Welcome to XCODEX - XCO2 Daily EXtractor

Hi there! My name is Henrique!

The creation of this Python package was intended to create a simple solution for extracting daily data from XCO2 retrieved from the GES DISC platform.

I will attach the links containing the GitHub profile of the researchers who helped me in the development of this package along with graphical visualization of the data and the citation of the OCO-2 project.

I hope it's useful to you. Long live science!

Installing the package

To install the package, use the command:

pip install xcodex

Using XCODEX

Importing the necessary libraries:

import os

import pandas as pd

from xcodex.main import xco2_extract
from xcodex.run_tests import run_all_tests

The new version of xcodex has a new feature that allows you to run tests to check if the package is working properly. To do this, use the command:

run_all_tests()

Setting the historical series:

start_date = "30 of January, 2015"
end_date = "22 of February, 2015" 

Setting the locations:

locations = dict(Mauna_loa=[19.479488, -155.602829],
                 New_York=[40.712776, -74.005974],
                 Paris=[48.856613, 2.352222])

Extracting the data and organizing it in a pandas.Dataframe:

df = xco2_extract(
                  start=start_date,
                  end=end_date,
                  missing_data=False,
                  **locations)      

Note1: The location used in this example was Mauna Loa, New York and Paris. Any location can be used
as long the format "Location[lat, lon]" is respected. The values of
latitude and longitude must be in decimal degrees.

for more information, please execute the command:

help(xco2_extractor)

Finally, you will have a pandas.Dataframe as result. Now it's up to you how you'll
handle it. I recomend checking the Github profiles below for data visualization.

Data visualization

Here we can plot in a map the locations:

## set mapbox access token

import plotly.express as px
import plotly.graph_objs as go

px.set_mapbox_access_token('pk.eyJ1Ijoic2FnYXJsaW1idTAiLCJhIjoiY2t2MXhhMm5mNnE5ajJ3dDl2eDZvNTM2NiJ9.1bwmb8HPgFZWwR8kcO5rOA')

# Plotly configs

fig= px.scatter_mapbox(df,
                              lat= 'lat',
                              lon= 'lon',
                              color= 'xco2',
                              zoom= .85,
                              width=960,
                              height=540,
                              size_max=10,
                              hover_name='city',
                              color_continuous_scale=px.colors.cyclical.IceFire)

fig.update_layout(mapbox_style="dark") #"open-street-map"


layout = go.Layout(margin=go.layout.Margin(
    l=0,
    r=0,
    b=0,
    t=0))


fig.update_layout(layout,
                  autosize=False,
                  height=540,
                  width=960,
                  hovermode="closest")

# Saving the output image

#fig.write_html('xcodex_map.html')
#fig.write_image("xcodex_map.png", scale=2)

fig.show()

And finally a way to observe the XCO2 behavior during the time serie:

# Showing XCO2 behavior in time serie

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10,5))

sns.set_theme(font_scale=1, style="white")

sns.lineplot(data=df,
             x="jd",
             y='xco2',
             hue='city',
             errorbar=('ci',0),
             palette="tab10")

plt.xlabel("")
plt.ylabel("XCO2 (ppm)")

plt.xlim(min(df.jd), max(df.jd))
plt.ylim(min(df.xco2), max(df.xco2))

sns.despine(right=False,
            top=False)

plt.legend(ncol=3)

plt.tight_layout()

#plt.savefig("xcodex_locations.png", dpi=300)

plt.show()

GitHub profiles:

https://github.com/GlaucoRolim (Co-author)
https://github.com/kyuenjpl/ARSET_XCO2
https://github.com/sagarlimbu0/OCO2-OCO3

Please, cite this package as:

Laurito, H., La Scala, N., Rolim, G. S., 2023. Extracting XCO2-NASA Daily data with XCODEX: A Python package designed for data extraction and structuration. Jaboticabal, SP, BR, (...)

Data source citation:

Brad Weir, Lesley Ott and OCO-2 Science Team (2022), OCO-2 GEOS Level 3 daily, 0.5x0.625 assimilated CO2 V10r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 10/31/2022, doi: 10.5067/Y9M4NM9MPCGH