have a complete build for secondary docs, but tables and imgs not fully integrated

4 files changed, 79 insertions, 43 deletions

diff --git a/py/data2.py b/py/data2.py
index 505a79d..a073f4b 100644
--- a/py/data2.py
+++ b/py/data2.py
@@ -2,6 +2,7 @@
 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'}
@@ -38,6 +39,9 @@ class Trial:
   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]),
@@ -53,6 +57,16 @@ class Trial:
 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], [
@@ -135,8 +149,8 @@ trials = [
     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)])
+    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),
@@ -149,6 +163,5 @@ trials = [
     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)]) ]),
+  ], fake=[ Obstacle([(10,2),(11,9),(3,8),(3,5)]) ]),
 ]
-
diff --git a/py/deathtable2.py b/py/deathtable2.py
index 401d42e..ec0f171 100644
--- a/py/deathtable2.py
+++ b/py/deathtable2.py
@@ -6,7 +6,7 @@ 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),
+  fancy[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/pvals.py b/py/pvals.py
new file mode 100644
index 0000000..9f46826
--- /dev/null
+++ b/py/pvals.py
@@ -0,0 +1,61 @@
+from data2 import trials, fancy
+from scipy.stats import cramervonmises
+from bisect import bisect_left
+from random import random
+
+def distance(pit1, pit2):
+        out = ((pit2[1]-pit1[1])**2 + (pit2[0]-pit1[0])**2)**(1/2)
+        if (out == 0):
+            return 100
+        else:
+            return out
+
+def cdf(a,nnsim,tup):
+    return [bisect_left(nnsim, x)/len(nnsim) for x in a]
+
+def calcpval(trialno):
+    trial = trials[trialno]
+    size = trial.size[0]
+    pits = len(trial.pits)
+
+    nnsim = []
+    for j in range(1000):
+        curpits = []
+        k = 0
+        while k < pits:
+            add = (random()*size,random()*size)
+            if trial.method == 'obstacles':
+                for obs in trial.fake:
+                    if obs.contains(add):
+                        continue
+            if trial.method == 'pits':
+                for pit in trial.fake:
+                    if distance(add,pit.loc) < pit.diam/2:
+                        continue
+            curpits.append(add)
+            k += 1
+        for pit in curpits:
+            l = size
+            for pit2 in curpits:
+                l = min(l,distance(pit,pit2))
+            nnsim.append(l)
+
+    nnsim.sort()
+
+    nnreal = trials[trialno].nearest_neighbor()
+    stat = cramervonmises(nnreal,cdf,(nnsim,()))
+    return stat.pvalue
+
+sizes = [[12,12],[24,24]]
+meths = ['obstacles','pits','trails']
+pvals = [['']*len(sizes) for x in range(len(meths))]
+for n in range(len(trials)):
+    trial = trials[n]
+    pvals[meths.index(trial.method)][sizes.index(trial.size)] = '%.4f' % calcpval(n)
+
+print('\\tabrule $12\\times12$&$24\\times24$\\cr\\tabrule')
+for row in range(len(meths)):
+    print(fancy[meths[row]],end='')
+    for col in range(len(sizes)):
+        print('&'+pvals[row][col],end='')
+    print('\\cr\\tabrule')
diff --git a/py/stats.py b/py/stats.py
deleted file mode 100644
index b79e56f..0000000
--- a/py/stats.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from data2 import trials
-from scipy.stats import cramervonmises
-from bisect import bisect_left
-from random import random
-
-def distance(pit1, pit2):
-        out = ((pit2[1]-pit1[1])**2 + (pit2[0]-pit1[0])**2)**(1/2)
-        if (out == 0):
-            return 100
-        else:
-            return out
-
-def cdf(a,nnsim,tup):
-    return [bisect_left(nnsim, x)/len(nnsim) for x in a]
-
-def calcpval(trialno):
-    size = trials[trialno].size[0]
-    pits = len(trials[trialno].pits)
-
-    nnsim = []
-    for j in range(10000):
-        curpits = []
-        for k in range(pits):
-            curpits.append([random()*size,random()*size])
-        for pit in curpits:
-            l = size
-            for pit2 in curpits:
-                l = min(l,distance(pit,pit2))
-            nnsim.append(l)
-
-    nnsim.sort()
-
-    nnreal = trials[trialno].nearest_neighbor()
-    stat = cramervonmises(nnreal,cdf,(nnsim,()))
-    return stat.pvalue
-
-for n in range(len(trials)):
-    print(calcpval(n))