Module see.tests.test_genetic_search

"""This runs unit tests for functions that can be found in GeneticSearch.py."""
import numpy as np
from see import Segmentors
from see import GeneticSearch
from see import base_classes


def test_two_point_copy():
    """Unit test for twoPointCopy function. Checks test individuals to see
     if copy took place successfully."""
    np1 = ['FB', 0, 0, 984, 0.09, 92, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           (1, 2), 0, "checkerboard", "checkerboard", 0, 0, 0, 0, 0, 0]
    np2 = ['CT', 0, 0, 0, 0, 0, 0, 0, 8, 10, 12, 0, 0, 0, 0,
           (1, 2), 0, "checkerboard", "checkerboard", 0, 0, 0, 0, 0, 0]
    new_np1, new_np2 = GeneticSearch.twoPointCopy(np1, np2, True)
    assert new_np1 == ['FB', 0, 0, 984, 0.09, 92, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                       (1, 2), 0, 'checkerboard', 'checkerboard', 0, 0, 0, 0, 0, 0]
    assert new_np2 == ['CT', 0, 0, 0, 0, 0, 0, 0, 8, 10, 12, 0, 0, 0, 0,
                       (1, 2), 0, 'checkerboard', 'checkerboard', 0, 0, 0, 0, 0, 0]


def test_skimage_cross_random():
    """Unit test for skimageCrossRandom function. Checks test individuals to see if crossover
     took place successfully."""
    np1 = ['FB', 0, 0, 984, 0.09, 92, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           (1, 2), 0, "checkerboard", "checkerboard", 0, 0, 0, 0, 0, 0]
    np2 = ['CT', 0, 0, 0, 0, 0, 0, 0, 8, 10, 12, 0, 0, 0, 0,
           (1, 2), 0, "checkerboard", "checkerboard", 0, 0, 0, 0, 0, 0]
    new_np1, new_np2 = GeneticSearch.skimageCrossRandom(np1, np2)
    assert new_np1 == ['FB', 0, 0, 984, 0.09, 92, 0, 0, 8, 0, 0, 0, 0, 0, 0,
                       (1, 2), 0, 'checkerboard', 'checkerboard', 0, 0, 0, 0, 0, 0]
    assert new_np2 == ['CT', 0, 0, 0, 0, 0, 0, 0, 0, 10, 12, 0, 0, 0, 0,
                       (1, 2), 0, 'checkerboard', 'checkerboard', 0, 0, 0, 0, 0, 0]

def test_make_toolbox():
    """Unit test for makeToolbox function. Checks that a toolbox of the
     correct size was made."""
    assert GeneticSearch.makeToolbox(
        10, Segmentors.segmentor).population.keywords['n'] == 10


def test_newpopulation():
    """Unit test for newpopulation function. Checks the type and length of
     the new population."""
    img = np.zeros((20, 20, 3))
    img[4:10, 4:10, :] = 1
    mask = img[:, :, 0]

    data = base_classes.pipedata()
    data.img = img
    data.gmask = mask
    data.fitness = 2
    evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
    assert isinstance(evolv.tool.population(), list)
    assert len(evolv.tool.population()) == 10

# def test_popfitness():
#     """Unit test for popfitness function. Checks the type and length of the fitness
#      values and population."""
#     img = np.zeros((20, 20, 3))
#     img[4:10, 4:10, :] = 1
#     mask = img[:, :, 0]
#     evolv = GeneticSearch.Evolver(img, mask)
#     fits, tpop = evolv.popfitness(evolv.tool.population())
#     assert isinstance(fits, list)
#     assert len(fits) == 10
#     assert isinstance(tpop, list)
#     assert len(tpop) == 10

# TODO: Failing test. Segmentors has no attribute 'parameters'.
# def test_mutate():
#     """Unit test for mutate function. Checks output type and checks test individual
#      to see if mutation took place successfully."""
#     copy_child = ['FB', 0, 0, 984, 0.09, 92, 0, 0, 0, 0, 0, 0, 0, 0, 0,
#                   (1, 2), 0, "checkerboard", "checkerboard", 0, 0, 0, 0, 0, 0]
#     all_vals = []
#     params = Segmentors.parameters()
#     for key in params.pkeys:
#         all_vals.append(eval(params.ranges[key]))
#     assert isinstance(GeneticSearch.mutate(
#         copy_child, all_vals, 0.5, True), list)
#     assert GeneticSearch.mutate(copy_child, all_vals, 0.5, True) ==\
#         ['FB', 1390, 0.173, 984, 0.09, 9927, 587, 0, 0.55, 0, 0, 0, 0, 1000, 0,
#          (1, 2), 0, 'disk', 'checkerboard', 9, 2907, -47, (0.0, 0.0, 0.0), 0, 0]

def test_mutate():
    """Unit test for mutate function. Checks type and length of the
     new population after mutation."""
    img = np.zeros((20, 20, 3))
    img[4:10, 4:10, :] = 1
    mask = img[:, :, 0]

    data = base_classes.pipedata()
    data.img = img
    data.gmask = mask
    evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
    tpop = evolv.mutate(evolv.tool.population())
    assert isinstance(tpop, list)
    assert len(tpop) == 10


def test_nextgen():
    """Unit test for nextgen function. Checks the type and length of the new population,
     and checks that the population is evolving."""
    img = np.zeros((20, 20, 3))
    img[4:10, 4:10, :] = 1
    mask = img[:, :, 0]

    data = base_classes.pipedata()
    data.img = img
    data.gmask = mask
    evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
    pop = evolv.tool.population()
    tpop = evolv.mutate(pop)
    assert isinstance(tpop, list)
    assert len(tpop) == 10
    assert tpop != pop


def test_run():
    """Unit test for run function. Checks the type and length of the final population,
     and checks that the population evolved."""
    img = np.zeros((20, 20, 3))
    img[4:10, 4:10, :] = 1
    mask = img[:, :, 0]

    data = base_classes.pipedata()
    data.img = img
    data.gmask = mask
    data.fitness = 2
    evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
    start_pop = evolv.tool.population()
    final_pop = evolv.run()
    assert isinstance(final_pop, list)
    assert len(final_pop) == 10
    assert final_pop != start_pop