From 193ea64d922b3a00c954dba8427ff753ac3f61d6 Mon Sep 17 00:00:00 2001
From: Nicolas Mantilla <nicolas2210707@correo.uis.edu.co>
Date: Mon, 4 Nov 2024 18:15:55 -0500
Subject: [PATCH] =?UTF-8?q?Carga=20espec=C3=ADfica=20code=20and=20data?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../code/CargaEspecifica.ipynb                | 307 ++++++++++++++++++
 LabAvanzado1/CargaEspecifica/data/data.csv    |  17 +
 2 files changed, 324 insertions(+)
 create mode 100644 LabAvanzado1/CargaEspecifica/code/CargaEspecifica.ipynb
 create mode 100644 LabAvanzado1/CargaEspecifica/data/data.csv

diff --git a/LabAvanzado1/CargaEspecifica/code/CargaEspecifica.ipynb b/LabAvanzado1/CargaEspecifica/code/CargaEspecifica.ipynb
new file mode 100644
index 0000000..9545aea
--- /dev/null
+++ b/LabAvanzado1/CargaEspecifica/code/CargaEspecifica.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Carga Especifica del Electrón"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importamos las librerias necesarias\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      I  R      V   I^2\n",
+      "12  0.8  2  105.3  0.64\n",
+      "8   1.1  2  143.2  1.21\n",
+      "0   1.3  2  166.1  1.69\n",
+      "4   2.6  2  159.5  6.76\n",
+      "      I  R      V   I^2\n",
+      "13  0.8  3  131.6  0.64\n",
+      "9   1.1  3  181.0  1.21\n",
+      "1   1.3  3  190.1  1.69\n",
+      "5   2.6  3  295.7  6.76\n",
+      "      I  R      V   I^2\n",
+      "14  0.8  4  123.4  0.64\n",
+      "10  1.1  4  176.5  1.21\n",
+      "2   1.3  4  207.6  1.69\n",
+      "6   2.6  4  308.3  6.76\n",
+      "      I  R      V   I^2\n",
+      "15  0.8  5    NaN  0.64\n",
+      "11  1.1  5  204.2  1.21\n",
+      "3   1.3  5  243.6  1.69\n",
+      "7   2.6  5    NaN  6.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cargamos los datos\n",
+    "data = pd.read_csv('../data/data.csv')\n",
+    "\n",
+    "# Ordenamos los datos por el valor de la corriente\n",
+    "data = data.sort_values(by='I')\n",
+    "\n",
+    "# Dropeamos los valores para la corriente 2.6\n",
+    "# data = data[data['I'] != 2.6]\n",
+    "\n",
+    "# Añadimos una columna con la corriente cuadrada\n",
+    "data['I^2'] = data['I']**2\n",
+    "\n",
+    "# Separamos los datos por radio de la trayectoria de los electrones\n",
+    "datos = [data[data['R'] == i] for i in range(2, 6)]\n",
+    "\n",
+    "# Verificamos que los datos se hayan separado correctamente\n",
+    "for i in range(4):\n",
+    "    print(datos[i].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Realizamos una regresión lineal entre el potencial anódico y el cuadrado de la corriente\n",
+    "regresiones = []\n",
+    "for i in range(4):\n",
+    "    x = datos[i]['I^2']\n",
+    "    y = datos[i]['V']\n",
+    "    try:\n",
+    "        m, b = np.polyfit(x, y, 1)\n",
+    "        regresiones.append((m, b))\n",
+    "    except:\n",
+    "        # print(f'No se pudo realizar la regresión para R = {i+2} cm')\n",
+    "        # Si no converge es porque solo tiene 2 puntos, entonces se hace con lo que se tiene\n",
+    "        y = datos[i].dropna()['V']\n",
+    "        x = datos[i].dropna()['I^2']\n",
+    "        m = (y.iloc[1] - y.iloc[0]) / (x.iloc[1] - x.iloc[0])\n",
+    "        b = y.iloc[0] - m*x.iloc[0]\n",
+    "        regresiones.append((m, b))\n",
+    "\n",
+    "# Graficamos el potencial anódico en función del cuadrado de la corriente de las bovinas\n",
+    "colors = ['m', 'g', 'c', 'orange']\n",
+    "Irange = np.linspace(0.6, 7, 100)\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "for i in range(4):\n",
+    "    plt.scatter(datos[i]['I^2'], datos[i]['V'], c=colors[i], label=f'R = {i+2} cm')\n",
+    "    plt.plot(Irange, np.polyval(regresiones[i], Irange), c=colors[i], linestyle='--')\n",
+    "\n",
+    "plt.xlabel('Corriente cuadrada $[A^2]$', fontsize=13)\n",
+    "plt.ylabel('Potencial anódico $[V]$', fontsize=13)\n",
+    "plt.title('Potencial anódico en función de la corriente cuadrada', fontsize=15)\n",
+    "plt.grid(alpha=0.5)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pendiente experim para R = 2 cm: 5.07 V/A^2\n",
+      "Pendiente teórica para R = 2 cm: 16.84 V/A^2\n",
+      "Error porcentual para R = 2 cm: 69.87%\n",
+      "Pendiente experim para R = 3 cm: 23.74 V/A^2\n",
+      "Pendiente teórica para R = 3 cm: 37.89 V/A^2\n",
+      "Error porcentual para R = 3 cm: 37.33%\n",
+      "Pendiente experim para R = 4 cm: 26.22 V/A^2\n",
+      "Pendiente teórica para R = 4 cm: 67.35 V/A^2\n",
+      "Error porcentual para R = 4 cm: 61.07%\n",
+      "Pendiente experim para R = 5 cm: 82.08 V/A^2\n",
+      "Pendiente teórica para R = 5 cm: 105.24 V/A^2\n",
+      "Error porcentual para R = 5 cm: 22.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Hallamos la pendiente teórica de esta relación como funcion del radio en cm\n",
+    "mu0 = 4*np.pi*1e-7 # permeabilidad magnética del vacío T*m/A\n",
+    "N = 154 # Número de espiras\n",
+    "Re = 0.2 # Radio de las espiras en metros\n",
+    "e = 1.6e-19 # Carga del electrón en Coulombs\n",
+    "m = 9.11e-31 # Masa del electrón en kg\n",
+    "def pendiente(R):\n",
+    "    R = R*10**-2 # Pasamos el radio a metros\n",
+    "    return (4/5)**3 /2 * (mu0*N*R/Re)**2 * e/m\n",
+    "\n",
+    "# Presentamos las pendientes teóricas\n",
+    "for i in range(4):\n",
+    "    print(f'Pendiente experim para R = {i+2} cm: {regresiones[i][0]:.2f} V/A^2')\n",
+    "    print(f'Pendiente teórica para R = {i+2} cm: {pendiente(i+2):.2f} V/A^2')\n",
+    "    print(f'Error porcentual para R = {i+2} cm: {abs((regresiones[i][0] - pendiente(i+2))/pendiente(i+2))*100:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Carga específica teórica: 1.756e+11 C/kg\n",
+      "Carga específica para R = 2 cm: 5.292e+10 C/kg\n",
+      "Error porcentual para R = 2 cm: 69.87%\n",
+      "Carga específica para R = 3 cm: 1.101e+11 C/kg\n",
+      "Error porcentual para R = 3 cm: 37.33%\n",
+      "Carga específica para R = 4 cm: 6.837e+10 C/kg\n",
+      "Error porcentual para R = 4 cm: 61.07%\n",
+      "Carga específica para R = 5 cm: 1.370e+11 C/kg\n",
+      "Error porcentual para R = 5 cm: 22.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculamos la carga específica del electrón a partir de cada pendiente\n",
+    "print(f'Carga específica teórica: {e/m:.3e} C/kg')\n",
+    "for i in range(4):\n",
+    "    cargaEsp = (5/4)**3 / (mu0*N*(i+2)*1e-2/Re)**2 * 2 * regresiones[i][0]\n",
+    "    print(f'Carga específica para R = {i+2} cm: {cargaEsp:.3e} C/kg')\n",
+    "    print(f'Error porcentual para R = {i+2} cm: {abs((cargaEsp - e/m)/(e/m))*100:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{rrr}\n",
+      "\\toprule\n",
+      "R [cm] & e/m [C/kg] & Error porcentual [\\%] \\\\\n",
+      "\\midrule\n",
+      "     2 &  5.292e+10 &                69.87 \\\\\n",
+      "     3 &  1.101e+11 &                37.33 \\\\\n",
+      "     4 &  6.837e+10 &                61.07 \\\\\n",
+      "     5 &  1.370e+11 &                22.00 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_36492\\1285771561.py:6: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
+      "  print(tabla.to_latex(index=False, formatters=[lambda x: f'{x:.0f}', lambda x: f'{x:.3e}', lambda x: f'{x:.2f}']))\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Imprimimos una tabla en latex con las columnas R, e/m experimental, error porcentual con 3 decimales\n",
+    "tabla = pd.DataFrame({'R [cm]': [i+2 for i in range(4)],\n",
+    "                        'e/m [C/kg]': [(5/4)**3 / (mu0*N*(i+2)*1e-2/Re)**2 * 2 * regresiones[i][0] for i in range(4)],\n",
+    "                        'Error porcentual [%]': [abs((cargaEsp - e/m)/(e/m))*100 for cargaEsp in [((5/4)**3 / (mu0*N*(i+2)*1e-2/Re)**2 * 2 * regresiones[i][0]) for i in range(4)]]\n",
+    "                    })\n",
+    "print(tabla.to_latex(index=False, formatters=[lambda x: f'{x:.0f}', lambda x: f'{x:.3e}', lambda x: f'{x:.2f}']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{rrr}\n",
+      "\\toprule\n",
+      "  I &  R &     V \\\\\n",
+      "\\midrule\n",
+      "0.8 &  2 & 105.3 \\\\\n",
+      "0.8 &  3 & 131.6 \\\\\n",
+      "0.8 &  4 & 123.4 \\\\\n",
+      "0.8 &  5 &   NaN \\\\\n",
+      "1.1 &  2 & 143.2 \\\\\n",
+      "1.1 &  3 & 181.0 \\\\\n",
+      "1.1 &  4 & 176.5 \\\\\n",
+      "1.1 &  5 & 204.2 \\\\\n",
+      "1.3 &  2 & 166.1 \\\\\n",
+      "1.3 &  3 & 190.1 \\\\\n",
+      "1.3 &  4 & 207.6 \\\\\n",
+      "1.3 &  5 & 243.6 \\\\\n",
+      "2.6 &  2 & 159.5 \\\\\n",
+      "2.6 &  3 & 295.7 \\\\\n",
+      "2.6 &  4 & 308.3 \\\\\n",
+      "2.6 &  5 &   NaN \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_36492\\1147736184.py:2: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
+      "  print(data.to_latex(index=False, columns=['I', 'R', 'V']))\n"
+     ]
+    }
+   ],
+   "source": [
+    "#imprimimos los datos de voltaje y radio para cada corriente en una tabla latex\n",
+    "print(data.to_latex(index=False, columns=['I', 'R', 'V']))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/LabAvanzado1/CargaEspecifica/data/data.csv b/LabAvanzado1/CargaEspecifica/data/data.csv
new file mode 100644
index 0000000..57d89a3
--- /dev/null
+++ b/LabAvanzado1/CargaEspecifica/data/data.csv
@@ -0,0 +1,17 @@
+I,R,V
+1.3,2,166.1
+1.3,3,190.1
+1.3,4,207.6
+1.3,5,243.6
+2.6,2,159.5
+2.6,3,295.7
+2.6,4,308.3
+2.6,5,
+1.1,2,143.2
+1.1,3,181
+1.1,4,176.5
+1.1,5,204.2
+0.8,2,105.3
+0.8,3,131.6
+0.8,4,123.4
+0.8,5,
-- 
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