Interpolation with `PyDynamic.uncertainty.interpolate.interp1d_unc`¶

In this series of notebooks we illustrate the use of our method interp1d_unc, which is very much inspired by SciPy’s interp1d method and therefore closely aligned with its signature and corresponding capabilities. The main features are:

interpolation of measurement values and associated uncertainties
available interpolation methods are linear, next, nearest, previous (aligned with from Scipy’s interp1d
extrapolation of measurement values and associated uncertainties based on the values at the original data’s bounds or by custom input
returning sensitivity coefficients
performance oriented implementation supported by optional parameters
copy
assume_sorted

Comprehensive details about the parameters meanings, you find on pydynamic.readthedocs.io.

Content¶

The examples proceed according to the following scheme.

01 Basic measurement data pre-processing.ipynb¶

Set up the appropriate execution and plotting environment.
Download an example data set of real sensor recordings from Zenodo.org.
Visualize the relevant part of the contained data.

02 Basic interpolation.ipynb¶

Conduct a simple interpolation.

03 Basic extrapolation.ipynb¶

Conduct simple interpolation and extrapolation outside the original bounds.
Demonstrate returning the sensitivity coefficients

Setup the Python environment¶

[1]:

import holoviews as hv
import numpy as np
import h5py
import pickle
from download import download

Setup plotting environment and labels¶

[2]:

# Set one of the available plotting backends ('plotly', 'bokeh', 'maplotlib').
hv.extension("bokeh")

# Define labels and units for plots.
timestamp_labels = hv.Dimension("relative measurement time", unit="s")
measurement_labels = hv.Dimension("Primary Nominal Current", unit="A")

Interpolation with PyDynamic.uncertainty.interpolate.interp1d_unc¶