Examples

Below, we have compiled a list of relevant examples and linked their corresponding jupyter notebooks with detailed commentary. All the available tools can be used on their own or in conjunction with other modules. For additional information regarding individual packages, refer to the signposted sources throughout the website.

📦 Punpy as a Standalone Package

🗸 General use cases

An example showcasing the capabilitites of the punpy package can be found here.

This jupyter notebook covers the following concepts:

1. Calibration of L0 data to L1
2. Propagation of various types of uncertainties: - uncorrelated (random) uncertainties - fully correlated (systematic) uncertainties - uncertainties associated with structured errors
3. Correlation along one, two, or more dimensions of a variable
4. Multidimensional input quantities, where a certain correlation structure is known along one dimension, while the other dimensions are random or systematic.

🗸 Punpy vs. NIST

We also have compiled some validation examples here, where the punpy results are compared against the NIST uncertainty machine.

Here, we have replicated the following examples available on the NIST uncertainty machine user manual.

1. End-gauge calibration
2. Dynamic viscosity
3. Resistance
4. Stefan-Boltzmann constant
5. Voltage reflection coefficient

❕ All the obtained results are fully consistent with the results of the NIST uncertainty machine.

📋 Digital Effects Tables (DET)

🗸 Defining DET

A notebook containing examples that define digital effects tables is available here.

It covers the following concepts:

• How obsarray can be used as a templater for efficiently making xarray datasets (both with and without uncertainties)?
• How, using obsarray’s special variable types (uncertainties and flags), datasets including detailed uncertainty and covariance information as well as quality flags can be created?
• An example for a digital effects table quantifying the uncertainties and error-correlation of the gas temperature, pressure, and the number of moles. Here, the uncertainties can be efficiently and easily propagated through a measurement function using punpy (learn more).

🗸 Utilising obsarray & punpy

1. An example showcasing the application of obsarray can be found here.

2. An example of using punpy with digital effects tables created with obsarray is explained here.

This notebook outlines how digital effects tables that are created with obsarray, can be propagated through a measurement function using punpy.

• At first, we calculate the uncertainties in a volume of gas, using the ideal gas law and a digital effects table.
• Then we quantify the uncertainties and error-correlation of the gas temperature, pressure and amount of substance.

☄️ Comet_maths interpolation

🗸 How to interpolate data with uncertainties?

A jupyter notebook for interpolation with uncertainties can be found here.

This example covers the following concepts:

• Interpolation
  1. Linear
  2. Quadratic
  3. Cubic
• Unknown input uncertainties (e.g. model uncertainties)
• Known measurment uncertainties
• Monte Carlo uncertainty propagation
• Extrapolation
• 1D interpolation along high-resolution example

🗂️ Project specific examples

In this section, we have compiled a list of external projects and examples that have utilised the CoMet Toolkit.

🗸 HYPERNETS example

• 🛰️ LANDHYPERNET flags and uncertainty propagation (through band integration over S2 SRF) is available here.