Revision: 4450
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4450&view=rev
Author: jswhit
Date: 2007-11-26 10:48:01 -0800 (Mon, 26 Nov 2007)
Log Message:
-----------
add missing file to dap package
Added Paths:
-----------
trunk/toolkits/basemap/lib/dap/proxy.py
Added: trunk/toolkits/basemap/lib/dap/proxy.py
===================================================================
--- trunk/toolkits/basemap/lib/dap/proxy.py (rev 0)
+++ trunk/toolkits/basemap/lib/dap/proxy.py 2007-11-26 18:48:01 UTC (rev
4450)
@@ -0,0 +1,147 @@
+from __future__ import division
+
+"""Proxy class to DAP data.
+
+This module implements a proxy object that behaves like an array and
+downloads data transparently from a DAP server when sliced. It is used
+when building a representation of the dataset, and should be not
+directly used.
+"""
+
+__author__ = "Roberto De Almeida <[EMAIL PROTECTED]>"
+
+import sys
+
+from dap.xdr import DapUnpacker
+from dap.helper import fix_slice
+from dap.util.http import openurl
+
+try:
+ from numpy import ndarray as ndarray_
+except ImportError:
+ ndarray_ = None
+
+
+class Proxy(object):
+ """
+ A proxy to data stored in a DODS server.
+ """
+ def __init__(self, url, id, shape, type, filters=None, cache=None,
username=None, password=None):
+ self.url = url
+ self.id = id
+ self.shape = shape
+ self.type = type
+
+ self.filters = filters or []
+
+ self.cache = cache
+ self.username = username
+ self.password = password
+
+ def __iter__(self):
+ return iter(self[:])
+
+ def __getitem__(self, index):
+ """
+ Download data from DAP server.
+
+ When the proxy object is sliced, it build an URL from the slice
+ and retrieves the data from the DAP server.
+ """
+ # Build the base URL.
+ url = '%s.dods?%s' % (self.url, self.id)
+
+ if self.shape:
+ # Fix the index for incomplete slices or ellipsis.
+ index = fix_slice(len(self.shape), index)
+
+ # Force index to tuple, to iterate over the slices.
+ if not isinstance(index, tuple):
+ index = index,
+
+ # Iterate over the sliced dimensions.
+ i = 0
+ outshape = []
+ for dimension in index:
+ # If dimension is a slice, get start, step and stop.
+ if isinstance(dimension, slice):
+ start = dimension.start or 0
+ step = dimension.step or 1
+ if dimension.stop:
+ stop = dimension.stop-1
+ else:
+ stop = self.shape[i]-1
+
+ # Otherwise, retrieve a single value.
+ else:
+ start = dimension
+ stop = dimension
+ step = 1
+
+ # When stop is not specified, use the shape.
+ if stop == sys.maxint or stop > self.shape[i]-1:
+ stop = self.shape[i]-1
+ # Negative slices.
+ elif stop < 0:
+ stop = self.shape[i]+stop
+
+ # Negative starting slices.
+ if start < 0: start = self.shape[i]+start
+
+ # Build the URL used to retrieve the data.
+ url = '%s[%s:%s:%s]' % (url, str(start), str(step), str(stop))
+
+ # outshape is a list of the slice dimensions.
+ outshape.append(1+(stop-start)//step)
+
+ # Update to next dimension.
+ i += 1
+
+ else:
+ # No need to resize the data.
+ outshape = None
+
+ # Make the outshape consistent with the numpy and pytables conventions.
+ if outshape is not None:
+ outshape = self._reduce_outshape(outshape)
+
+ # Check for filters.
+ if self.filters:
+ ce = '&'.join(self.filters)
+ url = '%s&%s' % (url, ce)
+
+ # Fetch data.
+ resp, data = openurl(url, self.cache, self.username, self.password)
+
+ # First lines are ASCII information that end with 'Data:\n'.
+ start = data.index('Data:\n') + len('Data:\n')
+ xdrdata = data[start:]
+
+ # Unpack data.
+ output = DapUnpacker(xdrdata, self.shape, self.type,
outshape).getvalue()
+
+ # Convert length 1 arrays to scalars
+ if ndarray_:
+ if outshape == () and isinstance(output, ndarray_):
+ output = output[0]
+
+ return output
+
+
+ def _reduce_outshape(self, outshape):
+ """Make the outshape consistent with the numpy and pytables
conventions.
+
+ (1, N) -> (N,)
+ (N, 1) -> (N,)
+ (N, M, 1) -> (N, M)
+
+ Basically, all ones are removed from the shape.
+ """
+ return tuple([index for index in outshape if index != 1])
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == "__main__":
+ _test()
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