Basic interpolation with PyDynamic.uncertainty.interpolate.interp1d_unc

This is the second notebook in the series to illustrate the use of our method interp1d_unc. We will conduct a simple interpolation and return the sensitivity coeffients.


First we setup our Python and plotting environment and collect all previously extracted measurement data and their visualization.

Setup the Python environment

import warnings


import holoviews as hv
import numpy as np
import pickle

from PyDynamic.uncertainty import interp1d_unc

Setup plotting environment and labels

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

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