# LeapGraph

Used to visualize a 1D signal.

```python
import numpy.typing as npt
from code_loader.contract.enums import LeapDataType

@dataclass
class LeapGraph:
    data: npt.NDArray[np.float32]
    type: LeapDataType = LeapDataType.Graph
    x_label: Optional[str] = None
    y_label: Optional[str] = None
    x_range: Optional[Tuple[float,float]] = None
    legend: Optional[List[str]] = None
```

<table><thead><tr><th width="167.39065467110788">Args</th><th></th></tr></thead><tbody><tr><td><code>data</code></td><td>an np.ndarray float32 [N,1] array where N is the number of points in the graph.</td></tr><tr><td><code>x_label</code></td><td>(optional) - the x label for the graph</td></tr><tr><td><code>y_label</code></td><td>(optional) - the y label for the graph</td></tr><tr><td><code>x_range</code></td><td>(optional) - controlling the ticks on the x axis by supplying a minimum and maximum value</td></tr><tr><td><code>legend</code></td><td>(optional) - a legend for distinguishing between different lines plotted in the same  graph</td></tr></tbody></table>

## Examples

#### Basic Usage

```python
from code_loader.contract.visualizer_classes import LeapGraph
from code_loader.contract.enums import LeapDataType
import numpy as np
...

@tensorleap_custom_visualizer("diff", LeapDataType.Graph)
def diff_per_channel_visualizer(prediction: np.ndarray, ground_truth: np.array) -> LeapGraph:
    diff = pred - ground_truth
    return LeapGraph(diff)
```


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