Analysis GUI
============

This Graphical User Interface enables to plot measurement data and display analysis results.

Environment
-----------

.. include:: ../_snippets/important_gui_extra.rst

We use the code from the test of the :class:`~cohesivm.gui.AnalysisGUI` for demonstrating its functionality:

.. code-block:: python

    import cohesivm
    import numpy as np


    class DemoAnalysis(cohesivm.analysis.Analysis):

        def __init__(self, dataset, contact_positions=None):
            functions = {
                'Maximum': self.max,
                'Minimum': self.min,
                'Sum': self.sum,
                'Dot Product': self.dot_product,
                'Average': self.average
            }
            plots = {
                'Measurement': self.measurement,
                'Semilog': self.semilog
            }
            Analysis.__init__(self, functions, plots, dataset, contact_positions)
            self.x_name = 'a'
            self.y_name = 'b'

        @result_buffer
        def max(self, contact):
            return max(self.data[contact][self.y_name])

        @result_buffer
        def min(self, contact):
            return min(self.data[contact][self.y_name])

        @result_buffer
        def sum(self, contact):
            return sum(self.data[contact][self.y_name])

        @result_buffer
        def dot_product(self, contact):
            return sum(self.data[contact][self.x_name] * self.data[contact][self.y_name])

        @result_buffer
        def average(self, contact):
            return sum(self.data[contact][self.y_name]) / len(self.data[contact])

        def measurement(self, contact_id):
            plot = XYPlot()
            plot.make_plot()
            data = copy.deepcopy(self.data[contact_id])
            plot.update_plot(data)
            return plot.figure

        def semilog(self, contact_id):
            plot = XYPlot()
            plot.make_plot()
            data = copy.deepcopy(self.data[contact_id])
            data[data.dtype.names[1]] = np.log(data[data.dtype.names[1]])
            plot.update_plot(data)
            return plot.figure


    x_list = np.linspace(-5, 5, 100)
    dtype = [('a', float), ('b', float)]
    dataset = {
        '1': np.array([(x, x**2) for x in x_list], dtype=dtype),
        '2': np.array([(x, np.sin(x)) for x in x_list], dtype=dtype),
        '3': np.array([(x, np.cos(x)) for x in x_list], dtype=dtype),
        '4': np.array([(x, np.exp(x)) for x in x_list], dtype=dtype),
        #'5': np.array([(x+10, np.log(x+10)) for x in x_list], dtype=dtype),
        '6': np.array([(x+5, np.sqrt(x+10)) for x in x_list], dtype=dtype)
    }

    contact_positions = {
        '1': (0., 0.),
        '2': (1., 0.),
        '3': (0., 1.),
        '4': (1., 1.),
        '5': (0., 2.),
        '6': (1., 2.)
    }

    analysis = DemoAnalysis(dataset, contact_positions)

    analysis_gui = cohesivm.gui.AnalysisGUI(analysis)
    analysis_gui.display()

In order to work with this GUI, an :class:`~cohesivm.analysis.Analysis` must be implemented first, which is covered in
detail in the tutorial :doc:`/tutorials/analysis`. Then, we generate the dataset where we set the ``dtype`` according
to how it's defined in the ``DemoAnalysis``. Since we do not implement an :class:`~cohesivm.interfaces.Interface` and
do not have a :class:`~cohesivm.database.Metadata` object, we must define ``contact_positions`` for initializing
the :class:`~cohesivm.analysis.Analysis`.

Usage
-----

When you run this code in a Jupyter Notebook, the following GUI should be displayed:

.. image:: /_static/img/gui_analysis_1.png

Similar to the :class:`~cohesivm.gui.ExperimentGUI`, we have an `Interface` and a `Plot` panel on the left and right
side, respectively. The former one only contains the representation of the ``contact_positions`` which we defined above
and the other one is currently empty.

The black dot, labelled '5', indicates that no data entry is available for this contact ID (which is how we defined
the ``dataset`` above). If you click on a gray dot, however, it will turn blue and the measurement data will appear in
the plot panel. Additionally, the functions and their results are tabulated in the bottom of the GUI:

.. image:: /_static/img/gui_analysis_2.png

If you look back at the implementation of the ``DemoAnalysis``, you can see that these functions are exactly the ones
we specified in the ``__init__()``. The currently displayed plot corresponds to the ``measurement()`` which can be
changed to the ``semilog()`` in the `Plot` panel by clicking the right-arrow in the top right.