<jupyter_start><jupyter_text>EMD. EXAMPLE.<jupyter_code>import numpy as np
import scipy.io as sio
from time import time
from pyhht import EMD
import matplotlib.pyplot as plt
from pyhht.visualization import plot_imfs
HA1 = sio.loadmat('H-A-1.mat')
Channel1 = HA1['Channel_1']
canal1 = Channel1.T[0]
t = np.linspace(0, 9, len(canal1))
x = canal1
plt.figure(figsize=(7, 4))
plt.plot(t, x)
plt.xlabel('Time')
plt.ylabel('Amplitude')
time0 = time()
decomposer = EMD(x)
imfs = decomposer.decompose()
elapsed_time = time() - time0
plt.figure(figsize=(8,5))
plot_imfs(x, imfs, t,  fignum=1)
elapsed_time<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.144, 'fraction_numerical': 0.0304, 'mean_word_length': 6.195402298850575, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '401991', 'license': 'MIT', 'n_tokens_mistral': 278, 'n_tokens_neox': 270, 'n_words': 58, 'path': 'FailurePrediction/ExamplesEMD/pyhht_EMD_example_var.ipynb', 'repo_name': 'judithspd/predictive-maintenance', 'size': '625'}