#Jupyter matplotlib subplot size full

The macro is the easiest way to define complex layouts, using Julia's multidimensional Array construction as the basis for a custom layout syntax. Titles and labels can be easily added: plot(rand(100,4), layout = 4, label=, More complex grid layouts can be created with the grid(.) constructor: plot(rand(100, 4), layout = grid(4, 1, heights=)) Pass a tuple to layout to create a grid of that size: # create a 4x1 grid, and map each of the 4 series to one of the subplots Pass an integer to layout to allow it to automatically compute a grid size for that many subplots: # create a 2x2 grid, and map each of the 4 series to one of the subplots (For example: a line or a set of markers) Series: One distinct visualization of data.Plot Area: The part of a subplot where the data is shown.Axis: One axis of a subplot, containing axis guide (label), tick labels, and tick marks.Subplot: One subplot, containing a title, axes, colorbar, legend, and plot area.It's helpful at this point to review terminology: Just set the layout keyword in a call to plot(.)

#Jupyter matplotlib subplot size full

Care has been taken to keep the framework flexible and generic, so that backends need only support the ability to precisely define the absolute position of a subplot, and they get the full power of nesting, plot area alignment, and more. As of v0.7.0, Plots has taken control of subplot positioning, allowing complex, nested grids of subplots and components.