from __future__ import absolute_import

import collections
import numpy as np


class FTS(object):
    """Class to describe the Fourier Transform Spectrometer.
    """

    def __init__(self, parameters):
        self.parameters = parameters

        print parameters['substages']['FTSpectrograph'].keys()

        # read params from FTSpectrograph sheet
        ftsparm = parameters['substages']['FTSpectrograph']

        self.result = collections.OrderedDict()

        # slightly awkward getting values as they are keyed by 
        # the row number in the parent spreadsheet
        row = ftsparm['wn min [cm-1]'].keys()[0]
        self.result['wnmin'] = wnmin = ftsparm['wn min [cm-1]'][row]
        self.result['wnmax'] = wnmax = ftsparm['wn max [cm-1]'][row]
        self.result['specres'] = specres = ftsparm['Spectral Res'][row]

        # read params from FTSMechanical sheet
        ftsparm = parameters['substages']['FTSMechanical']
        row = ftsparm['V drive [cm/s]'].keys()[0]
        self.result['vdrive'] = ftsparm['V drive [cm/s]'][row]

        # derived parameters
        self.result['delta_wn'] = delta_wn = wnmin / specres
        self.result['delta_opd'] = 1.0 / (2.0 * wnmax * 100.0)
        self.result['ftsnsample'] = nsample = np.ceil(wnmax / delta_wn)
        self.result['opd_max'] = 1.0 / (2.0 * delta_wn)

        # spectral points sampled by FTS (wn=wavenumber[cm-1]
        # lambda=wavelength[m])
        i = np.arange(int(nsample))
        fts_wn = wnmax * i / float(nsample-1)
        # crop the spectrum below wnmin. This does not seem to be done
        # in the matlab version.
        fts_wn = fts_wn[fts_wn > wnmin]
        fts_lambda = 1.0 / (fts_wn * 100.0)

        self.result['fts_wn'] = fts_wn
        self.result['fts_lambda'] = fts_lambda

        print 'more to add in FTSDrive'

    def run(self):
        print 'FTS.run'        
        return self.result

    def __repr__(self):
        return '''
FTS:
  wnmin    : {wnmin}
  wnmax    : {wnmax}
  specres  : {specres}
  delta wn : {delta_wn}
  delta opd: {delta_opd}
  nsample  : {nsample}
  max opd  : {opd_max}
'''.format(
          wnmin=self.result['wnmin'],
          wnmax=self.result['wnmax'],
          specres=self.result['specres'],
          delta_wn=self.result['delta_wn'],
          delta_opd=self.result['delta_opd'],
          nsample=self.result['nsample'],
          opd_max=self.result['opd_max'])