Skip to main content

Oceanography - Data Conversion

stars - badge-generator

The Surface Ocean CO₂ Atlas (SOCAT) contains measurements of the fugacity of CO2 in seawater around the globe. But to calculate how much carbon the ocean is taking up from the atmosphere, these measurements need to be converted to the partial pressure of CO2. We will convert the units by combining measurements of the surface temperature and fugacity. Python libraries (xarray, pandas, numpy) and the pyseaflux package facilitate this process.

In this example tutorial, we will investigate the data and convert the workload so that it can be executed on the Bacalhau network, to take advantage of the distributed storage and compute resources.


Running oceanography dataset with Bacalhau


To get started, you need to install the Bacalhau client, see more information here

The Sample Data

The raw data is available on the SOCAT website. We will use the SOCATv2021 dataset in the "Gridded" format to perform this calculation. First, let's take a quick look at some data:

mkdir -p inputs
curl --output ./inputs/
curl --output ./inputs/

Next let's write the requirements.txt and install the dependencies. This file will also be used by the Dockerfile to install the dependencies.

%%writefile requirements.txt

Installing dependencies

pip install -r requirements.txt > /dev/null

Writing the Script

import fsspec # for reading remote files
import xarray as xr
with"./inputs/", compression='zip') as fp:
ds = xr.open_dataset(fp)
time_slice = slice("2010", "2020") # select a decade
res = ds['sst_ave_unwtd'].sel(tmnth=time_slice).mean(dim='tmnth') # average over time
res.plot() # plot the result

We can see that the dataset contains lat-long coordinates, the date, and a series of seawater measurements. Above you can see a plot of the average surface sea temperature (sst) between 2010-2020, where recording buoys and boats have traveled.

Data Conversion

To convert the data from fugacity of CO2 (fCO2) to partial pressure of CO2 (pCO2) we will combine the measurements of the surface temperature and fugacity. The conversion is performed by the pyseaflux package.

To execute this workload on the Bacalhau network we need to perform three steps:

  • Upload the data to IPFS
  • Create a docker image with the code and dependencies
  • Run a Bacalhau job with the docker image using the IPFS data

Upload the Data to IPFS

The first step is to upload the data to IPFS. The simplest way to do this is to use a third-party service to "pin" data to the IPFS network, to ensure that the data exists and is available. To do this you need an account with a pinning service like or Pinata. Once registered you can use their UI or API or SDKs to upload files.

For the purposes of this example:

  1. Downloaded the latest monthly data from the SOCAT website
  2. Downloaded the latest long-term global sea surface temperature data from NOAA - information about that dataset can be found here.
  3. Pinned the data to IPFS

This resulted in the IPFS CID of bafybeidunikexxu5qtuwc7eosjpuw6a75lxo7j5ezf3zurv52vbrmqwf6y.

import fsspec
import xarray as xr
import pandas as pd
import numpy as np
import pyseaflux

def lon_360_to_180(ds=None, lonVar=None):
lonVar = "lon" if lonVar is None else lonVar
return (ds.assign_coords({lonVar: (((ds[lonVar] + 180) % 360) - 180)})
.astype(dtype='float32', order='C'))

def center_dates(ds):
# start and end date
start_date = str(ds.time[0].dt.strftime('%Y-%m').values)
end_date = str(ds.time[-1].dt.strftime('%Y-%m').values)

# monthly dates centered on 15th of each month
dates = pd.date_range(start=f'{start_date}-01T00:00:00.000000000',
freq='MS') + np.timedelta64(14, 'D')

return ds.assign(time=dates)

def get_and_process_sst(url=None):
# get noaa sst
if url is None:
url = ("/inputs/")

with as fp:
ds = xr.open_dataset(fp)
ds = lon_360_to_180(ds)
ds = center_dates(ds)
return ds

def get_and_process_socat(url=None):
if url is None:
url = ("/inputs/")

with, compression='zip') as fp:
ds = xr.open_dataset(fp)
ds = ds.rename({"xlon": "lon", "ylat": "lat", "tmnth": "time"})
ds = center_dates(ds)
return ds

def main():
print("Load SST and SOCAT data")
ds_sst = get_and_process_sst()
ds_socat = get_and_process_socat()

print("Merge datasets together")
time_slice = slice("1981-12", "2022-05")
ds_out = xr.merge([ds_sst['sst'].sel(time=time_slice),

print("Calculate pco2 from fco2")
ds_out['pco2_ave_unwtd'] = xr.apply_ufunc(

print("Add metadata")
ds_out['pco2_ave_unwtd'].attrs['units'] = 'uatm'
ds_out['pco2_ave_unwtd'].attrs['notes'] = ("calculated using" +
"and pyseaflux package")

print("Save data")
import shutil
shutil.make_archive("/outputs/processed.zarr", 'zip', "/processed.zarr")
print("Zarr file written to disk, job completed successfully")

if __name__ == "__main__":

Setting up Docker Container

We will create a Dockerfile and add the desired configuration to the file. These commands specify how the image will be built, and what extra requirements will be included.

%%writefile Dockerfile
FROM python:slim

RUN apt-get update && apt-get -y upgrade \
&& apt-get install -y --no-install-recommends \
g++ \
&& rm -rf /var/lib/apt/lists/*

WORKDIR /project

COPY ./requirements.txt /project

RUN pip3 install -r requirements.txt

COPY ./ /project

CMD ["python",""]

Build the container

We will run docker build command to build the container;

docker build -t <hub-user>/<repo-name>:<tag> .

Before running the command replace;

  • hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create a Docker account, and use the username of the account you created

  • repo-name with the name of the container, you can name it anything you want

  • tag this is not required but you can use the latest tag

Now you can push this repository to the registry designated by its name or tag.

docker push <hub-user>/<repo-name>:<tag>

For more information about working with custom containers, see the custom containers example.

Running a Bacalhau Job

Now that we have the data in IPFS and the Docker image pushed, next is to run a job using the bacalhau docker run command

%%bash  --out job_id
bacalhau docker run \
--input ipfs://bafybeidunikexxu5qtuwc7eosjpuw6a75lxo7j5ezf3zurv52vbrmqwf6y \
--id-only \
--wait \ -- python

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

%env JOB_ID={job_id}

Checking the State of your Jobs

  • Job status: You can check the status of the job using bacalhau list.
bacalhau list --id-filter ${JOB_ID}

When it says Published or Completed, that means the job is done, and we can get the results.

  • Job information: You can find out more information about your job by using bacalhau describe.
bacalhau describe ${JOB_ID}
  • Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
rm -rf results
mkdir -p ./results # Temporary directory to store the results
bacalhau get --output-dir ./results ${JOB_ID} # Download the results

Viewing your Job Output

To view the file, run the following command:

cat results/stdout

Need Support?

For questions, and feedback, please reach out in our forum