Legacy lnPi array routines (maskedlnpi_legacy)

Classes:

MaskedlnPiLegacy([data, lnz, state_kws, ...])

Class to store masked version of \(\ln\Pi(N)\).

class lnPi.maskedlnpi_legacy.MaskedlnPiLegacy(data=None, lnz=None, state_kws=None, extra_kws=None, **kwargs)

Bases: MaskedArray, AccessorMixin

Class to store masked version of \(\ln\Pi(N)\). shape is (N0,N1,…) where Ni is the span of each dimension)

Constructor

Parameters:

• data (array-like) – data for lnPi

• lnz (array-like, optional) – if None, set lnz=np.zeros(data.ndim)

• state_kws (dict, optional) – dictionary of state values, such as volume and beta. These parameters will be pushed to self.xge coordinates.

• extra_kws (dict, optional) – this defines extra parameters to pass along. Note that for potential energy calculations, extra_kws should contain PE (total potential energy for each N vector).

• zeromax (bool, default False) – if True, shift lnPi = lnPi - lnPi.max()

• pad (bool, default False) – if True, pad masked region by interpolation

• **kwargs – Extra arguments to numpy.ma.MaskedArray e.g., mask=…

Attributes:

optinfo	All extra properties
state_kws	State specific parameters
extra_kws	All extra parameters
edge_distance_matrix	Matrix of distance from upper bound

Methods:

pad([axes, ffill, bfill, limit, inplace])	Pad nan values in underlying data to values
zeromax([inplace])	Shift so that lnpi.max() == 0
adjust([zeromax, pad, inplace])	Do multiple adjustments in one go
reweight(lnz[, zeromax, pad])	Get lnpi at new lnz
smooth([sigma, mode, truncate, inplace])	Apply gaussian filter smoothing to data
copy_shallow([mask])	Create shallow copy
or_mask(mask, **kwargs)	New object with logical or of self.mask and mask
and_mask(mask, **kwargs)	New object with logical and of self.mask and mask
from_table(path, lnz[, state_kws, sep, ...])	Create lnPi object from text file table with columns [n_0,...,n_ndim, lnpi]
from_dataarray(da[, state_as_attrs])	Create a lnPi object from xarray.DataArray
list_from_masks(masks[, convention])	Create list of lnpis corresponding to masks[i]
list_from_labels(labels[, features, ...])	Create list of lnpis corresponding to labels

property optinfo

All extra properties

property state_kws

State specific parameters

property extra_kws

All extra parameters

edge_distance_matrix

Matrix of distance from upper bound

pad(axes=None, ffill=True, bfill=False, limit=None, inplace=False)

Pad nan values in underlying data to values

Parameters:

• ffill (bool, default True) – do forward filling

• bfill (bool, default False) – do back filling

• limit (int, default None) – The maximum number of consecutive NaN values to forward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit.

• inplace (bool, default False)

Returns:

out (object) – padded object

zeromax(inplace=False)

Shift so that lnpi.max() == 0

adjust(zeromax=False, pad=False, inplace=False)

Do multiple adjustments in one go

reweight(lnz, zeromax=False, pad=False)

Get lnpi at new lnz

Parameters:

• lnz (array-like) – chem. pot. for new state point

• zeromax (bool, default False)

• pad (bool, default False)

• phases (dict)

Returns:

object

smooth(sigma=4, mode='nearest', truncate=4, inplace=False, **kwargs)

Apply gaussian filter smoothing to data

Parameters:

• inplace (bool, default False) – if True, do inplace modification.

• mode (str, default 'nearest') – Arguments to gaussian_filter

• truncate (int, default 4) – Argument to gaussian_filter

• **kwargs – Extra arguments to gaussian_filter.

See also

gaussian_filter

copy_shallow(mask=None, **kwargs)

Create shallow copy

Parameters:

mask (optional) – if specified, new object has this mask otherwise, at least copy old mask

or_mask(mask, **kwargs)

New object with logical or of self.mask and mask

and_mask(mask, **kwargs)

New object with logical and of self.mask and mask

classmethod from_table(path, lnz, state_kws=None, sep='\\s+', names=None, csv_kws=None, **kwargs)

Create lnPi object from text file table with columns [n_0,…,n_ndim, lnpi]

Parameters:

• path (path-like) – file object to be read

• lnz (array-like) – beta*(chemical potential) for each component

• state_kws (dict, optional) – define state variables, like volume, beta

• sep (string, optional) – separator for file read

• names (sequence of str)

• csv_kws (dict, optional) – optional arguments to pandas.read_csv

• **kwargs – Passed to lnPi constructor

classmethod from_dataarray(da, state_as_attrs=None, **kwargs)

Create a lnPi object from xarray.DataArray

list_from_masks(masks, convention='image')

Create list of lnpis corresponding to masks[i]

Parameters:

• masks (list) – masks[i] is the mask for lnpi index i.

• convention (str or bool) – convention of input masks

Returns:

lnpis (list) – list of lnpis corresponding to each mask

list_from_labels(labels, features=None, include_boundary=False, check_features=True, **kwargs)

Create list of lnpis corresponding to labels