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 _
from Thuban.Model.color import Color

from wxPython.wx 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 getNull(self):
        return self.NullData

    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

    def TreeInfo(self):
        items = []

        #
        # shouldn't print anything if there are no classifications
        #

        
        def color_string(color):
            if color is None:
                return "None"
            return "(%.3f, %.3f, %.3f)" % (color.red, color.green, color.blue)

        if self.NullData is not None:
            i = []
            for key, value in self.NullData.items():
                if isinstance(value, Color):
                    i.append((_("%s: %s") % (key, color_string(value)), value))
                else:
                    i.append(_("%s: %s") % (key, value))
            items.append((_("'NULL'"), i))

        for name, data in self.points.items():
            i = []
            for key, value in data.items():
                if isinstance(value, Color):
                    i.append((_("%s: %s") % (key, color_string(value)), value))
                else:
                    i.append(_("%s: %s") % (key, value))
            items.append((_("%s") % name, i))

        for p in self.ranges:
            i = []
            data = p[RANGE_DATA]
            for key, value in data.items():
                if isinstance(value, Color):
                    i.append((_("%s: %s") % (key, color_string(value)), value))
                else:
                    i.append(_("%s: %s") % (key, value))
            items.append((_("%s-%s") % (p[RANGE_MIN], p[RANGE_MAX], i)))
       
        return (_("Classifications"), items)
 
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	from Thuban.Model.color import Color
25
26	from wxPython.wx import *
27
28	# constants
29	RANGE_MIN = 0
30	RANGE_MAX = 1
31	RANGE_DATA = 2
32
33	class Classification:
34
35
36	def __init__(self, field = None):
37	"""Initialize a classification.
38
39	field -- the name of the data table field that
40	is to be used to classify layer properties
41	"""
42
43	self.points = {}
44	self.ranges = []
45	self.setField(field)
46	self.setNull(None)
47
48	def setField(self, field):
49	"""Set the name of the data table field to use.
50
51	field -- if None then all values map to NullData
52	"""
53
54	self.field = field
55
56	def setNull(self, data):
57	"""Set the data to be used when a value can't be classified.
58
59	data -- data that the value maps to. See class description.
60	"""
61
62	self.NullData = data
63
64	def getNull(self):
65	return self.NullData
66
67	def addRange(self, min, max, data):
68	"""Add a new range to the classification.
69
70	A range allows a value to be classified if it falls between
71	min and max. Specifically, min <= value < max
72
73	min -- the lower bound.
74
75	max -- the upper bound.
76
77	data -- data that the value maps to. See class description.
78	"""
79
80	if min >= max:
81	raise ValueError(_("Range minimum >= maximum!"))
82	self.ranges.append([min, max, data])
83
84	def addPoint(self, value, data):
85	"""Associate a single value with data.
86
87	When this value is to be classified data will be returned.
88
89	value -- classification value.
90
91	data -- data that the value maps to. See class description.
92	"""
93
94	self.points[value] = data
95
96	def getProperties(self, value):
97	"""Return the associated data, or the NullData.
98
99	The following search technique is used:
100	(1) if the field is None, return NullData
101	(2) check if the value exists as a single value
102	(3) check if the value falls within a range. Ranges
103	are checked in the order they were added to
104	the classification.
105
106	value -- the value to classify. If there is no mapping
107	return the NullData (which may be None)
108	"""
109
110	if self.field is not None:
111	#
112	# first check the discrete values
113	#
114	if self.points.has_key(value):
115	return self.points[value]
116
117	#
118	# now check the ranges
119	#
120	for p in self.ranges:
121	if (p[RANGE_MIN] <= value) and (value < p[RANGE_MAX]):
122	return p[RANGE_DATA]
123
124
125	return self.NullData
126
127	def TreeInfo(self):
128	items = []
129
130	#
131	# shouldn't print anything if there are no classifications
132	#
133
134
135	def color_string(color):
136	if color is None:
137	return "None"
138	return "(%.3f, %.3f, %.3f)" % (color.red, color.green, color.blue)
139
140	if self.NullData is not None:
141	i = []
142	for key, value in self.NullData.items():
143	if isinstance(value, Color):
144	i.append((_("%s: %s") % (key, color_string(value)), value))
145	else:
146	i.append(_("%s: %s") % (key, value))
147	items.append((_("'NULL'"), i))
148
149	for name, data in self.points.items():
150	i = []
151	for key, value in data.items():
152	if isinstance(value, Color):
153	i.append((_("%s: %s") % (key, color_string(value)), value))
154	else:
155	i.append(_("%s: %s") % (key, value))
156	items.append((_("%s") % name, i))
157
158	for p in self.ranges:
159	i = []
160	data = p[RANGE_DATA]
161	for key, value in data.items():
162	if isinstance(value, Color):
163	i.append((_("%s: %s") % (key, color_string(value)), value))
164	else:
165	i.append(_("%s: %s") % (key, value))
166	items.append((_("%s-%s") % (p[RANGE_MIN], p[RANGE_MAX], i)))
167
168	return (_("Classifications"), items)
169
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