def test_dict_to_string(self):
"""dict_to_string() should return the proper representation"""
self.assertEqual(
utils.dict_to_string({
'key1': 'value1',
'key2': 'value2'
}), 'key1: value1;key2: value2')
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'GMI is a dual-polarization, multi-channel, conical-scanning, passive '
'microwave radiometer with frequent revisit times.',
utils.SUMMARY_FIELDS['processing_level']: '3'
})
def get_summary(self, raw_metadata):
"""Get the dataset's summary"""
description_attributes = [
'Date', 'Instrument name', 'Mode', 'Satellite', 'Size',
'Timeliness Category'
]
summary_fields = {}
description = ', '.join([
f"{attribute}={raw_metadata[attribute]}"
for attribute in description_attributes
if attribute in list(raw_metadata.keys())
])
if not description:
raise MetadataNormalizationError(
f"Could not build a description from {raw_metadata}")
summary_fields[utils.SUMMARY_FIELDS['description']] = description
for attribute_name in ['Processing level', 'Product level']:
if attribute_name in raw_metadata.keys():
processing_level = re.match(
'^(L|Level-)?([0-9A-Z]+)$',
raw_metadata[attribute_name]).group(2)
summary_fields[utils.SUMMARY_FIELDS[
'processing_level']] = processing_level
return utils.dict_to_string(summary_fields)
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']: (
'This v2.1 SST_cci Climatology Data Record (CDR) consists of Level 4 daily'
' climatology files gridded on a 0.05 degree grid.'
),
utils.SUMMARY_FIELDS['processing_level']: '4',
utils.SUMMARY_FIELDS['product']: 'ESA SST CCI Climatology'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
"Real Time Ocean Forecast System (RTOFS) Global is a data-assimilating "
"nowcast-forecast system operated by the National Weather Service's National "
"Centers for Environmental Prediction (NCEP).",
utils.SUMMARY_FIELDS['processing_level']: '4',
utils.SUMMARY_FIELDS['product']: 'RTOFS'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'This product is a NRT L4 global total velocity field at 0m and 15m.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'MULTIOBS_GLO_PHY_NRT_015_003'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'Altimeter satellite gridded Sea Level Anomalies (SLA) computed with '
'respect to a twenty-year mean.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'The Operational Mercator global ocean analysis and forecast system at '
'1/12 degree is providing 10 days of 3D global ocean forecasts updated daily.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'GLOBAL_ANALYSIS_FORECAST_PHY_001_024'
})
def get_summary(self, raw_metadata):
summary_fields = {}
summary_fields[
utils.SUMMARY_FIELDS['description']] = raw_metadata['summary']
processing_level = raw_metadata['processing_level'].lstrip('Ll')
summary_fields[
utils.SUMMARY_FIELDS['processing_level']] = processing_level
return utils.dict_to_string(summary_fields)
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'The physical component of the Mediterranean Forecasting System '
'(Med-Currents) is a coupled hydrodynamic-wave model implemented over the whole '
'Mediterranean Basin.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'MEDSEA_ANALYSISFORECAST_PHY_006_013'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'The operational IBI (Iberian Biscay Irish) Ocean Analysis and Forecasting'
' system provides a 5-day hydrodynamic forecast including high frequency '
'processes of paramount importance to characterize regional scale marine '
'processes.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'IBI_ANALYSISFORECAST_PHY_005_001'
})
def get_summary(self, raw_metadata):
"""Get the dataset's summary if it is available in the
metadata, otherwise use a default
"""
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']: (
'The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea '
'ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive '
'triangular mesh of 10 km average cell length.'),
utils.SUMMARY_FIELDS['processing_level']: '4',
utils.SUMMARY_FIELDS['product']: 'ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011'
})
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
'This system provides 4-day forecasts at 3-hour time steps, updated at 00Z '
'daily. Navy Global HYCOM has a resolution of 1/12 degree in the horizontal '
'and uses hybrid (isopycnal/sigma/z-level) coordinates in the vertical. The '
'output is interpolated onto a regular 1/12-degree grid horizontally and '
'40 standard depth levels.',
utils.SUMMARY_FIELDS['processing_level']:
'4',
utils.SUMMARY_FIELDS['product']:
'HYCOM'
})
def get_summary(self, raw_metadata):
description_attributes = ('sensorMode', 'platform', 'instrument',
'startDate')
summary_fields = {}
description = ', '.join([
f"{attribute}={raw_metadata[attribute]}"
for attribute in description_attributes
])
summary_fields[utils.SUMMARY_FIELDS['description']] = description
if 'processingLevel' in raw_metadata:
processing_level = raw_metadata['processingLevel'].replace(
'LEVEL', '')
summary_fields[
utils.SUMMARY_FIELDS['processing_level']] = processing_level
return utils.dict_to_string(summary_fields)
def get_summary(self, raw_metadata):
"""Get the dataset's summary if it is available in the
metadata, otherwise use a default
"""
description = None
raw_summary = raw_metadata.get('summary')
url = raw_metadata.get('url', '')
if raw_summary and raw_summary.strip():
description = raw_summary
if 'INSITU_GLO_NRT_OBSERVATIONS_013_030' in url:
product = 'INSITU_GLO_NRT_OBSERVATIONS_013_030'
if not description:
description = (
'Global Ocean - near real-time (NRT) in situ quality controlled '
'observations, hourly updated and distributed by INSTAC within 24-48 hours '
'from acquisition in average. Data are collected mainly through global '
'networks (Argo, OceanSites, GOSUD, EGO) and through the GTS'
)
elif 'INSITU_GLO_UV_NRT_OBSERVATIONS_013_048' in url:
product = 'INSITU_GLO_UV_NRT_OBSERVATIONS_013_048'
if not description:
description = (
'This product is entirely dedicated to ocean current data observed in '
'near-real time. Surface current data from 2 different types of instruments'
' are distributed: velocities calculated along the trajectories of drifting'
' buoys from the DBCP’s Global Drifter Program, and velocities measured by '
'High Frequency radars from the European High Frequency radar Network'
)
else:
product = utils.UNKNOWN
if not description:
description = 'CMEMS in situ TAC data'
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']:
description or '',
utils.SUMMARY_FIELDS['processing_level']:
'2',
utils.SUMMARY_FIELDS['product']:
product
})
def get_summary(self, raw_metadata):
summary_fields = {}
description = ''
umm = raw_metadata['umm']
for platform in umm['Platforms']:
description += (f"Platform={platform['ShortName']}, " +
', '.join(f"Instrument={i['ShortName']}"
for i in platform['Instruments']))
description += (
f", Start date={umm['TemporalExtent']['RangeDateTime']['BeginningDateTime']}"
)
summary_fields[utils.SUMMARY_FIELDS['description']] = description
processing_level = re.match(
r'^.*_L(\d[^_]*)_.*$',
umm['CollectionReference']['ShortName']).group(1)
summary_fields[
utils.SUMMARY_FIELDS['processing_level']] = processing_level
return utils.dict_to_string(summary_fields)
def get_summary(self, raw_metadata):
summary_fields = {}
summary_fields[
utils.SUMMARY_FIELDS['description']] = raw_metadata['abstract']
return utils.dict_to_string(summary_fields)
def test_empty_dict_to_string(self):
"""The representation of an empty dict is an empty string"""
self.assertEqual(utils.dict_to_string({}), '')
def get_summary(self, raw_metadata):
return utils.dict_to_string({
utils.SUMMARY_FIELDS['description']: 'GCOM-W AMSR2 data',
utils.SUMMARY_FIELDS['processing_level']: '3'
})