Source code for holoviews.operation.normalization

"""
Data normalization operations.

Normalizing input data into a valid range is a common operation and
often required before further processing. The semantics of
normalization are dependent on the element type being normalized
making it difficult to provide a general and consistent interface.

The Normalization class is used to define such an interface and
subclasses are used to implement the appropriate normalization
operations per element type. Unlike display normalization, data
normalizations result in transformations to the stored data within
each element.
"""

import param
from ..core.operation import ElementOperation
from ..element import Raster
from ..core import Overlay
from ..core.util import match_spec


[docs]class Normalization(ElementOperation): """ Base class for all normalization operation. This class standardizes how normalization is specified using the ranges and keys parameter. The ranges parameter is designed to be very flexible, allowing a concise desciption for simple normalization while allowing complex key- and element- specific normalization to also be specified. """ data_range = param.Boolean(default=False, doc=""" Whether normalization is allowed to use the minimum and maximum values of the existing data to infer an appropriate range""") ranges = param.ClassSelector(default={}, allow_None=True, class_=(dict, list), doc=""" The simplest value of this parameter is None to skip all normalization. The next simplest value is an empty dictionary to only applies normalization to Dimensions with explicitly declared ranges. The next most common specification is a dictionary of values and tuple ranges. The value keys are the names of the dimensions to be normalized and the tuple ranges are of form (lower-bound, upper-bound). For instance, you could specify: {'Height':(0, 200), 'z':(0,1)} In this case, any element with a 'Height' or 'z' dimension (or both) will be normalized to the supplied ranges. Finally, element-specific normalization may also be specified by supplying a match tuple of form (<type>, <group>, <label>). A 1- or 2-tuple may be supplied by omitting the <group>, <label> or just the <label> components respectively. This tuple key then uses the dictionary value-range specification decribed above. For instance, you could normalize only the Image elements of group pattern using: {('Image','Pattern'):{'Height':(0, 200), 'z':(0,1)}}) Key-wise normalization is possible for all these formats by supplying a list of such dictionary specification that will then be zipped with the keys parameter (if specified). """) keys = param.List(default=None, allow_None=True, doc=""" If supplied, this list of keys is zipped with the supplied list of ranges. These keys are used to supply key specific normalization for HoloMaps containing matching key values, enabling per-element normalization.""") def __call__(self, element, ranges={}, keys=None, **params): params = dict(params,ranges=ranges, keys=keys) return super(Normalization, self).__call__(element, **params) def process_element(self, element, key, ranges={}, keys=None, **params): params = dict(params,ranges=ranges, keys=keys) self.p = param.ParamOverrides(self, params) return self._process(element, key)
[docs] def get_ranges(self, element, key): """ Method to get the appropriate normalization range dictionary given a key and element. """ keys = self.p['keys'] ranges = self.p['ranges'] if ranges == {}: return {d.name: element.range(d.name, self.data_range) for d in element.dimensions()} if keys is None: specs = ranges elif keys and not isinstance(ranges, list): raise ValueError("Key list specified but ranges parameter" " not specified as a list.") elif len(keys) == len(ranges): # Unpack any 1-tuple keys try: index = keys.index(key) specs = ranges[index] except: raise KeyError("Could not match element key to defined keys") else: raise ValueError("Key list length must match length of supplied ranges") return match_spec(element, specs)
def _process(self, view, key=None): raise NotImplementedError("Normalization not implemented")
[docs]class raster_normalization(Normalization): """ Normalizes elements of type Raster. For Raster elements containing (NxM) data, this will normalize the array/matrix into the specified range if value_dimension matches a key in the ranges dictionary. For elements containing (NxMxD) data, the (NxM) components of the third dimensional are normalized independently if the corresponding value dimensions are selected by the ranges dictionary. """ def _process(self, raster, key=None): if isinstance(raster, Raster): return self._normalize_raster(raster, key) elif isinstance(raster, Overlay): overlay_clone = raster.clone(shared_data=False) for k, el in raster.items(): overlay_clone[k] = self._normalize_raster(el, key) return overlay_clone else: raise ValueError("Input element must be a Raster or subclass of Raster.") def _normalize_raster(self, raster, key): if not isinstance(raster, Raster): return raster norm_raster = raster.clone(raster.data.copy()) ranges = self.get_ranges(raster, key) for depth, name in enumerate(d.name for d in raster.vdims): depth_range = ranges.get(name, (None, None)) if None in depth_range: continue if depth_range and len(norm_raster.data.shape) == 2: depth_range = ranges[name] norm_raster.data[:,:] -= depth_range[0] range = (depth_range[1] - depth_range[0]) if range: norm_raster.data[:,:] /= range elif depth_range: norm_raster.data[:,:,depth] -= depth_range[0] range = (depth_range[1] - depth_range[0]) if range: norm_raster.data[:,:,depth] /= range return norm_raster