def get_phenomena(self):
phenomenon =\
{
"id" : "",
"attribute_count" : "",
"attributes" :[]
}
phen_attr =\
{
"name" : "",
"value": ""
}
try:
self.handler_id = "pp handler level 2."
pp_file_content=cdms.open(self.file_path)
var_ids = pp_file_content.listvariables()
# Filter long values and overwrite duplicates.
phen_list = []
for var_id in var_ids:
metadata_dict = pp_file_content[var_id].attributes
phen_attr_list = []
attr_count = 0
for key in metadata_dict.keys():
value = str(metadata_dict[key])
if len(key) < util.MAX_ATTR_LENGTH \
and len(value) < util.MAX_ATTR_LENGTH \
and util.is_valid_phen_attr(value):
phen_attr["name"] = str(key.strip())
phen_attr["value"] = str(unicode(value).strip())
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
# Dict of phenomenon attributes.
if len(phen_attr_list) > 0:
# phenomenon is a dictionary type
# phen_attr_list is a list
# attr_count is an INT
new_phenomenon = phenomenon.copy()
new_phenomenon["attributes"] = phen_attr_list
new_phenomenon["attribute_count"] = attr_count
# JAR 11-Oct-2016
# Append to list if new_phenomenon is NOT already in the phen_list
if new_phenomenon not in phen_list:
phen_list.append(new_phenomenon)
pp_file_content.close()
return (phen_list, )
except Exception as ex:
return None
def get_phenomena(self, netcdf):
"""
Construct list of Phenomena based on variables in NetCDF file.
:returns : List of metadata.product.Parameter objects.
"""
phen_list = []
phenomenon =\
{
"id" : "",
"attribute_count" : "",
"attributes" :[]
}
phen_attr =\
{
"name" : "",
"value": ""
}
#for all phenomena list.
for v_name, v_data in netcdf.variables.iteritems():
phen_attr_list = []
#for all attributtes in phenomenon.
attr_count = 0
for key, value in v_data.__dict__.iteritems():
if not self.is_valid_parameter(key, value) \
or not util.is_valid_phen_attr(value):
continue
phen_attr["name"] = str(key.strip())
phen_attr["value"] = str(unicode(value).strip())
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
phen_attr["name"] = "var_id"
phen_attr["value"] = str(v_name)
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
if len(phen_attr_list) > 0:
new_phenomenon = phenomenon.copy()
new_phenomenon["attributes"] = phen_attr_list
new_phenomenon["attribute_count"] = attr_count
phen_list.append(new_phenomenon)
return phen_list
def phenomena(self):
try:
na_fhandle = nappy.openNAFile(self.file_path)
variables = {}
for var in na_fhandle.getVariables():
if util.is_valid_phen_attr(var[1]):
variables.update({
var[0]: {
"name": var[0],
"units": var[1]
}
})
variables = [util.Parameter(k, other_params=var) for (k, var) in variables.iteritems()]
return variables
except Exception:
return None
def get_phenomena(self):
phen_list = []
phenomenon =\
{
"id" : "",
"attribute_count" : "",
"attributes" :[]
}
phen_keys = [
"paramId",
"cfNameECMF",
"cfName",
"cfVarName",
"units",
"nameECMF",
"name"
]
phen_attr =\
{
"name" : "",
"value": ""
}
try:
fd = open(self.file_path)
while 1:
gid = gapi.grib_new_from_file(fd)
if gid is None: break
phen_attr_list = []
attr_count = 0
for key in phen_keys:
if not gapi.grib_is_defined(gid, key):
continue
value = str(gapi.grib_get(gid, key))
if len(key) < util.MAX_ATTR_LENGTH \
and len(value) < util.MAX_ATTR_LENGTH \
and util.is_valid_phen_attr(value):
phen_attr["name"] = str(key.strip())
phen_attr["value"] = str(unicode(value).strip())
if phen_attr not in phen_attr_list:
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
if len(phen_attr_list) > 0:
new_phenomenon = phenomenon.copy()
new_phenomenon["attributes"] = phen_attr_list
new_phenomenon["attribute_count"] = attr_count
if new_phenomenon not in phen_list:
phen_list.append(new_phenomenon)
gapi.grib_release(gid)
fd.close()
return phen_list
except Exception:
return None
def get_metadata_level3(self):
phen_list = []
phenomenon =\
{
"id" : "",
"attribute_count" : "",
"attributes" :[]
}
phen_attr =\
{
"name" : "",
"value": ""
}
lat_f_l = []
lon_f_l = []
lat_l_l = []
lon_l_l = []
date_d_l = []
date_t_l = []
phen_keys = [
"paramId",
"cfNameECMF",
"cfName",
"cfVarName",
"units",
"nameECMF",
"name",
"Ni",
"Nj",
"latitudeOfFirstGridPointInDegrees",
"longitudeOfFirstGridPointInDegrees",
"latitudeOfLastGridPointInDegrees",
"longitudeOfLastGridPointInDegrees",
"dataDate",
"dataTime"
]
try:
fd = open(self.file_path)
while 1:
gid = gapi.grib_new_from_file(fd)
if gid is None: break
phen_attr_list = []
attr_count = 0
for key in phen_keys:
if not gapi.grib_is_defined(gid, key):
continue
value = str(gapi.grib_get(gid, key))
#So the file contains many records but all report the
#same spatial and temporal information. Only complete distinct records
#will be stored i.e the one that contain the full list of parameter
#and are unique. If evety record has got different spatial and temporal
#then th eindex must change because currently there is only on geo_shape_field.
if key == "latitudeOfFirstGridPointInDegrees":
lat_f_l.append(value)
elif key == "longitudeOfFirstGridPointInDegrees":
lon_f_l.append(value)
elif key == "latitudeOfLastGridPointInDegrees":
lat_l_l.append(value)
elif key =="longitudeOfLastGridPointInDegrees":
lon_l_l.append(value)
elif key == "dataDate":
date_d_l.append(value)
elif key == "dataTime":
date_t_l.append(value)
else:
if len(key) < util.MAX_ATTR_LENGTH \
and len(value) < util.MAX_ATTR_LENGTH \
and util.is_valid_phen_attr(value):
phen_attr["name"] = str(key.strip())
phen_attr["value"] = str(unicode(value).strip())
if phen_attr not in phen_attr_list:
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
if len(phen_attr_list) > 0:
new_phenomenon = phenomenon.copy()
new_phenomenon["attributes"] = phen_attr_list
new_phenomenon["attribute_count"] = attr_count
if new_phenomenon not in phen_list:
phen_list.append(new_phenomenon)
gapi.grib_release(gid)
fd.close()
if len(lat_f_l) > 0 \
and len(lon_f_l) > 0 \
and len(lat_l_l) > 0 \
and len(lon_l_l) > 0 \
and len(date_d_l) > 0 \
and len(date_t_l):
geospatial_dict = {}
geospatial_dict["type"] = "envelope"
temporal_dict = {}
lat_f = min(lat_f_l)
lon_f = min(lon_f_l)
lat_l = max(lat_l_l)
lon_l = max(lon_l_l)
date_d = min(date_d_l)
date_t = min(date_t_l)
if float(lon_l) > 180:
lon_l = (float(lon_l) -180) - 180
geospatial_dict["coordinates"] = [[round(float(lon_f), 3), round(float(lat_f), 3)], [round(float(lon_l), 3), round(float(lat_l), 3) ]]
temporal_dict["start_time"] = date_d
temporal_dict["end_time"] = date_t
return (phen_list, geospatial_dict, temporal_dict)
else:
return (phen_list,)
except Exception as ex:
return None
def get_metadata_pp_level3(self):
phenomenon =\
{
"id" : "",
"attribute_count" : "",
"attributes" :[]
}
phen_attr =\
{
"name" : "",
"value": ""
}
spatial = None
#Get basic file info.
file_info = self.get_metadata_generic_level1()
if file_info is not None:
try:
self.handler_id = "pp handler level 3."
lat_l = []
lat_u = []
lon_l = []
lon_u = []
start_time_l = []
end_time_l = []
pp_file_content=cdms.open(self.file_path)
var_ids = pp_file_content.listvariables()
phen_list = []
for var_id in var_ids:
metadata_dict = pp_file_content[var_id].attributes
phen_attr_list = []
attr_count = 0
for key in metadata_dict.keys():
value = str(metadata_dict[key])
if len(key) < util.MAX_ATTR_LENGTH \
and len(value) < util.MAX_ATTR_LENGTH \
and util.is_valid_phen_attr(value):
phen_attr["name"] = str(key.strip())
phen_attr["value"] = str(unicode(value).strip())
phen_attr_list.append(phen_attr.copy())
attr_count = attr_count + 1
#Dict of phenomenon attributes.
if len(phen_attr_list) > 0:
new_phenomenon = phenomenon.copy()
new_phenomenon["attributes"] = phen_attr_list
new_phenomenon["attribute_count"] = attr_count
phen_list.append(new_phenomenon)
try :
spatial = self.getBoundingBox(var_id, pp_file_content)
temporal = self.getTemporalDomain(var_id, pp_file_content)
#geospatial data.
lon_l.append(spatial[0])
lat_l.append(spatial[1])
lon_u.append(spatial[2])
lat_u.append(spatial[3])
#temporal
start_time_l.append(temporal[0])
end_time_l.append(temporal[1])
except Exception as ex:
continue
if len(lat_l) > 0 \
and len(lon_l) > 0 \
and len(lat_u) > 0 \
and len(lon_u) > 0:
min_lon_l = self.normalize_lon(min(lon_l))
min_lat_l = self.normalize_lat(min(lat_l))
max_lon_u = self.normalize_lon(max(lon_u))
max_lat_u = self.normalize_lat(max(lat_u))
spatial = {'coordinates': {'type': 'envelope', 'coordinates': [[round(min_lon_l, 3), round(min_lat_l, 3)], [round(max_lon_u, 3), round(max_lat_u, 3)]]}}
#kltsa 22/07/2016 change for issue 23341: validation of dates added.
if len(start_time_l) > 0 \
and len(end_time_l) > 0:
min_time = min(start_time_l)
max_time = max(end_time_l)
if util.is_date_valid(min_time.split("T")[0])\
and util.is_date_valid(max_time.split("T")[0]):
file_info[0]["info"]["temporal"] = {'start_time': min_time, 'end_time': max_time }
pp_file_content.close()
return file_info + (phen_list, spatial, )
except Exception as ex:
return file_info
else:
return None