/usr/lib/python3/dist-packages/pydap/handlers/lib.py is in python3-pydap 3.2.2+ds1-1ubuntu1.
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Pydap handlers are responsible for reading data in different formats -- NetCDF,
SQL databases, CSV files, etc. -- and convert them into the internal data model
so that the data may be served using different responses.
"""
from __future__ import division
import sys
import re
import operator
import itertools
import ast
import copy
import numpy as np
from webob import Request
import pkg_resources
from numpy.lib.arrayterator import Arrayterator
from six.moves import filter, map
from six import string_types, next
from ..responses.lib import load_responses
from ..responses.error import ErrorResponse
from ..parsers import parse_ce, parse_selection
from ..exceptions import (
ConstraintExpressionError, ExtensionNotSupportedError)
from ..lib import (walk, fix_shorthand, get_var, encode,
load_from_entry_point_relative)
from ..model import (DatasetType, BaseType,
SequenceType, StructureType,
GridType)
# buffer size in bytes, for streaming data
BUFFER_SIZE = 2**27
CORS_RESPONSES = ['dds', 'das', 'dods', 'ver', 'json']
def load_handlers(working_set=pkg_resources.working_set):
r"""Load all handlers, returning them on a list.
Passing ``working_set`` is used only for unit testing. Check the following
discussion for an explanation about this:
http://grokbase.com/t/python/distutils-sig/074rc4a6hb/ \
distutils-programmatically-adding-entry-points
"""
# Relative import of handlers:
package = 'pydap'
entry_points = 'pydap.handler'
base_dict = dict(load_from_entry_point_relative(r, package)
for r in working_set.iter_entry_points(entry_points)
if r.module_name.startswith(package))
opts_dict = dict((r.name, r.load())
for r in working_set.iter_entry_points(entry_points)
if not r.module_name.startswith(package))
base_dict.update(opts_dict)
return base_dict.values()
def get_handler(filepath, handlers=None):
"""Given a filepath, return the corresponding instantiated handler."""
# Check each handler to see which one handles this file.
for handler in handlers or load_handlers():
p = re.compile(handler.extensions)
if p.match(filepath):
return handler(filepath)
raise ExtensionNotSupportedError(
'No handler available for file {filepath}.'.format(filepath=filepath))
class BaseHandler(object):
"""Base class for Pydap handlers.
Handlers are WSGI applications that parse the client request and build the
corresponding dataset. The dataset is passed to proper Response (DDS, DAS,
etc.)
"""
# load all available responses
responses = load_responses()
def __init__(self, dataset=None):
self.dataset = dataset
self.additional_headers = []
def __call__(self, environ, start_response):
req = Request(environ)
path, response = req.path.rsplit('.', 1)
if response == 'das':
req.query_string = ''
projection, selection = parse_ce(req.query_string)
buffer_size = environ.get('pydap.buffer_size', BUFFER_SIZE)
try:
# build the dataset and pass it to the proper response, returning a
# WSGI app
dataset = self.parse(projection, selection, buffer_size)
app = self.responses[response](dataset)
app.close = self.close
# now build a Response and set additional headers
res = req.get_response(app)
for key, value in self.additional_headers:
res.headers.add(key, value)
# CORS for Javascript requests
if response in CORS_RESPONSES:
res.headers.add('Access-Control-Allow-Origin', '*')
res.headers.add(
'Access-Control-Allow-Headers',
'Origin, X-Requested-With, Content-Type')
return res(environ, start_response)
except:
# should the exception be catched?
if environ.get('x-wsgiorg.throw_errors'):
raise
else:
res = ErrorResponse(info=sys.exc_info())
return res(environ, start_response)
def parse(self, projection, selection, buffer_size=BUFFER_SIZE):
"""Parse the constraint expression, returning a new dataset."""
if self.dataset is None:
raise NotImplementedError(
"Subclasses must define a ``dataset`` attribute pointing to a"
"``DatasetType`` object.")
# make a copy of the dataset, so we can filter sequences inplace
dataset = copy.copy(self.dataset)
# apply the selection to the dataset, inplace
apply_selection(selection, dataset)
# wrap data in Arrayterator, to optimize projection/selection
dataset = wrap_arrayterator(dataset, buffer_size)
# fix projection
if projection:
projection = fix_shorthand(projection, dataset)
else:
projection = [[(key, ())] for key in dataset.keys()]
dataset = apply_projection(projection, dataset)
return dataset
def close(self):
"""Optional method for closing the dataset."""
pass
def wrap_arrayterator(dataset, size):
"""Wrap `BaseType` objects in an Arrayterator.
This function is used to optimize access to huge datasets. It returns a new
dataset with data wrapped in Arrayterators. This way the data is read in
blocks instead of buffering everything in memory.
Since the buffer size of the Arrayterator is in elements, not bytes, we
convert according to the data item size.
"""
for var in walk(dataset, BaseType):
if (not isinstance(var.data, Arrayterator) and
var.data.dtype.itemsize and var.data.shape):
elements = size // var.data.dtype.itemsize
var.data = Arrayterator(var.data, elements)
return dataset
def apply_selection(selection, dataset):
"""Apply a given selection to a dataset, modifying it inplace.
Returns the original dataset.
"""
for seq in walk(dataset, SequenceType):
# apply only relevant selections
conditions = [
condition for condition in selection
if re.match(
r'%s\.[^\.]+(<=|<|>=|>|=|!=)' % re.escape(seq.id), condition)]
for condition in conditions:
id1, op, id2 = parse_selection(condition, dataset)
seq.data = seq[op(id1, id2)].data
return dataset
def degenerate_grid_to_structure(candidate):
if isinstance(candidate, GridType):
candidate = StructureType(
candidate.name, candidate.attributes)
return candidate
def apply_projection(projection, dataset):
"""Apply a given projection to a dataset.
This function builds and returns a new dataset by adding those variables
that were requested on the projection.
"""
out = DatasetType(name=dataset.name, attributes=dataset.attributes)
# first collect all the variables
for p in projection:
target, template = out, dataset
for i, (name, slice_) in enumerate(p):
candidate = template[name]
# add variable to target
if isinstance(candidate, StructureType):
if name not in target.keys():
if i < len(p) - 1:
# if there are more children to add we need to clear
# the candidate so it has only explicitly added
# children; also, Grids are degenerated into Structures
candidate = degenerate_grid_to_structure(candidate)
candidate._visible_keys = []
target[name] = candidate
target, template = target[name], template[name]
else:
target[name] = candidate
# fix sequence data to include only variables that are in the sequence
for seq in walk(out, SequenceType):
seq.data = get_var(dataset, seq.id)[tuple(seq.keys())].data
# apply slices
for p in projection:
target = out
for name, slice_ in p:
target, parent = target[name], target
if slice_:
if isinstance(target, BaseType):
target.data = target[slice_]
elif isinstance(target, SequenceType):
parent[name] = target[slice_[0]]
elif isinstance(target, GridType):
parent[name] = target[slice_]
else:
raise ConstraintExpressionError("Invalid projection!")
return out
class ConstraintExpression(object):
"""An object representing a selection on a constraint expression.
These can be accumulated so that they are evaluated only once.
"""
def __init__(self, value):
self.value = value
def __str__(self):
return str(self.value)
def __and__(self, other):
"""Join two CEs together, returning a new object."""
return self.__class__(self.value + '&' + str(other))
def __or__(self, other):
raise ConstraintExpressionError(
"OR constraints not allowed in the Opendap specification.")
class IterData(object):
"""Class for manipulating data streams as structured arrays.
A structured array is a Numpy construct that has some very interesting
properties for working with tabular data.
"""
shape = ()
def __init__(self, stream, template, ifilter=None, imap=None, islice=None,
level=0):
self.stream = stream
self.template = template
self.level = level
# these are used to lazily evaluate the data stream
self.ifilter = ifilter or []
self.imap = imap or [fix_nested(template)]
self.islice = islice or []
@property
def dtype(self):
"""Return Numpy dtype of the object."""
def array_dtype(x, template):
if (hasattr(template, 'keys') and
len(list(template.keys())) > 1):
peek = x
if isinstance(x, IterData):
peek = next(iter(x))
return np.dtype([(col, array_dtype(val, template[col]))
for col, val
in zip(template.keys(), peek)])
else:
return np.array(x).dtype
return array_dtype(next(iter(self)), self.template)
def iterdata(self):
"""Included for code symmetry with Types"""
return iter(self)
def __iter__(self):
data = iter(self.stream)
for f in self.ifilter:
data = filter(f, data)
for m in self.imap:
data = map(m, data)
for s in self.islice:
data = itertools.islice(data, s.start, s.stop, s.step)
return data
def __copy__(self):
"""Return a lightweight copy of the object."""
return IterData(self.stream, copy.copy(self.template), self.ifilter[:],
self.imap[:], self.islice[:], self.level)
def __repr__(self):
return "<IterData to stream %r>" % self.stream
def __getitem__(self, key):
out = copy.copy(self)
# return a child, and adjust the data so that only the corresponding
# column is returned
if isinstance(key, string_types):
try:
col = list(self.template.keys()).index(key)
except ValueError:
raise KeyError(key)
out.level += 1
out.template = out.template[key]
out.imap.append(deep_map(operator.itemgetter(col), out.level))
# return a new sequence with the selected children
elif isinstance(key, list):
cols = [list(self.template.keys()).index(k) for k in key]
out.template._visible_keys = key
out.imap.append(deep_map(
lambda row: tuple(row[i] for i in cols), out.level+1))
# slice the data
elif isinstance(key, (int, slice)):
if isinstance(key, int):
out.islice.append(slice(key, key+1))
else:
out.islice.append(key)
# filter the data; like slicing it, we can use ``itertools.ifilter``
# only if the selection is applied to the outermost sequence, otherwise
# we need to do a deep map
elif isinstance(key, ConstraintExpression):
f, m = build_filter(key, self.template)
out.ifilter.append(f)
out.imap.append(m)
else:
raise KeyError(key)
return out
def __eq__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s=%s' % (self.template.id, right))
def __ne__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s!=%s' % (self.template.id, right))
def __ge__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s>=%s' % (self.template.id, right))
def __le__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s<=%s' % (self.template.id, right))
def __gt__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s>%s' % (self.template.id, right))
def __lt__(self, other):
if isinstance(other, self.__class__):
right = other.template.id
else:
right = encode(other)
return ConstraintExpression('%s<%s' % (self.template.id, right))
def fix_nested(template):
"""Apply ``IterData`` to nested sequences on iteration."""
def func(row):
return tuple(
IterData(col, child) if isinstance(child, SequenceType)
else col
for col, child in zip(row, template.children()))
return func
def deep_map(function, level):
"""Map a function inside a nested list, returning the modified data."""
def out(row, level=level):
if level == 1:
return function(row)
else:
return [out(value, level-1) for value in row]
return out
def build_filter(expression, template):
"""Return a filter function based on a comparison expression."""
id1, op, id2 = re.split('(<=|>=|!=|=~|>|<|=)', str(expression), 1)
# calculate the column index were filtering and how deep it is
try:
id1 = id1[len(template.id)+1:]
target = template
for level, token in enumerate(id1.split(".")):
parent1 = target.id
keys = list(target._all_keys())
col = keys.index(token)
target = target[token]
a = operator.itemgetter(col)
except:
raise ConstraintExpressionError(
'Invalid constraint expression: "{expression}" '
'("{id}" is not a valid variable)'.format(
expression=expression, id=id1))
# if we're comparing two variables they must be on the same sequence, so
# ``parent1`` must be equal to ``parent2``
if id2.rsplit(".", 1)[0] == parent1: # parent2 == parent1
keys = list(template._all_keys())
col = keys.index(id2.split(".")[-1])
b = operator.itemgetter(col)
else:
try:
value = ast.literal_eval(id2)
def b(row):
return value
except:
raise ConstraintExpressionError(
'Invalid constraint expression: "{expression}" '
'("{id}" is not valid)'.format(
expression=expression, id=id2))
op = {
'<': operator.lt,
'>': operator.gt,
'!=': operator.ne,
'=': operator.eq,
'>=': operator.ge,
'<=': operator.le,
'=~': lambda a, b: re.match(b, a),
}[op]
# if the filtering is applied in the outermost sequence we can simply pass
# a filter, and ignore the map
if level == 0:
def f(row):
return op(a(row), b(row))
def m(row):
return row
# if the filtering is applied to a nested sequence we actually need to map
# the outer data so that the inner data is filtered
else:
f = bool
def recurse(row, tokens, target):
token = tokens.pop(0)
# return the filtered inner data
if not tokens:
return [col for col in row if op(a(col), b(col))]
# navigate inside the sequence
col = list(target.keys()).index(token)
target = target[col]
# modify data in place; we need to convert tuple to list
row = list(row)
row[col] = recurse(row[col], tokens, target)
return tuple(row)
def m(row):
tokens = id1.split(".")
return recurse(row, tokens, template)
return f, m
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