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Tools for geochemical data analysis.

Project description

pyrolite

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Install

pip install pyrolite

Build Status

License: CSIRO Modified BSD/MIT License

Maintainer: Morgan Williams (morgan.williams at csiro.au)

master develop
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Usage Examples

Note: Examples for compositional data yet to come.

Elements and Oxides

Index Generators

All Elements up to U

>>> import pyrolite.common_elements as ce
>>> ce()  # periodictable.core.Element return
[H, He, Li, Be, ...,  Th, Pa, U]
>>> ce(output='str')  # string return
['H', 'He', 'Li', 'Be', ...,  'Th', 'Pa', 'U']

Oxides for Elements with Positive Charges (up to U)

>>> import pyrolite.common_oxides as co
>>> co()  # periodictable.formulas.Formula return
[H, He, Li, Be, ...,  Th, Pa, U]
>>> co(output='str')  # string return
['H2O', 'He2O', 'HeO', 'Li2O', 'Be2O', 'BeO', 'B2O', 'BO', 'B2O3', ...,
'U2O', 'UO', 'U2O3', 'UO2', 'U2O5', 'UO3']

REE Elements

>>> from pyrolite.geochem import REE
>>> REE(output='str')
['La', 'Ce', 'Pr', 'Nd', 'Pm', ..., 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu']

Data Cleaning

Some simple utilities for cleaning up data tables are included. Assuming you're importing data into pd.DataFrame:

import pandas as pd
df = pd.DataFrame({'label':'basalt', 'ID': 19076,
                   'mgo':20.0, 'SIO2':30.0, 'cs':5.0, 'TiO2':2.0},
                  index=[0])
>>> df.columns
Index(['label', 'ID', 'mgo', 'SIO2', 'cs', 'TiO2'], dtype='object')
from pyrolite.textutil import titlecase
from pyrolite.geochem import tochem

>>> df.columns = [titlecase(h, abbrv=['ID']) for h in df.columns]
Index(['Label', 'ID', 'Mgo', 'Sio2', 'Cs', 'Tio2'], dtype='object')
>>> df.columns = tochem(df.columns)
Index(['Label', 'ID', 'MgO', 'SiO2', 'Cs', 'TiO2'], dtype='object')

Normalisation

A selection of reference compositions are included:

>>> from pyrolite.normalisation import ReferenceCompositions
>>> refcomp = ReferenceCompositions()
{
'Chondrite_PON': Model of Chondrite (Palme2014),
'D-DMM_WH': Model of DepletedDepletedMORBMantle (Workman2005),
'DMM_WH': Model of DepletedMORBMantle (Workman2005),
'DM_SS': Model of DepletedMantle (Salters2004),
'E-DMM_WH': Model of EnrichedDepletedMORBMantle (Workman2005),
'PM_PON': Model of PrimitiveMantle (Palme2014)
}
>>> CH = refcomp['Chondrite_PON']
>>> PM = refcomp['PM_PON']
>>> reels = REE(output='str')
>>> CH[reels]
      value  unc_2sigma units
var                           
La    0.2414    0.014484   ppm
Ce    0.6194    0.037164   ppm
...
Tm   0.02609    0.001565   ppm
Yb    0.1687    0.010122   ppm
Lu   0.02503    0.001502   ppm

The normalize method can be used to normalise dataframes to a given reference (e.g. for spiderplots):

>>> from pyrolite.plot import spiderplot
>>> refcomp = ReferenceCompositions()
>>> CH = refcomp['Chondrite_PON']
>>> DMM = refcomp['DMM_WH']
>>>
>>> reels = ree(output='str')
>>> df = DMM.data.loc[reels, ['value']]
>>> spiderplot(CH.normalize(df), label=f'{DMM.Reference}')

Classification

Some simple discrimination methods are implemented, including the Total Alkali-Silica (TAS) classification:

>>> from pyrolite.classification import Geochemistry
>>>
>>> cm = Geochemistry.TAS()
>>> df.TotalAlkali = df.Na2O + df.K2O
>>> df['TAS'] = cm.classify(df)

This classifier can be quickly added to a bivariate plot, assuming you have data in a pandas DataFrame:

>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>>
>>> df['TotalAlkali'] = df['Na2O'] + df['K2O']
>>>
>>> fig, ax = plt.subplots(1, figsize=(6, 4))
>>> cm.add_to_axes(ax, facecolor='0.9', edgecolor='k',
>>>                linewidth=0.5, zorder=-1)
>>> classnames = cm.clsf.fclasses + ['none']
>>> df['TAScolors'] = df['TAS'].map(lambda x: classnames.index(x))
>>> ax.scatter(df.SiO2, df.TotalAlkali, c=df.TAScolors,
>>>            alpha=0.5, marker='D', s=8, cmap='tab20c')

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