# mpyfuzzy

Evaluate simulation goodness using fuzzy logic. This tool analyzes simulation
results by comparing them with validation data using statistical metrics
(Pearson correlation coefficient and mean relative error) and applies fuzzy
logic to determine an overall goodness score.

The evaluation process:

- Calculates Pearson correlation coefficient (PCC) for linear correlation
- Calculates mean relative error (MRE) for deviation magnitude
- Applies fuzzy logic rules to combine metrics
- Determines overall goodness score
- Outputs results for each validation field to CSV files

Note that the simulation case has to be equipped with a valid
[`case.yml`](../case-metadata) and a `validation` section.

For more information refer to

> Hänsch, S., Evdokimov, I., Schlegel, F., & Lucas, D. (2021). A workflow for
> the sustainable development of closure models for bubbly flows. Chemical
> Engineering Science, 244, 116807.

## Usage

```bash
mpyfuzzy [options] case_directory
```

## Examples

### Basic Evaluation

Evaluate a case with default fuzzy logic parameters:

```bash
mpyfuzzy ./my-simulation-case
```

### Custom PCC Parameters

Provide custom parameters for Pearson correlation:

```bash
mpyfuzzy -pcc '{"low": 0.5, "medium": 0.7, "high": 0.9}' ./case
```

### Custom MRE Parameters

Adjust mean relative error thresholds:

```bash
mpyfuzzy -mre '{"low": 0.1, "medium": 0.05, "high": 0.01}' ./case
```

### Custom Goodness Parameters

Define custom goodness classification:

```bash
mpyfuzzy -goodness '{"poor": 0.3, "fair": 0.5, "good": 0.7,
"excellent": 0.9}' ./case
```

## Output

Results are written to `postProcessing/mpyfuzzy/<field>.csv` for each
validation field.

Example output:

```text
field,pcc,mre,goodness
alpha,0.95,0.023,0.87
velocity,0.89,0.045,0.76
pressure,0.92,0.031,0.82
```