transition_rate_methods.py

from sklearn.cluster import DBSCAN
from scipy.stats import gaussian_kde

#### Examples based on scikit learn tutorial:
#### http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py

def run_DBSCAN(X, eps = 0.8, min_samples = 200):
    db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

    print('Estimated number of clusters: %d' % n_clusters_)
    return labels, n_clusters_, core_samples_mask

def plot_DBSCAN(X,labels, core_samples_mask):
    unique_labels = set(labels)
    colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = 'k'

        class_member_mask = (labels == k)

        xy = X[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=4, markeredgewidth = 0)

        xy = X[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=4, markeredgewidth = 0)

## Plot density
def plot_density(x,y,s = 10):
    xy = np.vstack([x,y])
    z = gaussian_kde(xy)(xy)
    fig = plt.figure(0,)
    fig.set_size_inches(10, 10)
    ax = fig.add_subplot(111, )
    ax.scatter(x, y, c=z, s=s, edgecolor='')