be_odf.py 65.8 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, 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
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
    def translate(self, file_path, show_plots=True, save_plots=True, do_histogram=False, verbose=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
118
119
120
121
122
123
124
125
126
127
128
129
130
131
        """
        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
132
133
        verbose : (optional) Boolean
            Whether or not to print statements
Somnath, Suhas's avatar
Somnath, Suhas committed
134
135
136
137
138
139
            
        Returns
        ----------
        h5_path : String / Unicode
            Absolute path of the resultant .h5 file
        """
140
        file_path = path.abspath(file_path)
Somnath, Suhas's avatar
Somnath, Suhas committed
141
        (folder_path, basename) = path.split(file_path)
142
        (basename, path_dict) = self._parse_file_path(file_path)
Unknown's avatar
Unknown committed
143

Somnath, Suhas's avatar
Somnath, Suhas committed
144
        h5_path = path.join(folder_path, basename + '.h5')
Somnath, Suhas's avatar
Somnath, Suhas committed
145
146
        tot_bins_multiplier = 1
        udvs_denom = 2
Unknown's avatar
Unknown committed
147

Somnath, Suhas's avatar
Somnath, Suhas committed
148
        if 'parm_txt' in path_dict.keys():
149
150
            if verbose:
                print('\treading parameters from text file')
Unknown's avatar
Unknown committed
151
            (isBEPS, parm_dict) = parmsToDict(path_dict['parm_txt'])
Somnath, Suhas's avatar
Somnath, Suhas committed
152
        elif 'old_mat_parms' in path_dict.keys():
153
154
            if verbose:
                print('\treading parameters from old mat file')
Somnath, Suhas's avatar
Somnath, Suhas committed
155
            parm_dict = self.__get_parms_from_old_mat(path_dict['old_mat_parms'])
156
157
158
159
            if parm_dict['VS_steps_per_full_cycle'] == 0:
                isBEPS=False
            else:
                isBEPS=True
Somnath, Suhas's avatar
Somnath, Suhas committed
160
        else:
161
            raise IOError('No parameters file found! Cannot translate this dataset!')
162
163
        if verbose:
            print('\tisBEPS = {}'.format(isBEPS))
Unknown's avatar
Unknown committed
164

Somnath, Suhas's avatar
Somnath, Suhas committed
165
166
167
        ignored_plt_grps = []
        if isBEPS:
            parm_dict['data_type'] = 'BEPSData'
Unknown's avatar
Unknown committed
168

Somnath, Suhas's avatar
Somnath, Suhas committed
169
170
            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
171

Somnath, Suhas's avatar
Somnath, Suhas committed
172
            if not std_expt:
173
                raise ValueError('This translator does not handle user defined voltage spectroscopy')
Unknown's avatar
Unknown committed
174
175
176

            spec_label = getSpectroscopicParmLabel(parm_dict['VS_mode'])

Somnath, Suhas's avatar
Somnath, Suhas committed
177
            if parm_dict['VS_mode'] in ['DC modulation mode', 'current mode']:
Somnath, Suhas's avatar
Somnath, Suhas committed
178
179
180
181
182
183
184
185
186
187
188
                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
189

Somnath, Suhas's avatar
Somnath, Suhas committed
190
191
192
        else:
            spec_label = 'None'
            parm_dict['data_type'] = 'BELineData'
Unknown's avatar
Unknown committed
193

Somnath, Suhas's avatar
Somnath, Suhas committed
194
        # Check file sizes:
195
196
197
        if verbose:
            print('\tChecking sizes of real and imaginary data files')

Somnath, Suhas's avatar
Somnath, Suhas committed
198
        if 'read_real' in path_dict.keys():
Somnath, Suhas's avatar
Somnath, Suhas committed
199
200
            real_size = path.getsize(path_dict['read_real'])
            imag_size = path.getsize(path_dict['read_imag'])
Somnath, Suhas's avatar
Somnath, Suhas committed
201
202
203
        else:
            real_size = path.getsize(path_dict['write_real'])
            imag_size = path.getsize(path_dict['write_imag'])
Unknown's avatar
Unknown committed
204

Somnath, Suhas's avatar
Somnath, Suhas committed
205
206
207
        if real_size != imag_size:
            raise ValueError("Real and imaginary file sizes DON'T match!. Ending")

208
        # Check here if a second channel for current is present
209
210
        # Look for the file containing the current data

211
212
        if verbose:
            print('\tLooking for secondary channels')
213
214
        file_names = listdir(folder_path)
        aux_files = []
Unknown's avatar
Unknown committed
215
        current_data_exists = False
216
217
218
219
220
221
222
        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
223
        add_pix = False
Somnath, Suhas's avatar
Somnath, Suhas committed
224
225
        num_rows = int(parm_dict['grid_num_rows'])
        num_cols = int(parm_dict['grid_num_cols'])
226
227
        if verbose:
            print('\tRows: {}, Cols: {}'.format(num_rows, num_cols))
Unknown's avatar
Unknown committed
228
229
        num_pix = num_rows * num_cols
        tot_bins = real_size / (num_pix * 4)
Chris Smith's avatar
Chris Smith committed
230
        # Check for case where only a single pixel is missing.
231
232
233
234
        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
235

236
237
238
239
        if verbose:
            print('\tChecking bins: Total: {}, actual: {}'.format(tot_bins,
                                                                  check_bins))

Unknown's avatar
Unknown committed
240
        if tot_bins % 1 and check_bins % 1:
241
242
            raise ValueError('Aborting! Some parameter appears to have '
                             'changed in-between')
Somnath, Suhas's avatar
Somnath, Suhas committed
243
        elif not tot_bins % 1:
Chris Smith's avatar
Chris Smith committed
244
            # Everything's ok
Somnath, Suhas's avatar
Somnath, Suhas committed
245
246
247
            pass
        elif not check_bins % 1:
            tot_bins = check_bins
248
249
            warn('Warning:  A pixel seems to be missing from the data. '
                 'File will be padded with zeros.')
Unknown's avatar
Unknown committed
250
251
252
253
            add_pix = True

        tot_bins = int(tot_bins) * tot_bins_multiplier

Somnath, Suhas's avatar
Somnath, Suhas committed
254
        if 'parm_mat' in path_dict.keys():
255
256
257
            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
258
        elif 'old_mat_parms' in path_dict.keys():
259
260
261
            if verbose:
                print('\treading BE arrays from old mat text file')
            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
262
        else:
263
264
            if verbose:
                print('\tGenerating dummy BE arrays')
Unknown's avatar
Unknown committed
265
            band_width = parm_dict['BE_band_width_[Hz]'] * (0.5 - parm_dict['BE_band_edge_trim'])
Somnath, Suhas's avatar
Somnath, Suhas committed
266
            st_f = parm_dict['BE_center_frequency_[Hz]'] - band_width
Unknown's avatar
Unknown committed
267
            en_f = parm_dict['BE_center_frequency_[Hz]'] + band_width
Somnath, Suhas's avatar
Somnath, Suhas committed
268
            bin_freqs = np.linspace(st_f, en_f, tot_bins, dtype=np.float32)
Unknown's avatar
Unknown committed
269

270
            warn('No parms .mat file found.... Filling dummy values into ancillary datasets.')
Somnath, Suhas's avatar
Somnath, Suhas committed
271
272
273
            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
274

Somnath, Suhas's avatar
Somnath, Suhas committed
275
276
277
278
279
        # 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)
280

Somnath, Suhas's avatar
Somnath, Suhas committed
281
        self.FFT_BE_wave = bin_FFT
282

Somnath, Suhas's avatar
Somnath, Suhas committed
283
        if isBEPS:
284
            if verbose:
285
                print('\tBuilding UDVS table for BEPS')
286
            UDVS_labs, UDVS_units, UDVS_mat = self.__build_udvs_table(parm_dict, verbose=verbose)
Unknown's avatar
Unknown committed
287

288
            if verbose:
289
                print('\tTrimming UDVS table to remove unused plot group columns')
290
            UDVS_mat, UDVS_labs, UDVS_units = trimUDVS(UDVS_mat, UDVS_labs, UDVS_units, ignored_plt_grps)
Unknown's avatar
Unknown committed
291

292
            old_spec_inds = np.zeros(shape=(2, tot_bins), dtype=INDICES_DTYPE)
Unknown's avatar
Unknown committed
293

294
            # Will assume that all excitation waveforms have same num of bins
Unknown's avatar
Unknown committed
295
296
            num_actual_udvs_steps = UDVS_mat.shape[0] / udvs_denom
            bins_per_step = tot_bins / num_actual_udvs_steps
297
298
299
            if verbose:
                print('\t# UDVS steps: {}, # bins/step: {}'
                      ''.format(num_actual_udvs_steps, bins_per_step))
Unknown's avatar
Unknown committed
300

Somnath, Suhas's avatar
Somnath, Suhas committed
301
            if bins_per_step % 1:
Somnath, Suhas's avatar
Somnath, Suhas committed
302
303
                print('UDVS mat shape: {}, total bins: {}, bins per step: {}'.format(UDVS_mat.shape, tot_bins,
                                                                                     bins_per_step))
304
                raise ValueError('Non integer number of bins per step!')
Unknown's avatar
Unknown committed
305

Somnath, Suhas's avatar
Somnath, Suhas committed
306
307
            bins_per_step = int(bins_per_step)
            num_actual_udvs_steps = int(num_actual_udvs_steps)
Unknown's avatar
Unknown committed
308
309
310

            stind = 0
            for step_index in range(UDVS_mat.shape[0]):
Unknown's avatar
Unknown committed
311
312
313
                if UDVS_mat[step_index, 2] < 1E-3:  # invalid AC amplitude
                    continue
                # Bin step
314
                old_spec_inds[0, stind:stind + bins_per_step] = np.arange(bins_per_step, dtype=INDICES_DTYPE)
Unknown's avatar
Unknown committed
315
                # UDVS step
316
                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
317
                stind += bins_per_step
Somnath, Suhas's avatar
Somnath, Suhas committed
318
            del stind, step_index
Unknown's avatar
Unknown committed
319

Somnath, Suhas's avatar
Somnath, Suhas committed
320
        else:  # BE Line
321
322
            if verbose:
                print('\tPreparing supporting variables since BE-Line')
Somnath, Suhas's avatar
Somnath, Suhas committed
323
            self.signal_type = 1
Somnath, Suhas's avatar
Somnath, Suhas committed
324
            self.expt_type = 1  # Stephen has not used this index for some reason
Somnath, Suhas's avatar
Somnath, Suhas committed
325
326
            num_actual_udvs_steps = 1
            bins_per_step = tot_bins
Somnath, Suhas's avatar
Somnath, Suhas committed
327
            UDVS_labs = ['step_num', 'dc_offset', 'ac_amp', 'wave_type', 'wave_mod', 'be-line']
Somnath, Suhas's avatar
Somnath, Suhas committed
328
            UDVS_units = ['', 'V', 'A', '', '', '']
Somnath, Suhas's avatar
Somnath, Suhas committed
329
330
            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
331

Chris Smith's avatar
Chris Smith committed
332
333
            old_spec_inds = np.vstack((np.arange(tot_bins, dtype=INDICES_DTYPE),
                                       np.zeros(tot_bins, dtype=INDICES_DTYPE)))
Unknown's avatar
Unknown committed
334

Somnath, Suhas's avatar
Somnath, Suhas committed
335
336
337
        # Some very basic information that can help the processing / analysis crew
        parm_dict['num_bins'] = tot_bins
        parm_dict['num_pix'] = num_pix
338
        parm_dict['num_udvs_steps'] = num_actual_udvs_steps
Rama Vasudevan's avatar
Rama Vasudevan committed
339
        parm_dict['num_steps'] = num_actual_udvs_steps
Unknown's avatar
Unknown committed
340

341
342
        if verbose:
            print('\tPreparing UDVS slices for region references')
Somnath, Suhas's avatar
Somnath, Suhas committed
343
        udvs_slices = dict()
Somnath, Suhas's avatar
Somnath, Suhas committed
344
        for col_ind, col_name in enumerate(UDVS_labs):
Unknown's avatar
Unknown committed
345
346
            udvs_slices[col_name] = (slice(None), slice(col_ind, col_ind + 1))

Somnath, Suhas's avatar
Somnath, Suhas committed
347
        # Need to add the Bin Waveform type - infer from UDVS        
Unknown's avatar
Unknown committed
348
        exec_bin_vec = self.signal_type * np.ones(len(bin_inds), dtype=np.int32)
Somnath, Suhas's avatar
Somnath, Suhas committed
349
350

        if self.expt_type == 2:
351
352
            if verbose:
                print('\tExperiment type = 2. Doubling BE vectors')
Unknown's avatar
Unknown committed
353
            exec_bin_vec = np.hstack((exec_bin_vec, -1 * exec_bin_vec))
Somnath, Suhas's avatar
Somnath, Suhas committed
354
355
            bin_inds = np.hstack((bin_inds, bin_inds))
            bin_freqs = np.hstack((bin_freqs, bin_freqs))
Somnath, Suhas's avatar
Somnath, Suhas committed
356
            # This is wrong but I don't know what else to do
Somnath, Suhas's avatar
Somnath, Suhas committed
357
            bin_FFT = np.hstack((bin_FFT, bin_FFT))
Unknown's avatar
Unknown committed
358

Somnath, Suhas's avatar
Somnath, Suhas committed
359
        # Create Spectroscopic Values and Spectroscopic Values Labels datasets
360
        # This is an old and legacy way of doing things. Ideally, all we would need ot do is just get the unit values
361
362
        if verbose:
            print('\tCalculating spectroscopic values')
Somnath, Suhas's avatar
Somnath, Suhas committed
363
        spec_vals, spec_inds, spec_vals_labs, spec_vals_units, spec_vals_labs_names = createSpecVals(UDVS_mat,
364
                                                                                                     old_spec_inds,
Somnath, Suhas's avatar
Somnath, Suhas committed
365
366
367
368
369
                                                                                                     bin_freqs,
                                                                                                     exec_bin_vec,
                                                                                                     parm_dict,
                                                                                                     UDVS_labs,
                                                                                                     UDVS_units)
370
371
372
373
        # 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
374

Somnath, Suhas's avatar
Somnath, Suhas committed
375
376
377
        spec_vals_slices = dict()

        for row_ind, row_name in enumerate(spec_vals_labs):
Unknown's avatar
Unknown committed
378
            spec_vals_slices[row_name] = (slice(row_ind, row_ind + 1), slice(None))
Somnath, Suhas's avatar
Somnath, Suhas committed
379

380
        if path.exists(h5_path):
381
382
            if verbose:
                print('\tRemoving existing / old translated file: ' + h5_path)
383
            remove(h5_path)
Chris Smith's avatar
Chris Smith committed
384

385
        # First create the file
ssomnath's avatar
ssomnath committed
386
        h5_f = h5py.File(h5_path, mode='w')
Somnath, Suhas's avatar
Somnath, Suhas committed
387

388
        # Then write root level attributes
389
        global_parms = dict()
Somnath, Suhas's avatar
Somnath, Suhas committed
390
391
        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
392
393
394
395
        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
396

Somnath, Suhas's avatar
Somnath, Suhas committed
397
        # assuming that the experiment was completed:
Unknown's avatar
Unknown committed
398
399
        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
400
        global_parms['data_type'] = parm_dict['data_type']
Somnath, Suhas's avatar
Somnath, Suhas committed
401
        global_parms['translator'] = 'ODF'
402
403
        if verbose:
            print('\tWriting attributes to HDF5 file root')
404
        write_simple_attrs(h5_f, global_parms)
405
        write_book_keeping_attrs(h5_f)
Unknown's avatar
Unknown committed
406

407
408
        # Then create the measurement group
        h5_meas_group = create_indexed_group(h5_f, 'Measurement')
Unknown's avatar
Unknown committed
409

410
        # Write attributes at the measurement group level
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
        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]})

431
432
        if verbose:
            print('\twriting attributes to Measurement group')
433
434
435
436
            keys = list(parm_dict.keys())
            keys.sort()
            for key in keys:
                print('\t\t{} : {}'.format(key, parm_dict[key]))
437
        write_simple_attrs(h5_meas_group, parm_dict)
Unknown's avatar
Unknown committed
438

439
440
        # Create the Channel group
        h5_chan_grp = create_indexed_group(h5_meas_group, 'Channel')
Unknown's avatar
Unknown committed
441

442
        # Write channel group attributes
Rama Vasudevan's avatar
Rama Vasudevan committed
443
444
        write_simple_attrs(h5_chan_grp, {'Channel_Input': 'IO_Analog_Input_1',
                                         'channel_type': 'BE'})
Unknown's avatar
Unknown committed
445

446
        # Now the datasets!
447
448
        if verbose:
            print('\tCreating ancillary datasets')
Chris Smith's avatar
Chris Smith committed
449
        h5_chan_grp.create_dataset('Excitation_Waveform', data=ex_wfm)
Unknown's avatar
Unknown committed
450

451
452
453
        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)
454

Chris Smith's avatar
Chris Smith committed
455
        h5_chan_grp.create_dataset('UDVS_Indices', data=old_spec_inds[1])
456

Chris Smith's avatar
Chris Smith committed
457
458
        h5_chan_grp.create_dataset('Bin_Step', data=np.arange(bins_per_step, dtype=INDICES_DTYPE),
                                   dtype=INDICES_DTYPE)
459

Chris Smith's avatar
Chris Smith committed
460
461
462
463
        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)
464

465
466
467
468
469
        if verbose:
            print('\tWriting Position datasets')

        pos_dims = [Dimension('X', 'm', np.arange(num_cols)),
                    Dimension('Y', 'm', np.arange(num_rows))]
470
        h5_pos_ind, h5_pos_val = write_ind_val_dsets(h5_chan_grp, pos_dims, is_spectral=False, verbose=verbose)
471
472
        if verbose:
            print('\tPosition datasets of shape: {}'.format(h5_pos_ind.shape))
473

474
        if verbose:
475
            print('\tWriting Spectroscopic datasets of shape: {}'.format(spec_inds.shape))
476
477
478
479
480
        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)
481
            write_simple_attrs(dset, spec_dim_dict)
482
483

        # Noise floor should be of shape: (udvs_steps x 3 x positions)
484
485
        if verbose:
            print('\tWriting noise floor dataset')
Chris Smith's avatar
Chris Smith committed
486
487
        h5_chan_grp.create_dataset('Noise_Floor', (num_pix, num_actual_udvs_steps), dtype=nf32,
                                   chunks=(1, num_actual_udvs_steps))
488
489
490
491
492
493
494
495
496
497
498

        """
        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))
499
500
        if verbose:
            print('\tHDF5 dataset will have chunks of size: {}'.format(BEPS_chunks))
501
            print('\tCreating empty main dataset of shape: ({}, {})'.format(num_pix, tot_bins))
502
503
504
505
        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
506

507
508
509
510
        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
511

512
513
        if verbose:
            print('\tGenerating plot groups')
514
        generatePlotGroups(self.h5_raw, self.mean_resp, folder_path, basename,
Somnath, Suhas's avatar
Somnath, Suhas committed
515
                           self.max_resp, self.min_resp, max_mem_mb=self.max_ram,
Somnath, Suhas's avatar
Somnath, Suhas committed
516
                           spec_label=spec_label, show_plots=show_plots, save_plots=save_plots,
Unknown's avatar
Unknown committed
517
                           do_histogram=do_histogram, debug=verbose)
518
519
        if verbose:
            print('\tUpgrading to USIDataset')
520
        self.h5_raw = USIDataset(self.h5_raw)
Unknown's avatar
Unknown committed
521
522
523

        # Go ahead and read the current data in the second (current) channel
        if current_data_exists:                     #If a .dat file matches
524
525
            if verbose:
                print('\tReading data in secondary channels (current)')
526
527
            self._read_secondary_channel(h5_meas_group, aux_files)

528
529
        if verbose:
            print('\tClosing HDF5 file')
530
        h5_f.close()
Unknown's avatar
Unknown committed
531

Somnath, Suhas's avatar
Somnath, Suhas committed
532
        return h5_path
Chris Smith's avatar
Chris Smith committed
533

534
535
    def _read_data(self, UDVS_mat, parm_dict, path_dict, real_size, isBEPS,
                   add_pix, verbose=False):
Chris Smith's avatar
Chris Smith committed
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
        """
        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
554
555
        verbose : bool, optional. Default = False
            Whether or not to print logs
Chris Smith's avatar
Chris Smith committed
556
557
558
559
560
561
562
563

        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)
564
565
            if verbose:
                print('\t\tReading all raw data for BE-Line in one shot')
566
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
567
568
        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)
569
570
            if verbose:
                print('\t\tReading all raw BEPS (out-of-field) data in one shot')
571
            self.__quick_read_data(path_dict['read_real'], path_dict['read_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
572
573
        elif real_size < self.max_ram and parm_dict['VS_measure_in_field_loops'] == 'in-field':
            # Do this for in-field only
574
575
            if verbose:
                print('\t\tReading all raw BEPS (in-field only) data in one shot')
576
            self.__quick_read_data(path_dict['write_real'], path_dict['write_imag'], parm_dict['num_udvs_steps'])
Chris Smith's avatar
Chris Smith committed
577
578
        else:
            # Large BEPS datasets OR those with in-and-out of field
579
580
            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
581
            self.__read_beps_data(path_dict, UDVS_mat.shape[0], parm_dict['VS_measure_in_field_loops'], add_pix)
582
        self.h5_raw.file.flush()
Chris Smith's avatar
Chris Smith committed
583

Somnath, Suhas's avatar
Somnath, Suhas committed
584
    def __read_beps_data(self, path_dict, udvs_steps, mode, add_pixel=False):
Somnath, Suhas's avatar
Somnath, Suhas committed
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
        """
        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
603

Somnath, Suhas's avatar
Somnath, Suhas committed
604
        print('---- reading pixel-by-pixel ----------')
Unknown's avatar
Unknown committed
605
606
607
608

        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
609
        if mode == 'out-of-field':
Unknown's avatar
Unknown committed
610
            parsers = [BEodfParser(path_dict['read_real'], path_dict['read_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
611
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
612
        elif mode == 'in-field':
Unknown's avatar
Unknown committed
613
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
614
                                   self.h5_raw.shape[0], bytes_per_pix)]
Somnath, Suhas's avatar
Somnath, Suhas committed
615
616
        elif mode == 'in and out-of-field':
            # each file will only have half the udvs steps:
Unknown's avatar
Unknown committed
617
            if 0.5 * udvs_steps % 1:
618
619
                raise ValueError('Odd number of UDVS')

Unknown's avatar
Unknown committed
620
            udvs_steps = int(0.5 * udvs_steps)
Somnath, Suhas's avatar
Somnath, Suhas committed
621
            # be careful - each pair contains only half the necessary bins - so read half
Unknown's avatar
Unknown committed
622
            parsers = [BEodfParser(path_dict['write_real'], path_dict['write_imag'],
Somnath, Suhas's avatar
Somnath, Suhas committed
623
                                   self.h5_raw.shape[0], int(bytes_per_pix / 2)),
Unknown's avatar
Unknown committed
624
625
626
                       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
627
            if step_size % 1:
628
629
                raise ValueError('strange number of bins per UDVS step. Exiting')

Somnath, Suhas's avatar
Somnath, Suhas committed
630
            step_size = int(step_size)
631

632
633
        rand_spectra = self.__get_random_spectra(parsers, self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
634
        take_conjugate = requires_conjugate(rand_spectra, cores=self._cores)
635

Somnath, Suhas's avatar
Somnath, Suhas committed
636
637
638
639
        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
640
        numpix = self.h5_raw.shape[0]
Somnath, Suhas's avatar
Somnath, Suhas committed
641
642
643
        """ 
        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
644
645
646
647
        """
        if add_pixel:
            numpix -= 1

Somnath, Suhas's avatar
Somnath, Suhas committed
648
        for pix_indx in range(numpix):
Somnath, Suhas's avatar
Somnath, Suhas committed
649
            if self.h5_raw.shape[0] > 5:
Unknown's avatar
Unknown committed
650
651
652
                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
653
654
655
            # get the raw stream from each parser
            pxl_data = list()
            for prsr in parsers:
Somnath, Suhas's avatar
Somnath, Suhas committed
656
                pxl_data.append(prsr.read_pixel())
Unknown's avatar
Unknown committed
657

Somnath, Suhas's avatar
Somnath, Suhas committed
658
659
660
661
662
            # 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
663

Somnath, Suhas's avatar
Somnath, Suhas committed
664
665
                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
666
                raw_mat = np.empty((udvs_steps * 2, step_size), dtype=out_fld.dtype)
Somnath, Suhas's avatar
Somnath, Suhas committed
667
668
                raw_mat[0::2, :] = in_fld_2
                raw_mat[1::2, :] = out_fld_2
Somnath, Suhas's avatar
Somnath, Suhas committed
669
670
                raw_vec = raw_mat.reshape(in_fld.size + out_fld.size).transpose()
            else:
Somnath, Suhas's avatar
Somnath, Suhas committed
671
                raw_vec = pxl_data[0]  # only one parser
Somnath, Suhas's avatar
Somnath, Suhas committed
672
673
            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
674
            self.mean_resp = (1 / (pix_indx + 1)) * (raw_vec + pix_indx * self.mean_resp)
675
676
677

            if take_conjugate:
                raw_vec = np.conjugate(raw_vec)
678
            self.h5_raw[pix_indx, :] = np.complex64(raw_vec[:])
679
            self.h5_raw.file.flush()
Unknown's avatar
Unknown committed
680

Somnath, Suhas's avatar
Somnath, Suhas committed
681
        # Add zeros to main_data for the missing pixel. 
Unknown's avatar
Unknown committed
682
683
684
        if add_pixel:
            self.h5_raw[-1, :] = 0 + 0j

Somnath, Suhas's avatar
Somnath, Suhas committed
685
        print('---- Finished reading files -----')
686
687

    def __quick_read_data(self, real_path, imag_path, udvs_steps):
Somnath, Suhas's avatar
Somnath, Suhas committed
688
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
689
690
691
692
693
694
695
696
        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
697
698
        udvs_steps : unsigned int
            Number of UDVS steps
Somnath, Suhas's avatar
Somnath, Suhas committed
699
        """
Unknown's avatar
Unknown committed
700
        print('---- reading all data at once ----------')
Somnath, Suhas's avatar
Somnath, Suhas committed
701

Unknown's avatar
Unknown committed
702
        parser = BEodfParser(real_path, imag_path, self.h5_raw.shape[0], self.h5_raw.shape[1] * 4)
703
704

        step_size = self.h5_raw.shape[1] / udvs_steps
705
706
        rand_spectra = self.__get_random_spectra([parser], self.h5_raw.shape[0], udvs_steps, step_size,
                                                 num_spectra=self.num_rand_spectra)
707
        take_conjugate = requires_conjugate(rand_spectra, cores=self._cores)
Somnath, Suhas's avatar
Somnath, Suhas committed
708
        raw_vec = parser.read_all_data()
709
        if take_conjugate:
710
            print('Taking conjugate to ensure positive Quality factors')
711
            raw_vec = np.conjugate(raw_vec)
Unknown's avatar
Unknown committed
712

Rama Vasudevan's avatar
Rama Vasudevan committed
713
714
715
716
717
718
719
720
721
722
723
724
        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
725

Somnath, Suhas's avatar
Somnath, Suhas committed
726
        # Write to the h5 dataset:
Somnath, Suhas's avatar
Somnath, Suhas committed
727
728
729
        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)
730
        self.h5_raw[:, :] = np.complex64(raw_mat)
731
        self.h5_raw.file.flush()
Somnath, Suhas's avatar
Somnath, Suhas committed
732

Unknown's avatar
Unknown committed
733
734
        print('---- Finished reading files -----')

735
736
    @staticmethod
    def _parse_file_path(data_filepath):
Somnath, Suhas's avatar
Somnath, Suhas committed
737
738
739
740
741
742
743
        """
        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
744
            Absolute path of any file in the same directory as the .dat files
Somnath, Suhas's avatar
Somnath, Suhas committed
745
746
747
748
749
750
751
752
753
        
        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
754
        (super_folder, basename) = path.split(folder_path)
Somnath, Suhas's avatar
Somnath, Suhas committed
755

756
757
        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
758
759
760
761
762
763
764
765
            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
766

Somnath, Suhas's avatar
Somnath, Suhas committed
767
        for file_name in listdir(folder_path):
Chris Smith's avatar
Chris Smith committed
768
            abs_path = path.join(folder_path, file_name)
Somnath, Suhas's avatar
Somnath, Suhas committed
769
770
771
772
773
            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
774
                elif file_name == (basename + '.mat'):
Somnath, Suhas's avatar
Somnath, Suhas committed
775
776
777
778
779
780
781
782
783
784
785
786
                    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
787
        return basename, path_dict
Somnath, Suhas's avatar
Somnath, Suhas committed
788

789
790
791
792
793
794
795
796
797
798
799
800
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
    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
832
        h5_current_spec_inds, h5_current_spec_values = write_reduced_anc_dsets(h5_current_channel_group,
833
                                                        self.h5_raw.h5_spec_inds,
834
                                                        self.h5_raw.h5_spec_vals, 'Frequency', is_spec=True)
835
836
837
838
839
840
841
842
843
844
845
846
847
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


        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
890
891
    @staticmethod
    def __read_old_mat_be_vecs(file_path):
Somnath, Suhas's avatar
Somnath, Suhas committed
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
        """
        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
915
        matread = loadmat(file_path, squeeze_me=True)
Somnath, Suhas's avatar
Somnath, Suhas committed
916
        BE_wave = matread['BE_wave']
Unknown's avatar
Unknown committed
917
        bin_inds = matread['bin_ind'] - 1  # Python base 0
Somnath, Suhas's avatar
Somnath, Suhas committed
918
919
920
        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
921
922
        bin_FFT = np.conjugate(FFT_full[bin_inds])
        return bin_inds, bin_w, bin_FFT, BE_wave, dc_amp_vec_full
Unknown's avatar
Unknown committed
923

924
    @staticmethod
925
926
927
928
929
930
931
932
933
934
935
936
937
938
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
    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
974
975
    @staticmethod
    def __get_parms_from_old_mat(file_path):
Somnath, Suhas's avatar
Somnath, Suhas committed
976
977
978
        """
        Formats parameters found in the old parameters .mat file into a dictionary
        as though the dataset had a parms.txt describing it
979
980

        Parameters
Somnath, Suhas's avatar
Somnath, Suhas committed
981
982
983
        --------------------
        file_path : Unicode / String
            absolute filepath of the .mat file containing the parameters
984
985

        Returns
Somnath, Suhas's avatar
Somnath, Suhas committed
986
987
988
989
990
991
        --------------------
        parm_dict : dictionary
            Parameters describing experiment
        """
        parm_dict = dict()
        matread = loadmat(file_path, squeeze_me=True)
Unknown's avatar
Unknown committed
992
993
994

        parm_dict['IO_rate'] = str(int(matread['AO_rate'] / 1E+6)) + ' MHz'

Somnath, Suhas's avatar
Somnath, Suhas committed
995
996
997
998
999
        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]
Unknown's avatar
Unknown committed
1000