f3/i2_Estacionarias_datos_B1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \"Estudio de Ondas Estacionarias en Cuerdas I2\"\n",
    "Karoll Johana Rueda Olave 2180088 - Est. Química.\n",
    "\n",
    "Paula Andrea Rueda Ruiz 2172041 - Est. Química.\n",
    "\n",
    "Juan Sebastian Carrillo Rojas 2180875 - Est. Química."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis de datos experimentales\n",
    "En\testa experiencia se\testudian los modos de vibración de una cuerda sujeta a dos extremos en función de la fuerza o tensión, para esto, se tiene una cuerda horizontal tensa de longitud l, entre dos puntos, y densidad lineal µ. Uno de los extremos se fija a un motor que produce una perturbación periódica de frecuencia 60 (Hz) de forma que se produzca una onda, el otro extremo la cuerda pasa por una polea sujeta a un porta masa, donde en ciertos casos se varió esa masa y/o la tensión de la cuerda.\n",
    "\n",
    "Mediante este análisis se logra observar el fenomeno de ondas estacionarias en una cuerda y la formación de varios armonicos por efecto de la longitud de onda y la tensión. Así mismo se comprueba que la masa es decir la tensión, influye en la velocidad de onda siendo directamente proporcionales, como también el tipo de material y el grosor\n",
    "de la cuerda, haciendo referencia a la densidad siendo inversamente proporcionales.\n",
    "\n",
    "Para comprender mejor los conceptos y las relaciones se recomienda ver la simulación anterior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos experimentales para Relación Tensión vs # de Armónicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Masa [Kg] $\\hspace{0.4cm}$ |  # de Armónicos $\\hspace{0.4cm}$|\n",
    "|------------------------------------|--------------------------------------|\n",
    "|$0.07   \\hspace{1.5cm}$           |$2\\hspace{1.0cm}       $                |\n",
    "|$0.06    \\hspace{1.5cm}$                    |$3\\hspace{1.0cm}$                 |\n",
    "              "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos para Relación entre Longitud de Onda vs Tensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m</th>\n",
       "      <th>L</th>\n",
       "      <th>λ</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.614</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.645</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.613</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.645</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.675</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.810</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.840</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.613</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.633</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.11</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.753</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.140</td>\n",
       "      <td>0.760</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.880</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.230</td>\n",
       "      <td>0.615</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.285</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.11</td>\n",
       "      <td>1.350</td>\n",
       "      <td>0.675</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.470</td>\n",
       "      <td>0.735</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.670</td>\n",
       "      <td>0.835</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.510</td>\n",
       "      <td>0.604</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.580</td>\n",
       "      <td>0.632</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.11</td>\n",
       "      <td>1.680</td>\n",
       "      <td>0.672</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.840</td>\n",
       "      <td>0.736</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.15</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.844</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       m      L      λ  n\n",
       "0   0.07  0.307  0.614  1\n",
       "1   0.09  0.323  0.645  1\n",
       "2   0.11  0.338  0.675  1\n",
       "3   0.13  0.405  0.810  1\n",
       "4   0.15  0.420  0.840  1\n",
       "5   0.07  0.613  0.613  2\n",
       "6   0.09  0.645  0.645  2\n",
       "7   0.11  0.675  0.675  2\n",
       "8   0.13  0.810  0.810  2\n",
       "9   0.15  0.840  0.840  2\n",
       "10  0.07  0.920  0.613  3\n",
       "11  0.09  0.950  0.633  3\n",
       "12  0.11  1.130  0.753  3\n",
       "13  0.13  1.140  0.760  3\n",
       "14  0.15  1.320  0.880  3\n",
       "15  0.07  1.230  0.615  4\n",
       "16  0.09  1.285  0.643  4\n",
       "17  0.11  1.350  0.675  4\n",
       "18  0.13  1.470  0.735  4\n",
       "19  0.15  1.670  0.835  4\n",
       "20  0.07  1.510  0.604  5\n",
       "21  0.09  1.580  0.632  5\n",
       "22  0.11  1.680  0.672  5\n",
       "23  0.13  1.840  0.736  5\n",
       "24  0.15  2.110  0.844  5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#          m[kg]  L[m]  λ[m]   n\n",
    "\n",
    "datos = [ (0.07, 0.307, 0.614, 1) ,\n",
    "          (0.09, 0.323, 0.645 , 1),\n",
    "          (0.11, 0.338, 0.675, 1),\n",
    "          (0.13, 0.405, 0.810, 1),\n",
    "          (0.15, 0.420, 0.840, 1),\n",
    "          (0.07, 0.613, 0.613, 2),\n",
    "          (0.09, 0.645, 0.645, 2),\n",
    "          (0.11, 0.675, 0.675, 2),\n",
    "          (0.13, 0.810, 0.810, 2),\n",
    "          (0.15, 0.840, 0.840, 2),\n",
    "          (0.07, 0.920, 0.613, 3),\n",
    "          (0.09, 0.950, 0.633, 3),\n",
    "          (0.11, 1.130, 0.753, 3),\n",
    "          (0.13, 1.140, 0.760, 3),\n",
    "          (0.15, 1.320, 0.880, 3),\n",
    "          (0.07, 1.230, 0.615, 4),\n",
    "          (0.09, 1.285, 0.643, 4),\n",
    "          (0.11, 1.350, 0.675, 4),\n",
    "          (0.13, 1.470, 0.735, 4),\n",
    "          (0.15, 1.670, 0.835, 4),\n",
    "          (0.07, 1.510, 0.604, 5),\n",
    "          (0.09, 1.580, 0.632, 5),\n",
    "          (0.11, 1.680, 0.672, 5),\n",
    "          (0.13, 1.840, 0.736, 5),\n",
    "          (0.15, 2.110, 0.844, 5)]\n",
    " \n",
    "#Crear dataframe con Pandas\n",
    "tabla = pd.DataFrame(datos, columns = ['m' , 'L', 'λ' , 'n']) \n",
    "tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfica Longitud de Onda vs Tensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fe4ccd7288>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as mp\n",
    "import numpy as np\n",
    "\n",
    "mp.xlabel('Tensión [N]')\n",
    "mp.ylabel('Longitud de Onda ')\n",
    "\n",
    "mp.plot([0.686, 0.882, 1.078, 1.274, 1.470],[0.612, 0.640, 0.690, 0.770, 0.848])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos para Relación entre Velocidad y Tensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>λprom</th>\n",
       "      <th>v experimental</th>\n",
       "      <th>v teórica</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.686</td>\n",
       "      <td>0.612</td>\n",
       "      <td>36.712</td>\n",
       "      <td>29.656</td>\n",
       "      <td>23.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.882</td>\n",
       "      <td>0.640</td>\n",
       "      <td>38.374</td>\n",
       "      <td>33.627</td>\n",
       "      <td>14.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.078</td>\n",
       "      <td>0.690</td>\n",
       "      <td>41.404</td>\n",
       "      <td>37.176</td>\n",
       "      <td>11.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.274</td>\n",
       "      <td>0.770</td>\n",
       "      <td>46.212</td>\n",
       "      <td>40.415</td>\n",
       "      <td>14.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.470</td>\n",
       "      <td>0.848</td>\n",
       "      <td>50.868</td>\n",
       "      <td>43.412</td>\n",
       "      <td>17.175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T  λprom  v experimental  v teórica       %\n",
       "0  0.686  0.612          36.712     29.656  23.792\n",
       "1  0.882  0.640          38.374     33.627  14.117\n",
       "2  1.078  0.690          41.404     37.176  11.373\n",
       "3  1.274  0.770          46.212     40.415  14.345\n",
       "4  1.470  0.848          50.868     43.412  17.175"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#          T[N]  λprom[m]  v[m/s]   v[m/s] %Error\n",
    "\n",
    "datos = [ (0.686, 0.612, 36.712, 29.656, 23.792) ,\n",
    "          (0.882, 0.640, 38.374 , 33.627, 14.117),\n",
    "        (1.078, 0.690, 41.404, 37.176, 11.373),\n",
    "        (1.274, 0.770, 46.212, 40.415, 14.345),\n",
    "        (1.470, 0.848, 50.868, 43.412, 17.175)]\n",
    " \n",
    "#Crear dataframe con Pandas\n",
    "tabla = pd.DataFrame(datos, columns = ['T' , 'λprom', 'v experimental' , 'v teórica', '%']) \n",
    "tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfica Velocidad Experimental vs Tensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Velocidad Experimental [m/s]')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.plot([0.686, 0.882, 1.078, 1.274, 1.470],[36.71, 38.37, 41.40, 46.21, 50.87])\n",
    "mp.xlabel('Tensión [N]')\n",
    "mp.ylabel('Velocidad Experimental [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfica Velocidad Teórica vs Tensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Velocidad Teórica [m/s]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.plot([0.686, 0.882, 1.078, 1.274, 1.470],[29.66, 33.63, 37.17, 40.42, 43.41])\n",
    "mp.xlabel('Tensión [N]')\n",
    "mp.ylabel('Velocidad Teórica [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SUBGRUPO B1 LABORATORIO DE FÍSICA III GRUPO H2B "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}