Adding observation data#

This tutorial demonstrates how OpenGHG can be used to process new measurement data, search the data present and to retrieve this for analysis and visualisation.

What is an object store?#

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 specified by the path given in the configuration file at ~/.config/openghg/openghg.conf.

Using the tutorial object store#

An object store is a folder with a fixed structure within which openghg can read and write data. To avoid adding the example data we use in this tutorial to your normal object store, we need to tell OpenGHG to use a separate sandboxed object store that we’ll call the tutorial store. To do this we use the use_tutorial_store function from openghg.tutorial. This sets the OPENGHG_TUT_STORE environment variable for this session and won’t affect your use of OpenGHG outside of this tutorial.

from openghg.tutorial import use_tutorial_store


1. Adding and standardising data#


Outside of this tutorial, if you have write access to multiple object stores you will need to pass the name of the object store you wish to write to to the store argument of the standardise functions.

Source formats#

OpenGHG can process and store several source formats in the object store, including data from the AGAGE, DECC, NOAA, LondonGHG, BEAC2ON networks. The process of adding data to the object store is called standardisation.

To standardise a new data file, you must specify the source format and other details about the data. For the full list of accepted observation inputs and source formats, call the function summary_source_formats:

In [1]: from openghg.standardise import summary_source_formats

In [2]: summary = summary_source_formats()

# import pandas as pd; pd.set_option('display.max_rows', None)
In [3]: summary
     Source format Site code  ...                     Long name      Platform
0              BTT       BTT  ...                  BT Tower, UK  surface site
1             CRDS       RPB  ...        Ragged Point, Barbados  surface site
2             CRDS       HFD  ...                Heathfield, UK  surface site
3             CRDS       BSD  ...                  Bilsdale, UK  surface site
4             CRDS       TTA  ...               Angus Tower, UK  surface site
..             ...       ...  ...                           ...           ...
317           NOAA       YON  ...           Yonagunijima, Japan  surface site
318           NOAA       ZEP  ...  Zeppelin, Ny Alesund, Norway  surface site
319  THAMESBARRIER       TMB  ...            Thames Barrier, UK  surface site
320      CRANFIELD       TMB  ...            Thames Barrier, UK  surface site
321            NPL       NPL  ...  National Physical Laboratory  surface site

[322 rows x 7 columns]

There may be multiple source formats for a given site. For instance, the Tacolneston site in the UK (site code “TAC”) has four entries:

In [4]: summary[summary["Site code"] == "TAC"]
    Source format Site code  ...              Long name      Platform
6            CRDS       TAC  ...  Tacolneston Tower, UK  surface site
33        GCWERKS       TAC  ...  Tacolneston Tower, UK  surface site
35        GCWERKS       TAC  ...  Tacolneston Tower, UK  surface site
279          NOAA       TAC  ...  Tacolneston Tower, UK  surface site

[4 rows x 7 columns]

Let’s see what data is available for a given source. First, we’ll list all source formats.

In [5]: summary["Source format"].unique()
       'CRANFIELD', 'NPL'], dtype=object)

Now we’ll find all data with source format "CRDS".

In [6]: summary[summary["Source format"] == "CRDS"]
  Source format Site code  ...               Long name      Platform
1          CRDS       RPB  ...  Ragged Point, Barbados  surface site
2          CRDS       HFD  ...          Heathfield, UK  surface site
3          CRDS       BSD  ...            Bilsdale, UK  surface site
4          CRDS       TTA  ...         Angus Tower, UK  surface site
5          CRDS       RGL  ...          Ridge Hill, UK  surface site
6          CRDS       TAC  ...   Tacolneston Tower, UK  surface site

[6 rows x 7 columns]

DECC network#

We will start by adding data to the object store from Tacolneston, which is a surface site in the DECC network. (Data at surface sites is measured in-situ.)

First we retrieve the raw data.

from openghg.tutorial import retrieve_example_data

data_url = ""

tac_data = retrieve_example_data(url=data_url)

Now we add this data to the object store using standardise_surface, passing the following arguments:

  • filepaths: list of paths to .dat files

  • site: "TAC", the site code for Tacolneston

  • source_format: "CRDS", the type of data we want to process

  • network: "DECC"

In [7]: from openghg.standardise import standardise_surface

In [8]: decc_results = standardise_surface(filepaths=tac_data, source_format="CRDS", site="TAC", network="DECC")

In [9]: decc_results
Out[9]: {'processed': {'tac.picarro.hourly.54m.dat': {'ch4': {'uuid': 'e2339fdf-c0d5-46b8-b5b9-3d682610e9fe', 'new': True}, 'co2': {'uuid': '1b4603e6-cac2-458c-b47e-e441864b29eb', 'new': True}},
'tac.picarro.hourly.100m.dat': {'ch4': {'uuid': '2e5935cc-07e3-4c0f-bd7c-8c6e4e2b13b7', 'new': True}, 'co2': {'uuid': '64c020b8-35dd-483f-b38c-99de83ea412d', 'new': True}},
'tac.picarro.hourly.185m.dat': {'ch4': {'uuid': '13172db7-7859-4f38-90cf-219c1fbe3b99', 'new': True}, 'co2': {'uuid': 'c79a3473-9f50-47d8-83d8-66a62fd085f7', 'new': True}}}}

This extracts the data and metadata from the files, standardises them, and adds them to our object store.

The returned decc_results dictionary shows how the data has been stored: each file has been split into several entries, each with a unique ID (UUID). Each entry is known as a Datasource (see Note on Datasources).

The decc_results output includes details of the processed data and tells us that the data has been stored correctly. This will also tell us if any errors have been encountered when trying to access and standardise this data.

Multiple stores#

If you have write access to more than one object store you’ll need to pass in the name of that store to the store argument. So instead of the standardise_surface call above, we’ll tell it to write to our default user object store. This is our default local object store created when we run openghg --quickstart.

from openghg.standardise import standardise_surface

decc_results = standardise_surface(filepaths=tac_data, source_format="CRDS", site="TAC", network="DECC", store="user")

The store argument can be passed to any of the standardise functions in OpenGHG and is required if you have write access to more than one store.

AGAGE data#

OpenGHG can also process data from the AGAGE network.

The functions that process the AGAGE data expect data to have an accompanying precisions file. For each data file we create a tuple with the data filename and the precisions filename.

First we retrieve example data from the Cape Grim station in Australia (site code “CGO””).

cgo_url = ""

capegrim_data = retrieve_example_data(url=cgo_url)

capegrim_data is a list of two file paths, one for the data file and one for the precisions file:


We put the data file and precisions file into a tuple:

capegrim_tuple = (capegrim_data[0], capegrim_data[1])

We can add these files to the object store in the same way as the DECC data by including the right arguments:

  • filepaths: tuple (or list of tuples) with paths to data and precision files

  • site (site code): "CGO"

  • source_format (data type): "GCWERKS"

  • network: "AGAGE"

  • instrument: "medusa"

agage_results = standardise_surface(filepaths=capegrim_tuple, source_format="GCWERKS", site="CGO",
                              network="AGAGE", instrument="medusa")

When viewing agage_results there will be a large number of Datasource UUIDs shown due to the large number of gases in each data file

In [10]: agage_results
{'processed': {'capegrim.18.C': {'ch4_70m': {'uuid': '200d8a1b-bc41-4f9f-86c4-448c2427d780',
'new': True},
'cfc12_70m': {'uuid': 'e507358e-ade3-4c83-914e-e486628640ce', 'new': True},
'n2o_70m': {'uuid': 'ad381148-76af-4d8c-aaec-f7cc2a0088b7', 'new': True},
'cfc11_70m': {'uuid': '2563a11b-2a54-4287-8705-670f34330e33', 'new': True},
'cfc113_70m': {'uuid': '6a6e28d9-4242-4c6f-a71a-0d56915a485b', 'new': True},
'chcl3_70m': {'uuid': '36af68d9-f421-4feb-9bfd-c719ec603f05', 'new': True},
'ch3ccl3_70m': {'uuid': 'f096f4c3-e86f-4d99-8a92-e35dd193cfbc',
'new': True},
'ccl4_70m': {'uuid': '396be43c-f29a-408e-9a88-c16ffd79da3b', 'new': True},
'h2_70m': {'uuid': '62045a91-bac9-4b7d-84b8-696ec8484002', 'new': True},
'co_70m': {'uuid': 'a1bd7ab9-4ae0-46aa-8570-ec961f929431', 'new': True},
'ne_70m': {'uuid': '950e94fe-6cf9-48e3-b920-275935761885', 'new': True}}}}

Note on Datasources#

Datasources are objects that are stored in the object store that hold the data and metadata associated with each measurement we upload to the platform.

For example, if we upload a file that contains readings for three gas species from a single site at a specific inlet height OpenGHG will assign this data to three different Datasources, one for each species. Metadata such as the site, inlet height, species, network etc are stored alongside the measurements for easy searching.

Datasources can also handle multiple versions of data from a single site, so if scales or other factors change multiple versions may be stored for easy future comparison.

3. Searching for data#

Searching the object store#

We can search the object store by property using the search_surface(...) function. This function retrieves all of the metadata associated with the search query from the data in the object store.

For example we can find all sites which have measurements for carbon tetrafluoride (“cf4”) using the species keyword:

from openghg.retrieve import search_surface

cfc_results = search_surface(species="cfc11")

We could also look for details of all the data measured at the Tacolneston (“TAC”) site using the site keyword:

tac_results = search_surface(site="tac")

For this site you can see this contains details of each of the species as well as the inlet heights these were measured at.

Quickly retrieve data#

Say we want to retrieve all the co2 data from Tacolneston, we can perform perform a search and expect a SearchResults object to be returned. If no results are found None is returned.

results = search_surface(site="tac", species="co2")

We can retrieve either some or all of the data easily using the retrieve function.

inlet_54m_data = results.retrieve(inlet="54m")

Or we can retrieve all of the data and get a list of ObsData objects.

all_co2_data = results.retrieve_all()

4. Retrieving data#

To retrieve the standardised data from the object store there are several functions we can use which depend on the type of data we want to access.

To access the surface data we have added so far we can use the get_obs_surface function and pass keywords for the site code, species and inlet height to retrieve our data. Using get_* functions will only allow one set of data to be returned and will give details if this is not the case.

In this case we want to extract the carbon dioxide (“co2”) data from the Tacolneston data (“TAC”) site measured at the “185m” inlet:

from openghg.retrieve import get_obs_surface

co2_data = get_obs_surface(site="tac", species="co2", inlet="185m")

If we view our returned obs_data variable this will contain:

  • data - The standardised data (accessed using e.g. This is returned as an xarray Dataset.

  • metadata - The associated metadata (accessed using e.g. obs_data.metadata).


We can now make a simple plot using the plot_timeseries method of the ObsData object.

NOTE: the plot created below may not show up on the online documentation version of this notebook.


You can also pass any of title, xlabel, ylabel and units to the plot_timeseries function to modify the labels.

5. Cleanup#

If you’re finished with the data in this tutorial you can cleanup the tutorial object store using the clear_tutorial_store function.

from openghg.tutorial import clear_tutorial_store