From 2ec4dfc489c67d76a9e6078eef69cf3fdc68eca3 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Fri, 20 Feb 2015 11:00:44 +0100 Subject: [PATCH 01/16] =?UTF-8?q?Primer=20Interfaz=20de=20comunicaci=C3=B3?= =?UTF-8?q?n=20m=C3=ADnimo?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Compatible con Python 3 --- aeropy/Xfoil_Interaction/minimal_interface.py | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 aeropy/Xfoil_Interaction/minimal_interface.py diff --git a/aeropy/Xfoil_Interaction/minimal_interface.py b/aeropy/Xfoil_Interaction/minimal_interface.py new file mode 100644 index 0000000..82af40a --- /dev/null +++ b/aeropy/Xfoil_Interaction/minimal_interface.py @@ -0,0 +1,20 @@ +import subprocess +import sys + +commands = ['naca 6715', + 'oper', + 'mach 0.2', + 're 3500', + 'alfa 3'] + +p = subprocess.Popen(["xfoil.exe",], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE) + +for command in commands: + p.stdin.write((command + '\n').encode()) + +p.stdin.write("\nquit\n".encode()) +p.stdin.close() +for line in p.stdout.readlines(): + print(line.decode(), end='') From df5d40c0883e40a0715df89c8900c8f9aac64a03 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Fri, 20 Feb 2015 12:39:48 +0100 Subject: [PATCH 02/16] Readme para Xfoil --- aeropy/Xfoil_Interaction/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 aeropy/Xfoil_Interaction/README.md diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md new file mode 100644 index 0000000..fc2c146 --- /dev/null +++ b/aeropy/Xfoil_Interaction/README.md @@ -0,0 +1,9 @@ +aeropy - Xfoil Interaction tool +====== + +Python tools for Aeronautical calculations. + +Genetic Optimization Algorithm in progress + +More info: +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing \ No newline at end of file From c6832f801125f72a83385dc2f37dbec31b534132 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Fri, 20 Feb 2015 19:42:42 +0100 Subject: [PATCH 03/16] Notebook con creacion de perfiles con bezier MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Añadido un notebook interactivo que explica cómo se construye el perfil a partir del genoma. Actualizado readme. --- .../Genetic_description_Bezier.ipynb | 543 ++++++++++++++++++ aeropy/Xfoil_Interaction/README.md | 5 + 2 files changed, 548 insertions(+) create mode 100644 aeropy/Xfoil_Interaction/Genetic_description_Bezier.ipynb diff --git a/aeropy/Xfoil_Interaction/Genetic_description_Bezier.ipynb b/aeropy/Xfoil_Interaction/Genetic_description_Bezier.ipynb new file mode 100644 index 0000000..0c4c4b1 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_description_Bezier.ipynb @@ -0,0 +1,543 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:726edeb8f702691c94f7c4337cf8c0d20522413cb5465f6f4aa0677c1ab14bbd" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "puntos_control = np.array([[0,0],\n", + " [0,1],\n", + " [3,5],\n", + " [3,0]])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "num = 100" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "parametro_u = np.linspace(0,1,num)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "curva = np.zeros([num,2])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def bernstein(u):\n", + " b = np.zeros([4,2])\n", + " b[0,:] = (1-u)**3\n", + " b[1,:] = (u * (1-u)**2)*3\n", + " b[2,:] = (u**2 * (1-u) )*3\n", + " b[3,:] = u**3\n", + " \n", + " return b" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for contador in np.arange(num):\n", + " _ = bernstein(parametro_u[contador])*puntos_control\n", + " curva[contador,] = sum (_)\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "plt.plot(curva[:,0],curva[:,1])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "bernstein(0.2)*puntos_control" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "array([[ 0. , 0. ],\n", + " [ 0. , 0.384],\n", + " [ 0.288, 0.48 ],\n", + " [ 0.024, 0. ]])" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sum(puntos_control)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + "array([6, 6])" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "curva[:,1]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 12, + "text": [ + "array([ 0. , 0.03120894, 0.06418022, 0.09883964, 0.13511299,\n", + " 0.17292608, 0.21220469, 0.25287463, 0.29486169, 0.33809166,\n", + " 0.38249035, 0.42798354, 0.47449704, 0.52195663, 0.57028813,\n", + " 0.61941731, 0.66926999, 0.71977195, 0.77084899, 0.8224269 ,\n", + " 0.87443149, 0.92678855, 0.97942387, 1.03226325, 1.08523249,\n", + " 1.13825738, 1.19126372, 1.24417731, 1.29692394, 1.3494294 ,\n", + " 1.4016195 , 1.45342003, 1.50475678, 1.55555556, 1.60574215,\n", + " 1.65524235, 1.70398197, 1.75188679, 1.79888261, 1.84489523,\n", + " 1.88985045, 1.93367405, 1.97629184, 2.01762962, 2.05761317,\n", + " 2.09616829, 2.13322079, 2.16869645, 2.20252108, 2.23462046,\n", + " 2.2649204 , 2.29334669, 2.31982513, 2.3442815 , 2.36664162,\n", + " 2.38683128, 2.40477626, 2.42040237, 2.43363541, 2.44440116,\n", + " 2.45262543, 2.45823401, 2.4611527 , 2.46130729, 2.45862358,\n", + " 2.45302737, 2.44444444, 2.43280061, 2.41802166, 2.40003339,\n", + " 2.3787616 , 2.35413208, 2.32607062, 2.29450303, 2.25935511,\n", + " 2.22055263, 2.17802141, 2.13168724, 2.08147592, 2.02731323,\n", + " 1.96912498, 1.90683696, 1.84037498, 1.76966481, 1.69463227,\n", + " 1.61520315, 1.53130324, 1.44285834, 1.34979424, 1.25203674,\n", + " 1.14951165, 1.04214474, 0.92986183, 0.8125887 , 0.69025115,\n", + " 0.56277498, 0.43008598, 0.29210996, 0.1487727 , 0. ])" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.arange(10)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 13, + "text": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "puntos_perfil = np.zeros([13,2])\n", + "puntos_perfil[0,] = [1,0]\n", + "puntos_perfil[6,] = [1,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "genes = np.array([150*np.pi/180, #ang s1\n", + " 0.2, #dist s1\n", + " 0.5, #x 1\n", + " 0.12, #y 1\n", + " 0, #ang 1\n", + " 0.2, #dist b1\n", + " 0.2, #dist c1\n", + " 0.1, #dist a1\n", + " 0.05, #dist a2\n", + " 0.4, #x 2\n", + " 0.05, #y 2\n", + " 5*np.pi/180, #ang 2\n", + " 0.2, #dist b2\n", + " 0.2, #dist c2\n", + " 160*np.pi/180, #ang s2\n", + " 0.2]) #dist s2" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def punto_pendiente(a,dist,ang):\n", + " punto = np.array(a)+ np.array(dist,dist)* [np.cos(ang),np.sin(ang)]\n", + " return punto" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def generador_puntos(genes):\n", + " puntos = np.zeros([13,2])\n", + " puntos[0,:] = [1,0]\n", + "# puntos[1,:] = [1,0] + np.array([genes[1],genes[1]]) * [-np.cos(genes[0]),np.sin(genes[0])]\n", + " puntos[1,:] = punto_pendiente([1,0],genes[1],genes[0])\n", + " puntos[3,:] = [genes[2],genes[3]]\n", + "# puntos[2,:] = [genes[2],genes[3]] + genes[5] * [np.cos(genes[4]),np.sin(genes[4])]\n", + " puntos[2,:] = punto_pendiente([genes[2],genes[3]],genes[5],genes[4])\n", + "# puntos[4,:] = [genes[2],genes[3]] + genes[6] * [-np.cos(genes[4]),np.sin(genes[4])]\n", + " puntos[4,:] = punto_pendiente([genes[2],genes[3]],genes[6],genes[4]+np.pi)\n", + " puntos[5,:] = [0, genes[7]]\n", + " puntos[6,:] = [0,0]\n", + " puntos[7,:] = [0, -genes[8]]\n", + " puntos[9,:] = [genes[9],genes[10]]\n", + "# puntos[8,:] = [genes[9],genes[10]] + genes[12] * [np.cos(genes[11]),np.sin(genes[11])]\n", + " puntos[8,:] = punto_pendiente([genes[9],genes[10]], genes[12], genes[11]+np.pi)\n", + "# puntos[10,:] = [genes[9],genes[10]] + genes[13] * [-np.cos(genes[11]),np.sin(genes[11])]\n", + " puntos[10,:] = punto_pendiente([genes[9],genes[10]], genes[13], genes[11])\n", + "# puntos[11,:] = [1,0] + genes[15] * [-np.cos(genes[14]),np.sin(genes[14])]\n", + " puntos[11,:] = punto_pendiente([1,0], genes[15], genes[14])\n", + " puntos[12,:] = [1,0]\n", + " return puntos" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "puntos_control = generador_puntos(genes)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot(puntos_control[:,0],puntos_control[:,1])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "curva = np.zeros([num,2])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def bezier(num, puntos_control):\n", + " \n", + " parametro_u = np.linspace(0,1,num)\n", + " curva = np.zeros([num,2])\n", + "\n", + " for contador in np.arange(num):\n", + " _ = bernstein(parametro_u[contador])*puntos_control\n", + " curva[contador,] = sum (_)\n", + " return curva\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "num = 100\n", + "\n", + "perfil = np.zeros([(4*num), 2])\n", + "\n", + "perfil[0:num,:] = bezier(num,puntos_control[0:4,:])\n", + "perfil[num:2*num,:] = bezier(num,puntos_control[3:7,:])\n", + "perfil[2*num:3*num,:] = bezier(num,puntos_control[6:10,:])\n", + "perfil[3*num:4*num,:] = bezier(num,puntos_control[9:13,:])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 22 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k')\n", + "plt.plot(perfil[:,0],perfil[:,1])\n", + "plt.plot(puntos_control[:,0],puntos_control[:,1])\n", + "plt.gca().set_aspect(1)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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OCgrC/v372/yx9TFMoI5OksRcz/pBw6lT4ouDr6+ooB0RIbZTp4r97t0Buxb9lhMREVFH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\njAMHDlxzzubNm42HH37YMAzDyMvLM5KTk62ICljCnRrZsWO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+ "text": [ + "" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from IPython.html.widgets import interactive" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def pintar(genes):\n", + " num = 100\n", + "\n", + " perfil = np.zeros([(4*num), 2])\n", + " \n", + " puntos_control = generador_puntos(genes)\n", + "\n", + " perfil[0:num,:] = bezier(num,puntos_control[0:4,:])\n", + " perfil[num:2*num,:] = bezier(num,puntos_control[3:7,:])\n", + " perfil[2*num:3*num,:] = bezier(num,puntos_control[6:10,:])\n", + " perfil[3*num:4*num,:] = bezier(num,puntos_control[9:13,:])\n", + " \n", + " plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.plot(perfil[:,0],perfil[:,1])\n", + " plt.plot(puntos_control[:,0],puntos_control[:,1])\n", + " plt.gca().set_aspect(1)\n", + "\n", + "\n", + " return 0\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def intermedio(g0,g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15):\n", + " return np.array([g0,g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15])\n", + "\n", + "def pintar2(g0,g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15):\n", + " pintar(intermedio(g0,g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15))\n", + " return 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "interactive(pintar2,\n", + " g0 = (130*np.pi/180,180*np.pi/180,5*np.pi/180), #ang s1\n", + " g1 = (0, 0.5, 0.01), #dist s1\n", + " g2 = (0.2, 0.8, 0.05), #x 1\n", + " g3 = (0, 0.4, 0.02), #y 1\n", + " g4 = (-25*np.pi/180,25*np.pi/180,5*np.pi/180), #ang 1\n", + " g5 = (0,0.4, 0.05), #dist b1\n", + " g6 = (0,0.4, 0.05), #dist c1\n", + " g7 = (0, 0.5, 0.01), #dist a1\n", + " g8 = (0, 0.5, 0.01), #dist a2\n", + " g9 = (0.2, 0.8, 0.05), #x 2\n", + " g10 = (-0.4, 0.2, 0.02), #y 2\n", + " g11 = (-25*np.pi/180,25*np.pi/180,5*np.pi/180), #ang 2\n", + " g12 = (0,0.4, 0.05), #dist b2\n", + " g13 = (0,0.4, 0.05), #dist c2\n", + " g14 = 160*np.pi/180, #ang s2\n", + " g15 = (0,0.4, 0.05))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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PN8vyPfkk/OEPZpqJiy6yu2J7eUXoWrnSzBfyv//ZXZGIiIjvKSgs4Mv1XxK3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\nYWlWPEcaLabpib4MauniviE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T9IPpRROVJu9Mpn/r/ricLoa3H07j2o3tLk9EREQqmEKX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\npcED1lDwAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 27 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index fc2c146..e13ad44 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -5,5 +5,10 @@ Python tools for Aeronautical calculations. Genetic Optimization Algorithm in progress +Includes: + +-Minimal working interface between Python and Xfoil +-Interactive Ipython notebook showing how te genome-to-profile decodification works (in progress). + More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing \ No newline at end of file From 62e550a0c6450bd4c9128d57bd5dcb243505bf63 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Sat, 21 Feb 2015 01:33:31 +0100 Subject: [PATCH 04/16] Prototipo incompleto genetico MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Incluye los módulos finales de traducción de genoma a puntos de perfil, y comunicación de los perfiles con xfoil. Incluye 2 perfiles de ejemplo, iguales entre sí. No incluye bucle de optimización. --- .../Genetic_algorithm_files/interfaz.py | 91 +++++++++++ .../Genetic_algorithm_files/main.py | 57 +++++++ .../Genetic_algorithm_files/transcript.py | 146 ++++++++++++++++++ aeropy/Xfoil_Interaction/README.md | 11 ++ 4 files changed, 305 insertions(+) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py new file mode 100644 index 0000000..21f36cc --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -0,0 +1,91 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Juan Luis Cano, Alberto Lorenzo, Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script requires from the main program a serie of profile genomes. +The number of profiles is "num_pop" +This subprogram, first, uses the script "transcript.py" in order to translate +the genomes into a series of points that Xfoil can understand. + +Then, sends them to Xfoil, and ask it to analize them. + +At last, it sends back to the main program the results obtained. + +''' + + + +import subprocess +import sys +import os +import transcript as trans +import numpy as np + + +def xfoil_calculate_profile(generation,profile_number,genome): + + profile_root = 'profiles\gen' + str(generation) + '\profile' + str(profile_number) + '.txt' + profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) + + + commands = ['load', + profile_root, + 'oper', + 'mach 0.2', + 're 3500', + 'visc', + 'pacc', + "aerodata\data" + profile_name + '.txt', + '', + 'aseq', + '0', + '20', + '1', + '', + 'quit'] + + + perfil = trans.decode_genome(genome) + + try: + os.remove(profile_root) + except : + pass + try: + os.remove("aerodata\data" + profile_name + '.txt') + except : + pass + + + archivo = open(profile_root, mode = 'x') + archivo.write(profile_name + '\n\n\n') + + + for i in np.arange(0,100,1): + texto = str(round(perfil[i,0],6)) + ' ' + str(round(perfil[i,1],6)) +'\n' + archivo.write(texto) + archivo.close() + + p = subprocess.Popen(["xfoil.exe",], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE) + + for command in commands: + p.stdin.write((command + '\n').encode()) + + p.stdin.write("\nquit\n".encode()) + p.stdin.close() + for line in p.stdout.readlines(): + print(line.decode(), end='') + +def xfoil_calculate_population(generation, genome_matrix): + num_pop = genome_matrix.shape[0] + for profile_number in np.arange(1,num_pop+1,1): + xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:]) + + diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py new file mode 100644 index 0000000..e507b1c --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -0,0 +1,57 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script requires from the main program a serie of profile genomes. +The number of profiles is "num_pop" +This subprogram, first, uses the script "transcript.py" in order to translate +the genomes into a series of points that Xfoil can understand. + +Then, sends them to Xfoil, and ask it to analize them. + +At last, it sends back to the main program the results obtained. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np + + + + + +genes = np.array([150*np.pi/180, #ang s1 + 0.2, #dist s1 + 0.5, #x 1 + 0.12, #y 1 + 0, #ang 1 + 0.2, #dist b1 + 0.2, #dist c1 + 0.1, #dist a1 + 0.05, #dist a2 + 0.4, #x 2 + 0.05, #y 2 + 5*np.pi/180, #ang 2 + 0.2, #dist b2 + 0.2, #dist c2 + 160*np.pi/180, #ang s2 + 0.2]) #dist s2 + +generation = 0 +#profile_number = 1 +genome = np.zeros([2,16]) +genome[0,:] = genes +genome[1,:] = genes + + +interfaz.xfoil_calculate_population(generation,genome) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py new file mode 100644 index 0000000..3b96bc5 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py @@ -0,0 +1,146 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is the python script that contains those functions that are necesary +in order to get a xfoil-compatible description of a profile from the +genetic information provided by the interface script. + +When this spript is run independly, will return an draw of a profile +calculated from an example genome. + +When used inside the algorithm, the interface will import these functions +and call the "decode_genome" function with an array of genes. The function shall +return an xfoil-compatible description. + +The complete Genetic Algorithm here used is explained at: +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +""" + + +import numpy as np + + +def bernstein(u): + '''This function returns a 4x2 array. Both columns are equal. + In each row, the value of a 3rd degree Bernstein polinome is + calculated for the given value of the parameter u. + This structure allows the result array to be multiplied element + by element with another 4x2 array containing the coordinates + of the control points. + ''' + b = np.zeros([4,2]) + b[0,:] = (1-u)**3 + b[1,:] = (u * (1-u)**2)*3 + b[2,:] = (u**2 * (1-u) )*3 + b[3,:] = u**3 + + return b + + +def punto_pendiente(a,dist,ang): + '''Given a point "a", calculates the coordinates of another one + placed at a distance "dist" in the direction"ang" (in radians). + ''' + punto = np.array(a)+ np.array(dist,dist)* [np.cos(ang),np.sin(ang)] + return punto + + +def generador_puntos(genes): + '''This function is the first step decoding the profile genome: + it generates the 13x2 coordinates of the points used in the 4 + bezier curves that describe the profile. + ''' + puntos = np.zeros([13,2]) + puntos[0,:] = [1,0] + puntos[1,:] = punto_pendiente([1,0],genes[1],genes[0]) + puntos[2,:] = punto_pendiente([genes[2],genes[3]],genes[5],genes[4]) + puntos[3,:] = [genes[2],genes[3]] + puntos[4,:] = punto_pendiente([genes[2],genes[3]],genes[6],genes[4]+np.pi) + puntos[5,:] = [0, genes[7]] + puntos[6,:] = [0,0] + puntos[7,:] = [0, -genes[8]] + puntos[8,:] = punto_pendiente([genes[9],genes[10]], genes[12], genes[11]+np.pi) + puntos[9,:] = [genes[9],genes[10]] + puntos[10,:] = punto_pendiente([genes[9],genes[10]], genes[13], genes[11]) + puntos[11,:] = punto_pendiente([1,0], genes[15], genes[14]) + puntos[12,:] = [1,0] + return puntos + +def bezier(num, puntos_control): + '''This function calculates a Bezier curve using as control + points those given in the imput, with a resolution of "num" points. + ''' + + parametro_u = np.linspace(0,1,num) + curva = np.zeros([num,2]) + + for contador in np.arange(num): + _ = bernstein(parametro_u[contador])*puntos_control + curva[contador,] = sum (_) + return curva + +def profile(num, puntos_control): + + '''This is the second stage of the decoding process. + This will return a line made of 4 bezier curves whose control points + are given in the imput. + Each curve will have a number of points equal to "num", so the total + number of points will be 4 * num''' + + perfil = np.zeros([(4*num), 2]) + + perfil[0:num,:] = bezier(num,puntos_control[0:4,:]) + perfil[num:2*num,:] = bezier(num,puntos_control[3:7,:]) + perfil[2*num:3*num,:] = bezier(num,puntos_control[6:10,:]) + perfil[3*num:4*num,:] = bezier(num,puntos_control[9:13,:]) + + return perfil +def decode_genome(genome): + ''' + ''' + num = 25 + epsilon = 0.001 + profile_points = profile(num, generador_puntos(genome)) + + profile_points[0, 1] = epsilon + profile_points[4*num-1,1] = -epsilon + return profile_points + +''' +The following code contains an example and will be used with test purposes only, +when this script is run whole and won't be used in the standard function of the +genetic algorithm. +''' +# +#import matplotlib.pyplot as plt +# +# +#genes = np.array([150*np.pi/180, #ang s1 +# 0.2, #dist s1 +# 0.5, #x 1 +# 0.12, #y 1 +# 0, #ang 1 +# 0.2, #dist b1 +# 0.2, #dist c1 +# 0.1, #dist a1 +# 0.05, #dist a2 +# 0.4, #x 2 +# 0.05, #y 2 +# 5*np.pi/180, #ang 2 +# 0.2, #dist b2 +# 0.2, #dist c2 +# 160*np.pi/180, #ang s2 +# 0.2]) #dist s2 +# + +# +#perfil = decode_genome(genes) +# +# +#plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k') +#plt.plot(perfil[:,0],perfil[:,1]) +#plt.gca().set_aspect(1) diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index e13ad44..0970043 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -8,7 +8,18 @@ Genetic Optimization Algorithm in progress Includes: -Minimal working interface between Python and Xfoil + -Interactive Ipython notebook showing how te genome-to-profile decodification works (in progress). +Genetic algorithm modules: + +-interfaz + +-transcript + +-main (in progress) + +All 3 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will decode an example genoma for 2 profiles, and then analize them with xfoil. + More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing \ No newline at end of file From 3e2157c1e6ff32720fcceefce28a17794fbfbd5f Mon Sep 17 00:00:00 2001 From: AunSiro Date: Sat, 21 Feb 2015 01:42:19 +0100 Subject: [PATCH 05/16] Readme updated --- aeropy/Xfoil_Interaction/README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index 0970043..8804fec 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -3,13 +3,13 @@ aeropy - Xfoil Interaction tool Python tools for Aeronautical calculations. -Genetic Optimization Algorithm in progress +Genetic Optimization Algorithm in progress (python 3.4, xfoil6.99) Includes: -Minimal working interface between Python and Xfoil --Interactive Ipython notebook showing how te genome-to-profile decodification works (in progress). +-Interactive Ipython notebook showing how the genome-to-profile decodification works (in progress). Genetic algorithm modules: @@ -22,4 +22,7 @@ Genetic algorithm modules: All 3 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will decode an example genoma for 2 profiles, and then analize them with xfoil. More info: -https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing \ No newline at end of file +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +You can download Xfoil for free from its official page: +http://web.mit.edu/drela/Public/web/xfoil/ \ No newline at end of file From f90a40bd3b1ebca69537160aab4ed063bc137691 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Sat, 21 Feb 2015 11:55:47 +0100 Subject: [PATCH 06/16] Version primitiva inicializador MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Versión primitiva del iniciador de la población añadido. Readme actualizado. --- .../Genetic_algorithm_files/initial.py | 85 +++++++++++++++++++ .../Genetic_algorithm_files/main.py | 40 ++------- aeropy/Xfoil_Interaction/README.md | 6 +- 3 files changed, 97 insertions(+), 34 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py new file mode 100644 index 0000000..c2fd879 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -0,0 +1,85 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script requires from the main program a serie of profile genomes. +The number of profiles is "num_pop" +This subprogram, first, uses the script "transcript.py" in order to translate +the genomes into a series of points that Xfoil can understand. + +Then, sends them to Xfoil, and ask it to analize them. + +At last, it sends back to the main program the results obtained. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np + + + +def start_pop(pop_num): + + genes = np.array([150*np.pi/180, #ang s1 + 0.2, #dist s1 + 0.5, #x 1 + 0.12, #y 1 + 0, #ang 1 + 0.2, #dist b1 + 0.2, #dist c1 + 0.1, #dist a1 + 0.05, #dist a2 + 0.4, #x 2 + 0.05, #y 2 + 5*np.pi/180, #ang 2 + 0.2, #dist b2 + 0.2, #dist c2 + 160*np.pi/180, #ang s2 + 0.2]) #dist s2 + +# generation = 0 +#profile_number = 1 + genome = np.zeros([pop_num,16]) + + gen_deviation = np.array([10*np.pi/180, #ang s1 + 0.15, #dist s1 + 0.2, #x 1 + 0.1, #y 1 + 10*np.pi/180, #ang 1 + 0.2, #dist b1 + 0.2, #dist c1 + 0.1, #dist a1 + 0.1, #dist a2 + 0.4, #x 2 + 0.05, #y 2 + 10*np.pi/180, #ang 2 + 0.2, #dist b2 + 0.2, #dist c2 + 30*np.pi/180, #ang s2 + 0.15]) #dist s2 + + + for profile in np.arange(0, pop_num, 1): + deviation = 0.2 * np.random.randn(16) * gen_deviation + genome[profile,:] = genes + deviation + genome[profile, 14] = genome[profile, 0] + abs(deviation[14]) + +# for gen in np.arange(0,16,1): +# genome[profile, gen] = genome[profile, gen] * (1 + 0.1 * np.random.randn()) +# +# genome[1,:] = genes + + return genome + + +#interfaz.xfoil_calculate_population(generation,genome) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index e507b1c..ec432c5 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -7,14 +7,9 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script requires from the main program a serie of profile genomes. -The number of profiles is "num_pop" -This subprogram, first, uses the script "transcript.py" in order to translate -the genomes into a series of points that Xfoil can understand. - -Then, sends them to Xfoil, and ask it to analize them. - -At last, it sends back to the main program the results obtained. +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. ''' @@ -25,33 +20,14 @@ import os import interfaz as interfaz import numpy as np +import initial as initial - - -genes = np.array([150*np.pi/180, #ang s1 - 0.2, #dist s1 - 0.5, #x 1 - 0.12, #y 1 - 0, #ang 1 - 0.2, #dist b1 - 0.2, #dist c1 - 0.1, #dist a1 - 0.05, #dist a2 - 0.4, #x 2 - 0.05, #y 2 - 5*np.pi/180, #ang 2 - 0.2, #dist b2 - 0.2, #dist c2 - 160*np.pi/180, #ang s2 - 0.2]) #dist s2 - generation = 0 -#profile_number = 1 -genome = np.zeros([2,16]) -genome[0,:] = genes -genome[1,:] = genes +starting_profiles = 5 + +genome = initial.start_pop(starting_profiles) +interfaz.xfoil_calculate_population(generation,genome) -interfaz.xfoil_calculate_population(generation,genome) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index 8804fec..b74f1dd 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -3,7 +3,7 @@ aeropy - Xfoil Interaction tool Python tools for Aeronautical calculations. -Genetic Optimization Algorithm in progress (python 3.4, xfoil6.99) +Genetic Optimization Algorithm in progress (python 3.4, xfoil 6.99) Includes: @@ -19,7 +19,9 @@ Genetic algorithm modules: -main (in progress) -All 3 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will decode an example genoma for 2 profiles, and then analize them with xfoil. +-initial (in progress) + +All 4 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will generate and decode an example genoma for 5 profiles, and then analize them with xfoil. More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing From 274e17335ba09fc9b5f39ffc4aa3a9bf39e09808 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Sun, 22 Feb 2015 20:18:42 +0100 Subject: [PATCH 07/16] =?UTF-8?q?A=C3=B1adido=20testing=20al=20script=20de?= =?UTF-8?q?=20generaci=C3=B3n=20inicial?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Ahora el rango de perfiles iniciales es mucho mayor, ya que los perfiles son analizados para evitar inconsistencias. --- .../Genetic_algorithm_files/initial.py | 12 ++- .../Genetic_algorithm_files/main.py | 2 +- .../Genetic_algorithm_files/testing.py | 98 +++++++++++++++++++ aeropy/Xfoil_Interaction/README.md | 4 +- 4 files changed, 110 insertions(+), 6 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py index c2fd879..a4e3e23 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -25,7 +25,7 @@ import os import interfaz as interfaz import numpy as np - +import testing as test def start_pop(pop_num): @@ -40,11 +40,11 @@ def start_pop(pop_num): 0.1, #dist a1 0.05, #dist a2 0.4, #x 2 - 0.05, #y 2 + -0.07, #y 2 5*np.pi/180, #ang 2 0.2, #dist b2 0.2, #dist c2 - 160*np.pi/180, #ang s2 + 270*np.pi/180, #ang s2 0.2]) #dist s2 # generation = 0 @@ -70,9 +70,13 @@ def start_pop(pop_num): for profile in np.arange(0, pop_num, 1): - deviation = 0.2 * np.random.randn(16) * gen_deviation + deviation = 0.7 * np.random.randn(16) * gen_deviation genome[profile,:] = genes + deviation genome[profile, 14] = genome[profile, 0] + abs(deviation[14]) + while not(test.test_perfil(genome[profile,:])): + deviation = 0.7 * np.random.randn(16) * gen_deviation + genome[profile,:] = genes + deviation + genome[profile, 14] = genome[profile, 0] + abs(deviation[14]) # for gen in np.arange(0,16,1): # genome[profile, gen] = genome[profile, gen] * (1 + 0.1 * np.random.randn()) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index ec432c5..5880780 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -25,7 +25,7 @@ generation = 0 -starting_profiles = 5 +starting_profiles = 20 genome = initial.start_pop(starting_profiles) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py new file mode 100644 index 0000000..500feb6 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py @@ -0,0 +1,98 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import transcript as transcript +import numpy as np +import initial as initial +from scipy import interpolate + +def prueba_creciente(array): + numele = np.shape(array)[0] + if np.array_equal(np.argsort(array), np.arange(0, numele, 1)): + return True + else: + return False + + +def prueba_decreciente(array): + numele = np.shape(array)[0] + if np.array_equal(np.argsort(array), np.arange(numele-1, -1, -1)): + return True + else: + return False + +def prueba_perfil_x (genome): + perfil = transcript.decode_genome(genome[:]) + test1 = prueba_decreciente(perfil[0:25, 0]) + test2 = prueba_decreciente(perfil[25:50, 0]) + test3 = prueba_creciente(perfil[50:75, 0]) + test4 = prueba_creciente(perfil[75:100, 0]) + + if (test1 * test2 * test3 * test4): + return True + else: + return False + +def test_simple (genome): + test1 = (genome[14] - genome [0]) > (5*np.pi/180) + test2 = genome[3] > genome[10] + test3 = genome[7] > 0.01 + test4 = genome[8] > 0.01 + test5 = genome[1] > 0.01 + test6 = genome[15] > 0.01 + test7 = genome[0] > (np.pi * 0.6) + test8 = genome[0] < (np.pi * 1.4) + + if (test1 * test2 * test3 * test4 * test5 * test6 * test7 * test8): + return True + else: + return False + +def collision_test(genome): + perfil = transcript.decode_genome(genome[:]) + + extrax = np.append(perfil[40:25:-1,0],perfil[24:5:-1,0]) + extray = np.append(perfil[40:25:-1,1],perfil[24:5:-1,1]) + + intrax = np.append(perfil[60:74,0],perfil[75:98,0]) + intray = np.append(perfil[60:74,1],perfil[75:98,1]) + + extrados = interpolate.InterpolatedUnivariateSpline(extrax, extray, k=1) + intrados = interpolate.InterpolatedUnivariateSpline(intrax, intray, k=1) + + ver = np.linspace(0.1, 0.9, 50) + very = extrados(ver) - intrados(ver) + + return (very > 0.01 ). all() + +def test_perfil (genome): + if test_simple(genome): + if prueba_perfil_x(genome): + if collision_test(genome): + return True + else: + return False + else: + return False + else: + return False + + \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index b74f1dd..6f167f1 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -17,11 +17,13 @@ Genetic algorithm modules: -transcript +-testing + -main (in progress) -initial (in progress) -All 4 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will generate and decode an example genoma for 5 profiles, and then analize them with xfoil. +All 5 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will generate and decode an example genoma for 20 profiles, test them in order to avoid inoperable profiles, and regenerate thosa that don't pass the test until there are 20. Then, the programe analize them with xfoil. More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing From caea19972d7c108f47bbd3b48e6c0e64f979ea84 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Mon, 23 Feb 2015 04:58:01 +0100 Subject: [PATCH 08/16] Algoritmo Funcional MK1 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit El algoritmo, aunque mejorable y con la documentación sin terminar, ya se encuentra totalmente operativo. --- .../Genetic_algorithm_files/analice.py | 73 +++++++++++++++++++ .../Genetic_algorithm_files/cross.py | 38 ++++++++++ .../Genetic_algorithm_files/genetics.py | 39 ++++++++++ .../Genetic_algorithm_files/initial.py | 4 +- .../Genetic_algorithm_files/interfaz.py | 3 + .../Genetic_algorithm_files/main.py | 19 ++++- .../Genetic_algorithm_files/mutation.py | 60 +++++++++++++++ .../Genetic_algorithm_files/selection.py | 33 +++++++++ aeropy/Xfoil_Interaction/README.md | 24 +++++- 9 files changed, 285 insertions(+), 8 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py new file mode 100644 index 0000000..a8e6fcf --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py @@ -0,0 +1,73 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np +import initial as initial + + + +#generation = 0 +#starting_profiles = 20 +# +#genome = initial.start_pop(starting_profiles) +# +#interfaz.xfoil_calculate_population(generation,genome) +# +#num_pop = genome_matrix.shape[0] + +def pop_analice (generation, num_pop): + pop_results = np.zeros([num_pop,2]) + for profile_number in np.arange(1,num_pop+1,1): + pop_results[profile_number - 1, :] = profile_analice (generation, profile_number) + + return pop_results + + +def profile_analice (generation, profile_number): + profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) + data_root = "aerodata\data" + profile_name + '.txt' + datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2]) + + read_dim = np.array(datos.shape) + #print('read_dim = ', read_dim, read_dim.shape) + if ((read_dim.shape[0]) != 2): + return np.array ([0,0]) + + + pos_clmax = np.argmax(datos[:,0]) + clmax = datos[pos_clmax,0] + efic = datos[:,0] / datos[:,1] + pos_maxefic = np.argmax(efic) + maxefic = efic[pos_maxefic] + return np.array([clmax , maxefic]) + +def adimension(array): + max_value = max(array) + adim = array / max_value + return adim + +def score(generation, num_pop): + + pop_results = pop_analice (generation, num_pop) + cl_score = adimension(pop_results[:,0]) + efic_score = adimension(pop_results[:,1]) + total_score = 0.6 * cl_score + 0.4 * efic_score + return total_score diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py new file mode 100644 index 0000000..aeb5b10 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py @@ -0,0 +1,38 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np +import initial as initial + + + + +def cross(parents, num_pop): + children = np.zeros([num_pop, 16]) + num_parents = parents.shape[0] + children[0:num_parents] = parents + for i in np.arange(num_parents, num_pop, 1): + coef = np.random.rand(num_parents) + coef = coef/sum(coef) + children[i,:]= np.dot(coef, parents) + + return children + diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py new file mode 100644 index 0000000..e0c223a --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py @@ -0,0 +1,39 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np +import initial as initial +import analice as analice +import selection as selection +import cross as cross +import mutation as mutation + + + + +def genetic_step(genome,generation,num_parent): + num_pop = genome.shape[0] + + scores = analice.score(generation,num_pop) + parents = selection.selection(scores, genome, num_parent) + children = cross.cross(parents, num_pop) + children = mutation.mutation(children, generation, num_parent) + return children \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py index a4e3e23..7d76ce3 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -44,7 +44,7 @@ def start_pop(pop_num): 5*np.pi/180, #ang 2 0.2, #dist b2 0.2, #dist c2 - 270*np.pi/180, #ang s2 + 190*np.pi/180, #ang s2 0.2]) #dist s2 # generation = 0 @@ -72,11 +72,9 @@ def start_pop(pop_num): for profile in np.arange(0, pop_num, 1): deviation = 0.7 * np.random.randn(16) * gen_deviation genome[profile,:] = genes + deviation - genome[profile, 14] = genome[profile, 0] + abs(deviation[14]) while not(test.test_perfil(genome[profile,:])): deviation = 0.7 * np.random.randn(16) * gen_deviation genome[profile,:] = genes + deviation - genome[profile, 14] = genome[profile, 0] + abs(deviation[14]) # for gen in np.arange(0,16,1): # genome[profile, gen] = genome[profile, gen] * (1 + 0.1 * np.random.randn()) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index 21f36cc..3793fc4 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -52,6 +52,8 @@ def xfoil_calculate_profile(generation,profile_number,genome): perfil = trans.decode_genome(genome) + if not os.path.exists('profiles\gen' + str(generation)): + os.makedirs('profiles\gen' + str(generation)) try: os.remove(profile_root) except : @@ -62,6 +64,7 @@ def xfoil_calculate_profile(generation,profile_number,genome): pass + archivo = open(profile_root, mode = 'x') archivo.write(profile_name + '\n\n\n') diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index 5880780..0e26017 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -21,13 +21,28 @@ import interfaz as interfaz import numpy as np import initial as initial +import genetics as genetics - +if not os.path.exists('aerodata'): + os.makedirs('aerodata') generation = 0 -starting_profiles = 20 +starting_profiles = 30 +total_generations = 10 +num_parent = 4 genome = initial.start_pop(starting_profiles) interfaz.xfoil_calculate_population(generation,genome) +for generation in np.arange(0,total_generations,1): + + genome = genetics.genetic_step(genome,generation,num_parent) + + interfaz.xfoil_calculate_population(generation + 1,genome) + + + + + + \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py new file mode 100644 index 0000000..158669e --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py @@ -0,0 +1,60 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np +import initial as initial +import testing as test + +def mutation(children, generation, num_parent): + coeff = 0.5 / (1 + generation**0.5) + gen_deviation = np.array([10*np.pi/180, #ang s1 + 0.15, #dist s1 + 0.2, #x 1 + 0.1, #y 1 + 10*np.pi/180, #ang 1 + 0.2, #dist b1 + 0.2, #dist c1 + 0.1, #dist a1 + 0.1, #dist a2 + 0.4, #x 2 + 0.05, #y 2 + 10*np.pi/180, #ang 2 + 0.2, #dist b2 + 0.2, #dist c2 + 30*np.pi/180, #ang s2 + 0.15]) #dist s2 + + pop_num = children.shape[0] + + children_n = children.copy() + + for i in np.arange(num_parent, pop_num, 1): + deviation = coeff * np.random.randn(16) * gen_deviation + children_n[i,:] = children[i,:] + deviation + n = 0 + while not(test.test_perfil(children_n[i,:])): + n = n + 1 + deviation = coeff * np.random.randn(16) * gen_deviation + children_n[i,:] = children[i,:] + deviation + print('mutando perfil viable, intento',n) + children[i,:] = children_n[i,:] + + return children diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py new file mode 100644 index 0000000..a71f504 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py @@ -0,0 +1,33 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + +import subprocess +import sys +import os +import interfaz as interfaz +import numpy as np +import initial as initial + + +def selection(score, genome, num_parent): + invscore = 1- score + positions = np.argsort(invscore) + parents = np.zeros([num_parent,16]) + for i in np.arange(0,num_parent,1): + parents[i,:] = genome[positions[i],:] + + return parents \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index 6f167f1..54dd650 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -19,11 +19,29 @@ Genetic algorithm modules: -testing --main (in progress) +-main --initial (in progress) +-initial -All 5 files must be in the same folder as xfoil.exe. Then, you can try the early version by executing main.py. It will generate and decode an example genoma for 20 profiles, test them in order to avoid inoperable profiles, and regenerate thosa that don't pass the test until there are 20. Then, the programe analize them with xfoil. +-genetics + +-analice + +-cross + +-mutation + +-selection + +All 10 files must be in the same folder as xfoil.exe. + +Execute the main.py file in order to start the algorithm. + +It will randomly generate 30 profiles and test if they are viable, those wich aren't will be regenerated until they are. + +They will be analiced with xfoil, and scored and sorted depending on the values they achieve. + +Then, the best 3 will be selected as parents for the next generation. More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing From 15017f413eadcb1a4bf73c6746780a2ff885b423 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Wed, 25 Feb 2015 17:56:16 +0100 Subject: [PATCH 09/16] Funcional MK2 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Ambiente Personalizable: Planeta, mach/velocidad, cuerda y altura de vuelo. Dibujador de perfiles guardado automático de datos permite continuar desde un punto anterior. --- .../Genetic_algorithm_files/ambient.py | 247 +++++++++++ .../Genetic_algorithm_files/analice.py | 2 +- .../Genetic_algorithm_files/genetics.py | 23 +- .../Genetic_algorithm_files/initial.py | 18 + .../Genetic_algorithm_files/interfaz.py | 17 +- .../Genetic_algorithm_files/main.py | 15 +- aeropy/Xfoil_Interaction/README.md | 8 +- aeropy/Xfoil_Interaction/result_drawer.ipynb | 405 ++++++++++++++++++ 8 files changed, 722 insertions(+), 13 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py create mode 100644 aeropy/Xfoil_Interaction/result_drawer.ipynb diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py new file mode 100644 index 0000000..4b8b5ca --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py @@ -0,0 +1,247 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + + +import numpy as np + + + + + +def ventana (x, inicio=0, fin=1, f=1.): + _1 = (np.sign(x-inicio)) + _2 = (np.sign(-x+fin)) + return 0.25 * (_1+1) * (_2+1) * f + +def etc (x, inicio=0, f=1.): + _1 = (np.sign(x-inicio)) + return 0.5 * (_1+1) * f + +def earth_conditions(): + heights = np.array([-1.000, + 11.019, + 20.063, + 32.162, + 47.350, + 51.413, + 71.802, + 86.000, + 90.000]) + + + # an es el gradiente válido entre H(n-1) y H(n) + temp_gradient = np.array([-6.49, + 0, + 0.99, + 2.77, + 0, + -2.75, + -1.96, + 0, + 0]) + + + + atm_layer = temp_gradient.shape[0] + + temp_points = np.zeros([atm_layer]) + temp_points[0] = 294.64 + for layer in np.arange(1, atm_layer, 1): + temp_points[layer] = temp_points[layer-1] + temp_gradient[layer-1]*(heights[layer]-heights[layer-1]) + gravity = 9.8 + gas_R = 287 + planet_radius = 6371 + p0 = 113922 + + conditions = (heights, temp_gradient, temp_points, gravity, gas_R, planet_radius, p0) + + return conditions + + +def temperatura(h, conditions, dT = 0): + '''Calcula la temperatura a una altura h en Km sobre el nivel del mar''' + grad = 0 + heights = conditions[0] + gradient = conditions[1] + atm_layer = gradient.shape[0] + temp_points = conditions[2] + + + + for layer in np.arange(0, atm_layer-1,1): + increase = temp_points[layer] + gradient[layer] * (h - heights[layer]) + grad = grad + ventana(h, heights[layer],heights[layer+1], increase) + grad = grad + etc(h, heights[atm_layer-1],temp_points[atm_layer-1] + gradient[atm_layer-1]*(h - heights[atm_layer-1])) + return grad + dT + + +def segmento_presion_1(z, pi, z0, dT, conditions): + '''calcula la presión en un segmento de atmósfera de temperatura constante''' + g = conditions[3] + R = conditions[4] + radius = conditions[5] + h = (radius * z) /(radius + z) + h0 = (radius * z0)/(radius + z0) + _ = 1000*(h-h0) * g / (R * temperatura(z, conditions, dT)) + return pi * np.e ** -_ + +def segmento_presion_2(z, pi, Ti, a, dT, conditions): + '''calcula la presión en un segmento de atmósfera con gradiente de temperatura "a" ''' + g = conditions[3] + R = conditions[4] + _ = g / (a*R/1000) + return pi * (temperatura(z, conditions, dT)/(Ti + dT)) ** -_ + +def presion (h, conditions, dT = 0): + '''Calcula la presion en Pa a una altura h en m sobre el nivel del mar''' + + heights = conditions[0] + gradient = conditions[1] + temp_points = conditions[2] + atm_layer = gradient.shape[0] + #Primero, calculamos la presion de cada punto de cambio de capa para la condición de dT pedida + #Suponemos que la presión es siempre constante a 101325 Pa a nivel del mar + + + pressure_points = np.zeros([atm_layer]) + pressure_points[0] = conditions[6] + + for layer in np.arange(1, atm_layer, 1): + if (abs(gradient[layer-1]) < 1e-8): + pressure_points[layer] = segmento_presion_1(heights[layer], + pressure_points[layer - 1], + heights[layer - 1], + dT, conditions) + else: + pressure_points[layer] = segmento_presion_2(heights[layer], + pressure_points[layer - 1], + temp_points[layer - 1], + gradient[layer-1], + dT, conditions) +# + + #A partir de estos datos, construímos la atmósfera en cada capa + + grad = 0 + for layer in np.arange(1, atm_layer, 1): + if (abs(gradient[layer-1]) < 1e-8): + funcion = segmento_presion_1(h, + pressure_points[layer - 1], + heights[layer - 1], + dT, conditions) + else: + funcion = segmento_presion_2(h, + pressure_points[layer - 1], + temp_points[layer - 1], + gradient[layer-1], + dT, conditions) + grad = grad + ventana(h, heights[layer-1], heights[layer], funcion) + if (abs(gradient[layer-1])< 10e-8): + funcion = segmento_presion_1(h, + pressure_points[layer - 1], + heights[layer - 1], + dT, conditions) + else: + funcion = segmento_presion_2(h, + pressure_points[layer - 1], + temp_points[layer - 1], + gradient[layer-1], + dT, conditions) + + grad = grad + etc(h, heights[atm_layer - 1], funcion) + return grad + + +def densidad(h, conditions, dT = 0): + '''Calcula la densidad a una altura h en m sobre el nivel del mar''' + R = conditions[4] + return presion(h, conditions, dT)/(R * temperatura(h, conditions, dT)) + + +def mars_conditions(): + heights = np.array([-8.3, + 8.85, + 30]) + + # an es el gradiente válido entre H(n-1) y H(n) + temp_gradient = np.array([-2.22, + -0.998, + -0.998]) + + + atm_layer = temp_gradient.shape[0] + + temp_points = np.zeros([atm_layer]) + temp_points[0] = 268.77 + for layer in np.arange(1, atm_layer, 1): + temp_points[layer] = temp_points[layer-1] + temp_gradient[layer-1]*(heights[layer]-heights[layer-1]) + gravity = 3.711 + gas_R = 192.1 + planet_radius = 3389 + p0 = 1131.67 + + conditions = (heights, temp_gradient, temp_points, gravity, gas_R, planet_radius, p0) + + return conditions + + + +def viscosidad(temp, planet): + if (planet == 'Earth'): + c = 120 + lamb = 1.512041288 + elif(planet == 'Mars'): + c = 240 + lamb = 1.572085931 + + visc = lamb * temp**1.5 / (temp + c) + return visc + + +def Reynolds(dens, longitud, vel, visc): + re = 1000000 * dens * longitud * vel / visc + return re + + +def aero_conditions(ambient_data): + (planet, chord, height, speed_type, speed) = ambient_data + planet_dic = {'Mars':mars_conditions(), 'Earth':earth_conditions()} + + + sound = (1.4 *presion(height, planet_dic[planet]) / densidad(height,planet_dic[planet]))**0.5 + + if (speed_type == 'mach'): + mach = speed + vel = mach * sound + elif (speed_type == 'speed'): + mach = speed / sound + vel = speed + else: + print('error in the data, invalid speed parameter') + + + + re = Reynolds(densidad(height, planet_dic[planet]), chord, vel, viscosidad(temperatura(height, planet_dic[planet]), planet)) + + + + return [mach, re] +# +#ambient_data = ('Earth', 03.0003, 11, 'speed', 30.1) +# +#result = aero_conditions(('Earth', 0.03, 11, 'mach', 0.1)) +#print(result) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py index a8e6fcf..0eabbf9 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py @@ -69,5 +69,5 @@ def score(generation, num_pop): pop_results = pop_analice (generation, num_pop) cl_score = adimension(pop_results[:,0]) efic_score = adimension(pop_results[:,1]) - total_score = 0.6 * cl_score + 0.4 * efic_score + total_score = 0.3 * cl_score + 0.7 * efic_score return total_score diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py index e0c223a..3fb47f2 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py @@ -29,11 +29,32 @@ -def genetic_step(genome,generation,num_parent): +def genetic_step(generation,num_parent): + + genome_parent_root = 'genome\generation'+ str(generation) + '.txt' + genome = np.loadtxt(genome_parent_root, skiprows=1) num_pop = genome.shape[0] scores = analice.score(generation,num_pop) parents = selection.selection(scores, genome, num_parent) children = cross.cross(parents, num_pop) children = mutation.mutation(children, generation, num_parent) + + profile_number = children.shape[0] + genome_root = 'genome\generation'+ str(generation + 1) + '.txt' + title = 'generation' + str(generation + 1) + 'genome' + + try: + os.remove(genome_root) + except : + pass + archivo = open(genome_root, mode = 'x') + archivo.write(title + '\n') + + for profile in np.arange(0, profile_number, 1): + line = '' + for gen in np.arange(0, 16,1): + line = line + str(children[profile, gen]) +' ' + line = line + '\n' + archivo.write(line) return children \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py index 7d76ce3..d8b5deb 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -81,6 +81,24 @@ def start_pop(pop_num): # # genome[1,:] = genes + profile_number = genome.shape[0] + genome_root = 'genome\generation0.txt' + title = 'generation 0 genome' + + try: + os.remove(genome_root) + except : + pass + archivo = open(genome_root, mode = 'x') + archivo.write(title + '\n') + + for profile in np.arange(0, profile_number, 1): + line = '' + for gen in np.arange(0, 16,1): + line = line + str(genome[profile, gen]) +' ' + line = line + '\n' + archivo.write(line) + return genome diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index 3793fc4..18b5b84 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -25,19 +25,22 @@ import os import transcript as trans import numpy as np +import ambient as ambient -def xfoil_calculate_profile(generation,profile_number,genome): +def xfoil_calculate_profile(generation,profile_number, genome, ambient_data): profile_root = 'profiles\gen' + str(generation) + '\profile' + str(profile_number) + '.txt' profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) + genome_root = 'genome\generation'+ str(generation) + '.txt' + aerodynamics = ambient.aero_conditions(ambient_data) commands = ['load', profile_root, 'oper', - 'mach 0.2', - 're 3500', + 'mach ' + str(aerodynamics[0]), + 're ' + str(aerodynamics[1]), 'visc', 'pacc', "aerodata\data" + profile_name + '.txt', @@ -86,9 +89,13 @@ def xfoil_calculate_profile(generation,profile_number,genome): for line in p.stdout.readlines(): print(line.decode(), end='') -def xfoil_calculate_population(generation, genome_matrix): +def xfoil_calculate_population(generation, ambient_data): + + genome_root = 'genome\generation'+ str(generation) + '.txt' + genome_matrix = np.loadtxt(genome_root, skiprows=1) num_pop = genome_matrix.shape[0] + for profile_number in np.arange(1,num_pop+1,1): - xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:]) + xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:], ambient_data) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index 0e26017..a005024 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -25,21 +25,28 @@ if not os.path.exists('aerodata'): os.makedirs('aerodata') + +if not os.path.exists('genome'): + os.makedirs('genome') generation = 0 starting_profiles = 30 total_generations = 10 -num_parent = 4 +num_parent = 3 +ambient_data = ('Earth', 0.3, 11, 'mach', 0.5) genome = initial.start_pop(starting_profiles) -interfaz.xfoil_calculate_population(generation,genome) +interfaz.xfoil_calculate_population(generation, ambient_data) + + +#arange antes en 0 for generation in np.arange(0,total_generations,1): - genome = genetics.genetic_step(genome,generation,num_parent) + genome = genetics.genetic_step(generation,num_parent) - interfaz.xfoil_calculate_population(generation + 1,genome) + interfaz.xfoil_calculate_population(generation + 1, ambient_data) diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index 54dd650..c551565 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -11,6 +11,8 @@ Includes: -Interactive Ipython notebook showing how the genome-to-profile decodification works (in progress). +-Ipython notebook that can easily be used to save drawings of the airfoils generate. (Must be placed with the .py genetic algorithm files) + Genetic algorithm modules: -interfaz @@ -33,9 +35,11 @@ Genetic algorithm modules: -selection -All 10 files must be in the same folder as xfoil.exe. +-ambient + +All 11 files must be in the same folder as xfoil.exe. -Execute the main.py file in order to start the algorithm. +Execute the main.py file in order to start the algorithm. The main control paraparameters are defined here, and secondary parameters will be automatically calculated. It will randomly generate 30 profiles and test if they are viable, those wich aren't will be regenerated until they are. diff --git a/aeropy/Xfoil_Interaction/result_drawer.ipynb b/aeropy/Xfoil_Interaction/result_drawer.ipynb new file mode 100644 index 0000000..b7679c3 --- /dev/null +++ b/aeropy/Xfoil_Interaction/result_drawer.ipynb @@ -0,0 +1,405 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:e927dd9f6de7a4a31613a90d6653167c6e56dc97e4d104555f737435e268faa2" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", + " \n", + " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.ylim(-0.15, 0.15)\n", + " plt.xlim(-0.05, 1.05)\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " plt.gca().set_aspect(1)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + '.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for i in np.arange(0,19,1):\n", + " drawing(i,1)\n", + " drawing(i,2)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:423: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_num_figures`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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0ySfSLbcQ5gAAAAA7WJb06afSwoVmCNSkSSbc9etnd83OH8bQNZH8fHPB/OMf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\n5Sn9mb4fwPlGoEONtm0zrXY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C3LffmjDXrp3VFQEAAADWOH5cmjZNunhReucdKTjY6ooaHoZcNiB2u/TQQ4Q5\nAAAAQDLX0n3yiXTjjVJkpFm/Ds6jh66e/elP5oTdsoUwBwAAAJSUkCBNmWLC3f/8j9SypdUVNQz0\n0DUQS5ZI779vFg0nzAEAAAClDR5sJgw8cUIaNEjat8/qiho+Al09WbtWWrTIXPjp42N1NQAAAEDD\n5OUlvfeemXPi+uulf/6TNeuqwpDLevD552atjc8+kyIirK4GAAAAcA1790p33il17SqtWNF016xj\nyKWFkpJMmFu7ljAHAAAAXIkePaSvv5auucZ8lo6Ls7qihoceujr088/S8OHSyy9Lt95qdTUAAACA\n6/riC2n6dOmmm6TFi6VWrayuqP7QQ2eBEyek0aPNrJaEOQAAAKBmRoyQdu+WTp6U+vc3I+FAD12d\nyMszJ1xUlPTcc1ZXAwAAADQu77wjxcZKjz4q/eEPUvPmVldUt6rKRAS6OvDww9LBg9KGDVIz+kAB\nAACAWnfkiDRtmmSzSatXS507W11R3WHIZT1au1Zav156803CHAAAAFBXunQxs8mPGydFRppeu6aI\nHrpatG+fWSvjk0+kfv2srgYAAABoGpKSpKlTpT59pFdeMWvZNSb00NWDs2fN5CeLFhHmAAAAgPrU\nt6/07bdSx45meYP4eKsrqj/00NUCu12aMkXy8DAr2QMAAACwxubN0n33mR67//5vqUULqyuqOXro\n6tiqVdIPP0hLllhdCQAAANC0jR0rJSdLBw5IgwZJ339vdUV1i0BXQzk50pNPmp65prS4IQAAANBQ\n+fhIH34o/e53ZimxJUvMqLrGiCGXNfTII9Lp0wy1BAAAABqiH3+U7rrLTJSyapXk7291RVeOIZd1\nZN8+s+bFX/5idSUAAAAAKhIaKm3fLg0ebCZP+fBDqyuqXfTQVZPdbsbnjhpleukAAAAANGxffSXd\nfbf0619Lf/+75OlpdUXOqdMeuri4OIWHhyssLEyLFi2qcJ+HHnpIYWFhioiIUFJS0hUd21B98YV0\n+LA0Z47VlQAAAABwxpAhZs06m8301n31ldUV1VyNAl1BQYHmzJmjuLg47d27V2vWrNG+fftK7bNp\n0yb9+OOPOnjwoF577TXNnj3b6WMbsg0bpGnTpKuusroSAAAAAM7y9DTzX/z1r9Itt0jz50v5+VZX\nVX01CnSJiYkKDQ1VSEiI3N3dNXnyZK1fv77UPhs2bND06dMlSYMGDVJOTo7S09OdOrYh+/xzaeRI\nq6sAAACBZY2nAAAZHElEQVQAUB233GJ66xISpOuuM5OnuKIaBbrU1FQFBwcXPQ4KClJqaqpT+6Sl\npV322IbqxAnp6FGpf3+rKwEAAABQXf7+ZiHyqVPNcMz33rO6oivnVpODbTabU/s1xElNamLnTvMf\n3K1Gvz0AAAAAVrPZzHp1w4ZJv/xidTVXrkaRJDAwUCkpKUWPU1JSFBQUVOU+x44dU1BQkPLy8i57\nrMP8+fOL7kdFRSkqKqomZddYmzZSbq6lJQAAAABwQl6elJEhpaUVt9TU0o/T0qQzZ6SnnpKio62u\nWIqPj1d8fLxT+9Zo2YL8/Hx169ZNW7duVUBAgAYOHKg1a9aoe/fuRfts2rRJS5cu1aZNm5SQkKDY\n2FglJCQ4dazUMJct+OEHafx46eBBqysBAAAAmqaCAikzs3wwK9uysiQfHykgoOrWsaPUrIGu0l1V\nJqpRD52bm5uWLl2q0aNHq6CgQDNnzlT37t21fPlySdKsWbM0btw4bdq0SaGhoWrTpo1WrVpV5bGu\nIDhYSkkxa9E5OeoUAAAAgJMKCqTjx80yYYcOSUeOlA9qJ05I7duXD2b9+0sTJhQ/7tRJat7c6ndU\nd1hYvJq8vaW9e80JAgAAAMB5hYVmGOShQya0OYKb435KitShgxQSInXtKnXuLAUFlQ5uvr5NZwmx\nOuuha8o6dzYnG4EOAAAAKM1uNz1oFYW1Q4fMjPFt25qwFhJiWmSkdNtt5n6XLlLLlha+ARdCoKum\n664zU5wOHGh1JQAAAED9stulkyfNUMjKetnatCkOa127Sr17SxMnmvtdukitW1v5DhoPhlxW0zff\nSFOmmIlRuI4OAAAAjYnjGrYjR8q3w4dND1uLFiaYde1a3NPmuO3SRfL0tPhNNCJVZSICXTXZ7VKP\nHtLKldLQoVZXAwAAADgvN9dcp1ZRYDtyRDp2zFzD1qVLcXMENUcjsNUfAl0def558+3EsmVWVwIA\nAAAUO3++dI9a2cB24oTk718+pDla585cw9aQEOjqyNGjUt++5n8KDw+rqwEAAEBTUFho1l87etS0\nI0eK7zsenzljQlllPWyBgZIbs2m4DAJdHZo+3SxC+OKLVlcCAACAxuDCBTMcsrLAlpJihjt27lx5\n8/VtuItk48oR6OrQyZNSz57Sxx+bqVYBAACAyjim8y/bo1by8alTUnBw5WEtONjMIImmg0BXx958\n0/TQffMNXdcAAABN2ZkzpgfN0Rw9aiV71zw8iq9To3cNziDQ1TG7XRo1Sho9WvrDH6yuBgAAAHXh\n0iUz+2PJoFY2vOXmmh60ss0R4OhdQ3UQ6OrBTz9JgwdL69ZJw4ZZXQ0AAACuhGPdtarCWna2FBBQ\nHMzKts6dzVT/rFGM2kagqydxcWaSlC1bpF69rK4GAAAAkpkV8sQJ07vm6GErOyQyPV3y9q44pDnu\n+/pKzZtb/W7QFBHo6tG775phl19+Kf3qV1ZXAwAA0LgVFkoZGcVBzRHaSj5OS5PatZOCgkwwc9yW\nbIGB0lVXWf1ugIpVlYmYwqOWTZ5suuNHjZK2b5f8/KyuCAAAwDUVFFQc1kreP35c8vIqHdaCgqSI\niOL7gYEsko3Gi0BXB2bPln75xYS6TZvMHxIAAAAUy883YSw1teJeNUdY69ChfFjr27d0WGvRwup3\nA1iHQFdHnnrKdNsPHCitXStdd53VFQEAANSP8+eLg1pltydPmmvWHKHMEdj69Su+HxBAWAMuh2vo\n6phjopRnn5VmzbK6GgAAgOqz282lJZcLa+fPm5AWGFgc2Mre+vlJ7u5WvyPANTApisUOHpRuvtn0\n0i1ZwgW3AACg4cnPN9erpaYWt5IhzXH/qquqDmpBQVLHjkzdD9QmAl0DcOaMdPfdZsrc11+XrrnG\n6ooAAEBTYLdLp06ZmR5LhrWyjx1DIAMCqg5sHh5WvyOg6SHQNRCFhdLLL0vPPSfFxkqPPUZvHQAA\nqL7cXDNxyOXCWvPmxUGtZCu5zc9PcmN2BaBBItA1MEeOSA8+KB06JL32mjRsmNUVAQCAhsRuNzNm\np6VVHdays6VOnaoOaoGBkqen1e8IQE0Q6Bogu1364APTUzdxovT882YNFQAA0HiVHP5YVTt+3Axt\nDAgwzd+/fGgLDDRhrnlzq98VgLpGoGvAcnKkxx+X1q+X5s41M2G2amV1VQAA4EqdO3f5oJaWVjz8\nsarm78/nAQDFCHQuYPduad486ZtvpCeekGJiWHcFAICG4Px502PmuFat5H3H8Me0NHM9W2UBzTEM\n0t+f4Y8ArhyBzoV8+60Jdrt3m8XJ772XiVMAAKgLjh61smGt7O3FiyaIOVrJIZAlQ5uXF1P1A6gb\nBDoX9PXXJtjt32+GZN51F9MEAwDgjDNnqg5ojvt5eaUDWtlbx/327QlqAKxFoHNhO3ZIf/2r9J//\nmHXsHniANewAAE2P3S5lZRUHMkdLTy/9OC3N7FtVQHPctmtHUAPgGgh0jcCRI9Krr0orV0p9+phl\nD8aPZ2YrAIBry8uTTpy4fFDLyJBaty499NHPr/RjR2vblqAGoHEh0DUiFy9K778v/eMf5h+4+++X\npk833zQCANBQnDtXPpRVFNSysyUfn4oDWsnHfn5Sy5ZWvysAsEadBLqsrCzdcccdOnLkiEJCQrR2\n7Vp5VbCQWlxcnGJjY1VQUKD77rtPc+fOlSTNnz9f//znP+Xj4yNJev755zVmzJgrKr6p+/Zb6ZVX\npH//W+rbV7rzTunWW81YfwAAalt+vulNS08v3xxhzdEKCsoHsrI9aX5+Jswx2gQAqlYnge6xxx6T\nt7e3HnvsMS1atEjZ2dlauHBhqX0KCgrUrVs3bdmyRYGBgRowYIDWrFmj7t2765lnnpGnp6ceeeSR\nahcP4+JFadMmac0a6dNPpV//2oS7CROkNm2srg4A0JA5FrquLJiVbFlZkre3CWKO5ghmZZunJ8Me\nAaC2VJWJ3Kr7ohs2bNC2bdskSdOnT1dUVFS5QJeYmKjQ0FCFhIRIkiZPnqz169ere/fukkRQqyUt\nW0qTJpl2+rS0bp30+utmOOa4cdLtt0s33MAsmQDQlDiGPGZkVHxbsrVoUXFI69699DZvb3rTAKCh\nqXagy8jIkK+vryTJ19dXGRkZ5fZJTU1VcHBw0eOgoCB9/fXXRY+XLFmi1atXKzIyUi+88EKFQzZx\nZdq2laZNM+3ECemDD6SlS80MmUOHSjfeaNrVV1tdKQDgSl24YAJZyVBWWWBzDHn09S1927u3FB1d\nPOzR19dMNgIAcE1VBrro6Gilp6eX275gwYJSj202m2wVjKuoaJvD7Nmz9fTTT0uS/vznP+vRRx/V\nypUrnSoazunUySxz8MADpuduyxZp40bpL38xi586wt1117F4OQBY5eLF4pBWslUU0i5eLB/QfH2l\nHj2kESNKb2fIIwA0DVUGus8++6zS53x9fZWeni4/Pz8dP35cnTp1KrdPYGCgUlJSih6npKQoKChI\nkkrtf99992nChAmV/qz58+cX3Y+KilJUVFRVZaMCbdsWD8ssLJR27TLh7vHHpYMHzQeBqCjTevaU\nmjWzumIAcF1nz5YOZydOVBz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2YPp0M8mKEzVrC11aWpoSEhIUHx+vhx9+uNZjbr/9dsXHxyspKUkbNmxo0Lkt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\nNG+JjDSTkABAS0Sgg988Hmn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6SHr6aWnUKKlGDburAgAAgB2ysszwy2nTpOuvl556Smrd2u6qUFLltm2Bp6en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\nnrswRsHHHh75c63q1bvw/dz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Dd9VV0uzZZj3od9+VKiq8XV3b4Rm6ZqqokIYONT10117r7WoAAACAzs31nN0f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\n0qWX8lNxoL0qK6u5UPSZM+7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BDgAAAABsikAHAAAAADZFoAMAAAAAmyLQAQAAAIBNhQS7AgAABMKypMpK6cIF6fx5z7Ky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\ncXFxKigoUHx8vPLz89WvX78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mzJmF29HR0YqOjq5O2bZw6JB0//2Sn5+0dav5VAcAAAD1R7duZrmD2bPNZCq/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\nAwAMVxjLyHAvjJX3vsNhQlX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iQrrnHunjj818CMCFNK7OyWFhYUot1Tecmpqq8PDwSo85fPiwwsLCJEmdOnWS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\nXewaO3HihEaNGqX+/furd+/eeuWVV2q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Siy9KWVmBrhEAAADQvkpKpD//WXruOalPH2nePNNyN2hQoGtmPwyKEkD79km3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\npC+/NBeartdc66dOSQMHNh3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6zZo1S+vXr1dJSYmcTqcCAwN16tQpjRo1So8//rhGjRrlWWAbmhSlpMR8ovPP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\nUmKm/j97cpIDB8zom7PXcUt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MWgkAAIDWJztbeucdE+42b5auusqEu+uvl3r2tLs62IlA147s3y+tWiW98IK5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\nJGnp0qX6+9//rh49ekiS/vC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toEDavl3auFFautT01k2caALe8OG8B8SlsbB4E5OWZv5yp6dLq1dzrxwAAEBL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\n+EfphhusrggAmp6K96xVF9Y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zAl0zsmWLdOed0oIF0s9+xi8WAAAArry8POnjj00L3ocfmkFXJkyQJk40AY85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\nVFhYWO156enpSk5OVlJSkp5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6tJltfPx404vXoYPd1cJpWFjc4SxLWrFCevRRqV076YknzGKYDK0EAABo/Pbt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\nwEm8e8cqmrTDl21hYcVDEH3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Tpeuvt7siAAAAeJSXS3v2mIDnCXnnz3vD3Te+YSaxY9FzNCYWFm+mSkvNrEs7\nd0qrVkndu9tdEQAAAC4nM9P03H32mZl8JSNDuuUW6bbbpORk6cYbpcBAu6tES0Kga4bOnzdjty3L\n9M516mR3RQAAAKiP3FwT7tLSpE8/lY4cMWvh3XabaUlJZoIWoL4IdM1Mbq50771mCt3/+i++wQEA\nAGhJzpypGvAOHpRGjDC9d7fdZmbdbN/e7irhJAS6ZuTIEenOO03v3C9+wQ21AAAALV1+ftWAt3+/\ndNNN3oA3fDgBD3Uj0DUT+/ZJ3/ym9NOfSk8+aXc1AAAAsENBgZlgxRPw9uwxvXbf/KY0erTZZwQX\nKiPQNQOnT5uu9hdekKZOtbsaAAAANBeFhSbgrVtn2tGjZpmE0aNNGzBAatPG7iphJwKdzS5elO64\nw/zF/Pd/t7saAAAANGenTkmffOINeGfPenvvRo9mmavWiEBnI8uSvvtds0TB3/7GtysAAAC4MkeP\nesPd//t/UlCQN9x985tSeLjdFaKxEehs9NJL0ocfmm9ZgoLsrgYAAABOZlnSrl3egLd+vRQT4w14\nt90mde5sd5W42gh0NnnrLenf/k3atIlvTgAAAHD1lZVJW7Z4A156ujR4sDRmjJlZfehQqW1bu6tE\nQxHobLB+vfTAA2b2ov797a4GAAAArUFxsfkc+uGHpp06ZeZyuPNOE/KiouyuEPVBoGtiR4+a9UTe\nfltKSbG7GgAAALRWmZkm2H30kbR2rQl0d95p2qhR0jXX2F0h/EGga0KWJY0bJ918s1lvDgAAAGgO\n3G7piy+8vXc7d0q33OINeAkJkstld5XwhUDXhFaskJ5/XtqxQ2rXzu5qAAAAAN/y8819d56AJ3nv\nvUtJkUJD7a0PXgS6JlJcLN1wg7R4sRmrDAAAADiBZUn79plgl5pqFjofOlS65x7p3nul+Hh67+xE\noGsiL74oHTggLVtmdyUAAABA/RUXmzXvVq6UVq2SAgNNsLvnHrM0AvfeNS0CXRPYv9/cN/fVV8we\nBAAAgJbDssz9dp5w9/XX0u23m4B3991SZKTdFbZ8BLpGZlnem0mfe87uagAAAIDGc/q0GZa5cqWZ\nPbNXL+/QzKQkqU0buytseQh0jex//1d6+WVp2zbTHQ0AAAC0BqWl0saNpudu5UrpzBnTa3fPPaaz\nIzjY7gpbBgJdIxs0SPr1r82sQAAAAEBrdeSICXf//Kf0r39J3/iGNGGCWdarRw+7q3MuAl0j2rHD\nXKBHjtBB+y9ZAAAYbklEQVS9DAAAAHgUFkqrV0vvv2+GZg4aZMLd/febYZrwH4GuET3/vBQQIP3q\nV3ZXAgAAADRPFy+aNe/ef9+s2xwZacLdhAnSwIEsiXA5BLpG4nZL114rffyx1L+/3dUAAAAAzZ/b\nbe67e/9909q0Mb12EyZII0dKbdvaXWHzQ6BrJOvWST/+sbR1q92VAAAAAM5jWeYWJk+4O3lSGj/e\nhLs77pDatbO7wuaBQNdIpkwxXcTPPmt3JQAAAIDzHT4sffCB9N570p490n33SQ8+KI0e3bpnkyfQ\nNYLiYrOA+O7dUkSE3dUAAAAALcvx49Lf/y4tWyYdPGh67b79bem228wcFq0Jga4R/O//SosXmxl7\nAAAAADSeY8ekd9814e74celb3zLh7tZbW8dM83Vlolbw9hvHV1+ZCwgAAABA47ruOjN3xZdfShs2\nmJFyTz8txcRIzzxjJlkpL7e7SnsQ6Orp2DEzwyUAAACAphMXJ/30p9L27WaSwq5dpWnTpNhY6Uc/\nkr74wky20loQ6OopI8N8UwAAAADAHvHx0r/9m7Rrl1nEPChIevhhE+6eeUb65BOprMzuKhtXgwNd\namqq4uPj1adPH7322ms+j3n66afVp08fJSYmatu2bVd0bnNFDx0AAADQfAwYIP3iF9K+fSbc9ehh\nhmn27Ck98oi0fLmZ2LCladCkKG63W/369dPatWsVFRWlYcOGaenSpUpISKg4ZvXq1VqwYIFWr16t\nzZs365lnntGmTZv8OldqnpOiuN0m/Z89K7Vvb3c1AAAAAGqTkWHC3AcfmHvwRo82C5nfe68UFmZ3\ndf5ptElR0tPTFRcXp9jYWAUGBmrSpElavnx5lWNWrFihyZMnS5KGDx+ugoICnTx50q9zm6vsbKlb\nN8IcAAAA0Nxde6301FPmfrvDh83adu+/b4Zljh4tzZ8vZWbaXWX9NSjQZWVlKSYmpuJxdHS0srKy\n/DomOzv7suc2V9w/BwAAADhP167S5Mkm0J08aYLel19KgwdLSUlmaTKnadCSfC6Xy6/jmtuQyYbK\nzjZjcgEAAADYy7KkixeloiJzS5Rna1lS27ZmEfLatomJ0tCh5t67jRudMwSzsgYFuqioKGVW6p/M\nzMxUdHR0ncccP35c0dHRKi0tvey5HnPmzKnYT05OVnJyckPKbrAbbzTrXpSXt46FDAEAAICrzRPE\nCgtNKyjw7ldu1YOar21AgBQcLHXubLbBweZzelmZmf/Cs6287+u5GTPMMEy7paWlKS0tza9jGzQp\nSllZmfr166d169YpMjJSN910U52TomzatEmzZs3Spk2b/DpXap6TokhS//7Sm29KN91kdyUAAABA\n07Ms6cIFE8Ty803ztV89qFV+3KaNFBJiWmiod9/zuHNnb/MENc9+5efatbP7T6Nx1ZWJGtRDFxAQ\noAULFujOO++U2+3W1KlTlZCQoEWLFkmSpk+frrvvvlurV69WXFycOnbsqDfeeKPOc51i3Dhp6VIC\nHQAAAJzt0iUpL8+0/HzvfuXnagttLpfUpYsJX1261NyPjjbLCfgKbCEh0jXX2P3una9BPXRNobn2\n0B0/Lt1yi/Tyy2ZdCwAAAMBOxcXSmTOm5eV5t3UFtbw8qbTU3DtWvXlCWVhY7YGNQNY0Gq2HrjWL\njpY++khKTjbfLkyYYHdFAAAAaAnKyqoGMk9Iqx7Wqj+WzCyOYWFm69kPCzNLbvXt6w1qlYNbhw6m\npw3ORKBrgH79pFWrpLFjTVf1t7/NXwYAAAB4lZR4g9fp0/5tz571hi5PMKsc0K67ruZzXbuaYIbW\nhyGXV8EXX5j1LHr1kv74R7NIIQAAAFqWsjJv8Lpc8xx34YI3eHXr5t82NJSZ1FFVXZmIQHeVlJRI\n//Ef0u9+Jz3/vPTDH0qBgXZXBQAAAF8sy8yymJtbM4z5eu70aW/PWbduVVv37lVDWeX9zp0ZwYWG\nI9A1oUOHpCeeMIuPv/CC9MADLX8aVQAAALuVlppesdxcbyCra//MGSkoyBvIPKGstta9Oz1nsA+B\nrolZlrRypfSf/ynt3i09/rg0fbrUs6fdlQEAADjDhQveEFa5nTpVNZh5tufOmXvJKoez6vuVn+va\nVWrf3u53CfiHQGejr7+WFiyQli2T7rlHeuopafhwu6sCAABoOpZlApevgFZbKy31hq8ePbz71Ru9\nZ2gNCHTNQH6+9Je/mElTOnQwyxxMnCgNHsy4agAA4CyWJZ0/X7XHzLOt7TmXq2oQqyukde8uBQfz\nGQnwINA1I+XlUnq69N570j/+YR5PnGgC3siRUtu2dlcIAABao9oCWm2BzeWqGsout9+xo93vEHAu\nAl0zZVnSzp0m3L33nvmf4/33S3fdJY0aZcaBAwAA1Efle9DqCmme/fJyb/jq0YOABjQnBDqHOHhQ\nev996eOPpX/9S4qLk5KTTSPgAQDQupWWVg1olcOYr/2SkprhrK5tx44McQSaKwKdA5WUSFu2SGlp\npm3cSMADAKAlcbulvDz/wllurlkDzTMBSPWQ5qtHjfXPgJaDQNcC+Ap44eFSUpI0dKhpN95oZngC\nAABNz7NQdW0Brfrj/HwpJMQbxMLDaw9r3bubBa2ZxRFonQh0LZDbLe3bZ0Lel1+a7fbtUkSEN+Ql\nJZmQ17mz3dUCAOA8npkcq4exunrRgoJq9pjV9rhrVykgwO53CcAJCHSthNst7d3rDXhffint2GFC\n3g03mNa/v2n9+pnlEwAAaE08E4XUNcSx8nNt2vjuLastrLFQNYDGQKBrxcrKpP37pd27Tdu1y2wP\nHpQiI024qxz04uOlTp3srhoAAP9cuuTfVPuerWex6tpCWvXAxkyOAJoDAh1qKCuTDh3yBjxP27fP\njOGPi5N6967ZgoPtrhwA0JJ5Alr1Vtui1Rcu1JxOv7ZeNBarBuBUBDr4ze2WDh82Ya96O3zY9N75\nCnq9e5sgyD+SAIDKLl6sO5xVf3zhgncmx+rrn/nqRQsJ4d8eAC0fgQ5XhWVJJ054w131wHf+vBQT\nU3djghYAcC7LMlPn5+ZKp0/77knzNM/rJSVVp9qvHs6qPyagAUBNBDo0iXPnpMxMb8vIqPo4M1MK\nDPQd9KKjpZ49TQsL4x9zAGgKpaXSmTPeAOZplR9XDmenT5tZGSuHsO7dq/aoVW8McQSAhiPQoVmw\nLLPmjq+gl5UlnTxpWnGxGb7pCXgREd796o+vucbudwUAzYNnkerTp01Iq7z17FcPaufOmanzu3Xz\nhrLKW1/7QUF2v1MAaH0IdHCUixe94a5yO3Gi5nNBQSbg9ejh+8NH9daxI98UA2j+LlwwASwvz/e2\nckjz7BcVSaGh3oDma1s9sIWGslA1ADgBgQ4tkqfH78SJmsOFfA0bOn3afINdW+gLC5O6dDEfcKpv\ng4IIggCujGWZkJWf7215eTUfnzlTM7SVl5sAFhbme+v5/1blsNali9S2rd3vGgDQGAh0wP8pLq76\njXbl4JeXJxUUmA9Znq1nv7zcG/BqC32ebUiIuWckONhMAuPZ54MW4Cxut5kApLDQ2woKfG89+5WD\nWmGh+TLI82WRp1V+XFto69C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fmLUSAACgoeXlmZk0160zbf9+M1IqMdG0vn35sh11q7JMVKseOj8/Pz377LO6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\nUqDhWJa5T7Z42CovgJW3ffa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vN+3gQTNSKinJtH79+LIdl1d1mahePXQBAQFasmSJxo0bJ7fbrZkzZyo+Pl5L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\nFsAq2z53zgQsT9iqLpyVf4w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5UvrFL6QxY6QnnjDT6QIAAMA53G7p889NuFu/3mwPG2bWx5s4UUpMZP071I5A\n5zB5edLdd5uLb//yF9OdDwAAAOc7c8YscP6vf0kpKdKpU95wl5wsde9ud4Vojgh0DrJ5s/TjH5uF\nL5cuNWO0AQAA0DJ9+6033KWmmrWEJ040bfhwKbBBM16gpSDQOUBpqfT449Kf/yw9/7w0dardFQEA\nAKApXbhgFjZPSTEtI8P02k2caHrx/m/FL7RCBLpmLidHuv126dw56bXXpJgYuysCAACA3bKypA8/\nNOFu3TozuYqn927MGKldO7srRFMh0DVj69ebxSnvvFNatIhudQAAAFTmdktbt3p77/buNTNmTpwo\nTZ4sxcbaXSEaE4GuGSopkX7/e+nll6VXXmHiEwAAAPjv5EnTa7d2rWk9e0rXXy9dd500ejSdBC0N\nga6Zyc6WbrpJCgmR/vEP8w8QAAAAqA/P0ggffCCtXi0dOWKuubvuOtOD162b3RWioQh0zci330rj\nx5tr5n73O9YdAQAAwMWVmSmtWWMCXmqqNGiQt/du4EDJ5bK7QtQVga6Z2LXLfEvy0EPSvffaXQ0A\nAABauqIiE+pWrzYBr7TUBLvrrjOX/HToYHeF8AeBrhnYskX64Q/NsgQzZthdDQAAAFobyzKTqXjC\n3fbt0tVXe3vvmGm9+SLQ2WzdOrNY+EsvmX8sAAAAgN3y8syi5qtXm4lVYmKkG24wnRCJiQzNbE4I\ndDZ65x1p3jxzO3as3dUAAAAAlZWUSJs3SytXSitWmIlWbrjBtLFjpaAguyts3Qh0NnnhBTPxyerV\n0pAhdlcDAAAA1M6ypN27veHu22/NWnc33GDmgwgOtrvC1odAZ4N335V+8QtpwwapXz+7qwEAAADq\n5+hRadUqE/A+/dT02P3wh9KUKVJEhN3VtQ4Euia2d6+5wHTtWmnYMLurAQAAAC6OU6fMZ9yVK6WU\nFCkhwTs0Mz7e7upaLgJdEyookEaMkB54QLrzTrurAQAAABrHhQtmSYQVK0wPXnCw6bm7+WbpiiuY\nVOViItA1EcuSbrpJ6tlTeu45u6sBAAAAmkZpqfTFF9J770lvv23C3s03mzZihBQQYHeFzkagayJL\nlphvKDZulNq1s7saAAAAoOlZlrRrlwl2b70lnT5tOj1uvlm68krCXX0Q6JrAhx9KM2dKW7dK0dF2\nVwMAAAA0D3v2mHD39tvSiRPecHfVVVKbNnZX5wwEukaWnS0NHiy9/rqUlGR3NQAAAEDz9PXXZn3m\nt9+WsrI9F17SAAAXfUlEQVSkG2804e7735cCA+2urvki0DWyBQvMYoxPPWV3JQAAAIAzfPONN9wd\nOuSdUOUHP2Ah84oIdI3o+HGpf39p506GWgIAAAD1cfiwWcf5rbekAwdMsJsxw6x5xzV3BLpG9dBD\nUm4us1oCAAAAF8ORI+ZSptdeM5+zp0834W7IkNa7FAKBrpHk5UlxcWaK1thYu6sBAAAAWpbdu6Xl\ny024a9vWBLsZM6R+/eyurGkR6BrJww+b7uEXX7S7EgAAAKDlsiwpLc0EuzfeMJc6/fjH0q23SlFR\ndlfX+Ah0jaCgQLr0UumTT1rfNwQAAACAXUpKpNRUE+5WrJASE024u+kmqWtXu6trHAS6RvDGG9Ir\nr0gffGB3JQAAAEDrVFQkrV1rwt2HH5rlD6ZPl66/Xurc2e7qLp6aMhFzxtTTrl3S0KF2VwEAAAC0\nXu3bm7Xs3npLysgws2O+9poZknn99ebSqNxcu6tsXA0OdCkpKYqPj1ffvn21dOnSKvf5+c9/rr59\n+yoxMVHbt2+v07HN1e7d0mWX2V0FAAAAAMn0yP30p2YEXUaGGYb5/vtm8sLx46Vly6ScHLurvPga\nFOjcbrfuu+8+paSkaM+ePVq+fLn27t3rs8+aNWt08OBBHThwQH/96181b948v49tzvbsIdABAAAA\nzVFoqAl0774rHTsm3X23ue6uf38zLPPpp6XMTLurvDgaFOjS0tIUFxen2NhYBQUFafr06Vq5cqXP\nPqtWrdLMmTMlSSNHjlR+fr6ys7P9Ora5On/erI/BZCgAAABA89apkxmKuXy5lJ0t/epXZtmxyy+X\nRo+W/vu/pUOH7K6y/hoU6DIzMxUTE1N2Pzo6WpkVom51+2RlZdV6bHP19ddSnz5mLQwAAAAAztC+\nvTRlivTSS6bnbvFiaf9+aeRIMz/GW2/ZXWHdBTbkYJefS7U3x1kqG+LgQel737O7CgAAAAD11bat\nubbummukJ56QNmyQQkLsrqruGhTooqKilJGRUXY/IyND0dHRNe5z9OhRRUdHq7i4uNZjPRYvXly2\nnZSUpKSkpIaU3WCDBknbtpkFDv3MtAAAAADqqLhYys+X8vJqbmfOSBcuVN3On6/+uQsXpIAAE+7a\ntpV+8QsT8OyWmpqq1NRUv/Zt0Dp0JSUl6t+/vzZs2KDIyEiNGDFCy5cvV0JCQtk+a9as0TPPPKM1\na9Zoy5Ytmj9/vrZs2eLXsVLzXYeuXz/p9ddZugAAAACoyYULNYexmgLbuXNSWJjUpUvNLSREatfO\nG8z8bUFBUps2dv+EaldTJmpQD11gYKCeeeYZTZgwQW63W7Nnz1ZCQoKWLVsmSZo7d64mT56sNWvW\nKC4uTp06ddKLL75Y47FOMWWK9Nxz0l//anclAAAAQOOqLZRV13JzzbFVhTLPYxER0oAB1Qc1RsTV\nrEE9dE2hufbQ5eVJV10lzZ4t3X+/3dUAAAAANTt/vn6hLC/PhLLaesmqa8HBhLKGqikTEegaID1d\nGjPGTHV66612VwMAAICW7mKEsq5d6x7KOnUilNmJQNeIdu6Urr1WWrDAtMAGDWIFAABAS0coQ10R\n6BrZ4cNm6OWZM2ZNCwddCggAAIB68CeU5eZW/XhxsW/Qqks4I5S1TgS6JlBaKi1bJv3ud9IDD0i/\n/KWZNQcAAADNU20TfVQXyGq7pqy2gEYoQ10R6JrQoUPSvHnS3r3Sr35leu46drS7KgAAgJbJs05Z\nTeGruufOn6/bkMXyQY1QhqZEoLNBWpq0ZIn0ySfSf/2X9LOfmX/8AAAA8FVSUnsoqy6YedYpq6lX\nrLrnmH0RTkGgs9HevdLSpdKqVdIdd0izZkmDBtldFQAAwMXldnsXiK5rb1lhoRQa6t+1ZBX3YZ0y\ntAYEumYgPd0sRP7Pf5pvkX7yE2nGDCk62u7KAAAADLdbOnXK/yGL5R8/e1bq3Ln2XrGqHg8JkQIC\n7H73QPNFoGtGSkuljz+WXnlFeucdacgQE+5uusn8JwgAANAQpaVSQUHdwpinFRSYcFVTAKsqkHXt\naj7HEMqAxkGga6aKiqTVq6VXX5U2bJBGjZImT5YmTZL69WP4AAAArZVlmeWQKoYuf+4XFJgJ2erT\nUxYWJrVpY/e7B1ARgc4BCgpMqFuzxrT27b3hLimJmTIBAHAayzLXhtU1kOXlmWvR2rWrvVesqsfC\nwqTAQLvfPYCLiUDnMJYlffWVN9xt3y6NHStdc4101VXS0KGscQcAQFMpKqp7T5nnscDA+g1fDAuT\n2ra1+50DaC4IdA6Xny+tWyelpprr7779VhoxwoS7sWPNUM3gYLurBACg+aq4gHRNgazic263b/Cq\nbruq++3b2/3OAbQEBLoWJi9P+vRTadMm07ZvlwYMMOHuqquk4cPN7JlcgwcAaEk80+JXF75qCmVF\nRdUHr9pCWceO/E4FYC8CXQtXVCRt3Wp67z7+WPr8czNsc+hQ6YorvO2SS/iFBACwV00zMNa2feaM\nWausPsGMtcoAOBmBrpWxLCkrS/riC2/bts0MN/GEvKFDTevThymGAQB145nso7YAVtVjp055Z2D0\nN4x5tkND+Z0FoHUi0EGSdOyYb8Dbts38co2Ply67zLddcgm/NAGgpSsqqvvQRc/9oCD/esoqBrWw\nMCb2AoC6ItChWqdOSXv2SLt3+7aCAikhoXLQi44m6AFAc1JSUv11ZbU95nbXLZCV327Xzu53DgCt\nB4EOdZaXVzno7d1rHv/e96S4OKlvX99bwh4A1E9drysr/9jZs2YooidsVRXAqgtlTPYBAM5AoMNF\nc/asdPCgaQcOeG8PHDDfEFcMe3FxUmysFBPDejoAWjbLks6dqxy6auot82x7riurayjr2tVM9sGX\naQDQshHo0CQqhj1P4DtyxEzS0qOH1Lu3t8XG+t7v1MnudwAAUnFx9T1itQ1lDAjwL4RVfI7rygAA\nNSHQwXYlJSbUHTli2uHD3u0jR6T0dBPoyge8qCgpMtI0zzahD4A/PEMY6xrIPOuVhYX5H8bKb3fo\nYPc7BwC0RAQ6NHuWJX33nTfspaebAJiVJWVmerfbtfMNeOW3PbcREXzTDbQUniGMtQWzirenTpkv\ngGoKY9Xdsl4ZAKC5IdChRbAs80GtYsjzbHtuv/vOfCDr2VMKDze3FVv5x0ND+fAGNKaKszDW5VYy\nQau2EFbxsbAwKTDQ3vcNAMDFQqBDq1Jaaj4Ifvdd9S0nx7tdVGSu7/MEvG7dTPN8iKxqOzRUatPG\n7ncKNB3LMtfJlg9bVQWwqrbPnDEByxO2agpnFR9jCCMAAAQ6oEZFRdLx496g5/kgevJk9dunT3un\nCa8Y+sLCzHOhoVLnzlVvd+pEryCalmWZYJWfb4Yj5uf7v52f77uQdHUBrLqgFhrKLIwAADQEgQ64\nyNxu8yG3fNDzhD3Ph+GCAnPraeXvnz9vhoVWF/yCg03r1Mm06rbL36fHsGUpLTU9YmfOeFvF+xVb\nxec9ocxz3rVvb75w8HzpUP62tse6dDHHAwCApkegA5qZ4mLTy1dd4PN8OPe08ver2i4sNL0n5cNe\nx47mA3hDW9u25lqkoCBv8+e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AALSWykqzPp4r4GVlSQcPmlBXPeTFxzOfQkdGoGuD8vKkBx6Q9u41vXIjR/q7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\nZwLfiRPmsWY7ccIEl4AAqVu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VQ17//ma4MQCg9SHQAaiT/HwT1CqGt8JCE9oGDvS2AQPMjJEALg/LMkstVAx5e/aYL0ji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\nh9yyQc8T9jwfhvPzza2nlb1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3262BAwdq/fr1iomJ0ahRo/T6668rKSmp6phTp07pyJEjWrlypUJDQ6sCnT/nXqryAFpe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\nKMiM2Ne1q2lF8RTfx77rdDG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MsLsiAACAtqljR+/smI8/Lp06ZYZnbthgJl25cME7PPOGG6Q+feyuGK0ZC4u3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\n6HnCnufDcGGhufW0ivcvXDD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BAACgfSgtNT13tXvx+vat34vHvXg4Wwh0DvHFF9Kdd0put/SnP0nf+Y7dFQEA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\n3XrCWo8edv9EAAC0DQIdzgr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28cUXZqKTqirTvTIlxd81AgAAQFtZlvT55ybYuUtwMAEPviPQ2cCrr0qPPmrW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\nXf/fe0SECWpRUVKvXv7+yQA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TzfV6ffp4W2ysuQ0OtrtqAEBjQqADcFWcPm1C29693rZvnwlm/fpJ119veh08we2aa0ov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\nZ85Uvn3+vHea8LKhLzjYPNe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5hnfuNAHvww+lZ5812/36mWA3bJh00UWeEhHhn88BAOja2hToCgsLlZCQULsdHx+vDRs2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\naXDcqlWrNG/ePDmdTt1zzz1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UHu7vSmEXBDobOXZMevRR6W9/k378Y+m++6Tevf1dFQAAANqipMTTi7dhg1kI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\ns5AQ01MdGmr2zxbUQkO55gE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D9doh78orpS5dgl0pOgsCnU243WaUpkWLpB/9SJo7l18UAAAAwVZVZUbVrB3y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\neYbx7t3bPMbESKGhwf7uAPu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IlZddJj39tJSYGOyKAAAAECwnT5pwt3mzCXi5uWawFU/Au/pqKS4u2FWivRDo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\nVYDazXNloLFWVmbmefKcnEZ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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file From 172a9cea653189b3eaf6e708abdfe60a42acf246 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 3 Mar 2015 14:34:30 +0100 Subject: [PATCH 10/16] Funcional MK2.5 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Unificadas funciones y variables en inglés, código correctamente comentado. --- .../result_drawer-checkpoint.ipynb | 466 ++++++++++++ .../Genetic_algorithm_files/ambient.py | 101 +-- .../{analice.py => analyze.py} | 41 +- .../Genetic_algorithm_files/cross.py | 20 +- .../Genetic_algorithm_files/genetics.py | 26 +- .../Genetic_algorithm_files/initial.py | 34 +- .../Genetic_algorithm_files/interfaz.py | 21 +- .../Genetic_algorithm_files/main.py | 54 +- .../Genetic_algorithm_files/mutation.py | 23 +- .../Genetic_algorithm_files/selection.py | 17 +- .../Genetic_algorithm_files/testing.py | 49 +- .../Genetic_algorithm_files/transcript.py | 69 +- aeropy/Xfoil_Interaction/README.md | 6 +- aeropy/Xfoil_Interaction/result_drawer.ipynb | 700 ++++++++++-------- 14 files changed, 1105 insertions(+), 522 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb rename aeropy/Xfoil_Interaction/Genetic_algorithm_files/{analice.py => analyze.py} (58%) diff --git a/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb b/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb new file mode 100644 index 0000000..4df0d4b --- /dev/null +++ b/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb @@ -0,0 +1,466 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:a2cae234c199207c88d4df8cf19f19cdb7a268841519e80d49d5905d30ac7851" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "from transcript import *" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "No module named 'transcript'", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtranscript\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mImportError\u001b[0m: No module named 'transcript'" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Result Drawer for the Xfoil Genetic Algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are described some useful functions that can help you to graphically display your results.\n", + "\n", + "Place this notebook in the same folder that the Xfoil and the genetic algorithm files.\n", + "\n", + "\n", + "Here is a list of the included functions:\n", + "\n", + "**profile_read_aero** (generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and return the Cd and Cl.\n", + "\n", + "**profile_read_aero_generic** (root)\n", + "\n", + "Reads a file located in root (ej: calculations/naca6715.txt) and return the Cd and Cl.\n", + "\n", + "**profile_read_alpha** (generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and return the alpha angle and Cl.\n", + "\n", + "**profile_read_alpha_generic** (root)\n", + "\n", + "Reads a file located in root (ej: calculations/naca6715.txt) and return the alpha angle and Cl.\n", + "\n", + "**drawing**(generation, profile_number) \n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and draws it with matplotlib.\n", + "\n", + "**drawing_bezier**(generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and draws it, along with its bezier points with matplotlib. Useful for understanding the genome of a certain profile.\n", + "\n", + "**drawing_polar**(generation, profile_number)\n", + "\n", + "Plots the Cd against the Cl for a certain profile generated by the algorithm\n", + "\n", + "**drawing_polar_compare**(generation_imput, profile_number_imput)\n", + "\n", + "Plots the Cd against the Cl for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", + "\n", + "**drawing_alpha_compare**(generation_imput, profile_number_imput)\n", + "\n", + "Plots the Cl against the alpha for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", + "\n", + "**drawing_polar_compare_generic**(profileroots) and **drawing_alpha_compare_generic**(root)\n", + "\n", + "Do the same as the equivalent non-generic functions, but must be fed with an array of file directions (ej: calculations/naca6715.txt)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_aero (generation, profile_number): \n", + " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", + " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " #print('read_dim = ', read_dim, read_dim.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_aero_generic (root): \n", + " profile_name = root\n", + " data_root = root\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " #print('read_dim = ', read_dim, read_dim.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_alpha (generation, profile_number): \n", + " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", + " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[0,1])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " #print('read_dim = ', read_dim, read_dim.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_alpha_generic (root): \n", + " \n", + " \n", + " datos = np.loadtxt(root, skiprows=12, usecols=[0,1])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " #print('read_dim = ', read_dim, read_dim.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", + " \n", + " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.ylim(-0.15, 0.15)\n", + " plt.xlim(-0.05, 1.05)\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " plt.gca().set_aspect(1)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + '.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_bezier(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " genome_root = 'genome\\generation'+ str(generation) + '.txt'\n", + " \n", + " genome_matrix = np.loadtxt(genome_root, skiprows=1)\n", + " \n", + " genome = genome_matrix[profile_number-1,:]\n", + " \n", + " bezier_points = generador_puntos(genome)\n", + " \n", + " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", + " \n", + " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.ylim(-0.15, 0.15)\n", + " plt.xlim(-0.05, 1.05)\n", + " plt.scatter(bezier_points[:,0],bezier_points[:,1])\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " plt.plot(bezier_points[0:2 , 0] , bezier_points[0:2 , 1])\n", + " plt.plot(bezier_points[2:5 , 0] , bezier_points[2:5 , 1])\n", + " plt.plot(bezier_points[5:8 , 0] , bezier_points[5:8 , 1])\n", + " plt.plot(bezier_points[8:11 , 0] , bezier_points[8:11 , 1])\n", + " plt.plot(bezier_points[11:13 , 0] , bezier_points[11:13 , 1])\n", + " plt.gca().set_aspect(1)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'bezierpoints.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " datos = profile_read_aero(generation, profile_number)\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'polar.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar_compare(generation_imput, profile_number_imput): \n", + " \n", + " num_prof = generation_imput.shape[0]\n", + " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " \n", + " for i in np.arange(0,num_prof,1):\n", + " generation = generation_imput[i]\n", + " profile_number = profile_number_imput[i]\n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " \n", + " \n", + " datos = profile_read_aero(generation, profile_number)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'polar.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_alpha_compare(generation_imput, profile_number_imput): \n", + " \n", + " num_prof = generation_imput.shape[0]\n", + " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " \n", + " ideal = np.array([[0,0],\n", + " [15, (15*np.pi/180)*np.pi*2]])\n", + " \n", + " plt.plot(ideal[:,0], ideal[:,1])\n", + " for i in np.arange(0,num_prof,1):\n", + " generation = generation_imput[i]\n", + " profile_number = profile_number_imput[i]\n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " \n", + " \n", + " datos = profile_read_alpha(generation, profile_number)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'alpha.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar_compare_generic(profileroots): \n", + " \n", + " num_prof = profileroots.shape[0]\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.rc('font', size = 20)\n", + " plt.title ('Profile comparison')\n", + " \n", + " for i in np.arange(0,num_prof,1):\n", + " \n", + " \n", + " profile_root = profileroots[i]\n", + " \n", + " \n", + " \n", + " datos = profile_read_aero_generic(profile_root)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " plt.legend(loc = 2, fontsize =14) \n", + " plt.grid()\n", + " plt.minorticks_on()\n", + " plt.xlabel('Lift coefficient') \n", + " plt.ylabel('Drag coefficient')\n", + " nombre_grafico = 'graficos\\comparepolar.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_alpha_compare_generic(root): \n", + " \n", + " num_prof = root.shape[0]\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.rc('font', size = 20)\n", + " plt.title ('profile comparison')\n", + " \n", + " ideal = np.array([[0,0],\n", + " [15, (15*np.pi/180)*np.pi*2]])\n", + " \n", + " plt.plot(ideal[:,0], ideal[:,1], label = ' ideal, cl alpha = 2$\\pi$')\n", + " for i in np.arange(0,num_prof,1):\n", + " \n", + " profile_root = root[i]\n", + " \n", + " \n", + " \n", + " datos = profile_read_alpha_generic(profile_root)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " plt.legend(loc = 2, fontsize =14) \n", + " plt.grid() \n", + " plt.minorticks_on()\n", + " plt.xlabel('Attack angle') \n", + " plt.ylabel('Lift coefficient')\n", + " nombre_grafico = 'graficos\\compare_alpha.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py index 4b8b5ca..4b417cd 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py @@ -7,31 +7,37 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. - -''' - +This script is the ambient subprogram. Its objective is to calculate the +Mach and Reynolds numbers that XFoil uses to calculate the +aerodynamics of airfoils. +This subprogram consist mainly in models for the International Standard Atmosphere +and an equivalent atmosphere model for Mars. +''' import numpy as np - - -def ventana (x, inicio=0, fin=1, f=1.): - _1 = (np.sign(x-inicio)) - _2 = (np.sign(-x+fin)) +def window (x, start=0, end=1, f=1.): + '''Returns a function f between 'start' and 'endn'. + Returns 0 outside this interval''' + _1 = (np.sign(x-start)) + _2 = (np.sign(-x+end)) return 0.25 * (_1+1) * (_2+1) * f -def etc (x, inicio=0, f=1.): - _1 = (np.sign(x-inicio)) +def etc (x, start=0, f=1.): + '''Returns 0 until 'start', returns 'f' after. + ''' + _1 = (np.sign(x-start)) return 0.5 * (_1+1) * f def earth_conditions(): + + '''Returns an array of atmospheric and physical data from which the complete + model of the atmosphere can be built. + ''' heights = np.array([-1.000, 11.019, 20.063, @@ -43,7 +49,7 @@ def earth_conditions(): 90.000]) - # an es el gradiente válido entre H(n-1) y H(n) + temp_gradient = np.array([-6.49, 0, 0.99, @@ -72,8 +78,8 @@ def earth_conditions(): return conditions -def temperatura(h, conditions, dT = 0): - '''Calcula la temperatura a una altura h en Km sobre el nivel del mar''' +def temperature(h, conditions, dT = 0): + '''Calculates the value for temperature in Kelvin at a certain altitude''' grad = 0 heights = conditions[0] gradient = conditions[1] @@ -84,37 +90,35 @@ def temperatura(h, conditions, dT = 0): for layer in np.arange(0, atm_layer-1,1): increase = temp_points[layer] + gradient[layer] * (h - heights[layer]) - grad = grad + ventana(h, heights[layer],heights[layer+1], increase) + grad = grad + window(h, heights[layer],heights[layer+1], increase) grad = grad + etc(h, heights[atm_layer-1],temp_points[atm_layer-1] + gradient[atm_layer-1]*(h - heights[atm_layer-1])) return grad + dT -def segmento_presion_1(z, pi, z0, dT, conditions): - '''calcula la presión en un segmento de atmósfera de temperatura constante''' +def pressure_segment_1(z, pi, z0, dT, conditions): + '''Calculates pressure throught a constant temperature segment of the atmosphere''' g = conditions[3] R = conditions[4] radius = conditions[5] h = (radius * z) /(radius + z) h0 = (radius * z0)/(radius + z0) - _ = 1000*(h-h0) * g / (R * temperatura(z, conditions, dT)) + _ = 1000*(h-h0) * g / (R * temperature(z, conditions, dT)) return pi * np.e ** -_ -def segmento_presion_2(z, pi, Ti, a, dT, conditions): - '''calcula la presión en un segmento de atmósfera con gradiente de temperatura "a" ''' +def pressure_segment_2(z, pi, Ti, a, dT, conditions): + '''Calculates pressure throught a variant temperature segment of the atmosphere ''' g = conditions[3] R = conditions[4] _ = g / (a*R/1000) - return pi * (temperatura(z, conditions, dT)/(Ti + dT)) ** -_ + return pi * (temperature(z, conditions, dT)/(Ti + dT)) ** -_ -def presion (h, conditions, dT = 0): - '''Calcula la presion en Pa a una altura h en m sobre el nivel del mar''' +def pressure (h, conditions, dT = 0): + '''Calculates the value for pressure in Pascal at a certain altitude''' heights = conditions[0] gradient = conditions[1] temp_points = conditions[2] atm_layer = gradient.shape[0] - #Primero, calculamos la presion de cada punto de cambio de capa para la condición de dT pedida - #Suponemos que la presión es siempre constante a 101325 Pa a nivel del mar pressure_points = np.zeros([atm_layer]) @@ -122,12 +126,12 @@ def presion (h, conditions, dT = 0): for layer in np.arange(1, atm_layer, 1): if (abs(gradient[layer-1]) < 1e-8): - pressure_points[layer] = segmento_presion_1(heights[layer], + pressure_points[layer] = pressure_segment_1(heights[layer], pressure_points[layer - 1], heights[layer - 1], dT, conditions) else: - pressure_points[layer] = segmento_presion_2(heights[layer], + pressure_points[layer] = pressure_segment_2(heights[layer], pressure_points[layer - 1], temp_points[layer - 1], gradient[layer-1], @@ -139,24 +143,24 @@ def presion (h, conditions, dT = 0): grad = 0 for layer in np.arange(1, atm_layer, 1): if (abs(gradient[layer-1]) < 1e-8): - funcion = segmento_presion_1(h, + funcion = pressure_segment_1(h, pressure_points[layer - 1], heights[layer - 1], dT, conditions) else: - funcion = segmento_presion_2(h, + funcion = pressure_segment_2(h, pressure_points[layer - 1], temp_points[layer - 1], gradient[layer-1], dT, conditions) - grad = grad + ventana(h, heights[layer-1], heights[layer], funcion) + grad = grad + window(h, heights[layer-1], heights[layer], funcion) if (abs(gradient[layer-1])< 10e-8): - funcion = segmento_presion_1(h, + funcion = pressure_segment_1(h, pressure_points[layer - 1], heights[layer - 1], dT, conditions) else: - funcion = segmento_presion_2(h, + funcion = pressure_segment_2(h, pressure_points[layer - 1], temp_points[layer - 1], gradient[layer-1], @@ -166,13 +170,16 @@ def presion (h, conditions, dT = 0): return grad -def densidad(h, conditions, dT = 0): - '''Calcula la densidad a una altura h en m sobre el nivel del mar''' +def density(h, conditions, dT = 0): + '''Calculates the value for density in Kg/m3 at a certain altitude''' R = conditions[4] - return presion(h, conditions, dT)/(R * temperatura(h, conditions, dT)) + return pressure(h, conditions, dT)/(R * temperature(h, conditions, dT)) def mars_conditions(): + '''Returns an array of atmospheric and physical data from which the complete + model of the atmosphere can be built. + ''' heights = np.array([-8.3, 8.85, 30]) @@ -200,7 +207,10 @@ def mars_conditions(): -def viscosidad(temp, planet): +def viscosity(temp, planet): + '''Calculates the value for viscosity in microPascal*second + for a certain temperature''' + if (planet == 'Earth'): c = 120 lamb = 1.512041288 @@ -213,16 +223,20 @@ def viscosidad(temp, planet): def Reynolds(dens, longitud, vel, visc): + '''Calculates the Reynolds number''' re = 1000000 * dens * longitud * vel / visc return re def aero_conditions(ambient_data): + '''Given a certain conditions, return the value of the Mach and Reynolds + numbers, in that order. + ''' (planet, chord, height, speed_type, speed) = ambient_data planet_dic = {'Mars':mars_conditions(), 'Earth':earth_conditions()} - sound = (1.4 *presion(height, planet_dic[planet]) / densidad(height,planet_dic[planet]))**0.5 + sound = (1.4 *pressure(height, planet_dic[planet]) / density(height,planet_dic[planet]))**0.5 if (speed_type == 'mach'): mach = speed @@ -234,14 +248,9 @@ def aero_conditions(ambient_data): print('error in the data, invalid speed parameter') - - re = Reynolds(densidad(height, planet_dic[planet]), chord, vel, viscosidad(temperatura(height, planet_dic[planet]), planet)) + visc = viscosity(temperature(height, planet_dic[planet]), planet) + re = Reynolds(density(height, planet_dic[planet]), chord, vel, visc) return [mach, re] -# -#ambient_data = ('Earth', 03.0003, 11, 'speed', 30.1) -# -#result = aero_conditions(('Earth', 0.03, 11, 'mach', 0.1)) -#print(result) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py similarity index 58% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py rename to aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py index 0eabbf9..132e367 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py @@ -7,33 +7,25 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the analysis subprogram. Its objective is to assign a score to +every airfoil by reading the aerodynamic characteristics that XFoil +has calculated. ''' -import subprocess -import sys -import os -import interfaz as interfaz + import numpy as np -import initial as initial -#generation = 0 -#starting_profiles = 20 -# -#genome = initial.start_pop(starting_profiles) -# -#interfaz.xfoil_calculate_population(generation,genome) -# -#num_pop = genome_matrix.shape[0] def pop_analice (generation, num_pop): + '''For a given generation and number of airfoils, returns an array which + contains the maximun Lift Coefficient and Maximum Aerodinamic Efficiency + for every airfoil. + ''' pop_results = np.zeros([num_pop,2]) for profile_number in np.arange(1,num_pop+1,1): pop_results[profile_number - 1, :] = profile_analice (generation, profile_number) @@ -41,7 +33,12 @@ def pop_analice (generation, num_pop): return pop_results -def profile_analice (generation, profile_number): +def profile_analice (generation, profile_number): + '''For a given generation and profile, searches for the results of the + aerodynamic analysis made in Xfoil. Then, searches for the maximum + values of the Lift Coefficient and Aerodynamic Efficiency and returns them + as an 1x2 array. + ''' profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) data_root = "aerodata\data" + profile_name + '.txt' datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2]) @@ -60,14 +57,18 @@ def profile_analice (generation, profile_number): return np.array([clmax , maxefic]) def adimension(array): + '''Adimensionalyzes an array with its maximun value + ''' max_value = max(array) adim = array / max_value return adim -def score(generation, num_pop): - +def score(generation, num_pop, weights): + '''For a given generation, number of airfoils and weight parameters, returns + an array of the scores of all airfoils. + ''' pop_results = pop_analice (generation, num_pop) cl_score = adimension(pop_results[:,0]) efic_score = adimension(pop_results[:,1]) - total_score = 0.3 * cl_score + 0.7 * efic_score + total_score = weights[0] * cl_score + weights[1] * efic_score return total_score diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py index aeb5b10..d2f5cf6 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py @@ -7,25 +7,27 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the cross subprogramme. Its objective is to generate +a whole new population genome from the genome of the previous generation +winners (parents). + +In order to eliminate the chance of randomly getting worse results, +the parents are preserved as the first elements of the new population. ''' -import subprocess -import sys -import os -import interfaz as interfaz -import numpy as np -import initial as initial +import numpy as np def cross(parents, num_pop): + '''Generates a population of (num_pop) airfoil genomes by mixing randomly + the genomes of the given parents. + The parents are preserved as the first elements of the new population. + ''' children = np.zeros([num_pop, 16]) num_parents = parents.shape[0] children[0:num_parents] = parents diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py index 3fb47f2..ff6c498 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py @@ -7,21 +7,18 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the Genetic Step Subprograme. After the XFoil analysis of the +'N' generation, this subprogramme will calculate the population of the +'N+1' generation. ''' -import subprocess -import sys + import os -import interfaz as interfaz import numpy as np -import initial as initial -import analice as analice +import analyze as analyze import selection as selection import cross as cross import mutation as mutation @@ -29,13 +26,14 @@ -def genetic_step(generation,num_parent): - +def genetic_step(generation,num_parent, weights): + '''Returns the genome of the (n+1)generation + ''' genome_parent_root = 'genome\generation'+ str(generation) + '.txt' genome = np.loadtxt(genome_parent_root, skiprows=1) num_pop = genome.shape[0] - scores = analice.score(generation,num_pop) + scores = analyze.score(generation,num_pop, weights) parents = selection.selection(scores, genome, num_parent) children = cross.cross(parents, num_pop) children = mutation.mutation(children, generation, num_parent) @@ -48,13 +46,13 @@ def genetic_step(generation,num_parent): os.remove(genome_root) except : pass - archivo = open(genome_root, mode = 'x') - archivo.write(title + '\n') + genome_file = open(genome_root, mode = 'x') + genome_file.write(title + '\n') for profile in np.arange(0, profile_number, 1): line = '' for gen in np.arange(0, 16,1): line = line + str(children[profile, gen]) +' ' line = line + '\n' - archivo.write(line) + genome_file.write(line) return children \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py index d8b5deb..52e1e5e 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -7,28 +7,24 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script requires from the main program a serie of profile genomes. -The number of profiles is "num_pop" -This subprogram, first, uses the script "transcript.py" in order to translate -the genomes into a series of points that Xfoil can understand. - -Then, sends them to Xfoil, and ask it to analize them. - -At last, it sends back to the main program the results obtained. +This script creates the initial population for the genetic algorithm. +It does so by adding a random deviation to a default profile genome. ''' -import subprocess -import sys + import os -import interfaz as interfaz import numpy as np import testing as test def start_pop(pop_num): + '''Creates a randomly generated population of the size (pop_num) + ''' + + genome = np.zeros([pop_num,16]) genes = np.array([150*np.pi/180, #ang s1 0.2, #dist s1 @@ -47,9 +43,8 @@ def start_pop(pop_num): 190*np.pi/180, #ang s2 0.2]) #dist s2 -# generation = 0 -#profile_number = 1 - genome = np.zeros([pop_num,16]) + + gen_deviation = np.array([10*np.pi/180, #ang s1 0.15, #dist s1 @@ -72,14 +67,14 @@ def start_pop(pop_num): for profile in np.arange(0, pop_num, 1): deviation = 0.7 * np.random.randn(16) * gen_deviation genome[profile,:] = genes + deviation - while not(test.test_perfil(genome[profile,:])): + while not(test.airfoil_test(genome[profile,:])): + + # Here we check tat our airfoil actually makes sense + deviation = 0.7 * np.random.randn(16) * gen_deviation genome[profile,:] = genes + deviation -# for gen in np.arange(0,16,1): -# genome[profile, gen] = genome[profile, gen] * (1 + 0.1 * np.random.randn()) -# -# genome[1,:] = genes + profile_number = genome.shape[0] genome_root = 'genome\generation0.txt' @@ -102,4 +97,3 @@ def start_pop(pop_num): return genome -#interfaz.xfoil_calculate_population(generation,genome) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index 18b5b84..a271252 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -21,18 +21,19 @@ import subprocess -import sys import os import transcript as trans import numpy as np import ambient as ambient -def xfoil_calculate_profile(generation,profile_number, genome, ambient_data): +def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aero_domain): + + '''Starts Xfoil and analyzes the given airfoil. Saves the results. + ''' profile_root = 'profiles\gen' + str(generation) + '\profile' + str(profile_number) + '.txt' profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) - genome_root = 'genome\generation'+ str(generation) + '.txt' aerodynamics = ambient.aero_conditions(ambient_data) @@ -46,9 +47,9 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data): "aerodata\data" + profile_name + '.txt', '', 'aseq', - '0', - '20', - '1', + str(aero_domain[0]), + str(aero_domain[1]), + str(aero_domain[2]), '', 'quit'] @@ -89,13 +90,17 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data): for line in p.stdout.readlines(): print(line.decode(), end='') -def xfoil_calculate_population(generation, ambient_data): +def xfoil_calculate_population(generation, ambient_data, aero_domain): + '''Given a generation number and ambiental conditions, reads the file + which contains the genome information of the generation, and uses xfoil to + analyze each airfoil. + ''' genome_root = 'genome\generation'+ str(generation) + '.txt' genome_matrix = np.loadtxt(genome_root, skiprows=1) num_pop = genome_matrix.shape[0] for profile_number in np.arange(1,num_pop+1,1): - xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:], ambient_data) + xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:], ambient_data, aero_domain) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index a005024..b91a3f0 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -15,8 +15,7 @@ -import subprocess -import sys + import os import interfaz as interfaz import numpy as np @@ -29,24 +28,57 @@ if not os.path.exists('genome'): os.makedirs('genome') + +####---------Primary Variables----- + + generation = 0 -starting_profiles = 30 -total_generations = 10 -num_parent = 3 -ambient_data = ('Earth', 0.3, 11, 'mach', 0.5) +airfoils_per_generation = 30 +total_generations = 15 +num_parent = 4 +ambient_data = ('Earth', 0.1, 3, 'mach', 0.1) + +# We give the algorithm the conditions at wich we want to optimize our airofil +# through the "ambient data" tuple. The first position is for the planet, +# only 'Mars' and 'Earth are available at the moment. +# The second position is for the lenght of the airfoil, in metres. +# The third is for the flying height, in kilometers, above sea level +# on Earth and avobe the zero reference in Mars. +# The fourth especifies the type of speed we are introducing, and can +# have the values 'speed' or 'mach'. +# The last one is for the value of the parameter selected in the previous one. + + +####--------Secondary Variables------ +#-- Analysis domain +start_alpha_angle = 0 +finish_alpha_angle = 20 +alpha_angle_step = 1 + +aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) +#-- Optimization objectives + +lift_coefficient_weight = 0.3 +efficiency_weight = 0.7 + +weighting_parameters = (lift_coefficient_weight, efficiency_weight) + +####--- Starting the population, analysis of the starting population + + +genome = initial.start_pop(airfoils_per_generation) -genome = initial.start_pop(starting_profiles) +interfaz.xfoil_calculate_population(generation, ambient_data, aero_domain) -interfaz.xfoil_calculate_population(generation, ambient_data) +##--- Genetic Algorithm -#arange antes en 0 for generation in np.arange(0,total_generations,1): - genome = genetics.genetic_step(generation,num_parent) + genome = genetics.genetic_step(generation,num_parent, weighting_parameters) - interfaz.xfoil_calculate_population(generation + 1, ambient_data) + interfaz.xfoil_calculate_population(generation + 1, ambient_data, aero_domain) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py index 158669e..02b14f8 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py @@ -7,23 +7,26 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the Mutation subprogramme. Its objective is to add diversity +to the population, in order to avoid stagnation in a not good solution. + +Intensity of the mutation is propotional to the square root of the generation +number, in order to refine the search for an optimal solution. + +The parents of the generation are left unmutated in order to avoid the chance +of decreasing the quality obtained in a previous step. ''' -import subprocess -import sys -import os -import interfaz as interfaz + import numpy as np -import initial as initial import testing as test def mutation(children, generation, num_parent): + '''Given a genome, mutates it in order to have a diverse population + ''' coeff = 0.5 / (1 + generation**0.5) gen_deviation = np.array([10*np.pi/180, #ang s1 0.15, #dist s1 @@ -50,11 +53,11 @@ def mutation(children, generation, num_parent): deviation = coeff * np.random.randn(16) * gen_deviation children_n[i,:] = children[i,:] + deviation n = 0 - while not(test.test_perfil(children_n[i,:])): + while not(test.airfoil_test(children_n[i,:])): n = n + 1 deviation = coeff * np.random.randn(16) * gen_deviation children_n[i,:] = children[i,:] + deviation - print('mutando perfil viable, intento',n) + print('mutating into viable airfoil, try #',n) children[i,:] = children_n[i,:] return children diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py index a71f504..ebaf785 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py @@ -7,23 +7,24 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the selection subprogramme. Given a population and a score array, +it just selects the (num_parent) best and ignores the rest. + +This has its own subprogramme because other genetic algorithms have +other selection parameters, so this can be easily found and changed here +if you wanted. ''' -import subprocess -import sys -import os -import interfaz as interfaz + import numpy as np -import initial as initial def selection(score, genome, num_parent): + '''Select the genome of the (num_parent) best airfoils. + ''' invscore = 1- score positions = np.argsort(invscore) parents = np.zeros([num_parent,16]) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py index 500feb6..2e1bb3a 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py @@ -7,24 +7,22 @@ This is a submodule for the genetic algorithm that is explained in https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing -This script is the main program. It will call the different submodules -and manage the data transfer between them in order to achieve the -genetic optimization of the profile. +This script is the testing subprogramme. Its objective is to check if a +new airfoil genome would make a real, viable one. In ordr to do so, +it runs 3 test in increasing level of complexity. ''' -import subprocess -import sys -import os -import interfaz as interfaz + import transcript as transcript import numpy as np -import initial as initial from scipy import interpolate -def prueba_creciente(array): +def increasing_test(array): + '''Checks wheter a certain array is monotone increasing. + ''' numele = np.shape(array)[0] if np.array_equal(np.argsort(array), np.arange(0, numele, 1)): return True @@ -32,19 +30,25 @@ def prueba_creciente(array): return False -def prueba_decreciente(array): +def decreasing_test(array): + '''Checks wheter a certain array is monotone decreasing. + ''' numele = np.shape(array)[0] if np.array_equal(np.argsort(array), np.arange(numele-1, -1, -1)): return True else: return False -def prueba_perfil_x (genome): +def x_coordinate_test(genome): + '''Cheks wheter the x coordinate of the points that a given genome + would generate get smaller at the airfoil upper side, as we go + from the tail to the nose, and viceversa for the lower side. + ''' perfil = transcript.decode_genome(genome[:]) - test1 = prueba_decreciente(perfil[0:25, 0]) - test2 = prueba_decreciente(perfil[25:50, 0]) - test3 = prueba_creciente(perfil[50:75, 0]) - test4 = prueba_creciente(perfil[75:100, 0]) + test1 = decreasing_test(perfil[0:25, 0]) + test2 = decreasing_test(perfil[25:50, 0]) + test3 = increasing_test(perfil[50:75, 0]) + test4 = increasing_test(perfil[75:100, 0]) if (test1 * test2 * test3 * test4): return True @@ -52,6 +56,9 @@ def prueba_perfil_x (genome): return False def test_simple (genome): + '''Test simple genome characteristics, such as the upper surface point + being above the lower surface point. + ''' test1 = (genome[14] - genome [0]) > (5*np.pi/180) test2 = genome[3] > genome[10] test3 = genome[7] > 0.01 @@ -67,6 +74,12 @@ def test_simple (genome): return False def collision_test(genome): + '''Interpolates two curves for the upper and the lower surfaces. + Then, check that the distance between them is larger than 0.01 times + the chord. + ''' + + perfil = transcript.decode_genome(genome[:]) extrax = np.append(perfil[40:25:-1,0],perfil[24:5:-1,0]) @@ -83,9 +96,11 @@ def collision_test(genome): return (very > 0.01 ). all() -def test_perfil (genome): +def airfoil_test(genome): + '''Run 3 test of increasing complexity to detect inviable airfoils + ''' if test_simple(genome): - if prueba_perfil_x(genome): + if x_coordinate_test(genome): if collision_test(genome): return True else: diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py index 3b96bc5..d0eb06b 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py @@ -8,8 +8,8 @@ in order to get a xfoil-compatible description of a profile from the genetic information provided by the interface script. -When this spript is run independly, will return an draw of a profile -calculated from an example genome. +When this spript is run independly, and the final lines ar de-commented, + will return an draw of a profile calculated from an example genome. When used inside the algorithm, the interface will import these functions and call the "decode_genome" function with an array of genes. The function shall @@ -41,7 +41,7 @@ def bernstein(u): return b -def punto_pendiente(a,dist,ang): +def point_slope(a,dist,ang): '''Given a point "a", calculates the coordinates of another one placed at a distance "dist" in the direction"ang" (in radians). ''' @@ -49,41 +49,41 @@ def punto_pendiente(a,dist,ang): return punto -def generador_puntos(genes): +def point_generator(genes): '''This function is the first step decoding the profile genome: it generates the 13x2 coordinates of the points used in the 4 bezier curves that describe the profile. ''' - puntos = np.zeros([13,2]) - puntos[0,:] = [1,0] - puntos[1,:] = punto_pendiente([1,0],genes[1],genes[0]) - puntos[2,:] = punto_pendiente([genes[2],genes[3]],genes[5],genes[4]) - puntos[3,:] = [genes[2],genes[3]] - puntos[4,:] = punto_pendiente([genes[2],genes[3]],genes[6],genes[4]+np.pi) - puntos[5,:] = [0, genes[7]] - puntos[6,:] = [0,0] - puntos[7,:] = [0, -genes[8]] - puntos[8,:] = punto_pendiente([genes[9],genes[10]], genes[12], genes[11]+np.pi) - puntos[9,:] = [genes[9],genes[10]] - puntos[10,:] = punto_pendiente([genes[9],genes[10]], genes[13], genes[11]) - puntos[11,:] = punto_pendiente([1,0], genes[15], genes[14]) - puntos[12,:] = [1,0] - return puntos - -def bezier(num, puntos_control): + points = np.zeros([13,2]) + points[0,:] = [1,0] + points[1,:] = point_slope([1,0],genes[1],genes[0]) + points[2,:] = point_slope([genes[2],genes[3]],genes[5],genes[4]) + points[3,:] = [genes[2],genes[3]] + points[4,:] = point_slope([genes[2],genes[3]],genes[6],genes[4]+np.pi) + points[5,:] = [0, genes[7]] + points[6,:] = [0,0] + points[7,:] = [0, -genes[8]] + points[8,:] = point_slope([genes[9],genes[10]], genes[12], genes[11]+np.pi) + points[9,:] = [genes[9],genes[10]] + points[10,:] = point_slope([genes[9],genes[10]], genes[13], genes[11]) + points[11,:] = point_slope([1,0], genes[15], genes[14]) + points[12,:] = [1,0] + return points + +def bezier(num, control_points): '''This function calculates a Bezier curve using as control points those given in the imput, with a resolution of "num" points. ''' - parametro_u = np.linspace(0,1,num) + parameter_u = np.linspace(0,1,num) curva = np.zeros([num,2]) - for contador in np.arange(num): - _ = bernstein(parametro_u[contador])*puntos_control - curva[contador,] = sum (_) + for counter in np.arange(num): + _ = bernstein(parameter_u[counter])*control_points + curva[counter,] = sum (_) return curva -def profile(num, puntos_control): +def profile(num, control_points): '''This is the second stage of the decoding process. This will return a line made of 4 bezier curves whose control points @@ -93,18 +93,19 @@ def profile(num, puntos_control): perfil = np.zeros([(4*num), 2]) - perfil[0:num,:] = bezier(num,puntos_control[0:4,:]) - perfil[num:2*num,:] = bezier(num,puntos_control[3:7,:]) - perfil[2*num:3*num,:] = bezier(num,puntos_control[6:10,:]) - perfil[3*num:4*num,:] = bezier(num,puntos_control[9:13,:]) + perfil[0:num,:] = bezier(num,control_points[0:4,:]) + perfil[num:2*num,:] = bezier(num,control_points[3:7,:]) + perfil[2*num:3*num,:] = bezier(num,control_points[6:10,:]) + perfil[3*num:4*num,:] = bezier(num,control_points[9:13,:]) return perfil def decode_genome(genome): - ''' + '''Calculates the x and y coordinates of 100 points describing + an airfoil from the given genome ''' num = 25 epsilon = 0.001 - profile_points = profile(num, generador_puntos(genome)) + profile_points = profile(num, point_generator(genome)) profile_points[0, 1] = epsilon profile_points[4*num-1,1] = -epsilon @@ -112,8 +113,10 @@ def decode_genome(genome): ''' The following code contains an example and will be used with test purposes only, -when this script is run whole and won't be used in the standard function of the +when this script is run alone, and won't be used in the standard function of the genetic algorithm. + +De-Comment the lines in order to use them. ''' # #import matplotlib.pyplot as plt diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index c551565..bb6a1fa 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -27,7 +27,7 @@ Genetic algorithm modules: -genetics --analice +-analyze -cross @@ -41,11 +41,7 @@ All 11 files must be in the same folder as xfoil.exe. Execute the main.py file in order to start the algorithm. The main control paraparameters are defined here, and secondary parameters will be automatically calculated. -It will randomly generate 30 profiles and test if they are viable, those wich aren't will be regenerated until they are. -They will be analiced with xfoil, and scored and sorted depending on the values they achieve. - -Then, the best 3 will be selected as parents for the next generation. More info: https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing diff --git a/aeropy/Xfoil_Interaction/result_drawer.ipynb b/aeropy/Xfoil_Interaction/result_drawer.ipynb index b7679c3..e6d8709 100644 --- a/aeropy/Xfoil_Interaction/result_drawer.ipynb +++ b/aeropy/Xfoil_Interaction/result_drawer.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:e927dd9f6de7a4a31613a90d6653167c6e56dc97e4d104555f737435e268faa2" + "signature": "sha256:2e17f8dbbc7de9bb287937db82fd91e0cac6bf14382e705ed36a69f75776b27e" }, "nbformat": 3, "nbformat_minor": 0, @@ -19,19 +19,160 @@ "language": "python", "metadata": {}, "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "from transcript import *" + ], + "language": "python", + "metadata": {}, + "outputs": [], "prompt_number": 2 }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Result Drawer for the Xfoil Genetic Algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are described some useful functions that can help you to graphically display your results.\n", + "\n", + "Place this notebook in the same folder that the Xfoil and the genetic algorithm files.\n", + "\n", + "\n", + "Here is a list of the included functions:\n", + "\n", + "**profile_read_aero** (generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and return the Cd and Cl.\n", + "\n", + "**profile_read_aero_generic** (root)\n", + "\n", + "Reads a file located in root (ej: calculations/naca6715.txt) and return the Cd and Cl.\n", + "\n", + "**profile_read_alpha** (generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and return the alpha angle and Cl.\n", + "\n", + "**profile_read_alpha_generic** (root)\n", + "\n", + "Reads a file located in root (ej: calculations/naca6715.txt) and return the alpha angle and Cl.\n", + "\n", + "**drawing**(generation, profile_number) \n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and draws it with matplotlib.\n", + "\n", + "**drawing_bezier**(generation, profile_number)\n", + "\n", + "Searches for the file of a certain profile generated by the algorithm and draws it, along with its bezier points with matplotlib. Useful for understanding the genome of a certain profile.\n", + "\n", + "**drawing_polar**(generation, profile_number)\n", + "\n", + "Plots the Cd against the Cl for a certain profile generated by the algorithm\n", + "\n", + "**drawing_polar_compare**(generation_imput, profile_number_imput)\n", + "\n", + "Plots the Cd against the Cl for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", + "\n", + "**drawing_alpha_compare**(generation_imput, profile_number_imput)\n", + "\n", + "Plots the Cl against the alpha for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", + "\n", + "**drawing_polar_compare_generic**(profileroots) and **drawing_alpha_compare_generic**(root)\n", + "\n", + "Do the same as the equivalent non-generic functions, but must be fed with an array of file directions (ej: calculations/naca6715.txt)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_aero (generation, profile_number): \n", + " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", + " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, { "cell_type": "code", "collapsed": false, "input": [ - "import os" + "def profile_read_aero_generic (root): \n", + " profile_name = root\n", + " data_root = root\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_alpha (generation, profile_number): \n", + " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", + " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", + " datos = np.loadtxt(data_root, skiprows=12, usecols=[0,1])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def profile_read_alpha_generic (root): \n", + " \n", + " \n", + " datos = np.loadtxt(root, skiprows=12, usecols=[0,1])\n", + " \n", + " read_dim = np.array(datos.shape)\n", + " if ((read_dim.shape[0]) != 2):\n", + " return np.array ([0,0])\n", + " else:\n", + " return datos" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, { "cell_type": "code", "collapsed": false, @@ -62,333 +203,249 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 6 + "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ - "for i in np.arange(0,19,1):\n", - " drawing(i,1)\n", - " drawing(i,2)" + "def drawing_bezier(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " genome_root = 'genome\\generation'+ str(generation) + '.txt'\n", + " \n", + " genome_matrix = np.loadtxt(genome_root, skiprows=1)\n", + " \n", + " genome = genome_matrix[profile_number-1,:]\n", + " \n", + " bezier_points = generador_puntos(genome)\n", + " \n", + " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", + " \n", + " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.ylim(-0.15, 0.15)\n", + " plt.xlim(-0.05, 1.05)\n", + " plt.scatter(bezier_points[:,0],bezier_points[:,1])\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " plt.plot(bezier_points[0:2 , 0] , bezier_points[0:2 , 1])\n", + " plt.plot(bezier_points[2:5 , 0] , bezier_points[2:5 , 1])\n", + " plt.plot(bezier_points[5:8 , 0] , bezier_points[5:8 , 1])\n", + " plt.plot(bezier_points[8:11 , 0] , bezier_points[8:11 , 1])\n", + " plt.plot(bezier_points[11:13 , 0] , bezier_points[11:13 , 1])\n", + " plt.gca().set_aspect(1)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'bezierpoints.png'\n", + " plt.savefig(nombre_grafico)" ], "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:423: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_num_figures`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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0ySfSLbcQ5gAAAAA7WJb06afSwoVmCNSkSSbc9etnd83OH8bQNZH8fHPB/OMf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\n5Sn9mb4fwPlGoEONtm0zrXY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C3LffmjDXrp3VFQEAAADWOH5cmjZNunhReucdKTjY6ooaHoZcNiB2u/TQQ4Q5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\nmjWzumIAcF1nz5YOZydOVBz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2YPp0M8mKEzVrC11aWpoSEhIUHx+vhx9+uNZjbr/9dsXHxyspKUkbNmxo0Lkt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\nNG+JjDSTkABAS0Sgg988Hmn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6SHr6aWnUKKlGDburAgAAgB2ysszwy2nTpOuvl556Smrd2u6qUFLltm2Bp6en\n5syZoz59+qht27a68847FR4ertjYWMXGxkqS+vfvr8svv1zBwcGKiYnRq6++Kkk6fPiwevXqpQ4d\nOqhbt266+eabzwtzcF9WlvTqq1J4uJkb98svUnQ0YQ4AAKA68/SUYmLMGgpt2pg1FR58UDpwwO7K\ncKmwsXgVsGaNNH681KSJ9PLLUvv2dlcEAACAyujYMWnWLOlf/5JGjDAL5zVvbndVuJhy3Vgc9klM\nlIYNk4YPN8Msv/iCMAcAAIALa9pUmjnTrIaZkWEWz/vLX6S0NLsrQ2kR6BwoM1P6+9+lDh2kkBCz\nn9wdd7AxOAAAANzj5yfNmSN9951ZDT0kxLy/ZA875yHQOcz335uxz2vWSBs3momtbEMAAACA0ggK\nkv79b+nrr837zOBg6aWXpDNn7K4M7iLQOURGhjR1qhQVZbYjWLnS7DUCAAAAlFVoqNnDbuVKadUq\ns4DKvHlmZBgqNwKdA3z3ndS5s7Rpk7R5szRyJMMrAQAAcOl16GD2r1u82AS8tm2lBQuknBy7K8OF\nsMplJXbmjBlS+cYb0vPPS3ffTZADAABAxVm9Wpoyxcyte/pp6ZZbeD9qh+IyEYGukvrmG9MTFxZm\n9pfz9bW7IgAAAFRHliUtW2ZWVa9VS3rmGTMNiGBXcQh0DvL772bp2HffNXvKsXolAAAAKoOcHOm/\n/5X++lezSuazz0o9e9pdVfXAPnQOsW6dGbd84ID044/SkCGEOQAAAFQOHh7SnXdKP/9s9kG+6y6p\nf38pIcHuyqo3Al0lkJNjxiTffrs0Y4aZgNq8ud1VAQAAAOfz9JRGjZJ27JBuukm67TapXz/pf/+z\nu7LqiSGXNjt2TLrnHjPRdNEiqWVLuysCAAAA3Hf2rPTmm2Zj8tBQMyTzmmvsrqpqYchlJZWQIHXq\nJLVrZ1YQIswBAADAaWrVkh58UNq500wZGj5cuv56ac0auyurHuihs4FlSXPnStOmSbGx0qBBdlcE\nAAAAXBqZmWaBv2eflfz9palTpeuuY22IsmCVy0rk1CkpJkb66Sfp/felkBC7KwIAAAAuvawssyn5\nM89IPj5mKOaNNxLsSoMhl5XEL79I3bpJNWuaSaOEOQAAAFRVnp5m+OXWrWZI5vjx0tVXS3FxZsQa\nLg166CrI4sXSuHFmsujo0XwyAQAAgOolO9vsY/f001L9+qbHrn9/3he7gyGXNnvuOenVV6UPPpCu\nusruagCoWwKxAAAZjUlEQVQAAAD75OSY98VPPWVGrv31r9LAgQS74hDobGJZ5kJduFBatUoKCLC7\nIgAAAKByyMmRPv7YvF+WTLC79VazgTkKI9DZwLKkyZPNGOHPPzcTQQEAAAAUZlnSJ59I06ebFTL/\n8hezWTnBLh+BroLl5JhJn998YwJd06Z2VwQAAABUbpYlrVhhgt3p0ybY3XGHVKOG3ZXZj0BXgbKz\npQcekLZvl5Yvlxo1srsiAAAAwDksS/r0UxPs0tKkiROlu++W6tSxuzL7EOgqSGamNGKElJwsLVli\nVu8BAAAAUHKWJX3xhfTCC9K335qtD8aOrZ5TmdiHrgJYlnTvvdLx49KyZYQ5AAAAoCxcLumGG8yo\ntzVrTKdJWJg0cqT0ww92V1d5EOgukblzpZ07pQ8/rN7dwQAAAMClFhYmvfaatGuXFBIi9esn3Xij\nCXs5OXZXZy+GXF4CP/8sRUZKX38thYbaXQ0AAABQtWVkSO+9Z4ZjpqdLEyZIw4dLdevaXVn5YA5d\nOTpzRura1axqef/9dlcDAAAAVB+WJa1dK734orRunRQdLT30kOTvb3dllxZz6MrRY49JbdpIo0fb\nXQkAAABQvbhc0h//aDYo/9//pJMnpfbtzdoWmzbZXV3FoIeuDFaulGJipC1bpCZN7K4GAAAAQGqq\n9K9/SS+/LF1+ufTnP0sDBjh7PzuGXJaD7Gzpssukd9+VrrvO7moAAAAAFJSZKX3wgRmOeeyY9H//\nZ1bIdOJq9OU65DIuLk5hYWEKCQnRzJkzizxm/PjxCgkJUUREhDZv3lyicyurjRtNrxxhDgAAAKh8\nvLykoUOlDRuk+fPNXLugIOnRR6X9++2u7tIpU6DLzs7WuHHjFBcXp61bt2rhwoX65ZdfCh2zYsUK\n7dq1Szt37tTrr7+uMWPGuH1uZfbpp1LfvnZXAQAAAKA4Lpd09dXSf/9rOmWys6WOHU3Y++Ybu6sr\nuzIFuoSEBAUHBysoKEheXl4aOnSolixZUuiYpUuXasSIEZKkbt26KS0tTYcPH3br3MosLo5ABwAA\nADhJq1Zmq4M9e6Tu3U2o69lTev99KSvL7upKp0yBLikpSYGBgXmPAwIClJSU5NYxBw8evOi5ldWx\nY2bvuWuusbsSAAAAACXVsKHZu27nTunhh6V//lMKDja9eE7jWZaTXS6XW8dVxkVNymL9erP3XK1a\ndlcCAAAAoLQ8PaXbbjNt/Xrp1Cm7Kyq5MgU6f39/JSYm5j1OTExUQEBAscccOHBAAQEByszMvOi5\nuaZNm5Z3PzIyUpGRkWUpu8zCwqStW81Ghm5mWgAAAAA2y8yU9u0zPXPntgMHpClTpN697a5Sio+P\nV3x8vFvHlmnbgqysLIWGhmr16tVq2bKlunbtqoULFyo8PDzvmBUrVmjOnDlasWKFNmzYoAkTJmjD\nhg1unStV3m0L2rSRFi2SrrrK7koAAAAA5MrKyg9tu3YVDm2JiZKfnxQSkt+Cg81tq1ZSzZp2V1+0\n4jJRmXroPD09NWfOHPXp00fZ2dkaPXq0wsPDFRsbK0mKiYlR//79tWLFCgUHB6tevXp68803iz3X\nKW67TXr2WTPO1qPMmz8AAAAAKAnLMgHthx+kH3/Mbzt3Sj4+hUPbDTeY28svr3rTpthYvJTOnDHd\nsd26Sc89Z3c1AAAAQNWVlib99JMJbLkB7qefpLp1pfbtC7fwcKlOHbsrvrSKy0QEujJISTF7Wowd\nK40fb3c1AAAAgLOlp0vbt0u//FI4vKWmSldccX54a9bM7oorBoGuHO3ZI0VFmf0rXnpJ8va2uyIA\nAACgcjt6VNq2zQS33LZtm3T4sJnTFh5eOLi1alW9pzkR6MrZqVPSo49Ky5dL//63dOONdlcEAAAA\n2Csnx8xxKxjYcu9nZprQFhZmbnNbq1ZSjRp2V175EOgqyKefSvffL918szR1quTra3dFAAAAQPmx\nLOnQocIrSeauLLl7txm9VjCw5QY4X1+2/yoJAl0FSk01Ye7dd6W77pImTZIuu8zuqgAAAIDSyQ1t\nBbcAyL2/a5dUv37h5f9zW+vWUsOGdldfNRDobHD4sPTCC9K//iUNGiQ99pi5sAEAAIDK5tQpszZE\nwbZ3r7ndvdusJllUaAsOJrRVBAKdjVJSpJdfll55Rbr2WmnECKlfP8nLy+7KAAAAUF2cPWs2275Q\naDt9WgoKMq1Vq8KtdWupUSObf4BqjkBXCZw4IS1eLL39trRjhzRsmDR8uHTVVYwfBgAAQOnl5Ei/\n/Sbt328WIUlMzL+fe3vsmOTvf35Yy20+PrwnrcwIdJXM7t3SO+9I8+ebTQ+HD5fuvlsKCLC7MgAA\nAFQmOTkmjB08KCUlFR3aDhwwwx4vu0wKDDTt3Pt+fpKnp90/DUqLQFdJ5eRI69aZYPfBB6aL+6ab\nTOvShSVbAQAAqirLko4fN0Ht3JaUlH//8GGz6Ii/vwlluUGtYGALCDBz3FB1EegcIDNTWr/e7GW3\nfLl05IiZa3fTTVLv3mxYDgAA4ARnzkjJySaIJSfnt4KPDx0yYa1GDally8LN37/wYz8/qXZtu38q\n2I1A50B79+aHu6++kjp1kvr2lXr1kjp3lmrVsrtCAACAqi872yxyd+SIdPRo4duiQtuZM1KLFmaf\nNR+f/FbwsZ+fCWsNGtj908EpCHQOl54uffGF9PnnJtzt2GFCXa9epvXowR8EAACAi8nONsMcU1JM\nO3bMtNyAVlRoS0szI6WaNZOaNzetWTPTCoa03Pve3iwugkuPQFfFHD9uhmd+9ZVpmzZJbduacHft\ntdI115g/MgAAAFWNZZkPu9PSCrfU1MJBLfd+wedOnDAfgjdtKjVpkt8KBrVz7zduzGIisB+Broo7\nc0ZKSMgPeOvXmz9UnTqZdtVV5paQBwAA7GRZ0u+/m2B1bjt+PP/2+PHzA1vBVrOm6Qk7t50b1Jo0\nKfyctzeLzsGZCHTVTHa2tHOn6bn77jvTNm82f8QKBrxOncwYbwAAgKJYlpSRIZ06ZdrJk+ffL+62\nqODm6WmW2D+3NWqUf79x46IDm7e3Oa5mTbt/M0DFItBBOTlm/7vvvssPeps2SfXqmYDXrp10xRVm\n6GZoKEvfAgDgBJZlRuqkp7vfTp8+v506VfTzp0+b+WD165uhiiW5rV+/cEjLbYQxoOQIdCiSZUl7\n9phgt3WraT//LO3aZZbMbds2v11xhRQWZgIgAAA4n2WZbYjOnDHDCnNvL3T/Qq+np+ffL+pxwefO\nnDErX9et616rU8f8X16SRgAD7EegQ4lkZZnevNyAlxv2duwwqzflBrzQUKl1a9P8/SUPD7srBwDA\nyO25Ki4UFWzFhayS3Hp4mD3D6tTJv3XnfsHncoNXwVbUc7mNOWFA1UegwyWRnS39+mt+0NuxwwS/\n3bvNylJBQSbcBQfnB73Wrc3z7JsHACjIsqSzZ/PnY+UO+7vQ/YLDBYsaQnjuc2fPmp4ld0NRcSHM\n3ddq12Y1RADlg0CHcnf6tAl7uQGvYEtMNHuz5Aa84GCpVSspMNA0Pz8+XQQAJym4bHxqatG3uSsW\nnrswRsHHHh75c63q1bvw/dz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Dd9VV0uzZZj3od9+VKiq8XV3b4Rm6ZqqokIYONT10117r7WoAAACAzs31nN0f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\n0qWX8lNxoL0qK6u5UPSZM+7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BDgAAAABsikAHAAAAADZFoAMAAAAAmyLQAQAAAIBNhQS7AgAABMKypMpK6cIF6fx5z7Ky\nUqqqMsV73bs0ZXt1dWB1CURIiBQaWrv421bfPuHhUkSE1LOnKV27tuxvCADoeAh0AIAmsywTqM6e\nlc6c8Sy91323nT1bN4y5l/62+Vt27Sr16CF17+5ZdutmApC7uAORv1Lfe97bu3SRHI7G/waN7WNZ\nntDoXfxtq6+cO2f+fuXlZtmjhwl2ERGeoOdvPSZGio2V+vUzxb3eq1dgnw0AYB8Oywr0PmNwOBwO\ntfMqAkC75nKZMFBWJp0+bYp73d+2QMNZ164mRISH+1/6bgsPrxvGmrrszC1UlmUCnjvclZf7Xz99\nWiopkY4fN6WoyLN+4YIn3LmXcXFSUpLUv79nGRfXuf/WANDeNJSJCHQA0E5VV5uQVVpauzQUxvxt\nO3PGhKnISNNC47303ebdva++YOZehtDHw3bOn68d8IqKpPx8KS9Pys2Vjh41y+JiKT6+dsjzXiYl\nmfcJfQDQNgh0ABAELpdpNSktNS0mvsHMXep7r7zcBK2oKE/p3dsEr8aCmfcyIsJ0IwQCVVFhQp47\n4B09Wnu9odDnvU7oA4CLg0AHAC3gcpmAdfJk48U7nJWVmZYsdxiLjq4dzryLv/ciI7kYRvtF6AOA\ntkOgA4D/79y5wIKZdzl1yoSrPn0aL9HRnnDWqxfdEtG5BRr64uIkp9MEPH9Lp9M8RwkAnRWBDkCH\nde5c3WeC3Ou+r0+cMK1tjYWymJi6IY1gBrSOigrp2DET/Nzhz3eZn29uqtQX9tzrUVGM4gmgYyLQ\nAbCNykoTvAIJaMePm4tB99Ds3iP3+Xvdt6/pAskFH2AvLpf5XvANe77Br7q6/rDnXjKCJwA7ItAB\nCCrLMt2q8vNrl4ICz3phoQlop0+bVjF/4czftshIAhoAo6zME/Tqa+0rKTHfHU6nlJjoWXqvO51m\nACK+WwC0FwQ6AK2iqsoEMd9w5hvaCgpMy1hCgqfEx9d+HRdnLrJiYhiREUDrqagw303Hjnm6evqu\n5+WZ77eGAp97W1hYsD8RgM6AQAegSaqrTVBzD2CQl+e/Va242LSmeQczf6EtPp6LHgD2cvq0Z46+\n+oLfsWNmXsb6gl9CglnGxUndugX7EwGwMwIdgBpVVSaUucOa7+hz7gEIYmI8w4u7L0x8S2wsg4UA\n6Lwsy4yEW1/gc7cEHj9uBmzxDnnu71HvbfHxUvfuwf5UANojAh3QSVRWmguIhsJaYaEZHMQd1nzn\nhnIHOC4qAODiqK42g7p4hzx3TwfvbYWF5rnghoKfu9DrAehcCHRAB3H6tHT4cN3iDmtFReY5NN+A\n5h3cEhKk0NCgfgwAgB8ul6fFz1/wO3bM81xy9+51u7b7Pp8cH2+6xTO4C2B/BDrAJsrKpCNH/Ie2\nw4el8+el5OTaZcAA6ZJLTGCLj6cLJAB0dJYllZbWfq7Zd+leLy83z/DVF/7cJS6OVj+gPSPQAe1E\nWZn/oOYOcRcu1A1s3oU7rQCApjh/vu5oxL5TxhQWmm1hYSbYeQc978DnXu/Xj5uHQFsj0AFtxOUy\nXWIOHDBl/36zPHTIBLaKCgIbAKD9cbf6ubt0FhR4gp7vthMnpOhoT8jzXvpu69uXidyBi4FAB1xE\nLpcZwcwd1ryD2w8/mMloBw+WUlI85dJLCWwAgI7BPciLdwufO/z5rpeUmFGT6wt83ut9+jAPKVCf\nVg10mzZt0uLFi1VdXa37779fS5YsqbPPgw8+qI0bNyo8PFyvvPKKRo8eLUlKTk5Wr1691LVrV4WG\nhio7O7tJlQdaS3W1GWTEO6y51w8eNP9zSkmpHdwGD5YGDZIiIoJdewAA2oeqKjNgl3fI8w1+7tdl\nZaZFr77A5/3sX69e3CBF59JQJmpRD+jq6motWrRImzdvltPp1JgxYzRjxgwNGzasZp8NGzbowIED\n2r9/v7Zv366FCxcqKyurpmKZmZmKiYlpSTWAZjtxQtq9W/r+e2nvXk9wO3TI3Cn0DmzXXGOWgwaZ\niWQBAEDDQkI8o242pqLCzNnnG/QOHpS2bfNsy883N17rG+EzPt4zwXvfvrT6oeNrUaDLzs5WSkqK\nkpOTJUmzZ8/WunXragW6999/X/fcc48kaezYsSotLVVhYaHi4uIkidY3tDrLMl/+u3d7wpt7WVEh\nXXaZKUOGSBMmmBB36aVSeHiwaw4AQOfRrZtnmp3GlJfXDnju5aef1p7m4fRpzzx+TqenJCaaqXwu\nucS8Zjof2FmLAl1eXp769+9f8zopKUnbt29vdJ+8vDzFxcXJ4XBo0qRJ6tq1qxYsWKD58+e3pDro\n5FwuM1qkO7B5h7cePUxoGzZMuvxy6fbbzev4eLpsAABgNxERnh40DTl/3hPu8vJMOXZM2rFDys2V\ncnJMGIyLM+HOPRXQgAGmpKSYZ+C7dWuTjwU0S4sCnSPAK+H6WuE+/fRTJSYmqqioSBkZGUpNTdWE\nCRNaUiV0AlVVZvAR78C2e7fpMhkT4wlu48ZJ991n1vv0CXatAQBAW+vRw/S6ufTS+veprDQhLyfH\n3BjOyZF27ZLefddcbxw9alrxvAc7S0kxN4gHDqRLJ4KvRYHO6XQqNze35nVubq6SfNrJffc5evSo\nnE6nJCkxMVGSFBsbq1tvvVXZ2dl+A92yZctq1tPT05Went6SasNGTp0yX6reZc8e88U6bJgJb5Mm\nSQ8+KKWmmoekAQAAAhUa6mmR89euUFFhgp77OfsDB6SPPjI3k0+e9PT+GT7clCuuMF06gZbIzMxU\nZmZmQPu2aJTLqqoqDR06VFu2bFFiYqKuvvpqrV27ts6gKKtWrdKGDRuUlZWlxYsXKysrS2fPnlV1\ndbUiIyN15swZTZ48WUuXLtXkyZNrV5BRLjsFyzJdH3zD2/Hj5otx1ChPGT6c59sAAEDwlZWZYPft\nt6Z89525fgkNlcaMkdLSPMu+fYNdW9hZq05bsHHjxpppC+bNm6fHHntMq1evliQtWLBAkrRo0SJt\n2rRJPXv21Msvv6wrr7xSBw8e1KxZsySZYHjXXXfpsccea1LlYU+VlaarpG9469GjdnAbNcqMKMmE\npAAAwC4sy3Tb/OILU/7+d+nLL81jIdddJ/3oR9L115tumzzHj0AxsTiCprxc2rnTBDb3cs8e063B\nN7z9/4FPAQAAOhSXyzzr/8kn0tatprhcJtj96EfSxIlmlG0CHupDoEObsCwz8fbnn0tZWWa5f780\nYoQ0enTtLpPM4wYAADoryzJz3n78sZSZaZ7J695dmjpVmjJFuvFGKTIy2LVEe0KgQ6s4dUrKzjbh\nzV0iI83okuPHm+WoUeYLCgAAAP5Zlnn+btMmU7Zvl666Spo2TZoxwwz8Rutd50agQ4u5XKarpHfr\n2+HD0pVXesLbuHFm8k4AAAA035kzpuVuwwbp/felsDDplltMGT+e8QU6IwIdmqy0tHZ4y842ozN5\nh7crrjCjOAEAAKB1WJaZCH3dOlPy86Wbb5ZmzpQmTzaDyqHjI9ChUefPS9u2SVu2SJs3m1Eo09JM\ngBs/Xho7VoqNDXYtAQAAOrdDh0yr3bvvmsHmbrpJ+od/MM/fhYUFu3ZoLQQ61FFdbUaddAe4rCwz\nWMmkSWakpfHjefYNAACgPSsoMMHu7bfN1AhTp0q3325CHnP2diwEOtSMQOkOcJmZUny8CW+TJpkh\nc3v3DnYtAQAA0BzHj0vvvWfC3fbtZrTM2bPNwCq03Nkfga6TKijwBLgtW8zAJpMmmXLjjVJiYrBr\nCAAAgIvtxAnTcvfGG+b5u5tvNuEuI0Pq1i3YtUNzEOg6CcuSdu82d2b+8hcpJ0e64QZPK9yQIQx5\nCwAA0JkUFJhrwzfeMCOWz5xpwl16uhQSEuzaIVAEug7MsswDsW+/Lb3zjnT2rHTbbaYwrC0AAADc\ncnKkP//ZhLucHDOYyp13mmtGbvq3bwS6DsaypC++8IQ4yQS422+XxozhHyQAAAAaduCACXZr1pgG\ngdmzTbgbMYJryfaIQNcBuFxmWoF33jElPNwEuNtuk0aN4h8eAAAAms6ypK+/ltauNSUiwgS7OXOk\nSy8Ndu3gRqCzKcsyIW7NGvNga9++npa4yy4jxAEAAODicbmkzz83we6tt6SBA024u+MOKSEh2LXr\n3Ah0NnP2rAlxq1aZ9XvvNSFuyJBg1wwAAACdQVWVGSV9zRozkflVV5lwN2uWFBUV7Np1PgQ6mzh4\nUHr+eenll6VrrpEWLTKjU3bpEuyaAQAAoLM6d07asMGEu82bzfRXd95ppkNgjru2QaBrx1wu8w9j\n5UrTxD13rrRwIX2WAQAA0P6UlppHgdaskf7+d2nGDBPuJk5kGoTWRKBrh06dkv74R+n3vzd3NhYt\nMv8YwsODXTMAAACgcQUF5lm7NWukQ4fMNAhz5phpEOhhdnER6NqRoiLp//wf6fXXpcmTTZC79loG\nOAEAAIB9/fCDmQbhT3/yTIMwZ450xRVc514MDWUisnMbqa42rXGXX27uWHz7rTnpr7uOkxwAAAD2\nNmiQ9Pjj0nffSevWmW0zZkjDh0u/+Y0JfGgdtNC1gW3bpAcekHr3NiNXDh8e7BoBAAAArct7GoQ/\n/1kaMMAzDUJiYrBrZy90uQySwkJpyRIz6Mn//b+m6ZnWOAAAAHQ2VVXS//yPed5u3Tpp9GjTJfO2\n26SYmGDXrv2jy2Ubq6qSnnvOtMT16yd9/705YQlzAAAA6IxCQsz4Ea+8IuXnm3EkPvzQTF4+Y4Zp\nxTtzJti1tCda6C6y774z4a1fPzMVwbBhwa4RAAAA0D6VlUnvvWcC3bZt0rRpplfb1KlSjx7Brl37\nQZfLNvLtt1JGhvTUU2Y+OVrkAAAAgMCcOCG9844ZOPCrr6RbbjHh7sYbpdDQYNcuuAh0bcAd5n73\nO3PiAQAAAGievDwzkMobb0gHD0q3326usa+7rnPOcUega2Xffmv6BD/7LGEOAAAAuJgOHpTefNOE\nu5MnzSiZc+ZIaWmdp0ccga4VuVvmnn3WnFgAAAAAWsfu3SbYrV1rpkVwT2De0acFI9C1kt27pUmT\npH//dzOnBgAAAIDWZ1nSzp0m2L35phQWJt18synXXdfxnrkj0LWSmTOlH/1I+vnPg10TAAAAoHNy\nuUy4++ADUw4ckKZMMeFu2jSpT59g17DlWnUeuk2bNik1NVWDBw/W008/7XefBx98UIMHD9bIkSO1\nc+fOJh3bXpWVmckR584Ndk0AAACAzqtLF+mqq6SlS6UvvvD0onv7bTPP3YQJ0tNPm+nF2mk7UYu0\nqIWuurpaQ4cO1ebNm+V0OjVmzBitXbtWw7wmX9uwYYNWrVqlDRs2aPv27frnf/5nZWVlBXSs1H5b\n6NaulV5/XVq/Ptg1AQAAAODP+fNSZqZpufvrX6WuXT1dM3/0I6l792DXMDCt1kKXnZ2tlJQUJScn\nKzQ0VLNnz9a6detq7fP+++/rnnvukSSNHTtWpaWlKigoCOjY9uzPf5b+4R+CXQsAAAAA9enRw0xS\nvmqVdPiwtG6dlJAgPfGEFBcn3Xab9PLLUmFhsGvafC0KdHl5eerfv3/N66SkJOXl5QW0z7Fjxxo9\ntr0qL5e2bDGTHQIAAABo/xwOacQI6bHHpM8+k/bvN9fzGzdKQ4dKY8eabpp2E9KSgx0BTvzQHrtM\ntsSnn0qDBknR0cGuCQAAAIDmiI2V7r7blIoK0zXTjrGlRYHO6XQqNze35nVubq6SkpIa3Ofo0aNK\nSkpSZWVlo8e6LVu2rGY9PT1d6enpLal2i02YYFrp3n7bzFoPAAAAwF7On5eys6VPPpE+/lj6/HPp\nF78wI2QGW2ZmpjIzMwPat0WDolRVVWno0KHasmWLEhMTdfXVVzc4KEpWVpYWL16srKysgI6V2u+g\nKNu3SzNmmIcrr7462LUBAAAA0JDTp6Vt20x4++QTaccO6bLLpOuvNw02113Xfqc4aCgTtaiFLiQk\nRKtWrdKUKVNUXV2tefPmadiwYVq9erUkacGCBbrpppu0YcMGpaSkqGfPnnr55ZcbPNYuxo6VnntO\nmjVLGj9eeuopaciQYNcKAAAAgCSVlEhbt5oA9/HH0p49UlqaCW+/+pW5ho+ICHYtW46JxVvo7Flp\n5Urp3/9RKFi6AAAWS0lEQVTdhLulS6XExGDXCgAAAOhczp83LXCbN5uyZ490zTVmeoLrrzdhzi7T\nFPhqKBMR6C6S4mJp+XLpD38wJ8xdd0nTp0vh4cGuGQAAANDxuFzSrl2eAPf559Lw4WZS8UmTpHHj\n7BvgfBHo2tDp09K770pr1pjn7KZPl+6805xUIS3q4AoAAAB0XpYlHTxopg/bvFn6n/+R+vWTJk40\n19rp6VLv3sGuZesg0AVJYaH05psm3B06JP3kJ9LMmdK113acuwUAAABAa6iokHbuNHPGbdtmll26\neALcxImS0xnsWrYNAl07cOCAtHattGGD9N13ZhSdKVOkyZOl1FQz0SEAAADQWRUXm26Tn31mypdf\nmrmfr73WUwYM6JzXzQS6dqakxDQRf/ih9Le/SdXVJthNnmzuNPTtG+waAgAAAK2nutoMWvLFF54A\nl5trpgNzh7dx4zpuF8qmItC1Y5Yl7d9vwt2HH5qhVYcMkW64wZzI48ebvsEAAACAHVVVmfD25Zee\n8tVXUny8GXnSHeCuuIIxJ+pDoLORigrT1Pzxx6avcFaWabG75hpPuewyqWvXYNcUAAAAqK2qSvr+\n+9rh7euvpYQE6aqrPOXKK6WoqGDX1j4IdDbmcpl/FNu2eR4GPX7cTGzuDnhjx0q9egW7pgAAAOhM\njh+Xvv1W+uYbz/Kbb8xAJd7hbfRowltLEeg6mKIi04rnDnk7dpgHRtPSzN2OK6+URo5kDjwAAAC0\nXFmZGdTPHdzcpapKGjHCzP02fLh0+eXmGpTn3i4+Al0HV1FhJlX88ksT7nbsMK16l17qCXhXXimN\nGkVLHgAAAOqyLDPl1r59puzdK+3ebYLbyZPmkR93cHOXhITOOeJkMBDoOqGKCnMnxR3wduww/Zed\nTk/AczeBx8QEu7YAAABoC2VlntDmW3r0MIPzDRkiDR5sWtyGD5eSk838bwgeAh0keUYY8g55u3aZ\nQOfdXD58uJkbj8nPAQAA7MXlkgoKpEOHapcDB0xoO33aE9rcZehQE+Cio4Nde9SHQId6uVzSDz/U\n7g/9zTfSwYPSwIF1g96gQYywCQAAECyWZSbg9g1shw+b5ZEj5hGbgQNNSU42y5QUE9wSE+kmaUcE\nOjTZhQum77R30Pv2W3PHJzW1bh/q/v35cgAAAGgJy5JOnTITbB896iner3Nzzc11d2DzDW7JyVLP\nnsH+JLjYCHS4aMrLPQ/IepfycnPXx7ukpprm+7CwYNcaAAAguM6dk/Lzzc1xd8nPrxvYunQxN8qT\nkkzxXXc6mQKgMyLQodUVF5sWPe+yZ4/puhkf7wl43oHP6aRVDwAA2NeFC2Y6qaIiMyebO6T5hraC\nArNvfLynJCSYpW9wY0Ry+EOgQ9BUVZk+3e6A5x32zp71PIjr26pHVwEAANCWXC4zAmRxsXTihCeo\n+Svu98+fl2Jjpb59pX79PCHNO7C5S1QUN7LRfAQ6tEulpbUDnnv9wAEzytKgQaakpHjWBw2S+vTh\nCxEAANRlWaZrY2mpeRattFQqKTEhrbFSWipFRJhrkL59TYmNbbj06sU1CdoGgQ624nJJeXlm9E13\nOXDAs25Z/oPeoEGmqwLzpAAAYD/V1WZI/bIyU9zr3ttOnfIEtfqWoaFS796mRax3bzM9UyAlKkoK\nCQn2XwHwj0CHDsM9VK932PMOfMXFZoQn36CXnCwNGGDuvAEAgJZxucyjE+Xl0pkzppSX119On/a/\nzTu8nT9v/j/dq5cUGWmW3uuRkZ6g5g5rvsvevZlHFx0TgQ6dxtmzZiAW38B3+LCZl6VnT0+487fs\n3Tuo1QcA4KKwLBOQzpwx/2/0Xga6zTus+Zbz580o1j17ekpkpAlk3sXfNu/3vANbeDi9bID6EOgA\nmf+5HT9ugp074Pkuu3atP/ANGMDzewCAi8eyzMiHvq1X3sHJX6hyb/MXxrxfd+tmglZ4eO1lU7Z5\nl4gIz3pYGOELaEsEOiAA7u6cR47UH/oqK2uHPO+5YZKSzFQM4eHB/RwAgNZVXe15lsv7uS5/6+7u\nhO7Q5h3eTp82NxJ9W7H8BSh/r32DmPd6WBjPgwEdCYEOuEhOnaod+PLyak8Impdn/kfqOxGob+FZ\nPgBoH86dM703vMvJk+YGn3t0RO9REktKTBBzP8/Vu7fpLui99N3mDmy+y4gI04oGAI0h0AFtxLLM\n3DRHjzZcQkPrD3vu0rs33TsBoLnKyjw32ryX+fm1w1tlpZk/zF3cc4q5Rz6Mjq69jIkxIa1r12B/\nQgCdCYEOaEcsywyr3FDgy801k7InJHgmJnWv+26LjeU5BgCdz4UL0qFDnlGODxww5cgR8z3qctXt\nEp+UZL434+I8AS4igptnANo/Ah1gQ+XlUkGBuZvsXXy3nTplQl1jwS8+nqGcAdhLebkZudg7tLmX\n+fnSJZeYOUm9i/v5ZiZ8BtCREOiADqyiQiosbDz8FRaa5zbc4S4+3tO9yHvpXueuNYDW5j23qL/Q\nVlZm5hZNSTFzinovL7nEdF8HgM6AQAdALpd50N8d9AoKpKIi8wyJv2V1dd2Q11AAZHRPAP5Ylvne\n8RfYfvjBfDe5W9cGDaod3BIT6VIOAFIrBbri4mL95Cc/0ZEjR5ScnKy33npLUVFRdfbbtGmTFi9e\nrOrqat1///1asmSJJGnZsmX6r//6L8XGxkqS/u3f/k1Tp05tUuUBtJ4zZ0yw8w17/gLg8eNmeGzf\nkBcbawYQ6NPHM5iA93pYWLA/JYCWcN8oOnbMlLw8z/qxY+YZtx9+ML0D/LWyDRrE/J4AEIhWCXSP\nPPKI+vbtq0ceeURPP/20SkpKtHz58lr7VFdXa+jQodq8ebOcTqfGjBmjtWvXatiwYXriiScUGRmp\nhx56qNmVB9A+WJZ51sU37BUVeYb6dg8D7l4/edKMEucv6Plb997Wo0ewPzHQsVmWeT7XO5z5C20F\nBSasJSbWLk6n6d49cKB06aVmHwBA8zWUiZo95eT777+vrVu3SpLuuecepaen1wl02dnZSklJUXJy\nsiRp9uzZWrdunYYNGyZJBDWgg3A4zAVbZKS54x4IyzLzP/kGPfd6UZG0d6//MBgSUjvwRUV55n4K\npPToQYsAOh+Xy4Q0978l97+n/Hz/wa1rV084cwe1QYOkCRM82+PjucECAMHW7EBXWFiouLg4SVJc\nXJwKCwvr7JOXl6f+/fvXvE5KStL27dtrXq9cuVKvvvqq0tLS9Mwzz/jtsgmgY3I4zHN34eFmRLpA\nWZZ09mztoFdSYi5U3SU3V/r229rbvIvL1bQA6D1ZcESEmTyeCYERLC6XGSzE382OhtZLS81569v6\nHR9vAtpVV3mCW0ICrWoAYBcNBrqMjAwVFBTU2f7UU0/Veu1wOOTwc7vb3za3hQsX6te//rUk6Ve/\n+pUefvhhvfTSSwFVGkDn5XCYQNWzZ9OCoLcLF+oPe+6Slyft3l13+5kzppSXm5/lHfDc9fLd1tjS\nd1tYGC2IHY27Rdp97riL92t/750+XbdFrbTUnCv1dU8eMEC68sq626OiGBUSADqiBgPdRx99VO97\ncXFxKigoUHx8vPLz89WvX78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mzJmF29HR0YqOjq5O2bZw6JB0//2Sn5+0dav5VAcAAAD1R7duZrmD2bPNZCq/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\nAwAMVxjLyHAvjJX3vsNhQlX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iQrrnHunjj818CMCFNK7OyWFhYUot1Tecmpqq8PDwSo85fPiwwsLCJEmdOnWS\nJHXo0EG33XabkpKSNGzYsHLfZ/bs2cX7cXFximPsYLGiImnZMumPf5Ruvln64QepY0e7qwIAAMCl\nat5cuuMO044fNwuXP/64ufful780Aa93b7urRG1ISEhQQkKCT8dWa1KUwsJC9ejRQxs3blSnTp00\naNCgSidFSUxM1MyZM5WYmKhz587J7XardevWOnv2rEaOHKmnnnpKI0eO9C6QSVEuaMsW6eGHpWbN\npOeeMzfUAgAAoH754Qfp9dfNrOUdO5qJVCZMYPHyhqRG16Fbt26dZs6cKbfbrSlTpujxxx/X4sWL\nJUnTpk2TJM2YMUPx8fFq2bKlli1bpp/85Cf68ccfdfvtt0sywfDuu+/W448/fknFN1RpadKsWVJC\ngjRvnnTXXQytBAAAqO/cbvP+77XXpJUrza02991nlkVo2tTu6lCTWFi8nsjPNzNWPvusNG2a6YJv\n1cruqgAAAFDbzpyR3ntPevllafduMyTzvvvMjJmofwh09cC330qTJkmdO0v/+IeZGQkAAADYs0d6\n5RUzO2Z4uDRlilkOoW1buyvD5UKgc7D8fGnOHGnRItMz98tfMrwSAAAA5RUWSuvXm167DRuksWNN\nr90110h+1ZrbHnYj0DlUcnJJr9zixdJ/JgUFAAAAKpWRIf3rX9LSpdK5c9LkydLEiWY5BDgPgc5h\nSvfK/e//milq6ZUDAADApbIs6ZtvTK/d229LAweaXrtbbjEzpcMZCHQOsm2b6ZULDze9cv9Zsg8A\nAAColtxc6YMPTLjbts3MlD51qtSnj92V4WIqy0SMpq0jCgulp5+WRo6Ufvtb6cMPCXMAAAC4fPz9\nTYjbsEH6+mspIEAaPVoaOlRatkw6e9buClEV9NDVAcePS+PHS40amRmKCHIAAACoDYWFUny89M9/\nSp9/bhYsnzpV6t/f7spQGj10dVhSkhQbaz4ZiY8nzAEAAKD2NG4s3XyztGqV9N13UnCwmR1z0CDp\npZfMeneo2+ihs9GSJdITT5hPRG691e5qAAAAAMntlj76yLxH3bxZGjdO+tWvpJ/8xO7KGi4mRalj\nzp+XZsyQvvxS+ve/pR497K4IAAAAKO/IEXN/3ZIlUlCQCXYTJkitW9tdWcNCoKtDDh2S7rhD6tLF\nzDDUqpXdFQEAAACVc7vNZCr//Kf0ySfm/eyvfmVuHWJ5rZrHPXR1REKCNHiwmQDl7bcJcwAAAHCG\nRo2kG26i/K9SAAAdFUlEQVSQ3n9f2rFD6tpVuvNO6aqrTO8dM2Tahx66WrJunXTvvdLy5dL119td\nDQAAAFA9RUXSxx9LixZJn30m3X23NH261LOn3ZXVP/TQ2eyDD8xi4atWEeYAAABQP/j5mV67FSvM\nQuVt20rXXisNHy69+65UUGB3hQ0DPXQ17K23pJkzpbVrmRkIAAAA9Vt+vgl4L7wg7dkj3X+/udcu\nPNzuypyNHjqbvPKK9MgjpiuaMAcAAID6rmlTs8xBQoJ5D5yVJfXtK912m7R+vRmmicuLHroa8uKL\n0p//bGYDiomxuxoAAADAHmfOSG++aXrtzp6VHnhAmjxZCgy0uzLnYNmCWvbSSybMbdwodetmdzUA\nAACA/SxLSkw0wW71aunWW80kKgMHsvTBxRDoalFiojR2rPT551L37nZXAwAAANQ9GRlmwfIXXzQ9\ndb/+tVkGoXlzuyurmwh0teT4cbMWx8KF0i232F0NAAAAULcVFZnlvRYskJKTzSQq06cziUpZTIpS\nCwoLzYLh995LmAMAAAB84ecn3XSTFB8vbd4snT5tJlH5xS/MY4f069iKHrrLZNYs6dtvzcXYqJHd\n1QAAAADOdPq09OqrZtRbs2ZmOOZdd0ktWthdmX0YclnD/v1vszzB119LQUF2VwMAAAA4X1GRmTF+\nwQLpyy/NzJgPPSRFRtpdWe1jyGUNOnPGjPN95x3CHAAAAHC5+PlJI0dKH34oJSWZgBcba25v2rCB\n4Zge9NBV0/z5ZqjlW2/ZXQkAAABQv509K/3rX6bXzu2WZswwc1i0amV3ZTWLIZc15MwZs87cJ59I\nV15pdzUAAABAw2BZ0qZNJtglJEj33CP96ldSr152V1YzGHJZQ154QRo+nDAHAAAA1CaXS4qLk95/\n3yx30LKldP310tVXS6+8Ip07Z3eFtYceuiqidw4AAACoOwoLpTVrpCVLpC1bzJJiU6dKAwbYXVn1\n0UNXA157TbrmGsIcAAAAUBc0bmwmTFm9Wtq+XQoJkW691UyksnixlJNjd4U1o9qBLj4+XjExMYqO\njta8efMqPObhhx9WdHS0+vXrp+Tk5Es6t67aulUaNcruKgAAAACUFREh/elP0o8/Sn/+s7R+vXTF\nFdKUKVJiYv2aIbNagc7tdmvGjBmKj4/Xjh07tHz5cu3cudPrmLVr12rfvn3au3ev/vnPf2r69Ok+\nn1uXffedWcUeAAAAQN3UqJHphHn/fWnnTql7dzOBSt++0nPPSZmZdldYfdUKdElJSYqKilJkZKSa\nNGmi8ePHa+XKlV7HrFq1ShMnTpQkDR48WNnZ2Tp27JhP59ZVBQXS7t0MtwQAAACcIiREmjVL2rPH\nhLnERKlrV+mXvzQzZjq1165agS4tLU0RERHFj8PDw5WWlubTMUeOHLnouXXV3r1SeLjUooXdlQAA\nAADwsCyzALnbfeFjXC4zU/2bb0r795t77B58UOrRQ3r33dqr9XJpXJ2TXS6XT8fVxVkqq2PnTikm\nxu4qAAAAgNqVny+dPm2WBaio5eZW/lpenvka+flm1Jtnv7JWUFAS1C62La15cykwsPLWvr0ZfvnG\nG6bnLijInn/X6qhWoAsLC1Nqamrx49TUVIWHh1d6zOHDhxUeHq6CgoKLnusxe/bs4v24uDjFxcVV\np+xq69xZOnTI1hIAAAAAnxUVSadOSVlZUna22WZlmZkfc3JMSCu9vdBzhYVS69ZSq1ZmtJq/v9lW\n1DyvtW9vJinx95eaNZOaNvW9NWliZq9s1Mj0rPn5mW3p/bJbl8sEvNxcc4/chdqBA+WfmzjR9N7Z\nLSEhQQkJCT4dW6116AoLC9WjRw9t3LhRnTp10qBBg7R8+XL17Nmz+Ji1a9dq4cKFWrt2rRITEzVz\n5kwlJib6dK5UN9ehy8uTAgKkEycYdgkAAIDaU1AgnTxp3oeeOFGy7wloF2qnT5sQFhAgtWtntgEB\nUtu2JqC1aVN+W9FzzZubwITaVVkmqlYPXePGjbVw4ULdcMMNcrvdmjJlinr27KnFixdLkqZNm6Yb\nb7xRa9euVVRUlFq2bKlly5ZVeq4TNGsm9epl1rcYOtTuagAAAOBElmWCVnq6aZ6QVlk7e9YMFQwK\nKmnt25cEtK5dS/ZLt7ZtTS8X6p9q9dDVhrrYQydJDzxgZrn89a/trgQAAAB1RVGR6RHzhDRPO368\n4ucaNZI6dpSCg822dFCrqLVta4YWomGpsR66hiw2VvrkEwIdAABAQ1BYaALY0aOmHTlSsl/68fHj\nUsuWJqB5miewDRrk/Tg42BwLVAc9dFV0/LjUu7e0YQMLjAMAADiVZZn70A4fltLSSrZlA9uJE2Zo\nY2hoSevUqfzj4GBzew5wOVWWiQh01bBkibR0qbRlC13fAAAAdU1hoXTsWPmwVnbbooUUFmbWGQ4L\nM61TJ+/AFhxsZlsE7ECgqyFFRdI110h33y1Nn253NQAAAA2HZUkZGVJKipSaWvH2+HGpQwfvsBYe\n7r0fFsas5aj7CHQ16IcfpLg46bvvzKc3AAAAqL5z58y6vykpFYe1w4dNEOvc2axxVnrr2Q8NNeuY\nAU5HoKthf/iDtHOn9N57TAcLAADgi1OnTGA7eNBsS+8fPGim8/eEs4pCW0QEE4qg4SDQ1bDcXGn0\naDPO+tVX+SQIAAAgK8sEswMHvIOaZ1tYKF1xhRQZabZl94ODmaMA8CDQ1YLcXOkXv5BcLumddyR/\nf7srAgAAqDnnzpUEtoqa2y116WJaZGT54BYYaN43Abg4Al0tKSiQ7r3XzKa0apXUurXdFQEAAFSN\n223uU/vxR2n//vKBLTvbhDNPaCvbCGzA5UOgq0Vut/TQQ9K330rr1pn1SgAAAOqi06dNYPO0/ftL\n9lNSpKAgqVs3E9C6dvUObKGhDIkEaguBrpZZlvT730sffii9+abUv7/dFQEAgIbIssyi2Pv3S/v2\nlQ9uZ86YoNa1qwlupfcjI6Xmze3+CQBIBDrbvPKK9Nhj0q9+JT35pNSsmd0VAQCA+qaw0Ezlv29f\nSXArHeBatTIBrXTzBLeQEIZFAk5AoLPR0aPSgw9Ku3dLS5dKQ4faXREAAHCa/Hxz39q+fSXNE9pS\nUqSOHaWoKBPWSm+7dpXatLG7egDVRaCzmWWZNeoefli6805pzhzWTQEAAN7y8kxo27vXBLXS26NH\nzbpr3bpJ0dHewa1LF4ZGAvUdga6OOHlSeuQR6bPPpH/+U7r+ersrAgAAtSkvzwyDLBvY9u0rCW3R\n0Sasld5ecQXr3AINGYGujlm3zgzDjI4299YNG2Z3RQAA4HIpLDTrs+3dK+3ZY7ae/dKhrWxwI7QB\nuBACXR2Uny+9/rr0l7+YX+x/+pM0fDg3JgMA4ARFRWYikopCW0qKmdI/Olrq3r0kvHXvTmgDUDUE\nujqssNAsbTBnjlnr5cknpRtuINgBAGA3y5IyMkxIK9v27zdrzZYNbdHRZiIS7mkDcDkR6BzA7Zbe\nfVf6n/8xE6Y8+aR0880EOwAAalpOTknvWtnWpIkJbJ7QVjq8McEZgNpCoHOQoiLpgw9MsCsqMmvY\n3XWXFBhod2UAADhXfr7pVasotOXklIS10i062vTCAYDdCHQOZFnSxo3Syy9La9dKo0dL990nXXed\n5Odnd3UAANQ9RUVSWpp3WNu922wPH5Y6dy4f2rp3lzp14m8rgLqNQOdwmZnmPruXXzZLH0yebNoV\nV9hdGQAAtS87uySolQ5te/dKbdt6h7UePcy2SxepaVO7KweAqiHQ1SPJySbYLV8uDRggTZki3Xor\nN18DAOqX0kMkd+8uCW27d0u5uSaoRUebrSe0RUdLbdrYXTkAXH4Eunro/HlpxQpp6VIT8m69Vbrl\nFrNYub+/3dUBAHBxliUdOVJxaCs9RNIT2Dzb0FAmDQPQsBDo6rlDh8xEKitWmHB33XUm3N18Mzdz\nAwDsd/p0xaFt716pRQvvXjbPtmtXhkgCgAeBrgE5cUJas0ZaudJMqjJggAl3t9xi/jgCAFATCgqk\nAwe872nzbE+dKplFsnR4695datfO7soBoO4j0DVQubnShg0m3K1aJYWElIS7q65iuAoA4NJYlpSe\nXnFoO3jQDIWsqLctPJxZJAGgOgh0kNstJSaacLdihfm0NC5OGj7ctO7dCXgAAOPMGTMcsmxoK73Q\ndtnQFhXFBF0AUFMIdCjn4EHp009LmttdEu6GDzfDMwl4AFB/FRaavwUVTf+fmWkCWunA5mncmw0A\nta9GAl1mZqbuvPNOHTp0SJGRkXrnnXfUroKB8PHx8Zo5c6bcbrfuv/9+zZo1S5I0e/ZsvfTSS+rQ\noYMk6a9//atGjRp1ScXj8rAs6ccfvQNe48beAY817wDAeSxLOnrUe6FtTzt40Cyo7Znuv/RQyYgI\nhkgCQF1SI4HuscceU1BQkB577DHNmzdPWVlZmjt3rtcxbrdbPXr00IYNGxQWFqaBAwdq+fLl6tmz\np55++mm1bt1ajzzySJWLR82wLPPH3hPuEhKkli3NEM2hQ6WBA6XevU3oAwDYLzvbDJGsKLi1aOHd\nw+bpdevalSGSAOAUlWWiKr8lX7VqlTZt2iRJmjhxouLi4soFuqSkJEVFRSkyMlKSNH78eK1cuVI9\ne/aUJIJaHeVylXxS+8ADJuDt2GGC3eefS3/7m5SaKvXvb8LdoEFm260bwzQBoKacOyft22dCmie8\neba5uSWzSHbvLt10k/TII+Y5ZpEEgPqtyoEuPT1dwcHBkqTg4GClp6eXOyYtLU0RERHFj8PDw7V1\n69bixwsWLNBrr72m2NhYPfvssxUO2YT9XC7pyitNe+gh89ypU9I330hffSW99540a5Z09qwUG1sS\n8AYONDOeAQB8k59fMvV/2dB24oTpVfMMkbz6amnSJPM4JIQP1ACgoao00I0YMULHjh0r9/ycOXO8\nHrtcLrkq+EtS0XMe06dP15/+9CdJ0pNPPqlHH31US5cu9alo2K9tW+naa03zOHbMBLyvvpJeeMFs\nW7QoCXj9+pmhmuHhvPEA0DBZlpSVZe5b3r/fbEvvHz1q7l/zhLY+faSf/9zsR0RIjRrZ/RMAAOqa\nSgPdxx9/fMHXgoODdezYMYWEhOjo0aPq2LFjuWPCwsKUmppa/Dg1NVXh4eGS5HX8/fffrzFjxlzw\ne82ePbt4Py4uTnFxcZWVDZuEhEhjxpgmlUy24gl5f/+79H//Z3ryevcu3/4zPw4AOFphoRmW7glp\nZYObZZkh6t26mR632FjpzjvNfkSEWRYAANCwJSQkKCEhwadjqzUpSvv27TVr1izNnTtX2dnZ5e6h\nKywsVI8ePbRx40Z16tRJgwYNKp4U5ejRowr9z3i8v//97/rqq6/05ptvli+QSVHqnZMnpR9+MOHO\n077/XmratHzIu/JKqU0buysGAG+nTpXvXfPsHz5sPuDq2rUktHm2XbtKgYGMUgAAXJoaW7Zg3Lhx\nSklJ8Vq24MiRI5o6darWrFkjSVq3bl3xsgVTpkzR448/Lkm69957tW3bNrlcLnXp0kWLFy8uvifP\n1+JRf3im1i4d8v7v/8xkLO3bm2DXs6dZF8nTOndm+BGAy6+oyAwhT0017fDhkv2DB01wO3/+woHt\niiukZs3s/ikAAPUJC4vDsYqKzBuo7783C97u21fSjh83b5xKhzxPi4xk2BKA8ixLysgoH9RKt6NH\npYAAc79vRIR3u+IKE946dKCXDQBQewh0qJdyc81scKVD3r59JUOewsNLAl63bt49e61a2V09gMvN\nM+HIhYJaaqqUlmbW1aworHlaWBg9bACAuoVAhwYnP186dKh82Nu3T0pJMYvpVvRGzvMmLzxc8ve3\n+6cAIEl5eVJ6umnHjpVsS++np0tHjkiNG3v/v1zR/+MtWtj9EwEAcGkIdEAplmUmZqlsyFVampmM\npWzQK/spftOmdv80gDMVFpph02VDWUVB7cwZKTjYtJAQ0zz7pZ8LCWESJQBA/USgAy5RUVHJfTYV\ntcOHTW9A69bmDWXHjhfftmrFPTeonyxLysmRMjPNhyUVbU+c8A5s2dlSUFDFIa1sYAsIkPz87P4p\nAQCwD4EOqAFFReaN6vHj5o3qxbaWVXng69DBvHFt185s27ZlFk/ULssy60RWFswq2mZlmWHM7dub\nKflLb0vvl+5Ja9+e6xsAAF8R6IA64OzZygPfiRPmjXF2ttnm5JhevYAA76Dn6z6TOjQclmWm0T99\n2lw3nm3p/Ys9l5lpWqNGFYexyraBgQw/BgCgJhHoAAcqKjJvtLOyvINe6f2KnvO0Ro3M/UQtW5a0\nVq0u7XFFzzVvztDRS1VUZGZlPX/ebMvuV/ba+fPmw4CyIazstlEjMwS4TZvy24qeK/uaJ5gxGRAA\nAHUPgQ5oYCzLhIHTp00YOHPGbMvuX+rjM2fMZBZNm9ZM8/MzYfFC7WKvV3as221aYeGFW2WvV/Za\nQUHlwaygwATh5s1NYPL3v/B+Ra+1aFF5MGvdmh5ZAADqMwIdgMvGE2Dy8y9vy8szPVmWVXnz5ZiK\njm3UyExpX1Gr7LWLvd6okVnEvrKg1qwZvZoAAKDqCHQAAAAA4FCVZSImggYAAAAAhyLQAQAAAIBD\nEegAAAAAwKEIdAAAAADgUAQ6AAAAAHAoAh0AAAAAOBSBDgAAAAAcikAHAAAAAA5FoAMAAAAAhyLQ\nAQAAAIBDEegAAAAAwKEIdAAAAADgUAQ6AAAAAHAoAh0AAAAAOBSBDgAAAAAcikAHAAAAAA5FoAMA\nAAAAhyLQAQAAAIBDEegAAAAAwKGqHOgyMzM1YsQIde/eXSNHjlR2dnaFx913330KDg5Wnz59qnQ+\nAAAAAKBiVQ50c+fO1YgRI7Rnzx5dd911mjt3boXHTZ48WfHx8VU+HwAAAABQMZdlWVZVToyJidGm\nTZsUHBysY8eOKS4uTrt27arw2IMHD2rMmDH6/vvvL/l8l8ulKpYIAAAAAI5XWSaqcg9denq6goOD\nJUnBwcFKT0+v1fMBAAAAoKFrXNmLI0aM0LFjx8o9P2fOHK/HLpdLLperykVU93wAAAAAaIgqDXQf\nf/zxBV/zDJUMCQnR0aNH1bFjx0v6xpdy/uzZs4v34+LiFBcXd0nfCwAAAACcIiEhQQkJCT4dW+V7\n6B577DG1b99es2bN0ty5c5WdnX3BiU0quofO1/O5hw4AAABAQ1ZZJqpyoMvMzNS4ceOUkpKiyMhI\nvfPOO2rXrp2OHDmiqVOnas2aNZKkCRMmaNOmTTp58qQ6duyoZ555RpMnT77g+ZdSPAAAAADUdzUS\n6GoLgQ4AAABAQ1Yjs1wCAAAAAOxFoAMAAAAAhyLQAQAAAIBDEegAAAAAwKEIdAAAAADgUAQ6AAAA\nAHAoAh0AAAAAOBSBDgAAAAAcikAHAAAAAA5FoAMAAAAAhyLQAQAAAIBDEegAAAAAwKEIdAAAAADg\nUAQ6AAAAAHAoAh0AAAAAOBSBDgAAAAAcikAHAAAAAA5FoAMAAAAAhyLQAQAAAIBDEegAAAAAwKEI\ndAAAAADgUAQ6AAAAAHAoAh0AAAAAOBSBDgAAAAAcikAHAAAAAA5FoAMAAAAAhyLQAQAAAIBDEegA\nAAAAwKEIdAAAAADgUFUOdJmZmRoxYoS6d++ukSNHKjs7u8Lj7rvvPgUHB6tPnz5ez8+ePVvh4eEa\nMGCABgwYoPj4+KqWAgAAAAANUpUD3dy5czVixAjt2bNH1113nebOnVvhcZMnT64wrLlcLj3yyCNK\nTk5WcnKyRo0aVdVSbJGQkGB3CajHuL5Qk7i+UNO4xlCTuL5Qk5x4fVU50K1atUoTJ06UJE2cOFEr\nVqyo8Lhhw4YpICCgwtcsy6rqt7edE/9jwzm4vlCTuL5Q07jGUJO4vlCTnHh9VTnQpaenKzg4WJIU\nHBys9PT0S/4aCxYsUL9+/TRlypQLDtkEAAAAAFSs0kA3YsQI9enTp1xbtWqV13Eul0sul+uSvvH0\n6dN14MABbdu2TaGhoXr00UcvvXoAAAAAaMBcVhXHPcbExCghIUEhISE6evSohg8frl27dlV47MGD\nBzVmzBh9//33l/z6pQZFAAAAAKhvLhTbGlf1C44dO1avvvqqZs2apVdffVW33nrrJZ1/9OhRhYaG\nSpI++OCDcrNgejj5PjsAAAAAqElV7qHLzMzUuHHjlJKSosjISL3zzjtq166djhw5oqlTp2rNmjWS\npAkTJmjTpk06efKkOnbsqGeeeUaTJ0/Wvffeq23btsnlcqlLly5avHhx8T15AAAAAICLq3KgAwAA\nAADYq8qzXDYU8fHxiomJUXR0tObNm1fhMQ8//LCio6PVr18/JScn13KFcLKLXV//+te/1K9fP/Xt\n21dXX321vvvuOxuqhFP58vtLkr766is1btxY//73v2uxOjidL9dXQkKCBgwYoN69eysuLq52C4Tj\nXewaO3HihEaNGqX+/furd+/eeuWVV2q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Siy9KWVmBrhEAAADQvkpKpD//WXruOalPH2nePNNyN2hQoGtmPwyKEkD79km3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\npC+/NBeartdc66dOSQMHNh3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6zZo1S+vXr1dJSYmcTqcCAwN16tQpjRo1So8//rhGjRrlWWAbmhSlpMR8ovPP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\nUmKm/j97cpIDB8zom7PXcUt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MWgkAAIDWJztbeucdE+42b5auusqEu+uvl3r2tLs62IlA147s3y+tWiW98IK5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\nJGnp0qX6+9//rh49ekiS/vC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toEDavl3auFFautT01k2caALe8OG8B8SlsbB4E5OWZv5yp6dLq1dzrxwAAEBL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\n+EfphhusrggAmp6K96xVF9Y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zAl0zsmWLdOed0oIF0s9+xi8WAAAArry8POnjj00L3ocfmkFXJkyQJk40AY85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\nVFhYWO156enpSk5OVlJSkp5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6tJltfPx404vXoYPd1cJpWFjc4SxLWrFCevRRqV076YknzGKYDK0EAABo/Pbt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\nwEm8e8cqmrTDl21hYcVDEH3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Tpeuvt7siAAAAeJSXS3v2mIDnCXnnz3vD3Te+YSaxY9FzNCYWFm+mSkvNrEs7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\nWmGh+TLI82WRp1V+XFto69C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vN+3gQTNSKinJtH79+LIdl1d1mahePXQBAQFasmSJxo0bJ7fbrZkzZyo+Pl5L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\nFsAq2z53zgQsT9iqLpyVf4w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AALSWykqzPp4r4GVlSQcPmlBXPeTFxzOfQkdGoGuD8vKkBx6Q9u41vXIjR/q7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\nZwLfiRPmsWY7ccIEl4AAqVu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VQ17//ma4MQCg9SHQAaiT/HwT1CqGt8JCE9oGDvS2AQPMjJEALg/LMkstVAx5e/aYL0ji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\nh9yyQc8T9jwfhvPzza2nlb1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3262BAwdq/fr1iomJ0ahRo/T6668rKSmp6phTp07pyJEjWrlypUJDQ6sCnT/nXqryAFpe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\nKMiM2Ne1q2lF8RTfx77rdDG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MsLsiAACAtqljR+/smI8/Lp06ZYZnbthgJl25cME7PPOGG6Q+feyuGK0ZC4u3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\n6HnCnufDcGGhufW0ivcvXDD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BAACgfSgtNT13tXvx+vat34vHvXg4Wwh0DvHFF9Kdd0put/SnP0nf+Y7dFQEA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\n3XrCWo8edv9EAAC0DQIdzgr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28cUXZqKTqirTvTIlxd81AgAAQFtZlvT55ybYuUtwMAEPviPQ2cCrr0qPPmrW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\nXf/fe0SECWpRUVKvXv7+yQA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TzfV6ffp4W2ysuQ0OtrtqAEBjQqADcFWcPm1C29693rZvnwlm/fpJ119veh08we2aa0ov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\nZ85Uvn3+vHea8LKhLzjYPNe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5hnfuNAHvww+lZ5812/36mWA3bJh00UWeEhHhn88BAOja2hToCgsLlZCQULsdHx+vDRs2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\naXDcqlWrNG/ePDmdTt1zzz1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UHu7vSmEXBDobOXZMevRR6W9/k378Y+m++6Tevf1dFQAAANqipMTTi7dhg1kI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\ns5AQ01MdGmr2zxbUQkO55gE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D9doh78orpS5dgl0pOgsCnU243WaUpkWLpB/9SJo7l18UAAAAwVZVZUbVrB3y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\neYbx7t3bPMbESKGhwf7uAPu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IlZddJj39tJSYGOyKAAAAECwnT5pwt3mzCXi5uWawFU/Au/pqKS4u2FWivRDo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\nVYDazXNloLFWVmbmefKcnEZ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75J0+7emiOXYsA67g8iPQ2Vh6ujR/vnlA97nnTKADAABA65GXZ7pofvqpCXjb\ntklxcd5dNa++WgoI8HdNYVcEOhvav1967DHzDdAzz0h33cUDuQAAAHZQUSF98YV3K96xY+b5u3Hj\nzBf0Y8dKvXr5u6awCwKdjZw6JT31lPS3v0mPPio99JDUubO/awUAAICmKCz0tOJt3mwmQo+KMuFu\n3DhTBg2iFQ+1I9DZgGVJr7xiRqz8znekRYuk8HB/1woAAADNwemUdu824c4d8goKzPN37pA3ZowU\nEuLvmqI1INC1ckeOSGlp0smT0quvmkktAQAA0L4UFJjume6Q99lnUny8p5vmuHHSgAGMqNkeEeha\nKZdL+tOfpCeekB5+2HSxDAryd60AAADQGpSXS7t2eQLep5+aETXdXTSvvda04nXt6u+aorkR6Fqh\ngwel++6Tzp0zXS2vvtrfNQIAAEBrd/y4p4vm5s1mXryBA6VvfMNTYmL8XUtcbgS6VsTlkv7nf6Qn\nnzQTgz/8sBQY6O9aAQAAwI7KykzXzE2bTNm8WerWzTvgDR7MYCt2R6BrJY4fl2bMMM3nr7wiJSb6\nu0YAAABoSyxL2rfPBDt3yDt+3HTNdAe8MWOk7t39XVM0BIGuFcjMlO64w3SzfPxxviUBAABAyzh5\n0jvgZWWZhgV3wLvuOik21t+1RH0IdH72179KP/mJ9Oc/S7fe6u/aAAAAoD27cEHavt2Eu08+Mctu\n3aTx4024Gz9eSkqSHA5/1xRuBDo/qagwI1e+/7703nsMfAIAAIDWx91Nc+NGUz75RDpzxoQ7d8Ab\nPpzR2P2JQOcHp05J3/2u6Vr51ltSaKi/awQAAAD45uhRE+w++cSEvIMHzaTn48ebMnYs0yW0JAJd\nCzt0SJo4Ubr9duk3v2EUSwAAANhbUZF5Ds8d8HbskAYN8gS88eOlXr38Xcu2i0DXgo4fNxf0Qw9J\n/+//+bs2AAAAwOVXViZt3WrC3YYNZm68vn2l66835ZvflPr08Xct2w4CXQspLJRSUqS77jIjWQIA\nAADtQUWFGT1z/XpTPvlEioryBLzrrzfbaBwCXQs4d850s7z2Wum//otRgQAAANB+OZ3Srl1SeroJ\neBs3mi6ZVQNeXJy/a2kfBLpmduGCNG2auSj/8hfCHAAAAFCVyyV98YWnBW/DBjNVwvXXSxMmSDfc\nwFx49SHQNbPZs6XTp6W332YAFAAAAOBSLEvas8eEu48/NiU01AS7G24wIS883N+1bD0IdM1owwbp\ne9+TsrOXbBtTAAAYoklEQVTNtwwAAAAAGsbdgvfvf5uyYYN0xRXSjTeagPfNb0o9e/q7lv5DoGsm\nFRXSiBHSE09I3/mOv2sDAAAAtA0VFdK2bZ6Al5Fhpklwt+B94xtSly7+rmXLIdA1kxdekP75T+mj\nj3huDgAAAGguFy6YUOcOeDt2mInOJ00yJTlZ6tDB37VsPgS6ZpCXJw0ZYpqDBw70d20AAACA9uPM\nGfP83b/+JX34oVRcLKWmmnCXmtr2pkioLxM1OceuWbNGSUlJ6t+/v5555plaj3nwwQfVv39/JScn\nKysrq0HntlavvCLdeSdhDgAAAGhp3btLN98svfiitG+ftGWLec5u5UrTNTM5WXr0UdOT7vx5f9e2\neTWphc7pdCoxMVFr165VTEyMRo0apTfffFMDq6Sc1atXa8mSJVq9erW2bNmihx56SBkZGT6dK7Xe\nFrpbbpG+/30T6gAAAAC0DhUV0tatpuXuww/NfHhjxpj5oseNM+thYf6uZcM0WwtdZmamEhISFB8f\nr6CgIE2fPl0rVqzwOmblypWaOXOmJGnMmDEqLi5WXl6eT+e2VpZlvgUYM8bfNQEAAABQVWCgCW4L\nF0qbNkk5OdJDD5nJzn/7W6lvXykpSZo1S/rTn6TPPzev2VWTAl1ubq7iqkzxHhsbq9zcXJ+OOXbs\n2CXPba2+/loKCGDyQwAAAKC1CwmRpk2Tnn7aDKhSVCS99ZZpnNm0SbrjDtNiN3Gi9Pe/+7u2Ddek\nabAdPg7t2Bq7TDZFVpaZroCRLQEAANDeWJbp1lhWJl28WH8pL7/0MRcvmverrzidl37dssxUBvWV\nrl096wMGSMOGSfPnS6WlZh48u3XFlJoY6GJiYpSTk1O5nZOTo9hqzVbVjzl69KhiY2NVXl5+yXPd\nFi1aVLmekpKilJSUplS7ycLDpZMn/VoFAAAAtHMulwlVpaVm4I/z5+ter7pdVmbKhQue9erb9b1W\nVmZ6q3XqZErHjt4lKKjmvvpKUJApgYFS585m6S4BAd7btRX3MZLnc1Yt585J+fm176++Ly3NzHPn\nb+np6UpPT/fp2CYNilJRUaHExEStW7dO0dHRGj16dL2DomRkZGjevHnKyMjw6VypdQ6KUlRkZq4v\nKaGVDgAAADVZlidcnDvnCQ/1rdf3em1B7eJFE4CCg02LU3Cwd6m+z73dubN36dSp7u3aXuvUyROg\n0DLqy0RN+qcIDAzUkiVLdNNNN8npdGrOnDkaOHCgli5dKkmaO3eupk6dqtWrVyshIUFdu3bVq6++\nWu+5dhAaaoZKPXLEPFQJAAAA+yovl86erbucO1f/67WV0lITfNzd/Nyl6nb19bAwKS6u9nNqC2ed\nOtG4ACYWb7TUVOnhh6WpU/1dEwAAgPbF5TIh68yZhpWSktr3l5dL3bqZL+y7dfO9dO1a9/6uXU1X\nQOByaLYWuvbs6qvNg5MEOgAAAN84nab1qqREOn3aLH0p1Y89e9Z0/+vevfbSo4dnPS6u7uPcJTiY\nli7YFy10jbR2rfTAA9Lu3eYhTgAAgLasosIEq8aWkhLTDbFrV6lnTxO6fCm1HdutG89woX2pLxMR\n6Jpg0iTpttuk++/3d00AAADqZllmZMLiYt9L9UBWVuYJWJcqISHe2+7zunWTOjRpFmSgfSLQNZOs\nLNPlcv9+01wPAADQXMrLzUjbRUUmcLnX3dt1hTL3umQGdgsJuXSpLaR160a3RMBfCHTN6HvfkxIS\npCpT5QEAANTq/HkTwAoLaw9l9QW2sjITtkJDPcGs+nr1UFZ1u3Nnf396AI1FoGtGhw9L11wjffaZ\n1K+fv2sDAACam9PpCVyFhZ5l1fW6li6XJ4RVL1UDWm3btJAB7ReBrpn94Q/Sf/2XtH69GUkJAAC0\nfu4ujO4wduqUZ722bXc5e9Y8ExYWZoKWe1l1va5lly6EMgANx7QFzeyBB6QLF6QbbpDS06WYGH/X\nCACA9sPdYnbqVM1SX0A7d86ErF69TOByF/f21Vd7tt2hLCzMdGVkYA8ArQWB7jJ5+GHzTd8NN5iW\nushIf9cIAAD7OX++9mBWXzl92rSY9erlXdxh7OqrvUObe717d4IZAPujy+Vl9p//Kb3xhvTxx1JE\nhL9rAwCA/5SWmsB18qRnean1igqpd++a4ay+EhoqBQT4+9MCQPOhy2ULevxxM9fLNddIf/yjdPPN\n/q4RAABNV17uCV4nT0oFBd7LqsUd0lwuE87cpVcvz3r//tK4cd77evUyk07zjBkA+I4WumaSni7N\nni1df730u9+ZkaoAAGgNLEs6c8aEMXepL6gVFJjnzaqGr/Bw72VtwY0BQADg8mCUSz85e1ZasEBa\nuVL685+lyZP9XSMAQFvkcpnRGqsGtPrKyZNSx44mjLlLfUEtPJyBQADAnwh0frZunTRnjpSaKv32\nt7TWAQDqZ1lSSYl04oSnFBTUvV1YaOYoqxrQ6iu9e0vBwf7+lAAAXxHoWoGSEtNa98470ty50rx5\nUp8+/q4VAKClXLhgwld+vveytpBWUCB16mT+P+Eu4eG1b7sDWlCQvz8hAKC5EOhakUOHTCvdW29J\n3/ue9JOfSFdc4e9aAQAayv0cWl6edzirHtjcy9JSTxiLiPAsw8M9y6phrXNnf39CAEBrQaBrhY4f\nN4Ol/OUv0re/bVrvEhP9XSsAaN/cXR3z801xh7W6tgMDvcNZ1fXqy5AQBggBADQOga4VKyyUliwx\n5brrpJkzpSlTzMPqAIDLo6zME8guVdwhLSJCioz0rNe23bWrvz8ZAKA9INDZwNmz0t/+ZiYl/+IL\n6Y47pP/4D+mb32RUMQCojWVJp0+bHg+1FXdAO37cDLnvDmRRUWZZWyGkAQBaIwKdzeTkmGfs3njD\nPBg/Y4YJd8OG0V0HQNvnfjYtN1c6dqxmqRraAgJMQKuvREZKoaF8OQYAsC8CnY1lZ5tg98YbZsSz\n73xHmjRJGjOGEc0A2E9pac1w5l6vGuAkKSZGio6uWaqGtW7d/Pt5AABoCQS6NsCypC1bpH/8w8xr\nd+CANH68NHGiKVdfTesdAP8pK/MOZ1XXq5aystoDWvXw1r07f9MAAHAj0LVBp05J//63tHatKefO\necLdxIlSbKy/awjA7qoOy+8e3dG9Xj2onT1rgpk7oNXWshYdzUiPAAA0BoGuHTh40LTcrV1rlmFh\n0qhR0siRpgwfTtckAEZpac2AVtd6UJBnMBH3oCGRkaZFrWpw69WLoAYAQHMh0LUzLpe0e7f02Wee\n8vnnUr9+Jtxdc41ZDhsmdeni79oCaCqXSyoqMoMonThRc1l1/rS8PKm83DukVQ1qVdcZ8REAgNaB\nQAddvGhC3rZtnpCXnS0lJJiAN2SImdg8MVGKjzfzMAHwD8uSiotrhrO6AtupU6YFvk8fKTy85rJ6\nUOvRg9Y0AADshECHWl24YOa8c4e7vXulffvMt/hXXmnCXVKSJ+glJpqhvwH4zuk04ezUqdrLyZM1\nt0+eNK3ntYWz6svwcKl3b6ljR39/UgAA0FwIdGiQ0lIziua+faa4g96+feYmMzFRGjBA6ttXiouT\nrrjCLOPipM6d/V17oHk4nVJ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- "text": [ - "" - ] - } + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar(generation, profile_number): \n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", + " \n", + " datos = profile_read_aero(generation, profile_number)\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'polar.png'\n", + " plt.savefig(nombre_grafico)" ], - "prompt_number": 7 + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar_compare(generation_imput, profile_number_imput): \n", + " \n", + " num_prof = generation_imput.shape[0]\n", + " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " \n", + " for i in np.arange(0,num_prof,1):\n", + " generation = generation_imput[i]\n", + " profile_number = profile_number_imput[i]\n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " \n", + " \n", + " datos = profile_read_aero(generation, profile_number)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'polar.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_alpha_compare(generation_imput, profile_number_imput): \n", + " \n", + " num_prof = generation_imput.shape[0]\n", + " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.title (profile_name)\n", + " \n", + " ideal = np.array([[0,0],\n", + " [15, (15*np.pi/180)*np.pi*2]])\n", + " \n", + " plt.plot(ideal[:,0], ideal[:,1])\n", + " for i in np.arange(0,num_prof,1):\n", + " generation = generation_imput[i]\n", + " profile_number = profile_number_imput[i]\n", + " \n", + " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", + " \n", + " \n", + " \n", + " datos = profile_read_alpha(generation, profile_number)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1])\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " \n", + " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'alpha.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_polar_compare_generic(profileroots): \n", + " \n", + " num_prof = profileroots.shape[0]\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.rc('font', size = 20)\n", + " plt.title ('Profile comparison')\n", + " \n", + " for i in np.arange(0,num_prof,1):\n", + " \n", + " \n", + " profile_root = profileroots[i]\n", + " \n", + " \n", + " \n", + " datos = profile_read_aero_generic(profile_root)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " plt.legend(loc = 2, fontsize =14) \n", + " plt.grid()\n", + " plt.minorticks_on()\n", + " plt.xlabel('Lift coefficient') \n", + " plt.ylabel('Drag coefficient')\n", + " nombre_grafico = 'graficos\\comparepolar.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def drawing_alpha_compare_generic(root): \n", + " \n", + " num_prof = root.shape[0]\n", + " \n", + " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", + " plt.rc('font', size = 20)\n", + " plt.title ('profile comparison')\n", + " \n", + " ideal = np.array([[0,0],\n", + " [15, (15*np.pi/180)*np.pi*2]])\n", + " \n", + " plt.plot(ideal[:,0], ideal[:,1], label = ' ideal, cl alpha = 2$\\pi$')\n", + " for i in np.arange(0,num_prof,1):\n", + " \n", + " profile_root = root[i]\n", + " \n", + " \n", + " \n", + " datos = profile_read_alpha_generic(profile_root)\n", + " \n", + " \n", + " \n", + " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", + " \n", + " \n", + " if not os.path.exists('graficos'):\n", + " os.makedirs('graficos')\n", + " \n", + " plt.legend(loc = 2, fontsize =14) \n", + " plt.grid() \n", + " plt.minorticks_on()\n", + " plt.xlabel('Attack angle') \n", + " plt.ylabel('Lift coefficient')\n", + " nombre_grafico = 'graficos\\compare_alpha.png'\n", + " plt.savefig(nombre_grafico)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 }, { "cell_type": "code", @@ -396,7 +453,8 @@ "input": [], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "prompt_number": 13 } ], "metadata": {} From 32756564a67506c1599d36f5d3c8af22ccf37c5e Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 3 Mar 2015 20:02:22 +0100 Subject: [PATCH 11/16] Funcional MK2.6 Compatible con Linux --- .../result_drawer-checkpoint.ipynb | 466 ------------------ .../Genetic_algorithm_files/analyze.py | 5 +- .../Genetic_algorithm_files/genetics.py | 9 +- .../Genetic_algorithm_files/initial.py | 2 +- .../Genetic_algorithm_files/interfaz.py | 16 +- 5 files changed, 18 insertions(+), 480 deletions(-) delete mode 100644 aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb diff --git a/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb b/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb deleted file mode 100644 index 4df0d4b..0000000 --- a/aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb +++ /dev/null @@ -1,466 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:a2cae234c199207c88d4df8cf19f19cdb7a268841519e80d49d5905d30ac7851" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "from transcript import *" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "No module named 'transcript'", - "output_type": "pyerr", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtranscript\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mImportError\u001b[0m: No module named 'transcript'" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Result Drawer for the Xfoil Genetic Algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are described some useful functions that can help you to graphically display your results.\n", - "\n", - "Place this notebook in the same folder that the Xfoil and the genetic algorithm files.\n", - "\n", - "\n", - "Here is a list of the included functions:\n", - "\n", - "**profile_read_aero** (generation, profile_number)\n", - "\n", - "Searches for the file of a certain profile generated by the algorithm and return the Cd and Cl.\n", - "\n", - "**profile_read_aero_generic** (root)\n", - "\n", - "Reads a file located in root (ej: calculations/naca6715.txt) and return the Cd and Cl.\n", - "\n", - "**profile_read_alpha** (generation, profile_number)\n", - "\n", - "Searches for the file of a certain profile generated by the algorithm and return the alpha angle and Cl.\n", - "\n", - "**profile_read_alpha_generic** (root)\n", - "\n", - "Reads a file located in root (ej: calculations/naca6715.txt) and return the alpha angle and Cl.\n", - "\n", - "**drawing**(generation, profile_number) \n", - "\n", - "Searches for the file of a certain profile generated by the algorithm and draws it with matplotlib.\n", - "\n", - "**drawing_bezier**(generation, profile_number)\n", - "\n", - "Searches for the file of a certain profile generated by the algorithm and draws it, along with its bezier points with matplotlib. Useful for understanding the genome of a certain profile.\n", - "\n", - "**drawing_polar**(generation, profile_number)\n", - "\n", - "Plots the Cd against the Cl for a certain profile generated by the algorithm\n", - "\n", - "**drawing_polar_compare**(generation_imput, profile_number_imput)\n", - "\n", - "Plots the Cd against the Cl for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", - "\n", - "**drawing_alpha_compare**(generation_imput, profile_number_imput)\n", - "\n", - "Plots the Cl against the alpha for some profiles generated by the algorithm. Both 'generation_imput' and 'profile_number_imput' must be numpy arrays of the same dimension.\n", - "\n", - "**drawing_polar_compare_generic**(profileroots) and **drawing_alpha_compare_generic**(root)\n", - "\n", - "Do the same as the equivalent non-generic functions, but must be fed with an array of file directions (ej: calculations/naca6715.txt)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def profile_read_aero (generation, profile_number): \n", - " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", - " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", - " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", - " \n", - " read_dim = np.array(datos.shape)\n", - " #print('read_dim = ', read_dim, read_dim.shape)\n", - " if ((read_dim.shape[0]) != 2):\n", - " return np.array ([0,0])\n", - " else:\n", - " return datos" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def profile_read_aero_generic (root): \n", - " profile_name = root\n", - " data_root = root\n", - " datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])\n", - " \n", - " read_dim = np.array(datos.shape)\n", - " #print('read_dim = ', read_dim, read_dim.shape)\n", - " if ((read_dim.shape[0]) != 2):\n", - " return np.array ([0,0])\n", - " else:\n", - " return datos" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def profile_read_alpha (generation, profile_number): \n", - " profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)\n", - " data_root = \"aerodata\\data\" + profile_name + '.txt'\n", - " datos = np.loadtxt(data_root, skiprows=12, usecols=[0,1])\n", - " \n", - " read_dim = np.array(datos.shape)\n", - " #print('read_dim = ', read_dim, read_dim.shape)\n", - " if ((read_dim.shape[0]) != 2):\n", - " return np.array ([0,0])\n", - " else:\n", - " return datos" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def profile_read_alpha_generic (root): \n", - " \n", - " \n", - " datos = np.loadtxt(root, skiprows=12, usecols=[0,1])\n", - " \n", - " read_dim = np.array(datos.shape)\n", - " #print('read_dim = ', read_dim, read_dim.shape)\n", - " if ((read_dim.shape[0]) != 2):\n", - " return np.array ([0,0])\n", - " else:\n", - " return datos" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing(generation, profile_number): \n", - " \n", - " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", - " \n", - " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", - " \n", - " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", - " \n", - " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.title (profile_name)\n", - " plt.ylim(-0.15, 0.15)\n", - " plt.xlim(-0.05, 1.05)\n", - " plt.plot(datos[:,0], datos[:,1])\n", - " plt.gca().set_aspect(1)\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " \n", - " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + '.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_bezier(generation, profile_number): \n", - " \n", - " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", - " \n", - " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", - " \n", - " genome_root = 'genome\\generation'+ str(generation) + '.txt'\n", - " \n", - " genome_matrix = np.loadtxt(genome_root, skiprows=1)\n", - " \n", - " genome = genome_matrix[profile_number-1,:]\n", - " \n", - " bezier_points = generador_puntos(genome)\n", - " \n", - " datos = np.loadtxt(profile_root, skiprows=3, usecols=[0,1])\n", - " \n", - " plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.title (profile_name)\n", - " plt.ylim(-0.15, 0.15)\n", - " plt.xlim(-0.05, 1.05)\n", - " plt.scatter(bezier_points[:,0],bezier_points[:,1])\n", - " plt.plot(datos[:,0], datos[:,1])\n", - " plt.plot(bezier_points[0:2 , 0] , bezier_points[0:2 , 1])\n", - " plt.plot(bezier_points[2:5 , 0] , bezier_points[2:5 , 1])\n", - " plt.plot(bezier_points[5:8 , 0] , bezier_points[5:8 , 1])\n", - " plt.plot(bezier_points[8:11 , 0] , bezier_points[8:11 , 1])\n", - " plt.plot(bezier_points[11:13 , 0] , bezier_points[11:13 , 1])\n", - " plt.gca().set_aspect(1)\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " \n", - " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'bezierpoints.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_polar(generation, profile_number): \n", - " \n", - " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", - " \n", - " profile_name = 'Generaci\u00f3n ' + str(generation) + ', perfil ' + str(profile_number)\n", - " \n", - " datos = profile_read_aero(generation, profile_number)\n", - " \n", - " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.title (profile_name)\n", - " plt.plot(datos[:,0], datos[:,1])\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " \n", - " nombre_grafico = 'graficos\\gen' + str(generation) + 'profile' + str(profile_number) + 'polar.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_polar_compare(generation_imput, profile_number_imput): \n", - " \n", - " num_prof = generation_imput.shape[0]\n", - " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", - " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.title (profile_name)\n", - " \n", - " for i in np.arange(0,num_prof,1):\n", - " generation = generation_imput[i]\n", - " profile_number = profile_number_imput[i]\n", - " \n", - " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", - " \n", - " \n", - " \n", - " datos = profile_read_aero(generation, profile_number)\n", - " \n", - " \n", - " \n", - " plt.plot(datos[:,0], datos[:,1])\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " \n", - " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'polar.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_alpha_compare(generation_imput, profile_number_imput): \n", - " \n", - " num_prof = generation_imput.shape[0]\n", - " profile_name = 'Generaci\u00f3n ' + str(generation_imput) + ', perfil ' + str(profile_number_imput)\n", - " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.title (profile_name)\n", - " \n", - " ideal = np.array([[0,0],\n", - " [15, (15*np.pi/180)*np.pi*2]])\n", - " \n", - " plt.plot(ideal[:,0], ideal[:,1])\n", - " for i in np.arange(0,num_prof,1):\n", - " generation = generation_imput[i]\n", - " profile_number = profile_number_imput[i]\n", - " \n", - " profile_root = 'profiles\\gen' + str(generation) + '\\profile' + str(profile_number) + '.txt'\n", - " \n", - " \n", - " \n", - " datos = profile_read_alpha(generation, profile_number)\n", - " \n", - " \n", - " \n", - " plt.plot(datos[:,0], datos[:,1])\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " \n", - " nombre_grafico = 'graficos\\gen' + str(generation_imput) + 'profile' + str(profile_number_imput) + 'alpha.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_polar_compare_generic(profileroots): \n", - " \n", - " num_prof = profileroots.shape[0]\n", - " \n", - " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.rc('font', size = 20)\n", - " plt.title ('Profile comparison')\n", - " \n", - " for i in np.arange(0,num_prof,1):\n", - " \n", - " \n", - " profile_root = profileroots[i]\n", - " \n", - " \n", - " \n", - " datos = profile_read_aero_generic(profile_root)\n", - " \n", - " \n", - " \n", - " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " plt.legend(loc = 2, fontsize =14) \n", - " plt.grid()\n", - " plt.minorticks_on()\n", - " plt.xlabel('Lift coefficient') \n", - " plt.ylabel('Drag coefficient')\n", - " nombre_grafico = 'graficos\\comparepolar.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def drawing_alpha_compare_generic(root): \n", - " \n", - " num_prof = root.shape[0]\n", - " \n", - " plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k')\n", - " plt.rc('font', size = 20)\n", - " plt.title ('profile comparison')\n", - " \n", - " ideal = np.array([[0,0],\n", - " [15, (15*np.pi/180)*np.pi*2]])\n", - " \n", - " plt.plot(ideal[:,0], ideal[:,1], label = ' ideal, cl alpha = 2$\\pi$')\n", - " for i in np.arange(0,num_prof,1):\n", - " \n", - " profile_root = root[i]\n", - " \n", - " \n", - " \n", - " datos = profile_read_alpha_generic(profile_root)\n", - " \n", - " \n", - " \n", - " plt.plot(datos[:,0], datos[:,1], label = profile_root)\n", - " \n", - " \n", - " if not os.path.exists('graficos'):\n", - " os.makedirs('graficos')\n", - " \n", - " plt.legend(loc = 2, fontsize =14) \n", - " plt.grid() \n", - " plt.minorticks_on()\n", - " plt.xlabel('Attack angle') \n", - " plt.ylabel('Lift coefficient')\n", - " nombre_grafico = 'graficos\\compare_alpha.png'\n", - " plt.savefig(nombre_grafico)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py index 132e367..597174b 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py @@ -17,6 +17,7 @@ import numpy as np +import os @@ -39,8 +40,8 @@ def profile_analice (generation, profile_number): values of the Lift Coefficient and Aerodynamic Efficiency and returns them as an 1x2 array. ''' - profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) - data_root = "aerodata\data" + profile_name + '.txt' + profile_name = 'datagen' + str(generation) + 'prof' + str(profile_number)+ '.txt' + data_root = os.path.join("aerodata", profile_name) datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2]) read_dim = np.array(datos.shape) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py index ff6c498..e1d3075 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py @@ -25,11 +25,11 @@ - def genetic_step(generation,num_parent, weights): '''Returns the genome of the (n+1)generation ''' - genome_parent_root = 'genome\generation'+ str(generation) + '.txt' + file_parent_name = 'generation'+ str(generation) + '.txt' + genome_parent_root = os.path.join('genome', file_parent_name) genome = np.loadtxt(genome_parent_root, skiprows=1) num_pop = genome.shape[0] @@ -38,8 +38,9 @@ def genetic_step(generation,num_parent, weights): children = cross.cross(parents, num_pop) children = mutation.mutation(children, generation, num_parent) - profile_number = children.shape[0] - genome_root = 'genome\generation'+ str(generation + 1) + '.txt' + profile_number = children.shape[0] + file_name = 'generation'+ str(generation + 1) + '.txt' + genome_root = os.path.join('genome', file_name) title = 'generation' + str(generation + 1) + 'genome' try: diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py index 52e1e5e..35bdada 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py @@ -77,7 +77,7 @@ def start_pop(pop_num): profile_number = genome.shape[0] - genome_root = 'genome\generation0.txt' + genome_root = os.path.join('genome','generation0.txt') title = 'generation 0 genome' try: diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index a271252..ff42361 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -32,8 +32,11 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer '''Starts Xfoil and analyzes the given airfoil. Saves the results. ''' - profile_root = 'profiles\gen' + str(generation) + '\profile' + str(profile_number) + '.txt' profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) + geo_file_name = 'profile' + str(profile_number) + '.txt' + profile_root = os.path.join('profiles','gen' + str(generation) , geo_file_name ) + data_root = os.path.join("aerodata","data" + profile_name + '.txt') + aerodynamics = ambient.aero_conditions(ambient_data) @@ -44,7 +47,7 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer 're ' + str(aerodynamics[1]), 'visc', 'pacc', - "aerodata\data" + profile_name + '.txt', + data_root, '', 'aseq', str(aero_domain[0]), @@ -56,14 +59,13 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer perfil = trans.decode_genome(genome) - if not os.path.exists('profiles\gen' + str(generation)): - os.makedirs('profiles\gen' + str(generation)) + try: os.remove(profile_root) except : pass try: - os.remove("aerodata\data" + profile_name + '.txt') + os.remove(data_root) except : pass @@ -78,7 +80,7 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer archivo.write(texto) archivo.close() - p = subprocess.Popen(["xfoil.exe",], + p = subprocess.Popen(["xfoil",], stdin=subprocess.PIPE, stdout=subprocess.PIPE) @@ -96,7 +98,7 @@ def xfoil_calculate_population(generation, ambient_data, aero_domain): analyze each airfoil. ''' - genome_root = 'genome\generation'+ str(generation) + '.txt' + genome_root = os.path.join('genome','generation'+ str(generation) + '.txt') genome_matrix = np.loadtxt(genome_root, skiprows=1) num_pop = genome_matrix.shape[0] From c94d5cb484b385306a942a16fb1bc59a89cc2c67 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 3 Mar 2015 20:32:59 +0100 Subject: [PATCH 12/16] Funcional MK2.7 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Corregido bug de creación de carpetas --- .../Xfoil_Interaction/Genetic_algorithm_files/interfaz.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index ff42361..81e1067 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -68,8 +68,7 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer os.remove(data_root) except : pass - - + archivo = open(profile_root, mode = 'x') archivo.write(profile_name + '\n\n\n') @@ -102,6 +101,10 @@ def xfoil_calculate_population(generation, ambient_data, aero_domain): genome_matrix = np.loadtxt(genome_root, skiprows=1) num_pop = genome_matrix.shape[0] + profile_folder = os.path.join('profiles', 'gen' + str(generation)) + if not os.path.exists(profile_folder): + os.makedirs(profile_folder) + for profile_number in np.arange(1,num_pop+1,1): xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:], ambient_data, aero_domain) From 2402b0aec1e6e4d1b54dbbd9208f753141346446 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 3 Mar 2015 21:24:25 +0100 Subject: [PATCH 13/16] Funcional MK2.8 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Corregido bug que impedía la lectura de los perfiles en el Xfoil de Linux --- aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index 81e1067..93eab6f 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -71,7 +71,7 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer archivo = open(profile_root, mode = 'x') - archivo.write(profile_name + '\n\n\n') + archivo.write(profile_name + '\n') for i in np.arange(0,100,1): From 74bac35af2e225be9dd0376e6369348ebbfa1d93 Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 28 Apr 2015 09:51:32 +0200 Subject: [PATCH 14/16] Funcional MK2.9 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Añadidas opciones de final report, como imágenes y texto. --- .../Genetic_algorithm_files/ender.py | 551 ++++++++++++++++++ .../Genetic_algorithm_files/ender_report.py | 351 +++++++++++ .../Genetic_algorithm_files/genetics.py | 22 + .../Genetic_algorithm_files/graphics.py | 45 ++ .../Genetic_algorithm_files/interfaz.py | 2 +- .../Genetic_algorithm_files/main.py | 144 +++-- 6 files changed, 1075 insertions(+), 40 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py create mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py new file mode 100644 index 0000000..8c0c31c --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py @@ -0,0 +1,551 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + + +import os +import interfaz as interfaz +import numpy as np +import initial as initial +import genetics as genetics +import analyze as analyze +import selection as selection +import matplotlib.pyplot as plt +import transcript as transcript +import ambient as ambient +import subprocess +import ender_report + + +def finish (all_parameters): + + + generation = all_parameters[1] + num_winners = all_parameters[3] + weights = all_parameters[4] + end_options = all_parameters[5] + + + must_draw_winners = end_options[0] + must_draw_polars = end_options[1] + must_draw_evolution = end_options[2] + must_compare_naca_standard = end_options[3] + must_compare_naca_custom = end_options[4] + must_create_report = end_options[5] + ambient_data = end_options[6] + aero_domain = end_options[7] + + analyze_final(generation, num_winners, weights) + + compare = must_compare_naca_standard + final_xfoil(generation, ambient_data, aero_domain, compare) + analyze_winners(generation, num_winners, weights) + calculate_evolution(generation, num_winners, compare) + if (must_draw_winners): + draw_winners(compare) + if (must_draw_polars): + draw_aero_comparison(num_winners, compare) + if (must_draw_evolution): + draw_evolution(compare, aero_domain) + if (must_create_report): + ender_report.create_report(all_parameters) + + +def analyze_final(generation, num_winners, weights): + '''Analyze the data of the last generation + ''' + file_parent_name = 'generation'+ str(generation) + '.txt' + genome_parent_root = os.path.join('genome', file_parent_name) + genome = np.loadtxt(genome_parent_root, skiprows=1) + num_pop = genome.shape[0] + results_data = analyze.pop_analice(generation, num_pop) + + scores = analyze.score(generation,num_pop, weights) + winners = selection.selection(scores, genome, num_winners) + + + + + file_name = 'winners.txt' + genome_root = os.path.join('genome', file_name) + title = 'winners genome' + + results_name = 'results_data_generation'+ str(generation) + '.txt' + results_root = os.path.join('results', 'data', results_name ) + results_title = 'generation' + str(generation) + 'results:' + + + try: + os.remove(genome_root) + except: + pass + + try: + os.remove(results_root) + except : + pass + + genome_file = open(genome_root, mode = 'x') + results_file = open(results_root, mode = 'x') + genome_file.write(title + '\n') + results_file.write(results_title + '\n') + results_file.write('Cl max Eficciency Score' + '\n') + + for profile in np.arange(0, num_pop, 1): + result = str(results_data[profile, 0]) + ' ' + result = result + str(results_data[profile, 1]) + ' ' + result = result + str(scores[profile]) + '\n' + results_file.write(result) + for profile in np.arange(0, num_winners, 1): + line = '' + for gen in np.arange(0, 16,1): + line = line + str(winners[profile, gen]) +' ' + line = line + '\n' + genome_file.write(line) + genome_file.close() + results_file.close() + + +def analyze_winners(generation, num_winners, weights): + '''Analyze the data of the winners + ''' + + results = np.zeros([num_winners, 3]) + + for i in np.arange(0, num_winners, 1): + dataname = 'datagen' + str(generation) + 'prof' + str(i + 1) + '.txt' + data_root = os.path.join('aerodata', dataname) + data = np.loadtxt(data_root, skiprows = 12, usecols=[1,2]) + clmax = max(data[:,0]) + efimax = max(data[:,0] / data[:,1]) + results[i, 0:2] = [clmax, efimax] + + cl_score = analyze.adimension(results[:,0]) + efic_score = analyze.adimension(results[:,1]) + results[:,2] = weights[0] * cl_score + weights[1] * efic_score + + + results_name = 'results_winners.txt' + results_root = os.path.join('results', 'data', results_name ) + results_title = 'Winners results:' + + + try: + os.remove(results_root) + except : + pass + + results_file = open(results_root, mode = 'x') + results_file.write(results_title + '\n') + results_file.write('Cl max Eficciency Score' + '\n') + + for profile in np.arange(0, num_winners, 1): + result = str(results[profile, 0]) + ' ' + result = result + str(results[profile, 1]) + ' ' + result = result + str(results[profile, 2]) + '\n' + results_file.write(result) + +#--- Drawing Airfoils + + + +def draw_winners(options): + + winners_root = os.path.join('genome', 'winners.txt') + winners_genome = np.loadtxt(winners_root, skiprows=1) + + num_winners = winners_genome.shape[0] + + for winner in np.arange(0, num_winners, 1): + + graph_name = 'winner ' + str(winner + 1) + graph_root = os.path.join('results','graphics',graph_name + '.png') + point_data = transcript.decode_genome(winners_genome[winner,:]) + draw_figure(graph_name, graph_root, point_data) + + if (options): + graph_name = 'NACA 5615' + graph_root = os.path.join('results','graphics',graph_name + '.png') + data_root = os.path.join('profiles','winners',graph_name + '.txt') + point_data = np.loadtxt(data_root, skiprows = 1) + draw_figure(graph_name, graph_root, point_data) + + +def draw_figure(graph_name, graph_root, point_data): + try: + os.remove(graph_root) + except : + pass + + plt.figure(num=None, figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k') + plt.title (graph_name) + plt.ylim(-0.15, 0.15) + plt.xlim(-0.05, 1.05) + plt.plot(point_data[:,0], point_data[:,1]) + plt.gca().set_aspect(1) + + + plt.savefig(graph_root) + + +# Running Xfoil for more detailed analysis of the winners + +def xfoil_calculate_profile(profile_name, profile_root, + ambient_data, aero_domain, + profile_type): + + '''Starts Xfoil and analyzes the given airfoil. Saves the results. + ''' + + data_root = os.path.join("results","data" , profile_name + 'aerodata.txt') + + aerodynamics = ambient.aero_conditions(ambient_data) + + + commands = [profile_type, + profile_root] + commands2 = ['oper', + 'mach ' + str(aerodynamics[0]), + 're ' + str(aerodynamics[1]), + 'visc', + 'pacc', + data_root, + '', + 'aseq', + str(aero_domain[0]-1), + str(aero_domain[1]+5), + str(aero_domain[2]/2), + '', + 'quit'] + if (profile_type == 'NACA'): + naca_root = os.path.join('profiles', 'winners', profile_name + '.txt') + commands.extend(['save',naca_root]) + commands.extend(commands2) + + + + try: + os.remove(data_root) + except : + pass + + + + p = subprocess.Popen(["xfoil",], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE) + + for command in commands: + p.stdin.write((command + '\n').encode()) + + + p.stdin.close() + for line in p.stdout.readlines(): + print(line.decode(), end='') + +def final_xfoil(total_generations, ambient_data, aero_domain, compare): + + profile_folder = os.path.join('profiles','winners') + if not os.path.exists(profile_folder): + os.makedirs(profile_folder) + + genome_root = os.path.join('genome','winners.txt') + genome_matrix = np.loadtxt(genome_root, skiprows=1) + num_winners = genome_matrix.shape[0] + + + for profile in np.arange(0, num_winners, 1): + genome = genome_matrix[profile,:] + profile_name = 'winner ' + str(profile + 1) + profile_root = os.path.join('profiles','winners', profile_name + '.txt') + + perfil = transcript.decode_genome(genome) + try: + os.remove(profile_root) + except : + pass + + + archivo = open(profile_root, mode = 'x') + archivo.write(profile_name + '\n') + for i in np.arange(0,100,1): + texto = str(round(perfil[i,0],6)) + ' ' + str(round(perfil[i,1],6)) +'\n' + archivo.write(texto) + archivo.close() + + xfoil_calculate_profile(profile_name, profile_root, + ambient_data, aero_domain, + 'load') + if (compare): + + profile_name = 'NACA 5615' + profile_root = '5615' + + try: + os.remove(os.path.join('profiles','winners', profile_name + '.txt')) + except : + pass + + xfoil_calculate_profile(profile_name, profile_root, + ambient_data, aero_domain, + 'NACA') + +# Drawing polars + +def draw_alpha(data): + + if not os.path.exists(os.path.join('results','graphics')): + os.makedirs(os.path.join('results','graphics')) + graph_name = 'Cl_vs_Alpha_Graphic' + graph_root = os.path.join('results','graphics',graph_name + '.png') + + root = data[:,1] + name = data[:,0] + + num_prof = root.shape[0] + + plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k') + plt.rc('font', size = 20) + plt.title (graph_name) + + ideal = np.array([[0,0], + [15, (15*np.pi/180)*np.pi*2]]) + + plt.plot(ideal[:,0], ideal[:,1], label = ' ideal, cl alpha = 2$\pi$') + for i in np.arange(0,num_prof,1): + + profile_root = root[i] + profile_name = name[i] + datos = np.loadtxt(profile_root, skiprows=12, usecols=[0,1]) + read_dim = np.array(datos.shape) + if ((read_dim.shape[0]) == 2): + plt.plot(datos[:,0], datos[:,1], label = profile_name) + + + + + plt.legend(loc = 2, fontsize =14) + plt.grid() + plt.minorticks_on() + plt.xlabel('Attack angle') + plt.ylabel('Lift coefficient') + plt.savefig(graph_root) + +def draw_polar(data): + + if not os.path.exists(os.path.join('results','graphics')): + os.makedirs(os.path.join('results','graphics')) + graph_name = 'Cl_vs_Cd_Graphic' + graph_root = os.path.join('results','graphics',graph_name + '.png') + + root = data[:,1] + name = data[:,0] + + num_prof = root.shape[0] + + plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k') + plt.rc('font', size = 20) + plt.title (graph_name) + + for i in np.arange(0,num_prof,1): + + profile_root = root[i] + profile_name = name[i] + datos = np.loadtxt(profile_root, skiprows=12, usecols=[1,2]) + read_dim = np.array(datos.shape) + if ((read_dim.shape[0]) == 2): + plt.plot(datos[:,1], datos[:,0], label = profile_name) + + + + + plt.legend(loc = 4, fontsize =14) + plt.grid() + plt.minorticks_on() + plt.xlabel('Drag coefficient') + plt.ylabel('Lift coefficient') + plt.savefig(graph_root) + + +def draw_aero_comparison(num_winners, compare): + + num_profiles = num_winners + if (compare): + num_profiles = num_profiles + 1 + + data = [] + + for i in np.arange(0, num_winners, 1): + name = 'winner ' + str(i + 1) + root = os.path.join('results','data', name + 'aerodata.txt') + data.append([name, root]) + if compare : + name = 'NACA 5615' + root = os.path.join('results','data', name + 'aerodata.txt') + data.append([name, root]) + + data = np.array(data) + draw_alpha(data) + draw_polar(data) + + +# Drawing the Evolution Diagrams + +def calculate_evolution(max_generations, num_winners, options): + lift = [] + effic = [] + + + + for gen in np.arange(0,max_generations + 1,1): + lift.append([]) + effic.append([]) + data_root = os.path.join('results','data','results_data_generation'+ str(gen) + '.txt') + data = np.loadtxt(data_root, skiprows = 2) + invscore = 1- data[:, 2] + positions = np.argsort(invscore) + for win in np.arange(0, num_winners, 1): + win_cl = data[positions[win], 0] + win_effic = data[positions[win], 1] + lift[gen].append(win_cl) + effic[gen].append(win_effic) + + lift = np.array(lift) + effic = np.array(effic) + + lift_name = 'lift history.txt' + lift_root = os.path.join('results', 'data', lift_name ) + effic_name = 'efficiency history.txt' + effic_root = os.path.join('results', 'data', effic_name ) + + + + try: + os.remove(lift_root) + except : + pass + try: + os.remove(effic_root) + except : + pass + + lift_file = open(lift_root, mode = 'x') + lift_file.write('Values of Cl of the best airfoils of each generation\n') + lift_file.write('generation ') + for i in np.arange(1, num_winners + 1, 1): + lift_file.write('winner ' + str(i) + ' ') + lift_file.write('\n') + + for gen in np.arange(0, max_generations + 1, 1): + lift_file.write(' '+ str(gen) + ' ') + + for win in np.arange(0, num_winners, 1): + lift_file.write(str(lift[gen,win])+ ' ') + lift_file.write('\n') + + lift_file.close() + + effic_file = open(effic_root, mode = 'x') + effic_file.write('Values of efficiency of the best airfoils of each generation\n') + effic_file.write('generation ') + for i in np.arange(1, num_winners + 1, 1): + effic_file.write('winner ' + str(i) + ' ') + effic_file.write('\n') + + for gen in np.arange(0, max_generations + 1, 1): + effic_file.write(' '+ str(gen) + ' ') + + for win in np.arange(0, num_winners, 1): + effic_file.write(str(effic[gen,win])+ ' ') + effic_file.write('\n') + + effic_file.close() + +def draw_evolution(options, aero_domain): + + if options : + naca_root = os.path.join('results','data','NACA 5615aerodata.txt') + naca_data = np.loadtxt(naca_root, skiprows = 12, usecols=[1,2]) + max_angle = 1 + 2 * round((aero_domain[1]-aero_domain[0])/aero_domain[2]) + naca_cl = max(naca_data[0:max_angle,0]) + naca_effic = max(naca_data[0:max_angle,0] / naca_data[0:max_angle,1]) + + lift_name = 'lift history.txt' + lift_root = os.path.join('results', 'data', lift_name ) + lift_data = np.loadtxt(lift_root, skiprows = 2) + + effic_name = 'efficiency history.txt' + effic_root = os.path.join('results', 'data', effic_name ) + effic_data = np.loadtxt(effic_root, skiprows = 2) + + + if not os.path.exists(os.path.join('results','graphics')): + os.makedirs(os.path.join('results','graphics')) + + # Draw the history of lift coefficient + graph_name = 'History of lift coefficient' + graph_root = os.path.join('results','graphics',graph_name + '.png') + + num_winners = lift_data.shape[1] - 1 + + plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k') + plt.rc('font', size = 20) + plt.title (graph_name) + + for i in np.arange(0,num_winners,1): + label = 'winner' + str(i + 1) + plt.plot(lift_data[:,0], lift_data[:,i + 1], label = label) + if options : + label = 'NACA 5615' + value = naca_cl * np.ones_like(effic_data[:,0]) + plt.plot(lift_data[:,0], value, label = label) + + + + plt.legend(loc = 4, fontsize =14) + plt.grid() + plt.minorticks_on() + plt.xlabel('Generation') + plt.ylabel('Lift coefficient') + plt.savefig(graph_root) + + # Draw the history of efficiency + graph_name = 'History of efficiency' + graph_root = os.path.join('results','graphics',graph_name + '.png') + + num_winners = effic_data.shape[1] - 1 + + plt.figure(num=None, figsize=(15, 8), dpi=80, facecolor='w', edgecolor='k') + plt.rc('font', size = 20) + plt.title (graph_name) + + for i in np.arange(0,num_winners,1): + label = 'winner' + str(i + 1) + plt.plot(effic_data[:,0], effic_data[:,i + 1], label = label) + if options : + label = 'NACA 5615' + value = naca_effic * np.ones_like(effic_data[:,0]) + plt.plot(effic_data[:,0], value, label = label) + + + + plt.legend(loc = 4, fontsize =14) + plt.grid() + plt.minorticks_on() + plt.xlabel('Generation') + plt.ylabel('Efficiency') + plt.savefig(graph_root) + diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py new file mode 100644 index 0000000..01a9570 --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py @@ -0,0 +1,351 @@ +''' + +Created on Fri Feb 20 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is the main program. It will call the different submodules +and manage the data transfer between them in order to achieve the +genetic optimization of the profile. + +''' + + + + +import os +import interfaz as interfaz +import numpy as np +import initial as initial +import genetics as genetics +import IPython.nbformat.current as nbf + + +###### Some commented code below is used to build and test this module + + +# +####---------Primary Variables----- +# +# +#airfoils_per_generation = 10 +#total_generations = 10 +#num_parent = 3 +# +## We give the algorithm the conditions at wich we want to optimize our airofil +## through the "ambient data" tuple. +# +#planet = 'Mars' # For the moment we have 'Earth' and 'Mars' +#chord_length = 0.2 # In metres +#altitude = -7.5 # In Kilometres above sea level or reference altitude +#speed_parameter = 'speed' # 'speed' or 'mach' +#speed_value = 30 # Value of the previous magnitude +#ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) +# +# +# +#####--------Secondary Variables------ +##-- Analysis domain +#start_alpha_angle = 0 +#finish_alpha_angle = 15 +#alpha_angle_step = 1 +# +#aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) +##-- Optimization objectives +# +#lift_coefficient_weight = 0.3 +#efficiency_weight = 0.7 +# +#weighting_parameters = (lift_coefficient_weight, efficiency_weight) +# +##-- Final results options +# +#num_winners = 3 +#draw_winners = True +#draw_polars = True +#draw_evolution = True +#compare_naca_standard = True +#compare_naca_custom = True +#create_report = True +# +#end_options = (draw_winners, draw_polars, draw_evolution, +# compare_naca_standard, compare_naca_custom, +# create_report, +# ambient_data, aero_domain) +# +# +# +#all_parameters = (airfoils_per_generation, total_generations, num_parent, +# num_winners, weighting_parameters, end_options ) +# +# +# +##### FIN PARA PROBAR + +def create_report(all_parameters): + + generation = all_parameters[1] + num_winners = all_parameters[3] + weights = all_parameters[4] + end_options = all_parameters[5] + + + must_draw_winners = end_options[0] + must_draw_polars = end_options[1] + must_draw_evolution = end_options[2] + must_compare_naca_standard = end_options[3] + must_compare_naca_custom = end_options[4] + must_create_report = end_options[5] + ambient_data = end_options[6] + aero_domain = end_options[7] + compare = must_compare_naca_standard + + try: + import urllib + url = 'https://avatars3.githubusercontent.com/u/6246900?v=3&s=200' + logo_root = os.path.join('graphics','logo.png') + logo_complete_root = os.path.join('results', logo_root) + urllib.request.urlretrieve(url, logo_complete_root) + logo = True + except: + logo = False + logo_root = '' + + + + report_root = os.path.join('results','results.ipynb') + file_root = os.path.join('results','ejemplo.py') + + try: + os.remove(report_root) + except : + pass + + try: + os.remove(file_root) + except : + pass + text_01_a = '''[AeroPython](https://github.com/AeroPython) \n ''' + + text_01_d = '''

Aeropython Xfoil Genetic + Algorithm Report

''' + + if logo: + text_01 = text_01_a + text_01_b + text_01_c + text_01_d + else: + text_01 = text_01_d + + cell_01 = nbf.new_text_cell('heading', source = text_01) + + text_02 = '''Thank you for using our Genetic algorithm! Now we will present a + short report displaying some data generated by the algorithm''' + + cell_02 = nbf.new_text_cell('markdown', source= text_02) + + text_03 = ('''The algorithm has searched for an optimal airfoil for flying in **''' + + str(ambient_data[0]) + '''**, at an altitude of **''' + + str(ambient_data[2]) + '''** Kilometers over the sea level or + reference level. The chord of the profile is **''' + + str(ambient_data[1]) + ''' metres** long, and the **''' + + str(ambient_data[3]) +'** has a value of **' +str(ambient_data[4])) + if (str(ambient_data[3]) == 'speed'): + text_03 = text_03 + ' meters per second' + text_03 = text_03 + '**.' + + cell_03 = nbf.new_text_cell('markdown', source= text_03) + + text_04 =('''The airfoils were tested in XFoil between **''' + + str(aero_domain[0]) + '** and **' + str(aero_domain[1]) + + '** derees, increasing in increments of **' + str(aero_domain[2]) + + '** degrees. The ' + str(num_winners) + ''' best airfoils of the + last generation, or "winners", were aditionally tested between ''' + + str(aero_domain[0] - 1) + ' and ' + str(aero_domain[1] + 5) + + ' derees, in increments of ' + str(aero_domain[2] / 2) + + ' degrees.''') + + cell_04 = nbf.new_text_cell('markdown', source= text_04) + + text_05 =('''In this execution of the algorithm, optimizing the Lift + Coefficient had a weight of **''' + str(weights[0]) + + '''**, while the efficiency , or Lift Coefficient + divided by Drag Coefficient, had a weight of **''' + + str(weights[1]) + ' **.\n\n' + + 'The algorithm ran along **' + str(generation) + + ' generations**, each composed of **' + str(all_parameters[0]) + + ' airfoils**. For every generation, the best **' + str(all_parameters[2]) + + '''** airfoils were selected and used as parents of the + next generation.''' ) + numbers = [all_parameters[1],all_parameters[0],all_parameters[2]] + + text_05 = text_05 + '\n\n' + analyze_algorithm_numbers(numbers) + + text_05 = text_05 +('''\n\nIf you want more details about how this algorithm works, check + the documentation that can be found at + https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing''') + + + cell_05 = nbf.new_text_cell('markdown', source= text_05) + + text_06 = ('''Now, let's see the results, starting with the values + of Cl and efficiency achieved''') + if compare: + text_06 = text_06 + ''', along with the results for a typical airfoil + (NACA 5615) in order to compare their quality''' + text_06 = text_06 + ':\n\n' + cell_06 = nbf.new_text_cell('markdown', source= text_06) + + + outputs = [nbf.new_output(output_type="stream", stream="stdout", output_text="a"), + nbf.new_output(output_type="text", output_text="b"), + nbf.new_output(output_type="stream", stream="stdout", output_text="c"), + nbf.new_output(output_type="stream", stream="stdout", output_text="d"), + nbf.new_output(output_type="stream", stream="stderr", output_text="e"), + nbf.new_output(output_type="stream", stream="stderr", output_text="f"), + nbf.new_output(output_type="png", output_png='Zw==')] # g + out = nbf.new_output(output_type="application/pdf") + out['application/pdf'] = 'aA==' # h + outputs.append(out) + cells=[cell_01, + cell_02, + cell_03, + cell_04, + cell_05, + cell_06, + nbf.new_code_cell(input="$ e $", prompt_number=1,outputs=outputs), + nbf.new_text_cell('markdown', source="$ e $"), + nbf.new_text_cell('markdown', source="Esto es una prueba"), + nbf.new_text_cell('heading', source="Esto es una segunda prueba")] + worksheets = [nbf.new_worksheet(cells=cells)] + + + nb = nbf.new_notebook(name = 'hola', worksheets = worksheets ) + + nbf.write(nb, open(report_root, 'x'), 'ipynb') + + + +def analyze_algorithm_numbers(numbers): + generation = numbers[0] + num_pop = numbers[1] + num_parents = numbers[2] + + + amount = [] + + if generation < 3 : + amount.append('very low') + elif generation < 6 : + amount.append('low') + elif generation < 15 : + amount.append('intermediate') + elif generation < 30 : + amount.append('high') + else: + amount.append('very high') + + + if num_pop < 5 : + amount.append('very low') + elif num_pop < 10 : + amount.append('low') + elif num_pop < 30 : + amount.append('intermediate') + elif num_pop < 70 : + amount.append('high') + else: + amount.append('very high') + + + if num_parents < 2 : + amount.append('very low') + elif num_parents < 3 : + amount.append('low') + elif num_parents < 5 : + amount.append('intermediate') + elif num_parents < 7 : + amount.append('high') + else: + amount.append('very high') + + text = ('According to these data, the number of **generations** is **' + + str(amount[0]) + '** , the number of **airfoils per generation** is **' + + str(amount[1]) + '** and the number of **parents** is **'+ str(amount[2]) + + '**.\n') + dicc = {'very low' : 1, + 'low' : 2, + 'intermediate' : 4, + 'high' : 8, + 'very high' : 16} + + parents_size = dicc[amount[2]] + airfoils_size = dicc[amount[1]] + generation_size = dicc[amount[0]] + size = airfoils_size * generation_size + + if size < 4: + text_2 = ('These specifications can just prove that the algorithm' + + ''' works, but won't produce any interesting data.''') + elif size < 16: + text_2 = ('These specifications can serve to test the algorithm,' + + '''and a little evolution can probably be seen, but won't''' + + 'produce any really useful data.') + elif size < 64: + text_2 = ('These specifications can work fine for a first approach.' + + ''' The algorithm should be producing a decent set of''' + + ' solutions, while in a moderate amount of time.') + elif size < 252: + text_2 = ('These specifications should provide a fairly good result.' + + ''' If the Reynolds number is high, (a very large'''+ + ''' airfoil flying very fast or very low), maybe you'''+ + ''' couldn't go a lot further without spending an impractical''' + + ' amount of time.' ) + else : + text_2 = ('''you are working with an amount of data that will'''+ + ' probably wring the capacity of this algorithm up to its limits.' + + ' We hope that the amount of time that this calculations probably ' + + 'have required will be rewarded with an excellent set of airfoils' + + ' specificaly optimized to your conditions. If you wanted to go' + + ' further in the design of airfoils, maybe you should search for a ' + + 'more specific software or a professional solution.') + + + if (parents_size < airfoils_size): + text_3 = (' For your next run, maybe you should **increase the number' + + ' of parents.** Too few parents may reduce the freedom' + + ' of the algorithm to try new ways to get better.') + elif (parents_size > airfoils_size): + text_3 = (' For your next run, maybe you should **decrease the number' + + ' of parents.** Too much parents may left little room in'+ + ' the airfoil number to properly generate new airfoils' + + ' in each generation.') + else: + text_3 = (' For this amount of profiles, the **amount of parents is' + + ' equilibrated.**') + + if (generation_size < airfoils_size): + text_4 = (' For your next run, maybe you should **increase the number' + + ' of generations**. The fine adjustment of the airfoil' + + ' happens in the later generations, and winners probably' + + ''' won't be well polished until at least generation number 15.''') + elif (generation_size > airfoils_size): + text_4 = (' For your next run, maybe you should **increase the number' + + ' of airfoils** per generation. Too few airfoils may result '+ + ' in a fine adjustment of a not so good local solution. '+ + 'For that amount of generations, a larger number of airfoils' + + ' would allow the algorithm to explore a wider range of ' + + 'possible solutions, specially in the early generations.') + else: + text_4 = (' For this amount of generations, the **amount of airfoils' + + ' per generation is equilibrated.**') + + return text + text_2 + text_3 + text_4 + +#### Uncomment to continue testing +# +#create_report(all_parameters) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py index e1d3075..fd79050 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py @@ -32,6 +32,7 @@ def genetic_step(generation,num_parent, weights): genome_parent_root = os.path.join('genome', file_parent_name) genome = np.loadtxt(genome_parent_root, skiprows=1) num_pop = genome.shape[0] + results_data = analyze.pop_analice(generation, num_pop) scores = analyze.score(generation,num_pop, weights) parents = selection.selection(scores, genome, num_parent) @@ -39,16 +40,30 @@ def genetic_step(generation,num_parent, weights): children = mutation.mutation(children, generation, num_parent) profile_number = children.shape[0] + + file_name = 'generation'+ str(generation + 1) + '.txt' genome_root = os.path.join('genome', file_name) title = 'generation' + str(generation + 1) + 'genome' + results_name = 'results_data_generation'+ str(generation) + '.txt' + results_root = os.path.join('results', 'data', results_name ) + results_title = 'generation' + str(generation) + 'results:' try: os.remove(genome_root) except : pass + + try: + os.remove(results_root) + except : + pass + genome_file = open(genome_root, mode = 'x') + results_file = open(results_root, mode = 'x') genome_file.write(title + '\n') + results_file.write(results_title + '\n') + results_file.write('Cl max Eficciency Score' + '\n') for profile in np.arange(0, profile_number, 1): line = '' @@ -56,4 +71,11 @@ def genetic_step(generation,num_parent, weights): line = line + str(children[profile, gen]) +' ' line = line + '\n' genome_file.write(line) + result = str(results_data[profile, 0]) + ' ' + result = result + str(results_data[profile, 1]) + ' ' + result = result + str(scores[profile]) + '\n' + results_file.write(result) + + genome_file.close() + results_file.close() return children \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py new file mode 100644 index 0000000..e86052f --- /dev/null +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py @@ -0,0 +1,45 @@ + +import sys +from PyQt4 import QtGui, QtCore + +class Example(QtGui.QWidget): + + def __init__(self): + super(Example, self).__init__() + + self.initUI() + + def initUI(self): + + self.lbl = QtGui.QLabel("Ubuntu", self) + + combo = QtGui.QComboBox(self) + combo.addItem("Ubuntu") + combo.addItem("Mandriva") + combo.addItem("Fedora") + combo.addItem("Red Hat") + combo.addItem("Gentoo") + + combo.move(50, 50) + self.lbl.move(50, 150) + + combo.activated[str].connect(self.onActivated) + + self.setGeometry(300, 300, 300, 200) + self.setWindowTitle('QtGui.QComboBox') + self.show() + + def onActivated(self, text): + + self.lbl.setText(text) + self.lbl.adjustSize() + +def main(): + + app = QtGui.QApplication(sys.argv) + ex = Example() + sys.exit(app.exec_()) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py index 93eab6f..ca36f9b 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py @@ -86,7 +86,7 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer for command in commands: p.stdin.write((command + '\n').encode()) - p.stdin.write("\nquit\n".encode()) + p.stdin.close() for line in p.stdout.readlines(): print(line.decode(), end='') diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py index b91a3f0..0105a2e 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py @@ -21,67 +21,133 @@ import numpy as np import initial as initial import genetics as genetics +import ender as ender -if not os.path.exists('aerodata'): - os.makedirs('aerodata') - -if not os.path.exists('genome'): - os.makedirs('genome') +#First, the main function is defined. This allows us to call it from a future +#different starting file (like a PyQT graphic interface). - -####---------Primary Variables----- +#If this is the starting file, it will call the main function with the +#parameters described below. -generation = 0 -airfoils_per_generation = 30 -total_generations = 15 -num_parent = 4 -ambient_data = ('Earth', 0.1, 3, 'mach', 0.1) -# We give the algorithm the conditions at wich we want to optimize our airofil -# through the "ambient data" tuple. The first position is for the planet, -# only 'Mars' and 'Earth are available at the moment. -# The second position is for the lenght of the airfoil, in metres. -# The third is for the flying height, in kilometers, above sea level -# on Earth and avobe the zero reference in Mars. -# The fourth especifies the type of speed we are introducing, and can -# have the values 'speed' or 'mach'. -# The last one is for the value of the parameter selected in the previous one. +def main_program(all_parameters): + '''The main function of the program, calls ir order all the rest''' + + airfoils_per_generation = all_parameters[0] + total_generations = all_parameters[1] + num_parent = all_parameters[2] +# num_winners = all_parameters[3] + weighting_parameters = all_parameters[4] +# end_options = all_parameters[5] + +###--- Creating work directories + + if not os.path.exists('aerodata'): + os.makedirs('aerodata') + + if not os.path.exists('genome'): + os.makedirs('genome') + + if not os.path.exists('results'): + os.makedirs('results') -####--------Secondary Variables------ -#-- Analysis domain -start_alpha_angle = 0 -finish_alpha_angle = 20 -alpha_angle_step = 1 + if not os.path.exists(os.path.join('results', 'graphics')): + os.makedirs(os.path.join('results', 'graphics')) + + if not os.path.exists(os.path.join('results', 'data')): + os.makedirs(os.path.join('results', 'data')) -aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) -#-- Optimization objectives -lift_coefficient_weight = 0.3 -efficiency_weight = 0.7 -weighting_parameters = (lift_coefficient_weight, efficiency_weight) ####--- Starting the population, analysis of the starting population -genome = initial.start_pop(airfoils_per_generation) + generation = 0 -interfaz.xfoil_calculate_population(generation, ambient_data, aero_domain) + initial.start_pop(airfoils_per_generation) -##--- Genetic Algorithm + interfaz.xfoil_calculate_population(generation, ambient_data, aero_domain) -for generation in np.arange(0,total_generations,1): - - genome = genetics.genetic_step(generation,num_parent, weighting_parameters) +####--- Genetic Algorithm + + + for generation in np.arange(0,total_generations,1): - interfaz.xfoil_calculate_population(generation + 1, ambient_data, aero_domain) + + genetics.genetic_step(generation,num_parent, weighting_parameters) + interfaz.xfoil_calculate_population(generation + 1, ambient_data, aero_domain) + + + ender.finish(all_parameters) + + +#If this is the file from which we are starting, we define here the parameters: + +if __name__ == '__main__': + +####---------Primary Variables----- + + + airfoils_per_generation = 3 + total_generations = 3 + num_parent = 1 + +# We give the algorithm the conditions at wich we want to optimize our airofil +# through the "ambient data" tuple. + + planet = 'Mars' # For the moment we have 'Earth' and 'Mars' + chord_length = 0.2 # In metres + altitude = -7.5 # In Kilometres above sea level or reference altitude + speed_parameter = 'speed' # 'speed' or 'mach' + speed_value = 30 # Value of the previous magnitude (speed - m/s) + ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) + + + +####--------Secondary Variables------ +#-- Analysis domain + + start_alpha_angle = 0 + finish_alpha_angle = 15 + alpha_angle_step = 1 + + aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) - \ No newline at end of file +#-- Optimization objectives + + lift_coefficient_weight = 0.3 + efficiency_weight = 0.7 + + weighting_parameters = (lift_coefficient_weight, efficiency_weight) + +#-- Final results options + + num_winners = 3 + draw_winners = True + draw_polars = True + draw_evolution = True + compare_naca_standard = True + compare_naca_custom = True #Work in progress + create_report = True #Work in progress + + end_options = (draw_winners, draw_polars, draw_evolution, + compare_naca_standard, compare_naca_custom, + create_report, + ambient_data, aero_domain) + + + + all_parameters = (airfoils_per_generation, total_generations, num_parent, + num_winners, weighting_parameters, end_options ) + + + main_program(all_parameters) \ No newline at end of file From 4dafc75bd0701cde16d1f927282f464256d0092d Mon Sep 17 00:00:00 2001 From: AunSiro Date: Tue, 10 Nov 2015 12:12:32 +0100 Subject: [PATCH 15/16] Reorganizado el paquete --- .../Genetic_algorithm_files/graphics.py | 45 ----- aeropy/Xfoil_Interaction/README.md | 31 ++- .../Xfoil_Interaction/algoritmo/__init__.py | 0 .../ambient.py | 0 .../analyze.py | 0 .../cross.py | 0 .../ender.py | 137 +++++++++++-- .../ender_report.py | 4 +- .../genetics.py | 8 +- .../initial.py | 2 +- .../interfaz.py | 4 +- .../main.py | 30 +-- .../mutation.py | 2 +- .../selection.py | 0 .../testing.py | 2 +- .../transcript.py | 0 aeropy/Xfoil_Interaction/launcher.py | 190 ++++++++++++++++++ 17 files changed, 356 insertions(+), 99 deletions(-) delete mode 100644 aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py create mode 100644 aeropy/Xfoil_Interaction/algoritmo/__init__.py rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/ambient.py (100%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/analyze.py (100%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/cross.py (100%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/ender.py (81%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/ender_report.py (99%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/genetics.py (94%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/initial.py (98%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/interfaz.py (97%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/main.py (86%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/mutation.py (98%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/selection.py (100%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/testing.py (98%) rename aeropy/Xfoil_Interaction/{Genetic_algorithm_files => algoritmo}/transcript.py (100%) create mode 100644 aeropy/Xfoil_Interaction/launcher.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py b/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py deleted file mode 100644 index e86052f..0000000 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/graphics.py +++ /dev/null @@ -1,45 +0,0 @@ - -import sys -from PyQt4 import QtGui, QtCore - -class Example(QtGui.QWidget): - - def __init__(self): - super(Example, self).__init__() - - self.initUI() - - def initUI(self): - - self.lbl = QtGui.QLabel("Ubuntu", self) - - combo = QtGui.QComboBox(self) - combo.addItem("Ubuntu") - combo.addItem("Mandriva") - combo.addItem("Fedora") - combo.addItem("Red Hat") - combo.addItem("Gentoo") - - combo.move(50, 50) - self.lbl.move(50, 150) - - combo.activated[str].connect(self.onActivated) - - self.setGeometry(300, 300, 300, 200) - self.setWindowTitle('QtGui.QComboBox') - self.show() - - def onActivated(self, text): - - self.lbl.setText(text) - self.lbl.adjustSize() - -def main(): - - app = QtGui.QApplication(sys.argv) - ex = Example() - sys.exit(app.exec_()) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/README.md b/aeropy/Xfoil_Interaction/README.md index bb6a1fa..f647e2a 100644 --- a/aeropy/Xfoil_Interaction/README.md +++ b/aeropy/Xfoil_Interaction/README.md @@ -11,35 +11,44 @@ Includes: -Interactive Ipython notebook showing how the genome-to-profile decodification works (in progress). --Ipython notebook that can easily be used to save drawings of the airfoils generate. (Must be placed with the .py genetic algorithm files) +-Ipython notebook that can easily be used to save drawings of the airfoils generated. (Must be placed with the launcher.py genetic algorithm file)(Work in progress) Genetic algorithm modules: --interfaz +-ambient --transcript +-analyze --testing +-cross --main +-ender --initial +-ender_report (Work in progress) -genetics --analyze +-initial --cross +-interfaz + +-main -mutation -selection --ambient +-testing + +-transcript + +-launcher + +##Instructions: + +Place xfoil in the same folder as "launcher.py" -All 11 files must be in the same folder as xfoil.exe. -Execute the main.py file in order to start the algorithm. The main control paraparameters are defined here, and secondary parameters will be automatically calculated. +Execute "launcher.py" diff --git a/aeropy/Xfoil_Interaction/algoritmo/__init__.py b/aeropy/Xfoil_Interaction/algoritmo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py b/aeropy/Xfoil_Interaction/algoritmo/ambient.py similarity index 100% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py rename to aeropy/Xfoil_Interaction/algoritmo/ambient.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py b/aeropy/Xfoil_Interaction/algoritmo/analyze.py similarity index 100% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py rename to aeropy/Xfoil_Interaction/algoritmo/analyze.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py b/aeropy/Xfoil_Interaction/algoritmo/cross.py similarity index 100% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py rename to aeropy/Xfoil_Interaction/algoritmo/cross.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py b/aeropy/Xfoil_Interaction/algoritmo/ender.py similarity index 81% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py rename to aeropy/Xfoil_Interaction/algoritmo/ender.py index 8c0c31c..c62efd9 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender.py +++ b/aeropy/Xfoil_Interaction/algoritmo/ender.py @@ -17,17 +17,17 @@ import os -import interfaz as interfaz +import algoritmo.interfaz as interfaz import numpy as np -import initial as initial -import genetics as genetics -import analyze as analyze -import selection as selection +import algoritmo.initial as initial +import algoritmo.genetics as genetics +import algoritmo.analyze as analyze +import algoritmo.selection as selection import matplotlib.pyplot as plt -import transcript as transcript -import ambient as ambient +import algoritmo.transcript as transcript +import algoritmo.ambient as ambient import subprocess -import ender_report +#import algoritmo.ender_report def finish (all_parameters): @@ -36,8 +36,9 @@ def finish (all_parameters): generation = all_parameters[1] num_winners = all_parameters[3] weights = all_parameters[4] - end_options = all_parameters[5] - + end_options = all_parameters[5] + ambient_data = all_parameters[6] + aero_domain = all_parameters[7] must_draw_winners = end_options[0] must_draw_polars = end_options[1] @@ -45,8 +46,6 @@ def finish (all_parameters): must_compare_naca_standard = end_options[3] must_compare_naca_custom = end_options[4] must_create_report = end_options[5] - ambient_data = end_options[6] - aero_domain = end_options[7] analyze_final(generation, num_winners, weights) @@ -60,8 +59,8 @@ def finish (all_parameters): draw_aero_comparison(num_winners, compare) if (must_draw_evolution): draw_evolution(compare, aero_domain) - if (must_create_report): - ender_report.create_report(all_parameters) + #if (must_create_report): + # ender_report.create_report(all_parameters) def analyze_final(generation, num_winners, weights): @@ -183,6 +182,12 @@ def draw_winners(options): point_data = np.loadtxt(data_root, skiprows = 1) draw_figure(graph_name, graph_root, point_data) + graph_name = 'NACA 5603' + graph_root = os.path.join('results','graphics',graph_name + '.png') + data_root = os.path.join('profiles','winners',graph_name + '.txt') + point_data = np.loadtxt(data_root, skiprows = 1) + draw_figure(graph_name, graph_root, point_data) + def draw_figure(graph_name, graph_root, point_data): try: @@ -225,9 +230,9 @@ def xfoil_calculate_profile(profile_name, profile_root, data_root, '', 'aseq', - str(aero_domain[0]-1), - str(aero_domain[1]+5), - str(aero_domain[2]/2), + str(aero_domain[0]), + str(aero_domain[1]), + str(aero_domain[2]), '', 'quit'] if (profile_type == 'NACA'): @@ -302,6 +307,18 @@ def final_xfoil(total_generations, ambient_data, aero_domain, compare): xfoil_calculate_profile(profile_name, profile_root, ambient_data, aero_domain, 'NACA') + + profile_name2 = 'NACA 5603' + profile_root2 = '5603' + + try: + os.remove(os.path.join('profiles','winners', profile_name2 + '.txt')) + except : + pass + + xfoil_calculate_profile(profile_name2, profile_root2, + ambient_data, aero_domain, + 'NACA') # Drawing polars @@ -371,7 +388,7 @@ def draw_polar(data): - + plt.xlim(xmin=0) plt.legend(loc = 4, fontsize =14) plt.grid() plt.minorticks_on() @@ -397,6 +414,10 @@ def draw_aero_comparison(num_winners, compare): root = os.path.join('results','data', name + 'aerodata.txt') data.append([name, root]) + name = 'NACA 5603' + root = os.path.join('results','data', name + 'aerodata.txt') + data.append([name, root]) + data = np.array(data) draw_alpha(data) draw_polar(data) @@ -483,6 +504,12 @@ def draw_evolution(options, aero_domain): naca_cl = max(naca_data[0:max_angle,0]) naca_effic = max(naca_data[0:max_angle,0] / naca_data[0:max_angle,1]) + naca_root2 = os.path.join('results','data','NACA 5603aerodata.txt') + naca_data2 = np.loadtxt(naca_root2, skiprows = 12, usecols=[1,2]) + max_angle = 1 + 2 * round((aero_domain[1]-aero_domain[0])/aero_domain[2]) + naca_cl2 = max(naca_data2[0:max_angle,0]) + naca_effic2 = max(naca_data2[0:max_angle,0] / naca_data2[0:max_angle,1]) + lift_name = 'lift history.txt' lift_root = os.path.join('results', 'data', lift_name ) lift_data = np.loadtxt(lift_root, skiprows = 2) @@ -512,6 +539,10 @@ def draw_evolution(options, aero_domain): label = 'NACA 5615' value = naca_cl * np.ones_like(effic_data[:,0]) plt.plot(lift_data[:,0], value, label = label) + + label = 'NACA 5603' + value = naca_cl2 * np.ones_like(effic_data[:,0]) + plt.plot(lift_data[:,0], value, label = label) @@ -539,6 +570,10 @@ def draw_evolution(options, aero_domain): label = 'NACA 5615' value = naca_effic * np.ones_like(effic_data[:,0]) plt.plot(effic_data[:,0], value, label = label) + + label = 'NACA 5603' + value = naca_effic2 * np.ones_like(effic_data[:,0]) + plt.plot(effic_data[:,0], value, label = label) @@ -549,3 +584,69 @@ def draw_evolution(options, aero_domain): plt.ylabel('Efficiency') plt.savefig(graph_root) + + + + + + +if __name__ == '__main__': + +####---------Primary Variables----- + + + airfoils_per_generation = 30 + total_generations = 30 + num_parent = 4 + +# We give the algorithm the conditions at wich we want to optimize our airofil +# through the "ambient data" tuple. + + planet = 'Mars' # For the moment we have 'Earth' and 'Mars' + chord_length = 0.4 # In metres + altitude = -7.5 # In Kilometres above sea level or reference altitude + speed_parameter = 'speed' # 'speed' or 'mach' + speed_value = 28.284 # Value of the previous magnitude (speed - m/s) + ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) + + + +####--------Secondary Variables------ +#-- Analysis domain + + start_alpha_angle = 0 + finish_alpha_angle = 20 + alpha_angle_step = 2 + + aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) + + +#-- Optimization objectives + + lift_coefficient_weight = 0.0 + efficiency_weight = 1.0 + + weighting_parameters = (lift_coefficient_weight, efficiency_weight) + +#-- Final results options + + num_winners = 3 + vdraw_winners = True + vdraw_polars = True + vdraw_evolution = True + vcompare_naca_standard = True + vcompare_naca_custom = True #Work in progress + vcreate_report = True #Work in progress + + end_options = (vdraw_winners, vdraw_polars, vdraw_evolution, + vcompare_naca_standard, vcompare_naca_custom, + vcreate_report, + ambient_data, aero_domain) + + + + all_parameters = (airfoils_per_generation, total_generations, num_parent, + num_winners, weighting_parameters, end_options ) + + + finish(all_parameters) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py b/aeropy/Xfoil_Interaction/algoritmo/ender_report.py similarity index 99% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py rename to aeropy/Xfoil_Interaction/algoritmo/ender_report.py index 01a9570..1f520a2 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/ender_report.py +++ b/aeropy/Xfoil_Interaction/algoritmo/ender_report.py @@ -19,8 +19,8 @@ import os import interfaz as interfaz import numpy as np -import initial as initial -import genetics as genetics +import algoritmo.initial as initial +import algoritmo.genetics as genetics import IPython.nbformat.current as nbf diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py b/aeropy/Xfoil_Interaction/algoritmo/genetics.py similarity index 94% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py rename to aeropy/Xfoil_Interaction/algoritmo/genetics.py index fd79050..7f2c17d 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/genetics.py +++ b/aeropy/Xfoil_Interaction/algoritmo/genetics.py @@ -18,10 +18,10 @@ import os import numpy as np -import analyze as analyze -import selection as selection -import cross as cross -import mutation as mutation +import algoritmo.analyze as analyze +import algoritmo.selection as selection +import algoritmo.cross as cross +import algoritmo.mutation as mutation diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py b/aeropy/Xfoil_Interaction/algoritmo/initial.py similarity index 98% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py rename to aeropy/Xfoil_Interaction/algoritmo/initial.py index 35bdada..dbb19f0 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/initial.py +++ b/aeropy/Xfoil_Interaction/algoritmo/initial.py @@ -17,7 +17,7 @@ import os import numpy as np -import testing as test +import algoritmo.testing as test def start_pop(pop_num): diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py similarity index 97% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py rename to aeropy/Xfoil_Interaction/algoritmo/interfaz.py index ca36f9b..5d13b68 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/interfaz.py +++ b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py @@ -22,9 +22,9 @@ import subprocess import os -import transcript as trans +import algoritmo.transcript as trans import numpy as np -import ambient as ambient +import algoritmo.ambient as ambient def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aero_domain): diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py b/aeropy/Xfoil_Interaction/algoritmo/main.py similarity index 86% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py rename to aeropy/Xfoil_Interaction/algoritmo/main.py index 0105a2e..4bd53da 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/main.py +++ b/aeropy/Xfoil_Interaction/algoritmo/main.py @@ -17,11 +17,11 @@ import os -import interfaz as interfaz +import algoritmo.interfaz as interfaz import numpy as np -import initial as initial -import genetics as genetics -import ender as ender +import algoritmo.initial as initial +import algoritmo.genetics as genetics +import algoritmo.ender as ender #First, the main function is defined. This allows us to call it from a future #different starting file (like a PyQT graphic interface). @@ -40,6 +40,8 @@ def main_program(all_parameters): # num_winners = all_parameters[3] weighting_parameters = all_parameters[4] # end_options = all_parameters[5] + ambient_data = all_parameters[6] + aero_domain = all_parameters[7] ###--- Creating work directories @@ -96,18 +98,18 @@ def main_program(all_parameters): ####---------Primary Variables----- - airfoils_per_generation = 3 - total_generations = 3 - num_parent = 1 + airfoils_per_generation = 6 + total_generations = 6 + num_parent = 2 # We give the algorithm the conditions at wich we want to optimize our airofil # through the "ambient data" tuple. planet = 'Mars' # For the moment we have 'Earth' and 'Mars' - chord_length = 0.2 # In metres + chord_length = 0.1 # In metres altitude = -7.5 # In Kilometres above sea level or reference altitude speed_parameter = 'speed' # 'speed' or 'mach' - speed_value = 30 # Value of the previous magnitude (speed - m/s) + speed_value = 18 # Value of the previous magnitude (speed - m/s) ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) @@ -116,8 +118,8 @@ def main_program(all_parameters): #-- Analysis domain start_alpha_angle = 0 - finish_alpha_angle = 15 - alpha_angle_step = 1 + finish_alpha_angle = 20 + alpha_angle_step = 2 aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) @@ -141,13 +143,13 @@ def main_program(all_parameters): end_options = (draw_winners, draw_polars, draw_evolution, compare_naca_standard, compare_naca_custom, - create_report, - ambient_data, aero_domain) + create_report) all_parameters = (airfoils_per_generation, total_generations, num_parent, - num_winners, weighting_parameters, end_options ) + num_winners, weighting_parameters, end_options, + ambient_data, aero_domain ) main_program(all_parameters) \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py b/aeropy/Xfoil_Interaction/algoritmo/mutation.py similarity index 98% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py rename to aeropy/Xfoil_Interaction/algoritmo/mutation.py index 02b14f8..2da007a 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/mutation.py +++ b/aeropy/Xfoil_Interaction/algoritmo/mutation.py @@ -22,7 +22,7 @@ import numpy as np -import testing as test +import algoritmo.testing as test def mutation(children, generation, num_parent): '''Given a genome, mutates it in order to have a diverse population diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py b/aeropy/Xfoil_Interaction/algoritmo/selection.py similarity index 100% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/selection.py rename to aeropy/Xfoil_Interaction/algoritmo/selection.py diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py b/aeropy/Xfoil_Interaction/algoritmo/testing.py similarity index 98% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py rename to aeropy/Xfoil_Interaction/algoritmo/testing.py index 2e1bb3a..1463922 100644 --- a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/testing.py +++ b/aeropy/Xfoil_Interaction/algoritmo/testing.py @@ -16,7 +16,7 @@ -import transcript as transcript +import algoritmo.transcript as transcript import numpy as np from scipy import interpolate diff --git a/aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py b/aeropy/Xfoil_Interaction/algoritmo/transcript.py similarity index 100% rename from aeropy/Xfoil_Interaction/Genetic_algorithm_files/transcript.py rename to aeropy/Xfoil_Interaction/algoritmo/transcript.py diff --git a/aeropy/Xfoil_Interaction/launcher.py b/aeropy/Xfoil_Interaction/launcher.py new file mode 100644 index 0000000..9fbba99 --- /dev/null +++ b/aeropy/Xfoil_Interaction/launcher.py @@ -0,0 +1,190 @@ +''' + +Created on Wed Nov 4 20:57:16 2015 + +@author: Siro Moreno + +This is a submodule for the genetic algorithm that is explained in +https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing + +This script is a command prompt launcher. + +''' + + + + +import algoritmo.main as main + + + +####---------Primary Variables----- + + +def cuestionario(min_x): + correct = False + while not correct: + x = input() + try: + x = int(x) + except: + print('Por favor, introduzca un número válido') + continue + if x < min_x: + print('número demasiado bajo, vuelva a intentarlo') + else: + correct = True + return x +def cuestionario_float(min_x): + correct = False + while not correct: + x = input() + try: + x = float(x) + except: + print('Por favor, introduzca un número válido') + continue + if x < min_x: + print('número demasiado bajo, vuelva a intentarlo') + else: + correct = True + return x + +def cuestionario_s_n(): + correct = False + while not correct: + x = input() + try: + x = str(x) + except: + continue + if x == 's' or x == 'y' or x == 'S' or x == 'Y': + r = True + correct = True + elif x =='n' or x =='N': + r = False + correct = True + else: + print('Por favor, responda con "s" o "n"') + correct = False + return r + +print() +print('--- Parámetros del algoritmo genético ---') +print() +print('introduzca un número de perfiles por generación') +airfoils_per_generation = cuestionario(2) +print('introduzca un número de generaciones') +total_generations = cuestionario(2) +print('introduzca un número de parents') +num_parent = cuestionario(1) + +# We give the algorithm the conditions at wich we want to optimize our airofil +# through the "ambient data" tuple. +print() +print('--- Parámetros ambientales ---') +print() + +print('¿En qué planeta desea otimizar? Mars / Earth') +correct = False +while not correct: + x = input() + try: + x = str(x) + except: + continue + if not(x == 'Earth' or x == 'Mars'): + print('planeta incorrecto, elija "Earth" o "Mars"') + else: + correct = True +planet = x # For the moment we have 'Earth' and 'Mars' +print('introduzca la longitud de la cuerda (en metros)') +chord_length = cuestionario_float(0) # In metres +print('introduzca la altitud de vuelo (en kilómetros)') +if planet == 'Earth': + altitude = cuestionario_float(0) +else: + altitude = cuestionario_float(-7.5)# In Kilometres above sea level or reference altitude +print('¿Cómo va a introducir la velocidad? speed / mach') +correct = False +while not correct: + x = input() + try: + x = str(x) + except: + continue + if not(x == 'speed' or x == 'mach'): + print('parámetro de velocidad incorrecto') + else: + correct = True +speed_parameter = x # 'speed' or 'mach' +if speed_parameter == 'speed': + print('Introduzca el valor de la velocidad (m/s)') + speed_value = cuestionario_float(0) # Value of the previous magnitude (speed - m/s) +else: + print('Introduzca el valor del número de Mach') + speed_value = cuestionario_float(0) +ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) + + + +####--------Secondary Variables------ +#-- Analysis domain +print() +print('--- Dominio Aerodinámico ---') +print() +print('Ángulo de ataque inicial(grados):') +start_alpha_angle = cuestionario_float(-20) +print('Ángulo de ataque final:') +finish_alpha_angle = cuestionario_float(start_alpha_angle) +print('Espaciado entre ángulos de ataque de estudio:') +alpha_angle_step = cuestionario_float(0) + +aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) + + +#-- Optimization objectives +print() +print('--- Objetivos de optimización ---') +print() +print('Importancia del coeficiente de sustentación:') +lift_coefficient_weight = cuestionario_float(-1) +print('Importancia de la eficiencia aerodinámica:') +efficiency_weight = cuestionario_float(-1) + +weighting_parameters = (lift_coefficient_weight, efficiency_weight) + +#-- Final results options +print() +print('--- Dominio Aerodinámico ---') +print() +print('Número de ganadores del algoritmo') +num_winners = cuestionario(1) +print('Desea generar dibujos de los ganadores? (s/n)') +draw_winners = cuestionario_s_n() +print('Desea generar gráficas de las polares? (s/n)') +draw_polars = cuestionario_s_n() +print('Desea generar gráficas de la evolución? (s/n)') +draw_evolution = cuestionario_s_n() +print('Desea comparar los resultados con dos perfiles NACA?') +compare_naca_standard = cuestionario_s_n() +print() +compare_naca_custom = True #Work in progress +create_report = True #Work in progress + +end_options = (draw_winners, draw_polars, draw_evolution, + compare_naca_standard, compare_naca_custom, + create_report) + + + +all_parameters = (airfoils_per_generation, total_generations, num_parent, + num_winners, weighting_parameters, end_options, + ambient_data, aero_domain ) + +print() +print('--- Iniciando algoritmo ---') +print() + +main.main_program(all_parameters) + From 01d7bf95624f544fc2f3bb553a873a80f220c75b Mon Sep 17 00:00:00 2001 From: AunSiro Date: Wed, 27 Apr 2016 08:27:50 +0200 Subject: [PATCH 16/16] Cambio a OOP MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Creo que evitaré muchos bugs con una estrategia de programación orientada a objetos --- aeropy/Xfoil_Interaction/algoritmo/analyze.py | 56 ++-- aeropy/Xfoil_Interaction/algoritmo/cross.py | 33 ++- aeropy/Xfoil_Interaction/algoritmo/ender.py | 280 ++++++++++-------- .../Xfoil_Interaction/algoritmo/genetics.py | 42 +-- aeropy/Xfoil_Interaction/algoritmo/initial.py | 48 ++- .../Xfoil_Interaction/algoritmo/interfaz.py | 51 ++-- aeropy/Xfoil_Interaction/algoritmo/main.py | 28 +- .../Xfoil_Interaction/algoritmo/mutation.py | 17 +- .../Xfoil_Interaction/algoritmo/selection.py | 13 +- .../Xfoil_Interaction/algoritmo/transcript.py | 61 ++-- aeropy/Xfoil_Interaction/launcher-express.py | 64 ++++ 11 files changed, 433 insertions(+), 260 deletions(-) create mode 100644 aeropy/Xfoil_Interaction/launcher-express.py diff --git a/aeropy/Xfoil_Interaction/algoritmo/analyze.py b/aeropy/Xfoil_Interaction/algoritmo/analyze.py index 597174b..96d71c9 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/analyze.py +++ b/aeropy/Xfoil_Interaction/algoritmo/analyze.py @@ -22,32 +22,33 @@ -def pop_analice (generation, num_pop): +def pop_analice (generation, population, num_parent): '''For a given generation and number of airfoils, returns an array which contains the maximun Lift Coefficient and Maximum Aerodinamic Efficiency for every airfoil. ''' - pop_results = np.zeros([num_pop,2]) - for profile_number in np.arange(1,num_pop+1,1): - pop_results[profile_number - 1, :] = profile_analice (generation, profile_number) + pop_len = len(population) + for airfoil_number in range(1, pop_len+1): + airfoil = population[airfoil_number - 1] + if not hasattr(airfoil, 'clmax'): + airfoil_analice(generation, airfoil_number, airfoil) - return pop_results + -def profile_analice (generation, profile_number): - '''For a given generation and profile, searches for the results of the +def airfoil_analice (generation, airfoil_number, airfoil): + '''For a given generation and airfoil, searches for the results of the aerodynamic analysis made in Xfoil. Then, searches for the maximum - values of the Lift Coefficient and Aerodynamic Efficiency and returns them - as an 1x2 array. + values of the Lift Coefficient and Aerodynamic Efficiency. ''' - profile_name = 'datagen' + str(generation) + 'prof' + str(profile_number)+ '.txt' - data_root = os.path.join("aerodata", profile_name) + airfoil_name = airfoil.name + data_root = os.path.join("aerodata","data" + airfoil_name + '.txt') datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2]) + #Chequear integridad de los datos read_dim = np.array(datos.shape) - #print('read_dim = ', read_dim, read_dim.shape) if ((read_dim.shape[0]) != 2): - return np.array ([0,0]) + datos = np.zeros([2,2]) pos_clmax = np.argmax(datos[:,0]) @@ -55,21 +56,22 @@ def profile_analice (generation, profile_number): efic = datos[:,0] / datos[:,1] pos_maxefic = np.argmax(efic) maxefic = efic[pos_maxefic] - return np.array([clmax , maxefic]) + airfoil.clmax = clmax + airfoil.maxefic = maxefic -def adimension(array): - '''Adimensionalyzes an array with its maximun value +def score(generation, population, weights): ''' - max_value = max(array) - adim = array / max_value - return adim -def score(generation, num_pop, weights): - '''For a given generation, number of airfoils and weight parameters, returns - an array of the scores of all airfoils. ''' - pop_results = pop_analice (generation, num_pop) - cl_score = adimension(pop_results[:,0]) - efic_score = adimension(pop_results[:,1]) - total_score = weights[0] * cl_score + weights[1] * efic_score - return total_score + max_cl = -100 + max_efic = -100 + + for airfoil in population: + max_cl = max(airfoil.clmax, max_cl) + max_efic = max(airfoil.maxefic, max_efic) + for airfoil in population: + cl_score = airfoil.clmax / max_cl + efic_score = airfoil.maxefic / max_efic + total_score = weights[0] * cl_score + weights[1] * efic_score + airfoil.score = total_score + diff --git a/aeropy/Xfoil_Interaction/algoritmo/cross.py b/aeropy/Xfoil_Interaction/algoritmo/cross.py index d2f5cf6..c409ca9 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/cross.py +++ b/aeropy/Xfoil_Interaction/algoritmo/cross.py @@ -20,21 +20,36 @@ import numpy as np +from algoritmo.initial import Airfoil - -def cross(parents, num_pop): +def cross(parents, pop_len, generation): '''Generates a population of (num_pop) airfoil genomes by mixing randomly the genomes of the given parents. The parents are preserved as the first elements of the new population. ''' - children = np.zeros([num_pop, 16]) - num_parents = parents.shape[0] - children[0:num_parents] = parents - for i in np.arange(num_parents, num_pop, 1): - coef = np.random.rand(num_parents) - coef = coef/sum(coef) - children[i,:]= np.dot(coef, parents) + children = [] + parents_len = len(parents) + + for parent_num in range(len(parents)): + parent = parents[parent_num] + parent.copy_data(generation + 1, parent_num) + children.append(parent) + + for child_num in range(parents_len, pop_len): + parent_1 = parents[np.random.choice(parents_len)] + parent_2 = parents[np.random.choice(parents_len)] + genome_1 = parent_1.genome + genome_2 = parent_2.genome + child_genome = [] + coefs = np.random.rand(len(genome_1)) + for gen_num in range(len(genome_1)): + gen = genome_1[gen_num] * coefs[gen_num] + gen += genome_2[gen_num] * (1- coefs[gen_num]) + child_genome.append(gen) + child_genome = np.array(child_genome) + child = Airfoil(child_genome) + children.append(child) return children diff --git a/aeropy/Xfoil_Interaction/algoritmo/ender.py b/aeropy/Xfoil_Interaction/algoritmo/ender.py index c62efd9..ddea0a4 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/ender.py +++ b/aeropy/Xfoil_Interaction/algoritmo/ender.py @@ -27,13 +27,15 @@ import algoritmo.transcript as transcript import algoritmo.ambient as ambient import subprocess +import shutil #import algoritmo.ender_report -def finish (all_parameters): +def finish (population, all_parameters): generation = all_parameters[1] + num_parent = all_parameters[2] num_winners = all_parameters[3] weights = all_parameters[4] end_options = all_parameters[5] @@ -47,14 +49,18 @@ def finish (all_parameters): must_compare_naca_custom = end_options[4] must_create_report = end_options[5] - analyze_final(generation, num_winners, weights) +# analyze_final(generation, num_winners, weights) + analyze.pop_analice (generation, population, num_parent) + analyze.score(generation, population, weights) + save_last_gen(population, generation) + winners = selection.selection(population, num_winners) compare = must_compare_naca_standard final_xfoil(generation, ambient_data, aero_domain, compare) - analyze_winners(generation, num_winners, weights) + analyze_winners(winners) calculate_evolution(generation, num_winners, compare) if (must_draw_winners): - draw_winners(compare) + draw_winners(winners, compare) if (must_draw_polars): draw_aero_comparison(num_winners, compare) if (must_draw_evolution): @@ -62,129 +68,167 @@ def finish (all_parameters): #if (must_create_report): # ender_report.create_report(all_parameters) - -def analyze_final(generation, num_winners, weights): - '''Analyze the data of the last generation - ''' - file_parent_name = 'generation'+ str(generation) + '.txt' - genome_parent_root = os.path.join('genome', file_parent_name) - genome = np.loadtxt(genome_parent_root, skiprows=1) - num_pop = genome.shape[0] - results_data = analyze.pop_analice(generation, num_pop) - - scores = analyze.score(generation,num_pop, weights) - winners = selection.selection(scores, genome, num_winners) - +def save_last_gen(population, generation): - - file_name = 'winners.txt' - genome_root = os.path.join('genome', file_name) - title = 'winners genome' - results_name = 'results_data_generation'+ str(generation) + '.txt' results_root = os.path.join('results', 'data', results_name ) results_title = 'generation' + str(generation) + 'results:' - - - try: - os.remove(genome_root) - except: - pass - + try: os.remove(results_root) except : pass - genome_file = open(genome_root, mode = 'x') + results_file = open(results_root, mode = 'x') - genome_file.write(title + '\n') results_file.write(results_title + '\n') - results_file.write('Cl max Eficciency Score' + '\n') + results_file.write('Cl max Eficciency Score\n') - for profile in np.arange(0, num_pop, 1): - result = str(results_data[profile, 0]) + ' ' - result = result + str(results_data[profile, 1]) + ' ' - result = result + str(scores[profile]) + '\n' + for airfoil in population: + result = str(airfoil.clmax) + ' ' + result += str(airfoil.maxefic) + ' ' + result += str(airfoil.score) + '\n' results_file.write(result) - for profile in np.arange(0, num_winners, 1): - line = '' - for gen in np.arange(0, 16,1): - line = line + str(winners[profile, gen]) +' ' - line = line + '\n' - genome_file.write(line) - genome_file.close() - results_file.close() - - -def analyze_winners(generation, num_winners, weights): + + results_file.close() + +#def analyze_final(generation, population, num_winners, weights): +# '''Analyze the data of the last generation +# ''' +# file_parent_name = 'generation'+ str(generation) + '.txt' +# genome_parent_root = os.path.join('genome', file_parent_name) +# genome = np.loadtxt(genome_parent_root, skiprows=1) +# num_pop = genome.shape[0] +# results_data = analyze.pop_analice(generation, num_pop) +# +# scores = analyze.score(generation,num_pop, weights) +# winners = selection.selection(scores, genome, num_winners) +# +# +# +# +# file_name = 'winners.txt' +# genome_root = os.path.join('genome', file_name) +# title = 'winners genome' +# +# results_name = 'results_data_generation'+ str(generation) + '.txt' +# results_root = os.path.join('results', 'data', results_name ) +# results_title = 'generation' + str(generation) + 'results:' +# +# +# try: +# os.remove(genome_root) +# except: +# pass +# +# try: +# os.remove(results_root) +# except : +# pass +# +# genome_file = open(genome_root, mode = 'x') +# results_file = open(results_root, mode = 'x') +# genome_file.write(title + '\n') +# results_file.write(results_title + '\n') +# results_file.write('Cl max Eficciency Score' + '\n') +# +# +# for profile in np.arange(0, num_pop, 1): +# result = str(results_data[profile, 0]) + ' ' +# result = result + str(results_data[profile, 1]) + ' ' +# result = result + str(scores[profile]) + '\n' +# results_file.write(result) +# for profile in np.arange(0, num_winners, 1): +# line = '' +# for gen in np.arange(0, 16,1): +# line = line + str(winners[profile, gen]) +' ' +# line = line + '\n' +# genome_file.write(line) +# genome_file.close() +# results_file.close() + + +def analyze_winners(winners): '''Analyze the data of the winners ''' - results = np.zeros([num_winners, 3]) - - for i in np.arange(0, num_winners, 1): - dataname = 'datagen' + str(generation) + 'prof' + str(i + 1) + '.txt' - data_root = os.path.join('aerodata', dataname) - data = np.loadtxt(data_root, skiprows = 12, usecols=[1,2]) - clmax = max(data[:,0]) - efimax = max(data[:,0] / data[:,1]) - results[i, 0:2] = [clmax, efimax] - - cl_score = analyze.adimension(results[:,0]) - efic_score = analyze.adimension(results[:,1]) - results[:,2] = weights[0] * cl_score + weights[1] * efic_score +# results = np.zeros([num_winners, 3]) +# +# for i in np.arange(0, num_winners, 1): +# dataname = 'datagen' + str(generation) + 'prof' + str(i + 1) + '.txt' +# data_root = os.path.join('aerodata', dataname) +# data = np.loadtxt(data_root, skiprows = 12, usecols=[1,2]) +# clmax = max(data[:,0]) +# efimax = max(data[:,0] / data[:,1]) +# results[i, 0:2] = [clmax, efimax] +# +# cl_score = analyze.adimension(results[:,0]) +# efic_score = analyze.adimension(results[:,1]) +# results[:,2] = weights[0] * cl_score + weights[1] * efic_score results_name = 'results_winners.txt' results_root = os.path.join('results', 'data', results_name ) results_title = 'Winners results:' + file_name = 'winners.txt' + genome_root = os.path.join('genome', file_name) + title = 'winners genome' try: os.remove(results_root) except : pass + try: + os.remove(genome_root) + except : + pass + genome_file = open(genome_root, mode = 'x') results_file = open(results_root, mode = 'x') + genome_file.write(title + '\n') results_file.write(results_title + '\n') - results_file.write('Cl max Eficciency Score' + '\n') + results_file.write('Cl max Eficciency Score\n') - for profile in np.arange(0, num_winners, 1): - result = str(results[profile, 0]) + ' ' - result = result + str(results[profile, 1]) + ' ' - result = result + str(results[profile, 2]) + '\n' + for airfoil_num in range(len(winners)): + airfoil = winners[airfoil_num] + airfoil.copy_winner(airfoil_num + 1) + line = '' + for gen in airfoil.genome: + line = line + str(gen) +' ' + line = line + '\n' + genome_file.write(line) + result = str(airfoil.clmax) + ' ' + result += str(airfoil.maxefic) + ' ' + result += str(airfoil.score) + '\n' results_file.write(result) #--- Drawing Airfoils -def draw_winners(options): +def draw_winners(winners, options): - winners_root = os.path.join('genome', 'winners.txt') - winners_genome = np.loadtxt(winners_root, skiprows=1) + num_winners = len(winners) - num_winners = winners_genome.shape[0] - - for winner in np.arange(0, num_winners, 1): - + for winner in range(num_winners): + genome = winners[winner].genome graph_name = 'winner ' + str(winner + 1) graph_root = os.path.join('results','graphics',graph_name + '.png') - point_data = transcript.decode_genome(winners_genome[winner,:]) + point_data = transcript.decode_genome(genome) draw_figure(graph_name, graph_root, point_data) if (options): graph_name = 'NACA 5615' graph_root = os.path.join('results','graphics',graph_name + '.png') - data_root = os.path.join('profiles','winners',graph_name + '.txt') + data_root = os.path.join('airfoils','winners',graph_name + '.txt') point_data = np.loadtxt(data_root, skiprows = 1) draw_figure(graph_name, graph_root, point_data) graph_name = 'NACA 5603' graph_root = os.path.join('results','graphics',graph_name + '.png') - data_root = os.path.join('profiles','winners',graph_name + '.txt') + data_root = os.path.join('airfoils','winners',graph_name + '.txt') point_data = np.loadtxt(data_root, skiprows = 1) draw_figure(graph_name, graph_root, point_data) @@ -236,7 +280,7 @@ def xfoil_calculate_profile(profile_name, profile_root, '', 'quit'] if (profile_type == 'NACA'): - naca_root = os.path.join('profiles', 'winners', profile_name + '.txt') + naca_root = os.path.join('airfoils', 'winners', profile_name + '.txt') commands.extend(['save',naca_root]) commands.extend(commands2) @@ -263,60 +307,60 @@ def xfoil_calculate_profile(profile_name, profile_root, def final_xfoil(total_generations, ambient_data, aero_domain, compare): - profile_folder = os.path.join('profiles','winners') - if not os.path.exists(profile_folder): - os.makedirs(profile_folder) - - genome_root = os.path.join('genome','winners.txt') - genome_matrix = np.loadtxt(genome_root, skiprows=1) - num_winners = genome_matrix.shape[0] - - - for profile in np.arange(0, num_winners, 1): - genome = genome_matrix[profile,:] - profile_name = 'winner ' + str(profile + 1) - profile_root = os.path.join('profiles','winners', profile_name + '.txt') - - perfil = transcript.decode_genome(genome) - try: - os.remove(profile_root) - except : - pass - - - archivo = open(profile_root, mode = 'x') - archivo.write(profile_name + '\n') - for i in np.arange(0,100,1): - texto = str(round(perfil[i,0],6)) + ' ' + str(round(perfil[i,1],6)) +'\n' - archivo.write(texto) - archivo.close() - - xfoil_calculate_profile(profile_name, profile_root, - ambient_data, aero_domain, - 'load') + airfoil_folder = os.path.join('airfoils','winners') + if not os.path.exists(airfoil_folder): + os.makedirs(airfoil_folder) + +# genome_root = os.path.join('genome','winners.txt') +# genome_matrix = np.loadtxt(genome_root, skiprows=1) +# num_winners = genome_matrix.shape[0] +# +# +# for profile in np.arange(0, num_winners, 1): +# genome = genome_matrix[profile,:] +# profile_name = 'winner ' + str(profile + 1) +# profile_root = os.path.join('profiles','winners', profile_name + '.txt') +# +# perfil = transcript.decode_genome(genome) +# try: +# os.remove(profile_root) +# except : +# pass +# +# +# archivo = open(profile_root, mode = 'x') +# archivo.write(profile_name + '\n') +# for i in np.arange(0,100,1): +# texto = str(round(perfil[i,0],6)) + ' ' + str(round(perfil[i,1],6)) +'\n' +# archivo.write(texto) +# archivo.close() +# +# xfoil_calculate_profile(profile_name, profile_root, +# ambient_data, aero_domain, +# 'load') if (compare): - profile_name = 'NACA 5615' - profile_root = '5615' + airfoil_name = 'NACA 5615' + airfoil_root = '5615' try: - os.remove(os.path.join('profiles','winners', profile_name + '.txt')) + os.remove(os.path.join('airfoils','winners', airfoil_name + '.txt')) except : pass - xfoil_calculate_profile(profile_name, profile_root, + xfoil_calculate_profile(airfoil_name, airfoil_root, ambient_data, aero_domain, 'NACA') - profile_name2 = 'NACA 5603' - profile_root2 = '5603' + airfoil_name2 = 'NACA 5603' + airfoil_root2 = '5603' try: - os.remove(os.path.join('profiles','winners', profile_name2 + '.txt')) + os.remove(os.path.join('airfoils','winners', airfoil_name2 + '.txt')) except : pass - xfoil_calculate_profile(profile_name2, profile_root2, + xfoil_calculate_profile(airfoil_name2, airfoil_root2, ambient_data, aero_domain, 'NACA') @@ -431,7 +475,7 @@ def calculate_evolution(max_generations, num_winners, options): - for gen in np.arange(0,max_generations + 1,1): + for gen in range(max_generations): lift.append([]) effic.append([]) data_root = os.path.join('results','data','results_data_generation'+ str(gen) + '.txt') @@ -470,7 +514,7 @@ def calculate_evolution(max_generations, num_winners, options): lift_file.write('winner ' + str(i) + ' ') lift_file.write('\n') - for gen in np.arange(0, max_generations + 1, 1): + for gen in np.arange(0, max_generations, 1): lift_file.write(' '+ str(gen) + ' ') for win in np.arange(0, num_winners, 1): @@ -486,7 +530,7 @@ def calculate_evolution(max_generations, num_winners, options): effic_file.write('winner ' + str(i) + ' ') effic_file.write('\n') - for gen in np.arange(0, max_generations + 1, 1): + for gen in np.arange(0, max_generations, 1): effic_file.write(' '+ str(gen) + ' ') for win in np.arange(0, num_winners, 1): diff --git a/aeropy/Xfoil_Interaction/algoritmo/genetics.py b/aeropy/Xfoil_Interaction/algoritmo/genetics.py index 7f2c17d..c6fa395 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/genetics.py +++ b/aeropy/Xfoil_Interaction/algoritmo/genetics.py @@ -1,5 +1,5 @@ +# -*- coding: utf-8 -*- ''' - Created on Fri Feb 20 20:57:16 2015 @author: Siro Moreno @@ -25,21 +25,22 @@ -def genetic_step(generation,num_parent, weights): +def genetic_step(generation,population, num_parent, weights): '''Returns the genome of the (n+1)generation ''' - file_parent_name = 'generation'+ str(generation) + '.txt' - genome_parent_root = os.path.join('genome', file_parent_name) - genome = np.loadtxt(genome_parent_root, skiprows=1) - num_pop = genome.shape[0] - results_data = analyze.pop_analice(generation, num_pop) +# file_parent_name = 'generation'+ str(generation) + '.txt' +# genome_parent_root = os.path.join('genome', file_parent_name) +# genome = np.loadtxt(genome_parent_root, skiprows=1) +# num_pop = genome.shape[0] + pop_len = len(population) + + analyze.pop_analice(generation, population, num_parent) + analyze.score(generation, population, weights) + parents = selection.selection(population, num_parent) + children = cross.cross(parents, pop_len, generation) + mutation.mutation(children, generation, num_parent) - scores = analyze.score(generation,num_pop, weights) - parents = selection.selection(scores, genome, num_parent) - children = cross.cross(parents, num_pop) - children = mutation.mutation(children, generation, num_parent) - profile_number = children.shape[0] file_name = 'generation'+ str(generation + 1) + '.txt' @@ -59,21 +60,24 @@ def genetic_step(generation,num_parent, weights): except : pass + if os.path.exists(genome_root): + os.remove(genome_root) + genome_file = open(genome_root, mode = 'x') results_file = open(results_root, mode = 'x') genome_file.write(title + '\n') results_file.write(results_title + '\n') - results_file.write('Cl max Eficciency Score' + '\n') + results_file.write('Cl max Eficciency Score\n') - for profile in np.arange(0, profile_number, 1): + for airfoil in population: line = '' - for gen in np.arange(0, 16,1): - line = line + str(children[profile, gen]) +' ' + for gen in airfoil.genome: + line = line + str(gen) +' ' line = line + '\n' genome_file.write(line) - result = str(results_data[profile, 0]) + ' ' - result = result + str(results_data[profile, 1]) + ' ' - result = result + str(scores[profile]) + '\n' + result = str(airfoil.clmax) + ' ' + result += str(airfoil.maxefic) + ' ' + result += str(airfoil.score) + '\n' results_file.write(result) genome_file.close() diff --git a/aeropy/Xfoil_Interaction/algoritmo/initial.py b/aeropy/Xfoil_Interaction/algoritmo/initial.py index dbb19f0..8c91c4a 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/initial.py +++ b/aeropy/Xfoil_Interaction/algoritmo/initial.py @@ -18,13 +18,33 @@ import os import numpy as np import algoritmo.testing as test +import shutil +class Airfoil(object): + '''This object represents a single airfoil''' + results_root = '' + + def __init__(self, genome): + self.genome = genome + def copy_data(self, gen, num): + airfoil_name = 'gen' + str(gen) + 'airf' + str(num) + new_root = os.path.join("aerodata","data" + airfoil_name + '.txt') + shutil.copy(self.results_root, new_root) + self.results_root = new_root + self.name = airfoil_name + def copy_winner(self, num): + new_root = os.path.join("results","data", 'winner '+str(num) + 'aerodata.txt') + shutil.copy(self.results_root, new_root) + self.results_root = new_root + + def start_pop(pop_num): '''Creates a randomly generated population of the size (pop_num) ''' - genome = np.zeros([pop_num,16]) + population = [] + genes = np.array([150*np.pi/180, #ang s1 0.2, #dist s1 @@ -64,19 +84,20 @@ def start_pop(pop_num): 0.15]) #dist s2 - for profile in np.arange(0, pop_num, 1): + for airfoil in range(pop_num): + genome = np.zeros(16) deviation = 0.7 * np.random.randn(16) * gen_deviation - genome[profile,:] = genes + deviation - while not(test.airfoil_test(genome[profile,:])): + genome = genes + deviation + while not(test.airfoil_test(genome)): - # Here we check tat our airfoil actually makes sense - + # Here we check that our airfoil actually makes sense deviation = 0.7 * np.random.randn(16) * gen_deviation - genome[profile,:] = genes + deviation - + genome = genes + deviation + airfoil = Airfoil(genome) + population.append(airfoil) - profile_number = genome.shape[0] + genome_root = os.path.join('genome','generation0.txt') title = 'generation 0 genome' @@ -87,13 +108,14 @@ def start_pop(pop_num): archivo = open(genome_root, mode = 'x') archivo.write(title + '\n') - for profile in np.arange(0, profile_number, 1): + for airfoil in population: line = '' - for gen in np.arange(0, 16,1): - line = line + str(genome[profile, gen]) +' ' + genome = airfoil.genome + for gen in genome: + line = line + str(gen) +' ' line = line + '\n' archivo.write(line) - return genome + return population diff --git a/aeropy/Xfoil_Interaction/algoritmo/interfaz.py b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py index 5d13b68..e7411d8 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/interfaz.py +++ b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py @@ -1,5 +1,5 @@ +# -*- coding: utf-8 -*- ''' - Created on Fri Feb 20 20:57:16 2015 @author: Juan Luis Cano, Alberto Lorenzo, Siro Moreno @@ -27,21 +27,24 @@ import algoritmo.ambient as ambient -def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aero_domain): +def xfoil_calculate_profile(generation, airfoil_number, + airfoil, ambient_data, aero_domain): '''Starts Xfoil and analyzes the given airfoil. Saves the results. ''' - profile_name = 'gen' + str(generation) + 'prof' + str(profile_number) - geo_file_name = 'profile' + str(profile_number) + '.txt' - profile_root = os.path.join('profiles','gen' + str(generation) , geo_file_name ) - data_root = os.path.join("aerodata","data" + profile_name + '.txt') + airfoil_name = 'gen' + str(generation) + 'airf' + str(airfoil_number) + geo_file_name = 'airfoil' + str(airfoil_number) + '.txt' + airfoil_root = os.path.join('airfoils','gen' + str(generation) , geo_file_name ) + data_root = os.path.join("aerodata","data" + airfoil_name + '.txt') + airfoil.name = airfoil_name + airfoil.results_root = data_root aerodynamics = ambient.aero_conditions(ambient_data) commands = ['load', - profile_root, + airfoil_root, 'oper', 'mach ' + str(aerodynamics[0]), 're ' + str(aerodynamics[1]), @@ -57,11 +60,12 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer 'quit'] - perfil = trans.decode_genome(genome) + genome = airfoil.genome + airf_points = trans.decode_genome(genome) try: - os.remove(profile_root) + os.remove(airfoil_root) except : pass try: @@ -70,12 +74,12 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer pass - archivo = open(profile_root, mode = 'x') - archivo.write(profile_name + '\n') - + archivo = open(airfoil_root, mode = 'x') + archivo.write(airfoil_name + '\n') for i in np.arange(0,100,1): - texto = str(round(perfil[i,0],6)) + ' ' + str(round(perfil[i,1],6)) +'\n' + texto = str(round(airf_points[i,0],6)) + texto += ' ' + str(round(airf_points[i,1],6)) +'\n' archivo.write(texto) archivo.close() @@ -91,21 +95,24 @@ def xfoil_calculate_profile(generation,profile_number, genome, ambient_data, aer for line in p.stdout.readlines(): print(line.decode(), end='') -def xfoil_calculate_population(generation, ambient_data, aero_domain): +def xfoil_calculate_population(generation, population, + ambient_data, aero_domain, + num_parent = 0): '''Given a generation number and ambiental conditions, reads the file which contains the genome information of the generation, and uses xfoil to analyze each airfoil. ''' - genome_root = os.path.join('genome','generation'+ str(generation) + '.txt') - genome_matrix = np.loadtxt(genome_root, skiprows=1) - num_pop = genome_matrix.shape[0] - profile_folder = os.path.join('profiles', 'gen' + str(generation)) - if not os.path.exists(profile_folder): - os.makedirs(profile_folder) + pop_len = len(population) + + airfoils_folder = os.path.join('airfoils', 'gen' + str(generation)) + if not os.path.exists(airfoils_folder): + os.makedirs(airfoils_folder) - for profile_number in np.arange(1,num_pop+1,1): - xfoil_calculate_profile(generation, profile_number, genome_matrix[profile_number-1,:], ambient_data, aero_domain) + for airfoil_number in range(num_parent, pop_len): + xfoil_calculate_profile(generation, airfoil_number, + population[airfoil_number], + ambient_data, aero_domain) diff --git a/aeropy/Xfoil_Interaction/algoritmo/main.py b/aeropy/Xfoil_Interaction/algoritmo/main.py index 4bd53da..eee958d 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/main.py +++ b/aeropy/Xfoil_Interaction/algoritmo/main.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- ''' Created on Fri Feb 20 20:57:16 2015 @@ -17,8 +18,8 @@ import os + import algoritmo.interfaz as interfaz -import numpy as np import algoritmo.initial as initial import algoritmo.genetics as genetics import algoritmo.ender as ender @@ -70,24 +71,28 @@ def main_program(all_parameters): generation = 0 - initial.start_pop(airfoils_per_generation) + population = initial.start_pop(airfoils_per_generation) - interfaz.xfoil_calculate_population(generation, ambient_data, aero_domain) + interfaz.xfoil_calculate_population(generation, population, + ambient_data, aero_domain) ####--- Genetic Algorithm - for generation in np.arange(0,total_generations,1): + for generation in range(0,total_generations): - genetics.genetic_step(generation,num_parent, weighting_parameters) + population = genetics.genetic_step(generation, population, + num_parent, weighting_parameters) - interfaz.xfoil_calculate_population(generation + 1, ambient_data, aero_domain) + interfaz.xfoil_calculate_population(generation + 1, population, + ambient_data, aero_domain, + num_parent) - ender.finish(all_parameters) + ender.finish(population, all_parameters) @@ -97,9 +102,14 @@ def main_program(all_parameters): ####---------Primary Variables----- + + import interfaz + import initial + import genetics + import ender - airfoils_per_generation = 6 - total_generations = 6 + airfoils_per_generation = 4 + total_generations = 4 num_parent = 2 # We give the algorithm the conditions at wich we want to optimize our airofil diff --git a/aeropy/Xfoil_Interaction/algoritmo/mutation.py b/aeropy/Xfoil_Interaction/algoritmo/mutation.py index 2da007a..4be62bd 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/mutation.py +++ b/aeropy/Xfoil_Interaction/algoritmo/mutation.py @@ -45,19 +45,20 @@ def mutation(children, generation, num_parent): 30*np.pi/180, #ang s2 0.15]) #dist s2 - pop_num = children.shape[0] + len_pop = len(children) - children_n = children.copy() + #children_n = children.copy() - for i in np.arange(num_parent, pop_num, 1): + for airfoil_num in range(num_parent, len_pop): deviation = coeff * np.random.randn(16) * gen_deviation - children_n[i,:] = children[i,:] + deviation + airfoil = children[airfoil_num] + proposed_genome = airfoil.genome + deviation n = 0 - while not(test.airfoil_test(children_n[i,:])): + while not(test.airfoil_test(proposed_genome)): n = n + 1 deviation = coeff * np.random.randn(16) * gen_deviation - children_n[i,:] = children[i,:] + deviation + proposed_genome = airfoil.genome + deviation print('mutating into viable airfoil, try #',n) - children[i,:] = children_n[i,:] + airfoil.genome = proposed_genome - return children + diff --git a/aeropy/Xfoil_Interaction/algoritmo/selection.py b/aeropy/Xfoil_Interaction/algoritmo/selection.py index ebaf785..ead62d6 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/selection.py +++ b/aeropy/Xfoil_Interaction/algoritmo/selection.py @@ -22,13 +22,18 @@ import numpy as np -def selection(score, genome, num_parent): +def selection(population, num_parent): '''Select the genome of the (num_parent) best airfoils. ''' + pop_len = len(population) + score = np.zeros(pop_len) + for airfoil_num in range(pop_len): + airfoil = population[airfoil_num] + score[airfoil_num] = airfoil.score invscore = 1- score positions = np.argsort(invscore) - parents = np.zeros([num_parent,16]) - for i in np.arange(0,num_parent,1): - parents[i,:] = genome[positions[i],:] + parents =[] + for parent_count in range(num_parent): + parents.append(population[positions[parent_count]]) return parents \ No newline at end of file diff --git a/aeropy/Xfoil_Interaction/algoritmo/transcript.py b/aeropy/Xfoil_Interaction/algoritmo/transcript.py index d0eb06b..2b0c576 100644 --- a/aeropy/Xfoil_Interaction/algoritmo/transcript.py +++ b/aeropy/Xfoil_Interaction/algoritmo/transcript.py @@ -115,35 +115,34 @@ def decode_genome(genome): The following code contains an example and will be used with test purposes only, when this script is run alone, and won't be used in the standard function of the genetic algorithm. - -De-Comment the lines in order to use them. ''' -# -#import matplotlib.pyplot as plt -# -# -#genes = np.array([150*np.pi/180, #ang s1 -# 0.2, #dist s1 -# 0.5, #x 1 -# 0.12, #y 1 -# 0, #ang 1 -# 0.2, #dist b1 -# 0.2, #dist c1 -# 0.1, #dist a1 -# 0.05, #dist a2 -# 0.4, #x 2 -# 0.05, #y 2 -# 5*np.pi/180, #ang 2 -# 0.2, #dist b2 -# 0.2, #dist c2 -# 160*np.pi/180, #ang s2 -# 0.2]) #dist s2 -# - -# -#perfil = decode_genome(genes) -# -# -#plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k') -#plt.plot(perfil[:,0],perfil[:,1]) -#plt.gca().set_aspect(1) +if __name__ == '__main__': + + import matplotlib.pyplot as plt + + + genes = np.array([150*np.pi/180, #ang s1 + 0.2, #dist s1 + 0.5, #x 1 + 0.12, #y 1 + 0, #ang 1 + 0.2, #dist b1 + 0.2, #dist c1 + 0.1, #dist a1 + 0.05, #dist a2 + 0.4, #x 2 + 0.05, #y 2 + 5*np.pi/180, #ang 2 + 0.2, #dist b2 + 0.2, #dist c2 + 160*np.pi/180, #ang s2 + 0.2]) #dist s2 + + + + perfil = decode_genome(genes) + + + plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k') + plt.plot(perfil[:,0],perfil[:,1]) + plt.gca().set_aspect(1) diff --git a/aeropy/Xfoil_Interaction/launcher-express.py b/aeropy/Xfoil_Interaction/launcher-express.py new file mode 100644 index 0000000..1e56ead --- /dev/null +++ b/aeropy/Xfoil_Interaction/launcher-express.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 26 18:52:51 2016 + +@author: Usuario +""" + +import algoritmo.main as main + +airfoils_per_generation = 3 +total_generations = 2 +num_parent = 1 + +# We give the algorithm the conditions at wich we want to optimize our airofil +# through the "ambient data" tuple. + +planet = 'Mars' # For the moment we have 'Earth' and 'Mars' +chord_length = 0.1 # In metres +altitude = -7.5 # In Kilometres above sea level or reference altitude +speed_parameter = 'speed' # 'speed' or 'mach' +speed_value = 18 # Value of the previous magnitude (speed - m/s) +ambient_data = (planet, chord_length, altitude, speed_parameter, speed_value) + + + +####--------Secondary Variables------ +#-- Analysis domain + +start_alpha_angle = 0 +finish_alpha_angle = 20 +alpha_angle_step = 2 + +aero_domain = (start_alpha_angle, finish_alpha_angle, alpha_angle_step) + + +#-- Optimization objectives + +lift_coefficient_weight = 0.3 +efficiency_weight = 0.7 + +weighting_parameters = (lift_coefficient_weight, efficiency_weight) + +#-- Final results options + +num_winners = 1 +draw_winners = True +draw_polars = True +draw_evolution = True +compare_naca_standard = True +compare_naca_custom = True #Work in progress +create_report = True #Work in progress + +end_options = (draw_winners, draw_polars, draw_evolution, + compare_naca_standard, compare_naca_custom, + create_report) + + + +all_parameters = (airfoils_per_generation, total_generations, num_parent, + num_winners, weighting_parameters, end_options, + ambient_data, aero_domain ) + + +main.main_program(all_parameters) \ No newline at end of file