# load_gam()


Load a fitted GAM from a `.npz` archive created by [save_gam](save_gam.md#whittaker.save_gam).


Usage

``` python
load_gam(path)
```


Reads back every piece of state that [save_gam](save_gam.md#whittaker.save_gam) wrote -- the formula, family, fitted coefficients and fit statistics, training design matrix and penalties, and each smooth's basis state (restored via an internal `_basis_from_state` helper that reconstructs the original `~whittaker.smooths.base.SmoothBasis` subclass without calling its constructor) -- and assembles them into a fully fitted `~whittaker.gam.GAM`. The returned model behaves exactly as it did before saving: `predict()`, `summary()`, [plot()](GAM.md#whittaker.GAM.plot), and [check()](check.md#whittaker.check) all work immediately, with no re-fitting or basis refitting performed.


## Parameters


`path: str or pathlib.Path`  
Path to the `.npz` file written by [save_gam](save_gam.md#whittaker.save_gam).


## Returns


`GAM`  
A fitted `~whittaker.gam.GAM` ready for prediction and inference.


## Examples


``` python
import numpy as np
import whittaker as wt
from whittaker.io import save_gam, load_gam

rng = np.random.default_rng(1)
x = np.sort(rng.uniform(0, 1, 150))
y = np.cos(3 * x) + rng.normal(scale=0.15, size=150)

model = wt.GAM("y ~ s(x)").fit({"x": x, "y": y})
save_gam(model, "gam_model.npz")

reloaded = load_gam("gam_model.npz")
reloaded.is_fitted
```


    True