diff --git a/wetb/prepost/windIO.py b/wetb/prepost/windIO.py
index 69fc6926133cc86dfc3380c99d14f17319f7b05e..92aacaaf0a5882603f514b5ab78421ff9da65420 100755
--- a/wetb/prepost/windIO.py
+++ b/wetb/prepost/windIO.py
@@ -49,9 +49,11 @@ from wetb.prepost import misc
# 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
+from wetb.hawc2.Hawc2io import ReadHawc2
+from wetb.fatigue_tools import fatigue
-class LoadResults(object):
+class LoadResults(ReadHawc2):
"""Read a HAWC2 result data file
Usage:
@@ -133,211 +135,17 @@ class LoadResults(object):
# FIXME: since HAWC2 will always have lower case output files, convert
# any wrongly used upper case letters to lower case here
self.file_name = file_name.lower()
- 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 = []
+ FileName = os.path.join(self.file_path, self.file_name)
+ print('readdata', readdata)
+ ReadOnly = 0 if readdata else 1
+ super(LoadResults, self).__init__(FileName, ReadOnly=ReadOnly)
+ ChVec = [] if usecols is None else usecols
+ self.sig = super(LoadResults, self).__call__(ChVec=ChVec)
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
@@ -996,7 +804,7 @@ class LoadResults(object):
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):
+ def calc_stats(self, sig, i0=0, i1=None):
stats = {}
# calculate the statistics values: