Sasdata

A package for importing and exporting reduced small angle scattering data.

The data loaders provided are usable as a standalone package, or in conjunction with the sasview analysis package.

Install

The easiest way to use sasdata is by using SasView.

View the latest release on the sasdata pypi page and install using pip install sasdata.

To run sasdata from the source, create a python environment using python 3.8 or higher and install all dependencies

• Using a python virtual environment::

  $ python -m venv sasdata $ .\sasdata\Scripts\activate (Windows) -or- source sasdata/bin/activate (Mac/Linux) (sasdata) $ python -m pip install -r requirements.txt (sasdata) $ python /path/to/sasdata/setup.py clean build (sasdata) $ python -m pip install .

• Using miniconda or anaconda::

  $ conda create -n sasdata $ conda activate sasdata (sasdata) $ python -m pip install -r requirements.txt (sasdata) $ python /path/to/sasdata/setup.py clean build (sasdata) $ python -m pip install .

Data Formats

The Loader() class is directly callable so a transient call can be made to the class or, for cases where repeated calls are necessary, the Loader() instance can be assigned to a python variable.

The Loader.load() method accepts a string or list of strings to load a single or multiple data sets simultaneously. The strings passed to load() can be any combination of file path representations, or URIs. A list of Data1D/Data2D objects is returned. An optional format parameter can be passed to specify the expected file extension associated with a reader. If format is passed, it must either be a single value, or a list of values of the same length as the file path list.

• Load format options include:
  • .xml: canSAS XML format
  • .h5, .hdf, .hdf5, .nxs: NXcanSAS format
  • .txt: Multi-column ascii format
  • .csv: Comma delimited text format
  • .ses, .sesans: Multi-column SESANS data
  • .dat: 2D NIST format
  • .abs, .cor: 1D NIST format for SAS and USAS
  • .pdh: Anton Paar reduced SAXS format

The save() method accepts 3 arguments; the file path to save the file as, a Data1D or Data2D object, and, optionally, a file extension. If an extension is passed to save, any file extension in the file path will be superseded. If no file extension is given in the filename or format, a ValueError will be thrown.

• Save format options include:
  • .xml: for the canSAS XML format
  • .h5: for the NXcanSAS format
  • .txt: for the multi-column ascii format
  • .csv: for a comma delimited text format

Save argument examples and data output:

More information on the recognized data formats is available on the sasview website.

Example Data

A number of data files are included with this package available in sasdata.example_data.

• Each subdirectory has a specific type of data.
  • 1d_data: 1-dimensional SAS data. A few examples are apoferritin.txt which is SANS from apoferritin, 3 files starting with AOT_ that are contrast variations for a mircroemulsion, and latex_smeared.xml with SANS and USANS data for spherical latex particles.
  • 2d_data: 2-dimensional SAS data. Examples include 3 P123_....dat files for a polymer concentration series.
  • convertibles_files: A series of data sets that can be converted via the data conversion tool in the sasdata.file_converter package.
  • dls_data: NOTE Not loadable by sasdata. Two example DLS data sets that will be loadable in a future release.
  • image_data: Image file loadable from sasdata.dataloader.readers.tiff_reader. The files are all the same image, but in different image formats.
  • sesans_data: SESANS data sets. sphere_isis.ses is spin-echo SANS from a sample with spherical particles.
• To directly access this data via a python prompt, import data_path from sasdata returns the absolute path to sasdata.example_data

Usage

Accessing example data

(sasdata) $ python
>>> from sasdata import data_path
>>> data = Loader().load(os.path.join(data_path, '1d_data', 'apoferritin.txt'))

Loading and saving data sets using a fixed Loader instance:

(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loader_module = Loader()
>>> loaded_data_sets = loader_module.load(path="/path/to/file.ext")
>>> loaded_data_set = loaded_data_sets[0]
>>> loader_module.save(path='/path/to/new/file.ext', data=loaded_data_set, format=None)

Loading and saving data sets using a transient Loader instance (more scriptable):

(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loaded_data_sets = Loader().load(path="/path/to/file.ext")
>>> Loader().save(path='/path/to/new/file.ext', data=loaded_data_sets[0], format=None)