PCA plot.py

import sys
import os
import os.path as osp
import numpy as np
from glob import glob
from tqdm import tqdm
import pandas as pd
import pyvista as pv
pv.set_plot_theme("Document")
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA, FastICA
import descriptors_utils as dut
from scipy import interpolate



## ---> SETTINGS
preprocDir = r'D:/InletProfileStudy/SSM/Output_2024/Circular/'
figureDir = osp.join(preprocDir, 'Figure')

os.makedirs(figureDir, exist_ok=True)

#synthOutDir = osp.join(preprocDir, 'sampled_profiles')
#meanProfDir = osp.join(preprocDir, 'mean_profile_SV')
#os.makedirs(synthOutDir, exist_ok=True)
#os.makedirs(meanProfDir, exist_ok=True)


V = pd.read_csv(osp.join(preprocDir, 'matrixV.csv')).to_numpy()
n_pat, n_frames, n_nodes = np.shape(V)[0], 20, np.shape(V)[1]//20//3



#-----------------------------------------------------------------------------------------------------------------------
## ---> PRINCIPAL COMPONENT ANALYSIS
print('Performing PCA...')


# 2. Compute individual and cumulative variance
pca = PCA(n_components=33)                                 # total n. of components has to be equal to min(n_samples, n_variables)
pca.fit(V)
var_components = pca.explained_variance_ratio_                # individual variance assciated to each component (mode)
var = np.sum(pca.explained_variance_ratio_[:26])              # total variance

cum_explained_var = []                                        # cumulative variance: # sum of invidual variances for subsequent components up to 10th mode.

for i in range(0, len(pca.explained_variance_ratio_)):
    if i == 0:
        cum_explained_var.append(pca.explained_variance_ratio_[i])
    else:
        cum_explained_var.append(pca.explained_variance_ratio_[i] + cum_explained_var[i-1])
cum_explained_var = np.asarray(cum_explained_var)


# Create figure and axes
fig, ax1 = plt.subplots()

# Plot individual values on the left y-axis
ax1.bar(np.arange(len(pca.explained_variance_ratio_)), pca.explained_variance_ratio_, color='k', alpha=0.5, label='Individual Values')
ax1.set_ylabel('Individual Variance (%)', color='b')
ax1.set_xlabel('Principal componants', color='k')
ax1.tick_params('y', colors='b')

# Create second y-axis on the right
ax2 = ax1.twinx()

# Plot cumulative values as bar chart on the right y-axis
ax2.plot(np.arange(len(pca.explained_variance_ratio_)), cum_explained_var, color='r', alpha=0.5, label='Cumulative Values')
ax2.set_ylabel('Cumulative Variance (%)', color='r')
ax2.tick_params('y', colors='r')


plt.xlabel('Principal componants')


# Add legend and title
ax1.legend(loc='center right', frameon=False)

#save the figure
plt.savefig(osp.join(figureDir,'culmulative variance.png'))


# Display the plot
plt.show()