def parse_selection(cond, dataset):
"""
Convert a URL selection to 3 tokens.
"""
id_, op, other = re.split("(<=|>=|!=|=~|>|<|=)", cond)
op = {
"<=": operator.le,
">=": operator.ge,
"!=": operator.ne,
"=": operator.eq,
">": operator.gt,
"<": operator.lt,
}[op]
try:
names = [dataset] + id_.split(".")
id_ = reduce(operator.getitem, names)
except:
id_ = expr_eval(id_)
try:
names = [dataset] + other.split(".")
other = reduce(operator.getitem, names)
except:
other = expr_eval(other)
return id_, op, other
def parse_selection(cond, dataset):
"""
Convert a URL selection to 3 tokens.
"""
id_, op, other = re.split('(<=|>=|!=|=~|>|<|=)', cond)
op = {
'<=': operator.le,
'>=': operator.ge,
'!=': operator.ne,
'=': operator.eq,
'>': operator.gt,
'<': operator.lt
}[op]
try:
names = [dataset] + id_.split('.')
id_ = reduce(operator.getitem, names)
except:
id_ = expr_eval(id_)
try:
names = [dataset] + other.split('.')
other = reduce(operator.getitem, names)
except:
other = expr_eval(other)
return id_, op, other
def _attribute(self):
type_ = self.consume('\w+')
name = self.consume('[^\s]+')
values = []
# One attribute for MLS data needs special handling since it breaks the
# parser (doesn't meet the DAP 2.0 standard):
if type_.lower() in ['string'] and name == 'PCF1':
endstrindx = self.buffer.index('";')
value = self.buffer[1:endstrindx]
self.buffer = self.buffer[endstrindx+1:]
self.consume(';')
return name, value
while not self.peek(';'):
value = self.consume(
r'''
"" # empty attribute
| # or
".*?[^\\]" # from quote up to an unquoted quote
| # or
[^;,]+ # up to semicolon or comma
'''
)
if type_.lower() in ['string', 'url']:
value = expr_eval(repr(value))
value = value.strip('"')
elif type_.lower() == 'alias':
# Support for Alias is not documented in the DAP spec. I based
# this on the Java documentation from the OPeNDAP website at:
# http://www.opendap.org/api/javaDocs/dods/dap/Alias.html
# Check if we should start from the root dataset or from
# the current item.
if value.startswith('.'):
tokens = value[1:].split('.')
target = self.dataset
else:
tokens = value.split('.')
target = self._target
# Run over tokens to get the value.
for token in tokens:
if (isinstance(target, StructureType) and
token in target):
value = target = target[token]
else:
value = target = target.attributes.get(token)
else:
if value.lower() in ['nan', 'nan.']:
value = numpy.NaN
else:
# Convert to proper type. This is specially important for floats,
# since this preserves the resolution even though Python has no
# difference between float 32 vs. 64.
dtype = {'float64': 'd',
'float32': 'f',
'int32' : 'l',
'int16' : 'h',
'uint32' : 'L',
'uint16' : 'H',
'byte' : 'B'}[type_.lower()]
if dtype in ['d', 'f']:
value = array.array(dtype, [float(value)])[0]
else:
value = expr_eval(value)
try:
value = int(value)
except OverflowError:
value = long(value)
values.append(value)
if self.peek(','):
self.consume(',')
self.consume(';')
if len(values) == 1:
values = values[0]
return name, values
def _attribute(self):
type_ = self.consume('\w+')
name = self.consume('[^\s]+')
values = []
# One attribute for MLS data needs special handling since it breaks the
# parser (doesn't meet the DAP 2.0 standard):
if type_.lower() in ['string'] and name == 'PCF1':
endstrindx = self.buffer.index('";')
value = self.buffer[1:endstrindx]
self.buffer = self.buffer[endstrindx + 1:]
self.consume(';')
return name, value
while not self.peek(';'):
value = self.consume(r'''
"" # empty attribute
| # or
".*?[^\\]" # from quote up to an unquoted quote
| # or
[^;,]+ # up to semicolon or comma
''')
if type_.lower() in ['string', 'url']:
value = expr_eval(repr(value))
value = value.strip('"')
elif type_.lower() == 'alias':
# Support for Alias is not documented in the DAP spec. I based
# this on the Java documentation from the OPeNDAP website at:
# http://www.opendap.org/api/javaDocs/dods/dap/Alias.html
# Check if we should start from the root dataset or from
# the current item.
if value.startswith('.'):
tokens = value[1:].split('.')
target = self.dataset
else:
tokens = value.split('.')
target = self._target
# Run over tokens to get the value.
for token in tokens:
if (isinstance(target, StructureType) and token in target):
value = target = target[token]
else:
value = target = target.attributes.get(token)
else:
if value.lower() in ['nan', 'nan.']:
value = numpy.NaN
else:
# Convert to proper type. This is specially important for floats,
# since this preserves the resolution even though Python has no
# difference between float 32 vs. 64.
dtype = {
'float64': 'd',
'float32': 'f',
'int32': 'l',
'int16': 'h',
'uint32': 'L',
'uint16': 'H',
'byte': 'B'
}[type_.lower()]
if dtype in ['d', 'f']:
value = array.array(dtype, [float(value)])[0]
else:
value = expr_eval(value)
try:
value = int(value)
except OverflowError:
value = long(value)
values.append(value)
if self.peek(','):
self.consume(',')
self.consume(';')
if len(values) == 1:
values = values[0]
return name, values