This file is indexed.

/usr/share/pyshared/traits/trait_numeric.py is in python-traits 4.4.0-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
#------------------------------------------------------------------------------
#
#  Copyright (c) 2005, Enthought, Inc.
#  All rights reserved.
#
#  This software is provided without warranty under the terms of the BSD
#  license included in enthought/LICENSE.txt and may be redistributed only
#  under the conditions described in the aforementioned license.  The license
#  is also available online at http://www.enthought.com/licenses/BSD.txt
#
#  Thanks for using Enthought open source!
#
#  Author: David C. Morrill
#  Date:   12/13/2004
#
#------------------------------------------------------------------------------

""" Trait definitions related to the numpy library.
"""

#-------------------------------------------------------------------------------
#  Imports:
#-------------------------------------------------------------------------------

from __future__ import absolute_import

import warnings

from .trait_base import SequenceTypes
from .trait_errors import TraitError
from .trait_handlers import TraitType, OBJECT_IDENTITY_COMPARE
from .trait_types import Str, Any, Int as TInt, Float as TFloat

#-------------------------------------------------------------------------------
#  Deferred imports from numpy:
#-------------------------------------------------------------------------------

ndarray = None
asarray = None

#-------------------------------------------------------------------------------
#  numpy dtype mapping:
#-------------------------------------------------------------------------------

def dtype2trait ( dtype ):
    """ Get the corresponding trait for a numpy dtype.
    """

    import numpy

    if dtype.char in numpy.typecodes['Float']:
        return TFloat

    elif dtype.char in numpy.typecodes['AllInteger']:
        return TInt

    elif dtype.char[0] == 'S':
        return Str

    else:
        return Any

#-------------------------------------------------------------------------------
#  'AbstractArray' trait base class:
#-------------------------------------------------------------------------------

class AbstractArray ( TraitType ):
    """ Abstract base class for defining numpy-based arrays.
    """

    def __init__ ( self, dtype = None, shape = None, value = None,
                         coerce = False, typecode = None, **metadata ):
        """ Returns an AbstractArray trait.
        """
        global ndarray, asarray

        try:
            import numpy
        except ImportError:
            raise TraitError( "Using Array or CArray trait types requires the "
                              "numpy package to be installed." )

        from numpy import array, asarray, ndarray, zeros

        # Mark this as being an 'array' trait:
        metadata[ 'array' ] = True

        # Normally use object identity to detect array values changing:
        metadata.setdefault( 'comparison_mode', OBJECT_IDENTITY_COMPARE )

        if typecode is not None:
            warnings.warn( 'typecode is a deprecated argument; use dtype '
                           'instead', DeprecationWarning )

            if (dtype is not None) and (dtype != typecode):
                raise TraitError( 'Inconsistent usage of the dtype and '
                                  'typecode arguments; use dtype alone.' )
            else:
                dtype = typecode

        if dtype is not None:
            try:
                # Convert the argument into an actual numpy dtype object:
                dtype = numpy.dtype( dtype )
            except TypeError:
                raise TraitError( 'could not convert %r to a numpy dtype' %
                                  dtype )

        if shape is not None:
            if isinstance( shape, SequenceTypes ):
                for item in shape:
                    if ((item is None) or (type( item ) is int) or
                        (isinstance( item, SequenceTypes ) and
                         (len( item ) == 2) and
                         (type( item[0] ) is int) and (item[0] >= 0) and
                         ((item[1] is None) or ((type( item[1] ) is int) and
                           (item[0] <= item[1]))))):
                        continue

                    raise TraitError, "shape should be a list or tuple"
            else:
                raise TraitError, "shape should be a list or tuple"

        if value is None:
            if dtype is None:
                # Compatibility with the default of Traits 2.0
                dt = int
            else:
                dt = dtype
            if shape is None:
                value = zeros( ( 0, ), dt )
            else:
                size = []
                for item in shape:
                    if item is None:
                        item = 1
                    elif type( item ) in SequenceTypes:
                        # XXX: what is this supposed to do?
                        item = item[0]
                    size.append( item )
                value = zeros( size, dt )

        self.dtype  = dtype
        self.shape  = shape
        self.coerce = coerce

        super( AbstractArray, self ).__init__( value, **metadata )

    def validate ( self, object, name, value ):
        """ Validates that the value is a valid array.
        """
        try:
            # Make sure the value is an array:
            type_value = type( value )
            if not isinstance( value, ndarray ):
                if not isinstance( value, SequenceTypes ):
                    self.error( object, name, value )
                if self.dtype is not None:
                    value = asarray( value, self.dtype )
                else:
                    value = asarray( value )

            # Make sure the array is of the right type:
            if ((self.dtype is not None) and
                (value.dtype != self.dtype)):
                if self.coerce:
                    value = value.astype( self.dtype )
                else:
                    # XXX: this also coerces.
                    value = asarray( value, self.dtype )

            # If no shape requirements, then return the value:
            trait_shape = self.shape
            if trait_shape is None:
                return value

            # Else make sure that the value's shape is compatible:
            value_shape = value.shape
            if len( trait_shape ) == len( value_shape ):
                for i, dim in enumerate( value_shape ):
                    item = trait_shape[i]
                    if item is not None:
                        if type( item ) is int:
                            if dim != item:
                                break
                        elif ((dim < item[0]) or
                              ((item[1] is not None) and (dim > item[1]))):
                            break
                else:
                    return value
        except:
            pass

        self.error( object, name, value )

    def info ( self ):
        """ Returns descriptive information about the trait.
        """
        dtype = shape = ''

        if self.shape is not None:
            shape = []
            for item in self.shape:
                if item is None:
                    item = '*'
                elif type( item ) is not int:
                    if item[1] is None:
                        item = '%d..' % item[0]
                    else:
                        item = '%d..%d' % item
                shape.append( item )
            shape = ' with shape %s' % ( tuple( shape ), )

        if self.dtype is not None:
            # FIXME: restore nicer descriptions of dtypes.
            dtype = ' of %s values' % self.dtype

        return 'an array%s%s' % ( dtype, shape )

    def get_editor ( self, trait = None ):
        """ Returns the default UI editor for the trait.
        """
        editor = None

        auto_set = False
        if self.auto_set is None:
            auto_set = True
        enter_set = self.enter_set or False

        if self.shape is not None and len( self.shape ) == 2:
            from traitsui.api import ArrayEditor
            editor = ArrayEditor( auto_set=auto_set, enter_set=enter_set )
        else:
            from traitsui.api import TupleEditor

            if self.dtype is None:
                types = Any
            else:
                types = dtype2trait( self.dtype )
            editor = TupleEditor( types     = types,
                                  labels    = self.labels or [],
                                  cols      = self.cols or 1,
                                  auto_set  = auto_set,
                                  enter_set = enter_set  )
        return editor

    #-- Private Methods --------------------------------------------------------

    def get_default_value ( self ):
        """ Returns the default value constructor for the type (called from the
            trait factory.
        """
        return ( 7, ( self.copy_default_value,
                 ( self.validate( None, None, self.default_value ), ), None ) )

    def copy_default_value ( self, value ):
        """ Returns a copy of the default value (called from the C code on
            first reference to a trait with no current value).
        """
        return value.copy()

#-------------------------------------------------------------------------------
#  'Array' trait:
#-------------------------------------------------------------------------------

class Array ( AbstractArray ):
    """ Defines a trait whose value must be a numpy array.
    """

    def __init__ ( self, dtype = None, shape = None, value = None,
                   typecode = None, **metadata ):
        """ Returns an Array trait.

        Parameters
        ----------
        dtype : a numpy dtype (e.g., int32)
            The type of elements in the array; if omitted, no type-checking is
            performed on assigned values.
        shape : a tuple
            Describes the required shape of any assigned value. Wildcards and
            ranges are allowed. The value None within the *shape* tuple means
            that the corresponding dimension is not checked. (For example,
            ``shape=(None,3)`` means that the first dimension can be any size,
            but the second must be 3.) A two-element tuple within the *shape*
            tuple means that the dimension must be in the specified range. The
            second element can be None to indicate that there is no upper
            bound. (For example, ``shape=((3,5),(2,None))`` means that the
            first dimension must be in the range 3 to 5 (inclusive), and the
            second dimension must be at least 2.)
        value : numpy array
            A default value for the array.

        Default Value
        -------------
        *value* or ``zeros(min(shape))``, where ``min(shape)`` refers to the
        minimum shape allowed by the array. If *shape* is not specified, the
        minimum shape is (0,).

        Description
        -----------
        An Array trait allows only upcasting of assigned values that are
        already numpy arrays. It automatically casts tuples and lists of the
        right shape to the specified *dtype* (just like numpy's **array**
        does).
        """
        super( Array, self ).__init__( dtype, shape, value, False,
                                       typecode = typecode, **metadata )

#-------------------------------------------------------------------------------
#  'CArray' trait:
#-------------------------------------------------------------------------------

class CArray ( AbstractArray ):
    """ Defines a trait whose value must be a numpy array, with casting
        allowed.
    """

    def __init__ ( self, dtype = None, shape = None, value = None,
                   typecode = None, **metadata ):
        """ Returns a CArray trait.

        Parameters
        ----------
        dtype : a numpy dtype (e.g., int32)
            The type of elements in the array.
        shape : a tuple
            Describes the required shape of any assigned value. Wildcards and
            ranges are allowed. The value None within the *shape* tuple means
            that the corresponding dimension is not checked. (For example,
            ``shape=(None,3)`` means that the first dimension can be any size,
            but the second must be 3.) A two-element tuple within the *shape*
            tuple means that the dimension must be in the specified range. The
            second element can be None to indicate that there is no upper
            bound. (For example, ``shape=((3,5),(2,None))`` means that the
            first dimension must be in the range 3 to 5 (inclusive), and the
            second dimension must be at least 2.)
        value : numpy array
            A default value for the array.

        Default Value
        -------------
        *value* or ``zeros(min(shape))``, where ``min(shape)`` refers to the
        minimum shape allowed by the array. If *shape* is not specified, the
        minimum shape is (0,).

        Description
        -----------
        The trait returned by CArray() is similar to that returned by Array(),
        except that it allows both upcasting and downcasting of assigned values
        that are already numpy arrays. It automatically casts tuples and
        lists of the right shape to the specified *dtype* (just like
        numpy's **array** does).
        """
        super( CArray, self ).__init__( dtype, shape, value, True,
                                        typecode = typecode, **metadata )