Version 1.6.0
Introduction
@@ -1595,7 +1595,7 @@
In-memory (diskless) Datasets
the parallel IO example, which is in
examples/mpi_example.py.
Unit tests are in the
test directory.
-
contact: Jeffrey Whitaker jeffrey.s.whitaker@noaa.gov
+
contact: Jeffrey Whitaker jeffrey.s.whitaker@noaa.gov
copyright: 2008 by Jeffrey Whitaker.
@@ -1725,8 +1725,10 @@
In-memory (diskless) Datasets
mode: access mode. r means read-only; no data can be
modified. w means write; a new file is created, an existing file with
-the same name is deleted. a and r+ mean append (in analogy with
-serial files); an existing file is opened for reading and writing.
+the same name is deleted. 'x' means write, but fail if an existing
+file with the same name already exists. a and r+ mean append;
+an existing file is opened for reading and writing, if
+file does not exist already, one is created.
Appending s to modes r, w, r+ or a will enable unbuffered shared
access to NETCDF3_CLASSIC, NETCDF3_64BIT_OFFSET or
NETCDF3_64BIT_DATA formatted files.
@@ -1737,7 +1739,8 @@
In-memory (diskless) Datasets
clobber: if True (default), opening a file with mode='w'
will clobber an existing file with the same name. if False, an
-exception will be raised if a file with the same name already exists.
+exception will be raised if a file with the same name already exists.
+mode='x' is identical to mode='w' with clobber=False.
format: underlying file format (one of 'NETCDF4',
'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC', 'NETCDF3_64BIT_OFFSET' or
@@ -2062,8 +2065,9 @@
In-memory (diskless) Datasets
Data from netCDF variables is presented to python as numpy arrays with
the corresponding data type.
-
dimensions must be a tuple containing dimension names (strings) that
-have been defined previously using Dataset.createDimension. The default value
+
dimensions must be a tuple containing Dimension instances and/or
+dimension names (strings) that have been defined
+previously using Dataset.createDimension. The default value
is an empty tuple, which means the variable is a scalar.
If the optional keyword zlib is True, the data will be compressed in
@@ -2813,7 +2817,7 @@
In-memory (diskless) Datasets
(for a variable-length string array). Numpy string and unicode datatypes with
length greater than one are aliases for
str.
-
dimensions: a tuple containing the variable's dimension names
+
dimensions: a tuple containing the variable's Dimension instances
(defined previously with createDimension). Default is an empty tuple
which means the variable is a scalar (and therefore has no dimensions).
diff --git a/setup.py b/setup.py
index 17e7afc46..b4cd2d350 100644
--- a/setup.py
+++ b/setup.py
@@ -616,6 +616,9 @@ def _populate_hdf5_info(dirstosearch, inc_dirs, libs, lib_dirs):
library_dirs=lib_dirs,
include_dirs=inc_dirs + ['include'],
runtime_library_dirs=runtime_lib_dirs)]
+ # set language_level directive to 3
+ for e in ext_modules:
+ e.cython_directives = {'language_level': "3"} #
else:
ext_modules = None
diff --git a/src/netCDF4/_netCDF4.pyx b/src/netCDF4/_netCDF4.pyx
index d84f74e4f..0dcd64626 100644
--- a/src/netCDF4/_netCDF4.pyx
+++ b/src/netCDF4/_netCDF4.pyx
@@ -1909,6 +1909,7 @@ cdef _get_vars(group):
grp = grp.parent
free(dimids)
# create new variable instance.
+ dimensions = tuple(_find_dim(group,d) for d in dimensions)
if endianness == '>':
variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian='big')
elif endianness == '<':
@@ -2408,9 +2409,9 @@ version 4.1.2 or higher of the netcdf C lib, and rebuild netcdf4-python."""
raise ValueError(msg)
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
ncdump = [repr(type(self))]
dimnames = tuple(_tostr(dimname)+'(%s)'%len(self.dimensions[dimname])\
for dimname in self.dimensions.keys())
@@ -2653,8 +2654,9 @@ length greater than one are aliases for `str`.
Data from netCDF variables is presented to python as numpy arrays with
the corresponding data type.
-`dimensions` must be a tuple containing dimension names (strings) that
-have been defined previously using `Dataset.createDimension`. The default value
+`dimensions` must be a tuple containing `Dimension` instances and/or
+dimension names (strings) that have been defined
+previously using `Dataset.createDimension`. The default value
is an empty tuple, which means the variable is a scalar.
If the optional keyword `zlib` is `True`, the data will be compressed in
@@ -2757,6 +2759,18 @@ is the number of variable dimensions."""
group = self
else:
group = self.createGroup(dirname)
+ # if dimensions is a single string or Dimension instance,
+ # convert to a tuple.
+ # This prevents a common error that occurs when
+ # dimensions = 'lat' instead of ('lat',)
+ if isinstance(dimensions, (str, bytes, Dimension)):
+ dimensions = dimensions,
+ # convert elements of dimensions tuple to Dimension
+ # instances if they are strings.
+ # _find_dim looks for dimension in this group, and if not
+ # found there, looks in parent (and it's parent, etc, back to root).
+ dimensions =\
+ tuple(_find_dim(group,d) if isinstance(d,(str,bytes)) else d for d in dimensions)
# create variable.
group.variables[varname] = Variable(group, varname, datatype,
dimensions=dimensions, zlib=zlib, complevel=complevel, shuffle=shuffle,
@@ -3454,9 +3468,9 @@ Read-only class variables:
raise AttributeError("size cannot be altered")
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
if not dir(self._grp):
return 'Dimension object no longer valid'
if self.isunlimited():
@@ -3616,7 +3630,7 @@ behavior is similar to Fortran or Matlab, but different than numpy.
(for a variable-length string array). Numpy string and unicode datatypes with
length greater than one are aliases for `str`.
- **`dimensions`**: a tuple containing the variable's dimension names
+ **`dimensions`**: a tuple containing the variable's Dimension instances
(defined previously with `createDimension`). Default is an empty tuple
which means the variable is a scalar (and therefore has no dimensions).
@@ -3674,7 +3688,7 @@ behavior is similar to Fortran or Matlab, but different than numpy.
is replaced with this value. If fill_value is set to `False`, then
the variable is not pre-filled. The default netCDF fill values can be found
in the dictionary `netCDF4.default_fillvals`.
-
+
**`chunk_cache`**: If specified, sets the chunk cache size for this variable.
Persists as long as Dataset is open. Use `set_var_chunk_cache` to
change it when Dataset is re-opened.
@@ -3696,11 +3710,6 @@ behavior is similar to Fortran or Matlab, but different than numpy.
# if complevel is set to zero, set zlib to False.
if not complevel:
zlib = False
- # if dimensions is a string, convert to a tuple
- # this prevents a common error that occurs when
- # dimensions = 'lat' instead of ('lat',)
- if type(dimensions) == str or type(dimensions) == bytes or type(dimensions) == unicode:
- dimensions = dimensions,
self._grpid = grp._grpid
# make a weakref to group to avoid circular ref (issue 218)
# keep strong reference the default behaviour (issue 251)
@@ -3784,17 +3793,9 @@ behavior is similar to Fortran or Matlab, but different than numpy.
ndims = len(dimensions)
# find dimension ids.
if ndims:
- dims = []
dimids =
malloc(sizeof(int) * ndims)
for n from 0 <= n < ndims:
- dimname = dimensions[n]
- # look for dimension in this group, and if not
- # found there, look in parent (and it's parent, etc, back to root).
- dim = _find_dim(grp, dimname)
- if dim is None:
- raise KeyError("dimension %s not defined in group %s or any group in it's family tree" % (dimname, grp.path))
- dimids[n] = dim._dimid
- dims.append(dim)
+ dimids[n] = dimensions[n]._dimid
# go into define mode if it's a netCDF 3 compatible
# file format. Be careful to exit define mode before
# any exceptions are raised.
@@ -3862,8 +3863,8 @@ behavior is similar to Fortran or Matlab, but different than numpy.
raise ValueError('chunksizes must be a sequence with the same length as dimensions')
chunksizesp = malloc(sizeof(size_t) * ndims)
for n from 0 <= n < ndims:
- if not dims[n].isunlimited() and \
- chunksizes[n] > dims[n].size:
+ if not dimensions[n].isunlimited() and \
+ chunksizes[n] > dimensions[n].size:
msg = 'chunksize cannot exceed dimension size'
raise ValueError(msg)
chunksizesp[n] = chunksizes[n]
@@ -3923,9 +3924,7 @@ behavior is similar to Fortran or Matlab, but different than numpy.
if grp.data_model != 'NETCDF4': grp._enddef()
# count how many unlimited dimensions there are.
self._nunlimdim = 0
- for dimname in dimensions:
- # look in current group, and parents for dim.
- dim = _find_dim(self._grp, dimname)
+ for dim in dimensions:
if dim.isunlimited(): self._nunlimdim = self._nunlimdim + 1
# set ndim attribute (number of dimensions).
with nogil:
@@ -3964,9 +3963,9 @@ behavior is similar to Fortran or Matlab, but different than numpy.
return self[...]
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
cdef int ierr, no_fill
if not dir(self._grp):
return 'Variable object no longer valid'
@@ -5559,9 +5558,9 @@ the user.
self.name = dtype_name
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
return "%r: name = '%s', numpy dtype = %s" %\
(type(self), self.name, self.dtype)
@@ -5841,9 +5840,9 @@ the user.
self.name = dtype_name
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
if self.dtype == str:
return '%r: string type' % (type(self),)
else:
@@ -5951,9 +5950,9 @@ the user.
self.enum_dict = enum_dict
def __repr__(self):
- return self.__unicode__()
+ return self.__str__()
- def __unicode__(self):
+ def __str__(self):
return "%r: name = '%s', numpy dtype = %s, fields/values =%s" %\
(type(self), self.name, self.dtype, self.enum_dict)
diff --git a/src/netCDF4/utils.py b/src/netCDF4/utils.py
index 7a858ef42..c96cc7575 100644
--- a/src/netCDF4/utils.py
+++ b/src/netCDF4/utils.py
@@ -45,7 +45,10 @@ def _find_dim(grp, dimname):
group = group.parent
except:
raise ValueError("cannot find dimension %s in this group or parent groups" % dimname)
- return dim
+ if dim is None:
+ raise KeyError("dimension %s not defined in group %s or any group in it's family tree" % (dimname, grp.path))
+ else:
+ return dim
def _walk_grps(topgrp):
"""Iterate through all (sub-) groups of topgrp, similar to os.walktree.
diff --git a/test/tst_dims.py b/test/tst_dims.py
index 026361f48..7af5d2ad7 100644
--- a/test/tst_dims.py
+++ b/test/tst_dims.py
@@ -19,7 +19,11 @@
TIME_LEN = None
TIME_LENG = None
GROUP_NAME='forecasts'
-VAR_NAME='temp'
+VAR_NAME1='temp1'
+VAR_NAME2='temp2'
+VAR_NAME3='temp3'
+VAR_NAME4='temp4'
+VAR_NAME5='temp5'
VAR_TYPE='f8'
@@ -28,11 +32,19 @@ class DimensionsTestCase(unittest.TestCase):
def setUp(self):
self.file = FILE_NAME
f = netCDF4.Dataset(self.file, 'w')
- f.createDimension(LAT_NAME,LAT_LEN)
- f.createDimension(LON_NAME,LON_LEN)
- f.createDimension(LEVEL_NAME,LEVEL_LEN)
- f.createDimension(TIME_NAME,TIME_LEN)
- f.createVariable(VAR_NAME,VAR_TYPE,(LEVEL_NAME, LAT_NAME, LON_NAME, TIME_NAME))
+ lat_dim=f.createDimension(LAT_NAME,LAT_LEN)
+ lon_dim=f.createDimension(LON_NAME,LON_LEN)
+ lev_dim=f.createDimension(LEVEL_NAME,LEVEL_LEN)
+ time_dim=f.createDimension(TIME_NAME,TIME_LEN)
+ # specify dimensions with names
+ fv1 = f.createVariable(VAR_NAME1,VAR_TYPE,(LEVEL_NAME, LAT_NAME, LON_NAME, TIME_NAME))
+ # specify dimensions with instances
+ fv2 = f.createVariable(VAR_NAME2,VAR_TYPE,(lev_dim,lat_dim,lon_dim,time_dim))
+ # specify dimensions using a mix of names and instances
+ fv3 = f.createVariable(VAR_NAME3,VAR_TYPE,(lev_dim, LAT_NAME, lon_dim, TIME_NAME))
+ # single dim instance for name (not in a tuple)
+ fv4 = f.createVariable(VAR_NAME4,VAR_TYPE,time_dim)
+ fv5 = f.createVariable(VAR_NAME5,VAR_TYPE,TIME_NAME)
g = f.createGroup(GROUP_NAME)
g.createDimension(LAT_NAME,LAT_LENG)
g.createDimension(LON_NAME,LON_LENG)
@@ -40,7 +52,7 @@ def setUp(self):
# (did not work prior to alpha 18)
#g.createDimension(LEVEL_NAME,LEVEL_LENG)
#g.createDimension(TIME_NAME,TIME_LENG)
- g.createVariable(VAR_NAME,VAR_TYPE,(LEVEL_NAME, LAT_NAME, LON_NAME, TIME_NAME))
+ gv = g.createVariable(VAR_NAME1,VAR_TYPE,(LEVEL_NAME, LAT_NAME, LON_NAME, TIME_NAME))
f.close()
def tearDown(self):
@@ -51,7 +63,11 @@ def runTest(self):
"""testing dimensions"""
# check dimensions in root group.
f = netCDF4.Dataset(self.file, 'r+')
- v = f.variables[VAR_NAME]
+ v1 = f.variables[VAR_NAME1]
+ v2 = f.variables[VAR_NAME2]
+ v3 = f.variables[VAR_NAME3]
+ v4 = f.variables[VAR_NAME4]
+ v5 = f.variables[VAR_NAME5]
isunlim = [dim.isunlimited() for dim in f.dimensions.values()]
dimlens = [len(dim) for dim in f.dimensions.values()]
names_check = [LAT_NAME, LON_NAME, LEVEL_NAME, TIME_NAME]
@@ -65,6 +81,15 @@ def runTest(self):
# check that dimension names are correct.
for name in f.dimensions.keys():
self.assertTrue(name in names_check)
+ for name in v1.dimensions:
+ self.assertTrue(name in names_check)
+ for name in v2.dimensions:
+ self.assertTrue(name in names_check)
+ for name in v3.dimensions:
+ self.assertTrue(name in names_check)
+ self.assertTrue(v4.dimensions[0] == TIME_NAME)
+ self.assertTrue(v5.dimensions[0] == TIME_NAME)
+ # check that dimension lengths are correct.
# check that dimension lengths are correct.
for name,dim in f.dimensions.items():
self.assertTrue(len(dim) == lensdict[name])
@@ -75,7 +100,7 @@ def runTest(self):
# make sure length of dimensions change correctly.
nadd1 = 2
nadd2 = 4
- v[0:nadd1,:,:,0:nadd2] = uniform(size=(nadd1,LAT_LEN,LON_LEN,nadd2))
+ v1[0:nadd1,:,:,0:nadd2] = uniform(size=(nadd1,LAT_LEN,LON_LEN,nadd2))
lensdict[LEVEL_NAME]=nadd1
lensdict[TIME_NAME]=nadd2
# check that dimension lengths are correct.
@@ -83,7 +108,7 @@ def runTest(self):
self.assertTrue(len(dim) == lensdict[name])
# check dimensions in subgroup.
g = f.groups[GROUP_NAME]
- vg = g.variables[VAR_NAME]
+ vg = g.variables[VAR_NAME1]
isunlim = [dim.isunlimited() for dim in g.dimensions.values()]
dimlens = [len(dim) for dim in g.dimensions.values()]
names_check = [LAT_NAME, LON_NAME, LEVEL_NAME, TIME_NAME]