mitgcm_tools

Python toolbox for working with MITgcm output. Depends heavily on the excellent xarray and xgcm packages.

open_ncfile(file_pattern,doconform_axes=True,chunking=None,strange_axes=dict(),grid=[]):

    Read in data from a netcdf file (file_pattern) using xarray, which can be chunked via dask by
      setting chunking to a dictionary of chunks (e.g. {'T':2,'X':10,'Y':10,'Z':2}).
    For compatability with xgcm, the axes may need to be conformed to certain specifications. 
      set conform_axes=False to override this. We can handle conversions between many axis names, but
      if there is a particularly difficult set (thanks, `pkg/diagnostics`) set the conversion within the
      "strange_axes" dictionary strange_axes={'Xnp':'XN','Ynp':'YN'}

open_bnfile(fname,sizearr=(12,15,64,128),prec='>f4'):

open_bnfile(fname,sizearr,prec) reads a binary file and returns it as a
    numpy array.
    
    fname is the file name,
    prec  is the precision and dtype argument ('>f4' works usually)
    sizearr is the anticipated size of the returned array

loadgrid(fname='grid.glob.nc'):

loadgrid(fname,sizearr,prec) reads a netcdf grid file and returns it as a
    xarray, with a few additional items.
    
    fname is the file name,

getparm(path_to_namelist,usef90nml=True,flatten=True):

    Read in Namelist file to a dictionary as strings or floats
    Works best with package `f90nml`

get_dicpco2(data_parms,data_dic,grid,path='./'):

get_dicpco2 loads output from the DIC package relating to the atmospheric
   pco2 boundary condition (constant, read in from a file, or dynamic).
   It interogates a bunch of different sources (text file, pickup, etc).

saturated_oxygen(t,s,gsw=False):

Compute oxygen saturation from temperature and salinity; best with `gsw` package.
Oxygen saturation value is the volume of oxygen gas absorbed from humidity-saturated
air at a total pressure of one atmosphere (mol/m3)

calc_pco2(dic,alk,po4,sit,theta,salt,pressure):

Compute seawater pCO2 concentration from dic and alkalinity
at local pressure, temperature, salinity and nutrient conc.
Written to work with my python fork of the `co2sys` package.

calc_carbon(pco2,alk,po4,sit,theta,salt,pressure):

Compute DIC concentration from seawater pCO2 and alkalinity
at local pressure, temperature, salinity and nutrient conc.

For saturated carbon, use preformed alkalinity and nutrients, as
well at a 3d field of atmospheric pCO2.