def validate_levels(name, levels):
"""
Validate containers and mappings as well as contained objects
The present function is meant to valdiate the 'levels' of a variable. That
is, the value of the variable itself, the values of the second level (in
case the value is a list, tuple, or dict), the values of the third level,
and so on.
Parameters
----------
name : None
The name of the variable to be validated.
levels : list or tuple
The list of levels.
See Also
--------
magni.utils.validation.validate_generic : Validate non-numeric objects.
magni.utils.validation.validate_numeric : Validate numeric objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`levels` is a list containing the levels. The value of the variable is
validated against the first level. In case the value is a list, tuple, or
dict, the values contained in this are validated against the second level
and so on. Each level is itself a list with the first value being either
'generic' or 'numeric' followed by the arguments that should be passed to
the respective function (with the exception of `name` which is
automatically prepended by the present function).
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('levels', levels)
if isinstance(name, (list, tuple)):
name = list(name)
else:
name = [name]
if not isinstance(levels, (list, tuple)):
_report(TypeError,
'must be in {!r}.', (list, tuple),
var_name='levels',
var_value=levels,
expr='type({})',
prepend='Invalid validation call: ')
if len(levels) == 0:
_report(ValueError,
'must be > 0.',
var_name='levels',
var_value=levels,
expr='len({})',
prepend='Invalid validation call: ')
_validate_level(name, _get_var(name), levels)
def validate_numeric(name, type_, range_='[-inf;inf]', shape=(),
precision=None, ignore_none=False, var=None):
"""
Validate numeric objects.
The present function is meant to valdiate the type or class of an object.
Furthermore, if the object may only take on a connected range of values,
the object can be validated against this range. Also, the shape of the
object can be validated. Finally, the precision used to represent the
object can be validated.
If the present function is called with `name` set to None, an iterable with
the value 'numeric' followed by the remaining arguments passed in the call
is returned. This is useful in combination with the validation function
`magni.utils.validation.validate_levels`.
Parameters
----------
name : None
The name of the variable to be validated.
type_ : None
One or more references to groups of types.
range_ : None
The range of accepted values. (the default is '[-inf;inf]', which
implies that all values are accepted)
shape : list or tuple
The accepted shape. (the default is (), which implies that only scalar
values are accepted)
precision : None
One or more precisions.
ignore_none : bool
A flag indicating if the variable is allowed to be none. (the default
is False)
var : None
The value of the variable to be validated.
See Also
--------
magni.utils.validation.validate_generic : Validate non-numeric objects.
magni.utils.validation.validate_levels : Validate contained objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`type_` is either a single value treated as a list with one value or a
set-like object containing at least one value. Each value refers to a
number of data types depending if the string value is 'boolean', 'integer',
'floating', or 'complex'.
- 'boolean' tests if the variable is a bool or has the data type
`numpy.bool8`.
- 'integer' tests if the variable is an int or has the data type
`numpy.int8`, `numpy.int16`, `numpy.int32`, or `numpy.int64`.
- 'floating' tests if the variable is a float or has the data type
`numpy.float16`, `numpy.float32`, `numpy.float64`, or `numpy.float128`.
- 'complex' tests if the variable is a complex or has the data type
`numpy.complex32`, `numpy.complex64`, or `numpy.complex128`.
`range_` is either a list with two strings or a single string. In the
latter case, the default value of the argument is used as the second
string. The first value represents the accepted range of real values
whereas the second value represents the accepted range of imaginary values.
Each string consists of the following parts:
- One of the following delimiters: '[', '(', ']'.
- A numeric value (or '-inf').
- A semi-colon.
- A numeric value (or 'inf').
- One of the following delimiters: ']', ')', '['.
`shape` is either None meaning that any shape is accepted or a list of
integers. In the latter case, the integer -1 may be used to indicate that
the given axis may have any length.
`precision` is either an integer treated as a list with one value or a
set-like object containing at least one integer. Each value refers to an
accepted number of bits used to store each value of the variable.
`var` can be used to pass the value of the variable to be validated. This
is useful either when the variable cannot be looked up by `name` (for
example, if the variable is a property of the argument of a function) or to
remove the overhead of looking up the value.
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('numeric', type_, range_, shape, precision, ignore_none)
if var is None:
var = _get_var(name)
if var is None:
if not isinstance(ignore_none, bool):
_report(TypeError, 'must be {!r}.', bool, var_name='ignore_none',
var_value=ignore_none, expr='type({})',
prepend='Invalid validation call: ')
if ignore_none:
return
else:
_report(ValueError, 'must not be {!r}.', None, var_name=name)
dtype, bounds, dshape = _examine_var(name, var)
if isinstance(type_, str) or not hasattr(type_, '__iter__'):
type_ = (type_,)
_check_type(name, dtype, type_)
_check_range(name, bounds, range_)
_check_shape(name, dshape, shape)
_check_precision(name, dtype, type_, precision)
def validate_generic(name, type_, value_in=None, len_=None, keys_in=None,
has_keys=None, superclass=None, ignore_none=False,
var=None):
"""
Validate non-numeric objects.
The present function is meant to validate the type or class of an object.
Furthermore, if the object may only take on a limited number of values, the
object can be validated against this list. In the case of collections (for
example lists and tuples) and mappings (for example dictionaries), a
specific length can be required. Furthermore, in the case of mappings, the
keys can be validated by requiring and/or only allowing certain keys.
If the present function is called with `name` set to None, an iterable with
the value 'generic' followed by the remaining arguments passed in the call
is returned. This is useful in combination with the validation function
`magni.utils.validation.validate_levels`.
Parameters
----------
name : None
The name of the variable to be validated.
type_ : None
One or more references to groups of types, specific types, and/or
specific classes.
value_in : set-like
The list of values accepted. (the default is None, which implies that
all values are accepted)
len_ : int
The length required. (the default is None, which implies that all
lengths are accepted)
keys_in : set-like
The list of accepted keys. (the default is None, which implies that all
keys are accepted)
has_keys : set-like
The list of required keys. (the default is None, which implies that no
keys are required)
superclass : class
The required superclass. (the default is None, which implies that no
superclass is required)
ignore_none : bool
A flag indicating if the variable is allowed to be none. (the default
is False)
var : None
The value of the variable to be validated.
See Also
--------
magni.utils.validation.validate_levels : Validate contained objects.
magni.utils.validation.validate_numeric : Validate numeric objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`type_` is either a single value treated as a list with one value or a
set-like object containing at least one value. Each value is either a
specific type or class, or it refers to one or more types by having one of
the string values 'string', 'explicit collection', 'implicict collection',
'collection', 'mapping', 'function', 'class'.
- 'string' tests if the variable is a str.
- 'explicit collection' tests if the variable is a list or tuple.
- 'implicit collection' tests if the variable is iterable.
- 'collection' is a combination of the two above.
- 'mapping' tests if the variable is a dict.
- 'function' tests if the variable is a function.
- 'class' tests if the variable is a type.
`var` can be used to pass the value of the variable to be validated. This
is useful either when the variable cannot be looked up by `name` (for
example, if the variable is a property of the argument of a function) or to
remove the overhead of looking up the value.
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('generic', type_, value_in, len_, keys_in, has_keys,
superclass, ignore_none)
if var is None:
var = _get_var(name)
if var is None:
if not isinstance(ignore_none, bool):
_report(TypeError, 'must be {!r}.', bool, var_name='ignore_none',
var_value=ignore_none, expr='type({})',
prepend='Invalid validation call: ')
if ignore_none:
return
else:
_report(ValueError, 'must not be {!r}.', None, var_name=name)
if isinstance(type_, (str, type)) or not hasattr(type_, '__iter__'):
type_ = (type_,)
_check_type(name, var, type_)
_check_value(name, var, value_in)
_check_len(name, var, len_)
_check_keys(name, var, keys_in, has_keys)
_check_inheritance(name, var, superclass)
def validate_numeric(name,
type_,
range_='[-inf;inf]',
shape=(),
precision=None,
ignore_none=False,
var=None):
"""
Validate numeric objects.
The present function is meant to valdiate the type or class of an object.
Furthermore, if the object may only take on a connected range of values,
the object can be validated against this range. Also, the shape of the
object can be validated. Finally, the precision used to represent the
object can be validated.
If the present function is called with `name` set to None, an iterable with
the value 'numeric' followed by the remaining arguments passed in the call
is returned. This is useful in combination with the validation function
`magni.utils.validation.validate_levels`.
Parameters
----------
name : None
The name of the variable to be validated.
type_ : None
One or more references to groups of types.
range_ : None
The range of accepted values. (the default is '[-inf;inf]', which
implies that all values are accepted)
shape : list or tuple
The accepted shape. (the default is (), which implies that only scalar
values are accepted)
precision : None
One or more precisions.
ignore_none : bool
A flag indicating if the variable is allowed to be none. (the default
is False)
var : None
The value of the variable to be validated.
See Also
--------
magni.utils.validation.validate_generic : Validate non-numeric objects.
magni.utils.validation.validate_levels : Validate contained objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`type_` is either a single value treated as a list with one value or a
set-like object containing at least one value. Each value refers to a
number of data types depending if the string value is 'boolean', 'integer',
'floating', or 'complex'.
- 'boolean' tests if the variable is a bool or has the data type
`numpy.bool8`.
- 'integer' tests if the variable is an int or has the data type
`numpy.int8`, `numpy.int16`, `numpy.int32`, or `numpy.int64`.
- 'floating' tests if the variable is a float or has the data type
`numpy.float16`, `numpy.float32`, `numpy.float64`, or `numpy.float128`.
- 'complex' tests if the variable is a complex or has the data type
`numpy.complex32`, `numpy.complex64`, or `numpy.complex128`.
Because `bool` is a subclass of `int`, a `bool` will pass validation as an
'integer'. This, however, is not the case for `numpy.bool8`.
`range_` is either a list with two strings or a single string. In the
latter case, the default value of the argument is used as the second
string. The first value represents the accepted range of real values
whereas the second value represents the accepted range of imaginary values.
Each string consists of the following parts:
- One of the following delimiters: '[', '(', ']'.
- A numeric value (or '-inf').
- A semi-colon.
- A numeric value (or 'inf').
- One of the following delimiters: ']', ')', '['.
`shape` is either None meaning that any shape is accepted or a list of
integers. In the latter case, the integer -1 may be used to indicate that
the given axis may have any length.
`precision` is either an integer treated as a list with one value or a
list or tuple containing at least one integer. Each value refers to an
accepted number of bits used to store each value of the variable.
`var` can be used to pass the value of the variable to be validated. This
is useful either when the variable cannot be looked up by `name` (for
example, if the variable is a property of the argument of a function) or to
remove the overhead of looking up the value.
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('numeric', type_, range_, shape, precision, ignore_none)
if var is None:
var = _get_var(name)
if var is None:
if not isinstance(ignore_none, bool):
_report(TypeError,
'must be {!r}.',
bool,
var_name='ignore_none',
var_value=ignore_none,
expr='type({})',
prepend='Invalid validation call: ')
if ignore_none:
return
else:
_report(ValueError, 'must not be {!r}.', None, var_name=name)
if not isinstance(var, (_Number, np.generic, np.ndarray, _MatrixBase)):
_report(TypeError,
'>>{}<<, {!r}, must be numeric.', (name, var),
prepend='The value(s) of ')
dtype, bounds, dshape = _examine_var(name, var)
if isinstance(type_, str) or not hasattr(type_, '__iter__'):
type_ = (type_, )
_check_type(name, dtype, type_)
_check_range(name, bounds, range_)
_check_shape(name, dshape, shape)
_check_precision(name, dtype, type_, precision)
def validate_levels(name, levels):
"""
Validate containers and mappings as well as contained objects
The present function is meant to valdiate the 'levels' of a variable. That
is, the value of the variable itself, the values of the second level (in
case the value is a list, tuple, or dict), the values of the third level,
and so on.
Parameters
----------
name : None
The name of the variable to be validated.
levels : list or tuple
The list of levels.
See Also
--------
magni.utils.validation.validate_generic : Validate non-numeric objects.
magni.utils.validation.validate_numeric : Validate numeric objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`levels` is a list containing the levels. The value of the variable is
validated against the first level. In case the value is a list, tuple, or
dict, the values contained in this are validated against the second level
and so on. Each level is itself a list with the first value being either
'generic' or 'numeric' followed by the arguments that should be passed to
the respective function (with the exception of `name` which is
automatically prepended by the present function).
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('levels', levels)
if isinstance(name, (list, tuple)):
name = list(name)
elif isinstance(name, str):
name = [name]
elif hasattr(name, '__iter__'):
name = [value for value in name]
else:
name = [name]
if not isinstance(levels, (list, tuple)):
_report(TypeError, 'must be in {!r}.', (list, tuple),
var_name='levels', var_value=levels, expr='type({})',
prepend='Invalid validation call: ')
_validate_level(name, _get_var(name), levels)
def validate_generic(name, type_, value_in=None, len_=None, keys_in=None,
has_keys=None, ignore_none=False, var=None):
"""
Validate non-numeric objects.
The present function is meant to validate the type or class of an object.
Furthermore, if the object may only take on a limited number of values, the
object can be validated against this list. In the case of collections (for
example lists and tuples) and mappings (for example dictionaries), a
specific length can be required. Furthermore, in the case of mappings, the
keys can be validated by requiring and/or only allowing certain keys.
If the present function is called with `name` set to None, an iterable with
the value 'generic' followed by the remaining arguments passed in the call
is returned. This is useful in combination with the validation function
`magni.utils.validation.validate_levels`.
Parameters
----------
name : None
The name of the variable to be validated.
type_ : None
One or more references to groups of types, specific types, and/or
specific classes.
value_in : set-like
The list of values accepted. (the default is None, which implies that
all values are accepted)
len_ : int
The length required. (the default is None, which implies that all
lengths are accepted)
keys_in : set-like
The list of accepted keys. (the default is None, which implies that all
keys are accepted)
has_keys : set-like
The list of required keys. (the default is None, which implies that no
keys are required)
ignore_none : bool
A flag indicating if the variable is allowed to be none. (the default
is False)
var : None
The value of the variable to be validated.
See Also
--------
magni.utils.validation.validate_levels : Validate contained objects.
magni.utils.validation.validate_numeric : Validate numeric objects.
Notes
-----
`name` must refer to a variable in the parent scope of the function or
method decorated by `magni.utils.validation.decorate_validation` which is
closest to the top of the call stack. If `name` is a string then there must
be a variable of that name in that scope. If `name` is a set-like object
then there must be a variable having the first value in that set-like
object as name. The remaining values are used as keys/indices on the
variable to obtain the variable to be validated. For example, the `name`
('name', 0, 'key') refers to the variable "name[0]['key']".
`type_` is either a single value treated as a list with one value or a
set-like object containing at least one value. Each value is either a
specific type or class, or it refers to one or more types by having one of
the string values 'string', 'explicit collection', 'implicict collection',
'collection', 'mapping', 'function', 'class'.
- 'string' tests if the variable is a str.
- 'explicit collection' tests if the variable is a list or tuple.
- 'implicit collection' tests if the variable is iterable.
- 'collection' is a combination of the two above.
- 'mapping' tests if the variable is a dict.
- 'function' tests if the variable is a function.
- 'class' tests if the variable is a type.
`var` can be used to pass the value of the variable to be validated. This
is useful either when the variable cannot be looked up by `name` (for
example, if the variable is a property of the argument of a function) or to
remove the overhead of looking up the value.
Examples
--------
Every public function and method of the present package (with the exception
of the functions of this subpackage itself) validates every argument and
keyword argument using the functionality of this subpackage. Thus, for
examples of how to use the present function, browse through the code.
"""
if name is None:
return ('generic', type_, value_in, len_, keys_in, has_keys,
ignore_none)
if var is None:
var = _get_var(name)
if var is None:
if not isinstance(ignore_none, bool):
_report(TypeError, 'must be {!r}.', bool, var_name='ignore_none',
var_value=ignore_none, expr='type({})',
prepend='Invalid validation call: ')
if ignore_none:
return
else:
_report(ValueError, 'must not be {!r}.', None, var_name=name)
if isinstance(type_, (str, type)) or not hasattr(type_, '__iter__'):
type_ = (type_,)
_check_type(name, var, type_)
_check_value(name, var, value_in)
_check_len(name, var, len_)
_check_keys(name, var, keys_in, has_keys)
