csw.version
# <codecell>
for oper in csw.operations:
if oper.name == 'GetRecords':
cnstr = oper.constraints['SupportedISOQueryables']['values']
print('\nISO Queryables:%s\n' % '\n'.join(cnstr))
# <markdowncell>
# ### Convert User Input into FES filters.
# <codecell>
start, stop = dateRange(start_date, stop_date)
bbox = fes.BBox(box)
# <codecell>
or_filt = fes.Or([fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
escapeChar='\\',
wildCard='*',
singleChar='?') for val in name_list])
# <markdowncell>
# ROMS model output often has Averages and History files. The Averages files are usually averaged over a tidal cycle or more, while the History files are snapshots at that time instant. We are not interested in averaged data for this test, so in the cell below we remove any Averages files here by removing any datasets that have the term "Averages" in the metadata text. A better approach would be to look at the `cell_methods` attributes propagated through to some term in the ISO metadata, but this is not implemented yet, as far as I know
# <codecell>
csw = CatalogueServiceWeb(endpoint, timeout=60)
for oper in csw.operations:
if oper.name == 'GetRecords':
print('\nISO Queryables:\n%s' %
'\n'.join(oper.constraints['SupportedISOQueryables']['values']))
# Put the names in a dict for ease of access.
data_dict = {}
data_dict["water"] = {"names": name_list,
"sos_name": sos_name}
# <codecell>
# Convert User Input into FES filters.
start, stop = dateRange(start_date, end_date)
box = []
box.append(bounding_box[0][0])
box.append(bounding_box[0][1])
box.append(bounding_box[1][0])
box.append(bounding_box[1][1])
bbox = fes.BBox(box)
or_filt = fes.Or([fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
escapeChar='\\',
wildCard='*',
singleChar='?') for val in name_list])
val = 'Averages'
not_filt = fes.Not([fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
if oper.name == 'GetRecords':
print('\nISO Queryables:\n%s' %
'\n'.join(oper.constraints['SupportedISOQueryables']['values']))
# Put the names in a dict for ease of access.
data_dict = {}
data_dict["water"] = {"names": name_list,
"sos_name": sos_name}
# <markdowncell>
# #### Convert User Input into FES filters.
# <codecell>
start, stop = dateRange(start_date, end_date)
box = []
box.append(bounding_box[0][0])
box.append(bounding_box[0][1])
box.append(bounding_box[1][0])
box.append(bounding_box[1][1])
bbox = fes.BBox(box)
or_filt = fes.Or([fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
escapeChar='\\',
wildCard='*',
singleChar='?') for val in name_list])
val = 'Averages'
not_filt = fes.Not([fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
csw.version
# <codecell>
for oper in csw.operations:
if oper.name == 'GetRecords':
cnstr = oper.constraints['SupportedISOQueryables']['values']
print('\nISO Queryables:%s\n' % '\n'.join(cnstr))
# <markdowncell>
# ### Convert User Input into FES filters.
# <codecell>
start, stop = dateRange(start_date, stop_date)
bbox = fes.BBox(box)
# <codecell>
or_filt = fes.Or([
fes.PropertyIsLike(propertyname='apiso:AnyText',
literal=('*%s*' % val),
escapeChar='\\',
wildCard='*',
singleChar='?') for val in name_list
])
# <markdowncell>
# ROMS model output often has Averages and History files. The Averages files are usually averaged over a tidal cycle or more, while the History files are snapshots at that time instant. We are not interested in averaged data for this test, so in the cell below we remove any Averages files here by removing any datasets that have the term "Averages" in the metadata text. A better approach would be to look at the `cell_methods` attributes propagated through to some term in the ISO metadata, but this is not implemented yet, as far as I know
