cf.Field.moving_window¶

Field.
moving_window
(method, window_size=None, axis=None, weights=None, mode=None, cval=None, origin=0, scale=None, radius='earth', great_circle=False, inplace=False)[source]¶ Perform moving window calculations along an axis.
Moving mean, sum, and integral calculations are possible.
By default moving means are unweighted, but weights based on the axis cell sizes (or custom weights) may applied to the calculation via the weights parameter.
By default moving integrals must be weighted.
When appropriate, a new cell method construct is created to describe the calculation.
Note
The
moving_window
method can not, in general, be emulated by theconvolution_filter
method, as the latter i) can not change the window weights as the filter passes through the axis; and ii) does not update the cell method constructs.New in version 3.3.0.
See also
 Parameters
 method:
str
Define the moving window method. The method is given by one of the following strings (see https://ncascms.github.io/cfpython/analysis.html#collapsemethods for precise definitions):
method
Description
Weighted
'sum'
The sum of the values.
Never
'mean'
The weighted or unweighted mean of the values.
May be
'integral'
The integral of values.
Always
Methods that are “Never” weighted ignore the weights parameter, even if it is set.
Methods that “May be” weighted will only be weighted if the weights parameter is set.
Methods that are “Always” weighted require the weights parameter to be set.
 window_size:
int
Specify the size of the window used to calculate the moving window.
 Parameter example:
A 5point moving window is set with
window_size=5
.
 axis:
str
orint
Select the domain axis over which the filter is to be applied, defined by that which would be selected by passing the given axis description to a call of the field construct’s
domain_axis
method. For example, for a value of'X'
, the domain axis construct returned byf.domain_axis('X')
is selected. weights: optional
Specify the weights for the moving window. The weights are, those that would be returned by this call of the field construct’s
weights
method:f.weights(weights, axes=axis, radius=radius, great_circle=great_circle, data=True)
. See the axis, radius and great_circle parameters andcf.Field.weights
for details.Note
By default weights is
None
, resulting in unweighted calculations.Note
Setting weights to
True
is generally a good way to ensure that the moving window calculations are appropriately weighted according to the field construct’s metadata. In this case, if it is not possible to create weights for the selected axis then an exception will be raised. Parameter example:
To specify weights on the cell sizes of the selected axis:
weights=True
.
 mode:
str
, optional The mode parameter determines how the input array is extended when the filter overlaps an array border. The default value is
'constant'
or, if the dimension being convolved is cyclic (as ascertained by theiscyclic
method),'wrap'
. The valid values and their behaviours are as follows:mode
Description
Behaviour
'reflect'
The input is extended by reflecting about the edge
(c b a  a b c  c b a)
'constant'
The input is extended by filling all values beyond the edge with the same constant value (
k
), defined by the cval parameter.(k k k  a b c  k k k)
'nearest'
The input is extended by replicating the last point
(a a a  a b c  c c c)
'mirror'
The input is extended by reflecting about the centre of the last point.
(c b  a b c  b a)
'wrap'
The input is extended by wrapping around to the opposite edge.
(a b c  a b c  a b c)
The position of the window relative to each value can be changed by using the origin parameter.
 cval: scalar, optional
Value to fill past the edges of the array if mode is
'constant'
. Ignored for other modes. Defaults toNone
, in which case the edges of the array will be filled with missing data. The only other valid value is0
. Parameter example:
To extend the input by filling all values beyond the edge with zero:
cval=0
 origin:
int
, optional Controls the placement of the filter. Defaults to 0, which is the centre of the window. If the window size, defined by the window_size parameter, is even then then a value of 0 defines the index defined by
window_size/2 1
. Parameter example:
For a window size of 5, if
origin=0
then the window is centred on each point. Iforigin=2
then the window is shifted to include the previous four points. Iforigin=1
then the window is shifted to include the previous point and the and the next three points.
 radius: optional
Specify the radius used for calculating the areas of cells defined in spherical polar coordinates. The radius is that which would be returned by this call of the field construct’s
radius
method:f.radius(radius)
. See thecf.Field.radius
for details.By default radius is
'earth'
which means that if and only if the radius can not found from the datums of any coordinate reference constucts, then the default radius taken as 6371229 metres. great_circle:
bool
, optional If True then allow, if required, the derivation of i) area weights from polygon geometry cells by assuming that each cell part is a spherical polygon composed of great circle segments; and ii) and the derivation of linelength weights from line geometry cells by assuming that each line part is composed of great circle segments.
 scale: number, optional
If set to a positive number then scale the weights so that they are less than or equal to that number. By default the weights are scaled to lie between 0 and 1 (i.e. scale is 1).
Ignored if the moving window method is not weighted. The scale parameter can not be set for moving integrals.
 Parameter example:
To scale all weights so that they lie between 0 and 0.5:
scale=0.5
.
 inplace:
bool
, optional If True then do the operation inplace and return
None
.
 method:
 Returns
Examples:
>>> f = cf.example_field(0) >>> print(f) Field: specific_humidity (ncvar%q)  Data : specific_humidity(latitude(5), longitude(8)) 1 Cell methods : area: mean Dimension coords: latitude(5) = [75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : time(1) = [20190101 00:00:00] >>> print(f.array) [[0.007 0.034 0.003 0.014 0.018 0.037 0.024 0.029] [0.023 0.036 0.045 0.062 0.046 0.073 0.006 0.066] [0.11 0.131 0.124 0.146 0.087 0.103 0.057 0.011] [0.029 0.059 0.039 0.07 0.058 0.072 0.009 0.017] [0.006 0.036 0.019 0.035 0.018 0.037 0.034 0.013]] >>> print(f.coordinate('X').bounds.array) [[ 0. 45.] [ 45. 90.] [ 90. 135.] [135. 180.] [180. 225.] [225. 270.] [270. 315.] [315. 360.]] >>> f.iscyclic('X') True >>> f.iscyclic('Y') False
Create a weighted 3point running mean for the cyclic ‘X’ axis:
>>> g = f.moving_window('mean', 3, axis='X', weights=True) >>> print(g) Field: specific_humidity (ncvar%q)  Data : specific_humidity(latitude(5), longitude(8)) 1 Cell methods : area: mean longitude(8): mean Dimension coords: latitude(5) = [75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : time(1) = [20190101 00:00:00] >>> print(g.array) [[0.02333 0.01467 0.017 0.01167 0.023 0.02633 0.03 0.02 ] [0.04167 0.03467 0.04767 0.051 0.06033 0.04167 0.04833 0.03167] [0.084 0.12167 0.13367 0.119 0.112 0.08233 0.057 0.05933] [0.035 0.04233 0.056 0.05567 0.06667 0.04633 0.03267 0.01833] [0.01833 0.02033 0.03 0.024 0.03 0.02967 0.028 0.01767]] >>> print(g.coordinate('X').bounds.array) [[45. 90.] [ 0. 135.] [ 45. 180.] [ 90. 225.] [135. 270.] [180. 315.] [225. 360.] [270. 405.]]
Create an unweighted 3point running mean for the cyclic ‘X’ axis:
>>> g = f.moving_window('mean', 3, axis='X')
Create an weighted 4point running integral for the noncyclic ‘Y’ axis:
>>> g = f.moving_window('integral', 4, axis='Y', weights=True) >>> g.Units <Units: 0.0174532925199433 rad> >>> print(g.array) [[        ] [ 8.37 11.73 10.05 13.14 8.88 11.64 4.59 4.02] [ 8.34 11.79 10.53 13.77 8.88 11.64 4.89 3.54] [        ] [        ]] >>> print(g.coordinate('Y').bounds.array) [[90. 30.] [90. 60.] [60. 90.] [30. 90.] [ 30. 90.]] >>> g = f.moving_window('integral', 4, axis='Y', weights=True, cval=0) >>> print(g.array) [[ 7.5 9.96 8.88 11.04 7.14 9.48 4.32 3.51] [ 8.37 11.73 10.05 13.14 8.88 11.64 4.59 4.02] [ 8.34 11.79 10.53 13.77 8.88 11.64 4.89 3.54] [ 7.65 10.71 9.18 11.91 7.5 9.45 4.71 1.56] [ 1.05 2.85 1.74 3.15 2.28 3.27 1.29 0.9 ]]