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import numpy as np
import pandas as pd
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import aaaaa
# checked; it is ok
# To generate distorsed covariance matrixes
def genS(sds, R, distor=1):
# sds: standard deviations
# R: correlation matrix
# distor: level of distorsion
sdsdis = sds * distor
S = R * np.outer(sdsdis, sdsdis)
np.fill_diagonal(S, sdsdis ** 2)
return S
original_data_path = "/home/scpmaotj/Github/hcr/reference_data/"
synthetic_data_path = "/home/scpmaotj/Github/hcr/synthetic_data/"
plots_path = "/home/scpmaotj/Github/hcr/plots/"
nkey = 21
#Nivel distorsion
# 1 - Distorsionamos las desviaciones sin distorsionar la estructura de correlacion
# Funcion genS()
d = [1, 1.01, 1.05, 1.1, 1.2, 1.5, 1.7, 2]
# 2 - Distorsionamos las desviaciones y la estrucctura de correlacion
# Funcion???
# Intento sacar una mano al azar de una normal multivariante.
# Sacaremos m=100 observaciones
m = 100
#gesto <- 1
#for gesto in range(1, 43):
for gesto in range(1, 2):
print(gesto)
datXYZ = aaaaa.get_datXYZ(gesto, original_data_path)
# Hacemos un grafico con ndib= 16 lecturas, no las 16 primeras sino desplazadas pos=4 posiciones
ndib = 16
pos = 4
hands = datXYZ[pos:pos+ndib].reshape(ndib, nkey, 3)
fig, axes = plt.subplots(4, 4, subplot_kw={'projection': '3d'})
for i, ax in enumerate(axes.flat):
ax.scatter(hands[i, :, 0], hands[i, :, 1], hands[i, :, 2], c='b', marker='o')
ax.set_title(f"Original {i+pos}")
plt.savefig(plots_path + f"{gesto}orig.pdf")
plt.close()
obsmean = np.mean(datXYZ, axis=0)
obssd = np.std(datXYZ, axis=0)
R = np.corrcoef(datXYZ, rowvar=False)
id = len(d)
np.random.seed(3663)
for i in range(id):
Sberria = genS(obssd, R, distor=d[i])
z = multivariate_normal.rvs(mean=obsmean, cov=Sberria, size=m)
np.savetxt(synthetic_data_path + f"gesto{gesto}d{i-1}.csv", z, delimiter=',', fmt='%.6f')
simhands = z[pos:pos+ndib].reshape(ndib, nkey, 3).transpose(0, 2, 1)
fig, axes = plt.subplots(4, 4, subplot_kw={'projection': '3d'})
for j, ax in enumerate(axes.flat):
ax.scatter(simhands[j, 0, :], simhands[j, 1, :], simhands[j, 2, :], c='b', marker='o')
ax.set_title(f"Simulated, d={i-1}, It. {j+4}")
plt.savefig(plots_path + f"gesto{gesto}_3coord_d{i-1}.pdf")
plt.close()
# Selecciono un gesto, y miro algunas configuraciones simuladas segun grado de distorsion
gesto = 4
gd = 0
mirar = synthetic_data_path + f"gesto{gesto}d{gd}.csv"
z = np.loadtxt(mirar, delimiter=',')
m = z.shape[0]
simhands = np.zeros((nkey, 3, m))
for i in range(m):
simhands[:, :, i] = z[i].reshape(nkey, 3)
simhands = simhands.transpose(2, 0, 1)
simhands = np.swapaxes(simhands, 1, 2)
fig, axes = plt.subplots(4, 4, subplot_kw={'projection': '3d'})
for i, ax in enumerate(axes.flat):
ax.scatter(simhands[i, 0, :], simhands[i, 1, :], simhands[i, 2, :], c='b', marker='o')
ax.set_title(str(i+1))
plt.show()
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