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fusion_pickles_prob_prod_3.py
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fusion_pickles_prob_prod_3.py
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#
# Fusion Pickles Probability * Productivity 3 Parts
#
# Peter Turney, July 14, 2021
#
# From the 20 runs, extract all of the pickled two-part seeds
# that are stored in the 20 "fusion_storage.bin" pickle files.
# Read the pickles and run each pickle, recording the results in
# a numpy tensor:
#
# tensor = num_seeds x num_steps x num_colours x num_parts
#
# num_seeds = to be determined
# num_steps = 1001
# num_colours = 5 (white, red, orange, blue, green)
# num_parts = 3
#
# After this tensor has been filled with values, generate
# a table of the form:
#
# <prob * prod N M> = <probability * productivity for N managers and M workers>
#
# row in table = <step number> <p*p 3 0> <p*p 2 1> <p*p 1 2> <p*p 0 3>
#
import golly as g
import model_classes as mclass
import model_functions as mfunc
import model_parameters as mparam
import numpy as np
import scipy.stats as st
import copy
import time
import pickle
import os
import re
import sys
#
# Parameter values for making the graphs.
#
max_seeds = 2000 # probably won't need more seeds than this
num_steps = 1001 # number of time steps in the game
num_colours = 5 # 5 colours [white, red, blue, orange, green]
num_parts = 3 # number of parts
num_files = 20 # number of fusion pickle files
step_size = 20 # number of time steps between each plot point
#
# Location of fusion_storage.bin files -- the input pickles.
#
fusion_dir = "C:/Users/peter/Peter's Projects" + \
"/management-theory-revised/Experiments"
# list of pickle files
fusion_files = []
# loop through the fusion files and record the file paths
# -- we assume the folders have the form "run1", "run2", ...
for i in range(num_files):
fusion_files.append(fusion_dir + "/run" + str(i + 1) + \
"/fusion_storage.bin")
#
# Loop through the pickles, loading them into fusion_list.
# Each fusion file will contain several pickles.
#
seed_list = mfunc.read_fusion_pickles(fusion_files)
#
# Given a list of seeds, fill a tensor with counts of the growth of colours
# generated by running the Management Game.
#
[tensor, num_seeds] = mfunc.growth_tensor(g, seed_list, step_size,
max_seeds, num_steps, num_colours, num_parts)
#
# now the tensor is full, so let's make the graph for 3 parts
#
graph_file = fusion_dir + "/fusion_pickles_prob_prod_3.txt"
graph_handle = open(graph_file, "w")
graph_handle.write("\n\nNOTE: {} Seeds -- {} Parts per seed\n\n".format(
num_seeds, num_parts))
header = ["step num", \
"3 managers and 0 workers", \
"2 managers and 1 worker", \
"1 manager and 2 workers", \
"0 managers and 3 workers", \
"error bars for 3 managers and 0 workers", \
"error bars for 2 managers and 1 workers", \
"error bars for 1 manager and 2 worker", \
"error bars for 0 managers and 3 workers"]
graph_handle.write("\t".join(header) + "\n")
#
for step_num in range(0, num_steps, step_size):
# initialize growth
growth_3m0w = [] # 3 managers, 0 workers
growth_2m1w = [] # 2 managers, 1 worker
growth_1m2w = [] # 1 manager, 2 workers
growth_0m3w = [] # 0 managers, 3 workers
# iterate over seed_num
for seed_num in range(num_seeds):
# iterate over parts
manager_count = 0
for part_num in range(num_parts):
# extract colours
red = tensor[seed_num, step_num, 1, part_num]
blue = tensor[seed_num, step_num, 2, part_num]
orange = tensor[seed_num, step_num, 3, part_num]
green = tensor[seed_num, step_num, 4, part_num]
# we focus on the current part (part_num) only
# -- the current part is always red, by convention
red_manager = (orange > green) # true or false
manager_count += red_manager # will increment by 0 or 1
# calculate growth
growth = red + blue + orange + green
#
# increment counts
if (manager_count == 3):
growth_3m0w.append(growth)
elif (manager_count == 2):
growth_2m1w.append(growth)
elif (manager_count == 1):
growth_1m2w.append(growth)
else:
growth_0m3w.append(growth)
#
# calculate stats for 3 managers, 0 workers (3m0w)
if (len(growth_3m0w) > 1) and (step_num > 0):
probability_3m0w = len(growth_3m0w) / num_seeds
prob_prod_3m0w = [probability_3m0w * growth for growth in growth_3m0w]
mean_pp_3m0w = np.mean(prob_prod_3m0w)
interval = st.t.interval(alpha=0.95, df=len(prob_prod_3m0w)-1, \
loc=np.mean(prob_prod_3m0w), scale=st.sem(prob_prod_3m0w))
error_bar_3m0w = (interval[1] - interval[0]) / 2.0
else:
mean_pp_3m0w = 0.0
error_bar_3m0w = 0.0
# calculate stats for 2 managers, 1 workers (2m1w)
if (len(growth_2m1w) > 1) and (step_num > 0):
probability_2m1w = len(growth_2m1w) / num_seeds
prob_prod_2m1w = [probability_2m1w * growth for growth in growth_2m1w]
mean_pp_2m1w = np.mean(prob_prod_2m1w)
interval = st.t.interval(alpha=0.95, df=len(prob_prod_2m1w)-1, \
loc=np.mean(prob_prod_2m1w), scale=st.sem(prob_prod_2m1w))
error_bar_2m1w = (interval[1] - interval[0]) / 2.0
else:
mean_pp_2m1w = 0.0
error_bar_2m1w = 0.0
# calculate stats for 1 manager, 2 worker (1m2w)
if (len(growth_1m2w) > 1) and (step_num > 0):
probability_1m2w = len(growth_1m2w) / num_seeds
prob_prod_1m2w = [probability_1m2w * growth for growth in growth_1m2w]
mean_pp_1m2w = np.mean(prob_prod_1m2w)
interval = st.t.interval(alpha=0.95, df=len(prob_prod_1m2w)-1, \
loc=np.mean(prob_prod_1m2w), scale=st.sem(prob_prod_1m2w))
error_bar_1m2w = (interval[1] - interval[0]) / 2.0
else:
mean_pp_1m2w = 0.0
error_bar_1m2w = 0.0
# calculate stats for 0 managers, 2 workers (0m3w)
if (len(growth_0m3w) > 1) and (step_num > 0):
probability_0m3w = len(growth_0m3w) / num_seeds
prob_prod_0m3w = [probability_0m3w * growth for growth in growth_0m3w]
mean_pp_0m3w = np.mean(prob_prod_0m3w)
interval = st.t.interval(alpha=0.95, df=len(prob_prod_0m3w)-1, \
loc=np.mean(prob_prod_0m3w), scale=st.sem(prob_prod_0m3w))
error_bar_0m3w = (interval[1] - interval[0]) / 2.0
else:
mean_pp_0m3w = 0.0
error_bar_0m3w = 0.0
#
graph_handle.write(("{}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}" +
"\t{:.3f}\t{:.3f}\t{:.3f}\n").format(
step_num, mean_pp_3m0w, mean_pp_2m1w, mean_pp_1m2w, mean_pp_0m3w,
error_bar_3m0w, error_bar_2m1w, error_bar_1m2w, error_bar_0m3w))
#
#
graph_handle.close()
#
#