"""
Plot a significance mask
========================
Compute a linear regression using :func:`pygeode.regress` and use :func:`pygeode.vsigmask` to plot significance
"""
import pygeode as pyg, numpy as np
import pylab as pyl
pyl.ioff()

from pygeode.tutorial import t3

# Compute regression 
reg = pyg.regress(t3.X1e, t3.Y1, ['time'], output='m,b,r2,p')


pyl.ioff()

# The significance mask routine takes a signed significance mask as it's main
# argument. This ensures that regions of significance on opposite sides of the
# distribution are separated by a filled region. It can be computed as follows
# from the p-value of the linear regression.
sigmask = (1 - 0.5*reg.p) * pyg.sign(reg.m)

ax1 = pyg.showvar(reg.m, fig=1)
pyg.vsigmask(sigmask, ax1.axes[0], mjsig = 0.99)

ax1.axes[0].setp(title = '(a) Significant regions (99%) stippled')

ax2 = pyg.showvar(reg.m, fig=1)
pyg.vsigmask(sigmask, ax2.axes[0], \
                mjsig = 0.95, mjsigp = dict(visible = False), \
                nsigp = dict(alpha = 0.4, hatch = None, color = 'k'))

ax2.axes[0].setp(title = '(b) Non-significant regions (95%) shaded')

ax = pyg.plot.grid([[ax1], [ax2]])
pyl.ion()

ax.render()

############################################################################
# ``reg`` contains a dataset of values from the regression
#

print(reg)