Interval Data - Sink Float

Intervals are encountered in Metallurgy, aka fractions, e.g. size fractions. In that case the typical nomenclature is size_retained, size passing, since the data originates from a sieve stack.

The Sink Float metallurgical test splits/fractionates samples by density. The density fraction is often conducted by size fraction, resulting in 2D fractionation (interval) data.

import logging
from functools import partial
from pathlib import Path

import pandas as pd
# noinspection PyUnresolvedReferences
import numpy as np
import plotly

from elphick.mass_composition import MassComposition
from elphick.mass_composition.datasets import datasets
from elphick.mass_composition.datasets.sample_data import size_by_assay
from elphick.mass_composition.flowsheet import Flowsheet
from elphick.mass_composition.utils.partition import napier_munn

logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
                    datefmt='%Y-%m-%dT%H:%M:%S%z')

Load Data

We load some real data.

df_data: pd.DataFrame = datasets.load_nordic_iron_ore_sink_float()
df_data

	size_retained	size_passing	density_lo	density_hi	mass_pct	Fe	SiO2	P	TiO2	V
0	1.000	NaN	NaN	NaN	0.4	22.7	50.9	0.042	0.170	0.0049
1	0.100	1.000	NaN	NaN	67.1	27.3	47.7	0.107	0.178	0.0062
2	0.063	0.100	NaN	NaN	12.7	18.0	57.8	0.440	0.200	0.0056
3	0.040	0.063	NaN	NaN	8.2	16.9	57.1	0.610	0.235	0.0057
4	0.000	0.040	NaN	NaN	11.6	19.4	51.6	0.650	0.310	0.0072
5	0.100	NaN	NaN	2.7	54.2	1.2	1.6	0.180	0.084	0.0030
6	0.100	NaN	2.7	3.3	9.7	16.6	42.6	0.980	0.380	0.0100
7	0.100	NaN	3.3	NaN	36.1	68.0	78.8	0.033	0.285	0.0120
8	0.063	0.100	NaN	2.7	24.0	1.2	79.5	0.015	0.060	0.0070
9	0.063	0.100	2.7	3.3	11.9	10.2	54.0	2.320	0.280	0.0080
10	0.063	0.100	3.3	NaN	64.1	67.1	1.4	0.174	0.530	0.0020
11	0.040	0.063	NaN	2.7	76.6	3.1	71.3	0.850	0.145	0.0130
12	0.040	0.063	2.7	3.3	4.3	28.5	24.5	2.780	0.460	0.1100
13	0.040	0.063	3.3	NaN	19.1	68.6	0.6	0.069	0.480	0.0130

The dataset contains size x assay, plus size x density x assay data. We’ll drop the size x assay data to leave the sink / float data.

# df_sink_float: pd.DataFrame = df_data.query('density_lo != np.nan and density_hi != np.nan')
# df_sink_float