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Lab1/molla/dinamica/mollaDinamica2.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "a80529e4",
"metadata": {},
"source": [
"# Analisi dei dati con il sonar\n",
"Minimo Indice:\n",
"- Analisi dei dati statici\n",
"- Analisi dei dati dinamici\n",
" - Sonar\n",
" - Cronometro"
]
},
{
"cell_type": "markdown",
"id": "32702b8f",
"metadata": {},
"source": [
"## Import delle librerie e set di variabili gloabali"
]
},
{
"cell_type": "code",
"execution_count": 603,
"id": "f34c5b88",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import scipy as sc\n",
"from scipy.stats import chi2\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import statsmodels.api as sm\n",
"\n",
"\n",
"g = 9.807\n",
"ug = 0.001\n",
"\n",
"m_mol = 15.43\n",
"um_mol = 0.01"
]
},
{
"cell_type": "markdown",
"id": "fd0b8b1d",
"metadata": {},
"source": [
"## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
"- Lettura del CSV\n",
"- Creazione del data frame\n",
"- Deviazioni standard\n",
"\n",
"ATTENZIONE: Linea cursed ~17"
]
},
{
"cell_type": "code",
"execution_count": 604,
"id": "08efb2be",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(r'dinamica2.csv')\n",
"\n",
"def calcola_stats(df, prefix, err_arbitrario):\n",
" cols = [col for col in df.columns if col.startswith(prefix)]\n",
"\n",
" def riga_stats(row):\n",
" valori = row[cols].dropna()\n",
" n = len(valori)\n",
"\n",
" if n == 0:\n",
" return pd.Series({prefix: np.nan, f\"u{prefix}\": np.nan})\n",
" elif n == 1:\n",
" return pd.Series({prefix: valori.iloc[0], f\"u{prefix}\": err_arbitrario})\n",
" else:\n",
" media = valori.mean()\n",
" sigma = valori.std(ddof=1)\n",
" return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
"\n",
" stats = df.apply(riga_stats, axis=1)\n",
" df[prefix] = stats[prefix]\n",
" df[f\"u{prefix}\"] = stats[f\"u{prefix}\"]\n",
"\n",
" return df\n",
"\n",
"\n",
"df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
"df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n",
"df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
"df = calcola_stats(df, \"a\", err_arbitrario=0.01)\n",
"df = calcola_stats(df, \"t\", err_arbitrario=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 605,
"id": "5494409f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>m1</th>\n",
" <th>a1</th>\n",
" <th>ua1</th>\n",
" <th>w1</th>\n",
" <th>uw1</th>\n",
" <th>c1</th>\n",
" <th>uc1</th>\n",
" <th>t1</th>\n",
" <th>a2</th>\n",
" <th>ua2</th>\n",
" <th>w2</th>\n",
" <th>uw2</th>\n",
" <th>c2</th>\n",
" <th>uc2</th>\n",
" <th>t2</th>\n",
" <th>a3</th>\n",
" <th>ua3</th>\n",
" <th>w3</th>\n",
" <th>uw3</th>\n",
" <th>c3</th>\n",
" <th>uc3</th>\n",
" <th>t3</th>\n",
" <th>a4</th>\n",
" <th>ua4</th>\n",
" <th>w4</th>\n",
" <th>uw4</th>\n",
" <th>c4</th>\n",
" <th>uc4</th>\n",
" <th>t4</th>\n",
" <th>w</th>\n",
" <th>uw</th>\n",
" <th>m</th>\n",
" <th>um</th>\n",
" <th>c</th>\n",
" <th>uc</th>\n",
" <th>a</th>\n",
" <th>ua</th>\n",
" <th>t</th>\n",
" <th>ut</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49.25</td>\n",
" <td>9.7171</td>\n",
" <td>0.016</td>\n",
" <td>7.65650</td>\n",
" <td>0.00040</td>\n",
" <td>484.4550</td>\n",
" <td>0.011</td>\n",
" <td>15.62</td>\n",
" <td>8.9110</td>\n",
" <td>0.015</td>\n",
" <td>7.65690</td>\n",
" <td>0.00040</td>\n",
" <td>484.516</td>\n",
" <td>0.011</td>\n",
" <td>15.58</td>\n",
" <td>10.446</td>\n",
" <td>0.027</td>\n",
" <td>7.66030</td>\n",
" <td>0.00050</td>\n",
" <td>485.082</td>\n",
" <td>0.019</td>\n",
" <td>15.76</td>\n",
" <td>8.377</td>\n",
" <td>0.016</td>\n",
" <td>7.65820</td>\n",
" <td>0.00040</td>\n",
" <td>484.752</td>\n",
" <td>0.011</td>\n",
" <td>15.87</td>\n",
" <td>7.657975</td>\n",
" <td>0.001711</td>\n",
" <td>49.25</td>\n",
" <td>0.002887</td>\n",
" <td>484.70125</td>\n",
" <td>0.284314</td>\n",
" <td>9.362775</td>\n",
" <td>0.908254</td>\n",
" <td>15.7075</td>\n",
" <td>0.133010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>69.28</td>\n",
" <td>9.8600</td>\n",
" <td>0.016</td>\n",
" <td>6.55968</td>\n",
" <td>0.00029</td>\n",
" <td>423.3520</td>\n",
" <td>0.011</td>\n",
" <td>18.31</td>\n",
" <td>10.3900</td>\n",
" <td>0.012</td>\n",
" <td>6.55891</td>\n",
" <td>0.00022</td>\n",
" <td>423.154</td>\n",
" <td>0.009</td>\n",
" <td>18.27</td>\n",
" <td>10.491</td>\n",
" <td>0.013</td>\n",
" <td>6.56002</td>\n",
" <td>0.00024</td>\n",
" <td>423.697</td>\n",
" <td>0.010</td>\n",
" <td>18.34</td>\n",
" <td>10.968</td>\n",
" <td>0.019</td>\n",
" <td>6.56000</td>\n",
" <td>0.00030</td>\n",
" <td>423.465</td>\n",
" <td>0.014</td>\n",
" <td>18.16</td>\n",
" <td>6.559652</td>\n",
" <td>0.000519</td>\n",
" <td>69.28</td>\n",
" <td>0.002887</td>\n",
" <td>423.41700</td>\n",
" <td>0.226641</td>\n",
" <td>10.427250</td>\n",
" <td>0.454472</td>\n",
" <td>18.2700</td>\n",
" <td>0.078740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>88.97</td>\n",
" <td>11.5840</td>\n",
" <td>0.014</td>\n",
" <td>5.84417</td>\n",
" <td>0.00020</td>\n",
" <td>363.2290</td>\n",
" <td>0.010</td>\n",
" <td>20.27</td>\n",
" <td>10.1763</td>\n",
" <td>0.017</td>\n",
" <td>5.84585</td>\n",
" <td>0.00028</td>\n",
" <td>363.354</td>\n",
" <td>0.012</td>\n",
" <td>20.44</td>\n",
" <td>12.044</td>\n",
" <td>0.018</td>\n",
" <td>5.84500</td>\n",
" <td>0.00026</td>\n",
" <td>363.183</td>\n",
" <td>0.013</td>\n",
" <td>20.54</td>\n",
" <td>11.224</td>\n",
" <td>0.016</td>\n",
" <td>5.84513</td>\n",
" <td>0.00025</td>\n",
" <td>363.233</td>\n",
" <td>0.011</td>\n",
" <td>20.49</td>\n",
" <td>5.845038</td>\n",
" <td>0.000689</td>\n",
" <td>88.97</td>\n",
" <td>0.002887</td>\n",
" <td>363.24975</td>\n",
" <td>0.073109</td>\n",
" <td>11.257075</td>\n",
" <td>0.794837</td>\n",
" <td>20.4350</td>\n",
" <td>0.117331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>108.61</td>\n",
" <td>11.5420</td>\n",
" <td>0.026</td>\n",
" <td>5.32780</td>\n",
" <td>0.00030</td>\n",
" <td>303.5502</td>\n",
" <td>0.019</td>\n",
" <td>22.49</td>\n",
" <td>8.4240</td>\n",
" <td>0.017</td>\n",
" <td>5.32820</td>\n",
" <td>0.00030</td>\n",
" <td>303.581</td>\n",
" <td>0.012</td>\n",
" <td>22.27</td>\n",
" <td>10.501</td>\n",
" <td>0.022</td>\n",
" <td>5.32960</td>\n",
" <td>0.00030</td>\n",
" <td>303.842</td>\n",
" <td>0.016</td>\n",
" <td>22.55</td>\n",
" <td>9.959</td>\n",
" <td>0.014</td>\n",
" <td>5.32822</td>\n",
" <td>0.00020</td>\n",
" <td>303.445</td>\n",
" <td>0.010</td>\n",
" <td>22.15</td>\n",
" <td>5.328455</td>\n",
" <td>0.000787</td>\n",
" <td>108.61</td>\n",
" <td>0.002887</td>\n",
" <td>303.60455</td>\n",
" <td>0.168669</td>\n",
" <td>10.106500</td>\n",
" <td>1.299853</td>\n",
" <td>22.3650</td>\n",
" <td>0.187172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>128.64</td>\n",
" <td>11.5740</td>\n",
" <td>0.020</td>\n",
" <td>4.92663</td>\n",
" <td>0.00023</td>\n",
" <td>242.9620</td>\n",
" <td>0.014</td>\n",
" <td>24.33</td>\n",
" <td>11.5920</td>\n",
" <td>0.023</td>\n",
" <td>4.92537</td>\n",
" <td>0.00029</td>\n",
" <td>242.876</td>\n",
" <td>0.017</td>\n",
" <td>24.38</td>\n",
" <td>10.264</td>\n",
" <td>0.023</td>\n",
" <td>4.92430</td>\n",
" <td>0.00030</td>\n",
" <td>242.789</td>\n",
" <td>0.017</td>\n",
" <td>25.09</td>\n",
" <td>9.118</td>\n",
" <td>0.021</td>\n",
" <td>4.92610</td>\n",
" <td>0.00030</td>\n",
" <td>243.115</td>\n",
" <td>0.015</td>\n",
" <td>24.26</td>\n",
" <td>4.925600</td>\n",
" <td>0.001009</td>\n",
" <td>128.64</td>\n",
" <td>0.002887</td>\n",
" <td>242.93550</td>\n",
" <td>0.138954</td>\n",
" <td>10.637000</td>\n",
" <td>1.188344</td>\n",
" <td>24.5150</td>\n",
" <td>0.386480</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" m1 a1 ua1 w1 uw1 c1 uc1 t1 a2 \\\n",
"0 49.25 9.7171 0.016 7.65650 0.00040 484.4550 0.011 15.62 8.9110 \n",
"1 69.28 9.8600 0.016 6.55968 0.00029 423.3520 0.011 18.31 10.3900 \n",
"2 88.97 11.5840 0.014 5.84417 0.00020 363.2290 0.010 20.27 10.1763 \n",
"3 108.61 11.5420 0.026 5.32780 0.00030 303.5502 0.019 22.49 8.4240 \n",
"4 128.64 11.5740 0.020 4.92663 0.00023 242.9620 0.014 24.33 11.5920 \n",
"\n",
" ua2 w2 uw2 c2 uc2 t2 a3 ua3 w3 \\\n",
"0 0.015 7.65690 0.00040 484.516 0.011 15.58 10.446 0.027 7.66030 \n",
"1 0.012 6.55891 0.00022 423.154 0.009 18.27 10.491 0.013 6.56002 \n",
"2 0.017 5.84585 0.00028 363.354 0.012 20.44 12.044 0.018 5.84500 \n",
"3 0.017 5.32820 0.00030 303.581 0.012 22.27 10.501 0.022 5.32960 \n",
"4 0.023 4.92537 0.00029 242.876 0.017 24.38 10.264 0.023 4.92430 \n",
"\n",
" uw3 c3 uc3 t3 a4 ua4 w4 uw4 c4 \\\n",
"0 0.00050 485.082 0.019 15.76 8.377 0.016 7.65820 0.00040 484.752 \n",
"1 0.00024 423.697 0.010 18.34 10.968 0.019 6.56000 0.00030 423.465 \n",
"2 0.00026 363.183 0.013 20.54 11.224 0.016 5.84513 0.00025 363.233 \n",
"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 c uc \\\n",
"0 0.011 15.87 7.657975 0.001711 49.25 0.002887 484.70125 0.284314 \n",
"1 0.014 18.16 6.559652 0.000519 69.28 0.002887 423.41700 0.226641 \n",
"2 0.011 20.49 5.845038 0.000689 88.97 0.002887 363.24975 0.073109 \n",
"3 0.010 22.15 5.328455 0.000787 108.61 0.002887 303.60455 0.168669 \n",
"4 0.015 24.26 4.925600 0.001009 128.64 0.002887 242.93550 0.138954 \n",
"\n",
" a ua t ut \n",
"0 9.362775 0.908254 15.7075 0.133010 \n",
"1 10.427250 0.454472 18.2700 0.078740 \n",
"2 11.257075 0.794837 20.4350 0.117331 \n",
"3 10.106500 1.299853 22.3650 0.187172 \n",
"4 10.637000 1.188344 24.5150 0.386480 "
]
},
"execution_count": 605,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.width', None)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "9b8d8cb4",
"metadata": {},
"source": [
"# Analisi statica"
]
},
{
"cell_type": "markdown",
"id": "fd1b1164",
"metadata": {},
"source": [
"## Permutazioni"
]
},
{
"cell_type": "code",
"execution_count": 606,
"id": "976d5531",
"metadata": {},
"outputs": [
{
"name": "stdout",
"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",
"[ 61.28425 121.4515 181.0967 241.76575 60.16725 119.81245 180.4815\n",
" 59.6452 120.31425 60.66905]\n",
"[0.36359352 0.29356288 0.33058029 0.31645313 0.23814054 0.28251562\n",
" 0.26584645 0.18383143 0.15701353 0.21853469]\n",
"############################################################\n",
"[-20.03 -39.72 -59.36 -79.39 -19.69 -39.33 -59.36 -19.64 -39.67 -20.03]\n",
"[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248\n",
" 0.00408248 0.00408248 0.00408248 0.00408248]\n"
]
}
],
"source": [
"perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
"\n",
"este = np.array([df.c[x] - df.c[j] for x,j in perm])\n",
"ueste = np.array([np.sqrt(df.uc[x]**2 + df.uc[j]**2) for x,j in perm])\n",
"\n",
"masse = np.array([df.m[x] - df.m[j] for x,j in perm])\n",
"umasse = np.array([np.sqrt(df.um[x]**2 + df.um[j]**2) for x,j in perm])\n",
"\n",
"\n",
"print(perm)\n",
"print(len(perm))\n",
"\n",
"print(\"#\"* 60)\n",
"print(este)\n",
"print(ueste)\n",
"\n",
"print(\"#\"* 60)\n",
"print(masse)\n",
"print(umasse)"
]
},
{
"cell_type": "markdown",
"id": "d37fbd7c",
"metadata": {},
"source": [
"## Calcolo dei K e media pesata\n",
"- Calcolo del K\n",
"- Calcolo della media pesata (sui K)"
]
},
{
"cell_type": "code",
"execution_count": 607,
"id": "2ad19283",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3.20529679 3.20732177 3.21454516 3.2203806 3.20938434 3.21927571\n",
" 3.22550245 3.22925365 3.23356286 3.23779934]\n",
"[0.01903074 0.00776638 0.00588125 0.00423125 0.01272429 0.00760543\n",
" 0.00476765 0.00998087 0.00424581 0.01168613]\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": 608,
"id": "5f59d6c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio: 3.22307834012753\n",
"sigmaC: 0.0020227911977321114\n"
]
}
],
"source": [
"def mediaPesata(x, ux, dim = 0):\n",
" x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
" sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
"\n",
" w = 1.0 / (sigma_arr ** 2)\n",
"\n",
" num = np.nansum(w * x_arr, axis=dim)\n",
" den = np.nansum(w, axis=dim)\n",
"\n",
" media = num / den\n",
" sigma = np.sqrt(1.0 / den)\n",
"\n",
" return media, sigma\n",
"\n",
"media, uA = mediaPesata(K, uK)\n",
"print(\"K-medio:\", media)\n",
"print(\"sigmaC: \", uA)"
]
},
{
"cell_type": "markdown",
"id": "21baebb4",
"metadata": {},
"source": [
"## Analisi con regressioni e grafici\n",
"Ogni blocco ha:\n",
"- Regressione\n",
"- grafico\n",
"- chi^2\n",
"\n",
"Nota: Ax + B"
]
},
{
"cell_type": "code",
"execution_count": 609,
"id": "5be377eb",
"metadata": {},
"outputs": [],
"source": [
"sns.set_theme(style=\"whitegrid\")\n",
"\n",
"data = pd.DataFrame({\n",
" \"x\": este,\n",
" \"ux\": ueste,\n",
" \"y\": - F,\n",
" \"uy\": uF\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "6ec60a92",
"metadata": {},
"source": [
"### Regressione lineare \"OLS\"\n",
"\n",
"Non tiene conto dei nostri errori, un risultato puro e semplice"
]
},
{
"cell_type": "code",
"execution_count": 610,
"id": "aefe7756",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 3.114e+05\n",
"Date: Sun, 05 Apr 2026 Prob (F-statistic): 1.19e-19\n",
"Time: 21:52:51 Log-Likelihood: -14.036\n",
"No. Observations: 10 AIC: 32.07\n",
"Df Residuals: 8 BIC: 32.68\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.0101 0.779 0.013 0.990 -1.785 1.805\n",
"x 3.2201 0.006 558.014 0.000 3.207 3.233\n",
"==============================================================================\n",
"Omnibus: 0.921 Durbin-Watson: 0.565\n",
"Prob(Omnibus): 0.631 Jarque-Bera (JB): 0.614\n",
"Skew: 0.062 Prob(JB): 0.736\n",
"Kurtosis: 1.792 Cond. No. 302.\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\n",
"RISULTATI REGRESSIONE:\n",
"Aols = 3.22008 ± 0.00577\n",
"Bols = 0.01011 ± 0.77853\n",
"R² = 0.99997\n"
]
}
],
"source": [
"X = sm.add_constant(data[\"x\"])\n",
"model = sm.OLS(data[\"y\"], X).fit()\n",
"\n",
"print(model.summary())\n",
"\n",
"# Estrazione parametri\n",
"Bols = model.params[\"const\"]\n",
"Aols = model.params[\"x\"]\n",
"uBols = model.bse[\"const\"]\n",
"uAols = model.bse[\"x\"]\n",
"R2 = model.rsquared\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE:\")\n",
"print(f\"Aols = {Aols:.5f} ± {uAols:.5f}\")\n",
"print(f\"Bols = {Bols:.5f} ± {uBols:.5f}\")\n",
"print(f\"R² = {R2:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 611,
"id": "8d795186",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dTlfk48XNRikXuF7KVgw/8/333y90DY3Bg1+YCx79KKi49SrL+ha3DYsybtw4nDp1CiNGjECDBg3g4eEBvV6PV155xS7bFkCpp6a3dIZSvV6PgIAAzJ8/v8jnHzwiVPCI1oNKeq40XFxczLpm70Fff/01Jk6ciCeffBIjRoxAQEAAlEolPvvssyKP/JW1XqKKiI0XEVmNv78/PD09odfrizzCU1CNGjVw+fJlyLJs8uXoxo0bJssZTpG6ceOG8ZRB4P6F5Ldu3TJpCmrXro2//voLbdq0MfteQIafc/36dZO/Xms0Gty8eRP169c3azxLGGqIj483OcqWn5+PmzdvGrepYbmrV68W+kv71atXTU4rA+5/+Zw/fz5ef/11vPXWW4iJiTE2POa8Z0WZMGGCxUdOSrNNr1+/Xuixa9eumRzt9PX1LfKLoeGIgSWsdS+pB+uXZRnXr1835tbwPnt5eVm0/a2ltNuwuO2SlpaGuLg4jB071uQUQMMRvdKMUVq1a9fG0aNHkZqaWuxRrxo1akCv1+P69euoW7eu8fHExESkp6cXOlpellri4uLQvHlzqzci5n7OS/Lff/8hOzvbpIk3vDeGbfH999+jVq1aWLp0qcl7tHjxYktXgYgewGu8iMhqlEolunXrhu+//77QlN+A6SlrkZGRuHv3Lg4cOGB8LC8vD19++aXJa8LDw+Hn54cvv/wSWq3W+Pi3335b6DSg7t274+7du4XGAIDc3NwS72sTHh4Of39/bN261WS2wV27dgk5Ba0obdu2hVqtxhdffGFyhGDHjh3IyMjAE088Yaw1ICCgUK0///wzrly5go4dOxYa28XFBUuXLkVERARGjx5tnD7dnPesKOHh4Wjbtq1F/5VmFrQff/zRZOrsM2fO4PTp0+jQoYPxsVq1aiE+Pt6k1r/++gt//PHHQ8cvjru7O4Cyn463e/du47TgwP1rgu7du2esPzw8HLVr18aaNWuMU/sX9LDtby2l3YbFbZfijhitX7++0GOGMSw9/bBr166QZRlLly4t9Jzhc2P4rDz489euXWvyfFl1794dOp2uyBlTtVptmfJjyee8OFqt1uRU4vz8fGzbtg3+/v5o1KgRgP97Dwvue06fPo0///zT4nUgIlM84kVEZvvqq69w5MiRQo8PHToU7777Lk6cOIF+/frhhRdeQL169ZCWlobz588jLi7OeFpa//79sXHjRrz77rsYOnQoAgMD8e233xpPozL8xdXFxQVjx47FRx99hGHDhqF79+64desWdu7cWej6jWeffRZ79+7F9OnTceLECTRv3hw6nQ7x8fHYt28fPv/8c+NU0w9Sq9UYN24cpk2bhmHDhiEqKgo3b97Ezp07y3yNV2n5+/vj1VdfxdKlS/HKK6+gc+fOuHr1KjZv3oyIiAj06tXLWOv48eMxadIkDB48GD169DBOM12jRo1ip393c3PDZ599hqFDh2LkyJH44osvEBoaWur3zB5q166NgQMHYuDAgcjPz8eGDRvg5+eHV155xbhM3759sW7dOowYMQJ9+/ZFUlIStm7dinr16hXZzJSG4cvorFmzEBkZCaVSiR49epg9jq+vL1588UX06dPHOJ38I488YpxERqFQYNasWRg5ciSeeeYZ9OnTB0FBQbh79y5OnDgBLy8vrFy50qJ1MEdpt6Gbmxvq1auHvXv3ok6dOvDz88Ojjz6K0NBQtGzZEp9//jk0Gg2CgoJw7Ngx4/25CjJs2+joaERFRUGtVqNTp06FTqksTuvWrfHss8/iiy++wPXr19G+fXvo9Xr8/vvvaNWqFQYPHoz69eujd+/e2LZtG9LT09GyZUucPXsWu3btwpNPPmly9LwsHn/8cfTv3x+fffYZLl68iHbt2kGtVuPatWvYt28fPvjgAzz99NMWjW3p57woVapUQUxMDG7duoU6deogNjYWFy9exEcffWSc7r5jx47Yv38/3njjDXTs2BE3b940ZqC83aibyF7YeBGR2R688aZBnz59ULVqVWzfvh3Lli3DDz/8gC1btsDPzw/16tUzuaGnp6cn1q9fj1mzZmHDhg3w8PDAc889h2bNmmHs2LEm17EMHjwYsixj7dq1+Pjjj1G/fn2sWLECs2bNMllOoVBg2bJlWLduHb7++mv88MMPcHd3R82aNTFkyJAiL1IvqH///tDpdFi9ejXmzZuH0NBQrFixAosWLSrjFiu9sWPHwt/fHxs3bsScOXPg6+uLfv364Z133jG5H1CfPn3g5uaGmJgYzJ8/Hx4eHnjyySfx3nvvFXsPL+D+KW2rV6/G4MGD8fLLL2PTpk145JFHSvWe2cNzzz0HhUKB9evXIykpCY0bN8bUqVNRpUoV4zJ169bFxx9/jMWLF2POnDmoV68e5s2bhz179ljcNHbt2hVDhgzBd999h2+++QayLFvUeI0ePRp///03Vq1ahaysLLRp0wbTp083HvUBgFatWmHbtm1Yvnw5Nm7ciOzsbAQGBqJx48YmM9GJZM42nDVrFj766CPMmTMHGo0GY8aMQWhoKD799FN89NFH2Lx5M2RZRrt27RATE1NoFr/GjRvjrbfewtatW3HkyBHo9XocOHCg1I0XAMyZMwdhYWHYsWMH5s2bB29vb4SHh5vM6jhr1izUrFkTu3btwo8//ojKlSvj1VdfLXI2xLKYOXMmwsPDsXXrVkRHR0OpVKJGjRro1asXmjdvXqaxLf2cP8jX1xdz587FrFmz8OWXX6Jy5cqYNm2aySyyffr0QWJiIrZt24ajR4+iXr16+OSTT7Bv3z67/vGFqDyRZHtd8UpEVIR169Zhzpw5OHz4sMk04g/S6/Vo06YNnnrqKcyaNcuGFZIt3Lx5E126dMH777+PESNG2Lscs504cQJDhw7FokWLLD7iQURE5Quv8SIiu8nNzTX5d15eHrZt24Y6deqYNF15eXmFZkXbvXs3UlNT8fjjj9ukViIiIqKy4KmGRGQ3Y8aMQfXq1VG/fn1kZmbim2++QXx8fKGpmf/880/MmTMHTz/9NPz8/HDhwgXs2LEDoaGhPJpAREREToGNFxHZTWRkJHbs2IFvv/0WOp0O9erVM15wX1CNGjVQtWpVfPHFF0hLS4Ovry+effZZjB8//qE34CUiIiJyBLzGi4iIiIiISDBe40VERERERCQYGy8iIiIiIiLBeI2XGU6dOgVZlk3upUNERERERBWPRqOBJEkm9xAsCRsvM8iyXGhKayIiIiIiqnjM7QvYeJlBrVZDlmWEh4dDkiR7l0PljCzL0Gg0UKvVzBcJwYyRSMwXicaMkUiW5Ovs2bNm/Qxe40VERERERCQYGy8iIiIiIiLBHK7xOnDgAF544QU0a9YMkZGReOutt5CQkFBoue3bt6Nbt26IiIhAr169cOjQoULLZGRkYPLkyXj88cfRrFkzvPnmm/jvv/9ssRpERERERERGDtV4nThxAmPGjEG9evWwbNkyTJ48GX/99Rdefvll5ObmGpf77rvvMHXqVHTv3h0xMTFo2rQpxowZgz///NNkvHHjxuHYsWOYMWMG5s+fj6tXr2LkyJHQarU2XjMiIiIiIqrIHGpyje+++w7Vq1fH7NmzjRe1+fv7Y9iwYTh37hxatGgBAFi8eDF69OiBcePGAQBat26NS5cuYdmyZYiJiQFwf+r3o0ePYvXq1YiMjAQABAcHIyoqCvv370dUVJRFNfJiThKJtyog0ZgxEon5ItGYMRJJdL4cqvHSarXw9PQ0aW68vb0B/N90jQkJCbh27Rree+89k9dGRUVh3rx5yM/Ph4uLCw4fPgwfHx+0a9fOuExISAgaNGiAw4cPW9x4AQ9vvnQ6HTQajcXjE1mDWq2GUqm0dxnkQPiHIxKJ+SLRmDESyRb5cqjGq0+fPvj666+xadMm9OrVC6mpqViwYAEaNmyI5s2bAwDi4+MB3D96VVDdunWh0WiQkJCAunXrIj4+HsHBwYU2YkhIiHEMS8myXOSbI8sy7ty5g9TU1DKNTxVXcdmylJ+fH6pWrcpfVgTgfr50Oh2USiUzQVbHfJFozBiJZIt8OVTj1aJFCyxduhTvvvsuZs6cCQBo0KABPv/8c+Nf7tPS0gAAPj4+Jq81/NvwfHp6uvFoWUG+vr44d+6cxTUWdxNlSZJw+/ZtpKWlITAwEB4eHsY3zfC/xd1kTZKkEp8ry2s5rthxrV2TofEqy/oYMpqdnY179+5BlmVUq1atwmzDijhuaV9r+KWiUCiE18RxHbcmkeMWzJe1xi1rTRzXMWuyZFxZlqHX6wtlzFHrddaanG1ca9VUcB9WlnFL4lCN1x9//IH3338f/fr1Q8eOHZGamorly5dj1KhR2Lx5M9zc3OxdIgBAo9GYdMKGNygtLQ2VK1eGv7+/yfKGHURRTVvBL9nmPFfW1xpq0uv1FXpcS19reM7aNcmybMxTWcd1c3ODLMu4d+8e/P39jZ8frVZb6LUqlcr4M3U6nclzCoUCKtX9XUVRp9AabjSo0+kK1WwYV5blQpPaGMaVZbnYcQEUOa5SqYRSqSxyXEmSjK8tal0NzxW1rmUZtyzb0MXFpdh1Lc24pd2GBddLpVKVeRta+72xdBuKfG9KyndJ61qacQHbbkPDuEXVZK1taKi34O9I7iNM19WR9xHWWldR4xryq9frC43LfUTZ19UW+whH/h5h+P+SJBk/cw/bR8iyeWcqOVTjNWvWLLRu3RoTJ040Pta0aVN07NgRX3/9Nfr37w9fX18A96eKDwwMNC6Xnp4OAMbnfXx8cOfOnUI/Iy0tzbiMpYq6o3VeXh4AFLpG7UGWPFeaN9TaP7OijFvwMUepyZrjenp6IjEx0WSnYdj5FUWhUBT7l0Sg6ItODT+r4A67qGWKu2C1pOfKMi5g+bqKGhco+cLdkta1LPUW/AVjqMHwvpVlXe3x3gAlb0NHy3dx4xqUt22oUqkK/Y7kPqL04wL23UdY8lpb7iMM+zCFQlHiNczcR9zniPsIR/4eYchXwfV72Lqa03QBDtZ4XblyBV26dDF5rGrVqqhUqRJu3LgB4P41WsD9a70M/9/wb7VajVq1ahmXi4uLK9SJXr16FaGhoWWqs+DRjgeV9CY465d3jmubmoo6glXWcQueUmbNcR3ltRzX/Nca9l8FGy8RNXFcx61J5LgP5ssRauK4jllTWce15LtWWWrie+O441qzpgezVZaaiuJQ9/GqXr06Lly4YPLYrVu3kJKSgho1agAAatWqhTp16mDfvn0my8XGxqJNmzbGQ4MdOnRAWloa4uLijMtcvXoVFy5cQIcOHSyu0ZKNTFRazBeJxpkuSSTmi0Rjxkgk0flyqMZrwIAB+PHHHzFr1iz88ssviI2NxejRoxEQEIDu3bsblxs7diz27NmDxYsX48SJE5g+fTrOnDmD119/3bhMs2bNEBkZicmTJ2Pv3r04ePAg3nzzTYSFhaFr165lqrO8fzlesmQJwsLCCv33zDPPAADCwsKwevVq4/I7d+7Et99+W6qxO3fubJw4BQAmTpxoHLe8OXz4MIYPH44WLVqgSZMmeO6557Bhw4ZC5yjv3LkTYWFhSElJKfaveCkpKZg9eza6du2KiIgItGnTBgMHDsS6detstDZUHkiSxNnASBjmi0RjxkgkW+TLoU41HDp0KFxcXLBlyxZ89dVX8PT0RNOmTbFw4UJUqlTJuNwzzzyDnJwcxMTEYNWqVQgODsbSpUvRrFkzk/EWLlyIOXPmYNq0adBqtYiMjMSUKVNKPF+zNMy9kM4Zubm5Yf369YUeA4Bt27ahevXqxsd37doFDw8P9OzZ0+yf8/rrryM7O7tsxTqgNWvW4OOPP8ZTTz2FefPmwd3dHYcPH8a8efNw/PhxLF26tMhzoA2nGxbMl1arxbBhw5CRkYFRo0YhJCQEiYmJ+OOPP3Do0CEMHz7cVqtFTs4w8UpJp+kQWYr5ItGYMRLJFvlyqMZLkiQMHDgQAwcOfOiyL7zwAl544YUSl/H29sbs2bMxe/Zsa5Vo0dSRzkihUKBp06ZFPlfc45aoXbu21caylvz8fKhUqhIvDi3JhQsXMH/+fPTu3Rtz5841Pt6mTRvUq1cPkydPxqZNmzBkyJBCry2qqf/111/x999/Y+PGjWjZsqXx8R49ehQ58yFRSbRabYkXIROVBfNFojFjJJLofDnUqYbkHAqeajhkyBD8+uuv+Omnn4ynJC5ZsqTUYz14qqHhtLsLFy7glVdeQdOmTdG1a1fs3r270Gt/+uknvPDCC2jcuDFat26N6dOnmxw9y87OxsyZM9GtWzc0adIEnTt3xrRp05CRkWEyjuH0x5iYGHTq1AmNGzc23gR7586d6NmzJyIiItC+fXtER0cXmib1QV988QUkScLYsWMLPde7d2/UqVOn0NHEkhjuTVdwFk8DS5tDIiIiIrItfmujYmm1WpP/ijraN336dDRs2BDNmzfHtm3bsG3btoceiSyN8ePHIzIyEsuWLUODBg0wceJEXLlyxfj8vn378NprryE0NBRLly7Fe++9hx9++AEffPCBcZnc3FzodDq8/fbbiImJwVtvvYXffvvN5FpAg/379+Onn37CBx98gOXLl8PDwwNr167FlClTEBkZiZUrV2LkyJHYsGEDoqOjS6z9t99+Q1hYmHFCmIIUCgU6deqEhIQE3L17t1TbokGDBlAoFJgyZQri4uKQn59fqtcRERERObucnBycPXsWycnJOHv2LHJycuxdksUc6lRDchzZ2dlo1KiRyWPz5s3Ds88+a/JYvXr14OXlBQ8PD6uegjho0CAMGjQIwP2JUn7++Wd8//33eP311yHLMubNm4eoqCj873//M74mMDAQo0aNwuuvv45HH30U/v7++PDDD43Pa7Va1KxZEy+++CKuXr2K4OBg43MajQYxMTHw8PAAAGRmZmLx4sV45ZVX8M477wAA2rVrB7Vajblz52LEiBEm1x0WdPfuXYSFhRW7btWqVQMA3LlzB0FBQQ/dFnXq1MHEiRPxySefYPjw4VCr1WjcuDG6d++OgQMHlvmaRSIiIiJHlZOTg3PnzsHLywvnzp1DjRo14O7ubu+yLMJvbGay9GI7zbV/oU/LePiCVqbw9Ya6TvWHL/gANzc3bNy40eQxwz3SbCEyMtL4/z08PFC9enXjDbGvXr2KW7duYfLkySYzBD7++ONQKBQ4d+4cHn30UQDA7t27sW7dOly/ft3kNMRr166ZNF6tWrUyNl0AcOrUKWRnZ+Ppp582+Rlt27ZFbm4u/vnnHzz++OPWX/FiDBs2DFFRUTh48CB+/fVXxMXFYdasWdi/fz/Wr1/PUw6p1HhBOonEfJFozFjFpNHqcONOBjTaki/3KCvR+WLjZQFz3xRdUiputBoI2GMiBKUSdc7vhjLAz6yXKRQKREREiKmpFLy9vU3+rVarjafYpaSkAADeeOONIl97+/ZtAMAPP/yACRMmoH///nj77bfh5+eHe/fu4Y033kBeXp7JawICAkz+bfgZvXv3LvFnFCUoKKjE5w3PVa1atdBzJTVQgYGB6N+/P/r37w+NRoNp06Zh586dOHToUKEbjxMVRZIkXpROwjBfJBozVrEkJycjLS0Nd+8lIfnkJfxx8j/4VgEuXY5HUGAafH194e/vb7WfZ4t8sfGyAWWAH2qf2GK3I17mNl2Ozs/PDwAwbdo0NG7cuNDzVapUAXD/OrAGDRqY3Dfs119/LXLMB5tpX19fAMDSpUuLbJBq1qxZbH0tW7bEt99+i9u3bxtPKzSQZRk///wzatWqVarTDIujVqsxfPhw7Ny5E1euXGHjRUREROXK8ePHEX/8dzQ6eA49//4XJzqE4e9KtXDo4AGoVUo0bNgQUVFR9i7TLGy8LGDJfbwsOd3PWajV6kJHkEQKCQlB1apVkZCQYLwOrCi5ubmF/nJR2hs9N2vWDO7u7rhz5w6eeuops+obMmQIvv76ayxevBhz5swxee7rr79GfHw8pk6dWuRr9Xp9oftHpKamwsvLq9C1XNeuXQNQ9GyHREWRZRlarRYqlYqn65DVMV8kGjNWccj5GjQ9+y/C1v0MvasaR7s2xy81Q1FVnYdOnTsgKDDA+Edyq/1MG+SLjZeZKsp9vMwREhKC3bt34+DBgwgMDESVKlXKdDTnYSRJwsSJEzF+/HhkZ2ejY8eOcHd3x7///ouff/4Zb7/9NoKDg9G2bVvMnDkTy5YtM07QERcXV6qf4ePjgzfffBOffPIJ7ty5g8cffxxKpRIJCQk4cOAAlixZUuyFnQ0bNsT48ePx8ccfIzMzE3369IGbmxuOHj2K9evXo0uXLnjxxRcLve7gwYPw9PQ0riMAPProo/jnn3+M9wVr3LgxVCoVLl68iM8++wzVq1c3uzGkio37MBKJ+SLRmLHyL/un35A4aSE0V2/Bd0QfYORzkI4cxqB6jXHt8hmE1gux6imGBYnOFxsvKrORI0fixo0bmDBhAtLT0zFmzJgi72FlTd27d4ePjw9WrlxpPIpVo0YNtG/fHpUrVwYADBgwADdv3sTGjRuxevVqREZG4tNPP0W/fv1K9TNefvllBAUFYe3atdi4cSNUKhVq166Njh07PvQc4Jdffhl169bF2rVrMX78eGg0GgQHB+P999/Hiy++WOS1XAWnwjd466230Lt3b3Tr1g0HDhzA+vXrkZeXh6pVq6Jnz54YNWoUvLy8SrU+RERERI5Kc/MukqYuQdaen+HWugmCPv8Qro3qITk5GWqVErWqeuPWNaW9yywTSeafDkrt7NmzkGUZERERhQ5B5ubmGqcod3Nzs1OF5MxkWTaexmqtQ9zMJRUkyzI0Gg3UajVP0yGrY75INGasfJLz8pG6bCtSFm6AwtsTAR++Aa/nnzK+xzk5Obh8+TJq1KiBW7duoV69ekKmk7ckX2fPngWAUk9IxyNeRERERERkc1k/HkfS5EXQJNyG76gX4D9+OBTenibLuLu7GxsbUacY2gobLzPxLywkEvNFonEqZhKJ+SLRmLHyQXP9XyROXYLsvUfhFtkcVb+YDZew4Ie/UDBOJ++A+OWYRGCuSDRmjERivkg0Zsz56XPykLp0M1IXb4Siki+CYj6E57OdHOK9tUUNbLwsYMl08kQPU/ByS+aLRJBlGXq9HgqFghkjq2O+SDRmzLllfX8MiVMWQ3vrP/iN7o9K7wyFwsvD3mUZ2SJfbLzMxLlISCQ29SSaTqcrclZNImtgvkg0Zsz5aK7eQuIHi5D9QxzcO7ZEtS2fwKVebXuXVSTR+WLjZWVszMiRMI9ERERkD/rsXKQu3ojUpVugDKyEoLWz4NmjQ4X+AzMbLysxXIyXnZ0tZIpLIktkZ2cD4MXIREREZBuyLCMr9giSpi6B9m4S/N4YiErjhkDhwdvasPGyEqVSCT8/P/z3338AAA8Pjwrd0ZP5rHkfL1mWkZ2djf/++w9+fn5QKp37hoNERETk+PKv3EDipEXIOfQrPLq0RrXtC+BSt5a9y3IYbLzMVNIX4qpVqwKAsfkiMpe1r/Hy8/Mz5pIIAJtwEor5ItGYMcekz8pByoL1SF2xDapqgaj6xRx4dGvndAchROeLjZcFiguRJEmoVq0aqlSpAo1GY+OqiEyp1Wr+giITkiQxEyQM80WiMWOOR5ZlZH3zExKnLYU+KRWVxg2B39hBULi72rs0s9kiX2y8LPCwoxJKpZI7BjKbNU81JCoKM0YiMV8kGjPmWPIvXUPipIXIOfw7PLq1Q+VZb0Jdp7q9y7KYLfLFxstMnCWORNJqtZwIg4Rixkgk5otEY8bsT5+ZjeT5a5H22XaoalZF1U0fw7NrW3uXZRWi88XGi4iIiIiISiTLMjJ3HUDS9GXQp2XAf/xL8H1jABRuzndaob2w8SIiIiIiomLlXYxH4sRo5P7yJzx7dEDAR2OhrsXJu8zFxouIiIiIiArRpWciZd4apH2+E+o61VFt23x4dG5l77KcFhsvM/FiThKJ+SLRmDESifki0Zgx25BlGZnbv0fSjBXQZ2XDf9Ir8BvdD5Kri71LE0p0vth4WYAfehJBkiReMExCMWMkEvNFojFjtpF37jISJyxA7q9n4flsZ1T+8HWoagTZuyzhbJEvNl5ERERERBWcLi0DyXM+R/ra3VDXq4VqX0XDo0MLe5dVrrDxssDD7uNFZAlZlqHVaqFSqZgvEoIZI5GYLxKNGRND1uuRsWUvkmathJyTh4Bpo+E7si8kl4p1dNEW+WLjZSbex4tEYr5INGaMRGK+SDRmzLryTv+NexOjkXfyPLyefwoBM16Hqmple5dlN6LzxcaLiIiIiKgC0aWkI3n2KqSv/wYu9YNRffdiuLdrZu+yyj02XkREREREFYCs0yFj03dI+t8qQKNFwEdj4ftyb0hqtgS2wK1MRERERFTO5f5xAYkTopH351/w6vc0AqaNhioowN5lVShsvMzEizlJJJWKH0kSixkjkZgvEo0ZM58uMQVJ/1uFjE3fwaVhXVTfswzurRrbuyyHJDpfTK8F2HyRCJIkMVskFDNGIjFfJBozZh5Zp0P6+m+QPCcGkGVUnjMOPsN6QWLzWiRb5Itb3gKcTp5EkGUZer0eCoWC+SIhmDESifki0Zix0sv97RzuTViA/LP/wPvFHvCf8ipUgZXsXZZDs0W+2HiZidOYkkg6nQ4KhcLeZVA5xoyRSMwXicaMlUz7XzKSZ65AxrZ9cGkcihp7V8KtRSN7l+U0ROeLjRcRERERkROTtVqkrdmNlI9XA0oFKs8fD5/Bz0BSKu1dGhXAxouIiIiIyEnl/PInEidFI//iVfgM6Qn/D0ZB6e9r77KoCGy8iIiIiIicjPZOIpI+XI7MHT/AtXkD1Ni/Cm5N69u7LCoBGy8z8WJOEonnrZNozBiJxHyRaMwYIGu0SPt8B5LnrYXkqkZg9AR4vxgFidumzETni42XBdh8kQiSJPH+JCQUM0YiMV8kGjMG5Bw7hXsTo6G5dB0+w5+D/8QRUFbysXdZ5YIt8lWx02shTidPIhScMZP5IhGYMRKJ+SLRKnLGtLfvIWn6MmTuOgC3luEI+iEGro1D7V1WuWKLfLHxMhOnkyeRNBoN1Gq1vcugcowZI5GYLxKtomVMztcg9bMvkTJ/PRSebghcMhne/brxtEJBROfLoRqvIUOG4Ndffy3yuQULFqBHjx4AgO3bt+Pzzz/Hv//+i+DgYLz99tvo1KmTyfIZGRmYM2cOfvzxR2g0GrRv3x5TpkxBlSpVhK8HEREREVFZZP98EomTFkITfxO+I/qg0vsvQenrbe+yqAwcqvGaPn06MjMzTR5bv3499u/fjzZt2gAAvvvuO0ydOhWjR49G69atERsbizFjxmDTpk1o2rSp8XXjxo3D5cuXMWPGDLi6umLhwoUYOXIkvvrqqwp/fjAREREROSbNzbtImroEWXt+hlvrJgiKmQHXRvXsXRZZgUN1IPXqFQ7Vu+++i3bt2sHf3x8AsHjxYvTo0QPjxo0DALRu3RqXLl3CsmXLEBMTAwA4deoUjh49itWrVyMyMhIAEBwcjKioKOzfvx9RUVG2WSEiIiIiolKQ8/KRunwrUhZ+AYWXB6qsmAqv55+qcNezlWcOfYLoH3/8gZs3b6Jnz54AgISEBFy7dg3du3c3WS4qKgpxcXHIz88HABw+fBg+Pj5o166dcZmQkBA0aNAAhw8ftt0KEBERERE9RPaBE0joMAzJ89bAZ/hzqH18M7z7dmXTVc441BGvB+3ZswceHh7o0qULACA+Ph7A/aNXBdWtWxcajQYJCQmoW7cu4uPjERwcXCisISEhxjEsJUkSPwQkhCRJcHFxsXcZVI4xYyQS80WilceMaW7cRuKUxcjeexRukc1RdcNsuIQFP/yFZHW2yJfDNl5arRZ79+5F586d4eHhAQBIS0sDAPj4mN6vwPBvw/Pp6enw9i588aGvry/OnTtX5tqKmtlQkqRiZzw0NGolPS/qtRyX742jj+uINTnbuI5YE8d13JqcbVxHrMnZxnXEmir6uPrcPKQt24LURRuhqOSLKqumw/PZzibLl5d1FT2uo9VUEodtvI4dO4bk5GQ888wz9i7FhCzLyM/PNznqpVAojBN2aDSaQq8xdM86nQ56vd7kOZVKBUmSoNfrodPpTJ4zjCvLcpHjGqa7LGpcpVIJpVIJWZah1WpNnpMkyfharVZbKDiixi3NugKFt6GocQ3rKkmS2etamnEB87ahLMvQ6/Vwc3ODJElmr6th3KJqstc2NIxb0ja0V75L2oa2zDdgu32EIWOG13Ef4Vz7iLKuq+h9hCzLyMvLg0KhMPkdyX2E6bo68j7CWusqalzD6wAUGteZ9hE5+39B6owV0P77H/xG94Pn2Beh8HQ3WaY87iOKGtewro6wjzD8jlQqlcbP3MP2EbJs3r19Hbbx2rNnD/z8/IyTYwD3j1gB96eKDwwMND6enp5u8ryPjw/u3LlTaMy0tDTjMmVhCEhxzxWnYKAfpFAooCjmngwFw27uuA97bUkzPIoat6R1BUrehtYe1/A+lmVdrfXePLjjqCjb0F75LstnTtR7I3ofYchYwX0Y9xElj+tI+4gHOeI2VCgUhX5Hch9R+nEBfo8oaVxZlqHT6Yx/OCqOo+4jNFdvIWnKYmT/EAf3J1qg2pZ5cHn0kRKPnJS3fYQjf48w/I4suH4PW1dzmi7AQRuv3Nxc/Pjjj+jVq5fJhgwJCQFw/1ovw/83/FutVqNWrVrG5eLi4gp1oVevXkVoaNnv8i1JRV/n9bCNX9Lzol7LccWOa+2aivqyYs2f6YjjOmJNzjauOa817L8KNl4iauK4jluTyHEfzJcj1MRxHbOmso5b3POOWK+ck4fUxRuRunQLlJX9ELTmI3g+80Sp9sPO+N7Yelxr1vRgtspSU1EcclbDgwcPIjs72ziboUGtWrVQp04d7Nu3z+Tx2NhYtGnTxnhYsEOHDkhLS0NcXJxxmatXr+LChQvo0KGD+BUgIiIiogpNlmVkfncYCZFDkLJkM3xfH4BaxzbCq2dHi760k/NzyCNe3377LapXr47HHnus0HNjx47F+PHjUbt2bbRq1QqxsbE4c+YMNm7caFymWbNmiIyMxOTJkzFhwgS4uroiOjoaYWFh6Nq1qy1XhYiIiIgqmPwrN5A4aRFyDv0Kjy6tUW37ArjUrWXvssjOHK7xSktLw5EjRzBs2LAi/xrwzDPPICcnBzExMVi1ahWCg4OxdOlSNGvWzGS5hQsXYs6cOZg2bRq0Wi0iIyMxZcqUEs/VLA3+hYJEKms+iR6GGSORmC8SzdEzps/KQcqC9UhdsQ2qaoGoumE2PJ6O5PdHJyE6X5JsyVyIFdTZs2cBABEREXauhIiIiIgchSzLyPrmJyROWwp9Uir83hwEv7GDoHB3tXdpJJC5vYFj/9nAQZk7dSRRaRSc6pv5IhGYMRKJ+SLRHDVj+ZeuIXHSQuQc/h0e3dqh8qw3oa5T3d5lkZlskS82XmbiAUISSafTlTgNK1FZMWMkEvNFojlSxvSZ2UievxZpn22HqmZVVN30MTy7trV3WVQGovPFxouIiIiIqJRkWUbmrgNImr4M+rQM+I9/Cb5vDIDCjacVUsnYeBERERERlULexXgkToxG7i9/wrNHBwR8NBbqWlXtXRY5CTZeREREREQl0KVnImXeGqR9vhPqOtVRbdt8eHRuZe+yyMmw8TKTI13MSeWPo5y3TuUXM0YiMV8kmq0zJssyMrd/j6QZK6DPyob/pFfgN7ofJFcXm9ZBtiE6X2y8LMDmi0SQJMnh709Czo0ZI5GYLxLN1hnLO3cZiRMWIPfXs/B8tjMqf/g6VDWCbPbzybZskS/uIYkcCG9VQKIxYyQS80Wi2SJjurQMJM/5HOlrd0NdrxaqfRUNjw4thP5Mcgyi88XGy0yyLPMXCwkhyzI0Gg3UajXzRUIwYyQS80Wiic6YrNcjY8teJM1aCTknDwHTRsN3ZF9ILmqr/yxyPLbYh7HxIiIiIqIKLe/037g3MRp5J8/D6/mnEDDjdaiqVrZ3WVTOsPEiIiIiogpJl5KO5NmrkL7+G7jUD0b13Yvh3q6ZvcuicoqNFxERERFVKLJej4xNe5A0axWg0SLgo7Hwfbk3JDW/GpM4TBcRERERVRi5f1xA4sSFyDt1EV79nkbAtNFQBQXYuyyqANh4mYkXDJNIajUv4CWxmDESifki0cqSMV1SKpJmfYaMTd/BpWFdVN+zDO6tGluxOnJ2ovdhbLwswOaLRGCuSDRmjERivkg0SzMm63RI3/ANkmfHALKMynPGwWdYL0i87xwVYIt9GBNnAU4nTyLIsgydTgelUsl8kRDMGInEfJFolmQs97dzuDdhAfLP/gPvF3vAf8qrUAVWElwpOSNb7MPYeJlJlmV7l0DlmF6vh1KptHcZVI4xYyQS80WilTZj2nspSJ65Ahlb98KlcShq7F0JtxaNbFAhOTPR+zA2XkRERERULshaLdLX7kby3NWAUoHK88fDZ/AzkPgHAXIAbLyIiIiIyOnlxJ1G4qRo5F+Ih8+QnvD/YBSU/r72LovIiI0XERERETkt7Z1EJM1cgczt++HavAFq7F8Ft6b17V0WUSFsvMzEC4ZJJF4bQaIxYyQS80WiFcyYrNEi7fMdSJ63FpKLGoEL3of3oB6QFAo7VkjOTPQ+jI2XBdh8kQiSJPFLCwnFjJFIzBeJVjBjOcdO4d7EaGguXYfPsGfhP+kVKCv52LlCcma22Iex8bIAp5MnEWRZNmaL+SIRmDESifki0WRZhubf/5D84Qpk7ToA15bhqPlDDFwbh9q7NCoHbLEPY+NlJk4nTyJptVrhd02nio0ZI5GYLxJFztcgdeWXSPl0HRQe7ghcMhne/brxtEKyKtH7MDZeREREROSwsn8+icRJC6G5kgCvl55FwMRXoPLjaYXkfNh4EREREZHD0dy8i6SpS5C152e4tW6CKqtmQBFaG0oeVSUnxcaLiIiIiByGnJeP1OVbkbLwCyi8PFBlxVR4Pf8UAECj0di5OiLLsfEyEy8YJpGYLxKNGSORmC8qq+wDJ5A4eSE012/Dd1Rf+L/3EhTengA4uRmJJzpfbLwswA89iSBJEi9KJ6GYMRKJ+aKy0Ny4jcQpi5G99yjcIpuj6vrZcKkfbLIMM0Yi2SJfbLyIiIiIyC70uXlIXboZqYs2QlHJF0GrZsDzuc78IzeVS2y8LMBD3SSCLMvQarVQqVTMFwnBjJFIzBeZK+v7Y0icshjaW//Bb3Q/VHpnGBReHsUuz4yRSLbIFxsvM/E+XiQS80WiMWMkEvNFpaG5eguJHyxC9g9xcO/YEtU2z4PLo4+U6rXMGIkkOl9svIiIiIhIOH12LlIXb0Tq0i1QVvZD0JqP4PnMEzx6RRUGGy8iIiIiEkaWZWTFHkHS1CXQ3k2C3xsDUemtwVB4utu7NCKbYuNFRERERELkX7mBxEmLkHPoV3h0aY1q2xfApW4te5dFZBdsvMzEw+EkEqfJJdGYMRKJ+SIDfVYOUhasR+qKbVBVC0TVDbPh8XRkmb9HMWMkEqeTd0BsvkgE5opEY8ZIJOaLgP9/WuE3PyFx2lLok1JRadwQ+I0dBIW7a5nHZsZIJFvki42XBTidPIkgyzJ0Oh2USiXzRUIwYyQS80X5l64hcdJC5Bz+HR7d2qHyrDehrlPdauMzYySSLfLFxstMnMaURNLr9VAqlfYug8oxZoxEYr4qJn1mNpLnr0XaZ9uhqlkVVTd9DM+ubcX8LGaMBBKdLzZeRERERGQ2WZaRuesAkqYvgz4tA/7jX4LvGwOgcCv7aYVE5REbLyIiIiIyS97FeCROjEbuL3/Cs0cHBHw0FupaVe1dFpFDY+NFRERERKWiS89EyidrkRbzFdR1qqPatvnw6NzK3mUROQU2XmbixZwkEs9bJ9GYMRKJ+Sq/ZFlG5vbvkTRjBfRZ2fCf9Ar8RveD5Opi0zqYMRJJdL4UQke30K5du/Dcc88hIiICrVq1wiuvvILc3Fzj8wcPHkSvXr0QERGBbt264auvvio0Rn5+Pj7++GO0a9cOTZs2xUsvvYT4+Hir1Mfmi0SQJIkzNZFQzBiJxHyVX3nnLuPfnmPw3xv/g1vbpqj9y0ZUemuwzZsuZoxEskW+HO6I14oVKxATE4PRo0ejadOmSElJQVxcHHQ6HQDg5MmTGDNmDPr27YvJkyfj+PHj+OCDD+Dp6Ymnn37aOM6sWbMQGxuLiRMnIigoCCtXrsTw4cPx3Xffwdvbu0w1cjp5EkGWZWO2mC8SgRkjkZiv8keXloGUuauRtmYX1PVqodpX0fDo0MJu9TBjJJIt8iXJDjQ/enx8PHr27Inly5fjiSeeKHKZESNGICsrC1u3bjU+9u677+LixYuIjY0FANy5cwedO3fG9OnT0b9/fwBAamoqOnXqhNdffx0jR460qL6zZ89ClmVERETwA09WJ8syNBoN1Go180VCMGMkEvNVfsh6PTK27kXSRysh5+TB/72X4DuyLyQXtX3rYsZIIEvydfbsWQBAREREqZZ3qFMNd+7ciZo1axbbdOXn5+PEiRMmR7YAICoqCleuXMHNmzcBAEePHoVerzdZzs/PD+3atcPhw4fFrQARERGRE8s7/Tdu9Xgd996aC48nWqL28c3we2Og3ZsuovLAoRqv06dPIzQ0FMuXL0ebNm0QHh6OAQMG4PTp0wCAGzduQKPRICQkxOR1devWBQDjNVzx8fEICAiAr69voeWsdZ0XERERUXmhS0nHvffm4+ZTIyFn5aD67sUIWjkNqqqV7V0aUbnhUNd43bt3D+fOncOlS5cwffp0uLu7Y+XKlXj55Zexf/9+pKWlAQB8fHxMXmf4t+H59PT0Iq/j8vHxMS5TFkWdnSlJUpGPG54r7nWiX8txnee9MZxbXPDf1hhXVL3WGtcRa3K2cUv7WkPGDP92xnV1tnEdsSaR4z64H7PGuGWtieOW/FrIMtI37kHy/1ZB1mgRMPMN+LzcB5Ja9dCfWZaaLBm34L7MmuOKqtdZa3K2ca1VU8F9WFnGLYlDNV6yLCM7OxuLFi1C/fr1AQBNmjRB586dsXHjRkRGRtq5wvs0Go3JuZ8KhQIqlcr43INcXO7P+qPT6aDX602eU6lUkCQJer3eOIHIg+Mazjl9kFqtLnZcpVIJpVIJWZah1WpNnpMkyfharVZbKDiixi3NugKFt6GocQ3rKkmS2etamnEB87ahLMsmy5q7roZxi6rJXtvQMG5J29Be+S5pG9oy34Dt9hGyLBvHUKlU3Ec8ZFzDujrKPqKs62qLfYThuYK/I7mPMF1XR9pH6M7+g6RJi5B36iI8nn8KflNGQVnFH1rIUOr1DrePMMw4p9frC43LfUTZ17Wif48w/I6UJMn4mXvYPqJgk1YaDtV4+fj4wM/Pz9h0AfevzWrYsCEuX76MHj16AAAyMjJMXpeeng4AxlMLfXx8kJmZWWj89PT0Qqcfmqvgm1EUw5tRlIKBfpBCoYBCUfSZnwXDbu64D3ut4YNgy3FLWleg5G1o7XENH5ayrKu13xtDTRVlG9or32X5zIl6b7iPuK885bu4cQ3K0zaUJAnu7u5FPg5wH1GacQHb7CN0SalI/t8qZGz6Di4N66L6N0vh1rpxqV77IFvvIwxflku63xL3Efc52j6iuHGdeR9hTtMFOFjjVa9ePdy4caPI5/Ly8lC7dm2o1WrEx8ejffv2xucM120Zrv0KCQlBYmIi0tLSTBqt+Pj4QteHWaK4jfywjV/S86Jey3HFjuuINTnbuI5Yk7ON64g1cVzHrcnZxnXEmpxtXMPzsk6H9A3fIHl2DKCXUXn2W/AZ/iykEr5cOtu6Otu4jliTs43rqDUVxaEm1+jUqRNSU1Nx8eJF42MpKSk4f/48GjVqBBcXF7Rq1Qrff/+9yetiY2NRt25d1KxZEwAQGRkJhUKB/fv3G5dJS0vD0aNH0aFDhzLXack5nUQPYzhUznyRKMwYicR8Obbc387h5lMjkfj+AnhGdUCt45vh+8rzJTZdjoYZI5FskS+H+rQ9+eSTiIiIwJtvvom3334brq6uWLVqFVxcXPDiiy8CAF577TUMHToUM2bMQPfu3XHixAns2bMH0dHRxnGqVq2Kvn37Yt68eVAoFAgKCsJnn30Gb29vDBgwoEw18sNOIjFfJBozRiIxX45Hey8FyTNXIGPrXrg0DkWNvSvh1qKRvcuyGDNGIonOl0PdQBkAkpOTMWfOHBw6dAgajQYtWrTApEmTUK9ePeMyBw4cwMKFC3H16lVUr14do0aNQt++fU3Gyc/PR3R0NL7++mtkZWWhefPmmDJlinHqeUvwBsokkuEvLbwxJInCjJFIzJdjkbVapK/djeS5qwGFBP8PRsFnSE9IJVwb5eiYMRLJknyZewNlh2u8HBkbLxKJv1BINGaMRGK+bCsnJweXL19GQEAA/vzzTzRt2hRJSUn3/1D95yUkTopG/oV4+AzpCf/JI6EM8LN3yWXGjJFItmi8HOpUQyIiIiJ6uJycHJw7dw6NGjXChQsXULNmTVz65Vf4LfwS+d/8BNfmDVDj+8/g1qyBvUslov+PjZeZ+BcWEqmkaUuJrIEZI5GYL9vSaHX4914m9BodstfFIuLL/dC4uyFwwfvwHtQDUgnTejsrZoxEEp0vptcCbL5IBEmSmC0SihkjkZgv20hOTkZaWhquXruBK9f+xZ0D59A57k/4pmThUngdeL79IqTw+tCmpsLf39/e5VoVM0Yi2SJfbLwsYO5dqolKQ5Zl6PV6i27IR1QazBiJxHzZxvHjx3HhwgXgXiraH/kLwf/cxX9VfbG33+NIruwD5Z+/4+Tf59GwYUNERUXZu1yrYsZIJFvki42XmTgXCYmk0+lKvOM7UVkxYyQS8yVeq+aPof5v8dBtikOeAtj/RHPcCa+EPEUluEp5aNe+FYIfqQ1fX197lyoEM0Yiic4XGy8iIiIiJ5D980lkTVoI+UoC3AdF4df6lRFStwHSfz+Gto+1xN1/49G0cUS5O8WQqLzgnwyIiIiIHJjm5l3ceWkKbvd9G8oAP9Q8uBoeU0ZC4e2BaoFeUCokVAv0glrlvPfoIqoIeMSLiIiIyAHJeflIXb4VKQu/gMLLA1VWTIXX809BkiToc3IQHh6OgIAANGzYEJUrV0Z4eDjc3d3tXTYRFYONl5l4MSeJxPPWSTRmjERivqwn+8AJJE5eCM312/Ad1Rf+770Ehben8Xl3d3fjTVurV69u8r/lGTNGIonOFxsvC7D5IhEkSeL9SUgoZoxEYr6sQ3PjNhKnLEb23qNwi2yOqutnw6V+sL3LcgjMGIlki3wxvRbgdPIkQsEZM5kvEoEZI5GYr7LR5+YhdelmpC7aCEUlXwStmgHP5zpzWxbAjJFItsgXGy8zcTp5Ekmj0UCtVtu7DCrHmDESifmyTNb3x5A4ZTG0t/6D3+h+qPTOMCi8POxdlkNixkgk0fli40VERERkB5qrt5D4wSJk/xAH944tUW3zPLg8+oi9yyIiQdh4EREREdmQPjsXqYs3InXpFigr+yFozUfwfOYJnj5HVM6x8SIiIiKyAVmWkb33CBKnLIH2bhL83hiISm8NhsKTU8ATVQRsvMzEv0aRSMwXicaMkUjMV/Hyr9xA4uTFyDl4Ah5dWqPa9gVwqVvL3mU5HWaMRBKdLzZeFuCHnkSQJIkXDJNQzBiJxHwVTZ+Vg5ToDUhdsQ2qqpVRdcNseDwdye8SFmDGSCRb5IuNFxEREZGVybKMrG9/QtK0pdAlpqLSW4PhN3YQFO6u9i6NiOyEjZcFeB8vEkGWZWi1WqhUKuaLhGDGSCTm6//kX7qGxMmLkPPzSXh0a4fKs96Euk51e5fl9JgxEskW+WLjZSbex4tEYr5INGaMRKro+dJnZiPl03VIXfklVDWrouqmj+HZta29yypXKnrGSCzR+WLjRURERFQGsiwjc9cBJE1fBn1aBvzHvwTfNwZA4cbTCono/7DxIiIiIrJQ3sV4JE5aiNxjp+DZowMCZo6BunY1e5dFRA6IjRcRERGRmXTpmUj5ZC3SYr6C+pFqqLZtPjw6t7J3WUTkwNh4mYkXc5JIKhU/kiQWM0YiVYR8ybKMzO3fI2nGCuizsuE/6RX4je4HydXF3qVVCBUhY2Q/ovPF9FqAzReJIEkSs0VCMWMkUkXIV965y0icGI3cE2fg2asTKs98A6oaQfYuq8KoCBkj+7FFvth4WYDTyZMIsixDr9dDoVAwXyQEM0Yiled86dIykDJ3NdLW7IK6bi1U2xENjyda2LusCqc8Z4zszxb5YuNlJk5jSiLpdDooFAp7l0HlGDNGIpW3fMl6PTK27kXSRysh5+QhYNpo+I7sC8lFbe/SKqzyljFyLKLzxcaLiIiI6AF5p//GvYnRyDt5Hl59nkTAjNehqhZo77KIyImx8SIiIiL6/3Qp6UievQrp67+BOqwOqu9eDPd2zexdFhGVA2y8iIiIqMKT9XpkbNqDpFmrAI0WATPHwHdEH0hqflUiIuvg3sRMvJiTROJ56yQaM0YiOWu+cv+4gMSJC5F36iK8+nVDwLTXoAoKsHdZVARnzRg5B9H5YuNlATZfJIIkSbw/CQnFjJFIzpgvXVIqkmZ9hoxN38GlYV1U/3YZ3Fs3tndZVAxnzBg5D1vki+m1AKeTJxEKzpjJfJEIzBiJ5Ez5knU6pG/4BsmzYwC9jMqz34LP8Gch8Uu9Q3OmjJHzsUW+uIcxE6eTJ5E0Gg3Uak5TTOIwYySSM+Qr97dzuDdhAfLP/gPvF3vAf8qrUAVWsndZVErOkDFyXqLzxcaLiIiIyj3tvRQkz1yBjK174dI4FDX2roRbi0b2LouIKhA2XkRERFRuyVot0tfuRvLc1YBCQuVP3oXPkJ6QlEp7l0ZEFQwbLyIiIiqXcuJOI3FSNPIvxMNnSE/4Tx4JZYCfvcsiogqKjRcRERGVK9o7iUiauQKZ2/fDtXkD1Pj+M7g1a2DvsoiogmPjZSZJkjiTDgkhSRLUajXzRcIwYySSI+RL1miR9vkOJM9bC8lFjcAF78N7UA9IvPdTueAIGaPyyxb5YuNF5ED4y4REY8ZIJHvmK+fYKdybGA3NpevwGfYs/Ce9AmUlH7vVQ2JwH0Yiic4XGy8L8D5eJIIsy9DpdFAqlcwXCcGMkUj2ypf29j0kTV+GzF0H4NoyHDV/iIFr41Cb/XyyHe7DSCRb5IuNl5l4Hy8SSa/XQ8mZtkggZoxEsmW+5HwNUldtR8r8dVB4uCFwyWR49+vG0wrLOe7DSCTR+WLjRURERE4l++eTSJy0EJorCfAd0QeVJrwMpa+3vcsiIioRGy8iIiJyCtpbd5E4dSmyvv0Jbq2bIChmBlwb1bN3WUREpeJQx+N37tyJsLCwQv/Nnz/fZLnt27ejW7duiIiIQK9evXDo0KFCY2VkZGDy5Ml4/PHH0axZM7z55pv477//bLUqREREZCVyXj5SFn6BG20HI/fEGVRZMRXVv1nCpouInIpDHvH6/PPP4e39f6cMBAUFGf//d999h6lTp2L06NFo3bo1YmNjMWbMGGzatAlNmzY1Ljdu3DhcvnwZM2bMgKurKxYuXIiRI0fiq6++gkpl+WrzYk4Sieetk2jMGIkkIl/ZB04gcfJCaK7fhu+ovvB/7yUovD2t/nPIOXAfRiKJzpdDNl6NGjWCv79/kc8tXrwYPXr0wLhx4wAArVu3xqVLl7Bs2TLExMQAAE6dOoWjR49i9erViIyMBAAEBwcjKioK+/fvR1RUVJnqY/NFIkiSxF8oJBQzRiJZO1+aG7eRNHUJsmKPwC2yOaqunw2X+sFWG5+cD/dhJJIt8uVQpxo+TEJCAq5du4bu3bubPB4VFYW4uDjk5+cDAA4fPgwfHx+0a9fOuExISAgaNGiAw4cPl7kOzmxIIsiyDL1ez3yRMMwYiWStfOlz85A8fy0S2g1G7qm/ELRqBqrvXMimi7gPI6FskS+HbLyeeeYZNGjQAF26dMFnn30GnU4HAIiPjwdw/+hVQXXr1oVGo0FCQoJxueDg4EJHpkJCQoxjWIofdhJJq9XauwQq55gxEqms+crafwwJ7Yci5dP18B3ZF7V/2Qiv3l14pgkZcR9GIonOl0OdahgYGIixY8eiSZMmkCQJBw8exMKFC3H37l1MmzYNaWlpAAAfH9M70Rv+bXg+PT3d5BoxA19fX5w7d67MdRbVfEmSVGxTZviFUdLzol7LcZ3nvZFl2eQxR1tXZ9iGFXXc0r7WkDHDv51xXZ1tXEesSeS4D+7HSjtufvxNJE1dguz9v8C9QwtU3fQxXB59xKHX1ZnGdcSaLBm34L7MGep11pqcbVxr1VRwH1aWcUviUI1X+/bt0b59e+O/IyMj4erqivXr12P06NF2rMyURqMx+eubQqEwTtih0WgKLe/i4gIA0Ol00Ov1Js+pVCpIkgS9Xm88svfguLIsFzmuWq0udlylUgmlUglZlgt175IkGV+r1WoLBUfUuKVZV6DwNhQ1rmFdJUkye11LMy5g3jaUZdmkfnPX1TBuUTXZaxsaxi1pG9or3yVtQ1vmG7DdPqLgeqlUKu4jHjKuYV0dZR9R1nW1xT7CUG/B35ElbUNFvhbpSzcjdekWKAL8ELBqOtyj2huX5z7iPn6P+L/XAvffm0JZ4j6izOta0b9HGP6/JEnGz9zD9hEFm7TSMLvxunnzJg4cOIA//vgDV65cQUpKCiRJQqVKlRASEoLmzZujc+fOqFWrlrlDF6l79+5Ys2YNLl68CF9fXwD3p4oPDAw0LpOeng4Axud9fHxw586dQmOlpaUZlykLQ0CKe644BQP9IIVCAYWi6DM/C4bd3HEf9tqSZngUNW5J6wqUvA2tPa7hfSzLulrrvSlqJ1ac8rQN7ZXvsnzmRL03ovcRhowV3IdxH1HyuI60j3iQI25DlUpV6HdkUdtQlmVk7z2KpKlLoL2bBL/XB8DvrcFQeLoXGpP7iP9T0b9HGPZhCoWixEkQuI+4zxH3EY78PcKQr4Lr97B1NafpAsxovA4dOoQ1a9bg999/hyzLqF27NmrWrInQ0FDIsoz09HT89ddf2L9/P+bOnYvHHnsMI0aMQKdOncwqqCQhISEA7l/DZfj/hn+r1WpjsxcSEoK4uLhCXejVq1cRGhpaphokSTL+V9RzD3utJc+V5bUcV+y41q6p4I7B0dbVWbZhRRzXnNcqlUqTfZizrauzjeuINYkc98F8FfXa/Cs3kDh5MXIOnoB751aotn0BXOqW/MdaR1xXZxrXEWuydFzDl93inne0ep2xJmcb15o1GfZh1qipKKVqvPr164e//voLXbp0wcKFC9G2bVt4eXkVuWxmZiaOHTuG77//HuPGjUP9+vWxbds2swsziI2NhVKpRMOGDREYGIg6depg3759ePLJJ02WadOmjfGwYIcOHbB8+XLExcWhbdu2AO43XRcuXMArr7xicS0GlmxoooeRJKlM95gjehhmjER6WL70WTlIid6A1BXboKpaGVU3zIbH05H8nUqlxn0YiWSLfJVq9FatWmH58uWoXLnyQ5f18vJCt27d0K1bN9y7dw8bNmwodTEjRoxAq1atEBYWBgA4cOAAvvzySwwdOtR4auHYsWMxfvx41K5dG61atUJsbCzOnDmDjRs3Gsdp1qwZIiMjMXnyZEyYMAGurq6Ijo5GWFgYunbtWup6iIiIqGxkWUbWtz8hadpS6BJTUenNQfB7czAU7q72Lo2IyKYk2ZIpOQSZNWsWjhw5gjt37kCv16NOnTp44YUXMGTIEJO/iG3fvh0xMTH4999/ERwcjHfeeafQKY0ZGRmYM2cOfvjhB2i1WkRGRmLKlCkICgqyuL6zZ89ClmVERETwL3RkdYaLQ0u6hpCoLJgxEqmofOVfuobEyYuQ8/NJeHRrh8ofjYU6uIadKyVnxX0YiWRJvs6ePQsAiIiIKNXyDtV4OTo2XiQSf6GQaMwYiVQwX3JWDlI+XYfUlV9CVTMIlf/3Fjy7trV3ieTkuA8jkWzReJX6RMbz58+XdlGjRo0amf0aIiIick6yLCNz549ImrEc+tR0VBo/HH5vDITCjacVEhGVuvF6/vnnzfrrgiRJuHDhgkVFERERkXPJvxiPexOjkRd3Gp49OiBg5hioa1ezd1lERA6j1I3XnDlzHrpMbm4uvvzyS1y8eLFMRREREZFz0KVnIuWTtUiL+Qqq2lVRdet8eHZpZe+yiIgcTqkbr969exf7XH5+PrZu3YqYmBjcu3cPLVu2xNixY61SoKPhOcUkUkk3ACSyBmaMrEWWZWRu/x5JM1ZAn5UN/0kj4PtqP0iuLvYujcox7sNIJNH5KtNk9fn5+diyZQs+//xz3Lt3D48//jgWLFiAli1bWqs+h8Tmi0Rgrkg0ZoysJe/cZSROjEbuiTPw7NUJlWe+AVUNy2cNJioN7sNIJFvky6LGKz8/H5s3b8bq1atx7949tGrVqkI0XAayLPPDT1YnyzJ0Ol2hu6YTWQszRmWlS8tAytzVSFuzC+q6tVBtRzQ8nmgBgPki8ZgxEskW+TKr8crLyzMe4UpMTESrVq0QHR2NFi1aCCnOEXH2fRJJr9dDqVTauwwqx5gxsoSs1yNj614kfbQSck4eAqaNhu/IvpBcTE/LYb5INGaMRBKdr1I3XuvWrcPnn3+OpKQktG7dGosWLcJjjz0mrDAiIiKyv7zTf9+frfDkeXj1eRIBM16HqlqgvcsiInI6pW685s6dC0mS0KBBA9StWxd79+7F3r17S3zNlClTylwgERER2Z4uJR3Js1chff03UIfVQfXdi+Herpm9yyIiclpmnWooyzIuXLhQqvtzSZLExouIiMjJyHo9MjbtQdKsVYBGi4CZY+A7og8kdZnm4yIiqvBKvRf966+/RNbhNHgxJ4nE89ZJNGaMSpL7xwUkTlyIvFMX4dWvGwKmvQZVUECpX898kWjMGIkkOl/885UF2HyRCJIk8RcKCcWMUXF0SalImvUZMjZ9B5eGdVH922Vwb93YrDGYLxKNGSORbJEvNl4W4HTyJIIsy8ZsMV8kAjNGD5J1OqRv+AbJs2MAvYzKs9+Cz/BnIanM/3rAfJFozBiJZIt8Wdx4ff311/jqq69w8+ZNpKWlFZpmXZIk/P7772Uu0NFwOnkSSavVCr9rOlVszBgZ5P52DvcmRiP/zCV4v9gD/lNehSqwUpnGZL5INGaMRBKdL4sar08++QRr1qxBUFAQwsPD4e3tbe26iIiISADtvRQkf7QSGVti4dI4FDX2roRbi0b2LouIqNyzqPHavn07OnbsiGXLlkGhUFi7JiIiIrIyWatF+trdSJ67GlBIqPzJu/AZ0hMSr5khIrIJi081fOKJJ9h0EREROYGc42eQOHEB8i/Ew2dIT/hPHgllgJ+9yyIiqlAs6pw6duxYLq/fKg1ezEkiMV8kGjNWsWjvJOLu6x/h355vQHJ1QY3vP0Pgp+8Ja7qYLxKNGSORROdLki2YLSIjIwOjR49GWFgYnn/+eVSrVq3Io19+fn7WqNFhnD17FgAQERFh50qIiIiKJ2u0SFv9FZI/XgPJRY2AKa/Ce1APSDxThYjIasztDSw61dDd3R3NmjXD6tWrsWXLlmKXu3jxoiXDExERkYVyjp3CvYnR0Px9DT7Dn4P/pFegrORj77KIiCo8ixqvmTNnYvv27WjSpAmaNGlS4WY15H28SARZlqHVaqFSqZgvEoIZK9+0t+8hafoyZO46ANeW4aj5Qwxcm4TZ7OczXyQaM0Yi2SJfFjVee/fuxbPPPou5c+daux6Hx/t4kUjMF4nGjJU/cr4Gqau2I2X+OkjurghcPAne/Z+2y2mFzBeJxoyRSKLzZVHjpVKp0KRJE2vXQkRERGbI/vkkEicthOZKAnxH9EGlCS9D6VuxzkIhInIWFv05rEePHjh06JC1ayEiIqJS0N66izsvT8Xtvm9D6e+LmgdXo/Lst9h0ERE5MIuOeHXv3h2zZs3CqFGjjLMaKou4AWOjRo3KXCARERHdJ+flI3XFNqREb4DCywNVlk+BV9+uvN6FiMgJWNR4DRo0CMD9WQuPHDlS6HnD5BPlcVZD/nIjkVQqi+9pTlQqzJjzyj5wAomTF0Jz/TZ8R/WF/3svQeHtae+yTDBfJBozRiKJzpdFo8+ZM8fadTgVNl8kgiRJzBYJxYw5J82N20iaugRZsUfg1q4Zqq6fDZf6wfYuqxDmi0RjxkgkW+TLosard+/e1q7DqXA6eRJBlmXo9XooFArmi4RgxpyLPjcPqUs3I3XRRij8fFBl1XR4PdfFYd875otEY8ZIJFvki8drzcRpTEkknU4HhR2mgKaKgxlzDln7jyHxg8XQ3rwLv9f6o9I7w6Dw8rB3WQ/FfJFozBiJJDpfpRp52rRpSEhIMHvwGzduYNq0aWa/joiIqCLSXL2F24Mm4M6giVA/Uh21Dq9HwLTXnKLpIiKikpXqiNft27fRvXt3tG7dGlFRUWjTpg2qVatW5LI3b95EXFwc9u7dixMnTqBdu3ZWLZiIiKi80WfnInXxRqQu3QJlZT8ErfkIns88wdOpiIjKkVI1XjExMfj999+xZs0aTJs2DTqdDn5+fqhRowZ8fX0hyzLS0tJw8+ZNpKenQ6lUokOHDli/fj1atGgheh2IiIickizLyN57BIlTlkB7Nwl+rw9ApXFDoPB0t3dpRERkZaW+xuuxxx7DY489huTkZBw6dAh//vkn4uPjcefOHQCAn58funbtiqZNm6Jjx44ICAgQVrQ98a+PJFJR98MjsiZmzHHkX7mBxMmLkXPwBNw7t0K17QvgUreWvcsqE+aLRGPGSCTR+ZJkzhZRamfPngUARERE2LkSIiJyVvqsHKREb0Dqim1QVa2MyrPGwuPpSP5hj4jIyZjbG3BWQwtwOnkSoeDfQJgvEoEZsy9ZlpH17U9ImrYUusRUVHpzEPzeHAyFu6u9S7MK5otEY8ZIJFvki42XmXiAkETSaDRQq9X2LoPKMWbMPvIvXUPi5EXI+fkkPLq1Q+WPxkIdXMPeZVkd80WiMWMkkuh8sfEiIiISRJ+ZjZRP1yF15ZdQ1QxC1U0fw7NrW3uXRUREdsDGi4iIyMpkWUbmrgNImr4M+tR0VBo/HH5vDITCrXycVkhEROZj40VERGRFeRfjkThpIXKPnYJnjw4ImDkG6tpF3/uSiIgqDjZeZuLFnCQS80WiMWPi6DOykDxvDdJivoL6kWqotm0+PDq3sndZNsV8kWjMGIkkOl+K0i64YMEC/PXXXyJrcRr80JMIkiRBrVYzXyQMMyaGLMvI2P49brR+EekbvoH/pFdQ6/D6Ctl0MV8kEjNGItkiX6VuvFatWoV//vnH+O+UlBQ0aNAAcXFxQgojIiJydHnnLuPfnmPw3+uz4Na6CWr/shGV3hoMydXF3qUREZGDKdOphhV1anXex4tEkGUZWq0WKpWK+SIhmDHr0aVlIGXuaqSt2QV13VqotiMaHk+0sHdZdsV8kWjMGIlki3yV+oiXrWVlZaFDhw4ICwsz3hXaYPv27ejWrRsiIiLQq1cvHDp0qNDrMzIyMHnyZDz++ONo1qwZ3nzzTfz3339lrquiNptkG8wXicaMlY2s1yN9SywS2gxC+pZYBEwbjVo/ra3wTZcB80WiMWMkkuh8OWzjtXz5cuh0ukKPf/fdd5g6dSq6d++OmJgYNG3aFGPGjMGff/5psty4ceNw7NgxzJgxA/Pnz8fVq1cxcuRIaLVaG60BERGVJ3mn/8atZ97AvTfnwL39Y6gdtwl+bwyE5MKbuRIR0cOZdarhrVu3cP78eQD3jygBwPXr1+Hj41Pk8o0aNbKoqCtXrmDz5s2YMGECpk+fbvLc4sWL0aNHD4wbNw4A0Lp1a1y6dAnLli1DTEwMAODUqVM4evQoVq9ejcjISABAcHAwoqKisH//fkRFRVlUFxERVTy6lHQkz4lB+rqvoQ6rg+q7F8O9XTN7l0VERE7GrMZr0aJFWLRokcljH374YaHlDNdAXbx40aKiZs2ahQEDBiA4ONjk8YSEBFy7dg3vvfeeyeNRUVGYN28e8vPz4eLigsOHD8PHxwft2rUzLhMSEoIGDRrg8OHDbLyIiOihZL0eGZv2IGnWKkCjRcDMMfAd0QeSmndiISIi85X6t8ecOXNE1mG0b98+XLp0CUuWLDEeXTOIj48HgEINWd26daHRaJCQkIC6desiPj4ewcHBhS6MCwkJMY5hKV7MSSKpVPxCR2IxY6WTe+oiEidEI+/URXj164aAaa9BFRRg77IcHvNFojFjJJLofJV69N69e4usAwCQk5ODuXPn4u2334aXl1eh59PS0gCg0KmNhn8bnk9PT4e3t3eh1/v6+uLcuXNWqfXBi+8kSSr2gjxDs1bS86Jey3Gd672RJMlh19VZtmFFHNec1xZc3hnXVfS4uqRUJM+OQcbGPXBpEILq3y6De+vGkGW52M+ss66rtcctbgzuI+w/riPWZOm4CoWiXH0eHbEmZxvXmjU9eIDFknFL4lB/NlixYgUCAgLw/PPP27uUYsmyjPz8fJM3RqFQGDtkjUZT6DUuLvfv56LT6aDX602eM0xZqdfrC00mYhhXluUix1Wr1cWOq1QqoVQqjVNjFmS4QRwAaLXaQsERNW5p1hUovA1FjWtYV0mSzF7X0owLmLcNDb9IXF1dIUmS2etqGLeomuy1DQ3jlrQN7ZXvkrahLfMN2G4fYciYJElQqVTcRxQYV9bpkLp2N9LmrYGs18PvozHwGtITLu5uFq2riH1EWddV9D6i4O/Hgr8juY8wXVdH3kdYa11FjWvIryzLhcbl94iyr2tF/x5h+B2pUCiMn7mH7SMMv1NLy2Ear1u3bmHNmjVYtmyZceKO7Oxs4/9mZWXB19cXwP2JPQIDA42vTU9PBwDj8z4+Prhz506hn5GWlmZcpixKuqu14c0oSsFAP0ihUEChKHqSyYJhN3fch722pEOqosYtaV2Bkrehtcc1vI9lWVdrvTcP7jgqyja0V77L8pkT9d6I3kcYMlZwH8Z9BJD72zncmxiN/DOX4DUwCgFTXoUysBIAx9pHPMiRtqGBoeaCvyO5jyj9uAC/R5Q0ruFLsuEPR8Vxpt+BxY1rUN72EY78PcLwO7Lg+j1sXc1pugAHarxu3rwJjUaDUaNGFXpu6NChaNKkCT799FMA96/1CgkJMT4fHx8PtVqNWrVqAbh/LVdcXFyhLvTq1asIDQ0tc60P/jWv4OMPe50lz5XltRxX7LjWrqmoLyvW/JmOOK4j1uRs45rzWsP+q2DjJaImRxk3JycHFy5cAHD/+uBbt26hRo0auHXrFoL9ApD9yXpkbImFS+NQ1IhdAbeW4cJrKs/jPpgvR6iJ4zpmTWUdt7jnHbVeZ6rJ2ca1Zk0PZqssNRXFYRqvBg0aYMOGDSaPXbx4EXPmzMGHH36IiIgI1KpVC3Xq1MG+ffvw5JNPGpeLjY1FmzZtjIcFO3TogOXLlyMuLg5t27YFcL/punDhAl555RXbrRQREdlVTk6O8dpef39/nDt3Dp5u7ri3bDM895+CpFSg8ifvwmdIT0gl/AWdiIiorBym8fLx8UGrVq2KfK5Ro0bGe4KNHTsW48ePR+3atdGqVSvExsbizJkz2Lhxo3H5Zs2aITIyEpMnT8aECRPg6uqK6OhohIWFoWvXrjZZHyIisp+cnBxcvnwZnp6eSE9Ph0LlhqycfLhduom8BV+j9vVbUL/QFdVnjoUywM/e5RIRUQXgMI1XaT3zzDPIyclBTEwMVq1aheDgYCxduhTNmjUzWW7hwoWYM2cOpk2bBq1Wi8jISEyZMqXM00RacliRqLRKOi+ayBoqSsZu376NEydOoFr1mkhKToYiTYtbW2aj8cV43AmshN/7dcDjQ7tDm54CX0kPf39/e5dcLlSUfJH9MGMkkuh8SbIlcyH+fxqNBvHx8cjIyChySsWWLVuWqThHc/bsWQBARESEnSshIqKS7Ny5E+fPn4dCllDvj3g0/jUeeqUCf7aph38aVAckCa5ublCrlGjYsCGioqLsXTIRETkZc3sDiw7/6PV6fPrpp9i8eTNyc3OLXe7ixYuWDO/wzJ06kqg0irrvDZE1VYSMJScnIy0tDX7+AQi4mYGWP51HpeR0/NWwJv544jHILjokZbvC312LTp07IigwwCqz3VLFyBfZFzNGItkiXxY1XitXrsTq1avRv39/PPbYY3j//fcxfvx4+Pj4YPPmzZAkCe+99561a3UIZThASPRQhqm+iUQp7xk7fvw44k/8jkYHzqHb3//iv6q+iO33OFKDfOHiooWLqys6demMa5fPILReCE8xtLLyni+yP2aMRBKdL4tOZNy1axe6d++ODz/8EO3btwdwfwKMfv364csvv4QkSTh+/LhVCyUiIiqJnK9B03P/ovu6w6h5Jx3HnmqOL3p0RXIVP/hVqoQOHTogwN8ftap6Q63iDIZERGRbFjVed+7cQevWrQH8393U8/Pzjf/u1asXvv76ayuVSEREVLLsn08ioeNLyPn0C/gOegaVfvwM6N4Sz0e1ReXKAWjapAnq16+P5s2bw9fXF+Hh4XB3d7d32UREVIFYdKqhn58fsrOzAQCenp7w8vJCQkKCyTLp6ellr46IiKgE2lt3kTh1KbK+/QlurRojKGYGXBvVQ3JyMtQqJUJqBeC/W5UQHh4Of39/46mFPMWQiIhszaLGq2HDhsZZPACgVatWWL9+PRo0aABZlrFhwwaEhYVZrUgiIqKC5Lx8pK7YhpToDVB4eaDK8inw6tvVeEG0u7s7wsPDeXSLiIgchkWNV79+/bBr1y7k5+fDxcUFb7/9NgYNGoTBgwdDlmX4+vpi4sSJ1q7VIUiSxJl0SAhJkqBWq5kvEqa8ZCz7wAkkTl4IzfXb8B3VF/7vvQSFt6fJMu7u7sbpfXl0yzbKS77IcTFjJJIt8lWm+3gVlJGRgRMnTkCpVKJZs2bw8/OzxrAOhffxIiKyH82N20iaugRZsUfg1q4ZAue+DZf6wfYui4iIKiib3MerKN7e3njyySetNZxD4328SARZlqHT6aBUKpkvEsJZM6bPzUPq0s1IXbQRCj8fVFk1HV7PdXGqdagInDVf5DyYMRLJFvmyaFbDLl26oH///oiPjy/y+R9//BFdunQpU2GOivfxIpH0er29S6ByztkylrX/GBLaD0XKp+vhO7IvasdtgnfvJ/mly0E5W77I+TBjJJLofFnUeN26dQvnz5/HCy+8gB9//LHQ89nZ2fj333/LXBwREVVMmqu3cHvQBNwZNBHqR6qj1uH1CJj2GhReHvYujYiIyCIWNV4AMGnSJLRs2RJjx47FwoULrVgSERFVVPrsXCTP/RwJ7Yci//xlBK35CNW2L4DLo4/YuzQiIqIysfgaLx8fH6xcuRJLly7F8uXLceHCBXz66afw9va2Zn1ERFQByLKM7L1HkDh1KbR3EuH3+gBUGjcECk9OA09EROWDxUe8DMaMGYOVK1fi9OnT6Nu3L/755x9r1OWweF0BiaRSWW2+G6IiOWLG8q8k4PaA93Bn2AdQ16t9/7TCD0ax6XJCjpgvKl+YMRJJdL7K3HgBQIcOHbBjxw64u7ujX79+OHDggDWGdVhsvkgESZKgUCiYLxLG0TKmz8pB0v9WIaHDMGgu30DVDbNRbesncKlby96lkQUcLV9U/jBjJJIt8mWVxgsAatWqhW3btqFr1674/vvvrTWsQ+LMhiSCLMvQ6/XMFwnjKBmTZRmZ3xxCQrvBSFuxDZXeHIRaR7+AZ/f2/ELlxBwlX1R+MWMkki3yZdHxtA0bNqBevXqFHnd1dcXHH3+MqKgoJCcnl7k4R8QPO4mk1WqhVqvtXQaVY/bOWP4/15E4aSFyfj4Jj27tUPmjsVAH17BbPWRd9s4XlX/MGIkkOl9mN145OTmYO3cuXnjhBQwcOLDIZZ544okyF0ZEROWHPjMbKZ+uQ+rKL6GqGYSqmz6GZ9e29i6LiIjIZsxuvNzd3XHz5k2eDkJERA8lyzIydx9A0rRl0Kemo9L44fB7YyAUbq72Lo2IiMimLLrGq3379jh69Ki1ayEionIk/6+r+LfPOPw36kO4PdYQtY5thP+7w9l0ERFRhWRR4/X666/j2rVreO+993Dy5EncvXsXqamphf4rj3ikj0RSKKw23w1RkWyRMX1GFhKnLkFCx5eg+/c/VNs2H1XX/Q/q2tWE/2yyL+7DSDRmjEQSnS+LJtfo0aMHAODy5cvYs2dPsctdvHjRsqocHJsvEkGSJN6fhIQSnTFZlpG5Yz+SZiyHPjMb/hNHwO+1/pBcXYT9THIc3IeRaMwYiWSLfFk0+htvvMHmg0gAWZb52SKhRGUs79xlJE6MRu6JM/Ds2REBM8dAXTPI6j+HHBv3YSQaM0Yiic6XRY3X2LFjrV2H05BlmR96EkKWZWg0GqjVauaLhBCRMV1aBlLmrkbaml1Q162Fajui4fFEC6uMTc6F+zASjRkjkWyRL6scT8vNzQUAuLm5WWM4IiJycLJej4xt+5D80Uros3LhP/VV+I16AZIL769DRERUFIsbr3///RdLlizBzz//jJSUFABApUqV8MQTT2DMmDGoUYM3xCQiKo/yTv+Ne5MWIu+3c/Dq8yQCZrwOVbVAe5dFRETk0CxqvK5cuYIXX3wRGRkZaNu2LerWrQsAiI+Px9dff41Dhw5h8+bNCAkJsWqxRERkP7qUdCTPiUH6uq+hDquD6rsXw71dM3uXRURE5BQsarw+/fRTKBQK7Nq1C2FhYSbPXbp0CcOHD8enn36KZcuWWaVIIiKyH1mvR8amPUiatQpyvgYBM9+A74jnIak5uxgREVFpWfRb87fffsNLL71UqOkCgNDQUAwaNAjr1q0ra20OiRdzkkhqNa+PIbHMzVjuqYtInBCNvFMX4dWvGwKmvQZVUICg6sjZcR9GojFjJJLofFnUeGm12hIn0nB3d4dWq7W4KEfH5otEYK5INHMypktKRdL/ViFj4x64NAxB9W+Xwb11Y4HVkbPjPoxEY8ZIJFvky6LbMzdo0ADbt29HRkZGoecyMzOxY8cONGzYsMzFOSpZlu1dApVDsixDq9UyXyRMaTIm63RIW7sLN1q/iKyvD6Hy7LdQ88fP2XTRQ3EfRqIxYySSLfJl8X28Ro4cie7du6NPnz6oU6cOAODq1avYtWsXUlNTMW3aNGvW6TD4YSeR9Ho9lEqlvcugcqykjOX+dg73JkYj/8wleA+Mgv/U0VAFVrJxheTMuA8j0ZgxEkl0vixqvNq0aYNVq1Zh3rx5WLVqlclzDRo0wCeffILWrVtbpUAiIhJLey8FyR+tRMaWWLg0DkWN2BVwaxlu77KIiIjKFYunpGrbti12796Ne/fu4d9//wUAVK9eHYGBvJcLEZEzkLVapK/djeS5qwGFhMqfvAufIT0h8a/JREREVlfqxmvBggWIiopC/fr1TR4PDAxks0VE5GRyjp9B4sQFyL8QD+/BzyDgg1FQBvjZuywiIqJyq9STa6xatQr//POP8d8pKSlo0KAB4uLihBTmqDijDonE89ZJNDkxFf+9Pgv/9nwDkosLanz/GaoseJ9NF1kF92EkGjNGIonOV5nufllRJ5pg80UiSJLEXygkjKzRIm31V0j+eA0kFzUCF7wP70E9ICksmtyWqBDuw0g0ZoxEskW+ytR4VVSyLLP5IquTZdmYLeaLrCnn2CncmxgNzd/X4D2sF/wnjYTK39feZVE5w30YicaMkUi2yBcbLzNV1KN8ZBtarVb4XdOp4tDevoek6cuQuesAXFuGo8YPq6BoEAIlM0aCcB9GojFjJJLofJnVeN26dQvnz58HAOPNk69fvw4fH58il2/UqFEZyyMiInPJ+RqkrtqOlPnrILm7InDxJHj3fxqQJGg0GnuXR0REVCFJcikP4dSvX7/QYbfiTrkzPH7x4kXrVOkgzp49C1mWERERwUPcZHWyLEOj0UCtVjNfZLHswyeROHEhNFcS4DuiDypNeBlKX28AzBiJxXyRaMwYiWRJvs6ePQsAiIiIKNXypT7iNWfOnNIuSkRENqa9dReJ05Yh65tDcGvVGEExM+DaqJ69yyIiIqL/r9SNV+/evUXW4TT4FxYSifkic8l5+UhdsQ0p0Rug8PJAleVT4NW3a7FZYsZIJOaLRGPGSCTR+eLkGhbgh55EkCSJFwyTWbIPnkDipIXQXL8N31F94f/eS1B4exa7PDNGIjFfJBozRiLZIl8OdQOXn3/+GYMHD0br1q0RHh6OLl26YM6cOcaJPAwOHjyIXr16ISIiAt26dcNXX31VaKz8/Hx8/PHHaNeuHZo2bYqXXnoJ8fHxtloVIiJhNDdu486wybjdfzyU1QJR66e1qDxzTIlNFxEREdmXQx3xSk1NRePGjTFkyBD4+fnhn3/+wZIlS/DPP/9gzZo1AICTJ09izJgx6Nu3LyZPnozjx4/jgw8+gKenJ55++mnjWLNmzUJsbCwmTpyIoKAgrFy5EsOHD8d3330Hb2/vMtXJ+3iRCLIsQ6vVQqVSMV9UJH1uHlKXbUHqwi+g8PNBlVXT4fVcl1LnhRkjkZgvEo0ZI5FskS+HaryeffZZk3+3atUKLi4umDp1Ku7evYugoCCsWLECjRs3xsyZMwEArVu3RkJCAhYvXmxsvO7cuYMdO3Zg+vTp6Nu3L4D7s4106tQJW7duxciRIy2ukffxIpGYLypO1v5fkPjBYmhv3oHfa/1R6Z1hUHh5mD0OM0YiMV8kGjNGIonOl0OdalgUPz8/AIBGo0F+fj5OnDhhcmQLAKKionDlyhXcvHkTAHD06FHo9XqT5fz8/NCuXTscPnzYZrUTEZWV5tq/uD1oAu4MmgD1I9VQ6/B6BEx7zaKmi4iIiOzHIRsvnU6HvLw8nD9/HsuWLUPnzp1Rs2ZN3LhxAxqNBiEhISbL161bFwCM13DFx8cjICAAvr6+hZbjdV5E5Az0OXlI/ng1EiKHIP/8ZQStnolq2xfA5dFH7F0aERERWcChTjU06NSpE+7evQsAaN++PT799FMAQFpaGgDAx8fHZHnDvw3Pp6enF3kdl4+Pj3GZsijqMKQkScUenjScJ1rS86Jey3Gd572RZdnkMUdbV2fYhuVhXFmWkb33KJKmLYH2diL8Xh+ASm8PheThVuTPNqcmw/iGf9t7XSvCuI5Yk8hxH9yPWWPcstbEcR2zJkvGLbgvc4Z6nbUmZxvXWjUV3IeVZdySOGTjtWrVKuTk5ODy5ctYsWIFRo8ejbVr19q7LAD3N7RGozG56E6hUEClur8pNRpNode4uLgAuH8kT6/XmzxnuIBPr9dDp9OZPGcYV5blIsc1THlZ1LhKpRJKpdJ4oeCD62B4rVarLRQcUeOWZl2BwttQ1LiGdZUkyex1Lc24gHnbsOAH3ZJ1NYxbVE322oaGcUvahvbKd0nb0Jb5Bv5vH5H7z3UkT1mM3EO/wa1TS1TeOBfuYcFQKBTQ6XRl3kcYatZoNFCpVNxHPGRcw7o6yj6irOtqi32EYeyC+zLuI0zXtSz7iIr+PUKpVEKtVhc5LvcRZV/Xiv49wrBOWq3W+Jl72D7iwe9uD+OQjVf9+vUBAM2aNUNERASeffZZ/PDDD6hXrx4AFJpePj09HQCMpxb6+PggMzOz0Ljp6emFTj+0hOHNKEpJ8/8XDPSDFAoFFIqiz/wsGHZzx33Yawv+srTVuCWtK1DyNrT2uIYPS1nW1drvjaGmirIN7ZXvsnzmrL0N9Vk5SFn4BVKXb4UqKABB6/4Hj+6Rhf7Aw33Efc6U7+LGNShP21CSpCJ/P3IfUfpxAX6PeNi4kiSVOC7AfYSBo+0jihvXmfcR5jRdgIM2XgWFhYVBrVbjxo0b6Ny5M9RqNeLj49G+fXvjMobrtgzXfoWEhCAxMRFpaWkmjVZ8fHyh68MsVdSGftjGL+l5Ua/luGLHtWZNsixDr9cbP8iOtq7OsA2dbVxZlpH17U9ImrYUusRUVHpzEPzGDoLi/59WaO2aHsyYtcY197mKNK4j1iRyXR/Ml71r4riOW5Ml48qyDJ1OV+IXXkeq11lrcrZxrVVTwd+R1qipKA45uUZBp0+fhkajQc2aNeHi4oJWrVrh+++/N1kmNjYWdevWRc2aNQEAkZGRUCgU2L9/v3GZtLQ0HD16FB06dChTPZacz0lUWg8enqfyK/+f67j9wju4O2IaXBrVQ60jG+A/YUSRTZc1MWMkEvNFojFjJJLofDnUEa8xY8YgPDwcYWFhcHNzw19//YXVq1cjLCwMTz75JADgtddew9ChQzFjxgx0794dJ06cwJ49exAdHW0cp2rVqujbty/mzZsHhUKBoKAgfPbZZ/D29saAAQPstXpERNBnZiPl03VIXfklVDWDUHXTXHh2bWfvsoiIiEgwh2q8GjdujNjYWKxatQqyLKNGjRp44YUXMGLECON54y1atMCSJUuwcOFC7NixA9WrV8esWbPQvXt3k7GmTJkCT09PfPrpp8jKykLz5s2xdu3aImc7JCISTZZlZO4+gKRpy6BPTUel8cPh98ZAKNxc7V0aERER2YAk89y5Ujt79ixkWUZERIRF53USlcQwK49hdh8qP/L/uop7kxYi9+gf8Ixqj4CPxkJdu5rN62DGSCTmi0RjxkgkS/J19uxZAEBERESplneoI17OgB90EqmkWZrI+egzspA8bw3SYr6C+pFqqLZ1Pjy6tLJrTcwYicR8kWjMGIkkOl9svCzA5otEMEyRS85PlmVk7tiPpBnLoc/Mhv/EEfB7rT8k1+JvRWELzBiJxHyRaMwYiWSLfLHxsoC5N0sjKo2Cd0tnvpxX3rnLSJwYjdwTZ+DZsyMCZo6BumaQvcsCwIyRWMwXicaMkUi2yBcbLzPxkjgSSavVlngjP3JcurQMpMxdjbQ1u6CuWwvVdkTD44kW9i6rEGaMRGK+SDRmjEQSnS82XkREZSDr9cjYtg/JH62EPisX/lNfhd+oFyC58IsBERER/R82XkREFso7/TfuTVqIvN/OwavPkwiY8TpU1QLtXRYRERE5IDZeRERm0qWkI3lODNLXfQ11WB1U370Y7u2a2bssIiIicmBsvMzEizlJJObLscl6PTI27UHSrFWQ8zUImPkGfEc8D0ntPLtSZoxEYr5INGaMRBKdL+f5tuBA+KEnESRJ4gXDDiz31EUkTohG3qmL8OrXDQHTXoMqKMDeZZmFGSORmC8SjRkjkWyRLzZeREQl0CWlIul/q5CxcQ9cGoag+rfL4N66sb3LIiIiIifDxssCvI8XiSDLMrRaLVQqFfPlAGSdDukbvkHy7BhAL6Py7LfgM/xZSCrn3W0yYyQS80WiMWMkki3y5bzfIOyE9/EikZgvx5B78jzuTViA/DOX4D0wCv5TR0MVWMneZVkFM0YiMV8kGjNGIonOFxsvIqL/T3svBckfrUTGlli4NA5FjdgVcGsZbu+yiIiIqBxg40VEFZ6s1SJ93ddInvs5IEmo/Mm78BnSE5JSae/SiIiIqJxg40VEFVrO8TNInBiN/AtX4D34GQR8MArKAD97l0VERETlDBsvM/FiThJJ5cQTNzgb7d0kJM1cgcwvv4drswao8f1ncGvWwN5lCceMkUjMF4nGjJFIovPF9FqAzReJIEkSs2UDskaLtNU7kTJvDaBWIXDB+/Ae1AOSQmHv0oRjxkgk5otEY8ZIJFvki42XBTidPIkgyzL0ej0UCgXzJUjOsVNInLQQ+X9dhc/wZ+E/aSSUlXzsXZbNMGMkEvNFojFjJJIt8sXGy0ycxpRE0ul0UFSAIy+2pr2TiKTpy5C580e4tgxHzR9i4NokzN5l2QUzRiIxXyQaM0Yiic4XGy8iKrfkfA1SV21Hyvx1kNxdEbh4Erz7P10hTiskIiIix8LGi4jKpezDJ5E4cSE0VxLg+3JvVJo4Akpfb3uXRURERBUUGy8iKle0t+4icdoyZH1zCG6tGiNo1Qy4htezd1lERERUwbHxMhMv5iSReN665eS8fKSu2IaU6A1QeHmgyvIp8OrblZ/ZBzBjJBLzRaIxYySS6Hyx8bIAv8iRCJIk8f4kFso+eAKJkxZCc/02fEc+D//3X4bC29PeZTkcZoxEYr5INGaMRLJFvpheC3A6eRKh4IyZzFfpaG7cRtLUJciKPQK3tk0RtO5/cG0QYu+yHBYzRiIxXyQaM0Yi2SJfbLzMxOnkSSSNRgO1Wm3vMhyePjcPqcu2IHXhF1D4+aDKqunweq4LfxGXAjNGIjFfJBozRiKJzhcbLyJyKln7f0HiB4uhvXkHfqP7odK7w6Hw8rB3WUREREQlYuNFRE5Bc+1fJH6wCNn7f4H7Ey1QbfPHcHn0EXuXRURERFQqbLyIyKHpc/KQungjUpdshrKyH4JWz4Rnz448rZCIiIicChsvInJIsiwje+8RJE5dCu3te/B7fQAqvT0UCk93e5dGREREZDY2XmaSJIl/aSchJEmCi4uLvctwCPlXEpA4eRFyDp6Ae+dWqPblfLjUrW3vspweM0YiMV8kGjNGItkiX2y8iMhh6LNykLLwC6Qu3wpVUACqrv8fPLq35x87iIiIyOmx8bIA7+NFIsiyDJ1OB6VSWeHyJcsysr79CUnTlkKXmIpKbw6C39hBUHi42bu0cqUiZ4zEY75INGaMRLJFvth4mYn38SKR9Ho9lEqlvcuwqfx/riNx0kLk/HwSHl3bovKsN6EOrmHvssqtipgxsh3mi0Rjxkgk0fli40VEdqHPzEbKp+uQuvJLqGoGoeqmufDs2s7eZREREREJwcaLiGxKlmVk7j6ApGnLoE9NR6Xxw+H3xkAo3FztXRoRERGRMGy8iMhm8v+6inuTFiL36B/wjGqPgI/GQl27mr3LIiIiIhKOjZeZeDEniaRSlc+PpD4jC8nz1iAt5iuoH6mGalvnw6NLK3uXVSGV14yRY2C+SDRmjEQSnS+m1wJsvkiE8niPOFmWkbljP5JmLIc+Mxv+E0fA77X+kFx5HxZ7KI8ZI8fBfJFozBiJZIt8sfGyAKeTJxFkWYZer4dCoSgX+co7fxmJExci9/hpePbsiICZY6CuGWTvsiq08pYxcizMF4nGjJFItsgXGy8zcTp5Ekmn00GhUNi7jDLRpWUg5eM1SFuzC+qQmqi2IxoeT7Swd1n0/5WHjJHjYr5INGaMRBKdLzZeRGQVsl6PjG37kPzRSuizcuE/ZRT8Rr0AyUVt79KIiIiI7I6NFxGVWd6ZS7g3MRp5v52DV58nETDjdaiqBdq7LCIiIiKH4VDHavfu3YvXXnsNHTp0QNOmTfHss89ix44dhU7v2759O7p164aIiAj06tULhw4dKjRWRkYGJk+ejMcffxzNmjXDm2++if/++89Wq0JUIehS0nHv/QW4+dRI6DOyUH33YgR9Np1NFxEREdEDHKrxWrduHdzd3TFx4kSsWLECHTp0wNSpU7Fs2TLjMt999x2mTp2K7t27IyYmBk2bNsWYMWPw559/mow1btw4HDt2DDNmzMD8+fNx9epVjBw5Elqttkw18mJOEslZzluX9Xqkf/EtbrR5ERnbv0fAh6+j1sE1cG/XzN6l0UM4S8bIOTFfJBozRiKJzpckO9BsEcnJyfD39zd5bOrUqYiNjcVvv/0GhUKBbt26ITw8HJ9++qlxmQEDBsDb2xsxMTEAgFOnTmHAgAFYvXo1IiMjAQDx8fGIiorCggULEBUVZVF9Z8+eBQBERERY9Hqi8iD3z7+QOGEB8v64CK9+3RAw7TWoggLsXRYRERGRTZnbGzjUnw0ebLoAoEGDBsjMzER2djYSEhJw7do1dO/e3WSZqKgoxMXFIT8/HwBw+PBh+Pj4oF27dsZlQkJC0KBBAxw+fFjsShCVgQP9HaQQXVIq/ntnHm51HQU5Lx/Vv12GoGVT2HQ5GUfOGDk/5otEY8ZIJNH5cvjJNX7//XcEBQXBy8sLv//+OwAgODjYZJm6detCo9EgISEBdevWRXx8PIKDgwudFhgSEoL4+Pgy1SPLMu/jRULIsgyNRgO1Wm2XfOXk5ODPP//EvXv3EBkZCXd3d1y+fBl1g4Oh2fEjkmfHADo9Ks9+Cz7Dn4Uk+O7uZH32zhiVb8wXicaMkUi2yJdDf3M6efIkYmNjMWHCBABAWloaAMDHx8dkOcO/Dc+np6fD29u70Hi+vr44d+5cmesqqhuWJKnYLtnw5pX0vKjXclzneW8MTX3Bf1tj3NLWm5OTg/PnzyM5ORnh4eGQZRlXv/0B3t+fhu5CPLwGRiFgyqtQBlYyGceRtmFFHbe0rzVkrOB752zr6mzjOmJNIsd9cD9mjXHLWhPHdcyaLBm34L7MGep11pqcbVxr1VRwH1aWcUvisI3XnTt38Pbbb6NVq1YYOnSovcsxodFoTDphhUIB1f//679Goym0vIuLC4D7N2XT6/Umz6lUKkiSBL1eD51OZ/KcYVxDB/4gtVpd7LhKpRJKpRKyLBeaUESSJONrtVptoeCIGrc06woU3oaixjWsqyRJZq9racYFzNuGsiyb1G/uuhrGLaqm4rZhTk4Ozpw5g6SkJISFhSEzMwtanR6JV+8gZ20MGn1/FHKDYNSIXQHXFo2g0Wigf2Dskrahod6StqG98l3SNrRlvgHb7SMKrpdKpeI+4iHjGtbVUfYRZV1XS/YRpRm34Loa6i34O5L7CNN1deR9hLXWVdS4hvzq9fpC43IfUfZ1tcU+wtxtaMt9hOH/S5Jk/Mw9bB9RsEkrDYdsvNLT0zFy5Ej4+flhyZIlxhlGfH19AdyfKj4wMNBk+YLP+/j44M6dO4XGTUtLMy5TFiUdgjS8GUUpGOgHKRSKYmdSKRh2c8d92GsNHwRbjlvSugIlb0Nrj2t4H8uyrtZ6b4raiRXHWtswIyMD58+fR0pKCrJzcpGWkox6Z2/Ba9l+yJBwvGMEqo5+AajsCZ8iJr8xrAdg+Ta0V77L8pkT9d6I3kcYMlZwH8Z9RMnjOtI+4kGOuA1VKlWh35HcR5R+XIDfI0oa17APUygUxY4LcB9h4Ij7CBHb0Fr5NuSr4Po9bF3NaboAB2y8cnNz8eqrryIjIwPbtm0zOWUwJCQEwP0ZCg3/3/BvtVqNWrVqGZeLi4sr1IVevXoVoaGhZa5RkqQiN/TDNn5Jz4t6LccVO661ayrqy4o1f2ZRPyMjIwP5+flI/fkEon76C5WSMnG5YQ382aYuct1cEP/LMZw69TsaNmxY4oygjrINK+K45rzWsP8q2HiJqInjOm5NIsd9MF+OUBPHdcyayjpucc87ar3OVJOzjWvNmh7MVllqKopDzWqo1Woxbtw4xMfH4/PPP0dQUJDJ87Vq1UKdOnWwb98+k8djY2PRpk0b42HBDh06IC0tDXFxccZlrl69igsXLqBDhw5lqtGSjUxUWiX9xcaakpOTcfr0acQd/xXaexlovf8cun11EnqlAt893xKHO7dAskc1yEp3tGvfHs8//zxat25tk9pILFtljCom5otEY8ZIJNH5cqgjXh9++CEOHTqEiRMnIjMz0+SmyA0bNoSLiwvGjh2L8ePHo3bt2mjVqhViY2Nx5swZbNy40bhss2bNEBkZicmTJ2PChAlwdXVFdHQ0wsLC0LVr1zLXyeaLRLBlro4fP44/T/6Ouqeu4blfr0CvUCCuYwNcaVQdkCS4K3JQq04NKOCHpo0jijzFkJwP910kEvNFojFjJJIt8uVQjdexY8cAAHPnzi303IEDB1CzZk0888wzyMnJQUxMDFatWoXg4GAsXboUzZo1M1l+4cKFmDNnDqZNmwatVovIyEhMmTKlxHM1S8vcC+mISsMwuYZSqRSer8cUXqj37Vkort/GpUa18fvjwcjxDICrnA6lUonWrVsjJCQEJ06cEFoH2ZYtM0YVD/NFojFjJJIt8iXJlsyFWEGdPXsWsiwjIiKCH3iyOsOsPCLvH6G9k4ik6cuQufNHuLZoBJfJI7D74ikkJiWjUZPWuHHlHKpWDUKXLl2M9/GqV68e3N3dhdRDtmWLjFHFxXyRaMwYiWRJvs6ePQsAiIiIKNXyDnXEi4jEkPM1SF21HSnz10Fyd0XgoonwHtAdKampUPx9GiqlAsE1/ZGdFoAuXboYTy0s7Y6EiIiIiErGxouonMs+fBKJExdCcyUBvi/3RqWJI6D0vT9bqLu7Oxo1aoR79+6hcuXKCA8P59EtIiIiIgHYeBGVU9pbd5E4bRmyvjkEt1aNEbRqBlzD65ks4+7ujjZt2hj/Xb16dVuXSURERFQhsPEyE88pJpFKuiFkacl5+UhdsQ0p0Rug8PJAleVT4NW3K7NLAKyTMaLiMF8kGjNGIonOFxsvC/ALLIkgSVKZP/DZB08gcdJCaK7fhu/I5+H//stQeHtaqUJydtbIGFFxmC8SjRkjkWyRLzZeFuB08iSCLMvGbJmbL82N20iaugRZsUfg1rYpgtb9D64NQgRVSs6qLBkjehjmi0RjxkgkW+SLjZeZOPs+iaTVas26a7o+Nw+py7YgdeEXUPj5oMqq6fB6rgt/IVGxzM0YkTmYLxKNGSORROeLjReRk8ra/wsSP1gM7c078BvdD5XeHQ6Fl4e9yyIiIiKiIrDxInIymmv/IvGDRcje/wvcn2iBaps/hsujj9i7LCIiIiIqARsvIiehz8lD6uKNSF2yGcrKfghaPROePTvytEIiIiIiJ8DGy0z8kksiFZUvWZaRvfcIEqcuhfb2Pfi9PgCV3h4KhSdvdEzm4z6MRGK+SDRmjEQSnS82Xhbgh55EkCSp0AWd+VcSkDh5EXIOnoB751ao9uV8uNStbacKydkVlTEia2G+SDRmjESyRb7YeBE5IH1WDlIWfoHU5VuhCgpA1fX/g0f39mz6iYiIiJwUGy8L8D5eJIIsy9BoNMjbdwzJ05ZCl5iKSm8Ogt/YQVB4uNm7PCoHZFmGVquFSqXiPoysjvki0ZgxEskW+WLjZSbex4tEyf/nOu5NWoi8w7/Do2tbVJ71JtTBNexdFpUz3IeRSMwXicaMkUii88XGi8jO9JnZSFmwHqkrv4SyeiCCNs6FV7d29i6LiIiIiKyIjReRnciyjKzdB5E4fRn0KWmo9M4weI7qCxdvT3uXRkRERERWxsaLyA7y/7qKe5MWIvfoH/CMao+Aj8ZCVasqNBqNvUsjIiIiIgHYeJmJF3NSWegzspD8yVqkxeyAunY1VNs6Hx5dWgG4fwSM0+SSaMwYicR8kWjMGInE6eQdEJsvMpcsy8jcsR9JM5ZDn5kN/wkj4Pdaf0iuLsZlmCsSjRkjkZgvEo0ZI5FskS82XhbgdPJkjrzzl5E4cSFyj5+GZ8+OCJg5BuqaQYWWk2UZOp0OSqWS+SIhmDESifki0ZgxEskW+WLjZSZOY0qlpUvLQMrHa5C2ZhfUwTVQbfsCeHRsWeJr9Ho9lEqljSqkiogZI5GYLxKNGSORROeLjReRlcl6PTK27UPyRyuhz8qF/5RR8Bv1AiQXnpdOREREVFGx8SKyorwzl3BvYjTyfjsHr95dEPDhG1BVC7R3WURERERkZ2y8iKxAl5KO5DmfI33911A/WhvVdy2Ce2Rze5dFRERERA6CjZeZeDEnFSTr9cjY9B2S/vcZ5DwNAj58Hb4jnoektuyjxfPWSTRmjERivkg0ZoxEEp0vNl4WYPP1/9q78/Co6rvv45+ZZBIgZEJYBGSRhAqCoIkVEFkUUGwAQVu5pYqoRQQtUOCxJiIgIFSkFgmIUiIuLL0tKFaQSLGKoICtVLwpqEWTYCPcLIEwWSAkmTnPHzyZh5CErL/Mwvt1XVxXcubM73zPzCe/mS8z5xxIUsFX3yorcZHOffmNGo8crGazHlNoq+Y1Hs9ms/GCAqPIGEwiXzCNjMGk+sgXjVcNcDr5y5v75GmdnL9CuWveV1jXWF258SU17H19rce1LMubLfIFE8gYTCJfMI2MwaT6yBeNVzVxOvnLl+V2K2f1Jp36XYrk9qj5/MlyPnyXbKF192dUXFxs/KrpuLyRMZhEvmAaGYNJpvNF4wVUQcGeAzqRuEiF+w4qclSCms6coNArmvq6LAAAAAQIGi/gEopPZOvUs8uV+9+pCut+tdqkvqIGPbr5uiwAAAAEGBovoBxWcbFy3nhPpxa8Ktlsar5wmpxjhsvGQb0AAACoARqvauJgzuB39vN9ykp6UYVfpyly9DA1e/pRhTRrUi/bJl8wjYzBJPIF08gYTDKdLxqvGuCPPjgVHzupk3NfUd66vyo8vovabFmuBjd0rbft22w2DhiGUWQMJpEvmEbGYFJ95IvGC5c9q6hYrpUblL3wNckRqhaLfqvI+4fJZrf7ujQAAAAECRqvGuA6XsHj7M69ynpqsQq/zZDzoRFq+tQ4hUQ7fVKLZVkqLi5WaGgo+YIRZAwmkS+YRsZgUn3ki8armriOV3AoPpqlk88sU96Gvyn8xmvV9sMUhV/f2ddlkS8YR8ZgEvmCaWQMJpnOF40XLitWYZFOr1iv7BfekK1huFokJylyVAJfKwQAAIBRNF64bJzZsUdZSYtVlJapqF/dreiksQqJivR1WQAAALgM0Hgh6BUfPqasWcuUv3GbGvS6Ti1XzFZ4t5/4uiwAAABcRmi8qomDOQOHda5Qp5evU/aiN2Vv3EhXvDxDje8Z7NfPYWgof5Iwi4zBJPIF08gYTDKdL9JbA/78xh3nnfn478qanqyiQ0cUNe4Xavrkr2SPjPB1WZdks9nIFowiYzCJfME0MgaT6iNfNF41wOnk/VdR5lGdnLlU+Zt3qMHNcWr5+jyFd4n1dVlVYlmWPB6P7HY7+YIRZAwmkS+YRsZgUn3ki8armjiNqX/yFJyTa9lbyk5eLXtUpK5Y8Ywa3zUo4CZmt9stO2dYhEFkDCaRL5hGxmCS6Xz5VXJ/+OEHzZo1SyNGjFDXrl01bNiwctdbv3697rjjDnXv3l3Dhw/Xtm3byqyTm5ur6dOnq2fPnoqPj9fkyZN1/Phx07sAH8jfukuZ/R7UqRdeV9TYn6v97rWKvPu2gGu6AAAAELz8qvH67rvvtH37dl111VXq2LFjuets3rxZM2fOVEJCglJSUhQXF6eJEyfqq6++KrXelClTtHPnTs2ePVsvvPCCMjIyNG7cOBUXF9fDnqA+FB06ov8dnaSj9yfKcVVrtdvxppo987jsjRv5ujQAAACgFL/6quHAgQN12223SZKSkpK0f//+MussWbJEQ4cO1ZQpUyRJN910kw4ePKhly5YpJSVFkrR371599tlnWrlypfr27StJiomJ0ZAhQ7R161YNGTKkfnYIRnjOntPpJWt0eumfFNK8iVqunKuIO2/lEy4AAAD4Lb/6xKuy71RmZmbq0KFDSkhIKLV8yJAh2r17twoLCyVJO3bskNPpVJ8+fbzrxMbGqkuXLtqxY0etauTNve9YlqX8Dz5VZt8HlJ28RlET/kvtdq5R4+EDguZ54XvrMI2MwSTyBdPIGEwynS+/+sSrMunp6ZLOf3p1oY4dO6qoqEiZmZnq2LGj0tPTFRMTU+bNeGxsrHeM2giWN/mBpDAtUyefXqIzH32uhgN7qfW6FxTWsb2vy6pTNpuN65PAKDIGk8gXTCNjMKk+8hVQ6XW5XJIkp9NZannJ7yW35+TkKDIyssz9o6Kiyv36YnV5PJ4yzZfNZqvwjIcl617qdlP3DfRxPflndTp5jU6//JZCWzZTyzfmq1FC3zLr+qre2tz34ttKfi+5joS/Pzd1Na4/1hRo41b1vhf/zQTivgbauP5Yk6lxPR5PmbHqYtza3Jdx/bemmoxbcjmfii7r42/1BmpNgTZuXdV04c8ln3zVZNxLCajGyx9YlqWioqJSf/B2u93bIRcVFZW5T1hYmKTzp6i88IVJOn+F7JIXLLfbXeq2knFLtnkxh8NR4bghISEKCQmRZVllTihis9m89y0uLi4THFPjVmVfpfOPoWVZOpv6qU7PfkXuk9mKnnSfmkweLbcjpMx2qzPuxRwOh2w2W7X3tSrjStV7DC3LktvtVsOGDSVV/zEsGbe8muryuSlvXyt6DEvGvdRj6Kt8X+oxNJ3vi9XXHFGyX6GhoQoNDQ3oOaI+xi3ZV3+ZI2q7r/UxR5w9e9a77oX7yhwRGHNEXe2rqXFDQs6/B7Db7WXGZY6o/b5e7u8jSn52OBzev7nK5oiK/hOgIgHVeEVFRUk6f6r4Fi1aeJfn5OSUut3pdOro0aNl7u9yubzr1EZJQCq6rSIXBvpidru9wu+VXhj26o5b2X0v9ZGqqXEvta+SZB06opPTk3V2+x41ur23ms2brLDYtue3e4n/Xahs3PLqLXkea7OvdfXclDeJVaQm+2pq3No+hr7Kd23+5kw9N6bniJKMXTiHBeIcEUj5rmjcEsH2GIaGhpZ5jWSOqPq4Eu8jLjVuyRxmt9srHFdijijhj3OEP7+PKMnXhftX2b5Wp+mSAqzxio2NlXT+WK+Sn0t+dzgcateunXe93bt3l+lCMzIy1KlTp1rXYbPZKvyIu7L71eS22tw3kMb15J1R9qI3dXr5OoW2uUKt1i5QxOA+VbqvL+o1UVN5b1bqcpv+OK4/1hRo41bnviXz14WNl4maGNd/azI57sX58oeaGNc/a6rtuBXd7q/1BlJNgTZuXdZ0cbZqU1N5AurUMO3atVOHDh20ZcuWUstTU1PVu3dv78eC/fv3l8vl0u7du73rZGRk6Ouvv1b//v3rtWZUzrIs5b37kf5z82i5Ut5W9LQxavfpqjJNFwAAABCo/OoTr7Nnz2r79u2SpMOHDysvL8/bZPXs2VNNmzbVpEmT9MQTT6h9+/bq1auXUlNTtW/fPq1Zs8Y7Tnx8vPr27avp06crMTFR4eHhevHFF9W5c2cNHjy4VjXWpLtFxQq/zdCJpxar4LMvFTGkn5rNnSjHVVf6uiyfIV8wjYzBJPIF08gYTDKdL5tVk1NyGPLjjz9q0KBB5d62atUq9erVS5K0fv16paSk6MiRI4qJidG0adM0YMCAUuvn5ubqueee04cffqji4mL17dtXM2bMUMuWLWtc37/+9S9JUvfu3Ws8Bs7z5Obr1O9flyvlbTnatVbz56ao0aBevi4LAAAAqJLq9gZ+1Xj5Oxqv2rMsS3lvb9XJ2S/Lk5uv6Klj1OTxUbKFh/m6NAAAAKDKqtsb+NVXDQNFdU8difPOHfheWUmLVfD5/yjizlvPf62wbc0/gQw2F57qm3zBBDIGk8gXTCNjMKk+8kXjVU18QFh9bleusp9/Ta7X3pUjpo1ar1+kRrf28HVZfol8wTQyBpPIF0wjYzDJdL5ovGCM5fEo989bdOrZ5fLkF6jpjEfV5NGRsoVVfK0FAAAAIBjReMGIc/sO6kTSizr3xX41vnuQms35tUJbt6j8jgAAAEAQovFCnXJn5+jUc68q58335Li6va58N1kN+97g67IAAAAAn6LxqiYO5iyf5fEod+1mnZz/R1nnitRszuOKGvsL2RxErDpCQ3m8YBYZg0nkC6aRMZhkOl+ktwZovkor+OpbZSUu0rkvv1HjkYPVbNZjCm3V3NdlBRybzUa2YBQZg0nkC6aRMZhUH/mi8aoBTid/nvvkaZ2cv0K5a95XWNdYXbnxJTXsfb2vywpYlmXJ4/HIbreTLxhBxmAS+YJpZAwm1Ue+aLyqidOYSpbbrZzVm3TqdymS26Pm8yfL+fBdsvHxf6253W7Z7XZfl4EgRsZgEvmCaWQMJpnOF++UUS0Few7oROIiFe47qMhRCWo6c4JCr2jq67IAAAAAv0bjhSopPpGtU88uV+5/pyqs+9Vqk/qKGvTo5uuyAAAAgIBA44VLsoqLlfPGezq14FXJZlPzhdPkHDNctpAQX5cGAAAABAwar2q6nA7mPPv3fcpKfFGFX6cpcvQwNXv6UYU0a+LrsoIa31uHaWQMJpEvmEbGYJLpfNF41UCwN1/Fx07q5Nzlylu3ReHxXdRmy3I1uKGrr8sKejabjeuTwCgyBpPIF0wjYzCpPvJFemsgWE8nbxUXy/XqBmUvfE1yhKrFot8q8v5hsvG/S/XiwjNmBmO+4HtkDCaRL5hGxmBSfeSLxquagvV08md37lXWU4tV+G2GnA+NUNOnxikk2unrsi47RUVFcjgcvi4DQYyMwSTyBdPIGEwynS8ar8tc8dEsnXxmmfI2/E3hN16rth+mKPz6zr4uCwAAAAgqNF6XKauoWK4V63Xq96/L1jBcLZKTFDkqga8VAgAAAAbQeF2GzuzYo6ynFqvo+0xF/epuRSeNVUhUpK/LAgAAAIIWjddlpPjwMWXNWqb8jdvUoNd1avnRbIV3+4mvywIAAACCHo1XNdlstoA7k451rlCnl69T9qI3ZY9opCuWPa3GI+8IuP0IdjabTQ6Hg+cFxpAxmES+YBoZg0n1kS8aryB35uO/K2t6sooOHVHUIz9X9JO/Uoizsa/LQgV4MYFpZAwmkS+YRsZgkul80XjVQCBcx6so86hOzlyq/M071ODmOLV8fZ7Cu8T6uixcgmVZcrvdCgkJ8ft8ITCRMZhEvmAaGYNJ9ZEvGq9q8vfreHkKzsm17C1lJ6+WPSpSV/zxGTW+exATVIDweDwKCQnxdRkIYmQMJpEvmEbGYJLpfNF4BZH8rbuU9fQSFf94VE0m/Jei/89Dsjdu5OuyAAAAgMsejVcQKDp0RFkzlujMX3eqYf+fqvXaBQrr1MHXZQEAAAD4f2i8Apjn7DmdXrJGp5f+SfZmTdTy1bmKGH4rXysEAAAA/AyNVzX5Q1NjWZbObPlMWTOWqvh/T6jJ46MUPXWM7BENfV0aaonvrcM0MgaTyBdMI2MwyXS+aLxqwJfNV2Fapk4+vURnPvpcDQf0VOt1LyisY3uf1YO6Y7PZeEGBUWQMJpEvmEbGYFJ95IvGqwZ8cTp5T/5ZZS9erdMvv6XQls3U6s35apTQzy8+gUPdsCzLmy2eV5hAxmAS+YJpZAwm1Ue+aLyqqb5PJ29ZlvLf366TM5fKnXVa0ZPuU5PJo2Vv1KBe60D9KC4ulsPh8HUZCGJkDCaRL5hGxmCS6XzRePmxwu9+UNb0ZJ395As1ur23ms//jRwxbXxdFgAAAIBqovHyQ568M8pe9KZOL1+n0DZXqNXaBYoY3MfXZQEAAACoIRovP2JZlvL/8rGynlkmT7ZL0dPGqMnE+2RvEO7r0gAAAADUAo1XNZk62K7w2wydeGqxCj77UhFD+qnZ3IlyXHWlkW3Bf9ntdl+XgCBHxmAS+YJpZAwmmc4XjVcN1GXz5cnN16nfvy5XyttytGut1m+9oEaDetXZ+AgcNptNoaH8ScIcMgaTyBdMI2MwqT7yRXp9xLIs5b29VSdnvyxPbr6aPvkrNXl8lGzhYb4uDQAAAEAdo/GqpgvP8V9T5w58r6ykxSr4/H8Uceet579W2LZlHVaJQGRZloqKiuRwOLg+CYwgYzCJfME0MgaT6iNfNF71yO3KVfbzr8n12rtyxLRR6/WL1OjWHr4uCwAAAIBhNF71wPJ4lLvurzo19xV58gvUdMajavLoSNnCuAAgAAAAcDmg8TLs3L6Dykp6UQVf7Ffjuwep2ZxfK7R1C1+XBQAAAKAe0XgZ4j6dq1O/S1HOm+/JcXV7Xflushr2vcHXZQEAAADwARqvaqrsYDvL41Hun1J1ct5yWeeK1GzO44oa+wvZHDzUqJzDwddPYRYZg0nkC6aRMZhkOl9B3Q2kpaVp3rx52rt3ryIiIjRixAhNmTJFYWG1O2V7Rc1XwVffKitxkc59+Y0ajxysZrMeU2ir5rXaFi4fnKEJppExmES+YBoZg0n1ka+gbbxcLpcefPBBdejQQUuXLtWxY8e0YMECFRQUaNasWbUa++LTybtPuXRq/grlrN6ksK6xunLjS2rY+/ra7gIuM5Zlye12KyQkhBcXGEHGYBL5gmlkDCbVR76CtvF66623lJ+fr5deeklNmjSRJLndbs2ZM0fjx49Xy5Y1u26WZVn//2e3WzmrN+nU71Ikt0fN50+W8+G7ZOOq6qghj8ejkJAQX5eBIEbGYBL5gmlkDCaZzpfd2Mg+tmPHDvXu3dvbdElSQkKCPB6Pdu7cWevxC/Yc0OE7xivrt39QxB191G73WkWNu4emCwAAAEAZQdt4paenKzY2ttQyp9OpFi1aKD09veYDn87ViSkLdDhhgiyPR21SX9EVS6cr9IqmtawYAAAAQLAK2o9ncnJy5HQ6yyyPioqSy+Wq0ZhFRUWKfHCOcmw2Ff1mlM4M66vTIZb0r3/VtlxAUtnjB4G6RsZgEvmCaWQMJlU3X4WFhdVaP2gbLxNsNpty31nAqUxhDC8mMI2MwSTyBdPIGEyqbr5sNhuNl3T+a4W5ublllrtcLkVFRdVozPj4+NqWBQAAAOAyFLTHeMXGxpY5lis3N1cnTpwoc+wXAAAAAJgUtI1X//79tWvXLuXk5HiXbdmyRXa7XX369PFhZQAAAAAuNzbrwgtTBRGXy6WhQ4cqJiZG48eP915A+c4776z1BZQBAAAAoDqCtvGSpLS0ND377LPau3evIiIiNGLECE2dOlVhYWG+Lg0AAADAZSSoGy8AAAAA8AdBe4wXAAAAAPgLGi8AAAAAMIzGCwAAAAAMo/ECAAAAAMNovAAAAADAMBovAAAAADCMxgsAAAAADKPxqoK0tDQ9/PDDiouLU58+fbRw4UIVFhb6uiwEoA0bNqhz585l/r3wwgul1lu/fr3uuOMOde/eXcOHD9e2bdt8VDH82Q8//KBZs2ZpxIgR6tq1q4YNG1buelXJU25urqZPn66ePXsqPj5ekydP1vHjx03vAvxYVfL1wAMPlDunpaWllVqPfOFiH3zwgR577DH1799fcXFxGjFihN5++21dfHlZ5i/UVFUyVt9zWGit9yrIuVwuPfjgg+rQoYOWLl2qY8eOacGCBSooKNCsWbN8XR4C1KuvvqrIyEjv7y1btvT+vHnzZs2cOVMTJkzQTTfdpNTUVE2cOFFr165VXFycD6qFv/ruu++0fft2XX/99fJ4PGXesEhVz9OUKVP0/fffa/bs2QoPD9fixYs1btw4vfPOOwoN5aXiclSVfEnSDTfcoMTExFLL2rZtW+p38oWLvfHGG2rTpo2SkpIUHR2tXbt2aebMmTp69KgmTpwoifkLtVOVjEn1PIdZuKTly5dbcXFxVnZ2tnfZW2+9ZXXp0sU6evSo7wpDQHrnnXesTp06WSdPnqxwncGDB1vTpk0rtezee++1HnnkEdPlIcC43W7vz4mJidbQoUPLrFOVPH355ZdWp06drE8//dS7LC0tzercubO1efNmA5UjEFQlX6NHj7YeffTRS45DvlCe8l4HZ8yYYd1www3e7DF/oTaqkrH6nsP4qmElduzYod69e6tJkybeZQkJCfJ4PNq5c6fvCkNQyszM1KFDh5SQkFBq+ZAhQ7R7926+4opS7PZLT+FVzdOOHTvkdDrVp08f7zqxsbHq0qWLduzYUfeFIyBUlq+qIl8oT9OmTcss69Kli/Ly8nTmzBnmL9RaZRmrqrrMGI1XJdLT0xUbG1tqmdPpVIsWLZSenu6jqhDohg0bpi5dumjQoEH64x//KLfbLUneTMXExJRav2PHjioqKlJmZma914rAVdU8paenKyYmRjabrdR6sbGxzHOo1D/+8Q/FxcWpe/fuGj16tL744otSt5MvVNU///lPtWzZUo0bN2b+ghEXZqxEfc5hfPG1Ejk5OXI6nWWWR0VFyeVy+aAiBLIWLVpo0qRJuv7662Wz2fTxxx9r8eLFOnbsmGbNmuXN1MWZK/mdzKE6qpqnnJycUsccloiKitL+/fsNV4lA1qNHD40YMUIdOnTQ8ePHtXLlSj388MNavXq14uPjJZEvVM2ePXuUmprqPdaG+Qt17eKMSfU/h9F4AfWoX79+6tevn/f3vn37Kjw8XG+++aYmTJjgw8oAoPomT55c6vdbb71Vw4YN08svv6yUlBQfVYVAc/ToUU2dOlW9evXSmDFjfF0OglBFGavvOYyvGlbC6XQqNze3zHKXy6WoqCgfVIRgk5CQILfbrW+++cabqYszl5OTI0lkDtVS1Tw5nU7l5eWVuT/zHKqrUaNGuuWWW3TgwAHvMvKFS8nJydG4cePUpEkTLV261HtsIfMX6kpFGSuP6TmMxqsS5X1/Mzc3VydOnChz7BdQWyWZujhz6enpcjgcateunS/KQoCqap5iY2OVkZFR5nThGRkZzHOoNfKFihQUFGj8+PHKzc0tc5kV5i/UhUtlrKrqMmM0XpXo37+/du3a5f0fFknasmWL7HZ7qbObADWVmpqqkJAQde3aVe3atVOHDh20ZcuWMuv07t1bYWFhPqoSgaiqeerfv79cLpd2797tXScjI0Nff/21+vfvX681I7CdOXNGn3zyibp37+5dRr5QnuLiYk2ZMkXp6el69dVXS13PUmL+Qu1VlrHymJ7DOMarEqNGjdLq1av161//WuPHj9exY8e0cOFCjRo1qkpPIHChsWPHqlevXurcubMk6aOPPtK6des0ZswYtWjRQpI0adIkPfHEE2rfvr169eql1NRU7du3T2vWrPFl6fBDZ8+e1fbt2yVJhw8fVl5envdNSs+ePdW0adMq5Sk+Pl59+/bV9OnTlZiYqPDwcL344ovq3LmzBg8e7JN9g+9Vlq+SNzO333672rRpo+PHj+v111/XiRMnlJyc7B2HfKE8c+bM0bZt25SUlKS8vDx99dVX3tu6du2qsLAw5i/USmUZ27dvX73PYTbr4s/NUEZaWpqeffZZ7d27VxERERoxYoSmTp3Kpw+otnnz5unTTz/V0aNH5fF41KFDB40cOVIPPPBAqdOUrl+/XikpKTpy5IhiYmI0bdo0DRgwwIeVwx/9+OOPGjRoULm3rVq1Sr169ZJUtTzl5ubqueee04cffqji4mL17dtXM2bM4D+YLmOV5atVq1aaO3eu/v3vf+v06dNq2LCh4uPjNXHiRF133XWl1idfuNjAgQN1+PDhcm/76KOP1LZtW0nMX6i5yjLmdrvrfQ6j8QIAAAAAwzjGCwAAAAAMo/ECAAAAAMNovAAAAADAMBovAAAAADCMxgsAAAAADKPxAgAAAADDaLwAAAAAwDAaLwAAypGfn6/evXtr48aNvi7FKzs7W3Fxcdq+fbuvSwEAVBONFwAg4Kxdu1adO3fWyJEjjW1j1apVioiI0NChQ41to7qio6N1zz33KDk52delAACqicYLABBwNm3aJIfDoX379umHH36o8/GLioq0atUqjRw5UiEhIXU+fm388pe/1IEDB7R7925flwIAqAYaLwBAQMnMzNTevXv12GOPyeFwaNOmTXW+jU8++USnTp1SQkJCnY9dWx07dlSnTp307rvv+roUAEA10HgBAALKpk2bFBISonvvvVc333xzuY3XkiVLdM0115T5VGjmzJnq1q2bvv3220tu429/+5vatGmj9u3bl1qelJSk+Ph4HTlyROPHj1d8fLz69euntWvXSpL+/e9/a8yYMYqLi9OAAQPK1LZhwwZ17txZe/bs0bx583TTTTfpxhtv1KxZs1RYWKicnBw9+eST6tGjh3r06KGFCxfKsqwy9d18883atm1bubcBAPwTjRcAIKBs2rRJN954o5o3b66EhAQdOnRI+/btK7XOY489pi5duujpp59WXl6eJOnTTz/VunXr9Pjjj+uaa6655Db27t2ra6+9ttzb3G63xo0bp1atWumJJ55QmzZtNHfuXG3YsEGPPPKIunXrpieeeEIRERFKTExUZmZmmTHmzZunQ4cOadKkSRo4cKD+/Oc/Kzk5WRMmTJDb7dbUqVP105/+VCtXrtR7771X5v7XXnutcnJy9N1331X1YQMA+BiNFwAgYOzfv1/p6ekaMmSIJOm2224r9+uGDodDzz//vI4fP64FCxYoJydHTz/9tLp166ZHH330ktsoLi7Wf/7zH7Vt27bc28+dO6fhw4drzpw5uv/++7VixQo1aNBA06dP11NPPaUnn3xSo0eP1pIlS+R2u/WXv/ylzBjNmjVTSkqK7r//fi1cuFDx8fFauXKlrr76av3hD3/Qfffdp2XLlqlVq1Z65513yty/Xbt2kqTvv/++Kg8bAMAP0HgBAALGpk2bFBoaqsGDB0uSIiMj1a9fP6Wmpsrtdpdat1OnTpo8ebLWr1+vsWPHKjs7W88//7xCQ0MvuQ2XyyXLsuR0Oitc58KzKTqdTsXExKhhw4aljgmLjY2V0+ks9xOve+65Rzabzfv7ddddJ8uydM8993iXhYSEqFu3buXev6S27OzsS+4LAMB/0HgBAAKC2+3W5s2bddNNN6lp06be5UOGDFFWVla5Z/kbO3asrrnmGu3bt08TJ07UT37ykypvr6Ljp8LDw0ttXzrfALZq1apUM1WyPCcnp8wYV155ZZn1JKl169ZllrtcrgprvHh7AAD/ReMFAAgIn3/+uU6cOFHmTIMDBw5UgwYNyj3JRmZmpvd08wcPHqzSdqKiomSz2cptmCRVeHr5ipaX18DZ7eW//Fa0/GIlzVh0dHSV1gcA+B6NFwAgIJRcu+v2228vtTwiIkK33HKLPvzwQxUUFHiXezweJSUlqXHjxpowYYLef/99bd26tdLthIaGqn379vrxxx/rfB/qSkltHTt29HElAICqovECAPi9goICbd26VTfffLOioqLK3P6zn/1M+fn5+vjjj73LXn/9de3du1dz587Vb37zG8XHx2v27Nk6depUpduLi4vT/v3763Qf6tKBAwcUGRmpq6++2telAACq6NJHGAMA4Ac+/vhj5efnS5JWrFhR5vazZ89KkjZu3KghQ4YoLS1NycnJ+vnPf66BAwdKkhYsWKC77rpLc+bMUXJy8iW3N2jQIL333nvKyMhQTExMHe9N7e3atUsDBgzgGC8ACCA0XgAAv7dx40ZJ0vbt27V9+/YK1/vss8+UnZ2txMRERUdHa/r06d7bOnTooGnTpmn+/PlKTU31npK+PAMGDFB0dLQ++OADPf7443W3I3UgLS1NBw8eLLVvAAD/Z7O47D0AAGUsW7ZMGzZs0NatWys8cYYvzJ8/X3v27NGGDRv4xAsAAgjHeAEAUI6HHnpIZ86c0ebNm31dild2drbefvttTZkyhaYLAAIMn3gBAAAAgGF84gUAAAAAhtF4AQAAAIBhNF4AAAAAYBiNFwAAAAAYRuMFAAAAAIbReAEAAACAYTReAAAAAGAYjRcAAAAAGEbjBQAAAACG0XgBAAAAgGE0XgAAAABg2P8FO2erRbSDRWAAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot( data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = Bols + Aols * xfit\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare OLS\"\n",
")\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 612,
"id": "1d42b009",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 19.38156\n",
"GdL = 8\n",
"Chi² rid = 2.42269\n",
"P(0, chi²)= 0.98705\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.611114\n",
"1 2.711326\n",
"2 0.899704\n",
"3 0.003816\n",
"4 0.723766\n",
"5 0.013567\n",
"6 1.272428\n",
"7 0.818954\n",
"8 10.045360\n",
"9 2.281521\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = Bols + Aols * data[\"x\"]\n",
"sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
"\n",
"chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
"\n",
"N = len(data)\n",
"nu = N - 2\n",
"\n",
"chi2_red = chi2_val / nu\n",
"Pols = chi2.cdf(chi2_val, df=nu)\n",
"\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nu}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {Pols:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((data[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "9f26f65e",
"metadata": {},
"source": [
"### Regressione lineare Carpi"
]
},
{
"cell_type": "code",
"execution_count": 613,
"id": "6a910226",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B : \n",
"AC = 3.2199 +- 0.0046\n",
"BC = 0.4357 +- 0.5627\n",
"cov_ABC = -0.002350\n",
"P(0, chi²)= 0.9661\n"
]
}
],
"source": [
"def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
" # Stima iniziale di A con sola sigma_y\n",
" sy2 = sigma_y**2\n",
" Sw = np.sum(1 / sy2)\n",
" Sx = np.sum(x / sy2)\n",
" Sxx = np.sum(x**2 / sy2)\n",
" Sy = np.sum(y / sy2)\n",
" Sxy = np.sum(x * y / sy2)\n",
" delta = Sxx * Sw - Sx**2\n",
" A_est = (Sxy * Sw - Sx * Sy) / delta\n",
"\n",
" # Propagazione\n",
" sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
" return sigma_eq\n",
"\n",
"\n",
"def reg_lin(x, y, sigma_x, sigma_y):\n",
" x = np.asarray(x, dtype=float)\n",
" y = np.asarray(y, dtype=float)\n",
" sigma_x = np.asarray(sigma_x, dtype=float)\n",
" sigma_y = np.asarray(sigma_y, dtype=float)\n",
"\n",
" # Propagazione errore x → y\n",
" sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
"\n",
" # Somme pesate\n",
" w = 1.0 / sigma_y_eq**2\n",
" Sw = np.sum(w)\n",
" Sx = np.sum(w * x)\n",
" Sxx = np.sum(w * x**2)\n",
" Sy = np.sum(w * y)\n",
" Sxy = np.sum(w * x * y)\n",
" delta = Sxx * Sw - Sx**2\n",
"\n",
" # Parametri\n",
" A = (Sxy * Sw - Sx * Sy) / delta\n",
" B = (Sxx * Sy - Sxy * Sx) / delta\n",
" sigma_A = np.sqrt(Sw / delta)\n",
" sigma_B = np.sqrt(Sxx / delta)\n",
" cov_AB = -Sx / delta\n",
"\n",
" # Chi quadro\n",
" x2 = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
" dof = len(x) - 2\n",
" chi = sc.stats.chi2.cdf(x2, dof) # P(X² > x2)\n",
"\n",
" return A, B, sigma_A, sigma_B, cov_AB, chi\n",
"\n",
"\n",
"AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\n",
"print(\"Ax + B : \")\n",
"print(f\"AC = {AC:.4f} +- {uAC:.4f}\")\n",
"print(f\"BC = {BC:.4f} +- {uBC:.4f}\")\n",
"print(f\"cov_ABC = {covABC:.6f}\")\n",
"print(f\"P(0, chi²)= {chiC:.4f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 614,
"id": "b49ec5a3",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = BC + AC * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare Carpi\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 615,
"id": "538ddb98",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 16.65269\n",
"GdL = 8\n",
"Chi² rid = 2.08159\n",
"P(0, chi²)= 0.96606\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 1.288557\n",
"1 4.292682\n",
"2 1.730222\n",
"3 0.095836\n",
"4 1.932287\n",
"5 0.311997\n",
"6 0.452119\n",
"7 0.042871\n",
"8 5.653755\n",
"9 0.852360\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = BC + AC * data[\"x\"]\n",
"sigma = np.sqrt(data[\"uy\"]**2 + (AC * data[\"ux\"])**2)\n",
"\n",
"\n",
"chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
"\n",
"N = len(data)\n",
"nu = N - 2\n",
"\n",
"chi2_red = chi2_val / nu\n",
"PC = chi2.cdf(chi2_val, df=nu)\n",
"\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nu}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {PC:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((data[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "4ec42f61",
"metadata": {},
"source": [
"### Regressione lineare pesata Yorkfit "
]
},
{
"cell_type": "code",
"execution_count": 616,
"id": "c7f54cf3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AY = 3.2199987 ± 0.0046395\n",
"BY = 0.4236310 ± 0.5627057\n",
"cov_ABY = -0.0023499728735626884\n",
"chi² = 16.65212\n",
"chi² rid = 2.08151\n",
"P(0, chi²)= 0.96606\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": 617,
"id": "fc824066",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot( data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = BY + AY * xfit\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare York\"\n",
")\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "15a6fec5",
"metadata": {},
"source": [
"## Raccolta finale dei dati caso statico"
]
},
{
"cell_type": "code",
"execution_count": 618,
"id": "1203e1a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.22308 ± 0.00202\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Aols = 3.22008 ± 0.00577\n",
"Bols = 0.01011 ± 0.77853\n",
"P(0, chi²)= 0.98705\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"Ac = 3.21989 ± 0.00464\n",
"Bc = 0.43570 ± 0.56269\n",
"P(0, chi²)= 0.96606\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"Ay = 3.22000 ± 0.00464\n",
"By = 0.42363 ± 0.56271\n",
"P(0, chi²)= 0.96606\n"
]
}
],
"source": [
"print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
"print(f\"Media pesata K = {media:.5f} ± {uA:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
"print(f\"Aols = {Aols:.5f} ± {uAols:.5f}\")\n",
"print(f\"Bols = {Bols:.5f} ± {uBols:.5f}\")\n",
"print(f\"P(0, chi²)= {Pols:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
"print(f\"Ac = {AC:.5f} ± {uAC:.5f}\")\n",
"print(f\"Bc = {BC:.5f} ± {uBC:.5f}\")\n",
"print(f\"P(0, chi²)= {PC:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE York:\")\n",
"print(f\"Ay = {AY:.5f} ± {uAY:.5f}\")\n",
"print(f\"By = {BY:.5f} ± {uBY:.5f}\")\n",
"print(f\"P(0, chi²)= {PY:.5f}\")"
]
},
{
"cell_type": "markdown",
"id": "2dcfef24",
"metadata": {},
"source": [
"## Aggiunta degli errori strumentali"
]
},
{
"cell_type": "code",
"execution_count": 619,
"id": "aaa95426",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"u_strum_m = 0.004082\n",
"u_strum_Dx = 0.020000\n",
"uF_strum = 0.088914\n",
"uK_strum = 0.019742\n"
]
}
],
"source": [
"res_b = 0.01\n",
"res_c = 0.02\n",
"\n",
"# Incertezza strumentale su una differenza: res/sqrt(12) * sqrt(2) = res/sqrt(6)\n",
"u_strum_m = res_b / np.sqrt(6)\n",
"u_strum_Dx = res_c #incertezza delle misure di C DISCUTERE\n",
"\n",
"# Massimo tra campionario e strumentale, punto per punto\n",
"ueste_strum = np.maximum(ueste, u_strum_Dx)\n",
"umasse_strum = np.maximum(umasse, u_strum_m)\n",
"\n",
"# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
"uF_strum = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.max(ueste_strum)\n",
"\n",
"uK_strum = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"\n",
"print(f\"u_strum_m = {u_strum_m:.6f}\")\n",
"print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n",
"print(f\"uF_strum = {uF_strum:.6f}\")\n",
"print(f\"uK_strum = {uK_strum:.6f}\")"
]
},
{
"cell_type": "markdown",
"id": "23be271e",
"metadata": {},
"source": [
"### Propapazione dell'errore strumentale max"
]
},
{
"cell_type": "code",
"execution_count": 620,
"id": "a9f2a9fc",
"metadata": {},
"outputs": [],
"source": [
"# Media pesata\n",
"uA_fin = np.sqrt(uA**2 + uK_strum**2)\n",
"\n",
"# OLS\n",
"uAols_fin = np.sqrt(uAols**2 + uK_strum**2)\n",
"uBols_fin = np.sqrt(uBols**2 + uK_strum**2)\n",
"\n",
"# Carpi\n",
"uAC_fin = np.sqrt(uAC**2 + uK_strum**2)\n",
"uBC_fin = np.sqrt(uBC**2 + uK_strum**2)\n",
"\n",
"# York\n",
"uAY_fin = np.sqrt(uAY**2 + uK_strum**2)\n",
"uBY_fin = np.sqrt(uBY**2 + uK_strum**2)\n",
"\n",
"\n",
"# Nuovo uy e ux per ricalcolare i chi^2\n",
"uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
"ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
"\n",
"# OLS\n",
"F_fit_ols = Bols + Aols * data[\"x\"]\n",
"sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
"chi2_ols = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
"GdL = len(data) - 2\n",
"\n",
"# Carpi\n",
"F_fit_c = BC + AC * data[\"x\"]\n",
"sigma_c = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
"chi2_c = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
"\n",
"# York\n",
"F_fit_y = BY + AY * data[\"x\"]\n",
"sigma_y_ = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
"chi2_y = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
]
},
{
"cell_type": "markdown",
"id": "41baee7a",
"metadata": {},
"source": [
"## Risultati della propagazione dell'errore strumentale massimo"
]
},
{
"cell_type": "code",
"execution_count": 621,
"id": "c95874fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.22308 ± 0.01985\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Aols = 3.22008 ± 0.02057\n",
"Bols = 0.01011 ± 0.77878\n",
"Chi² OLS = 4.80671 | rid = 0.60084 | P = 0.22198\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AC = 3.21989 ± 0.02028\n",
"BC = 0.43570 ± 0.56303\n",
"Chi² Carpi = 5.14076 | rid = 0.64260 | P = 0.25757\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AY = 3.22000 ± 0.02028\n",
"BY = 0.42363 ± 0.56305\n",
"Chi² York = 5.14358 | rid = 0.64295 | P = 0.25787\n"
]
}
],
"source": [
"print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
"print(f\"Media pesata K = {media:.5f} ± {uA_fin:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
"print(f\"Aols = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
"print(f\"Bols = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
"print(f\"Chi² OLS = {chi2_ols:.5f} | rid = {chi2_ols/GdL:.5f}\" f\" | P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
"\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
"print(f\"AC = {AC:.5f} ± {uAC_fin:.5f}\")\n",
"print(f\"BC = {BC:.5f} ± {uBC_fin:.5f}\")\n",
"print(f\"Chi² Carpi = {chi2_c:.5f} | rid = {chi2_c/GdL:.5f}\" f\" | P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
"\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE York:\")\n",
"print(f\"AY = {AY:.5f} ± {uAY_fin:.5f}\")\n",
"print(f\"BY = {BY:.5f} ± {uBY_fin:.5f}\")\n",
"print(f\"Chi² York = {chi2_y:.5f} | rid = {chi2_y/GdL:.5f}\" f\" | P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
]
},
{
"cell_type": "markdown",
"id": "fb210b61",
"metadata": {},
"source": [
"# Dinamica Sonar\n",
"Qui specifico la formula perché non è così ovvia:\n",
"- $M_{eq} = M + \\frac m 3$\n",
"- $\\omega = \\frac{2\\pi}{T} \\quad \\Rightarrow \\quad T = \\frac{2\\pi}{\\omega}$\n",
"- A: coefficiente della regressione lineare $A = \\frac{T^2}{M_{eq}}$\n",
"- $K = \\frac{4 \\pi^2}{A}$\n",
"\n",
"Nota: nelle formule comparirà un $/1000$, questo serve a riportare il valore in unità di misura standard: N/m"
]
},
{
"cell_type": "code",
"execution_count": 622,
"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 0.000301\n",
"1 0.000145\n",
"2 0.000272\n",
"3 0.000411\n",
"4 0.000667\n",
"dtype: float64\n"
]
}
],
"source": [
"Meq = df.m + ( m_mol / 3 )\n",
"uMeq = np.sqrt( df.um**2 + um_mol**2 / 9 )\n",
"\n",
"T2 = ( 2 * np.pi / df.w )**2\n",
"uT2 = np.sqrt( (8*np.pi**2 / df.w**3)**2 * df.uw**2 )\n",
"\n",
"print(T2)\n",
"print(uT2)"
]
},
{
"cell_type": "markdown",
"id": "7bd743d4",
"metadata": {},
"source": [
"## Calcolo dei K e media pesata\n",
"- Calcolo della Meq (massa equivalente)\n",
"- Calcolo della media pesata sui K"
]
},
{
"cell_type": "code",
"execution_count": 623,
"id": "8c9f5b44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3.189874\n",
"1 3.202365\n",
"2 3.215332\n",
"3 3.229734\n",
"4 3.245789\n",
"dtype: float64\n",
"0 0.001449\n",
"1 0.000541\n",
"2 0.000772\n",
"3 0.000963\n",
"4 0.001334\n",
"dtype: float64\n"
]
}
],
"source": [
"Ad = T2 / Meq\n",
"uAd = np.sqrt( (1/Meq)**2 * uT2**2 + (T2/Meq**2)**2 * uMeq**2 )\n",
"\n",
"Kd = 4 * np.pi**2 / Ad\n",
"uKd = np.sqrt( (4 * np.pi**2 / Ad**2)**2 * uAd**2 )\n",
"\n",
"Kd /= 1000\n",
"uKd /= 1000\n",
"\n",
"print(Kd)\n",
"print(uKd)"
]
},
{
"cell_type": "code",
"execution_count": 624,
"id": "0d899f17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio: 3.212034524173708\n",
"sigmaC: 0.0003724442799985059\n"
]
}
],
"source": [
"def mediaPesata(x, ux, dim = 0):\n",
" x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
" sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
"\n",
" w = 1.0 / (sigma_arr ** 2)\n",
"\n",
" num = np.nansum(w * x_arr, axis=dim)\n",
" den = np.nansum(w, axis=dim)\n",
"\n",
" media = num / den\n",
" sigma = np.sqrt(1.0 / den)\n",
"\n",
" return media, sigma\n",
"\n",
"KdM, uKdM = mediaPesata(Kd, uKd)\n",
"print(\"K-medio:\", KdM)\n",
"print(\"sigmaC: \", uKdM)"
]
},
{
"cell_type": "markdown",
"id": "6741544e",
"metadata": {},
"source": [
"## Analisi con regressioni e grafici\n",
"Ogni blocco ha:\n",
"- Regressione\n",
"- grafico\n",
"- chi^2\n",
"\n",
"Nota: Ax + B"
]
},
{
"cell_type": "code",
"execution_count": 625,
"id": "4b4c2682",
"metadata": {},
"outputs": [],
"source": [
"sns.set_theme(style=\"whitegrid\")\n",
"\n",
"datad = pd.DataFrame({\n",
" \"x\": Meq,\n",
" \"ux\": uMeq,\n",
" \"y\": T2,\n",
" \"uy\": uT2\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "ad203fbd",
"metadata": {},
"source": [
"### Regressione lineare \"OLS\"\n",
"\n",
"Non tiene conto dei nostri errori, un risultato puro e semplice"
]
},
{
"cell_type": "code",
"execution_count": 626,
"id": "90d191d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 7.624e+04\n",
"Date: Sun, 05 Apr 2026 Prob (F-statistic): 1.05e-07\n",
"Time: 21:52:52 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",
"x 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",
"Adols = 0.01202 ± 0.00004\n",
"Bdols = 0.02190 ± 0.00427\n",
"R² = 0.99996\n",
"Kdols = 3.28476 ± 0.01190\n",
"KBdols = 1.80288 ± 0.35183\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": [
"X = sm.add_constant(datad[\"x\"])\n",
"model = sm.OLS(datad[\"y\"], X).fit()\n",
"\n",
"print(model.summary())\n",
"\n",
"# Estrazione parametri\n",
"Bdols = model.params[\"const\"]\n",
"Adols = model.params[\"x\"]\n",
"uBdols = model.bse[\"const\"]\n",
"uAdols = model.bse[\"x\"]\n",
"R2 = model.rsquared\n",
"\n",
"# Calcolo del K dai parametri\n",
"Kdols = (2*np.pi)**2 / Adols / 1000\n",
"uKdols = np.sqrt( (4*np.pi**2 / Adols**2)**2 * uAdols**2 ) / 1000\n",
"KBdols = (2*np.pi)**2 / Bdols / 1000\n",
"uKBdols = np.sqrt( (4*np.pi**2 / Bdols**2)**2 * uBdols**2 ) / 1000\n",
"\n",
"\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE:\")\n",
"print(f\"Adols = {Adols:.5f} ± {uAdols:.5f}\")\n",
"print(f\"Bdols = {Bdols:.5f} ± {uBdols:.5f}\")\n",
"print(f\"R² = {R2:.5f}\")\n",
"print(f\"Kdols = {Kdols:.5f} ± {uKdols:.5f}\")\n",
"print(f\"KBdols = {KBdols:.5f} ± {uKBdols:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 627,
"id": "a0ba5c8d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot( data=datad,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" datad[\"x\"],\n",
" datad[\"y\"],\n",
" xerr=datad[\"ux\"],\n",
" yerr=datad[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
"yfit = Bdols + Adols * xfit\n",
"\n",
"plt.plot(\n",
" xfit, yfit,\n",
" color=\"crimson\", linewidth=1, zorder=10, label=\"Fit lineare OLS\"\n",
")\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T^2 (s^2)\")\n",
"plt.title(\"Legge di ??\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 628,
"id": "3c7c05e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 226.04638\n",
"GdL = 3\n",
"Chi² rid = 75.34879\n",
"P(0, chi²)= 1.00000\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 64.351872\n",
"1 52.297643\n",
"2 83.029405\n",
"3 11.357915\n",
"4 15.009549\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = Bdols + Adols * datad[\"x\"]\n",
"sigma = np.sqrt(datad[\"uy\"]**2 + (Adols * datad[\"ux\"])**2)\n",
"\n",
"chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
"\n",
"N = len(datad)\n",
"nu = N - 2\n",
"\n",
"chi2_red = chi2_val / nu\n",
"Pdols = chi2.cdf(chi2_val, df=nu)\n",
"\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nu}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {Pdols:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((datad[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "a1296a66",
"metadata": {},
"source": [
"### Regressione lineare Carpi"
]
},
{
"cell_type": "code",
"execution_count": 629,
"id": "698e3c48",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B : \n",
"AdC = 0.01205 ± 0.00001\n",
"BdC = 0.01985 ± 0.00053\n",
"cov_ABdC = -0.000000\n",
"P(0,chi²)= 1.00000\n",
"KdC = 3.27519 ± 0.00175\n",
"KBdC = 1.98906 ± 0.05297\n"
]
}
],
"source": [
"def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
" # Stima iniziale di A con sola sigma_y\n",
" sy2 = sigma_y**2\n",
" Sw = np.sum(1 / sy2)\n",
" Sx = np.sum(x / sy2)\n",
" Sxx = np.sum(x**2 / sy2)\n",
" Sy = np.sum(y / sy2)\n",
" Sxy = np.sum(x * y / sy2)\n",
" delta = Sxx * Sw - Sx**2\n",
" A_est = (Sxy * Sw - Sx * Sy) / delta\n",
"\n",
" # Propagazione\n",
" sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
" return sigma_eq\n",
"\n",
"\n",
"def reg_lin(x, y, sigma_x, sigma_y):\n",
" x = np.asarray(x, dtype=float)\n",
" y = np.asarray(y, dtype=float)\n",
" sigma_x = np.asarray(sigma_x, dtype=float)\n",
" sigma_y = np.asarray(sigma_y, dtype=float)\n",
"\n",
" # Propagazione errore x → y\n",
" sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
"\n",
" # Somme pesate\n",
" w = 1.0 / sigma_y_eq**2\n",
" Sw = np.sum(w)\n",
" Sx = np.sum(w * x)\n",
" Sxx = np.sum(w * x**2)\n",
" Sy = np.sum(w * y)\n",
" Sxy = np.sum(w * x * y)\n",
" delta = Sxx * Sw - Sx**2\n",
"\n",
" # Parametri\n",
" A = (Sxy * Sw - Sx * Sy) / delta\n",
" B = (Sxx * Sy - Sxy * Sx) / delta\n",
" sigma_A = np.sqrt(Sw / delta)\n",
" sigma_B = np.sqrt(Sxx / delta)\n",
" cov_AB = -Sx / delta\n",
"\n",
" # Chi quadro\n",
" x2 = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
" dof = len(x) - 2\n",
" chi = sc.stats.chi2.cdf(x2, dof) # P(X² > x2)\n",
"\n",
" return A, B, sigma_A, sigma_B, cov_AB, chi\n",
"\n",
"\n",
"AdC, BdC, uAdC, uBdC, covABdC, chidC = reg_lin(datad[\"x\"], datad[\"y\"], datad[\"ux\"], datad[\"uy\"])\n",
"\n",
"# Calcolo del K dai parametri\n",
"KdC = (2*np.pi)**2 / AdC / 1000\n",
"uKdC = np.sqrt( (4*np.pi**2 / AdC**2)**2 * uAdC**2 ) / 1000\n",
"KBdC = (2*np.pi)**2 / BdC / 1000\n",
"uKBdC = np.sqrt( (4*np.pi**2 / BdC**2)**2 * uBdC**2 ) / 1000\n",
"\n",
"print(\"Ax + B : \")\n",
"print(f\"AdC = {AdC:.5f} ± {uAdC:.5f}\")\n",
"print(f\"BdC = {BdC:.5f} ± {uBdC:.5f}\")\n",
"print(f\"cov_ABdC = {covABdC:.6f}\")\n",
"print(f\"P(0,chi²)= {chidC:.5f}\")\n",
"print(f\"KdC = {KdC:.5f} ± {uKdC:.5f}\")\n",
"print(f\"KBdC = {KBdC:.5f} ± {uKBdC:.5f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 630,
"id": "a0ab8534",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=datad,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" datad[\"x\"],\n",
" datad[\"y\"],\n",
" xerr=datad[\"ux\"],\n",
" yerr=datad[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
"yfit = BdC + AdC * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare Carpi\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T^2 (s^2)\")\n",
"plt.title(\"Legge di ??\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 631,
"id": "12bb0479",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 154.21640\n",
"GdL = 3\n",
"Chi² rid = 51.40547\n",
"P(0, chi²)= 1.00000\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 57.239249\n",
"1 12.808689\n",
"2 21.014741\n",
"3 1.756058\n",
"4 61.397667\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = BdC + AdC * datad[\"x\"]\n",
"sigma = np.sqrt(datad[\"uy\"]**2 + (AdC * datad[\"ux\"])**2)\n",
"\n",
"\n",
"chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
"\n",
"N = len(datad)\n",
"nu = N - 2\n",
"\n",
"chi2_red = chi2_val / nu\n",
"PdC = chi2.cdf(chi2_val, df=nu)\n",
"\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nu}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {PdC:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((datad[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "f3bf75d6",
"metadata": {},
"source": [
"### Regressione lineare pesata Yorkfit"
]
},
{
"cell_type": "code",
"execution_count": 632,
"id": "385db415",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AdY = 0.0120538 ± 0.0000065\n",
"BdY = 0.0198466 ± 0.0005286\n",
"cov_ABdY = -3.327136085608824e-09\n",
"chi² = 154.21640\n",
"chi² rid = 51.40547\n",
"P(0, chi²)= 1.00000\n",
"KdY = 3.27519 ± 0.00175\n",
"KBdY = 1.98918 ± 0.05298\n"
]
}
],
"source": [
"def york_fit(x, y, sx, sy, max_iter=50, tol=1e-15):\n",
" # Pesi iniziali\n",
" wx = 1 / sx**2\n",
" wy = 1 / sy**2\n",
"\n",
" # Stima iniziale della pendenza (OLS y|x)\n",
" A = np.cov(x, y, aweights=wy)[0,1] / np.cov(x, x, aweights=wy)[0,1]\n",
"\n",
" for _ in range(max_iter):\n",
" A_old = A\n",
"\n",
" # 1) Pesi combinati\n",
" Wi = (wx * wy) / (A**2 * wy + wx)\n",
"\n",
" # 2) x e y pesati (eq. 10)\n",
" X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
" Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
"\n",
" # 3) Variabili centrate\n",
" Ui = x - X_bar\n",
" Vi = y - Y_bar\n",
"\n",
" # 4) beta\n",
" beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
"\n",
" # 5) Aggiornamento pendenza\n",
" A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
"\n",
" # Convergenza\n",
" if abs(A - A_old) < tol:\n",
" break\n",
"\n",
" # 6) Intercetta\n",
" B = Y_bar - A * X_bar\n",
"\n",
" # S = somma Wi (xi - x)**2\n",
" S = np.sum(Wi * Ui**2)\n",
"\n",
" sA = np.sqrt(1 / S)\n",
" sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n",
"\n",
" # cov(A,B)\n",
" cov_AB = -X_bar * sA**2\n",
"\n",
" # CHI QUADRATO\n",
" chi2 = np.sum(Wi * (Vi - A * Ui)**2)\n",
" dof = len(x) - 2\n",
" chi2_red = chi2 / dof\n",
"\n",
" return A, B, sA, sB, cov_AB, chi2, chi2_red\n",
"\n",
"AdY, BdY, uAdY, uBdY, cov_ABdY, chi2_val, chi2_red = york_fit(datad.x, datad.y, datad.ux, datad.uy)\n",
"PdY = chi2.cdf(chi2_val, df=nu)\n",
"\n",
"# Calcolo del K dai parametri\n",
"KdY = (2*np.pi)**2 / AdY / 1000\n",
"uKdY = np.sqrt( (4*np.pi**2 / AdY**2)**2 * uAdY**2 ) / 1000\n",
"KBdY = (2*np.pi)**2 / BdY / 1000\n",
"uKBdY = np.sqrt( (4*np.pi**2 / BdY**2)**2 * uBdY**2 ) / 1000\n",
"\n",
"\n",
"print(f\"AdY = {AdY:.7f} ± {uAdY:.7f}\")\n",
"print(f\"BdY = {BdY:.7f} ± {uBdY:.7f}\")\n",
"print(f\"cov_ABdY = {cov_ABdY}\")\n",
"print(f\"chi² = {chi2_val:.5f}\")\n",
"print(f\"chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {PdY:.5f}\")\n",
"print(f\"KdY = {KdY:.5f} ± {uKdY:.5f}\")\n",
"print(f\"KBdY = {KBdY:.5f} ± {uKBdY:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 633,
"id": "698bc57c",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=datad,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" datad[\"x\"],\n",
" datad[\"y\"],\n",
" xerr=datad[\"ux\"],\n",
" yerr=datad[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
"yfit = BdY + AdY * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare York\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T^2 (s^2)\")\n",
"plt.title(\"Legge di ??\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "2e612bc6",
"metadata": {},
"source": [
"## Raccolta finale di dati dinamici"
]
},
{
"cell_type": "code",
"execution_count": 634,
"id": "fc32f9e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.21203 ± 0.00037\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Adols = 0.01202 ± 0.00004\n",
"Bdols = 0.02190 ± 0.00427\n",
"P(0,chi²)= 1.00000\n",
"Kdols = 3.28476 ± 0.01190\n",
"KBdols = 1.80288 ± 0.35183\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AdC = 0.01205 ± 0.00001\n",
"BdC = 0.01985 ± 0.00053\n",
"P(0,chi²)= 1.00000\n",
"KdC = 3.27519 ± 0.00175\n",
"KBdC = 1.98906 ± 0.05297\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"Ady = 0.01205 ± 0.00001\n",
"Bdy = 0.01985 ± 0.00053\n",
"P(0,chi²)= 1.00000\n",
"KdY = 3.27519 ± 0.00175\n",
"KBdY = 1.98918 ± 0.05298\n"
]
}
],
"source": [
"print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
"print(f\"Media pesata K = {KdM:.5f} ± {uKdM:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
"print(f\"Adols = {Adols:.5f} ± {uAdols:.5f}\")\n",
"print(f\"Bdols = {Bdols:.5f} ± {uBdols:.5f}\")\n",
"print(f\"P(0,chi²)= {Pdols:.5f}\")\n",
"print(f\"Kdols = {Kdols:.5f} ± {uKdols:.5f}\")\n",
"print(f\"KBdols = {KBdols:.5f} ± {uKBdols:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
"print(f\"AdC = {AdC:.5f} ± {uAdC:.5f}\")\n",
"print(f\"BdC = {BdC:.5f} ± {uBdC:.5f}\")\n",
"print(f\"P(0,chi²)= {PdC:.5f}\")\n",
"print(f\"KdC = {KdC:.5f} ± {uKdC:.5f}\")\n",
"print(f\"KBdC = {KBdC:.5f} ± {uKBdC:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE York:\")\n",
"print(f\"Ady = {AdY:.5f} ± {uAdY:.5f}\")\n",
"print(f\"Bdy = {BdY:.5f} ± {uBdY:.5f}\")\n",
"print(f\"P(0,chi²)= {PdY:.5f}\")\n",
"print(f\"KdY = {KdY:.5f} ± {uKdY:.5f}\")\n",
"print(f\"KBdY = {KBdY:.5f} ± {uKBdY:.5f}\")"
]
},
{
"cell_type": "markdown",
"id": "7caf197a",
"metadata": {},
"source": [
"## Aggiunta degli errori strumentali"
]
},
{
"cell_type": "code",
"execution_count": 635,
"id": "f4897de3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res_T = 0.001000\n",
"res_T2 = 0.002551\n",
"res_b = 0.004082\n",
"res_uAd = 0.000047\n",
"uKd_strum= 0.012519\n"
]
}
],
"source": [
"res_b = 0.01 / np.sqrt(6) # riso bilancia\n",
"res_T = 0.001 # riso T\n",
"\n",
"T = 2 * np.pi / df.w\n",
"res_T2 = np.max(2 * T * res_T)\n",
"\n",
"res_uAd = np.max(np.sqrt( (1 / Meq)**2 * res_T2**2 + (T2 / Meq**2)**2 * res_b**2))\n",
"\n",
"uKd_strum = np.max(np.abs(4 * np.pi**2 / Ad**2) * res_uAd) / 1000\n",
"\n",
"print(f\"res_T = {res_T:.6f}\")\n",
"print(f\"res_T2 = {res_T2:.6f}\")\n",
"print(f\"res_b = {res_b:.6f}\")\n",
"print(f\"res_uAd = {res_uAd:.6f}\")\n",
"print(f\"uKd_strum= {uKd_strum:.6f}\")"
]
},
{
"cell_type": "code",
"execution_count": 636,
"id": "37d88464",
"metadata": {},
"outputs": [],
"source": [
"uKdM_fin = np.sqrt(uKdM**2 + uKd_strum**2)\n",
"\n",
"uAdols_fin = np.sqrt(uAdols**2 + res_uAd**2)\n",
"uBdols_fin = np.sqrt(uBdols**2 + res_uAd**2)\n",
"uKdols_fin = np.sqrt(uKdols**2 + uKd_strum**2)\n",
"\n",
"uAdC_fin = np.sqrt(uAdC**2 + res_uAd**2)\n",
"uBdC_fin = np.sqrt(uBdC**2 + res_uAd**2)\n",
"uKdC_fin = np.sqrt(uKdC**2 + uKd_strum**2)\n",
"\n",
"uAdY_fin = np.sqrt(uAdY**2 + res_uAd**2)\n",
"uBdY_fin = np.sqrt(uBdY**2 + res_uAd**2)\n",
"uKdY_fin = np.sqrt(uKdY**2 + uKd_strum**2)\n",
"\n",
"\n",
"uy_fin_d = np.sqrt(datad[\"uy\"]**2 + res_T2**2)\n",
"ux_fin_d = np.sqrt(datad[\"ux\"]**2 + res_b**2)\n",
"\n",
"GdLd = len(datad) - 2\n",
"\n",
"\n",
"F_fit_dols = Bdols + Adols * datad[\"x\"]\n",
"sigma_dols = np.sqrt(uy_fin_d**2 + (Adols * ux_fin_d)**2)\n",
"chi2_dols = np.sum(((datad[\"y\"] - F_fit_dols) / sigma_dols)**2)\n",
"\n",
"F_fit_dC = BdC + AdC * datad[\"x\"]\n",
"sigma_dC = np.sqrt(uy_fin_d**2 + (AdC * ux_fin_d)**2)\n",
"chi2_dC = np.sum(((datad[\"y\"] - F_fit_dC) / sigma_dC)**2)\n",
"\n",
"F_fit_dY = BdY + AdY * datad[\"x\"]\n",
"sigma_dY = np.sqrt(uy_fin_d**2 + (AdY * ux_fin_d)**2)\n",
"chi2_dY = np.sum(((datad[\"y\"] - F_fit_dY) / sigma_dY)**2)\n"
]
},
{
"cell_type": "markdown",
"id": "42c27c50",
"metadata": {},
"source": [
"## Risultati sulla propagazione dell'errore"
]
},
{
"cell_type": "code",
"execution_count": 637,
"id": "9075e52d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata Kd = 3.21203 ± 0.01252\n",
"\n",
"RISULTATI OLS:\n",
"Adols = 0.01202 ± 0.00006\n",
"Bdols = 0.02190 ± 0.00427\n",
"Kdols = 3.28476 ± 0.01727\n",
"Chi² = 3.32681 | rid = 1.10894 | P = 0.65607\n",
"\n",
"RISULTATI Carpi:\n",
"AdC = 0.01205 ± 0.00005\n",
"BdC = 0.01985 ± 0.00053\n",
"KdC = 3.27519 ± 0.01264\n",
"Chi² = 5.09243 | rid = 1.69748 | P = 0.83485\n",
"\n",
"RISULTATI York:\n",
"AdY = 0.01205 ± 0.00005\n",
"BdY = 0.01985 ± 0.00053\n",
"KdY = 3.27519 ± 0.01264\n",
"Chi² = 5.09342 | rid = 1.69781 | P = 0.83492\n"
]
}
],
"source": [
"print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
"print(f\"Media pesata Kd = {KdM:.5f} ± {uKdM_fin:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI OLS:\")\n",
"print(f\"Adols = {Adols:.5f} ± {uAdols_fin:.5f}\")\n",
"print(f\"Bdols = {Bdols:.5f} ± {uBdols_fin:.5f}\")\n",
"print(f\"Kdols = {Kdols:.5f} ± {uKdols_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_dols:.5f} | rid = {chi2_dols/GdLd:.5f} | P = {chi2.cdf(chi2_dols, GdLd):.5f}\")\n",
"\n",
"print(\"\\nRISULTATI Carpi:\")\n",
"print(f\"AdC = {AdC:.5f} ± {uAdC_fin:.5f}\")\n",
"print(f\"BdC = {BdC:.5f} ± {uBdC_fin:.5f}\")\n",
"print(f\"KdC = {KdC:.5f} ± {uKdC_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_dC:.5f} | rid = {chi2_dC/GdLd:.5f} | P = {chi2.cdf(chi2_dC, GdLd):.5f}\")\n",
"\n",
"print(\"\\nRISULTATI York:\")\n",
"print(f\"AdY = {AdY:.5f} ± {uAdY_fin:.5f}\")\n",
"print(f\"BdY = {BdY:.5f} ± {uBdY_fin:.5f}\")\n",
"print(f\"KdY = {KdY:.5f} ± {uKdY_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_dY:.5f} | rid = {chi2_dY/GdLd:.5f} | P = {chi2.cdf(chi2_dY, GdLd):.5f}\")"
]
},
{
"cell_type": "markdown",
"id": "6f4edfa1",
"metadata": {},
"source": [
"# Dinamica Cronometro"
]
},
{
"cell_type": "code",
"execution_count": 638,
"id": "0f407f81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.616814\n",
"1 0.834482\n",
"2 1.043973\n",
"3 1.250483\n",
"4 1.502463\n",
"Name: t, dtype: float64\n",
"0 0.010446\n",
"1 0.007193\n",
"2 0.011988\n",
"3 0.020931\n",
"4 0.047373\n",
"dtype: float64\n"
]
}
],
"source": [
"T2t = (df.t / 20)**2\n",
"uT2t = 2 * (df.t / 20) * (df.ut / 20)\n",
"\n",
"print(T2t)\n",
"print(uT2t)"
]
},
{
"cell_type": "markdown",
"id": "8d097869",
"metadata": {},
"source": [
"## Calcolo dei K e media pesata"
]
},
{
"cell_type": "code",
"execution_count": 639,
"id": "99bc83a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3481.378680\n",
"1 3520.884276\n",
"2 3558.947648\n",
"3 3591.253438\n",
"4 3515.263991\n",
"dtype: float64\n",
"0 58.960824\n",
"1 30.349344\n",
"2 40.869090\n",
"3 60.110320\n",
"4 110.836582\n",
"dtype: float64\n"
]
}
],
"source": [
"Adt = T2t / Meq\n",
"uAdt = np.sqrt( (1/Meq)**2 * uT2t**2 + (T2t/Meq**2)**2 * uMeq**2 )\n",
"\n",
"Kdt = 4 * np.pi**2 / Adt\n",
"uKdt = (4 * np.pi**2 / Adt**2) * uAdt\n",
"\n",
"Kdt\n",
"uKdt\n",
"\n",
"print(Kdt)\n",
"print(uKdt)"
]
},
{
"cell_type": "code",
"execution_count": 640,
"id": "ae55d12a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio (t): 3.533948573536872\n",
"sigmaC (t): 0.02071589620414334\n"
]
}
],
"source": [
"KdtM, uKdtM = mediaPesata(Kdt, uKdt)\n",
"KdtM /= 1e3\n",
"uKdtM /= 1e3\n",
"print(\"K-medio (t):\", KdtM)\n",
"print(\"sigmaC (t):\", uKdtM)"
]
},
{
"cell_type": "markdown",
"id": "634a06e9",
"metadata": {},
"source": [
"## Analisi con regressioni e grafici"
]
},
{
"cell_type": "code",
"execution_count": 641,
"id": "75e94b0a",
"metadata": {},
"outputs": [],
"source": [
"datat = pd.DataFrame({\n",
" \"x\": Meq,\n",
" \"ux\": uMeq,\n",
" \"y\": T2t,\n",
" \"uy\": uT2t\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "e7761130",
"metadata": {},
"source": [
"### Regressione lineare \"OLS\"\n",
"Non tiene conto dei nostri errori, un risultato puro e semplice"
]
},
{
"cell_type": "code",
"execution_count": 642,
"id": "896388e2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.999\n",
"Model: OLS Adj. R-squared: 0.998\n",
"Method: Least Squares F-statistic: 2631.\n",
"Date: Sun, 05 Apr 2026 Prob (F-statistic): 1.63e-05\n",
"Time: 21:52:54 Log-Likelihood: 15.713\n",
"No. Observations: 5 AIC: -27.43\n",
"Df Residuals: 3 BIC: -28.21\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.0107 0.021 0.506 0.648 -0.057 0.078\n",
"x 0.0110 0.000 51.298 0.000 0.010 0.012\n",
"==============================================================================\n",
"Omnibus: nan Durbin-Watson: 2.074\n",
"Prob(Omnibus): nan Jarque-Bera (JB): 0.262\n",
"Skew: -0.213 Prob(JB): 0.877\n",
"Kurtosis: 1.963 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 OLS (t):\n",
"Atols = 0.01104 ± 0.00022\n",
"Btols = 0.01070 ± 0.02113\n",
"R² = 0.99886\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 3.69021 ± 7.28924\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": [
"X = sm.add_constant(datat[\"x\"])\n",
"model = sm.OLS(datat[\"y\"], X).fit()\n",
"\n",
"print(model.summary())\n",
"\n",
"Btols = model.params[\"const\"]\n",
"Atols = model.params[\"x\"]\n",
"uBtols = model.bse[\"const\"]\n",
"uAtols = model.bse[\"x\"]\n",
"R2t = model.rsquared\n",
"\n",
"Kdtols = 4 * np.pi**2 / Atols / 1000\n",
"uKdtols = (4 * np.pi**2 / Atols**2) * uAtols / 1000\n",
"KBdtols = 4 * np.pi**2 / Btols / 1000\n",
"uKBdtols = (4 * np.pi**2 / Btols**2) * uBtols / 1000\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE OLS (t):\")\n",
"print(f\"Atols = {Atols:.5f} ± {uAtols:.5f}\")\n",
"print(f\"Btols = {Btols:.5f} ± {uBtols:.5f}\")\n",
"print(f\"R² = {R2t:.5f}\")\n",
"print(f\"Kdtols = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
"print(f\"KBdtols = {KBdtols:.5f} ± {uKBdtols:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 643,
"id": "61e40e4c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
"\n",
"plt.errorbar(\n",
" datat[\"x\"], datat[\"y\"],\n",
" xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
" fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
")\n",
"\n",
"xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
"yfit = Btols + Atols * xfit\n",
"\n",
"plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit OLS t\")\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T² (s²)\")\n",
"plt.title(\"Legge di ?? — cronometro\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 644,
"id": "354687ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 1.29720\n",
"GdL = 3\n",
"Chi² rid = 0.43240\n",
"P(0, chi²)= 0.27020\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.279626\n",
"1 0.079511\n",
"2 0.241451\n",
"3 0.601922\n",
"4 0.094687\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = Btols + Atols * datat[\"x\"]\n",
"sigma = np.sqrt(datat[\"uy\"]**2 + (Atols * datat[\"ux\"])**2)\n",
"\n",
"chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
"\n",
"N = len(datat)\n",
"nut = N - 2\n",
"\n",
"chi2_red = chi2_val / nut\n",
"Ptols = chi2.cdf(chi2_val, df=nut)\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nut}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {Ptols:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((datat[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "c28f2d34",
"metadata": {},
"source": [
"### Regressione lineare Carpi"
]
},
{
"cell_type": "code",
"execution_count": 645,
"id": "8d6cd1df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B (Carpi, t):\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.03084 ± 0.02321\n",
"cov_ABtC = -0.000007\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 1.28007 ± 0.96319\n"
]
}
],
"source": [
"AtC, BtC, uAtC, uBtC, covABtC, chitC = reg_lin(datat[\"x\"], datat[\"y\"], datat[\"ux\"], datat[\"uy\"])\n",
"\n",
"KdtC = 4 * np.pi**2 / AtC / 1000\n",
"uKdtC = (4 * np.pi**2 / AtC**2) * uAtC / 1000\n",
"KBdtC = 4 * np.pi**2 / BtC / 1000\n",
"uKBdtC = (4 * np.pi**2 / BtC**2) * uBtC / 1000\n",
"\n",
"print(\"Ax + B (Carpi, t):\")\n",
"print(f\"AtC = {AtC:.5f} ± {uAtC:.5f}\")\n",
"print(f\"BtC = {BtC:.5f} ± {uBtC:.5f}\")\n",
"print(f\"cov_ABtC = {covABtC:.6f}\")\n",
"print(f\"P(0,chi²)= {chitC:.5f}\")\n",
"print(f\"KdtC = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
"print(f\"KBdtC = {KBdtC:.5f} ± {uKBdtC:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 646,
"id": "592c257c",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
"\n",
"plt.errorbar(\n",
" datat[\"x\"], datat[\"y\"],\n",
" xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
" fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
")\n",
"\n",
"xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
"yfit = BtC + AtC * xfit\n",
"\n",
"plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit Carpi (t)\")\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T² (s²)\")\n",
"plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 647,
"id": "12f276cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 0.53371\n",
"GdL = 3\n",
"Chi² rid = 0.17790\n",
"P(0, chi²)= 0.08857\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.003198\n",
"1 0.022511\n",
"2 0.021599\n",
"3 0.113281\n",
"4 0.373119\n",
"dtype: float64\n"
]
}
],
"source": [
"F_fit = BtC + AtC * datat[\"x\"]\n",
"sigma = np.sqrt(datat[\"uy\"]**2 + (AtC * datat[\"ux\"])**2)\n",
"chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
"chi2_red = chi2_val / nut\n",
"PtC = chi2.cdf(chi2_val, df=nut)\n",
"\n",
"print(f\"Chi² = {chi2_val:.5f}\")\n",
"print(f\"GdL = {nut}\")\n",
"print(f\"Chi² rid = {chi2_red:.5f}\")\n",
"print(f\"P(0, chi²)= {PtC:.5f}\")\n",
"print(\"\\n\\n\" + \"#\"*60)\n",
"print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
"print(((datat[\"y\"] - F_fit) / sigma)**2)"
]
},
{
"cell_type": "markdown",
"id": "0823cca1",
"metadata": {},
"source": [
"### Regressione lineare pesata Yorkfit"
]
},
{
"cell_type": "code",
"execution_count": 648,
"id": "d04ee20e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AtY = 0.0107837 ± 0.0002969\n",
"BtY = 0.0308408 ± 0.0232061\n",
"cov_ABtY = -6.719211797637951e-06\n",
"chi² = 0.53371\n",
"chi² rid = 0.17790\n",
"P(0, chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 1.28007 ± 0.96319\n"
]
}
],
"source": [
"AtY, BtY, uAtY, uBtY, cov_ABtY, chi2_val_t, chi2_red_t = york_fit(datat.x, datat.y, datat.ux, datat.uy)\n",
"PtY = chi2.cdf(chi2_val_t, df=nut)\n",
"\n",
"KdtY = 4 * np.pi**2 / AtY / 1000\n",
"uKdtY = (4 * np.pi**2 / AtY**2) * uAtY / 1000\n",
"KBdtY = 4 * np.pi**2 / BtY / 1000\n",
"uKBdtY = (4 * np.pi**2 / BtY**2) * uBtY / 1000\n",
"\n",
"print(f\"AtY = {AtY:.7f} ± {uAtY:.7f}\")\n",
"print(f\"BtY = {BtY:.7f} ± {uBtY:.7f}\")\n",
"print(f\"cov_ABtY = {cov_ABtY}\")\n",
"print(f\"chi² = {chi2_val_t:.5f}\")\n",
"print(f\"chi² rid = {chi2_red_t:.5f}\")\n",
"print(f\"P(0, chi²)= {PtY:.5f}\")\n",
"print(f\"KdtY = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
"print(f\"KBdtY = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 649,
"id": "e795e732",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
"\n",
"plt.errorbar(\n",
" datat[\"x\"], datat[\"y\"],\n",
" xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
" fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
")\n",
"\n",
"xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
"yfit = BtY + AtY * xfit\n",
"\n",
"plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit York (t)\")\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T² (s²)\")\n",
"plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "038b2684",
"metadata": {},
"source": [
"## Raccolta finale di dati dinamici col cronometro"
]
},
{
"cell_type": "code",
"execution_count": 650,
"id": "46ef07bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.53395 ± 0.02072\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Atols = 0.01104 ± 0.00022\n",
"Btols = 0.01070 ± 0.02113\n",
"P(0,chi²)= 0.27020\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 3.69021 ± 7.28924\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.03084 ± 0.02321\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 1.28007 ± 0.96319\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AtY = 0.01078 ± 0.00030\n",
"BtY = 0.03084 ± 0.02321\n",
"P(0,chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 1.28007 ± 0.96319\n"
]
}
],
"source": [
"print(\"RISULTATI senza ERRORE STRUMENTALE (cronometro):\")\n",
"print(f\"Media pesata K = {KdtM:.5f} ± {uKdtM:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
"print(f\"Atols = {Atols:.5f} ± {uAtols:.5f}\")\n",
"print(f\"Btols = {Btols:.5f} ± {uBtols:.5f}\")\n",
"print(f\"P(0,chi²)= {Ptols:.5f}\")\n",
"print(f\"Kdtols = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
"print(f\"KBdtols = {KBdtols:.5f} ± {uKBdtols:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
"print(f\"AtC = {AtC:.5f} ± {uAtC:.5f}\")\n",
"print(f\"BtC = {BtC:.5f} ± {uBtC:.5f}\")\n",
"print(f\"P(0,chi²)= {PtC:.5f}\")\n",
"print(f\"KdtC = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
"print(f\"KBdtC = {KBdtC:.5f} ± {uKBdtC:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI REGRESSIONE York:\")\n",
"print(f\"AtY = {AtY:.5f} ± {uAtY:.5f}\")\n",
"print(f\"BtY = {BtY:.5f} ± {uBtY:.5f}\")\n",
"print(f\"P(0,chi²)= {PtY:.5f}\")\n",
"print(f\"KdtY = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
"print(f\"KBdtY = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
]
},
{
"cell_type": "markdown",
"id": "70201336",
"metadata": {},
"source": [
"## Aggiungi gli errori strumentali"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfcd391e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res_t = 0.000500\n",
"res_t2 = 0.024515\n",
"res_uAdt = 0.000451\n",
"uKdt_strum = 0.147238\n"
]
}
],
"source": [
"res_t = 0.01 / 20 # risoluzione cronometro\n",
"res_t2 = np.max(2 * df.t * res_t)\n",
"\n",
"res_uAdt = np.max(np.sqrt( (1 / Meq)**2* res_t2**2 +(T2t / Meq**2)**2 * res_b**2))\n",
"uKdt_strum = np.max(np.abs(4 * np.pi**2 / Adt**2) * res_uAdt) / 1000\n",
"\n",
"print(f\"res_t = {res_t:.6f}\")\n",
"print(f\"res_t2 = {res_t2:.6f}\")\n",
"print(f\"res_uAdt = {res_uAdt:.6f}\")\n",
"print(f\"uKdt_strum = {uKdt_strum:.6f}\")"
]
},
{
"cell_type": "code",
"execution_count": 652,
"id": "b52fea3d",
"metadata": {},
"outputs": [],
"source": [
"uKdtM_fin = np.sqrt(uKdtM**2 + uKdt_strum**2)\n",
"\n",
"uAtols_fin = np.sqrt(uAtols**2 + res_uAdt**2)\n",
"uBtols_fin = np.sqrt(uBtols**2 + res_uAdt**2)\n",
"uKdtols_fin = np.sqrt(uKdtols**2 + uKdt_strum**2)\n",
"\n",
"uAtC_fin = np.sqrt(uAtC**2 + res_uAdt**2)\n",
"uBtC_fin = np.sqrt(uBtC**2 + res_uAdt**2)\n",
"uKdtC_fin = np.sqrt(uKdtC**2 + uKdt_strum**2)\n",
"\n",
"uAtY_fin = np.sqrt(uAtY**2 + res_uAdt**2)\n",
"uBtY_fin = np.sqrt(uBtY**2 + res_uAdt**2)\n",
"uKdtY_fin = np.sqrt(uKdtY**2 + uKdt_strum**2)\n",
"\n",
"uy_fin_t = np.sqrt(datat[\"uy\"]**2 + res_t2**2)\n",
"ux_fin_t = np.sqrt(datat[\"ux\"]**2 + res_b**2)\n",
"\n",
"GdLt = len(datat) - 2\n",
"\n",
"F_fit_tols = Btols + Atols * datat[\"x\"]\n",
"sigma_tols = np.sqrt(uy_fin_t**2 + (Atols * ux_fin_t)**2)\n",
"chi2_tols = np.sum(((datat[\"y\"] - F_fit_tols) / sigma_tols)**2)\n",
"\n",
"F_fit_tC = BtC + AtC * datat[\"x\"]\n",
"sigma_tC = np.sqrt(uy_fin_t**2 + (AtC * ux_fin_t)**2)\n",
"chi2_tC = np.sum(((datat[\"y\"] - F_fit_tC) / sigma_tC)**2)\n",
"\n",
"F_fit_tY = BtY + AtY * datat[\"x\"]\n",
"sigma_tY = np.sqrt(uy_fin_t**2 + (AtY * ux_fin_t)**2)\n",
"chi2_tY = np.sum(((datat[\"y\"] - F_fit_tY) / sigma_tY)**2)"
]
},
{
"cell_type": "markdown",
"id": "858bbff7",
"metadata": {},
"source": [
"## Risultati della propagazione dell'errore"
]
},
{
"cell_type": "code",
"execution_count": 653,
"id": "0b1e4a1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.53395 ± 0.14869\n",
"\n",
"RISULTATI OLS:\n",
"Atols = 0.01104 ± 0.00050\n",
"Btols = 0.01070 ± 0.02114\n",
"Kdtols = 3.57541 ± 0.16290\n",
"Chi² = 0.42434 | rid = 0.14145 | P = 0.06483\n",
"\n",
"RISULTATI Carpi:\n",
"AtC = 0.01078 ± 0.00054\n",
"BtC = 0.03084 ± 0.02321\n",
"KdtC = 3.66092 ± 0.17842\n",
"Chi² = 0.34851 | rid = 0.11617 | P = 0.04934\n",
"\n",
"RISULTATI York:\n",
"AtY = 0.01078 ± 0.00054\n",
"BtY = 0.03084 ± 0.02321\n",
"KdtY = 3.66092 ± 0.17842\n",
"Chi² = 0.34851 | rid = 0.11617 | P = 0.04934\n"
]
}
],
"source": [
"print(\"RISULTATI CON ERRORE STRUMENTALE (cronometro):\")\n",
"print(f\"Media pesata K = {KdtM:.5f} ± {uKdtM_fin:.5f}\")\n",
"\n",
"print(\"\\nRISULTATI OLS:\")\n",
"print(f\"Atols = {Atols:.5f} ± {uAtols_fin:.5f}\")\n",
"print(f\"Btols = {Btols:.5f} ± {uBtols_fin:.5f}\")\n",
"print(f\"Kdtols = {Kdtols:.5f} ± {uKdtols_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_tols:.5f} | rid = {chi2_tols/GdLt:.5f} | P = {chi2.cdf(chi2_tols, GdLt):.5f}\")\n",
"\n",
"print(\"\\nRISULTATI Carpi:\")\n",
"print(f\"AtC = {AtC:.5f} ± {uAtC_fin:.5f}\")\n",
"print(f\"BtC = {BtC:.5f} ± {uBtC_fin:.5f}\")\n",
"print(f\"KdtC = {KdtC:.5f} ± {uKdtC_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_tC:.5f} | rid = {chi2_tC/GdLt:.5f} | P = {chi2.cdf(chi2_tC, GdLt):.5f}\")\n",
"\n",
"print(\"\\nRISULTATI York:\")\n",
"print(f\"AtY = {AtY:.5f} ± {uAtY_fin:.5f}\")\n",
"print(f\"BtY = {BtY:.5f} ± {uBtY_fin:.5f}\")\n",
"print(f\"KdtY = {KdtY:.5f} ± {uKdtY_fin:.5f}\")\n",
"print(f\"Chi² = {chi2_tY:.5f} | rid = {chi2_tY/GdLt:.5f} | P = {chi2.cdf(chi2_tY, GdLt):.5f}\")"
]
}
],
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