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"""A collection of functions designed to help I/O with ascii files."""
__docformat__ = "restructuredtext en"
import numpy as np
import numpy.core.numeric as nx
from __builtin__ import bool, int, long, float, complex, object, unicode, str
def _is_string_like(obj):
"""
Check whether obj behaves like a string.
"""
try:
obj + ''
except (TypeError, ValueError):
return False
return True
def _to_filehandle(fname, flag='r', return_opened=False):
"""
Returns the filehandle corresponding to a string or a file.
If the string ends in '.gz', the file is automatically unzipped.
Parameters
----------
fname : string, filehandle
Name of the file whose filehandle must be returned.
flag : string, optional
Flag indicating the status of the file ('r' for read, 'w' for write).
return_opened : boolean, optional
Whether to return the opening status of the file.
"""
if _is_string_like(fname):
if fname.endswith('.gz'):
import gzip
fhd = gzip.open(fname, flag)
elif fname.endswith('.bz2'):
import bz2
fhd = bz2.BZ2File(fname)
else:
fhd = file(fname, flag)
opened = True
elif hasattr(fname, 'seek'):
fhd = fname
opened = False
else:
raise ValueError('fname must be a string or file handle')
if return_opened:
return fhd, opened
return fhd
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError : If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
def flatten_dtype(ndtype, flatten_base=False):
"""
Unpack a structured data-type by collapsing nested fields and/or fields
with a shape.
Note that the field names are lost.
Parameters
----------
ndtype : dtype
The datatype to collapse
flatten_base : {False, True}, optional
Whether to transform a field with a shape into several fields or not.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
... ('block', int, (2, 3))])
>>> np.lib._iotools.flatten_dtype(dt)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
dtype('int32')]
"""
names = ndtype.names
if names is None:
if flatten_base:
return [ndtype.base] * int(np.prod(ndtype.shape))
return [ndtype.base]
else:
types = []
for field in names:
(typ, _) = ndtype.fields[field]
flat_dt = flatten_dtype(typ, flatten_base)
types.extend(flat_dt)
return types
class LineSplitter:
"""
Object to split a string at a given delimiter or at given places.
Parameters
----------
delimiter : str, int, or sequence of ints, optional
If a string, character used to delimit consecutive fields.
If an integer or a sequence of integers, width(s) of each field.
comment : str, optional
Character used to mark the beginning of a comment. Default is '#'.
autostrip : bool, optional
Whether to strip each individual field. Default is True.
"""
def autostrip(self, method):
"""
Wrapper to strip each member of the output of `method`.
Parameters
----------
method : function
Function that takes a single argument and returns a sequence of
strings.
Returns
-------
wrapped : function
The result of wrapping `method`. `wrapped` takes a single input
argument and returns a list of strings that are stripped of
white-space.
"""
return lambda input: [_.strip() for _ in method(input)]
#
def __init__(self, delimiter=None, comments='#', autostrip=True):
self.comments = comments
# Delimiter is a character
if (delimiter is None) or _is_string_like(delimiter):
delimiter = delimiter or None
_handyman = self._delimited_splitter
# Delimiter is a list of field widths
elif hasattr(delimiter, '__iter__'):
_handyman = self._variablewidth_splitter
idx = np.cumsum([0] + list(delimiter))
delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
# Delimiter is a single integer
elif int(delimiter):
(_handyman, delimiter) = (self._fixedwidth_splitter, int(delimiter))
else:
(_handyman, delimiter) = (self._delimited_splitter, None)
self.delimiter = delimiter
if autostrip:
self._handyman = self.autostrip(_handyman)
else:
self._handyman = _handyman
#
def _delimited_splitter(self, line):
line = line.split(self.comments)[0].strip(" \r\n")
if not line:
return []
return line.split(self.delimiter)
#
def _fixedwidth_splitter(self, line):
line = line.split(self.comments)[0]
if not line:
return []
fixed = self.delimiter
slices = [slice(i, i + fixed) for i in range(len(line))[::fixed]]
return [line[s] for s in slices]
#
def _variablewidth_splitter(self, line):
line = line.split(self.comments)[0]
if not line:
return []
slices = self.delimiter
return [line[s] for s in slices]
#
def __call__(self, line):
return self._handyman(line)
class NameValidator:
"""
Object to validate a list of strings to use as field names.
The strings are stripped of any non alphanumeric character, and spaces
are replaced by '_'. During instantiation, the user can define a list of
names to exclude, as well as a list of invalid characters. Names in the
exclusion list are appended a '_' character.
Once an instance has been created, it can be called with a list of names,
and a list of valid names will be created.
The `__call__` method accepts an optional keyword "default" that sets
the default name in case of ambiguity. By default this is 'f', so
that names will default to `f0`, `f1`, etc.
Parameters
----------
excludelist : sequence, optional
A list of names to exclude. This list is appended to the default list
['return', 'file', 'print']. Excluded names are appended an underscore:
for example, `file` becomes `file_` if supplied.
deletechars : str, optional
A string combining invalid characters that must be deleted from the
names.
casesensitive : {True, False, 'upper', 'lower'}, optional
* If True, field names are case-sensitive.
* If False or 'upper', field names are converted to upper case.
* If 'lower', field names are converted to lower case.
The default value is True.
Notes
-----
Calling an instance of `NameValidator` is the same as calling its method
`validate`.
Examples
--------
>>> validator = np.lib._iotools.NameValidator()
>>> validator(['file', 'field2', 'with space', 'CaSe'])
['file_', 'field2', 'with_space', 'CaSe']
>>> validator = np.lib._iotools.NameValidator(excludelist=['excl'],
deletechars='q',
case_sensitive='False')
>>> validator(['excl', 'field2', 'no_q', 'with space', 'CaSe'])
['excl_', 'field2', 'no_', 'with_space', 'case']
"""
#
defaultexcludelist = ['return', 'file', 'print']
defaultdeletechars = set("""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
#
def __init__(self, excludelist=None, deletechars=None, case_sensitive=None):
# Process the exclusion list ..
if excludelist is None:
excludelist = []
excludelist.extend(self.defaultexcludelist)
self.excludelist = excludelist
# Process the list of characters to delete
if deletechars is None:
delete = self.defaultdeletechars
else:
delete = set(deletechars)
delete.add('"')
self.deletechars = delete
# Process the case option .....
if (case_sensitive is None) or (case_sensitive is True):
self.case_converter = lambda x: x
elif (case_sensitive is False) or ('u' in case_sensitive):
self.case_converter = lambda x: x.upper()
elif 'l' in case_sensitive:
self.case_converter = lambda x: x.lower()
else:
self.case_converter = lambda x: x
def validate(self, names, defaultfmt="f%i", nbfields=None):
"""
Validate a list of strings to use as field names for a structured array.
Parameters
----------
names : sequence of str
Strings to be validated.
defaultfmt : str, optional
Default format string, used if validating a given string reduces its
length to zero.
nboutput : integer, optional
Final number of validated names, used to expand or shrink the initial
list of names.
Returns
-------
validatednames : list of str
The list of validated field names.
Notes
-----
A `NameValidator` instance can be called directly, which is the same as
calling `validate`. For examples, see `NameValidator`.
"""
# Initial checks ..............
if (names is None):
if (nbfields is None):
return None
names = []
if isinstance(names, basestring):
names = [names, ]
if nbfields is not None:
nbnames = len(names)
if (nbnames < nbfields):
names = list(names) + [''] * (nbfields - nbnames)
elif (nbnames > nbfields):
names = names[:nbfields]
# Set some shortcuts ...........
deletechars = self.deletechars
excludelist = self.excludelist
case_converter = self.case_converter
# Initializes some variables ...
validatednames = []
seen = dict()
nbempty = 0
#
for item in names:
item = case_converter(item)
item = item.strip().replace(' ', '_')
item = ''.join([c for c in item if c not in deletechars])
if item == '':
item = defaultfmt % nbempty
while item in names:
nbempty += 1
item = defaultfmt % nbempty
nbempty += 1
elif item in excludelist:
item += '_'
cnt = seen.get(item, 0)
if cnt > 0:
validatednames.append(item + '_%d' % cnt)
else:
validatednames.append(item)
seen[item] = cnt + 1
return tuple(validatednames)
#
def __call__(self, names, defaultfmt="f%i", nbfields=None):
return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)
def str2bool(value):
"""
Tries to transform a string supposed to represent a boolean to a boolean.
Parameters
----------
value : str
The string that is transformed to a boolean.
Returns
-------
boolval : bool
The boolean representation of `value`.
Raises
------
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 : sequence of str, optional
Sequence of strings indicating a missing value.
locked : bool, optional
Whether the StringConverter should be locked to prevent automatic
upgrade or not. Default is False.
"""
#
_mapper = [(nx.bool_, str2bool, False),
(nx.integer, int, -1),
(nx.floating, float, nx.nan),
(complex, complex, nx.nan + 0j),
(nx.string_, str, '???')]
(_defaulttype, _defaultfunc, _defaultfill) = zip(*_mapper)
#
@classmethod
def _getsubdtype(cls, val):
"""Returns the type of the dtype of the input variable."""
return np.array(val).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 = datetustil.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))
#
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
ttype = np.bool
else:
# Is the input a np.dtype ?
try:
self.func = None
ttype = np.dtype(dtype_or_func).type
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"\
" or 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
ttype = self._getsubdtype(default)
# Set the status according to the dtype
_status = -1
for (i, (deftype, func, default_def)) in enumerate(self._mapper):
if np.issubdtype(ttype, deftype):
_status = i
if default is None:
self.default = default_def
else:
self.default = default
break
if _status == -1:
# We never found a match in the _mapper...
_status = 0
self.default = default
self._status = _status
# 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 smthg more robust
if self.func == self._mapper[1][1]:
self.func = lambda x : int(float(x))
# Store the list of strings corresponding to missing values.
if missing_values is None:
self.missing_values = set([''])
else:
if isinstance(missing_values, basestring):
missing_values = missing_values.split(",")
self.missing_values = set(list(missing_values) + [''])
#
self._callingfunction = self._strict_call
self.type = ttype
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:
return self.func(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 upgrade(self, value):
"""
Try to 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:
self._strict_call(value)
except ValueError:
# 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
self.upgrade(value)
def iterupgrade(self, value):
self._checked = True
if not hasattr(value, '__iter__'):
value = (value,)
_strict_call = self._strict_call
try:
map(_strict_call, value)
except ValueError:
# 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:
raise ConverterError("Could not find a valid conversion function")
elif _status < _statusmax - 1:
_status += 1
(self.type, self.func, default) = self._mapper[_status]
if self._initial_default is not None:
self.default = self._initial_default
else:
self.default = default
self._status = _status
self.iterupgrade(value)
def update(self, func, default=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.
missing_values : sequence of str, optional
Sequence of strings indicating a missing value.
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._getsubdtype(default)
else:
try:
tester = func('1')
except (TypeError, ValueError):
tester = None
self.type = self._getsubdtype(tester)
# Add the missing values to the existing set
if missing_values is not None:
if _is_string_like(missing_values):
self.missing_values.add(missing_values)
elif hasattr(missing_values, '__iter__'):
for val in missing_values:
self.missing_values.add(val)
else:
self.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, basestring):
names = names.split(",")
names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)
ndtype = np.dtype(dict(formats=ndtype, names=names))
else:
nbtypes = len(ndtype)
# Explicit names
if names is not None:
validate = NameValidator(**validationargs)
if isinstance(names, basestring):
names = names.split(",")
# Simple dtype: repeat to match the nb of names
if nbtypes == 0:
formats = tuple([ndtype.type] * len(names))
names = validate(names, defaultfmt=defaultfmt)
ndtype = np.dtype(zip(names, formats))
# Structured dtype: just validate the names as needed
else:
ndtype.names = validate(names, nbfields=nbtypes,
defaultfmt=defaultfmt)
# No implicit names
elif (nbtypes > 0):
validate = NameValidator(**validationargs)
# Default initial names : should we change the format ?
if (ndtype.names == tuple("f%i" % i for i in range(nbtypes))) and \
(defaultfmt != "f%i"):
ndtype.names = validate([''] * nbtypes, defaultfmt=defaultfmt)
# Explicit initial names : just validate
else:
ndtype.names = validate(ndtype.names, defaultfmt=defaultfmt)
return ndtype
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