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authorHolden Rohrer <hr@hrhr.dev>2021-01-10 21:15:58 -0500
committerHolden Rohrer <hr@hrhr.dev>2021-01-10 21:15:58 -0500
commit50ad3052a0b0c2245b6f3f481f12322391737aa5 (patch)
tree30890317d114c876e6c09565b234fa307a103815
parent2fb9d493a9c08d1e75e51eee2740fc79ff407717 (diff)
working on new graphs
-rw-r--r--py/boxplot.py13
-rw-r--r--py/data2.py154
-rw-r--r--py/deathtable.py4
-rw-r--r--py/deathtable2.py12
-rw-r--r--py/img.py7
5 files changed, 188 insertions, 2 deletions
diff --git a/py/boxplot.py b/py/boxplot.py
new file mode 100644
index 0000000..7d2cf1e
--- /dev/null
+++ b/py/boxplot.py
@@ -0,0 +1,13 @@
+from data2 import trials
+import matplotlib.pyplot as plt
+
+vecs = []
+names = []
+for trial in trials:
+ vecs.append(trial.nearest_neighbor())
+ names.append(str(trial))
+
+plt.boxplot(vecs, labels=names)
+plt.title('Nearest Neighbor for Each Pit in Different Trials')
+plt.ylabel('Distance to Nearest Neighbor (cm)')
+plt.show()
diff --git a/py/data2.py b/py/data2.py
new file mode 100644
index 0000000..505a79d
--- /dev/null
+++ b/py/data2.py
@@ -0,0 +1,154 @@
+# Data
+from scipy.spatial import Voronoi, voronoi_plot_2d, KDTree, distance
+from enum import Enum
+import matplotlib.pyplot as plt
+
+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)
+ 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
+
+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),(21,4),(21,7)]),
+ Obstacle([(17,15),(18,20),(23,19),(21,20)])
+ ]),
+ 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),(3,5),(3,8)]) ]),
+]
+
diff --git a/py/deathtable.py b/py/deathtable.py
index 45e4f08..fc9728e 100644
--- a/py/deathtable.py
+++ b/py/deathtable.py
@@ -1,4 +1,6 @@
from data import trials
for trial in trials:
- print('& '.join(['$\\times$'.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('& '.join(['$\\times$'.join([str(el) for el in trial.size]),
+ str(trial.date), str(trial.intro), str(trial.dead),
+ str(len(trial.pits))])+'\\cr\\noalign{\\hrule}')
diff --git a/py/deathtable2.py b/py/deathtable2.py
new file mode 100644
index 0000000..401d42e
--- /dev/null
+++ b/py/deathtable2.py
@@ -0,0 +1,12 @@
+from statistics import mean
+from data2 import trials, fancy
+
+print('\\tabrule Size & Interruption & Reclusive & Dead & Pits formed &\
+Nearest Neighbor (avg) & Width (avg) & Depth (avg)\\cr\\tabrule')
+
+for trial in trials:
+ print('& '.join(['$'+'\\times'.join([str(el) for el in trial.size])+'$',
+ names[trial.method], str(trial.recl), str(trial.dead),
+ str(len(trial.pits)), '%.2f' % mean(trial.nearest_neighbor()),
+ '%.2f' % mean([pit.diam for pit in trial.pits]),
+ '%.2f' % mean([pit.depth for pit in trial.pits])])+'\\cr\\tabrule')
diff --git a/py/img.py b/py/img.py
index 177a7a2..c28bed0 100644
--- a/py/img.py
+++ b/py/img.py
@@ -1,4 +1,9 @@
from data import trials
for trial in trials:
- trial.plot(save=True);
+ trial.plot(save=True)
+
+from data2 import trials
+
+for trial in trials:
+ trial.plot(save=True)