UML Diagram for Spectra Fitting Data Classes

Here is an UML diagram representing the interoperability between several internal classes of the package that comprise the fluctuation generator CalibrationProblem and OnePointSpectra. Please refer to specific class documentations for details. The following diagram is interactive – try zooming and panning to resize for your convenience.

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classDiagram direction LR class CalibrationProblem { <<class>> - output_directory : Path - device : str init_device() update_parameters() eval() eval_grad() calibrate() plot() plot_loss() } class NNParameters { <<dataclass>> - nlayers : int - input_size : int - hidden_layer_size : int - hidden_layer_sizes : list[int] - activations : list[str] - output_size : int } class ProblemParameters { <<dataclass>> - learning_rate : float - tol : float - nepochs : int - init_with_noise : bool - noise_magnitude : float - data_type : DataType - eddy_lifetime : EddyLifetimeType - power_spectra : PowerSpectraType - learn_nu : bool } class PhysicalParameters { <<dataclass>> - L : float - Gamma : float - sigma : float - Uref : float - zref : float - domain : torch.Tensor } class LossParameters { <<dataclass>> - alpha_pen : float - alpha_reg : float - beta_pen : float } class OnePointSpectra { <<class>> - grid_k2 : torch.Tensor - grid_k3 : torch.Tensor - meshgrid23 : torch.Tensor - logLengthScale : nn.Parameter - logTimeScale : nn.Parameter - logMagnitude : nn.Parameter - tauNet : nn.Module** exp_scales() forward(k1_input) EddyLifetime(k) PowerSpectra() quad23() get_div() } CalibrationProblem ..> ProblemParameters CalibrationProblem ..> PhysicalParameters OnePointSpectra ..> LossParameters CalibrationProblem ..> OnePointSpectra OnePointSpectra ..> ProblemParameters OnePointSpectra ..> NNParameters