physics module

Define useful relations.

powers_to_reflection(forward_power, reflected_power, name, warn_reflected_higher_than_forward=True, warn_gamma_too_close_to_unity=True, tol=0.05)

Compute the reflection coefficient \(R\).

We use the definition:

\[R = \frac{V_r}{V_f} = \sqrt{\frac{P_r}{P_f}}\]

Parameters:

• forward_power (ndarray[tuple[Any, ...], dtype[double]])

• reflected_power (ndarray[tuple[Any, ...], dtype[double]])

• name (str)

• warn_reflected_higher_than_forward (bool, default: True)

• warn_gamma_too_close_to_unity (bool, default: True)

• tol (float, default: 0.05)

Return type:

Series

diff(data, name)

Return data differential. Add a nan at the start.

Currently used for DiffPenning creation.

Parameters:

• data (ndarray[tuple[Any, ...], dtype[double]])

• name (str)

Return type:

Series

reflection_to_swr(reflection_coefficient, name='')

Overloads:

• reflection_coefficient (NDArray[np.float64]), name (str) → pd.Series

• reflection_coefficient (float), name (str) → float

Compute the \(SWR\).

We use the definition:

\[SWR = \frac{1 + R}{1 - R}\]

where \(R\) is the reflection coefficient.

Parameters:

• reflection_coefficient (ndarray[tuple[Any, ...], dtype[double]] | float)

• name (str, default: '')

swr_to_reflection(swr, name='')

Overloads:

• swr (NDArray[np.float64]), name (str) → pd.Series

• swr (float), name (str) → float

Compute the reflection coefficient \(R\).

We use the relation:

\[R = \frac{SWR - 1}{SWR + 1}\]

Parameters:

• swr (ndarray[tuple[Any, ...], dtype[double]] | float)

• name (str, default: '')