PSD Optimizer

Example for the derivation of an equivalent PSD signal for shaker testing or other purposes. See the docu of the pylife psd_smoother function in the psdSignal class for more details

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as op
import sys
from pylife.stress.frequencysignal import psdSignal
import ipywidgets as wd
%matplotlib inline

psd = pd.DataFrame(pd.read_csv("data/PSD_values.csv",index_col = 0).iloc[5:1500,0])

#fsel = np.array([30,50,80,460,605,1000])
fsel = []
fig = plt.figure()
plt.loglog(psd)
txt = wd.Textarea(
    value='',
    placeholder='',
    description='fsel = ',
    disabled=False
)
display(txt)
def onclick(event):
    yval = psd.values[psd.index.get_loc(event.xdata,method ='nearest')]
    plt.vlines(event.xdata,0,yval)
    fsel.append(event.xdata)
    txt.value = "{:.2f}".format(event.xdata)
cid = fig.canvas.mpl_connect('button_press_event', onclick)

fsel = np.asarray(fsel)
# please uncomment the following line. It is just for utomatization purpose
fsel = np.asarray([5,29,264,1035,1300,])
print(fsel)

[   5   29  264 1035 1300]

def rms_psd(psd):
    return (sum(((psd.diff()+psd).dropna().values.flatten()*np.diff(psd.index.values)))**0.5)
plt.loglog(psd, label="rms = {:.2f}".format(rms_psd(psd)))
for i in np.linspace(0,1,4):
    psd_fit = psdSignal.psd_smoother(psd, fsel, i)
    plt.loglog(psd_fit, label="rms = {:.2f}".format(rms_psd(psd_fit)))
plt.legend()

<matplotlib.legend.Legend at 0x7f2207b7e850>