feat: separate logic from display

lab3/main.py

@@ -7,8 +7,6 @@ import sys
 import os
 import time
 import tempfile
-# run pylint with:
-# pylint --generated-members=cv2.* .\main.py
 import cv2
 import matplotlib.pyplot as plt
 import numpy as np
@@ -45,7 +43,6 @@ def generate(population,
     population = children + mutation
     return fitness, population
 
-
 def evolution_strategy(
         number_of_parents=5,
         size_of_population=20,
@@ -53,19 +50,19 @@ def evolution_strategy(
         number_of_generations=123,
         min_max=(-5.12, 5.12),
         number_of_outputs = 10,
-        no_display = False
+        no_display = False,
+        save_results = False
 ):
     """ Define the Evolutionary Strategy (μ, λ) algorithm """
     # Initialize the population
-    total_time = 0
-    start_time = time.perf_counter()
+    print_info = []
     population = np.random.uniform(
         low=min_max[0], high=min_max[1], size=(
             size_of_population, 2))
 
     summary = []
-    output(population, 0, f"0:nop-{number_of_parents}:sop-{size_of_population}:ms-{mutation_strength}:nog-{number_of_generations}:min-max-{min_max}:noo-{number_of_outputs}")
-
+    if not no_display:
+        print_info.append((population, 0, f"0:nop-{number_of_parents}:sop-{size_of_population}:ms-{mutation_strength}:nog-{number_of_generations}:min-max-{min_max}:noo-{number_of_outputs}"))
     number_of_outputs = min([number_of_outputs-1, number_of_generations])
 
     # Iterate until we reach max number of generate and terminate
@@ -79,24 +76,17 @@ def evolution_strategy(
             if number_of_generations % number_of_outputs == 0 \
             else number_of_generations//(number_of_outputs-1)
         offset = number_of_generations % step
-        end_time = time.perf_counter()
-        total_time += end_time - start_time
         if (generation_number - offset) % step == 0 and not no_display:
-            output(population, generation_number, f"{generation_number}:nop_{number_of_parents}:sop_{size_of_population}:ms_{mutation_strength}:nog_{number_of_generations}:min_max_{min_max}:noo_{number_of_outputs}")
+            print_info.append((population, generation_number, f"{generation_number}:nop_{number_of_parents}:sop_{size_of_population}:ms_{mutation_strength}:nog_{number_of_generations}:min_max_{min_max}:noo_{number_of_outputs}"))
             summary.append(population)
 
-    if not no_display:
-        print_summary(summary, f"{generation_number}:nop-{number_of_parents}:sop-{size_of_population}:ms-{mutation_strength}:nog-{number_of_generations}:min-max-{min_max}:noo-{number_of_outputs}")
-    start_time = time.perf_counter()
     # Evaluate the fitness of the final population
     fitness = np.array([rastrigin(x_point_value, y_point_value)
                        for x_point_value, y_point_value in population])
 
     # Return the best individual found
     best_idx = np.argmin(fitness)
-    end_time = time.perf_counter()
-    total_time += end_time - start_time
-    return population[best_idx], fitness[best_idx], population, total_time
+    return population[best_idx], fitness[best_idx], population, total_time, print_info, summary
 
 
 def print_help():
@@ -126,6 +116,7 @@ def print_help():
     Those arguments can be given in any order and any argument which was not entered will be replaced with default value,
     Additional flags:
     -nd, --no-display (does not show the plots)
+    -s, --save (if issued WILL save the files)
     exemplary use:
     python main.py -nop 5 -sop 20 -ms 0.1 -i 100 -min -5.12 -max 5.12 -noo 100
     """)
@@ -159,7 +150,7 @@ def get_output_bounds(x_data, y_data):
     return x_bounds, y_bounds
 
 
-def output(population_output, generation_number, file_name = "temp"):
+def output(population_output, generation_number, file_name = "temp", save_results = False):
     """ Draw result of our function """
 
     # define the visualization params
@@ -188,7 +179,8 @@ def output(population_output, generation_number, file_name = "temp"):
 
         # show the image, provide window name first
         cv2.imshow(f"Generation {generation_number}", image)
-        cv2.imwrite(file_name + ".jpg", image)
+        if save_results:
+            cv2.imwrite(file_name + ".jpg", image)
         # add wait key. window waits until user presses a key and quits if
         # the key is 'q'
         if cv2.waitKey(0) == 113:
@@ -201,7 +193,7 @@ def output(population_output, generation_number, file_name = "temp"):
     os.unlink(file_.name)
 
 
-def print_summary(populations, file_name = "temp_summary"):
+def print_summary(populations, file_name = "temp_summary", save_results = False):
     """ Draw result of our function for chosen generations """
 
     # define the visualization params
@@ -235,7 +227,8 @@ def print_summary(populations, file_name = "temp_summary"):
 
         # read image
         image = cv2.imread(file_.name)
-        cv2.imwrite("SUMMARY:" + file_name + ".jpg", image)
+        if save_results:
+            cv2.imwrite("SUMMARY:" + file_name + ".jpg", image)
 
         # show the image, provide window name first
         cv2.imshow(f"Summary", image)
@@ -263,7 +256,8 @@ def user_input():
         "min": -5.12,
         "max": 5.12,
         "number_of_outputs": 10,
-        "no_display": False}
+        "no_display": False,
+        "save": False}
     for index, argument in enumerate(sys.argv):
         if argument in ('-h', '--help'):
             print_help()
@@ -284,26 +278,39 @@ def user_input():
             arguments["number_of_outputs"] = int(sys.argv[index + 1])
         if argument in ('-nd', '--no_display'):
             arguments["no_display"] = True
+        if argument in ('-s', '--save'):
+            arguments["save"] = True
 
     return arguments
 
+def print_output(print_info, save_results, summary):
+    for population, file_name, generation_number in print_info:
+        output(population, generation_number, file_name, save_results)
+    print_summary(summary)
 
 # Ran first in the code
 if __name__ == "__main__":
     # Run the Evolutionary Strategy algorithm
     ARGUMENTS = user_input()
-    best_individual, best_fitness, output_population, generation_time = evolution_strategy(
+    total_time = 0
+    start_time = time.perf_counter()
+    best_individual, best_fitness, output_population, generation_time, print_info, summary = evolution_strategy(
         ARGUMENTS["number_of_parents"],
         ARGUMENTS["size_of_population"],
         ARGUMENTS["mutation_strength"],
         ARGUMENTS["number_of_generations"],
         (ARGUMENTS["min"], ARGUMENTS["max"]),
         ARGUMENTS["number_of_outputs"],
-        ARGUMENTS["no_display"])
-    time_per_generation = generation_time / \
+        ARGUMENTS["no_display"],
+        ARGUMENTS["save"])
+    end_time = time.perf_counter()
+    if not ARGUMENTS["no_display"]:
+        print_output(print_info, ARGUMENTS["save"], summary)
+    total_time = end_time - start_time
+    time_per_generation = total_time / \
         ARGUMENTS["number_of_generations"]
 
     print("Best individual found:", best_individual)
     print("Best fitness found:", best_fitness)
-    print("total_generation_time: ", generation_time)
+    print("total_generation_time: ", total_time)
     print("time_per_generation: ", time_per_generation)

lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.fdb_latexmk

@@ -1,11 +1,11 @@
 # Fdb version 4
-["xdvipdfmx"] 1681756905 "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" "EARIN_LAB_3_RUDNICKI_KLISZKO.pdf" "EARIN_LAB_3_RUDNICKI_KLISZKO" 1681756905 0
-  "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" 1681756905 30192 bd831cc7fba44cf2e46d93256fd6cbfe "xelatex"
+["xdvipdfmx"] 1681757884 "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" "EARIN_LAB_3_RUDNICKI_KLISZKO.pdf" "EARIN_LAB_3_RUDNICKI_KLISZKO" 1681757884 0
+  "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" 1681757884 33124 09e315a7c52dfa256c17b04adeecd4ac "xelatex"
   (generated)
   "EARIN_LAB_3_RUDNICKI_KLISZKO.pdf"
   (rewritten before read)
-["xelatex"] 1681756905 "/home/kuchy/earin/EARIN/lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.tex" "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" "EARIN_LAB_3_RUDNICKI_KLISZKO" 1681756905 0
-  "/home/kuchy/earin/EARIN/lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.tex" 1681756904 3239 1c19d3382eb02eca12f28f266179f0f0 ""
+["xelatex"] 1681757884 "/home/kuchy/earin/EARIN/lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.tex" "EARIN_LAB_3_RUDNICKI_KLISZKO.xdv" "EARIN_LAB_3_RUDNICKI_KLISZKO" 1681757884 0
+  "/home/kuchy/earin/EARIN/lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.tex" 1681757883 3477 8ed5d7943c9fec7b536ad469925926ad ""
   "/usr/share/texmf-dist/fonts/map/fontname/texfonts.map" 1679564905 3524 cb3e574dea2d1052e39280babc910dc8 ""
   "/usr/share/texmf-dist/fonts/tfm/adobe/zapfding/pzdr.tfm" 1679564905 1528 f853c4d1b4e0550255e02831fdc8496f ""
   "/usr/share/texmf-dist/fonts/tfm/public/cm/cmmi12.tfm" 1679564905 1524 4414a8315f39513458b80dfc63bff03a ""
@@ -66,9 +66,9 @@
   "/usr/share/texmf-dist/tex/latex/url/url.sty" 1679564905 12796 8edb7d69a20b857904dd0ea757c14ec9 ""
   "/usr/share/texmf-dist/web2c/texmf.cnf" 1679564905 39911 2da6c67557ec033436fe5418a70a8a61 ""
   "/var/lib/texmf/web2c/xetex/xelatex.fmt" 1680438638 11046058 c421129080a65de742470a82b56f326d ""
-  "EARIN_LAB_3_RUDNICKI_KLISZKO.aux" 1681756905 1319 29ad114a76f2d8d01aa31fa0dc1b211c "xelatex"
-  "EARIN_LAB_3_RUDNICKI_KLISZKO.out" 1681756905 454 50b668959753031c85a7636eb970de0c "xelatex"
-  "EARIN_LAB_3_RUDNICKI_KLISZKO.tex" 1681756904 3239 1c19d3382eb02eca12f28f266179f0f0 ""
+  "EARIN_LAB_3_RUDNICKI_KLISZKO.aux" 1681757884 1319 29ad114a76f2d8d01aa31fa0dc1b211c "xelatex"
+  "EARIN_LAB_3_RUDNICKI_KLISZKO.out" 1681757884 454 50b668959753031c85a7636eb970de0c "xelatex"
+  "EARIN_LAB_3_RUDNICKI_KLISZKO.tex" 1681757883 3477 8ed5d7943c9fec7b536ad469925926ad ""
   "example_halfway.jpg" 1681756460 43787 f0809821c60091352d61146053f795f2 ""
   "example_summary.jpg" 1681756506 17901 286cc778e6a6b564c8027d51b98a968f ""
   (generated)

lab3/report/EARIN_LAB_3_RUDNICKI_KLISZKO.tex

@@ -50,6 +50,11 @@ Exemplary use (settings all values to default values):
     python main.py -nop 5 -sop 20 -s 0.1
     -i 100 -min -5.12 -max 5.12 -noo 10
 \end{lstlisting}
+There are additional flags for quality of life with the program
+\begin{lstlisting}
+    -nd --no_display (If used will NOT print out the plots)
+    -s --save (If used WILL save plot files to code folder)
+\end{lstlisting}
 To print help info about program user can issue help flag:
 \begin{lstlisting}[language=bash]
     python main.py -h
@@ -66,7 +71,7 @@ At the end summary of results on plot will display with red gradient showing res
     \includegraphics[width=8cm]{example_summary.jpg}
     \centering
     \end{figure}
-Results will be displayed and saved in the same folder as code directory for further inspection, with file name containing information about input parameters 
+Results will be displayed and if user requested saved in the same folder as code directory for further inspection, with file name containing information about input parameters 
 \section{Results}
 We have successfully implemented ES($\mu$, $\lambda$) to optimize Rastrigin function \\ 
 Rastrigin function is used to test optimization algorithms as it contains a lot of local minima