Source code for holoviews.core.data

from __future__ import absolute_import

try:
    import itertools.izip as zip
except ImportError:
    pass

import numpy as np
import param

from .. import util
from ..dimension import redim, Dimension, process_dimensions
from ..element import Element
from ..ndmapping import OrderedDict
from ..spaces import HoloMap, DynamicMap
from .interface import Interface, iloc, ndloc
from .array import ArrayInterface
from .dictionary import DictInterface
from .grid import GridInterface
from .multipath import MultiInterface         # noqa (API import)
from .image import ImageInterface             # noqa (API import)

default_datatype = 'dictionary'
datatypes = ['dictionary', 'grid']

try:
    import pandas as pd # noqa (Availability import)
    from .pandas import PandasInterface
    default_datatype = 'dataframe'
    datatypes = ['dataframe', 'dictionary', 'grid']
    DFColumns = PandasInterface
except ImportError:
    pd = None
except Exception as e:
    pd = None
    param.main.warning('Pandas interface failed to import with '
                       'following error: %s' % e)

try:
    from .xarray import XArrayInterface # noqa (Conditional API import)
    datatypes.append('xarray')
except ImportError:
    pass

try:
    from .dask import DaskInterface   # noqa (Conditional API import)
    datatypes.append('dask')
except ImportError:
    pass

if 'array' not in datatypes:
    datatypes.append('array')
if 'multitabular' not in datatypes:
    datatypes.append('multitabular')


[docs]def concat(datasets, datatype=None): """Concatenates collection of datasets along NdMapping dimensions. Concatenates multiple datasets wrapped in an NdMapping type along all of its dimensions. Before concatenation all datasets are cast to the same datatype, which may be explicitly defined or implicitly derived from the first datatype that is encountered. For columnar data concatenation adds the columns for the dimensions being concatenated along and then concatenates all the old and new columns. For gridded data a new axis is created for each dimension being concatenated along and then hierarchically concatenates along each dimension. Args: datasets: NdMapping of Datasets to concatenate datatype: Datatype to cast data to before concatenation Returns: Concatenated dataset """ return Interface.concatenate(datasets, datatype)
[docs]class DataConversion(object): """ DataConversion is a very simple container object which can be given an existing Dataset Element and provides methods to convert the Dataset into most other Element types. """ def __init__(self, element): self._element = element def __call__(self, new_type, kdims=None, vdims=None, groupby=None, sort=False, **kwargs): """ Generic conversion method for Dataset based Element types. Supply the Dataset Element type to convert to and optionally the key dimensions (kdims), value dimensions (vdims) and the dimensions. to group over. Converted Columns can be automatically sorted via the sort option and kwargs can be passed through. """ element_params = new_type.params() kdim_param = element_params['kdims'] vdim_param = element_params['vdims'] if isinstance(kdim_param.bounds[1], int): ndim = min([kdim_param.bounds[1], len(kdim_param.default)]) else: ndim = None nvdim = vdim_param.bounds[1] if isinstance(vdim_param.bounds[1], int) else None if kdims is None: kd_filter = groupby or [] if not isinstance(kd_filter, list): kd_filter = [groupby] kdims = [kd for kd in self._element.kdims if kd not in kd_filter][:ndim] elif kdims and not isinstance(kdims, list): kdims = [kdims] if vdims is None: vdims = [d for d in self._element.vdims if d not in kdims][:nvdim] if vdims and not isinstance(vdims, list): vdims = [vdims] # Checks Element type supports dimensionality type_name = new_type.__name__ for dim_type, dims in (('kdims', kdims), ('vdims', vdims)): min_d, max_d = new_type.params(dim_type).bounds if ((min_d is not None and len(dims) < min_d) or (max_d is not None and len(dims) > max_d)): raise ValueError("%s %s must be between length %s and %s." % (type_name, dim_type, min_d, max_d)) if groupby is None: groupby = [d for d in self._element.kdims if d not in kdims+vdims] elif groupby and not isinstance(groupby, list): groupby = [groupby] if self._element.interface.gridded: dropped_kdims = [kd for kd in self._element.kdims if kd not in groupby+kdims] if dropped_kdims: selected = self._element.reindex(groupby+kdims, vdims) else: selected = self._element else: if pd and issubclass(self._element.interface, PandasInterface): ds_dims = self._element.dimensions() ds_kdims = [self._element.get_dimension(d) if d in ds_dims else d for d in groupby+kdims] ds_vdims = [self._element.get_dimension(d) if d in ds_dims else d for d in vdims] selected = self._element.clone(kdims=ds_kdims, vdims=ds_vdims) else: selected = self._element.reindex(groupby+kdims, vdims) params = {'kdims': [selected.get_dimension(kd, strict=True) for kd in kdims], 'vdims': [selected.get_dimension(vd, strict=True) for vd in vdims], 'label': selected.label} if selected.group != selected.params()['group'].default: params['group'] = selected.group params.update(kwargs) if len(kdims) == selected.ndims or not groupby: element = new_type(selected, **params) return element.sort() if sort else element group = selected.groupby(groupby, container_type=HoloMap, group_type=new_type, **params) if sort: return group.map(lambda x: x.sort(), [new_type]) else: return group
[docs]class Dataset(Element): """ Dataset provides a general baseclass for Element types that contain structured data and supports a range of data formats. The Dataset class supports various methods offering a consistent way of working with the stored data regardless of the storage format used. These operations include indexing, selection and various ways of aggregating or collapsing the data with a supplied function. """ datatype = param.List(datatypes, doc=""" A priority list of the data types to be used for storage on the .data attribute. If the input supplied to the element constructor cannot be put into the requested format, the next format listed will be used until a suitable format is found (or the data fails to be understood).""") group = param.String(default='Dataset', constant=True) # In the 1D case the interfaces should not automatically add x-values # to supplied data _auto_indexable_1d = False # Define a class used to transform Datasets into other Element types _conversion_interface = DataConversion # Whether the key dimensions are specified as bins _binned = False _vdim_reductions = {} _kdim_reductions = {} def __init__(self, data, kdims=None, vdims=None, **kwargs): if isinstance(data, Element): pvals = util.get_param_values(data) kwargs.update([(l, pvals[l]) for l in ['group', 'label'] if l in pvals and l not in kwargs]) kwargs.update(process_dimensions(kdims, vdims)) kdims, vdims = kwargs.get('kdims'), kwargs.get('vdims') validate_vdims = kwargs.pop('_validate_vdims', True) initialized = Interface.initialize(type(self), data, kdims, vdims, datatype=kwargs.get('datatype')) (data, self.interface, dims, extra_kws) = initialized super(Dataset, self).__init__(data, **dict(kwargs, **dict(dims, **extra_kws))) self.interface.validate(self, validate_vdims) self.redim = redim(self, mode='dataset')
[docs] def closest(self, coords=[], **kwargs): """Snaps coordinate(s) to closest coordinate in Dataset Args: coords: List of coordinates expressed as tuples **kwargs: Coordinates defined as keyword pairs Returns: List of tuples of the snapped coordinates Raises: NotImplementedError: Raised if snapping is not supported """ if self.ndims > 1: raise NotImplementedError("Closest method currently only " "implemented for 1D Elements") if kwargs: if len(kwargs) > 1: raise NotImplementedError("Closest method currently only " "supports 1D indexes") samples = list(kwargs.values())[0] coords = samples if isinstance(samples, list) else [samples] xs = self.dimension_values(0) if xs.dtype.kind in 'SO': raise NotImplementedError("Closest only supported for numeric types") idxs = [np.argmin(np.abs(xs-coord)) for coord in coords] return [xs[idx] for idx in idxs]
[docs] def sort(self, by=None, reverse=False): """ Sorts the data by the values along the supplied dimensions. Args: by: Dimension(s) to sort by reverse (bool, optional): Reverse sort order Returns: Sorted Dataset """ if by is None: by = self.kdims elif not isinstance(by, list): by = [by] sorted_columns = self.interface.sort(self, by, reverse) return self.clone(sorted_columns)
[docs] def range(self, dim, data_range=True, dimension_range=True): """Return the lower and upper bounds of values along dimension. Args: dimension: The dimension to compute the range on. data_range (bool): Compute range from data values dimension_range (bool): Include Dimension ranges Whether to include Dimension range and soft_range in range calculation Returns: Tuple containing the lower and upper bound """ dim = self.get_dimension(dim) if dim is None or (not data_range and not dimension_range): return (None, None) elif all(util.isfinite(v) for v in dim.range) and dimension_range: return dim.range elif dim in self.dimensions() and data_range and len(self): lower, upper = self.interface.range(self, dim) else: lower, upper = (np.NaN, np.NaN) if not dimension_range: return lower, upper return util.dimension_range(lower, upper, dim.range, dim.soft_range)
[docs] def add_dimension(self, dimension, dim_pos, dim_val, vdim=False, **kwargs): """Adds a dimension and its values to the Dataset Requires the dimension name or object, the desired position in the key dimensions and a key value scalar or array of values, matching the length o shape of the Dataset. Args: dimension: Dimension or dimension spec to add dim_pos (int) Integer index to insert dimension at dim_val (scalar or ndarray): Dimension value(s) to add vdim: Disabled, this type does not have value dimensions **kwargs: Keyword arguments passed to the cloned element Returns: Cloned object containing the new dimension """ if isinstance(dimension, (util.basestring, tuple)): dimension = Dimension(dimension) if dimension.name in self.kdims: raise Exception('{dim} dimension already defined'.format(dim=dimension.name)) if vdim: dims = self.vdims[:] dims.insert(dim_pos, dimension) dimensions = dict(vdims=dims) dim_pos += self.ndims else: dims = self.kdims[:] dims.insert(dim_pos, dimension) dimensions = dict(kdims=dims) if issubclass(self.interface, ArrayInterface) and np.asarray(dim_val).dtype != self.data.dtype: element = self.clone(datatype=[default_datatype]) data = element.interface.add_dimension(element, dimension, dim_pos, dim_val, vdim) else: data = self.interface.add_dimension(self, dimension, dim_pos, dim_val, vdim) return self.clone(data, **dimensions)
[docs] def select(self, selection_specs=None, **selection): """Applies selection by dimension name Applies a selection along the dimensions of the object using keyword arguments. The selection may be narrowed to certain objects using selection_specs. For container objects the selection will be applied to all children as well. Selections may select a specific value, slice or set of values: * value: Scalar values will select rows along with an exact match, e.g.: ds.select(x=3) * slice: Slices may be declared as tuples of the upper and lower bound, e.g.: ds.select(x=(0, 3)) * values: A list of values may be selected using a list or set, e.g.: ds.select(x=[0, 1, 2]) Args: selection_specs: List of specs to match on A list of types, functions, or type[.group][.label] strings specifying which objects to apply the selection on. **selection: Dictionary declaring selections by dimension Selections can be scalar values, tuple ranges, lists of discrete values and boolean arrays Returns: Returns an Dimensioned object containing the selected data or a scalar if a single value was selected """ if selection_specs is not None and not isinstance(selection_specs, (list, tuple)): selection_specs = [selection_specs] selection = {dim: sel for dim, sel in selection.items() if dim in self.dimensions()+['selection_mask']} if (selection_specs and not any(self.matches(sp) for sp in selection_specs) or not selection): return self data = self.interface.select(self, **selection) if np.isscalar(data): return data else: return self.clone(data)
[docs] def reindex(self, kdims=None, vdims=None): """Reindexes Dataset dropping static or supplied kdims Creates a new object with a reordered or reduced set of key dimensions. By default drops all non-varying key dimensions.x Args: kdims (optional): New list of key dimensionsx vdims (optional): New list of value dimensions Returns: Reindexed object """ gridded = self.interface.gridded scalars = [] if gridded: coords = [(d, self.interface.coords(self, d.name)) for d in self.kdims] scalars = [d for d, vs in coords if len(vs) == 1] if kdims is None: # If no key dimensions are defined and interface is gridded # drop all scalar key dimensions key_dims = [d for d in self.kdims if (not vdims or d not in vdims) and not d in scalars] elif not isinstance(kdims, list): key_dims = [self.get_dimension(kdims, strict=True)] else: key_dims = [self.get_dimension(k, strict=True) for k in kdims] dropped = [d for d in self.kdims if not d in key_dims and not d in scalars] new_type = None if vdims is None: val_dims = [d for d in self.vdims if not kdims or d not in kdims] else: val_dims = [self.get_dimension(v, strict=True) for v in vdims] new_type = self._vdim_reductions.get(len(val_dims), type(self)) data = self.interface.reindex(self, key_dims, val_dims) datatype = self.datatype if gridded and dropped: datatype = [dt for dt in datatype if not self.interface.interfaces[dt].gridded] return self.clone(data, kdims=key_dims, vdims=val_dims, new_type=new_type, datatype=datatype)
def __getitem__(self, slices): """ Allows slicing and selecting values in the Dataset object. Supports multiple indexing modes: (1) Slicing and indexing along the values of each dimension in the columns object using either scalars, slices or sets of values. (2) Supplying the name of a dimension as the first argument will return the values along that dimension as a numpy array. (3) Slicing of all key dimensions and selecting a single value dimension by name. (4) A boolean array index matching the length of the Dataset object. """ slices = util.process_ellipses(self, slices, vdim_selection=True) if isinstance(slices, np.ndarray) and slices.dtype.kind == 'b': if not len(slices) == len(self): raise IndexError("Boolean index must match length of sliced object") return self.clone(self.select(selection_mask=slices)) elif slices in [(), Ellipsis]: return self if not isinstance(slices, tuple): slices = (slices,) value_select = None if len(slices) == 1 and slices[0] in self.dimensions(): return self.dimension_values(slices[0]) elif len(slices) == self.ndims+1 and slices[self.ndims] in self.dimensions(): selection = dict(zip(self.dimensions('key', label=True), slices)) value_select = slices[self.ndims] elif len(slices) == self.ndims+1 and isinstance(slices[self.ndims], (Dimension,str)): raise IndexError("%r is not an available value dimension" % slices[self.ndims]) else: selection = dict(zip(self.dimensions(label=True), slices)) data = self.select(**selection) if value_select: if data.shape[0] == 1: return data[value_select][0] else: return data.reindex(vdims=[value_select]) return data
[docs] def sample(self, samples=[], bounds=None, closest=True, **kwargs): """Samples values at supplied coordinates. Allows sampling of element with a list of coordinates matching the key dimensions, returning a new object containing just the selected samples. Supports multiple signatures: Sampling with a list of coordinates, e.g.: ds.sample([(0, 0), (0.1, 0.2), ...]) Sampling a range or grid of coordinates, e.g.: 1D: ds.sample(3) 2D: ds.sample((3, 3)) Sampling by keyword, e.g.: ds.sample(x=0) Args: samples: List of nd-coordinates to sample bounds: Bounds of the region to sample Defined as two-tuple for 1D sampling and four-tuple for 2D sampling. closest: Whether to snap to closest coordinates **kwargs: Coordinates specified as keyword pairs Keywords of dimensions and scalar coordinates Returns: Element containing the sampled coordinates """ if kwargs and samples != []: raise Exception('Supply explicit list of samples or kwargs, not both.') elif kwargs: sample = [slice(None) for _ in range(self.ndims)] for dim, val in kwargs.items(): sample[self.get_dimension_index(dim)] = val samples = [tuple(sample)] elif isinstance(samples, tuple) or util.isscalar(samples): if self.ndims == 1: xlim = self.range(0) lower, upper = (xlim[0], xlim[1]) if bounds is None else bounds edges = np.linspace(lower, upper, samples+1) linsamples = [(l+u)/2.0 for l,u in zip(edges[:-1], edges[1:])] elif self.ndims == 2: (rows, cols) = samples if bounds: (l,b,r,t) = bounds else: l, r = self.range(0) b, t = self.range(1) xedges = np.linspace(l, r, cols+1) yedges = np.linspace(b, t, rows+1) xsamples = [(lx+ux)/2.0 for lx,ux in zip(xedges[:-1], xedges[1:])] ysamples = [(ly+uy)/2.0 for ly,uy in zip(yedges[:-1], yedges[1:])] Y,X = np.meshgrid(ysamples, xsamples) linsamples = list(zip(X.flat, Y.flat)) else: raise NotImplementedError("Regular sampling not implemented " "for elements with more than two dimensions.") samples = list(util.unique_iterator(self.closest(linsamples))) # Note: Special handling sampling of gridded 2D data as Curve # may be replaced with more general handling # see https://github.com/ioam/holoviews/issues/1173 from ...element import Table, Curve if len(samples) == 1: sel = {kd.name: s for kd, s in zip(self.kdims, samples[0])} dims = [kd for kd, v in sel.items() if not np.isscalar(v)] selection = self.select(**sel) # If a 1D cross-section of 2D space return Curve if self.interface.gridded and self.ndims == 2 and len(dims) == 1: new_type = Curve kdims = [self.get_dimension(kd) for kd in dims] else: new_type = Table kdims = self.kdims if np.isscalar(selection): selection = [samples[0]+(selection,)] else: reindexed = selection.clone(new_type=Dataset).reindex(kdims) selection = tuple(reindexed.columns(kdims+self.vdims).values()) datatype = list(util.unique_iterator(self.datatype+['dataframe', 'dict'])) return self.clone(selection, kdims=kdims, new_type=new_type, datatype=datatype) lens = set(len(util.wrap_tuple(s)) for s in samples) if len(lens) > 1: raise IndexError('Sample coordinates must all be of the same length.') if closest: try: samples = self.closest(samples) except NotImplementedError: pass samples = [util.wrap_tuple(s) for s in samples] return self.clone(self.interface.sample(self, samples), new_type=Table)
[docs] def reduce(self, dimensions=[], function=None, spreadfn=None, **reductions): """Applies reduction along the specified dimension(s). Allows reducing the values along one or more key dimension with the supplied function. Supports two signatures: Reducing with a list of dimensions, e.g.: ds.reduce(['x'], np.mean) Defining a reduction using keywords, e.g.: ds.reduce(x=np.mean) Args: dimensions: Dimension(s) to apply reduction on Defaults to all key dimensions function: Reduction operation to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **reductions: Keyword argument defining reduction Allows reduction to be defined as keyword pair of dimension and function Returns: The Dataset after reductions have been applied. """ if any(dim in self.vdims for dim in dimensions): raise Exception("Reduce cannot be applied to value dimensions") function, dims = self._reduce_map(dimensions, function, reductions) dims = [d for d in self.kdims if d not in dims] return self.aggregate(dims, function, spreadfn)
[docs] def aggregate(self, dimensions=None, function=None, spreadfn=None, **kwargs): """Aggregates data on the supplied dimensions. Aggregates over the supplied key dimensions with the defined function. Args: dimensions: Dimension(s) to aggregate on Default to all key dimensions function: Aggregation function to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **kwargs: Keyword arguments passed to the aggregation function Returns: Returns the aggregated Dataset """ if function is None: raise ValueError("The aggregate method requires a function to be specified") if dimensions is None: dimensions = self.kdims elif not isinstance(dimensions, list): dimensions = [dimensions] kdims = [self.get_dimension(d, strict=True) for d in dimensions] if not len(self): if spreadfn: spread_name = spreadfn.__name__ vdims = [d for vd in self.vdims for d in [vd, vd('_'.join([vd.name, spread_name]))]] else: vdims = self.vdims return self.clone([], kdims=kdims, vdims=vdims) vdims = self.vdims aggregated, dropped = self.interface.aggregate(self, kdims, function, **kwargs) aggregated = self.interface.unpack_scalar(self, aggregated) vdims = [vd for vd in vdims if vd not in dropped] ndims = len(dimensions) min_d, max_d = self.params('kdims').bounds generic_type = (min_d is not None and ndims < min_d) or (max_d is not None and ndims > max_d) if spreadfn: error, _ = self.interface.aggregate(self, dimensions, spreadfn) spread_name = spreadfn.__name__ ndims = len(vdims) error = self.clone(error, kdims=kdims, new_type=Dataset) combined = self.clone(aggregated, kdims=kdims, new_type=Dataset) for i, d in enumerate(vdims): dim = d('_'.join([d.name, spread_name])) dvals = error.dimension_values(d, flat=False) combined = combined.add_dimension(dim, ndims+i, dvals, True) return combined.clone(new_type=Dataset if generic_type else type(self)) if np.isscalar(aggregated): return aggregated else: try: # Should be checking the dimensions declared on the element are compatible return self.clone(aggregated, kdims=kdims, vdims=vdims) except: datatype = self.params('datatype').default return self.clone(aggregated, kdims=kdims, vdims=vdims, new_type=Dataset if generic_type else None, datatype=datatype)
[docs] def groupby(self, dimensions=[], container_type=HoloMap, group_type=None, dynamic=False, **kwargs): """Groups object by one or more dimensions Applies groupby operation over the specified dimensions returning an object of type container_type (expected to be dictionary-like) containing the groups. Args: dimensions: Dimension(s) to group by container_type: Type to cast group container to group_type: Type to cast each group to dynamic: Whether to return a DynamicMap **kwargs: Keyword arguments to pass to each group Returns: Returns object of supplied container_type containing the groups. If dynamic=True returns a DynamicMap instead. """ if not isinstance(dimensions, list): dimensions = [dimensions] if not len(dimensions): dimensions = self.dimensions('key', True) if group_type is None: group_type = type(self) dimensions = [self.get_dimension(d, strict=True) for d in dimensions] dim_names = [d.name for d in dimensions] if dynamic: group_dims = [kd for kd in self.kdims if kd not in dimensions] kdims = [self.get_dimension(d) for d in kwargs.pop('kdims', group_dims)] drop_dim = len(group_dims) != len(kdims) group_kwargs = dict(util.get_param_values(self), kdims=kdims) group_kwargs.update(kwargs) def load_subset(*args): constraint = dict(zip(dim_names, args)) group = self.select(**constraint) if np.isscalar(group): return group_type(([group],), group=self.group, label=self.label, vdims=self.vdims) data = group.reindex(kdims) if drop_dim and self.interface.gridded: data = data.columns() return group_type(data, **group_kwargs) dynamic_dims = [d(values=list(self.interface.values(self, d.name, False))) for d in dimensions] return DynamicMap(load_subset, kdims=dynamic_dims) return self.interface.groupby(self, dim_names, container_type, group_type, **kwargs)
def __len__(self): "Number of values in the Dataset." return self.interface.length(self) def __nonzero__(self): "Whether the Dataset contains any values" return self.interface.nonzero(self) __bool__ = __nonzero__ @property def shape(self): "Returns the shape of the data." return self.interface.shape(self)
[docs] def dimension_values(self, dimension, expanded=True, flat=True): """Return the values along the requested dimension. Args: dimension: The dimension to return values for expanded (bool, optional): Whether to expand values Whether to return the expanded values, behavior depends on the type of data: * Columnar: If false returns unique values * Geometry: If false returns scalar values per geometry * Gridded: If false returns 1D coordinates flat (bool, optional): Whether to flatten array Returns: NumPy array of values along the requested dimension """ dim = self.get_dimension(dimension, strict=True) return self.interface.values(self, dim, expanded, flat)
[docs] def get_dimension_type(self, dim): """Get the type of the requested dimension. Type is determined by Dimension.type attribute or common type of the dimension values, otherwise None. Args: dimension: Dimension to look up by name or by index Returns: Declared type of values along the dimension """ dim_obj = self.get_dimension(dim) if dim_obj and dim_obj.type is not None: return dim_obj.type return self.interface.dimension_type(self, dim_obj)
[docs] def dframe(self, dimensions=None, multi_index=False): """Convert dimension values to DataFrame. Returns a pandas dataframe of columns along each dimension, either completely flat or indexed by key dimensions. Args: dimensions: Dimensions to return as columns multi_index: Convert key dimensions to (multi-)index Returns: DataFrame of columns corresponding to each dimension """ if dimensions is None: dimensions = [d.name for d in self.dimensions()] else: dimensions = [self.get_dimension(d, strict=True).name for d in dimensions] df = self.interface.dframe(self, dimensions) if multi_index: df = df.set_index([d for d in dimensions if d in self.kdims]) return df
[docs] def columns(self, dimensions=None): """Convert dimension values to a dictionary. Returns a dictionary of column arrays along each dimension of the element. Args: dimensions: Dimensions to return as columns Returns: Dictionary of arrays for each dimension """ if dimensions is None: dimensions = self.dimensions() else: dimensions = [self.get_dimension(d, strict=True) for d in dimensions] return OrderedDict([(d.name, self.dimension_values(d)) for d in dimensions])
@property def to(self): "Returns the conversion interface with methods to convert Dataset" return self._conversion_interface(self)
[docs] def clone(self, data=None, shared_data=True, new_type=None, *args, **overrides): """Clones the object, overriding data and parameters. Args: data: New data replacing the existing data shared_data (bool, optional): Whether to use existing data new_type (optional): Type to cast object to *args: Additional arguments to pass to constructor **overrides: New keyword arguments to pass to constructor Returns: Cloned object """ if 'datatype' not in overrides: datatypes = [self.interface.datatype] + self.datatype overrides['datatype'] = list(util.unique_iterator(datatypes)) return super(Dataset, self).clone(data, shared_data, new_type, *args, **overrides)
@property def iloc(self): """Returns iloc indexer with support for columnar indexing. Returns an iloc object providing a convenient interface to slice and index into the Dataset using row and column indices. Allow selection by integer index, slice and list of integer indices and boolean arrays. Examples: * Index the first row and column: dataset.iloc[0, 0] * Select rows 1 and 2 with a slice: dataset.iloc[1:3, :] * Select with a list of integer coordinates: dataset.iloc[[0, 2, 3]] """ return iloc(self) @property def ndloc(self): """Returns ndloc indexer with support for gridded indexing. Returns an ndloc object providing nd-array like indexing for gridded datasets. Follows NumPy array indexing conventions, allowing for indexing, slicing and selecting a list of indices on multi-dimensional arrays using integer indices. The order of array indices is inverted relative to the Dataset key dimensions, e.g. an Image with key dimensions 'x' and 'y' can be indexed with ``image.ndloc[iy, ix]``, where ``iy`` and ``ix`` are integer indices along the y and x dimensions. Examples: * Index value in 2D array: dataset.ndloc[3, 1] * Slice along y-axis of 2D array: dataset.ndloc[2:5, :] * Vectorized (non-orthogonal) indexing along x- and y-axes: dataset.ndloc[[1, 2, 3], [0, 2, 3]] """ return ndloc(self)
# Aliases for pickle backward compatibility Columns = Dataset ArrayColumns = ArrayInterface DictColumns = DictInterface GridColumns = GridInterface