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 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 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.""") 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 = [] @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' # Adjust width and height depending on pixel ratio pixel_ratio = self._get_pixel_ratio() width = int(width * pixel_ratio) height = int(height * pixel_ratio) # 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) ) 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)