def dataset(self, prepare: bool = True):
data = self._observations
if self._file_data_format == DataFileType.time_explicit:
data = data.T
dataset = xr.DataArray(data,
coords=[('time', self._times),
('spectral', self._spectral_indices)])
if prepare:
dataset = prepare_time_trace_dataset(dataset)
return dataset
def read_sdt(
file_path: str,
index: np.ndarray = None,
flim: bool = False,
dataset_index: int = None,
swap_axis: bool = False,
orig_time_axis_index: int = 2,
) -> xr.Dataset:
"""
Reads a `*.sdt` file and returns a pd.DataFrame (`return_dataframe==True`), a
SpectralTemporalDataset (`type_of_data=='st'`) or a FLIMDataset (`type_of_data=='flim'`).
Parameters
----------
file_path: str
Path to the sdt file which should be read.
index: list, np.ndarray
This is only needed if `type_of_data=="st"`, since `*.sdt` files,
which only contain spectral temporal data, lack the spectral information.
Thus for the spectral axis data need to be given by the user.
flim:
Set true if reading a result from a FLIM measurement.
dataset_index: int: default 0
If the `*.sdt` file contains multiple datasets the index will used
to select the wanted one
swap_axis: bool, default False
Flag to switch a wavelength explicit `input_df` to time explicit `input_df`,
before generating the SpectralTemporalDataset.
orig_time_axis_index: int
Index of the axis which corresponds to the time axis.
I.e. for data of shape (64, 64, 256), which are a 64x64 pixel map
with 256 time steps, orig_time_axis_index=2.
Raises
______
IndexError:
If the length of the index array is incompatible with the data.
"""
sdt_parser = SdtFile(file_path)
if not dataset_index:
# looking at the source code of SdtFile, times and data
# always have the same len, so only one needs to be checked
nr_of_datasets = len(sdt_parser.times)
if nr_of_datasets > 1:
warnings.warn(
UserWarning(
f"The file '{file_path}' contains {nr_of_datasets} Datasets.\n "
f"By default only the first Dataset will be read. "
f"If you only need the first Dataset and want get rid of "
f"this warning you can set dataset_index=0."))
dataset_index = 0
times = sdt_parser.times[dataset_index]
data = sdt_parser.data[dataset_index]
if index and len(index) is not data.shape[0]:
raise IndexError(
f"The Dataset contains {data.shape[0]} measurements, but the "
f"indices supplied are {len(index)}.")
elif not index and not flim:
warnings.warn(
UserWarning(
f"There was no `index` provided."
f"That for the indices will be a entry count(integers)."
f"To prevent this warning from being shown, provide "
f"a list of indices, with len(index)={data.shape[0]}"))
if flim:
if orig_time_axis_index != 2:
np.swapaxes(data, 2, orig_time_axis_index)
full_data = xr.DataArray(data,
coords={'time': times},
dims=['x', 'y', 'time'])
data = full_data.stack(pixel=('x', 'y')).to_dataset(name='data')
data['full_data'] = full_data.rename({'x': 'pixel_x', 'y': 'pixel_y'})
data['data_intensity_map'] = \
data.data.groupby('pixel').sum().unstack().rename({'x': 'pixel_x', 'y': 'pixel_y'})
else:
if swap_axis:
data = data.T
if not index:
index = np.array(range(data[0]))
data = xr.DataArray(data.T,
coords=[('time', times), ('spectral', index)])
data = prepare_time_trace_dataset(data)
return data