diff --git a/Proyecto_Experimento.ipynb b/Proyecto_Experimento.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c9962fc87c8513e1cbb39d0790c97a2383190d87
--- /dev/null
+++ b/Proyecto_Experimento.ipynb
@@ -0,0 +1,1382 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b74eb188-c431-42ff-8416-5f1f5c0cfa78",
+ "metadata": {},
+ "source": [
+ "Experimetto Servilleta Extendida"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 218,
+ "id": "9a078fe2-8988-4fd3-82ba-41eedefec172",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy.interpolate import interp1d\n",
+ "from scipy.optimize import curve_fit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 265,
+ "id": "6af40689-0f2e-4633-8723-9fb859d63d87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datos1=np.genfromtxt(\"1E.txt\")\n",
+ "datos2=np.genfromtxt(\"2E.txt\")\n",
+ "datos3=np.genfromtxt(\"3E.txt\")\n",
+ "datos4=np.genfromtxt(\"4E.txt\")\n",
+ "datos5=np.genfromtxt(\"5E.txt\")\n",
+ "datos6=np.genfromtxt(\"6E.txt\")\n",
+ "datos7=np.genfromtxt(\"7E.txt\")\n",
+ "datos8=np.genfromtxt(\"8E.txt\")\n",
+ "datos9=np.genfromtxt(\"9E.txt\")\n",
+ "datos10=np.genfromtxt(\"10E.txt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 243,
+ "id": "77971793-3d19-48ff-8b48-2a011aff45c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0. -0.297]\n",
+ " [ 0.433 -0.97 ]\n",
+ " [ 0.433 -0.97 ]\n",
+ " [ 0.467 -0.97 ]\n",
+ " [ 0.5 -4.62 ]\n",
+ " [ 0.533 -6.43 ]\n",
+ " [ 0.567 -8.21 ]\n",
+ " [ 0.6 -10.4 ]\n",
+ " [ 0.633 -12.78 ]\n",
+ " [ 0.667 -16.65 ]\n",
+ " [ 0.7 -15.76 ]\n",
+ " [ 0.733 -16.35 ]\n",
+ " [ 0.767 -17.24 ]\n",
+ " [ 0.8 -19.32 ]\n",
+ " [ 0.833 -21.7 ]\n",
+ " [ 0.867 -24.97 ]\n",
+ " [ 0.9 -27.65 ]\n",
+ " [ 0.933 -30.62 ]\n",
+ " [ 0.967 -32.4 ]\n",
+ " [ 1. -35.38 ]\n",
+ " [ 1.033 -38.94 ]\n",
+ " [ 1.067 -41.32 ]\n",
+ " [ 1.1 -46.67 ]\n",
+ " [ 1.133 -47.86 ]\n",
+ " [ 1.167 -51.43 ]\n",
+ " [ 1.2 -51.73 ]\n",
+ " [ 1.233 -52.62 ]\n",
+ " [ 1.267 -53.21 ]\n",
+ " [ 1.3 -56.48 ]\n",
+ " [ 1.333 -59.16 ]\n",
+ " [ 1.367 -61.24 ]\n",
+ " [ 1.433 -64.81 ]\n",
+ " [ 1.467 -68.08 ]\n",
+ " [ 1.5 -68.08 ]\n",
+ " [ 1.533 -68.08 ]\n",
+ " [ 1.567 -71.05 ]\n",
+ " [ 1.6 -73.13 ]\n",
+ " [ 1.633 -73.13 ]\n",
+ " [ 1.667 -79.07 ]\n",
+ " [ 1.7 -82.64 ]\n",
+ " [ 1.733 -85.02 ]\n",
+ " [ 1.767 -88.59 ]\n",
+ " [ 1.8 -94.3 ]\n",
+ " [ 1.833 -100.8 ]\n",
+ " [ 1.844 -103.5 ]\n",
+ " [ 1.867 -106.4 ]\n",
+ " [ 1.9 -113. ]\n",
+ " [ 1.933 -123.4 ]\n",
+ " [ 1.967 -130.8 ]\n",
+ " [ 2. -137.9 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 244,
+ "id": "ac826179-2755-4f09-9f1b-dc1e94e82b9d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 2.880e-01]\n",
+ " [ 3.300e-02 2.880e-01]\n",
+ " [ 6.600e-02 2.880e-01]\n",
+ " [ 1.000e-01 2.880e-01]\n",
+ " [ 1.330e-01 -1.731e+00]\n",
+ " [ 1.660e-01 -4.904e+00]\n",
+ " [ 1.990e-01 -6.057e+00]\n",
+ " [ 2.330e-01 -7.211e+00]\n",
+ " [ 2.660e-01 -9.519e+00]\n",
+ " [ 2.990e-01 -1.540e+00]\n",
+ " [ 3.320e-01 -1.183e+01]\n",
+ " [ 3.660e-01 -1.211e+01]\n",
+ " [ 3.990e-01 -1.442e+01]\n",
+ " [ 4.320e-01 -1.558e+01]\n",
+ " [ 4.650e-01 -1.817e+01]\n",
+ " [ 4.980e-01 -1.933e+01]\n",
+ " [ 5.320e-01 -2.135e+01]\n",
+ " [ 5.650e-01 -2.394e+01]\n",
+ " [ 5.980e-01 -2.654e+01]\n",
+ " [ 6.310e-01 -2.942e+01]\n",
+ " [ 6.650e-01 -3.058e+01]\n",
+ " [ 6.980e-01 -3.404e+01]\n",
+ " [ 7.310e-01 -3.490e+01]\n",
+ " [ 7.640e-01 -3.577e+01]\n",
+ " [ 7.980e-01 -3.577e+01]\n",
+ " [ 8.310e-01 -3.721e+01]\n",
+ " [ 8.640e-01 -3.923e+01]\n",
+ " [ 8.970e-01 -4.154e+01]\n",
+ " [ 9.310e-01 -4.442e+01]\n",
+ " [ 9.970e-01 -5.250e+01]\n",
+ " [ 1.030e+00 nan]\n",
+ " [ 1.063e+00 -5.596e+01]\n",
+ " [ 1.097e+00 -5.711e+01]\n",
+ " [ 1.130e+00 -5.798e+01]\n",
+ " [ 1.163e+00 -6.144e+01]\n",
+ " [ 1.196e+00 -6.577e+01]\n",
+ " [ 1.230e+00 -7.038e+01]\n",
+ " [ 1.263e+00 -7.298e+01]\n",
+ " [ 1.296e+00 -7.557e+01]\n",
+ " [ 1.329e+00 -7.932e+01]\n",
+ " [ 1.363e+00 -8.221e+01]\n",
+ " [ 1.396e+00 -8.827e+01]\n",
+ " [ 1.429e+00 -9.028e+01]\n",
+ " [ 1.462e+00 -9.144e+01]\n",
+ " [ 1.496e+00 -9.403e+01]\n",
+ " [ 1.529e+00 -9.980e+01]\n",
+ " [ 1.562e+00 -1.050e+02]\n",
+ " [ 1.595e+00 -1.087e+02]\n",
+ " [ 1.628e+00 -1.145e+02]\n",
+ " [ 1.662e+00 -1.263e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 268,
+ "id": "b70fadb6-e99a-4a82-b5fd-f66cd995d669",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 -4.444e+00]\n",
+ " [ 3.300e-02 -5.926e+00]\n",
+ " [ 6.700e-02 -8.518e+00]\n",
+ " [ 1.000e-01 -1.000e+00]\n",
+ " [ 1.330e-01 -1.590e+00]\n",
+ " [ 1.670e-01 -1.370e+01]\n",
+ " [ 2.000e-01 -1.630e+01]\n",
+ " [ 2.340e-01 -1.815e+01]\n",
+ " [ 2.670e-01 -2.037e+01]\n",
+ " [ 3.000e-01 -2.296e+01]\n",
+ " [ 3.340e-01 -2.556e+01]\n",
+ " [ 3.670e-01 -2.815e+01]\n",
+ " [ 4.000e-01 -2.926e+01]\n",
+ " [ 4.340e-01 -3.074e+01]\n",
+ " [ 4.670e-01 -3.370e+01]\n",
+ " [ 5.010e-01 -3.667e+01]\n",
+ " [ 5.340e-01 -4.074e+01]\n",
+ " [ 5.670e-01 -4.481e+01]\n",
+ " [ 6.010e-01 -4.704e+01]\n",
+ " [ 6.340e-01 -5.037e+01]\n",
+ " [ 6.670e-01 -5.111e+01]\n",
+ " [ 7.010e-01 -5.444e+01]\n",
+ " [ 7.340e-01 -5.407e+01]\n",
+ " [ 7.670e-01 -5.556e+01]\n",
+ " [ 8.010e-01 -6.481e+01]\n",
+ " [ 8.340e-01 -6.148e+01]\n",
+ " [ 8.680e-01 -6.272e+01]\n",
+ " [ 9.010e-01 -6.593e+01]\n",
+ " [ 9.340e-01 -7.185e+01]\n",
+ " [ 9.680e-01 -7.481e+01]\n",
+ " [ 1.001e+00 -7.815e+01]\n",
+ " [ 1.034e+00 -8.111e+01]\n",
+ " [ 1.068e+00 -8.444e+01]\n",
+ " [ 1.101e+00 -8.741e+01]\n",
+ " [ 1.168e+00 -9.185e+01]\n",
+ " [ 1.201e+00 -9.481e+01]\n",
+ " [ 1.235e+00 -9.815e+01]\n",
+ " [ 1.268e+00 -1.004e+02]\n",
+ " [ 1.301e+00 -1.030e+02]\n",
+ " [ 1.335e+00 -1.056e+02]\n",
+ " [ 1.368e+00 -1.096e+02]\n",
+ " [ 1.401e+00 -1.141e+02]\n",
+ " [ 1.435e+00 -1.156e+02]\n",
+ " [ 1.468e+00 -1.181e+02]\n",
+ " [ 1.502e+00 -1.233e+02]\n",
+ " [ 1.535e+00 -1.256e+02]\n",
+ " [ 1.568e+00 -1.293e+02]\n",
+ " [ 1.602e+00 -1.315e+02]\n",
+ " [ 1.635e+00 -1.341e+02]\n",
+ " [ 1.668e+00 -1.367e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 246,
+ "id": "43a2d558-f93c-451f-a561-a59b94981450",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 0.000e+00]\n",
+ " [ 3.300e-02 0.000e+00]\n",
+ " [ 6.700e-02 -4.874e+00]\n",
+ " [ 1.000e-01 -4.874e+00]\n",
+ " [ 1.330e-01 nan]\n",
+ " [ 1.670e-01 -7.499e+00]\n",
+ " [ 2.000e-01 -7.499e+00]\n",
+ " [ 2.330e-01 -9.374e+00]\n",
+ " [ 2.670e-01 -1.162e+01]\n",
+ " [ 3.000e-01 -1.275e+01]\n",
+ " [ 3.330e-01 -1.387e+01]\n",
+ " [ 3.670e-01 -1.650e+01]\n",
+ " [ 4.000e-01 -1.950e+01]\n",
+ " [ 4.330e-01 -2.100e+01]\n",
+ " [ 4.670e-01 -2.250e+01]\n",
+ " [ 5.000e-01 -2.475e+01]\n",
+ " [ 5.330e-01 -2.850e+01]\n",
+ " [ 5.670e-01 -3.112e+01]\n",
+ " [ 6.000e-01 -3.562e+01]\n",
+ " [ 6.330e-01 -3.825e+01]\n",
+ " [ 6.670e-01 -3.825e+01]\n",
+ " [ 7.000e-01 -4.162e+01]\n",
+ " [ 7.330e-01 -4.199e+01]\n",
+ " [ 7.670e-01 -4.499e+01]\n",
+ " [ 8.000e-01 -5.137e+01]\n",
+ " [ 8.330e-01 -5.324e+01]\n",
+ " [ 8.670e-01 -5.774e+01]\n",
+ " [ 9.000e-01 -5.849e+01]\n",
+ " [ 9.330e-01 -5.999e+01]\n",
+ " [ 9.670e-01 -5.999e+01]\n",
+ " [ 1.000e+00 -5.999e+01]\n",
+ " [ 1.033e+00 -6.224e+01]\n",
+ " [ 1.067e+00 -6.787e+01]\n",
+ " [ 1.100e+00 -7.124e+01]\n",
+ " [ 1.133e+00 -7.274e+01]\n",
+ " [ 1.167e+00 -7.349e+01]\n",
+ " [ 1.200e+00 -7.574e+01]\n",
+ " [ 1.233e+00 -7.987e+01]\n",
+ " [ 1.267e+00 -8.249e+01]\n",
+ " [ 1.300e+00 -8.586e+01]\n",
+ " [ 1.333e+00 -9.074e+01]\n",
+ " [ 1.400e+00 -9.711e+01]\n",
+ " [ 1.433e+00 -1.001e+02]\n",
+ " [ 1.467e+00 -1.016e+02]\n",
+ " [ 1.500e+00 -1.031e+02]\n",
+ " [ 1.533e+00 -1.046e+02]\n",
+ " [ 1.567e+00 -1.057e+02]\n",
+ " [ 1.600e+00 -1.095e+02]\n",
+ " [ 1.633e+00 -1.114e+02]\n",
+ " [ 1.667e+00 -1.159e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 247,
+ "id": "e2868bf7-17fd-4dfc-a605-d00aebbfa6b0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 3.610e-01]\n",
+ " [ 3.300e-02 3.610e-01]\n",
+ " [ 6.700e-02 3.610e-01]\n",
+ " [ 1.000e-01 -1.445e+01]\n",
+ " [ 1.330e-01 -1.518e+01]\n",
+ " [ 1.670e-01 -1.843e+01]\n",
+ " [ 2.000e-01 -2.240e+01]\n",
+ " [ 2.330e-01 -2.421e+01]\n",
+ " [ 2.670e-01 -2.710e+01]\n",
+ " [ 3.000e-01 -2.927e+01]\n",
+ " [ 3.330e-01 -3.541e+01]\n",
+ " [ 3.670e-01 -3.830e+01]\n",
+ " [ 4.000e-01 -4.083e+01]\n",
+ " [ 4.330e-01 -4.083e+01]\n",
+ " [ 4.670e-01 -4.083e+01]\n",
+ " [ 5.000e-01 -4.300e+01]\n",
+ " [ 5.330e-01 -4.373e+01]\n",
+ " [ 5.670e-01 -4.373e+01]\n",
+ " [ 6.000e-01 -4.373e+01]\n",
+ " [ 6.330e-01 -4.553e+01]\n",
+ " [ 6.670e-01 -4.842e+01]\n",
+ " [ 7.000e-01 -5.348e+01]\n",
+ " [ 7.330e-01 -5.854e+01]\n",
+ " [ 7.670e-01 -6.179e+01]\n",
+ " [ 8.330e-01 -6.974e+01]\n",
+ " [ 8.670e-01 -7.300e+01]\n",
+ " [ 9.000e-01 -7.625e+01]\n",
+ " [ 9.330e-01 -7.878e+01]\n",
+ " [ 9.670e-01 -7.986e+01]\n",
+ " [ 1.000e+00 -8.167e+01]\n",
+ " [ 1.033e+00 -8.167e+01]\n",
+ " [ 1.067e+00 -8.203e+01]\n",
+ " [ 1.100e+00 -8.311e+01]\n",
+ " [ 1.133e+00 -8.673e+01]\n",
+ " [ 1.167e+00 -8.926e+01]\n",
+ " [ 1.200e+00 -9.143e+01]\n",
+ " [ 1.233e+00 -9.540e+01]\n",
+ " [ 1.267e+00 -9.829e+01]\n",
+ " [ 1.300e+00 nan]\n",
+ " [ 1.333e+00 -1.037e+02]\n",
+ " [ 1.367e+00 -1.062e+02]\n",
+ " [ 1.400e+00 -1.091e+02]\n",
+ " [ 1.433e+00 -1.120e+02]\n",
+ " [ 1.467e+00 -1.167e+02]\n",
+ " [ 1.500e+00 -1.171e+02]\n",
+ " [ 1.533e+00 -1.178e+02]\n",
+ " [ 1.567e+00 -1.207e+02]\n",
+ " [ 1.600e+00 -1.211e+02]\n",
+ " [ 1.633e+00 -1.218e+02]\n",
+ " [ 1.667e+00 -1.236e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 248,
+ "id": "5b3ba155-41ed-4c08-b9be-9d3e2269186d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 3.370e-01]\n",
+ " [ 3.300e-02 3.700e-01]\n",
+ " [ 6.700e-02 -1.348e+00]\n",
+ " [ 1.000e-01 -5.055e+00]\n",
+ " [ 1.330e-01 -6.740e+00]\n",
+ " [ 1.670e-01 -7.520e+00]\n",
+ " [ 2.000e-01 -1.146e+01]\n",
+ " [ 2.330e-01 -1.213e+01]\n",
+ " [ 2.670e-01 -1.382e+01]\n",
+ " [ 3.000e-01 -1.753e+01]\n",
+ " [ 3.330e-01 -1.921e+01]\n",
+ " [ 3.670e-01 -2.325e+01]\n",
+ " [ 4.000e-01 -2.460e+01]\n",
+ " [ 4.330e-01 -2.662e+01]\n",
+ " [ 4.670e-01 -2.831e+01]\n",
+ " [ 5.000e-01 -3.168e+01]\n",
+ " [ 5.330e-01 -3.370e+01]\n",
+ " [ 5.670e-01 -3.640e+01]\n",
+ " [ 6.000e-01 -3.640e+01]\n",
+ " [ 6.330e-01 -3.876e+01]\n",
+ " [ 6.670e-01 -4.011e+01]\n",
+ " [ 7.000e-01 -4.314e+01]\n",
+ " [ 7.330e-01 -4.516e+01]\n",
+ " [ 7.670e-01 -4.786e+01]\n",
+ " [ 8.000e-01 -5.022e+01]\n",
+ " [ 8.330e-01 -5.258e+01]\n",
+ " [ 8.670e-01 -5.696e+01]\n",
+ " [ 9.000e-01 -6.066e+01]\n",
+ " [ 9.330e-01 -6.403e+01]\n",
+ " [ 9.670e-01 -6.471e+01]\n",
+ " [ 1.000e+00 -6.639e+01]\n",
+ " [ 1.067e+00 -6.909e+01]\n",
+ " [ 1.100e+00 -7.212e+01]\n",
+ " [ 1.133e+00 -7.583e+01]\n",
+ " [ 1.167e+00 -7.752e+01]\n",
+ " [ 1.200e+00 -8.021e+01]\n",
+ " [ 1.233e+00 -8.392e+01]\n",
+ " [ 1.267e+00 -8.661e+01]\n",
+ " [ 1.300e+00 -8.999e+01]\n",
+ " [ 1.333e+00 -9.430e+01]\n",
+ " [ 1.367e+00 -1.014e+02]\n",
+ " [ 1.400e+00 -1.065e+02]\n",
+ " [ 1.433e+00 -1.146e+02]\n",
+ " [ 1.467e+00 -1.183e+02]\n",
+ " [ 1.500e+00 -1.196e+02]\n",
+ " [ 1.533e+00 -1.217e+02]\n",
+ " [ 1.567e+00 -1.217e+02]\n",
+ " [ 1.600e+00 -1.237e+02]\n",
+ " [ 1.633e+00 -1.281e+02]\n",
+ " [ 1.667e+00 -1.321e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 249,
+ "id": "63cabe50-08f0-4afc-a539-436533298e67",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 -2.530e+00]\n",
+ " [ 3.300e-02 -4.337e+00]\n",
+ " [ 6.700e-02 -1.120e+01]\n",
+ " [ 1.000e-01 -1.193e+01]\n",
+ " [ 1.330e-01 -1.337e+01]\n",
+ " [ 1.670e-01 -1.590e+01]\n",
+ " [ 2.000e-01 -1.952e+01]\n",
+ " [ 2.330e-01 -2.060e+01]\n",
+ " [ 2.670e-01 -2.530e+01]\n",
+ " [ 3.000e-01 -2.711e+01]\n",
+ " [ 3.330e-01 -2.819e+01]\n",
+ " [ 3.670e-01 -3.036e+01]\n",
+ " [ 4.000e-01 -3.108e+01]\n",
+ " [ 4.330e-01 -3.144e+01]\n",
+ " [ 4.670e-01 -3.325e+01]\n",
+ " [ 5.000e-01 -3.542e+01]\n",
+ " [ 5.330e-01 -3.795e+01]\n",
+ " [ 5.670e-01 -3.939e+01]\n",
+ " [ 6.000e-01 -3.975e+01]\n",
+ " [ 6.330e-01 -4.337e+01]\n",
+ " [ 6.670e-01 -4.915e+01]\n",
+ " [ 7.000e-01 -5.132e+01]\n",
+ " [ 7.330e-01 -6.035e+01]\n",
+ " [ 7.670e-01 -6.831e+01]\n",
+ " [ 8.000e-01 -6.794e+01]\n",
+ " [ 8.330e-01 -7.120e+01]\n",
+ " [ 8.670e-01 -7.373e+01]\n",
+ " [ 9.000e-01 -7.626e+01]\n",
+ " [ 9.330e-01 -7.951e+01]\n",
+ " [ 9.670e-01 -8.168e+01]\n",
+ " [ 1.000e+00 -8.312e+01]\n",
+ " [ 1.033e+00 -8.782e+01]\n",
+ " [ 1.067e+00 -9.107e+01]\n",
+ " [ 1.100e+00 -9.252e+01]\n",
+ " [ 1.133e+00 -9.650e+01]\n",
+ " [ 1.167e+00 -1.001e+02]\n",
+ " [ 1.200e+00 -1.052e+02]\n",
+ " [ 1.233e+00 -1.102e+02]\n",
+ " [ 1.267e+00 -1.153e+02]\n",
+ " [ 1.300e+00 -1.128e+02]\n",
+ " [ 1.333e+00 -1.120e+02]\n",
+ " [ 1.367e+00 -1.120e+02]\n",
+ " [ 1.400e+00 -1.124e+02]\n",
+ " [ 1.433e+00 -1.167e+02]\n",
+ " [ 1.467e+00 -1.175e+02]\n",
+ " [ 1.500e+00 -1.243e+02]\n",
+ " [ 1.533e+00 -1.254e+02]\n",
+ " [ 1.567e+00 -1.272e+02]\n",
+ " [ 1.600e+00 -1.279e+02]\n",
+ " [ 1.633e+00 -1.279e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 250,
+ "id": "fc4e4a4b-e225-4ec5-9174-29e650b1a417",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 -4.444e+00]\n",
+ " [ 3.300e-02 -5.926e+00]\n",
+ " [ 6.700e-02 -8.518e+00]\n",
+ " [ 1.000e-01 -1.000e+00]\n",
+ " [ 1.330e-01 -1.590e+00]\n",
+ " [ 1.670e-01 -1.370e+01]\n",
+ " [ 2.000e-01 -1.630e+01]\n",
+ " [ 2.340e-01 -1.815e+01]\n",
+ " [ 2.670e-01 -2.037e+01]\n",
+ " [ 3.000e-01 -2.296e+01]\n",
+ " [ 3.340e-01 -2.556e+01]\n",
+ " [ 3.670e-01 -2.815e+01]\n",
+ " [ 4.000e-01 -2.926e+01]\n",
+ " [ 4.340e-01 -3.074e+01]\n",
+ " [ 4.670e-01 -3.370e+01]\n",
+ " [ 5.010e-01 -3.667e+01]\n",
+ " [ 5.340e-01 -4.074e+01]\n",
+ " [ 5.670e-01 -4.481e+01]\n",
+ " [ 6.010e-01 -4.704e+01]\n",
+ " [ 6.340e-01 -5.037e+01]\n",
+ " [ 6.670e-01 -5.111e+01]\n",
+ " [ 7.010e-01 -5.444e+01]\n",
+ " [ 7.340e-01 -5.407e+01]\n",
+ " [ 7.670e-01 -5.556e+01]\n",
+ " [ 8.010e-01 -6.481e+01]\n",
+ " [ 8.340e-01 -6.148e+01]\n",
+ " [ 8.680e-01 -6.272e+01]\n",
+ " [ 9.010e-01 -6.593e+01]\n",
+ " [ 9.340e-01 -7.185e+01]\n",
+ " [ 9.680e-01 -7.481e+01]\n",
+ " [ 1.001e+00 -7.815e+01]\n",
+ " [ 1.034e+00 -8.111e+01]\n",
+ " [ 1.068e+00 -8.444e+01]\n",
+ " [ 1.101e+00 -8.741e+01]\n",
+ " [ 1.134e+00 -8.815e+01]\n",
+ " [ 1.168e+00 -9.185e+01]\n",
+ " [ 1.235e+00 -9.815e+01]\n",
+ " [ 1.268e+00 -1.004e+02]\n",
+ " [ 1.301e+00 -1.030e+02]\n",
+ " [ 1.335e+00 -1.056e+02]\n",
+ " [ 1.368e+00 -1.096e+02]\n",
+ " [ 1.401e+00 -1.141e+02]\n",
+ " [ 1.435e+00 -1.156e+02]\n",
+ " [ 1.468e+00 -1.181e+02]\n",
+ " [ 1.502e+00 -1.233e+02]\n",
+ " [ 1.535e+00 -1.256e+02]\n",
+ " [ 1.568e+00 -1.293e+02]\n",
+ " [ 1.602e+00 -1.315e+02]\n",
+ " [ 1.635e+00 -1.341e+02]\n",
+ " [ 1.668e+00 -1.367e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 266,
+ "id": "14ed3d41-1894-4863-94b4-5208bd3d0489",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 3.370e-01]\n",
+ " [ 3.300e-02 3.700e-01]\n",
+ " [ 6.700e-02 -1.348e+00]\n",
+ " [ 1.000e-01 -5.055e+00]\n",
+ " [ 1.330e-01 -6.740e+00]\n",
+ " [ 1.670e-01 -7.520e+00]\n",
+ " [ 2.000e-01 -1.146e+01]\n",
+ " [ 2.330e-01 -1.213e+01]\n",
+ " [ 2.670e-01 -1.382e+01]\n",
+ " [ 3.000e-01 -1.753e+01]\n",
+ " [ 3.330e-01 -1.921e+01]\n",
+ " [ 3.670e-01 -2.325e+01]\n",
+ " [ 4.000e-01 -2.460e+01]\n",
+ " [ 4.330e-01 -2.662e+01]\n",
+ " [ 4.670e-01 -2.831e+01]\n",
+ " [ 5.000e-01 -3.168e+01]\n",
+ " [ 5.330e-01 -3.370e+01]\n",
+ " [ 5.670e-01 -3.640e+01]\n",
+ " [ 6.000e-01 -3.640e+01]\n",
+ " [ 6.330e-01 -3.876e+01]\n",
+ " [ 7.000e-01 -4.314e+01]\n",
+ " [ 7.330e-01 -4.516e+01]\n",
+ " [ 7.670e-01 -4.786e+01]\n",
+ " [ 8.000e-01 -5.022e+01]\n",
+ " [ 8.330e-01 -5.258e+01]\n",
+ " [ 8.670e-01 -5.696e+01]\n",
+ " [ 9.000e-01 -6.066e+01]\n",
+ " [ 9.330e-01 -6.403e+01]\n",
+ " [ 9.670e-01 -6.471e+01]\n",
+ " [ 1.000e+00 -6.639e+01]\n",
+ " [ 1.033e+00 -6.639e+01]\n",
+ " [ 1.067e+00 -6.909e+01]\n",
+ " [ 1.100e+00 -7.212e+01]\n",
+ " [ 1.133e+00 -7.583e+01]\n",
+ " [ 1.167e+00 -7.752e+01]\n",
+ " [ 1.200e+00 -8.021e+01]\n",
+ " [ 1.233e+00 -8.392e+01]\n",
+ " [ 1.267e+00 -8.661e+01]\n",
+ " [ 1.300e+00 -8.999e+01]\n",
+ " [ 1.333e+00 -9.430e+01]\n",
+ " [ 1.367e+00 -1.014e+02]\n",
+ " [ 1.400e+00 -1.065e+02]\n",
+ " [ 1.433e+00 -1.146e+02]\n",
+ " [ 1.467e+00 -1.183e+02]\n",
+ " [ 1.500e+00 -1.196e+02]\n",
+ " [ 1.533e+00 -1.217e+02]\n",
+ " [ 1.567e+00 -1.217e+02]\n",
+ " [ 1.600e+00 -1.237e+02]\n",
+ " [ 1.633e+00 -1.281e+02]\n",
+ " [ 1.667e+00 -1.321e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos9)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 252,
+ "id": "c50d09d8-50f7-4cc6-9c87-e832f737f7d3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 7.220e-01]\n",
+ " [ 3.300e-02 7.220e-01]\n",
+ " [ 6.700e-02 -1.084e+00]\n",
+ " [ 1.000e-01 -3.251e+00]\n",
+ " [ 1.330e-01 -3.973e+00]\n",
+ " [ 1.670e-01 -5.057e+00]\n",
+ " [ 2.000e-01 -5.418e+00]\n",
+ " [ 2.330e-01 -6.863e+00]\n",
+ " [ 2.670e-01 -7.947e+00]\n",
+ " [ 3.000e-01 -1.048e+01]\n",
+ " [ 3.330e-01 -1.120e+01]\n",
+ " [ 3.670e-01 -1.589e+01]\n",
+ " [ 4.000e-01 -1.662e+01]\n",
+ " [ 4.330e-01 -1.914e+01]\n",
+ " [ 4.670e-01 -2.131e+01]\n",
+ " [ 5.000e-01 -2.276e+01]\n",
+ " [ 5.330e-01 -2.492e+01]\n",
+ " [ 5.670e-01 -2.745e+01]\n",
+ " [ 6.000e-01 -2.998e+01]\n",
+ " [ 6.330e-01 -3.034e+01]\n",
+ " [ 6.670e-01 -3.034e+01]\n",
+ " [ 7.000e-01 -3.143e+01]\n",
+ " [ 7.330e-01 -3.287e+01]\n",
+ " [ 7.670e-01 -3.504e+01]\n",
+ " [ 8.000e-01 -3.612e+01]\n",
+ " [ 8.670e-01 -4.046e+01]\n",
+ " [ 9.000e-01 -4.912e+01]\n",
+ " [ 9.330e-01 -5.490e+01]\n",
+ " [ 9.670e-01 -5.563e+01]\n",
+ " [ 1.000e+00 -5.599e+01]\n",
+ " [ 1.033e+00 -6.104e+01]\n",
+ " [ 1.067e+00 -6.177e+01]\n",
+ " [ 1.100e+00 -6.357e+01]\n",
+ " [ 1.133e+00 -6.357e+01]\n",
+ " [ 1.167e+00 -6.682e+01]\n",
+ " [ 1.200e+00 -6.935e+01]\n",
+ " [ 1.233e+00 -7.477e+01]\n",
+ " [ 1.267e+00 -7.910e+01]\n",
+ " [ nan -8.199e+01]\n",
+ " [ 1.333e+00 -8.597e+01]\n",
+ " [ 1.367e+00 -8.777e+01]\n",
+ " [ 1.400e+00 -9.102e+01]\n",
+ " [ 1.433e+00 -9.391e+01]\n",
+ " [ 1.467e+00 -9.825e+01]\n",
+ " [ 1.500e+00 -1.009e+02]\n",
+ " [ 1.533e+00 -1.069e+02]\n",
+ " [ 1.567e+00 -1.113e+02]\n",
+ " [ 1.600e+00 -1.178e+02]\n",
+ " [ 1.633e+00 -1.214e+02]\n",
+ " [ 1.667e+00 -1.264e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 269,
+ "id": "fd37104a-bbda-4f60-abda-b99995edda0b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datos_promedio=(datos1+datos2+datos3+datos4+datos5+datos6+datos7+datos8+datos9+datos10)/10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 270,
+ "id": "90394cd7-5675-4dd7-9fb5-e86c65bf7608",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.00000e+00 -9.67000e-01]\n",
+ " [ 7.30000e-02 -1.50480e+00]\n",
+ " [ 1.03500e-01 -3.72110e+00]\n",
+ " [ 1.36700e-01 -4.72970e+00]\n",
+ " [ 1.69700e-01 nan]\n",
+ " [ 2.03500e-01 -1.00660e+01]\n",
+ " [ 2.36600e-01 -1.24624e+01]\n",
+ " [ 2.69900e-01 -1.39218e+01]\n",
+ " [ 3.03500e-01 -1.62646e+01]\n",
+ " [ 3.36600e-01 -1.78780e+01]\n",
+ " [ 3.69800e-01 -2.05800e+01]\n",
+ " [ 4.03500e-01 -2.32310e+01]\n",
+ " [ 4.36600e-01 -2.47410e+01]\n",
+ " [ 4.69800e-01 -2.62030e+01]\n",
+ " [ 5.03400e-01 -2.81780e+01]\n",
+ " [ 5.36700e-01 -3.06930e+01]\n",
+ " [ 5.69800e-01 -3.32980e+01]\n",
+ " [ 6.03400e-01 -3.58670e+01]\n",
+ " [ 6.36700e-01 -3.74900e+01]\n",
+ " [ 6.69700e-01 -4.00550e+01]\n",
+ " [ 7.06700e-01 -4.21150e+01]\n",
+ " [ 7.40000e-01 -4.50390e+01]\n",
+ " [ 7.73100e-01 -4.76480e+01]\n",
+ " [ 8.06600e-01 -5.02960e+01]\n",
+ " [ 8.43300e-01 -5.44790e+01]\n",
+ " [ 8.79900e-01 -5.59340e+01]\n",
+ " [ 9.13400e-01 -5.91750e+01]\n",
+ " [ 9.46500e-01 -6.19730e+01]\n",
+ " [ 9.79900e-01 -6.48330e+01]\n",
+ " [ 1.01670e+00 -6.71710e+01]\n",
+ " [ 1.04980e+00 nan]\n",
+ " [ 1.08980e+00 -7.15030e+01]\n",
+ " [ 1.12340e+00 -7.43930e+01]\n",
+ " [ 1.15640e+00 -7.66600e+01]\n",
+ " [ 1.19320e+00 -7.89880e+01]\n",
+ " [ 1.22660e+00 -8.18270e+01]\n",
+ " [ 1.26320e+00 -8.58760e+01]\n",
+ " [ 1.29660e+00 -8.87590e+01]\n",
+ " [ nan nan]\n",
+ " [ 1.36310e+00 -9.50090e+01]\n",
+ " [ 1.39660e+00 -9.85940e+01]\n",
+ " [ 1.43320e+00 -1.02729e+02]\n",
+ " [ 1.46640e+00 -1.06339e+02]\n",
+ " [ 1.49990e+00 -1.09829e+02]\n",
+ " [ 1.53110e+00 -1.12193e+02]\n",
+ " [ 1.56310e+00 -1.15440e+02]\n",
+ " [ 1.59660e+00 -1.18310e+02]\n",
+ " [ 1.62990e+00 -1.21810e+02]\n",
+ " [ 1.66300e+00 -1.25220e+02]\n",
+ " [ 1.69660e+00 -1.29560e+02]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos_promedio)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 271,
+ "id": "1c2f8d48-e7f6-4d25-a9a2-41216f7db585",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50"
+ ]
+ },
+ "execution_count": 271,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(datos_promedio)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 272,
+ "id": "a9056daf-231d-43d6-a247-4fd0d84acde5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t=datos_promedio [:,0]\n",
+ "y=datos_promedio [:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 273,
+ "id": "b1eb8438-0031-4269-a7f6-10d1fec98840",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0. 0.073 0.1035 0.1367 0.1697 0.2035 0.2366 0.2699 0.3035 0.3366\n",
+ " 0.3698 0.4035 0.4366 0.4698 0.5034 0.5367 0.5698 0.6034 0.6367 0.6697\n",
+ " 0.7067 0.74 0.7731 0.8066 0.8433 0.8799 0.9134 0.9465 0.9799 1.0167\n",
+ " 1.0498 1.0898 1.1234 1.1564 1.1932 1.2266 1.2632 1.2966 nan 1.3631\n",
+ " 1.3966 1.4332 1.4664 1.4999 1.5311 1.5631 1.5966 1.6299 1.663 1.6966]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 274,
+ "id": "05de5324-df51-4642-bafa-38fae5680db8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50"
+ ]
+ },
+ "execution_count": 274,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 275,
+ "id": "9c9980d2-aa67-4e48-b1e2-e5c849f2ca00",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ -0.967 -1.5048 -3.7211 -4.7297 nan -10.066 -12.4624\n",
+ " -13.9218 -16.2646 -17.878 -20.58 -23.231 -24.741 -26.203\n",
+ " -28.178 -30.693 -33.298 -35.867 -37.49 -40.055 -42.115\n",
+ " -45.039 -47.648 -50.296 -54.479 -55.934 -59.175 -61.973\n",
+ " -64.833 -67.171 nan -71.503 -74.393 -76.66 -78.988\n",
+ " -81.827 -85.876 -88.759 nan -95.009 -98.594 -102.729\n",
+ " -106.339 -109.829 -112.193 -115.44 -118.31 -121.81 -125.22\n",
+ " -129.56 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 276,
+ "id": "020ec687-ec83-4ca1-89c2-14f7957345a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50"
+ ]
+ },
+ "execution_count": 276,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 277,
+ "id": "99a974cc-6801-4eef-9aad-342485b29855",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure()\n",
+ "plt.plot(t,y,'.')\n",
+ "plt.title(\"Servilleta extendida\")\n",
+ "plt.savefig('graphic_servilletaext.pdf', bbox_inches = 'tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "id": "652ff9a7-4e36-461d-87d3-902f501732f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y = np.log(np.abs(y[1:]))\n",
+ "x = np.log(t[1:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 279,
+ "id": "713f6a1d-87b5-440d-b0b0-8bae5fb43f63",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure()\n",
+ "plt.plot(x,y,'.')\n",
+ "plt.xlabel(r'$ln(t)$')\n",
+ "plt.ylabel(r'$ln(y)$')\n",
+ "plt.title(\"Linealización servilleta extendida\")\n",
+ "plt.savefig('linealization_servilletaext.pdf', bbox_inches = 'tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 294,
+ "id": "a241fa95-6bca-41ff-9ef9-8e6022f563b4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Grafica"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 293,
+ "id": "661682a7-9402-4b1b-a08b-f3d89976c0fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Error en las mediciones "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 280,
+ "id": "28f892a7-fbf9-475b-bbee-492768117ef9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a1=datos1[:,0]\n",
+ "a2=datos2[:,0]\n",
+ "a3=datos3[:,0]\n",
+ "a4=datos4[:,0]\n",
+ "a5=datos5[:,0]\n",
+ "a6=datos6[:,0]\n",
+ "a7=datos7[:,0]\n",
+ "a8=datos8[:,0]\n",
+ "a9=datos9[:,0]\n",
+ "a10=datos10[:,0]\n",
+ "\n",
+ "b1=datos1[:,1]\n",
+ "b2=datos2[:,1]\n",
+ "b3=datos3[:,1]\n",
+ "b4=datos4[:,1]\n",
+ "b5=datos5[:,1]\n",
+ "b6=datos6[:,1]\n",
+ "b7=datos7[:,1]\n",
+ "b8=datos8[:,1]\n",
+ "b9=datos9[:,1]\n",
+ "b10=datos10[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 281,
+ "id": "e4f5bce1-379b-46f8-b422-7e4671890f38",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ -0.297 -0.97 -0.97 -0.97 -4.62 -6.43 -8.21 -10.4\n",
+ " -12.78 -16.65 -15.76 -16.35 -17.24 -19.32 -21.7 -24.97\n",
+ " -27.65 -30.62 -32.4 -35.38 -38.94 -41.32 -46.67 -47.86\n",
+ " -51.43 -51.73 -52.62 -53.21 -56.48 -59.16 -61.24 -64.81\n",
+ " -68.08 -68.08 -68.08 -71.05 -73.13 -73.13 -79.07 -82.64\n",
+ " -85.02 -88.59 -94.3 -100.8 -103.5 -106.4 -113. -123.4\n",
+ " -130.8 -137.9 ]\n",
+ " [ 0.288 0.288 0.288 0.288 -1.731 -4.904 -6.057 -7.211\n",
+ " -9.519 -1.54 -11.83 -12.11 -14.42 -15.58 -18.17 -19.33\n",
+ " -21.35 -23.94 -26.54 -29.42 -30.58 -34.04 -34.9 -35.77\n",
+ " -35.77 -37.21 -39.23 -41.54 -44.42 -52.5 nan -55.96\n",
+ " -57.11 -57.98 -61.44 -65.77 -70.38 -72.98 -75.57 -79.32\n",
+ " -82.21 -88.27 -90.28 -91.44 -94.03 -99.8 -105. -108.7\n",
+ " -114.5 -126.3 ]\n",
+ " [ -4.444 -5.926 -8.518 -1. -1.59 -13.7 -16.3 -18.15\n",
+ " -20.37 -22.96 -25.56 -28.15 -29.26 -30.74 -33.7 -36.67\n",
+ " -40.74 -44.81 -47.04 -50.37 -51.11 -54.44 -54.07 -55.56\n",
+ " -64.81 -61.48 -62.72 -65.93 -71.85 -74.81 -78.15 -81.11\n",
+ " -84.44 -87.41 -91.85 -94.81 -98.15 -100.4 -103. -105.6\n",
+ " -109.6 -114.1 -115.6 -118.1 -123.3 -125.6 -129.3 -131.5\n",
+ " -134.1 -136.7 ]\n",
+ " [ 0. 0. -4.874 -4.874 nan -7.499 -7.499 -9.374\n",
+ " -11.62 -12.75 -13.87 -16.5 -19.5 -21. -22.5 -24.75\n",
+ " -28.5 -31.12 -35.62 -38.25 -38.25 -41.62 -41.99 -44.99\n",
+ " -51.37 -53.24 -57.74 -58.49 -59.99 -59.99 -59.99 -62.24\n",
+ " -67.87 -71.24 -72.74 -73.49 -75.74 -79.87 -82.49 -85.86\n",
+ " -90.74 -97.11 -100.1 -101.6 -103.1 -104.6 -105.7 -109.5\n",
+ " -111.4 -115.9 ]\n",
+ " [ 0.361 0.361 0.361 -14.45 -15.18 -18.43 -22.4 -24.21\n",
+ " -27.1 -29.27 -35.41 -38.3 -40.83 -40.83 -40.83 -43.\n",
+ " -43.73 -43.73 -43.73 -45.53 -48.42 -53.48 -58.54 -61.79\n",
+ " -69.74 -73. -76.25 -78.78 -79.86 -81.67 -81.67 -82.03\n",
+ " -83.11 -86.73 -89.26 -91.43 -95.4 -98.29 nan -103.7\n",
+ " -106.2 -109.1 -112. -116.7 -117.1 -117.8 -120.7 -121.1\n",
+ " -121.8 -123.6 ]\n",
+ " [ 0.337 0.37 -1.348 -5.055 -6.74 -7.52 -11.46 -12.13\n",
+ " -13.82 -17.53 -19.21 -23.25 -24.6 -26.62 -28.31 -31.68\n",
+ " -33.7 -36.4 -36.4 -38.76 -40.11 -43.14 -45.16 -47.86\n",
+ " -50.22 -52.58 -56.96 -60.66 -64.03 -64.71 -66.39 -69.09\n",
+ " -72.12 -75.83 -77.52 -80.21 -83.92 -86.61 -89.99 -94.3\n",
+ " -101.4 -106.5 -114.6 -118.3 -119.6 -121.7 -121.7 -123.7\n",
+ " -128.1 -132.1 ]\n",
+ " [ -2.53 -4.337 -11.2 -11.93 -13.37 -15.9 -19.52 -20.6\n",
+ " -25.3 -27.11 -28.19 -30.36 -31.08 -31.44 -33.25 -35.42\n",
+ " -37.95 -39.39 -39.75 -43.37 -49.15 -51.32 -60.35 -68.31\n",
+ " -67.94 -71.2 -73.73 -76.26 -79.51 -81.68 -83.12 -87.82\n",
+ " -91.07 -92.52 -96.5 -100.1 -105.2 -110.2 -115.3 -112.8\n",
+ " -112. -112. -112.4 -116.7 -117.5 -124.3 -125.4 -127.2\n",
+ " -127.9 -127.9 ]\n",
+ " [ -4.444 -5.926 -8.518 -1. -1.59 -13.7 -16.3 -18.15\n",
+ " -20.37 -22.96 -25.56 -28.15 -29.26 -30.74 -33.7 -36.67\n",
+ " -40.74 -44.81 -47.04 -50.37 -51.11 -54.44 -54.07 -55.56\n",
+ " -64.81 -61.48 -62.72 -65.93 -71.85 -74.81 -78.15 -81.11\n",
+ " -84.44 -87.41 -88.15 -91.85 -98.15 -100.4 -103. -105.6\n",
+ " -109.6 -114.1 -115.6 -118.1 -123.3 -125.6 -129.3 -131.5\n",
+ " -134.1 -136.7 ]\n",
+ " [ 0.337 0.37 -1.348 -5.055 -6.74 -7.52 -11.46 -12.13\n",
+ " -13.82 -17.53 -19.21 -23.25 -24.6 -26.62 -28.31 -31.68\n",
+ " -33.7 -36.4 -36.4 -38.76 -43.14 -45.16 -47.86 -50.22\n",
+ " -52.58 -56.96 -60.66 -64.03 -64.71 -66.39 -66.39 -69.09\n",
+ " -72.12 -75.83 -77.52 -80.21 -83.92 -86.61 -89.99 -94.3\n",
+ " -101.4 -106.5 -114.6 -118.3 -119.6 -121.7 -121.7 -123.7\n",
+ " -128.1 -132.1 ]\n",
+ " [ 0.722 0.722 -1.084 -3.251 -3.973 -5.057 -5.418 -6.863\n",
+ " -7.947 -10.48 -11.2 -15.89 -16.62 -19.14 -21.31 -22.76\n",
+ " -24.92 -27.45 -29.98 -30.34 -30.34 -31.43 -32.87 -35.04\n",
+ " -36.12 -40.46 -49.12 -54.9 -55.63 -55.99 -61.04 -61.77\n",
+ " -63.57 -63.57 -66.82 -69.35 -74.77 -79.1 -81.99 -85.97\n",
+ " -87.77 -91.02 -93.91 -98.25 -100.9 -106.9 -111.3 -117.8\n",
+ " -121.4 -126.4 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "Alturas=np.array([b1,b2,b3,b4,b5,b6,b7,b8,b9,b10])\n",
+ "print(Alturas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 282,
+ "id": "ec721414-d8a9-451e-8eac-af126ea20118",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.433 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733\n",
+ " 0.767 0.8 0.833 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133\n",
+ " 1.167 1.2 1.233 1.267 1.3 1.333 1.367 1.433 1.467 1.5 1.533 1.567\n",
+ " 1.6 1.633 1.667 1.7 1.733 1.767 1.8 1.833 1.844 1.867 1.9 1.933\n",
+ " 1.967 2. ]\n",
+ " [0. 0.033 0.066 0.1 0.133 0.166 0.199 0.233 0.266 0.299 0.332 0.366\n",
+ " 0.399 0.432 0.465 0.498 0.532 0.565 0.598 0.631 0.665 0.698 0.731 0.764\n",
+ " 0.798 0.831 0.864 0.897 0.931 0.997 1.03 1.063 1.097 1.13 1.163 1.196\n",
+ " 1.23 1.263 1.296 1.329 1.363 1.396 1.429 1.462 1.496 1.529 1.562 1.595\n",
+ " 1.628 1.662]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.234 0.267 0.3 0.334 0.367\n",
+ " 0.4 0.434 0.467 0.501 0.534 0.567 0.601 0.634 0.667 0.701 0.734 0.767\n",
+ " 0.801 0.834 0.868 0.901 0.934 0.968 1.001 1.034 1.068 1.101 1.168 1.201\n",
+ " 1.235 1.268 1.301 1.335 1.368 1.401 1.435 1.468 1.502 1.535 1.568 1.602\n",
+ " 1.635 1.668]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733 0.767\n",
+ " 0.8 0.833 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133 1.167\n",
+ " 1.2 1.233 1.267 1.3 1.333 1.4 1.433 1.467 1.5 1.533 1.567 1.6\n",
+ " 1.633 1.667]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733 0.767\n",
+ " 0.833 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133 1.167 1.2\n",
+ " 1.233 1.267 1.3 1.333 1.367 1.4 1.433 1.467 1.5 1.533 1.567 1.6\n",
+ " 1.633 1.667]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733 0.767\n",
+ " 0.8 0.833 0.867 0.9 0.933 0.967 1. 1.067 1.1 1.133 1.167 1.2\n",
+ " 1.233 1.267 1.3 1.333 1.367 1.4 1.433 1.467 1.5 1.533 1.567 1.6\n",
+ " 1.633 1.667]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733 0.767\n",
+ " 0.8 0.833 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133 1.167\n",
+ " 1.2 1.233 1.267 1.3 1.333 1.367 1.4 1.433 1.467 1.5 1.533 1.567\n",
+ " 1.6 1.633]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.234 0.267 0.3 0.334 0.367\n",
+ " 0.4 0.434 0.467 0.501 0.534 0.567 0.601 0.634 0.667 0.701 0.734 0.767\n",
+ " 0.801 0.834 0.868 0.901 0.934 0.968 1.001 1.034 1.068 1.101 1.134 1.168\n",
+ " 1.235 1.268 1.301 1.335 1.368 1.401 1.435 1.468 1.502 1.535 1.568 1.602\n",
+ " 1.635 1.668]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.7 0.733 0.767 0.8\n",
+ " 0.833 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133 1.167 1.2\n",
+ " 1.233 1.267 1.3 1.333 1.367 1.4 1.433 1.467 1.5 1.533 1.567 1.6\n",
+ " 1.633 1.667]\n",
+ " [0. 0.033 0.067 0.1 0.133 0.167 0.2 0.233 0.267 0.3 0.333 0.367\n",
+ " 0.4 0.433 0.467 0.5 0.533 0.567 0.6 0.633 0.667 0.7 0.733 0.767\n",
+ " 0.8 0.867 0.9 0.933 0.967 1. 1.033 1.067 1.1 1.133 1.167 1.2\n",
+ " 1.233 1.267 nan 1.333 1.367 1.4 1.433 1.467 1.5 1.533 1.567 1.6\n",
+ " 1.633 1.667]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tiempo=np.array([a1,a2,a3,a4,a5,a6,a7,a8,a9,a10])\n",
+ "print(tiempo)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 283,
+ "id": "38e5691e-1fea-420d-a85a-0270cab4cb36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tiempo_prom=tiempo.mean(axis=0)\n",
+ "tiempo_error=tiempo.std(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 284,
+ "id": "6b4ecf5d-b427-4d02-9f9b-54c0c76113f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50"
+ ]
+ },
+ "execution_count": 284,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(tiempo_prom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 285,
+ "id": "1f2ee57a-6280-4dad-ae4a-2958a4e21661",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_prom=Alturas.mean(axis=0)\n",
+ "y_error=Alturas.std(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 286,
+ "id": "e52d230c-b0e4-4a57-bbd2-a6876fb45fa9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50"
+ ]
+ },
+ "execution_count": 286,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(y_prom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 295,
+ "id": "f635ebef-5183-47b3-bf97-80d7f2200439",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Grafica del Error"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 287,
+ "id": "28eccaa4-a463-434a-a626-b2881cb6a663",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<function matplotlib.pyplot.show(close=None, block=None)>"
+ ]
+ },
+ "execution_count": 287,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure()\n",
+ "plt.errorbar(tiempo_prom, y_prom, y_error, tiempo_error,fmt=\"o\")\n",
+ "plt.title(\"Error servilleta extendida\")\n",
+ "plt.savefig('Error_servilletaext.pdf', bbox_inches = 'tight')\n",
+ "plt.show"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 296,
+ "id": "44082b56-9ee9-42df-bea1-51b4074ebcc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Calcular el coeficiente de rosamiento con el aire"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 288,
+ "id": "d5e71c2d-c350-40ec-8227-39a270e269ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g=9.8 # hay que poner con el valor que nos da aquÃ\n",
+ "b=2\n",
+ "y0=0\n",
+ "v0=0\n",
+ "dt=0.01\n",
+ "t0=0\n",
+ "#instante1\n",
+ "v1=g*dt+v0\n",
+ "y1=g*(dt**2)/2+v0*dt+y0\n",
+ "#instante2\n",
+ "a11=g-b*v1\n",
+ "v2=v1+a11*dt\n",
+ "y2=(a11*dt**2)/2+v1*dt + y1\n",
+ "#instante 3\n",
+ "a21=g-b*v2\n",
+ "v3=v1+a21*dt\n",
+ "y3=(a21*dt**2)/2+v2*dt + y2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 289,
+ "id": "5cb7d5cc-8c21-4dab-8e5f-d769c9d2b395",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t0=0\n",
+ "t1=1.0\n",
+ "dt=0.1\n",
+ "b=11\n",
+ "g=5.6\n",
+ "#condiciones iniciales\n",
+ "v0=0\n",
+ "y0=0\n",
+ "\n",
+ "tiempo= np.arange(t0,t1,dt)\n",
+ "y=[y0]\n",
+ "vy=[v0]\n",
+ "\n",
+ "for i in np.arange(1,len(tiempo)):\n",
+ " ai=g-b*vy[i-1]\n",
+ " vi=ai*dt+vy[i-1]\n",
+ " yi=(ai*dt**2)/2+vy[i-1]+y[i-1]\n",
+ " y.append(yi)\n",
+ " vy.append(vi)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1002e347-8fdf-4228-b2bc-06c104615462",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}