Source code for

import sys

import numpy as np

from .dask import DaskInterface
from .interface import Interface
from .spatialpandas import SpatialPandasInterface

[docs]class DaskSpatialPandasInterface(SpatialPandasInterface): base_interface = DaskInterface datatype = 'dask_spatialpandas'
[docs] @classmethod def loaded(cls): return 'spatialpandas.dask' in sys.modules
@classmethod def data_types(cls): from spatialpandas.dask import DaskGeoDataFrame, DaskGeoSeries return (DaskGeoDataFrame, DaskGeoSeries) @classmethod def series_type(cls): from spatialpandas.dask import DaskGeoSeries return DaskGeoSeries @classmethod def frame_type(cls): from spatialpandas.dask import DaskGeoDataFrame return DaskGeoDataFrame @classmethod def init(cls, eltype, data, kdims, vdims): import dask.dataframe as dd data, dims, params = super().init( eltype, data, kdims, vdims ) if not isinstance(data, cls.frame_type()): data = dd.from_pandas(data, npartitions=1) return data, dims, params @classmethod def partition_values(cls, df, dataset, dimension, expanded, flat): ds = dataset.clone(df, datatype=['spatialpandas']) return ds.interface.values(ds, dimension, expanded, flat)
[docs] @classmethod def values(cls, dataset, dimension, expanded=True, flat=True, compute=True, keep_index=False): if compute and not keep_index: dtype = cls.dtype(dataset, dimension) meta = np.array([], dtype=dtype.base) return cls.partition_values, meta=meta, dataset=dataset, dimension=dimension, expanded=expanded, flat=flat ).compute() values = super().values( dataset, dimension, expanded, flat, compute, keep_index ) if compute and not keep_index and hasattr(values, 'compute'): return values.compute() return values
[docs] @classmethod def split(cls, dataset, start, end, datatype, **kwargs): ds = dataset.clone(, datatype=['spatialpandas']) return ds.interface.split(ds, start, end, datatype, **kwargs)
@classmethod def iloc(cls, dataset, index): rows, cols = index if rows is not None: raise NotImplementedError return super().iloc(dataset, index) @classmethod def add_dimension(cls, dataset, dimension, dim_pos, values, vdim): return cls.base_interface.add_dimension(dataset, dimension, dim_pos, values, vdim) @classmethod def dframe(cls, dataset, dimensions): if dimensions: return[dimensions].compute() else: return