Changelog

v3.3.0

Control system plotting
- Fixed bug with missing title of plot_root_locus.
- Implemented sgrid based on Matplotlib projection.
- Improved sgrid support on Bokeh interactive plots.
- Improved labeling of MIMO systems.
- step_response, impulse_response, ramp_response are now able to compute an appropriate upper time limit for the simulation.
- Updated code to use control 0.10.0. As a consequence, Python 3.9 is no longer supported by this module.
Improvements of interactive plots:
- Added support for multiple-values widgets, like RangeSlider.
- Improvements of spb.interactive.panel:
  - Simplified underlying architecture. Previously, InteractivePlot inherited from param.Parameterized: widgets were class attributes. Instantiating a new interactive plot would make the previous instance completely unusable. This inheritance has been removed. Now, widgets are instance attributes. Multiple instances work perfectly fine.
  - Added support for widgets of Holoviz Panel.
  - Updated plotgrid to work with the new architecture.
- Improvements to documentation.
Fixed bug with legend of plot_vector.

v3.2.0

add update_event keyword argument to enable/disable auto-update on panning for plots created with MatplotlibBackend, PlotlyBackend and BokehBackend. By default, this functionality is turned off, update_event=False.
Improved the logic handling the creation of sliders on interactive-widget plots. Consider this code: params = {k: (1, 0, 5, formatter, label). It now works both with ipywidgets as well as panel. Previously, formatter was not supported by ipywidgets.
Added arrows= keyword argument to nichols and plot_nichols.
Added show_minus_one= keyword argument to mcircles.
Implemented renderers for Bokeh in order to deal with control system plotting.
Improved tooltips in BokehBackend.
Breaking: refactoring of NicholsLineSeries. Previously, it returned data about the open-loop transfer function. Now, it also returns data about the closed-loop transfer function, which can be used on tooltips.

v3.1.1

Fix incorrect behavior of “arrow_3d”.

v3.1.0

User can now specify an existing figure over which symbolic expressions will be plotted. Just use the ax= or fig= keyword argument.
Added arrow_3d to spb.graphics.vectors in order to plot a single arrow in a three-dimensional space.
Enhanced capabilities of line plots with the steps keyword argument. Possible values are "pre", "post", "mid", mimicking Matplotlib’s step function.
New features on the spb.graphics.control sub-module:
- It now depends on the python-control module. This dependency allows the implementation of new plotting functions and to seamlessly deal with transfer functions from sympy, control and scipy.signal, supporting both continous-time and discrete-time systems.
- Added support for MIMO systems.
- Created sgrid, zgrid, ngrid, mcircles functions to easily create grids for control system plots. Appropriate keyword arguments have been created on all major plot functions in order to activate these grids.
- Added plot_root_locus to the control submodule.
- plot_bode now auto-computes an appropriate frequency range.
- Removed the transfer function’s Latex representation from the title of plots related to control systems. This decision is motivated from practical experience, where most of the transfer functions have floating point coefficients, which makes their Latex representation too big to fit into the small width of a plot.
Refactoring of the series.py sub-module:
- code has been re-organized to make extensibility easier and slightly improve performance. In particular, the mixin class CommonUniformEvaluation has been introduced, which handles all the machinery necessary to evaluate symbolic expressions. Series classes may or may not inherit from it. CommonUniformEvaluation allows for a better separation of scopes: data series that don’t need that code are faster to instantiate.
- Breaking: refactoring of NyquistLineSeries in order to use the control module. In particular, the get_data method now returns many more arrays.
- Breaking: removed attribute use_quiver_solid_color from Vector2DSeries.
Fixed bug with labels of 2D vector fields.

v3.0.1

Added new coloring option to domain_coloring. Setting coloring="k+log" will apply a logarithm to the magnitude of the complex function. This improves the visibility of zeros in complex functions that have very steep poles.
Added the hyper function to the list of functions to be evaluated with real numbers. This avoids unexpected errors.
Set unwrap=True as defaul option for plot_bode: this helps to get a continous phase plot.
Enabled plot_bode to deal with system containing time delays.
Enabled panel’s interactive applications to render Latex labels on widgets when served on a new window.
Fixed bug with evaluation of user-defined python’s function.
Fixed bug with labels of plot_implicit.
Fixed bug with labels of plot_piecewise.
Fixed bug with difficult to render labels on Matplotlib. If Matplotlib detects an error while parsing legend’s entries, the plot won’t show the legend.
Fixed bug with plot_bode_phase when phase_units="deg" and unwrap=True.
Added settings for bode plot’s phase_unit and freq_unit to the defaults submodule.
Fixed bug with title of Bode plots.
Fixed title of plot_step_response.
Implemented workaround for holoviz’s Panel interactive applications to be able to work with a currently open bug.

v3.0.0

Introducing the graphics module, which aims to solve the following problems about ordinary plotting function (whose name’s start with plot_):
1. Some functions perform too many tasks, making them difficult and confusing to use.
2. The documentation is difficult to maintain because many keywords arguments are repeated on all plotting functions.
3. The procedures to combine multiple plots together is far from ideal.
The graphics module implements new functions into appropriate submodules. Each function solves a very specific task and is able to plot only one symbolic expression. Each function returns a list containing one or more data series, depending on the required visualization. In order to render the data series on the screen, they must be passed into the graphics function. Plenty of examples about its usage are available on the documentation.
Added arrow_2d to spb.graphics.vectors in order to plot a single arrow in a two-dimensional space.
Reorganized old plotting functions (whose name’s start with plot_) into a new submodule: spb.plot_functions. In particular:
- Deprecated spb.vectors. Its content is now into spb.plot_functions.vectors.
- Deprecated spb.functions. Its content is now into spb.plot_functions.functions_2d and spb.plot_functions.functions_3d.
- Deprecated spb.control. Its content is now into spb.plot_functions.control.
- Deprecated spb.ccomplex.complex. Its content is now into spb.plot_functions.complex_analysis.
- Deprecated spb.ccomplex.wegert. Its content is now into spb.wegert.
Under the hood, many of these plotting functions now uses the graphics module.
Bug fix on MatplotlibBackend about updating y-axis limits. Thanks to Chrillebon for the fix.
Improved performance of the evaluation with adaptive=False (the default one). Removed np.vectorize when the evaluation module is NumPy/Scipy in order to take full advantage of Numpy’s vectorized operations.
Keyword argument is_point now has an alias: scatter. Setting scatter=True will render a sequence of points as a scatter rather than a line.
Improved warning messages to provide more useful information.
Fixed import-related bug with older versions of SymPy.

v2.4.3

Bug fix: set axis scales only if the appropriate keyword arguments are provided. This allows to create symbolic plots with categorical axis.
Fixed deprecation warning of one example using Holoviz panel and Bokeh formatters.
Added new tutorial to documentation.
Added the unwrap keyword argument to plot_bode in order to get a continous phase plot.

v2.4.2

Fixed bug with renderers and the extend and append methods of plot objects.

v2.4.1

Fixed bug with conda package.

v2.4.0

Enabled interactive-widgets plotgrid. In particular, this allows to create interactive widget plots with plot_bode and plot_riemann_sphere.
Enabled support for plotting applied undefined functions.
Implemented parametric text for titles and axis labels.
Implemented the exclude keyword argument for plot and plot_parametric. It accepts a list of values at which a discontinuity will be introduced. This complementes the poles detection algorithm.
Bug fixes
- fixed bug with axis labels of plot_real_imag when creating contour plots.
- fixed bug with colorbar label of 3d plots with lambda functions.

v2.3.0

Improvements to the plot function:
- Implemented reversed x-axis. Usually, a plot range is given with the form (symbol, min_val, max_val), with min_val on the left of the plot. If a range is given with (symbol, max_val, min_val), then the x-axis will be reversed.
- The plot function is now able to show vertical lines at discontinuities when detect_poles="symbolic", at least for simple symbolic expressions.
Introducing the Renderer class. Up to version 2.2.0, all the rendering logic was located into each backend class, making it very difficult if not impossible to extend the capabilities for final users. From this version, each data series is going to be paired with an instance of Renderer: users can create new data series and renderers. Then, by informing the backend of their existance, users can create new plot functions or modify the rendering of the old ones.
Introducing the control module, which contains plotting functions for some of the common plots used in control system. This is an improved version of what is currently present on SymPy (version 1.12), because:
- it allows to plot multiple systems simultaneously, making it easier to compare different transfer functions.
- it works both on Matplotlib, Plotly and Bokeh.
- it allows to create interactive-widgets plots, allowing the study of parametric systems.
Thanks to all SymPy developers that worked on the sympy.physics.control.control_plots module.

Further, it includes plot_nyquist and plot_nichols, which currently only works with Matplotlib. Their underlying rendering logic comes from the python-control package. Huge thanks to all the python-control developers that worked on those functions.
Upgrading dependency of Holoviz’s Panel to version greater or equal than 1.0.0.
Bug fixes:
- complex surfaces can now be plotted with plot_contour.
- custom rendering keyword arguments can be passed to plot_geometry.

v2.2.0

Improved complex domain coloring and added plot_riemann_sphere.
Added imagegrid keyword argument to plotgrid.
Enabled support for plotting indexed objects.
Implemented colorbar keyword argument to show/hide colorbar.
Implemented show_in_legend keyword argument to show/hide a specific series on the legend of a plot.
Improved logic about legend.
Fixed bug with PlotlyBackend when creating 3D analytic landscapes.

v2.1.0

Improved plot_implicit:
- implemented the color keyword argument, to set the color of line or region being plotted.
- implemented the border_color keyword argument: this will add a new data series to represent a limiting border when plotting inequalities (>, >=, <, <=).
- reduced the number of discretization points from 1000 to 100. Thanks to improvements to the backend and data generation, same quality can be achieved much more efficiently.
Improved plot_complex and domain coloring plots:
- User can now set a different colormap.
- Added new coloring schemes.
- User can change the label of the colorbar.
Bug fixes on MatplotlibBackend:
- fixed bad behavior when plotting filled geometries with interactive widgets.
- fixed missing legend entries when combining different types of plots.
Bug fixes on K3DBackend:
- it is now possible to plot 3D quivers with custom colormaps.
- fixed color bar visibility when plotting 3D complex plots.
MatplotlibBackend and PlotlyBackend are now able to visualize legend entries for 3D surface plots using solid colors.

v2.0.2

Bug fix: included static files necessary for serving interactive application on a new browser window.
Improved documentation.

v2.0.1

Improved import statements on spb.interactive.ipywidgets: now, this module can be used even when only matplotlib and ipywidgets are installed.

v2.0.0

If you are upgrading from a previous version, you should run the following code to load the new configuration settings:

from spb.defaults import reset
reset()

Breaking changes:
- Refactoring of *Series classes. All *InteractiveSeries classes have been removed. The interactive functionalities have been integrated on regular *Series. This greatly simplifies the code base, meaning bug fixes should take less time to implement.
- Refactoring of iplot to take into account the aforementioned changes. In particular, interactive widget plots are now tighly integrated into the usual plotting functions. This improves user experience and simplifies the code base.
- The spb.interactive.create_series function has been removed.
Changed the default evaluation algorithm to a uniform sampling strategy, instead of the adaptive algorithm. The latter is still available, just set adaptive=True on the plotting functions that support it. The motivation behind this change is that the adaptive algorithm is usually much slower to produce comparable results: by default, the uniform sampling strategy uses 1000 discretization points over the specified range (users can increase it or decrease it), which is usually enough to smoothly capture the function.

It also simplifies the dependencies of the module: now, the adaptive algorithm is not required by the plotting module to successfully visualize symbolic expressions, hence it is not installed. If users need the adaptive algorithm, they’ll have to follow the adaptive module installation instructions.
Improved support for plotting summations.
Implemented wireframe lines for 3D complex plots.
Interactive widget plots.
- Users can now chose the interactive module to be used:
  - ipywidgets: new in this release. It is the default one.
  - panel: the same, old one.
  Please, read the documentation about the interactive sub-module to learn more about them, and how to chose one or the other.
- Implemented the template keyword argument for interactive widget plots with Holoviz’s Panel and servable=True: user can further customize the layout of the web application, or can provide their own Panel’s templates.
- The module is now fully interactive. Thanks to the prange class, it is possible to specify parametric ranges. Explore the examples in the module documentation to find out how to use it.
color_func now support symbolic expressions.
line_color and surface_color are now deprecated in favor of color_func.
plot_implicit:
- now it supports interactive-widget plots, when adaptive=False.
- not it support rendering_kw for plots created with adaptive=True.
- improved logic dealing with legends. When plotting multiple regions, rectangles will be visible on the legend. When plotting multiple lines, lines will be visible on the legend.
Removed tutorials folder containing Jupyter notebooks. The documentation contains plently of examples: the notebooks were just reduntant and difficult to maintain.
MatplotlibBackend: implemented support for ipywidgets.
PlotlyBackend:
- fixed bug with interactive update of lines.
- implemented support for ipywidgets.
BokehBackend:
- improved support for Bokeh 3.0.
- removed update_event because it became a redundant feature now that the module is fully parametric.
plot_contour: added the clabels keyword argument to show/hide contour labels.
Documentation is now able to show interactive widget plots with K3D-Jupyter.
conda package is now built and made available through the conda-forge channel. This greatly simplify the workflow and should allow an easier installation with conda.

v1.6.7

Fixed bugs related to evaluation with complex numbers and parameters. Thanks to Michele Ceccacci for the fix!

v1.6.6

Fixed bug with PlaneSeries’s data generation. Thanks to Crillebon for the fix!

v1.6.5

Refinements and bug correction on plot_polar: now it supports both cartesian and polar axis. Set polar_axis=True to enable polar axis.
Added polar axis support to plot_contour with MatplotlibBackend.
3D complex plots uses an auto aspect ratio by default.

v1.6.4

MatplotlibBackend:
- improved aspect logic. It is now able to support the new values for 3D plots for Matplotlib>=3.6.0.
- exposed the ax attribute to easily retrieve the plot axis.
Added camera keyword arguments to backends in order to set the 3D view position. Refer to each backend documentation to get more information about its usage.
improved documentation.

v1.6.3

Fixed bug with plot_geometry and 3D geometric entities.
Added tutorial about combining plots together.

v1.6.2

Added plot3d_list function to plot list of coordinates on 3D space.
Changed value to default setting: cfg["matplotlib"]["show_minor_grid"]=False. Set it to True in order to visualize minor grid lines.
Improved documentation.
Enabled color_func keyword argument on plot_vector.
PlotlyBackend:
- if the number of points of a line is greater than some threshold, the backend will switch to go.Scattergl. This improves performance.
- Fixed bug with interactive widget contour plot and update of colorbar.
MatplotlibBackend can now combine 3d plots with contour plots.
Fixed bug with addition of interactive plots.

v1.6.1

Improvements to documentation. In particular, ReadTheDocs now shows pictures generated with PlotlyBackend, K3DBackend as well as interactive plots with widgets.
Default settings:
- Changed cgf["interactive"]["theme"] to "light": interactive plots served on a new browser window will use a light theme.
- Changed cgf["bokeh"]["update_event"] to False: Bokeh won’t update the plot with new data as dragging or zooming operations are performed.
- Added new option cgf["k3d"]["camera_mode"].
Improvements to MatplotlibBackend:
- Added label capability to plot_implicit.
- show() method now accepts keyword arguments. This is useful to detach the plot from a non-interactive console.
Added dots keyword argument to plot_piecewise to choose wheter to show circular markers on endpoints.
Fixed bug with plotting 3D vectors.

v1.6.0

Added new plotting functions:
- plot3d_revolution to create surface of revolution.
- plot_parametric_region, still in development.
MatplotlibBackend:
- Fixed bug with colormaps and normalization.
- Improved update speed when dealing with parametric domain coloring plots.
Improved zlim support on K3DBackend for interactive widget plots.
Fixed bug with parametric interactive widget plots and PlotlyBackend: the update speed is now decent.
Series:
- Moved LineOver1DRangeSeries._detect_poles to _detect_poles_helper.
- plot_complex and plot_real_imag: the input expression is no longer wrapped by symbolic re() or im(). Instead, the necessary processing is done on the series after the complex function has been evaluated. This improves performance.
Parametric2DLineSeries now support detect_poles.
Implemented support for color_func keyword argument on plot_list and plot_complex_list.
Added extras_require to setup.py:
- by default, pip install sympy_plot_backends will install only the necessary requirements to get non-interactive plotting to work with Matplotlib.
- use pip install sympy_plot_backends[all] to install all other packages: panel, bokeh, plotly, k3d, vtk, …
Documentation:
- Improved examples.
- Added examples with PlotlyBackend.

v1.5.0

Implemented the plot3d_spherical function to plot functions in spherical coordinates.
Added the wireframe option to plot3d, plot3d_parametric_surface and plot3d_spherical to add grid lines over the surface.
Fixed bug with plot3d and plot_contour when dealing with instances of BaseScalar.
Added normalize keyword argument to plot_vector and plot_complex_vector to visualize quivers with unit length.
Improve documentation of plot_vector and plot_complex_vector.
Improved test coverage on complex and vector plotting functions.
Improvements on PlotlyBackend:
- it is now be able to plot more than 14 2d/3d parametric lines when use_cm=False.
- improved logic to show colorbars on 3D surface plots.
- added support for custom aspect ratio on 3D plots.
Improved support for xlim, ylim, zlim on K3DBackend.
Series:
- Fixed bug with uniform evaluation while plotting numerical functions.
- Fixed bug with color_func.
- Added transformation keyword arguments tx, ty, tz to parametric series.
Breaks:
- Inside plot_parametric and plot3d_parametric_line, the tz keyword argument has been renamed to tp.
- Removed Mayavi from setup dependencies. Mayavi is difficult to install: can’t afford the time it requires for proper setup and testing. MayaviBackend is still available to be used “as is”.

v1.4.0

Reintroduced MayaviBackend to plot 3D symbolic expressions with Mayavi. Note that interactive widgets are still not supported by this backend.
plot_contour is now able to create filled contours or line contours on backends that supports such distinction. Set the is_filled keyword argument to choose the behaviour.
Implemented interactive widget support for plot_list.
Implemented back-compatibility-related features with SymPy.
Fixed bugs with PlaneSeries:
- Data generation for vertical planes is now fixed.
- K3DBackend is now able to plot this series.
- Similar to other 3D surfaces, planes will be plotted with a solid color.
Fixed bug with Vector3DSeries: the discretized volume is now created with Numpy’s meshgrid with indexing='ij'. This improves the generation of 3D streamlines.
Fixed bug with plot3d and plot_contour: when params is provided the specified backend will be instantiated.
Fixed bug with K3DBackend and plot3d_implicit.

v1.3.0

Added support for plotting numerical vectorized functions. Many of the plotting functions exposed by this module are now able to deal with both symbolic expressions as well as numerical functions. This extends the scope of this module, as it is possible to use it directly with numpy and lambda functions. For example, the following is now supported:
```
import numpy as np
plot(lambda t: np.cos(x) * np.exp(-x / 5), ("t", 0, 10))
```
Added support for vector from the sympy.physics.mechanics module in the plot_vector function.
Implemented keyword argument validator: if a user writes a misspelled keyword arguments, a warning message will be raised showing one possible alternative.

v1.2.1

Added used_by_default inside default options for adaptive algorithm. This let the user decide wheter to use adaptive algorithm or uniform meshing by default for line plots.
Fix the axis labels for the plot_complex_vector function.
Improved a few examples in the docstring of plot_vector and plot_complex_vector.
Fixed bug with interactive update of plot_vector inside MatplotlibBackend.
Improvements to the code in preparation for merging this module into Sympy:
- Small refactoring about the label generation: previously, the string and latex representations were generated at different times and in different functions. Now, they are generated simultaneously inside the __init__ method of a data series.
- Changes in names of functions that are meant to remain private:
  - adaptive_eval -> _adaptive_eval.
  - _uniform_eval -> _uniform_eval_helper
  - uniform_eval -> _uniform_eval
  - _correct_size -> _correct_shape
  - get_points -> _get_points

v1.2.0

Replaced the line_kw, surface_kw, image_kw, fill_kw keyword arguments with rendering_kw. This simplifies the usage between different plotting functions.
Plot functions now accepts a new argument: rendering_kw, a dictionary of options that will be passed directly to the backend to customize the appearance. In particular:
- Possibility to plot and customize multiple expressions with a single function call. For example, for line plots:
```
plot(
  (expr1, range1 [opt], label1 [opt], rendering_kw1 [opt]),
  (expr2, range2 [opt], label2 [opt], rendering_kw2 [opt]),
  **kwargs
)
```
- Possibility to achieve the same result using the label and rendering_kw keyword arguments by providing lists of elements (one element for each expression). For example, for line plots:
```
plot(expr1, expr2, range [opt],
    label=["label1", "label2"],
    rendering_kw=[dict(...), dict(...)],
    **kwargs
)
```
Interactive submodule:
- Fixed bug with spb.interactive.create_widgets.
- Integration of the interactive-widget plot iplot into the most important plotting functions. To activate the interactive-widget plot users need to provide the params dictionary to the plotting function. For example, to create a line interactive-widget plot:
```
plot(cos(u * x), (x, -5, 5), params={u: (1, 0, 2)})
```
Series:
- Fixed a bug with line series when plotting complex-related function with adaptive=False.
- Fixed bug with lambdify and modules="sympy".
- Fixed bug with the number of discretization points of vector series.
- Enabled support for Python’s built-in sum() function, which can now be used to combine multiple plots.
Backends:
- Fixed a bug with MatplotlibBackend and string-valued color maps.
- Fixed a bug with BokehBackend about the update of quivers color when using iplot.
Updated tutorials and documentation.

v1.1.7

Fixed bug with plot_complex_list.
Added new tutorial about singularity-dections.

v1.1.6

Fixed bug with label keyword argument.
Added error message to plot3d.
Updated documentation.

v1.1.5

Implemented line_color and surface_color: this plotting module should now be back-compatible with the current sympy.plotting.

v1.1.4

color_func is back-compatible with sympy.plotting’s line_color and surface_color.

v1.1.3

Added color_func support to parametric line series.
Improved docstring.

v1.1.2

iplot:
- Added servable keyword argument: servable=True will serves the application to a new browser windows,
- Added name keyword argument: if used with servable=True it will add a title to the interactive application.
Default settings:
- Added servable and theme to interactive section.
Fixed a bug when plotting lines with BokehBackend.
Improved the way of setting the number of discretization points: n can now be a two (or three) elements tuple, which will override n1 and n2.
It is now possible to pass a float number of discretization points, for example n=1e04.
added label keyword argument to plot functions.

v1.1.1

Added color_func keyword argument to:
- plot to apply custom coloring to lines.
- plot3d and plot3d_parametric_surface to apply custom coloring to 3D
  surfaces.
- to accomodate color_func, ParametricSurfaceSeries.get_data() now returns 5 elements instead of 3.
Added plot range to default settings.
Implemented a custom printer for interval math to be used inside ImplicitSeries.
Added plot3d_implicit to visualize implicit surfaces.
MatplotlibBackend now uses default colorloop from plt.rcParams['axes.prop_cycle'].

v1.1.0

polar_plot:
- a polar chart will be generated if a backend support such feature, otherwise the backend will apply a polar transformation and plot a cartesian chart.
- iplot changes the keyword argument to request a 2D polar chart. Use is_polar=True instead of polar=True.
plot3d:
- Setting is_polar=True enables polar discretization.
3d vector plots:
- Keyword argument slice can now acccept instances of surface-related series (as well as surface interactive series).
- Improved PlotlyBackend and K3DBackend support for 3D vector-quiver interactive series.
Default setting:
- Added adaptive "goal".
- Added use_cm for 3D plots.
Added tx, ty, tz keyword arguments. Now it is possible to apply transformation functions to the numerical data, for example converting the domain of a function from radians to degrees.
Added Latex support and a the use_latex keyword argument to toggle on/off the use of latex labels. Plot functions will use latex labels on the axis by default, if the backend supports such feature. The behaviour can be changed on the default settings.
Fixed bug within iplot and K3DBackend when setting use_cm=False.
iplot parameters can accept symbolic numerical values (of type Integer, Float, Rational).
Removed plot_data module.

v1.0.4

Bug fix for plotting real/imag of complex functions.

v1.0.3

Deprecated get_plot_data function.
Exposed create_series function from the spb.interactive module.
Removed dependency on sympy.plotting.experimental_lambdify. Now this plotting module relies only on lambdify.
Improved testing of plot_implicit.
Added quickstart tutorials to ReadTheDocs.

v1.0.2

Added backend’s aliases into __init__.py.
Added example to the plot function.
Improved docstring and examples of plot_implicit.
Fixed bug with PlotlyBackend in which axis labels were not visible.
Added throttled to default settings of interactive.
Added grid to defaults settings of all backends.

v1.0.1

Exiting development status Beta
Updated K3DBackend documentation.
Updated tutorial

v1.0.0

Data series:
- Integrated adaptive module with SymPy Plotting Backends.
  - Implemented adaptive algorithm for 3D parametric lines and 3D surfaces.
  - added adaptive_goal and loss_fn keyword arguments to control the behaviour of adaptive algorithm.
- Improved support for integer discretization.
- Integrated lambdify into data series to generate numerical data.
  - partially removed dependency sympy.plotting.experimental_lambdify. Only ImplicitSeries still uses it for its adaptive implementation with interval arithmetic.
  - Added modules keyword argument to data series in order to choose the lambdify module (except ImplicitSeries).
- Line series now implements the _detect_poles algorithm.
- Added rendering_kw attribute to all data series.
- Refactoring of InteractiveSeries:
  - InteractiveSeries is now a base class.
  - Implemented several child classes to deal with specific tasks.
  - Removed update_data method.
  - Added params attribute as a property.
  - Fixed the instantiation of subclasses in __new__.
Functions:
- removed aliases of plotting functions.
- Added complex-related plotting functions:
  - plot_complex now plots the absolute value of a function colored by its argument.
  - plot_real_imag: plot the real and imaginary parts.
  - plot_complex_list: plot list of complex points.
  - plot_complex_vector: plot the vector field [re(f(z)), im(f(z))] of a complex function f.
- plotgrid is now fully functioning.
- added plot_list to visualize lists of numerical data.
- added sum_bound keyword argument to plot: now it is possible to plot summations.
- removed process_piecewise keyword argument from plot. Now, plot is unable to correctly display Piecewise expressions and their discontinuities.
- added plot_piecewise to correctly visualize Piecewise expressions and their discontinuities.
- added is_point and is_filled keyword arguments to plot and plot_list in order to visualize filled/empty points.
- replaced fill keyword argument with is_filled inside plot_geometry.
- iplot:
  - implemented addition between instances of InteractivePlot and Plot.
  - fixed bug with MatplotlibBackend in which the figure would show up twice.
- Deprecation of smart_plot.
- plot_parametric and plot3d_parametric_line: the colorbar now shows the name of the parameter, not the name of the expression.
Backends:
- Plot:
  - improved support for addition between instances of Plot.
  - improved instantiation of child classes in __new__ method.
  - removed _kwargs instance attribute.
- MatplotlibBackend:
  - fig attribute now returns only the figure. The axes can be retrieved from its figure.
  - Dropped support for jupyterthemes.
  - Fix bug in which the figure would show up twice on Jupyter Notebook.
  - Added colorbar when plotting only 2D streamlines.
- PlotlyBackend:
  - removed the wireframe keyword argument and dropped support for 3D wireframes.
  - dropped support for plot_implicit.
- BokehBackend:
  - add update_event keyword argument to enable/disable auto-update on panning for line plots.
  - dropped support for plot_implicit.
- K3DBackend:
  - fixed bug with zlim.
- All backends:
  - Generates numerical data and add it to the figure only when show() or fig are called.
  - colorloop, colormaps class attributes are now empty lists. User can set them to use custom coloring. Default coloring is implemented inside __init__ method of each backend.
Performance:
- Improved module’s load time by replacing from sympy import somethig with from sympy.module import somethig.
- Improved module’s load time by loading backend’s dependencies not at the beginning of the module, but only when they are required.
Default settings:
- Change backend’s themes to light themes.
- Added options to show grid and minor grid on bokeh, plotly and matplotlib.
- Added interactive section and the use_latex option.
- Added update_event to bokeh.
Documentation:
- Improved examples in docstring of plotting functions.
- Removed tutorials from the Tutorials section as they slowed down the pages.
- Improved organization.