# Input data

The normal production input is a fitted PyMC-Marketing 1.x MMM saved with `mmm.save(...)`.

## Required

### `model.nc` or `model.zarr`

A fitted PyMC-Marketing 1.x MMM DataTree containing at least a `/posterior` group and serialised model configuration. The recommended form is the normal full save:

```python
mmm.fit(X, y)
mmm.save("inputs/model.nc", engine="h5netcdf")
```

A normal full 1.x DataTree save also contains `/fit_data`, which lets PyMC-Marketing reconstruct the model for approximate future predictions. A posterior-only NetCDF may be enough for the prior reweighting sweep if the model can still be loaded, but it is not sufficient for every prediction workflow.

## Optional

### `future.csv`

New predictor data for the recommended candidate. It must contain the same columns PyMC-Marketing expects for prediction: the date column, all media channel columns, control columns, and any custom panel dimensions such as `geo` or `brand`.

The sweep first importance-resamples the existing joint posterior under the candidate prior, then calls the loaded model's own `predict_posterior` method. This is an approximation to a refit under the new prior. Always perform a real validation fit before production promotion.

## Not required

The sweep does not need the original raw marketing dataset as a separate file when the full saved MMM already contains `fit_data`. It also does not require one NetCDF file per candidate prior. The point of the package is to test many candidate priors against one fitted reference posterior.
