Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/_iotools.py
"""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 numpy.compat import asbytes, asunicode
def _decode_line(line, encoding=None):
"""Decode bytes from binary input streams.
Defaults to decoding from 'latin1'. That differs from the behavior of
np.compat.asunicode that decodes from 'ascii'.
Parameters
----------
line : str or bytes
Line to be decoded.
encoding : str
Encoding used to decode `line`.
Returns
-------
decoded_line : str
"""
if type(line) is bytes:
if encoding is None:
encoding = "latin1"
line = line.decode(encoding)
return line
def _is_string_like(obj):
"""
Check whether obj behaves like a string.
"""
try:
obj + ''
except (TypeError, ValueError):
return False
return True
def _is_bytes_like(obj):
"""
Check whether obj behaves like a bytes object.
"""
try:
obj + b''
except (TypeError, ValueError):
return False
return True
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 is not None:
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 : bool, optional
If True, transform a field with a shape into several fields. Default is
False.
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('int64')]
>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
[dtype('S4'),
dtype('float64'),
dtype('float64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64')]
"""
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:
info = ndtype.fields[field]
flat_dt = flatten_dtype(info[0], 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.
comments : 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,
encoding=None):
delimiter = _decode_line(delimiter)
comments = _decode_line(comments)
self.comments = comments
# Delimiter is a character
if (delimiter is None) or isinstance(delimiter, str):
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
self.encoding = encoding
def _delimited_splitter(self, line):
"""Chop off comments, strip, and split at delimiter. """
if self.comments is not None:
line = line.split(self.comments)[0]
line = line.strip(" \r\n")
if not line:
return []
return line.split(self.delimiter)
def _fixedwidth_splitter(self, line):
if self.comments is not None:
line = line.split(self.comments)[0]
line = line.strip("\r\n")
if not line:
return []
fixed = self.delimiter
slices = [slice(i, i + fixed) for i in range(0, len(line), fixed)]
return [line[s] for s in slices]
def _variablewidth_splitter(self, line):
if self.comments is not None:
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(_decode_line(line, self.encoding))
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.
case_sensitive : {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.
replace_space : '_', optional
Character(s) used in replacement of white spaces.
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_Q', 'WITH_SPACE', 'CASE')
"""
defaultexcludelist = ['return', 'file', 'print']
defaultdeletechars = set(r"""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
def __init__(self, excludelist=None, deletechars=None,
case_sensitive=None, replace_space='_'):
# 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 case_sensitive.startswith('u'):
self.case_converter = lambda x: x.upper()
elif case_sensitive.startswith('l'):
self.case_converter = lambda x: x.lower()
else:
msg = 'unrecognized case_sensitive value %s.' % case_sensitive
raise ValueError(msg)
self.replace_space = replace_space
def validate(self, names, defaultfmt="f%i", nbfields=None):
"""
Validate a list of strings 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.
nbfields : 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, str):
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
replace_space = self.replace_space
# Initializes some variables ...
validatednames = []
seen = dict()
nbempty = 0
for item in names:
item = case_converter(item).strip()
if replace_space:
item = item.replace(' ', replace_space)
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 : {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
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