be_odf.py 47.7 KB
Newer Older
Somnath, Suhas's avatar
Somnath, Suhas committed
1
2
3
4
5
6
7
# -*- coding: utf-8 -*-
"""
Created on Tue Nov  3 15:24:12 2015

@author: Suhas Somnath, Stephen Jesse
"""

8
from __future__ import division, print_function, absolute_import, unicode_literals
9

Somnath, Suhas's avatar
Somnath, Suhas committed
10
from os import path, listdir, remove
11
from warnings import warn
12

Somnath, Suhas's avatar
Somnath, Suhas committed
13
14
import numpy as np
from scipy.io.matlab import loadmat  # To load parameters stored in Matlab .mat file
15

16
from .df_utils.be_utils import trimUDVS, getSpectroscopicParmLabel, parmsToDict, generatePlotGroups, \
17
    createSpecVals, requires_conjugate, nf32
Somnath, Suhas's avatar
Somnath, Suhas committed
18
from .translator import Translator
19
from .utils import generate_dummy_main_parms, build_ind_val_dsets
Somnath, Suhas's avatar
Somnath, Suhas committed
20
21
22
from ..hdf_utils import getH5DsetRefs, linkRefs, calc_chunks
from ..io_hdf5 import ioHDF5
from ..microdata import MicroDataGroup, MicroDataset
23

Chris Smith's avatar
Chris Smith committed
24
25
26
# nf32 = np.dtype([('super_band', np.float32), ('inter_bin_band', np.float32),
#                  ('sub_band', np.float32)])

27

28

Somnath, Suhas's avatar
Somnath, Suhas committed
29
30
31
32
33
class BEodfTranslator(Translator):
    """
    Translates either the Band Excitation (BE) scan or Band Excitation 
    Polarization Switching (BEPS) data format from the old data format(s) to .h5
    """
Chris Smith's avatar
Chris Smith committed
34
35
36
37
38
    def __init__(self, *args, **kwargs):
        super(BEodfTranslator, self).__init__(*args, **kwargs)

        self.hdf = None
        self.h5_raw = None
39
        self.num_rand_spectra = kwargs.pop('num_rand_spectra', 1000)
Chris Smith's avatar
Chris Smith committed
40

Somnath, Suhas's avatar
Somnath, Suhas committed
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
    def translate(self, file_path, show_plots=True, save_plots=True, do_histogram=False):
        """
        Translates .dat data file(s) to a single .h5 file
        
        Parameters
        -------------
        file_path : String / Unicode
            Absolute file path for one of the data files. 
            It is assumed that this file is of the OLD data format.
        show_plots : (optional) Boolean
            Whether or not to show intermediate plots
        save_plots : (optional) Boolean
            Whether or not to save plots to disk
        do_histogram : (optional) Boolean
            Whether or not to construct histograms to visualize data quality. Note - this takes a fair amount of time
            
        Returns
        ----------
        h5_path : String / Unicode
            Absolute path of the resultant .h5 file
        """
        (folder_path, basename) = path.split(file_path)
63
        (basename, path_dict) = self._parse_file_path(file_path)
Somnath, Suhas's avatar
Somnath, Suhas committed
64
            
Somnath, Suhas's avatar
Somnath, Suhas committed
65
        h5_path = path.join(folder_path, basename + '.h5')
Somnath, Suhas's avatar
Somnath, Suhas committed
66
67
68
69
70
71
72
        tot_bins_multiplier = 1
        udvs_denom = 2
        
        if 'parm_txt' in path_dict.keys():
            (isBEPS,parm_dict) = parmsToDict(path_dict['parm_txt'])
        elif 'old_mat_parms' in path_dict.keys():
            isBEPS = True
Somnath, Suhas's avatar
Somnath, Suhas committed
73
            parm_dict = self.__get_parms_from_old_mat(path_dict['old_mat_parms'])
Somnath, Suhas's avatar
Somnath, Suhas committed
74
        else:
75
            raise IOError('No parameters file found! Cannot translate this dataset!')
Somnath, Suhas's avatar
Somnath, Suhas committed
76
77
78
79
80
81
82
83
84
          
        ignored_plt_grps = []
        if isBEPS:
            parm_dict['data_type'] = 'BEPSData'
            
            field_mode = parm_dict['VS_measure_in_field_loops']
            std_expt = parm_dict['VS_mode'] != 'load user defined VS Wave from file'
            
            if not std_expt:
85
                raise ValueError('This translator does not handle user defined voltage spectroscopy')
Somnath, Suhas's avatar
Somnath, Suhas committed
86
87
88
            
            spec_label = getSpectroscopicParmLabel(parm_dict['VS_mode']) 
            
Somnath, Suhas's avatar
Somnath, Suhas committed
89
            if parm_dict['VS_mode'] in ['DC modulation mode', 'current mode']:
Somnath, Suhas's avatar
Somnath, Suhas committed
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
                if field_mode == 'in and out-of-field':
                    tot_bins_multiplier = 2
                    udvs_denom = 1
                else:
                    if field_mode == 'out-of-field':
                        ignored_plt_grps = ['in-field']
                    else:
                        ignored_plt_grps = ['out-of-field']
            else:
                tot_bins_multiplier = 1
                udvs_denom = 1
                    
        else:
            spec_label = 'None'
            parm_dict['data_type'] = 'BELineData'
            
        # Check file sizes:
        if 'read_real' in path_dict.keys():
Somnath, Suhas's avatar
Somnath, Suhas committed
108
109
            real_size = path.getsize(path_dict['read_real'])
            imag_size = path.getsize(path_dict['read_imag'])
Somnath, Suhas's avatar
Somnath, Suhas committed
110
111
112
113
114
115
116
117
        else:
            real_size = path.getsize(path_dict['write_real'])
            imag_size = path.getsize(path_dict['write_imag'])
            
        if real_size != imag_size:
            raise ValueError("Real and imaginary file sizes DON'T match!. Ending")

        add_pix = False         
Somnath, Suhas's avatar
Somnath, Suhas committed
118
119
120
121
        num_rows = int(parm_dict['grid_num_rows'])
        num_cols = int(parm_dict['grid_num_cols'])
        num_pix = num_rows*num_cols
        tot_bins = real_size/(num_pix*4)
Chris Smith's avatar
Chris Smith committed
122
123
        # Check for case where only a single pixel is missing.
        check_bins = real_size/((num_pix-1)*4)
Somnath, Suhas's avatar
Somnath, Suhas committed
124
125
        
        if tot_bins % 1 and check_bins % 1: 
126
            raise ValueError('Aborting! Some parameter appears to have changed in-between')
Somnath, Suhas's avatar
Somnath, Suhas committed
127
        elif not tot_bins % 1:
Chris Smith's avatar
Chris Smith committed
128
            # Everything's ok
Somnath, Suhas's avatar
Somnath, Suhas committed
129
130
131
132
133
134
135
136
137
            pass
        elif not check_bins % 1:
            tot_bins = check_bins
            warn('Warning:  A pixel seems to be missing from the data.  File will be padded with zeros.') 
            add_pix = True 
         
        tot_bins = int(tot_bins)*tot_bins_multiplier
        
        if 'parm_mat' in path_dict.keys():
Somnath, Suhas's avatar
Somnath, Suhas committed
138
            (bin_inds, bin_freqs, bin_FFT, ex_wfm) = self.__read_parms_mat(path_dict['parm_mat'], isBEPS)
Somnath, Suhas's avatar
Somnath, Suhas committed
139
        elif 'old_mat_parms' in path_dict.keys():
Somnath, Suhas's avatar
Somnath, Suhas committed
140
            (bin_inds, bin_freqs, bin_FFT, ex_wfm, dc_amp_vec) = self.__read_old_mat_be_vecs(path_dict['old_mat_parms'])
Somnath, Suhas's avatar
Somnath, Suhas committed
141
142
143
144
145
146
        else:
            band_width = parm_dict['BE_band_width_[Hz]']*(0.5 - parm_dict['BE_band_edge_trim'])
            st_f = parm_dict['BE_center_frequency_[Hz]'] - band_width
            en_f = parm_dict['BE_center_frequency_[Hz]'] + band_width            
            bin_freqs = np.linspace(st_f, en_f, tot_bins, dtype=np.float32)
            
147
            warn('No parms .mat file found.... Filling dummy values into ancillary datasets.')
Somnath, Suhas's avatar
Somnath, Suhas committed
148
149
150
151
152
153
154
155
156
            bin_inds = np.zeros(shape=tot_bins, dtype=np.int32)
            bin_FFT = np.zeros(shape=tot_bins, dtype=np.complex64)
            ex_wfm = np.zeros(shape=100, dtype=np.float32)
            
        # Forcing standardized datatypes:
        bin_inds = np.int32(bin_inds)
        bin_freqs = np.float32(bin_freqs)
        bin_FFT = np.complex64(bin_FFT)
        ex_wfm = np.float32(ex_wfm)
157
158
159

        ds_ex_wfm = MicroDataset('Excitation_Waveform', ex_wfm)

Somnath, Suhas's avatar
Somnath, Suhas committed
160
        self.FFT_BE_wave = bin_FFT
161

Unknown's avatar
Unknown committed
162
        ds_pos_ind, ds_pos_val = build_ind_val_dsets([num_rows, num_cols], is_spectral=False,
163
                                                     labels=['X', 'Y'], units=['m', 'm'], verbose=False)
Somnath, Suhas's avatar
Somnath, Suhas committed
164
165
        
        if isBEPS:
Somnath, Suhas's avatar
Somnath, Suhas committed
166
            (UDVS_labs, UDVS_units, UDVS_mat) = self.__build_udvs_table(parm_dict)
Somnath, Suhas's avatar
Somnath, Suhas committed
167
168
169
170
           
#             Remove the unused plot group columns before proceeding:
            (UDVS_mat, UDVS_labs, UDVS_units) = trimUDVS(UDVS_mat, UDVS_labs, UDVS_units, ignored_plt_grps)
           
Somnath, Suhas's avatar
Somnath, Suhas committed
171
            spec_inds = np.zeros(shape=(2, tot_bins), dtype=np.uint)
Somnath, Suhas's avatar
Somnath, Suhas committed
172
173
174
175
176
177
                      
#             Will assume that all excitation waveforms have same number of bins
            num_actual_udvs_steps = UDVS_mat.shape[0]/udvs_denom
            bins_per_step = tot_bins/num_actual_udvs_steps
           
            if bins_per_step % 1:
Somnath, Suhas's avatar
Somnath, Suhas committed
178
179
                print('UDVS mat shape: {}, total bins: {}, bins per step: {}'.format(UDVS_mat.shape, tot_bins,
                                                                                     bins_per_step))
180
                raise ValueError('Non integer number of bins per step!')
Somnath, Suhas's avatar
Somnath, Suhas committed
181
182
183
184
185
            
            bins_per_step = int(bins_per_step)
            num_actual_udvs_steps = int(num_actual_udvs_steps)
               
            stind = 0           
Somnath, Suhas's avatar
Somnath, Suhas committed
186
187
188
189
190
            for step_index in range(UDVS_mat.shape[0]):  
                if UDVS_mat[step_index, 2] < 1E-3: # invalid AC amplitude
                    continue  # skip
                spec_inds[0, stind:stind+bins_per_step] = np.arange(bins_per_step, dtype=np.uint32) # Bin step
                spec_inds[1, stind:stind+bins_per_step] = step_index * np.ones(bins_per_step, dtype=np.uint32) # UDVS step
Somnath, Suhas's avatar
Somnath, Suhas committed
191
                stind += bins_per_step
Somnath, Suhas's avatar
Somnath, Suhas committed
192
            del stind, step_index
Somnath, Suhas's avatar
Somnath, Suhas committed
193
           
Somnath, Suhas's avatar
Somnath, Suhas committed
194
        else:  # BE Line
Somnath, Suhas's avatar
Somnath, Suhas committed
195
            self.signal_type = 1
Somnath, Suhas's avatar
Somnath, Suhas committed
196
            self.expt_type = 1  # Stephen has not used this index for some reason
Somnath, Suhas's avatar
Somnath, Suhas committed
197
198
            num_actual_udvs_steps = 1
            bins_per_step = tot_bins
Somnath, Suhas's avatar
Somnath, Suhas committed
199
            UDVS_labs = ['step_num', 'dc_offset', 'ac_amp', 'wave_type', 'wave_mod', 'be-line']
Somnath, Suhas's avatar
Somnath, Suhas committed
200
            UDVS_units = ['', 'V', 'A', '', '', '']
Somnath, Suhas's avatar
Somnath, Suhas committed
201
202
            UDVS_mat = np.array([1, 0, parm_dict['BE_amplitude_[V]'], 1, 1, 1],
                                dtype=np.float32).reshape(1, len(UDVS_labs))
Somnath, Suhas's avatar
Somnath, Suhas committed
203
204
205
206
207
208

            spec_inds = np.vstack((np.arange(tot_bins, dtype=np.uint), np.zeros(tot_bins, dtype=np.uint32)))
        
        # Some very basic information that can help the processing / analysis crew
        parm_dict['num_bins'] = tot_bins
        parm_dict['num_pix'] = num_pix
209
        parm_dict['num_udvs_steps'] = num_actual_udvs_steps
Somnath, Suhas's avatar
Somnath, Suhas committed
210
        
Somnath, Suhas's avatar
Somnath, Suhas committed
211
        udvs_slices = dict()
Somnath, Suhas's avatar
Somnath, Suhas committed
212
        for col_ind, col_name in enumerate(UDVS_labs):
Somnath, Suhas's avatar
Somnath, Suhas committed
213
            udvs_slices[col_name] = (slice(None), slice(col_ind, col_ind+1))
Somnath, Suhas's avatar
Somnath, Suhas committed
214
215
216
217
218
219
220
        ds_UDVS = MicroDataset('UDVS', UDVS_mat)
        ds_UDVS.attrs['labels'] = udvs_slices
        ds_UDVS.attrs['units'] = UDVS_units
#         ds_udvs_labs = MicroDataset('UDVS_Labels',np.array(UDVS_labs))
        ds_UDVS_inds = MicroDataset('UDVS_Indices', spec_inds[1])        
        
#         ds_spec_labs = MicroDataset('Spectroscopic_Labels',np.array(['Bin','UDVS_Step']))
Somnath, Suhas's avatar
Somnath, Suhas committed
221
        ds_bin_steps = MicroDataset('Bin_Step', np.arange(bins_per_step, dtype=np.uint32), dtype=np.uint32)
Somnath, Suhas's avatar
Somnath, Suhas committed
222
223
224
225
226
227
        
        # Need to add the Bin Waveform type - infer from UDVS        
        exec_bin_vec = self.signal_type*np.ones(len(bin_inds), dtype=np.int32)

        if self.expt_type == 2:
            # Need to double the vectors:
Somnath, Suhas's avatar
Somnath, Suhas committed
228
229
230
            exec_bin_vec = np.hstack((exec_bin_vec, -1*exec_bin_vec))
            bin_inds = np.hstack((bin_inds, bin_inds))
            bin_freqs = np.hstack((bin_freqs, bin_freqs))
Somnath, Suhas's avatar
Somnath, Suhas committed
231
            # This is wrong but I don't know what else to do
Somnath, Suhas's avatar
Somnath, Suhas committed
232
            bin_FFT = np.hstack((bin_FFT, bin_FFT))
Somnath, Suhas's avatar
Somnath, Suhas committed
233
234
235
236
237
238
239
        
        ds_bin_inds = MicroDataset('Bin_Indices', bin_inds, dtype=np.uint32)       
        ds_bin_freq = MicroDataset('Bin_Frequencies', bin_freqs)        
        ds_bin_FFT = MicroDataset('Bin_FFT', bin_FFT)
        ds_wfm_typ = MicroDataset('Bin_Wfm_Type', exec_bin_vec)
        
        # Create Spectroscopic Values and Spectroscopic Values Labels datasets
Somnath, Suhas's avatar
Somnath, Suhas committed
240
241
242
243
244
245
246
        spec_vals, spec_inds, spec_vals_labs, spec_vals_units, spec_vals_labs_names = createSpecVals(UDVS_mat,
                                                                                                     spec_inds,
                                                                                                     bin_freqs,
                                                                                                     exec_bin_vec,
                                                                                                     parm_dict,
                                                                                                     UDVS_labs,
                                                                                                     UDVS_units)
Chris Smith's avatar
Chris Smith committed
247

Somnath, Suhas's avatar
Somnath, Suhas committed
248
249
250
251
252
253
        spec_vals_slices = dict()
#         if len(spec_vals_labs) == 1:
#             spec_vals_slices[spec_vals_labs[0]]=(slice(0,1,None),)
#         else:

        for row_ind, row_name in enumerate(spec_vals_labs):
Somnath, Suhas's avatar
Somnath, Suhas committed
254
            spec_vals_slices[row_name] = (slice(row_ind, row_ind+1), slice(None))
Somnath, Suhas's avatar
Somnath, Suhas committed
255
256
257
258

        ds_spec_mat = MicroDataset('Spectroscopic_Indices', spec_inds, dtype=np.uint32)
        ds_spec_mat.attrs['labels'] = spec_vals_slices
        ds_spec_mat.attrs['units'] = spec_vals_units                   
Somnath, Suhas's avatar
Somnath, Suhas committed
259
        ds_spec_vals_mat = MicroDataset('Spectroscopic_Values', np.array(spec_vals, dtype=np.float32))
Somnath, Suhas's avatar
Somnath, Suhas committed
260
261
262
        ds_spec_vals_mat.attrs['labels'] = spec_vals_slices
        ds_spec_vals_mat.attrs['units'] = spec_vals_units
        for entry in spec_vals_labs_names:
Chris Smith's avatar
Chris Smith committed
263
            label = entry[0]+'_parameters'
Somnath, Suhas's avatar
Somnath, Suhas committed
264
            names = entry[1]
Somnath, Suhas's avatar
Somnath, Suhas committed
265
266
            ds_spec_mat.attrs[label] = names
            ds_spec_vals_mat.attrs[label] = names
Chris Smith's avatar
Chris Smith committed
267

Somnath, Suhas's avatar
Somnath, Suhas committed
268
        # Noise floor should be of shape: (udvs_steps x 3 x positions)
Somnath, Suhas's avatar
Somnath, Suhas committed
269
270
        ds_noise_floor = MicroDataset('Noise_Floor', np.zeros(shape=(num_pix, num_actual_udvs_steps), dtype=nf32),
                                      chunking=(1, num_actual_udvs_steps))
Somnath, Suhas's avatar
Somnath, Suhas committed
271
272

        """
Chris Smith's avatar
Chris Smith committed
273
        New Method for chunking the Main_Data dataset.  Chunking is now done in N-by-N squares
Somnath, Suhas's avatar
Somnath, Suhas committed
274
        of UDVS steps by pixels.  N is determined dynamically based on the dimensions of the
Chris Smith's avatar
Chris Smith committed
275
        dataset.  Currently it is set such that individual chunks are less than 10kB in size.
Somnath, Suhas's avatar
Somnath, Suhas committed
276
277
278
        
        Chris Smith -- csmith55@utk.edu
        """
Chris Smith's avatar
Chris Smith committed
279
280
281
282
283
284
285
286
        BEPS_chunks = calc_chunks([num_pix, tot_bins],
                                  np.complex64(0).itemsize,
                                  unit_chunks=(1, bins_per_step))
        ds_main_data = MicroDataset('Raw_Data', data=[],
                                    maxshape=(num_pix, tot_bins),
                                    dtype=np.complex64,
                                    chunking=BEPS_chunks,
                                    compression='gzip')
Somnath, Suhas's avatar
Somnath, Suhas committed
287
288
289
290
291
        
        chan_grp = MicroDataGroup('Channel_')
        chan_grp.attrs['Channel_Input'] = parm_dict['IO_Analog_Input_1']
        chan_grp.addChildren([ds_main_data, ds_noise_floor])
        chan_grp.addChildren([ds_ex_wfm, ds_pos_ind, ds_pos_val, ds_spec_mat, ds_UDVS,
Chris Smith's avatar
Chris Smith committed
292
293
                              ds_bin_steps, ds_bin_inds, ds_bin_freq, ds_bin_FFT,
                              ds_wfm_typ, ds_spec_vals_mat, ds_UDVS_inds])
Somnath, Suhas's avatar
Somnath, Suhas committed
294
295
296
297
298
299
300
        
        # technically should change the date, etc.
        meas_grp = MicroDataGroup('Measurement_')
        meas_grp.attrs = parm_dict
        meas_grp.addChildren([chan_grp])
        
        spm_data = MicroDataGroup('')
301
        global_parms = generate_dummy_main_parms()
Somnath, Suhas's avatar
Somnath, Suhas committed
302
303
        global_parms['grid_size_x'] = parm_dict['grid_num_cols']
        global_parms['grid_size_y'] = parm_dict['grid_num_rows']
Somnath, Suhas's avatar
Somnath, Suhas committed
304
305
306
307
        try:
            global_parms['experiment_date'] = parm_dict['File_date_and_time']
        except KeyError:
            global_parms['experiment_date'] = '1:1:1'
Chris Smith's avatar
Chris Smith committed
308

Somnath, Suhas's avatar
Somnath, Suhas committed
309
        # assuming that the experiment was completed:
Somnath, Suhas's avatar
Somnath, Suhas committed
310
311
312
        global_parms['current_position_x'] = parm_dict['grid_num_cols']-1
        global_parms['current_position_y'] = parm_dict['grid_num_rows']-1
        global_parms['data_type'] = parm_dict['data_type']
Somnath, Suhas's avatar
Somnath, Suhas committed
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
        global_parms['translator'] = 'ODF'
            
        spm_data.attrs = global_parms
        spm_data.addChildren([meas_grp])
        
        if path.exists(h5_path):
            remove(h5_path)
        
        # Write everything except for the main data.
        self.hdf = ioHDF5(h5_path)
        
        h5_refs = self.hdf.writeData(spm_data)
                    
        self.h5_raw = getH5DsetRefs(['Raw_Data'], h5_refs)[0]
            
Somnath, Suhas's avatar
Somnath, Suhas committed
328
329
330
331
        # Now doing linkrefs:
        aux_ds_names = ['Excitation_Waveform', 'Position_Indices', 'Position_Values',
                        'Spectroscopic_Indices', 'UDVS', 'Bin_Step', 'Bin_Indices', 'UDVS_Indices',
                        'Bin_Frequencies', 'Bin_FFT', 'Bin_Wfm_Type', 'Noise_Floor', 'Spectroscopic_Values']
332
        linkRefs(self.h5_raw, getH5DsetRefs(aux_ds_names, h5_refs))
Somnath, Suhas's avatar
Somnath, Suhas committed
333

Chris Smith's avatar
Chris Smith committed
334
        self._read_data(UDVS_mat, parm_dict, path_dict, real_size, isBEPS, add_pix)
Somnath, Suhas's avatar
Somnath, Suhas committed
335
336
337
        
        generatePlotGroups(self.h5_raw, self.hdf, self.mean_resp, folder_path, basename,
                           self.max_resp, self.min_resp, max_mem_mb=self.max_ram,
Somnath, Suhas's avatar
Somnath, Suhas committed
338
                           spec_label=spec_label, show_plots=show_plots, save_plots=save_plots,
Somnath, Suhas's avatar
Somnath, Suhas committed
339
340
341
342
343
                           do_histogram=do_histogram)
        
        self.hdf.close()
        
        return h5_path
Chris Smith's avatar
Chris Smith committed
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371

    def _read_data(self, UDVS_mat, parm_dict, path_dict, real_size, isBEPS, add_pix):
        """
        Checks if the data is BEPS or BELine and calls the correct function to read the data from
        file

        Parameters
        ----------
        UDVS_mat : numpy.ndarray of float
            UDVS table
        parm_dict : dict
            Experimental parameters
        path_dict : dict
            Dictionary of data files to be read
        real_size : dict
            Size of each data file
        isBEPS : boolean
            Is the data BEPS
        add_pix : boolean
            Does the reader need to add extra pixels to the end of the dataset

        Returns
        -------
        None
        """
        # Now read the raw data files:
        if not isBEPS:
            # Do this for all BE-Line (always small enough to read in one shot)
372
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
373
374
        elif real_size < self.max_ram and parm_dict['VS_measure_in_field_loops'] == 'out-of-field':
            # Do this for out-of-field BEPS ONLY that is also small (256 MB)
375
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
376
377
        elif real_size < self.max_ram and parm_dict['VS_measure_in_field_loops'] == 'in-field':
            # Do this for in-field only
378
            self.__quick_read_data(path_dict['write_real'], path_dict['write_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
379
380
        else:
            # Large BEPS datasets OR those with in-and-out of field
Somnath, Suhas's avatar
Somnath, Suhas committed
381
            self.__read_beps_data(path_dict, UDVS_mat.shape[0], parm_dict['VS_measure_in_field_loops'], add_pix)
Chris Smith's avatar
Chris Smith committed
382
383
        self.hdf.file.flush()

Somnath, Suhas's avatar
Somnath, Suhas committed
384
    def __read_beps_data(self, path_dict, udvs_steps, mode, add_pixel=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
        """
        Reads the imaginary and real data files pixelwise and writes to the H5 file 
        
        Parameters 
        --------------------
        path_dict : dictionary
            Dictionary containing the absolute paths of the real and imaginary data files
        udvs_steps : unsigned int
            Number of UDVS steps
        mode : String / Unicode
            'in-field', 'out-of-field', or 'in and out-of-field'
        add_pixel : boolean. (Optional; default is False)
            If an empty pixel worth of data should be written to the end             
        
        Returns 
        -------------------- 
        None
        """
        
        print('---- reading pixel-by-pixel ----------')
        
        bytes_per_pix = self.h5_raw.shape[1]*4 
        step_size = self.h5_raw.shape[1]/udvs_steps          
        
        if mode == 'out-of-field':
Somnath, Suhas's avatar
Somnath, Suhas committed
410
411
            parsers = [BEodfParser(path_dict['read_real'], path_dict['read_imag'], 
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
412
        elif mode == 'in-field':
Somnath, Suhas's avatar
Somnath, Suhas committed
413
414
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'], 
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
415
416
417
        elif mode == 'in and out-of-field':
            # each file will only have half the udvs steps:
            if 0.5*udvs_steps % 1:
418
419
                raise ValueError('Odd number of UDVS')

Somnath, Suhas's avatar
Somnath, Suhas committed
420
421
            udvs_steps = int(0.5*udvs_steps)
            # be careful - each pair contains only half the necessary bins - so read half
Somnath, Suhas's avatar
Somnath, Suhas committed
422
423
424
425
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'], 
                                   self.h5_raw.shape[0], int(bytes_per_pix / 2)),
                       BEodfParser(path_dict['read_real'], path_dict['read_imag'], 
                                   self.h5_raw.shape[0], int(bytes_per_pix/2))]
Somnath, Suhas's avatar
Somnath, Suhas committed
426
427
            
            if step_size % 1:
428
429
                raise ValueError('strange number of bins per UDVS step. Exiting')

Somnath, Suhas's avatar
Somnath, Suhas committed
430
            step_size = int(step_size)
431

432
433
        rand_spectra = self.__get_random_spectra(parsers, self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
434
        take_conjugate = requires_conjugate(rand_spectra)
435

Somnath, Suhas's avatar
Somnath, Suhas committed
436
437
438
439
440
441
442
443
444
445
        self.mean_resp = np.zeros(shape=(self.h5_raw.shape[1]), dtype=np.complex64)
        self.max_resp = np.zeros(shape=(self.h5_raw.shape[0]), dtype=np.float32)
        self.min_resp = np.zeros(shape=(self.h5_raw.shape[0]), dtype=np.float32)

        numpix = self.h5_raw.shape[0] 
        """ 
        Don't try to do the last step if a pixel is missing.   
        This will be handled after the loop. 
        """ 
        if add_pixel: 
Somnath, Suhas's avatar
Somnath, Suhas committed
446
            numpix -= 1 
Somnath, Suhas's avatar
Somnath, Suhas committed
447
        
Somnath, Suhas's avatar
Somnath, Suhas committed
448
        for pix_indx in range(numpix):
Somnath, Suhas's avatar
Somnath, Suhas committed
449
450
            if self.h5_raw.shape[0] > 5:
                if pix_indx % int(round(self.h5_raw.shape[0]/10)) == 0:
Somnath, Suhas's avatar
Somnath, Suhas committed
451
                    print('Reading... {} complete'.format(round(100*pix_indx / self.h5_raw.shape[0])))
Somnath, Suhas's avatar
Somnath, Suhas committed
452
453
454
455
                    
            # get the raw stream from each parser
            pxl_data = list()
            for prsr in parsers:
Somnath, Suhas's avatar
Somnath, Suhas committed
456
                pxl_data.append(prsr.read_pixel())
Somnath, Suhas's avatar
Somnath, Suhas committed
457
458
459
460
461
462
463
            
            # interleave if both in and out of field
            # we are ignoring user defined possibilities...
            if mode == 'in and out-of-field':
                in_fld = pxl_data[0]
                out_fld = pxl_data[1]
                
Somnath, Suhas's avatar
Somnath, Suhas committed
464
465
466
467
468
                in_fld_2 = in_fld.reshape(udvs_steps, step_size)
                out_fld_2 = out_fld.reshape(udvs_steps, step_size)
                raw_mat = np.empty((udvs_steps*2, step_size), dtype=out_fld.dtype)
                raw_mat[0::2, :] = in_fld_2
                raw_mat[1::2, :] = out_fld_2
Somnath, Suhas's avatar
Somnath, Suhas committed
469
470
                raw_vec = raw_mat.reshape(in_fld.size + out_fld.size).transpose()
            else:
Somnath, Suhas's avatar
Somnath, Suhas committed
471
                raw_vec = pxl_data[0]  # only one parser
Somnath, Suhas's avatar
Somnath, Suhas committed
472
473
            self.max_resp[pix_indx] = np.max(np.abs(raw_vec))
            self.min_resp[pix_indx] = np.min(np.abs(raw_vec))
Somnath, Suhas's avatar
Somnath, Suhas committed
474
            self.mean_resp = (1/(pix_indx+1))*(raw_vec + pix_indx * self.mean_resp)
475
476
477

            if take_conjugate:
                raw_vec = np.conjugate(raw_vec)
478
            self.h5_raw[pix_indx, :] = np.complex64(raw_vec[:])
Somnath, Suhas's avatar
Somnath, Suhas committed
479
480
481
482
            self.hdf.file.flush()
            
        # Add zeros to main_data for the missing pixel. 
        if add_pixel: 
Somnath, Suhas's avatar
Somnath, Suhas committed
483
            self.h5_raw[-1, :] = 0+0j             
Somnath, Suhas's avatar
Somnath, Suhas committed
484
485
            
        print('---- Finished reading files -----')
486
487

    def __quick_read_data(self, real_path, imag_path, udvs_steps):
Somnath, Suhas's avatar
Somnath, Suhas committed
488
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
489
490
491
492
493
494
495
496
        Returns information about the excitation BE waveform present in the .mat file

        Parameters
        -----------
        real_path : String / Unicode
            Absolute file path of the real data file
        imag_path : String / Unicode
            Absolute file path of the real data file
497
498
        udvs_steps : unsigned int
            Number of UDVS steps
Somnath, Suhas's avatar
Somnath, Suhas committed
499
500
501
        """
        print('---- reading all data at once ----------')  

502
503
504
        parser = BEodfParser(real_path, imag_path, self.h5_raw.shape[0], self.h5_raw.shape[1]*4)

        step_size = self.h5_raw.shape[1] / udvs_steps
505
506
        rand_spectra = self.__get_random_spectra([parser], self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
507
        take_conjugate = requires_conjugate(rand_spectra)
Somnath, Suhas's avatar
Somnath, Suhas committed
508
        raw_vec = parser.read_all_data()
509
        if take_conjugate:
510
            print('Taking conjugate to ensure positive Quality factors')
511
            raw_vec = np.conjugate(raw_vec)
Somnath, Suhas's avatar
Somnath, Suhas committed
512
                                      
Somnath, Suhas's avatar
Somnath, Suhas committed
513
        raw_mat = raw_vec.reshape(self.h5_raw.shape[0], self.h5_raw.shape[1])
Somnath, Suhas's avatar
Somnath, Suhas committed
514
515
                
        # Write to the h5 dataset:
Somnath, Suhas's avatar
Somnath, Suhas committed
516
517
518
        self.mean_resp = np.mean(raw_mat, axis=0)
        self.max_resp = np.amax(np.abs(raw_mat), axis=0)
        self.min_resp = np.amin(np.abs(raw_mat), axis=0)
519
        self.h5_raw[:, :] = np.complex64(raw_mat)
Somnath, Suhas's avatar
Somnath, Suhas committed
520
521
522
523
        self.hdf.file.flush()

        print('---- Finished reading files -----')       
        
524
    def _parse_file_path(self, data_filepath):
Somnath, Suhas's avatar
Somnath, Suhas committed
525
526
527
528
529
530
531
        """
        Returns the basename and a dictionary containing the absolute file paths for the
        real and imaginary data files, text and mat parameter files in a dictionary
        
        Parameters 
        --------------------
        data_filepath: String / Unicode
Somnath, Suhas's avatar
Somnath, Suhas committed
532
            Absolute path of any file in the same directory as the .dat files
Somnath, Suhas's avatar
Somnath, Suhas committed
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
        
        Returns 
        --------------------
        basename : String / Unicode
            Basename of the dataset      
        path_dict : Dictionary
            Dictionary containing absolute paths of all necessary data and parameter files
        """
        (folder_path, basename) = path.split(data_filepath)
        (super_folder, basename) = path.split(folder_path) 

        if basename.endswith('_d'):
            # Old old data format where the folder ended with a _d for some reason
            basename = basename[:-2]
        """
        A single pair of real and imaginary files are / were generated for:
            BE-Line and BEPS (compiled version only generated out-of-field or 'read')
        Two pairs of real and imaginary files were generated for later BEPS datasets
            These have 'read' and 'write' prefixes to denote out or in field respectively
        """
        path_dict = dict()
        
        for file_name in listdir(folder_path):
Chris Smith's avatar
Chris Smith committed
556
            abs_path = path.join(folder_path, file_name)
Somnath, Suhas's avatar
Somnath, Suhas committed
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
            if file_name.endswith('.txt') and file_name.find('parm') > 0:
                path_dict['parm_txt'] = abs_path
            elif file_name.find('.mat') > 0:
                if file_name.find('more_parms') > 0:
                    path_dict['parm_mat'] = abs_path
                elif file_name == (basename + '.mat'):                   
                    path_dict['old_mat_parms'] = abs_path
            elif file_name.endswith('.dat'):
                # Need to account for the second AI channel here
                file_tag = 'read'
                if file_name.find('write') > 0:
                    file_tag = 'write'
                if file_name.find('real') > 0:
                    file_tag += '_real'
                elif file_name.find('imag') > 0:
                    file_tag += '_imag'
                path_dict[file_tag] = abs_path

Chris Smith's avatar
Chris Smith committed
575
        return basename, path_dict
Somnath, Suhas's avatar
Somnath, Suhas committed
576
577
578

    @staticmethod
    def __read_old_mat_be_vecs(file_path):
Somnath, Suhas's avatar
Somnath, Suhas committed
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
        """
        Returns information about the excitation BE waveform present in the 
        more parms.mat file
        
        Parameters 
        --------------------
        filepath : String or unicode
            Absolute filepath of the .mat parameter file
        
        Returns 
        --------------------
        bin_inds : 1D numpy unsigned int array
            Indices of the excited and measured frequency bins
        bin_w : 1D numpy float array
            Excitation bin Frequencies
        bin_FFT : 1D numpy complex array
            FFT of the BE waveform for the excited bins
        BE_wave : 1D numpy float array
            Band Excitation waveform
        dc_amp_vec_full : 1D numpy float array
            spectroscopic waveform. 
            This information will be necessary for fixing the UDVS for AC modulation for example
        """
        matread = loadmat(file_path, squeeze_me=True)    
        BE_wave = matread['BE_wave']
Somnath, Suhas's avatar
Somnath, Suhas committed
604
        bin_inds = matread['bin_ind'] -1  # Python base 0
Somnath, Suhas's avatar
Somnath, Suhas committed
605
606
607
        bin_w = matread['bin_w']
        dc_amp_vec_full = matread['dc_amp_vec_full']
        FFT_full = np.fft.fftshift(np.fft.fft(BE_wave))
Somnath, Suhas's avatar
Somnath, Suhas committed
608
609
        bin_FFT = np.conjugate(FFT_full[bin_inds])
        return bin_inds, bin_w, bin_FFT, BE_wave, dc_amp_vec_full
Somnath, Suhas's avatar
Somnath, Suhas committed
610
        
Somnath, Suhas's avatar
Somnath, Suhas committed
611
612
    @staticmethod
    def __get_parms_from_old_mat(file_path):
Somnath, Suhas's avatar
Somnath, Suhas committed
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
        """
        Formats parameters found in the old parameters .mat file into a dictionary
        as though the dataset had a parms.txt describing it
        
        Parameters 
        --------------------
        file_path : Unicode / String
            absolute filepath of the .mat file containing the parameters
            
        Returns 
        --------------------
        parm_dict : dictionary
            Parameters describing experiment
        """
        parm_dict = dict()
        matread = loadmat(file_path, squeeze_me=True)
        
        parm_dict['IO_rate'] = str(int(matread['AO_rate']/1E+6)) + ' MHz'
        
        position_vec = matread['position_vec']
        parm_dict['grid_current_row'] = position_vec[0]
        parm_dict['grid_current_col'] = position_vec[1]
        parm_dict['grid_num_rows'] = position_vec[2]
        parm_dict['grid_num_cols'] = position_vec[3]
        
        if position_vec[0] != position_vec[1] or position_vec[2] != position_vec[3]:
            warn('WARNING: Incomplete dataset. Translation not guaranteed!')
Somnath, Suhas's avatar
Somnath, Suhas committed
640
            parm_dict['grid_num_rows'] = position_vec[0]  # set to number of present cols and rows
Somnath, Suhas's avatar
Somnath, Suhas committed
641
642
643
644
645
646
647
648
649
650
651
            parm_dict['grid_num_cols'] = position_vec[1]
    
        BE_parm_vec_1 = matread['BE_parm_vec_1']
        # Not required for translation but necessary to have
        if BE_parm_vec_1[0] == 3:
            parm_dict['BE_phase_content'] = 'chirp-sinc hybrid'
        else:
            parm_dict['BE_phase_content'] = 'Unknown'
        parm_dict['BE_center_frequency_[Hz]'] = BE_parm_vec_1[1]
        parm_dict['BE_band_width_[Hz]'] = BE_parm_vec_1[2]
        parm_dict['BE_amplitude_[V]'] = BE_parm_vec_1[3]
Somnath, Suhas's avatar
Somnath, Suhas committed
652
653
        parm_dict['BE_band_edge_smoothing_[s]'] = BE_parm_vec_1[4]  # 150 most likely
        parm_dict['BE_phase_variation'] = BE_parm_vec_1[5]  # 0.01 most likely
Somnath, Suhas's avatar
Somnath, Suhas committed
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
        parm_dict['BE_window_adjustment'] = BE_parm_vec_1[6] 
        parm_dict['BE_points_per_step'] = 2**int(BE_parm_vec_1[7])
        parm_dict['BE_repeats'] = 2**int(BE_parm_vec_1[8])
        try:
            parm_dict['BE_bins_per_read'] = matread['bins_per_band_s']
        except KeyError:
            parm_dict['BE_bins_per_read'] = len(matread['bin_w'])
    
        assembly_parm_vec = matread['assembly_parm_vec']
        
        if assembly_parm_vec[2] == 0:
            parm_dict['VS_measure_in_field_loops'] = 'out-of-field'
        elif assembly_parm_vec[2] == 1:
            parm_dict['VS_measure_in_field_loops'] = 'in and out-of-field'
        else:
            parm_dict['VS_measure_in_field_loops'] = 'in-field'
        
        parm_dict['IO_Analog_Input_1'] = '+/- 10V, FFT'
        if assembly_parm_vec[3] == 0:
            parm_dict['IO_Analog_Input_2'] = 'off'
        else:
            parm_dict['IO_Analog_Input_2'] = '+/- 10V, FFT'
            
Somnath, Suhas's avatar
Somnath, Suhas committed
677
678
        # num_driving_bands = assembly_parm_vec[0]  # 0 = 1, 1 = 2 bands
        # band_combination_order = assembly_parm_vec[1]  # 0 parallel 1 series
Somnath, Suhas's avatar
Somnath, Suhas committed
679
680
681
682
683
684
685
        
        VS_parms = matread['SS_parm_vec']
        dc_amp_vec_full = matread['dc_amp_vec_full']
           
        VS_start_V = VS_parms[4] 
        VS_start_loop_amp = VS_parms[5] 
        VS_final_loop_amp = VS_parms[6] 
Somnath, Suhas's avatar
Somnath, Suhas committed
686
        # VS_read_write_ratio = VS_parms[8]  # 1 <- SS_read_write_ratio
Somnath, Suhas's avatar
Somnath, Suhas committed
687
        
Somnath, Suhas's avatar
Somnath, Suhas committed
688
        parm_dict['VS_set_pulse_amplitude_[V]'] = VS_parms[9]  # 0 <- SS_set_pulse_amp
Somnath, Suhas's avatar
Somnath, Suhas committed
689
690
691
692
693
        parm_dict['VS_read_voltage_[V]'] = VS_parms[3] 
        parm_dict['VS_steps_per_full_cycle'] = VS_parms[7]
        parm_dict['VS_cycle_fraction'] = 'full'
        parm_dict['VS_cycle_phase_shift'] = 0 
        parm_dict['VS_number_of_cycles'] = VS_parms[2]
Somnath, Suhas's avatar
Somnath, Suhas committed
694
695
696
697
698
        parm_dict['FORC_num_of_FORC_cycles'] = 1
        parm_dict['FORC_V_high1_[V]'] = 0
        parm_dict['FORC_V_high2_[V]'] = 0
        parm_dict['FORC_V_low1_[V]'] = 0
        parm_dict['FORC_V_low2_[V]'] = 0
Somnath, Suhas's avatar
Somnath, Suhas committed
699
700
701
        
        if VS_parms[0] == 0:
            parm_dict['VS_mode'] = 'DC modulation mode'
Somnath, Suhas's avatar
Somnath, Suhas committed
702
            parm_dict['VS_amplitude_[V]'] = 0.5*(max(dc_amp_vec_full) - min(dc_amp_vec_full))  # VS_parms[1] # SS_max_offset_amplitude
Somnath, Suhas's avatar
Somnath, Suhas committed
703
704
705
706
            parm_dict['VS_offset_[V]'] = max(dc_amp_vec_full) + min(dc_amp_vec_full)     
        elif VS_parms[0] == 1:
            # FORC
            parm_dict['VS_mode'] = 'DC modulation mode'
Somnath, Suhas's avatar
Somnath, Suhas committed
707
            parm_dict['VS_amplitude_[V]'] = 1  # VS_parms[1] # SS_max_offset_amplitude
Somnath, Suhas's avatar
Somnath, Suhas committed
708
709
            parm_dict['VS_offset_[V]'] = 0
            parm_dict['VS_number_of_cycles'] = 1                             
Somnath, Suhas's avatar
Somnath, Suhas committed
710
711
712
713
714
            parm_dict['FORC_num_of_FORC_cycles'] = VS_parms[2]
            parm_dict['FORC_V_high1_[V]'] = VS_start_V
            parm_dict['FORC_V_high2_[V]'] = VS_start_V
            parm_dict['FORC_V_low1_[V]'] = VS_start_V - VS_start_loop_amp
            parm_dict['FORC_V_low2_[V]'] = VS_start_V - VS_final_loop_amp
Somnath, Suhas's avatar
Somnath, Suhas committed
715
716
717
        elif VS_parms[0] == 2:
            # AC mode 
            parm_dict['VS_mode'] = 'AC modulation mode with time reversal'
Somnath, Suhas's avatar
Somnath, Suhas committed
718
719
            parm_dict['VS_amplitude_[V]'] = 0.5 * VS_final_loop_amp
            parm_dict['VS_offset_[V]'] = 0  # this is not correct. Fix manually when it comes to UDVS generation?
Somnath, Suhas's avatar
Somnath, Suhas committed
720
721
722
723
724
        else:
            parm_dict['VS_mode'] = 'Custom'
    
        return parm_dict
        
Somnath, Suhas's avatar
Somnath, Suhas committed
725
726
    @staticmethod
    def __read_parms_mat(file_path, is_beps):
Somnath, Suhas's avatar
Somnath, Suhas committed
727
728
729
730
731
        """
        Returns information about the excitation BE waveform present in the more parms.mat file
        
        Parameters 
        --------------------
Somnath, Suhas's avatar
Somnath, Suhas committed
732
        file_path : String / Unicode
Somnath, Suhas's avatar
Somnath, Suhas committed
733
            Absolute filepath of the .mat parameter file
Somnath, Suhas's avatar
Somnath, Suhas committed
734
        is_beps : Boolean
Somnath, Suhas's avatar
Somnath, Suhas committed
735
736
737
738
739
740
741
742
743
744
745
746
747
            Whether or not this is BEPS or BE-Line
        
        Returns 
        --------------------
        BE_bin_ind : 1D numpy unsigned int array
            Indices of the excited and measured frequency bins
        BE_bin_w : 1D numpy float array
            Excitation bin Frequencies
        BE_bin_FFT : 1D numpy complex array
            FFT of the BE waveform for the excited bins
        ex_wfm : 1D numpy float array
            Band Excitation waveform
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
748
        if not path.exists(file_path):
749
            raise IOError('NO "More parms" file found')
Somnath, Suhas's avatar
Somnath, Suhas committed
750
        if is_beps:
Somnath, Suhas's avatar
Somnath, Suhas committed
751
752
753
            fft_name = 'FFT_BE_wave'
        else:
            fft_name = 'FFT_BE_rev_wave'
Somnath, Suhas's avatar
Somnath, Suhas committed
754
755
756
757
        matread = loadmat(file_path, variable_names=['BE_bin_ind', 'BE_bin_w', fft_name])
        BE_bin_ind = np.squeeze(matread['BE_bin_ind']) - 1   # From Matlab (base 1) to Python (base 0)
        BE_bin_w = np.squeeze(matread['BE_bin_w'])
        FFT_full = np.complex64(np.squeeze(matread[fft_name]))
Somnath, Suhas's avatar
Somnath, Suhas committed
758
        # For whatever weird reason, the sign of the imaginary portion is flipped. Correct it:
Somnath, Suhas's avatar
Somnath, Suhas committed
759
760
761
762
        #BE_bin_FFT = np.conjugate(FFT_full[BE_bin_ind])
        BE_bin_FFT = np.zeros(len(BE_bin_ind), dtype=np.complex64)
        BE_bin_FFT.real = np.real(FFT_full[BE_bin_ind])
        BE_bin_FFT.imag = -1*np.imag(FFT_full[BE_bin_ind])
Somnath, Suhas's avatar
Somnath, Suhas committed
763
764
        
        ex_wfm = np.real(np.fft.ifft(np.fft.ifftshift(FFT_full)))
Somnath, Suhas's avatar
Somnath, Suhas committed
765
766

        return BE_bin_ind, BE_bin_w, BE_bin_FFT, ex_wfm
Somnath, Suhas's avatar
Somnath, Suhas committed
767
        
Somnath, Suhas's avatar
Somnath, Suhas committed
768
    def __build_udvs_table(self, parm_dict):
Somnath, Suhas's avatar
Somnath, Suhas committed
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
        """
        Generates the UDVS table using the parameters
        
        Parameters 
        --------------------
        parm_dict : dictionary
            Parameters describing experiment
        
        Returns 
        --------------------      
        UD_VS_table_label : List of strings
            Labels for columns in the UDVS table
        UD_VS_table_unit : List of strings
            Units for the columns in the UDVS table
        UD_VS_table : 2D numpy float array
            UDVS data table
        """
    
Somnath, Suhas's avatar
Somnath, Suhas committed
787
        def translate_val(target, strvals, numvals):
Somnath, Suhas's avatar
Somnath, Suhas committed
788
789
            """
            Internal function - Interprets the provided value using the provided lookup table
Somnath, Suhas's avatar
Somnath, Suhas committed
790
791
792
793
794
795
796
797
798

            Parameters
            ----------
            target : String
                Item we are looking for in the strvals list
            strvals : list of strings
                List of source values
            numvals : list of numbers
                List of results
Somnath, Suhas's avatar
Somnath, Suhas committed
799
800
801
802
            """
        
            if len(strvals) is not len(numvals):
                return None    
Somnath, Suhas's avatar
Somnath, Suhas committed
803
            for strval, fltval in zip(strvals, numvals):
Somnath, Suhas's avatar
Somnath, Suhas committed
804
805
                if target == strval:
                    return fltval
Somnath, Suhas's avatar
Somnath, Suhas committed
806
            return None  # not found in list
Somnath, Suhas's avatar
Somnath, Suhas committed
807
808
            
        #% Extract values from parm text file    
Somnath, Suhas's avatar
Somnath, Suhas committed
809
        BE_signal_type = translate_val(parm_dict['BE_phase_content'], ['chirp-sinc hybrid','1/2 harmonic excitation','1/3 harmonic excitation','pure sine'],[1,2,3,4])
Somnath, Suhas's avatar
Somnath, Suhas committed
810
811
812
813
814
815
816
817
818
819
820
821
        # This is necessary when normalzing the AI by the AO
        self.harmonic = BE_signal_type
        self.signal_type = BE_signal_type
        if BE_signal_type is 4:
            self.harmonic = 1
        BE_amp = parm_dict['BE_amplitude_[V]']
        
        VS_amp = parm_dict['VS_amplitude_[V]']
        VS_offset = parm_dict['VS_offset_[V]']
        #VS_read_voltage = parm_dict['VS_read_voltage_[V]']
        VS_steps = parm_dict['VS_steps_per_full_cycle']
        VS_cycles = parm_dict['VS_number_of_cycles']
Somnath, Suhas's avatar
Somnath, Suhas committed
822
823
824
        VS_fraction = translate_val(parm_dict['VS_cycle_fraction'],
                                    ['full', '1/2', '1/4', '3/4'],
                                    [1., 0.5, 0.25, 0.75])
Somnath, Suhas's avatar
Somnath, Suhas committed
825
826
        VS_shift = parm_dict['VS_cycle_phase_shift']
        if VS_shift is not 0:
Somnath, Suhas's avatar
Somnath, Suhas committed
827
828
829
830
831
832
833
            VS_shift = translate_val(VS_shift, ['1/4', '1/2', '3/4'], [0.25, 0.5, 0.75])
        VS_in_out_cond = translate_val(parm_dict['VS_measure_in_field_loops'],
                                       ['out-of-field', 'in-field', 'in and out-of-field'], [0, 1, 2])
        VS_ACDC_cond = translate_val(parm_dict['VS_mode'],
                                     ['DC modulation mode', 'AC modulation mode with time reversal',
                                      'load user defined VS Wave from file', 'current mode'],
                                     [0, 2, 3, 4])
Somnath, Suhas's avatar
Somnath, Suhas committed
834
835
836
837
838
839
840
841
842
843
        self.expt_type = VS_ACDC_cond
        FORC_cycles = parm_dict['FORC_num_of_FORC_cycles']
        FORC_A1 = parm_dict['FORC_V_high1_[V]']
        FORC_A2 = parm_dict['FORC_V_high2_[V]']
        #FORC_repeats = parm_dict['# of FORC repeats']
        FORC_B1 = parm_dict['FORC_V_low1_[V]']
        FORC_B2 = parm_dict['FORC_V_low2_[V]']
            
        #% build vector of voltage spectroscopy values
        
Somnath, Suhas's avatar
Somnath, Suhas committed
844
845
        if VS_ACDC_cond == 0 or VS_ACDC_cond == 4:  # DC voltage spectroscopy or current mode
            VS_amp_vec_1 = np.arange(0, 1+1/(VS_steps/4), 1/(VS_steps/4))
Somnath, Suhas's avatar
Somnath, Suhas committed
846
847
848
            VS_amp_vec_2 = np.flipud(VS_amp_vec_1[:-1])
            VS_amp_vec_3 = -VS_amp_vec_1[1:]
            VS_amp_vec_4 =  VS_amp_vec_1[1:-1]-1
Somnath, Suhas's avatar
Somnath, Suhas committed
849
850
851
852
853
854
855
856
857
858
859
860
861
            vs_amp_vec = VS_amp*(np.hstack((VS_amp_vec_1, VS_amp_vec_2,  VS_amp_vec_3, VS_amp_vec_4)))
            vs_amp_vec = np.roll(vs_amp_vec, int(np.floor(VS_steps/VS_fraction*VS_shift)))  # apply phase shift to VS wave
            vs_amp_vec = vs_amp_vec[:int(np.floor(VS_steps*VS_fraction))]  # cut VS waveform
            vs_amp_vec = np.tile(vs_amp_vec, VS_cycles)  # repeat VS waveform
            vs_amp_vec = vs_amp_vec+VS_offset
            
        elif VS_ACDC_cond == 2:  # AC voltage spectroscopy with time reversal
            vs_amp_vec = VS_amp * np.arange(1/(VS_steps/2/VS_fraction), 1 + 1/(VS_steps/2/VS_fraction),
                                            1/(VS_steps/2/VS_fraction))
            vs_amp_vec = np.roll(vs_amp_vec,
                                 int(np.floor(VS_steps/VS_fraction*VS_shift)))  # apply phase shift to VS wave
            vs_amp_vec = vs_amp_vec[:int(np.floor(VS_steps*VS_fraction/2))]  # cut VS waveform
            vs_amp_vec = np.tile(vs_amp_vec, VS_cycles * 2)  # repeat VS waveform
Somnath, Suhas's avatar
Somnath, Suhas committed
862
863
            
        if FORC_cycles > 1:
Somnath, Suhas's avatar
Somnath, Suhas committed
864
            vs_amp_vec = vs_amp_vec/np.max(np.abs(vs_amp_vec))
Somnath, Suhas's avatar
Somnath, Suhas committed
865
866
867
868
869
870
            FORC_cycle_vec = np.arange(0, FORC_cycles+1, FORC_cycles/(FORC_cycles-1))
            FORC_A_vec = FORC_cycle_vec*(FORC_A2-FORC_A1)/FORC_cycles + FORC_A1
            FORC_B_vec = FORC_cycle_vec*(FORC_B2-FORC_B1)/FORC_cycles + FORC_B1
            FORC_amp_vec = (FORC_A_vec-FORC_B_vec)/2
            FORC_off_vec = (FORC_A_vec+FORC_B_vec)/2
            
Somnath, Suhas's avatar
Somnath, Suhas committed
871
872
873
            VS_amp_mat = np.tile(vs_amp_vec, [FORC_cycles, 1])
            FORC_amp_mat = np.tile(FORC_amp_vec, [len(vs_amp_vec), 1]).transpose()
            FORC_off_mat = np.tile(FORC_off_vec, [len(vs_amp_vec), 1]).transpose()
Somnath, Suhas's avatar
Somnath, Suhas committed
874
            VS_amp_mat = VS_amp_mat*FORC_amp_mat + FORC_off_mat
Somnath, Suhas's avatar
Somnath, Suhas committed
875
            vs_amp_vec = VS_amp_mat.reshape(int(FORC_cycles*VS_cycles*VS_fraction*VS_steps))
Somnath, Suhas's avatar
Somnath, Suhas committed
876
            
Somnath, Suhas's avatar
Somnath, Suhas committed
877
878
        # Build UDVS table:
        if VS_ACDC_cond is 0 or VS_ACDC_cond is 4:  # DC voltage spectroscopy or current mode
Somnath, Suhas's avatar
Somnath, Suhas committed
879
880
            
            if VS_ACDC_cond is 0:
Somnath, Suhas's avatar
Somnath, Suhas committed
881
                UD_dc_vec = np.vstack((vs_amp_vec, np.zeros(len(vs_amp_vec))))
Somnath, Suhas's avatar
Somnath, Suhas committed
882
            if VS_ACDC_cond is 4:
Somnath, Suhas's avatar
Somnath, Suhas committed
883
                UD_dc_vec = np.vstack((vs_amp_vec, vs_amp_vec))
Somnath, Suhas's avatar
Somnath, Suhas committed
884
        
Somnath, Suhas's avatar
Somnath, Suhas committed
885
            UD_dc_vec = UD_dc_vec.transpose().reshape(UD_dc_vec.size)
Somnath, Suhas's avatar
Somnath, Suhas committed
886
887
            num_VS_steps = UD_dc_vec.size
                        
Somnath, Suhas's avatar
Somnath, Suhas committed
888
            UD_VS_table_label = ['step_num', 'dc_offset', 'ac_amp', 'wave_type', 'wave_mod', 'in-field', 'out-of-field']
Somnath, Suhas's avatar
Somnath, Suhas committed
889
            UD_VS_table_unit = ['', 'V', 'A', '', '', 'V', 'V']
Somnath, Suhas's avatar
Somnath, Suhas committed
890
            udvs_table = np.zeros(shape=(num_VS_steps, 7), dtype=np.float32)
Somnath, Suhas's avatar
Somnath, Suhas committed
891
            
Somnath, Suhas's avatar
Somnath, Suhas committed
892
893
            udvs_table[:, 0] = np.arange(0, num_VS_steps)  # Python base 0
            udvs_table[:, 1] = UD_dc_vec
Somnath, Suhas's avatar
Somnath, Suhas committed
894
            
Somnath, Suhas's avatar
Somnath, Suhas committed
895
896
            BE_IF_switch = np.abs(np.imag(np.exp(1j*np.pi/2*np.arange(1, num_VS_steps+1))))
            BE_OF_switch = np.abs(np.real(np.exp(1j*np.pi/2*np.arange(1, num_VS_steps+1))))
Somnath, Suhas's avatar
Somnath, Suhas committed
897
            
Somnath, Suhas's avatar
Somnath, Suhas committed
898
899
900
901
902
903
            if VS_in_out_cond is 0:  # out of field only
                udvs_table[:, 2] = BE_amp * BE_OF_switch
            elif VS_in_out_cond is 1:  # in field only
                udvs_table[:, 2] = BE_amp * BE_IF_switch
            elif VS_in_out_cond is 2:  # both in and out of field
                udvs_table[:, 2] = BE_amp * np.ones(num_VS_steps)
Somnath, Suhas's avatar
Somnath, Suhas committed
904
            
Somnath, Suhas's avatar
Somnath, Suhas committed
905
906
            udvs_table[:, 3] = np.ones(num_VS_steps)  # wave type
            udvs_table[:, 4] = np.ones(num_VS_steps) * BE_signal_type  # wave mod
Somnath, Suhas's avatar
Somnath, Suhas committed
907
            
Somnath, Suhas's avatar
Somnath, Suhas committed
908
909
            udvs_table[:, 5] = float('NaN')*np.ones(num_VS_steps)
            udvs_table[:, 6] = float('NaN')*np.ones(num_VS_steps)
Somnath, Suhas's avatar
Somnath, Suhas committed
910
                            
Somnath, Suhas's avatar
Somnath, Suhas committed
911
912
            udvs_table[BE_IF_switch == 1, 5] = udvs_table[BE_IF_switch == 1, 1]
            udvs_table[BE_OF_switch == 1, 6] = udvs_table[BE_IF_switch == 1, 1]
Somnath, Suhas's avatar
Somnath, Suhas committed
913
            
Somnath, Suhas's avatar
Somnath, Suhas committed
914
        elif VS_ACDC_cond is 2:  # AC voltage spectroscopy
Somnath, Suhas's avatar
Somnath, Suhas committed
915
        
Somnath, Suhas's avatar
Somnath, Suhas committed
916
            num_VS_steps = vs_amp_vec.size
Somnath, Suhas's avatar
Somnath, Suhas committed
917
918
919
            half = int(0.5*num_VS_steps)
            
            if num_VS_steps is not half * 2:
920
                raise ValueError('Odd number of UDVS steps found. Exiting!')
Somnath, Suhas's avatar
Somnath, Suhas committed
921
922
                
            UD_dc_vec = VS_offset*np.ones(num_VS_steps)
Somnath, Suhas's avatar
Somnath, Suhas committed
923
            UD_VS_table_label = ['step_num', 'dc_offset', 'ac_amp', 'wave_type', 'wave_mod', 'forward', 'reverse']
Somnath, Suhas's avatar
Somnath, Suhas committed
924
            UD_VS_table_unit = ['', 'V', 'A', '', '', 'A', 'A']
Somnath, Suhas's avatar
Somnath, Suhas committed
925
926
927
928
929
930
931
932
933
934
935
936
937
938
            udvs_table = np.zeros(shape=(num_VS_steps, 7), dtype=np.float32)
            udvs_table[:, 0] = np.arange(1, num_VS_steps+1)
            udvs_table[:, 1] = UD_dc_vec
            udvs_table[:, 2] = vs_amp_vec
            udvs_table[:, 3] = np.ones(num_VS_steps)
            udvs_table[:half, 4] = BE_signal_type*np.ones(half)
            udvs_table[half:, 4] = -1*BE_signal_type*np.ones(half)
            udvs_table[:, 5] = float('NaN')*np.ones(num_VS_steps)
            udvs_table[:, 6] = float('NaN')*np.ones(num_VS_steps)
            udvs_table[:half, 5] = vs_amp_vec[:half]
            udvs_table[half:, 6] = vs_amp_vec[half:]
            
        return UD_VS_table_label, UD_VS_table_unit, udvs_table

939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
    @staticmethod
    def __get_random_spectra(parsers, num_pixels, num_udvs_steps, num_bins, num_spectra=100, verbose=False):
        """
        Parameters
        ----------
        parsers : list of BEodfParser objects
            parsers to seek into files to grab spectra
        num_pixels : unsigned int
            Number of spatial positions in the image
        num_udvs_steps : unsigned int
            Number of UDVS steps
        num_bins : unsigned int
            Number of frequency bins in every UDVS step
        num_spectra : unsigned int
            Total number of spectra to be extracted
        verbose : Boolean, optional
            Whether or not to print debugging statements

        Returns
        -------
        chosen_spectra : 2D complex numpy array
            spectrogram or spectra arranged as [instance, spectrum]
        """
        num_pixels = int(num_pixels)
        num_udvs_steps = int(num_udvs_steps)
        num_bins = int(num_bins)

        num_spectra = min(num_spectra, len(parsers) * num_pixels * num_udvs_steps)
        selected_pixels = np.random.randint(0, num_pixels, size=num_spectra)
        selected_steps = np.random.randint(0, num_udvs_steps, size=num_spectra)
        selected_parsers = np.random.randint(0, len(parsers), size=num_spectra)

        if verbose:
            print('Selecting the following random pixels, UDVS steps, parsers')
            print(np.vstack((selected_pixels, selected_steps, selected_parsers)))

        chosen_spectra = np.zeros(shape=(num_spectra, num_bins), dtype=np.complex64)

        for spectra_index in range(num_spectra):
            prsr = parsers[selected_parsers[spectra_index]]
            prsr.seek_to_pixel(selected_pixels[spectra_index])
            raw_vec = prsr.read_pixel()
            spectrogram = raw_vec.reshape(num_udvs_steps, -1)
            chosen_spectra[spectra_index] = spectrogram[selected_steps[spectra_index]]

        for prsr in parsers:
            prsr.reset()

        return chosen_spectra
Somnath, Suhas's avatar
Somnath, Suhas committed
988

989

Somnath, Suhas's avatar
Somnath, Suhas committed
990
class BEodfParser(object):
991

992
    def __init__(self, real_path, imag_path, num_pix, bytes_per_pix):
Somnath, Suhas's avatar
Somnath, Suhas committed
993
994
995
996
997
998
999
1000
        """
        This object reads the two binary data files (real and imaginary data).
        Use separate parser instances for in-field and out-field data sets.
        
        Parameters 
        --------------------
        real_path : String / Unicode
            absolute path of the binary file containing the real portion of the data