11. Utilities

11.1. MITgcmutils

This Python package includes a number of helpful functions and scripts for dealing with MITgcm output. You can install it from the model repository (in directory utils/python/MITgcmutils) or from the Python Package Index:

pip install --user MITgcmutils

The following functions are exposed at the package level:

The package also includes a standalone script for joining tiled mnc files: gluemncbig.

For more functions, see the individual modules:

11.1.1. mds

exception MITgcmutils.mds.ParseError[source]
MITgcmutils.mds.parsemeta(metafile)[source]

parses metafile (file object or filename) into a dictionary of lists of floats, ints or strings

MITgcmutils.mds.rdmds(fnamearg, itrs=-1, machineformat='b', rec=None, fill_value=0, returnmeta=False, astype=<class 'float'>, region=None, lev=(), usememmap=False, mm=False, squeeze=True, verbose=False)[source]

Read meta-data files as written by MITgcm.

Call signatures:

a = rdmds(fname,…)

a,its,meta = rdmds(fname,…,returnmeta=True)

Parameters

  • fname (string) –

    name of file to read, without the ‘.data’ or ‘.meta’ suffix. If itrs is given, the iteration number is added to fname as well. fname may contain shell wildcards, which is useful for tile files organized into directories, e.g.,

    T = rdmds(‘prefix*/T’, 2880)

    will read prefix0000/T.0000002880.*, prefix0001/T.0000002880.*, … (and any others that match the wildcard, so be careful how you name things!)

  • itrs (int or list of ints or np.nan or np.inf) –

    Iteration number(s). With itrs=-1, will try to read

    fname.meta or fname.001.001.meta, …

    If itrs is a list of integers of an integer, it will read the corresponding

    fname.000000iter.meta, …

    If itrs is np.nan, it will read all iterations for which files are found. If itrs is np.inf, it will read the highest iteration found.

  • machineformat (int) – endianness (‘b’ or ‘l’, default ‘b’)

  • rec (list of int or None) – list of records to read (default all) useful for pickups and multi-field diagnostics files

  • fill_value (float) – fill value for missing (blank) tiles (default 0)

  • astype (data type) – data type to return (default: double precision) None: keep data type/precision of file

  • region (tuple of int) – (x0,x1,y0,y1) read only this region (default (0,nx,0,ny))

  • lev (list of int or tuple of lists of int) – list of levels to read, or, for multiple dimensions (excluding x,y), tuple(!) of lists (see examples below)

  • usememmap (bool) – if True, use a memory map for reading data (default False) recommended when using lev, or region with global files to save memory and, possibly, time

Returns

  • a (array_like) – numpy array of the data read

  • its (list of int) – list of iteration numbers read (only if returnmeta=True)

  • meta (dict) – dictionary of metadata (only if returnmeta=True)

Examples

>>> XC = rdmds('XC')
>>> XC = rdmds('res_*/XC')
>>> T = rdmds('T.0000002880')
>>> T = rdmds('T',2880)
>>> T2 = rdmds('T',[2880,5760])
>>> T,its = rdmds('T',numpy.inf)
>>> VVEL = rdmds('pickup',2880,rec=range(50,100))
>>> a5 = rdmds('diags',2880,rec=0,lev=[5])
>>> a = rdmds('diags',2880,rec=0,lev=([0],[0,1,5,6,7]))
>>> from numpy import r_
>>> a = rdmds('diags',2880,rec=0,lev=([0],r_[:2,5:8]))  # same as previous
>>> a = rdmds('diags',2880,rec=0)[0, [0,1,5,6,7], ...]  # same, but less efficient
>>> a = rdmds('diags',2880)[0, 0, [0,1,5,6,7], ...]     # even less efficient
MITgcmutils.mds.readmeta(f)[source]

read meta file and extract tile/timestep-specific parameters

MITgcmutils.mds.scanforfiles(fname)[source]

return list of iteration numbers for which metafiles with base fname exist

MITgcmutils.mds.strip_comments(text)[source]

strips C and C++ style comments from text

MITgcmutils.mds.wrmds(fbase, arr, itr=None, dataprec='float32', ndims=None, nrecords=None, times=None, fields=None, simulation=None, machineformat='b', deltat=None, dimlist=None)[source]

Write an array to an mds meta/data file set.

If itr is given, the files will be named fbase.0000000itr.data and fbase.0000000itr.meta, otherwise just fbase.data and fbase.meta.

Parameters

  • fbase (string) – Name of file to write, without the ‘.data’ or ‘.meta’ suffixes, and without the iteration number if itr is give

  • arr (array_like) – Numpy array to write

  • itr (int or None) – If given, this iteration number will be appended to the file name

  • dataprec (string) – precision of resulting file (‘float32’ or ‘float64’)

  • ndims (int) – number of non-record dimensions; extra (leading) dimensions will be folded into 1 record dimension

  • nrecords (int) – number of records; will fold as many leading dimensions as necessary (has to match shape!)

  • times (float or list of floats) – times to write into meta file. Either a single float or a list of two for a time interval

  • fields (list of strings) – list of fields

  • simulation (string) – string describing the simulation

  • machineformat (string) – ‘b’ or ‘l’ for big or little endian

  • deltat (float) – time step; provide in place of either times or itr to have one computed from the other

  • dimlist (tuple) – dimensions as will be stored in file (only useful when passing meta data from an existing file to wrmds as keyword args)

11.1.2. mnc

class MITgcmutils.mnc.MNC(fpatt, layout=None, multitime=False)[source]

A file object for MNC (tiled NetCDF) data.

Should behave mostly like scipy.io.netcdf.netcdf_file in ‘r’ mode.

Parameters

  • fpatt (string) – glob pattern for tile files

  • layout (string) –

    which global layout to use:

    ’model’

    use layout implied by Nx, Ny

    ’exch2’

    use exch2 global layout

    ’faces’

    variables are lists of exch2 faces

    default is to use exch2 layout if present, model otherwise

Example

>>> nc = mnc_files('mnc_*/state.0000000000.t*.nc')
>>> temp = nc.variables['Temp'][:]
>>> salt = nv.variables['S'][:]
>>> nc.close()
temp and salt are now assembled (global) arrays of shape (Nt, Nr, Ny, Nx)
where Nt is the number iterations found in the file (in this case probably 1).

Notes

The multitime option is not implemented, i.e., MNC cannot read files split in time.

close()[source]

Close tile files

MITgcmutils.mnc.mnc_files(fpatt, layout=None)[source]

A file object for MNC (tiled NetCDF) data.

Should behave mostly like scipy.io.netcdf.netcdf_file in ‘r’ mode.

Parameters

  • fpatt (string) – glob pattern for tile files

  • layout (string) –

    which global layout to use:

    ’model’

    use layout implied by Nx, Ny

    ’exch2’

    use exch2 global layout

    ’faces’

    variables are lists of exch2 faces

    default is to use exch2 layout if present, model otherwise

Example

>>> nc = mnc_files('mnc_*/state.0000000000.t*.nc')
>>> temp = nc.variables['Temp'][:]
>>> salt = nv.variables['S'][:]
>>> nc.close()
temp and salt are now assembled (global) arrays of shape (Nt, Nr, Ny, Nx)
where Nt is the number iterations found in the file (in this case probably 1).

Notes

The multitime option is not implemented, i.e., MNC cannot read files split in time.

MITgcmutils.mnc.rdmnc(fpatt, varnames=None, iters=None, slices=Ellipsis, layout=None)[source]

Read one or more variables from an mnc file set.

Parameters

  • fpatt (string) – glob pattern for netcdf files comprising the set

  • varnames (list of strings, optional) – list of variables to read (default all)

  • iters (list of int, optional) – list of iterations (not time) to read

  • slices (tuple of slice objects) – tuple of slices to read from each variable (typically given as numpy.s_[…])

Returns

dictionary of variable arrays

Return type

dict of numpy arrays

Example

>>> S = rdmnc("mnc_*/state.0000000000.*', ['U', 'V'], slices=numpy.s_[..., 10:-10, 10:-10])
>>> u = S['U']
>>> v = S['V']

Notes

Can currently read only one file set (i.e., 1 file per tile), not several files split in time.

Consider using mnc_files for more control (and similar convenience). The same restriction about multiple files applies, however.

11.1.3. diagnostics

MITgcmutils.diagnostics.readstats(fname)[source]

statsPerLayer,statsVertInt,itrs = readstats(fname)

Read a diagstats text file into record arrays (or dictionaries).

Parameters

fname (string) – name of diagstats file to read

Returns

  • statsPerLayer (record array or dict of arrays) – statistics per layer, shape (len(itrs), len(nReg), Nr, 5)

  • statsVertInt (record array or dict of arrays) – column integrals, shape (len(itrs), len(nReg), 5)

  • itrs (list of int) – iteration numbers found in the file

Notes

  • The 5 columns of the resulting arrays are average, std.dev, min, max and total volume.

  • There is a record (or dictionary key) for each field found in the file.

  • Regional axis is omitted if nReg == 1

11.1.4. ptracers

MITgcmutils.ptracers.iolabel(i)[source]

Map tracer number (1..3843) to 2-character I/O label:

1..99 => 01..99
100..619 => 0a..0Z,1a..9Z
620..3843 => aa..ZZ
Parameters

i (int) – ptracer number (1..3843)

Returns

2-character I/O label

Return type

string

MITgcmutils.ptracers.iolabel2num(s)[source]

Map 2-character IO label to tracer number, the inverse of iolabel()

11.1.5. density

Density of Sea Water using linear EOS and POLY3 method. Density of Sea Water using the Jackett and McDougall 1995 (JAOT 12) polynomial Density of Sea Water using the UNESCO equation of state formula (IES80) of Fofonoff and Millard (1983) [FRM83]. Density of Sea Water using the EOS-10 48-term polynomial.

MITgcmutils.density.bulkmodjmd95(salt, theta, p)[source]

Compute bulk modulus

MITgcmutils.density.bulkmodunesco(salt, theta, p)[source]

Compute bulk modulus

MITgcmutils.density.ini_poly3(fpath='POLY3.COEFFS')[source]

Reads the file fpath (default ‘POLY3.COEFFS’) and returns coefficients in poly

MITgcmutils.density.jmd95(salt, theta, p)[source]

Computes in-situ density of sea water

Density of Sea Water using Jackett and McDougall 1995 (JAOT 12) polynomial (modified UNESCO polynomial).

Parameters

  • salt (array_like) – salinity [psu (PSS-78)]

  • theta (array_like) – potential temperature [degree C (IPTS-68)]; same shape as s

  • p (array_like) – sea pressure [dbar]. p may have dims 1x1, mx1, 1xn or mxn for s(mxn)

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> dens.jmd95(35.5, 3., 3000.)
1041.83267

Notes

  • Jackett and McDougall, 1995, JAOT 12(4), pp. 381-388

  • Source code written by Martin Losch 2002-08-09

  • Converted to python by jahn on 2010-04-29

MITgcmutils.density.linear(salt, theta, sref=30, tref=20, sbeta=0.00074, talpha=0.0002, rhonil=999.8)[source]

Computes in-situ density of water

Density of water using the linear EOS of McDougall (1987).

Parameters

  • salt (array_like) – salinity [psu (PSS-78)]

  • theta (array_like) – potential temperature [degree C (IPTS-68)] same shape as salt

  • sref (reference salinity) – default 30 [psu (PSS-78)]

  • tref (reference potential temperature) – default 20 [degree C (IPTS-68)]

  • sbeta (haline expansion coefficient) – default 7.4e-04 [1/C]

  • talpha (therma expansion coefficient) – default 2.0e-04 [(g/Kg)-1]

  • rhonil (density of water) – default 999.8 [(g/Kg)-1];

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> dens.linear(35.5, 3.)
1007.268506

Notes

  • Source code written by Martin Losch 2002-08-09

  • Converted to python by Gavilan on 2024-07-18

MITgcmutils.density.mdjwf(salt, theta, p, epsln=0)[source]

Computes in-situ density of sea water

Density of Sea Water using the McDougall et al. 2003 (JAOT 20) polynomial.

Parameters

  • salt (array_like) – salinity [psu (PSS-78)]

  • theta (array_like) – potential temperature [degree C (IPTS-68)]; same shape as salt

  • p (array_like) – sea pressure [dbar]. p may have dims 1x1, mx1, 1xn or mxn for salt(mxn)

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> dens.mdjwf(35.5, 3., 3000.)
1041.83305

Notes

  • McDougall et al., 2003, JAOT 20(5), pp. 730-741

  • Converted to python by Gavilan on 2024-07-18

MITgcmutils.density.poly3(poly3, salt, theta)[source]

Calculates in-situ density as approximated by the POLY3 method based on the Knudsen formula (see Bryan and Cox 1972).

Parameters

  • poly3 (coefficients read from file) – ‘POLY3.COEFFS’ using INI_POLY3

  • salt (array_like) – salinity [psu (PSS-78)]

  • theta (array_like) – potential temperature [degree C (IPTS-68)]; same shape as salt

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> p=ini_poly3()
>>> T=rdmds('T',100)
>>> S=rdmds('S',100)
>>> D=poly3(p,salt,theta)
>>> or to work within a single model level
>>> D=poly3(P[3,:],S[3,:,:],T[3,:,:])

Notes

  • Source code written by Martin Losch 2002-08-09

  • Converted to python by Gavilan on 2024-07-18

MITgcmutils.density.teos10(salt, theta, p, epsln=0)[source]

Computes in-situ density of sea water

Density of Sea Water using TEOS-10.

Parameters

  • salt (array_like) – absolute salinity [g/kg]

  • theta (array_like) – conservative temperature [degree C (IPTS-68)]; same shape as s

  • p (array_like) – sea pressure [dbar]. p may have dims 1x1, mx1, 1xn or mxn for s(mxn)

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> dens.teos10(35.5, 3., 3000.)
1041.70578

Notes

  • Converted to python by Gavilan on 2024-07-18

MITgcmutils.density.unesco(salt, theta, p)[source]

Computes in-situ density of sea water

Density of Sea Water using Fofonoff and Millard (1983) polynomial.

Parameters

  • salt (array_like) – salinity [psu (PSS-78)]

  • theta (array_like) – potential temperature [degree C (IPTS-68)]; same shape as s

  • p (array_like) – sea pressure [dbar]. p may have dims 1x1, mx1, 1xn or mxn for s(mxn)

Returns

dens – density [kg/m^3]

Return type

array

Example

>>> dens.unesco(35.5, 3., 3000.)
1041.87663

Notes

  • Source code written by Martin Losch 2002-08-09

  • Converted to python by Gavilan on 2024-07-18

11.1.6. miscellaneous utilities

Utilities for MITgcm.

MITgcmutils.utils.gen_blanklist(depth, sNx, sNy, tilemap=False, fill_value=0)[source]

Computes blanklist for data.exch2

Parameters

  • depth (2D array_like) – depth [m].

  • sNx (int) – x point in each tile.

  • sNy (int) – y point in each tile.

  • tilemap (bool) – True : output tile contourplot, default False.

Returns

  • blank (list of int) – empty tiles numbers

  • fig(optional) (matplotlib figure) – tile plot

  • Usage

  • —–

  • >>> blank=gen_blanklist(bathy, 5, 5, tilemap=False)

  • 10,11,12,..,103

  • >>> [blank,fig]=gen_blanklist(bathy, 5, 5, tilemap=True)

  • 10,11,12,..,103

Example

>>> eg_blanklist()
10,11,12,..,103
MITgcmutils.utils.hfac(depth, rF, hFacMin=0.3, hFacMinDr=50, htype='C')[source]

Computes hFacC,W,S

Parameters

  • depth (2D array_like) – Depth [m].

  • rF (1D array_like) – Depth at the f point.

  • hFacMin (float) – Min fraction for partial vertical levels.

  • hFacMinDr (float) – Min depth for partial vertical levels.

  • htype (string) – Types of hfac: one or more of ‘C’,’S’,’W’, default=’C’.

Returns

  • hFacC,W,S (tuple of array_like) – The hfac arrays requested in htype.

  • Usage

  • —–

  • >>> [hFacC]=mit.hfac(depth,rF,0.3,50,’C’)

Example

>>> [hFacC]=eg_hfac()

Notes

The first row and column are filled with zeros for the hFacS and hFacW, respectively.

MITgcmutils.utils.readbin(fname, ndims, dataprec='float32', machineformat='b')[source]

Read meta-data files as written by MITgcm.

Parameters

  • fname (string) – name of file to read

  • ndims (int) – dimension of the file

  • dataprec (string) – precision of resulting file (‘float32’ or ‘float64’)

  • machineformat (string) – endianness (‘b’ or ‘l’, default ‘b’)

Returns

  • arr (array_like) – numpy array of the data read

  • Usage

  • —–

  • >>> arr=readbin(‘bathy.bin’,[Y,X])

MITgcmutils.utils.tilecmap(arr, sNx, sNy, tilen=None, sel_zoom=5, fill_value=0)[source]

Pseudocolor plot of land mask with tiles superimposed, optionally showing the values of arr for a single tile.

Parameters

  • arr (2D array_like) – values to plot, land mask is taken as arr==fill_value

  • sNx (int) – number of x points in each tile

  • sNy (int) – number of y points in each tile

  • tilen (int or None) – plot a specific tile, default None

  • sel_zoom (int) – zooming range, default 5

  • fill_value (float) – default 0

Returns

  • figure (matplotlib figure) – Plot of land mask, tiles and arr values

  • Usage

  • —–

  • >>> [fig]=tilecmap(bathy, 5, 5)

  • >>> [fig]=tilecmap(bathy, 5, 5, 66, sel_zoom=4)

Example

>>> eg_tilemap()
MITgcmutils.utils.writebin(fname, arr, dataprec='float32', machineformat='b')[source]

Write an array to a bin format for MITgcm

Parameters

  • fbase (string) – Name of file to write

  • arr (array_like) – Numpy array to write

  • dataprec (string) – precision of resulting file (‘float32’ or ‘float64’) (‘float32’ by default)

  • machineformat (string) – ‘b’ or ‘l’ for big or little endian (‘b’ by default)

  • Usage –

  • ----- –

  • writebin('data.bin' (>>>) –

  • arr) –

11.1.7. conversion

MITgcmutils.conversion.pfromz(rC, rF0=0.0, lat=None, rhoConst=1027.5, eosRefP0=101325.0, top_Pres=0.0, surf_pRef=101325.0)[source]

Computes pressure (dbar) of sea water from depth.

Parameters

  • rC (float or array_like) – Depth at c point [m].

  • rF0 (float or array_like) – Depth at rF[k=0], default 0.

  • lat (array_like or None) – Latitude to compute unesco gravity. If None, use gravity = 9.81. Default None.

  • rhoConst (float) – Density of seawater, default 1027.5.

  • eosRefP0 (float) – EOS reference pressure (Pa), default 1.01325e+5.

  • top_Pres (float) – Reference pressure at the top, default 0.

  • surf_pRef (float) – Surface pressure (Pa), default 1.01325e+5.

Returns

p – Pressure [dbar]. If rC and lat are both 1-dimensional, a 2-dimensional array corresponding to the outer product will be returned.

Return type

array_like

Example

>>> pfromz(-1000)
1007.9775
>>> pfromz([-100,-1000])
100.7978, 1007.9775
>>> pfromz([-100,-1000],lat=90)
101.0256, 1010.2562
>>> pfromz(-1000,lat=[70,90])
1009.6304, 1010.2562

11.1.8. cs

MITgcmutils.cs.pcol(x, y, data, projection=None, vmin=None, vmax=None, **kwargs)[source]

Plots 2D scalar fields on the MITgcm cubed sphere grid with pcolormesh.

Parameters

  • x (array_like) – ‘xg’, that is, x coordinate of the points one half grid cell to the left and bottom, that is vorticity points for tracers, etc.

  • y (array_like) – ‘yg’, that is, y coordinate of same points

  • data (array_like) – scalar field at tracer points

  • projection (Basemap instance, optional) – used to transform if present. Unfortunatly, cylindrical and conic maps are limited to the [-180 180] range. projection = ‘sphere’ results in a 3D visualization on the sphere without any specific projection. Good for debugging.

Example

>>> from mpl_toolkits.basemap import Basemap
>>> import MITgcmutils as mit
>>> import matplotlib.pyplot as plt
>>> from sq import sq
>>>
>>> x=mit.rdmds('XG'); y=mit.rdmds('YG'); e=mit.rdmds('Eta',np.inf)
>>> fig = plt.figure();
>>> mp = Basemap(projection='moll',lon_0 = 0.,
>>>              resolution = 'l', area_thresh = 1000.)
>>> plt.clf()
>>> h = mit.cs.pcol(x,y,sq(e), projection = mp)
>>> mp.fillcontinents(color = 'grey')
>>> mp.drawmapboundary()
>>> mp.drawmeridians(np.arange(0, 360, 30))
>>> mp.drawparallels(np.arange(-90, 90, 30))
>>> plt.show()

11.1.9. llc

MITgcmutils.llc.contour(*arguments, **kwargs)[source]

Create a contour plot of a 2-D llc array (with tricontour).

Call signatures:

contour(X, Y, C, N, **kwargs)

contour(X, Y, C, V, **kwargs)
Parameters

  • X (array-like) – x coordinates of the grid points

  • Y (array-like) – y coordinates of the grid points

  • C (array-like) – array of color values.

  • N (int) – number of levels

  • V (list of float) – list of levels

  • kwargs – passed to tricontour.

MITgcmutils.llc.contourf(*arguments, **kwargs)[source]

Create a contourf plot of a 2-D llc array (with tricontour).

Call signatures:

contourf(X, Y, C, N, **kwargs)

contourf(X, Y, C, V, **kwargs)
Parameters

  • X (array-like) – x coordinates of the grid points

  • Y (array-like) – y coordinates of the grid points

  • C (array-like) – array of color values.

  • N (int) – number of levels

  • V (list of float) – list of levels

  • kwargs – passed to tricontour.

MITgcmutils.llc.div(u, v, dxg=None, dyg=None, rac=None, hfw=None, hfs=None)[source]

Compute divergence of vector field (U,V) on llc grid

Call signatures:

divergence = div(U, V, DXG, DYG, RAC, HFW, HFS)
divergence = div(U, V)
divergence = div(U, V, DXG, DYG)
divergence = div(U, V, DXG, DYG, RAC)
divergence = div(U, V, DXG, DYG, hfw=HFW, hfs=HFS)
Parameters

  • u (array-like (timelevel,depthlevel,jpoint,ipoint)) – x-component of vector field at u-point

  • v (array-like (timelevel,depthlevel,jpoint,ipoint)) – y-component of vector field at v-point

  • dxg (array-like (jpoint,ipoint), optional) – grid spacing in x across v-point, defaults to one

  • dyg (array-like (jpoint,ipoint), optional) – grid spacing in y across u-point, defaults to one

  • rac (array-like (jpoint,ipoint), optional) – grid cell area, defaults to dxg*dyg

  • hfw (array-like (depthlevel,jpoint,ipoint), optional) – hFac at u-point, defaults to one

  • hfs (array-like (depthlevel,jpoint,ipoint), optional) – hFac at v-point, defaults to one

MITgcmutils.llc.faces(fld)[source]

convert mds multidimensional data into a list with 6 faces

MITgcmutils.llc.faces2mds(ff)[source]

convert 6 faces to mds 2D data, inverse opertation of llc.faces

MITgcmutils.llc.flat(fld, **kwargs)[source]

convert mds data into global 2D field only fields with 2 to 5 dimensions are allowed

MITgcmutils.llc.grad(X, dxc=None, dyc=None, hfw=None, hfs=None)[source]

Compute horizontal gradient of scalar field X on llc grid

Call signatures:

dXdx, dXdy = div(X, DXC, DYC, HFW, HFS)
dXdx, dXdy = div(X)
dXdx, dXdy = div(X, DXC, DYC)
dXdx, dXdy = div(X, hfw=HFW, hfs=HFS)
Parameters

  • X (array-like (timelevel,depthlevel,jpoint,ipoint)) – scalar field at c-point

  • dxc (array-like (jpoint,ipoint), optional) – grid spacing in x across u-point, defaults to one

  • dyc (array-like (jpoint,ipoint), optional) – grid spacing in y across v-point, defaults to one

  • hfw (array-like (depthlevel,jpoint,ipoint), optional) – hFac at u-point, defaults to one

  • hfs (array-like (depthlevel,jpoint,ipoint), optional) – hFac at v-point, defaults to one

MITgcmutils.llc.pcol(*arguments, **kwargs)[source]

Create a pseudo-color plot of a 2-D llc array (with plt.pcolormesh).

Call signatures:

pcol(X, Y, C, **kwargs)

pcol(X, Y, C, m, **kwargs)
Parameters

  • X (array-like) – x coordinates of the grid point corners (G-points)

  • Y (array-like) – y coordinates of the grid point corners (G-points)

  • C (array-like) – array of color values.

  • m (Basemap instance, optional) – map projection to use. NOTE: currently not all projections work

  • kwargs – passed to plt.pcolormesh.

MITgcmutils.llc.uv2c(u, v)[source]

Average vector component (u,v) to center points on llc grid

Call signatures:

uc,vc = uv2c(U,V)
Parameters

  • U (array-like (timelevel,depthlevel,jpoint,ipoint)) – x-component of vector field at u-point

  • V (array-like (timelevel,depthlevel,jpoint,ipoint)) – y-component of vector field at v-point

11.1.10. examples

MITgcmutils.examples.eg_blanklist()[source]

Example blanklist generator

MITgcmutils.examples.eg_hfac()[source]

Example grid mask generator

MITgcmutils.examples.eg_tilemap()[source]

Example tilemap plot distribution

11.1.11. gluemncbig

This command line script is part of MITgcmutils and provides a convenient method for stitching together NetCDF files into a single file covering the model domain. Be careful though - the resulting files can get very large.

Usage: gluemncbig [-2] [-q] [--verbose] [--help] [--many] [-v <vars>] -o <outfile> <files>

 -v <vars>  comma-separated list of variable names or glob patterns
 -2         write a NetCDF version 2 (64-Bit Offset) file allowing for large records
 --many     many tiles: assemble only along x in memory; less efficient
            on some filesystems, but opens fewer files simultaneously and
            uses less memory
 -q         suppress progress messages
 --verbose  report variables
 --help     show this help text

All files must have the same variables.
Each variable (or 1 record of it) must fit in memory.
With --many, only a row of tiles along x must fit in memory.

Examples:

gluemncbig -o ptr.nc mnc_*/ptr_tave.*.nc
gluemncbig -o BIO.nc -v 'BIO_*' mnc_*/ptr_tave.*.nc