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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_gallery_sparse_vector_normals.py>`
        to download the full example code

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

.. _sphx_glr_gallery_sparse_vector_normals.py:


A sparse model vector generator
=================================


Demonstrates how to create sparse model vectors with small number of non-zero entries sampled from Gaussian distribution

.. GENERATED FROM PYTHON SOURCE LINES 10-11

Let's import necessary libraries 

.. GENERATED FROM PYTHON SOURCE LINES 11-22

.. code-block:: default

    import matplotlib as mpl
    import matplotlib.pyplot as plt
    from jax import random
    import jax.numpy as jnp
    import cr.sparse as crs
    import cr.sparse.data as crdata
    from cr.nimble.dsp import (
        nonzero_indices,
        nonzero_values
    )








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Let's define the size of model and number of sparse entries

.. GENERATED FROM PYTHON SOURCE LINES 24-30

.. code-block:: default


    # Model size
    N = 1000
    # Number of non-zero entries in the sparse model
    K = 30








.. GENERATED FROM PYTHON SOURCE LINES 31-32

Let's generate a random sparse model

.. GENERATED FROM PYTHON SOURCE LINES 32-36

.. code-block:: default

    key = random.PRNGKey(1)
    x, omega = crdata.sparse_normal_representations(key, N, K, 1)
    x = jnp.squeeze(x)








.. GENERATED FROM PYTHON SOURCE LINES 37-38

We can easily find the locations of non-zero entries

.. GENERATED FROM PYTHON SOURCE LINES 38-40

.. code-block:: default

    print(nonzero_indices(x))





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

 .. code-block:: none

    [ 45  69 114 133 159 185 199 233 259 338 348 377 393 425 490 504 521 533
     537 627 722 730 782 790 817 846 891 947 949 968]




.. GENERATED FROM PYTHON SOURCE LINES 41-42

We can  extract corresponding non-zero values in a compact vector

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: default

    print(nonzero_values(x))





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

 .. code-block:: none

    [-0.64617896 -1.2449833  -0.9160154   2.7065587   0.99159986  1.7746289
      0.8708357  -0.28911775  0.30582124 -1.0570332  -0.03854743 -2.4557247
     -0.04477294 -2.2054574  -1.835958    1.8202777  -0.3686923  -0.54682994
     -0.82268345  1.5236915   0.24205726 -0.01583949  1.150074   -0.00814029
     -0.6977474  -0.88545537  0.4539182   1.7888712  -0.24443631 -0.39677632]




.. GENERATED FROM PYTHON SOURCE LINES 45-46

Let's plot the vector to see where the non-zero entries are

.. GENERATED FROM PYTHON SOURCE LINES 46-48

.. code-block:: default

    plt.figure(figsize=(8,6), dpi= 100, facecolor='w', edgecolor='k')
    plt.stem(x, markerfmt='.');



.. image-sg:: /gallery/images/sphx_glr_sparse_vector_normals_001.png
   :alt: sparse vector normals
   :srcset: /gallery/images/sphx_glr_sparse_vector_normals_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <StemContainer object of 3 artists>




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

   **Total running time of the script:** (0 minutes 1.457 seconds)


.. _sphx_glr_download_gallery_sparse_vector_normals.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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