import numpy as np
from matplotlib import pyplot
import power1d





#(0) Construct primitives:
np.random.seed(1)
J,Q       = 10,101





noise     = []
### basic types:
noise.append(   power1d.noise.ConstantUniform( J, Q, x0=0, x1=1 )   )
noise.append(   power1d.noise.ConstantGaussian( J, Q, mu=0, sigma=1 )   )
noise.append(   power1d.noise.Uniform( J, Q, x0=0, x1=1 )   )
noise.append(   power1d.noise.Gaussian( J, Q, mu=0, sigma=1 )   )
noise.append(   power1d.noise.Skewed( J, Q, mu=0, sigma=1, alpha=3 )   )
noise.append(   power1d.noise.SmoothGaussian( J, Q, mu=0, sigma=1, fwhm=20, pad=False )   )
noise.append(   power1d.noise.SmoothSkewed( J, Q, mu=0, sigma=1, alpha=3, fwhm=20, pad=False )   )
### compound type (Additive):
noise0    = power1d.noise.Gaussian( J, Q, mu=0, sigma=0.1 )
noise1    = power1d.noise.SmoothGaussian( J, Q, mu=0, sigma=1, fwhm=40, pad=False )
noise.append(   power1d.noise.Additive( noise0, noise1 )    )
### compound type (Mixture):
noise0    = power1d.noise.Gaussian( 2, Q, mu=0, sigma=1 )
noise1    = power1d.noise.SmoothGaussian( 40, Q, mu=3.5, sigma=1, fwhm=20, pad=False )
noise.append(   power1d.noise.Mixture( noise0, noise1 )    )
### compound type (Scaled):
noise0    = power1d.noise.Gaussian( J, Q, mu=0, sigma=1 )
scale     = np.linspace(0, 1, Q)
noise.append(   power1d.noise.Scaled( noise0, scale )   )
### compound type (SignalDependent):
noise0    = power1d.noise.Gaussian(J=J, Q=101, sigma=0.1)
signal    = power1d.geom.GaussianPulse(Q=101, q=65, amp=2.5, fwhm=20)
fn        = lambda nvalue,svalue: nvalue + nvalue*svalue**2
noise.append(   power1d.noise.SignalDependent(noise0, signal, fn=fn)   )






#(1) Plot:
pyplot.close('all')
pyplot.figure(figsize=(12,6))
for i,x in enumerate(noise):
	ax = pyplot.subplot(3, 5, i+1)
	x.plot(ax, color='b', lw=0.5)
	ax.text(0.05, 0.9, '%s' %x.__class__.__name__, transform=ax.transAxes, bbox=dict(facecolor='w', alpha=0.5))
pyplot.suptitle('All power1d.noise models', size=20)
# pyplot.show()