cleaned up python directory structure

9 files changed, 182 insertions, 191 deletions

diff --git a/Makefile b/Makefile
index 2145259..dade053 100644
--- a/Makefile
+++ b/Makefile
@@ -10,16 +10,17 @@ all: outputs/posterboard.pdf outputs/report.pdf outputs/logbook.pdf
 png: outputs/posterboard.png
 .PHONY: clean
 clean:
-	rm -rf imgs graph outputs
+	rm -rf imgs graph outputs data/tables.tex
 
-$(IMGS): data.py
+$(IMGS): python/data.py python/img.py
 	cd imgs && \
-	python3 ../data.py img
+	python3 ../python/img.py
 
-$(GRAPHS): data.py 
-	cd graph && \
-	python3 ../data.py depwid && \
-	python3 ../data.py nei
+graph/depth_width.png: python/data.py python/depwid.py
+	cd graph && python3 ../python/depwid.py
+
+graph/nearest_neighbor.png: python/data.py python/neighbor.py
+	cd graph && python3 ../python/neighbor.py
 
 outputs/posterboard.png: outputs/posterboard.pdf 
 	gs -sDEVICE=pngalpha -r288 -o outputs/posterboard.png outputs/posterboard.pdf
@@ -45,6 +46,5 @@ outputs/qr.png:
 outputs/qr-source.png:
 	qrencode https://git.hrhr.dev/scifair -o outputs/qr-source.png
 
-
-data/tables.tex:
+data/tables.tex: data/maketables.sh data/tables.orig.tex
 	data/maketables.sh
diff --git a/data.py b/data.py
deleted file mode 100644
index 9aead79..0000000
--- a/data.py
+++ /dev/null
@@ -1,179 +0,0 @@
-# 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)
-    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)
-
-if 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')
-
-if 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('\\vtable{')
-  table = ['Dimensions (in)', 'Pit Depth (cm)', 'Pit Width (cm)', 'Nearest Neighbor (cm)',''];
-  lastsize = 0;
-  for trial in trials:
-    size = trial.size
-    table[0] += '&\\multispan{' + str(len(trial.pits)) + '}\\vfil\\line{\\hfil' + '$\\times$'.join([str(el) for el in size]) + '\\hfil}\\vfil\\hrule'
-    nei = trial.nearest_neighbor()
-    for pitind in range(len(trial.pits)):
-      pit = trial.pits[pitind]
-      table[1] += '&' + '%.1f' % pit.depth
-      table[2] += '&' + '%.1f' % pit.diam
-      table[3] += '&' + '%.2f' % nei[pitind]
-  print('\\cr\\noalign{\\vrule}\n'.join(table) + '}', end='');
-
-if arg == 'deathtable':
-  print('\\table{')
-  print('Trial Size& Date& Introduced& Deaths& Pits formed\\cr\\noalign{\\hrule}')
-  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('}', end='')
diff --git a/data/maketables.sh b/data/maketables.sh
index 0f043ad..dffc937 100755
--- a/data/maketables.sh
+++ b/data/maketables.sh
@@ -2,8 +2,7 @@
 
 export TABLE=`mktemp`
 export DEATHTABLE=`mktemp`
-echo $TABLE
-python data.py deathtable > $DEATHTABLE
-python data.py table > $TABLE
+python python/deathtable.py > $DEATHTABLE
+python python/table.py > $TABLE
 sed -e "/%%DEATHTABLE%%/{r $DEATHTABLE" -e "d}" -e "/%%MAINTABLE%%/{r $TABLE" -e "d}" <data/tables.orig.tex >data/tables.tex
 rm $DEATHTABLE $TABLE
diff --git a/python/data.py b/python/data.py
new file mode 100644
index 0000000..de850e8
--- /dev/null
+++ b/python/data.py
@@ -0,0 +1,102 @@
+# Data
+arg = ''
+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)
+    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),
+  ]),
+]
diff --git a/python/deathtable.py b/python/deathtable.py
new file mode 100644
index 0000000..f393cc8
--- /dev/null
+++ b/python/deathtable.py
@@ -0,0 +1,7 @@
+from data import trials
+
+print('\\table{')
+print('Trial Size& Date& Introduced& Deaths& Pits formed\\cr\\noalign{\\hrule}')
+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('}', end='')
diff --git a/python/depwid.py b/python/depwid.py
new file mode 100644
index 0000000..081167f
--- /dev/null
+++ b/python/depwid.py
@@ -0,0 +1,22 @@
+from data import trials
+from math import sqrt
+from scipy.stats import pearsonr
+from collections import Counter
+import matplotlib.pyplot as plt
+
+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')
diff --git a/python/img.py b/python/img.py
new file mode 100644
index 0000000..177a7a2
--- /dev/null
+++ b/python/img.py
@@ -0,0 +1,4 @@
+from data import trials
+
+for trial in trials:
+  trial.plot(save=True);
diff --git a/python/neighbor.py b/python/neighbor.py
new file mode 100644
index 0000000..004979a
--- /dev/null
+++ b/python/neighbor.py
@@ -0,0 +1,21 @@
+from data import trials
+from scipy.stats import pearsonr
+from numpy import poly1d, polyfit
+from math import sqrt
+import matplotlib.pyplot as plt
+
+x = []
+y = []
+for trial in trials:
+  size = sqrt(trial.size[0]*trial.size[1])
+  nei = trial.nearest_neighbor()
+  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')
diff --git a/python/table.py b/python/table.py
new file mode 100644
index 0000000..1d11779
--- /dev/null
+++ b/python/table.py
@@ -0,0 +1,15 @@
+from data import trials
+
+print('\\vtable{')
+table = ['Dimensions (in)', 'Pit Depth (cm)', 'Pit Width (cm)', 'Nearest Neighbor (cm)',''];
+lastsize = 0;
+for trial in trials:
+  size = trial.size
+  table[0] += '&\\multispan{' + str(len(trial.pits)) + '}\\vfil\\line{\\hfil' + '$\\times$'.join([str(el) for el in size]) + '\\hfil}\\vfil\\hrule'
+  nei = trial.nearest_neighbor()
+  for pitind in range(len(trial.pits)):
+    pit = trial.pits[pitind]
+    table[1] += '&' + '%.1f' % pit.depth
+    table[2] += '&' + '%.1f' % pit.diam
+    table[3] += '&' + '%.2f' % nei[pitind]
+print('\\cr\\noalign{\\vrule}\n'.join(table) + '}', end='');