Implement grid concatenation and standardize datatype casting

holoviews/core/data/__init__.py

@@ -17,12 +17,14 @@
 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
-    datatypes = ['dataframe', 'dictionary', 'grid', 'array']
+    default_datatype = 'dataframe'
+    datatypes = ['dataframe', 'dictionary', 'grid']
     DFColumns = PandasInterface
 except ImportError:
     pd = None
@@ -64,6 +66,27 @@
 from .. import util
 
 
+def concat(datasets, datatype=None):
+    """
+    Concatenates multiple datasets wrapped in an NdMapping type
+    along all of its dimensions. Before concatenation all datasets
+    are cast to the same datatype. 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.
+
+    Signature
+    ---------
+
+    datasets: NdMapping of Datasets defining dimensions to concatenate on
+    datatype: Datatype to cast data to before concatenation
+
+    Returns: Dataset
+    """
+    return Interface.concatenate(datasets, datatype)
+
+
 class DataConversion(object):
     """
     DataConversion is a very simple container object which can be
@@ -291,7 +314,7 @@ def add_dimension(self, dimension, dim_pos, dim_val, vdim=False, **kwargs):
             dimensions = dict(kdims=dims)
 
         if issubclass(self.interface, ArrayInterface) and np.asarray(dim_val).dtype != self.data.dtype:
-            element = self.clone(datatype=['pandas', 'dictionary'])
+            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)

holoviews/core/data/array.py

@@ -104,12 +104,6 @@ def add_dimension(cls, dataset, dimension, dim_pos, values, vdim):
         return np.insert(data, dim_pos, values, axis=1)
 
 
-    @classmethod
-    def concat(cls, dataset_objs):
-        cast_objs = cls.cast(dataset_objs)
-        return np.concatenate([col.data for col in cast_objs])
-
-
     @classmethod
     def sort(cls, dataset, by=[], reverse=False):
         data = dataset.data

holoviews/core/data/dask.py

@@ -244,9 +244,14 @@ def add_dimension(cls, columns, dimension, dim_pos, values, vdim):
         return data
 
     @classmethod
-    def concat(cls, columns_objs):
-        cast_objs = cls.cast(columns_objs)
-        return dd.concat([col.data for col in cast_objs])
+    def concat(cls, datasets, dimensions, vdims):
+        dataframes = []
+        for key, ds in datasets:
+            data = ds.data.copy()
+            for d, k in zip(dimensions, key):
+                data[d.name] = k
+            dataframes.append(data)
+        return dd.concat(dataframes)
 
     @classmethod
     def dframe(cls, columns, dimensions):

holoviews/core/data/dictionary.py

@@ -1,5 +1,4 @@
-from collections import OrderedDict
-
+from collections import OrderedDict, defaultdict
 try:
     import itertools.izip as zip
 except ImportError:
@@ -139,7 +138,10 @@ def unpack_scalar(cls, dataset, data):
         key = list(data.keys())[0]
 
         if len(data[key]) == 1 and key in dataset.vdims:
-            return data[key][0]
+            scalar = data[key][0]
+            return scalar.compute() if hasattr(scalar, 'compute') else scalar
+        return data
+
 
     @classmethod
     def isscalar(cls, dataset, dim):
@@ -185,17 +187,19 @@ def redim(cls, dataset, dimensions):
             renamed.append((k, v))
         return OrderedDict(renamed)
 
+
     @classmethod
-    def concat(cls, dataset_objs):
-        cast_objs = cls.cast(dataset_objs)
-        cols = set(tuple(c.data.keys()) for c in cast_objs)
-        if len(cols) != 1:
-            raise Exception("In order to concatenate, all Dataset objects "
-                            "should have matching set of columns.")
-        concatenated = OrderedDict()
-        for column in cols.pop():
-            concatenated[column] = np.concatenate([obj[column] for obj in cast_objs])
-        return concatenated
+    def concat(cls, datasets, dimensions, vdims):
+        columns = defaultdict(list)
+        for key, ds in datasets:
+            for k, vals in ds.data.items():
+                columns[k].append(vals)
+            for d, k in zip(dimensions, key):
+                columns[d.name].append(np.full(len(ds), k))
+
+        template = datasets[0][1]
+        dims = dimensions+template.dimensions()
+        return OrderedDict([(d.name, np.concatenate(columns[d.name])) for d in dims])
 
 
     @classmethod

holoviews/core/data/grid.py

@@ -17,13 +17,15 @@
 except ImportError:
     da = None
 
+def is_dask(array):
+    return da and isinstance(array, da.Array)
 
 from .dictionary import DictInterface
 from .interface import Interface, DataError
 from ..dimension import Dimension
 from ..element import Element
 from ..dimension import OrderedDict as cyODict
-from ..ndmapping import NdMapping, item_check
+from ..ndmapping import NdMapping, item_check, sorted_context
 from .. import util
 
 
@@ -116,14 +118,47 @@ def init(cls, eltype, data, kdims, vdims):
         return data, {'kdims':kdims, 'vdims':vdims}, {}
 
 
+    @classmethod
+    def concat(cls, datasets, dimensions, vdims):
+        from . import Dataset
+        with sorted_context(False):
+            datasets = NdMapping(datasets, kdims=dimensions)
+            datasets = datasets.clone([(k, v.data if isinstance(v, Dataset) else v)
+                                       for k, v in datasets.data.items()])
+        if len(datasets.kdims) > 1:
+            items = datasets.groupby(datasets.kdims[:-1]).data.items()
+            return cls.concat([(k, cls.concat(v, v.kdims, vdims=vdims)) for k, v in items],
+                              datasets.kdims[:-1], vdims)
+        return cls.concat_dim(datasets, datasets.kdims[0], vdims)
+
+
+    @classmethod
+    def concat_dim(cls, datasets, dim, vdims):
+        values, grids = zip(*datasets.items())
+        new_data = {k: v for k, v in grids[0].items() if k not in vdims}
+        new_data[dim.name] = np.array(values)
+        for vdim in vdims:
+            arrays = [grid[vdim.name] for grid in grids]
+            shapes = set(arr.shape for arr in arrays)
+            if len(shapes) > 1:
+                raise DataError('When concatenating gridded data the shape '
+                                'of arrays must match. %s found that arrays '
+                                'along the %s dimension do not match.' %
+                                (cls.__name__, vdim.name))
+            stack = np.stack if any(is_dask(arr) for arr in arrays) else da.stack
+            new_data[vdim.name] = stack(arrays, -1)
+        return new_data
+
+
     @classmethod
     def irregular(cls, dataset, dim):
         return dataset.data[dim.name if isinstance(dim, Dimension) else dim].ndim > 1
 
 
     @classmethod
     def isscalar(cls, dataset, dim):
-        return np.unique(cls.values(dataset, dim, expanded=False)) == 1
+        values = cls.values(dataset, dim, expanded=False)
+        return values.shape in ((), (1,)) or len(np.unique(values)) == 1
 
 
     @classmethod
@@ -541,9 +576,9 @@ def aggregate(cls, dataset, kdims, function, **kwargs):
         axes = tuple(dataset.ndims-dataset.get_dimension_index(kdim)-1
                      for kdim in dataset.kdims if kdim not in kdims)
         for vdim in dataset.vdims:
-            data[vdim.name] = np.atleast_1d(function(dataset.data[vdim.name],
-                                                      axis=axes, **kwargs))
-
+            values = dataset.data[vdim.name]
+            atleast_1d = da.atleast_1d if is_dask(values) else np.atleast_1d
+            data[vdim.name] = atleast_1d(function(values, axis=axes, **kwargs))
         return data
 
 

holoviews/core/data/interface.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from ..element import Element
-from ..ndmapping import OrderedDict
+from ..ndmapping import OrderedDict, NdMapping
 from .. import util
 
 
@@ -107,30 +107,20 @@ class Interface(param.Parameterized):
     def register(cls, interface):
         cls.interfaces[interface.datatype] = interface
 
-
     @classmethod
-    def cast(cls, dataset, datatype=None, cast_type=None):
+    def cast(cls, datasets, datatype=None, cast_type=None):
         """
         Given a list of Dataset objects, cast them to the specified
         datatype (by default the format matching the current interface)
         with the given cast_type (if specified).
         """
-        if len({type(c) for c in dataset}) > 1 and cast_type is None:
-            raise Exception("Please supply the common cast type")
-
-        if datatype is None:
-           datatype = cls.datatype
-
-        unchanged = all({c.interface==cls for c in dataset})
-        if unchanged and cast_type is None:
-            return dataset
-        elif unchanged:
-            return [cast_type(co, **dict(util.get_param_values(co)))
-                    for co in dataset]
-
-        return [co.clone(co.columns(), datatype=[datatype], new_type=cast_type)
-                for co in dataset]
-
+        datatype = datatype or cls.datatype
+        cast = []
+        for ds in datasets:
+            if cast_type is not None or ds.interface.datatype != datatype:
+                ds = ds.clone(ds, datatype=[datatype], new_type=cast_type)
+            cast.append(ds)
+        return cast
 
     @classmethod
     def error(cls):
@@ -167,7 +157,7 @@ def initialize(cls, eltype, data, kdims, vdims, datatype=None):
 
             if data.interface.datatype in datatype and data.interface.datatype in eltype.datatype:
                 data = data.data
-            elif data.interface.gridded:
+            elif data.interface.gridded and any(cls.interfaces[dt].gridded for dt in datatype):
                 gridded = OrderedDict([(kd.name, data.dimension_values(kd.name, expanded=False))
                                        for kd in data.kdims])
                 for vd in data.vdims:
@@ -307,25 +297,35 @@ def range(cls, dataset, dimension):
                 return column[0], column[-1]
 
     @classmethod
-    def concatenate(cls, dataset, datatype=None):
+    def concatenate(cls, datasets, datatype=None, new_type=None):
         """
-        Utility function to concatenate a list of Column objects,
-        returning a new Dataset object. Note that this is unlike the
-        .concat method which only concatenates the data.
+        Utility function to concatenate an NdMapping of Dataset objects.
         """
-        if len(set(type(c) for c in dataset)) != 1:
-               raise Exception("All inputs must be same type in order to concatenate")
-
-        interfaces = set(c.interface for c in dataset)
-        if len(interfaces)!=1 and datatype is None:
-            raise Exception("Please specify the concatenated datatype")
-        elif len(interfaces)!=1:
-            interface = cls.interfaces[datatype]
-        else:
-            interface = interfaces.pop()
-
-        concat_data = interface.concat(dataset)
-        return dataset[0].clone(concat_data)
+        from . import Dataset, default_datatype
+        new_type = new_type or Dataset
+        if isinstance(datasets, NdMapping):
+            dimensions = datasets.kdims
+            datasets = datasets.data
+        if isinstance(datasets, (dict, OrderedDict)):
+            datasets = datasets.items()
+            keys, datasets = zip(*datasets)
+        elif isinstance(datasets, list) and not any(isinstance(v, tuple) for v in datasets):
+            keys = [()]*len(datasets)
+            dimensions = []
+        template = datasets[0]
+        datatype = datatype or template.interface.datatype
+
+        # Handle non-general datatypes by casting to general type
+        if datatype == 'array':
+            datatype = default_datatype
+        elif datatype == 'image':
+            datatype = 'grid'
+
+        datasets = template.interface.cast(datasets, datatype)
+        template = datasets[0]
+        data = list(zip(keys, datasets)) if keys else datasets
+        concat_data = template.interface.concat(data, dimensions, vdims=template.vdims)
+        return template.clone(concat_data, kdims=dimensions+template.kdims, new_type=new_type)
 
     @classmethod
     def reduce(cls, dataset, reduce_dims, function, **kwargs):

holoviews/core/data/iris.py

@@ -4,6 +4,9 @@
 from itertools import product
 
 import iris
+from iris.coords import DimCoord
+from iris.cube import CubeList
+from iris.experimental.equalise_cubes import equalise_attributes
 from iris.util import guess_coord_axis
 
 import numpy as np
@@ -230,6 +233,20 @@ def groupby(cls, dataset, dims, container_type=HoloMap, group_type=None, **kwarg
         else:
             return container_type(data)
 
+    @classmethod
+    def concat_dim(cls, datasets, dim, vdims):
+        """
+        Concatenates datasets along one dimension
+        """
+        cubes = []
+        for c, cube in datasets.items():
+            cube = cube.copy()
+            cube.add_aux_coord(DimCoord([c], var_name=dim.name))
+            cubes.append(cube)
+        cubes = CubeList(cubes)
+        equalise_attributes(cubes)
+        return cubes.merge_cube()
+
 
     @classmethod
     def range(cls, dataset, dimension):
@@ -261,7 +278,7 @@ def length(cls, dataset):
         """
         Returns the total number of samples in the dataset.
         """
-        return np.product([len(d.points) for d in dataset.data.coords()])
+        return np.product([len(d.points) for d in dataset.data.coords(dim_coords=True)])
 
 
     @classmethod

holoviews/core/data/pandas.py

@@ -169,9 +169,14 @@ def range(cls, columns, dimension):
 
 
     @classmethod
-    def concat(cls, columns_objs):
-        cast_objs = cls.cast(columns_objs)
-        return pd.concat([col.data for col in cast_objs])
+    def concat(cls, datasets, dimensions, vdims):
+        dataframes = []
+        for key, ds in datasets:
+            data = ds.data.copy()
+            for d, k in zip(dimensions, key):
+                data[d.name] = k
+            dataframes.append(data)
+        return pd.concat(dataframes)
 
 
     @classmethod

holoviews/core/data/xarray.py

@@ -360,13 +360,10 @@ def ndloc(cls, dataset, indices):
         else:
             return dataset.data.isel(**isel)
 
-
     @classmethod
-    def concat(cls, dataset_objs):
-        #cast_objs = cls.cast(dataset_objs)
-        # Reimplement concat to automatically add dimensions
-        # once multi-dimensional concat has been added to xarray.
-        return xr.concat([col.data for col in dataset_objs], dim='concat_dim')
+    def concat_dim(cls, datasets, dim, vdims):
+        return xr.concat([ds.assign_coords(**{dim.name: c}) for c, ds in datasets.items()],
+                         dim=dim.name)
 
     @classmethod
     def redim(cls, dataset, dimensions):