diff --git a/servilleta_extendida.ipynb b/servilleta_extendida.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..389c832fe865b5b19d8d5fc3e6f52dbea9ee61bd
--- /dev/null
+++ b/servilleta_extendida.ipynb
@@ -0,0 +1,1394 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wtRsnXhFDur6"
+ },
+ "source": [
+ "***EXPERIMENTO SERVILLETA CON FUERZA DE ROZAMIENTO (SERVILLETA LISA.***"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "raRMWxo7KE_-"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "Zvh6fwrF96Sc"
+ },
+ "outputs": [],
+ "source": [
+ "datos1=np.genfromtxt(\"datos servi extendida 1.txt\")\n",
+ "datos2=np.genfromtxt(\"datos servi extendida 2.txt\")\n",
+ "datos3=np.genfromtxt(\"datos servi extendida 3.txt\")\n",
+ "datos4=np.genfromtxt(\"datos servi extendida 4.txt\")\n",
+ "datos5=np.genfromtxt(\"datos servi extendida 5.txt\")\n",
+ "datos6=np.genfromtxt(\"datos servi extendida 6.txt\")\n",
+ "datos7=np.genfromtxt(\"datos servi extendida 7.txt\")\n",
+ "datos8=np.genfromtxt(\"datos servi extendida 8.txt\")\n",
+ "datos9=np.genfromtxt(\"datos servi extendida 9.txt\")\n",
+ "datos10=np.genfromtxt(\"datos servi extendida 10.txt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3k5p459pDMQt",
+ "outputId": "a27e896e-6c6c-4276-b82b-2ccabe5b26a9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.04386]\n",
+ " [0.04 0.05354]\n",
+ " [0.08 0.08819]\n",
+ " [0.12 0.105 ]\n",
+ " [0.16 0.128 ]\n",
+ " [0.2 0.148 ]\n",
+ " [0.24 0.179 ]\n",
+ " [0.28 0.202 ]\n",
+ " [0.32 0.241 ]\n",
+ " [0.36 0.271 ]\n",
+ " [0.4 0.302 ]\n",
+ " [0.44 0.332 ]\n",
+ " [0.48 0.362 ]\n",
+ " [0.52 0.387 ]\n",
+ " [0.561 0.402 ]\n",
+ " [0.601 0.425 ]\n",
+ " [0.641 0.453 ]\n",
+ " [0.681 0.486 ]\n",
+ " [0.721 0.527 ]\n",
+ " [0.761 0.566 ]\n",
+ " [0.801 0.601 ]\n",
+ " [0.841 0.622 ]\n",
+ " [0.881 0.626 ]\n",
+ " [0.921 0.638 ]\n",
+ " [0.961 0.655 ]\n",
+ " [1.001 0.685 ]\n",
+ " [1.041 0.725 ]\n",
+ " [1.081 0.774 ]\n",
+ " [1.121 0.828 ]\n",
+ " [1.161 0.889 ]\n",
+ " [1.201 0.936 ]\n",
+ " [1.241 0.964 ]\n",
+ " [1.281 0.979 ]\n",
+ " [1.321 0.991 ]\n",
+ " [1.361 0.994 ]\n",
+ " [1.401 1.005 ]\n",
+ " [1.441 1.021 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zJFm15HbjwUZ",
+ "outputId": "91ff9691-fd01-41a5-9016-ee8dbccdca5d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.01408]\n",
+ " [0.04 0.0224 ]\n",
+ " [0.08 0.02348]\n",
+ " [0.12 0.04094]\n",
+ " [0.16 0.06097]\n",
+ " [0.2 0.07753]\n",
+ " [0.24 0.102 ]\n",
+ " [0.28 0.132 ]\n",
+ " [0.32 0.159 ]\n",
+ " [0.36 0.191 ]\n",
+ " [0.4 0.217 ]\n",
+ " [0.44 0.242 ]\n",
+ " [0.48 0.275 ]\n",
+ " [0.52 0.296 ]\n",
+ " [0.56 0.314 ]\n",
+ " [0.601 0.358 ]\n",
+ " [0.641 0.371 ]\n",
+ " [0.681 0.395 ]\n",
+ " [0.721 0.431 ]\n",
+ " [0.761 0.48 ]\n",
+ " [0.801 0.578 ]\n",
+ " [0.841 0.62 ]\n",
+ " [0.881 0.686 ]\n",
+ " [0.921 0.739 ]\n",
+ " [0.961 0.781 ]\n",
+ " [1.001 0.831 ]\n",
+ " [1.041 0.84 ]\n",
+ " [1.081 0.856 ]\n",
+ " [1.121 0.872 ]\n",
+ " [1.161 0.911 ]\n",
+ " [1.201 0.955 ]\n",
+ " [1.241 1.001 ]\n",
+ " [1.281 1.057 ]\n",
+ " [1.321 1.124 ]\n",
+ " [1.361 1.15 ]\n",
+ " [1.401 1.167 ]\n",
+ " [1.441 1.173 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "47PBPa7ljw7x",
+ "outputId": "4a140d29-fe46-498c-9fa9-991e2049a4f1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.02132]\n",
+ " [0.04 0.02402]\n",
+ " [0.08 0.04794]\n",
+ " [0.12 0.07765]\n",
+ " [0.16 0.102 ]\n",
+ " [0.2 0.127 ]\n",
+ " [0.24 0.152 ]\n",
+ " [0.28 0.172 ]\n",
+ " [0.32 0.177 ]\n",
+ " [0.36 0.202 ]\n",
+ " [0.4 0.245 ]\n",
+ " [0.44 0.276 ]\n",
+ " [0.48 0.314 ]\n",
+ " [0.521 0.343 ]\n",
+ " [0.561 0.38 ]\n",
+ " [0.601 0.418 ]\n",
+ " [0.641 0.431 ]\n",
+ " [0.681 0.442 ]\n",
+ " [0.721 0.447 ]\n",
+ " [0.761 0.478 ]\n",
+ " [0.801 0.523 ]\n",
+ " [0.841 0.557 ]\n",
+ " [0.881 0.612 ]\n",
+ " [0.921 0.648 ]\n",
+ " [0.961 0.693 ]\n",
+ " [1.001 0.755 ]\n",
+ " [1.041 0.832 ]\n",
+ " [1.081 0.877 ]\n",
+ " [1.121 0.927 ]\n",
+ " [1.161 0.973 ]\n",
+ " [1.201 1.006 ]\n",
+ " [1.241 1.029 ]\n",
+ " [1.281 1.057 ]\n",
+ " [1.321 1.054 ]\n",
+ " [1.361 1.082 ]\n",
+ " [1.401 1.113 ]\n",
+ " [1.441 1.122 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ovLZ85Ezjytx",
+ "outputId": "c0c1a604-3626-48fe-f97e-efa184ca07b1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.000e+00 -2.527e-03]\n",
+ " [ 4.000e-02 -4.005e-03]\n",
+ " [ 8.000e-02 -6.829e-04]\n",
+ " [ 1.200e-01 1.399e-02]\n",
+ " [ 1.600e-01 3.424e-02]\n",
+ " [ 2.000e-01 5.185e-02]\n",
+ " [ 2.400e-01 8.084e-02]\n",
+ " [ 2.800e-01 9.654e-02]\n",
+ " [ 3.200e-01 1.240e-01]\n",
+ " [ 3.600e-01 1.510e-01]\n",
+ " [ 4.000e-01 1.810e-01]\n",
+ " [ 4.400e-01 2.080e-01]\n",
+ " [ 4.800e-01 2.360e-01]\n",
+ " [ 5.200e-01 2.330e-01]\n",
+ " [ 5.600e-01 2.490e-01]\n",
+ " [ 6.010e-01 3.210e-01]\n",
+ " [ 6.410e-01 4.450e-01]\n",
+ " [ 6.810e-01 5.170e-01]\n",
+ " [ 7.210e-01 5.850e-01]\n",
+ " [ 7.610e-01 6.340e-01]\n",
+ " [ 8.010e-01 6.400e-01]\n",
+ " [ 8.410e-01 6.310e-01]\n",
+ " [ 8.810e-01 6.340e-01]\n",
+ " [ 9.210e-01 6.540e-01]\n",
+ " [ 9.610e-01 6.890e-01]\n",
+ " [ 1.001e+00 7.290e-01]\n",
+ " [ 1.041e+00 7.670e-01]\n",
+ " [ 1.081e+00 8.240e-01]\n",
+ " [ 1.121e+00 8.610e-01]\n",
+ " [ 1.161e+00 8.870e-01]\n",
+ " [ 1.201e+00 9.070e-01]\n",
+ " [ 1.241e+00 9.300e-01]\n",
+ " [ 1.281e+00 9.470e-01]\n",
+ " [ 1.321e+00 9.670e-01]\n",
+ " [ 1.361e+00 1.011e+00]\n",
+ " [ 1.401e+00 1.042e+00]\n",
+ " [ 1.441e+00 1.056e+00]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5Y_3dYqPjybh",
+ "outputId": "c6ef5edd-80d4-4379-f2d4-06a1b598c3b6"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.02048]\n",
+ " [0.04 0.02919]\n",
+ " [0.08 0.04365]\n",
+ " [0.12 0.07058]\n",
+ " [0.16 0.09657]\n",
+ " [0.2 0.09288]\n",
+ " [0.24 0.128 ]\n",
+ " [0.28 0.16 ]\n",
+ " [0.32 0.187 ]\n",
+ " [0.36 0.199 ]\n",
+ " [0.4 0.229 ]\n",
+ " [0.44 0.251 ]\n",
+ " [0.48 0.286 ]\n",
+ " [0.52 0.321 ]\n",
+ " [0.561 0.363 ]\n",
+ " [0.601 0.405 ]\n",
+ " [0.641 0.444 ]\n",
+ " [0.681 0.488 ]\n",
+ " [0.721 0.512 ]\n",
+ " [0.761 0.541 ]\n",
+ " [0.801 0.573 ]\n",
+ " [0.841 0.608 ]\n",
+ " [0.881 0.64 ]\n",
+ " [0.921 0.659 ]\n",
+ " [0.961 0.677 ]\n",
+ " [1.001 0.696 ]\n",
+ " [1.041 0.717 ]\n",
+ " [1.081 0.747 ]\n",
+ " [1.121 0.784 ]\n",
+ " [1.161 0.819 ]\n",
+ " [1.201 0.854 ]\n",
+ " [1.241 0.923 ]\n",
+ " [1.281 0.944 ]\n",
+ " [1.321 0.979 ]\n",
+ " [1.361 1.006 ]\n",
+ " [1.401 1.024 ]\n",
+ " [1.441 1.04 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wlGmWHBJjyJh",
+ "outputId": "ca84024c-de05-4b83-f825-4587bad5e313"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.01954]\n",
+ " [0.04 0.01682]\n",
+ " [0.08 0.01893]\n",
+ " [0.12 0.04444]\n",
+ " [0.16 0.05556]\n",
+ " [0.2 0.08611]\n",
+ " [0.24 0.1 ]\n",
+ " [0.28 0.117 ]\n",
+ " [0.32 0.142 ]\n",
+ " [0.36 0.15 ]\n",
+ " [0.4 0.167 ]\n",
+ " [0.44 0.2 ]\n",
+ " [0.48 0.239 ]\n",
+ " [0.52 0.289 ]\n",
+ " [0.56 0.328 ]\n",
+ " [0.601 0.381 ]\n",
+ " [0.641 0.419 ]\n",
+ " [0.681 0.467 ]\n",
+ " [0.721 0.506 ]\n",
+ " [0.761 0.539 ]\n",
+ " [0.801 0.569 ]\n",
+ " [0.841 0.603 ]\n",
+ " [0.881 0.628 ]\n",
+ " [0.921 0.656 ]\n",
+ " [0.961 0.669 ]\n",
+ " [1.001 0.694 ]\n",
+ " [1.041 0.733 ]\n",
+ " [1.081 0.789 ]\n",
+ " [1.121 0.85 ]\n",
+ " [1.161 0.903 ]\n",
+ " [1.201 0.956 ]\n",
+ " [1.241 0.989 ]\n",
+ " [1.281 1.022 ]\n",
+ " [1.321 1.042 ]\n",
+ " [1.361 1.028 ]\n",
+ " [1.401 1.019 ]\n",
+ " [1.441 1.019 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mVg_N-OZjxth",
+ "outputId": "ef3959ba-07f0-4f1e-d5bb-f235a1b56e16"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.02226]\n",
+ " [0.04 0.02522]\n",
+ " [0.08 0.06379]\n",
+ " [0.12 0.04883]\n",
+ " [0.16 0.06481]\n",
+ " [0.2 0.09463]\n",
+ " [0.24 0.126 ]\n",
+ " [0.28 0.159 ]\n",
+ " [0.32 0.183 ]\n",
+ " [0.36 0.213 ]\n",
+ " [0.4 0.25 ]\n",
+ " [0.44 0.291 ]\n",
+ " [0.48 0.323 ]\n",
+ " [0.52 0.363 ]\n",
+ " [0.56 0.401 ]\n",
+ " [0.601 0.428 ]\n",
+ " [0.641 0.449 ]\n",
+ " [0.681 0.463 ]\n",
+ " [0.721 0.479 ]\n",
+ " [0.761 0.482 ]\n",
+ " [0.801 0.495 ]\n",
+ " [0.841 0.522 ]\n",
+ " [0.881 0.554 ]\n",
+ " [0.921 0.616 ]\n",
+ " [0.961 0.678 ]\n",
+ " [1.001 0.762 ]\n",
+ " [1.041 0.829 ]\n",
+ " [1.081 0.888 ]\n",
+ " [1.121 0.939 ]\n",
+ " [1.161 0.969 ]\n",
+ " [1.201 1.023 ]\n",
+ " [1.241 1.047 ]\n",
+ " [1.281 1.066 ]\n",
+ " [1.321 1.098 ]\n",
+ " [1.361 1.122 ]\n",
+ " [1.401 1.141 ]\n",
+ " [1.441 1.146 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "LG9RK9RYj0Wh",
+ "outputId": "dc85f09e-c702-4d9e-dcbe-d41b8e466db2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.03664]\n",
+ " [0.033 0.05981]\n",
+ " [0.067 0.08268]\n",
+ " [0.1 0.117 ]\n",
+ " [0.133 0.139 ]\n",
+ " [0.167 0.162 ]\n",
+ " [0.2 0.185 ]\n",
+ " [0.233 0.208 ]\n",
+ " [0.267 0.233 ]\n",
+ " [0.3 0.261 ]\n",
+ " [0.333 0.291 ]\n",
+ " [0.367 0.319 ]\n",
+ " [0.4 0.346 ]\n",
+ " [0.433 0.38 ]\n",
+ " [0.467 0.409 ]\n",
+ " [0.5 0.435 ]\n",
+ " [0.533 0.454 ]\n",
+ " [0.567 0.467 ]\n",
+ " [0.6 0.478 ]\n",
+ " [0.633 0.487 ]\n",
+ " [0.667 0.491 ]\n",
+ " [0.7 0.5 ]\n",
+ " [0.733 0.523 ]\n",
+ " [0.767 0.557 ]\n",
+ " [0.8 0.6 ]\n",
+ " [0.833 0.651 ]\n",
+ " [0.867 0.7 ]\n",
+ " [0.9 0.743 ]\n",
+ " [0.933 0.788 ]\n",
+ " [0.967 0.815 ]\n",
+ " [1. 0.862 ]\n",
+ " [1.033 0.879 ]\n",
+ " [1.067 0.899 ]\n",
+ " [1.1 0.922 ]\n",
+ " [1.133 0.938 ]\n",
+ " [1.167 0.957 ]\n",
+ " [1.2 0.963 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-TGNWLydj0vB",
+ "outputId": "6f5a771d-120a-436a-ee38-aa3b30d209fb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.07714]\n",
+ " [0.033 0.09663]\n",
+ " [0.067 0.122 ]\n",
+ " [0.1 0.149 ]\n",
+ " [0.133 0.17 ]\n",
+ " [0.167 0.191 ]\n",
+ " [0.2 0.212 ]\n",
+ " [0.233 0.235 ]\n",
+ " [0.267 0.271 ]\n",
+ " [0.3 0.298 ]\n",
+ " [0.333 0.344 ]\n",
+ " [0.367 0.377 ]\n",
+ " [0.4 0.407 ]\n",
+ " [0.433 0.43 ]\n",
+ " [0.467 0.452 ]\n",
+ " [0.5 0.472 ]\n",
+ " [0.533 0.498 ]\n",
+ " [0.567 0.528 ]\n",
+ " [0.6 0.561 ]\n",
+ " [0.633 0.597 ]\n",
+ " [0.667 0.633 ]\n",
+ " [0.7 0.666 ]\n",
+ " [0.733 0.695 ]\n",
+ " [0.767 0.705 ]\n",
+ " [0.8 0.718 ]\n",
+ " [0.833 0.721 ]\n",
+ " [0.867 0.731 ]\n",
+ " [0.9 0.744 ]\n",
+ " [0.933 0.777 ]\n",
+ " [0.966 0.813 ]\n",
+ " [1. 0.852 ]\n",
+ " [1.033 0.905 ]\n",
+ " [1.066 0.967 ]\n",
+ " [1.1 1.03 ]\n",
+ " [1.133 1.085 ]\n",
+ " [1.166 1.128 ]\n",
+ " [1.2 1.197 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos9)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2iWS4GwFjxTE",
+ "outputId": "51d82612-ec58-46c8-f69c-66a003977b23"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.0566 ]\n",
+ " [0.033 0.07698]\n",
+ " [0.067 0.09283]\n",
+ " [0.1 0.106 ]\n",
+ " [0.133 0.127 ]\n",
+ " [0.167 0.145 ]\n",
+ " [0.2 0.165 ]\n",
+ " [0.233 0.192 ]\n",
+ " [0.267 0.222 ]\n",
+ " [0.3 0.254 ]\n",
+ " [0.333 0.29 ]\n",
+ " [0.367 0.315 ]\n",
+ " [0.4 0.344 ]\n",
+ " [0.433 0.376 ]\n",
+ " [0.467 0.398 ]\n",
+ " [0.5 0.405 ]\n",
+ " [0.533 0.442 ]\n",
+ " [0.567 0.455 ]\n",
+ " [0.6 0.455 ]\n",
+ " [0.633 0.469 ]\n",
+ " [0.667 0.516 ]\n",
+ " [0.7 0.53 ]\n",
+ " [0.733 0.562 ]\n",
+ " [0.767 0.623 ]\n",
+ " [0.8 0.695 ]\n",
+ " [0.833 0.749 ]\n",
+ " [0.867 0.788 ]\n",
+ " [0.9 0.804 ]\n",
+ " [0.933 0.822 ]\n",
+ " [0.967 0.845 ]\n",
+ " [1. 0.874 ]\n",
+ " [1.033 0.91 ]\n",
+ " [1.067 0.955 ]\n",
+ " [1.1 0.996 ]\n",
+ " [1.133 1.014 ]\n",
+ " [1.167 1.028 ]\n",
+ " [1.2 1.046 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CS_s2LR5kGoZ"
+ },
+ "source": [
+ "Se realizara el promedio de todos los datos para optener una mejor presición en los datos y comparar dichas medidas con el modelo teórico estipulado."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "id": "t4dmIsJ_gdb7"
+ },
+ "outputs": [],
+ "source": [
+ "datos_promedio=(datos1+datos2+datos3+datos4+datos5+datos6+datos7+datos8+datos9+datos10)/10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t9roRLEZgsyk",
+ "outputId": "58c2854f-e30b-4b1b-9221-f17e68d7041c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.0309393 ]\n",
+ " [0.0379 0.0400605 ]\n",
+ " [0.0761 0.05828071]\n",
+ " [0.114 0.077343 ]\n",
+ " [0.1519 0.097815 ]\n",
+ " [0.1901 0.1176 ]\n",
+ " [0.228 0.142984 ]\n",
+ " [0.2659 0.167354 ]\n",
+ " [0.3041 0.1939 ]\n",
+ " [0.342 0.219 ]\n",
+ " [0.3799 0.2516 ]\n",
+ " [0.4181 0.2811 ]\n",
+ " [0.456 0.3132 ]\n",
+ " [0.494 0.3418 ]\n",
+ " [0.5324 0.3696 ]\n",
+ " [0.5707 0.4048 ]\n",
+ " [0.6086 0.4406 ]\n",
+ " [0.6468 0.4708 ]\n",
+ " [0.6847 0.4981 ]\n",
+ " [0.7226 0.5273 ]\n",
+ " [0.7608 0.5619 ]\n",
+ " [0.7987 0.5859 ]\n",
+ " [0.8366 0.616 ]\n",
+ " [0.8748 0.6495 ]\n",
+ " [0.9127 0.6855 ]\n",
+ " [0.9506 0.7273 ]\n",
+ " [0.9888 0.7662 ]\n",
+ " [1.0267 0.8046 ]\n",
+ " [1.0646 0.8448 ]\n",
+ " [1.1027 0.8824 ]\n",
+ " [1.1407 0.9225 ]\n",
+ " [1.1786 0.9577 ]\n",
+ " [1.2167 0.9893 ]\n",
+ " [1.2547 1.0203 ]\n",
+ " [1.2926 1.043 ]\n",
+ " [1.3307 1.0624 ]\n",
+ " [1.3687 1.0783 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datos_promedio)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UIjtRtEok-3o",
+ "outputId": "204f0685-8950-405a-ae31-ae18defd289f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "37"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(datos_promedio)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "id": "X0EgXFeiD3qO"
+ },
+ "outputs": [],
+ "source": [
+ " t=datos_promedio[:,0]\n",
+ " y=datos_promedio[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vuQKQbTcEaeN",
+ "outputId": "106bae8f-ad89-4c5d-a50d-d2f0595e4e61"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0. 0.0379 0.0761 0.114 0.1519 0.1901 0.228 0.2659 0.3041 0.342\n",
+ " 0.3799 0.4181 0.456 0.494 0.5324 0.5707 0.6086 0.6468 0.6847 0.7226\n",
+ " 0.7608 0.7987 0.8366 0.8748 0.9127 0.9506 0.9888 1.0267 1.0646 1.1027\n",
+ " 1.1407 1.1786 1.2167 1.2547 1.2926 1.3307 1.3687]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "XcjRKIfxNxgX",
+ "outputId": "a50919c1-ac60-4ad8-bbce-bc9f40bde170"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "37"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "IrsTycR6EhJt",
+ "outputId": "e5925ea6-1e76-439d-95fe-d8831fc9fd51"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.0309393 0.0400605 0.05828071 0.077343 0.097815 0.1176\n",
+ " 0.142984 0.167354 0.1939 0.219 0.2516 0.2811\n",
+ " 0.3132 0.3418 0.3696 0.4048 0.4406 0.4708\n",
+ " 0.4981 0.5273 0.5619 0.5859 0.616 0.6495\n",
+ " 0.6855 0.7273 0.7662 0.8046 0.8448 0.8824\n",
+ " 0.9225 0.9577 0.9893 1.0203 1.043 1.0624\n",
+ " 1.0783 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rWYPI7x5N3SX",
+ "outputId": "a1c33aa8-800c-40df-e6ce-2b98b3ac4c29"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "37"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 452
+ },
+ "id": "aCAgeiYyE1Xs",
+ "outputId": "91773417-57af-424a-c3e8-1e8af74965c1"
+ },
+ "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": 42,
+ "metadata": {
+ "id": "c1FEh7cKFJgW"
+ },
+ "outputs": [],
+ "source": [
+ "y = np.log(np.abs(y[1:]))\n",
+ "x = np.log(t[1:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 474
+ },
+ "id": "qR-Nx0YhFPi-",
+ "outputId": "c6209b4a-379d-4421-b90f-8fdeee8d4026"
+ },
+ "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": "markdown",
+ "metadata": {
+ "id": "sJ9O5cwzLp8l"
+ },
+ "source": [
+ "Cálculo del error y gráfica"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HeNISxEJMC9N"
+ },
+ "source": [
+ "Se dividiran los datos en instantes y tiempos respectivos para sacar el error correspondiente al experimento."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "id": "RboN6cxaLpR2"
+ },
+ "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": 45,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "I8H8-RmJL-cA",
+ "outputId": "fb6c6f7d-e1c7-4592-e298-80c16cab80fb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 4.386e-02 5.354e-02 8.819e-02 1.050e-01 1.280e-01 1.480e-01\n",
+ " 1.790e-01 2.020e-01 2.410e-01 2.710e-01 3.020e-01 3.320e-01\n",
+ " 3.620e-01 3.870e-01 4.020e-01 4.250e-01 4.530e-01 4.860e-01\n",
+ " 5.270e-01 5.660e-01 6.010e-01 6.220e-01 6.260e-01 6.380e-01\n",
+ " 6.550e-01 6.850e-01 7.250e-01 7.740e-01 8.280e-01 8.890e-01\n",
+ " 9.360e-01 9.640e-01 9.790e-01 9.910e-01 9.940e-01 1.005e+00\n",
+ " 1.021e+00]\n",
+ " [ 1.408e-02 2.240e-02 2.348e-02 4.094e-02 6.097e-02 7.753e-02\n",
+ " 1.020e-01 1.320e-01 1.590e-01 1.910e-01 2.170e-01 2.420e-01\n",
+ " 2.750e-01 2.960e-01 3.140e-01 3.580e-01 3.710e-01 3.950e-01\n",
+ " 4.310e-01 4.800e-01 5.780e-01 6.200e-01 6.860e-01 7.390e-01\n",
+ " 7.810e-01 8.310e-01 8.400e-01 8.560e-01 8.720e-01 9.110e-01\n",
+ " 9.550e-01 1.001e+00 1.057e+00 1.124e+00 1.150e+00 1.167e+00\n",
+ " 1.173e+00]\n",
+ " [ 2.132e-02 2.402e-02 4.794e-02 7.765e-02 1.020e-01 1.270e-01\n",
+ " 1.520e-01 1.720e-01 1.770e-01 2.020e-01 2.450e-01 2.760e-01\n",
+ " 3.140e-01 3.430e-01 3.800e-01 4.180e-01 4.310e-01 4.420e-01\n",
+ " 4.470e-01 4.780e-01 5.230e-01 5.570e-01 6.120e-01 6.480e-01\n",
+ " 6.930e-01 7.550e-01 8.320e-01 8.770e-01 9.270e-01 9.730e-01\n",
+ " 1.006e+00 1.029e+00 1.057e+00 1.054e+00 1.082e+00 1.113e+00\n",
+ " 1.122e+00]\n",
+ " [-2.527e-03 -4.005e-03 -6.829e-04 1.399e-02 3.424e-02 5.185e-02\n",
+ " 8.084e-02 9.654e-02 1.240e-01 1.510e-01 1.810e-01 2.080e-01\n",
+ " 2.360e-01 2.330e-01 2.490e-01 3.210e-01 4.450e-01 5.170e-01\n",
+ " 5.850e-01 6.340e-01 6.400e-01 6.310e-01 6.340e-01 6.540e-01\n",
+ " 6.890e-01 7.290e-01 7.670e-01 8.240e-01 8.610e-01 8.870e-01\n",
+ " 9.070e-01 9.300e-01 9.470e-01 9.670e-01 1.011e+00 1.042e+00\n",
+ " 1.056e+00]\n",
+ " [ 2.048e-02 2.919e-02 4.365e-02 7.058e-02 9.657e-02 9.288e-02\n",
+ " 1.280e-01 1.600e-01 1.870e-01 1.990e-01 2.290e-01 2.510e-01\n",
+ " 2.860e-01 3.210e-01 3.630e-01 4.050e-01 4.440e-01 4.880e-01\n",
+ " 5.120e-01 5.410e-01 5.730e-01 6.080e-01 6.400e-01 6.590e-01\n",
+ " 6.770e-01 6.960e-01 7.170e-01 7.470e-01 7.840e-01 8.190e-01\n",
+ " 8.540e-01 9.230e-01 9.440e-01 9.790e-01 1.006e+00 1.024e+00\n",
+ " 1.040e+00]\n",
+ " [ 1.954e-02 1.682e-02 1.893e-02 4.444e-02 5.556e-02 8.611e-02\n",
+ " 1.000e-01 1.170e-01 1.420e-01 1.500e-01 1.670e-01 2.000e-01\n",
+ " 2.390e-01 2.890e-01 3.280e-01 3.810e-01 4.190e-01 4.670e-01\n",
+ " 5.060e-01 5.390e-01 5.690e-01 6.030e-01 6.280e-01 6.560e-01\n",
+ " 6.690e-01 6.940e-01 7.330e-01 7.890e-01 8.500e-01 9.030e-01\n",
+ " 9.560e-01 9.890e-01 1.022e+00 1.042e+00 1.028e+00 1.019e+00\n",
+ " 1.019e+00]\n",
+ " [ 2.226e-02 2.522e-02 6.379e-02 4.883e-02 6.481e-02 9.463e-02\n",
+ " 1.260e-01 1.590e-01 1.830e-01 2.130e-01 2.500e-01 2.910e-01\n",
+ " 3.230e-01 3.630e-01 4.010e-01 4.280e-01 4.490e-01 4.630e-01\n",
+ " 4.790e-01 4.820e-01 4.950e-01 5.220e-01 5.540e-01 6.160e-01\n",
+ " 6.780e-01 7.620e-01 8.290e-01 8.880e-01 9.390e-01 9.690e-01\n",
+ " 1.023e+00 1.047e+00 1.066e+00 1.098e+00 1.122e+00 1.141e+00\n",
+ " 1.146e+00]\n",
+ " [ 3.664e-02 5.981e-02 8.268e-02 1.170e-01 1.390e-01 1.620e-01\n",
+ " 1.850e-01 2.080e-01 2.330e-01 2.610e-01 2.910e-01 3.190e-01\n",
+ " 3.460e-01 3.800e-01 4.090e-01 4.350e-01 4.540e-01 4.670e-01\n",
+ " 4.780e-01 4.870e-01 4.910e-01 5.000e-01 5.230e-01 5.570e-01\n",
+ " 6.000e-01 6.510e-01 7.000e-01 7.430e-01 7.880e-01 8.150e-01\n",
+ " 8.620e-01 8.790e-01 8.990e-01 9.220e-01 9.380e-01 9.570e-01\n",
+ " 9.630e-01]\n",
+ " [ 7.714e-02 9.663e-02 1.220e-01 1.490e-01 1.700e-01 1.910e-01\n",
+ " 2.120e-01 2.350e-01 2.710e-01 2.980e-01 3.440e-01 3.770e-01\n",
+ " 4.070e-01 4.300e-01 4.520e-01 4.720e-01 4.980e-01 5.280e-01\n",
+ " 5.610e-01 5.970e-01 6.330e-01 6.660e-01 6.950e-01 7.050e-01\n",
+ " 7.180e-01 7.210e-01 7.310e-01 7.440e-01 7.770e-01 8.130e-01\n",
+ " 8.520e-01 9.050e-01 9.670e-01 1.030e+00 1.085e+00 1.128e+00\n",
+ " 1.197e+00]\n",
+ " [ 5.660e-02 7.698e-02 9.283e-02 1.060e-01 1.270e-01 1.450e-01\n",
+ " 1.650e-01 1.920e-01 2.220e-01 2.540e-01 2.900e-01 3.150e-01\n",
+ " 3.440e-01 3.760e-01 3.980e-01 4.050e-01 4.420e-01 4.550e-01\n",
+ " 4.550e-01 4.690e-01 5.160e-01 5.300e-01 5.620e-01 6.230e-01\n",
+ " 6.950e-01 7.490e-01 7.880e-01 8.040e-01 8.220e-01 8.450e-01\n",
+ " 8.740e-01 9.100e-01 9.550e-01 9.960e-01 1.014e+00 1.028e+00\n",
+ " 1.046e+00]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "Alturas=np.array([b1,b2,b3,b4,b5,b6,b7,b8,b9,b10])\n",
+ "print(Alturas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7CrQFq8NMBRt",
+ "outputId": "9ee5b968-ec66-487a-f0d5-d86be7f72d3b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.561 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.56 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.521 0.561 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.56 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.561 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.56 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\n",
+ " [0. 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44\n",
+ " 0.48 0.52 0.56 0.601 0.641 0.681 0.721 0.761 0.801 0.841 0.881 0.921\n",
+ " 0.961 1.001 1.041 1.081 1.121 1.161 1.201 1.241 1.281 1.321 1.361 1.401\n",
+ " 1.441]\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 ]\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 0.966 1. 1.033 1.066 1.1 1.133 1.166\n",
+ " 1.2 ]\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 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tiempo=np.array([a1,a2,a3,a4,a5,a6,a7,a8,a9,a10])\n",
+ "print(tiempo)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "W7wdXZRuMNY2"
+ },
+ "outputs": [],
+ "source": [
+ "tiempo_prom=tiempo.mean(axis=0)\n",
+ "tiempo_error=tiempo.std(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uZXhJlInMP4P",
+ "outputId": "fa7486f5-cf3f-48fa-d372-5ecd5e07426e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "37"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(tiempo_prom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "id": "qqv159BYMbRT"
+ },
+ "outputs": [],
+ "source": [
+ "y_prom=Alturas.mean(axis=0)\n",
+ "y_error=Alturas.std(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ag-zM9cxMe3O",
+ "outputId": "ca3a33e4-8466-415a-ed14-bb47f7e09c30"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "37"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(y_prom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pqtD9FdmMhMl"
+ },
+ "source": [
+ "Gráfica del error:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 470
+ },
+ "id": "CUOF0CZUMgln",
+ "outputId": "0c84b974-2bb2-4b6c-a58d-975607c25e0e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<function matplotlib.pyplot.show(close=None, block=None)>"
+ ]
+ },
+ "execution_count": 111,
+ "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": "markdown",
+ "metadata": {
+ "id": "PpKui_ncPp5G"
+ },
+ "source": [
+ "Cálculo Constante de fricción del aire."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "id": "aSQbYYXbPn6E"
+ },
+ "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": 128,
+ "metadata": {
+ "id": "act3P7KuQD8d"
+ },
+ "outputs": [],
+ "source": [
+ "t0=0\n",
+ "t1=1.0\n",
+ "dt=0.1\n",
+ "b=3.5\n",
+ "g=10\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": "markdown",
+ "metadata": {
+ "id": "4a_lVruPF45z"
+ },
+ "source": [
+ "Gráfica Modelado"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "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(tiempo,y)\n",
+ "plt.savefig('Modeladoservilletaextendida.pdf', bbox_inches = 'tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "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": 4
+}