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 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	jonathan	371	# 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	jan	374	from Thuban import _
24	jonathan	381	from Thuban.Model.color import Color
25	jan	374
26	jonathan	381	from wxPython.wx import *
27
28	jonathan	371	# 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	jonathan	378	self.points = {}
44			self.ranges = []
45	jonathan	371	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 addRange(self, min, max, data):
65			"""Add a new range to the classification.
66
67			A range allows a value to be classified if it falls between
68			min and max. Specifically, min <= value < max
69
70			min -- the lower bound.
71
72			max -- the upper bound.
73
74			data -- data that the value maps to. See class description.
75			"""
76
77			if min >= max:
78	jan	374	raise ValueError(_("Range minimum >= maximum!"))
79	jonathan	378	self.ranges.append([min, max, data])
80	jonathan	371
81			def addPoint(self, value, data):
82			"""Associate a single value with data.
83
84			When this value is to be classified data will be returned.
85
86			value -- classification value.
87
88			data -- data that the value maps to. See class description.
89			"""
90
91	jonathan	378	self.points[value] = data
92	jonathan	371
93			def getProperties(self, value):
94			"""Return the associated data, or the NullData.
95
96			The following search technique is used:
97			(1) if the field is None, return NullData
98			(2) check if the value exists as a single value
99			(3) check if the value falls within a range. Ranges
100			are checked in the order they were added to
101			the classification.
102
103			value -- the value to classify. If there is no mapping
104			return the NullData (which may be None)
105			"""
106
107			if self.field is not None:
108			#
109			# first check the discrete values
110			#
111	jonathan	378	if self.points.has_key(value):
112			return self.points[value]
113	jonathan	371
114			#
115			# now check the ranges
116			#
117	jonathan	378	for p in self.ranges:
118	jonathan	371	if (p[RANGE_MIN] <= value) and (value < p[RANGE_MAX]):
119			return p[RANGE_DATA]
120
121
122			return self.NullData
123
124	jonathan	381	def TreeInfo(self):
125			items = []
126	jonathan	378
127	jonathan	381	#
128			# shouldn't print anything if there are no classifications
129			#
130
131
132			def color_string(color):
133			if color is None:
134			return "None"
135			return "(%.3f, %.3f, %.3f)" % (color.red, color.green, color.blue)
136
137			if self.NullData is not None:
138			i = []
139			for key, value in self.NullData.items():
140			if isinstance(value, Color):
141			i.append((_("%s: %s") % (key, color_string(value)), value))
142			else:
143			i.append(_("%s: %s") % (key, value))
144			items.append((_("'NULL'"), i))
145
146			for name, data in self.points.items():
147			i = []
148			for key, value in data.items():
149			if isinstance(value, Color):
150			i.append((_("%s: %s") % (key, color_string(value)), value))
151			else:
152			i.append(_("%s: %s") % (key, value))
153			items.append((_("%s") % name, i))
154
155			for p in self.ranges:
156			i = []
157			data = p[RANGE_DATA]
158			for key, value in data.items():
159			if isinstance(value, Color):
160			i.append((_("%s: %s") % (key, color_string(value)), value))
161			else:
162			i.append(_("%s: %s") % (key, value))
163			items.append((_("%s-%s") % (p[RANGE_MIN], p[RANGE_MAX], i)))
164
165			return (_("Classifications"), items)
166
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