import copy import types from contextlib import contextmanager from functools import wraps import numpy as np import pandas as pd # noqa import param from param.parameterized import ParameterizedMetaclass from .. import util as core_util from ..accessors import Redim from ..dimension import ( Dimension, Dimensioned, LabelledData, dimension_name, process_dimensions, ) from ..element import Element from ..ndmapping import MultiDimensionalMapping from ..spaces import DynamicMap, HoloMap from .array import ArrayInterface from .cudf import cuDFInterface # noqa (API import) from .dask import DaskInterface # noqa (API import) from .dictionary import DictInterface # noqa (API import) from .grid import GridInterface # noqa (API import) from .ibis import IbisInterface # noqa (API import) from .image import ImageInterface # noqa (API import) from .interface import Interface, iloc, ndloc from .multipath import MultiInterface # noqa (API import) from .pandas import PandasAPI, PandasInterface # noqa (API import) from .spatialpandas import SpatialPandasInterface # noqa (API import) from .spatialpandas_dask import DaskSpatialPandasInterface # noqa (API import) from .xarray import XArrayInterface # noqa (API import) default_datatype = 'dataframe' datatypes = ['dataframe', 'dictionary', 'grid', 'xarray', 'multitabular', 'spatialpandas', 'dask_spatialpandas', 'dask', 'cuDF', 'array', 'ibis'] 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) class DataConversion: """ 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.param.objects() 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 = element_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(f"{type_name} {dim_type} must be between length {min_d} and {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 elif issubclass(self._element.interface, PandasAPI): 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.param.objects('existing')['group'].default: params['group'] = selected.group params.update(kwargs) if len(kdims) == selected.ndims or not groupby: # Propagate dataset params['dataset'] = self._element.dataset params['pipeline'] = self._element._pipeline 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 @contextmanager def disable_pipeline(): """ Disable PipelineMeta class from storing pipelines. """ PipelineMeta.disable = True try: yield finally: PipelineMeta.disable = False class PipelineMeta(ParameterizedMetaclass): # Public methods that should not be wrapped blacklist = ['__init__', 'clone'] disable = False def __new__(mcs, classname, bases, classdict): for method_name in classdict: method_fn = classdict[method_name] if method_name in mcs.blacklist or method_name.startswith('_'): continue elif isinstance(method_fn, types.FunctionType): classdict[method_name] = mcs.pipelined(method_fn, method_name) inst = type.__new__(mcs, classname, bases, classdict) return inst @staticmethod def pipelined(method_fn, method_name): @wraps(method_fn) def pipelined_fn(*args, **kwargs): from ...operation.element import method as method_op inst = args[0] inst_pipeline = copy.copy(getattr(inst, '_pipeline', None)) in_method = inst._in_method if not in_method: inst._in_method = True try: result = method_fn(*args, **kwargs) if PipelineMeta.disable: return result op = method_op.instance( input_type=type(inst), method_name=method_name, args=list(args[1:]), kwargs=kwargs, ) if not in_method: if isinstance(result, Dataset): result._pipeline = inst_pipeline.instance( operations=inst_pipeline.operations + [op], output_type=type(result), ) elif isinstance(result, MultiDimensionalMapping): for key, element in result.items(): if isinstance(element, Dataset): getitem_op = method_op.instance( input_type=type(result), method_name='__getitem__', args=[key] ) element._pipeline = inst_pipeline.instance( operations=inst_pipeline.operations + [ op, getitem_op ], output_type=type(result), ) finally: if not in_method: inst._in_method = False return result return pipelined_fn class Dataset(Element, metaclass=PipelineMeta): """ 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(default=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 __new__(cls, data=None, kdims=None, vdims=None, **kwargs): """ Allows casting a DynamicMap to an Element class like hv.Curve, by applying the class to each underlying element. """ if isinstance(data, DynamicMap): class_name = cls.__name__ repr_kdims = f'kdims={kdims!r}' if kdims else None repr_vdims = f'vdims={vdims!r}' if vdims else None repr_kwargs = (', '.join(f'{k}={v!r}' for k,v in kwargs.items()) if kwargs else None) extras = ', '.join([el for el in [repr_kdims, repr_vdims, repr_kwargs] if el is not None]) extras = ', ' + extras if extras else '' apply_args= f'hv.{class_name}{extras}' msg = "Cannot construct a {class_name} from the supplied object of type DynamicMap. Implicitly creating a DynamicMap of {class_name} objects, but instead please explicitly call .apply({apply_args}) on the supplied DynamicMap." cls.param.warning(cls, msg.format(class_name=class_name, apply_args=apply_args)) return data.apply(cls, per_element=True, kdims=kdims, vdims=vdims, **kwargs) else: return super().__new__(cls) def __init__(self, data, kdims=None, vdims=None, **kwargs): from ...operation.element import ( chain as chain_op, factory, ) self._in_method = False input_data = data dataset_provided = 'dataset' in kwargs input_dataset = kwargs.pop('dataset', None) input_pipeline = kwargs.pop('pipeline', None) input_transforms = kwargs.pop('transforms', None) if isinstance(data, Element): if 'kdims' in kwargs: kwargs['kdims'] = [ data.get_dimension(kd) if isinstance(kd, str) else kd for kd in kwargs['kdims'] ] if 'kdims' in kwargs: kwargs['vdims'] = [ data.get_dimension(vd) if isinstance(vd, str) else vd for vd in kwargs['vdims'] ] pvals = core_util.get_param_values(data) kwargs.update([(l, pvals[l]) for l in ['group', 'label'] if l in pvals and l not in kwargs]) if isinstance(data, Dataset): if not dataset_provided and data._dataset is not None: input_dataset = data._dataset if input_pipeline is None: input_pipeline = data.pipeline if input_transforms is None: input_transforms = data._transforms 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().__init__(data, **dict(kwargs, **dict(dims, **extra_kws))) self.interface.validate(self, validate_vdims) # Handle _pipeline property if input_pipeline is None: input_pipeline = chain_op.instance() kwargs['kdims'] = self.kdims kwargs['vdims'] = self.vdims init_op = factory.instance( output_type=type(self), args=[], kwargs=dict(kwargs, kdims=self.kdims, vdims=self.vdims), ) self._pipeline = input_pipeline.instance( operations=input_pipeline.operations + [init_op], output_type=type(self), ) self._transforms = input_transforms or [] # On lazy interfaces this allows keeping an evaluated version # of the dataset in memory self._cached = None # Handle initializing the dataset property. self._dataset = input_dataset if self._dataset is None and isinstance(input_data, Dataset) and not dataset_provided: if input_data.data is self.data: self._dataset = {'kdims': input_data.kdims, 'vdims': input_data.vdims} else: self._dataset = Dataset(input_data, dataset=None, pipeline=None, transforms=None, _validate_vdims=False) if hasattr(self, '_binned'): self._dataset._binned = self._binned def __getstate__(self): "Ensures pipelines are dropped" obj_dict = super().__getstate__() if '_pipeline' in obj_dict: pipeline = obj_dict['_pipeline'] obj_dict['_pipeline'] = pipeline.instance(operations=pipeline.operations[:1]) if '_transforms' in obj_dict: obj_dict['_transforms'] = [] return obj_dict @property def redim(self): return Redim(self, mode='dataset') @property def dataset(self): """ The Dataset that this object was created from """ if self._dataset is None: if type(self) is Dataset: return self datatype = list(core_util.unique_iterator(self.datatype+Dataset.datatype)) dataset = Dataset(self, dataset=None, pipeline=None, transforms=None, _validate_vdims=False, datatype=datatype) if hasattr(self, '_binned'): dataset._binned = self._binned return dataset elif not isinstance(self._dataset, Dataset): return Dataset(self, _validate_vdims=False, **self._dataset) return self._dataset @property def pipeline(self): """ Chain operation that evaluates the sequence of operations that was used to create this object, starting with the Dataset stored in dataset property """ return self._pipeline def compute(self): """ Computes the data to a data format that stores the daata in memory, e.g. a Dask dataframe or array is converted to a Pandas DataFrame or NumPy array. Returns: Dataset with the data stored in in-memory format """ return self.interface.compute(self) def persist(self): """ Persists the results of a lazy data interface to memory to speed up data manipulation and visualization. If the particular data backend already holds the data in memory this is a no-op. Unlike the compute method this maintains the same data type. Returns: Dataset with the data persisted to memory """ persisted = self.interface.persist(self) if persisted.interface is self.interface: return persisted self._cached = persisted return self def closest(self, coords=None, **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 coords is None: coords = [] 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 = next(iter(kwargs.values())) 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 [type(s)(xs[idx]) for s, idx in zip(coords, idxs)] 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) 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(core_util.isfinite(v) for v in dim.range) and dimension_range: return dim.range elif dim in self.dimensions() and data_range and bool(self): lower, upper = self.interface.range(self, dim) else: lower, upper = (np.nan, np.nan) if not dimension_range: return lower, upper return core_util.dimension_range(lower, upper, dim.range, dim.soft_range) 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 or 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, (str, tuple)): dimension = Dimension(dimension) if dimension.name in self.kdims: raise Exception(f'{dimension.name} dimension already defined') 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) def select(self, selection_expr=None, 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]) * predicate expression: A holoviews.dim expression, e.g.: from holoviews import dim ds.select(selection_expr=dim('x') % 2 == 0) Args: selection_expr: holoviews.dim predicate expression specifying selection. 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 """ from ...util.transform import dim if selection_expr is not None and not isinstance(selection_expr, dim): raise ValueError("""\ The first positional argument to the Dataset.select method is expected to be a holoviews.util.transform.dim expression. Use the selection_specs keyword argument to specify a selection specification""") if selection_specs is not None and not isinstance(selection_specs, (list, tuple)): selection_specs = [selection_specs] selection = {dim_name: sel for dim_name, sel in selection.items() if dim_name in self.dimensions()+['selection_mask']} if (selection_specs and not any(self.matches(sp) for sp in selection_specs) or (not selection and not selection_expr)): return self # Handle selection dim expression if selection_expr is not None: mask = selection_expr.apply(self, compute=False, keep_index=True) selection = {'selection_mask': mask} # Handle selection kwargs if selection: data = self.interface.select(self, **selection) else: data = self.data if np.isscalar(data): return data else: return self.clone(data) 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 d not 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 d not in key_dims and d not 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: interfaces = self.interface.interfaces datatype = [dt for dt in datatype if not getattr(interfaces.get(dt, None), 'gridded', True)] 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 = core_util.process_ellipses(self, slices, vdim_selection=True) if getattr(getattr(slices, 'dtype', None), 'kind', None) == '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 (isinstance(slices, ()) and len(slices) == 1) or slices is 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(f"{slices[self.ndims]!r} is not an available value dimension") 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 def sample(self, samples=None, 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 samples is None: samples = [] 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 core_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(core_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/holoviz/holoviews/issues/1173 from ...element import Curve, Table datatype = ['dataframe', 'dictionary', 'dask', 'ibis', 'cuDF'] 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, datatype=datatype).reindex(kdims) selection = tuple(reindexed.columns(kdims+self.vdims).values()) datatype = list(core_util.unique_iterator(self.datatype+['dataframe', 'dict'])) return self.clone(selection, kdims=kdims, new_type=new_type, datatype=datatype) lens = {len(core_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 = [core_util.wrap_tuple(s) for s in samples] sampled = self.interface.sample(self, samples) return self.clone(sampled, new_type=Table, datatype=datatype) def reduce(self, dimensions=None, 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 dimensions is None: dimensions = [] 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) 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 or dim_transform specified as a tuple of the transformed dimension name and dim transform. Args: dimensions: Dimension(s) to aggregate on Default to all key dimensions function: Aggregation function or transform to apply Supports both simple functions and dimension transforms spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **kwargs: Keyword arguments either passed to the aggregation function or to create new names for the transformed variables Returns: Returns the aggregated Dataset """ from ...util.transform import dim if dimensions is None: dimensions = self.kdims elif not isinstance(dimensions, list): dimensions = [dimensions] if isinstance(function, tuple) or any(isinstance(v, dim) for v in kwargs.values()): dataset = self.clone(new_type=Dataset) if dimensions: dataset = dataset.groupby(dimensions) args = () if function is None else (function,) transformed = dataset.apply.transform(*args, drop=True, **kwargs) if not isinstance(transformed, Dataset): transformed = transformed.collapse() return transformed.clone(new_type=type(self)) ndims = len(dimensions) min_d, max_d = self.param.objects('existing')['kdims'].bounds generic_type = (min_d is not None and ndims < min_d) or (max_d is not None and ndims > max_d) new_type = Dataset if generic_type else None # Handle functions kdims = [self.get_dimension(d, strict=True) for d in dimensions] if not self: if spreadfn: spread_name = spreadfn.__name__ vdims = [d for vd in self.vdims for d in [vd, vd.clone(f'{vd.name}_{spread_name}')]] else: vdims = self.vdims if not kdims and len(vdims) == 1: return np.nan return self.clone([], kdims=kdims, vdims=vdims, new_type=new_type) 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] 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 d in vdims: dim = d.clone(f'{d.name}_{spread_name}') dvals = error.dimension_values(d, flat=False) idx = vdims.index(d) combined = combined.add_dimension(dim, idx+1, dvals, True) vdims = combined.vdims return combined.clone(new_type=new_type) 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 Exception: datatype = self.param.objects('existing')['datatype'].default return self.clone(aggregated, kdims=kdims, vdims=vdims, new_type=new_type, datatype=datatype) def groupby(self, dimensions=None, 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 dimensions is None: dimensions = [] 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(core_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.clone(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 transform(self, *args, **kwargs): """Transforms the Dataset according to a dimension transform. Transforms may be supplied as tuples consisting of the dimension(s) and the dim transform to apply or keyword arguments mapping from dimension(s) to dim transforms. If the arg or kwarg declares multiple dimensions the dim transform should return a tuple of values for each. A transform may override an existing dimension or add a new one in which case it will be added as an additional value dimension. Args: args: Specify the output arguments and transforms as a tuple of dimension specs and dim transforms drop (bool): Whether to drop all variables not part of the transform keep_index (bool): Whether to keep indexes Whether to apply transform on datastructure with index, e.g. pandas.Series or xarray.DataArray, (important for dask datastructures where index may be required to align datasets). kwargs: Specify new dimensions in the form new_dim=dim_transform Returns: Transformed dataset with new dimensions """ drop = kwargs.pop('drop', False) keep_index = kwargs.pop('keep_index', True) transforms = {} for s, transform in list(args)+list(kwargs.items()): transforms[core_util.wrap_tuple(s)] = transform new_data = {} for signature, transform in transforms.items(): applied = transform.apply( self, compute=False, keep_index=keep_index ) if len(signature) == 1: new_data[signature[0]] = applied else: for s, vals in zip(signature, applied): new_data[s] = vals new_dims = [] for d in new_data: if self.get_dimension(d) is None: new_dims.append(d) ds = self if ds.interface.datatype in ('image', 'array'): ds = ds.clone(datatype=[dt for dt in ds.datatype if dt != ds.interface.datatype]) if drop: kdims = [ds.get_dimension(d) for d in new_data if d in ds.kdims] vdims = [ds.get_dimension(d) or d for d in new_data if d not in ds.kdims] data = dict([(dimension_name(d), values) for d, values in new_data.items()]) return ds.clone(data, kdims=kdims, vdims=vdims) else: new_data = dict([(dimension_name(d), values) for d, values in new_data.items()]) data = ds.interface.assign(ds, new_data) data, drop = data if isinstance(data, tuple) else (data, []) kdims = [kd for kd in self.kdims if kd.name not in drop] return ds.clone(data, kdims=kdims, vdims=ds.vdims+new_dims) def __len__(self): "Number of values in the Dataset." return self.interface.length(self) def __bool__(self): "Whether the Dataset contains any values" return self.interface.nonzero(self) @property def shape(self): "Returns the shape of the data." return self.interface.shape(self) 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) values = self.interface.values(self, dim, expanded, flat) if dim.nodata is not None: # Ensure nodata applies to boolean data in py2 values = np.where(values==dim.nodata, np.nan, values) return values 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) 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 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 dict([(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) def clone(self, data=None, shared_data=True, new_type=None, link=True, *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 link (bool, optional): Whether clone should be linked Determines whether Streams and Links attached to original object will be inherited. *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(core_util.unique_iterator(datatypes)) if data is None: overrides['_validate_vdims'] = False # Allows datatype conversions if shared_data: data = self if link: overrides['plot_id'] = self._plot_id elif self._in_method and 'dataset' not in overrides: overrides['dataset'] = self.dataset return super().clone(data, shared_data, new_type, *args, **overrides) # Overrides of superclass methods that are needed so that PipelineMeta # will find them to wrap with pipeline support @wraps(Dimensioned.options) def options(self, *args, **kwargs): return super().options(*args, **kwargs) @wraps(LabelledData.map) def map(self, *args, **kwargs): return super().map(*args, **kwargs) @wraps(LabelledData.relabel) def relabel(self, *args, **kwargs): return super().relabel(*args, **kwargs) @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)