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How to use get_cluster_count method in lisa

Best Python code snippet using lisa_python

analyze_serialized.py

Source:analyze_serialized.py

...7from math import sqrt, degrees8from analysis.minipix import *9cluster_types = ["SMALL_BLOB", "HEAVY_TRACK", "HEAVY_BLOB", "MEDIUM_BLOB", "STRAIGHT_TRACK", "LIGHT_TRACK"]10utc = pytz.UTC11def get_cluster_count(file, data):12    for frame in range(file.num_frames):13        frame_data = data.get(frame, None)14        if frame_data:15            acq_time = data[frame]["acq_time"].replace(tzinfo=utc)16            clusters = data[frame]["clusters"]17            # Cluster types18            types = [cluster[5] for cluster in clusters]19            type_freq = {c_type: types.count(c_type) for c_type in cluster_types}20            print(acq_time, end=",")21            print(",".join(map(str, type_freq.values())))22        else:23            acq_time = file.timestamps[frame]24            print(acq_time, end=",")25            print("0,0,0,0,0,0")26def get_frame_energy(file, data):27    for frame in range(file.num_frames):28        frame_data = data.get(frame, None)29        if frame_data:30            acq_time = data[frame]["acq_time"].replace(tzinfo=utc)31            clusters = data[frame]["clusters"]32            # Total energy33            cluster_energy = [cluster[2] for cluster in clusters]34            total_energy = sum(cluster_energy)35            print(acq_time, end=",")36            print(total_energy)37        else:38            acq_time = file.timestamps[frame]39            print(acq_time, end=",")40            print(0)41def get_LET(file, data):42    for frame in range(file.num_frames):43        frame_data = data.get(frame, None)44        if frame_data:45            acq_time = data[frame]["acq_time"].replace(tzinfo=utc)46            clusters = data[frame]["clusters"]47            # Total energy48            for cluster in clusters:49                trklen = cluster[7]50                truetrkln = sqrt((55*trklen)**2 + 500**2)51                aoi = degrees(arctan(500/(55*trklen)))52                print("{},{}".format(truetrkln, aoi), end=",")53            total_energy = sum(cluster_energy)54            print(acq_time)55        else:56            acq_time = file.timestamps[frame]57            print(acq_time, end=",")58            print(0)59def get_cluster_energy(file, data):60    cluster_types = {61        "SMALL_BLOB": [],62        "HEAVY_TRACK": [],63        "HEAVY_BLOB": [],64        "MEDIUM_BLOB": [],65        "STRAIGHT_TRACK": [],66        "LIGHT_TRACK": []67    }68    for frame in range(file.num_frames):69        frame_data = data.get(frame, None)70        if frame_data:71            acq_time = data[frame]["acq_time"].replace(tzinfo=utc)72            clusters = data[frame]["clusters"]73            for cluster in clusters:74                type = cluster[5]75                energy = cluster[2]76                cluster_types[type].append((str(acq_time), type, str(energy)))77    for cluster_type in cluster_types:78        filename = cluster_type + ".csv"79        csvfile = open(filename, "w")80        csvwriter = csv.writer(csvfile)81        csvwriter.writerow(["Timestamp", "Type", "Energy"])82        for cluster in cluster_types[cluster_type]:83            csvwriter.writerow(cluster)84def get_energy_distribution(file, data):85    cluster_types = {86        "SMALL_BLOB": [],87        "HEAVY_TRACK": [],88        "HEAVY_BLOB": [],89        "MEDIUM_BLOB": [],90        "STRAIGHT_TRACK": [],91        "LIGHT_TRACK": []92    }93    for frame in range(file.num_frames):94        frame_data = data.get(frame, None)95        if frame_data:96            acq_time = data[frame]["acq_time"].replace(tzinfo=utc)97            clusters = data[frame]["clusters"]98            for cluster in clusters:99                type = cluster[5]100                energy = cluster[2]101                cluster_types[type].append((str(acq_time), type, str(energy)))102    """103    for cluster_type in cluster_types:104        filename = cluster_type + "_dist" + ".csv"105        csvfile = open(filename, "w")106        csvwriter = csv.writer(csvfile)107        energy = [float(cluster[2]) for cluster in cluster_types[cluster_type]]108        energy_distribution = normalized_distribution(energy, 50)109        csvwriter.writerow(["Range", "Count"])110        for r in energy_distribution:111            csvwriter.writerow([r, energy_distribution[r]])112    """113    fig, ax = plt.subplots()114    for cluster_type in cluster_types:115        energy = [float(cluster[2]) for cluster in cluster_types[cluster_type]]116        mu = sum(energy)/len(energy)117        sigma = std(energy)118        n, bins, patches = ax.hist(energy, bins=arange(0, 10000, 5))119        y = mlab.normpdf(bins, mu, sigma)120        ax.plot(bins, y, '--')121    ax.set_xlabel('Energy')122    ax.set_ylabel('Probability Density')123    ax.set_title('Light Track Frequency Distribution')124    fig.tight_layout()125    plt.show()126def normalized_distribution(data, window):127    data.sort()128    distributed_data = {}129    range = 0130    for point in data:131        if not distributed_data.get(range, None):132            distributed_data[range] = 0133        if point < range + window:134            distributed_data[range] += 1135        else:136            range += window137    # Normalize the distribution138    total = sum(distributed_data.values())139    for key in distributed_data:140        distributed_data[key] /= total141    return distributed_data142if __name__ == "__main__":143    serialized = open('clusters.pkl', 'rb')144    data = pickle.load(serialized)145    pmffile = PmfFile("HASPDATA/thurs_test36.pmf")146    pmffile.load_dsc()147    get_cluster_count(pmffile, data)148    # get_energy_distribution(pmffile, data)149    #get_cluster_energy(file, data)150    # get_cluster_count(file, data)...

external.py

Source:external.py

1from math import log2# count the size of a cluster3def get_cluster_count(data, index):4    num_class = len(data)5    count = 06    for i in range(0, num_class):7        count += data[i][index]8    return count9# get a list of cluster counts10def get_all_cluster_counts(data):11    counts = []12    num_cluster = len(data[0])13    num_class = len(data)14    for i in range(0, num_cluster):15        counts.append(get_cluster_count(data, i))16    return counts17# count the size of each class18def get_class_count(data, index):19    num_cluster = len(data[0])20    count = 021    for i in range(0, num_cluster):22        count += data[index][i]23    return count24# get a list of class counts25def get_all_class_counts(data):26    counts = []27    num_class = len(data)28    num_cluster = len(data[0])29    for i in range(0, num_class):30        counts.append(get_class_count(data, i))31    return counts32# total value in the list33def get_all_counts(count_list):34    total = 0.035    for i in range(0, len(count_list)):36        total += count_list[i]37    return total38# compute n chooses 239def get_combination_two(n):40    return n * (n - 1) / 2.041# compute nomalized mutual information42def nmi(data):43    num_class = len(data)44    num_cluster = len(data[0])45    class_counts = get_all_class_counts(data)46    cluster_counts = get_all_cluster_counts(data)47    N = get_all_counts(class_counts)48    #print "N: {0}".format(N)49    Hc = 0.050    for j in range(0, num_class):51        c = class_counts[j]52        Hc -= (c / N) * log(c / N, 2)53    #print "Hc: {0}".format(Hc)54    Hw = 0.055    I = 0.056    for k in range(0, num_cluster):57        w = cluster_counts[k]58        Hw -= (w / N) * log(w / N, 2)59        for j in range(0, num_class):60            i = 0.061            wc = data[j][k]62            if wc > 0:63                i = (wc / N) * log(N * wc / (class_counts[j] * w), 2)64                I += i65                #print "wc: {0}, i: {1}".format(wc, i)66    #print "Hw: {0}".format(Hw)67    #print "I: {0}".format(I)68    return I * 2 / (Hw + Hc)69# compute the Rand index which penalizes both false positive and false negative decisions during clustering70# TP - assigns two similar documents to the same cluster71# TN - assigns two dissimilar documents to different clusters.72# FP - assigns two dissimilar documents to the same cluster.73# FN - assigns two similar documents to different clusters.74def rand_index(data):75    num_class = len(data)76    num_cluster = len(data[0])77    TP_FP = 0.078    TP = 0.079    cluster_counts = []80    for k in range(0, num_cluster):81        w = get_cluster_count(data, k)82        cluster_counts.append(w)83        c = get_combination_two(w)  # choose two from each clusters84        TP_FP += c85        #print "c: {0}, TP+FP: {1}".format(c, TP_FP)86        for j in range(0, num_class):87            wc = data[j][k]88            if wc > 1:89                TP += get_combination_two(wc)  # choose two from each class within a cluster90    FP = TP_FP - TP91    #print cluster_counts92    TN_FN = 0.093    for i in range(0, num_cluster):94        for j in range(i + 1, num_cluster):95            TN_FN += cluster_counts[i] * cluster_counts[j]  # choose one from different clusters...

kmeans_clustering.py

Source:kmeans_clustering.py

...20plt.xlabel('k')21plt.ylim(0, 425)22plt.savefig('kmeans_clustering_error.png')23kmeans = KMeans(n_clusters=4, random_state=0).fit(data)24def get_cluster_count(cluster_num):25  return list(kmeans.labels_).count(cluster_num)26result_df['cluster'] = list(kmeans.labels_)27result_df['count'] = result_df['cluster'].apply(get_cluster_count)28cluster_df = result_df.groupby(['cluster']).mean()...

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