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 2.443686 3.927448
1 3.284488 -10.290790
2 -2.262977 1.336638
3 -3.657237 -1.365633
4 -5.360220 2.735193
... ... ...
997 -2.746358 -0.133583
998 -4.381293 0.345181
999 -1.607172 -1.902053
1000 7.902405 -7.793638
1001 -0.685121 5.986917

1002 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                        to                                        
(-20.285284031951928, -17.878069922305432]  (-21.86891534766978, -19.15115596276683]      0.0
                                            (-19.15115596276683, -16.433396577863878]     0.0
                                            (-16.433396577863878, -13.715637192960926]    0.0
                                            (-13.715637192960926, -10.997877808057975]    0.0
                                            (-10.997877808057975, -8.280118423155024]     0.0
                                                                                         ... 
(15.822927612745513, 18.230141722392013]    (8.026437886262684, 10.744197271165635]       0.0
                                            (10.744197271165635, 13.461956656068583]      0.0
                                            (13.461956656068583, 16.179716040971538]      0.0
                                            (16.179716040971538, 18.897475425874493]      0.0
                                            (18.897475425874493, 21.61523481077744]       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