Tutorial for Analyzing Datasets using Distortion Metrics

import distorted as dt
import pandas as pd

This file will demonstrate how to compute various existing metrics for distortion from datasets using Distorted

df = pd.read_csv('data/SAEFigure21.csv')
sample = dt.Distortion(df)

To compute each distortion metric, simply run their associated function from the Distortion class

Refer to function documentation for each metric’s formulas and description of variables.

sample.plot_quantity('Total Pressure')

Examples

sample.pDeltaPavg1()

0.18400613353778453

sample.pDeltaPavg2()

0.11097036567885596

sample.PrattAndWhitneyKD2()

723.8214687328376

sample.ARP1420() #ARP1420 Returns a table of distortion intensity and extent information

	Span/ Ring #	Circumferential Intensity	Circumferential Extent (deg)	Multiple Per Rev	Radial Intensity
0	1	0.072311	70.630631	1.928822	-0.007284
1	2	0.077349	69.189189	1.994193	-0.009034
2	3	0.071547	70.630631	1.950068	-0.008284
3	4	0.071375	72.792793	1.930127	0.000133
4	5	0.062398	73.513514	1.894335	0.024467

Some Distortion metrics require information on dynamic pressure for computation, and thus Static Pressure or Velocity data must be included in the dataset.

sample.RollsRoyceDC60()

Rolls Royce DC60 metric requires either Static Pressure or Velocity data

df2 = pd.read_csv('data/sample.csv') # Sample dataset with static pressure data
sample2 = dt.Distortion(df2)

sample2.RollsRoyceDC60()

0.02152726931274783