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plot_living_average.py
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plot_living_average.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 11 07:25:46 2022
@author: jam
"""
#%% global packages
import sys
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.colors as colors
import matplotlib.figure as figure
mpl.rc('text', usetex=True)
mpl.rc('font', family='serif')
mpl.rc('font', size=10)
from IPython.core.display import display
#%%
if len (sys.argv) > 1:
data_file = sys.argv[1]
else:
data_file = 'living_data.csv'
exp_desc = data_file[:-4] # remove prefix
cols = ['rand', 'sync', 'p', 'second_threshold', 'living']
data = pd.read_csv(exp_desc + '.csv', usecols = cols)
probs = []
for p in range(0,101):
probs.append(p/100)
# used for coarsing the data
skip = 5
#%% calculate average
df = pd.DataFrame(columns=cols[:-1] + ['living_mean', 'living_min', 'living_max', 'living_std'])
for st in range(4,10):
for r in [True, False]:
for s in [True, False]:
for p in probs:
df.loc[len(df.index)] = [
r, s, p, st,
data[
(data['second_threshold'] == st) &
(data['p'] == p) &
(data['rand'] == r) &
(data['sync'] == s)
]['living'].mean(),
data[
(data['second_threshold'] == st) &
(data['p'] == p) &
(data['rand'] == r) &
(data['sync'] == s)
]['living'].min(),
data[
(data['second_threshold'] == st) &
(data['p'] == p) &
(data['rand'] == r) &
(data['sync'] == s)
]['living'].max(),
data[
(data['second_threshold'] == st) &
(data['p'] == p) &
(data['rand'] == r) &
(data['sync'] == s)
]['living'].std()
]
#%% check length
# df_len = pd.DataFrame(columns=cols)
# for st in range(4,10):
# for r in [True, False]:
# for s in [True, False]:
# for p in probs:
# df_len.loc[len(df_len.index)] = [
# r, s, p, st,
# len(data[
# (data['second_threshold'] == st) &
# (data['p'] == p) &
# (data['rand'] == r) &
# (data['sync'] == s)
# ]['living'])]
#%%
#plot_style[(rand,sync)] = 'k'
plot_style = {}
plot_style[(True,True)] = 'k:'
plot_style[(True,False)] = 'g--'
plot_style[(False,True)] = 'r-.'
plot_style[(False,False)] = 'b-'
plot_label = {}
plot_label[(True,True)] = 'rand sync'
plot_label[(True,False)] = 'rand async'
plot_label[(False,True)] = 'detrm sync'
plot_label[(False,False)] = 'detrm async'
plot_marker = {}
plot_marker[(True,True)] = 'x'
plot_marker[(True,False)] = 's'
plot_marker[(False,True)] = 'o'
plot_marker[(False,False)] = 'v'
fig = mpl.figure.Figure(figsize=(6, 4))
# l = []
for i in range(1,7):
axs = fig.add_subplot(2,3,i)
axs.set_xticks(np.arange(0, 1.01, 0.2))
axs.set_ylim(-5, 105)
axs.set_yticks(np.arange(0, 101, 20))
axs.grid(True, linestyle=':', linewidth=0.25, c='k')
for r in [True,False]:
for s in [True,False]:
plot_data_mean = df[(df['second_threshold'] == 3+i) & (df['rand'] == r) & (df['sync'] == s)]['living_mean'].to_numpy()
# plot_data_min = df[(df['second_threshold'] == 3+i) & (df['rand'] == r) & (df['sync'] == s)]['living_min'].to_numpy()
# plot_data_max = df[(df['second_threshold'] == 3+i) & (df['rand'] == r) & (df['sync'] == s)]['living_max'].to_numpy()
axs.plot(
probs[0:-1:skip], plot_data_mean[0:-1:skip],
plot_style[(r,s)], linewidth=0.75,
marker = plot_marker[(r,s)], fillstyle='none', ms = 5,
label=plot_label[(r,s)]
)
# axs.fill_between(
# probs[0:-1:skip], plot_data_min[0:-1:skip], plot_data_max[0:-1:skip],
# linewidth=0.75, color=plot_style[(r,s)][0], alpha=0.35
# )
axs.text(0.21,85, r"$r$={}".format(3+i), rasterized=False, usetex=True)
if i not in [4,5,6]:
axs.set_xticklabels([])
else:
axs.set_xlabel('probability')
if i not in [1,4]:
axs.set_yticklabels([])
# else:
axs.set_ylabel('% of living cells')
# axs.legend(l,loc="upper", labels=plot_label.values())
lines, labels = fig.axes[-1].get_legend_handles_labels()
fig.legend(lines, labels, loc = 'upper center', ncol=4, bbox_to_anchor=(0.5, 1.05))
fig.tight_layout()
display(fig)
#%% save the firs plot
fName = "plots/plot_" + exp_desc + "_all.pdf"
print("[INFO] Saving " + fName)
fig.savefig(fName, format="pdf", bbox_inches='tight')
#%% plot all random and sync on one plot
plot_rs_style = {}
plot_rs_style[4] = ['k',':']
plot_rs_style[5] = ['g','--']
plot_rs_style[6] = ['r','-.']
plot_rs_style[7] = ['c','-']
plot_rs_style[8] = ['m','--']
plot_rs_style[9] = ['y','-.']
plot_rs_marker = {}
plot_rs_marker[4] = '+'
plot_rs_marker[5] = '^'
plot_rs_marker[6] = 'o'
plot_rs_marker[7] = 'x'
plot_rs_marker[8] = 'D'
plot_rs_marker[9] = 's'
fig_rs = mpl.figure.Figure(figsize=(6, 3))
plot_rs_text = {}
plot_rs_text[(True,True)] = 'random synchronous'
plot_rs_text[(True,False)] = 'random asynchronous'
plot_rs_text[(False,True)] = 'deterministic synchronous'
plot_rs_text[(False,False)] = 'deterministic asynchronous'
for r in [True]:
for s in [True,False]:
axs = fig_rs.add_subplot(122- int(str(int(s)),base=2))
for st in range(4,10):
plot_data_mean = df[(df['second_threshold'] == st) & (df['rand'] == r) & (df['sync'] == s)]['living_mean'].to_numpy()
# plot_data_min = df[(df['second_threshold'] == st) & (df['rand'] == r) & (df['sync'] == s)]['living_min'].to_numpy()
# plot_data_max = df[(df['second_threshold'] == st) & (df['rand'] == r) & (df['sync'] == s)]['living_max'].to_numpy()
# axs.fill_between(
# probs[0:-1:skip], plot_data_min[0:-1:skip], plot_data_max[0:-1:skip],
# linewidth=0.75, color=plot_rs_style[st][0],alpha=0.35
# )
axs.plot(
probs[0:-1:skip], plot_data_mean[0:-1:skip],
color=plot_rs_style[st][0], linestyle=plot_rs_style[st][1],
marker = plot_rs_marker[st], fillstyle='none', ms = 5,
linewidth=0.75, label = r'$r={}$'.format(st)
)
axs.set_title( plot_rs_text[(r,s)], rasterized=False, usetex=True)
axs.set_xticks(np.arange(0, 1.01, 0.2))
axs.set_yticks(np.arange(0, 101, 20))
axs.set_ylim(-5, 105)
axs.grid(True, linestyle=':', linewidth=0.25, c='k')
if int(str(int(s)),base=2) == 1:
axs.set_ylabel('percentage of living cells')
if int(str(int(s)),base=2) == 0:
#axs.set_ylabel('percentage of living cells')
axs.set_yticklabels([])
axs.set_xlabel('probability')
lines, labels = fig_rs.axes[-1].get_legend_handles_labels()
fig_rs.legend(lines, labels, loc = 'upper center', ncol=6, bbox_to_anchor=(0.5, 1.1))
fig_rs.tight_layout()
display(fig_rs)
# if i not in [4,5,6]:
# axs.set_xticklabels([])
# if i not in [1,4]:
# axs.set_yticklabels([])
#%% save the firs plot
fName = "plots/plot_" + exp_desc + "_rand_sync.pdf"
print("[INFO] Saving " + fName)
fig_rs.savefig(fName, format="pdf", bbox_inches='tight')