Source code for datashader.core

from __future__ import absolute_import, division, print_function

from numbers import Number
from math import log10

import numpy as np
import pandas as pd
import dask.dataframe as dd
import dask.array as da
from six import string_types
from xarray import DataArray, Dataset
from collections import OrderedDict

from datashader.spatial.points import SpatialPointsFrame
from .utils import Dispatcher, ngjit, calc_res, calc_bbox, orient_array, \
    compute_coords, dshape_from_xarray_dataset
from .utils import get_indices, dshape_from_pandas, dshape_from_dask
from .utils import Expr # noqa (API import)
from .resampling import resample_2d, resample_2d_distributed
from . import reductions as rd


class Axis(object):
    """Interface for implementing axis transformations.

    Instances hold implementations of transformations to and from axis space.
    The default implementation is equivalent to:

    >>> def forward_transform(data_x):
    ...     scale * mapper(data_x) + t
    >>> def inverse_transform(axis_x):
    ...     inverse_mapper((axis_x - t)/s)

    Where ``mapper`` and ``inverse_mapper`` are elementwise functions mapping
    to and from axis-space respectively, and ``scale`` and ``transform`` are
    parameters describing a linear scale and translate transformation, computed
    by the ``compute_scale_and_translate`` method.
    """

    def compute_scale_and_translate(self, range, n):
        """Compute the scale and translate parameters for a linear transformation
        ``output = s * input + t``, mapping from data space to axis space.

        Parameters
        ----------
        range : tuple
            A tuple representing the range ``[min, max]`` along the axis, in
            data space. Both min and max are inclusive.
        n : int
            The number of bins along the axis.

        Returns
        -------
        s, t : floats
        """
        start, end = map(self.mapper, range)
        s = n/(end - start)
        t = -start * s
        return s, t

    def compute_index(self, st, n):
        """Compute a 1D array representing the axis index.

        Parameters
        ----------
        st : tuple
            A tuple of ``(scale, translate)`` parameters.
        n : int
            The number of bins along the dimension.

        Returns
        -------
        index : ndarray
        """
        px = np.arange(n)+0.5
        s, t = st
        return self.inverse_mapper((px - t)/s)

    def mapper(val):
        """A mapping from data space to axis space"""
        raise NotImplementedError

    def inverse_mapper(val):
        """A mapping from axis space to data space"""
        raise NotImplementedError

    def validate(self, range):
        """Given a range (low,high), raise an error if the range is invalid for this axis"""
        pass


class LinearAxis(Axis):
    """A linear Axis"""
    @staticmethod
    @ngjit
    def mapper(val):
        return val

    @staticmethod
    @ngjit
    def inverse_mapper(val):
        return val


class LogAxis(Axis):
    """A base-10 logarithmic Axis"""
    @staticmethod
    @ngjit
    def mapper(val):
        return log10(val)

    @staticmethod
    @ngjit
    def inverse_mapper(val):
        y = 10 # temporary workaround for https://github.com/numba/numba/issues/3135 (numba 0.39.0)
        return y**val

    def validate(self, range):
        low, high = map(self.mapper, range)
        if not (np.isfinite(low) and np.isfinite(high)):
            raise ValueError('Range values must be >0 for a LogAxis')


_axis_lookup = {'linear': LinearAxis(), 'log': LogAxis()}


[docs]class Canvas(object): """An abstract canvas representing the space in which to bin. Parameters ---------- plot_width, plot_height : int, optional Width and height of the output aggregate in pixels. x_range, y_range : tuple, optional A tuple representing the bounds inclusive space ``[min, max]`` along the axis. x_axis_type, y_axis_type : str, optional The type of the axis. Valid options are ``'linear'`` [default], and ``'log'``. """ def __init__(self, plot_width=600, plot_height=600, x_range=None, y_range=None, x_axis_type='linear', y_axis_type='linear'): self.plot_width = plot_width self.plot_height = plot_height self.x_range = None if x_range is None else tuple(x_range) self.y_range = None if y_range is None else tuple(y_range) self.x_axis = _axis_lookup[x_axis_type] self.y_axis = _axis_lookup[y_axis_type] def points(self, source, x, y, agg=None): """Compute a reduction by pixel, mapping data to pixels as points. Parameters ---------- source : pandas.DataFrame, dask.DataFrame, or xarray.DataArray/Dataset The input datasource. x, y : str Column names for the x and y coordinates of each point. agg : Reduction, optional Reduction to compute. Default is ``count()``. """ from .glyphs import Point from .reductions import count as count_rdn if agg is None: agg = count_rdn() if (isinstance(source, SpatialPointsFrame) and source.spatial is not None and source.spatial.x == x and source.spatial.y == y and self.x_range is not None and self.y_range is not None): source = source.spatial_query( x_range=self.x_range, y_range=self.y_range) return bypixel(source, self, Point(x, y), agg) def line(self, source, x, y, agg=None, axis=0): """Compute a reduction by pixel, mapping data to pixels as one or more lines. For aggregates that take in extra fields, the interpolated bins will receive the fields from the previous point. In pseudocode: >>> for i in range(len(rows) - 1): # doctest: +SKIP ... row0 = rows[i] ... row1 = rows[i + 1] ... for xi, yi in interpolate(row0.x, row0.y, row1.x, row1.y): ... add_to_aggregate(xi, yi, row0) Parameters ---------- source : pandas.DataFrame, dask.DataFrame, or xarray.DataArray/Dataset The input datasource. x, y : str or number or list or tuple or np.ndarray Specification of the x and y coordinates of each vertex * str or number: Column labels in source * list or tuple: List or tuple of column labels in source * np.ndarray: When axis=1, a literal array of the coordinates to be used for every row agg : Reduction, optional Reduction to compute. Default is ``any()``. axis : 0 or 1, default 0 Axis in source to draw lines along * 0: Draw lines using data from the specified columns across all rows in source * 1: Draw one line per row in source using data from the specified columns Examples -------- Define a canvas and a pandas DataFrame with 6 rows >>> import pandas as pd # doctest: +SKIP ... import numpy as np ... import datashader as ds ... from datashader import Canvas ... import datashader.transfer_functions as tf ... cvs = Canvas() ... df = pd.DataFrame({ ... 'A1': [1, 1.5, 2, 2.5, 3, 4], ... 'A2': [1.5, 2, 3, 3.2, 4, 5], ... 'B1': [10, 12, 11, 14, 13, 15], ... 'B2': [11, 9, 10, 7, 8, 12], ... }, dtype='float64') Aggregate one line across all rows, with coordinates df.A1 by df.B1 >>> agg = cvs.line(df, x='A1', y='B1', axis=0) # doctest: +SKIP ... tf.spread(tf.shade(agg)) Aggregate two lines across all rows. The first with coordinates df.A1 by df.B1 and the second with coordinates df.A2 by df.B2 >>> agg = cvs.line(df, x=['A1', 'A2'], y=['B1', 'B2'], axis=0) # doctest: +SKIP ... tf.spread(tf.shade(agg)) Aggregate two lines across all rows where the lines share the same x coordinates. The first line will have coordinates df.A1 by df.B1 and the second will have coordinates df.A1 by df.B2 >>> agg = cvs.line(df, x='A1', y=['B1', 'B2'], axis=0) # doctest: +SKIP ... tf.spread(tf.shade(agg)) Aggregate 6 length-2 lines, one per row, where the ith line has coordinates [df.A1[i], df.A2[i]] by [df.B1[i], df.B2[i]] >>> agg = cvs.line(df, x=['A1', 'A2'], y=['B1', 'B2'], axis=1) # doctest: +SKIP ... tf.spread(tf.shade(agg)) Aggregate 6 length-4 lines, one per row, where the x coordinates of every line are [0, 1, 2, 3] and the y coordinates of the ith line are [df.A1[i], df.A2[i], df.B1[i], df.B2[i]]. >>> agg = cvs.line(df, # doctest: +SKIP ... x=np.arange(4), ... y=['A1', 'A2', 'B1', 'B2'], ... axis=1) ... tf.spread(tf.shade(agg)) Aggregate RaggedArrays of variable length lines, one per row (requires pandas >= 0.24.0) >>> df_ragged = pd.DataFrame({ # doctest: +SKIP ... 'A1': pd.array([ ... [1, 1.5], [2, 2.5, 3], [1.5, 2, 3, 4], [3.2, 4, 5]], ... dtype='Ragged[float32]'), ... 'B1': pd.array([ ... [10, 12], [11, 14, 13], [10, 7, 9, 10], [7, 8, 12]], ... dtype='Ragged[float32]'), ... 'group': pd.Categorical([0, 1, 2, 1]) ... }) ... ... agg = cvs.line(df_ragged, x='A1', y='B1', axis=1) ... tf.spread(tf.shade(agg)) Aggregate RaggedArrays of variable length lines by group column, one per row (requires pandas >= 0.24.0) >>> agg = cvs.line(df_ragged, x='A1', y='B1', # doctest: +SKIP ... agg=ds.count_cat('group'), axis=1) ... tf.spread(tf.shade(agg)) """ from .glyphs import (LineAxis0, LinesAxis1, LinesAxis1XConstant, LinesAxis1YConstant, LineAxis0Multi, LinesAxis1Ragged) from .reductions import any as any_rdn if agg is None: agg = any_rdn() # Broadcast column specifications to handle cases where # x is a list and y is a string or vice versa orig_x, orig_y = x, y x, y = _broadcast_column_specifications(x, y) if axis == 0: if (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types))): glyph = LineAxis0(x, y) elif (isinstance(x, (list, tuple)) and isinstance(y, (list, tuple))): glyph = LineAxis0Multi(tuple(x), tuple(y)) else: raise ValueError(""" Invalid combination of x and y arguments to Canvas.line when axis=0. Received: x: {x} y: {y} See docstring for more information on valid usage""".format( x=repr(orig_x), y=repr(orig_y))) elif axis == 1: if isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)): glyph = LinesAxis1(tuple(x), tuple(y)) elif (isinstance(x, np.ndarray) and isinstance(y, (list, tuple))): glyph = LinesAxis1XConstant(x, tuple(y)) elif (isinstance(x, (list, tuple)) and isinstance(y, np.ndarray)): glyph = LinesAxis1YConstant(tuple(x), y) elif (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types))): glyph = LinesAxis1Ragged(x, y) else: raise ValueError(""" Invalid combination of x and y arguments to Canvas.line when axis=1. Received: x: {x} y: {y} See docstring for more information on valid usage""".format( x=repr(orig_x), y=repr(orig_y))) else: raise ValueError(""" The axis argument to Canvas.line must be 0 or 1 Received: {axis}""".format(axis=axis)) return bypixel(source, self, glyph, agg) def area(self, source, x, y, agg=None, axis=0, y_stack=None): """Compute a reduction by pixel, mapping data to pixels as a filled area region Parameters ---------- source : pandas.DataFrame, dask.DataFrame, or xarray.DataArray/Dataset The input datasource. x, y : str or number or list or tuple or np.ndarray Specification of the x and y coordinates of each vertex of the line defining the starting edge of the area region. * str or number: Column labels in source * list or tuple: List or tuple of column labels in source * np.ndarray: When axis=1, a literal array of the coordinates to be used for every row agg : Reduction, optional Reduction to compute. Default is ``count()``. axis : 0 or 1, default 0 Axis in source to draw lines along * 0: Draw area regions using data from the specified columns across all rows in source * 1: Draw one area region per row in source using data from the specified columns y_stack: str or number or list or tuple or np.ndarray or None Specification of the y coordinates of each vertex of the line defining the ending edge of the area region, where the x coordinate is given by the x argument described above. If y_stack is None, then the area region is filled to the y=0 line If y_stack is not None, then the form of y_stack must match the form of y. Examples -------- Define a canvas and a pandas DataFrame with 6 rows >>> import pandas as pd # doctest: +SKIP ... import numpy as np ... import datashader as ds ... from datashader import Canvas ... import datashader.transfer_functions as tf ... cvs = Canvas() ... df = pd.DataFrame({ ... 'A1': [1, 1.5, 2, 2.5, 3, 4], ... 'A2': [1.6, 2.1, 2.9, 3.2, 4.2, 5], ... 'B1': [10, 12, 11, 14, 13, 15], ... 'B2': [11, 9, 10, 7, 8, 12], ... }, dtype='float64') Aggregate one area region across all rows, that starts with coordinates df.A1 by df.B1 and is filled to the y=0 line >>> agg = cvs.area(df, x='A1', y='B1', # doctest: +SKIP ... agg=ds.count(), axis=0) ... tf.shade(agg) Aggregate one area region across all rows, that starts with coordinates df.A1 by df.B1 and is filled to the line with coordinates df.A1 by df.B2 >>> agg = cvs.area(df, x='A1', y='B1', y_stack='B2', # doctest: +SKIP ... agg=ds.count(), axis=0) ... tf.shade(agg) Aggregate two area regions across all rows. The first starting with coordinates df.A1 by df.B1 and the second with coordinates df.A2 by df.B2. Both regions are filled to the y=0 line >>> agg = cvs.area(df, x=['A1', 'A2'], y=['B1', 'B2'], agg=ds.count(), axis=0) # doctest: +SKIP ... tf.shade(agg) Aggregate two area regions across all rows where the regions share the same x coordinates. The first region will start with coordinates df.A1 by df.B1 and the second will start with coordinates df.A1 by df.B2. Both regions are filled to the y=0 line >>> agg = cvs.area(df, x='A1', y=['B1', 'B2'], agg=ds.count(), axis=0) # doctest: +SKIP ... tf.shade(agg) Aggregate 6 length-2 area regions, one per row, where the ith region starts with coordinates [df.A1[i], df.A2[i]] by [df.B1[i], df.B2[i]] and is filled to the y=0 line >>> agg = cvs.area(df, x=['A1', 'A2'], y=['B1', 'B2'], agg=ds.count(), axis=1) # doctest: +SKIP ... tf.shade(agg) Aggregate 6 length-4 area regions, one per row, where the starting x coordinates of every region are [0, 1, 2, 3] and the starting y coordinates of the ith region are [df.A1[i], df.A2[i], df.B1[i], df.B2[i]]. All regions are filled to the y=0 line >>> agg = cvs.area(df, # doctest: +SKIP ... x=np.arange(4), ... y=['A1', 'A2', 'B1', 'B2'], ... agg=ds.count(), ... axis=1) ... tf.shade(agg) Aggregate RaggedArrays of variable length area regions, one per row. The starting coordinates of the ith region are df_ragged.A1 by df_ragged.B1 and the regions are filled to the y=0 line. (requires pandas >= 0.24.0) >>> df_ragged = pd.DataFrame({ # doctest: +SKIP ... 'A1': pd.array([ ... [1, 1.5], [2, 2.5, 3], [1.5, 2, 3, 4], [3.2, 4, 5]], ... dtype='Ragged[float32]'), ... 'B1': pd.array([ ... [10, 12], [11, 14, 13], [10, 7, 9, 10], [7, 8, 12]], ... dtype='Ragged[float32]'), ... 'B2': pd.array([ ... [6, 10], [9, 10, 18], [9, 5, 6, 8], [4, 5, 11]], ... dtype='Ragged[float32]'), ... 'group': pd.Categorical([0, 1, 2, 1]) ... }) ... ... agg = cvs.area(df_ragged, x='A1', y='B1', agg=ds.count(), axis=1) ... tf.shade(agg) Instead of filling regions to the y=0 line, fill to the line with coordinates df_ragged.A1 by df_ragged.B2 >>> agg = cvs.area(df_ragged, x='A1', y='B1', y_stack='B2', # doctest: +SKIP ... agg=ds.count(), axis=1) ... tf.shade(agg) (requires pandas >= 0.24.0) """ from .glyphs import ( AreaToZeroAxis0, AreaToLineAxis0, AreaToZeroAxis0Multi, AreaToLineAxis0Multi, AreaToZeroAxis1, AreaToLineAxis1, AreaToZeroAxis1XConstant, AreaToLineAxis1XConstant, AreaToZeroAxis1YConstant, AreaToLineAxis1YConstant, AreaToZeroAxis1Ragged, AreaToLineAxis1Ragged, ) from .reductions import any as any_rdn if agg is None: agg = any_rdn() # Broadcast column specifications to handle cases where # x is a list and y is a string or vice versa orig_x, orig_y, orig_y_stack = x, y, y_stack x, y, y_stack = _broadcast_column_specifications(x, y, y_stack) if axis == 0: if y_stack is None: if (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types))): glyph = AreaToZeroAxis0(x, y) elif (isinstance(x, (list, tuple)) and isinstance(y, (list, tuple))): glyph = AreaToZeroAxis0Multi(tuple(x), tuple(y)) else: raise ValueError(""" Invalid combination of x and y arguments to Canvas.area when axis=0. Received: x: {x} y: {y} See docstring for more information on valid usage""".format( x=repr(x), y=repr(y))) else: # y_stack is not None if (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types)) and isinstance(y_stack, (Number, string_types))): glyph = AreaToLineAxis0(x, y, y_stack) elif (isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)) and isinstance(y_stack, (list, tuple))): glyph = AreaToLineAxis0Multi( tuple(x), tuple(y), tuple(y_stack)) else: raise ValueError(""" Invalid combination of x, y, and y_stack arguments to Canvas.area when axis=0. Received: x: {x} y: {y} y_stack: {y_stack} See docstring for more information on valid usage""".format( x=repr(orig_x), y=repr(orig_y), y_stack=repr(orig_y_stack))) elif axis == 1: if y_stack is None: if (isinstance(x, (list, tuple)) and isinstance(y, (list, tuple))): glyph = AreaToZeroAxis1(tuple(x), tuple(y)) elif (isinstance(x, np.ndarray) and isinstance(y, (list, tuple))): glyph = AreaToZeroAxis1XConstant(x, tuple(y)) elif (isinstance(x, (list, tuple)) and isinstance(y, np.ndarray)): glyph = AreaToZeroAxis1YConstant(tuple(x), y) elif (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types))): glyph = AreaToZeroAxis1Ragged(x, y) else: raise ValueError(""" Invalid combination of x and y arguments to Canvas.area when axis=1. Received: x: {x} y: {y} See docstring for more information on valid usage""".format( x=repr(x), y=repr(y))) else: if (isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)) and isinstance(y_stack, (list, tuple))): glyph = AreaToLineAxis1( tuple(x), tuple(y), tuple(y_stack)) elif (isinstance(x, np.ndarray) and isinstance(y, (list, tuple)) and isinstance(y_stack, (list, tuple))): glyph = AreaToLineAxis1XConstant( x, tuple(y), tuple(y_stack)) elif (isinstance(x, (list, tuple)) and isinstance(y, np.ndarray) and isinstance(y_stack, np.ndarray)): glyph = AreaToLineAxis1YConstant(tuple(x), y, y_stack) elif (isinstance(x, (Number, string_types)) and isinstance(y, (Number, string_types)) and isinstance(y_stack, (Number, string_types))): glyph = AreaToLineAxis1Ragged(x, y, y_stack) else: raise ValueError(""" Invalid combination of x, y, and y_stack arguments to Canvas.area when axis=1. Received: x: {x} y: {y} y_stack: {y_stack} See docstring for more information on valid usage""".format( x=repr(orig_x), y=repr(orig_y), y_stack=repr(orig_y_stack))) else: raise ValueError(""" The axis argument to Canvas.line must be 0 or 1 Received: {axis}""".format(axis=axis)) return bypixel(source, self, glyph, agg) def quadmesh(self, source, x=None, y=None, agg=None): """Samples a recti- or curvi-linear quadmesh by canvas size and bounds. Parameters ---------- source : xarray.DataArray or Dataset The input datasource. x, y : str Column names for the x and y coordinates of each point. agg : Reduction, optional Reduction to compute. Default is ``mean()``. Returns ------- data : xarray.DataArray """ from .glyphs import QuadMeshRectilinear, QuadMeshCurvialinear # Determine reduction operation from .reductions import mean as mean_rnd if isinstance(source, Dataset): if agg is None or agg.column is None: name = list(source.data_vars)[0] else: name = agg.column # Keep as dataset so that source[agg.column] works source = source[[name]] elif isinstance(source, DataArray): # Make dataset so that source[agg.column] works name = source.name source = source.to_dataset() else: raise ValueError("Invalid input type") if agg is None: agg = mean_rnd(name) if x is None and y is None: y, x = source[name].dims elif not x or not y: raise ValueError("Either specify both x and y coordinates" "or allow them to be inferred.") yarr, xarr = source[y], source[x] if (yarr.ndim > 1 or xarr.ndim > 1) and xarr.dims != yarr.dims: raise ValueError("Ensure that x- and y-coordinate arrays " "share the same dimensions. x-coordinates " "are indexed by %s dims while " "y-coordinates are indexed by %s dims." % (xarr.dims, yarr.dims)) if (name is not None and agg.column is not None and agg.column != name): raise ValueError('DataArray name %r does not match ' 'supplied reduction %s.' % (source.name, agg)) if xarr.ndim == 1: glyph = QuadMeshRectilinear(x, y, name) elif xarr.ndim == 2: glyph = QuadMeshCurvialinear(x, y, name) else: raise ValueError("""\ x- and y-coordinate arrays must have 1 or 2 dimensions. Received arrays with dimensions: {dims}""".format( dims=list(xarr.dims))) return bypixel(source, self, glyph, agg) # TODO re 'untested', below: Consider replacing with e.g. a 3x3 # array in the call to Canvas (plot_height=3,plot_width=3), then # show the output as a numpy array that has a compact # representation def trimesh(self, vertices, simplices, mesh=None, agg=None, interp=True, interpolate=None): """Compute a reduction by pixel, mapping data to pixels as a triangle. >>> import datashader as ds >>> verts = pd.DataFrame({'x': [0, 5, 10], ... 'y': [0, 10, 0], ... 'weight': [1, 5, 3]}, ... columns=['x', 'y', 'weight']) >>> tris = pd.DataFrame({'v0': [2], 'v1': [0], 'v2': [1]}, ... columns=['v0', 'v1', 'v2']) >>> cvs = ds.Canvas(x_range=(verts.x.min(), verts.x.max()), ... y_range=(verts.y.min(), verts.y.max())) >>> untested = cvs.trimesh(verts, tris) Parameters ---------- vertices : pandas.DataFrame, dask.DataFrame The input datasource for triangle vertex coordinates. These can be interpreted as the x/y coordinates of the vertices, with optional weights for value interpolation. Columns should be ordered corresponding to 'x', 'y', followed by zero or more (optional) columns containing vertex values. The rows need not be ordered. The column data types must be floating point or integer. simplices : pandas.DataFrame, dask.DataFrame The input datasource for triangle (simplex) definitions. These can be interpreted as rows of ``vertices``, aka positions in the ``vertices`` index. Columns should be ordered corresponding to 'vertex0', 'vertex1', and 'vertex2'. Order of the vertices can be clockwise or counter-clockwise; it does not matter as long as the data is consistent for all simplices in the dataframe. The rows need not be ordered. The data type for the first three columns in the dataframe must be integer. agg : Reduction, optional Reduction to compute. Default is ``mean()``. mesh : pandas.DataFrame, optional An ordered triangle mesh in tabular form, used for optimization purposes. This dataframe is expected to have come from ``datashader.utils.mesh()``. If this argument is not None, the first two arguments are ignored. interpolate : str, optional default=linear Method to use for interpolation between specified values. ``nearest`` means to use a single value for the whole triangle, and ``linear`` means to do bilinear interpolation of the pixels within each triangle (a weighted average of the vertex values). For backwards compatibility, also accepts ``interp=True`` for ``linear`` and ``interp=False`` for ``nearest``. """ from .glyphs import Triangles from .reductions import mean as mean_rdn from .utils import mesh as create_mesh source = mesh # 'interp' argument is deprecated as of datashader=0.6.4 if interpolate is not None: if interpolate == 'linear': interp = True elif interpolate == 'nearest': interp = False else: raise ValueError('Invalid interpolate method: options include {}'.format(['linear','nearest'])) # Validation is done inside the [pd]d_mesh utility functions if source is None: source = create_mesh(vertices, simplices) verts_have_weights = len(vertices.columns) > 2 if verts_have_weights: weight_col = vertices.columns[2] else: weight_col = simplices.columns[3] if agg is None: agg = mean_rdn(weight_col) elif agg.column is None: agg.column = weight_col cols = source.columns x, y, weights = cols[0], cols[1], cols[2:] return bypixel(source, self, Triangles(x, y, weights, weight_type=verts_have_weights, interp=interp), agg) def raster(self, source, layer=None, upsample_method='linear', # Deprecated as of datashader=0.6.4 downsample_method=rd.mean(), # Deprecated as of datashader=0.6.4 nan_value=None, agg=None, interpolate=None, chunksize=None, max_mem=None): """Sample a raster dataset by canvas size and bounds. Handles 2D or 3D xarray DataArrays, assuming that the last two array dimensions are the y- and x-axis that are to be resampled. If a 3D array is supplied a layer may be specified to resample to select the layer along the first dimension to resample. Missing values (those having the value indicated by the "nodata" attribute of the raster) are replaced with `NaN` if floats, and 0 if int. Also supports resampling out-of-core DataArrays backed by dask Arrays. By default it will try to maintain the same chunksize in the output array but a custom chunksize may be provided. If there are memory constraints they may be defined using the max_mem parameter, which determines how large the chunks in memory may be. Parameters ---------- source : xarray.DataArray or xr.Dataset 2D or 3D labelled array (if Dataset, the agg reduction must define the data variable). layer : float For a 3D array, value along the z dimension : optional default=None ds_method : str (optional) Grid cell aggregation method for a possible downsampling. us_method : str (optional) Grid cell interpolation method for a possible upsampling. nan_value : int or float, optional Optional nan_value which will be masked out when applying the resampling. agg : Reduction, optional default=mean() Resampling mode when downsampling raster. options include: first, last, mean, mode, var, std, min, max Accepts an executable function, function object, or string name. interpolate : str, optional default=linear Resampling mode when upsampling raster. options include: nearest, linear. chunksize : tuple(int, int) (optional) Size of the output chunks. By default this the chunk size is inherited from the *src* array. max_mem : int (optional) The maximum number of bytes that should be loaded into memory during the regridding operation. Returns ------- data : xarray.Dataset """ # For backwards compatibility if agg is None: agg=downsample_method if interpolate is None: interpolate=upsample_method upsample_methods = ['nearest','linear'] downsample_methods = {'first':'first', rd.first:'first', 'last':'last', rd.last:'last', 'mode':'mode', rd.mode:'mode', 'mean':'mean', rd.mean:'mean', 'var':'var', rd.var:'var', 'std':'std', rd.std:'std', 'min':'min', rd.min:'min', 'max':'max', rd.max:'max'} if interpolate not in upsample_methods: raise ValueError('Invalid interpolate method: options include {}'.format(upsample_methods)) if not isinstance(source, (DataArray, Dataset)): raise ValueError('Expected xarray DataArray or Dataset as ' 'the data source, found %s.' % type(source).__name__) column = None if isinstance(agg, rd.Reduction): agg, column = type(agg), agg.column if (isinstance(source, DataArray) and column is not None and source.name != column): agg_repr = '%s(%r)' % (agg.__name__, column) raise ValueError('DataArray name %r does not match ' 'supplied reduction %s.' % (source.name, agg_repr)) if isinstance(source, Dataset): data_vars = list(source.data_vars) if column is None: raise ValueError('When supplying a Dataset the agg reduction ' 'must specify the variable to aggregate. ' 'Available data_vars include: %r.' % data_vars) elif column not in source.data_vars: raise KeyError('Supplied reduction column %r not found ' 'in Dataset, expected one of the following ' 'data variables: %r.' % (column, data_vars)) source = source[column] if agg not in downsample_methods.keys(): raise ValueError('Invalid aggregation method: options include {}'.format(list(downsample_methods.keys()))) ds_method = downsample_methods[agg] if source.ndim not in [2, 3]: raise ValueError('Raster aggregation expects a 2D or 3D ' 'DataArray, found %s dimensions' % source.ndim) res = calc_res(source) ydim, xdim = source.dims[-2:] xvals, yvals = source[xdim].values, source[ydim].values left, bottom, right, top = calc_bbox(xvals, yvals, res) if layer is not None: source=source.sel(**{source.dims[0]: layer}) array = orient_array(source, res) dtype = array.dtype if nan_value is not None: mask = array==nan_value array = np.ma.masked_array(array, mask=mask, fill_value=nan_value) fill_value = nan_value else: fill_value = np.NaN if self.x_range is None: self.x_range = (left,right) if self.y_range is None: self.y_range = (bottom,top) # window coordinates xmin = max(self.x_range[0], left) ymin = max(self.y_range[0], bottom) xmax = min(self.x_range[1], right) ymax = min(self.y_range[1], top) width_ratio = min((xmax - xmin) / (self.x_range[1] - self.x_range[0]), 1) height_ratio = min((ymax - ymin) / (self.y_range[1] - self.y_range[0]), 1) if np.isclose(width_ratio, 0) or np.isclose(height_ratio, 0): raise ValueError('Canvas x_range or y_range values do not match closely enough with the data source to be able to accurately rasterize. Please provide ranges that are more accurate.') w = max(int(round(self.plot_width * width_ratio)), 1) h = max(int(round(self.plot_height * height_ratio)), 1) cmin, cmax = get_indices(xmin, xmax, xvals, res[0]) rmin, rmax = get_indices(ymin, ymax, yvals, res[1]) kwargs = dict(w=w, h=h, ds_method=ds_method, us_method=interpolate, fill_value=fill_value) if array.ndim == 2: source_window = array[rmin:rmax+1, cmin:cmax+1] if ds_method in ['var', 'std']: source_window = source_window.astype('f') if isinstance(source_window, da.Array): data = resample_2d_distributed( source_window, chunksize=chunksize, max_mem=max_mem, **kwargs) else: data = resample_2d(source_window, **kwargs) layers = 1 else: source_window = array[:, rmin:rmax+1, cmin:cmax+1] if ds_method in ['var', 'std']: source_window = source_window.astype('f') arrays = [] for arr in source_window: if isinstance(arr, da.Array): arr = resample_2d_distributed( arr, chunksize=chunksize, max_mem=max_mem, **kwargs) else: arr = resample_2d(arr, **kwargs) arrays.append(arr) data = np.dstack(arrays) layers = len(arrays) if w != self.plot_width or h != self.plot_height: num_height = self.plot_height - h num_width = self.plot_width - w lpad = xmin - self.x_range[0] rpad = self.x_range[1] - xmax lpct = lpad / (lpad + rpad) if lpad + rpad > 0 else 0 left = max(int(np.ceil(num_width * lpct)), 0) right = max(num_width - left, 0) lshape, rshape = (self.plot_height, left), (self.plot_height, right) if layers > 1: lshape, rshape = lshape + (layers,), rshape + (layers,) left_pad = np.full(lshape, fill_value, source_window.dtype) right_pad = np.full(rshape, fill_value, source_window.dtype) tpad = ymin - self.y_range[0] bpad = self.y_range[1] - ymax tpct = tpad / (tpad + bpad) if tpad + bpad > 0 else 0 top = max(int(np.ceil(num_height * tpct)), 0) bottom = max(num_height - top, 0) tshape, bshape = (top, w), (bottom, w) if layers > 1: tshape, bshape = tshape + (layers,), bshape + (layers,) top_pad = np.full(tshape, fill_value, source_window.dtype) bottom_pad = np.full(bshape, fill_value, source_window.dtype) concat = da.concatenate if isinstance(data, da.Array) else np.concatenate if top_pad.shape[0] > 0: data = concat((top_pad, data, bottom_pad), axis=0) if left_pad.shape[1] > 0: data = concat((left_pad, data, right_pad), axis=1) # Reorient array to original orientation if res[1] > 0: data = data[::-1] if res[0] < 0: data = data[:, ::-1] # Restore nan_value from masked array if nan_value is not None: data = data.filled() # Restore original dtype if dtype != data.dtype: data = data.astype(dtype) # Compute DataArray metadata xs, ys = compute_coords(self.plot_width, self.plot_height, self.x_range, self.y_range, res) coords = {xdim: xs, ydim: ys} dims = [ydim, xdim] attrs = dict(res=res[0]) if source._file_obj is not None and hasattr(source._file_obj, 'nodata'): attrs['nodata'] = source._file_obj.nodata # Handle DataArray with layers if data.ndim == 3: data = data.transpose([2, 0, 1]) layer_dim = source.dims[0] coords[layer_dim] = source.coords[layer_dim] dims = [layer_dim]+dims return DataArray(data, coords=coords, dims=dims, attrs=attrs) def validate(self): """Check that parameter settings are valid for this object""" self.x_axis.validate(self.x_range) self.y_axis.validate(self.y_range)
def bypixel(source, canvas, glyph, agg): """Compute an aggregate grouped by pixel sized bins. Aggregate input data ``source`` into a grid with shape and axis matching ``canvas``, mapping data to bins by ``glyph``, and aggregating by reduction ``agg``. Parameters ---------- source : pandas.DataFrame, dask.DataFrame Input datasource canvas : Canvas glyph : Glyph agg : Reduction """ # Convert 1D xarray DataArrays and DataSets into Dask DataFrames if isinstance(source, DataArray) and source.ndim == 1: if not source.name: source.name = 'value' source = source.reset_coords() if isinstance(source, Dataset) and len(source.dims) == 1: columns = list(source.coords.keys()) + list(source.data_vars.keys()) cols_to_keep = _cols_to_keep(columns, glyph, agg) source = source.drop([col for col in columns if col not in cols_to_keep]) source = source.to_dask_dataframe() if isinstance(source, pd.DataFrame): # Avoid datashape.Categorical instantiation bottleneck # by only retaining the necessary columns: # https://github.com/bokeh/datashader/issues/396 # Preserve column ordering without duplicates cols_to_keep = _cols_to_keep(source.columns, glyph, agg) if len(cols_to_keep) < len(source.columns): source = source[cols_to_keep] dshape = dshape_from_pandas(source) elif isinstance(source, dd.DataFrame): dshape = dshape_from_dask(source) elif isinstance(source, Dataset): # Multi-dimensional Dataset dshape = dshape_from_xarray_dataset(source) else: raise ValueError("source must be a pandas or dask DataFrame") schema = dshape.measure glyph.validate(schema) agg.validate(schema) canvas.validate() # All-NaN objects (e.g. chunks of arrays with no data) are valid in Datashader with np.warnings.catch_warnings(): np.warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered') return bypixel.pipeline(source, schema, canvas, glyph, agg) def _cols_to_keep(columns, glyph, agg): cols_to_keep = OrderedDict({col: False for col in columns}) for col in glyph.required_columns(): cols_to_keep[col] = True if hasattr(agg, 'values'): for subagg in agg.values: if subagg.column is not None: cols_to_keep[subagg.column] = True elif agg.column is not None: cols_to_keep[agg.column] = True return [col for col, keepit in cols_to_keep.items() if keepit] def _broadcast_column_specifications(*args): lengths = {len(a) for a in args if isinstance(a, (list, tuple))} if len(lengths) != 1: # None of the inputs are lists/tuples, return them as is return args else: n = lengths.pop() return tuple( (arg,) * n if isinstance(arg, (Number, string_types)) else arg for arg in args ) bypixel.pipeline = Dispatcher()