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Roberto De Almeida  committed 4971de5

Imported code from svn.

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+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/guide_15.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()
+"""
+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/guide_15.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
+
+from numpy import fromstring, ndarray, dtype, empty, array, asarray, prod
+from numpy import little_endian as 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=True, version=1):
+        if hasattr(filename, 'seek'):
+            self.fp = filename
+            self.filename = 'None'
+            self.use_mmap = False
+        else:
+            self.filename = filename
+            self.fp = open(self.filename, '%sb' % mode)
+            self.use_mmap = mmap
+
+        self.version_byte = version
+
+        assert mode in 'rw', "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 is '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)
+        assert magic == 'CDF', "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()
+            if shape and shape[0] is None:
+                rec_vars.append(name)
+                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:
+                if self.use_mmap:
+                    mm = mmap(self.fp.fileno(), begin_+vsize, 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(vsize), dtype=dtype_)
+                    data = fromstring(self.fp.read(size*prod(shape)), 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):
+        if self.data.shape:
+            return self.data[:]
+        else:
+            return self.data.item()
+
+    def assignValue(self, value):
+        if self.data.shape:
+            self.data[:] = value[:]
+        else:
+            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
+RELEASE = True
+
+from setuptools import setup, find_packages
+import sys, os
+
+classifiers = """\
+Development Status :: 5 - Production/Stable
+Environment :: Console
+Intended Audience :: Developers
+Intended Audience :: Science/Research
+License :: OSI Approved :: MIT License
+Operating System :: OS Independent
+Programming Language :: Python
+Topic :: Scientific/Engineering
+Topic :: Software Development :: Libraries :: Python Modules
+"""
+
+version = '1.0.13'
+
+setup(
+        name='pupynere',
+        version=version,
+        description="NetCDF file reader and writer.",
+        long_description="""\
+Pupynere is a Python module for reading and writing NetCDF files,
+using the same API as Scientific.IO.NetCDF and pynetcdf. It depends only
+on Numpy, so you don't need to have the NetCDF library installed.
+
+Changelog:
+
+1.0.13
+    Fixed bug when reading character variables without mmap.
+
+1.0.12
+  Fixed bug.
+
+1.0.11
+  Fixed bug.
+
+1.0.10
+  Fixed bug when packing integer attributes in 64-bit systems.
+
+1.0.9
+  Should work with Python 2.3.
+  Accepts file objects instead of only filenames.
+
+1.0.8
+  Allow writing version 2 files (Large Files).
+
+1.0.7
+  Removed reads from asserts to allow PYTHONOPTIMIZE.
+
+1.0.6
+  Allows zero-length record variables.
+
+1.0.5
+  Added the option to open files without using mmap, since mmap can't
+  handle huge files on Windows.
+
+1.0.4
+  Fixed packing of dimensions when writing a file. The order was being
+  read from a dictionary (essentially unordered), instead of from the list
+  with the proper order.
+
+1.0.3
+  Fixed bug so that it can write scalar variables.
+
+1.0.2
+  Fixed broken 1.0.1, ``var.shape`` was returning the current number
+  of records in the first dimension, breaking the detection of record
+  variables.
+
+1.0.1
+  Changed the code to read the variable shape from the underlying
+  data object.
+
+1.0.0
+  Initial stable release. Handles record arrays properly (using a single
+  mmap for all record variables) and writes files.
+""",
+        classifiers=filter(None, classifiers.split("\n")),
+        keywords='netcdf data array math',
+        author='Roberto De Almeida',
+        author_email='roberto@dealmeida.net',
+        url='http://bitbucket.org/robertodealmeida/pupynere/',
+        download_url = "http://cheeseshop.python.org/packages/source/p/pupynere/pupynere-%s.tar.gz" % version,
+        license='MIT',
+        py_modules=['pupynere'],
+        include_package_data=True,
+        zip_safe=True,
+        test_suite = 'nose.collector',
+        install_requires=[
+            'numpy',
+        ],
+        extras_require={
+            'test': ['nose'],
+        },
+)