def get_lowest_resolution(ts_type, *ts_types):
"""Get the lowest resolution from several ts_type codes
Parameters
----------
ts_type : str
first ts_type
*ts_types
one or more additional ts_type codes
Returns
-------
str
the ts_type that corresponds to the lowest resolution
Raises
------
ValueError
if one of the input ts_type codes is not supported
"""
#all_ts_types = const.GRID_IO.TS_TYPES
from pyaerocom.tstype import TsType
lowest = TsType(ts_type)
for freq in ts_types:
# =============================================================================
# if not freq in all_ts_types:
# raise ValueError('Invalid input, only valid ts_type codes are '
# 'supported: {}'.format(all_ts_types))
# =============================================================================
_temp = TsType(freq)
if _temp < lowest:
lowest = _temp
return lowest.val
def _init_data_default_frequencies(coldata, colocation_settings):
to_ts_types = ['daily', 'monthly', 'yearly']
data_arrs = dict.fromkeys(to_ts_types)
jsdate = dict.fromkeys(to_ts_types)
tt = TsType(coldata.ts_type)
if tt < TsType('monthly'):
raise TemporalResolutionError(
'Temporal resolution ({}) is too low for '
'web processing, need monthly or higher'.format(tt))
elif tt > TsType('daily'):
# resolution is higher than daily -> convert to daily
coldata = _resample_time_coldata(coldata, 'daily', colocation_settings)
tt = TsType('daily')
for freq in to_ts_types:
tt_freq = TsType(freq)
if tt < tt_freq: # skip (coldata is in lower resolution)
#data_arrs[freq] = None
continue
elif tt == tt_freq:
data_arrs[freq] = coldata.copy()
jsdate[freq] = _get_jsdate(coldata)
else:
cd = _resample_time_coldata(coldata, freq, colocation_settings)
data_arrs[freq] = cd
jsdate[freq] = _get_jsdate(cd)
return (data_arrs, jsdate)
def test_next_lower():
try:
TsType('yearly').next_lower
except Exception as e:
assert type(e) == IndexError
assert str(TsType('3minutely').next_lower) == 'hourly'
def test_next_higher():
try:
TsType('minutely').next_higher
except Exception as e:
assert type(e) == IndexError
assert str(TsType('3minutely').next_higher) == 'minutely'
assert str(TsType('hourly').next_higher) == 'minutely'
assert str(TsType('monthly').next_higher) == 'weekly'
def test_basic_operators_pandas():
monthly = TsType('MS')
yearly = TsType('AS')
daily = TsType('D')
assert monthly < daily
assert monthly <= daily
assert monthly != daily
assert yearly < daily
assert not (yearly == daily)
assert monthly > yearly
assert monthly >= yearly
def test_basic_operators():
monthly = TsType('monthly')
yearly = TsType('yearly')
daily = TsType('daily')
assert monthly < daily
assert monthly <= daily
assert monthly != daily
assert yearly < daily
assert not (yearly == daily)
assert monthly > yearly
assert monthly >= yearly
def get_tot_number_of_seconds(ts_type, dtime=None):
from pyaerocom.tstype import TsType
ts_tpe = TsType(ts_type)
if ts_tpe >= TsType('montly'):
if dtime is None:
raise AttributeError(
'For frequncies larger than or eq. monthly you' +
'need to provide dtime in order to compute the number of second. '
)
else:
# find seconds from dtime
return None
else:
return TS_TYPE_SECS[ts_type]
def correct_time_coord(cube, ts_type, year):
"""Method that corrects the time coordinate of an iris Cube
Parameters
----------
cube : Cube
cube containing data
ts_type : TsType or str
temporal resolution of data (e.g. "hourly", "daily"). This information
is e.g. encrypted in the filename of a NetCDF file and may be
accessed using :class:`pyaerocom.io.FileConventionRead`
year : int
interger specifying start year, e.g. 2017
Returns
-------
Cube
the same instance of the input cube with corrected time dimension axis
"""
tindex_cube = None
dim_lens = []
if isinstance(ts_type, str):
ts_type = TsType(ts_type)
for i, coord in enumerate(cube.dim_coords):
dim_lens.append(len(coord.points))
if coord.name() == 'time':
tindex_cube = i
if tindex_cube is None:
if cube.ndim != len(cube.dim_coords): #one dimension is missing
for idx, dim_len in enumerate(cube.shape):
if not dim_len in dim_lens: #candidate
tindex_cube = idx
if tindex_cube is None:
raise NetcdfError('Failed to identify data index of time dimension in '
'cube {}'.format(repr(cube)))
tres_str = ts_type.cf_base_unit
conv = ts_type.datetime64_str
tunit_str = '%s since %s-01-01 00:00:00' % (tres_str, year)
num = cube.shape[tindex_cube]
tunit = cf_units.Unit(tunit_str, calendar=cf_units.CALENDAR_STANDARD)
tres_np = ts_type.timedelta64_str #TSTR_TO_NP_TD[ts_type]
base = datetime64("%s-01-01 00:00:00" % year).astype(conv)
times = base + arange(0, num, 1).astype(tres_np)
# see this thread https://github.com/matplotlib/matplotlib/issues/2259/
times_dt = times.astype("datetime64[s]").astype(datetime)
# timestamps = datetime64(str(year)) +
time_nums = [tunit.date2num(t) for t in times_dt]
tcoord = iris.coords.DimCoord(time_nums, standard_name='time', units=tunit)
#tcoord_dim = cube.coord_dims('time')
try:
cube.remove_coord('time')
except Exception:
pass
cube.add_dim_coord(tcoord, tindex_cube)
return cube
def check_validity(self, file):
"""Check if filename is valid"""
info = self.get_info_from_file(file)
year = info["year"]
if not TsType.valid(info['ts_type']):
raise FileConventionError("Invalid ts_type %s in filename %s" %
(info['ts_type'], basename(file)))
elif not (const.MIN_YEAR <= year <= const.MAX_YEAR):
raise FileConventionError("Invalid year %d in filename %s" %
(info['year'], basename(file)))
def get_tot_number_of_seconds(ts_type, dtime=None):
from pyaerocom.tstype import TsType
ts_tpe = TsType(ts_type)
if ts_tpe >= TsType('monthly'):
if dtime is None:
raise AttributeError(
'For frequncies larger than or eq. monthly you' +
' need to provide dtime in order to compute the number of second.'
)
else:
# find seconds from dtime
# TODO generalize this
days_in_month = dtime.dt.daysinmonth
if ts_type == 'monthly':
monthly_to_sec = days_in_month * 24 * 60 * 60
return monthly_to_sec
else:
return TS_TYPE_SECS[ts_type]
def _check_correct_time_dim(cube, file, file_convention=None):
if file_convention is None:
try:
file_convention = FileConventionRead(from_file=file)
except Exception:
pass
if not isinstance(file_convention, FileConventionRead):
raise FileConventionError(
'Unknown file convention: {}'.format(file_convention))
finfo = file_convention.get_info_from_file(file)
try:
ts_type = TsType(finfo['ts_type'])
except Exception:
raise FileConventionError(
'Invalid ts_type in file: {}'.format(ts_type))
year = finfo['year']
if not const.MIN_YEAR <= year <= const.MAX_YEAR:
raise FileConventionError('Invalid year in file: {}'.format(year))
try:
check_time_coord(cube, ts_type, year)
except UnresolvableTimeDefinitionError as e:
raise UnresolvableTimeDefinitionError(repr(e))
except Exception:
msg = ("Invalid time dimension coordinate in file {}. ".format(
os.path.basename(file)))
logger.warning(msg)
if const.GRID_IO.CORRECT_TIME_FILENAME:
logger.warning("Attempting to correct time coordinate "
"using information in file name")
try:
cube = correct_time_coord(cube,
ts_type=finfo["ts_type"],
year=finfo["year"])
except Exception:
pass
if const.WRITE_FILEIO_ERR_LOG:
add_file_to_log(file, 'Invalid time dimension')
return cube
def test_to_timedelta64(ts_type, ref_time_str, np_dt_str, output_str):
tref = np.datetime64(ref_time_str, np_dt_str)
assert str(tref + TsType(ts_type).to_timedelta64()) == output_str
def _run_gridded_ungridded(self, var_name=None):
"""Analysis method for gridded vs. ungridded data"""
model_reader = ReadGridded(self.model_id)
obs_reader = ReadUngridded(self.obs_id)
obs_vars_supported = obs_reader.get_reader(
self.obs_id).PROVIDES_VARIABLES
obs_vars = list(np.intersect1d(self.obs_vars, obs_vars_supported))
if len(obs_vars) == 0:
raise DataCoverageError(
'No observation variable matches found for '
'{}'.format(self.obs_id))
var_matches = self._find_var_matches(obs_vars, model_reader, var_name)
if self.read_opts_ungridded is not None:
ropts = self.read_opts_ungridded
else:
ropts = {}
obs_data = obs_reader.read(datasets_to_read=self.obs_id,
vars_to_retrieve=obs_vars,
**ropts)
if 'obs_filters' in self:
remaining_filters = self._eval_obs_filters()
obs_data = obs_data.apply_filters(**remaining_filters)
if self.remove_outliers:
self._update_var_outlier_ranges(var_matches)
#all_ts_types = const.GRID_IO.TS_TYPES
data_objs = {}
for model_var, obs_var in var_matches.items():
ts_type = self.ts_type
start, stop = start_stop(self.start, self.stop)
print_log.info('Running {} / {} ({}, {})'.format(
self.model_id, self.obs_id, model_var, obs_var))
try:
model_data = self._read_gridded(reader=model_reader,
var_name=model_var,
start=start,
stop=stop,
is_model=True)
except Exception as e:
msg = (
'Failed to load gridded data: {} / {}. Reason {}'.format(
self.model_id, model_var, repr(e)))
const.print_log.warning(msg)
self._write_log(msg + '\n')
if self.raise_exceptions:
self._close_log()
raise Exception(msg)
else:
continue
ts_type_src = model_data.ts_type
# =============================================================================
# if not model_data.ts_type in all_ts_types:
# raise TemporalResolutionError('Invalid temporal resolution {} '
# 'in model {}'.format(model_data.ts_type,
# self.model_id))
# =============================================================================
ignore_stats = None
if self.ignore_station_names is not None:
ignore_stats = self.ignore_station_names
if isinstance(ignore_stats, dict):
if obs_var in ignore_stats:
ignore_stats = ignore_stats[obs_var]
else:
ignore_stats = None
#ts_type_src = model_data.ts_type
if TsType(ts_type_src) < TsType(
ts_type): # < all_ts_types.index(ts_type_src):
print_log.info('Updating ts_type from {} to {} (highest '
'available in model {})'.format(
ts_type, ts_type_src, self.model_id))
ts_type = ts_type_src
if self.save_coldata:
savename = self._coldata_savename(model_data,
start,
stop,
ts_type,
var_name=model_var)
file_exists = self._check_coldata_exists(
model_data.data_id, savename)
out_dir = chk_make_subdir(self.basedir_coldata, self.model_id)
if file_exists:
if not self.reanalyse_existing:
if self._log:
self._write_log('SKIP: {}\n'.format(savename))
print_log.info('Skip {} (file already '
'exists)'.format(savename))
self.file_status[savename] = 'skipped'
continue
else:
print_log.info(
'Deleting and recomputing existing '
'colocated data file {}'.format(savename))
print_log.info('REMOVE: {}\n'.format(savename))
os.remove(os.path.join(out_dir, savename))
try:
by = None
if self.model_use_climatology:
by = start.year
coldata = colocate_gridded_ungridded(
gridded_data=model_data,
ungridded_data=obs_data,
ts_type=ts_type,
start=start,
stop=stop,
var_ref=obs_var,
filter_name=self.filter_name,
regrid_res_deg=self.regrid_res_deg,
remove_outliers=self.remove_outliers,
vert_scheme=self.vert_scheme,
harmonise_units=self.harmonise_units,
var_outlier_ranges=self.var_outlier_ranges,
var_ref_outlier_ranges=self.var_ref_outlier_ranges,
update_baseyear_gridded=by,
ignore_station_names=ignore_stats,
apply_time_resampling_constraints=self.
apply_time_resampling_constraints,
min_num_obs=self.min_num_obs,
colocate_time=self.colocate_time,
var_keep_outliers=self.model_keep_outliers,
var_ref_keep_outliers=self.obs_keep_outliers)
if self.save_coldata:
self._save_coldata(coldata, savename, out_dir, model_var,
model_data, obs_var)
data_objs[model_var] = coldata
except Exception as e:
msg = ('Colocation between model {} / {} and obs {} / {} '
'failed: Reason {}'.format(self.model_id, model_var,
self.obs_id, obs_var,
repr(e)))
const.print_log.warning(msg)
self._write_log(msg + '\n')
if self.raise_exceptions:
self._close_log()
raise Exception(msg)
return data_objs
def resample(self, to_ts_type, input_data=None, from_ts_type=None,
how=None, apply_constraints=None,
min_num_obs=None, **kwargs):
"""Resample input data
Parameters
----------
to_ts_type : str or pyaerocom.tstype.TsType
output resolution
input_data : pandas.Series or xarray.DataArray
data to be resampled
how : str
string specifying how the data is to be aggregated, default is mean
apply_constraints : bool, optional
if True, hierarchical resampling is applied using input
`samping_constraints` (if provided) or else, using constraints
specified in :attr:`pyaerocom.const.OBS_MIN_NUM_RESAMPLE`
min_num_obs : dict or int, optinal
integer or nested dictionary specifying minimum number of
observations required to resample from higher to lower frequency.
For instance, if `input_data` is hourly and `to_ts_type` is
monthly, you may specify something like::
min_num_obs =
{'monthly' : {'daily' : 7},
'daily' : {'hourly' : 6}}
to require at least 6 hours per day and 7 days per month.
**kwargs
additional input arguments passed to resampling method
Returns
-------
pandas.Series or xarray.DataArray
resampled data object
"""
if how is None:
how = 'mean'
if not isinstance(to_ts_type, TsType):
to_ts_type = TsType(to_ts_type)
if input_data is not None:
self.input_data = input_data
if self.input_data is None:
raise ValueError('Please provide data (Series or DataArray)')
if apply_constraints is None:
apply_constraints = self.APPLY_CONSTRAINTS
self.last_setup = dict(apply_constraints=False,
min_num_obs=None,
how=how)
if not apply_constraints or from_ts_type is None:
freq = to_ts_type.to_pandas_freq()
if not isinstance(how, str):
raise ValueError('Temporal resampling without constraints can '
'only use string type argument how (e.g. '
'how=mean). Got {}'.format(how))
return self.fun(self.input_data, freq=freq,
how=how, **kwargs)
# =============================================================================
# elif from_ts_type is None:
# self.last_setup = dict(apply_constraints=False,
# min_num_obs=None)
# freq = to_ts_type.to_pandas_freq()
# return self.fun(self.input_data, freq=freq,
# how=how, **kwargs)
# =============================================================================
if isinstance(from_ts_type, str):
from_ts_type = TsType(from_ts_type)
if not isinstance(from_ts_type, TsType):
raise ValueError('Invalid input for from_ts_type: {}. Need valid '
'str or TsType. Input arg from_ts_type is '
'required if resampling using hierarchical '
'constraints (arg apply_constraints) is activated'
.format(from_ts_type.val))
if to_ts_type > from_ts_type:
raise TemporalResolutionError('Cannot resample time-series from {} '
'to {}'
.format(from_ts_type, to_ts_type))
elif to_ts_type == from_ts_type:
const.logger.info('Input time frequency {} equals current frequency '
'of data. Resampling will be applied anyways '
'which will introduce NaN values at missing '
'time stamps'.format(to_ts_type.val))
freq = to_ts_type.to_pandas_freq()
return self.fun(self.input_data, freq=freq, how='mean',
**kwargs)
if min_num_obs is None:
min_num_obs = self.SAMPLING_CONSTRAINTS
_idx = self._gen_idx(from_ts_type, to_ts_type, min_num_obs, how)
data = self.input_data
for to_ts_type, mno, rshow in _idx:
const.logger.info('TO: {} ({}, {})'.format(to_ts_type, mno, rshow))
freq = TsType(to_ts_type).to_pandas_freq()
data = self.fun(data, freq=freq, how=rshow,
min_num_obs=mno)
self.last_setup = dict(apply_constraints=True,
min_num_obs=min_num_obs,
how=how)
return data
def __init__(self, input_data=None):
self.last_setup = None
self._input_data = None
self.input_data = input_data
self.valid_base_ts_types = [x for x in const.GRID_IO.TS_TYPES if
TsType(x).mulfac==1]
def resample(self,
to_ts_type,
input_data=None,
from_ts_type=None,
how='mean',
apply_constraints=None,
sampling_constraints=None,
**kwargs):
"""Resample input data
Parameters
----------
input_data : pandas.Series or xarray.DataArray
data to be resampled
to_ts_type : str or pyaerocom.tstype.TsType
output resolution
how : str
string specifying how the data is to be aggregated, default is mean
apply_constraints : bool, optional
if True, hierarchical resampling is applied using input
`samping_constraints` (if provided) or else, using constraints
specified in :attr:`pyaerocom.const.OBS_MIN_NUM_RESAMPLE`
sampling_constraints : dict
nested dictionary specifying sampling constraints to be applied to
data. For instance, if `input_data` is hourly and `to_ts_type` is
monthly, you may specify something like::
sampling_constraints =
{'monthly' : {'daily' : 7},
'daily' : {'hourly' : 6}}
to require at least 6 hours per day and 7 days per month.
**kwargs
additional input arguments passed to resampling method
Returns
-------
pandas.Series or xarray.DataArray
resampled data object
"""
if not isinstance(to_ts_type, TsType):
to_ts_type = TsType(to_ts_type)
if not to_ts_type.val in self.FREQS_SUPPORTED:
raise NotImplementedError('Cannot resample to input frequency '
'{}. Choose from: {}'.format(
to_ts_type,
self.FREQS_SUPPORTED.keys()))
if input_data is not None:
self.input_data = input_data
if self.input_data is None:
raise ValueError('Please provide data (Series or DataArray)')
if apply_constraints is None:
apply_constraints = self.APPLY_CONSTRAINTS
if not apply_constraints:
return self.fun(self.input_data,
freq=to_ts_type.val,
how=how,
**kwargs)
if isinstance(from_ts_type, str):
from_ts_type = TsType(from_ts_type)
if not isinstance(from_ts_type, TsType):
raise ValueError(
'Invalid input for from_ts_type: {}. Need valid '
'str or TsType. Input arg from_ts_type is '
'required if resampling using hierarchical '
'constraints (arg apply_constraints) is activated'.format(
from_ts_type))
if sampling_constraints is None:
sampling_constraints = self.SAMPLING_CONSTRAINTS
_idx = self._gen_idx(from_ts_type, to_ts_type, sampling_constraints)
data = self.input_data
for to_ts_type, min_num_obs in _idx:
data = self.fun(data,
freq=to_ts_type,
how=how,
min_num_obs=min_num_obs)
return data
def colocate_gridded_ungridded(gridded_data,
ungridded_data,
ts_type=None,
start=None,
stop=None,
filter_name=None,
regrid_res_deg=None,
remove_outliers=True,
vert_scheme=None,
harmonise_units=True,
regrid_scheme='areaweighted',
var_ref=None,
var_outlier_ranges=None,
var_ref_outlier_ranges=None,
update_baseyear_gridded=None,
ignore_station_names=None,
apply_time_resampling_constraints=None,
min_num_obs=None,
colocate_time=False,
var_keep_outliers=True,
var_ref_keep_outliers=False,
use_climatology_ref=False,
resample_how=None,
**kwargs):
"""Colocate gridded with ungridded data (low level method)
For high-level colocation see :class:`pyaerocom.colocation_auto.Colocator`
and :class:`pyaerocom.colocation_auto.ColocationSetup`
Note
----
Uses the variable that is contained in input :class:`GriddedData` object
(since these objects only contain a single variable). If this variable
is not contained in observation data (or contained but using a different
variable name) you may specify the obs variable to be used via input arg
`var_ref`
Parameters
----------
gridded_data : GriddedData
gridded data object (e.g. model results).
ungridded_data : UngriddedData
ungridded data object (e.g. observations).
ts_type : str
desired temporal resolution of colocated data (must be valid AeroCom
ts_type str such as daily, monthly, yearly.).
start : :obj:`str` or :obj:`datetime64` or similar, optional
start time for colocation, if None, the start time of the input
:class:`GriddedData` object is used.
stop : :obj:`str` or :obj:`datetime64` or similar, optional
stop time for colocation, if None, the stop time of the input
:class:`GriddedData` object is used
filter_name : str
string specifying filter used (cf. :class:`pyaerocom.filter.Filter` for
details). If None, then it is set to 'WORLD-wMOUNTAINS', which
corresponds to no filtering (world with mountains).
Use WORLD-noMOUNTAINS to exclude mountain sites.
regrid_res_deg : int or dict, optional
regrid resolution in degrees. If specified, the input gridded data
object will be regridded in lon / lat dimension to the input
resolution (if input is integer, both lat and lon are regridded to that
resolution, if input is dict, use keys `lat_res_deg` and `lon_res_deg`
to specify regrid resolutions, respectively).
remove_outliers : bool
if True, outliers are removed from model and obs data before colocation,
else not. Outlier ranges can be specified via input args
`var_outlier_ranges` and `var_ref_outlier_ranges`.
vert_scheme : str
string specifying scheme used to reduce the dimensionality in case
input grid data contains vertical dimension. Example schemes are
`mean, surface, altitude`, for details see
:func:`GriddedData.to_time_series`.
harmonise_units : bool
if True, units are attempted to be harmonised (note: raises Exception
if True and units cannot be harmonised).
var_ref : :obj:`str`, optional
variable against which data in :attr:`gridded_data` is supposed to be
compared. If None, then the same variable is used
(i.e. `gridded_data.var_name`).
var_outlier_ranges : dict, optional
dictionary specifying outlier ranges for dataset to be analysed
(e.g. dict(od550aer = [-0.05, 10], ang4487aer=[0,4])). If None, then
the pyaerocom default outlier ranges are used for the input variable.
Defaults to None.
var_ref_outlier_ranges : dict, optional
like `var_outlier_ranges` but for reference dataset.
update_baseyear_gridded : int, optional
optional input that can be set in order to re-define the time dimension
in the gridded data object to be analysed. E.g., if the data object
is a climatology (one year of data) that has set the base year of the
time dimension to a value other than the specified input start / stop
time this may be used to update the time in order to make colocation
possible.
ignore_station_names : str or list, optional
station name or pattern or list of station names or patterns that should
be ignored
apply_time_resampling_constraints : bool, optional
if True, then time resampling constraints are applied as provided via
:attr:`min_num_obs` or if that one is unspecified, as defined in
:attr:`pyaerocom.const.OBS_MIN_NUM_RESAMPLE`. If None, than
:attr:`pyaerocom.const.OBS_APPLY_TIME_RESAMPLE_CONSTRAINTS` is used
(which defaults to True !!).
min_num_obs : int or dict, optional
minimum number of observations for resampling of time
colocate_time : bool
if True and if original time resolution of data is higher than desired
time resolution (`ts_type`), then both datasets are colocated in time
*before* resampling to lower resolution.
var_keep_outliers : bool
if True, then no outliers will be removed from dataset to be analysed,
even if `remove_outliers` is True. That is because for model evaluation
often only outliers are supposed to be removed in the observations but
not in the model.
var_ref_keep_outliers : bool
if True, then no outliers will be removed from the reference dataset,
even if `remove_outliers` is True.
use_climatology_ref : bool
if True, climatological timeseries are used from observations
resample_how : str or dict
string specifying how data should be aggregated when resampling in time.
Default is "mean". Can also be a nested dictionary, e.g.
resample_how={'daily': {'hourly' : 'max'}} would use the maximum value
to aggregate from hourly to daily, rather than the mean.
**kwargs
additional keyword args (passed to
:func:`UngriddedData.to_station_data_all`)
Returns
-------
ColocatedData
instance of colocated data
Raises
------
VarNotAvailableError
if grid data variable is not available in ungridded data object
AttributeError
if instance of input :class:`UngriddedData` object contains more than
one dataset
TimeMatchError
if gridded data time range does not overlap with input time range
ColocationError
if none of the data points in input :class:`UngriddedData` matches
the input colocation constraints
"""
if var_outlier_ranges is None:
var_outlier_ranges = {}
if var_ref_outlier_ranges is None:
var_ref_outlier_ranges = {}
if filter_name is None:
filter_name = const.DEFAULT_REG_FILTER
try:
gridded_data.check_dimcoords_tseries()
except DimensionOrderError:
gridded_data.reorder_dimensions_tseries()
var = gridded_data.var_name
aerocom_var = gridded_data.var_name_aerocom
_check_var_registered(var, aerocom_var, gridded_data)
if var_ref is None:
if aerocom_var is not None:
var_ref = aerocom_var
else:
var_ref = var
if remove_outliers:
low, high, low_ref, high_ref = None, None, None, None
if var in var_outlier_ranges:
low, high = var_outlier_ranges[var]
if var_ref in var_ref_outlier_ranges:
low_ref, high_ref = var_ref_outlier_ranges[var_ref]
if not var_ref in ungridded_data.contains_vars:
raise VarNotAvailableError('Variable {} is not available in ungridded '
'data (which contains {})'.format(
var_ref, ungridded_data.contains_vars))
elif len(ungridded_data.contains_datasets) > 1:
raise AttributeError('Colocation can only be performed with '
'ungridded data objects that only contain a '
'single dataset. Use method `extract_dataset` of '
'UngriddedData object to extract single datasets')
dataset_ref = ungridded_data.contains_datasets[0]
if update_baseyear_gridded is not None:
# update time dimension in gridded data
gridded_data.base_year = update_baseyear_gridded
grid_ts_type_src = gridded_data.ts_type
grid_ts_type = TsType(gridded_data.ts_type)
if isinstance(ts_type, str):
ts_type = TsType(ts_type)
if ts_type is None or grid_ts_type < ts_type:
ts_type = grid_ts_type
elif grid_ts_type > ts_type and not colocate_time:
gridded_data = gridded_data.resample_time(
str(ts_type),
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
how=resample_how)
grid_ts_type = ts_type
# get start / stop of gridded data as pandas.Timestamp
grid_start = to_pandas_timestamp(gridded_data.start)
grid_stop = to_pandas_timestamp(gridded_data.stop)
if start is None:
start = grid_start
else:
start = to_pandas_timestamp(start)
if stop is None:
stop = grid_stop
else:
stop = to_pandas_timestamp(stop)
if start < grid_start:
start = grid_start
if stop > grid_stop:
stop = grid_stop
# check overlap
if stop < grid_start or start > grid_stop:
raise TimeMatchError('Input time range {}-{} does not '
'overlap with data range: {}-{}'.format(
start, stop, grid_start, grid_stop))
# create instance of Filter class (may, in the future, also include all
# filter options, e.g. start, stop, variables, only land, only oceans, and
# may also be linked with other data object, e.g. if data is only supposed
# to be used if other data object exceeds a certain threshold... but for
# now, only region and altitude range)
regfilter = Filter(name=filter_name)
# apply filter to data
ungridded_data = regfilter.apply(ungridded_data)
#crop time
gridded_data = regfilter.apply(gridded_data)
if start > grid_start or stop < grid_stop:
gridded_data = gridded_data.crop(time_range=(start, stop))
if regrid_res_deg is not None:
gridded_data = _regrid_gridded(gridded_data, regrid_scheme,
regrid_res_deg)
if remove_outliers and not var_ref_keep_outliers: #called twice if used via Colocator, this should go out here
ungridded_data.remove_outliers(var_ref,
inplace=True,
low=low_ref,
high=high_ref)
if use_climatology_ref:
col_freq = 'monthly'
obs_start = const.CLIM_START
obs_stop = const.CLIM_STOP
else:
col_freq = str(grid_ts_type) #TS_TYPE_TO_PANDAS_FREQ[grid_ts_type]
obs_start = start
obs_stop = stop
latitude = gridded_data.latitude.points
longitude = gridded_data.longitude.points
lat_range = [np.min(latitude), np.max(latitude)]
lon_range = [np.min(longitude), np.max(longitude)]
ungridded_data = ungridded_data.filter_by_meta(latitude=lat_range,
longitude=lon_range)
# get timeseries from all stations in provided time resolution
# (time resampling is done below in main loop)
all_stats = ungridded_data.to_station_data_all(
vars_to_convert=var_ref,
start=obs_start,
stop=obs_stop,
by_station_name=True,
ignore_index=ignore_station_names,
**kwargs)
obs_stat_data = all_stats['stats']
ungridded_lons = all_stats['longitude']
ungridded_lats = all_stats['latitude']
if len(obs_stat_data) == 0:
raise VarNotAvailableError('Variable {} is not available in specified '
'time interval ({}-{})'.format(
var_ref, start, stop))
# make sure the gridded data is in the right dimension
if gridded_data.ndim > 3:
if vert_scheme is None:
vert_scheme = 'mean'
if not vert_scheme in gridded_data.SUPPORTED_VERT_SCHEMES:
raise ValueError(
'Vertical scheme {} is not supported'.format(vert_scheme))
grid_stat_data = gridded_data.to_time_series(longitude=ungridded_lons,
latitude=ungridded_lats,
vert_scheme=vert_scheme)
pd_freq = TsType(col_freq).to_pandas_freq()
time_idx = make_datetime_index(start, stop, pd_freq)
coldata = np.empty((2, len(time_idx), len(obs_stat_data)))
lons = []
lats = []
alts = []
station_names = []
ungridded_unit = None
ts_type_src_ref = None
if not harmonise_units:
gridded_unit = str(gridded_data.units)
else:
gridded_unit = None
# loop over all stations and append to colocated data object
for i, obs_stat in enumerate(obs_stat_data):
# ToDo: consider removing to keep ts_type_src_ref (this was probably
# introduced for EBAS were the original data frequency is not constant
# but can vary from site to site)
if ts_type_src_ref is None:
ts_type_src_ref = obs_stat['ts_type_src']
elif obs_stat['ts_type_src'] != ts_type_src_ref:
spl = ts_type_src_ref.split(';')
if not obs_stat['ts_type_src'] in spl:
spl.append(obs_stat['ts_type_src'])
ts_type_src_ref = ';'.join(spl)
if ungridded_unit is None:
try:
ungridded_unit = obs_stat['var_info'][var_ref]['units']
except KeyError as e: #variable information or unit is not defined
logger.exception(repr(e))
try:
unit = obs_stat['var_info'][var_ref]['units']
except Exception:
unit = None
if not unit == ungridded_unit:
raise ValueError(
'Cannot perform colocation. Ungridded data '
'object contains different units ({})'.format(var_ref))
# get observations (Note: the index of the observation time series
# is already in the specified frequency format, and thus, does not
# need to be updated, for details (or if errors occur), cf.
# UngriddedData.to_station_data, where the conversion happens)
# get model station data
grid_stat = grid_stat_data[i]
if harmonise_units:
grid_unit = grid_stat.get_unit(var)
obs_unit = obs_stat.get_unit(var_ref)
if not grid_unit == obs_unit:
grid_stat.convert_unit(var, obs_unit)
if gridded_unit is None:
gridded_unit = obs_unit
if remove_outliers and not var_keep_outliers:
# don't check if harmonise_units is active, because the
# remove_outliers method checks units based on AeroCom default
# variables, and a variable mapping might be active, i.e.
# sometimes models use abs550aer for absorption coefficients
# with units [m-1] and not for AAOD (which is the AeroCom default
# and unitless. Hence, unit check in remove_outliers works only
# if the variable name (and unit) corresonds to AeroCom default)
#chk_unit = not harmonise_units
grid_stat.remove_outliers(var, low=low, high=high, check_unit=True)
_df = _colocate_site_data_helper(
stat_data=grid_stat,
stat_data_ref=obs_stat,
var=var,
var_ref=var_ref,
ts_type=col_freq,
resample_how=resample_how,
apply_time_resampling_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
use_climatology_ref=use_climatology_ref)
# this try/except block was introduced on 23/2/2021 as temporary fix from
# v0.10.0 -> v0.10.1 as a result of multi-weekly obsdata (EBAS) that
# can end up resulting in incorrect number of timestamps after resampling
# (the error was discovered using EBASMC, concpm10, 2019 and colocation
# frequency monthly)
try:
# assign the unified timeseries data to the colocated data array
coldata[0, :, i] = _df['ref'].values
coldata[1, :, i] = _df['data'].values
except ValueError as e:
const.print_log.warning(
f'Failed to colocate time for station {obs_stat.station_name}. '
f'This station will be skipped (error: {e})')
lons.append(obs_stat.longitude)
lats.append(obs_stat.latitude)
alts.append(obs_stat.altitude)
station_names.append(obs_stat.station_name)
try:
revision = ungridded_data.data_revision[dataset_ref]
except Exception:
try:
revision = ungridded_data._get_data_revision_helper(dataset_ref)
except MetaDataError:
revision = 'MULTIPLE'
except Exception:
revision = 'n/a'
files = [os.path.basename(x) for x in gridded_data.from_files]
meta = {
'data_source': [dataset_ref, gridded_data.name],
'var_name': [var_ref, var],
'ts_type': col_freq, # will be updated below if resampling
'filter_name': filter_name,
'ts_type_src': [ts_type_src_ref, grid_ts_type_src],
'start_str': to_datestring_YYYYMMDD(start),
'stop_str': to_datestring_YYYYMMDD(stop),
'var_units': [ungridded_unit, gridded_unit],
'vert_scheme': vert_scheme,
'data_level': 3,
'revision_ref': revision,
'from_files': files,
'from_files_ref': None,
'stations_ignored': ignore_station_names,
'colocate_time': colocate_time,
'obs_is_clim': use_climatology_ref,
'pyaerocom': pya_ver,
'apply_constraints': apply_time_resampling_constraints,
'min_num_obs': min_num_obs,
'outliers_removed': remove_outliers
}
meta.update(regfilter.to_dict())
# create coordinates of DataArray
coords = {
'data_source': meta['data_source'],
'var_name': ('data_source', meta['var_name']),
'var_units': ('data_source', meta['var_units']),
'ts_type_src': ('data_source', meta['ts_type_src']),
'time': time_idx,
'station_name': station_names,
'latitude': ('station_name', lats),
'longitude': ('station_name', lons),
'altitude': ('station_name', alts)
}
dims = ['data_source', 'time', 'station_name']
data = ColocatedData(data=coldata,
coords=coords,
dims=dims,
name=var,
attrs=meta)
# add correct units for lat / lon dimensions
data.latitude.attrs['standard_name'] = gridded_data.latitude.standard_name
data.latitude.attrs['units'] = str(gridded_data.latitude.units)
data.longitude.attrs[
'standard_name'] = gridded_data.longitude.standard_name
data.longitude.attrs['units'] = str(gridded_data.longitude.units)
if col_freq != str(ts_type):
data = data.resample_time(
to_ts_type=ts_type,
colocate_time=colocate_time,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
how=resample_how,
**kwargs)
return data
def resample_time_dataarray(arr, freq, how='mean', min_num_obs=None):
"""Resample the time dimension of a :class:`xarray.DataArray`
Note
----
The dataarray must have a dimension coordinate named "time"
Parameters
----------
arr : DataArray
data array to be resampled
freq : str
new temporal resolution (can be pandas freq. string, or pyaerocom
ts_type)
how : str
choose from mean or median
min_num_obs : :obj:`int`, optional
minimum number of observations required per period (when downsampling).
E.g. if input is in daily resolution and freq is monthly and
min_num_obs is 10, then all months that have less than 10 days of data
are set to nan.
Returns
-------
DataArray
resampled data array object
Raises
------
IOError
if data input `arr` is not an instance of :class:`DataArray`
DataDimensionError
if time dimension is not available in dataset
"""
if not isinstance(arr, xray.DataArray):
raise IOError('Invalid input for arr: need DataArray, got {}'.format(
type(arr)))
elif not 'time' in arr.dims:
raise DataDimensionError('Cannot resample time: input DataArray has '
'no time dimension')
from pyaerocom.tstype import TsType
from pyaerocom.time_config import XARR_TIME_GROUPERS
to = TsType(freq)
pd_freq = to.to_pandas()
invalid = None
if min_num_obs is not None:
if not pd_freq in XARR_TIME_GROUPERS:
raise ValueError(
'Cannot infer xarray grouper for ts_type {}'.format(to.val))
gr = XARR_TIME_GROUPERS[pd_freq]
# 2D mask with shape of resampled data array
invalid = arr.groupby(
'time.{}'.format(gr)).count(dim='time') < min_num_obs
freq, loffset = _get_pandas_freq_and_loffset(freq)
arr = arr.resample(time=pd_freq, loffset=loffset).mean(dim='time')
if invalid is not None:
arr.data[invalid.data] = np.nan
return arr
def check_time_coordOLD(cube, ts_type, year):
"""Method that checks the time coordinate of an iris Cube
This method checks if the time dimension of a cube is accessible and
according to the standard (i.e. fully usable). It only checks, and does not
correct. For the latter, please see :func:`correct_time_coord`.
Parameters
----------
cube : Cube
cube containing data
ts_type : str
temporal resolution of data (e.g. "hourly", "daily"). This information
is e.g. encrypted in the filename of a NetCDF file and may be
accessed using :class:`pyaerocom.io.FileConventionRead`
year : int
interger specifying year of observation, e.g. 2017
Returns
-------
bool
True, if time dimension is ok, False if not
"""
ok = True
ts_type = TsType(ts_type)
test_idx = [
0, 1, 2, 7
] #7, since last accessible index in a 3hourly dataset of one day is 7
try:
try:
t = cube.coord("time")
except Exception:
raise AttributeError("Cube does not contain time dimension")
if not isinstance(t, iris.coords.DimCoord):
raise AttributeError("Time is not a DimCoord instance")
try:
cftime_to_datetime64(0, cfunit=t.units)
except Exception:
raise ValueError("Could not convert time unit string")
# =============================================================================
# tres_np = TSTR_TO_NP_TD[ts_type]
# conv = TSTR_TO_NP_DT[ts_type]
# =============================================================================
tres_np = ts_type.timedelta64_str
conv = ts_type.datetime64_str_str
base = datetime64("{}-01-01 00:00:00".format(year)).astype(conv)
test_datenums = asarray(test_idx)
ts_nominal = base + test_datenums.astype(tres_np)
dts_nominal = ts_nominal[1:] - ts_nominal[:-1]
ts_values = cftime_to_datetime64(t[test_idx].points,
cfunit=t.units).astype(conv)
dts_values = ts_values[1:] - ts_values[:-1]
if not all(ts_values == ts_nominal):
raise ValueError(
"Time match error, nominal dates for test array"
"%s (unit=%s): %s\nReceived values after "
"conversion: %s" %
(test_datenums, t.units.origin, ts_nominal, ts_values))
elif not all(dts_values == dts_nominal):
raise ValueError(
"Time match error, time steps for test array"
"%s (unit=%s): %s\nReceived values after "
"conversion: %s" %
(test_datenums, t.units.origin, dts_nominal, dts_values))
except Exception as e:
logger.warning("Invalid time dimension.\n"
"Error message: {}".format(repr(e)))
ok = False
return ok
def test_to_pandas_freq():
assert TsType('3hourly').to_pandas_freq() == '3H'
assert TsType('daily').to_pandas_freq() == 'D'
def resample(self, to_ts_type, input_data=None, from_ts_type=None,
how='mean', apply_constraints=False,
min_num_obs=None, **kwargs):
"""Resample input data
Parameters
----------
input_data : pandas.Series or xarray.DataArray
data to be resampled
to_ts_type : str or pyaerocom.tstype.TsType
output resolution
how : str
string specifying how the data is to be aggregated, default is mean
apply_constraints : bool, optional
if True, hierarchical resampling is applied using input
`samping_constraints` (if provided) or else, using constraints
specified in :attr:`pyaerocom.const.OBS_MIN_NUM_RESAMPLE`
min_num_obs : dict or int, optinal
integer or nested dictionary specifying minimum number of
observations required to resample from higher to lower frequency.
For instance, if `input_data` is hourly and `to_ts_type` is
monthly, you may specify something like::
min_num_obs =
{'monthly' : {'daily' : 7},
'daily' : {'hourly' : 6}}
to require at least 6 hours per day and 7 days per month. Or, if
data is daily and output is monthly and
**kwargs
additional input arguments passed to resampling method
Returns
-------
pandas.Series or xarray.DataArray
resampled data object
"""
if not isinstance(to_ts_type, TsType):
to_ts_type = TsType(to_ts_type)
if not to_ts_type.val in self.FREQS_SUPPORTED:
raise NotImplementedError('Cannot resample to input frequency '
'{}. Choose from: {}'
.format(to_ts_type,
self.FREQS_SUPPORTED.keys()))
if input_data is not None:
self.input_data = input_data
if self.input_data is None:
raise ValueError('Please provide data (Series or DataArray)')
if apply_constraints is None:
apply_constraints = self.APPLY_CONSTRAINTS
if not apply_constraints:
self.last_setup = dict(apply_constraints=False,
min_num_obs=None)
return self.fun(self.input_data, freq=to_ts_type.val,
how=how, **kwargs)
elif from_ts_type is None:
const.print_log.warn('Cannot apply time resampling constraints, '
'since input from_ts_type is None. Applying '
'resampling to {} without any constraints'
.format(to_ts_type))
self.last_setup = dict(apply_constraints=False,
min_num_obs=None)
return self.fun(self.input_data, freq=to_ts_type.val,
how=how, **kwargs)
if isinstance(from_ts_type, str):
from_ts_type = TsType(from_ts_type)
if not isinstance(from_ts_type, TsType):
raise ValueError('Invalid input for from_ts_type: {}. Need valid '
'str or TsType. Input arg from_ts_type is '
'required if resampling using hierarchical '
'constraints (arg apply_constraints) is activated'
.format(from_ts_type))
if to_ts_type > from_ts_type:
raise TemporalResolutionError('Cannot resample time-series from {} '
'to {}'
.format(from_ts_type, to_ts_type))
elif to_ts_type == from_ts_type:
const.logger.info('Input time frequency equals current frequency '
'of data, ignoring any resampling constraints')
self.last_setup = dict(apply_constraints=False,
min_num_obs=None)
return self.fun(self.input_data, freq=to_ts_type.val, how=how,
**kwargs)
if min_num_obs is None:
min_num_obs = self.SAMPLING_CONSTRAINTS
_idx = self._gen_idx(from_ts_type, to_ts_type, min_num_obs)
data = self.input_data
for to_ts_type, mno in _idx:
data = self.fun(data, freq=to_ts_type, how=how,
min_num_obs=mno)
self.last_setup = dict(apply_constraints=True,
min_num_obs=min_num_obs)
return data
def colocate_gridded_gridded(gridded_data,
gridded_data_ref,
ts_type=None,
start=None,
stop=None,
filter_name=None,
regrid_res_deg=None,
remove_outliers=True,
vert_scheme=None,
harmonise_units=True,
regrid_scheme='areaweighted',
var_outlier_ranges=None,
var_ref_outlier_ranges=None,
update_baseyear_gridded=None,
apply_time_resampling_constraints=None,
min_num_obs=None,
colocate_time=False,
var_keep_outliers=True,
var_ref_keep_outliers=False,
resample_how=None,
**kwargs):
"""Colocate 2 gridded data objects
Todo
----
- think about vertical dimension (vert_scheme input not used at the moment)
Parameters
----------
gridded_data : GriddedData
gridded data (e.g. model results)
gridded_data_ref : GriddedData
reference dataset that is used to evaluate
:attr:`gridded_data` (e.g. gridded observation data)
ts_type : str
desired temporal resolution of colocated data (must be valid AeroCom
ts_type str such as daily, monthly, yearly..)
start : :obj:`str` or :obj:`datetime64` or similar, optional
start time for colocation, if None, the start time of the input
:class:`GriddedData` object is used
stop : :obj:`str` or :obj:`datetime64` or similar, optional
stop time for colocation, if None, the stop time of the input
:class:`GriddedData` object is used
filter_name : str
string specifying filter used (cf. :class:`pyaerocom.filter.Filter` for
details). If None, then it is set to 'WORLD-wMOUNTAINS', which
corresponds to no filtering (world with mountains).
Use WORLD-noMOUNTAINS to exclude mountain sites.
regrid_res_deg : int or dict, optional
regrid resolution in degrees. If specified, the input gridded data
objects will be regridded in lon / lat dimension to the input
resolution (if input is integer, both lat and lon are regridded to that
resolution, if input is dict, use keys `lat_res_deg` and `lon_res_deg`
to specify regrid resolutions, respectively).
remove_outliers : bool
if True, outliers are removed from model and obs data before colocation,
else not.
vert_scheme : str
string specifying scheme used to reduce the dimensionality in case
input grid data contains vertical dimension. Example schemes are
`mean, surface, altitude`, for details see
:func:`GriddedData.to_time_series`.
harmonise_units : bool
if True, units are attempted to be harmonised (note: raises Exception
if True and units cannot be harmonised).
regrid_scheme : str
iris scheme used for regridding (defaults to area weighted regridding)
var_outlier_ranges : :obj:`dict`, optional
dictionary specifying outlier ranges for dataset to be analysed
(e.g. dict(od550aer = [-0.05, 10], ang4487aer=[0,4])). If None, then
the pyaerocom default outlier ranges are used for the input variable.
Defaults to None.
var_ref_outlier_ranges : dict, optional
like `var_outlier_ranges` but for reference dataset.
update_baseyear_gridded : int, optional
optional input that can be set in order to redefine the time dimension
in the gridded data object to be analysed. E.g., if the data object
is a climatology (one year of data) that has set the base year of the
time dimension to a value other than the specified input start / stop
time this may be used to update the time in order to make colocation
possible.
apply_time_resampling_constraints : bool, optional
if True, then time resampling constraints are applied as provided via
:attr:`min_num_obs` or if that one is unspecified, as defined in
:attr:`pyaerocom.const.OBS_MIN_NUM_RESAMPLE`. If None, than
:attr:`pyaerocom.const.OBS_APPLY_TIME_RESAMPLE_CONSTRAINTS` is used
(which defaults to True !!).
min_num_obs : int or dict, optional
minimum number of observations for resampling of time
colocate_time : bool
if True and if original time resolution of data is higher than desired
time resolution (`ts_type`), then both datasets are colocated in time
*before* resampling to lower resolution.
var_keep_outliers : bool
if True, then no outliers will be removed from dataset to be analysed,
even if `remove_outliers` is True. That is because for model evaluation
often only outliers are supposed to be removed in the observations but
not in the model.
var_ref_keep_outliers : bool
if True, then no outliers will be removed from the reference dataset,
even if `remove_outliers` is True.
resample_how : str or dict
string specifying how data should be aggregated when resampling in time.
Default is "mean". Can also be a nested dictionary, e.g.
resample_how={'daily': {'hourly' : 'max'}} would use the maximum value
to aggregate from hourly to daily, rather than the mean.
**kwargs
additional keyword args (not used here, but included such that factory
class can handle different methods with different inputs)
Returns
-------
ColocatedData
instance of colocated data
"""
if vert_scheme is not None:
raise NotImplementedError(
f'This type of colocation is not implemented '
f'for gridded / gridded colocation... ({vert_scheme})')
if var_outlier_ranges is None:
var_outlier_ranges = {}
if var_ref_outlier_ranges is None:
var_ref_outlier_ranges = {}
if filter_name is None:
filter_name = const.DEFAULT_REG_FILTER
if harmonise_units and gridded_data.var_info.has_unit:
if not gridded_data.units == gridded_data_ref.units:
try:
gridded_data_ref.convert_unit(gridded_data.units)
except Exception:
raise DataUnitError('Failed to merge data unit of reference '
'gridded data object ({}) to data unit '
'of gridded data object ({})'.format(
gridded_data.units,
gridded_data_ref.units))
var, var_ref = gridded_data.var_name, gridded_data_ref.var_name
aerocom_var = gridded_data.var_name_aerocom
_check_var_registered(var, aerocom_var, gridded_data)
if remove_outliers:
low, high, low_ref, high_ref = None, None, None, None
if var in var_outlier_ranges:
low, high = var_outlier_ranges[var]
if var_ref in var_ref_outlier_ranges:
low_ref, high_ref = var_ref_outlier_ranges[var_ref]
if not var_keep_outliers:
gridded_data.remove_outliers(low, high, inplace=True)
if not var_ref_keep_outliers:
gridded_data_ref.remove_outliers(low_ref, high_ref, inplace=True)
if update_baseyear_gridded is not None:
# update time dimension in gridded data
gridded_data.base_year = update_baseyear_gridded
if regrid_res_deg is not None:
gridded_data_ref = _regrid_gridded(gridded_data_ref, regrid_scheme,
regrid_res_deg)
# perform regridding
if gridded_data.lon_res < gridded_data_ref.lon_res: #obs has lower resolution
gridded_data = gridded_data.regrid(gridded_data_ref,
scheme=regrid_scheme)
else:
gridded_data_ref = gridded_data_ref.regrid(gridded_data,
scheme=regrid_scheme)
# get start / stop of gridded data as pandas.Timestamp
grid_start = to_pandas_timestamp(gridded_data.start)
grid_stop = to_pandas_timestamp(gridded_data.stop)
grid_start_ref = to_pandas_timestamp(gridded_data_ref.start)
grid_stop_ref = to_pandas_timestamp(gridded_data_ref.stop)
# time resolution of dataset to be analysed
grid_ts_type = grid_ts_type_src = gridded_data.ts_type
ref_ts_type = ref_ts_type_src = gridded_data_ref.ts_type
if ref_ts_type != grid_ts_type:
# ref data is in higher resolution
if TsType(ref_ts_type) > TsType(grid_ts_type):
gridded_data_ref = gridded_data_ref.resample_time(
grid_ts_type,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
how=resample_how)
else:
gridded_data = gridded_data.resample_time(
ref_ts_type,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
how=resample_how)
grid_ts_type = ref_ts_type
# now both are in same temporal resolution
# input ts_type is not specified or model is in lower resolution
# than input ts_type -> use model frequency to colocate
if ts_type is None or TsType(grid_ts_type) < TsType(ts_type):
ts_type = grid_ts_type
if start is None:
start = grid_start
else:
start = to_pandas_timestamp(start)
if stop is None:
stop = grid_stop
else:
stop = to_pandas_timestamp(stop)
if grid_start_ref > start:
start = grid_start_ref
if grid_stop_ref < stop:
stop = grid_stop_ref
# check overlap
if stop < grid_start or start > grid_stop:
raise TimeMatchError('Input time range {}-{} does not '
'overlap with data range: {}-{}'.format(
start, stop, grid_start, grid_stop))
gridded_data = gridded_data.crop(time_range=(start, stop))
gridded_data_ref = gridded_data_ref.crop(time_range=(start, stop))
# perform region extraction (if applicable)
regfilter = Filter(name=filter_name)
gridded_data = regfilter(gridded_data)
gridded_data_ref = regfilter(gridded_data_ref)
if not gridded_data.shape == gridded_data_ref.shape:
raise ColocationError('Shape mismatch between two colocated data '
'arrays, please debug')
files_ref = [os.path.basename(x) for x in gridded_data_ref.from_files]
files = [os.path.basename(x) for x in gridded_data.from_files]
meta = {
'data_source': [gridded_data_ref.data_id, gridded_data.data_id],
'var_name': [var_ref, var],
'ts_type': grid_ts_type,
'filter_name': filter_name,
'ts_type_src': [ref_ts_type_src, grid_ts_type_src],
'start_str': to_datestring_YYYYMMDD(start),
'stop_str': to_datestring_YYYYMMDD(stop),
'var_units': [str(gridded_data_ref.units),
str(gridded_data.units)],
'vert_scheme': vert_scheme,
'data_level': 3,
'revision_ref': gridded_data_ref.data_revision,
'from_files': files,
'from_files_ref': files_ref,
'colocate_time': colocate_time,
'obs_is_clim': False,
'pyaerocom': pya_ver,
'apply_constraints': apply_time_resampling_constraints,
'min_num_obs': min_num_obs
}
meta.update(regfilter.to_dict())
data = gridded_data.grid.data
if isinstance(data, np.ma.core.MaskedArray):
data = data.filled(np.nan)
data_ref = gridded_data_ref.grid.data
if isinstance(data_ref, np.ma.core.MaskedArray):
data_ref = data_ref.filled(np.nan)
arr = np.asarray((data_ref, data))
time = gridded_data.time_stamps().astype('datetime64[ns]')
lats = gridded_data.latitude.points
lons = gridded_data.longitude.points
# create coordinates of DataArray
coords = {
'data_source': meta['data_source'],
'var_name': ('data_source', meta['var_name']),
'var_units': ('data_source', meta['var_units']),
'ts_type_src': ('data_source', meta['ts_type_src']),
'time': time,
'latitude': lats,
'longitude': lons
}
dims = ['data_source', 'time', 'latitude', 'longitude']
data = ColocatedData(data=arr,
coords=coords,
dims=dims,
name=gridded_data.var_name,
attrs=meta)
# add correct units for lat / lon dimensions
data.latitude.attrs['standard_name'] = gridded_data.latitude.standard_name
data.latitude.attrs['units'] = str(gridded_data.latitude.units)
data.longitude.attrs[
'standard_name'] = gridded_data.longitude.standard_name
data.longitude.attrs['units'] = str(gridded_data.longitude.units)
if grid_ts_type != ts_type:
data = data.resample_time(
to_ts_type=ts_type,
colocate_time=colocate_time,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
how=resample_how,
**kwargs)
return data
def test_cf_base_unit():
assert TsType('daily').cf_base_unit == 'days'
assert TsType('monthly').cf_base_unit == 'days'
assert TsType('hourly').cf_base_unit == 'hours'
def check_time_coord(cube, ts_type, year):
"""Method that checks the time coordinate of an iris Cube
This method checks if the time dimension of a cube is accessible and
according to the standard (i.e. fully usable). It only checks, and does not
correct. For the latter, please see :func:`correct_time_coord`.
Parameters
----------
cube : Cube
cube containing data
ts_type : str
pyaerocom ts_type
year :
year of data
Returns
-------
bool
True, if time dimension is ok, False if not
"""
if isinstance(ts_type, str):
ts_type = TsType(ts_type)
try:
t = cube.coord("time")
except Exception:
raise AttributeError("Cube does not contain time dimension")
if not isinstance(t, iris.coords.DimCoord):
raise AttributeError("Time is not a DimCoord instance")
try:
cftime_to_datetime64(0, cfunit=t.units)
except Exception:
raise ValueError("Could not convert time unit string")
freq = ts_type.to_pandas_freq()
tidx = make_datetimeindex_from_year(freq, year)
num_per = len(tidx)
num = len(t.points)
if not num == num_per:
if tidx[0].is_leap_year:
if not _check_leap_year(num, num_per, ts_type):
raise UnresolvableTimeDefinitionError(
'Expected {} timestamps but '
'data has {}'.format(len(tidx), num))
else:
raise UnresolvableTimeDefinitionError('Expected {} timestamps but '
'data has {}'.format(
len(tidx), num))
# ToDo: check why MS is not working for period conversion
if freq == 'MS':
freq = 'M'
# convert first and last timestamps of index array into periods
# (e.g. January and December for monthly data)
per0 = tidx[0].to_period(freq)
per1 = tidx[-1].to_period(freq)
# first and last timestamp in data
t0, t1 = cftime_to_datetime64([t.points[0], t.points[-1]], cfunit=t.units)
if not per0.start_time <= t0 <= per0.end_time:
raise ValueError('First timestamp of data {} does not lie in first '
'period: {}'.format(t0, per0))
elif not per1.start_time <= t1 <= per1.end_time:
raise ValueError('Last timestamp of data {} does not lie in last '
'period: {}'.format(t1, per1))
def test_to_numpy_freq():
assert TsType('3hourly').to_numpy_freq() == '3h'
assert TsType('daily').to_numpy_freq() == '1D'
def _run_gridded_ungridded(self, var_name=None):
"""Analysis method for gridded vs. ungridded data"""
print_log.info('PREPARING colocation of {} vs. {}'.format(
self.model_id, self.obs_id))
model_reader = self.instantiate_gridded_reader(what='model')
obs_reader = ReadUngridded(self.obs_id, data_dir=self.obs_data_dir)
obs_vars = obs_reader.get_vars_supported(self.obs_id, self.obs_vars)
if len(obs_vars) == 0:
raise DataCoverageError(
'No observation variable matches found for '
'{}'.format(self.obs_id))
var_matches = self._find_var_matches(obs_vars, model_reader, var_name)
print_log.info(
'The following variable combinations will be colocated\n'
'MODEL-VAR\tOBS-VAR')
for key, val in var_matches.items():
print_log.info('{}\t{}'.format(key, val))
# get list of unique observation variables
obs_vars = np.unique(list(var_matches.values())).tolist()
if self.remove_outliers:
self._update_var_outlier_ranges(var_matches)
if self.read_opts_ungridded is not None:
ropts = self.read_opts_ungridded
else:
ropts = {}
data_objs = {}
if self.start is None:
self._infer_start_stop(model_reader)
start, stop = start_stop(self.start, self.stop)
for model_var, obs_var in var_matches.items():
# ToDo: consider removing outliers already here.
#if 'obs_filters' in self:
ts_type = self.ts_type
print_log.info('Running {} / {} ({}, {})'.format(
self.model_id, self.obs_id, model_var, obs_var))
try:
model_data = self._read_gridded(reader=model_reader,
var_name=model_var,
start=start,
stop=stop,
is_model=True)
except Exception as e:
msg = (
'Failed to load gridded data: {} / {}. Reason {}'.format(
self.model_id, model_var, repr(e)))
const.print_log.warning(msg)
self._write_log(msg + '\n')
if self.raise_exceptions:
self._close_log()
raise Exception(msg)
else:
continue
ts_type_src = model_data.ts_type
rshow = self._eval_resample_how(model_var, obs_var)
if ts_type is None:
# if colocation frequency is not specified
ts_type = ts_type_src
ignore_stats = None
if self.ignore_station_names is not None:
ignore_stats = self.ignore_station_names
if isinstance(ignore_stats, dict):
if obs_var in ignore_stats:
ignore_stats = ignore_stats[obs_var]
else:
ignore_stats = None
#ts_type_src = model_data.ts_type
if TsType(ts_type_src) < TsType(
ts_type): # < all_ts_types.index(ts_type_src):
print_log.info('Updating ts_type from {} to {} (highest '
'available in model {})'.format(
ts_type, ts_type_src, self.model_id))
ts_type = ts_type_src
really_do_reanalysis = True
if self.save_coldata:
really_do_reanalysis = False
savename = self._coldata_savename(model_data,
start,
stop,
ts_type,
var_name=model_var)
file_exists = self._check_coldata_exists(
model_data.data_id, savename)
out_dir = chk_make_subdir(self.basedir_coldata, self.model_id)
if file_exists:
if not self.reanalyse_existing:
if self._log:
self._write_log('SKIP: {}\n'.format(savename))
print_log.info('Skip {} (file already '
'exists)'.format(savename))
self.file_status[savename] = 'skipped'
continue
else:
really_do_reanalysis = True
print_log.info(
'Deleting and recomputing existing '
'colocated data file {}'.format(savename))
print_log.info('REMOVE: {}\n'.format(savename))
os.remove(os.path.join(out_dir, savename))
else:
really_do_reanalysis = True
if really_do_reanalysis:
#Reading obs data only if the co-located data file does
#not already exist.
#This part of the method has been changed by @hansbrenna to work better with
#large observational data sets. Only one variable is loaded into
# the UngriddedData object at a time. Currently the variable is
#re-read a lot of times, which is a weakness.
obs_data = obs_reader.read(vars_to_retrieve=obs_var,
only_cached=self._obs_cache_only,
**ropts)
# ToDo: consider removing outliers already here.
if 'obs_filters' in self:
remaining_filters = self._eval_obs_filters()
obs_data = obs_data.apply_filters(**remaining_filters)
try:
try:
by = self.update_baseyear_gridded
stop = None
except AttributeError:
by = None
if self.model_use_climatology:
by = start.year
coldata = colocate_gridded_ungridded(
gridded_data=model_data,
ungridded_data=obs_data,
ts_type=ts_type,
start=start,
stop=stop,
var_ref=obs_var,
filter_name=self.filter_name,
regrid_res_deg=self.regrid_res_deg,
remove_outliers=self.remove_outliers,
vert_scheme=self.vert_scheme,
harmonise_units=self.harmonise_units,
var_outlier_ranges=self.var_outlier_ranges,
var_ref_outlier_ranges=self.var_ref_outlier_ranges,
update_baseyear_gridded=by,
ignore_station_names=ignore_stats,
apply_time_resampling_constraints=self.
apply_time_resampling_constraints,
min_num_obs=self.min_num_obs,
colocate_time=self.colocate_time,
var_keep_outliers=self.model_keep_outliers,
var_ref_keep_outliers=self.obs_keep_outliers,
use_climatology_ref=self.obs_use_climatology,
resample_how=rshow)
if self.model_to_stp:
coldata = correct_model_stp_coldata(coldata)
if self.save_coldata:
self._save_coldata(coldata, savename, out_dir, model_var,
model_data, obs_var)
data_objs[model_var] = coldata
except Exception:
msg = ('Colocation between model {} / {} and obs {} / {} '
'failed.\nTraceback:\n{}'.format(
self.model_id, model_var, self.obs_id, obs_var,
traceback.format_exc()))
const.print_log.warning(msg)
self._write_log(msg + '\n')
if self.raise_exceptions:
self._close_log()
raise Exception(msg)
return data_objs
