# find Dimension instance given group and name.

# look in current group, and parents.

group = grp

dim = None

while 1:

try:

dim = group.dimensions[dimname]

break

except:

try:

group = group.parent

except:

raise ValueError("cannot find dimension %s in this group or parent groups" % dimname)

"""Iterate through all (sub-) groups of topgrp, similar to os.walktree.

"""

grps = topgrp.groups.values()

yield grps

for grp in topgrp.groups.values():

for children in _walk_grps(grp):

"""

quantize data to improve compression. data is quantized using

around(scale*data)/scale, where scale is 2**bits, and bits is determined

from the least_significant_digit. For example, if

least_significant_digit=1, bits will be 4.

"""

precision = pow(10.,-least_significant_digit)

exp = np.log10(precision)

if exp < 0:

exp = int(np.floor(exp))

else:

exp = int(np.ceil(exp))

bits = np.ceil(np.log2(pow(10.,-exp)))

scale = pow(2.,bits)

datout = np.around(scale*data)/scale

if ma.isMA(datout):

datout.set_fill_value(data.fill_value)

return datout

else:

put=False):

"""Return start, count, stride and indices needed to store/extract data

into/from a netCDF variable.

This function is used to convert a slicing expression into a form that is

compatible with the nc_get_vars function. Specifically, it needs

to interpret integers, slices, Ellipses, and 1-d sequences of integers

and booleans.

Numpy uses "broadcasting indexing" to handle array-valued indices.

"Broadcasting indexing" (a.k.a "fancy indexing") treats all multi-valued

indices together to allow arbitrary points to be extracted. The index

arrays can be multidimensional, and more than one can be specified in a

slice, as long as they can be "broadcast" against each other.

This style of indexing can be very powerful, but it is very hard

to understand, explain, and implement (and can lead to hard to find bugs).

Most other python packages and array processing

languages (such as netcdf4-python, xray, biggus, matlab and fortran)

use "orthogonal indexing" which only allows for 1-d index arrays and

treats these arrays of indices independently along each dimension.

The implementation of "orthogonal indexing" used here requires that

index arrays be 1-d boolean or integer. If integer arrays are used,

the index values must be sorted and contain no duplicates.

In summary, slicing netcdf4-python variable objects with 1-d integer or

boolean arrays is allowed, but may give a different result than slicing a

numpy array.

Numpy also supports slicing an array with a boolean array of the same

shape. For example x[x>0] returns a 1-d array with all the positive values of x.

This is also not supported in netcdf4-python, if x.ndim > 1.

Orthogonal indexing can be used in to select netcdf variable slices

using the dimension variables. For example, you can use v[lat>60,lon<180]

to fetch the elements of v obeying conditions on latitude and longitude.

Allow for this sort of simple variable subsetting is the reason we decided to

deviate from numpy's slicing rules.

This function is used both by the __setitem__ and __getitem__ method of

the Variable class.

Parameters

----------

elem : tuple of integer, slice, ellipsis or 1-d boolean or integer

sequences used to slice the netCDF Variable (Variable[elem]).

shape : tuple containing the current shape of the netCDF variable.

dimensions : sequence

The name of the dimensions. This is only useful to find out

whether or not some dimensions are unlimited. Only needed within

__setitem__.

grp : netCDF Group

The netCDF group to which the variable being set belongs to.

Only needed within __setitem__.

datashape : sequence

The shape of the data that is being stored. Only needed by __setitime__

put : True|False (default False). If called from __setitem__, put is True.

Returns

-------

start : ndarray (..., n)

A starting indices array of dimension n+1. The first n

dimensions identify different independent data chunks. The last dimension

can be read as the starting indices.

count : ndarray (..., n)

An array of dimension (n+1) storing the number of elements to get.

stride : ndarray (..., n)

An array of dimension (n+1) storing the steps between each datum.

indices : ndarray (..., n)

An array storing the indices describing the location of the

data chunk in the target/source array (__getitem__/__setitem__).

Notes:

netCDF data is accessed via the function:

nc_get_vars(grpid, varid, start, count, stride, data)

Assume that the variable has dimension n, then

start is a n-tuple that contains the indices at the beginning of data chunk.

count is a n-tuple that contains the number of elements to be accessed.

stride is a n-tuple that contains the step length between each element.

"""

# Adapted from pycdf (http://pysclint.sourceforge.net/pycdf)

# by Andre Gosselin..

# Modified by David Huard to handle efficiently fancy indexing with

# sequences of integers or booleans.

nDims = len(shape)

if nDims == 0:

nDims = 1

shape = (1,)

# When a single array or (non-tuple) sequence of integers is given

# as a slice, assume it applies to the first dimension,

# and use ellipsis for remaining dimensions.

if np.iterable(elem):

if type(elem) == np.ndarray or (type(elem) != tuple and \

np.array([_is_int(e) for e in elem]).all()):

elem = [elem]

for n in range(len(elem)+1,nDims+1):

elem.append(slice(None,None,None))

else: # Convert single index to sequence

elem = [elem]

# replace sequence of integer indices with boolean arrays

newElem = []

IndexErrorMsg=\

"only integers, slices (`:`), ellipsis (`...`), and 1-d integer or boolean arrays are valid indices"

for i, e in enumerate(elem):

# string-like object try to cast to int

# needs to be done first, since strings are iterable and

# hard to distinguish from something castable to an iterable numpy array.

if type(e) in [str,bytes,unicode]:

try:

e = int(e)

except:

raise IndexError(IndexErrorMsg)

ea = np.asarray(e)

# Raise error if multidimensional indexing is used.

if ea.ndim > 1:

raise IndexError("Index cannot be multidimensional")

# set unlim to True if dimension is unlimited and put==True

# (called from __setitem__)

if put and (dimensions is not None and grp is not None) and len(dimensions):

try:

dimname = dimensions[i]

# is this dimension unlimited?

# look in current group, and parents for dim.

dim = _find_dim(grp, dimname)

unlim = dim.isunlimited()

except IndexError: # more slices than dimensions (issue 371)

unlim = False

else:

unlim = False

# an iterable (non-scalar) integer array.

if np.iterable(ea) and ea.dtype.kind == 'i':

# convert negative indices in 1d array to positive ones.

ea = np.where(ea < 0, ea + shape[i], ea)

if np.any(ea < 0):

raise IndexErro("integer index out of range")

if not np.all(np.diff(ea) > 0): # same but cheaper than np.all(np.unique(ea) == ea)

# raise an error when new indexing behavior is different

# (i.e. when integer sequence not sorted, or there are

# duplicate indices in the sequence)

msg = "integer sequences in slices must be sorted and cannot have duplicates"

raise IndexError(msg)

# convert to boolean array.

# if unlim, let boolean array be longer than current dimension

# length.

elen = shape[i]

if unlim:

elen = max(ea.max()+1,elen)

else:

if ea.max()+1 > elen:

msg="integer index exceeds dimension size"

raise IndexError(msg)

eb = np.zeros(elen,np.bool)

eb[ea] = True

newElem.append(eb)

# an iterable (non-scalar) boolean array

elif np.iterable(ea) and ea.dtype.kind =='b':

# check that boolen array not too long

if not unlim and shape[i] != len(ea):

msg="""

Boolean array must have the same shape as the data along this dimension."""

raise IndexError(msg)

newElem.append(ea)

# integer scalar

elif ea.dtype.kind == 'i':

newElem.append(e)

# slice or ellipsis object

elif type(e) == slice or type(e) == type(Ellipsis):

newElem.append(e)

else: # castable to a scalar int, otherwise invalid

try:

e = int(e)

newElem.append(e)

except:

raise IndexError(IndexErrorMsg)

elem = newElem

# replace Ellipsis and boolean arrays with slice objects, if possible.

hasEllipsis = False

newElem = []

for e in elem:

ea = np.asarray(e)

# Replace ellipsis with slices.

if type(e) == type(Ellipsis):

if hasEllipsis:

raise IndexError("At most one ellipsis allowed in a slicing expression")

# The ellipsis stands for the missing dimensions.

newElem.extend((slice(None, None, None),) * (nDims - len(elem) + 1))

hasEllipsis = True

# Replace boolean array with slice object if possible.

elif ea.dtype.kind == 'b':

if ea.any():

indices = np.flatnonzero(ea)

start = indices[0]

stop = indices[-1] + 1

if len(indices) >= 2:

step = indices[1] - indices[0]

else:

step = None

if np.array_equal(indices, np.arange(start, stop, step)):

newElem.append(slice(start, stop, step))

else:

newElem.append(ea)

else:

newElem.append(slice(0, 0))

else:

newElem.append(e)

elem = newElem

# If slice doesn't cover all dims, assume ellipsis for rest of dims.

if len(elem) < nDims:

for n in range(len(elem)+1,nDims+1):

elem.append(slice(None,None,None))

# make sure there are not too many dimensions in slice.

if len(elem) > nDims:

raise ValueError("slicing expression exceeds the number of dimensions of the variable")

# Compute the dimensions of the start, count, stride and indices arrays.

# The number of elements in the first n dimensions corresponds to the

# number of times the _get method will be called.

sdim = []

for i, e in enumerate(elem):

# at this stage e is a slice, a scalar integer, or a 1d boolean array.

# Booleans --- _get call for each True value

if np.asarray(e).dtype.kind == 'b':

sdim.append(e.sum())

# Scalar int or slice, just a single _get call

else:

sdim.append(1)

# Create the start, count, stride and indices arrays.

sdim.append(max(nDims, 1))

start = np.empty(sdim, dtype=int)

count = np.empty(sdim, dtype=int)

stride = np.empty(sdim, dtype=int)

indices = np.empty(sdim, dtype=object)

for i, e in enumerate(elem):

ea = np.asarray(e)

# set unlim to True if dimension is unlimited and put==True

# (called from __setitem__). Note: grp and dimensions must be set.

if put and (dimensions is not None and grp is not None) and len(dimensions):

dimname = dimensions[i]

# is this dimension unlimited?

# look in current group, and parents for dim.

dim = _find_dim(grp, dimname)

unlim = dim.isunlimited()

else:

unlim = False

# SLICE #

if type(e) == slice:

# determine length parameter for slice.indices.

# shape[i] can be zero for unlim dim that hasn't been written to

# yet.

# length of slice may be longer than current shape

# if dimension is unlimited (and we are writing, not reading).

if unlim and e.stop is not None and e.stop > shape[i]:

length = e.stop

elif unlim and e.stop is None and datashape != ():

if e.start is None:

length = datashape[i]

else:

length = e.start+datashape[i]

else:

if unlim and datashape == () and len(dim) == 0:

# writing scalar along unlimited dimension using slicing

# syntax (var[:] = 1, when var.shape = ())

length = 1

else:

length = shape[i]

beg, end, inc = e.indices(length)

n = len(range(beg,end,inc))

start[...,i] = beg

count[...,i] = n

stride[...,i] = inc

indices[...,i] = slice(None)

# BOOLEAN ITERABLE #

elif ea.dtype.kind == 'b':

e = np.arange(len(e))[e] # convert to integer array

start[...,i] = np.apply_along_axis(lambda x: e*x, i, np.ones(sdim[:-1]))

indices[...,i] = np.apply_along_axis(lambda x: np.arange(sdim[i])*x, i, np.ones(sdim[:-1], int))

count[...,i] = 1

stride[...,i] = 1

# all that's left is SCALAR INTEGER #

else:

if e >= 0:

start[...,i] = e

elif e < 0 and (-e <= shape[i]) :

start[...,i] = e+shape[i]

else:

raise IndexError("Index out of range")

count[...,i] = 1

stride[...,i] = 1

indices[...,i] = -1 # Use -1 instead of 0 to indicate that

# this dimension shall be squeezed.

"""Return the output array shape given the count array created by getStartCountStride"""

s = list(count.shape[:-1])

out = []

for i, n in enumerate(s):

if n == 1:

c = count[..., i].ravel()[0] # All elements should be identical.

out.append(c)

else:

out.append(n)

# is object container-like? (can test for

# membership with "is in", but not a string)

try: 1 in a

except: return False

if type(a) == type(basestring): return False

try:

return int(a) == a

except:

try:

ss = str(s)

except:

ss = s

import posixpath

grps = p.split("/")

for gname in grps:

if gname == "": continue

g = g.groups[gname]

from netCDF4 import Dataset

usage = """

Print summary information about a netCDF file.

usage: %s [-h] [-g grp or --group=grp] [-v var or --variable=var] [-d dim or --dimension=dim] filename

-h -- Print usage message.

-g <group name> or --group=<group name> -- Print info for this group

(default is root group). Nested groups specified

using posix paths ("group1/group2/group3").

-v <variable name> or --variable=<variable name> -- Print info for this variable.

-d <dimension name> or --dimension=<dimension name> -- Print info for this dimension.

netcdf filename must be last argument.

\n""" % os.path.basename(sys.argv[0])

try:

opts, pargs = getopt.getopt(sys.argv[1:],'hv:g:d:',

['group=',

'variable=',

'dimension='])

except:

(type, value, traceback) = sys.exc_info()

sys.stdout.write("Error parsing the options. The error was: %s\n" % value)

sys.stderr.write(usage)

sys.exit(0)

# Get the options

group = None; var = None; dim=None

for option in opts:

if option[0] == '-h':

sys.stderr.write(usage)

sys.exit(0)

elif option[0] == '--group' or option[0] == '-g':

group = option[1]

elif option[0] == '--variable' or option[0] == '-v':

var = option[1]

elif option[0] == '--dimension' or option[0] == '-d':

dim = option[1]

else:

sys.stdout.write("%s: Unrecognized option\n" % option[0])

sys.stderr.write(usage)

sys.exit(0)

# filename passed as last argumenbt

filename = pargs[-1]

f = Dataset(filename)

if group is None:

if var is None and dim is None:

print(f)

else:

if var is not None:

print(f.variables[var])

if dim is not None:

print(f.dimensions[dim])

else:

if var is None and dim is None:

print(getgrp(f,group))

else:

g = getgrp(f,group)

if var is not None:

print(g.variables[var])

if dim is not None:

print(g.dimensions[var])

"""convert a netcdf 4 file (filename4) in NETCDF4_CLASSIC format

to a netcdf 3 file (filename3) in NETCDF3_64BIT format."""

ncfile4 = Dataset(filename4,'r')

if ncfile4.file_format != 'NETCDF4_CLASSIC':

raise IOError('input file must be in NETCDF4_CLASSIC format')

ncfile3 = Dataset(filename3,'w',clobber=clobber,format=format)

# create dimensions. Check for unlimited dim.

unlimdimname = False

unlimdim = None

# create global attributes.

if not quiet: sys.stdout.write('copying global attributes ..\n')

#for attname in ncfile4.ncattrs():

# setattr(ncfile3,attname,getattr(ncfile4,attname))

ncfile3.setncatts(ncfile4.__dict__)

if not quiet: sys.stdout.write('copying dimensions ..\n')

for dimname,dim in ncfile4.dimensions.items():

if dim.isunlimited():

unlimdimname = dimname

unlimdim = dim

ncfile3.createDimension(dimname,None)

else:

ncfile3.createDimension(dimname,len(dim))

# create variables.

for varname,ncvar in ncfile4.variables.items():

if not quiet:

sys.stdout.write('copying variable %s\n' % varname)

# is there an unlimited dimension?

if unlimdimname and unlimdimname in ncvar.dimensions:

hasunlimdim = True

else:

hasunlimdim = False

if hasattr(ncvar, '_FillValue'):

FillValue = ncvar._FillValue

else:

FillValue = None

var = ncfile3.createVariable(varname,ncvar.dtype,ncvar.dimensions,fill_value=FillValue)

# fill variable attributes.

attdict = ncvar.__dict__

if '_FillValue' in attdict:

del attdict['_FillValue']

var.setncatts(attdict)

#for attname in ncvar.ncattrs():

# if attname == '_FillValue': continue

# setattr(var,attname,getattr(ncvar,attname))

# fill variables with data.

if hasunlimdim: # has an unlim dim, loop over unlim dim index.

# range to copy

if nchunk:

start = 0; stop = len(unlimdim); step = nchunk

if step < 1:

step = 1

for n in range(start, stop, step):

nmax = n+nchunk

if nmax > len(unlimdim):

nmax=len(unlimdim)

var[n:nmax] = ncvar[n:nmax]

else:

var[0:len(unlimdim)] = ncvar[:]

else: # no unlim dim or 1-d variable, just copy all data at once.

var[:] = ncvar[:]

ncfile3.sync() # flush data to disk

# close files.

ncfile3.close()

usage = """

Convert a netCDF 4 file (in NETCDF4_CLASSIC format) to netCDF 3 format.

usage: %s [-h] [-o] [--chunk] netcdf4filename netcdf3filename

-h -- Print usage message.

-o -- Overwite destination file (default is to raise an error if output file already exists).

--quiet=(0|1) -- if 1, don't print diagnostic information.

--format -- netcdf3 format to use (NETCDF3_64BIT by default, can be set to NETCDF3_CLASSIC)

--chunk=(integer) -- number of records along unlimited dimension to

write at once. Default 10. Ignored if there is no unlimited

dimension. chunk=0 means write all the data at once.

\n""" % os.path.basename(sys.argv[0])

try:

opts, pargs = getopt.getopt(sys.argv[1:], 'ho',

['format=','chunk=','quiet='])

except:

(type, value, traceback) = sys.exc_info()

sys.stdout.write("Error parsing the options. The error was: %s\n" % value)

sys.stderr.write(usage)

sys.exit(0)

# default options

quiet = 0

chunk = 1000

format = 'NETCDF3_64BIT'

overwritefile = 0

# Get the options

for option in opts:

if option[0] == '-h':

sys.stderr.write(usage)

sys.exit(0)

elif option[0] == '-o':

overwritefile = 1

elif option[0] == '--quiet':

quiet = int(option[1])

elif option[0] == '--format':

format = option[1]

elif option[0] == '--chunk':

chunk = int(option[1])

else:

sys.stdout.write("%s : Unrecognized option\n" % options[0])

sys.stderr.write(usage)

sys.exit(0)

# if we pass a number of files different from 2, abort

if len(pargs) < 2 or len(pargs) > 2:

sys.stdout.write("You need to pass both source and destination!\n.")

sys.stderr.write(usage)

sys.exit(0)

# Catch the files passed as the last arguments

filename4 = pargs[0]

filename3 = pargs[1]

# copy the data from filename4 to filename3.

zlib=True,complevel=6,shuffle=True,fletcher32=False,

clobber=False,lsd_dict=None,nchunk=10,quiet=False,classic=0,

vars=None,istart=0,istop=-1):

"""convert a netcdf 3 file (filename3) to a netcdf 4 file

The default format is 'NETCDF4', but can be set

to NETCDF4_CLASSIC if classic=1.

If unpackshort=True, variables stored as short

integers with a scale and offset are unpacked to floats.

in the netcdf 4 file. If the lsd_dict is not None, variable names

corresponding to the keys of the dict will be truncated to the decimal place

specified by the values of the dict. This improves compression by

making it 'lossy'..

If vars is not None, only variable names in the list

will be copied (plus all the dimension variables).

The zlib, complevel and shuffle keywords control

how the compression is done."""

from netCDF4 import Dataset

ncfile3 = Dataset(filename3,'r')

if classic:

ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4_CLASSIC')

else:

ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4')

mval = 1.e30 # missing value if unpackshort=True

# create dimensions. Check for unlimited dim.

unlimdimname = False

unlimdim = None

# create global attributes.

if not quiet: sys.stdout.write('copying global attributes ..\n')

#for attname in ncfile3.ncattrs():

# setattr(ncfile4,attname,getattr(ncfile3,attname))

ncfile4.setncatts(ncfile3.__dict__)

if not quiet: sys.stdout.write('copying dimensions ..\n')

for dimname,dim in ncfile3.dimensions.items():

if dim.isunlimited():

unlimdimname = dimname

unlimdim = dim

ncfile4.createDimension(dimname,None)

if istop == -1: istop=len(unlimdim)

else:

ncfile4.createDimension(dimname,len(dim))

# create variables.

if vars is None:

varnames = ncfile3.variables.keys()

else:

# variables to copy specified

varnames = vars

# add dimension variables

for dimname in ncfile3.dimensions.keys():

if dimname in ncfile3.variables.keys() and\

dimname not in varnames:

varnames.append(dimname)

for varname in varnames:

ncvar = ncfile3.variables[varname]

if not quiet: sys.stdout.write('copying variable %s\n' % varname)

# quantize data?

if lsd_dict is not None and lsd_dict.has_key(varname):

lsd = lsd_dict[varname]

if not quiet: sys.stdout.write('truncating to least_significant_digit = %d\n'%lsd)

else:

lsd = None # no quantization.

# unpack short integers to floats?

if unpackshort and hasattr(ncvar,'scale_factor') and hasattr(ncvar,'add_offset'):

dounpackshort = True

datatype = 'f4'

else:

dounpackshort = False

datatype = ncvar.dtype

# is there an unlimited dimension?

if unlimdimname and unlimdimname in ncvar.dimensions:

hasunlimdim = True

else:

hasunlimdim = False

if dounpackshort:

if not quiet: sys.stdout.write('unpacking short integers to floats ...\n')

sys.stdout.write('')

if hasattr(ncvar, '_FillValue'):

FillValue = ncvar._FillValue

else:

FillValue = None

var = ncfile4.createVariable(varname,datatype,ncvar.dimensions, fill_value=FillValue, least_significant_digit=lsd,zlib=zlib,complevel=complevel,shuffle=shuffle,fletcher32=fletcher32)

# fill variable attributes.

attdict = ncvar.__dict__

if '_FillValue' in attdict: del attdict['_FillValue']

if dounpackshort and 'add_offset' in attdict:

del attdict['add_offset']

if dounpackshort and 'scale_factor' in attdict:

del attdict['scale_factor']

if dounpackshort and 'missing_value' in attdict:

attdict['missing_value']=mval

var.setncatts(attdict)

#for attname in ncvar.ncattrs():

# if attname == '_FillValue': continue

# if dounpackshort and attname in ['add_offset','scale_factor']: continue

# if dounpackshort and attname == 'missing_value':

# setattr(var,attname,mval)

# else:

# setattr(var,attname,getattr(ncvar,attname))

# fill variables with data.

if hasunlimdim: # has an unlim dim, loop over unlim dim index.

# range to copy

if nchunk:

start = istart; stop = istop; step = nchunk

if step < 1: step = 1

for n in range(start, stop, step):

nmax = n+nchunk

if nmax > istop: nmax=istop

idata = ncvar[n:nmax]

if dounpackshort:

tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')

if hasattr(ncvar,'missing_value'):

tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)

else:

tmpdata = idata

var[n-istart:nmax-istart] = tmpdata

else:

idata = ncvar[:]

if dounpackshort:

tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')

if hasattr(ncvar,'missing_value'):

tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)

else:

tmpdata = idata

var[0:len(unlimdim)] = tmpdata

else: # no unlim dim or 1-d variable, just copy all data at once.

idata = ncvar[:]

if dounpackshort:

tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')

if hasattr(ncvar,'missing_value'):

tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)

else:

tmpdata = idata

var[:] = tmpdata

ncfile4.sync() # flush data to disk

# close files.

ncfile3.close()

usage = """

Convert a netCDF 3 file to netCDF 4 format, optionally

unpacking variables packed as short integers (with scale_factor and add_offset)

to floats, and adding zlib compression (with the HDF5 shuffle filter and fletcher32 checksum).

Data may also be quantized (truncated) to a specified precision to improve compression.

usage: %s [-h] [-o] [--vars=var1,var2,..] [--zlib=(0|1)] [--complevel=(1-9)] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--unpackshort=(0|1)] [--quantize=var1=n1,var2=n2,..] netcdf3filename netcdf4filename

-h -- Print usage message.

-o -- Overwite destination file (default is to raise an error if output file already exists).

--vars -- comma separated list of variable names to copy (default is to copy

all variables)

--classic=(0|1) -- use NETCDF4_CLASSIC format instead of NETCDF4 (default 1)

--zlib=(0|1) -- Activate (or disable) zlib compression (default is activate).

--complevel=(1-9) -- Set zlib compression level (6 is default).

--shuffle=(0|1) -- Activate (or disable) the shuffle filter (active by default).

--fletcher32=(0|1) -- Activate (or disable) the fletcher32 checksum (not

active by default).

--unpackshort=(0|1) -- Unpack short integer variables to float variables

using scale_factor and add_offset netCDF variable attributes (active by default).

--quantize=(comma separated list of "variable name=integer" pairs) --

Truncate the data in the specified variables to a given decimal precision.

For example, 'speed=2, height=-2, temp=0' will cause the variable

'speed' to be truncated to a precision of 0.01, 'height' to a precision of 100

and 'temp' to 1. This can significantly improve compression. The default

is not to quantize any of the variables.

--quiet=(0|1) -- if 1, don't print diagnostic information.

--chunk=(integer) -- number of records along unlimited dimension to

write at once. Default 10. Ignored if there is no unlimited

dimension. chunk=0 means write all the data at once.

--istart=(integer) -- number of record to start at along unlimited dimension.

Default 0. Ignored if there is no unlimited dimension.

--istop=(integer) -- number of record to stop at along unlimited dimension.

Default -1. Ignored if there is no unlimited dimension.

\n""" % os.path.basename(sys.argv[0])

try:

opts, pargs = getopt.getopt(sys.argv[1:], 'ho',

['classic=',

'vars=',

'zlib=',

'quiet=',

'complevel=',

'shuffle=',

'fletcher32=',

'unpackshort=',

'quantize=',

'chunk=',

'istart=',

'istop='])

except:

(type, value, traceback) = sys.exc_info()

sys.stdout.write("Error parsing the options. The error was: %s\n" % value)

sys.stderr.write(usage)

sys.exit(0)

# default options

overwritefile = 0

complevel = 6

classic = 1

zlib = 1

shuffle = 1

fletcher32 = 0

unpackshort = 1

vars = None

quantize = None

quiet = 0

chunk = 1000

istart = 0

istop = -1

# Get the options

for option in opts:

if option[0] == '-h':

sys.stderr.write(usage)

sys.exit(0)

elif option[0] == '-o':

overwritefile = 1

elif option[0] == '--classic':

classic = int(option[1])

elif option[0] == '--zlib':

zlib = int(option[1])

elif option[0] == '--quiet':

quiet = int(option[1])

elif option[0] == '--complevel':

complevel = int(option[1])

elif option[0] == '--shuffle':

shuffle = int(option[1])

elif option[0] == '--fletcher32':

fletcher32 = int(option[1])

elif option[0] == '--unpackshort':

unpackshort = int(option[1])

elif option[0] == '--chunk':

chunk = int(option[1])

elif option[0] == '--vars':

vars = option[1]

elif option[0] == '--quantize':

quantize = option[1]

elif option[0] == '--istart':

istart = int(option[1])

elif option[0] == '--istop':

istop = int(option[1])

else:

sys.stdout.write("%s: Unrecognized option\n" % option[0])

sys.stderr.write(usage)

sys.exit(0)

# if we pass a number of files different from 2, abort

if len(pargs) < 2 or len(pargs) > 2:

sys.stdout.write("You need to pass both source and destination!.\n")

sys.stderr.write(usage)

sys.exit(0)

# Catch the files passed as the last arguments

filename3 = pargs[0]

filename4 = pargs[1]

# Parse the quantize option, create a dictionary from key/value pairs.

if quantize is not None:

lsd_dict = {}

for p in quantize.split(','):

kv = p.split('=')

lsd_dict[kv[0]] = int(kv[1])

else:

lsd_dict=None

# Parse the vars option, create a list of variable names.

if vars is not None:

vars = vars.split(',')

# copy the data from filename3 to filename4.

_nc3tonc4(filename3,filename4,unpackshort=unpackshort,

zlib=zlib,complevel=complevel,shuffle=shuffle,

fletcher32=fletcher32,clobber=overwritefile,lsd_dict=lsd_dict,

nchunk=chunk,quiet=quiet,vars=vars,classic=classic,