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# Data
from scipy.spatial import Voronoi, voronoi_plot_2d, KDTree, distance
import matplotlib.pyplot as plt
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 Date:
def __init__(self, year, month, day):
self.year = year
self.month = month
self.day = day
def __repr__(self):
return '%d-%d-%d' % (self.year, self.month, self.day)
class Trial:
def __init__(self, date, intro, dead, size, pits):
self.date = date
self.intro = intro
self.dead = dead
self.size = size
self.pits = pits
self.pitlocs = [pit.loc for pit in self.pits]
def __repr__(self):
return str(self.date) + ' ' + str(self.pits)
def plot(self, save=False):
vor = Voronoi([pit.loc for pit in self.pits])
voronoi_plot_2d(vor)
for pit in self.pits:
plt.text(pit[0], pit[1], pit.disp(), ha='center', va='bottom', size='xx-small')
plt.xlabel('%s (dimension %dx%dcm)' % (str(self.date), self.size[0], self.size[1]))
if save:
plt.savefig(str(self.date)+'.png')
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]]
trials = [
Trial(Date(2019, 10, 16), 31, 6, [33,32], [
Pit([4,25],1.3,4.2),
Pit([3,13],1.4,3.7),
Pit([10,25],1.1,3.0),
Pit([18,18],1.8,2.3),
Pit([30,14],2.2,3.1),
Pit([29,11],1.4,2.5),
Pit([27,10],1.2,2.1),
Pit([29,6],2.4,3.9),
Pit([26,5],1.8,3.6),
]),
Trial(Date(2019, 10, 30), 27, 3, [24,24], [
Pit([4,21],2.0,7.0),
Pit([7,22],2.5,4.1),
Pit([2,9],0.5,2.0),
Pit([12,18],1.2,2.5),
Pit([12,11],1.2,3.0),
Pit([20,11],1.0,3.0),
Pit([19,2],1.5,4.0),
]),
Trial(Date(2019, 12, 3), 19, 3, [17, 16], [
Pit([14,5],1.3,4.1),
Pit([12,2],1.2,3.8),
Pit([5,1],0.9,3.2),
Pit([15,17],2.2,3.8),
Pit([12,17],1.2,2.5),
Pit([7,17],2.0,5.0),
Pit([1,17],1.8,3.6),
]),
Trial(Date(2019, 12, 5), 10, 0, [17, 16], [
Pit([17,4],1.3,3.1),
Pit([10,4],1.5,3.1),
Pit([18,9],1.4,2.9),
]),
Trial(Date(2019, 12, 19), 12, 4, [8,7], [
Pit([4,7],.8,.9),
Pit([3,5],.9,.8),
Pit([8,2],2,3),
]),
Trial(Date(2019, 12, 20), 5, 0, [8,7], [
Pit([6,7],.8,.8),
Pit([2,2],.8,.8),
Pit([8,6],.8,.8),
Pit([2,9],.8,.8),
]),
]
import sys
import math
import numpy as np
from scipy.stats import pearsonr
if len(sys.argv) < 2:
print('You must provide at least one argument to choose the function of this program: `img` or `nei` or `depwid')
quit()
arg = sys.argv[1]
if arg == 'img':
for trial in trials:
trial.plot(save=True)
elif arg == 'nei':
x = []
y = []
for trial in trials:
size = math.sqrt(trial.size[0]*trial.size[1])
nei = trial.nearest_neighbor()
#for n in range(len(nei)):
# nei[n] += n*.05
#x += [math.sqrt(trial.size[0]*trial.size[1])]*len(nei)
#y += nei
x.append(size)
y.append(sum(nei)/len(nei))
print('p-value', pearsonr(x,y))
plt.xlabel('Square root of Trial Area (cm)')
plt.ylabel('Nearest Neighbor for Individual Pits (cm)')
plt.plot(x, y, 'bo')
plt.plot(x, np.poly1d(np.polyfit(x, y, 1))(x))
plt.show()
elif arg == 'depwid':
for trial in trials:
size = math.sqrt(trial.size[0]*trial.size[1])
for pit in trial.pits:
plt.plot(size, pit.depth, 'bo')
plt.plot(size, pit.diam, 'ro')
plt.xlabel('Square root of Trial Area (cm)')
plt.ylabel('Depth/Width of Antlion Pits (cm)')
plt.show()
if arg == 'table':
print('Dimensions (in)\tPit Depth (cm)\tPit Width (cm)\tNearest Neighbor (cm)')
for trial in trials:
size = trial.size
nei = trial.nearest_neighbor()
for pitind in range(len(trial.pits)):
pit = trial.pits[pitind]
print('\t'.join(['x'.join([str(el) for el in size]), str(pit.depth), str(pit.diam), str(nei[pitind])]))
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