ccdfstd : computes the standard deviation of any variable ----------------------------------------------------------- Computes the standard deviation of any variable. This computation is direct and does not required a pre-processing with any of the cdfmoy tools. This is the wrapping around the native cdfstd operator assuming its usage is:: cdfstd [-save] **[-spval0]** **[-nomissincl]** **[-stdopt]** ... list_of_files CliMAF optional arguments are the ones surrounded with '**'. See also: - :doc:`ccdfstdmoy ` for also getting the mean value of the field **References** : http://www.drakkar-ocean.eu/tools **Provider / contact** : climaf at meteo dot fr for the wrapping **Inputs** (in the order of CliMAF call): - any dataset forming a time-series (but only one) **Mandatory arguments**: None **Optional arguments**: - ``opt`` may be used to pass following keys : - ``-spval0`` : set missing_value attribute to 0 for all output variables and take care of the input missing_value. This option is usefull if missing_values differ from files to files - ``-nomissincl`` : with this option, the output std and mean are set to missing value at any gridpoint where the variable contains a missing value for at least one timestep. You should combine with -spval0 if missing values are not 0 in all the input files - ``-stdopt`` : use a more optimal algorithm to compute std and std is unbiased **Required files**: None **Outputs**: - main output : a netcdf file (variable : IN-var_std, same units than input variables) **Climaf call example**:: For more examples which are systematically tested, see :download:`cdftools.py <../../examples/cdftools.py>` >>> dmld=ds(simulation="PRE6CPLCr2alb", variable="omlmax", period="199807-199810", realm="O") # some dataset, with whatever variable >>> my_cdfstd=ccdfstd(dmld) >>> cfile(my_cdfstd) # to compute the standard deviation of variable "omlmax" >>> my_cdfstd2=ccdfstd(dmld,opt='-full') >>> cfile(my_cdfstd2) **Implementation**: The operator is implemented as a binary using cdftools cdfstd operator.