<gh_stars>0
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from collections import deque
import statistics
import sys
import pandas as pd
import seaborn as sns
import bisect

from sklearn.svm import SVC
from sklearn import decomposition


class Parser(object):
    """
    Given filename, this static class could parse them into useful information.
    """
    PAGESIZE = 100
    TOP_PEAK_PERCENT = 10
    DATA_FOLDER_PATH = 'recorded_original_data//'
    MEAN_GAP_DIM = 1

    @staticmethod
    def read(filename):
        """
        Load file(.csv) and store them.

        :param filename: filename string "without" extension.
        :return: n*1 dimension list
        """
        records = Parser.__load_csv(filename)
        return np.array(records)

    @staticmethod
    def parse(buffer):
        """
        Do PCA with some filters; e.g. discard a noise axis.

        :param buffer: n*1 dimension list
        :return: n*1 dimension list
        """
        pca, means, components = Parser.__get_pca(buffer, 1)
        return pca, means, components

    @staticmethod
    def __find_gaps(peaks, valleys):
        pos = int(Parser.PAGESIZE * Parser.TOP_PEAK_PERCENT / 100)
        peak_ave = np.mean(peaks[-pos:])
        valley_ave = np.mean(valleys[:pos])
        return peak_ave - valley_ave

    @staticmethod
    def get_gaps_curve(raw_data):
        """
        Find gaps for the input data.

        :param raw_data:
        :return:
        """
        peaks = []
        valleys = []
        gaps = []
        # process the first window; i.e., the first PAGESIZE rows of data
        for j in range(1, Parser.PAGESIZE):
            if raw_data[j] > raw_data[j - 1] and raw_data[j] > raw_data[j + 1]:
                bisect.insort_left(peaks, raw_data[j], bisect.bisect_left(peaks, raw_data[j]))
            elif raw_data[j] < raw_data[j - 1] and raw_data[j] < raw_data[j + 1]:
                bisect.insort_left(valleys, raw_data[j], bisect.bisect_left(valleys, raw_data[j]))

        gaps.append(Parser.__find_gaps(peaks, valleys))

        # slide from start to end
        for j in range(Parser.PAGESIZE, len(raw_data)):
            s = j - Parser.PAGESIZE + 1
            if raw_data[s] > raw_data[s - 1] and raw_data[s] > raw_data[s + 1]:
                del peaks[bisect.bisect_left(peaks, raw_data[s])]
            elif raw_data[s] < raw_data[s - 1] and raw_data[s] < raw_data[s + 1]:
                del valleys[bisect.bisect_left(valleys, raw_data[s])]

            e = j - 1
            if raw_data[e] > raw_data[e - 1] and raw_data[e] > raw_data[e + 1]:
                bisect.insort_left(peaks, raw_data[e], bisect.bisect_left(peaks, raw_data[e]))
            elif raw_data[e] < raw_data[e - 1] and raw_data[e] < raw_data[e + 1]:
                bisect.insort_left(valleys, raw_data[e], bisect.bisect_left(valleys, raw_data[e]))
            gaps.append(Parser.__find_gaps(peaks, valleys))

        return gaps

    @staticmethod
    def __get_pca(records, n):
        pca = decomposition.PCA(n_components=n)
        pca.fit(records)
        print('mean = ' + str(pca.mean_))
        print('components = ' + str(pca.components_))
        return pca, pca.mean_, pca.components_

    @staticmethod
    def __load_csv(filename):
        """
        spider from csv which we experiment, then stored them into a list (n*3 dimension)

        :param filename: filename string "without" extension.
        """
        fp = open(Parser.DATA_FOLDER_PATH + filename + '.csv', 'r')
        records = []
        for line in fp:
            items = line.strip().split(',')
            x, y, z = '0', '0', '0'
            if len(items) > 1:
                x = items[1]
            if len(items) > 2:
                y = items[2]
            if len(items) > 3:
                z = items[3]

            values = [x, y, z]
            records.append(values)

        # Discard some beginning data which may be noisy
        # del records[:int(len(records) / 30)]
        n = len(records)

        for i in range(n):
            rec = []
            # Consider X, Y, Z axes
            for k in range(3):
                # If can convert string to float
                try:
                    val = float(records[i][k])
                except ValueError:
                    val = 0
                rec.append(val)

            # Replace it
            records[i] = rec
        return records

    @staticmethod
    def find_peaks_sorted(xs, ratio=TOP_PEAK_PERCENT):
        peaks = []
        pagesize = len(xs)

        for j in range(1, pagesize - 1):
            now = xs[j]
            prevv = xs[j - 1]
            nextt = xs[j + 1]
            # peak detected
            if now > prevv and now > nextt:
                # stored absolute value
                peaks.append(now)
        if len(peaks) == 0:
            peaks.append(0)
        peaks.sort()
        peaks.reverse()
        peaks = peaks[:int(pagesize * ratio / 100)]
        return peaks

    @staticmethod
    def find_valley_sorted(xs, ratio=TOP_PEAK_PERCENT):
        valleys = []
        pagesize = len(xs)

        for j in range(1, pagesize - 1):
            now = xs[j]
            prevv = xs[j - 1]
            nextt = xs[j + 1]
            # valley detected
            if now < prevv and now < nextt:
                valleys.append(now)
        if len(valleys) == 0:
            valleys.append(0)
        valleys.sort()
        valleys = valleys[:int(pagesize * ratio / 100)]
        return valleys


class Model(object):
    """

    """

    _FOLD_COUNT = 5
    _SAMPLE_RATE = 20

    def __init__(self, filename, labels):
        self._filename = filename
        self._labels = labels
        self._mode = len(self._labels)

        file_list = []
        for i in range(self._mode):
            file_list.append(filename + '_' + self._labels[i])

        self._original_data = []
        for i in range(self._mode):
            self._original_data.append(Parser.read(file_list[i]))

        self._raw_data = []
        self._components = []
        self._means = []

    def run(self, time_interval):
        """
        PCA

        :return:
        """
        if self._mode > 1:
            print('Error: Only accept at only 1 file.')
            sys.exit(2)

        pca, mean, comp = Parser.parse(self._original_data[0][:time_interval * Model._SAMPLE_RATE])
        self._raw_data.append(pca.transform(self._original_data[0][:time_interval * Model._SAMPLE_RATE]))
        self._means.append(mean)
        self._components.append(comp)

        gaps = Parser.get_gaps_curve(self._raw_data[0])
        mean = statistics.mean(gaps)
        std = statistics.pstdev(gaps)
        print(mean, std)

        sns.distplot(gaps, bins=20)
        PresentationModel.write_to_file(self._components, mean, std)
        return mean, std


class PresentationModel(object):
    """
    """
    TRAINING_MODEL_FILE = 'motorcycle.txt'
    TARGET_FILE = 'prediction.txt'

    _POOL_SIZE = 20
    _BUFFER_SIZE = 20

    def __init__(self, training_model_file, pool_size=_POOL_SIZE, buffer_size=_BUFFER_SIZE):
        self._pool_size = pool_size
        self._buffer_size = buffer_size

        # read feature to build SVM
        fp = open(training_model_file, 'r')

        self._components = []
        for token in fp.readline().split(','):
            self._components.append(float(token))

        self._mean = float(fp.readline())
        self._std = float(fp.readline())

    @staticmethod
    def write_to_file(components, mean, std):
        fp = open(PresentationModel.TRAINING_MODEL_FILE, 'w')
        for i in range(len(components)):
            PresentationModel.__write_by_line(fp, components[i][0])
        fp.write(str(mean) + '\n')
        fp.write(str(std) + '\n')
        fp.close()

    @staticmethod
    def __write_by_line(fp, xs):
        n = len(xs)
        for i in range(n):
            fp.write(str(xs[i]))
            fp.write(',') if i < n - 1 else fp.write('\n')

    def pca_combine(self, data_list):
        pcas = []
        for i in range(len(data_list[0])):
            pca = 0
            for k in range(3):
                pca += data_list[k][i] * self._components[k]
            pcas.append(pca)
        return pcas

    def predict(self, x, k):
        if abs(x - self._mean) <= k * self._std:
            return 0
        return 1


class AnalogData(object):
    """
    class that holds analog data for N samples
    """
    # con-str
    def __init__(self, max_len):
        self.ax = deque([0.0] * max_len)
        self.ay = deque([0.0] * max_len)
        self.az = deque([0.0] * max_len)
        self.maxLen = max_len

    # ring buffer
    def add_tp_buf(self, buf, val):
        if len(buf) < self.maxLen:
            buf.append(val)
        else:
            buf.pop()
            buf.appendleft(val)

    # add data
    def add(self, data):
        assert(len(data) == 3)
        self.add_tp_buf(self.ax, data[0])
        self.add_tp_buf(self.ay, data[1])
        self.add_tp_buf(self.az, data[2])

    def merge_to_list(self):
        tmps = [[], [], []]
        tmps[0] = list(self.ax)
        tmps[1] = list(self.ay)
        tmps[2] = list(self.az)
        return tmps


class Drawer(object):

    COLORS = ['blue', 'orange', 'green', 'red']

    @staticmethod
    def plot_envelope_prob(mean, std, gaps):
        xs = []
        ys = []
        for K in range(0, 50 + 1, 2):
            K /= 10
            hit = 0
            for j in range(len(gaps)):
                if abs(gaps[j] - mean) <= K * std:
                    hit += 1
            hit_ratio = hit / len(gaps)
            xs.append(K)
            ys.append(hit_ratio)

        df = pd.DataFrame(data={
            'K': xs,
            'hitRatio': ys
        })
        print(df)
        f, ax = plt.subplots(1, 1)
        ax.set_title('Probability of gaps dropping within range(motor_0504_4Y7M_2_HOOK)')
        sns.pointplot('K', 'hitRatio', data=df, title='sss')
        plt.savefig('hitRatio(motor_0504_4Y7M_2_HOOK)240s.png')
        plt.show()

    @staticmethod
    def plot_2d_scatter_origin(raw_data, index, title='', suffix=''):
        # pre-process
        dim = len(raw_data)

        rd = [[], [], []]
        for k in range(dim):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        marks = ['X', 'Y', 'Z']
        for i in range(3):
            for j in range(i + 1, 3):
                fig, ax = plt.subplots()
                x_label = 'acceleration at ' + marks[i] + ' axis (mg)'
                y_label = 'acceleration at ' + marks[j] + ' axis (mg)'
                plt.xlabel(x_label)
                plt.ylabel(y_label)
                ax.set_title('Scatters of Original Data in 2D (' + title + '_' + suffix + ')'
                             + '[' + marks[i] + marks[j] + ']')
                x = rd[i]
                y = rd[j]
                plt.scatter(x, y, label=marks[i] + marks[j], color=Drawer.COLORS[index], alpha=0.2)
                ax.legend()
                plt.savefig(title + '_' + suffix + '[' + marks[i] + marks[j] + ']' + '2d.png')

    @staticmethod
    def plot_2d_scatter_mean_gap(raw_data, title='', labels=[], suffix=''):
        fig, ax = plt.subplots()
        plt.xlabel('meanGap1 (mg)')
        plt.ylabel('meanGap2 (mg)')
        ax.set_title('Scatters of Mean Gaps in 2D (' + title + ')' + '[' + suffix + ']')

        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        for i in range(dim):
            gap_list = []
            for k in range(len(data_list[i])):
                gap_list.append(Parser.get_gaps_curve(data_list[i][k]))

            now_list = [[], []]
            for j in range(len(gap_list)):
                for k in range(2):
                    now_list[k].append(gap_list[j][k])

            plt.scatter(now_list[0], now_list[1], label=labels[i])

        ax.legend()

        plt.savefig(title + '[' + suffix + ']' + '2D-mean-gap.png')
        # plt.show()

    @staticmethod
    def plot_3d_scatter_origin(raw_data, index, title='', suffix=''):
        fig = plt.figure()
        ax = Axes3D(fig)
        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        ax.set_xlabel('acceleration at X axis (mg)')
        ax.set_ylabel('acceleration at Y axis (mg)')
        ax.set_zlabel('acceleration at Z axis (mg)')
        ax.set_title('Scatters of Original Data in 3D (' + title + '_' + suffix + ')')

        rd = [[], [], []]
        for k in range(len(raw_data)):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        ax.scatter(rd[0], rd[1], rd[2], color=Drawer.COLORS[index], label='XYZ')

        ax.legend()

        plt.savefig(title + '[' + suffix + ']' + '3D-origin.png')
        # plt.show()

    @staticmethod
    def plot_3d_scatter(raw_data, title='', labels=[], suffix=''):
        fig = plt.figure()
        ax = Axes3D(fig)
        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        ax.set_xlabel('meanGap1 (mg)')
        ax.set_ylabel('meanGap2 (mg)')
        ax.set_zlabel('meanGap3 (mg)')
        ax.set_title('Scatters of Mean Gaps in 3D (' + title + ')' + '[' + suffix + ']')

        for i in range(dim):
            gap_list = []
            for k in range(len(data_list[i])):
                gap_list.append(Parser.get_gaps_curve(data_list[i][k]))

            now_list = [[], [], []]
            for j in range(len(gap_list)):
                for k in range(3):
                    now_list[k].append(gap_list[j][k])

            ax.scatter(now_list[0], now_list[1], now_list[2], color=Drawer.COLORS[i], label=labels[i])

        ax.legend()
        # plt.show()

    @staticmethod
    def draw_xyz(raw_data, index, filename='', label='', suffix=''):
        x_label = 'time_stamp (s/20)'
        y_label = 'acceleration (mg)'
        title = 'Original Data of X,Y,Z (' + filename + '_' + label + ') [' + suffix + ']'

        fig, ax = plt.subplots(3, sharex='all', sharey='all')

        rd = [[], [], []]
        for k in range(len(raw_data)):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        plt.xlabel(x_label)
        plt.ylabel(y_label)
        # plt.ylim(-50, 50)
        ax[0].set_title(title)

        axis_labels = ['X', 'Y', 'Z']
        for i in range(3):
            x = np.arange(0, len(rd[i]))
            y = rd[i]

            ax[i].plot(x, y, color=Drawer.COLORS[index], label=axis_labels[i])
            ax[i].legend()

        plt.savefig(title + 'xyz.png')
        # plt.show()

    @staticmethod
    def draw_line_chart(raw_data, filename='', labels=[], suffix=''):
        title = 'PCA Value (' + filename + ') [' + suffix + ']'
        fig, ax = plt.subplots()

        data_list = []
        # pre-process
        for i in range(len(raw_data)):
            data_list.append(Parser.sliding(raw_data[i]))

        plt.xlabel('time_stamp (20/s)')
        plt.ylabel('PCA_value (mg)')

        for i in range(len(raw_data)):
            peaks_list = []
            valleys_list = []
            for k in range(len(data_list[i])):
                fragment = data_list[i][k]
                peaks = Parser.find_peaks_sorted(fragment)
                valleys = Parser.find_valley_sorted(fragment)
                if len(peaks) == 0:
                    peaks.append(0)
                if len(valleys) == 0:
                    valleys.append(0)
                peaks_list.append(np.mean(peaks))
                valleys_list.append(np.mean(valleys))

            X = np.arange(0, len(peaks_list))
            ax.plot(X, peaks_list, label='peak_' + labels[i], color=Drawer.COLORS[i])
            ax.plot(X, valleys_list, '--', label='valley_' + labels[i], color=Drawer.COLORS[i])
        ax.legend()
        ax.set_title(title)

        plt.savefig(title + 'line_chart.png')
        # plt.show()

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from collections import deque
import statistics
import sys
import pandas as pd
import seaborn as sns
import bisect

from sklearn.svm import SVC
from sklearn import decomposition


class Parser(object):
    """
    Given filename, this static class could parse them into useful information.
    """
    PAGESIZE = 100
    TOP_PEAK_PERCENT = 10
    DATA_FOLDER_PATH = 'recorded_original_data//'
    MEAN_GAP_DIM = 1

    @staticmethod
    def read(filename):
        """
        Load file(.csv) and store them.

        :param filename: filename string "without" extension.
        :return: n*1 dimension list
        """
        records = Parser.__load_csv(filename)
        return np.array(records)

    @staticmethod
    def parse(buffer):
        """
        Do PCA with some filters; e.g. discard a noise axis.

        :param buffer: n*1 dimension list
        :return: n*1 dimension list
        """
        pca, means, components = Parser.__get_pca(buffer, 1)
        return pca, means, components

    @staticmethod
    def __find_gaps(peaks, valleys):
        pos = int(Parser.PAGESIZE * Parser.TOP_PEAK_PERCENT / 100)
        peak_ave = np.mean(peaks[-pos:])
        valley_ave = np.mean(valleys[:pos])
        return peak_ave - valley_ave

    @staticmethod
    def get_gaps_curve(raw_data):
        """
        Find gaps for the input data.

        :param raw_data:
        :return:
        """
        peaks = []
        valleys = []
        gaps = []
        # process the first window; i.e., the first PAGESIZE rows of data
        for j in range(1, Parser.PAGESIZE):
            if raw_data[j] > raw_data[j - 1] and raw_data[j] > raw_data[j + 1]:
                bisect.insort_left(peaks, raw_data[j], bisect.bisect_left(peaks, raw_data[j]))
            elif raw_data[j] < raw_data[j - 1] and raw_data[j] < raw_data[j + 1]:
                bisect.insort_left(valleys, raw_data[j], bisect.bisect_left(valleys, raw_data[j]))

        gaps.append(Parser.__find_gaps(peaks, valleys))

        # slide from start to end
        for j in range(Parser.PAGESIZE, len(raw_data)):
            s = j - Parser.PAGESIZE + 1
            if raw_data[s] > raw_data[s - 1] and raw_data[s] > raw_data[s + 1]:
                del peaks[bisect.bisect_left(peaks, raw_data[s])]
            elif raw_data[s] < raw_data[s - 1] and raw_data[s] < raw_data[s + 1]:
                del valleys[bisect.bisect_left(valleys, raw_data[s])]

            e = j - 1
            if raw_data[e] > raw_data[e - 1] and raw_data[e] > raw_data[e + 1]:
                bisect.insort_left(peaks, raw_data[e], bisect.bisect_left(peaks, raw_data[e]))
            elif raw_data[e] < raw_data[e - 1] and raw_data[e] < raw_data[e + 1]:
                bisect.insort_left(valleys, raw_data[e], bisect.bisect_left(valleys, raw_data[e]))
            gaps.append(Parser.__find_gaps(peaks, valleys))

        return gaps

    @staticmethod
    def __get_pca(records, n):
        pca = decomposition.PCA(n_components=n)
        pca.fit(records)
        print('mean = ' + str(pca.mean_))
        print('components = ' + str(pca.components_))
        return pca, pca.mean_, pca.components_

    @staticmethod
    def __load_csv(filename):
        """
        spider from csv which we experiment, then stored them into a list (n*3 dimension)

        :param filename: filename string "without" extension.
        """
        fp = open(Parser.DATA_FOLDER_PATH + filename + '.csv', 'r')
        records = []
        for line in fp:
            items = line.strip().split(',')
            x, y, z = '0', '0', '0'
            if len(items) > 1:
                x = items[1]
            if len(items) > 2:
                y = items[2]
            if len(items) > 3:
                z = items[3]

            values = [x, y, z]
            records.append(values)

        # Discard some beginning data which may be noisy
        # del records[:int(len(records) / 30)]
        n = len(records)

        for i in range(n):
            rec = []
            # Consider X, Y, Z axes
            for k in range(3):
                # If can convert string to float
                try:
                    val = float(records[i][k])
                except ValueError:
                    val = 0
                rec.append(val)

            # Replace it
            records[i] = rec
        return records

    @staticmethod
    def find_peaks_sorted(xs, ratio=TOP_PEAK_PERCENT):
        peaks = []
        pagesize = len(xs)

        for j in range(1, pagesize - 1):
            now = xs[j]
            prevv = xs[j - 1]
            nextt = xs[j + 1]
            # peak detected
            if now > prevv and now > nextt:
                # stored absolute value
                peaks.append(now)
        if len(peaks) == 0:
            peaks.append(0)
        peaks.sort()
        peaks.reverse()
        peaks = peaks[:int(pagesize * ratio / 100)]
        return peaks

    @staticmethod
    def find_valley_sorted(xs, ratio=TOP_PEAK_PERCENT):
        valleys = []
        pagesize = len(xs)

        for j in range(1, pagesize - 1):
            now = xs[j]
            prevv = xs[j - 1]
            nextt = xs[j + 1]
            # valley detected
            if now < prevv and now < nextt:
                valleys.append(now)
        if len(valleys) == 0:
            valleys.append(0)
        valleys.sort()
        valleys = valleys[:int(pagesize * ratio / 100)]
        return valleys


class Model(object):
    """

    """

    _FOLD_COUNT = 5
    _SAMPLE_RATE = 20

    def __init__(self, filename, labels):
        self._filename = filename
        self._labels = labels
        self._mode = len(self._labels)

        file_list = []
        for i in range(self._mode):
            file_list.append(filename + '_' + self._labels[i])

        self._original_data = []
        for i in range(self._mode):
            self._original_data.append(Parser.read(file_list[i]))

        self._raw_data = []
        self._components = []
        self._means = []

    def run(self, time_interval):
        """
        PCA

        :return:
        """
        if self._mode > 1:
            print('Error: Only accept at only 1 file.')
            sys.exit(2)

        pca, mean, comp = Parser.parse(self._original_data[0][:time_interval * Model._SAMPLE_RATE])
        self._raw_data.append(pca.transform(self._original_data[0][:time_interval * Model._SAMPLE_RATE]))
        self._means.append(mean)
        self._components.append(comp)

        gaps = Parser.get_gaps_curve(self._raw_data[0])
        mean = statistics.mean(gaps)
        std = statistics.pstdev(gaps)
        print(mean, std)

        sns.distplot(gaps, bins=20)
        PresentationModel.write_to_file(self._components, mean, std)
        return mean, std


class PresentationModel(object):
    """
    """
    TRAINING_MODEL_FILE = 'motorcycle.txt'
    TARGET_FILE = 'prediction.txt'

    _POOL_SIZE = 20
    _BUFFER_SIZE = 20

    def __init__(self, training_model_file, pool_size=_POOL_SIZE, buffer_size=_BUFFER_SIZE):
        self._pool_size = pool_size
        self._buffer_size = buffer_size

        # read feature to build SVM
        fp = open(training_model_file, 'r')

        self._components = []
        for token in fp.readline().split(','):
            self._components.append(float(token))

        self._mean = float(fp.readline())
        self._std = float(fp.readline())

    @staticmethod
    def write_to_file(components, mean, std):
        fp = open(PresentationModel.TRAINING_MODEL_FILE, 'w')
        for i in range(len(components)):
            PresentationModel.__write_by_line(fp, components[i][0])
        fp.write(str(mean) + '\n')
        fp.write(str(std) + '\n')
        fp.close()

    @staticmethod
    def __write_by_line(fp, xs):
        n = len(xs)
        for i in range(n):
            fp.write(str(xs[i]))
            fp.write(',') if i < n - 1 else fp.write('\n')

    def pca_combine(self, data_list):
        pcas = []
        for i in range(len(data_list[0])):
            pca = 0
            for k in range(3):
                pca += data_list[k][i] * self._components[k]
            pcas.append(pca)
        return pcas

    def predict(self, x, k):
        if abs(x - self._mean) <= k * self._std:
            return 0
        return 1


class AnalogData(object):
    """
    class that holds analog data for N samples
    """
    # con-str
    def __init__(self, max_len):
        self.ax = deque([0.0] * max_len)
        self.ay = deque([0.0] * max_len)
        self.az = deque([0.0] * max_len)
        self.maxLen = max_len

    # ring buffer
    def add_tp_buf(self, buf, val):
        if len(buf) < self.maxLen:
            buf.append(val)
        else:
            buf.pop()
            buf.appendleft(val)

    # add data
    def add(self, data):
        assert(len(data) == 3)
        self.add_tp_buf(self.ax, data[0])
        self.add_tp_buf(self.ay, data[1])
        self.add_tp_buf(self.az, data[2])

    def merge_to_list(self):
        tmps = [[], [], []]
        tmps[0] = list(self.ax)
        tmps[1] = list(self.ay)
        tmps[2] = list(self.az)
        return tmps


class Drawer(object):

    COLORS = ['blue', 'orange', 'green', 'red']

    @staticmethod
    def plot_envelope_prob(mean, std, gaps):
        xs = []
        ys = []
        for K in range(0, 50 + 1, 2):
            K /= 10
            hit = 0
            for j in range(len(gaps)):
                if abs(gaps[j] - mean) <= K * std:
                    hit += 1
            hit_ratio = hit / len(gaps)
            xs.append(K)
            ys.append(hit_ratio)

        df = pd.DataFrame(data={
            'K': xs,
            'hitRatio': ys
        })
        print(df)
        f, ax = plt.subplots(1, 1)
        ax.set_title('Probability of gaps dropping within range(motor_0504_4Y7M_2_HOOK)')
        sns.pointplot('K', 'hitRatio', data=df, title='sss')
        plt.savefig('hitRatio(motor_0504_4Y7M_2_HOOK)240s.png')
        plt.show()

    @staticmethod
    def plot_2d_scatter_origin(raw_data, index, title='', suffix=''):
        # pre-process
        dim = len(raw_data)

        rd = [[], [], []]
        for k in range(dim):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        marks = ['X', 'Y', 'Z']
        for i in range(3):
            for j in range(i + 1, 3):
                fig, ax = plt.subplots()
                x_label = 'acceleration at ' + marks[i] + ' axis (mg)'
                y_label = 'acceleration at ' + marks[j] + ' axis (mg)'
                plt.xlabel(x_label)
                plt.ylabel(y_label)
                ax.set_title('Scatters of Original Data in 2D (' + title + '_' + suffix + ')'
                             + '[' + marks[i] + marks[j] + ']')
                x = rd[i]
                y = rd[j]
                plt.scatter(x, y, label=marks[i] + marks[j], color=Drawer.COLORS[index], alpha=0.2)
                ax.legend()
                plt.savefig(title + '_' + suffix + '[' + marks[i] + marks[j] + ']' + '2d.png')

    @staticmethod
    def plot_2d_scatter_mean_gap(raw_data, title='', labels=[], suffix=''):
        fig, ax = plt.subplots()
        plt.xlabel('meanGap1 (mg)')
        plt.ylabel('meanGap2 (mg)')
        ax.set_title('Scatters of Mean Gaps in 2D (' + title + ')' + '[' + suffix + ']')

        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        for i in range(dim):
            gap_list = []
            for k in range(len(data_list[i])):
                gap_list.append(Parser.get_gaps_curve(data_list[i][k]))

            now_list = [[], []]
            for j in range(len(gap_list)):
                for k in range(2):
                    now_list[k].append(gap_list[j][k])

            plt.scatter(now_list[0], now_list[1], label=labels[i])

        ax.legend()

        plt.savefig(title + '[' + suffix + ']' + '2D-mean-gap.png')
        # plt.show()

    @staticmethod
    def plot_3d_scatter_origin(raw_data, index, title='', suffix=''):
        fig = plt.figure()
        ax = Axes3D(fig)
        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        ax.set_xlabel('acceleration at X axis (mg)')
        ax.set_ylabel('acceleration at Y axis (mg)')
        ax.set_zlabel('acceleration at Z axis (mg)')
        ax.set_title('Scatters of Original Data in 3D (' + title + '_' + suffix + ')')

        rd = [[], [], []]
        for k in range(len(raw_data)):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        ax.scatter(rd[0], rd[1], rd[2], color=Drawer.COLORS[index], label='XYZ')

        ax.legend()

        plt.savefig(title + '[' + suffix + ']' + '3D-origin.png')
        # plt.show()

    @staticmethod
    def plot_3d_scatter(raw_data, title='', labels=[], suffix=''):
        fig = plt.figure()
        ax = Axes3D(fig)
        # pre-process
        dim = len(raw_data)
        data_list = []
        for i in range(dim):
            data_list.append(Parser.sliding(raw_data[i]))

        ax.set_xlabel('meanGap1 (mg)')
        ax.set_ylabel('meanGap2 (mg)')
        ax.set_zlabel('meanGap3 (mg)')
        ax.set_title('Scatters of Mean Gaps in 3D (' + title + ')' + '[' + suffix + ']')

        for i in range(dim):
            gap_list = []
            for k in range(len(data_list[i])):
                gap_list.append(Parser.get_gaps_curve(data_list[i][k]))

            now_list = [[], [], []]
            for j in range(len(gap_list)):
                for k in range(3):
                    now_list[k].append(gap_list[j][k])

            ax.scatter(now_list[0], now_list[1], now_list[2], color=Drawer.COLORS[i], label=labels[i])

        ax.legend()
        # plt.show()

    @staticmethod
    def draw_xyz(raw_data, index, filename='', label='', suffix=''):
        x_label = 'time_stamp (s/20)'
        y_label = 'acceleration (mg)'
        title = 'Original Data of X,Y,Z (' + filename + '_' + label + ') [' + suffix + ']'

        fig, ax = plt.subplots(3, sharex='all', sharey='all')

        rd = [[], [], []]
        for k in range(len(raw_data)):
            for j in range(3):
                rd[j].append(raw_data[k][j])

        plt.xlabel(x_label)
        plt.ylabel(y_label)
        # plt.ylim(-50, 50)
        ax[0].set_title(title)

        axis_labels = ['X', 'Y', 'Z']
        for i in range(3):
            x = np.arange(0, len(rd[i]))
            y = rd[i]

            ax[i].plot(x, y, color=Drawer.COLORS[index], label=axis_labels[i])
            ax[i].legend()

        plt.savefig(title + 'xyz.png')
        # plt.show()

    @staticmethod
    def draw_line_chart(raw_data, filename='', labels=[], suffix=''):
        title = 'PCA Value (' + filename + ') [' + suffix + ']'
        fig, ax = plt.subplots()

        data_list = []
        # pre-process
        for i in range(len(raw_data)):
            data_list.append(Parser.sliding(raw_data[i]))

        plt.xlabel('time_stamp (20/s)')
        plt.ylabel('PCA_value (mg)')

        for i in range(len(raw_data)):
            peaks_list = []
            valleys_list = []
            for k in range(len(data_list[i])):
                fragment = data_list[i][k]
                peaks = Parser.find_peaks_sorted(fragment)
                valleys = Parser.find_valley_sorted(fragment)
                if len(peaks) == 0:
                    peaks.append(0)
                if len(valleys) == 0:
                    valleys.append(0)
                peaks_list.append(np.mean(peaks))
                valleys_list.append(np.mean(valleys))

            X = np.arange(0, len(peaks_list))
            ax.plot(X, peaks_list, label='peak_' + labels[i], color=Drawer.COLORS[i])
            ax.plot(X, valleys_list, '--', label='valley_' + labels[i], color=Drawer.COLORS[i])
        ax.legend()
        ax.set_title(title)

        plt.savefig(title + 'line_chart.png')
        # plt.show()
Given filename, this static class could parse them into useful information. Load file(.csv) and store them.          :param filename: filename string "without" extension.         :return: n*1 dimension list Do PCA with some filters; e.g. discard a noise axis.          :param buffer: n*1 dimension list         :return: n*1 dimension list Find gaps for the input data.          :param raw_data:         :return: # process the first window; i.e., the first PAGESIZE rows of data # slide from start to end spider from csv which we experiment, then stored them into a list (n*3 dimension)          :param filename: filename string "without" extension. # Discard some beginning data which may be noisy # del records[:int(len(records) / 30)] # Consider X, Y, Z axes # If can convert string to float # Replace it # peak detected # stored absolute value # valley detected PCA          :return: # read feature to build SVM class that holds analog data for N samples # con-str # ring buffer # add data # pre-process # pre-process # plt.show() # pre-process # plt.show() # pre-process # plt.show() # plt.ylim(-50, 50) # plt.show() # pre-process # plt.show()