| 1 | from __future__ import absolute_import
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| 2 |
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| 3 | import collections
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| 4 | import numpy as np
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| 5 |
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| 6 |
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| 7 | class FTS(object):
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| 8 | """Class to describe the Fourier Transform Spectrometer.
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| 9 | """
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| 10 |
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| 11 | def __init__(self, parameters):
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| 12 | self.parameters = parameters
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| 13 |
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| 14 | print parameters['substages']['FTSpectrograph'].keys()
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| 15 |
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| 16 | # read params from FTSpectrograph sheet
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| 17 | ftsparm = parameters['substages']['FTSpectrograph']
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| 18 |
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| 19 | self.result = collections.OrderedDict()
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| 20 |
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| 21 | # slightly awkward getting values as they are keyed by
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| 22 | # the row number in the parent spreadsheet
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| 23 | row = ftsparm['wn min [cm-1]'].keys()[0]
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| 24 | self.result['wnmin'] = wnmin = ftsparm['wn min [cm-1]'][row]
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| 25 | self.result['wnmax'] = wnmax = ftsparm['wn max [cm-1]'][row]
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| 26 | self.result['specres'] = specres = ftsparm['Spectral Res'][row]
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| 27 |
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| 28 | # read params from FTSMechanical sheet
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| 29 | ftsparm = parameters['substages']['FTSMechanical']
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| 30 | row = ftsparm['V drive [cm/s]'].keys()[0]
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| 31 | self.result['vdrive'] = ftsparm['V drive [cm/s]'][row]
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| 32 |
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| 33 | # derived parameters
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| 34 | self.result['delta_wn'] = delta_wn = wnmin / specres
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| 35 | self.result['delta_opd'] = 1.0 / (2.0 * wnmax * 100.0)
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| 36 | self.result['ftsnsample'] = nsample = np.ceil(wnmax / delta_wn)
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| 37 | self.result['opd_max'] = 1.0 / (2.0 * delta_wn)
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| 38 |
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| 39 | # spectral points sampled by FTS (wn=wavenumber[cm-1]
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| 40 | # lambda=wavelength[m])
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| 41 | i = np.arange(int(nsample))
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| 42 | fts_wn = wnmax * i / float(nsample-1)
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| 43 | # crop the spectrum below wnmin. This does not seem to be done
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| 44 | # in the matlab version.
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| 45 | fts_wn = fts_wn[fts_wn > wnmin]
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| 46 | fts_lambda = 1.0 / (fts_wn * 100.0)
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| 47 |
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| 48 | self.result['fts_wn'] = fts_wn
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| 49 | self.result['fts_lambda'] = fts_lambda
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| 50 |
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| 51 | print 'more to add in FTSDrive'
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| 52 |
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| 53 | def run(self):
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| 54 | print 'FTS.run'
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| 55 | return self.result
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| 56 |
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| 57 | def __repr__(self):
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| 58 | return '''
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| 59 | FTS:
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| 60 | wnmin : {wnmin}
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| 61 | wnmax : {wnmax}
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| 62 | specres : {specres}
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| 63 | delta wn : {delta_wn}
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| 64 | delta opd: {delta_opd}
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| 65 | nsample : {nsample}
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| 66 | max opd : {opd_max}
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| 67 | '''.format(
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| 68 | wnmin=self.result['wnmin'],
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| 69 | wnmax=self.result['wnmax'],
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| 70 | specres=self.result['specres'],
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| 71 | delta_wn=self.result['delta_wn'],
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| 72 | delta_opd=self.result['delta_opd'],
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| 73 | nsample=self.result['nsample'],
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| 74 | opd_max=self.result['opd_max'])
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| 75 |
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