.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/rsa/1_rsa_intro.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_rsa_1_rsa_intro.py>`
        to download the full example code.

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

.. _sphx_glr_examples_rsa_1_rsa_intro.py:


1. Computing RDMs
=================

As we did for classification, we will use an MEG dataset of humans performing a visual categorization task. Briefly, participants saw a list of 92 images. Effectively, these images are faces, not faces, human, not human, artificial, etc. For more information, consult `MNE's documentation <https://mne.tools/1.8/auto_examples/decoding/decoding_rsa_sgskip.html>`_ or the `original paper <https://dx.doi.org/10.1038/nn.3635>`_. Our goal here will be to create a neural RDM and a hypothesis RDM based on the neural data and the corresponding categories and see if we find some similarity between the two.

First, we will have to load the data---To do this, we use MNE's sample code. Be aware that this will download roughly 6GB of data, which may take a while. As we did in the classification example, we will again be using only gradiometers (for convenience).

.. GENERATED FROM PYTHON SOURCE LINES 10-65

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np
    from pandas import read_csv

    import mne
    from mne.datasets import visual_92_categories
    from mne.io import concatenate_raws, read_raw_fif

    print(__doc__)

    data_path = visual_92_categories.data_path()

    # Define stimulus - trigger mapping
    fname = data_path / "visual_stimuli.csv"
    conds = read_csv(fname)
    print(conds.head(5))

    max_trigger = 92
    conds = conds[:max_trigger]  # take only the first 24 rows

    conditions = []
    for c in conds.values:
        cond_tags = list(c[:2])
        cond_tags += [
            ("not-" if i == 0 else "") + conds.columns[k] for k, i in enumerate(c[2:], 2)
        ]
        conditions.append("/".join(map(str, cond_tags)))
    print(conditions[:10])

    event_id = dict(zip(conditions, conds.trigger + 1))
    event_id["0/human bodypart/human/not-face/animal/natural"]

    n_runs = 4  # 4 for full data (use less to speed up computations)
    fnames = [data_path / f"sample_subject_{b}_tsss_mc.fif" for b in range(n_runs)]
    raws = [
        read_raw_fif(fname, verbose="error", on_split_missing="ignore") for fname in fnames
    ]  # ignore filename warnings
    raw = concatenate_raws(raws)

    events = mne.find_events(raw, min_duration=0.002)

    events = events[events[:, 2] <= max_trigger]

    picks = mne.pick_types(raw.info, meg=True)
    epochs = mne.Epochs(
        raw,
        events=events,
        event_id=event_id,
        baseline=None,
        picks='grad',
        tmin=-0.1,
        tmax=0.500,
        preload=True,
    )





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

 .. code-block:: none


    Using default location ~/mne_data for visual_92_categories...
    Creating /home/runner/mne_data
    Downloading file 'MNE-visual_92_categories-data-part1.tar.gz' from 'https://osf.io/8ejrs/download?version=1' to '/home/runner/mne_data'.

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    Untarring contents of '/home/runner/mne_data/MNE-visual_92_categories-data-part1.tar.gz' to '/home/runner/mne_data'
    Downloading file 'MNE-visual_92_categories-data-part2.tar.gz' from 'https://osf.io/t4yjp/download?version=1' to '/home/runner/mne_data'.

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    Untarring contents of '/home/runner/mne_data/MNE-visual_92_categories-data-part2.tar.gz' to '/home/runner/mne_data'
    Attempting to create new mne-python configuration file:
    /home/runner/.mne/mne-python.json
    Could not read the /home/runner/.mne/mne-python.json json file during the writing. Assuming it is empty. Got: Expecting value: line 1 column 1 (char 0)
    Download complete in 02m44s (5468.7 MB)
       trigger       condition  human  face  animal  natural
    0        0  human bodypart      1     0       1        1
    1        1  human bodypart      1     0       1        1
    2        2  human bodypart      1     0       1        1
    3        3  human bodypart      1     0       1        1
    4        4  human bodypart      1     0       1        1
    ['0/human bodypart/human/not-face/animal/natural', '1/human bodypart/human/not-face/animal/natural', '2/human bodypart/human/not-face/animal/natural', '3/human bodypart/human/not-face/animal/natural', '4/human bodypart/human/not-face/animal/natural', '5/human bodypart/human/not-face/animal/natural', '6/human bodypart/human/not-face/animal/natural', '7/human bodypart/human/not-face/animal/natural', '8/human bodypart/human/not-face/animal/natural', '9/human bodypart/human/not-face/animal/natural']
    Finding events on: STI101
    4142 events found on stim channel STI101
    Event IDs: [  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
      19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
      37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
      55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
      73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
      91  92  93 200 222 244]
    Not setting metadata
    2760 matching events found
    No baseline correction applied
    0 projection items activated
    Loading data for 2760 events and 601 original time points ...
    2 bad epochs dropped




.. GENERATED FROM PYTHON SOURCE LINES 66-67

Before we can compute RDMs, we would like to also get some data to base our hypothesis RDMs on. Specifically, we will use the epoch categories (human/nonhuman/natural/artificial, bodypart/face/inanimate, human/not-human, face/not-face, natural/not-natural) to build features vectors of our images, like so:

.. GENERATED FROM PYTHON SOURCE LINES 67-114

.. code-block:: Python


    # generate some vectors for conditions based on labels
    condition_types = {}

    for i, cond in enumerate(conditions):
        items = '/'.join(cond.split(' ')).split('/')[1:]
    
        for j, item in enumerate(items):
            if j not in condition_types:
                condition_types[j] = []
        
            if item not in condition_types[j]:
                condition_types[j].append(item)

    N = np.array([len(condition_types[i]) for i in range(len(condition_types))]).sum()
    condition_vecs = np.zeros((len(conditions), N))

    for i, cond in enumerate(conditions):
        items = '/'.join(cond.split(' ')).split('/')[1:]
    
        indx_i = 0
        for j, item in enumerate(items):
            indx_j = np.where(np.array(condition_types[j]) == item)[0]
            condition_vecs[i,indx_i+indx_j[0]] = 1
            indx_i += len(condition_types[j])

    # generate vectors for trials based on condition labels
    epochs, indc = epochs.equalize_event_counts()

    X = epochs.get_data()
    y = np.zeros((X.shape[0], N, X.shape[-1]))
    L = np.zeros((X.shape[0],))

    for i, event in enumerate(epochs.events):
        y[i,:,:] = condition_vecs[event[2] - 1,:,None]
        L[i] = event[2] - 1

    # group by condition
    unq, counts = np.unique(L, return_counts = True)
    X_g = np.zeros((counts[0], len(unq), X.shape[1], X.shape[2]))
    y_g = np.zeros((counts[0], len(unq), y.shape[1], y.shape[2]))

    for i, unq_i in enumerate(unq):
        indc = np.where(L == unq_i)[0]
        X_g[:,i] = X[indc]
        y_g[:,i] = y[indc]





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

 .. code-block:: none

    Dropped 90 epochs: 930, 1478, 1479, 1480, 1481, 1482, 1486, 1492, 1493, 1499, 1504, 1505, 1518, 1520, 1521, 1525, 1539, 1657, 1659, 1669, 1709, 1713, 1730, 1742, 1743, 1745, 1749, 1752, 1754, 1757, 1758, 1761, 1763, 1768, 1770, 1771, 1772, 1774, 1777, 1778, 1779, 1780, 1781, 1782, 1784, 1785, 1787, 1788, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1798, 1800, 1803, 1805, 1806, 1807, 1808, 1810, 1812, 1814, 1815, 1817, 1818, 1819, 1820, 1821, 1823, 1824, 1825, 1826, 1827, 1830, 1831, 1832, 1833, 1836, 1837, 1838, 1946, 1959, 2001, 2007, 2011, 2013, 2016




.. GENERATED FROM PYTHON SOURCE LINES 115-116

Now that we have our data, let's move everything to torch. Note that, by default, this will try to look for a GPU.

.. GENERATED FROM PYTHON SOURCE LINES 116-123

.. code-block:: Python


    import torch

    # convert data
    device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
    X_g, y_g = torch.from_numpy(X_g).to(torch.float32).to(device), torch.from_numpy(y_g).to(torch.float32).to(device)








.. GENERATED FROM PYTHON SOURCE LINES 124-125

With the setup out of the way, let's create a quick pipeline to compute our neural and hypothesis RDMs:

.. GENERATED FROM PYTHON SOURCE LINES 125-151

.. code-block:: Python


    from mvpy.estimators import Scaler, RSA
    from mvpy.math import *
    from sklearn.pipeline import make_pipeline

    # compute the neural RDM with the following pipeline:
    #   1. Scale the data (zero mean and unit variance)
    #   2. Compute the neural RDM using the Pearson correlation as our similarity measure
    n_rsa = make_pipeline(Scaler().to_torch(),
                          RSA(estimator = pearsonr, 
                              verbose = True, 
                              n_jobs = None).to_torch())
    n_rsa.fit(X_g.mean(0))
    n_rsa.transform(X_g.mean(0))

    # compute the hypothesis RDM; pipeline is same as neural RDM
    h_rsa = make_pipeline(Scaler().to_torch(),
                             RSA(estimator = pearsonr, 
                                 verbose = True, 
                                 n_jobs = None).to_torch())
    h_rsa.fit(y_g.mean(0))
    h_rsa.transform(y_g.mean(0))

    # Now, let's compare the two RDMs we have so far by computing their spearman correlations
    rho_pr = spearmanr(n_rsa[1].rdm_.T, h_rsa[1].rdm_.T)





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

 .. code-block:: none


    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]
    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]

    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]
    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]

    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]
    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]

    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]
    Computing RDM...:   0%|          | 0/601 [00:00<?, ?it/s]




.. GENERATED FROM PYTHON SOURCE LINES 152-153

With our result in hand, we may find ourselves wanting to look at the results and some of the raw RSMs we computed. Let's do that now:

.. GENERATED FROM PYTHON SOURCE LINES 153-173

.. code-block:: Python


    fig, ax = plt.subplots(nrows = 1, ncols = 3, figsize = (9.0, 4.0))
    max_tp = rho_pr.argmax().cpu().numpy()

    t = np.arange(-0.1, 0.5+1e-3, 1e-3)     # time points
    ax[0].plot(t, 0*t, color = 'red')       # plot chance level
    ax[0].plot(t, rho_pr.cpu().numpy())     # plot spearman correlation
    ax[0].set_xlabel(r'Time ($s$)')
    ax[0].set_ylabel(r'Spearman $\rho$')

    ax[1].imshow(n_rsa[1].full_rdm().cpu().numpy()[:,:,max_tp], vmin = -1, vmax = 1, cmap = 'RdBu_r')   # obtain and plot the full neural RDM from our class
    ax[1].set_title(fr'nRDM at $t={np.round(max_tp*1e-3 - 0.1, 3)}s$')
    ax[1].set_ylabel('Categories')
    ax[1].set_xlabel('Categories')

    ax[2].imshow(h_rsa[1].full_rdm().cpu().numpy()[:,:,max_tp], vmin = -1, vmax = 1, cmap = 'RdBu_r')   # obtain and plot the full hypothesis RDM from our class
    ax[2].set_title(fr'hRDM at $t={np.round(max_tp*1e-3 - 0.1, 3)}s$')
    ax[2].set_ylabel('Categories')
    ax[2].set_xlabel('Categories')

    plt.tight_layout()


.. image-sg:: /examples/rsa/images/sphx_glr_1_rsa_intro_001.png
   :alt: nRDM at $t=0.151s$, hRDM at $t=0.151s$
   :srcset: /examples/rsa/images/sphx_glr_1_rsa_intro_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (3 minutes 27.923 seconds)


.. _sphx_glr_download_examples_rsa_1_rsa_intro.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 1_rsa_intro.ipynb <1_rsa_intro.ipynb>`

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

      :download:`Download Python source code: 1_rsa_intro.py <1_rsa_intro.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 1_rsa_intro.zip <1_rsa_intro.zip>`


.. only:: html

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

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