Unit Scaling

import numpy as np
import pandas as pd

import pyrolite.geochem

pd.set_option("display.precision", 3)  # smaller outputs

Here we create an example dataframe to work from, containing some synthetic data in the form of oxides and elements, each with different units (wt% and ppm, respectively).

from pyrolite.util.synthetic import normal_frame

df = normal_frame(
    columns=["CaO", "MgO", "SiO2", "FeO", "Ni", "Ti", "La", "Lu"], seed=22
)
df.pyrochem.oxides *= 100  # oxides in wt%
df.pyrochem.elements *= 10000  # elements in ppm

In this case, we might want to transform the Ni and Ti into their standard oxide equivalents NiO and TiO2:

df.pyrochem.convert_chemistry(to=["NiO", "TiO2"]).head(2)

	NiO	TiO2
0	1235.188	2388.196
1	1301.086	2164.601

But here because Ni and Ti have units of ppm, the results are a little non-sensical, especially when it’s combined with the other oxides:

df.pyrochem.convert_chemistry(to=df.pyrochem.list_oxides + ["NiO", "TiO2"]).head(2)

	CaO	MgO	SiO2	FeO	NiO	TiO2
0	4.887	5.527	33.705	3.236	1235.188	2388.196
1	5.323	6.056	33.733	2.760	1301.086	2164.601

There are multiple ways we could convert the units, but here we’re going to first convert the elemental ppm data to wt%, then perform our oxide-element conversion. To do this, we’ll use the built-in function scale():

from pyrolite.util.units import scale

df.pyrochem.elements *= scale("ppm", "wt%")

We can see that this then gives us numbers which are a bit more sensible:

df.pyrochem.convert_chemistry(to=df.pyrochem.list_oxides + ["NiO", "TiO2"]).head(2)

	CaO	MgO	SiO2	FeO	NiO	TiO2
0	4.887	5.527	33.705	3.236	0.124	0.239
1	5.323	6.056	33.733	2.760	0.130	0.216

Dealing with Units in Column Names

Often our dataframes will start containing column names which pyrolite doesn’t recognize natively by default (work in progress, this is an item on the roadmap). Here we can create an example of that, and go through some key steps for using this data in pyrolite:

df = normal_frame(
    columns=["CaO", "MgO", "SiO2", "FeO", "Ni", "Ti", "La", "Lu"], seed=22
)
df.pyrochem.oxides *= 100  # oxides in wt%
df.pyrochem.elements *= 10000  # elements in ppm
df = df.rename(
    columns={
        **{c: c + "_wt%" for c in df.pyrochem.oxides},
        **{c: c + "_ppm" for c in df.pyrochem.elements},
    }
)
df.head(2)

	CaO_wt%	MgO_wt%	SiO2_wt%	FeO_wt%	Ni_ppm	Ti_ppm	La_ppm	Lu_ppm
0	4.887	5.527	33.705	3.236	970.613	1431.363	2454.951	407.684
1	5.323	6.056	33.733	2.760	1022.395	1297.351	2543.004	350.085

If you just wanted to rescale some columns, you can get away without renaming your columns, e.g. converting all of the ppm columns to wt%:

df.loc[:, [c for c in df.columns if "_ppm" in c]] *= scale("ppm", "wt%")
df.head(2)

	CaO_wt%	MgO_wt%	SiO2_wt%	FeO_wt%	Ni_ppm	Ti_ppm	La_ppm	Lu_ppm
0	4.887	5.527	33.705	3.236	0.097	0.143	0.245	0.041
1	5.323	6.056	33.733	2.760	0.102	0.130	0.254	0.035

However, to access the full native capability of pyrolite, we’d need to rename these columns to use things like convert_chemistry():

units = {  # keep a copy of the units, we can use these to map back later
    c: c[c.find("_") + 1 :] if "_" in c else None for c in df.columns
}
df = df.rename(
    columns={c: c.replace("_wt%", "").replace("_ppm", "") for c in df.columns}
)
df.head(2)

	CaO	MgO	SiO2	FeO	Ni	Ti	La	Lu
0	4.887	5.527	33.705	3.236	0.097	0.143	0.245	0.041
1	5.323	6.056	33.733	2.760	0.102	0.130	0.254	0.035

We could then perform our chemistry conversion, rename our columns to include units, and optionally export to e.g. CSV:

converted_wt_pct = df.pyrochem.convert_chemistry(
    to=df.pyrochem.list_oxides + ["NiO", "TiO2"]
)
converted_wt_pct.head(2)

	CaO	MgO	SiO2	FeO	NiO	TiO2
0	4.887	5.527	33.705	3.236	0.124	0.239
1	5.323	6.056	33.733	2.760	0.130	0.216

Here we rename the columns before we export them, just so we know explicitly what the units are:

converted_wt_pct = converted_wt_pct.rename(
    columns={c: c + "_wt%" for c in converted_wt_pct.pyrochem.list_oxides}
)
converted_wt_pct.head(2)

	CaO_wt%	MgO_wt%	SiO2_wt%	FeO_wt%	NiO_wt%	TiO2_wt%
0	4.887	5.527	33.705	3.236	0.124	0.239
1	5.323	6.056	33.733	2.760	0.130	0.216

converted_wt_pct.to_csv("converted_wt_pct.csv")