I created an evolution simulator. It takes random chance and applies it to phenotypes of species. This was very much for fun, and I would love any input on:

  • Readability of code
  • Efficiency of generation generating
  • Better ways to create dynamic GUI elements
  • Future mod-ability
  • My usage of classes, something I'm historically not great at
  • Any tips on how to improve how the program looks as well as behaves

Also, please feel completely free to just run the program for fun! It is (I hope) cool to see how natural disasters will affect certain phenotypes in a population and how changing the chances of things like mutations and natural disasters affects the population as a whole! I had a lot of fun playing around with the different outcomes.

A quick overview of the buttons in the GUI:

  • Quit: quits the program
  • Export profile: Saves all the current settings to a file that you can access later, using...
  • Load profile: loads a presaved .profile file
  • NUM_ORG: original number of organisms in the population
  • OPT_OFF_NUM: optimal number of offspring
  • NAT_DIS_FREQ: Frequency of natural disasters, use number between 0 and 100
  • GEN_FREQ: how fast the generations reproduce, in seconds
  • POP_LIM: upper limit of the population (between 1000 and 9999)
  • FREQ_MUT: likelihood of a mutation occurring in an organism
  • MAX_MUT: maximum number of mutations in the population
  • GEN_NUM: Number of generations (it works pretty quickly, but results may vary)
  • EXECUTE MAIN: Runs the main function, generation a population list
  • GRAPH: generates the graph based off of the settings above the button
  • Checkboxes: allows you to control what is graphed. For example, unchecking the first box removes the "heat-resistant" organisms from the graph
  • Show Natural Disaster Lines?: Draws a line straight down from a natural disaster to show what generation it occurred at

Resultant graph:

Graph that results from program execution. Note the names of natural disasters and the blue dotted population line.

Program in action:

Program in action

# Written by Joseph Farah
# Started: 7/30/16
# Last updated: 8/15/16
# Evolution simulator
# User should be able to pick number of organisms, frequency of natural disasters,
# frequency of generation, population limit, maximum number of mutations per cycle, etc. 
# -------------------------------------------------

import random
import math
import time
import string
import matplotlib.pyplot as plt
import numpy
from Tkinter import *
from tkFileDialog import askopenfilename as selectFILE
import tkMessageBox as tkmb

# Constants (or defaults, depending on whether or not the program accepts input)
# mainloop 
main = Tk()

# constant dictionary
c = {'NUM_ORG':10, 'OPT_OFF_NUM':5, 'NAT_DIS_FREQ':10, 'GEN_FREQ':1, "POP_LIM":1000,'FREQ_MUT':45, 'MAX_MUT':3, 'GEN_NUM':100}
no = IntVar()
oon = IntVar() 
ndf = IntVar()
gf = IntVar()
pl = IntVar()
fm = IntVar()
mm = IntVar()
pop = IntVar()
natcheck = IntVar()


# classes
class element_input:
    def __init__(self, parent, CONSTANT):

        top = self.top = Toplevel(parent)
        con = self.con = c[CONSTANT]
        Label(top, text="Current value is: {0}\nPlease enter new value for {1}".format(con, CONSTANT)).pack()

        self.e = Entry(top)

        b = Button(top, text="submit", command=self.enter_element)

    def enter_element(self):
        new_value = self.e.get()
        var_idx = gui_element_names.index(self.CONSTANT)
        c[self.CONSTANT] = int(new_value)


class generation_lists:
    def __init__(self, parent):

        top = self.top = Toplevel(parent)
        self.listgen = Listbox(top)
        self.listgen.insert(END, "none")
        for generation in population_MASTER:

        b = Button(top, text="submit", command=self.select)

    def select(self):


    def CurSelet(self, evt):

# functions

def defining_stuff():
    global weighted_char_list, population_MASTER, char_effect, char_list, natural_disasters, natural_disaster_chance, mutation_chance,NUM_ORG, OPT_OFF_NUM, NAT_DIS_FREQ, GEN_FREQ, POP_LIM, FREQ_MUT, MAX_MUT, GEN_NUM, c
    # defining the weighted characteristics list
    weighted_char_list = []
    # generating a weighted characteristics list that will make some chars more probable than others
    for i in xrange(1,len(characteristics)+1):
        num_char = len(characteristics)-i+1
        for x in range(num_char):
    # natural disasters and who survives
    nat_dist_list = [1,2,3,4,5,6]
    natural_disaster_names = {1:'landslide', 2:'blizzard', 3:'drought', 4:'lightning strike', 5:'hurricane', 6:'earthquake'}
    natural_disasters = {1:'4 6 7', 2:'2 3 7', 3:'1 3 7', 4:'1 4 7', 5:'2 3 6', 6:'1 3 4 6'}
    # list for the natural disaster frequency
    natural_disaster_chance = [0 for i in xrange(100)]
    for i in xrange(0,100):
        if i >= c['NAT_DIS_FREQ']:
            natural_disaster_chance[i] = 1
    # mutation chance list
    mutation_chance = [0 for i in xrange(100)]
    for i in xrange(0,100):
        if i >= c['FREQ_MUT']:
            mutation_chance[i] = 1

def generate(generation):
    '''this function generates the organisms, their characteristics, and their lifetimes'''
    global weighted_char_list, population_MASTER, char_effect, char_list, natural_disasters, natural_disaster_chance, mutation_chance
    # current gen should be an empty list at the beginning because the current generation doesn't exist yet
    current_gen = []
    # if we are on the first generation, create the first generation without any previous data
    if generation == 0:
        # pick a random character from the list--all the organisms will share this characteristic
        characteristic = random.choice(char_list)
        # iterate through the number of organinisms in the initial generations, established by NUM_ORG
        for org in xrange(c['NUM_ORG']):
            # create smaller lists for each organism
            # first item: organism number, denoted by org
            # second item: characteristic, denoted by characteristic
            # third item: name of the characteristic, selected from the characteristics list
            current_gen.append([org, characteristic, characteristics[characteristic]])
        return current_gen  
    # if we aren't on the first generation, generate a new generation
    # begin by iterating through each organism in the PREVIOUS GENERATION
    for organism in population_MASTER[generation-1]:
        # examine the current organisms characteristic
        # this will determine the success of the organism's reproductive cycle
        org_char = organism[1]
        # value essentially represents the deviation from the optimal offspring, set by OPT_OFF_NUM
        off_change = char_effect[org_char]
        # checking if the deviation is positive or negative
        if off_change == '-':
            # if negative, subtract the optimal offspring number
            number_of_offspring = c['OPT_OFF_NUM'] - random.randint(0,c['OPT_OFF_NUM'])
        elif off_change == '+':
            # if positive, add to the optimal offspring number
            number_of_offspring = c['OPT_OFF_NUM'] + random.randint(0,c['OPT_OFF_NUM'])
        # generating the offspring for each parent
        # iterates through the number of offspring, denoted by number_of_offspring
        for offspring in xrange(0,number_of_offspring):
            # randomly selects from the weighted mutation chance list
            # 1 denotes a successful mutation, 0 denotes no mutation
            will_mutate = random.choice(mutation_chance)
            if will_mutate == 1:
                # if mutation is successful, pick a random characteristic 
                # DIFFERENT from the parent's characteristic
                mutation = random.choice(weighted_char_list)
                while mutation == org_char:
                    mutation = random.choice(weighted_char_list)
                # add it to the current generation
            # if the organism does not mutate, it is identical to the parent. 
            # duplicate the parent and append it to the current gen
            elif will_mutate == 0:
        # find out the size of the current generation
        population_size = len(current_gen)
        # if the population is larger than the limit, denoted by POP_LIM, make it within the limit
        # splice time!
        if population_size >= c['POP_LIM']:
            current_gen = current_gen[:c['POP_LIM']]
    return current_gen

def get_per(generation):

    global population_MASTER, char_list, characteristics
    # initiliaze the percentages list
    percentages = []

    # find the length of the current generation
    length = float(len(population_MASTER[generation]))
    # iterate through all possible characteristics, tally up organisms, and divide to find the percentages 
    for attribute in char_list:
        tmp_count = 0
        for organism in population_MASTER[generation]:
            if organism[1] == attribute:
        percentages.append([characteristics[attribute], 100*(tmp_count/length)])
    return percentages

def get_final_per():
    get the percentages of each characteristic for the current generation.
    Call this function only!!! after population_MASTER has been filled.   
    global population_MASTER, per_list
    per_list = []
    for gen in population_MASTER:
        gen_num = population_MASTER.index(gen)

def natural_disaster(generation):
    global population_MASTER, char_list, natural_disasters, natural_disaster_names, nat_dist_list, natlist
    gen = population_MASTER[generation]
    nat_dist_type = random.choice(nat_dist_list)
    who_survives = natural_disasters[nat_dist_type].split()
    who_survives = [int(s) for s in who_survives]
    for organism in population_MASTER[generation]:
        if organism[1] not in who_survives:
    return population_MASTER

# button functions

def constant_change(constant):
    element_input(main, constant)

def graph():
    graph the evolution according to the specifications set by the user
    global per_list, natlist
    y = []
    approved_list = ['null']
    if no.get() == 1:
    if oon.get() == 1:
    if ndf.get() == 1:
    if gf.get() == 1:
    if pl.get() == 1:
    if fm.get() == 1:
    if mm.get() == 1:

    x = []
    print characteristics[1]
    fig, ax = plt.subplots()
    for char in char_list:
        y_list = []
        for gen in per_list: 
    for i in range(0,len(population_MASTER)):
    population_num = []
    for generation in population_MASTER:
    if pop.get() == 1:
        ax.plot(x,population_num, linestyle='dashed', label='Population (scaled)')
    for dataset in y:
        if dataset[0] in approved_list:
    for ND in natlist:
        ax.text(ND[1],90, '-{0}'.format(ND[0]),rotation=45)
        if natcheck.get() == 1:
            ax.axvline(x=ND[1], linewidth=1, color='k')

    # making the legend
    legend = ax.legend(loc='upper right', shadow=True)
    for label in legend.get_texts():

    # generation_lists(main)

def main_function():
    '''generate population master, including all effects to the population'''
    global c, population_MASTER, nat_dist_check, natural_disaster_chance, per_list
    del population_MASTER[:]
    for i in range(0,c['GEN_NUM']):
        nat_dist_check = random.choice(natural_disaster_chance)
        if nat_dist_check == 1:
            population_MASTER = natural_disaster(i)
    tkmb.showinfo("Process Completed","Process complete, EVOLUTION terminated")

def get_profile():
    '''load profile from file'''
    global gui_element_names
    profile_filepath = selectFILE()
    with open(profile_filepath) as f:
        profile = f.readlines()
    for i in range(len(gui_element_names)):
        c[gui_element_names[i]] = int(profile[i])
    tkmb.showinfo("Process Completed","Current Profile Loaded")

def export_profile():
    '''export profile to file'''
    global gui_element_names
    profilename = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(5))+'.profile'
    with open(profilename, 'w') as o:
        for i in range(len(gui_element_names)):
            o.write("%s\n" % c[gui_element_names[i]])
    tkmb.showinfo("Process Completed","Profile Exported")

# main portion of program

# Lists and dictionaries
characteristics = {1:'heat-resistant', 2:'cold-resistant', 3:'energy-efficient', 4:'fast', 5:'slow', 6:'big', 7:'small', 8:'attractive'}
# character list for iteration
char_list = [1,2,3,4,5,6,7]
# defining which characteristics lead to an increase, decrease, or no change in reproductive activity
char_effect = {1:'-', 2:'+',3:'+',4:'+',5:'-',6:'+',7:'-',8:'+'}
nat_dist_list = [1,2,3,4,5,6]
natural_disaster_names = {1:'landslide', 2:'blizzard', 3:'drought', 4:'lightning strike', 5:'hurricane', 6:'earthquake'}
natural_disasters = {1:'4 6 7', 2:'2 3 7', 3:'1 3 7', 4:'1 4 7', 5:'2 3 6', 6:'1 3 4 6'}
# the master list that contains all generations
population_MASTER = []
# percentage lists
per_list = []
natlist = []
# defining of GUI elements
gui_element_names = ['NUM_ORG', 'OPT_OFF_NUM', 'NAT_DIS_FREQ', 'GEN_FREQ', 'POP_LIM', 'FREQ_MUT', 'MAX_MUT', 'GEN_NUM']
r = 0
cc = 0
for element in gui_element_names:
    Label(main, text=element).grid(row=r,column=cc)
    r += 1
r = 0
for char in char_list:
    Label(main, text=characteristics[char]+'?').grid(row=r, column=3)
    r +=1

menubar = Menu(main)
menubar.add_command(label="Quit!", command=main.quit)
menubar.add_command(label="Load Profile", command=get_profile)
menubar.add_command(label="Export Profile", command=export_profile)

Button(main,text='NUM_ORG', command=lambda:constant_change('NUM_ORG')).grid(row = 0, column=1)
Button(main,text='OPT_OFF_NUM', command=lambda:constant_change('OPT_OFF_NUM')).grid(row = 1, column=1)
Button(main,text='NAT_DIS_FREQ', command=lambda:constant_change('NAT_DIS_FREQ')).grid(row = 2, column=1)
Button(main,text='GEN_FREQ', command=lambda:constant_change('GEN_FREQ')).grid(row = 3, column=1)
Button(main,text='POP_LIM', command=lambda:constant_change('POP_LIM')).grid(row = 4, column=1)
Button(main,text='FREQ_MUT', command=lambda:constant_change('FREQ_MUT')).grid(row = 5, column=1)
Button(main,text='MAX_MUT', command=lambda:constant_change('MAX_MUT')).grid(row = 6, column=1)
Button(main,text='GEN_NUM', command=lambda:constant_change('GEN_NUM')).grid(row = 7, column=1)
Button(main,text='EXECUTE MAIN',command=main_function).grid(row=8,column=0)
a = Checkbutton(main, text="<---Graph", variable=no)
a.grid(row=0, column=2, sticky=W)
b=Checkbutton(main, text="<---Graph", variable=oon)
b.grid(row=1, column=2, sticky=W)
c1 = Checkbutton(main, text="<---Graph", variable=ndf)
c1.grid(row=2, column=2, sticky=W)
k1=Checkbutton(main, text="<---Graph", variable=gf)
k1.grid(row=3, column=2, sticky=W)
d1=Checkbutton(main, text="<---Graph", variable=pl)
d1.grid(row=4, column=2, sticky=W)
e1=Checkbutton(main, text="<---Graph", variable=fm)
e1.grid(row=5, column=2, sticky=W)
f1=Checkbutton(main, text="<---Graph", variable=mm)
f1.grid(row=6, column=2, sticky=W)
g1=Checkbutton(main, text="Graph pop", variable=pop)
g1.grid(row=7, column=2, sticky=W)
h1=Checkbutton(main, text="Show natural disaster lines?", variable=natcheck)
h1.grid(row=8, column=2, sticky=W, columnspan=2)


Github Page

  • \$\begingroup\$ In line 389, you probably meant g1.toggle(). \$\endgroup\$
    – Graipher
    Aug 18 '16 at 16:21
  • \$\begingroup\$ @Graipher that was a problem with my copy-pasting, updated to include the correction. Thanks! \$\endgroup\$ Aug 18 '16 at 16:38
  • 1
    \$\begingroup\$ This is pretty cool man, nicely done. \$\endgroup\$ Aug 18 '16 at 17:13
  • \$\begingroup\$ @YoYoYoI'mAwesome thanks so much! glad you like it! XD \$\endgroup\$ Aug 18 '16 at 17:22

For python an official styleguide exists, PEP 8. It recommends:

  • 4 spaces per indentation level (there are a few instances where you have 5)
  • descriptive variable names (no single letter) in lower_case
  • in argument lists, a space after a comma
  • exactly 2 blank lines before a function and class definition
  • 80 characters max per line
  • whitespace around operators

There are automatic tools to check where your code violates these recommendation pep8, which is commonly installed when you install python.

I would use collections.namedtuple for your IntVars. This allows you to get rid of quite a lot of duplication:

from collections import namedtuple
Enum = namedtuple("Enum", "no oon ndf gf pl fm mm pop natcheck")
variables = Enum(*(IntVar() for _ in range(9)))
def graph():
    approved_list = ['null']
    approved_list += [characteristics[i+1] for i, var in enumerate(variables) if var.get() == 1]

Now you can do variables.no to get the variable no. You will have to change every appearance of the variables, though.

Your Checkbutton code can be replaced now with something like:

buttons = []
for row, var in enumerate(variables):
    if row <= 6:
        text = "<---Graph"
    elif row == 7:
        text = "Graph pop"
    elif row == 8:
        text ="Show natural disaster lines?"
    buttons.append(Checkbutton(main, text=text, variable=var))
    if row <= 7:
        buttons[-1].grid(row=row, column=2, sticky=W)
        buttons[-1].grid(row=row, column=2, sticky=W, columnspan=2)

And other instances, where you have special behaviour for your different variables

range starts by default at 0, so there is no need to manually set it (unless you want to use step, without using a keyword, like range(3, step=2)).

You should avoid unnecessary globals, whenever you can. They are hard to keep track off, even worse when they are actually modified by functions. In all of your cases you could just make them parameters of the function instead of relying on global.

In class element_input, the button b is not assigned to self.b and will therefore probably not persist (or will at least not be accessible).

In python it is normal to iterate over the elements of a list, not its indices:

with open(profilename, 'w') as out_file:
    for element_name in gui_element_names:
        out_file.write("%s\n" % c[element_name])

Use lists instead of dicts here:

char_effect = {1:'-', 2:'+',3:'+',4:'+',5:'-',6:'+',7:'-',8:'+'}
char_effect = ['-', '+', '+', '+', '-', '+', '-', '+']

The only difference: index - 1 Or even better, make a disaster class (and an entity class):

import random
from itertools import count

ADVANTAGES = "heat-resistant cold-resistant energy-efficient fast slow big small attractive".split()

class Disaster():
    def __init__(self, name, immunities):
        self.name = name
        self.immunities = immunities

    def survivors(self, population):
        return [entity for entity in population if entity.characteristic in self.immunities]

class Entity():
    ID = count()

    def __init__(self, characteristic):
        self.characteristic = characteristic
        self.id = next(Entity.ID)

population = (Entity(random.choice(ADVANTAGES)) for _ in range(100))
landslide = Disaster("landslide", ["fast", "big", "small"])
population = landslide.survivors(population)
  • \$\begingroup\$ Wow! Thanks so much for the detailed response! I definitely learned a lot. Regarding self.b, is it even worth using it? The window is a popup, and I never intended for the button to persist. \$\endgroup\$ Aug 19 '16 at 13:07
  • \$\begingroup\$ I don't know, because honestly I could not (or rather did not take the time to) follow every detail of the code, the GUI especially. \$\endgroup\$
    – Graipher
    Aug 19 '16 at 13:51
  • \$\begingroup\$ @Graipher Doesn't Python throw an indentation exception if there's more then 4 spaces..? \$\endgroup\$ Aug 19 '16 at 17:42
  • \$\begingroup\$ @YoYoYoI'mAwesome Nope, it will only complain if your indentation does not match an outer indentation. So after an if block you accidentally put 5 tabs instead of the 4 the code before the block was indented. But using a consistent offset (within a block's surrounding) does not produce an error. You could indent your whole code with 1 space per level and it would work! \$\endgroup\$
    – Graipher
    Aug 20 '16 at 15:42

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