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gluemncbig
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gluemncbig
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Usage: gluemncbig [-2] [-q] [--verbose] [--help] [--many] [-v <vars>] -o <outfile> <files>
-v <vars> comma-separated list of variable names or glob patterns
-2 write a NetCDF version 2 (64-Bit Offset) file allowing for large records
--many many tiles: assemble only along x in memory; less efficient
on some filesystems, but opens fewer files simultaneously and
uses less memory
-q suppress progress messages
--verbose report variables
--help show this help text
All files must have the same variables.
Each variable (or 1 record of it) must fit in memory.
With --many, only a row of tiles along x must fit in memory.
Examples:
gluemncbig -o ptr.nc mnc_*/ptr_tave.*.nc
gluemncbig -o BIO.nc -v 'BIO_*' mnc_*/ptr_tave.*.nc
"""
from __future__ import print_function
# NetCDF reader/writer module modified from pupynere,
# https://bitbucket.org/robertodealmeida/pupynere/
# to allow delayed reading/writing of variable data.
# MIT license
moduledoc = u"""
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:
>>> f = netcdf_file('simple.nc', 'w')
>>> f.history = 'Created for a test'
>>> f.location = u'北京'
>>> f.createDimension('time', 10)
>>> time = f.createVariable('time', 'i', ('time',))
>>> time[:] = range(10)
>>> time.units = u'µs 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:
>>> f = netcdf_file('simple.nc', 'r')
>>> print(f.history)
Created for a test
>>> print(f.location)
北京
>>> time = f.variables['time']
>>> print(time.units)
µs 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']
from operator import mul
try:
from collections import OrderedDict
except ImportError:
OrderedDict = dict
from mmap import mmap, ACCESS_READ
import numpy as np
from numpy import frombuffer, ndarray, dtype, empty, array, asarray
from numpy import little_endian as LITTLE_ENDIAN
import sys
# the following are mostly from numpy.compat.py3k
if sys.version_info[0] >= 3:
from functools import reduce
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
long = int
unicode = str
basestring = str
else:
# python 2
asbytes = str
asstr = str
long = long
unicode = unicode
basestring = basestring
ABSENT = b'\x00\x00\x00\x00\x00\x00\x00\x00'
ZERO = b'\x00\x00\x00\x00'
NC_BYTE = b'\x00\x00\x00\x01'
NC_CHAR = b'\x00\x00\x00\x02'
NC_SHORT = b'\x00\x00\x00\x03'
NC_INT = b'\x00\x00\x00\x04'
NC_FLOAT = b'\x00\x00\x00\x05'
NC_DOUBLE = b'\x00\x00\x00\x06'
NC_DIMENSION = b'\x00\x00\x00\n'
NC_VARIABLE = b'\x00\x00\x00\x0b'
NC_ATTRIBUTE = b'\x00\x00\x00\x0c'
TYPEMAP = { NC_BYTE: dtype(np.byte),
NC_CHAR: dtype('c'),
NC_SHORT: dtype(np.int16).newbyteorder('>'),
NC_INT: dtype(np.int32).newbyteorder('>'),
NC_FLOAT: dtype(np.float32).newbyteorder('>'),
NC_DOUBLE: dtype(np.float64).newbyteorder('>'),
}
REVERSE = { dtype(np.byte): NC_BYTE,
dtype('c'): NC_CHAR,
dtype(np.int16): NC_SHORT,
dtype(np.int32): NC_INT,
dtype(np.int64): NC_INT, # will be converted to int32
dtype(np.float32): NC_FLOAT,
dtype(np.float64): NC_DOUBLE,
}
class NetCDFError(Exception):
pass
class unmapped_array(object):
def __init__(self, shape, dtype_):
self.shape = shape
self.dtype = 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]
def __getitem__(self, indx):
raise NetCDFError('netcdf_file: delay is True, use read_var or read_recvar to read variable data')
def __setitem__(self, indx, val):
raise NetCDFError('netcdf_file: delay is True, use write_var or write_recvar to assign variable data')
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 delay is True, variable data is only read/written on demand.
In this case, if mode="w", write_metadata() needs to be called after
all dimensions, variables and attributes have been defined, but before
any variable data is written.
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
delay : bool, optional
Whether to delay reading of variable data. Default is False.
This is an alternative to mmap for more efficient reading.
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: # 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
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."""
try:
closed = self.fp.closed
except AttributeError:
pass
else:
if not 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 not isinstance(type, dtype): type = dtype(type)
if type.newbyteorder('=') not in REVERSE:
raise ValueError("NetCDF 3 does not support type %s" % type)
if self.delay:
data = unmapped_array(shape_, type)
else:
data = empty(shape_, type)
self.variables[name] = netcdf_variable(data, type, shape, dimensions)
return self.variables[name]
def flush(self):
"""
Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.
See Also
--------
sync : Identical function
"""
if getattr(self, 'mode', None) == 'w':
if self.delay:
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!'''
if self.delay:
self._map()
else:
raise UserWarning('write_metadata is void unless delay is True')
def _map(self):
self.fp.seek(0)
self.fp.write(b'CDF')
self.fp.write(array(self.version_byte, '>b').tobytes())
# 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))
# Separate record variables from non-record ones, keep order
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 variable, so we can calculate their position in the file
pos0 = pos = self.fp.tell()
# 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
recstart = pos
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
self.__dict__['_recsize'] = pos - recstart
# 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.dtype.newbyteorder('=')]
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, j0=None, iY=-2):
if not self.delay:
raise NetCDFError('netcdf_file: delay is False, need to assign to variables')
if not self._mapped:
raise NetCDFError('netcdf_file: need to call write_metadata first')
pos = self._begins[name]
var = self.variables[name]
count = var.data.size * var.data.itemsize
if j0 is not None:
if iY == 0:
idxs = [()]
else:
idxs = np.ndindex(*var.data.shape[:iY])
for idx in idxs:
ii = idx + (j0,) + len(var.data.shape[iY+1:])*(0,)
offset = np.ravel_multi_index(ii, var.data.shape)*var.data.itemsize
end = offset + val[idx].size*var.data.itemsize
if end > count:
raise NetCDFError('array too large: {} > {}'.format(end, count))
self.fp.seek(pos+offset)
np.asanyarray(val[idx], var.data.dtype.newbyteorder('>')).tofile(self.fp)
else:
end = val.size*var.data.itemsize
if end != count:
raise NetCDFError('array too large: {} > {}'.format(end, count))
self.fp.seek(pos)
np.asanyarray(val, var.data.dtype.newbyteorder('>')).tofile(self.fp)
# pad
self.fp.write(b'\x00' * (var._vsize - count))
def write_recvar(self, name, rec, val, j0=None, iY=-2):
if not self.delay:
raise NetCDFError('netcdf_file: delay is False, need to assign to variables')
if not self._mapped:
raise NetCDFError('netcdf_file: need to call write_metadata first')
pos = self._begins[name] + rec*self._recsize
var = self.variables[name]
count = reduce(mul, var.data.shape[1:], 1) * var.data.itemsize
if j0 is not None:
if iY == 0:
idxs = [()]
else:
idxs = np.ndindex(*var.data.shape[1:iY])
for idx in idxs:
ii = idx + (j0,) + len(var.data.shape[iY+1:])*(0,)
offset = np.ravel_multi_index(ii, var.data.shape[1:])*var.data.itemsize
end = offset + val[idx].size*var.data.itemsize
if end > count:
raise NetCDFError('array too large: {} > {}'.format(end, count))
self.fp.seek(pos+offset)
np.asanyarray(val[idx], var.data.dtype.newbyteorder('>')).tofile(self.fp)
else:
end = val.size*var.data.itemsize
if end != count:
raise ValueError('netcdf_file.write_recvar: array too large')
self.fp.seek(pos)
np.asanyarray(val, var.data.dtype.newbyteorder('>')).tofile(self.fp)
# pad
self.fp.write(b'\x00' * (var._vsize - count))
if rec >= self._recs:
self.__dict__['_recs'] = rec + 1
def _write(self):
self.fp.seek(0)
self.fp.write(b'CDF')
self.fp.write(array(self.version_byte, '>b').tobytes())
# 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]
# Separate record variables from non-record ones, keep order
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 ]
variables = nonrec_vars + rec_vars
# 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.dtype.newbyteorder('=')]
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:
if (var.data.dtype.byteorder == '<' or
(var.data.dtype.byteorder == '=' and LITTLE_ENDIAN)):
var.data = var.data.byteswap()
self.fp.write(var.data.tobytes())
count = var.data.size * var.data.itemsize
self.fp.write(b'\x00' * (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:
if (rec.dtype.byteorder == '<' or
(rec.dtype.byteorder == '=' and LITTLE_ENDIAN)):
rec = rec.byteswap()
self.fp.write(rec.tobytes())
# Padding
count = rec.size * rec.itemsize
self.fp.write(b'\x00' * (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.newbyteorder('=')]
else:
types = [
(int, NC_INT),
(long, NC_INT),
(float, NC_FLOAT),
(basestring, NC_CHAR),
]
try:
sample = values[0]
except (IndexError, TypeError):
sample = values
if isinstance(sample, unicode):
if not isinstance(values, unicode):
raise ValueError(
"NetCDF requires that text be encoded as UTF-8")
values = values.encode('utf-8')
for class_, nc_type in types:
if isinstance(sample, class_): break
if nc_type == NC_CHAR:
if len(values) == 0:
# only this can represent zero-length strings
dtype_ = dtype('c')
else:
# this avoids double encoding in python 3
dtype_ = dtype('S')
else:
dtype_ = TYPEMAP[nc_type]
values = asarray(values, 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.tobytes())
count = values.size * values.itemsize
self.fp.write(b'\x00' * (-count % 4)) # pad
def _read(self):
# 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()
def _read_numrecs(self):
self.__dict__['_recs'] = self._unpack_int()
def _read_dim_array(self):
header = self.fp.read(4)
if not header 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
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)
if not header 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_values()
return attributes
def _read_var_array(self):
header = self.fp.read(4)
if not header in [ZERO, NC_VARIABLE]:
raise ValueError("Unexpected header.")
begin = 0
dtypes = {'names': [], 'formats': []}
rec_vars = []
count = self._unpack_int()
rec_vsizes = []
for var in range(count):
name, dimensions, shape, attributes, type, 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
rec_vsizes.append(vsize)
if begin == 0: begin = begin_
dtypes['names'].append(name)
dtypes['formats'].append(str(shape[1:]) + '>' + type.char)
# Handle padding with a virtual variable.
if type.char in 'bch':
actual_size = reduce(mul, (1,) + shape[1:]) * type.itemsize
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:], type)
else:
data = None
else: # not a record variable
# Calculate size to avoid problems with vsize (above)
a_size = reduce(mul, shape, 1) * type.itemsize
pos = self.fp.tell()
if self.use_mmap:
mm = mmap(self.fp.fileno(), begin_+a_size, access=ACCESS_READ)
data = ndarray.__new__(ndarray, shape, dtype=type,
buffer=mm, offset=begin_, order=0)
elif self.delay:
self._begins[name] = begin_
data = unmapped_array(shape, type)
else:
self.fp.seek(begin_)
data = frombuffer(self.fp.read(a_size), type)
data.shape = shape
self.fp.seek(pos)
# Add variable.
self.variables[name] = netcdf_variable(data, type, shape, dimensions, attributes)
if rec_vars and not self.delay:
dtypes['formats'] = [f.replace('()', '').replace(' ', '') for f in dtypes['formats']]
# 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.
pos = self.fp.tell()
rec_arrays = []
if self.use_mmap:
mm = mmap(self.fp.fileno(), begin+self._recs*self._recsize, access=ACCESS_READ)
if self._recsize >= 1<<31:
# need to work around limitation of numpy.dtype.itemsize to 32 bit
i = 0
while i < len(rec_vsizes):
ends = np.cumsum(rec_vsizes[i:])
n = np.searchsorted(ends, 1<<31)
dtype1 = dict(names=dtypes['names'][i:i+n], formats=dtypes['formats'][i:i+n])
rec_array = ndarray.__new__(ndarray, (self._recs,), dtype=dtype1,
buffer=mm, offset=begin, order=0)
rec_arrays.append(rec_array)
begin += ends[n-1]
i += n
else:
rec_array = ndarray.__new__(ndarray, (self._recs,), dtype=dtypes,
buffer=mm, offset=begin, order=0)
rec_arrays = [ rec_array ]
else:
self.fp.seek(begin)
rec_array = frombuffer(self.fp.read(self._recs*self._recsize), dtype=dtypes)
rec_array.shape = (self._recs,)
rec_arrays = [ rec_array ]
self.fp.seek(pos)
for rec_array in rec_arrays:
for var in rec_array.dtype.names:
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 = frombuffer(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 = frombuffer(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]()
type = TYPEMAP[nc_type]
return name, dimensions, shape, attributes, type, begin, vsize
def _read_values(self):
nc_type = self.fp.read(4)
n = self._unpack_int()
type = TYPEMAP[nc_type]
count = n*type.itemsize
values = self.fp.read(int(count))
self.fp.read(-count % 4) # read padding
if type.char != 'c':
values = frombuffer(values, type)
if values.shape == (1,): values = values[0]
else:
## text values are encoded via UTF-8, per NetCDF standard
values = values.rstrip(b'\x00').decode('utf-8', 'replace')
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').tobytes())
_pack_int32 = _pack_int
def _unpack_int(self):