Examples

Note

To run the example-notebooks, you also need the additional dependencies matplotlib and ipympl.

Basics

• Model definition and analysis.
• Linear combinations of distribution functions

Parameter Retrieval

What are the retrieval examples doing?

The retrieval examples show how to use rt1_model package together with scipy.optimize to retrieve model parameters from datasets via non-linear least squares optimization.

All examples follow the same basic structure (e.g. “closed-loop experiments”):

1. Select a model configuration.

2. Simulate a dataset using a set of random input-parameters.

3. Add some random noise to the data.

4. Use an optimization procedure to retrieve the parameters from the simulated dataset.

5. Check if the retrieved parameters are similar to the ones used for creating the dataset.

• Retrieval 1: Static parameters
• Retrieval 2: Timeseries
• Retrieval 3: Multi-parameter timeseries
• Retrieval 4: Using parameter functions
• Retrieval 5: Timeseries with auxiliary data
• Retrieval 6: Multi-temporal parameter timeseries

Notes on first-order corrections

• Calculation of the fn-coefficients
• Number of expansion coefficients