From 5aa40ce1fc882fab16b15a4022e4ecf30bcc4f9c Mon Sep 17 00:00:00 2001
From: "Mads M. Pedersen" <mmpe@dtu.dk>
Date: Mon, 22 May 2017 14:05:04 +0200
Subject: [PATCH] small updates
---
wetb/fatigue_tools/fatigue.py | 19 ++++++-------------
wetb/signal/__init__.py | 3 ++-
wetb/signal/filters/_despike.py | 4 ++--
wetb/wind/__init__.py | 2 +-
4 files changed, 11 insertions(+), 17 deletions(-)
diff --git a/wetb/fatigue_tools/fatigue.py b/wetb/fatigue_tools/fatigue.py
index e98aaf7..0aa8713 100644
--- a/wetb/fatigue_tools/fatigue.py
+++ b/wetb/fatigue_tools/fatigue.py
@@ -14,13 +14,11 @@ 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
@@ -96,7 +94,7 @@ def eq_load_and_cycles(signals, no_bins=46, m=[3, 4, 6, 8, 10, 12], neq=[10 ** 6
eq_loads : array-like
List of lists of equivalent loads for the corresponding equivalent number(s) and Wohler exponents
cycles : array_like
- 2d array with shape = (no_ampl_bins, no_mean_bins)
+ 2d array with shape = (no_ampl_bins, 1)
ampl_bin_mean : array_like
mean amplitude of the bins
ampl_bin_edges
@@ -106,8 +104,7 @@ def eq_load_and_cycles(signals, no_bins=46, m=[3, 4, 6, 8, 10, 12], neq=[10 ** 6
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()
- 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)]
+ 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)]
return eq_loads, cycles, ampl_bin_mean, ampl_bin_edges
@@ -150,8 +147,7 @@ def cycle_matrix(signals, ampl_bins=10, mean_bins=10, rainflow_func=rainflow_win
>>> cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges = cycle_matrix(signal)
>>> cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges = cycle_matrix([(.4, signal), (.6,signal)])
"""
- if len(signals)==0:
- print ("here")
+
if isinstance(signals[0], tuple):
weights, ampls, means = np.array([(np.zeros_like(ampl)+weight,ampl,mean) for weight, signal in signals for ampl,mean in rainflow_func(signal[:]).T], dtype=np.float64).T
else:
@@ -162,12 +158,9 @@ 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]
- 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)
+ 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
diff --git a/wetb/signal/__init__.py b/wetb/signal/__init__.py
index 42d8da6..57d5e28 100644
--- a/wetb/signal/__init__.py
+++ b/wetb/signal/__init__.py
@@ -3,7 +3,8 @@ d = None
d = dir()
from .interpolation import interpolate
-
+from .fit import *
+from .fix import *
__all__ = [m for m in set(dir()) - set(d)]
diff --git a/wetb/signal/filters/_despike.py b/wetb/signal/filters/_despike.py
index bdeb845..0a23195 100644
--- a/wetb/signal/filters/_despike.py
+++ b/wetb/signal/filters/_despike.py
@@ -33,8 +33,8 @@ def thresshold_finder(data, thresshold, plt=None):
def univeral_thresshold_finder(data, variation='mad', plt=None):
## Three variation measures in decreasing order of sensitivity to outliers
- variation = {'std': np.sqrt(np.mean((data - np.mean(data)) ** 2)), # standard deviation
- 'abs': np.mean(np.abs(data - np.mean(data))), # mean abs deviation
+ variation = {'std': np.nanstd(data), # standard deviation
+ 'abs': np.nanmean(np.abs(data - np.nanmean(data))), # mean abs deviation
'mad': nanmedian(np.abs(data - nanmedian(data))) # median abs deviation (mad)
}.get(variation, variation)
diff --git a/wetb/wind/__init__.py b/wetb/wind/__init__.py
index ae17e9a..36e41c9 100644
--- a/wetb/wind/__init__.py
+++ b/wetb/wind/__init__.py
@@ -1 +1 @@
-from .turbulence import *
\ No newline at end of file
+from .turbulence import *
--
GitLab