from __future__ import annotations
import numpy as np
from matplotlib.cm import ScalarMappable
from pylbo.visualisation.modes.mode_data import ModeVisualisationData
from pylbo.visualisation.modes.mode_figure import ModeFigure
[docs]
class TemporalEvolutionPlot1D(ModeFigure):
"""
Main class for temporal evolutions of the eigenfunction.
Parameters
----------
data : ModeVisualisationData
Data object containing all data associated with the selected eigenmode.
u2 : float
The data for the :math:`u_2` coordinate.
u3 : float
The data for the :math:`u_3` coordinate.
time : np.ndarray
The data for the time.
figsize : tuple[int, int]
The size of the figure.
show_ef_panel : bool
Whether to show the eigenfunction panel.
**kwargs
Additional keyword arguments to be passed to :meth:`matplotlib.pyplot.imshow`.
"""
def __init__(
self,
data: ModeVisualisationData,
u2: float,
u3: float,
time: np.ndarray,
figsize: tuple[int, int],
show_ef_panel: bool,
**kwargs,
) -> None:
self._u1 = data.ds.ef_grid
self._u2 = self._check_if_number(u2, "u2")
self._u3 = self._check_if_number(u3, "u3")
self._time = self._check_if_array(time, "time")
self._kwargs = kwargs
super().__init__(figsize, data, show_ef_panel)
[docs]
def set_plot_arrays(self) -> None:
self.solution_shape = (len(self._u1), len(self._time))
for ef, omega in zip(self.data.eigenfunction, self.data.omega):
# transpose here so data[:, i] gives eigenfunction data at time i
data = np.broadcast_to(ef, shape=reversed(self.solution_shape)).transpose()
self.ef_data.append({"ef": data, "omega": omega})
x_2d, time_2d = np.meshgrid(self.data.ds.ef_grid, self._time, indexing="ij")
self.time_data = time_2d
self.u1_data = x_2d
self.u2_data = self._u2
self.u3_data = self._u3
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def draw_solution(self) -> None:
self._view = self.ax.pcolormesh(
self.u1_data,
self.time_data,
self.solutions,
**self._kwargs,
)
self.cbar = self.fig.colorbar(
ScalarMappable(norm=self._view.norm, cmap=self._view.cmap),
cax=self.cbar_ax,
)
[docs]
def get_view_ylabel(self) -> str:
return "time"