import mne
import glob
import argparse
import shutil
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import math
import pandas
import os
import re
from scipy import signal
from scipy.fftpack import fft, ifft
import pywt
from PyEMD import EMD


class WaveletTransform(object):
    """
    Class used to help create wavelet transform of EEG data
    """
    def __init__(self, **kwargs):
        """
        Default constructor
        """
        self.classes = kwargs['state']
        self.root_path = kwargs['root_path']
        self.data_helper = kwargs['data_helper']
        self.excluded_channels = kwargs['excluded_channels']

        # TODO: can make the following parameters passed as options
        #       for wavelet transform
        self.segment_time_size = 2
        self.scales = np.arange(1, 64)
        self.band_filter = [0.5, 40.0]
        self.vmin_vmax = [0.0, 4.0]

    def generate_wavelet_transform(self):
        """
        Function used to genereate the wavelet transform images from data
        """
        self.data_helper.clean_create_dir(self.root_path)

        for class_label in self.data_helper.all_files.keys():
            class_files = self.data_helper.all_files[class_label]

            class_root = os.path.join(self.root_path, str(class_label))
            self.data_helper.clean_create_dir(class_root)

            for eeg_file in class_files:
                # Load EEG data
                raw = self.data_helper.load_data(eeg_file)

                # Apply filter to data
                raw.filter(self.band_filter[0], self.band_filter[1], fir_design='firwin')

                # Create output dir to patient data
                filename_dir = eeg_file.split(os.path.sep)[-4:-3]
                patient_path = os.path.join(class_root, '/'.join(filename_dir))
                self.data_helper.clean_create_dir(patient_path)

                # iterate EEG data for patient
                self.iterate_eeg_data(data=raw, output_dir=patient_path)

    def iterate_eeg_data(self, **kwargs):
        """
        Function used to iterate data, and generate the wavelet transform images
        """
        raw_data = kwargs['data'].copy()
        kwargs['data'].load_data()
        data = kwargs['data'].to_data_frame()

        # Get list of channel  names
        channel_names = raw_data.ch_names

        # Sample Frequency
        fs = int(raw_data.info['sfreq'])

        # Status channel
        status_data = data['Status'].values

        # Wavelet Transform Parameters
        segment_size = fs * self.segment_time_size

        for channel in channel_names:
            if channel in self.excluded_channels:
                continue

            # Create output directory for channel image data
            channel_path = os.path.join(kwargs['output_dir'], channel)
            self.data_helper.clean_create_dir(channel_path)

            # counter for image names
            image_counter = 0

            # determine number of segments we are going to look at
            # to create the wavelet transformation for
            channel_data = data[channel].values
            size = len(channel_data)
            segments = int(size // segment_size)
            image_counter = 0

            for index in range(segments):
                lower_point = index * segment_size
                upper_point = lower_point + segment_size
                current_segment = channel_data[lower_point : upper_point]

                # cmor0.4-1.0
                coef, freq = pywt.cwt(np.array(current_segment), self.scales, 'cmor0.4-1.0')
                # flip image information along the x-axis
                #coef = np.flip(coef, axis=0)

                try:
                    output_file = os.path.join(channel_path, str(image_counter))

                    coef = np.log(abs(coef))

                    plt.pcolormesh(coef, vmin=self.vmin_vmax[0], vmax=self.vmin_vmax[1])
                    plt.show()

                    """
                        Modifying Plot settings
                    """
                    plt.axis('off')
                    figure = plt.gcf()
                    plt.savefig(output_file, bbox_inches='tight', pad_inches=0, dpi=100)
                    plt.clf()

                    image_counter += 1
                except FloatingPointError as e:
                    print('Caught divide by 0 error: {0}'.format(output_filepath))

import mne
import glob
import argparse
import shutil
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import math
import pandas
import os
import re
from scipy import signal
from scipy.fftpack import fft, ifft
import pywt
from PyEMD import EMD


class WaveletTransform(object):
    """
    Class used to help create wavelet transform of EEG data
    """
    def __init__(self, **kwargs):
        """
        Default constructor
        """
        self.classes = kwargs['state']
        self.root_path = kwargs['root_path']
        self.data_helper = kwargs['data_helper']
        self.excluded_channels = kwargs['excluded_channels']

        # TODO: can make the following parameters passed as options
        #       for wavelet transform
        self.segment_time_size = 2
        self.scales = np.arange(1, 64)
        self.band_filter = [0.5, 40.0]
        self.vmin_vmax = [0.0, 4.0]

    def generate_wavelet_transform(self):
        """
        Function used to genereate the wavelet transform images from data
        """
        self.data_helper.clean_create_dir(self.root_path)

        for class_label in self.data_helper.all_files.keys():
            class_files = self.data_helper.all_files[class_label]

            class_root = os.path.join(self.root_path, str(class_label))
            self.data_helper.clean_create_dir(class_root)

            for eeg_file in class_files:
                # Load EEG data
                raw = self.data_helper.load_data(eeg_file)

                # Apply filter to data
                raw.filter(self.band_filter[0], self.band_filter[1], fir_design='firwin')

                # Create output dir to patient data
                filename_dir = eeg_file.split(os.path.sep)[-4:-3]
                patient_path = os.path.join(class_root, '/'.join(filename_dir))
                self.data_helper.clean_create_dir(patient_path)

                # iterate EEG data for patient
                self.iterate_eeg_data(data=raw, output_dir=patient_path)

    def iterate_eeg_data(self, **kwargs):
        """
        Function used to iterate data, and generate the wavelet transform images
        """
        raw_data = kwargs['data'].copy()
        kwargs['data'].load_data()
        data = kwargs['data'].to_data_frame()

        # Get list of channel  names
        channel_names = raw_data.ch_names

        # Sample Frequency
        fs = int(raw_data.info['sfreq'])

        # Status channel
        status_data = data['Status'].values

        # Wavelet Transform Parameters
        segment_size = fs * self.segment_time_size

        for channel in channel_names:
            if channel in self.excluded_channels:
                continue

            # Create output directory for channel image data
            channel_path = os.path.join(kwargs['output_dir'], channel)
            self.data_helper.clean_create_dir(channel_path)

            # counter for image names
            image_counter = 0

            # determine number of segments we are going to look at
            # to create the wavelet transformation for
            channel_data = data[channel].values
            size = len(channel_data)
            segments = int(size // segment_size)
            image_counter = 0

            for index in range(segments):
                lower_point = index * segment_size
                upper_point = lower_point + segment_size
                current_segment = channel_data[lower_point : upper_point]

                # cmor0.4-1.0
                coef, freq = pywt.cwt(np.array(current_segment), self.scales, 'cmor0.4-1.0')
                # flip image information along the x-axis
                #coef = np.flip(coef, axis=0)

                try:
                    output_file = os.path.join(channel_path, str(image_counter))

                    coef = np.log(abs(coef))

                    plt.pcolormesh(coef, vmin=self.vmin_vmax[0], vmax=self.vmin_vmax[1])
                    plt.show()

                    """
                        Modifying Plot settings
                    """
                    plt.axis('off')
                    figure = plt.gcf()
                    plt.savefig(output_file, bbox_inches='tight', pad_inches=0, dpi=100)
                    plt.clf()

                    image_counter += 1
                except FloatingPointError as e:
                    print('Caught divide by 0 error: {0}'.format(output_filepath))

Class used to help create wavelet transform of EEG data Default constructor # TODO: can make the following parameters passed as options #       for wavelet transform Function used to genereate the wavelet transform images from data # Load EEG data # Apply filter to data # Create output dir to patient data # iterate EEG data for patient Function used to iterate data, and generate the wavelet transform images # Get list of channel  names # Sample Frequency # Status channel # Wavelet Transform Parameters # Create output directory for channel image data # counter for image names # determine number of segments we are going to look at # to create the wavelet transformation for # cmor0.4-1.0 # flip image information along the x-axis #coef = np.flip(coef, axis=0) Modifying Plot settings