From 841930f0a8d4ae54a20a9c14c2840be1dbd1a465 Mon Sep 17 00:00:00 2001
From: Basso Ricci Jacopo <bassoriccijacopo@gmail.com>
Date: Fri, 10 Apr 2026 22:24:11 +0200
Subject: [PATCH] =?UTF-8?q?Ora=20la=20molla=201=20=C3=A8=20analizzata=20in?=
 =?UTF-8?q?=20modo=20veramente=20chiaro?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 mollaDefinitiva/molla-1/molla1.ipynb    | 1164 ++++++++++++++++++++++-
 mollaDefinitiva/molla-1/molla1Sonar.csv |    5 +
 2 files changed, 1150 insertions(+), 19 deletions(-)
 create mode 100644 mollaDefinitiva/molla-1/molla1Sonar.csv

diff --git a/mollaDefinitiva/molla-1/molla1.ipynb b/mollaDefinitiva/molla-1/molla1.ipynb
index 947604f..04129bd 100644
--- a/mollaDefinitiva/molla-1/molla1.ipynb
+++ b/mollaDefinitiva/molla-1/molla1.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 59,
    "id": "3a34cc3f",
    "metadata": {},
    "outputs": [],
@@ -35,12 +35,20 @@
     "uG = 0.004"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0eaece9c",
+   "metadata": {},
+   "source": [
+    "# Analisi dati del calibro"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "960e68a5",
    "metadata": {},
    "source": [
-    "## Import dei dati (criterio di Chauvenet)\n",
+    "## Import dei dati calibro (criterio di Chauvenet)\n",
     "- Primo test di implementazione 1\n",
     "- Scrittura con logica chiara   2\n",
     "- Codice pulito per bene        3 (sono abbastanza soddisfatto)"
@@ -48,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 60,
    "id": "1e2fabea",
    "metadata": {},
    "outputs": [],
@@ -136,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 61,
    "id": "a5ff5ff5",
    "metadata": {},
    "outputs": [
@@ -180,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 62,
    "id": "e649eddc",
    "metadata": {},
    "outputs": [],
@@ -202,14 +210,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 63,
    "id": "42ff21d6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e9d979d582d48549847d52a98f7dae6",
+       "model_id": "145bf1a6c8534204b2aa7b50bad128a2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -239,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 64,
    "id": "dfab4331",
    "metadata": {},
    "outputs": [],
@@ -296,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 65,
    "id": "5d7ca0a0",
    "metadata": {},
    "outputs": [
@@ -352,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 66,
    "id": "cd68b201",
    "metadata": {},
    "outputs": [
@@ -381,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 67,
    "id": "f98a3612",
    "metadata": {},
    "outputs": [],
@@ -407,12 +415,12 @@
    "id": "5de53d0e",
    "metadata": {},
    "source": [
-    "### Grafici"
+    "### Grafici calibro"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 68,
    "id": "68eb66dc",
    "metadata": {},
    "outputs": [
@@ -465,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 69,
    "id": "831a4ac6",
    "metadata": {},
    "outputs": [
@@ -521,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 70,
    "id": "17cbeaea",
    "metadata": {},
    "outputs": [
@@ -553,14 +561,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 71,
    "id": "0bc01605",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataFrame  CALIBRO\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2cd706cd740242dc8c4f0d77578ff0cf",
+       "model_id": "fa8fc6ccb620432abcaf6c87e53ff735",
        "version_major": 2,
        "version_minor": 0
       },
@@ -571,10 +586,18 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "dataFrame  CALIBRO TOLTO DATO 1\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05f2a07809a448b9a2b8c385f180ec43",
+       "model_id": "d661c6a9648a4cadbc73d47d6b177805",
        "version_major": 2,
        "version_minor": 0
       },
@@ -601,15 +624,18 @@
     "   uAC2, uBC2, covABC2\n",
     ")\n",
     "\n",
+    "print(\"dataFrame  CALIBRO\")\n",
     "sheet = ipysheet.from_dataframe(dfCalibro)\n",
     "display(sheet)\n",
+    "\n",
+    "print(\"\\ndataFrame  CALIBRO TOLTO DATO 1\")\n",
     "sheet = ipysheet.from_dataframe(dfCalibro_senza1)\n",
     "display(sheet)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 72,
    "id": "d2c1a550",
    "metadata": {},
    "outputs": [
@@ -652,6 +678,1106 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8f79348f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit senza un dato\n",
+    "SCALA = 10\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "x_data = -dfCalibro_senza1[\"dx\"]\n",
+    "y_data =  dfCalibro_senza1[\"F\"]\n",
+    "\n",
+    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
+    "y_fit = AC2 * x_fit + BC2\n",
+    "\n",
+    "ax.errorbar(\n",
+    "    x_data, y_data,\n",
+    "    xerr=SCALA * dfCalibro_senza1[\"udx\"], yerr=SCALA * dfCalibro_senza1[\"uF\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x_fit, y_fit,\n",
+    "    color='red', linewidth=1.5,\n",
+    "    label=f\"Fit: $A={AC2:.3f}\\\\pm{uAC2:.3f}$\\n\"\n",
+    "          f\"     $B={BC2:.2f}\\\\pm{uBC2:.2f}$\\n\"\n",
+    ")\n",
+    "\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
+    "ax.set_ylabel(\"F  [mN]\")\n",
+    "ax.set_title(\"Fit lineare — Molla 1 (calibro tolto dato 1)\")\n",
+    "ax.legend(fontsize=9)\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b952c206",
+   "metadata": {},
+   "source": [
+    "# Analisi dati sonar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e347644",
+   "metadata": {},
+   "source": [
+    "## Import dei dati sonar (semidispersioni)\n",
+    "- masse trattate come prima\n",
+    "- $\\omega$, c trattati con semidispersione"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "d6195b96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfSonar = pd.read_csv(r'molla1Sonar.csv')\n",
+    "\n",
+    "# Bilancia trattata esattamente come prima\n",
+    "dfSonar = calcola_stats(dfSonar, \"m\",  ERR_BILANCIA)\n",
+    "\n",
+    "# Semidispersioni su w e uw\n",
+    "dfSonar['w']  = (dfSonar['w1'] + dfSonar['w2']) / 2\n",
+    "dfSonar['uw'] = np.maximum((dfSonar['w1'] - dfSonar['w2']).abs() / 2,\n",
+    "                            np.sqrt(dfSonar['uw1']**2 + dfSonar['uw2']**2))\n",
+    "\n",
+    "# Semidispersione su c e uc\n",
+    "dfSonar['c']  = (dfSonar['c1'] + dfSonar['c2']) / 2\n",
+    "dfSonar['uc'] = np.maximum((dfSonar['c1'] - dfSonar['c2']).abs() / 2,\n",
+    "                            np.sqrt(dfSonar['uc1']**2 + dfSonar['uc2']**2))\n",
+    "\n",
+    "# Aggiunta delle forze\n",
+    "dfSonar[\"F\"]  = F_fn(dfSonar[\"m\"],  G)\n",
+    "dfSonar[\"uF\"] = uF_fn(dfSonar[\"m\"], dfSonar[\"um\"], G, uG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "02e2d183",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "440939c283e448e3952454bb3231a234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'[{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":9.21,\"ua1\":0.029,\"w1\":14.2459,\"uw1\":0.0008,\"c1\":268.151,\"uc1\":0.02,\"a2\":10.69,\"ua2\":0.04,\"w2\":14.2434,\"uw2\":0.0009,\"c2\":268.326,\"uc2\":0.026,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":14.24465,\"uw\":0.00125,\"c\":268.2385,\"uc\":0.0875,\"F\":1065.02966,\"uF\":0.4353612606},{\"m1\":128.64,\"m2\":128.63,\"m3\":128.64,\"a1\":10.037,\"ua1\":0.025,\"w1\":13.1939,\"uw1\":0.0007,\"c1\":259.605,\"uc1\":0.018,\"a2\":9.744,\"ua2\":0.027,\"w2\":13.1913,\"uw2\":0.0009,\"c2\":259.745,\"uc2\":0.02,\"alp\":0.333333,\"ualp\":0.000001,\"m\":128.6366666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":13.1926,\"uw\":0.0013,\"c\":259.675,\"uc\":0.07,\"F\":1261.4111533333,\"uF\":0.5163603432},{\"m1\":148.38,\"m2\":148.38,\"m3\":148.38,\"a1\":9.93,\"ua1\":0.03,\"w1\":12.3469,\"uw1\":0.0007,\"c1\":251.525,\"uc1\":0.021,\"a2\":11.41,\"ua2\":0.03,\"w2\":12.3461,\"uw2\":0.0006,\"c2\":251.542,\"uc2\":0.023,\"alp\":0.333333,\"ualp\":0.000001,\"m\":148.38,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":12.3465,\"uw\":0.0009219544,\"c\":251.5335,\"uc\":0.031144823,\"F\":1455.01428,\"uF\":0.5941946685},{\"m1\":168.53,\"m2\":168.53,\"m3\":168.53,\"a1\":11.34,\"ua1\":0.03,\"w1\":11.6345,\"uw1\":0.0005,\"c1\":243.211,\"uc1\":0.021,\"a2\":11.5,\"ua2\":0.03,\"w2\":11.6344,\"uw2\":0.0006,\"c2\":243.13,\"uc2\":0.021,\"alp\":0.333333,\"ualp\":0.000001,\"m\":168.53,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":11.63445,\"uw\":0.000781025,\"c\":243.1705,\"uc\":0.0405,\"F\":1652.60518,\"uF\":0.6747140787}]'"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sheet = ipysheet.from_dataframe(dfSonar)\n",
+    "\n",
+    "display(sheet)\n",
+    "dfSonar.head(4).to_json(orient=\"records\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e12f76c",
+   "metadata": {},
+   "source": [
+    "## Regressione lineare Carpi dati del sonar Caso statico\n",
+    "chi² Come persone normali"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "06f9ccef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ax + B : \n",
+      "AS      = 23.5225 ± 0.0853\n",
+      "BS      = 7371.9487 ± 21.4728\n",
+      "cov_ABS = 1.830281\n",
+      "chi²    = 3.8872\n",
+      "P(chi², ∞)= 0.1432\n"
+     ]
+    }
+   ],
+   "source": [
+    "AS, BS, uAS, uBS, covABS, chiS, PS = reg_lin(-dfSonar[\"c\"], dfSonar[\"F\"], dfSonar[\"uc\"], dfSonar[\"uF\"])\n",
+    "print(\"Ax + B : \")\n",
+    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
+    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
+    "print(f\"cov_ABS = {covABS:.6f}\")\n",
+    "print(f\"chi²    = {chiS:.4f}\")\n",
+    "print(f\"P(chi², ∞)= {PS:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "09445aac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calcolo numerico residui sul dfSonar\n",
+    "dfSonar[\"r\"] = r_fn(\n",
+    "  -dfSonar[\"c\"],\n",
+    "  dfSonar[\"F\"],\n",
+    "  AS,\n",
+    "  BS\n",
+    ")\n",
+    "\n",
+    "dfSonar[\"ur\"] = sigma_r_fn(\n",
+    "  -dfSonar[\"c\"], dfSonar[\"F\"],\n",
+    "  AS, BS,\n",
+    "  dfSonar[\"uc\"], dfSonar[\"uF\"],\n",
+    "  uAS, uBS, covABS\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6641e08",
+   "metadata": {},
+   "source": [
+    "### Grafici sonar valori statici"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "75653d7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit\n",
+    "SCALA = 10\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "x_data = -dfSonar[\"c\"]\n",
+    "y_data =  dfSonar[\"F\"]\n",
+    "\n",
+    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
+    "y_fit = AS * x_fit + BS\n",
+    "\n",
+    "ax.errorbar(\n",
+    "    x_data, y_data,\n",
+    "    xerr=SCALA * dfSonar[\"uc\"], yerr=SCALA * dfSonar[\"uF\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x_fit, y_fit,\n",
+    "    color='red', linewidth=1.5,\n",
+    "    label=f\"Fit: $A={AS:.3f}\\\\pm{uAS:.3f}$\\n\"\n",
+    "          f\"     $B={BS:.2f}\\\\pm{uBS:.2f}$\\n\"\n",
+    ")\n",
+    "\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
+    "ax.set_ylabel(\"F  [mN]\")\n",
+    "ax.set_title(\"Fit lineare — Molla 1 (sonar)\")\n",
+    "ax.legend(fontsize=9)\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "1ecf9ab2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Grafico Residui\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Residui con barre d'errore\n",
+    "ax.errorbar(\n",
+    "    dfSonar[\"F\"], dfSonar[\"r\"],\n",
+    "    xerr=dfSonar[\"uF\"], yerr=dfSonar[\"ur\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=\"Residui\"\n",
+    ")\n",
+    "\n",
+    "# Linea dello zero\n",
+    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
+    "\n",
+    "# Estetica\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(\"F  [mN]\")\n",
+    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
+    "ax.set_title(\"Residui della regressione — Molla 1 (sonar statico)\")\n",
+    "ax.legend()\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eebafaf",
+   "metadata": {},
+   "source": [
+    "### Sezione non espressamente richiesta\n",
+    "La velocità del suono è chiaramente un fattore moltiplicativo che viene ignorato.\n",
+    "\n",
+    "Formula di Cramer 1993"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4088453c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c = 0.00301841231512225*c_raw*v0/sqrt(0.00366099212886692*T + 1)\n",
+      "sigma_c = v0*sqrt(3.05277451303515e-11*c_raw**2*sigma_T**2 + 9.11081290408163e-6*sigma_c_raw**2*(0.00366099212886692*T + 1)**2)/(0.00366099212886692*T + 1)**(3/2)\n",
+      "\n",
+      "Formula LaTeX:\n",
+      "c = \\frac{0.00301841231512225 c_{raw} v_{0}}{\\sqrt{0.00366099212886692 T + 1}}\n",
+      "sigma_c = \\frac{v_{0} \\sqrt{3.05277451303515 \\cdot 10^{-11} c_{raw}^{2} \\sigma_{T}^{2} + 9.11081290408163 \\cdot 10^{-6} \\sigma_{c raw}^{2} \\left(0.00366099212886692 T + 1\\right)^{2}}}{\\left(0.00366099212886692 T + 1\\right)^{\\frac{3}{2}}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# propagazione dell'errore con la temperatura\n",
+    "# simboli\n",
+    "v0, T, c_raw = sp.symbols('v0 T c_raw', positive=True)\n",
+    "sigma_T, sigma_c_raw = sp.symbols('sigma_T sigma_c_raw', positive=True)\n",
+    "\n",
+    "# velocità corretta e posizione\n",
+    "v = 331.3 * sp.sqrt(1 + T / 273.15)\n",
+    "c = c_raw * v0 / v\n",
+    "\n",
+    "# derivate parziali\n",
+    "dc_dcraw = sp.diff(c, c_raw)\n",
+    "dc_dT    = sp.diff(c, T)\n",
+    "\n",
+    "sigma_c = sp.sqrt((dc_dcraw * sigma_c_raw)**2 + (dc_dT * sigma_T)**2)\n",
+    "\n",
+    "print(\"c =\", c)\n",
+    "print(\"sigma_c =\", sigma_c.simplify())\n",
+    "print(\"\\nFormula LaTeX:\")\n",
+    "print(f\"c = {sp.latex(c)}\")\n",
+    "print(f\"sigma_c = {sp.latex(sigma_c.simplify())}\")\n",
+    "\n",
+    "# funzioni numeriche\n",
+    "c_fn  = sp.lambdify((c_raw, T, v0), c, 'numpy')\n",
+    "uc_fn = sp.lambdify((c_raw, sigma_c_raw, T, sigma_T, v0), sigma_c, 'numpy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "1584a5d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Temperatura = 22.6\n",
+    "uTemperatura = 0.7 / np.sqrt(12)\n",
+    "Velocita_impostata = 343.0\n",
+    "\n",
+    "dfSonarCorrezione = dfSonar.copy()\n",
+    "\n",
+    "dfSonarCorrezione['c']  =  c_fn(dfSonarCorrezione['c'], Temperatura, Velocita_impostata)\n",
+    "dfSonarCorrezione['uc'] = uc_fn(dfSonarCorrezione['c'], dfSonarCorrezione['uc'],\n",
+    "                      Temperatura, uTemperatura,\n",
+    "                      Velocita_impostata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ac4860fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b64011848560421ba1ea35f3a415cd00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'[{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":9.21,\"ua1\":0.029,\"w1\":14.2459,\"uw1\":0.0008,\"c1\":268.151,\"uc1\":0.02,\"a2\":10.69,\"ua2\":0.04,\"w2\":14.2434,\"uw2\":0.0009,\"c2\":268.326,\"uc2\":0.026,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":14.24465,\"uw\":0.00125,\"c\":268.2385,\"uc\":0.0875,\"F\":1065.02966,\"uF\":0.4353612606,\"r\":-0.1151157394,\"ur\":0.1108576},{\"m1\":128.64,\"m2\":128.63,\"m3\":128.64,\"a1\":10.037,\"ua1\":0.025,\"w1\":13.1939,\"uw1\":0.0007,\"c1\":259.605,\"uc1\":0.018,\"a2\":9.744,\"ua2\":0.027,\"w2\":13.1913,\"uw2\":0.0009,\"c2\":259.745,\"uc2\":0.02,\"alp\":0.333333,\"ualp\":0.000001,\"m\":128.6366666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":13.1926,\"uw\":0.0013,\"c\":259.675,\"uc\":0.07,\"F\":1261.4111533333,\"uF\":0.5163603432,\"r\":0.0997008545,\"ur\":0.0835641852},{\"m1\":148.38,\"m2\":148.38,\"m3\":148.38,\"a1\":9.93,\"ua1\":0.03,\"w1\":12.3469,\"uw1\":0.0007,\"c1\":251.525,\"uc1\":0.021,\"a2\":11.41,\"ua2\":0.03,\"w2\":12.3461,\"uw2\":0.0006,\"c2\":251.542,\"uc2\":0.023,\"alp\":0.333333,\"ualp\":0.000001,\"m\":148.38,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":12.3465,\"uw\":0.0009219544,\"c\":251.5335,\"uc\":0.031144823,\"F\":1455.01428,\"uF\":0.5941946685,\"r\":0.010632976,\"ur\":0.0484884905},{\"m1\":168.53,\"m2\":168.53,\"m3\":168.53,\"a1\":11.34,\"ua1\":0.03,\"w1\":11.6345,\"uw1\":0.0005,\"c1\":243.211,\"uc1\":0.021,\"a2\":11.5,\"ua2\":0.03,\"w2\":11.6344,\"uw2\":0.0006,\"c2\":243.13,\"uc2\":0.021,\"alp\":0.333333,\"ualp\":0.000001,\"m\":168.53,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":11.63445,\"uw\":0.000781025,\"c\":243.1705,\"uc\":0.0405,\"F\":1652.60518,\"uF\":0.6747140787,\"r\":-0.0264654245,\"ur\":0.0645469948}]'"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sheet = ipysheet.from_dataframe(dfSonarCorrezione)\n",
+    "\n",
+    "display(sheet)\n",
+    "dfSonar.head(4).to_json(orient=\"records\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "8917049c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit completo:   chi²=3.89,  P=0.1432\n",
+      "Temperatura :  chi²=1.632,  P=0.4422\n",
+      "\n",
+      "A:  23.5225 ± 0.0853  to  23.6160 ± 0.1404\n",
+      "B:  7371.9487 ± 21.4728  to  7365.6372 ± 35.4592\n"
+     ]
+    }
+   ],
+   "source": [
+    "AS2, BS2, uAS2, uBS2, covABS2, chiS2, PS2 = reg_lin(-dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"])\n",
+    "\n",
+    "\n",
+    "print(f\"Fit completo:   chi²={chiS:.2f},  P={PS:.4f}\")\n",
+    "print(f\"Temperatura :  chi²={chiS2:.3f},  P={PS2:.4f}\")\n",
+    "print()\n",
+    "print(f\"A:  {AS:.4f} ± {uAS:.4f}  to  {AS2:.4f} ± {uAS2:.4f}\")\n",
+    "print(f\"B:  {BS:.4f} ± {uBS:.4f}  to  {BS2:.4f} ± {uBS2:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "e35a9456",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calcolo numerico residui sul dfSonar\n",
+    "dfSonarCorrezione[\"r\"] = r_fn(\n",
+    "  -dfSonarCorrezione[\"c\"],\n",
+    "  dfSonarCorrezione[\"F\"],\n",
+    "  AS2,\n",
+    "  BS2\n",
+    ")\n",
+    "\n",
+    "dfSonarCorrezione[\"ur\"] = sigma_r_fn(\n",
+    "  -dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"],\n",
+    "  AS2, BS2,\n",
+    "  dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"],\n",
+    "  uAS2, uBS2, covABS2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "d5646b8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit\n",
+    "SCALA = 10\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "x_data = -dfSonarCorrezione[\"c\"]\n",
+    "y_data =  dfSonarCorrezione[\"F\"]\n",
+    "\n",
+    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
+    "y_fit = AS2 * x_fit + BS2\n",
+    "\n",
+    "ax.errorbar(\n",
+    "    x_data, y_data,\n",
+    "    xerr=SCALA * dfSonarCorrezione[\"uc\"], yerr=SCALA * dfSonarCorrezione[\"uF\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x_fit, y_fit,\n",
+    "    color='red', linewidth=1.5,\n",
+    "    label=f\"Fit: $A={AS2:.3f}\\\\pm{uAS2:.3f}$\\n\"\n",
+    "          f\"     $B={BS2:.2f}\\\\pm{uBS2:.2f}$\\n\"\n",
+    ")\n",
+    "\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
+    "ax.set_ylabel(\"F  [mN]\")\n",
+    "ax.set_title(\"Fit lineare — Molla 1 (sonar Correzione della temperatura)\")\n",
+    "ax.legend(fontsize=9)\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "8de2a48e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Hh9tXvXA+ISEBs2bNwuLFi/HRRx9h0KBBuP7663HbbbfZFh0TJ07EW2+9hWnTpuGRRx7BsGHDMHbsWNx00022X5Sqq/olz9HPSefOnfHtt9+isLAQgYGBttNbt25tt13VL6rZ2dkICQnRfJscKSsrQ1ZWVq3n18XHx8ducVSXnj17olOnTvj4448RFhaGmJgY28KxulOnTqFDhw41voedO3e2nV8fBw8exBNPPIHNmzfX+GX8/NfLuIpy8GLfOXPmYNOmTejTpw/at2+PESNGYNKkSRgwYAAAID09HUVFRbX+nFitVpw5cwaXXnqp7fS6fk6q1Od74ei+6oivry8WLVqEBx98EM2bN8eVV16Ja6+9FnfccYem16dUvRZi+fLlOHHihN3rPZo1a3bByzty/PhxtGjRos6fz6rHk+qPHzExMQgLC3P4c6aU0vxYR1RfXFgQ6ahPnz6258mOGTMGAwcOxKRJk3D48GEEBQXZ/oI8e/ZsjBw50uF1VP0fyuDBg3H8+HF88cUX+O677/DWW29hyZIlWLFiBaZNm9bgWWv7PyItLx5tiPp8D7T48ccfcf3112Pw4MFYvnw5YmNj4e3tjXfffbfGi05rc/5fTM+fs2vXrli8eLHDy7Rq1UrzjBfan9VqhclkwjfffOPwnZrOX6y9/PLLmDJliu3nYubMmVi4cCF++eUXtGzZEv7+/vjhhx+wZcsWfPXVV9iwYQNWrVqFv/3tb/juu++c9k5QtV1P1S+s9blN1W3fvv2i3wY1MTHR7gMVL2TSpEl4/fXXERwcjIkTJ9a6+HKmnJwcJCYmIiQkBE899RTatWsHPz8/7N69G3PmzNF8pOliNWvWzOFf8Tt37ozDhw9j/fr12LBhA9asWYPly5dj3rx5WLBgwUXt60I/J/X9Xji6r9bm/vvvx3XXXYd169bh22+/xdy5c7Fw4UJs3rz5gq9HeO655zB37lz8/e9/x9NPP42IiAh4eHjg/vvvd3kfQPsfRYBz38PIyEgXTkNNGRcWRAbh6emJhQsXYujQoXjttdfwyCOP2J4+4O3t7fAdWaqLiIjA1KlTMXXqVBQUFGDw4MF48skna11YtGnTBlarFcePH7f7q6Kj91IPDw93+I5N9f3L6/n7/v7771FQUGD3S2P1fdf3e3Aha9asgZ+fH7799lu7o0Pvvvtug663Xbt2+P333zFs2LA6/0++6nt+4sQJu7/OHzt2rF77UkohISEBl1xyyQW379q1K7p27YonnngC27dvx4ABA7BixQo888wzAM69DeWwYcMwbNgwLF68GM899xwef/xxbNmyxeH3vE2bNgAc/5z8+eefiIyMtDta4YrbdL7u3bvbvftZfTh6ik9dJk2ahHnz5iElJQUffPBBrdu1adMG+/btg9VqtVt8VD1Vp+p7qMXWrVuRmZmJtWvXYvDgwbbTT5w4Ua/ZL1anTp3w0UcfITc313akq0pgYCAmTpyIiRMnoqysDGPHjsWzzz6LRx99FFFRUQgICKj158TDw6PeC25Xfy/atWuHBx98EA8++CCOHj2KHj164OWXX8aHH34IoPZf4D/77DMMHToUb7/9tt3p1X+Jr88CoF27dvj222+RlZVV61GLqseTo0eP2o6GAUBaWhpycnJq/JyZzWaUlZXZbUvkTHyNBZGBDBkyBH369MHSpUtRUlKC6OhoDBkyBG+88QZSUlJqbH/+2zBmZmbanRcUFIT27ds7fMvBKqNHjwYA/Otf/7I7fenSpTW2bdeuHXJzc7Fv3z7baSkpKTXedUSrq6++GhUVFXj99ddtp1VWVuLVV1+1264+3wMtPD09YTKZ7I60nDx50u5dmy7GhAkTYDabHX4wYHFxse3zJ6qOulT/kKrqt7suY8eOhaenJxYsWFDjKSpKKdvPQl5eHioqKuzO79q1Kzw8PGw/F46eQtSjRw8AqPVnJzY2Fj169MD7779vt9g8cOAAvvvuO1x99dWab0t9b5Mj4eHhGD58+EV9Vb1Ljlbt2rXD0qVLsXDhQvTp06fW7a6++mqkpqbavZtRRUUFXn31VQQFBSExMVHzPqv+in/+96WsrKzRPuisX79+UEohKSnJ7vTqTXx8fNClSxcopVBeXg5PT0+MGDECX3zxhe0d74Bzv/R+/PHHGDhwIEJCQuo1i6u+F0VFRTU+mLRdu3YIDg62ux8EBgY6/AOLp6dnjZ/b1atX13j9V9WCu7a31T7fuHHjoJRyePSnal9V97Xqj9lVR06rv2NZVcO63omKqCF4xILIYB566CGMHz8e7733Hu6++24sW7YMAwcORNeuXXHXXXehbdu2SEtLw44dO3D27Fnb+6R36dIFQ4YMQa9evRAREYFdu3bhs88+w4wZM2rdV48ePXDLLbdg+fLlyM3NRf/+/fH99987/Ov5zTffjDlz5uDGG2/EzJkzUVRUhNdffx2XXHLJRb048brrrsOAAQPwyCOP4OTJk+jSpQvWrl3r8PniWr8HWlxzzTVYvHgxRo0ahUmTJsFisWDZsmVo37693aKpvm6//XZ8+umnuPvuu7FlyxYMGDAAlZWV+PPPP/Hpp5/i22+/Re/evdGrVy+MGzcOS5cuRWZmpu3tZo8cOQJA218027Vrh2eeeQaPPvooTp48iTFjxiA4OBgnTpzA559/junTp2P27NnYvHkzZsyYgfHjx+OSSy5BRUUFPvjgA3h6emLcuHEAzn36+w8//IBrrrkGbdq0gcViwfLly9GyZUu7z8yo7sUXX8To0aPRr18/3Hnnnba3mw0NDbX7LA6ttN4mI9DyVrHTp0/HG2+8gSlTpiApKQnx8fH47LPP8PPPP2Pp0qUIDg7WvL/+/fsjPDwckydPxsyZM2EymfDBBx/U60POXnvtNeTk5CA5ORkA8N///tf2eTX33XdfjSMR5xs4cCCaNWuGTZs22b2mZMSIEYiJicGAAQPQvHlzHDp0CK+99hquueYa2+175plnbJ+T8o9//ANeXl544403UFpaihdeeEHz/M78Xjhy5MgRDBs2DBMmTECXLl3g5eWFzz//HGlpabj55ptt2/Xq1Quvv/46nnnmGbRv3x7R0dH429/+hmuvvRZPPfUUpk6div79+2P//v346KOP7F60Dpz7OQ8LC8OKFSsQHByMwMBA9O3bt8brqABg6NChuP322/Gvf/0LR48exahRo2C1WvHjjz9i6NChmDFjBrp3747Jkydj5cqVtqeJ7dy5E++//z7GjBlT4ymCGzduROvWrflWs+Q6jfgOVET0f6reWvG3336rcV5lZaVq166dateunaqoqFBKKXX8+HF1xx13qJiYGOXt7a3i4uLUtddeqz777DPb5Z555hnVp08fFRYWpvz9/VWnTp3Us88+q8rKymzbOHpr2OLiYjVz5kzVrFkzFRgYqK677jp15syZGm+BqZRS3333nbrsssuUj4+P6tixo/rwww8v+u1mlVIqMzNT3X777SokJESFhoaq22+/3fYWqdXfjlHL90Dr282+/fbbqkOHDsrX11d16tRJvfvuuw5vhyOJiYm1vsVmWVmZWrRokbr00kuVr6+vCg8PV7169VILFixQubm5tu0KCwvVvffeqyIiIlRQUJAaM2aMOnz4sAKgnn/+edt257/VqSNr1qxRAwcOVIGBgSowMFB16tRJ3Xvvverw4cNKKaX++usv9fe//121a9dO+fn5qYiICDV06FC1adMm23V8//336oYbblAtWrRQPj4+qkWLFuqWW25RR44csW3j6O1mlVJq06ZNasCAAcrf31+FhISo6667Tv3xxx9229R2Gxy9paqW29TYLtSgCqq93axSSqWlpampU6eqyMhI5ePjo7p27erwbUar39ccfW9+/vlndeWVVyp/f3/VokUL9fDDD6tvv/22xs97bdq0aaMAOPyq3sCRmTNn2r3VrlJKvfHGG2rw4MGqWbNmytfXV7Vr10499NBDdj/rSim1e/duNXLkSBUUFKQCAgLU0KFD1fbt2+22qe0x0dF9Wuv3oq77anUZGRnq3nvvVZ06dVKBgYEqNDRU9e3bV3366ad226WmpqprrrlGBQcHKwC2t54tKSlRDz74oIqNjVX+/v5qwIABaseOHSoxMbHG29N+8cUXqkuXLsrLy8vufuXosaqiokK9+OKLqlOnTsrHx0dFRUWp0aNHq6SkJNs25eXlasGCBSohIUF5e3urVq1aqUcffbTG2xBXVlaq2NhY9cQTT2j6nhBdDJNS/Fx3IiK97d27Fz179sSHH36IW2+9Ve9xiOz89ddf6NSpE7755hsMGzZM73HoIqxbtw6TJk3C8ePHERsbq/c45Kb4GgsiokZWXFxc47SlS5fCw8PD7gWpREbRtm1b3HnnnXj++ef1HoUu0qJFizBjxgwuKsileMSCiKiRLViwAElJSRg6dCi8vLzwzTff4JtvvrE9L5+IiEgiLiyIiBrZxo0bsWDBAvzxxx8oKChA69atcfvtt+Pxxx+HlxffU4OIiGTiwoKIiIiIiBqMr7EgIiIiIqIG48KCiIiIiIgajAsLJ1FKIS8vr8Ef0kNEREREJBEXFk6Sn5+P0NBQ5Ofn6z2K4ZSVlek9AmnEVrKwlyzsJQdbycJexsGFBblcZWWl3iOQRmwlC3vJwl5ysJUs7GUcXFiQyxUVFek9AmnEVrKwlyzsJQdbycJexsGFBbkc35dfDraShb1kYS852EoW9jIOLizI5Tw8+GMmBVvJwl6ysJccbCULexkHS5DLlZSU6D0CacRWsrCXLOwlB1vJwl7GwWNH5HIhISF6j0AasZUs7CULe8nBVo2rsrIS5eXlF315X19fLi4awNvbG56enk65Li4syOUyMjIQFxen9xikAVvJwl6ysJccbNU4lFJITU1FTk5Og66noqKCr7NooLCwMMTExMBkMjXoekyKn+jmFHl5eQgNDUVubi7/0kFERER0ASkpKcjJyUF0dDQCAgIa/Est1Z9SCkVFRbBYLAgLC0NsbGyDro/LO3I5s9nMv/wIwVaysJcs7CUHW7leZWWlbVHRrFmzBl1XWVkZfHx8nDRZ0+Pv7w8AsFgsiI6ObtDTovjibXK5Fi1a6D0CacRWsrCXLOwlB1u5XtVrKgICAhp8Xd7e3g2+jqauqkNDXusC8IgFNYLk5GT+5UcItpKFvWRhLznYqvFc7NOf8vPzUVBQAODcL8PVFxdBQUEIDg5u8HxNhbOehsaFBblcZGSk3iOQRmwlC3vJwl5ysJXxJSUlYdu2bbWen5iYiCFDhjTeQASACwtqBHl5eYiKitJ7DNKArWRhL1nYSw62Mr5evXqhY8eOAIDU1FR8+eWXGDt2rG1RGBQUpOd4F+3kyZNISEjAnj170KNHD4fbbN26FUOHDkV2djbCwsI0Xe+UKVOQk5ODdevWOW1WR7iwIJfz9fXVewTSiK1kYS9Z2EsOtjK+4OBg21OdKisrAZw70tTQdzXSYsqUKXj//fcBAF5eXmjZsiXGjx+Pp556Cn5+fg267latWiElJcXpR81eeeUVNMYbwfLF20RERERkePn5+di6dSvy8/Ntp53IKMSK7SnYWpaA5T+bcSKj8IKXcYZRo0YhJSUFf/31F5YsWYI33ngD8+fPb/D1enp6IiYmxumfyxEaGqr56EZDcGFBLldWVqb3CKQRW8nCXrKwlxxsZUwFBQXYtm2b7UXbn+46g2Evb8V/9lhwsjICHyelYdjLW7F615laL+Msvr6+iImJQatWrTBmzBgMHz4cGzduBABYrVYsXLgQCQkJ8Pf3R/fu3fHZZ5/ZLpudnY1bb70VUVFR8Pf3R4cOHfDuu+8COPdUKJPJhL1799q2//rrr3HJJZfA398fQ4cOxcmTJ+1mefLJJ2s8bWrp0qWIj4+3/XvKlCkYM2aMM78FDvGpUORygYGBeo9AGrGVLOwlC3vJwVbGdyKjEI+s2Qer7dk9JlT+33/PWbMPV8RHID6ycToeOHAA27dvR5s2bQAACxcuxIcffogVK1agQ4cO+OGHH3DbbbchKioKiYmJmDt3Lv744w988803iIyMxLFjx1BcXOzwus+cOYOxY8fi3nvvxfTp07Fr1y48+OCDjXK7LgYXFuRy2dnZtg9fIWNrqq3Of9tCR4z6toVNtZdU7CUHWxlbRkYGPj1sRl1vkPr21kP4x4A4ZGRkuGSG9evXIygoCBUVFSgtLYWHhwdee+01lJaW4rnnnsOmTZvQr18/AEDbtm3x008/4Y033kBiYiJOnz6Nnj17onfv3gBgd2Shutdffx3t2rXDyy+/DADo2LEj9u/fj0WLFrnkdjUUFxbkcjExMXqPQBo11VZS37awqfaSir3kYCtjW7t2LX4qS4BVRQAOlhdKKfy05w94HfzKZTMMHToUr7/+OgoLC7FkyRJ4eXlh3LhxOHjwIIqKinDVVVfZbV9WVoaePXsCAO655x6MGzcOu3fvxogRIzBmzBj079/f4X4OHTqEvn372p1WtWAxIi4syOVSUlL4QUNCNNVW579tYUZGBtauXSvibQubai+p2EsOtjK2sWPHouJwKU4npdme/nQ+k8mEgT27YPqAq2yP6c4WGBiI9u3bAwDeeecddO/eHW+//TYuu+wyAMBXX31V42eo6t3GRo8ejVOnTuHrr7/Gxo0bMWzYMNx777146aWXLmoWDw+PGu/41NBP0L5YXFiQy/HBWY6m2ur8ty2s0lhvW9gQTbWXVOwlB1sZW2RkJP4eG4KPktJq3ebOIZ0R20ivsfDw8MBjjz2GWbNm4ciRI/D19cXp06eRmJhY62WioqIwefJkTJ48GYMGDcJDDz3kcGHRuXNnfPnll3an/fLLLzWuKzU1FUop2ydon//i78bEd4UilzObzXqPQBqxlSzsJQt7ycFWxpcQGYhF47rBwwR4mgATFDxMgIcJWDSuW6O9cLvK+PHj4enpiTfeeAOzZ8/GAw88gPfffx/Hjx/H7t278eqrr9o++2LevHn44osvcOzYMRw8eBDr169H586dHV7v3XffjaNHj+Khhx7C4cOH8fHHH+O9996z22bIkCFIT0/HCy+8gOPHj2PZsmX45ptvXH2THeLCglyOn14qB1vJwl6ysJccbGVMQUFBSExMtD09dXzvVtj84BDccnk04j2zcGuv5tj84BCM792q1su4ipeXF2bMmIEXXngBjz76KObOnYuFCxeic+fOGDVqFL766iskJCQAAHx8fPDoo4+iW7duGDx4MDw9PfHJJ584vN7WrVtjzZo1WLduHbp3744VK1bgueees9umc+fOWL58OZYtW4bu3btj586dmD17tktvb21MqjE+hq8JyMvLQ2hoKHJzcxESEqL3OIZisVgQHR2t9xikAVude271ypUrMX36dMM/FYq9ZGEvOdjK9UpKSnDixAkkJCQ0+NOqz5w5g3feeUfE47ZROasHX2NBLhcQEKD3CKQRW8nCXrKwlxxsZXznv014ZmYmANi9taxR3ybc3XFhQS5XUVGh9wikEVvJwl6ysJccbGV8jt4m/Px3fzLq24S7Oy4syOWsVqveI5BGbCULe8nCXnKwlfGd/zbhFRUV8PKy/5XWqG8T7u64sCCXa+hzJ6nxsJUs7CULe8nBVsZ3/tuEV1ZWwtPTU+eJCOC7QlEjyMvL03sE0oitZGEvWdhLDraShUeYjIMLC3K5qk8vJuNjK1nYSxb2koOtGo8zFgXVnwZF9eesxRlLkMulpaXxU0yFYCtZ2EsW9pKDrVzPx8cHHh4eSE5ORlRUFHx8fGyfGl1fZWVl8PHxcfKETYNSCmVlZUhPT4eHh0eDv49cWJDL8cFZDraShb1kYS852Mr1PDw8kJCQgJSUFCQnJ+s9TpMXEBCA1q1bw8OjYU9m4sKCXM5sNvNBWgi2koW9ZGEvOdiqcfj4+KB169aoqKhAZWXlRV9PWloamjdv7sTJmhZPT094eXld9BGj83FhQS7HO7scbCULe8nCXnKwVeMxmUzw9vaGt7f3RV9HXFwcX2dhEHzxNrnc+Z+EScbGVrKwlyzsJQdbycJexsGFBblcSEiI3iOQRmwlC3vJwl5ysJUs7GUcXFiQy5WUlOg9AmnEVrKwlyzsJQdbycJexsGFBbmcM14MRI2DrWRhL1nYSw62koW9jIMLC3K5hrwgixoXW8nCXrKwlxxsJQt7GQcXFuRyhYWFeo9AGrGVLOwlC3vJwVaysJdxcGFBLhcREaH3CKQRW8nCXrKwlxxsJQt7GQcXFuRyFotF7xFII7aShb1kYS852EoW9jIOLizI5fjppXKwlSzsJQt7ycFWsrCXcbjtwmLZsmWIj4+Hn58f+vbti507d9a67ZtvvolBgwYhPDwc4eHhGD58eJ3bU/2YzWa9RyCN2EoW9pKFveRgK1nYyzjccmGxatUqzJo1C/Pnz8fu3bvRvXt3jBw5stZDZVu3bsUtt9yCLVu2YMeOHWjVqhVGjBjBH1QniY2N1XsE0oitZGEvWdhLDraShb2Mwy0XFosXL8Zdd92FqVOnokuXLlixYgUCAgLwzjvvONz+o48+wj/+8Q/06NEDnTp1wltvvQWr1Yrvv/++kSd3T6mpqXqPQBqxlSzsJQt7ycFWsrCXcbjdwqKsrAxJSUkYPny47TQPDw8MHz4cO3bs0HQdRUVFKC8vr/NdBkpLS5GXl2f3RY6Fh4frPQJpxFaysJcs7CUHW8nCXsbhdguLjIwMVFZWonnz5nanN2/eXPOKds6cOWjRooXd4qS6hQsXIjQ01PbVqlUrAOc+Vj45ORlWq9X2VCqz2YyysjJYLBYUFBQgJycHWVlZKC4uRmpqKioqKuy2LS8vR2pqKoqKipCVlYXs7GwUFhYiLS0N5eXldttWVlYiOTkZxcXFyMzMRG5uLvLz85Geno7S0lK7bZVSMJvNKC0tRXp6um1BlJGRgeLiYs1zFxUVOZy7oqKixtwFBQW223T+tlar9YJzV81bfe78/Hzk5uYiMzOz1rnLy8uRlpaGgoICZGdn12vunJwcFBQUwGKxoKysrNa5MzIybN9DR3NXXaauuSsrKx3OXVhYWGPu6t/DqrmLi4s1z11SUuJw7uTkZCilcOrUKbu5LRZLjblTUlIuOPeFfmZTUlLs5s7Pz6/X3BkZGRe8r+Xn59vd17TOXVxcDAA1fk7On/v8n1mLxeLwvpacnOzwvubMx4isrCy3eIxwNLc7Pkbk5OSIf4yoPre7PkYUFRU1aG6jPEa4y+8RF3qMSElJcYvHCCP/HqGVSSmlNG8tQHJyMuLi4rB9+3b069fPdvrDDz+Mbdu24ddff63z8s8//zxeeOEFbN26Fd26dat1u9LSUpSWltr+nZeXh1atWiE3NxchISENvyFuJDc3F6GhoXqPQRqwFZCSkoKVK1di+vTphn/eLnvJwl5ysJUs7GUcXnoP4GyRkZHw9PREWlqa3elpaWmIiYmp87IvvfQSnn/+eWzatKnORQUA+Pr6wtfXt8HzNgUeHm53YMxtsZUs7CULe8nBVrKwl3G4XQkfHx/06tXL7oXXVS/EPv8IRnUvvPACnn76aWzYsAG9e/dujFGbjJKSEr1HII3YShb2koW95GArWdjLONzuiAUAzJo1C5MnT0bv3r3Rp08fLF26FIWFhZg6dSoA4I477kBcXBwWLlwIAFi0aBHmzZuHjz/+GPHx8bbXYgQFBSEoKEi32+Eu+NQwOdhKFvaShb3kYCtZ2Ms43O6IBQBMnDgRL730EubNm4cePXpg79692LBhg+0F3adPn0ZKSopt+9dffx1lZWW46aabEBsba/t66aWX9LoJbiUjI0PvEUgjtpKFvWRhLznYShb2Mg63e/G2XvLy8hAaGsoXbzuglILJZNJ7DNKArWS9eJu9ZGEvOdhKFvYyDrc8YkHGkpycrPcIpBFbycJesrCXHGwlC3sZBxcW5HItWrTQewTSiK1kYS9Z2EsOtpKFvYyDCwtyOf4lQQ62koW9ZGEvOdhKFvYyDi4syOUiIyP1HoE0YitZ2EsW9pKDrWRhL+PgwoJcLi8vT+8RSCO2koW9ZGEvOdhKFvYyDi4syOX8/Pz0HoE0YitZ2EsW9pKDrWRhL+PgwoJczmq16j0CacRWsrCXLOwlB1vJwl7GwYUFuVxFRYXeI5BGbCULe8nCXnKwlSzsZRxcWJDLBQQE6D0CacRWsrCXLOwlB1vJwl7GwYUFuVx2drbeI5BGbCULe8nCXnKwlSzsZRxcWJDLxcTE6D0CacRWsrCXLOwlB1vJwl7GwYUFuVxKSoreI5BGbCULe8nCXnKwlSzsZRxcWJDLxcXF6T0CacRWsrCXLOwlB1vJwl7GwYUFuZzZbNZ7BNKIrWRhL1nYSw62koW9jIMLC3K56OhovUcgjdhKFvaShb3kYCtZ2Ms4uLAgl8vKytJ7BNKIrWRhL1nYSw62koW9jIMLC3K5wMBAvUcgjdhKFvaShb3kYCtZ2Ms4uLAglysvL9d7BNKIrWRhL1nYSw62koW9jIMLC3I5pZTeI5BGbCULe8nCXnKwlSzsZRxcWJDL+fn56T0CacRWsrCXLOwlB1vJwl7GwYUFuVxeXp7eI5BGbCULe8nCXnKwlSzsZRxcWJDLRUZG6j0CacRWsrCXLOwlB1vJwl7GwYUFuVxaWpreI5BGbCULe8nCXnKwlSzsZRxcWJDLxcXF6T0CacRWsrCXLOwlB1vJwl7GwYUFuZzZbNZ7BNKIrWRhL1nYSw62koW9jIMLC3K55s2b6z0CacRWsrCXLOwlB1vJwl7GwYUFuVxGRobeI5BGbCULe8nCXnKwlSzsZRxcWJDLhYSE6D0CacRWsrCXLOwlB1vJwl7GwYUFuVxJSYneI5BGbCULe8nCXnKwlSzsZRxcWJDLeXjwx0wKtpKFvWRhLznYShb2Mg6WIJfz8vLSewTSiK1kYS9Z2EsOtpKFvYyDCwtyuaKiIr1HII3YShb2koW95GArWdjLOLiwIJcLCwvTewTSiK1kYS9Z2EsOtpKFvYyDCwtyufT0dL1HII3YShb2koW95GArWdjLOLiwIJeLi4vTewTSiK1kYS9Z2EsOtpKFvYyDCwtyObPZrPcIpBFbycJesrCXHGwlC3sZBxcW5HKxsbF6j0AasZUs7CULe8nBVrKwl3FwYUEul5qaqvcIpBFbycJesrCXHGwlC3sZBxcW5HLh4eF6j0AasZUs7CULe8nBVrKwl3FwYUEuV1hYqPcIpBFbycJesrCXHGwlC3sZBxcW5HI+Pj56j0AasZUs7CULe8nBVrKwl3FwYUFERERERA3GhQW5XGlpqd4jkEZsJQt7ycJecrCVLOxlHFxYkMuFhIToPQJpxFaysJcs7CUHW8nCXsbBhQW5XEZGht4jkEZsJQt7ycJecrCVLOxlHFxYkMu1aNFC7xFII7aShb1kYS852EoW9jIOLizI5ZKTk/UegTRiK1nYSxb2koOtZGEv4+DCglwuLi5O7xFII7aShb1kYS852EoW9jIOLizI5cxms94jkEZsJQt7ycJecrCVLOxlHFxYkMtFRkbqPQJpxFaysJcs7CUHW8nCXsbBhQW5XF5ent4jkEZsJQt7ycJecrCVLOxlHF56D0Duz8/PT+8RSCO2koW9ZHHnXvn5+SgoKKj1/KCgIAQHBzfiRA3jzq3cEXsZBxcW5HJWq1XvEUgjtpKFvWRx515JSUnYtm1brecnJiZiyJAhjTdQA7lzK3fEXsbBhQW5XEVFhd4jkEZsJQt7yeLOvXr16oWOHTsCOPdhZWvXrsXYsWNtz30PCgrSc7x6c+dW7oi9jIMLC3K5gIAAvUcgjdhKFvaSxZ17BQcH13iqU2RkJGJjY3WaqGHcuZU7Yi/j4Iu3yeWys7P1HoE0YitZ2EsW9pKDrWRhL+PgwoJcrnnz5nqPQBo19VYnMgrxr60nsbUsAY9/uhMHTln0HqlOTb2XNOwlB1vJwl7GcdFPhfryyy/rfZmrrroK/v7+F7tLEio1NZWfiilEU2716a4zeGTNPgCAUhE4mQJsXfEbFo3rhvG9W+k8nWNNuZdE7CUHW8nCXsZx0QuLMWPG1Gt7k8mEo0ePom3bthe7SxKKd3Y5mmqrExmFeGTNPlhV1SkmAIBSwJw1+3BFfATiIwN1m682TbWXVOwlB1vJwl7G0aCnQqWmpsJqtWr64gtrmi6z2az3CKRRU2316a4zMJlMDs8zmUxYtetMI0+kTVPtJRV7ycFWsrCXcVz0wmLy5Mn1elrTbbfdhpCQkIvdHQkWHR2t9wikUVNtdTa7GEoph+cppXA2u7iRJ9KmqfaSir3kYCtZ2Ms4Lnph8e6779brUzRff/112/tZU9OSlZWl9wikUVNt1TLcv84jFi3DjfnasKbaSyr2koOtZGEv43Dbd4VatmwZ4uPj4efnh759+2Lnzp21bnvw4EGMGzcO8fHxMJlMWLp0aeMN2gRI+2CkpqyptprQu1WdRywmGvTF2021l1TsJQdbycJexuG0D8grKSnBvn37YLFYany0+vXXX++s3WiyatUqzJo1CytWrEDfvn2xdOlSjBw5EocPH3Z4uKyoqAht27bF+PHj8cADDzTqrE1BWVkZAgON98JXqqmptkqIDMSicd0wx/auUOcWGSaTCYvGdTPkC7eBpttLKvaSg61kYS/jcMrCYsOGDbjjjjuQkZFR4zyTyYTKykpn7EazxYsX46677sLUqVMBACtWrMBXX32Fd955B4888kiN7a+44gpcccUVAODwfGqY2v4STMbTlFuN790KV8RH4I2N+/DLgaNIiA7Fg2P64dI2xn3ublPuJRF7ycFWsrCXcTjlqVD33Xcfxo8fj5SUlBrvBtXYi4qysjIkJSVh+PDhttM8PDwwfPhw7Nixo1FnoXP8/Pz0HoE0auqt4iMDMXNIPIb4nMCzE/oYelEBsJc07CUHW8nCXsbhlIVFWloaZs2aZYhPPszIyEBlZWWNWZo3b47U1FSn7ae0tBR5eXl2X+QYvzdysJUs7CULe8nBVrKwl3E4ZWFx0003YevWrc64KjEWLlyI0NBQ21erVude3FlSUoLk5GRYrVbb+yqbzWaUlZXBYrGgoKAAOTk5yMrKQnFxMVJTU1FRUWG3bXl5OVJTU1FUVISsrCxkZ2ejsLAQaWlpKC8vt9u2srISycnJKC4uRmZmJnJzc5Gfn4/09HSUlpbabauUgtlsRmlpKdLT020LooyMDBQXF2ueu6ioyOHcFRUVNeYuKCiA1WqtMbfVar3g3FXzVp87Pz8fubm5yMzMrHXu8vJypKWloaCgANnZ2fWaOycnBwUFBbBYLCgrK6t17oyMDNv30NHcVZepa+7KykqHcxcWFtaYu/r3sGru4uJizXOXlJQ4nDs5ORlKKZSWltrNbbFYasydkpJywbkv9DObkpJiN3d+fn695s7IyLjgfS0/P9/uvqZ17uLic28tW/3n5Py5z/+ZtVgsDu9rycnJDu9rznyM8Pf3d4vHCEdzu+NjRFBQkPjHiOpzO3qMqHpatMViEfsY0axZswbNbZTHCHf5PeJCjxEmk8ktHiOM/HuEViblhCemFRUVYfz48YiKikLXrl3h7e1td/7MmTMbugvNysrKEBAQgM8++8zu08EnT56MnJwcfPHFF3VePj4+Hvfffz/uv//+OrcrLS21/RIGnFstt2rVCrm5ufy8jmrMZjM/FVMItgJSUlKwcuVKTJ8+HbGxsXqPUyf2kqWp9JJ0H6pNU2nlLtjLOJzy4u3//Oc/+O677+Dn54etW7favR+8yWRq1IWFj48PevXqhe+//962sLBarfj+++8xY8YMp+3H19cXvr6+Trs+d8Y7uxxsJQt7ycJecrCVLOxlHE55KtTjjz+OBQsWIDc3FydPnsSJEydsX3/99ZczdlEvs2bNwptvvon3338fhw4dwj333IPCwkLbu0TdcccdePTRR23bl5WVYe/evdi7d6/t0M/evXtx7NixRp/dHdXnEBrpi61kYS9Z2EsOtpKFvYzDKUcsysrKMHHiRHh4GOPz9iZOnIj09HTMmzcPqamp6NGjBzZs2GB7Qffp06ftZk1OTkbPnj1t/37ppZfw0ksvITExscm9dsQVjPCiftKGrWRhL1nYSw62koW9jMMpC4vJkydj1apVeOyxx5xxdU4xY8aMWp/6VH2xEB8fz/dAdqGMjAzExMToPQZpwFaysJcs7CUHW8nijr3y8/NRUFBQ6/lBQUEIDg5uxIm0ccrCorKyEi+88AK+/fZbdOvWrcaLtxcvXuyM3ZBQoaGheo9AGrGVLOwlC3vJwVayuGOvpKQkbNu2rdbzExMTMWTIkMYbSCOnLCz2799veyrRgQMH7M47/4Xc1DQVFxfD399f7zFIA7aShb1kYS852EoWd+zVq1cvdOzYEcC5IzJr167F2LFjERkZCeDcEQsjcsrCYsuWLc64GnJTRnntDV0YW8nCXrKwlxxsJYs79goODq7xVKfIyEjDv4WzUxYWwLkPhtu3bx8sFgusVqvtdJPJhOuuu85ZuyGBvLyc9mNGLsZWsrCXLOwlB1vJwl7G4ZQSGzZswO23347MzMwa55lMJlRWVjpjNyRUUVGRYQ/ZkT22koW9ZGEvOdhKFvYyDqccO7rvvvswYcIEpKSkwGq12n1xUUFhYWF6j0AasZUs7CULe8nBVrKwl3E4ZWGRlpaGWbNm8X2EyaH09HS9RyCN2EoW9pKFveRgK1nYyzicsrC46aab+EFyVKu4uDi9RyCN2EoW9pKFveRgK1nYyzic8hqL1157DePHj8ePP/6Irl271vgci5kzZzpjNySU2WzmnV4ItpKFvWRhLznYShb2Mg6nLCz+85//4LvvvoOfnx+2bt1q99kVJpOJC4smzuhvjUb/j61kYS9Z2EsOtpKFvYzDKU+Fevzxx7FgwQLk5ubi5MmTOHHihO3rr7/+csYuSLDU1FS9RyCN2EoW9pKFveRgK1nYyzicsrAoKyvDxIkT3fIDSqjhIiIi9B6BNGIrWdhLFvaSg61kYS/jcMpKYPLkyVi1apUzrorcUEFBgd4jkEZsJQt7ycJecrCVLOxlHE55jUVlZSVeeOEFfPvtt+jWrVuNF28vXrzYGbshoXx8fPQegTRiK1nYSxb2koOtZGEv43DKwmL//v3o2bMnAODAgQN2553/Qm4iIiIiInJPTllYbNmyxRlXQ26qtLRU7xFII7aShb1kYS852EoW9jIOvtqaXC4kJETvEUgjtpKFvWRhLznYShb2Mo6LXljs27cPVqtV8/YHDx5ERUXFxe6OBMvMzNR7BNKIrWRhL1nYSw62koW9jOOinwrVs2dPpKamIioqStP2/fr1w969e9G2bduL3SVVk5+fX+c7IQQFBSE4OLgRJ3KMH1wjB1vJwl6ysJccbCULexnHRS8slFKYO3cuAgICNG1fVlZ2sbuiWiQlJWHbtm21np+YmIghQ4Y03kC1SE5ORlxcnN5jkAZsJQt7ycJecrCVLOxlHBe9sBg8eDAOHz6seft+/frB39//YndHDvTq1QsdO3YEAGRkZGDt2rUYO3YsIiMjAZw7YmEEvLPLwVaysJcs7CUHW8nCXsZx0QuLrVu3OnEMuhjBwcE1nuoUGRlpuEOCZrOZd3oh2EoW9pKFveRgK1nYyzj4rlDkclVHUMj42EoW9pKFveRgK1nYyzi4sCCXy83N1XsE0oitZGEvWdhLDraShb2MgwsLcjm+tkYOtpKFvWRhLznYShb2Mg4uLMjl6vN5J6QvtpKFvWRhLznYShb2Mg4uLMjl+MGIcrCVLOwlC3vJwVaysJdxcGFBLqf1s05If2wlC3vJwl5ysJUs7GUcXFiQy+Xk5Og9AmnEVrKwlyzsJQdbycJexsGFBblcdHS03iOQRmwlC3vJwl5ysJUs7GUcXFiQy6Wmpuo9AmnEVrKwlyzsJQdbycJexsGFBbkcPw1TDraShb1kYS852EoW9jIOr4ZcuLS0FJs3b8bmzZtx9uxZWCwWFBcXIyoqCtHR0ejRoweuvvpqJCQkOGteEshsNvNOLwRbycJesrCXHGwlC3sZx0UtLMxmMxYsWIDvv/8eHTt2xGWXXYbu3bsjJCQEvr6+yMvLQ35+Pnbt2oW3334bXl5emDNnDsaNG+fs+UkAPvdRDraShb1kYS852EoW9jKOei8s/vWvf+Hs2bOYPn06Vq5cqekyeXl5WLVqFe6++2489thjaN26db0HJbmysrLQvHlzvccgDdhKFvaShb3kYCtZ2Ms46rWwWL9+Pa677rp6P7UpJCQEd911F6ZNm4ZPP/0UgYGBaNasWb2ug+QKCgrSewTSiK1kYS9Z2EsOtpKFvYyjXi/evvbaaxv0egmTyYSJEydyUdHElJWV6T0CacRWsrCXLOwlB1vJwl7G4ZJ3hfL09HTF1RIRERERkUG5ZGGhlHLF1ZJQPj4+eo9AGrGVLOwlC3vJwVaysJdx1HthUVRUdMFtTCbTRQ1D7qmgoEDvEUgjtpKFvWRhLznYShb2Mo56vXh7xowZeO+999C+fXt89tlnWLx4MSwWC4YNG4Z77rnH4WXS0tLwxx9/2L4OHjyIQ4cOIS0tzSk3gIwvIiJC7xFII7aShb1kYS852EoW9jKOeh2x+Oabb5CRkYHXX38dAwcORMuWLXHHHXfghx9+wNy5c23bKaUwaNAgNG/eHIMHD8aSJUtw6tQprF69Gi+88AL+/PNPp98QMi6LxaL3CKQRW8nCXrKwlxxsJQt7GUe9jliEhobCz88P/fr1Q2hoKB577DEAwDXXXIO+ffvi6aeftm3bokULVFZWYuHChUhMTAQArF69Gn369HHi+CQBPw1TDraShb1kYS852EoW9jKOeh2xSE9Px7p163DixAkEBgbaTvf09LR7wbbJZMKqVavwxhtvYOnSpRgxYgR+/fVXvvaiiTKbzXqPQBqxlSzsJQt7ycFWsrCXcdTriMWsWbPw3//+FwsXLsRff/2F/v37o2PHjujYsSMyMzNrbN+1a1d8/vnn2LVrF+bNm4e0tDT8+uuv6Nu3r9NuABlfTEyM3iOQRmwlC3vJwl5ysJUs7GUc9VpYPPDAA3b/PnHiBA4cOIADBw5gwIABttOrv91s79698fXXX+Pnn3/GY489BpPJhE2bNjVgbJLEYrEgNjZW7zFIA7aShb1kYS852EoW9jKOei0sqktISEBCQgKuu+46u9OtVqvD7QcMGIDvv/8eW7ZsachuSZiwsDC9RyCN2EoW9pKFveRgK1nYyzhc8gF5FzJ06FA9dks6KS4u1nsE0oitZGEvWdhLDraShb2MQ5eFBTUtHh78MZOCrWRhL1nYSw62koW9jMPpJY4cOYKKigpnXy0J5unpqfcIpBFbycJesrCXHGwlC3sZh9MXFp07d8Zff/3l7KslwXiIUg62koW9ZGEvOdhKFvYyDqcvLKq/IxQRX1QlB1vJwl6ysJccbCULexkHn5RGLpeenq73CKQRW8nCXrKwlxxsJQt7GQcXFuRycXFxeo9AGrGVLOwlC3vJwVaysJdxcGFBLmc2m/UegTRiK1nYSxb2koOtZGEv4+DCglyOn4YpB1vJwl6yNIVeB05Z8PinO7G1LAH/2noSJzIK9R7pojSFVu6EvYyDCwtyudTUVL1HII3YShb2ksXde3266wyuX/EbtqR44mRlBFbty8Kwl7di9a4zeo9Wb+7eyt2wl3FwYUEuFxERofcIpBFbycJesrhzrxMZhXhkzT5YFaBggoIJVgVYFTBnzT6cFHbkwp1buSP2Mg6nLyzmzJmDZs2aOftqSbCCggK9RyCN2EoW9pLFnXt9uusMTCaTw/NMJhNWCTtq4c6t3BF7GYeXs69w4cKFzr5KEs7Hx0fvEUgjtpKFvWRx515ns4tr/RwrpRTOZsv6ADN3buWO2Ms4+FQoIiIiapCW4f51HrFoGe7fyBMRkR64sCCXKysr03sE0oitZGEvWdy514Tereo8YjGxd6tGnqhh3LmVO2Iv4+DCglwuKChI7xFII7aShb1kcedeCZGBWDSuGzxM+L+Xbit4mAAPE7BoXDfERwbqPWK9uHMrd8RexuGShYWnp6crrrZeli1bhvj4ePj5+aFv377YuXNnnduvXr0anTp1gp+fH7p27Yqvv/66kSZ1f1lZWXqPQBqxlSzsJYu79xrfuxX+e/cVGBpbiXjPLEzsFoHNDw7BeGFHKwD3b+Vu2Ms4XLKwqO1waGNZtWoVZs2ahfnz52P37t3o3r07Ro4cCYvF4nD77du345ZbbsGdd96JPXv2YMyYMRgzZgwOHDjQyJO7p5iYGL1HII3YShb2kqUp9Lq0TTSendAHQ3xOYOaQeHFHKqo0hVbuhL2M46IWFs888wwWLVqEb775xuHHqNf2Aq7GsnjxYtx1112YOnUqunTpghUrViAgIADvvPOOw+1feeUVjBo1Cg899BA6d+6Mp59+Gpdffjlee+21Rp7cPaWkpOg9AmnEVrKwlyzsJQdbycJexnFRC4snnngC48ePR2lpKd5++238z//8T63bpqWlYcuWLVi2bBnuvfdeDBkyBM2bN7/ogS+krKwMSUlJGD58uO00Dw8PDB8+HDt27HB4mR07dthtDwAjR46sdXuqn7i4OL1HII3YShb2koW95GArWdjLOOr1ORZ33303nnrqKURHR6Nt27Zo27YtxowZU2M7pRQGDRqEI0eOICwsDB07dkSnTp2wevVqrF+/Hh06dHDW/DVkZGSgsrKyxuKlefPm+PPPPx1eJjU11eH2dX1EfGlpKUpLS23/zsvLO/cfe/cC57+IKDwcSEgASkqAP/6oeUWXX37ufw8fBgqrfTJpfDwQEQGkpwNnqn24UHAw0KEDUFkJ/P47vNLTEZOcDK99+4CUFKBrV8DbGzh+HMjNtb9sXBzQvDmQnQ2cOGF/nr8/0Lnzuf/esweo/rS2zp3PbXPqFJCZaX9e8+bnrjs/Hzh61HayxWJBdFzcuZkAYP9+oLzc/rIdOpy7TWYzkJZmf16zZkCbNkBxMXDokP15JhPQs+e5/z506Nw250tIONcgLe3cdZ8vNBRo1+7cLPv3o4bu3QFPz3O3JT/f/rxWrYCoKCArCzh50v68wECgY8dz/717d83r7dIF8PM7973PzrY/Lzb23FdeHnDsmP15vr7ApZee++99+4CKCvvzL7nk3M/e2bNA9af9RUYCrVsDRUVA9fuBhwfQowcAIG3LFjQPDbU/v21bICwMSE0FkpPtzwsLO3d+WRng6KmDPXqcu/4jR4DqH2DUuvW5uTIygNOn7c8LCjp3e6zWc/ep6i67DPDxAf76C8jJsT+vRQsgJubc6X/9ZX+en9+57z9w7nqtVvvzO3UCAITk5Pz/falKdDTQsuW523HkiP3lvLyAbt3O/ffBg8B5jw0AgPbtgZCQc9dX/S9rDXiMSPbxQYvLLtP0GFGDgR4jAJybxc0fIywWC6Kjo0U/RuCPP879rJ6v2mOE3f8fFReLfIwwm82IS093/BgREHBunowM+/MM+BhRn98jahD0GGGxWBA9YID4xwibao8RNX7H0+Mxourn7EJUPaxbt0716tVLzZ8/XxUUFNS6nclkUhMmTFD9+vVTW7dutZ0eHx9fn91dFLPZrACo7du3253+0EMPqT59+ji8jLe3t/r444/tTlu2bJmKjo6udT/z589XAGp85Z67C/3/1623qrNnz6rSgwftT/+/r5SUFFVeXq5KL7+8xnklb72lMjMzVeELL9S87IgR6uzZs6oiK8vh9ab/8YcqKSlRRVddVeO88kWLlMViUYXvvVfjPGvPnurs2bNKKaWsPj41zs/5+WdVWFioCidNqnFe5cMPq5SUFFX8zTc1rzcuzna9FTExNc7P/+9/VV5eniqcObPmZf/+93Pfw927a57n46PMZrOqrKxUpZddVuP8on//W2VlZanCp5+u+X267jp19uxZVZ6c7PB7aDl2TJWWlqrixMQa55UuXqzS09NV0cqVNWe68krbbXV0vVk7d6qioiJVOHZsjfMqnnhCpaamquJ162peb7t2//89jIiocX7et9+q/Px8Vfg//1Pzsvfco8xmsyrZvr3mecHBymw2K6vVqko7dKhxfuEnn6icnBxV8MQTNW/PTTeps2fPqrK//nJ4W1NPnVJlZWWq5Mora34Ply079/P9yis1L5uYqM6ePasqi4ocXm/m77+r4uJiVXTNNTV/vp9+WqWlpanCTz6peVu7dLF9DyuDgmqcn7tlizp+/LjaecUVNX++//lPlZycrEq2bKl5vZGRtustb9OmxvkFa9ao3NxcVfjQQzVvTwMeIwpWrOBjBPgYYbveRniMKLvkkhrnu+NjRGlpaa2PEQUFBapwypSaP98GfIzg7xF8jLC73gY8RmhlOjezdpWVlVi5ciVWrFiBe+65B9OnT4eHh/0zqjw9PVFZWYn9+/dj3rx5KCwsxNNPP41bbrkFf1X/64CTlZWVISAgAJ999pnd0ZTJkycjJycHX3zxRY3LtG7dGrNmzcL9999vO23+/PlYt24dfne0iofjIxatWrVC7rZtCNHhiEV6ejrWrl2LsWPHIioqylB/acjKykJE8+Zu/9dIG8F/jcz88Uc0C6z2YssmdsQiJTcXn7zwAm4bNercfamKAf8amR4YiKiOHd3+r5EA3OIxIisrCxEREaIfI7QcsbD7/6MOHUQ+RlgsFkQnJ/OIhZDHiKysLET07Sv+McKm2mNEjd/xDHzEot4LCwA4ffo0Nm/ejNmzZyMyMhIvvvgirrvuOtv5VQuLKrt27cK8efOwbds2bN68GX379q3vLuulb9++6NOnD1599VUAgNVqRevWrTFjxgw88sgjNbafOHEiioqK8N///td2Wv/+/dGtWzesWLFC0z7z8vIQGhqK3NxchISEOOeG1ENKSgpWrlyJ6dOnIzY2ttH3X5f8/HwEBwfrPQZpwFbGvi9Vx16yNJVeku5DtWkqrdyFu/eSdJ+q12ssRo0ahUOHDqFVq1a2X9wvueQSLF++HN9//z2WLl3q8HK9e/fG119/jZ9//hmPPfYYTCYTNm3a5Iz5HZo1axYmT56M3r17o0+fPli6dCkKCwsxdepUAMAdd9yBuLg4LFy4EADwz3/+E4mJiXj55ZdxzTXX4JNPPsGuXbuwcuVKl83YlJy/yCRjYytZ2EsW9pKDrWRhL+Oo18Li+eefR9euXWt8AN7bb7+NTv/3okcAqO0gyIABA/D9999jy5YtFzGqdhMnTkR6ejrmzZuH1NRU9OjRAxs2bLC9QPv06dN2T9/q378/Pv74YzzxxBN47LHH0KFDB6xbtw6XXXaZS+dsKqzVDyWTYbGVLOwlC3vJwVaysJdx1GthkZmZiYqKCoefrH3+J1XXFXjnzp3oVvU8QxeaMWMGZsyY4fC8rVu31jht/PjxGD9+vIunapr8/f31HoE0YitZ2EsW9pKDrWRhL+Oo1+dY9O/fH/Pnz8d7772HwmovEGrbtm2dl01KSsJDDz2E0tJSNGvWrP6Tklg51V84R4bFVrKwlyzsJQdbycJexlGvIxb+/v54/vnn8e233+Lqq69GUFAQevToga5duyIiIgIhISHw8fFBfn4+8vLycPLkSezduxd79+7FiBEj8NhjjyE8PNxVt4UMKjo6Wu8RSCO2koW9ZGEvOdhKFvYyjnotLKqMHDkSI0eOxO7du/HNN9/gzTffxNmzZ2GxWFBcXIzIyEhER0eje/fuGD16NF5++WWEhYU5eXSSIjU1lZ+KKQRbycJesrCXHGwlC3sZx0UtLKpcfvnluPzyy/H44487ax5yQ7yzy8FWsrCXLOwlB1vJwl7GUa/XWBBdDHP1D5Qhw2IrWdhLFvaSg61kYS/j4MKCXI7PfZSDrWRhL1nYSw62koW9jIMLC3K5rKwsvUcgjdhKFvaShb3kYCtZ2Ms4uLAglwsKCtJ7BNKIrWRhL1nYSw62koW9jIMLC3K5srIyvUcgjdhKFvaShb3kYCtZ2Ms4GvSuUI4cOXIEbdu2hZeX06+aiMgl8vPzUVBQAADIyMiw+1/g3F/DgoODdZmNiIhICqf/9t+5c2ccOnQIl1xyibOvmoTy8fHRewTSqKm2SkpKwrZt2+xOW7t2re2/ExMTMWTIkEae6sKaai+p2EsOtpKFvYzD6QsLpZSzr5KEKygoQGBgoN5jkAZNtVWvXr3QsWPHWs836vN3m2ovqdhLDraShb2Mg89XIpeLiIjQewTSqKm2Cg4OFvlUp6baSyr2koOtZGEv4+CLt8nlLBaL3iOQRmwlC3vJwl5ysJUs7GUcXFiQy8XFxek9AmnEVrKwlyzsJQdbycJexsGFBbmc2WzWewTSiK1kYS9Z2EsOtpLFnXsdOGXB45/uxNayBPxr60mcyCjUe6Q6cWFBLhcTE6P3CKQRW8nCXrKwlxxsJYu79vp01xlcv+I3bEnxxMnKCKzal4VhL2/F6l1n9B6tVlxYkMvxuY9ysJUs7CULe8nBVrK4Y68TGYV4ZM0+WBWgYIKCCVYFWBUwZ80+nDTokQunLyzmzJmDZs2aOftqSbCwsDC9RyCN2EoW9pKFveRgK1ncsdenu87AZDI5PM9kMmGVQY9aOH1hsXDhQi4syE5RUZHeI5BGbCULe8nCXnKwlSzu2OtsdnGtnw2nlMLZ7OJGnkgbPhWKXM7Lix+XIgVbycJesrCXHGwlizv2ahnuX+cRi5bh/o08kTZcWJDLeXjwx0wKtpKFvWRhLznYShZ37DWhd6s6j1hM7N2qkSfSxv1KkOEUFxvzcB3VxFaysJcs7CUHW8nijr0SIgOxaFw3eJjwfy/dVvAwAR4mYNG4boiPDNR7RIecvrBYsmQJAODgwYOorKx09tWTQKGhoXqPQBqxlSzsJQt7ycFWsrhrr/G9W+G/d1+BobGViPfMwsRuEdj84BCMN+jRCsAFC4sePXoAAB577DF06dIFPXr0wK233ornn38e69evd/buSICMjAy9RyCN2EoW9pKFveRgK1ncudelbaLx7IQ+GOJzAjOHxBv2SEUVp7zaJT8/H8HBwQCAoUOHAgC++OILAEBBQQEOHjyI/fv3Y9OmTbj22mudsUsSJC4uTu8RSCO2koW9ZGEvOdhKFvYyDqccsRg0aBBSU1MdnhcUFIS+ffti2rRpWLp0qTN2R8KYzWa9RyCN2EoW9pKFveRgK1nYyzicsrDo2bMn+vbtiz///NPu9L179+Lqq692xi5IsBYtWug9AmnEVrKwlyzsJQdbycJexuGUhcW7776LKVOmYODAgfjpp59w5MgRTJgwAb169YKnp6czdkGCpaSk6D0CacRWsrCXLOwlB1vJwl7G4bRPFFmwYAF8fX1x1VVXobKyEsOGDcOOHTvQp08fZ+2ChOInscvBVrKwlyzsJQdbycJexuGUIxZpaWn45z//iWeeeQZdunSBt7c3pkyZwkUFAQDy8vL0HoE0YitZ2EsW9pKDrWRhL+NwysIiISEBP/zwA1avXo2kpCSsWbMG06dPx4svvuiMqyfhfH199R6BNGIrWdhLFvaSg61kYS/jcMpTod555x3cfPPNtn+PGjUKW7ZswbXXXouTJ09i2bJlztgNEREREREZlFOOWJy/qKhy+eWXY/v27di8ebMzdkGClZWV6T0CacRWsrCXLOwlB1vJwl7G4fRP3j5ffHw8tm/f7spdkABBQUF6j0AasZUs7CULe8nBVrKwl3G4dGEBAOHh4a7eBRlcVlaW3iOQRmwlC3vJwl5ysJUs7GUcLl9YEMXExOg9AmnEVrKwlyzsJQdbycJexsGFBbkcP7hGDraShb1kYS852EoW9jIOLizI5eLi4vQegTRiK1nYSxb2koOtZGEv43DawiInJwcvv/wypk2bhmnTpmHJkiXIzc111tWTYGazWe8RSCO2koW9ZGEvOdhKFvYyDqcsLHbt2oV27dphyZIlyMrKQlZWFhYvXox27dph9+7dztgFCRYVFaX3CKQRW8nCXrKwlxxsJQt7GYdTFhYPPPAArr/+epw8eRJr167F2rVrceLECVx77bW4//77nbELEiwnJ0fvEUgjtpKFvWRhLznYShb2Mg6nfPL2rl278Oabb8LL6/+vzsvLCw8//DB69+7tjF2QYAEBAXqPQBqxlSzsJQt7ycFWsrCXcTjliEVISAhOnz5d4/QzZ84gODjYGbsgwSoqKvQegTRiK1nYSxb2koOtZGEv43DKwmLixIm48847sWrVKpw5cwZnzpzBJ598gmnTpuGWW25xxi5IMKvVqvcIpBFbycJesrCXHGwlC3sZh1OeCvXSSy/BZDLhjjvuQEVFBZRS8PHxwT333IPnn3/eGbsgwfz9/fUegTRiK1nYSxb2koOtZGEv43DKEQsfHx+88soryM7Oxt69e/H7778jKysLS5Ysga+vrzN2QYLxbYflYCtZ2EsW9pKDrWRhL+NwyhGLp556qs7z582b54zdkFCRkZF6j0AasZUs7CULe8nBVrKwl3E4ZWHx+eef2/27vLwcJ06cgJeXF9q1a8eFRROXlpbGT8UUgq1kYS9Z2EsOtpKFvYzDKQuLPXv21DgtLy8PU6ZMwY033uiMXZBgvLPLwVaysJcs7CUHW8nCXsbhlNdYOBISEoIFCxZg7ty5rtoFCWE2m/UegTRiK1nYSxb2koOtZGEv43DZwgI492IavqCGoqOj9R6BNGIrWdhLFvaSg61kYS/jcMpTof71r3/Z/VsphZSUFHzwwQcYPXq0M3ZBgmVmZiImJkbvMUgDtpKFvWRhLznYShb2Mg6nLCyWLFli928PDw9ERUVh8uTJePTRR52xCxIsJCRE7xFII7aShb1kYS852EoW9jIOpywsTpw44YyrITdVUlKCgIAAvccgDdhKFvaShb3kYCtZ2Ms4XPoaCyIAMJlMeo9AGrGVLOwlC3vJwVaysJdxXPQRi1mzZmnedvHixRe7G3IDPj4+eo9AGrGVLOwlC3vJwVaysJdxXPTCovpnV+zevRsVFRXo2LEjAODIkSPw9PREr169GjYhiVdQUIDAwEC9xyAN2EoW9pKFveRgK1nYyzguemGxZcsW238vXrwYwcHBeP/99xEeHg4AyM7OxtSpUzFo0KCGT0miRURE6D0CacRWsrCXLOwlB1vJwl7G4ZTXWLz88stYuHChbVEBAOHh4XjmmWfw8ssvO2MXJJjFYtF7BNKIrWRhL1nYSw62koW9jMMpC4u8vDykp6fXOD09PR35+fnO2AUJFhcXp/cIpBFbycJesrCXHGwlC3sZh1MWFjfeeCOmTp2KtWvX4uzZszh79izWrFmDO++8E2PHjnXGLqgOB05Z8PinO7G1LAH/2noSJzIK9R7Jjtls1nsE0oitZGEvWdhLDraShb2MwymfY7FixQrMnj0bkyZNQnl5+bkr9vLCnXfeiRdffNEZu6BafLrrDB5Zsw9KeQKIwKl9WVi1bysWjeuG8b1b6T0eAPDTMAVhK1nYSxb2koOtZGEv43DKEYuAgAAsX74cmZmZ2LNnD/bs2YOsrCwsX7680V+ln5WVhVtvvRUhISEICwvDnXfeiYKCgjovs3LlSgwZMgQhISEwmUzIyclpnGEb6ERGIR5Zsw9WBSiYoGCCVQFWBcxZsw8nDXLkIi0tTe8RSCO2koW9ZGEvOdhKFvYyDqd+QF5gYCC6deuGbt266fa2X7feeisOHjyIjRs3Yv369fjhhx8wffr0Oi9TVFSEUaNG4bHHHmukKZ3j011nav1QGJPJhFW7zjTyRI6d/6J+Mja2koW9ZHHnXvn5+UhJSUFKSgoyMjIAABkZGbbTpL3e0p1buSP2Mo4GfUDe008/jcDAwAt+WF5jfUDeoUOHsGHDBvz222/o3bs3AODVV1/F1VdfjZdeegktWrRweLn7778fALB169ZGmdNZzmYXQynl8DylFM5mFzfyRI4VFRXB399f7zFIA7aShb1kcedeSUlJ2LZtm91pa9eutf13YmIihgwZ0shTXTx3buWO2Ms4GvQBeVWvp6j+YXnna8yPWd+xYwfCwsJsiwoAGD58ODw8PPDrr7/ixhtvbLRZGkPLcP9z318HiwuTyYSW4ca4k3l5OeWlPNQI2EoW9pLFnXv16tXL9gG5jgQFBTXiNA3nzq3cEXsZh1M+IO/8/9ZTamoqoqOj7U7z8vJCREQEUlNTnbqv0tJSlJaW2v6dl5fn1OvXYkLvVnhj23GH5ymlMNEgL9728HDqM+7IhdhKFvaSxZ17BQcHIzg4WO8xnMadW7kj9jIOp5QoLi5GUVGR7d+nTp3C0qVL8d133znj6vHII4/AZDLV+fXnn386ZV9aLVy4EKGhobavVq3O/RJfUlKC5ORkWK1W29ufmc1mlJWVwWKxoKCgADk5OcjKykJxcTFSU1NRUVFht215eTlSU1NRVFSErKwsZGdno7CwEGlpaSgvL7dt61Oag4VjL4MJ+L+Xbit4mAAPE/DEiATEBnvZXa9SCmazGaWlpUhPT0deXh7y8vKQkZGB4uJizXMXFRU5nLuioqLG3AUFBUhJSbGb22w2w2q1Ijk5GcXFxcjMzERubi7y8/ORnp6O0tJSu3mrz52fn4/c3FxkZmbWOnd5eTnS0tJQUFCA7Ozses2dk5ODgoICWCwWlJWV1Tp3RkaG7XvoaO6qy9Q1d2VlpcO5CwsLa8xd/XtYNXdxcbHmuUtKShzOnZycfO7pc2fP2s1tsVhqzJ2SknLBuWv7mTWbzaisrERKSord3Pn5+fWaOyMj44L3tfz8fLv7mjPnPv9n1mKx2Nqff19LTk52eF9z5mNE1XYXmvtC9zW9HyMcze2OjxF5eXniHyOqz+2ujxElJSVu8Rhxod8j3OUxwmKxuMVjRG2/R5SVlQEACgsLdXuM0MqkanuSfj2MGDECY8eOxd13342cnBx07NgRPj4+yMjIwOLFi3HPPfc06PrT09ORmZlZ5zZt27bFhx9+iAcffBDZ2dm20ysqKuDn54fVq1df8KlQW7duxdChQ5GdnY2wsLA6t3V0xKJVq1bIzc1FSEjIhW+UEx08ZcHL63bghCUXV17WAf9zVTfER+rz4nlHSktL4evrq/cYpAFbycJesrCXHGwli7v3SklJwcqVKzF9+nTExsbqPU6dnHLEYvfu3Rg0aBAA4LPPPkNMTAxOnTqFf//73/jXv/7V4OuPiopCp06d6vzy8fFBv379kJOTg6SkJNtlN2/eDKvVir59+zZ4jvP5+voiJCTE7ksvl7aJxrMT+mCIzwnMHBJvqEUFANs7hJDxsZUs7CULe8nBVrKwl3E4ZWFRVFRke27ld999h7Fjx8LDwwNXXnklTp065YxdaNK5c2eMGjUKd911F3bu3Imff/4ZM2bMwM0332x7Ryiz2YxOnTph586dtsulpqZi7969OHbsGABg//792Lt3L7KyshptdncWFxen9wikEVvJwl6ysJccbCULexmHUxYW7du3x7p163DmzBl8++23GDFiBADAYrE0+l/yP/roI3Tq1AnDhg3D1VdfjYEDB2LlypW288vLy3H48GG714SsWLECPXv2xF133QUAGDx4MHr27Ikvv/yyUWd3V/V5bh7pi61kYS9Z2EsOtpKFvYzDKe/PNW/ePEyaNAkPPPAAhg0bhn79+gE4d/SiZ8+eztiFZhEREfj4449rPT8+Pr7GZz88+eSTePLJJ108WdNV2+eHkPGwlSzsJQt7ycFWsrCXcTjliMVNN92E06dPY9euXdiwYYPt9GHDhmHJkiXO2AUJlpycrPcIpBFbycJesrCXHGwlC3sZh9M+USQmJgYxMTF2p/Xp08dZV0+CRUZG6j0CacRWsrCXLOwlB1vJwl7G4bRPFPnxxx9x2223oV+/frbnun3wwQf46aefnLULEkqPDw+ki8NWsrCXLOwlB1vJwl7G4ZSFxZo1azBy5Ej4+/tjz549ts93yM3NxXPPPeeMXZBg7vze0u6GrWRhL1nYSw62koW9jMMpC4tnnnkGK1aswJtvvglvb2/b6QMGDMDu3budsQsiIiIiIjIwpywsDh8+jMGDB9c4PTQ0FDk5Oc7YBQlW9VH0ZHxsJQt7ycJecrCVLOxlHE5ZWMTExNg+XO58P/30E9q2beuMXZBggYHG+iRwqh1bycJesrCXHGwlC3sZh1MWFnfddRf++c9/4tdff4XJZEJycjI++ugjzJ49G/fcc48zdkGCZWdn6z0CacRWsrCXLOwlB1vJwl7G4ZS3m33kkUdgtVoxbNgwFBUVYfDgwfD19cXs2bNx3333OWMXJFj1tyEm42IrWdhLFvaSg61kYS/jcMoRC5PJhMcffxxZWVk4cOAAfvnlF6Snp+Ppp59GcXGxM3ZBgqWkpOg9AmnEVrKwlyzsJQdbycJexuG0z7EAAB8fH3Tp0gV9+vSBt7c3Fi9ejISEBGfuggSKi4vTewTSiK1kYS9Z2EsOtpKFvYyjQQuL0tJSPProo+jduzf69++PdevWAQDeffddJCQkYMmSJXjggQecMScJVvWBiWR8bCULe8nCXnKwlSzsZRwNeo3FvHnz8MYbb2D48OHYvn07xo8fj6lTp+KXX37B4sWLMX78eHh6ejprVhIqKipK7xFII7aShb1kYS852EoW9jKOBh2xWL16Nf7973/js88+w3fffYfKykpUVFTg999/x80338xFBQEAP8tEELaShb1kYS852EoW9jKOBi0szp49i169egEALrvsMvj6+uKBBx6AyWRyynDkHgICAvQegTRiK1nYSxb2koOtZGEv42jQwqKyshI+Pj62f3t5eSEoKKjBQ5F7qaio0HsE0oitZGEvWdhLDraShb2Mo0GvsVBKYcqUKfD19QUAlJSU4O67767xCYhr165tyG5IOKvVqvcIpBFbycJesrCXHGwlC3sZR4MWFpMnT7b792233dagYcg9+fn56T0CacRWsrCXLOwlB1vJwl7G0aCFxbvvvuusOciN5eXl8fmPQrCVLOwlC3vJwVaysJdxOPUD8ogciYyM1HsE0oitZGEvWdhLDraShb2MgwsLcrm0tDS9RyCN2EoW9pKFveRgK1nYyzi4sCCXi4uL03sE0oitZGEvWdhLDraShb2MgwsLcjmz2az3CKQRW8nCXrKwlxxsJQt7GQcXFuRyzZs313sE0oitZGEvWdhLDraShb2MgwsLcrmMjAy9RyCN2EoW9pKFveRgK1nYyzi4sCCXCwkJ0XsE0oitZGEvWdhLDraShb2MgwsLcrmSkhK9RyCN2EoW9pKFveRgK1nYyzi4sCCXM5lMeo9AGrGVLOwlC3vJwVaysJdxcGFBLuft7a33CKQRW8nCXrKwlxxsJQt7GQcXFuRyhYWFeo9AGrGVLOwlC3vJwVaysJdxcGFBLhcREaH3CKQRW8nCXrKwlxxsJQt7GQcXFuRyFotF7xFII7aShb1kYS852EoW9jIOLizI5eLi4vQegTRiK1nYSxb2koOtZGEv4+DCglzObDbrPQJpxFaysJcs7CUHW8nCXsbBhQW5XGxsrN4jkEZsJQt7ycJecrCVLOxlHFxYkMulpqbqPQJpxFaysJcs7CUHW8nCXsbBhQW5XHh4uN4jkEZsJQt7ycJecrCVLOxlHF56D0Dur6ioCP7+/nqPQRqwlSzsJQt7ycFWsrhjr/z8fBQUFAAAMjIy7P4XAIKCghAcHKzLbHXhwoJczsuLP2ZSsJUs7CULe8nBVrK4Y6+kpCRs27bN7rS1a9fa/jsxMRFDhgxp5KkuzP1KkOF4ePAZd1KwlSzsJQt7ycFWsrhjr169eqFjx461nh8UFNSI02jHhQW5XElJiSEP11FNbCULe8nCXnKwlSzu2Cs4OFjkbXK/JR4ZTkhIiN4jkEZsJQt7ycJecrCVLOxlHFxYkMud/2IjMja2koW9ZGEvOdhKFvYyDi4syOVatGih9wikEVvJwl6ysJccbCULexkHFxbkcsnJyXqPQBqxlSzsJQt7ycFWsrCXcXBhQS7HvyTIwVaysJcs7CUHW8nCXsbBhQW5HP+SIAdbycJesrCXHGwlC3sZBxcW5HKRkZF6j0AasZUs7CULe8nBVrKwl3FwYUEul5eXp/cIpBFbycJesrCXHGwlC3sZBxcW5HJ+fn56j0AasZUs7CULe8nBVrKwl3FwYUEuZ7Va9R6BNGIrWdhLFvaSg61kYS/j4MKCXK6iokLvEUgjtpKFvWRhLznYShb2Mg4uLMjlAgIC9B6BNGIrWdhLFvaSg61kYS/j4MKCXC47O1vvEUgjtpKFvWRhLznYShb2Mg4uLMjlYmJi9B6BNGIrWdhLFvaSg61kYS/j4MKCXC4lJUXvEUgjtpKFvWRhLznYShb2Mg4uLMjl4uLi9B6BNGIrWdhLFvaSg61kYS/j4MKCXM5sNus9AmnEVrKwlyzsJQdbycJexsGFBblcdHS03iOQRmwlC3vJwl5ysJUs7GUcXFiQy2VlZek9AmnEVrKwlyzsJQdbycJexsGFBblcYGCg3iOQRmwlC3vJwl5ysJUs7GUcXFiQy5WXl+s9AmnEVrKwlyzsJQdbycJexsGFBbmcUkrvEUgjtpKFvWRhLznYShb2Mg63W1hkZWXh1ltvRUhICMLCwnDnnXeioKCgzu3vu+8+dOzYEf7+/mjdujVmzpyJ3NzcRpzavfn5+ek9AmnEVrKwlyzsJQdbycJexuF2C4tbb70VBw8exMaNG7F+/Xr88MMPmD59eq3bJycnIzk5GS+99BIOHDiA9957Dxs2bMCdd97ZiFO7t7y8PL1HII3YShb2koW95GArWdjLOEzKjY4fHTp0CF26dMFvv/2G3r17AwA2bNiAq6++GmfPnkWLFi00Xc/q1atx2223obCwEF5eXpouk5eXh9DQUOTm5iIkJOSib8PFSklJwcqVKzF9+nTExsY2+v7rUlFRofn7SPpiK1nYSxb2koOtZGEv43CrIxY7duxAWFiYbVEBAMOHD4eHhwd+/fVXzddTtTio64e0tLQUeXl5dl/kWFpamt4jkEZsJQt7ycJecrCVLOxlHG61sEhNTa3xISleXl6IiIhAamqqpuvIyMjA008/XefTpwBg4cKFCA0NtX21atUKAFBSUoLk5GRYrVbbJ0GazWaUlZXBYrGgoKAAOTk5yMrKQnFxMVJTU1FRUWG3bXl5OVJTU1FUVISsrCxkZ2ejsLAQaWlpKC8vt9u2srISycnJKCkpAQAUFBQgPz8f6enpKC0ttdtWKQWz2YzS0lKkp6fbFkQZGRkoLi7WPHdRUZHDuSsqKmrMXVBQAC8vrxpzW61WJCcno7i4GJmZmcjNza0xd9W81efOz89Hbm4uMjMza527vLwcaWlpKCgoQHZ2dr3mzsnJQUFBASwWC8rKymqdOyMjw/Y9dDR31WXqmruystLh3IWFhTXmrv49rJq7uLhY89wlJSUO505OTrZ78VvV3BaLpcbcKSkpF5z7Qj+zKSkpdnPn5+fXa+6MjIwL3tfy8/Pt7mvOnPv8n1mLxeLwvpacnOzwvubMx4jw8HDNjxF13df0foxwNLc7PkZERkaKf4yoPre7PkbExcW5xWNEfX6PkPwY4efn5xaPEUb+PUIrEU+FeuSRR7Bo0aI6tzl06BDWrl2L999/H4cPH7Y7Lzo6GgsWLMA999xT53Xk5eXhqquuQkREBL788kt4e3vXum1paSlKS0vtLtuqVSs+FcqBqgdpMj62koW9ZGEvOdhKFvYyDhFPSHvwwQcxZcqUOrdp27YtYmJiYLFY7E6vqKhAVlYWYmJi6rx8fn4+Ro0aheDgYHz++ed1LioAwNfXF76+vprmb+qaN2+u9wikEVvJwl6ysJccbCULexmHiIVFVFQUoqKiLrhdv379kJOTg6SkJPTq1QsAsHnzZlitVvTt27fWy+Xl5WHkyJHw9fXFl19+ybctc7KMjIwLLuzIGNhKFvaShb3kYCtZ2Ms43Oo1Fp07d8aoUaNw1113YefOnfj5558xY8YM3HzzzbZ3hDKbzejUqRN27twJ4NyiYsSIESgsLMTbb7+NvLw8pKamIjU1FZWVlXreHLehx1PD6OKwlSzsJQt7ycFWsrCXcbjVwgIAPvroI3Tq1AnDhg3D1VdfjYEDB2LlypW288vLy3H48GEUFRUBAHbv3o1ff/0V+/fvR/v27REbG2v7OnPmjF43w61UvbCcjI+tZGEvWdhLDraShb2MQ8RToeojIiICH3/8ca3nx8fH2737zZAhQ/hR8C7m4eF261e3xVaysJcs7CUHW8nCXsbBEuRy/NAaOdhKFvaShb3kYCtZ2Ms4uLAgl6t62hkZH1vJwl6ysJccbCULexkHFxbkcmFhYXqPQBqxlSzsJQt7ycFWsrCXcXBhQS6Xnp6u9wikEVvJwl6ysJccbCULexkHFxbkcvw0TDnYShb2koW95GArWdjLOLiwIJczm816j0AasZUs7CULe8nBVrKwl3FwYUEuFxsbq/cIpBFbycJesrCXHGwlC3sZBxcW5HKpqal6j0AasZUs7CULe8nBVrKwl3FwYUEuFx4ervcIpBFbycJesrCXHGwlC3sZBxcW5HKFhYV6j0AasZUs7CULe8nBVrKwl3FwYUEu5+Pjo/cIpBFbycJesrCXHGwlC3sZBxcWRERERETUYFxYkMuVlpbqPQJpxFaysJcs7CUHW8nCXsbBhQW5XEhIiN4jkEZsJQt7ycJecrCVLOxlHFxYkMtlZGToPQJpxFaysJcs7CUHW8nCXsbBhQW5XIsWLfQegTRiK1nYSxb2koOtZGEv4+DCglwuOTlZ7xFII7aShb1kYS852EoW9jIOLizI5eLi4vQegTRiK1nYSxb2koOtZGEv4+DCglzObDbrPQJpxFaysJcs7CUHW8nCXsbBhQW5XGRkpN4jkEZsJQt7ycJecrCVLOxlHFxYkMvl5eXpPQJpxFaysJcs7CUHW8nCXsbBhQW5nJ+fn94jkEZsJQt7ycJecrCVLOxlHFxYkMtZrVa9RyCN2EoW9pKFveRgK1nYyzi4sCCXq6io0HsE0oitZGEvWdhLDraShb2MgwsLcrmAgAC9RyCN2EoW9pKFveRgK1nYyzi4sCCXy87O1nsE0oitZGEvWdhLDraShb2MgwsLcrnmzZvrPQJpxFaysJcs7CUHW8nCXsbBhQW5XGpqqt4jkEZsJQt7ycJecrCVLOxlHFxYkMvFxcXpPQJpxFaysJcs7CUHW8nCXsbBhQW5nNls1nsE0oitZGEvWdhLDraShb2MgwsLcrno6Gi9RyCN2EoW9pKFveRgK1nYyzi4sCCXy8rK0nsE0oitZGEvWdhLDraShb2MgwsLcrmgoCC9RyCN2EoW9pKFveRgK1nYyzi4sCCXKysr03sE0oitZGEvWdhLDraShb2MgwsLcjmllN4jkEZsJQt7ycJecrCVLOxlHF56D0AXLz8/HwUFBQCAjIwMu/8Fzh0aDA4O1mW28/n5+ek9AmnEVrKwlyzsJQdbycJexsGFhWBJSUnYtm2b3Wlr1661/XdiYiKGDBnSyFPVlJeXh4CAAL3HIA3YShb2koW95GArWdjLOEyKx4+cIi8vD6GhocjNzUVISEij7PP8IxaOGOWIRXl5Oby9vfUegzRgK1nYSxb2koOtZGEv4+ARC8GCg4MNsXC4EIvFwk/FFIKtZGEvWdhLDraShb2Mg0csnESPIxZEREREREbBd4UilzObzXqPQBqxlSzsJQt7ycFWsrCXcfCIhZPwiEXtKioq4OXFZ91JwFaysJcs7CUHW8nCXsbBIxbkcue/BS4ZG1vJwl6ysJccbCULexkHFxbkcqGhoXqPQBqxlSzsJQt7ycFWsrCXcXBhQS5XXFys9wikEVvJwl6ysJccbCULexkHFxbkch4e/DGTgq1kYS9Z2EsOtpKFvYyDJcjl+IIqOdhKFvaShb3kYCtZ2Ms4uLAglysqKtJ7BNKIrWRhL1nYSw62koW9jIMLC3K5sLAwvUcgjdhKFvaShb3kYCtZ2Ms4uLAgl0tPT9d7BNKIrWRhL1nYSw62koW9jIMfkOck/IA8IiIiImrKeMSCXM5sNus9AmnEVrKwlyzsJQdbycJexsEjFk7CIxa1s1qtfCs4IdhKFvaShb3kYCtZ2Ms4WIFcLjU1Ve8RSCO2koW9ZGEvOdhKFvYyDr7xr5NUHfjJy8vTeRLj8fLy4vdFCLaShb1kYS852EoW9mocwcHBMJlMdW7DhYWT5OfnAwBatWql8yRERERERM6l5en+fI2Fk1itViQnJ2tazTUleXl5aNWqFc6cOcPXnhgcW8nCXrKwlxxsJQt7NR4esWhEHh4eaNmypd5jGFZISAjv8EKwlSzsJQt7ycFWsrCXMfDF20RERERE1GBcWBARERERUYNxYUEu5evri/nz58PX11fvUegC2EoW9pKFveRgK1nYy1j44m0iIiIiImowHrEgIiIiIqIG48KCiIiIiIgajAsLIiIiIiJqMC4sqE4//PADrrvuOrRo0QImkwnr1q2zO3/t2rUYMWIEmjVrBpPJhL1799a4jpKSEtx7771o1qwZgoKCMG7cOKSlpdltc/r0aVxzzTUICAhAdHQ0HnroIVRUVLjwlrmnunqVl5djzpw56Nq1KwIDA9GiRQvccccdSE5OtruOrKws3HrrrQgJCUFYWBjuvPNOFBQU2G2zb98+DBo0CH5+fmjVqhVeeOGFxrh5budC968nn3wSnTp1QmBgIMLDwzF8+HD8+uuvdtuwV+O5UK/z3X333TCZTFi6dKnd6ezVOC7UasqUKTCZTHZfo0aNstuGrRqPlvvWoUOHcP311yM0NBSBgYG44oorcPr0adv5/F3DGLiwoDoVFhaie/fuWLZsWa3nDxw4EIsWLar1Oh544AH897//xerVq7Ft2zYkJydj7NixtvMrKytxzTXXoKysDNu3b8f777+P9957D/PmzXP67XF3dfUqKirC7t27MXfuXOzevRtr167F4cOHcf3119ttd+utt+LgwYPYuHEj1q9fjx9++AHTp0+3nZ+Xl4cRI0agTZs2SEpKwosvvognn3wSK1eudPntczcXun9dcskleO2117B//3789NNPiI+Px4gRI5Cenm7bhr0az4V6Vfn888/xyy+/oEWLFjXOY6/GoaXVqFGjkJKSYvv6z3/+Y3c+WzWeC/U6fvw4Bg4ciE6dOmHr1q3Yt28f5s6dCz8/P9s2/F3DIBSRRgDU559/7vC8EydOKABqz549dqfn5OQob29vtXr1attphw4dUgDUjh07lFJKff3118rDw0Olpqbatnn99ddVSEiIKi0tdfrtaCrq6lVl586dCoA6deqUUkqpP/74QwFQv/32m22bb775RplMJmU2m5VSSi1fvlyFh4fbtZkzZ47q2LGj829EE6KlV25urgKgNm3apJRiLz3V1uvs2bMqLi5OHThwQLVp00YtWbLEdh576cNRq8mTJ6sbbrih1suwlX4c9Zo4caK67bbbar0Mf9cwDh6xIJdKSkpCeXk5hg8fbjutU6dOaN26NXbs2AEA2LFjB7p27YrmzZvbthk5ciTy8vJw8ODBRp+5KcnNzYXJZEJYWBiAcy3CwsLQu3dv2zbDhw+Hh4eH7Sk4O3bswODBg+Hj42PbZuTIkTh8+DCys7Mbdf6mpKysDCtXrkRoaCi6d+8OgL2Mxmq14vbbb8dDDz2ESy+9tMb57GUsW7duRXR0NDp27Ih77rkHmZmZtvPYyjisViu++uorXHLJJRg5ciSio6PRt29fu6dL8XcN4+DCglwqNTUVPj4+tl9cqzRv3hypqam2bc6/o1edX3UeuUZJSQnmzJmDW265BSEhIQDOfb+jo6PttvPy8kJERAR76WT9+vUICgqCn58flixZgo0bNyIyMhIAexnNokWL4OXlhZkzZzo8n72MY9SoUfj3v/+N77//HosWLcK2bdswevRoVFZWAmArI7FYLCgoKMDzzz+PUaNG4bvvvsONN96IsWPHYtu2bQD4u4aReOk9ABE1vvLyckyYMAFKKbz++ut6j0N1GDp0KPbu3YuMjAy8+eabmDBhAn799dcav/SQvpKSkvDKK69g9+7dMJlMeo9DF3DzzTfb/rtr167o1q0b2rVrh61bt2LYsGE6TkbVWa1WAMANN9yABx54AADQo0cPbN++HStWrEBiYqKe41E1PGJBLhUTE4OysjLk5OTYnZ6WloaYmBjbNtXfuaHq31XbkPNULSpOnTqFjRs32o5WAOe+3xaLxW77iooKZGVlsZdOAgMD0b59e1x55ZV4++234eXlhbfffhsAexnJjz/+CIvFgtatW8PLywteXl44deoUHnzwQcTHxwNgLyNr27YtIiMjcezYMQBsZSSRkZHw8vJCly5d7E7v3Lmz7V2h+LuGcXBhQS7Vq1cveHt74/vvv7eddvjwYZw+fRr9+vUDAPTr1w/79++3exCv+oW3+gMJNUzVouLo0aPYtGkTmjVrZnd+v379kJOTg6SkJNtpmzdvhtVqRd++fW3b/PDDDygvL7dts3HjRnTs2BHh4eGNc0OaMKvVitLSUgDsZSS333479u3bh71799q+WrRogYceegjffvstAPYysrNnzyIzMxOxsbEA2MpIfHx8cMUVV+Dw4cN2px85cgRt2rQBwN81DEXvV4+TseXn56s9e/aoPXv2KABq8eLFas+ePbZ3EcrMzFR79uxRX331lQKgPvnkE7Vnzx6VkpJiu467775btW7dWm3evFnt2rVL9evXT/Xr1892fkVFhbrsssvUiBEj1N69e9WGDRtUVFSUevTRRxv99kpXV6+ysjJ1/fXXq5YtW6q9e/eqlJQU29f574gxatQo1bNnT/Xrr7+qn376SXXo0EHdcssttvNzcnJU8+bN1e23364OHDigPvnkExUQEKDeeOMNPW6yaHX1KigoUI8++qjasWOHOnnypNq1a5eaOnWq8vX1VQcOHLBdB3s1ngs9HlZX/V2hlGKvxlJXq/z8fDV79my1Y8cOdeLECbVp0yZ1+eWXqw4dOqiSkhLbdbBV47nQfWvt2rXK29tbrVy5Uh09elS9+uqrytPTU/3444+26+DvGsbAhQXVacuWLQpAja/JkycrpZR69913HZ4/f/5823UUFxerf/zjHyo8PFwFBASoG2+80W7hoZRSJ0+eVKNHj1b+/v4qMjJSPfjgg6q8vLwRb6l7qKtX1VsCO/rasmWL7ToyMzPVLbfcooKCglRISIiaOnWqys/Pt9vP77//rgYOHKh8fX1VXFycev755xv5lrqHunoVFxerG2+8UbVo0UL5+Pio2NhYdf3116udO3faXQd7NZ4LPR5W52hhwV6No65WRUVFasSIESoqKkp5e3urNm3aqLvuusvubUiVYqvGpOW+9fbbb6v27dsrPz8/1b17d7Vu3Tq76+DvGsZgUkop5x8HISIiIiKipoSvsSAiIiIiogbjwoKIiIiIiBqMCwsiIiIiImowLiyIiIiIiKjBuLAgIiIiIqIG48KCiIiIiIgajAsLIiIiIiJqMC4siIiIiIiowbiwICIiQzKZTDCZTAgLC2u0fT755JO2/S5durTR9ktE5A64sCAiIqebMmWK7Rf087+OHTtWr+t59913ceTIkQbPEx8fD5PJhF9++cXu9Pvvvx9Dhgyx/Xv27NlISUlBy5YtG7xPIqKmhgsLIiJyiVGjRiElJcXuKyEhoV7XERYWhujoaKfM4+fnhzlz5tS5TVBQEGJiYuDp6emUfRIRNSVcWBARkUv4+voiJibG7quhv7A/+eST6NGjB9555x20bt0aQUFB+Mc//oHKykq88MILiImJQXR0NJ599tkal50+fTp++eUXfP311w2agYiIHPPSewAiIqL6OH78OL755hts2LABx48fx0033YS//voLl1xyCbZt24bt27fj73//O4YPH46+ffvaLpeQkIC7774bjz76KEaNGgUPD/5tjYjImfioSkRELrF+/XoEBQXZvsaPH++U67VarXjnnXfQpUsXXHfddRg6dCgOHz6MpUuXomPHjpg6dSo6duyILVu21LjsE088gRMnTuCjjz5yyixERPT/eMSCiIhcYujQoXj99ddt/w4MDHTK9cbHxyM4ONj27+bNm8PT09PuCETz5s1hsVhqXDYqKgqzZ8/GvHnzMHHiRKfMQ0RE5/CIBRERuURgYCDat29v+4qNjXXK9Xp7e9v922QyOTzNarU6vPysWbNQXFyM5cuXO2UeIiI6hwsLIiJqUoKCgjB37lw8++yzyM/P13scIiK3wYUFERE1OdOnT0doaCg+/vhjvUchInIbXFgQEVGT4+3tjaeffholJSV6j0JE5DZMSiml9xBERETVmUwmfP755xgzZkyj7zs+Ph73338/7r///kbfNxGRVDxiQUREhnXLLbegZcuWjba/5557DkFBQTh9+nSj7ZOIyF3wiAURERnSsWPHAACenp5ISEholH1mZWUhKysLwLm3pg0NDW2U/RIRuQMuLIiIiIiIqMH4VCgiIiIiImowLiyIiIiIiKjBuLAgIiIiIqIG48KCiIiIiIgajAsLIiIiIiJqMC4siIiIiIiowbiwICIiIiKiBuPCgoiIiIiIGowLCyIiIiIiarD/BRUG2bItrcuUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Grafico Residui\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Residui con barre d'errore\n",
+    "ax.errorbar(\n",
+    "    dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"r\"],\n",
+    "    xerr=dfSonarCorrezione[\"uF\"], yerr=dfSonarCorrezione[\"ur\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=\"Residui\"\n",
+    ")\n",
+    "\n",
+    "# Linea dello zero\n",
+    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
+    "\n",
+    "# Estetica\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(\"F  [mN]\")\n",
+    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
+    "ax.set_title(\"Residui della regressione — Molla 1 (sonar statico)\")\n",
+    "ax.legend()\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9c20bd0",
+   "metadata": {},
+   "source": [
+    "## Analisi dati sonar dinamica\n",
+    "Ancora non abbiamo il parametro $\\alpha$, quindi useremo $\\alpha = \\frac 1 3$. Lo imposto nel csv così è più facile da cambiare con il metodo della molla"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "af6143df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Massa molla = 29.8933 ± 0.0044\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calcolo della massa della molla per bene\n",
+    "dfMasse = pd.DataFrame([[29.90, 29.89, 29.89]], columns=[\"m1\", \"m2\", \"m3\"])\n",
+    "dfMasse = calcola_stats(dfMasse, \"m\",  ERR_BILANCIA)\n",
+    "\n",
+    "massaMolla  = dfMasse.m[0]\n",
+    "umassaMolla = dfMasse.um[0]\n",
+    "print(f\"Massa molla = {massaMolla:.4f} ± {umassaMolla:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "a1e69bd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meq = 1000/(alp*m_molla + m_osc)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigma_Meq = 1000*sqrt(alp**2*sigma_m_molla**2 + m_molla**2*sigma_alp**2 + sigma_m_osc**2)/(alp*m_molla + m_osc)**2\n",
+      "\n",
+      "Formula LaTeX:\n",
+      "Meq = \\frac{1000}{alp m_{molla} + m_{osc}}\n",
+      "sigma_Meq = \\frac{1000 \\sqrt{alp^{2} \\sigma_{m molla}^{2} + m_{molla}^{2} \\sigma_{alp}^{2} + \\sigma_{m osc}^{2}}}{\\left(alp m_{molla} + m_{osc}\\right)^{2}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#massa della molla equivalente\n",
+    "# simboli\n",
+    "m_osc, alp, m_molla = sp.symbols('m_osc alp m_molla', positive=True)\n",
+    "sigma_m_osc, sigma_alp, sigma_m_molla = sp.symbols('sigma_m_osc sigma_alp sigma_m_molla', positive=True)\n",
+    "\n",
+    "# massa equivalente\n",
+    "Meq = 1 / (m_osc + alp * m_molla) * 1000\n",
+    "\n",
+    "# derivate parziali\n",
+    "dMeq_dm_osc   = sp.diff(Meq, m_osc)\n",
+    "dMeq_dalp     = sp.diff(Meq, alp)\n",
+    "dMeq_dm_molla = sp.diff(Meq, m_molla)\n",
+    "\n",
+    "sigma_Meq = sp.sqrt(\n",
+    "    (dMeq_dm_osc   * sigma_m_osc  )**2 +\n",
+    "    (dMeq_dalp     * sigma_alp    )**2 +\n",
+    "    (dMeq_dm_molla * sigma_m_molla)**2\n",
+    ")\n",
+    "\n",
+    "print(\"Meq =\", Meq)\n",
+    "print(\"sigma_Meq =\", sigma_Meq.simplify())\n",
+    "print(\"\\nFormula LaTeX:\")\n",
+    "print(f\"Meq = {sp.latex(Meq)}\")\n",
+    "print(f\"sigma_Meq = {sp.latex(sigma_Meq.simplify())}\")\n",
+    "\n",
+    "# funzioni numeriche\n",
+    "Meq_fn  = sp.lambdify((m_osc, alp, m_molla), Meq, 'numpy')\n",
+    "uMeq_fn = sp.lambdify((m_osc, sigma_m_osc, alp, sigma_alp, m_molla, sigma_m_molla), sigma_Meq, 'numpy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "56679fc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w2 = w**2\n",
+      "sigma_w2 = 2*sigma_w*w\n",
+      "\n",
+      "Formula LaTeX:\n",
+      "w2 = w^{2}\n",
+      "sigma_w2 = 2 \\sigma_{w} w\n"
+     ]
+    }
+   ],
+   "source": [
+    "# w in w^2\n",
+    "# simboli\n",
+    "w = sp.symbols('w', positive=True)\n",
+    "sigma_w = sp.symbols('sigma_w', positive=True)\n",
+    "\n",
+    "# omega^2\n",
+    "w2 = w**2\n",
+    "\n",
+    "# derivata parziale\n",
+    "dw2_dw = sp.diff(w2, w)\n",
+    "\n",
+    "sigma_w2 = sp.sqrt((dw2_dw * sigma_w)**2)\n",
+    "\n",
+    "print(\"w2 =\", w2)\n",
+    "print(\"sigma_w2 =\", sigma_w2.simplify())\n",
+    "print(\"\\nFormula LaTeX:\")\n",
+    "print(f\"w2 = {sp.latex(w2)}\")\n",
+    "print(f\"sigma_w2 = {sp.latex(sigma_w2.simplify())}\")\n",
+    "\n",
+    "# funzioni numeriche\n",
+    "w2_fn  = sp.lambdify((w), w2, 'numpy')\n",
+    "uw2_fn = sp.lambdify((w, sigma_w), sigma_w2, 'numpy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "455271a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e41f715ecf9427790522ae267be1968",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'[{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":9.21,\"ua1\":0.029,\"w1\":14.2459,\"uw1\":0.0008,\"c1\":268.151,\"uc1\":0.02,\"a2\":10.69,\"ua2\":0.04,\"w2\":14.2434,\"uw2\":0.0009,\"c2\":268.326,\"uc2\":0.026,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":14.24465,\"uw\":0.00125,\"c\":268.2385,\"uc\":0.0875,\"F\":1065.02966,\"uF\":0.4353612606,\"r\":-0.1151157394,\"ur\":0.1108576,\"Meq\":8.4335211413,\"uMeq\":0.000230411,\"Omega2\":202.9100536225,\"uOmega2\":0.035611625},{\"m1\":128.64,\"m2\":128.63,\"m3\":128.64,\"a1\":10.037,\"ua1\":0.025,\"w1\":13.1939,\"uw1\":0.0007,\"c1\":259.605,\"uc1\":0.018,\"a2\":9.744,\"ua2\":0.027,\"w2\":13.1913,\"uw2\":0.0009,\"c2\":259.745,\"uc2\":0.02,\"alp\":0.333333,\"ualp\":0.000001,\"m\":128.6366666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":13.1926,\"uw\":0.0013,\"c\":259.675,\"uc\":0.07,\"F\":1261.4111533333,\"uF\":0.5163603432,\"r\":0.0997008545,\"ur\":0.0835641852,\"Meq\":7.2149498938,\"uMeq\":0.0002419647,\"Omega2\":174.04469476,\"uOmega2\":0.03430076},{\"m1\":148.38,\"m2\":148.38,\"m3\":148.38,\"a1\":9.93,\"ua1\":0.03,\"w1\":12.3469,\"uw1\":0.0007,\"c1\":251.525,\"uc1\":0.021,\"a2\":11.41,\"ua2\":0.03,\"w2\":12.3461,\"uw2\":0.0006,\"c2\":251.542,\"uc2\":0.023,\"alp\":0.333333,\"ualp\":0.000001,\"m\":148.38,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":12.3465,\"uw\":0.0009219544,\"c\":251.5335,\"uc\":0.031144823,\"F\":1455.01428,\"uF\":0.5941946685,\"r\":0.010632976,\"ur\":0.0484884905,\"Meq\":6.3153466889,\"uMeq\":0.0001292051,\"Omega2\":152.43606225,\"uOmega2\":0.0227658211},{\"m1\":168.53,\"m2\":168.53,\"m3\":168.53,\"a1\":11.34,\"ua1\":0.03,\"w1\":11.6345,\"uw1\":0.0005,\"c1\":243.211,\"uc1\":0.021,\"a2\":11.5,\"ua2\":0.03,\"w2\":11.6344,\"uw2\":0.0006,\"c2\":243.13,\"uc2\":0.021,\"alp\":0.333333,\"ualp\":0.000001,\"m\":168.53,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":11.63445,\"uw\":0.000781025,\"c\":243.1705,\"uc\":0.0405,\"F\":1652.60518,\"uF\":0.6747140787,\"r\":-0.0264654245,\"ur\":0.0645469948,\"Meq\":5.6024155762,\"uMeq\":0.0001016801,\"Omega2\":135.3604268025,\"uOmega2\":0.0181735919}]'"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfSonar['Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
+    "dfSonar['uMeq']= uMeq_fn(dfSonar['m'], dfSonar['um'], dfSonar['alp'], dfSonar['ualp'], massaMolla, umassaMolla)\n",
+    "\n",
+    "dfSonar['Omega2'] = w2_fn(dfSonar['w'])\n",
+    "dfSonar['uOmega2']= uw2_fn(dfSonar['w'], dfSonar['uw'])\n",
+    "\n",
+    "\n",
+    "sheet = ipysheet.from_dataframe(dfSonar)\n",
+    "display(sheet)\n",
+    "dfSonar.head(4).to_json(orient=\"records\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dda09cd",
+   "metadata": {},
+   "source": [
+    "## Regressione lineare Carpi dati del sonar Caso Dinamico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "02119d66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ax + B : \n",
+      "AD      = 23.8854 ± 0.0133\n",
+      "BD      = 1.5710 ± 0.0856\n",
+      "cov_ABD = -0.001129\n",
+      "chi²    = 27.0310\n",
+      "P(chi², ∞)= 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "AD, BD, uAD, uBD, covABD, chiD, PD = reg_lin( dfSonar[\"Meq\"], dfSonar[\"Omega2\"], dfSonar[\"uMeq\"], dfSonar[\"uOmega2\"])\n",
+    "\n",
+    "print(\"Ax + B : \")\n",
+    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
+    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
+    "print(f\"cov_ABD = {covABD:.6f}\")\n",
+    "print(f\"chi²    = {chiD:.4f}\")\n",
+    "print(f\"P(chi², ∞)= {PD:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "96f12fe6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calcolo numerico residui sul dfSonar caso dinamico\n",
+    "dfSonar[\"R\"] = r_fn(\n",
+    "  dfSonar[\"Meq\"],\n",
+    "  dfSonar[\"Omega2\"],\n",
+    "  AD,\n",
+    "  BD\n",
+    ")\n",
+    "\n",
+    "dfSonar[\"uR\"] = sigma_r_fn(\n",
+    "  dfSonar[\"Meq\"], dfSonar[\"Omega2\"],\n",
+    "  AD, BD,\n",
+    "  dfSonar[\"uMeq\"], dfSonar[\"uOmega2\"],\n",
+    "  uAD, uBD, covABD\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "de571f32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit\n",
+    "SCALA = 10\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "x_data =  dfSonar[\"Meq\"]\n",
+    "y_data =  dfSonar[\"Omega2\"]\n",
+    "\n",
+    "x_fit = np.linspace(0, x_data.max(), 300)\n",
+    "y_fit = AD * x_fit + BD\n",
+    "\n",
+    "ax.errorbar(\n",
+    "    x_data, y_data,\n",
+    "    xerr=SCALA * dfSonar[\"uMeq\"], yerr=SCALA * dfSonar[\"uOmega2\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x_fit, y_fit,\n",
+    "    color='red', linewidth=1.5,\n",
+    "    label=f\"Fit: $A={AD:.3f}\\\\pm{uAD:.3f}$\\n\"\n",
+    "          f\"     $B={BD:.2f}\\\\pm{uBD:.2f}$\\n\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "plt.xlim(left=0)\n",
+    "plt.ylim(bottom=0)\n",
+    "\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(r\"Meq  [Kg$^{-1}$]\")\n",
+    "ax.set_ylabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
+    "ax.set_title(\"Fit lineare — Molla 1 (sonar Dinamico)\")\n",
+    "ax.legend(fontsize=9)\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "c87033e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Grafico Residui\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Residui con barre d'errore\n",
+    "ax.errorbar(\n",
+    "    dfSonar[\"Omega2\"], dfSonar[\"R\"],\n",
+    "    xerr=dfSonar[\"uOmega2\"], yerr=dfSonar[\"uR\"],\n",
+    "    fmt='o', color=sns.color_palette()[0],\n",
+    "    ecolor='gray', elinewidth=1, capsize=3,\n",
+    "    markersize=5, label=\"Residui\"\n",
+    ")\n",
+    "\n",
+    "# Linea dello zero\n",
+    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
+    "\n",
+    "# Estetica\n",
+    "sns.despine(ax=ax)\n",
+    "ax.set_xlabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
+    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [Kg$^{-1}$]\")\n",
+    "ax.set_title(\"Residui della regressione — Molla 1 (sonar dinamico)\")\n",
+    "ax.legend()\n",
+    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5ca1727",
+   "metadata": {},
+   "source": [
+    "# Raccolta finale di tutti i dati"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "e87e24b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calibro Con tutti i dati\n",
+      "AC      = 23.9437 ± 0.0424\n",
+      "BC      = 2719.8777 ± 2.8546\n",
+      "cov_ABC = 0.119914\n",
+      "chi²    = 55.1017\n",
+      "GdL     = 5\n",
+      "P(chi², ∞)= 0.0000\n",
+      "\n",
+      "Calibro tolto il dato 1\n",
+      "AC      = 23.8835 ± 0.0432\n",
+      "BC      = 2718.1378 ± 2.8613\n",
+      "cov_ABC = 0.121602\n",
+      "chi²    = 1.7942\n",
+      "GdL     = 4\n",
+      "P(chi², ∞)= 0.4078\n",
+      "\n",
+      "Sonar caso statico (temperatura non considerata)\n",
+      "AS      = 23.5225 ± 0.0853\n",
+      "BS      = 7371.9487 ± 21.4728\n",
+      "cov_ABS = 1.830281\n",
+      "chi²    = 3.8872\n",
+      "GdL     = 4\n",
+      "P(chi², ∞)= 0.1432\n",
+      "\n",
+      "Sonar caso statico (temperatura considerata)\n",
+      "AS      = 23.6160 ± 0.1404\n",
+      "BS      = 7365.6372 ± 35.4592\n",
+      "cov_ABS = 4.976670\n",
+      "chi²    = 1.6321\n",
+      "GdL     = 4\n",
+      "P(chi², ∞)= 0.4422\n",
+      "\n",
+      "Sonar caso Dinamico\n",
+      "AD      = 23.8854 ± 0.0133\n",
+      "BD      = 1.5710 ± 0.0856\n",
+      "cov_ABD = -0.001129\n",
+      "chi²    = 27.0310\n",
+      "GdL     = 4\n",
+      "P(chi², ∞)= 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Calibro Con tutti i dati\")\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\"chi²    = {chiC:.4f}\")\n",
+    "print(f\"GdL     = {len(dfCalibro)}\")\n",
+    "print(f\"P(chi², ∞)= {PC:.4f}\")\n",
+    "\n",
+    "print(\"\\nCalibro tolto il dato 1\")\n",
+    "print(f\"AC      = {AC2:.4f} ± {uAC2:.4f}\")\n",
+    "print(f\"BC      = {BC2:.4f} ± {uBC2:.4f}\")\n",
+    "print(f\"cov_ABC = {covABC2:.6f}\")\n",
+    "print(f\"chi²    = {chiC2:.4f}\")\n",
+    "print(f\"GdL     = {len(dfCalibro_senza1)}\")\n",
+    "print(f\"P(chi², ∞)= {PC2:.4f}\")\n",
+    "\n",
+    "print(\"\\nSonar caso statico (temperatura non considerata)\")\n",
+    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
+    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
+    "print(f\"cov_ABS = {covABS:.6f}\")\n",
+    "print(f\"chi²    = {chiS:.4f}\")\n",
+    "print(f\"GdL     = {len(dfSonar)}\")\n",
+    "print(f\"P(chi², ∞)= {PS:.4f}\")\n",
+    "\n",
+    "print(\"\\nSonar caso statico (temperatura considerata)\")\n",
+    "print(f\"AS      = {AS2:.4f} ± {uAS2:.4f}\")\n",
+    "print(f\"BS      = {BS2:.4f} ± {uBS2:.4f}\")\n",
+    "print(f\"cov_ABS = {covABS2:.6f}\")\n",
+    "print(f\"chi²    = {chiS2:.4f}\")\n",
+    "print(f\"GdL     = {len(dfSonar)}\")\n",
+    "print(f\"P(chi², ∞)= {PS2:.4f}\")\n",
+    "\n",
+    "print(\"\\nSonar caso Dinamico\")\n",
+    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
+    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
+    "print(f\"cov_ABD = {covABD:.6f}\")\n",
+    "print(f\"chi²    = {chiD:.4f}\")\n",
+    "print(f\"GdL     = {len(dfSonar)}\")\n",
+    "print(f\"P(chi², ∞)= {PD:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "d4af85a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def p_t_student_compatibilita(x, ux, gx, y, uy, gy):\n",
+    "  GdL = gx + gy - 2\n",
+    "  s2 = ( (gx - 1) * ux**2  +  (gy - 1) * uy**2 ) / GdL\n",
+    "  sigma2 = ( s2 / gx )  + ( s2 / gy )\n",
+    "  t = abs(x - y) / np.sqrt( sigma2 )\n",
+    "\n",
+    "  return {\"p\": 2 * (1 - stats.t.cdf(abs(t), df=GdL)), \"t\": t}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "57de948b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compatibilità: Calibro (tolto dato 1) vs Sonar statico (T considerata)\n",
+      "Sigma             t    p-value\n",
+      "------------------------------\n",
+      "1σ           2.5752     0.1235\n",
+      "2σ           1.2876     0.3268\n",
+      "3σ           0.8584     0.4811\n",
+      "\n",
+      "\n",
+      "Compatibilità: Calibro (tolto dato 1) vs Sonar dinamico\n",
+      "Sigma             t    p-value\n",
+      "------------------------------\n",
+      "1σ           0.0591     0.9582\n",
+      "2σ           0.0296     0.9791\n",
+      "3σ           0.0197     0.9861\n",
+      "\n",
+      "\n",
+      "Compatibilità: Sonar Dinamico vs Sonar statico (T considerata)\n",
+      "Sigma             t    p-value\n",
+      "------------------------------\n",
+      "1σ           2.7009     0.1141\n",
+      "2σ           1.3505     0.3094\n",
+      "3σ           0.9003     0.4630\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ringraziate Cloude per questo incredibile print\n",
+    "print(\"Compatibilità: Calibro (tolto dato 1) vs Sonar statico (T considerata)\")\n",
+    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
+    "print(\"-\" * 30)\n",
+    "for n in [1, 2, 3]:\n",
+    "    res = p_t_student_compatibilita(AC2, uAC2*n, len(dfCalibro_senza1)-2,\n",
+    "                                    AS2,  uAS2*n, len(dfSonar)-2)\n",
+    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
+    "\n",
+    "print(\"\\n\\nCompatibilità: Calibro (tolto dato 1) vs Sonar dinamico\")\n",
+    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
+    "print(\"-\" * 30)\n",
+    "for n in [1, 2, 3]:\n",
+    "    res = p_t_student_compatibilita(AC2, uAC2*n, len(dfCalibro_senza1)-2,\n",
+    "                                    AD,  uAD*n,  len(dfSonar)-2)\n",
+    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
+    "\n",
+    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs Sonar statico (T considerata)\")\n",
+    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
+    "print(\"-\" * 30)\n",
+    "for n in [1, 2, 3]:\n",
+    "    res = p_t_student_compatibilita(AD,  uAD*n,  len(dfSonar)-2,\n",
+    "                                    AS2, uAS2*n, len(dfSonar)-2)\n",
+    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b66ccc91",
+   "metadata": {},
+   "source": [
+    "# Note finali\n",
+    "Chiaramente i dati presi con il sonar statico soffrono di altri errori che dovremmo discutere come introdurre, sicuramente soffrono della precisione con cui abbiamo puntato il sonar.\n",
+    "\n",
+    "La temperatura sfortunatamente è stata presa con troppa poca attenzione e questo fa perdere di integrità hai nostri dati, ma altro per ora non mi viene su 2 piedi.\n",
+    "\n",
+    "Abbiamo almeno una buona basa su cui poter ragionare."
+   ]
   }
  ],
  "metadata": {
diff --git a/mollaDefinitiva/molla-1/molla1Sonar.csv b/mollaDefinitiva/molla-1/molla1Sonar.csv
new file mode 100644
index 0000000..2fc4189
--- /dev/null
+++ b/mollaDefinitiva/molla-1/molla1Sonar.csv
@@ -0,0 +1,5 @@
+m1,m2,m3,a1,ua1,w1,uw1,c1,uc1,a2,ua2,w2,uw2,c2,uc2,alp,ualp
+108.61,108.61,108.61,9.21,0.029,14.2459,0.0008,268.151,0.02,10.69,0.04,14.2434,0.0009,268.326,0.026,0.333333,0.000001
+128.64,128.63,128.64,10.037,0.025,13.1939,0.0007,259.605,0.018,9.744,0.027,13.1913,0.0009,259.745,0.02,0.333333,0.000001
+148.38,148.38,148.38,9.93,0.03,12.3469,0.0007,251.525,0.021,11.41,0.03,12.3461,0.0006,251.542,0.023,0.333333,0.000001
+168.53,168.53,168.53,11.34,0.03,11.6345,0.0005,243.211,0.021,11.5,0.03,11.6344,0.0006,243.13,0.021,0.333333,0.000001