.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_2D_1_examples_plot_2_TEM.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_2D_1_examples_plot_2_TEM.py:


Transmission Electron Microscopy (TEM) dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The following `TEM dataset <https://doi.org/10.1371/journal.pbio.1000502>`_ is
a section of an early larval brain of *Drosophila melanogaster* used in the
analysis of neuronal microcircuitry. The dataset was obtained
from the `TrakEM2 tutorial <http://www.ini.uzh.ch/~acardona/data.html>`_ and
subsequently converted to the CSD model file-format.

Let's import the CSD model data-file and look at its data structure.


.. code-block:: default

    import matplotlib.pyplot as plt

    import csdmpy as cp

    filename = "https://osu.box.com/shared/static/3w5iqkx15fayan1u6g6sn5woc2ublkyh.csdf"
    TEM = cp.load(filename)
    print(TEM.data_structure)





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

 Out:

 .. code-block:: none

    {
      "csdm": {
        "version": "1.0",
        "read_only": true,
        "timestamp": "2016-03-12T16:41:00Z",
        "description": "TEM image of the early larval brain of Drosophila melanogaster used in the analysis of neuronal microcircuitry.",
        "dimensions": [
          {
            "type": "linear",
            "count": 512,
            "increment": "4.0 nm",
            "quantity_name": "length",
            "reciprocal": {
              "quantity_name": "wavenumber"
            }
          },
          {
            "type": "linear",
            "count": 512,
            "increment": "4.0 nm",
            "quantity_name": "length",
            "reciprocal": {
              "quantity_name": "wavenumber"
            }
          }
        ],
        "dependent_variables": [
          {
            "type": "internal",
            "numeric_type": "uint8",
            "quantity_type": "scalar",
            "components": [
              [
                "126, 107, ..., 164, 171"
              ]
            ]
          }
        ]
      }
    }




This dataset consists of two linear dimensions and one single-component
dependent variable. The tuple of the dimension and the dependent variable
instances from this example are


.. code-block:: default

    x = TEM.dimensions
    y = TEM.dependent_variables








and the respective coordinates (viewed only for the first ten coordinates),


.. code-block:: default

    print(x[0].coordinates[:10])





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

 Out:

 .. code-block:: none

    [ 0.  4.  8. 12. 16. 20. 24. 28. 32. 36.] nm





.. code-block:: default

    print(x[1].coordinates[:10])





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

 Out:

 .. code-block:: none

    [ 0.  4.  8. 12. 16. 20. 24. 28. 32. 36.] nm




For convenience, let's convert the coordinates from `nm` to `µm` using the
:meth:`~csdmpy.Dimension.to` method of the respective :ref:`dim_api` instance,


.. code-block:: default

    x[0].to("µm")
    x[1].to("µm")








and plot the data.


.. code-block:: default

    plt.figure(figsize=(6, 4))
    cp.plot(TEM)
    plt.tight_layout()
    plt.show()



.. image:: /auto_examples/2D_1_examples/images/sphx_glr_plot_2_TEM_001.png
    :alt: plot 2 TEM
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  1.235 seconds)


.. _sphx_glr_download_auto_examples_2D_1_examples_plot_2_TEM.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_2_TEM.py <plot_2_TEM.py>`



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

     :download:`Download Jupyter notebook: plot_2_TEM.ipynb <plot_2_TEM.ipynb>`


.. only:: html

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

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