jacopoBR/Lab1
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
1c8f6fb41870f192eaacac3b36015023cdacedfc
Lab1/molla/dinamica/mollaDinamica2.ipynb

3112 lines
587 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
{
"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": 2,
"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": 3,
"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": 4,
"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": 4,
"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": 5,
"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": 6,
"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": 7,
"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": 8,
"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": 9,
"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: Mon, 06 Apr 2026 Prob (F-statistic): 1.19e-19\n",
"Time: 10:29:56 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": 10,
"id": "8d795186",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot( data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = Bols + Aols * xfit\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare OLS\"\n",
")\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"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": 13,
"id": "b49ec5a3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = BC + AC * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare Carpi\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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": 15,
"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": 16,
"id": "fc824066",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot( data=data,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" data[\"x\"],\n",
" data[\"y\"],\n",
" xerr=data[\"ux\"],\n",
" yerr=data[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
"yfit = BY + AY * xfit\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare York\"\n",
")\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Δx (mm)\")\n",
"plt.ylabel(\"Forza F (mN)\")\n",
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "15a6fec5",
"metadata": {},
"source": [
"## Raccolta finale dei dati caso statico"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"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": 19,
"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": 20,
"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": 21,
"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": 22,
"id": "8c9f5b44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 2.888246\n",
"1 2.981052\n",
"2 3.039612\n",
"3 3.083702\n",
"4 3.121004\n",
"dtype: float64\n",
"0 0.001317\n",
"1 0.000508\n",
"2 0.000732\n",
"3 0.000920\n",
"4 0.001283\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": 23,
"id": "0d899f17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio: 3.0134007705961383\n",
"sigmaC: 0.0003513307107337363\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": 24,
"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": 25,
"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: Mon, 06 Apr 2026 Prob (F-statistic): 1.05e-07\n",
"Time: 10:29:59 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 0.0837 0.004 20.623 0.000 0.071 0.097\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. 310.\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 = 0.08371 ± 0.00406\n",
"R² = 0.99996\n",
"Kdols = 3.28476 ± 0.01190\n",
"KBdols = 0.47159 ± 0.02287\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": 26,
"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": 27,
"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": 28,
"id": "698e3c48",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B : \n",
"AdC = 0.01205 ± 0.00001\n",
"BdC = 0.08184 ± 0.00050\n",
"cov_ABdC = -0.000000\n",
"P(0,chi²)= 1.00000\n",
"KdC = 3.27519 ± 0.00175\n",
"KBdC = 0.48236 ± 0.00292\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": 29,
"id": "a0ab8534",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=datad,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" datad[\"x\"],\n",
" datad[\"y\"],\n",
" xerr=datad[\"ux\"],\n",
" yerr=datad[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
"yfit = BdC + AdC * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare Carpi\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T^2 (s^2)\")\n",
"plt.title(\"Legge di ??\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 31,
"id": "385db415",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AdY = 0.0120538 ± 0.0000065\n",
"BdY = 0.0818432 ± 0.0004963\n",
"cov_ABdY = -3.1129590737835483e-09\n",
"chi² = 154.21640\n",
"chi² rid = 51.40547\n",
"P(0, chi²)= 1.00000\n",
"KdY = 3.27519 ± 0.00175\n",
"KBdY = 0.48237 ± 0.00292\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": 32,
"id": "698bc57c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"# Seaborn scatter\n",
"sns.scatterplot(\n",
" data=datad,\n",
" x=\"x\",\n",
" y=\"y\",\n",
" s=7\n",
")\n",
"\n",
"# Barre d’errore\n",
"plt.errorbar(\n",
" datad[\"x\"],\n",
" datad[\"y\"],\n",
" xerr=datad[\"ux\"],\n",
" yerr=datad[\"uy\"],\n",
" fmt=\"none\",\n",
" ecolor=\"gray\",\n",
" elinewidth=1,\n",
" capsize=3,\n",
" alpha=0.7\n",
")\n",
"\n",
"# Linea di regressione\n",
"xfit = np.linspace(0, datad[\"x\"].max(), 200)\n",
"yfit = BdY + AdY * xfit\n",
"\n",
"\n",
"plt.plot(\n",
" xfit,\n",
" yfit,\n",
" color=\"crimson\",\n",
" linewidth=1,\n",
" zorder=10,\n",
" label=\"Fit lineare York\"\n",
")\n",
"\n",
"\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"\n",
"\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T^2 (s^2)\")\n",
"plt.title(\"Legge di ??\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "2e612bc6",
"metadata": {},
"source": [
"## Raccolta finale di dati dinamici"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "fc32f9e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.01340 ± 0.00035\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Adols = 0.01202 ± 0.00004\n",
"Bdols = 0.08371 ± 0.00406\n",
"P(0,chi²)= 1.00000\n",
"Kdols = 3.28476 ± 0.01190\n",
"KBdols = 0.47159 ± 0.02287\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AdC = 0.01205 ± 0.00001\n",
"BdC = 0.08184 ± 0.00050\n",
"P(0,chi²)= 1.00000\n",
"KdC = 3.27519 ± 0.00175\n",
"KBdC = 0.48236 ± 0.00292\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"Ady = 0.01205 ± 0.00001\n",
"Bdy = 0.08184 ± 0.00050\n",
"P(0,chi²)= 1.00000\n",
"KdY = 3.27519 ± 0.00175\n",
"KBdY = 0.48237 ± 0.00292\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": 34,
"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 = 0.000052\n",
"uKd_strum= 0.012784\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": 35,
"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": 36,
"id": "9075e52d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata Kd = 3.01340 ± 0.01279\n",
"\n",
"RISULTATI OLS:\n",
"Adols = 0.01202 ± 0.00007\n",
"Bdols = 0.08371 ± 0.00406\n",
"Kdols = 3.28476 ± 0.01746\n",
"Chi² = 3.32681 | rid = 1.10894 | P = 0.65607\n",
"\n",
"RISULTATI Carpi:\n",
"AdC = 0.01205 ± 0.00005\n",
"BdC = 0.08184 ± 0.00050\n",
"KdC = 3.27519 ± 0.01290\n",
"Chi² = 5.09243 | rid = 1.69748 | P = 0.83485\n",
"\n",
"RISULTATI York:\n",
"AdY = 0.01205 ± 0.00005\n",
"BdY = 0.08184 ± 0.00050\n",
"KdY = 3.27519 ± 0.01290\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": 37,
"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": 38,
"id": "99bc83a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3152.185929\n",
"1 3277.558955\n",
"2 3364.449659\n",
"3 3428.875660\n",
"4 3380.118798\n",
"dtype: float64\n",
"0 53.385731\n",
"1 28.252029\n",
"2 38.635615\n",
"3 57.392460\n",
"4 106.575447\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": 39,
"id": "ae55d12a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio (t): 3.3035179495747626\n",
"sigmaC (t): 0.019369943676623744\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": 40,
"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": 41,
"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: Mon, 06 Apr 2026 Prob (F-statistic): 1.63e-05\n",
"Time: 10:30:02 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 0.0675 0.020 3.362 0.044 0.004 0.131\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. 310.\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 = 0.06749 ± 0.02007\n",
"R² = 0.99886\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 0.58496 ± 0.17399\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": 42,
"id": "61e40e4c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIsCAYAAAAeUFNGAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAnXFJREFUeJzs3Xd4FOXaBvB7tqQnmwRICAFE4AQRAoiIUkRFVLAh0pUmUvQgKtgQG1URERWk915UQMUDiA0UFFERgWOhCQgacDfZ9GTL+/3ht3tSN5tkk5l5c/+uy8uL2Z03z8w9M9knO0URQggQERERERFRpRjULoCIiIiIiEgGbK6IiIiIiIgCgM0VERERERFRALC5IiIiIiIiCgA2V0RERERERAHA5oqIiIiIiCgA2FwREREREREFAJsrIiIiIiKiAGBzRUREREREFABsroiIqNoMHjwYgwcP9v77jz/+QLNmzbBlyxYVqyIiIgoMNldERCrYsmULmjVrhiNHjqhdii788ccfmDZtGm677Ta0bt0aPXr0wIYNGwq957fffsOzzz6Lbt26oU2bNujZsyc+/vhjlSomf+zZswdz585VuwwiooAxqV0AERHVXImJifjpp59gMvn+dfT222/jxx9/RM+ePVGrVi3s2rULkyZNQnBwMO69914AwNSpU5GdnY0BAwYgLCwM7733Hh599FGsXLkS1113XXUsDpXTnj17sG7dOowdO1btUoiIAoLNFRERqUZRFAQHB5f5vvvvvx/Tp0+H0WgEAPTu3RvdunXDhx9+6G2unnzySbRu3do7z913340OHTpg+/btumqusrOzERYWpnYZmuN0OuF2uxEUFKR2KUREpeJpgUREGpaSkoJnn30WHTt2RMuWLXHHHXfg3XffLfa+8+fP46GHHkKbNm3QoUMHvPzyy/jyyy/RrFkzHDhwoNB7161bh5tvvhmtWrVCnz598N133xW7FgoA8vPzMWfOHNxyyy1o2bIlbrjhBsycORP5+fl+1b5p0yZ069at0M8pyt9rrpKTk72NFQCYTCaYTCY4HA7vtIKNFQAEBQXBYDAUek91S0lJwcSJE9G5c2e0bNkSXbt2xUsvveRdh57TQ7/99ltMmjQJHTp0wA033OCdf926dbjjjjvQsmVLdO7cGZMnT0Z6enqhnzF48GDceeedOHHiBAYPHozWrVvj+uuvx5IlS4rVY7VaMXHiRHTs2BHJycm4++67sXXr1kLv8WSybNky77bSunVrDB8+HH/++SeEEJg3bx66dOmCVq1a4eGHH0ZaWlqxn7Vnzx7cd999aNOmDa666iqMGjUKx48f974+YcIErFu3DgDQrFkz739Fa1i5ciW6deuG5ORknDx5EgDw9ddfe8du164dHn74Ye9rRERq4jdXREQa9ffff6Nfv35QFAX3338/YmNjsXfvXjz33HPIzMzEsGHDAPzzTcfQoUNx6dIlDBkyBLVr18b27duLNVUAsH79ekyZMgXt2rXDsGHDcP78eYwZMwZRUVGoW7eu931utxsPP/wwvv/+e/Tr1w9NmjTBb7/9hlWrVuH333/H/Pnzfdb+zjvv4MUXX8RVV12FoUOH4ty5c3j44YdhsViQkJBQ6XWzZs0anDt3DmPGjCn1PbNnz0ZeXp73m63qlpKSgj59+iAjIwP9+vVD48aNkZKSgl27diE3N7fQNzCTJ09GbGwsxowZg+zsbADA3Llz8fbbb6Njx44YOHAgTp8+jQ0bNuDIkSPYsGEDzGazd3673Y4RI0bglltuQY8ePbBr1y7MmjULSUlJ3mYtNzcXgwcPxtmzZ3H//fejfv362LlzJyZMmID09HQMHTq0UP0ffvghHA4HBg8ejLS0NCxduhSPP/44rrvuOhw4cAAjR47EmTNnsHbtWrz66qt45ZVXvPNu27YNEyZMQOfOnfHkk08iJycHGzZswH333YetW7eifv366N+/Py5evIh9+/Zh5syZJa7DLVu2IC8vD/369UNQUBAsFgv279+PkSNHon79+njkkUeQm5uLtWvXYuDAgdiyZQvq168fsAyJiMpNEBFRtXvvvfdEUlKS+Omnn0p9z8SJE0WnTp2EzWYrNH3cuHHi6quvFjk5OUIIIZYvXy6SkpLE7t27ve/Jzc0V3bt3F0lJSeKbb74RQgiRl5cn2rdvL3r37i0cDof3vVu2bBFJSUli0KBB3mnbtm0TV1xxhTh48GChn71hwwaRlJQkvv/++1Lrzs/PFx06dBA9e/YUeXl53umbNm0q9nPOnTsnkpKSxHvvvVfqeEW9//77olmzZuLFF18s9T0LFy4USUlJYtGiRX6PG2hPP/20uOKKK0rM2O12CyH+tx0MHDhQOJ1O7+tWq1W0aNFCDB8+XLhcLu/0tWvXiqSkJPHuu+96pw0aNEgkJSWJrVu3eqfl5eWJTp06ibFjx3qnrVy5UiQlJYn333/fOy0/P1/0799ftGnTRmRkZAgh/pfJddddJ9LT073vff3110VSUpK4++67C20/48ePFy1atPBmnZmZKdq1ayeef/75Qst86dIlcfXVVxeaPnnyZJGUlFRs/XhqaNu2rbBarYVe69mzp+jQoYNITU31Tvv555/FFVdcIZ5++uliYxERVSeeFkhEpEFCCHz88cfo2rUrhBCw2Wze/zp37oyMjAwcO3YMAPDll18iPj4eN998s3f+4OBg9OvXr9CYR48eRVpaGvr161foBhJ33XUXLBZLoffu3LkTTZo0QePGjQv9bM+1SyV9K1bw51itVgwYMKDQtzO9evVCZGRkxVcKgJMnT2LixIm4+eab8dJLL5X4nv3792P27NkYPHgwRo0aVamfV1FutxuffPIJbrrpJiQnJxd7XVGUQv/u169fodMe9+/fD4fDgSFDhsBg+N+v6r59+yIiIgJ79uwpNH9YWBh69uzp/XdQUBCSk5Nx7tw577S9e/eiTp06uPPOO73TzGYzBg8ejOzsbBw8eLDQmN27dy+UV6tWrQD8cy1bwe2nVatWcDgcSElJ8daenp6OO+64o9C2YzAY0Lp1a5/bTlG33norYmNjvf++ePEifv75Z/Tq1QvR0dHe6VdccQU6duxYbL0QEVU3nhZIRKRBNpsN6enp2LRpEzZt2lTqe4B/rrdq2LBhsQ/sDRs2LPTvCxculDjdZDIhMTGx0LQzZ87g5MmT6NChQ4k/22q1llq75+dcdtllhaabzWY0aNCg1Pn88Z///AcOhwMvvfRSoaajoA8++ADh4eF4+umnyxzP5XJ512N5GY3GQh/8C7LZbMjMzMS//vUvv8YqeiqbZx02bty40PSgoCA0aNAA58+fLzS9bt26xfK3WCz49ddfvf8+f/48LrvssmLrrUmTJoV+pkfR0zc9jVZp0+12Oxo0aIDff/8dAIqdZugRERFR4vSSlLZeLr/88mLvbdKkCb766iveEISIVMXmiohIg9xuN4B/viXo1atXie/xXPxfVT8/KSkJzz77bImvF7w+qzp5bpwQFxfn8z3R0dF+3VXuzz//LPSNX3kkJibis88+q9C8Rflzx0RfCn7rFSiljVlaUyuEKPT/mTNnok6dOn6PW5KQkBC/30tEpAVsroiINCg2Nhbh4eFwu93o2LGjz/cmJibixIkTEEIU+vbi7Nmzhd5Xr1497/SCtyZ3Op04f/58oWatYcOG+OWXX9ChQ4di34iUxfNzzpw5U+ibL4fDgT/++ANXXHFFucYrqF27dt4P76W54YYbcOWVV/o1Xp06dbBixYoK1eKrIYqNjUVEREShu+OVh2cdnjp1qtC3ffn5+fjjjz/K3CZKkpiYiF9//RVut7tQg3Tq1KlCP7OyPPXWqlWrzDorum2dPn262GunTp1CTEwMv7UiIlXxmisiIg0yGo247bbbsGvXLvz222/FXi94Klvnzp2RkpKCTz/91DstLy8PmzdvLjRPy5YtER0djc2bN8PpdHqnf/jhh7Db7YXe26NHD6SkpBQbA/jnrnOeO9qVpGXLloiNjcXGjRsL3bZ969atxW4jXl5XX3017r//fp/vueGGG3DPPff4NV5wcDA6duxYof+uvvrqUsc1GAzo1q0bPv/8cxw5cqTY62U1iB07doTZbMaaNWsKvffdd99FRkZGodu1+6tLly64dOkS/vOf/3inOZ1OrFmzBmFhYbjmmmvKPWZJrr/+ekRERGDRokUl3ga/4LYbGhoKAH5vF3FxcWjevDm2bdtWaJ7ffvsN+/btq9B6ISIKJH5zRUSkovfeew9ffvllselDhgzBE088gQMHDqBfv37o27cvmjZtCrvdjmPHjuHrr7/Gt99+CwDo378/1q5diyeeeAJDhgxBnTp18OGHH3q/WfF8OxAUFISxY8di6tSpGDp0KHr06IHz589jy5Ytxa7D6tmzJ3bs2IGXXnoJBw4cQNu2beFyuXDq1Cns3LkTS5cuLfFGDcA/11Y9/vjjePHFFzF06FDcfvvt+OOPP7Bly5ZKX3M1e/ZsbN26tdC1REU988wzOH/+fMBO2auo8ePHY9++fRg8eLD3dvaXLl3Czp07sX79ekRFRZU6b2xsLEaPHo23334bI0aMQNeuXXH69GmsX7/e+3yq8urfvz82bdqECRMm4NixY0hMTMSuXbvwww8/YOLEieW6FsqXiIgITJo0CU8//TTuvfde3H777YiNjcWFCxewZ88etG3bFi+++CIAoEWLFgCAadOmoXPnzjAajbjjjjt8jv/0009j5MiR6N+/P/r06eO9FXtkZCQeeeSRgCwDEVFFsbkiIlLRhg0bSpx+7733om7dunjnnXcwb9487N69Gxs2bEB0dDSaNm2KJ5980vve8PBwrFq1CtOmTcPq1asRFhaGe+65B1dddRXGjh1b6PS1QYMGQQiBFStW4NVXX8UVV1yBBQsWYNq0aYXeZzAYMG/ePKxcuRLvv/8+du/ejdDQUNSvXx+DBw8u8YYCBfXv3x8ulwvLli3DzJkzkZSUhAULFuCtt96q5BrTj/j4eGzevBlvvfUWPvzwQ2RmZiI+Ph5dunTx61qisWPHIjY2FmvXrsUrr7wCi8WCfv36Yfz48YWeceWvkJAQrFmzBrNmzcLWrVuRmZmJyy+/HK+88krAnwV21113IS4uDosXL8ayZcuQn5+P+Ph4tGvXrtDPuvXWWzF48GB89NFH+OCDDyCEKLO56tixI5YuXYo5c+Zgzpw5MJlMuOaaa/DUU09VunknIqosRZR1bgIREenSypUr8corr2Dv3r2Ij48v9X1utxsdOnTALbfcgmnTplVjhURERHLhNVdERBLIzc0t9O+8vDxs2rQJjRo1KtRY5eXlFbveZ9u2bUhLS0P79u2rpVYiIiJZ8bRAIiIJPPLII6hXrx6uuOIKZGZm4oMPPsCpU6cwa9asQu/78ccf8corr6B79+6Ijo7Gf//7X7z77rtISkpC9+7dVaqeiIhIDmyuiIgk0LlzZ7z77rv48MMP4XK50LRpU7zxxhu4/fbbC70vMTERdevWxZo1a2C322GxWNCzZ088+eSTfj0XioiIiErHa66IiIiIiIgCgNdcERERERERBQCbKyIiIiIiogDgNVelOHToEIQQFXqWCBERERERycPhcEBRFFx11VU+38fmqhRCiGK3KyYiIiIioprH375AU83VmTNnsGzZMhw+fBjHjx9H48aNsX37dr/mTUlJwezZs7Fnzx5kZ2cjMTERDz/8MO6+++4K1WI2myGEQMuWLaEoSoXGIG0QQsDhcMBsNjNLHWOO8mCW8mCW8mCWcmCOVefIkSN+vU9TzdXx48exZ88etG7dGm632+8O8eLFi+jfvz8uv/xyTJ06FRERETh+/Djy8/OruGIiIiIiIqJ/aKq56tq1K7p16wYAmDBhAo4ePerXfK+99hrq1q2LpUuXwmg0AgA6dOhQZXUSEREREREVpam7BRoM5S8nMzMTO3bswH333edtrIiIiIiIiKqbppqrijh27BgcDgdMJhMGDRqEFi1aoFOnTnjttdfgcDjULo+IiIiIiGoITZ0WWBF///03AOD5559Hv3798Mgjj+Cnn37CnDlzYDAY8MQTT1R4bEVRSrzuq7TpnteA0u8oUpXzctySXxNCwGQyFfq32jXV1HErW5PJZCr2Hq0uq1bXoRbGLbhP6qFePddU1eMW3S+5DtUft6LzAijxGBuImvS2DrWWTXnGLWmf1HK9eq+pJLpvrtxuNwCgY8eOmDBhAgDguuuuQ1ZWFpYvX44xY8YgJCSkwuM7nc5C/zYYDN4PBZ5vxlwuF5xOJ4QQCAoK8s7nqc3DZDLBYDDA5XLB5XKVOK7nLi9Fee76UtK4RqMRRqMRbre7WL2Konif1eVwOIptHFU1bkWXtarG9WdZg4ODoShKqeN6aippXOCf7aC0dSiE8LkOPdtP0WVVFAVut7tYTZ5xS6qpMuP6s6yedVTS9u05AJW23/jKBvC9Dj3LU55l9bzmax0GOpuy1qHnGOFrHfoatzLrsCLLWtXbt699GdDO9u1rWXmMKLysah0jKrN98xih3WNEedchjxH+LSuPEf4ta8FjhBDC24j5ovvmKioqCsA/DVVBHTp0wMKFC3HmzBk0a9aswuN7gi7ttb/++gt2u73C41PVK/oXVV+MRiPq1KkDi8VS4vt9PVS64IGqqII7b0kKfrNWlMFg8Hk9YnWO61knFV3WstaDr3E98xuNxhKzqeiyqpWNr2WtTL1VtR0GclwhBFwuFxRFkWr7Lm1cDxmPEZ4sPfulmseIqlqHNeUY4cmyrM89FalXr9t3UXo4RhTdJwG51qGaxwh/PkcCEjRXTZs29fl6Xl5ehcf2dKglrUxFUbyNVVxcHMLCwvxe6VS9hBA+s/S8x+l0Ij09HX/99Rdyc3ORkJBQ7H2+Mq7oazVp3MrWVNY+GeifqcVxtVhTRcb1548eWqpXrzVVx7gl7Zdq11STx63ovP78rtTasupt3OqqqWiOWq9XhpoK0n1zlZiYiKSkJOzfvx+DBg3yTt+/fz9CQkLKbL4qyuVyIS0tDXFxcahVq1aV/AwKDH9+YXhERkYiODgYf//9N+Li4ngHSiIiIiLym6aaq5ycHOzZswcAcP78eWRmZmLnzp0AgPbt2yM2NhZDhw7FhQsXsHv3bu9848aNw7///W9Mnz4dN954I44cOYLly5fjwQcfRFhYWJXU6jnfs6rGJ/WEh4fj0qVLcDgcbK6IiIiIyG+aaq6sVisee+yxQtM8/169ejWuvfbaEi+e69q1K2bPno358+djw4YNiIuLw9ixYzFq1Kgqr5mnAsqHmRIRERFRRWiquapfvz5+/fVXn+9Zs2ZNidNvv/123H777QGthx+y5cEs5cBvEuXBLOXBLOXBLOXAHNWl+4cIV7Wa8KF87ty5aNasWbH/7rzzTgBAs2bNsGzZMu/7t2zZgg8//NDv8XNzczF//nzcfvvtSE5ORvv27fHQQw/hxx9/LPbewYMHY/To0T7H+/HHHzFixAh06tQJrVq1QteuXfHoo4/i8OHDJb7fc63V1q1by1U3aYuilH6nQNIXZikPZikPZikH5qg+TX1zpUX+3tNe70JCQrBq1api0wBg06ZNqFevnnf61q1bERYWhrvuuqvMcbOzszFs2DAcP34cI0aMQLt27ZCWloa1a9fivvvuw6xZs8r1jeP333+PIUOG4Prrr8fkyZMRHh6OM2fO4JNPPsFPP/2E1q1bF5vHc1eyLVu2IDw83K+6SXvKc2MS0jZmKQ9mKQ9mKQfmqD42Vz6U52nMemcwGNCmTZsSXyttuj/eeustHD58GKtWrSr0LLJu3bph+PDheO6559CuXTvExcX5Nd6GDRuQmJiIefPmeb/27tChAwYMGFDsoXEF1aQsZeZ0On0+o4L0g1nKg1nKg1nKgTmqi6cFUpkKnhY4ePBgfPvtt/jiiy+8pw/OnTu3xPlyc3OxefNmdOrUqdhDno1GIx599FFkZ2fjnXfe8buW9PR0xMbGlng+sa8H2Q0ZMgQHDx70q24iIiIioorgN1fk5XQ6C/27pHN2X3rpJTz11FMICQnBM888AwCoW7duieMdPXoU2dnZuOmmm0p8/eqrr0Z0dDS+++47v2ts0aIF5s+fjzfffBN33XUXmjRp4td8L774Ip555hm/6iYiIiIiqgg2VwTgn2ujWrRoUWjazJkz0bNnz0LTmjZtioiICISFhZV5umBKSgoAICEhodT3JCQk4K+//vK7zgcffBCHDx/GggULsGDBAkRHR6Nz584YOHAg2rVrV+p8TZs2RXh4OMLDwyt1miMRERERUWnYXPlQ0QsBHb9fgNueEeBqymawRMLcqF7ZbyxBSEgI1q5dW2hagwYNAlFWQEVERGD58uX46aef8MUXX+D777/Hrl278NFHH2Hq1Kno27ev2iVSFeLFufJglvJglvJglnLQa445OTnIyckp9fXQ0FCEhoZWY0UVw+aqDOXdQF3WNJy9diDg4+YKVcZoRKNj22CsFV3uWQ0GA5KTkwNaTnx8PADgzz//LPU9f/75J6688spyj92qVSu0atUKAHDu3DkMHjwYs2bNKrG58twxR68HG/qHoii8QFcSzFIezFIezFIOes7xxIkTOHr0KADA5XIhKysL4eHh3uvsW7ZsGfDPqlWBzVWAGWtFo+GBDap9c1WRxqqqtGzZEmFhYfjiiy8wePDgYq8fOnQIaWlpPk/n80eDBg3QvXt3rFixAn///Tdq165dqfGIiIiIqHo1bdoUiYmJAAC73Y69e/eiU6dOsFgsAKCLb60ANldlqshzrip6ap5emM1m5OXllfm+kJAQ9OvXDytXrsTBgwdxzTXXeF9zu92YM2cOwsLCynUqX2nN0++//46goCBERUUVe83zzAeTyeRX3aRNQgg4nU6YTCZ+C6lzzFIezFIezFIOes6x6Gl/QUFBsFgsiI2NVbGq8mNz5QOfjVSyxo0bY9u2bfjss89Qp04dxMXFeU8BLOqxxx7DoUOHMGrUKIwcOdL7EOF169bh4MGDmDVrVrFnXF26dAk7d+4sNtaNN96I559/Hi6XC7feeisaNWqEzMxM7Nq1C59//jmGDh2KoKCgUutu0qSJ33WTNnGflAezlAezlAezlANzVBebKyq3kSNH4uzZs3jmmWeQnp6ORx55BGPHji3xvWFhYVi9ejWWL1+O7du3Y8GCBQgNDUXbtm2xbt06XHXVVcXmOXbsGB577LFi0/fs2YP7778f27Ztw6JFi3Dp0iWEhISgYcOGmD59Onr16uWz7hEjRvhdNxERERFReSmC7W2Jjhw5AiEEkpOTS/xaNTc3F6dPn8bll1+OkJAQFSokf3lOC/T3phbMVpuEEHA4HDCbzbo71YEKY5byYJbyYJZykCVHm82GXbt24bbbbtPMaYFHjhwBgDJvqmGojmKIiIiIiIhkx+bKBz13/FQYs5SDXm8vS8UxS3kwS3kwSzkwR3Xxmqsy8EO5/jFDOTBHeTBLeTBLeTBLOTBH9fGbqzLwkjT981xzxSz1TQgBl8vFHCXALOXBLOXBLOXAHNXH5soHbpjyYJZycLlcapdAAcIs5cEs5cEs5SBDjmlpaUhJSUFaWprapZQbm6tK4od2+TBTIiIiIvVkZmZ6/9MbNlcV5LlYMDs7W+VKKNCysrKgKAovCCUiIiJSQXZuPoQQyM7NV7uUcuMNLSrIaDQiOjoaFy9eBPDPw3J5EaE2+fOcKyEEnE4n0tPTkZ6ejujoaBiNxmqulIiIiKhmS8/Mw/Z9v8PoEti+73dceWUyoiKC1S7Lb2yufCirWapbty4AeBss0i5Pc1UWo9GIhIQEWCyWaqiKyosNrzyYpTyYpTyYpRz0nuOf1iyc+zMDjcKAc39m4C9rNpsrmfj6QK4oChISEhAXFweHw1GNVVFVMJlMMBqN/AZSoxRF0f0vDPoHs5QHs5QHs5SDDDkm1ApHg4RIwA40SIhE3VphapdULmyuyuDPNx5Go1H3G7LM/DktkLSPOcqDWcqDWcqDWcpBhhyjIoJxZ6dG2L3rZ/To1EhX31oBvKGFT7xrnDycTqfaJVAAMEd5MEt5MEt5MEs56D1HV3omLJt2o8mx8wgLCVK7nHJjc0VERERERKoS+Q7Yl7yLs+0HIGjTJwgKDUFERITaZZUbTwskIiIiIiJVCCGQ9cEXsE5bBOfZPxE5sAcMo3vD8eN3iI6OVru8cmNzRURERERE1S7n68OwTp6PvO//i7BbOqDu6pcR3LwxbDab2qVVGJsrH/R6ISAVxyzlwBzlwSzlwSzlwSzloIcc84+fgXXqQmTv+ArBrZuh3ta3ENq5rdplBQSbqzLoYQMl3xRFgdlsVrsMqiTmKA9mKQ9mKQ9mKQet5+hMsSL1tRVIX7sdpsQ4xC16CRH3dIVikOc2EGyuiIiIiIioyrgzs5E2fyPS5m2EEmRCrUkPw/JALyjB+rsbYFnYXJXBn+dckbYJIeB0OmEymZiljjFHeTBLeTBLeTBLOWgtR+F0In3dR0h9dTnc6ZmwjOqD6EcHwRgdqXZpVYbNlQ98zpU8mKUcmKM8mKU8mKU8mKUctJCjEALZO7+CdcpCOE6eQ0Tf2xD77AiY68erXVqVY3NFREREREQBkfvdMVgnzUfugZ8QekM7xC96CcGtksqcLycnBzk5OQAAu92O/Px82O127+uhoaEIDQ2tsroDhc0VERERERFVSv7Jc7BNX4ysD79AUIsmSNj8OsJuau/3/CdOnMDRo0cBAC6XC7m5udi3bx+MRiMAoGXLlkhOTq6K0gOKzRUREREREVWI6+9UpL6+CvaV22CMq4U6cycisu+tUP6/KfJX06ZNkZiYWOrrevjWCmBz5ZMWLgSkwDCZuKnLgDnKg1nKg1nKg1nKobpydGfnwr5oM1LnrIOiKIh9diQsI/vAEBpcofH0ctpfWbgXlYENlv4pisIcJcAc5cEs5cEs5cEs5VAdOQqXCxkbd8I2Yylc1jRYhvdCzLghMNaKrtKfqxdsrsrAW7HrnxACbrcbBoOBWeoYc5QHs5QHs5QHs5RDVeYohED2pwdgm7IA+T+fQkSvmxE7cRTMjeoF9OfoHZsrH7RwK0sKDJfLBYNET/+uqZijPJilPJilPJilHKoix7zDv8I6eT5yvvwBIR3bIPHjxQi5qnlAf4Ys2FwREREREVExjrN/wvbyEmS+txvmpMtQd90MhN3Skd9u+sDmioiIiIiIvFyp6Uh9YzXsy7bAGBOFOrOfRuTAHlB405MycQ0RERERERHcuXlIX7YFqW+shnC6EDN+CKIf6g9DuP7v4ldd2Fz5wK885cFzyOXAHOXBLOXBLOXBLOVQkRyF243M93bD9vISOP/8G1FD7kbMk8Ngioutggrlpqm96MyZM3jxxRfRs2dPXHnllbjzzjvLPcbKlSvRrFkzjB49OiA1scHSP0VRYDKZmKXOMUd5MEt5MEt5MEs5VCTH7D3f4Y9uI3Dx39MQ3PoKNPhqNerMHM/GqoI09c3V8ePHsWfPHrRu3Rput7vcd+u7dOkS5s2bh1q1agWsJt6KXf8KbkfMUr+YozyYpTyYpTyYpRzKk2PesROwTlmInM8OIPialkj8aD5C2idXdYnS01Rz1bVrV3Tr1g0AMGHCBBw9erRc87/22mvo2rUrLly4EJB6eCt2eTgcDpjNZrXLoEpijvJglvJglvJglnIoK0fn+RTYZixDxqadMF+eiPgV0xB+Rxc21QGiqeaqMuf6fvfdd/jkk0+wc+dOPPHEEwGsioiIiIhI31zpmUh7ay3si9+BEhGG2jPGIWrwXVDMmmoHdE+KtelyuTB16lQ89NBDiIuLU7scIiIiIiJNEPkO2Fe+j9TXV0Lk5iF6zEBEjxkIQ2S42qVJSYrmav369cjJycGwYcMCPnZJpwYqilLqKYOer1R9vV5V83Lckl8TQhSapoWaauq4la2paJaBGreq6q2KcbVYU3nHLZijHurVc01VPW7R/ZLrUP1xKzqvZ77SPvdUpia9rUOtZVOecQvuk0IIZH/wBazTFsF57i9E3nc7Yp4eDlPd2t751a63Ouetqv2mKN03V1arFXPmzMGrr76KoKCggI/vcDgKnYNqMBhg+v8HqDkcjmLv99TgcrngdrsLvea5e4vb7YbL5Sr0mmdcIUSJ43rOnS1pXKPRCKPRCCEEnE5nodcURfHO63Q6i20cVTWuP8sKFF+HVTGup36z2QxFUcq9rGVlrsY69IxbUk1VlY1nWUtbh55xfa3DQG3fnn3S3+3b1zqszu0b4DECKPwBTgvHiILLymNE+dZh0f1SK8cIf5eVx4j/LavBYPDOW/BzDz9HFF5WrR8jPO/L++YnpE9fgrxDPyP0lg6ovWIqzM0aQfx/DTX9c4S/y1rwGCGEfze5031z9dZbb6FZs2Zo164d0tPTAfyzcpxOJ9LT0xEWFuYNubwURfHZsPm6WLDgRluUwWAo9fqygiGXd9yy5vW1HqpqXF/LCvheh4EcNygoyLtDVGZZ1chGK+sQQKXXYWW2b4PBgLCwsFLnreiyqpUNjxH/zCfT9l3auB6yHiMK/p5U8xhRVeuwphwjFEVBcHBwqfMBNftzBKCPY0T+r78jfdpCZO/aj+A2V6DetjkI6dim1PnKqklr61DNY4Q/jRUgQXN1+vRpHDx4ENdcc02x16655hosWbIEXbp0qfD4pa3Islawr9eral6OW7XjarEmvY2rxZr0Nq4Wa+K42q1Jb+NqsSa9javFmjhu1dfkSrHCNnM5MtZ9BFODeMQvnoTwnjdBKeNmcVyHgZm3IN03VxMnTvR+Y+Xx8ssvIyQkBOPHj0ezZs0qNb6/XwGSdgkh4HK5YDQamaWOMUd5MEt5MEt5MEt9cmdmI23eBqTN3wglOAi1Jv8b4YPvgikslDmqRFPNVU5ODvbs2QMAOH/+PDIzM7Fz504AQPv27REbG4uhQ4fiwoUL2L17NwCgefPmxcaJiopCWFgYrr322krVU56L10jb3G53qV8Tk34wR3kwS3kwS3kwS/0QDifS136I1NdWwJ2eBcuovoh+7H4YoiJKvCaJqo+mmiur1YrHHnus0DTPv1evXo1rr722xIvniIiIiIhkJ4RA9o4vYZ26CI6T5xDZ7zbETBgBc/147+ukLk01V/Xr18evv/7q8z1r1qwpcxx/3kNEREREpBe5B4/COmk+cr89gtCb2iN+yWQEt2yqdllUhKaaKyIiIiIi+p/8k+dgm7YIWdv3IKhFUyS8MxthNxa/kRtpA5srH3ghoDwqejt+0hbmKA9mKQ9mKQ9mqS3OS6lInbUS6avfhym+FuLmPYeIPreWeQdA5qgurv0ysMHSP0VRmKMEmKM8mKU8mKU8mKV2uLNzYV+4Galz10ExGBA7cSQsI/vAEOL7OWQAc9QCNldl4K3Y9U8IAbfbXa4HwJH2MEd5MEt5MEt5MEv1CZcLGRt2wPbqMrisabA8eC9ixg2BMdbi/xjMUXVsrnzgHVfk4XK5fD5FnPSBOcqDWcqDWcqDWapDCIHs3V/DOnUhHL+cRsS93RA7cSTMl9Wr0HjMUV1sroiIiIiIVJD74y+wTl6A3K9+QEinqxC3ewlC2lyhdllUCWyuiIiIiIiqkePMBdheXoLMLZ/AfMXlqLt+JsK6XcdT+STA5oqIiIiIqBq4bHakvrEa9mVbYIy1oM4bzyByQHcovMOfNJikD/zrgTx47rEcmKM8mKU8mKU8mGXVcefkwb70XaS9uRbC7Ubskw/AMrovDOGhAf9ZzFFdbK7KwAZL/xRF4TMfJMAc5cEs5cEs5cEsq4Zwu5H57sewvbwEzhQroob0RMyTw2CqE1MlP485qo9rn2oE3lJfDsxRHsxSHsxSHswysLK/OAjr5AXIP3oc4XfegITnRyGoScMq/7nMUV1srnwQQnADlYAQAg6HA2azmVnqGHOUB7OUB7OUB7MMnLyjJ2CdPB85XxxESPtkJP5nAUKuaVktP5s5qo/NFRERERFRJTnPp8D2ylJkbN4Fc+P6qLtqOsJ6XM8mp4Zhc0VEREREVEEuewbS3loH++J3YIgKR+1XxyFq0F1QzPyYXRMxdSIiIiKichJ5+bCv2IbU2asg8vIRPfY+RI8ZCENEmNqlkYrYXBERERER+Um43ch6/3NYpy+C81wKogbdiZinHoCpbm21SyMNYHPlA8+RlYfZbFa7BAoA5igPZikPZikPZlm2nH2HYJ28AHmHfkbYbZ2QsO5VBDW7XO2yCmGO6mJzVQY2WPrHDOXAHOXBLOXBLOXBLH3L/+U0rFMXIvvj/Qi+qjnqbZuD0E5XqV1WMcxRfWyuysBbseufEAIulwtGo5FZ6hhzlAezlAezlAezLJnzr79he3UZMtb/B6aGdRG/ZDLCe96k2XXEHNXH5soHIYTaJVCAuN1uGI1GtcugSmKO8mCW8mCW8mCW/+POzEba3PVIW7ARSmgIak0dC8uwnlCCtH/KHXNUF5srIiIiIiIAwuFE+poPYXttOURmNiyj+iL60fthtESqXRrpBJsrIiIiIqrRhBDI+mgvbFMXwnH6PCL7d0fshAdhSoxXuzTSGTZXRERERFRj5X57BNZJ85F78ChCu16L+OVTEdyiqdplkU6xufKBFwLKg+cey4E5yoNZyoNZyqOmZZl/8ixsUxcj66M9CEr+FxLefQNhN7RTu6xKq2k5ag2bqzKwwdI/RVF4oJEAc5QHs5QHs5RHTcrSedGG1Fkrkb76A5gSaiNu/vOI6H0LFINB7dIqrSblqFVsrsrAW7HrnxDCmyOz1C/mKA9mKQ9mKY+akKU7KwdpCzchbe56KCYjar0wGlEP3gtDSLDapQVMTchR69hc+cBbscvD6XTyieUSYI7yYJbyYJbykDVL4XQiY8MO2F5dBldqOiwP3ouYcUNgjIlSu7QqIWuOesHmioiIiIikI4RA9u79sE5ZCMevvyOi9y2IfXYEzJfVU7s0khibKyIiIiKSSu6hn/+5A+D+HxHSuS3i5z2P4NbN1C6LagA2V0RERESkSzk5OcjJyfH+23XuL+S8sRaOj76EIekyxK6ahugeXXj9EVUbNlc+cEeUB7OUA3OUB7OUB7OUhx6zPHHiBI4ePQpTVi7q7jiIuvuOwRkRgj/uuwl/t2+Glg1iEaPD5aoMPeYoEzZXZeAGqn+KovDCTgkwR3kwS3kwS3noNcsm9Rug1kffIHfxe3C7XPi5UzM0fHokrv5XEwBAaGioyhVWL73mKBM2V0RERESkK8LlQsY7H8P2ylK4LlphGXYPLvS4Bof3fo7LQoMRGxurdolUQ7G5KgOfc6V/Qgg4nU6YTCZmqWPMUR7MUh7MUh56yjL7829hnbwA+cdOIPyuG1Hr+dEwN66PE99+988dAnPz1S5RNXrKUVZsrnzgc67kwSzlwBzlwSzlwSzlofUs844ch3XKAuR8cRAh17ZC4o6FCGnXAgCQnpmH7ft+h9ElsH3f77jyymRERcjzcODy0HqOsmNzRURERESa5Tj3F2wzliLznY9hbtIAdVe/jLDunQt9M/OnNQvn/sxAozDg3J8Z+MuaXWObK1IXmysiIiIi0hyXPQNpb66Bfcl7MERFoPZrTyDq/jugmIp/fE2oFY4GCZGAHWiQEIm6tcJUqJiIzRURERERaYjIy4d9xVakzl4NkedA9KP3I/rfA2CIKL1hiooIxp2dGmH3rp/Ro1MjfmtFqmFz5QMvBJQHb0sqB+YoD2YpD2YpD7WzFG43Mrd9Btv0xXCev4ioQXci5slhMNWt7df8YSFBUBQFYSFBVVyptqmdY03H5qoMbLD0jxnKgTnKg1nKg1nKQ+0sc776AdZJ85F3+FeE9eiMhA0zEZTUqFxjREREeP+rqdTOkdhclYm3Ytc/IQRcLheMRiOz1DHmKA9mKQ9mKQ+1ssz/5TSsUxYge/fXCG7bHPXen4vQjm38nj8nJwc5OTkA/mksYmJioCgKbDYbgH8eIlyTHiTMfVJ9bK584K0s5eF2u2E0GtUugyqJOcqDWcqDWcqjOrN0/nkJtleXIWPDDpgaJiB+6RSE331juRuCEydO4OjRowAAl8uF3Nxc7Nu3z7scLVu2RHJycsDr1zLuk+pic0VERERE1cKdkYXUuethX7gJSlgIak8bi6ihPaEEVew6oaZNmyIxMbHU12vSt1akDWyuiIiIiKhKCYcT6aveh23WCoisHFge6o/osffBGFW566Nq2ml/pH2aaq7OnDmDZcuW4fDhwzh+/DgaN26M7du3+5zn4sWLWLlyJfbt24ezZ88iMjIS11xzDcaPH+/zLxlEREREVLWEEMjavge2aYvgOH0ekQN6IHbCgzDVi1O7NKIqoanm6vjx49izZw9at24Nt9vt1zVPx44dw+7du9G7d2+0bt0aqampWLBgAfr27Yvt27cjNja2wvXwQkB58NxjOTBHeTBLeTBLeQQ6y5wDP/1zB8DvjiHs5usQv2Iagq9sEtCfQcVxn1SXppqrrl27olu3bgCACRMmeC9Q9OXqq6/Gjh07YCrwtO62bdvixhtvxLZt2zB8+PBK1cQGS/8UReGBRgLMUR7MUh7MUh6BzDL/xFnYpi1C1kd7EdQqCQnvvYGwLu0CMjb5xn1SfZpqrgwGQ7nniYqKKjatbt26iI2NxcWLFytdE2/Frn9CCG+OzFK/mKM8mKU8mKU8ApGl86INqbNWIH31hzDVq4O4hS8iotfNUCrw+Y4qhvuk+jTVXAXK6dOnYbVa0aRJ5b565q3Y5eF0OvnEcgkwR3kwS3kwS3lUNEt3ZjbSFm5C2twNUMxG1HrxIUQN7wVDSHAVVEll4T6pLumaKyEEpk2bhri4ONxxxx0BGa8oRVFKbbw8fyXw9XpVzctxS37N81ecgv9Wu6aaOm5layqaZaDGrap6q2JcLdZU3nEL5qiHevVcU1WPW3S/5DpUf9yKzuuZr7TPPSWNK5xOZKz/D1JnLocrLQOWEb0R/dggGGOivO/X4zrUWjblGbekfVLL9eq9ppJI11zNnTsX33zzDZYuXYqwsLBKj+dwOAp9rWowGLzXdzkcjmLvDwoKAvDPg+zcbneh10wmExRFgdvthsvlKvSaZ1whRInjev4CUdK4RqMRRqMRQgg4nc5CrymK4p3X6XQW2ziqalx/lhUovg6rYlxP/WazGYqilHtZy8pcjXXoGbekmqoqG8+ylrYOPeP6WoeB2r49+6S/27evdVid2zfAYwRQ+AOcFo4RBZeVx4jyrcOi+6VWjhH+LiuPEf9bVoPB4J234OeekpZVCIHc3V8j7eUlcB4/i4g+t8Dy9HAYEuPgBuD+/58vy+eIgutQ68eIgu/z1MvPEYE5Rnj+WFAWqZqrzZs3Y968eZg+fTo6dOgQkDE9G0Fpr5Wm4EZblMFgKPX6soIhl3fcsuYteNOP6hrX17ICvtdhoMYt+hfVyiyrGtloYR16VHYdVnb7NplMpe6TFV1WtbKpyceIgr8cZdq+SxvXQ8ZjhOf46tkv1T5GVMWy1sRjRGmfe0wmE3J/+C9skxcg9+vDCLm+LeIXvICQ1lf4/Mu+XrfvovRwjCi6TwJyrUM1jxH+NFaARM3V7t27MWnSJDz66KPo06dPQMb0/KIoaWWWtYJ9vV5V83Lc0l8ruPNrpaaaOG5la/Ic3Mq7T3Idam9czz6pl3r1WlN1jFvSfql2TTV53IrOK4TweYx1nD4P6/TFyHr/MwRd2RgJG2chtGt773tlWoday6a84xbNUev1ylBTQVI0VwcOHMD48ePRt29fjBkzJqBjl2dlkjaV9ZcK0gfmKA9mKQ9mKY/SsnRZ05D6+irYV26DsXYM6sx5FpH9boPC231rEvdJ9WmqucrJycGePXsAAOfPn0dmZiZ27twJAGjfvj1iY2MxdOhQXLhwAbt37wYAnDx5EmPGjEGjRo3Qs2dP/Pjjj97xYmNj0bBhw2pfDiIiIiI9c+fkwb74HaS9tRYAEPvMg7CM7ANDWIjKlRFpm6aaK6vViscee6zQNM+/V69ejWuvvbbYxXOHDx9GRkYGMjIyMHDgwELz9urVCzNmzKhUTf5evEba5bmA0XOhJOkTc5QHs5QHs5SHJ0ujoiDznY9hm7EMrotWWB7ohZjxQ2CsHaN2ieQH7pPqU0R57i1Ygxw5cgRCCCQnJ3Pj1DnPxfO+LtIl7WOO8mCW8mCW8nC73cjYvR/p05cg/+dTCO/ZFbUmjoS5cX21S6Ny4D5ZdY4cOQIASE5O9vk+TX1zRURERETVK++n32CdNB85X36PkOtaIXHnQoRc3ULtsoh0ic0VERERUQ3kOPcXbK8sQeY7H8P8r4aovWIqIm/v4vP22kTkG5srIiIiohrElZaB1DdXw77kPRijI1Hn9acQMbAHnLzOnKjS2Fz5wAOMPHw9GI70gznKg1nKg1nqh8jLh33ZFqS+sRoi34mYcYMR/VB/GCLCIISAiZfhS4H7pLq49svABkv/SnsgIukLc5QHs5QHs9QH4XYjc+unsL28BM7zFxE15C7EPDEMpvha3vcwSzkwR/WxuSoDb8Wuf0IIuN1u7xPLSZ+YozyYpTyYpfZlf/k9rJPmI/+n3xB++/VI2Pgagv51WbH3MUs5MEf1sbnygXepl4fL5eIFuhJgjvJglvJgltqU99+TsE1ZiOxPv0Fwuxao9+E8hF7Xyuc8zFIOzFFdbK6IiIiIJOG8cBG2GcuQsXEHzI0SEb9sCsLvupHfYhBVEzZXRERERDrnSs9E2tz1sC/aDCUsBLVffgxRQ+6GEmRWuzSiGoXNFREREZFOiXwH0le9D9vrKyGyc2F5qD9ixt4HQ2S42qUR1UhsrnzgV+jy4LnHcmCO8mCW8mCW6hBCIOuDL2CdtgjOs38icmAPxD7zIEwJdSo8JrOUA3NUF5urMrDB0j9FUfjMBwkwR3kwS3kwS3XkfH0Y1snzkff9fxHW7TrUXTUdwVc2qdSYzFIOzFF9XPtl4K3Y9a/gXR+ZpX4xR3kwS3kwy+qVf/wMrFMXInvHVwhqlYSELW8i7PqrAzI2s5QDc1QfmysfeCt2eTgcDpjNvKhX75ijPJilPJhl1XOmWJE6awXS12yHKTEOcYteQsQ9XaEE+PQvZikH5qguNldEREREGuTOzEbagk1Ie3sDlCATak16GJYHekEJDlK7NCIqBZsrIiIiIg0RTifS132E1FeXw52eCcvI3oh+bDCM0ZFql0ZEZWBzRURERKQBQghk7/wK1qmL4DhxFhF9b0XshBEwN6irdmlE5Cc2Vz7wQkB5MEs5MEd5MEt5MMvAyP3+GKyTFiD3m8MIvaEd4he+iOBWSdVaA7OUA3NUF5urMnAD1T9FUXhhpwSYozyYpTyYZeU5Tv0B6/TFyPrgcwS1aIKETbMQelP7av/8wSzlwBzVx+aKiIiIqJq5/k5F6uurYF+5Dca4WqgzdyIi+94KxWhUuzQiqgQ2V2Xgc670TwgBp9MJk8nELHWMOcqDWcqDWZafOzsX9kWbkTpnHRRFQeyzI2EZ2QeG0GBV62KWcmCO6mNz5QOfcyUPZikH5igPZikPZukf4XIhY9NO2GYsg+vvVFiG90LMuCEw1opWuzQvZikH5qguNldEREREVUQIgexPD8A2ZQHyfz6FiHu6InbiKJgvT1S7NCKqAmyuiIiIiKpA3uFfYZ08Hzlf/oCQDq2RuGsRQtpeqXZZRFSF2FwRERERBZDj7J+wvbwEme/thjnpMtRdNwNht3TkNTBENQCbKx94EJSHycRNXQbMUR7MUh7M8n9cqelIfXMN7EvfgzE6EnVmP4XIgbdD0ck6YpZyYI7q4tovAxss/VMUhTlKgDnKg1nKg1n+w52bh/RlW5D6xmoIpwsx44cg+qH+MISHql2a35ilHJij+thclYG3Ytc/IQTcbjcMBgOz1DHmKA9mKY+anqVwu5H53m7YXl4C559/I2rI3Yh5chhMcbFql1ZuNT1LWTBH9bG58oG3spSHy+WCwWBQuwyqJOYoD2Ypj5qaZfbe72CdNB/5R44j/I4bkPDOKAQ1bah2WZVSU7OUDXNUF5srIiIiIj/lHTsB65SFyPnsAIKvaYl62+ch9NpWapdFRBrB5oqIiIioDM4LF2F7ZSkyNu2E+fJExK+YhvA7uvDUKyIqhM0VERERUSlc6ZlIe2st7IvfgRIRhtozxiFq8F1QzPwIRUTF8cjgA/8aJQ+eeywH5igPZikPWbMU+Q7YV76P1NdXQuTkIvrfAxH9yEAYIsPVLq3KyJplTcMc1cXmqgxssPRPURQ+80ECzFEezFIees8yJycHOTk5AAC73Y5Dhw6hTZs2CNt/FDmz18B9/iIi77sdsc88CFPd2ipXW7X0niX9gzmqj2u/DLwVu/4VvOsjs9Qv5igPZikPvWd54sQJHD16FACQmZkJ1/f/RdrUNRB//I3UFpchZNJENLn7NpWrrB56z5L+wRzVx+bKB96KXR4OhwNms1ntMqiSmKM8mKU89Jxl06ZNkZiYCNfJc/jrxbcR9t0vyGuSiIhVUxFzXSuEhurnIcCBoOcs6X+Yo7rYXBEREVGNZLZnIfO1FUhfux2uqAjsvTUZJ6++CU936YCoiGC1yyMiHWJzRURERDWKOzMbafM2IG3+RijBQXA9/gDm/O1Eg8gLOPdXJv6yZrO5IqIKYXNFRERENYJwOJG+bjtSZ66AOz0TllF9Ef3Y/cgyBqHekg8B+wU0SIhE3VphapdKRDrF5oqIiIikJoRA9o4vYZ26CI6T5xDR9zbEPjsC5vrxAIAoAHd2aoTdu35Gj06N+K0VEVUYmysfFEXhnVYkoCgKzGYzs9Q55igPZikPPWSZe/AorJPmI/fbIwi98RrEL56E4OR/ASh8K3a3M9/7f5vNBgAIDQ2tMTe10EOWVDbmqD42V1Qj8CAjB+YoD2YpD61mmX/yHGzTFiFr+x4EtWiKhM2vI+ym9oXeU/BW7Lm5uQgJCcGvv/6KM2fOAABatmyJ5OTkaq9dLVrNksqHOaqLzVUZ+Jwr/RNCwOVywWg0MksdY47yYJby0GKWzkupSH19JdJXvQ9TfC3Evf0cIvrcAsVoLPZez63YS1NTvrUCtJkllR9zVB+bKx/4nCt5uN1uGEv4xUr6whzlwSzloZUs3dm5sC/cjNS566AYDIidOBKWEX1gCC39+qmadNqfP7SSJVUOc1SXppqrM2fOYNmyZTh8+DCOHz+Oxo0bY/v27WXOJ4TAkiVLsH79ethsNjRv3hzPPvss2rRpU/VFExERkWqEy4WMDTtge3UZXNY0WB68FzHjhsAYa1G7NCKqgQxqF1DQ8ePHsWfPHlx22WVo0qSJ3/MtWbIEc+bMwbBhw7Bo0SLUqVMHw4cPx7lz56qwWiIiIlKLEAJZu7/GuRsfwKVxryK0Yxs03L8OtaeOZWNFRKrRVHPVtWtX7NmzB3PmzEGLFi38micvLw+LFi3C8OHDMWzYMHTo0AGzZ89GdHQ0li1bVsUVExERUXXL/fEXXLj3cfx139MwxlqQ+PFixC96CeZG9dQujYhqOE2dFmgwlL/X++GHH5CZmYkePXp4pwUFBeGWW27B7t27K1UPLwSUB889lgNzlAezlEd1Zuk4cwG2l5cgc8snMDdrhLrrXkXYLR34+zpAuF/KgTmqS1PNVUWcOnUKANC4ceNC05s0aYJVq1Z5b61aUTxg65+iKDzQSIA5yoNZyqO6snTZ7Eh9YzXsy7fCGBOFOm88g8gB3aGYdP8xRjO4X8qBOapP90el9PR0BAUFITi48N2AoqKiIISA3W6vVHPldruLNViKopR6J0HPe329XlXzctySXxNCQAgBg8GgmZpq6riVmdftdnsfjVBwn9TqsmpxHWpl3IL7ZGm0VK+ea6rqcT1ZevbLQK9Dd24e0pduQdqbayBcLsSMHwLL6H4whIcWeq+e12Ggx63ovMD/PvOU9LmnMjXpbR1qLZvyjFvSPqnlevVeU0l031xVJSEEHA5HoYOMwWCA6f//UuZwOIrNExQUBABwuVxwu92FXjOZTN4PiS6Xq9BrnnE9P7Mos9lc6rhGoxFGoxFCCDidzkKvKYrindfpdBbbOKpqXH+WFSi+DqtiXE/9oaGhUBSl3MtaVuZqrEPPuCXVVFXZeJa1tHXoGdfXOqzs9p2bm+v9Of4sq+c1X+uwOrdvgMcI4H+//IODgzVxjCi4rDxGlG8deur2zBOoY4Rwu5G95VPYZy6HK8WKqCF3I+bJB4CYSLjcbrgKLFMgtm8eI4wwGAzezzxF/4BV0z9HFFxWrR8jCu6Tnnr5OSIwxwhP01oW3TdXUVFRyM/PR15eXqFvr9LT06EoCiyWyt0xyLMRlPZaaQputEUZDIZS/2JbMOTyjlvWvJ6NvTrH9bWsgO91GKhxC/5lE6jcsqqRjRbWoUdl12Flt++CvyyKquiyqpVNTT5GFPzlKNP2Xdq4HjIeIzzHV89+GYhjhGPfj7BNWYj8o8cRfucNiHluFIKaNPB+6KrudVgTjxGlfe6pqZ8jPPRwjCi6TwJyrUO1fgd6zn7yh+6bK8+1VqdPn8YVV1zhnX7q1CnUq1evUqcEAijx63HP9LLmq8hrlZmX45b+WtG/wmmhppo4bmVrKvhfIMf1RWvjarGmiozrmaaXevVaU3WMW9J+WZFx846egHXKAuR8/i1CrmmJxI/mI6R9ckDqrcy8ehu3ovOWdHpnoGrS2zrUWjblHbdojlqvV4aaCtJ9c9W2bVtERERgx44d3ubK4XDg448/RpcuXVSujoiIiHxxnk+B7ZWlyNi8C+bG9RG/cjrCb7++XB9miIi0QlPNVU5ODvbs2QMAOH/+PDIzM7Fz504AQPv27REbG4uhQ4fiwoUL3tusBwcHY/To0Zg7dy5iY2ORlJSEDRs2IC0tDQ8++GCl6uGBXR4Vuc0/aQ9zlAezlEdFs3TZM5D21jrYF78DQ1Q4ar86DlGD7oJi1tRHkxqF+6UcmKO6NHUEs1qteOyxxwpN8/x79erVuPbaa0u8eG7kyJEQQmD58uWw2Wxo3rw5li1bhgYNGlS6JjZY+qcois/zaEkfmKM8mKU8KpKlyMuHfcU2pM5eBZGXj+ix9yF6zEAYIsKqqEryB/dLOTBH9SmiPPcWrEGOHDkCAEhOTi7jnURERFQWIQSytn0G6/RFcJ5LQeT9dyD26eEw1a2tdmlERGXytzdga+tDwWcFkH557kzm6w5IpH3MUR7MUh7+Zpmz7xCskxcg79DPCLutExLWvYqgZpdXY6VUFu6XcmCO6mNzRURERFUi/9fTsE5ZiOyP9yP4quaot20OQjtdpXZZRERVhs0VERERBZTzr79hm7kcGes+gqlhXcQvmYzwnjfxL+lEJD02V0RERBQQ7sxspL29HmkLNkEJDkKtKY/AMqwnlOAgtUsjIqoWbK6IiIioUoTDifS122F7bTlEZjYso/oi+tH7YbREql0aEVG1YnPlA09fkIfZbFa7BAoA5igPZikHIQTyP/4af01bBMepPxDZvztiJzwIU2K82qVRBXC/lANzVBebqzKwwdI/ZigH5igPZimH3G+PwDppPnIPHkXoTe0Rv3QKgls2VbssqiDul3Jgjupjc1UG3opd/4QQcLlcMBqNzFLHmKM8mKW+5Z88C9vUxcj6aA+CWv4LcRtfQ0TXa5mlznG/lANzVB+bKx/4fGV5uN1uGI1GtcugSmKO8mCW+uO8aEPqrJVIX/0BTAm1ETf/eYTf2w1Ol0vt0ihAuF/KgTmqi80VERERlcqdlYO0hZuQNnc9FKMRsc+PgmVEbxhCgv/5IySbKyIiLzZXREREVIxwOpGxYQdsry6DKzUdlgfvRczjg2GMtahdGhGRZrG5IiIiIi8hBLJ374d1ykI4fv0dEb1vQeyzI2C+rJ7apRERaR6bKx94IaA8eO6xHJijPJilNuUe+hnWyQuQu+8QQjq3Rfy85xHcupnPeZilPJilHJijuthclYENlv4pisIDjQSYozyYpfY4fr8A28uLkbn1U5ivuBx1189EWLfryvwdyCzlwSzlwBzVx+aqDLwVu/4JIbw5Mkv9Yo7yYJba4bLZkTp7FezLt8JYKxp13pyAyAHdofj54YxZyoNZyoE5qo/NlQ+8Fbs8nE4nn1guAeYoD2apLndOHuxL3kXaW2sh3G7EPvUALKP7wRAWUu6xmKU8mKUcmKO62FwRERHVEMLlQua7u2F7ZQmcKVZEDe2JmCeGwVQnRu3SiIikwOaKiIioBsj+/FtYJy9A/rETCL/rRiQ8NwpBTRqoXRYRkVTYXBEREUks78hxWKcsQM4XBxHSPhmJOxYipF0LtcsiIpISmysfeCGgPJilHJijPJhl1XP8kQLbK0uR+c4umJs0QN3VLyOse+eAr3tmKQ9mKQfmqC42V2XgBqp/iqLwwk4JMEd5MMuq5bJnIO2ttbAvfheGqHDUnjkeUYPuhGIK/K98ZikPZikH5qg+NldEREQSEHn5sK/YitTZqyHy8hH96P2I/vcAGCLC1C6NiKjGYHNVBj7nSv+EEHA6nTCZTMxSx5ijPJhlYAm3G5nbPoNt+mI4z19E1KA7EfPkMJjq1q76n80spcEs5cAc1Vep5spmsyE1NRWKoiAmJgYxMXLdypXPuZIHs5QDc5QHswyMnH2HYJ00H3k//oKw7p2RsGEmgpIaVWsNzFIezFIOzFFd5WqusrOzsXPnTnz66ac4dOgQUlNTC70eExODNm3aoFu3bujevTvCwngqAhERUaDl/3Ia1ikLkL37awS3bY56789FaMc2apdFRFTj+dVcpaamYvHixdi4cSPy8/PRrFkz3HzzzWjQoAGioqIghEB6ejr++OMPHDt2DC+88AKmTp2KAQMGYOTIkYiNja3q5SAiIpKe889LsL26DBkbdsDUMAHxS6cg/O4befoPEZFG+NVcde3aFZdddhmefvpp3HbbbWU2SzabDbt27cLmzZuxadMm/PDDDwEploiIqCZyZ2Qhde562BdughIagtrTxiJqaE8oQbwrGBGRlijCjxMzv/zyS1x//fUV+gGVmVdNR44cAQC0bNmSfxHUOSGE98YkzFK/mKM8mKX/hMOJ9NUfwDZrBURmNiyj+yH60fthjIpQuzQAzFImzFIOzLHqeHqD5ORkn+/z65uryjRHemysCuKGqX88wMiBOcqDWZZNCIGs7Xtgm7YIjtPnETmgB2InPAhTvTi1SyuEWcqDWcqBOaqv0rdiz8/PR1BQUCBq0STeil3/hBBwu90wGAzMUseYozyYpW85B36CdfIC5B08irCbr0P88qkIbtFU7bJKxCzlwSzlwBzVZ/D3jYcOHcLNN9+MW2+9FZ9//rl3+ogRI6qkMC3grSzl4XK51C6BAoA5yoNZFpd/4iz+GvYcLtw5BiI3DwnvvYGEja9ptrHyYJbyYJZyYI7q8vubq5dffhlDhgyBEAJPPfUUxo4di6FDh7IBISIiqgTnRRtSZ61A+uoPYapXB3ELXkDEvd2gGPz++ycREWmE382VyWTC0KFDAQAdO3bEqFGjEBQUxK8ciYiIKsCdmY20hZuQ9vYGKCYjar34EKKG94IhJFjt0oiIqIIqdM1VUlIS1qxZg6FDhyIrKyvQNREREUlLOJ3IWP8f2F5dBldaBiwjeiPm8cEwxkSpXRoREVWS3+ccXH311Thz5oz33w0aNMCqVavQuHHjKilMC/itnDyMRqPaJVAAMEd51MQshRDI2rUP524YhktPvIbQLlej4dfrUHvyGF03VjUxS1kxSzkwR3X59Zyrmsjfe9kTERGVJfeH/8I6aT5yvz6M0OvbotZL/0Zw62Zql0VERH4K6HOu/JGTk4OPPvoI+fn5uOGGG5CYmBiooVXFW7HrX8G/HzBL/WKO8pAxy5ycHPz3v/8FAFx55ZUIDQ0FADhOn4ft5cXI3PYZgq5sjISNsxDatb00yy1jljUVs5QDc1RfhZqriRMn4qeffsL27dsB/POsq379+uH48eMAgMjISKxatQpXXnll4CpVAb/Uk4fD4YDZbFa7DKok5igPGbLMyclBTk4OAMBut+PQoUMAgNjYWES6FDiXbEHWmg9hrB2DOm9NQGT/7lAkPF1HhizpH8xSDsxRXRVqrg4cOIC7777b++/t27fj+PHjmDVrFq644gqMHTsWb7/9NubPnx+wQomIiLTkxIkTOHr0KAAgMysbKSkXESSAU5Pmov5nh2E0GFDr6eGwjOoLQ1iIytUSEVF1qFBz9ffffxc67e+TTz5By5YtceeddwIA+vXrh2XLlgWmQiIiIg1q2rQpEhMTkZmdjyWb9qLJTwfR9ttTCMvJR8h9PRA9fggi6ieoXSYREVWjCjVXoaGhyMjIAAA4nU58++23GDRokPf18PBw7+tEREQyCg0NRUhICP7++FN0X7QOdVNtONWkLmpPegJNu1+ndnlERKSCCjVXLVq0wObNm3Httdfis88+Q1ZWFrp27ep9/ezZs6hVq1bAilQLLwSUB7OUA3OUhwxZph88gtRpi2DcfxhoUA8fdm2P1Pr10LdhDGw2G0JDQ703tpCZDFnSP5ilHJijuirUXD3++OMYMWIEevfuDSEEbrvtNrRq1cr7+u7du9G2bduAFakmbqD6pygKL+yUAHOUh96zdJz7C7ZXliDznY+REx+NcyN74I/L45B18SJCTU58f/AbHAkKQsuWLaV/nIfes6T/YZZyYI7qq1BzlZycjB07duCHH35AVFQU2rdv730tPT0d9913X6FpREREeudKy0DaW2tgX/IeDFERsLzyGKLvvgH1TEZcYbfj008/BQB06dIFFoulRnxrRUREhWnuIcInT57EtGnTcOjQIYSHh6Nnz554/PHHERQU5HO+1NRUvPHGG9i7dy/S0tJQv3593H///Rg4cGCF6vA8KKxly5b89krnhBBwOp0wmUzMUseYozz0lqXIy4d9+Rakzl4Nke9E9CMDEf1wfxgiwrzvKe05V7LTW5ZUOmYpB+ZYdQL6EOE///wTCQkVu+NReea12+0YOnQoGjVqhLlz5yIlJQUzZsxAbm4uXnzxRZ/zPvbYYzh16hTGjx+PhIQE7N27F5MmTYLRaES/fv0qVLvG+k6qBGYpB+YoDz1kKdxuZG79FLaXl8B5/iKiBt+JmCcfgCm++DXFoaGhuPrqq1WoUn16yJL8wyzlwBzV5Vdzdcstt+Cuu+7CwIEDC11b5csPP/yAjRs3YseOHd5OrywbN25EVlYW3n77bURHRwMAXC4XJk+ejNGjRyM+Pr7E+S5duoQDBw7glVdewb333gsA6NChA44cOYKPPvqows0VERHVTNlffg/rpPnI/+k3hN9+PRI2voagf12mdllERKRxfjVX69evx5tvvol+/fqhXr16uO6669CiRQvUr18fUVFREEIgPT0df/zxB44ePYpvvvkGKSkpuPbaa7Fu3Tq/i9m7dy86dOjgbawAoEePHnjppZewb98+b+NUlNPpBABERkYWmh4REYHs7Gy/fz4REdVseT+fgm3yAmR/+g2C27VAvQ/nIfQ6//6oSERE5Fdz1apVKyxfvhw///wz3nvvPXz22WfYsmULgP/dTc/zFWRCQgK6deuG3r17o3nz5uUq5tSpU+jdu3ehaVFRUahTpw5OnTpV6nwJCQno3LkzFi5ciMsvvxx169bF3r17sW/fPsyaNatcNRARUc3j/PMSbDOWIWPjDpgvq4f4ZVMQfteNvGaBiIjKpVx3C2zevDmef/55PP/880hJScGpU6eQlpYGAIiOjkbjxo1LPXXPH+np6YiKiio23WKxwG63+5x37ty5GDduHO644w4AgNFoxPPPP4/bbrutwvUoilLieaulTfe8BpR+vmtVzstxS35NCAGj0Vjo32rXVFPHrWxNRqOx2Hu0uqxaXYdaGLfgPql2ve70TKS9vQH2RZuhhIWg9vRHETn4LihB5mI/Q0vrUCvjFt0vuX2rP25F5wVQ4jE2EDXpbR1qLZvyjFvSPqnlevVeU0kqdCt2AIiPj69UIxVIQgg8++yz+P333/H666+jTp062L9/P15++WVYLBZvw1URnlMOPQwGA0ymf1abw+Eo9n7PXQ1dLhfcbneh1zx3bnG73XC5XCWOK4QocVzPMwtKGtdoNHp3pKL1FnzegdPpLLZxVNW4/iwrUHwdVtW4ntcrsqxljavGOvSMW1JNVbkOzWYzFEXxuX37WoeV2b49r5dnWT2v+VqH1bl9AzxGFF1Wl8ulyjEiPysbmWu3I/2NNRDZuYgc1Qexjw2CMSoCTqcT7iI/l8cI/5dVrWNEZbZvHiP+WVZFUTR3jNDS5whf2zePEf4vq16PEUIIbyPmS4Wbq6oQFRWFjIyMYtPtdjssFkup833xxRfYuXMnPvjgAzRr1gwAcO2118JqtWLGjBmVaq583crS10PaCm60RRkMBu8H/aIKhlzeccua17OxV+e4vpYV8L0OAzWuEKLQjlaZZVUjGy2sQw/PvlDRZa3M9l2w5pL2yYouq1rZ1ORjhGefVBSl2rdvIQQyP/wCtmmL4Pz9AiIH9kDM08NhqhdX6e27rHplPEZ4svTsl2oeI6pqHdaUY4QnS1+fe2rq5wgPPRwjiu6TgFzrUM1jhD+NFaCx5qpx48bFrq3KyMjApUuX0Lhx41LnO3HiBIxGI5KSkgpNb968Od555x3k5ORU6Jkjng61pJVZ1gr29XpVzctxS3/N7XZ7dzat1FQTx61sTZ4cy7tPch1qb1xfWVZmXF+v5XzzE6yT5iHv+/8irNt1qLtyOoKvbFLpcQMxr57HLSlLtWuqyeNWdF7Ph3Jf+6XWllVv41ZXTUVz1Hq9MtRUUOntpgq6dOmC/fv3Iz093Ttt586dMBgM6NSpU6nzJSYmwuVy4ddffy00/dixY6hVq1aNeZgjEREVl3/8DP4c8iwu3DUGwuFEwpY3kbDhtRIbKyIiosrQ1DdXAwYMwJo1azBmzBiMHj0aKSkpmDlzJgYMGFDo+q6hQ4fiwoUL2L17N4B/mrJ69erh0UcfxZgxYxAXF4evvvoKW7duxdixY9VaHCIiUpEzxYrUWSuQvmY7TPXqIG7hi4jodTMUH6exEBERVYammiuLxYJVq1Zh6tSpGDNmDMLDw9GnTx+MGzeu0PuKXkAXERGBlStX4o033sCsWbOQkZGB+vXrY8KECRg0aFB1LwYREanInZmNtAWbkPb2BihBJtR66SFYht8LJThI7dKIiEhyiijPvQVrkCNHjgAAWrZsWa7zLEl7hBBwuVxlXt9B2sYc5VFVWQqnE+nrPkLqq8vhTs+EZWRvRD82GMboyLJnpgrhfikPZikH5lh1PL1BcnKyz/f5fW7EqVOnMHToUDzwwAM4duyYd/qYMWMqWKI+cMPUP0VRfN79iPSBOcoj0FkKIZC140uc6zIMfz85C6E3tkODr9eh1kv/ZmNVxbhfyoNZyoE5qs/v5uqll15Cs2bN0LRpUwwaNAi7du0CgEI3n5ARv9jTPyGE9z/SL+Yoj0Bmmfv9MVy4eyz+GjIRpoTaqP/pMsTPfwHmBnUDUCmVhfulPJilHJij+vy+5iovLw8TJ04EAHTt2hXjxo2TvjPmhikPh8Ph89kGpA/MUR6VzdJx6g9Ypy9G1gefI+jKxkjYNAuhN7WX+neSVnG/lAezlANzVJffzVXBB2t16NABixcvxr///W+fDw8jIiIKJNffqUidvRr2ldtgrBOLOnMnIrLvrVB8PHyaiIiouvjdGdWvXx9nzpzx/rtVq1aYP39+lRRFRERUkDs7F6lvrsHZ9gORsXEHYp95EA2/WY+oAT3YWBERkWb4/c1V3759ERlZ+MLgVq1aYe/evQEvioiICACEy4WMTTthm7EMrr9TYXmgF2LGD4GxVrTapRERERXj9zdXQ4YMwb59+6qyFiIiIgD/fwfAT77BHzcNx6XHZiCkfTIa7luL2tMfZWNFRESa5fc3VzXx5g6KovDiaAkoigKz2cwsdY45yqOsLPMO/wrr5PnI+fIHhHRojcSdCxFydYtqrpL8wf1SHsxSDsxRfX43V0R6xoOMHJijPErK0nH2T9heWYLMd3fDnHQZ6q6dgbBbOzJ3jWM+8mCWcmCO6ipXc1UTwxJC1MjllgmfVi4H5iiPolm6UtOR+uYa2Je+B2N0JOrMfgqRA2+HYuLf/7SO+6U8mKUcmKP6yvWb66mnnsJTTz3l13sVRcF///vfChWlFTXxVEhZud1uGHlHMd1jjvJwu91QHE5kLN+K1DdWQzhdiBk/BNEP9YchPFTt8qgcuF/Kg1nKgTmqq1zNVceOHdGoUaMqKoWIiGoC4XYja8snSJ+5As4LlxA15C7EPPkATHGxapdGRERUKeVqru655x7cddddVVULERFJLnvvd7BOmo/8I8cRdvv1SNj8OoKaNlS7LCIiooDgCe1ERFTl8o6dgHXKQuR8dgDB7VogbttbiOh4Fa8JICIiqbC58oG/9OVh4oXxUmCO+uO8cBG2V5YiY9NOmC9PRPyKaQi7/Xq1y6IA4n4pD2YpB+aoLq79MrDB0j8+r0wOzFFfXOmZSJuzDvZFm6FEhKH2K48jasjdUMz8tSMT7pfyYJZyYI7q8/u33C+//FKVdWgWb8Wuf0IIb47MUr+Yoz6IfAfsK99H6usrIXJyEf3vgYh+ZCAMkeH/ew+zlAazlAezlANzVB//hOgDb8UuD6fTCbPZrHYZVEnMUbuEEMh6/3NYpy+C8+xfiLzvdsQ+PRymhDolvp9ZyoNZyoNZyoE5qovNFRERVUrO/h9hnTwfeT/8jLBbOyJhzQwEXXG52mURERFVOzZXRERUIfm//Q7r1EXI3vkVgls3Q72tbyG0c1u1yyIiIlINmysiIioX519/I/W1FUhfux2mBvGIW/wSInp2hWIwqF0aERGRqthc+cALAeVh4Ic+KTBHdbkzs5E2bwPS5m+EEmRGrcn/huWBXlCCg8o9FrOUB7OUB7OUA3NUF5urMrDB0j9FUfjMBwkwR/UIhxPp67YjdeYKuNMzYRnVB9GPDoIxOrJC4zFLeTBLeTBLOTBH9XHtU43AW+rLgTlWLyEEsnd8CevURXCcPIeIvrch9tkRMNePD8jYzFIOzFIezFIOzFFdbK58KPisANIvIQQcDgfMZjOz1DHmWL1yvzsG66T5yD3wE0JvvAbxiychOPlfARmbWcqDWcqDWcqBOaqPzRUREXnlnzwH2/TFyPrwCwS1aIqEza8j7Kb2apdFRESkC2yuiIgIzkupSH19JdJXvQ9TfC3Evf0cIvrcAsVoVLs0IiIi3WBzRURUg7mzc2FfuBmpc9dBMRgQO3EkLCP6wBAarHZpREREusPmioioBhIuFzI27oRtxlK4rGmwDO+FmPFDYYy1qF0aERGRbrG58oEXAsrDbDarXQIFAHOsPCEEsj/5BtYpC+D45TQiet2M2ImjYG5Ur1rrYJbyYJbyYJZyYI7qYnNVBjZY+scM5cAcKy/3x19gnbwAuV/9gJCObRD38WKEXNW82utglvJglvJglnJgjupjc1UG3opd/4QQcLlcMBqNzFLHmGPFOc5cgO3lJcjc8gnMzRqh7rpXEXZLB9XWI7OUB7OUB7OUA3NUH5srH4QQapdAAeJ2u2HkXc90jzmWjys1HalvrIZ92RYYY6JQZ/bTiBzYA4pJ/UM/s5QHs5QHs5QDc1SX+r9hiYgooNy5ebAvfQ9pb66BcLoQO34oLA/1gyE8VO3SiIiIpMbmiohIEsLtRua7H8P2ylI4//obUUN6IubJYTDViVG7NCIiohqBzRURkQSy93wH66T5yD96HOF33ICEd0chqElDtcsiIiKqUdhc+cALAeXBc4/lwByLyzt6AtYpC5Dz+bcIuaYlEj+aj5D2yWqXVSZmKQ9mKQ9mKQfmqC42V2Vgg6V/iqLwQCMB5liY83wKbK8sRcbmXTA3ro/4ldMRfvv1ujhmMUt5MEt5MEs5MEf1sbkqA2/Frn9CCG+OzFK/mOM/XPYMpL21DvYl78AQGY7ar45D1KC7oJj1czhnlvJglvJglnJgjurTz29jFfBW7PJwOp18YrkEanKOIt8B+4ptSJ29CiI3D9FjBiL6kftgiAhTu7QKqclZyoZZyoNZyoE5qovNFRGRhgkhkLXtM1hfXgzn2b8Qef8diH16OEx1a6tdGhERERXB5oqIqJrl5OQgJycHAGC323Ho0CFcddVVsFgsAIDQ0FCEhoYiZ98hWCcvQN6hnxF2WyckrJ2BoGaXq1k6ERER+cDmioiomp04cQJHjx4FAGRmZuKvv/6C1WpFREQEACA5ohZqbfoM2bv2Ifiq5qi3bQ5CO12lZslERETkBzZXPvBCQHkwSznIkmPTpk2RmJgIAPjx8BH8+eefaNI0Ca0SEpEzdwPy312I/AbxiF88CeH3dJVmuQuScZlqKmYpD2YpB+aoLs01VydPnsS0adNw6NAhhIeHo2fPnnj88ccRFBRU5rwpKSmYPXs29uzZg+zsbCQmJuLhhx/G3XffXeF6uIHqn6IovLBTAjLl6DntLz0zD5/9eAnBuU6kvvUB7D/8BENIEGpNeQSWYT2hBJd93NMjmbKs6ZilPJilHJij+jTVXNntdgwdOhSNGjXC3LlzkZKSghkzZiA3Nxcvvviiz3kvXryI/v374/LLL8fUqVMRERGB48ePIz8/v5qqJyIqnz9T7Ij/7FvcdPh7mPNdcA+6B41eeBBGS6TapREREVEFaKq52rhxI7KysvD2228jOjoaAOByuTB58mSMHj0a8fHxpc772muvoW7duli6dKn34WkdOnSodE18zpX+CSHgdDphMpmYpY7JlKMQAln/+RLBkxegx+k/cPKKBPxwc3eMefJ+GCOC1S6vysmUZU3HLOXBLOXAHNVnULuAgvbu3YsOHTp4GysA6NGjB9xuN/bt21fqfJmZmdixYwfuu+++gD6Vms+5kgezlIMMOeZ+ewTnejyElGHPQUmsgz9njsGBW1ri2huawJmfBZvN5r2ToMxkyJL+wSzlwSzlwBzVpalvrk6dOoXevXsXmhYVFYU6derg1KlTpc537NgxOBwOmEwmDBo0CIcOHUJ0dDTuuecePP744zz3lIhUl3/yLGxTFyProz1wN62P3/59F9KvaIDMzAwoioKTJ35Dyl8XAAAtW7ZEcnKyyhUTERFReWmquUpPT0dUVFSx6RaLBXa7vdT5/v77bwDA888/j379+uGRRx7BTz/9hDlz5sBgMOCJJ56ocE0ldf+KopT6VwHPV7C+Xq+qeTluya8JIQpN00JNNXXcytZUNMtAjVtV9QKA61IqUl9fifTVH8AYXwtx856D8Y7rEZubC6D4c64URUFISEipxx4tL6u/4xbMUQ/16rmmqh636H7Jdaj+uBWd1zOfzMcetcetjppK2ie1XK/eayqJppqrinK73QCAjh07YsKECQCA6667DllZWVi+fDnGjBmDkJCQCo3tcDgKnbNqMBhgMpm8rxXluauhy+Xy1uXhOf/V7XbD5XIVes0zrhCixHE9376VNK7RaITRaPSeZ1tQwbvGOJ3OYhtHVY3rz7ICxddhVYzrqd9sNkNRlHIva1mZq7EOPeOWVFNVZeNZ1tLWoWdcX+swUNu3Z5/0d/v2tQ6ravt2Z+XANn8jMuZvBIwGWCY8iMgHeiE46p9nWQUFB8PtdiMyMhL169f3jmswGOByuYqtJ5mOEQV/8WvhGFFwWXmMKN86LLpfauUY4e+yqnmMALT1OcJgMHjnLfi5h58jCi+r1o8RBd/nqZefIwJzjBDCv/swaKq5ioqKQkZGRrHpdrsdFovF53zAPw1VQR06dMDChQtx5swZNGvWrNz1FPxFURJfpxsW3GiLMhgMMBhKvtytYMjlHbeseT0be3WO62tZAd/rMFDjCiEK5ViZZVUjGy2sQ4/KrsPKbt+hoaGF6iioossa6GyE04n0NR/C9uoyuFLTYRneC9GPD4YxtvAxrCYfIwr+8pJp+y5tXA8ZjxGe4yvwzzKofYyoimVVKxs1jhGexq60zz019XOEhx6OEUX3SUCudajmMcKfxgrQWHPVuHHjYtdWZWRk4NKlS2jcuHGp8zVt2tTnuHl5eRWuydfByxdfr1fVvBy35NfK+rcaNdXUcSszr68DeGXGDVS9Qghk7/4a1qkL4fjlNCLu7YbYiSNhvqyeajVpdVx/f0FppV4911TV45b2HhmXVS/jarEmjlt9NZX0Pi3XK0tNBWnqboFdunTB/v37kZ6e7p22c+dOGAwGdOrUqdT5EhMTkZSUhP379xeavn//foSEhJTZfPlSnnMsSZuEEHC5XMxS57ScY+6Pv+BCr8fw1/3PwFgrGom7lyB+0UulNlY1nZazpPJhlvJglnJgjurTVHM1YMAAhIeHY8yYMfjqq6/w3nvvYebMmRgwYEChZ1wNHToUt9xyS6F5x40bh88++wzTp0/Hvn37sHDhQixfvhzDhg1DWFhYherhhimPoucEkz5pLUfH7xeQMmoSzt8yEi5rGuqun4l6W99CSJsr1C5N87SWJVUcs5QHs5QDc1SXpk4LtFgsWLVqFaZOnYoxY8YgPDwcffr0wbhx4wq9r6QL6Lp27YrZs2dj/vz52LBhA+Li4jB27FiMGjWqOheBiGoAl82O1NmrYF++FcZa0ajz5gREDugOJYDP2SMiIiL9UQS/ninRkSNHIIRAcnJyuc6zJO3x3FnG181JSPu0kKM7Jw/2Je8i7a21EG43YsbeD8vovjCEh6pSj15pIUsKDGYpD2YpB+ZYdY4cOQIAZT6HUlPfXBERaZFwuZD57m7YXlkCZ4oVUUN7IuaJYTDViVG7NCIiItIQNlc+sOOXR2m35SR9USPH7M+/hXXyAuQfO4Hwu25EwnOjENSkQbXXIRvuk/JglvJglnJgjupic1UGNlj6pygKDzQSqO4c844ch3XKAuR8cRAh7ZOR+J8FCLmmZbX9fJlxn5QHs5QHs5QDc1Qfm6sy+Ps0ZtIuzxPLFUVhljpWXTk6/kiB7ZWlyHxnF8yN66PuqukI63E9t50A4j4pD2YpD2YpB+aoPjZXPvBeH/JwOp0+n8pN+lCVObrsGUh7ay3si9+FISoctWeOR9T9d0Ix8zBZFbhPyoNZyoNZyoE5qoufGoioRhN5+bCv2IrU2ash8vIR/ej9iP73ABgiKvZ8PCIiIqq52FwRUY0k3G5kvv8ZbNMXw3kuBVGD7kTMUw/AVLe22qURERGRTrG5IqIaJ2ffIVgnzUfej78grHtnJKyfiaCkRmqXRURERDrH5soHXggoD2Yph8rmmP/LaVinLED27q8R3LY56r0/F6Ed2wSmOCoX7pPyYJbyYJZyYI7qYnNVBm6g+qcoCi/slEBlcnT+9Tdsry5Dxvr/wNQwAfFLpyD87hu5f6uE+6Q8mKU8mKUcmKP62FwRkbTcGVlIe3sD0hZshBIaglpTx8IyrCeUIP7iISIiosBjc1UGPudK/4QQcDqdMJlMzFLHypOjcDiRvvoD2GatgMjMhmV0P0Q/ej+MURHVVC35wn1SHsxSHsxSDsxRfWyufOBzruTBLOVQVo5CCGRt3wPbtEVwnD6PyP7dEfvsCJjqxVVTheQv7pPyYJbyYJZyYI7qYnNFRFLIOfATrJMXIO/gUYR2vRbxy6ciuEVTtcsiIiKiGoTNFRHpWv6Js7BNW4Ssj/YiKPlfSHj3DYTd0E7tsoiIiKgGYnNFRLrkvGhD6qwVSF/9IUwJtRG34AVE3NsNisGgdmlERERUQ7G58oEXAsrDZOKmLgOTyQR3Vg7sizYjbe56KCYjar34EKKG94IhJFjt8qgcuE/Kg1nKg1nKgTmqi2u/DGyw9E9RFOYoA5cLmRv+A9uMZXClZcAyojdiHh8MY0yU2pVROXGflAezlAezlANzVB+bqzLwVuz6J4SA2+2GwWBgljokhED2x/thnbIAjt/OIKL3LYidOBLmhglql0YVxH1SHsxSHsxSDsxRfWyufOCtLOXhcrlg4LU4upP7w39hnbwAuft/RMj1bRH71gSEX92CvzAkwH1SHsxSHsxSDsxRXWyuiEhzHKfPw/byYmRu+wxBzRuj7obXENq1PZxOp9qlEREREZWKzRURaYbLmobU2athX7EVxtoxqPPWBET27w7FaOQ3yURERKR5bK6ISHXunDzYF7+DtLfWQgiB2KeHwzKqLwxhIWqXRkREROQ3Nlc+8LoOefDcY20SLhcyNu/65w6AF62wDLsHMU8MhbF2TInvZ47yYJbyYJbyYJZyYI7qYnNVBjZY+qcoCp/5oDFCCOR8/i2sUxYg/9hJhN99E2o9NwrmxvVLnYc5yoNZyoNZyoNZyoE5qo9rvwy8Fbv+FbxWh1mqL++n32CdsgA5e75DyLWtkLhjIULatShzPuYoD2YpD2YpD2YpB+aoPjZXPvACenk4HA6YzWa1y6jRHOf+gm3GUmS+8zHMTRqg7uqXEda9c7kO/sxRHsxSHsxSHsxSDsxRXWyuiKhKudIykPbWGtiXvAdDVARqv/YEou6/AwpPWyAiIiLJ8NMNEVUJkZcP+/ItSJ29GiLfiejHBiH64f4wRISpXRoRERFRlWBzRUQBJdxuZG79FLaXl8B5/iKiBt2JmKcegCm+ltqlEREREVUpNldEFDA5X/0A66T5yDv8K8J6dEbChpkISmqkdllERERE1YLNlQ+KovBOKxJQFAVBQUFqlyG1vJ9PwTZlAbI/+QbBV1+Jeh+8jdAOrQP6M5ijPJilPJilPJilHJij+thcEVGFOf+8BNuMZcjYuAOmhgmIXzYF4XfdyD9KEBERUY3E5qoMfM6V/gkh4HK5YDQamWWAuDOykDp3PewLN0EJC0Ht6Y8iasjdUIKq7tavzFEezFIezFIezFIOzFF9bK584HOu5OF2u2E0GtUuQ/dEvgPpqz+AbdYKiKwcWB7qj+ix98EYFVEtP585yoNZyoNZyoNZyoE5qovNFRGVSQiBrA+/gG3aYjh+P4/Igbcj9pnhMNWLU7s0IiIiIs1gc0VEPuV88xOsk+cj77tjCLv5OsSvnIbgK5uoXRYRERGR5rC5IqIS5R8/A+vUhcje8RWCWiUh4b03ENalndplEREREWkWmysfeCGgPEwmbur+cqZYkTprBdLXbIepXh3ELXwREb1uhmIwqF0ac5QIs5QHs5QHs5QDc1QX134Z2GDpH59X5h93ZjbSFmxC2tsboASZUOulhxD1QC8YQoLVLg0Ac5QJs5QHs5QHs5QDc1Qfm6sy8Fbs+ieEgNvthsFgYJYlEE4n0td9hNRXl8OdngnLyN6IfmwwjNGRapdWCHOUB7OUB7OUB7OUA3NUH5srH3grdnm4XC4YNHBam5YIIZC9ax+sUxbCcfwMIvreithnR8LcoK7apZWKOcqDWcqDWcqDWcqBOaqLzRVRDZT7/TFYJy1A7jeHEdrlasQveAHBrZupXRYRERGRrrG5IqpBHKf+gHX6YmR98DmCrmyMhE2zEHpTe546QERERBQAmvvO8OTJk3jggQfQpk0bdOrUCTNnzkR+fn65xli5ciWaNWuG0aNHV1GVRPri+jsVf098C2c7D0buwaOoM+dZ1P9sOcK6XsvGioiIiChANPXNld1ux9ChQ9GoUSPMnTsXKSkpmDFjBnJzc/Hiiy/6NcalS5cwb9481KpVq9L18EOnPGrqucfu7FzYF7+DtDnrAACxzzwIy6i+MIRq4w6A5VVTc5QRs5QHs5QHs5QDc1SXppqrjRs3IisrC2+//Taio6MB/HNR3uTJkzF69GjEx8eXOcZrr72Grl274sKFCwGpiQ2W/imKUuOe+SBcLmRs2gnbjGVw/Z0KywO9EDN+CIy1otUurcJqYo6yYpbyYJbyYJZyYI7q01Rru3fvXnTo0MHbWAFAjx494Ha7sW/fvjLn/+677/DJJ5/giSeeqMIqSY9qyp0fhRDI/vQA/ug6HJcem4GQ9slouG8tak9/VNeNlUdNybEmYJbyYJbyYJZyYI7q0lRre+rUKfTu3bvQtKioKNSpUwenTp3yOa/L5cLUqVPx0EMPIS4uLiD1eJ4VUPTbK0VRSt1wPe/19XpVzctxS35NCAGHw4GgoCDN1FQV4+b99Ctskxci58vvEXJdayTuXIjgtleWOL5a9VZmXrfbDYfDAbPZXGif1EM2gRxXizWVd9yC+2RptFSvnmuq6nE9WXr2S65D9cet6LwAkJ+fX+wYG4ia9LYOtZZNecYtaZ/Ucr16r6kkmmqu0tPTERUVVWy6xWKB3W73Oe/69euRk5ODYcOGBbQmh8NR6CBjMBi8X7c6HI5i7/d8WHC5XHC73YVeM5lM3g+JLper0GuecT07RVFms7nUcY1GI4xGI4QQcDqdhV5TFMU7r9PpLLZxVNW4/iwrUHwdVsW4nvo9B5ryLmtZmauxDj3jAkDOqXOwz1yO7C2fwvSvhqi9chqibu8CRVHgcDgClo1nWUtbh55xfa3DQG3fnn3S3+3b1zqszu0b4DEC+Gef9PxbC8eIgssq4zGiKtdh0f1SK8cIf5eVx4j/LavBYPDOW/QPWDX9c0TBZdX6MaLg+zz1qnmMKGlZ9XqMEEIU+8NDSTTVXFWU1WrFnDlz8Oqrr/r8S2hFlPQXnIKvlabgRluUwWAo9WLDgiGXd9yy5vV1Dm5VjetrWQHf6zBQ43p2FE+OlVlWNbIpbVldaRlIfWM17EvfgzE6ErVnPYnI+26H8v8Hp4qO61FSvZVdh5Xdvk0mU6n7ZEWXVa3tuyYfIwr+ctTCMcJDtmOEPzVVdlzP8bXoN1dqHSOqYllr4jGitM89NfVzhIcejhFF90lArnWo5jHCn8YK0FhzFRUVhYyMjGLT7XY7LBZLqfO99dZbaNasGdq1a4f09HQA/3SfTqcT6enpCAsLq/DFfQV/WRSdXtZ8FXmtMvNy3NJfK/pXOC3UVNF53bl5SF++FalvrIbIdyJm3GBEP9QfhogwTdYbyJoK/hfIcX3R2rharKki4xb89jGQ41b2Ndlqqo5xS9ov1a6pJo9b0Xk9f5Uv7RhbmZr0tg61lk15xy2ao9brlaGmgjTVXDVu3LjYtVUZGRm4dOkSGjduXOp8p0+fxsGDB3HNNdcUe+2aa67BkiVL0KVLl4DXS1SdhNuNzC2fwPbyEjgvXELUkLsQ8+QDMMXFql0aEREREUFjzVWXLl2wcOHCQtde7dy5EwaDAZ06dSp1vokTJ3q/sfJ4+eWXERISgvHjx6NZs2YVqqc8XSppm6+vgfUge+93sE6aj/wjxxF++/VI2Pw6gpo2VLusaqf3HOl/mKU8mKU8mKUcmKO6NNVcDRgwAGvWrMGYMWMwevRopKSkYObMmRgwYEChZ1wNHToUFy5cwO7duwEAzZs3LzZWVFQUwsLCcO2111aqJjZY+qfnDPP+exLWyQuQ89kBBLdrgXofzkPoda3ULksVes6RCmOW8mCW8mCWcmCO6tNUc2WxWLBq1SpMnToVY8aMQXh4OPr06YNx48YVel9JdyepKv7eGYS0SwgBl8sFo9GomyydFy7CNmMZMjbugLlRIuKXT0X4nTfopv6qoMccqWTMUh7MUh7MUg7MUX2KKM+N22uQI0eOQAiB5ORkbpw6V/SZD1rmSs9E2px1sC/aDCUiDLFPPoCoIXdDMWvq7yCq0FOO5BuzlAezlAezlANzrDpHjhwBACQnJ/t8Hz+xEWmAyHfAvvJ9pL6+EiInF9H/HojoRwbCEBmudmlERERE5Cc2V0QqEkIg64MvYJ22CM6zfyJyYA/EPvMgTAl11C6NiIiIiMqJzRWRSnK+PgzrpHnI++FnhN3SAXVXv4zg5qU/coCIiIiItI3NlQ88V1UepT2tWw35v/0O69RFyN75FYJbN0O9rW8htHNbtcvSBS3lSJXDLOXBLOXBLOXAHNXF5qoMbLD0T1EUTRxonClWpM5cjvS122GqH4+4RS8h4p6uUAwGtUvTBa3kSJXHLOXBLOXBLOXAHNXH5qoMvBW7/gkhvDmqkaU7Mxtp8zcibd5GKEEm1Jr8b1ge6AUlOKjaa9EztXOkwGGW8mCW8mCWcmCO6mNz5QPvUi8Pp9NZ7U8sFw4n0tdtR+rMFXCnZ8Iyqg+iHx0EY3RktdYhEzVypKrBLOXBLOXBLOXAHNXF5ooowIQQyN7xJaxTF8Fx8hwi+t6G2GdHwFw/Xu3SiIiIiKgKsbkiCqDc747BOmk+cg/8hNAb2iF+0UsIbpWkdllEREREVA3YXBEFQP7Jc7BNX4ysD79AUIsmSNj8OsJuaq92WURERERUjdhc+cALAeVRVVm6/k6FbdZKpK96H8a4WqgzdyIi+94KhXfqqRLcJ+XBLOXBLOXBLOXAHNXF5qoM3ED1T1GUgF/Y6c7OhX3RZqTOWQdFURD77EhYRvaBITQ4oD+H/qcqciR1MEt5MEt5MEs5MEf1sbkiKgfhciFj407YZiyFy5oGy/BeiBk3BMZa0WqXRkREREQqY3NVBj7nSv+EEHA6nTCZTBXOUgiB7E++gW3qQuT/fAoRvW5G7MRRMDeqF+BqqTSByJG0gVnKg1nKg1nKgTmqj82VD3zOlTwqk2Xe4V/x96T5yP3qB4R0bIPEjxcj5KrmAayO/MV9Uh7MUh7MUh7MUg7MUV1srohK4ThzAbZXliLzvd0wJ12GuutmIOyWjvxLEBERERGViM0VURGu1HSkvrEa9mVbYIyJQp3ZTyNyYA8oJu4uRERERFQ6flok+n/u3DzYl76HtDfXQDhdiB0/FJaH+sEQHqp2aURERESkA2yufODpX/LwdVtS4XYj873dsL28BM4//0bUkLsR89QDMNWJqcYKyR+8vaw8mKU8mKU8mKUcmKO62FyVgQ2W/vnKMHvPd7BOmo/8o8cRfscNSHhnFIKaNqzG6shf3BflwSzlwSzlwSzlwBzVx+aqDLwVu/4JIeByuWA0Gr1Z5h09AeuUBcj5/FsEX9MSiR/NR0j7ZJUrJV9KypH0iVnKg1nKg1nKgTmqj82VD7yVpTzcbjeMRiOc51Nge2UpMjbvgvnyRMSvmIbwO7rwAKQTnhxJ/5ilPJilPJilHJijuthcUY3gTs+EbcFm2Be/AyUiDLVnjEPU4LugmLkLEBEREVFg8JMlSU3kO2BfsRWpr6+CyMtH9JiBiB4zEIbIcLVLIyIiIiLJsLkiKQkhkLXtM1hfXgzn2b8QPrAHaj3zIMwJddQujYiIiIgkxebKB16Ho085+w7BOnkB8g79jLBbO6Lu6ldgSroMBoNB7dKokngOuTyYpTyYpTyYpRyYo7rYXJWBDZZ+5P96Gtapi5C9ax+C21yBetvmILTTVWqXRQGiKAp/YUiCWcqDWcqDWcqBOaqPzVUZeCt27XP+9TdsM5cjY91HMDWIR/ziSQjveROU//+mSgjhzZFZ6hdzlAezlAezlAezlANzVB+bKx94K3Ztc2dmI+3t9UhbsAlKcBBqTR4DywP3QAkOKvZep9PJJ5ZLgDnKg1nKg1nKg1nKgTmqi80V6Y5wOJG+9kOkvrYC7vQsWEb1RfRj98NoiVS7NCIiIiKqwdhckW4IIZD1ny9hm7oQjlN/ILLfbYiZMALm+vFql0ZERERExOaK9CH32yOwTpqP3INHEXpTe8QvnYLglk3VLouIiIiIyIvNlQ+8EFB9+SfPwjZtMbK270FQi6ZIeGc2wm68ptzjMEs5MEd5MEt5MEt5MEs5MEd1sbkqAzdQdTgvpSJ11kqkr34fpvhaiJv3HCL63Oq9A2B5KIrCCzslwBzlwSzlwSzlwSzlwBzVx+aKNMWdlQP7ws1InbsOitGI2IkjYRnZB4aQYLVLIyIiIiLyic1VGficq+ohnE5kbNgB26vL4LLZYXnwXsSMGwJjrKXyYwsBp9MJk8nELHWMOcqDWcqDWcqDWcqBOaqPzZUPfM5V1RNCIHv317BOXQjHL6cRcW83xE4cCfNl9QL+c0j/mKM8mKU8mKU8mKUcmKO62FyRanJ//OWfOwDuO4SQTlchbvcShLS5Qu2yiIiIiIgqhM0VVTvHmQuwTV+MzK2fwnzF5ai7fibCul3Hr6+JiIiISNfYXFG1cdnsSH1jNezLtsAYa0GdN55B5IDuUEzcDImIiIhI//ip1gd+kxIY7pw82Je+i7Q310K43Yh98gFYRveFITy02mowsYGTAnOUB7OUB7OUB7OUA3NUF9d+GdhgVZxwu5H5zsewvbIEzhQroob2RMwTw2CqE1OtdSiKwhwlwBzlwSzlwSzlwSzlwBzVx+aqDLwVe8Vkf3EQ1knzkX/sBMLvvAEJz49GUJMGqtQihIDb7YbBYGCWOsYc5cEs5cEs5cEs5cAc1ae55urkyZOYNm0aDh06hPDwcPTs2ROPP/44goKCSp3n4sWLWLlyJfbt24ezZ88iMjIS11xzDcaPH4/ExMQK18JbWZZf3tETsE6ej5wvDiKkfTIS/7MAIde0VLssuFwuGAwGtcugSmKO8mCW8mCW8mCWcmCO6tJUc2W32zF06FA0atQIc+fORUpKCmbMmIHc3Fy8+OKLpc537Ngx7N69G71790br1q2RmpqKBQsWoG/fvti+fTtiY2OrcSlqJscfKbC9shSZ7+yCuXF91F01HWE9rudfTYiIiIioxtBUc7Vx40ZkZWXh7bffRnR0NIB/uu/Jkydj9OjRiI+PL3G+q6++Gjt27Ch0AV/btm1x4403Ytu2bRg+fHh1lF8juewZSHtrLeyL34UhKhy1Xx2HqEF3QTFratMiIiIiIqpymvrOcO/evejQoYO3sQKAHj16wO12Y9++faXOFxUVVezOKHXr1kVsbCwuXrxYVeXWaCIvH2kLN+PsNQNgX7YF0WPvQ8NvN8LyQC82VkRERERUI2nqU/CpU6fQu3fvQtOioqJQp04dnDp1qlxjnT59GlarFU2aNKlwPTylrTjhdiPz/c9gm74YznMpiBp0J2KeegCmurXVLs0nnnssB+YoD2YpD2YpD2YpB+aoLk01V+np6YiKiio23WKxwG63+z2OEALTpk1DXFwc7rjjjkrXVfTGFoqilHqzC09D5uv1qpq3qsfN2XcItskLkPfjLwjr3gkJ615FULPLIYQocX616y3IaDRKnY1exq3svEajsdgYWl1WLa5DLY3r2Sf1Uq9ea6qOcQvul1yH6o9bmXlLOsYGYly9rUMtZlOecYvuk1qvV881lURTzVWgzJ07F9988w2WLl2KsLCwSo2Vn59f6Bssg8HgPQXR4XAUe7/nroYulwtut7vQayaTCYqiwO12w+VyFXrNM64QosRxzWZzqeMajUYYjUYIIeB0Ogu9piiKd16n01ls4/Bn3LyfT8E6dSFyP/kGQW2aoc67sxHasY3Pcf1ZVqD4Oiyr3oqM6xnDbDbDYDCUex2WlXlVZeNrWT3jllRTVazDgsuqKIrP7dvXOqzM9u12u73jevZJf7dvX+sw0NnUxGNEeccVQsBkMsFgMGjiGFFwWUvbvnmMKHlZi/6hQ81jRGW2bx4jjN790el0Fvrco8Yxwt9lBXiMKLqsBZfXUy8/RwTmGFHwD0i+aKq5ioqKQkZGRrHpdrsdFovFrzE2b96MefPmYfr06ejQoUOl6hFCeDeCknhWeEkKbrRFGQyGUr+yLRhyeccta15fT+wuaVznX3/DOnM5Mtb/B6YGdRG3ZBLC776p2PrwNa6vZQV8r8NAjevZEQt++1HRdahGNlpYhx6VXYeV3b49dZW0T1Z0WdXKRoZjREXH9eyTQUFBUm3fpY3rIeMxwpOlZ79U+xhRFctak44RTqfT5+ee6jpGFMRjhH/jepa16D4JyLUO1TxG+NNYARprrho3blzs2qqMjAxcunQJjRs3LnP+3bt3Y9KkSXj00UfRp0+fgNRU8JdF0ellzVeR1yozb6DGdWdkIe3tDUhbsBFKaAhqTR0Ly7CeUIJK39DVrNef14r+FU4LNdXEcStbU8H/AjmuL1obV4s1VWTcgt8+BnLcyr4mW03VMW5J+6XaNdXkcSs6r+ev8qUdYytTk97WodayKe+4RXPUer0y1FSQppqrLl26YOHChYWuvdq5cycMBgM6derkc94DBw5g/Pjx6Nu3L8aMGVMd5UpHOJxIX/0BbLNWQGRmwzKqL6IfvR9GS6TapRERERERaZ6mmqsBAwZgzZo1GDNmDEaPHo2UlBTMnDkTAwYMKPSMq6FDh+LChQvYvXs3AODkyZMYM2YMGjVqhJ49e+LHH3/0vjc2NhYNGzas7kXRFSEEsj7aC9vUhXCcPo/I/t0RO+FBmBJLfq4YEREREREVp6nmymKxYNWqVZg6dSrGjBmD8PBw9OnTB+PGjSv0vqIX0B0+fBgZGRnIyMjAwIEDC723V69emDFjRoXqKc9XgHqVc+AnWCcvQN7Bowjtei3il09FcIumapcVcDUhy5qAOcqDWcqDWcqDWcqBOapLEeW5t2ANcuTIEQBAcnKyypVUjfwTZ2GbtghZH+1FUPK/UOulfyPshnZql0VEREREpDn+9gaa+uaKqp7zog2ps1YiffUHMCXURtz85xHR+xYofOAcEREREVGlsLkqg7/3tNc6d1YO0hZuQtrc9VBMRtR6YTSiHrwXhpBgtUurcp7nGnien0D6xBzlwSzlwSzlwSzlwBzVx+bKBxnOmBROJzI2/Ae2GcvgSsuA5cF7ETNuCIwxUWqXVq1kyJKYo0yYpTyYpTyYpRyYo7rYXElKCIHsj/fDOmUBHL+dQUTvWxD77AiYL6undmlERERERFJicyWh3EM/wzppPnL3/4iQzm0RP/8FBLdupnZZRERERERSY3MlEcfp87C9vBiZ2z5DUPPGqLvhNYTdfC3PuSUiIiIiqgZsrnzQS1PistmR+voq2FdshbFWNOq8NQGR/btDMRrVLk0zTCZu6jJgjvJglvJglvJglnJgjuri2i+Dlhssd04e7EveRdpbayHcbsQ+9QAso/vBEBaidmmaoiiKpnMk/zBHeTBLeTBLeTBLOTBH9bG5KoMWb8UuXC5kvPMxbK8sheuiFZZh9yDmiaEw1o5RuzRNEkLA7XbDYDBoLkvyH3OUB7OUB7OUB7OUA3NUH5srH7R4K8vszw7AOmUB8o+dRPjdN6HWc6Ngblxf7bI0z+VywcAHJesec5QHs5QHs5QHs5QDc1QXmyudyPvpN1inLkTOFwcRcm0rJO5YiJB2LdQui4iIiIiI/h+bK41znPsLthlLkfnOxzA3aYC6q19GWPfO/KqXiIiIiEhj2FxplCstA2lvrYF9yXswREWg9mtPIOr+O6DwDjBERERERJrET+o+qPHtkMjLh335FqTOXg2R70T0o/cj+t8DYIgIq/ZaZMJzj+XAHOXBLOXBLOXBLOXAHNXF5qoM1dVgCbcbmds+g236YjjPX0TUoDsR8+QwmOrWrpafLzNFUfjMBwkwR3kwS3kwS3kwSzkwR/Vx7ZehOm7FnvPVD7BOmo+8w78irEdnJGyYiaCkRlX6M2uSgnd95LVq+sUc5cEs5cEs5cEs5cAc1cfmyoeqvhV73s+nYJuyANmffIPgq69EvQ/eRmiH1lX6M2sqh8MBs9msdhlUScxRHsxSHsxSHsxSDsxRXWyuVOD88xJsry5DxoYdMDVMQPzSKQi/+0b+hYGIiIiISMfYXFUjd0YWUueuh33hJihhIag9bSyihvaEEsS/LhARERER6R2bq2og8h1IX/0BbLNWQGTlwPJQf0SPvQ/GqAi1SyMiIiIiogBhc1WFhBDI+vAL2KYthuP384gc0AOxEx6EqV6c2qUREREREVGAsbnyQVGUCl8HlfPNT7BOno+8744h7ObrEL9yGoKvbBLgCskfiqLAbDbzmjadY47yYJbyYJbyYJZyYI7qY3MVYPnHz8A2bRGy/vMlglolIeG9NxDWpZ3aZdV4PMjIgTnKg1nKg1nKg1nKgTmqi81VGfx9zpUzxYrUWSuQvmY7TPXqIG7hi4jodTMUPiVbdUIIuFwuGI1GHnB0jDnKg1nKg1nKg1nKgTmqj82VD/4858qdmY20hZuQNncDFLMRtV58CFHDe8EQElwNFZK/3G43jEaj2mVQJTFHeTBLeTBLeTBLOTBHdbG5qiDhdCJj/X9ge3UZXGkZsIzsjZjHBsMYE6V2aUREREREpAI2V+UkhED2rn2wTlkIx/EziOhzC2KfHQlzwwS1SyMiIiIiIhWxuSqH3O+PwTppAXK/OYzQ69sifsELCG7dTO2yiIiIiIhIA9hc+eC5ENBx+jys0xYh64PPEXRlYyRsnIXQru15oaCO8NxjOTBHeTBLeTBLeTBLOTBHdbG58sWeCetzc2BfuQ3G2jGoM+dZRPa7DQo3Wl1RFIUHGgkwR3kwS3kwS3kwSzkwR/WxufIhdNCLSDcYEfvMg7CM7ANDWIjaJVEFCCG8t9Tnt436xRzlwSzlwSzlwSzlwBzVx+bKB8cdnfGvSY/CVCdG7VKokpxOJ8xms9plUCUxR3kwS3kwS3kwSzkwR3XxCbc+OEb3grF2tNplEBERERGRDrC5IiIiIiIiCgA2V0RERERERAHA5soHXggoD4OBm7oMmKM8mKU8mKU8mKUcmKO6eEOLMrDB0j9FUWAycVPXO+YoD2YpD2YpD2YpB+aoPra2REREREREAcDmygfPswJI34QQyM/PZ5Y6xxzlwSzlwSzlwSzlwBzVx+aKiIiIiIgoANhcERERERERBQCbKyIiIiIiogBgc0VERERERBQAbK584G3Y5WE2m9UugQKAOcqDWcqDWcqDWcqBOapLc83VyZMn8cADD6BNmzbo1KkTZs6cifz8/DLnE0Jg8eLFuPHGG9GqVSv0798fP/74Y6XrYYOlf4qieP8j/WKO8mCW8mCW8mCWcmCO6tNUc2W32zF06FA4HA7MnTsX48aNw+bNmzFjxowy512yZAnmzJmDYcOGYdGiRahTpw6GDx+Oc+fOVaom3spS/4QQcDqdzFLnmKM8mKU8mKU8mKUcmKP6NPUI540bNyIrKwtvv/02oqOjAQAulwuTJ0/G6NGjER8fX+J8eXl5WLRoEYYPH45hw4YBAK6++mp0794dy5Ytw6RJkypUDzdMebjdbhiNRrXLoEpijvJglvJglvJglnJgjurS1DdXe/fuRYcOHbyNFQD06NEDbrcb+/btK3W+H374AZmZmejRo4d3WlBQEG655Rbs3bu3KksmIiIiIiICoLHm6tSpU2jcuHGhaVFRUahTpw5OnTrlcz4AxeZt0qQJLly4gNzc3MAXS0REREREVICmTgtMT09HVFRUsekWiwV2u93nfEFBQQgODi40PSoqCkII2O12hISElKsWh8MBIQSOHj1arvlIm4QQvLhTAsxRHsxSHsxSHsxSDsyxauTn5/u1XjXVXGkJN0q5ME85MEd5MEt5MEt5MEs5MMeq4e9dGDXVXEVFRSEjI6PYdLvdDovF4nO+/Px85OXlFfr2Kj09HYqi+Jy3NFdddVW55yEiIiIioppLU9dcNW7cuNi1VRkZGbh06VKx66mKzgcAp0+fLjT91KlTqFevXrlPCSQiIiIiIiovTTVXXbp0wf79+5Genu6dtnPnThgMBnTq1KnU+dq2bYuIiAjs2LHDO83hcODjjz9Gly5dqrRmIiIiIiIiQGOnBQ4YMABr1qzBmDFjMHr0aKSkpGDmzJkYMGBAoWdcDR06FBcuXMDu3bsBAMHBwRg9ejTmzp2L2NhYJCUlYcOGDUhLS8ODDz6o1uIQEREREVENoqnmymKxYNWqVZg6dSrGjBmD8PBw9OnTB+PGjSv0PrfbDZfLVWjayJEjIYTA8uXLYbPZ0Lx5cyxbtgwNGjSozkUgIiIiIqIaShFCCLWLICIiIiIi0jtNXXNFRERERESkV2yuiIiIiIiIAoDNFRERERERUQCwuSIiIiIiIgoANldEREREREQBwOaKiIiIiIgoANhcERERERERBQCbqyJOnjyJBx54AG3atEGnTp0wc+ZM5Ofnq10W+bBjxw48/PDD6NKlC9q0aYOePXvi3XffRdFHuL3zzju47bbbkJycjLvvvhuff/65ShWTv7KystClSxc0a9YMR44cKfQa89SHrVu34p577kFycjKuvfZajBgxArm5ud7XP/vsM9x9991ITk7Gbbfdhvfee0/Faqkkn376Kfr27YurrroKnTt3xmOPPYZz584Vex/3SW05c+YMXnzxRfTs2RNXXnkl7rzzzhLf509uGRkZmDhxItq3b4+rrroKjz76KC5evFjVi0D/r6wsMzMzMXfuXPTp0wft2rVDx44d8dBDD+HXX38tNhazrHpsrgqw2+0YOnQoHA4H5s6di3HjxmHz5s2YMWOG2qWRDytXrkRoaCgmTJiABQsWoEuXLnjhhRcwb94873s++ugjvPDCC+jRoweWLFmCNm3a4JFHHsGPP/6oXuFUpvnz58PlchWbzjz1YcGCBZg6dSpuv/12LFu2DFOmTEH9+vW9mX733Xd45JFH0KZNGyxZsgQ9evTAc889h507d6pcOXkcOHAAjzzyCJo2bYp58+Zh4sSJ+OWXXzB8+PBCTTL3Se05fvw49uzZg8suuwxNmjQp8T3+5vb4449j3759mDRpEmbNmoXTp09j5MiRcDqd1bAkVFaWFy5cwKZNm9CpUye8+eabmDp1KjIyMtC/f3+cPHmy0HuZZTUQ5LVw4ULRpk0bkZqa6p22ceNG0bx5c/HXX3+pVxj5ZLVai017/vnnRdu2bYXL5RJCCHHrrbeK8ePHF3pP//79xYgRI6qlRiq/EydOiDZt2ogNGzaIpKQk8dNPP3lfY57ad/LkSXHllVeKL774otT3DB8+XPTv37/QtPHjx4sePXpUdXnkpxdeeEF07dpVuN1u77Svv/5aJCUliYMHD3qncZ/UHs/vPyGEeOaZZ8Qdd9xR7D3+5PbDDz+IpKQk8eWXX3qnnTx5UjRr1kx89NFHVVA5FVVWlllZWSI7O7vQtMzMTNG+fXsxZcoU7zRmWT34zVUBe/fuRYcOHRAdHe2d1qNHD7jdbuzbt0+9wsin2NjYYtOaN2+OzMxMZGdn49y5c/j999/Ro0ePQu+5/fbb8fXXX/O0T42aNm0aBgwYgMsvv7zQdOapD1u2bEH9+vVxww03lPh6fn4+Dhw4gO7duxeafvvtt+PkyZP4448/qqNMKoPT6UR4eDgURfFOi4yMBADvqdfcJ7XJYPD9Ec/f3Pbu3YuoqCh06tTJ+57GjRujefPm2Lt3b+ALp2LKyjIsLAyhoaGFpoWHh6Nhw4aFTvljltWDzVUBp06dQuPGjQtNi4qKQp06dXDq1CmVqqKK+P777xEfH4+IiAhvdkU/pDdp0gQOh6PEawdIXTt37sRvv/2GMWPGFHuNeerD4cOHkZSUhPnz56NDhw5o2bIlBgwYgMOHDwMAzp49i/9r7/5joq7/OIA/QTmcwHHimgTCJFkQJ2gExw8dBGhBSqQ5wyawsKA0RJoZ/ghFWJhKRIX4g7kQtaZmGsRwxlBXGoXFAs8tfoYgNAOOuwvBw7vvH46b50Ggncfdt+djuz/u/X5/3p8X99rnw73u80ulUuntc4dPeeE+1zQsW7YMTU1NOHr0KBQKBa5fv46PPvoIXl5e8PX1BcBt0lyNN2/Nzc1wc3PTKbCBu1/KuZ2aLrlcjoaGBp19LHNpHCyu7iGXyyEUCvXa7e3t0dfXNwER0cOoqalBeXk5EhMTAUCbu/tzO/yeuTUtt27dws6dO5GWlgZbW1u9fubTPNy8eRPff/89zpw5g23btqGgoAAWFhZITExEd3c382gm/Pz88NlnnyE3Nxd+fn5YuHAhuru7cfDgQUyaNAkAt0lzNd68yeVy7dHKe/G7kWnbvXs3LCwssHLlSm0bc2kcLK7o/0pXVxfS0tIQEBCA+Pj4iQ6HHkJhYSGmT5+Ol19+eaJDoX9Bo9Ggv78f+fn5iIyMRGhoKAoLC6HRaHDkyJGJDo/G6ZdffsHGjRuxYsUKFBcXIz8/H2q1GklJSTo3tCAi0/HVV1/h+PHjyMjIgKOj40SH85/D4uoeQqEQCoVCr72vrw/29vYTEBE9CLlcjjfeeAMikQiffvqp9hzl4dzdn1u5XK7TTxOvo6MDhw4dwrp166BQKCCXy9Hf3w8A6O/vx99//818mgmhUAiRSARPT09tm0gkgpeXFxobG5lHM5GdnY3AwECkp6cjMDAQkZGROHDgAKRSKc6cOQOA+1hzNd68CYVCKJVKveX53cg0XbhwARkZGVizZg2WLl2q08dcGgeLq3uMdM6pQqHAzZs39a4LINMyMDCA5ORkKBQKFBUV6Rz2Hs7d/bltbm6GlZUVXFxcjBorja69vR0qlQpJSUnw9/eHv78/3nzzTQBAfHw8XnvtNebTTLi7u4/aNzg4CFdXV1hZWY2YRwDc55qIpqYmnQIZABwdHTFt2jS0tbUB4D7WXI03b0888QRaWlr0nh3Z0tLC7dTE1NbWIjU1FS+99BJSU1P1+plL42BxdY+QkBBcunRJ+6sNcPfCektLS507q5BpGRoawvr169Hc3IyioiLMmDFDp9/FxQWzZs3Se3ZOeXk5goKCIBAIjBku/YOnnnoKhw8f1nlt2rQJAJCZmYlt27Yxn2YiLCwMMpkM165d07b19vbi6tWrEIvFEAgECAgIwNmzZ3WWKy8vx+zZszFz5kxjh0wjcHJyglQq1Wnr6OhAb28vnJ2dAXAfa67Gm7eQkBD09fXh8uXL2jEtLS2QSqUICQkxasw0usbGRiQnJyMwMBCZmZkjjmEujWPyRAdgSmJjY1FSUoK1a9ciOTkZf/75J3bt2oXY2Fi9L+xkOjIzM1FVVYX09HQolUqdhx96eXlBIBAgJSUFGzZsgKurKwICAlBeXo7ffvuN136YGKFQiICAgBH7xGIxxGIxADCfZmDhwoXw9vbGunXrkJaWBmtraxw4cAACgQCvvvoqAOCtt95CfHw8tm/fjqioKFRXV6OsrAx5eXkTHD0Ni42NxQcffIDs7GyEh4dDJpNpr4u89xbe3CZNz61bt3DhwgUAdwtipVKpLaQkEgkcHBzGlbenn34aCxYswObNm/Hee+/B2toaeXl58PDwwHPPPTchf9t/zVi51Gg0WL16NaytrZGQkID6+nrtsra2ttozCZhL47DQ3H9s8D+uqakJWVlZ+PXXX2FjY4OYmBikpaXxlzcTFh4ejo6OjhH7Kisrtb+AnzhxAgcPHsSNGzfg5uaGd955B2FhYcYMlR5CdXU14uPjcfLkSXh7e2vbmU/T19PTg5ycHFRVVUGlUsHPzw+bNm3SOWWwsrISH3/8MVpaWuDk5ISkpCQsX758AqOme2k0Gnz55Zf44osvcP36ddjY2GDevHlIS0vT3jZ/GLdJ09Le3o6IiIgR+w4fPqz9IWs8eVMoFMjJycG5c+cwNDSEBQsWYOvWrfzh2UjGyiWAUW/iJZFIUFJSon3PXD56LK6IiIiIiIgMgNdcERERERERGQCLKyIiIiIiIgNgcUVERERERGQALK6IiIiIiIgMgMUVERERERGRAbC4IiIiIiIiMgAWV0RERERERAbA4oqIiMiAOjs74e3tjStXrjzwso2NjfDy8sLvv//+CCIjIqJHjcUVERGZhVOnTsHDwwMeHh6oqanR69doNAgNDYWHhweSk5MnIMK7CgoKMHfuXDzzzDMPvKy7uztCQ0PxySefPILIiIjoUWNxRUREZsXa2hplZWV67T/99BO6urogEAgmIKq7enp6cPr0acTGxj70HLGxsTh37hza2toMGBkRERkDiysiIjIroaGhqKiowNDQkE57WVkZxGIxHnvssQmKDPjmm28wadIkhIWFPfQcwcHBsLe3x9dff23AyIiIyBhYXBERkVlZvHgxZDIZfvjhB23b7du3cfbsWURHR4+4jFqtxueff47FixfD29sbwcHByMjIQF9fn844jUaDvXv3IiQkBHPnzkVcXBwaGhoQHh6O9PT0MWP77rvv4OPjAxsbG72+o0ePIiIiAj4+Pli+fDlqamoQFxeHuLg4nXFWVlaQSCSorKwcz8dBREQmhMUVERGZFWdnZ8ybNw/ffvuttu3ixYtQKBR44YUXRlwmIyMDu3fvhq+vL7Zs2YJly5ahtLQUq1evhkql0o7Lz89Hfn4+PD09sXHjRri4uCAxMRH9/f1jxqVSqVBXVwexWKzXd+zYMezYsQOOjo5499134efnh7Vr16Krq2vEucRiMRoaGqBUKsdcLxERmY7JEx0AERHRg4qOjkZubi4GBgYwZcoUlJaWwt/fHzNmzNAbW1NTgxMnTmDPnj06R7YCAgLw+uuvo6KiAtHR0ejp6UFRURGeffZZ7Nu3DxYWFgCAvLw87Nu3b8yYOjs7MTAwgJkzZ+q03759G/n5+fD29kZxcTEmT777r9fDwwPp6elwdHTUm8vFxQVqtRrNzc3w8fF5oM+GiIgmDo9cERGR2YmKisLg4CCqqqqgVCpx/vz5UU8JrKiogJ2dHebPn4+enh7tSywWY+rUqaiurgYAXLp0CSqVCqtWrdIWVgCQkJAwrphkMhkAQCgU6rTX19dDJpNhxYoV2sIKuFsg2tvbjzjX8By9vb3jWjcREZkGHrkiIiKz4+DggKCgIJSVlWFgYAB37tzB888/P+LYP/74AwqFAkFBQSP2d3d3AwBu3LgBAJg1a5beukYrgkai0Wh03g/P6+rqqtM+efJkODs7j2sOIiIyDyyuiIjILC1ZsgTvv/8+/vrrL4SEhOgdMRqmVqsxffp07NmzZ8R+BwcHg8QjEokAAHK5/F/PNTzHtGnT/vVcRERkPDwtkIiIzNKiRYtgaWmJ2tpaLFmyZNRxrq6ukMlk8PX1RXBwsN7L09MTAODk5AQAaG1t1Vm+p6dH766CI3n88ccxZcoUtLe367QPz3v/c6uGhobQ0dEx4lzt7e2wtLSEm5vbmOslIiLTweKKiIjMko2NDbZv346UlBSEh4ePOi4qKgp37tzB3r179fqGhoa0R4mCg4NhZWWFI0eO6JyWV1xcPK54rKysMGfOHNTX1+u0z5kzByKRCMePH9d5NldpaemoRdvVq1fh7u4OOzu7ca2biIhMA08LJCIis7V06dIxx0gkErzyyivYv38/rl27hvnz58PKygqtra2oqKjAli1bEBkZCQcHByQmJmL//v1ITk5GaGgopFIpLl68OO7T8yIiIpCXlwelUglbW1sAgEAgQEpKCrKyspCQkICoqCh0dHTg1KlTetdhAXdv6f7zzz9j5cqVD/ZhEBHRhOORKyIi+r+3Y8cOZGVlobu7G3l5ecjNzcWPP/6IF198Eb6+vtpx69evR0pKCqRSKXbt2oW2tjYcOnQIU6dOHdd6YmJioFar9R4AvGrVKmzduhWdnZ348MMPUVNTg8LCQtjZ2cHa2lpn7OXLlyGTycZVOBIRkWmx0PCWRERERP8oPDwcEokEO3fuHHPs5s2b0draimPHjv3jOLVajaCgICxatAjZ2dna9jVr1sDCwgIFBQX/Om4iIjIuHrkiIiIyoLfffht1dXW4cuWKtm1wcFDv9uqnT5+GTCaDRCLRtjU1NeH8+fNITU01WrxERGQ4vOaKiIjIgJycnFBXV6fTVltbi5ycHERGRkIkEkEqleLkyZN48sknERkZqR03e/ZsSKVSY4dMREQGwuKKiIjoEXN2doajoyNKSkrQ19cHe3t7xMTEYMOGDRAIBBMdHhERGQivuSIiIiIiIjIAXnNFRERERERkACyuiIiIiIiIDIDFFRERERERkQGwuCIiIiIiIjIAFldEREREREQGwOKKiIiIiIjIAFhcERERERERGQCLKyIiIiIiIgNgcUVERERERGQA/wMTv7ldeOLZqgAAAABJRU5ErkJggg==",
"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": 43,
"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": 44,
"id": "8d6cd1df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B (Carpi, t):\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.08631 ± 0.02172\n",
"cov_ABtC = -0.000006\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 0.45743 ± 0.11512\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": 45,
"id": "592c257c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"\n",
"sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
"\n",
"plt.errorbar(\n",
" datat[\"x\"], datat[\"y\"],\n",
" xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
" fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
")\n",
"\n",
"xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
"yfit = BtC + AtC * xfit\n",
"\n",
"plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit Carpi (t)\")\n",
"plt.xlim(left=0)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel(\"Meq (g)\")\n",
"plt.ylabel(\"T² (s²)\")\n",
"plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
"plt.legend()\n",
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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": 47,
"id": "d04ee20e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AtY = 0.0107837 ± 0.0002969\n",
"BtY = 0.0863052 ± 0.0217195\n",
"cov_ABtY = -6.265968158785223e-06\n",
"chi² = 0.53371\n",
"chi² rid = 0.17790\n",
"P(0, chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 0.45743 ± 0.11512\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": 48,
"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": 49,
"id": "46ef07bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.30352 ± 0.01937\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Atols = 0.01104 ± 0.00022\n",
"Btols = 0.06749 ± 0.02007\n",
"P(0,chi²)= 0.27020\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 0.58496 ± 0.17399\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.08631 ± 0.02172\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 0.45743 ± 0.11512\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AtY = 0.01078 ± 0.00030\n",
"BtY = 0.08631 ± 0.02172\n",
"P(0,chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 0.45743 ± 0.11512\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": 50,
"id": "dfcd391e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res_t = 0.000500\n",
"res_t2 = 0.024515\n",
"res_uAdt = 0.000498\n",
"uKdt_strum = 0.148242\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": 51,
"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": 52,
"id": "0b1e4a1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.30352 ± 0.14950\n",
"\n",
"RISULTATI OLS:\n",
"Atols = 0.01104 ± 0.00054\n",
"Btols = 0.06749 ± 0.02008\n",
"Kdtols = 3.57541 ± 0.16381\n",
"Chi² = 0.42434 | rid = 0.14145 | P = 0.06483\n",
"\n",
"RISULTATI Carpi:\n",
"AtC = 0.01078 ± 0.00058\n",
"BtC = 0.08631 ± 0.02173\n",
"KdtC = 3.66092 ± 0.17925\n",
"Chi² = 0.34851 | rid = 0.11617 | P = 0.04934\n",
"\n",
"RISULTATI York:\n",
"AtY = 0.01078 ± 0.00058\n",
"BtY = 0.08631 ± 0.02173\n",
"KdtY = 3.66092 ± 0.17925\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}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink