Iron Ore Assays

Demonstrate with some real data

import pandas as pd
import plotly
from elphick.mass_composition.datasets.sample_data import iron_ore_sample_data
from plotly.graph_objs import Figure

from elphick.mass_composition import MassComposition

Create a MassComposition object

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

df_data: pd.DataFrame = iron_ore_sample_data()
name = 'A072391'

print(df_data.shape)
df_data.head()

(126, 15)

	mass_dry	H2O	MgO	MnO	Al2O3	P	Fe	SiO2	TiO2	CaO	Na2O	K2O	DHID	interval_from	interval_to
index
6	2.12	0.35	0.07	0.0	1.48	0.019	64.30	3.23	0.080	0.04	0.01	0.03	CBS02	26.60	26.85
7	2.06	0.23	0.06	0.0	1.28	0.017	64.91	2.90	0.082	0.04	0.01	0.03	CBS02	26.85	27.10
9	1.91	0.23	0.06	0.0	1.01	0.016	65.09	2.39	0.059	0.03	0.01	0.02	CBS02	27.70	28.00
10	1.96	0.36	0.06	0.0	0.99	0.022	65.03	2.22	0.057	0.04	0.01	0.02	CBS02	28.00	28.30
12	2.06	0.40	0.05	0.0	0.75	0.016	65.87	1.69	0.040	0.03	0.01	0.01	CBS02	28.60	28.95

…and create a MassComposition from the DataFrame.

obj_mc: MassComposition = MassComposition(df_data, name=name)
obj_mc.aggregate(group_var='DHID')

	mass_wet	mass_dry	H2O	MgO	MnO	Al2O3	P	Fe	SiO2	TiO2	CaO	Na2O	K2O
DHID
CBS02	46.614043	46.310	0.652257	0.055636	0.000000	1.029685	0.022764	64.656675	2.589849	0.053834	0.029160	0.010436	0.017296
CBS03	229.414089	226.250	1.379204	0.132968	0.038134	1.399500	0.046587	60.297271	3.653106	0.052016	0.699112	0.030318	0.019871
CBS04	347.440438	344.680	0.794507	0.088788	0.126484	1.513147	0.038461	60.289083	3.319658	0.050986	0.093096	0.001773	0.011788
CBS10	306.500146	304.690	0.590586	0.044836	0.091520	1.963728	0.059062	61.001839	2.949455	0.061461	0.060045	0.014310	0.012509
CBS12	506.098042	493.968	2.396777	0.090723	0.247394	1.854824	0.032990	60.572344	2.491186	0.067683	0.045545	0.016941	0.014323
CBS13	593.551050	565.790	4.677112	0.066832	0.165065	1.969400	0.051777	59.839565	2.443914	0.072125	0.027301	0.019054	0.010321

Parallel plots

obj_mc: MassComposition = MassComposition(df_data.reset_index().set_index(['DHID', 'interval_from', 'interval_to']),
                                          name=name)

fig: Figure = obj_mc.plot_parallel(color='Fe')
fig

fig: Figure = obj_mc.plot_parallel(color='Fe', plot_interval_edges=True)
fig

# with selected variables
fig: Figure = obj_mc.plot_parallel(color='Fe', vars_include=['mass_wet', 'H2O', 'Fe', 'SiO2'])
# noinspection PyTypeChecker
plotly.io.show(fig)  # this call to show will set the thumbnail for the gallery