import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

import numpy as np


def split_title_line(title_text, max_words=5):
	"""
	A function that splits any string based on specific character
	(returning it with the string), with maximum number of words on it
	"""
	seq = title_text.split()
	return '\n'.join([' '.join(seq[i:i + max_words]) for i in range(0, len(seq), max_words)])

def plot_alignment(alignment, path, info=None, split_title=False, max_len=None):
	if max_len is not None:
		alignment = alignment[:, :max_len]

	fig = plt.figure(figsize=(8, 6))
	ax = fig.add_subplot(111)

	im = ax.imshow(
		alignment,
		aspect='auto',
		origin='lower',
		interpolation='none')
	fig.colorbar(im, ax=ax)
	xlabel = 'Decoder timestep'
	if info is not None:
		if split_title:
			title = split_title_line(info)
		else:
			title = info
	plt.xlabel(xlabel)
	plt.title(title)
	plt.ylabel('Encoder timestep')
	plt.tight_layout()
	plt.savefig(path, format='png')
	plt.close()


def plot_spectrogram(pred_spectrogram, path, info=None, split_title=False, target_spectrogram=None, max_len=None, auto_aspect=False):
	if max_len is not None:
		target_spectrogram = target_spectrogram[:max_len]
		pred_spectrogram = pred_spectrogram[:max_len]

	if info is not None:
		if split_title:
			title = split_title_line(info)
		else:
			title = info

	fig = plt.figure(figsize=(10, 8))
	# Set common labels
	fig.text(0.5, 0.18, title, horizontalalignment='center', fontsize=16)

	#target spectrogram subplot
	if target_spectrogram is not None:
		ax1 = fig.add_subplot(311)
		ax2 = fig.add_subplot(312)

		if auto_aspect:
			im = ax1.imshow(np.rot90(target_spectrogram), aspect='auto', interpolation='none')
		else:
			im = ax1.imshow(np.rot90(target_spectrogram), interpolation='none')
		ax1.set_title('Target Mel-Spectrogram')
		fig.colorbar(mappable=im, shrink=0.65, orientation='horizontal', ax=ax1)
		ax2.set_title('Predicted Mel-Spectrogram')
	else:
		ax2 = fig.add_subplot(211)

	if auto_aspect:
		im = ax2.imshow(np.rot90(pred_spectrogram), aspect='auto', interpolation='none')
	else:
		im = ax2.imshow(np.rot90(pred_spectrogram), interpolation='none')
	fig.colorbar(mappable=im, shrink=0.65, orientation='horizontal', ax=ax2)

	plt.tight_layout()
	plt.savefig(path, format='png')
	plt.close()
