# Requesting an output validity mask

This notebook shows how to obtain a binary validity mask for the resampled output. Output pixels are flagged invalid when the resampling could not produce a meaningful value (out-of-domain grid nodes, masked input pixels, etc.). Setting `nodata_out` lets you detect them in the image; an explicit mask makes the validity check unambiguous.

**What you'll learn**

- How to open a dedicated dataset for the output mask
- How to pass it to `basic_grid_resampling_chain` through `mask_out_ds`
- The GridR mask convention (`1 = valid`, `0 = invalid`)
- The 1-bit GeoTIFF storage option

## Setting things up

We build on top of the `(8, 8)` zoom case from the *Getting Started* notebook.

## Adding a mask output dataset

Pass an opened writable dataset through `mask_out_ds`. GridR's convention is `1 = valid` / `0 = invalid`. To minimise disk usage, the mask is stored as a 1-bit GeoTIFF (`nbits=1` GDAL option).


```python
with rasterio.open(GRID_IN_F64, "r") as grid_in_ds, \
        rasterio.open(RASTER_IN, "r") as array_src_ds, \
        rasterio.open(output_raster_path, "w", **raster_out_open_args) as array_out_ds, \
        rasterio.open(output_mask_path, "w", **mask_out_open_args) as mask_out_ds:

    basic_grid_resampling_chain(
        grid_ds              = grid_in_ds,
        grid_row_coords_band = 1,
        grid_col_coords_band = 2,
        grid_resolution      = grid_resolution,
        array_src_ds         = array_src_ds,
        array_src_bands      = 1,
        array_out_ds         = array_out_ds,
        interp               = "cubic",
        nodata_out           = 0,
        mask_out_ds          = mask_out_ds,    # <= dedicated mask dataset
    )
```


![002_mask_out.png](grid_resampling_chain_002_output_mask_files/002_mask_out.png)


The output mask makes it easy to filter or composite the resampled image downstream. Subsequent notebooks add input-side masks (grid mask, source raster mask, geometry masks) that all propagate to this output mask.