# -*- coding: utf-8 -*-
# pylint: disable=C0103
"""
======================
Plot cluster centroids
======================

This example shows how to plot centroids of the clusters of rhythmic patterns.
"""

# Code source: Martín Rocamora
# License: MIT

##############################################
# Imports
#   - matplotlib for visualization
#
from __future__ import print_function
import matplotlib.pyplot as plt
import carat

##############################################
# We group rhythmic patterns into clusters and plot their centroids.
#
# First, we'll load one of the audio files included in `carat`.
audio_path = carat.util.example_audio_file(num_file=1)

y, sr = carat.audio.load(audio_path)

##############################################
# Next, we'll load the annotations provided for the example audio file.
annotations_path = carat.util.example_beats_file(num_file=1)

beats, beat_labs = carat.annotations.load_beats(annotations_path)
downbeats, downbeat_labs = carat.annotations.load_downbeats(annotations_path)

##############################################
# Then, we'll compute the accentuation feature.
#
# **Note:** This example is tailored towards the rhythmic patterns of the lowest
# sounding of the three drum types taking part in the recording, so the analysis
# focuses on the low frequencies (20 to 200 Hz).
acce, times, _ = carat.features.accentuation_feature(y, sr, minfreq=20, maxfreq=200)

##############################################
# Next, we'll compute the feature map.
n_beats = int(round(beats.size/downbeats.size))
n_tatums = 4

map_acce, _, _, _ = carat.features.feature_map(acce, times, beats, downbeats, n_beats=n_beats,
                                               n_tatums=n_tatums)

##############################################
# Then, we'll group rhythmic patterns into clusters. This is done using the classical
# K-means method with Euclidean distance (but other clustering methods and distance
# measures can be used too).
#
# **Note:** The number of clusters n_clusters has to be specified as an input parameter.
n_clusters = 4

cluster_labs, centroids, _ = carat.clustering.rhythmic_patterns(map_acce, n_clusters=n_clusters)

##############################################
# Finally we plot the centroids of the clusters of rhythmic patterns.

fig = plt.figure(figsize=(8, 8))
carat.display.centroids_plot(centroids, n_tatums=n_tatums)

plt.tight_layout()

plt.show()