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.
"""
  
from Thuban import _

# 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	from Thuban import _
24
25	# constants
26	RANGE_MIN = 0
27	RANGE_MAX = 1
28	RANGE_DATA = 2
29
30	class Classification:
31
32
33	def __init__(self, field = None):
34	"""Initialize a classification.
35
36	field -- the name of the data table field that
37	is to be used to classify layer properties
38	"""
39
40	self.points = {}
41	self.ranges = []
42	self.setField(field)
43	self.setNull(None)
44
45	def setField(self, field):
46	"""Set the name of the data table field to use.
47
48	field -- if None then all values map to NullData
49	"""
50
51	self.field = field
52
53	def setNull(self, data):
54	"""Set the data to be used when a value can't be classified.
55
56	data -- data that the value maps to. See class description.
57	"""
58
59	self.NullData = data
60
61	def addRange(self, min, max, data):
62	"""Add a new range to the classification.
63
64	A range allows a value to be classified if it falls between
65	min and max. Specifically, min <= value < max
66
67	min -- the lower bound.
68
69	max -- the upper bound.
70
71	data -- data that the value maps to. See class description.
72	"""
73
74	if min >= max:
75	raise ValueError(_("Range minimum >= maximum!"))
76	self.ranges.append([min, max, data])
77
78	def addPoint(self, value, data):
79	"""Associate a single value with data.
80
81	When this value is to be classified data will be returned.
82
83	value -- classification value.
84
85	data -- data that the value maps to. See class description.
86	"""
87
88	self.points[value] = data
89
90	def getProperties(self, value):
91	"""Return the associated data, or the NullData.
92
93	The following search technique is used:
94	(1) if the field is None, return NullData
95	(2) check if the value exists as a single value
96	(3) check if the value falls within a range. Ranges
97	are checked in the order they were added to
98	the classification.
99
100	value -- the value to classify. If there is no mapping
101	return the NullData (which may be None)
102	"""
103
104	if self.field is not None:
105	#
106	# first check the discrete values
107	#
108	if self.points.has_key(value):
109	return self.points[value]
110
111	#
112	# now check the ranges
113	#
114	for p in self.ranges:
115	if (p[RANGE_MIN] <= value) and (value < p[RANGE_MAX]):
116	return p[RANGE_DATA]
117
118
119	return self.NullData
120
121
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