43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
data = [[float(y) for y in x.strip().split(', ')] for x in open('block_datadump.csv').readlines()]
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for i in range(0, 2283416, 200000):
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print 'Checking 200k blocks from %d' % i
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dataset = []
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for j in range(i, min(i + 200000, 2283400), 100):
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gas = 0
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nonzeroes = 0
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txs = 0
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uncs = 0
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zeroes = 0
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for num, _uncs, uncrew, uncgas, _txs, _gas, _length, _zeroes in data[j:j+100]:
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txs += _txs
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gas += _gas
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nonzeroes += _length - _zeroes
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zeroes += _zeroes
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uncs += _uncs
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dataset.append([gas, txs, nonzeroes, zeroes, uncs])
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mean_x = sum([x[0] for x in dataset]) * 1.0 / len(dataset)
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mean_y = sum([x[-1] for x in dataset]) * 1.0 / len(dataset)
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covar = sum([(x[0] - mean_x) * (x[-1] - mean_y) for x in dataset])
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var = sum([(x[0] - mean_x) ** 2 for x in dataset])
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for d in dataset:
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d.append(d[-1] - covar / var * (d[0] - mean_x))
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mean_x1 = sum([x[1] for x in dataset]) * 1.0 / len(dataset)
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mean_x2 = sum([x[2] for x in dataset]) * 1.0 / len(dataset)
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mean_x3 = sum([x[3] for x in dataset]) * 1.0 / len(dataset)
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mean_y2 = sum([x[-1] for x in dataset]) * 1.0 / len(dataset)
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covar1 = sum([(x[1] - mean_x1) * (x[-1] - mean_y2) for x in dataset])
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var1 = sum([(x[1] - mean_x1) ** 2 for x in dataset])
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covar2 = sum([(x[2] - mean_x2) * (x[-1] - mean_y2) for x in dataset])
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var2 = sum([(x[2] - mean_x2) ** 2 for x in dataset])
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covar3 = sum([(x[3] - mean_x2) * (x[-1] - mean_y2) for x in dataset])
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var3 = sum([(x[3] - mean_x2) ** 2 for x in dataset])
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print 'Base m =', covar / var
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print 'Base b =', mean_y - mean_x * (covar / var)
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print 'Excess m for txs=', covar1 / var1
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print 'Excess m for nonzero bytes=', covar2 / var2
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print 'Excess m for zero bytes=', covar3 / var3
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