util
get_implementations(f)
Returns the implemented type signatures for an operator.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
f
|
An operator function registered via @operator. |
required |
Returns:
| Type | Description |
|---|---|
|
list[tuple[str, ...]]: List of type signature tuples for each |
|
|
implementation. |
Example
from jaxdf.operators import gradient get_implementations(gradient) [('Continuous',), ('FiniteDifferences',), ('FourierSeries',)]
get_implemented(f)
Prints the implemented methods of an operator
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
f
|
Callable
|
The operator to get the implemented methods of. |
required |
Returns:
| Type | Description |
|---|---|
|
None |
has_implementation(f, *types)
Check if an operator has an implementation for the given types.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
f
|
An operator function registered via @operator. |
required | |
*types
|
The types to check for. |
()
|
Returns:
| Name | Type | Description |
|---|---|---|
bool |
True if an implementation exists for the given types. |
Example
from jaxdf.operators import gradient from jaxdf.discretization import FourierSeries has_implementation(gradient, FourierSeries) True
update_dictionary(old, new_entries)
Update a dictionary with new entries.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
old
|
dict
|
The dictionary to update |
required |
new_entries
|
dict
|
The new entries to add to the dictionary |
required |
Returns:
| Name | Type | Description |
|---|---|---|
dict |
The updated dictionary |