/usr/lib/python3/dist-packages/pydap/handlers/dap.py is in python3-pydap 3.2.2+ds1-1ubuntu1.
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DAP handlers convert from different data formats (NetCDF, eg) to the internal
Pydap model. The Pydap client is just a handler that converts from a remote
dataset to the internal model.
"""
import io
import sys
import pprint
import copy
import re
from itertools import chain
# handlers should be set by the application
# http://docs.python.org/2/howto/logging.html#configuring-logging-for-a-library
import logging
import numpy as np
from six.moves.urllib.parse import urlsplit, urlunsplit, quote
from six import text_type, string_types
from pydap.model import (BaseType,
SequenceType, StructureType,
GridType)
from ..net import GET, raise_for_status
from ..lib import (
encode, combine_slices, fix_slice, hyperslab,
START_OF_SEQUENCE, walk, StreamReader, BytesReader,
DEFAULT_TIMEOUT, DAP2_ARRAY_LENGTH_NUMPY_TYPE)
from .lib import ConstraintExpression, BaseHandler, IterData
from ..parsers.dds import build_dataset
from ..parsers.das import parse_das, add_attributes
from ..parsers import parse_ce
from ..responses.dods import DAP2_response_dtypemap
logger = logging.getLogger('pydap')
logger.addHandler(logging.NullHandler())
BLOCKSIZE = 512
class DAPHandler(BaseHandler):
"""Build a dataset from a DAP base URL."""
def __init__(self, url, application=None, session=None, output_grid=True,
timeout=DEFAULT_TIMEOUT):
# download DDS/DAS
scheme, netloc, path, query, fragment = urlsplit(url)
ddsurl = urlunsplit((scheme, netloc, path + '.dds', query, fragment))
r = GET(ddsurl, application, session, timeout=timeout)
raise_for_status(r)
if not r.charset:
r.charset = 'ascii'
dds = r.text
dasurl = urlunsplit((scheme, netloc, path + '.das', query, fragment))
r = GET(dasurl, application, session, timeout=timeout)
raise_for_status(r)
if not r.charset:
r.charset = 'ascii'
das = r.text
# build the dataset from the DDS and add attributes from the DAS
self.dataset = build_dataset(dds)
add_attributes(self.dataset, parse_das(das))
# remove any projection from the url, leaving selections
projection, selection = parse_ce(query)
url = urlunsplit((scheme, netloc, path, '&'.join(selection), fragment))
# now add data proxies
for var in walk(self.dataset, BaseType):
var.data = BaseProxy(url, var.id, var.dtype, var.shape,
application=application,
session=session)
for var in walk(self.dataset, SequenceType):
template = copy.copy(var)
var.data = SequenceProxy(url, template, application=application,
session=session)
# apply projections
for var in projection:
target = self.dataset
while var:
token, index = var.pop(0)
target = target[token]
if isinstance(target, BaseType):
target.data.slice = fix_slice(index, target.shape)
elif isinstance(target, GridType):
index = fix_slice(index, target.array.shape)
target.array.data.slice = index
for s, child in zip(index, target.maps):
target[child].data.slice = (s,)
elif isinstance(target, SequenceType):
target.data.slice = index
# retrieve only main variable for grid types:
for var in walk(self.dataset, GridType):
var.set_output_grid(output_grid)
class BaseProxy(object):
"""A proxy for remote base types.
This class behaves like a Numpy array, proxying the data from a base type
on a remote dataset.
"""
def __init__(self, baseurl, id, dtype, shape, slice_=None,
application=None, session=None, timeout=DEFAULT_TIMEOUT):
self.baseurl = baseurl
self.id = id
self.dtype = dtype
self.shape = shape
self.slice = slice_ or tuple(slice(None) for s in self.shape)
self.application = application
self.session = session
self.timeout = timeout
def __repr__(self):
return 'BaseProxy(%s)' % ', '.join(
map(repr, [
self.baseurl, self.id, self.dtype, self.shape, self.slice]))
def __getitem__(self, index):
# build download url
index = combine_slices(self.slice, fix_slice(index, self.shape))
scheme, netloc, path, query, fragment = urlsplit(self.baseurl)
url = urlunsplit((
scheme, netloc, path + '.dods',
quote(self.id) + hyperslab(index) + '&' + query,
fragment)).rstrip('&')
# download and unpack data
logger.info("Fetching URL: %s" % url)
r = GET(url, self.application, self.session, timeout=self.timeout)
raise_for_status(r)
dds, data = r.body.split(b'\nData:\n', 1)
dds = dds.decode(r.content_encoding or 'ascii')
# Parse received dataset:
dataset = build_dataset(dds)
dataset.data = unpack_data(BytesReader(data), dataset)
return dataset[self.id].data
def __len__(self):
return self.shape[0]
def __iter__(self):
return iter(self[:])
# Comparisons return a boolean array
def __eq__(self, other):
return self[:] == other
def __ne__(self, other):
return self[:] != other
def __ge__(self, other):
return self[:] >= other
def __le__(self, other):
return self[:] <= other
def __gt__(self, other):
return self[:] > other
def __lt__(self, other):
return self[:] < other
class SequenceProxy(object):
"""A proxy for remote sequences.
This class behaves like a Numpy structured array, proxying the data from a
sequence on a remote dataset. The data is streamed from the dataset,
meaning it can be treated one record at a time before the whole data is
downloaded.
"""
shape = ()
def __init__(self, baseurl, template, selection=None, slice_=None,
application=None, session=None, timeout=DEFAULT_TIMEOUT):
self.baseurl = baseurl
self.template = template
self.selection = selection or []
self.slice = slice_ or (slice(None),)
self.application = application
self.session = session
self.timeout = timeout
# this variable is true when only a subset of the children are selected
self.sub_children = False
@property
def dtype(self):
return self.template.dtype
def __repr__(self):
return 'SequenceProxy(%s)' % ', '.join(
map(repr, [
self.baseurl, self.template, self.selection, self.slice]))
def __copy__(self):
"""Return a lightweight copy of the object."""
return self.__class__(self.baseurl, self.template, self.selection[:],
self.slice[:], self.application)
def __getitem__(self, key):
"""Return a new object representing a subset of the data."""
out = copy.copy(self)
# return the data for a children
if isinstance(key, string_types):
out.template = out.template[key]
# return a new object with requested columns
elif isinstance(key, list):
out.sub_children = True
out.template._visible_keys = key
# return a copy with the added constraints
elif isinstance(key, ConstraintExpression):
out.selection.extend(str(key).split('&'))
# slice data
else:
if isinstance(key, int):
key = slice(key, key+1)
out.slice = combine_slices(self.slice, (key,))
return out
@property
def url(self):
"""Return url from where data is fetched."""
scheme, netloc, path, query, fragment = urlsplit(self.baseurl)
url = urlunsplit((
scheme, netloc, path + '.dods',
self.id + hyperslab(self.slice) + '&' +
'&'.join(self.selection), fragment)).rstrip('&')
return url
@property
def id(self):
"""Return the id of this sequence."""
if self.sub_children:
id_ = ','.join(
quote(child.id) for child in self.template.children())
else:
id_ = quote(self.template.id)
return id_
def __iter__(self):
# download and unpack data
r = GET(self.url, self.application, self.session, timeout=self.timeout)
raise_for_status(r)
i = r.app_iter
if not hasattr(i, '__next__'):
i = iter(i)
# Fast forward past the DDS header
# the pattern could span chunk boundaries though so make sure to check
pattern = b'Data:\n'
last_chunk = find_pattern_in_string_iter(pattern, i)
if last_chunk is None:
raise ValueError("Could not find data segment in response from {}"
.format(self.url))
# Then construct a stream consisting of everything from
# 'Data:\n' to the end of the chunk + the rest of the stream
def stream_start():
yield last_chunk
stream = StreamReader(chain(stream_start(), i))
return unpack_sequence(stream, self.template)
def __eq__(self, other):
return ConstraintExpression('%s=%s' % (self.id, encode(other)))
def __ne__(self, other):
return ConstraintExpression('%s!=%s' % (self.id, encode(other)))
def __ge__(self, other):
return ConstraintExpression('%s>=%s' % (self.id, encode(other)))
def __le__(self, other):
return ConstraintExpression('%s<=%s' % (self.id, encode(other)))
def __gt__(self, other):
return ConstraintExpression('%s>%s' % (self.id, encode(other)))
def __lt__(self, other):
return ConstraintExpression('%s<%s' % (self.id, encode(other)))
def unpack_sequence(stream, template):
"""Unpack data from a sequence, yielding records."""
# is this a sequence or a base type?
sequence = isinstance(template, SequenceType)
# if there are no children, we use the template as the only column
cols = list(template.children()) or [template]
# if there are no strings and no nested sequences we can unpack record by
# record easily
simple = all(isinstance(c, BaseType) and c.dtype.char not in "SU"
for c in cols)
if simple:
dtype = np.dtype([("", c.dtype, c.shape) for c in cols])
marker = stream.read(4)
while marker == START_OF_SEQUENCE:
rec = np.fromstring(stream.read(dtype.itemsize), dtype=dtype)[0]
if not sequence:
rec = rec[0]
yield rec
marker = stream.read(4)
else:
marker = stream.read(4)
while marker == START_OF_SEQUENCE:
rec = unpack_children(stream, template)
if not sequence:
rec = rec[0]
else:
rec = tuple(rec)
yield rec
marker = stream.read(4)
def unpack_children(stream, template):
"""Unpack children from a structure, returning their data."""
cols = list(template.children()) or [template]
out = []
for col in cols:
# sequences and other structures
if isinstance(col, SequenceType):
out.append(IterData(list(unpack_sequence(stream, col)), col))
elif isinstance(col, StructureType):
out.append(tuple(unpack_children(stream, col)))
# unpack arrays
else:
out.extend(convert_stream_to_list(stream, col.dtype, col.shape,
col.id))
return out
def convert_stream_to_list(stream, parser_dtype, shape, id):
out = []
response_dtype = DAP2_response_dtypemap(parser_dtype)
if shape:
n = np.fromstring(stream.read(4), DAP2_ARRAY_LENGTH_NUMPY_TYPE)[0]
count = response_dtype.itemsize * n
if response_dtype.char in 'S':
# Consider on 'S' and not 'SU' because
# response_dtype.char should never be
data = []
for _ in range(n):
k = np.fromstring(stream.read(4),
DAP2_ARRAY_LENGTH_NUMPY_TYPE)[0]
data.append(stream.read(k))
stream.read(-k % 4)
out.append(np.array([text_type(x.decode('ascii'))
for x in data], 'S').reshape(shape))
else:
stream.read(4) # read additional length
try:
out.append(
np.fromstring(
stream.read(count), response_dtype)
.astype(parser_dtype).reshape(shape))
except ValueError as e:
if str(e) == 'total size of new array must be unchanged':
# server-side failure.
# it is expected that the user should be mindful of this:
raise RuntimeError(
('variable {0} could not be properly '
'retrieved. To avoid this '
'error consider using open_url(..., '
'output_grid=False).').format(quote(id)))
else:
raise
if response_dtype.char == "B":
# Unsigned Byte type is packed to multiples of 4 bytes:
stream.read(-n % 4)
# special types: strings and bytes
elif response_dtype.char in 'S':
# Consider on 'S' and not 'SU' because
# response_dtype.char should never be
# 'U'
k = np.fromstring(stream.read(4), DAP2_ARRAY_LENGTH_NUMPY_TYPE)[0]
out.append(text_type(stream.read(k).decode('ascii')))
stream.read(-k % 4)
# usual data
else:
out.append(
np.fromstring(stream.read(response_dtype.itemsize), response_dtype)
.astype(parser_dtype)[0])
if response_dtype.char == "B":
# Unsigned Byte type is packed to multiples of 4 bytes:
stream.read(3)
return out
def unpack_data(xdr_stream, dataset):
"""Unpack a string of encoded data, returning data as lists."""
return unpack_children(xdr_stream, dataset)
def find_pattern_in_string_iter(pattern, i):
last_chunk = b''
length = len(pattern)
for this_chunk in i:
last_chunk += this_chunk
m = re.search(pattern, last_chunk)
if m:
return last_chunk[m.end():]
last_chunk = last_chunk[-length:]
def dump(): # pragma: no cover
"""Unpack dods response into lists.
Return pretty-printed data.
"""
dods = sys.stdin.read()
dds, xdrdata = dods.split(b'\nData:\n', 1)
dataset = build_dataset(dds)
xdr_stream = io.BytesIO(xdrdata)
data = unpack_data(xdr_stream, dataset)
data = dataset.data
pprint.pprint(data)
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