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 1.746781 1.648603
1 6.262732 8.320329
2 2.801580 -2.177818
3 2.804332 5.134315
4 3.065899 2.098992
... ... ...
1030 6.854375 6.484520
1031 8.194538 -10.995772
1032 -2.833096 6.257419
1033 -3.230922 10.886451
1034 4.128377 5.654207

1035 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                       to                                        
(-16.29320270590753, -13.859738760511423]  (-18.66415325029162, -16.011616919540018]     0.0
                                           (-16.011616919540018, -13.359080588788416]    0.0
                                           (-13.359080588788416, -10.706544258036814]    0.0
                                           (-10.706544258036814, -8.054007927285213]     0.0
                                           (-8.054007927285213, -5.401471596533611]      0.0
                                                                                        ... 
(20.208756475034047, 22.64222042043015]    (10.513746387976, 13.1662827187276]           0.0
                                           (13.1662827187276, 15.818819049479202]        0.0
                                           (15.818819049479202, 18.471355380230804]      0.0
                                           (18.471355380230804, 21.123891710982406]      0.0
                                           (21.123891710982406, 23.77642804173401]       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