cf.CoordinateBounds¶

class cf.CoordinateBounds(properties={})¶

Bases: cf.variable.Variable

A CF coordinate’s bounds object containing cell boundaries or intervals of climatological time. The parent coordinate’s climatology attribute indicates which type of bounds are present.

Initialization

Parameters :	properties : dict, optional Initialize a new instance with CF properties from the dictionary’s key/value pairs. Values are deep copied.

CoordinateBounds CF Properties

`add_offset`	The add_offset CF property.
`calendar`	The calendar CF property.
`comment`	The comment CF property.
`_FillValue`	The _FillValue CF property.
`history`	The history CF property.
`leap_month`	The leap_month CF property.
`leap_year`	The leap_year CF property.
`long_name`	The long_name CF property.
`missing_value`	The missing_value CF property.
`month_lengths`	The month_lengths CF property.
`scale_factor`	The scale_factor CF property.
`standard_name`	The standard_name CF property.
`units`	The units CF property.
`valid_max`	The valid_max CF property.
`valid_min`	The valid_min CF property.
`valid_range`	The valid_range CF property.

CoordinateBounds data attributes

`array`	A numpy array deep copy of the data array.
`Data`	The Data object containing the data array.
`dtype`	The numpy data type of the data array.
`_FillValue`	The _FillValue CF property.
`first_datum`	The first element of the data array.
`hardmask`	Whether the mask is hard (True) or soft (False).
`isscalar`	True if and only if the data array is a scalar array.
`last_datum`	The last element of the data array.
`mask`	The mask of the data array.
`ndim`	Number of dimensions in the data array.
`shape`	Tuple of the data array’s dimension sizes.
`size`	Number of elements in the data array.
`Units`	The Units object containing the units of the data array.
`varray`	A numpy array view of the data array.

CoordinateBounds miscellaneous attributes

`hasData`	True if and only if there is a data array.
`properties`	A dictionary of the CF properties.

CoordinateBounds methods

`binary_mask`	Return a binary missing data mask of the data array.
`chunk`	Partition the data array using Large Amounts of Massive Arrays (LAMA)
`clip`	Clip (limit) the values in the data array in place.
`copy`	Return a deep copy.
`cos`	Take the trigonometric cosine of the data array in place.
`delprop`	Delete a CF property.
`dump`	Return a string containing a full description of the instance.
`equals`	True if two variables are logically equal, False otherwise.
`expand_dims`	axis is an integer
`flip`	Flip dimensions of the data array in place.
`getprop`	Get a CF property.
`hasprop`	Return True if the variable has a CF property.
`identity`	Return the variable’s identity.
`match`	Determine whether or not a variable satisfies conditions.
`name`	Return a name for the variable.
`override_units`	Override the data array units in place.
`setitem`	Set selected elements of the data array in place.
`setmask`	Set selected elements of the data array’s mask in place.
`setprop`	Set a CF property.
`sin`	Take the trigonometric sine of the data in place.
`squeeze`	Remove size 1 dimensions from the data array in place.
`subset`	Return the instance if it matches the given conditions.
`subspace`	Return a new variable whose data is subspaced.
`transpose`	Permute the dimensions of the data array in place.

binary_mask()¶

Return a binary missing data mask of the data array.

The binary mask’s data array comprises dimensionless 32-bit integers and has 0 where the data array has missing data and 1 otherwise.

Returns :	out : Variable The binary mask.

Examples

>>> print f.mask.array
[[ True  False  True False]]
>>> b = f.binary_mask().array
>>> print b.array
[[0 1 0 1]]

chunk(chunksize=None, extra_boundaries=[], pdim=[])¶

Partition the data array using Large Amounts of Massive Arrays (LAMA) functionality.

Parameters :

chunksize : int

extra_boundaries : list

pdim : list

Returns :

extra_boundaries, pdim : {list, list}

clip(a_min, a_max, units=None)¶

Clip (limit) the values in the data array in place.

Given an interval, values outside the interval are clipped to the interval edges.

Parameters :

a_min : scalar

a_max : scalar

units : str or Units

Returns :	None

Examples

copy(_omit_Data=False, _omit_special=())¶

Return a deep copy.

Equivalent to copy.deepcopy(f).

Returns :	out : The deep copy.

Examples

>>> g = f.copy()

cos()¶

Take the trigonometric cosine of the data array in place.

Units are accounted for in the calcualtion, so that the the cosine of 90 degrees_east is 0.0, as is the sine of 1.57079632 radians. If the units are not equivalent to radians (such as Kelvin) then they are treated as if they were radians.

The Units are changed to ‘1’ (nondimensionsal).

Returns :	None

Examples

>>> f.Units
<CF Units: degrees_east>
>>> print f.array
[[-90 0 90 --]]
>>> f.cos()
>>> f.Units
<CF Units: 1>
>>> print f.array
[[0.0 1.0 0.0 --]]

>>> f.Units
<CF Units: m s-1>
>>> print f.array
[[1 2 3 --]]
>>> f.cos()
>>> f.Units
<CF Units: 1>
>>> print f.array
[[0.540302305868 -0.416146836547 -0.9899924966 --]]

delprop(prop)¶

Delete a CF property.

Parameters :	prop : str The name of the property to delete.
Returns :	None
Raises :	AttributeError : If the variable does not have the named CF property.

Examples

>>> f.delprop('standard_name')
>>> f.delprop('foo')
AttributeError: Field doesn't have CF property 'foo'

dump(id=None, omit=())¶

Return a string containing a full description of the instance.

Parameters :	id : str, optional Set the common prefix of component names. By default the instance’s class name is used. omit : sequence of strs Omit the given CF properties from the description.
Returns :	out : str A string containing the description.

Examples

>>> x = v.dump()
>>> print v.dump()
>>> print v.dump(id='variable1')
>>> print v.dump(omit=('long_name',))

equals(other, rtol=None, atol=None, traceback=False, ignore=())¶

True if two variables are logically equal, False otherwise.

Parameters :

other :: The object to compare for equality.
atol : float, optional: The absolute tolerance for all numerical comparisons, By default the value returned by the ATOL function is used.
rtol : float, optional: The relative tolerance for all numerical comparisons, By default the value returned by the RTOL function is used.
traceback : bool, optional: If True then print a traceback highlighting where the two instances differ.
ignore : iterable, optional: Omit these CF properties from the comparison.

Returns :

out : bool: Whether or not the two instances are equal.

Examples

>>> f.equals(f)
True
>>> g = f + 1
>>> f.equals(g)
False
>>> g -= 1
>>> f.equals(g)
True
>>> f.setprop('name', 'name0')
>>> g.setprop('name', 'name1')
>>> f.equals(g)
False
>>> f.equals(g, ignore=set('name',))
True

expand_dims(axis, dim, direction)¶: axis is an integer dim is a string direction is a boolean

flip(axes=None)¶

Flip dimensions of the data array in place.

Parameters :	axes : int or sequence of ints Flip the dimensions whose positions are given. By default all dimensions are flipped.
Returns :	out : list of ints The axes which were flipped, in arbitrary order.

Examples

>>> f.flip()
>>> f.flip(1)
>>> f.flip([0, 1])

>>> g = f[::-1, :, ::-1]
>>> f.flip([2, 0]).equals(g)
True

getprop(prop, *default)¶

Get a CF property.

When a default argument is given, it is returned when the attribute doesn’t exist; without it, an exception is raised in that case.

Parameters :	prop : str The name of the property to get. default : optional Return default if and only if the variable does not have the named property.
Returns :	out : The value of the named property, or the default value.
Raises :	AttributeError : If the variable does not have the named property and a default value has not been set.

Examples

>>> f.getprop('standard_name')
>>> f.getprop('standard_name', None)
>>> f.getprop('foo')
AttributeError: Field doesn't have CF property 'foo'
>>> f.getprop('foo', 'bar')
'bar'

hasprop(prop)¶

Return True if the variable has a CF property.

Parameters :	prop : str The name of the property.
Returns :	out : bool True if the instance has the property.

Examples

>>> f.hasprop('standard_name')
True
>>> f.hasprop('foo')
False

identity(default=None)¶

Return the variable’s identity.

The idendity is the value of the standard_name property or, if that does not exist, the id attribute.

Parameters :	default : optional If neither the standard_name nor id attributes exist then return default. By default, default is None.
Returns :	out : The identity.

Examples

>>> f.standard_name = 'Kelvin'
>>> f.identity
'Kelvin'
>>> del f.standard_name
>>> f.id = 'foo'
>>> f.identity
'foo'
>>> del f.id
>>> f.identity()
None
>>> f.identity('bar')
'bar'

match(prop={}, attr={})¶

Determine whether or not a variable satisfies conditions.

Conditions may be specified on the variable’s CF properties and attributes.

Parameters :

prop : dict, optional

Dictionary for which each key/value pair is a CF property name and a condition for the property to be tested against. If the value is a sequence of conditions then the attribute matches if at least one of the conditions is passed.

In general, a condition is any object and it is passed if the attribute is equal to the object, with the following exception:

If the property is string-valued, then the condition may be a regular expression pattern recognised by the re module and the condition is passed if the property matches the regular expression. Special characters for the start and end of the string are assumed and need not be included. For example, ‘.*wind’ is equivalent to ‘^.*wind$’.

attr : dict, optional

Dictionary for which each key/value pair is an attribute name and a condition for the attribute to be tested against. If the value is a sequence of conditions then the attribute matches if at least one of the conditions is passed.

In general, a condition behaves as for attr, with the following exception:

For the Units attribute, the condition is passed if the attribute is equivalent (rather than equal) to the object. (Note that this behaviour does not apply to the units attribute.)

Returns :

out : bool: Whether or not the variable matches the given criteria.

Examples

name(long_name=False, ncvar=False, default=None)¶

Return a name for the variable.

Returns the standard_name, long_name (if requested) or netCDF variable name (if requested), whichever it finds first, otherwise returns a default name.

Parameters :

long_name : bool, optional

If True, return the long_name if standard_name does not exist.

ncvar : bool, optional

If True, return ncvar if neither the standard_name nor long_name has already been returned.

default : str, optional

Return default if neither standard_name, long_name nor ncvar has already been returned.

Returns :

name : str: The name of the variable.

override_units(new_units)¶

Override the data array units in place.

Not to be confused with setting the Units attribute to units which are equivalent to the original units.

This is different to setting the Units attribute, as the new units need not be equivalent to the original ones and the data array elements will not be changed to reflect the new units.

Parameters :	new_units : str or Units The new units for the data array.
Returns :	None

Examples

>>> f.Units
<CF Units: hPa>
>>> f.first_datum
100000.0
>>> f.override_units('km')
>>> f.Units
<CF Units: km>
>>> f.first_datum
100000.0
>>> f.override_units(cf.Units('watts'))
>>> f.Units
<CF Units: watts>
>>> f.first_datum
100000.0

setitem(value, indices=Ellipsis, condition=None, masked=None, other_mask=None, hardmask=None)¶

Set selected elements of the data array in place.

The value to which the selected elements of the data array will be set may be any object which is broadcastable across the selected elements.

Note the following:

f.setitem(value) is equivalent to f.subspace[...]=value.
f.setitem(value, indices) is equivalent to f.subspace[indices]=value.
If and only if the value to be assigned is the scalar cf.masked (or numpy.ma.masked) then the selected elements of data array’s mask will be set to True (masked). For example, f.setitem(cf.masked, indices) is equivalent to f.setmask(True, indices). This is consistent with the behaviour of numpy masked arrays.

Parameters :

value : array-like: The value to which the selected elements of the data array will be set. Must be an object which is broadcastable across the selected elements.
indices : optional: Any indices as would be accepted by f.subspace. Only elements of the data array described by the indices, and where other criteria (if any) are met, are set to value. By default, the entire data array is considered. How masked elements of the data array are treated depends on the hardmask parameter. The value must be any object broadcastable across the shape implied by indices. Note that f.setitem(value, indices) is equivalent to f.subspace[indices]=value.
condition : scalar or Comparison, optional: A condition applied to each element of the data array specified by indices which determines whether or not that element is set to the logically scalar value. The condition is evaluated by checking if each element equals the condition, and if it does then that element is set to value. How masked elements of the data array are treated depends on the hardmask parameter. Note that if condition is a scalar, x, then this is equivalent to the Comparison object cf.eq(x).
masked : bool, optional: If False then each unmasked element of the data array specified by indices is set to the logically scalar value. If True then each unmasked element of the data array specified by indices is unmasked and set to the logically scalar value, regardless of the hardmask is parameter.
other_mask : array-like, optional: Each element of the data array specified by indices and which corresponds to an element which evaluates to False from other_mask is set to the logically scalar value. other_mask may be any object which is broadcastable across the shape implied by indices. How masked elements of the data array are treated depends on the hardmask parameter.
hardmask : bool, optional: If True then any selected elements of the data array which are masked will not be unmasked and assigned to. If False then any selected elements of the data array which are masked will be unmasked and assigned to. By default, the value of the instance’s hardmask attribute is used.

Returns :

None

Examples

setmask(value, indices=Ellipsis)¶

Set selected elements of the data array’s mask in place.

The value to which the selected elements of the mask will be set may be any object which is broadcastable across the selected elements. The broadcasted value may be of any data type but will be evaluated as boolean.

Unmasked elements are set to the fill value.

The mask may be effectively removed by setting every element to False with f.setmask(False).

Note that if and only if the value to be assigned is logically scalar and evaluates to True then f.setmask(value, indices) is equivalent to f.setitem(cf.masked, indices). This is consistent with the behaviour of numpy masked arrays.

Parameters :	value : array-like The value to which the selected element s of the mask will be set. Must be an object which is broadcastable across the selected elements. indices : optional Indices of the data array. Only elements of the mask described by the indices are set to value. By default, the entire mask is considered.
Returns :	None

Examples

setprop(prop, value)¶

Set a CF property.

Parameters :	prop : str The name of the property to set. value : The value for the property.
Returns :	None

Examples

>>> f.setprop('standard_name', 'time')
>>> f.setprop('foo', 12.5)

sin()¶

Take the trigonometric sine of the data in place.

Units are accounted for in the calculation. For example, the the sine of 90 degrees_east is 1.0, as is the sine of 1.57079632 radians. If the units are not equivalent to radians (such as Kelvin) then they are treated as if they were radians.

The Units are changed to ‘1’ (nondimensionsal).

Returns :	None

Examples

>>> f.Units
<CF Units: degrees_north>
>>> print f.array
[[-90 0 90 --]]
>>> f.sin()
>>> f.Units
<CF Units: 1>
>>> print f.array
[[-1.0 0.0 1.0 --]]

>>> f.Units
<CF Units: m s-1>
>>> print f.array
[[1 2 3 --]]
>>> f.sin()
>>> f.Units
<CF Units: 1>
>>> print f.array
[[0.841470984808 0.909297426826 0.14112000806 --]]

squeeze(axes=None)¶

Remove size 1 dimensions from the data array in place.

Parameters :	axes : int or sequence of ints, optional The size 1 axes to remove. By default, all size 1 axes are removed. Size 1 axes for removal may be identified by the integer positions of dimensions in the data array.
Returns :	None

Examples

>>> v.squeeze()
>>> v.squeeze(1)
>>> v.squeeze([1, 2])

subset(*args, **kwargs)¶

Return the instance if it matches the given conditions.

Equivalent to:

def subset(f, *args, **kwargs):
    if f.match(*args, **kwargs):
        return f
    raise ValueError('')

Parameters :	args, kwargs : As for the match method.
Returns :	out : The variable as an object identity, if it matches the given conditions.
Raises :	ValueError : If the variable does not match the conditions.

transpose(axes=None)¶

Permute the dimensions of the data array in place.

Parameters :	axes : sequence of ints, optional The new order of the data array. By default, reverse the dimensions’ order, otherwise the axes are permuted according to the values given. The values of the sequence comprise the integer positions of the dimensions in the data array in the desired order.
Returns :	None

Examples

>>> v.transpose()
>>> v.ndim
3
>>> v.transpose([1, 2, 0])

Data¶

The Data object containing the data array.

Examples

>>> f.Data
<CF Data: >

Units¶

The Units object containing the units of the data array.

Stores the units and calendar CF properties in an internally consistent manner. These are mirrored by the units and calendar CF properties respectively.

Examples

>>> f.Units
<CF Units: K>

add_offset¶

The add_offset CF property.

This property is only used when writing to a file on disk.

Examples

>>> f.add_offset = -4.0
>>> f.add_offset
-4.0
>>> del f.add_offset

>>> f.setprop('add_offset', 10.5)
>>> f.getprop('add_offset')
10.5
>>> f.delprop('add_offset')

array¶

A numpy array deep copy of the data array.

Changing the returned numpy array does not change the data array.

Examples

>>> a = f.array
>>> type(a)
<type 'numpy.ndarray'>
>>> print a
[0 1 2 3 4]
>>> a[0] = 999
>>> print a
[999 1 2 3 4]
>>> print f.array
[0 1 2 3 4]

calendar¶

The calendar CF property.

This property is a mirror of the calendar stored in the Units attribute.

Examples

>>> f.calendar = 'noleap'
>>> f.calendar
'noleap'
>>> del f.calendar

>>> f.setprop('calendar', 'proleptic_gregorian')
>>> f.getprop('calendar')
'proleptic_gregorian'
>>> f.delprop('calendar')

comment¶

The comment CF property.

Examples

>>> f.comment = 'a comment'
>>> f.comment
'a comment'
>>> del f.comment

>>> f.setprop('comment', 'a comment')
>>> f.getprop('comment')
'a comment'
>>> f.delprop('comment')

dtype¶

The numpy data type of the data array.

By default this is the data type with the smallest size and smallest scalar kind to which all data array partitions may be safely cast. For example, if the partitions have data types ‘int64’ and ‘float32’ then the data array’s data type will be ‘float64’.

Setting the data type to a numpy.dtype object, or any object convertible to a numpy.dtype object, will change the interpretation of the underlying data array elements. Note that the underlying data are not altered, so reinstating the original data type results in no loss of information.

Deleting the data type after setting it will reinstate the default behaviour. Deleting the data type when the default behaviour is in place will have no effect.

Examples

>>> f.dtype
dtype('float64')
>>> type(f.dtype)
<type 'numpy.dtype'>
>>> print f.array
[0.5 1.5 2.5]

>>> print f.array
[0.5 1.5 2.5]
>>> import numpy
>>> f.dtype = numpy.dtype(int)
>>> print f.array
[0 1 2]
>>> f.dtype = bool
>>> print f.array
[False  True  True]
>>> f.dtype = 'float64'
>>> print f.array
[0.5 1.5 2.5]

first_datum¶

The first element of the data array.

Examples

>>> print f.array
[[1 2 3 4]]
>>> f.first_datum
1

>>> print f.array
[[-- 2 3 4]]
>>> f.first_datum
--

hardmask¶

Whether the mask is hard (True) or soft (False).

When the mask is hard, masked entries of the data array can not be unmasked by assignment.

By default, the mask is hard.

Examples

>>> f.hardmask = False
>>> f.hardmask
False

hasData¶

True if and only if there is a data array.

Examples

>>> hasattr(f, 'Data')
True
>>> f.hasData
True
>>> del f.Data
>>> f.hasData
False

history¶

The history CF property.

Examples

>>> f.history = 'created on 2012/10/01'
>>> f.history
'created on 2012/10/01'
>>> del f.history

>>> f.setprop('history', 'created on 2012/10/01')
>>> f.getprop('history')
'created on 2012/10/01'
>>> f.delprop('history')

isscalar¶

True if and only if the data array is a scalar array.

Examples

>>> print f.array
2
>>> f.isscalar
True

>>> print f.array
[2]
>>> f.isscalar
False

>>> print f.array
[[2, 3]]
>>> f.isscalar
False

>>> f.hasData
False
>>> f.isscalar
False

last_datum¶

The last element of the data array.

Examples

>>> print f.array
[[1 2 3 4]]
>>> f.last_datum
4

>>> print f.array
[[1 2 3 --]]
>>> f.last_datum
--

leap_month¶

The leap_month CF property.

Examples

>>> f.leap_month = 2
>>> f.leap_month
2
>>> del f.leap_month

>>> f.setprop('leap_month', 2)
>>> f.getprop('leap_month')
2
>>> f.delprop('leap_month')

leap_year¶

The leap_year CF property.

Examples

>>> f.leap_year = 1984
>>> f.leap_year
1984
>>> del f.leap_year

>>> f.setprop('leap_year', 1984)
>>> f.getprop('leap_year')
1984
>>> f.delprop('leap_year')

long_name¶

The long_name CF property.

Examples

>>> f.long_name = 'zonal_wind'
>>> f.long_name
'zonal_wind'
>>> del f.long_name

>>> f.setprop('long_name', 'surface air temperature')
>>> f.getprop('long_name')
'surface air temperature'
>>> f.delprop('long_name')

mask¶

The mask of the data array.

Examples

>>> f.shape
(12, 73, 96)
>>> m = f.mask
<CF Field: mask()>
>>> m.shape
(12, 73, 96)
>>> m.dtype
dtype('bool')

missing_value¶

The missing_value CF property.

This property is forced to be consistent with the _FillValue property as follows:

Assigning a value to missing_value also assigns the same value to _FillValue whether the latter has been previously set or not.

Assigning a value to _FillValue also assigns the same value to missing_value, but only if the latter has previously been defined.

Examples

>>> f.missing_value = 1e30
>>> f.missing_value
1e30
>>> f._FillValue
1e30
>>> del f.missing_value
>>> f._FillValue
1e30

month_lengths¶

The month_lengths CF property.

Stored as a tuple but may be set as any array-like object.

Examples

>>> f.month_lengths = numpy.array([34, 31, 32, 30, 29, 27, 28, 28, 28, 32, 32, 34])
>>> f.month_lengths
(34, 31, 32, 30, 29, 27, 28, 28, 28, 32, 32, 34)
>>> del f.month_lengths

>>> f.setprop('month_lengths', [34, 31, 32, 30, 29, 27, 28, 28, 28, 32, 32, 34])
>>> f.getprop('month_lengths')
(34, 31, 32, 30, 29, 27, 28, 28, 28, 32, 32, 34)
>>> f.delprop('month_lengths')

ndim¶

Number of dimensions in the data array.

Examples

>>> f.shape
(73, 96)
>>> f.ndim
2

properties¶

A dictionary of the CF properties.

Note that modifying the returned dictionary will not change the CF properties.

Examples

>>> f.properties
{'_FillValue': 1e+20,
 'foo': 'bar',
 'long_name': 'Surface Air Temperature',
 'standard_name': 'air_temperature',
 'units': 'K'}

scale_factor¶

The scale_factor CF property.

This property is only used when writing to a file on disk.

Examples

>>> f.scale_factor = 10.0
>>> f.scale_factor
10.0
>>> del f.scale_factor

>>> f.setprop('scale_factor', 10.0)
>>> f.getprop('scale_factor')
10.0
>>> f.delprop('scale_factor')

shape¶

Tuple of the data array’s dimension sizes.

Examples

>>> f.shape
(73, 96)

size¶

Number of elements in the data array.

Examples

>>> f.shape
(73, 96)
>>> f.size
7008

standard_name¶

The standard_name CF property.

Examples

>>> f.standard_name = 'time'
>>> f.standard_name
'time'
>>> del f.standard_name

>>> f.setprop('standard_name', 'time')
>>> f.getprop('standard_name')
'time'
>>> f.delprop('standard_name')

subspace¶

Return a new variable whose data is subspaced.

This attribute may be indexed to select a subspace from dimension index values.

Subspacing by indexing

Subspacing by dimension indices uses an extended Python slicing syntax, which is similar numpy array indexing. There are two extensions to the numpy indexing functionality:

Size 1 dimensions are never removed.

An integer index i takes the i-th element but does not reduce the rank of the output array by one.
When advanced indexing is used on more than one dimension, the advanced indices work independently.

When more than one dimension’s slice is a 1-d boolean array or 1-d sequence of integers, then these indices work independently along each dimension (similar to the way vector subscripts work in Fortran), rather than by their elements.

Examples

units¶

The units CF property.

This property is a mirror of the units stored in the Units attribute.

Examples

>>> f.units = 'K'
>>> f.units
'K'
>>> del f.units

>>> f.setprop('units', 'm.s-1')
>>> f.getprop('units')
'm.s-1'
>>> f.delprop('units')

valid_max¶

The valid_max CF property.

Examples

>>> f.valid_max = 100.0
>>> f.valid_max
100.0
>>> del f.valid_max

>>> f.setprop('valid_max', 100.0)
>>> f.getprop('valid_max')
100.0
>>> f.delprop('valid_max')

valid_min¶

The valid_min CF property.

Examples

>>> f.valid_min = 100.0
>>> f.valid_min
100.0
>>> del f.valid_min

>>> f.setprop('valid_min', 100.0)
>>> f.getprop('valid_min')
100.0
>>> f.delprop('valid_min')

valid_range¶

The valid_range CF property.

Stored as a tuple but may be set as any array-like object.

Examples

>>> f.valid_range = numpy.array([100., 400.])
>>> f.valid_range
(100.0, 400.0)
>>> del f.valid_range

>>> f.setprop('valid_range', [100.0, 400.0])
>>> f.getprop('valid_range')
(100.0, 400.0)
>>> f.delprop('valid_range')

varray¶

A numpy array view of the data array.

Changing the elements of the returned view changes the data array.

Examples

>>> a = f.varray
>>> type(a)
<type 'numpy.ndarray'>
>>> a
array([0, 1, 2, 3, 4])
>>> a[0] = 999
>>> f.varray
array([999, 1, 2, 3, 4])

cf.CoordinateBounds¶

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cf.CoordinateBounds¶

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