Hi all,
I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of course). Is there some easy way to do that by any chance ?
dtype1 = np.dtype( [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')]) ] )
dtype2 = np.dtype( [ ('vertex', 'f4', 3), ('normal', 'f4', 3), ('color', 'f4', 4)] )
Nicolas
On Wed, Dec 26, 2012 at 8:09 PM, Nicolas Rougier Nicolas.Rougier@inria.fr wrote:
Hi all,
I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of course). Is there some easy way to do that by any chance ?
dtype1 = np.dtype( [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')]) ] )
dtype2 = np.dtype( [ ('vertex', 'f4', 3), ('normal', 'f4', 3), ('color', 'f4', 4)] )
If you have an array whose dtype is dtype1, and you want to convert it into an array with dtype2, then you just do my_dtype2_array = my_dtype1_array.view(dtype2)
If you have dtype1 and you want to programmaticaly construct dtype2, then that's a little more fiddly and depends on what exactly you're trying to do, but start by poking around with dtype1.names and dtype1.fields, which contain information on how dtype1 is put together in the form of regular python structures.
-n
Yep, I'm trying to construct dtype2 programmaticaly and was hoping for some function giving me a "canonical" expression of the dtype. I've started playing with fields but it's just a bit harder than I though (lot of different cases and recursion).
Thanks for the answer.
Nicolas
On Dec 27, 2012, at 1:32 , Nathaniel Smith wrote:
On Wed, Dec 26, 2012 at 8:09 PM, Nicolas Rougier Nicolas.Rougier@inria.fr wrote:
Hi all,
I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of course). Is there some easy way to do that by any chance ?
dtype1 = np.dtype( [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')]) ] )
dtype2 = np.dtype( [ ('vertex', 'f4', 3), ('normal', 'f4', 3), ('color', 'f4', 4)] )
If you have an array whose dtype is dtype1, and you want to convert it into an array with dtype2, then you just do my_dtype2_array = my_dtype1_array.view(dtype2)
If you have dtype1 and you want to programmaticaly construct dtype2, then that's a little more fiddly and depends on what exactly you're trying to do, but start by poking around with dtype1.names and dtype1.fields, which contain information on how dtype1 is put together in the form of regular python structures.
-n _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
I ended coding the dtype reduction, it's not foolproof but it might be useful for others as well.
Nicolas
import numpy as np
def dtype_reduce(dtype, level=0, depth=0): """ Try to reduce dtype up to a given level when it is possible
dtype = [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')])]
level 0: ['color,vertex,normal,', 10, 'float32'] level 1: [['color', 4, 'float32'] ['normal', 3, 'float32'] ['vertex', 3, 'float32']] """ dtype = np.dtype(dtype) fields = dtype.fields
# No fields if fields is None: if dtype.shape: count = reduce(mul, dtype.shape) else: count = 1 size = dtype.itemsize/count if dtype.subdtype: name = str( dtype.subdtype[0] ) else: name = str( dtype ) return ['', count, name] else: items = [] name = '' # Get reduced fields for key,value in fields.items(): l = dtype_reduce(value[0], level, depth+1) if type(l[0]) is str: items.append( [key, l[1], l[2]] ) else: items.append( l ) name += key+','
# Check if we can reduce item list ctype = None count = 0 for i,item in enumerate(items): # One item is a list, we cannot reduce if type(item[0]) is not str: return items else: if i==0: ctype = item[2] count += item[1] else: if item[2] != ctype: return items count += item[1] if depth >= level: return [name, count, ctype] else: return items
if __name__ == '__main__':
# Fully reductible dtype = [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')])] print 'level 0:' print dtype_reduce(dtype,level=0) print 'level 1:' print dtype_reduce(dtype,level=1) print
# Not fully reductible dtype = [ ('vertex', [('x', 'i4'), ('y', 'i4'), ('z', 'i4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')])] print 'level 0:' print dtype_reduce(dtype,level=0) print
# Not reductible at all dtype = [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'i4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'i4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'i4'), ('a', 'f4')])] print 'level 0:' print dtype_reduce(dtype,level=0)
On Dec 27, 2012, at 9:11 , Nicolas Rougier wrote:
Yep, I'm trying to construct dtype2 programmaticaly and was hoping for some function giving me a "canonical" expression of the dtype. I've started playing with fields but it's just a bit harder than I though (lot of different cases and recursion).
Thanks for the answer.
Nicolas
On Dec 27, 2012, at 1:32 , Nathaniel Smith wrote:
On Wed, Dec 26, 2012 at 8:09 PM, Nicolas Rougier Nicolas.Rougier@inria.fr wrote:
Hi all,
I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of course). Is there some easy way to do that by any chance ?
dtype1 = np.dtype( [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')]) ] )
dtype2 = np.dtype( [ ('vertex', 'f4', 3), ('normal', 'f4', 3), ('color', 'f4', 4)] )
If you have an array whose dtype is dtype1, and you want to convert it into an array with dtype2, then you just do my_dtype2_array = my_dtype1_array.view(dtype2)
If you have dtype1 and you want to programmaticaly construct dtype2, then that's a little more fiddly and depends on what exactly you're trying to do, but start by poking around with dtype1.names and dtype1.fields, which contain information on how dtype1 is put together in the form of regular python structures.
-n _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion