def _get_existing_datasets(self, index=-1):
"""
Extracts references to the existing datasets that hold the results
:param index: which existing dataset to get
:type index:
"""
if not self.override:
self.h5_results_grp = find_dataset(self.h5_main.parent,
'Inst_Freq')[index].parent
self.h5_new_spec_vals = self.h5_results_grp['Spectroscopic_Values']
self.h5_tfp = self.h5_results_grp['tfp']
self.h5_shift = self.h5_results_grp['shift']
self.h5_if = self.h5_results_grp['Inst_Freq']
self.h5_amp = self.h5_results_grp['Amplitude']
self.h5_phase = self.h5_results_grp['Phase']
try:
self.h5_pwrdis = self.h5_results_grp['PowerDissipation']
self.h5_tfp_cal = self.h5_results_grp['tfp_cal']
except:
pass
return
def test_legal(self):
with h5py.File(data_utils.std_beps_path, mode='r') as h5_f:
h5_group = h5_f['/Raw_Measurement/']
expected_dsets = [
h5_f['/Raw_Measurement/Spectroscopic_Indices'], h5_f[
'/Raw_Measurement/source_main-Fitter_000/Spectroscopic_Indices'],
h5_f[
'/Raw_Measurement/source_main-Fitter_001/Spectroscopic_Indices']
]
ret_val = hdf_utils.find_dataset(h5_group, 'Spectroscopic_Indices')
self.assertEqual(set(ret_val), set(expected_dsets))
def is_valid_file(file_path):
"""
Checks whether the provided file can be read by this translator
Parameters
----------
file_path : str
Path to raw data file
Returns
-------
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
"""
if not isinstance(file_path, (str, unicode)):
raise TypeError('file_path should be a string object')
if not os.path.isfile(file_path):
return None
file_path = os.path.abspath(file_path)
extension = os.path.splitext(file_path)[1][1:]
if extension not in ['h5', 'hdf5']:
return None
try:
h5_f = h5py.File(file_path, 'r+')
except:
return None
# TODO: Make this check as lot stronger. Currently brittle
if 'DAQ_software_version_name' not in h5_f.attrs.keys():
return None
if len(find_dataset(h5_f, 'Raw_Data')) < 1:
return None
return file_path
def plot_svd(h5_main, savefig=False, num_plots=16, **kwargs):
'''
Replots the SVD showing the skree, abundance maps, and eigenvectors.
If h5_main is a Dataset, it will default to the most recent SVD group from that
Dataset.
If h5_main is the results group, then it will plot the values for that group.
:param h5_main:
:type h5_main: USIDataset or h5py Dataset or h5py Group
:param savefig: Saves the figures to disk with some default names
:type savefig: bool, optional
:param num_plots: Default number of eigenvectors and abundance plots to show
:type num_plots: int
:param kwargs: keyword arguments for svd filtering
:type kwarrgs: dict, optional
'''
if isinstance(h5_main, h5py.Group):
_U = find_dataset(h5_main, 'U')[-1]
_V = find_dataset(h5_main, 'V')[-1]
units = 'arbitrary (a.u.)'
h5_spec_vals = np.arange(_V.shape[1])
h5_svd_group = _U.parent
else:
h5_svd_group = find_results_groups(h5_main, 'SVD')[-1]
units = h5_main.attrs['quantity']
h5_spec_vals = h5_main.get_spec_values('Time')
h5_U = h5_svd_group['U']
h5_V = h5_svd_group['V']
h5_S = h5_svd_group['S']
_U = USIDataset(h5_U)
[num_rows, num_cols] = _U.pos_dim_sizes
abun_maps = np.reshape(h5_U[:, :16], (num_rows, num_cols, -1))
eigen_vecs = h5_V[:16, :]
skree_sum = np.zeros(h5_S.shape)
for i in range(h5_S.shape[0]):
skree_sum[i] = np.sum(h5_S[:i]) / np.sum(h5_S)
plt.figure()
plt.plot(skree_sum, 'bo')
plt.title('Cumulative Variance')
plt.xlabel('Total Components')
plt.ylabel('Total variance ratio (a.u.)')
if savefig:
plt.savefig('Cumulative_variance_plot.png')
fig_skree, axes = plot_utils.plot_scree(h5_S, title='Scree plot')
fig_skree.tight_layout()
if savefig:
plt.savefig('Scree_plot.png')
fig_abun, axes = plot_utils.plot_map_stack(abun_maps,
num_comps=num_plots,
title='SVD Abundance Maps',
color_bar_mode='single',
cmap='inferno',
reverse_dims=True,
fig_mult=(3.5, 3.5),
facecolor='white',
**kwargs)
fig_abun.tight_layout()
if savefig:
plt.savefig('Abundance_maps.png')
fig_eigvec, axes = plot_utils.plot_curves(h5_spec_vals * 1e3,
eigen_vecs,
use_rainbow_plots=False,
x_label='Time (ms)',
y_label=units,
num_plots=num_plots,
subtitle_prefix='Component',
title='SVD Eigenvectors',
evenly_spaced=False,
**kwargs)
fig_eigvec.tight_layout()
if savefig:
plt.savefig('Eigenvectors.png')
return
def translate(self, h5_path, force_patch=False, **kwargs):
"""
Add the needed references and attributes to the h5 file that are not created by the
LabView data aquisition program.
Parameters
----------
h5_path : str
path to the h5 file
force_patch : bool, optional
Should the check to see if the file has already been patched be ignored.
Default False.
Returns
-------
h5_file : h5py.File
patched hdf5 file
"""
# Open the file and check if a patch is needed
h5_file = h5py.File(os.path.abspath(h5_path), 'r+')
if h5_file.attrs.get('translator') is not None and not force_patch:
print('File is already Pycroscopy ready.')
return h5_file
'''
Get the list of all Raw_Data Datasets
Loop over the list and update the needed attributes
'''
raw_list = find_dataset(h5_file, 'Raw_Data')
for h5_raw in raw_list:
if 'quantity' not in h5_raw.attrs:
h5_raw.attrs['quantity'] = 'quantity'
if 'units' not in h5_raw.attrs:
h5_raw.attrs['units'] = 'a.u.'
# Grab the channel and measurement group of the data to check some needed attributes
h5_chan = h5_raw.parent
try:
c_type = get_attr(h5_chan, 'channel_type')
except KeyError:
warn_str = "'channel_type' was not found as an attribute of {}.\n".format(
h5_chan.name)
warn_str += "If this is BEPS or BELine data from the LabView aquisition software, " + \
"please run the following piece of code. Afterwards, run this function again.\n" + \
"CODE: " \
"hdf.file['{}'].attrs['channel_type'] = 'BE'".format(h5_chan.name)
warn(warn_str)
return h5_file
except:
raise
if c_type != 'BE':
continue
h5_meas = h5_chan.parent
h5_meas.attrs['num_UDVS_steps'] = h5_meas.attrs['num_steps']
# Get the object handles for the Indices and Values datasets
h5_pos_inds = h5_chan['Position_Indices']
h5_pos_vals = h5_chan['Position_Values']
h5_spec_inds = h5_chan['Spectroscopic_Indices']
h5_spec_vals = h5_chan['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_spec_inds = create_spec_inds_from_vals(h5_spec_vals[()])
if not np.allclose(ds_spec_inds, h5_spec_inds[()]):
h5_spec_inds[:, :] = ds_spec_inds[:, :]
h5_file.flush()
# Get the labels and units for the Spectroscopic datasets
h5_spec_labels = h5_spec_inds.attrs['labels']
inds_and_vals = [
h5_pos_inds, h5_pos_vals, h5_spec_inds, h5_spec_vals
]
for dset in inds_and_vals:
spec_labels = dset.attrs['labels']
try:
spec_units = dset.attrs['units']
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
dset['units'] = ['' for _ in spec_labels]
except:
raise
for ilabel, label in enumerate(h5_spec_labels):
label_slice = (slice(ilabel, ilabel + 1), slice(None))
if label == '':
label = 'Step'
h5_spec_inds.attrs[label] = h5_spec_inds.regionref[label_slice]
h5_spec_vals.attrs[label] = h5_spec_vals.regionref[label_slice]
# Link the references to the Indices and Values datasets to the Raw_Data
link_as_main(h5_raw, h5_pos_inds, h5_pos_vals, h5_spec_inds,
h5_spec_vals)
# Also link the Bin_Frequencies and Bin_Wfm_Type datasets
h5_freqs = h5_chan['Bin_Frequencies']
aux_dset_names = ['Bin_Frequencies']
aux_dset_refs = [h5_freqs.ref]
check_and_link_ancillary(h5_raw,
aux_dset_names,
anc_refs=aux_dset_refs)
'''
Get all SHO_Fit groups for the Raw_Data and loop over them
Get the Guess and Spectroscopic Datasets for each SHO_Fit group
'''
sho_list = find_results_groups(h5_raw, 'SHO_Fit')
for h5_sho in sho_list:
h5_sho_guess = h5_sho['Guess']
h5_sho_spec_inds = h5_sho['Spectroscopic_Indices']
h5_sho_spec_vals = h5_sho['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_sho_spec_inds = create_spec_inds_from_vals(
h5_sho_spec_inds[()])
if not np.allclose(ds_sho_spec_inds, h5_sho_spec_inds[()]):
h5_sho_spec_inds[:, :] = ds_sho_spec_inds[:, :]
# Get the labels and units for the Spectroscopic datasets
h5_sho_spec_labels = get_attr(h5_sho_spec_inds, 'labels')
link_as_main(h5_sho_guess, h5_pos_inds, h5_pos_vals,
h5_sho_spec_inds, h5_sho_spec_vals)
sho_inds_and_vals = [h5_sho_spec_inds, h5_sho_spec_vals]
for dset in sho_inds_and_vals:
spec_labels = get_attr(dset, 'labels')
try:
spec_units = get_attr(dset, 'units')
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
spec_units = [''.encode('utf-8') for _ in spec_labels]
dset.attrs['units'] = spec_units
except:
raise
# Make region references in the
for ilabel, label in enumerate(h5_sho_spec_labels):
label_slice = (slice(ilabel, ilabel + 1), slice(None))
if label == '':
label = 'Step'.encode('utf-8')
h5_sho_spec_inds.attrs[label] = h5_sho_spec_inds.regionref[
label_slice]
h5_sho_spec_vals.attrs[label] = h5_sho_spec_vals.regionref[
label_slice]
h5_file.flush()
h5_file.attrs['translator'] = 'V3patcher'.encode('utf-8')
return h5_file
def translate(self, h5_path, force_patch=False, **kwargs):
"""
Add the needed references and attributes to the h5 file that are not created by the
LabView data aquisition program.
Parameters
----------
h5_path : str
path to the h5 file
force_patch : bool, optional
Should the check to see if the file has already been patched be ignored.
Default False.
Returns
-------
h5_file : str
path to the patched dataset
"""
# Open the file and check if a patch is needed
h5_file = h5py.File(os.path.abspath(h5_path), 'r+')
if h5_file.attrs.get('translator') is not None and not force_patch:
print('File is already Pycroscopy ready.')
h5_file.close()
return h5_path
'''
Get the list of all Raw_Data Datasets
Loop over the list and update the needed attributes
'''
raw_list = find_dataset(h5_file, 'Raw_Data')
for h5_raw in raw_list:
if 'quantity' not in h5_raw.attrs:
h5_raw.attrs['quantity'] = 'quantity'
if 'units' not in h5_raw.attrs:
h5_raw.attrs['units'] = 'a.u.'
# Grab the channel and measurement group of the data to check some needed attributes
h5_chan = h5_raw.parent
try:
c_type = get_attr(h5_chan, 'channel_type')
except KeyError:
warn_str = "'channel_type' was not found as an attribute of {}.\n".format(
h5_chan.name)
warn_str += "If this is BEPS or BELine data from the LabView aquisition software, " + \
"please run the following piece of code. Afterwards, run this function again.\n" + \
"CODE: " \
"hdf.file['{}'].attrs['channel_type'] = 'BE'".format(h5_chan.name)
warn(warn_str)
h5_file.close()
return h5_path
except:
raise
if c_type != 'BE':
continue
h5_meas = h5_chan.parent
h5_meas.attrs['num_UDVS_steps'] = h5_meas.attrs['num_steps']
# Get the object handles for the Indices and Values datasets
h5_pos_inds = h5_chan['Position_Indices']
h5_pos_vals = h5_chan['Position_Values']
h5_spec_inds = h5_chan['Spectroscopic_Indices']
h5_spec_vals = h5_chan['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_spec_inds = create_spec_inds_from_vals(h5_spec_vals[()])
if not np.allclose(ds_spec_inds, h5_spec_inds[()]):
h5_spec_inds[:, :] = ds_spec_inds[:, :]
h5_file.flush()
# Get the labels and units for the Spectroscopic datasets
h5_spec_labels = h5_spec_inds.attrs['labels']
inds_and_vals = [
h5_pos_inds, h5_pos_vals, h5_spec_inds, h5_spec_vals
]
for dset in inds_and_vals:
spec_labels = dset.attrs['labels']
try:
spec_units = dset.attrs['units']
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
dset['units'] = ['' for _ in spec_labels]
except:
raise
""""
In early versions, too many spectroscopic dimension labels and
units were listed compared to the number of rows. Remove here:
"""
remove_non_exist_spec_dim_labs(h5_spec_inds,
h5_spec_vals,
h5_meas,
verbose=False)
"""
Add back some standard metadata to be consistent with older
BE data
"""
missing_metadata = dict()
if 'File_file_name' not in h5_meas.attrs.keys():
missing_metadata['File_file_name'] = os.path.split(
h5_raw.file.filename)[-1].replace('.h5', '')
if 'File_date_and_time' not in h5_meas.attrs.keys():
try:
date_str = get_attr(h5_raw.file, 'date_string')
time_str = get_attr(h5_raw.file, 'time_string')
full_str = date_str.strip() + ' ' + time_str.strip()
"""
convert:
date_string : 2018-12-05
time_string : 3:41:45 PM
to:
File_date_and_time: 19-Jun-2009 18:44:56
"""
try:
dt_obj = datetime.datetime.strptime(
full_str, "%Y-%m-%d %I:%M:%S %p")
missing_metadata[
'File_date_and_time'] = dt_obj.strftime(
'%d-%b-%Y %H:%M:%S')
except ValueError:
pass
except KeyError:
pass
# Now write to measurement group:
if len(missing_metadata) > 0:
write_simple_attrs(h5_meas, missing_metadata)
# Link the references to the Indices and Values datasets to the Raw_Data
link_as_main(h5_raw, h5_pos_inds, h5_pos_vals, h5_spec_inds,
h5_spec_vals)
# Also link the Bin_Frequencies and Bin_Wfm_Type datasets
h5_freqs = h5_chan['Bin_Frequencies']
aux_dset_names = ['Bin_Frequencies']
aux_dset_refs = [h5_freqs.ref]
check_and_link_ancillary(h5_raw,
aux_dset_names,
anc_refs=aux_dset_refs)
'''
Get all SHO_Fit groups for the Raw_Data and loop over them
Get the Guess and Spectroscopic Datasets for each SHO_Fit group
'''
sho_list = find_results_groups(h5_raw, 'SHO_Fit')
for h5_sho in sho_list:
h5_sho_guess = h5_sho['Guess']
h5_sho_spec_inds = h5_sho['Spectroscopic_Indices']
h5_sho_spec_vals = h5_sho['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_sho_spec_inds = create_spec_inds_from_vals(
h5_sho_spec_inds[()])
if not np.allclose(ds_sho_spec_inds, h5_sho_spec_inds[()]):
h5_sho_spec_inds[:, :] = ds_sho_spec_inds[:, :]
# Get the labels and units for the Spectroscopic datasets
h5_sho_spec_labels = get_attr(h5_sho_spec_inds, 'labels')
link_as_main(h5_sho_guess, h5_pos_inds, h5_pos_vals,
h5_sho_spec_inds, h5_sho_spec_vals)
sho_inds_and_vals = [h5_sho_spec_inds, h5_sho_spec_vals]
for dset in sho_inds_and_vals:
spec_labels = get_attr(dset, 'labels')
try:
spec_units = get_attr(dset, 'units')
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
spec_units = [''.encode('utf-8') for _ in spec_labels]
dset.attrs['units'] = spec_units
except:
raise
h5_file.flush()
h5_file.attrs['translator'] = 'V3patcher'.encode('utf-8')
h5_file.close()
return h5_path
def translate(self, h5_path, force_patch=False, **kwargs):
"""
Add the needed references and attributes to the h5 file that are not created by the
LabView data aquisition program.
Parameters
----------
h5_path : str
path to the h5 file
force_patch : bool, optional
Should the check to see if the file has already been patched be ignored.
Default False.
Returns
-------
h5_file : h5py.File
patched hdf5 file
"""
# Open the file and check if a patch is needed
h5_file = h5py.File(os.path.abspath(h5_path), 'r+')
if h5_file.attrs.get('translator') is not None and not force_patch:
print('File is already Pycroscopy ready.')
return h5_file
'''
Get the list of all Raw_Data Datasets
Loop over the list and update the needed attributes
'''
raw_list = find_dataset(h5_file, 'Raw_Data')
for h5_raw in raw_list:
if 'quantity' not in h5_raw.attrs:
h5_raw.attrs['quantity'] = 'quantity'
if 'units' not in h5_raw.attrs:
h5_raw.attrs['units'] = 'a.u.'
# Grab the channel and measurement group of the data to check some needed attributes
h5_chan = h5_raw.parent
try:
c_type = get_attr(h5_chan, 'channel_type')
except KeyError:
warn_str = "'channel_type' was not found as an attribute of {}.\n".format(h5_chan.name)
warn_str += "If this is BEPS or BELine data from the LabView aquisition software, " + \
"please run the following piece of code. Afterwards, run this function again.\n" + \
"CODE: " \
"hdf.file['{}'].attrs['channel_type'] = 'BE'".format(h5_chan.name)
warn(warn_str)
return h5_file
except:
raise
if c_type != 'BE':
continue
h5_meas = h5_chan.parent
h5_meas.attrs['num_UDVS_steps'] = h5_meas.attrs['num_steps']
# Get the object handles for the Indices and Values datasets
h5_pos_inds = h5_chan['Position_Indices']
h5_pos_vals = h5_chan['Position_Values']
h5_spec_inds = h5_chan['Spectroscopic_Indices']
h5_spec_vals = h5_chan['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_spec_inds = create_spec_inds_from_vals(h5_spec_vals[()])
if not np.allclose(ds_spec_inds, h5_spec_inds[()]):
h5_spec_inds[:, :] = ds_spec_inds[:, :]
h5_file.flush()
# Get the labels and units for the Spectroscopic datasets
h5_spec_labels = h5_spec_inds.attrs['labels']
inds_and_vals = [h5_pos_inds, h5_pos_vals, h5_spec_inds, h5_spec_vals]
for dset in inds_and_vals:
spec_labels = dset.attrs['labels']
try:
spec_units = dset.attrs['units']
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
dset['units'] = ['' for _ in spec_labels]
except:
raise
for ilabel, label in enumerate(h5_spec_labels):
label_slice = (slice(ilabel, ilabel + 1), slice(None))
if label == '':
label = 'Step'
h5_spec_inds.attrs[label] = h5_spec_inds.regionref[label_slice]
h5_spec_vals.attrs[label] = h5_spec_vals.regionref[label_slice]
# Link the references to the Indices and Values datasets to the Raw_Data
link_as_main(h5_raw, h5_pos_inds, h5_pos_vals, h5_spec_inds, h5_spec_vals)
# Also link the Bin_Frequencies and Bin_Wfm_Type datasets
h5_freqs = h5_chan['Bin_Frequencies']
aux_dset_names = ['Bin_Frequencies']
aux_dset_refs = [h5_freqs.ref]
check_and_link_ancillary(h5_raw, aux_dset_names, anc_refs=aux_dset_refs)
'''
Get all SHO_Fit groups for the Raw_Data and loop over them
Get the Guess and Spectroscopic Datasets for each SHO_Fit group
'''
sho_list = find_results_groups(h5_raw, 'SHO_Fit')
for h5_sho in sho_list:
h5_sho_guess = h5_sho['Guess']
h5_sho_spec_inds = h5_sho['Spectroscopic_Indices']
h5_sho_spec_vals = h5_sho['Spectroscopic_Values']
# Make sure we have correct spectroscopic indices for the given values
ds_sho_spec_inds = create_spec_inds_from_vals(h5_sho_spec_inds[()])
if not np.allclose(ds_sho_spec_inds, h5_sho_spec_inds[()]):
h5_sho_spec_inds[:, :] = ds_sho_spec_inds[:, :]
# Get the labels and units for the Spectroscopic datasets
h5_sho_spec_labels = get_attr(h5_sho_spec_inds, 'labels')
link_as_main(h5_sho_guess, h5_pos_inds, h5_pos_vals, h5_sho_spec_inds, h5_sho_spec_vals)
sho_inds_and_vals = [h5_sho_spec_inds, h5_sho_spec_vals]
for dset in sho_inds_and_vals:
spec_labels = get_attr(dset, 'labels')
try:
spec_units = get_attr(dset, 'units')
if len(spec_units) != len(spec_labels):
raise KeyError
except KeyError:
spec_units = [''.encode('utf-8') for _ in spec_labels]
dset.attrs['units'] = spec_units
except:
raise
# Make region references in the
for ilabel, label in enumerate(h5_sho_spec_labels):
label_slice = (slice(ilabel, ilabel + 1), slice(None))
if label == '':
label = 'Step'.encode('utf-8')
h5_sho_spec_inds.attrs[label] = h5_sho_spec_inds.regionref[label_slice]
h5_sho_spec_vals.attrs[label] = h5_sho_spec_vals.regionref[label_slice]
h5_file.flush()
h5_file.attrs['translator'] = 'V3patcher'.encode('utf-8')
return h5_file
def save_CSV_from_file(h5_file,
h5_path='/',
append='',
mirror=False,
offset=0):
"""
Saves the tfp, shift, and fixed_tfp as CSV files
:param h5_file: Reminder you can always type: h5_svd.file or h5_avg.file for this
:type h5_file: H5Py file of FFtrEFM class
:param h5_path: specific folder path to search for the tfp data. Usually not needed.
:type h5_path: str, optional
:param append: text to append to file name
:type append: str, optional
:param mirror:
:type mirror: bool, optional
:param offset: if calculating tFP with a fixed offset for fitting, this subtracts it out
:type offset: float
"""
h5_ff = h5_file
if isinstance(h5_file, ffta.hdf_utils.process.FFtrEFM):
print('Saving from FFtrEFM Class')
h5_ff = h5_file.h5_main.file
h5_path = h5_file.h5_results_grp.name
elif not isinstance(h5_file, h5py.File):
print('Saving from pyUSID object')
h5_ff = h5_file.file
tfp = find_dataset(h5_ff[h5_path], 'tfp')[0][()]
shift = find_dataset(h5_ff[h5_path], 'shift')[0][()]
try:
tfp_cal = find_dataset(h5_ff[h5_path], 'tfp_cal')[0][()]
except:
tfp_cal = None
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
if isinstance(tfp_cal, np.ndarray):
tfp_cal_fixed, _ = badpixels.fix_array(tfp_cal, threshold=2)
tfp_cal_fixed = np.array(tfp_cal_fixed)
print(find_dataset(h5_ff[h5_path], 'shift')[0].parent.name)
path = h5_ff.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
if mirror:
np.savetxt('tfp-' + append + '.csv',
np.fliplr(tfp - offset).T,
delimiter=',')
np.savetxt('shift-' + append + '.csv',
np.fliplr(shift).T,
delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv',
np.fliplr(tfp_fixed - offset).T,
delimiter=',')
else:
np.savetxt('tfp-' + append + '.csv', (tfp - offset).T, delimiter=',')
np.savetxt('shift-' + append + '.csv', shift.T, delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv', (tfp_fixed - offset).T,
delimiter=',')
if isinstance(tfp_cal, np.ndarray):
if mirror:
np.savetxt('tfp_cal-' + append + '.csv',
np.fliplr(tfp_cal - offset).T,
delimiter=',')
np.savetxt('tfp_cal_fixed-' + append + '.csv',
np.fliplr(tfp_cal_fixed - offset).T,
delimiter=',')
else:
np.savetxt('tfp_cal-' + append + '.csv', (tfp_cal - offset).T,
delimiter=',')
np.savetxt('tfp_cal_fixed-' + append + '.csv',
(tfp_cal_fixed - offset).T,
delimiter=',')
return