# coding=utf-8
"""Data manager for THREDDS server"""
import os
from datetime import datetime
from time import strptime
import iris
from iris.coords import DimCoord
import netCDF4
import numpy as np
from bscearth.utils.date import parse_date, chunk_start_date, chunk_end_date
from bscearth.utils.log import Log
from cf_units import Unit
from earthdiagnostics.datafile import DataFile, StorageStatus, LocalStatus
from earthdiagnostics.datamanager import DataManager
from earthdiagnostics.utils import TempFile, Utils
from earthdiagnostics.variable import VariableType
[docs]class THREDDSManager(DataManager):
"""
Data manager class for THREDDS
Parameters
----------
config: Config
"""
def __init__(self, config):
super(THREDDSManager, self).__init__(config)
self.server_url = config.thredds.server_url
data_folders = self.config.data_dir.split(':')
self.config.data_dir = None
for data_folder in data_folders:
if os.path.isdir(os.path.join(data_folder, self.config.data_type, self.experiment.institute.lower(),
self.experiment.model.lower())):
self.config.data_dir = data_folder
break
if not self.config.data_dir:
raise Exception('Can not find model data')
if self.config.data_type in ('obs', 'recon') and self.experiment.chunk_size != 1:
raise Exception('For obs and recon data chunk_size must be always 1')
# noinspection PyUnusedLocal
[docs] def file_exists(self, domain, var, startdate, member, chunk, grid=None, box=None, frequency=None,
vartype=VariableType.MEAN, possible_versions=None):
"""
Check if a file exists in the storage
Creates a THREDDSSubset and checks if it is accesible
Parameters
----------
domain: ModelingRealm
var: str
startdate: str
member: int
chunk: int
grid: str or None
box: Box or None
frequency: Frequency or None
vartype: VariableType
Returns
-------
THREDDSSubset
"""
aggregation_path = self.get_var_url(var, startdate, frequency, box, vartype)
start_chunk = chunk_start_date(parse_date(startdate), chunk, self.experiment.chunk_size, 'month',
self.experiment.calendar)
end_chunk = chunk_end_date(start_chunk, self.experiment.chunk_size, 'month', self.experiment.calendar)
thredds_subset = THREDDSSubset(aggregation_path, "", var, start_chunk, end_chunk)
return thredds_subset
[docs] def get_file_path(self, startdate, domain, var, frequency, vartype,
box=None, grid=None):
"""
Return the path to a concrete file
Parameters
----------
startdate: str
domain: ModelingRealm
var: str
frequency: Frequency
vartype: VariableType
box: Box or None, optional
grid: str or None, optional
Returns
-------
str
"""
if frequency is None:
frequency = self.config.frequency
var = self._get_final_var_name(box, var)
folder_path = self._get_folder_path(frequency, domain, var, grid, vartype)
file_name = self._get_file_name(var, startdate)
filepath = os.path.join(folder_path, file_name)
return filepath
def _get_folder_path(self, frequency, domain, variable, grid, vartype):
if self.config.data_type == 'exp':
var_folder = domain.get_varfolder(variable, self.config.experiment.ocean_timestep,
self.config.experiment.atmos_timestep, grid=grid)
else:
var_folder = variable
folder_path = os.path.join(self.config.data_dir, self.config.data_type,
self.experiment.institute.lower(),
self.experiment.model.lower(),
frequency.folder_name(vartype),
var_folder)
return folder_path
# noinspection PyUnusedLocal
[docs] def get_year(self, domain, var, startdate, member, year, grid=None, box=None, vartype=VariableType.MEAN):
"""
Ge a file containing all the data for one year for one variable
:param domain: variable's domain
:type domain: str
:param var: variable's name
:type var: str
:param startdate: startdate to retrieve
:type startdate: str
:param member: member to retrieve
:type member: int
:param year: year to retrieve
:type year: int
:param grid: variable's grid
:type grid: str
:param box: variable's box
:type box: Box
:param vartype: Variable type (mean, statistic)
:type vartype: VariableType
:return:
"""
aggregation_path = self.get_var_url(var, startdate, None, box, vartype)
thredds_subset = THREDDSSubset(aggregation_path, "", var, datetime(year, 1, 1), datetime(year + 1, 1, 1))
return thredds_subset.download()
[docs] def get_var_url(self, var, startdate, frequency, box, vartype):
"""
Get url for dataset
:param var: variable to retrieve
:type var: str
:param startdate: startdate to retrieve
:type startdate: str
:param frequency: frequency to get:
:type frequency: Frequency | None
:param box: box to get
:type box: Box
:param vartype: type of variable
:type vartype: VariableType
:return:
"""
if frequency is None:
frequency = self.config.frequency
var = self._get_final_var_name(box, var)
full_path = os.path.join(self.server_url, 'dodsC', self.config.data_type, self.experiment.institute,
self.experiment.model, frequency.folder_name(vartype))
if self.config.data_type == 'exp':
full_path = os.path.join(full_path, var, self._get_file_name(var, startdate))
else:
full_path = os.path.join(full_path, self._get_file_name(var, None))
return full_path
def _get_file_name(self, var, startdate):
if startdate:
if self.config.data_type != 'exp':
startdate = startdate[0:6]
return '{0}_{1}.nc'.format(var, startdate)
else:
return '{0}.nc'.format(var)
[docs] def request_chunk(self, domain, var, startdate, member, chunk, grid=None, box=None, frequency=None,
vartype=VariableType.MEAN):
"""
Request a given file from the CMOR repository to the scratch folder and returns the path to the scratch's copy
Parameters
----------
domain: ModelingRealm
var: str
startdate: str
member: int
chunk: int
grid: str or None
box: Box or None
frequency: Frequency or None
vartype: VariableType or None
Returns
-------
DataFile
"""
aggregation_path = self.get_var_url(var, startdate, frequency, box, vartype)
file_path = self.get_file_path(startdate, domain, var, frequency, vartype, box=box)
start_chunk = chunk_start_date(parse_date(startdate), chunk, self.experiment.chunk_size, 'month',
self.experiment.calendar)
end_chunk = chunk_end_date(start_chunk, self.experiment.chunk_size, 'month', self.experiment.calendar)
thredds_subset = THREDDSSubset(aggregation_path, file_path, var, start_chunk, end_chunk)
thredds_subset.local_status = LocalStatus.PENDING
self.requested_files[file_path] = thredds_subset
return thredds_subset
# noinspection PyUnusedLocal
[docs] def declare_chunk(self, domain, var, startdate, member, chunk, grid=None, region=None, box=None, frequency=None,
vartype=VariableType.MEAN, diagnostic=None):
"""
Copy a given file from the CMOR repository to the scratch folder and returns the path to the scratch's copy
:param diagnostic:
:param region:
:param domain: CMOR domain
:type domain: Domain
:param var: variable name
:type var: str
:param startdate: file's startdate
:type startdate: str
:param member: file's member
:type member: int
:param chunk: file's chunk
:type chunk: int
:param grid: file's grid (only needed if it is not the original)
:type grid: str|None
:param box: file's box (only needed to retrieve sections or averages)
:type box: Box
:param frequency: file's frequency (only needed if it is different from the default)
:type frequency: Frequency|None
:param vartype: Variable type (mean, statistic)
:type vartype: VariableType
:return: path to the copy created on the scratch folder
:rtype: str
"""
aggregation_path = self.get_var_url(var, startdate, frequency, box, vartype)
file_path = self.get_file_path(startdate, domain, var, frequency, vartype, box=box)
start_chunk = chunk_start_date(parse_date(startdate), chunk, self.experiment.chunk_size, 'month',
self.experiment.calendar)
end_chunk = chunk_end_date(start_chunk, self.experiment.chunk_size, 'month', self.experiment.calendar)
final_name = self._get_final_var_name(box, var)
if file_path in self.requested_files:
thredds_subset = self.requested_files[file_path]
else:
thredds_subset = THREDDSSubset(aggregation_path, file_path, var, start_chunk, end_chunk)
self.requested_files[file_path] = thredds_subset
thredds_subset.final_name = final_name
thredds_subset.diagnostic = diagnostic
thredds_subset.storage_status = StorageStatus.PENDING
return thredds_subset
[docs]class THREDDSError(Exception):
"""Exception to be launched when a THREDDS related error is encounteredd"""
pass
[docs]class THREDDSSubset(DataFile):
"""
Implementation of DataFile for the THREDDS server
Parameters
----------
thredds_path: str
file_path: str
var: str
start_time: datetime
end_time: datetime
"""
def __init__(self, thredds_path, file_path, var, start_time, end_time):
super(THREDDSSubset, self).__init__()
self.thredds_path = thredds_path
self.remote_file = file_path
self.local_file = None
if '_f' in var:
self.var = var[:var.index('_f')]
self.hourly = var[var.index('_f'):]
else:
self.var = var
self.hourly = ''
self.dimension_indexes = {}
self.handler = None
self.start_time = start_time
self.end_time = end_time
def __str__(self):
return 'THREDDS {0.thredds_path} ({0.start_time}-{0.end_time})'.format(self)
[docs] def download(self):
"""
Get data from the THREDDS server
Raises
------
THREDDSError
If the data can not be downloaded
"""
try:
Log.debug('Downloading thredds subset {0}...', self)
iris.FUTURE.netcdf_promote = True
iris.FUTURE.netcdf_no_unlimited = True
with iris.FUTURE.context(cell_datetime_objects=True):
time_constraint = iris.Constraint(time=lambda cell: self.start_time <= cell.point <= self.end_time)
var_cube = iris.load_cube(self.thredds_path, constraint=time_constraint, callback=self._correct_cube)
if not self.local_file:
self.local_file = TempFile.get()
iris.save(var_cube, self.local_file, zlib=True)
if not Utils.check_netcdf_file(self.local_file):
raise THREDDSError('netcdf check for downloaded file failed')
Log.info('Request {0} ready!', self)
self.local_status = LocalStatus.READY
except THREDDSError as ex:
Log.error('Can not retrieve {0} from server: {1}'.format(self, ex))
self.local_status = LocalStatus.FAILED
# noinspection PyUnusedLocal
@staticmethod
def _correct_cube(cube, field, filename):
if not cube.coords('time'):
return
time = cube.coord('time')
if time.units.origin.startswith('month'):
ref = strptime(time.units.origin[time.units.origin.index(' since ') + 7:], '%Y-%m-%d %H:%M:%S')
helper = np.vectorize(lambda x: datetime(year=ref.tm_year + int(x) / 12,
month=int(x - 1) % 12 + 1,
day=ref.tm_mday))
times = np.round(time.points + ref.tm_mon)
dates = helper(times)
dates = netCDF4.date2num(dates, units='days since 1850-01-01', calendar=time.units.calendar)
new_time = DimCoord(dates, standard_name=time.standard_name, long_name=time.long_name,
var_name=time.var_name, attributes=time.attributes,
units=Unit('days since 1850-01-01', time.units.calendar))
[dimension] = cube.coord_dims(time)
cube.remove_coord(time)
cube.add_dim_coord(new_time, dimension)