from pathlib import Path
from typing import Any
from pandas import Timedelta
import warnings
from openghg.objectstore import get_writable_bucket
from openghg.util import sort_by_filenames
from openghg.types import multiPathType
from numcodecs import Blosc
import logging
logger = logging.getLogger("openghg.standardise")
def standardise(
data_type: str,
filepath: multiPathType,
store: str | None = None,
**kwargs: Any,
) -> list[dict]:
"""Generic standardise function, used by data-type specific versions.
Args:
data_type: type of data to standardise
filepath: path to file(s) to standardise
store: Name of object store to write to, required if user has access to more than one
writable store
**kwargs: data type specific arguments, see specific implementations below.
Returns:
dict: Dictionary of result data.
"""
from openghg.store import get_data_class
dclass = get_data_class(data_type)
bucket = get_writable_bucket(name=store)
compression = kwargs.get("compression", True)
compressor = kwargs.get("compressor")
if compression:
if compressor is None:
compressor = Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)
else:
logger.info("Compression disabled")
compressor = None
kwargs["compressor"] = compressor
try:
del kwargs["compression"]
except KeyError:
pass
with dclass(bucket=bucket) as dc:
result = dc.read_file(filepath=filepath, **kwargs)
return result
[docs]
def standardise_surface(
source_format: str,
network: str,
site: str,
filepath: multiPathType,
precision_filepath: str | Path | list[str | Path] | None = None,
inlet: str | None = None,
height: str | None = None,
instrument: str | None = None,
data_level: str | int | float | None = None,
data_sublevel: str | float | None = None,
dataset_source: str | None = None,
sampling_period: Timedelta | str | None = None,
calibration_scale: str | None = None,
platform: str | None = None,
measurement_type: str | None = None,
verify_site_code: bool = True,
site_filepath: str | Path | None = None,
tag: str | list | None = None,
store: str | None = None,
update_mismatch: str = "never",
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compression: bool = True,
compressor: Any | None = None,
filters: Any | None = None,
chunks: dict | None = None,
info_metadata: dict | None = None,
sort_files: bool = False,
) -> list[dict]:
"""Standardise surface measurements and store the data in the object store.
Args:
filepath: Filepath(s)
source_format: Data format, for example CRDS, GCWERKS
site: Site code/name
network: Network name
inlet: Inlet height. Format 'NUMUNIT' e.g. "10m".
If retrieve multiple files pass None, OpenGHG will attempt to
extract this from the file.
height: Alias for inlet.
instrument: Instrument name
data_level: The level of quality control which has been applied to the data.
This should follow the convention of:
- "0": raw sensor output
- "1": automated quality assurance (QA) performed
- "2": final data set
- "3": elaborated data products using the data
data_sublevel: Typically used for "L1" data depending on different QA performed
before data is finalised.
dataset_source: Dataset source name, for example "ICOS", "InGOS", "European ObsPack", "CEDA 2023.06".
sampling_period: Sampling period as pandas time code, e.g. 1m for 1 minute, 1h for 1 hour
calibration_scale: Calibration scale for data
platform: Type of measurement platform e.g. "surface-insitu", "surface-flask"
measurement_type: Type of measurement. For some source_formats this value is added
to the attributes. Platform should be used in preference.
If platform is specified and measurement_type is not, this will be
set to match the platform.
verify_site_code: Verify the site code
site_filepath: Alternative site info file (see openghg/openghg_defs repository for format).
Otherwise will use the data stored within openghg_defs/data/site_info JSON file by default.
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
store: Name of object store to write to, required if user has access to more than one
writable store
update_mismatch: This determines how mismatches between the internal data
"attributes" and the supplied / derived "metadata" are handled.
This includes the options:
- "never" - don't update mismatches and raise an AttrMismatchError
- "from_source" / "attributes" - update mismatches based on input attributes
- "from_definition" / "metadata" - update mismatches based on input metadata
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
force: Force adding of data even if this is identical to data stored.
compression: Enable compression in the store
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking.
To disable chunking pass an empty dictionary.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
sort_files: Sorts multiple files date-wise.
Returns:
dict: Dictionary of result data
"""
from openghg.standardise.surface import check_gcwerks_input
from openghg.util import check_filepath
if source_format.lower() == "gcwerks":
filepath, precision_filepath = check_gcwerks_input(filepath, precision_filepath)
else:
filepath = check_filepath(filepath, source_format)
if sort_files:
# Don't sort filepaths for gcwerks because this needs to map in order to precision_filepaths
if source_format.lower() != "gcwerks":
filepath = sort_by_filenames(filepath=filepath)
return standardise(
store=store,
data_type="surface",
filepath=filepath,
precision_filepath=precision_filepath,
source_format=source_format,
network=network,
site=site,
inlet=inlet,
height=height,
instrument=instrument,
data_level=data_level,
data_sublevel=data_sublevel,
dataset_source=dataset_source,
sampling_period=sampling_period,
calibration_scale=calibration_scale,
platform=platform,
measurement_type=measurement_type,
overwrite=overwrite,
verify_site_code=verify_site_code,
site_filepath=site_filepath,
tag=tag,
update_mismatch=update_mismatch,
if_exists=if_exists,
save_current=save_current,
force=force,
compression=compression,
compressor=compressor,
filters=filters,
chunks=chunks,
info_metadata=info_metadata,
)
def standardise_column(
filepath: str | Path | list[str | Path],
species: str,
platform: str = "satellite",
obs_region: str | None = None,
site: str | None = None,
satellite: str | None = None,
domain: str | None = None,
selection: str | None = None,
network: str | None = None,
instrument: str | None = None,
source_format: str = "openghg",
tag: str | list | None = None,
store: str | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compression: bool = True,
compressor: Any | None = None,
filters: Any | None = None,
pressure_weights_method: str | None = None,
chunks: dict | None = None,
info_metadata: dict | None = None,
) -> list[dict]:
"""Read column observation file
Args:
filepath: Path to the input observation file.
species: Species name or synonym (e.g., "ch4").
platform: Type of platform (default is "satellite"). Can be one of:
- "satellite"
- "site"
obs_region: The geographic region covered by the data ("BRAZIL", "INDIA", "UK").
site: Site name or code, if platform is site-based.
satellite: Satellite name (if platform is satellite-based).
domain: For satellite data, specifies the geographical domain identifier.
This can either map to an existing domain or define a new domain.
selection: A unique identifier for a data selection (e.g., "land", "glint").
If not specified, `domain` is used.
network: Name of the satellite or in-situ measurement network (e.g., "TCCON").
instrument: Instrument name used to collect data (e.g., "TANSO-FTS").
source_format: Format of the input data (default is "openghg").
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
store: The name of the store to write the processed data to.
if_exists: Determines behavior if data already exists in the store.
Can be one of:
- "auto" (default): Checks for overlap and decides whether to add or raise an error.
- "new": Only includes new data, ignoring existing data.
- "combine": Replaces and inserts new data into the existing timeseries.
save_current: Decides whether to save the current version of the data:
- "auto" (default): Automatically saves based on `if_exists` behavior.
- "yes" or "y": Save current data as a separate version.
- "no" or "n": Allow updates or deletion of current data.
overwrite: Deprecated. Replaced by `if_exists="new"`.
force: Forces the addition of data even if it's identical to the existing data.
compression: Enables or disables compression during data storage (default is True).
compressor: Custom compression method. Defaults to `Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.)`.
filters: Filters to apply during data storage. Defaults to no filtering.
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
pressure_weights_method: method to use to derive TCCON pressure_weights.
chunks: Specifies chunking schema for data storage (default is None). It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking
To disable chunking pass an empty dictionary.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
Returns:
dict: Dictionary containing confirmation of standardisation process.
"""
return standardise(
store=store,
data_type="column",
filepath=filepath,
species=species,
platform=platform,
obs_region=obs_region,
satellite=satellite,
domain=domain,
selection=selection,
site=site,
network=network,
instrument=instrument,
source_format=source_format,
tag=tag,
overwrite=overwrite,
if_exists=if_exists,
save_current=save_current,
force=force,
compression=compression,
compressor=compressor,
filters=filters,
pressure_weights_method=pressure_weights_method,
chunks=chunks,
info_metadata=info_metadata,
)
[docs]
def standardise_bc(
filepath: str | Path | list[str | Path],
species: str,
bc_input: str,
domain: str,
source_format: str = "openghg",
period: str | tuple | None = None,
continuous: bool = True,
tag: str | list | None = None,
store: str | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compression: bool = True,
compressor: Any | None = None,
filters: Any | None = None,
chunks: dict | None = None,
info_metadata: dict | None = None,
) -> list[dict]:
"""Standardise boundary condition data and store it in the object store.
Args:
filepath: Path of boundary conditions file
species: Species name
bc_input: Input used to create boundary conditions. For example:
- a model name such as "MOZART" or "CAMS"
- a description such as "UniformAGAGE" (uniform values based on AGAGE average)
domain: Region for boundary conditions
source_format : Type of data being input e.g. openghg (internal format).
period: Period of measurements, if not passed this is inferred from the time coords
continuous: Whether time stamps have to be continuous.
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
store: Name of store to write to
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
force: Force adding of data even if this is identical to data stored.
compression: Enable compression in the store
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking
To disable chunking pass an empty dictionary.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
returns:
dict: Dictionary containing confirmation of standardisation process.
"""
return standardise(
store=store,
data_type="boundary_conditions",
filepath=filepath,
species=species,
bc_input=bc_input,
domain=domain,
source_format=source_format,
period=period,
continuous=continuous,
tag=tag,
overwrite=overwrite,
if_exists=if_exists,
save_current=save_current,
force=force,
compression=compression,
compressor=compressor,
filters=filters,
chunks=chunks,
info_metadata=info_metadata,
)
[docs]
def standardise_flux(
filepath: str | Path | list[str | Path],
species: str,
source: str,
domain: str,
database: str | None = None,
source_format: str = "openghg",
database_version: str | None = None,
model: str | None = None,
time_resolved: bool = False,
high_time_resolution: bool = False,
period: str | tuple | None = None,
chunks: dict | None = None,
continuous: bool = True,
tag: str | list | None = None,
store: str | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compression: bool = True,
compressor: Any | None = None,
filters: Any | None = None,
info_metadata: dict | None = None,
) -> list[dict]:
"""Process flux / emissions data
Args:
filepath: Path of flux / emissions file
species: Species name
source: Flux / Emissions source
domain: Flux / Emissions domain
source_format: Data format, for example openghg, intem
date: Date as a string e.g. "2012" or "201206" associated with emissions as a string.
Only needed if this can not be inferred from the time coords
time_resolved: If this is a high resolution file
high_time_resolution: This argument is deprecated and will be replaced in future versions with time_resolved.
period: Period of measurements, if not passed this is inferred from the time coords
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking.
To disable chunking pass an empty dictionary.
continuous: Whether time stamps have to be continuous.
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
store: Name of store to write to
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
force: Force adding of data even if this is identical to data stored.
compression: Enable compression in the store
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
returns:
dict: Dictionary of Datasource UUIDs data assigned to
"""
if high_time_resolution:
warnings.warn(
"This argument is deprecated and will be replaced in future versions with time_resolved.",
DeprecationWarning,
)
time_resolved = high_time_resolution
return standardise(
data_type="flux",
store=store,
filepath=filepath,
species=species,
source=source,
domain=domain,
database=database,
database_version=database_version,
model=model,
time_resolved=time_resolved,
period=period,
continuous=continuous,
chunks=chunks,
tag=tag,
overwrite=overwrite,
if_exists=if_exists,
save_current=save_current,
force=force,
compression=compression,
compressor=compressor,
filters=filters,
info_metadata=info_metadata,
)
def standardise_eulerian(
filepath: str | Path | list[str | Path],
model: str,
species: str,
source_format: str = "openghg",
start_date: str | None = None,
end_date: str | None = None,
setup: str | None = None,
tag: str | list | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
store: str | None = None,
force: bool = False,
compression: bool = True,
compressor: Any | None = None,
filters: Any | None = None,
chunks: dict | None = None,
info_metadata: dict | None = None,
) -> list[dict]:
"""Read Eulerian model output
Args:
filepath: Path of Eulerian model species output
model: Eulerian model name
species: Species name
source_format: Data format, for example openghg (internal format)
start_date: Start date (inclusive) associated with model run
end_date: End date (exclusive) associated with model run
setup: Additional setup details for run
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
store: Name of object store to write to, required if user has access to more than one
writable store
force: Force adding of data even if this is identical to data stored.
compression: Enable compression in the store
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking.
To disable chunking pass an empty dictionary.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
Returns:
dict: Dictionary of result data
"""
return standardise(
store=store,
data_type="eulerian_model",
filepath=filepath,
source_format=source_format,
model=model,
species=species,
start_date=start_date,
end_date=end_date,
setup=setup,
tag=tag,
overwrite=overwrite,
if_exists=if_exists,
force=force,
save_current=save_current,
compression=compression,
compressor=compressor,
filters=filters,
chunks=chunks,
info_metadata=info_metadata,
)
def standardise_from_binary_data(
store: str,
data_type: str,
binary_data: bytes,
metadata: dict,
file_metadata: dict,
**kwargs: Any,
) -> list[dict] | None:
"""Standardise binary data from serverless function.
The data dictionary should contain sub-dictionaries that contain
data and metadata keys.
args:
store: Name of object store to write to, required if user has access to more than one
writable store
data_type: type of data to standardise
binary_data: Binary measurement data
metadata: Metadata
file_metadata: File metadata such as original filename
**kwargs: data type specific arguments, see specific implementations in data classes.
returns:
Dictionary of result data.
"""
from openghg.store import get_data_class
dclass = get_data_class(data_type)
bucket = get_writable_bucket(name=store)
with dclass(bucket) as dc:
result = dc.read_data(
binary_data=binary_data, metadata=metadata, file_metadata=file_metadata, **kwargs
)
return result
[docs]
def standardise_flux_timeseries(
filepath: str | Path,
species: str,
source: str,
region: str = "UK",
source_format: str = "crf",
domain: str | None = None,
database: str | None = None,
database_version: str | None = None,
model: str | None = None,
tag: str | list | None = None,
store: str | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compressor: Any | None = None,
filters: Any | None = None,
period: str | tuple | None = None,
continuous: bool | None = None,
info_metadata: dict | None = None,
) -> list[dict]:
"""Process one dimension timeseries file
Args:
filepath: Path of flux timeseries file
species: Species name
source: Flux / Emissions source
region: Region/Country of the CRF data
source_format : Type of data being input e.g. openghg (internal format)
period: Period of measurements. Only needed if this can not be inferred from the time coords
domain: If flux is related to pre-existing domain (e.g. "EUROPE") with defined latitude-longitude bounds this can be used to flag that. Otherwise, use `region` input to describe the name of a region (e.g. "UK").
database: Name of database source for this input (if relevant)
database_version: Name of database version (if relevant)
model: Model name (if relevant)
If specified, should be one of:
- "yearly", "monthly"
- suitable pandas Offset Alias
- tuple of (value, unit) as would be passed to pandas.Timedelta function
tag: Special tagged values to add to the Datasource. This will be added to any
current values if the tag key already exists in a list.
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking.
To disable chunking pass in an empty dictionary.
continuous: Whether time stamps have to be continuous.
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
force: Force adding of data even if this is identical to data stored.
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
info_metadata: Allows to pass in additional tags to describe the data. e.g {"comment":"Quality checks have been applied"}
Returns:
dict: Dictionary of datasource UUIDs data assigned to
"""
if domain is not None:
logger.warning(
"Geographic domain, default is 'None'. Instead region is used to identify area,"
"Please supply region in future instances"
)
region = domain
return standardise(
data_type="flux_timeseries",
store=store,
filepath=filepath,
species=species,
source=source,
source_format=source_format,
domain=domain,
region=region,
database=database,
database_version=database_version,
model=model,
tag=tag,
overwrite=overwrite,
if_exists=if_exists,
save_current=save_current,
force=force,
compressor=compressor,
filters=filters,
period=period,
continuous=continuous,
info_metadata=info_metadata,
)
