def _init_log(self):
logbase = chk_make_subdir(self.out_basedir, 'log_files_analysis')
logdir = chk_make_subdir(logbase, datetime.today().strftime('%Y%m%d'))
if self.start is None:
start_str = 'ModelStart'
else:
start_str = to_datestring_YYYYMMDD(self.start)
if self.stop is None:
if isinstance(self.start, int): #is year
stop_str = to_datestring_YYYYMMDD(self.start + 1)
else:
stop_str = 'None'
else:
stop_str = to_datestring_YYYYMMDD(self.stop)
fname = ('result_log_{}_{}_{}.csv'
.format(self.obs_id, start_str, stop_str))
self._log = log = open(os.path.join(logdir, fname), 'w+')
log.write('Analysis configuration\n')
for k, v in self._setup.items():
if k == 'model_id':
continue
elif k == 'ts_type_setup':
log.write('TS_TYPES (<read>: <analyse>)\n')
for key, val in v.items():
if key == 'read_alt':
continue
log.write(' {}:{}\n'.format(key, val))
if v['read_alt']:
log.write(' Alternative TS_TYPES (read)\n')
for key, val in v['read_alt'].items():
log.write(' {}:{}\n'.format(key, val))
else:
log.write('{}: {}\n'.format(k, v))
def _coldata_save_name(self, model_data, ts_type_ana, start=None,
stop=None):
"""Based on current setup, get savename of colocated data file
"""
if start is None:
start = model_data.start
else:
start = to_pandas_timestamp(start)
if stop is None:
stop = model_data.stop
else:
stop = to_pandas_timestamp(stop)
start_str = to_datestring_YYYYMMDD(start)
stop_str = to_datestring_YYYYMMDD(stop)
ts_type_src = model_data.ts_type
coll_data_name = ColocatedData._aerocom_savename(model_data.var_name,
self.obs_id,
self.model_id,
ts_type_src,
start_str,
stop_str,
ts_type_ana,
self.filter_name)
return coll_data_name + '.nc'
def _coldata_savename(self,
model_data,
start=None,
stop=None,
ts_type=None,
var_name=None):
"""Based on current setup, get savename of colocated data file
"""
if start is None:
start = model_data.start
else:
start = to_pandas_timestamp(start)
if stop is None:
stop = model_data.stop
else:
stop = to_pandas_timestamp(stop)
if ts_type is None:
ts_type = model_data.ts_type
if var_name is None:
var_name = model_data.var_name
start_str = to_datestring_YYYYMMDD(start)
stop_str = to_datestring_YYYYMMDD(stop)
if isinstance(self.obs_name, str):
obs_id = self.obs_name
else:
obs_id = self.obs_id
if isinstance(self.model_name, str):
model_id = self.model_name
else:
model_id = model_data.data_id
col_data_name = ColocatedData._aerocom_savename(
var_name=var_name,
obs_id=obs_id,
model_id=model_id,
start_str=start_str,
stop_str=stop_str,
ts_type=ts_type,
filter_name=self.filter_name)
return col_data_name + '.nc'
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,
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,
**kwargs):
"""Colocate gridded with ungridded data
Note
----
Uses the variable that is contained in input :class:`GriddedData` object
(since these objects only contain a single variable)
Parameters
----------
gridded_data : GriddedData
gridded data (e.g. model results)
ungridded_data : UngriddedData
ungridded data (e.g. observations)
ts_type : str
desired temporal resolution of colocated data (must be valid AeroCom
ts_type str such as daily, monthly, yearly.). The colocation itself is
done in the highest available resolution and resampling to `ts_type` is
done afterwards. You may change this behaviour by setting input param
`resample_first=True` (default is False).
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 : :obj:`int`, optional
regrid resolution in degrees. If specified, the input gridded data
object will be regridded in lon / lat dimension to the input
resolution. (BETA feature)
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).
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.
**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 = 'WORLD-wMOUNTAINS'
var = gridded_data.var_name
aerocom_var = gridded_data.var_name_aerocom
if var_ref is None:
var_ref = aerocom_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
# 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_ts_type = gridded_data.ts_type
if ts_type is None:
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 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(ungridded_data)
#crop time
gridded_data = gridded_data.crop(time_range=(start, stop))
if regrid_res_deg is not None:
lons = gridded_data.longitude.points
lats = gridded_data.latitude.points
lons_new = np.arange(lons.min(), lons.max(), regrid_res_deg)
lats_new = np.arange(lats.min(), lats.max(), regrid_res_deg)
gridded_data = gridded_data.interpolate(latitude=lats_new,
longitude=lons_new)
ungridded_freq = None # that keeps ungridded data in original resolution
if not colocate_time:
gridded_data = gridded_data.resample_time(to_ts_type=ts_type)
ungridded_freq = ts_type # converts ungridded data directly to desired resolution
# ts_type that is used for colocation
col_ts_type = gridded_data.ts_type
# pandas frequency string that corresponds to col_ts_type
col_freq = TS_TYPE_TO_PANDAS_FREQ[col_ts_type]
if remove_outliers and not var_ref_keep_outliers:
ungridded_data.remove_outliers(var_ref,
inplace=True,
low=low_ref,
high=high_ref)
all_stats = ungridded_data.to_station_data_all(
vars_to_convert=var_ref,
start=start,
stop=stop,
freq=ungridded_freq,
by_station_name=True,
ignore_index=ignore_station_names,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
**kwargs)
obs_stat_data = all_stats['stats']
ungridded_lons = all_stats['longitude']
ungridded_lats = all_stats['latitude']
# resampling constraints may have been altered in case input was None,
# thus overwrite
vi = obs_stat_data[0]['var_info'][var_ref]
if 'apply_constraints' in vi:
apply_time_resampling_constraints = vi['apply_constraints']
min_num_obs = vi['min_num_obs']
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
try:
gridded_data.check_dimcoords_tseries()
except DimensionOrderError:
gridded_data.reorder_dimensions_tseries()
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)
# Generate time index of ColocatedData object
time_idx = pd.DatetimeIndex(start=start, end=stop, freq=col_freq)
#periods = time_idx.to_period(col_freq)
# =============================================================================
# if col_freq in PANDAS_RESAMPLE_OFFSETS:
# offs = np.timedelta64(1, '[{}]'.format(PANDAS_RESAMPLE_OFFSETS[col_freq]))
# time_idx = time_idx + offs
# =============================================================================
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):
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:
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 data corresponding to station
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)
# get grid and obs timeseries data (that may be sampled in arbitrary
# time resolution, particularly the obs data)
grid_ts = grid_stat[var]
obs_ts = obs_stat[var_ref]
# resample to the colocation frequency
obs_ts1 = obs_ts.resample(col_freq).mean()
grid_ts1 = grid_ts.resample(col_freq).mean()
# fill up missing time stamps
_df = pd.concat([obs_ts1, grid_ts1], axis=1, keys=['o', 'm'])
# assign the unified timeseries data to the colocated data array
coldata[0, :, i] = _df['o'].values
coldata[1, :, i] = _df['m'].values
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:
try:
revision = ungridded_data._get_data_revision_helper(dataset_ref)
except MetaDataError:
revision = 'MULTIPLE'
except:
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_ts_type,
'filter_name': filter_name,
'ts_type_src': [ts_type_src_ref, grid_ts_type],
'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,
'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)
if colocate_time and grid_ts_type != ts_type:
data = data.resample_time(
to_ts_type=ts_type,
colocate_time=True,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
**kwargs)
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,
**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 : :obj:`int`, optional
regrid resolution in degrees. If specified, the input gridded data
objects will be regridded in lon / lat dimension to the input
resolution. (BETA feature)
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.
**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('Input vert_scheme cannot yet be handled '
'for gridded / gridded colocation...')
if ts_type is None:
ts_type = 'monthly'
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 = 'WORLD-wMOUNTAINS'
if gridded_data.var_info.has_unit:
if harmonise_units and not gridded_data.units == gridded_data_ref.units:
try:
gridded_data_ref.convert_unit(gridded_data.units)
except:
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
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 update_baseyear_gridded is not None:
# update time dimension in gridded data
gridded_data.base_year = update_baseyear_gridded
# 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)
grid_ts_type = gridded_data.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))
if regrid_res_deg is not None:
lons = gridded_data_ref.longitude.points
lats = gridded_data_ref.latitude.points
lons_new = np.arange(lons.min(), lons.max(), regrid_res_deg)
lats_new = np.arange(lats.min(), lats.max(), regrid_res_deg)
gridded_data_ref = gridded_data_ref.interpolate(latitude=lats_new,
longitude=lons_new)
# get both objects in same time resolution
if not colocate_time:
gridded_data = gridded_data.resample_time(ts_type)
gridded_data_ref = gridded_data_ref.resample_time(ts_type)
# guess bounds (for area weighted regridding, which is the default)
gridded_data._check_lonlat_bounds()
gridded_data_ref._check_lonlat_bounds()
# perform regridding
gridded_data = gridded_data.regrid(gridded_data_ref, scheme=regrid_scheme)
# 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': ts_type,
'filter_name': filter_name,
'ts_type_src': [gridded_data_ref.ts_type, grid_ts_type],
'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,
'apply_constraints': apply_time_resampling_constraints,
'min_num_obs': min_num_obs
}
meta.update(regfilter.to_dict())
if remove_outliers:
if not var_keep_outliers:
gridded_data.remove_outliers(low, high)
if not var_ref_keep_outliers:
gridded_data_ref.remove_outliers(low_ref, high_ref)
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)
if colocate_time and grid_ts_type != ts_type:
data = data.resample_time(
to_ts_type=ts_type,
colocate_time=True,
apply_constraints=apply_time_resampling_constraints,
min_num_obs=min_num_obs,
**kwargs)
return data
def test_to_datestring_YYYYMMDD(input, expected):
assert helpers.to_datestring_YYYYMMDD(input) == expected
def colocate_gridded_gridded(gridded_data,
gridded_data_ref,
ts_type=None,
start=None,
stop=None,
filter_name=None,
regrid_scheme='areaweighted',
vert_scheme=None,
**kwargs):
"""Colocate 2 gridded data objects
Todo
----
- Complete docstring
- think about vertical dimension (vert_scheme input not used at the moment)
"""
if ts_type is None:
ts_type = 'yearly'
if filter_name is None:
filter_name = 'WORLD-wMOUNTAINS'
if gridded_data.var_info.has_unit:
if not gridded_data.unit == gridded_data_ref.unit:
try:
gridded_data_ref.convert_unit(gridded_data.unit)
except:
raise DataUnitError('Failed to merge data unit of reference '
'gridded data object ({}) to data unit '
'of gridded data object ({})'.format(
gridded_data.unit,
gridded_data_ref.unit))
# 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_ts_type = gridded_data.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)
# 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))
# get both objects in same time resolution
gridded_data = gridded_data.downscale_time(ts_type)
gridded_data_ref = gridded_data_ref.downscale_time(ts_type)
# guess bounds (for area weighted regridding, which is the default)
gridded_data._check_lonlat_bounds()
gridded_data_ref._check_lonlat_bounds()
# perform regridding
gridded_data = gridded_data.regrid(gridded_data_ref, scheme=regrid_scheme)
# 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')
meta = {
'data_source': [gridded_data_ref.name, gridded_data.name],
'var_name': [gridded_data.var_name, gridded_data_ref.var_name],
'ts_type': ts_type,
'filter_name': filter_name,
'ts_type_src': grid_ts_type,
'ts_type_src_ref': gridded_data_ref.ts_type,
'start_str': to_datestring_YYYYMMDD(start),
'stop_str': to_datestring_YYYYMMDD(stop),
'unit': str(gridded_data.unit),
'data_level': 'colocated',
'revision_ref': gridded_data_ref.data_revision
}
meta.update(regfilter.to_dict())
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, gridded_data.grid.data))
time = gridded_data.time_stamps().astype('datetime64[ns]')
# create coordinates of DataArray
coords = {
'data_source': meta['data_source'],
'var_name': ('data_source', meta['var_name']),
'time': time,
'longitude': gridded_data.longitude.points,
'latitude': gridded_data.latitude.points
}
dims = ['data_source', 'time', 'latitude', 'longitude']
return ColocatedData(data=arr,
coords=coords,
dims=dims,
name=gridded_data.var_name,
attrs=meta)
def colocate_gridded_ungridded(gridded_data,
ungridded_data,
ts_type='daily',
start=None,
stop=None,
filter_name='WORLD-wMOUNTAINS',
var_ref=None,
vert_scheme=None,
**kwargs):
"""Colocate gridded with ungridded data of 2D data
2D means, that the vertical direction is only sampled at one altitude or
the variable is of integrated nature (or averaged) so that the dimensionality
of the grid data is (or can be -> cf. input parameter `vert_scheme`)
reduced to dimensionality time, lat, lon.
Note
----
Uses the variable that is contained in input :class:`GriddedData` object
(since these objects only contain a single variable)
Parameters
----------
gridded_data : GriddedData
gridded data (e.g. model results)
ungridded_data : UngriddedData
ungridded data (e.g. observations)
var_name : str
variable to be colocated
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). Default is 'WORLD-wMOUNTAINS', which corresponds to no
filtering (world with mountains). Use WORLD-noMOUNTAINS to exclude
stations at altitudes exceeding 1000 m.
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`).
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`.
**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
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
"""
var = gridded_data.var_info.var_name
if var_ref is None:
var_ref = var
if gridded_data.var_info.has_unit:
if not gridded_data.unit == ungridded_data.unit[var_ref]:
try:
gridded_data.convert_unit(ungridded_data.unit[var_ref])
except:
raise DataUnitError('Failed to merge data unit of '
'gridded data object ({}) to data unit '
'of ungridded data object ({})'.format(
gridded_data.unit,
ungridded_data.unit[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]
# 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_ts_type = gridded_data.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)
# 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(ungridded_data)
ungridded_lons = ungridded_data.longitude
ungridded_lats = ungridded_data.latitude
#crop time
gridded_data = gridded_data.crop(time_range=(start, stop))
# downscale time (if applicable)
grid_data = gridded_data.downscale_time(to_ts_type=ts_type)
# conver
grid_stat_data = grid_data.to_time_series(longitude=ungridded_lons,
latitude=ungridded_lats,
vert_scheme=vert_scheme)
# pandas frequency string for TS type
freq_pd = TS_TYPE_TO_PANDAS_FREQ[ts_type]
freq_np = TS_TYPE_TO_NUMPY_FREQ[ts_type]
start = pd.Timestamp(start.to_datetime64().astype(
'datetime64[{}]'.format(freq_np)))
#stop = pd.Timestamp(stop.to_datetime64().astype('datetime64[{}]'.format(freq_np)))
obs_stat_data = ungridded_data.to_station_data_all(vars_to_convert=var_ref,
start=start,
stop=stop,
freq=freq_pd,
interp_nans=False)
obs_vals = []
grid_vals = []
lons = []
lats = []
alts = []
station_names = []
# TIME INDEX ARRAY FOR COLLOCATED DATA OBJECT
TIME_IDX = pd.DatetimeIndex(freq=freq_pd, start=start, end=stop)
ts_type_src_ref = None
for i, obs_data in enumerate(obs_stat_data):
if obs_data is not None:
if ts_type_src_ref is None:
ts_type_src_ref = obs_data['ts_type_src']
elif not obs_data['ts_type_src'] == ts_type_src_ref:
raise ValueError(
'Cannot perform colocation. Ungridded data '
'object contains different source frequencies')
# 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)
obs_tseries = obs_data[var_ref]
# get model data corresponding to station
grid_tseries = grid_stat_data[i][var]
if sum(grid_tseries.isnull()) > 0:
raise Exception('DEVELOPER: PLEASE DEBUG AND FIND SOLUTION')
elif not len(grid_tseries) == len(TIME_IDX):
raise Exception('DEVELOPER: PLEASE DEBUG AND FIND SOLUTION')
# make sure, time index is defined in the right way (i.e.
# according to TIME_INDEX, e.g. if ts_type='monthly', it should
# not be the mid or end of month)
grid_tseries = pd.Series(grid_tseries.values, index=TIME_IDX)
# the following command takes care of filling up with NaNs where
# data is missing
df = pd.DataFrame(
{
'ungridded': obs_tseries,
'gridded': grid_tseries
},
index=TIME_IDX)
grid_vals_temp = df['gridded'].values
obs_vals.append(df['ungridded'].values)
grid_vals.append(grid_vals_temp)
lons.append(obs_data.longitude)
lats.append(obs_data.latitude)
alts.append(obs_data.altitude)
station_names.append(obs_data.station_name)
if len(obs_vals) == 0:
raise ColocationError('No observations could be found that match '
'the colocation constraints')
try:
revision = ungridded_data.data_revision[dataset_ref]
except:
revision = 'n/a'
meta = {
'data_source': [dataset_ref, gridded_data.name],
'var_name': [var, var_ref],
'ts_type': ts_type,
'filter_name': filter_name,
'ts_type_src': grid_ts_type,
'ts_type_src_ref': ts_type_src_ref,
'start_str': to_datestring_YYYYMMDD(start),
'stop_str': to_datestring_YYYYMMDD(stop),
'unit': str(gridded_data.unit),
'data_level': 'colocated',
'revision_ref': revision
}
meta.update(regfilter.to_dict())
grid_vals = np.asarray(grid_vals)
obs_vals = np.asarray(obs_vals)
stat_dim, time_dim = grid_vals.shape
arr = np.array((obs_vals, grid_vals))
arr = np.swapaxes(arr, 1, 2)
#.reshape((2, time_dim, stat_dim))
# create coordinates of DataArray
coords = {
'data_source': meta['data_source'],
'var_name': ('data_source', meta['var_name']),
'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=arr,
coords=coords,
dims=dims,
name=var,
attrs=meta)
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 start(self, value):
self._start = to_datestring_YYYYMMDD(value)
def stop(self, value):
self._stop = to_datestring_YYYYMMDD(value)