The FourPointDetector class

class pylife.stress.rainflow.FourPointDetector(recorder)[source]

Implements four point rainflow counting algorithm.

from pylife.stress.timesignal import TimeSignalGenerator
import pylife.stress.rainflow as RF

ts = TimeSignalGenerator(10, {
    'number': 50,
    'amplitude_median': 1.0, 'amplitude_std_dev': 0.5,
    'frequency_median': 4, 'frequency_std_dev': 3,
    'offset_median': 0, 'offset_std_dev': 0.4}, None, None).query(10000)

rfc = RF.FourPointDetector(recorder=RF.LoopValueRecorder())
rfc.process(ts)

rfc.recorder.collective
from to
0 -6.631055 3.641915
1 4.638039 5.643112
2 7.299681 -5.980538
3 1.994762 1.472573
4 3.731940 -0.127724
... ... ...
1095 12.414769 -8.568888
1096 14.520373 -13.034524
1097 -1.440109 -7.664336
1098 -0.778939 4.204315
1099 1.461007 -0.220545

1100 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                       to                                        
(-19.18775856305451, -16.886403588714458]  (-16.969067413570947, -14.770686463437272]    0.0
                                           (-14.770686463437272, -12.572305513303595]    0.0
                                           (-12.572305513303595, -10.373924563169918]    0.0
                                           (-10.373924563169918, -8.175543613036243]     0.0
                                           (-8.175543613036243, -5.977162662902568]      0.0
                                                                                        ... 
(15.33256605204624, 17.63392102638629]     (7.213123037899489, 9.411503988033164]        0.0
                                           (9.411503988033164, 11.60988493816684]        0.0
                                           (11.60988493816684, 13.808265888300518]       0.0
                                           (13.808265888300518, 16.006646838434193]      0.0
                                           (16.006646838434193, 18.205027788567868]      0.0
Length: 256, dtype: float64

We take four turning points into account to detect closed hysteresis loops.

Consider four consecutive peak/valley points say, A, B, C, and D If B and C are contained within A and B, then a cycle is counted from B to C; otherwise no cycle is counted.

i.e, If X ≥ Y AND Z ≥ Y then a cycle exist FROM = B and TO = C where, ranges X = |D–C|, Y = |C–B|, and Z = |B–A|

Load -----------------------------
|        x B               F x
--------/-\-----------------/-----
|      /   \   x D         /
------/-----\-/-\---------/-------
|    /     C x   \       /
--\-/-------------\-----/---------
|  x A             \   /
--------------------\-/-----------
|                    x E
----------------------------------
|              Time

So, if a cycle exsist from B to C then delete these peaks from the turns array and perform next iteration by joining A&D else if no cylce exsists, then B would be the next strarting point.

__init__(recorder)[source]

Instantiate a FourPointDetector.

Parameters:

recorder (subclass of AbstractRecorder) – The recorder that the detector will report to.

process(samples)[source]

Process a sample chunk.

Parameters:

samples (array_like, shape (N, )) – The samples to be processed

Returns:

self – The self object so that processing can be chained

Return type:

FourPointDetector