pyrolite.geochem.pyrochem (Pandas Interface)

class pyrolite.geochem.pyrochem(obj)[source]
property list_elements

Get the subset of columns which are element names.

Return type

list

Notes

The list will have the same ordering as the source DataFrame.

property list_isotope_ratios

Get the subset of columns which are isotope ratios.

Return type

list

Notes

The list will have the same ordering as the source DataFrame.

property list_REE

Get the subset of columns which are Rare Earth Element names.

Return type

list

Notes

The returned list will reorder REE based on atomic number.

property list_REY

Get the subset of columns which are Rare Earth Element names.

Return type

list

Notes

The returned list will reorder REE based on atomic number.

property list_oxides

Get the subset of columns which are oxide names.

Return type

list

Notes

The list will have the same ordering as the source DataFrame.

property list_compositional
property elements

Get an elemental subset of a DataFrame.

Return type

pandas.Dataframe

property REE

Get a Rare Earth Element subset of a DataFrame.

Return type

pandas.Dataframe

property REY

Get a Rare Earth Element + Yttrium subset of a DataFrame.

Return type

pandas.Dataframe

property oxides

Get an oxide subset of a DataFrame.

Return type

pandas.Dataframe

property isotope_ratios

Get an isotope ratio subset of a DataFrame.

Return type

pandas.Dataframe

property compositional

Get an oxide & elemental subset of a DataFrame.

Return type

pandas.Dataframe

Notes

This wil not include isotope ratios.

parse_chem(abbrv=['ID', 'IGSN'], split_on='[\\s_]+')[source]

Convert column names to pyrolite-recognised elemental, oxide and isotope ratio column names where valid names are found.

check_multiple_cation_inclusion(exclude=['LOI', 'FeOT', 'Fe2O3T'])[source]

Returns cations which are present in both oxide and elemental form.

Parameters

exclude (list, ["LOI", "FeOT", "Fe2O3T"]) – List of components to exclude from the duplication check.

Returns

Set of elements for which multiple components exist in the dataframe.

Return type

set

to_molecular(renorm=True)[source]

Converts mass quantities to molar quantities.

Parameters

renorm (bool, True) – Whether to renormalise the dataframe after converting to relative moles.

Notes

Does not convert units (i.e. mass% –> mol%; mass-ppm –> mol-ppm).

Returns

Transformed dataframe.

Return type

pandas.DataFrame

to_weight(renorm=True)[source]

Converts molar quantities to mass quantities.

Parameters

renorm (bool, True) – Whether to renormalise the dataframe after converting to relative moles.

Notes

Does not convert units (i.e. mol% –> mass%; mol-ppm –> mass-ppm).

Returns

Transformed dataframe.

Return type

pandas.DataFrame

devolatilise(exclude=['H2O', 'H2O_PLUS', 'H2O_MINUS', 'CO2', 'LOI'], renorm=True)[source]

Recalculates components after exclusion of volatile phases (e.g. H2O, CO2).

Parameters

  • exclude (list) – Components to exclude from the dataset.

  • renorm (bool, True) – Whether to renormalise the dataframe after devolatilisation.

Returns

Transformed dataframe.

Return type

pandas.DataFrame

elemental_sum(component=None, to=None, total_suffix='T', logdata=False, molecular=False)[source]

Sums abundance for a cation to a single series, starting from a dataframe containing multiple componnents with a single set of units.

Parameters

  • component (str) – Component indicating which element to aggregate.

  • to (str) – Component to cast the output as.

  • logdata (bool, False) – Whether data has been log transformed.

  • molecular (bool, False) – Whether to perform a sum of molecular data.

Returns

Series with cation aggregated.

Return type

pandas.Series

aggregate_element(to, total_suffix='T', logdata=False, renorm=False, molecular=False)[source]

Aggregates cation information from oxide and elemental components to either a single species or a designated mixture of species.

Parameters

  • to (str | Element | Formula | dict) – Component(s) to convert to. If one component is specified, the element will be converted to the target species.

    If more than one component is specified with proportions in a dictionary (e.g. {'FeO': 0.9, 'Fe2O3': 0.1}), the components will be split as a fraction of the elemental sum.

  • renorm (bool, True) – Whether to renormalise the dataframe after recalculation.

  • total_suffix (str, ‘T’) – Suffix of ‘total’ variables. E.g. ‘T’ for FeOT, Fe2O3T.

  • logdata (bool, False) – Whether the data has been log transformed.

  • molecular (bool, False) – Whether to perform a sum of molecular data.

Notes

This won’t convert units, so need to start from single set of units.

Returns

Series with cation aggregated.

Return type

pandas.Series

recalculate_Fe(to='FeOT', renorm=False, total_suffix='T', logdata=False, molecular=False)[source]

Recalculates abundances of iron, and normalises a dataframe to contain either a single species, or multiple species in certain proportions.

Parameters

  • to (str | Element | Formula | dict) – Component(s) to convert to.

    If one component is specified, all iron will be converted to the target species.

    If more than one component is specified with proportions in a dictionary (e.g. {'FeO': 0.9, 'Fe2O3': 0.1}), the components will be split as a fraction of Fe.

  • renorm (bool, False) – Whether to renormalise the dataframe after recalculation.

  • total_suffix (str, ‘T’) – Suffix of ‘total’ variables. E.g. ‘T’ for FeOT, Fe2O3T.

  • logdata (bool, False) – Whether the data has been log transformed.

  • molecular (bool, False) – Flag that data is in molecular units, rather than weight units.

Returns

Transformed dataframe.

Return type

pandas.DataFrame

get_ratio(ratio: str, alias: Optional[str] = None, norm_to=None, molecular=False)[source]

Add a ratio of components A and B, given in the form of string ‘A/B’. Returned series be assigned an alias name.

Parameters

  • ratio (str) – String decription of ratio in the form A/B[_n].

  • alias (str) – Alternate name for ratio to be used as column name.

  • norm_to (str | pyrolite.geochem.norm.Composition, None) – Reference composition to normalise to.

  • molecular (bool, False) – Flag that data is in molecular units, rather than weight units.

Returns

Dataframe with ratio appended.

Return type

pandas.DataFrame

See also

add_MgNo()

add_ratio(ratio: str, alias: Optional[str] = None, norm_to=None, molecular=False)[source]

Add a ratio of components A and B, given in the form of string ‘A/B’. Returned series be assigned an alias name.

Parameters

  • ratio (str) – String decription of ratio in the form A/B[_n].

  • alias (str) – Alternate name for ratio to be used as column name.

  • norm_to (str | pyrolite.geochem.norm.Composition, None) – Reference composition to normalise to.

  • molecular (bool, False) – Flag that data is in molecular units, rather than weight units.

Returns

Dataframe with ratio appended.

Return type

pandas.DataFrame

See also

add_MgNo()

add_MgNo(molecular=False, use_total_approx=False, approx_Fe203_frac=0.1, name='Mg#')[source]

Append the magnesium number to a dataframe.

Parameters

  • molecular (bool, False) – Whether the input data is molecular.

  • use_total_approx (bool, False) – Whether to use an approximate calculation using total iron rather than just FeO.

  • approx_Fe203_frac (float) – Fraction of iron which is oxidised, used in approximation mentioned above.

  • name (str) – Name to use for the Mg Number column.

Returns

Dataframe with ratio appended.

Return type

pandas.DataFrame

See also

add_ratio()

lambda_lnREE(norm_to='ChondriteREE_ON', exclude=['Pm', 'Eu', 'Ce', 'Eu', 'Eu', 'Ce'], params=None, degree=4, scale='ppm', sigmas=None, **kwargs)[source]

Calculates orthogonal polynomial coefficients (lambdas) for a given set of REE data, normalised to a specific composition 1. Lambda factors are given for the radii vs. ln(REE/NORM) polynomial combination.

Parameters

  • norm_to (str | Composition | numpy.ndarray) – Which reservoir to normalise REE data to (defaults to "ChondriteREE_ON").

  • exclude (list, ["Pm", "Eu"]) – Which REE elements to exclude from the fit. May wish to include Ce for minerals in which Ce anomalies are common.

  • params (list | str, None) – Pre-computed parameters for the orthogonal polynomials (a list of tuples). Optionally specified, otherwise defaults the parameterisation as in O’Neill (2016). If a string is supplied, "O'Neill (2016)" or similar will give the original defaults, while "full" will use all of the REE (including Eu) as a basis for the orthogonal polynomials.

  • degree (int, 4) – Maximum degree polynomial fit component to include.

  • scale (str) – Current units for the REE data, used to scale the reference dataset.

  • sigmas (float | numpy.ndarray | pandas.Series) – Value or 1D array of fractional REE uncertaintes (i.e. \(\sigma_{REE}/REE\)).

References

1

O’Neill HSC (2016) The Smoothness and Shapes of Chondrite-normalized Rare Earth Element Patterns in Basalts. J Petrology 57:1463–1508. doi: 10.1093/petrology/egw047

See also

get_ionic_radii(), calc_lambdas(), orthogonal_polynomial_constants(), REE_radii_plot()

convert_chemistry(to=[], logdata=False, renorm=False, molecular=False)[source]

Attempts to convert a dataframe with one set of components to another.

Parameters

  • to (list) – Set of columns to try to extract from the dataframe.

    Can also include a dictionary for iron speciation. See pyrolite.geochem.recalculate_Fe().

  • logdata (bool, False) – Whether chemical data has been log transformed. Necessary for aggregation functions.

  • renorm (bool, False) – Whether to renormalise the data after transformation.

  • molecular (bool, False) – Flag that data is in molecular units, rather than weight units.

Returns

Dataframe with converted chemistry.

Return type

pandas.DataFrame

Todo

  • Check for conflicts between oxides and elements

  • Aggregator for ratios

  • Implement generalised redox transformation.

  • Add check for dicitonary components (e.g. Fe) in tests

normalize_to(reference=None, units=None, convert_first=False)[source]

Normalise a dataframe to a given reference composition.

Parameters

  • reference (str | Composition | numpy.ndarray) – Reference composition to normalise to.

  • units (str) – Units of the input dataframe, to convert the reference composition.

  • convert_first (bool) – Whether to first convert the referenece compostion before normalisation. This is useful where elements are presented as different components (e.g. Ti, TiO2).

Returns

Dataframe with normalised chemistry.

Return type

pandas.DataFrame

Notes

This assumes that dataframes have a single set of units.

denormalize_from(reference=None, units=None)[source]

De-normalise a dataframe from a given reference composition.

Parameters

  • reference (str | Composition | numpy.ndarray) – Reference composition which the composition is normalised to.

  • units (str) – Units of the input dataframe, to convert the reference composition.

Returns

Dataframe with normalised chemistry.

Return type

pandas.DataFrame

Notes

This assumes that dataframes have a single set of units.

scale(in_unit, target_unit='ppm')[source]

Scale a dataframe from one set of units to another.

Parameters

  • in_unit (str) – Units to be converted from

  • target_unit (str, "ppm") – Units to scale to.

Returns

Dataframe with new scale.

Return type

pandas.DataFrame