"""

.. _decode_discrete:

============================
Discrete functional decoding
============================

Perform meta-analytic functional decoding on regions of interest.

We can use the methods in ``nimare.decode.discrete`` to apply functional
characterization analysis to regions of interest or subsets of the Dataset.
"""
import os

import nibabel as nib
import numpy as np
from nilearn.plotting import plot_roi

from nimare.dataset import Dataset
from nimare.decode import discrete
from nimare.utils import get_resource_path

###############################################################################
# Load dataset with abstracts
# -----------------------------------------------------------------------------
# We'll load a small dataset composed only of studies in Neurosynth with
# Angela Laird as a coauthor, for the sake of speed.
dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))
dset.annotations.head(5)

###############################################################################
# Create a region of interest
# -----------------------------------------------------------------------------

# First we'll make an ROI
arr = np.zeros(dset.masker.mask_img.shape, int)
arr[65:75, 50:60, 50:60] = 1
mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine)
plot_roi(mask_img, draw_cross=False)

# Get studies with voxels in the mask
ids = dset.get_studies_by_mask(mask_img)

###############################################################################
#
# .. _brain-map-decoder-example:
#
# Decode an ROI image using the BrainMap method
# -----------------------------------------------------------------------------

# Run the decoder
decoder = discrete.BrainMapDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by="probReverse", ascending=False).head()

###############################################################################
#
# .. _neurosynth-chi2-decoder-example:
#
# Decode an ROI image using the Neurosynth chi-square method
# -----------------------------------------------------------------------------

# Run the decoder
decoder = discrete.NeurosynthDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by="probReverse", ascending=False).head()

###############################################################################
#
# .. _neurosynth-roi-decoder-example:
#
# Decode an ROI image using the Neurosynth ROI association method
# -----------------------------------------------------------------------------

# This method decodes the ROI image directly, rather than comparing subsets of the Dataset like the
# other two.
decoder = discrete.ROIAssociationDecoder(mask_img)
decoder.fit(dset)

# The `transform` method doesn't take any parameters.
decoded_df = decoder.transform()

decoded_df.sort_values(by="r", ascending=False).head()