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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='small')
plt.xlabel('%s (dimension %dx%dcm)' % (str(self.date), self.size[0], self.size[1]))
if save:
plt.savefig(str(self.date)+'.png', 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]]
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([16,9],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),
]),
]
from sys import argv
from math import sqrt
from numpy import poly1d, polyfit
from scipy.stats import pearsonr
from collections import Counter
if len(argv) < 2:
print('You must provide at least one argument to choose the function of this program: `img` or `nei` or `depwid')
quit()
arg = argv[1]
if arg == 'img':
for trial in trials:
trial.plot(save=True)
elif arg == 'nei':
x = []
y = []
for trial in trials:
size = sqrt(trial.size[0]*trial.size[1])
nei = trial.nearest_neighbor()
#for n in range(len(nei)):
# nei[n] += n*.05
#x += [sqrt(trial.size[0]*trial.size[1])]*len(nei)
#y += nei
x.append(size)
y.append(sum(nei)/len(nei))
fig = plt.figure()
ax = fig.add_subplot(111)
plt.text(0.1, 0.9, 'R^2 = %.3f\np=%.3f' % (pearsonr(x,y)[0]**2, pearsonr(x,y)[1]), ha='center', va='center', transform=ax.transAxes)
plt.xlabel('Square root of Trial Area (cm)')
plt.ylabel('Nearest Neighbor for Individual Pits (cm)')
plt.plot(x, y, 'bo')
plt.plot(x, poly1d(polyfit(x, y, 1))(x))
plt.savefig('nearest_neighbor.png', bbox_inches='tight')
elif arg == 'depwid':
depths, widths, sizes = [], [], []
for trial in trials:
size = sqrt(trial.size[0]*trial.size[1])
for pit in trial.pits:
sizes.append(size)
depths.append(pit.depth)
widths.append(pit.diam)
weights = [20*i for i in Counter(depths).values() for j in range(i)]
plt.scatter([size-.5 for size in sizes], depths, weights, 'b','o', label='depth')
weights = [20*i for i in Counter(widths).values() for j in range(i)]
plt.scatter(sizes, widths, weights, 'r', 'o', label='width')
plt.xlabel('Square root of Trial Area (cm)')
plt.ylabel('Depth/Width of Antlion Pits (cm)')
plt.legend(loc='upper right')
plt.text(10, 7, 'R^2 = %.3f\np=%.3f' % (pearsonr(sizes,depths)[0], pearsonr(sizes,depths)[1]), ha='center', va='center')
plt.savefig('depth_width.png', bbox_inches='tight')
if arg == 'table':
print('\\table{')
print('Dimensions (in)& Pit Depth (cm)& Pit Width (cm)& Nearest 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('& '.join(['x'.join([str(el) for el in size]), '%.1f' % pit.depth, '%.1f' % pit.diam, '%.2f' % nei[pitind]])+'\\cr\\noalign{\\hrule}')
print('}')
if arg == 'deathtable':
print('\\table{')
print('Trial Size& Date& Introduced& Deaths& Pits formed\\cr\\noalign{\\hrule}')
for trial in trials:
print('& '.join(['x'.join([str(el) for el in trial.size]), str(trial.date), str(trial.intro), str(trial.dead), str(len(trial.pits))])+'\\cr\\noalign{\\hrule}')
print('}')
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