[MITgcm-support] gluemnc problems
Oliver Jahn
jahn at MIT.EDU
Tue Sep 4 10:28:44 EDT 2012
Hi Andrea,
I have a python script that does what gluemnc does. It is still in
development, so I am sending it only to you. Should work on any size
files, though. Only requirement is numpy (netcdf reader is inlined from
scipy.io.netcdf). Make the file executable and run
./gluemnc
for a short help message. Usage is slightly different from the nco
shell script: you have to give all the input files on the command line
(or a glob pattern) and put a -o before the output file name. Let me
know if you have questions or find bugs!
Cheers,
Oliver
On 2012-09-04 09:37, cimatori wrote:
> Hi everybody,
> gluemnc has suddenly stopped to work, apparently after system
> administrators have updated nco tools to the (not so new, in fact)
> 4.0.8. Basically, gluemnc fails due to a "core dumped" in ncrcat, while
> processing a grid with 300x150 point. I was thinking of trying to
> manually install a more recent version of nco, but maybe you have more
> clever suggestions. I also read that a python tool equivalent to gluemnc
> may be available (where?), could that help?
> Thanks!
--
Oliver Jahn tel: +1 617 253 2454
Earth System Initiative and fax: +1 617 253 4464
Dept. of Earth, Atmospheric and Planetary Sciences
Massachusetts Institute of Technology
77 Massachusetts Ave., Bldg. 54-1510 skype: oliver.jahn
Cambridge, MA 02139-4307 USA email: jahn at mit.edu
-------------- next part --------------
#!/usr/bin/env python
"""Usage: gluemnc [--verbose] [-v <vars>] -o <outfile> <files>
-v <vars> comma-separated list of variable names or glob patterns
--verbose report variables being read
All files must have the same variables.
Each variable (or 1 record of it) must fit in memory.
Examples:
gluemnc -o ptr.nc mnc_*/ptr_tave.*.nc
gluemnc -o BIO.nc -v 'BIO_*' mnc_*/ptr_tave.*.nc
"""
# Based on the netcdf module in scipy.io, which in turn is based on pupynere
# Modified netcdf module is included here, script follows
moduledoc = """
NetCDF reader/writer module.
This module is used to read and create NetCDF files. NetCDF files are
accessed through the `netcdf_file` object. Data written to and from NetCDF
files are contained in `netcdf_variable` objects. Attributes are given
as member variables of the `netcdf_file` and `netcdf_variable` objects.
Notes
-----
NetCDF files are a self-describing binary data format. The file contains
metadata that describes the dimensions and variables in the file. More
details about NetCDF files can be found `here
<http://www.unidata.ucar.edu/software/netcdf/docs/netcdf.html>`_. There
are three main sections to a NetCDF data structure:
1. Dimensions
2. Variables
3. Attributes
The dimensions section records the name and length of each dimension used
by the variables. The variables would then indicate which dimensions it
uses and any attributes such as data units, along with containing the data
values for the variable. It is good practice to include a
variable that is the same name as a dimension to provide the values for
that axes. Lastly, the attributes section would contain additional
information such as the name of the file creator or the instrument used to
collect the data.
When writing data to a NetCDF file, there is often the need to indicate the
'record dimension'. A record dimension is the unbounded dimension for a
variable. For example, a temperature variable may have dimensions of
latitude, longitude and time. If one wants to add more temperature data to
the NetCDF file as time progresses, then the temperature variable should
have the time dimension flagged as the record dimension.
This module implements the Scientific.IO.NetCDF API to read and create
NetCDF files. The same API is also used in the PyNIO and pynetcdf
modules, allowing these modules to be used interchangeably when working
with NetCDF files. The major advantage of this module over other
modules is that it doesn't require the code to be linked to the NetCDF
libraries.
In addition, the NetCDF file header contains the position of the data in
the file, so access can be done in an efficient manner without loading
unnecessary data into memory. It uses the ``mmap`` module to create
Numpy arrays mapped to the data on disk, for the same purpose.
Examples
--------
To create a NetCDF file:
>>> from scipy.io import netcdf
>>> f = netcdf.netcdf_file('simple.nc', 'w')
>>> f.history = 'Created for a test'
>>> f.createDimension('time', 10)
>>> time = f.createVariable('time', 'i', ('time',))
>>> time[:] = range(10)
>>> time.units = 'days since 2008-01-01'
>>> f.close()
Note the assignment of ``range(10)`` to ``time[:]``. Exposing the slice
of the time variable allows for the data to be set in the object, rather
than letting ``range(10)`` overwrite the ``time`` variable.
To read the NetCDF file we just created:
>>> from scipy.io import netcdf
>>> f = netcdf.netcdf_file('simple.nc', 'r')
>>> print f.history
Created for a test
>>> time = f.variables['time']
>>> print time.units
days since 2008-01-01
>>> print time.shape
(10,)
>>> print time[-1]
9
>>> f.close()
"""
#TODO:
# * properly implement ``_FillValue``.
# * implement Jeff Whitaker's patch for masked variables.
# * fix character variables.
# * implement PAGESIZE for Python 2.6?
#The Scientific.IO.NetCDF API allows attributes to be added directly to
#instances of ``netcdf_file`` and ``netcdf_variable``. To differentiate
#between user-set attributes and instance attributes, user-set attributes
#are automatically stored in the ``_attributes`` attribute by overloading
#``__setattr__``. This is the reason why the code sometimes uses
#``obj.__dict__['key'] = value``, instead of simply ``obj.key = value``;
#otherwise the key would be inserted into userspace attributes.
__all__ = ['netcdf_file', 'netcdf_variable']
from operator import mul
from collections import OrderedDict
from mmap import mmap, ACCESS_READ
import numpy as np
from numpy.compat import asbytes, asstr
from numpy import fromstring, ndarray, dtype, empty, array, asarray
from numpy import little_endian as LITTLE_ENDIAN
ABSENT = asbytes('\x00\x00\x00\x00\x00\x00\x00\x00')
ZERO = asbytes('\x00\x00\x00\x00')
NC_BYTE = asbytes('\x00\x00\x00\x01')
NC_CHAR = asbytes('\x00\x00\x00\x02')
NC_SHORT = asbytes('\x00\x00\x00\x03')
NC_INT = asbytes('\x00\x00\x00\x04')
NC_FLOAT = asbytes('\x00\x00\x00\x05')
NC_DOUBLE = asbytes('\x00\x00\x00\x06')
NC_DIMENSION = asbytes('\x00\x00\x00\n')
NC_VARIABLE = asbytes('\x00\x00\x00\x0b')
NC_ATTRIBUTE = asbytes('\x00\x00\x00\x0c')
TYPEMAP = { NC_BYTE: ('b', 1),
NC_CHAR: ('c', 1),
NC_SHORT: ('h', 2),
NC_INT: ('i', 4),
NC_FLOAT: ('f', 4),
NC_DOUBLE: ('d', 8) }
REVERSE = { 'b': NC_BYTE,
'c': NC_CHAR,
'h': NC_SHORT,
'i': NC_INT,
'f': NC_FLOAT,
'd': NC_DOUBLE,
# these come from asarray(1).dtype.char and asarray('foo').dtype.char,
# used when getting the types from generic attributes.
'l': NC_INT,
'S': NC_CHAR }
class unmapped_array(object):
def __init__(self, shape, dtype_):
self.shape = shape
self.dtype = np.dtype(dtype_)
@property
def itemsize(self):
return self.dtype.itemsize
@property
def size(self):
return reduce(mul, self.shape, 1)
@property
def nbytes(self):
return self.size * self.itemsize
def __len__(self):
return self.shape[0]
class netcdf_file(object):
"""
A file object for NetCDF data.
A `netcdf_file` object has two standard attributes: `dimensions` and
`variables`. The values of both are dictionaries, mapping dimension
names to their associated lengths and variable names to variables,
respectively. Application programs should never modify these
dictionaries.
All other attributes correspond to global attributes defined in the
NetCDF file. Global file attributes are created by assigning to an
attribute of the `netcdf_file` object.
If mode='w' and mmap=True, creates a file on disk and attaches
mmaps to variables before assigning values. You need to call
write_metadata after defining all attributes, dimensions and variables,
but before assigning any values to variables.
Parameters
self.delay = delay
----------
filename : string or file-like
string -> filename
mode : {'r', 'w'}, optional
read-write mode, default is 'r'
mmap : None or bool, optional
Whether to mmap `filename` when reading. Default is True
when `filename` is a file name, False when `filename` is a
file-like object
version : {1, 2}, optional
version of netcdf to read / write, where 1 means *Classic
format* and 2 means *64-bit offset format*. Default is 1. See
`here <http://www.unidata.ucar.edu/software/netcdf/docs/netcdf/Which-Format.html>`_
for more info.
"""
def __init__(self, filename, mode='r', mmap=None, version=1, delay=False):
"""Initialize netcdf_file from fileobj (str or file-like)."""
if delay:
if mmap is None:
mmap = False
else:
raise ValueError('Cannot delay variables for mmap')
if hasattr(filename, 'seek'): # file-like
self.fp = filename
self.filename = 'None'
if mmap is None:
mmap = False
elif mmap and not hasattr(filename, 'fileno'):
raise ValueError('Cannot use file object for mmap')
else: # maybe it's a string
self.filename = filename
if mmap and mode == 'w':
fmode = 'wb+'
else:
fmode = '%sb' % mode
self.fp = open(self.filename, fmode)
if mmap is None:
mmap = True
self.use_mmap = mmap
self.version_byte = version
self.delay = delay
if not mode in 'rw':
raise ValueError("Mode must be either 'r' or 'w'.")
self.mode = mode
self.dimensions = OrderedDict()
self.variables = OrderedDict()
self._dims = []
self._recs = 0
self._recsize = 0
self._mapped = False
self._begins = OrderedDict()
self._attributes = OrderedDict()
if mode == 'r':
self._read()
def __setattr__(self, attr, value):
# Store user defined attributes in a separate dict,
# so we can save them to file later.
try:
self._attributes[attr] = value
except AttributeError:
pass
self.__dict__[attr] = value
def close(self):
"""Closes the NetCDF file."""
if not self.fp.closed:
try:
self.flush()
finally:
self.fp.close()
__del__ = close
def createDimension(self, name, length):
"""
Adds a dimension to the Dimension section of the NetCDF data structure.
Note that this function merely adds a new dimension that the variables can
reference. The values for the dimension, if desired, should be added as
a variable using `createVariable`, referring to this dimension.
Parameters
----------
name : str
Name of the dimension (Eg, 'lat' or 'time').
length : int
Length of the dimension.
See Also
--------
createVariable
"""
self.dimensions[name] = length
self._dims.append(name)
def createVariable(self, name, type, dimensions):
"""
Create an empty variable for the `netcdf_file` object, specifying its data
type and the dimensions it uses.
Parameters
----------
name : str
Name of the new variable.
type : dtype or str
Data type of the variable.
dimensions : sequence of str
List of the dimension names used by the variable, in the desired order.
Returns
-------
variable : netcdf_variable
The newly created ``netcdf_variable`` object.
This object has also been added to the `netcdf_file` object as well.
See Also
--------
createDimension
Notes
-----
Any dimensions to be used by the variable should already exist in the
NetCDF data structure or should be created by `createDimension` prior to
creating the NetCDF variable.
"""
shape = tuple([self.dimensions[dim] for dim in dimensions])
shape_ = tuple([dim or 0 for dim in shape]) # replace None with 0 for numpy
if isinstance(type, basestring): type = np.dtype(type)
typecode, size = type.char, type.itemsize
dtype_ = '>%s' % typecode
if size > 1: dtype_ += str(size)
data = unmapped_array(shape_, dtype_)
self.variables[name] = netcdf_variable(data, typecode, shape, dimensions)
return self.variables[name]
def flush(self):
"""
Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.
Close mmaps if used.
See Also
--------
sync : Identical function
"""
if getattr(self, 'mode', None) is 'w':
if self.use_mmap:
if not self._mapped:
self._map()
self.update_numrecs(self._recs)
else:
self._write()
sync = flush
def write_metadata(self):
'''This needs to be called before assigning any data to variables!'''
self._map()
def _map(self):
self.fp.seek(0)
self.fp.write(asbytes('CDF'))
self.fp.write(array(self.version_byte, '>b').tostring())
# Write headers
self._write_numrecs()
self._write_dim_array()
self._write_gatt_array()
self._map_var_array()
self._mapped = True
def _map_var_array(self):
if self.variables:
self.fp.write(NC_VARIABLE)
self._pack_int(len(self.variables))
# Sort variables non-recs first, then recs.
# keep order from variable creation otherwise
nonrec_vars = [ k for k,v in self.variables.items() if not v.isrec ]
rec_vars = [ k for k,v in self.variables.items() if v.isrec ]
# Set the metadata for all variables.
for name in nonrec_vars + rec_vars:
self._map_var_metadata(name)
# Now that we have the metadata, we know the vsize of
# each record variable, so we can calculate recsize.
nonrecsize = sum([
var._vsize for var in self.variables.values()
if not var.isrec])
self.__dict__['_recsize'] = sum([
var._vsize for var in self.variables.values()
if var.isrec])
# fill file
pos0 = pos = self.fp.tell()
# end = pos0 + nonrecsize + self._recs*self._recsize
# self.fp.seek(end-1)
# self.fp.write('\x00')
# self.fp.flush()
# self.fp.seek(pos0)
# set file pointers for all variables.
for name in nonrec_vars:
var = self.variables[name]
# Set begin in file header.
self.fp.seek(var._begin)
self._pack_begin(pos)
self._begins[name] = pos
pos += var._vsize
for name in rec_vars:
var = self.variables[name]
# Set begin in file header.
self.fp.seek(var._begin)
self._pack_begin(pos)
self._begins[name] = pos
pos += var._vsize
# first var
self.fp.seek(pos0)
else:
self.fp.write(ABSENT)
def _map_var_metadata(self, name):
var = self.variables[name]
self._pack_string(name)
self._pack_int(len(var.dimensions))
for dimname in var.dimensions:
dimid = self._dims.index(dimname)
self._pack_int(dimid)
self._write_att_array(var._attributes)
nc_type = REVERSE[var.typecode()]
self.fp.write(asbytes(nc_type))
if not var.isrec:
vsize = var.data.size * var.data.itemsize
vsize += -vsize % 4
else: # record variable
if 1: #var.data.shape[0]:
size = reduce(mul, var.data.shape[1:], 1)
vsize = size * var.data.itemsize
else:
vsize = 0
rec_vars = len([var for var in self.variables.values()
if var.isrec])
if rec_vars > 1:
vsize += -vsize % 4
self.variables[name].__dict__['_vsize'] = vsize
self._pack_int(vsize)
# Pack a bogus begin, and set the real value later.
self.variables[name].__dict__['_begin'] = self.fp.tell()
self._pack_begin(0)
@property
def numrecs(self):
return self._recs
@property
def attributes(self):
return self._attributes
@property
def begins(self):
return [(name,pos,self.variables[name].isrec) for name,pos in self._begins.items()]
def write_var(self, name, val):
var = self.variables[name]
pos = self._begins[name]
self.fp.seek(pos)
np.asanyarray(val, var.data.dtype).tofile(self.fp)
# pad
count = var.data.size * var.data.itemsize
self.fp.write(asbytes('0') * (var._vsize - count))
def write_recvar(self, name, rec, val):
var = self.variables[name]
pos = self._begins[name] + rec*self._recsize
self.fp.seek(pos)
np.asanyarray(val, var.data.dtype).tofile(self.fp)
# pad
count = var.data.size * var.data.itemsize
self.fp.write(asbytes('0') * (var._vsize - count))
if rec >= self._recs:
self.__dict__['_recs'] = rec + 1
def _write(self):
self.fp.write(asbytes('CDF'))
self.fp.write(array(self.version_byte, '>b').tostring())
# Write headers and data.
self._write_numrecs()
self._write_dim_array()
self._write_gatt_array()
self._write_var_array()
def _write_numrecs(self):
# Get highest record count from all record variables.
for var in self.variables.values():
if var.isrec and len(var.data) > self._recs:
self.__dict__['_recs'] = len(var.data)
self.__dict__['_numrecs_begin'] = self.fp.tell()
self._pack_int(self._recs)
def update_numrecs(self, numrecs):
self.__dict__['_recs'] = numrecs
self.fp.seek(self._numrecs_begin)
self._pack_int(numrecs)
def _write_dim_array(self):
if self.dimensions:
self.fp.write(NC_DIMENSION)
self._pack_int(len(self.dimensions))
for name in self._dims:
self._pack_string(name)
length = self.dimensions[name]
self._pack_int(length or 0) # replace None with 0 for record dimension
else:
self.fp.write(ABSENT)
def _write_gatt_array(self):
self._write_att_array(self._attributes)
def _write_att_array(self, attributes):
if attributes:
self.fp.write(NC_ATTRIBUTE)
self._pack_int(len(attributes))
for name, values in attributes.items():
self._pack_string(name)
self._write_values(values)
else:
self.fp.write(ABSENT)
def _write_var_array(self):
if self.variables:
self.fp.write(NC_VARIABLE)
self._pack_int(len(self.variables))
# Sort variables non-recs first, then recs. We use a DSU
# since some people use pupynere with Python 2.3.x.
deco = [ (v._shape and not v.isrec, k) for (k, v) in self.variables.items() ]
deco.sort()
variables = [ k for (unused, k) in deco ][::-1]
# Set the metadata for all variables.
for name in variables:
self._write_var_metadata(name)
# Now that we have the metadata, we know the vsize of
# each record variable, so we can calculate recsize.
self.__dict__['_recsize'] = sum([
var._vsize for var in self.variables.values()
if var.isrec])
# Set the data for all variables.
for name in variables:
self._write_var_data(name)
else:
self.fp.write(ABSENT)
def _write_var_metadata(self, name):
var = self.variables[name]
self._pack_string(name)
self._pack_int(len(var.dimensions))
for dimname in var.dimensions:
dimid = self._dims.index(dimname)
self._pack_int(dimid)
self._write_att_array(var._attributes)
nc_type = REVERSE[var.typecode()]
self.fp.write(asbytes(nc_type))
if not var.isrec:
vsize = var.data.size * var.data.itemsize
vsize += -vsize % 4
else: # record variable
try:
vsize = var.data[0].size * var.data.itemsize
except IndexError:
vsize = 0
rec_vars = len([var for var in self.variables.values()
if var.isrec])
if rec_vars > 1:
vsize += -vsize % 4
self.variables[name].__dict__['_vsize'] = vsize
self._pack_int(vsize)
# Pack a bogus begin, and set the real value later.
self.variables[name].__dict__['_begin'] = self.fp.tell()
self._pack_begin(0)
def _write_var_data(self, name):
var = self.variables[name]
# Set begin in file header.
the_beguine = self.fp.tell()
self.fp.seek(var._begin)
self._pack_begin(the_beguine)
self.fp.seek(the_beguine)
# Write data.
if not var.isrec:
self.fp.write(var.data.tostring())
count = var.data.size * var.data.itemsize
self.fp.write(asbytes('0') * (var._vsize - count))
else: # record variable
# Handle rec vars with shape[0] < nrecs.
if self._recs > len(var.data):
shape = (self._recs,) + var.data.shape[1:]
var.data.resize(shape)
pos0 = pos = self.fp.tell()
for rec in var.data:
# Apparently scalars cannot be converted to big endian. If we
# try to convert a ``=i4`` scalar to, say, '>i4' the dtype
# will remain as ``=i4``.
if not rec.shape and (rec.dtype.byteorder == '<' or
(rec.dtype.byteorder == '=' and LITTLE_ENDIAN)):
rec = rec.byteswap()
self.fp.write(rec.tostring())
# Padding
count = rec.size * rec.itemsize
self.fp.write(asbytes('0') * (var._vsize - count))
pos += self._recsize
self.fp.seek(pos)
self.fp.seek(pos0 + var._vsize)
def _write_values(self, values):
if hasattr(values, 'dtype'):
nc_type = REVERSE[values.dtype.char]
else:
types = [
(int, NC_INT),
(long, NC_INT),
(float, NC_FLOAT),
(basestring, NC_CHAR),
]
try:
sample = values[0]
except (TypeError, IndexError):
sample = values
for class_, nc_type in types:
if isinstance(sample, class_): break
typecode, size = TYPEMAP[nc_type]
if typecode is 'c':
dtype_ = '>c'
else:
dtype_ = '>%s' % typecode
if size > 1: dtype_ += str(size)
values = asarray(values, dtype=dtype_)
self.fp.write(asbytes(nc_type))
if values.dtype.char == 'S':
nelems = values.itemsize
else:
nelems = values.size
self._pack_int(nelems)
if not values.shape and (values.dtype.byteorder == '<' or
(values.dtype.byteorder == '=' and LITTLE_ENDIAN)):
values = values.byteswap()
self.fp.write(values.tostring())
count = values.size * values.itemsize
self.fp.write(asbytes('0') * (-count % 4)) # pad
def _read(self):
# Check magic bytes and version
magic = self.fp.read(3)
if not magic == asbytes('CDF'):
raise TypeError("Error: %s is not a valid NetCDF 3 file" %
self.filename)
self.__dict__['version_byte'] = fromstring(self.fp.read(1), '>b')[0]
# Read file headers and set data.
self._read_numrecs()
self._read_dim_array()
self._read_gatt_array()
self._read_var_array()
def _read_numrecs(self):
self.__dict__['_recs'] = self._unpack_int()
def _read_dim_array(self):
header = self.fp.read(4)
assert header in [ZERO, NC_DIMENSION]
count = self._unpack_int()
for dim in range(count):
name = asstr(self._unpack_string())
length = self._unpack_int() or None # None for record dimension
self.dimensions[name] = length
self._dims.append(name) # preserve order
def _read_gatt_array(self):
for k, v in self._read_att_array().items():
self.__setattr__(k, v)
def _read_att_array(self):
header = self.fp.read(4)
assert header in [ZERO, NC_ATTRIBUTE]
count = self._unpack_int()
attributes = OrderedDict()
for attr in range(count):
name = asstr(self._unpack_string())
attributes[name] = self._read_values()
return attributes
def _read_var_array(self):
header = self.fp.read(4)
assert header in [ZERO, NC_VARIABLE]
begin = 0
dtypes = {'names': [], 'formats': []}
rec_vars = []
count = self._unpack_int()
for var in range(count):
(name, dimensions, shape, attributes,
typecode, size, dtype_, begin_, vsize) = self._read_var()
# http://www.unidata.ucar.edu/software/netcdf/docs/netcdf.html
# Note that vsize is the product of the dimension lengths
# (omitting the record dimension) and the number of bytes
# per value (determined from the type), increased to the
# next multiple of 4, for each variable. If a record
# variable, this is the amount of space per record. The
# netCDF "record size" is calculated as the sum of the
# vsize's of all the record variables.
#
# The vsize field is actually redundant, because its value
# may be computed from other information in the header. The
# 32-bit vsize field is not large enough to contain the size
# of variables that require more than 2^32 - 4 bytes, so
# 2^32 - 1 is used in the vsize field for such variables.
if shape and shape[0] is None: # record variable
rec_vars.append(name)
# The netCDF "record size" is calculated as the sum of
# the vsize's of all the record variables.
self.__dict__['_recsize'] += vsize
if begin == 0: begin = begin_
dtypes['names'].append(name)
dtypes['formats'].append(str(shape[1:]) + dtype_)
# Handle padding with a virtual variable.
if typecode in 'bch':
actual_size = reduce(mul, (1,) + shape[1:]) * size
padding = -actual_size % 4
if padding:
dtypes['names'].append('_padding_%d' % var)
dtypes['formats'].append('(%d,)>b' % padding)
# Data will be set later.
if self.delay:
self._begins[name] = begin_
data = unmapped_array((self._recs,)+shape[1:], dtype_)
else:
data = None
else: # not a record variable
# Calculate size to avoid problems with vsize (above)
a_size = reduce(mul, shape, 1) * size
if self.use_mmap:
mm = mmap(self.fp.fileno(), begin_+a_size, access=ACCESS_READ)
data = ndarray.__new__(ndarray, shape, dtype=dtype_,
buffer=mm, offset=begin_, order=0)
elif self.delay:
self._begins[name] = begin_
data = unmapped_array(shape, dtype_)
else:
pos = self.fp.tell()
self.fp.seek(begin_)
data = fromstring(self.fp.read(a_size), dtype=dtype_)
data.shape = shape
self.fp.seek(pos)
# Add variable.
self.variables[name] = netcdf_variable(
data, typecode, shape, dimensions, attributes)
if rec_vars and not self.delay:
# Remove padding when only one record variable.
if len(rec_vars) == 1:
dtypes['names'] = dtypes['names'][:1]
dtypes['formats'] = dtypes['formats'][:1]
# Build rec array.
if self.use_mmap:
mm = mmap(self.fp.fileno(), begin+self._recs*self._recsize, access=ACCESS_READ)
rec_array = ndarray.__new__(ndarray, (self._recs,), dtype=dtypes,
buffer=mm, offset=begin, order=0)
else:
pos = self.fp.tell()
self.fp.seek(begin)
rec_array = fromstring(self.fp.read(self._recs*self._recsize), dtype=dtypes)
rec_array.shape = (self._recs,)
self.fp.seek(pos)
for var in rec_vars:
self.variables[var].__dict__['data'] = rec_array[var]
def read_var(self, name):
var = self.variables[name]
pos = self._begins[name]
self.fp.seek(pos)
data = fromstring(self.fp.read(var.data.nbytes), dtype=var.data.dtype)
data.shape = var.data.shape
return data
def read_recvar(self, name, rec):
var = self.variables[name]
pos = self._begins[name] + rec*self._recsize
self.fp.seek(pos)
count = reduce(mul, var.data.shape[1:], 1) * var.data.itemsize
data = fromstring(self.fp.read(count), dtype=var.data.dtype)
data.shape = var.data.shape[1:]
return data
def _read_var(self):
name = asstr(self._unpack_string())
dimensions = []
shape = []
dims = self._unpack_int()
for i in range(dims):
dimid = self._unpack_int()
dimname = self._dims[dimid]
dimensions.append(dimname)
dim = self.dimensions[dimname]
shape.append(dim)
dimensions = tuple(dimensions)
shape = tuple(shape)
attributes = self._read_att_array()
nc_type = self.fp.read(4)
vsize = self._unpack_int()
begin = [self._unpack_int, self._unpack_int64][self.version_byte-1]()
typecode, size = TYPEMAP[nc_type]
if typecode is 'c':
dtype_ = '>c'
else:
dtype_ = '>%s' % typecode
if size > 1: dtype_ += str(size)
return name, dimensions, shape, attributes, typecode, size, dtype_, begin, vsize
def _read_values(self):
nc_type = self.fp.read(4)
n = self._unpack_int()
typecode, size = TYPEMAP[nc_type]
count = n*size
values = self.fp.read(int(count))
self.fp.read(-count % 4) # read padding
if typecode is not 'c':
values = fromstring(values, dtype='>%s%d' % (typecode, size))
if values.shape == (1,): values = values[0]
else:
values = values.rstrip(asbytes('\x00'))
return values
def _pack_begin(self, begin):
if self.version_byte == 1:
self._pack_int(begin)
elif self.version_byte == 2:
self._pack_int64(begin)
def _pack_int(self, value):
self.fp.write(array(value, '>i').tostring())
_pack_int32 = _pack_int
def _unpack_int(self):
return int(fromstring(self.fp.read(4), '>i')[0])
_unpack_int32 = _unpack_int
def _pack_int64(self, value):
self.fp.write(array(value, '>q').tostring())
def _unpack_int64(self):
return fromstring(self.fp.read(8), '>q')[0]
def _pack_string(self, s):
count = len(s)
self._pack_int(count)
self.fp.write(asbytes(s))
self.fp.write(asbytes('0') * (-count % 4)) # pad
def _unpack_string(self):
count = self._unpack_int()
s = self.fp.read(count).rstrip(asbytes('\x00'))
self.fp.read(-count % 4) # read padding
return s
class netcdf_variable(object):
"""
A data object for the `netcdf` module.
`netcdf_variable` objects are constructed by calling the method
`netcdf_file.createVariable` on the `netcdf_file` object. `netcdf_variable`
objects behave much like array objects defined in numpy, except that their
data resides in a file. Data is read by indexing and written by assigning
to an indexed subset; the entire array can be accessed by the index ``[:]``
or (for scalars) by using the methods `getValue` and `assignValue`.
`netcdf_variable` objects also have attribute `shape` with the same meaning
as for arrays, but the shape cannot be modified. There is another read-only
attribute `dimensions`, whose value is the tuple of dimension names.
All other attributes correspond to variable attributes defined in
the NetCDF file. Variable attributes are created by assigning to an
attribute of the `netcdf_variable` object.
Parameters
----------
data : array_like
The data array that holds the values for the variable.
Typically, this is initialized as empty, but with the proper shape.
typecode : dtype character code
Desired data-type for the data array.
shape : sequence of ints
The shape of the array. This should match the lengths of the
variable's dimensions.
dimensions : sequence of strings
The names of the dimensions used by the variable. Must be in the
same order of the dimension lengths given by `shape`.
attributes : dict, optional
Attribute values (any type) keyed by string names. These attributes
become attributes for the netcdf_variable object.
Attributes
----------
dimensions : list of str
List of names of dimensions used by the variable object.
isrec, shape
Properties
See also
--------
isrec, shape
"""
def __init__(self, data, typecode, shape, dimensions, attributes=None):
self.data = data
self._typecode = typecode
self._shape = shape
self.dimensions = dimensions
self._attributes = attributes or OrderedDict()
for k, v in self._attributes.items():
self.__dict__[k] = v
def __setattr__(self, attr, value):
# Store user defined attributes in a separate dict,
# so we can save them to file later.
try:
self._attributes[attr] = value
except AttributeError:
pass
self.__dict__[attr] = value
@property
def attributes(self):
return self._attributes
def isrec(self):
return self.data.shape and not self._shape[0]
isrec = property(isrec)
def shape(self):
return self.data.shape
shape = property(shape)
def getValue(self):
"""
Retrieve a scalar value from a `netcdf_variable` of length one.
Raises
------
ValueError
If the netcdf variable is an array of length greater than one,
this exception will be raised.
"""
return self.data.item()
def assignValue(self, value):
"""
Assign a scalar value to a `netcdf_variable` of length one.
Parameters
----------
value : scalar
Scalar value (of compatible type) to assign to a length-one netcdf
variable. This value will be written to file.
Raises
------
ValueError
If the input is not a scalar, or if the destination is not a length-one
netcdf variable.
"""
self.data.itemset(value)
def typecode(self):
"""
Return the typecode of the variable.
Returns
-------
typecode : char
The character typecode of the variable (eg, 'i' for int).
"""
return self._typecode
def __getitem__(self, index):
return self.data[index]
def __setitem__(self, index, data):
# Expand data for record vars?
if self.isrec:
if isinstance(index, tuple):
rec_index = index[0]
else:
rec_index = index
if isinstance(rec_index, slice):
recs = (rec_index.start or 0) + len(data)
else:
recs = rec_index + 1
if recs > len(self.data):
shape = (recs,) + self._shape[1:]
self.data.resize(shape)
self.data[index] = data
NetCDFFile = netcdf_file
NetCDFVariable = netcdf_variable
######################################################################
import sys
import re
import glob
import fnmatch
from getopt import gnu_getopt as getopt
from collections import OrderedDict
import numpy as np
#from netcdf import netcdf_file
tilepatt = re.compile(r'(\.t[0-9]{3}\.nc)$')
iterpatt = re.compile(r'(\.[0-9]{10})$')
# parse command-line arguments
try:
optlist,args = getopt(sys.argv[1:], 'o:v:', ['verbose'])
opts = dict(optlist)
assert '-o' in opts
fnames = args
assert len(fnames) > 0
except (ValueError, AssertionError):
sys.exit(__doc__)
outname = opts.get('-o')
verbose = '--verbose' in opts
tname = 'T'
# turn into list of compiled regular expressions
varpatt = opts.get('-v', '').split(',')
varpatt = [ re.compile(fnmatch.translate(patt.strip())) for patt in varpatt ]
readopts = dict(delay=True)
writeopts = dict(mmap=True)
if len(fnames) == 1 and any(s in fnames[0] for s in '*?[]'):
fnames = glob.glob(fnames[0])
fnames.sort()
# Get list of iterations
itfiles = {}
for fname in fnames:
base = tilepatt.sub('', fname)
m = iterpatt.search(base)
if m:
itfiles.setdefault(m.group(1), []).append((fname))
its = itfiles.keys()
its.sort()
for it in its:
itfiles[it].sort()
filess = [ itfiles[it] for it in its ]
try:
files0 = filess[0]
except IndexError:
files0 = fnames
if verbose:
print 'Files to be read:'
for files in filess:
print files[0], '... ({})'.format(len(files))
nc = netcdf_file(files0[0], 'r', **readopts)
gatt = OrderedDict(nc.attributes)
#gatt['tile_number'] = 1
#gatt['bi'] = 1
#gatt['bj'] = 1
del gatt['tile_number']
del gatt['bi']
del gatt['bj']
for k in gatt.keys():
if k.startswith('exch2_'):
del gatt[k]
sNx = gatt['sNx']
sNy = gatt['sNy']
Nx = gatt['Nx']
Ny = gatt['Ny']
ntx = gatt['nSx']*gatt['nPx']
nty = gatt['nSy']*gatt['nPy']
ntiles = ntx*nty
for it in its:
if len(itfiles[it]) != ntiles:
raise ValueError('Error: found {} tiles for iteration {}, need {}'.format(
len(itfiles[it]), it[1:], ntiles))
Xslice = []
Yslice = []
for tn in range(ntx*nty):
bj,bi = divmod(tn, ntx)
ie = sNx*(bi+1-ntx) or None
je = sNy*(bj+1-nty) or None
Xslice.append(slice(sNx*bi, ie))
Yslice.append(slice(sNy*bj, je))
dims = OrderedDict()
for k,n in nc.dimensions.items():
if k[0] == 'X':
n += Nx - sNx
elif k[0] == 'Y':
n += Ny - sNy
dims[k] = n
print 'Tiled dimensions:', ' '.join([k for k in nc.dimensions if k[0] in 'XY'])
havetime = tname in dims
if havetime:
assert dims[tname] is None
nrec = nc.numrecs
print 'Record dimension:', tname
else:
assert len(its) <= 1
nrec = None
if verbose:
print 'Variables:'
varprops = OrderedDict()
for name,var in nc.variables.items():
if name in dims or any(patt.search(name) for patt in varpatt) or len(varpatt) == 0:
varprops[name] = {}
varprops[name]['dtype'] = var.data.dtype
varprops[name]['dimensions'] = var.dimensions
varprops[name]['ncattrs'] = OrderedDict(var.attributes)
iX = None
iY = None
for i,dim in enumerate(var.dimensions):
if dim[0] == 'X':
iX = i
elif dim[0] == 'Y':
iY = i
varprops[name]['iX'] = iX
varprops[name]['iY'] = iY
dimstrs = len(var.dimensions)*[':']
if tname in var.dimensions: dimstrs[var.dimensions.index(tname)] = tname
if iX is not None: dimstrs[iX] = var.dimensions[iX]
if iY is not None: dimstrs[iY] = var.dimensions[iY]
if verbose: print '{} {}({})'.format(var.typecode(), name, ','.join(dimstrs))
######################################################################
# create global netcdf file
ncout = netcdf_file(outname, 'w', **writeopts)
# global attributes
for name,att in gatt.items():
setattr(ncout, name, att)
# create dimensions
for name,n in dims.items():
ncout.createDimension(name, n)
# create variables with attributes
vars = {}
for name,var in varprops.items():
dtype = np.dtype(var['dtype']).newbyteorder('>')
# if verbose: print 'Creating variable', name, dtype, var['dimensions']
vars[name] = ncout.createVariable(name, dtype, var['dimensions'])
for attname,att in var['ncattrs'].items():
setattr(vars[name], attname, att)
ncout.write_metadata()
ncs = {files0[0]: nc}
for fname in files0:
if fname not in ncs:
ncs[fname] = nc = netcdf_file(fname, 'r', **readopts)
# assemble non-record variable data
if verbose: print 'Writing variable data:'
indstrings = {}
for name,pos,isrec in ncout.begins:
if not isrec:
if verbose: print name
prop = varprops[name]
vardims = prop['dimensions']
var = vars[name]
indx = len(vardims)*[slice(None)]
iX = prop['iX']
iY = prop['iY']
data = np.empty(var.shape, var.data.dtype)
for fname,nc in ncs.items():
tn = nc.tile_number - 1
if iX is not None: indx[iX] = Xslice[tn]
if iY is not None: indx[iY] = Yslice[tn]
data[tuple(indx)] = nc.read_var(name)
ncout.write_var(name, data)
del data
else: # isrec
if not havetime:
raise ValueError('record variables, but no time')
# assemble record variable data
if havetime:
irec = 0
for fnames in filess:
if irec:
ncs = {}
for fname in fnames:
ncs[fname] = nc = netcdf_file(fname, 'r', **readopts)
nrec = nc.numrecs
for irecin in range(nrec):
for name,pos,isrec in ncout.begins:
if isrec:
if verbose: print irec+irecin, name
prop = varprops[name]
vardims = prop['dimensions'][1:]
var = vars[name]
indx = len(vardims)*[slice(None)]
iX = prop['iX']
iY = prop['iY']
data = np.empty(var.data.shape[1:], var.data.dtype)
for fname,nc in ncs.items():
tn = nc.tile_number - 1
if iX is not None: indx[iX-1] = Xslice[tn]
if iY is not None: indx[iY-1] = Yslice[tn]
data[tuple(indx)] = nc.read_recvar(name, irecin)
ncout.write_recvar(name, irec+irecin, data)
del data
irec += nrec
for nc in ncs.values():
nc.close()
ncout.close()
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