Application: gvSIG desktop » gvSIG 3D

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Structured Arrays (aka Record Arrays)

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Introduction

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Numpy provides powerful capabilities to create arrays of structs or records.

These arrays permit one to manipulate the data by the structs or by fields of

the struct. A simple example will show what is meant.: ::

 >>> x = np.zeros((2,),dtype=('i4,f4,a10'))

 >>> x[:] = [(1,2.,'Hello'),(2,3.,"World")]

 >>> x

 array([(1, 2.0, 'Hello'), (2, 3.0, 'World')],

      dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])

Here we have created a one-dimensional array of length 2. Each element of

this array is a record that contains three items, a 32-bit integer, a 32-bit

float, and a string of length 10 or less. If we index this array at the second

position we get the second record: ::

 >>> x[1]

 (2,3.,"World")

Conveniently, one can access any field of the array by indexing using the

string that names that field. In this case the fields have received the

default names 'f0', 'f1' and 'f2'.

 >>> y = x['f1']

 >>> y

 array([ 2.,  3.], dtype=float32)

 >>> y[:] = 2*y

 >>> y

 array([ 4.,  6.], dtype=float32)

 >>> x

 array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],

       dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])

In these examples, y is a simple float array consisting of the 2nd field

in the record. But, rather than being a copy of the data in the structured

array, it is a view, i.e., it shares exactly the same memory locations.

Thus, when we updated this array by doubling its values, the structured

array shows the corresponding values as doubled as well. Likewise, if one

changes the record, the field view also changes: ::

 >>> x[1] = (-1,-1.,"Master")

 >>> x

 array([(1, 4.0, 'Hello'), (-1, -1.0, 'Master')],

       dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])

 >>> y

 array([ 4., -1.], dtype=float32)

Defining Structured Arrays

==========================

One defines a structured array through the dtype object.  There are

**several** alternative ways to define the fields of a record.  Some of

these variants provide backward compatibility with Numeric, numarray, or

another module, and should not be used except for such purposes. These

will be so noted. One specifies record structure in

one of four alternative ways, using an argument (as supplied to a dtype

function keyword or a dtype object constructor itself).  This

argument must be one of the following: 1) string, 2) tuple, 3) list, or

4) dictionary.  Each of these is briefly described below.

1) String argument (as used in the above examples).

In this case, the constructor expects a comma-separated list of type

specifiers, optionally with extra shape information.

The type specifiers can take 4 different forms: ::

  a) b1, i1, i2, i4, i8, u1, u2, u4, u8, f4, f8, c8, c16, a<n>

     (representing bytes, ints, unsigned ints, floats, complex and

      fixed length strings of specified byte lengths)

  b) int8,...,uint8,...,float32, float64, complex64, complex128

     (this time with bit sizes)

  c) older Numeric/numarray type specifications (e.g. Float32).

     Don't use these in new code!

  d) Single character type specifiers (e.g H for unsigned short ints).

     Avoid using these unless you must. Details can be found in the

     Numpy book

These different styles can be mixed within the same string (but why would you

want to do that?). Furthermore, each type specifier can be prefixed

with a repetition number, or a shape. In these cases an array

element is created, i.e., an array within a record. That array

is still referred to as a single field. An example: ::

 >>> x = np.zeros(3, dtype='3int8, float32, (2,3)float64')

 >>> x

 array([([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),

        ([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),

        ([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])],

       dtype=[('f0', '|i1', 3), ('f1', '>f4'), ('f2', '>f8', (2, 3))])

By using strings to define the record structure, it precludes being

able to name the fields in the original definition. The names can

be changed as shown later, however.

2) Tuple argument: The only relevant tuple case that applies to record

structures is when a structure is mapped to an existing data type. This

is done by pairing in a tuple, the existing data type with a matching

dtype definition (using any of the variants being described here). As

an example (using a definition using a list, so see 3) for further

details): ::

 >>> x = zeros(3, dtype=('i4',[('r','u1'), ('g','u1'), ('b','u1'), ('a','u1')]))

 >>> x

 array([0, 0, 0])

 >>> x['r']

 array([0, 0, 0], dtype=uint8)

In this case, an array is produced that looks and acts like a simple int32 array,

but also has definitions for fields that use only one byte of the int32 (a bit

like Fortran equivalencing).

3) List argument: In this case the record structure is defined with a list of

tuples. Each tuple has 2 or 3 elements specifying: 1) The name of the field

('' is permitted), 2) the type of the field, and 3) the shape (optional).

For example:

 >>> x = np.zeros(3, dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])

 >>> x

 array([(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),

        (0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),

        (0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]])],

       dtype=[('x', '>f4'), ('y', '>f4'), ('value', '>f4', (2, 2))])

4) Dictionary argument: two different forms are permitted. The first consists

of a dictionary with two required keys ('names' and 'formats'), each having an

equal sized list of values. The format list contains any type/shape specifier

allowed in other contexts. The names must be strings. There are two optional

keys: 'offsets' and 'titles'. Each must be a correspondingly matching list to

the required two where offsets contain integer offsets for each field, and

titles are objects containing metadata for each field (these do not have

to be strings), where the value of None is permitted. As an example: ::

 >>> x = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})

 >>> x

 array([(0, 0.0), (0, 0.0), (0, 0.0)],

       dtype=[('col1', '>i4'), ('col2', '>f4')])

The other dictionary form permitted is a dictionary of name keys with tuple

values specifying type, offset, and an optional title.

 >>> x = np.zeros(3, dtype={'col1':('i1',0,'title 1'), 'col2':('f4',1,'title 2')})

 array([(0, 0.0), (0, 0.0), (0, 0.0)],

       dtype=[(('title 1', 'col1'), '|i1'), (('title 2', 'col2'), '>f4')])

Accessing and modifying field names

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The field names are an attribute of the dtype object defining the record structure.

For the last example: ::

 >>> x.dtype.names

 ('col1', 'col2')

 >>> x.dtype.names = ('x', 'y')

 >>> x

 array([(0, 0.0), (0, 0.0), (0, 0.0)],

      dtype=[(('title 1', 'x'), '|i1'), (('title 2', 'y'), '>f4')])

 >>> x.dtype.names = ('x', 'y', 'z') # wrong number of names

 <type 'exceptions.ValueError'>: must replace all names at once with a sequence of length 2

Accessing field titles

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The field titles provide a standard place to put associated info for fields.

They do not have to be strings.

 >>> x.dtype.fields['x'][2]

 'title 1'

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