Default weighting function normalization

[aywagner]

The default linear weighting function is being normalized per diagram rather than being fixed or normalized per experiment. As a consequence, the norm of a persistence image corresponding to a diagram with one point very close to the diagonal is not close to zero.

persim/persim/images.py

Lines 151 to 160 in 76b2b5a

	if landscape is not None:
	if len(landscape) > 0:
	maxy = np.max(landscape[:, 1])
	else:
	maxy = 1
	def linear(interval):
	# linear function of y such that f(0) = 0 and f(max(y)) = 1
	d = interval[1]
	return (1 / maxy) * d if landscape is not None else d