def test_moc_order_param(self, moc_order):
moc_region = MOC.from_json({'0': [1]})
result = cds.query_region(region=moc_region,
# return a mocpy obj
return_moc=True,
max_norder=moc_order,
get_query_payload=False)
assert isinstance(result, MOC)
def test_moc_order_param(self, moc_order):
moc_region = MOC.from_json({'0': [1]})
result = cds.query_region(region=moc_region,
# return a mocpy obj
return_moc=True,
max_norder=moc_order,
get_query_payload=False)
assert isinstance(result, MOC)
def _parse_result(self, response, verbose=False):
"""
Parsing of the response returned by the MOCServer.
Parameters
----------
response : `~requests.Response`
The HTTP response returned by the MOCServer.
verbose : bool, optional
False by default.
Returns
-------
result : `astropy.table.Table` or `mocpy.MOC`
By default an astropy table of the data-sets matching the query. If ``return_moc`` is set to True, it gives
a MOC object corresponding to the union of the MOCs from all the matched data-sets.
"""
if not verbose:
commons.suppress_vo_warnings()
result = response.json()
if not self.return_moc:
"""
The user will get `astropy.table.Table` object whose columns refer to the returned data-set meta-datas.
"""
# cast the data-sets meta-datas values to their correct Python type.
typed_result = []
for d in result:
typed_d = {k: self._cast_to_float(v) for k, v in d.items()}
typed_result.append(typed_d)
# looping over all the record's keys to find all the existing keys
column_names_l = []
for d in typed_result:
column_names_l.extend(d.keys())
# remove all the doubles
column_names_l = list(set(column_names_l))
# init a dict mapping all the meta-data's name to an empty list
table_d = {key: [] for key in column_names_l}
type_d = {key: None for key in column_names_l}
masked_array_d = {key: [] for key in column_names_l}
# fill the dict with the value of each returned data-set one by one.
for d in typed_result:
row_table_d = {key: None for key in column_names_l}
row_table_d.update(d)
for k, mask_l in masked_array_d.items():
entry_masked = False if k in d.keys() else True
mask_l.append(entry_masked)
for k, v in row_table_d.items():
if v:
type_d[k] = type(v)
table_d[k].append(v)
# define all the columns using astropy.table.MaskedColumn objects
columns_l = []
for k, v in table_d.items():
try:
if k != '#':
columns_l.append(MaskedColumn(v, name=k, mask=masked_array_d[k], dtype=type_d[k]))
except ValueError:
# some metadata can be of multiple types when looking on all the datasets.
# this can be due to internal typing errors of the metadatas.
columns_l.append(MaskedColumn(v, name=k, mask=masked_array_d[k], dtype=object))
pass
# return an `astropy.table.Table` object created from columns_l
return Table(columns_l)
"""
The user will get `mocpy.MOC` object.
"""
# remove
empty_order_removed_d = {}
for order, ipix_l in result.items():
if len(ipix_l) > 0:
empty_order_removed_d.update({order: ipix_l})
# return a `mocpy.MOC` object. See https://github.com/cds-astro/mocpy and the MOCPy's doc
return MOC.from_json(empty_order_removed_d)
def _parse_result(self, response, verbose=False):
"""
Parsing of the response returned by the MOCServer.
Parameters
----------
response : `~requests.Response`
The HTTP response returned by the MOCServer.
verbose : bool, optional
False by default.
Returns
-------
result : `astropy.table.Table` or `mocpy.MOC`
By default an astropy table of the data-sets matching the query. If ``return_moc`` is set to True, it gives
a MOC object corresponding to the union of the MOCs from all the matched data-sets.
"""
if not verbose:
commons.suppress_vo_warnings()
result = response.json()
if not self.return_moc:
"""
The user will get `astropy.table.Table` object whose columns refer to the returned data-set meta-datas.
"""
# cast the data-sets meta-datas values to their correct Python type.
typed_result = []
for d in result:
typed_d = {k: self._cast_to_float(v) for k, v in d.items()}
typed_result.append(typed_d)
# looping over all the record's keys to find all the existing keys
column_names_l = []
for d in typed_result:
column_names_l.extend(d.keys())
# remove all the doubles
column_names_l = list(set(column_names_l))
# init a dict mapping all the meta-data's name to an empty list
table_d = {key: [] for key in column_names_l}
type_d = {key: None for key in column_names_l}
masked_array_d = {key: [] for key in column_names_l}
# fill the dict with the value of each returned data-set one by one.
for d in typed_result:
row_table_d = {key: None for key in column_names_l}
row_table_d.update(d)
for k, mask_l in masked_array_d.items():
entry_masked = False if k in d.keys() else True
mask_l.append(entry_masked)
for k, v in row_table_d.items():
if v:
type_d[k] = type(v)
table_d[k].append(v)
# define all the columns using astropy.table.MaskedColumn objects
columns_l = []
for k, v in table_d.items():
try:
if k != '#':
columns_l.append(
MaskedColumn(v,
name=k,
mask=masked_array_d[k],
dtype=type_d[k]))
except ValueError:
# some metadata can be of multiple types when looking on all the datasets.
# this can be due to internal typing errors of the metadatas.
columns_l.append(
MaskedColumn(v,
name=k,
mask=masked_array_d[k],
dtype=object))
pass
# return an `astropy.table.Table` object created from columns_l
return Table(columns_l)
"""
The user will get `mocpy.MOC` object.
"""
# remove
empty_order_removed_d = {}
for order, ipix_l in result.items():
if len(ipix_l) > 0:
empty_order_removed_d.update({order: ipix_l})
# return a `mocpy.MOC` object. See https://github.com/cds-astro/mocpy and the MOCPy's doc
return MOC.from_json(empty_order_removed_d)
