diff options
author | Holden Rohrer <hr@hrhr.dev> | 2020-07-06 18:20:32 -0400 |
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committer | Holden Rohrer <hr@hrhr.dev> | 2020-07-06 18:41:47 -0400 |
commit | a1d245cfd1979ec78bbc01e5125af80071f8cc42 (patch) | |
tree | 9bf0ec38631e4f0879940dc4a0b133d78fc2f17c /py | |
parent | 5a18c8a33b90003a2c930a207f766800883c3622 (diff) |
File reorganization
More makefile-friendly
Diffstat (limited to 'py')
-rw-r--r-- | py/data.py | 102 | ||||
-rw-r--r-- | py/deathtable.py | 7 | ||||
-rw-r--r-- | py/depwid.py | 22 | ||||
-rw-r--r-- | py/img.py | 4 | ||||
-rw-r--r-- | py/neighbor.py | 21 | ||||
-rw-r--r-- | py/table.py | 15 |
6 files changed, 171 insertions, 0 deletions
diff --git a/py/data.py b/py/data.py new file mode 100644 index 0000000..de850e8 --- /dev/null +++ b/py/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/py/deathtable.py b/py/deathtable.py new file mode 100644 index 0000000..f393cc8 --- /dev/null +++ b/py/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/py/depwid.py b/py/depwid.py new file mode 100644 index 0000000..081167f --- /dev/null +++ b/py/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/py/img.py b/py/img.py new file mode 100644 index 0000000..177a7a2 --- /dev/null +++ b/py/img.py @@ -0,0 +1,4 @@ +from data import trials + +for trial in trials: + trial.plot(save=True); diff --git a/py/neighbor.py b/py/neighbor.py new file mode 100644 index 0000000..004979a --- /dev/null +++ b/py/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/py/table.py b/py/table.py new file mode 100644 index 0000000..1d11779 --- /dev/null +++ b/py/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=''); |