2008/10/12 Linda Seltzer <[EMAIL PROTECTED]>: >> Here is an example that works for any working numpy installation: >> >> import numpy as npy >> npy.zeros((256, 256)) > This suggestion from David did work so far, and removing the other import > line enabled the program to run. > However, the data types the program used as defaults for variables has > changed, and now I am getting error messages about data types. It seems > that some variables are getting a default designation as floats. Before I > installed numpy and needed 2-D arrays, the program was working with the > default types, and I did not have to specify types. > Is there a clear tutorial that describes a means to assign data types for > each variable as in C, so that I don't obtain error messages about data > types?
Python is a dynamically-typed language (unlike C), so variables do not have type. That is, a variable can refer to an object of any type; if you need to know what type of object a variable currently refers to you must inspect the object. You may want to go through one of the brief introduction-to-python tutorials that are on the python website just to get comfortable with the language. (For example, understanding the meaning and syntax of import statements.) When you create a numpy array, you can specify its type. You can also explicitly or implicitly convert the types of numpy arrays. I recommend you select a data type, let's say np.uint32, and make sure various arrays are created containing that type: np.zeros((m,n),dtype=np.uint32) np.arange(10,dtype=np.uint32) x.astype(np.uint32) np.array([1,2,3,4.5], dtype=np.uint32) et cetera. Most operations (addition, multiplication, maximum) will preserve the data type of arrays they are given (but if you supply two different data types numpy will attempt to choose the "larger"). > Because I am simulating code for a DSP processor, the data types I need > are unsigned bytes, unsigned 32-bit ints, and signed 32-bit ints. In some > cases I can use unsigned and signed 16-bit ints. > Also, what data types are valid for use with local operations such as > exclusive or? The numpy data types you want are described by "dtype" objects. These can in principle become quite complicated, but the ones you need are given names already: np.uint8 np.uint32 np.int32 np.uint16 np.int16 You can specify any of these as "dtype=" arguments to the various numpy functions. If you need really rigid typing, python may not be the ideal language for you. Good luck, Anne _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
