elphick.geomet.base.MassComposition

class elphick.geomet.base.MassComposition(data=None, name=None, moisture_in_scope=True, mass_wet_var=None, mass_dry_var=None, moisture_var=None, component_vars=None, composition_units='%', components_as_symbols=True, ranges=None, config_file=None)[source]

__init__(data=None, name=None, moisture_in_scope=True, mass_wet_var=None, mass_dry_var=None, moisture_var=None, component_vars=None, composition_units='%', components_as_symbols=True, ranges=None, config_file=None)[source]

Parameters:

data (Optional[DataFrame]) – The input data
name (Optional[str]) – The name of the sample
moisture_in_scope (bool) – Whether the moisture is in scope. If False, only dry mass is processed.
mass_wet_var (Optional[str]) – The name of the wet mass column
mass_dry_var (Optional[str]) – The name of the dry mass column
moisture_var (Optional[str]) – The name of the moisture column
component_vars (Optional[list[str]]) – The names of the chemical columns
components_as_symbols (bool) – If True, convert the composition variables to symbols, e.g. Fe
ranges (Optional[dict[str, list]]) – The range of valid data for each column in the data
config_file (Optional[Path]) – The configuration file

Methods

`__init__`([data, name, moisture_in_scope, ...])
`add`(other[, name, include_supplementary_data])	Add two objects together
`create_congruent_object`(name[, ...])	Create an object with the same attributes
`div`(other[, name, include_supplementary_data])	Divide two objects
`filter_by_index`(index)	Update the data by index
`from_mass_dataframe`(mass_df[, mass_wet, ...])	Class method to create a MassComposition object from a mass dataframe.
`plot_comparison`(other[, color, ...])	Create an interactive parallel plot
`plot_parallel`([color, vars_include, ...])	Create an interactive parallel plot
`plot_ternary`(variables[, color, title])	Plot a ternary diagram
`query`(expr[, name])	Reduce the data by a query expression
`reset_index`(index_name)
`split`(fraction[, name_1, name_2, ...])	Split the object by mass
`sub`(other[, name, include_supplementary_data])	Subtract other from self
`to_stream`()
`update_mass_data`(value)
`weight_average`([group_by])

Attributes

`aggregate`
`composition_columns`
`data`
`data_columns`
`mass_columns`
`mass_data`
`moisture_column`
`supplementary_columns`
`variable_map`	A map from lower case standard names to the actual column names

__add__(other)[source]

Add two objects

Perform the addition with the mass-composition variables only and then append any attribute variables. Presently ignores any attribute vars in other :type other: TypeVar(MC, bound= MassComposition) :param other: object to add to self

Returns:

Return type:: Stream

add(other, name=None, include_supplementary_data=False)[source]

Add two objects together

Parameters:

other (TypeVar(MC, bound= MassComposition)) – The other object
name (Optional[str]) – The name of the new object
include_supplementary_data (bool) – Whether to include the supplementary data

Return type:

Stream

Returns:

The new object

create_congruent_object(name, include_mc_data=False, include_supp_data=False)[source]

Create an object with the same attributes

Return type:: TypeVar(MC, bound= MassComposition)

div(other, name=None, include_supplementary_data=False)[source]

Divide two objects

Divides self by other, with optional name of the returned object :type other: TypeVar(MC, bound= MassComposition) :param other: the denominator (or reference) object :type name: Optional[str] :param name: name of the returned object :type include_supplementary_data: bool :param include_supplementary_data: Whether to include the supplementary data

Returns:

Return type:: TypeVar(MC, bound= MassComposition)

filter_by_index(index)[source]: Update the data by index

classmethod from_mass_dataframe(mass_df, mass_wet='mass_wet', mass_dry='mass_dry', moisture_column_name=None, component_columns=None, composition_units='%', **kwargs)[source]

Class method to create a MassComposition object from a mass dataframe.

Parameters:

mass_df (DataFrame) – DataFrame with mass data.
**kwargs – Additional arguments to pass to the MassComposition constructor.

Returns:

A new MassComposition object.

plot_comparison(other, color=None, vars_include=None, vars_exclude=None, facet_col_wrap=3, trendline=False, trendline_kwargs=None, title=None)[source]

Create an interactive parallel plot

Useful to compare the difference in component values between two objects.

Parameters:

other (TypeVar(MC, bound= MassComposition)) – the object to compare with self.
color (Optional[str]) – Optional color variable
vars_include (Optional[list[str]]) – Optional List of variables to include in the plot
vars_exclude (Optional[list[str]]) – Optional List of variables to exclude in the plot
trendline (bool) – If True and trendlines
trendline_kwargs (Optional[dict]) – Allows customising the trendline: ref: https://plotly.com/python/linear-fits/
title (Optional[str]) – Optional plot title
facet_col_wrap (int) – The number of subplot columns per row.

Return type:

Figure

Returns:

plot_parallel(color=None, vars_include=None, vars_exclude=None, title=None, include_dims=True, plot_interval_edges=False)[source]

Create an interactive parallel plot

Useful to explore multidimensional data like mass-composition data

Parameters:

color (Optional[str]) – Optional color variable
vars_include (Optional[list[str]]) – Optional list of variables to include in the plot
vars_exclude (Optional[list[str]]) – Optional list of variables to exclude in the plot
title (Optional[str]) – Optional plot title
include_dims (Union[bool, list[str], None]) – Optional boolean or list of dimension to include in the plot. True will show all dims.
plot_interval_edges (bool) – If True, interval edges will be plotted instead of interval mid

Return type:

Figure

Returns:

plot_ternary(variables, color=None, title=None)[source]

Plot a ternary diagram

variables: List of 3 components to plot color: Optional color variable title: Optional plot title

Return type:: Figure

query(expr, name=None)[source]

Reduce the data by a query expression

Parameters:

expr (str) – A pandas query expression
name (Optional[str]) – name of the new object

Return type:

TypeVar(MC, bound= MassComposition)

Returns:

A new object with the reduced data

split(fraction, name_1=None, name_2=None, include_supplementary_data=False)[source]

Split the object by mass

A simple mass split maintaining the same composition

Parameters:

fraction (float) – A constant in the range [0.0, 1.0]
name_1 (Optional[str]) – The name of the reference object created by the split
name_2 (Optional[str]) – The name of the complement object created by the split
include_supplementary_data (bool) – Whether to inherit the supplementary variables

Return type:

tuple[Stream, Stream]

Returns:

tuple of two objects, the first with the mass fraction specified, the other the complement

sub(other, name=None, include_supplementary_data=False)[source]

Subtract other from self

Parameters:

other (TypeVar(MC, bound= MassComposition)) – The other object
name (Optional[str]) – The name of the new object
include_supplementary_data (bool) – Whether to include the supplementary data

Return type:

Stream

Returns:

The new object

property variable_map: dict[str, str] | None: A map from lower case standard names to the actual column names