Source code for datashader.glyphs.points

from __future__ import absolute_import, division
import numpy as np
from toolz import memoize

from datashader.glyphs.glyph import Glyph
from datashader.utils import isreal, ngjit

from numba import cuda

try:
    import cudf
    from ..transfer_functions._cuda_utils import cuda_args
except ImportError:
    cudf = None
    cuda_args = None


def values(s):
    if isinstance(s, cudf.Series):
        return s.to_gpu_array(fillna=np.nan)
    else:
        return s.values


class _GeometryLike(Glyph):
    def __init__(self, geometry):
        self.geometry = geometry

    @property
    def ndims(self):
        return 1

    @property
    def inputs(self):
        return (self.geometry,)

    @property
    def geom_dtypes(self):
        from spatialpandas.geometry import GeometryDtype
        return (GeometryDtype,)

    def validate(self, in_dshape):
        if not isinstance(in_dshape[str(self.geometry)], self.geom_dtypes):
            raise ValueError(
                '{col} must be an array with one of the following types: {typs}'.format(
                    col=self.geometry,
                    typs=', '.join(typ.__name__ for typ in self.geom_dtypes)
                ))

    @property
    def x_label(self):
        return 'x'

    @property
    def y_label(self):
        return 'y'

    def required_columns(self):
        return [self.geometry]

    def compute_x_bounds(self, df):
        bounds = df[self.geometry].array.total_bounds_x
        return self.maybe_expand_bounds(bounds)

    def compute_y_bounds(self, df):
        bounds = df[self.geometry].array.total_bounds_y
        return self.maybe_expand_bounds(bounds)

    @memoize
    def compute_bounds_dask(self, ddf):
        total_bounds = ddf[self.geometry].total_bounds
        x_extents = (total_bounds[0], total_bounds[2])
        y_extents = (total_bounds[1], total_bounds[3])

        return (self.maybe_expand_bounds(x_extents),
                self.maybe_expand_bounds(y_extents))


class _PointLike(Glyph):
    """Shared methods between Point and Line"""
    def __init__(self, x, y):
        self.x = x
        self.y = y

    @property
    def ndims(self):
        return 1

    @property
    def inputs(self):
        return (self.x, self.y)

    def validate(self, in_dshape):
        if not isreal(in_dshape.measure[str(self.x)]):
            raise ValueError('x must be real')
        elif not isreal(in_dshape.measure[str(self.y)]):
            raise ValueError('y must be real')

    @property
    def x_label(self):
        return self.x

    @property
    def y_label(self):
        return self.y

    def required_columns(self):
        return [self.x, self.y]

    def compute_x_bounds(self, df):
        bounds = self._compute_bounds(df[self.x])
        return self.maybe_expand_bounds(bounds)

    def compute_y_bounds(self, df):
        bounds = self._compute_bounds(df[self.y])
        return self.maybe_expand_bounds(bounds)

    @memoize
    def compute_bounds_dask(self, ddf):

        r = ddf.map_partitions(lambda df: np.array([[
            np.nanmin(df[self.x].values).item(),
            np.nanmax(df[self.x].values).item(),
            np.nanmin(df[self.y].values).item(),
            np.nanmax(df[self.y].values).item()]]
        )).compute()

        x_extents = np.nanmin(r[:, 0]), np.nanmax(r[:, 1])
        y_extents = np.nanmin(r[:, 2]), np.nanmax(r[:, 3])

        return (self.maybe_expand_bounds(x_extents),
                self.maybe_expand_bounds(y_extents))


[docs]class Point(_PointLike): """A point, with center at ``x`` and ``y``. Points map each record to a single bin. Points falling exactly on the upper bounds are treated as a special case, mapping into the previous bin rather than being cropped off. Parameters ---------- x, y : str Column names for the x and y coordinates of each point. """ @memoize def _build_extend(self, x_mapper, y_mapper, info, append): x_name = self.x y_name = self.y @ngjit @self.expand_aggs_and_cols(append) def _perform_extend_points(i, sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols): x = xs[i] y = ys[i] # points outside bounds are dropped; remainder # are mapped onto pixels if (xmin <= x <= xmax) and (ymin <= y <= ymax): xx = int(x_mapper(x) * sx + tx) yy = int(y_mapper(y) * sy + ty) xi, yi = (xx - 1 if x == xmax else xx, yy - 1 if y == ymax else yy) append(i, xi, yi, *aggs_and_cols) @ngjit @self.expand_aggs_and_cols(append) def extend_cpu(sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols): for i in range(xs.shape[0]): _perform_extend_points(i, sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols) @cuda.jit @self.expand_aggs_and_cols(append) def extend_cuda(sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols): i = cuda.grid(1) if i < xs.shape[0]: _perform_extend_points(i, sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols) def extend(aggs, df, vt, bounds): aggs_and_cols = aggs + info(df) sx, tx, sy, ty = vt xmin, xmax, ymin, ymax = bounds if cudf and isinstance(df, cudf.DataFrame): xs = df[x_name].to_gpu_array(fillna=np.nan) ys = df[y_name].to_gpu_array(fillna=np.nan) do_extend = extend_cuda[cuda_args(xs.shape[0])] else: xs = df[x_name].values ys = df[y_name].values do_extend = extend_cpu do_extend( sx, tx, sy, ty, xmin, xmax, ymin, ymax, xs, ys, *aggs_and_cols ) return extend
class MultiPointGeometry(_GeometryLike): @property def geom_dtypes(self): from spatialpandas.geometry import PointDtype, MultiPointDtype return PointDtype, MultiPointDtype @memoize def _build_extend(self, x_mapper, y_mapper, info, append): geometry_name = self.geometry @ngjit @self.expand_aggs_and_cols(append) def _perform_extend_points( i, j, sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, *aggs_and_cols ): x = values[j] y = values[j + 1] # points outside bounds are dropped; remainder # are mapped onto pixels if (xmin <= x <= xmax) and (ymin <= y <= ymax): xx = int(x_mapper(x) * sx + tx) yy = int(y_mapper(y) * sy + ty) xi, yi = (xx - 1 if x == xmax else xx, yy - 1 if y == ymax else yy) append(i, xi, yi, *aggs_and_cols) @ngjit @self.expand_aggs_and_cols(append) def extend_point_cpu( sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, missing, eligible_inds, *aggs_and_cols ): for i in eligible_inds: if missing[i] is True: continue _perform_extend_points( i, 2 * i, sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, *aggs_and_cols ) @ngjit @self.expand_aggs_and_cols(append) def extend_multipoint_cpu( sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, missing, offsets, eligible_inds, *aggs_and_cols ): for i in eligible_inds: if missing[i] is True: continue start = offsets[i] stop = offsets[i + 1] for j in range(start, stop, 2): _perform_extend_points( i, j, sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, *aggs_and_cols ) def extend(aggs, df, vt, bounds): from spatialpandas.geometry import PointArray aggs_and_cols = aggs + info(df) sx, tx, sy, ty = vt xmin, xmax, ymin, ymax = bounds geometry = df[geometry_name].array # Compute indices of potentially intersecting polygons using # geometry's R-tree eligible_inds = geometry.sindex.intersects((xmin, ymin, xmax, ymax)) missing = geometry.isna() if isinstance(geometry, PointArray): values = geometry.flat_values extend_point_cpu( sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, missing, eligible_inds, *aggs_and_cols ) else: values = geometry.buffer_values offsets = geometry.buffer_offsets[0] extend_multipoint_cpu( sx, tx, sy, ty, xmin, xmax, ymin, ymax, values, missing, offsets, eligible_inds, *aggs_and_cols ) return extend