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Example Gallery
===============

Explore practical examples showcasing essential features and workflows,
including data handling, interpolation, advanced geospatial analysis, and
integration with Pangeo.



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Core Usage & Data Structures
----------------------------

These examples introduce the fundamental data structures and basic usage
patterns of pyinterp, including axes and object handling.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example explains how to create the fundamental objects required for interpolation with pyinterp: the axes and the grid. These objects are the building blocks for defining the space and data on which interpolation will be performed.">

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  .. image:: /auto_examples/core/images/thumb/sphx_glr_ex_objects_thumb.png
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  :ref:`sphx_glr_auto_examples_core_ex_objects.py`

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      <div class="sphx-glr-thumbnail-title">Interpolator Objects</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In pyinterp, axes are fundamental components for defining grids and performing interpolations. This example demonstrates how to create and manipulate both regular and irregular axes.">

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  .. image:: /auto_examples/core/images/thumb/sphx_glr_ex_axis_thumb.png
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  :ref:`sphx_glr_auto_examples_core_ex_axis.py`

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      <div class="sphx-glr-thumbnail-title">Manipulating Axes</div>
    </div>


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Gridded Interpolation & Multidimensional Examples
-------------------------------------------------

Explore pyinterp’s capabilities for interpolation in 2D, 3D, and 4D, as well as
handling unstructured grids.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to perform 2D interpolation of a variable on a regular grid. The pyinterp library provides several interpolation methods, and this guide will walk you through bivariate and bicubic interpolation.">

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  .. image:: /auto_examples/grid/images/thumb/sphx_glr_ex_2d_thumb.png
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  :ref:`sphx_glr_auto_examples_grid_ex_2d.py`

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      <div class="sphx-glr-thumbnail-title">2D Interpolation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to perform 3D interpolation on a regular grid. The pyinterp library supports both trivariate and bicubic interpolation for 3D data.">

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  .. image:: /auto_examples/grid/images/thumb/sphx_glr_ex_3d_thumb.png
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  :ref:`sphx_glr_auto_examples_grid_ex_3d.py`

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      <div class="sphx-glr-thumbnail-title">3D Interpolation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to perform 4D interpolation on a regular grid. The pyinterp library supports both quadrivariate and bicubic interpolation for 4D data.">

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  .. image:: /auto_examples/grid/images/thumb/sphx_glr_ex_4d_thumb.png
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  :ref:`sphx_glr_auto_examples_grid_ex_4d.py`

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      <div class="sphx-glr-thumbnail-title">4D Interpolation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the various interpolation methods available for unstructured grids, such as data from satellite tracks or other non-uniform sources.">

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  .. image:: /auto_examples/grid/images/thumb/sphx_glr_ex_unstructured_thumb.png
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  :ref:`sphx_glr_auto_examples_grid_ex_unstructured.py`

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      <div class="sphx-glr-thumbnail-title">Unstructured Grid Interpolation</div>
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Binning & Statistics
--------------------

Learn how to perform data binning and compute descriptive statistics using
pyinterp’s built-in tools.



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    <div class="sphx-glr-thumbcontainer" tooltip="While NumPy provides a wide range of statistical functions, calculating multiple statistical variables from the same array often requires multiple passes over the data. The :pypyinterp.DescriptiveStatistics class offers a more efficient solution by computing several statistical variables in a single pass. This approach is not only faster but also more numerically stable, thanks to its incremental calculation algorithm.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_ex_descriptive_statistics_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_ex_descriptive_statistics.py`

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      <div class="sphx-glr-thumbnail-title">Descriptive Statistics</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Binning is a technique used to group continuous values into a smaller number of bins. This is particularly useful when you have irregularly distributed data and want to analyze it on a regular grid. In this example, we will use pyinterp&#x27;s 2D binning functionality to calculate drifter velocity statistics in the Black Sea over a 9-year period.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_ex_binning_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_ex_binning.py`

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      <div class="sphx-glr-thumbnail-title">Binning</div>
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Geospatial & Geodetic Operations
--------------------------------

Demonstrates geospatial operations such as geodetic calculations and geohash
encoding/decoding.



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    <div class="sphx-glr-thumbcontainer" tooltip="The library provides utilities to manage geodetic coordinates. While other libraries offer more exhaustive geodetic functionalities, pyinterp includes these objects because its C++ core requires geodetic information to be passed from Python.">

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  .. image:: /auto_examples/geo/images/thumb/sphx_glr_ex_geodetic_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_geo_ex_geodetic.py`

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      <div class="sphx-glr-thumbnail-title">Geodetic Objects</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Geohashing is a geocoding method used to encode geographic coordinates (latitude and longitude) into a short string of digits and letters. This string, also known as a geohash, delineates a rectangular area on a map, called a cell. The longer the string, the smaller the cell and the more precise the location.">

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  .. image:: /auto_examples/geo/images/thumb/sphx_glr_ex_geohash_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_geo_ex_geohash.py`

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      <div class="sphx-glr-thumbnail-title">Geohash</div>
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Utilities & Date Handling
-------------------------

Covers utility functions for date manipulation and handling undefined values in
datasets.



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    <div class="sphx-glr-thumbcontainer" tooltip="The pyinterp.dateutils module provides a set of utility functions for working with dates and times in NumPy arrays. These functions are designed to be fast and efficient, making it easy to perform common date and time calculations.">

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  .. image:: /auto_examples/utilities/images/thumb/sphx_glr_ex_dateutils_thumb.png
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  :ref:`sphx_glr_auto_examples_utilities_ex_dateutils.py`

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      <div class="sphx-glr-thumbnail-title">Date and Time Utilities</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="When working with gridded data, undefined values (NaNs) can be problematic for interpolation, especially near land/sea masks. If any of the grid points used for interpolation are undefined, the result will also be undefined. This example demonstrates how to fill these undefined values in a grid to allow for seamless interpolation.">

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  .. image:: /auto_examples/utilities/images/thumb/sphx_glr_ex_fill_undef_thumb.png
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  :ref:`sphx_glr_auto_examples_utilities_ex_fill_undef.py`

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      <div class="sphx-glr-thumbnail-title">Filling Undefined Values</div>
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Orbit & Temporal Analysis
-------------------------

Examples focused on orbit-related computations and temporal data analysis.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use pyinterp to interpolate satellite orbits. The library can propagate orbit ephemerides from a template file that contains satellite positions for a single orbit cycle. This is useful for simulations and other applications that require accurate orbit propagation over time.">

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  .. image:: /auto_examples/orbit/images/thumb/sphx_glr_ex_orbit_thumb.png
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  :ref:`sphx_glr_auto_examples_orbit_ex_orbit.py`

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      <div class="sphx-glr-thumbnail-title">Orbit Interpolation</div>
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Pangeo Integration & Advanced Workflows
---------------------------------------

Showcases advanced workflows and integration with Pangeo for time series and
unstructured grid processing.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to interpolate data from the high-resolution LLC4320 ocean model, which uses a curvilinear grid. This type of grid is a prime example of unstructured data that pyinterp can handle efficiently.">

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  .. image:: /auto_examples/pangeo/images/thumb/sphx_glr_pangeo_unstructured_grid_thumb.png
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  :ref:`sphx_glr_auto_examples_pangeo_pangeo_unstructured_grid.py`

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      <div class="sphx-glr-thumbnail-title">LLC4320 Ocean Model Interpolation</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Interpolation of a time series">

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  .. image:: /auto_examples/pangeo/images/thumb/sphx_glr_pangeo_time_series_thumb.png
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  :ref:`sphx_glr_auto_examples_pangeo_pangeo_time_series.py`

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      <div class="sphx-glr-thumbnail-title">Time series</div>
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.. toctree::
   :hidden:
   :includehidden:


   /auto_examples/core/index.rst
   /auto_examples/grid/index.rst
   /auto_examples/stats/index.rst
   /auto_examples/geo/index.rst
   /auto_examples/utilities/index.rst
   /auto_examples/orbit/index.rst
   /auto_examples/pangeo/index.rst


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      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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