Developer API¶

The functions and methods documented in this section are the internal workings of the OpenGHG library. They are subject to change without warning due to the early stages of development of the project.

Warning

Normal users should not use any of the functions shown here directly as they may be removed or their functionality may change.

modules¶

Base class¶

This provides the functionality required by all data storage and processing classes, namely the saving, retrieval and loading of data from the object store.

BaseStore: Base class which the other core processing modules inherit

Data processing¶

These classes are used for the processing of data by the ObsSurface processing class.

CRANFIELD: For processing data from Cranfield
CRDS: For processing data from CRDS (cavity ring-down spectroscopy) data from the DECC network.
EUROCOM: For processing data from the EUROCOM network
GCWERKS: For processing data in the form expected by the GCWERKS package
ICOS: For processing data from the ICOS network
NOAA: For processing data from the NOAA network
THAMESBARRIER: For processing data from the Thames Barrier measurement sites

Datasource¶

The Datasource is the smallest data provider within the OpenGHG topology. A Datasource represents a data provider such as an instrument measuring a specific gas at a specific height at a specific site. For an instrument measuring three gas species at an inlet height of 100m at a site we would have three Datasources.

Datasource: Handles the storage of data, metadata and version information for measurements

objectstore¶

These functions handle the storage of data in the object store, in JSON or binary format. Each object and piece of data in the object store is stored at a specific key, which can be thought of as the address of the data. The data is stored in a bucket which in the cloud is a section of the OpenGHG object store. Locally a bucket is just a normal directory in the user’s filesystem specific by the OPENGHG_PATH environment variable.

delete_object(): Delete an object in the store
exists(): Check if an object exists at that key
get_bucket(): Get path to bucket
get_local_bucket(): Get path to local bucket
get_object(): Get object at given key
get_object_from_json(): Get object from JSON
set_object_from_file(): Set data at a key from a given filepath
set_object_from_json(): Set data at a key from JSON

util¶

This module contains all the helper functions used throughout OpenGHG.

Exporting¶

These are used to export data to a format readable by the OpenGHG data dashboard.

to_dashboard(): Export timeseries data to JSON
to_dashboard_mobile(): Export mobile observations data to JSON

Hashing¶

These handle hashing of data (usually with SHA1)

hash_file(): Calculate the SHA1 hash of a file
hash_string(): Calculate the SHA1 hash of a UTF-8 encoded string

String manipulation¶

String cleaning and formatting functions

clean_string(): Return a lowercase cleaned string
to_lowercase(): Converts a string to lowercase

Time¶

Helpers to deal with all things datetime.

timestamp_tzaware(): Create a Timestamp with a UTC timezone
timestamp_now(): Create a timezone aware timestamp for now
timestamp_epoch(): Create a timezone aware timestamp for the UNIX epoch (1970-01-01)
daterange_from_str(): Create a daterange from two timestamp strings
daterange_to_str(): Convert a daterange to string
create_daterange_str(): Create a daterange string from two timestamps or strings
create_daterange(): Create a pandas DatetimeIndex from two timestamps
daterange_overlap(): Check if two dateranges overlap
combine_dateranges(): Combine a list of dateranges
split_daterange_str(): Split a daterange string to the component start and end Timestamps
closest_daterange(): Finds the closest daterange in a list of dateranges
valid_daterange(): Check if the passed daterange is valid
find_daterange_gaps(): Find the gaps in a list of dateranges
trim_daterange(): Removes overlapping dates from to_trim
split_encompassed_daterange(): Checks if one of the passed dateranges contains the other, if so, then split the larger daterange into three sections.
daterange_contains(): Checks if one daterange contains another
sanitise_daterange(): Make sure the daterange is correct and return tzaware daterange.
check_nan(): Check if the given value is NaN, is so return an NA string
check_date(): Check if the passed string is a valid date or not, if not returns NA

Iteration¶

Our own personal itertools

pairwise(): Return a zip of an iterable where a is the iterable and b is the iterable advanced one step.
unanimous(): Checks that all values in an iterable object are the same

Site Checks¶

These perform checks to ensure data processed for each site is correct

valid_site(): Check if the passed site is a valid one
multiple_inlets(): Check if the passed site has more than one inlet

User API