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How to use tap_ok method in autotest

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DTW.py

Source:DTW.py

1import json2import numpy as np3from dtw import *4import matplotlib5matplotlib.use('tkAgg')6import matplotlib.pyplot as plt7import pandas as pd8sys.path.append("../")9from data_dict import data_dict10def get_data(filename):11    df = pd.read_csv(filename)12    df['time'] = pd.to_datetime(df['time'], format='%Y-%m-%d %H:%M:%S.%f')13    return df['time'].to_numpy(), df['value'].to_numpy()14def get_hits(x_signal, signal, x_template, template):15    len_signal = signal.shape[0]16    len_template = template.shape[0]17    x = []18    dist = []19    for i in range(0, len_signal-len_template, int(len(template)/3)):20        print(i / (len_signal - len_template))21        al = dtw(signal[i:i+len_template], template)22        di = al.distance23        xi = x_signal[i+len_template]24        dist.append(di)25        x.append(xi)26    return np.array(x), np.array(dist)27def get_similarity(signal, template):28    if len(template) > len(signal):29        return float('inf')30    al = dtw(signal[-len(template):], template)31    return al.normalizedDistance32def default_filter_func(a):33    return a.to_numpy()34def fft_filter_func(a):35    T = 1.0 / 50.036    N = len(a)37    f = np.linspace(0, 1.0/(2.0*T), int(N/2))38    res = np.fft.rfft(a - np.mean(a))39    my_filter = np.bitwise_or(f < 0.1, f > 25)40    power_filter = np.abs(res[:int(N/2)]) < 341    ind = np.where(my_filter)42    res[ind] = 043    ind2 = np.where(power_filter)44    res[ind2] = 045    ires = np.fft.irfft(res)46    return ires47def analysis(x_wc, wc, x_tap, tap, filter_func=default_filter_func):48    print("Performing DTW analysis")49    wc = filter_func(wc)50    x_wc = x_wc[:len(wc)]51    tap = filter_func(tap)52    x_tap = x_tap[:len(tap)]53    for entry in data_dict:54        if len(entry['wc']) == 0 and len(entry['tap water']) == 0:55            continue56        x_signal, signal = get_data('../data/dataFull/' + entry['filename'])57        signal = filter_func(signal)58        x_signal = x_signal[:len(signal)]59        x1, dist_wc = get_hits(x_signal, signal, x_wc, wc)60        x2, dist_tap = get_hits(x_signal, signal, x_tap, tap)61        fig, axs = plt.subplots(3)62        axs[0].plot(x_signal, signal, color='b')63        for wc_entry in entry['wc']:64            x_wc_entry, y_wc_entry = x_signal[wc_entry["start"]*50:wc_entry["end"]*50], signal[wc_entry["start"]*50:wc_entry["end"]*50]65            axs[0].plot(x_wc_entry, y_wc_entry, color='r', label='wc entry')66        for tap_entry in entry['tap water']:67            x_tap_entry, y_tap_entry = x_signal[tap_entry["start"]*50:tap_entry["end"]*50], signal[tap_entry["start"]*50:tap_entry["end"]*50]68            axs[0].plot(x_tap_entry, y_tap_entry, color='g', label='tap water entry')69        dist_wc = dist_wc / len(wc)70        dist_tap = dist_tap / len(tap)71        my_max = max(max(dist_wc), max(dist_tap))72        axs[1].plot(x1, dist_wc, color='r', label='normalized dist to wc')73        axs[1].plot(x2, dist_tap, color='g', label='normalized dist to tap water')74        axs[1].set_ylim(0, my_max)75        #fig.legend()76        ind = np.where(dist_tap < 0.017)77        is_tap_signal = np.zeros(len(dist_tap))78        is_tap_signal[ind] = 179        axs[2].plot(x2, is_tap_signal)80        fig.savefig(entry['filename'] + ".png")81def fft_analysis(x_signal, signal):82    print("Performing FFT analysis")83    T = 1.0 / 50.084    N = len(signal)85    f = np.linspace(0, 1.0/(2.0*T), int(N/2))86    res = np.fft.rfft(signal - np.mean(signal))87    fig_noisy, axs_noisy = plt.subplots(2)88    axs_noisy[0].plot(x_signal, signal)89    axs_noisy[1].plot(f, 2.0 / N * np.abs(res[:int(N/2)]))90    my_filter = np.bitwise_or(f < 0.1, f > 15)91    power_filter = np.abs(res[:int(N/2)]) < 1092    ind = np.where(my_filter)93    res[ind] = 094    ind2 = np.where(power_filter)95    res[ind2] = 096    ires = np.fft.irfft(res)97    fig_filt, axs_filt = plt.subplots(2)98    axs_filt[0].plot(x_signal, ires)99    axs_filt[1].plot(f, 2.0 / N * np.abs(res[:int(N/2)]))100    plt.legend()101    plt.show()102def old_dtw():103    x_source_wc, source_wc = get_data('data2.csv')104    wc_start, wc_end = 492, 573105    x_wc, wc = x_source_wc.iloc[wc_start*50:wc_end*50], source_wc.iloc[wc_start*50:wc_end*50]106    x_source_tap, source_tap = get_data('data3.csv')107    tap_start, tap_end = 216, 236108    x_tap, tap = x_source_tap.iloc[tap_start*50:tap_end*50], source_tap.iloc[tap_start*50:tap_end*50]109    analysis(x_wc, wc, x_tap, tap)110def show_dtw():111    sub_len = 20112    sub_signal = np.zeros(sub_len)113    for i in range(sub_len):114        sub_signal[i] = 1/10 * 2.73**(-(i-(sub_len/2))**2 / 10)115    x_source_tap, source_tap = get_data('data3.csv')116    my_len = 100117    template_start, template_end = 216, (216 + my_len)118    tap_start, tap_end = 500, (500+my_len)119    template = source_tap[template_start:template_end]120    tap = source_tap[tap_start:tap_end]121    template[10:10+sub_len] = template[10:10+sub_len] + sub_signal122    template[40:40+sub_len] = template[40:40+sub_len] + sub_signal123    tap[30:30+sub_len] = tap[30:30+sub_len] + sub_signal124    tap[60:60+sub_len] = tap[60:60+sub_len] + sub_signal125    alignment = dtw(tap, template, keep_internals=True)126    #alignment.plot(type="twoway", offset=-1)127    alignment.plot(type="twoway", offset=-1, xlab="Sample index", ylab=r"magnetic intensity ($\mu$T)")128    print(np.linalg.norm(tap-template))129    print(alignment.normalizedDistance)130    #plt.plot(x_source_tap, source_tap, color='b')131    #plt.plot(x_source_tap[template_start:template_end], source_tap[template_start:template_end], color='r')132    #plt.plot(x_source_tap[tap_start:tap_end], source_tap[tap_start:tap_end], color='r')133    #plt.show()134    #x_tap, tap = x_source_tap.iloc[tap_start*50:tap_end*50], source_tap.iloc[tap_start*50:tap_end*50]135def get_templates():136    folder = '../data/dataFull/'137    data_file_list = ['1625807700000000000-1625808600000000000.csv', '1625631240-1625632140.csv', '1625770800000000000-1625771700000000000.csv']138    taps, wcs = [], []139    for data_file in data_file_list:140        _, source_data = get_data(folder + data_file)141        for dd in data_dict:142            if dd['filename'] == data_file:143                for tap_water in dd['tap water']:144                    taps.append(source_data[int(tap_water['start']*50):int(tap_water['end']*50)])145                for wc in dd['wc']:146                    wcs.append(source_data[int(wc['start']*50):int(wc['end']*50)])147    res = []148    for tap in taps:149        res.append({150            "class": "tap water",151            "template": tap,152        })153    for wc in wcs:154        res.append({155            "class": "wc",156            "template": wc,157        })158    return res159def create_similarity_measures(templates):160    similarities = []161    i = 0162    for data in data_dict:163        print(str(i) + " / " + str(len(data_dict)))164        i += 1165        filepath = "../data/dataFull/" + data["filename"]166        _, signal = get_data(filepath)167        for entry_class in ["wc", "tap water"]:168            for entry in data[entry_class]:169                min_dists = {"wc": float('inf'), "tap water": float('inf')}170                for template in templates:171                    dist = get_similarity(signal[:int(entry["end"]*50)], template["template"])172                    if template["class"] == "wc" and dist < min_dists["wc"]:173                        min_dists["wc"] = dist174                    elif template["class"] == "tap water" and dist < min_dists["tap water"]:175                        min_dists["tap water"] = dist176                similarities.append({177                    "filepath": filepath,178                    "min_dists": min_dists,179                    "class": entry_class,180                })181    with open("simil.txt", "w+") as f:182        for sim in similarities:183            f.write(str(sim) + "\n")184def parse_similarity_measures(filepath="simil.txt"):185    similarities = []186    with open(filepath, "r") as f:187        for line in f.read().splitlines():188            s = json.loads(line)189            similarities.append(s)190    return similarities191min_diff = 1000192max_diff = -1000193def predict(similarity, thresh = 0):194    global min_diff195    global max_diff196    diff = similarity["min_dists"]["wc"] - similarity["min_dists"]["tap water"]197    if diff < min_diff:198        min_diff = diff199    if  diff > max_diff and diff < 10:200        max_diff = diff201    if diff > thresh:202        return "wc"203    else:204        return "tap water"205def test_similarity_measures(similarities, thresh=0):206    wc_ok, wc_nok, tap_ok, tap_nok = 0, 0, 0, 0207    for simil in similarities:208        prediction = predict(simil, thresh)209        if prediction == "wc" and simil["class"] == "wc":210            wc_ok += 1211        elif prediction == "tap water" and simil["class"] == "wc":212            wc_nok += 1213        elif prediction == "tap water" and simil["class"] == "tap water":214            tap_ok += 1215        elif prediction == "wc" and simil["class"] == "tap water":216            tap_nok += 1217        else:218            print("ERRROR")219    res = {220        "wc_ok": wc_ok,221        "wc_nok": wc_nok,222        "tap_ok": tap_ok,223        "tap_nok": tap_nok,224        "accuracy": (wc_ok + tap_ok) / (wc_ok + tap_ok + wc_nok + tap_nok),225    }226    return res227def test_templates(templates):228    wc_ok, wc_nok, tap_ok, tap_nok = 0, 0, 0, 0229    i = 0230    for data in data_dict:231        print(str(i) + " / " + str(len(data_dict)))232        i += 1233        _, signal = get_data("../data/dataFull/" + data["filename"])234        for entry_class in ["wc", "tap water"]:235            for entry in data[entry_class]:236                min_dist = float('inf')237                best_class = ""238                for template in templates:239                    dist = get_similarity(signal[:int(entry["end"]*50)], template["template"])240                    if dist < min_dist:241                        min_dist = dist242                        best_class = template["class"]243                if entry_class == "wc" and entry_class == best_class:244                    wc_ok += 1245                elif entry_class == "wc" and entry_class != best_class:246                    wc_nok += 1247                elif entry_class == "tap water" and entry_class == best_class:248                    tap_ok += 1249                else:250                    tap_nok += 1251    print("wc correct: " + str(wc_ok) + " / (" + str(wc_ok) + " + " + str(wc_nok) + ") = " + str(wc_ok / (wc_ok + wc_nok)))252    print("tap correct: " + str(tap_ok) + " / (" + str(tap_ok) + " + " + str(tap_nok) + ") = " + str(tap_ok / (tap_ok + tap_nok)))253    return (wc_ok + tap_ok) / (wc_ok + tap_ok + wc_nok + tap_nok)254def dtw_classifier(similarities):255    best = None256    best_thresh = 0257    for thresh in np.linspace(-0.00863405061896113*10,0.010766850570994357*10, 10):258        acc = test_similarity_measures(similarities, thresh = thresh)259        if best is None or acc["accuracy"] > best["accuracy"]:260            best = acc261            best_thresh = thresh262    print(best)263    print("Best thresh: " + str(thresh))264def clean_simil():265    with open("simil.txt", "rt") as file:266        data = file.read()267        data = data.replace("'", "\"")268        data = data.replace("inf", str(10000))269    with open("simil.txt", "wt") as file:270        file.write(data)271if __name__ == '__main__':272    print("Performing DTW test")273    #show_dtw()274    generate_similarities = False275    if generate_similarities:276        templates = get_templates()277        create_similarity_measures(templates)278        clean_simil()279    else:280        similarities = parse_similarity_measures()...

k-NN.py

Source:k-NN.py

1import matplotlib2import numpy as np3import sys4sys.path.append("../recording")5sys.path.append("../")6from SineFit import guess_fft, sin_fit7from math import sqrt8matplotlib.use('tkAgg')9import matplotlib.pyplot as plt10import pandas as pd11from scipy import optimize as opt12from sklearn.neighbors import KNeighborsClassifier13from sklearn.model_selection import train_test_split14from dtw import *15from data_dict import data_dict16second_size = 5017def get_data(filename):18    df = pd.read_csv(filename)19    #for i in range(0, df['time'].size):20    #    df.loc[i, 'time'] = pd.to_datetime(df.loc[i, 'time'], format='%Y-%m-%d %H:%M:%S.%f').timestamp()21    df['time'] = pd.to_datetime(df['time'], format='%Y-%m-%d %H:%M:%S.%f').values.astype(float)22    return df['time'], df['value']23def fft_filter_func(a):24    T = 1.0 / 50.025    N = len(a)26    f = np.linspace(0, 1.0/(2.0*T), int(N/2))27    res = np.fft.rfft(a - np.mean(a))28    my_filter = np.bitwise_or(f < 0.1, f > 25)29    power_filter = np.abs(res[:int(N/2)]) < 330    ind = np.where(my_filter)31    res[ind] = 032    ind2 = np.where(power_filter)33    res[ind2] = 034    ires = np.fft.irfft(res)35    return ires36def fit_sine_curve_freq(x, y, guess_f=guess_fft):37    """38    1st method to get period from data39    :param data: path of file to read data from40    :param guess_f: function to estimate the points41    :return:42    """43    x = np.array(x.astype(float).values)44    y = np.array(y.astype(float).values)45    x0 = x[0]46    for i in range(0, x.size):47        x[i] -= x048    guess = guess_f(x, y)49    try:50        params, params_covariance = opt.curve_fit(f=sin_fit, xdata=x, ydata=y, p0=guess)51        amplitude = params[0]52        freq = params[1]53        phase = params[2]54        offset = params[3]55        return freq56    except:57        return -158def fit_sine_curve_knn(x, y, freqs=[1, 10, 15, 25]):59    n_features = len(freqs) - 160    res = np.zeros(n_features)61    for i in range(0,len(x) - second_size, second_size):62        f = fit_sine_curve_freq(x[i:i+second_size], y[i:i+second_size]) * 1e963        if f > 0:64            for i in range(n_features):65                if f > freqs[i] and f < freqs[i+1]:66                    res[i] = res[i] + 167    res = res / (len(x) / second_size)68    return res69"""70Return a list of N tuples corresponding to (frequency - amplitude) for71frequencies between 1 and 25Hz72x is considered to be at 50Hz73"""74def fft_knn(x, y, freqs=[1, 10, 15, 25]):75    T = 1.0 / 50.076    N = len(y)77    f = np.linspace(0, 1.0/(2.0*T), int(N/2))78    res_fft = np.fft.rfft(y - np.mean(y))79    highpass_filter = f < 0.980    ind = np.where(highpass_filter)81    res_fft[ind] = 082    n_features = len(freqs) - 183    res = np.zeros(n_features)84    for idx in range(n_features):85        filt = np.bitwise_and(f > freqs[i], f < freqs[i+1])86        ind = np.where(filt)87        filtered_values = res_fft[ind]88        res[idx] = np.mean(filtered_values)89    return res90def knn_view(knn_func=fft_knn, freqs=[0, 8.33, 16.66, 25]):91    fig = plt.figure()92    ax = fig.add_subplot(projection='3d')93    for entry in data_dict:94        if len(entry["wc"]) == 0 and len(entry["tap water"]) == 0:95            continue96        x_signal, signal = get_data('../data/' + entry['filename'])97        for wc_entry in entry["wc"]:98            x_wc, wc = x_signal[int(wc_entry["start"]*50):int(wc_entry["end"]*50)], signal[int(wc_entry["start"]*50):int(wc_entry["end"]*50)]99            xyz = knn_func(x_wc, wc, freqs=freqs)100            ax.scatter([xyz[0]], [xyz[1]], [xyz[2]], marker='o', color='r')101        for tap_entry in entry["tap water"]:102            x_tap, tap = x_signal[int(tap_entry["start"]*50):int(tap_entry["end"]*50)], signal[int(tap_entry["start"]*50):int(tap_entry["end"]*50)]103            xyz = knn_func(x_tap, tap, freqs=freqs)104            ax.scatter([xyz[0]], [xyz[1]], [xyz[2]], marker='^', color='g')105    ax.set_xlabel('f1 mean amplitude')106    ax.set_ylabel('f2 mean amplitude')107    ax.set_zlabel('f3 mean amplitude')108    plt.show()109def read_data_dict(folderPath, knn_func, freqs, data_dict):110    x, y = [], []111    for data in data_dict:112        entry = data["data"]113        clas = data["class"]114        x_signal, signal = get_data(folderPath + data['filename'])115        tmp_x, tmp_y = x_signal[int(entry["start"]*50):int(entry["end"]*50)], signal[int(entry["start"]*50):int(entry["end"]*50)]116        xyz = knn_func(tmp_x, tmp_y, freqs)117        x.append(xyz)118        y.append(clas)119    return x, y120def parse_dict(data_dict):121    entries = []122    for entry in data_dict:123        for wc_entry in entry["wc"]:124            entries.append({"data": wc_entry,"class": "wc","filename":entry["filename"]})125        for tap_entry in entry["tap water"]:126            entries.append({"data": tap_entry,"class": "tap","filename":entry["filename"]})127    return entries128def knn_getdata(knn_func=fft_knn, freqs=[0,8.33, 16.66, 25], test_size=0.5):129    entries = parse_dict(data_dict)130    dict_train, dict_test = train_test_split(entries, test_size=test_size, random_state=2)131    X_train, y_train = read_data_dict('../data/dataFull/', knn_func, freqs, dict_train)132    X_test, y_test = read_data_dict('../data/dataFull/', knn_func, freqs, dict_test)133    return X_train, y_train, X_test, y_test134def evaluate_knn(neigh, X_test, y_test):135    wc_ok, wc_nok, tap_ok, tap_nok = 0, 0, 0, 0136    for X,y in zip(X_test, y_test):137        pred = neigh.predict([X])138        if (str(pred[0]) == str(y)):139            if y == 'wc':140                wc_ok = wc_ok + 1141            elif y == 'tap':142                tap_ok = tap_ok + 1143        else:144            if y == 'wc':145                wc_nok = wc_nok + 1146            elif y == 'tap':147                tap_nok = tap_nok + 1148    print("wc correct: " + str(wc_ok) + " / (" + str(wc_ok) + " + " + str(wc_nok) + ") = " + str(wc_ok / (wc_ok + wc_nok)))149    print("tap correct: " + str(tap_ok) + " / (" + str(tap_ok) + " + " + str(tap_nok) + ") = " + str(tap_ok / (tap_ok + tap_nok)))150    return (wc_ok + tap_ok) / (wc_ok + tap_ok + wc_nok + tap_nok)151def full_knn(pre_process=fit_sine_curve_knn, metric=None, freqs=[0,8.33,16.66,25], test_size=0.5, K=5):152    X_train, y_train, X_test, y_test = knn_getdata(pre_process, freqs=freqs, test_size=test_size)153    if metric is None:154        metric = 'minkowski'155    neigh = KNeighborsClassifier(n_neighbors=K, metric=metric)156    neigh.fit(X_train, y_train)157    return evaluate_knn(neigh, X_test, y_test)158def my_euclidean(X, Y):159    total = 0160    for x,y in zip(X, Y):161        total += (x-y)**2162    return sqrt(total)163def my_dtw(X, Y):164    al = dtw(X, Y)165    return al.distance166def tune_parameters():167    n_dim = [20,25,50]168    K = [1,3,5]169    metrics = [my_euclidean, my_dtw]170    max_success_rate = 0171    best_K = 0172    best_dim = 0173    best_metric = "euclidean"174    for dim in n_dim:175        for k in K:176            for metric in metrics:177                success_rate = full_knn(metric=metric, freqs=np.linspace(0, 25, dim), test_size=0.3, K=k)178                if success_rate > max_success_rate:179                    max_success_rate = success_rate180                    best_K = k181                    best_dim = dim182                    if metric == my_euclidean:183                        best_metric = "euclidean"184                    else:185                        best_metric = "dtw"186    retval = {187        "K": best_K,188        "dim": dim,189        "success_rate": max_success_rate,190        "metric": best_metric,191    }192    return retval193if __name__ == '__main__':194    params = tune_parameters()...

tap_processor.py

Source:tap_processor.py

1# -*- coding: utf-8 -*-2import re3TAP_NG = 04TAP_OK = 15TAP_SKIP = 26TAP_TODO = 37class TAP_Processor(object):8    #===============================================================================9    #                 |-----result------|10    # _resultListæ ¼å¼ï¼ï¼»ï¼»msgTypeï¼messageï¼½ï¼ï¼»msgType ,messageï¼½.......ï¼½11    #                     ^                        ^                      12    #                   TAP_NG                   TAP_OK                   13    #===============================================================================   14    def __init__(self):15        self._resultList = []16    17    def clear(self):18        self._resultList =  []19    20    #addæä½ï¼ by list21    def addResultList(self , result_List):   22        self._resultList.extend(result_List)23     24    #addæä½ï¼ by simple result      25    def addResult(self ,msgType , message):26        self._resultList.append([msgType, message])27    28    #loadæä½ï¼ by tap file29    def loadTap(self , tapFile):30        with open(tapFile , 'rb') as f:31            for line in f:32                if re.match(r'\S+', line):33                    if re.match(r'[^\d+..\d+]',line):34                        self._resultList.append(self.tapLine2result(line))35    #dumpæä½ to file36    def dump2file(self, outPathName):   37        result_list = self.dump2strList()38        39        with open(outPathName , 'wb') as f:40            f.writelines(result_list)41      42    #dumpæä½ to string list       43    def dump2strList(self):44        result_list = ['1..%d\n'%len(self._resultList)]45        46        for i in range(0, len(self._resultList)):47            result_list.append(self.result2tapLine(self._resultList[i], i+1)+'\n')48            49        return result_list50           51    #è·åææresult52    def getAllResult(self):53        result_list = self._resultList54        55        return result_list56      57    #è·åææresult,è½¬æåå¸ç±»å58    def getAllResultDict(self):59        result_list = self._resultList60        result_dict = {}61        result_map = ["NG","OK","Not executed","NG"]62        for item in result_list:63            result_dict[item[1]] = result_map[item[0]]64            65        return result_dict66    67    #searchæä½ all successï½fail|skip|TODO result68    def searchByType(self, resultType):69        result_list = []70        71        for result in self._resultList:72            if result[0] == resultType:73                result_list.append(result)74        75        return result_list76    77    #searchæä½ message match one REGEX78    def searchByMessage(self , REGEX):79        result_list = []80        81        for result in self._resultList:82            if re.search(REGEX, result[1]):83                result_list.append(result)84                85        return result_list     86    #result to tapLine87    def result2tapLine(self, result , i):88        tapPrintLine = ''89        90        if result[0] == TAP_NG:91            tapPrintLine = "not ok %d - %s"%(i,result[1])92        elif result[0] == TAP_OK:93            tapPrintLine = "ok %d - %s"%(i,result[1])94        elif result[0] == TAP_SKIP:95            tapPrintLine = "not ok %d - %s # SKIP skip this test"%(i,result[1])96        elif result[0] == TAP_TODO:97            tapPrintLine = "not ok %d - %s # TODO need redo, source list error"%(i,result[1])98        else:99            raise NameError 100        101        return tapPrintLine102    103    #tapLine to result 104    def tapLine2result(self , tapLine):        105        result = []106        pattern = re.compile(r'(\s+$)|(^\s+)')107        108        if re.match('^ok', tapLine) :  109            message = re.search(r'(?<=-)(.*)', tapLine).group()110            message = pattern.sub('', message)111            result = [TAP_OK,message]  112        elif re.match('^not.*#\sSKIP\s.*', tapLine):113            message = re.search(r'(?<=-)(.*)(?=#)', tapLine).group()114            message = pattern.sub('', message)               115            result = [TAP_SKIP,message]116        elif re.match('^not.*#\sTODO\s.*', tapLine):117            message = re.search(r'(?<=-)(.*)(?=#)', tapLine).group()118            message = pattern.sub('', message)119            result = [TAP_TODO,message]120        elif re.match('^not',tapLine):121            message = re.search(r'(?<=-)(.*)', tapLine).group()122            message = pattern.sub('', message)123            result = [TAP_NG,message]124        else:125            raise NameError126                127        return result128    129 130  131        132                133    134    135                136    137    138                139                140                141                142    143                144            145                146         147                148                149        150        151        152        153        154        155        156        157        158        159        160        161        162        163        164        165        166        167        168        169        170        171        172        173        174        175        ...

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