grapharray.functions module

Functions for treating graph variables.

grapharray.functions.apply_element_wise_function(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray], function: Callable) → Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]

Execute a element-wise function for np.ndarray to NodeVar or EdgeVar.

Parameters

• var – A variable to apply function

• function – A function for np.ndarray to apply.

Returns

An instance of the same class as var’s, whose array is the result of the function passed i.e., function(var.array).

grapharray.functions.exp(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]) → Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]

Element-wise exponential

grapharray.functions.get_representative_value(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray], function: Callable) → float

Apply a function for np.ndarray that returns a scalar to Node/EdgeArray

Parameters

• var – A variable to apply function

• function – A function for np.ndarray to apply.

Returns

(float) The result of “function(var)”

grapharray.functions.log(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]) → Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]

Element-wise natural logarithm

grapharray.functions.max(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]) → float

The maximum of all variables

grapharray.functions.min(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]) → float

The minimum of all variables

grapharray.functions.sum(var: Union[grapharray.classes.NodeArray, grapharray.classes.EdgeArray]) → float

Sum up all variables