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from ragGen import *
import csv
import numpy as np
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# Read in the data
data = open('../../data/data.csv', 'r').readlines()
fieldnames = ['x', 'y', 'z', 'unmasked', 'synapses']
reader = csv.reader(data)
reader.next()
rows = [[int(col) for col in row] for row in reader]
# These will come in handy later
sorted_x = sorted(list(set([r[0] for r in rows])))
sorted_y = sorted(list(set([r[1] for r in rows])))
sorted_z = sorted(list(set([r[2] for r in rows])))
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real_volume = np.zeros((len(sorted_y), len(sorted_x), len(sorted_z)))
for r in rows:
real_volume[sorted_y.index(r[1]), sorted_x.index(r[0]), sorted_z.index(r[2])] = r[-1]
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test_im = real_volume[1]
ragu = generate_rag(test_im, False)
ragu.number_of_edges()
# ragu.adjacency_list()
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