Thuban/Model/classification.py

# Copyright (c) 2001 by Intevation GmbH
# Authors:
# Jonathan Coles <[email protected]>
#
# This program is free software under the GPL (>=v2)
# Read the file COPYING coming with Thuban for details.

__version__ = "$Revision$"

"""
A Classification provides a mapping from an input value
to data. This mapping can be specified in two ways. 
First, specific values can be associated with data. 
Second, ranges can be associated with data such that if 
an input value falls with a range that data is returned. 
If no mapping can be found then a NullData data will 
be returned. Input values must be hashable objects

See the description of getProperties() for more information 
on the mapping algorithm.
"""
  
# constants
RANGE_MIN  = 0
RANGE_MAX  = 1
RANGE_DATA = 2

class Classification:


    def __init__(self, field = None):
        """Initialize a classification.

           field -- the name of the data table field that 
                    is to be used to classify layer properties
        """
 
        self.__points = {}
        self.__ranges = []
        self.setField(field)
        self.setNull(None)
        
    def setField(self, field):
        """Set the name of the data table field to use.
         
           field -- if None then all values map to NullData 
        """

        self.field = field

    def setNull(self, data):
        """Set the data to be used when a value can't be classified.

           data -- data that the value maps to. See class description.
        """

        self.NullData = data

    def addRange(self, min, max, data):
        """Add a new range to the classification.

           A range allows a value to be classified if it falls between
           min and max. Specifically, min <= value < max
         
           min -- the lower bound.

           max -- the upper bound.

           data -- data that the value maps to. See class description.
        """

        if min >= max:
            raise ValueError("Range minimum >= maximum!")
        self.__ranges.append([min, max, data])

    def addPoint(self, value, data):
        """Associate a single value with data.

           When this value is to be classified data will be returned.

           value -- classification value.

           data  -- data that the value maps to. See class description.
        """

        self.__points[value] = data

    def getProperties(self, value):
        """Return the associated data, or the NullData.

           The following search technique is used:
               (1) if the field is None, return NullData
               (2) check if the value exists as a single value
               (3) check if the value falls within a range. Ranges
                   are checked in the order they were added to 
                   the classification.

           value -- the value to classify. If there is no mapping
                    return the NullData (which may be None)
        """

        if self.field is not None:
            #
            # first check the discrete values
            #
            if self.__points.has_key(value):
                return self.__points[value]

            #
            # now check the ranges
            #
            for p in self.__ranges:
                if (p[RANGE_MIN] <= value) and (value < p[RANGE_MAX]):
                    return p[RANGE_DATA]


        return self.NullData

1	# Copyright (c) 2001 by Intevation GmbH
2	# Authors:
3	# Jonathan Coles <[email protected]>
4	#
5	# This program is free software under the GPL (>=v2)
6	# Read the file COPYING coming with Thuban for details.
7
8	__version__ = "$Revision$"
9
10	"""
11	A Classification provides a mapping from an input value
12	to data. This mapping can be specified in two ways.
13	First, specific values can be associated with data.
14	Second, ranges can be associated with data such that if
15	an input value falls with a range that data is returned.
16	If no mapping can be found then a NullData data will
17	be returned. Input values must be hashable objects
18
19	See the description of getProperties() for more information
20	on the mapping algorithm.
21	"""
22
23	# constants
24	RANGE_MIN = 0
25	RANGE_MAX = 1
26	RANGE_DATA = 2
27
28	class Classification:
29
30
31	def __init__(self, field = None):
32	"""Initialize a classification.
33
34	field -- the name of the data table field that
35	is to be used to classify layer properties
36	"""
37
38	self.__points = {}
39	self.__ranges = []
40	self.setField(field)
41	self.setNull(None)
42
43	def setField(self, field):
44	"""Set the name of the data table field to use.
45
46	field -- if None then all values map to NullData
47	"""
48
49	self.field = field
50
51	def setNull(self, data):
52	"""Set the data to be used when a value can't be classified.
53
54	data -- data that the value maps to. See class description.
55	"""
56
57	self.NullData = data
58
59	def addRange(self, min, max, data):
60	"""Add a new range to the classification.
61
62	A range allows a value to be classified if it falls between
63	min and max. Specifically, min <= value < max
64
65	min -- the lower bound.
66
67	max -- the upper bound.
68
69	data -- data that the value maps to. See class description.
70	"""
71
72	if min >= max:
73	raise ValueError("Range minimum >= maximum!")
74	self.__ranges.append([min, max, data])
75
76	def addPoint(self, value, data):
77	"""Associate a single value with data.
78
79	When this value is to be classified data will be returned.
80
81	value -- classification value.
82
83	data -- data that the value maps to. See class description.
84	"""
85
86	self.__points[value] = data
87
88	def getProperties(self, value):
89	"""Return the associated data, or the NullData.
90
91	The following search technique is used:
92	(1) if the field is None, return NullData
93	(2) check if the value exists as a single value
94	(3) check if the value falls within a range. Ranges
95	are checked in the order they were added to
96	the classification.
97
98	value -- the value to classify. If there is no mapping
99	return the NullData (which may be None)
100	"""
101
102	if self.field is not None:
103	#
104	# first check the discrete values
105	#
106	if self.__points.has_key(value):
107	return self.__points[value]
108
109	#
110	# now check the ranges
111	#
112	for p in self.__ranges:
113	if (p[RANGE_MIN] <= value) and (value < p[RANGE_MAX]):
114	return p[RANGE_DATA]
115
116
117	return self.NullData
118
Name	Value
svn:eol-style	native
svn:keywords	Author Date Id Revision