y=array([1,2,3], '1') y
array([1, 2, 3],'1')
y.astype(Int32)
array([1, 2, 3],'i')
-----Original Message----- From: numpy-discussion-admin@lists.sourceforge.net [mailto:numpy-discussion-admin@lists.sourceforge.net]On Behalf Of Chris Barker Sent: Thursday, November 02, 2000 1:46 PM Cc: numpy-discussion@sourceforge.net Subject: [Numpy-discussion] "formstring()" in place?
I have a narge array of type "1" (single bytes). I need to convert it to Int32, in the manner that fromstring() would. Right now, I am doing:
Array = fromstring(Array.tostring(),'f')
This works fine, but what concerns me is that I need to do this on potentially HUGE arrays, and if I understand this right, I am going to create a copy with tostring, and then another copy with fromstring, that then gets referenced to Array, at which point the first original copy gets de-referenced, and should be deleted, and the temporary one gets deleted at some point in this process. I don't know when stuff created in the middle of a statement gets deleted, so I could potentially have three copies of the data around at the same time, and at least two. Since it is exactly the same C array, I'd like to be able to do this without making any copies at all. Is it possible? It seems like it should be a simple matter of changing the typecode and shape, but is this possible?
While I'm asking questions: can I byteswap in place as well?
The greater problem:
To give a little background, and to see if anyone has a better idea of how to do what I am doing, I thought I'd run through the task that I really need to do.
I am reading a binary file full of a lot of data. I have some control over the form of the file, but it needs to be compact, so I can't just make everything the same large type. The file is essentially a whole bunch of records, each of which is a collection of a couple of different types, and which I would eventually like to get into a couple of NumPy arrays. My first cut at the problem was to read each record one at a time in a loop, and use the struct module to convert everything. This worked fine, but was pretty darn slow, so I am now doing it all with NumPy like this (one example, I have more complex ones):
num_bytes = 9 # number of bytes in a record: two longs and a char
# read all the data into a single byte array data = fromstring(file.read(num_bytes*num_timesteps*num_LEs),'1')
# rearrange into 3-d array data.shape = (num_timesteps,num_LEs,num_bytes)
# extract LE data: LEs = data[:,:,:8] # extract flag data flags = data[:,:,8]
# convert LE data to longs LEs = fromstring(LEs.tostring(),Int32)
if endian == 'L': # byteswap if required LEs = LEs.byteswapped()
# convert to 3-d array LEs.shape = (num_timesteps,num_LEs,2)
Anyone have any better ideas on how to do this?
Thanks,
-Chris