[Numpy-discussion] type conversion question

K.-Michael Aye kmichael.aye@gmail....
Thu Apr 18 18:31:36 CDT 2013


I don't understand why sometimes a direct assignment of a new dtype is 
possible (but messes up the values), and why at other times a seemingly 
harmless upcast (in my potentially ignorant point of view) is not 
possible.
So, maybe a direct assignment of a new dtype is actually never a good 
idea? (I'm asking), and one should always go the route of newarray= 
array(oldarray, dtype=newdtype), but why then sometimes the upcast 
provides an error and forbids it and sometimes not?


Examples:

In [140]: slope.read_center_window()

In [141]: slope.data.dtype
Out[141]: dtype('float32')

In [142]: slope.data[1,1]
Out[142]: 10.044398

In [143]: val = slope.data[1,1]

In [144]: slope.data.dtype='float64'

In [145]: slope.data[1,1]
Out[145]: 586.98938070189865

#-----
#Here, the value of data[1,1] has completely changed (and so has the 
rest of the array), and no error was given.
# But then...
#----

In [146]: val.dtype
Out[146]: dtype('float32')

In [147]: val
Out[147]: 10.044398

In [148]: val.dtype='float64'
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-148-52a373a41cac> in <module>()
----> 1 val.dtype='float64'

AttributeError: attribute 'dtype' of 'numpy.generic' objects is not writable

=== end of code

So why is there an error in the 2nd case, but no error in the first 
case? Is there a logic to it?

Thanks,
Michael





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