.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ex_objects.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_ex_objects.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ex_objects.py:


***************************
Create interpolator objects
***************************

In this example, we are going to build the basic objects allowing to carry out
interpolations.

Before starting, we will examine the properties of a Cartesian grid and the
different classes associated with these objects.


Step-by-step creation of grids
##############################

.. GENERATED FROM PYTHON SOURCE LINES 16-33

.. code-block:: Python

    import timeit

    import numpy
    import pandas

    import pyinterp
    import pyinterp.backends.xarray
    import pyinterp.tests

    ds = pyinterp.tests.load_grid3d()
    lon, lat, time, tcw = (
        ds['longitude'].values,
        ds['latitude'].values,
        ds['time'].values,
        ds['tcw'].values,
    )








.. GENERATED FROM PYTHON SOURCE LINES 34-43

This regular 3-dimensional grid is associated with three axes:

* longitudes,
* latitudes and
* time.

To perform the calculations quickly, we will build three objects that will be
used by the interpolator to search for the data to be used. Let's start with
the y-axis representing the latitude axis.

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: Python

    y_axis = pyinterp.Axis(lat)
    print(y_axis)





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

 .. code-block:: none

    <pyinterp.core.Axis>
      min_value: -90
      max_value: 90
      step     : -2.5
      is_circle: false




.. GENERATED FROM PYTHON SOURCE LINES 47-49

For example, you can search for the closest point to 0.12 degrees north
latitude.

.. GENERATED FROM PYTHON SOURCE LINES 49-51

.. code-block:: Python

    y_axis.find_index([0.12])





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

 .. code-block:: none


    array([36])



.. GENERATED FROM PYTHON SOURCE LINES 52-54

Then, the x-axis representing the longitudinal axis. In this case, the axis is
an axis representing a 360 degree circle.

.. GENERATED FROM PYTHON SOURCE LINES 54-57

.. code-block:: Python

    x_axis = pyinterp.Axis(lon, is_circle=True)
    print(x_axis)





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

 .. code-block:: none

    <pyinterp.core.Axis>
      min_value: 0
      max_value: 357.5
      step     : 2.5
      is_circle: true




.. GENERATED FROM PYTHON SOURCE LINES 58-59

The values -180 and 180 degrees represent the same point on the axis.

.. GENERATED FROM PYTHON SOURCE LINES 59-61

.. code-block:: Python

    print(x_axis.find_index([-180]) == x_axis.find_index([180]))





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

 .. code-block:: none

    [ True]




.. GENERATED FROM PYTHON SOURCE LINES 62-63

Finally, we create the time axis

.. GENERATED FROM PYTHON SOURCE LINES 63-66

.. code-block:: Python

    t_axis = pyinterp.TemporalAxis(time)
    print(t_axis)





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

 .. code-block:: none

    <pyinterp.core.TemporalAxis>
      min_value: 2002-07-01T12:00:00.000000000
      max_value: 2002-07-31T12:00:00.000000000
      step     : 86400000000000 nanoseconds




.. GENERATED FROM PYTHON SOURCE LINES 67-71

As these objects must communicate in C++ memory space, we use objects specific
to the library much faster than other data models and manage the axes
representing a circle. For example if we compare these objects to Pandas
indexes:

.. GENERATED FROM PYTHON SOURCE LINES 71-84

.. code-block:: Python

    values = lon[10:20] + 1 / 3
    index = pandas.Index(lon)
    print('pandas.Index: %f' % timeit.timeit('index.searchsorted(values)',
                                             globals={
                                                 'index': index,
                                                 'values': values
                                             }))
    print('pyinterp.Axis %f' % timeit.timeit('x_axis.find_index(values)',
                                             globals={
                                                 'x_axis': x_axis,
                                                 'values': values
                                             }))





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

 .. code-block:: none

    pandas.Index: 0.983544
    pyinterp.Axis 0.665903




.. GENERATED FROM PYTHON SOURCE LINES 85-86

This time axis is also very efficient compared to the pandas index.

.. GENERATED FROM PYTHON SOURCE LINES 86-99

.. code-block:: Python

    index = pandas.Index(time)
    values = time + numpy.timedelta64(1, 'ns')
    print('pandas.Index: %f' % timeit.timeit('index.searchsorted(values)',
                                             globals={
                                                 'index': index,
                                                 'values': values
                                             }))
    print('pyinterp.Axis %f' % timeit.timeit('t_axis.find_index(values)',
                                             globals={
                                                 't_axis': t_axis,
                                                 'values': values
                                             }))





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

 .. code-block:: none

    pandas.Index: 14.089318
    pyinterp.Axis 4.317738




.. GENERATED FROM PYTHON SOURCE LINES 100-102

Before constructing the tensor for pyinterp, we must begin to organize the
tensor data so that it is properly stored in memory for pyinterp.

.. GENERATED FROM PYTHON SOURCE LINES 104-105

* The shape of the tensor must be (len(x_axis), len(y_axis), len(t_axis))

.. GENERATED FROM PYTHON SOURCE LINES 105-106

.. code-block:: Python

    tcw = tcw.T







.. GENERATED FROM PYTHON SOURCE LINES 107-112

.. warning::

  If the array handled is a masked array, the masked values must be set to
  nan.


.. GENERATED FROM PYTHON SOURCE LINES 114-120

Now we can build the object handling the regular 3-dimensional grid.

.. note::
  Grid data are not copied, the Grid3D class just keeps a reference on the
  handled array. Axis data are copied for non-uniform axes, and only examined
  for regular axes.

.. GENERATED FROM PYTHON SOURCE LINES 120-123

.. code-block:: Python

    grid_3d = pyinterp.Grid3D(x_axis, y_axis, t_axis, tcw)
    print(grid_3d)





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

 .. code-block:: none

    <pyinterp.grid.Grid3D>
    array([[[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [12.08470387,  9.64372637, 12.96766807, ..., 10.47133655,
             12.06850269, 24.25583891],
            [15.77317208, 10.12436132, 11.59731843, ...,  9.53976881,
             11.00327523, 21.59209521],
            ...,
            [ 0.38745328,  0.44010711,  0.65747292, ...,  0.19438924,
              0.57376683,  0.36045132],
            [ 0.39420377,  0.35910122,  0.57106663, ...,  0.18763875,
              0.3739523 ,  0.27269494],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]],

           [[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [12.19541192,  9.65722735, 12.9460665 , ..., 10.34847761,
             12.54643744, 24.19508449],
            [15.72996894, 10.18511573, 11.41775537, ...,  9.63562578,
             11.89839032, 21.96202211],
            ...,
            [ 0.38340299,  0.43065642,  0.64937233, ...,  0.19573934,
              0.56026585,  0.34695034],
            [ 0.38880338,  0.35505093,  0.56566624, ...,  0.18763875,
              0.36855191,  0.26729454],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]],

           [[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [12.30611997,  9.67072833, 12.92311483, ..., 10.22561868,
             13.02707239, 24.13433007],
            [15.68946599, 10.24316996, 11.23954241, ...,  9.73283285,
             12.79485551, 22.33194901],
            ...,
            [ 0.37935269,  0.41985564,  0.63992164, ...,  0.19978964,
              0.54406467,  0.33344936],
            [ 0.38475309,  0.34830043,  0.56026585, ...,  0.18763875,
              0.36315151,  0.26324425],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]],

           ...,

           [[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [11.72422765,  9.73148275, 12.9447164 , ..., 10.88716679,
             11.01542611, 24.1167788 ],
            [15.4585992 , 10.30257428, 11.62972078, ...,  9.36560614,
              9.72743245, 19.62230197],
            ...,
            [ 0.40500456,  0.47250947,  0.71417704, ...,  0.21599081,
              0.62642066,  0.40770475],
            [ 0.40635466,  0.3739523 ,  0.57376683, ...,  0.21329062,
              0.38880338,  0.28889612],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]],

           [[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [11.84438639,  9.70178059, 12.95281699, ..., 10.74810667,
             11.36645164, 24.16403223],
            [15.56390686, 10.24316996, 11.61892   , ...,  9.42501046,
             10.15406348, 20.27844969],
            ...,
            [ 0.39825407,  0.46170868,  0.69527567, ...,  0.20789022,
              0.60886938,  0.39285367],
            [ 0.40230436,  0.36855191,  0.57376683, ...,  0.20383993,
              0.38475309,  0.28484582],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]],

           [[10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844],
            [11.96454513,  9.67342853, 12.96091758, ..., 10.61039666,
             11.71612706, 24.20858547],
            [15.66786442, 10.18241554, 11.60676911, ...,  9.48306468,
             10.5779943 , 20.9345974 ],
            ...,
            [ 0.39285367,  0.45225799,  0.67502419, ...,  0.20248983,
              0.59131811,  0.3766525 ],
            [ 0.39825407,  0.36450161,  0.57376683, ...,  0.19573934,
              0.37800259,  0.27944543],
            [ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973]]])
    Axis:
     * x: <pyinterp.core.Axis>
           min_value: 0
           max_value: 357.5
           step     : 2.5
           is_circle: true
     * y: <pyinterp.core.Axis>
           min_value: -90
           max_value: 90
           step     : -2.5
           is_circle: false
     * z: <pyinterp.core.TemporalAxis>
           min_value: 2002-07-01T12:00:00.000000000
           max_value: 2002-07-31T12:00:00.000000000
           step     : 86400000000000 nanoseconds




.. GENERATED FROM PYTHON SOURCE LINES 124-131

xarray backend
##############

The construction of these objects manipulating the :py:class:`regular grids
<pyinterp.backends.xarray.RegularGridInterpolator>` can be done more easily
using the `xarray <http://xarray.pydata.org/>`_ library and `CF
<https://cfconventions.org/>`_ convention usually found in NetCDF files.

.. GENERATED FROM PYTHON SOURCE LINES 131-134

.. code-block:: Python

    interpolator = pyinterp.backends.xarray.RegularGridInterpolator(
        pyinterp.tests.load_grid3d().tcw)
    print(interpolator.grid)




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

 .. code-block:: none

    <pyinterp.backends.xarray.Grid3D>
    array([[[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.39420377,  0.35910122,  0.57106663, ...,  0.18763875,
              0.3739523 ,  0.27269494],
            [ 0.38745328,  0.44010711,  0.65747292, ...,  0.19438924,
              0.57376683,  0.36045132],
            ...,
            [15.77317208, 10.12436132, 11.59731843, ...,  9.53976881,
             11.00327523, 21.59209521],
            [12.08470387,  9.64372637, 12.96766807, ..., 10.47133655,
             12.06850269, 24.25583891],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]],

           [[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.38880338,  0.35505093,  0.56566624, ...,  0.18763875,
              0.36855191,  0.26729454],
            [ 0.38340299,  0.43065642,  0.64937233, ...,  0.19573934,
              0.56026585,  0.34695034],
            ...,
            [15.72996894, 10.18511573, 11.41775537, ...,  9.63562578,
             11.89839032, 21.96202211],
            [12.19541192,  9.65722735, 12.9460665 , ..., 10.34847761,
             12.54643744, 24.19508449],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]],

           [[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.38475309,  0.34830043,  0.56026585, ...,  0.18763875,
              0.36315151,  0.26324425],
            [ 0.37935269,  0.41985564,  0.63992164, ...,  0.19978964,
              0.54406467,  0.33344936],
            ...,
            [15.68946599, 10.24316996, 11.23954241, ...,  9.73283285,
             12.79485551, 22.33194901],
            [12.30611997,  9.67072833, 12.92311483, ..., 10.22561868,
             13.02707239, 24.13433007],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]],

           ...,

           [[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.40635466,  0.3739523 ,  0.57376683, ...,  0.21329062,
              0.38880338,  0.28889612],
            [ 0.40500456,  0.47250947,  0.71417704, ...,  0.21599081,
              0.62642066,  0.40770475],
            ...,
            [15.4585992 , 10.30257428, 11.62972078, ...,  9.36560614,
              9.72743245, 19.62230197],
            [11.72422765,  9.73148275, 12.9447164 , ..., 10.88716679,
             11.01542611, 24.1167788 ],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]],

           [[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.40230436,  0.36855191,  0.57376683, ...,  0.20383993,
              0.38475309,  0.28484582],
            [ 0.39825407,  0.46170868,  0.69527567, ...,  0.20789022,
              0.60886938,  0.39285367],
            ...,
            [15.56390686, 10.24316996, 11.61892   , ...,  9.42501046,
             10.15406348, 20.27844969],
            [11.84438639,  9.70178059, 12.95281699, ..., 10.74810667,
             11.36645164, 24.16403223],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]],

           [[ 0.33344936,  0.28214562,  0.35640102, ...,  0.2227413 ,
              0.23759238,  0.20113973],
            [ 0.39825407,  0.36450161,  0.57376683, ...,  0.19573934,
              0.37800259,  0.27944543],
            [ 0.39285367,  0.45225799,  0.67502419, ...,  0.20248983,
              0.59131811,  0.3766525 ],
            ...,
            [15.66786442, 10.18241554, 11.60676911, ...,  9.48306468,
             10.5779943 , 20.9345974 ],
            [11.96454513,  9.67342853, 12.96091758, ..., 10.61039666,
             11.71612706, 24.20858547],
            [10.15271338, 10.66710078, 11.29219624, ..., 10.60634637,
             15.88388013, 19.57369844]]])
    Axis:
     * x: <pyinterp.core.Axis>
           min_value: 0
           max_value: 357.5
           step     : 2.5
           is_circle: true
     * y: <pyinterp.core.Axis>
           min_value: -90
           max_value: 90
           step     : 2.5
           is_circle: false
     * z: <pyinterp.core.TemporalAxis>
           min_value: 2002-07-01T12:00:00.000000000
           max_value: 2002-07-31T12:00:00.000000000
           step     : 86400000000000 nanoseconds





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

   **Total running time of the script:** (0 minutes 20.138 seconds)


.. _sphx_glr_download_auto_examples_ex_objects.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/CNES/pangeo-pyinterp/master?urlpath=lab/tree/notebooks/auto_examples/ex_objects.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: ex_objects.zip <ex_objects.zip>`


.. only:: html

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

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