records.py - Record Arrays = Record arrays expose the...

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""" Record Arrays ============= Record arrays expose the fields of structured arrays as properties. Most commonly, ndarrays contain elements of a single type, e.g. floats, integers, bools etc. However, it is possible for elements to be combinations of these using structured types, such as:: >>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', int), ('y', float)]) >>> a array([(1, 2.0), (1, 2.0)], dtype=[('x', '<i4'), ('y', '<f8')]) Here, each element consists of two fields: x (and int), and y (a float). This is known as a structured array. The different fields are analogous to columns in a spread-sheet. The different fields can be accessed as one would a dictionary:: >>> a['x'] array([1, 1]) >>> a['y'] array([ 2., 2.]) Record arrays allow us to access fields as properties:: >>> ar = np.rec.array(a) >>> ar.x array([1, 1]) >>> ar.y array([ 2., 2.]) """ from __future__ import division, absolute_import, print_function import sys import os import warnings from . import numeric as sb from . import numerictypes as nt from numpy.compat import isfileobj, bytes, long from .arrayprint import get_printoptions # All of the functions allow formats to be a dtype __all__ = ['record', 'recarray', 'format_parser'] ndarray = sb.ndarray _byteorderconv = {'b':'>', 'l':'<', 'n':'=', 'B':'>', 'L':'<', 'N':'=',
'S':'s', 's':'s', '>':'>', '<':'<', '=':'=', '|':'|', 'I':'|', 'i':'|'} # formats regular expression # allows multidimension spec with a tuple syntax in front # of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 ' # are equally allowed numfmt = nt.typeDict def find_duplicate(list): """Find duplication in a list, return a list of duplicated elements""" dup = [] for i in range(len(list)): if (list[i] in list[i + 1:]): if (list[i] not in dup): dup.append(list[i]) return dup class format_parser(object): """ Class to convert formats, names, titles description to a dtype. After constructing the format_parser object, the dtype attribute is the converted data-type: ``dtype = format_parser(formats, names, titles).dtype`` Attributes ---------- dtype : dtype The converted data-type. Parameters ---------- formats : str or list of str The format description, either specified as a string with comma-separated format descriptions in the form ``'f8, i4, a5'``, or a list of format description strings in the form ``['f8', 'i4', 'a5']``. names : str or list/tuple of str The field names, either specified as a comma-separated string in the form ``'col1, col2, col3'``, or as a list or tuple of strings in the form ``['col1', 'col2', 'col3']``. An empty list can be used, in that case default field names ('f0', 'f1', ...) are used. titles : sequence Sequence of title strings. An empty list can be used to leave titles out. aligned : bool, optional If True, align the fields by padding as the C-compiler would. Default is False. byteorder : str, optional If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is used. For all available string specifiers, see `dtype.newbyteorder`. See Also -------- dtype, typename, sctype2char Examples -------- >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... ['T1', 'T2', 'T3']).dtype dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), '|S5')]) `names` and/or `titles` can be empty lists. If `titles` is an empty list, titles will simply not appear. If `names` is empty, default field names will be used.

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