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""" 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.
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.
Only NetCDF3 is supported here; for NetCDF4 see `netCDF4-python <http://unidata.github.io/netcdf4-python/>`__, which has a similar API.
"""
# TODO: # * properly implement ``_FillValue``. # * 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.
NC_CHAR: ('c', 1), NC_SHORT: ('h', 2), NC_INT: ('i', 4), NC_FLOAT: ('f', 4), NC_DOUBLE: ('d', 8)}
NC_CHAR: FILL_CHAR, NC_SHORT: FILL_SHORT, NC_INT: FILL_INT, NC_FLOAT: FILL_FLOAT, NC_DOUBLE: FILL_DOUBLE}
('B', 1): NC_CHAR, ('c', 1): NC_CHAR, ('h', 2): NC_SHORT, ('i', 4): NC_INT, ('f', 4): NC_FLOAT, ('d', 8): NC_DOUBLE,
# these come from asarray(1).dtype.char and asarray('foo').dtype.char, # used when getting the types from generic attributes. ('l', 4): NC_INT, ('S', 1): NC_CHAR}
""" 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.
Parameters ---------- filename : string or file-like string -> filename mode : {'r', 'w', 'a'}, optional read-write-append 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. Note that when mmap is in use, data arrays returned refer directly to the mmapped data on disk, and the file cannot be closed as long as references to it exist. 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 <https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html#select_format>`__ for more info. maskandscale : bool, optional Whether to automatically scale and/or mask data based on attributes. Default is False.
Notes ----- The major advantage of this module over other modules is that it doesn't require the code to be linked to the NetCDF libraries. This module is derived from `pupynere <https://bitbucket.org/robertodealmeida/pupynere/>`_.
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 <https://www.unidata.ucar.edu/software/netcdf/docs/user_guide.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.
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.
Note that when `netcdf_file` is used to open a file with mmap=True (default for read-only), arrays returned by it refer to data directly on the disk. The file should not be closed, and cannot be cleanly closed when asked, if such arrays are alive. You may want to copy data arrays obtained from mmapped Netcdf file if they are to be processed after the file is closed, see the example below.
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[:] = np.arange(10) >>> time.units = 'days since 2008-01-01' >>> f.close()
Note the assignment of ``arange(10)`` to ``time[:]``. Exposing the slice of the time variable allows for the data to be set in the object, rather than letting ``arange(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) b'Created for a test' >>> time = f.variables['time'] >>> print(time.units) b'days since 2008-01-01' >>> print(time.shape) (10,) >>> print(time[-1]) 9
NetCDF files, when opened read-only, return arrays that refer directly to memory-mapped data on disk:
>>> data = time[:] >>> data.base.base <mmap.mmap object at 0x7fe753763180>
If the data is to be processed after the file is closed, it needs to be copied to main memory:
>>> data = time[:].copy() >>> f.close() >>> data.mean() 4.5
A NetCDF file can also be used as context manager:
>>> from scipy.io import netcdf >>> with netcdf.netcdf_file('simple.nc', 'r') as f: ... print(f.history) b'Created for a test'
""" maskandscale=False): """Initialize netcdf_file from fileobj (str or file-like).""" raise ValueError("Mode must be either 'r', 'w' or 'a'.")
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 # Mmapped files on PyPy cannot be usually closed # before the GC runs, so it's better to use mmap=False # as the default.
# Cannot read write-only files
self._mm = mm.mmap(self.fp.fileno(), 0, access=mm.ACCESS_READ) self._mm_buf = np.frombuffer(self._mm, dtype=np.int8)
self._read()
# Store user defined attributes in a separate dict, # so we can save them to file later.
"""Closes the NetCDF file.""" finally: ref = weakref.ref(self._mm_buf) self._mm_buf = None if ref() is None: # self._mm_buf is gc'd, and we can close the mmap self._mm.close() else: # we cannot close self._mm, since self._mm_buf is # alive and there may still be arrays referring to it warnings.warn(( "Cannot close a netcdf_file opened with mmap=True, when " "netcdf_variables or arrays referring to its data still exist. " "All data arrays obtained from such files refer directly to " "data on disk, and must be copied before the file can be cleanly " "closed. (See netcdf_file docstring for more information on mmap.)" ), category=RuntimeWarning)
return self
self.close()
""" 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
""" raise ValueError("Only first dimension may be unlimited!")
""" 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.
"""
raise ValueError("NetCDF 3 does not support type %s" % type)
data, typecode, size, shape, dimensions, maskandscale=self.maskandscale)
""" Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.
See Also -------- sync : Identical function
"""
# Write headers and data.
# Get highest record count from all record variables. self.__dict__['_recs'] = len(var.data)
else: self.fp.write(ABSENT)
else:
# Sort variable names non-recs first, then recs. return (-1,)
# Set the metadata for all variables. # Now that we have the metadata, we know the vsize of # each record variable, so we can calculate recsize. var._vsize for var in self.variables.values() if var.isrec]) # Set the data for all variables. else: self.fp.write(ABSENT)
else: # record variable try: vsize = var.data[0].size * var.data.itemsize except IndexError: vsize = 0 rec_vars = len([v for v in self.variables.values() if v.isrec]) if rec_vars > 1: vsize += -vsize % 4
# Pack a bogus begin, and set the real value later.
# Set begin in file header.
# Write data. else: # record variable # Handle rec vars with shape[0] < nrecs. if self._recs > len(var.data): shape = (self._recs,) + var.data.shape[1:] # Resize in-place does not always work since # the array might not be single-segment try: var.data.resize(shape) except ValueError: var.__dict__['data'] = np.resize(var.data, shape).astype(var.data.dtype)
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._write_var_padding(var, var._vsize - count) pos += self._recsize self.fp.seek(pos) self.fp.seek(pos0 + var._vsize)
nc_type = REVERSE[values.dtype.char, values.dtype.itemsize] else: (float, NC_FLOAT), (str, NC_CHAR) ] # bytes index into scalars in py3k. Check for "string" types else:
# asarray() dies with bytes and '>c' in py3k. Change to 'S'
else:
(values.dtype.byteorder == '=' and LITTLE_ENDIAN)): values = values.byteswap()
# Check magic bytes and version magic = self.fp.read(3) if not magic == b'CDF': raise TypeError("Error: %s is not a valid NetCDF 3 file" % self.filename) self.__dict__['version_byte'] = frombuffer(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()
self.__dict__['_recs'] = self._unpack_int()
header = self.fp.read(4) if header not in [ZERO, NC_DIMENSION]: raise ValueError("Unexpected header.") 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
for k, v in self._read_att_array().items(): self.__setattr__(k, v)
header = self.fp.read(4) if header not in [ZERO, NC_ATTRIBUTE]: raise ValueError("Unexpected header.") count = self._unpack_int()
attributes = OrderedDict() for attr in range(count): name = asstr(self._unpack_string()) attributes[name] = self._read_att_values() return attributes
header = self.fp.read(4) if header not in [ZERO, NC_VARIABLE]: raise ValueError("Unexpected header.")
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() # https://www.unidata.ucar.edu/software/netcdf/docs/user_guide.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: data = self._mm_buf[begin_:begin_+a_size].view(dtype=dtype_) data.shape = shape else: pos = self.fp.tell() self.fp.seek(begin_) data = frombuffer(self.fp.read(a_size), dtype=dtype_ ).copy() data.shape = shape self.fp.seek(pos)
# Add variable. self.variables[name] = netcdf_variable( data, typecode, size, shape, dimensions, attributes, maskandscale=self.maskandscale)
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: rec_array = self._mm_buf[begin:begin+self._recs*self._recsize].view(dtype=dtypes) rec_array.shape = (self._recs,) else: pos = self.fp.tell() self.fp.seek(begin) rec_array = frombuffer(self.fp.read(self._recs*self._recsize), dtype=dtypes).copy() rec_array.shape = (self._recs,) self.fp.seek(pos)
for var in rec_vars: self.variables[var].__dict__['data'] = rec_array[var]
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] dtype_ = '>%s' % typecode
return name, dimensions, shape, attributes, typecode, size, dtype_, begin, vsize
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 = frombuffer(values, dtype='>%s' % typecode).copy() if values.shape == (1,): values = values[0] else: values = values.rstrip(b'\x00') return values
elif self.version_byte == 2: self._pack_int64(begin)
return int(frombuffer(self.fp.read(4), '>i')[0])
self.fp.write(array(value, '>q').tostring())
return frombuffer(self.fp.read(8), '>q')[0]
count = self._unpack_int() s = self.fp.read(count).rstrip(b'\x00') self.fp.read(-count % 4) # read padding return s
""" 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. size : int Desired element size 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. maskandscale : bool, optional Whether to automatically scale and/or mask data based on attributes. Default is False.
Attributes ---------- dimensions : list of str List of names of dimensions used by the variable object. isrec, shape Properties
See also -------- isrec, shape
""" attributes=None, maskandscale=False):
self.__dict__[k] = v
# Store user defined attributes in a separate dict, # so we can save them to file later.
"""Returns whether the variable has a record dimension or not.
A record dimension is a dimension along which additional data could be easily appended in the netcdf data structure without much rewriting of the data file. This attribute is a read-only property of the `netcdf_variable`.
"""
"""Returns the shape tuple of the data variable.
This is a read-only attribute and can not be modified in the same manner of other numpy arrays. """ return self.data.shape
""" 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()
""" 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.
""" if not self.data.flags.writeable: # Work-around for a bug in NumPy. Calling itemset() on a read-only # memory-mapped array causes a seg. fault. # See NumPy ticket #1622, and SciPy ticket #1202. # This check for `writeable` can be removed when the oldest version # of numpy still supported by scipy contains the fix for #1622. raise RuntimeError("variable is not writeable")
self.data.itemset(value)
""" Return the typecode of the variable.
Returns ------- typecode : char The character typecode of the variable (eg, 'i' for int).
"""
""" Return the itemsize of the variable.
Returns ------- itemsize : int The element size of the variable (eg, 8 for float64).
"""
if not self.maskandscale: return self.data[index]
data = self.data[index].copy() missing_value = self._get_missing_value() data = self._apply_missing_value(data, missing_value) scale_factor = self._attributes.get('scale_factor') add_offset = self._attributes.get('add_offset') if add_offset is not None or scale_factor is not None: data = data.astype(np.float64) if scale_factor is not None: data = data * scale_factor if add_offset is not None: data += add_offset
return data
missing_value = ( self._get_missing_value() or getattr(data, 'fill_value', 999999)) self._attributes.setdefault('missing_value', missing_value) self._attributes.setdefault('_FillValue', missing_value) data = ((data - self._attributes.get('add_offset', 0.0)) / self._attributes.get('scale_factor', 1.0)) data = np.ma.asarray(data).filled(missing_value) if self._typecode not in 'fd' and data.dtype.kind == 'f': data = np.round(data)
# Expand data for record vars? 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:] # Resize in-place does not always work since # the array might not be single-segment try: self.data.resize(shape) except ValueError: self.__dict__['data'] = np.resize(self.data, shape).astype(self.data.dtype)
""" The default encoded fill-value for this Variable's data type. """
""" Returns the encoded fill value for this variable as bytes.
This is taken from either the _FillValue attribute, or the default fill value for this variable's data type. """ fill_value = np.array(self._attributes['_FillValue'], dtype=self.data.dtype).tostring() if len(fill_value) == self.itemsize(): return fill_value else: return self._default_encoded_fill_value() else:
""" Returns the value denoting "no data" for this variable.
If this variable does not have a missing/fill value, returns None.
If both _FillValue and missing_value are given, give precedence to _FillValue. The netCDF standard gives special meaning to _FillValue; missing_value is just used for compatibility with old datasets. """
if '_FillValue' in self._attributes: missing_value = self._attributes['_FillValue'] elif 'missing_value' in self._attributes: missing_value = self._attributes['missing_value'] else: missing_value = None
return missing_value
def _apply_missing_value(data, missing_value): """ Applies the given missing value to the data array.
Returns a numpy.ma array, with any value equal to missing_value masked out (unless missing_value is None, in which case the original array is returned). """
if missing_value is None: newdata = data else: try: missing_value_isnan = np.isnan(missing_value) except (TypeError, NotImplementedError): # some data types (e.g., characters) cannot be tested for NaN missing_value_isnan = False
if missing_value_isnan: mymask = np.isnan(data) else: mymask = (data == missing_value)
newdata = np.ma.masked_where(mymask, data)
return newdata
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