API Reference

dewloosh.mpl.triplot.triplot(triobj, *args, hinton=False, data=None, title=None, label=None, fig=None, ax=None, axes=None, fig_kw: Optional[dict] = None, **kwargs)[source]

Creates plots over triangulations using matplotlib.

Parameters

  • triobj (TriMeshLike) – This is either a tuple of mesh data (coordinates and topology) or a triangulation object understood by dewloosh.tri.trimesh.triangulate.

  • hinton (bool, Optional) – Creates a hinton-like plot. Only wors if the provided data is along the cells. Default is False.

  • data (numpy.ndarray, Optional) – Some data to plot as an 1d or 2d numpy array. Default is None.

  • title (str, Optional) – Title of the plot. See matplotlib for further details. Default is None.

  • label (str, Optional) – Title of the plot. See matplotlib for further details. Default is None.

  • fig (matplotlib.Figure, Optional) – A matplotlib figure to plot on. Default is None.

  • ax (matplotlib.axes.Axes or a collection of it, Optional.) – A matplotlib axis, or a collection of such objects to plot on. Default is None.

  • kwargs (dict, Optional) –

    The following keyword arguments are understood and forwarded to the appropriate function in matplotlib:

    ’cmap’ - colormap (if data is provided) ‘lw’ ‘xlim’ ‘ylim’ ‘axis’ ‘suptitle’

  • fig_kw (dict, Optional) – If there is no figure instance provided, these parameters are forwarded to the matplotlib.pyplot.figure call.

Examples

Create a triangulation

>>> from polymesh.grid import grid
>>> from polymesh.topo.tr import Q4_to_T3
>>> from polymesh.tri.trimesh import triangulate
>>> from dewloosh.mpl import triplot
>>> import numpy as np
>>> gridparams = {
>>>     'size' : (1200, 600),
>>>     'shape' : (30, 15),
>>>     'eshape' : (2, 2),
>>>     'origo' : (0, 0),
>>>     'start' : 0
>>> }
>>> coordsQ4, topoQ4 = grid(**gridparams)
>>> points, triangles = Q4_to_T3(coordsQ4, topoQ4, path='grid')
>>> triobj = triangulate(points=points[:, :2], triangles=triangles)[-1]

If you just want to plot the mesh itself, do this

>>> triplot(triobj)

Plot the mesh with random data over the cells

>>> data = np.random.rand(len(triangles))
>>> triplot(triobj, data=data)

>>> data = np.random.rand(len(triangles))
>>> triplot(triobj, hinton=True, data=data)

Plot the mesh with random data over the points

>>> data = np.random.rand(len(points))
>>> triplot(triobj, data=data, cmap='bwr')

You can play with the arguments sent to matplotlib

>>> triplot(triobj, data=data, cmap='Set1', axis='off')
dewloosh.mpl.parallel.parallel(data, *args, labels=None, padding=0.05, colors=None, lw=0.2, bezier=True, figsize=None, title=None, **kwargs)[source]
Parameters

  • data (list of arrays) – A list of numpy.ndarray for each column. Each array is 1d with a length of N, where N is the number of data records (the number of lines).

  • labels (Iterable, Optinal) – Labels for the columns. If provided, it must have the same length as data.

  • padding (float, Optional) – Controls the padding around the plot.

  • colors (list of float, Optional) – A value for each record. Default is None.

  • lw (float, Optional) – Linewidth.

  • bezier (bool, Optional) – If True, bezier curves are created instead of a linear polinomials. Default is True.

  • figsize (tuple, Optional) – A tuple to control the size of the figure. Default is None.

  • title (str, Optional) – The title of the figure.

Example

>>> from dewloosh.mpl import parallel
>>> colors = np.random.rand(150, 3)
>>> labels = [str(i) for i in range(10)]
>>> values = [np.random.rand(150) for i in range(10)]
>>> parallel(values, labels=labels, padding=0.05, lw=0.2,
>>>         colors=colors, title='Parallel Plot with Random Data')
dewloosh.mpl.parallel.aligned_parallel(data, datapos, *args, yticks=None, labels=None, sharelimits=False, texlabels=None, xticksrotation=0, suptitle=None, slider=False, slider_label=None, hlines=None, vlines=None, y=None, xoffset=0.0, yoffset=0.0, **kwargs)[source]
Parameters

  • data (numpy.ndarray or dict) – The values to plot. If it is a numpy array, labels must be provided with the argument labels, if it is a sictionary, the keys of the dictionary are used as labels.

  • datapos (Iterable) – Positions of the provided data values.

  • yticks (Iterable, Optional) – Positions of ticks on the vertical axes. Default is None.

  • labels (Iterable, Optional) – An iterable of strings specifying labels for the datasets. Default is None.

  • sharelimits (bool, Optional) – If True, the axes share limits of the vertical axes. Default is False.

  • texlabels (Itrable, Optional) – TeX-formatted labels. If provided, it must have the same length as labels. Default is None.

  • xticksrotation (int, Optional) – Rotation of the ticks along the vertical axes. Expected in degrees. Default is 0.

  • suptitle (str, Optional) – See Matplotlib’s docs for the details. Default is None.

  • slider (bool, Optional) – If True, a slider is added to the figure for interactive plots. Default is False.

  • slider_label (str, Optional) – A label for the slider. Only if slider is true. Default is None.

  • hlines (Iterable, Optional) – A list of data values where horizontal lines are to be added to the axes. Default is None.

  • vlines (Iterable, Optional) – A list of data values where vertical lines are to be added to the axes. Default is None.

  • y (float or int, Optional) – Value for the vertical axis. Default is the average of the limits of the vertical axis (0.5*(datapos[0] + datapos[-1])).

  • xoffset (float, Optional) – Margin of the plot in the vertical direction. Default is 0.

  • yoffset (float, Optional) – Margin of the plot in the horizontal direction. Default is 0.

  • **kwargs (dict, Optional) – Extra keyword arguments are forwarded to the creator of the matplotlib figure. Default is None.

Example

>>> from dewloosh.mpl import aligned_parallel
>>> labels = ['a', 'b', 'c']
>>> values = np.array([np.random.rand(150) for _ in labels]).T
>>> datapos = np.linspace(-1, 1, 150)
>>> aligned_parallel(values, datapos, labels=labels, yticks=[-1, 1])