Transfer functions

Transfer functions can be customised too depending on needs. There are built-in transfer functions, which have the added benefit of having plotting functions meaning the results can be visualised correctly, for example the impedance tensor.

However, if a completely custom transfer function is required, this can be done with the caveat that there will be no plotting function available. A better solution might be to write a custom transfer function if required. For more about writing custom transfer functions, see the advanced usage.

from resistics.transfunc import TransferFunction

To initialise a new transfer function, the input and channels need to be defined.

tf = TransferFunction(in_chans=["Cat", "Dog"], out_chans=["Tiger", "Wolf"])
print(tf)
tf.summary()

Out:

| Tiger | = | Tiger_Cat   Tiger_Dog   | | Cat   |
| Wolf  |   | Wolf_Cat    Wolf_Dog    | | Dog   |
{
    'name': 'TransferFunction',
    'variation': 'generic',
    'out_chans': ['Tiger', 'Wolf'],
    'in_chans': ['Cat', 'Dog'],
    'cross_chans': ['Cat', 'Dog'],
    'n_out': 2,
    'n_in': 2,
    'n_cross': 2
}

It is also possible to set the channels that will be used to calculate out the cross spectra. Note that these channels should be available in the input site, output site and cross site respectively.

tf = TransferFunction(
    name="Jungle",
    in_chans=["Cat", "Dog"],
    out_chans=["Tiger", "Wolf"],
    cross_chans=["Lizard", "Crocodile"],
)
print(tf)
tf.summary()

Out:

| Tiger | = | Tiger_Cat   Tiger_Dog   | | Cat   |
| Wolf  |   | Wolf_Cat    Wolf_Dog    | | Dog   |
{
    'name': 'Jungle',
    'variation': 'generic',
    'out_chans': ['Tiger', 'Wolf'],
    'in_chans': ['Cat', 'Dog'],
    'cross_chans': ['Lizard', 'Crocodile'],
    'n_out': 2,
    'n_in': 2,
    'n_cross': 2
}

In scenarios where the core transfer function stays the same (input and output channels), but the cross channels will be changed, there is an additional variation property that helps separate them.

tf = TransferFunction(
    name="Jungle",
    variation="Birds",
    in_chans=["Cat", "Dog"],
    out_chans=["Tiger", "Wolf"],
    cross_chans=["Owl", "Eagle"],
)
print(tf)
tf.summary()

Out:

| Tiger | = | Tiger_Cat   Tiger_Dog   | | Cat   |
| Wolf  |   | Wolf_Cat    Wolf_Dog    | | Dog   |
{
    'name': 'Jungle',
    'variation': 'Birds',
    'out_chans': ['Tiger', 'Wolf'],
    'in_chans': ['Cat', 'Dog'],
    'cross_chans': ['Owl', 'Eagle'],
    'n_out': 2,
    'n_in': 2,
    'n_cross': 2
}