Plot Demo

Demonstrating the plot methods.

import pandas as pd
import plotly
from plotly.graph_objs import Figure
from elphick.geomet import Sample
from elphick.geomet.utils.data import sample_data

Load Data

We get some demo data in the form of a pandas DataFrame

df_data: pd.DataFrame = sample_data()
df_data.head()

	wet_mass	mass_dry	FE	SIO2	al2o3	LOI	group
index
0	100.0	90.0	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	61.0	2.2	0.9	3.0	grp_2

Create Sample

obj_smpl: Sample = Sample(df_data)
print(obj_smpl)

Sample: None
{'wet_mass': {0: 300.0}, 'mass_dry': {0: 260.0}, 'H2O': {0: 13.333333333333334}, 'Fe': {0: 59.0}, 'SiO2': {0: 3.5153846153846153}, 'Al2O3': {0: 1.8730769230769235}, 'LOI': {0: 4.0}}

Parallel Plots

Create an interactive parallel plot. Great for visualising and interactively filtering mass-composition data.

fig: Figure = obj_smpl.plot_parallel()
fig

Create a parallel plot with only selected components and color

fig2 = obj_smpl.plot_parallel(vars_include=['wet_mass', 'H2O', 'Fe', 'group'], color='group')
fig2

Ternary Diagram

Create a ternary diagram for any 3 composition variables.

fig3 = obj_smpl.plot_ternary(variables=['SiO2', 'Al2O3', 'LOI'], color='group')
# noinspection PyTypeChecker
plotly.io.show(fig3)  # this call to show will set the thumbnail for use in the gallery