spexread 0.2.2

A module for reading Princeton Instruments SPE files (version 2.x and 3.0) captured by WinSpec or LightField

spexread

A package for reading Princeton Instruments SPE files captured by WinSpec (version 2.x) or LightField(version 3.0).

It relies on xarray to handle the many different possible image shapes, ROIs, etc. that can be stored in an SPE file and reads them into a single xarray.Dataset, or a list of xarray.DataArrays.

Important metadata for both format versions is parsed and validated to a consistent schema using pydantic and stored in the attrs attribute of the dataset.

This has a number of key benefits:

• Data is described and indexed as a function of dimensions or coordinates.

• Access per-frame tracking information such as exposure_time or gate_width trivially (when stored, SPE v3.0 only) as coordinates of your data, alongside the core dimension x,y and frame.

• The xarray.Dataset supports multiple regions of interest (ROI's) that can be accessed like a python dict.

  • You can handle files in the same way, regardless of amount of ROI's.

• Metadata remains closely associated with the data and can be easily accessed.

[!IMPORTANT] spexread is functional, but some features and metadata that you use may be missing. Please file an issue and provide a sample file to add support for them. Found a bug? Please raise an issue as well!

Installing

spexread can be installed easily with pip, from PyPI:

pip install spexread

To install the latest version from GitHub, you can run the following command:

pip install git+https://github.com/AntoineTUE/spexread

Example usage

The example below demonstrates how to plot a kinetic series.

In LightField it is possible to acquire a kinetic series with varying gate width and gate delay, which can be stored to the file.

You can change the frame coordinate with e.g. gate_width in this example, to plot as a function of this coordinate.

from spexread import read_spe_file
from spexread.data_models import SPEType
from pathlib import Path
import matplotlib.pyplot as plt

# read data as a xarray.Dataset
data = read_spe_file(Path("./my_data.spe"))
print(data.coords._names) # lists available coordinate names

# plot spectra
plt.figure()
for name,roi in data.items():
    roi.mean(['frame','y']).plot(x='wavelength', label=name)

# Plot trends over time. You can replace `frame` with e.g. `gate_width` as well.
plt.figure()
for name,roi in data.items():
    roi.mean(['y','x']).plot(x='frame',label=name)

# easily convert to numpy arrays if needed, other formats possible as well, see xarray docs.
image = data['ROI 0'].mean('frame').to_numpy()
plt.figure()
plt.imshow(image)

# Convert the SPE metadata to a SPEType pydantic model, allowing attribute access
metadata = SPEType.model_validate(data.attrs)
print(metadata.GeneralInfo)
print(metadata.Calibrations.WavelengthCalib)
print(metadata.Calibrations.WavelengthCalib.wavelength)

License

spexread is licensed under the MIT license.