.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_2D_1_examples_plot_3_labeled.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_2D_1_examples_plot_3_labeled.py:


Labeled Dataset
^^^^^^^^^^^^^^^

The CSD model also supports labeled dimensions. In the following example, we
present a mixed `linear` and `labeled` two-dimensional dataset representing
the population of the country as a function of time. The dataset is
obtained from `The World Bank <https://data.worldbank.org/indicator/SP.POP.TOTL?view=chart>`_.

Import the `csdmpy` model and load the dataset.


.. code-block:: default

    import csdmpy as cp

    filename = "https://osu.box.com/shared/static/e81to3izj5yv5m7mjq9xw7gmqez2blto.csdf"
    labeled_data = cp.load(filename)








The tuple of dimension and dependent variable objects from
``labeled_data`` instance are


.. code-block:: default


    x = labeled_data.dimensions
    y = labeled_data.dependent_variables








Since one of the dimensions is a `labeled` dimension, let's make use of the
:attr:`~csdmpy.Dimension.type` attribute of the dimension instances
to find out which dimension is `labeled`.


.. code-block:: default

    x[0].type





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    'linear'




.. code-block:: default

    x[1].type





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    'labeled'



Here, the second dimension is the `labeled` dimension with [#f1]_


.. code-block:: default

    x[1].count





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    263



labels, where the first five labels are


.. code-block:: default

    print(x[1].labels[:5])





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    ['Aruba' 'Afghanistan' 'Angola' 'Albania' 'Andorra']




.. note::
    For labeled dimensions, the :attr:`~csdmpy.Dimension.coordinates`
    attribute is an alias of the :attr:`~csdmpy.Dimension.labels`
    attribute.


.. code-block:: default

    print(x[1].coordinates[:5])





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    ['Aruba' 'Afghanistan' 'Angola' 'Albania' 'Andorra']




The coordinates along the first dimension, viewed up to the first ten
points, are


.. code-block:: default


    print(x[0].coordinates[:10])
    # [1960. 1961. 1962. 1963. 1964. 1965. 1966. 1967. 1968. 1969.] yr





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [1960. 1961. 1962. 1963. 1964. 1965. 1966. 1967. 1968. 1969.] yr




**Plotting the dataset**

You may plot this dataset however you like. Here, we use a bar graph to
represent the population of countries in the year 2017. The data
corresponding to this year is a cross-section of the dependent variable
at index 57 along the ``x[0]`` dimension.


.. code-block:: default

    print(x[0].coordinates[57])





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    2017.0 yr




To keep the plot simple, we only plot the first 20 country labels along
the ``x[1]`` dimension.


.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    x_data = x[1].coordinates[:20]
    x_pos = np.arange(20)
    y_data = y[0].components[0][:20, 57]

    plt.bar(x_data, y_data, align="center", alpha=0.5)
    plt.xticks(x_pos, x_data, rotation=90)
    plt.ylabel(y[0].axis_label[0])
    plt.yscale("log")
    plt.title(y[0].name)
    plt.tight_layout()
    plt.show()




.. image:: /auto_examples/2D_1_examples/images/sphx_glr_plot_3_labeled_001.png
    :class: sphx-glr-single-img





.. rubric:: Footnotes

.. [#f1] In the CSD model, the attribute count is only valid for the
         :ref:`linearDimension_uml`. In `csdmpy`, however, the
         :attr:`~csdmpy.Dimension.count` attribute is valid for all
         dimension objects and returns an integer with the number of grid
         points along the dimension.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  1.541 seconds)


.. _sphx_glr_download_auto_examples_2D_1_examples_plot_3_labeled.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_3_labeled.py <plot_3_labeled.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_3_labeled.ipynb <plot_3_labeled.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_