#! /usr/bin/env python

import openturns as ot

ot.TESTPREAMBLE()


size = 10
dimension = 2
sample = ot.Sample(size, dimension)
# Fill-in the sample
for i in range(size):
    p = ot.Point(dimension)
    for j in range(dimension):
        p[j] = i + j * 1.0 / dimension
    sample[i] = p
print("sample=", repr(sample))
# History using the Null strategy
nullStrategy = ot.Null()
for i in range(size):
    nullStrategy.store(sample[i])
print("Null strategy sample=", repr(nullStrategy.getSample()))
# History using the Full strategy
fullStrategy = ot.Full()
fullStrategy.setDimension(dimension)
for i in range(size):
    fullStrategy.store(sample[i])
print("Full strategy sample=", repr(fullStrategy.getSample()))
# History using the Last strategy, large storage
lastStrategy = ot.Last(3 * size)
lastStrategy.setDimension(dimension)
for i in range(size):
    lastStrategy.store(sample[i])
print("Last strategy sample (large storage)=", repr(lastStrategy.getSample()))
lastStrategy = ot.Last(size // 3)
lastStrategy.setDimension(dimension)
# History using the Last strategy, small storage
for i in range(size):
    lastStrategy.store(sample[i])
print("Last strategy sample (small storage)=", repr(lastStrategy.getSample()))
# History using the Compact strategy, large storage
compactStrategy = ot.Compact(3 * size)
compactStrategy.setDimension(dimension)
for i in range(size):
    compactStrategy.store(sample[i])
print("Compact strategy sample (large storage)=", repr(compactStrategy.getSample()))
compactStrategy = ot.Compact(size // 3)
compactStrategy.setDimension(dimension)
# History using the Compact strategy, small storage
for i in range(size):
    compactStrategy.store(sample[i])
print("Compact strategy sample (small storage)=", repr(compactStrategy.getSample()))