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exp1x2x-mobility-patch_cont-patch_infect.py
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exp1x2x-mobility-patch_cont-patch_infect.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#%%
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.colors as colors
mpl.rc('text', usetex=True)
mpl.rc('font', family='serif')
mpl.rc('font', size=10)
#%%
# file with data from the experiment
# Note: header=6 is for NetLogo data
exp1_desc = 'exp1-mobility-patch_cont-patch_infect'
exp2_desc = 'exp2x-mobility-patch_cont-patch_infect'
exp_desc = 'v3w_exp1x2x'
data1 = pd.read_csv(exp1_desc + '.csv', header=6)
data2 = pd.read_csv(exp2_desc + '.csv', header=6)
#%%
# select variables for the analysis
# this depends on the experiment
# variables
# 1st is different for each plot
# 2nd and 3rd vars provide axes for plots
# 4th variable is visualized
# variant 1
# v = ['mobility-prob', 'patch-infection-weight', 'patch-contamination-prob','mean-infected']
# variant 2
v = ['patch-infection-prob', 'mobility-prob', 'patch-contamination-prob','mean-infected']
vl = [r'$w_3$', r'$\mu$', r'$p^{\mathrm{patch}}$']
# selection for plotting
# selected values of the 1st variable
# mrs = [0.1, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.75, 0.9, 1]
#var0s = [0.1, 0.2, 0.3, 0.4, 0.5, 0.55, 0.6, 0.7, 0.75]
var0s = [0.15, 0.30, 0.45, 0.75]
# all vaues for 2nd dna 3rd variable
var1s = data1[v[1]].unique()
var2s = data1[v[2]].unique()
df1 = pd.DataFrame(columns=v)
df2 = pd.DataFrame(columns=v)
#%%
# calculate mean for the presented variable
for v0 in var0s:
for v1 in var1s:
for v2 in var2s:
df1.loc[len(df1.index)] = [
v0,
v1,
v2,
data1[ (data1[v[0]] == v0) & (data1[v[1]] == v1) & (data1[v[2]] == v2) ] ['%infected'].mean()
]
df2.loc[len(df2.index)] = [
v0,
v1,
v2,
data2[ (data2[v[0]] == v0) & (data2[v[1]] == v1) & (data2[v[2]] == v2) ] ['%infected'].mean()
]
#%%
fig = mpl.figure.Figure(figsize=(6,3.5))
levels = [1,10,20,30,40,50,60,70]
for i,v0 in enumerate(var0s):
axs = fig.add_subplot(241+i);
plot_data = df1[df1[v[0]] == v0][[v[1], v[2], v[3]]].to_numpy()
axs.contour(
plot_data.T[0].reshape(len(var1s), len(var1s)),
plot_data.T[1].reshape(len(var2s), len(var2s)),
plot_data.T[2].reshape(21,21),
levels = levels,
colors = 'k',linestyles='dotted'
)
im=axs.contourf(
plot_data.T[0].reshape(len(var1s), len(var1s)),
plot_data.T[1].reshape(len(var2s), len(var2s)),
plot_data.T[2].reshape(21,21),
levels = levels,
cmap = 'Oranges',
norm=colors.Normalize(vmin=0, vmax=70),
)
axs.grid(True,linestyle=':', linewidth=0.5, c='k')
axs.set_title("abcd"[i]+") "+vl[0]+'='+str(v0))
axs.set_xticks(np.arange(0,1.01,0.2))
axs.set_xticks([0,0.25,0.5,0.75,1.0])
axs.set_xticklabels([])
axs.set_yticks([0,0.25,0.5,0.75,1.0])
axs.set_yticklabels([0,0.25,0.5,0.75,1.0])
if i not in [0]:
axs.set_yticklabels([])
if i == 0:
axs.set_ylabel(vl[2])
# lower panels (from experiment exp2x)
for i,v0 in enumerate(var0s):
axs = fig.add_subplot(245+i);
plot_data = df2[df2[v[0]] == v0][[v[1], v[2], v[3]]].to_numpy()
axs.contour(
plot_data.T[0].reshape(len(var1s), len(var1s)),
plot_data.T[1].reshape(len(var2s), len(var2s)),
plot_data.T[2].reshape(21,21),
levels = levels,
colors = 'k',linestyles='dotted'
)
im=axs.contourf(
plot_data.T[0].reshape(len(var1s), len(var1s)),
plot_data.T[1].reshape(len(var2s), len(var2s)),
plot_data.T[2].reshape(21,21),
levels = levels,
cmap = 'Oranges',
norm=colors.Normalize(vmin=0, vmax=70),
)
axs.grid(True,linestyle=':', linewidth=0.5, c='k')
axs.set_title("efgh"[i]+") "+vl[0]+'='+str(v0))
axs.set_xticks([0,0.25,0.5,0.75,1.0])
axs.set_xticklabels([0,0.25,0.5,0.75,1.0])
axs.set_yticks([0,0.25,0.5,0.75,1.0])
axs.set_yticklabels([0,0.25,0.5,0.75,1.0])
#if i not in [0,3,6]:
# axs.set_yticklabels([])
axs.set_xlabel(vl[1])
if i not in [0]:
axs.set_yticklabels([])
if i == 0:
axs.set_ylabel(vl[2])
# fig.colorbar(p)
# fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.125, 1.035, 0.8, 0.025])
cbar = fig.colorbar(im, cax=cbar_ax, orientation="horizontal")
cbar.set_ticklabels([str(l)+"\%" for l in levels])
fig.tight_layout()
display(fig)
#%%
fName = "plot_" + exp_desc + ".pdf"
print("INFO] Saving " + fName)
fig.savefig(fName, format="pdf", bbox_inches='tight')