/usr/lib/python2.7/dist-packages/traits/trait_numeric.py is in python-traits 4.6.0-1.
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#
# 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 asarray, ndarray
# 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:
value = self._default_for_dtype_and_shape( dtype, shape )
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 create_editor ( self ):
""" 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()
def _default_for_dtype_and_shape ( self, dtype, shape ):
""" Invent a suitable default value for a given dtype and shape. """
from numpy import zeros
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:
# Given a (minimum-allowed-length, maximum-allowed_length)
# pair for a particular axis, use the minimum.
item = item[0]
size.append( item )
value = zeros( size, dt )
return value
#-------------------------------------------------------------------------------
# '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 )
#-------------------------------------------------------------------------------
# 'ArrayOrNone' trait
#-------------------------------------------------------------------------------
class ArrayOrNone ( CArray ):
""" A trait whose value may be either a NumPy array or None, with
casting allowed. The default is None.
"""
def __init__ ( self, *args, **metadata ):
# Normally use object identity to detect array values changing:
metadata.setdefault( 'comparison_mode', OBJECT_IDENTITY_COMPARE )
super( ArrayOrNone, self ).__init__( *args, **metadata )
def validate (self, object, name, value ):
if value is None:
return value
return super( ArrayOrNone, self ).validate( object, name, value )
def get_default_value ( self ):
dv = self.default_value
if dv is None:
return ( 0, dv )
else:
return ( 7, ( self.copy_default_value,
( self.validate( None, None, dv ), ), None ) )
def _default_for_dtype_and_shape ( self, dtype, shape ):
# For ArrayOrNone, if no default is explicitly specified, we
# always default to `None`.
return None
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