Collection of lnPi objects (lnpiseries)

Collection of lnPi objects (`lnpiseries`)#

Classes:

lnPiCollection(data[, index, xarray_output, ...])

Wrapper around pandas.Series for collection of lnPiMasked objects.

class lnpy.lnpiseries.lnPiCollection(data, index=None, xarray_output=True, concat_dim=None, concat_coords=None, unstack=True, name=None, base_class='first', dtype=None)[source]#

Bases: AccessorMixin

Wrapper around pandas.Series for collection of lnPiMasked objects.

Parameters:

data (sequence of lnPiMasked) – \(\ln \Pi(N)\) instances to consider.
index (array-like, pandas.Index, pandas.MultiIndex, optional) – Index to apply to Series.
xarray_output (bool, default True) – If True, then wrap lnPiCollection outputs in DataArray
concat_dim (str, optional) – Name of dimensions to concat results along. Also Used by xGrandCanonical.
concat_coords (string, optional) – parameters coords `to :func:`xarray.concat
unstack (bool, default True) – If True, then outputs will be unstacked using xarray.DataArray.unstack()
single_state (bool, default True) – If True, verify that all data has same shape, and value of state_kws. That is, all lnpi are for a single state.
*args **kwargs – Extra arguments to Series constructor

Methods:

`new_like`([data, index])	Create new object with optional new data/index
`xs`(key[, axis, level, drop_level, wrap])	Interface to `pandas.Series.xs()`
`append`(to_append[, ignore_index, ...])	Interface to `pandas.concat()`
`droplevel`(level)	New object with dropped level
`apply`(func[, convert_dtype, args, wrap])	Interface to `pandas.Series.apply()`
`sort_index`(args, *kwargs)	Interface to `pandas.Series.sort_index()`
`groupby`([by, level, as_index, sort, ...])	Wrapper around `pandas.Series.groupby()`.
`groupby_allbut`(drop, *[, wrap])	Groupby all but columns in drop
`concat_like`(objs, **concat_kws)	Concat a sequence of objects like self
`concat`(objs[, concat_kws])	Create collection from sequence of objects
`get_index_level`([level])	Get index values for specified level
`wrap_list_results`(items)	Utility to wrap output in :class:xarray.DataArray
`from_list`(items, index, **kwargs)	Create collection from list of `lnPiMasked` objects.
`from_builder`(lnzs, build_phases[, ref, ...])	Build collection from scalar builder
`to_dataarray`([dtype, reset_index])	Convert collection to a `DataArray`
`from_labels`(ref, labels, lnzs[, features, ...])	Create from reference `lnPiMasked` and labels array
`from_dataarray`(ref, da[, grouper, ...])	Create a collection from DataArray of labels

Attributes:

`series`	View of the underlying `pandas.Series`
`s`	Alias to `series()`
`values`	Series values
`items`	Alias to `values`
`index`	Series index
`name`	Series name
`state_kws`	state_kws from first `lnPiMasked`
`nlnz`	Number of unique lnzs
`index_frame`	Values (from `xarray.DataArray`) for each sample.
`xge`	Accessor to `xGrandCanonical`.
`wfe`	Accessor to `wFreeEnergyPhases` from `wfe_phases`.
`wfe_phases`	Accessor to `wFreeEnergyCollection` from `wfe`.
`wlnPi`	Deprecated accessor to `wFreeEnergyCollection` from `wlnPi`.
`wlnPi_single`	Deprecated accessor to `wFreeEnergyPhases` from `wlnPi_single`.
`spinodal`	Accessor to `Spinodals`
`binodal`	Accessor to `Binodals`

new_like(data=None, index=None, **kwargs)[source]#: Create new object with optional new data/index

property series#: View of the underlying pandas.Series

property s#: Alias to series()

property values#: Series values

property items#: Alias to values

property index#: Series index

property name#: Series name

xs(key, axis=0, level=None, drop_level=False, wrap=True)[source]#: Interface to pandas.Series.xs()

append(to_append, ignore_index=False, verify_integrity=True, concat_kws=None, inplace=False)[source]#

Interface to pandas.concat()

Parameters:

to_append (object) – Object to append
ignore_index (bool, default False)
verify_integrity (bool, default True)
concat_kws (mapping, optional) – Extra arguments to
inplace (bool, default False)

See also

pandas.concat

droplevel(level)[source]#: New object with dropped level

See also

pandas.Series.droplevel

apply(func, convert_dtype=True, args=(), wrap=False, **kwds)[source]#: Interface to pandas.Series.apply()

sort_index(*args, **kwargs)[source]#: Interface to pandas.Series.sort_index()

groupby(by=None, *, level=None, as_index=True, sort=True, group_keys=True, observed=False, dropna=True, wrap=False)[source]#

Wrapper around pandas.Series.groupby().

Parameters:: wrap (bool, default False) – if True, try to wrap output in class of self

See also

pandas.Series.groupby

groupby_allbut(drop, *, wrap=False, **kwargs)[source]#: Groupby all but columns in drop

concat_like(objs, **concat_kws)[source]#: Concat a sequence of objects like self

classmethod concat(objs, concat_kws=None, *args, **kwargs)[source]#: Create collection from sequence of objects

property state_kws#: state_kws from first lnPiMasked

property nlnz#: Number of unique lnzs

index_frame[source]#

Values (from xarray.DataArray) for each sample.

includes a column ‘lnz_index’ which is the unique lnz values regardless of phase

get_index_level(level='phase')[source]#: Get index values for specified level

wrap_list_results(items)[source]#: Utility to wrap output in :class:xarray.DataArray

classmethod from_list(items, index, **kwargs)[source]#

Create collection from list of lnPiMasked objects.

Parameters:

items (sequence of lnPiMasked) – Sequence of lnPi
index (sequence) – Sequence of phases ID for each lnPi
*args – Extra positional arguments to cls
**kwargs – Extra keyword arguments to cls

Returns:

lnPiCollection

classmethod from_builder(lnzs, build_phases, ref=None, build_kws=None, nmax=None, base_class='first', **kwargs)[source]#

Build collection from scalar builder

Parameters:

lnzs (sequence of float) – One dimensional array of lnz value for the varying component.
ref (lnPiMasked) – lnpi_phases to reweight to get list of lnpi’s
build_phases (callable()) – Typically one of PhaseCreator.build_phases_mu or PhaseCreator.build_phases_dmu
build_kws (optional) – optional arguments to build_phases

Returns:

lnPiCollection

to_dataarray(dtype=None, reset_index=True)[source]#

Convert collection to a DataArray

Parameters:

dtype (numpy.dtype, optional) – Default to numpy.uint8.
reset_index (bool, default True)

classmethod from_labels(ref, labels, lnzs, features=None, include_boundary=False, labels_kws=None, check_features=True, **kwargs)[source]#

Create from reference lnPiMasked and labels array

Parameters:

ref (lnPiMasked)
labels (sequence of ndarray of int) – Each labels[i] is a labels array for each value of lnzs[i]. That is, the labels for different phases at a given value of lnz.
lnzs (sequence) – Each lnzs[i] will be passed to ref.reweight.
features (sequence of int) – If specified, extract only those locations where labels == feature for all values feature in features. That is, select a subset of unique label values.
include_boundary (bool) – if True, include boundary regions in output mask
labels_kws (mapping, optional)
check_features (bool) – if True, then make sure each feature is in labels
**kwargs – Extra arguments past to from_list()

See also

labels_to_masks, from_list

classmethod from_dataarray(ref, da, grouper='sample', include_boundary=False, labels_kws=None, features=None, check_features=True, **kwargs)[source]#

Create a collection from DataArray of labels

Parameters:

ref (lnPiMasked)
da (DataArray or int) – Labels.
grouper (Hashable) – Name of dimension(s) to group along to give a single label array
features (sequence of int) – If specified, extract only those locations where labels == feature for all values feature in features. That is, select a subset of unique label values.
check_features (bool) – if True, then make sure each feature is in labels