.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_figure_1_c.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_figure_1_c.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_figure_1_c.py:


Figure 1 C
==============================================================
recreate figure 1C from the paper

.. GENERATED FROM PYTHON SOURCE LINES 9-20

.. code-block:: default


    # This notebook compares the psychometric performance of mice on the cloud of tones task,
    # according to muscimol injections in the tail of the striatum and dorsomedial striatum

    # Commented out IPython magic to ensure Python compatibility.
    # run this on Colab
    # !rm -rf APE_paper/
    # !git clone https://github.com/HernandoMV/APE_paper.git
    # %pip install mouse-behavior-analysis-tools
    # %cd APE_paper/docs/figures_notebooks








.. GENERATED FROM PYTHON SOURCE LINES 21-26

1. Import libraries
~~~~~~~~~~~~~~~~~~~~
Commented out IPython magic to ensure Python compatibility.
%load_ext autoreload
%autoreload 2

.. GENERATED FROM PYTHON SOURCE LINES 26-37

.. code-block:: default

    import pandas as pd
    import urllib.request
    from os.path import exists

    from mouse_behavior_analysis_tools.plot import make_figures
    from mouse_behavior_analysis_tools.utils import custom_functions as cuf
    from IPython.display import clear_output

    import warnings
    warnings.filterwarnings('ignore')








.. GENERATED FROM PYTHON SOURCE LINES 38-40

2. Download data
~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 40-56

.. code-block:: default


    # download data from server
    dataset_name = 'Muscimol-injections_dataframe.csv'
    url = "https://zenodo.org/record/7261639/files/" + dataset_name
    dataset_path = '../data/' + dataset_name
    # download if data is not there
    if not exists(dataset_path):
        print('Downloading data...')
        urllib.request.urlretrieve(url, dataset_path)
    else:
        print('Data already in directory')
    # load
    mus_df = pd.read_csv(dataset_path, index_col=0)







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

 .. code-block:: none

    Downloading data...




.. GENERATED FROM PYTHON SOURCE LINES 57-59

3. Analyse the psychometric performance
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 59-74

.. code-block:: default


    # Analyse the different conditions
    muscimol_conditions = ['StrTail-bilat-Control', 'StrTail-bilat', 'DMS-bilat']
    # specify the text for the figure (this is the text that will appear in the legend)
    text_for_figure = ['Saline (str tail)', 'Muscimol (str tail)', 'Muscimol (DMS)']
    PP_array = []
    for muscond in muscimol_conditions:
        # process the psychometric performance
        predictDif, PsyPer, fakePredictions, predictPer, EB = \
        cuf.PP_ProcessExperiment(mus_df[mus_df['Muscimol'] == muscond], 500, error_bars='SessionTime')
        # save to array
        PP_array.append([predictDif, PsyPer, fakePredictions, predictPer, EB])

    clear_output()





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

 .. code-block:: none

    
    



.. GENERATED FROM PYTHON SOURCE LINES 75-77

4. Create **Figures 1 C** from the paper
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 77-89

.. code-block:: default


    # prepare colors
    colorlist = [(0.24715576, 0.49918708, 0.57655991),
                 (160/255, 11/255 , 11/255),
                 (46/255, 122/255 , 11/255)]

    fig = make_figures.make_figure_muscimol_psychometric(PP_array, text_for_figure, colorlist)
    fig
    # uncomment here to save the plot
    # data_directory = ''
    # plt.savefig(data_directory + 'Figure1C.pdf',
    #             transparent=True, bbox_inches='tight')



.. image-sg:: /auto_examples/images/sphx_glr_plot_figure_1_c_001.png
   :alt: plot figure 1 c
   :srcset: /auto_examples/images/sphx_glr_plot_figure_1_c_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <Figure size 500x500 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 90-92

5. See a summary of the training and performance of the mice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 92-99

.. code-block:: default


    # see a summary of the dataset
    fig = make_figures.make_figure_muscimol_sessions_overview(mus_df)
    fig
    # uncomment here to save the plot
    # data_directory = ''
    # plt.savefig(data_directory + 'Muscimol-sessions-per-mouse.pdf',
    #             transparent=True, bbox_inches='tight')


.. image-sg:: /auto_examples/images/sphx_glr_plot_figure_1_c_002.png
   :alt: pdyn01 - ForMuscimol  , pdyn03 - ForMuscimol  , pdyn04 - ForMuscimol  , T01 - ForMuscimol  , T03 - ForMuscimol  
   :srcset: /auto_examples/images/sphx_glr_plot_figure_1_c_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <Figure size 700x2500 with 10 Axes>




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

   **Total running time of the script:** ( 1 minutes  44.644 seconds)


.. _sphx_glr_download_auto_examples_plot_figure_1_c.py:

.. only:: html

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


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

      :download:`Download Python source code: plot_figure_1_c.py <plot_figure_1_c.py>`

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

      :download:`Download Jupyter notebook: plot_figure_1_c.ipynb <plot_figure_1_c.ipynb>`


.. only:: html

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

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