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# Copyright 2014 Jason Heeris, [e-mail]
# 
# This file is part of the gammatone toolkit, and is licensed under the 3-clause
# BSD license: https://github.com/detly/gammatone/blob/master/COPYING
from __future__ import division
import numpy as np

from .filters import make_erb_filters, centre_freqs, erb_filterbank

"""
This module contains functions for rendering "spectrograms" which use gammatone
filterbanks instead of Fourier transforms.
"""
'''
Modified from the original toolbox. Jan 2015. --Mi

'''

def round_half_away_from_zero(num):
	""" Implement the round-half-away-from-zero rule, where fractional parts of
	0.5 result in rounding up to the nearest positive integer for positive
	numbers, and down to the nearest negative number for negative integers.
	"""
	return np.sign(num) * np.floor(np.abs(num) + 0.5)


def gtgram_strides(fs, gammatoneLen, step_rate, filterbank_cols):
	"""
	Calculates the window size for a gammatonegram.
	
	@return a tuple of (window_size, hop_samples, output_columns)
	"""
	step_samples = int(gammatoneLen * step_rate)
	columns = (1 + int(np.floor((filterbank_cols - gammatoneLen)/ step_samples)))
		
	return (step_samples, columns)


def gtgram_xe(wave, fs, f_max, channels, f_min):
	""" Calculate the intermediate ERB filterbank processed matrix """
	cfs = centre_freqs(f_max, channels, f_min)
	fcoefs = np.flipud(make_erb_filters(fs, cfs))
	xf = erb_filterbank(wave, fcoefs)
	return xf
	# xe = np.power(xf, 2)
	# return xe


def gtgram(wave, fs, gammatoneLen, step_rate, channels, f_max, f_min):
	"""
	Calculate a spectrogram-like time frequency magnitude array based on
	gammatone subband filters. The waveform ``wave`` (at sample rate ``fs``) is
	passed through an multi-channel gammatone auditory model filterbank, with
	lowest frequency ``f_min`` and highest frequency ``f_max``. The outputs of
	each band then have their energy integrated over windows of ``window_time``
	seconds, advancing by ``hop_time`` secs for successive columns. These
	magnitudes are returned as a nonnegative real matrix with ``channels`` rows.
	
	| 2009-02-23 Dan Ellis [e-mail]
	|
	| (c) 2013 Jason Heeris (Python implementation)
	"""
	xe = gtgram_xe(wave, fs, f_max, channels, f_min)	 
	# print 'xe', xe.shape
	step_samples, ncols = gtgram_strides(fs, gammatoneLen, step_rate, xe.shape[1])
	# print gammatoneLen, step_samples, channels, ncols
	y = np.zeros((channels, ncols))
	
	for cnum in range(ncols):
		segment = xe[:, cnum * step_samples + np.arange(gammatoneLen)]
		segment = np.power(segment, 2)
		y[:, cnum] = np.sqrt(segment.mean(1))
	
	return y.T