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": [
+        "[<matplotlib.lines.Line2D at 0x6730eb8>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1Q5bczzork0cUEQmnWrWgdu3Efz6jNfeGDR0bNkD9+hk5pIhIqPnJw0JQcz/2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\nXUQkgv4ftseZQvfedoYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x66d4358>"
+       ]
+      }
+     ],
+     "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": [
+        "[<matplotlib.lines.Line2D at 0x69eddd8>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Q3907xh7fAJRXH2w2sweAEncfGXtcCpzs7mtqXKsf8IW7D67xvMYUCkxdJaMV6wpl60bp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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x6758278>"
+       ]
+      }
+     ],
+     "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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\ntEVumWnBygAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x69cff28>"
+       ]
+      }
+     ],
+     "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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\npcED1lDwAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x6c0a400>"
+       ]
+      }
+     ],
+     "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
new file mode 100644
index 0000000..f647e2a
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/README.md
@@ -0,0 +1,59 @@
+aeropy - Xfoil Interaction tool
+======
+
+Python tools for Aeronautical calculations.
+
+Genetic Optimization Algorithm in progress (python 3.4, xfoil 6.99)
+
+Includes:
+
+-Minimal working interface between Python and Xfoil
+
+-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 generated. (Must be placed with the launcher.py genetic algorithm file)(Work in progress)
+
+Genetic algorithm modules:
+
+-ambient
+
+-analyze
+
+-cross
+
+-ender
+
+-ender_report (Work in progress)
+
+-genetics
+
+-initial 
+
+-interfaz
+
+-main 
+
+-mutation
+
+-selection
+
+-testing
+
+-transcript
+
+-launcher
+
+##Instructions:
+
+Place xfoil in the same folder as "launcher.py"
+
+
+Execute "launcher.py" 
+
+
+
+More info: 
+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
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/algoritmo/ambient.py b/aeropy/Xfoil_Interaction/algoritmo/ambient.py
new file mode 100644
index 0000000..4b417cd
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/ambient.py
@@ -0,0 +1,256 @@
+'''
+
+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 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 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, 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,
+                        32.162,
+                        47.350,
+                        51.413,
+                        71.802,
+                        86.000,
+                        90.000])
+  
+
+    
+    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 temperature(h, conditions, dT = 0):
+        '''Calculates the value for temperature in Kelvin at a certain altitude'''
+        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 + 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 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 * temperature(z, conditions, dT))
+        return pi * np.e ** -_
+            
+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 * (temperature(z, conditions, dT)/(Ti + dT)) ** -_
+                
+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]
+        
+        
+        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] = pressure_segment_1(heights[layer],
+                                                            pressure_points[layer - 1],
+                                                            heights[layer - 1],
+                                                            dT, conditions)
+            else:
+                pressure_points[layer] = pressure_segment_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 = pressure_segment_1(h,
+                                             pressure_points[layer - 1],
+                                             heights[layer - 1],
+                                             dT, conditions)
+            else:
+                funcion = pressure_segment_2(h,
+                                             pressure_points[layer - 1],
+                                             temp_points[layer - 1],
+                                             gradient[layer-1],
+                                             dT, conditions)
+            grad = grad + window(h, heights[layer-1], heights[layer], funcion)
+        if (abs(gradient[layer-1])< 10e-8):
+            funcion = pressure_segment_1(h,
+                                         pressure_points[layer - 1],
+                                         heights[layer - 1],
+                                         dT, conditions)
+        else:
+            funcion = pressure_segment_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 density(h, conditions, dT = 0):
+        '''Calculates the value for density in Kg/m3 at a certain altitude'''
+        R = conditions[4]
+        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]) 
+
+    # 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 viscosity(temp, planet):
+    '''Calculates the value for viscosity in microPascal*second
+    for a certain temperature'''
+    
+    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):
+    '''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 *pressure(height, planet_dic[planet]) / density(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')
+        
+    
+    visc = viscosity(temperature(height, planet_dic[planet]), planet)
+    re = Reynolds(density(height, planet_dic[planet]), chord, vel, visc)
+    
+  
+    
+    return [mach, re]
diff --git a/aeropy/Xfoil_Interaction/algoritmo/analyze.py b/aeropy/Xfoil_Interaction/algoritmo/analyze.py
new file mode 100644
index 0000000..96d71c9
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/analyze.py
@@ -0,0 +1,77 @@
+'''
+
+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 analysis subprogram. Its objective is to assign a score to
+every airfoil by reading the aerodynamic characteristics that XFoil
+has calculated.
+
+'''
+
+
+
+
+import numpy as np
+import os
+
+
+
+
+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_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)
+    
+   
+    
+    
+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.
+    '''    
+    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)
+    if ((read_dim.shape[0]) != 2):
+        datos = np.zeros([2,2])
+    
+    
+    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]
+    airfoil.clmax = clmax
+    airfoil.maxefic = maxefic
+    
+def score(generation, population, weights):
+    '''
+    
+    '''
+    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
new file mode 100644
index 0000000..c409ca9
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/cross.py
@@ -0,0 +1,55 @@
+'''
+
+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 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 numpy as np
+from algoritmo.initial import Airfoil
+
+
+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 = []
+    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
new file mode 100644
index 0000000..ddea0a4
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/ender.py
@@ -0,0 +1,696 @@
+'''
+
+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 algoritmo.interfaz as interfaz
+import numpy as np
+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 algoritmo.transcript as transcript
+import algoritmo.ambient as ambient
+import subprocess
+import shutil
+#import algoritmo.ender_report
+
+
+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]    
+    ambient_data = all_parameters[6]
+    aero_domain = all_parameters[7]
+    
+    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]
+    
+#    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(winners)
+    calculate_evolution(generation, num_winners, compare)
+    if (must_draw_winners):
+        draw_winners(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 save_last_gen(population, generation):
+    
+    
+    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(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 airfoil in population:
+        result = str(airfoil.clmax) + '   ' 
+        result += str(airfoil.maxefic) + '   '
+        result += str(airfoil.score) + '\n'
+        results_file.write(result)
+    
+    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_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')
+    
+    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(winners, options):
+    
+    num_winners = len(winners)
+    
+    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(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('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('airfoils','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]),
+            str(aero_domain[1]),
+            str(aero_domain[2]),
+            '',
+            'quit']
+    if (profile_type == 'NACA'):
+        naca_root = os.path.join('airfoils', '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):
+    
+    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):
+        
+        airfoil_name = 'NACA 5615'
+        airfoil_root = '5615'
+        
+        try:
+            os.remove(os.path.join('airfoils','winners', airfoil_name + '.txt'))
+        except :
+            pass 
+        
+        xfoil_calculate_profile(airfoil_name, airfoil_root,
+                                ambient_data, aero_domain,
+                                'NACA')
+                                
+        airfoil_name2 = 'NACA 5603'
+        airfoil_root2 = '5603'
+        
+        try:
+            os.remove(os.path.join('airfoils','winners', airfoil_name2 + '.txt'))
+        except :
+            pass 
+        
+        xfoil_calculate_profile(airfoil_name2, airfoil_root2,
+                                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.xlim(xmin=0)    
+    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])
+        
+        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)
+    
+    
+# Drawing the Evolution Diagrams
+
+def calculate_evolution(max_generations, num_winners, options):
+    lift = []
+    effic = []
+    
+   
+    
+    for gen in range(max_generations):
+        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):
+        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):
+        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])
+        
+        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)
+    
+    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)
+        
+        label = 'NACA 5603' 
+        value = naca_cl2 * 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)
+        
+        label = 'NACA 5603' 
+        value = naca_effic2 * 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)
+  
+
+
+
+
+
+
+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/algoritmo/ender_report.py b/aeropy/Xfoil_Interaction/algoritmo/ender_report.py
new file mode 100644
index 0000000..1f520a2
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/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 algoritmo.initial as initial
+import algoritmo.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 = '''[<img src="'''
+    text_01_b = logo_root
+    text_01_c = '''" alt="AeroPython" style="width: 
+            100px; float: center"/>](https://github.com/AeroPython) \n '''
+
+    text_01_d = '''<p style='text-align: center;'>Aeropython Xfoil Genetic 
+            Algorithm Report</p>'''
+
+    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/algoritmo/genetics.py b/aeropy/Xfoil_Interaction/algoritmo/genetics.py
new file mode 100644
index 0000000..c6fa395
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/genetics.py
@@ -0,0 +1,85 @@
+# -*- coding: utf-8 -*-
+'''
+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 Genetic Step Subprograme. After the XFoil analysis of the
+'N' generation, this subprogramme will calculate the population of the 
+'N+1' generation. 
+
+'''
+
+
+
+
+import os
+import numpy as np
+import algoritmo.analyze as analyze
+import algoritmo.selection as selection
+import algoritmo.cross as cross
+import algoritmo.mutation as mutation
+
+
+
+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]
+    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)
+    
+    
+    
+    
+    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
+    
+    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')
+    
+    for airfoil in population:
+        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)
+    
+    genome_file.close()
+    results_file.close()    
+    return children
\ No newline at end of file
diff --git a/aeropy/Xfoil_Interaction/algoritmo/initial.py b/aeropy/Xfoil_Interaction/algoritmo/initial.py
new file mode 100644
index 0000000..8c91c4a
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/initial.py
@@ -0,0 +1,121 @@
+'''
+
+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 creates the initial population for the genetic algorithm.
+It does so by adding a random deviation to a default profile genome.
+
+'''
+
+
+
+
+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)
+    '''
+    
+    population = []
+        
+    
+    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
+                  -0.07,          #y 2
+                  5*np.pi/180,   #ang 2
+                  0.2,           #dist b2 
+                  0.2,           #dist c2
+                  190*np.pi/180, #ang s2
+                  0.2])          #dist s2
+
+
+    
+    
+    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 airfoil in range(pop_num):
+        genome = np.zeros(16)
+        deviation = 0.7 * np.random.randn(16) * gen_deviation
+        genome = genes + deviation
+        while not(test.airfoil_test(genome)):
+            
+            # Here we check that our airfoil actually makes sense        
+            deviation = 0.7 * np.random.randn(16) * gen_deviation
+            genome = genes + deviation
+        airfoil = Airfoil(genome)
+        population.append(airfoil)
+
+    
+       
+    genome_root = os.path.join('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 airfoil in population:
+        line = ''
+        genome = airfoil.genome
+        for gen in genome:
+            line = line + str(gen) +'    '
+        line = line + '\n'
+        archivo.write(line)
+    
+    return population
+
+
diff --git a/aeropy/Xfoil_Interaction/algoritmo/interfaz.py b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py
new file mode 100644
index 0000000..e7411d8
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/interfaz.py
@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+'''
+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 os
+import algoritmo.transcript as trans
+import numpy as np
+import algoritmo.ambient as ambient
+
+
+def xfoil_calculate_profile(generation, airfoil_number,
+                            airfoil, ambient_data, aero_domain):
+    
+    '''Starts Xfoil and analyzes the given airfoil. Saves the results.
+    '''
+    
+    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',
+            airfoil_root,
+            'oper',
+            'mach ' + str(aerodynamics[0]),
+            're ' + str(aerodynamics[1]),
+            'visc',
+            'pacc',
+            data_root,
+            '',
+            'aseq',
+            str(aero_domain[0]),
+            str(aero_domain[1]),
+            str(aero_domain[2]),
+            '',
+            'quit']
+
+
+    genome = airfoil.genome    
+    airf_points = trans.decode_genome(genome)
+    
+
+    try:
+        os.remove(airfoil_root)
+    except :
+        pass
+    try:
+        os.remove(data_root)
+    except :
+        pass
+   
+    
+    archivo = open(airfoil_root, mode = 'x')
+    archivo.write(airfoil_name + '\n')
+
+    for i in np.arange(0,100,1):
+        texto = str(round(airf_points[i,0],6))
+        texto += '   ' + str(round(airf_points[i,1],6)) +'\n'
+        archivo.write(texto)
+    archivo.close()
+
+    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 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.
+    '''
+    
+    
+    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 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
new file mode 100644
index 0000000..eee958d
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/main.py
@@ -0,0 +1,165 @@
+# -*- coding: utf-8 -*-
+'''
+
+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 algoritmo.interfaz as interfaz
+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).
+
+#If this is the starting file, it will call the main function with the 
+#parameters described below.
+
+
+
+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]
+    ambient_data = all_parameters[6]
+    aero_domain = all_parameters[7]
+
+    
+###--- 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')
+
+    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'))
+
+
+
+
+####--- Starting the population, analysis of the starting population
+
+
+    generation = 0
+
+
+    population = initial.start_pop(airfoils_per_generation)
+
+    interfaz.xfoil_calculate_population(generation, population,
+                                        ambient_data, aero_domain)
+
+
+####--- Genetic Algorithm
+
+
+    for generation in range(0,total_generations):
+    
+
+        population = genetics.genetic_step(generation, population, 
+                              num_parent, weighting_parameters)
+    
+        interfaz.xfoil_calculate_population(generation + 1, population,
+                                            ambient_data, aero_domain,
+                                            num_parent)
+    
+   
+
+    ender.finish(population, all_parameters)    
+    
+
+
+#If this is the file from which we are starting, we define here the parameters:
+
+if __name__ == '__main__':
+
+####---------Primary Variables-----
+
+        
+    import interfaz
+    import initial
+    import genetics
+    import ender
+
+    airfoils_per_generation = 4
+    total_generations = 4
+    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 = 0.1 # 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 = 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)
+             
+             
+             
+    all_parameters = (airfoils_per_generation, total_generations, num_parent,
+                      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/algoritmo/mutation.py b/aeropy/Xfoil_Interaction/algoritmo/mutation.py
new file mode 100644
index 0000000..4be62bd
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/mutation.py
@@ -0,0 +1,64 @@
+'''
+
+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 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 numpy as np
+import algoritmo.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
+                  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
+    
+    len_pop = len(children)
+    
+    #children_n = children.copy()
+    
+    for airfoil_num in range(num_parent, len_pop):
+        deviation = coeff * np.random.randn(16) * gen_deviation
+        airfoil = children[airfoil_num]
+        proposed_genome = airfoil.genome + deviation
+        n = 0
+        while not(test.airfoil_test(proposed_genome)):
+            n = n + 1
+            deviation = coeff * np.random.randn(16) * gen_deviation
+            proposed_genome = airfoil.genome + deviation
+            print('mutating into viable airfoil, try #',n)
+        airfoil.genome = proposed_genome
+    
+   
diff --git a/aeropy/Xfoil_Interaction/algoritmo/selection.py b/aeropy/Xfoil_Interaction/algoritmo/selection.py
new file mode 100644
index 0000000..ead62d6
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/selection.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 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 numpy as np
+
+
+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 =[]
+    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/testing.py b/aeropy/Xfoil_Interaction/algoritmo/testing.py
new file mode 100644
index 0000000..1463922
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/testing.py
@@ -0,0 +1,113 @@
+'''
+
+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 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 algoritmo.transcript as transcript
+import numpy as np
+from scipy import interpolate
+
+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
+    else:
+        return False
+    
+
+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 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 = 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
+    else:
+        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
+    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):
+    '''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])
+    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 airfoil_test(genome):
+    '''Run 3 test of increasing complexity to detect inviable airfoils
+    '''
+    if test_simple(genome):
+        if x_coordinate_test(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/algoritmo/transcript.py b/aeropy/Xfoil_Interaction/algoritmo/transcript.py
new file mode 100644
index 0000000..2b0c576
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/algoritmo/transcript.py
@@ -0,0 +1,148 @@
+# -*- 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, 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
+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 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).
+    '''
+    punto = np.array(a)+ np.array(dist,dist)* [np.cos(ang),np.sin(ang)]
+    return punto
+
+
+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.
+    '''
+    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.
+    '''
+   
+    parameter_u = np.linspace(0,1,num)
+    curva = np.zeros([num,2])
+
+    for counter in np.arange(num):
+        _ = bernstein(parameter_u[counter])*control_points
+        curva[counter,] = sum (_)
+    return curva
+
+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
+    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,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, point_generator(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 alone, and won't be used in the standard function of the 
+genetic algorithm.
+'''
+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
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)
+
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='')
diff --git a/aeropy/Xfoil_Interaction/result_drawer.ipynb b/aeropy/Xfoil_Interaction/result_drawer.ipynb
new file mode 100644
index 0000000..e6d8709
--- /dev/null
+++ b/aeropy/Xfoil_Interaction/result_drawer.ipynb
@@ -0,0 +1,463 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:2e17f8dbbc7de9bb287937db82fd91e0cac6bf14382e705ed36a69f75776b27e"
+ },
+ "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": [],
+     "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": [
+      "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,
+     "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": 7
+    },
+    {
+     "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": [],
+     "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)"
+     ],
+     "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",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file