sasdata 0.8.1

Sas Data Loader application

Sasdata

A package for loading and handling 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.

You can also install sasdata as a standalone package in python. To create a python environment with the sasdata dependencies

• Using a python virtual environment::

  $ python -m venv sasdata $ python -m pip install numpy lxml h5py xmlrunner "pytest<6"

  • Activate the environment:: $ .\sasdata\Scripts\activate

• Using miniconda or anaconda::

  $ conda create -n sasdata -c conda-forge numpy lxml h5py xmlrunner "pytest<6"

  • Activate the environment and install sasdata::

    $ conda activate sasdata $ (sasdata) $ pip install sasdata

View the latest release on the sasdata pypi page.

Usage

Loading data sets:

(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loader_module = Loader()
>>> loaded_data_sets = loader_module.load(path="/path/to/file.ext")

• The Loader() class is not callable and must be instantiated prior to use.
• The load() method returns a list of Data1/2D objects as loaded from the specified path.

Saving loaded data:

>>> loaded_data_set = loaded_data_sets[0]
>>> loader.save(path='/path/to/new/file.ext', data=loaded_data_set, format=None)

• The save() method accepts three (3) arguments:
  • path: The file name and path to save the data.
  • data: A Data1D or Data2D object.
  • format (optional): The expected file extension for the file. 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.
• The file extension specified in the save path will be superseded by the format value.