Funcional MK2.5

Unificadas funciones y variables en inglés, código correctamente
comentado.

Author: AunSiro

aeropy/Xfoil_Interaction/.ipynb_checkpoints/result_drawer-checkpoint.ipynb

@@ -7,31 +7,37 @@
 This is a submodule for the genetic algorithm that is explained in
 https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing
 
-This script is the main program. It will call the different submodules
-and manage the data transfer between them in order to achieve the
-genetic optimization of the profile.
-
-'''
-
+This script is the ambient subprogram. Its objective is to calculate the 
+Mach and Reynolds numbers that XFoil uses to calculate the 
+aerodynamics of airfoils.
 
 
+This subprogram consist mainly in  models for the International Standard Atmosphere
+and an equivalent atmosphere model for Mars.
+'''
 
 import numpy as np
 
 
 
-
-
-def ventana (x, inicio=0, fin=1, f=1.):
-    _1 = (np.sign(x-inicio))
-    _2 = (np.sign(-x+fin))
+def window (x, start=0, end=1, f=1.):
+    '''Returns a function f between 'start' and 'endn'.
+    Returns 0 outside this interval'''
+    _1 = (np.sign(x-start))
+    _2 = (np.sign(-x+end))
     return 0.25 * (_1+1) * (_2+1) * f
 
-def etc (x, inicio=0, f=1.):
-    _1 = (np.sign(x-inicio))
+def etc (x, start=0, f=1.):
+    '''Returns 0 until 'start', returns 'f' after.
+    '''
+    _1 = (np.sign(x-start))
     return 0.5 * (_1+1) * f
 
 def earth_conditions():
+    
+    '''Returns an array of atmospheric and physical data from which the complete
+    model of the atmosphere can be built.
+    '''
     heights = np.array([-1.000,
                         11.019,
                         20.063,
@@ -43,7 +49,7 @@ def earth_conditions():
                         90.000])
 
 
-    # an es el gradiente válido entre H(n-1) y H(n)
+    
     temp_gradient = np.array([-6.49,
                                0,
                                0.99,
@@ -72,8 +78,8 @@ def earth_conditions():
     return conditions
 
 
-def temperatura(h, conditions, dT = 0):
-        '''Calcula la temperatura a una altura h en Km sobre el nivel del mar'''
+def temperature(h, conditions, dT = 0):
+        '''Calculates the value for temperature in Kelvin at a certain altitude'''
         grad = 0
         heights = conditions[0]
         gradient = conditions[1]
@@ -84,50 +90,48 @@ def temperatura(h, conditions, dT = 0):
 
         for layer in np.arange(0, atm_layer-1,1):
             increase = temp_points[layer] + gradient[layer] * (h - heights[layer])
-            grad = grad + ventana(h, heights[layer],heights[layer+1], increase)
+            grad = grad + window(h, heights[layer],heights[layer+1], increase)
         grad = grad + etc(h, heights[atm_layer-1],temp_points[atm_layer-1] + gradient[atm_layer-1]*(h - heights[atm_layer-1]))
         return grad + dT
 
 
-def segmento_presion_1(z, pi, z0, dT, conditions):
-        '''calcula la presión en un segmento de atmósfera de temperatura constante'''
+def pressure_segment_1(z, pi, z0, dT, conditions):
+        '''Calculates pressure throught a constant temperature segment of the atmosphere'''
         g = conditions[3]
         R = conditions[4]
         radius = conditions[5]
         h  = (radius * z) /(radius + z)
         h0 = (radius * z0)/(radius + z0)
-        _ = 1000*(h-h0) * g / (R * temperatura(z, conditions, dT))
+        _ = 1000*(h-h0) * g / (R * temperature(z, conditions, dT))
         return pi * np.e ** -_
 
-def segmento_presion_2(z, pi, Ti, a, dT, conditions):
-        '''calcula la presión en un segmento de atmósfera con gradiente de temperatura "a" '''
+def pressure_segment_2(z, pi, Ti, a, dT, conditions):
+        '''Calculates pressure throught a variant temperature segment of the atmosphere '''
         g = conditions[3]
         R = conditions[4]
         _ = g / (a*R/1000)
-        return pi * (temperatura(z, conditions, dT)/(Ti + dT)) ** -_
+        return pi * (temperature(z, conditions, dT)/(Ti + dT)) ** -_
 
-def presion (h, conditions,  dT = 0):
-        '''Calcula la presion en Pa a una altura h en m sobre el nivel del mar'''
+def pressure (h, conditions,  dT = 0):
+        '''Calculates the value for pressure in Pascal at a certain altitude'''
 
         heights = conditions[0]
         gradient = conditions[1]
         temp_points = conditions[2]
         atm_layer = gradient.shape[0]
-        #Primero, calculamos la presion de cada punto de cambio de capa para la condición de dT pedida
-        #Suponemos que la presión es siempre constante a 101325 Pa a nivel del mar
 
 
         pressure_points = np.zeros([atm_layer])
         pressure_points[0] = conditions[6]
 
         for layer in np.arange(1, atm_layer, 1):
             if (abs(gradient[layer-1]) < 1e-8):
-                pressure_points[layer] = segmento_presion_1(heights[layer],
+                pressure_points[layer] = pressure_segment_1(heights[layer],
                                                             pressure_points[layer - 1],
                                                             heights[layer - 1],
                                                             dT, conditions)
             else:
-                pressure_points[layer] = segmento_presion_2(heights[layer],
+                pressure_points[layer] = pressure_segment_2(heights[layer],
                                                             pressure_points[layer - 1],
                                                             temp_points[layer - 1],
                                                             gradient[layer-1],
@@ -139,24 +143,24 @@ def presion (h, conditions,  dT = 0):
         grad = 0
         for layer in np.arange(1, atm_layer, 1):
             if (abs(gradient[layer-1]) < 1e-8):
-                funcion = segmento_presion_1(h,
+                funcion = pressure_segment_1(h,
                                              pressure_points[layer - 1],
                                              heights[layer - 1],
                                              dT, conditions)
             else:
-                funcion = segmento_presion_2(h,
+                funcion = pressure_segment_2(h,
                                              pressure_points[layer - 1],
                                              temp_points[layer - 1],
                                              gradient[layer-1],
                                              dT, conditions)
-            grad = grad + ventana(h, heights[layer-1], heights[layer], funcion)
+            grad = grad + window(h, heights[layer-1], heights[layer], funcion)
         if (abs(gradient[layer-1])< 10e-8):
-            funcion = segmento_presion_1(h,
+            funcion = pressure_segment_1(h,
                                          pressure_points[layer - 1],
                                          heights[layer - 1],
                                          dT, conditions)
         else:
-            funcion = segmento_presion_2(h,
+            funcion = pressure_segment_2(h,
                                          pressure_points[layer - 1],
                                          temp_points[layer - 1],
                                          gradient[layer-1],
@@ -166,13 +170,16 @@ def presion (h, conditions,  dT = 0):
         return grad
 
 
-def densidad(h, conditions, dT = 0):
-        '''Calcula la densidad a una altura h en m sobre el nivel del mar'''
+def density(h, conditions, dT = 0):
+        '''Calculates the value for density in Kg/m3 at a certain altitude'''
         R = conditions[4]
-        return presion(h, conditions, dT)/(R * temperatura(h, conditions, dT))
+        return pressure(h, conditions, dT)/(R * temperature(h, conditions, dT))
 
 
 def mars_conditions():
+    '''Returns an array of atmospheric and physical data from which the complete
+    model of the atmosphere can be built.
+    '''
     heights = np.array([-8.3,
                          8.85,
                          30]) 
@@ -200,7 +207,10 @@ def mars_conditions():
 
 
 
-def viscosidad(temp, planet):
+def viscosity(temp, planet):
+    '''Calculates the value for viscosity in microPascal*second
+    for a certain temperature'''
+    
     if (planet == 'Earth'):
         c = 120
         lamb = 1.512041288
@@ -213,16 +223,20 @@ def viscosidad(temp, planet):
 
 
 def Reynolds(dens, longitud, vel, visc):
+    '''Calculates the Reynolds number'''
     re = 1000000 * dens * longitud * vel / visc
     return re
 
 
 def aero_conditions(ambient_data):
+    '''Given a certain conditions, return the value of the Mach and Reynolds
+    numbers, in that order.
+    '''
     (planet, chord, height, speed_type, speed) = ambient_data
     planet_dic = {'Mars':mars_conditions(), 'Earth':earth_conditions()}
 
 
-    sound = (1.4 *presion(height, planet_dic[planet]) / densidad(height,planet_dic[planet]))**0.5
+    sound = (1.4 *pressure(height, planet_dic[planet]) / density(height,planet_dic[planet]))**0.5
 
     if (speed_type == 'mach'):
         mach = speed
@@ -234,14 +248,9 @@ def aero_conditions(ambient_data):
         print('error in the data, invalid speed parameter')
 
 
-    
-    re = Reynolds(densidad(height, planet_dic[planet]), chord, vel, viscosidad(temperatura(height, planet_dic[planet]), planet))
+    visc = viscosity(temperature(height, planet_dic[planet]), planet)
+    re = Reynolds(density(height, planet_dic[planet]), chord, vel, visc)
 
 
 
     return [mach, re]
-#
-#ambient_data = ('Earth', 03.0003, 11, 'speed', 30.1) 
-#
-#result = aero_conditions(('Earth', 0.03, 11, 'mach', 0.1))       
-#print(result)      

aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py

@@ -7,41 +7,38 @@
 This is a submodule for the genetic algorithm that is explained in
 https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing
 
-This script is the main program. It will call the different submodules
-and manage the data transfer between them in order to achieve the
-genetic optimization of the profile.
+This script is the analysis subprogram. Its objective is to assign a score to
+every airfoil by reading the aerodynamic characteristics that XFoil
+has calculated.
 
 '''
 
 
 
-import subprocess
-import sys
-import os
-import interfaz as interfaz
+
 import numpy as np
-import initial as initial
 
 
 
-#generation = 0
-#starting_profiles = 20
-#
-#genome = initial.start_pop(starting_profiles)
-#
-#interfaz.xfoil_calculate_population(generation,genome)
-#
-#num_pop = genome_matrix.shape[0]
 
 def pop_analice (generation, num_pop):
+    '''For a given generation and number of airfoils, returns an array which
+    contains the maximun Lift Coefficient and Maximum Aerodinamic Efficiency
+    for every airfoil.
+    '''
     pop_results = np.zeros([num_pop,2])
     for profile_number in np.arange(1,num_pop+1,1):
        pop_results[profile_number - 1, :] = profile_analice (generation, profile_number)
 
     return pop_results
 
 
-def profile_analice (generation, profile_number):    
+def profile_analice (generation, profile_number):
+    '''For a given generation and profile, searches for the results of the
+    aerodynamic analysis made in Xfoil. Then, searches for the maximum
+    values of the Lift Coefficient and Aerodynamic Efficiency and returns them
+    as an 1x2 array.
+    '''    
     profile_name = 'gen' + str(generation) + 'prof' + str(profile_number)
     data_root = "aerodata\data" + profile_name + '.txt'
     datos = np.loadtxt(data_root, skiprows=12, usecols=[1,2])
@@ -60,14 +57,18 @@ def profile_analice (generation, profile_number):
     return np.array([clmax , maxefic])
 
 def adimension(array):
+    '''Adimensionalyzes an array with its maximun value
+    '''
     max_value = max(array)
     adim = array / max_value
     return adim
 
-def score(generation, num_pop):
-    
+def score(generation, num_pop, weights):
+    '''For a given generation, number of airfoils and weight parameters, returns
+    an array of the scores of all airfoils.
+    '''
     pop_results = pop_analice (generation, num_pop)
     cl_score = adimension(pop_results[:,0])
     efic_score = adimension(pop_results[:,1])
-    total_score = 0.3 * cl_score + 0.7 * efic_score
+    total_score = weights[0] * cl_score + weights[1] * efic_score
     return total_score

…ction/Genetic_algorithm_files/analice.py …ction/Genetic_algorithm_files/analyze.pyaeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py renamed to aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py aeropy/Xfoil_Interaction/Genetic_algorithm_files/analice.py renamed to aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.py

@@ -7,25 +7,27 @@
 This is a submodule for the genetic algorithm that is explained in
 https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing
 
-This script is the main program. It will call the different submodules
-and manage the data transfer between them in order to achieve the
-genetic optimization of the profile.
+This script is the cross subprogramme. Its objective is to generate 
+a whole new population genome from the genome of the previous generation
+winners (parents).
+
+In order to eliminate the chance of randomly getting worse results,
+the parents are preserved as the first elements of the new population.
 
 '''
 
 
 
-import subprocess
-import sys
-import os
-import interfaz as interfaz
-import numpy as np
-import initial as initial
 
+import numpy as np
 
 
 
 def cross(parents, num_pop):
+    '''Generates a population of (num_pop) airfoil genomes by mixing randomly
+    the genomes of the given parents.
+    The parents are preserved as the first elements of the new population.
+    '''
     children = np.zeros([num_pop, 16])
     num_parents = parents.shape[0]
     children[0:num_parents] = parents