def _ddsdas(baseurl, cache, username, password):
ddsurl, dasurl = '%s.dds' % baseurl, '%s.das' % baseurl
# Get metadata.
respdds, dds = openurl(ddsurl, cache, username, password)
respdas, das = openurl(dasurl, cache, username, password)
if respdds['status'] == '200' and respdas['status'] == '200':
from dap.parsers.dds import DDSParser
from dap.parsers.das import DASParser
# Build dataset.
dataset = DDSParser(dds, ddsurl, cache, username, password).parse()
# Add attributes.
dataset = DASParser(das, dasurl, dataset).parse()
return dataset
def _ddsdas(baseurl, cache, username, password):
ddsurl, dasurl = '%s.dds' % baseurl, '%s.das' % baseurl
# Get metadata.
respdds, dds = openurl(ddsurl, cache, username, password)
respdas, das = openurl(dasurl, cache, username, password)
if respdds['status'] == '200' and respdas['status'] == '200':
from dap.parsers.dds import DDSParser
from dap.parsers.das import DASParser
# Build dataset.
dataset = DDSParser(dds, ddsurl, cache, username, password).parse()
# Add attributes.
dataset = DASParser(das, dasurl, dataset).parse()
return dataset
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 __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