def _create_guess_datasets(self):
"""
Creates the h5 group, guess dataset, corresponding spectroscopic datasets and also
links the guess dataset to the spectroscopic 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, self.parms_dict)
# If writing to a new HDF5 file:
# Add back the data_type attribute - still being used in the visualizer
if self.h5_results_grp.file != self.h5_main.file:
write_simple_attrs(
self.h5_results_grp.file,
{'data_type': get_attr(self.h5_main.file, 'data_type')})
ret_vals = write_reduced_anc_dsets(self.h5_results_grp,
self.h5_main.h5_spec_inds,
self.h5_main.h5_spec_vals,
self._fit_dim_name,
verbose=self.verbose)
h5_sho_inds, h5_sho_vals = ret_vals
self._h5_guess = write_main_dataset(
self.h5_results_grp, (self.h5_main.shape[0], self.num_udvs_steps),
'Guess',
'SHO',
'compound',
None,
None,
h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=h5_sho_inds,
h5_spec_vals=h5_sho_vals,
chunks=(1, self.num_udvs_steps),
dtype=sho32,
main_dset_attrs=self.parms_dict,
verbose=self.verbose)
# Does not make sense to propagate region refs - nobody uses them
# copy_region_refs(self.h5_main, self._h5_guess)
self._h5_guess.file.flush()
if self.verbose and self.mpi_rank == 0:
print('Finished creating Guess dataset')
def _create_guess_datasets(self):
"""
Creates the h5 group, guess dataset, corresponding spectroscopic datasets and also
links the guess dataset to the spectroscopic datasets.
"""
self.h5_results_grp = create_results_group(self.h5_main, self.process_name)
write_simple_attrs(self.h5_results_grp, self.parms_dict)
h5_sho_inds, h5_sho_vals = write_reduced_anc_dsets(self.h5_results_grp, self.h5_main.h5_spec_inds,
self.h5_main.h5_spec_vals, self._fit_dim_name)
self._h5_guess = write_main_dataset(self.h5_results_grp, (self.h5_main.shape[0], self.num_udvs_steps), 'Guess', 'SHO',
'compound', None, None, h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals, h5_spec_inds=h5_sho_inds,
h5_spec_vals=h5_sho_vals, chunks=(1, self.num_udvs_steps), dtype=sho32,
main_dset_attrs=self.parms_dict, verbose=self.verbose)
copy_region_refs(self.h5_main, self._h5_guess)
self._h5_guess.file.flush()
if self.verbose and self.mpi_rank == 0:
print('Finished creating Guess dataset')
def _setup_h5(self, data_gen_parms):
"""
Setups up the hdf5 file structure before doing the actual generation
Parameters
----------
data_gen_parms : dict
Dictionary containing the parameters to write to the Measurement Group as attributes
Returns
-------
"""
'''
Build the group structure down to the channel group
'''
# Set up the basic group structure
root_parms = dict()
root_parms['translator'] = 'FAKEBEPS'
root_parms['data_type'] = data_gen_parms['data_type']
# Write the file
self.h5_f = h5py.File(self.h5_path, 'w')
write_simple_attrs(self.h5_f, root_parms)
meas_grp = create_indexed_group(self.h5_f, 'Measurement')
chan_grp = create_indexed_group(meas_grp, 'Channel')
write_simple_attrs(meas_grp, data_gen_parms)
# Create the Position and Spectroscopic datasets for the Raw Data
h5_pos_dims, h5_spec_dims = self._build_ancillary_datasets()
h5_raw_data = write_main_dataset(chan_grp,
(self.n_pixels, self.n_spec_bins),
'Raw_Data',
'Deflection',
'Volts',
h5_pos_dims,
h5_spec_dims,
slow_to_fast=True,
dtype=np.complex64,
verbose=True)
'''
Build the SHO Group
'''
sho_grp = create_results_group(h5_raw_data, 'SHO_Fit')
# Build the Spectroscopic datasets for the SHO Guess and Fit
h5_sho_spec_inds, h5_sho_spec_vals = write_reduced_anc_dsets(
sho_grp,
h5_raw_data.h5_spec_inds,
h5_raw_data.h5_spec_vals,
'Frequency',
is_spec=True)
h5_sho_fit = write_main_dataset(
sho_grp, (self.n_pixels, int(self.n_spec_bins // self.n_bins)),
'Fit',
'SHO Parameters',
'a.u.',
None,
None,
h5_pos_inds=h5_raw_data.h5_pos_inds,
h5_pos_vals=h5_raw_data.h5_pos_vals,
h5_spec_inds=h5_sho_spec_inds,
h5_spec_vals=h5_sho_spec_vals,
slow_to_fast=True,
dtype=sho32)
h5_sho_guess = copy_dataset(h5_sho_fit, sho_grp, alias='Guess')
'''
Build the loop group
'''
loop_grp = create_results_group(h5_sho_fit, 'Loop_Fit')
# Build the Spectroscopic datasets for the loops
h5_loop_spec_inds, h5_loop_spec_vals = write_reduced_anc_dsets(
loop_grp,
h5_sho_fit.h5_spec_inds,
h5_sho_fit.h5_spec_vals,
'DC_Offset',
is_spec=True)
h5_loop_fit = write_main_dataset(loop_grp,
(self.n_pixels, self.n_loops),
'Fit',
'Loop Fitting Parameters',
'a.u.',
None,
None,
h5_pos_inds=h5_raw_data.h5_pos_inds,
h5_pos_vals=h5_raw_data.h5_pos_vals,
h5_spec_inds=h5_loop_spec_inds,
h5_spec_vals=h5_loop_spec_vals,
slow_to_fast=True,
dtype=loop_fit32)
h5_loop_guess = copy_dataset(h5_loop_fit, loop_grp, alias='Guess')
copy_all_region_refs(h5_loop_guess, h5_loop_fit)
self.h5_raw = h5_raw_data
self.h5_sho_guess = h5_sho_guess
self.h5_sho_fit = h5_sho_fit
self.h5_loop_guess = h5_loop_guess
self.h5_loop_fit = h5_loop_fit
self.h5_spec_vals = h5_raw_data.h5_spec_vals
self.h5_spec_inds = h5_raw_data.h5_spec_inds
self.h5_sho_spec_inds = h5_sho_fit.h5_spec_inds
self.h5_sho_spec_vals = h5_sho_fit.h5_spec_vals
self.h5_loop_spec_inds = h5_loop_fit.h5_spec_inds
self.h5_loop_spec_vals = h5_loop_fit.h5_spec_vals
self.h5_file = h5_raw_data.file
return
def _create_results_datasets(self):
"""
Setup the Loop_Fit Group and the loop projection datasets
"""
# First grab the spectroscopic indices and values and position indices
# TODO: Avoid unnecessary namespace pollution
# self._sho_spec_inds = self.h5_main.h5_spec_inds
# self._sho_spec_vals = self.h5_main.h5_spec_vals
# self._sho_pos_inds = self.h5_main.h5_pos_inds
# Which row in the spec datasets is DC offset?
self._fit_spec_index = self.h5_main.spec_dim_labels.index(
self._fit_dim_name)
# TODO: Unkown usage of variable. Waste either way
# self._fit_offset_index = 1 + self._fit_spec_index
# Calculate the number of loops per position
cycle_start_inds = np.argwhere(
self.h5_main.h5_spec_inds[self._fit_spec_index, :] == 0).flatten()
tot_cycles = cycle_start_inds.size
if self.verbose:
print('Found {} cycles starting at indices: {}'.format(
tot_cycles, cycle_start_inds))
# Make the results group
self.h5_results_grp = create_results_group(self.h5_main,
self.process_name)
write_simple_attrs(self.h5_results_grp, self.parms_dict)
# Write datasets
self.h5_projected_loops = create_empty_dataset(
self.h5_main,
np.float32,
'Projected_Loops',
h5_group=self.h5_results_grp)
h5_loop_met_spec_inds, h5_loop_met_spec_vals = write_reduced_anc_dsets(
self.h5_results_grp,
self.h5_main.h5_spec_inds,
self.h5_main.h5_spec_vals,
self._fit_dim_name,
basename='Loop_Metrics',
verbose=self.verbose)
self.h5_loop_metrics = write_main_dataset(
self.h5_results_grp, (self.h5_main.shape[0], tot_cycles),
'Loop_Metrics',
'Metrics',
'compound',
None,
None,
dtype=loop_metrics32,
h5_pos_inds=self.h5_main.h5_pos_inds,
h5_pos_vals=self.h5_main.h5_pos_vals,
h5_spec_inds=h5_loop_met_spec_inds,
h5_spec_vals=h5_loop_met_spec_vals)
# Copy region reference:
# copy_region_refs(self.h5_main, self.h5_projected_loops)
# copy_region_refs(self.h5_main, self.h5_loop_metrics)
self.h5_main.file.flush()
self._met_spec_inds = self.h5_loop_metrics.h5_spec_inds
if self.verbose and self.mpi_rank == 0:
print('Finished creating Guess dataset')