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BlockSci UTXO age distribution
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import blocksci | |
import pandas as pd | |
import numpy as np | |
chain = blocksci.Blockchain("/blocksci/bitcoin") | |
block_day = 6 * 24 | |
block_year = 365 * block_day | |
block_week = 7 * block_day | |
block_month = block_year // 12 | |
block_3month = block_year // 4 | |
block_6month = block_year // 2 | |
cutoffs = [0, | |
block_day, | |
block_week, | |
block_month, | |
block_3month, | |
block_6month, | |
block_year, | |
block_year + block_6month, | |
block_year * 2, | |
block_year * 3, | |
block_year * 5, | |
block_year * 20 | |
] | |
buckets = list(zip(cutoffs[:-1], cutoffs[1:])) | |
bucket_names = [ | |
"<1d", | |
"1d-1w", | |
"1w-1m", | |
"1-3m", | |
"3-6m", | |
"6-12m", | |
"12-18m", | |
"18-24m", | |
"2-3y", | |
"3-5y", | |
">5y" | |
] | |
def calculateImpl(blocks): | |
totals = np.zeros((len(chain), len(buckets))) | |
for block in blocks: | |
never_spent = np.sum(block.outputs.unspent.value) | |
spent_outputs = block.outputs.spending_tx.has_value | |
output_heights = block.outputs.spending_tx.block_height.with_value | |
output_ages = output_heights - block.height | |
output_values = block.outputs.value[spent_outputs] | |
age_sort = np.argsort(output_ages) | |
sorted_ages = output_ages[age_sort] | |
if len(output_values) > 0: | |
sorted_values = output_values[age_sort] | |
cuts = np.searchsorted(sorted_ages, cutoffs, side="left") | |
out = np.add.reduceat(np.concatenate((sorted_values, np.zeros(1, dtype=int))), cuts) | |
stop_points = np.empty_like(cuts) | |
stop_points[:-1] = cuts[1:] | |
stop_points[-1] = len(sorted_values) | |
out[cuts == stop_points] = 0 | |
for i, cut in enumerate(zip(cuts[:-1], cuts[1:])): | |
np.subtract.at(totals, (output_heights[age_sort][cut[0]:cut[1]], i), sorted_values[cut[0]:cut[1]]) | |
else: | |
out = np.zeros(len(cutoffs)) | |
out[-1] = never_spent | |
out = out[::-1].cumsum()[::-1] | |
for i, (start, end) in enumerate(buckets): | |
if block.height + start < len(chain): | |
totals[block.height + start, i] += out[i] | |
if i > 0: | |
totals[block.height + start, i - 1] -= out[i] | |
return totals | |
def calculateNet(blocks): | |
return calculateImpl(blocks).cumsum(axis=0) | |
def calculateNetMulti(chain): | |
def mapFunc(blocks): | |
return [calculateImpl(blocks)] | |
def reduceFunc(accum, new_val): | |
accum.extend(new_val) | |
return accum | |
parts = chain.mapreduce_block_ranges(mapFunc, reduceFunc) | |
return sum(parts).cumsum(axis=0) | |
data = calculateNetMulti(chain) | |
df = pd.DataFrame(data, index=chain.blocks.time) | |
total = df.sum(axis=1) | |
for i in range(11): | |
df[i] /= total | |
df.columns = bucket_names | |
df.plot.area() |
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