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.

Preparation

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

Setup the Python environment

[1]:
import warnings

warnings.filterwarnings("ignore")
warnings.simplefilter("ignore")

import holoviews as hv
import numpy as np
import pickle

from PyDynamic.uncertainty import interp1d_unc

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(("Time", "relative measurement time"), unit="s")
measurement_labels = hv.Dimension(("Current", "Primary Nominal Current"), unit="A")