import pandas as pd
import matplotlib.pyplot as plt
import scipy as sci
from sklearn import preprocessing
from sklearn.cluster import DBSCAN

df = pd.read_csv('mystery.csv', header=None)
scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))
scaler.fit(df)
df = pd.DataFrame(scaler.transform(df))


def ezScatter(x, y):
    plt.scatter(df[x], df[y])
    plt.xlabel(x)
    plt.ylabel(y)
    plt.title((str(x) + "->" + str(y)))
    plt.show()


def ezDB(x, y):
    toFit = df[[x, y]].copy()
    dbscan = DBSCAN(eps=.10, min_samples=4)
    model = dbscan.fit(toFit)
    clusterLabels = model.labels_

    plt.scatter(df[x], df[y], c=clusterLabels, cmap='hsv')
    plt.xlabel(x)
    plt.ylabel(y)
    plt.title((str(x) + "->" + str(y) + " DBSCAN"))
    plt.show()


pd.plotting.scatter_matrix(df, cmap='prism')
plt.show()

print("ctrl c to quit. Too lazy for an escape ")
while True:
    xFig = int(input("X to examine: "))
    yFig = int(input("Y to examine: "))
    ezScatter(xFig, yFig)
    ezDB(xFig, yFig)