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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,132 @@ | ||
| import torch | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
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| from fadin.kernels import DiscreteKernelFiniteSupport | ||
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| def plot(solver, plotfig=False, bl_noise=False, title=None, ch_names=None, | ||
| savefig=None): | ||
| """ | ||
| Plots estimated kernels and baselines of `FaDIn` solver. | ||
| Should be called after calling the `fit` method on solver. | ||
| Parameters | ||
| ---------- | ||
| solver: `FaDIn` solver. | ||
| `fit` method should be called on the solver before calling `plot`. | ||
| plotfig: bool (default `False`) | ||
| If set to `True`, the figure is plotted. | ||
| bl_noise: bool (default`False`) | ||
| Whether to plot the baseline of noisy activations. | ||
| Only works if the solver has 'baseline_noise' attribute. | ||
| title: `str` or `None`, default=`None` | ||
| Title of the plot. If set to `None`, the title text is generic. | ||
| ch_names: list of `str` (default `None`) | ||
| Channel names for subplots. If set to `None`, will be set to | ||
| `np.arange(solver.n_dim).astype('str')`. | ||
| savefig: str or `None`, default=`None` | ||
| Path for saving the figure. If set to `None`, the figure is not saved. | ||
| Returns | ||
| ------- | ||
| fig, axs : matplotlib.pyplot Figure | ||
| n_dim x n_dim subplots, where subplot of coordinates (i, j) shows the | ||
| kernel component $\\alpha_{i, j}\\phi_{i, j}$ and the baseline $\\mu_i$ | ||
| of the intensity function $\\lambda_i$. | ||
| """ | ||
| # Recover kernel time values and y values for kernel plot | ||
| discretization = torch.linspace(0, solver.kernel_length, 200) | ||
| kernel = DiscreteKernelFiniteSupport(solver.delta, | ||
| solver.n_dim, | ||
| kernel=solver.kernel, | ||
| kernel_length=solver.kernel_length) | ||
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| kappa_values = kernel.kernel_eval(solver.params_intens[-2:], | ||
| discretization).detach() | ||
| # Plot | ||
| if ch_names is None: | ||
| ch_names = np.arange(solver.n_dim).astype('str') | ||
| fig, axs = plt.subplots(nrows=solver.n_dim, | ||
| ncols=solver.n_dim, | ||
| figsize=(4 * solver.n_dim, 4 * solver.n_dim), | ||
| sharey=True, | ||
| sharex=True, | ||
| squeeze=False) | ||
| for i in range(solver.n_dim): | ||
| for j in range(solver.n_dim): | ||
| # Plot baseline | ||
| label = (rf'$\mu_{{{ch_names[i]}}}$=' + | ||
| f'{round(solver.baseline[i].item(), 2)}') | ||
| axs[i, j].hlines( | ||
| y=solver.baseline[i].item(), | ||
| xmin=0, | ||
| xmax=solver.kernel_length, | ||
| label=label, | ||
| color='orange', | ||
| linewidth=4 | ||
| ) | ||
| if bl_noise: | ||
| # Plot noise baseline | ||
| mutilde = round(solver.baseline_noise[i].item(), 2) | ||
| label = rf'$\tilde{{\mu}}_{{{ch_names[i]}}}$={mutilde}' | ||
| axs[i, j].hlines( | ||
| y=solver.baseline_noise[i].item(), | ||
| xmin=0, | ||
| xmax=solver.kernel_length, | ||
| label=label, | ||
| color='green', | ||
| linewidth=4 | ||
| ) | ||
| # Plot kernel (i, j) | ||
| phi_values = solver.alpha[i, j].item() * kappa_values[i, j, 1:] | ||
| axs[i, j].plot( | ||
| discretization[1:], | ||
| phi_values, | ||
| label=rf'$\phi_{{{ch_names[i]},{ch_names[j]}}}$', | ||
| linewidth=4 | ||
| ) | ||
| if solver.kernel == 'truncated_gaussian': | ||
| # Plot mean of gaussian kernel | ||
| mean = round(solver.params_intens[-2][i, j].item(), 2) | ||
| axs[i, j].vlines( | ||
| x=mean, | ||
| ymin=0, | ||
| ymax=torch.max(phi_values).item(), | ||
| label=rf'mean={mean}', | ||
| color='pink', | ||
| linestyles='dashed', | ||
| linewidth=3, | ||
| ) | ||
| # Handle text | ||
| axs[i, j].set_xlabel('Time', size='x-large') | ||
| axs[i, j].tick_params( | ||
| axis='both', | ||
| which='major', | ||
| labelsize='x-large' | ||
| ) | ||
| axs[i, j].set_title( | ||
| f'{ch_names[j]}-> {ch_names[i]}', | ||
| size='x-large' | ||
| ) | ||
| axs[i, j].legend(fontsize='large', loc='best') | ||
| # Plot title | ||
| if title is None: | ||
| fig_title = 'Hawkes influence ' + solver.kernel + ' kernel' | ||
| else: | ||
| fig_title = title | ||
| fig.suptitle(fig_title, size=20) | ||
| fig.tight_layout() | ||
| # Save figure | ||
| if savefig is not None: | ||
| fig.savefig(savefig) | ||
| # Plot figure | ||
| if plotfig: | ||
| fig.show() | ||
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| return fig, axs |