"""
Glotarans module to read files
"""
from __future__ import annotations
import warnings
import numpy as np
import xarray as xr
from sdtfile import SdtFile
from glotaran.io import DataIoInterface
from glotaran.io import register_data_io
from glotaran.io.prepare_dataset import prepare_time_trace_dataset
[docs]@register_data_io("sdt")
class SdtDataIo(DataIoInterface):
[docs] def load_dataset(
self,
file_name: str,
*,
index: np.ndarray | None = None,
flim: bool = False,
dataset_index: int | None = 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_name: 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_name)
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_name}' 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."
),
stacklevel=4,
)
dataset_index = 0
times: np.ndarray = sdt_parser.times[dataset_index]
raw_data: np.ndarray = sdt_parser.data[dataset_index]
if index and len(index) is not raw_data.shape[0]:
raise IndexError(
f"The Dataset contains {raw_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)={raw_data.shape[0]}"
),
stacklevel=4,
)
if flim:
if orig_time_axis_index != 2:
np.swapaxes(raw_data, 2, orig_time_axis_index)
full_data = xr.DataArray(raw_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:
raw_data = raw_data.T
if not index:
index = np.arange(raw_data.shape[0])
data = xr.DataArray(raw_data.T, coords=[("time", times), ("spectral", index)])
data = prepare_time_trace_dataset(data)
return data