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| { | |
| "metadata": { | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.9.2" | |
| }, | |
| "orig_nbformat": 2, | |
| "kernelspec": { | |
| "name": "python392jvsc74a57bd036cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930", | |
| "display_name": "Python 3.9.2 64-bit" | |
| }, | |
| "metadata": { | |
| "interpreter": { | |
| "hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930" | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2, | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(array([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100.]),\n", | |
| " array([ 32., 53., 69., 88., 102., 115., 126., 137., 154., 177., 182.]))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 1 | |
| } | |
| ], | |
| "source": [ | |
| "from csv import reader\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "with open('TP2E9.csv', encoding='iso-8859-1') as f_tabla:\n", | |
| " x, y = [*reader(f_tabla, delimiter=';')]\n", | |
| " x_label, x, y_label, y = x[0], [*map(float, x[1:])], y[0], [*map(float, y[1:])]\n", | |
| "x, y = np.array(x), np.array(y)\n", | |
| "x, y" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 432x288 with 1 Axes>", | |
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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "from matplotlib import pyplot as plt\n", | |
| "\n", | |
| "fig, lin = plt.subplots()\n", | |
| "dx, dy = np.diff(x), np.diff(y)\n", | |
| "dy_dx = dy/dx\n", | |
| "lin.plot(x, y)\n", | |
| "for diff_n, xn, yn in zip(dy_dx, x[1:], y[1:]):\n", | |
| " x_central = xn-(x[1]/2)\n", | |
| " #print((xn, yn), diff_n)\n", | |
| " xs = np.array([x_central - 10, x_central + 10])\n", | |
| " ys = yn + diff_n * (xs - xn)\n", | |
| " lin.plot(xs, ys, linestyle='--')\n", | |
| "lin.set_xlabel(x_label)\n", | |
| "lin.set_ylabel(y_label)\n", | |
| "lin.grid()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 0. , 2.79182928],\n", | |
| " [ 10. , 1.62908536],\n", | |
| " [ 20. , 1.79182928],\n", | |
| " [ 30. , 1.70359751],\n", | |
| " [ 40. , 1.29378068],\n", | |
| " [ 50. , 1.22127976],\n", | |
| " [ 60. , 1.02110027],\n", | |
| " [ 70. , 1.29431916],\n", | |
| " [ 80. , 2.2016231 ],\n", | |
| " [ 90. , 1.89918845],\n", | |
| " [100. , -1.3983769 ]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 432x288 with 1 Axes>", | |
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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "from scipy import interpolate\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "x_resamp = np.linspace(min(x), max(x), 101)\n", | |
| "params = interpolate.splrep(x, y, s=0)\n", | |
| "\n", | |
| "fig, ax = plt.subplots()\n", | |
| "\n", | |
| "ax.plot(x_resamp, interpolate.splev(x_resamp, params, der=0))\n", | |
| "ax.scatter(x, y, marker='x', color='gray')\n", | |
| "ax.grid()\n", | |
| "ax.set_xlabel(x_label)\n", | |
| "ax.set_ylabel(y_label)\n", | |
| "ax.set_ylim(ymin=-5)\n", | |
| "ax.set_xlim(xmin=-5)\n", | |
| "\n", | |
| "dy_dx_interp = interpolate.splev(x, params, der=1)\n", | |
| "\n", | |
| "for diff_n, xn, yn in zip(dy_dx_interp, x, y):\n", | |
| " xs = np.array([xn - 5, xn + 5])\n", | |
| " ys = yn + diff_n * (xs - xn)\n", | |
| " ax.annotate(f\"{diff_n:.2f}\", (xn, yn),\n", | |
| " horizontalalignment='left',\n", | |
| " verticalalignment='top' if diff_n > 0 else 'bottom')\n", | |
| " ax.plot(xs, ys, linestyle='--')\n", | |
| "\n", | |
| "fig.tight_layout()\n", | |
| "\n", | |
| "np.array([x, dy_dx_interp]).transpose()" | |
| ] | |
| } | |
| ] | |
| } |
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| % Evaporado ;0;10;20;30;40;50;60;70;80;90;100 | |
| Temperatura (°C) ;32;53;69;88;102;115;126;137;154;177;182 |
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