arctichoke.dataset.example_dataset
==================================

.. py:module:: arctichoke.dataset.example_dataset


Functions
---------

.. autoapisummary::

   arctichoke.dataset.example_dataset.make_example_dataset


Module Contents
---------------

.. py:function:: make_example_dataset(save_as: str = None, n: int = 10, offset: (int, float) = 0, test_var_name: str = 'test_var', time_axis: (bool, int) = False, overwrite: bool = True)

   Create an example dataset for testing.

   Construct a dataset such that it is minimal in size yet contains all notable features of the datasets of HighResMIP data, and save it to a netCDF file.

   :param save_as: The absolute file path to which to save the example dataset.
                   Default is `None`, which doesn't save the dataset to a file.
   :type save_as: `str`, `None`, optional
   :param n: The number of values in each dimension.
             Default is `10`.
   :type n: `int`, optional
   :param offset: A constant offset value to add to every value in the dataset.
                  Default is `0`.
   :type offset: `int`, `float`, optional
   :param test_var_name: The name to give the test variable.
                         Default is `test_var`.
   :type test_var_name: `str`, optional
   :param time_axis: Whether to include a time axis in the example dataset.
                     If an integer is given, that will be used as the year for the datetimes.
                     Default is `False`.
   :type time_axis: `bool`, `int`, optional
   :param overwrite: Whether to overwrite an existing file at the given filepath in `save_as`.
                     Default is `True`.
   :type overwrite: `bool`, optional

   :returns: **example_dataset** -- A list, sorted alphabetically, of the names of the available models.
   :rtype: `xr.Dataset`

   .. rubric:: Examples

   >>> from arctichoke.dataset.example_dataset import make_example_dataset
   >>> dataset = make_example_dataset(n=3, time_axis=True)
   >>> dataset['test_var'].values
   array([[[0., 1., 2.],
           [3., 4., 5.],
           [6., 7., 8.]],

          [[0., 1., 2.],
           [3., 4., 5.],
           [6., 7., 8.]]])