elphick.mass_composition.stream.Stream

class elphick.mass_composition.stream.Stream(mc, **kwargs)[source]

__init__(mc, **kwargs)[source]

Parameters:

mc (MassComposition) – MassComposition object to be used as a stream.
**kwargs – The key word arguments for the Mass Composition object

Methods

`__init__`(mc, **kwargs)	type mc: `MassComposition`
`add`(other[, name])	Adds the stream to another stream or a tuple of streams.
`aggregate`([group_var, group_bins, ...])	Calculate the weight average.
`binned_mass_composition`(cutoff_var, bin_width)	A.K.A "The Grade-Tonnage" curve.
`calculate_partition`(ref)	Calculate the partition of the ref stream relative to self
`compare`(other[, comparisons, ...])	rtype: `Union`[`DataFrame`, `Dataset`]
`constrain`([clip_mass, clip_composition, ...])	Constrain the mass-composition
`div`(other[, name])	Divide two objects
`from_mass_composition`(mc, **kwargs)
`from_xarray`(ds[, name])
`get_common_prefix`(columns)	rtype: `str`
`get_constraint_bounds`(constraints)	rtype: `Dict`[`str`, `List`]
`ideal_incremental_composition`([discard_from])	Incrementally separate a fractionated sample.
`ideal_incremental_recovery`([discard_from, ...])	Incrementally separate a fractionated sample.
`ideal_incremental_separation`([discard_from])	Incrementally separate a fractionated sample.
`plot_amenability`(target_analyte[, ...])	The yield-recovery plot.
`plot_bins`(variables, cutoff_var, bin_width)	Plot "The Grade-Tonnage" curve.
`plot_comparison`(other[, color, ...])	Create an interactive parallel plot
`plot_grade_recovery`(target_analyte[, ...])	The grade-recovery plot.
`plot_intervals`(variables[, cumulative, ...])	Plot "The Grade-Tonnage" curve.
`plot_parallel`([color, vars_include, ...])	Create an interactive parallel plot
`plot_ternary`(variables[, color, title])	Plot a ternary diagram
`query`(queries)	rtype: `MassComposition`
`rename`(new_name)	rtype: `MassComposition`
`resample_1d`(interval_edges[, precision, ...])	Resample a 1D fractional dim/index
`set_child_node`(child)	rtype: `MassComposition`
`set_data`(data[, constraints])
`set_parent_node`(parent)	rtype: `MassComposition`
`set_stream_nodes`(nodes)	rtype: `MassComposition`
`split`(fraction[, name_1, name_2])	Splits the stream into two streams.
`split_by_estimator`(estimator[, name_1, ...])	Split an object using a sklearn estimator.
`split_by_function`(split_function[, name_1, ...])	Split an object using a function.
`split_by_partition`(partition_definition[, ...])	Partition the object along a given dimension.
`sub`(other[, name])	Subtract two streams
`to_xarray`()	Returns the mc compliant xr.Dataset
`update_data`(values)

Attributes

`data`
`destination_node`
`name`
`source_node`

add(other, name=None)[source]

Adds the stream to another stream or a tuple of streams.

Parameters:

other (Union[Stream, Tuple[Stream, ...]]) – The other stream, or a tuple of other streams
name (Optional[str]) – The name of the new stream.

Return type:

Stream

Returns:

A Stream object.

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

Splits the stream into two streams.

Parameters:

fraction (float) – The fraction of the stream to be assigned to the first stream.
name_1 (Optional[str]) – The name of the first stream.
name_2 (Optional[str]) – The name of the second stream.

Return type:

tuple[Stream, Stream]

Returns:

A tuple of two Stream objects.

split_by_estimator(estimator, name_1=None, name_2=None, extra_features=None, allow_prefix_mismatch=False, mass_recovery_column=None, mass_recovery_max=1.0)[source]

Split an object using a sklearn estimator.

This method applies the function to self, resulting in two new objects. The object returned with name_1 is the result of the estimator.predict() method. The object returned with name_2 is the complement.

See also: split, split_by_function, split_by_partition

Parameters:

estimator (sklearn.base.BaseEstimator) – Any sklearn estimator that transforms the dataframe from a MassComposition object into a new dataframe with values representing a new (output) stream using the predict method. The returned dataframe structure must be identical to the input dataframe.
name_1 (Optional[str]) – The name of the stream created by the estimator.
name_2 (Optional[str]) – The name of the complement stream created by the split, which is calculated automatically. extra_features: Optional additional features to pass to the estimator as features.
allow_prefix_mismatch (bool) – If True, allow feature names to be different and log an info message. If False, raise an error when feature names are different.
mass_recovery_column (Optional[str]) – If provided, this indicates that the model has estimated mass recovery, not mass explicitly. This will execute a transformation of the predicted dry mass recovery to dry mass.
mass_recovery_max (float) – The maximum mass recovery value, used to scale the mass recovery to mass. Only applicable if mass_recovery_column is provided. Should be either 1.0 or 100.0.

Return type:

tuple[Stream, Stream]

Returns:

tuple of two Stream objects, the first the output of the estimator, the other the complement

split_by_function(split_function, name_1=None, name_2=None)[source]

Split an object using a function.

This method applies the function to self, resulting in two new objects. The object returned with name_1 is the result of the function. The object returned with name_2 is the complement.

See also: split, split_by_estimator, split_by_partition

Parameters:

split_function (Callable) – Any function that transforms the dataframe from a MassComposition object into a new dataframe with values representing a new (output) stream. The returned dataframe structure must be identical to the input dataframe.
name_1 (Optional[str]) – The name of the stream created by the function
name_2 (Optional[str]) – The name of the complement stream created by the split, which is calculated automatically.

Return type:

tuple[Stream, Stream]

Returns:

A generator of two Streams,

split_by_partition(partition_definition, name_1=None, name_2=None)[source]

Partition the object along a given dimension.

This method applies the defined separation resulting in two new objects.