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"""A collection of functions designed to help I/O with ascii files.
"""
else: from __builtin__ import bool, int, float, complex, object, unicode, str
"""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.
Returns ------- decoded_line : unicode Unicode in Python 2, a str (unicode) in Python 3.
""" else: line = line.decode(encoding)
""" Check whether obj behaves like a string. """ return True
""" Check whether obj behaves like a bytes object. """ try: obj + b'' except (TypeError, ValueError): return False return True
""" 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
""" 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
""" 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('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: info = ndtype.fields[field] flat_dt = flatten_dtype(info[0], flatten_base) types.extend(flat_dt) return types
""" 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.
"""
""" 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)] #
delimiter = _decode_line(delimiter) comments = _decode_line(comments)
self.comments = comments
# Delimiter is a character if (delimiter is None) or isinstance(delimiter, basestring): 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 #
"""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) #
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] #
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] #
return self._handyman(_decode_line(line, self.encoding))
""" 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_', 'with_space', 'case']
""" # #
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
""" 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, 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 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) #
return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)
""" 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")
""" Exception raised when an error occurs in a converter for string values.
"""
""" Exception raised when an attempt is made to upgrade a locked converter.
"""
""" 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.
"""
""" 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.
""" # (nx.integer, int, -1)]
# On 32-bit systems, we need to make sure that we explicitly include # nx.int64 since ns.integer is nx.int32. _mapper.append((nx.int64, int, -1))
(nx.complexfloating, complex, nx.nan + 0j), (nx.longdouble, nx.longdouble, nx.nan), (nx.unicode_, asunicode, '???'), (nx.string_, asbytes, '???')])
def _getdtype(cls, val): """Returns the dtype of the input variable.""" return np.array(val).dtype #
def _getsubdtype(cls, val): """Returns the type of the dtype of the input variable.""" return np.array(val).dtype.type # # 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").
def _dtypeortype(cls, dtype): """Returns dtype for datetime64 and type of dtype otherwise.""" if dtype.type == np.datetime64: return dtype return dtype.type #
""" 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)) #
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) # Set the status according to the dtype _status = -1 for (i, (deftype, func, default_def)) in enumerate(self._mapper): if np.issubdtype(dtype.type, deftype): _status = i if default is None: self.default = default_def else: self.default = default break # if a converter for the specific dtype is available use that last_func = func for (i, (deftype, func, default_def)) in enumerate(self._mapper): if dtype.type == deftype: _status = i last_func = func if default is None: self.default = default_def else: self.default = default break func = last_func 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 # 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, basestring): 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 #
try: return self.func(value) except ValueError: return self.default #
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) #
return self._callingfunction(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: # 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 return self.upgrade(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: # 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)
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, basestring) for v in missing_values): raise TypeError("missing_values must be strings or unicode") self.missing_values.update(missing_values)
""" 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(list(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 |