def _generic_load_ccems(self, stride=None):
# Generalize attribute value lookup
plt_name = name.split('_')[1]
platformName = plt_name[-3:].upper()
base = getattr(self, plt_name + '_base')
files = getattr(self, plt_name + '_files')
parms = getattr(self, plt_name + '_parms')
nominal_depth = getattr(self, plt_name + '_nominal_depth')
start_datetime = getattr(self, plt_name + '_start_datetime', None)
end_datetime = getattr(self, plt_name + '_end_datetime', None)
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in files], files):
url = os.path.join(base, f)
# Monkeypatch featureType depending on file name (or parms...)
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[plt_name],
platformTypeName = 'mooring',
stride = stride,
startDatetime = start_datetime,
endDatetime = end_datetime,
dataStartDatetime = None)
loader.include_names = parms
loader.auxCoords = {}
if 'adcp' in f.lower() or 'aquadopp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
for p in parms:
# The timeseries variables 'Hdg_1215', 'Ptch_1216', 'Roll_1217' should have a coordinate of
# a singleton depth variable, but EPIC files has this as a sensor_depth variable attribute.
# Need special handling in the loader for these data.
if p in ['u_1205', 'v_1206', 'w_1204', 'AGC_1202']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon', 'depth': 'depth'}
if p in ['Hdg_1215', 'Ptch_1216', 'Roll_1217']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon'}
else:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat',
'longitude': 'lon', 'depth': 'depth'}
loader.process_data()
def loadCCESIN(self, stride=None):
'''
Mooring CCESIN specific load functions
'''
platformName = 'CCESIN'
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in self.ccesin_files], self.ccesin_files):
url = os.path.join(self.ccesin_base, f)
ccesin_start_datetime = getattr(self, 'ccesin_start_datetime', None)
ccesin_end_datetime = getattr(self, 'ccesin_end_datetime', None)
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[platformName.lower()],
platformTypeName = 'mooring',
stride = stride,
startDatetime = ccesin_start_datetime,
endDatetime = ccesin_end_datetime,
command_line_args = self.args)
loader.include_names = self.ccesin_parms
loader.auxCoords = {}
if 'adcp' in f.lower() or 'aquadopp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
# The timeseries variables 'Hdg_1215', 'Ptch_1216', 'Roll_1217' should have a coordinate of
# a singleton depth variable, but EPIC files has this as a sensor_depth variable attribute.
# Need special handling in the loader for these data.
for p in ['u_1205', 'v_1206', 'w_1204', 'AGC_1202']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon', 'depth': 'depth'}
for p in ['Hdg_1215', 'Ptch_1216', 'Roll_1217']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon'}
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
try:
loader.process_data()
except NoValidData as e:
self.logger.info(str(e))
continue
# For timeseriesProfile data we need to pass the nominaldepth of the plaform
# so that the model is put at the correct depth in the Spatial -> 3D view.
try:
self.addPlatformResources('https://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName, nominaldepth=self.ccesin_nominaldepth)
except AttributeError:
self.addPlatformResources('https://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName)
def loadCCESIN(self, stride=None):
'''
Mooring CCESIN specific load functions
'''
platformName = 'CCESIN'
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in self.ccesin_files], self.ccesin_files):
url = os.path.join(self.ccesin_base, f)
ccesin_start_datetime = getattr(self, 'ccesin_start_datetime', None)
ccesin_end_datetime = getattr(self, 'ccesin_end_datetime', None)
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[platformName.lower()],
platformTypeName = 'mooring',
stride = stride,
startDatetime = ccesin_start_datetime,
endDatetime = ccesin_end_datetime,
command_line_args = self.args)
loader.include_names = self.ccesin_parms
loader.auxCoords = {}
if 'adcp' in f.lower() or 'aquadopp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
# The timeseries variables 'Hdg_1215', 'Ptch_1216', 'Roll_1217' should have a coordinate of
# a singleton depth variable, but EPIC files has this as a sensor_depth variable attribute.
# Need special handling in the loader for these data.
for p in ['u_1205', 'v_1206', 'w_1204', 'AGC_1202']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon', 'depth': 'depth'}
for p in ['Hdg_1215', 'Ptch_1216', 'Roll_1217']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon'}
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader.process_data()
# For timeseriesProfile data we need to pass the nominaldepth of the plaform
# so that the model is put at the correct depth in the Spatial -> 3D view.
try:
self.addPlatformResources('https://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName, nominaldepth=self.ccesin_nominaldepth)
except AttributeError:
self.addPlatformResources('https://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName)
def loadCCEBIN(self, stride=None):
'''
Mooring CCEBIN specific load functions
'''
platformName = 'CCEBIN'
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in self.ccebin_files], self.ccebin_files):
url = os.path.join(self.ccebin_base, f)
dataStartDatetime = None
if self.args.append:
# Return datetime of last timevalue - if data are loaded from multiple
# activities return the earliest last datetime value
dataStartDatetime = InstantPoint.objects.using(self.dbAlias).filter(
activity__name=aName).aggregate(
Max('timevalue'))['timevalue__max']
if dataStartDatetime:
# Subract an hour to fill in missing_values at end from previous load
dataStartDatetime = dataStartDatetime - timedelta(seconds=3600)
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[platformName.lower()],
platformTypeName = 'mooring',
stride = stride,
startDatetime = self.ccebin_start_datetime,
endDatetime = self.ccebin_end_datetime,
dataStartDatetime = dataStartDatetime)
loader.include_names = self.ccebin_parms
loader.auxCoords = {}
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
for p in ['u_1205', 'v_1206', 'w_1204', 'AGC_1202', 'Hdg_1215', 'Ptch_1216', 'Roll_1217']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon', 'depth': 'depth'}
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader.process_data()
# For timeseriesProfile data we need to pass the nominaldepth of the plaform
# so that the model is put at the correct depth in the Spatial -> 3D view.
try:
self.addPlatformResources('http://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName, nominaldepth=self.ccebin_nominaldepth)
except AttributeError:
self.addPlatformResources('http://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName)
def _generic_load_ccems(self, stride=None):
# Generalize attribute value lookup
plt_name = name.split('_')[1]
platformName = plt_name[-3:].upper()
base = getattr(self, plt_name + '_base')
files = getattr(self, plt_name + '_files')
parms = getattr(self, plt_name + '_parms')
nominal_depth = getattr(self, plt_name + '_nominal_depth')
start_datetime = getattr(self, plt_name + '_start_datetime')
end_datetime = getattr(self, plt_name + '_end_datetime')
stride = stride or self.stride
for (aName, f) in zip([a + getStrideText(stride) for a in files],
files):
url = os.path.join(base, f)
# Monkeypatch featureType depending on file name (or parms...)
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader = Mooring_Loader(
url=url,
campaignName=self.campaignName,
campaignDescription=self.campaignDescription,
dbAlias=self.dbAlias,
activityName=aName,
activitytypeName='Mooring Deployment',
platformName=platformName,
platformColor=self.colors[plt_name],
platformTypeName='mooring',
stride=stride,
startDatetime=start_datetime,
endDatetime=end_datetime,
dataStartDatetime=None)
loader.include_names = parms
loader.auxCoords = {}
for p in parms:
loader.auxCoords[p] = {
'time': 'time',
'latitude': 'lat',
'longitude': 'lon',
'depth': 'depth'
}
loader.process_data()
def _generic_load_ccems(self, stride=None):
# Generalize attribute value lookup
plt_name = name.split('_')[1]
platformName = plt_name[-3:].upper()
base = getattr(self, plt_name + '_base')
files = getattr(self, plt_name + '_files')
parms = getattr(self, plt_name + '_parms')
nominal_depth = getattr(self, plt_name + '_nominal_depth')
start_datetime = getattr(self, plt_name + '_start_datetime')
end_datetime = getattr(self, plt_name + '_end_datetime')
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in files], files):
url = os.path.join(base, f)
# Monkeypatch featureType depending on file name (or parms...)
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[plt_name],
platformTypeName = 'mooring',
stride = stride,
startDatetime = start_datetime,
endDatetime = end_datetime,
dataStartDatetime = None)
loader.include_names = parms
loader.auxCoords = {}
for p in parms:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat',
'longitude': 'lon', 'depth': 'depth'}
loader.process_data()
def loadStationData(self, stride=1):
'''Crawl the OceanSITES Mooring data TDS for OPeNDAP links and load into STOQS
'''
urls = []
strides = {}
for dataSet in self.dataSets:
c = Crawl(dataSet[0], select=dataSet[1], debug=self.args.verbose)
dsUrls = [s.get("url") for d in c.datasets for s in d.services if s.get("service").lower() == "opendap"]
for dsu in dsUrls:
strides[dsu] = dataSet[2]
urls += dsUrls
# First pass through urls matching OceanSITES pattern to collect platform names to get colors
# Use OceanSITES naming convention for platform "OS_<platformName>_xxx_R|D_<type>.nc"
pNames = set()
platfrormTypeNames = set()
for url in urls:
platfrormTypeNames.add(url.split('/')[-2])
if url.find('MOVE1_') != -1:
# Special hack for MOVE PlatformCode
newUrl = url.replace('MOVE1_', 'MOVE1-')
pNames.add(newUrl.split('/')[-1].split('.')[0].split('_')[1])
else:
pNames.add(url.split('/')[-1].split('.')[0].split('_')[1])
# Assign colors by platformTypeName
pColors = {}
for ptName, color in zip(sorted(platfrormTypeNames), self.getColor(len(platfrormTypeNames))) :
pColors[ptName] = color
# Now loop again, this time loading the data
for url in urls:
logger.info("Executing runMooringLoader with url = %s", url)
if self.args.optimal_stride and strides[url]:
stride = strides[url]
elif self.args.test:
stride = strides[url] * 2
fixedUrl = url
if url.find('OS_IMOS-EAC_EAC') != -1:
# Special fix to get platform name
fixedUrl = url.replace('OS_IMOS-EAC_EAC', 'OS_IMOS-EAC-EAC')
if stride > 1:
aName = fixedUrl.split('/')[-1].split('.')[0] + '(stride=%d)' % stride
else:
aName = fixedUrl.split('/')[-1].split('.')[0]
pName = aName.split('_')[1]
ptName = url.split('/')[-2]
logger.debug("Instantiating Mooring_Loader for url = %s", url)
ml = Mooring_Loader(
url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = pName,
platformColor = pColors[ptName],
platformTypeName = ptName,
stride = stride,
startDatetime = self.startDatetime,
dataStartDatetime = None,
endDatetime = self.endDatetime)
# Special fixes for non standard metadata and if files don't contain the standard TEMP and PSAL parameters
if url.find('MBARI-') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH'}
elif url.find('OS_PAPA_2009PA003_D_CTD_10min') != -1:
ml.include_names = ['TEMP']
elif url.find('OS_PAPA_2009PA003_D_PSAL_1hr') != -1:
ml.include_names = ['PSAL']
elif url.find('OS_SOTS_SAZ-15-2012_D_microcat-4422m') != -1:
ml.include_names = ['TEMP', 'PSAL']
# DEPTH_CN_PR_PS_TE coordinate missing standard_name attribute
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH_CN_PR_PS_TE'}
# Only global attribute is 'cdm_data_type: Time-series'; monkey-patch the method
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
elif url.find('D_MICROCAT-PART') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH'}
elif url.find('D_RDI-WORKHORSE-ADCP-') != -1:
ml.include_names = ['UCUR', 'VCUR', 'WCUR']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'HEIGHT_ABOVE_SENSOR'}
# Metadata in file states 'timeseries', but it's really something different; monkey-patch the getFeatureType() method
Mooring_Loader.getFeatureType = lambda self: 'trajectoryprofile'
elif url.find('TVSM_dy.nc') != -1:
##ml.include_names = ['UCUR', 'VCUR', 'TEMP', 'PSAL', 'CSPD', 'CDIR']
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ('UCUR', 'VCUR', 'CSPD', 'CDIR'):
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPCUR'}
for v in ('TEMP',):
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH'}
for v in ('PSAL',):
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPPSAL'}
# These PIRATA daily files are timeSeriesProfile which hsa no featureType attribute
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
elif url.find('CCE') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH'}
elif url.find('NOG') != -1:
ml.include_names = ['TEMP', 'PSAL']
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
elif url.find('Stratus') != -1:
# Variable attrubute coordinates: TIME, DEPTH, LATITUDE, LONGITUDE; it should not contain commas
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {'time': 'TIME', 'latitude': 'LATITUDE', 'longitude': 'LONGITUDE', 'depth': 'DEPTH'}
else:
ml.include_names = ['TEMP', 'PSAL']
try:
(nMP, path, parmCountHash) = ml.process_data()
logger.debug("Loaded Activity with name = %s", aName)
except NoValidData, e:
logger.warning(e)
def loadStationData(self, stride=1):
'''Crawl the OceanSITES Mooring data TDS for OPeNDAP links and load into STOQS
'''
urls = []
strides = {}
for dataSet in self.dataSets:
c = Crawl(dataSet[0], select=dataSet[1], debug=self.args.verbose)
dsUrls = [
s.get("url") for d in c.datasets for s in d.services
if s.get("service").lower() == "opendap"
]
for dsu in dsUrls:
strides[dsu] = dataSet[2]
urls += dsUrls
# First pass through urls matching OceanSITES pattern to collect platform names to get colors
# Use OceanSITES naming convention for platform "OS_<platformName>_xxx_R|D_<type>.nc"
pNames = set()
platfrormTypeNames = set()
for url in urls:
platfrormTypeNames.add(url.split('/')[-2])
if url.find('MOVE1_') != -1:
# Special hack for MOVE PlatformCode
newUrl = url.replace('MOVE1_', 'MOVE1-')
pNames.add(newUrl.split('/')[-1].split('.')[0].split('_')[1])
else:
pNames.add(url.split('/')[-1].split('.')[0].split('_')[1])
# Assign colors by platformTypeName
pColors = {}
for ptName, color in zip(sorted(platfrormTypeNames),
self.getColor(len(platfrormTypeNames))):
pColors[ptName] = color
# Now loop again, this time loading the data
for url in urls:
logger.info("Executing runMooringLoader with url = %s", url)
if self.args.optimal_stride and strides[url]:
stride = strides[url]
elif self.args.test:
stride = strides[url] * 2
fixedUrl = url
if url.find('OS_IMOS-EAC_EAC') != -1:
# Special fix to get platform name
fixedUrl = url.replace('OS_IMOS-EAC_EAC', 'OS_IMOS-EAC-EAC')
if stride > 1:
aName = fixedUrl.split('/')[-1].split(
'.')[0] + '(stride=%d)' % stride
else:
aName = fixedUrl.split('/')[-1].split('.')[0]
pName = aName.split('_')[1]
ptName = url.split('/')[-2]
logger.debug("Instantiating Mooring_Loader for url = %s", url)
try:
ml = Mooring_Loader(
url=url,
campaignName=self.campaignName,
campaignDescription=self.campaignDescription,
dbAlias=self.dbAlias,
activityName=aName,
activitytypeName='Mooring Deployment',
platformName=pName,
platformColor=pColors[ptName],
platformTypeName=ptName,
stride=stride,
startDatetime=self.startDatetime,
dataStartDatetime=None,
endDatetime=self.endDatetime)
except UnicodeDecodeError as e:
logger.warn(str(e))
logger.warn(f'Cannot read data from {url}')
continue
# Special fixes for non standard metadata and if files don't contain the standard TEMP and PSAL parameters
if url.find('MBARI-') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH'
}
elif url.find('OS_PAPA_2009PA003_D_CTD_10min') != -1:
ml.include_names = ['TEMP']
elif url.find('OS_PAPA_2009PA003_D_PSAL_1hr') != -1:
ml.include_names = ['PSAL']
elif url.find('OS_SOTS_SAZ-15-2012_D_microcat-4422m') != -1:
ml.include_names = ['TEMP', 'PSAL']
# DEPTH_CN_PR_PS_TE coordinate missing standard_name attribute
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH_CN_PR_PS_TE'
}
# Only global attribute is 'cdm_data_type: Time-series'; monkey-patch the method
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
elif url.find('D_MICROCAT-PART') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH'
}
elif url.find('D_RDI-WORKHORSE-ADCP-') != -1:
ml.include_names = ['UCUR', 'VCUR', 'WCUR']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'HEIGHT_ABOVE_SENSOR'
}
# Metadata in file states 'timeseries', but it's really something different; monkey-patch the getFeatureType() method
Mooring_Loader.getFeatureType = lambda self: 'trajectoryprofile'
elif url.find('TVSM_dy.nc') != -1:
##ml.include_names = ['UCUR', 'VCUR', 'TEMP', 'PSAL', 'CSPD', 'CDIR']
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ('UCUR', 'VCUR', 'CSPD', 'CDIR'):
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPCUR'
}
for v in ('TEMP', ):
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH'
}
for v in ('PSAL', ):
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPPSAL'
}
# These PIRATA daily files are timeSeriesProfile which hsa no featureType attribute
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
elif url.find('CCE') != -1:
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH'
}
elif url.find('NOG') != -1:
ml.include_names = ['TEMP', 'PSAL']
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
elif url.find('Stratus') != -1:
# Variable attrubute coordinates: TIME, DEPTH, LATITUDE, LONGITUDE; it should not contain commas
ml.include_names = ['TEMP', 'PSAL']
ml.auxCoords = {}
for v in ml.include_names:
ml.auxCoords[v] = {
'time': 'TIME',
'latitude': 'LATITUDE',
'longitude': 'LONGITUDE',
'depth': 'DEPTH'
}
else:
ml.include_names = ['TEMP', 'PSAL']
try:
(nMP, path, parmCountHash) = ml.process_data()
logger.debug("Loaded Activity with name = %s", aName)
except NoValidData as e:
logger.warning(e)
def _generic_load_ccems(self, stride=None):
# Generalize attribute value lookup
plt_name = name.split('_')[1]
platformName = plt_name[-3:].upper()
base = getattr(self, plt_name + '_base')
files = getattr(self, plt_name + '_files')
parms = getattr(self, plt_name + '_parms')
nominal_depth = getattr(self, plt_name + '_nominal_depth')
start_datetime = getattr(self, plt_name + '_start_datetime', None)
end_datetime = getattr(self, plt_name + '_end_datetime', None)
stride = stride or self.stride
for (aName, f) in zip([ a + getStrideText(stride) for a in files], files):
if '_ProcessedWaves' in f:
if stride != 1:
# Most files are high time frequency ADCP data, so accept the generic stride
# Override for files like MBCCE_MS0_AWAC_20160408_ProcessedWaves.nc, which has 2 hour sampling
stride = 1
aName = f
self.logger.info(f"Overriding stride -> = {stride} for file {f}")
else:
self.logger.info(f"Skipping {f} with stride = 1 as the entire 2 hour data set is loaded")
url = os.path.join(base, f)
# Monkeypatch featureType depending on file name (or parms...)
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader = Mooring_Loader(url = url,
campaignName = self.campaignName,
campaignDescription = self.campaignDescription,
dbAlias = self.dbAlias,
activityName = aName,
activitytypeName = 'Mooring Deployment',
platformName = platformName,
platformColor = self.colors[plt_name],
platformTypeName = 'mooring',
stride = stride,
startDatetime = start_datetime,
endDatetime = end_datetime,
dataStartDatetime = None)
loader.include_names = parms
loader.auxCoords = {}
if 'adcp' in f.lower() or 'aquadopp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
for p in parms:
# The timeseries variables 'Hdg_1215', 'Ptch_1216', 'Roll_1217' should have a coordinate of
# a singleton depth variable, but EPIC files has this as a sensor_depth variable attribute.
# Need special handling in the loader for these data.
if p in ['u_1205', 'v_1206', 'w_1204', 'AGC_1202']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon', 'depth': 'depth'}
if p in ['Hdg_1215', 'Ptch_1216', 'Roll_1217']:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat', 'longitude': 'lon'}
else:
loader.auxCoords[p] = {'time': 'time', 'latitude': 'lat',
'longitude': 'lon', 'depth': 'depth'}
try:
loader.process_data()
except NoValidData as e:
self.logger.info(str(e))
continue
def loadCCEBIN(self, stride=None):
'''
Mooring CCEBIN specific load functions
'''
platformName = 'CCEBIN'
stride = stride or self.stride
for (aName,
f) in zip([a + getStrideText(stride) for a in self.ccebin_files],
self.ccebin_files):
url = os.path.join(self.ccebin_base, f)
dataStartDatetime = None
if self.args.append:
# Return datetime of last timevalue - if data are loaded from multiple
# activities return the earliest last datetime value
dataStartDatetime = InstantPoint.objects.using(
self.dbAlias).filter(activity__name=aName).aggregate(
Max('timevalue'))['timevalue__max']
if dataStartDatetime:
# Subract an hour to fill in missing_values at end from previous load
dataStartDatetime = dataStartDatetime - timedelta(
seconds=3600)
loader = Mooring_Loader(
url=url,
campaignName=self.campaignName,
campaignDescription=self.campaignDescription,
dbAlias=self.dbAlias,
activityName=aName,
activitytypeName='Mooring Deployment',
platformName=platformName,
platformColor=self.colors[platformName.lower()],
platformTypeName='mooring',
stride=stride,
startDatetime=self.ccebin_start_datetime,
endDatetime=self.ccebin_end_datetime,
dataStartDatetime=dataStartDatetime)
loader.include_names = self.ccebin_parms
loader.auxCoords = {}
if 'adcp' in f.lower():
Mooring_Loader.getFeatureType = lambda self: 'timeseriesprofile'
for p in [
'u_1205', 'v_1206', 'w_1204', 'AGC_1202', 'Hdg_1215',
'Ptch_1216', 'Roll_1217'
]:
loader.auxCoords[p] = {
'time': 'time',
'latitude': 'lat',
'longitude': 'lon',
'depth': 'depth'
}
else:
Mooring_Loader.getFeatureType = lambda self: 'timeseries'
loader.process_data()
# For timeseriesProfile data we need to pass the nominaldepth of the plaform
# so that the model is put at the correct depth in the Spatial -> 3D view.
try:
self.addPlatformResources(
'http://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName,
nominaldepth=self.ccebin_nominaldepth)
except AttributeError:
self.addPlatformResources(
'http://stoqs.mbari.org/x3d/cce_bin_assem/cce_bin_assem_src_scene.x3d',
platformName)
