from __future__ import division, print_function

## steps 1. read wav file 2. get spec 3. get peaks of spectrogram 4. get those corresponding row/columns MFCC's

## import numpy because NUMPY

import numpy as np

## wav read imports

import scipy.io.wavfile as wav

##SPEC and MFCC imports

from features import mfcc

from features import logfbank

###plotting imports

from mpl_toolkits.mplot3d import Axes3D

from matplotlib import cm

from matplotlib.ticker import LinearLocator, FormatStrFormatter

import matplotlib.pyplot as plt

### peaks imports

import sys

sys.path.insert(1, r'./../functions') # add to pythonpath

from detect_peaks import detect_peaks

#get wav

(rate,sig) = wav.read("BF4.wav")

#MFCC

mfcc_feat_not_norm = mfcc(sig,rate)

max_mfcc = np.amax(mfcc_feat_not_norm)

mfcc_feat = (1/max_mfcc) * mfcc_feat_not_norm

mfcc_size = len(mfcc_feat[:,1]) # x dimensions MFCC

#Log Spec

fbank_feat_not_norm = logfbank(sig,rate)

max_log = np.amax(fbank_feat_not_norm)

fbank_feat = (1/max_log) * fbank_feat_not_norm

logSizeX = len(fbank_feat[1,:])# y dimensions log spec

logSizeY =len(fbank_feat[:,1])# x dimensions log spec

'''

#plotting Log Spec

fig = plt.figure(1)

ax = fig.add_subplot(2, 1, 1, projection='3d')

X = np.arange(0, logSizeX, 1)

Y = np.arange(0, logSizeY, 1)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = fbank_feat

ax.set_xlabel('Bank')

ax.set_ylabel('Time')

surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,

linewidth=0, antialiased=False)

#plt.show()

#PLOTTING mfcc

#fig = plt.figure()

ax = fig.add_subplot(2, 1, 2, projection='3d')

X2 = np.arange(1,14 , 1)

Y2 = np.arange(0, mfcc_size, 1)

X2, Y2 = np.meshgrid(X2, Y2)

Z2 = mfcc_feat

surf = ax.plot_surface(Y2, X2, Z2, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)

ax.set_xlabel('Time')

ax.set_ylabel('MFCC')

plt.show()

'''

#getting peaks from spec, this will give us an array with all the time slices we need to get MFCCs for

spec_peaks_array = [];

print(len(fbank_feat[1,:]))

print(len(fbank_feat[:,1]))

for n in range (0,logSizeX):

ind = detect_peaks(fbank_feat[:,n], mph = 0.8, mpd = 10)

spec_peaks_array = np.concatenate((ind, spec_peaks_array), axis = 0)

print(spec_peaks_array)

print(len(spec_peaks_array))

#spec_peaks_array is a list of the time coordinates of the peaks for the call. Theses are the locations we need to get the MFCC's for

##get rid of duplications in spec_peaks array

spec_peaks = list(set(spec_peaks_array))

print(spec_peaks)

print(len(spec_peaks))

#ratio wanted 5peaks/100time = 0.05 peaks/time

ratio = len(spec_peaks)/logSizeY

print(ratio)

#for quiet samples lower the min peak height to 0.6

if ratio < 0.05:

#getting peaks from spec, this will give us an array with all the time slices we need to get MFCCs for

spec_peaks_array = [];

print(len(fbank_feat[1,:]))

print(len(fbank_feat[:,1]))

for n in range (0,logSizeX):

ind = detect_peaks(fbank_feat[:,n], mph = 0.6, mpd = 10)

spec_peaks_array = np.concatenate((ind, spec_peaks_array), axis = 0)

print(spec_peaks_array)

print(len(spec_peaks_array))

#spec_peaks_array is a list of the time coordinates of the peaks for the call. Theses are the locations we need to get the MFCC's for

##get rid of duplications in spec_peaks array

spec_peaks = list(set(spec_peaks_array))

#print(spec_peaks)

#print(len(spec_peaks))

MFCC_features = np.empty([len(spec_peaks), 13]);

for counter in range(0, len(spec_peaks)):

time_slice = spec_peaks[counter]

temp =mfcc_feat[time_slice, :]

#print(temp.transpose())

MFCC_features[counter] = temp.transpose()

MFCC_features = np.array(MFCC_features)

np.savetxt('new_call_mfcc_noUI.txt', MFCC_features, fmt = '%7.7f')