"""

.. _annotations_cogat:

===================
The Cognitive Atlas
===================

Download the Cognitive Atlas and extract CogAt terms from text.
"""
import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from nimare import annotate, extract
from nimare.dataset import Dataset
from nimare.utils import get_resource_path

###############################################################################
# Load dataset with abstracts
# -----------------------------------------------------------------------------
dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))

###############################################################################
# Download Cognitive Atlas
# -----------------------------------------------------------------------------
cogatlas = extract.download_cognitive_atlas(data_dir=get_resource_path(), overwrite=False)
id_df = pd.read_csv(cogatlas["ids"])
rel_df = pd.read_csv(cogatlas["relationships"])

###############################################################################
# ID DataFrame
id_df.head()

###############################################################################
# Relationships DataFrame
rel_df.head()

###############################################################################
# Extract Cognitive Atlas terms from text
# -----------------------------------------------------------------------------
counts_df, rep_text_df = annotate.cogat.extract_cogat(dset.texts, id_df, text_column="abstract")

###############################################################################
# Expand counts
# -------------
weights = {"isKindOf": 1, "isPartOf": 1, "inCategory": 1}
expanded_df = annotate.cogat.expand_counts(counts_df, rel_df, weights)
# Sort by total count and reduce for better visualization
series = expanded_df.sum(axis=0)
series = series.sort_values(ascending=False)
series = series[series > 0]
columns = series.index.tolist()

###############################################################################
# Make some plots
# -----------------------------------------------------------------------------
# We will reduce the dataframes to only columns with at least one count to make
# visualization easier.

# Raw counts
fig1, ax1 = plt.subplots(figsize=(16, 8))
pos = ax1.imshow(counts_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values))
fig1.colorbar(pos, ax=ax1)
ax1.set_title("Counts Before Expansion", fontsize=20)
ax1.set_yticks(range(counts_df.shape[0]))
ax1.set_yticklabels(counts_df.index)
ax1.set_ylabel("Study", fontsize=16)
ax1.set_xticks(range(len(columns)))
ax1.set_xticklabels(columns, rotation=90)
ax1.set_xlabel("Cognitive Atlas Term", fontsize=16)
fig1.tight_layout()
fig1.show()

# Expanded counts
fig2, ax2 = plt.subplots(figsize=(16, 8))
pos = ax2.imshow(
    expanded_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values)
)
fig2.colorbar(pos, ax=ax2)
ax2.set_title("Counts After Expansion", fontsize=20)
ax2.set_yticks(range(counts_df.shape[0]))
ax2.set_yticklabels(counts_df.index)
ax2.set_ylabel("Study", fontsize=16)
ax2.set_xticks(range(len(columns)))
ax2.set_xticklabels(columns, rotation=90)
ax2.set_xlabel("Cognitive Atlas Term", fontsize=16)
fig2.tight_layout()
fig2.show()