| Current File : //proc/self/root/proc/self/root/lib64/python2.7/site-packages/numpy/lib/_iotools.pyc |
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Check whether obj behaves like a string.
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Check whether obj behaves like a bytes object.
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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.
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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
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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')]
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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.
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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.
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NameValidatorc B s\ e Z d Z d d d g Z e d � Z d
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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.
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_', 'with_space', 'case']
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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`.
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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
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Exception raised when an error occurs in a converter for string values.
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Exception raised when an attempt is made to upgrade a locked converter.
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Warning issued when a string converter has a problem.
Notes
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In `genfromtxt` a `ConversionWarning` is issued if raising exceptions
is explicitly suppressed with the "invalid_raise" keyword.
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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.
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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)
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Try to find the best converter for a given string, and return the result.
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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
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Parameters
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value : str
The string to convert.
Returns
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The result of converting `value` with the appropriate converter.
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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, 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.
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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')])
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