ogstools.plot package#

Plotting utilities for simple access.

ogstools.plot.animate(fig, plot_func, *args, **kwargs)[source]#

Create an animation by applying a plot function on a sequence of meshes.

Parameters:

  • fig (Figure) – The figure on which the animation is rendered

  • plot_func (Callable) – The function which is applied for all timevalues. Expects to read a time value and a mesh.

Return type:

FuncAnimation

Positional Arguments:

Sequences where each element corresponds to a frame. plot_func has to accept the individual elements of these sequences as arguments. Most common choice here would be a MeshSeries as an iterator for the meshes and possibly its timevalues for labeling.

Keyword Arguments:

  • interval: Delay between frames in milliseconds (default=50).

  • repeat: Whether the animation repeats at the end.

ogstools.plot.compute_levels(lower, upper, n_ticks)[source]#

Return an array in the interval [lower, upper] with terminating decimals.

The length of the arrays will be close to n_ticks. At the boundaries the tickspacing may differ from the remaining array.

Return type:

ndarray

ogstools.plot.contourf(meshes, variable, fig=None, ax=None, interactive=None, **kwargs)[source]#

Plot the variable field of meshes with default settings.

The resulting figure adheres to the configurations in plot.setup. For 2D meshes matplotlib backend is used, for 3D pyvista backend.

Parameters:

  • meshes (list[UnstructuredGrid] | ndarray | UnstructuredGrid) – Singular mesh of 2D numpy array of meshes

  • variable (Variable | str) – The field to be visualized on all meshes

  • fig (Figure | None) – matplotlib figure to use for plotting

  • ax (Axes | None) – matplotlib axis to use for plotting

  • interactive (bool | None) – By default: 2D mesh -> matplotlib, 3D mesh -> pyvista, if True: always use interactive pyvista plot if False and 3D mesh: -> pyvista static Image

Return type:

Figure | Plotter | Image | list[Plotter] | list[Image] | None

Keyword Arguments:

  • arrowsize scaling factor for arrowsize

  • cbar: If True (default), draw a colorbar

  • cb_labelsize: colorbar labelsize

  • cb_loc: colorbar location (‘left’ or ‘right’)

  • cb_pad: colorbar padding

  • cmap: colormap

  • dpi: resolution

  • figsize: figure size

  • fontsize size for labels and captions

  • levels: user defined levels

  • log_scaled: logarithmic scaling

  • min_ax_aspect: minimum axes aspect ratio

  • max_ax_aspect: maximum axes aspect ratio

  • show_edges: show element edges

  • show_max: mark the location of the maximum value

  • show_min: mark the location of the minimum value

  • show_region_bounds: show the edges of the different regions

  • vmin: minimum value

  • vmax: maximum value

ogstools.plot.contourf_pv(mesh, variable, show_edges=True, opacities=None, lighting=False, categoric=None, **kwargs)[source]#

General 3D plot using pyvista

Parameters:

  • mesh (UnstructuredGrid) – The mesh to be plotted with pyvista.

  • variable (str | Variable) – The variable which should be shown.

  • show_edges (bool) – If True, draw edges as black lines

  • opacities (dict[int, float] | None) – Dictionary, mapping opacities to material ids. Default None (all opaque), Example: {1:0.0 # transparent, 2:1 # opaque, 3:0.5} All not provided ids are rendered as opaque.

  • lighting (bool) – If True, use lighting in the visualization.

  • categoric (bool | None) – If True, use a categoric colormap. By default it uses the variable to determine if this should be True.

Returns:

A pyvista Plotter object. Use .show() to display the scene.

Return type:

Plotter

ogstools.plot.heatmap(data, variable, fig=None, ax=None, x_vals=None, y_vals=None, **kwargs)[source]#

Create a heatmap plot of given data.

Parameters:

  • data (ndarray) – The two-dimensional data of interest.

  • variable (Variable) – Provides the label and colormap for the colorbar.

  • fig (Figure | None) – Optionally plot into this figure.

  • ax (Axes | None) – Optionally plot into this Axes.

  • x_vals (ndarray | None) – one-dimensional x_values of the data.

  • y_vals (ndarray | None) – one-dimensional y_values of the data.

Return type:

Figure | None

Keyword Arguments:

  • figsize: figure size

  • dpi: resolution

  • vmin: minimum value of the colorbar

  • vmax: maximum value of the colorbar

  • num_levels: Number of levels (approximation)

  • log_scaled: If True, use logarithmic scaling

  • aspect: Aspect ratio of the plt.Axes (y/x)

  • fontsize: fontsize

Returns:

A figure with a heatmap

Return type:

Figure | None

ogstools.plot.line(dataset, var1=None, var2=None, ax=None, sort=True, outer_legend=False, **kwargs)[source]#

Plot some data of a (1D) dataset.

You can pass “x”, “y” or “z” to either of x_var or y_var to specify which spatial dimension should be used for the corresponding axis. By passing “time” the timevalues will be use for this axis. You can also pass two data variables for a phase plot. if no value is given, automatic detection of spatial axis is tried.

>>> line(ms, ot.variables.temperature)          # temperature over time
>>> line(ms, ot.variables.temperature, "time")  # time over temperature
>>> line(ms, "pressure", "temperature")     # temperature over pressure
>>> line(mesh, ot.variables.temperature)    # temperature over x, y or z
>>> line(mesh, "y", "temperature")          # temperature over y
>>> line(mesh, ot.variables.pressure, "y")  # y over pressure
>>> line(mesh)  # z=const: y over x, y=const: z over x, x=const: z over y
Parameters:

  • dataset (DataSet | Sequence[DataSet]) – The mesh or meshseries which contains the data to plot.

  • var1 (str | Variable | None) – Variable for the x-axis if var2 is given else for y-axis.

  • var2 (str | Variable | None) – Variable for the y-axis if var1 is given.

  • ax (Axes | None) – The matplotlib axis to use for plotting, if None a new figure will be created.

  • sort (bool) – Automatically sort the values along the dimension of the mesh with the largest extent (only for pointclouds).

Outer_legend:

Draw legend to the right next to the plot area. By default False (legend stays inside). User can pass a tuple of two floats (x, y), which will be passed to bbox_to_anchor parameter in matplotlib legend call. True will pass the default values (1.05, 1.0).

Return type:

Figure | None

Keyword Arguments:

  • figsize: figure size (default=[16, 10])

  • dpi: resolution of the figure

  • color: color of the line

  • linewidth: width of the line

  • linestyle: style of the line

  • label: label in the legend

  • grid: if True, show grid

  • monospace: if True, the legend uses a monospace font

  • loc: location of the legend (default=”upper right”)

  • clip_on: If True, clip the output to stay within the Axes.

    (default=False)

  • all other kwargs get passed to matplotlib’s plot function

Note:

Using loc=”best” will take a long time, if you plot lines on top of a contourplot, as matplotlib is calculating the best position against all the underlying cells.

ogstools.plot.quiver(mesh, ax, variable, arrowsize=None, projection=None, glyph_type='arrow')[source]#

Plot arrows or lines corresponding to vectors on a matplotlib axis.

Parameters:

  • mesh (DataSet) – Mesh containing the vector variable

  • ax (Axes) – Matplotlib axis to plot onto

  • variable (Vector) – Vector variable to visualize

  • arrowsize (float | None) – Sets size of arrows in the plot.

  • projection (int | None) – Index of flat dimension (e.g. 2 for z axis), gets automatically determined if not given

  • glyph_type (Literal['arrow', 'line']) – Whether to plot arrows or lines.

ogstools.plot.shape_on_top(ax, surf, contour, scaling=1.0)[source]#
ogstools.plot.streamlines(mesh, ax, variable, arrowsize=None, projection=None)[source]#

Plot the vector streamlines on a matplotlib axis.

Parameters:

  • mesh (DataSet) – Mesh containing the vector variable

  • ax (Axes) – Matplotlib axis to plot onto

  • variable (Vector) – Vector variable to visualize

  • arrowsize (float | None) – Sets size of arrows in the plot.

  • projection (int | None) – Index of flat dimension (e.g. 2 for z axis), gets automatically determined if not given

ogstools.plot.subplot(mesh, variable, ax, levels=None, **kwargs)[source]#

Plot the variable field of a mesh on a matplotlib.axis.

Parameters:

  • mesh (UnstructuredGrid) – singular mesh

  • variable (Variable | str) – the field to be visualized on all meshes

  • ax (Axes) – matplotlib ax to use for plotting

  • levels (ndarray | None) – value levels

Matplotlib kwargs are also accepted.

Submodules#