def parse_constraints(self, environ):
dataset = copy.deepcopy(self.dataset)
projection, selection = parse_qs(environ.get('QUERY_STRING', ''))
list_of_var = []
if projection:
for var in projection:
var_name = var[len(var)-1][0]
if var_name in list_of_var:
continue
list_of_var.append(var_name)
if var_name in ['lat', 'lon']:
if var_name == 'lon':
dataset[var_name] = BaseType(name=var_name,
data=self.lons,
shape=self.lons.shape,
dimensions=('lon',),
type=self.lons.dtype.char)
elif var_name == 'lat':
dataset[var_name] = BaseType(name=var_name,
data=self.lats,
shape=self.lats.shape,
dimensions=('lat',),
type=self.lats.dtype.char)
else:
data = self.get_data_for_parameter(var_name, None)
dataset[var_name] = BaseType(name=var_name,
data=data,
shape=data.shape,
dimensions=('lat', 'lon'),
type=data.dtype.char)
return constrain(dataset, environ.get('QUERY_STRING', ''))
def parse_constraints(self, environ):
dataset = copy.deepcopy(self.dataset)
projection, selection = parse_qs(environ.get('QUERY_STRING', ''))
list_of_var = []
if projection:
for var in projection:
var_name = var[len(var) - 1][0]
if var_name in list_of_var:
continue
list_of_var.append(var_name)
if var_name in ['lat', 'lon']:
if var_name == 'lon':
dataset[var_name] = BaseType(name=var_name,
data=self.lons,
shape=self.lons.shape,
dimensions=('lon', ),
type=self.lons.dtype.char)
elif var_name == 'lat':
dataset[var_name] = BaseType(name=var_name,
data=self.lats,
shape=self.lats.shape,
dimensions=('lat', ),
type=self.lats.dtype.char)
else:
data = self.get_data_for_parameter(var_name, None)
dataset[var_name] = BaseType(name=var_name,
data=data,
shape=data.shape,
dimensions=('lat', 'lon'),
type=data.dtype.char)
return constrain(dataset, environ.get('QUERY_STRING', ''))
def parse_constraints(self, environ):
"""
Parse the request.
This method uses the magic ``constrain`` function, which handles
arbitrary datasets/constraint expressions according to the DAP spec.
"""
environ['x-wsgiorg.throw_errors'] = self.debug
new_dataset = constrain(self.dataset, environ.get('QUERY_STRING', ''))
if hasattr(self.dataset, 'close'): self.dataset.close()
return new_dataset
def parse_constraints(self, environ):
"""
Parse the request.
This method uses the magic ``constrain`` function, which handles
arbitrary datasets/constraint expressions according to the DAP spec.
"""
environ['x-wsgiorg.throw_errors'] = self.debug
new_dataset = constrain(self.dataset, environ.get('QUERY_STRING', ''))
if hasattr(self.dataset, 'close'): self.dataset.close()
return new_dataset
def parse_constraints(self, environ):
try:
f = fCNV(self.filepath)
except:
message = 'Unable to open file %s.' % self.filepath
print self.filepath
raise OpenFileError(message)
# ====
#s = StructureType(name='s')
dataset = SequenceType(name=f.attributes['filename'],
attributes=f.attributes)
for k in f.keys():
dataset[k] = BaseType(name=k, data=f[k], shape=f[k].shape,
type=f[k].dtype.char) #, attributes=f.attributes)
# ====
return constrain(dataset, environ.get('QUERY_STRING', ''))
def parse_constraints(self, environ):
try:
f = fCNV(self.filepath)
except:
message = 'Unable to open file %s.' % self.filepath
print self.filepath
raise OpenFileError(message)
# ====
#s = StructureType(name='s')
dataset = SequenceType(name=f.attributes['filename'],
attributes=f.attributes)
for k in f.keys():
dataset[k] = BaseType(
name=k, data=f[k], shape=f[k].shape,
type=f[k].dtype.char) #, attributes=f.attributes)
# ====
return constrain(dataset, environ.get('QUERY_STRING', ''))
dataset[seq_name][name] = BaseType(name=name, data=data, type=self.get_numpy_type(data), attributes=attrs)
if isinstance(pc.param_type,RecordType):
try:
result = []
for ddict in data:
d = ['_'.join([k,v]) for k,v in ddict.iteritems()]
result = result + d
result = np.asanyarray(result)
except Exception, e:
result = np.asanyarray(['None' for d in data])
ttype = self.get_numpy_type(result)
dataset[seq_name][name] = BaseType(name=name, data=result, type=ttype, attributes=attrs)
except Exception, e:
log.exception('Problem reading cov %s %s', cov.name, e)
continue
return constrain(dataset, environ.get('QUERY_STRING', ''))
def get_numpy_type(self, data):
result = data.dtype.char
if self.is_basestring(data):
result = 'S'
elif self.is_float(data):
result = 'd'
elif self.is_int(data):
result = 'i'
elif result not in ('d', 'f', 'h', 'i','b','H','I','B','S'):
raise TypeNotSupportedError()
return result
def is_basestring(self, data):
for d in data:
