[Numpy-tickets] [NumPy] #728: numpy.r_ incorrectly casts with mixed types

[NumPy]

#728: numpy.r_ incorrectly casts with mixed types
------------------------+---------------------------------------------------
 Reporter:  bsouthey    |       Owner:  somebody
     Type:  defect      |      Status:  new     
 Priority:  high        |   Milestone:  1.0.5   
Component:  numpy.core  |     Version:  none    
 Severity:  normal      |    Keywords:          
------------------------+---------------------------------------------------
 Numpy.r_ appears to use the type of the array argument in determining the
 return types. So when arguments have mixed types, there can be a loss of
 precision:

 Correct when all arguments have the same type:
 {{{
 import numpy
 ra=numpy.r_[-10, numpy.array([2, 3, 4]), 10]
 type(ra), type(ra[0]), type(ra[1])
 }}}
 Provides: (<type 'numpy.ndarray'>, <type 'numpy.int64'>, <type
 'numpy.int64'>)


 It is also correct if at least one array has the 'correct' type:

 {{{
 ra=numpy.r_[-10.1, numpy.array([1]), numpy.array([2, 3, 4], dtype=float),
 10]
 type(ra), type(ra[0]), type(ra[1]), ra[0], ra[4]
 }}}
 Provides: (<type 'numpy.ndarray'>, <type 'numpy.float64'>, <type
 'numpy.float64'>, -10.1, 4.0)

 But if mixed types are used:

 {{{
 import numpy
 ra=numpy.r_[-10.1, numpy.array([2, 3, 4]), 10.0]
 type(ra), type(ra[0]), type(ra[1]), ra[0], ra[4]
 }}}

 Provides: (<type 'numpy.ndarray'>, <type 'numpy.int64'>, <type
 'numpy.int64'>, -10, 10)[[BR]]

 So the float has been down cast to a integer so 10.1 becomes 10.

 The workaround is to ensure that all numpy arrays have the same type as
 required:

 {{{
 import numpy
 ra=numpy.r_[-10.1, numpy.array([2, 3, 4], dtype=float), 10]
 type(ra), type(ra[0]), type(ra[1]), ra[0], ra[4]
 }}}
 Provides:
 (<type 'numpy.ndarray'>, <type 'numpy.float64'>, <type 'numpy.float64'>,
 -10.1, 10.0)

-- 
Ticket URL: <http://scipy.org/scipy/numpy/ticket/728>
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