# cumulative_sum

Graph.cumulative_sum(x, axis=0, *, name=None)

Calculate the cumulative sum of a tensor along a specified dimension.

Parameters:
• x (np.ndarray or Tensor) – The tensor whose elements you want to sum. It must have at least one dimension.

• axis (int, optional) – The dimension along which you want to sum the tensor. Defaults to 0.

• name (str or None, optional) – The name of the node.

Returns:

The cumulative sum of x along dimension axis.

Return type:

Tensor

Examples

```>>> x = np.array([1, 2, 3])
>>> y = np.array([[1, 2, 3], [4, 5, 6]])
```

Calculate the cumulative sum of an array.

```>>> graph.cumulative_sum(x, axis=0, name="a")
<Tensor: name="a", operation_name="cumulative_sum", shape=(3,)>
>>> result = qctrl.functions.calculate_graph(graph=graph, output_node_names=["a"])
>>> result.output["a"]["value"]
array([1, 3, 6])
```

Calculate the cumulative sum of a 2D array along its first dimension.

```>>> graph.cumulative_sum(y, axis=0, name="b")
<Tensor: name="b", operation_name="cumulative_sum", shape=(2, 3)>
>>> result = qctrl.functions.calculate_graph(graph=graph, output_node_names=["b"])
>>> result.output["b"]["value"]
array([[1, 2, 3],
[5, 7, 9]])
```

Calculate the cumulative sum of a 2D array along its second dimension.

```>>> graph.cumulative_sum(y, axis=1, name="c")
<Tensor: name="c", operation_name="cumulative_sum", shape=(2, 3)>
>>> result = qctrl.functions.calculate_graph(graph=graph, output_node_names=["c"])
>>> result.output["c"]["value"]
array([[ 1,  3,  6],
[ 4,  9, 15]])
```