def metadata_index_responder(sesh, network, climo=False):
'''The function creates a pydap csv response which lists variable metadata out of the database. It returns an generator for the contents of the file
:param sesh: database session
:type sesh: sqlalchemy.orm.session.Session
:param network: Name of the network for which variables should be listed
:type network: str
:rtype: generator
'''
maxlen = 256
if climo:
climo_filt = or_(Variable.cell_method.like('%within%'), Variable.cell_method.like('%over%'))
else:
climo_filt = not_(or_(Variable.cell_method.like('%within%'), Variable.cell_method.like('%over%')))
rv = sesh.query(Variable).join(Network).filter(Network.name == network).filter(climo_filt)
a = np.array([(var.name, var.standard_name, var.cell_method, var.unit) for var in rv],
dtype=np.dtype({'names': ['variable', 'standard_name', 'cell_method', 'unit'],
'formats':[(str, maxlen), (str, maxlen), (str, maxlen), (str, maxlen)]})
)
dst = DatasetType('Variable metadata')
seq = SequenceType('variables')
seq['variable'] = BaseType('variable')
seq['standard_name'] = BaseType('standard_name', reference='http://llnl.gov/')
seq['cell_method'] = BaseType('cell_method', reference='http://llnl.gov/')
seq['unit'] = BaseType('unit')
seq.data = a
dst['variables'] = seq
responder = BaseHandler(dst)
environ = {'PATH_INFO': '/variables.foo.ascii', 'REQUEST_METHOD': 'GET'}
return responder(environ, null_start_response)
def density(dataset, salinity, temperature, pressure):
"""Calculate in-situ density.
This function calculated in-situ density from absolute salinity and
conservative temperature, using the `gsw.rho` function. Returns a new
sequence with the data.
"""
# find sequence
for sequence in walk(dataset, SequenceType):
break
else:
raise ConstraintExpressionError(
'Function "bounds" should be used on a Sequence.')
selection = sequence[salinity.name, temperature.name, pressure.name]
rows = [tuple(row) for row in selection]
data = np.rec.fromrecords(rows,
names=['salinity', 'temperature', 'pressure'])
rho = gsw.rho(data['salinity'], data['temperature'], data['pressure'])
out = SequenceType("result")
out['rho'] = BaseType("rho", units="kg/m**3")
out.data = np.rec.fromrecords(rho.reshape(-1, 1), names=['rho'])
return out
def density(dataset, salinity, temperature, pressure):
"""Calculate in-situ density.
This function calculated in-situ density from absolute salinity and
conservative temperature, using the `gsw.rho` function. Returns a new
sequence with the data.
"""
# find sequence
for sequence in walk(dataset, SequenceType):
break
else:
raise ConstraintExpressionError(
'Function "bounds" should be used on a Sequence.')
selection = sequence[salinity.name, temperature.name, pressure.name]
rows = [tuple(row) for row in selection]
data = np.rec.fromrecords(
rows, names=['salinity', 'temperature', 'pressure'])
rho = gsw.rho(data['salinity'], data['temperature'], data['pressure'])
out = SequenceType("result")
out['rho'] = BaseType("rho", units="kg/m**3")
out.data = np.rec.fromrecords(rho.reshape(-1, 1), names=['rho'])
return out
def test_regexp(self):
sequence = SequenceType("sequence")
sequence["name"] = BaseType("name")
sequence.data = IterData([
("John", "Paul", "George", "Ringo"),
], sequence)
filtered = sequence[ConstraintExpression('sequence.name=~"J.*"')]
self.assertEqual(list(filtered.iterdata()), [("John", )])
def test_regexp(self):
sequence = SequenceType("sequence")
sequence["name"] = BaseType("name")
sequence.data = IterData([
("John", "Paul", "George", "Ringo"),
], sequence)
filtered = sequence[ConstraintExpression('sequence.name=~"J.*"')]
self.assertEqual(list(filtered.iterdata()), [("John",)])
def sequence_example():
"""Create a standard sequence from the DAP spec."""
example = SequenceType("example")
example["index"] = BaseType("index")
example["temperature"] = BaseType("temperature")
example["site"] = BaseType("site")
example.data = np.rec.fromrecords([
(10, 15.2, "Diamond_St"),
(11, 13.1, 'Blacktail_Loop'),
(12, 13.3, 'Platinum_St'),
(13, 12.1, 'Kodiak_Trail')], names=list(example.keys()))
return example
def sequence_example():
"""Create a standard sequence from the DAP spec."""
example = SequenceType("example")
example["index"] = BaseType("index")
example["temperature"] = BaseType("temperature")
example["site"] = BaseType("site")
example.data = np.rec.fromrecords([
(10, 15.2, "Diamond_St"),
(11, 13.1, 'Blacktail_Loop'),
(12, 13.3, 'Platinum_St'),
(13, 12.1, 'Kodiak_Trail')], names=list(example.keys()))
return example
def sequence_type_data():
"""
Simple sequence test data
"""
data = [(10, 15.2, 'Diamond_St'), (11, 13.1, 'Blacktail_Loop'),
(12, 13.3, 'Platinum_St'), (13, 12.1, 'Kodiak_Trail')]
dtype = [('index', '<i4'), ('temperature', '<f8'), ('station', 'S40')]
seq = SequenceType('sequence')
seq['index'] = BaseType('index')
seq['temperature'] = BaseType('temperature')
seq['station'] = BaseType('station')
seq.data = np.array(data, dtype=dtype)
return seq
def sequence_type_data():
"""
Simple sequence test data
"""
data = [(10, 15.2, 'Diamond_St'),
(11, 13.1, 'Blacktail_Loop'),
(12, 13.3, 'Platinum_St'),
(13, 12.1, 'Kodiak_Trail')]
dtype = [('index', '<i4'),
('temperature', '<f8'),
('station', 'S40')]
seq = SequenceType('sequence')
seq['index'] = BaseType('index')
seq['temperature'] = BaseType('temperature')
seq['station'] = BaseType('station')
seq.data = np.array(data, dtype=dtype)
return seq
def double(dataset, var):
"""A dummy function that doubles a value.
The value must be in a sequence. Return a new sequence with the value
doubled.
"""
# sequence is the first variable
sequence = next(dataset.children())
# get a single variable and double its value
selection = sequence[var.name]
rows = [(value * 2, ) for value in selection]
# create output sequence
out = SequenceType("result")
out["double"] = BaseType("double")
out.data = np.rec.fromrecords(rows, names=["double"])
return out
def double(dataset, var):
"""A dummy function that doubles a value.
The value must be in a sequence. Return a new sequence with the value
doubled.
"""
# sequence is the first variable
sequence = next(dataset.children())
# get a single variable and double its value
selection = sequence[var.name]
rows = [(value*2,) for value in selection]
# create output sequence
out = SequenceType("result")
out["double"] = BaseType("double")
out.data = np.rec.fromrecords(rows, names=["double"])
return out
# A very simple sequence: flat and with no strings. This sequence can be mapped
# directly to a Numpy structured array, and can be easily encoded and decoded
# in the DAP spec.
VerySimpleSequence = DatasetType("VerySimpleSequence")
VerySimpleSequence["sequence"] = SequenceType("sequence")
VerySimpleSequence["sequence"]["byte"] = BaseType("byte")
VerySimpleSequence["sequence"]["int"] = BaseType("int")
VerySimpleSequence["sequence"]["float"] = BaseType("float")
VerySimpleSequence["sequence"].data = np.array([
(0, 1, 10.),
(1, 2, 20.),
(2, 3, 30.),
(3, 4, 40.),
(4, 5, 50.),
(5, 6, 60.),
(6, 7, 70.),
(7, 8, 80.),
],
dtype=[('byte', 'b'),
('int', 'i4'),
('float', 'f4')])
# A nested sequence.
NestedSequence = DatasetType("NestedSequence")
NestedSequence["location"] = SequenceType("location")
NestedSequence["location"]["lat"] = BaseType("lat")
NestedSequence["location"]["lon"] = BaseType("lon")
NestedSequence["location"]["elev"] = BaseType("elev")
NestedSequence["location"]["time_series"] = SequenceType("time_series")
NestedSequence["location"]["time_series"]["time"] = BaseType("time")
from collections import OrderedDict
# A very simple sequence: flat and with no strings. This sequence can be mapped
# directly to a Numpy structured array, and can be easily encoded and decoded
# in the DAP spec.
VerySimpleSequence = DatasetType("VerySimpleSequence")
VerySimpleSequence["sequence"] = SequenceType("sequence")
VerySimpleSequence["sequence"]["byte"] = BaseType("byte")
VerySimpleSequence["sequence"]["int"] = BaseType("int")
VerySimpleSequence["sequence"]["float"] = BaseType("float")
VerySimpleSequence["sequence"].data = np.array([
(0, 1, 10.),
(1, 2, 20.),
(2, 3, 30.),
(3, 4, 40.),
(4, 5, 50.),
(5, 6, 60.),
(6, 7, 70.),
(7, 8, 80.),
], dtype=[('byte', 'b'), ('int', 'i4'), ('float', 'f4')])
# A nested sequence.
NestedSequence = DatasetType("NestedSequence")
NestedSequence["location"] = SequenceType("location")
NestedSequence["location"]["lat"] = BaseType("lat")
NestedSequence["location"]["lon"] = BaseType("lon")
NestedSequence["location"]["elev"] = BaseType("elev")
NestedSequence["location"]["time_series"] = SequenceType("time_series")
NestedSequence["location"]["time_series"]["time"] = BaseType("time")
NestedSequence["location"]["time_series"]["slp"] = BaseType("slp")
