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# Data
from scipy.spatial import Voronoi, voronoi_plot_2d, KDTree, distance
from enum import Enum
import matplotlib.pyplot as plt
from matplotlib.path import Path
fancy = {'pits':'Artificial Pits', 'obstacles':'Artificial Obstacles',
'trails':'Trail Erasure'}
class Pit:
def __init__(self, loc, depth, diam):
self.loc = loc
self.depth = depth
self.diam = diam
def __repr__(self):
return '%s @ %.1fcm deep, %.1fcm wide' % (str(self.loc), self.depth, self.diam)
def disp(self):
return '%.1fcm dp\n %.1fcm wd' % (self.depth, self.diam)
def __getitem__(self,ind):
return self.loc[ind]
class Trial:
def __init__(self, method, recl, dead, size, pits, fake=None):
self.method = method
# intro = 15
self.recl = recl
self.dead = dead
self.size = size
self.pits = pits
self.pitlocs = [pit.loc for pit in self.pits]
self.fake = fake
def __repr__(self):
return f'{fancy[self.method]} {self.size[0]}'
def plot(self, save=False):
vor = Voronoi([pit.loc for pit in self.pits])
voronoi_plot_2d(vor)
if self.method == 'obstacles':
for obstacle in self.fake:
plt.fill(*obstacle.unzip())
plt.xlabel('%s (dimension %dx%dcm)' % (fancy[self.method], self.size[0], self.size[1]))
if save:
plt.savefig('%s-%dx%d.png'%(self.method,self.size[0],self.size[1]),
bbox_inches='tight')
else:
plt.show()
def nearest_neighbor(self):
tree = KDTree(self.pitlocs)
sumnn = 0
return [dists[1] for dists in tree.query(self.pitlocs,2)[0]]
class Obstacle:
def __init__(self, coords):
self.coords = coords
self.path = Path(coords)
def contains(self, point):
return self.path.contains_point(point)
def multicontains(self, points):
return self.path.contains_points(points)
def unzip(self):
return ([x[0] for x in self.coords],[x[1] for x in self.coords])
trials = [
Trial('trails', 1, 2, [24,24], [
Pit((24,1),1,2),
Pit((21,6),1,1),
Pit((18,18),2,3),
Pit((18,24),1,1),
Pit((23,23),5,3),
Pit((23,13),2,1),
Pit((20,6),1,1),
Pit((4,4),2,3),
Pit((14,5),4,5),
Pit((16,9),1,1),
Pit((9,10),3,4),
Pit((13,12),1,1),
]),
Trial('trails', 2, 3, [12,12], [
Pit((12,12),1,2),
Pit((12,0),1,1),
Pit((11,8),1,1),
Pit((11,5),4,7),
Pit((9,7),2,2),
Pit((9,9),3,4),
Pit((5,11),1,3),
Pit((5,9),1,3),
Pit((1,11),1,1),
Pit((3,4),1,3),
]),
Trial('pits', 1, 7, [24,24], [
Pit((1,23),1,1),
Pit((3,12),3,5),
Pit((17,5),2,5),
Pit((10,17),2,4),
Pit((23,0),1,1),
Pit((23,9),5,8),
Pit((24,24),1,1)
], fake=[
Pit((5,4),5,8),
Pit((7,2),5,8),
Pit((20,2),5,8),
Pit((23,4),5,8),
Pit((12,7),5,8),
Pit((7,18),5,8),
Pit((20,18),5,8),
Pit((5,17),5,8),
Pit((12,23),5,8),
Pit((14,23),5,8),
Pit((18,23),5,8),
Pit((14,18),5,8),
]),
Trial('pits', 4, 4, [12,12], [
Pit((2,12),1,2),
Pit((6,4),2,3),
Pit((6,7),5,8),
Pit((8,12),2,3),
Pit((12,5),1,1),
Pit((11,9),1,1),
Pit((2,9),1,1),
], fake=[
Pit((2,3),5,8),
Pit((2,12),5,8),
Pit((6,3),5,8),
Pit((6,7),5,8),
Pit((11,3),5,8),
Pit((11,11),5,8),
]),
Trial('obstacles', 0, 4, [24,24], [
Pit((1,20),1,1),
Pit((2,12),1,2),
Pit((12,0),1,1),
Pit((12,12),1,2),
Pit((20,15),5,5),
Pit((20,24),2,3),
Pit((11,22),3,5),
Pit((20,5),3,5),
Pit((21,8),2,6),
Pit((20,5),3,5),
Pit((21,8),2,6),
Pit((22,14),1,1),
Pit((22,16),2,4),
], fake=[
Obstacle([(8,3),(1,7),(7,16)]),
Obstacle([(18,1),(16,7),(21,7),(21,4)]),
Obstacle([(17,15),(18,20),(21,20),(23,19)])
]),
Trial('obstacles', 3, 0, [12,12], [
Pit((2,11),2,3),
Pit((2,1),1,1),
Pit((5,3),1,3),
Pit((6,10),1,1),
Pit((12,4),3,6),
Pit((11,6),1,3),
Pit((11,9),1,2),
Pit((10,11),2,4),
Pit((6,3),3,5),
Pit((12,9),1,2),
], fake=[ Obstacle([(10,2),(11,9),(3,8),(3,5)]) ]),
]
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