTATI REGRESSIONE:\n",
+      "B) = 0.0120187 ± 0.0000435\n",
+      "intercetta  = 0.02190 ± 0.00427\n",
+      "R² = 0.99996\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; 5 samples were given.\n",
+      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "dfDin = pd.DataFrame({\n",
+    "    \"masse\": Meq,\n",
+    "    \"umasse\": uMeq,\n",
+    "    \"tmp2\": T2,\n",
+    "    \"utmp2\": uT2\n",
+    "})\n",
+    "\n",
+    "\n",
+    "X = sm.add_constant(dfDin[\"masse\"])\n",
+    "model = sm.OLS(dfDin[\"tmp2\"], X).fit()\n",
+    "\n",
+    "print(model.summary())\n",
+    "\n",
+    "# Estrazione parametri\n",
+    "intercetta = model.params[\"const\"]\n",
+    "pente = model.params[\"masse\"]\n",
+    "u_intercetta = model.bse[\"const\"]\n",
+    "u_pente = model.bse[\"masse\"]\n",
+    "R2 = model.rsquared\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
+    "print(f\"B) = {pente:.7f} ± {u_pente:.7f}\")\n",
+    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
+    "print(f\"R² = {R2:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "67d16b84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K :  3.284762657868823\n",
+      "uK : 3.786606732225535e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "K = (2*np.pi)**2 / pente / 1000\n",
+    "uK = np.sqrt( (4*np.pi / pente**2)**2 * u_pente**2 ) / 1e6\n",
+    "\n",
+    "print(\"K : \", K)\n",
+    "print(\"uK :\", uK)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a0ba5c8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eeQc1atRAu3btHE5/5cqVQg/nq1Onju356OhoXLhwAbVr13Z6Nb7vv/8eCxYswJ9//ml37smtOVi98847uHDhAjZu3FjoYVUZGRl44YUXEBERgRkzZtjGSUpKQnZ2NmrXrl1gmrp168JiseDff//FHXfcUej7JiUlwWw2O/wsRTEYDE6vnHdrzoqi4Pbbb8fly5dL9Z4AUL16dbu/g4ODAaDAeTiucGv9gYGBqFKliq3+c+fOAQAGDBhQ6PT5myjg5vqU/5Df/G49RNc67a2vt/5o4erPe+XKFcTFxeG7774rcF6cKxv64qxDly9fRtWqVQvMv7p169qez6+ww5tDQkKczqOSbsvyN8u3uvU567bOug2x8vHxwW233VbgM0RERNj9yJDf7bffXmDbUatWLQA350WVKlVgsViwevVqrF+/HpcuXbJbr62HNBf3sxBRQWzGiMjGYrEAAJ544gk8+eSThb4m/0n1arx/dHS07XycWxX1ZdMd9Hq97aICzZo1Q//+/bF161anzZgrHTx4EMOHD0fLli0xYcIEVKlSBd7e3tiyZQu2bdtW4PWrVq3Ctm3bMHPmzCIvNz5mzBhcv34dmzdvLvAFtSy6detW6sbo22+/VfULndlsLrTZK+qKb+KWCzu4g/U9Z8yYgSpVqhR4/tb68++9cPZaZ48X5/MWNQ8Le92zzz6L1NRUDBkyBHXq1EFAQACuXbuGMWPG2LY5nqq086ik2zJHe5LKeiXJsu6lWrhwIWbPno2nn34aL730EkJCQqDT6TB16tRC5wP3ihGVDJsxIrIxGAwIDAyExWJx2mRERUXhn3/+gRDC7pfV/FctA/63t+HChQu2Q3SAmxccsF6VzqpmzZr466+/0LZt2yL39BTF+j7nz5+3+zXaaDTi0qVLaNCgQYnGc6Rx48YAbt5byaqoeqtXr46zZ88WePzMmTN2ddesWROHDx+G0Wgs8oT3r776Cr6+vli2bJndL91btmwp8NqDBw9ixowZGDBgAJ544olCx1u8eDG++eYbzJ0717ZnwMpgMMDf37/I2nU6ncOLosycORO5ublFPu9IYc3HrfJf5RC4+eX4/PnzdstTUXswrly5Uuq9pSVdLoty/vx5u/UhMzMTN27cwL333gsAtvrCwsLc2vDfqrjzsKj58vfff+PcuXOYPn06unbtant83759BV5b1nlbnHXIelGejIwMux8frOtjYVdOLW0tpd2WOWPdZpw5c8Yug7y8PFy6dKlEy8v58+cLbMOte2Wt8+Krr75C69atMXXqVLtp09LSHF7EhIiKh+eMEZGNXq/Hww8/jK+++gp///13geethygCQIcOHXDt2jV8++23tsdyc3MLnEvVuHFjhIaG4pNPPoHJZLI9vnXr1gKHLHXq1AnXrl0r9HysnJwc25XECtO4cWMYDAZs3LjR7vC9zz//vNSHXR08eBBGo7HA49ZzMvIfwufv71/o+/znP//BkSNHcOjQIdtjWVlZ+OSTTxAVFWU7T+Shhx5CcnKy7fLe+Vl/fdbr9VAUxe4woUuXLtllANxsEl9++WXbJdMLs3//fsyaNQvDhg1Dx44dCzyv1+vRvn17fPvtt3aXO09ISMC2bdvQokULh3vSWrRogXbt2pXqv+LsCYiPj7c7xG3Xrl12zQxws6E5fPiw3fLw/fffF7gEfkn4+/sjPT291NNbbdq0yW7Z2rBhA0wmk63+e+65B0FBQVi0aFGhy2D+dVFNxZ2H1vtW3boOWPfW5d+DIoQo9IqNRY1RXMVZh+69916YzeYCr1m5ciUURbFbfsqiLNsyZ9q1awdvb2+sWbPGbr5++umnSE9Pt7v8vTPXr1/H7t27bX9nZGQgPj4eDRs2tP0ootfrC+wB27lzJ65du1bqz0BE/8M9Y0QV0JYtW/Djjz8WeLx///549dVXceDAAfTo0QPdu3dHvXr1kJqaiuPHj+Pnn3/Gr7/+CuDmBQjWrl2LV199Ff3790eVKlWwdetW2xdp6y+tPj4+GDVqFCZNmoQBAwagU6dOuHz5Mj777LMC55F16dIFO3fuxIQJE3DgwAHcddddMJvNOHPmDHbt2oWlS5ciJiam0M/k7e2Nl19+GePHj8eAAQPw6KOP4tKlS/jss89KvRdkyZIlOH78OB588EHbHpcTJ04gPj4eoaGhdufzNGrUCBs2bMD8+fNx++23w2AwoG3btnj++eexfft2PPfcc+jXrx9CQkIQHx+PS5cuYc6cObYvq127dkV8fDzef/99HDlyBC1atEB2djZ+/vln9OrVCx07dsR//vMfrFixAkOGDMFjjz2GxMRErF+/HjVr1rTdNwoAJk+ejKSkJAwZMgTbt2+3+0z169dHgwYN8Morr8BgMKBWrVr44osv7F7Tvn17hIeH4+WXX8b+/fvRu3dv9O7dG3q9Hps2bUJeXh5ef/31Us1TVwkJCUHv3r3x1FNP2S5tf/vtt9tdQKZ79+746quvMGTIEHTq1AkXLlzA1q1bCyx3JdGoUSPs2LED77//PmJiYhAQEGC711hJGI1GDBw4EJ06dcLZs2exfv16tGjRwnZBnKCgIEycOBFvvPEGnnrqKTz66KMwGAy4cuUK9uzZg7vuugvjx48v9ecoruLOw5o1ayI4OBgbN25EYGAgAgIC0KRJE9SpUwc1a9bE9OnTce3aNQQFBeGrr74qtOFq1KgRgJvLb4cOHaDX69G5c+di11qcdSg2NhatW7fGxx9/bNszv2/fPnz77bcYMGBAmZaN/MqyLXPGYDBg6NChmDt3LoYMGYLY2FjbMhQTE1PknvDC1KpVC2+99RaOHj2KsLAwbNmyBYmJiXj//fdtr7nvvvswb948jB07Fs2bN8fff/+NrVu3lulcXCL6HzZjRBXQhg0bCn38qaeeQmRkJDZv3ox58+Zh9+7d2LBhA0JDQ1GvXj3bjWSBmxccWLVqFSZPnozVq1cjICAAXbt2RfPmzTFq1Ci7vRt9+/aFEAIrVqzA9OnT0aBBAyxYsACTJ0+2e51Op8O8efOwcuVKfPHFF9i9ezf8/f1Ro0YN9OvXr9CLSeTXs2dPmM1mLFu2DDNmzEB0dDQWLFiA2bNnl2o+DR06FNu2bcNvv/2GrVu3IicnB1WqVEHnzp0xYsQIuy8jI0eOxJUrV7B06VJkZmaiVatWaNu2LcLDw7Fx40bMnDkTa9euRW5uLurXr4+FCxfa7gsG3Pz1ecmSJViwYAG2bduGr7/+GqGhobjrrrtsjWDbtm0xZcoULFmyBFOnTkWNGjXw2muv4fLly3bNWHJyMsxms90XKqsXXngBDRo0QHJyMgDgzTffLPCa1atXIzw8HHfccQfWrVuHDz/8EIsWLYIQAk2aNMHMmTM1vccYcPOeXydPnsTixYuRmZmJtm3bYsKECba9K8DNvUtjxozBihUrMHXqVDRu3BgLFy7E9OnTS/2+vXv3xp9//onPPvsMK1euRFRUVKmasfHjx2Pr1q2Ii4uD0WhE586d8fbbb9sdLvb444+jatWqWLx4MZYtW4a8vDxERETg7rvvLvLqeK5W3Hno7e2NadOm4aOPPsLEiRNhMpnw/vvv46mnnsLChQsxefJkLFq0CL6+vnjwwQfRp08fdOnSxW6Mhx56CP369cP27dvx5ZdfQghRomasOOuQTqfDggULEBcXhx07duCzzz5DVFQU3njjDQwaNKjsM+z/lXVb5syoUaNgMBiwdu1avP/++wgJCUGPHj3wyiuvlOi+XrVq1cI777yDGTNm4OzZs6hRo4bths1Ww4YNQ3Z2NrZu3YodO3bgzjvvxKJFi/Dhhx+W6TMQ0U2K0OLMZCIqt1auXIn3338fe/fuLXAz0PwsFgvatm2LBx98EJMnT3ZjhSSzAwcOoH///pg9ezYeeeQRrcspsc8++wxjx47Fp59+Wuo9I0REVH7wnDEiKrWcnBy7v3Nzc7Fp0ybUqlXLrhHLzc0tcM5BfHw8UlJS0KpVK7fUSkRERORpeJgiEZXaCy+8gOrVq6NBgwbIyMjAl19+iTNnzuCDDz6we90ff/yB999/H4888ghCQ0Nx4sQJfPrpp4iOjpZy7wYRERGRK7AZI6JS69ChAz799FNs3boVZrMZ9erVw8cff4xHH33U7nVRUVGIjIzEmjVrkJqaipCQEHTp0gWvvfZakTcjJSIiIirveM4YERERERGRBnjOGBERERERkQbYjBEREREREWmA54wV4dChQxBClOh+HUREREREVP4YjUYoioLmzZu7dFw2Y0UQQhS4FDcREREREVU8avUFbMaK4O3tDSEEGjduDEVRtC6HikkIAaPRCG9vb+YmGWYnJ+YmJ+YmL2YnJ+Ymp/y5HTt2TJX34DljREREREREGmAzRkREREREpAE2Y0RERERERBpgM0ZERERERKQBNmMO8ARLOfF2BPJidnJibnJibvJidnJibnJSOzdeTdGJ4jRkZrMZRqPRDdWQJ/H29oZer9e6jHKDP37IibnJibnJi9nJibnJyR25sRlzQghRZBBCCFy9ehUpKSnuLYoccpSZq4WGhiIyMpIbWRcQQsBsNkOv13N+SoS5yYm5yYvZyYm5ySl/bmphM+aAs5u7WRuxqlWrIiAggCuXB7DerFtRFFXzEEIgKysL169fBwBUq1ZNtfeqSCwWC/c2Soi5yYm5yYvZyYm5yUnt3NiMlZLZbLY1YmFhYVqXQ//PXc0YAPj7+wMArl+/jqpVq3IDS0REREQlwgt4lJL1HLGAgACNKyEtWfPnOYNEREREVFJsxsqIhyZWbMyfiIiIiEqLzZgD/KItJ+YmLx7qKSfmJifmJi9mJyfmJie1c2Mz5kRF+GI/Z84c1K9fv8B/jz32GACgfv36WLZsme31n332GbZu3VqssWNjY/Hee+/Z/h4zZoxtXDVYzxWrCLmVN4qi8CpTEmJucmJu8mJ2cmJucnJHbryAhxPuvEy6lvz8/LBq1aoCjwHApk2bUL16ddvjn3/+OQICAvD444+X+H1GjBiBrKysshXrQP4rYFaE3MoTd158hVyHucmJucmL2cmJuckpf25qYTPmgLNL25cnOp0OzZo1K/S5oh4vjZo1a7psrKKUdKXJy8uDl5cXdDruKNaayWRS/U735HrMTU7MTV7MTk7MTU5q58Zvn+RU/sMU+/Xrh19//RU//PCD7XDGOXPmFHusWw9T/Oyzz1C/fn2cOHECQ4YMQbNmzfDQQw8hPj6+wLQ//PADunfvjiZNmqBNmzaYMGGC3V62rKwsvPfee+jUqROaNWuG2NhYjB8/Hunp6XbjWA+dXLJkCe6//340adLEduPuzz77DI8//jhiYmJwzz334OOPP4bZbC7B3CIiIiIiKh7uGSMbk8lk93dhx8hOmDABr7/+Ovz8/PDmm28CACIjI8v83q+99hp69OiBZ599Fp988gnGjBmDmJgY1K1bFwCwa9cujB49Gk899RRGjRqFGzdu4MMPP0RaWho+/vhjAEBOTg4sFgtefvllGAwGXL16FQsXLsSIESOwZs0au/f7+uuvcfvtt+Ott96CTqdDQEAAVqxYgZkzZ2LAgAEYM2YMTp8+bWvGXnvttTJ/RiIiIiKi/NiMEYCbe5UaNWpk99iMGTPQpUsXu8fq1auHoKAgBAQEuPTwxT59+qBPnz4AgObNm2PPnj346quvMGLECAghMGPGDDz66KOYMmWKbZoqVarg+eefx4gRI3DHHXfAYDBg4sSJtsMUzWYzatSogd69e+Ps2bOoXbu2bVqj0YglS5bY7hOWkZGBuLg4DBkyBK+88goAoH379vD29sa0adMwePBgVK5c2WWfl4iIiIgKl5SUhF9++QVt2rSBwWDQuhxVsRlzoLQn6xnPXYElNd35C11MF1IJ3rWqO39hIfz8/LB27Vq7x2677TZXlFUsHTp0sP1/QEAAqlevjqtXrwIAzp49i8uXL2PcuHF2e+9atWoFnU6HY8eO4Y477gAAfPHFF1i5ciXOnz9vdwjjuXPn7Jqx1q1b292w+9ChQ8jKysIjjzxi9x7t2rVDTk4OTp06hVatWrn+g5MdntQsJ+YmJ+YmL2YnJ+ZWfKmpqThx4gQaNmyoeTOmdm5sxpwoaQDmxBRcaN0LsFhUqsgBvR61jsdDHxZa4kl1Oh1iYmJcX1MxVapUye5vb29v5OXlAQCSk5MBACNHjix02n///RcAsHv3brz55pvo2bMnRo8ejdDQUNy4cQMjR45Ebm6u3TRhYWF2f1vf48knn3T4HqQeRVF4YrOEmJucmJu8mJ2cmJtz2dnZyM7OhhACyV98j5Bz13HtRiJCQkIAAP7+/vD393drTe7Ijc2Yi+nDQlHzwAbN9oyVphHzdKGhoQCA8ePHo0mTJgWer1q1KoCb55U1bNjQ7r5mv/76a6Fj3tpkW1f0uXPnFnoOXI0aNUpVOxERERE5988//+D0Vz+g5uYfEXLmX1RuXQc7d/+Iv0/+BW8vPRo3bqzpjgO1sBlzojT3FijtoYKy8Pb2LrCnSU116tRBZGQkLl68aDuvrDA5OTnw9vaGxWKx3cejuDenbt68Ofz9/XH16lU8+OCDriqdSkAIAZPJBC8vLx7KIRHmJifmJi9mJyfm5pg5OQ2Vl25FzMZdMFarik8evg859bxwNd0XD9RrgtsiK9l+OHen/LmpxaOasfPnz2PZsmU4fPgwTp06hTp16mDbtm3FmvbatWv46KOPsGfPHmRlZSEqKgrDhw/HE088Uep6KtJ9xkqiTp06iI+Px3fffYcqVaqgatWqiIiIUO39FEXBmDFj8NprryErKwv33Xcf/P39ceXKFezZswejR49G7dq10a5dO7z33nuYP38+mjdvjr179+Lnn38u1nsEBwfjxRdfxMyZM3H16lW0atUKer0eFy9exLfffos5c+a4fdd4RcR1Tk7MTU7MTV7MTk7MrSBhNiNt7TYkTV0Cc04uLj7RFpfa3YmcK0mAMQ2RlXJx7p8juHzu5p4xLc4fUzs3j2rGTp06hT179qBp06awWCzF/vDXr19Hz549Ubt2bUyaNAlBQUE4deqU7Zwjcq3nnnsOFy5cwJtvvom0tDS88MILGDVqlKrv2alTJwQHB2PhwoW2vV1RUVG45557EB4eDgB45plncPHiRaxbtw7Lly9Hhw4d8OGHH6JHjx7Feo9BgwYhIiICK1aswNq1a+Hl5YWaNWvivvvu43HeRERERC6UfeAIEsbOQt7RU6jU8xEEvD4Qhkr+aArg73/O4Ptvv8H9D9yD6Hp1AKDc/iiuCA9q0y0WC3S6m/ehHjNmDI4dO1asPWOvv/667Uu4Xq93SS1Hjx6FEAIxMTGF7k7OycmxXS7dz8/PJe9JZSeEsB1a6o7DALgcuI4QAkajEd7e3jyEQyLMTU7MTV7MTk7M7X9MVxOQ+O58ZHy6G75N6yP8/Zfh17Kx3WvOnj2LLVu24Omnn7a7Gra75c/t2LFjAODy89Y8as+YtREriYyMDOzcuRNTp051WSNGRERERESuI3LzkLJoM5I/XAXF3wdVPnoDlfp0hlLI9/+QkBDceeedmpwn5m4e1YyVxvHjx2E0GuHl5YW+ffvi0KFDCA0NRdeuXfHyyy+X6fCyiv7LhayYm7x4OKicmJucmJu8mJ2cKnJumd/8gsS3ZsN4/l+EDHoSld8YBH1opSJfbzAY8Oijj7qxwqLx0vZOJCQkAADefvtt9OjRAy+88AKOHDmCuLg46HQ6vPrqq2V+j1uP5FQUxfaY9bC4/M8VNk1h0xb2XFmm5bgFs3HluIVNm//v8jgPXT1ucad1tM65uibZxvXEmtRa52SbD55YU3HGLew1nIdyjFvR1znZxrVO60nrnNrjGs9cQuL4ucj6ej/82jdHxIrJ8L2zbqnngytqcuW4riB9M2b5/5srt2vXDmPGjAEAtGnTBpmZmVi+fDlGjhxZ6nN5hBDIy8uz29Oi0+lsl7e0LkiFNWPW5/MrznOOFs7SjHvr2OV93KL+gXI2dmnrzf+32Wy2LY9Wer0eer0eQty8NOqt41p/bTGZTAXGtl7+1mKxwGw2FzouABiNRpeNm3/5vnVcALZj3Qv7rNZxC/us1nGFEEWOa13frJ/h1s9amnlofc7RPHR1Ns7moY+PD4DCl5fijOtoHhY1rivmYVHj6nQ6mEwm2y0lijtuaeehVsu3o3lYnHEBz9pGeHl5FTqds3G13EYA6i3fMm0jrNMJYX/rHa3noTuXbxm3EXq9HhaLBSaTyS4367iAZ20jyjoPLZnZSPxoFdIXbYY+vDLCFo6H/2P3FuvfQE/aRli3ldbv57dm5wrSN2PBwcEAbjZg+bVt2xYLFy7E+fPnUb9+/VKP7+hES0VRbP8V9XxRSjNNWcYtztjlZdyiVpZbvyi6oqbCmoaipnO0m9vR/St0Op3D8yndOa7185b2szqbD9b3LSo/NT6rVtk4modlqVet5dDRuBaLxeG2sqIs30WNa+Vp2wiz2ezwnkeeuI3QYh564jZCCFHqbaUW2wj+G3iT2Wx2uK0sD/NQCIH0z79B4oT5sCSlIvSF3gh9sQ90ATd3jMi2jRBCON1WlpX0zVi9evUcPl/WmxMX1Ww5+2IvU/NSnsYtai+lljVx3OI9b22ii7POubIm2cb1tJqc5VaWmmSaD55ak7NtpRq5aTVtRRmX65yc43rqOufKcXOP/YOEcbOQ8/NhBD56D8LefQHetaprWpM7xi2rkl++0MNERUUhOjoa+/fvt3t8//798PPzc9qslZa1s87KylJlfJKDNf+KfFIuERERVVzm5DTceOMjXHpgMMw3klHtkw8RuWpqkY0Y2fOoPWPZ2dnYs2cPAODy5cvIyMjArl27AACtWrWCwWDAgAEDcOXKFezevds23ejRozFixAhMmTIF9913H44ePYrly5dj8ODBCAgIUKVWvV6P0NBQXL9+HQAQEBCgatdMxZP/mF418xBCICsrC9evX0doaChvq0BEREQVijCbkbZ2G5KmLoHIMyJswnCEDHkaig9/oC4Jj2rGEhMT8dJLL9k9Zv179erVaN26daEnC8bGxuKjjz7C/PnzsWHDBlStWhWjRo3C888/X6Z6nH2Zj4yMBABbQ0aeQa0TLAsTGhpqWw6o7NjUyom5yYm5yYvZyak85ZZ94AgSxs5C3tFTqNTzERjeHgqvyHCty1KF2rkpQs1rNUrs6NGjAIp3l22z2VzolV2ofPP29i5XG1YiIiIiR0xXE5D43gJkbP4avk3rI/z9l+HXsrHWZblFSXqDkvCoPWOeqDh7WRxduYXcy12HKZLrMTs5MTc5MTd5MTs5yZ6byDMiZdEnSP5wFRQ/H1T56A1U6v0olHL+/VfNS9pbsRlzgDsN5WQymXhBDUkxOzkxNzkxN3kxOznJmlvmN78g8e04GM9dQcigJ1H5jUHQh1bSuiy3UTs3NmNERERERGTHeOYSEt6Zg6yv98OvfXNELJ8E3zvral1WucNmjIiIiIiIAACWzGwkz1qDlPkb4VWlMiKWvofAJ+6T8vBKGbAZIyIiIiKq4IQQyIj/FokT5sOSlIrKo3ojdFQf6AL9tS6tXGMz5gB/AZATc5MXs5MTc5MTc5MXs5OTJ+eWe/wfJIydhZyfDyOgUweEvzeKN23+f2rnxmbMCU9ecaggRVGkPDmWmJ2smJucmJu8mJ2cPDU3c3IakqYtQ9rKeHjXqYFqmz5AQGxrrcvyGO7Ijc0YEREREVEFIsxmpK3dhqSpSyDyjAibMBwhQ56G4uN5DWN5x2bMCbXvLUCuJYSAyWSCl5cXc5MMs5MTc5MTc5MXs5OTJ+WWfeAIEsbOQt7RU6jU8xEY3h4Kr8hwTWvyVPlzUwubMQd4nzE5MTd5MTs5MTc5MTd5MTs5aZ2b6WoCEt9bgIzNX8O3aX1E7VgAv5aNNa1JBmrnxmaMiIiIiKicEnlGpCz6BMkfroLi54MqH72BSr0fhaLXa10agc0YEREREVG5lPnNL0h8Ow7Gc1cQ8mxXVH5zMPShlbQui/JhM0ZEREREVI4Yz15GwjtzkPXVPvi1b46I5ZPge2ddrcuiQrAZc0DrEyypdNQ8yZLUxezkxNzkxNzkxezk5I7cLJnZSJ61BinzN8KrSmVELH0PgU/cx++0ZaB2blybneDCKxdFUZiZpJidnJibnJibvJidnNTOTQiBjPhvkThxASyJKag8qjdCR/WBLtBftfesCNyxvrEZc4KXtpeLEAIWiwU6nY65SYbZyYm5yYm5yYvZyUnN3HKP/4OEsbOQ8/NhBHTqgPD3RsG7VnWXvkdFlT83tbAZc0DrS5BS6ZjNZlVXGlIPs5MTc5MTc5MXs5OTq3MzJ6chadoypK2Mh3edGqi26QMExLZ22fh0k9rrG5sxIiIiIiJJCLMZaWu3IWnqEojcPISNH4aQ57pB8fHWujQqBTZjREREREQSyPn1KG6MnYW8I38jqMcjCHtnKLwiw7Uui8qAzRgRERERkQczXU1A4nsLkLH5a/g2rY+oHQvg17Kx1mWRC7AZc4AnxsqJx9HLi9nJibnJibnJi9nJqTS5iTwjUhZvRvIHK6H4+aDKR2+gUu9Hoej1KlRIhVF7fWMz5gQbMrkoisL7r0iK2cmJucmJucmL2cmpNLllfXsACW/NhvHcFYQ82xWV3xwMfWgllSqkwrhjfePa7AQvbS+X/FfAZG5yYXZyYm5yYm7yYnZyKkluxrOXkfDOHGR9tQ9+7ZsjYvkk+N5ZV+0SqRDuuLI6mzEHeGl7ORmNRnh784pCMmJ2cmJucmJu8mJ2cnKWmyUzG8mz1iBl/kbowysjYsm7COxyP5tujam9vrEZIyIiIiLSiBACmfHfIWHifFgSU1D5hd4IfbEPdIH+WpdGbsBmjIiIiIhIA7nH/0HCuNnI2f8HAjp1QPh7o+Bdq7rWZZEbsRkjIiIiInIjc3IakqYvQ9qKeHjXqYFqmz5AQGxrrcsiDbAZIyIiIiJyA2E2I339DiROWQyRm4ew8cMQ8lw3KD48B7CiYjPmgKIoPGlSMoqiwMfHR+syqBSYnZyYm5yYm7yYnZwURYHlj5O4PHYW8o78jaAejyDsnaHwigzXujRywB3rG5sxIiIiIiKVmK4mIPG9BcjY/DV8mkQjavt8+LWK0bos8hBsxpzgfcbkIoSA2WyGXq9nbpJhdnJibnJibvJidvIQeUakLN6M5A9WQvHzgWHmqwjp+xh0vGm3NPKvb2rh0uAA7zMmJ4vFoupKQ+phdnJibnJibvJidp4v69sDSHhrNoznriDk2a4IfWMQLIF+UJibdNRe39iMERERERG5gPHsZSSMn4usXT/Br31zRCyfBN8760IIAYvRqHV55IHYjBERERERlYElMxvJs9cidf5G6MJCEbHkXQR2uZ+HkpJTbMaIiIiIiEpBCIHM+O+QMHE+LIkpCB3ZC6Ev9oEu0F/r0kgSbMYc4K8ZcvLiibHSYnZyYm5yYm7yYnaeIff4P0gYNxs5+/9AQKcOCH9vFLxrVS/y9cxNTmrnxqXCCTZkcuG94eTF7OTE3OTE3OTF7LRnTk5D0vRlSFsRD+/aUai28QMEPNDa4TTMTU7uyI3NmBO8tL1chBCwWCzQ6XTMTTLMTk7MTU7MTV7MTjvCbEb6uu1InLIYIjcPYeOHIeS5blB8vJ1Py9yklD83tbAZc4CXtpeT2WxWdaUh9TA7OTE3OTE3eTE798v59ShujJ2FvCN/I6jHIwh7Zyi8IsNLNAZzk5PaubEZIyIiIiIqhOlqAhInLUTGJ1/Bp0k0orbPh1+rGK3LonKEzRgRERERUT4iz4iUxZuR/MFKKH4+qPLR66jUuzNv2kwu51H7Ss+fP4/x48ejS5cuuPPOO/HYY4+VeIyVK1eifv36GDp0qAoVEhEREVF5lvXtAVy8dwCSJi1CcK9HUfOXDQju9wQbMVKFR+0ZO3XqFPbs2YOmTZvCYrGU+JytGzduYN68eQgLC3NJPTzBUk48HltezE5OzE1OzE1ezE4dxrOXkTB+LrJ2/QS/ds0QsXwSfO+s67LxmZuc1M7No5qx2NhYdOzYEQAwZswYHDt2rETTz5w5E7Gxsbhy5YrLamJDJhdFUXgfD0kxOzkxNzkxN3kxO9ezZGYjefZapM7fCF1YKCKWvIvALve79Dsgc5OTO3LzqBa9LJ3nwYMH8c033+DVV191YUUkI14FU17MTk7MTU7MTV7MzjWEEMj4/FtcaNcXqfM3InRkL9TcvxZBXWNV+TGeuclJ7dzKRYtuNpsxadIkDBs2DFWrVnXZuEII3mdMMkIIGI1GeHt7MzfJMDs5MTc5MTd5MTvXyD1xGgnjZiNn3yEEdOqA8PdGwbtWddXej7nJKX9uaikXzdj69euRnZ2NgQMHunzswrphRVGK7JKtK5ij59WaluPCNl1RuZWl3rJMy3GdT2t9zt3rnGzjelpN1unUWOdkmg+eWpOzX3Q9rV5PrMnTxrU+x3XO+bhZWVk4ceIEAODOO++Ev78/zCnpSJ6+DGkr4uFdOwqRG2ciILa1rQ41t5WlnbY8ZiNLTdbH1Nw7Jn0zlpiYiLi4OEyfPh0+Pj4uH99oNNr9gqHT6WzHjhqNxgKvt9ZgNpthsVjsnvPy8oKiKLBYLDCbzXbPWce1duC3snbkhY2r1+uh1+shhIDJZLJ7TlEU27Qmk6nAwqTWuMX5rEDBeVjWcfX/f6WjW3OzflZFUUr8WZ1lrsU8tI5bWE1qZWP9rEXNQ+u4juahs+XbYrEUyM4Vy7ejeejO5Rsof9sInU5nqyl/bp66jSjt8l3ethHWem99T2fjar2NUGv5lm0bUdg6p/U89MTvERkZGbbrD9SoXh05m75C6vRlEDl5qPz28wh8tisUH2/bGGpuI5x9P3H3PCyv3yPUmIcAbBcWvDU7V5C+GZs9ezbq16+Pu+++G2lpaQBuzkyTyYS0tDQEBASU6cQ7R7uTHe2yzL+Q30qn0xV5flz+haKk4zqb1tF8UGtcR58VcDwPyzrurblZ/y7LZ9UiGy3n4a3KOg+dzQedTlfkOqfWPNQqm/K4jShqW1lRlu+ixrXy1G1EUbl54jZCi3noidsIR+ucJ28jSjNuaeZhdnY2srOzkZGRgdy8PAScvo60JW9A//d5eHe5D4a3hyLw9qgixyxqXFdtI4pa5zxpHqo1rmzbCGtzptPpVGnEgHLQjJ09exa//fYbWrZsWeC5li1bYsmSJbj33ntLPb6iKEVu7JxNV5rnyjItx/3fIQZF5aZFTRy3eM87y062z1pRthFqrnMyzQdPrclRbtbnPWlbWZZpK8q4XOecP3fixAkcO3YMlhspqB2/H3VPXkFi1VBcGN4ZmXWroXHiNbSoVcOt9XrqOifbuJ5aU1lI34yNGzfOtkfMaurUqfDz88Mrr7yC+vXrl3psNWc8qUfNkyxJXcxOTsxNTsxNXszOCaMJUd8fwW27/wuTTsHP9zXAkeh6qB0VhgANy2JuclI7N49qxrKzs7Fnzx4AwOXLl5GRkYFdu3YBAFq1agWDwYABAwbgypUr2L17NwCgYcOGBcYJDg5GQEAAWrduXeaa2JDJhXnJi9nJibnJibnJi9k5lvXdAYSPm4XQs1dg6nof4qv6IwU6+FZpiM6dWyIowAf+/v5ur4u5yckduXlUM5aYmIiXXnrJ7jHr36tXr0br1q0LPVlQTWqdrEfqEELAbDZDr9czN8kwOzkxNzkxN3kxu8IZz11BwjtzkLXrJ/i1a4bI5ZORGVkZkfFfwGC0oHPnlqhZI1Kz+pibnPLnphaPasZq1KiBkydPOnzNmjVrnI5TnNcUh5qXsST1WCwWVVcaUg+zkxNzkxNzkxez+x9LZjZS4tYhZd4G6MJCEbHkXQR2uR+KoiAzKQleeh289DoEBbj+itslrpW5SUnt3DyqGSMiIiIickYIgcwvvkfixHkw3UhG6MheqPxSX+gC/3cIor+/Pxo3bmz7fyJPxGaMiIiIiKSRe+I0EsbNRs6+Qwh4pAOqv/cCvGsXvFS9v78/WrRooUGFRMXHZoyIiIiIPJ45JR1J05YibUU8vGtHodrGDxDwQNkv1kakJTZjDvAESznxeGx5MTs5MTc5MTd5VbTshNmM9PXbkThlMUROHgzvDEXo892h+Mh1qfiKllt5oXZubMacYEMmF0VRuLGTFLOTE3OTE3OTV0XLLue3Y0gYOwu5h08iqMfDCHtnGLwiw7Uuq8QqWm7lhTtyYzPmBC9tLxchhC0z5iYXZicn5iYn5iavipKd6WoCEictQsYnu+DTJBpR2+fDr1WM1mWVWkXJrbzJn5ta2Iw5wEvby8lkMvEu95JidnJibnJibvIqz9mJPCNSl3yKpA9WQvHxRpUPX0elPp2hlIO9SuU5t/JM7dzYjBERERGR5rK+O4CEt+JgPHMJwc92hWHMEOhDK2ldFpGq2IwRERERkWaM564g4Z05yNr1E/zaNUPE0nfh26ie1mURuQWbMSIiIiJyO0tmNlLi1iFl3gbowkIRsXgiArvG8pwqqlDYjDnAjYGcmJu8mJ2cmJucmJu8ZM9OCIHML75H4sR5MN1IRujIXqj8Ul/oAv21Lk1VsudWUamdG5sxJ7jiyEVRFJ4cKylmJyfmJifmJi/Zs8s9cRoJ42YjZ98hBDzSAdXfewHetaO0Lkt1sudWUbkjNzZjRERERKQqc0o6kqcvQ+qKeHjXqo5qGz9AwAOttS6LSHNsxpzgfcbkIoSAyWSCl5cXc5MMs5MTc5MTc5OXbNkJsxnp67cjccpiiJw8GN5+HqHPd4fiU7H2EsmWG92UPze1sBlzgPcZkxNzkxezkxNzkxNzk5cs2eX8dgwJY2ch9/BJBPV4GGHvDINXZLjWZWlGltzIntq5sRkjIiIiIpcxXU1A4qRFyPhkF3xi7kD1bfPg37qJ1mUReSQ2Y0RERERUZiLPiNQlnyLpg5VQfLxR5cPXUalPZyh6vdalEXksNmNEREREVCZZ3x1AwltxMJ65hOBnu8IwZgj0oZW0LovI47EZc4AnWMqJl46VF7OTE3OTE3OTlydlZzx3BQnj5yBr50/wa9cMEUvfhW+jelqX5ZE8KTcqPl7aXmNsyOTCvOTF7OTE3OTE3OTlKdlZsnKQMnstUuZtgC4sFBGLJyKwa6zH1OdpOF/k5I7c2Iw5wUvby0UIAbPZDL1ez9wkw+zkxNzkxNzkpXV2QghkfvE9EifOg+lGMkJH9kLll/pCF+jv9lpkonVuVDr5c1MLmzEHeAlSOVksFlVXGlIPs5MTc5MTc5OXVtnlnjiNhHGzkbPvEAIebo/qk0bBu3aU2+uQFdc5OamdG5sxIiIiIiqSOSUdydOXIXVFPLxrVUfkhpkI7NhG67KIygU2Y0RERERUgDCbkb5+OxKnLIbIyYPh7ecR+nx3KD68EAWRq7AZIyIiIiI7Ob8dQ8LYWcg9fBJBPR5G2DvD4BUZrnVZROUOmzEHeIKlnHg8tryYnZyYm5yYm7zUzM50LRGJ7y1Exie74BNzB6pvmwf/1k1Ue7+KhOucnNTOjc2YE2zI5KIoCjd2kmJ2cmJucmJu8lIrO5FnROqST5H0wUooPt6o8uHrqNSnMxQuJy7BdU5O7siNzZgTvLS9XIQQtsyYm1yYnZyYm5yYm7zUyC7ruwNIeCsOxjOXEDywKwxjBkNfOdglY9NNXOfklD83tbAZc4CXtpeTyWTiXe4lxezkxNzkxNzk5arsjOeuIGH8HGTt/Al+7ZohYum78G1UzwUVUmG4zslJ7dzYjBERERFVIJasHKTMXouUeRugCwtFxOKJCOwayz02RBpgM0ZERERUAQghkPnF90icOA+mG8kIHdkLlV/qC12gv9alEVVYbMaIiIiIyrncP88gYdxs5Pz0XwQ83B7VJ42Cd+0orcsiqvDYjDnA3fVyYm7yYnZyYm5yYm7yKkl25pR0JE9fhtQV8fCuVR2RG2YisGMbFaujonCdk5PaubEZc4IrjlwUReHJsZJidnJibnJibvIqbnbCbEb6+h1InLIIIicPhreeQ+jQHlB8mLsWuM7JyR25sRkjIiIiKkdyfjuGhLGzkHv4JIK6P4Sw8cPhFRmudVlEVAg2Y07wPmNyEULAZDLBy8uLuUmG2cmJucmJucnLUXama4lIfG8hMj7ZBZ+YO1B92zz4t26iUaWUH9c5OeXPTS1sxhzgfcbkxNzkxezkxNzkxNzkdWt2Is+I1CWfIumDlVB8vFHlw9dRqU9nKHq9RhVSYbjOyUnt3NiMEREREUkq6/tfkTBuNoxnLiF4YFcYxgyGvnKw1mURUTGxGSMiIiKSjPHcFSROmIusnT/Br21TRCx9F76N6mldFhGVEJsxIiIiIklYsnKQOms10hd8Ap0hBFUXT0BQ1wd4HhKRpNiMOcANm5zUPMmS1MXs5MTc5MTc5CKEQOaXPyBhwlyYbyQjdMQzqPxyP+gC/bUujYqJ65yc1M7No5aK8+fPY9myZTh8+DBOnTqFOnXqYNu2bQ6nuX79OlauXIl9+/bhwoULqFSpElq2bIlXXnkFUVFlv7M8GzK5KIrCzCTF7OTE3OTE3OSS++cZJIybjZyf/ouAh9sjfNIoeNcu+3ccch+uc3JyR24e1YydOnUKe/bsQdOmTWGxWIp19ZLjx49j9+7dePrpp9G0aVMkJydjwYIF6N69O7Zt2waDwVCmmnhpe7kIIWCxWKDT6ZibZJidnJibnJibHMwp6UievgypK+LhXas6IjfMRMADrW3fkZidPLjOySl/bmrxqGYsNjYWHTt2BACMGTMGx44dczpNixYtsHPnTrtdiHfddRfuu+8+xMfHY9CgQaWuh5cglZPZbFZ1pSH1MDs5MTc5MTfPJcxmpK/fgcQpiyBy8mB46zmEDu0BxccbQghmJynmJie1c/OoZqw0HzQ4uODlWyMjI2EwGHD9+nVXlEVERETkFjkHjyNhzMfIPXwSQd0fQtj44fCKDNe6LCJSiUc1Y65y9uxZJCYmom7dulqXQkREROSU6VoikiYtRPqmXfCJuQPVt82Df+smWpdFRCord82YEAKTJ09G1apV0blzZ5eMdytFUYo8hNF6HLCj59WaluPCNl1RuZWl3rJMy3GdT2t9zt3rnGzjelpN1unUWOdkmg+eWpOzw+09rV5PrEntcUWeEalLtyD5g5VQfLwR/sFrCO77GKDTFTk91zn5xrXypJpkHNfdNdnWUxVPXSp3zdicOXPwyy+/YOnSpQgICCjzeEaj0e5ES51OZzs/zWg0Fni9j48PgJvHl1osFrvnvLy8oCgKLBYLzGaz3XPWcYUQhY7r7e1d5Lh6vR56vR5CCJhMJrvnFEWxTWsymQosTGqNW5zPChSch2UdV6/XQ6fTFcjN+lkVRSnxZ3WWuRbz0DpuYTWplY31sxY1D63jOpqHzpZv6/P5s3PF8u1oHrpz+QbK3zbCejL6rbl56jaitMt3edtGeHl5QafTFXhPZ+NqvY1Qa/nWahuR/s3PSB4/D6YzlxDU/3EEvzYQ/hHhtnGLmodA0d9PPG0bIdv3CLW2Ec6+nwCetY2Q9XuEGvNQp9OpetGcctWMffLJJ5g3bx6mTJmCtm3blnk8RVFsX5wKYw2vMPkX8lvpdLoiz4/Lv1CUdFxn0zq6T4Ja4zr6rIDjeViaca1fMgpjXYHK8lm1yMbd89DRuGWdh86e8/X1LbIeteahVtmUl22EszGdjVuelu+ixrXypG2EdVvp6NdeT9tGlGVcwLO2EcZzV5Dwzhxk7foJfm2bImLxRPg2rmf3GkfrnKPvJp62jSjruOVlG2Fd54qarrTjWlWEeejsubKMCxT9Wa3bSjUaMaAcNWO7d+/GxIkT8eKLL6Jbt24uHbuwme8sEEfPqzUtx7XfjVzUa8rLZ5VxXEfPO8tOts9aUbYRaq5zMs0HT61Jtm1lWaaVYVxLVg5S4tYiZe4G6AwhqLp4AoK6PlCibR7XOTnH9dR1TrZx3V1TUYcEu1K5aMYOHDiAV155Bd27d8fIkSNdNq7aM5/UYTQanf5ST56J2cmJucmJubmPEAKZX/6AxAlzYbqRjNARz6Dyy/2gC/Qv1XjMTk7MTU5q5+ZRzVh2djb27NkDALh8+TIyMjKwa9cuAECrVq1gMBgwYMAAXLlyBbt37wYAnD59GiNHjkStWrXQpUsX/PHHH7bxDAYDatas6fbPQURERAQAuX+eQcK42cj56b8IeLg9qk8aBe/aUVqXRUQewqOascTERLz00kt2j1n/Xr16NVq3bl3gZMHDhw8jPT0d6enp6NWrl920Tz75JKZNm6Z+4URERET5mFPTkTx9OVKXfw7v26shcsNMBHZso3VZRORhFMFj8Qp19OhRCCEQExOj2gl75HrWq+xYr9ZD8mB2cmJucmJu6hFmM9LX70DilEUQOXmo/OoAhA7tAcXHNYc5MTs5MTc55c/t2LFjAICYmBiXvodH7RnzNFxZ5MTc5MXs5MTc5MTcXC/n4HEkjPkYuYdPIqj7QwgbPxxekeEufx9mJyfmJie1c2Mz5gRXHLk4u2wpeS5mJyfmJifmVjpJSUn45Zdf0KZNGxgMBtvjpmuJSJq0EOmbdsEn5g5U3zYP/q2bqFIDs5MTc5OTO3JjM0ZERERUDKmpqThx4gQaNmwIg8EAkWdE6tItSJq5AoqPN8I/eA3BfR+D4uBeUkRE+bEZc0LNm7yR61nvrm69izvJg9nJibnJibmVTmZ2HkxmCzKz85D1/a9IeCsOxtMXETywKwxjBkNfOVj1GpidnJibnPLnphY2Yw7w2iZyYm7yYnZyYm5yYm7Fk52djezsbGRk5WHjV0cRdDUF1597H/5/n4ZXy0aosmMegls0dmtNzE5OzE1OvOkzERERkUZOnDiBY8eOISctB02++gXN/jiDbD8fHOlxL9Ja3oHGyEULrYskImmxGSMiIiIqihAIO3wGtb88AO+MbBxvVhPHWjVG9VpV4cXDzYiojNiMERERERUi988zqDppJUL3H4b3/S1xtc9D+OvEH3jwgQcQXa8OAMDf31/jKolIZmzGHOAJlnJS8yRLUhezkxNzkxNzK5o5NR3J05cjdfnn8L69GiI3zERgxzYwnz0L71PHEFElzO7S9u7G7OTE3OSkdm5cKpxgQyYXRVGYmaSYnZyYm5yYW+GExYL0dduROGURRE4eDG89h9ChPaD43LzPUEhICO68806EhIRoViOzkxNzk5M7cmMz5gQvbS8XIQQsFgt0Oh1zkwyzkxNzkxNzKyjn4HEkjJ2F3D/+QlD3hxA2fji8IsPtXmMwGPDoo49qVOFNzE5OzE1O+XNTC5sxB3gJUjmZzWZVVxpSD7OTE3OTE3O7yXQtEUmTFiJ90y74NL4D1bfNg3/rJlqX5RCzkxNzk5PaubEZIyIiogpH5BmRunQLkmaugOLthfAPXkNw38eg6PVal0ZEFQibMSIiIqpQsr7/FQlvxcF4+iKCB3aFYcxg6CsHa10WEVVAbMaIiIioQjCev4LE8XORueNH+LVtiojFE+HbuJ7WZRFRBcZmzAGeYCknHo8tL2YnJ+Ymp4qUmyUrBylxa5EydwN0hhBUXTwBQV0fkPbf+YqUXXnC3OSkdm5sxpyQdUNdUSmKwvt4SIrZyYm5yami5CaEQObWH5A4YR5M15MQOuIZVH6pL3RBAVqXVmoVJbvyhrnJyR25calwgpe2l0v+K2AyN7kwOzkxNzlVhNzy/jqLhHGzkP3jfxHwUDtU3zIL3nVqaF1WmVWE7Moj5iYnd1xZnc2YA7y0vZyMRiO8vb21LoNKgdnJibnJqbzmZk5NR/KMFUhd9hm8b6+GyPUzEPhgW63Lcqnyml15x9zkpHZubMaIiIhIesJiQfr6HUicsggiOxeGt55D6PPdofj6aF0aEVGR2IwRERGR1HIOHkfC2FnI/eMvBHV/CGHjh8MrMlzrsoiInGIzRkRERFIyXUtE0qSFSN+0Cz6N70D1bfPg37qJ1mURERUbmzEiIiKSijCakLr0UyTNWAHF2wvhH7yG4L6PQdHrtS6NiKhE2Iw5oCgKr3gjGUVR4O3tzdwkxOzkxNzkJHNuWT/8hoRxs2E8fRHBA7rAMHYI9JWDtS7LbWTOriJjbnJyR25sxqjc4YZOXsxOTsxNTrLlZjx/BYkT5iFz+174tWmKiMUT4du4ntZlaUK27Ogm5iYntXNjM+YE7zMmFyEEzGYz9Ho9c5MMs5MTc5OTTLlZsnKQMmcdUuauh65yCKounoCgrg94fN1qkSk7+h/mJqf8uamFzZgDvM+YnCwWi6orDamH2cmJucnJ03MTQiBz6w9InDAPputJCB3xDCq/1Be6oACtS9Ocp2dHhWNuclI7NzZjRERE5FHy/jqLhHGzkP3jfxHwUDtU3zIL3nVqaF0WEZHLsRkjIiIij2BOTUfyjBVIXfYZvG+vhsj1MxD4YFutyyIiUg2bMSIiItKUsFiQvn4HEqcsgsjKhWHccwgd2h2Kr4/WpRERqYrNmAM8wVJOPB5bXsxOTsxNTp6SW87vx5EwZhZy//gLQd0eRNiEEfCKDNe6LI/mKdlRyTA3OamdG5sxJ9iQyUVRFG7sJMXs5MTc5OQJuZmuJyFp0kKkb9wJn8Z3oPrWefBv00TTmmTgCdlRyTE3ObkjNzZjTvDS9nIRQtgyY25yYXZyYm5y0jI3YTQhdemnSJ65EvDSI3zmqwju9zgUflEtFq5zcmJucsqfm1rYjDnAS9vLyWQywdvbW+syqBSYnZyYm5y0yC3rh9+QMG42jKcvInhAFxjGDoG+crBbaygPuM7JibnJSe3c2IwRERGRqoznryBxwjxkbt8LvzZNEbF4Inwb19O6LCIizbEZIyIiIlVYsnKQMmcdUuauh65yCKoumoCgJx/gYVpERP+PzRgRERG5lBACmVt/QOKEeTBdT0Lo8J6o/HI/6IICtC6NiMijsBlzgL/cyUmn02ldApUSs5MTc5OTWrnl/XUWCeNmIfvH/yLgoXaovmUWvOvUUOW9Kiquc3JibnJSOzc2Y06wIZOLoijw8uJiLSNmJyfmJic1cjOnpiN5xgqkLvsM3rdXQ+T6GQh8sK1L34O4zsmKucnJHblxqSAiIqJSExYL0tfvQOKURRBZuTCMew6hQ7tD8fXRujQiIo/HZswBd9xbgFxLCAGj0Qhvb2/mJhlmJyfmJidX5Zbz+3EkjJmF3D/+QlC3BxE2YQS8IsNdWCndiuucnJibnPLnphaPasbOnz+PZcuW4fDhwzh16hTq1KmDbdu2OZ1OCIElS5Zg/fr1SEpKQsOGDTF27Fg0a9ZM/aKJiIgqGNP1JCRNWoj0jTvh0/gOVN86D/5tmmhdFhGRdDzqTMJTp05hz549uP3221G3bt1iT7dkyRLExcVh4MCBWLRoEapUqYJBgwbh4sWLKlZLRERUsQijCSkLNuJim97I/Gofwme+ihrfLGEjRkRUSh7VjMXGxmLPnj2Ii4tDo0aNijVNbm4uFi1ahEGDBmHgwIFo27YtPvroI4SGhmLZsmUqV0xERFQxZP3wGy7eNxCJExcgqNtDqPnLeoQM7ApFr9e6NCIiaXnUYYqluXTkf//7X2RkZKBTp062x3x8fPDggw9i9+7driyPiIiowjFe+BeJ4+cic/te+LVpiohvJsA35g6tyyIiKhdK1Yz99ddf+P3333H69GkkJydDURRUrlwZderUwV133YWGDRu6us4inTlzBgBQp04du8fr1q2LVatWIScnB35+fqUamydYyknNkyxJXcxOTsxNTs5ys2TlIGXOOqTMXQ9d5RBUXTQBQU8+wH8bPQDXOTkxNzmpnVuxm7HExESsX78e8fHxuHLlCoQQ8Pb2RkhICIQQSEtLg9FohKIoqFatGp588kn06tUL4eHqXlUpLS0NPj4+8PX1tXs8ODgYQgikpqaWuhmzEkLY/a0oSoHH8j9X2DTumJbj2j9XWG5lGdcVNVX0cYs7rTvXOdnG9cSa1FrnZJsPnlhTcca99TVCCGRt34vE8XNhup6E0OE9EfpSX+iCAuxeo0a9ZZm2Io5b0dc52ca1TlvYa8rbZ60o2whXKFYzNnPmTKxfvx6BgYF45JFH0K5dOzRq1AgRERF2r7t27RqOHz+Offv24ZNPPsHy5cvRt29fvPrqq6oUrzYhBPLy8ux+BdTpdLabvxmNxgLT+PjcvK+K2WyGxWKxe87LywuKosBiscBsNts9Zx3XegnNW1m78sLG1ev10Ov1EELAZDLZPacoim1ak8lUYGFSa9zifFag4Dws67h6vR4mkwkWi6XAr7fWy8mW9LM6y1yLeWgdt7Ca1MrG+lmLmofWcR3NQ0fLtxACubm5UBTFLjtXLN+O5qE7l2+g/G0jdDodjEYjhLC/DYinbiNKu3yXt22El5dXofUYT55Dyvh5yPnpv/B/sC3CN86Ed+0omAGYjUZNtxGAesu3TNsIAMjLy7PVceu4nraNkO17hFrbCL1eD7PZDLPZXOj3E8CzthEyfo8A1JmH1s94679zrlKsZuzgwYOYOXMmHnjA8eEJERERiIiIQGxsLN5++218++23WLp0qcuKLUxwcDDy8vKQm5trt3csLS0NiqIgJCSkTOM7uh+Eo92W+RfyW+l0uiLPj8u/UJR0XGfTOrqDuFrjOvqsgON5WNpxLRZLoblZ/y7LZ9UiGy3mYVHjlnUeOpsP1ucLW+fUmodaZVOethFCCIfbyoqyfBc1rpWnbSMsFovty485NR3JM1cgbdnn8KoZicj1MxDQsU2RYxY1rtrbCC3moSduI6zTlmZbye8RpR+3rMt3Ud9PrDgP//caT9lGCCHstpVqKFYztmnTphIPrCgKOnbsiI4dO5Z42pKwnit29uxZNGjQwPb4mTNnUL169TIfonjrr/T5H3c2XWmeK8u0HPd/v1oUlZsWNXHc4j3vLDvZPmtF2Uaouc7JNB88tSZHuf3//yB9/Q4kTlkEkZULw7jnEDq0OxRfn1LXU5Z6yzJtRRmX65yc41rXOX4/KZ81lYVHXU2xNO666y4EBQVh586dtmbMaDTi66+/xr333qtxdURERJ4p979/4vr4ucg99BeCnn4QYROGw6taFa3LIiKqUDyqGcvOzsaePXsAAJcvX0ZGRgZ27doFAGjVqhUMBgMGDBiAK1eu2C5b7+vri6FDh2LOnDkwGAyIjo7Ghg0bkJKSgsGDB2v2WYiIiDyR6XoSEictRMbGnfBpfAeqb53HmzYTEWmkRM3YP//8g8WLF+P06dOoXLkyOnfujK5duxbYdffll1/izTffxJ9//lmiYhITE/HSSy/ZPWb9e/Xq1WjdunWhJws+99xzEEJg+fLlSEpKQsOGDbFs2TLcdtttJXr/W6m5S5LU4+gYe/JszE5OzE0OwmhC6rItSJ6xAvDSwzB9NEL6PwGdg3NCyDNxnZMTc5OT2rkpopjXajx37hyefPJJmM1m1KtXD4mJibh27RruuusuzJ49G1Wq/O/QhtI2Y57k6NGjAICYmBiNKyEiIiqbrB9+Q8Jbs2H85yKCB3SBYcxg6A1lu8AVEVFFolZvUOyfw2bNmoXAwECsW7cOt99+OwDgiy++wKRJk9CzZ08sXbq0wI2XywO1LmNJ6rDev8PRCbLkmZidnJibZzNe+BeJ4+cic/te+LVpiohvJsA35g7bFcKYm3y4zsmJuckpf25qKfp6lLc4fPgw+vbta2vEAKBLly7YtGkTdDodevfujSNHjqhSpFbUvMEbqefWe0iQPJidnJib57Fk5SBp+jJcbN8XOb+fQNVFE1D9yznwjbnD9hrmJi9mJyfmJie1cyt2M5aSkoLw8PACj9etWxcbN25EZGQkBgwYgB9//NGlBRIREVHxCCGQsfUHXGzfF8lx6xDyfHfU/HkdKj3Vkb/GExF5oGIfphgVFYWTJ08W+lx4eDjWrl2LoUOHYvjw4bykPBERkZvl/XUWCW/NRvbe3xHwUDtU+/Rj+NQt24WsiIhIXcXeM9aqVSvs2rWryF11QUFBWLFiBe655x589913LiuQiIiIimZOTUfC23G4eN+zMF28hsj1M1Bt3XQ2YkREEij2nrEnn3wSCQkJOHbsGJo1a1boa3x8fDBv3jy8//77+Ouvv1xVo2Z4SIecmJu8mJ2cmJs2hMWC9A07kTRlESyZOTCMew6hQ7tD8fUp1vTMTV7MTk7MTU5q51bsS9tXNLy0PREReaqc348jYexs5B76E0FPP4iwCcPhVa2K8wmJiKhUNL+0PREREWnLdD0JSZMWIn3jTvg0vgPVt86Df5smWpdFRESlVOpmLDs7G/7+/q6sxSPxPmNyEULAZDLBy8uLuUmG2cmJubmHMJqQumwLkmesALz0CJ/xCoL7PwFFry/deMxNWsxOTsxNTvlzU0uxL+CRX0JCAvr06ePqWjwOj+CUE3OTF7OTE3NTV9aeg7h430AkTpiPoG4PouYv6xHy7JOlbsSsmJu8mJ2cmJuc1M6txG3e2bNnMWTIkELvOUZERESuYbzwLxLHz0Pm9j3wa9MUEd9MsLtpMxERya9Ee8Z+//13PPPMMwgLC8OyZcvUqomIiKhCSEpKwo4dO5CUlGR7zJKVg6QZy3GxfV/k/H4cVRdNQPUv57ARIyIqh4rdjO3atQvPPvssbrvtNixbtgxBQUFq1kVERFTupaam4sSJE0hNTYUQAhlbf8DF9n2RPHstQp7vjpo/r0OlpzryHBMionKq2Icpjh49GnfeeSdWrFiBSpUqqVmTx+A/fnJS8yRLUhezkxNzK73M7DyYzBZknTiLf1+bjey9vyPgwbao9unHqt+0mbnJi9nJibnJSe3cij26n58fEhISkJKSUmGaMYANmWwURWFmkmJ2cmJuJZednY3s7GxkZOXhsy8Ootk3R+Dz4TZk14hA0KJ3ENzpHviofLVi5iYvZicn5iYnd+RW7MMU161bB7PZjP79++PSpUtq1uRReOUbuQghYDabmZuEmJ2cmFvJnThxAlu/+BKH3puFJxcsR/SJS/itVTR2D34Qu9L/xYkTJ1SvgbnJi9nJibnJyR25FbsZu/POO7Fp0yb4+/ujX79+uHz5smpFeQquMHIym81al0ClxOzkxNxKRn/yPJrO+RKNP9+HqzWrIr5PW5xs0xDe/t5urYO5yYvZyYm5yUnt3Ep0EGRUVBQ2btyI4cOHo1+/fvjuu+/UqouIiKhcMV1PQtLkRai0YQf0DWsjYN0rqBTqB/HtN+j0wH8QXa8OAMBf5UMUiYjIc5T4jLTg4GCsWLECb775phr1EBERlSvCaELqsi1InrEC8NIjfMYrCO7/BBS9Hplnz8Lb2wsRVcJgMBi0LpWIiNysVJcH8fHxwccff+zqWoiIiMqVrD0HkTBuFoz/XETwgCdgGDMEekOI7fmQkBDceeedCAkJcTAKERGVV7zGpgO86o2c9Hq91iVQKTE7OTG3gowX/kXi+HnI3L4Hfq2bIOKbCYXetNlgMODRRx/VoELmJjNmJyfmJie1c3NpM3bx4kXk5eWhbt26rhxWU2zI5KIoCjd2kmJ2cmJu9izZuUiZsw4pc9ZBFxqMqgvHI8gDb9rM3OTF7OTE3OTkjtxK1YytXr0ahw4dsjtUcezYsYiPjwcANGzYEEuWLEFYWJhLitSSEMLj/hGlouW/AiZzkwuzkxNzu0kIgcxte5A4YR5M1xIROqwHKo/uD11QgNalFYq5yYvZyYm5yckdV1Yv9qXt89u8ebNdo/Xjjz/i888/R48ePfD222/j0qVLmDt3rsuK1AovbS8no9GodQlUSsxOThU9t7yTZ/Fvt9G4Nugd+DSojdv2rkLYO8M8thGzqui5yYzZyYm5yUnt3Eq1Z+zKlSt2hyLu3LkTNWrUwLvvvgsASEhIwBdffOGaComIiDyQOS0DyTOWI3XpZ/CuWQ2R66Yj8KF2WpdFREQSKVUzduseo3379uGBBx6w/R0VFYWEhISyVUZEROSBhMWC9A07kTRlESyZOTCMHYLQYT2g+PpoXRoREUmmVIcp1qpVC9988w2Am4coXr9+Hffee6/t+atXryI4ONg1FRIREXmInP+ewOVHhuHGy9Pgf+/dqPnLOlR+qS8bMSIiKpVS7RkbPHgwXn31VbRs2RLZ2dmoW7cuOnToYHv+wIEDaNCggcuK1ApPsJQTc5MXs5NTRcjNdD0JSZMXIX3DDvg0qofqX86Ff9umWpdVJhUht/KK2cmJuclJ7dxK1Yx17twZoaGh2LNnD4KDg9G7d294ed0cKiUlBSEhIejSpYtLC9UKVxy5KIoCb29vrcugUmB2cirvuQmjCanLtiB5xgrAS4/wGa8guP8TUCS/RHV5z608Y3ZyYm5yckduiuAlAwt19OhRAEBMTIzGlRARkRay9h5EwrjZMJ66gOABT8AwZgj0hhCtyyIiIg2o1Ru49KbP5RHvMyYXIQRMJhO8vLyYm2SYnZzKY27GC/8icfw8ZG7fA7/WTRDxzVL4xtyhdVkuVR5zqyiYnZyYm5zy56aWYl3A49FHH0V8fDzy8vKKPXBeXh62bNmCRx99tNTFaY07DeXE3OTF7ORUXnKzZOciaeYKXGzfFzm/H0fVheNRfevccteIWZWX3CoiZicn5iYntXMrVpv35JNP4v3338eUKVMQGxuLtm3bolGjRqhRowb8/f0BAFlZWbh06RKOHTuG/fv34/vvv4e3tzcGDx6s6gcgIiIqCyEEMrftQeKEeTBdTUDo8J6oPLq/x9+0mYiI5FesZuy5555Dr1698Omnn+Lzzz/HF198YdvFqv//k5jNZjOAm/+o3XHHHRg1ahS6deuGoKAglUonIiIqm7yTZ5Ewbjay9/6OgI5tUG3zR/Cpe5vWZRERUQVR7AMgg4KCMHDgQAwcOBCXLl3CoUOHcObMGaSkpAAAQkNDUadOHTRr1gy33cZ/yIiIyHOZ0zKQPGM5Upd+Bu+a1RC5bjoCH2qndVlERFTBlOpstBo1aqBGjRqursXj8ARLOal5kiWpi9nJSabchMWC9I07kTR5ESyZOTCMHYLQYT0q5E2bZcqN7DE7OTE3OamdG5cKJ9iQyUVRFGYmKWYnJ5lyy/nvCSSMnYXc//6JoKcfRNiE4fCqVkXrsjQhU25kj9nJibnJyR25sRlzgpe2l4sQAhaLBTqdjrlJhtnJSYbcTNeTkDRlMdLXb4dPo3qo/uVc+LdtqnVZmpIhNyocs5MTc5NT/tzUwmbMAV6CVE5ms1nVlYbUw+zk5Km5CaMJqcs+Q/KM5YCXHuEzXkFw/yeg/P+Fpyo6T82NnGN2cmJuclI7NzZjRERU7mTtPYiEcbNhPHUBwQOegGHMEOgNIVqXRUREZIfNGBERlRvGC/8icfw8ZG7fA7/WTRCxewl8m0RrXRYREVGhPK4ZO336NCZPnoxDhw4hMDAQXbp0wcsvvwwfH8dXukpOTsbHH3+MvXv3IiUlBTVq1ECfPn3Qq1cvN1VORERasWTnImXueqTErYUuNBhVF45H0FMdeW4GERF5NFWasZycHCQlJaF69eolmi41NRUDBgxArVq1MGfOHFy7dg3Tpk1DTk4Oxo8f73Dal156CWfOnMErr7yCatWqYe/evZg4cSL0ej169OhRqs/Bf8TlxOOx5cXs5KRlbkIIZG7fi8Txc2G6moDQ4T1ReXR/6IICNKtJFlzf5MXs5MTc5KR2biVqxn7++WfMnTsXp0+fRuXKldG5c2cMHjwY/v7+dq/7+uuv8eabb+LPP/8sUTEbN25EZmYm5s6di9DQUAA3T5p79913MXToUERERBQ63Y0bN3DgwAG8//77eOqppwAAbdu2xdGjR7F9+/ZSN2MAGzLZKIrC+3hIitnJScvc8k6eRcJbccjecxABHdug2uaP4FP3Nk1qkQ3XN3kxOzkxNzm5I7dit3rHjh3DkCFDcPbsWbRq1QqhoaGYN28eunbtitOnT7ukmL1796Jt27a2RgwAOnXqBIvFgn379hU5nclkAgBUqlTJ7vGgoKAyXxGRV1SUixDC9h/JhdnJSYvczGkZSHhnDi7e9yxMF64ict10VNswk41YCXB9kxezkxNzk5M7cit2MzZnzhzUqFEDO3bsQFxcHDZs2IA1a9YgJycHvXr1wsGDB8tczJkzZ1CnTh27x4KDg1GlShWcOXOmyOmqVauGDh06YOHChfjnn3+QkZGBHTt2YN++fejTp0+p6+EKIyej0ah1CVRKzE5O7spNWCxIW78dF9v0RtrqrTCMGYLbflyFwIfaueX9yxuub/JidnJibnJSO7di73c7fvw4Bg0aZLfX6u6778bnn3+O559/HoMHD8aHH36Ijh07lrqYtLQ0BAcHF3g8JCQEqampDqedM2cORo8ejc6dOwMA9Ho93n77bTz88MOlrgcovCFTFKXIRs16WKOj59WaluPCNl1RuZWl3rJMy3GdT2t9zt3rnGzjelpN1unUWOfyP5fz3xNIHDcbuf/9E0FPdUTYhOHQV6tS6PjMxvlzVp5WryfW5GnjWp9Te53juK4d18qTapJxXHfXZH1MzR00xW7GsrKyChwGCAAGgwFr1qzBCy+8gJdffhkTJkyAr6+vS4t0RgiBsWPH4ty5c/jwww9RpUoV7N+/H1OnTkVISIitQSsNo9Fod96YTqezHTtaWKdsveqj2WyGxWKxe87LywuKosBiscBsNts9Zx1XCFHouN7e3kWOq9frodfrIYSwHbJppSiKbVqTyVRgYVJr3OJ8VqDgPCzruPr/v5nrrblZP6uiKCX+rM4y12IeWsctrCa1srF+1qLmoXVcR/PQ2fJtsVgKZOeK5dvRPHTn8g2Uv22ETqez1ZQ/N1fNw5wr15A6bRkyN+6C9511UfXzWajUoQWAm/PXXct3edtGWOu99T2djav1NkKt5Vu2bURh65zW87C8fI9Qaxvh7PsJ4FnbCFm/R6gxD4Gb2wghRIHsXKHYzVjNmjVx5MgRdO/evcBz/v7+WLhwId544w2MHz8ezZo1K1UxwcHBSE9PL/B4amoqQkJCipzuhx9+wK5du/Dll1+ifv36AIDWrVsjMTER06ZNK1MzZl1oinquKPkX8lvpdLoir8ySf6Eo6bjOpnV0AqJa4zr6rIDjeVjWcW/Nzfp3WT6rFtloOQ9vVdZ56Gw+6HS6Itc5teahVtmUx21EUdvK0sxDYTQhddkWJM1YAcVLj/Dpr6BSv8eg5BvL05bvosa18tRtRFG5eeI2Qot56InbCEfrnCdvI0ozbnn6N9A6blHrHOfh/17jKdsIa3Om0+lUacSAEjRj7dq1w5YtWzBu3LgCV08Ebs7wjz76CKGhodiwYUOpCq5Tp06Bc8PS09Nx48aNAueS5ffPP/9Ar9cjOtr+xp4NGzbE5s2bkZ2dXWjNxaEoSpEbO2fTlea5skzLcWH71aKo3LSoieMW73ln2cn2WSvKNkKNdS5r70EkjJsN46kLCB7wBAxjhkBvKPiDnCfNB0+tyVFu1uc9aVtZlmkryrhq/jvnaZ+1PI3rqeucbON6ak1lUexmrFu3bhBC4OzZs7jzzjsLfY2iKJgwYQJuv/12nDx5ssTF3HvvvVi4cKHduWO7du2CTqdD+/bti5wuKioKZrMZJ0+eRIMGDWyPHz9+HGFhYS5vxMhzWX/1YG7yYXZycmVuxotXkTh+LjK37YFf6yaI2L0Evk2inU9IJcb1TV7MTk7MTU7uyE0Rap6RVkKpqano3LkzateujaFDh9pu+vz444/b3fR5wIABuHLlCnbv3g0AyMjIwOOPPw5vb2+MHDkSVatWxU8//YTly5dj1KhRGDFiRIlrOXr0KAAgJibGNR+OiIgKZcnORcrc9UiJWwtdaDDCJo5A0FMd+aWFiIg8hlq9gUfdfS4kJASrVq3CpEmTMHLkSAQGBqJbt24YPXq03etuPWEwKCgIK1euxMcff4wPPvgA6enpqFGjBsaMGYO+ffuWqSa1TtYjdQghYDabodfrmZtkmJ2cypKbEAKZ2/cicfxcmK4mIHRYT1R+pT90QQEqVUtWXN/kxezkxNzklD83tZR6z1hCQgLCw8NdXY/HOHr0KIQQiImJ4UojEetVdngogHyYnZxKm1veybNIeCsO2XsOIqBjG4RNfpE3bXYjrm/yYnZyYm5yyp/bsWPHALh+z1ixb/qc3+nTp9GjRw+XFkJEROWfOS0DCe/MwcX7noXpwlVErpuOahtmshEjIqIKqcSHKR48eBAjR45EixYt1KiHiIjKIWGxIH3jTiRNXgRLZg4MY4YgdFgPKL4+WpdGRESkmRI1Yzt37sSYMWPQtm1bxMXFqVUTERGVIzmH/kTC2FnI/f0Egp7qiLAJw+FVvarWZREREWmu2M3Y8uXLMXPmTPznP//B3LlzHd4ErrzgMb1yqgjLZnnF7ORUVG6mG8lImrwI6eu3w6dRPVT/ci782zZ1c3VUFK5v8mJ2cmJuclI7t2KPPmPGDLRs2RJz5sypUAsTGzK58N5w8mJ2ciosN2E0IXXZZ0ieuQLQKQif/gqC+z8OpQL92+HpuL7Ji9nJibnJyR25FftfxvDwcPz111/4888/0aRJEzVr8ii8tL1chBC2zJibXJidnG7NLWvvQSSMmw3j3+cRPOAJGMY+B70hROsy6RZc3+TF7OTE3OSUPze1FPtqip988gmqVq2KwYMH2y7tWN550P2wqQRMJpPWJVApMTs5mUwmGC9exdVn38a/T4+GPqQSanyzFFVmvsZGzINxfZMXs5MTc5OT2rkVuxmrXr06NmzYgPr162PQoEE4fvy4mnUREZEELNm5SP1oNS6174uc346h6oJ3UH3bPPg2ida6NCIiIo9XovuMBQcHY/ny5Wjfvj0GDRqkVk1EROThhBDI2L4Xl+7ph7S4dQh+rhtq/rIelbo9xENwiIiIiqnEZ1P7+Pjg448/xowZM9Soh4iIPFze3+eQMG42svcchH/HNghfNx0B9WuzCSMiIiqhUl/a6o033nBlHR6JXyzkpNOVaIcveRBm59nMaRlInrkCqUu3wKtGJCLXTUPAg+1gNpu1Lo1KgeubvJidnJibnNTOrdjN2G+//Ya6devCYDCoWY/HYUMmF0VRKtStF8oTZue5hMWC9I07kTR5ESyZ2TC8ORihw3tC8fUBwHvnyIjrm7yYnZyYm5zckVuxW73+/ftj3759atZC5BK8Cqa8mJ3nyTn0Jy4/Ohw3XpoG/3taoObP61D55X62RgxgbrJibvJidnJibnJSO7dit3oVcQFyx70FyLWEEDAajfD29mZukmF2nsV0IxlJkxchff12+DSqi+pfzIF/u2YFXsfc5MTc5MXs5MTc5JQ/N7VwfykREdkIowmpyz9H8ozlgE5B+PRXENz/cSg8vIaIiMjlSvSvKzt5IqLyK+vH35EwbjaMJ88heMATMIx9jjdtJiIiUlGJmrHXX38dr7/+erFeqygKTpw4UaqiiIjIfYwXryJxwjxkbv0Bfq1iEPHNUt60mYiIyA1K1Iy1a9cOtWrVUqkUIiJyJ0t2LlLmrUdK3DrogoNQdcE7CHr6QR4FQURE5CYlasa6du2Kxx9/XK1aPA6/kMhJzZMsSV3Mzj2EEMjc8SMSx8+F6d8bCB3WE5Vf6Q9dUECpxmNucmJu8mJ2cmJuclI7N56R7QQbMrkwL3kxO/fI+/scEsbNRvaegwh4oA2qffIBfOrWLPV4zE1OzE1ezE5OzE1O7siNzZgTvLS9XIQQMJvN0Ov1zE0yzE5d5rQMJH+wEqlLPoVXjUhErpuGwIfal3lc5iYn5iYvZicn5ian/Lmphc2YAxXx3mrlgcViUXWlIfUwO9cTFgvSN+1C0qRFsGRmwfDmYIQO72l30+ayYm5yYm7yYnZyYm5yUju3Yjdjf/31l2pFEBGR6+Uc+hMJY2ch9/cTCHqqI8ImDIdX9apal0VERET/j3vGiIgkl5SUhF9++QVt2rSBwWCA6UYykqYsQvr6HfC5sw6qfzEH/u2aaV0mERER3YLNGBGR5FJTU3HixAk0uOMO6DZ/i+QZywGdgvBpoxHc/3EoXtzUExEReSL+C+0AT7CUE4/HlhezK53M7DxUPnsd5j5vI/Hcvwju/wQMY4dAHxbqlvdnbnJibvJidnJibnJSOzc2Y06wIZOLoijc2EmK2ZVMdnY2srOzkf7PZVx99SPc99c/uFajGqLWT4f+rvrI8/eFvxvqYG5yYm7yYnZyYm5yckdubMac4KXt5SKEsGXG3OTC7ErmxKE/kDpvI2p8fxhVvb3wY8dG+LPu7ah1+gQCLv+Nxo0bo0WLFqrXwdzkxNzkxezkxNzklD83tbAZc4CXtpeTyWTiXe4lxeycE0Igc8ePqPTmhwhOSMGlexrhp4Y1YdLlws/LBz7eOrfXxNzkxNzkxezkxNzkpHZubMaIiCSR9/c5JLwVh+wffkPA/S3h++azCKsdBf9/zuD7b7/B/Q/8B9H16gAA/P3dcZAiERERlQWbMSIiD2dOy0DyByuRuuRTeNWIROS6aQh4sJ3tsImIKqnw9vZCRJUwGAwGjaslIiKi4mIzRkTkoYTFgvRNu5A0aREsmVkwvDEIIcN7Qufna/e6kJAQ3HnnnQgJCdGoUiIiIioNNmMO8ARLOTE3eTG7/8k59CcSxs5C7u8nEPRUR4RNGA6v6lULfa3BYMCjjz7q5gr/h7nJibnJi9nJibnJSe3c2Iw5wRVHLoqi8ORYSTG7m0w3kpE0ZRHS1++Az511UP2LOfBv10zrsorE3OTE3OTF7OTE3OTkjtzYjBEReQBhNCF1+edInrEc0CkInzYawf0fh+LFzTQREVF5xX/lneB9xuQihIDJZIKXlxdzk0xFzi7rx9+RMG42jCfPIbj/EzCMHQJ9WKjWZRVLRc5NZsxNXsxOTsxNTvlzUwubMQd4nzE5MTd5VbTsjJeuIXH8XGRu/QF+rWIQ8c1S+DaJ1rqsEqtouZUXzE1ezE5OzE1OaufGZoyIyM0s2blImbceKXHroAsOQtX5byOo20P8tZSIiKiCYTNGROQmQghk7vgRiePnwvTvDYQO64nKr/SHLihA69KIiIhIA2zGiIjcIO/vc0h4Kw7ZP/yGgAfaoNonH8Cnbk2tyyIiIiINsRlzgIcMyYmXjpVXeczOkp6JpJkrkLrkU3jViETkumkIeLBdudq+lMfcKgLmJi9mJyfmJqcKd2n706dPY/LkyTh06BACAwPRpUsXvPzyy/Dx8XE67bVr1/DRRx9hz549yMrKQlRUFIYPH44nnnii1PWUpy9MFQHzkld5y05YLEjftAtJkxbBkpkFwxuDEDK8J3R+vlqX5lLlLbeKgrnJi9nJibnJyR25eVQzlpqaigEDBqBWrVqYM2cOrl27hmnTpiEnJwfjx493OO3169fRs2dP1K5dG5MmTUJQUBBOnTqFvLy8MtXES9vLRQgBi8UCnU7H3CRTnrLL+eMvJIydhdyDxxH05AMImzgCXtWral2WKspTbhUJc5MXs5MTc5NT/tzU4lHN2MaNG5GZmYm5c+ciNDQUAGA2m/Huu+9i6NChiIiIKHLamTNnIjIyEkuXLoVerwcAtG3btkz18BKkcjKbzaquNKQe2bMz3UhG0pRFSF+/Az4Na6N6fBz82zfXuizVyZ5bRcXc5MXs5MTc5KR2bh61ROzduxdt27a1NWIA0KlTJ1gsFuzbt6/I6TIyMrBz50707t3b1ogREbmLMJqQsvhTXGzTG5nb9yL8/ZdR49tlFaIRIyIiotLzqGbszJkzqFOnjt1jwcHBqFKlCs6cOVPkdMePH4fRaISXlxf69u2LRo0aoX379pg5cyaMRqPaZRNRBZb14++4GDsIiW/HIejJB1Dzl/UIGfwUFC+POvCAiIiIPJBHfVtIS0tDcHBwgcdDQkKQmppa5HQJCQkAgLfffhs9evTACy+8gCNHjiAuLg46nQ6vvvpqqWsq7FBFRVGKPITRehywo+fVmpbjwjZdUbmVpd6yTMtxnU9rfc7d61xpxzVdvo7E8XORufUH+LaKQdQ3S+AbE233WdSotyzTyrbOyTQfPLUmZ4fbe1q9nliTp41rfY7rnFzjWnlSTTKO6+6arI+peeqSRzVjpWWxWAAA7dq1w5gxYwAAbdq0QWZmJpYvX46RI0fCz8+vVGMbjUa7Ey11Oh28/v8X78L2ulmv+mg2m211WXl5eUFRFFgsFpjNZrvnrOMKIQod13pZzcLG1ev10Ov1EELAZDLZPacoim1ak8lUYGFSa9zifFag4Dws67jWmm/NzfpZFUUp8Wd1lrkW89A6bmE1qZWN9bMWNQ+t4zqah86Wb+v75s/OFcu3o3lY0nEt2bnIXLwZqXPWQxccBEPcGAQ81RGKosBoNFa4bYROp4NOpyuQm6duI0q7fJe3bYSXlxf0en2B93Q2rtbbCLWWb1duI/J/1tLMw+JsI6zbm8K+n3jaNkK27xFqbSOcfT8BPGsbIev3CDXmoV6vh8VigRDqXNTPo5qx4OBgpKenF3g8NTUVISEhDqcDbjZg+bVt2xYLFy7E+fPnUb9+/RLXoyiKw0vqO7rvQP6F/FbWLy9FvWdpx3U2rXXlcOe4jj4r4HgelmZcRVGK/CzWFagsn1WLbNw9Dx2NW9Z56Ow5R+ubWvOwuOMKIZC18yckjp8D078JCB3WA6Gj+0MXFFDktBVhG2H9Yu9IRVm+ixrXypO2EdZtpaPpPG0bUZZxAfW3ESUdFyjd8uKsHk/bRpR13PKyjSjOOsd5+L/XeNI2QqfTqdaIAR7WjNWpU6fAuWHp6em4ceNGgXPJ8qtXr57DcXNzc8tUV2Ez31kgjp5Xa1qO+79DN6y/HHpCTRy3eM87y07Lz5p36jwSxs1G9g+/wT+2Napt+hA+9WqWuh5X1OQp46q5zsk0Hzy1Jtm2lWWZtqKMy3VOznE9dZ2TbVx31yTEzUvbq9WIAR52AY97770X+/fvR1pamu2xXbt2QafToX379kVOFxUVhejoaOzfv9/u8f3798PPz89ps1YUNY8PJfUUdtgNycHTsrOkZyJhwjxcvHcAjOcuI3LtNFTbONNpI1bReFpuVDzMTV7MTk7MTU5q5+ZRzdgzzzyDwMBAjBw5Ej/99BO2bNmCGTNm4JlnnrG7x9iAAQPw4IMP2k07evRofPfdd5gyZQr27duHhQsXYvny5Rg4cCACAoo+jIiI6FbCYkHahh240Lo30lbGw/DGINz242oEPtxe1V/HiIiIqGLxqMMUQ0JCsGrVKkyaNAkjR45EYGAgunXrhtGjR9u9rrATBmNjY/HRRx9h/vz52LBhA6pWrYpRo0bh+eefd+dHICLJ5fzxFxLGzkLuweMIevIBhE0cAa/qVbUui4iIiMohRfBYvEIdPXoUQgjExMTwl3CJWK+yY71aD8lD6+xMN5KRNGUR0tfvgE/D2gif+jJv2lwMWudGpcPc5MXs5MTc5JQ/t2PHjgEAYmJiXPoeHrVnzNNwZZETc5OXFtkJkwmpy+ORPH0ZoFMQ/v7LCB7wBG/aXAJc5+TE3OTF7OTE3OSkdm78tuEEVxy5KIrjy5aS59Iiu+yf/osbY2fBePIcgvs/AcPYIdCHhbq1BtlxnZMTc5MXs5MTc5OTO3JjM0ZEFY7x0jUkTpiHzC+/h1/LxojYvQS+TUt+L0IiIiKismAz5oSaN3kj17PeXd16F3eShzuys2TnImX+BqTMXgtdcBCqzn8bQd0e4rJSBlzn5MTc5MXs5MTc5JQ/N7WwGXOA1zaRE3OTl1rZCSGQtfNHJLwzF6Z/byB0WA9UfmUAdEG87YUrcJ2TE3OTF7OTE3OTk9q5sRkjonIt79R5JIybjewffoN/bGtU2/QBb9pMREREHoHNGBGVS5b0TCR9sBKpizfDq0YEItdOQ8BD7Xh4CBEREXkMNmNEVK4IiwXpn3yFpPcWwpKZBcMbgxAyvCd0fr5al0ZERERkh82YA/wFXU5qnmRJ6iprdjl//IWEsbOQe/A4grrGImziCHhFRbioOioK1zk5MTd5MTs5MTc5qZ0blwon2JDJRVEUZiapsmRnTkhG4pTFSF+3HT4Na6N6fBz82zd3cYVUGK5zcmJu8mJ2cmJucnJHbmzGnOCl7eUihIDFYoFOp2NukilNdsJkQuryeCRPXwboFIS//zKCBzwBhb8+ug3XOTkxN3kxOzkxNznlz00t/MbiAC9BKiez2azqSkPqKUl22T/9FzfGzoLx5DkE938ChrFDoA8LVbdAKhTXOTkxN3kxOzkxNzmpnRubMSKSivHSNSROmIfML7+HX8vGiNi9BL5N62tdFhEREVGJsRkjIilYcnKRMm8DUmavhS44CFXnv42gbg/xcA8iIiKSFpsxIvJoQghk7fwRCePnwnTlBkKHdkflVwdCFxSgdWlEREREZcJmzAH+4i4nHo8tr1uzyzt1HgnjZiP7h9/gH9sa1TZ+AJ96NTWqjorCdU5OzE1ezE5OzE1OaufGZswJNmRyURSF9/GQVP7sLOmZSPpgJVIXb4ZXjQhErp2GgIfacX30QFzn5MTc5MXs5MTc5OSO3LhUOMFL28sl/xUwmZtchBAQFgsyPvkKSZMWwZKZBcMbgxAyvCd0fr5al0dF4DonJ+YmL2YnJ+YmJ3dcWZ3NmAO8tL2cjEYjvL29tS6DSij3j79wY8zHyPvvnwjqGouwiSPgFRWhdVlUDFzn5MTc5MXs5MTc5KR2bmzGiEhT5oRkJE5ZjPR12+HdoBaqxcchoH1zrcsiIiIiUh2bMSLShDCZkLo8HsnTlwEKEDb1Rfj37gwffz+tSyMiIiJyCzZjROR22T/9FwnjZiPvr7MI7vc4DOOeg84QAqPRqHVpRERERG7DZoyI3MZ46RoSJ8xD5pffw69lY9TYvQS+TesD4DmaREREVPGwGXNAURRe8UYyiqLAx8dH6zLoFpacXKTM24CU2WuhCw5C1flvI6jbQ3brF7OTE3OTE3OTF7OTE3OTkztyYzNGRKoRQiBr549IGD8Xpis3EDq0Oyq/OhC6oACtSyMiIiLSHJsxJ3ifMbkIIWA2m6HX65mbxvJOnUfCuNnI/uE3+Me2RrWNH8CnXs0iX8/s5MTc5MTc5MXs5MTc5JQ/N7WwGXOA57DIyWKxqLrSkGOW9EwkfbASqYs3w6tGBCLXvI+Ah9sX6x8fZicn5iYn5iYvZicn5iYntXNjM0ZELiEsFmRs/hqJ7y2AJSMLhtcHIWRET+j8fLUujYiIiMgjsRkjojLLPXwSN8bOQu5vxxDUNRZhE0fAKypC67KIiIiIPBqbMSIqNXNCMhKnLkH62m3waVgb1ePj4N++udZlEREREUmBzZgDPMFSTl5eXKzVJkwmpC6PR/L0ZYAChE99CcEDu0Ap47xndnJibnJibvJidnJibnJSOzcuFU6wIZML7w2nvuyf/ouEcbOR99dZBPd7HIZxz0EfFlrmcZmdnJibnJibvJidnJibnNyRG5sxJ3hpe7kIIWCxWKDT6ZibixkvXUPihHnI/PJ7+LVsjBq7l8C3aX2Xjc/s5MTc5MTc5MXs5MTc5JQ/N7WwGXOAl7aXk9lsVnWlqWgsOblInbcRybPXQFcpEFXnvYWg7g+r8o8Js5MTc5MTc5MXs5MTc5OT2rmxGSOiQgkhkLXrJyS8MwemKzcQOrQ7Kr86ELqgAK1LIyIiIioX2IwRUQF5p84j4a04ZH//K/zvb4VqGz+AT72aWpdFREREVK6wGSMiG0t6JpI+XInURZvhVSMCkWveR8DD7Xl8OxEREZEK2Iw5wC+gcuLx2CUnLBZkbP4aie8tgCUjC4bXByFkRE/o/HzdWgezkxNzkxNzkxezkxNzk5PaubEZc4INmVwUReF9PEoo9/BJ3Bg7C7m/HUNQ11iETRwBr6gIt9fB7OTE3OTE3OTF7OTE3OTkjty4VFC5w9sRFI85IRmJU5cgfe02+DSsjerxcfBv31zTmpidnJibnJibvJidnJibnNTOjc2YA0IIrjiSEULAaDTC29ubuRVBmExIWxGPpOnLAADhU19C8MAuUDT+xY7ZyYm5yYm5yYvZyYm5ySl/bmrxuINXT58+jWeffRbNmjVD+/btMWPGDOTl5ZVojJUrV6J+/foYOnSoSlUSySl73yFcih2MhLfiENQlFjV/WY+QIU9r3ogRERERVUQe9Q0sNTUVAwYMQK1atTBnzhxcu3YN06ZNQ05ODsaPH1+sMW7cuIF58+YhLCxM5WqJ5GG6fA0JE+Yj84vv4NuyMWrsXgLfpvW1LouIiIioQvOoZmzjxo3IzMzE3LlzERoaCuDmXa/fffddDB06FBERzi8qMHPmTMTGxuLKlSsqV0vk+Sw5uUidtxHJs9dAVykQVee9haDuD/MQCSIiIiIP4FGHKe7duxdt27a1NWIA0KlTJ1gsFuzbt8/p9AcPHsQ333yDV199VcUqiTyfEAKZO3/ExQ79kPThSoQMfgo1D2xApR6PsBEjIiIi8hAetWfszJkzePrpp+0eCw4ORpUqVXDmzBmH05rNZkyaNAnDhg1D1apVXVIPv7TKSc2TLGWQd+o8Et6KQ/b3v8L//laotvED+NSrqXVZxVLRs5MVc5MTc5MXs5MTc5OT2rl5VDOWlpaG4ODgAo+HhIQgNTXV4bTr169HdnY2Bg4c6PK6hBB2fyuKUuCx/M8VNo07puW49s8VlltZxnVFTWqOa0nPRPKHq5C6eDO8akQgcs378H+oXaFjaFVvcad15zon27ieWJNa65xs88ETayrOuIW9hvNQjnEr+jon27jWaT1pnZNtXE+ryRU8qhkrrcTERMTFxWH69Onw8fFx2bhCCOTl5dntIdPpdLabvxmNxgLTWN/fbDbDYrHYPefl5QVFUWCxWGA2m+2es45rvYTmraxdeWHj6vV66PV6CCFgMpnsnlMUxTatyWQqsDCpNW5xPitQcB6WdVy9Xg+TyQSLxVJgz6b1crIl/azOMtdiHlrHBYC83FxkbfkGKVOXQKRlIuTVATC80Bs6P18YjUaXZWP9rEXNQ+u4juaho+VbCIHc3FwoimKXnSuWb0fz0J3LN1D+thE6nc62nOXPzVO3EaVdvmXeRhQ2D728vAqtx9m4Wm4jAPWWb5m2EQBsV5ku7PuJp20jZPseodY2Qq/Xw2w2w2w2F/r9BPCsbYQW89ATtxHWbSWg3v3GPKoZCw4ORnp6eoHHU1NTERISUuR0s2fPRv369XH33XcjLS0NwM0ZajKZkJaWhoCAgFLfPdvR/SAc7bbMv5DfSqfTQacr/HS9/AtFScd1Nq2jeaDWuI4+K+B4HpZ2XIvFUmhu1r/L8lm1yMbRZ809fBIJY2ch9+BxBHaNRdiE4fCKirB9VldnU9Z56Gw+WJ8vbJ1Tax5qtXyXp22EEMLhttLTthFFjVsetxGOarJYLLYvP64aV+1thBbz0BO3EdZpS7Ot5PeI0o9b1uW7qO8nVpyH/3uNp2wjhBBOt5Vl5VHNWJ06dQqcG5aeno4bN26gTp06RU539uxZ/Pbbb2jZsmWB51q2bIklS5bg3nvvLVVNt/5Kn/9xZ9OV5rmyTMtx//erRVG5aVGTGuOaE5KROHUJ0tdug0/D2qgeHwf/9s09tt7iPO8sO1myUXtcT6tJzXVOpvngqTU5ys36vCdtK8sybUUZl+ucnON66jon27ieWlNZeFQzdu+992LhwoV2547t2rULOp0O7du3L3K6cePG2faIWU2dOhV+fn545ZVXUL8+76dE5YMwmZC2Ih5J05cBAMKnvoTggV1402YiIiIiCXnUN7hnnnkGa9aswciRIzF06FBcu3YNM2bMwDPPPGN3j7EBAwbgypUr2L17NwCgYcOGBcYKDg5GQEAAWrdu7bb6idSUve8QEsbOQt5fZxHc73EYxg6BPryy1mURERERUSl5VDMWEhKCVatWYdKkSRg5ciQCAwPRrVs3jB492u51hZ0wqAY1d0mSehwdYy8j0+VrSJgwH5lffAfflo1RY/cS+DYtn3t7y1t2FQVzkxNzkxezkxNzk5PauSlCzWs1Suzo0aMAgJiYGI0roYrKkpOL1HkbkTx7DXSVAhE2YTiCuj0ExcHJskRERETkemr1Bh61Z8wTqXUZS1KH9f4djk6Q9XRCCGTt+gkJ78yB6fJ1hAztDsOrA6GrFKh1aaoqD9lVRMxNTsxNXsxOTsxNTvlzUwubMQe401BOJpNJ2rvc5/1zAQnjZiP7+1/hf38rVNswEz533K51WW4jc3YVGXOTE3OTF7OTE3OTk9q5sRkj8gCW9EwkfbgSqYs2w6tGBCLXvI+Ah9vz1zMiIiKicozNGJGGhMWCjM1fI/G9BbCkZ8Lw+iCEjOgJnZ+v1qURERERkcrYjBFpJPfwSdwYOwu5vx1DYJdYhL87Al5REc4nJCIiIqJygc2YAzxETE6enps5IRmJU5cgfe02+DSojerxcfBv31zrsjyCp2dHhWNucmJu8mJ2cmJuclI7NzZjTnDFkYuiKB57cqwwmZC2Ih5J05cBAMKnvIjgZ7tC8eJqCHh2dlQ05iYn5iYvZicn5iYnd+TGb4FEbpC97xASxs1C3p9nEdzvcRjGDoE+vLLWZRERERGRhtiMOcH7jMlFCAGTyQQvLy+PyM10+RoSJsxH5hffwbdlY9TYvQS+TetrXZZH8rTsqHiYm5yYm7yYnZyYm5zy56YWNmMO8D5jcvKE3Cw5uUidvwnJs9dAFxSAqvPeQlC3h6DodFqX5tE8ITsqOeYmJ+YmL2YnJ+YmJ7VzYzNG5EJCCGTt+gkJ78yB6fJ1hAztDsOrA6GrFKh1aURERETkYdiMEblI3j8XkDBuNrK//xX+97dCtQ0z4XPH7VqXRUREREQeis0YURlZ0jOR9OFKpC7aDK+oqohcPRUBj3TgMeFERERE5BCbMQf4ZVpO7rp0rLBYkPHp10h8dwEs6ZkwvPYsQkY+A52fr1vevzziZX/lxNzkxNzkxezkxNzkxEvba4wNmVzclVfu4ZO4MXYWcn87hsAusQh/dwS8oiLc8t7lFdc1OTE3OTE3eTE7OTE3ObkjNzZjTvDS9nIRQsBsNkOv16uSmzkhGUnvL0Xamq3waVAb1ePj4N++ucvfpyJSOztSB3OTE3OTF7OTE3OTU/7c1MJmzAFeglROFovF5SuNMJmQtiIeSdOXAQDCp7yI4Ge7QlHxvhMVkRrZkfqYm5yYm7yYnZyYm5zUzo3fJImcyN53CAnjZiHvz7Oo1PcxhI17DvrwylqXRURERESSYzNGVATT5WtImDAfmV98B9+7GyHq68Xwa9ZA67KIiIiIqJxgM0Z0C0tOLlLnb0Ly7DXQBQag6ty3ENT9ISg6ndalEREREVE5wmbMAZ5gKafSHtcrhEDWV/uQ8M4cmC5dQ8jQ7jC8OhC6SoEurpCKwmPp5cTc5MTc5MXs5MTc5KR2bmzGnGBDJhdFUUq10uT9cwEJb8Uh+7sD8L+/FaqtnwGfO25XoUIqSmmzI20xNzkxN3kxOzkxNzm5Izc2Y07w0vZyEULYMitObpb0TCR/tAopizbDq3oVRK6eioBHOjBzDZQ0O/IMzE1OzE1ezE5OzE1O+XNTC5sxB3hpezmZTCand0sXFgsyPv0aie8ugCU9E4ZXByJk5DPQ+fm6qUoqTHGyI8/D3OTE3OTF7OTE3OSkdm5sxqjCyT18EjfGzkLub8cQ2CUWYRNHwLtGhNZlEREREVEFw2aMKgxzYgqSpi5B2pqt8GlQG9U/nw3/DndpXRYRERERVVBsxqjcEyYT0lZ+gaRpSwEA4VNeRPCzXaF4cfEnIiIiIu3w26gDPMFSTvlzy953CAnjZiHvz7Oo1PcxhI17DvrwyhpWR45wnZMTc5MTc5MXs5MTc5OT2rmxGXOCK45cFEWBt7c3TJevIXHifGTEfwffuxsh6uvF8GvWQOvyyAFrdiQX5iYn5iYvZicn5iYnd+TGZozKFUtOLlLnb0Ly7DXQBQag6ty3ENT9ISg6ndalERERERHZYTPmBO8zJgchBLK+2oeEt+fAdPkaQp7vDsNrA6GrFKh1aVRMQgiYTCZ4eXlxnZMIc5MTc5MXs5MTc5NT/tzUwmbMAd5nTA55/1xAwltxyP7uAPzva4nw1VMQ0LAuN3YS4jonJ+YmJ+YmL2YnJ+YmJ7VzYzNG0rJkZCH5w5VIWbQZXtWrIHL1VPg/3B4mk0nr0oiIiIiInGIzRtIRQiBj81dIfHcBLOmZMLw6ECEjn4HOz5e/OhERERGRNNiMkVRyD59EwthZyPntGAK7xCJs4gh414jQuiwiIiIiohJjM+YAzznyHObEFCRNXYK0NVvh06A2qn8+G/4d7ir0tWqeZEnqYnZyYm5yYm7yYnZyYm5yUjs3LhVOsCHTljCZkLbyCyRNWwoACJ/yIoKf7QqliBVDURRmJilmJyfmJifmJi9mJyfmJid35MZmzAle2l472fsOIWHcLOT9eRaV+nRG2FvPQx9e2eE0QghYLBbodDrmJhlmJyfmJifmJi9mJyfmJqf8uamFzZgDvBiENkyXryFx4nxkxH8H37sbIerrxfBr1qDY05vNZlVXGlIPs5MTc5MTc5MXs5MTc5OT2rmxGSOPYcnJRer8TUievQa6wABUnfsWgro/BIUbLiIiIiIqh9iMkeaEEMj6ej8S3o6D6dI1hAztDsOrA6GrFKh1aUREREREqvG4Zuz06dOYPHkyDh06hMDAQHTp0gUvv/wyfHx8ipzm+vXrWLlyJfbt24cLFy6gUqVKaNmyJV555RVERUW5sXoqqbzTF5AwLg7Z3x2A/30tUW39DPjccbvWZRERERERqc6jmrHU1FQMGDAAtWrVwpw5c3Dt2jVMmzYNOTk5GD9+fJHTHT9+HLt378bTTz+Npk2bIjk5GQsWLED37t2xbds2GAyGUtXDEyzVY8nIQvJHq5Cy8BN4Va+CyNVTEfBIB5fMcx6PLS9mJyfmJifmJi9mJyfmJie1c/OoZmzjxo3IzMzE3LlzERoaCuDmSXPvvvsuhg4dioiIwm/u26JFC+zcudPuPgB33XUX7rvvPsTHx2PQoEGlrokNmWsJIZCx+SskvrsAlvRMVH51AEJH9ILO39cl4yuKwvt4SIrZyYm5yYm5yYvZyYm5yckduXlUi7537160bdvW1ogBQKdOnWCxWLBv374ipwsODi4woyIjI2EwGHD9+vUy1cQrKrpO7uGTuNJ5BK6PnAK/Nk1x2/51N88Nc1EjBtzMy/ofyYXZyYm5yYm5yYvZyYm5yckduXlUM3bmzBnUqVPH7rHg4GBUqVIFZ86cKdFYZ8+eRWJiIurWrVvqerjCuIY5MQU3Xp2JSw8+B0tGFqp/PhuRy96Dd43C93SWldFoVGVcUh+zkxNzkxNzkxezkxNzk5PauXnU/tK0tDQEBwcXeDwkJASpqanFHkcIgcmTJ6Nq1aro3LlzmWoqrCFTFKXIRs16WKOj59Wa1tPGhdmM1JVfIHn6MkAIhE15EcEDu0D5/72YatRrna6o3Eo7bnnLRqtxHU1rfc7d65xs43paTdbp1FjnZJoPnlqTsx8VPa1eT6zJ08a1Psd1Tq5xrTypJhnHdXdN1sfU3EHjUc2Yq8yZMwe//PILli5dioCAgDKNZTQa7c4b0+l0tkMiC+uUrVd9NJvNsFgsds95eXlBURRYLBaYzWa756zjCiEKHdfb27vIcfV6PfR6PYQQMJlMds8pimKb1mQyFViY1BrX+OsxJL0dh7wTZxDYqxNCxgyGPiwUJiGgM5uLnIfOxnU2D/V6vW3cW8/38/b2hqIoJf6szjJXax46+qzWcQurqazz0NlnLWoeWsd1NA+dLd8Wi6VAdq5Yvh3NQ1dnU9G2ETqdzlZT/tzUmodaLd/lbRthrffW93Q2rtbbCLWWb9m2EYWtc1rPQ3cu3zJuI5x9PwE8axsh6/cINeYhcHMbIYQokJ0reFQzFhwcjPT09AKPp6amIiQkpFhjfPLJJ5g3bx6mTJmCtm3blrkm60JT1HNFyb+Q30qn0xV5ZZb8C0VJx3U2raMTEF01runyNSS+txCZn38L37sbIeqrRfBt1qDIaUtbr6N5aB331tysf5fls2qRTXE+q7vGLes8dDYfdDpdkeucWvNQq2zK4zaiqG1lRVm+ixrXylO3EUXl5onbCC3moSduIxytc568jSjNuOVxG1HUOsd5+L/XeMo2wtqc6XQ6VRoxwMOasTp16hQ4Nyw9PR03btwocC5ZYXbv3o2JEyfixRdfRLdu3cpcj6Iotv8Ke87ZtKV5rizTajmuJScXqfM3IXn2GugCA1BlzjhU6vEwFAcrqRr1CiFsK0xRr/HUeVgRxnX0vLPsZPusFWUboeY6J9N88NSaHOXm6N+4srynVtNWlHG5zsk5rqeuc7KNq0VNjjJzBY9qxu69914sXLjQ7tyxXbt2QafToX379g6nPXDgAF555RV0794dI0eOdFlNas788kAIgayv9yPh7TiYLl1DyPPdYXhtIHSVAjWpx9mvHuS5mJ2cmJucmJu8mJ2cmJuc3JGbRzVjzzzzDNasWYORI0di6NChuHbtGmbMmIFnnnnG7h5jAwYMwJUrV7B7924AwOnTpzFy5EjUqlULXbp0wR9//GF7rcFgQM2aNd39USqEvNMXkDAuDtnfHYD/fS1Rbd10+ETX0rosIiIiIiIpeFQzFhISglWrVmHSpEkYOXIkAgMD0a1bN4wePdrudbeeMHj48GGkp6cjPT0dvXr1snvtk08+iWnTppW6JrVO1pOZJSMLyR+tQsrCT+BVrQoiV01BQKd7PGI+WU/OdHQeBHkmZicn5iYn5iYvZicn5ian/LmpRRFqXqtRYkePHoUQAjExMVxp/p8QAhmffo3EdxfAkpaB0Jf6InREL5fetLmsrFfZcXSCLHkmZicn5iYn5iYvZicn5ian/LkdO3YMABATE+PS9/CoPWPkuXIPn0TC2FnI+e0YAp+4H2HvjlTtps1ERERERBUBmzFyyJyYgqT3lyJt9Zfwrl8L1T6bhYB7WmhdFhERERGR9NiMUaGEyYS0VV8iadpSwCIQNvlFhAzqCkXFY2aJiIiIiCoSfrN2oKIe05u97xAS3pqNvBNnUKlPZxjGPQ+vKpW1LqvY1DzJktTF7OTE3OTE3OTF7OTE3OSkdm5cKpyoSA2Z6cp1JE6cj4zPv4Xv3Y0Q9dUi+DVvqHVZJaL2jflIPcxOTsxNTsxNXsxOTsxNTu7Ijc2YExXh0vaWnFykLtiE5FlroAsMQJU541Cpx8NQdDqtSysxIQQsFgt0Ol25z628YXZyYm5yYm7yYnZyYm5yyp+bWtiMOVDer/ovhEDW1/uR8PYcmC5dRcjz3WF4bSB0lQK1Lq1MzGazqisNqYfZyYm5yYm5yYvZyYm5yUnt3NiMVVB5py8g8a05yPr2F/jf1xLV1k2DT3QtrcsiIiIiIqow2IxVMJaMLCR/tAopC/+vvXuPiqrc3wD+DDqgoAPiYYkXPCouUBBDVBQxVMxkvNexxEpJvFDesU4pXkIx9ZiGRor3E5ppXivRMDWVc9QsNEujUzp4Q9SU28yIwAyzf3/4c5bjIKAx7Hnz+azFH7773Xu+8LiH+bJv21C7sQc8U96Hs/pZHjInIiIiIqphbMaeEpIkQb/jG+TOTYZJq0eDt6LgNn44HOo6yV0aEREREdFTic1YBf4qR4tKfvoNt2csQ/EP5+AyqBcaxo+H0stT7rJshudji4vZiYm5iYm5iYvZiYm5icnWubEZq4TIDVlZbgHyFq6DduNXUPq2QONdy+D8bEe5y7IphULB53gIitmJibmJibmJi9mJibmJqSZy4/+KSoh4a3vJaIQ25SvkLVoHmCQ0nD8ZrtFDoHgK3gQevAOmaLk97ZidmJibmJibuJidmJibmGrizup//U/nf4KIt7a/e/wMbsctQ2lmFuq/2h/uceNQ26OB3GXVKIPBAKVSKXcZ9ASYnZiYm5iYm7iYnZiYm5hsnRubsb8IY84fyI1fCf3uQ3Dq6Iem+1ejToe2cpdFRERERESPwGZMcKbiEhQmf478ZZvg4OIMj6Q41H+5LxS8SJSIiIiIyK6xGROUJEko+uY4bs9KgjH7BlzHvYQGb0Whlqqe3KUREREREVEVsBkTUKnmCnJnJqHo0Heo27MzGm9eBEefFnKXRUREREREj4HNWAUUCoVd3fHGpC9C/ocpKFi1DbUbe8Az5X04q5+1qxrlplAooFQq+TMRELMTE3MTE3MTF7MTE3MTU03kxmZMAJIkQb/zAHLjV8Kk1aPBW1FwGz8cDnWd5C7NLvGNTlzMTkzMTUzMTVzMTkzMTUy2zo3NWCXkfs5Yyc+/4/aMZSj+/ixcBvVCw/jxUHp5ylaPvZMkCWVlZahVqxbf9ATD7MTE3MTE3MTF7MTE3MT0YG62wmasAnI+Z6wstwB5C9dBu/ErKH1boPGuZXB+tqNs9YjEZDLZdKch22F2YmJuYmJu4mJ2YmJuYrJ1bmzG7IxkNEKb8hXyFq0DTBIazp8M11FDoFAyKiIiIiKivxJ+wrcjd4+fwe24ZSjNzEL9V/vDPW4cans0kLssIiIiIiKyATZjdsCY8wdy41dCv/sQnDr6oen+1ajToa3cZRERERERkQ2xGauArS+wlEpKUZD8OfITN8LBxRkeSXGo/3JfKBwcbPq6f3U8H1tczE5MzE1MzE1czE5MzE1Mts6NzVglbNWQ3fnmGG7PTIIx+wZcx72EBm9FoZaqnk1e62miUCj4ZicoZicm5iYm5iYuZicm5iammsiNzVglqvvW9qWaK8idlYSig9+hbs/OaLx5ERx9WlTb9p92kiSZM+OtY8XC7MTE3MTE3MTF7MTE3MT0YG62wmasAtV5a3uTvgj5H6agYNU21G7sAc+U9+GsfpY7pA0YjUYolUq5y6AnwOzExNzExNzExezExNzEZOvc2IzZmCRJ0O88gNz4lTBp9WjwVhTcxg+HQ10nuUsjIiIiIiIZsRmzoZKff8ftGctQ/P1ZuAzqhYbx46H08pS7LCIiIiIisgNsxmygLK8QeQvXQpvyFZS+LdB41zI4P9tR7rKIiIiIiMiOsBmrwONezyUZjdBu3IO8hWsBk4SG8yfDddQQKJT8MdckBz4aQFjMTkzMTUzMTVzMTkzMTUy2zo1dQiWq2pDdPX4Gt+OWozRTg/qv9IP7zBjU9mhg4+roYQqFArVr87+1iJidmJibmJibuJidmJibmGoiN/6v+JOMOX8gd24y9LsOwqmjH5ruX406HdrKXRYREREREdk5NmMVqOjZAlJJKQqSP0d+4kY4uNSFx0czUH9YBBQ8BC0rSZJgMBigVCr52ADBMDsxMTcxMTdxMTsxMTcxPZibrbAZewJ3vjmG2zOTYMy+AdexQ9Hg7ddRS1VP7rKIiIiIiEggbMYeQ6nmCnJnJaHo4Heo27MzGm9eBEefFnKXRUREREREAmIzVgUmfRHyP9yIglWfo3ZjD3imvA9n9bM8zExERERERE+MzVhFJAm6Hd8gb94qmAq0aDBtJNwmvAKHuk5yV0ZERERERIJjM1aBOrGJuHVWA5eBPdFw7gQovTzlLomqwJYXWZJtMTsxMTcxMTdxMTsxMTcx2To3u7v1n0ajwahRoxAYGIjQ0FAsXrwYpaWlla4nSRLWrFmDnj17on379hg2bBjOnDnzp2pRFJWg8c5EeG5IYCMmCIVCYf4isTA7MTE3MTE3cTE7MTE3MdVEbnbVjBUWFiIqKgoGgwFJSUmIjY3Ftm3bsGjRokrXXbt2LT766CO8/vrrWL16NTw8PBAdHY2rV68+cT3Fa+NQ99mOT7w+1TxJkmA0GiFJktyl0GNidmJibmJibuJidmJibmKqidzs6jTFrVu34s6dO/j444/h5uYGACgrK8PcuXMRExODRo0albteSUkJVq9ejejoaLz++usAgI4dOyIiIgLr169HfHz8E9XDHUZMJpMJtWrVkrsMegLMTkzMTUzMTVzMTkzMTUy2zs2ujoylp6cjJCTE3IgBgFqthslkwrFjxx653unTp6HX66FWq81jjo6O6NOnD9LT021ZMhERERER0ROxq2YsKysLrVq1shhTqVTw8PBAVlZWhesBsFrX29sbOTk5KC4urv5iiYiIiIiI/gS7Ok1Rq9VCpVJZjbu6uqKwsLDC9RwdHeHkZHnLeZVKBUmSUFhYiDp16jxWLQaDAZIk4dy5c4+1HslPkiReICsoZicm5iYm5iYuZicm5iam+7mVlpbaJD+7asbsCXcWcTE7cTE7MTE3MTE3cTE7MTE3Md3PzVZ3VbSrZkylUkGn01mNFxYWwtXVtcL1SktLUVJSYnF0TKvVQqFQVLjuo3To0OGx1yEiIiIiIqoqu7pmrFWrVlbXhul0Oty6dcvqerCH1wOAixcvWoxnZWWhSZMmj32KIhERERERka3ZVTMWFhaG48ePQ6vVmsfS0tLg4OCA0NDQR64XFBSEevXq4euvvzaPGQwGfPPNNwgLC7NpzURERERERE/Crk5TjIyMxKZNmzBhwgTExMTg5s2bWLx4MSIjIy2eMRYVFYWcnBwcOHAAAODk5ISYmBgkJSXB3d0dPj4+2LJlCwoKCjB69Gi5vh0iIiIiIqJHsqtmzNXVFSkpKUhISMCECRPg4uKCoUOHIjY21mKeyWRCWVmZxdjYsWMhSRI2bNiAvLw8tG3bFuvXr4eXl1dNfgtERERERERVopAkSZK7CCIiIiIioqeNXV0zRkRERERE9LRgM0ZERERERCQDNmNEREREREQyYDNGREREREQkAzZjREREREREMmAzRkREREREJAM2Y0RERERERDJgM/YQjUaDUaNGITAwEKGhoVi8eDFKS0vlLose8PXXX+PNN99EWFgYAgMDMXjwYOzYsQMPPzJv+/bt6Nu3LwICAjBo0CAcPnxYpoqpPHfu3EFYWBh8fX1x9uxZi2XMzv7s3r0bQ4YMQUBAALp06YIxY8aguLjYvPzbb7/FoEGDEBAQgL59+2Lnzp0yVkv3HTp0CC+99BI6dOiA7t27Y8qUKbh69arVPO5z8rl8+TLmzJmDwYMHw8/PDwMGDCh3XlUy0ul0iIuLQ3BwMDp06IDJkyfjjz/+sPW38FSqLDe9Xo+kpCQMHToUnTp1Qrdu3fDGG2/gt99+s9oWc6tZVd3n7jt48CB8fX3LnVcd2bEZe0BhYSGioqJgMBiQlJSE2NhYbNu2DYsWLZK7NHrAJ598grp162L69OlITk5GWFgYZs+ejRUrVpjn7N27F7Nnz4ZarcbatWsRGBiIiRMn4syZM/IVThZWrlyJsrIyq3FmZ3+Sk5ORkJCAfv36Yf369Zg3bx6aNWtmzi8jIwMTJ05EYGAg1q5dC7VajZkzZyItLU3myp9uJ0+exMSJE9G6dWusWLECcXFx+N///ofo6GiLRpr7nLzOnz+Po0eP4u9//zu8vb3LnVPVjKZOnYpjx44hPj4eS5YswcWLFzF27FgYjcYa+E6eLpXllpOTg88//xyhoaFYtmwZEhISoNPpMGzYMGg0Gou5zK1mVWWfu6+4uBgLFizA3/72t3KXV0t2EpmtWrVKCgwMlPLz881jW7duldq2bSvduHFDvsLIQm5urtXYrFmzpKCgIKmsrEySJEl6/vnnpWnTplnMGTZsmDRmzJgaqZEqduHCBSkwMFDasmWL5OPjI/3888/mZczOvmg0GsnPz086cuTII+dER0dLw4YNsxibNm2apFarbV0eVWD27NlSeHi4ZDKZzGMnTpyQfHx8pB9++ME8xn1OXvd/b0mSJL377rtS//79reZUJaPTp09LPj4+0n/+8x/zmEajkXx9faW9e/faoPKnW2W53blzRyoqKrIY0+v1UnBwsDRv3jzzGHOreVXZ5+5btmyZ9Oqrr5Y7r7qy45GxB6SnpyMkJARubm7mMbVaDZPJhGPHjslXGFlwd3e3Gmvbti30ej2Kiopw9epVXLp0CWq12mJOv379cOLECZ52agfmz5+PyMhItGzZ0mKc2dmfXbt2oVmzZujRo0e5y0tLS3Hy5ElERERYjPfr1w8ajQbZ2dk1USaVw2g0wsXFBQqFwjxWv359ADCf1s19Tn4ODhV/FKtqRunp6VCpVAgNDTXPadWqFdq2bYv09PTqL/wpV1luzs7OqFu3rsWYi4sLmjdvbnEaG3OreZVld9+VK1fw73//G7NmzSp3eXVlx2bsAVlZWWjVqpXFmEqlgoeHB7KysmSqiqri1KlTaNSoEerVq2fO6uEP+t7e3jAYDOVeL0E1Jy0tDb///jsmTJhgtYzZ2Z+ffvoJPj4+WLlyJUJCQtCuXTtERkbip59+AnDvl5XBYLB677x/6gffO+Xz4osvQqPRYPPmzdDpdLh69So+/PBD+Pn5ISgoCAD3ORFUNaOsrCy0bNnSovkG7n045H5oH7RaLc6fP2/xfsnc7Nf777+PwYMHo02bNuUur67s2Iw9QKvVQqVSWY27urqisLBQhoqoKjIyMrBv3z5ER0cDgDmrh7O8/29mKZ+7d+9i0aJFiI2NRb169ayWMzv7c+vWLfz3v//Fl19+iffeew8rVqyAQqFAdHQ0cnNzmZkd69SpEz7++GMsXboUnTp1wnPPPYfc3FysXbsWtWrVAsB9TgRVzUir1ZqPfD6In2HsxwcffACFQoHhw4ebx5ibffr222/x448/YsqUKY+cU13ZsRkjod24cQOxsbHo0qULRo4cKXc5VInk5GQ0bNgQ//jHP+QuhapIkiQUFRVh+fLliIiIQI8ePZCcnAxJkvDpp5/KXR5V4PTp03jnnXfw8ssvIyUlBcuXL4fJZMK4ceMsbuBBRLa3c+dObNu2DXPmzIGnp6fc5VAFSkpKsGDBAkyaNKncS2OqG5uxB6hUKuh0OqvxwsJCuLq6ylARVUSr1WLs2LFwc3NDUlKS+Rzg+1k9nKVWq7VYTjXr2rVr2LBhAyZPngydTgetVouioiIAQFFREe7cucPs7JBKpYKbm5vFaRpubm7w8/PDhQsXmJkdmz9/Prp27Yrp06eja9euiIiIwJo1a5CZmYkvv/wSAN8vRVDVjFQqFfR6vdX6/Awjv6NHj2LOnDkYP348XnjhBYtlzM3+pKSkwMHBAf3794dWq4VWq4XBYIDJZIJWqzVfp1ld2bEZe0B553jqdDrcunXL6noIkldxcTFiYmKg0+mwbt06i8PE97N6OMusrCwolUp4eXnVaK10T3Z2NgwGA8aNG4fOnTujc+fOeOONNwAAI0eOxKhRo5idHWrduvUjl5WUlKB58+ZQKpXlZgaA750y0mg0Vtc6eHp6okGDBrhy5QoAvl+KoKoZtWrVChcvXrR65ubFixe5H8rozJkzmDJlCoYMGVLuKW/Mzf5kZWXh8uXLCAkJMX9eSU1NhUajQefOnc3P0ayu7NiMPSAsLAzHjx83/7UJuHezAQcHB4s7pZC8jEYjpk6diqysLKxbtw6NGjWyWO7l5YUWLVpYPeNo3759CAkJgaOjY02WS/+vbdu22Lhxo8XXjBkzAABz587Fe++9x+zsUK9evVBQUIBff/3VPJafn49ffvkF/v7+cHR0RJcuXbB//36L9fbt2wdvb280a9aspkum/9ekSRNkZmZajF27dg35+flo2rQpAL5fiqCqGYWFhaGwsBAnTpwwz7l48SIyMzMRFhZWozXTPRcuXEBMTAy6du2KuXPnljuHudmfsWPHWn1e6d69O5o2bYqNGzciPDwcQPVlV7vavwOBRUZGYtOmTZgwYQJiYmJw8+ZNLF68GJGRkVYf+Ek+c+fOxeHDhzF9+nTo9XqLh176+fnB0dERkyZNwttvv43mzZujS5cu2LdvH37++Wde4yIjlUqFLl26lLvM398f/v7+AMDs7Mxzzz2HgIAATJ48GbGxsXBycsKaNWvg6OiIV155BQDw5ptvYuTIkYiPj4darcbJkyeRmpqKxMREmat/ukVGRmLBggWYP38+wsPDUVBQYL5u88HbpHOfk9fdu3dx9OhRAPeaZb1eb268goOD4e7uXqWMOnTogO7duyMuLg7vvvsunJyckJiYCF9fXzz//POyfG9/ZZXlJkkSRo8eDScnJ0RFReHcuXPmdevVq2c+64C51bzKsvP29rZ6GPTu3btx8+ZNi88x1ZWdQnr42NpTTqPRICEhAT/++CNcXFwwePBgxMbG8q+DdiQ8PBzXrl0rd9mhQ4fMf4nfvn071q5di5ycHLRs2RLTpk1Dr169arJUqsTJkycxcuRI7NixAwEBAeZxZmdf8vLysHDhQhw+fBgGgwGdOnXCjBkzLE5hPHToEJYtW4aLFy+iSZMmGDduHIYOHSpj1SRJErZu3YotW7bg6tWrcHFxQWBgIGJjY60+aHCfk092djZ69+5d7rKNGzeaP/xVJSOdToeFCxfiwIEDMBqN6N69O2bNmsU/KNtAZbkBeOSNxYKDg7Fp0ybzv5lbzarqPveg6dOn49y5c0hNTbUYr47s2IwRERERERHJgNeMERERERERyYDNGBERERERkQzYjBEREREREcmAzRgREREREZEM2IwRERERERHJgM0YERERERGRDNiMERERERERyYDNGBERUTW6fv06AgICcOrUqcde98KFC/Dz88Pvv/9ug8qIiMjesBkjIiIh7Nq1C76+vvD19UVGRobVckmS0KNHD/j6+iImJkaGCu9ZsWIFnnnmGXTs2PGx123dujV69OiBjz76yAaVERGRvWEzRkREQnFyckJqaqrV+Pfff48bN27A0dFRhqruycvLwxdffIHIyMgn3kZkZCQOHDiAK1euVGNlRERkj9iMERGRUHr06IG0tDQYjUaL8dTUVPj7+8PDw0OmyoCvvvoKtWrVQq9evZ54G926dYOrqyt2795djZUREZE9YjNGRERC6d+/PwoKCnDs2DHzWGlpKfbv34+BAweWu47JZMInn3yC/v37IyAgAN26dcOcOXNQWFhoMU+SJKxcuRJhYWF45plnMGLECJw/fx7h4eGYPn16pbUdPHgQ7du3h4uLi9WyzZs3o3fv3mjfvj2GDh2KjIwMjBgxAiNGjLCYp1QqERwcjEOHDlXlx0FERAJjM0ZEREJp2rQpAgMDsXfvXvNYeno6dDod+vXrV+46c+bMwQcffICgoCDMnDkTL774Ivbs2YPRo0fDYDCY5y1fvhzLly9HmzZt8M4778DLywvR0dEoKiqqtC6DwYCzZ8/C39/fatlnn32GefPmwdPTE//85z/RqVMnTJgwATdu3Ch3W/7+/jh//jz0en2lr0tEROKqLXcBREREj2vgwIFYunQpiouLUadOHezZswedO3dGo0aNrOZmZGRg+/btWLJkicWRsy5dumDMmDFIS0vDwIEDkZeXh3Xr1qFnz55YtWoVFAoFACAxMRGrVq2qtKbr16+juLgYzZo1sxgvLS3F8uXLERAQgJSUFNSufe9Xr6+vL6ZPnw5PT0+rbXl5ecFkMiErKwvt27d/rJ8NERGJg0fGiIhIOGq1GiUlJTh8+DD0ej2OHDnyyFMU09LSUL9+fYSGhiIvL8/85e/vD2dnZ5w8eRIAcPz4cRgMBrz22mvmRgwAoqKiqlRTQUEBAEClUlmMnzt3DgUFBXj55ZfNjRhwr6F0dXUtd1v3t5Gfn1+l1yYiIjHxyBgREQnH3d0dISEhSE1NRXFxMcrKytC3b99y516+fBk6nQ4hISHlLs/NzQUA5OTkAABatGhh9VqPaprKI0mSxb/vb7d58+YW47Vr10bTpk2rtA0iIvprYjNGRERCGjBgAGbPno3bt28jLCzM6ojUfSaTCQ0bNsSSJUvKXe7u7l4t9bi5uQEAtFrtn97W/W00aNDgT2+LiIjsF09TJCIiIfXp0wcODg44c+YMBgwY8Mh5zZs3R0FBAYKCgtCtWzerrzZt2gAAmjRpAgC4dOmSxfp5eXlWd10sT+PGjVGnTh1kZ2dbjN/f7sPPDTMajbh27Vq528rOzoaDgwNatmxZ6esSEZG42IwREZGQXFxcEB8fj0mTJiE8PPyR89RqNcrKyrBy5UqrZUaj0XwUqlu3blAqlfj0008tThNMSUmpUj1KpRLt2rXDuXPnLMbbtWsHNzc3bNu2zeLZaHv27Hlkk/fLL7+gdevWqF+/fpVem4iIxMTTFImISFgvvPBCpXOCg4MxbNgwrF69Gr/++itCQ0OhVCpx6dIlpKWlYebMmYiIiIC7uzuio6OxevVqxMTEoEePHsjMzER6enqVTxfs3bs3EhMTodfrUa9ePQCAo6MjJk2ahISEBERFRUGtVuPatWvYtWuX1XVkwL1b5P/www8YPnz44/0wiIhIODwyRkREf3nz5s1DQkICcnNzkZiYiKVLl+K7777DoEGDEBQUZJ43depUTJo0CZmZmVi8eDGuXLmCDRs2wNnZuUqvM3jwYJhMJqsHNr/22muYNWsWrl+/jn/961/IyMhAcnIy6tevDycnJ4u5J06cQEFBQZUaTSIiEptC4i2biIiIKhQeHo7g4GAsWrSo0rlxcXG4dOkSPvvsswrnmUwmhISEoE+fPpg/f755fPz48VAoFFixYsWfrpuIiOwbj4wRERFVo4kTJ+Ls2bM4deqUeaykpMTqdvVffPEFCgoKEBwcbB7TaDQ4cuQIpkyZUmP1EhGRfHjNGBERUTVq0qQJzp49azF25swZLFy4EBEREXBzc0NmZiZ27NgBHx8fREREmOd5e3sjMzOzpksmIiKZsBkjIiKysaZNm8LT0xObNm1CYWEhXF1dMXjwYLz99ttwdHSUuzwiIpIJrxkjIiIiIiKSAa8ZIyIiIiIikgGbMSIiIiIiIhmwGSMiIiIiIpIBmzEiIiIiIiIZsBkjIiIiIiKSAZsxIiIiIiIiGbAZIyIiIiIikgGbMSIiIiIiIhmwGSMiIiIiIpLB/wG7UyfoZbIeIwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "# Seaborn scatter\n",
+    "sns.scatterplot(\n",
+    "    data=dfDin,\n",
+    "    x=\"masse\",\n",
+    "    y=\"tmp2\",\n",
+    "    s=7\n",
+    ")\n",
+    "\n",
+    "# Barre d’errore\n",
+    "plt.errorbar(\n",
+    "    dfDin[\"masse\"],\n",
+    "    dfDin[\"tmp2\"],\n",
+    "    xerr=dfDin[\"umasse\"],\n",
+    "    yerr=dfDin[\"utmp2\"],\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, dfDin[\"masse\"].max(), 200)\n",
+    "yfit = intercetta + pente * xfit\n",
+    "\n",
+    "\n",
+    "plt.plot(\n",
+    "    xfit,\n",
+    "    yfit,\n",
+    "    color=\"crimson\",\n",
+    "    linewidth=1,\n",
+    "    zorder=10,\n",
+    "    label=\"Fit lineare\"\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 Stocazzo — 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": 45,
+   "id": "859f2337",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ax + B : \n",
+      "A      = 0.0120540 +- 0.0000313\n",
+      "B      = 0.0198573 +- 0.0025536\n",
+      "cov_AB = -0.000000\n",
+      "p-value chi² = 0.9131\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",
+    "A, B, sA, sB, covAB, chi = reg_lin(dfDin[\"masse\"], dfDin[\"tmp2\"], dfDin[\"umasse\"], dfDin[\"utmp2\"])\n",
+    "print(\"Ax + B : \")\n",
+    "print(f\"A      = {A:.7f} +- {sA:.7f}\")\n",
+    "print(f\"B      = {B:.7f} +- {sB:.7f}\")\n",
+    "print(f\"cov_AB = {covAB:.6f}\")\n",
+    "print(f\"p-value chi² = {chi:.4f}\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "fef04a85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K nostro:  3.275133343668113\n",
+      "uK nostro: 0.0001776827257612153\n"
+     ]
+    }
+   ],
+   "source": [
+    "K = (2*np.pi)**2 / A / 1000\n",
+    "uK = np.sqrt( (4*np.pi / A**2)**2 * uA**2 ) / 1e6\n",
+    "\n",
+    "print(\"K nostro: \", K)\n",
+    "print(\"uK nostro:\", uK)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5b191469",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 = 6.57093\n",
+      "Gradi di libertà = 3\n",
+      "Chi^2 ridotto = 2.19031\n",
+      "Probabilità P(0 → chi^2) = 0.91309\n"
+     ]
+    }
+   ],
+   "source": [
+    "F_fit = B + A * dfDin[\"masse\"]\n",
+    "sigma = np.sqrt(dfDin[\"utmp2\"]**2 + (A * dfDin[\"umasse\"])**2)\n",
+    "\n",
+    "\n",
+    "chi2_val = np.sum( ((dfDin[\"tmp2\"] - F_fit) / sigma)**2 )\n",
+    "\n",
+    "N = len(dfDin)\n",
+    "nu = N - 2\n",
+    "\n",
+    "chi2_red = chi2_val / nu\n",
+    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "\n",
+    "\n",
+    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
+    "print(f\"Gradi di libertà = {nu}\")\n",
+    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
+    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ee9d9744",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2.486198\n",
+       "1    0.533770\n",
+       "2    0.859257\n",
+       "3    0.083522\n",
+       "4    2.608185\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((dfDin[\"tmp2\"] - F_fit) / sigma)**2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "59281ecb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== REGRESSIONE T^2 vs Meq (ODR/York) ===\n",
+      "A = 0.0120540 ± 0.0000463\n",
+      "B = 0.0198570 ± 0.0037792\n",
+      "cov(A,B) = -7.804236195803344e-08\n",
+      "chi² = 6.57093\n",
+      "chi² ridotto = 2.19031\n",
+      "\n",
+      "=== COSTANTE ELASTICA DINAMICA DA ODR ===\n",
+      "k = 3.275132 ± 0.012588 N/m\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# --- DATI DELLA TUA ANALISI ---\n",
+    "x = Meq.to_numpy()      # masse equivalenti\n",
+    "sx = uMeq.to_numpy()    # errori masse equivalenti\n",
+    "y = T2.to_numpy()       # T^2\n",
+    "sy = uT2.to_numpy()     # errori di T^2\n",
+    "\n",
+    "# --- MODELLO LINEARE ---\n",
+    "def f(B, x):\n",
+    "    return B[0] * x + B[1]   # A*x + B\n",
+    "\n",
+    "# Pacchetto \"RealData\" con errori anche su x\n",
+    "dati = RealData(x, y, sx=sx, sy=sy)\n",
+    "\n",
+    "# Modello lineare\n",
+    "modello = Model(f)\n",
+    "\n",
+    "# Esecuzione ODR (metodo di York)\n",
+    "odr = ODR(dati, modello, beta0=[0.01, 0])   # stima iniziale: A≈0.01, B≈0\n",
+    "out = odr.run()\n",
+    "\n",
+    "A, B = out.beta\n",
+    "sA, sB = out.sd_beta\n",
+    "cov_AB = out.cov_beta[0, 1]\n",
+    "chi2 = out.sum_square\n",
+    "chi2_red = out.res_var\n",
+    "\n",
+    "print(\"=== REGRESSIONE T^2 vs Meq (ODR/York) ===\")\n",
+    "print(f\"A = {A:.7f} ± {sA:.7f}\")\n",
+    "print(f\"B = {B:.7f} ± {sB:.7f}\")\n",
+    "print(f\"cov(A,B) = {cov_AB}\")\n",
+    "print(f\"chi² = {chi2:.5f}\")\n",
+    "print(f\"chi² ridotto = {chi2_red:.5f}\")\n",
+    "\n",
+    "# --- COSTANTE ELASTICA ---\n",
+    "K = (2*np.pi)**2 / A / 1000     # k in N/m → convertito a N/mm?\n",
+    "uK = (2*np.pi)**2 / (A**2) * sA / 1000\n",
+    "\n",
+    "print(\"\\n=== COSTANTE ELASTICA DINAMICA DA ODR ===\")\n",
+    "print(f\"k = {K:.6f} ± {uK:.6f} N/m\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a53e863f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== REGRESSIONE T^2 vs Meq (Metodo di Y0RK 2004) ===\n",
+      "A = 0.0120540 ± 0.0000313\n",
+      "B = 0.0198570 ± 0.0025536\n",
+      "cov(A,B) = -7.804416866416002e-08\n",
+      "chi² = 6.57093\n",
+      "chi² ridotto = 2.19031\n",
+      "\n",
+      "=== COSTANTE ELASTICA DINAMICA (York 2004) ===\n",
+      "k = 3.275132 ± 0.008506 N/m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- DATI ---\n",
+    "x = Meq.to_numpy().astype(float)\n",
+    "sx = uMeq.to_numpy().astype(float)\n",
+    "y = T2.to_numpy().astype(float)\n",
+    "sy = uT2.to_numpy().astype(float)\n",
+    "\n",
+    "# --- METODO DI YORK 2004 ---\n",
+    "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 (eq. 9)\n",
+    "    Wi = (wx * wy) / (A**2 * wy + wx)\n",
+    "\n",
+    "    # 2) x̄ e ȳ 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) β (eq. 11)\n",
+    "    beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
+    "\n",
+    "    # 5) Aggiornamento pendenza (eq. 12)\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 (eq. 13)\n",
+    "  B = Y_bar - A * X_bar\n",
+    "\n",
+    "  # --- ERRORI (eq. 14–16) ---\n",
+    "  # S = Σ Wi (xi - x̄)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 (eq. 17)\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",
+    "A, B, sA, sB, cov_AB, chi2, chi2_red = york_fit(x, y, sx, sy)\n",
+    "\n",
+    "print(\"=== REGRESSIONE T^2 vs Meq (Metodo di Y0RK 2004) ===\")\n",
+    "print(f\"A = {A:.7f} ± {sA:.7f}\")\n",
+    "print(f\"B = {B:.7f} ± {sB:.7f}\")\n",
+    "print(f\"cov(A,B) = {cov_AB}\")\n",
+    "print(f\"chi² = {chi2:.5f}\")\n",
+    "print(f\"chi² ridotto = {chi2_red:.5f}\")\n",
+    "K = (2*np.pi)**2 / A / 1000\n",
+    "uK = (2*np.pi)**2 / (A**2) * sA / 1000\n",
+    "\n",
+    "print(\"\\n=== COSTANTE ELASTICA DINAMICA (York 2004) ===\")\n",
+    "print(f\"k = {K:.6f} ± {uK:.6f} N/m\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/molla/statica/mollaStatica.ipynb b/molla/statica/mollaStatica.ipynb
index b8ca40c..9209379 100644
--- a/molla/statica/mollaStatica.ipynb
+++ b/molla/statica/mollaStatica.ipynb
@@ -1,76 +1,112 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4b8ea041",
-   "metadata": {},
-   "source": [
-    "# Statistica della molla statica\n",
-    "Passo passo logica dello studio di una molla in condizione statica"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "6bf32773",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: matplotlib in ./.venv/lib/python3.13/site-packages (3.10.8)\n",
-      "Requirement already satisfied: numpy in ./.venv/lib/python3.13/site-packages (2.4.3)\n",
-      "Requirement already satisfied: pandas in ./.venv/lib/python3.13/site-packages (3.0.1)\n",
-      "Requirement already satisfied: scipy in ./.venv/lib/python3.13/site-packages (1.17.1)\n",
-      "Requirement already satisfied: seaborn in ./.venv/lib/python3.13/site-packages (0.13.2)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.13/site-packages (from matplotlib) (1.3.3)\n",
-      "Requirement already satisfied: cycler>=0.10 in ./.venv/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in ./.venv/lib/python3.13/site-packages (from matplotlib) (4.62.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.3.1 in ./.venv/lib/python3.13/site-packages (from matplotlib) (1.5.0)\n",
-      "Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.13/site-packages (from matplotlib) (26.0)\n",
-      "Requirement already satisfied: pillow>=8 in ./.venv/lib/python3.13/site-packages (from matplotlib) (12.1.1)\n",
-      "Requirement already satisfied: pyparsing>=3 in ./.venv/lib/python3.13/site-packages (from matplotlib) (3.3.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.13/site-packages (from matplotlib) (2.9.0.post0)\n",
-      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.13/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install matplotlib numpy pandas scipy seaborn"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "2e1b4f68",
+   "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",
-    "g = 9.805\n",
-    "ug = 0.001\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9986ac20",
-   "metadata": {},
-   "source": [
-    "## Sezione inutile all'ultilizzo in lab: creazione di dati pseudo random"
+    "\n",
+    "g = 9.806\n",
+    "ug = 0.001"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "id": "a5b574c7",
+   "execution_count": null,
+   "id": "08efb2be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\ndf[\"K\"] = df.m * g / df.Dx\\ndf[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\\n\\ndf[\"F\"] = df.m * g\\ndf[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\\nmedia_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\\n\\n\\n#chi 2\\nchi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\\ndof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\\nchi2_rid = chi2_val / dof\\np_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\\n\\nprint(\"#\"*60)\\nprint(\"Valori di K\")\\nprint(\"media pesata K:\", media_K)\\nprint(\"sigma K:\", sigma_K)\\nprint(f\"Chi2           : {chi2_val:.4f}\")\\nprint(f\"DOF            : {dof}\")\\nprint(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\\nprint(f\"p-value        : {p_value:.4f}\")\\n'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(r'statica2.csv')\n",
+    "\n",
+    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
+    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
+    "\n",
+    "  def riga_stats(row):\n",
+    "    valori = row[dx_cols].dropna()\n",
+    "    n = len(valori)\n",
+    "\n",
+    "    if n == 0:\n",
+    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
+    "    elif n == 1:\n",
+    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
+    "    else:\n",
+    "      media = valori.mean()\n",
+    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
+    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
+    "\n",
+    "  stats = df.apply(riga_stats, axis=1)\n",
+    "  df['Dx'] = stats['Dx']\n",
+    "  df['uDx'] = stats['uDx']\n",
+    "\n",
+    "  return df\n",
+    "\n",
+    "def calcola_m_stats(df, err_arbitrario_m):\n",
+    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
+    "\n",
+    "  def riga_stats(row):\n",
+    "    valori = row[m_cols].dropna()\n",
+    "    n = len(valori)\n",
+    "\n",
+    "    if n == 0:\n",
+    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
+    "    elif n == 1:\n",
+    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
+    "    else:\n",
+    "      media = valori.mean()\n",
+    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
+    "      return pd.Series({'m': media, 'um': sigma})\n",
+    "\n",
+    "  stats = df.apply(riga_stats, axis=1)\n",
+    "  df['m'] = stats['m']\n",
+    "  df['um'] = stats['um']\n",
+    "\n",
+    "  return df\n",
+    "\n",
+    "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",
+    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
+    "df = calcola_m_stats(df, err_arbitrario_m=0.0028867513)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5494409f",
    "metadata": {},
    "outputs": [
     {
@@ -95,486 +131,190 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>m1</th>\n",
-       "      <th>m2</th>\n",
-       "      <th>m3</th>\n",
-       "      <th>m4</th>\n",
-       "      <th>m5</th>\n",
        "      <th>Dx1</th>\n",
        "      <th>Dx2</th>\n",
        "      <th>Dx3</th>\n",
        "      <th>Dx4</th>\n",
        "      <th>Dx5</th>\n",
        "      <th>Dx6</th>\n",
-       "      <th>Dx7</th>\n",
+       "      <th>Dx</th>\n",
+       "      <th>uDx</th>\n",
+       "      <th>m</th>\n",
+       "      <th>um</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.050</td>\n",
-       "      <td>0.052432</td>\n",
-       "      <td>0.038498</td>\n",
-       "      <td>0.041740</td>\n",
-       "      <td>0.066845</td>\n",
-       "      <td>0.018047</td>\n",
-       "      <td>0.025734</td>\n",
-       "      <td>0.031742</td>\n",
-       "      <td>0.022223</td>\n",
-       "      <td>0.021998</td>\n",
-       "      <td>0.021180</td>\n",
-       "      <td>0.022974</td>\n",
+       "      <td>49.246667</td>\n",
+       "      <td>212.10</td>\n",
+       "      <td>211.64</td>\n",
+       "      <td>212.00</td>\n",
+       "      <td>212.18</td>\n",
+       "      <td>212.52</td>\n",
+       "      <td>212.04</td>\n",
+       "      <td>212.080000</td>\n",
+       "      <td>0.284816</td>\n",
+       "      <td>49.246667</td>\n",
+       "      <td>0.002887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.075</td>\n",
-       "      <td>0.072613</td>\n",
-       "      <td>0.062558</td>\n",
-       "      <td>0.077425</td>\n",
-       "      <td>0.015478</td>\n",
-       "      <td>0.042429</td>\n",
-       "      <td>0.040160</td>\n",
-       "      <td>0.037280</td>\n",
-       "      <td>0.033654</td>\n",
-       "      <td>0.042858</td>\n",
-       "      <td>0.037256</td>\n",
-       "      <td>0.043517</td>\n",
+       "      <td>69.276667</td>\n",
+       "      <td>150.92</td>\n",
+       "      <td>150.26</td>\n",
+       "      <td>150.02</td>\n",
+       "      <td>150.16</td>\n",
+       "      <td>150.40</td>\n",
+       "      <td>150.18</td>\n",
+       "      <td>150.323333</td>\n",
+       "      <td>0.317847</td>\n",
+       "      <td>69.276667</td>\n",
+       "      <td>0.002887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.100</td>\n",
-       "      <td>0.103347</td>\n",
-       "      <td>0.101605</td>\n",
-       "      <td>0.099361</td>\n",
-       "      <td>0.093900</td>\n",
-       "      <td>0.052411</td>\n",
-       "      <td>0.046097</td>\n",
-       "      <td>0.050659</td>\n",
-       "      <td>0.049701</td>\n",
-       "      <td>0.047669</td>\n",
-       "      <td>0.039527</td>\n",
-       "      <td>0.049989</td>\n",
+       "      <td>88.966667</td>\n",
+       "      <td>90.34</td>\n",
+       "      <td>90.34</td>\n",
+       "      <td>90.38</td>\n",
+       "      <td>90.52</td>\n",
+       "      <td>90.26</td>\n",
+       "      <td>90.28</td>\n",
+       "      <td>90.353333</td>\n",
+       "      <td>0.092664</td>\n",
+       "      <td>88.966667</td>\n",
+       "      <td>0.002887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.125</td>\n",
-       "      <td>0.118064</td>\n",
-       "      <td>0.150049</td>\n",
-       "      <td>0.124056</td>\n",
-       "      <td>0.153998</td>\n",
-       "      <td>0.059645</td>\n",
-       "      <td>0.056738</td>\n",
-       "      <td>0.066964</td>\n",
-       "      <td>0.065501</td>\n",
-       "      <td>0.064932</td>\n",
-       "      <td>0.051581</td>\n",
-       "      <td>0.068314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.150</td>\n",
-       "      <td>0.158329</td>\n",
-       "      <td>0.143522</td>\n",
-       "      <td>0.153235</td>\n",
-       "      <td>0.125121</td>\n",
-       "      <td>0.070399</td>\n",
-       "      <td>0.063578</td>\n",
-       "      <td>0.077659</td>\n",
-       "      <td>0.072795</td>\n",
-       "      <td>0.067691</td>\n",
-       "      <td>0.066485</td>\n",
-       "      <td>0.074349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.175</td>\n",
-       "      <td>0.174045</td>\n",
-       "      <td>0.152967</td>\n",
-       "      <td>0.181660</td>\n",
-       "      <td>0.176064</td>\n",
-       "      <td>0.087671</td>\n",
-       "      <td>0.090652</td>\n",
-       "      <td>0.088689</td>\n",
-       "      <td>0.087106</td>\n",
-       "      <td>0.078646</td>\n",
-       "      <td>0.081791</td>\n",
-       "      <td>0.080701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.200</td>\n",
-       "      <td>0.202672</td>\n",
-       "      <td>0.184025</td>\n",
-       "      <td>0.201895</td>\n",
-       "      <td>0.229997</td>\n",
-       "      <td>0.090162</td>\n",
-       "      <td>0.098133</td>\n",
-       "      <td>0.096154</td>\n",
-       "      <td>0.100708</td>\n",
-       "      <td>0.097934</td>\n",
-       "      <td>0.094379</td>\n",
-       "      <td>0.102352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.225</td>\n",
-       "      <td>0.228883</td>\n",
-       "      <td>0.260549</td>\n",
-       "      <td>0.226784</td>\n",
-       "      <td>0.201636</td>\n",
-       "      <td>0.104527</td>\n",
-       "      <td>0.111335</td>\n",
-       "      <td>0.105212</td>\n",
-       "      <td>0.113755</td>\n",
-       "      <td>0.108067</td>\n",
-       "      <td>0.108022</td>\n",
-       "      <td>0.107109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.250</td>\n",
-       "      <td>0.251224</td>\n",
-       "      <td>0.242992</td>\n",
-       "      <td>0.238627</td>\n",
-       "      <td>0.266212</td>\n",
-       "      <td>0.129012</td>\n",
-       "      <td>0.118645</td>\n",
-       "      <td>0.124443</td>\n",
-       "      <td>0.118103</td>\n",
-       "      <td>0.123003</td>\n",
-       "      <td>0.121608</td>\n",
-       "      <td>0.124631</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.275899</td>\n",
-       "      <td>0.251327</td>\n",
-       "      <td>0.280781</td>\n",
-       "      <td>0.312978</td>\n",
-       "      <td>0.136631</td>\n",
-       "      <td>0.140951</td>\n",
-       "      <td>0.132259</td>\n",
-       "      <td>0.137163</td>\n",
-       "      <td>0.137445</td>\n",
-       "      <td>0.130444</td>\n",
-       "      <td>0.140669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.293292</td>\n",
-       "      <td>0.293954</td>\n",
-       "      <td>0.296745</td>\n",
-       "      <td>0.308939</td>\n",
-       "      <td>0.153415</td>\n",
-       "      <td>0.151791</td>\n",
-       "      <td>0.151211</td>\n",
-       "      <td>0.146986</td>\n",
-       "      <td>0.148656</td>\n",
-       "      <td>0.147098</td>\n",
-       "      <td>0.150745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0.325</td>\n",
-       "      <td>0.324090</td>\n",
-       "      <td>0.330965</td>\n",
-       "      <td>0.334900</td>\n",
-       "      <td>0.357583</td>\n",
-       "      <td>0.160549</td>\n",
-       "      <td>0.158722</td>\n",
-       "      <td>0.160388</td>\n",
-       "      <td>0.156324</td>\n",
-       "      <td>0.157541</td>\n",
-       "      <td>0.162296</td>\n",
-       "      <td>0.161074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.350</td>\n",
-       "      <td>0.347289</td>\n",
-       "      <td>0.355742</td>\n",
-       "      <td>0.363132</td>\n",
-       "      <td>0.347237</td>\n",
-       "      <td>0.180310</td>\n",
-       "      <td>0.168808</td>\n",
-       "      <td>0.167861</td>\n",
-       "      <td>0.168199</td>\n",
-       "      <td>0.166944</td>\n",
-       "      <td>0.172948</td>\n",
-       "      <td>0.166314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0.375</td>\n",
-       "      <td>0.375010</td>\n",
-       "      <td>0.412261</td>\n",
-       "      <td>0.358674</td>\n",
-       "      <td>0.366582</td>\n",
-       "      <td>0.178874</td>\n",
-       "      <td>0.182060</td>\n",
-       "      <td>0.185182</td>\n",
-       "      <td>0.182708</td>\n",
-       "      <td>0.178580</td>\n",
-       "      <td>0.188249</td>\n",
-       "      <td>0.183653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0.400</td>\n",
-       "      <td>0.386501</td>\n",
-       "      <td>0.396172</td>\n",
-       "      <td>0.413298</td>\n",
-       "      <td>0.409323</td>\n",
-       "      <td>0.202518</td>\n",
-       "      <td>0.192107</td>\n",
-       "      <td>0.199646</td>\n",
-       "      <td>0.191132</td>\n",
-       "      <td>0.197280</td>\n",
-       "      <td>0.205252</td>\n",
-       "      <td>0.192378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0.425</td>\n",
-       "      <td>0.417214</td>\n",
-       "      <td>0.394087</td>\n",
-       "      <td>0.418190</td>\n",
-       "      <td>0.396204</td>\n",
-       "      <td>0.213574</td>\n",
-       "      <td>0.208489</td>\n",
-       "      <td>0.209244</td>\n",
-       "      <td>0.201062</td>\n",
-       "      <td>0.216948</td>\n",
-       "      <td>0.205209</td>\n",
-       "      <td>0.215061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>0.450</td>\n",
-       "      <td>0.448536</td>\n",
-       "      <td>0.480177</td>\n",
-       "      <td>0.439624</td>\n",
-       "      <td>0.472196</td>\n",
-       "      <td>0.226967</td>\n",
-       "      <td>0.217532</td>\n",
-       "      <td>0.216700</td>\n",
-       "      <td>0.220931</td>\n",
-       "      <td>0.220583</td>\n",
-       "      <td>0.228625</td>\n",
-       "      <td>0.223440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.475</td>\n",
-       "      <td>0.476295</td>\n",
-       "      <td>0.480740</td>\n",
-       "      <td>0.472676</td>\n",
-       "      <td>0.475891</td>\n",
-       "      <td>0.241500</td>\n",
-       "      <td>0.235796</td>\n",
-       "      <td>0.234180</td>\n",
-       "      <td>0.236741</td>\n",
-       "      <td>0.226907</td>\n",
-       "      <td>0.227570</td>\n",
-       "      <td>0.237174</td>\n",
+       "      <td>108.610000</td>\n",
+       "      <td>29.82</td>\n",
+       "      <td>30.18</td>\n",
+       "      <td>30.10</td>\n",
+       "      <td>30.20</td>\n",
+       "      <td>30.10</td>\n",
+       "      <td>30.40</td>\n",
+       "      <td>30.133333</td>\n",
+       "      <td>0.188750</td>\n",
+       "      <td>108.610000</td>\n",
+       "      <td>0.002887</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       m1        m2        m3        m4        m5       Dx1       Dx2  \\\n",
-       "0   0.050  0.052432  0.038498  0.041740  0.066845  0.018047  0.025734   \n",
-       "1   0.075  0.072613  0.062558  0.077425  0.015478  0.042429  0.040160   \n",
-       "2   0.100  0.103347  0.101605  0.099361  0.093900  0.052411  0.046097   \n",
-       "3   0.125  0.118064  0.150049  0.124056  0.153998  0.059645  0.056738   \n",
-       "4   0.150  0.158329  0.143522  0.153235  0.125121  0.070399  0.063578   \n",
-       "5   0.175  0.174045  0.152967  0.181660  0.176064  0.087671  0.090652   \n",
-       "6   0.200  0.202672  0.184025  0.201895  0.229997  0.090162  0.098133   \n",
-       "7   0.225  0.228883  0.260549  0.226784  0.201636  0.104527  0.111335   \n",
-       "8   0.250  0.251224  0.242992  0.238627  0.266212  0.129012  0.118645   \n",
-       "9   0.275  0.275899  0.251327  0.280781  0.312978  0.136631  0.140951   \n",
-       "10  0.300  0.293292  0.293954  0.296745  0.308939  0.153415  0.151791   \n",
-       "11  0.325  0.324090  0.330965  0.334900  0.357583  0.160549  0.158722   \n",
-       "12  0.350  0.347289  0.355742  0.363132  0.347237  0.180310  0.168808   \n",
-       "13  0.375  0.375010  0.412261  0.358674  0.366582  0.178874  0.182060   \n",
-       "14  0.400  0.386501  0.396172  0.413298  0.409323  0.202518  0.192107   \n",
-       "15  0.425  0.417214  0.394087  0.418190  0.396204  0.213574  0.208489   \n",
-       "16  0.450  0.448536  0.480177  0.439624  0.472196  0.226967  0.217532   \n",
-       "17  0.475  0.476295  0.480740  0.472676  0.475891  0.241500  0.235796   \n",
+       "           m1     Dx1     Dx2     Dx3     Dx4     Dx5     Dx6          Dx  \\\n",
+       "0   49.246667  212.10  211.64  212.00  212.18  212.52  212.04  212.080000   \n",
+       "1   69.276667  150.92  150.26  150.02  150.16  150.40  150.18  150.323333   \n",
+       "2   88.966667   90.34   90.34   90.38   90.52   90.26   90.28   90.353333   \n",
+       "3  108.610000   29.82   30.18   30.10   30.20   30.10   30.40   30.133333   \n",
        "\n",
-       "         Dx3       Dx4       Dx5       Dx6       Dx7  \n",
-       "0   0.031742  0.022223  0.021998  0.021180  0.022974  \n",
-       "1   0.037280  0.033654  0.042858  0.037256  0.043517  \n",
-       "2   0.050659  0.049701  0.047669  0.039527  0.049989  \n",
-       "3   0.066964  0.065501  0.064932  0.051581  0.068314  \n",
-       "4   0.077659  0.072795  0.067691  0.066485  0.074349  \n",
-       "5   0.088689  0.087106  0.078646  0.081791  0.080701  \n",
-       "6   0.096154  0.100708  0.097934  0.094379  0.102352  \n",
-       "7   0.105212  0.113755  0.108067  0.108022  0.107109  \n",
-       "8   0.124443  0.118103  0.123003  0.121608  0.124631  \n",
-       "9   0.132259  0.137163  0.137445  0.130444  0.140669  \n",
-       "10  0.151211  0.146986  0.148656  0.147098  0.150745  \n",
-       "11  0.160388  0.156324  0.157541  0.162296  0.161074  \n",
-       "12  0.167861  0.168199  0.166944  0.172948  0.166314  \n",
-       "13  0.185182  0.182708  0.178580  0.188249  0.183653  \n",
-       "14  0.199646  0.191132  0.197280  0.205252  0.192378  \n",
-       "15  0.209244  0.201062  0.216948  0.205209  0.215061  \n",
-       "16  0.216700  0.220931  0.220583  0.228625  0.223440  \n",
-       "17  0.234180  0.236741  0.226907  0.227570  0.237174  "
+       "        uDx           m        um  \n",
+       "0  0.284816   49.246667  0.002887  \n",
+       "1  0.317847   69.276667  0.002887  \n",
+       "2  0.092664   88.966667  0.002887  \n",
+       "3  0.188750  108.610000  0.002887  "
       ]
      },
-     "execution_count": 59,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rng = np.random.default_rng(42)\n",
-    "\n",
-    "m1 = np.arange(0.05, 0.5, 0.025)\n",
-    "rng = np.random.default_rng(50)\n",
-    "m2 = m1 + rng.normal(0, 0.0050, size=m1.size)\n",
-    "rng = np.random.default_rng(51)\n",
-    "m3 = m1 + rng.normal(0, 0.020, size=m1.size)\n",
-    "rng = np.random.default_rng(52)\n",
-    "m4 = m1 + rng.normal(0, 0.010, size=m1.size)\n",
-    "rng = np.random.default_rng(55)\n",
-    "m5 = m1 + rng.normal(0, 0.020, size=m1.size)\n",
-    "\n",
-    "\n",
-    "K_vera = 20.0\n",
-    "\n",
-    "Dx_vero = (m1 * g) / K_vera\n",
-    "Dx_errore1 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(43)\n",
-    "Dx_errore2 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(44)\n",
-    "Dx_errore3 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(45)\n",
-    "Dx_errore4 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(46)\n",
-    "Dx_errore5 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(47)\n",
-    "Dx_errore6 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "rng = np.random.default_rng(48)\n",
-    "Dx_errore7 = Dx_vero + rng.normal(0, 0.0050, size=Dx_vero.size)\n",
-    "\n",
-    "df = pd.DataFrame({\n",
-    "    'm1': m1,\n",
-    "    'm2': m2,\n",
-    "    'm3': m3,\n",
-    "    'm4': m4,\n",
-    "    'm5': m5,\n",
-    "    'Dx1': Dx_errore1,\n",
-    "    'Dx2': Dx_errore2,\n",
-    "    'Dx3': Dx_errore3,\n",
-    "    'Dx4': Dx_errore4,\n",
-    "    'Dx5': Dx_errore5,\n",
-    "    'Dx6': Dx_errore6,\n",
-    "    'Dx7': Dx_errore7,\n",
-    "})\n",
-    "\n",
     "df"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e90c3495",
-   "metadata": {},
-   "source": [
-    "## Realmente vorremmo prendere i dati da un csv\n",
-    "```python\n",
-    "df = pd.read_csv(r'molla.csv')\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3b7dfe0",
-   "metadata": {},
-   "source": [
-    "# Inzio dello studio statistico della molla"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "d98f6172",
+   "execution_count": 15,
+   "id": "976d5531",
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Cannot perform reduction 'mean' with string dtype",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 61\u001b[39m\n\u001b[32m     57\u001b[39m   sigma = np.sqrt(\u001b[32m1.0\u001b[39m / den)\n\u001b[32m     59\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m media, sigma\n\u001b[32m---> \u001b[39m\u001b[32m61\u001b[39m df = \u001b[43mcalcola_Dx_stats\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_arbitrario_DX\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     62\u001b[39m df = calcola_m_stats(df, err_arbitrario_m=\u001b[32m0.01\u001b[39m)\n\u001b[32m     64\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33mK\u001b[39m\u001b[33m\"\u001b[39m] = df.m * g / df.Dx\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 19\u001b[39m, in \u001b[36mcalcola_Dx_stats\u001b[39m\u001b[34m(df, err_arbitrario_DX)\u001b[39m\n\u001b[32m     16\u001b[39m     sigma = valori.std(ddof=\u001b[32m1\u001b[39m)  \u001b[38;5;66;03m# sigma sperimentale S\u001b[39;00m\n\u001b[32m     17\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m pd.Series({\u001b[33m'\u001b[39m\u001b[33mDx\u001b[39m\u001b[33m'\u001b[39m: media, \u001b[33m'\u001b[39m\u001b[33muDx\u001b[39m\u001b[33m'\u001b[39m: sigma})\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m stats = \u001b[43mdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mriga_stats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     20\u001b[39m df[\u001b[33m'\u001b[39m\u001b[33mDx\u001b[39m\u001b[33m'\u001b[39m] = stats[\u001b[33m'\u001b[39m\u001b[33mDx\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m     21\u001b[39m df[\u001b[33m'\u001b[39m\u001b[33muDx\u001b[39m\u001b[33m'\u001b[39m] = stats[\u001b[33m'\u001b[39m\u001b[33muDx\u001b[39m\u001b[33m'\u001b[39m]\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/frame.py:12419\u001b[39m, in \u001b[36mDataFrame.apply\u001b[39m\u001b[34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[39m\n\u001b[32m  12405\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnknown engine \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mengine\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m  12407\u001b[39m     op = frame_apply(\n\u001b[32m  12408\u001b[39m         \u001b[38;5;28mself\u001b[39m,\n\u001b[32m  12409\u001b[39m         func=func,\n\u001b[32m   (...)\u001b[39m\u001b[32m  12417\u001b[39m         kwargs=kwargs,\n\u001b[32m  12418\u001b[39m     )\n\u001b[32m> \u001b[39m\u001b[32m12419\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m.__finalize__(\u001b[38;5;28mself\u001b[39m, method=\u001b[33m\"\u001b[39m\u001b[33mapply\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m  12420\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(engine, \u001b[33m\"\u001b[39m\u001b[33m__pandas_udf__\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m  12421\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m result_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/apply.py:1015\u001b[39m, in \u001b[36mFrameApply.apply\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1012\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.raw:\n\u001b[32m   1013\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.apply_raw(engine=\u001b[38;5;28mself\u001b[39m.engine, engine_kwargs=\u001b[38;5;28mself\u001b[39m.engine_kwargs)\n\u001b[32m-> \u001b[39m\u001b[32m1015\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/apply.py:1167\u001b[39m, in \u001b[36mFrameApply.apply_standard\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1165\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m   1166\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.engine == \u001b[33m\"\u001b[39m\u001b[33mpython\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1167\u001b[39m         results, res_index = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1168\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1169\u001b[39m         results, res_index = \u001b[38;5;28mself\u001b[39m.apply_series_numba()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/apply.py:1183\u001b[39m, in \u001b[36mFrameApply.apply_series_generator\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1180\u001b[39m results = {}\n\u001b[32m   1182\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[32m-> \u001b[39m\u001b[32m1183\u001b[39m     results[i] = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1184\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[32m   1185\u001b[39m         \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[32m   1186\u001b[39m         \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[32m   1187\u001b[39m         results[i] = results[i].copy(deep=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36mcalcola_Dx_stats.<locals>.riga_stats\u001b[39m\u001b[34m(row)\u001b[39m\n\u001b[32m     13\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m pd.Series({\u001b[33m'\u001b[39m\u001b[33mDx\u001b[39m\u001b[33m'\u001b[39m: valori.iloc[\u001b[32m0\u001b[39m], \u001b[33m'\u001b[39m\u001b[33muDx\u001b[39m\u001b[33m'\u001b[39m: err_arbitrario_DX})\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m   media = \u001b[43mvalori\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     16\u001b[39m   sigma = valori.std(ddof=\u001b[32m1\u001b[39m)  \u001b[38;5;66;03m# sigma sperimentale S\u001b[39;00m\n\u001b[32m     17\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m pd.Series({\u001b[33m'\u001b[39m\u001b[33mDx\u001b[39m\u001b[33m'\u001b[39m: media, \u001b[33m'\u001b[39m\u001b[33muDx\u001b[39m\u001b[33m'\u001b[39m: sigma})\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/util/_decorators.py:336\u001b[39m, in \u001b[36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    330\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > num_allow_args:\n\u001b[32m    331\u001b[39m     warnings.warn(\n\u001b[32m    332\u001b[39m         msg.format(arguments=_format_argument_list(allow_args)),\n\u001b[32m    333\u001b[39m         klass,\n\u001b[32m    334\u001b[39m         stacklevel=find_stack_level(),\n\u001b[32m    335\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m336\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/series.py:8113\u001b[39m, in \u001b[36mSeries.mean\u001b[39m\u001b[34m(self, axis, skipna, numeric_only, **kwargs)\u001b[39m\n\u001b[32m   8063\u001b[39m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(Pandas4Warning, allowed_args=[\u001b[33m\"\u001b[39m\u001b[33mself\u001b[39m\u001b[33m\"\u001b[39m], name=\u001b[33m\"\u001b[39m\u001b[33mmean\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   8064\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mmean\u001b[39m(\n\u001b[32m   8065\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   8069\u001b[39m     **kwargs,\n\u001b[32m   8070\u001b[39m ) -> Any:\n\u001b[32m   8071\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   8072\u001b[39m \u001b[33;03m    Return the mean of the values over the requested axis.\u001b[39;00m\n\u001b[32m   8073\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m   8111\u001b[39m \u001b[33;03m    2.0\u001b[39;00m\n\u001b[32m   8112\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m8113\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   8114\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m   8115\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/generic.py:11831\u001b[39m, in \u001b[36mNDFrame.mean\u001b[39m\u001b[34m(self, axis, skipna, numeric_only, **kwargs)\u001b[39m\n\u001b[32m  11823\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mmean\u001b[39m(\n\u001b[32m  11824\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m  11825\u001b[39m     *,\n\u001b[32m   (...)\u001b[39m\u001b[32m  11829\u001b[39m     **kwargs,\n\u001b[32m  11830\u001b[39m ) -> Series | \u001b[38;5;28mfloat\u001b[39m:\n\u001b[32m> \u001b[39m\u001b[32m11831\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m  11832\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmean\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m  11833\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/generic.py:11785\u001b[39m, in \u001b[36mNDFrame._stat_function\u001b[39m\u001b[34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[39m\n\u001b[32m  11781\u001b[39m nv.validate_func(name, (), kwargs)\n\u001b[32m  11783\u001b[39m validate_bool_kwarg(skipna, \u001b[33m\"\u001b[39m\u001b[33mskipna\u001b[39m\u001b[33m\"\u001b[39m, none_allowed=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m> \u001b[39m\u001b[32m11785\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m  11786\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnumeric_only\u001b[49m\n\u001b[32m  11787\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/series.py:7480\u001b[39m, in \u001b[36mSeries._reduce\u001b[39m\u001b[34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[39m\n\u001b[32m   7476\u001b[39m     \u001b[38;5;28mself\u001b[39m._get_axis_number(axis)\n\u001b[32m   7478\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delegate, ExtensionArray):\n\u001b[32m   7479\u001b[39m     \u001b[38;5;66;03m# dispatch to ExtensionArray interface\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m7480\u001b[39m     result = \u001b[43mdelegate\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   7482\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   7483\u001b[39m     \u001b[38;5;66;03m# dispatch to numpy arrays\u001b[39;00m\n\u001b[32m   7484\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m numeric_only \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.dtype.kind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33miufcb\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m   7485\u001b[39m         \u001b[38;5;66;03m# i.e. not is_numeric_dtype(self.dtype)\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/uni/lab/.venv/lib/python3.13/site-packages/pandas/core/arrays/string_.py:967\u001b[39m, in \u001b[36mStringArray._reduce\u001b[39m\u001b[34m(self, name, skipna, keepdims, axis, **kwargs)\u001b[39m\n\u001b[32m    964\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._from_sequence([result], dtype=\u001b[38;5;28mself\u001b[39m.dtype)\n\u001b[32m    965\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[32m--> \u001b[39m\u001b[32m967\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCannot perform reduction \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m with string dtype\u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mTypeError\u001b[39m: Cannot perform reduction 'mean' with string dtype"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n",
+      "6\n",
+      "############################################################\n",
+      "[ 61.75666667 121.72666667 181.94666667  59.97       120.19\n",
+      "  60.22      ]\n",
+      "[0.42678644 0.29951071 0.34168211 0.33107904 0.36966652 0.21026967]\n",
+      "############################################################\n",
+      "[-20.03       -39.72       -59.36333333 -19.69       -39.33333333\n",
+      " -19.64333333]\n",
+      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248]\n"
      ]
     }
    ],
    "source": [
-    "df = pd.read_csv(r'statica1.csv')\n",
+    "perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
     "\n",
-    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
-    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[dx_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['Dx'] = stats['Dx']\n",
-    "  df['uDx'] = stats['uDx']\n",
-    "  \n",
-    "  return df\n",
+    "este = np.array([df.Dx[x] - df.Dx[j] for x,j in perm])\n",
+    "ueste = np.array([np.sqrt(df.uDx[x]**2 + df.uDx[j]**2) for x,j in perm])\n",
     "\n",
-    "def calcola_m_stats(df, err_arbitrario_m):\n",
-    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[m_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'m': media, 'um': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['m'] = stats['m']\n",
-    "  df['um'] = stats['um']\n",
-    "  \n",
-    "  return df\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": "code",
+   "execution_count": 5,
+   "id": "2ad19283",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3.18045307 3.19974522 3.19938176 3.21961214 3.2091078  3.19864707]\n",
+      "[0.02199135 0.00788665 0.00602107 0.01779022 0.00988124 0.01119321]\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": 6,
+   "id": "5f59d6c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-medio: 3.201234977342435\n",
+      "sigmaC:  0.003860115909468001\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",
@@ -589,57 +329,339 @@
     "\n",
     "  return media, sigma\n",
     "\n",
-    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
-    "df = calcola_m_stats(df, err_arbitrario_m=0.01)\n",
-    "\n",
-    "df[\"K\"] = df.m * g / df.Dx\n",
-    "df[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\n",
-    "\n",
-    "df[\"F\"] = df.m * g\n",
-    "df[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\n",
-    "media_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\n",
-    "\n",
-    "#chi 2\n",
-    "chi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\n",
-    "dof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\n",
-    "chi2_rid = chi2_val / dof\n",
-    "p_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\n",
-    "\n",
-    "print(\"#\"*60)\n",
-    "print(\"Valori di K\")\n",
-    "print(\"media pesata K:\", media_K)\n",
-    "print(\"sigma K:\", sigma_K)\n",
-    "print(f\"Chi2           : {chi2_val:.4f}\")\n",
-    "print(f\"DOF            : {dof}\")\n",
-    "print(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\n",
-    "print(f\"p-value        : {p_value:.4f}\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "215645fe",
-   "metadata": {},
-   "source": [
-    "## Fit lineare di K\n",
-    "Funzione fatta come quella spiegata dalla Carpi."
+    "media, uA = mediaPesata(K, uK)\n",
+    "print(\"K-medio:\", media)\n",
+    "print(\"sigmaC: \", uA)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "6b8e71d0",
+   "execution_count": 7,
+   "id": "aefe7756",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "############################################################\n",
-      "Calcolo della regressione lineare\n",
-      "A      = 19.8275 +- 0.4716\n",
-      "B      = 0.0194 +- 0.0685\n",
-      "cov_AB = -0.029082\n",
-      "p-value chi² = 0.0016\n"
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      F   R-squared:                       1.000\n",
+      "Model:                            OLS   Adj. R-squared:                  1.000\n",
+      "Method:                 Least Squares   F-statistic:                 1.277e+05\n",
+      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           3.68e-10\n",
+      "Time:                        14:04:37   Log-Likelihood:                -7.2416\n",
+      "No. Observations:                   6   AIC:                             18.48\n",
+      "Df Residuals:                       4   BIC:                             18.07\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const          0.0265      0.991      0.027      0.980      -2.724       2.777\n",
+      "este           3.2011      0.009    357.408      0.000       3.176       3.226\n",
+      "==============================================================================\n",
+      "Omnibus:                          nan   Durbin-Watson:                   1.155\n",
+      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.275\n",
+      "Skew:                          -0.058   Prob(JB):                        0.871\n",
+      "Kurtosis:                       1.957   Cond. No.                         271.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "RISULTATI REGRESSIONE:\n",
+      "k (pendenza) = 3.20112 ± 0.00896\n",
+      "intercetta  = 0.02655 ± 0.99066\n",
+      "R² = 0.99997\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": [
+    "data = pd.DataFrame({\n",
+    "    \"este\": este,\n",
+    "    \"ueste\": ueste,\n",
+    "    \"F\": - F,\n",
+    "    \"uF\": uF\n",
+    "})\n",
+    "\n",
+    "\n",
+    "X = sm.add_constant(data[\"este\"])\n",
+    "model = sm.OLS(data[\"F\"], X).fit()\n",
+    "\n",
+    "print(model.summary())\n",
+    "\n",
+    "# Estrazione parametri\n",
+    "intercetta = model.params[\"const\"]\n",
+    "pente = model.params[\"este\"]\n",
+    "u_intercetta = model.bse[\"const\"]\n",
+    "u_pente = model.bse[\"este\"]\n",
+    "R2 = model.rsquared\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
+    "print(f\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
+    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
+    "print(f\"R² = {R2:.5f}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d42b009",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "# Seaborn scatter\n",
+    "sns.scatterplot(\n",
+    "    data=data,\n",
+    "    x=\"este\",\n",
+    "    y=\"F\",\n",
+    "    s=7\n",
+    ")\n",
+    "\n",
+    "# Barre d’errore\n",
+    "plt.errorbar(\n",
+    "    data[\"este\"],\n",
+    "    data[\"F\"],\n",
+    "    xerr=data[\"ueste\"],\n",
+    "    yerr=data[\"uF\"],\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[\"este\"].max(), 200)\n",
+    "yfit = intercetta + pente * xfit\n",
+    "\n",
+    "\n",
+    "plt.plot(\n",
+    "    xfit,\n",
+    "    yfit,\n",
+    "    color=\"crimson\",\n",
+    "    linewidth=1,\n",
+    "    zorder=10,\n",
+    "    label=\"Fit lineare\"\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": 9,
+   "id": "986ff4a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 = 2.77690\n",
+      "Gradi di libertà = 4\n",
+      "Chi^2 ridotto = 0.69422\n",
+      "Probabilità P(0 → chi^2) = 0.40417\n"
+     ]
+    }
+   ],
+   "source": [
+    "F_fit = intercetta + pente * data[\"este\"]\n",
+    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
+    "\n",
+    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
+    "\n",
+    "N = len(data)\n",
+    "nu = N - 2\n",
+    "\n",
+    "chi2_red = chi2_val / nu\n",
+    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "\n",
+    "\n",
+    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
+    "print(f\"Gradi di libertà = {nu}\")\n",
+    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
+    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ef0817f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.908593\n",
+       "1    0.040832\n",
+       "2    0.097969\n",
+       "3    1.041093\n",
+       "4    0.620683\n",
+       "5    0.067728\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((data[\"F\"] - F_fit) / sigma)**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cebe6742",
+   "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>este</th>\n",
+       "      <th>ueste</th>\n",
+       "      <th>F</th>\n",
+       "      <th>uF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>61.756667</td>\n",
+       "      <td>0.426786</td>\n",
+       "      <td>196.414180</td>\n",
+       "      <td>0.044764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>121.726667</td>\n",
+       "      <td>0.299511</td>\n",
+       "      <td>389.494320</td>\n",
+       "      <td>0.056394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>181.946667</td>\n",
+       "      <td>0.341682</td>\n",
+       "      <td>582.116847</td>\n",
+       "      <td>0.071601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>59.970000</td>\n",
+       "      <td>0.331079</td>\n",
+       "      <td>193.080140</td>\n",
+       "      <td>0.044613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120.190000</td>\n",
+       "      <td>0.369667</td>\n",
+       "      <td>385.702667</td>\n",
+       "      <td>0.056123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60.220000</td>\n",
+       "      <td>0.210270</td>\n",
+       "      <td>192.622527</td>\n",
+       "      <td>0.044592</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         este     ueste           F        uF\n",
+       "0   61.756667  0.426786  196.414180  0.044764\n",
+       "1  121.726667  0.299511  389.494320  0.056394\n",
+       "2  181.946667  0.341682  582.116847  0.071601\n",
+       "3   59.970000  0.331079  193.080140  0.044613\n",
+       "4  120.190000  0.369667  385.702667  0.056123\n",
+       "5   60.220000  0.210270  192.622527  0.044592"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4b7144",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ax + B : \n",
+      "A      = 3.2002 +- 0.0092\n",
+      "B      = 0.1195 +- 0.9523\n",
+      "cov_AB = -0.007941\n",
+      "p-value chi² = 0.4020\n"
      ]
     }
    ],
@@ -692,9 +714,9 @@
     "\n",
     "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
     "\n",
-    "A, B, sA, sB, covAB, chi = reg_lin(df[\"Dx\"], df[\"F\"], df[\"uDx\"], df[\"uF\"])\n",
-    "print(\"#\"*60)\n",
-    "print(\"Calcolo della regressione lineare\")\n",
+    "\n",
+    "A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
+    "print(\"Ax + B : \")\n",
     "print(f\"A      = {A:.4f} +- {sA:.4f}\")\n",
     "print(f\"B      = {B:.4f} +- {sB:.4f}\")\n",
     "print(f\"cov_AB = {covAB:.6f}\")\n",
@@ -702,44 +724,53 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "bfbc49bf",
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "32e9948f",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 = 2.76835\n",
+      "Gradi di libertà = 4\n",
+      "Chi^2 ridotto = 0.69209\n",
+      "Probabilità P(0 → chi^2) = 0.40269\n"
+     ]
+    }
+   ],
    "source": [
-    "## Grafico potente dei dati"
+    "F_fit = B + A * data[\"este\"]\n",
+    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
+    "\n",
+    "\n",
+    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
+    "\n",
+    "N = len(data)\n",
+    "nu = N - 2\n",
+    "\n",
+    "chi2_red = chi2_val / nu\n",
+    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "\n",
+    "\n",
+    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
+    "print(f\"Gradi di libertà = {nu}\")\n",
+    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
+    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "3d1f0dc8",
+   "execution_count": 14,
+   "id": "e2407a04",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:25: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "<>:25: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "/tmp/ipykernel_2965/2212367346.py:25: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "  label=r\"Incertezza fit $\\pm\\,\\sigma$\")\n"
-     ]
-    },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Text(0.04, 0.95, '$k = A = (19.8 \\\\pm 0.5)\\\\ \\\\mathrm{N/m}$\\n$B = (0.020 \\\\pm 0.075)\\\\ \\\\mathrm{N}$')"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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qSHx8vH6ioldffRWvvfaawXa1nYC0tDSj2zL0xQAAODs7A0CDSTr8/f3x3//+F3PmzME333yDzZs344YbbsDQoUMxfPhwXHvttc3+eWrzCkDfETAkMjIShYWFjU5u09yfp+62br31VqPbrZ3sqLH30haVlpbi/PnzAIC1a9fis88+M9iuNmslJSUoLCyEj4+PvuMIoNHnigYEBOj3UZep69c9tuPHj8eECRMMrn81x7YptQUEBDR5ezI05bmdV3qWpil/O2T+3Tl58iTuuusu5ObmwsXFpdGa6x7nxvZdq639DlPbxM4nEZmNoVkSa2fmrP3AbUjdP+iNzeTZnA5STk4OHnzwQWi1WowcORLPPvuswXbV1dUYOnQo9u3bh6CgIMyePRs9evSAt7e3vk3tlTNRZ9bUpvr555+Rl5eHYcOGwc3NrdnrN9X48eMNzqxZ69Zbb8Xp06fx/vvvY926dUhISMDixYuxePFiBAYGYs6cOXj66aeNXk0ypu7xeuWVV654dbex7V9NB3jWrFm4+eabsXz5cmzZsgVHjhzBkSNHMHfuXHTv3h0LFy7EoEGDmry9uj9PUzIrK6+Xb2v79u0IDQ1ttL2rq2uTtx0REaGfuVim8PDwRmdYlqnu+zNp0iRMmzbtiuvUXqGqO2N1Y8dVqVQaXG7q+nVr//jjj9G3b1+j2wDQrBmxm1KbsbqsmSl/O2T+3Rk4cCAGDRqErVu34ty5c3juuefw4YcfNlozAJw4cQJ2do3fcFh7BwWRLWPnk4halIeHBwCgvLzcaJu6r9W2N4VOp8NDDz2E7OxsREVFYe3atUbbfvjhh9i3bx8cHBywd+9edO3a1eT911V7y+29994rdbtXIywsDG+//TYWLlyIX375BZs3b8b69euRlZWFmTNnIjs7G2+99Vaztln3eLm7u1/xaoc53HDDDbjhhhtQUVGB3bt3Y9OmTdiyZQtOnjyJwYMHY+fOnRg8eHCTtlX35ykvLzfawavNrIy8Gtp3YGCg1Pdy7969V3y8y9Uwx2ODjKn7/jg5OTXr/al7m2Rj5yJDdzzIWL9u7T4+PlKP7eW1GcqssbqsmSl/O2T+3Rk/fjzee+89fPjhh5g6dSpWrFiBoUOHYujQoUZrBmrOt4buBiJqazjmk4haVO2tj4ZuRatV97Xu3bubvM/XXnsNP/74IxwdHbFp06ZGPwDUjrmJiYmR3vEUQmDLli1wcHAwOB7WXLKzs1FYWGj0dTs7O9x8881YsmQJUlNTcd999wEAFi9erB871lQdO3bUX104d+6c0XYajQaffPIJdu7c2aztN4ezszNGjRqFr776CgkJCYiNjYUQAu+8806Tt1H3Vt3GMpucnAxATl5r1d1WY+9leno6Vq9ejRMnTjR52zExMejWrZv0f7Vjic2l7q3zrq6u+luhG3t/AGD9+vX4+uuv9f/fpUsX/X83dqXW2HM2TV2/qcf20qVLWL16NQ4dOmS0zdXUZsrzQy3FlL8dMv/uvPfeewCAp556CiNGjAAATJw4Ebm5uQ3aNvU4//PPP1i9evVVzyNA1Jqw80lELWrMmDEAah4K/8cffxhss3fvXgA1Hy6HDBli0v727duH+fPnA6j50HD99dfXez0vLw/33Xcf8vLyAPw7PszYLbWm3FL422+/ISMjAwMHDqx3G6+53XDDDXjmmWfqLQsICMCKFSsatHVxccHcuXMB1EwedPHixXqvOzjU3DBT9/05f/48NmzYgIKCAjg6OmLkyJEAgN27d+vHrF1u9+7dePzxxw1OdnS1fv31VwQEBBgcdxkaGoopU6YAALKyspq8zWHDhqFdu3YAarJkyLFjx/Sd+9p8y3DjjTciLCwMALBjxw6j7ZYsWYInnniiwbGyRQcPHqw3jrj2/T506FC9203rOnXqFB5++GFs375dv6xDhw76TtqPP/5ocL2MjAz9lwqXM3X90NBQ/Zjzxo7tF198gSeeeALx8fFG21xuyJAh+t9TY7X9/PPPTd6etTDlb4e5/u588sknCA4ORm5uLiZOnNjg9aFDh+qvPDd2nOfOnYtJkyZd1VAOotaGnU8ialGDBg3Sj7mbO3dugz+2+fn5WLJkCYCaMYOmdNIyMzPx8MMPQ6fT4cEHH9R3PuoqKyvD5s2b9TMS1s4CGRcXh2PHjjVo/8EHH1x1Pd9++y0A67jlNicnB1999ZW+s11X7Tf0ERERDSZ1qp2opG5H58svv8SDDz6IkpISAMDrr78Od3d3pKWl6Y9lXaWlpXjhhRfg4eHRpHF6TaVWq5GTk4NNmzYZfL325+rTp0+Tt+nt7a3vjC9evBgFBQX1XtfpdHj55ZcB1HzQvPPOO6+mdIMcHBzw9ttvAwC+/vpr/P777w3anDp1CitXrkTfvn2NzsZry55//nmEhISgoqICL730UoPXNRoNZsyYAaVSidmzZ9d7rfYK6rZt2/DXX381WPfVV19tdEyyqeu/8847cHBw0N/yfrnMzEz897//RXh4uNHJbwwJCgrCpEmTANR8MXF5p7yiogILFixo8vashSl/O8z1d8fHxwdffPEF7OzssHPnzgZjP729vfXnh+XLlxu88rpv3z5s3boV999/v9nvHCCyBhzzSUTNkpGRgcLCQmRkZOiXxcfH62d+jY2NveJkFl999RVGjBiB77//HoMHD8bTTz+NgIAA/PPPP3jjjTeQmZmJmTNn4vnnn9evU11djbi4OGRmZjbYb7t27RrMJKjVavHggw8iNzcXbm5uGD16NPbv39+glpycnHr/P23aNP0st0OGDMH//d//4YYbbkBxcTE2bNiALVu26NsmJyfD19cXkZGR+qtjjdm8eTPs7Ozwn//854ptDSktLdU/nqF2ZtSsrCyDP1ddl8+iWuvnn3/GoEGDMGnSJERERKCiogJHjhzBggUL4OTkhI8//rjBh+d77rkHv/zyCz777DP07t0bJSUl+Oijj9CjRw/9VboOHTpg69atGDVqFGbNmoW4uDiMGTMGbm5uOHPmDBYuXIi0tDRs3ry53qybycnJKCsr038RkJubi9OnT8Pf3x/+/v767F26dAlAzS2Jp0+fhpeXF4KDg/W1vvXWW8jLy8PIkSPRvn17FBQUYNu2bVi5ciUiIiL0V8IB4PTp0wD+HQOXkZFRb58AMGfOHGRkZGD58uXo168f5s2bhy5duiA7Oxvvv/8+vv/+ewwYMADr1q2r917Fx8dDrVY32HZISAg8PT2v+PMCwOjRo3HhwgXMmjULd955J+bMmYPbbrsNGo0Ghw8fxqJFi+Dv748NGzY0mgFrdPbsWWRmZtabnfWXX35BYmKi0XUu//Du7e2NXbt2YejQoVi+fDmys7MxceJE+Pn5ITExEYsXL8aJEyfw8ccfN5jt+P7778fzzz+Pd955B3feeSdeffVV3HDDDbh06RLWrl2L/Px89O/f3+hVQlPX79evH9atW4fx48fjoYcewowZMzB8+HA4ODjgjz/+wFtvvYXq6mrs2LEDTk5OTX1bAQBvv/02Tp8+jUOHDuGWW27B3LlzER0djeTkZCxcuBDXXnstTp06BeDf81hwcDC8vLya9DvRmNzcXOTm5ta76lu7j9rf1bpqf68Ntb98n1fzt8OUdcvKypCcnFyvttr3p1u3bqiuroafnx9Gjx6NDRs24Pnnn0dsbCzat2+v/7vw/PPPIysrC++99x769eun/5tSWlqKffv2YenSpejevbvBO1GIbJLFnjBKRK3SuHHj9A/ENvSvqQ+Jr66uFp988okYOHCg8PLyEkqlUgQGBop7773X4APGk5OTje5zwIABzWp/pbpLSkrEvHnzROfOnYWjo6NwdHQUHTt2FFOnThUpKSkN1jX2kPa6Tpw4IQCIm2++uUnvjyF//vlns36muv/GjRtXb1vnzp0TL7/8srj55puFn5+fcHBwEE5OTiImJkY8+eSTIj4+3mANarVazJkzR4SFhQkHBwcRGBgoRo8ebfC4Z2dnizlz5oiuXbsKFxcXoVKpRHR0tJg8ebJISEho0H7AgAEGa3/llVeEEMazV/dnO3jwoJg6daro0aOH8PDwEPb29sLNzU307NlTvPrqq6KwsLDePo29X7X7rOuHH34Q9957rwgICBBKpVJ4eXmJW2+9VaxZs0ZoNJoG7cPDww1ue+3atU36eev666+/xPjx40V4eLhQqVTCxcVFdO/eXbz66qtGH3Bv7a50Lmns3+WKiorE/PnzxfXXXy/c3NyEUqkUoaGh4uGHHxYnTpxotI7NmzeLW265Rbi6ugoXFxfRrVs38eabbwq1Wi1uvvlmAUC8+eabZls/KSlJTJ06VXTs2FE4OTkJJycn0blzZ/Hcc8+JrKyspr+hl6mqqhKLFi0S3bp1E46OjsLd3V307t1bfPDBB0Kr1TZ4T1esWCGEaN7vhCGvvPJKk89DQjSeA0P7bO7fDlPWPXDgQKMZbOzvzOXb++mnn8To0aNFUFCQUCqVws3NTfTt21csXrxYVFZWNum9JbIFCiF4gzkRkbnNnTsXb775JpYsWYIZM2ZYuhwiaoLaK4SrVq3C448/3uLrExHZGo75JCJqAbVjuq703EsiahmXLl3C3LlzG4zjrVVSUoK4uDgAwE033SR9fSKitoidTyKiFvDPP/9ACIHQ0FBLl0JEqOk8vvnmm/j4448Nvr548WKo1WoMGTIEnTp1kr4+EVFbxAmHiIiIqM2aN28ecnJyMGzYMPj6+iInJwdff/011q5di2uuuQZr16416/pERG0Jx3wSERFRm6PVarF3715s27YNR44cQU5ODgoKCuDi4oKuXbvivvvuw5NPPglnZ2ezrE9E1Bax80lERERERERmxzGfREREREREZHbsfBIREREREZHZsfNJREREREREZsfOJxEREREREZkdO59ERERERERkdux8EhERERERkdmx80lERERERERmx84nERERERERmR07n0RERERERGR27HwSERERERGR2bHzSURERERERGbHzicRERERERGZHTufREREREREZHbsfBIREREREZHZOVi6AKoREBCAsrIyhIWFWboUIiIiIiKiBtLS0tCuXTtkZ2df1fq88mklysrKoFarLV0G2RitVmvpEsjGMFMkGzNFsjFTJBsz9a/q6mqUlZVd9fq88mklwsLCUFVVhdOnT0OhUFi6HLIBQgjExcUhNjaWmSIpmCmSjZki2Zgpko2Zqq9r164mrc8rn0RERERERGR27HxaEaVSaekSyMYEBQVZugSyMcwUycZMkWzMFMnGTMnD226tiJ2dHS/nkzQKhQLu7u6WLoNsCDNFsjFTJBszRbIxU3LxyqcVqaqqghDC0mWQjRBC4Ny5c8wUScNMkWzMFMnGTJFszJRc7HwSERERERGR2bHzSURERERERGbHzqcV4XhPko2TWJFszBTJxkyRbMwUycZMyaMQvIHZKtQ+M+fMmTMWroSIiIiIiKghU/ssvPJpRbRaLQczkzRCCOTn5zNTJA0zRbIxUyQbM0WyMVNysfNpRTQajaVLIBuTn59v6RLIxjBTJBszRbIxUyQbMyUPO59ERERERERkdux8EhERERERkdmx82lFOJMWyRYYGGjpEsjGMFMkGzNFsjFTJBszJY+DpQugf9nZ2fFxKySNQqGAh4eHpcsgG8JMkWzMFMnGTJFszJRcvPJpRdRqNWfSImmEEIiLi2OmSBpmimRjpkg2ZopkY6bkYufTijDUJBszRbIxUyQbM0WyMVMkGzMlDzufREREREREZHYc82lFWnK8p06nQ2FhIfLz81FWVgaNRgOdTtdi+yfzE0KgoqIChYWFHEtsBvb29nBwcIC7uzt8fX3h7u7eJt5nBwf+2SC5mCmSjZki2ZgpeRSC15GtQteuXQEAZ86cMet+hBA4f/48cnJyoNVqzbovorZEpVIhJCQEwcHBli6FiIiIyCxM7bOwG29FtFothBBmu3pSO2A6Ly8PQM2HZV9fX3h5eUGpVMLOjndh2xqtVgt7e3tLl2GTtFot1Go1CgoKUFBQALVajfPnz0On0yE0NNTS5ZmFEAKFhYXw8vJqE1d5yfyYKZKNmSLZmCm52Pm0IhqNxmzbrtvxVCgUiI2Nha+vL3+JbJgQApWVlXBycuJxNiNfX1/odDqkp6cjLS0NKSkpUCgUCAkJsXRpZpGbmwsvLy9Ll0E2hJki2Zgpko2ZkoeXutqI0tJSfcezc+fO8PPzY4eESBI7OzuEh4cjPDwcAJCamsrb2omIiIguw85nG1F7q62vry98fHwsXA2RbQoNDYWzszN0Oh0uXrxo6XKIiIjISgmtFtm7tkKnVlu6lBbFzqcVMddMWkII5OfnA6jpfFLboVQqLV1Cm6JQKPS/Y7Vf+Nia9u3bW7oEsjHMFMnGTJFssjNVePx3/DH+PsTNfwkZ36yXum1rxzGfVsTe3t4st8JWVlaiqqoKdnZ2vF+9DVEoFJwa3AJ8fX2Rnp6OS5cuWboU6RQKBc8hJBUzRbIxUySbzExVXEhD0vJ3UHDoRwCAg5s7HFzdpGy7teCVTyuiVqthjiffVFdXA6iZ3ZYzn7YdtRMO8WlKLcvR0RFAzWy4tvbsXCEEEhISmCmShpki2Zgpkk1GpjSlJUj64F0ce+iemo6nvT2C73sIfTbuQuA990ms1vrxsogVMdeJsnYWXXY82x7+8W15da82azQaqFQqC1YjHydSItmYKZKNmSLZrjZTQqtF9o4tSP54GaoLa+aC8OrbH9FPz0a7yGiZJbYa7Hy2IZzdlsj8+HtGREREl04cReLShShLiAMAOIdFIvrpWfC+8eY2/VmBt9024uDBg1AoFI3+mzlzprT9WVsQi4uL8euvv+Kvv/6ydCkWsXfvXmRkZDRYXlJSgpdffhknTpywQFXNYy2ZSkxMRE5ODk6fPo1ff/3VIjXMmDED11xzjUX2bUvs7Phng+Ripkg2Zopka06mKjLSceb/ZuLktIkoS4iDg5s7omfMQa9138Kn3y1W89nMUvjbeQUODg6IjY01+k/m7FcqlcpqArlhwwbcfPPN6N+/v0U7n5MnT8b27dtbfL/z589HQkICgoOD9cvUajWWLl2KqKgovPHGGyguLja6fmJiIh5++GGMGzcOI0aMwH333Yfz5883ad8nT57Eo48+imnTpuGRRx7BzTffjHXr1jX7Z1AoFHBycmpWpoqLi/HMM8/giSeewJNPPom7774bR44cadZ+Z82a1eBLmkGDBsHf3x/dunXDDz/8gM8//7xJ29qwYQP69+8PhUIBV1dXnD59ukGb/Px8zJs3DwEBAVAoFBgwYAA2bNjQoN2WLVswatSoZv0sVJ9CoUBMTIzVnKeo9WOmSDZmimRraqY0ZaU4/+F7OPbQPcg/uB+ws0PQqAfQ5+udCBnzCOwc+AQCgLfdXlFwcDDOnTvXIvvS6XQQQljFCfOBBx5Aamoq/v77b9xxxx0WqeHkyZNYvXo1oqKiMHz48Bbb7+uvv47CwkLMnTu33vJTp07hgQcewJ9//onPPvvM6PpJSUno168fNm/ejJtvvhkA8NZbb+GGG27AkSNHEB1t/B7/I0eO4O6778Yvv/yCbt26AQDi4+PRo0cPqNVqTJw4sck/hxACWq22ybMol5aW4pZbbkHv3r2xatUqAMC+ffswcOBA/Pjjj+jfv/8Vt1FUVIRNmzZh0KBB+mUKhQLjx4/X1zBv3jyMHz8eXl5eVzyuDzzwAP7zn//g2muvRXx8PEaOHInjx4/D09NT38bX1xdvvPEG+vTpg4kTJ2L37t1wcXGpt53jx48jPT0d//nPf674M5BxQggUFRXBw8PDKs5T1PoxUyQbM0WyXSlTNc/r/A7JK5eh+mIBAMCr94014zqjO7Z0uVaPnU8rUjsrrbU4evQoYmNjERISYpH9z5w5EzqdDmfPnm2xfe7btw+ff/45zpw50+C1nj17AgAiIiIa3cZTTz2F0NBQfccTAGbPno1ly5bhySefxL59+4yuu2zZMnTs2FHf8QSAmJgY3H777fj444+b1fkEajLV1Imm5s2bh7///hs7duzQL7vzzjtxww03YMKECTh79uwVH93y0UcfYdq0aXj++ecbbbdkyRL06NEDffr0ueLdA46OjggMDER2djaSkpLw0EMPYceOHQ1ugYmNjUXHjh0bdDwB4Ntvv0VERASuu+66RvdFV5adnQ0PDw9Ll0E2hJki2Zgpks1Ypi79eRxJSxeiNP4fAIBzaDiips+CT/8B/PLDCN52SwbpdDocPHjQYlc9v/32W5SVlQGAwY6gOeh0OkybNg0zZszQPy6judLT07F37956nUegZqbhO+64A/v372+0M11SUoKUlBRUVlY2qK25NmzYgKlTpzapbXl5OVatWoVrrrmmwZcNQ4cORUJCQr1OqSG1tyWr1WocO3as0Zo9PT0xYsSIBleXG7NmzRo4Oztj9+7dmDdvXoPXVSqV0Zllt2zZwqueREREJEVF5gWceelZnJw6HqXx/8De1Q1R02eh17rv4HvTQHY8G8HO5xVUV1dj+fLluOmmmxAcHAx/f3/06tULL7zwgsHJaGzFn3/+iYsXL+L2229v8X1XVVVh7ty5+Pzzz6FQKHDu3LkWeWTIzp07ER8fj2HDhl31NmonIQoKCmrwWmxsLADghx9+MLr+wIEDUVBQgKefflr/MycnJ+PHH3/EtGnTmlXLuXPnGt1XXUePHkVZWRm6du3a4LXaSXp27drV6DbWrVuHrKwsvPTSS+jTpw+CgoKwYMEC/aN+Lnf33Xfj888/R25ubpNq7NmzJ1avXg2g5jbmLVu2NGm9f/75B+fOndN3PnNzc7FixQrceuutWLlyJfbv348+ffrA2dkZPXr0wLlz51BdXY233noLUVFR8PT0xNSpUzl1PxERURunKSvD+Y+W1IzrPLAXsLND4H9Go8/XOxH64DjYKTmu80p42+0VZGZmYs2aNZg9eza6deuGsrIybNu2DW+//TY++ugjbNy4EXfddVeTt2fowz1QM04wIiKiQSdLoVA0aVlT2goh9P99pbb79++Hvb09Bg4ciIqKCixbtgwvvPACRo0ahWnTpuHWW2/Vt01JSblibXUFBAToJ8Ix1Pa9997DHXfcgdjYWISFhSE1NRUpKSmN3u4qo4Zvv/0W7u7uBo9D3e0aej9rlZSUAKgZ+3j5az4+PgDQ4EHFdWuYOnUqtmzZglWrVuH8+fOYPHkylixZgs8++wz33Xeffr/GajO0/PJ1DLWtHdccEBDQoG1AQACAmivQtWOSDe1r4MCB2LZtG86ePYutW7fiyJEjePHFF/Hrr7/i22+/rXf7r0KhQPfu3aFWq7F582Y8+eSTDbZn6Gd48MEH8ddff+Htt9/GuHHjEBsbi86dO9f7GS+vbfPmzfD390e/fv0ghMCFCxfw559/4uDBgwCA3r1748MPP0R6ejrGjBmDxx57DJ07d8Y999yj/13/8MMPMWDAANx///1G67y83tpaGstiU7J6pbYyzxFNWe7r6yv9PGUM2zbetiWPvbnaCiGanClj2LbxttZ67M11jmCmGl/e2toa05JthRDw8fZG9s7vkLJyKdQF+QAAz559EfX0LLh2iNW3s4Z6m9u2ucfIVOx8NiI4OBgvvfQS5s2bV+82zL59+yIyMhJPPPEE7r33Xpw9exahoaFS9hkfH6//78DAQHh4eCA+Pl4fAKVSiejoaFy8eBF5eXn6tgEBAfD09ERCQoL+dkd7e3t07NgRpaWlqKqqgrOzMyorK6FUKuHg4ICqqqp6nVEnJydotVpUV1dj37596NGjB1xdXaFUKpGXl4cvv/wSI0eO1O9Tp9NBrVYjKiqqWT/j999/j9tuuw1KpRJqtbre7ZlFRUVYtmwZjhw5gsrKSsTExCA1NRVnz55FUFBQvba1x6Sqquqqarj11luhUqlQXV0NrVaLo0ePIjg4WP+eVFVV6dvb2dnB0dER1dXV+it5tbXb2dnpb5OtnQgnISEBGo2m3lW/2npLS0v17Wu3W7ftd999h0cffRTff/89fvjhB8ydOxdDhw5FVVUVnJycGmzX2PGsPUHUvYXXWNvCwkIANbeu1ravbav837d4+fn5+hpqc1LLwcEBUVFRCA4Oxh133IGnn34a+/fvx6RJk7B9+3a88847mDFjhr6tUqmEl5cXVCoVDh48iPHjx8PJyUmfqbrbVSqV0Ol0qKqqQmVlJV555RWcOnUK33//PUaOHIlDhw7Bw8NDX0/t8az13XffYfjw4frtdu7cGaNGjcKqVavQtWtXvPbaawBqvhi66aabcODAASxYsAC9evUCUDMW9vPPP8euXbvqTZBUOzu1oZwIIVBVVYXExETY29sjNDQUTk5OSEhI0Ld1cnJCREQE8vLycPHiRf3y4OBguLq6Ii4urt6+oqKiUFBQgPz8fP1yWeeIS5cuIScnR9/W398f3t7eSEpK0mdNoVAgNjYWSqWy3nnK19cXvr6+OH/+fL1MdOrUCSUlJcjMzNQv8/HxgZ+fH1JSUuq9bzExMSgvL8eFCxf0y7y8vNC+fXukpaWhoqJCv7xDhw5Qq9VIS0vTL/Pw8EBgYCDS09NRXl6uXx4dHQ2NRoPU1FT9Mnd3dwQFBSEjIwOlpaX65ZGRkQBq7jSo5erqipCQEGRlZdWb3To8PBwODg5ISkrSL3NxcUFYWBiys7NRVFSkXx4WFgaVSoXExET9MmdnZ4SHhyM3N1f/uwcAISEhcHFxqff+Ojo6IjIyEvn5+SgoKNAvDwoKgru7e72c1B77puTEwcEBHTp0QGFhYb27D9q3bw8vLy8kJibqf4/s7OwQExODoqIiZGdn69v6+fnBx8en3rGvzUlxcTGysrL0bWtzkpycXO93PDY2Fo6OjvV+Zm9vb/j7+yM1NbXe+atjx46orKxEenq6fpmnpycCAgIMHvvq6mqDOblw4YJ+WAcAREVFQafT1fsS083NDcHBwcjMzNR/qQjUjPm3s7OrN3t5u3btEBoaavDYK5VKgznJycnBpUuX9Mtt6Rxh7Ni35Dni8t8jniNqtNZzRGlpab27DVv6HOFbWoTU5e+g7H/jOu39A+A3fjJiRtyHzMxMZNR5j9vCOUKtVhsd5tQUCtES9zPaIK1Wi9jYWCQlJWHWrFlYtGiRSdvr2rUr1Go14uLi6n2rIOPbpYKCApw9exbt2rVDjx49rrjdqqoqeHt7Y8aMGXj55Zcxf/58jBkzpt4zEuuu/913312xtrpuuukm+Pr6Gmz7+OOPIyAgAPPnzwcAPPPMM1i6dCkWLlyIWbNmGd2mjBo8PDzQrVs3/PLLLwbXqW376quv4vXXX8ePP/6IgQMH1mtTWlqKoKAgVFRUIDk5ud74ySVLluDZZ5/F7NmzsWDBAqP1XrhwARMmTEBMTAw++eQTqNVqDB8+HF9//TWcnZ0N/mypqakNli9ZsgTffPMNDh8+3ODnaN++PZycnPTL3nvvPTz33HOYO3cuXn/99XptExMT0bFjR8TExODcuXPNyt/ff/+N3r17o2PHjvUek1LbNiAgACEhITh+/HiD7dVtO3DgQKxdu1Z/9buoqAg33HAD4uLiMHz4cHz33XdITU3FhAkTcODAAf26aWlpiIiIwI4dOzBkyBD98oMHD+K2227Dyy+/jFdffVW//NFHH8W6detw/vx5/b60Wi2USiUGDx6MnTt3Gq2zbr0///wzAKBPnz6NnqBb2zfbQM1dGlFRUVLPU8awbeNtrfVKRXOvfJ4/f75JmTKGbRtva63H3pxXPpkp48tbW1tjWqJtZXYmkj9YjLwf9wAA7Nu5Imz8ZATf9xDs/vcFtDXVe7Vtm3uMau/ivNo5WXjl8yrZ29ujZ8+eSEpK0n/QNJUQot4Vq1qGLnEbu+zdWNvLt22s7ZEjR1BRUYHo6Gjccsst+OabbxAeHm50u6ZM5FK3hj///BPbtm1DYmKifnmXLl0AAGfPnm30Ur+MGsrLy6FUKhvdT9330NCxcnNzw2uvvYbnnnsOjz76KNavXw9fX1/s27cPH3zwAYCab8WMHePU1FT0798f7733Hu6//3489dRTGD16NLZv346ZM2di5cqVBuur/UbWEENXhQ8cOFCv41x7a21lZWWD7dd+o+zt7V3vZzfk8uXdu3fH4MGDsW/fPoM/s6OjI4qLixt9z+u2r23n6emJrVu3om/fvti+fTtee+01TJgwoUEN3333Hdzc3HDHHXcYzP7lx7D21uC6y2tn+C0vL29SncZqbqxNc7bX1OXmaCuEgEajMct5yhi2bbxtSx17c7ZtTqaMYdvG21rrsTfXOYKZanx5a2trjLna6ioqkLbuE1z48lPo1FWAnR2cb74d3We9BEdvX6urt6VzYip2Pk0QGBgIAPVuc7AFtZPUbNu2DcePH8fhw4cNdj5r1b1VqSlqx1tebubMmfD19cWYMWP0y2pvN7nS41Zk1NCuXbt6t3pcrWeffRYuLi5YtmwZOnXqhLCwMDz00EO49tprkZSU1OgkTi+//DIUCoV+bGG3bt3w008/oUePHvjkk0/w5ptvwtfXt8F6hibf2bBhA3788UeDHdbLZ+MNCwsDgHq3y9SqXVb7RUBzde7cucHV11rV1dVXPR1+bGwsvvrqKwwbNgxvvPGGfkxtXd9++y2GDBly1bMXExERUdsgdDrk7NmO5BVLoc6vudXY8/o+iHp6NjJ0gMqr4ecMaj52Phvx/fffo3fv3gY/1AL/fij38vKSsj9zfcPQXPv378dNN92ErVu3okOHDliyZAnGjh1rtH1jV90MufyqGwB88803KCoqwunTp+tNTJOfnw8/Pz/8888/+ivD5qohMjKy3tgKUzz55JP1JtEpKCjA/Pnz0atXL8TExBhd79ixYw0edeLn54fJkyfj5ZdfRlJSksHOZ92xuLX+/PNPHDlyBCNHjrxitvr06QM3NzeDnfxTp04BwFU/diczMxPXX3+9wddKS0tNevbm4MGD8dZbb2HOnDl47rnn0L9/f/1reXl5OHz4ML788sur3j41ZC3nKbIdzBTJxkxRcxWd+hNJSxai5J+aIUJOQSGInv48fG6puWCgqDPOlkzDzmcjBg8ejDVr1uhv56tLp9PpH6vRr18/KfurncDEkoqKinD8+HHMnTsXCoUCTz/9NGbOnIl9+/bhzjvvRHx8fIPOU1MfeVHr8qtuFRUVmDVrFtatW1ev4wnUDED39/dHbm4uUlNTjc54a2oNQM1EUuvXr2+0k3u13nrrLVRUVNQbT6nRaLBlyxbceOON+g6nv78/4uLiGtTg7u4OoGYQeVPV3nLUlJ9FpVLh0UcfxYoVK5CTk4P27dvrX9u9ezf8/Pz0tzYbqhuoOY7Ozs71tltQUIAdO3bgiy++aLDP0tJSlJaWomfPnlesr6qqyuhV6dmzZ+PkyZMNOplbt26FUqmsN9aTTKNQKPSPDCKSgZki2Zgpao7K7Cwkr3gPuftqHidn79IOYeMnI2T0w7CrM2cDMyWRIKMAiFtuuUVotdoGr3322WcCgHB0dBSJiYkm76tLly6iU6dOQqfTmbytyxUUFIhDhw6JEydOXLHtd999JwCIn376SQghRFlZmQgMDBRdunQRx44dEytXrpRe36xZs8SQIUOMvt6vXz8BQHz55ZfS913X999/LwCIuLi4RttNmTJFABC7du1q0nb37dsnHBwcxOzZs+stX7FihQAg+vXrp1+2Z88eYW9vL5YtW6ZfVlFRIXr16iXGjx/fjJ9GiJdfflmEh4c3OVMFBQUiIiJCTJkyRb/s4MGDwt7eXmzatKnRuk+cOCHs7e1F586dxY8//iiEEKK4uFgMHTpUzJ8/3+D+fvvtNwFA/P77743WpVarhY+Pj9i8ebPRNuXl5aJnz55iwIAB+mWDBw82mqudO3cKAGLu3Ln1lj/44IMCgPj777/1y8rKygQA0b9//0brrOvQoUPi0KFDoqqqqsnrtAY6nU5cunTJLOcpapuYKZKNmaKm0JSXifMfvy8ODbheHLyxqzjYr5s499+XRVVBXoO2zFR9Xbp0EV26dLnq9dn5bIS9vb0AIO68806xe/dukZSUJE6fPi3efPNN4eTkJJydnRv9QNwcXbp0EdHR0RbvfE6fPl04OzvX+9C8fft24enpKUaPHi2qq6ul1aVWq8X06dOFQqEQPXv2FNu2bav3ekpKinj33XdFu3btBAARGxsrtmzZIm3/l9PpdKJTp07ivffeM/j633//LV577TV9PT169BArVqwQubm5Rre5fft24ebmJubNm9fg2B46dEh4enqKZ555pt7y33//XQwZMkSMGjVKTJkyRfznP/8RH3zwQbPf+08//VQ88MADzcpUdna2mDhxonjooYfEU089Je666y6xZ8+eK9ZdUlIiHn74YeHj4yOcnJzE4MGDxaRJk8SxY8eM7mv+/Pmib9++jdbzySefiN69ewsAwsPDQzzzzDOirKzMYNv09HQxZswYIYQQRUVFQqVSiVWrVjVo9+OPP4oBAwYIAKJz587iyy+/FGq1WqxevVoEBAQIAGLEiBHi8OHDIikpSUycOFEAEM7OzmLZsmWisLCw0Zpr3yNb7Xz+888//ANM0jBTJBszRY3RabUie/c28es9t9V0Om/sKv6cMk4UnztrfB1mqh5TO5981EojMjMz8dVXX2HPnj04ffo08vPzoVQqER4ejttvvx0zZ85EdHS0lH117doVVVVVSEhIkH7L58WLF3HmzBm4urrqH7VChu3YsQPPPvsszp49q5/ltLmEEDh27BiWLl2KjIwMzJ8/HzfddJPkSuszNOGS+N9jcxwdHRtkytikTy1FrVajU6dO+Oqrr9C3b1+L1WEutTNg9+3b16RnYVkbIQTi4uIQGxtr8SECZBuYKZKNmSJjik+fROLShSg58zcAwCkwGFHTnofvwDsazQozVR8ftWJGQUFBeO655/Dcc89ZuhRqIcOGDcP27duxcOFCvPTSS1e1jY0bN0IIgUWLFjVrjKYpZEy41JLmzZuHKVOm2GTHk4iIiKxHZU4WklcsQe7emmd127u4IOzRSQgZ8wjsOBt+i2Pn04pc7ZU2kmv58uUYN24ctm3bhnvuuafZ69d9VExLMTThkhACWq0W9vb2V3zUSktas2YNXF1dMWvWLIvVQFfP0GzLRKZgpkg2ZooAQFtZgfR1a5C+fi10VZWAQoGAoSMRMelpOPr6NWtbzJQ87O1YEUOdBGp5SqUS69atw7Jly5CVlaV/nqs1M/SoFWt0+vRp+Pj4YOLEiZYuha6CQqHgH2CSipki2ZgpEkIgd+9OJK94D1W5OQAA9+7Xo8OMOXDr1LXZ22Om5GLn04pUV1eb5TEf1Hx2dnaYOXOmpcswiRACarXaKh7hU6tbt24WvepKphFCIDk5GZGRkVaTKWrdmCmSjZlq24rPnkLiewtQcuYkAMAxIAjR056D7613XXUemCm52Pm0IjqdztIlkI1hpkg2Y89bJbpazBTJxky1PVV5OUhesQQ5328HANg5OyPs0ScQ8sCjsHc0fYJFZkoedj6JiIiIiKjV0VZW4MJXnyHti0+gq6wAALQfMgKRk2fA0c/fwtWRIex8EhERERFRqyGEQN7+3Tj/4WJU5WQDANyv7VEzrrMzh/dYM3Y+rYgjp3smySz5LE+yTbGxsZYugWwMM0WyMVO2rfjsKSQtXYjiU38BABzbByDqqWfhd8dgs43JZKbkYefTinB8Hsmm0+lgZ2dn6TLIhpSWlsLV1dXSZZANYaZINmbKNlXl5SL5oyXI2b0NAGDn5IywRx5HyEPjpIzrbAwzJQ8/lVqR6upqS5dANoYD5Em2jIwMS5dANoaZItmYKduirapE6qcrcfSBofqOZ/vB96DP1zsQPmGy2TueADMlE698Uquwd+9edO3aFcHBwZYuhUyQmJgINzc35OXlobi4GP369bN0SURERGSFhBDI++F7nP9gMapysgAA7t26I3rmC3Dvco2Fq6OrxSufZPXmz5+PhISEeh3P4uJiPPPMM3jiiSfw5JNP4u6778aRI0eatL3mrPv999+jX79+cHZ2hoeHB+655x78888/Jm1TNlP33ZT1X3zxRSgUCqP/tm/f3mC7s2bNatBu0KBB8Pf3R7du3fDDDz/g888/b1KNGzZsQP/+/aFQKODq6orTp083aJOfn4958+YhICAACoUCAwYMwIYNG5r8PhAREZF1KDl3Bn89NQ7/vDwLVTlZcPRvj86vLsJ1K9ex49nKKYQQwtJFENC1a1dotVr8888/0gdLX7x4EWfOnIGrqyt69OjRpHWysrKwYcMGLFiwALm5ufD19cWwYcNQUlKCs2fPok+fPpg3bx6io6Ol1nq5119/HYWFhXjvvff0y0pLS3HTTTehd+/eWLVqFQBg3759GDZsGH788Uf079/f6Paas+7+/fsxYsQI3H777VCpVPj111+RlZUFLy8v/PnnnwgPDze5HlM1tu8ffvgBffv2hYODg9FMNaX26upqRERE4Nprr21w5Tk5ORm//fYb8vLy4OLiol9eVFSE7t27o1OnTvplCoUC48ePx5gxY/TLxo8fj3vvvRfDhw+/4s9aVVWFa6+9FvHx8YiOjsbx48fh6enZoN327dsxceJEpKam1qupJf38888AgL59+0KlUlmkBnMQQiAvLw9+fn580DZJwUyRbMxU61aVn4fkj5YiZ/dWQAjYOTkjdOwEhI6dAHsnZ4vUxEzV17VrVwDAmTNnrm4DgqxCly5dRJcuXcyy7YKCAnHo0CFx4sSJZq979913CwDigw8+0C/7559/hIODgwgICBAlJSUyS61n7969Ijo6WlRWVtZbPnPmTKFQKER6enq95bfccovo2LGjqK6uNrrNpq6r0+lEr169RFxcnL5NaWmpGDRokAAgZsyYIaUeU5m676asv2nTJrFmzRqD68+YMUPce++9DZYvWLBAvP3221esv7CwUERERIjs7OwrthVCiAEDBgh3d3cBQAwePFhotdoGbeLi4sSNN97YpO2Zy6FDh8ShQ4dEVVWVResgIiJqDbSVlSLl05Xi0G29xMEbu4qDN3YVZ1+dIypzsixdGl3G1D4Lb7u1ItXV1RBWdCFap9Ph6NGjsLe3x/33369f3qlTJ4SFhSE7Oxt//PGH2fY9bdo0zJgxo94jaMrLy7Fq1Spcc801CAkJqbfO0KFDkZCQgB07dhjcZnPW/f333/HEE08gJiZG36Zdu3ZYtGgRAODcuXMm12PIhg0bMGnSpCa1bcq+t2zZYjRTTa39hhtuwIQJExqsL4TAN998g3vvvbfecrVajaVLl0KtVuPYsWONzuLs6emJESNGYO7cuU36mQFgzZo1cHZ2xu7duzFv3rwGr6tUKpu62mhNhBBISUmxqvMUtW7MFMnGTLUuQgjk/bgXxx66Bykrl0FXUQG3rteix8fr0fmVBXD0D7B0icyUZOx8WhFre9TKb7/9hosXL2LgwIHw8/PTL8/OzsaFCxegUqnQsWNHs+x7586diI+Px7Bhw+otP3r0KMrKyvSX/Ou65pqaMQC7du0yuM3mrBsdHW2ww1X78wYFBZlcjyHnzp3D3r17m9S2KfvevXu3Sevv2rWrQce01i+//IL8/HwMHTq03vJ169YhKysLL730Evr06YOgoCAsWLAAGo3G4HbuvvtufP7558jNzTVaa109e/bE6tWrAQBvvfUWtmzZ0qT1SI7KykpLl0A2hpki2ZgpyyivVDfrX86pv/H7UxNwYt4sXMrOhsqvPTq9sgA9Vq6De7fulv5x6mGm5OFst2RUbaepdoyeTqfD3r178eKLL6J9+/b48MMP9Z2wWikpKc3aR0BAAJycGk6RvWXLFri7uyMyMrLe8torjoGBgQ3WqV1m7B705qxbt7NdV20HaeTIkSbXY6qm7NvQ5EjNWb+x2jdt2oQ77rgD7u7u9ZYPHDgQ27dvx9mzZ/Hdd9/hyJEjePHFF/Hrr79iy5YtsLe3r9f+uuuug1qtxrfffosnn3zS6P7qeuihh3Dy5EksWrQI48aNQ6dOndC5c+cmrUtERETydRzb8G6kK2sPBNwNAEhb9wrsnS0zVwO1HF75JKN27twJOzs7bN26Ff369UO7du0wevRoPPXUU0hOTm5wVRIAIiMjm/Xvt99+M7jvo0ePGrziVlhYCAAGJ5Jp164dAKCgoMDgNk1Zt9b333+P3r176392Gdu8Wk3Z98WLF01a31jtxm65BYCoqCgMGzYMs2fPxq+//oo9e/YgICAA27dvx5IlSxq0b9++PVQqFQ4dOmS0VkPeeustDB48GCUlJRg5ciSKioqatT4RERFZD3Y82wZe+bQidcc2WlpmZib++usv9OrVSz9mMSMjA6NGjcKUKVPg4OBg8LbU5t4C2a1bN4PL09LSDL5W+x4Zmm3Mzq7muxRj9+Sbsi5QM9vqqlWr8Nlnn+nbm7JNQ1eJL126BI1GY/C1y68SN2XfjTGl9l9++QW5ubkYMWLEFfdz1113Yc+ePejVqxfWrFmD5557rt7rCoUCnp6eiIuLu+K2Lq/xq6++Qt++fREXF4dHHnkEW7dubdY2qPnMdas9tV3MFMnWFjJVXqk2aX0XJ/lzIySsf8Pg8rKKKlz3+HwAwIqcfXAUWrjFdkHk1Gfh0dW6bq81pi1kqqWw82lFrGnMZ+0tt7fffrt+WXBwMJYuXYobb7wRTz/9NO69994Gt1zW3o5qqvLyciiVygbLAwJqBp5XVFQYXAcAvL29DW7TlHUBYN68eXj99dfrjZE0ZZuX31J8pdcOHDiAgQMHNmvfXl5eRvdhSu0bN27EwIEDG32/6rr22msxePBg7Nu3z+Drjo6OKC4ubtK26vLw8MDWrVvRt29fbN++Ha+++qrBL0VInsrKSos9woZsEzNFsrWFTF3dLa7/yti8UFIl/zLUoS2NP4ekJQsB+AMA3Ly90WXK0/AfNAyKJnxRbi3aQqZaCjufVqS6utrSJejt3LkTAHDrrbfWW177nNDS0lLExcWhd+/e9V6XNeazXbt2UKsbfqsXFhYGoGbSo8vVLuvSpYvBfZmy7ooVK3DNNddgyJAh0rZp6Crxhg0b8OOPP+Ljjz9u8NrlV4Kbsu/Y2FiD+zaldiEENm/ebHCm2cZ07twZhw8fNvhadXU1PDw8mrW9WrGxsfjqq68wbNgwvPHGG/Dx8bmq7VDTpKenN5orouZipkg2Zsoy6l6NVRfmI3XNSmTv3ooq2AHt7wQAdFn5Jdy8PFGhbjgJoTmuxsrCTMnDzic1oFarsX//fjg4OKB///71Xrt06ZL+v93c3Bqs29jVPEMuv5pXdzu1YxLr6tOnD9zc3HD27NkGr506dQoAcMcddxjc19Wu++WXX8LOzg6PPPKI1HoMXSX+66+/8NtvvzXpCnJT9n3bbbeZtL6h2g8fPozs7Gz85z//uWKNdWVmZuL66683+FppaSmuu+66Zm2vrsGDB+Ott97CnDlz8NxzzzXILRERkS0xdotreaUa3R+ree3kJ/NatENn8Gps+0H1/vf6ae8aXd8cV2PJ+rDzSQ0cOnQIpaWl6N27N1xdXeu99u233wKouYrVqVOnBuvKGvPZt29frF+/HkKIemMSVSoVHn30UaxYsQI5OTlo3769/rXdu3fDz89P3ynSaDTYsmULbrzxRoSEhDRr3bo/T1ZWVoNxiuXl5fjggw8wa9asZm9Tlqb8PPfccw+Ahu9FU9c3VPumTZvQv3//eu3rqqiogLOzc71lBQUF2LFjB7744osG7UtLS1FaWoqePXte8WeuqqoyeEUcAGbPno2TJ0/iyy+/vOJ2iIiIWrOmdCpdnFQt1vnkMzCpyQRZhS5duoiYmBih0+mkb7ugoEAcOnRInDhxokntn3zySQFAPPLII/WWf/3118LV1VX4+vqKP/74Q3qddX3//fcCgIiLi2vwWkFBgYiIiBBTpkzRLzt48KCwt7cXmzZt0i9bsWKFACD69evX7HWFEGL37t3Cz89PjBkzpt6/+++/X0RGRop9+/Y1e5tX8sorr4jw8PAmt29s3xs3bhRqtVrodDqD78XV1K7VakVQUJBYsmSJwXpOnDgh7O3tRefOncWPP/4ohBCiuLhYDB06VMyfP9/gOr/99psAIH7//fdGf1a1Wi18fHzE5s2bjbYpLy8XPXv2FAMGDGh0W+Z26NAhcejQIVFVVWXROmTT6XQiKyvLLOcpapuYKZKtrWeqrKJKBI2aLYJGzRZlFS3zN6gk4Zz4a9pE8X2/a8X3/a4VPw6/TZzf8o0oLasQZRVVTf5nrdp6pi7XpUsX0aVLl6ten1c+rYiDg4PBmUdbSlFREd5991188sknAICvv/4aFRUV0Ol0SE1NhUKhwLPPPovp06fD19fXrLXcdddd6NSpE3bt2oWYmJh6r3l7e+O3337D//3f/2Hs2LHw9PREYmIidu3ahbvuukvfrmvXrvD09ETfvn2bve7vv/+OUaNGoaKiAl9//XWD+iIiIvSTMTV1m00RFRWFm266qcntTXkvrqb2X375BZmZmRg1apTBejp27IgHH3wQu3fvxpAhQ3DrrbciNDQUr776Knr16mVwnf3796Nv377o06eP0Z9zzZo1+Oijj1BQUICJEyfi8OHDmD9/foPB/87Ozvjuu+/w/PPPG90WXT2FQqGfqIpIBmaKZGOmWo76YgFSVi9H1rbNgE4HZ5UKoQ+OR9gjj8PehibnYabkUgjB6+TWoGvXrqiurkZcXJz0DujFixdx5swZuLq66icMag127NiBZ599FmfPnoWDg+19TyJrciZjhBBQq9VQqVQW/VKjMWq1Gp06ddI/MsVW/PzzzwBqbh9Xqax3AoXmEkIgPT0doaGhVpspal2YKZKtrWeqvFKtH3uZsP4Ns9x2q6uuRsam9Uhd+xG0ZaUAAL/bByHqqWfhFBgsfX+W1tYzdbnapz6cOXPmqta3vU/0rZg1PWrFGgwbNgzbt2/HwoUL8dJLL1m6HOlkTc7UGGvP1Lx58zBlyhSb6njautrH8BDJwkyRbMyUeQghUHD4IM6//zYqLqQBAFxjuyB6xhx4XnfleRtaM2ZKHnY+yaotX74c48aNw7Zt2/ST59gKWZMztVZr1qyBq6srZs2aZelSiIiIqBGlSfFIWrYIl479BgBQ+fgicvIMtB8yolU9r5Msj51PsmpKpRLr1q3DsmXLkJWVhcDAQEuXJE1THqdiq06fPg0fHx9MnDjR0qUQERGREerCi0hd/QEyt24CdDoolEqEPPAowh6dBId27SxdHrVC7HxaEVsaGyaTnZ0dZs6caekyWiVHR0dLl2BQt27dbO5KblsRHR1t6RLIxjBTJBszZTpddTUyN3+FlDUroC0tAQD4DrwTUdOeg3NQiIWra3nMlDzsfBLZMHHZc1KJTFVdXW2TE4CR5TBTJBszdfWEELj4609Iev8dVKSlAABcO3ZC9Mw58OzR27LFWRAzJQ9v0rYiarXaLNut7XxY++QzJJ+5MkXG1f09s8WOf1pamqVLIBvDTJFszNTVKUtOwqlnJuP0rGmoSEuB0ssbMS++huvXfN2mO54AMyUTu/BtQO03NRqNxsKVENm+ur9n/JaUiIisXXXRJaSs/gCZ320EtNqacZ2jH0HY+ElwaOdq6fLIxvCTURtQO+5PrVajqqrKascBEtmCkpKasTGOjo42eeWTiIhsg05TjczNG5C6ZgU0JcUAAJ9bbkP0tOfhHBJm4erIVrHzaUXs7e3Nsl2VSgV3d3cUFxcjPz8fwcG29wBgMsxcmSLj8vPzAQA+Pj4WrsQ8PDw8LF0C2RhmimRjpq6s4NdDSFr2NirSkgEA7TrEIHrGC/Dq2cfClVknZkoedj6tiIODg9mulPj6+qK4uBh5eXkICgriFZk2QKFQcAblFqbRaFBQUAAA8PPzs3A18ikUCpt63BFZHjNFsjFTjStLTkLS+2+j8LfDAAClpzciJk1H4PBRUPALa4OYKbk44ZAVqa6uhhDCLNv29fUFUHNLYEJCgtn2Q9ZDCAG1Ws1j3UK0Wi3OnDkDrVYLlUoFNzc3S5cknRAC6enpzBRJw0yRbMyUYdXFRUhc/F8cf3QUCn87DIWDA0IeGo8+G3ciaOT97Hg2gpmSi1c+rYg5Z6N1dHREp06dcO7cOeTk5ECj0SA4OBju7u68CmrDtFotlEqlpcuwaTqdDoWFhUhPT0dJSQkcHBzQpUsXm/29Kisrs3QJZGOYKZKNmfqXTlONrC0bkbL6g3/Hdd58G6Knc1xnczBT8rDz2YbU3gZ47tw5FBQUoKCgACqVCp6enlAqlbCzs7PZD8xtkRAC1dXVUCqVPK6SCSGg1WqhVqtRWFgIrVYLoObW+W7dutnkVU8iImpdLh79FVkfLUZ5ynkAQLvojoieMQdevW6wcGXUlrHz2cb4+flBpVIhOzsbBQUFUKvVyM3NtXRZZCa1nU8yL5VKBT8/PwQEBMDFxcXS5RAREeHMizPgJLRQenoh4olpCBx+LxR8BBhZGBNoRVpqchgPDw94eHhAp9Ph0qVLKC0thUajMettv2QZWq2WM96aib29PZRKJdzd3eHm5tZmri5HRUVZugSyMcwUydZWM1VdXISkVR/q/19h54CQ+x9G+ITJcHBzt2BlrV9bzZQ5sPPZhtnZ2cHb2xve3t6WLoXMQAihf65rW+kYkfnxSyqSjZki2dpapoRGg8ytm5CyajlKS0qBgLsBANev2QDfjh0tXJ1taGuZMifOdmtF1Gq1pUsgG5OSkmLpEsjGMFMkGzNFsrWlTF08+iuOj7sPie++CU1xEVzC/71C5xIabsHKbEtbypS58conEREREVErUp6ajKTl7+DiLz8BABw8PBHx+DR4DroHGPeahasjMo6dTyIiIiKiVqC6uAipa1ci85svIbQaKOwdEHTvAwifOAVKdw+UV/IuOrJu7HxaEU4MQ7LxkR8kGzNFsjFTJJstZkpoNMja9g2SVy2HpugSAMC73y2InvY8XCI4GY652WKmLIWdTyvi4ODAiWFIGoVCgeDgYEuXQTaEmSLZmCmSzRYzVXjsCJKWLkLZ+QQAgEtEFKKfngPvG/pbuLK2wRYzZUnsfFoRjUYDIQQ7oCSFEAKZmZkICgpipkgKZopkY6ZINlvKVHl6Ks6//zYKDh8EADi4eyDiiWkIGnE/KjQ6g7fY1l3W2C24Lk4t83g/W2BLmbIG7HxaEa1Wa+kSyMaUlJRYugSyMcwUycZMkWytPVOakmKkrl2JjG/WQ2g0gL09gkc9gPDHnoLS3QMA0HHMnCtup/tjbxh9LWPzQmn1tgWtPVPWhJ1PIiIiIiILE1otsrZvRsrH76P6UiEAwPvGmxE1/Xm0i4i2cHVEcrDzSURERERkQYXHf0fS0gUoS/rfuM7wSEQ9PRs+N95ssH3CeuNXNYmsGTufVkSl4v33JFdERISlSyAbw0yRbMwUydaaMlVxIQ1Jy99BwaEfAQAObu4If+wpBI0aAzsHpdH1OGazZbWmTFk7dj6JbJidnZ2lSyAbw0yRbMwUydYaMqUpLUHqZx8j4+sv9OM6g/4zBhGPPQWlh6ely6PLtIZMtRZ8J62IWs0HA5Nc58+ft3QJZGOYKZKNmSLZrDlTQqtF1tZvcHTMUFxYvxZCo4FXn37o9dlmdHz2/9jxtFLWnKnWhlc+iYiIiIjM7NKJo0hcuhBlCXEAAOewCERPnwXvfrfwER7UZrDzSURERERkJhUZ6Tj/wbvIP7gfwP/GdU6cgqBRD8BOaXxcJ5EtYufTivB+cpKtXbt2li6BbAwzRbIxUySbtWRKU1aKtM9W4cLXn0NUVwN2dggaORoRj0+F0tPL0uVRM1hLpmwBO59WRKlU8rYLkkahUCA0NNTSZZANYaZINmaKZLOGTAmtFtm7vkPyymWovlgAAPDqfSOin56NdtEdLVobNZ81ZMqWsPNpRTQaDYQQ7ICSFEIIZGdnIyAggJkiKZgpko2ZItksnalLfx5H0rJFKI07CwBwDg1H1PRZ8Ok/gBlvpSydKVvDzqcV0Wq1li6BbExRURECAgIsXQbZEGaKZGOmSDZLZKoi8wLOf7AY+Qf2AgDsXd0QMXEKgu59kOM6bQDPU/Kw80lEREREdBU0ZWVI+2IVLmz4HEKtBuzsEDjiPkQ8Pg0qL29Ll0dkddj5JCIiIiJqRHll/WexC50OuXt2IGXNh1D/b1yn5/U3IHLqM3CN7AgNAE2ddVycVC1ZLpHVYufTiqhUPDGRXGFhYZYugWwMM0WyMVMkmzky1XHsPMMvqHoDtXdjZgGY+7nBZhmbF0qviVoOz1Py8NkeRDZMyXEmJBkzRbIxUyQbM0WyMVPy8MqnFVGr1VduRNQMSUlJiI2NtXQZZEOYKZKNmSLZZGdKW16OfbcF4sKmdTXP61TYIWDICIRPmAyNsxu6P/YGAODkJ/N4e62N4nlKHnY+iYiIiIguI3Q65Hy/HckfLYE6Pw+OADyu740OM+bAtWMnAPXHgro4qdj5JLoCdj6JiIiIiOoo+vsEkpYsRMm5MwAAp6AQRE9/Hj633M5nPRKZgJ1PK2JnxyG4JJeLi4ulSyAbw0yRbMwUyWZKpiqzs3D+w8XI278bAGDv0g5h4ycjZPTDsOPEkG0Wz1PysPNpRZRKJb9NI2kUCgVnZyOpmCmSjZki2a42U9qKcqStW4ML69dCp64CFAoEDPsPIic9DZWPrxkqpdaC5ym5eKntKuzYsQMKhQIKhQIHDx6Utl2NRgMhhLTtUdsmhEB2djYzRdIwUyQbM0WyNTdTteM6jz4wHGlrP4JOXQWP63rh+jUbEfvi6+x4Es9TkvHKZzNdunQJkydPNsu2tVqtWbZLbdelS5fQvn17S5dBNoSZItmYKZKtqZkqPn0SiUsXouTM3wAAp8BgRE17Hr4D7+CdaFQPz1PysPPZTM8++yyf9UNERETUSlXmZCF5xRLk7t0JALB3cUHYo5MQMuYR2Dk6Wrg6ItvGzmcz7NmzB59++im+//57DBo0yNLlEBEREdmUuo8uuRrOjsYvEGgrK5C+bg3S16+FrqqyZlzn0JGImPQ0HH39TNovETUNO59NVFxcjCeeeALjx4/HXXfdZZZ98IoqyRYaGmrpEsjGMFMkGzNFdXUcO8+k9S98s6BBpoQQyN27E8kr3kNVbg4AwL379egwYw7cOnU1aX/UNvA8JQ87n000a9YsaDQaLF682Gz74KNWSDYnJydLl0A2hpki2Zgpkq1uporP/I3EJQtRcuYkAMAxIAjR056D7613cVwnNRnPU/Kw89kEP/zwA1atWoVvv/0Wnp6eZttPVVWV2bZNbVNCQgJiY2MtXQbZEGaKZGOmqK6E9W8YXF5eqUb3x2peO/nJPLg4GX/mZkJCAiK8PWvGde7ZAQCwc3ZG2KNPIPSBcRzXSc3G85Q87HxeQWlpKR5//HGMHj0aI0eONHl7Xbsavr0jKSkJISEhDaZxVigUTVpmzW2NYdvG28p434UQFs2UMWwrr21LH09zZMoYtm28rbWe85vTtjZPTd2GIWzbeFtrPfbG2ro4qYyee2o5Oyrh7Kg0uA1NRTlKt27EsT3boKusBAC0HzICkZNnQPW/cZ1Xek+b875fXmNj6/HYN7+tMS3Z1tbPU809RqZi5/MKXnjhBZSWluL99983+76EEIiLi9Mf2MDAQHh4eCA+Pl4fAKVSiejoaFy8eBF5eXn6dQMCAuDp6YmEhATodDoAgL29PTp27IhLly4hJydH39bf3x/e3t5ISkqCRqMBUBOm2NhYFBcXIysrS9/W19cXvr6+OH/+PKqrq/XLO3XqhJKSEmRmZuqX+fj4wM/PDykpKfWu4sbExKC8vBwXLlzQL/Py8kL79u2RlpaGiooK/fIOHTpArVYjLS1Nv8zDwwOBgYFIT09HeXm5fnl0dDQ0Gg1SU1P1y9zd3REUFISMjAyUlpbql0dGRgIAkpOT9ctcXV0REhKCrKwsFBcX65eHh4fDwcEBSUlJ+mUuLi4ICwtDdnY2ioqK9MvDwsKgUqmQmJioX+bs7Izw8HDk5uaisLBQvzwkJAQuLi6Ij4/XL3N0dERkZCTy8/NRUFCgXx4UFAR3d3fExcXpl9Ue+4KCAuTn5+uXG8qJg4MDoqOjUVlZWS9T7du3h5eXFxITE/WP9rGzs0NMTAyKioqQnZ2t366fnx98fHzqHfsr5SQ5ORlq9b+TRcTGxqK0tBQZGRn6Zd7e3vD390dqaioq//fBAAA6duyIyspKpKen65d5enoiICDA4LGvrq42mJMLFy6grKxMvzwqKgo6nQ4pKSn6ZW5ubggODkZmZiZKSkr0yyMiImBnZ4fz58/rl7Vr1w6hoaEGj71SqTSYk5ycHFy6dEm/PDQ0FE5OTkhISNAvc3JyQkREBPLy8nDx4kX98uDgYLi6utY79iqVClFRUU069uY8R8TExKCqqqpepniOqNEazxEdOnRAYWEhcnNz9W1b+hwRExMDtVpdL1M8R9RojecIc36OKCv/95jFJ8QjqL1/vXOEEAJVR39BxZYNUOfW5FTZoRPcHhwPj+t6wtHPH2lpadLPEd6+/vXqclYpeY74H1v5HBEZGQmtVlvvPNWWzxFqtRoqlfE7D65EIfjEVKN++ukn3HrrrVi3bh0eeuiheq/Vhu/AgQMYOHCgyfvq2rUrqqurce7cuXrfKljrN1Gt7Vur1thWxvuekpKC8PBwi2XKGLaV17alj6c5MmUM2zbe1lrP+c298pmamtqkTBnDto23tdZj39xzRHmlGjEPvwwAiF/3OlycVPq2Jf+cRtLShSg+9RcAwN7HDx2fngW/2+/W58pc73tFVbV+kqTaumRsl8e+cS195dOWz1PNPUa1d3GeOXOmSfu9HK98GlFeXo7HHnsMw4YNa9DxNBelUmlw0iFDl7iNXfa21rbGsG3jbU1932u/qZW9XR5762rbksfTXJkyhm0bb2ut5/ymtlUoFM3KlDFs23hbazz2prRVKBRQKBSoystF8kdLkLN7GwDAzskZYY88jpCHxsHeseEEMeZ63y+vS9Z2eewb11Jt28J5qjnHyFScXtWIY8eOISkpCXv37oWrq2uDf7UGDx6sX/bf//7XpH1qtdomf3tBdCVCCOTm5jJTJA0zRbIxU3Q1tFWVSP10JY4+MFTf8Ww/+B70+XoHwsZPQkFRMTNF0vA8JRevfBrRp0+fevdVX65jx44AgNWrV6Nv374Aau5BN0XtuAkiWS5evAg/Pz44m+Rhpkg2Zoqa64/x90ORUzMG0K1rd3R45gW4d7kGQE1HgZki2Zgpedj5NMLZ2RkdOnS4Yrvg4OAmtSMiIiKiq1MSf1b/31W52fDwb4+op56D352DzXZ7IBHJx84nERERkQ0rr1RfuVEjGptEx9yq8vOQ/NFSpH6/A2g/CAAQNm4SYh6dCHsnZ4vVRURXh53PZigqKqo35T9Qcxm+dlrpgIAAk7avVCpNWp/ocsHBwZYugWwMM0WyMVPmVzsb69XK2LxQUiVNp6uqQvqGz5D2+SroKioAhb3+tfBHn4B9Ix1iZopkY6bkYeezGWbMmIHPPvus3rJ7771X/9+mDkQ2NNMtkSnqTo5FJAMzRbIxU1SXEAL5B/Yiafm7qMqueQaoW9drETvlOeDNjU3ahjkyZezqcd3ljV1htuTVYzIdz1Py8DmfVqJr166oqqpCQkICxy6QFEIIxMXFITY2lpkiKZgpko2ZahmNdZy6P/YGAODkJ/OMdpBaquNUEncWSUsWoujkHwAAlV97RD31DPzvHIIKtUZ/BTdh/RtGazJXpoLvnWPS+pa4ekxy8DxVH5/zSURERERGNaXz6OKkstjVOXVBPpJXLkX2zu8AIWDn6ITQsRMQOnYC7J1dLFITEZkHO59ERERE1OJ0VVW48PUXSPv8Y2jLywEA/ncNReSUmXBqH2jh6upLWP+GpUsgsgnsfFoRjvkk2VQqjjEhuZgpko2ZanuEEMg/uB/nP3gXlZkXAABunbsheuYL8LjmOpO3b45Mccxm28bzlDzsfFoRpVLJe8lJGoVCgaioKEuXQTaEmSLZmKm2pzT+HBKXLUTRiWMAAJWvP6KmzIT/oGFQ2NlJmdiHmSKZeJ6Si51PK6LVaiGEYAeUpBBCoKCgAD4+PswUScFMkWzMVNuhvpiP5JXvI3vHtzXjOlWOCBk7AWFjJ8Le5d9xnU15LEztJEmGXPhmATNFUvE8JRc7n1ZEo9FYugSyMfn5+fDx8bF0GWRDmCmSjZmybTq1Ghmb1iF17Upoy8sAAH63342op56FU2CQWfbJTJFszJQ87HwSERERkVRCCBQc+hFJy99BZUY6AMCtU1dEz5wDj2uvN7oeJ/Yhsm3sfBIRERGRNKWJcUhaugiX/vgdAKDy9UPkkzPR/u7hUFxhckVTJ/bh4+uJrBs7n1ZEqVRaugSyMYGB1jVVPbV+zBTJxkzZDnXhRaSseh9Z2zYDOh0UKhVCHxyPsEcerzeu09yYKZKNmZKHnU8rYmdnx4HMJI1CoYCHh4elyyAbwkyRbMyUbdBVVyNj03qkrv0I2rJSAIDf7YP+N64zuEVrYaZINmZKLj5Y0oqo1WreLkLSCCEQFxfHTJE0zBTJxky1bkII5P98AMfHjsD55e9AW1YK19gu6P7hZ+jyxrst3vGsrYmZIpmYKbl45dOKMNQkGzNFsjFTJBsz1TqVJsUjadkiXDr2GwBA5eOLyMkz0H7IiCuO6zQ3ZopkY6bkYeeTiIiIiJpEXXgRqas/QObWTfpxnSEPPIqwR56AQ7t2li6PiKwcO59WhOM9STZOYkWyMVMkGzPVOuiqq5HxzZc14zpLSwAAvrfehaipz8I5KMTC1dXHTJFszJQ87HxaEZVKxQ4oSaNQKBAdHW3pMsiGMFMkGzNl/YQQuPjrT0h6/x1UpKUAAFw7dkL0zDnw7NHbssUZwEyRbMyUXOx8WhGtVgshBDugJIUQAhcvXoS3tzczRVIwUyQbM2Xdys4nImnpQhQeOwIAUHp5I/LJGQgYMhIKe3sLV2cYM0WyMVNysfNpRTQajaVLIBuTl5cHb29vS5dBNoSZItmYKetTfakQKbXjOrVaKJRKhIx+BGHjJ8Ghnauly7siZopkY6bkYeeTiIiIiKDTVCNz8wakrlkBTUkxAMB3wO2ImvocnEPCLFwdEdkCdj6JiIiI2riC3w8ja8V7qEhLBgC06xCD6BkvwKtnHwtXRkS2hJ1PK8KZtEi2gIAAS5dANoaZItmYKetw9v+egZPQQunpjYhJ0xE4fJTVjuu8EmaKZGOm5GHn04rY2dlxIDNJo1Ao4OnpaekyyIYwUyQbM2U51cVFSFq5XP//CnsHhIx+BOHjJ8PB1c2ClZmGmSLZmCm57CxdAP1LrVZDCGHpMshGCCEQHx/PTJE0zBTJxky1PJ2mGhmb1uPo/YOR+d1G/fKeazcietrzrbrjCTBTJB8zJRevfFoRhppk0+l0li6BbAwzRbIxUy3n4m+HkbRsEcpTzgMA2kXFABU1rzkHh1qwMrmYKZKNmZKHnU8iIiIiG1aech5J77+Ni0d+BgAoPb0QMWk6PO4YBox7zcLVEVFbws6nFeF4T5LNvpVOFkHWi5ki2Zgp86kuLkLqJx8i89uvIbQaKOwdEHz/WIRPmAwHN3eUV6otXaJZMFMkGzMlDzufVkSlUrEDStIoFAp07NjR0mWQDWGmSDZmyjyERoPMrZuQsmo5NMVFAACfm25F1LTn4BIWYdnizIyZItmYKbnY+bQiWq0WQgh2QEkKIQQuXboET09PZoqkYKZINmZKvou//1IzrjM5CQDgEtkB0TNmw7tPPwtX1jKYKZKNmZKLnU8rotFoLF0C2ZicnBxOD05SMVMkGzMlR3lqMpKWv4OLv/wEAHDw8ETkE9MQeM99UDi0rY97zBTJxkzJ07bORkREREQ2pLq4CKlrVyLzmy/14zqD7nsI4RMmQ+nuYenyiIjqYeeTiIiISBJTJ/FxcVI1qZ3QaJC17Rskr1oOTdElAIB3v1sQPX0WXMIjTaqBiMhc2Pm0Ig5t7LYYMj9/f39Ll0A2hpki2WwtUx3HzjNp/YzNC6/YpvDYESQtXYSy8wkAAJfIaERPnw3vG/qbtG9bYWuZIstjpuRhb8eK2NvbcyAzSaNQKODt7W3pMsiGMFMkGzPVPOXpqTj//jsoOHwAAODg7oGIx6ciaOToRsd1GrsaW3d5Y1dsm3o11howUyQbMyUXO59WRK1Wc7ZbkkYIgaSkJERHRzNTJAUzRbLZYqYS1r9hcHl5pRrdH6t57eQn85rVodOUFCN17UpkfLMeQqMB7O0RPOoBhD/2VJPGdTblamxtbYY05WqstbDFTJFlMVNysfNpRYQQli6BbAxnUCbZmCmSzdYy1ZROpYuTqknthFaLrO2bkfLx+6i+VAgA8L7xZkRNfx7tIqJNrtVW2VqmyPKYKXnY+SQiIiKyMoXHf0fS0gUoS/rfuM7wSEQ9PRs+N97c7G0ZuxpLRNTS2Pm0IryUT7IxUyQbM0WyMVP1VVxIQ9L776Dg5x8BAA5u7gh/7CkEjRoDOwflVW2zNY3ZlIGZItmYKXnY+bQiKpWK4SZpFAoFYmNjLV0G2RBmimRjpv6lKS1B6mcfI+PrL/TjOoP+MwYRjz0FpYenpctrNZgpko2ZkoudTyui0+k44RBJI4RAcXEx3N3dmSmSgpki2ZipmnGd2Tu2IPnjZaguvAgA8OrTD9FPz0a7qA4Wrq71YaZINmZKLnY+rUh1dbWlSyAbk5WVBXd3d0uXQTaEmSLZZGeqsUeGNEVL3qJ66cRRJC5diLKEOACAc1gEoqfPgne/W/gh1wQ8T5FszJQ87HwSERGRzWjKY0Ua0xKPFanIvIDk1cuQf3A/gP+N65w4BUGjHoCd8urGdRIRtQbsfBIRERFJYuzKa93lv054ACpNFWCnRODwUQgfPwlKDy9UagVc2PckIhvGzqcVcXDg4SC5fH19LV0C2RhmimSTnSljjxUpr1Sj+2M1r538ZJ7Zbq9typXXJ31v/fd/jpYCRxfr/7clrrzaOp6nSDZmSh72dqyIvb09x3iQNAqFgidLkoqZItnMkammdCpdnFRt7vEjbQXPUyQbMyUXO59WRK1Wc7ZbkkYIgfPnzyMqKoqZIimYKZLNFjNVe+W1MjsTyR+/j/yfasZ12rdzRdgjjyNo5GiO6zQjW8wUWRYzJRc7n1ZECGHpEsjGcAZlko2ZItlsLVOOOg3SPl+F9A2fQajVcLKzQ+CI+xDx+DSovLwtXV6bYGuZIstjpuRh55OIiIjIREKnQ87ubUj+aAnUBfkAAM+efRE9YzZcO/AB9UREADufRERERCYpOnkCiUsXoPTcWQCAU3AooqfPgs/Nt/I2PSKiOtj5tCKOjo78I0XSKBQKdOrUydJlkA1hpki21p6pyqxMnP/wXeT9sAdAzbjO8AmTEXzfWNipOKGRJbT2TJH1YabkYufTiuh0Ok44RNIIIVBSUgI3NzdmiqRgpki21popbXk50tZ9ggtffgqdugpQKBB4z32IeGIaVN4+li6vTWutmSLrxUzJZWfpAuhfHMxMsmVmZlq6BLIxzBTJ1poyJXQ6ZO/eiqMPDEPapyuhU1fB4/re6PnpJsTMeYUdTyvRmjJFrQMzJQ+vfBIRERFdQdGpP5G0ZCFK/jkNAHAKCkH09Ofhc8vtvBpCRNRE7HwSERERGVGZnYXkFe8hd98uAIC9SzuEjZ+MkNEPc1wnEVEzsfNpRRwceDhILh8f3gJGcjFTJJu1ZkpbUY60dWtwYf1a/bjOgGH/QeSkp6Hy8bV0edQIa80UtV7MlDzs7VgRe3t73rpD0igUCvj5+Vm6DLIhzBTJZo2ZEjodcvfuxPkVS6DOywEAeFzXC9Ez5sAttrOFq6MrscZMUevGTMnFCYesSHV1NYQQli6DbIQQAsnJycwUScNMkWzWlqni0yfx5+SHce71F6HOy4FTYDC6vPkeun+wlh3PVsLaMkWtHzMlF698WhGdTmfpEsjGVFVVWboEsjHMFMlmDZmqzMlC8oolyN27EwBg7+KCsEefQMiYR2Hn6Gjh6qi5rCFTZFuYKXnY+SQiIqI2SVtZgfT1a5G+bg10VZU14zqHjEDE5Blw9OVtdkREsrHzSURERG2KEAI5e3ci+cPFqMqtGdfp3v16dJgxB26dulq4OiIi28XOpxVx5K09JFlMTIylSyAbw0yRbJbI1Mnpj6H67F8AAMeAIERNfRZ+tw3ipH82gucpko2ZkoedTyvCMZ8kW3l5Odq1a2fpMsiGMFMkW0tlqio/V//fJf+cgouzM8IeeRwhD46DvaOT2fdPLYfnKZKNmZKHs91akerqakuXQDbmwoULli6BbAwzRbKZO1PaygqkrFmB44/eq1/mf9dQ9NmwE+HjJ7PjaYN4niLZmCl5eOWTiIiIbEZ5pRpAzbjO/AN7kbzqfVTl5qBKYa9vEzrj/6B1Uunb1uXipGqxWomI2hp2PomIiMhmdBw7r/4Cux5AQP1F3R97w+j6GZsXmqEqIiICeNutVbG3t79yI6Jm8PLysnQJZGOYKZJNZqaq8nKv3IhsHs9TJBszJQ+vfFoRBwcHzrRH0igUCrRv397SZZANYaZINlmZ0lZV4sJXnyHti9VY879baf3vHIKIx6fC0dff5O1T68HzFMnGTMnFzqcVqa6uhhCCHVCSQgiBtLQ0hIWFMVMkBTNFspmaKSEE8n7cg/MfLEZVdiYAwL9bd0TPfAHuXa6RXS61AjxPkWzMlFzsfFoRPmqFZKuoqLB0CWRjmCmS7WozVXLuDBKXLkTxyRMAAEf/9oh66jn43TmYHxDbOJ6nSDZmSh52PomIiKjVqMrPQ8rKpcjetRUQAnaOTgh95DGEPjQe9k7Oli6PiIgawc4nERERWT1dVRUufP050j5fBW15OYCa53VGPfUMHP0DrrA2ERFZA3Y+ryAnJwe7du3Crl278McffyAzMxNCCAQGBqJv37546qmnMGDAACn7cnR0lLIdolodOnSwdAlkY5gpku1Kmap5Xuc+nP/gXVRmZQAA3Lpeiw4z5sC9W/eWKJFaGZ6nSDZmSh4+auUKhg4diokTJ0KhUGDNmjU4d+4cjh49iunTp2PXrl0YOHAgli1bJmVfQggp2yGqpVY3fIA6kSmYKZKtsUyVxP2Dk1Mn4OzcZ1GZlQGVX3t0emUBeqxcx44nGcXzFMnGTMnDK59NcMcdd2Djxo31lnXv3h2enp54/PHH8X//93+YPHmyyVcuGWySLS0tDbGxsZYug2wIM0WyGcqUuiAfySuXInvndzXjOlWOCH14AkLHToS9s4tlCqVWg+cpko2ZkoedzytYtGgRgoKCDL7Wp08fAEBZWRnKysp42ywREZEJdFVVuLBxHdI++xja8jIANeM6I5+cCaeAQAtXR0REpmLn8wpuu+02o6/9+uuvAICePXvC29u7pUoiIiKyKUII5P+0H+eXv4vKzAsAALfO3RA98wV4XHOdZYsjIiJp2PlsJq1Wi8zMTHzzzTd46aWXcP3112PDhg1Stm1vby9lO0S1PDw8LF0C2RhmimRzvJiHv6cvRNGfxwAAKl9/RE2ZCf9Bw6Cw49QU1Hw8T5FszJQ87Hw2w7Rp07BixQrodDp4eXnhzTffxPTp0+Hg0PS3sWvXrgaXJyUlITo6GkD9iYcUCkWDiYgMLbPmtsawbeNtZRyjgICaxw9YKlPGsK28ti39e2+OTBnDto23tdZzft1l5ZVqo23VhflIXbsSObu3/W9cpzOCRz+MkAcehYNLO5RXVcPFSdVgn02pgW0bX97a2hpjrG1Tz1PN3W5ra2utx7M1/n2w5Uw19xiZSiE4xWqT5eXlobCwENnZ2dixYweWLl2KLl264Msvv0Tnzp2btI3GOp8hISHYvn27/sAGBgbCw8MDcXFx+gAolUpER0ejoKAAeXl5+vUDAgLg6emJ+Ph46HQ6ADVXUjt27IjCwkLk5OTo2/r7+8Pb2xuJiYnQaDQAasIUGxuLoqIiZGVl6dv6+vrC19cXSUlJqK6u1i/v1KkTiouLkZmZqV/m4+MDPz8/JCcno6qqSr88JiYG5eXluHDhgn6Zl5cX2rdvj9TUVFRUVOiXd+jQAWq1GmlpafplHh4eCAwMRFpaGsr/92w3AIiOjoZGo0Fqaqp+mbu7O4KCgnDhwgWUlpbql0dGRgIAkpOT9ctcXV0REhKCzMxMFBcX65eHh4fDwcEBSUlJ+mUuLi4ICwtDVlYWioqK9MvDwsKgUqmQmJioX+bs7Izw8HDk5OSgsLBQvzwkJAQuLi6Ij4/XL3N0dERkZCTy8vJQUFCgXx4UFAR3d3ecO3dOv6z22Ofn5yM/P1+/3FBOHBwcEB0drV+/NlPt27eHl5cXEhISoNVqAQB2dnaIiYnBpUuXkJ2drd+un58ffHx86h37K+Xk/Pnz9SbOio2NRWlpKTIyMvTLvL294e/vj5SUFFRWVuqXd+zYEZWVlUhPT9cv8/T0REBAgMFjX11dbTAn6enpKCsr0y+PioqCTqdDSkqKfpmbmxuCg4ORkZGBkpIS/fKIiAjY2dnh/Pnz+mXt2rVDaGiowWOvVCoN5iQ7OxuXLl3SLw8NDYWTkxMSEhL0y5ycnBAREYHc3FxcvHhRvzw4OBiurq6Ii4vTL1OpVIiKimrSsTfnOSImJka/r9pM8RxRozWeIzp06ICLFy8iNzdX31b2OeL2l9bCFD+8OUH/3zxH1LDmc4Q1fI6Ijo5GSkoKNBqN/jzFc0QNazxH1LLmzxGRkZG4cOEC1Gq1PlNt+RzRsWNHqFQqnDlzBleDnU8T7NixA8OHD4eHhwf++usvREREXPW2unbtiqqqKsTHx9f7VsFav4lqjd9atba2Mt73c+fOITY21mKZMoZt5bVt6eNpjkwZw7aNt7XWc37dZSH3vWDkp2qaC98suGIbaz5G1tC2NeSkKcsNMdRWCIG4uLgmnaeas93W2NZaj2dr+/tg65lq7jGqvZB2tZ1P3nZrgmHDhqFPnz44evQoFi1ahA8//NDkbSoUigaXtA1d4jZ22dta2xrDto23NeV9r706ZelMGcO28tq21PE0Z6aMYdvG21ZUVRtc3lTGbmuVlamE9W8AAErPxyP5g/dw6a/jAACdjz8eV14PAPjmxQdwbdcuzdrn1bZrq22t9bOBuc4RzTlPNWe7rbGttR7P1vb3wdYzZcr5t7nY+TRRr169cPToUf3Mt0RERC2l49h5Jq2fsXmhpEoMc6goRcqq95G1bTOg08FZpULoA+PgO3oc8MRbAAAnlQNcnFRm+6BDRETWg53PRmRmZmLbtm144oknjM5EW3tJuu7YhKulUl15YgWi5qidxIpIFmaKmkJXXY2MTeuRuvYjaMtqxs353TYIUVOfhVNgcL2JiKIioyxVJtkonqdINmZKHnY+GxEfH48pU6bg1ltvRWxsrME2p06dAlAzGJ7I2mg0mmbNxkx0JcyUdam9rfVy5ZVqdH+s5rWTn8xr0qyxMgghUHD4IM6//zYqLtRM5OEa2wXRM+bA87qeBtepnbCGSBaep0g2ZkoePkCrCT744AODy/fs2YPDhw8DACZMmGCwTXPUnd2LSIa6M/gRycBMWRcXJ5XRf81pI0NZUgL+nvkEzsyZjooLaVD5+CL2/97A9Z9sMNrxBIC09DSjrxFdDZ6nSDZmSh524RtR+w3H+++/j/z8fDz22GOIjIxEfn4+du/ejQULambhe/755zFy5EgLVkpERGQZ1ZcKkbJqOTK3bgJ0OihUKoQ88CjCHnkCDu3aWbo8IiKyIux8NuKmm27Cn3/+iQ0bNuDQoUMYM2YMioqK4ODggODgYNx3332YNGkSbr75ZkuXSkRE1KJ01dXI3PwVUtasgLa05jl3vgPvRNS05+AcFGLh6oiIyBqx83kF1113Ha677roW2ZexSY2Irpa7u7ulSyAbw0yREAIXf/0JSe+/g4q0FACAa8dOiJ45B549ejd7e+5uzBTJxfMUycZMycPOpxVxcHDgVPMkjUKhQFBQkKXLIBvCTFHZ+UQkLVuEwqM1jxdTenkj8skZCBgyEgoDX6DWndXW2HIPLx+jzyttqYmSyHbwPEWyMVNysfNpRTQajf4h7kSmEkIgIyMDwcHBzBRJwUy1XdWXCpHyyYfI/G4joNVCoVQiZPQjCBs/CQ7tXI2u15TnkNbOymuIuZ9DSraH5ymSjZmSi51PK6LVai1dAtmY0tJSS5dANoaZalt0mmpkbt6A1DUroCkpBgD4DrgdUVOfg3NImIWrIzKM5ymSjZmSh51PIiIiaqDg10NIWvY2KtKSAQDtOsQgesYL8OrZp8nbMPYc0lpCCMQnxCOmYwyvKBARtQHsfBIREZFeWXISkt5/G4W/1TzHWunpjYhJ0xE4fJTBcZ2NudKYTSEEnFVKuDip2PkkImoD2Pm0IioVJ1YguSIjIy1dAtkYZsp2VRcXIXX1B8jY8nXNuE4HBwSPfhjh4yfDwdXNbPtlpkg2ZopkY6bkYeeTiIioDdNpqpH13SakrP4AmuIiAIDPzbchatpzcAkNt3B1RERkS+wsXQD9S602PCU90dVKTk62dAlkY5gp23Lxt1/wx6P3InHxf6EpLkK7qI64dtlqdFu4rMU6nswUycZMkWzMlDy88klERNTGlKecR9L7b+PikZ8BAEpPL0Q8MQ2Bw++FwoEfDYiIyDz4F4aIiKiNqC4uQuqaFcjcvAFCq4HC3gHB949F+ITJcHBzt3R5RERk49j5tCL2zZxFkOhKXF2NP/yd6GowU62T0GiQuXUTUlYt/3dc500DETXtebiERVi0NmaKZGOmSDZmSh52Pq2Ig4MDp5onaRQKBUJCQixdBtkQZqp1unjsCLI+Wozy5CQAgEtkB0TPmA3vPv0sXBkzRfIxUyQbMyUXJxyyIhqNBkIIS5dBNkIIgczMTGaKpGGmWqczLzyN8uQkOHh4osNzc9Hrs2+souMJMFMkHzNFsjFTcrHzaUW0Wq2lSyAbU1xcbOkSyMYwU9avurgISR++p/9/hZ0Dgsc8gj5f70TwvQ9Y3YRCzBTJxkyRbMyUPNb1F4iIiIiuitBokLXtGySvWo7S4hIg4G4AwPWrv4RvbKyFqyMiImLnk4iIqNUrPHYESUsXoex8AgDAJaIDUFXzmkt4pAUrIyIi+hc7n1ZEpVJZugSyMeHhLfOQeGo7mCnrUp6eivPvv4OCwwcAAA7uHoh4fCo87x4JjHvNssU1ETNFsjFTJBszJQ87n0Q2zMHKxnZR68dMWQdNaQlS136EjE3rITQawN4ewaMeQPhjT0Hp7oHySrWlS2wyZopkY6ZINmZKHk44ZEXU6tbzYYFah6SkJEuXQDaGmbIsodUi87tNODp6KC589RmERgOvG25Cry++RYdnXoTS3cPSJTYbM0WyMVMkGzMlD7vxRERErUDh8d+RtGwhyhLjAQDOYZGInjEbPjfebOHKiIiImoadTyIiIitWcSENScvfQcGhHwEADm7uCH/sKQSNGgM7B6WFqyMiImo6dj6tiJ0d74ImuVxcXCxdAtkYZqrlaMpKkfrpSmRsXAdRXQ3Y2yPoP2MQ8dhTUHp4Wro8aZgpko2ZItmYKXnY+bQiSqUSCoXC0mWQjVAoFAgLC7N0GWRDmKmWIbRaZO/cguSVy1BdeBEA4NWnH6Kfno12UR0sXJ1czBTJxkyRbMyUXOx8WhGNRgMhBDugJIUQAtnZ2QgICGCmSApmyvwu/XkMSUsWojThHADAOSwC0dNnwbvfLTb5njNTJBszRbIxU3Kx82lFtFqtpUsgG1NUVISAgABLl0E2hJkyj4qMdJz/4F3kH9wP4H/jOidOQdC9D9j8uE5mimRjpkg2Zkoedj6JiIgsRFNWhrTPP8aFDZ/XjOu0s0PQyNGIeHwqlJ5eli6PiIhIKnY+iYiIWpjQapG9eyuSP1qK6osFAACv3jfWjOuM7mjh6oiIiMyDnU8rolKpLF0C2RgOkCfZ2lKmyivVJq3v4mT4nH7pz+NIWroQpfH/AACcQ8MRNX0WfPoPaJPjidpSpqhlMFMkGzMlDzufVqQtfugg8+IXGiRbW8pUx7HzTFo/Y/PCev9fmZWBpOXvIv/AXgCAvasbwic8ieD7HoKd0rbHdTamLWWKWgYzRbIxU/LwwZJWpKqqytIlkI1JTEy0dAlkY5ip5tOUleH8R0tw9MHhNR1POzsE/mc0+ny9E6EPjjOp41leqTb6rzltLImZItmYKZKNmZKHVz6JiIgMSFj/hsHl5ZVqdH+s5rWTn8wzenut0OmQs3sbkj9aAnVBPgDAs2dfRM+YDdcOsVJqbMrV2dpaDbn86iwREZE5sfNJRERkgLFO5eVtDLUrOnkC/yxdgNJzZwEATsGhiJ4+Cz4338ohFkRE1Gax82lF7Ox4FzTJ5ezsbOkSyMYwU42rzMrE+Q/fRd4PewAA9u1cET5hMoLvGws7M4wZMnZ1tjVhpkg2ZopkY6bkYefTiiiVSn4jTtIoFAqEh4dbugyyIcyUcdrycqSt+wQXvvwUOnUVoFAg8J77EPHENKi8fcy236ZcnbVmzBTJxkyRbMyUXOx8WhGNRgMhBDugJIUQArm5ufD392emSApmqiGh09U8r3PFUqjzcwEAHtf3RocZc+DasZOFq7N+zBTJxkyRbMyUXOx8WhGtVmvpEsjGFBYWwt/f39JlkA1hpuo7OW0Cqs+dAgA4BYUgatpz8B1wBz+gNAMzRbIxUyQbMyUPO59ERNRiTH28hzXcZlqZk63/75K4s2jn0g5h4ycjZPTDZhnXSUREZCvY+SQiohbTlEeDNMaSjwbRVpQjbd0aJK7/DPC5DQDQfvA96DJlBlQ+vhari4iIqLVg59OKKE140DiRISEhIZYugWxMW8yU0OmQu3cnzq9YAnVeDnQKe/1rMc/Pg8oKrsa2Zm0xU2RezBTJxkzJw86nFeGjVkg2FxcXS5dANsbUTBl7NEh5pRrdH6t57eQn86zi9loAKD59EolLF6LkzN8AAKfAYEROfgZYecDCldkOnqdINmaKZGOm5GFvx4pUVVVZugSyMfHx8ZYugWyMqZlycVIZ/decNuZWmZOFf16dgz8njUXJmb9h7+KCyCdnoveX2+B7y20tVkdbwPMUycZMkWzMlDy88klERPQ/2soKpK9fi/R1a6CrqgQUCgQMHYmISU/D0devppGJkyYRERG1VSZ3Pm+7zTzfAPv7+2PDhg1m2TYREVFdQgjk7tuF5A8Xoyo3BwDg3v16dJgxB26dulq4OiIiIttgcufz4MGDEspoqC0O7OWYT5LN0dHR0iWQjbHFTBWfPYXE9xag5MxJAIBjQBCipz0H31vv4vM6W4AtZoosi5ki2ZgpeaTcdjt16lT07NlT2h/pU6dOYdOmTVK21ZoolUp+0CFpFAoFIiMjLV0G2RBby1RVXg6SVyxBzvfbAQB2zs4Ie/QJhDzwKOwdnYw+k7Tu8saeW2otkyZZM1vLFFkeM0WyMVNymdz5jI2Nxfvvvy+jlnoOHGh7MwlqtVoIIdgBJSmEEMjPz4evry8zRVLYSqa0lRW48NVnSPviE+gqKwAA7YeMQOTkGXD089e3a8ozSWtn6DXEks8kbS1sJVNkPZgpko2Zksvkzqebm5uMOhpoi5e3NRqNpUsgG1NQUABfX19Ll0E2pDVnSgiBvP27cf7DxajKyQYAuF9zHaJnzIF7l2ssXF3b1ZozRdaJmSLZmCl5TO58CiFk1NFi2yUioran+OwpJC1diOJTfwEAHNsHImrqs/C7/W6j32QbeyYpERERXR2TO59r166VUUcDhw4dMst2iYio7ajKy0XyR0uQs3sbAMDOyRlhjzyOkIfGwd7RqdF1OWaTiIhILpM7n3fccQeys7Nl1FKPUqmUvk1r1xZ/ZjKvoKAgS5dANqa1ZEpbVYkLX36GtC9W/zuuc/A9iHxyBhz92lu4OqqrtWSKWg9mimRjpuQxufOZm5uLrVu3YsSIETLqadPs7Ow4kJmkUSgUcHd3t3QZZENaQ6aEEMj74Xuc/2AxqnKyAADu3bojeuYLHNdphVpDpqh1YaZINmZKLimPWrn33nvRq1cvREZGwsnJ6ao6UHZ2dli9erWMclqtqqoqznZL0gghEBcXh9jYWGaKpLD2TJWcO4PEpQtRfPIEAMDRvz2innoOfncOtsp6yfozRa0PM0WyMVNySel8KhQK2Nvb6///SpMFpaWl4cCBA/oD6OXlhXXr1skohYiI2piq/Dwkf7QUObu3AkLAztEJoY88htCHxsPeydnS5REREdH/SOl8btu2DYMHD25S2+3bt2PChAlQKBQQQqBHjx7YvHkzIiIiZJRCRERthK6qCukbPkPa56ugq6gZ1+l/11BEPfUMHP0DLFwdERERXc7kzmf79u2b1PHUaDSYPXs2li5dCqDm6uiECRPw4YcftslnehrCS/kkGyexItmsIVNCCOQf2IfzH7yLyqwMAIBb12vRYcYcuHfrbuHqqLmsIVNkW5gpko2ZksfkzueOHTuu2CYlJQVjxozB8ePHIYSAo6Mj3n//fTz++OOm7t6mqFQqdkBJGoVCgejoaEuXQTbEGjJVEvcPkpYuRNFfxwEAKr/2iHrqGfjfOQQKOzuL1kbNZw2ZItvCTJFszJRcJnc+e/bs2ejrmzdvxuOPP47i4mIIIRAeHo5vvvnmiuu1RVqtlhMOkTRCCBQUFMDHx4eZIiksmSl1QT6SVy5F9s7vasZ1qhwR+vAEhI6dCHtnlxatheTheYpkY6ZINmZKLiljPg1Rq9WYOXMmVq5cCaDmwN1111348ssv4e3tba7dtmoajcbSJZCNyc/Ph4+Pj6XLIBvS0pnSVVXhwsZ1SPvsY2jLywDUjOuMfHImnAICW6wOMh+ep0g2ZopkY6bkMUvnMyEhAaNHj8bff/+tv5I3b948vPrqq/zGgIiIrkgIgfyf9uP88ndRmXkBAODWuRuiZ74Aj2uus2xxREREdFWkdz7Xr1+PKVOmoKysDEIIeHl54YsvvsCQIUNk74qIiGxQaWIc4j9ajKITxwAAKl9/RE2ZCf9Bwziuk4iIqBWT1vmsqKjA1KlT8dlnnwGo+db6uuuuw+bNmxEZGSlrNzaNM2mRbIGBvC2R/lVeqTZpfWdHZYtk6s8nH4GTTgM7lSNCxk5A2NiJsHfhuE5bxfMUycZMkWzMlDxSOp9nzpzBmDFj8M8//0AIAQAYP348PvzwQzg5OTVpG++99x6eeeYZGeW0WnZ2drwtmaRRKBTw8PCwdBlkRTqOnWfS+hmbF5olUzq1Ghe+/vzfBULA747BiJryDJwCg6Tvj6wHz1MkGzNFsjFTcpl8/9Lq1avRt29ffcfT0dERK1euxJo1a5rc8dTpdFixYoWppbR6arVa33knMpUQAnFxccwUSSM7UzXjOn/AsbEjkPzx+/rl1y5dhS6vv82OZxvA8xTJxkyRbMyUXCZf+Zw0aZL+ap1KpcL8+fMRGxuLQ4cOXXHd6upqZGZmYuPGjUhKSjK1lFaPoSbZmCmqK2H9GwaXl1eq0f2xmtdOfjIPLk4qo9uQlanSxDgkLV2ES3/8DgBQ+bbXv+bR7Top+6DWgecpko2ZItmYKXmkjfkUQkCtVmPWrFlXtT5vNyUiMq/GOpV12xhrJ+OPr/piAVJWLUfW9s2ATgeFSoXQB8fD9/5HgSfeMnn7REREZL2kTBs4bNgwlJeXQ6fTNetfVVUVUlJSsHDhQhlltHrsgJNsDg5me5QvtVFXmylddTXSv/wUR8cMRdbWTYBOB7/bB6HPV9sROflpTijUhvE8RbIxUyQbMyWPye+kq6srvvrqqyaP76xLqVQiLCwMs2bNwnfffWdqKa2eSqViB5SkUSgU6NChg6XLIBtyNZkSQqDg8EGcf/9tVFxIAwC4xnZB9Iw58LyupznKpFaE5ymSjZki2ZgpuUzufM6YMQMuEr6xnjJlisnbaO20Wi2EEOyAkhRCCBQWFsLLy4uZIimam6nSpHgkLVuES8d+AwAovX0Q+eQMBAwZyed1EgCep0g+ZopkY6bkkjLhkAwPP/xwvf9PT09HaGiolG23FhqNxtIlkI3Jzc2Fl5eXpcsgG9KUTKkLLyJ19QfI/N/ttQqVCiEPPIqwR56AQ7t2LVQptRY8T5FszBTJxkzJY/JXz/fee6+MOhoYM2aMWbbbXBcuXMDChQtx6623wtfXF0qlEl5eXujfvz8WL16MiooKS5dIRGQVdNXVuLDhcxwdMxSZW74GdDr4DrwTvb/chqgnZ7LjSURE1MaZ3Pk019TD1jCl8U8//YSoqCi89NJL6N69O3bs2IH4+Hhs3LgRKpUKzz33HK6//npkZmZaulQiIosRQqDgl4M4/sh/kLRsEbSlJWjXMRbdl69B1/++B+egEEuXSERERFbA5NtuL126JKGM+jQaDYqLi6Vvt7kKCwtRXV2NN954A3PnztUvj4yMxG233YY+ffrgxIkTmDx5MrZv327y/jiTFsnWvn37KzciaobLM1V2PhFJyxah8OivAACllzciJz+NgKH/gcLevsH65ZVqg9utu9xYG6Bpj4uh1oXnKZKNmSLZmCl5TO7tJCUlYezYsYiNjZVRD3Q6HX7++WeUlJRI2Z6pFAoFJk+e3GC5vb09Jk+ejMmTJ2PXrl0oKSmBm5ubSfuyt7fnQGaSRqFQcHwCSVU3U9VFl5Cy+gNkfrcR0GqhUCoRMvoRhI2fBId2rka30XHsvCvup/tjbxh9LWMzH81lS3ieItmYKZKNmZJLyqW2DRs2yNiMnhACISGWv01r+PDhKCkpQTsj45RqJ0TS6XS4dOmSyZ1PtVrN2W5JGiEEEhMT0aFDB2aKpBBCIOHcObj8fRypa1ZAU1Jzh4rvgNsRNfU5OIeEWbhCam14niLZmCmSjZmSy+TO57hx42TU0YC3t7dZttsc9vb2RjueAPRjPX18fKR0lq1hnCvZFq1Wa+kSyIZc/PUQct99E9rsmnNfuw4xiJ7xArx69mnyNhLWG7+qSW0Tz1MkGzNFsjFT8pjc+Vy7dq2MOlqlHTt2AACefvrpJn8T0rVrV4PLk5KSEBIS0qADqlAomrTMmtsaw7aNt5XxvgshLJopY9hWXlvZx7M2M3WXlyUn4fzyt1H42y8AAKWnFyImTUfAsFH6cZ1NrcHFSWXSz2xsXWs+RuZqa63n/Oa0rc1bU7dhCNs23tZaj725/pYwU40vb21tjWnJtraeqeYeI1Nxhpur9Ndff2HHjh3o3r07Zs+eLWWbQgjExcXpD2xgYCA8PDwQHx+vD4BSqUR0dDQuXryIvLw8/boBAQHw9PREQkICdDodgJortx07dsSlS5eQk5Ojb+vv7w9vb28kJSXpny2qUCgQGxuL4uJiZGVl6dv6+vrC19cX58+fR3V1tX55p06dUFJSUm+mXx8fH/j5+SElJQVVVVX65TExMSgvL8eFCxf0y7y8vNC+fXukpaXVe1xNhw4doFarkZaWpl/m4eGBwMBApKeno7y8XL88OjoaGo0Gqamp+mXu7u4ICgpCRkYGSktL9csjIyMBAMnJyfplrq6uCAkJQVZWVr0JrsLDw+Hg4ICkpCT9MhcXF4SFhSE7OxtFRUX65WFhYVCpVEhMTNQvc3Z2Rnh4OHJzc1FYWKhfHhISAhcXF8THx+uXOTo6IjIyEvn5+SgoKNAvDwoKgru7O+Li4vTLao99QUEB8vPz9csN5cTBwQHR0dFQq9X1MtW+fXt4eXkhMTFR/y2enZ0dYmJiUFRUhOzsbP12/fz84OPjU+/YXyknycnJUKv/nSwmNjYWpaWlyMjI0C/z9vaGv78/UlNTUfn/7d13eBTV/gbwd9N7JSG9FwhN6VZQqoKIiGBDEAsWmiK2e73ItWJBKdarUhQbIKBeC3ClCKIICAJCSCMQUklCerLZ3fP7I79ds2Q32SRns5PN+3kennudOTtzdufNZL+ZOWdqaw3LExMTUVtbi7NnzxqW+fn5ISQkxOSxr6+vN5mTnJwcVFVVGZbHxcVBp9Ph9OnThmXe3t4IDw9Hbm6u0fjymJgYODg4IDMz07DM09MTkZGRJo+9s7OzyZwUFBQYTcYWGRkJNzc3pKWlGZa5ubkhJiYGRUVFKCkpMSwPDw+Hl5eX0bF3cXFBXFycRce+TeeIsr/7eirtFKIjwhEQEIC0I4dxYeOnqNnxY8PzOp2c4HLNGHhPuAXlnl4oT0/nOeL/dcZzREJCAkpLS1FYWGho29HniKSkJGg0GqPzFM8RDRR1juhE3yPi4+Oh1WqNMsVzRIPOeI5QwveI2NjYJt/Ru/I5Qq1Ww8Wl7ZP/qQTv9Wy1iooKDB06FCUlJfj555+RkJDQ7m3qr4geO3bMaLlS/xLV2f5q1RnbKvV48tgrq62M41lVU4ekO/8FADj1yb/h5qRC/ub1OP3BW4ZxnYFXXYP4OQvhFh4pvQ/msG3zbZX6c89jr6y2Sj32zIn12yr1ePLYK6tta4+RvmY5fvy4Rfu9GK98tlJNTQ3Gjx+PoqIi7Ny5U0rhqaf/S+PFl7RNXeI2d9lbqW3NYdvm27bncxdCoKysDL6+vjbNlDlsK6+tzONZ+vs+5L27FNWnG/5q6xmfiPh5T8B/4FCrZsoctm2+rVLP+dY6T5nDts23VeKxb21bc5ip5tsq9Xh2pt8PXSFTrTlG7cXisxUqKipw44034vTp09izZw+SkpKkbr/x7ShEMuTn58PX19fW3aBO5PhT8+AmtIZxnaHjJ0HV6BnEzBTJxkyRbMwUycZMycPi00Lnz5/Hddddh4qKCuzZs8fwmBUios6svrwMGf952/DfKgcnRNxyJ6LvngUnbx8b9oyIiIjsjYOtO9AZ5OTk4Oqrr4ZOp8PPP//cpPCcPn06tm3bZqPeERG1ntBocG7jZ9g/5XrkfvX3s5r7f/Q54ucuZOFJRERE0kkpPj/88EP8+eefhjGL9iQtLQ1XXnklgoKCsGPHDgQFBTVps2vXLqNZuNrKyYkXokkuU3klKvltLw5Mvxnpr78ATXkZPKLjDOs8IqObfS0zRbIxUyQbM0WyMVPySKl27rvvPqhUKgQHB+Oaa67BK6+8goiICBmbtqnjx4/j2muvRWFhIby8vHDttdeabNd4quj2cHR0tNrgXup6VCoVAgMDbd0NUpDq7CxkrHwNJXt3AQCcfP0Qc+9s+I2ZAExf3OLrmSmSjZki2Zgpko2ZkkvapbZZs2ZhxYoVcPz/B47bg23bthmebdTW6YRbQ61WQwjBApSkEEIgMzMTcXFxzFQXV19ehuxV7yF3w6cQWg1Ujk4Im3wbou9+AM4+vqiuVbe8ETBTJB8zRbIxUyQbMyWXtOJz3rx5dlV4AsD8+fMxf/78DtsfH7lKsnEG5a5NaDTI+3oDsv6zEpqyCwCAgMuvRvzsx+ARE9f8i81gpkg2ZopkY6ZINmZKHinFp6OjY5PHjvz2229QqVQYPHiwjF0QEVErlP6+DxnLXkFVZhoAwCMmDvFzn0DA0Cts3DMiIiLqqqRMOOTj49PkMnTPnj3xzTffICYmBnPnzsWOHTvsckIimXgpn2Rjprqe6rPZOPb4bPw57z5UZabByccXCQv+gYFrv5JSeDJTJBszRbIxUyQbMyWPlCuf7u7uTZb5+Pjgueeeg5+fHxYuXIiPP/4Yl1xyCfr374/XX39dxm7tjouLC8NN0qhUKiQnJ9u6G9RBNBXlyF71Hs5tWAeh0QCOjgifdCui73kIzj4ND8Y2N7az8fLmxn96uLkwUyQVz1MkGzNFsjFTcqmEhIGGYWFhyM3NNbnu7NmziI6OxvHjx9GzZ8/27spu9erVCzqdDn/99RcLUJJCCIHy8nKTdyaQ/RBaLfK+2YjT769A/YVSAEDAZVchbs5j8IyJN2obfvMT7dpXzoaXmSmSiucpko2ZItmYKWO9evUC0PbJWKVc+SwsLERubi7CwsKarAsPD4eLiwsLTwtwMDPJlpeXBx8fH1t3g6yk9MBvyFj2Mqoy/n9cZ3Qs4uY+jsDLrrLaPpkpko2ZItmYKZKNmZJHSvGp0+nQo0cPjBw5EmPGjMGYMWMQExMDAHBwcICHh4eM3RAREYCanDPIWPkainf/BABw8vZB9D0PIWzSVDg4OZt9Xdq65zqqi0RERERNSHvUSlVVFbZs2YItW7YAABISEjB27FiMHj3aokeI8PmWRGRvLH1+pjkebi5G/62pqkT26vdw7ouPDeM6w26aiph7HoKzr1+rt9dafBwUERERtYe04tPd3R01NTWGLydpaWlIT0/HypUrAQBXXnklRo0ahdGjR2PIkCFwcDCeaPe5557Dv/71L1nd6ZScnKQdDiIAQLdu3WzdhS4t8Y5n2vX6cxuXAGgY15n/7SZkvb8c9aUlAAD/wZcjfu7j8IxLaHc/W4OZItmYKZKNmSLZmCl5pFQ7Y8aMwffff4/q6mr88ssv2Lp1K3788UccPXrU0Gbfvn3Yt28f/v3vf8PX1xfXXHMNRo0ahZEjRyIhIQF79+6V0ZVOzdHRkVd/SRqVSsWTpR248MfvyHhzCSrTTgIA3KNiED9nIQIuv7rDzxfMFMnGTJFszBTJxkzJJWW22+effx7//Oc/mywvKCjA1q1bsXXrVmzfvh0FBQV/77jRlyZnZ2fU19dDq9W2tyudVq9evVBfX4/U1FQWoCSFEAJZWVmIjY1lpmykuUeb9LunYfzlkQ+fMXk7bE1uDvI/WI7zO7cD+P9xnTMfRNikW+HgbH5cpzUxUyQbM0WyMVMkGzNlTBGz3ZoqPAGge/fumDZtGqZNmwYAOHLkiOGq6N69e1FXVwcAUKvVPJhomLiJSCa1un1jDql9LBlj6eHmYtROU1WFM2vfR87nayHq6wEHB4RNnIKYex+Gs5+/NbtrEWaKZGOmSDZmimRjpuTp0EGG/fr1Q79+/bBw4ULU1NRg165d+P777/Hll1+isLCwI7tCRKQoQqtF/vdbkPXuMtSXFAMA/Add1jCuMz7Rxr0jIiIiaj+bzXDj7u6OsWPHYuzYsXjxxRdx9dVX26orREQ2deGPA8hYtgSVp04AANwjoxE/dyECLh/Gu0KIiIjIbihielVPT088+OCDtu6Gzbm6utq6C2RnkpOTbd0FasFfi59E1a6tAABHL29E3/0AwiffbrNxnS1hpkg2ZopkY6ZINmZKHoeWm3SMe++919ZdsDmO+STZKisrbd0FMkFTXWX4/8W7/9cwrvOmqRj8xX8Redt0xRaeADNF8jFTJBszRbIxU/IopvgkoL6+3tZdIDtz7tw5W3eBGhE6HfK/3YSD0282LPO7dBAGrNmAxIXPwMU/wIa9swwzRbIxUyQbM0WyMVPyKOK2WyIie1d25BDS33wZlal/Qa1yBEIalvd+9S14uvOWeyIiIrJ/LD6JiKyoNu8cMt9eiqL//QgAcPT0Quy0+4AtpwCAEwoRERFRl8HiU0GcnHg4SK6AAOXfxmmvtNXVOPPxBzj72WoItRpQqRB6w82IuX8ONB7ewJZnbN3FNmGmSDZmimRjpkg2ZkoeVjsK4ujoyKsgJI1KpUJwcLCtu9HlCJ0OBT98g6x334T6fBEAwLf/ICTMewJeiT0AAJrazvmwamaKZGOmSDZmimRjpuTihEMKUl9fDyGErbtBdkIIgdOnTzNTHajsz0P4497bkPr8P6A+XwS3sAj0eulN9FvxkaHw7MyYKZKNmSLZmCmSjZmSi1c+FYSPWiHZamtrbd2FLqE2P69hXOf27wEAjh6eiJoxCxFT7oSDi4uNeycXM0WyMVMkGzNFsjFT8rD4JCJqI21NNc6u+whnP1kFnboOUKkQMv4mxN4/Fy6B3WzdPSIiIiJFYfFJRNRKQqdD4db/IvOdN6EuKgAA+F4yEPHznoB3ck8b946IiIhImVh8KoirK5/1R3IlJibaugt2p/zYEaQvW4KK438CANxCwxE3+zF0Gz6yyYRh1WYmFmq83FwbAPBwU94tu8wUycZMkWzMFMnGTMnD4lNBOOaTZKutrYWHh4etu2EXagvykPXOmyjc+l8AgKOHB6Luuh8RU6fBwcwfjhLvaPlxKv3uec7sunMbl7Sts1bETJFszBTJxkyRbMyUPCw+FaS+vt7WXSA7c/bsWSQnJ9u6G9I0d5XQEm25kqitrcHZTz7C2XWroKurbRjXOW4iYu6fC9duQe3qT2dkb5ki22OmSDZmimRjpuRh8UlEnYYlVxKb05oriUIIFG79L7LeeQN1hQ3jOn369UfCvCfg3aOXRdtIW2f+qiYRERFRV8Pik4joIuXH/0T6m0tQcfwIAMA1JAzxsxeg2zWjm4zrbI4Sx2wSERER2QqLTwVxdHS0dRfIzvj5+dm6C1KZu5JYXas2jJ088uEzbS766ooKkPn2Gyj88VsAgIO7O6Luug+Rt043O66zq7G3TJHtMVMkGzNFsjFT8rD4VBAnJ6dWXVUhao5KpUJISIituyGVJUWlh5tLq4tPbW0Nzn66Gmc/+Qi62hoAQPfrJyL2gXldclynOfaYKbItZopkY6ZINmZKLgdbd4D+Vl9fDyGErbtBdkIIgTNnzjBTzRBCoHDbd/j9thuQ/cFb0NXWwKfvpej/4efo8c/nWXhehJki2Zgpko2ZItmYKbl45VNB+KgVkq26utrWXVCs8r+OImPZEpQfPQwAcO0eiriHH0XQiLG8A6EZzBTJxkyRbMwUycZMycPik4iksMVjUNqirqgQWe++iYLvvwYAOLi5I2ravYi4fTocXd06pA9EREREXRGLTyKSoiMfg9IW2rpa5Hy2Bmc+/gC6mv8f1zn2BsQ+OB+uQd2tum8iIiIiYvGpKC4ufCwDyRUfH2/rLticEAJF//sBmW8tRV1BHgDAp3c/xM9/Ej4pfWzcu86HmSLZmCmSjZki2ZgpeVh8Etmx+vp6ODl1zI+5tR+D0hYVJ48jfdkSlB85BABwDe6OuIcWIGjUdRzX2UYdmSnqGpgpko2ZItmYKXn4KSqIWt2+MXNEFztz5gySk5M7ZF/WegxKW9QVFSLrveUo+H4LIAQc3NwReedMRN4+A45u7lbfvz3ryExR18BMkWzMFMnGTMnD4pOI7MqZdR+h6NMPDeM6g8eMR9yD8+EazGd0EREREdkSi08i6vQaP3sr+6N34Ca08O7VFwnznoBP73427BkRERER6bH4VBBHR0dbd4HsjK+vr627YHUVqX/h+BuvAGiYsdalWzB6PDQPwaOuh8rBwbads0NdIVPUsZgpko2ZItmYKXlYfCqIk5MTJ0EhaVQqFUJDQ23dDatRF59H1nvLkP/fzaiFAxAyFgAwcPV6ePv72bZzdsreM0Udj5ki2Zgpko2ZkouXBRSkvr7e6PZBovYQQuDs2bN2lyldXR3OfPwh9k8dh/xvNwFCIGjEWMN6R3cPG/bOvtlrpsh2mCmSjZki2ZgpuXjlU0F0Op2tu0B2pqqqytZdkEYIgfO7tiNz5euozc0BAHj37I34+U/COTEFuOMZG/ewa7CnTJEyMFMkGzNFsjFT8rD4JCLFqzx1EunLl6Ds0O8AGsZ1xj04H8FjxkPl4IDqWj6miIiIiEjpWHwSkWKpS87j9PsrkffNxobndbq4IuKOuxF1x0w4evD2WiIiIqLOhMWngri4uNi6C2Rn4uLibN2FNtGp1Ti3/hNkr3oP2uqGW12CRoxF3EOPwi00zMa969o6a6ZIuZgpko2ZItmYKXlYfBLZsc42jlgIgeLdPyFj5WuoPXcWAODdoxfi5z8B3779bdw7Ajpfpkj5mCmSjZki2ZgpeVh8KohazXFrJNfp06eRnJxs625YpDI9FRnLXsGFg78BAFy6BSH2gfnoPvYGw/M6zY3tbLy8ufGfHm68u6C9OlOmqHNgpkg2ZopkY6bkYfFJRDalLinG6Q9WIu/rjYBOB5WLCyJvm4Goafc2GdeZaMGMtv3uec7sunMbl7S7v0RERETUNiw+icgmdPX1OLd+HbJXvQttVSUAIOjaMYh7+FG4hYbbuHdEREREJBuLTwVxdHS0dRfIznh7e9u6C00IIVC8ZycyV7yKmpwzAACv5BTEz3sCfpcMaPa1aevMX9WkjqHETFHnxkyRbMwUycZMycPiU0GcnJygUqls3Q2yEyqVCuHhyrqCWJmVhrR338CF338FALgEdkPsrHnofv2NhnGdzeGYTdtSYqaoc2OmSDZmimRjpuRq+dsedRiNRgMhhK27QXZCCIFz584pKlN/3HcnLvz+a8O4zrvuxaDP/4uQ8TdZVHiS7SkxU9S5MVMkGzNFsjFTcvHKp4JotVpbd4HsTEVFhU33r6uvx7kNn/69QOjQbfgoxM1eAPewCNt1jNrM1pki+8NMkWzMFMnGTMnD4pOIpBNCoOSXXchY8RpKz54FQsYCAPosfRdhQy6zce+IiIiIyBZYfBKRVFWZ6chYtgSlv+8DADj7dzOs8+vX/IRCRERERGS/WHwqiIsLJ1MhuWJiYjpsX/UXSnH6g7eQu2U9oNVC5eyMiCnT0O22GcB9fL6mvejITFHXwEyRbMwUycZMycPik8iOOXTARD46TT1yN36O7I/egaaiHADQbdgIxD28AO4RUaiuVVu9D9RxOiJT1LUwUyQbM0WyMVPy8JNUELWaX9JJrszMTKtuv/iX3Thw5yRkLFsCTUU5PBOS0HfFR+j10jK4R0RZdd9kG9bOFHU9zBTJxkyRbMyUPLzySUStVpWVgYwVr6L01z0AAGe/AMTcPwehN0yCytHRxr0jIiIiIiVi8UlEFqsvL0P2B2/h3KYvGsZ1OjkhfMqdiJ4xC2onV9TUa4F640cGNb7ttrlbcD3cOOaZiIiIyJ6x+FQQ3k9Osnl6ekrZjk5Tj7xNX+L0B28ZxnUGXnUt4uc8Zri9NvrmJ1rcTr97njO77txGTkrUGcjKFJEeM0WyMVMkGzMlD4tPBXF2doZKpbJ1N8hOqFQqREZGtns7Jb/uQcbyV1B9umG8g2d8IuLnPQH/gUPbvW3qXGRlikiPmSLZmCmSjZmSi8Wngmg0GgghWICSFEII5OfnIyQkpE2Zqj6diYwVr6Jk388AAGc//4ZxneMnQeXU9NSRts78VU2yD+3NFNHFmCmSjZki2ZgpuVh8KohWq225EVErlJWVISQkpFWvqS8vQ/aHbyP3qy8gtJqGcZ233IHoGbPg5O1j9nUcs9k1tCVTRM1hpkg2ZopkY6bkYfFJRAAAodEgd8t6nP7PSmjKywAAgVcOR9ychfCIjLZx74iIiIios2PxSUQo+W1vw7jOrAwAgEdsAuLnPY6AwZfbuGdEREREZC84vWorpKam4vLLL4dKpcLq1aulb9/FhbctklxRUVHNrq/OzsLRhQ/j6COzUJ2VASdfPyQ+9k8MXLOBhSeZ1FKmiFqLmSLZmCmSjZmSh1c+LaDVavH6669j0aJFqK2ttXV3iCzm7OxscrmmohynP3oXuRs+bRjX6eiEsMm3I/ruWXD28e3gXlJnYi5TRG3FTJFszBTJxkzJwyufLSguLsbll1+OV155BR999BGio6039k2tVltt29Q1ZWRkGP230GiQ+9Xn+G3K9Tj3xVoIrQYBl1+NgZ9sQsK8x1l4UosuzhRRezFTJBszRbIxU/LwymcLzp49i8jISHzzzTcIDg7GU089ZesuEbVJ6e/7kLHsFVRlpgEAPGLiED/3CQQMvcLGPSMiIiKiroDFZwv69OmDDRs22LobRG1WczYbmStfQ/GenQAAJx9fxNw3G2E33mLyeZ1ERERERNbAb54tcHR07LB9OTjwLmgyrbq29bdk11eUofTLT3Bm+/dw1dQBjo4In3Qrou95iLfXUpt5eHjYugtkZ5gpko2ZItmYKXlYfCqIs7MzVCqVrbtBCpR4xzNtf3HQSPwQU464OY/BMyZeXqeoy1GpVJzxj6Ripkg2ZopkY6bkYvHZwXr16mVyeUZGBqKjo6HT6YwKUJVKBSGEUVtTy5Tc1hy2bb5ta7bRkt6vvQ0AEELw2NtZ244+nvn5+ejevbvU85Q5bNt8W6We81vTVgiBgoICizJlDts231apx95a5whmqvnlna2tOR3Z1t4z1dpj1F4sPhVEo9EgNTXVcGBDQ0Ph6+uLU6dOGQLg7OyM+Ph4lJSUoKioyPDakJAQ+Pn5IS0tDTqdDkDDLcOJiYm4cOECCgoKDG2Dg4MREBCAjIwMaDQaAA1hSk5ORnl5OfLy8gxtu3Xrhm7duiEzMxP19fWG5T169EBFRQVyc3MNywIDAxEUFITTp0+jrq7OsDwpKQnV1dXIyckxLPP390f37t1x5swZ1NTUGJYnJCRArVbjzJkzhmW+vr4IDQ3F2bNnUV1dbVgeHx8PjUaD7OxswzIfHx+EhYXh3LlzqKysNCyPjY0FAGRlZRmWeXl5ISIiAnl5eSgvLzcsj46OhpOTk9HMZh4eHoiKikJ+fj7KysoMy6OiouDi4oL09HTDMnd3d0RHR6OwsBClpaWG5REREfDw8MCpU6cMy1xdXREbG4vz58+juLjYsDwsLAw+Pj5ITU0FAHy76E44OzsjLjYO54v/blur1mDyS58DAD4MLoLqz4MAAJWHJ2LvfgBFMYlw8/Q0bKd79+7w9/dHeno6tFotgIbbvZOSklBWVob8/HxDH4KCghAYGGh07FvKSVZWltGszcnJyaisrMS5c+cMywICAhAcHIzs7GyjRxclJiaitrYWZ8+eNSzz8/NDSEiIyWNfX19vMic5OTmoqqoyLI+Li4NOp8Pp06cNy7y9vREeHo7c3FxUVFQYlsfExMDBwQGZmZmGZZ6enoiMjDR57J2dnU3mpKCgABcuXDAsj4yMhJubG9LS0gzL3NzcEBMTg6KiIpSUlBiWh4eHw8vLy3DMgIZnAMfFxaG4uBjnz583LO/oc0RSUpLhvenPUzxHNLD1OQL4+9hbkhMnJyckJCSgtLQUhYWFhrYdfY5ISkpCYWGhUaZ4jmjQGc8RSvgeER8fj/PnzxtliueIBp3xHKGE7xGxsbEoLi42ylRXPkeo1Wq4uLigrVRC1qWVLiImJgbZ2dlYtWoVZsyYIW27vXr1Ql1dHU6dOsUrn2xrdvnFy8qLi5Ey61UAwEf5P8DNAQi7aSqiZz4IZ18/nDx5EsnJyTbLlDlsK69tRx9Pa2TKHLZtvq1Sz/mtvfKZmppqUabMYdvm2yr12FvzyiczZX55Z2trTkdf+bTnTLX2GOnv4jx+/LhF+70Yr3wqjEqlanJJ29QlbnOXvZXa1hy2bb6tuc9daLXI/3YTTry/EnAbAgDwHzgUveY/Ds/YhnGdQghDnmyZKXPYVl7bjjqe1syUOWzbfFulnvNb29bSTJnDts23VfKxt7StOcxU822Vejw72+8He89Ua45Re7H4VBBnZ2dbd4EUqvFstxcOH0Dm20tRlZGGOpUj4NawPPbZ16BydzW0FUIgMLg7qmvV8HR3tUW3yQ5FRkbaugtkZ5gpko2ZItmYKXlYfCoIH7VC5jSd7TYeCDGeufaSe583+/pzG5dYoVfUFbm5udm6C2RnmCmSjZki2ZgpeVjtKEjjwfVEeppGg96JbK3xZAdEMjBTJBszRbIxU/LwyieRQgmtFvnfb0HWu8vwUekFAIBf/0GIe+hReMYmtPx6IXAq7RSSEpOs3FMiIiIiopax+LRASUmJYdpn/bTSjaeTDggIaNeUw0QXu3D4IDLefBmVp04AAPwjoxE3ZyECrxhm8QBwIQTcXZzh4cZsEhEREZHtsfi0wKRJk7Br1y6jZfPnz8f8+fMBADt27MDw4cPbvR+O+aTavHPIfGspin76EQDg6OWN6LsfQPjk2+HQhgmpOEaBZGOmSDZmimRjpkg2ZkoePudTIdr7zBzq3LTV1Tiz9j84+/kaCLUacHBA6I2TEXPvbLj4B9i6e0REREREfM6nPdFqtYbn6FHXIHQ6FHz/NbLefRPq4vMAAL8BQxA/73F4JSS3b9tCoKioCEFBQcwUScFMkWzMFMnGTJFszJRcLD4VRKPR2LoL1AaNn8HZGmVHDyPz7dehSW34y5FbeCTi5yxE4FXXSDu5lZSUICgoSMq2iABmiuRjpkg2ZopkY6bkYfFJ1E5Nn8HZGjH4wjMb0XfPQvjkO+DAiauIiIiIyE6x+CSyscFf/BcuAYG27gYRERERkVWx+FQQ5zbMZkq2l7buOZPLq2vV6HdPw7pttw9EwZr3oC4uAgD49uuPuIcfhVd8Mlys+CiU8PBwq22buiZmimRjpkg2ZopkY6bkYfGpIHzUSudkyXM0zy59Hm5CC7+wCMTPeQyBV4/okEHrXl5eVt8HdS3MFMnGTJFszBTJxkzJw2pHQerq6mzdBZKkNj8PJ57/h+G/Hd09EPvQoxj06dfoNmxkh82Wlpqa2iH7oa6DmSLZmCmSjZki2ZgpeXjlk0gibU01znzyEXLWrUJ1vQYIGQsAGLh2I/zCwmzcOyIiIiIi22HxSSSB0OlQuPW/yHznTaiLCgAAvv0GAoUN610Cutmwd0REREREtsfiU0E45rNzKj92BOnLlqDi+J8AALfQcMTNfgweQ68G7vyXTfvmwke3kGTMFMnGTJFszBTJxkzJw+JTQZydnTtsLCC1X21BHrLeeROFW/8LAHD08EDUXfchYupdcHB1RXWt2qb9U6lUiIuLs2kfyL4wUyQbM0WyMVMkGzMlF4tPBdFqtRBCsABVOG1tDc6uW4Wzn3wEXV0toFIh5PobETNrHly7Bdm6ewZCCBQXFyMwMJCZIimYKZKNmSLZmCmSjZmSi8Wngmg0Glt3gZohhEDhtu+Q9fZS1BU2jOv06dcfCfOegHePXjbunWnnz59HYGCgrbtBdoSZItmYKZKNmSLZmCl5WHwSWaD8r6NIf+NlVBw/AgBwDQlD/OwF6HbNaP4VjIiIiIjIAiw+iZpRV1SArHfeRMEP3wAAHNzdG8Z13noXHF3dbNw7IiIiIqLOg8Wngjg7O9u6C/T/tLU1OPvp6oZxnbU1AIDu19+I2Fnz4BoUbOPeWS40NNTWXSA7w0yRbMwUycZMkWzMlDwsPhXEwcGBt3DamBACRdu/R+bbS1FXkA8A8Ol7acO4zp69bdy71lGpVPD19bV1N8iOMFMkGzNFsjFTJBszJRcfLKkgarUaQghbd6PLKv/rKA4/MA0nFj2OuoJ8uHYPRc/Fr+CSd9Z2usITaCikU1NTmSmShpki2Zgpko2ZItmYKbl45VNBGGrbqCsqRNa7b6Lg+68BAA5u7oiadi8ibp/e6cd1MlMkGzNFsjFTJBszRbIxU/Kw+KQuS1tXi5zP1uDMxx9AV/P/4zrH3oDYB+fDNai7jXtHRERERGRfWHwqCMd7dgwhBIp++hGZby1FXX4uAMCndz/Ez38SPil9bNw7uTiJFcnGTJFszBTJxkyRbMyUPCw+FcTFxYUFqJVVnDyO9GVLUH7kEADANbg7Yh96FMGjrre7z16lUiE+Pt7W3SA7wkyRbMwUycZMkWzMlFwsPhVEq9VCCGF3RZAS1J0vwun3liH/uy2AEHBwdUPknTMRecfdcHRzt3X3rEIIgZKSEgQEBDBTJAUzRbIxUyQbM0WyMVNysfhUEI1GY+sudLjqWnW7Xu/h5tLsel1dHXK+WIsza/8DbXU1ACB49DjEPfQIXIND2rXvzqCoqAgBAQG27gbZEWaKZGOmSDZmimRjpuRh8Uk2lXjHM+16/bmNS0wuF0Lg/I5tyHzrddTmnQMAePfqi4R5T8Cnd7927ZOIiIiIiFqPxSfZnYrUE8hYtgRlhw8AAFyCuiPuoUcaxnU68NG2RERERES2wOJTQbriTFpp654zuby6Vo1+9zSsO/LhMy3eXgsA6pLzyHpvOfK/3fT3uM477m4Y1+nuIbXfnUVIiP3fWkwdi5ki2Zgpko2ZItmYKXlYfCqIg4NDlxvIbElR6eHm0mw7nVqNnC8+xpk170NbXQWgYVxn7IPz4dY9VFpfOxuVSgU/Pz9bd4PsCDNFsjFTJBszRbIxU3LxHkQFUavVEELYuhudhhACRTu34ffbJyDrnTegra6Cd8/euOS9j9Hz2SVduvAEGj6fU6dOMVMkDTNFsjFTJBszRbIxU3LxyqeCMNSWqzx1EunLl6Ds0O8AAJduwYh7cD6Cx4znuM5GdDqdrbtAdoaZItmYKZKNmSLZmCl5WHxSp6IuOY/T769E3jcbG8Z1urgi4o67EXXHTDh6dM1xnUREREREnQGLTwXpauM9W0OnVuPc+k+Qveo9w7jOoBFjEffQo3ALDbNx75TL0dHR1l0gO8NMkWzMFMnGTJFszJQ8LD4VxMXFhQWoCef37EDee2+i9txZAIBXjxQkzH8Svn3727hnyqZSqZCYmGjrbpAdYaZINmaKZGOmSDZmSi4Wnwqi1WohhGABepETix6Hm9DCJbAbYh+Yj+7XTeC4TgsIIXDhwgX4+fkxUyQFM0WyMVMkGzNFsjFTcvEbvIJoNBpbd0ER1KUlSHvjRcN/q5ydEXXXfRj8xXcIGTeRhWcrFBQU2LoLZGeYKZKNmSLZmCmSjZmSh1c+STF09fU4t34dsle9i6rqGiBkLABg4OoNCIiJsW3niIiIiIioXVh8ks0JIVC8dxcyV7yKmrPZAACvxBSgsmG9WwgnFCIiIiIi6uxYfCqIk1PXOxxVGWlIX74EF37/FQDgHBCI2Afmweea64C7nrVt5+xAcHCwrbtAdoaZItmYKZKNmSLZmCl5ul61o2COjo5dZiBz/YVSnP7PSuRuWQ/odFA5OyNi6l2Imn4fnDy9UF2rtnUXOz2VSoWAgABbd4PsCDNFsjFTJBszRbIxU3Jx5hYFUavVEELYuhtWpauvR87na/HblOuRu+kLQKdDt+GjMOjTrxH30CNw8vSydRfthhAC6enpdp8p6jjMFMnGTJFszBTJxkzJxSufCmLPoRZCoOSXXchY8RpqzpwGAHgmJiNh3hPw6z/Ytp2zY5xBmWRjpkg2ZopkY6ZINmZKHhafZHVVmenIWP4KSvf/AgBw9g9A7Ky5CBl3E2rqtSZvsW28rLlbcD3cXOR3mIiIiIiIpGPxqSD2Nt6zvuwCTn/wFnI3fwlotQ3jOqdMQ9SM+w231yZOebrF7fS75zmz685tXCKtv/bI3jJFtsdMkWzMFMnGTJFszJQ8LD4VxMXFxS7CrdPUI3fj58j+6B1oKsoBAN2GjUDcwwvgHhFl4951HSqVCsnJybbuBtkRZopkY6ZINmaKZGOm5GLxqSA6nQ5CiE5dgBb/shsZy19FzZksAIBnQhLi5z4B/4FDTLZPW2f+qia1jxAC5eXl8PHx6dSZIuVgpkg2ZopkY6ZINmZKLhafClJfX2/rLrRZ1ekMZCx/FaW/7gEAOPsFIOb+OQi9YRJUjo5mX2cPYzbNjUlVwrjVvLw8+Pj4WG371PUwUyQbM0WyMVMkGzMlD4tPapf68jJkf/AWzm36omFcp5MTwqfciegZs+Dk5W3r7nWIxDueabENx60SERERUVfH4pPaRKepR97m9Tj9wVvQlJcBAAKvuhZxsxfAIzLaxr0jIiIiIiKlYfGpIE5OneNwlPy6FxnLl6D6dCYAwDMuEfHzn4D/wKE27pltKHncardu3WzdBbIzzBTJxkyRbMwUycZMydM5qp0uwtHRUdEDmatPZyJjxaso2fczAMDZzx8x9z6M0AmToeokhbM1KHXcqkql4smSpGKmSDZmimRjpkg2ZkouB1t3gP6mVqshhLB1N5qoLy9D+psv48C0SSjZ9zNUjk6IuHU6Bn/xX4RNurVLF55KJoRARkaGIjNFnRMzRbIxUyQbM0WyMVNysWpQEKWFWmg0yN2yHqf/s/LvcZ1XDkfc7MfgERVj286RRTrzDMqkTMwUycZMkWzMFMnGTMnD4pNMKvltLzKWv4LqrAwAgEdsAuLnPY6AwZfbuGdERERERNQZsfgkI9XZWchY+RpK9u4CADj5+iHm3tkIu7Frj+skIiIiIqL2UQml3evZRfXq1QsAcPz4cZvsv768DNmr3kPuhk8htBqoHJ0QNvk2RN/9AJx9fG3SJyIiIiIiUo721iy8lKUgOp0OQogOnfFWaDTI+3oDsv6zEpqyCwCAgMuvRvychfCIju2wfpB8QghUVFTA29tb0bMoU+fBTJFszBTJxkyRbMyUXJztVkE6ejBz6e/7cHDGLUh77Xloyi7AIzYefZa+hz6vvc3C007k5ubaugtkZ5gpko2ZItmYKZKNmZKHVz67oOqz2chc8SqK9+wEADj5+CLmvtkIu/EWjuskIiIiIiKrYKXRhWgqypG96j2c27AOQqMBHB0RPulWRN/zEMd1EhERERGRVbH4VBAnK111FFot8r7ZiNPvr0D9hVIAQMBlVyFuzmPwjIm3yj5JGQIDA23dBbIzzBTJxkyRbMwUycZMycPiU0EcHR2lD2QuPfAbMpa9jKqMNACAR3Qs4uY+jsDLrpK6H1IelUqFoKAgW3eD7AgzRbIxUyQbM0WyMVNyccIhBamvr4esJ9/U5JzBsSfn4s+596AqIw1O3j6In/8kBnz8FQvPLkIIgaysLGmZImKmSDZmimRjpkg2ZkouXvlUEJ1O1+5taCorkL3mfZz74mPDuM6wm6Yi5p6H4Ozr1/5OUqdSV1dn6y6QnWGmSDZmimRjpkg2ZkoeFp92Qmi1yP92E7LeX4760hIAgP/gyxE/93F4xiXYuHdERERERNTVsfi0AxcO7Uf6siWoSksFALhHxSB+zkIEXH41H4ZLRERERESKoBK8gVkRevXqBQA4duyYxQVjzbmzyHzrdZzfuR0A4OTtg+iZDyJs0q1wcHa2Wl+pcxBCQAgBlUrFP0KQFMwUycZMkWzMFMnGTBnT1yzHjx9v0+t55VNBLB3zqamqxJk1/0HOF2sh6usBBweETZyCmHsfhrOfv5V7SZ1JdXU1PD09bd0NsiPMFMnGTJFszBTJxkzJw9luFaS+vr7Z9frnde6fOg5nP/kQor4efoOGYuCajUh87J8sPKmJnJwcW3eB7AwzRbIxUyQbM0WyMVPy8MpnJ3HhjwPIWLYEladOAADcI6MRN2chAq8YxlsAiIiIiIhI8Xjl00K7d+/GhAkTEBQUBHd3d/To0QP//Oc/UVlZadX91uTm4Pg/HsWRh2eg8tQJOHp5I27OQgz8ZDO6XTmchScREREREXUKLD4tsGLFCgwfPhwlJSXYtGkTjh07hocffhivvfYaBgwYgMLCQin7cXR0NPx/TVUVMt99E7/fPgHnd2wFHBwQOvEWDP7iv4i8bTonFCKL+PvzVmySi5ki2Zgpko2ZItmYKXk4220L9u/fj6FDhyI8PBwnTpyAl5eXYd1bb72F2bNnY8yYMfjhhx/atR/DbLdHj6Lg+6+R9e6bUBefBwD4DRiC+HmPwyshuV37ICIiIiIiaqv2znbLK58t+Ne//gUhBB588EGjwhMA7r33Xvj7++PHH3/Ezz//3O59RQsNDt1zK1Jf+CfUxefhFh6JXi8vR9/lH7DwpFYTQiA7Oxv8+xLJwkyRbMwUycZMkWzMlFwsPptRXFyM7dsbnqE5cuTIJutdXV1x1VVXAQA+++yzdu/vCT9nVKb+BUdPL8TNXoBB67ag29XXclwntVlNTY2tu0B2hpki2Zgpko2ZItmYKXlYfDbjwIED0Gq1AICePXuabJOSkgIA+O2339q9P50QCJlwMwZ/8S0ib78bDi4u7d4mERERERGREvBRK81IT08HAHh4eMDb29tkm9DQUKO2LdHfJ32xjIwM/DsmEv97fBFUKpXh0n7j/69napmS25rDts23lfG5CyEs2gaPfedt29HH0xqZModtm2+r1HN+a9rq82TpNkxh2+bbKvXYW+scwUw1v7yztTWnI9vae6Zae4zai8VnM8rLywEA7u7uZtt4eHgAACoqKqDT6eDg0PaLybnCAampqYYDGxoaCl9fX5w6dcoQAGdnZ8THx6OkpARFRUWG14aEhMDPzw9paWnQ6XQAGmbPTUxMxIULF1BQUGBoGxwcjICAAGRkZECj0QBoCFNycjLKy8uRl5dnaNutWzd069YNmZmZqK+vNyzv0aMHKioqkJuba1gWGBiIoKAgnD59GnV1dYblSUlJqK6uNnpAr7+/P7p3744zZ84Y3cqQkJAAtVqNM2fOGJb5+voiNDQUZ8+eRXV1tWF5fHw8NBoNsrOzDct8fHwQFhaGc+fOGT0GJzY2FgCQlZVlWObl5YWIiAjk5eUZjjUAREdHw8nJCRkZGYZlHh4eiIqKQn5+PsrKygzLo6Ki4OLiYvTHB3d3d0RHR6OwsBClpaWG5REREfDw8MCpU6cMy1xdXREbG4vz58+juLjYsDwsLAw+Pj5ITU01LNMf++LiYpw/f96w3FROnJycEB8fj27duhllqnv37vD390d6errhqr6DgwOSkpJQVlaG/Px8w3aDgoIQGBhodOxbyklWVhbUarVheXJyMiorK3Hu3DnDsoCAAAQHByM7Oxu1tbWG5YmJiaitrcXZs2cNy/z8/BASEmLy2NfX15vMSU5ODqqqqgzL4+LioNPpcPr0acMyb29vhIeHIzc3FxUVFYblMTExcHBwQGZmpmGZp6cnIiMjTR57Z2dnkzkpKCjAhQsXDMsjIyPh5uaGtLQ0wzI3NzfExMSgqKgIJSUlhuXh4eHw8vIyOvYuLi6Ii4uz6Nhb8xyRlJSE4OBgo0zxHNGgM54jEhISUFpaajRje0efI5KSkhAaGmqUKZ4jGnTGc4QSvkfEx8cjPDzcKFM8RzTojOcIJXyPiI2NRWRkpFGmuvI5Qq1Ww6Udd2dytttmvPTSS3j66acRGBhodKAa++CDD3DfffdBpVJBo9G0ufjs1asXdDodjh8/bvRXBaX+Jaqz/dWqM7aV8blXV1fD3d3dZpkyh23lte3o42mNTJnDts23Veo5v7VXPmtqaizKlDls23xbpR57a175ZKbML+9sbc3p6Cuf9pyp1h6j9s52yyufzfDx8QHQ/CBj/V9RvLy82nXVEwDq6+uhUqmaXNI2dYnb3GVvpbY1h22bb9uez10IgbNnzyI5OdmmmTKHbeW17ajjac1MmcO2zbdV6jm/NW1bkylz2Lb5tko99tY6RzBTzS/vbG3N6ci29p6p1hyj9uKEQ81ISEgA0FBgNr6k3pj+loHExMQO6xcREREREVFnw+KzGQMHDoSjoyMA4MSJEybb/PXXXwCAIUOGdFi/iIiIiIiIOhsWn80IDAw0PN9T/7zPxurq6vDzzz8DAG677bZ2709f6BLJ4uvra+sukJ1hpkg2ZopkY6ZINmZKHhafLVi8eDFUKhXeeecdo1nPgIbJhkpLSzF69GhcddVV7d6Xk5OT1e6vpq5HpVIhNDSUmSJpmCmSjZki2Zgpko2ZkovFZwuGDBmCZcuW4dy5c7juuuuwd+9eZGZmYsWKFViwYAGSkpLw8ccfS9lXfX29xTNWEbVECIEzZ84wUyQNM0WyMVMkGzNFsjFTcrH4tMCcOXOwY8cO+Pr6YsKECUhJSTEUnwcPHkRwcLCU/eifq0UkS+NnWhHJwEyRbMwUycZMkWzMlDx81IqFhg0bhmHDhtm6G0RERERERJ0Sr3wSERERERGR1bH4VBAXFxdbd4HsTHx8vK27QHaGmSLZmCmSjZki2ZgpeVh8EtkxjUZj6y6QnWGmSDZmimRjpkg2ZkoeFp8Kolarbd0FsjPZ2dm27gLZGWaKZGOmSDZmimRjpuRh8UlERERERERWx+KTiIiIiIiIrI7Fp4I4OjraugtkZ3x8fGzdBbIzzBTJxkyRbMwUycZMycPnfCqIk5MTVCqVrbtBdkKlUiEsLMzW3SA7wkyRbMwUycZMkWzMlFy88qkgGo0GQghbd4PshBACOTk5zBRJw0yRbMwUycZMkWzMlFwsPhVEq9XaugtkZyorK23dBbIzzBTJxkyRbMwUycZMycPbbu2ETqfjX2TIiBACOp0OWq2Wt3OTFMwUycZMtY5KpYKDA68bEFHnxeKzEysrK0NeXh4KCwtRV1dn6+6QAmk0Gpw7d87W3SA7wkyRbMxU67i6uiI4OBihoaHw9fW1dXeIiFqFxaeCuLi4WNw2Pz8fx44d49VOapaTE3/ESS5mimRjplqnrq4OZ8+eRU5ODnr37o2QkBBbd0lxYmNjbd0FsjPMlDw843dCZWVlhsIzKCgIERER8PHx4aNayEjjP0zwdjaSgZki2Zip1tFqtSgvL0dOTg6Kiopw7NgxuLu78wooEXUaLD4VRK1WW9QuLy/PUHj269ePv7DJJCEENBoNH+FD0jBTJBsz1TqOjo7o1q0bAgMDceTIERQVFSEvL4/F50WysrKQnJxs626QHWGm5OGo9U6osLAQABAREcFf1kRERF2MSqVCREQEgL+/ExARdQYsPjsZnU5nmFzIx8fHxr0hIiIiW9B/B6irq4NOp7Nxb4iILMPiU0EsGbPZeHwMx3hSSzglP8nGTJFszFTbNP7cOPmgMS8vL1t3gewMMyUPx3wqCMe8kEwqlYp/oCCpmCmSjZlqO35fMK3xLclEMjBTcvHPjQqi0Wj410uSRggBrVbLTJE0zBTJxkyRbEII5ObmMlMkDTMlF4tPBdFqtbbuAtkZjgMi2Zgpko2ZItnKy8tt3QWyM8yUPCw+iYiIiIiIyOpYfBIREREREZHVsfhUEBcXF1t3geyMk5P15xTTP/qHlEMIgW3btrXptWq1utlxLR2RKVtjpjuWLTN18c9KS/mnziE6OtrWXSA7w0zJw+KT7N7w4cOhUqla9S8mJsbw+jVr1sDX1xcff/yxye1v3rwZzz77LHbu3Nkxb0hBduzYgccee8zkOp1Oh48//hijRo0y+/qqqio89dRTGDp0KK644gr069cPb7/9dqu+/FVXV+OVV17B8OHDMXLkSFx66aWYOnUqMjIyTLavrKzE008/jeTkZHh6eiIlJQUrVqzo8C+cH3/8MQYPHowrr7wSvXr1wty5c1FRUdHq7ezYsaNJfh0cHLBmzRqjdqWlpZg3bx6ioqLg4uKCqKgoLFiwAJWVlUbtzp49ixkzZrTrwfU//vgj7r333ja/3pZMZVrWsbJ0O5YeK9n9ay8Z/bB0G6tWrWrxPP7HH38YvcaSn5X25j8mJsZo+5dffjnq6+stbq//R+3TFf5IRh2LmZJIkCKkpKSI+Ph4odPpmm2n0WjE1q1bxdatW4VGo+mg3nVuxcXFIi8vT0yZMkUAEFOmTBF5eXlm/y1YsEBER0cbXn/99dcLAGLcuHEmtz99+nQBQCxatMhq72HXrl3i0UcfFQ4ODgKAcHBwEP379xdXX321GDp0qEhMTBSOjo4CgLjtttuEEELodDqhVqtbzFRbrVmzRowbN06o1eom6zZt2iR69eolABh9lo1VVVWJwYMHi1tvvdWQ5f379wtvb28xc+ZMi/pQVVUl+vbtK6677jpRUVEhhBBCrVaLmTNnCh8fH/HXX38Zta+pqREjR44Uzz//vNi/f7/YunWrGD58uAAgXnzxxVa8+/Z55plnhJubm9i7d68QQojS0lLRv39/0a9fP1FZWdmqbY0bN06EhoaK8PBww7+YmBjx+++/G9rU1dWJfv36CScnJxEZGSmcnZ0FAAFADBo0SNTW1hptMy0tTVxxxRXi7NmzRsstzdS4ceOEm5ubKCwsbNV7sTVTmZZ1rCzdTmuPlcwstUdb+9E4U63ZxhVXXCHc3NxEQkKCSE5ONvrn5+cnQkNDm+TUkp8VIczn3xKFhYVGv28AiAcffNCi9pdddpnh95Al+H3ANJ1OJ06cOGG1333U9TBTxlJSUkRKSkqbX8/iUyFYfFqfvkicPn16s+0WLVpkVDB99913YtCgQeK7775rdrvWLD71unfvLgCIhQsXNllXVFQkLrvsMvHaa68JIaxbfG7evFnExsaKCxcuNFl34sQJcezYMbF169Zmi88FCxYIlUolcnJyjJY/99xzAoD49NNPW+zH8uXLBQDx66+/Gi2vqKgQjo6OTYrYl19+WezZs8doWXl5ufDz8zPbT9l+/PFHAUDMmTPHaPmePXsEAPHQQw9ZvK2TJ0+KlJSUFo/xokWLxLRp00RpaakQoqFAf+mllwxfjleuXNnkNf/73//EgAEDRHV1tWGZJZlKT083/JHk3//+t8XvxdZMZVrWsWrNdlpzrGRmqT3a0w99pn744QeLt3Hy5EkxbNgwk+cfIYS47LLLxP3332+0zNKfFT1T+W+N6dOnCycnJ8NxW716dYvthw0b1qp98PuAaSwUSDZmyhiLTzvB4tP6LC0+q6qqRElJSau3a+3i8/z580KlUgkAYufOnSbbLF++XPz0009CCOsVnzk5OcLX11ds3ry52XZZWVlmi8+qqirh6ekpYmNjm6xLS0sTAETPnj1b7Mv9998vADS5ciGEEMHBweL66683WmbuDwiDBw8Wvr6+Le5PBv2VVv3VncaioqKEs7OzyM3NtWhbs2bNEm+//XazbXQ6nRgxYoSor69vsu7OO+8UAMTEiRNNvvbWW28Vjz32mNG2WsrU/PnzRUpKigAgQkJCRF1dnUXvRYbo6OgWf75NMZdpWcfK0u209ljJzFJ7tKcf+ky1ZhsbN24U586dM7m93NxcoVKpmvysW/KzcrGL898a+mJy8uTJAoBwd3cXhw4darF9a/D7gGksFEg2ZspYe4tPjvlUEAcHHg5bmjFjBoYPHw4PDw/4+/vj9OnTZseBAsDq1auhUqkM44UWL15s1H716tVS+/e///0PQgh4eHjgsssuM9kmLi4O/fv3N/y3NcYOPf300wgKCsKECRPavI3ff/8dVVVViIiIaLIuISEBvr6+OHHiBP76669mtxMaGgoA+OSTT4yWFxYW4vz587j00kuNll933XUmt5Obm4ubbrqpNW8BQMN4rRkzZljc/vz589i5cyccHR0xYMCAJusvu+wy1NfX46uvvmpxW8XFxVi7di1mz56NSy65BAsXLsSRI0eatMvIyMCsWbNMjlfRfx4ajcbkPmbPno0333wTmZmZhmXNZaqyshIff/wxtmzZgm7duiE/Px+fffZZi+/F1kxlWtaxas12WnOsZGapMVtkuri4uFXbmDRpEsLCwkxua9OmTfDy8sK1115rtH1LflYuZir/rbV69Wr06dMHNTU1mDRpEoqLi9u8LbKch4eHrbtAdoaZkofVjoI4OztzogEFiYyMRF5eHvbv329y/dSpU5GXl4cpU6YAABYsWIC8vDzDv6lTp0rtz48//ggAuPLKK41mRtbpdIaJSMaNGwdfX18ADUWCk5OT1Ezl5eXh008/xfjx49u13dzcXADmT+aRkZEAgEOHDjW7nTvvvBPOzs5Yvnw53n//fcPyhQsXomfPnliwYEGLfdmwYQMiIyOxbNkyS7vfZvosRUREwNXVtcn6+Ph4AMC+ffta3NaGDRug0Wig0+lw5MgRvPbaa7jkkkswd+5caLVaQ7uEhATccsstJrehz0pKSorJ9UOHDoWHhweWLl0KoOVMrV27Ftdccw0SEhIMEw69+eabLb4XWzKXaVnHqjXbac2xkpml9mhvP1QqleHnXMZ7+eqrrzB27Fij7Vj6s3Kxi/PfFp6enti8eTMCAgJw+vRp3H777dDpdG3eHrVMpVIhKiqK36dIGmZKLhafCqLRaKTMuCmEgLamulP/k/E5tJejoyNCQkIQFBRkcr27uztCQkLg7u4OAPDy8kJISIjhn345AOzduxchISG45JJLcP78+Tb1Z+vWrQCAESNGGC1fv349UlNTm7QXQkCr1WLEiBHo0aNHq/499dRTJvuwZcsWaDQao6urbaG/slNQUGByvZeXFwAgPz+/2e0kJCTggw8+gEqlwqxZs3Dbbbdh0qRJ8Pb2xq+//gp/f3+zrxVCYO3atbj77rsxZsyYDnnUUXZ2NgCYzVT37t0BAGlpaS1ua9asWVCr1cjKysI777yD5ORkAMCKFSswZ84ci/pz+vRpAMDtt99ucr2joyP69u2LL774Alqt1pApcz+fK1euNOz7gQcegKOjIw4fPmx2JuijR4/ihRdeQO/evREUFGT4fLKzszF8+HCEhYXh3LlzFr2XtjKXaVnHStZ2Lj5WMrPUHu3thxACWVlZ7dqGXklJCXbt2oUbb7zRaHlbf1Yuzn9bxcXF4fPPP4ejoyO2bt2Kf/7zn23eFrVMCIG8vDxFfI8g+8BMycV5gxWkPb/cGtPV1mDPiMFStmUrV/5vPxzdrXOLw7p167Bhw4Ymy+vq6nDFFVdYZZ+ff/45CgoKUFBQgF27duHmm29u1ev/+usv5OTkAIDhdjIhBH755Re8/vrrZq/O6nQ6ZGRkGL4gWiovL8/k8r179wJo//OuevToAQBIT09HTU2NUaEOwHDVwtztoI3dddddcHd3x1133YXPP/8c/v7++Pjjjw0FrCm//PILFi5ciF9++QUA8Oyzz+L777/Hjh07mvRFpgsXLgAA3NzcTK7X77usrMzibcbExOCBBx7AzJkz8eSTT+KNN97AO++8gxkzZmDw4ObPA19//TUmT56Mfv36Nbv9PXv24PDhw+jfvz90Op3JIQLbtm2Di4sLrr76agANGRk/fjy2bNmCN954A8OHD2/ymuTkZPzjH/9AWFgYZs6cifXr1+Oee+7B4sWLcc899+DQoUMIDAy0+LNoC3OZlnWsZG3n4mNljSy1hYx+lJaWtnsbQMNnBDTcAWJKW35WGuff1C3Blho1ahRefvllLFy4EC+//DIGDx6MiRMntnl71LyysjKEhITYuhtkR5gpeVh8UpczYcIELFmypMnyJ554wmrjcWbOnIkdO3YgJCSk2edemqO/5dbZ2RkPP/wwACAzMxPnz5/HI4880uxrs7KypN0qcurUKQBAt27d2rWdPn36oFevXjh+/Dg2b96M2267zWh9UVFRq/Zz9uxZXHHFFcjJyUFqaipuuOEGLF26FPPnzzfZ/vLLL8fevXtRWlqKTz75BE899RR+++03vPHGG3j66aebtB8xYoTJK3Dnzp3Dpk2b8OuvvzZZd9NNN+Gll14yuX9zx0Nf1LXlr6suLi5YunSpYXzbJ5980mzxefjwYfz55584ePBgs9vVX436888/m73ivXz58iZXkWbPno0tW7bg22+/RXp6OhISEpr0GWj4mVSpVPjll1+Qk5ODpUuXws/PD9OmTWu2bzK0lGlZx6o922nuWLV1u0rMdHu38dVXX2HYsGHw8/Nrtl1rflYa5789xScAPPbYY/jjjz/w6aefYvr06di/f7/hKiwRUVfB4tMOObi548r/mb4S1lk4uFnv6pO3t3eTL8EAsHHjRqvt89JLL8WxY8fa/Hp98Tl58mR8+umnABq+iN1yyy2YNGmSlD5aoqWrHK3x/vvvY+TIkVi4cCF69+6NPn36oLy8HK+99hrS09MBWHaF9cEHH8Tvv/+OPXv2QAiBOXPm4MMPP8QjjzyC2NjYJrfgNebv7485c+YgJiYGEyZMwHfffWey+Gzu6nF5eTnKy8ubLDd19Vj/pbi2ttbktvTL9eP72uKZZ57B2rVrDbcymlJfX4+5c+di/fr1Zidu0dNfddL/QcCUzMxM/Prrr/jyyy+Nlo8cORI9evTAyZMnsXz5cixfvtzk6wMDA9GjRw988803+Oabb1osHmQWTuYyLetYtXc75o5Ve7erpEzL2EZlZSW2bduGV1991Wybi1nys2JJ/lvjgw8+wIkTJ/DHH3/gpptuwv79+5u9S4OIyN6w+FQQWWPOVCqV1W5ZpY5XW1uL3bt3AwCuv/56w3KVSoXk5GRcfvnlZl/r6OgotS/6KxMyxj1cfvnl2LdvH1588UVMmTIFERERiIqKwm233YZXXnkFrq6uuPLKK5vdxsaNG/Huu+9i+/bthuLhgw8+QPfu3fHiiy/iX//6V7PFp94NN9yA8PBws2NQ9ePtLhYTE4Phw4dbPLOxfuIUc+N+9V9wTf1xxFIJCQnw9/dv9mr3nDlz8OCDD7b4+QJ/X3XSDwswlamVK1di5syZJm9ZfuihhzB37lysWrUKzz33nNkC4sorr8SJEyfQu3fvFvskq3ACzGda1rFq73bMHav2bldJmdava882vvvuO9TW1lr08954vy39rFyc//Zyd3fHpk2bMHDgQJw4cQJ333031q9fL2Xb9LeoqChbd4HsDDMlD4tPBeEsWmTK7t27UVNTAwcHB4wdO9Zo3UMPPdTsI3pUKhVGjhzZ6klbzF0l0o+/q6mpadX2zOnXrx+++OILo2Uff/wx6urqcNNNN7U4/lI/dnfo0KFGy//9739j3bp1+PPPP1FVVQVPT88W+xIREWH1xx0NHjwYDg4OyMnJQX19PZydnY3W6x/pYO5ROpbS6XRmf1EuWbIEvXr1anKrszl1dXUA/r46dfF5qqqqCmvXrjU7M/H06dPx9NNPo7KyEv/5z3/w2GOPNWmj1Wpx4sQJAMDPP//cYt9kFU6A+UzLOlbt2U5zx6qjstQSGf0YMmRIu7fx1VdfoX///oaZsi3V3M8K0DT/MkRHR2P9+vUYNWoUNmzYgFdeeUXatqlBR0wgR10LMyUPZ7tVEP0vOepcTBUsZWVlbZ7V9mL6W24HDRrUZFxaeHh4s6/VaDTIyMhAampqq/6Zu0qkf8yDuSuE7aXVag23zT3zzDMtttdPVHLx7XqOjo4YNGgQgIbiyBJnzpxpMpOwbH5+frj66qtRX1+Pw4cPN1m/b98+ODg4tOsZqllZWSgrK8P48eObrPvoo49QVVVlcoZP/djHi5WUlAAAkpKSADSdBGrt2rW4+uqrzX6B9/HxwV133QWgYXZRU1eQXnjhBcyaNQsAzM6May3mMi3rWLV1Oy0dq47IkiVk9MPLy6td26irq8N3333XqqueQPM/K3oX51+W4cOH4/XXXwfQ8JzZ3377Ter2uzr90A0iWZgpeVh8ErWT/i/ijYucu+66y+gW2T/++AO9e/fGyJEjTd4S2Bx98dl4e62RlZUFIUSr/pm7cqSfsdSSh65bMlPtxV599VUcPXoUjz/+OC699FKjdRUVFRg3bpzRlbMhQ4YAAPbs2dNkW+fOnUNcXByCg4MNy3Jzc01+/j/88APq6uosfkRJe+gniLr4iu+BAweQnp6Om2++2Wisq6n3rdFosGnTJsPVwsZefPFFXHvttU2ukm/ZsgUHDx7Ev//97yavOXHiBN577z2T/c3JyYGzszMGDhxocv3KlSsxduxYnDx50uy/kSNHAmgo8L/66iuj1+/evRteXl648847ERMTYyg+c3JyDLMRW1NzmZZxrNqyHUuPVWu3ay0yPif95GBteS/btm1DRUWFydlj2/Kz0lhL+W+PuXPnYsaMGdBqtTh58qT07RMRKZIgRUhJSRHx8fFCp9M1206j0YitW7eKrVu3Co1G00G969yKi4tFXl6emDJligAgpkyZIvLy8kReXp6oq6sz+zqNRiPy8vLE/v37BQAREREh8vLyRGFhoVG7r7/+WgAQvXv3FsePHxfbtm0Tnp6e4sknnzS0efjhhwUAAUBs2LDB4r4fP37c8Lpdu3a16n3rdDqhVqtbzFRrlJaWCnd3dzFr1qwW27777rsCgHB1dRV5eXkttl+/fr1wcHAQt99+u9BqtSbX6z+LoqIiQ3+Sk5NFfHy8SE1NNbT98MMPhaOjo/j6668Ny+rr64WHh4fw9vYWS5YsEdXV1UIIIQ4dOiQGDRokDhw40GIfLzZ06FDxyCOPtPp199xzj3B3dxf79u0TQghRXl4uhgwZIqKiokRubm6L73vnzp0CgHBwcBDz5s0TFRUVQqPRiNdee00MHTpUlJSUGG1j165dws3NTSQmJork5GSjf/Hx8cLR0VHs2LHDZF9DQkLEddddJ4Romqlt27YZ+mbpv8suu0x8/fXX4o477hCffPKJePDBB40+FwDizTffFA888ECrz3HR0dFi+vTprXpNS5lu77Fq7XZae6xa0z9LdHSmCwsLDZlq63u5++67RWxsrMl1rf1ZuVjj/FuisLDQ8PvmsssuM/yuMae2tlYMGjRIABDDhg2zeD9C8PuAOTqdTpw4cULq7z7q2pgpYykpKSIlJaXNr2fxqRApKSkiMTGRxacVDBs2zOwXYXNfuIUQIisry+RroqOjm7R97rnnRFRUlHBychLh4eHi4YcfFpWVlYb1u3fvFsHBwaJv375NvpSas3btWtGzZ0/DfidMmCA+/PBDi9+3TqcT9fX10k+WDz30kIiKijK73b1794qUlBSjz8zT01MMHjzYUPA1VldXJxYtWiQ8PDzESy+9ZHa/Z8+eFVFRUWLMmDFG+y4tLRXz588XCQkJon///mLQoEFi/Pjx4pdffmmyjccff1wEBwcLJycnERERIe644w7x2muviYqKijZ8Em2n0+nEO++8I/r37y+uuuoq0adPH3HfffeZ/JJq6n2r1WqxYMECERkZKZydnUX37t3F+PHjxbvvvivq6+uNXn/q1Cnh6+vbbEEYGxtr8ngeOXJEABDbt2839LtxpiZMmNDq4hOAePbZZ4Wbm5uYOHGi0c/Jr7/+Kvz8/MSwYcMs/jlprC3FpxDNZ7q9x6o122nLsWpN/6yprZ+TVqs1ZKot70Wj0Yhu3bqJ+fPnm1zfmp+Vi12cf0tER0ebPG7NycnJESEhISw+JdHpdOL06dMsFEgaZspYe4tPlRASpq2kduvVqxcA4Pjx482202q1+OmnnwAA1157rfTZTIlaUlBQgB49euCjjz7CTTfd1ObtHDlyBJs3b8b27dsxaNAgzJ07FzExMfI6KpG5R3s0p7lnInYW9913H4qLi5vcKqtUbZlwCJCX6c6kq2a6NZSef34fICJbsLRmMYez3SqIRqOBEIKz3pIUQgjodDo4ODhIzVT37t3xzjvv4LHHHsPo0aMtmknWnFmzZmHRokXS+mYtzT3awxxzkzZ1Fnv37sWuXbuwd+9ewzJrZUoWc7PgtkRmpjsLpWRaqZkylX/qHIQQKCwsRHBwsKIyRZ0XMyUXi08FkfUcMSI9/Zc62W699VacOXMG06ZNw4YNG9q0j379+knvl7W0tajprLKzs7Fw4UJ89913CAoKMlpnrUzZmoxMdyZKyrTSMtVc/qlzKC0tNZpsjqi9mCl5lHO2J6JO5fHHH8dNN93UITPEUsc5ffo0Fi9ejK+++goJCQm27k6HYqapK+efiKgj8MonEbXZtGnTrP4cQepYQUFB+PDDD7vsrUXMdNfW1fNPRGRtLD4VxNnZ2dZdIDvTERNQ+Pr6Wn0f1HFaGu/YFSY1YaY7lpIy1RXG+3YFERERtu4C2RlmSh7edqsgShrzQvaBf70n2Zgpko2ZItk8PDxs3QWyM8yUPKx2FKSurs7WXSA7o9FobN0FsjPMFMnGTJFsp06dsnUXyM4wU/Kw+CQiIiIiIiKrY/FJREREREREVsfiU0E45pNk41gqko2ZItmYKZLN1dXV1l0gO8NMycPZbhXE2dmZv4RJGpVKBScn/oiTPMwUycZMkWwqlQqxsbG27gbZEWZKLl5qUxCtVgshhK27QXZCCMFMkVTMFMnGTJFsQggUFRUxUyQNMyUX/9yoIEqa8a+6Vt2u13u4uUjqCbWHTqfj7dwkFTNFsjFTJFtxcTG6detm626QHWGm5GHxSSYl3vFMu15/buMSST0hIiIiIiJ7wD81EhERERERkdXxyqeCODs727oLBmnrnjO5vLpWjX73NKw78uEzvL1W4RwdHW3dBbIzzBTJxkyRbGFhYbbuAtkZZkoeFp8K4uDgoJjZbi0pKj3cXFh8KphKpVJMnsg+MFMkGzNFsqlUKvj4+Ni6G2RHmCm5eNutgtTV1XEmLZJGCIH6+npmqgvQf4G39hd5ZopkY6ZINiEETp48yUyRNMyUXCw+ya4NHz68yRdz/T9PT09ERERg5MiR+Ne//oX09HSr9GHz5s149tlnsXPnTrNttm/fjoCAALz00ktW6UNnkJeXh/j4eIwfP96uT/DPPvssnn32WVy4cEHaNvPy8pCXl2d2vRAC119/PRISEpCfny9tv0REREStweKT7NpXX32FvLw8TJkyBQAwZcoUwxf1Q4cO4YMPPsAll1yC5cuXIykpCXfeeafUogBoKD4XL17cbPG5adMmlJaWYt26dVL33Zns2bMHmZmZ+O9//4vi4mJbd8dqFi9ejMWLF0vNWUhICEJCQsyuP3/+PL7//ntkZGRgz5490vZLRERE1Boc86kgHPciX0BAAADA3d3d8L/6L+khISFITk7G2LFj8dRTT2Hq1KlYt24d9u/fjz179iA4OLjD+jlz5kwcPHgQs2bNkrrdzpSpUaNGYcyYMUhMTOSztCQLCgrC7NmzkZaWhpEjR7ZrW50pU9Q5MFMkm5ImcCT7wEzJw+JTQVxcXPhL2EYCAwPx9ddfY/DgwTh+/DhuueUW7Nq1q8P2P2DAAPz6669St6lSqeDk1Hl+xP38/PDDDz/Yuht2a8WKFe3eRmfLFCkfM0WyqVQqxMfH27obZEeYKbl4262CaLVaux7rpnQeHh6GMZe7d+/G119/bbQ+JycHS5cuxYgRIxAVFQUXFxcEBwdj/Pjx2LZtW5PtrV69GiqVCmvWrAHQcLtl4zGnq1evBtD+yWJ27dqFCRMmIDo6Gq6urggODsaYMWPw9ttvIzc315Cpi/dTU1ODxYsXo2fPnnB3d4e/vz/Gjx+P33//3ey+8vPz8eijjyIpKQlubm7w9fXFZZddhhUrVkCtVhu1HTlypNH+du7ciW3btmHEiBHw9/c3+hwuHpvb+Bbl559/3mjds88+i9TUVEycOBF+fn4ICAjADTfcgOPHjwMAamtr8cQTTyAyMhJubm7o27cvvvjii2Y/w6+++gqjR49GQEAAXF1dERUVhbvuugvHjh0zardnzx6jvgwfPhzl5eWYN28eIiMj4eLigri4ODz77LPQarVGr9W/R73Y2FiTx72urg4bN27EHXfcgZ49e8LLywteXl7o378/Xn75ZdTV1TX7Xi727LPPNvn82koIwfMUScVMkWxCCJw/f56ZImmYKblYfCqIRqOxdRe6vHHjxsHX1xcA8OWXXxqte/7557Fw4UIMGTIE33zzDdLS0rBx40b4+vpi9OjRTa4sTZ061Wi86YIFCwzjTfPy8jB16lQALU8W05yPPvoIw4cPh0ajwbp163Dq1Cls3rwZnp6eePjhh/Hkk08a2l68n3HjxiEnJweffvopTpw4gTfffBP79u3DFVdc0aTwBoA//vgD/fr1w/vvv4958+bh8OHD+OGHH5CSkoK5c+di1KhRqKmpMbRfv3498vLycNlllxn+e8mSJVi8eDEOHTqE5577+1my+rG5+raNPfLII8jLy8OCBQsAAOnp6Zg1axbmzZuHQ4cO4fXXX8eOHTswbNgw5OTk4N5770Xfvn3x888/4+uvv0ZVVRVuvfVWfP/99yY/w/vvvx8333wzgIaxt0ePHsXLL7+M7du3Y+DAgdi0aZOh7ZAhQ5CXl4eNGzcCaCh0b7rpJgwYMAA///wztm7dCi8vLyxevBhPPfWU0X7071Fv//79Rnlo/DlPnjwZOTk5WL58OY4dO4a9e/di2rRpeOmllzBixIhWFaCPPfaY0efXXjqdTsp2iPSYKZLt/Pnztu4C2RlmSiJBipCSkiLi4+OFTqdrtp1GoxFbt24VW7duFRqNpoN697eqmjoRNulxETbpcVFVU9fh+2+r6dOnCwBi+vTpLba95pprBAARGxtrtPzpp58Wjz32mMnXXHvttcLV1VXk5eWZ3feiRYua3S8A0dofyZiYGAFAFBUVGS3XaDSiR48eYtq0aU0ypd/P+PHjm2zvxx9/FACEn5+fKCwsNCyvqKgQkZGRAoDYtGlTk9dNnTpVADD5+QwbNkwAEOHh4aKmpsZoXVJSkvj888+btN2xY0eT7SxatEgAEE5OTiI7O9to3dNPPy0AiL59+4p169YZrfv+++8FADFmzJgm21y2bJkAIC699FKh1WqN1h07dkw4ODgIb29vkZuba7Rux44dhs9x7dq1Ruv++OMPAUB4eXmJurqmPyP612VlZTVZJ4QQhw4dEikpKaKqqqrJurVr1woA4o033jD52uYypP/8Wsphc3Q6nVCr1S2ep4gsxUy1na2/DyiVTqcTJ06cYKZIGmbKWEpKikhJSWnz63nlk+gi+omGCgoKjJa/8MILePXVV02+5vLLL0ddXR22bt1q9f41VlhYCADIzs42Wu7o6Ig33ngDd9xxh9nXzps3r8my0aNHIzk5GRcuXMCHH35oWP7hhx/i7NmzSEpKwsSJE5u87uGHHwYAvPvuu6itrTW5v/vuuw9ubm5Gy1JTUw1XgC2lv+25sUGDBgEAsrKycMsttxitGzJkCADg4MGDRss1Gg2ef/55AA1XBx0cjE+HvXr1wvDhw1FRUWH0WTTm5eXVpP/9+vWDu7s7KisrcerUqVa9NwC49NJLcfz4cXh4eDRZd/nllwOAySvTRERERErH4lNBOJOWMuhvATM1/vLbb7/FpEmTEBsbC19fX8N4vCVLlgBoGBfakUaNGmX43+effx5ZWVmGdWPGjDGsN6VXr14ml+sLuZ9++smwTD8R0ODBg02+JiEhAQBQWVmJP/74w2Sbnj17mu1La+j31Zj+VumoqKgmP0d+fn4AgJKSEqPlBw8eRFFREYC/C1Rz+/r5559NrteP/W1MpVLB398fAFBaWtrcWzErKysL8+bNQ79+/dCtWzdDzvr27Qug43PWmKOjo832TfaJmSLZQkNDbd0FsjPMlDwsPi2Um5uLG264od0TdjTHwcGBs90qgL4g6d69u9HyRx99FDfccAOOHTuG5557Drt378bhw4dx+PBhPPDAAwDQZNIda/vggw8wceJEXLhwAc888wzi4uLQu3dvPPvss8jJyWk2Uxe/Pz39ld/GBY7+yuoXX3xhKIQa/0tMTDS0PXfunMntBgUFtek9XiwwMLDJMv17bG7dxePKGl8t7tevn8n3tWrVKgDm35O5R8LoC+CLJx2yxC+//II+ffrgvffeww033IDNmzcbcvbdd98B6Pic6alUKp6nSCpmimRTqVTw9fVlpkgaZkouzm9ugdWrV+ORRx6R+lB4U9RqNYQQDLcN6XQ6HDp0CMDftzgCDVfJ3njjDbi4uOCHH35AXFyc0ev0zxPtaN26dcOmTZuQnp6OdevWYcOGDTh27BiOHz+OV199Fe+//z5uv/32VmVK/P9sbsLErG533XWX0SRGppgramXlurnttHUfv/zyi8nbXPXM3ZVgjZ/VBx54AFVVVXjuuefwz3/+02idrR9JIf5/ZlJHR0eep0gKZopkE0Lg1KlTSEpKYqZICmZKLhafzaivr8eNN96IXbt24cUXX8RXX32F3bt3W21/pr7sU8f6+uuvUV5eDgC49dZbDcv1j/5ISkpqUngqQUJCAhYtWoRFixbh6NGjWLhwIX788UfMmjUL119/veE20Mby8/MRFhbWZLl+HGlkZKRhWXR0NE6cOAGtVmvyttfOKDo62vD/fX19jf7bVoqLi3H06FEAwHXXXWfj3pjG8xTJxkyRbMwUycZMycPbbptRVVWFmpoaHD16FPPmzeNfO+xcVVUVnn76aQANz2QcN26cYV1LjwJobgzexRPZAEBZWZmUabtnzJiBw4cPGy3r06eP4XErVVVVZie9+euvv0wu1z/n85prrjEsGzt2LABg3759ZvvywgsvYPDgwaisrGzNW7CZAQMGGG4FNve+zp8/j/79+2P58uXS9mvqPJKfn4+qqqp25YyIiIhI6Vh8NsPX1xc//fRTh13pYnFrO+fPn8f48eNx4sQJJCUlNXnGp35CmpMnTyItLc1oXXl5Of773/+a3bZ+wpuqqirDsrvuugvXX399u/u9Zs0abNiwocly/a1sDg4OTWaG1Vu2bFmTZVu3bsWpU6fg5+eHe+65x7D8nnvuQWRkJFJTU7Fly5Ymr8vOzsZrr72GhIQEeHl5teMddRwnJyf84x//AAAsXbrU5HN2X3zxRRw5cgSjR4+Wtt+L81BaWorQ0FCsWbMGQUFBiI2NBQB88803TV772WefSetHW/E8RbIxUySbrYcokP1hpuRh8dkMlUrVob8UXVxc+EtYspKSEuTn56OmpgYAUFNTg/z8fOTn5+PUqVP48ccfsWDBAsTHx2PXrl2YNm0a9u/f32RynKuvvho33ngjNBoNxo8fj82bNyMjIwPbt2/H6NGjDbOaVlZWGu0P+PsK4g8//IC//voL27dvx//+9z+MGDECAAz90bv4v1vy6quv4vnnn8fhw4eRnZ2NnTt3YuLEiaitrcXjjz9udoa27t2747777jO8bu3atbj99tvh7OyMNWvWGCYeAhoeKbJlyxZ0794dd9xxB5YuXYrU1FSkpaXhk08+wfDhwxEeHo6VK1caXlNWVob8/HzD5Dj6Y1FWVmb2OF3cVq1WG46Z/opq489Yq9UiPz/fMJOtWq02altUVNTks9VPKAUAc+fOxf3334/ff/8dY8aMwU8//YQzZ87gt99+w3333Yc33ngDb7/9Nnr06AEAZvenf0/696yfaEj/PhrT52HNmjXIzMzEiy++CEdHRwwbNgwA8Nprr0GlUuGll17CCy+8gOPHj+PIkSNYuHChYQKki/vRXIb0n9fFn19brlCrVCo4OTnxPEXSMFMkm0qlQkJCAjNF0jBTkrXvMaNdy7Bhw9r9kHZzUlJSRHJycosPsLX1Q6WraupE2KTHRdikx0VVTV2H77+19MfM1D93d3cRFhYmrr32WvHMM8+ItLS0ZrelVqvFiy++KHr06CGcnZ2Fh4eHuPTSS8Xrr78u/vGPfxhte9WqVUavfe6550RUVJRwcnIS4eHh4uGHHxaVlZVCCGG2f5Y4ePCgePLJJ0X//v2Ft7e3cHZ2FlFRUWLcuHHiu+++E1qttkmm9NvXarVi6dKlonfv3sLd3V34+vqK66+/Xvz2229m91dQUCAWLlwoevToIdzc3IS/v7/o37+/WLJkiSgrKzNqO336dJPva/r06U22a+447dixQ6xatcrkulWrVomsrCyT6/Q/o9HR0U3WRUdHN9n/li1bxNixY0VgYKBwcXERsbGx4uabbxZ79+41amduf/r3ZO49N3bu3Dlx4403Cl9fX+Hm5iZ69+4tPvvsM6M227dvFyNGjDA6pjNnzhQ7duww2u6wYcOMjqmp/S5atKjZz6g1dDqdyUwRtRUz1Xa2/j6gVDqdThQXFzNTJA0zZSwlJUWkpKS0+fUqITiC1lLDhw/Hrl27sGjRojY/bsXcsxUzMjIQERGBU6dOGf1lRaVSGQ1y1mq12LFjB4CGKyiNn492cVtzy9rTtrpWjaQ7/wUAOPXJv+Hh1vQZh5ZGim2bb9ve4wkAGo2mya0i+jGojccXWrMPprCtvLYd8XPfmKlMMSe2aduRx14JmTKnq7a9+PuAk5OTzY+nEn6XCCGQmpqK5OTkZr9PtXa7nbGtUo9nZ/v9YO+Zau0x0tcyx48ft2i/F+MNzAqiD7c+2KGhofD19cWpU6cMAWhcbGo0GqPlKpUKWq3WsEx/O5P4//F/epa0ra5tuP3RwcEBjo6Ohn1V1/39fMGqmjqjcXKN27q7/v1oClN90Ldt3Ie2tNV/Dhe31el0RsVVe9vqP5+L2+o/SyW0vfh46rPSeBsX56fxdtuSk4s/y/YeTxltZR97c22VfOzbezxbkylzx8jZ2VkROTHXX54jrJcTWceI5wjL2mo0Gpw+fRoJCQkoLi42mszO1PcIZ2dnxMfHo6SkxGgYQkhICPz8/JCWlmbYn6OjIxITE3HhwgUUFBQY2gYHByMgIAAZGRlGv0uSk5NRXl6OvLw8Q9tu3bqhW7duyMzMRH19vWF5jx49UFFRgdzcXMOywMBABAUF4fTp06irqzMsT0pKQnV1tdGka/7+/ujevTvOnDljNNQlPj4eGo3G6PuUr68vQkNDcfbsWVRXVzdp2/i5zz4+PggLC8O5c+eMhifox+NnZWUZlnl5eSEiIgJ5eXmGmfKBhtnUnZyckJGRYVjm4eGBqKioJsNPoqKi4OLigvT0dMMyd3d3REdHo7Cw0DCsBwAiIiLg4eFhNJGgq6srYmNjcf78eRQXFxuWh4WFwcfHB6mpqYZl+mNvSU6cnJyQkJCA0tJSwyz4QMOQHX9/f6Snpxt+vhwcHJCUlGQYeqIXFBSEwMBAo2PfUk6ysrKMnmWdnJyMyspKo+dtBwQEIDg4GNnZ2aitrTUsT0xMRG1tLc6ePWtY5ufnh5CQEJPHvr6+HmfOnDEs0+ckJyfHaJ6O2NhYaLVao0x5e3sjPDwcubm5qKioMLSNiYmBg4MDMjMzDcs8PT0RGRlp8tg7OzubzElBQYHRIx4jIyPh5uZmNO+Im5sbYmJiUFRUZBiGAwDh4eHw8vIyOvYuLi6Ii4uTco5Qq9VwcTG++NQadnnlc/78+SYnU2nJsGHDDI/UMGW4hCuf5vTq1Qt1dXWKufIZMbn5Zzm2JGfDyy22UfJfgZTQ1lpXFHjl077advRfoHnlUzltlXqlwlqZMqertuWVz655lao1bZV6PDvb7wd7z1RrjxGvfJrQvXt3JCcnt/p15mYF7Sj6SRcaBxtAk6A3/v/NtW1uWWvbtpal22nN/rpi2/YcTyGE4Y8TKpWqycQ3+r9ih4SEWK0PzWFbeW2t9XPfUqZk9sEctm2+bUcde2u1bW2mzOmKbU19N7D18bRmW3NMtQ0JCbH4O1JrttsZ2yr1eHa23w/2nilr1gQXs8vi86mnnsJTTz1l6260mv5WGiVIW/ecrbtA7XTxSfLiWW/1/22HNz+QlZj6xUvUHswUyaZSqeDv72/rbpAdYabkssvis7NSq9UQQijiF/HFEwlR56Mf76T/owaLTGqvizNF1F7MFMkmhEB6ejofjUHSMFNy8TmfCsLigGRjpkg2ZopkY6ZItsYTTRHJwEzJw+KTiIiIiIiIrI633bagrKzMMIW3furnyspKw+Qtvr6+cHd3l7IvXson2Zgpko2ZItmYqbbhFWPz9LO6E8nCTMnDT7IF8+bNQ2hoKEJDQ7Fv3z4AwOuvv25Y9sUXX0jbl4uLS4u/hBuv5y0A1ByVSmWYQZlIBmaKZGOm2u7ix2VRA5VKhaSkJH4mJA0zJReLzxasXr0aQgiz/2bMmCFtXzqdrsW/ZDo4OMDV1RUAjB5oTHQxIYRFmSKyFDNFsjFTbaf/DuDq6sqrMo0IIXDhwgVmiqRhpuTi2UpB6uvrLWoXHBwMAMjJyeEPAjWLV8dJNmaKZGOmWk8IgZycHAB/fyegv138XGui9mKm5OGYz04oNDQUOTk5KCoqwpEjRxAREQEfHx84ODjwlgAy0D/CgM/RI1mYKZKNmbKc/ipxeXm54TuASqVq8gxnIiIlY/HZCfn6+qJ37944duwYioqKUFRUZOsuERERUQdSqVTo3bs3fH19bd0VIiKLsfhUECcnyw9HSEgI3N3dkZeXh8LCQtTV1VmxZ9RZ6XQ6jgUiqZgpko2Zah1XV1cEBwcjNDSUhacZQUFBtu4C2RlmSh4Wnwri6OjYqtuOfH194evrix49enDCBiIiIjunUqlYqLdApVIhMDDQ1t0gO8JMycXiU0HUajWEEG0a98JfRnQxIQQyMzMRFxfHsVQkBTNFsjFTJBszRbIxU3KxYlEQXrkk2SydQZnIUswUycZMkWzMFMnGTMnD4pOIiIiIiIisjsWngvBSPsnGTJFszBTJxkyRbMwUycZMyaMSvNdTEXr16gUAOH78uI17QkRERERE1FR7axZe+VQQzlhLMgkhUFZWxkyRNMwUycZMkWzMFMnGTMnF4lNBOJiZZMvLy7N1F8jOMFMkGzNFsjFTJBszJQ+LTyIiIiIiIrI6jvlUCG9vb9TV1SExMdHWXSE7UldXB1dXV1t3g+wIM0WyMVMkGzNFsjFTf8vIyICzszMqKira9Hpe+VSImpoaaLVaW3eD7EhGRgZycnJs3Q2yI8wUycZMkWzMFMnGTBlzdnaGp6dnm1/vJLEv1A7JyckAONstycMZlEk2ZopkY6ZINmaKZGOm5OKVTyIiIiIiIrI6Fp9ERERERERkdSw+iYiIiIiIyOpYfBIREREREZHVsfgkIiIiIiIiq+NzPomIiIiIiMjqeOWTiIiIiIiIrI7FJxEREREREVkdi08iIiIiIiKyOhafREREREREZHUsPomIiIiIiMjqWHwSERERERGR1bH4JCIiIiIiIqtj8UlERK3y3XffITIyEiqVCqdPn7Z1d8gOMFNERF0Di8822r17NyZMmICgoCC4u7ujR48e+Oc//4nKyso2bU8IgY8++giXXXYZfHx84OPjg6FDh+I///kPhBDNvvb06dO49957ERkZCVdXV0RERGDmzJnIzMxsU1/INpSSKZVK1ey/Sy65pI3vkDqa7EyVlpZixowZGD9+PHJyclr1Wp6n7INSMsXzlP2QmalDhw5hwYIFGDBgAHx8fODi4oLQ0FCMHz8eGzZsaPH158+fxyOPPIL4+Hi4ubkhJCQEU6ZMwR9//NGWt0Y2opRMxcTENHue8vPza+M77OQEtdry5cuFSqUSV1xxhfj5559Fenq6WL58uXB1dRVJSUmioKCgVdvTarVi6tSpAoC49957xdGjR8Xx48fFAw88IACIm2++WWg0GpOv3bdvn/D29hZhYWFi48aNIjMzU2zevFlERkYKT09PsWfPHhlvmaxMSZkCIOLi4kRycrLJfxMnTpTxlsnKZGfqt99+E6GhoaJPnz5iw4YNAoAAILKyslp8Lc9T9kFJmeJ5yj7IzNQLL7wgAIjAwEDxxhtviD/++EOkpqaKTz/9VMTExAgAYtKkSaK+vt7k69PS0kRoaKjw8fERq1atEpmZmWLbtm2iT58+wtnZWaxfv17W2yYrUlKmoqOjRWRkpNnz1KBBg2S97U6FxWcr/fbbb0KlUomIiAhRUVFhtG7lypUCgBgzZkyrtvnqq68KACZ/WU6ePFkAEC+99FKTdeXl5SIkJEQ4ODiIgwcPGq07cuSIcHR0FMHBweLChQut6g91LCVlSghh8Zc/Ui5rZOqtt94S//rXv4RarRZZWVkWFwo8T9kHJWVKCJ6n7IHsTM2bN08AED///HOTdWfOnBFeXl4CgHjttdearNdoNKJv374CgNi8ebPRutzcXOHj4yPc3NxEZmamxf2hjqekTAnRUHzu2LGjVe+hK2Dx2UpjxowRAMQLL7zQZF1tba3w9/cXAMTu3bst2l5NTY3w8/MTAMTevXubrP/tt98EAOHt7S2qqqqM1r300ksCgBg1apTJbV933XUCgPj3v/9tUV/INpSUKSH4pc4eyM6UEMLoSnlrCgWep+yDkjIlBM9T9kB2pubNmyd69epldv1tt90mAIiBAwc2WffZZ58JACIxMdHkax988EEBQMycOdOivpBtKClTQrD4NIdjPluhuLgY27dvBwCMHDmyyXpXV1dcddVVAIDPPvvMom3+8MMPuHDhAry8vDB06NAm6wcNGgQ/Pz9UVFTg22+/NVr3+eefm+0LAIwaNapVfaGOp7RMUednjUwBgKOjY5v6w/NU56e0TFHnZ41MvfLKKzhw4IDZ9ZGRkQCAkpKSJussPU9t2LAB9fX1FvWHOpbSMkXmsfhshQMHDkCr1QIAevbsabJNSkoKAOC3336zaJv6dklJSXBwaHo4VCoVevTo0WSb1dXVOHr0qEV9OXnyJMrLyy3qD3UsJWWqsW+++Qbjxo1DfHw8AgMD0bNnT8ycORMHDx60qA9kO9bIVFvxPGUflJSpxnie6ryskSkXFxe4ubmZXZ+bmwsAJiej0u+jpb6Ul5fj5MmTFvWHOpbSMqW3Z88eTJ48GcnJyQgICEBCQgJuu+02/PTTTxb1wR6x+GyF9PR0AICHhwe8vb1NtgkNDTVqa+k2Q0JCzLYxtc2srCzodLpmX6t/nRACGRkZFvWHOpaSMtXYCy+8gNGjR2PDhg3YsWMH5syZg++++w6DBw/GSy+9ZFE/yDaskam24nnKPigpU43xPNV5dXSm6uvrsXXrVgDAvHnzjNZVVVUhPz8fQMvnKVn9IfmUlKnGXnnlFVxyySX4+OOPsXv3bixatAgHDhzAiBEj8NBDD7X4RAt75GTrDnQm+r/Ku7u7m23j4eEBAKioqIBOpzN55amt2ywrK2vyuuZeq3/dxa8l5VBSpvTmzJmDxx57DFFRUYZlffv2xTXXXIO+ffvi6aefRo8ePXDTTTc12w+yDWtkqr19aa4/PE8pn5IypcfzVOfW0Zl67733UFhYiPvvvx9XX321yb401x+ep5RPSZnSmz59OiZNmoR+/foZlvXu3Rtjx45FUlIS3nnnHSQmJuKRRx5pcz86I6VEDosAAA20SURBVF757ETa89eRrviXFWqZqVwsX77c6AudXs+ePXHnnXcCAK8qkFXwPEWW4nmKLHX06FE88cQTuPLKK7Fs2bJ2b4/nKbI0U4sXLzYqPPWCgoIwf/58AMDLL7/c5TLF4rMVfHx8AAA1NTVm21RXVwMAvLy8LPqLSmu26evr2+R1zb1W/7qLX0vKoaRMWWLw4MEAGsZWNLd9sh1rZKq9fWmuPzxPKZ+SMmUJnqeUr6MylZ6ejtGjR6Nv37747rvvTI7f43nKPigpU5bQn6cKCwtx6tSpNm2js2Lx2QoJCQkAGsJbUVFhsk1eXh4AIDExsVXb1I83sHSbsbGxhh8cc6/Vv06lUhn2Q8qipExZovH4vNLS0la9ljqGNTLVVjxP2QclZcoSPE8pX0dk6uTJk7jmmmvQu3dvbNu2zew4QE9PT8NYz5bOU+3pD1mXkjJlicbjiIuLi9u8nc6IxWcrDBw40DA1/IkTJ0y2+euvvwAAQ4YMsWib+nanTp0yTMzRmBDCMLNa4216eHigT58+FvWlR48eRn/ZI+VQUqYAYP/+/UhLSzO7bf0vZpVKBT8/P4v6Qx3LGplqK56n7IOSMgXwPGUPrJ2pAwcO4KqrrsKgQYPw7bffwsvLq9n2+n201BcfHx/DbPGkLErL1NGjR/Hnn3+aXd/4Dx3+/v6t7k9nxuKzFQIDAw3PDtI/S6ixuro6/PzzzwCA2267zaJtjh07Fr6+vqisrMSvv/7aZP3vv/+OCxcuwNvbG+PGjTNad+utt5rtCwBs27atVX2hjqe0TD3++ON4/vnnzW5b/7yrfv36GU3AQMphjUy1B89TnZ/SMsXzVOdnzUzt3LkT1157LW644QasX78erq6uhnUFBQUYO3YsCgoKjF5j6Xlq8uTJcHZ2blV/qGMoLVOvv/465syZY3ab+vNUYGAgkpOTW9WfTk9Qq/z6669CpVKJiIgIUVFRYbRu5cqVAoAYPXq00fILFy6Ia6+9VvTq1UscOnSoyTZfffVVAUBMnDixybrJkycLAOKll15qsq68vFyEhIQIBweHJts9cuSIcHR0FMHBweLChQtteavUQZSUqWHDhonAwECRn5/fZF1aWppwcXERAMQnn3zS2rdJHcgamWosKytLABAARFZWVrNteZ6yD0rKFM9T9sEamdqyZYtwc3MT8+fPFzqdrsl6fc4uzphGoxF9+/YVAMTmzZuN1uXm5gofHx/h5uYmMjMz2/huqSMoKVPTp08Xrq6u4tixY01eU1JSIgIDAwUA8fzzz7fhnXZuLD7bYPny5UKlUokrr7xS7NmzR2RkZIjly5cLV1dXkZSUJAoKCozar1+/3vBLdfbs2U22p9VqxZQpUwQAce+994pjx46J48ePiwcffFAAEJMmTRIajcZkX3755Rfh5eUlwsPDxVdffSWysrLEli1bRGRkpPD09BR79uyxymdAciklUyNGjBAARFJSkvjkk0/EX3/9JTIyMsSaNWtEZGSkACAef/xxq30OJI/sTAkhRF5ensjLyxP79+83tN2/f79huTk8T9kHpWSK5yn7ITNTn376qXBychJOTk5iwIABJv/16dPH7B84Tp06JUJCQoSPj49YvXq1yMrKEtu3bxd9+vQRTk5O4ssvv7TmR0GSKCVT99xzjwAgwsLCxLvvviv+/PNPkZWVJTZu3ChSUlIEAHHbbbeZ/X5vz1h8ttHOnTvFuHHjREBAgHB1dRWJiYni6aefbvKXFiGEKCwsFH369BHdu3c3+yVLp9OJDz/8UAwZMkR4eXkJLy8vMWjQIPH++++b/EtLY1lZWWLmzJkiPDxcuLi4iLCwMDFjxgyRnp4u5b1Sx1BCpiorK8WaNWvELbfcIuLi4oSrq6twcXERUVFRYurUqWLnzp1S3zNZl+xM6X9Bm/vXHJ6n7IMSMsXzlH2Rlakbb7yxxTy1dHW9qKhIzJs3T8TGxgoXFxcRHBwsJk+e3OKVe1IWJWSqrq5ObNiwQUybNk0kJycLDw8P4ezsLEJDQ8WECROaXGHvSlRCdLGHyxAREREREVGH44RDREREREREZHUsPomIiIiIiMjqWHwSERERERGR1bH4JCIiIiIiIqtj8UlERERERERWx+KTiIiIiIiIrI7FJxEREREREVkdi08iIiIiIiKyOhafREREREREZHUsPomIiIiIiMjqWHwSERERERGR1bH4JCIiIiIiIqtj8UlERERERERWx+KTiIiIiIiIrI7FJxERUQcZMGAAjh07ZpN9z5gxAyqVCiqVCvHx8Rg4cCAGDhyI7Oxsq+xPrVYb9tGjRw/DvocPH26V/RERkfKx+CQiIuoAe/bswaFDh/Dmm2/atB9Tp05FRkYGDhw4gAMHDiA6Otoq+3FxcTHs4+TJk3j44Yetsh8iIuo8WHwSERF1gBUrVgAA1q1bh6KiIpv1o0ePHjbZ74ABA2yyXyIiUg4Wn0RERFZ27tw5bNq0CQBQW1uLd99918Y96ngqlcrWXSAiIhtj8UlERGRl77zzDq666ioMHToUAPD2229DrVbbuFdEREQdi8UnERGRFdXV1eH999/HvHnzMHfuXABAfn4+PvvsMxv3jIiIqGOx+CQiIrKiL774At7e3hg/fjwmT56M0NBQALD5xEONqdVq7NixA08++SQuvfRSLFq0CJWVlVi0aBGSk5Ph5uaG6OhovPPOO4bXrF+/Htdccw18fX3h7++PRx55BFqt1obvgoiIlI7FJxERkRWtWLECc+bMgYODA5ydnfHAAw8AAA4fPoydO3eafV11dTVWr16NiRMnIjg4GHFxcbhw4YJh/WeffYaYmBikpKSgtra2XX1MTU1FVlYW3nvvPRw+fBipqamYPHkyAgIC8Omnn+Kdd95BQUEBHnroIXz55ZeYNm0a8vPz8frrr2PNmjXw8vLCm2++iaVLl7arH0REZN9YfBIREVnJvn37kJqaipkzZxqWzZo1Cy4uLgCAN954w+xr6+rqMHToUKxduxahoaHIysrChg0bAACrV6/Gl19+ifj4eNTV1UGn07Wrn3369MHMmTNx7bXXAmgojD/99FPMmzcPAwYMwN13340pU6YAAO666y7MnTsXc+bMQf/+/TFx4kQsXrwYAPDRRx+1qx9ERGTfWHwSERFZyYoVKzBjxgz4+PgYlnXv3h1Tp04FAHz77bfIyMgw+Vp/f3/06NEDPj4+uPXWWwEAu3fvxuHDh3Hw4EFs2rQJ//vf/5CRkQEPDw8p/fX29gYADB06FAEBAUbrLr30UgBASEgIBg0aZHJdamoqampqpPSFiIjsD4tPIiIiK8jLy8NXX32FOXPmNFmnX6bT6bBs2bIWtzVs2DAAwJ49e7B48WK88sorcjtrAT8/vxbXCSFQWlraMR0iIqJOh8UnERGRFbz77rsYOXIkEhMTm6wbNGiQ4bErq1atQllZWbPbGjBgAFxdXZGVlYV77rkH7u7uVulzc5p7TmfjdXyEDBERmcPik4iISDK1Wo333nsP8+bNM9tG/9iVyspKfPDBB81uz9XV1XCrq5ubm7yOEhERdSAWn0RERJKtX78egYGBGDVqlNk2jR+7smLFimYfU1JeXo6cnBwADbfeEhERdUYsPomIiCRbsWKF4cqmOY0fu5KdnY1NmzaZbTt79mzDJEW7du2S11EiIqIOxOKTiIhIot9//x2nTp3CtGnTWmxryWNX1q5di+TkZDz99NNwdHTEr7/+ahhXWVJSgurqanmdJyIisiInW3eAiIjInixfvhxqtRr9+/e3qL1+sp5ffvkFv//+O/r374/q6mp4e3sjNTUV33zzDb788kuoVCpccsklOHjwIH755RdceeWVePrpp7F06VJpfa+trQXQMGvtxerr6wEAVVVVZl8HoN3PHCUiIvvF4pOIiEiSgoICfPnll1Cr1UhNTW3169944w14eHhg3bp1mDZtGlJTU7Fx40ZDgTpq1CgcPHgQjz76KLp3745nn31W2jM+a2pqcODAAQDAX3/9BZ1OBweHv2+QOn78OACguLgYf/zxh+HZnoDxrcCHDh1CXFyclD4REZF94W23REREkrz//vvtetTI+vXrERkZCZVKhcOHD+PDDz9Et27dDOtnzZqFqKgolJSUYPbs2RgyZIiMbmPlypUICwtDRkYGAODAgQMIDQ3FqlWrcPToUcTHxxueRyqEwJAhQzBhwgQAwNChQw1jVwFg6tSp6NGjh5R+ERGRfeGVTyIiIkmeeeYZPPPMM+3ezqJFi0wuj4mJQXZ2dru3f7HZs2dj9uzZZtfri1JTfv31V+n9ISIi+8Qrn0RERF0Ix2QSEZGtsPgkIiLqQn7++WfD5EEd6dixYx2+TyIiUhbedktERNQFBAQEIDw8HGlpaejbty98fX0BAF988QWio6Ol70+tVuPqq682/P+ioiKEh4cjKChI+r6IiKhzUAlT86kTERERERERScTbbomIiIiIiMjqWHwSERERERGR1bH4JCIiIiIiIqtj8UlERERERERWx+KTiIiIiIiIrI7FJxEREREREVkdi08iIiIiIiKyOhafREREREREZHUsPomIiIiIiMjqWHwSERERERGR1bH4JCIiIiIiIqtj8UlERERERERWx+KTiIiIiIiIrI7FJxEREREREVkdi08iIiIiIiKyOhafREREREREZHX/B8r2MNHh9HjAAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1050x750 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -747,67 +778,58 @@
     }
    ],
    "source": [
-    "#Colori e grafica in generale generati da LLM\n",
     "\n",
-    "mpl.rcParams.update({\n",
-    "    \"font.family\":       \"serif\",\n",
-    "    \"font.serif\":        [\"Times New Roman\", \"DejaVu Serif\"],\n",
-    "    \"mathtext.fontset\":  \"stix\",\n",
-    "    \"axes.labelsize\":    13,\n",
-    "    \"xtick.labelsize\":   11,\n",
-    "    \"ytick.labelsize\":   11,\n",
-    "    \"legend.fontsize\":   11,\n",
-    "    \"figure.dpi\":        150,\n",
-    "})\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(7, 5))\n",
+    "plt.figure(figsize=(10,6))\n",
     "\n",
-    "x_fit = np.linspace(-0.05, df[\"Dx\"].max() * 1.10, 100)\n",
-    "y_fit = A * x_fit + B\n",
-    "sigma_fit = np.sqrt((x_fit * sA)**2 + sB**2)\n",
-    "\n",
-    "'''\n",
-    "ax.fill_between(x_fit,\n",
-    "                y_fit - sigma_fit,\n",
-    "                y_fit + sigma_fit,\n",
-    "                alpha=0.20, color=\"#C0392B\",\n",
-    "                label=r\"Incertezza fit $\\pm\\,\\sigma$\")\n",
-    "'''\n",
-    "ax.plot(x_fit, y_fit,\n",
-    "        color=\"#C0392B\", linewidth=1, zorder=3,\n",
-    "        label=rf\"Fit: $F = ({A:.2f} \\pm {sA:.2f})\\,\\Delta x + ({B:.3f} \\pm {sB:.3f})$ N\")\n",
-    "\n",
-    "ax.errorbar(\n",
-    "    df[\"Dx\"], df[\"F\"],\n",
-    "    xerr=df[\"uDx\"],          # barre orizzontali  (errore su Δx)\n",
-    "    yerr=df[\"uF\"],           # barre verticali    (errore su F)\n",
-    "    fmt=\"none\",                 # simbolo cerchio per il punto\n",
-    "    color=\"#1A5276\",\n",
-    "    ecolor=\"#1A5276\",\n",
-    "    elinewidth=1.0,          # spessore delle barre\n",
-    "    capsize=4,               # cappelletto alle estremità\n",
-    "    capthick=1.0,\n",
-    "    markersize=5,\n",
-    "    zorder=5,\n",
-    "    label=\"Dati sperimentali\",\n",
+    "# Seaborn scatter\n",
+    "sns.scatterplot(\n",
+    "    data=data,\n",
+    "    x=\"este\",\n",
+    "    y=\"F\",\n",
+    "    s=7\n",
     ")\n",
     "\n",
-    "ax.set_xlabel(r\"$\\Delta x\\ \\mathrm{[m]}$\")\n",
-    "ax.set_ylabel(r\"$F\\ \\mathrm{[N]}$\")\n",
-    "ax.set_title(\"Forza / Estensione — Legge di Hooke\", pad=10)\n",
-    " \n",
-    "ax.legend(framealpha=0.9, edgecolor=\"0.7\")\n",
-    "ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.5)\n",
-    "ax.set_xlim(left=0)\n",
-    " \n",
-    "# Testo con i parametri del fit (opzionale)\n",
-    "textstr = (rf\"$k = A = ({A:.1f} \\pm {sA:.1f})\\ \\mathrm{{N/m}}$\" \"\\n\"\n",
-    "           rf\"$B = ({B:.3f} \\pm {sB:.3f})\\ \\mathrm{{N}}$\")\n",
-    "ax.text(0.04, 0.95, textstr,\n",
-    "        transform=ax.transAxes,\n",
-    "        verticalalignment=\"top\",\n",
-    "        bbox=dict(boxstyle=\"round,pad=0.4\", facecolor=\"white\", alpha=0.8, edgecolor=\"0.7\"),\n",
-    "        fontsize=10)\n"
+    "# Barre d’errore\n",
+    "plt.errorbar(\n",
+    "    data[\"este\"],\n",
+    "    data[\"F\"],\n",
+    "    xerr=data[\"ueste\"],\n",
+    "    yerr=data[\"uF\"],\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[\"este\"].max(), 200)\n",
+    "yfit = B + A * xfit\n",
+    "\n",
+    "\n",
+    "plt.plot(\n",
+    "    xfit,\n",
+    "    yfit,\n",
+    "    color=\"crimson\",\n",
+    "    linewidth=1,\n",
+    "    zorder=10,\n",
+    "    label=\"Fit lineare\"\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"
    ]
   }
  ],
diff --git a/molla/statica/mollaStatica1.ipynb b/molla/statica/mollaStatica1.ipynb
index 4ff2f93..eda7072 100644
--- a/molla/statica/mollaStatica1.ipynb
+++ b/molla/statica/mollaStatica1.ipynb
@@ -1,8 +1,24 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3a8dfc0e",
+   "metadata": {},
+   "source": [
+    "# Analisi della molla statica 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff20b69",
+   "metadata": {},
+   "source": [
+    "## Import delle librerie e set di variabili gloabali"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f34c5b88",
    "metadata": {},
    "outputs": [],
@@ -17,95 +33,60 @@
     "import statsmodels.api as sm\n",
     "\n",
     "\n",
-    "g = 9.806\n",
+    "g = 9.807\n",
     "ug = 0.001"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08efb2be",
+   "cell_type": "markdown",
+   "id": "39d2ffea",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\ndf[\"K\"] = df.m * g / df.Dx\\ndf[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\\n\\ndf[\"F\"] = df.m * g\\ndf[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\\nmedia_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\\n\\n\\n#chi 2\\nchi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\\ndof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\\nchi2_rid = chi2_val / dof\\np_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\\n\\nprint(\"#\"*60)\\nprint(\"Valori di K\")\\nprint(\"media pesata K:\", media_K)\\nprint(\"sigma K:\", sigma_K)\\nprint(f\"Chi2           : {chi2_val:.4f}\")\\nprint(f\"DOF            : {dof}\")\\nprint(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\\nprint(f\"p-value        : {p_value:.4f}\")\\n'"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df = pd.read_csv(r'statica1.csv')\n",
-    "\n",
-    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
-    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[dx_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['Dx'] = stats['Dx']\n",
-    "  df['uDx'] = stats['uDx']\n",
-    "  \n",
-    "  return df\n",
-    "\n",
-    "def calcola_m_stats(df, err_arbitrario_m):\n",
-    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[m_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'m': media, 'um': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['m'] = stats['m']\n",
-    "  df['um'] = stats['um']\n",
-    "  \n",
-    "  return df\n",
-    "\n",
-    "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",
-    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
-    "df = calcola_m_stats(df, err_arbitrario_m=0.0028867513)\n"
+    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
+    "- Lettura del CSV\n",
+    "- Creazione del data frame\n",
+    "- Deviazioni standard\n",
+    "- Permutazioni e calcolo dei Delta"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "id": "08efb2be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r'statica1.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",
+    "df = calcola_stats(df, \"Dx\", err_arbitrario=0.01)\n",
+    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "5494409f",
    "metadata": {},
    "outputs": [
@@ -234,7 +215,7 @@
        "4  168.53  0.002887  "
       ]
      },
-     "execution_count": 147,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "976d5531",
    "metadata": {},
    "outputs": [
@@ -254,6 +235,7 @@
      "output_type": "stream",
      "text": [
       "[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]\n",
+      "10\n",
       "############################################################\n",
       "[ 8.33       16.34666667 24.38       32.65333333  8.01666667 16.05\n",
       " 24.32333333  8.03333333 16.30666667  8.27333333]\n",
@@ -288,9 +270,19 @@
     "print(umasse)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5b3b6776",
+   "metadata": {},
+   "source": [
+    "## Calcolo dei K e media pesata\n",
+    "- Calcolo del K\n",
+    "- Calcolo della media pesata (sui K)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2ad19283",
    "metadata": {},
    "outputs": [
@@ -298,10 +290,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[23.12002881 23.79714641 23.89558901 23.89236505 24.50072931 24.29810717\n",
-      " 24.15686666 24.09590539 23.98781725 23.88286463]\n",
-      "[0.22417698 0.16284102 0.10114355 0.15187011 0.31272214 0.1404374\n",
-      " 0.20118598 0.36230687 0.31897871 0.61338857]\n"
+      "[23.12238655 23.79957321 23.89802584 23.89480155 24.50322786 24.30058505\n",
+      " 24.15933014 24.09836266 23.99026349 23.88530016]\n",
+      "[0.22419984 0.16285763 0.10115386 0.15188559 0.31275403 0.14045171\n",
+      " 0.2012065  0.36234381 0.31901124 0.61345112]\n"
      ]
     }
    ],
@@ -319,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "5f59d6c9",
    "metadata": {},
    "outputs": [
@@ -327,8 +319,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "K-medio: 23.948686783351924\n",
-      "sigmaC:  0.05731089207006433\n"
+      "K-medio: 23.951129031636384\n",
+      "sigmaC:  0.057316734630371916\n"
      ]
     }
    ],
@@ -352,9 +344,50 @@
     "print(\"sigmaC: \", uA)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "02fc45a9",
+   "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": null,
+   "execution_count": 7,
+   "id": "7e75ec05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "data = pd.DataFrame({\n",
+    "    \"x\": este,\n",
+    "    \"ux\": ueste,\n",
+    "    \"y\": - F,\n",
+    "    \"uy\": uF\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "313237da",
+   "metadata": {},
+   "source": [
+    "### Regressione lineare \"OLS\"\n",
+    "\n",
+    "Non tiene conto dei nostri errori, un risultato puro e semplice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "aefe7756",
    "metadata": {},
    "outputs": [
@@ -364,20 +397,20 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                      F   R-squared:                       1.000\n",
+      "Dep. Variable:                      y   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 2.238e+04\n",
-      "Date:                Wed, 01 Apr 2026   Prob (F-statistic):           4.46e-15\n",
-      "Time:                        11:03:56   Log-Likelihood:                -27.237\n",
-      "No. Observations:                  10   AIC:                             58.47\n",
+      "Date:                Fri, 03 Apr 2026   Prob (F-statistic):           4.46e-15\n",
+      "Time:                        15:28:32   Log-Likelihood:                -27.238\n",
+      "No. Observations:                  10   AIC:                             58.48\n",
       "Df Residuals:                       8   BIC:                             59.08\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "const          0.0999      2.914      0.034      0.973      -6.621       6.821\n",
-      "este          23.9663      0.160    149.602      0.000      23.597      24.336\n",
+      "const          0.0999      2.915      0.034      0.973      -6.621       6.821\n",
+      "x             23.9687      0.160    149.602      0.000      23.599      24.338\n",
       "==============================================================================\n",
       "Omnibus:                        0.134   Durbin-Watson:                   0.595\n",
       "Prob(Omnibus):                  0.935   Jarque-Bera (JB):                0.285\n",
@@ -389,49 +422,40 @@
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\n",
       "RISULTATI REGRESSIONE:\n",
-      "k (pendenza) = 23.96627 ± 0.16020\n",
-      "intercetta  = 0.09992 ± 2.91442\n",
+      "Aols  = 23.96871 ± 0.16022\n",
+      "Bols  = 0.09993 ± 2.91472\n",
       "R² = 0.99964\n"
      ]
     }
    ],
    "source": [
-    "data = pd.DataFrame({\n",
-    "    \"este\": este,\n",
-    "    \"ueste\": ueste,\n",
-    "    \"F\": - F,\n",
-    "    \"uF\": uF\n",
-    "})\n",
-    "\n",
-    "\n",
-    "X = sm.add_constant(data[\"este\"])\n",
-    "model = sm.OLS(data[\"F\"], X).fit()\n",
+    "X = sm.add_constant(data[\"x\"])\n",
+    "model = sm.OLS(data[\"y\"], X).fit()\n",
     "\n",
     "print(model.summary())\n",
     "\n",
     "# Estrazione parametri\n",
-    "intercetta = model.params[\"const\"]\n",
-    "pente = model.params[\"este\"]\n",
-    "u_intercetta = model.bse[\"const\"]\n",
-    "u_pente = model.bse[\"este\"]\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\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
-    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
-    "print(f\"R² = {R2:.5f}\")\n",
-    "\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": null,
+   "execution_count": 9,
    "id": "1d42b009",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -441,47 +465,41 @@
     }
    ],
    "source": [
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "\n",
     "plt.figure(figsize=(10,6))\n",
     "\n",
     "# Seaborn scatter\n",
-    "sns.scatterplot(\n",
-    "    data=data,\n",
-    "    x=\"este\",\n",
-    "    y=\"F\",\n",
-    "    s=7\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[\"este\"],\n",
-    "    data[\"F\"],\n",
-    "    xerr=data[\"ueste\"],\n",
-    "    yerr=data[\"uF\"],\n",
-    "    fmt=\"none\",\n",
-    "    ecolor=\"gray\",\n",
-    "    elinewidth=1,\n",
-    "    capsize=3,\n",
-    "    alpha=0.7\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[\"este\"].max(), 200)\n",
-    "yfit = intercetta + pente * xfit\n",
-    "\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\"\n",
+    "  xfit,\n",
+    "  yfit,\n",
+    "  color=\"crimson\",\n",
+    "  linewidth=1,\n",
+    "  zorder=10,\n",
+    "  label=\"Fit lineare OLS\"\n",
     ")\n",
     "\n",
-    "\n",
     "plt.xlim(left=0)\n",
     "plt.ylim(bottom=0)\n",
     "\n",
@@ -492,12 +510,12 @@
     "plt.legend()\n",
     "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "986ff4a6",
    "metadata": {},
    "outputs": [
@@ -505,197 +523,61 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Chi^2 = 25.02317\n",
-      "Gradi di libertà = 8\n",
-      "Chi^2 ridotto = 3.12790\n",
-      "Probabilità P(0 → chi^2) = 0.99846\n"
+      "Chi²      = 25.02317\n",
+      "GdL       = 8\n",
+      "Chi² rid  = 3.12790\n",
+      "P(0, chi²)= 0.99846\n",
+      "\n",
+      "\n",
+      "############################################################\n",
+      "capiamo quale dato sta contribuendo maggiormente\n",
+      "0    13.640283\n",
+      "1     1.141735\n",
+      "2     0.543390\n",
+      "3     0.255239\n",
+      "4     2.911852\n",
+      "5     5.525531\n",
+      "6     0.872959\n",
+      "7     0.105774\n",
+      "8     0.002341\n",
+      "9     0.024062\n",
+      "dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "F_fit = intercetta + pente * data[\"este\"]\n",
-    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
+    "F_fit = Bols + Aols * data[\"x\"]\n",
+    "sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
     "\n",
-    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\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",
-    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "Pols = chi2.cdf(chi2_val, df=nu)\n",
     "\n",
     "\n",
-    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
-    "print(f\"Gradi di libertà = {nu}\")\n",
-    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
-    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\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": "code",
-   "execution_count": null,
-   "id": "ef0817f4",
+   "cell_type": "markdown",
+   "id": "6015e6fd",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    13.640283\n",
-       "1     1.141735\n",
-       "2     0.543390\n",
-       "3     0.255239\n",
-       "4     2.911852\n",
-       "5     5.525530\n",
-       "6     0.872958\n",
-       "7     0.105774\n",
-       "8     0.002341\n",
-       "9     0.024062\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "((data[\"F\"] - F_fit) / sigma)**2"
+    "### Regressione lineare Carpi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "cebe6742",
-   "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>este</th>\n",
-       "      <th>ueste</th>\n",
-       "      <th>F</th>\n",
-       "      <th>uF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.330000</td>\n",
-       "      <td>0.080747</td>\n",
-       "      <td>192.58984</td>\n",
-       "      <td>0.044591</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>16.346667</td>\n",
-       "      <td>0.111833</td>\n",
-       "      <td>389.00402</td>\n",
-       "      <td>0.056359</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>24.380000</td>\n",
-       "      <td>0.103150</td>\n",
-       "      <td>582.57446</td>\n",
-       "      <td>0.071639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>32.653333</td>\n",
-       "      <td>0.207525</td>\n",
-       "      <td>780.16536</td>\n",
-       "      <td>0.089064</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8.016667</td>\n",
-       "      <td>0.102307</td>\n",
-       "      <td>196.41418</td>\n",
-       "      <td>0.044764</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>16.050000</td>\n",
-       "      <td>0.092736</td>\n",
-       "      <td>389.98462</td>\n",
-       "      <td>0.056429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>24.323333</td>\n",
-       "      <td>0.202550</td>\n",
-       "      <td>587.57552</td>\n",
-       "      <td>0.072063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8.033333</td>\n",
-       "      <td>0.120775</td>\n",
-       "      <td>193.57044</td>\n",
-       "      <td>0.044635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>16.306667</td>\n",
-       "      <td>0.216826</td>\n",
-       "      <td>391.16134</td>\n",
-       "      <td>0.056514</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>8.273333</td>\n",
-       "      <td>0.212477</td>\n",
-       "      <td>197.59090</td>\n",
-       "      <td>0.044818</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        este     ueste          F        uF\n",
-       "0   8.330000  0.080747  192.58984  0.044591\n",
-       "1  16.346667  0.111833  389.00402  0.056359\n",
-       "2  24.380000  0.103150  582.57446  0.071639\n",
-       "3  32.653333  0.207525  780.16536  0.089064\n",
-       "4   8.016667  0.102307  196.41418  0.044764\n",
-       "5  16.050000  0.092736  389.98462  0.056429\n",
-       "6  24.323333  0.202550  587.57552  0.072063\n",
-       "7   8.033333  0.120775  193.57044  0.044635\n",
-       "8  16.306667  0.216826  391.16134  0.056514\n",
-       "9   8.273333  0.212477  197.59090  0.044818"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "2d4b7144",
    "metadata": {},
    "outputs": [
@@ -704,10 +586,10 @@
      "output_type": "stream",
      "text": [
       "Ax + B : \n",
-      "A      = 24.04493 +- 0.13159\n",
-      "B      = -1.56967 +- 2.08087\n",
-      "cov_AB = -0.246320\n",
-      "p-value chi² = 0.9978741\n"
+      "AC      = 24.0474 +- 0.1316\n",
+      "BC      = -1.5698 +- 2.0811\n",
+      "cov_ABC = -0.246370\n",
+      "P(0, chi²)= 0.9979\n"
      ]
     }
    ],
@@ -761,62 +643,23 @@
     "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
     "\n",
     "\n",
-    "A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
+    "AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\n",
     "print(\"Ax + B : \")\n",
-    "print(f\"A      = {A:.5f} +- {sA:.5f}\")\n",
-    "print(f\"B      = {B:.5f} +- {sB:.5f}\")\n",
-    "print(f\"cov_AB = {covAB:.6f}\")\n",
-    "print(f\"p-value chi² = {chi:.7f}\")\n",
-    "\n",
-    "\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": null,
-   "id": "32e9948f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chi^2 = 24.13715\n",
-      "Gradi di libertà = 8\n",
-      "Chi^2 ridotto = 3.01714\n",
-      "Probabilità P(0 → chi^2) = 0.99783\n"
-     ]
-    }
-   ],
-   "source": [
-    "F_fit = B + A * data[\"este\"]\n",
-    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
-    "\n",
-    "\n",
-    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
-    "\n",
-    "N = len(data)\n",
-    "nu = N - 2\n",
-    "\n",
-    "chi2_red = chi2_val / nu\n",
-    "P = chi2.cdf(chi2_val, df=nu)\n",
-    "\n",
-    "\n",
-    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
-    "print(f\"Gradi di libertà = {nu}\")\n",
-    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
-    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "e2407a04",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -826,44 +669,41 @@
     }
    ],
    "source": [
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "\n",
     "plt.figure(figsize=(10,6))\n",
     "\n",
     "# Seaborn scatter\n",
     "sns.scatterplot(\n",
-    "    data=data,\n",
-    "    x=\"este\",\n",
-    "    y=\"F\",\n",
-    "    s=7\n",
+    "  data=data,\n",
+    "  x=\"x\",\n",
+    "  y=\"y\",\n",
+    "  s=7\n",
     ")\n",
     "\n",
     "# Barre d’errore\n",
     "plt.errorbar(\n",
-    "    data[\"este\"],\n",
-    "    data[\"F\"],\n",
-    "    xerr=data[\"ueste\"],\n",
-    "    yerr=data[\"uF\"],\n",
-    "    fmt=\"none\",\n",
-    "    ecolor=\"gray\",\n",
-    "    elinewidth=1,\n",
-    "    capsize=3,\n",
-    "    alpha=0.7\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[\"este\"].max(), 200)\n",
-    "yfit = B + A * xfit\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\"\n",
+    "  xfit,\n",
+    "  yfit,\n",
+    "  color=\"crimson\",\n",
+    "  linewidth=1,\n",
+    "  zorder=10,\n",
+    "  label=\"Fit lineare Carpi\"\n",
     ")\n",
     "\n",
     "\n",
@@ -879,6 +719,452 @@
     "\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "32e9948f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi²      = 24.13715\n",
+      "GdL       = 8\n",
+      "Chi² rid  = 3.01714\n",
+      "P(0, chi²)= 0.99783\n",
+      "\n",
+      "\n",
+      "############################################################\n",
+      "capiamo quale dato sta contribuendo maggiormente\n",
+      "0    9.978674\n",
+      "1    0.850746\n",
+      "2    0.696840\n",
+      "3    0.467438\n",
+      "4    4.507635\n",
+      "5    6.377901\n",
+      "6    0.776558\n",
+      "7    0.464361\n",
+      "8    0.014989\n",
+      "9    0.002006\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": "ad1c81fc",
+   "metadata": {},
+   "source": [
+    "### Regressione lineare pesata Yorkfit "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bfb895c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AY        = 24.0647990 ± 0.1318513\n",
+      "BY        = -1.8175805 ± 2.0850560\n",
+      "cov_ABY   = -0.2473126752648217\n",
+      "chi²      = 24.11969\n",
+      "chi² rid  = 3.01496\n",
+      "P(0, chi²)= 0.99781\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": 15,
+   "id": "202de438",
+   "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": "63442336",
+   "metadata": {},
+   "source": [
+    "## Raccolta finale dei dati"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "caf23dbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "RISULTATI REGRESSIONE OLS:\n",
+      "Aols  = 23.96871 ± 0.16022\n",
+      "Bols  = 0.09993 ± 2.91472\n",
+      "P(0, chi²)= 0.99846\n",
+      "\n",
+      "RISULTATI REGRESSIONE Carpi:\n",
+      "Ac  = 24.04738 ± 0.13160\n",
+      "Bc  = -1.56983 ± 2.08108\n",
+      "P(0, chi²)= 0.99783\n",
+      "\n",
+      "RISULTATI REGRESSIONE York:\n",
+      "Ay  = 24.06480 ± 0.13185\n",
+      "By  = -1.81758 ± 2.08506\n",
+      "P(0, chi²)= 0.99781\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\")\n",
+    "\n",
+    "print(\"RISULTATI 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": "7692f792",
+   "metadata": {},
+   "source": [
+    "## Aggiunta degli errori strumentali"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8f5c8bb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "u_strum_m    = 0.004082\n",
+      "u_strum_Dx   = 0.020412\n",
+      "uF_strum     = 0.089066\n",
+      "uK_strum     = 0.662828\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_b = 0.01\n",
+    "res_c = 0.05\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  = np.maximum(ueste,  u_strum_Dx)\n",
+    "umasse_strum = np.maximum(umasse, u_strum_m)\n",
+    "\n",
+    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
+    "uF_strum  = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
+    "uDx_strum = np.max(ueste_strum)\n",
+    "\n",
+    "uK_strum  = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
+    "\n",
+    "print(f\"u_strum_m    = {u_strum_m:.6f}\")\n",
+    "print(f\"u_strum_Dx   = {u_strum_Dx:.6f}\")\n",
+    "print(f\"uF_strum     = {uF_strum:.6f}\")\n",
+    "print(f\"uK_strum     = {uK_strum:.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8706126d",
+   "metadata": {},
+   "source": [
+    "### Propapazione dell'errore strumentale max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a1dc24c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Media pesata\n",
+    "uA_fin   = np.sqrt(uA**2      + uK_strum**2)\n",
+    "\n",
+    "# OLS\n",
+    "uAols_fin = np.sqrt(uAols**2  + uK_strum**2)\n",
+    "uBols_fin = np.sqrt(uBols**2  + uK_strum**2)\n",
+    "\n",
+    "# Carpi\n",
+    "uAC_fin  = np.sqrt(uAC**2     + uK_strum**2)\n",
+    "uBC_fin  = np.sqrt(uBC**2     + uK_strum**2)\n",
+    "\n",
+    "# York\n",
+    "uAY_fin  = np.sqrt(uAY**2     + uK_strum**2)\n",
+    "uBY_fin  = np.sqrt(uBY**2     + uK_strum**2)\n",
+    "\n",
+    "\n",
+    "# Nuovo uy e ux per ricalcolare i chi^2\n",
+    "uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
+    "ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
+    "\n",
+    "# OLS\n",
+    "F_fit_ols = Bols + Aols * data[\"x\"]\n",
+    "sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
+    "chi2_ols  = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
+    "GdL       = len(data) - 2\n",
+    "\n",
+    "# Carpi\n",
+    "F_fit_c   = BC + AC * data[\"x\"]\n",
+    "sigma_c   = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
+    "chi2_c    = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
+    "\n",
+    "# York\n",
+    "F_fit_y   = BY + AY * data[\"x\"]\n",
+    "sigma_y_   = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
+    "chi2_y     = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e57c7d8",
+   "metadata": {},
+   "source": [
+    "## Risutltati della propagazione dell'errore strumentale massimo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c8e264fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
+      "Media pesata K  = 23.95113 ± 0.66530\n",
+      "\n",
+      "RISULTATI REGRESSIONE OLS:\n",
+      "Aols  = 23.96871 ± 0.68192\n",
+      "Bols  = 0.09993 ± 2.98913\n",
+      "Chi² OLS   = 3.95368  |  rid = 0.49421  |  P = 0.13872\n",
+      "\n",
+      "RISULTATI REGRESSIONE Carpi:\n",
+      "AC  = 24.04738 ± 0.67577\n",
+      "BC  = -1.56983 ± 2.18409\n",
+      "Chi² Carpi = 4.03420  |  rid = 0.50428  |  P = 0.14598\n",
+      "\n",
+      "RISULTATI REGRESSIONE York:\n",
+      "AY  = 24.06480 ± 0.67582\n",
+      "BY  = -1.81758 ± 2.18788\n",
+      "Chi² York  = 4.06701  |  rid = 0.50838  |  P = 0.14897\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
+    "print(f\"Media pesata K  = {media:.5f} ± {uA_fin:.5f}\")\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
+    "print(f\"Aols  = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
+    "print(f\"Bols  = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
+    "print(f\"Chi² OLS   = {chi2_ols:.5f}  |  rid = {chi2_ols/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
+    "print(f\"AC  = {AC:.5f} ± {uAC_fin:.5f}\")\n",
+    "print(f\"BC  = {BC:.5f} ± {uBC_fin:.5f}\")\n",
+    "print(f\"Chi² Carpi = {chi2_c:.5f}  |  rid = {chi2_c/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
+    "print(f\"AY  = {AY:.5f} ± {uAY_fin:.5f}\")\n",
+    "print(f\"BY  = {BY:.5f} ± {uBY_fin:.5f}\")\n",
+    "print(f\"Chi² York  = {chi2_y:.5f}  |  rid = {chi2_y/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e38b0bd",
+   "metadata": {},
+   "source": [
+    "## Minima interpretazione (Mio commento -> Non necessario)\n",
+    "Sovrastimando l'errore in modo molto marcato ovviamente il Chi² viene ridotto.\n",
+    "Il Chi² che stiamo usando serve per stimare quale sia la probabilità di ottenere un Chi² minore o uguale a quello osservato.\n",
+    "Sapendo che il valore buono è ~50% stiamo un po' allargando le distribuzioni in modo un po' esagerato.\n",
+    "\n",
+    "In generale con il Chi² vale:\n",
+    "- \\~50%: Errori ottimamente stimati\n",
+    "- \\<5% : Fit troppo grande\n",
+    "- \\>95%: Fit troppo piccolo\n",
+    "\n",
+    "In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
+   ]
   }
  ],
  "metadata": {
diff --git a/molla/statica/mollaStatica2.ipynb b/molla/statica/mollaStatica2.ipynb
index f032cfe..d840a8c 100644
--- a/molla/statica/mollaStatica2.ipynb
+++ b/molla/statica/mollaStatica2.ipynb
@@ -1,8 +1,24 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3a8dfc0e",
+   "metadata": {},
+   "source": [
+    "# Analisi della molla statica 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff20b69",
+   "metadata": {},
+   "source": [
+    "## Import delle librerie e set di variabili gloabali"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 141,
    "id": "f34c5b88",
    "metadata": {},
    "outputs": [],
@@ -17,95 +33,60 @@
     "import statsmodels.api as sm\n",
     "\n",
     "\n",
-    "g = 9.806\n",
+    "g = 9.807\n",
     "ug = 0.001"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08efb2be",
+   "cell_type": "markdown",
+   "id": "39d2ffea",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\ndf[\"K\"] = df.m * g / df.Dx\\ndf[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\\n\\ndf[\"F\"] = df.m * g\\ndf[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\\nmedia_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\\n\\n\\n#chi 2\\nchi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\\ndof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\\nchi2_rid = chi2_val / dof\\np_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\\n\\nprint(\"#\"*60)\\nprint(\"Valori di K\")\\nprint(\"media pesata K:\", media_K)\\nprint(\"sigma K:\", sigma_K)\\nprint(f\"Chi2           : {chi2_val:.4f}\")\\nprint(f\"DOF            : {dof}\")\\nprint(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\\nprint(f\"p-value        : {p_value:.4f}\")\\n'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df = pd.read_csv(r'statica2.csv')\n",
-    "\n",
-    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
-    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[dx_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['Dx'] = stats['Dx']\n",
-    "  df['uDx'] = stats['uDx']\n",
-    "  \n",
-    "  return df\n",
-    "\n",
-    "def calcola_m_stats(df, err_arbitrario_m):\n",
-    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
-    "  \n",
-    "  def riga_stats(row):\n",
-    "    valori = row[m_cols].dropna()\n",
-    "    n = len(valori)\n",
-    "      \n",
-    "    if n == 0:\n",
-    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
-    "    elif n == 1:\n",
-    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
-    "    else:\n",
-    "      media = valori.mean()\n",
-    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
-    "      return pd.Series({'m': media, 'um': sigma})\n",
-    "  \n",
-    "  stats = df.apply(riga_stats, axis=1)\n",
-    "  df['m'] = stats['m']\n",
-    "  df['um'] = stats['um']\n",
-    "  \n",
-    "  return df\n",
-    "\n",
-    "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",
-    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
-    "df = calcola_m_stats(df, err_arbitrario_m=0.0028867513)\n"
+    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
+    "- Lettura del CSV\n",
+    "- Creazione del data frame\n",
+    "- Deviazioni standard\n",
+    "- Permutazioni e calcolo dei Delta"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 142,
+   "id": "08efb2be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r'statica2.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",
+    "df = calcola_stats(df, \"Dx\", err_arbitrario=0.01)\n",
+    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
    "id": "5494409f",
    "metadata": {},
    "outputs": [
@@ -218,7 +199,7 @@
        "3  0.188750  108.610000  0.002887  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 144,
    "id": "976d5531",
    "metadata": {},
    "outputs": [
@@ -272,9 +253,19 @@
     "print(umasse)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5b3b6776",
+   "metadata": {},
+   "source": [
+    "## Calcolo dei K e media pesata\n",
+    "- Calcolo del K\n",
+    "- Calcolo della media pesata (sui K)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 145,
    "id": "2ad19283",
    "metadata": {},
    "outputs": [
@@ -282,8 +273,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[3.18045307 3.19974522 3.19938176 3.21961214 3.2091078  3.19864707]\n",
-      "[0.02199135 0.00788665 0.00602107 0.01779022 0.00988124 0.01119321]\n"
+      "[3.18077741 3.20007153 3.19970803 3.21994047 3.20943506 3.19897326]\n",
+      "[0.02199359 0.00788745 0.00602168 0.01779203 0.00988225 0.01119435]\n"
      ]
     }
    ],
@@ -301,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 146,
    "id": "5f59d6c9",
    "metadata": {},
    "outputs": [
@@ -309,8 +300,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "K-medio: 3.201234977342435\n",
-      "sigmaC:  0.003860115909468001\n"
+      "K-medio: 3.2015614337677185\n",
+      "sigmaC:  0.003860508806765328\n"
      ]
     }
    ],
@@ -334,9 +325,50 @@
     "print(\"sigmaC: \", uA)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "02fc45a9",
+   "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": 7,
+   "execution_count": 147,
+   "id": "7e75ec05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "data = pd.DataFrame({\n",
+    "    \"x\": este,\n",
+    "    \"ux\": ueste,\n",
+    "    \"y\": - F,\n",
+    "    \"uy\": uF\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "313237da",
+   "metadata": {},
+   "source": [
+    "### Regressione lineare \"OLS\"\n",
+    "\n",
+    "Non tiene conto dei nostri errori, un risultato puro e semplice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
    "id": "aefe7756",
    "metadata": {},
    "outputs": [
@@ -346,11 +378,11 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                      F   R-squared:                       1.000\n",
+      "Dep. Variable:                      y   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 1.277e+05\n",
-      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           3.68e-10\n",
-      "Time:                        14:04:37   Log-Likelihood:                -7.2416\n",
+      "Date:                Fri, 03 Apr 2026   Prob (F-statistic):           3.68e-10\n",
+      "Time:                        15:05:08   Log-Likelihood:                -7.2422\n",
       "No. Observations:                   6   AIC:                             18.48\n",
       "Df Residuals:                       4   BIC:                             18.07\n",
       "Df Model:                           1                                         \n",
@@ -359,7 +391,7 @@
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0265      0.991      0.027      0.980      -2.724       2.777\n",
-      "este           3.2011      0.009    357.408      0.000       3.176       3.226\n",
+      "x              3.2014      0.009    357.408      0.000       3.177       3.226\n",
       "==============================================================================\n",
       "Omnibus:                          nan   Durbin-Watson:                   1.155\n",
       "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.275\n",
@@ -371,8 +403,8 @@
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\n",
       "RISULTATI REGRESSIONE:\n",
-      "k (pendenza) = 3.20112 ± 0.00896\n",
-      "intercetta  = 0.02655 ± 0.99066\n",
+      "Aols  = 3.20145 ± 0.00896\n",
+      "Bols  = 0.02655 ± 0.99076\n",
       "R² = 0.99997\n"
      ]
     },
@@ -386,42 +418,33 @@
     }
    ],
    "source": [
-    "data = pd.DataFrame({\n",
-    "    \"este\": este,\n",
-    "    \"ueste\": ueste,\n",
-    "    \"F\": - F,\n",
-    "    \"uF\": uF\n",
-    "})\n",
-    "\n",
-    "\n",
-    "X = sm.add_constant(data[\"este\"])\n",
-    "model = sm.OLS(data[\"F\"], X).fit()\n",
+    "X = sm.add_constant(data[\"x\"])\n",
+    "model = sm.OLS(data[\"y\"], X).fit()\n",
     "\n",
     "print(model.summary())\n",
     "\n",
     "# Estrazione parametri\n",
-    "intercetta = model.params[\"const\"]\n",
-    "pente = model.params[\"este\"]\n",
-    "u_intercetta = model.bse[\"const\"]\n",
-    "u_pente = model.bse[\"este\"]\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\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
-    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
-    "print(f\"R² = {R2:.5f}\")\n",
-    "\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": 8,
+   "execution_count": 149,
    "id": "1d42b009",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -431,47 +454,41 @@
     }
    ],
    "source": [
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "\n",
     "plt.figure(figsize=(10,6))\n",
     "\n",
     "# Seaborn scatter\n",
-    "sns.scatterplot(\n",
-    "    data=data,\n",
-    "    x=\"este\",\n",
-    "    y=\"F\",\n",
-    "    s=7\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[\"este\"],\n",
-    "    data[\"F\"],\n",
-    "    xerr=data[\"ueste\"],\n",
-    "    yerr=data[\"uF\"],\n",
-    "    fmt=\"none\",\n",
-    "    ecolor=\"gray\",\n",
-    "    elinewidth=1,\n",
-    "    capsize=3,\n",
-    "    alpha=0.7\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[\"este\"].max(), 200)\n",
-    "yfit = intercetta + pente * xfit\n",
-    "\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\"\n",
+    "  xfit,\n",
+    "  yfit,\n",
+    "  color=\"crimson\",\n",
+    "  linewidth=1,\n",
+    "  zorder=10,\n",
+    "  label=\"Fit lineare OLS\"\n",
     ")\n",
     "\n",
-    "\n",
     "plt.xlim(left=0)\n",
     "plt.ylim(bottom=0)\n",
     "\n",
@@ -482,12 +499,12 @@
     "plt.legend()\n",
     "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 150,
    "id": "986ff4a6",
    "metadata": {},
    "outputs": [
@@ -495,161 +512,57 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Chi^2 = 2.77690\n",
-      "Gradi di libertà = 4\n",
-      "Chi^2 ridotto = 0.69422\n",
-      "Probabilità P(0 → chi^2) = 0.40417\n"
+      "Chi²      = 2.77690\n",
+      "GdL       = 4\n",
+      "Chi² rid  = 0.69422\n",
+      "P(0, chi²)= 0.40417\n",
+      "\n",
+      "\n",
+      "############################################################\n",
+      "capiamo quale dato sta contribuendo maggiormente\n",
+      "0    0.908593\n",
+      "1    0.040832\n",
+      "2    0.097969\n",
+      "3    1.041094\n",
+      "4    0.620684\n",
+      "5    0.067728\n",
+      "dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "F_fit = intercetta + pente * data[\"este\"]\n",
-    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
+    "F_fit = Bols + Aols * data[\"x\"]\n",
+    "sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
     "\n",
-    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\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",
-    "P = chi2.cdf(chi2_val, df=nu)\n",
+    "Pols = chi2.cdf(chi2_val, df=nu)\n",
     "\n",
     "\n",
-    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
-    "print(f\"Gradi di libertà = {nu}\")\n",
-    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
-    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\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": "code",
-   "execution_count": 10,
-   "id": "ef0817f4",
+   "cell_type": "markdown",
+   "id": "6015e6fd",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0.908593\n",
-       "1    0.040832\n",
-       "2    0.097969\n",
-       "3    1.041093\n",
-       "4    0.620683\n",
-       "5    0.067728\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "((data[\"F\"] - F_fit) / sigma)**2"
+    "### Regressione lineare Carpi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "cebe6742",
-   "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>este</th>\n",
-       "      <th>ueste</th>\n",
-       "      <th>F</th>\n",
-       "      <th>uF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>61.756667</td>\n",
-       "      <td>0.426786</td>\n",
-       "      <td>196.414180</td>\n",
-       "      <td>0.044764</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>121.726667</td>\n",
-       "      <td>0.299511</td>\n",
-       "      <td>389.494320</td>\n",
-       "      <td>0.056394</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>181.946667</td>\n",
-       "      <td>0.341682</td>\n",
-       "      <td>582.116847</td>\n",
-       "      <td>0.071601</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>59.970000</td>\n",
-       "      <td>0.331079</td>\n",
-       "      <td>193.080140</td>\n",
-       "      <td>0.044613</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>120.190000</td>\n",
-       "      <td>0.369667</td>\n",
-       "      <td>385.702667</td>\n",
-       "      <td>0.056123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60.220000</td>\n",
-       "      <td>0.210270</td>\n",
-       "      <td>192.622527</td>\n",
-       "      <td>0.044592</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         este     ueste           F        uF\n",
-       "0   61.756667  0.426786  196.414180  0.044764\n",
-       "1  121.726667  0.299511  389.494320  0.056394\n",
-       "2  181.946667  0.341682  582.116847  0.071601\n",
-       "3   59.970000  0.331079  193.080140  0.044613\n",
-       "4  120.190000  0.369667  385.702667  0.056123\n",
-       "5   60.220000  0.210270  192.622527  0.044592"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 151,
    "id": "2d4b7144",
    "metadata": {},
    "outputs": [
@@ -658,10 +571,10 @@
      "output_type": "stream",
      "text": [
       "Ax + B : \n",
-      "A      = 3.2002 +- 0.0092\n",
-      "B      = 0.1195 +- 0.9523\n",
-      "cov_AB = -0.007941\n",
-      "p-value chi² = 0.4020\n"
+      "AC      = 3.2005 +- 0.0092\n",
+      "BC      = 0.1195 +- 0.9524\n",
+      "cov_ABC = -0.007942\n",
+      "P(0, chi²)= 0.4020\n"
      ]
     }
    ],
@@ -715,62 +628,23 @@
     "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
     "\n",
     "\n",
-    "A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
+    "AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\n",
     "print(\"Ax + B : \")\n",
-    "print(f\"A      = {A:.4f} +- {sA:.4f}\")\n",
-    "print(f\"B      = {B:.4f} +- {sB:.4f}\")\n",
-    "print(f\"cov_AB = {covAB:.6f}\")\n",
-    "print(f\"p-value chi² = {chi:.4f}\")\n",
-    "\n",
-    "\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": 13,
-   "id": "32e9948f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chi^2 = 2.76835\n",
-      "Gradi di libertà = 4\n",
-      "Chi^2 ridotto = 0.69209\n",
-      "Probabilità P(0 → chi^2) = 0.40269\n"
-     ]
-    }
-   ],
-   "source": [
-    "F_fit = B + A * data[\"este\"]\n",
-    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
-    "\n",
-    "\n",
-    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
-    "\n",
-    "N = len(data)\n",
-    "nu = N - 2\n",
-    "\n",
-    "chi2_red = chi2_val / nu\n",
-    "P = chi2.cdf(chi2_val, df=nu)\n",
-    "\n",
-    "\n",
-    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
-    "print(f\"Gradi di libertà = {nu}\")\n",
-    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
-    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 152,
    "id": "e2407a04",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -780,44 +654,41 @@
     }
    ],
    "source": [
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "\n",
     "plt.figure(figsize=(10,6))\n",
     "\n",
     "# Seaborn scatter\n",
     "sns.scatterplot(\n",
-    "    data=data,\n",
-    "    x=\"este\",\n",
-    "    y=\"F\",\n",
-    "    s=7\n",
+    "  data=data,\n",
+    "  x=\"x\",\n",
+    "  y=\"y\",\n",
+    "  s=7\n",
     ")\n",
     "\n",
     "# Barre d’errore\n",
     "plt.errorbar(\n",
-    "    data[\"este\"],\n",
-    "    data[\"F\"],\n",
-    "    xerr=data[\"ueste\"],\n",
-    "    yerr=data[\"uF\"],\n",
-    "    fmt=\"none\",\n",
-    "    ecolor=\"gray\",\n",
-    "    elinewidth=1,\n",
-    "    capsize=3,\n",
-    "    alpha=0.7\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[\"este\"].max(), 200)\n",
-    "yfit = B + A * xfit\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\"\n",
+    "  xfit,\n",
+    "  yfit,\n",
+    "  color=\"crimson\",\n",
+    "  linewidth=1,\n",
+    "  zorder=10,\n",
+    "  label=\"Fit lineare Carpi\"\n",
     ")\n",
     "\n",
     "\n",
@@ -833,6 +704,448 @@
     "\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "32e9948f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi²      = 2.76835\n",
+      "GdL       = 4\n",
+      "Chi² rid  = 0.69209\n",
+      "P(0, chi²)= 0.40269\n",
+      "\n",
+      "\n",
+      "############################################################\n",
+      "capiamo quale dato sta contribuendo maggiormente\n",
+      "0    0.961357\n",
+      "1    0.033834\n",
+      "2    0.061166\n",
+      "3    0.968819\n",
+      "4    0.642604\n",
+      "5    0.100572\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": "ad1c81fc",
+   "metadata": {},
+   "source": [
+    "### Regressione lineare pesata Yorkfit "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "bfb895c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AY        = 3.2006138 ± 0.0091837\n",
+      "BY        = 0.1126344 ± 0.9516509\n",
+      "cov_ABY   = -0.007930461708887697\n",
+      "chi²      = 2.76829\n",
+      "chi² rid  = 0.69207\n",
+      "P(0, chi²)= 0.40268\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": 155,
+   "id": "202de438",
+   "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": "63442336",
+   "metadata": {},
+   "source": [
+    "## Raccolta finale dei dati"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "caf23dbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "RISULTATI REGRESSIONE OLS:\n",
+      "Aols  = 3.20145 ± 0.00896\n",
+      "Bols  = 0.02655 ± 0.99076\n",
+      "P(0, chi²)= 0.40417\n",
+      "\n",
+      "RISULTATI REGRESSIONE Carpi:\n",
+      "Ac  = 3.20054 ± 0.00919\n",
+      "Bc  = 0.11948 ± 0.95235\n",
+      "P(0, chi²)= 0.40269\n",
+      "\n",
+      "RISULTATI REGRESSIONE York:\n",
+      "Ay  = 3.20061 ± 0.00918\n",
+      "By  = 0.11263 ± 0.95165\n",
+      "P(0, chi²)= 0.40268\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\")\n",
+    "\n",
+    "print(\"RISULTATI 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": "7692f792",
+   "metadata": {},
+   "source": [
+    "## Aggiunta degli errori strumentali"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "8f5c8bb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "u_strum_m    = 0.004082\n",
+      "u_strum_Dx   = 0.020412\n",
+      "uF_strum     = 0.071603\n",
+      "uK_strum     = 0.022946\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_b = 0.01\n",
+    "res_c = 0.05\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  = np.maximum(ueste,  u_strum_Dx)\n",
+    "umasse_strum = np.maximum(umasse, u_strum_m)\n",
+    "\n",
+    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
+    "uF_strum  = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
+    "uDx_strum = np.max(ueste_strum)\n",
+    "\n",
+    "uK_strum  = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
+    "\n",
+    "print(f\"u_strum_m    = {u_strum_m:.6f}\")\n",
+    "print(f\"u_strum_Dx   = {u_strum_Dx:.6f}\")\n",
+    "print(f\"uF_strum     = {uF_strum:.6f}\")\n",
+    "print(f\"uK_strum     = {uK_strum:.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8706126d",
+   "metadata": {},
+   "source": [
+    "### Propapazione dell'errore strumentale max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "a1dc24c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Media pesata\n",
+    "uA_fin   = np.sqrt(uA**2      + uK_strum**2)\n",
+    "\n",
+    "# OLS\n",
+    "uAols_fin = np.sqrt(uAols**2  + uK_strum**2)\n",
+    "uBols_fin = np.sqrt(uBols**2  + uK_strum**2)\n",
+    "\n",
+    "# Carpi\n",
+    "uAC_fin  = np.sqrt(uAC**2     + uK_strum**2)\n",
+    "uBC_fin  = np.sqrt(uBC**2     + uK_strum**2)\n",
+    "\n",
+    "# York\n",
+    "uAY_fin  = np.sqrt(uAY**2     + uK_strum**2)\n",
+    "uBY_fin  = np.sqrt(uBY**2     + uK_strum**2)\n",
+    "\n",
+    "\n",
+    "# Nuovo uy e ux per ricalcolare i chi^2\n",
+    "uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
+    "ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
+    "\n",
+    "# OLS\n",
+    "F_fit_ols = Bols + Aols * data[\"x\"]\n",
+    "sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
+    "chi2_ols  = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
+    "GdL       = len(data) - 2\n",
+    "\n",
+    "# Carpi\n",
+    "F_fit_c   = BC + AC * data[\"x\"]\n",
+    "sigma_c   = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
+    "chi2_c    = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
+    "\n",
+    "# York\n",
+    "F_fit_y   = BY + AY * data[\"x\"]\n",
+    "sigma_y_   = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
+    "chi2_y     = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e57c7d8",
+   "metadata": {},
+   "source": [
+    "## Risutltati della propagazione dell'errore strumentale massimo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "c8e264fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
+      "Media pesata K  = 3.20156 ± 0.02327\n",
+      "\n",
+      "RISULTATI REGRESSIONE OLS:\n",
+      "Aols  = 3.20145 ± 0.02463\n",
+      "Bols  = 0.02655 ± 0.99102\n",
+      "Chi² OLS   = 1.17582  |  rid = 0.29396  |  P = 0.11794\n",
+      "\n",
+      "RISULTATI REGRESSIONE Carpi:\n",
+      "AC  = 3.20054 ± 0.02472\n",
+      "BC  = 0.11948 ± 0.95263\n",
+      "Chi² Carpi = 1.17417  |  rid = 0.29354  |  P = 0.11767\n",
+      "\n",
+      "RISULTATI REGRESSIONE York:\n",
+      "AY  = 3.20061 ± 0.02472\n",
+      "BY  = 0.11263 ± 0.95193\n",
+      "Chi² York  = 1.17400  |  rid = 0.29350  |  P = 0.11764\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
+    "print(f\"Media pesata K  = {media:.5f} ± {uA_fin:.5f}\")\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
+    "print(f\"Aols  = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
+    "print(f\"Bols  = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
+    "print(f\"Chi² OLS   = {chi2_ols:.5f}  |  rid = {chi2_ols/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
+    "print(f\"AC  = {AC:.5f} ± {uAC_fin:.5f}\")\n",
+    "print(f\"BC  = {BC:.5f} ± {uBC_fin:.5f}\")\n",
+    "print(f\"Chi² Carpi = {chi2_c:.5f}  |  rid = {chi2_c/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
+    "print(f\"AY  = {AY:.5f} ± {uAY_fin:.5f}\")\n",
+    "print(f\"BY  = {BY:.5f} ± {uBY_fin:.5f}\")\n",
+    "print(f\"Chi² York  = {chi2_y:.5f}  |  rid = {chi2_y/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e38b0bd",
+   "metadata": {},
+   "source": [
+    "## Minima interpretazione (Mio commento -> Non necessario)\n",
+    "Sovrastimando l'errore in modo molto marcato ovviamente il Chi² viene ridotto.\n",
+    "Il Chi² che stiamo usando serve per stimare quale sia la probabilità di ottenere un Chi² minore o uguale a quello osservato.\n",
+    "Sapendo che il valore buono è ~50% stiamo un po' allargando le distribuzioni in modo un po' esagerato.\n",
+    "\n",
+    "In generale con il Chi² vale:\n",
+    "- \\~50%: Errori ottimamente stimati\n",
+    "- \\<5% : Fit troppo grande\n",
+    "- \\>95%: Fit troppo piccolo\n",
+    "\n",
+    "In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
+   ]
   }
  ],
  "metadata": {