kdlines 0.1

Kernel density estimation and heatmap visualisation on many lines, time series or 2D data using scikit-learn.

kdlines

Density heatmaps of many (time-)series using kernel density estimation using scikit-learn.

This package is at alpha stage and experimental.

Requirements

scikit-learn
numpy
matplotlib

Installation

pip install kdlines

Basic example

"""
A simple example based on numpy arrays and pandas DataFrame
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from kdlines import KernelDensityLines

# generate test data, 1000 series each of length 100
x_n = 100
y_n = 1000

# resolution of the heatmap y axis (i.e. number of grid cells)
y_res = 100

# arrays and generated data
x = np.linspace(0, x_n, x_n)
ys = np.empty((y_n, x_n))
randg = np.random.default_rng(seed=645)
for i in range(y_n):
    ys[i] = np.sin(-x / 3) + 0.5 * randg.standard_normal(1) + x * 0.1 + 11

# Data frames with datetime index to simulate time series with timestamps
ys_df = pd.DataFrame(index=x, data=ys.T)
ys_df.index = pd.date_range("2020-01-01", periods=x_n, freq="D")

# Estimation on the pandas DataFrame
kde_df = KernelDensityLines(
    y_res=y_res, kde_kernel='linear', bandwidth='scott'
)
kde_df.fit(y_lines=ys_df)

# Plotting as subplot
# sharex is False in this case since imshow is not plotted, only labeled, 
# with timestamps
fig, axs = plt.subplots(2, 1, figsize=(8, 8), sharey=True, sharex=False)
axs[0].plot(ys_df.index, ys_df.to_numpy(), lw=1)
kde_df.plot(ax=axs[1])
fig.tight_layout()

MIT License

Copyright (c) 2022 Reinert Huseby Karlsen

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.