# -*- coding: utf-8 -*-
# pylint: disable=C0103
"""
==============================
Plot low-dimensional embedding
==============================

This example shows how to plot a low-dimensional embedding of the rhythmic patterns.

This is based on the rhythmic patterns analysis proposed in [CIM2014]_.
"""

# Code source: Martín Rocamora
# License: MIT

##############################################
# Imports
#   - matplotlib for visualization
#   - Axes3D from mpl_toolkits.mplot3d for 3D plots
#
from __future__ import print_function
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import carat

##############################################
# We compute the feature map of rhythmic patterns and we
# learn a manifold in a low--dimensional space.
# The patterns are they shown in the low--dimensional space
# before and after being grouped into clusters.
#
# 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)

##############################################
# Next, we compute a low-dimensional embedding of the rhythmic pattern. This is mainly done for
# visualization purposes. This representation can be useful to select the number of clusters, or
# to spot outliers. There are several approaches for dimensionality reduction among which isometric
# mapping, Isomap, was selected (other embedding methods can be also applied).
# Isomap is preferred since it is capable of keeping the levels of similarity among the original
# patterns after being mapped to the lower dimensional space. Besides, it allows the projection of
# new patterns onto the low-dimensional space.
#
# **Note 1:** You have to provide the number of dimensions to map on.
# Although any number of dimensions can be used to compute the embedding, only 2- and 3-dimensions
# plots are available (for obvious reasons).
#
# **Note 2:** 3D plots need Axes3D from mpl_toolkits.mplot3d

n_dims = 3
map_emb = carat.clustering.manifold_learning(map_acce, method='isomap', n_components=n_dims)

##############################################
# Finally we plot the low-dimensional embedding of the rhythmic patterns and the clusters obtained.

fig1 = plt.figure(figsize=(10, 8))
ax1 = fig1.add_subplot(111, projection='3d')
carat.display.embedding_plot(map_emb, ax=ax1, clusters=cluster_labs, s=30)
plt.tight_layout()

fig2 = plt.figure(figsize=(10, 8))
ax2 = fig2.add_subplot(111, projection='3d')
carat.display.embedding_plot(map_emb, ax=ax2, s=30)
plt.tight_layout()

plt.show()