#!/usr/bin/env python 

from scipy import io
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os, glob
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages



flight_number = '670200106231735'
inMATFile = flight_number + '.mat'
raw_data = io.loadmat(inMATFile, struct_as_record=True, squeeze_me=False) # dict
test_matrix = np.array(raw_data) # ndarray


# Generate the time vector using a known 1Hz feature
abrk = raw_data['ABRK']['data'][0][0]
size = abrk.size
time = np.linspace(0,1,size)

# Generate Pressure Altitude data vector for reference
alt = raw_data['ALT']['data'][0][0]
alt = alt[0:alt.size:4]

# Generate the pages, one for each individual feature (total of 186 features/pages)
features = raw_data.keys()
with PdfPages(flight_number+'_plots.pdf') as pdf:
    for key in features:
        page = pdf.get_pagecount()
        page += 1
        
        # Filter out the 3 non-feature columns
        if key[0] == '_':
            continue
        
        # Assign the data vector and the sampling rate for the feature
        b1 = raw_data[key]['data'][0][0]
        rate = raw_data[key]['Rate'][0][0][0][0]
        long_description = raw_data[key]['Description'][0][0][0]
        
        # Create a figure instance (ie. a new page)
        fig = plt.figure(figsize=(11, 8.5), dpi=100)
        title = 'Feature ' + str(page) + ': ' + long_description
        fig.suptitle(title, fontsize=12, fontweight= 'bold')
        
        gs = gridspec.GridSpec(2,1, height_ratios=[5,1])
        
        # For those features with a sampling rate < 0, upsample
        if rate == 0.25:
            b1 = np.resize(b1,(size,1))
            ax1 = plt.subplot(gs[0])
            #plt.subplots_adjust(top=0.9)
            plt.plot(time,b1,'b')
            ax1.set_ylabel(key)

            ax2 = plt.subplot(gs[1])
            plt.plot(time,alt,'r')
            ax2.set_ylabel('altitude')
            #ax2.axis('off')
            ax2.set_yticklabels([])
            ax2.set_yticks([])
            ax2.set_xlabel('time (normalized)')
            ax2.text(0.5,0.01, flight_number, verticalalignment='bottom', 
            horizontalalignment='right', transform = ax2.transAxes, 
            color='green', fontsize=10)
            
            pdf.savefig()
            plt.close()
        
        # For those features with a sampling rate > 0, subsample    
        else:
            b1 = b1[0:b1.size:rate]
            ax1 = plt.subplot(gs[0])
            plt.plot(time,b1,'r')
            ax1.set_ylabel(key)
            
            ax2 = plt.subplot(gs[1])
            plt.plot(time,alt,'r')
            ax2.set_ylabel('altitude')
            #ax2.axis('off')
            ax2.set_yticklabels([])
            ax2.set_yticks([])
            ax2.set_xlabel('time (normalized)')
            ax2.text(0.5,0.01, flight_number, verticalalignment='bottom', 
            horizontalalignment='right', transform = ax2.transAxes, 
            color='green', fontsize=10)
            
            pdf.savefig()
            plt.close()