From cff1f6bdd03c0e53c9019f75527289b43ea256b7 Mon Sep 17 00:00:00 2001
From: "Mads M. Pedersen" <mmpe@dtu.dk>
Date: Wed, 17 May 2017 13:50:14 +0200
Subject: [PATCH] fixed cycle_matrix error
---
wetb/fatigue_tools/fatigue.py | 16 +++++++++++-----
1 file changed, 11 insertions(+), 5 deletions(-)
diff --git a/wetb/fatigue_tools/fatigue.py b/wetb/fatigue_tools/fatigue.py
index 684a598..e98aaf7 100644
--- a/wetb/fatigue_tools/fatigue.py
+++ b/wetb/fatigue_tools/fatigue.py
@@ -14,11 +14,13 @@ or
- 'rainflow_astm' (based on the c-implementation by Adam Nieslony found at the MATLAB Central File Exchange
http://www.mathworks.com/matlabcentral/fileexchange/3026)
'''
+from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
-from __future__ import absolute_import
+
from future import standard_library
+
standard_library.install_aliases()
import numpy as np
from wetb.fatigue_tools.rainflowcounting import rainflowcount
@@ -103,8 +105,9 @@ def eq_load_and_cycles(signals, no_bins=46, m=[3, 4, 6, 8, 10, 12], neq=[10 ** 6
cycles, ampl_bin_mean, ampl_bin_edges, _, _ = cycle_matrix(signals, no_bins, 1, rainflow_func)
if 0: #to be similar to windap
ampl_bin_mean = (ampl_bin_edges[:-1] + ampl_bin_edges[1:]) / 2
- cycles, ampl_bin_mean = cycles.flatten(), ampl_bin_mean.flatten()
- eq_loads = [[((np.nansum(cycles * ampl_bin_mean ** _m) / _neq) ** (1. / _m)) for _m in np.atleast_1d(m)] for _neq in np.atleast_1d(neq)]
+ cycles, ampl_bin_mean = cycles.flatten(), ampl_bin_mean.flatten()
+ mask = cycles>0
+ eq_loads = [[((np.sum(cycles[mask] * ampl_bin_mean[mask] ** _m) / _neq) ** (1. / _m)) for _m in np.atleast_1d(m)] for _neq in np.atleast_1d(neq)]
return eq_loads, cycles, ampl_bin_mean, ampl_bin_edges
@@ -159,9 +162,12 @@ def cycle_matrix(signals, ampl_bins=10, mean_bins=10, rainflow_func=rainflow_win
cycles, ampl_edges, mean_edges = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights)
ampl_bin_sum = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights * ampls)[0]
- ampl_bin_mean = np.nanmean(ampl_bin_sum / np.where(cycles,cycles,np.nan),1)
mean_bin_sum = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights * means)[0]
- mean_bin_mean = np.nanmean(mean_bin_sum / np.where(cycles, cycles, np.nan), 1)
+ import warnings
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", category=RuntimeWarning)
+ ampl_bin_mean = np.nanmean(ampl_bin_sum / np.where(cycles,cycles,np.nan),1)
+ mean_bin_mean = np.nanmean(mean_bin_sum / np.where(cycles, cycles, np.nan), 1)
cycles = cycles / 2 # to get full cycles
return cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges
--
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