Layout

All plots consist of a number of different “elements” arranged in a specific spatial “layout”. In fivecentplots, each plot component (such as the axes window, labels, legends, etc.) is defined as an Element class (or object) that contains all of the attributes that define its style (i.e., color, font, size, etc). The placement of these Element objects is handled by a Layout class. Element objects are spaced by either a user-defined or default whitespace. The specifics of the Layout class depend on the python plotting engine being used. Currently, only matplotlib is available as a plotting engine, but additional engines will be added in the future.

An example x-y plot and a schematic blueprint of its underlying layout is shown below. All Element attributes and whitespace parameters can be controlled via keyword arguments in the plot function call or from a predifined “theme” file that consists of dictionaries of preferred values.

Example Plot:

Layout Schematic:

We can extend the simple single-axis plot and make a grid of subplots. An example of the featured Element objects in this case is shown below:

Layout Schematic with Grid:

All keyword arguments for Element attributes and whitespace are defined in the next section of the guide.

Engine

In order to actually create a plot, fivecentplots must tap into a plotting “engine”. This engine is a standard Python plotting package like matplotlib or bokeh. One of the key advantages of fivecentplots is the ability to switch between plotting engines while using the same keyword-argument-based api. The engine itself is toggled by a keyword. fivecentplots uses matplotlib as its default engine.

For example, if you needed a high-quality plot for a paper and wanted to use matplotlib, you could do the following:

fcp.plot(df, x='Voltage', y='I [A]', legend='Die',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Alternatively, what if you needed to interactively manipulate the same data set? It may be more convenient to plot via bokeh. To switch engines, we simply add the keyword engine and set to “bokeh”:

fcp.plot(df, x='Voltage', y='I [A]', legend='Die', engine='bokeh',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Note

As of version 0.3.0, bokeh support is limited compared with matplotlib. More development is needed.