from sklearn.cluster import DBSCAN
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt

import csv_parser
import recog
import metric

if __name__ == '__main__':
    data = csv_parser.parse_data_from_csv('test.csv')
    entries = recog.recognize_entries(data)
    x = []
    for e in entries:
        x.append([e.lon, e.lat])
    x = np.array(x)
    db = DBSCAN(eps = 10/6400000, min_samples = 3,
            metric = lambda x,y:metric.spherical_distance(x,y)).fit(x)
    labels = db.labels_
    core_samples_mask = np.zeros_like(db.labels_, dtype = bool)
    core_samples_mask[db.core_sample_indices_] = True
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    n_noise_ = list(labels).count(-1)

    print('Estimated number of clusters: %d' % n_clusters_)
    print('Estimated number of noise points: %d' % n_noise_)
    if n_clusters_ == 0:
        print('can not get any clusters')
        plt.plot(x[:,0], x[:,1], 'o')
        plt.show()
        exit(0)
    print("Silhouette Coefficient: %0.3f"
              % metrics.silhouette_score(x, labels))

    unique_labels = set(labels)
    colors = [plt.cm.Spectral(each)
                      for each in np.linspace(0, 1, len(unique_labels))]
    for k, col in zip(unique_labels, colors):
            if k == -1:
                    # Black used for noise.
                    col = [0, 0, 0, 1]

            class_member_mask = (labels == k)

            xy = x[class_member_mask & core_samples_mask]
            plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                             markeredgecolor='k', markersize=14)

            xy = x[class_member_mask & ~core_samples_mask]
            plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                             markeredgecolor='k', markersize=6)
    plt.show()