"""
NetCDF reader/writer module.
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 interchangebly when working
with NetCDF files. The major advantage of ``scipy.io.netcdf`` over other
modules is that it doesn't require the code to be linked to the NetCDF
libraries as the other modules do.
The code is based on the `NetCDF file format specification
<http://www.unidata.ucar.edu/software/netcdf/docs/netcdf.html>`_. A
NetCDF file is a self-describing binary format, with a header followed
by data. The header contains metadata describing dimensions, variables
and the position of the data in the file, so access can be done in an
efficient manner without loading unnecessary data into memory. We use
the ``mmap`` module to create Numpy arrays mapped to the data on disk,
for the same purpose.
The structure of a NetCDF file is as follows:
C D F <VERSION BYTE> <NUMBER OF RECORDS>
<DIMENSIONS> <GLOBAL ATTRIBUTES> <VARIABLES METADATA>
<NON-RECORD DATA> <RECORD DATA>
Record data refers to data where the first axis can be expanded at
will. All record variables share a same dimension at the first axis,
and they are stored at the end of the file per record, ie
A[0], B[0], ..., A[1], B[1], ..., etc,
so that new data can be appended to the file without changing its original
structure. Non-record data are padded to a 4n bytes boundary. Record data
are also padded, unless there is exactly one record variable in the file,
in which case the padding is dropped. All data is stored in big endian
byte order.
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.
To create a NetCDF file::
>>> import time
>>> f = 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()
To read the NetCDF file we just created::
>>> f = 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?
"""
__all__ = ['netcdf_file', 'netcdf_variable']
from operator import mul
from mmap import mmap,ACCESS_READ
import numpy as np
from numpy import fromstring,ndarray,dtype,empty,array,asarray
from numpy import little_endian
ABSENT = '\x00\x00\x00\x00\x00\x00\x00\x00'
ZERO = '\x00\x00\x00\x00'
NC_BYTE = '\x00\x00\x00\x01'
NC_CHAR = '\x00\x00\x00\x02'
NC_SHORT = '\x00\x00\x00\x03'
NC_INT = '\x00\x00\x00\x04'
NC_FLOAT = '\x00\x00\x00\x05'
NC_DOUBLE = '\x00\x00\x00\x06'
NC_DIMENSION = '\x00\x00\x00\n'
NC_VARIABLE = '\x00\x00\x00\x0b'
NC_ATTRIBUTE = '\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 netcdf_file(object):
"""
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.
"""
def __init__(self, filename, mode='r', mmap=None, version=1):
''' Initialize netcdf_file from fileobjstringfilelike import
Parameters
----------
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
http://www.unidata.ucar.edu/software/netcdf/docs/netcdf/Which-Format.html#Which-Format
'''
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
self.fp = open(self.filename, '%sb' % mode)
if mmap is None:
mmap = True
self.use_mmap = mmap
self.version_byte = version
if not mode in 'rw':
raise ValueError("Mode must be either 'r' or 'w'.")
self.mode = mode
self.dimensions = {}
self.variables = {}
self._dims = []
self._recs = 0
self._recsize = 0
self._attributes = {}
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):
if not self.fp.closed:
try:
self.flush()
finally:
self.fp.close()
__del__ = close
def createDimension(self, name, length):
self.dimensions[name] = length
self._dims.append(name)
def createVariable(self, name, type, dimensions):
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 = dtype(type)
typecode, size = type.char, type.itemsize
dtype_ = '>%s' % typecode
if size > 1: dtype_ += str(size)
data = empty(shape_, dtype=dtype_)
self.variables[name] = netcdf_variable(data, typecode, shape, dimensions)
return self.variables[name]
def flush(self):
if self.mode is 'w':
self._write()
sync = flush
def _write(self):
self.fp.write('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._pack_int(self._recs)
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(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('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('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:
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(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('0' * (-count % 4)) # pad
def _read(self):
# Check magic bytes and version
magic = self.fp.read(3)
if not magic == '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 = 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 = {}
for attr in range(count):
name = 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.
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)
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:
# 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 = 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(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('\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 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(s)
self.fp.write('0' * (-count % 4)) # pad
def _unpack_string(self):
count = self._unpack_int()
s = self.fp.read(count).rstrip('\x00')
self.fp.read(-count % 4) # read padding
return s
class netcdf_variable(object):
"""
``netcdf_variable`` objects are constructed by calling the method
``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 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.
"""
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 {}
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
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):
return self.data.item()
def assignValue(self, value):
self.data.itemset(value)
def typecode(self):
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
|