Funcional MK2.5

Unificadas funciones y variables en inglés, código correctamente
comentado.

aeropy/Xfoil_Interaction/Genetic_algorithm_files/ambient.py

@@ -7,31 +7,37 @@
 This is a submodule for the genetic algorithm that is explained in
 https://docs.google.com/presentation/d/1_78ilFL-nbuN5KB5FmNeo-EIZly1PjqxqIB-ant-GfM/edit?usp=sharing
 
-This script is the main program. It will call the different submodules
-and manage the data transfer between them in order to achieve the
-genetic optimization of the profile.
-
-'''
-
+This script is the ambient subprogram. Its objective is to calculate the 
+Mach and Reynolds numbers that XFoil uses to calculate the 
+aerodynamics of airfoils.
 
 
+This subprogram consist mainly in models for the International Standard Atmosphere
+and an equivalent atmosphere model for Mars.
+'''
 
 import numpy as np
 
 
 
-
-
-def ventana (x, inicio=0, fin=1, f=1.):
- _1 = (np.sign(x-inicio))
- _2 = (np.sign(-x+fin))
+def window (x, start=0, end=1, f=1.):
+ '''Returns a function f between 'start' and 'endn'.
+ Returns 0 outside this interval'''
+ _1 = (np.sign(x-start))
+ _2 = (np.sign(-x+end))
  return 0.25 * (_1+1) * (_2+1) * f
 
-def etc (x, inicio=0, f=1.):
- _1 = (np.sign(x-inicio))
+def etc (x, start=0, f=1.):
+ '''Returns 0 until 'start', returns 'f' after.
+ '''
+ _1 = (np.sign(x-start))
  return 0.5 * (_1+1) * f
 
 def earth_conditions():
+
+ '''Returns an array of atmospheric and physical data from which the complete
+ model of the atmosphere can be built.
+ '''
  heights = np.array([-1.000,
  11.019,
  20.063,
@@ -43,7 +49,7 @@ def earth_conditions():
  90.000])
 
 
- # an es el gradiente válido entre H(n-1) y H(n)
+
  temp_gradient = np.array([-6.49,
  0,
  0.99,
@@ -72,8 +78,8 @@ def earth_conditions():
  return conditions
 
 
-def temperatura(h, conditions, dT = 0):
- '''Calcula la temperatura a una altura h en Km sobre el nivel del mar'''
+def temperature(h, conditions, dT = 0):
+ '''Calculates the value for temperature in Kelvin at a certain altitude'''
  grad = 0
  heights = conditions[0]
  gradient = conditions[1]
@@ -84,50 +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/analice.py → aeropy/Xfoil_Interaction/Genetic_algorithm_files/analyze.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

aeropy/Xfoil_Interaction/Genetic_algorithm_files/cross.py

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