Solvers Module

Solvers Module#

Solvers for wpt-tools.

wpt_tools.solver.series_lcr_xself(x, ls, cs)#

Series LCR model for self reactance.

wpt_tools.solver.series_lcr_rself(x, r)#

Series LCR model for self resistance.

wpt_tools.solver.series_lcr_xm(x, lm)#

Series LCR model for mutual reactance.

wpt_tools.solver.efficiency_calculator(rich_nw: RichNetwork, rx_port: Literal[1, 2], target_f: float | None, range_f: float | None) → EfficiencyResults#

Compute efficiency vectors and maxima.

Parameters:

  • rich_nw (RichNetwork) – The network to analyze.

  • rx_port (Literal[1, 2]) – The port to analyze.

  • target_f (Optional[float]) – The target frequency.

  • range_f (Optional[float]) – The range of the target frequency.

Returns:

The results of the efficiency solver.

Return type:

EfficiencyResults

wpt_tools.solver.compute_load_sweep(rich_nw: RichNetwork, rez_min: float, rez_max: float, rez_step: float, imz_min: float, imz_max: float, imz_step: float, rx_port: Literal[1, 2], input_voltage: float | None = 1, target_f: float | None = None, range_f: float | None = None) → OptimalLoadGridResults#

Compute efficiency, input and output power over a grid (load sweep).

wpt_tools.solver.compute_rxc_filter(rich_nw: RichNetwork, rx_port: Literal[1, 2], rload: float, *, c_network: Literal['CpCsRl'] = 'CpCsRl', target_f: float | None = None, range_f: float | None = None) → RXCFilterResults#

Compute receiver capacitor values for a target load at the optimal point.

wpt_tools.solver.lcr_fitting(rich_nw: RichNetwork, target_f: float | None = None, range_f: float | None = None) → LCRFittingResults#

Fit the LCR model to the network.

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