From b9d32b3affb5a208f0306c8ecfb1f09561d960d0 Mon Sep 17 00:00:00 2001
From: Alvaro Steven Robles Carvajal <alvaro2231814@correo.uis.edu.co>
Date: Sun, 7 May 2023 22:38:49 +0000
Subject: [PATCH] =?UTF-8?q?Delete=20Medidas=5Fy=5Fcomparaciones=5Fdel=5Fpr?=
=?UTF-8?q?oyecto=5Fde=5Festimaci=C3=B3n=5Fdel=5Frozamiento.ipynb?=
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---
...de_estimaci\303\263n_del_rozamiento.ipynb" | 373 ------------------
1 file changed, 373 deletions(-)
delete mode 100644 "Medidas_y_comparaciones_del_proyecto_de_estimaci\303\263n_del_rozamiento.ipynb"
diff --git "a/Medidas_y_comparaciones_del_proyecto_de_estimaci\303\263n_del_rozamiento.ipynb" "b/Medidas_y_comparaciones_del_proyecto_de_estimaci\303\263n_del_rozamiento.ipynb"
deleted file mode 100644
index 93bcb02..0000000
--- "a/Medidas_y_comparaciones_del_proyecto_de_estimaci\303\263n_del_rozamiento.ipynb"
+++ /dev/null
@@ -1,373 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Medidas y comparaciones del proyecto de estimación del rozamiento"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Importación de datos de los paquetes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Importación de los datos.txt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "co1 = np.genfromtxt('co1.txt')\n",
- "co2 = np.genfromtxt('co2.txt')\n",
- "co3 = np.genfromtxt('co3.txt')\n",
- "co4 = np.genfromtxt('co4.txt')\n",
- "co5 = np.genfromtxt('co5.txt')\n",
- "co6 = np.genfromtxt('co6.txt')\n",
- "ex1 = np.genfromtxt('ex1.txt')\n",
- "ex2 = np.genfromtxt('ex2.txt')\n",
- "ex3 = np.genfromtxt('ex3.txt')\n",
- "ex4 = np.genfromtxt('ex4.txt')\n",
- "ex5 = np.genfromtxt('ex5.txt')\n",
- "ex6 = np.genfromtxt('ex6.txt')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Seleccionar columnas de cada archivo importado."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "tco1 = co1[:,0]\n",
- "yco1 = co1[:,2]\n",
- "tco2 = co2[:,0]\n",
- "yco2 = co2[:,2]\n",
- "tco3 = co3[:,0]\n",
- "yco3 = co3[:,2]\n",
- "tco4 = co4[:,0]\n",
- "yco4 = co4[:,2]\n",
- "tco5 = co5[:,0]\n",
- "yco5 = co5[:,2]\n",
- "tco6 = co6[:,0]\n",
- "yco6 = co6[:,2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "tex1 = ex1[:,0]\n",
- "yex1 = ex1[:,2]\n",
- "tex2 = ex2[:,0]\n",
- "yex2 = ex2[:,2]\n",
- "tex3 = ex3[:,0]\n",
- "yex3 = ex3[:,2]\n",
- "tex4 = ex4[:,0]\n",
- "yex4 = ex4[:,2]\n",
- "tex5 = ex5[:,0]\n",
- "yex5 = ex5[:,2]\n",
- "tex6 = ex6[:,0]\n",
- "yex6 = ex6[:,2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Unir los tiempos de la comprimida en un mismo conjunto"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "tco = np.array([tco1, tco2, tco3, tco4, tco5, tco6])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "yco = np.array([yco1, yco2, yco3, yco4, yco5, yco6])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Graficar para verificar y establecer el promedio con la desviación estandar con la servilleta comprimida."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "tco_prom = tco.mean(axis=0)\n",
- "yco_prom = yco.mean(axis=0)\n",
- "tco_std = tco.std(axis=0)\n",
- "yco_std = yco.std(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "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.errorbar(tco_prom, yco_prom, tco_std, yco_std, fmt = 'm-', label = 'Servilleta comprimida')\n",
- "plt.legend(title = 'Caida de una servilleta')\n",
- "plt.xlabel('t(s)')\n",
- "plt.ylabel('h(m)')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Unir los tiempos de la extendida en un mismo conjunto."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "tex = np.array([tex1, tex2, tex3, tex4, tex5, tex6])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "yex = np.array([yex1, yex2, yex3, yex4, yex5, yex6])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Graficar para verificar y establecer el promedio con la desviación estandar con la servilleta extendida."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "tex_prom = tex.mean(axis=0)\n",
- "yex_prom = yex.mean(axis=0)\n",
- "tex_std = tex.std(axis=0)\n",
- "yex_std = yex.std(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "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.errorbar(tex_prom, yex_prom, tex_std, yex_std, fmt = 'm-', label = 'Servilleta extendida')\n",
- "plt.legend(title = 'Caida de una servilleta')\n",
- "plt.xlabel('t(s)')\n",
- "plt.ylabel('h(m)')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " # Determinando Instantes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "g = 9.8 # m/s^2\n",
- "B = 2\n",
- "dt = 0.033\n",
- "t0 = 0\n",
- "v0 = 0\n",
- "y0 = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- " # Instantes de la servilleta Comprimida"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Instante 1 \n",
- "v1 = g*dt\n",
- "y1 = g*dt**2/2 + v0*dt + y0\n",
- "\n",
- "# Instante 2\n",
- "a1 = g - B*v1\n",
- "v2 = v1 + a1*dt\n",
- "y2 = g*dt**2/2 + v1*dt + y1\n",
- "\n",
- "# Instante 3\n",
- "a2 = g - B*v2\n",
- "v3 = v2 + a2*dt\n",
- "y3 = g*dt**2/2 + v2*dt + y2\n",
- "\n",
- "# Instante 4\n",
- "a3 = g - B*v3\n",
- "v4 = v3 + a3*dt\n",
- "y4 = g*dt**2/2 + v3*dt + y3\n",
- "\n",
- "# Instante 5\n",
- "a4 = g - B*v4\n",
- "v5 = v4 + a4*dt\n",
- "y5 = g*dt**2/2 + v4*dt + y4\n",
- "\n",
- "# Instante 6\n",
- "a5 = g - B*v5\n",
- "v6 = v5 + a5*dt\n",
- "y6 = g*dt**2/2 + v5*dt + y5\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Instantes de la servilleta Extendida"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Instante 1 \n",
- "v1 = g*dt\n",
- "y1 = g*dt**2/2 + v0*dt + y0\n",
- "\n",
- "# Instante 2\n",
- "a1 = g - B*v1\n",
- "v2 = v1 + a1*dt\n",
- "y2 = g*dt**2/2 + v1*dt + y1\n",
- "\n",
- "# Instante 3\n",
- "a2 = g - B*v2\n",
- "v3 = v2 + a2*dt\n",
- "y3 = g*dt**2/2 + v2*dt + y2\n",
- "\n",
- "# Instante 4\n",
- "a3 = g - B*v3\n",
- "v4 = v3 + a3*dt\n",
- "y4 = g*dt**2/2 + v3*dt + y3\n",
- "\n",
- "# Instante 5\n",
- "a4 = g - B*v4\n",
- "v5 = v4 + a4*dt\n",
- "y5 = g*dt**2/2 + v4*dt + y4\n",
- "\n",
- "# Instante 6\n",
- "a5 = g - B*v5\n",
- "v6 = v5 + a5*dt\n",
- "y6 = g*dt**2/2 + v5*dt + y5\n"
- ]
- }
- ],
- "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": 4
-}
--
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