Added SWARII for python 2.7

# -*- coding: utf-8 -*-

"""

Copyright 2016 Julien Audiffren

This code is related to the paper :

[Preprocessing of the Nintendo Wii Board Signal to Derive More

Accurate Descriptors of Statokinesigrams, Audiffren and Contal] 

If you use this algorithm in your research work, please cite this paper.

Licence :

This program is free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or any later

 version.

This program is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this program.  If not, see <http://www.gnu.org/licenses/>.

"""

from resampling import SWARII

import pylab as plb

import numpy as np

def parse_wbb_file(file_adress):

    time = []

    signal = []

    f = open(file_adress,'rt+')

    l = f.readline()

    l = f.readline()

    while len(l)>0:

        data = l.split(" ")

        t = 0.001*float(data[0])

        x = float(data[1])

        y = float(data[2])

        time.append(t)

        signal.append([x,y])

        l=f.readline()

    return np.array(time),np.array(signal)

if __name__=='__main__':

    resampling_method = SWARII(window_size=0.25,desired_frequency=25)

    time,signal = parse_wbb_file('example_wbb_statokinesigram.txt')

    resampled_time, resampled_signal = resampling_method.resample(time,signal)

    fig,ax = plb.subplots(2)

    ax[0].plot(time,signal[:,0],c='b',label='original signal')

    ax[0].plot(resampled_time,resampled_signal[:,0],c='r',label='resampled signal')

    ax[0].set_xlabel("time")

    ax[0].set_ylabel("X coordinate")

    ax[0].set_title("X")

    ax[0].legend()

    ax[1].plot(time,signal[:,1],c='b',label='original signal')

    ax[1].plot(resampled_time,resampled_signal[:,1],c='r',label='resampled signal')

    ax[1].set_xlabel("time")

    ax[1].set_ylabel("Y coordinate")

    ax[1].set_title("Y")

    ax[1].legend()

    plb.show()

# -*- coding: utf-8 -*-

"""

Copyright 2016 Julien Audiffren

This code is related to the paper :

[Preprocessing of the Nintendo Wii Board Signal to Derive More

Accurate Descriptors of Statokinesigrams, Audiffren and Contal] 

If you use this algorithm in your research work, please cite this paper.

Licence :

This program is free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or any later

 version.

This program is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this program.  If not, see <http://www.gnu.org/licenses/>.

"""

import numpy as np

class SWARII:

    """

    Implementation of the Sliding Windows Weighted Averaged Interpolation method

    How To use :

        First instantiate the class with the desired parameters

        Then call resample on the desired signal

    """

    def __init__(self, window_size=1, desired_frequency=25):

        """

        Instantiate SWARII 

        Parameters :

            desired_frequency : The frequency desired for the output signal,

                                after the resampling.

            window_size : The size of the sliding window, in seconds.

        """

        self.desired_frequency = desired_frequency

        self.window_size = window_size

    def resample(self, time, signal):

        """

        Apply the SWARII to resample a given signal.

        Input :

            time:   The time stamps of the the data point in the signal. A 1-d

                    array of shape n, where n is the number of points in the

                    signal. The unit is seconds.

            signal: The data points representing the signal. A k-d array of

                    shape (n,k), where n is the number of points in the signal,

                    and k is the dimension of the signal (e.g. 2 for a

                    statokinesigram).

        Output: 

            resampled_time : The time stamps of the signal after the resampling

            resampled_signal : The resampled signal.

        """

        a_signal=np.array(signal)

        current_time = max(0.,time[0]) 

        output_time=[]

        output_signal = []

        while current_time < time[-1]:

            relevant_times = [t for t in range(len(time)) if abs(

                time[t] - current_time) < self.window_size * 0.5]

            assert len(relevant_times) > 0,"Trying to interpolate an empty window !"

            if len(relevant_times) == 1:

                value = a_signal[relevant_times[0]]

            else :

                value = 0

                weight = 0

                for i, t in enumerate(relevant_times):

                    if i == 0 or t==0:

                        left_border = max(

                            time[0], (current_time - self.window_size * 0.5))

                    else:

                        left_border = 0.5 * (time[t] + time[t - 1])

                    if i == len(relevant_times) - 1:

                        right_border = min(

                            time[-1], current_time + self.window_size * 0.5)

                    else:

                        right_border = 0.5 * (time[t + 1] + time[t])

                    w = right_border - left_border

                    value += a_signal[t] * w

                    weight += w

                value /= weight

            output_time.append(current_time)

            output_signal.append(value)

            current_time += 1. / self.desired_frequency

        return np.array(output_time),np.array(output_signal)

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