from operator import itemgetter
import numpy as np
import colorsys
import param
from ..core import util
from ..core.data import ImageInterface
from ..core import Dimension, Element2D, Overlay, Dataset
from ..core.boundingregion import BoundingRegion, BoundingBox
from ..core.sheetcoords import SheetCoordinateSystem, Slice
from ..core.util import max_range, dimension_range, compute_density, datetime_types
from .chart import Curve
from .tabular import Table
from .util import compute_edges, compute_slice_bounds, categorical_aggregate2d
[docs]class Raster(Element2D):
"""
Raster is a basic 2D element type for presenting either numpy or
dask arrays as two dimensional raster images.
Arrays with a shape of (N,M) are valid inputs for Raster whereas
subclasses of Raster (e.g. RGB) may also accept 3D arrays
containing channel information.
Raster does not support slicing like the Image or RGB subclasses
and the extents are in matrix coordinates if not explicitly
specified.
"""
kdims = param.List(default=[Dimension('x'), Dimension('y')],
bounds=(2, 2), constant=True, doc="""
The label of the x- and y-dimension of the Raster in form
of a string or dimension object.""")
group = param.String(default='Raster', constant=True)
vdims = param.List(default=[Dimension('z')],
bounds=(1, 1), doc="""
The dimension description of the data held in the matrix.""")
def __init__(self, data, kdims=None, vdims=None, extents=None, **params):
if extents is None:
(d1, d2) = data.shape[:2]
extents = (0, 0, d2, d1)
super(Raster, self).__init__(data, kdims=kdims, vdims=vdims, extents=extents, **params)
def __getitem__(self, slices):
if slices in self.dimensions(): return self.dimension_values(slices)
slices = util.process_ellipses(self,slices)
if not isinstance(slices, tuple):
slices = (slices, slice(None))
elif len(slices) > (2 + self.depth):
raise KeyError("Can only slice %d dimensions" % 2 + self.depth)
elif len(slices) == 3 and slices[-1] not in [self.vdims[0].name, slice(None)]:
raise KeyError("%r is the only selectable value dimension" % self.vdims[0].name)
slc_types = [isinstance(sl, slice) for sl in slices[:2]]
data = self.data.__getitem__(slices[:2][::-1])
if all(slc_types):
return self.clone(data, extents=None)
elif not any(slc_types):
return data
else:
return self.clone(np.expand_dims(data, axis=slc_types.index(True)),
extents=None)
def range(self, dim, data_range=True):
idx = self.get_dimension_index(dim)
if data_range and idx == 2:
dimension = self.get_dimension(dim)
lower, upper = np.nanmin(self.data), np.nanmax(self.data)
return dimension_range(lower, upper, dimension)
return super(Raster, self).range(dim, data_range)
[docs] def dimension_values(self, dim, expanded=True, flat=True):
"""
The set of samples available along a particular dimension.
"""
dim_idx = self.get_dimension_index(dim)
if not expanded and dim_idx == 0:
return np.array(range(self.data.shape[1]))
elif not expanded and dim_idx == 1:
return np.array(range(self.data.shape[0]))
elif dim_idx in [0, 1]:
values = np.mgrid[0:self.data.shape[1], 0:self.data.shape[0]][dim_idx]
return values.flatten() if flat else values
elif dim_idx == 2:
arr = self.data.T
return arr.flatten() if flat else arr
else:
return super(Raster, self).dimension_values(dim)
@classmethod
def collapse_data(cls, data_list, function, kdims=None, **kwargs):
if isinstance(function, np.ufunc):
return function.reduce(data_list)
else:
return function(np.dstack(data_list), axis=-1, **kwargs)
[docs] def sample(self, samples=[], **sample_values):
"""
Sample the Raster along one or both of its dimensions,
returning a reduced dimensionality type, which is either
a ItemTable, Curve or Scatter. If two dimension samples
and a new_xaxis is provided the sample will be the value
of the sampled unit indexed by the value in the new_xaxis
tuple.
"""
if isinstance(samples, tuple):
X, Y = samples
samples = zip(X, Y)
params = dict(self.get_param_values(onlychanged=True),
vdims=self.vdims)
params.pop('extents', None)
params.pop('bounds', None)
if len(sample_values) == self.ndims or len(samples):
if not len(samples):
samples = zip(*[c if isinstance(c, list) else [c] for _, c in
sorted([(self.get_dimension_index(k), v) for k, v in
sample_values.items()])])
table_data = [c+(self._zdata[self._coord2matrix(c)],)
for c in samples]
params['kdims'] = self.kdims
return Table(table_data, **params)
else:
dimension, sample_coord = list(sample_values.items())[0]
if isinstance(sample_coord, slice):
raise ValueError(
'Raster sampling requires coordinates not slices,'
'use regular slicing syntax.')
# Indices inverted for indexing
sample_ind = self.get_dimension_index(dimension)
if sample_ind is None:
raise Exception("Dimension %s not found during sampling" % dimension)
other_dimension = [d for i, d in enumerate(self.kdims) if
i != sample_ind]
# Generate sample slice
sample = [slice(None) for i in range(self.ndims)]
coord_fn = (lambda v: (v, 0)) if not sample_ind else (lambda v: (0, v))
sample[sample_ind] = self._coord2matrix(coord_fn(sample_coord))[abs(sample_ind-1)]
# Sample data
x_vals = self.dimension_values(other_dimension[0].name, False)
ydata = self._zdata[sample[::-1]]
if hasattr(self, 'bounds') and sample_ind == 0: ydata = ydata[::-1]
data = list(zip(x_vals, ydata))
params['kdims'] = other_dimension
return Curve(data, **params)
[docs] def reduce(self, dimensions=None, function=None, **reduce_map):
"""
Reduces the Raster using functions provided via the
kwargs, where the keyword is the dimension to be reduced.
Optionally a label_prefix can be provided to prepend to
the result Element label.
"""
function, dims = self._reduce_map(dimensions, function, reduce_map)
if len(dims) == self.ndims:
if isinstance(function, np.ufunc):
return function.reduce(self.data, axis=None)
else:
return function(self.data)
else:
dimension = dims[0]
other_dimension = [d for d in self.kdims if d.name != dimension]
oidx = self.get_dimension_index(other_dimension[0])
x_vals = self.dimension_values(other_dimension[0].name, False)
reduced = function(self._zdata, axis=oidx)
if oidx and hasattr(self, 'bounds'):
reduced = reduced[::-1]
data = zip(x_vals, reduced)
params = dict(dict(self.get_param_values(onlychanged=True)),
kdims=other_dimension, vdims=self.vdims)
params.pop('bounds', None)
params.pop('extents', None)
return Table(data, **params)
@property
def depth(self):
return len(self.vdims)
@property
def _zdata(self):
return self.data
def _coord2matrix(self, coord):
return int(round(coord[1])), int(round(coord[0]))
[docs]class Image(Dataset, Raster, SheetCoordinateSystem):
"""
Image is the atomic unit as which 2D data is stored, along with
its bounds object. The input data may be a numpy.matrix object or
a two-dimensional numpy array.
Allows slicing operations of the data in sheet coordinates or direct
access to the data, via the .data attribute.
"""
bounds = param.ClassSelector(class_=BoundingRegion, default=BoundingBox(), doc="""
The bounding region in sheet coordinates containing the data.""")
datatype = param.List(default=['image', 'grid', 'xarray', 'cube', 'dataframe', 'dictionary'])
group = param.String(default='Image', constant=True)
kdims = param.List(default=[Dimension('x'), Dimension('y')],
bounds=(2, 2), constant=True, doc="""
The label of the x- and y-dimension of the Raster in form
of a string or dimension object.""")
vdims = param.List(default=[Dimension('z')],
bounds=(1, 1), doc="""
The dimension description of the data held in the matrix.""")
def __init__(self, data, kdims=None, vdims=None, bounds=None, extents=None,
xdensity=None, ydensity=None, **params):
extents = extents if extents else (None, None, None, None)
if (data is None
or (isinstance(data, (list, tuple)) and not data)
or (isinstance(data, np.ndarray) and data.size == 0)):
data = np.zeros((2, 2))
Dataset.__init__(self, data, kdims=kdims, vdims=vdims, extents=extents, **params)
dim2, dim1 = self.interface.shape(self, gridded=True)[:2]
if bounds is None:
xvals = self.dimension_values(0, False)
l, r, xdensity, _ = util.bound_range(xvals, xdensity, self._time_unit)
yvals = self.dimension_values(1, False)
b, t, ydensity, _ = util.bound_range(yvals, ydensity, self._time_unit)
bounds = BoundingBox(points=((l, b), (r, t)))
elif np.isscalar(bounds):
bounds = BoundingBox(radius=bounds)
elif isinstance(bounds, (tuple, list, np.ndarray)):
l, b, r, t = bounds
bounds = BoundingBox(points=((l, b), (r, t)))
l, b, r, t = bounds.lbrt()
xdensity = xdensity if xdensity else compute_density(l, r, dim1, self._time_unit)
ydensity = ydensity if ydensity else compute_density(b, t, dim2, self._time_unit)
SheetCoordinateSystem.__init__(self, bounds, xdensity, ydensity)
if len(self.shape) == 3:
if self.shape[2] != len(self.vdims):
raise ValueError("Input array has shape %r but %d value dimensions defined"
% (self.shape, len(self.vdims)))
def __setstate__(self, state):
"""
Ensures old-style unpickled Image types without an interface
use the ImageInterface.
Note: Deprecate as part of 2.0
"""
self.__dict__ = state
if isinstance(self.data, np.ndarray):
self.interface = ImageInterface
super(Dataset, self).__setstate__(state)
def aggregate(self, dimensions=None, function=None, spreadfn=None, **kwargs):
agg = super(Image, self).aggregate(dimensions, function, spreadfn, **kwargs)
return Curve(agg) if isinstance(agg, Dataset) and len(self.vdims) == 1 else agg
[docs] def select(self, selection_specs=None, **selection):
"""
Allows selecting data by the slices, sets and scalar values
along a particular dimension. The indices should be supplied as
keywords mapping between the selected dimension and
value. Additionally selection_specs (taking the form of a list
of type.group.label strings, types or functions) may be
supplied, which will ensure the selection is only applied if the
specs match the selected object.
"""
if selection_specs and not any(self.matches(sp) for sp in selection_specs):
return self
selection = {self.get_dimension(k).name: slice(*sel) if isinstance(sel, tuple) else sel
for k, sel in selection.items() if k in self.kdims}
coords = tuple(selection[kd.name] if kd.name in selection else slice(None)
for kd in self.kdims)
shape = self.interface.shape(self, gridded=True)
if any([isinstance(el, slice) for el in coords]):
bounds = compute_slice_bounds(coords, self, shape[:2])
xdim, ydim = self.kdims
l, b, r, t = bounds.lbrt()
# Situate resampled region into overall slice
y0, y1, x0, x1 = Slice(bounds, self)
y0, y1 = shape[0]-y1, shape[0]-y0
selection = (slice(y0, y1), slice(x0, x1))
sliced = True
else:
y, x = self.sheet2matrixidx(coords[0], coords[1])
y = shape[0]-y-1
selection = (y, x)
sliced = False
datatype = list(util.unique_iterator([self.interface.datatype]+self.datatype))
data = self.interface.ndloc(self, selection)
if not sliced:
if np.isscalar(data):
return data
elif isinstance(data, tuple):
data = data[self.ndims:]
return self.clone(data, kdims=[], new_type=Dataset,
datatype=datatype)
else:
return self.clone(data, xdensity=self.xdensity, datatype=datatype,
ydensity=self.ydensity, bounds=bounds)
[docs] def sample(self, samples=[], **kwargs):
"""
Allows sampling of an Image as an iterator of coordinates
matching the key dimensions, returning a new object containing
just the selected samples. Alternatively may supply kwargs to
sample a coordinate on an object. On an Image the coordinates
are continuously indexed and will always snap to the nearest
coordinate.
"""
kwargs = {k: v for k, v in kwargs.items() if k != 'closest'}
if kwargs and samples:
raise Exception('Supply explicit list of samples or kwargs, not both.')
elif kwargs:
sample = [slice(None) for _ in range(self.ndims)]
for dim, val in kwargs.items():
sample[self.get_dimension_index(dim)] = val
samples = [tuple(sample)]
# If a 1D cross-section of 2D space return Curve
shape = self.interface.shape(self, gridded=True)
if len(samples) == 1:
dims = [kd for kd, v in zip(self.kdims, samples[0]) if not np.isscalar(v)]
if len(dims) == 1:
kdims = [self.get_dimension(kd) for kd in dims]
sel = {kd.name: s for kd, s in zip(self.kdims, samples[0])}
dims = [kd for kd, v in sel.items() if not np.isscalar(v)]
selection = self.select(**sel)
selection = tuple(selection.columns(kdims+self.vdims).values())
datatype = list(util.unique_iterator(self.datatype+['dataframe', 'dict']))
return self.clone(selection, kdims=kdims, new_type=Curve,
datatype=datatype)
else:
kdims = self.kdims
else:
kdims = self.kdims
xs, ys = zip(*samples)
yidx, xidx = self.sheet2matrixidx(np.array(xs), np.array(ys))
yidx = shape[0]-yidx-1
# Detect out-of-bounds indices
out_of_bounds= (yidx<0) | (xidx<0) | (yidx>=shape[0]) | (xidx>=shape[1])
if out_of_bounds.any():
coords = [samples[idx] for idx in np.where(out_of_bounds)[0]]
raise IndexError('Coordinate(s) %s out of bounds for %s with bounds %s' %
(coords, type(self).__name__, self.bounds.lbrt()))
data = self.interface.ndloc(self, (yidx, xidx))
return self.clone(data, new_type=Table, datatype=['dataframe', 'dict'])
[docs] def closest(self, coords=[], **kwargs):
"""
Given a single coordinate or multiple coordinates as
a tuple or list of tuples or keyword arguments matching
the dimension closest will find the closest actual x/y
coordinates.
"""
if kwargs and coords:
raise ValueError("Specify coordinate using as either a list "
"keyword arguments not both")
if kwargs:
coords = []
getter = []
for k, v in kwargs.items():
idx = self.get_dimension_index(k)
if np.isscalar(v):
coords.append((0, v) if idx else (v, 0))
else:
if isinstance(v, list):
coords = [(0, c) if idx else (c, 0) for c in v]
if len(coords) not in [0, len(v)]:
raise ValueError("Length of samples must match")
elif len(coords):
coords = [(t[abs(idx-1)], c) if idx else (c, t[abs(idx-1)])
for c, t in zip(v, coords)]
getter.append(idx)
else:
getter = [0, 1]
getter = itemgetter(*sorted(getter))
if len(coords) == 1:
coords = coords[0]
if isinstance(coords, tuple):
return getter(self.closest_cell_center(*coords))
else:
return [getter(self.closest_cell_center(*el)) for el in coords]
def range(self, dim, data_range=True):
idx = self.get_dimension_index(dim)
dimension = self.get_dimension(dim)
low, high = super(Image, self).range(dim, data_range)
if idx in [0, 1] and data_range and dimension.range == (None, None):
if self.interface.datatype == 'image':
l, b, r, t = self.bounds.lbrt()
return (b, t) if idx else (l, r)
density = self.ydensity if idx else self.xdensity
halfd = (1./density)/2.
if isinstance(low, datetime_types):
halfd = np.timedelta64(int(round(halfd)), self._time_unit)
return (low-halfd, high+halfd)
else:
return super(Image, self).range(dim, data_range)
[docs] def table(self, datatype=None):
"""
Converts the data Element to a Table, optionally may
specify a supported data type. The default data types
are 'numpy' (for homogeneous data), 'dataframe', and
'dictionary'.
"""
if datatype and not isinstance(datatype, list):
datatype = [datatype]
from ..element import Table
return self.clone(self.columns(), new_type=Table,
**(dict(datatype=datatype) if datatype else {}))
def _coord2matrix(self, coord):
return self.sheet2matrixidx(*coord)
class GridImage(Image):
def __init__(self, *args, **kwargs):
self.warning('GridImage is now deprecated. Please use Image element instead.')
super(GridImage, self).__init__(*args, **kwargs)
[docs]class RGB(Image):
"""
An RGB element is a Image containing channel data for the the
red, green, blue and (optionally) the alpha channels. The values
of each channel must be in the range 0.0 to 1.0.
In input array may have a shape of NxMx4 or NxMx3. In the latter
case, the defined alpha dimension parameter is appended to the
list of value dimensions.
"""
group = param.String(default='RGB', constant=True)
alpha_dimension = param.ClassSelector(default=Dimension('A',range=(0,1)),
class_=Dimension, instantiate=False, doc="""
The alpha dimension definition to add the value dimensions if
an alpha channel is supplied.""")
vdims = param.List(
default=[Dimension('R', range=(0,1)), Dimension('G',range=(0,1)),
Dimension('B', range=(0,1))], bounds=(3, 4), doc="""
The dimension description of the data held in the matrix.
If an alpha channel is supplied, the defined alpha_dimension
is automatically appended to this list.""")
_vdim_reductions = {1: Image}
@property
def rgb(self):
"""
Returns the corresponding RGB element.
Other than the updating parameter definitions, this is the
only change needed to implemented an arbitrary colorspace as a
subclass of RGB.
"""
return self
[docs] @classmethod
def load_image(cls, filename, height=1, array=False, bounds=None, bare=False, **kwargs):
"""
Returns an raster element or raw numpy array from a PNG image
file, using matplotlib.
The specified height determines the bounds of the raster
object in sheet coordinates: by default the height is 1 unit
with the width scaled appropriately by the image aspect ratio.
Note that as PNG images are encoded as RGBA, the red component
maps to the first channel, the green component maps to the
second component etc. For RGB elements, this mapping is
trivial but may be important for subclasses e.g. for HSV
elements.
Setting bare=True will apply options disabling axis labels
displaying just the bare image. Any additional keyword
arguments will be passed to the Image object.
"""
try:
from matplotlib import pyplot as plt
except:
raise ImportError("RGB.load_image requires matplotlib.")
data = plt.imread(filename)
if array: return data
(h, w, _) = data.shape
if bounds is None:
f = float(height) / h
xoffset, yoffset = w*f/2, h*f/2
bounds=(-xoffset, -yoffset, xoffset, yoffset)
rgb = cls(data, bounds=bounds, **kwargs)
if bare: rgb = rgb(plot=dict(xaxis=None, yaxis=None))
return rgb
def __init__(self, data, kdims=None, vdims=None, **params):
if isinstance(data, Overlay):
images = data.values()
if not all(isinstance(im, Image) for im in images):
raise ValueError("Input overlay must only contain Image elements")
shapes = [im.data.shape for im in images]
if not all(shape==shapes[0] for shape in shapes):
raise ValueError("Images in the input overlays must contain data of the consistent shape")
ranges = [im.vdims[0].range for im in images]
if any(None in r for r in ranges):
raise ValueError("Ranges must be defined on all the value dimensions of all the Images")
arrays = [(im.data - r[0]) / (r[1] - r[0]) for r,im in zip(ranges, images)]
data = np.dstack(arrays)
if vdims is None:
vdims = list(self.vdims)
else:
vdims = list(vdims) if isinstance(vdims, list) else [vdims]
if isinstance(data, np.ndarray):
if data.shape[-1] == 4 and len(vdims) == 3:
vdims.append(self.alpha_dimension)
super(RGB, self).__init__(data, kdims=kdims, vdims=vdims, **params)
[docs]class HSV(RGB):
"""
Example of a commonly used color space subclassed from RGB used
for working in a HSV (hue, saturation and value) color space.
"""
group = param.String(default='HSV', constant=True)
alpha_dimension = param.ClassSelector(default=Dimension('A',range=(0,1)),
class_=Dimension, instantiate=False, doc="""
The alpha dimension definition to add the value dimensions if
an alpha channel is supplied.""")
vdims = param.List(
default=[Dimension('H', range=(0,1), cyclic=True),
Dimension('S',range=(0,1)),
Dimension('V', range=(0,1))], bounds=(3, 4), doc="""
The dimension description of the data held in the array.
If an alpha channel is supplied, the defined alpha_dimension
is automatically appended to this list.""")
hsv_to_rgb = np.vectorize(colorsys.hsv_to_rgb)
@property
def rgb(self):
"""
Conversion from HSV to RGB.
"""
data = [self.dimension_values(d, flat=False)
for d in self.vdims]
hsv = self.hsv_to_rgb(*data[:3])
if len(self.vdims) == 4:
hsv += (data[3],)
params = util.get_param_values(self)
del params['vdims']
return RGB(np.dstack(hsv)[::-1], bounds=self.bounds,
xdensity=self.xdensity, ydensity=self.ydensity,
**params)
[docs]class QuadMesh(Raster):
"""
QuadMesh is a Raster type to hold x- and y- bin values
with associated values. The x- and y-values of the QuadMesh
may be supplied either as the edges of each bin allowing
uneven sampling or as the bin centers, which will be converted
to evenly sampled edges.
As a secondary but less supported mode QuadMesh can contain
a mesh of quadrilateral coordinates that is not laid out in
a grid. The data should then be supplied as three separate
2D arrays for the x-/y-coordinates and grid values.
"""
group = param.String(default="QuadMesh", constant=True)
kdims = param.List(default=[Dimension('x'), Dimension('y')],
bounds=(2, 2), constant=True)
vdims = param.List(default=[Dimension('z')], bounds=(1,1))
def __init__(self, data, kdims=None, vdims=None, **params):
data = self._process_data(data)
Element2D.__init__(self, data, kdims=kdims, vdims=vdims, **params)
self.data = self._validate_data(self.data)
self._grid = self.data[0].ndim == 1
@property
def depth(self): return 1
def _process_data(self, data):
if isinstance(data, Image):
x = data.dimension_values(0, expanded=False)
y = data.dimension_values(1, expanded=False)
zarray = data.dimension_values(2, flat=False)
else:
data = tuple(np.array(el) for el in data)
x, y, zarray = data
ys, xs = zarray.shape
if x.ndim == 1 and len(x) == xs:
x = compute_edges(x)
if y.ndim == 1 and len(y) == ys:
y = compute_edges(y)
return (x, y, zarray)
@property
def _zdata(self):
return self.data[2]
def _validate_data(self, data):
x, y, z = data
if not z.ndim == 2:
raise ValueError("Z-values must be 2D array")
ys, xs = z.shape
shape_errors = []
if x.ndim == 1 and xs+1 != len(x):
shape_errors.append('x')
if x.ndim == 1 and ys+1 != len(y):
shape_errors.append('y')
if shape_errors:
raise ValueError("%s-edges must match shape of z-array." %
'/'.join(shape_errors))
return data
def __getitem__(self, slices):
if slices in self.dimensions(): return self.dimension_values(slices)
slices = util.process_ellipses(self,slices)
if not self._grid:
raise KeyError("Indexing of non-grid based QuadMesh"
"currently not supported")
slices = util.wrap_tuple(slices)
if len(slices) == 1:
slices = slices+(slice(None),)
if len(slices) > (2 + self.depth):
raise KeyError("Can only slice %d dimensions" % (2 + self.depth))
elif len(slices) == 3 and slices[-1] not in [self.vdims[0].name, slice(None)]:
raise KeyError("%r is the only selectable value dimension" % self.vdims[0].name)
slices = slices[:2]
if not isinstance(slices, tuple): slices = (slices, slice(None))
slc_types = [isinstance(sl, slice) for sl in slices]
if not any(slc_types):
indices = []
for idx, data in zip(slices, self.data[:self.ndims]):
dig = np.digitize([idx], data)
indices.append(dig-1 if dig else dig)
return self.data[2][tuple(indices[::-1])][0]
else:
sliced_data, indices = [], []
for slc, data in zip(slices, self.data[:self.ndims]):
if isinstance(slc, slice):
low, high = slc.start, slc.stop
lidx = (None if low is None else
max((np.digitize([low], data)-1, 0))[0])
hidx = (None if high is None else
np.digitize([high], data)[0])
sliced_data.append(data[lidx:hidx])
indices.append(slice(lidx, (hidx if hidx is None else hidx-1)))
else:
index = (np.digitize([slc], data)-1)[0]
sliced_data.append(data[index:index+2])
indices.append(index)
z = np.atleast_2d(self.data[2][tuple(indices[::-1])])
if not all(slc_types) and not slc_types[0]:
z = z.T
return self.clone(tuple(sliced_data+[z]))
[docs] @classmethod
def collapse_data(cls, data_list, function, kdims=None, **kwargs):
"""
Allows collapsing the data of a number of QuadMesh
Elements with a function.
"""
if not all(data[0].ndim == 1 for data in data_list):
raise Exception("Collapsing of non-grid based QuadMesh"
"currently not supported")
xs, ys, zs = zip(data_list)
if isinstance(function, np.ufunc):
z = function.reduce(zs)
else:
z = function(np.dstack(zs), axis=-1, **kwargs)
return xs[0], ys[0], z
def _coord2matrix(self, coord):
return tuple((np.digitize([coord[i]], self.data[i])-1)[0]
for i in [1, 0])
def range(self, dimension, data_range=True):
idx = self.get_dimension_index(dimension)
dim = self.get_dimension(dimension)
if idx in [0, 1, 2] and data_range:
data = self.data[idx]
lower, upper = np.nanmin(data), np.nanmax(data)
return dimension_range(lower, upper, dim)
return super(QuadMesh, self).range(dimension, data_range)
def dimension_values(self, dimension, expanded=True, flat=True):
idx = self.get_dimension_index(dimension)
data = self.data[idx]
if idx in [0, 1]:
# Handle grid
if not self._grid:
return data.flatten()
odim = self.data[2].shape[idx] if expanded else 1
vals = np.tile(np.convolve(data, np.ones((2,))/2, mode='valid'), odim)
if idx:
return np.sort(vals)
else:
return vals
elif idx == 2:
# Value dimension
return data.flatten() if flat else data
else:
# Handle constant dimensions
return super(QuadMesh, self).dimension_values(idx)
[docs]class HeatMap(Dataset, Element2D):
"""
HeatMap is an atomic Element used to visualize two dimensional
parameter spaces. It supports sparse or non-linear spaces, dynamically
upsampling them to a dense representation, which can be visualized.
A HeatMap can be initialized with any dict or NdMapping type with
two-dimensional keys.
"""
group = param.String(default='HeatMap', constant=True)
kdims = param.List(default=[Dimension('x'), Dimension('y')],
bounds=(2, 2), constant=True)
vdims = param.List(default=[Dimension('z')], constant=True)
def __init__(self, data, kdims=None, vdims=None, **params):
super(HeatMap, self).__init__(data, kdims=kdims, vdims=vdims, **params)
self.gridded = categorical_aggregate2d(self)
@property
def raster(self):
self.warning("The .raster attribute on HeatMap is deprecated, "
"the 2D aggregate is now computed dynamically "
"during plotting.")
return self.gridded.dimension_values(2, flat=False)
