Functions returning ClimAF objects

Except for the first three paragraphs, this section is for advanced use. As a first step, you should consider using the built-in data definitions described at projects. You may need to come back to this section for reference

cscalar : returns a scalar value (float) in python (using cMA)

climaf.functions.cscalar(dat)[source]

Returns a scalar value using cMA (and not a masked array, to avoid the subsetting that is generally needed).

fadd : add two CliMAF objects or a CliMAF object and a constant

climaf.functions.fadd(dat1, dat2)[source]

Addition of two CliMAF object, or multiplication of the CliMAF object given as first argument and a constant as second argument (string, float or integer)

Shortcut to ccdo(dat,operator=’addc,’+str(c)) and ccdo2(dat1,dat2,operator=’add’)

>>> ds1= .... #some dataset, with whatever variable
>>> ds2= .... #some other, compatible dataset
>>> ds1_plus_ds2 = fadd(ds1,ds2) # ds1 + ds2
>>> ds1= .... #some dataset, with whatever variable
>>> c = '-1'  #a constant
>>> ds1_plus_c = fadd(ds1,c) # ds1 + c

fsub : subtract two CliMAF objects or a CliMAF object and a constant

climaf.functions.fsub(dat1, dat2)[source]

Substraction of two CliMAF object, or multiplication of the CliMAF object given as first argument and a constant as second argument (string, float or integer)

Shortcut to ccdo(dat,operator=’subc,’+str(c)) and ccdo2(dat1,dat2,operator=’sub’)

>>> ds1= .... #some dataset, with whatever variable
>>> ds2= .... #some other, compatible dataset
>>> ds1_minus_ds2 = fsub(ds1,ds2) # ds1 - ds2
>>> ds1= .... #some dataset, with whatever variable
>>> c = '-1'  #a constant
>>> ds1_minus_c = fsub(ds1,c) # ds1 - c
>>> just to check

fmul : multiply two CliMAF objects or a CliMAF object and a constant

climaf.functions.fmul(dat1, dat2)[source]

Multiplication of two CliMAF objects, or multiplication of the CliMAF object given as first argument and a constant as second argument (string, float or integer)

Shortcut to ccdo(dat1,dat2,operator=’mul’) and ccdo(dat,operator=’mulc,’+c)

>>> ds1= .... #some dataset, with whatever variable
>>> ds2= .... #some other, compatible dataset
>>> ds1_times_ds2 = fmul(ds1,ds2) # ds1 * ds2
>>> ds1= .... #some dataset, with whatever variable
>>> c = '-1'  #a constant
>>> ds1_times_c = fmul(ds1,c) # ds1 * c

fdiv : divide two CliMAF objects or a CliMAF object and a constant

climaf.functions.fdiv(dat1, dat2)[source]

Division of two CliMAF object, or multiplication of the CliMAF object given as first argument and a constant as second argument (string, float or integer)

Shortcut to ccdo(dat1,dat2,operator=’div’) and ccdo(dat,operator=’divc,’+c)

>>> ds1= .... #some dataset, with whatever variable
>>> ds2= .... #some other, compatible dataset
>>> ds1_by_ds2 = fdiv(ds1,ds2) # ds1 / ds2
>>> ds1= .... #some dataset, with whatever variable
>>> c = '-1'  #a constant
>>> ds1_times_c = fdiv(ds1,c) # ds1 * c

apply_scale_offset : Returns a CliMAF object after applying a scale and offset

climaf.functions.apply_scale_offset(dat, scale, offset)[source]

Returns a CliMAF object after applying a scale and offset ! Shortcut to: ccdo(ccdo(dat,operator=’mulc,’+str(float(scale))),operator=’addc,’+str(float(offset)))

Note : this function should be used parcimonioulsy because the model in CliMAF for dealing with scaling and offset is rather :

  • to automatically scale and offset the data to S.I. units at input/reading stage; this si done by declaring scaling for each relevant variable in a project using function calias(); it also allows to set the units
  • if the S.I. units are not suitable for a plot, to use plot operators arguments ‘scale’ and ‘offset’ to change the values

diff_regrid : Regrids dat1 on dat2 and returns the difference between dat1 and dat2

climaf.functions.diff_regrid(dat1, dat2)[source]

Regrids dat1 on dat2 and returns the difference between dat1 and dat2

>>> dat1= ....   # some dataset, with whatever variable
>>> dat2= ....   # some dataset, with the same variable as dat1
>>> diff_dat1_dat2 = diff_regrid(dat1,dat2)

diff_regridn : Regrids dat1 and dat2 on a chosen cdogrid (default is n90) and returns the difference between dat1 and dat2

climaf.functions.diff_regridn(data1, data2, cdogrid=u'n90', option=u'remapbil')[source]

Regrids dat1 and dat2 on a chosen cdogrid (default is n90) and returns the difference between dat1 and dat2 The user can specify the cdo regridding method with the argument option (as in regridn(); default is option=’remapbil’).

# some dataset, with whatever variable
>>> dat1= ....
# some dataset, with the same variable as dat1
>>> dat2= ....
# -> uses cdogrid=’n90’ and option=’remapbil’
>>> diff_dat1_dat2 = diff_regridn(dat1,dat2)
# -> Returns the difference on 2 deg grid
>>> diff_dat1_dat2 = diff_regridn(dat1,dat2,cdogrid='r180x90')
# -> Returns the difference on 2 deg grid regridded with the remapdis CDO method
>>> diff_dat1_dat2 = diff_regridn(dat1,dat2,cdogrid='r180x90', option='remapdis')

annual_cycle : Computes the annual cycle as the 12 climatological months of dat

climaf.functions.annual_cycle(dat)[source]

Computes the annual cycle as the 12 climatological months of dat (wrapper of ccdo with operator ymonavg)

>>> dat= ....   # some dataset, with whatever variable
>>> annual_cycle_dat = annual_cycle(dat)

clim_average : Computes climatological averages on the annual cycle of a dataset

climaf.functions.clim_average(dat, season)[source]

Computes climatological averages on the annual cycle of a dataset, on the months specified with ‘season’, either:

  • the annual mean climatology (season => ‘ann’,’annual’,’climato’,’clim’,’climatology’,’annual_average’,’anm’)
  • seasonal climatologies (e.g. season = ‘DJF’ or ‘djf’ to compute the seasonal climatology over December-January-February; available seasons: DJF, MAM, JJA, SON, JFM, JAS, JJAS
  • individual monthly climatologies (e.g. season = ‘january’, ‘jan’, ‘1’ or 1 to get the climatological January)
  • annual maximum or minimum (typically makes sense with the mixed layer depth)

Note that you can use upper case or lower case characters to specify the months or seasons.

clim_average computes the annual cycle for you.

>>> dat= ....   # some dataset, with whatever variable
>>> climds_JFM = clim_average(dat,'JFM')         # The climatology of dat over January-February-March
>>> climds_ANM = clim_average(dat,'annual_mean') # The annual mean climatology
>>> climds_September = clim_average(dat,'September') # The annual mean climatology of September
>>> climds_September = clim_average(dat,9) # Same as previous example, with a float

lonlatvert_interpolation : Interpolates a lon/lat/pres field (two possible ways)

climaf.functions.lonlatvert_interpolation(dat1, dat2=None, vertical_levels=None, cdo_horizontal_grid=u'r1x90', horizontal_regridding=True)[source]

Interpolates a lon/lat/pres field dat1 via two possible ways: - either by providing a target lon/lat/pres field dat2 => dat1 is regridded both horizontally and vertically on dat2 - or by providing a list of vertical levels => dat1 is regridded horizontally on the cdo_horizontal_grid (default=’r1x90’), and vertically on the list of vertical levels The user can provide the vertical levels (in Pa) like this: vertical_levels=[100000,85000,50000,20000,…] # or vertical_levels=’100000,85000,50000,20000’ Before the computations, the function checks the unit of the vertical axis; it is converted to Pa if necessary directly in the netcdf file(s) corresponding to dat1(2).

>>> dat = ds(project='CMIP5',model='IPSL-CM5A-LR',variable='ua',period='1980-1985',
             experiment='historical',table='Amon')
>>> ref = ds(project='ref_pcmdi',variable='ua',product='ERAINT')
>>> zonmean_dat = zonmean(time_average(dat))
>>> zonmean_ref = zonmean(time_average(ref))
>>> dat_interpolated_on_ref = lonlatvert_interpolation(zonmean_dat,zonmean_ref)
>>> dat_interpolated_on_list_of_levels = lonlatvert_interpolation(zonmean_dat,
...                                                               vertical_levels='100000,85000,50000,20000,'
...                                                                               '10000,5000,2000,1000')

zonmean : Return the zonal mean field of dat

climaf.functions.zonmean(dat)[source]

Return the zonal mean field of dat

Shortcut to the command ccdo(dat,operator=’zonmean’)

>>> ds= ....   # some dataset, with whatever variable
>>> ds_zonmean = zonmean(ds) # Zonal mean of ds()

diff_zonmean : Returns the zonal mean bias of dat1 against dat2 (based on lonlatpres_interpolation)

climaf.functions.diff_zonmean(dat1, dat2)[source]

Returns the zonal mean bias of dat1 against dat2

The function first computes the zonal means of dat1 and dat2. Then, it interpolates the zonal mean field of dat1 on the zonal mean field of dat2 with the function lonlatvert_interpolation. It finally returns the bias field.

>>> ds1= ....   # some dataset, with whatever variable
>>> ds2= ....   # some dataset, with the same variable as ds1
>>> diff_zonmean_ds1_ds2 = diff_zonmean(ds1,ds2) # Zonal mean difference between ds1 and ds2