898 lines
30 KiB
Python
898 lines
30 KiB
Python
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"""A collection of functions designed to help I/O with ascii files.
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"""
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__docformat__ = "restructuredtext en"
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import numpy as np
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import numpy.core.numeric as nx
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from numpy.compat import asbytes, asunicode
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def _decode_line(line, encoding=None):
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"""Decode bytes from binary input streams.
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Defaults to decoding from 'latin1'. That differs from the behavior of
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np.compat.asunicode that decodes from 'ascii'.
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Parameters
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----------
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line : str or bytes
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Line to be decoded.
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encoding : str
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Encoding used to decode `line`.
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Returns
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-------
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decoded_line : str
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"""
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if type(line) is bytes:
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if encoding is None:
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encoding = "latin1"
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line = line.decode(encoding)
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return line
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def _is_string_like(obj):
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"""
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Check whether obj behaves like a string.
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"""
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try:
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obj + ''
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except (TypeError, ValueError):
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return False
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return True
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def _is_bytes_like(obj):
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"""
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Check whether obj behaves like a bytes object.
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"""
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try:
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obj + b''
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except (TypeError, ValueError):
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return False
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return True
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def has_nested_fields(ndtype):
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"""
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Returns whether one or several fields of a dtype are nested.
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Parameters
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----------
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ndtype : dtype
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Data-type of a structured array.
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Raises
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------
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AttributeError
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If `ndtype` does not have a `names` attribute.
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Examples
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--------
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>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
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>>> np.lib._iotools.has_nested_fields(dt)
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False
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"""
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for name in ndtype.names or ():
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if ndtype[name].names is not None:
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return True
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return False
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def flatten_dtype(ndtype, flatten_base=False):
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"""
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Unpack a structured data-type by collapsing nested fields and/or fields
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with a shape.
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Note that the field names are lost.
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Parameters
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----------
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ndtype : dtype
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The datatype to collapse
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flatten_base : bool, optional
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If True, transform a field with a shape into several fields. Default is
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False.
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Examples
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--------
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>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
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... ('block', int, (2, 3))])
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>>> np.lib._iotools.flatten_dtype(dt)
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[dtype('S4'), dtype('float64'), dtype('float64'), dtype('int64')]
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>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
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[dtype('S4'),
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dtype('float64'),
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dtype('float64'),
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dtype('int64'),
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dtype('int64'),
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dtype('int64'),
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dtype('int64'),
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dtype('int64'),
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dtype('int64')]
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"""
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names = ndtype.names
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if names is None:
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if flatten_base:
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return [ndtype.base] * int(np.prod(ndtype.shape))
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return [ndtype.base]
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else:
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types = []
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for field in names:
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info = ndtype.fields[field]
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flat_dt = flatten_dtype(info[0], flatten_base)
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types.extend(flat_dt)
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return types
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class LineSplitter:
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"""
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Object to split a string at a given delimiter or at given places.
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Parameters
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----------
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delimiter : str, int, or sequence of ints, optional
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If a string, character used to delimit consecutive fields.
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If an integer or a sequence of integers, width(s) of each field.
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comments : str, optional
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Character used to mark the beginning of a comment. Default is '#'.
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autostrip : bool, optional
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Whether to strip each individual field. Default is True.
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"""
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def autostrip(self, method):
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"""
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Wrapper to strip each member of the output of `method`.
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Parameters
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----------
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method : function
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Function that takes a single argument and returns a sequence of
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strings.
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Returns
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-------
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wrapped : function
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The result of wrapping `method`. `wrapped` takes a single input
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argument and returns a list of strings that are stripped of
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white-space.
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"""
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return lambda input: [_.strip() for _ in method(input)]
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def __init__(self, delimiter=None, comments='#', autostrip=True,
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encoding=None):
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delimiter = _decode_line(delimiter)
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comments = _decode_line(comments)
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self.comments = comments
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# Delimiter is a character
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if (delimiter is None) or isinstance(delimiter, str):
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delimiter = delimiter or None
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_handyman = self._delimited_splitter
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# Delimiter is a list of field widths
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elif hasattr(delimiter, '__iter__'):
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_handyman = self._variablewidth_splitter
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idx = np.cumsum([0] + list(delimiter))
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delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
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# Delimiter is a single integer
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elif int(delimiter):
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(_handyman, delimiter) = (
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self._fixedwidth_splitter, int(delimiter))
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else:
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(_handyman, delimiter) = (self._delimited_splitter, None)
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self.delimiter = delimiter
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if autostrip:
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self._handyman = self.autostrip(_handyman)
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else:
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self._handyman = _handyman
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self.encoding = encoding
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def _delimited_splitter(self, line):
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"""Chop off comments, strip, and split at delimiter. """
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if self.comments is not None:
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line = line.split(self.comments)[0]
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line = line.strip(" \r\n")
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if not line:
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return []
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return line.split(self.delimiter)
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def _fixedwidth_splitter(self, line):
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if self.comments is not None:
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line = line.split(self.comments)[0]
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line = line.strip("\r\n")
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if not line:
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return []
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fixed = self.delimiter
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slices = [slice(i, i + fixed) for i in range(0, len(line), fixed)]
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return [line[s] for s in slices]
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def _variablewidth_splitter(self, line):
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if self.comments is not None:
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line = line.split(self.comments)[0]
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if not line:
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return []
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slices = self.delimiter
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return [line[s] for s in slices]
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def __call__(self, line):
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return self._handyman(_decode_line(line, self.encoding))
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class NameValidator:
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"""
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Object to validate a list of strings to use as field names.
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The strings are stripped of any non alphanumeric character, and spaces
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are replaced by '_'. During instantiation, the user can define a list
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of names to exclude, as well as a list of invalid characters. Names in
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the exclusion list are appended a '_' character.
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Once an instance has been created, it can be called with a list of
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names, and a list of valid names will be created. The `__call__`
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method accepts an optional keyword "default" that sets the default name
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in case of ambiguity. By default this is 'f', so that names will
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default to `f0`, `f1`, etc.
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Parameters
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----------
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excludelist : sequence, optional
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A list of names to exclude. This list is appended to the default
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list ['return', 'file', 'print']. Excluded names are appended an
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underscore: for example, `file` becomes `file_` if supplied.
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deletechars : str, optional
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A string combining invalid characters that must be deleted from the
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names.
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case_sensitive : {True, False, 'upper', 'lower'}, optional
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* If True, field names are case-sensitive.
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* If False or 'upper', field names are converted to upper case.
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* If 'lower', field names are converted to lower case.
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The default value is True.
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replace_space : '_', optional
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Character(s) used in replacement of white spaces.
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Notes
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-----
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Calling an instance of `NameValidator` is the same as calling its
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method `validate`.
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Examples
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--------
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>>> validator = np.lib._iotools.NameValidator()
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>>> validator(['file', 'field2', 'with space', 'CaSe'])
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('file_', 'field2', 'with_space', 'CaSe')
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>>> validator = np.lib._iotools.NameValidator(excludelist=['excl'],
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... deletechars='q',
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... case_sensitive=False)
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>>> validator(['excl', 'field2', 'no_q', 'with space', 'CaSe'])
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('EXCL', 'FIELD2', 'NO_Q', 'WITH_SPACE', 'CASE')
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"""
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defaultexcludelist = ['return', 'file', 'print']
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defaultdeletechars = set(r"""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
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def __init__(self, excludelist=None, deletechars=None,
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case_sensitive=None, replace_space='_'):
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# Process the exclusion list ..
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if excludelist is None:
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excludelist = []
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excludelist.extend(self.defaultexcludelist)
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self.excludelist = excludelist
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# Process the list of characters to delete
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if deletechars is None:
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delete = self.defaultdeletechars
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else:
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delete = set(deletechars)
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delete.add('"')
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self.deletechars = delete
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# Process the case option .....
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if (case_sensitive is None) or (case_sensitive is True):
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self.case_converter = lambda x: x
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elif (case_sensitive is False) or case_sensitive.startswith('u'):
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self.case_converter = lambda x: x.upper()
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elif case_sensitive.startswith('l'):
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self.case_converter = lambda x: x.lower()
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else:
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msg = 'unrecognized case_sensitive value %s.' % case_sensitive
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raise ValueError(msg)
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self.replace_space = replace_space
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def validate(self, names, defaultfmt="f%i", nbfields=None):
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"""
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Validate a list of strings as field names for a structured array.
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Parameters
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----------
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names : sequence of str
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Strings to be validated.
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defaultfmt : str, optional
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Default format string, used if validating a given string
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reduces its length to zero.
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nbfields : integer, optional
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Final number of validated names, used to expand or shrink the
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initial list of names.
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Returns
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-------
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validatednames : list of str
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The list of validated field names.
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Notes
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-----
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A `NameValidator` instance can be called directly, which is the
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same as calling `validate`. For examples, see `NameValidator`.
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"""
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# Initial checks ..............
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if (names is None):
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if (nbfields is None):
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return None
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names = []
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if isinstance(names, str):
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names = [names, ]
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if nbfields is not None:
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nbnames = len(names)
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if (nbnames < nbfields):
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names = list(names) + [''] * (nbfields - nbnames)
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elif (nbnames > nbfields):
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names = names[:nbfields]
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# Set some shortcuts ...........
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deletechars = self.deletechars
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excludelist = self.excludelist
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case_converter = self.case_converter
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replace_space = self.replace_space
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# Initializes some variables ...
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validatednames = []
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seen = dict()
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nbempty = 0
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for item in names:
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item = case_converter(item).strip()
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if replace_space:
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item = item.replace(' ', replace_space)
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item = ''.join([c for c in item if c not in deletechars])
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if item == '':
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item = defaultfmt % nbempty
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while item in names:
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nbempty += 1
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item = defaultfmt % nbempty
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nbempty += 1
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elif item in excludelist:
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item += '_'
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cnt = seen.get(item, 0)
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if cnt > 0:
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validatednames.append(item + '_%d' % cnt)
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else:
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validatednames.append(item)
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seen[item] = cnt + 1
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return tuple(validatednames)
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def __call__(self, names, defaultfmt="f%i", nbfields=None):
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return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)
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def str2bool(value):
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"""
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Tries to transform a string supposed to represent a boolean to a boolean.
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Parameters
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----------
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value : str
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The string that is transformed to a boolean.
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Returns
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-------
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boolval : bool
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The boolean representation of `value`.
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Raises
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|
|
------
|
||
|
|
ValueError
|
||
|
|
If the string is not 'True' or 'False' (case independent)
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> np.lib._iotools.str2bool('TRUE')
|
||
|
|
True
|
||
|
|
>>> np.lib._iotools.str2bool('false')
|
||
|
|
False
|
||
|
|
|
||
|
|
"""
|
||
|
|
value = value.upper()
|
||
|
|
if value == 'TRUE':
|
||
|
|
return True
|
||
|
|
elif value == 'FALSE':
|
||
|
|
return False
|
||
|
|
else:
|
||
|
|
raise ValueError("Invalid boolean")
|
||
|
|
|
||
|
|
|
||
|
|
class ConverterError(Exception):
|
||
|
|
"""
|
||
|
|
Exception raised when an error occurs in a converter for string values.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class ConverterLockError(ConverterError):
|
||
|
|
"""
|
||
|
|
Exception raised when an attempt is made to upgrade a locked converter.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class ConversionWarning(UserWarning):
|
||
|
|
"""
|
||
|
|
Warning issued when a string converter has a problem.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
In `genfromtxt` a `ConversionWarning` is issued if raising exceptions
|
||
|
|
is explicitly suppressed with the "invalid_raise" keyword.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class StringConverter:
|
||
|
|
"""
|
||
|
|
Factory class for function transforming a string into another object
|
||
|
|
(int, float).
|
||
|
|
|
||
|
|
After initialization, an instance can be called to transform a string
|
||
|
|
into another object. If the string is recognized as representing a
|
||
|
|
missing value, a default value is returned.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
func : function
|
||
|
|
Function used for the conversion.
|
||
|
|
default : any
|
||
|
|
Default value to return when the input corresponds to a missing
|
||
|
|
value.
|
||
|
|
type : type
|
||
|
|
Type of the output.
|
||
|
|
_status : int
|
||
|
|
Integer representing the order of the conversion.
|
||
|
|
_mapper : sequence of tuples
|
||
|
|
Sequence of tuples (dtype, function, default value) to evaluate in
|
||
|
|
order.
|
||
|
|
_locked : bool
|
||
|
|
Holds `locked` parameter.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
dtype_or_func : {None, dtype, function}, optional
|
||
|
|
If a `dtype`, specifies the input data type, used to define a basic
|
||
|
|
function and a default value for missing data. For example, when
|
||
|
|
`dtype` is float, the `func` attribute is set to `float` and the
|
||
|
|
default value to `np.nan`. If a function, this function is used to
|
||
|
|
convert a string to another object. In this case, it is recommended
|
||
|
|
to give an associated default value as input.
|
||
|
|
default : any, optional
|
||
|
|
Value to return by default, that is, when the string to be
|
||
|
|
converted is flagged as missing. If not given, `StringConverter`
|
||
|
|
tries to supply a reasonable default value.
|
||
|
|
missing_values : {None, sequence of str}, optional
|
||
|
|
``None`` or sequence of strings indicating a missing value. If ``None``
|
||
|
|
then missing values are indicated by empty entries. The default is
|
||
|
|
``None``.
|
||
|
|
locked : bool, optional
|
||
|
|
Whether the StringConverter should be locked to prevent automatic
|
||
|
|
upgrade or not. Default is False.
|
||
|
|
|
||
|
|
"""
|
||
|
|
_mapper = [(nx.bool_, str2bool, False),
|
||
|
|
(nx.int_, int, -1),]
|
||
|
|
|
||
|
|
# On 32-bit systems, we need to make sure that we explicitly include
|
||
|
|
# nx.int64 since ns.int_ is nx.int32.
|
||
|
|
if nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize:
|
||
|
|
_mapper.append((nx.int64, int, -1))
|
||
|
|
|
||
|
|
_mapper.extend([(nx.float64, float, nx.nan),
|
||
|
|
(nx.complex128, complex, nx.nan + 0j),
|
||
|
|
(nx.longdouble, nx.longdouble, nx.nan),
|
||
|
|
# If a non-default dtype is passed, fall back to generic
|
||
|
|
# ones (should only be used for the converter)
|
||
|
|
(nx.integer, int, -1),
|
||
|
|
(nx.floating, float, nx.nan),
|
||
|
|
(nx.complexfloating, complex, nx.nan + 0j),
|
||
|
|
# Last, try with the string types (must be last, because
|
||
|
|
# `_mapper[-1]` is used as default in some cases)
|
||
|
|
(nx.str_, asunicode, '???'),
|
||
|
|
(nx.bytes_, asbytes, '???'),
|
||
|
|
])
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _getdtype(cls, val):
|
||
|
|
"""Returns the dtype of the input variable."""
|
||
|
|
return np.array(val).dtype
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _getsubdtype(cls, val):
|
||
|
|
"""Returns the type of the dtype of the input variable."""
|
||
|
|
return np.array(val).dtype.type
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _dtypeortype(cls, dtype):
|
||
|
|
"""Returns dtype for datetime64 and type of dtype otherwise."""
|
||
|
|
|
||
|
|
# This is a bit annoying. We want to return the "general" type in most
|
||
|
|
# cases (ie. "string" rather than "S10"), but we want to return the
|
||
|
|
# specific type for datetime64 (ie. "datetime64[us]" rather than
|
||
|
|
# "datetime64").
|
||
|
|
if dtype.type == np.datetime64:
|
||
|
|
return dtype
|
||
|
|
return dtype.type
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def upgrade_mapper(cls, func, default=None):
|
||
|
|
"""
|
||
|
|
Upgrade the mapper of a StringConverter by adding a new function and
|
||
|
|
its corresponding default.
|
||
|
|
|
||
|
|
The input function (or sequence of functions) and its associated
|
||
|
|
default value (if any) is inserted in penultimate position of the
|
||
|
|
mapper. The corresponding type is estimated from the dtype of the
|
||
|
|
default value.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
func : var
|
||
|
|
Function, or sequence of functions
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import dateutil.parser
|
||
|
|
>>> import datetime
|
||
|
|
>>> dateparser = dateutil.parser.parse
|
||
|
|
>>> defaultdate = datetime.date(2000, 1, 1)
|
||
|
|
>>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
|
||
|
|
"""
|
||
|
|
# Func is a single functions
|
||
|
|
if hasattr(func, '__call__'):
|
||
|
|
cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))
|
||
|
|
return
|
||
|
|
elif hasattr(func, '__iter__'):
|
||
|
|
if isinstance(func[0], (tuple, list)):
|
||
|
|
for _ in func:
|
||
|
|
cls._mapper.insert(-1, _)
|
||
|
|
return
|
||
|
|
if default is None:
|
||
|
|
default = [None] * len(func)
|
||
|
|
else:
|
||
|
|
default = list(default)
|
||
|
|
default.append([None] * (len(func) - len(default)))
|
||
|
|
for fct, dft in zip(func, default):
|
||
|
|
cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _find_map_entry(cls, dtype):
|
||
|
|
# if a converter for the specific dtype is available use that
|
||
|
|
for i, (deftype, func, default_def) in enumerate(cls._mapper):
|
||
|
|
if dtype.type == deftype:
|
||
|
|
return i, (deftype, func, default_def)
|
||
|
|
|
||
|
|
# otherwise find an inexact match
|
||
|
|
for i, (deftype, func, default_def) in enumerate(cls._mapper):
|
||
|
|
if np.issubdtype(dtype.type, deftype):
|
||
|
|
return i, (deftype, func, default_def)
|
||
|
|
|
||
|
|
raise LookupError
|
||
|
|
|
||
|
|
def __init__(self, dtype_or_func=None, default=None, missing_values=None,
|
||
|
|
locked=False):
|
||
|
|
# Defines a lock for upgrade
|
||
|
|
self._locked = bool(locked)
|
||
|
|
# No input dtype: minimal initialization
|
||
|
|
if dtype_or_func is None:
|
||
|
|
self.func = str2bool
|
||
|
|
self._status = 0
|
||
|
|
self.default = default or False
|
||
|
|
dtype = np.dtype('bool')
|
||
|
|
else:
|
||
|
|
# Is the input a np.dtype ?
|
||
|
|
try:
|
||
|
|
self.func = None
|
||
|
|
dtype = np.dtype(dtype_or_func)
|
||
|
|
except TypeError:
|
||
|
|
# dtype_or_func must be a function, then
|
||
|
|
if not hasattr(dtype_or_func, '__call__'):
|
||
|
|
errmsg = ("The input argument `dtype` is neither a"
|
||
|
|
" function nor a dtype (got '%s' instead)")
|
||
|
|
raise TypeError(errmsg % type(dtype_or_func))
|
||
|
|
# Set the function
|
||
|
|
self.func = dtype_or_func
|
||
|
|
# If we don't have a default, try to guess it or set it to
|
||
|
|
# None
|
||
|
|
if default is None:
|
||
|
|
try:
|
||
|
|
default = self.func('0')
|
||
|
|
except ValueError:
|
||
|
|
default = None
|
||
|
|
dtype = self._getdtype(default)
|
||
|
|
|
||
|
|
# find the best match in our mapper
|
||
|
|
try:
|
||
|
|
self._status, (_, func, default_def) = self._find_map_entry(dtype)
|
||
|
|
except LookupError:
|
||
|
|
# no match
|
||
|
|
self.default = default
|
||
|
|
_, func, _ = self._mapper[-1]
|
||
|
|
self._status = 0
|
||
|
|
else:
|
||
|
|
# use the found default only if we did not already have one
|
||
|
|
if default is None:
|
||
|
|
self.default = default_def
|
||
|
|
else:
|
||
|
|
self.default = default
|
||
|
|
|
||
|
|
# If the input was a dtype, set the function to the last we saw
|
||
|
|
if self.func is None:
|
||
|
|
self.func = func
|
||
|
|
|
||
|
|
# If the status is 1 (int), change the function to
|
||
|
|
# something more robust.
|
||
|
|
if self.func == self._mapper[1][1]:
|
||
|
|
if issubclass(dtype.type, np.uint64):
|
||
|
|
self.func = np.uint64
|
||
|
|
elif issubclass(dtype.type, np.int64):
|
||
|
|
self.func = np.int64
|
||
|
|
else:
|
||
|
|
self.func = lambda x: int(float(x))
|
||
|
|
# Store the list of strings corresponding to missing values.
|
||
|
|
if missing_values is None:
|
||
|
|
self.missing_values = {''}
|
||
|
|
else:
|
||
|
|
if isinstance(missing_values, str):
|
||
|
|
missing_values = missing_values.split(",")
|
||
|
|
self.missing_values = set(list(missing_values) + [''])
|
||
|
|
|
||
|
|
self._callingfunction = self._strict_call
|
||
|
|
self.type = self._dtypeortype(dtype)
|
||
|
|
self._checked = False
|
||
|
|
self._initial_default = default
|
||
|
|
|
||
|
|
def _loose_call(self, value):
|
||
|
|
try:
|
||
|
|
return self.func(value)
|
||
|
|
except ValueError:
|
||
|
|
return self.default
|
||
|
|
|
||
|
|
def _strict_call(self, value):
|
||
|
|
try:
|
||
|
|
|
||
|
|
# We check if we can convert the value using the current function
|
||
|
|
new_value = self.func(value)
|
||
|
|
|
||
|
|
# In addition to having to check whether func can convert the
|
||
|
|
# value, we also have to make sure that we don't get overflow
|
||
|
|
# errors for integers.
|
||
|
|
if self.func is int:
|
||
|
|
try:
|
||
|
|
np.array(value, dtype=self.type)
|
||
|
|
except OverflowError:
|
||
|
|
raise ValueError
|
||
|
|
|
||
|
|
# We're still here so we can now return the new value
|
||
|
|
return new_value
|
||
|
|
|
||
|
|
except ValueError:
|
||
|
|
if value.strip() in self.missing_values:
|
||
|
|
if not self._status:
|
||
|
|
self._checked = False
|
||
|
|
return self.default
|
||
|
|
raise ValueError("Cannot convert string '%s'" % value)
|
||
|
|
|
||
|
|
def __call__(self, value):
|
||
|
|
return self._callingfunction(value)
|
||
|
|
|
||
|
|
def _do_upgrade(self):
|
||
|
|
# Raise an exception if we locked the converter...
|
||
|
|
if self._locked:
|
||
|
|
errmsg = "Converter is locked and cannot be upgraded"
|
||
|
|
raise ConverterLockError(errmsg)
|
||
|
|
_statusmax = len(self._mapper)
|
||
|
|
# Complains if we try to upgrade by the maximum
|
||
|
|
_status = self._status
|
||
|
|
if _status == _statusmax:
|
||
|
|
errmsg = "Could not find a valid conversion function"
|
||
|
|
raise ConverterError(errmsg)
|
||
|
|
elif _status < _statusmax - 1:
|
||
|
|
_status += 1
|
||
|
|
self.type, self.func, default = self._mapper[_status]
|
||
|
|
self._status = _status
|
||
|
|
if self._initial_default is not None:
|
||
|
|
self.default = self._initial_default
|
||
|
|
else:
|
||
|
|
self.default = default
|
||
|
|
|
||
|
|
def upgrade(self, value):
|
||
|
|
"""
|
||
|
|
Find the best converter for a given string, and return the result.
|
||
|
|
|
||
|
|
The supplied string `value` is converted by testing different
|
||
|
|
converters in order. First the `func` method of the
|
||
|
|
`StringConverter` instance is tried, if this fails other available
|
||
|
|
converters are tried. The order in which these other converters
|
||
|
|
are tried is determined by the `_status` attribute of the instance.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
value : str
|
||
|
|
The string to convert.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
out : any
|
||
|
|
The result of converting `value` with the appropriate converter.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self._checked = True
|
||
|
|
try:
|
||
|
|
return self._strict_call(value)
|
||
|
|
except ValueError:
|
||
|
|
self._do_upgrade()
|
||
|
|
return self.upgrade(value)
|
||
|
|
|
||
|
|
def iterupgrade(self, value):
|
||
|
|
self._checked = True
|
||
|
|
if not hasattr(value, '__iter__'):
|
||
|
|
value = (value,)
|
||
|
|
_strict_call = self._strict_call
|
||
|
|
try:
|
||
|
|
for _m in value:
|
||
|
|
_strict_call(_m)
|
||
|
|
except ValueError:
|
||
|
|
self._do_upgrade()
|
||
|
|
self.iterupgrade(value)
|
||
|
|
|
||
|
|
def update(self, func, default=None, testing_value=None,
|
||
|
|
missing_values='', locked=False):
|
||
|
|
"""
|
||
|
|
Set StringConverter attributes directly.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
func : function
|
||
|
|
Conversion function.
|
||
|
|
default : any, optional
|
||
|
|
Value to return by default, that is, when the string to be
|
||
|
|
converted is flagged as missing. If not given,
|
||
|
|
`StringConverter` tries to supply a reasonable default value.
|
||
|
|
testing_value : str, optional
|
||
|
|
A string representing a standard input value of the converter.
|
||
|
|
This string is used to help defining a reasonable default
|
||
|
|
value.
|
||
|
|
missing_values : {sequence of str, None}, optional
|
||
|
|
Sequence of strings indicating a missing value. If ``None``, then
|
||
|
|
the existing `missing_values` are cleared. The default is `''`.
|
||
|
|
locked : bool, optional
|
||
|
|
Whether the StringConverter should be locked to prevent
|
||
|
|
automatic upgrade or not. Default is False.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
`update` takes the same parameters as the constructor of
|
||
|
|
`StringConverter`, except that `func` does not accept a `dtype`
|
||
|
|
whereas `dtype_or_func` in the constructor does.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.func = func
|
||
|
|
self._locked = locked
|
||
|
|
|
||
|
|
# Don't reset the default to None if we can avoid it
|
||
|
|
if default is not None:
|
||
|
|
self.default = default
|
||
|
|
self.type = self._dtypeortype(self._getdtype(default))
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
tester = func(testing_value or '1')
|
||
|
|
except (TypeError, ValueError):
|
||
|
|
tester = None
|
||
|
|
self.type = self._dtypeortype(self._getdtype(tester))
|
||
|
|
|
||
|
|
# Add the missing values to the existing set or clear it.
|
||
|
|
if missing_values is None:
|
||
|
|
# Clear all missing values even though the ctor initializes it to
|
||
|
|
# set(['']) when the argument is None.
|
||
|
|
self.missing_values = set()
|
||
|
|
else:
|
||
|
|
if not np.iterable(missing_values):
|
||
|
|
missing_values = [missing_values]
|
||
|
|
if not all(isinstance(v, str) for v in missing_values):
|
||
|
|
raise TypeError("missing_values must be strings or unicode")
|
||
|
|
self.missing_values.update(missing_values)
|
||
|
|
|
||
|
|
|
||
|
|
def easy_dtype(ndtype, names=None, defaultfmt="f%i", **validationargs):
|
||
|
|
"""
|
||
|
|
Convenience function to create a `np.dtype` object.
|
||
|
|
|
||
|
|
The function processes the input `dtype` and matches it with the given
|
||
|
|
names.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ndtype : var
|
||
|
|
Definition of the dtype. Can be any string or dictionary recognized
|
||
|
|
by the `np.dtype` function, or a sequence of types.
|
||
|
|
names : str or sequence, optional
|
||
|
|
Sequence of strings to use as field names for a structured dtype.
|
||
|
|
For convenience, `names` can be a string of a comma-separated list
|
||
|
|
of names.
|
||
|
|
defaultfmt : str, optional
|
||
|
|
Format string used to define missing names, such as ``"f%i"``
|
||
|
|
(default) or ``"fields_%02i"``.
|
||
|
|
validationargs : optional
|
||
|
|
A series of optional arguments used to initialize a
|
||
|
|
`NameValidator`.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> np.lib._iotools.easy_dtype(float)
|
||
|
|
dtype('float64')
|
||
|
|
>>> np.lib._iotools.easy_dtype("i4, f8")
|
||
|
|
dtype([('f0', '<i4'), ('f1', '<f8')])
|
||
|
|
>>> np.lib._iotools.easy_dtype("i4, f8", defaultfmt="field_%03i")
|
||
|
|
dtype([('field_000', '<i4'), ('field_001', '<f8')])
|
||
|
|
|
||
|
|
>>> np.lib._iotools.easy_dtype((int, float, float), names="a,b,c")
|
||
|
|
dtype([('a', '<i8'), ('b', '<f8'), ('c', '<f8')])
|
||
|
|
>>> np.lib._iotools.easy_dtype(float, names="a,b,c")
|
||
|
|
dtype([('a', '<f8'), ('b', '<f8'), ('c', '<f8')])
|
||
|
|
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
ndtype = np.dtype(ndtype)
|
||
|
|
except TypeError:
|
||
|
|
validate = NameValidator(**validationargs)
|
||
|
|
nbfields = len(ndtype)
|
||
|
|
if names is None:
|
||
|
|
names = [''] * len(ndtype)
|
||
|
|
elif isinstance(names, str):
|
||
|
|
names = names.split(",")
|
||
|
|
names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)
|
||
|
|
ndtype = np.dtype(dict(formats=ndtype, names=names))
|
||
|
|
else:
|
||
|
|
# Explicit names
|
||
|
|
if names is not None:
|
||
|
|
validate = NameValidator(**validationargs)
|
||
|
|
if isinstance(names, str):
|
||
|
|
names = names.split(",")
|
||
|
|
# Simple dtype: repeat to match the nb of names
|
||
|
|
if ndtype.names is None:
|
||
|
|
formats = tuple([ndtype.type] * len(names))
|
||
|
|
names = validate(names, defaultfmt=defaultfmt)
|
||
|
|
ndtype = np.dtype(list(zip(names, formats)))
|
||
|
|
# Structured dtype: just validate the names as needed
|
||
|
|
else:
|
||
|
|
ndtype.names = validate(names, nbfields=len(ndtype.names),
|
||
|
|
defaultfmt=defaultfmt)
|
||
|
|
# No implicit names
|
||
|
|
elif ndtype.names is not None:
|
||
|
|
validate = NameValidator(**validationargs)
|
||
|
|
# Default initial names : should we change the format ?
|
||
|
|
numbered_names = tuple("f%i" % i for i in range(len(ndtype.names)))
|
||
|
|
if ((ndtype.names == numbered_names) and (defaultfmt != "f%i")):
|
||
|
|
ndtype.names = validate([''] * len(ndtype.names),
|
||
|
|
defaultfmt=defaultfmt)
|
||
|
|
# Explicit initial names : just validate
|
||
|
|
else:
|
||
|
|
ndtype.names = validate(ndtype.names, defaultfmt=defaultfmt)
|
||
|
|
return ndtype
|