This documention is made from a jupyter notebook available in
'Exemple/Documentation.ipynb'

It can be run interactively using binder :

.. image:: https://mybinder.org/badge_logo.svg
    :target: https://mybinder.org/v2/gh/ThomasChauve/aita/HEAD
    


Load data from G50 analyser
===========================

Loading data from G50 analyser after you convert binary file into ASCII
file with 5 columuns. The ‘micro\_test.bmp’ file should be a black and
white image with the grains boundaries in white.

.. code:: ipython3

    import AITAToolbox.loadData_aita as lda
    import matplotlib.pyplot as plt
    
    data=lda.aita5col('orientation_test.dat','micro_test.bmp')


.. parsed-literal::

    Sucessfull aita build !


Basic treatment
===============

Filter the data
---------------

This function filter the bad indexed value. Using G50 analyser a quelity
factor is given between 0 and 100. Usualy using data with a quality
factor higher than 75 is a good option.

.. code:: ipython3

    data.filter(75)

Croping
-------

Sometimes it can be usefull to select a sub area.

**Warning :** This function need an interaction with a figure. Hence it
cannot ne used with jupyter notebook such as '%matplotlib inline'

.. code:: ipython3

    data.crop()

Colormap
========

Plotting the colormap with the grains boundaries

.. code:: ipython3

    plt.figure(figsize=(10,10))
    data.plot()
    data.micro.plotBoundary(dilatation=4)
    plt.title('Colormap')




.. parsed-literal::

    Text(0.5, 1.0, 'Colormap')




.. image:: output_7_1.png


The associated colorwheel :

.. code:: ipython3

    plt.imshow(lda.aita.lut())
    plt.axis('off')
    plt.title('LUT')




.. parsed-literal::

    Text(0.5, 1.0, 'LUT')




.. image:: output_9_1.png


Pole figure
===========

There is various option to plot the pole figure here we focus on some of
them but to see all of them refer to the documentation of plotpdf
function.

The color coding of the pole figure is obtain using a Kernel Density
Estimation (KDE). This KDE has to be manipulating carrefully. If you
want to have a basic idea of what is a KDE you can look at
https://mathisonian.github.io/kde/.

Representation
--------------

Pole figure all sample
~~~~~~~~~~~~~~~~~~~~~~

Here some of the option are shown as contour plot, and with or without
circle for specific angle.

Be aware that to reduce the computation time we only used by default
10000 orientations selected randomly. You can modify this using 'nbp'
value. If you set nbp to 0 it use all the data.

.. code:: ipython3

    plt.figure(figsize=(20,20),dpi=160)
    plt.subplot(2,2,1)
    data.plotpdf(contourf=True,angle=0)
    plt.subplot(2,2,2)
    data.plotpdf(contourf=True)
    plt.subplot(2,2,3)
    data.plotpdf(angle=0)
    plt.subplot(2,2,4)
    data.plotpdf()




.. parsed-literal::

    array([0.29090318, 0.36842623, 0.3406706 ], dtype=float32)




.. image:: output_11_1.png


Kernel Density Estimation
-------------------------

If you want to have an idea of a basic KDE in one dimention refer to
https://mathisonian.github.io/kde/

Here there is some specificities du to the fact that we are computing
KDE on a sphere. To do so we are using sklearn.neighbors.KernelDensity
(https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html).

The 'metric' is set to 'haversine' for spherical computation (for
exemple see
https://scikit-learn.org/stable/auto\_examples/neighbors/plot\_species\_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py)

We are using a gaussian kernel.

**Warning :** The 'bandwidth' parameter is crutial parameter to set. It
can have a strong influence on your pole figure and you interpretation.
You should set it up carefully and be critic on your pole figure. Here
we show different pole figure for different bandwidth value using the
same data as input.

.. code:: ipython3

    plt.figure(figsize=(20,20),dpi=160)
    plt.subplot(2,2,1)
    data.plotpdf(contourf=True,angle=0,bw=0.05)
    plt.title('bw=0.05')
    plt.subplot(2,2,2)
    data.plotpdf(contourf=True,angle=0,bw=0.1)
    plt.title('bw=0.1')
    plt.subplot(2,2,3)
    data.plotpdf(contourf=True,angle=0,bw=0.3)
    plt.title('bw=0.3')
    plt.subplot(2,2,4)
    data.plotpdf(contourf=True,angle=0,bw=2.0)
    plt.title('bw=2')




.. parsed-literal::

    Text(0.5, 1.0, 'bw=2')




.. image:: output_13_1.png