import numpy as np
import param
from param.parameterized import bothmethod
from ..core import Dataset, Operation
from ..core.util import datetime_types, dt_to_int, isfinite, max_range
from ..element import Image
from ..streams import PlotSize, RangeX, RangeXY
[docs]class LinkableOperation(Operation):
"""
Abstract baseclass for operations supporting linked inputs.
"""
link_inputs = param.Boolean(default=True, doc="""
By default, the link_inputs parameter is set to True so that
when applying an operation, backends that support linked
streams update RangeXY streams on the inputs of the operation.
Disable when you do not want the resulting plot to be
interactive, e.g. when trying to display an interactive plot a
second time.""")
_allow_extra_keywords=True
[docs]class ResampleOperation1D(LinkableOperation):
"""
Abstract baseclass for resampling operations
"""
dynamic = param.Boolean(default=True, doc="""
Enables dynamic processing by default.""")
x_range = param.Tuple(default=None, length=2, doc="""
The x_range as a tuple of min and max x-value. Auto-ranges
if set to None.""")
x_sampling = param.Number(default=None, doc="""
Specifies the smallest allowed sampling interval along the x axis.""")
streams = param.ClassSelector(default=[PlotSize, RangeX], class_=(dict, list), doc="""
List or dictionary of streams that are applied if dynamic=True,
allowing for dynamic interaction with the plot.""")
width = param.Integer(default=400, doc="""
The width of the output image in pixels.""")
height = param.Integer(default=400, doc="""
The height of the output image in pixels.""")
pixel_ratio = param.Number(default=None, bounds=(0,None),
inclusive_bounds=(False,False), doc="""
Pixel ratio applied to the height and width. Useful for higher
resolution screens where the PlotSize stream reports 'nominal'
dimensions in pixels that do not match the physical pixels. For
instance, setting pixel_ratio=2 can give better results on Retina
displays. Also useful for using lower resolution for speed.
If not set explicitly, the zoom level of the browsers will be used,
if available.""")
[docs]class ResampleOperation2D(ResampleOperation1D):
"""
Abstract baseclass for resampling operations
"""
dynamic = param.Boolean(default=True, doc="""
Enables dynamic processing by default.""")
expand = param.Boolean(default=True, doc="""
Whether the x_range and y_range should be allowed to expand
beyond the extent of the data. Setting this value to True is
useful for the case where you want to ensure a certain size of
output grid, e.g. if you are doing masking or other arithmetic
on the grids. A value of False ensures that the grid is only
just as large as it needs to be to contain the data, which will
be faster and use less memory if the resulting aggregate is
being overlaid on a much larger background.""")
y_range = param.Tuple(default=None, length=2, doc="""
The y-axis range as a tuple of min and max y value. Auto-ranges
if set to None.""")
y_sampling = param.Number(default=None, doc="""
Specifies the smallest allowed sampling interval along the y axis.""")
target = param.ClassSelector(class_=Dataset, doc="""
A target Dataset which defines the desired x_range, y_range,
width and height.
""")
streams = param.ClassSelector(default=[PlotSize, RangeXY], class_=(dict, list), doc="""
List or dictionary of streams that are applied if dynamic=True,
allowing for dynamic interaction with the plot.""")
element_type = param.ClassSelector(class_=(Dataset,), instantiate=False,
is_instance=False, default=Image,
doc="""
The type of the returned Elements, must be a 2D Dataset type.""")
precompute = param.Boolean(default=False, doc="""
Whether to apply precomputing operations. Precomputing can
speed up resampling operations by avoiding unnecessary
recomputation if the supplied element does not change between
calls. The cost of enabling this option is that the memory
used to represent this internal state is not freed between
calls.""")
_transfer_options = []
[docs] @bothmethod
def instance(self_or_cls,**params):
filtered = {k:v for k,v in params.items() if k in self_or_cls.param}
inst = super().instance(**filtered)
inst._precomputed = {}
return inst
def _get_sampling(self, element, x, y, ndim=2, default=None):
target = self.p.target
if not isinstance(x, list) and x is not None:
x = [x]
if not isinstance(y, list) and y is not None:
y = [y]
if target:
x0, y0, x1, y1 = target.bounds.lbrt()
x_range, y_range = (x0, x1), (y0, y1)
height, width = target.dimension_values(2, flat=False).shape
else:
if x is None:
x_range = self.p.x_range or (-0.5, 0.5)
elif self.p.expand or not self.p.x_range:
if self.p.x_range and all(isfinite(v) for v in self.p.x_range):
x_range = self.p.x_range
else:
x_range = max_range([element.range(xd) for xd in x])
else:
x0, x1 = self.p.x_range
ex0, ex1 = max_range([element.range(xd) for xd in x])
x_range = (np.nanmin([np.nanmax([x0, ex0]), ex1]),
np.nanmax([np.nanmin([x1, ex1]), ex0]))
if (y is None and ndim == 2):
y_range = self.p.y_range or default or (-0.5, 0.5)
elif self.p.expand or not self.p.y_range:
if self.p.y_range and all(isfinite(v) for v in self.p.y_range):
y_range = self.p.y_range
elif default is None:
y_range = max_range([element.range(yd) for yd in y])
else:
y_range = default
else:
y0, y1 = self.p.y_range
if default is None:
ey0, ey1 = max_range([element.range(yd) for yd in y])
else:
ey0, ey1 = default
y_range = (np.nanmin([np.nanmax([y0, ey0]), ey1]),
np.nanmax([np.nanmin([y1, ey1]), ey0]))
width, height = self.p.width, self.p.height
(xstart, xend), (ystart, yend) = x_range, y_range
xtype = 'numeric'
if isinstance(xstart, str) or isinstance(xend, str):
raise ValueError("Categorical data is not supported")
elif isinstance(xstart, datetime_types) or isinstance(xend, datetime_types):
xstart, xend = dt_to_int(xstart, 'ns'), dt_to_int(xend, 'ns')
xtype = 'datetime'
elif not np.isfinite(xstart) and not np.isfinite(xend):
xstart, xend = 0, 0
if x and element.get_dimension_type(x[0]) in datetime_types:
xtype = 'datetime'
ytype = 'numeric'
if isinstance(ystart, str) or isinstance(yend, str):
raise ValueError("Categorical data is not supported")
elif isinstance(ystart, datetime_types) or isinstance(yend, datetime_types):
ystart, yend = dt_to_int(ystart, 'ns'), dt_to_int(yend, 'ns')
ytype = 'datetime'
elif not np.isfinite(ystart) and not np.isfinite(yend):
ystart, yend = 0, 0
if y and element.get_dimension_type(y[0]) in datetime_types:
ytype = 'datetime'
# Compute highest allowed sampling density
xspan = xend - xstart
yspan = yend - ystart
if self.p.x_sampling:
width = int(min([(xspan/self.p.x_sampling), width]))
if self.p.y_sampling:
height = int(min([(yspan/self.p.y_sampling), height]))
if xstart == xend or width == 0:
xunit, width = 0, 0
else:
xunit = float(xspan)/width
if ystart == yend or height == 0:
yunit, height = 0, 0
else:
yunit = float(yspan)/height
xs, ys = (np.linspace(xstart+xunit/2., xend-xunit/2., width),
np.linspace(ystart+yunit/2., yend-yunit/2., height))
pixel_ratio = self._get_pixel_ratio()
width = int(width * pixel_ratio)
height = int(height * pixel_ratio)
return ((xstart, xend), (ystart, yend)), (xs, ys), (width, height), (xtype, ytype)
def _get_pixel_ratio(self):
if self.p.pixel_ratio is None:
from panel import state
if state.browser_info and isinstance(state.browser_info.device_pixel_ratio, (int, float)):
return state.browser_info.device_pixel_ratio
else:
return 1
else:
return self.p.pixel_ratio
def _dt_transform(self, x_range, y_range, xs, ys, xtype, ytype):
(xstart, xend), (ystart, yend) = x_range, y_range
if xtype == 'datetime':
xstart, xend = np.array([xstart, xend]).astype('datetime64[ns]')
xs = xs.astype('datetime64[ns]')
if ytype == 'datetime':
ystart, yend = np.array([ystart, yend]).astype('datetime64[ns]')
ys = ys.astype('datetime64[ns]')
return ((xstart, xend), (ystart, yend)), (xs, ys)