elphick.geomet.block_model.BlockModel

class elphick.geomet.block_model.BlockModel(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)

__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)

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
balance_composition()	Balance the composition data
clip_composition([ranges])	Clip the components
clip_recovery(other[, recovery_bounds, ...])	Clip the recovery to the specified bounds and recalculate the estimate.
common_block_size()
create_congruent_object(name[, ...])	Create an object with the same attributes
create_structured_grid(imports)
create_unstructured_grid(imports)	Requires the index to be a pd.MultiIndex with names ['x', 'y', 'z', 'dx', 'dy', 'dz'].
create_voxels()
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.
from_omf(cls, omf_filepath, imports[, name, ...])
get_blocks()
get_mass_data([include_moisture])	Get the mass data
is_regular()	Determine if the grid spacing is complete and regular If it is, a pv.StructuredGrid is suitable.
plot(scalar, imports[, show_edges])
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)
set_moisture(moisture[, mass_to_adjust])	Set the moisture to the specified value
split(fraction[, name_1, name_2, ...])	Split the object by mass
sub(other[, name, include_supplementary_data])	Subtract other from self
to_omf(omf_filepath, imports[, name, ...])
to_stream()
update_mass_data(value)
voxelise(blocks, imports)
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

create_unstructured_grid(imports)

Requires the index to be a pd.MultiIndex with names [‘x’, ‘y’, ‘z’, ‘dx’, ‘dy’, ‘dz’]. :rtype: UnstructuredGrid :return:

is_regular()

Determine if the grid spacing is complete and regular If it is, a pv.StructuredGrid is suitable. If not, a pv.UnstructuredGrid is suitable.

Return type:

bool

Returns: