## loops.plate_loop()


Main loop to process a plate.


Usage

``` python
loops.plate_loop(
    conn,
    plate_id,
    segmentation_mode=False,
    stitch_mode=False,
    delete_existing=False
)
```


## Parameters


`conn: BlitzGateway`  
Connection to OMERO

`plate_id: int`  
ID of the plate

`segmentation_mode: bool = ``False`  
Only perform image segmentation

`stitch_mode: bool = ``False`  
Run stitched-well segmentation instead of per-field

`delete_existing: bool = ``False`  
Delete the plate's stored segmentation masks before running, forcing a fresh segmentation. Without this, both the stitched and per-field paths reuse whatever masks are already in the dataset and only recompute the measurements.


## Returns


`pd.DataFrame`  
Tuple\[DataFrame, DataFrame, DataFrame, Dict\]: Three DataFrames containing the final data and quality control data;

`pd.DataFrame | None`  
dictionary of matplotlib figures of the inference gallery keyed by class (can be None)