# Adding Custom Visualization Functions

If you want to visualize data or results from a specific pipeline step — for example, neuron activity, motion correction results, or event detections — you can easily integrate visualization libraries such as **Matplotlib**.

There are two main approaches depending on your experience level:

***

**1. Quick & Simple: Add a Custom Method in `pipeline.py`**

If you just want to visualize results quickly, you can define a new method directly in the `CIPipe` class inside:

ci\_pipe/
\
└─ pipeline.py <-- modify this file

For example:

```
def my_function_matplot_view_neurons(self, step_number):
    self._plotter.get_neurons_step(
        self._trace_repository.load(),
        self._branch_name,
        step_number
    )
```

Then, in your script or notebook, you can call:

```
pipeline.my_function_matplot_view_neurons(step_nro=3)
```

This approach uses the internal `Plotter` class to display formatted results (with Rich tables and panels) and can be easily extended to include graphical visualizations.

***

**2. Recommended for Developers: Create or Extend a Plotter**

If you are comfortable with programming and want a cleaner or more modular design, you can extend the visualization system directly in:

ci\_pipe/
\
└─ plotter.py <-- modify this file

You can add a new import for Matplotlib and define your own visualization function:

```
import matplotlib.pyplot as plt

class Plotter:
    ...
    def get_neurons_step(self, trace, branch, step_number):
        step = self._get_step(trace, step_number, branch)
        if not step:
            return

        neuron_data = step["outputs"].get("neurons", [])
        plt.figure(figsize=(8, 4))
        plt.plot(neuron_data)
        plt.title(f"Neuron activity – step {step_number}")
        plt.show()
```

**Best Practice**

* If you have **basic or limited Python knowledge**, simply add a helper method in `pipeline.py`.
* If you are a **developer or advanced user**, extend or create a new `Plotter` class — this keeps your visualization logic modular and easier to maintain.
* You can also build specialized plotters (e.g., `IsxPlotter`, `QCPlotter`, `EventPlotter`) and integrate them into the pipeline using the same internal `Plotter` interface.


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