.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/00_encodingdecoding/encoding_model.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_00_encodingdecoding_encoding_model.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_00_encodingdecoding_encoding_model.py:


Create a simple Gaussian Prf encoding model
=============================================

In this example, we create a Gaussian PRF model and plot the predictions.
We also simulate data and then estimate back the generating parameters.

.. GENERATED FROM PYTHON SOURCE LINES 9-33

.. code-block:: Python


    # Import necessary libraries
    from braincoder.models import GaussianPRF
    import pandas as pd
    import numpy as np

    # We set up two PRFS, one centered at 1 and one centered at -2
    # The first one has a sd of 1 and the second one has a sd of 1.5
    parameters = [{'mu':1.0, 'sd':1.0, 'amplitude':1.0, 'baseline':0.0},
                  {'mu':-2., 'sd':1.5, 'amplitude':1.0, 'baseline':0.0}
                  ]
    parameters = pd.DataFrame(parameters, index=['voxel 1', 'voxel 2'])

    # We have a virtual experimental paradigm where we go from -5 to 5
    paradigm = np.linspace(-5, 5, 100)

    # Set up the model.
    model = GaussianPRF(paradigm=paradigm, parameters=parameters)

    # Extract and plot the predictions
    predictions = model.predict()
    predictions.index = pd.Index(paradigm, name='Stimulus value')
    ax = predictions.plot()




.. image-sg:: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_001.png
   :alt: encoding model
   :srcset: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 34-38

.. code-block:: Python


    # We simulate data with a bit of noise
    data = model.simulate(noise=0.2)
    data.plot()



.. image-sg:: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_002.png
   :alt: encoding model
   :srcset: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <Axes: xlabel='frame'>



.. GENERATED FROM PYTHON SOURCE LINES 39-61

.. code-block:: Python


    # Import and set up a parameter fitter with the (simulated) data,
    # the paradigm, and the model
    from braincoder.optimize import ParameterFitter
    from braincoder.utils import get_rsq

    optimizer = ParameterFitter(model, data=data, paradigm=paradigm)

    # Set up a grid search over the parameters
    possible_mus = np.linspace(-5, 5, 10)
    possible_sds = np.linspace(0.1, 5, 10)

    # For the grid search we use a correlation cost function, so we can fit
    # the amplitude an baseline later using OLS
    possible_amplitudes = [1.0]
    possible_baselines = [0.0]

    # Fit the grid
    grid_pars = optimizer.fit_grid(possible_mus, possible_sds, possible_amplitudes, possible_baselines, use_correlation_cost=True, progressbar=False)

    # Show the results
    grid_pars




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

 .. code-block:: none

    Working with chunk size of 3333333
    Using correlation cost!

      0%|          | 0/1 [00:00<?, ?it/s]
    100%|██████████| 1/1 [00:00<00:00, 11.42it/s]


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>mu</th>
          <th>sd</th>
          <th>amplitude</th>
          <th>baseline</th>
        </tr>
        <tr>
          <th>source</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>voxel 1</th>
          <td>0.555556</td>
          <td>1.188889</td>
          <td>1.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>voxel 2</th>
          <td>-1.666667</td>
          <td>1.733333</td>
          <td>1.0</td>
          <td>0.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 62-80

.. code-block:: Python


    # We can now fit the amplitude and baseline using OLS
    grid_pars = optimizer.refine_baseline_and_amplitude(grid_pars)

    # Show the fitted timeseries
    import matplotlib.pyplot as plt
    import seaborn as sns
    palette = sns.color_palette()
    grid_pred = model.predict(parameters=grid_pars)

    # See how well the predictions align with the data
    # using the explained variance statistic
    r2_grid = get_rsq(data, grid_pred)

    plt.plot(paradigm, data)
    plt.plot(paradigm, grid_pred, ls='--', c='k', label='Grid prediction')
    plt.title(f'R2 = {r2_grid.mean():.2f}')




.. image-sg:: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_003.png
   :alt: R2 = 0.68
   :srcset: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Text(0.5, 1.0, 'R2 = 0.68')



.. GENERATED FROM PYTHON SOURCE LINES 81-93

.. code-block:: Python


    # Final optimisation using gradient descent:
    gd_pars = optimizer.fit(init_pars=grid_pars, progressbar=False)
    gd_pred = model.predict(parameters=gd_pars)

    r2_gd = get_rsq(data, gd_pred)

    plt.plot(paradigm, data)
    plt.plot(paradigm, grid_pred, ls='--', c='k', alpha=0.5, label='Grid prediction')
    plt.plot(paradigm, gd_pred, ls='--', c='k', label='Gradient descent prediction')
    plt.title(f'R2 = {r2_gd.mean():.2f}')




.. image-sg:: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_004.png
   :alt: R2 = 0.76
   :srcset: /auto_examples/00_encodingdecoding/images/sphx_glr_encoding_model_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Number of problematic voxels (mask): 0
    Number of voxels remaining (mask): 2

    Text(0.5, 1.0, 'R2 = 0.76')




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

   **Total running time of the script:** (0 minutes 6.255 seconds)


.. _sphx_glr_download_auto_examples_00_encodingdecoding_encoding_model.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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