from __future__ import annotations
from numbers import Number
from math import log10
import warnings
import numpy as np
import pandas as pd
import dask.dataframe as dd
import dask.array as da
from xarray import DataArray, Dataset
from collections import OrderedDict
from .utils import Dispatcher, ngjit, calc_res, calc_bbox, orient_array, \
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
try:
import cudf
except Exception:
cudf = None
try:
import dask_cudf
except Exception:
dask_cudf = None
try:
import spatialpandas
except Exception:
spatialpandas = None
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(float(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):
if range is None:
# Nothing to check if no range
return
if range[0] <= 0 or range[1] <= 0:
raise ValueError('Range values must be >0 for logarithmic axes')
_axis_lookup = {'linear': LinearAxis(), 'log': LogAxis()}
def validate_xy_or_geometry(glyph, x, y, geometry):
if (geometry is None and (x is None or y is None) or
geometry is not None and (x is not None or y is not None)):
raise ValueError("""
{glyph} coordinates may be specified by providing both the x and y arguments, or by
providing the geometry argument. Received:
x: {x}
y: {y}
geometry: {geometry}
""".format(glyph=glyph, x=repr(x), y=repr(y), geometry=repr(geometry)))
[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=None, y=None, agg=None, geometry=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. If provided,
the geometry argument may not also be provided.
agg : Reduction, optional
Reduction to compute. Default is ``count()``.
geometry: str
Column name of a PointsArray of the coordinates of each point. If provided,
the x and y arguments may not also be provided.
"""
from .glyphs import Point, MultiPointGeometry
from .reductions import count as count_rdn
validate_xy_or_geometry('Point', x, y, geometry)
if agg is None:
agg = count_rdn()
if geometry is None:
glyph = Point(x, y)
else:
if spatialpandas and isinstance(source, spatialpandas.dask.DaskGeoDataFrame):
# Downselect partitions to those that may contain points in viewport
x_range = self.x_range if self.x_range is not None else (None, None)
y_range = self.y_range if self.y_range is not None else (None, None)
source = source.cx_partitions[slice(*x_range), slice(*y_range)]
elif spatialpandas and isinstance(source, spatialpandas.GeoDataFrame):
pass
else:
raise ValueError(
"source must be an instance of spatialpandas.GeoDataFrame or \n"
"spatialpandas.dask.DaskGeoDataFrame.\n"
" Received value of type {typ}".format(typ=type(source)))
glyph = MultiPointGeometry(geometry)
return bypixel(source, self, glyph, agg)
def line(self, source, x=None, y=None, agg=None, axis=0, geometry=None,
line_width=0, antialias=False):
"""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
geometry : str
Column name of a LinesArray of the coordinates of each line. If provided,
the x and y arguments may not also be provided.
line_width : number, optional
Width of the line to draw, in pixels. If zero, the
default, lines are drawn using a simple algorithm with a
blocky single-pixel width based on whether the line passes
through each pixel or does not. If greater than one, lines
are drawn with the specified width using a slower and
more complex antialiasing algorithm with fractional values
along each edge, so that lines have a more uniform visual
appearance across all angles. Line widths between 0 and 1
effectively use a line_width of 1 pixel but with a
proportionate reduction in the strength of each pixel,
approximating the visual appearance of a subpixel line
width.
antialias : bool, optional
This option is kept for backward compatibility only.
``True`` is equivalent to ``line_width=1`` and
``False`` (the default) to ``line_width=0``. Do not specify
both ``antialias`` and ``line_width`` in the same call as a
``ValueError`` will be raised if they disagree.
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, LineAxis1Geometry)
validate_xy_or_geometry('Line', x, y, geometry)
if agg is None:
agg = rd.any()
if line_width is None:
line_width = 0
# Check and convert antialias kwarg to line_width.
if antialias and line_width != 0:
raise ValueError(
"Do not specify values for both the line_width and \n"
"antialias keyword arguments; use line_width instead.")
if antialias:
line_width = 1.0
if geometry is not None:
if spatialpandas and isinstance(source, spatialpandas.dask.DaskGeoDataFrame):
# Downselect partitions to those that may contain lines in viewport
x_range = self.x_range if self.x_range is not None else (None, None)
y_range = self.y_range if self.y_range is not None else (None, None)
source = source.cx_partitions[slice(*x_range), slice(*y_range)]
elif spatialpandas and isinstance(source, spatialpandas.GeoDataFrame):
pass
else:
raise ValueError(
"source must be an instance of spatialpandas.GeoDataFrame or \n"
"spatialpandas.dask.DaskGeoDataFrame.\n"
" Received value of type {typ}".format(typ=type(source)))
glyph = LineAxis1Geometry(geometry)
else:
# 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, str)) and
isinstance(y, (Number, str))):
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, str)) and
isinstance(y, (Number, str))):
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))
if (line_width > 0 and ((cudf and isinstance(source, cudf.DataFrame)) or
(dask_cudf and isinstance(source, dask_cudf.DataFrame)))):
warnings.warn(
"Antialiased lines are not supported for CUDA-backed sources, "
"so reverting to line_width=0")
line_width = 0
glyph.set_line_width(line_width)
if glyph.antialiased:
# This is required to identify and report use of reductions that do
# not yet support antialiasing.
non_cat_agg = agg
if isinstance(non_cat_agg, rd.by):
non_cat_agg = non_cat_agg.reduction
if not isinstance(non_cat_agg, (
rd.any, rd.count, rd.max, rd.min, rd.sum, rd.summary, rd._sum_zero,
rd.first, rd.last, rd.mean
)):
raise NotImplementedError(
f"{type(non_cat_agg)} reduction not implemented for antialiased lines")
return bypixel(source, self, glyph, agg, antialias=glyph.antialiased)
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, str)) and
isinstance(y, (Number, str))):
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, str)) and
isinstance(y, (Number, str)) and
isinstance(y_stack, (Number, str))):
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, str)) and
isinstance(y, (Number, str))):
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, str)) and
isinstance(y, (Number, str)) and
isinstance(y_stack, (Number, str))):
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.area must be 0 or 1
Received: {axis}""".format(axis=axis))
return bypixel(source, self, glyph, agg)
def polygons(self, source, geometry, agg=None):
"""Compute a reduction by pixel, mapping data to pixels as one or
more filled polygons.
Parameters
----------
source : xarray.DataArray or Dataset
The input datasource.
geometry : str
Column name of a PolygonsArray of the coordinates of each line.
agg : Reduction, optional
Reduction to compute. Default is ``any()``.
Returns
-------
data : xarray.DataArray
Examples
--------
>>> import datashader as ds # doctest: +SKIP
... import datashader.transfer_functions as tf
... from spatialpandas.geometry import PolygonArray
... from spatialpandas import GeoDataFrame
... import pandas as pd
...
... polygons = PolygonArray([
... # First Element
... [[0, 0, 1, 0, 2, 2, -1, 4, 0, 0], # Filled quadrilateral (CCW order)
... [0.5, 1, 1, 2, 1.5, 1.5, 0.5, 1], # Triangular hole (CW order)
... [0, 2, 0, 2.5, 0.5, 2.5, 0.5, 2, 0, 2], # Rectangular hole (CW order)
... [2.5, 3, 3.5, 3, 3.5, 4, 2.5, 3], # Filled triangle
... ],
...
... # Second Element
... [[3, 0, 3, 2, 4, 2, 4, 0, 3, 0], # Filled rectangle (CCW order)
... # Rectangular hole (CW order)
... [3.25, 0.25, 3.75, 0.25, 3.75, 1.75, 3.25, 1.75, 3.25, 0.25],
... ]
... ])
...
... df = GeoDataFrame({'polygons': polygons, 'v': range(len(polygons))})
...
... cvs = ds.Canvas()
... agg = cvs.polygons(df, geometry='polygons', agg=ds.sum('v'))
... tf.shade(agg)
"""
from .glyphs import PolygonGeom
from .reductions import any as any_rdn
if spatialpandas and isinstance(source, spatialpandas.dask.DaskGeoDataFrame):
# Downselect partitions to those that may contain polygons in viewport
x_range = self.x_range if self.x_range is not None else (None, None)
y_range = self.y_range if self.y_range is not None else (None, None)
source = source.cx_partitions[slice(*x_range), slice(*y_range)]
elif spatialpandas and isinstance(source, spatialpandas.GeoDataFrame):
pass
else:
raise ValueError(
"source must be an instance of spatialpandas.GeoDataFrame or \n"
"spatialpandas.dask.DaskGeoDataFrame.\n"
" Received value of type {typ}".format(typ=type(source)))
if agg is None:
agg = any_rdn()
glyph = PolygonGeom(geometry)
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()``. Note that agg is ignored when
upsampling.
Returns
-------
data : xarray.DataArray
"""
from .glyphs import QuadMeshRaster, QuadMeshRectilinear, QuadMeshCurvilinear
# 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:
xaxis_linear = self.x_axis is _axis_lookup["linear"]
yaxis_linear = self.y_axis is _axis_lookup["linear"]
even_yspacing = np.allclose(
yarr, np.linspace(yarr[0], yarr[-1], len(yarr))
)
even_xspacing = np.allclose(
xarr, np.linspace(xarr[0], xarr[-1], len(xarr))
)
if xaxis_linear and yaxis_linear and even_xspacing and even_yspacing:
# Source is a raster, where all x and y coordinates are evenly spaced
glyph = QuadMeshRaster(x, y, name)
upsample_width, upsample_height = glyph.is_upsample(
source, x, y, name, self.x_range, self.y_range,
self.plot_width, self.plot_height
)
if upsample_width and upsample_height:
# Override aggregate with more efficient one for upsampling
agg = rd._upsample(name)
return bypixel(source, self, glyph, agg)
elif not upsample_width and not upsample_height:
# Downsample both width and height
return bypixel(source, self, glyph, agg)
else:
# Mix of upsampling and downsampling
# Use general rectilinear quadmesh implementation
glyph = QuadMeshRectilinear(x, y, name)
return bypixel(source, self, glyph, agg)
else:
# Source is a general rectilinear quadmesh
glyph = QuadMeshRectilinear(x, y, name)
return bypixel(source, self, glyph, agg)
elif xarr.ndim == 2:
glyph = QuadMeshCurvilinear(x, y, name)
return bypixel(source, self, glyph, agg)
else:
raise ValueError("""\
x- and y-coordinate arrays must have 1 or 2 dimensions.
Received arrays with dimensions: {dims}""".format(
dims=list(xarr.dims)))
# 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. The supported
options include: first, last, mean, mode, var, std, min,
The agg can be specified as either a string name or as a
reduction function, but note that the function object will
be used only to extract the agg type (mean, max, etc.) and
the optional column name; the hardcoded raster code
supports only a fixed set of reductions and ignores the
actual code of the provided agg.
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)
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
elif np.issubdtype(source.dtype, np.integer):
fill_value = 0
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
arrays = (top_pad, data) if top_pad.shape[0] > 0 else (data,)
if bottom_pad.shape[0] > 0:
arrays += (bottom_pad,)
data = concat(arrays, axis=0) if len(arrays) > 1 else arrays[0]
arrays = (left_pad, data) if left_pad.shape[1] > 0 else (data,)
if right_pad.shape[1] > 0:
arrays += (right_pad,)
data = concat(arrays, axis=1) if len(arrays) > 1 else arrays[0]
# Reorient array to original orientation
if res[1] > 0: data = data[::-1]
if res[0] < 0: data = data[:, ::-1]
# Compute DataArray metadata
# To avoid floating point representation error,
# do not recompute x coords if same x_range and same plot_width,
# do not recompute y coords if same y_range and same plot_height
close_x = np.allclose([left, right], self.x_range) and np.size(xvals) == self.plot_width
close_y = np.allclose([bottom, top], self.y_range) and np.size(yvals) == self.plot_height
if close_x:
xs = xvals
else:
x_st = self.x_axis.compute_scale_and_translate(self.x_range, self.plot_width)
xs = self.x_axis.compute_index(x_st, self.plot_width)
if res[0] < 0:
xs = xs[::-1]
if close_y:
ys = yvals
else:
y_st = self.y_axis.compute_scale_and_translate(self.y_range, self.plot_height)
ys = self.y_axis.compute_index(y_st, self.plot_height)
if res[1] > 0:
ys = ys[::-1]
coords = {xdim: xs, ydim: ys}
dims = [ydim, xdim]
attrs = dict(res=res[0], x_range=self.x_range, y_range=self.y_range)
# Find nodata value if available in any of the common conventional locations
# See https://corteva.github.io/rioxarray/stable/getting_started/nodata_management.html
# and https://github.com/holoviz/datashader/issues/990
for a in ['_FillValue', 'missing_value', 'fill_value', 'nodata', 'NODATA']:
if a in source.attrs:
attrs['nodata'] = source.attrs[a]
break
if 'nodata' not in attrs:
try:
attrs['nodata'] = source.attrs['nodatavals'][0]
except:
pass
# 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_ranges(self, x_range, y_range):
self.x_axis.validate(x_range)
self.y_axis.validate(y_range)
def validate_size(self, width, height):
if width <= 0 or height <= 0:
raise ValueError("Invalid size: plot_width and plot_height must be bigger than 0")
def validate(self):
"""Check that parameter settings are valid for this object"""
self.validate_ranges(self.x_range, self.y_range)
self.validate_size(self.plot_width, self.plot_height)
def bypixel(source, canvas, glyph, agg, *, antialias=False):
"""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
"""
source, dshape = _bypixel_sanitise(source, glyph, agg)
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 warnings.catch_warnings():
warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
return bypixel.pipeline(source, schema, canvas, glyph, agg, antialias=antialias)
def _bypixel_sanitise(source, glyph, agg):
# 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_vars([col for col in columns if col not in cols_to_keep])
source = source.to_dask_dataframe()
if (isinstance(source, pd.DataFrame) or
(cudf and isinstance(source, cudf.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):
# If _sindex is set, ensure it is not dropped
# https://github.com/holoviz/datashader/issues/1121
sindex = None
from .glyphs.polygon import PolygonGeom
if isinstance(glyph, PolygonGeom):
sindex = getattr(source[glyph.geometry].array, "_sindex", None)
source = source[cols_to_keep]
if sindex is not None and getattr(source[glyph.geometry].array, "_sindex", None) is None:
source[glyph.geometry].array._sindex = sindex
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")
return source, dshape
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
def recurse(cols_to_keep, agg):
if hasattr(agg, 'values'):
for subagg in agg.values:
recurse(cols_to_keep, subagg)
elif hasattr(agg, 'columns'):
for column in agg.columns:
if column is not None:
cols_to_keep[column] = True
elif agg.column is not None:
cols_to_keep[agg.column] = True
recurse(cols_to_keep, agg)
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, str)) else arg
for arg in args
)
bypixel.pipeline = Dispatcher()
