def plotmask(maskwoutput=None, maxr=None, xray_image=None,
position_matrix=None):
"""
Plots the mask metrics allowing for evaluation of mask quality
"""
# highper,lowper,totalper, mask, imask,StatsA=maskwoutput
highper, lowper, totalper, mask, imask = maskwoutput
# May need matplotlib help from Tom on this
plt.ioff()
plt.clf()
# plot the graph of how the mask depends on radial distance
# TODO: use Q instead of r in the final product?
plt.plot(np.arange(0, maxr + 1, 1), highper[:], '-',
np.arange(0, maxr + 1, 1), lowper[:], 'o',
np.arange(0, maxr + 1, 1), totalper[:], '^')
plt.show()
# Plot the mask itself
# plot the overlay of the mask and the image
mask = plt.imshow(mask)
plt.show()
imagemask = plt.imshow(xray_image * imask)
plt.show()
t = np.arange(0, maxr, 1)
plt.plot(t, Calculate.mean1d(xray_image,
position_matrix=position_matrix * imask,
step=1), 'b',
# t, Median1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask), 'r',\
# t,Median1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask)-Mean1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask)
)
plt.show()
oldstd = np.sqrt(
Calculate.variance1d(xray_image, position_matrix=position_matrix,
max=maxr, step=1))
newstd = np.sqrt(Calculate.variance1d(xray_image,
position_matrix=position_matrix * imask,
max=maxr, step=1))
plt.plot(t, oldstd, 'b', t, newstd, 'r', t, oldstd - newstd, 'g')
plt.show()
