This is a low-level implementation of the Largest-Triangle-Three-Buckets (LTTB) downsampling algorithm written in Python.

The code has been translated from the work of Sveinn Steinarsson (https://github.com/sveinn-steinarsson/flot-downsample/).

The examples show the efficiency of the downsampling algorithm with a data set set of 5000 data points down sampled to 500 and 250 points.

Known features and requirements:

• The algorithm requires monotonically increasing x data (finite)
• The algorithm requires finite y data (otherwise problems might occur)
• x and y data have to be of same length (of course)
• The algorithm returns arrays of dtype double

You can also install it from PyPI to use in other environments with Python 3.5 or later:

pip install lttbc

The module lttbc differs in the standard input from other largest triangle three buckets implementations. The downsample function takes an input for x and y in addition to the threshold:

import lttbc
import numpy as np
ARRAY_SIZE = 10000
THRESHOLD = 1000
x = np.arange(ARRAY_SIZE, dtype=np.int32)
y = np.random.randint(1000, size=ARRAY_SIZE, dtype=np.uint64)
nx, ny = lttbc.downsample(x, y, THRESHOLD)
assert len(nx) == THRESHOLD
assert len(ny) == THRESHOLD
assert nx.dtype == np.double
assert ny.dtype == np.double
# List data or a mixture is accepted as well ...
x = list(range(ARRAY_SIZE))
y = [np.random.uniform(0, 1000) for _ in range(ARRAY_SIZE)]
assert isinstance(x, list)
assert isinstance(y, list)
nx, ny = lttbc.downsample(x, y, THRESHOLD)
assert len(nx) == THRESHOLD
assert len(ny) == THRESHOLD
assert nx.dtype == np.double
assert ny.dtype == np.double