text: # -*- coding: utf-8 -*-
  """
  Class for reading/writing analog signals in a text file.
  Each columns represents a AnalogSignal. All AnalogSignal have the same sampling rate.
  Covers many case when part of a file can be viewed as a CSV format.
  
  Supported : Read/Write
  
  Author: sgarcia
  
  """
  
  import csv
  import os
  
  import numpy as np
  import quantities as pq
  
  from neo.io.baseio import BaseIO
  from neo.core import AnalogSignal, Segment
  
  
  class AsciiSignalIO(BaseIO):
      """
  
      Class for reading signal in generic ascii format.
      Columns respresents signal. They share all the same sampling rate.
      The sampling rate is externally known or the first columns could hold the time
      vector.
  
      Usage:
          >>> from neo import io
          >>> r = io.AsciiSignalIO(filename='File_asciisignal_2.txt')
          >>> seg = r.read_segment(lazy=False, cascade=True)
          >>> print seg.analogsignals
          [<AnalogSignal(array([ 39.0625    ,   0.        ,   0.        , ..., -26.85546875 ...
  
      """
  
      is_readable        = True
      is_writable        = True
  
      supported_objects  = [ Segment , AnalogSignal]
      readable_objects   = [ Segment]
      writeable_objects  = [Segment]
  
      has_header         = False
      is_streameable     = False
  
      read_params        = {
                              Segment : [
                                          ('delimiter' , {'value' :  '\t', 'possible' : ['\t' , ' ' , ',' , ';'] }) ,
                                          ('usecols' , { 'value' : None , 'type' : int } ),
                                          ('skiprows' , { 'value' :0 } ),
                                          ('timecolumn' , { 'value' : None, 'type' : int } ) ,
                                          ('unit' , { 'value' : 'V', } ),
                                          ('sampling_rate' , { 'value' : 1000., } ),
                                          ('t_start' , { 'value' : 0., } ),
                                          ('method' , { 'value' : 'homemade', 'possible' : ['genfromtxt' , 'csv' , 'homemade' ] }) ,
                                          ]
                              }
      write_params       = {
                              Segment : [
                                          ('delimiter' , {'value' :  '\t', 'possible' : ['\t' , ' ' , ',' , ';'] }) ,
                                          ('writetimecolumn' , { 'value' : True,  } ) ,
                                          ]
                              }
  
      name               = None
      extensions          = [ 'txt' , 'asc', ]
  
      mode = 'file'
  
      def __init__(self , filename = None) :
          """
          This class read/write AnalogSignal in a text file.
          Each signal is a column.
          One of the column can be the time vector
  
          Arguments:
              filename : the filename to read/write
          """
          BaseIO.__init__(self)
          self.filename = filename
  
      def read_segment(self,
                                          lazy = False,
                                          cascade = True,
                                          delimiter = '\t',
                                          usecols = None,
                                          skiprows =0,
  
                                          timecolumn = None,
                                          sampling_rate = 1.*pq.Hz,
                                          t_start = 0.*pq.s,
  
                                          unit = pq.V,
  
                                          method = 'genfromtxt',
  
                                          ):
          """
          Arguments:
              delimiter  :  columns delimiter in file  '\t' or one space or two space or ',' or ';'
              usecols : if None take all columns otherwise a list for selected columns
              skiprows : skip n first lines in case they contains header informations
              timecolumn :  None or a valid int that point the time vector
              samplerate : the samplerate of signals if timecolumn is not None this is not take in account
              t_start : time of the first sample
              unit : unit of AnalogSignal can be a str or directly a Quantities
  
              method :  'genfromtxt' or 'csv' or 'homemade'
                          in case of bugs you can try one of this methods
  
                          'genfromtxt' use numpy.genfromtxt
                          'csv' use cvs module
                          'homemade' use a intuitive more robust but slow method
  
          """
          seg = Segment(file_origin = os.path.basename(self.filename))
          if not cascade:
              return seg
  
          if type(sampling_rate) == float or type(sampling_rate)==int:
              # if not quantitities Hz by default
              sampling_rate = sampling_rate*pq.Hz
  
          if type(t_start) == float or type(t_start)==int:
              # if not quantitities s by default
              t_start = t_start*pq.s
  
          unit = pq.Quantity(1, unit)
  
  
  
          #loadtxt
          if method == 'genfromtxt' :
              sig = np.genfromtxt(self.filename,
                                          delimiter = delimiter,
                                          usecols = usecols ,
                                          skiprows = skiprows,
                                          dtype = 'f')
              if len(sig.shape) ==1:
                  sig = sig[:, np.newaxis]
          elif method == 'csv' :
              tab = [l for l in  csv.reader( file(self.filename,'rU') , delimiter = delimiter ) ]
              tab = tab[skiprows:]
              sig = np.array( tab , dtype = 'f')
          elif method == 'homemade' :
              fid = open(self.filename,'rU')
              for l in range(skiprows):
                  fid.readline()
              tab = [ ]
              for line in fid.readlines():
                  line = line.replace('\r','')
                  line = line.replace('\n','')
                  l = line.split(delimiter)
                  while '' in l :
                      l.remove('')
                  tab.append(l)
              sig = np.array( tab , dtype = 'f')
  
          if timecolumn is not None:
              sampling_rate = 1./np.mean(np.diff(sig[:,timecolumn])) * pq.Hz
              t_start = sig[0,timecolumn] * pq.s
  
  
  
          for i in range(sig.shape[1]) :
              if timecolumn == i : continue
              if usecols is not None and i not in usecols: continue
  
              if lazy:
                  signal = [ ]*unit
              else:
                  signal = sig[:,i]*unit
  
              anaSig = AnalogSignal(signal, sampling_rate=sampling_rate,
                                    t_start=t_start, channel_index=i,
                                    name='Column %d'%i)
              if lazy:
                  anaSig.lazy_shape = sig.shape
              seg.analogsignals.append( anaSig )
  
          seg.create_many_to_one_relationship()
          return seg
  
      def write_segment(self, segment,
                                  delimiter = '\t',
  
                                  skiprows =0,
                                  writetimecolumn = True,
  
                                  ):
          """
          Write a segment and AnalogSignal in a text file.
  
           **Arguments**
              delimiter  :  columns delimiter in file  '\t' or one space or two space or ',' or ';'
              writetimecolumn :  True or Flase write time vector as first column
          """
          if skiprows:
              raise NotImplementedError('skiprows values other than 0 are not ' +
                                        'supported')
          l = [ ]
          if writetimecolumn is not None:
              l.append(segment.analogsignals[0].times[:, np.newaxis])
          for anaSig in segment.analogsignals:
              l.append(anaSig.magnitude[:, np.newaxis])
          sigs = np.concatenate(l, axis=1)
          #print sigs.shape
          np.savetxt(self.filename , sigs , delimiter = delimiter)
  
  
  