cf.FieldList.anchor

FieldList.anchor(axes, value, i=False, dry_run=False, **kwargs)[source]

For each field, roll a cyclic axis so that the given value lies in the first coordinate cell.

For each field, a unique axis is selected with the axes and kwargs parameters.

New in version 1.0.

See also

cf.Field.axis, cf.Field.cyclic, cf.Field.iscyclic, cf.Field.period, roll

Examples 1:

Anchor the cyclic X axis to a value of 180:

>>> g = f.anchor('X', 180)
Parameters:
axes, kwargs: optional

Select axes. The axes parameter may be one, or a sequence, of:

  • None. If no kwargs arguments have been set then all axes are selected. This is the default.

  • An integer. Explicitly selects the axis corresponding to the given position in the list of axes of the field’s data array.

    Example:

    To select the third data array axis: axes=2. To select the last axis: axes=-1.

  • A slice object. Explicitly selects the axes corresponding to the given positions in the list of axes of the field’s data array.

    Example:

    To select the last three data array axes: axes=slice(-3, None)

  • A domain axis identifier. Explicitly selects this axis.

    Example:

    To select axis “dim1”: axes='dim1'.

  • Any value accepted by the items parameter of the field’s items method. Used in conjunction with the kwargs parameters to select the axes which span the items that would be identified by this call of the field’s items method: f.items(items=axes, axes=None, **kwargs). See cf.Field.items for details.

    Example:

    To select the axes spanned by one dimensionsal time coordinates: f.anchor('T', ndim=1).

If axes is a sequence of any combination of the above then the selected axes are the union of those selected by each element of the sequence. If the sequence is empty then no axes are selected.

value: data-like object

Anchor the dimension coordinate values for the selected cyclic axis to the value. If value has units then they must be compatible with those of the dimension coordinates, otherwise it is assumed to have the same units as the dimension coordinates. The coordinate values are transformed so that value is “equal to or just before” the new first coordinate value. More specifically:

  • Increasing dimension coordinates with positive period, P, are transformed so that value lies in the half-open range (L-P, F], where F and L are the transformed first and last coordinate values, respectively.
  • Decreasing dimension coordinates with positive period, P, are transformed so that value lies in the half-open range (L+P, F], where F and L are the transformed first AND last coordinate values, respectively.
Example:

If the original dimension coordinates are 0, 5, ..., 355 (evenly spaced) and the period is 360 then value=0 implies transformed coordinates of 0, 5, ..., 355; value=-12 implies transformed coordinates of -10, -5, ..., 345; value=380 implies transformed coordinates of 380, 385, ..., 715.

Example:

If the original dimension coordinates are 355, 350, ..., 0 (evenly spaced) and the period is 360 then value=355 implies transformed coordinates of 355, 350, ..., 0; value=0 implies transformed coordinates of 0, -5, ..., -355; value=392 implies transformed coordinates of 390, 385, ..., 30.

A data-like object is any object containing array-like or scalar data which could be used to create a cf.Data object.

Example:

Instances, x, of following types are all examples of data-like objects (because cf.Data(x) creates a valid cf.Data object): int, float, str, tuple, list, numpy.ndarray, cf.Data, cf.Coordinate, cf.Field.

i: bool, optional

If True then update the field list in place. By default a new field list is created. In either case, a field list is returned.

dry_run: bool, optional

Return a dictionary of parameters which describe the anchoring process. The field is not changed, even if i is True.
Returns:

out: cf.FieldList
Examples 2: 
>>> f[0].iscyclic('X')
True
>>> f[0].dim('X').data
<CF Data: [0, ..., 315] degrees_east>
>>> print f[0].dim('X').array
[  0  45  90 135 180 225 270 315]
>>> g = f.anchor('X', 230)
>>> print g[0].dim('X').array
[270 315   0  45  90 135 180 225]
>>> g = f.anchor('X', cf.Data(590, 'degreesE'))
>>> print g[0].dim('X').array
[630 675 360 405 450 495 540 585]
>>> g = f.anchor('X', cf.Data(-490, 'degreesE'))
>>> print g[0].dim('X').array
[-450 -405 -720 -675 -630 -585 -540 -495]

>>> f[0].iscyclic('X')
True
>>> f[0].dim('X').data
<CF Data: [0.0, ..., 357.1875] degrees_east>
>>> f.anchor('X', 10000)[0].dim('X').data
<CF Data: [10001.25, ..., 10358.4375] degrees_east>
>>> d = f[0].anchor('X', 10000, dry_run=True)
>>> d
{'axis': 'dim2',
 'nperiod': <CF Data: [10080.0] 0.0174532925199433 rad>,
 'roll': 28}
>>> (f.roll(d['axis'], d['roll'])[0].dim(d['axis']) + d['nperiod']).data
<CF Data: [10001.25, ..., 10358.4375] degrees_east>