# -*- coding: utf-8 -*-
"""
Created on Thu Apr 3 19:53:59 2014
@author: dave
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import dict
from io import open as opent
from builtins import range
from builtins import str
from builtins import int
from future import standard_library
standard_library.install_aliases()
from builtins import object
# always devide as floats
#print(*objects, sep=' ', end='\n', file=sys.stdout)
__author__ = 'David Verelst'
__license__ = 'GPL'
__version__ = '0.5'
import os
import copy
import struct
import math
from time import time
import codecs
import scipy
import scipy.integrate as integrate
import array
import numpy as np
import pandas as pd
#import sympy
# misc is part of prepost, which is available on the dtu wind gitlab server:
# https://gitlab.windenergy.dtu.dk/dave/prepost
from wetb.prepost import misc
# wind energy python toolbox, available on the dtu wind redmine server:
# http://vind-redmine.win.dtu.dk/projects/pythontoolbox/repository/show/fatigue_tools
from wetb.fatigue_tools.rainflowcounting.rainflowcount import rainflow_astm as rainflow_astm
from wetb.fatigue_tools.rainflowcounting.rfc_hist import rfc_hist as rfc_hist
class LoadResults(object):
"""Read a HAWC2 result data file
Usage:
obj = LoadResults(file_path, file_name)
This class is called like a function:
HawcResultData() will read the specified file upon object initialization.
Available output:
obj.sig[timeStep,channel] : complete result file in a numpy array
obj.ch_details[channel,(0=ID; 1=units; 2=description)] : np.array
obj.error_msg: is 'none' if everything went OK, otherwise it holds the
error
The ch_dict key/values pairs are structured differently for different
type of channels. Currently supported channels are:
For forcevec, momentvec, state commands:
key:
coord-bodyname-pos-sensortype-component
global-tower-node-002-forcevec-z
local-blade1-node-005-momentvec-z
hub1-blade1-elem-011-zrel-1.00-state pos-z
value:
ch_dict[tag]['coord']
ch_dict[tag]['bodyname']
ch_dict[tag]['pos'] = pos
ch_dict[tag]['sensortype']
ch_dict[tag]['component']
ch_dict[tag]['chi']
ch_dict[tag]['sensortag']
ch_dict[tag]['units']
For the DLL's this is:
key:
DLL-dll_name-io-io_nr
DLL-yaw_control-outvec-3
DLL-yaw_control-inpvec-1
value:
ch_dict[tag]['dll_name']
ch_dict[tag]['io']
ch_dict[tag]['io_nr']
ch_dict[tag]['chi']
ch_dict[tag]['sensortag']
ch_dict[tag]['units']
For the bearings this is:
key:
bearing-bearing_name-output_type-units
bearing-shaft_nacelle-angle_speed-rpm
value:
ch_dict[tag]['bearing_name']
ch_dict[tag]['output_type']
ch_dict[tag]['chi']
ch_dict[tag]['units']
"""
# ch_df columns, these are created by LoadResults._unified_channel_names
cols = set(['bearing_name', 'sensortag', 'bodyname', 'chi', 'component',
'pos', 'coord', 'sensortype', 'radius', 'blade_nr', 'units',
'output_type', 'io_nr', 'io', 'dll', 'azimuth', 'flap_nr',
'direction'])
# start with reading the .sel file, containing the info regarding
# how to read the binary file and the channel information
def __init__(self, file_path, file_name, debug=False, usecols=None,
readdata=True):
self.debug = debug
# timer in debug mode
if self.debug:
start = time()
self.file_path = file_path
# remove .log, .dat, .sel extensions who might be accedental left
if file_name[-4:] in ['.htc','.sel','.dat','.log']:
file_name = file_name[:-4]
self.file_name = file_name
self.read_sel()
# create for any supported channel the
# continue if the file has been succesfully read
if self.error_msg == 'none':
# load the channel id's and scale factors
self.scale_factors = self.data_sel()
# with the sel file loaded, we have all the channel names to
# squeeze into a more consistant naming scheme
self._unified_channel_names()
# only read when asked for
if readdata:
# if there is sel file but it is empty or whatever else
# FilType will not exists
try:
# read the binary file
if self.FileType == 'BINARY':
self.read_bin(self.scale_factors, usecols=usecols)
# read the ASCII file
elif self.FileType == 'ASCII':
self.read_ascii(usecols=usecols)
else:
print('='*79)
print('unknown file type: ' + self.FileType)
print('='*79)
self.error_msg = 'error: unknown file type'
self.sig = []
except:
print('='*79)
print('couldn\'t determine FileType')
print('='*79)
self.error_msg = 'error: no file type'
self.sig = []
if self.debug:
stop = time() - start
print('time to load HAWC2 file:', stop, 's')
def read_sel(self):
# anticipate error on file reading
try:
# open file, read and close
go_sel = os.path.join(self.file_path, self.file_name + '.sel')
FILE = opent(go_sel, "r")
self.lines = FILE.readlines()
FILE.close()
self.error_msg = 'none'
# error message if the file does not exists
except:
# print(26*' ' + 'ERROR'
print(50*'=')
print(self.file_path)
print(self.file_name + '.sel could not be found')
print(50*'=')
self.error_msg = 'error: file not found'
def data_sel(self):
# scan through all the lines in the file
line_nr = 1
# channel counter for ch_details
ch = 0
for line in self.lines:
# on line 9 we can read following paramaters:
if line_nr == 9:
# remove the end of line character
line = line.replace('\n','').replace('\r', '')
settings = line.split(' ')
# delete all empty string values
for k in range(settings.count('')):
settings.remove('')
# and assign proper values with correct data type
self.N = int(settings[0])
self.Nch = int(settings[1])
self.Time = float(settings[2])
self.FileType = settings[3]
self.Freq = self.N/self.Time
# prepare list variables
self.ch_details = np.ndarray(shape=(self.Nch,3),dtype='<U100')
# it seems that float64 reeds the data correctly from the file
scale_factors = scipy.zeros(self.Nch, dtype='Float64')
#self.scale_factors_dec = scipy.zeros(self.Nch, dtype='f8')
i = 0
# starting from line 13, we have the channels info
if line_nr > 12:
# read the signal details
if line_nr < 13 + self.Nch:
# remove leading and trailing whitespaces from line parts
self.ch_details[ch,0] = str(line[12:43]).strip() # chID
self.ch_details[ch,1] = str(line[43:54]).strip() # chUnits
self.ch_details[ch,2] = str(line[54:-1]).strip() # chDescr
ch += 1
# read the signal scale parameters for binary format
elif line_nr > 14 + self.Nch:
scale_factors[i] = line
# print(scale_factors[i]
#self.scale_factors_dec[i] = D.Decimal(line)
i = i + 1
# stop going through the lines if at the end of the file
if line_nr == 2*self.Nch + 14:
self.scale_factors = scale_factors
if self.debug:
print('N ', self.N)
print('Nch ', self.Nch)
print('Time ', self.Time)
print('FileType', self.FileType)
print('Freq ', self.Freq)
print('scale_factors', scale_factors.shape)
return scale_factors
break
# counting the line numbers
line_nr = line_nr + 1
def read(self, usecols=False):
"""
This whole LoadResults needs to be refactered because it is crap.
Keep the old ones for backwards compatibility
"""
if self.FileType == 'ASCII':
self.read_ascii(usecols=usecols)
elif self.FileType == 'BINARY':
self.read_bin(self.scale_factors, usecols=usecols)
def read_bin(self, scale_factors, usecols=False):
if not usecols:
usecols = list(range(0, self.Nch))
fid = open(os.path.join(self.file_path, self.file_name) + '.dat', 'rb')
self.sig = np.zeros( (self.N, len(usecols)) )
for j, i in enumerate(usecols):
fid.seek(i*self.N*2,0)
self.sig[:,j] = np.fromfile(fid, 'int16', self.N)*scale_factors[i]
def read_bin_old(self, scale_factors):
# if there is an error reading the binary file (for instance if empty)
try:
# read the binary file
go_binary = os.path.join(self.file_path, self.file_name) + '.dat'
FILE = open(go_binary, mode='rb')
# create array, put all the binary elements as one long chain in it
binvalues = array.array('h')
binvalues.fromfile(FILE, self.N * self.Nch)
FILE.close()
# convert now to a structured numpy array
# sig = np.array(binvalues, np.float)
# sig = np.array(binvalues)
# this is faster! the saved bin values are only of type int16
sig = np.array(binvalues, dtype='int16')
if self.debug: print(self.N, self.Nch, sig.shape)
# sig = np.reshape(sig, (self.Nch, self.N))
# # apperently Nch and N had to be reversed to read it correctly
# # is this because we are reading a Fortran array with Python C
# # code? so now transpose again so we have sig(time, channel)
# sig = np.transpose(sig)
# reshape the array to 2D and transpose (Fortran to C array)
sig = sig.reshape((self.Nch, self.N)).T
# create diagonal vector of size (Nch,Nch)
dig = np.diag(scale_factors)
# now all rows of column 1 are multiplied with dig(1,1)
sig = np.dot(sig,dig)
self.sig = sig
# 'file name;' + 'lnr;msg;'*(len(MsgList)) + '\n'
except:
self.sig = []
self.error_msg = 'error: reading binary file failed'
print('========================================================')
print(self.error_msg)
print(self.file_path)
print(self.file_name)
print('========================================================')
def read_ascii(self, usecols=None):
try:
go_ascii = os.path.join(self.file_path, self.file_name) + '.dat'
# self.sig = np.genfromtxt(go_ascii)
self.sig = np.loadtxt(go_ascii, usecols=usecols)
# self.sig = np.fromfile(go_ascii, dtype=np.float32, sep=' ')
# self.sig = self.sig.reshape((self.N, self.Nch))
except:
self.sig = []
self.error_msg = 'error: reading ascii file failed'
print('========================================================')
print(self.error_msg)
print(self.file_path)
print(self.file_name)
print('========================================================')
# print '========================================================'
# print 'ASCII reading not implemented yet'
# print '========================================================'
# self.sig = []
# self.error_msg = 'error: ASCII reading not implemented yet'
def reformat_sig_details(self):
"""Change HAWC2 output description of the channels short descriptive
strings, usable in plots
obj.ch_details[channel,(0=ID; 1=units; 2=description)] : np.array
"""
# CONFIGURATION: mappings between HAWC2 and short good output:
change_list = []
change_list.append( ['original','new improved'] )
# change_list.append( ['Mx coo: hub1','blade1 root bending: flap'] )
# change_list.append( ['My coo: hub1','blade1 root bending: edge'] )
# change_list.append( ['Mz coo: hub1','blade1 root bending: torsion'] )
#
# change_list.append( ['Mx coo: hub2','blade2 root bending: flap'] )
# change_list.append( ['My coo: hub2','blade2 root bending: edge'] )
# change_list.append( ['Mz coo: hub2','blade2 root bending: torsion'] )
#
# change_list.append( ['Mx coo: hub3','blade3 root bending: flap'] )
# change_list.append( ['My coo: hub3','blade3 root bending: edge'] )
# change_list.append( ['Mz coo: hub3','blade3 root bending: torsion'] )
change_list.append( ['Mx coo: blade1','blade1 flap'] )
change_list.append( ['My coo: blade1','blade1 edge'] )
change_list.append( ['Mz coo: blade1','blade1 torsion'] )
change_list.append( ['Mx coo: blade2','blade2 flap'] )
change_list.append( ['My coo: blade2','blade2 edge'] )
change_list.append( ['Mz coo: blade2','blade2 torsion'] )
change_list.append( ['Mx coo: blade3','blade3 flap'] )
change_list.append( ['My coo: blade3','blade3 edeg'] )
change_list.append( ['Mz coo: blade3','blade3 torsion'] )
change_list.append( ['Mx coo: hub1','blade1 out-of-plane'] )
change_list.append( ['My coo: hub1','blade1 in-plane'] )
change_list.append( ['Mz coo: hub1','blade1 torsion'] )
change_list.append( ['Mx coo: hub2','blade2 out-of-plane'] )
change_list.append( ['My coo: hub2','blade2 in-plane'] )
change_list.append( ['Mz coo: hub2','blade2 torsion'] )
change_list.append( ['Mx coo: hub3','blade3 out-of-plane'] )
change_list.append( ['My coo: hub3','blade3 in-plane'] )
change_list.append( ['Mz coo: hub3','blade3 torsion'] )
# this one will create a false positive for tower node nr1
change_list.append( ['Mx coo: tower','tower top momemt FA'] )
change_list.append( ['My coo: tower','tower top momemt SS'] )
change_list.append( ['Mz coo: tower','yaw-moment'] )
change_list.append( ['Mx coo: chasis','chasis momemt FA'] )
change_list.append( ['My coo: chasis','yaw-moment chasis'] )
change_list.append( ['Mz coo: chasis','chasis moment SS'] )
change_list.append( ['DLL inp 2: 2','tower clearance'] )
self.ch_details_new = np.ndarray(shape=(self.Nch,3),dtype='<U100')
# approach: look for a specific description and change it.
# This approach is slow, but will not fail if the channel numbers change
# over different simulations
for ch in range(self.Nch):
# the change_list will always be slower, so this loop will be
# inside the bigger loop of all channels
self.ch_details_new[ch,:] = self.ch_details[ch,:]
for k in range(len(change_list)):
if change_list[k][0] == self.ch_details[ch,0]:
self.ch_details_new[ch,0] = change_list[k][1]
# channel description should be unique, so delete current
# entry and stop looking in the change list
del change_list[k]
break
# self.ch_details_new = ch_details_new
def _unified_channel_names(self):
"""
Make certain channels independent from their index.
The unified channel dictionary ch_dict holds consequently named
channels as the key, and the all information is stored in the value
as another dictionary.
The ch_dict key/values pairs are structured differently for different
type of channels. Currently supported channels are:
For forcevec, momentvec, state commands:
node numbers start with 0 at the root
element numbers start with 1 at the root
key:
coord-bodyname-pos-sensortype-component
global-tower-node-002-forcevec-z
local-blade1-node-005-momentvec-z
hub1-blade1-elem-011-zrel-1.00-state pos-z
value:
ch_dict[tag]['coord']
ch_dict[tag]['bodyname']
ch_dict[tag]['pos']
ch_dict[tag]['sensortype']
ch_dict[tag]['component']
ch_dict[tag]['chi']
ch_dict[tag]['sensortag']
ch_dict[tag]['units']
For the DLL's this is:
key:
DLL-dll_name-io-io_nr
DLL-yaw_control-outvec-3
DLL-yaw_control-inpvec-1
value:
ch_dict[tag]['dll_name']
ch_dict[tag]['io']
ch_dict[tag]['io_nr']
ch_dict[tag]['chi']
ch_dict[tag]['sensortag']
ch_dict[tag]['units']
For the bearings this is:
key:
bearing-bearing_name-output_type-units
bearing-shaft_nacelle-angle_speed-rpm
value:
ch_dict[tag]['bearing_name']
ch_dict[tag]['output_type']
ch_dict[tag]['chi']
ch_dict[tag]['units']
For many of the aero sensors:
'Cl', 'Cd', 'Alfa', 'Vrel'
key:
sensortype-blade_nr-pos
Cl-1-0.01
value:
ch_dict[tag]['sensortype']
ch_dict[tag]['blade_nr']
ch_dict[tag]['pos']
ch_dict[tag]['chi']
ch_dict[tag]['units']
"""
# save them in a dictionary, use the new coherent naming structure
# as the key, and as value again a dict that hols all the different
# classifications: (chi, channel nr), (coord, coord), ...
self.ch_dict = dict()
# some channel ID's are unique, use them
ch_unique = set(['Omega', 'Ae rot. torque', 'Ae rot. power',
'Ae rot. thrust', 'Time', 'Azi 1'])
ch_aero = set(['Cl', 'Cd', 'Alfa', 'Vrel', 'Tors_e', 'Alfa'])
ch_aerogrid = set(['a_grid', 'am_grid'])
# also safe as df
# cols = set(['bearing_name', 'sensortag', 'bodyname', 'chi',
# 'component', 'pos', 'coord', 'sensortype', 'radius',
# 'blade_nr', 'units', 'output_type', 'io_nr', 'io', 'dll',
# 'azimuth', 'flap_nr'])
df_dict = {col:[] for col in self.cols}
df_dict['ch_name'] = []
# scan through all channels and see which can be converted
# to sensible unified name
for ch in range(self.Nch):
items = self.ch_details[ch,2].split(' ')
# remove empty values in the list
items = misc.remove_items(items, '')
dll = False
# be carefull, identify only on the starting characters, because
# the signal tag can hold random text that in some cases might
# trigger a false positive
# -----------------------------------------------------------------
# check for all the unique channel descriptions
if self.ch_details[ch,0].strip() in ch_unique:
tag = self.ch_details[ch,0].strip()
channelinfo = {}
channelinfo['units'] = self.ch_details[ch,1]
channelinfo['sensortag'] = self.ch_details[ch,2]
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# or in the long description:
# 0 1 2 3 4 5 6 and up
# MomentMz Mbdy:blade nodenr: 5 coo: blade TAG TEXT
elif self.ch_details[ch,2].startswith('MomentM'):
coord = items[5]
bodyname = items[1].replace('Mbdy:', '')
# set nodenr to sortable way, include leading zeros
# node numbers start with 0 at the root
nodenr = '%03i' % int(items[3])
# skip the attached the component
#sensortype = items[0][:-2]
# or give the sensor type the same name as in HAWC2
sensortype = 'momentvec'
component = items[0][-1:len(items[0])]
# the tag only exists if defined
if len(items) > 6:
sensortag = ' '.join(items[6:])
else:
sensortag = ''
# and tag it
pos = 'node-%s' % nodenr
tagitems = (coord,bodyname,pos,sensortype,component)
tag = '%s-%s-%s-%s-%s' % tagitems
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = coord
channelinfo['bodyname'] = bodyname
channelinfo['pos'] = pos
channelinfo['sensortype'] = sensortype
channelinfo['component'] = component
channelinfo['chi'] = ch
channelinfo['sensortag'] = sensortag
channelinfo['units'] = self.ch_details[ch,1]
# -----------------------------------------------------------------
# 0 1 2 3 4 5 6 7 and up
# Force Fx Mbdy:blade nodenr: 2 coo: blade TAG TEXT
elif self.ch_details[ch,2].startswith('Force'):
coord = items[6]
bodyname = items[2].replace('Mbdy:', '')
nodenr = '%03i' % int(items[4])
# skipe the attached the component
#sensortype = items[0]
# or give the sensor type the same name as in HAWC2
sensortype = 'forcevec'
component = items[1][1]
if len(items) > 7:
sensortag = ' '.join(items[7:])
else:
sensortag = ''
# and tag it
pos = 'node-%s' % nodenr
tagitems = (coord,bodyname,pos,sensortype,component)
tag = '%s-%s-%s-%s-%s' % tagitems
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = coord
channelinfo['bodyname'] = bodyname
channelinfo['pos'] = pos
channelinfo['sensortype'] = sensortype
channelinfo['component'] = component
channelinfo['chi'] = ch
channelinfo['sensortag'] = sensortag
channelinfo['units'] = self.ch_details[ch,1]
# -----------------------------------------------------------------
# 0 1 2 3 4 5 6 7 8
# State pos x Mbdy:blade E-nr: 1 Z-rel:0.00 coo: blade
# 0 1 2 3 4 5 6 7 8 9+
# State_rot proj_ang tx Mbdy:bname E-nr: 1 Z-rel:0.00 coo: cname label
# State_rot omegadot tz Mbdy:bname E-nr: 1 Z-rel:1.00 coo: cname label
elif self.ch_details[ch,2].startswith('State'):
# or self.ch_details[ch,0].startswith('euler') \
# or self.ch_details[ch,0].startswith('ax') \
# or self.ch_details[ch,0].startswith('omega') \
# or self.ch_details[ch,0].startswith('proj'):
coord = items[8]
bodyname = items[3].replace('Mbdy:', '')
# element numbers start with 1 at the root
elementnr = '%03i' % int(items[5])
zrel = '%04.2f' % float(items[6].replace('Z-rel:', ''))
# skip the attached the component
#sensortype = ''.join(items[0:2])
# or give the sensor type the same name as in HAWC2
tmp = self.ch_details[ch,0].split(' ')
sensortype = tmp[0]
if sensortype.startswith('State'):
sensortype += ' ' + tmp[1]
component = items[2]
if len(items) > 8:
sensortag = ' '.join(items[9:])
else:
sensortag = ''
# and tag it
pos = 'elem-%s-zrel-%s' % (elementnr, zrel)
tagitems = (coord,bodyname,pos,sensortype,component)
tag = '%s-%s-%s-%s-%s' % tagitems
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = coord
channelinfo['bodyname'] = bodyname
channelinfo['pos'] = pos
channelinfo['sensortype'] = sensortype
channelinfo['component'] = component
channelinfo['chi'] = ch
channelinfo['sensortag'] = sensortag
channelinfo['units'] = self.ch_details[ch,1]
# -----------------------------------------------------------------
# DLL CONTROL I/O
# there are two scenario's on how the channel description is formed
# the channel id is always the same though
# id for all three cases:
# DLL out 1: 3
# DLL inp 2: 3
# description case 1 ("dll type2_dll b2h2 inpvec 30" in htc output)
# 0 1 2 3 4+
# yaw_control outvec 3 yaw_c input reference angle
# description case 2 ("dll inpvec 2 1" in htc output):
# 0 1 2 3 4 5 6+
# DLL : 2 inpvec : 4 mgen hss
# description case 3
# 0 1 2 4
# hawc_dll :echo outvec : 1
elif self.ch_details[ch,0].startswith('DLL'):
# case 3
if items[1][0] == ':echo':
# hawc_dll named case (case 3) is polluted with colons
items = self.ch_details[ch,2].replace(':','')
items = items.split(' ')
items = misc.remove_items(items, '')
dll = items[1]
io = items[2]
io_nr = items[3]
tag = 'DLL-%s-%s-%s' % (dll,io,io_nr)
sensortag = ''
# case 2: no reference to dll name
elif self.ch_details[ch,2].startswith('DLL'):
dll = items[2]
io = items[3]
io_nr = items[5]
sensortag = ' '.join(items[6:])
# and tag it
tag = 'DLL-%s-%s-%s' % (dll,io,io_nr)
# case 1: type2 dll name is given
else:
dll = items[0]
io = items[1]
io_nr = items[2]
sensortag = ' '.join(items[3:])
tag = 'DLL-%s-%s-%s' % (dll,io,io_nr)
# save all info in the dict
channelinfo = {}
channelinfo['dll'] = dll
channelinfo['io'] = io
channelinfo['io_nr'] = io_nr
channelinfo['chi'] = ch
channelinfo['sensortag'] = sensortag
channelinfo['units'] = self.ch_details[ch,1]
# -----------------------------------------------------------------
# BEARING OUTPUS
# bea1 angle_speed rpm shaft_nacelle angle speed
elif self.ch_details[ch,0].startswith('bea'):
output_type = self.ch_details[ch,0].split(' ')[1]
bearing_name = items[0]
units = self.ch_details[ch,1]
# there is no label option for the bearing output
# and tag it
tag = 'bearing-%s-%s-%s' % (bearing_name,output_type,units)
# save all info in the dict
channelinfo = {}
channelinfo['bearing_name'] = bearing_name
channelinfo['output_type'] = output_type
channelinfo['units'] = units
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# AERO CL, CD, CM, VREL, ALFA, LIFT, DRAG, etc
# Cl, R= 0.5 deg Cl of blade 1 at radius 0.49
# Azi 1 deg Azimuth of blade 1
elif self.ch_details[ch,0].split(',')[0] in ch_aero:
dscr_list = self.ch_details[ch,2].split(' ')
dscr_list = misc.remove_items(dscr_list, '')
sensortype = self.ch_details[ch,0].split(',')[0]
radius = dscr_list[-1]
# is this always valid?
blade_nr = self.ch_details[ch,2].split('blade ')[1][0]
# sometimes the units for aero sensors are wrong!
units = self.ch_details[ch,1]
# there is no label option
# and tag it
tag = '%s-%s-%s' % (sensortype,blade_nr,radius)
# save all info in the dict
channelinfo = {}
channelinfo['sensortype'] = sensortype
channelinfo['radius'] = float(radius)
channelinfo['blade_nr'] = int(blade_nr)
channelinfo['units'] = units
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# for the induction grid over the rotor
# a_grid, azi 0.00 r 1.74
elif self.ch_details[ch,0].split(',')[0] in ch_aerogrid:
items = self.ch_details[ch,0].split(',')
sensortype = items[0]
items2 = items[1].split(' ')
items2 = misc.remove_items(items2, '')
azi = items2[1]
radius = items2[3]
units = self.ch_details[ch,1]
# and tag it
tag = '%s-azi-%s-r-%s' % (sensortype,azi,radius)
# save all info in the dict
channelinfo = {}
channelinfo['sensortype'] = sensortype
channelinfo['radius'] = float(radius)
channelinfo['azimuth'] = float(azi)
channelinfo['units'] = units
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# INDUCTION AT THE BLADE
# 0: Induc. Vz, rpco, R= 1.4
# 1: m/s
# 2: Induced wsp Vz of blade 1 at radius 1.37, RP. coo.
# Induc. Vx, locco, R= 1.4 // Induced wsp Vx of blade 1 at radius 1.37, local ae coo.
# Induc. Vy, blco, R= 1.4 // Induced wsp Vy of blade 1 at radius 1.37, local bl coo.
# Induc. Vz, glco, R= 1.4 // Induced wsp Vz of blade 1 at radius 1.37, global coo.
# Induc. Vx, rpco, R= 8.4 // Induced wsp Vx of blade 1 at radius 8.43, RP. coo.
elif self.ch_details[ch,0].strip()[:5] == 'Induc':
items = self.ch_details[ch,2].split(' ')
items = misc.remove_items(items, '')
blade_nr = int(items[5])
radius = float(items[8].replace(',', ''))
items = self.ch_details[ch,0].split(',')
coord = items[1].strip()
component = items[0][-2:]
units = self.ch_details[ch,1]
# and tag it
rpl = (coord, blade_nr, component, radius)
tag = 'induc-%s-blade-%1i-%s-r-%03.02f' % rpl
# save all info in the dict
channelinfo = {}
channelinfo['blade_nr'] = blade_nr
channelinfo['sensortype'] = 'induction'
channelinfo['radius'] = radius
channelinfo['coord'] = coord
channelinfo['component'] = component
channelinfo['units'] = units
channelinfo['chi'] = ch
# TODO: wind speed
# some spaces have been trimmed here
# WSP gl. coo.,Vy m/s
# // Free wind speed Vy, gl. coo, of gl. pos 0.00, 0.00, -2.31
# WSP gl. coo.,Vdir_hor deg
# Free wind speed Vdir_hor, gl. coo, of gl. pos 0.00, 0.00, -2.31
# -----------------------------------------------------------------
# WATER SURFACE gl. coo, at gl. coo, x,y= 0.00, 0.00
elif self.ch_details[ch,2].startswith('Water'):
units = self.ch_details[ch,1]
# but remove the comma
x = items[-2][:-1]
y = items[-1]
# and tag it
tag = 'watersurface-global-%s-%s' % (x, y)
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = 'global'
channelinfo['pos'] = (float(x), float(y))
channelinfo['units'] = units
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# WIND SPEED
# WSP gl. coo.,Vx
elif self.ch_details[ch,0].startswith('WSP gl.'):
units = self.ch_details[ch,1]
direction = self.ch_details[ch,0].split(',')[1]
tmp = self.ch_details[ch,2].split('pos')[1]
x, y, z = tmp.split(',')
x, y, z = x.strip(), y.strip(), z.strip()
# and tag it
tag = 'windspeed-global-%s-%s-%s-%s' % (direction, x, y, z)
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = 'global'
channelinfo['pos'] = (x, y, z)
channelinfo['units'] = units
channelinfo['chi'] = ch
channelinfo['sensortype'] = 'windspeed'
channelinfo['component'] = direction[1:]
# WIND SPEED AT BLADE
# 0: WSP Vx, glco, R= 61.5
# 2: Wind speed Vx of blade 1 at radius 61.52, global coo.
elif self.ch_details[ch,0].startswith('WSP V'):
units = self.ch_details[ch,1].strip()
direction = self.ch_details[ch,0].split(' ')[1].strip()
blade_nr = self.ch_details[ch,2].split('blade')[1].strip()[:2]
radius = self.ch_details[ch,2].split('radius')[1].split(',')[0]
coord = self.ch_details[ch,2].split(',')[1].strip()
radius = radius.strip()
blade_nr = blade_nr.strip()
# and tag it
rpl = (direction, blade_nr, radius, coord)
tag = 'wsp-blade-%s-%s-%s-%s' % rpl
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = coord
channelinfo['direction'] = direction
channelinfo['blade_nr'] = int(blade_nr)
channelinfo['radius'] = float(radius)
channelinfo['units'] = units
channelinfo['chi'] = ch
# FLAP ANGLE
# 2: Flap angle for blade 3 flap number 1
elif self.ch_details[ch,0][:7] == 'setbeta':
units = self.ch_details[ch,1].strip()
blade_nr = self.ch_details[ch,2].split('blade')[1].strip()
blade_nr = blade_nr.split(' ')[0].strip()
flap_nr = self.ch_details[ch,2].split(' ')[-1].strip()
radius = radius.strip()
blade_nr = blade_nr.strip()
# and tag it
tag = 'setbeta-bladenr-%s-flapnr-%s' % (blade_nr, flap_nr)
# save all info in the dict
channelinfo = {}
channelinfo['coord'] = coord
channelinfo['flap_nr'] = int(flap_nr)
channelinfo['blade_nr'] = int(blade_nr)
channelinfo['units'] = units
channelinfo['chi'] = ch
# -----------------------------------------------------------------
# ignore all the other cases we don't know how to deal with
else:
# if we get here, we don't have support yet for that sensor
# and hence we can't save it. Continue with next channel
continue
# -----------------------------------------------------------------
# ignore if we have a non unique tag
if tag in self.ch_dict:
jj = 1
while True:
tag_new = tag + '_v%i' % jj
if tag_new in self.ch_dict:
jj += 1
else:
tag = tag_new
break
# msg = 'non unique tag for HAWC2 results, ignoring: %s' % tag
# logging.warn(msg)
# else:
self.ch_dict[tag] = copy.copy(channelinfo)
# -----------------------------------------------------------------
# save in for DataFrame format
cols_ch = set(channelinfo.keys())
for col in cols_ch:
df_dict[col].append(channelinfo[col])
# the remainder columns we have not had yet. Fill in blank
for col in (self.cols - cols_ch):
df_dict[col].append('')
df_dict['ch_name'].append(tag)
self.ch_df = pd.DataFrame(df_dict)
self.ch_df.set_index('chi', inplace=True)
def _ch_dict2df(self):
"""
Create a DataFrame version of the ch_dict, and the chi columns is
set as the index
"""
# identify all the different columns
cols = set()
for ch_name, channelinfo in self.ch_dict.items():
cols.update(set(channelinfo.keys()))
df_dict = {col:[] for col in cols}
df_dict['ch_name'] = []
for ch_name, channelinfo in self.ch_dict.items():
cols_ch = set(channelinfo.keys())
for col in cols_ch:
df_dict[col].append(channelinfo[col])
# the remainder columns we have not had yet. Fill in blank
for col in (cols - cols_ch):
df_dict[col].append('')
df_dict['ch_name'].append(ch_name)
self.ch_df = pd.DataFrame(df_dict)
self.ch_df.set_index('chi', inplace=True)
def _data_window(self, nr_rev=None, time=None):
"""
Based on a time interval, create a proper slice object
======================================================
The window will start at zero and ends with the covered time range
of the time input.
Paramters
---------
nr_rev : int, default=None
NOT IMPLEMENTED YET
time : list, default=None
time = [time start, time stop]
Returns
-------
slice_
window
zoomtype
time_range
time_range = [0, time[1]]
"""
# -------------------------------------------------
# determine zome range if necesary
# -------------------------------------------------
time_range = None
if nr_rev:
raise NotImplementedError
# input is a number of revolutions, get RPM and sample rate to
# calculate the required range
# TODO: automatich detection of RPM channel!
time_range = nr_rev/(self.rpm_mean/60.)
# convert to indices instead of seconds
i_range = int(self.Freq*time_range)
window = [0, time_range]
# in case the first datapoint is not at 0 seconds
i_zero = int(self.sig[0,0]*self.Freq)
slice_ = np.r_[i_zero:i_range+i_zero]
zoomtype = '_nrrev_' + format(nr_rev, '1.0f') + 'rev'
elif time.any():
time_range = time[1] - time[0]
i_start = int(time[0]*self.Freq)
i_end = int(time[1]*self.Freq)
slice_ = np.r_[i_start:i_end]
window = [time[0], time[1]]
zoomtype = '_zoom_%1.1f-%1.1fsec' % (time[0], time[1])
return slice_, window, zoomtype, time_range
# TODO: general signal method, this is not HAWC2 specific, move out
def calc_stats(self, sig, i0=0, i1=-1):
stats = {}
# calculate the statistics values:
stats['max'] = sig[i0:i1,:].max(axis=0)
stats['min'] = sig[i0:i1,:].min(axis=0)
stats['mean'] = sig[i0:i1,:].mean(axis=0)
stats['std'] = sig[i0:i1,:].std(axis=0)
stats['range'] = stats['max'] - stats['min']
stats['absmax'] = np.absolute(sig[i0:i1,:]).max(axis=0)
stats['rms'] = np.sqrt(np.mean(sig[i0:i1,:]*sig[i0:i1,:], axis=0))
stats['int'] = integrate.trapz(sig[i0:i1,:], x=sig[i0:i1,0], axis=0)
return stats
# TODO: general signal method, this is not HAWC2 specific, move out
def calc_fatigue(self, signal, no_bins=46, m=[3, 4, 6, 8, 10, 12], neq=1):
"""
Parameters
----------
signal: 1D array
One dimentional array containing the signal.
no_bins: int
Number of bins for the binning of the amplitudes.
m: list
Values of the slope of the SN curve.
neq: int
Number of equivalent cycles
Returns
-------
eq: list
Damage equivalent loads for each m value.
"""
try:
sig_rf = rainflow_astm(signal)
except (TypeError) as e:
print(e)
return []
if len(sig_rf) < 1 and not sig_rf:
return []
hist_data, x, bin_avg = rfc_hist(sig_rf, no_bins)
m = np.atleast_1d(m)
eq = []
for i in range(len(m)):
eq.append(np.power(np.sum(0.5 * hist_data *\
np.power(bin_avg, m[i])) / neq, 1. / m[i]))
return eq
# TODO: general signal method, this is not HAWC2 specific, move out
def cycle_matrix(self, signal, no_bins=46, m=[3, 4, 6, 8, 10, 12]):
# import fatigue_tools.fatigue as ft
# cycles, ampl_bin_mean, ampl_bin_edges, mean_bin_mean, mean_edges \
# = ft.cycle_matrix(signal, ampl_bins=no_bins, mean_bins=1,
# rainflow_func=ft.rainflow_windap)
# # in this case eq = sum( n_i*S_i^m )
# return [np.sum(cycles * ampl_bin_mean ** _m) for _m in m]
try:
sig_rf = rainflow_astm(signal)
except:
return []
if len(sig_rf) < 1 and not sig_rf:
return []
hist_data, x, bin_avg = rfc_hist(sig_rf, no_bins)
m = np.atleast_1d(m)
return [np.sum(0.5 * hist_data * bin_avg ** _m) for _m in m]
def blade_deflection(self):
"""
"""
# select all the y deflection channels
db = misc.DictDB(self.ch_dict)
db.search({'sensortype' : 'state pos', 'component' : 'z'})
# sort the keys and save the mean values to an array/list
chiz, zvals = [], []
for key in sorted(db.dict_sel.keys()):
zvals.append(-self.sig[:,db.dict_sel[key]['chi']].mean())
chiz.append(db.dict_sel[key]['chi'])
db.search({'sensortype' : 'state pos', 'component' : 'y'})
# sort the keys and save the mean values to an array/list
chiy, yvals = [], []
for key in sorted(db.dict_sel.keys()):
yvals.append(self.sig[:,db.dict_sel[key]['chi']].mean())
chiy.append(db.dict_sel[key]['chi'])
return np.array(zvals), np.array(yvals)
def save_csv(self, fname, fmt='%.18e', delimiter=','):
"""
Save to csv and use the unified channel names as columns
"""
map_sorting = {}
# first, sort on channel index
for ch_key, ch in self.ch_dict.items():
map_sorting[ch['chi']] = ch_key
header = []
# not all channels might be present...iterate again over map_sorting
for chi in map_sorting:
try:
sensortag = self.ch_dict[map_sorting[chi]]['sensortag']
header.append(map_sorting[chi] + ' // ' + sensortag)
except:
header.append(map_sorting[chi])
# and save
print('saving...', end='')
np.savetxt(fname, self.sig[:,list(map_sorting.keys())], fmt=fmt,
delimiter=delimiter, header=delimiter.join(header))
print(fname)
def save_df(self, fname):
"""
Save the HAWC2 data and sel file in a DataFrame that contains all the
data, and all the channel information (the one from the sel file
and the parsed from this function)
"""
self.sig
self.ch_details
self.ch_dict
def ReadOutputAtTime(fname):
"""Distributed blade loading as generated by the HAWC2 output_at_time
command.
"""
# because the formatting is really weird, we need to sanatize it a bit
with opent(fname, 'r') as f:
# read the header from line 3
f.readline()
f.readline()
header = f.readline().replace('\r', '').replace('\n', '')
cols = [k.strip().replace(' ', '_') for k in header.split('#')[1:]]
# data = pd.read_fwf(fname, skiprows=3, header=None)
# pd.read_table(fname, sep=' ', skiprows=3)
# data.index.names = cols
data = np.loadtxt(fname, skiprows=3)
return pd.DataFrame(data, columns=cols)
def ReadEigenBody(fname, debug=False):
"""
Read HAWC2 body eigenalysis result file
=======================================
Parameters
----------
file_path : str
file_name : str
Returns
-------
results : DataFrame
Columns: body, Fd_hz, Fn_hz, log_decr_pct
"""
#Body data for body number : 3 with the name :nacelle
#Results: fd [Hz] fn [Hz] log.decr [%]
#Mode nr: 1: 1.45388E-21 1.74896E-03 6.28319E+02
FILE = opent(fname)
lines = FILE.readlines()
FILE.close()
df_dict = {'Fd_hz':[], 'Fn_hz':[], 'log_decr_pct':[], 'body':[]}
for i, line in enumerate(lines):
if debug: print('line nr: %5i' % i)
# identify for which body we will read the data
if line[:25] == 'Body data for body number':
body = line.split(':')[2].rstrip().lstrip()
# remove any annoying characters
body = body.replace('\n','').replace('\r','')
if debug: print('modes for body: %s' % body)
# identify mode number and read the eigenfrequencies
elif line[:8] == 'Mode nr:':
linelist = line.replace('\n','').replace('\r','').split(':')
#modenr = linelist[1].rstrip().lstrip()
# text after Mode nr can be empty
try:
eigenmodes = linelist[2].rstrip().lstrip().split(' ')
except IndexError:
eigenmodes = ['0', '0', '0']
if debug: print(eigenmodes)
# in case we have more than 3, remove all the empty ones
# this can happen when there are NaN values
if not len(eigenmodes) == 3:
eigenmodes = linelist[2].rstrip().lstrip().split(' ')
eigmod = []
for k in eigenmodes:
if len(k) > 1:
eigmod.append(k)
#eigenmodes = eigmod
else:
eigmod = eigenmodes
# remove any trailing spaces for each element
for k in range(len(eigmod)):
eigmod[k] = float(eigmod[k])#.lstrip().rstrip()
df_dict['body'].append(body)
df_dict['Fd_hz'].append(eigmod[0])
df_dict['Fn_hz'].append(eigmod[1])
df_dict['log_decr_pct'].append(eigmod[2])
return pd.DataFrame(df_dict)
def ReadEigenStructure(file_path, file_name, debug=False, max_modes=500):
"""
Read HAWC2 structure eigenalysis result file
============================================
The file looks as follows:
#0 Version ID : HAWC2MB 11.3
#1 ___________________________________________________________________
#2 Structure eigenanalysis output
#3 ___________________________________________________________________
#4 Time : 13:46:59
#5 Date : 28:11.2012
#6 ___________________________________________________________________
#7 Results: fd [Hz] fn [Hz] log.decr [%]
#8 Mode nr: 1: 3.58673E+00 3.58688E+00 5.81231E+00
#...
#302 Mode nr:294: 0.00000E+00 6.72419E+09 6.28319E+02
Parameters
----------
file_path : str
file_name : str
debug : boolean, default=False
max_modes : int
Stop evaluating the result after max_modes number of modes have been
identified
Returns
-------
modes_arr : ndarray(3,n)
An ndarray(3,n) holding Fd, Fn [Hz] and the logarithmic damping
decrement [%] for n different structural eigenmodes
"""
#0 Version ID : HAWC2MB 11.3
#1 ___________________________________________________________________
#2 Structure eigenanalysis output
#3 ___________________________________________________________________
#4 Time : 13:46:59
#5 Date : 28:11.2012
#6 ___________________________________________________________________
#7 Results: fd [Hz] fn [Hz] log.decr [%]
#8 Mode nr: 1: 3.58673E+00 3.58688E+00 5.81231E+00
# Mode nr:294: 0.00000E+00 6.72419E+09 6.28319E+02
FILE = opent(os.path.join(file_path, file_name))
lines = FILE.readlines()
FILE.close()
header_lines = 8
# we now the number of modes by having the number of lines
nrofmodes = len(lines) - header_lines
modes_arr = np.ndarray((3,nrofmodes))
for i, line in enumerate(lines):
if i > max_modes:
# cut off the unused rest
modes_arr = modes_arr[:,:i]
break
# ignore the header
if i < header_lines:
continue
# split up mode nr from the rest
parts = line.split(':')
#modenr = int(parts[1])
# get fd, fn and damping, but remove all empty items on the list
modes_arr[:,i-header_lines]=misc.remove_items(parts[2].split(' '),'')
return modes_arr
class UserWind(object):
"""
"""
def __init__(self):
pass
def __call__(self, z_h, r_blade_tip, a_phi=None, shear_exp=None, nr_hor=3,
nr_vert=20, h_ME=500.0, fname=None, wdir=None):
"""
Parameters
----------
z_h : float
Hub height
r_blade_tip : float
Blade tip radius
a_phi : float, default=None
:math:`a_{\\varphi} \\approx 0.5` parameter for the modified
Ekman veer distribution. Values vary between -1.2 and 0.5.
shear_exp : float, default=None
nr_vert : int, default=3
nr_hor : int, default=20
h_ME : float, default=500
Modified Ekman parameter. Take roughly 500 for off shore sites,
1000 for on shore sites.
fname : str, default=None
When specified, the HAWC2 user defined veer input file will be
written.
wdir : float, default=None
A constant veer angle, or yaw angle. Equivalent to setting the
wind direction. Angle in degrees.
Returns
-------
None
"""
x, z = self.create_coords(z_h, r_blade_tip, nr_vert=nr_vert,
nr_hor=nr_hor)
if a_phi is not None:
phi_rad = self.veer_ekman_mod(z, z_h, h_ME=h_ME, a_phi=a_phi)
assert len(phi_rad) == nr_vert
else:
nr_vert = len(z)
phi_rad = np.zeros((nr_vert,))
# add any yaw error on top of
if wdir is not None:
# because wdir cw positive, and phi veer ccw positive
phi_rad -= (wdir*np.pi/180.0)
u, v, w, xx, zz = self.decompose_veer(phi_rad, x, z)
# scale the shear on top of that
if shear_exp is not None:
shear = self.shear_powerlaw(zz, z_h, shear_exp)
uu = u*shear[:,np.newaxis]
vv = v*shear[:,np.newaxis]
ww = w*shear[:,np.newaxis]
# and write to a file
if fname is not None:
self.write_user_defined_shear(fname, uu, vv, ww, xx, zz)
def create_coords(self, z_h, r_blade_tip, nr_vert=3, nr_hor=20):
"""
Utility to create the coordinates of the wind field based on hub heigth
and blade length.
"""
# take 15% extra space after the blade tip
z = np.linspace(0, z_h + r_blade_tip*1.15, nr_vert)
# along the horizontal, coordinates with 0 at the rotor center
x = np.linspace(-r_blade_tip*1.15, r_blade_tip*1.15, nr_hor)
return x, z
def shear_powerlaw(self, z, z_ref, a):
profile = np.power(z/z_ref, a)
# when a negative, make sure we return zero and not inf
profile[np.isinf(profile)] = 0.0
return profile
def veer_ekman_mod(self, z, z_h, h_ME=500.0, a_phi=0.5):
"""
Modified Ekman veer profile, as defined by Mark C. Kelly in email on
10 October 2014 15:10 (RE: veer profile)
.. math::
\\varphi(z) - \\varphi(z_H) \\approx a_{\\varphi}
e^{-\sqrt{z_H/h_{ME}}}
\\frac{z-z_H}{\sqrt{z_H*h_{ME}}}
\\left( 1 - \\frac{z-z_H}{2 \sqrt{z_H h_{ME}}}
- \\frac{z-z_H}{4z_H} \\right)
where:
:math:`h_{ME} \\equiv \\frac{\\kappa u_*}{f}`
and :math:`f = 2 \Omega \sin \\varphi` is the coriolis parameter,
and :math:`\\kappa = 0.41` as the von Karman constant,
and :math:`u_\\star = \\sqrt{\\frac{\\tau_w}{\\rho}}` friction velocity.
For on shore, :math:`h_{ME} \\approx 1000`, for off-shore,
:math:`h_{ME} \\approx 500`
:math:`a_{\\varphi} \\approx 0.5`
Parameters
----------
:math:`a_{\\varphi} \\approx 0.5` parameter for the modified
Ekman veer distribution. Values vary between -1.2 and 0.5.
returns
-------
phi_rad : ndarray
veer angle in radians
"""
t1 = np.exp(-math.sqrt(z_h / h_ME))
t2 = (z - z_h) / math.sqrt(z_h * h_ME)
t3 = ( 1.0 - (z-z_h)/(2.0*math.sqrt(z_h*h_ME)) - (z-z_h)/(4.0*z_h) )
return a_phi * t1 * t2 * t3
def decompose_veer(self, phi_rad, x, z):
"""
Convert a veer angle into u, v, and w components, ready for the
HAWC2 user defined veer input file.
Paramters
---------
phi_rad : ndarray
veer angle in radians
method : str, default=linear
'linear' for a linear veer, 'ekman_mod' for modified ekman method
Returns
-------
u, v, w, v_coord, w_coord
"""
nr_hor = len(x)
nr_vert = len(z)
assert len(phi_rad) == nr_vert
tan_phi = np.tan(phi_rad)
# convert veer angles to veer components in v, u. Make sure the
# normalized wind speed remains 1!
# u = sympy.Symbol('u')
# v = sympy.Symbol('v')
# tan_phi = sympy.Symbol('tan_phi')
# eq1 = u**2.0 + v**2.0 - 1.0
# eq2 = (tan_phi*u/v) - 1.0
# sol = sympy.solvers.solve([eq1, eq2], [u,v], dict=True)
# # proposed solution is:
# u2 = np.sqrt(tan_phi**2/(tan_phi**2 + 1.0))/tan_phi
# v2 = np.sqrt(tan_phi**2/(tan_phi**2 + 1.0))
# # but that gives the sign switch wrong, simplify/rewrite to:
u = np.sqrt(1.0/(tan_phi**2 + 1.0))
v = np.sqrt(1.0/(tan_phi**2 + 1.0))*tan_phi
# verify they are actually the same but the sign:
# assert np.allclose(np.abs(u), np.abs(u2))
# assert np.allclose(np.abs(v), np.abs(v2))
u_full = u[:,np.newaxis] + np.zeros((3,))[np.newaxis,:]
v_full = v[:,np.newaxis] + np.zeros((3,))[np.newaxis,:]
w_full = np.zeros((nr_vert,nr_hor))
return u_full, v_full, w_full, x, z
def load_user_defined_veer(self, fname):
"""
Load a user defined veer and shear file as used for HAWC2
Returns
-------
u_comp, v_comp, w_comp, v_coord, w_coord, phi_deg
"""
blok = 0
bloks = {}
with opent(fname) as f:
for i, line in enumerate(f.readlines()):
if line.strip()[0] == '#' and blok > 0:
bloks[blok] = i
blok += 1
elif line.strip()[0] == '#':
continue
elif blok == 0:
items = line.split(' ')
items = misc.remove_items(items, '')
nr_hor, nr_vert = int(items[0]), int(items[1])
blok += 1
# nr_lines = i
k = nr_hor + 4*nr_vert + 7
v_comp = np.genfromtxt(fname, skiprows=3, skip_footer=i-3-3-nr_vert)
u_comp = np.genfromtxt(fname, skiprows=3+1+nr_vert,
skip_footer=i-3-3-nr_vert*2)
w_comp = np.genfromtxt(fname, skiprows=3+2+nr_vert*2,
skip_footer=i-3-3-nr_vert*3)
v_coord = np.genfromtxt(fname, skiprows=3+3+nr_vert*3,
skip_footer=i-3-3-nr_vert*3-3)
w_coord = np.genfromtxt(fname, skiprows=3+3+nr_vert*3+4,
skip_footer=i-k)
phi_deg = np.arctan(v_comp[:,0]/u_comp[:,0])*180.0/np.pi
return u_comp, v_comp, w_comp, v_coord, w_coord, phi_deg
def write_user_defined_shear(self, fname, u, v, w, v_coord, w_coord,
fmt_uvw='% 08.05f', fmt_coord='% 8.02f'):
"""
"""
nr_hor = len(v_coord)
nr_vert = len(w_coord)
try:
assert u.shape == v.shape
assert u.shape == w.shape
assert u.shape[0] == nr_vert
assert u.shape[1] == nr_hor
except AssertionError:
raise ValueError('u, v, w shapes should be consistent with '
'nr_hor and nr_vert: u.shape: %s, nr_hor: %i, '
'nr_vert: %i' % (str(u.shape), nr_hor, nr_vert))
# and create the input file
with open(fname, 'wb') as fid:
fid.write(b'# User defined shear file\n')
fid.write(b'%i %i # nr_hor (v), nr_vert (w)\n' % (nr_hor, nr_vert))
h1 = b'normalized with U_mean, nr_hor (v) rows, nr_vert (w) columns'
fid.write(b'# v component, %s\n' % h1)
np.savetxt(fid, v, fmt=fmt_uvw, delimiter=' ')
fid.write(b'# u component, %s\n' % h1)
np.savetxt(fid, u, fmt=fmt_uvw, delimiter=' ')
fid.write(b'# w component, %s\n' % h1)
np.savetxt(fid, w, fmt=fmt_uvw, delimiter=' ')
h2 = b'# v coordinates (along the horizontal, nr_hor, 0 rotor center)'
fid.write(b'%s\n' % h2)
np.savetxt(fid, v_coord.reshape((v_coord.size,1)), fmt=fmt_coord)
h3 = b'# w coordinates (zero is at ground level, height, nr_hor)'
fid.write(b'%s\n' % h3)
np.savetxt(fid, w_coord.reshape((w_coord.size,1)), fmt=fmt_coord)
class WindProfiles(object):
def __init__(self):
pass
def powerlaw(self, z, z_ref, a):
profile = np.power(z/z_ref, a)
# when a negative, make sure we return zero and not inf
profile[np.isinf(profile)] = 0.0
return profile
def veer_ekman_mod(self, z, z_h, h_ME=500.0, a_phi=0.5):
"""
Modified Ekman veer profile, as defined by Mark C. Kelly in email on
10 October 2014 15:10 (RE: veer profile)
.. math::
\\varphi(z) - \\varphi(z_H) \\approx a_{\\varphi}
e^{-\sqrt{z_H/h_{ME}}}
\\frac{z-z_H}{\sqrt{z_H*h_{ME}}}
\\left( 1 - \\frac{z-z_H}{2 \sqrt{z_H h_{ME}}}
- \\frac{z-z_H}{4z_H} \\right)
where:
:math:`h_{ME} \\equiv \\frac{\\kappa u_*}{f}`
and :math:`f = 2 \Omega \sin \\varphi` is the coriolis parameter,
and :math:`\\kappa = 0.41` as the von Karman constant,
and :math:`u_\\star = \\sqrt{\\frac{\\tau_w}{\\rho}}` friction velocity.
For on shore, :math:`h_{ME} \\approx 1000`, for off-shore,
:math:`h_{ME} \\approx 500`
:math:`a_{\\varphi} \\approx 0.5`
Parameters
----------
:math:`a_{\\varphi} \\approx 0.5` parameter for the modified
Ekman veer distribution. Values vary between -1.2 and 0.5.
returns
-------
phi_rad : ndarray
veer angle in radians as function of height
"""
t1 = np.exp(-math.sqrt(z_h / h_ME))
t2 = (z - z_h) / math.sqrt(z_h * h_ME)
t3 = ( 1.0 - (z-z_h)/(2.0*math.sqrt(z_h*h_ME)) - (z-z_h)/(4.0*z_h) )
return a_phi * t1 * t2 * t3
class Turbulence(object):
def __init__(self):
pass
def read_hawc2(self, fpath, shape):
"""
Read the HAWC2 turbulence format
"""
fid = open(fpath, 'rb')
tmp = np.fromfile(fid, 'float32', shape[0]*shape[1]*shape[2])
turb = np.reshape(tmp, shape)
return turb
def read_bladed(self, fpath, basename):
fid = open(fpath + basename + '.wnd', 'rb')
R1 = struct.unpack('h', fid.read(2))[0]
R2 = struct.unpack('h', fid.read(2))[0]
turb = struct.unpack('i', fid.read(4))[0]
lat = struct.unpack('f', fid.read(4))[0]
rough = struct.unpack('f', fid.read(4))[0]
refh = struct.unpack('f', fid.read(4))[0]
longti = struct.unpack('f', fid.read(4))[0]
latti = struct.unpack('f', fid.read(4))[0]
vertti = struct.unpack('f', fid.read(4))[0]
dv = struct.unpack('f', fid.read(4))[0]
dw = struct.unpack('f', fid.read(4))[0]
du = struct.unpack('f', fid.read(4))[0]
halfalong = struct.unpack('i', fid.read(4))[0]
mean_ws = struct.unpack('f', fid.read(4))[0]
VertLongComp = struct.unpack('f', fid.read(4))[0]
LatLongComp = struct.unpack('f', fid.read(4))[0]
LongLongComp = struct.unpack('f', fid.read(4))[0]
Int = struct.unpack('i', fid.read(4))[0]
seed = struct.unpack('i', fid.read(4))[0]
VertGpNum = struct.unpack('i', fid.read(4))[0]
LatGpNum = struct.unpack('i', fid.read(4))[0]
VertLatComp = struct.unpack('f', fid.read(4))[0]
LatLatComp = struct.unpack('f', fid.read(4))[0]
LongLatComp = struct.unpack('f', fid.read(4))[0]
VertVertComp = struct.unpack('f', fid.read(4))[0]
LatVertComp = struct.unpack('f', fid.read(4))[0]
LongVertComp = struct.unpack('f', fid.read(4))[0]
points = np.fromfile(fid, 'int16', 2*halfalong*VertGpNum*LatGpNum*3)
fid.close()
return points
def convert2bladed(self, fpath, basename, shape=(4096,32,32)):
"""
Convert turbulence box to BLADED format
"""
u = self.read_hawc2(fpath + basename + 'u.bin', shape)
v = self.read_hawc2(fpath + basename + 'v.bin', shape)
w = self.read_hawc2(fpath + basename + 'w.bin', shape)
# mean velocity components at the center of the box
v1, v2 = (shape[1]/2)-1, shape[1]/2
w1, w2 = (shape[2]/2)-1, shape[2]/2
ucent = (u[:,v1,w1] + u[:,v1,w2] + u[:,v2,w1] + u[:,v2,w2]) / 4.0
vcent = (v[:,v1,w1] + v[:,v1,w2] + v[:,v2,w1] + v[:,v2,w2]) / 4.0
wcent = (w[:,v1,w1] + w[:,v1,w2] + w[:,v2,w1] + w[:,v2,w2]) / 4.0
# FIXME: where is this range 351:7374 coming from?? The original script
# considered a box of lenght 8192
umean = np.mean(ucent[351:7374])
vmean = np.mean(vcent[351:7374])
wmean = np.mean(wcent[351:7374])
ustd = np.std(ucent[351:7374])
vstd = np.std(vcent[351:7374])
wstd = np.std(wcent[351:7374])
# gives a slight different outcome, but that is that significant?
# umean = np.mean(u[351:7374,15:17,15:17])
# vmean = np.mean(v[351:7374,15:17,15:17])
# wmean = np.mean(w[351:7374,15:17,15:17])
# this is wrong since we want the std on the center point
# ustd = np.std(u[351:7374,15:17,15:17])
# vstd = np.std(v[351:7374,15:17,15:17])
# wstd = np.std(w[351:7374,15:17,15:17])
iu = np.zeros(shape)
iv = np.zeros(shape)
iw = np.zeros(shape)
iu[:,:,:] = (u - umean)/ustd*1000.0
iv[:,:,:] = (v - vmean)/vstd*1000.0
iw[:,:,:] = (w - wmean)/wstd*1000.0
# because MATLAB and Octave do a round when casting from float to int,
# and Python does a floor, we have to round first
np.around(iu, decimals=0, out=iu)
np.around(iv, decimals=0, out=iv)
np.around(iw, decimals=0, out=iw)
return iu.astype(np.int16), iv.astype(np.int16), iw.astype(np.int16)
def write_bladed(self, fpath, basename, shape):
"""
Write turbulence BLADED file
"""
# TODO: get these parameters from a HAWC2 input file
seed = 6
mean_ws = 11.4
turb = 3
R1 = -99
R2 = 4
du = 0.974121094
dv = 4.6875
dw = 4.6875
longti = 14
latti = 9.8
vertti = 7
iu, iv, iw = self.convert2bladed(fpath, basename, shape=shape)
fid = open(fpath + basename + '.wnd', 'wb')
fid.write(struct.pack('h', R1)) # R1
fid.write(struct.pack('h', R2)) # R2
fid.write(struct.pack('i', turb)) # Turb
fid.write(struct.pack('f', 999)) # Lat
fid.write(struct.pack('f', 999)) # rough
fid.write(struct.pack('f', 999)) # refh
fid.write(struct.pack('f', longti)) # LongTi
fid.write(struct.pack('f', latti)) # LatTi
fid.write(struct.pack('f', vertti)) # VertTi
fid.write(struct.pack('f', dv)) # VertGpSpace
fid.write(struct.pack('f', dw)) # LatGpSpace
fid.write(struct.pack('f', du)) # LongGpSpace
fid.write(struct.pack('i', shape[0]/2)) # HalfAlong
fid.write(struct.pack('f', mean_ws)) # meanWS
fid.write(struct.pack('f', 999.)) # VertLongComp
fid.write(struct.pack('f', 999.)) # LatLongComp
fid.write(struct.pack('f', 999.)) # LongLongComp
fid.write(struct.pack('i', 999)) # Int
fid.write(struct.pack('i', seed)) # Seed
fid.write(struct.pack('i', shape[1])) # VertGpNum
fid.write(struct.pack('i', shape[2])) # LatGpNum
fid.write(struct.pack('f', 999)) # VertLatComp
fid.write(struct.pack('f', 999)) # LatLatComp
fid.write(struct.pack('f', 999)) # LongLatComp
fid.write(struct.pack('f', 999)) # VertVertComp
fid.write(struct.pack('f', 999)) # LatVertComp
fid.write(struct.pack('f', 999)) # LongVertComp
# fid.flush()
# bladed2 = np.ndarray((shape[0], shape[2], shape[1], 3), dtype=np.int16)
# for i in xrange(shape[0]):
# for k in xrange(shape[1]):
# for j in xrange(shape[2]):
# fid.write(struct.pack('i', iu[i, shape[1]-j-1, k]))
# fid.write(struct.pack('i', iv[i, shape[1]-j-1, k]))
# fid.write(struct.pack('i', iw[i, shape[1]-j-1, k]))
# bladed2[i,k,j,0] = iu[i, shape[1]-j-1, k]
# bladed2[i,k,j,1] = iv[i, shape[1]-j-1, k]
# bladed2[i,k,j,2] = iw[i, shape[1]-j-1, k]
# re-arrange array for bladed format
bladed = np.ndarray((shape[0], shape[2], shape[1], 3), dtype=np.int16)
bladed[:,:,:,0] = iu[:,::-1,:]
bladed[:,:,:,1] = iv[:,::-1,:]
bladed[:,:,:,2] = iw[:,::-1,:]
bladed_swap_view = bladed.swapaxes(1,2)
bladed_swap_view.tofile(fid, format='%int16')
fid.flush()
fid.close()
class Bladed(object):
def __init__(self):
"""
Some BLADED results I have seen are just weird text files. Convert
them to a more convienent format.
path/to/file
channel 1 description
col a name/unit col b name/unit
a0 b0
a1 b1
...
path/to/file
channel 2 description
col a name/unit col b name/unit
...
"""
pass
def infer_format(self, lines):
"""
Figure out how many channels and time steps are included
"""
count = 1
for line in lines[1:]:
if line == lines[0]:
break
count += 1
iters = count - 3
chans = len(lines) / (iters + 3)
return int(chans), int(iters)
def read(self, fname, chans=None, iters=None, enc='cp1252'):
"""
Parameters
----------
fname : str
chans : int, default=None
iters : int, default=None
enc : str, default='cp1252'
character encoding of the source file. Usually BLADED is used on
windows so Western-European windows encoding is a safe bet.
"""
with codecs.opent(fname, 'r', enc) as f:
lines = f.readlines()
nrl = len(lines)
if chans is None and iters is None:
chans, iters = self.infer_format(lines)
if iters is not None:
chans = int(nrl / (iters + 3))
if chans is not None:
iters = int((nrl / chans) - 3)
# file_head = [ [k[:-2],0] for k in lines[0:nrl:iters+3] ]
# chan_head = [ [k[:-2],0] for k in lines[1:nrl:iters+3] ]
# cols_head = [ k.split('\t')[:2] for k in lines[2:nrl:iters+3] ]
data = {}
for k in range(chans):
# take the column header from the 3 comment line, but
head = lines[2 + (3 + iters)*k][:-2].split('\t')[1].encode('utf-8')
i0 = 3 + (3 + iters)*k
i1 = i0 + iters
data[head] = np.array([k[:-2].split('\t')[1] for k in lines[i0:i1:1]])
data[head] = data[head].astype(np.float64)
time = np.array([k[:-2].split('\t')[0] for k in lines[i0:i1:1]])
df = pd.DataFrame(data, index=time.astype(np.float64))
df.index.name = lines[0][:-2]
return df
if __name__ == '__main__':
pass