Loading in constraint using cell_methods?

[Seonaid Dey]

Dear all,

I have a pp file containing three cubes which have the same dimension coordinates and attributes but differ their cell method and forecast reference time coordinates (see below). I only want to load in the field without a specified cell method . Is this possible? If so how? The cubes are of 1.5m temperature (the one I want, no cell method specified), min hourly temperature and max hourly temperature.

air_temperature / K                 (realization: 2; time: 37; grid_latitude: 548; grid_longitude: 422)

     Dimension coordinates:

          realization                           x        -                  -                    -

          time                                  -        x                  -                    -

          grid_latitude                         -        -                  x                    -

          grid_longitude                        -        -                  -                    x

     Auxiliary coordinates:

          forecast_period                       -        x                  -                    -

     Scalar coordinates:

          forecast_reference_time: 2013-07-17 03:00:00

          height: 1.5 m

     Attributes:

          STASH: m01s03i236

          source: Data from Met Office Unified Model 8.02

air_temperature / K                 (realization: 2; time: 36; grid_latitude: 548; grid_longitude: 422)

     Dimension coordinates:

          realization                           x        -                  -                    -

          time                                  -        x                  -                    -

          grid_latitude                         -        -                  x                    -

          grid_longitude                        -        -                  -                    x

     Auxiliary coordinates:

          forecast_period                       -        x                  -                    -

          forecast_reference_time               -        x                  -                    -

     Scalar coordinates:

          height: 1.5 m

     Attributes:

          STASH: m01s03i236

          source: Data from Met Office Unified Model 8.02

     Cell methods:

          maximum: time (1 hour)

Any help much appreciated!

Thanks,

Seonaid

[esc24]

You can do this using the cube_func keyword arg of iris.Constraint. I believe the constraint you want is:

constraint = iris.Constraint(cube_func=lambda cube: not cube.cell_methods)

[Seonaid Dey]

Thanks, this seems to work. Just one more question. I already have a constraint defining the stash codes I want to read in (that works)

 for stash in stasharr:
        stash_constr.append(iris.AttributeConstraint(STASH=stash))

How do I combine the cell_method constraint with this so that I only load cubes which meet the stash and cell_method criteria?

I have tried
 
cell_constr=iris.Constraint(cube_func=lambda cube: not cube.cell_methods)
fields= iris.load(filearr,  stash_constr & cell_constr, callback=lagged_ensemble_callback)

But this ignores the stash constraint!

Thanks very much for your help,

Seonaid

[Seonaid Dey]

Dear all,

Don't worry problem solved!

    fields= iris.load(filearr, stash_constr, callback=lagged_ensemble_callback).extract(cell_constr)      

Thanks,
Seonaid

[Steven Chan]

Please pardon me for replying to an old question. I think the natural extension of the question is how to distinguish between different cell methods. For instance, you have a file with maximum, minimum and mean temperature, and they are distinguished by different information in Cell methods, for instance:

>>> print cubes 
0: air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
1: air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
2: air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
>>> print cubes[0]
air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
     Dimension coordinates:
          time                           x                  -                     -
          grid_latitude                  -                  x                     -
          grid_longitude                 -                  -                     x
     Auxiliary coordinates:
          forecast_period                x                  -                     -
     Scalar coordinates:
          forecast_reference_time: 1998-03-01 00:00:00

          height: 1.5 m
     Attributes:
          STASH: m01s03i236
          source: Data from Met Office Unified Model

          um_version: 10.1
     Cell methods:
          mean: time (1 hour)
>>> print cubes[1]
air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
     Dimension coordinates:
          time                           x                  -                     -
          grid_latitude                  -                  x                     -
          grid_longitude                 -                  -                     x
     Auxiliary coordinates:
          forecast_period                x                  -                     -
     Scalar coordinates:
          forecast_reference_time: 1998-03-01 00:00:00

          height: 1.5 m
     Attributes:
          STASH: m01s03i236
          source: Data from Met Office Unified Model

          um_version: 10.1
     Cell methods:
          minimum: time (1 hour)
>>> print cubes[2]
air_temperature / (K)               (time: 28; grid_latitude: 1536; grid_longitude: 1536)
     Dimension coordinates:
          time                           x                  -                     -
          grid_latitude                  -                  x                     -
          grid_longitude                 -                  -                     x
     Auxiliary coordinates:
          forecast_period                x                  -                     -
     Scalar coordinates:
          forecast_reference_time: 1998-03-01 00:00:00

          height: 1.5 m
     Attributes:
          STASH: m01s03i236
          source: Data from Met Office Unified Model

          um_version: 10.1

     Cell methods:
          maximum: time (1 hour)
>>>

Is there a straightforward constraint that would work here? I do find out what you can do:

>>> stuff = cubes[0].cell_methods
>>> stuff
(CellMethod(method='mean', coord_names=('time',), intervals=('1 hour',), comments=()),)
>>> constraint = iris.Constraint(cube_func=lambda cube: cube.cell_methods is stuff)
>>> cubes.extract(constraint)
[<iris 'Cube' of air_temperature / (K) (time: 28; grid_latitude: 1536; grid_longitude: 1536)>]

That is rather clumsy, and will only work if you save a sample cube with the correct method somewhere. (i.e. you save a small sample; when you need to do the extraction, you import the sample, and use the method from the sample to do the constraint).

Thank you
Steven

[RuthC]

Hi Steven,

you can create a cell method from scratch:

my_cell_method = iris.coords.CellMethod(method='mean', coords='time', intervals='1 hour')

Ruth

[Jonny Williams]

Hey Ruth

How would you use this as part of the loading process?

For example, how I would I use this type of cell_method constraint to only load the hourly mean data here?...

In [130]: files

Out[130]:

['/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950apr.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950aug.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950dec.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950feb.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950jan.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950jul.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950jun.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950mar.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950may.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950nov.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950oct.pp',

 '/home/williamsjh/cylc-run/u-bl658/share/data/History_Data/bl658a.pm1950sep.pp']

In [131]: constraint

Out[131]: AttributeConstraint({'STASH': 'm01s05i216'})

In [132]: cubes = iris.load(files,constraint)

In [133]: cubes

Out[133]:

[<iris 'Cube' of precipitation_flux / (kg m-2 s-1) (time: 12; latitude: 144; longitude: 192)>,

<iris 'Cube' of precipitation_flux / (kg m-2 s-1) (-- : 96; latitude: 144; longitude: 192)>]

In [134]: cubes[0].cell_methods

Out[134]: (CellMethod(method='mean', coord_names=('time',), intervals=('1 hour',), comments=()),)

In [135]: cubes[1].cell_methods

Out[135]: (CellMethod(method='mean', coord_names=('time',), intervals=('24 hour',), comments=()),)

Thanks!

Jonny

[RuthC]

Hi Jonny,

you should be able to create the constraints you want, and combine them using "&":

stash_constraint = iris.AttributeConstraint(STASH='m01s05i216')

desired_cell_method = iris.coords.CellMethod(method='mean', coords='time', intervals='1 hour')
cell_method_constraint = iris.Constraint(cube_func=lambda cube: desired_cell_method in cube.cell_methods)

cube = iris.load_cube(files, stash_constraint & cell_method_constraint)

Cheers,

Ruth

[Jonny Williams]

Hey Ruth.

Thanks a lot for this, this is super helpful! Now being used in earnest!

Cheers

Jonny

[Nick Dunstone]

Yes, very helpful, thanks Ruth! :-)