Sankey Plots

Related MassComposition objects are managed as a network.

import pandas as pd
import matplotlib.pyplot as plt
import plotly
from matplotlib import pyplot as plt
from plotly.graph_objs import Figure

from elphick.mass_composition import MassComposition
from elphick.mass_composition.flowsheet import Flowsheet
from elphick.mass_composition.datasets.sample_data import sample_data

# sphinx_gallery_thumbnail_number = -1

Create some MassComposition objects

Create an object, and split it to create two more objects.

df_data: pd.DataFrame = sample_data()
obj_mc: MassComposition = MassComposition(df_data, name='Feed')
obj_mc_1, obj_mc_2 = obj_mc.split(0.4)

Create a Flowsheet object

This requires passing an Iterable of MassComposition objects

fs: Flowsheet = Flowsheet().from_streams([obj_mc, obj_mc_1, obj_mc_2])

hf = fs.plot_network()
plt.show()

Sankey Plot

A sankey represents the network with the width of each edge representing its mass. The optional color of the edge represents the value of the selected composition analyte. In this example, grades are the same, so will not add any value.

fig: Figure = fs.plot_sankey()
# noinspection PyTypeChecker
plotly.io.show(fig)  # this call to show will set the thumbnail for use in the gallery

Filtered Data

It is convenient to filter the underlying data before plotting.

To verify the filtering, we’ll plot the sankey as part of the table_plot, which will tabulate the aggregated mass-composition for each edge/stream in the network.

fig: Figure = fs.table_plot(plot_type='sankey', table_pos='left')
fig

Now filter and plot again

fig: Figure = fs.query(mc_name='Feed', queries={'index': 'Fe>58'}).table_plot(plot_type='sankey', table_pos='left')
fig