Combined Plot of Raw and Cleaned CO₂ Data in the Time Domain

[dnxjay]

This pull request includes the time domain analysis and visualization of CO₂ data from the Mauna Loa Observatory. The analysis involves data processing techniques to clean and plot the data, showing both the raw and smoothed trends. The code passes linting checks. Files Changed: co2_analysis_notebook.ipynb (Updated with data processing code and plot) /workspaces/CP1-24-HW5/mauna-loa-data/flask_monthly.json (Read for CO₂ data analysis) This assignment task required the creation of a single plot combining the raw and cleaned CO₂ data in the time domain, with data processed using waveform modification techniques. Techniques used include padding, windowing, FFT, noise removal, inverse FFT, and undoing the window+padding.

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[laserlab]

@dnxjay As per homework description only one Jupyter notebook should be used since you are the first one you can choose the name, so please choose something more generic that fits for all plotting tasks.

[laserlab]

@ubsuny/cp1-24 Who wants to review this?

[dnxjay]

@dnxjay As per homework description only one Jupyter notebook should be used since you are the first one you can choose the name, so please choose something more generic that fits for all plotting tasks.

Okay will do

[iglesias-cardinale]

I can review this.

[laserlab]

@iglesias-cardinale please mark as approved or rejected, not just comment since otherwise it is unclear if this can gets merged. @dnxjay Filename is still an issue

[laserlab]

@iglesias-cardinale @dnxjay Also I just noticed your task was to plot the power spectrum as you mentioned yourself in #51

[jkblc]

It would be nice to generalize the filename

[laserlab]

It would be nice to generalize the filename

The first PR that is merged defines it, but it would be good to have a generic one as suggested in PR #52

[dnxjay]

The plot is clean! And the notebook used to produce it looks nice as well.

When I looked at your PR in a codespace, it looked like the preparation.py was an old version. I'm not sure if that will impact the current version should we merge this PR. Is your codespace that you're working with up to date with the main repository?

The only minor addition I might suggest is some documentation on how the data cleaning works and some benefits of using the cleaned data rather than the raw data.

For instance, we remove the noise by removing low frequencies from the FFT, but that also removes the seasonal oscillations. Is that something we want? Is that something we can adjust for by rescaling the threshold for low frequencies?

Overall it looks good! My main concern is the old version of the preparation.py file, but this might be a non-issue.

yes i had issues using the preparation.py which is why it wasn't implemented, my codespace was updated yesterday when i did the PR

[dnxjay]

what am i missing to get this merged? @laserlab @iglesias-cardinale