tdcpy.plot package#

Submodules#

tdcpy.plot.discretization_animation module#

Set of functions for animation of discretization#

tdcpy.plot.discretization_animation.discretization_animation(tds, discretization, s0=0j, **kwargs)#

Creates discretization animation

Parameters:

  • tds (RDDE | NDDE | DDAE) – system to discretize and animate

  • discretization (iterable) – iterable of discretization degrees to animate over

  • s0 (complex, optional) – point discretization is done around, default 0j

  • **kwargs –

    discretization_idealint, optional

    this discretization is assumed to be ‘correct’ and always present in animation, set None to turn off, default None

    xlimtuple, optional

    x axis limits, default None

    ylimtuple, optional

    y axis limits, default None

    intervalint, optional

    time in ms between updates

Returns:

ani – python Animation object, use plt.show() to see

Return type:

animation

Notes

  1. if this function does not suit you, copy and rewrite this

  2. if you want to save animation, use ani.save(…) method of returned Animation object

Examples

>>> from tdcpy.ddae import DDAE
>>> from tdcpy.plot.discretization_animation import discretization_animation
>>> import numpy as np
>>> E = np.array([[1, 0], [0, 0]])
>>> A = np.zeros((2,2,2))
>>> A[:,:,0] = np.array([[0, 1], [-2, -3]])
>>> A[:,:,1] = np.array([[0, 0], [0, -1]])
>>> hA = np.array([0.0, 1.0])
>>> tds = DDAE(E=E,A=A, hA=hA)
>>> ani = discretization_animation(tds, discretization=range(2, 100), discretization_ideal=50)
>>> # to show the animation, use plt.show()

tdcpy.plot.eigenvalues module#

Set of ploting eigenvalue plotting functions#

Mainly to provide similar functionality like the original TDS-CONTROl function:

https://gitlab.kuleuven.be/u0011378/tds-control/-/blob/main/tds-control/code/tds_eigenplot.m

tdcpy.plot.eigenvalues.complex_scatter_axplot(roots, ax, *args, **kwargs)#

Plots non-empty complex numbers, x: Re(z), y: Im(z)

Parameters:

  • roots (npt.NDArray) – array of complex numbers to visualize

  • ax (Axes) – matplotlib.axes.Axes object to plot to

  • *args – see matplotlib .scatter function

  • **kwargs – see matplotlib .scatter function

Return type:

None

Notes

  1. if roots is empty, does nothing

  2. if ax is None, creates new figure and axes

  3. uses matplotlib scatter function internally

  4. x axis is real part, y axis is imaginary part

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from tdcpy.plot.eigenvalues import complex_scatter_axplot
>>> roots = np.array([1+2j, -1-1j, 0+0j, 3+0j])
>>> fig, ax = plt.subplots()
>>> complex_scatter_axplot(roots, ax, c='r', marker='x')
>>> plt.show()
tdcpy.plot.eigenvalues.eigen_plot(roots, ax=None, **kwargs)#

Plots eigenvalues in complex plane

Parameters:

  • roots (npt.NDArray) – array of complex eigenvalues to plot

  • ax (Axes, optional) – matplotlib.axes.Axes object to plot to, default None

  • **kwargs – tol (float): tolerance for assuming Re(root) ~ 0, default 1e-10

Returns:

ax – matplotlib.axes.Axes object containing the plot

Return type:

Axes

Notes

  1. if ax is None, creates new figure and axes

  2. plots horizontal and vertical lines at 0

  3. colors eigenvalues based on their real part:

    • red for Re(root) > tol

    • blue for |Re(root)| <= tol

    • green for Re(root) < -tol

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from tdcpy.plot.eigenvalues import eigen_plot
>>> roots = np.array([1+2j, -1-1j, 0+0j, 3+0j])
>>> ax = eigen_plot(roots)
>>> plt.show()

Module contents#