#!/usr/bin/env python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import os

import essentia
import essentia.standard
import essentia.streaming
import essentia.streaming as ess



# https://stackoverflow.com/questions/9622163/save-plot-to-image-file-instead-of-displaying-it-using-matplotlib
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plot

#from pylab import show, figure, imshow

import pylab

argc = len(sys.argv)

input_audio_filename     = None
output_filename_root = None

if argc <= 1:
# Python3 syntax:
    print("Usage: "+sys.argv[0] +" input_file [output_file]\n",file=sys.stderr)    
#    print "Usage: "+sys.argv[0] +" input_file [output_file]\n"   
    sys.exit(1)
else:
    input_audio_filename    = sys.argv[1]
    if argc == 2:
        output_filename_root = os.path.splitext(input_audio_filename)[0]
    else:
        output_filename_root = sys.argv[2]



# Initialize algorithms we will use
loader = ess.MonoLoader(filename=input_audio_filename)

framecutter = ess.FrameCutter(frameSize=4096, hopSize=2048, silentFrames='noise')
windowing = ess.Windowing(type='blackmanharris62')
spectrum = ess.Spectrum()
spectralpeaks = ess.SpectralPeaks(orderBy='magnitude',
                                  magnitudeThreshold=0.00001,
                                  minFrequency=20,
                                  maxFrequency=3500,
                                  maxPeaks=60)

# Use default HPCP parameters for plots, however we will need higher resolution
# and custom parameters for better Key estimation

hpcp = ess.HPCP()
hpcp_key = ess.HPCP(size=36, # we will need higher resolution for Key estimation
                    referenceFrequency=440, # assume tuning frequency is 44100.
                    bandPreset=False,
                    minFrequency=20,
                    maxFrequency=3500,
                    weightType='cosine',
                    nonLinear=False,
                    windowSize=1.)

key = ess.Key(profileType='edma', # Use profile for electronic music
              numHarmonics=4,
              pcpSize=36,
              slope=0.6,
              usePolyphony=True,
              useThreeChords=True)

# Use pool to store data
pool = essentia.Pool()

# Connect streaming algorithms
loader.audio >> framecutter.signal
framecutter.frame >> windowing.frame >> spectrum.frame
spectrum.spectrum >> spectralpeaks.spectrum
spectralpeaks.magnitudes >> hpcp.magnitudes
spectralpeaks.frequencies >> hpcp.frequencies
spectralpeaks.magnitudes >> hpcp_key.magnitudes
spectralpeaks.frequencies >> hpcp_key.frequencies
hpcp_key.hpcp >> key.pcp
hpcp.hpcp >> (pool, 'tonal.hpcp')
key.key >> (pool, 'tonal.key_key')
key.scale >> (pool, 'tonal.key_scale')
key.strength >> (pool, 'tonal.key_strength')

# Run streaming network
essentia.run(loader)



# Plot HPCP
pylab.imshow(pool['tonal.hpcp'].T, aspect='auto', origin='lower', interpolation='none')
#plot.title("HPCPs in frames (the 0-th HPCP coefficient corresponds to A)")
#show()


#plt.matshow(np.log(np.abs(constantq)),origin='lower', aspect='auto')
#aspect='auto' fits axis with the dimensions of the matrix

output_filename_hpcp = output_filename_root + "-hpcp.png"

# https://stackoverflow.com/questions/47147146/save-an-image-only-content-without-axes-or-anything-else-to-a-file-using-matl
#fig,ax = plot.subplots(1)
#fig.subplots_adjust(left=0,right=1,bottom=0,top=1)
#ax.axis('tight')
#ax.axis('off')

plot.axis('off')
plot.savefig(output_filename_hpcp , bbox_inches="tight", pad_inches=0) # , transparent=True)

# plot.savefig(output_filename_hpcp, bbox_inches="tight", pad_inches=0, transparent=True)

# fig.savefig('sp_xyz.png', dpi=300, frameon='false')

print("Estimated key and scale:", pool['tonal.key_key'] + " " + pool['tonal.key_scale'])