Multiplot: drawing several plots on one page#

The Multiplot object is basically a layout of several figures where all drawn elements are linked together so that selecting an object in one plot selects this object on all plots, except for 2D representations (PrimitiveGroup) and Graph2D objects.

How to draw a Multiplot ?#

  1. Import the required packages

# Required packages
import random
import plot_data

  1. Create Data

In order to draw a Multiplot with random values, build a random vector of samples (stored as Python dict) with different attributes. Here 4 float attributes (mass, length, speed and power), 1 integer attribute (rank) and 1 discrete attribute (shape) are chosen to describe each sample.

# Vector construction
elements = []
SHAPES = ['round', 'square', 'triangle', 'ellipse']
for i in range(500):
    elements.append({"mass": random.uniform(0, 10),
                     "length": random.uniform(0, 100),
                     "speed": random.uniform(0, 3.6),
                     "shape": random.choice(SHAPES),
                     "rank": random.randint(1, 20),
                     'power': random.gauss(0, 3)})

  1. Build all plots to draw in the Multiplot

# ParallelPlot
parallelplot = pld.ParallelPlot(axes=['mass', 'length', 'speed', 'shape', 'rank', 'power'])

# Scatterplots
mass_vs_length = pld.Scatter(x_variable='mass', y_variable='length')
shape_vs_rank = pld.Scatter(x_variable='shape', y_variable='rank')

# 2D representation
drawing_2d = pld.PrimitiveGroup(primitives=[pld.Rectangle(0, 0, 12, 24), pld.Circle2D(12, 24, 5)])

histogram_power = pld.Histogram(x_variable='power')
histogram_speed = pld.Histogram(x_variable='speed')

# Creating the multiplot
plots = [parallelplot, mass_vs_length, shape_vs_rank, drawing_2d, histogram_power, histogram_speed]

  1. Build a Multiplot with all these plots

# Points sets creation as an example
point_families=[
            pld.PointFamily('rgb(25, 178, 200)', [1,2,3,4,5,6,7]),
            pld.PointFamily('rgb(225, 13, 200)', [10,20,30,41,45,46,47]),
            pld.PointFamily('rgb(146, 178, 78)', [11,21,31,41,25,26,27])
            ]

multiplot = pld.MultiplePlots(
            plots=plots,
            elements=elements,
            initial_view_on=True
            )

Once done, the figure can be displayed with the following command line :

pld.plot_canvas(plot_data_object=multiplot, canvas_id='my_mulitplot')

Multiplot Features#

  • All features available in alone figures are available in the Multiplot layout,

  • Cross selection between figures is available,

  • One can reorder figures by clicking on Resize Figures ,

  • One can select several lines with several mouse click by keeping pressed Ctrl key,

  • One can reset the view of the mouse hovered plot by pressing Ctrl + Space,

  • One can reset the whole figure by pressing Ctrl + Shift + Left Click.

How to write a method to draw a Multiplot in a DessiaObject ?#

For the pendulum example, a Multiplot can be designed for the PendulumDOE class to draw all relevant figures in one html page. As for other plots, a decorator @plot_data_view is added for a future platform usage.

  1. Before coding the Multiplot method, some re-arrangements need to be done in PendulumDOE class for minimizing the amount of produced data

class PendulumDOE(Dataset):
    :
    :
    :
    # To build only one vector elements
    def _to_sample(self):
    return [{
        "length": pendulum.length,
        "g": pendulum.g,
        "speed": pendulum.max_speed,
        "period": pendulum.period,
        } for pendulum in self.dessia_objects]

    # To draw all pendulums
  def _to_drawings(self):
    cmap = colormaps["jet"](npy.linspace(0, 1, len(self.dessia_objects)))
    return sum([pendulum.draw(Color(*(cmap[i][:-1]))).primitives
                for i, pendulum in enumerate(self.dessia_objects)], [])

  def _scatter_speed_period(self, elements = None):
    tooltip = pld.Tooltip(["length", "g"])
    return pld.Scatter(x_variable="period", y_variable="speed", tooltip=tooltip, elements=elements)

  def _parallel_plot(self, elements = None):
    return pld.ParallelPlot(axes=["g", "length", "period", "speed"], elements=elements)

  def _histogram(self, elements = None):
    return pld.Histogram(x_variable="speed", graduation_nb=20, elements=elements)

  @plot_data_view("max_speed")
  def scatter_speed_period(self, reference_path: str = "#"):
    return self._scatter_speed_period(elements=self._to_sample())

  @plot_data_view("parallelplot")
  def parallel_plot(self, reference_path: str = "#"):
    return self._parallel_plot(elements=self._to_sample())

  @plot_data_view("histogram")
  def histogram(self, reference_path: str = "#"):
    return self._histogram(elements=self._to_sample())

  @plot_data_view("draw")
  def draw(self):
    return pld.PrimitiveGroup(primitives=self._to_drawings())

  1. Write the Mutliplot method

class PendulumDOE(Dataset):
    :
    :
    :
    @plot_data_view("Multiplot")
    def multiplot(self, reference_path: str = "#"):
      scatter_plot = self._scatter_speed_period()
      y_vs_t_curves = self.all_y_vs_time()
      parallel_plot = self._parallel_plot()
      histogram = self._histogram()
      draw = self.draw()
      plots = [scatter_plot, parallel_plot, histogram, draw, y_vs_t_curves]
      elements = self._to_sample()
      return pld.MultiplePlots(elements=elements, plots=plots, name="Multiple Plot")

Once done, the figure can be displayed with the following command line :

multiplot = pendulum_doe.multiplot()
pld.plot_canvas(plot_data_object=multiplot, canvas_id='my_multiplot')