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visualizations.py
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visualizations.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 28 17:25:34 2018
@author: oscar
"""
import numpy as np
import scipy.signal as sg
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as pl
from mpl_toolkits.mplot3d import Axes3D
import pygmo as po
##
#pathdata = '/Users/p277634/python/kaoModel/optimResult/'
#filename = 'pso_nodes'
#extdata = '.npz'
##extfig = '.pdf'
#toLoad = pathdata + filename + extdata
##
#with np.load(toLoad) as data:
# vel = data['velocity']
# kL = data['kL']
# kG = data['kG']
# fit = data['fitness']
# kordL = data['KordrL']
# kordG = data['KordrG']
# kordLstd= data['KordrLstd']
# kordLstd= data['KordrLstd']
##
#vel90 = np.tile(vel,(90,1)).T.reshape((1,90*fit.shape[0]))
#kL90 = kL.reshape((1,90*fit.shape[0]))
#kG90 = np.tile(kG,(90,1)).T.reshape((1,90*fit.shape[0]))
#fit90 = np.tile(fit,(90,1)).T.reshape((1,90*fit.shape[0]))
###
vel90 = np.tile(velocity,(90,1)).T.reshape((1,90*fitness.shape[0]))
kL90 = kL.reshape((1,90*fitness.shape[0]))
kG90 = np.tile(kG,(90,1)).T.reshape((1,90*fitness.shape[0]))
fit90 = np.tile(fitness,(90,1)).T.reshape((1,90*fitness.shape[0]))
#bestIx = np.argmin(fitness)
#best = {'kL':kL[bestIx].round(1),'kG':kG[bestIx].round(1),'vel':vel[bestIx].round(1)}
def doScatter3D(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(111, projection='3d')
sc = ax.scatter(kL, kG, vel, c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('Global Coupling')
ax.set_zlabel('Velocity')
#title = 'Best, velocity: ' + str(best['vel'])+ '[m/s]; kL: ' + str(best['kL']) + '; kG: ' + str(best['kG'])
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def do3subScatterkLkGvel(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(131)
sc = ax.scatter(kL, kG, s=0.2, c=fit.ravel(), cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('Global Coupling')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
ax = fig.add_subplot(132)
sc = ax.scatter(kL, vel, s=0.2, c=fit.ravel(), cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('velocity')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
ax = fig.add_subplot(133)
sc = ax.scatter(kG, vel, s=0.2, c=fit.ravel(), cmap="hot")
ax.grid(True)
ax.set_xlabel('Global Coupling')
ax.set_ylabel('velocity')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def do2subScatterGLorder(Gord, GordSD, Lord, LordSD, fit, title):
fig = pl.figure()
ax = fig.add_subplot(121)
sc = ax.scatter(Gord, GordSD, s=0.2, c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Global Order')
ax.set_ylabel('Global Order SD')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
ax = fig.add_subplot(122)
sc = ax.scatter(Lord, LordSD, s=0.2, c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Order')
ax.set_ylabel('Local Order SD')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def doScatter2DkLkG(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(111)
sc = ax.scatter(kL, kG, s=vel, c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('Global Coupling')
#title = 'Best, velocity (size): ' + str(best['vel'])+ '[m/s]; kL: ' + str(best['kL']) + '; kG: ' + str(best['kG'])
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def doScatter2DkLvel(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(111)
sc = ax.scatter(kL, vel, s=kG/np.mean(kG), c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('velocity')
#title = 'Best, velocity: ' + str(best['vel'])+ '[m/s]; kL: ' + str(best['kL']) + '; kG (size): ' + str(best['kG'])
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def doScatter2DkGvel(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(111)
sc = ax.scatter(kG, vel, s=kL/np.mean(kL), c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Global Coupling')
ax.set_ylabel('velocity')
#title = 'Best, velocity: ' + str(best['vel'])+ '[m/s]; kL (size): ' + str(best['kL']) + '; kG: ' + str(best['kG'])
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def doScatter2DkL(vel, kL, kG, fit, title):
fig = pl.figure()
ax = fig.add_subplot(111)
sc = ax.scatter(kL, s=kG/np.mean(kG), c=fit, cmap="hot")
ax.grid(True)
ax.set_xlabel('Global Coupling')
ax.set_ylabel('velocity')
#title = 'Best, velocity: ' + str(best['vel'])+ '[m/s]; kL (size): ' + str(best['kL']) + '; kG: ' + str(best['kG'])
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def doFitGenrations(fit):
fig = pl.figure()
ax = fig.add_subplot(111)
ax.plot(-1*fit)
ax.grid(True)
ax.set_xlabel('genrations | iterations')
ax.set_ylabel('Fitness')
title = 'Fitness/correation to empirical data'
ax.set_title(title)
pl.show()
def doFuncionalConnectivty(z, fBands, fs, tMin):
coeFil, coeFilEnv = desingFilterBands(fBands,fs)
connecti = connectvityBands(z, coeFil, coeFilEnv, tMin, fs)
fig, axes = pl.subplots(2, int(np.ceil(fBands.shape[0]/2)))#,sharex=True,sharey=True)
axes = axes.ravel()
for ix in range(fBands.shape[0]):
ms = axes[ix].matshow(connecti[:,:,ix], origin='lower', cmap="coolwarm", vmin=-1, vmax=1)
title = 'frequencies: ' + str(fBands[ix,:]) + ' Hz'
axes[ix].set_title(title)
pl.colorbar(ms)
fig.show()
def desingFilterBands(fBands,fs):
nyq = fs / 2.0
trans = 2.0
coeFil = []
for freq in fBands:
# Filter frequency bands
passCut = freq / nyq
stopCut = [(freq[0] - trans) / nyq, (freq[1] + trans) / nyq]
coeFil.append(sg.iirdesign(passCut, stopCut, gpass=0.0025, gstop=30.0,
analog=False, ftype='cheby2', output='sos'))
#coeFil = np.dstack(coeFil)
# Filter envelops
coeFilEnv = sg.iirdesign(0.5 / nyq, (0.5+trans)/nyq , gpass=0.0025, gstop=30.0,
analog=False, ftype='cheby2', output='sos')
return coeFil, coeFilEnv
def connectvityBands(z, coeFil, coeFilEnv, tMin, fs):
envCo = []
for coefsos in coeFil:
# filter frequency bands
zFilt = sg.sosfiltfilt(coefsos, np.imag(z), axis=1, padtype='odd')
zEnv = np.abs(sg.hilbert(zFilt, axis=1))
# filter envelope
zEnvFilt= sg.sosfiltfilt(coeFilEnv, zEnv, axis=1, padtype='odd')
# Correlation discarding warmup time
envCo.append(np.corrcoef(zEnvFilt[:,int(tMin*fs):-int(tMin*fs)],
rowvar=True))
envCo = np.dstack(envCo)
return envCo
def simpleConnecti(connecti,fBands):
fig, axes = pl.subplots(2, int(np.ceil(fBands.shape[0]/2)))#,sharex=True,sharey=True)
axes = axes.ravel()
for ix in range(fBands.shape[0]):
ms = axes[ix].matshow(connecti[ix,:,:], origin='lower', cmap="coolwarm", vmin=-1, vmax=1)
title = 'frequencies: ' + str(fBands[ix,:]) + ' Hz'
axes[ix].set_title(title)
pl.colorbar(ms)
fig.show()
def dokLkGvelMultiObj(vel, kL, kG, fit, title):
fBands = 4
locSub = np.range(1,3*(fBands+1)+1).reshape(3,fBands+1)
fit_ = np.zeros(fBands+1,vel.shape[0])
for i in range(vel.shape[0]):
for j in range(fBands):
fit_[i,j] = fit[i][j]
fit_[:,-1]= getHypervolumenMOO(fit)
fig = pl.figure()
for i in range(fBands):
ax = fig.add_subplot(3,5,locSub[0, i])
sc = ax.scatter(kL, kG, s=0.2, c=fit_[:,i], cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('Global Coupling')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
ax = fig.add_subplot(3,5,locSub[1, i])
sc = ax.scatter(kL, vel, s=0.2, c=fit_[:,i], cmap="hot")
ax.grid(True)
ax.set_xlabel('Local Coupling')
ax.set_ylabel('velocity')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
ax = fig.add_subplot(3,5,locSub[2, i])
sc = ax.scatter(kG, vel, s=0.2, c=fit_[:,i], cmap="hot")
ax.grid(True)
ax.set_xlabel('Global Coupling')
ax.set_ylabel('velocity')
ax.set_title(title)
clb = pl.colorbar(sc)
clb.ax.set_title(' fitness')
pl.show()
def getHypervolumenMOO(fit):
sd = np.zeros(fit.shape)
for i in range(fit.shape[0]):
sd[i] = po.hypervolume(fit[i][None]).compute([1,1,1,1])
return sd