Metadata-Version: 2.1
Name: lttbc
Version: 0.2.1
Summary: Largest triangle three buckets module for Python written in C
Home-page: https://github.com/dgoeries/lttbc/
Author: European XFEL GmbH
Author-email: dennis.goeries@xfel.eu
Maintainer: Dennis Goeries
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.5
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy

# lttbc: Largest-Triangle-Three-Buckets (Python using a c implementation)
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/).

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**

## How to use on the field

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


