be_odf.py 68.6 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
import sys
12
import datetime
13
from warnings import warn
14
import h5py
Somnath, Suhas's avatar
Somnath, Suhas committed
15
16
import numpy as np
from scipy.io.matlab import loadmat  # To load parameters stored in Matlab .mat file
17

18
from .df_utils.be_utils import trimUDVS, getSpectroscopicParmLabel, parmsToDict, generatePlotGroups, \
19
    createSpecVals, requires_conjugate, generate_bipolar_triangular_waveform, nf32
20
from pyUSID.io.translator import Translator
21
22
from pyUSID.io.write_utils import INDICES_DTYPE, VALUES_DTYPE, Dimension, calc_chunks
from pyUSID.io.hdf_utils import write_ind_val_dsets, write_main_dataset, write_region_references, \
23
    create_indexed_group, write_simple_attrs, write_book_keeping_attrs, copy_attributes,\
24
    write_reduced_anc_dsets, get_unit_values
25
from pyUSID.io.usi_data import USIDataset
26
from pyUSID.processing.comp_utils import get_available_memory
27

28
29
30
if sys.version_info.major == 3:
    unicode = str

31

Somnath, Suhas's avatar
Somnath, Suhas committed
32
33
34
35
36
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
    """
Unknown's avatar
Unknown committed
37

Chris Smith's avatar
Chris Smith committed
38
39
40
    def __init__(self, *args, **kwargs):
        super(BEodfTranslator, self).__init__(*args, **kwargs)
        self.h5_raw = None
41
        self.num_rand_spectra = kwargs.pop('num_rand_spectra', 1000)
42
        self._cores = kwargs.pop('cores', None)
Unknown's avatar
Unknown committed
43
44
45
        self.FFT_BE_wave = None
        self.signal_type = None
        self.expt_type = None
Chris Smith's avatar
Chris Smith committed
46

47
    @staticmethod
48
    def is_valid_file(data_path):
49
50
51
52
53
        """
        Checks whether the provided file can be read by this translator

        Parameters
        ----------
54
        data_path : str
55
56
57
58
            Path to raw data file

        Returns
        -------
59
60
61
62
        obj : str
            Path to file that will be accepted by the translate() function if
            this translator is indeed capable of translating the provided file.
            Otherwise, None will be returned
63
        """
64
65
66
67
68
69
70
71
        if not isinstance(data_path, (str, unicode)):
            raise TypeError('data_path must be a string')

        ndf = 'newdataformat'

        data_path = path.abspath(data_path)

        if path.isfile(data_path):
72
73
74
75
            ext = data_path.split('.')[-1]
            if ext.lower() not in ['jpg', 'png', 'jpeg', 'tiff', 'mat', 'txt',
                                   'dat', 'xls', 'xlsx']:
                return None
76
77
            # we only care about the folder names at this point...
            data_path, _ = path.split(data_path)
78
79

        # Check if the data is in the new or old format:
80
81
82
83
84
85
86
        # Check one level up:
        _, dir_name = path.split(data_path)
        if dir_name == ndf:
            # Though this translator could also read the files but the NDF Translator is more robust...
            return None
        # Check one level down:
        if ndf in listdir(data_path):
87
            # Though this translator could also read the files but the NDF Translator is more robust...
88
89
90
            return None

        file_path = path.join(data_path, listdir(path=data_path)[0])
91
92

        _, path_dict = BEodfTranslator._parse_file_path(file_path)
93

94
95
        if any([x.find('bigtime_0') > 0 and x.endswith('.dat') for x in path_dict.values()]):
            # This is a G-mode Line experiment:
96
            return None
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
        if any([x.find('bigtime_0') > 0 and x.endswith('.dat') for x in
                path_dict.values()]):
            # This is a G-mode Line experiment:
            return None

        parm_found = any([piece in path_dict.keys() for piece in
                          ['parm_txt', 'old_mat_parms']])
        real_found = any([piece in path_dict.keys() for piece in
                          ['read_real', 'write_real']])
        imag_found = any([piece in path_dict.keys() for piece in
                          ['read_imag', 'write_imag']])

        if parm_found and real_found and imag_found:
            if 'parm_txt' in path_dict.keys():
                return path_dict['parm_txt']
            else:
                return path_dict['old_mat_parms']
114
        else:
115
            return None
116

117
118
    def translate(self, file_path, show_plots=True, save_plots=True,
                  do_histogram=False, verbose=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
119
120
121
122
123
124
125
126
127
128
129
130
131
132
        """
        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
133
134
        verbose : (optional) Boolean
            Whether or not to print statements
Somnath, Suhas's avatar
Somnath, Suhas committed
135
136
137
138
139
140
            
        Returns
        ----------
        h5_path : String / Unicode
            Absolute path of the resultant .h5 file
        """
141
        file_path = path.abspath(file_path)
Somnath, Suhas's avatar
Somnath, Suhas committed
142
        (folder_path, basename) = path.split(file_path)
143
        (basename, path_dict) = self._parse_file_path(file_path)
Unknown's avatar
Unknown committed
144

Somnath, Suhas's avatar
Somnath, Suhas committed
145
        h5_path = path.join(folder_path, basename + '.h5')
Somnath, Suhas's avatar
Somnath, Suhas committed
146
147
        tot_bins_multiplier = 1
        udvs_denom = 2
Unknown's avatar
Unknown committed
148

Somnath, Suhas's avatar
Somnath, Suhas committed
149
        if 'parm_txt' in path_dict.keys():
150
151
            if verbose:
                print('\treading parameters from text file')
Unknown's avatar
Unknown committed
152
            (isBEPS, parm_dict) = parmsToDict(path_dict['parm_txt'])
Somnath, Suhas's avatar
Somnath, Suhas committed
153
        elif 'old_mat_parms' in path_dict.keys():
154
155
            if verbose:
                print('\treading parameters from old mat file')
156
            parm_dict = self.__get_parms_from_old_mat(path_dict['old_mat_parms'], verbose=verbose)
157
158
159
160
            if parm_dict['VS_steps_per_full_cycle'] == 0:
                isBEPS=False
            else:
                isBEPS=True
Somnath, Suhas's avatar
Somnath, Suhas committed
161
        else:
162
            raise IOError('No parameters file found! Cannot translate this dataset!')
163
164
        if verbose:
            print('\tisBEPS = {}'.format(isBEPS))
Unknown's avatar
Unknown committed
165

Somnath, Suhas's avatar
Somnath, Suhas committed
166
167
168
        ignored_plt_grps = []
        if isBEPS:
            parm_dict['data_type'] = 'BEPSData'
Unknown's avatar
Unknown committed
169

Somnath, Suhas's avatar
Somnath, Suhas committed
170
171
            field_mode = parm_dict['VS_measure_in_field_loops']
            std_expt = parm_dict['VS_mode'] != 'load user defined VS Wave from file'
Unknown's avatar
Unknown committed
172

Somnath, Suhas's avatar
Somnath, Suhas committed
173
            if not std_expt:
174
                raise ValueError('This translator does not handle user defined voltage spectroscopy')
Unknown's avatar
Unknown committed
175
176
177

            spec_label = getSpectroscopicParmLabel(parm_dict['VS_mode'])

Somnath, Suhas's avatar
Somnath, Suhas committed
178
            if parm_dict['VS_mode'] in ['DC modulation mode', 'current mode']:
Somnath, Suhas's avatar
Somnath, Suhas committed
179
180
181
182
183
184
185
186
187
188
189
                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
Unknown's avatar
Unknown committed
190

Somnath, Suhas's avatar
Somnath, Suhas committed
191
192
193
        else:
            spec_label = 'None'
            parm_dict['data_type'] = 'BELineData'
Unknown's avatar
Unknown committed
194

Somnath, Suhas's avatar
Somnath, Suhas committed
195
        # Check file sizes:
196
197
198
        if verbose:
            print('\tChecking sizes of real and imaginary data files')

Somnath, Suhas's avatar
Somnath, Suhas committed
199
        if 'read_real' in path_dict.keys():
Somnath, Suhas's avatar
Somnath, Suhas committed
200
201
            real_size = path.getsize(path_dict['read_real'])
            imag_size = path.getsize(path_dict['read_imag'])
Somnath, Suhas's avatar
Somnath, Suhas committed
202
203
204
        else:
            real_size = path.getsize(path_dict['write_real'])
            imag_size = path.getsize(path_dict['write_imag'])
Unknown's avatar
Unknown committed
205

Somnath, Suhas's avatar
Somnath, Suhas committed
206
207
208
        if real_size != imag_size:
            raise ValueError("Real and imaginary file sizes DON'T match!. Ending")

209
        # Check here if a second channel for current is present
210
211
        # Look for the file containing the current data

212
213
        if verbose:
            print('\tLooking for secondary channels')
214
215
        file_names = listdir(folder_path)
        aux_files = []
Unknown's avatar
Unknown committed
216
        current_data_exists = False
217
218
219
220
221
222
223
        for fname in file_names:
            if 'AI2' in fname:
                if 'write' in fname:
                    current_file = path.join(folder_path, fname)
                    current_data_exists=True
                aux_files.append(path.join(folder_path, fname))

Unknown's avatar
Unknown committed
224
        add_pix = False
Somnath, Suhas's avatar
Somnath, Suhas committed
225
226
        num_rows = int(parm_dict['grid_num_rows'])
        num_cols = int(parm_dict['grid_num_cols'])
227
228
        if verbose:
            print('\tRows: {}, Cols: {}'.format(num_rows, num_cols))
Unknown's avatar
Unknown committed
229
230
        num_pix = num_rows * num_cols
        tot_bins = real_size / (num_pix * 4)
Chris Smith's avatar
Chris Smith committed
231
        # Check for case where only a single pixel is missing.
232
233
234
235
        if num_pix == 1:
            check_bins = real_size / (num_pix * 4)
        else:
            check_bins = real_size / ((num_pix - 1) * 4)
Unknown's avatar
Unknown committed
236

237
238
239
240
        if verbose:
            print('\tChecking bins: Total: {}, actual: {}'.format(tot_bins,
                                                                  check_bins))

Unknown's avatar
Unknown committed
241
        if tot_bins % 1 and check_bins % 1:
242
243
            raise ValueError('Aborting! Some parameter appears to have '
                             'changed in-between')
Somnath, Suhas's avatar
Somnath, Suhas committed
244
        elif not tot_bins % 1:
Chris Smith's avatar
Chris Smith committed
245
            # Everything's ok
Somnath, Suhas's avatar
Somnath, Suhas committed
246
247
248
            pass
        elif not check_bins % 1:
            tot_bins = check_bins
249
250
            warn('Warning:  A pixel seems to be missing from the data. '
                 'File will be padded with zeros.')
Unknown's avatar
Unknown committed
251
252
253
254
            add_pix = True

        tot_bins = int(tot_bins) * tot_bins_multiplier

Somnath, Suhas's avatar
Somnath, Suhas committed
255
        if 'parm_mat' in path_dict.keys():
256
257
258
            if verbose:
                print('\treading BE arrays from parameters text file')
            bin_inds, bin_freqs, bin_FFT, ex_wfm = self.__read_parms_mat(path_dict['parm_mat'], isBEPS)
Somnath, Suhas's avatar
Somnath, Suhas committed
259
        elif 'old_mat_parms' in path_dict.keys():
260
261
            if verbose:
                print('\treading BE arrays from old mat text file')
262
            bin_inds, bin_freqs, bin_FFT, ex_wfm, dc_amp_vec = self.__read_old_mat_be_vecs(path_dict['old_mat_parms'], verbose=verbose)
Somnath, Suhas's avatar
Somnath, Suhas committed
263
        else:
264
265
            if verbose:
                print('\tGenerating dummy BE arrays')
Unknown's avatar
Unknown committed
266
            band_width = parm_dict['BE_band_width_[Hz]'] * (0.5 - parm_dict['BE_band_edge_trim'])
Somnath, Suhas's avatar
Somnath, Suhas committed
267
            st_f = parm_dict['BE_center_frequency_[Hz]'] - band_width
Unknown's avatar
Unknown committed
268
            en_f = parm_dict['BE_center_frequency_[Hz]'] + band_width
Somnath, Suhas's avatar
Somnath, Suhas committed
269
            bin_freqs = np.linspace(st_f, en_f, tot_bins, dtype=np.float32)
Unknown's avatar
Unknown committed
270

271
            warn('No parms .mat file found.... Filling dummy values into ancillary datasets.')
Somnath, Suhas's avatar
Somnath, Suhas committed
272
273
274
            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)
Unknown's avatar
Unknown committed
275

Somnath, Suhas's avatar
Somnath, Suhas committed
276
277
278
279
280
        # 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)
281

Somnath, Suhas's avatar
Somnath, Suhas committed
282
        self.FFT_BE_wave = bin_FFT
283

Somnath, Suhas's avatar
Somnath, Suhas committed
284
        if isBEPS:
285
            if verbose:
286
                print('\tBuilding UDVS table for BEPS')
287
            UDVS_labs, UDVS_units, UDVS_mat = self.__build_udvs_table(parm_dict, verbose=verbose)
Unknown's avatar
Unknown committed
288

289
            if verbose:
290
                print('\tTrimming UDVS table to remove unused plot group columns')
291

292
            UDVS_mat, UDVS_labs, UDVS_units = trimUDVS(UDVS_mat, UDVS_labs, UDVS_units, ignored_plt_grps)
Unknown's avatar
Unknown committed
293

294
            old_spec_inds = np.zeros(shape=(2, tot_bins), dtype=INDICES_DTYPE)
Unknown's avatar
Unknown committed
295

296
            # Will assume that all excitation waveforms have same num of bins
Unknown's avatar
Unknown committed
297
298
            num_actual_udvs_steps = UDVS_mat.shape[0] / udvs_denom
            bins_per_step = tot_bins / num_actual_udvs_steps
299
300
301
            if verbose:
                print('\t# UDVS steps: {}, # bins/step: {}'
                      ''.format(num_actual_udvs_steps, bins_per_step))
Unknown's avatar
Unknown committed
302

Somnath, Suhas's avatar
Somnath, Suhas committed
303
            if bins_per_step % 1:
Somnath, Suhas's avatar
Somnath, Suhas committed
304
305
                print('UDVS mat shape: {}, total bins: {}, bins per step: {}'.format(UDVS_mat.shape, tot_bins,
                                                                                     bins_per_step))
306
                raise ValueError('Non integer number of bins per step!')
Unknown's avatar
Unknown committed
307

Somnath, Suhas's avatar
Somnath, Suhas committed
308
309
            bins_per_step = int(bins_per_step)
            num_actual_udvs_steps = int(num_actual_udvs_steps)
Unknown's avatar
Unknown committed
310
311
312

            stind = 0
            for step_index in range(UDVS_mat.shape[0]):
Unknown's avatar
Unknown committed
313
314
315
                if UDVS_mat[step_index, 2] < 1E-3:  # invalid AC amplitude
                    continue
                # Bin step
316
                old_spec_inds[0, stind:stind + bins_per_step] = np.arange(bins_per_step, dtype=INDICES_DTYPE)
Unknown's avatar
Unknown committed
317
                # UDVS step
318
                old_spec_inds[1, stind:stind + bins_per_step] = step_index * np.ones(bins_per_step, dtype=INDICES_DTYPE)
Somnath, Suhas's avatar
Somnath, Suhas committed
319
                stind += bins_per_step
Somnath, Suhas's avatar
Somnath, Suhas committed
320
            del stind, step_index
Unknown's avatar
Unknown committed
321

Somnath, Suhas's avatar
Somnath, Suhas committed
322
        else:  # BE Line
323
324
            if verbose:
                print('\tPreparing supporting variables since BE-Line')
Somnath, Suhas's avatar
Somnath, Suhas committed
325
            self.signal_type = 1
Somnath, Suhas's avatar
Somnath, Suhas committed
326
            self.expt_type = 1  # Stephen has not used this index for some reason
Somnath, Suhas's avatar
Somnath, Suhas committed
327
328
            num_actual_udvs_steps = 1
            bins_per_step = tot_bins
Somnath, Suhas's avatar
Somnath, Suhas committed
329
            UDVS_labs = ['step_num', 'dc_offset', 'ac_amp', 'wave_type', 'wave_mod', 'be-line']
Somnath, Suhas's avatar
Somnath, Suhas committed
330
            UDVS_units = ['', 'V', 'A', '', '', '']
Somnath, Suhas's avatar
Somnath, Suhas committed
331
332
            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
333

Chris Smith's avatar
Chris Smith committed
334
335
            old_spec_inds = np.vstack((np.arange(tot_bins, dtype=INDICES_DTYPE),
                                       np.zeros(tot_bins, dtype=INDICES_DTYPE)))
Unknown's avatar
Unknown committed
336

Somnath, Suhas's avatar
Somnath, Suhas committed
337
338
339
        # Some very basic information that can help the processing / analysis crew
        parm_dict['num_bins'] = tot_bins
        parm_dict['num_pix'] = num_pix
340
        parm_dict['num_udvs_steps'] = num_actual_udvs_steps
Rama Vasudevan's avatar
Rama Vasudevan committed
341
        parm_dict['num_steps'] = num_actual_udvs_steps
Unknown's avatar
Unknown committed
342

343
344
        if verbose:
            print('\tPreparing UDVS slices for region references')
Somnath, Suhas's avatar
Somnath, Suhas committed
345
        udvs_slices = dict()
Somnath, Suhas's avatar
Somnath, Suhas committed
346
        for col_ind, col_name in enumerate(UDVS_labs):
Unknown's avatar
Unknown committed
347
348
            udvs_slices[col_name] = (slice(None), slice(col_ind, col_ind + 1))

Somnath, Suhas's avatar
Somnath, Suhas committed
349
        # Need to add the Bin Waveform type - infer from UDVS        
Unknown's avatar
Unknown committed
350
        exec_bin_vec = self.signal_type * np.ones(len(bin_inds), dtype=np.int32)
Somnath, Suhas's avatar
Somnath, Suhas committed
351
352

        if self.expt_type == 2:
353
354
            if verbose:
                print('\tExperiment type = 2. Doubling BE vectors')
Unknown's avatar
Unknown committed
355
            exec_bin_vec = np.hstack((exec_bin_vec, -1 * exec_bin_vec))
Somnath, Suhas's avatar
Somnath, Suhas committed
356
357
            bin_inds = np.hstack((bin_inds, bin_inds))
            bin_freqs = np.hstack((bin_freqs, bin_freqs))
Somnath, Suhas's avatar
Somnath, Suhas committed
358
            # This is wrong but I don't know what else to do
Somnath, Suhas's avatar
Somnath, Suhas committed
359
            bin_FFT = np.hstack((bin_FFT, bin_FFT))
Unknown's avatar
Unknown committed
360

Somnath, Suhas's avatar
Somnath, Suhas committed
361
        # Create Spectroscopic Values and Spectroscopic Values Labels datasets
362
        # This is an old and legacy way of doing things. Ideally, all we would need ot do is just get the unit values
363
364
        if verbose:
            print('\tCalculating spectroscopic values')
Somnath, Suhas's avatar
Somnath, Suhas committed
365
        spec_vals, spec_inds, spec_vals_labs, spec_vals_units, spec_vals_labs_names = createSpecVals(UDVS_mat,
366
                                                                                                     old_spec_inds,
Somnath, Suhas's avatar
Somnath, Suhas committed
367
368
369
370
371
                                                                                                     bin_freqs,
                                                                                                     exec_bin_vec,
                                                                                                     parm_dict,
                                                                                                     UDVS_labs,
                                                                                                     UDVS_units)
372
373

        if verbose:
374
            print('\t\tspec_vals_labs: {}'.format(spec_vals_labs))
375
376
377
            unit_vals = get_unit_values(spec_inds, spec_vals,
                                        all_dim_names=spec_vals_labs,
                                        is_spec=True, verbose=False)
378
379
380
381
            print('\tUnit spectroscopic values')
            for key, val in unit_vals.items():
                print('\t\t{} : length: {}, values:\n\t\t\t{}'.format(key, len(val), val))

382

383
384
385
386
        # Not sure what is happening here but this should work.
        spec_dim_dict = dict()
        for entry in spec_vals_labs_names:
            spec_dim_dict[entry[0] + '_parameters'] = entry[1]
Chris Smith's avatar
Chris Smith committed
387

Somnath, Suhas's avatar
Somnath, Suhas committed
388
389
390
        spec_vals_slices = dict()

        for row_ind, row_name in enumerate(spec_vals_labs):
Unknown's avatar
Unknown committed
391
            spec_vals_slices[row_name] = (slice(row_ind, row_ind + 1), slice(None))
Somnath, Suhas's avatar
Somnath, Suhas committed
392

393
        if path.exists(h5_path):
394
395
            if verbose:
                print('\tRemoving existing / old translated file: ' + h5_path)
396
            remove(h5_path)
Chris Smith's avatar
Chris Smith committed
397

398
        # First create the file
ssomnath's avatar
ssomnath committed
399
        h5_f = h5py.File(h5_path, mode='w')
Somnath, Suhas's avatar
Somnath, Suhas committed
400

401
        # Then write root level attributes
402
        global_parms = dict()
Somnath, Suhas's avatar
Somnath, Suhas committed
403
404
        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
405
406
407
408
        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
409

Somnath, Suhas's avatar
Somnath, Suhas committed
410
        # assuming that the experiment was completed:
Unknown's avatar
Unknown committed
411
412
        global_parms['current_position_x'] = parm_dict['grid_num_cols'] - 1
        global_parms['current_position_y'] = parm_dict['grid_num_rows'] - 1
Somnath, Suhas's avatar
Somnath, Suhas committed
413
        global_parms['data_type'] = parm_dict['data_type']
Somnath, Suhas's avatar
Somnath, Suhas committed
414
        global_parms['translator'] = 'ODF'
415
416
        if verbose:
            print('\tWriting attributes to HDF5 file root')
417
        write_simple_attrs(h5_f, global_parms)
418
        write_book_keeping_attrs(h5_f)
Unknown's avatar
Unknown committed
419

420
421
        # Then create the measurement group
        h5_meas_group = create_indexed_group(h5_f, 'Measurement')
Unknown's avatar
Unknown committed
422

423
        # Write attributes at the measurement group level
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
        keys = list(parm_dict.keys())
        keys.sort()
        """
        for key in keys:
            print('{} : {}'.format(key, main_dsets[0].parent.parent.attrs[key]))
        """
        nest_parm_dict = dict()
        for key in ['FORC', 'VS', 'grid', 'BE', 'IO', 'File', 'Misc']:
            nest_parm_dict[key] = dict()
        for key in keys:
            parts = key.split('_')
            parent = 'Misc'
            rem_key = key
            if len(parts) > 1:
                if parts[0] in nest_parm_dict.keys():
                    parent = parts[0]
                    rem_key = '_'.join(parts[1:])
            nest_parm_dict[parent].update(
                {rem_key: parm_dict[key]})

444
445
        if verbose:
            print('\twriting attributes to Measurement group')
446
447
448
449
            keys = list(parm_dict.keys())
            keys.sort()
            for key in keys:
                print('\t\t{} : {}'.format(key, parm_dict[key]))
450
        write_simple_attrs(h5_meas_group, parm_dict)
Unknown's avatar
Unknown committed
451

452
453
        # Create the Channel group
        h5_chan_grp = create_indexed_group(h5_meas_group, 'Channel')
Unknown's avatar
Unknown committed
454

455
        # Write channel group attributes
Rama Vasudevan's avatar
Rama Vasudevan committed
456
457
        write_simple_attrs(h5_chan_grp, {'Channel_Input': 'IO_Analog_Input_1',
                                         'channel_type': 'BE'})
Unknown's avatar
Unknown committed
458

459
        # Now the datasets!
460
461
        if verbose:
            print('\tCreating ancillary datasets')
Chris Smith's avatar
Chris Smith committed
462
        h5_chan_grp.create_dataset('Excitation_Waveform', data=ex_wfm)
Unknown's avatar
Unknown committed
463

464
465
466
        h5_udvs = h5_chan_grp.create_dataset('UDVS', data=UDVS_mat)
        write_region_references(h5_udvs, udvs_slices, add_labels_attr=True, verbose=verbose)
        write_simple_attrs(h5_udvs, {'units': UDVS_units}, verbose=verbose)
467

Chris Smith's avatar
Chris Smith committed
468
        h5_chan_grp.create_dataset('UDVS_Indices', data=old_spec_inds[1])
469

Chris Smith's avatar
Chris Smith committed
470
471
        h5_chan_grp.create_dataset('Bin_Step', data=np.arange(bins_per_step, dtype=INDICES_DTYPE),
                                   dtype=INDICES_DTYPE)
472

Chris Smith's avatar
Chris Smith committed
473
474
475
476
        h5_chan_grp.create_dataset('Bin_Indices', data=bin_inds, dtype=INDICES_DTYPE)
        h5_chan_grp.create_dataset('Bin_Frequencies', data=bin_freqs)
        h5_chan_grp.create_dataset('Bin_FFT', data=bin_FFT)
        h5_chan_grp.create_dataset('Bin_Wfm_Type', data=exec_bin_vec)
477

478
479
480
481
482
        if verbose:
            print('\tWriting Position datasets')

        pos_dims = [Dimension('X', 'm', np.arange(num_cols)),
                    Dimension('Y', 'm', np.arange(num_rows))]
483
        h5_pos_ind, h5_pos_val = write_ind_val_dsets(h5_chan_grp, pos_dims, is_spectral=False, verbose=verbose)
484
485
        if verbose:
            print('\tPosition datasets of shape: {}'.format(h5_pos_ind.shape))
486

487
        if verbose:
488
            print('\tWriting Spectroscopic datasets of shape: {}'.format(spec_inds.shape))
489
490
491
492
493
        h5_spec_inds = h5_chan_grp.create_dataset('Spectroscopic_Indices', data=spec_inds, dtype=INDICES_DTYPE)        
        h5_spec_vals = h5_chan_grp.create_dataset('Spectroscopic_Values', data=np.array(spec_vals), dtype=VALUES_DTYPE)
        for dset in [h5_spec_inds, h5_spec_vals]:
            write_region_references(dset, spec_vals_slices, add_labels_attr=True, verbose=verbose)
            write_simple_attrs(dset, {'units': spec_vals_units}, verbose=verbose)
494
            write_simple_attrs(dset, spec_dim_dict)
495
496

        # Noise floor should be of shape: (udvs_steps x 3 x positions)
497
498
        if verbose:
            print('\tWriting noise floor dataset')
Chris Smith's avatar
Chris Smith committed
499
500
        h5_chan_grp.create_dataset('Noise_Floor', (num_pix, num_actual_udvs_steps), dtype=nf32,
                                   chunks=(1, num_actual_udvs_steps))
501
502
503
504
505
506
507
508
509
510
511

        """
        New Method for chunking the Main_Data dataset.  Chunking is now done in N-by-N squares
        of UDVS steps by pixels.  N is determined dynamically based on the dimensions of the
        dataset.  Currently it is set such that individual chunks are less than 10kB in size.

        Chris Smith -- csmith55@utk.edu
        """
        BEPS_chunks = calc_chunks([num_pix, tot_bins],
                                  np.complex64(0).itemsize,
                                  unit_chunks=(1, bins_per_step))
512
513
        if verbose:
            print('\tHDF5 dataset will have chunks of size: {}'.format(BEPS_chunks))
514
            print('\tCreating empty main dataset of shape: ({}, {})'.format(num_pix, tot_bins))
515
516
517
518
        self.h5_raw = write_main_dataset(h5_chan_grp, (num_pix, tot_bins), 'Raw_Data', 'Piezoresponse', 'V', None, None,
                                         dtype=np.complex64, chunks=BEPS_chunks, compression='gzip',
                                         h5_pos_inds=h5_pos_ind, h5_pos_vals=h5_pos_val, h5_spec_inds=h5_spec_inds,
                                         h5_spec_vals=h5_spec_vals, verbose=verbose)
Somnath, Suhas's avatar
Somnath, Suhas committed
519

520
521
522
523
        if verbose:
            print('\tReading data from binary data files into raw HDF5')
        self._read_data(UDVS_mat, parm_dict, path_dict, real_size, isBEPS,
                        add_pix, verbose=verbose)
Unknown's avatar
Unknown committed
524

525
526
        if verbose:
            print('\tGenerating plot groups')
527
        generatePlotGroups(self.h5_raw, self.mean_resp, folder_path, basename,
Somnath, Suhas's avatar
Somnath, Suhas committed
528
                           self.max_resp, self.min_resp, max_mem_mb=self.max_ram,
Somnath, Suhas's avatar
Somnath, Suhas committed
529
                           spec_label=spec_label, show_plots=show_plots, save_plots=save_plots,
Unknown's avatar
Unknown committed
530
                           do_histogram=do_histogram, debug=verbose)
531
532
        if verbose:
            print('\tUpgrading to USIDataset')
533
        self.h5_raw = USIDataset(self.h5_raw)
Unknown's avatar
Unknown committed
534
535
536

        # Go ahead and read the current data in the second (current) channel
        if current_data_exists:                     #If a .dat file matches
537
538
            if verbose:
                print('\tReading data in secondary channels (current)')
539
540
            self._read_secondary_channel(h5_meas_group, aux_files)

541
542
        if verbose:
            print('\tClosing HDF5 file')
543
        h5_f.close()
Unknown's avatar
Unknown committed
544

Somnath, Suhas's avatar
Somnath, Suhas committed
545
        return h5_path
Chris Smith's avatar
Chris Smith committed
546

547
548
    def _read_data(self, UDVS_mat, parm_dict, path_dict, real_size, isBEPS,
                   add_pix, verbose=False):
Chris Smith's avatar
Chris Smith committed
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
        """
        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
567
568
        verbose : bool, optional. Default = False
            Whether or not to print logs
Chris Smith's avatar
Chris Smith committed
569
570
571
572
573
574
575
576

        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)
577
578
            if verbose:
                print('\t\tReading all raw data for BE-Line in one shot')
579
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
580
581
        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)
582
583
            if verbose:
                print('\t\tReading all raw BEPS (out-of-field) data in one shot')
584
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
585
586
        elif real_size < self.max_ram and parm_dict['VS_measure_in_field_loops'] == 'in-field':
            # Do this for in-field only
587
588
            if verbose:
                print('\t\tReading all raw BEPS (in-field only) data in one shot')
589
            self.__quick_read_data(path_dict['write_real'], path_dict['write_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
590
591
        else:
            # Large BEPS datasets OR those with in-and-out of field
592
593
            if verbose:
                print('\t\tReading all raw data for in and out of filed OR very large file')
Somnath, Suhas's avatar
Somnath, Suhas committed
594
            self.__read_beps_data(path_dict, UDVS_mat.shape[0], parm_dict['VS_measure_in_field_loops'], add_pix)
595
        self.h5_raw.file.flush()
Chris Smith's avatar
Chris Smith committed
596

Somnath, Suhas's avatar
Somnath, Suhas committed
597
    def __read_beps_data(self, path_dict, udvs_steps, mode, add_pixel=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
        """
        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
        """
Unknown's avatar
Unknown committed
616

Somnath, Suhas's avatar
Somnath, Suhas committed
617
        print('---- reading pixel-by-pixel ----------')
Unknown's avatar
Unknown committed
618
619
620
621

        bytes_per_pix = self.h5_raw.shape[1] * 4
        step_size = self.h5_raw.shape[1] / udvs_steps

Somnath, Suhas's avatar
Somnath, Suhas committed
622
        if mode == 'out-of-field':
Unknown's avatar
Unknown committed
623
            parsers = [BEodfParser(path_dict['read_real'], path_dict['read_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
624
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
625
        elif mode == 'in-field':
Unknown's avatar
Unknown committed
626
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
627
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
628
629
        elif mode == 'in and out-of-field':
            # each file will only have half the udvs steps:
Unknown's avatar
Unknown committed
630
            if 0.5 * udvs_steps % 1:
631
632
                raise ValueError('Odd number of UDVS')

Unknown's avatar
Unknown committed
633
            udvs_steps = int(0.5 * udvs_steps)
Somnath, Suhas's avatar
Somnath, Suhas committed
634
            # be careful - each pair contains only half the necessary bins - so read half
Unknown's avatar
Unknown committed
635
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
636
                                   self.h5_raw.shape[0], int(bytes_per_pix / 2)),
Unknown's avatar
Unknown committed
637
638
639
                       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
640
            if step_size % 1:
641
642
                raise ValueError('strange number of bins per UDVS step. Exiting')

Somnath, Suhas's avatar
Somnath, Suhas committed
643
            step_size = int(step_size)
644

645
646
        rand_spectra = self.__get_random_spectra(parsers, self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
647
        take_conjugate = requires_conjugate(rand_spectra, cores=self._cores)
648

Somnath, Suhas's avatar
Somnath, Suhas committed
649
650
651
652
        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)

Unknown's avatar
Unknown committed
653
        numpix = self.h5_raw.shape[0]
Somnath, Suhas's avatar
Somnath, Suhas committed
654
655
656
        """ 
        Don't try to do the last step if a pixel is missing.   
        This will be handled after the loop. 
Unknown's avatar
Unknown committed
657
658
659
660
        """
        if add_pixel:
            numpix -= 1

Somnath, Suhas's avatar
Somnath, Suhas committed
661
        for pix_indx in range(numpix):
Somnath, Suhas's avatar
Somnath, Suhas committed
662
            if self.h5_raw.shape[0] > 5:
Unknown's avatar
Unknown committed
663
664
665
                if pix_indx % int(round(self.h5_raw.shape[0] / 10)) == 0:
                    print('Reading... {} complete'.format(round(100 * pix_indx / self.h5_raw.shape[0])))

Somnath, Suhas's avatar
Somnath, Suhas committed
666
667
668
            # get the raw stream from each parser
            pxl_data = list()
            for prsr in parsers:
Somnath, Suhas's avatar
Somnath, Suhas committed
669
                pxl_data.append(prsr.read_pixel())
Unknown's avatar
Unknown committed
670

Somnath, Suhas's avatar
Somnath, Suhas committed
671
672
673
674
675
            # 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]
Unknown's avatar
Unknown committed
676

Somnath, Suhas's avatar
Somnath, Suhas committed
677
678
                in_fld_2 = in_fld.reshape(udvs_steps, step_size)
                out_fld_2 = out_fld.reshape(udvs_steps, step_size)
Unknown's avatar
Unknown committed
679
                raw_mat = np.empty((udvs_steps * 2, step_size), dtype=out_fld.dtype)
Somnath, Suhas's avatar
Somnath, Suhas committed
680
681
                raw_mat[0::2, :] = in_fld_2
                raw_mat[1::2, :] = out_fld_2
Somnath, Suhas's avatar
Somnath, Suhas committed
682
683
                raw_vec = raw_mat.reshape(in_fld.size + out_fld.size).transpose()
            else:
Somnath, Suhas's avatar
Somnath, Suhas committed
684
                raw_vec = pxl_data[0]  # only one parser
Somnath, Suhas's avatar
Somnath, Suhas committed
685
686
            self.max_resp[pix_indx] = np.max(np.abs(raw_vec))
            self.min_resp[pix_indx] = np.min(np.abs(raw_vec))
Unknown's avatar
Unknown committed
687
            self.mean_resp = (1 / (pix_indx + 1)) * (raw_vec + pix_indx * self.mean_resp)
688
689
690

            if take_conjugate:
                raw_vec = np.conjugate(raw_vec)
691
            self.h5_raw[pix_indx, :] = np.complex64(raw_vec[:])
692
            self.h5_raw.file.flush()
Unknown's avatar
Unknown committed
693

Somnath, Suhas's avatar
Somnath, Suhas committed
694
        # Add zeros to main_data for the missing pixel. 
Unknown's avatar
Unknown committed
695
696
697
        if add_pixel:
            self.h5_raw[-1, :] = 0 + 0j

Somnath, Suhas's avatar
Somnath, Suhas committed
698
        print('---- Finished reading files -----')
699
700

    def __quick_read_data(self, real_path, imag_path, udvs_steps):
Somnath, Suhas's avatar
Somnath, Suhas committed
701
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
702
703
704
705
706
707
708
709
        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
710
711
        udvs_steps : unsigned int
            Number of UDVS steps
Somnath, Suhas's avatar
Somnath, Suhas committed
712
        """
Unknown's avatar
Unknown committed
713
        print('---- reading all data at once ----------')
Somnath, Suhas's avatar
Somnath, Suhas committed
714

Unknown's avatar
Unknown committed
715
        parser = BEodfParser(real_path, imag_path, self.h5_raw.shape[0], self.h5_raw.shape[1] * 4)
716
717

        step_size = self.h5_raw.shape[1] / udvs_steps
718
719
        rand_spectra = self.__get_random_spectra([parser], self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
720
        take_conjugate = requires_conjugate(rand_spectra, cores=self._cores)
Somnath, Suhas's avatar
Somnath, Suhas committed
721
        raw_vec = parser.read_all_data()
722
        if take_conjugate:
723
            print('Taking conjugate to ensure positive Quality factors')
724
            raw_vec = np.conjugate(raw_vec)
Unknown's avatar
Unknown committed
725

Rama Vasudevan's avatar
Rama Vasudevan committed
726
727
728
729
730
731
732
733
734
735
736
737
        if raw_vec.shape != np.prod(self.h5_raw.shape):
            percentage_padded = 100 * (np.prod(self.h5_raw.shape) - raw_vec.shape) / np.prod(self.h5_raw.shape)
            print('Warning! Raw data length {} is not matching placeholder length {}. '
                  'Padding zeros for {}% of the data!'.format(raw_vec.shape, np.prod(self.h5_raw.shape), percentage_padded))

            padded_raw_vec = np.zeros(np.prod(self.h5_raw.shape), dtype = np.complex64)

            padded_raw_vec[:raw_vec.shape[0]] = raw_vec
            raw_mat = padded_raw_vec.reshape(self.h5_raw.shape[0], self.h5_raw.shape[1])
        else:
            raw_mat = raw_vec.reshape(self.h5_raw.shape[0], self.h5_raw.shape[1])

Unknown's avatar
Unknown committed
738

Somnath, Suhas's avatar
Somnath, Suhas committed
739
        # Write to the h5 dataset:
Somnath, Suhas's avatar
Somnath, Suhas committed
740
741
742
        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)
743
        self.h5_raw[:, :] = np.complex64(raw_mat)
744
        self.h5_raw.file.flush()
Somnath, Suhas's avatar
Somnath, Suhas committed
745

Unknown's avatar
Unknown committed
746
747
        print('---- Finished reading files -----')

748
749
    @staticmethod
    def _parse_file_path(data_filepath):
Somnath, Suhas's avatar
Somnath, Suhas committed
750
751
752
753
754
755
756
        """
        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
757
            Absolute path of any file in the same directory as the .dat files
Somnath, Suhas's avatar
Somnath, Suhas committed
758
759
760
761
762
763
764
765
766
        
        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)
Unknown's avatar
Unknown committed
767
        (super_folder, basename) = path.split(folder_path)
Somnath, Suhas's avatar
Somnath, Suhas committed
768

769
770
        if basename.endswith('_d') or basename.endswith('_c'):
            # Old old data format where the folder ended with a _d or _c to denote a completed spectroscopic run
Somnath, Suhas's avatar
Somnath, Suhas committed
771
772
773
774
775
776
777
778
            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()
Unknown's avatar
Unknown committed
779

Somnath, Suhas's avatar
Somnath, Suhas committed
780
        for file_name in listdir(folder_path):
Chris Smith's avatar
Chris Smith committed
781
            abs_path = path.join(folder_path, file_name)
Somnath, Suhas's avatar
Somnath, Suhas committed
782
783
784
785
786
            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
Unknown's avatar
Unknown committed
787
                elif file_name == (basename + '.mat'):
Somnath, Suhas's avatar
Somnath, Suhas committed
788
789
790
791
792
793
794
795
796
797
798
799
                    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
800
        return basename, path_dict
Somnath, Suhas's avatar
Somnath, Suhas committed
801

802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
    def _read_secondary_channel(self, h5_meas_group, aux_file_path):
        """
        Reads secondary channel stored in AI .mat file
        Currently works for in-field measurements only, but should be updated to
        include both in and out of field measurements

        Parameters
        -----------
        h5_meas_group : h5 group
            Reference to the Measurement group
        aux_file_path : String / Unicode
            Absolute file path of the secondary channel file.
        """
        print('---- Reading Secondary Channel  ----------')
        if len(aux_file_path)>1:
            print('Detected multiple files, assuming in and out of field')
            aux_file_paths = aux_file_path
        else:
            aux_file_paths = list(aux_file_path)

        freq_index = self.h5_raw.spec_dim_labels.index('Frequency')
        num_pix = self.h5_raw.shape[0]
        spectral_len = 1

        for i in range(len(self.h5_raw.spec_dim_sizes)):
            if i == freq_index:
                continue
            spectral_len = spectral_len * self.h5_raw.spec_dim_sizes[i]

        #num_forc_cycles = self.h5_raw.spec_dim_sizes[self.h5_raw.spec_dim_labels.index("FORC")]
        #num_dc_steps =  self.h5_raw.spec_dim_sizes[self.h5_raw.spec_dim_labels.index("DC_Offset")]

        # create a new channel
        h5_current_channel_group = create_indexed_group(h5_meas_group, 'Channel')

        # Copy attributes from the main channel
        copy_attributes(self.h5_raw.parent, h5_current_channel_group)

        # Modify attributes that are different
        write_simple_attrs(h5_current_channel_group, {'Channel_Input': 'IO_Analog_Input_2',
                                                      'channel_type': 'Current'}, verbose=True)

        #Get the reduced dimensions
845
        h5_current_spec_inds, h5_current_spec_values = write_reduced_anc_dsets(h5_current_channel_group,
846
                                                        self.h5_raw.h5_spec_inds,
847
                                                        self.h5_raw.h5_spec_vals, 'Frequency', is_spec=True)
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902


        h5_current_main = write_main_dataset(h5_current_channel_group,  # parent HDF5 group
                                             (num_pix, spectral_len),  # shape of Main dataset
                                             'Raw_Data',  # Name of main dataset
                                             'Current',  # Physical quantity contained in Main dataset
                                             'nA',  # Units for the physical quantity
                                             None,  # Position dimensions
                                             None,  # Spectroscopic dimensions
                                             h5_pos_inds=self.h5_raw.h5_pos_inds,
                                             h5_pos_vals=self.h5_raw.h5_pos_vals,
                                             h5_spec_inds=h5_current_spec_inds,
                                             h5_spec_vals=h5_current_spec_values,
                                             dtype=np.float32,  # data type / precision
                                             main_dset_attrs={'IO_rate': 4E+6, 'Amplifier_Gain': 9})

        # Now calculate the number of positions that can be stored in memory in one go.
        b_per_position = np.float32(0).itemsize * spectral_len

        max_pos_per_read = int(np.floor((get_available_memory()) / b_per_position))

        # if self._verbose:
        print('Allowed to read {} pixels per chunk'.format(max_pos_per_read))

        #Open the read and write files and write them to the hdf5 file
        for aux_file in aux_file_paths:
            if 'write' in aux_file:
                infield = True
            else:
                infield=False

            cur_file = open(aux_file, "rb")

            start_pix = 0

            while start_pix < num_pix:
                end_pix = min(num_pix, start_pix + max_pos_per_read)

                # TODO: Fix for when it won't fit in memory.

                #if max_pos_per_read * b_per_position > num_pix * b_per_position:
                cur_data = np.frombuffer(cur_file.read(), dtype='f')
                #else:
                #cur_data = np.frombuffer(cur_file.read(max_pos_per_read * b_per_position), dtype='f')

                cur_data = cur_data.reshape(end_pix - start_pix, spectral_len//2)

                # Write to h5
                if infield:
                    h5_current_main[start_pix:end_pix, ::2] = cur_data
                else:
                    h5_current_main[start_pix:end_pix, 1::2] = cur_data
                start_pix = end_pix


Somnath, Suhas's avatar
Somnath, Suhas committed
903
    @staticmethod
904
    def __read_old_mat_be_vecs(file_path, verbose=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
        """
        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
        """
Unknown's avatar
Unknown committed
928
        matread = loadmat(file_path, squeeze_me=True)
929
        #TODO: What about key errors?
Somnath, Suhas's avatar
Somnath, Suhas committed
930
        BE_wave = matread['BE_wave']
Unknown's avatar
Unknown committed
931
        bin_inds = matread['bin_ind'] - 1  # Python base 0
Somnath, Suhas's avatar
Somnath, Suhas committed
932
933
        bin_w = matread['bin_w']
        dc_amp_vec_full = matread['dc_amp_vec_full']
934
935
936
937
938
939
940
941
        if verbose:
            for vec, var_name in zip([BE_wave, bin_inds, bin_w, dc_amp_vec_full],
                                     ['BE_wave', 'bin_inds', 'bin_w', 'dc_amp_vec_full']):
                print('\t\t{} has shape: {} and dtype: {}'.format(var_name, vec.shape, vec.dtype))
        try:
            FFT_full = np.fft.fftshift(np.fft.fft(BE_wave))
        except ValueError:
            FFT_full = BE_wave
942
943
944
945
        try:
            bin_FFT = np.conjugate(FFT_full[bin_inds])
        except IndexError:
            bin_FFT = FFT_full
Somnath, Suhas's avatar
Somnath, Suhas committed
946
        return bin_inds, bin_w, bin_FFT, BE_wave, dc_amp_vec_full
Unknown's avatar
Unknown committed
947

948
    @staticmethod
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
988
989
990
991
992
993
994
995
996
997
    def __infer_frac_phase(slopes):
        """
        Infers the VS cycle fraction and phase when parameters were
        stored in old mat files

        Parameters
        --------------------
        slopes : list / tuple
            Array of mean slopes of each fraction of a SINGLE cycle

        Returns
        --------------------
        tuple:
            fraction : float
                Fraction of VS cycle
            phase : float
                Phase offset for VS cycle
        """
        if all([_ > 0 for _ in slopes]):
            return 0.25, 0
        elif all([_ < 0 for _ in slopes]):
            return 0.25, 0.75
        elif all([_ > 0 for _ in slopes[:2]]) and all(
                [_ < 0 for _ in slopes[2:]]):
            return 0.5, 0
        elif all([_ < 0 for _ in slopes[:2]]) and all(
                [_ > 0 for _ in slopes[2:]]):
            return 0.5, 0.5
        elif all([_ > 0 for _ in slopes[:1]]) and all(
                [_ < 0 for _ in slopes[1:]]):
            return 0.75, 0
        elif all([_ > 0 for _ in slopes[:3]]) and all(
                [_ < 0 for _ in slopes[3:]]):
            return 0.75, 0.25
        elif all([_ < 0 for _ in slopes[:1]]) and all(
                [_ > 0 for _ in slopes[1:]]):
            return 0.75, 0.5
        elif all([_ < 0 for _ in slopes[:3]]) and all(
                [_ > 0 for _ in slopes[3:]]):
            return 0.75, 0.75
        elif slopes[0] > 0 and slopes[1] < 0 and slopes[2] < 0 and slopes[
            3] > 0:
            return 1, 0
        elif slopes[0] < 0 and slopes[1] > 0 and slopes[2] > 0 and slopes[
            3] < 0:
            return 1, 0.5
        else:
            return 0, 0

Somnath, Suhas's avatar
Somnath, Suhas committed
998
    @staticmethod
999
    def __get_parms_from_old_mat(file_path, verbose=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
1000
        """