import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from plotstyle import *

fig = plt.figure(figsize=cm_size(figure_width, 2.0*figure_height))
spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.5,
                         **adjust_fs(fig, left=5.5, top=0.5, bottom=2.7))
rng = np.random.RandomState(2981)
n = 200
for k, r  in enumerate([ 1.0, 0.6, 0.0, -0.9 ]) :
    x = rng.randn(n)
    y = r*x + np.sqrt(1.0-r*r)*rng.randn(n)
    ax = fig.add_subplot(spec[k//2, k%2])
    ax.text(-2, 2.5, 'r=%.1f' % r)
    if k == 0 :
        ax.text(2.8, -2.8, 'positively\ncorrelated', ha='right', va='bottom')
    elif k == 1 :
        ax.text(2.8, -2.8, 'weakly\ncorrelated', ha='right', va='bottom')
    elif k == 2 :
        ax.text(2.8, -2.8, 'not\ncorrelated', ha='right', va='bottom')
    elif k == 3 :
        ax.text(-2.8, -2.8, 'negatively\ncorrelated', ha='left', va='bottom')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_xlim(-3.0, 3.0)
    ax.set_ylim(-3.0, 3.0)
    ax.plot(x[(np.abs(x)<2.8)&(np.abs(y)<2.8)], y[(np.abs(x)<2.8)&(np.abs(y)<2.8)], **psAm)

plt.savefig('correlation.pdf')