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: 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: 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

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