def make_pos_vals_inds_dims(self):
x_range = float(self.params_dictionary['XScanRange'])
y_range = float(self.params_dictionary['YScanRange'])
x_center = float(self.params_dictionary['xCenter'])
y_center = float(self.params_dictionary['yCenter'])
x_start = x_center - (x_range / 2)
x_end = x_center + (x_range / 2)
y_start = y_center - (y_range / 2)
y_end = y_center + (y_range / 2)
dx = x_range / self.x_len
dy = y_range / self.y_len
#assumes y scan direction:down; scan angle: 0 deg
y_linspace = -np.arange(y_start, y_end, step=dy)
x_linspace = np.arange(x_start, x_end, step=dx)
pos_ind, pos_val = build_ind_val_matrices(unit_values=(x_linspace,
y_linspace),
is_spectral=False)
#Dimension uses ascii encoding, which can not encode
# micron symbol, so we replace it, if present, with the letter u.
pos_dims = [
Dimension('X',
self.params_dictionary['XPhysUnit'].replace('\xb5', 'u'),
self.x_len),
Dimension('Y',
self.params_dictionary['YPhysUnit'].replace('\xb5', 'u'),
self.y_len)
]
self.pos_ind, self.pos_val, self.pos_dims = pos_ind, pos_val, pos_dims
def save_cpd(h5_main, cpd_mat, cpd_sm):
'''
:param h5_main:
:type h5_main:
:param cpd_mat:
:type cpd_mat:
:param cpd_mat_sm:
:type cpd_mat_sm:
:returns:
:rtype:
'''
parm_dict = usid.hdf_utils.get_attributes(h5_main)
# Get relevant parameters
num_rows = parm_dict['num_rows']
num_cols = parm_dict['num_cols']
h5_gp = h5_main.parent
h5_meas_group = usid.hdf_utils.create_indexed_group(h5_gp, 'CPD')
# Create dimensions
pos_desc = [
Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'],
num_cols)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'],
num_rows))
]
# ds_pos_ind, ds_pos_val = build_ind_val_matrices(pos_desc, is_spectral=False)
spec_desc = [
Dimension('Time', 's',
np.linspace(0, parm_dict['total_time'], cpd_mat.shape[1]))
]
# ds_spec_inds, ds_spec_vals = build_ind_val_matrices(spec_desc, is_spectral=True)
# Writes main dataset
h5_cpd = usid.hdf_utils.write_main_dataset(
h5_meas_group,
cpd_mat,
'cpd', # Name of main dataset
'Contact Potential', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
)
# add smoothed dataset
h5_meas_group.create_dataset('cpd_sm', data=cpd_sm, dtype=np.float32)
usid.hdf_utils.copy_attributes(h5_main, h5_gp)
return h5_cpd
def _translate_image_stack(self, h5_meas_grp):
"""
Reads the scan images from the proprietary file and writes them to HDF5 datasets
Parameters
----------
h5_meas_grp : h5py.Group object
Reference to the measurement group
"""
# since multiple channels will share the same position and spectroscopic dimensions, why not share them?
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(h5_meas_grp,
Dimension(
'single', 'a. u.',
1),
is_spectral=True)
# Find out the size of the force curves from the metadata:
layer_info = None
for class_name in self.meta_data.keys():
if 'Ciao image list' in class_name:
layer_info = self.meta_data[class_name]
break
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(h5_meas_grp, [
Dimension('X', 'nm', layer_info['Samps/line']),
Dimension('Y', 'nm', layer_info['Number of lines'])
],
is_spectral=False)
for class_name in self.meta_data.keys():
if 'Ciao image list' in class_name:
layer_info = self.meta_data[class_name]
quantity = layer_info.pop('Image Data_2')
data = self._read_image_layer(layer_info)
h5_chan_grp = create_indexed_group(h5_meas_grp, 'Channel')
write_main_dataset(
h5_chan_grp,
np.reshape(data, (-1, 1)),
'Raw_Data',
# Quantity and Units needs to be fixed by someone who understands these files better
quantity,
'a. u.',
None,
None,
dtype=np.float32,
compression='gzip',
h5_pos_inds=h5_pos_inds,
h5_pos_vals=h5_pos_vals,
h5_spec_inds=h5_spec_inds,
h5_spec_vals=h5_spec_vals)
# Think about standardizing attributes for rows and columns
write_simple_attrs(h5_chan_grp, layer_info)
def _translate_force_curve(self, h5_meas_grp):
"""
Reads the force curves from the proprietary file and writes them to HDF5 datasets
Parameters
----------
h5_meas_grp : h5py.Group object
Reference to the measurement group
"""
# since multiple channels will share the same position and spectroscopic dimensions, why not share them?
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(h5_meas_grp,
Dimension(
'single', 'a. u.',
1),
is_spectral=False)
# Find out the size of the force curves from the metadata:
layer_info = None
for class_name in self.meta_data.keys():
if 'Ciao force image list' in class_name:
layer_info = self.meta_data[class_name]
break
tr_rt = [int(item) for item in layer_info['Samps/line'].split(' ')]
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(
h5_meas_grp,
Dimension('Z', 'nm', int(np.sum(tr_rt))),
is_spectral=True)
for class_name in self.meta_data.keys():
if 'Ciao force image list' in class_name:
layer_info = self.meta_data[class_name]
quantity = layer_info.pop('Image Data_4')
data = self._read_data_vector(layer_info)
h5_chan_grp = create_indexed_group(h5_meas_grp, 'Channel')
write_main_dataset(
h5_chan_grp,
np.expand_dims(data, axis=0),
'Raw_Data',
# Quantity and Units needs to be fixed by someone who understands these files better
quantity,
'a. u.',
None,
None,
dtype=np.float32,
compression='gzip',
h5_pos_inds=h5_pos_inds,
h5_pos_vals=h5_pos_vals,
h5_spec_inds=h5_spec_inds,
h5_spec_vals=h5_spec_vals)
# Think about standardizing attributes
write_simple_attrs(h5_chan_grp, layer_info)
def write_spectrograms(self):
if bool(self.spectrogram_desc):
for spectrogram_f, descriptors in self.spectrogram_desc.items():
channel_i = create_indexed_group(self.h5_meas_grp, 'Channel_')
spec_vals_i = self.spectrogram_spec_vals[spectrogram_f]
spectrogram_spec_dims = Dimension('Wavelength', descriptors[8],
spec_vals_i)
h5_raw = write_main_dataset(
channel_i, # parent HDF5 group
(self.x_len * self.y_len,
len(spec_vals_i)), # shape of Main dataset
'Raw_Data', # Name of main dataset
'Spectrogram', # Physical quantity contained in Main dataset
descriptors[3], # Units for the physical quantity
self.pos_dims, # Position dimensions
spectrogram_spec_dims, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs={
'Caption': descriptors[0],
'Bytes_Per_Pixel': descriptors[1],
'Scale': descriptors[2],
'Physical_Units': descriptors[3],
'Offset': descriptors[4],
'Datatype': descriptors[5],
'Bytes_Per_Reading': descriptors[6],
'Wavelength_File': descriptors[7],
'Wavelength_Units': descriptors[8]
})
h5_raw.h5_pos_vals[:, :] = self.pos_val
h5_raw[:, :] = self.spectrograms[spectrogram_f].reshape(
h5_raw.shape)
def write_ps_spectra(self):
if bool(self.pspectrum_desc):
for spec_f, descriptors in self.pspectrum_desc.items():
# create new measurement group for ea spectrum
self.h5_meas_grp = create_indexed_group(
self.h5_f, 'Measurement_')
x_name = self.spectra_x_y_dim_name[spec_f][0].split(' ')[0]
x_unit = self.spectra_x_y_dim_name[spec_f][0].split(' ')[1]
y_name = self.spectra_x_y_dim_name[spec_f][1].split(' ')[0]
y_unit = self.spectra_x_y_dim_name[spec_f][1].split(' ')[1]
spec_i_spec_dims = Dimension(x_name, x_unit,
self.spectra_spec_vals[spec_f])
spec_i_pos_dims = [
Dimension(
'X', self.params_dictionary['XPhysUnit'].replace(
'\xb5', 'u'), np.array([0])),
Dimension(
'Y', self.params_dictionary['YPhysUnit'].replace(
'\xb5', 'u'), np.array([0]))
]
# write data to a channel in the measurement group
spec_i_ch = create_indexed_group(self.h5_meas_grp,
'PowerSpectrum_')
h5_raw = write_main_dataset(
spec_i_ch, # parent HDF5 group
(1, len(self.spectra_spec_vals[spec_f])),
# shape of Main dataset
'Raw_Spectrum',
# Name of main dataset
y_name,
# Physical quantity contained in Main dataset
y_unit, # Units for the physical quantity
# Position dimensions
pos_dims=spec_i_pos_dims,
spec_dims=spec_i_spec_dims,
# Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs={
'XLoc': 0,
'YLoc': 0
})
h5_raw[:, :] = self.spectra[spec_f].reshape(h5_raw.shape)
def setUp(self):
self.h5_f = h5py.File(test_h5_file_path)
h5_raw_grp = self.h5_f.create_group('Raw_Measurement')
num_rows = 3
num_cols = 5
num_cycles = 2
num_cycle_pts = 7
# Create Main dataset and ancillaries
source_dset_name = 'source_main'
pos_dims = [Dimension('X', 'nm', num_rows),
Dimension('Y', 'nm', num_cols)]
spec_dims = [Dimension('Bias', 'V', num_cycle_pts),
Dimension('Cycle', 'a.u.', num_cycles)]
source_main_data = np.random.rand(num_rows * num_cols, num_cycle_pts * num_cycles)
h5_source_main = write_main_dataset(h5_raw_grp, source_main_data, source_dset_name,
'Current', 'A',
pos_dims, spec_dims)
# Create Guess dataset and ancillaries
h5_guess_grp = h5_raw_grp.create_group(source_dset_name+'-Fitter_000')
guess_data = np.random.rand(num_rows * num_cols, num_cycles)
guess_spec_dims = spec_dims[1]
self.h5_guess = write_main_dataset(h5_guess_grp, guess_data, 'Guess',
'Guess', 'a.u.',
pos_dims, guess_spec_dims)
self.fitter = Fitter(h5_source_main, variables=['Bias'])
self.h5_main = h5_source_main
self.h5_f.flush()
def _translate_force_map(self, h5_meas_grp):
"""
Reads the scan image + force map from the proprietary file and writes it to HDF5 datasets
Parameters
----------
h5_meas_grp : h5py.Group object
Reference to the measurement group
"""
# First lets write the image into the measurement group that has already been created:
image_parms = self.meta_data['Ciao image list']
quantity = image_parms.pop('Image Data_2')
image_mat = self._read_image_layer(image_parms)
h5_chan_grp = create_indexed_group(h5_meas_grp, 'Channel')
write_main_dataset(
h5_chan_grp,
np.reshape(image_mat, (-1, 1)),
'Raw_Data',
# Quantity and Units needs to be fixed by someone who understands these files better
quantity,
'a. u.',
[
Dimension('X', 'nm', image_parms['Samps/line']),
Dimension('Y', 'nm', image_parms['Number of lines'])
],
Dimension('single', 'a. u.', 1),
dtype=np.float32,
compression='gzip')
# Think about standardizing attributes for rows and columns
write_simple_attrs(h5_chan_grp, image_parms)
# Now work on the force map:
force_map_parms = self.meta_data['Ciao force image list']
quantity = force_map_parms.pop('Image Data_4')
force_map_vec = self._read_data_vector(force_map_parms)
tr_rt = [
int(item) for item in force_map_parms['Samps/line'].split(' ')
]
force_map_2d = force_map_vec.reshape(image_mat.size, np.sum(tr_rt))
h5_chan_grp = create_indexed_group(h5_meas_grp, 'Channel')
write_main_dataset(
h5_chan_grp,
force_map_2d,
'Raw_Data',
# Quantity and Units needs to be fixed by someone who understands these files better
quantity,
'a. u.',
[
Dimension('X', 'nm', image_parms['Samps/line']),
Dimension('Y', 'nm', image_parms['Number of lines'])
],
Dimension('Z', 'nm', int(np.sum(tr_rt))),
dtype=np.float32,
compression='gzip')
# Think about standardizing attributes
write_simple_attrs(h5_chan_grp, force_map_parms)
def load_pixel_averaged_from_raw(h5_file, verbose=True, loadverbose=True):
"""
Creates a new group FF_Avg where the FF_raw file is averaged together.
This is more useful as pixel-wise averages are more relevant in FF-processing
This Dataset is (n_pixels*n_rows, n_pnts_per_avg)
:param h5_file: H5 File to be examined. File typically set as h5_file = hdf.file
hdf = px.ioHDF5(h5_path), h5_path = path to disk
:type h5_file: h5py File
:param verbose: Display outputs of each function or not
:type verbose: bool, optional
:param loadverbose: Whether to print any simple "loading Line X" statements for feedback
:type loadverbose: bool, optional
:returns: The new averaged Dataset
:rtype: Dataset
"""
hdf = h5py.File(h5_file)
h5_main = usid.hdf_utils.find_dataset(hdf.file, 'FF_Raw')[0]
try:
ff_avg_group = h5_main.parent.create_group('FF_Avg')
except:
ff_avg_group = usid.hdf_utils.create_indexed_group(h5_main.parent, 'FF_Avg')
parm_dict = get_attributes(h5_main.parent)
num_rows = parm_dict['num_rows']
num_cols = parm_dict['num_cols']
pnts_per_avg = parm_dict['pnts_per_avg']
pnts_per_line = parm_dict['pnts_per_line']
pnts_per_pixel = parm_dict['pnts_per_pixel']
parm_dict['pnts_per_pixel'] = 1 # only 1 average per pixel now
parm_dict['pnts_per_line'] = num_cols # equivalent now with averaged data
n_pix = int(pnts_per_line / pnts_per_pixel)
dt = 1 / parm_dict['sampling_rate']
# Set up the position vectors for the data
pos_desc = [Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'], num_cols)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'], num_rows))]
spec_desc = [Dimension('Time', 's', np.linspace(0, parm_dict['total_time'], pnts_per_avg))]
for p in parm_dict:
ff_avg_group.attrs[p] = parm_dict[p]
ff_avg_group.attrs['pnts_per_line'] = num_cols # to change number of pnts in a line
ff_avg_group.attrs['pnts_per_pixel'] = 1 # to change number of pnts in a pixel
h5_avg = usid.hdf_utils.write_main_dataset(ff_avg_group, # parent HDF5 group
(num_rows * num_cols, pnts_per_avg), # shape of Main dataset
'FF_Avg', # Name of main dataset
'Deflection', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
compression='gzip',
main_dset_attrs=parm_dict)
# Uses get_line to extract line. Averages and returns to the Dataset FF_Avg
# We can operate on the dataset array directly, get_line is used for future_proofing if
# we want to add additional operation (such as create an Image class)
for i in range(num_rows):
if loadverbose == True:
print('#### Row:', i, '####')
_ll = get_utils.get_line(h5_main, pnts=pnts_per_line, line_num=i, array_form=False, avg=False)
_ll = _ll.pixel_wise_avg()
h5_avg[i * num_cols:(i + 1) * num_cols, :] = _ll[:, :]
if verbose == True:
usid.hdf_utils.print_tree(hdf.file, rel_paths=True)
h5_avg = usid.hdf_utils.find_dataset(hdf.file, 'FF_Avg')[0]
print('H5_avg of size:', h5_avg.shape)
hdf.flush()
return h5_avg
def _parse_sxm_parms(header_dict, signal_dict):
"""
Parse sxm files.
Parameters
----------
header_dict : dict
signal_dict : dict
Returns
-------
parm_dict : dict
"""
parm_dict = dict()
data_dict = dict()
# Create dictionary with measurement parameters
meas_parms = {key: value for key, value in header_dict.items()
if value is not None}
info_dict = meas_parms.pop('data_info')
parm_dict['meas_parms'] = meas_parms
# Create dictionary with channel parameters
channel_parms = dict()
channel_names = info_dict['Name']
single_channel_parms = {name: dict() for name in channel_names}
for field_name, field_value, in info_dict.items():
for channel_name, value in zip(channel_names, field_value):
single_channel_parms[channel_name][field_name] = value
for value in single_channel_parms.values():
if value['Direction'] == 'both':
value['Direction'] = ['forward', 'backward']
else:
direction = [value['Direction']]
scan_dir = meas_parms['scan_dir']
for name, parms in single_channel_parms.items():
for direction in parms['Direction']:
key = ' '.join((name, direction))
channel_parms[key] = dict(parms)
channel_parms[key]['Direction'] = direction
data = signal_dict[name][direction]
if scan_dir == 'up':
data = np.flip(data, axis=0)
if direction == 'backward':
data = np.flip(data, axis=1)
data_dict[key] = data
parm_dict['channel_parms'] = channel_parms
# Position dimensions
num_cols, num_rows = header_dict['scan_pixels']
width, height = header_dict['scan_range']
pos_names = ['X', 'Y']
pos_units = ['nm', 'nm']
pos_vals = np.vstack([
np.linspace(0, width, num_cols),
np.linspace(0, height, num_rows),
])
pos_vals *= 1e9
pos_dims = [Dimension(name, unit, values) for name, unit, values
in zip(pos_names, pos_units, pos_vals)]
data_dict['Position Dimensions'] = pos_dims
# Spectroscopic dimensions
spec_dims = Dimension('arb.', 'a. u.', np.arange(1, dtype=np.float32))
data_dict['Spectroscopic Dimensions'] = spec_dims
return parm_dict, data_dict
def translate(self, file_path):
"""
The main function that translates the provided file into a .h5 file
Parameters
----------
file_path : String / unicode
Absolute path of any file in the directory
Returns
-------
h5_path : String / unicode
Absolute path of the h5 file
"""
file_path = path.abspath(file_path)
# Figure out the basename of the data:
(basename, parm_paths, data_paths) = super(GTuneTranslator, self)._parse_file_path(file_path)
(folder_path, unused) = path.split(file_path)
h5_path = path.join(folder_path, basename + '.h5')
if path.exists(h5_path):
remove(h5_path)
# Load parameters from .mat file
matread = loadmat(parm_paths['parm_mat'],
variable_names=['AI_wave', 'BE_wave_AO_0', 'BE_wave_AO_1', 'BE_wave_train',
'BE_wave', 'total_cols', 'total_rows'])
be_wave = np.float32(np.squeeze(matread['BE_wave']))
be_wave_train = np.float32(np.squeeze(matread['BE_wave_train']))
num_cols = int(matread['total_cols'][0][0])
expected_rows = int(matread['total_rows'][0][0])
self.points_per_pixel = len(be_wave)
self.points_per_line = len(be_wave_train)
# Load parameters from .txt file - 'BE_center_frequency_[Hz]', 'IO rate'
is_beps, parm_dict = parmsToDict(parm_paths['parm_txt'])
# Get file byte size:
# For now, assume that bigtime_00 always exists and is the main file
file_size = path.getsize(data_paths[0])
# Calculate actual number of lines since the first few lines may not be saved
self.num_rows = 1.0 * file_size / (4 * self.points_per_pixel * num_cols)
if self.num_rows % 1:
warn('Error - File has incomplete rows')
return None
else:
self.num_rows = int(self.num_rows)
samp_rate = parm_dict['IO_rate_[Hz]']
ex_freq_nominal = parm_dict['BE_center_frequency_[Hz]']
# method 1 for calculating the correct excitation frequency:
pixel_duration = 1.0 * self.points_per_pixel / samp_rate
num_periods = pixel_duration * ex_freq_nominal
ex_freq_correct = 1 / (pixel_duration / np.floor(num_periods))
# correcting the excitation frequency - will be VERY useful during analysis and filtering
parm_dict['BE_center_frequency_[Hz]'] = ex_freq_correct
# Some very basic information that can help the processing crew
parm_dict['points_per_line'] = self.points_per_line
parm_dict['num_bins'] = self.points_per_pixel
parm_dict['grid_num_rows'] = self.num_rows
parm_dict['data_type'] = 'G_mode_line'
if self.num_rows != expected_rows:
print('Note: {} of {} lines found in data file'.format(self.num_rows, expected_rows))
# Calculate number of points to read per line:
self.__bytes_per_row__ = int(file_size / self.num_rows)
# First finish writing all global parameters, create the file too:
h5_file = h5py.File(h5_path, 'w')
global_parms = dict()
global_parms['data_type'] = 'G_mode_line'
global_parms['translator'] = 'G_mode_line'
write_simple_attrs(h5_file, global_parms)
# Next create the Measurement and Channel groups and write the appropriate parameters to them
meas_grp = create_indexed_group(h5_file, 'Measurement')
write_simple_attrs(meas_grp, parm_dict)
# Now that the file has been created, go over each raw data file:
"""
We only allocate the space for the main data here.
This does NOT change with each file. The data written to it does.
The auxiliary datasets will not change with each raw data file since
only one excitation waveform is used
"""
pos_desc = Dimension('Y', 'm', np.arange(self.num_rows))
spec_desc = Dimension('Excitation', 'V', np.tile(VALUES_DTYPE(be_wave), num_cols))
h5_pos_ind, h5_pos_val = write_ind_val_dsets(meas_grp, pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(meas_grp, spec_desc, is_spectral=True)
for f_index in data_paths.keys():
chan_grp = create_indexed_group(meas_grp, 'Channel')
h5_main = write_main_dataset(chan_grp, (self.num_rows, self.points_per_pixel * num_cols), 'Raw_Data',
'Deflection', 'V',
None, None,
h5_pos_inds=h5_pos_ind, h5_pos_vals=h5_pos_val,
h5_spec_inds=h5_spec_inds, h5_spec_vals=h5_spec_vals,
chunks=(1, self.points_per_pixel), dtype=np.float16)
# Now transfer scan data in the dat file to the h5 file:
super(GTuneTranslator, self)._read_data(data_paths[f_index], h5_main)
h5_file.close()
print('G-Tune translation complete!')
return h5_path
def load_FF(data_files, parm_dict, h5_path, verbose=False, loadverbose=True,
average=True, mirror=True):
"""
Generates the HDF5 file given path to data_files and parameters dictionary
Creates a Datagroup FFtrEFM_Group with a single dataset in chunks
:param data_files: List of the \*.ibw files to be invidually scanned. This is generated
by load_folder above
:type data_files: list
:param parm_dict: Scan parameters to be saved as attributes. This is generated
by load_folder above, or you can pass this explicitly.
:type parm_dict: dict
:param h5_path:
:type h5_path : string
:param verbose: Display outputs of each function or not
:type verbose: bool, optional
:param loadverbose: Whether to print any simple "loading Line X" statements for feedback
:type loadverbose: bool, optional
:param average: Whether to average each pixel before saving to H5. This saves both time and space
:type average: bool, optional
:param mirror: Mirrors the data when saving. This parameter is to match the FFtrEFM data
with the associate topography as FFtrEFM is acquired during a retrace while
topo is saved during a forward trace
:type mirror: bool, optional
:returns: The filename path to the H5 file created
:rtype: str
"""
# Prepare data for writing to HDF
num_rows = parm_dict['num_rows']
num_cols = parm_dict['num_cols']
pnts_per_avg = parm_dict['pnts_per_avg']
name = 'FF_Raw'
if average:
parm_dict['pnts_per_pixel'] = 1
parm_dict['pnts_per_line'] = num_cols
name = 'FF_Avg'
pnts_per_pixel = parm_dict['pnts_per_pixel']
pnts_per_line = parm_dict['pnts_per_line']
dt = 1 / parm_dict['sampling_rate']
def_vec = np.arange(0, parm_dict['total_time'], dt)
if def_vec.shape[0] != parm_dict['pnts_per_avg']:
def_vec = def_vec[:-1]
# warnings.warn('Time-per-point calculation error')
# To do: Fix the labels/attributes on the relevant data sets
try:
hdf = h5py.File(h5_path, 'r+')
except:
print('Creating HDF5 file...')
hdf = h5py.File(h5_path, 'w')
try:
ff_group = hdf.create_group('FF_Group')
except:
print('Group already exists, creating new one')
ff_group = usid.hdf_utils.create_indexed_group(hdf['/'], 'FF_Group')
# Set up the position vectors for the data
pos_desc = [Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'], num_cols * pnts_per_pixel)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'], num_rows))]
spec_desc = [Dimension('Time', 's', np.linspace(0, parm_dict['total_time'], pnts_per_avg))]
for p in parm_dict:
ff_group.attrs[p] = parm_dict[p]
ff_group.attrs['pnts_per_line'] = num_cols
h5_ff = usid.hdf_utils.write_main_dataset(ff_group, # parent HDF5 group
(num_rows * num_cols * pnts_per_pixel, pnts_per_avg),
# shape of Main dataset
name, # Name of main dataset
'Deflection', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
compression='gzip',
main_dset_attrs=parm_dict)
pnts_per_line = parm_dict['pnts_per_line']
# Cycles through the remaining files. This takes a while (~few minutes)
for k, num in zip(data_files, np.arange(0, len(data_files))):
if loadverbose:
fname = k.replace('/', '\\')
print('####', fname.split('\\')[-1], '####')
fname = str(num).rjust(4, '0')
line_file = load.signal(k)
if average:
_ll = line.Line(line_file, parm_dict, n_pixels=num_cols, pycroscopy=False)
_ll = _ll.pixel_wise_avg().T
else:
_ll = line_file.transpose()
f = hdf.file[h5_ff.name]
if mirror:
f[pnts_per_line * num:pnts_per_line * (num + 1), :] = np.flipud(_ll[:, :])
else:
f[pnts_per_line * num:pnts_per_line * (num + 1), :] = _ll[:, :]
if verbose == True:
usid.hdf_utils.print_tree(hdf.file, rel_paths=True)
hdf.flush()
return h5_ff
def _create_results_datasets(self):
"""
Creates hdf5 datasets and datagroups to hold the resutls
"""
# create all h5 datasets here:
num_pos = self.h5_main.shape[0]
if self.verbose and self.mpi_rank == 0:
print('Now creating the datasets')
self.h5_results_grp = create_results_group(self.h5_main,
self.process_name,
h5_parent_group=self._h5_target_group)
write_simple_attrs(self.h5_results_grp, {'algorithm_author': 'Kody J. Law', 'last_pixel': 0})
write_simple_attrs(self.h5_results_grp, self.parms_dict)
if self.verbose and self.mpi_rank == 0:
print('created group: {} with attributes:'.format(self.h5_results_grp.name))
print(get_attributes(self.h5_results_grp))
# One of those rare instances when the result is exactly the same as the source
self.h5_i_corrected = create_empty_dataset(self.h5_main, np.float32, 'Corrected_Current', h5_group=self.h5_results_grp)
if self.verbose and self.mpi_rank == 0:
print('Created I Corrected')
# print_tree(self.h5_results_grp)
# For some reason, we cannot specify chunks or compression!
# The resistance dataset requires the creation of a new spectroscopic dimension
self.h5_resistance = write_main_dataset(self.h5_results_grp, (num_pos, self.num_x_steps), 'Resistance', 'Resistance',
'GOhms', None, Dimension('Bias', 'V', self.num_x_steps),
dtype=np.float32, # chunks=(1, self.num_x_steps), #compression='gzip',
h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals)
if self.verbose and self.mpi_rank == 0:
print('Created Resistance')
# print_tree(self.h5_results_grp)
assert isinstance(self.h5_resistance, USIDataset) # only here for PyCharm
self.h5_new_spec_vals = self.h5_resistance.h5_spec_vals
# The variance is identical to the resistance dataset
self.h5_variance = create_empty_dataset(self.h5_resistance, np.float32, 'R_variance')
if self.verbose and self.mpi_rank == 0:
print('Created Variance')
# print_tree(self.h5_results_grp)
# The capacitance dataset requires new spectroscopic dimensions as well
self.h5_cap = write_main_dataset(self.h5_results_grp, (num_pos, 1), 'Capacitance', 'Capacitance', 'pF', None,
Dimension('Direction', '', [1]), h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals, dtype=cap_dtype, #compression='gzip',
aux_spec_prefix='Cap_Spec_')
if self.verbose and self.mpi_rank == 0:
print('Created Capacitance')
# print_tree(self.h5_results_grp)
print('Done creating all results datasets!')
if self.mpi_size > 1:
self.mpi_comm.Barrier()
self.h5_main.file.flush()
def _create_results_datasets(self):
'''
Creates the datasets an Groups necessary to store the results.
'''
print('Creating CPD results datasets')
# Get relevant parameters
num_rows = self.parm_dict['num_rows']
num_cols = self.parm_dict['num_cols']
pnts_per_avg = self.parm_dict['pnts_per_avg']
ds_shape = [num_rows * num_cols, pnts_per_avg]
cpd_ds_shape = [num_rows * num_cols, self.cpd_dict['num_CPD']]
self.h5_results_grp = usid.hdf_utils.create_results_group(
self.h5_main, self.process_name)
self.h5_cpd_grp = usid.hdf_utils.create_results_group(
self.h5_main, self.process_name + '_CPD')
usid.hdf_utils.copy_attributes(self.h5_main.parent,
self.h5_results_grp)
usid.hdf_utils.copy_attributes(self.h5_main.parent, self.h5_cpd_grp)
# Create dimensions
pos_desc = [
Dimension('X', 'm',
np.linspace(0, self.parm_dict['FastScanSize'],
num_cols)),
Dimension('Y', 'm',
np.linspace(0, self.parm_dict['SlowScanSize'], num_rows))
]
# ds_pos_ind, ds_pos_val = build_ind_val_matrices(pos_desc, is_spectral=False)
spec_desc = [
Dimension(
'Time', 's',
np.linspace(0, self.parm_dict['total_time'], pnts_per_avg))
]
cpd_spec_desc = [
Dimension(
'Time', 's',
np.linspace(0, self.parm_dict['total_time'],
self.cpd_dict['num_CPD']))
]
# ds_spec_inds, ds_spec_vals = build_ind_val_matrices(spec_desc, is_spectral=True)
# Writes main dataset
self.h5_force = usid.hdf_utils.write_main_dataset(
self.h5_results_grp,
ds_shape,
'force', # Name of main dataset
'Force', # Physical quantity contained in Main dataset
'N', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_cpd = usid.hdf_utils.write_main_dataset(
self.h5_cpd_grp,
cpd_ds_shape,
'CPD', # Name of main dataset
'Potential', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
None, # Position dimensions
cpd_spec_desc, # Spectroscopic dimensions
h5_pos_inds=self.h5_main.h5_pos_inds, # Copy Pos Dimensions
h5_pos_vals=self.h5_main.h5_pos_vals,
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_cap = usid.hdf_utils.write_main_dataset(
self.h5_cpd_grp,
cpd_ds_shape,
'capacitance', # Name of main dataset
'Capacitance', # Physical quantity contained in Main dataset
'F', # Units for the physical quantity
None, # Position dimensions
None,
h5_pos_inds=self.h5_main.h5_pos_inds, # Copy Pos Dimensions
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=self.h5_cpd.h5_spec_inds,
# Copy Spectroscopy Dimensions
h5_spec_vals=self.h5_cpd.h5_spec_vals,
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_cpd.file.flush()
return
def translate(self, parm_path):
"""
Basic method that translates .mat data files to a single .h5 file
Parameters
------------
parm_path : string / unicode
Absolute file path of the parameters .mat file.
Returns
----------
h5_path : string / unicode
Absolute path of the translated h5 file
"""
self.parm_path = path.abspath(parm_path)
(folder_path, file_name) = path.split(parm_path)
(file_name, base_name) = path.split(folder_path)
h5_path = path.join(folder_path, base_name + '.h5')
# Read parameters
parm_dict = readGmodeParms(parm_path)
# Add the w^2 specific parameters to this list
parm_data = loadmat(parm_path, squeeze_me=True, struct_as_record=True)
freq_sweep_parms = parm_data['freqSweepParms']
parm_dict['freq_sweep_delay'] = np.float(
freq_sweep_parms['delay'].item())
gen_sig = parm_data['genSig']
parm_dict['wfm_fix_d_fast'] = np.int32(gen_sig['restrictT'].item())
freq_array = np.float32(parm_data['freqArray'])
# prepare and write spectroscopic values
samp_rate = parm_dict['IO_down_samp_rate_[Hz]']
num_bins = int(parm_dict['wfm_n_cycles'] * parm_dict['wfm_p_slow'] *
samp_rate)
w_vec = np.arange(-0.5 * samp_rate, 0.5 * samp_rate,
np.float32(samp_rate / num_bins))
# There is most likely a more elegant solution to this but I don't have the time... Maybe np.meshgrid
spec_val_mat = np.zeros((len(freq_array) * num_bins, 2),
dtype=VALUES_DTYPE)
spec_val_mat[:, 0] = np.tile(w_vec, len(freq_array))
spec_val_mat[:, 1] = np.repeat(freq_array, num_bins)
spec_ind_mat = np.zeros((2, len(freq_array) * num_bins),
dtype=np.int32)
spec_ind_mat[0, :] = np.tile(np.arange(num_bins), len(freq_array))
spec_ind_mat[1, :] = np.repeat(np.arange(len(freq_array)), num_bins)
num_rows = parm_dict['grid_num_rows']
num_cols = parm_dict['grid_num_cols']
parm_dict['data_type'] = 'GmodeW2'
num_pix = num_rows * num_cols
global_parms = dict()
global_parms['grid_size_x'] = parm_dict['grid_num_cols']
global_parms['grid_size_y'] = parm_dict['grid_num_rows']
# assuming that the experiment was completed:
global_parms['current_position_x'] = parm_dict['grid_num_cols'] - 1
global_parms['current_position_y'] = parm_dict['grid_num_rows'] - 1
global_parms['data_type'] = parm_dict[
'data_type'] # self.__class__.__name__
global_parms['translator'] = 'W2'
# Now start creating datasets and populating:
if path.exists(h5_path):
remove(h5_path)
h5_f = h5py.File(h5_path, 'w')
write_simple_attrs(h5_f, global_parms)
meas_grp = create_indexed_group(h5_f, 'Measurement')
chan_grp = create_indexed_group(meas_grp, 'Channel')
write_simple_attrs(chan_grp, parm_dict)
pos_dims = [
Dimension('X', 'nm', num_rows),
Dimension('Y', 'nm', num_cols)
]
spec_dims = [
Dimension('Response Bin', 'a.u.', num_bins),
Dimension('Excitation Frequency ', 'Hz', len(freq_array))
]
# Minimize file size to the extent possible.
# DAQs are rated at 16 bit so float16 should be most appropriate.
# For some reason, compression is more effective on time series data
h5_main = write_main_dataset(chan_grp, (num_pix, num_bins),
'Raw_Data',
'Deflection',
'V',
pos_dims,
spec_dims,
chunks=(1, num_bins),
dtype=np.float32)
h5_ex_freqs = chan_grp.create_dataset('Excitation_Frequencies',
freq_array)
h5_bin_freq = chan_grp.create_dataset('Bin_Frequencies', w_vec)
# Now doing link_h5_objects_as_attrs:
link_h5_objects_as_attrs(h5_main, [h5_ex_freqs, h5_bin_freq])
# Now read the raw data files:
pos_ind = 0
for row_ind in range(1, num_rows + 1):
for col_ind in range(1, num_cols + 1):
file_path = path.join(
folder_path,
'fSweep_r' + str(row_ind) + '_c' + str(col_ind) + '.mat')
print('Working on row {} col {}'.format(row_ind, col_ind))
if path.exists(file_path):
# Load data file
pix_data = loadmat(file_path, squeeze_me=True)
pix_mat = pix_data['AI_mat']
# Take the inverse FFT on 2nd dimension
pix_mat = np.fft.ifft(np.fft.ifftshift(pix_mat, axes=1),
axis=1)
# Verified with Matlab - no conjugate required here.
pix_vec = pix_mat.transpose().reshape(pix_mat.size)
h5_main[pos_ind, :] = np.float32(pix_vec)
h5_f.flush() # flush from memory!
else:
print('File not found for: row {} col {}'.format(
row_ind, col_ind))
pos_ind += 1
if (100.0 * pos_ind / num_pix) % 10 == 0:
print('completed translating {} %'.format(
int(100 * pos_ind / num_pix)))
h5_f.close()
return h5_path
def _create_results_datasets(self):
"""
Creates all the datasets necessary for holding all parameters + data.
"""
self.h5_results_grp = create_results_group(
self.h5_main,
self.process_name,
h5_parent_group=self._h5_target_group)
self.parms_dict.update({
'last_pixel': 0,
'algorithm': 'pycroscopy_SignalFilter'
})
write_simple_attrs(self.h5_results_grp, self.parms_dict)
assert isinstance(self.h5_results_grp, h5py.Group)
if isinstance(self.composite_filter, np.ndarray):
h5_comp_filt = self.h5_results_grp.create_dataset(
'Composite_Filter', data=np.float32(self.composite_filter))
if self.verbose and self.mpi_rank == 0:
print(
'Rank {} - Finished creating the Composite_Filter dataset'.
format(self.mpi_rank))
# First create the position datsets if the new indices are smaller...
if self.num_effective_pix != self.h5_main.shape[0]:
# TODO: Do this part correctly. See past solution:
"""
# need to make new position datasets by taking every n'th index / value:
new_pos_vals = np.atleast_2d(h5_pos_vals[slice(0, None, self.num_effective_pix), :])
pos_descriptor = []
for name, units, leng in zip(h5_pos_inds.attrs['labels'], h5_pos_inds.attrs['units'],
[int(np.unique(h5_pos_inds[:, dim_ind]).size / self.num_effective_pix)
for dim_ind in range(h5_pos_inds.shape[1])]):
pos_descriptor.append(Dimension(name, units, np.arange(leng)))
ds_pos_inds, ds_pos_vals = build_ind_val_dsets(pos_descriptor, is_spectral=False, verbose=self.verbose)
h5_pos_vals.data = np.atleast_2d(new_pos_vals) # The data generated above varies linearly. Override.
"""
h5_pos_inds_new, h5_pos_vals_new = write_ind_val_dsets(
self.h5_results_grp,
Dimension('pixel', 'a.u.', self.num_effective_pix),
is_spectral=False,
verbose=self.verbose and self.mpi_rank == 0)
if self.verbose and self.mpi_rank == 0:
print('Rank {} - Created the new position ancillary dataset'.
format(self.mpi_rank))
else:
h5_pos_inds_new = self.h5_main.h5_pos_inds
h5_pos_vals_new = self.h5_main.h5_pos_vals
if self.verbose and self.mpi_rank == 0:
print('Rank {} - Reusing source datasets position datasets'.
format(self.mpi_rank))
if self.noise_threshold is not None:
self.h5_noise_floors = write_main_dataset(
self.h5_results_grp, (self.num_effective_pix, 1),
'Noise_Floors',
'Noise',
'a.u.',
None,
Dimension('arb', '', [1]),
dtype=np.float32,
aux_spec_prefix='Noise_Spec_',
h5_pos_inds=h5_pos_inds_new,
h5_pos_vals=h5_pos_vals_new,
verbose=self.verbose and self.mpi_rank == 0)
if self.verbose and self.mpi_rank == 0:
print('Rank {} - Finished creating the Noise_Floors dataset'.
format(self.mpi_rank))
if self.write_filtered:
# Filtered data is identical to Main_Data in every way - just a duplicate
self.h5_filtered = create_empty_dataset(
self.h5_main,
self.h5_main.dtype,
'Filtered_Data',
h5_group=self.h5_results_grp)
if self.verbose and self.mpi_rank == 0:
print(
'Rank {} - Finished creating the Filtered dataset'.format(
self.mpi_rank))
self.hot_inds = None
if self.write_condensed:
self.hot_inds = np.where(self.composite_filter > 0)[0]
self.hot_inds = np.uint(self.hot_inds[int(0.5 *
len(self.hot_inds)):]
) # only need to keep half the data
condensed_spec = Dimension('hot_frequencies', '',
int(0.5 * len(self.hot_inds)))
self.h5_condensed = write_main_dataset(
self.h5_results_grp,
(self.num_effective_pix, len(self.hot_inds)),
'Condensed_Data',
'Complex',
'a. u.',
None,
condensed_spec,
h5_pos_inds=h5_pos_inds_new,
h5_pos_vals=h5_pos_vals_new,
dtype=np.complex,
verbose=self.verbose and self.mpi_rank == 0)
if self.verbose and self.mpi_rank == 0:
print(
'Rank {} - Finished creating the Condensed dataset'.format(
self.mpi_rank))
if self.mpi_size > 1:
self.mpi_comm.Barrier()
self.h5_main.file.flush()
def _write_results_chunk(self):
"""
Writes the provided SVD results to file
Parameters
----------
"""
comp_dim = Dimension('Principal Component', 'a. u.', len(self.__s))
h5_svd_group = create_results_group(
self.h5_main,
self.process_name,
h5_parent_group=self._h5_target_group)
self.h5_results_grp = h5_svd_group
self._write_source_dset_provenance()
write_simple_attrs(h5_svd_group, self.parms_dict)
write_simple_attrs(h5_svd_group, {'svd_method': 'sklearn-randomized'})
h5_u = write_main_dataset(h5_svd_group,
np.float32(self.__u),
'U',
'Abundance',
'a.u.',
None,
comp_dim,
h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals,
dtype=np.float32,
chunks=calc_chunks(self.__u.shape,
np.float32(0).itemsize))
# print(get_attr(self.h5_main, 'quantity')[0])
h5_v = write_main_dataset(h5_svd_group,
self.__v,
'V',
get_attr(self.h5_main, 'quantity')[0],
'a.u.',
comp_dim,
None,
h5_spec_inds=self.h5_main.h5_spec_inds,
h5_spec_vals=self.h5_main.h5_spec_vals,
chunks=calc_chunks(
self.__v.shape,
self.h5_main.dtype.itemsize))
# No point making this 1D dataset a main dataset
h5_s = h5_svd_group.create_dataset('S', data=np.float32(self.__s))
'''
Check h5_main for plot group references.
Copy them into V if they exist
'''
for key in self.h5_main.attrs.keys():
if '_Plot_Group' not in key:
continue
ref_inds = get_indices_for_region_ref(self.h5_main,
self.h5_main.attrs[key],
return_method='corners')
ref_inds = ref_inds.reshape([-1, 2, 2])
ref_inds[:, 1, 0] = h5_v.shape[0] - 1
svd_ref = create_region_reference(h5_v, ref_inds)
h5_v.attrs[key] = svd_ref
# Marking completion:
self._status_dset_name = 'completed_positions'
self._h5_status_dset = h5_svd_group.create_dataset(
self._status_dset_name,
data=np.ones(self.h5_main.shape[0], dtype=np.uint8))
# keeping legacy option:
h5_svd_group.attrs['last_pixel'] = self.h5_main.shape[0]
def save_Yout(h5_main, Yout, yout):
'''
Writes the results to teh HDF5 file
:param h5_main:
:type h5_main: h5py dataset of USIDataset
:param Yout:
:type Yout:
:param yout:
:type yout:
'''
parm_dict = usid.hdf_utils.get_attributes(h5_main)
# Get relevant parameters
num_rows = parm_dict['num_rows']
num_cols = parm_dict['num_cols']
pnts_per_avg = parm_dict['pnts_per_avg']
h5_gp = h5_main.parent
h5_meas_group = usid.hdf_utils.create_indexed_group(
h5_gp, 'GKPFM_Frequency')
# Create dimensions
pos_desc = [
Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'],
num_cols)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'],
num_rows))
]
# ds_pos_ind, ds_pos_val = build_ind_val_matrices(pos_desc, is_spectral=False)
spec_desc = [
Dimension('Frequency', 'Hz',
np.linspace(0, parm_dict['sampling_rate'], pnts_per_avg))
]
# ds_spec_inds, ds_spec_vals = build_ind_val_matrices(spec_desc, is_spectral=True)
# Writes main dataset
h5_y = usid.hdf_utils.write_main_dataset(
h5_meas_group,
Yout,
'Y', # Name of main dataset
'Deflection', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.cdouble, # data type / precision
main_dset_attrs=parm_dict)
usid.hdf_utils.copy_attributes(h5_y, h5_gp)
h5_meas_group = usid.hdf_utils.create_indexed_group(h5_gp, 'GKPFM_Time')
spec_desc = [
Dimension('Time', 's',
np.linspace(0, parm_dict['total_time'], pnts_per_avg))
]
h5_y = usid.hdf_utils.write_main_dataset(
h5_meas_group,
yout,
'y_time', # Name of main dataset
'Deflection', # Physical quantity contained in Main dataset
'V', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs=parm_dict)
usid.hdf_utils.copy_attributes(h5_y, h5_gp)
h5_y.file.flush()
return
def write_images(self):
if bool(self.img_desc):
for img_f, descriptors in self.img_desc.items():
#check for existing spectrogram or image and link position/spec inds/vals
#at most two channels worth of need to be checked (Fwd and Bwd)
try:
str_main = str(
get_all_main(self.h5_f['Measurement_000/Channel_000']))
i_beg = str_main.find('located at: \n\t') + 14
i_end = str_main.find('\nData contains') - 1
data_loc = str_main[i_beg:i_end]
channel_data = USIDataset(self.h5_f[data_loc])
h5_pos_inds = channel_data.h5_pos_inds
h5_pos_vals = channel_data.h5_pos_vals
pos_dims = None
write_pos_vals = False
if channel_data.spec_dim_sizes[0] == 1:
h5_spec_inds = channel_data.h5_spec_inds
h5_spec_vals = channel_data.h5_spec_vals
spec_dims = None
#if channel 000 is spectrogram, check next dataset
elif channel_data.spec_dim_sizes[0] != 1:
str_main = str(
get_all_main(
self.h5_f['Measurement_000/Channel_001']))
i_beg = str_main.find('located at: \n\t') + 14
i_end = str_main.find('\nData contains') - 1
data_loc = str_main[i_beg:i_end]
channel_data = USIDataset(self.h5_f[data_loc])
#channel data is an image, & we link their spec inds/vals
if channel_data.spec_dim_sizes[0] == 1:
h5_spec_inds = channel_data.h5_spec_inds
h5_spec_vals = channel_data.h5_spec_vals
spec_dims = None
else: # If a forward/bwd spectrogram exist
h5_spec_inds = None
h5_spec_vals = None
spec_dims = Dimension('arb', 'a.u', 1)
#in case where channel does not exist, we make new spec/pos inds/vals
except KeyError:
#pos dims
h5_pos_inds = None
h5_pos_vals = None
pos_dims = self.pos_dims
write_pos_vals = True
#spec dims
h5_spec_inds = None
h5_spec_vals = None
spec_dims = Dimension('arb', 'a.u', 1)
channel_i = create_indexed_group(self.h5_meas_grp, 'Channel_')
h5_raw = write_main_dataset(
channel_i, #parent HDF5 group
(self.x_len * self.y_len, 1), # shape of Main dataset
'Raw_' + descriptors[0].replace('-', '_'),
# Name of main dataset
descriptors[0],
# Physical quantity contained in Main dataset
descriptors[2], # Units for the physical quantity
h5_pos_inds=h5_pos_inds,
h5_pos_vals=h5_pos_vals,
# Position dimensions
pos_dims=pos_dims,
# Spectroscopic dimensions
h5_spec_inds=h5_spec_inds,
h5_spec_vals=h5_spec_vals,
spec_dims=spec_dims,
dtype=np.float32, # data type / precision
main_dset_attrs={
'Caption': descriptors[0],
'Scale': descriptors[1],
'Physical_Units': descriptors[2],
'Offset': descriptors[3]
})
h5_raw[:, :] = self.imgs[img_f].reshape(h5_raw.shape)
if write_pos_vals:
h5_raw.h5_pos_vals[:, :] = self.pos_val
def _parse_3ds_parms(header_dict, signal_dict):
"""
Parse 3ds files.
Parameters
----------
header_dict : dict
signal_dict : dict
Returns
-------
parm_dict : dict
"""
parm_dict = dict()
data_dict = dict()
# Create dictionary with measurement parameters
meas_parms = {key: value for key, value in header_dict.items()
if value is not None}
channels = meas_parms.pop('channels')
for key, parm_grid in zip(meas_parms.pop('fixed_parameters')
+ meas_parms.pop('experimental_parameters'),
signal_dict['params'].T):
# Collapse the parm_grid along one axis if it's constant
# along said axis
if parm_grid.ndim > 1:
dim_slice = list()
# Find dimensions that are constant
for idim in range(parm_grid.ndim):
tmp_grid = np.moveaxis(parm_grid.copy(), idim, 0)
if np.all(np.equal(tmp_grid[0], tmp_grid[1])):
dim_slice.append(0)
else:
dim_slice.append(slice(None))
# print(key, dim_slice)
# print(parm_grid[tuple(dim_slice)])
parm_grid = parm_grid[tuple(dim_slice)]
meas_parms[key] = parm_grid
parm_dict['meas_parms'] = meas_parms
# Create dictionary with channel parameters and
# save channel data before renaming keys
data_channel_parms = dict()
for chan_name in channels:
splitted_chan_name = chan_name.split(maxsplit=2)
if len(splitted_chan_name) == 2:
direction = 'forward'
elif len(splitted_chan_name) == 3:
direction = 'backward'
splitted_chan_name.pop(1)
name, unit = splitted_chan_name
key = ' '.join((name, direction))
data_channel_parms[key] = {'Name': name,
'Direction': direction,
'Unit': unit.strip('()'),
}
data_dict[key] = signal_dict.pop(chan_name)
parm_dict['channel_parms'] = data_channel_parms
# Add remaining signal_dict elements to data_dict
data_dict.update(signal_dict)
# Position dimensions
nx, ny = header_dict['dim_px']
if 'X (m)' in parm_dict:
row_vals = parm_dict.pop('X (m)')
else:
row_vals = np.arange(nx, dtype=np.float32)
if 'Y (m)' in parm_dict:
col_vals = parm_dict.pop('Y (m)')
else:
col_vals = np.arange(ny, dtype=np.float32)
pos_vals = np.hstack([row_vals.reshape(-1, 1),
col_vals.reshape(-1, 1)])
pos_names = ['X', 'Y']
pos_dims = [Dimension(label, 'nm', values)
for label, values in zip(pos_names, pos_vals.T)]
data_dict['Position Dimensions'] = pos_dims
# Spectroscopic dimensions
sweep_signal = header_dict['sweep_signal']
spec_label, spec_unit = sweep_signal.split(maxsplit=1)
spec_unit = spec_unit.strip('()')
# parm_dict['sweep_signal'] = (sweep_name, sweep_unit)
dc_offset = data_dict['sweep_signal']
spec_dim = Dimension(spec_label, spec_unit, dc_offset)
data_dict['Spectroscopic Dimensions'] = spec_dim
return parm_dict, data_dict
def translate(self, raw_data_path):
"""
The main function that translates the provided file into a .h5 file
Parameters
------------
raw_data_path : string / unicode
Absolute file path of the data .mat file.
Returns
----------
h5_path : string / unicode
Absolute path of the translated h5 file
"""
raw_data_path = path.abspath(raw_data_path)
folder_path, file_name = path.split(raw_data_path)
h5_path = path.join(folder_path, file_name[:-4] + '.h5')
if path.exists(h5_path):
remove(h5_path)
h5_f = h5py.File(h5_path, 'w')
self.h5_read = True
try:
h5_raw = h5py.File(raw_data_path, 'r')
except:
self.h5_read = False
h5_raw = loadmat(raw_data_path)
try:
excite_cell = h5_raw['dc_amp_cell3']
except KeyError:
excite_cell = [h5_raw['VS_amp_vec']]
test = excite_cell[0][0]
if self.h5_read:
excitation_vec = h5_raw[test]
else:
excitation_vec = np.float32(np.squeeze(test))
try:
current_cell = h5_raw['current_cell3']
except KeyError:
current_cell = h5_raw['IV_dat']
num_rows = current_cell.shape[0]
num_cols = current_cell.shape[1]
num_iv_pts = excitation_vec.size
num_cycles = 0
if len(current_cell.shape) == 4:
num_cycles = current_cell.shape[-1]
current_data = np.zeros(shape=(num_rows * num_cols,
num_iv_pts * num_cycles),
dtype=np.float32)
else:
current_data = np.zeros(shape=(num_rows * num_cols, num_iv_pts),
dtype=np.float32)
for row_ind in range(num_rows):
for col_ind in range(num_cols):
pix_ind = row_ind * num_cols + col_ind
if self.h5_read:
curr_val = np.squeeze(
h5_raw[current_cell[row_ind][col_ind]].value)
else:
curr_val = np.float32(
np.squeeze(current_cell[row_ind][col_ind]))
curr_val = curr_val.reshape(current_data[0, :].shape)
current_data[pix_ind, :] = 1E+9 * curr_val
parm_dict = self._read_parms(h5_raw)
parm_dict.update({'translator': 'FORC_IV'})
pos_desc = [
Dimension('Y', 'm', np.arange(num_rows)),
Dimension('X', 'm', np.arange(num_cols))
]
if num_cycles > 0:
spec_desc = [
Dimension('DC Bias', 'V', excitation_vec),
Dimension('Cycles', 'number', np.arange(num_cycles))
]
else:
spec_desc = [Dimension('DC Bias', 'V', excitation_vec)]
meas_grp = create_indexed_group(h5_f, 'Measurement')
chan_grp = create_indexed_group(meas_grp, 'Channel')
write_simple_attrs(chan_grp, parm_dict)
h5_main = write_main_dataset(chan_grp, current_data, 'Raw_Data',
'Current', '1E-9 A', pos_desc, spec_desc)
return h5_main
def _create_results_datasets(self):
'''
Creates the datasets an Groups necessary to store the results.
Parameters
----------
h5_if : 'Inst_Freq' h5 Dataset
Contains the Instantaneous Frequencies
tfp : 'tfp' h5 Dataset
Contains the time-to-first-peak data as a 1D matrix
shift : 'shift' h5 Dataset
Contains the frequency shift data as a 1D matrix
'''
print('Creating results datasets')
# Get relevant parameters
num_rows = self.parm_dict['num_rows']
num_cols = self.parm_dict['num_cols']
pnts_per_avg = self.parm_dict['pnts_per_avg']
ds_shape = [num_rows * num_cols, pnts_per_avg]
self.h5_results_grp = create_results_group(self.h5_main,
self.process_name)
copy_attributes(self.h5_main.parent, self.h5_results_grp)
# Create dimensions
pos_desc = [
Dimension('X', 'm',
np.linspace(0, self.parm_dict['FastScanSize'],
num_cols)),
Dimension('Y', 'm',
np.linspace(0, self.parm_dict['SlowScanSize'], num_rows))
]
# ds_pos_ind, ds_pos_val = build_ind_val_matrices(pos_desc, is_spectral=False)
spec_desc = [
Dimension(
'Time', 's',
np.linspace(0, self.parm_dict['total_time'], pnts_per_avg))
]
# ds_spec_inds, ds_spec_vals = build_ind_val_matrices(spec_desc, is_spectral=True)
# Writes main dataset
self.h5_if = write_main_dataset(
self.h5_results_grp,
ds_shape,
'Inst_Freq', # Name of main dataset
'Frequency', # Physical quantity contained in Main dataset
'Hz', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_amp = write_main_dataset(
self.h5_results_grp,
ds_shape,
'Amplitude', # Name of main dataset
'Amplitude', # Physical quantity contained in Main dataset
'nm', # Units for the physical quantity
None, # Position dimensions
None, # Spectroscopic dimensions
h5_pos_inds=self.h5_main.h5_pos_inds, # Copy Pos Dimensions
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=self.h5_main.h5_spec_inds,
# Copy Spectroscopy Dimensions
h5_spec_vals=self.h5_main.h5_spec_vals,
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_phase = write_main_dataset(
self.h5_results_grp,
ds_shape,
'Phase', # Name of main dataset
'Phase', # Physical quantity contained in Main dataset
'degrees', # Units for the physical quantity
None, # Position dimensions
None, # Spectroscopic dimensions
h5_pos_inds=self.h5_main.h5_pos_inds, # Copy Pos Dimensions
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=self.h5_main.h5_spec_inds,
# Copy Spectroscopy Dimensions
h5_spec_vals=self.h5_main.h5_spec_vals,
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
self.h5_pwrdis = write_main_dataset(
self.h5_results_grp,
ds_shape,
'PowerDissipation', # Name of main dataset
'Power', # Physical quantity contained in Main dataset
'W', # Units for the physical quantity
None, # Position dimensions
None, # Spectroscopic dimensions
h5_pos_inds=self.h5_main.h5_pos_inds, # Copy Pos Dimensions
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=self.h5_main.h5_spec_inds,
# Copy Spectroscopy Dimensions
h5_spec_vals=self.h5_main.h5_spec_vals,
dtype=np.float32, # data type / precision
main_dset_attrs=self.parm_dict)
_arr = np.zeros([num_rows * num_cols, 1])
self.h5_tfp = self.h5_results_grp.create_dataset('tfp',
data=_arr,
dtype=np.float32)
self.h5_shift = self.h5_results_grp.create_dataset('shift',
data=_arr,
dtype=np.float32)
self.h5_if.file.flush()
return
def translate(self, parm_path):
"""
The main function that translates the provided file into a .h5 file
Parameters
------------
parm_path : string / unicode
Absolute file path of the parameters .mat file.
Returns
----------
h5_path : string / unicode
Absolute path of the translated h5 file
"""
parm_path = path.abspath(parm_path)
parm_dict, excit_wfm = self._read_parms(parm_path)
folder_path, base_name = path.split(parm_path)
waste, base_name = path.split(folder_path)
# Until a better method is provided....
with h5py.File(path.join(folder_path, 'line_1.mat'),
'r') as h5_mat_line_1:
num_ai_chans = h5_mat_line_1['data'].shape[1]
h5_path = path.join(folder_path, base_name + '.h5')
if path.exists(h5_path):
remove(h5_path)
with h5py.File(h5_path) as h5_f:
h5_meas_grp = create_indexed_group(h5_f, 'Measurement')
global_parms = dict()
global_parms.update({'data_type': 'gIV', 'translator': 'gIV'})
write_simple_attrs(h5_meas_grp, global_parms)
# Only prepare the instructions for the dimensions here
spec_dims = Dimension('Bias', 'V', excit_wfm)
pos_dims = Dimension(
'Y', 'm',
np.linspace(0, parm_dict['grid_scan_height_[m]'],
parm_dict['grid_num_rows']))
self.raw_datasets = list()
for chan_index in range(num_ai_chans):
h5_chan_grp = create_indexed_group(h5_meas_grp, 'Channel')
write_simple_attrs(h5_chan_grp, parm_dict)
"""
Minimize file size to the extent possible.
DAQs are rated at 16 bit so float16 should be most appropriate.
For some reason, compression is effective only on time series data
"""
h5_raw = write_main_dataset(
h5_chan_grp, (parm_dict['grid_num_rows'], excit_wfm.size),
'Raw_Data',
'Current',
'1E-{} A'.format(parm_dict['IO_amplifier_gain']),
pos_dims,
spec_dims,
dtype=np.float16,
chunks=(1, excit_wfm.size),
compression='gzip')
self.raw_datasets.append(h5_raw)
# Now that the N channels have been made, populate them with the actual data....
self._read_data(parm_dict, folder_path)
return h5_path
def _build_ancillary_datasets(self):
"""
Parameters
----------
None
Returns
-------
ds_pos_inds : VirtualDataset
Position Indices
ds_pos_vals : VirtualDataset
Position Values
ds_spec_inds : VirtualDataset
Spectrosocpic Indices
ds_spec_vals : VirtualDataset
Spectroscopic Values
"""
# create spectrogram at each pixel from the coefficients
spec_step = np.arange(0, 1, 1 / self.n_steps)
V_vec = 10 * np.arcsin(np.sin(
self.n_fields * np.pi * spec_step)) * 2 / np.pi
# build DC vector for typical BEPS
Vdc_mat = np.vstack(
(V_vec, np.full(np.shape(V_vec),
np.nan))) # Add out-of-field values
IF_vec = Vdc_mat.T.flatten() # Base DC vector
IF_vec = np.tile(IF_vec, self.n_cycles) # Now with Cycles
IF_vec = np.dot(1 + np.arange(self.forc_cycles)[:, None],
IF_vec[None, :]) # Do a single FORC
IF_vec = np.tile(IF_vec.flatten(),
self.forc_repeats) # Repeat the FORC
IF_inds = np.logical_not(np.isnan(IF_vec))
Vdc_vec = np.where(IF_inds, IF_vec, 0)
# build AC vector
Vac_vec = np.ones(np.shape(Vdc_vec))
# Build the Spectroscopic Values matrix
spec_dims = [
self.n_fields, self.n_steps, self.n_cycles, self.forc_cycles,
self.forc_repeats, self.n_bins
]
spec_labs = [
'Field', 'DC_Offset', 'Cycle', 'FORC', 'FORC_repeat', 'Frequency'
]
spec_units = ['', 'V', '', '', '', 'Hz']
spec_start = [0, 0, 0, 0, 0, self.start_freq]
spec_steps = [
1, 1, 1, 1, 1, (self.end_freq - self.start_freq) / self.n_bins
]
# Remove dimensions with single values
real_dims = np.argwhere(np.array(spec_dims) != 1).squeeze()
spec_dims = [spec_dims[idim] for idim in real_dims]
spec_labs = [spec_labs[idim] for idim in real_dims]
spec_units = [spec_units[idim] for idim in real_dims]
spec_start = [spec_start[idim] for idim in real_dims]
spec_steps = [spec_steps[idim] for idim in real_dims]
# Correct the DC Offset dimension
spec_dims_corrected = list()
for dim_size, dim_name, dim_units, step_size, init_val in zip(
spec_dims, spec_labs, spec_units, spec_steps, spec_start):
if dim_name == 'DC_Offset':
value = Vdc_vec[::2]
else:
value = np.arange(dim_size) * step_size + init_val
spec_dims_corrected.append(Dimension(dim_name, dim_units, value))
pos_dims = list()
for dim_size, dim_name, dim_units, step_size, init_val in zip(
[self.N_y, self.N_x], ['Y', 'X'], ['um', 'um'],
[10 / self.N_y, 10 / self.N_x], [-5, -5]):
pos_dims.append(
Dimension(dim_name, dim_units,
np.arange(dim_size) * step_size + init_val))
return pos_dims, spec_dims_corrected
def translate(self,
data_filepath,
out_filename,
verbose=False,
debug=False):
'''
The main function that translates the provided file into a .h5 file
Parameters
----------------
data_filepath : String / unicode
Absolute path of the data file
out_filename : String / unicode
Name for the new generated hdf5 file. The new file will be
saved in the same folder of the input file with
file name "out_filename".
NOTE: the .h5 extension is automatically added to "out_filename"
debug : Boolean (Optional. default is false)
Whether or not to print log statements
Returns
----------------
h5_path : String / unicode
Absolute path of the generated .h5 file
'''
self.debug = debug
# Open the datafile
try:
data_filepath = os.path.abspath(data_filepath)
ARh5_file = h5py.File(data_filepath, 'r')
except:
print('Unable to open the file', data_filepath)
raise
# Get info from the origin file like Notes and Segments
self.notes = ARh5_file.attrs['Note']
self.segments = ARh5_file['ForceMap']['Segments'] #shape: (X, Y, 4)
self.segments_name = list(ARh5_file['ForceMap'].attrs['Segments'])
self.map_size['X'] = ARh5_file['ForceMap']['Segments'].shape[0]
self.map_size['Y'] = ARh5_file['ForceMap']['Segments'].shape[1]
self.channels_name = list(ARh5_file['ForceMap'].attrs['Channels'])
try:
self.points_per_sec = np.float(
self.note_value('ARDoIVPointsPerSec'))
except NameError:
self.points_per_sec = np.float(self.note_value('NumPtsPerSec'))
if self.debug:
print('Map size [X, Y]: ', self.map_size)
print('Channels names: ', self.channels_name)
# Only the extension 'Ext' segment can change size
# so we get the shortest one and we trim all the others
extension_idx = self.segments_name.index('Ext')
short_ext = np.amin(np.array(self.segments[:, :, extension_idx]))
longest_ext = np.amax(np.array(self.segments[:, :, extension_idx]))
difference = longest_ext - short_ext # this is a difference between integers
tot_length = (np.amax(self.segments) - difference) + 1
# +1 otherwise array(tot_length) will be of 1 position shorter
points_trimmed = np.array(self.segments[:, :,
extension_idx]) - short_ext
if self.debug:
print('Data were trimmed in the extension segment of {} points'.
format(difference))
# Open the output hdf5 file
folder_path = os.path.dirname(data_filepath)
h5_path = os.path.join(folder_path, out_filename + '.h5')
h5_file = h5py.File(h5_path, 'w')
# Create the measurement group
h5_meas_group = create_indexed_group(h5_file, 'Measurement')
# Create all channels and main datasets
# at this point the main dataset are just function of time
x_dim = np.linspace(0, np.float(self.note_value('FastScanSize')),
self.map_size['X'])
y_dim = np.linspace(0, np.float(self.note_value('FastScanSize')),
self.map_size['Y'])
z_dim = np.arange(tot_length) / np.float(self.points_per_sec)
pos_dims = [
Dimension('Cols', 'm', x_dim),
Dimension('Rows', 'm', y_dim)
]
spec_dims = [Dimension('Time', 's', z_dim)]
# This is quite time consuming, but on magnetic drive is limited from the disk, and therefore is not useful
# to parallelize these loops
for index, channel in enumerate(self.channels_name):
cur_chan = create_indexed_group(h5_meas_group, 'Channel')
main_dset = np.empty(
(self.map_size['X'], self.map_size['Y'], tot_length))
for column in np.arange(self.map_size['X']):
for row in np.arange(self.map_size['Y']):
AR_pos_string = str(column) + ':' + str(row)
seg_start = self.segments[column, row,
extension_idx] - short_ext
main_dset[column,
row, :] = ARh5_file['ForceMap'][AR_pos_string][
index, seg_start:]
# Reshape with Fortran order to have the correct position indices
main_dset = np.reshape(main_dset, (-1, tot_length), order='F')
if index == 0:
first_main_dset = cur_chan
quant_unit = self.get_def_unit(channel)
h5_raw = write_main_dataset(
cur_chan, # parent HDF5 group
main_dset, # 2D array of raw data
'Raw_' + channel, # Name of main dset
channel, # Physical quantity
self.get_def_unit(channel), # Unit
pos_dims, # position dimensions
spec_dims, #spectroscopy dimensions
)
else:
h5_raw = write_main_dataset(
cur_chan, # parent HDF5 group
main_dset, # 2D array of raw data
'Raw_' + channel, # Name of main dset
channel, # Physical quantity
self.get_def_unit(channel), # Unit
pos_dims, # position dimensions
spec_dims, #spectroscopy dimensions
# Link Ancilliary dset to the first
h5_pos_inds=first_main_dset['Position_Indices'],
h5_pos_vals=first_main_dset['Position_Values'],
h5_spec_inds=first_main_dset['Spectroscopic_Indices'],
h5_spec_vals=first_main_dset['Spectroscopic_Values'],
)
# Make Channels with IMAGES.
# Position indices/values are the same of all other channels
# Spectroscopic indices/valus are they are just one single dimension
img_spec_dims = [Dimension('arb', 'a.u.', [1])]
for index, image in enumerate(ARh5_file['Image'].keys()):
main_dset = np.reshape(np.array(ARh5_file['Image'][image]),
(-1, 1),
order='F')
cur_chan = create_indexed_group(h5_meas_group, 'Channel')
if index == 0:
first_image_dset = cur_chan
h5_raw = write_main_dataset(
cur_chan, # parent HDF5 group
main_dset, # 2D array of image (shape: P*Q x 1)
'Img_' + image, # Name of main dset
image, # Physical quantity
self.get_def_unit(image), # Unit
pos_dims, # position dimensions
img_spec_dims, #spectroscopy dimensions
# Link Ancilliary dset to the first
h5_pos_inds=first_main_dset['Position_Indices'],
h5_pos_vals=first_main_dset['Position_Values'],
)
else:
h5_raw = write_main_dataset(
cur_chan, # parent HDF5 group
main_dset, # 2D array of image (shape: P*Q x 1)
'Img_' + image, # Name of main dset
image, # Physical quantity
self.get_def_unit(image), # Unit
pos_dims, # position dimensions
img_spec_dims, #spectroscopy dimensions
# Link Ancilliary dset to the first
h5_pos_inds=first_main_dset['Position_Indices'],
h5_pos_vals=first_main_dset['Position_Values'],
h5_spec_inds=first_image_dset['Spectroscopic_Indices'],
h5_spec_vals=first_image_dset['Spectroscopic_Values'],
)
# Create the new segments that will be stored as attribute
new_segments = {}
for seg, name in enumerate(self.segments_name):
new_segments.update({name: self.segments[0, 0, seg] - short_ext})
write_simple_attrs(
h5_meas_group, {
'Segments': new_segments,
'Points_trimmed': points_trimmed,
'Notes': self.notes
})
write_simple_attrs(
h5_file, {
'translator':
'ARhdf5',
'instrument':
'Asylum Research ' + self.note_value('MicroscopeModel'),
'AR sftware version':
self.note_value('Version')
})
if self.debug:
print(print_tree(h5_file))
print('\n')
for key, val in get_attributes(h5_meas_group).items():
if key != 'Notes':
print('{} : {}'.format(key, val))
else:
print('{} : {}'.format(
key, 'notes string too long to be written here.'))
# Clean up
ARh5_file.close()
h5_file.close()
self.translated = True
return h5_path
def write_results(self, verbose=False, name='inst_freq_masked'):
'''
Writes a new main data set
:param verbose:
:type verbose: bool
:param name:
:type name: str
:returns:
:rtype:
'''
h5_dist_clust_group = px.hdf_utils.create_indexed_group(
self.h5_main.parent, 'dist-cluster')
# Create dimensions
pos_desc = [Dimension('Grain Distance', 'm', self.data_dist)]
ds_pos_ind, ds_pos_val = build_ind_val_dsets(pos_desc,
is_spectral=False,
verbose=verbose)
spec_desc = [
Dimension(
'Time', 's',
np.linspace(0, self.pxl_time, self.parms_dict['pnts_per_avg']))
]
ds_spec_inds, ds_spec_vals = build_ind_val_dsets(spec_desc,
is_spectral=True,
verbose=verbose)
# Writes main dataset
h5_clust = px.hdf_utils.write_main_dataset(
h5_dist_clust_group,
self.data_scatter[:, 1:],
name, # Name of main dataset
'Frequency', # Physical quantity contained in Main dataset
'Hz', # Units for the physical quantity
pos_desc, # Position dimensions
spec_desc, # Spectroscopic dimensions
dtype=np.float32, # data type / precision
main_dset_attrs=self.parms_dict)
# Adds mask and grain min distances and mean distances
grp = px.io.VirtualGroup(h5_dist_clust_group.name)
mask = px.io.VirtualDataset('mask',
self.mask,
parent=self.h5_main.parent)
dist_min = px.io.VirtualDataset('dist_min',
self.data_dist,
parent=self.h5_main.parent)
dist_mean = px.io.VirtualDataset('dist_mean',
self.data_avg_dist,
parent=self.h5_main.parent)
data_pos = px.io.VirtualDataset('coordinates',
self.mask_off_1D_pos,
parent=self.h5_main.parent)
data_avg = px.io.VirtualDataset('data_avg',
self.data_avg_1D_vals,
parent=self.h5_main.parent)
grp.add_children([mask])
grp.add_children([dist_min])
grp.add_children([dist_mean])
grp.add_children([data_pos])
grp.add_children([data_avg])
# Find folder, write to it
hdf = px.io.HDFwriter(self.h5_main.file)
h5_refs = hdf.write(grp, print_log=verbose)
return h5_clust
def _write_results_chunk(self):
"""
Writes the labels and mean response to the h5 file
Returns
---------
h5_group : HDF5 Group reference
Reference to the group that contains the clustering results
"""
print('Writing clustering results to file.')
num_clusters = self.__mean_resp.shape[0]
self.h5_results_grp = create_results_group(
self.h5_main,
self.process_name,
h5_parent_group=self._h5_target_group)
self._write_source_dset_provenance()
write_simple_attrs(self.h5_results_grp, self.parms_dict)
h5_labels = write_main_dataset(self.h5_results_grp,
np.uint32(self.__labels.reshape([-1,
1])),
'Labels',
'Cluster ID',
'a. u.',
None,
Dimension('Cluster', 'ID', 1),
h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals,
aux_spec_prefix='Cluster_',
dtype=np.uint32)
if self.num_comps != self.h5_main.shape[1]:
'''
Setup the Spectroscopic Indices and Values for the Mean Response if we didn't use all components
Note that a sliced spectroscopic matrix may not be contiguous. Let's just lose the spectroscopic data
for now until a better method is figured out
'''
"""
if isinstance(self.data_slice[1], np.ndarray):
centroid_vals_mat = h5_centroids.h5_spec_vals[self.data_slice[1].tolist()]
else:
centroid_vals_mat = h5_centroids.h5_spec_vals[self.data_slice[1]]
ds_centroid_values.data[0, :] = centroid_vals_mat
"""
if isinstance(self.data_slice[1], np.ndarray):
vals_slice = self.data_slice[1].tolist()
else:
vals_slice = self.data_slice[1]
vals = self.h5_main.h5_spec_vals[:, vals_slice].squeeze()
new_spec = Dimension('Original_Spectral_Index', 'a.u.', vals)
h5_inds, h5_vals = write_ind_val_dsets(self.h5_results_grp,
new_spec,
is_spectral=True)
else:
h5_inds = self.h5_main.h5_spec_inds
h5_vals = self.h5_main.h5_spec_vals
# For now, link centroids with default spectroscopic indices and values.
h5_centroids = write_main_dataset(self.h5_results_grp,
self.__mean_resp,
'Mean_Response',
get_attr(self.h5_main,
'quantity')[0],
get_attr(self.h5_main, 'units')[0],
Dimension('Cluster', 'a. u.',
np.arange(num_clusters)),
None,
h5_spec_inds=h5_inds,
aux_pos_prefix='Mean_Resp_Pos_',
h5_spec_vals=h5_vals)
# Marking completion:
self._status_dset_name = 'completed_positions'
self._h5_status_dset = self.h5_results_grp.create_dataset(
self._status_dset_name,
data=np.ones(self.h5_main.shape[0], dtype=np.uint8))
# keeping legacy option:
self.h5_results_grp.attrs['last_pixel'] = self.h5_main.shape[0]
return self.h5_results_grp
def reshape_from_lines_to_pixels(h5_main, pts_per_cycle, scan_step_x_m=None):
"""
Breaks up the provided raw G-mode dataset into lines and pixels (from just lines)
Parameters
----------
h5_main : h5py.Dataset object
Reference to the main dataset that contains the raw data that is only broken up by lines
pts_per_cycle : unsigned int
Number of points in a single pixel
scan_step_x_m : float
Step in meters for pixels
Returns
-------
h5_resh : h5py.Dataset object
Reference to the main dataset that contains the reshaped data
"""
if not check_if_main(h5_main):
raise TypeError('h5_main is not a Main dataset')
h5_main = USIDataset(h5_main)
if pts_per_cycle % 1 != 0 or pts_per_cycle < 1:
raise TypeError('pts_per_cycle should be a positive integer')
if scan_step_x_m is not None:
if not isinstance(scan_step_x_m, Number):
raise TypeError('scan_step_x_m should be a real number')
else:
scan_step_x_m = 1
if h5_main.shape[1] % pts_per_cycle != 0:
warn(
'Error in reshaping the provided dataset to pixels. Check points per pixel'
)
raise ValueError
num_cols = int(h5_main.shape[1] / pts_per_cycle)
# TODO: DO NOT assume simple 1 spectral dimension!
single_ao = np.squeeze(h5_main.h5_spec_vals[:, :pts_per_cycle])
spec_dims = Dimension(
get_attr(h5_main.h5_spec_vals, 'labels')[0],
get_attr(h5_main.h5_spec_vals, 'units')[0], single_ao)
# TODO: DO NOT assume simple 1D in positions!
pos_dims = [
Dimension('X', 'm', np.linspace(0, scan_step_x_m, num_cols)),
Dimension('Y', 'm',
np.linspace(0, h5_main.h5_pos_vals[1, 0], h5_main.shape[0]))
]
h5_group = create_results_group(h5_main, 'Reshape')
# TODO: Create empty datasets and then write for very large datasets
h5_resh = write_main_dataset(h5_group,
(num_cols * h5_main.shape[0], pts_per_cycle),
'Reshaped_Data',
get_attr(h5_main, 'quantity')[0],
get_attr(h5_main, 'units')[0],
pos_dims,
spec_dims,
chunks=(10, pts_per_cycle),
dtype=h5_main.dtype,
compression=h5_main.compression)
# TODO: DON'T write in one shot assuming small datasets fit in memory!
print('Starting to reshape G-mode line data. Please be patient')
h5_resh[()] = np.reshape(h5_main[()], (-1, pts_per_cycle))
print('Finished reshaping G-mode line data to rows and columns')
return USIDataset(h5_resh)
def translate(self, file_path):
"""
The main function that translates the provided file into a .h5 file
Parameters
----------
file_path : String / unicode
Absolute path of any file in the directory
Returns
-------
h5_path : String / unicode
Absolute path of the h5 file
"""
file_path = path.abspath(file_path)
# Figure out the basename of the data:
(basename, parm_paths, data_paths) = self._parse_file_path(file_path)
(folder_path, unused) = path.split(file_path)
h5_path = path.join(folder_path, basename + '.h5')
if path.exists(h5_path):
remove(h5_path)
# Load parameters from .mat file - 'BE_wave', 'FFT_BE_wave', 'total_cols', 'total_rows'
matread = loadmat(parm_paths['parm_mat'],
variable_names=[
'BE_wave', 'FFT_BE_wave', 'total_cols',
'total_rows'
])
be_wave = np.float32(np.squeeze(matread['BE_wave']))
# Need to take the complex conjugate if reading from a .mat file
# FFT_BE_wave = np.conjugate(np.complex64(np.squeeze(matread['FFT_BE_wave'])))
num_cols = int(matread['total_cols'][0][0])
expected_rows = int(matread['total_rows'][0][0])
self.points_per_pixel = len(be_wave)
# Load parameters from .txt file - 'BE_center_frequency_[Hz]', 'IO rate'
is_beps, parm_dict = parmsToDict(parm_paths['parm_txt'])
# Get file byte size:
# For now, assume that bigtime_00 always exists and is the main file
file_size = path.getsize(data_paths[0])
# Calculate actual number of lines since the first few lines may not be saved
self.num_rows = 1.0 * file_size / (4 * self.points_per_pixel *
num_cols)
if self.num_rows % 1:
warn('Error - File has incomplete rows')
return None
else:
self.num_rows = int(self.num_rows)
samp_rate = parm_dict['IO_rate_[Hz]']
ex_freq_nominal = parm_dict['BE_center_frequency_[Hz]']
# method 1 for calculating the correct excitation frequency:
pixel_duration = 1.0 * self.points_per_pixel / samp_rate
num_periods = pixel_duration * ex_freq_nominal
ex_freq_correct = 1 / (pixel_duration / np.floor(num_periods))
# method 2 for calculating the exact excitation frequency:
"""
fft_ex_wfm = np.abs(np.fft.fftshift(np.fft.fft(be_wave)))
w_vec = np.linspace(-0.5 * samp_rate, 0.5 * samp_rate - 1.0*samp_rate / self.points_per_pixel,
self.points_per_pixel)
hot_bins = np.squeeze(np.argwhere(fft_ex_wfm > 1E+3))
ex_freq_correct = w_vec[hot_bins[-1]]
"""
# correcting the excitation frequency - will be VERY useful during analysis and filtering
parm_dict['BE_center_frequency_[Hz]'] = ex_freq_correct
# Some very basic information that can help the processing crew
parm_dict['num_bins'] = self.points_per_pixel
parm_dict['grid_num_rows'] = self.num_rows
parm_dict['data_type'] = 'G_mode_line'
if self.num_rows != expected_rows:
print('Note: {} of {} lines found in data file'.format(
self.num_rows, expected_rows))
# Calculate number of points to read per line:
self.__bytes_per_row__ = int(file_size / self.num_rows)
# First finish writing all global parameters, create the file too:
h5_f = h5py.File(h5_path, 'w')
global_parms = dict()
global_parms['data_type'] = 'G_mode_line'
global_parms['translator'] = 'G_mode_line'
write_simple_attrs(h5_f, global_parms)
meas_grp = create_indexed_group(h5_f, 'Measurement')
write_simple_attrs(meas_grp, parm_dict)
pos_desc = Dimension('Y', 'm', np.arange(self.num_rows))
spec_desc = Dimension('Excitation', 'V',
np.tile(VALUES_DTYPE(be_wave), num_cols))
first_dat = True
for key in data_paths.keys():
# Now that the file has been created, go over each raw data file:
# 1. write all ancillary data. Link data. 2. Write main data sequentially
""" We only allocate the space for the main data here.
This does NOT change with each file. The data written to it does.
The auxiliary datasets will not change with each raw data file since
only one excitation waveform is used"""
chan_grp = create_indexed_group(meas_grp, 'Channel')
if first_dat:
if len(data_paths) > 1:
# All positions and spectra are shared between channels
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(
meas_grp, pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(
meas_grp, spec_desc, is_spectral=True)
elif len(data_paths) == 1:
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(
chan_grp, pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(
chan_grp, spec_desc, is_spectral=True)
first_dat = False
else:
pass
h5_main = write_main_dataset(
chan_grp, (self.num_rows, self.points_per_pixel * num_cols),
'Raw_Data',
'Deflection',
'V',
None,
None,
h5_pos_inds=h5_pos_inds,
h5_pos_vals=h5_pos_vals,
h5_spec_inds=h5_spec_inds,
h5_spec_vals=h5_spec_vals,
chunks=(1, self.points_per_pixel),
dtype=np.float16)
# Now transfer scan data in the dat file to the h5 file:
self._read_data(data_paths[key], h5_main)
h5_f.close()
print('G-Line translation complete!')
return h5_path
