├── .gitignore ├── 001-anywidget ├── README.md ├── reactive_quak.py └── tldraw_colorpicker.py ├── 002-stem-probes ├── README.md ├── layouts │ └── stem-probes.slides.json └── stem-probes.py ├── 003-bennet-meyers ├── README.md ├── assets │ └── solar_power_soiling_reference.png ├── layouts │ └── notebook.slides.json ├── modules │ ├── __init__.py │ ├── components.py │ ├── dataloaders.py │ ├── explainer.py │ ├── intro_problem.py │ ├── layout.py │ ├── problems.py │ └── solutions.py └── notebook.py ├── 004-xdsl ├── README.md └── xdsl.py ├── 005-cvxpy-nasa ├── README.md ├── aircraft-design.py ├── comparing-regularizers-in-regression.py ├── dcp-analysis.py ├── diet-problem.py ├── energy-storage.py ├── fuel-use.py ├── portfolio-optimization.py ├── robust-kalman-filter-and-sensitivity-analysis.py ├── systems-biology.py └── trajectory-planning.py ├── 006-vrtnis ├── K-d_tree_art.py ├── README.md ├── lmsys.pdf ├── lmsys.py └── ping-pong.py ├── 007-haleshot ├── README.md └── goodreads-eda.py ├── 008-marimo-tutorials └── README.md ├── 009-WE3-Lab ├── README.md ├── figures │ ├── pdf │ │ ├── figure1.pdf │ │ ├── figure2.pdf │ │ ├── figure3.pdf │ │ ├── figure4.pdf │ │ └── figure5.pdf │ └── png │ │ ├── figure2.png │ │ ├── figure3a.png │ │ ├── figure3b.png │ │ ├── figure3c.png │ │ ├── figure4.png │ │ └── figure5.png ├── marimonotebook │ ├── casestudies │ │ ├── houston │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── WSD │ │ │ │ ├── Annualized │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ └── WWT │ │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ └── radar.json │ │ │ │ ├── Summer │ │ │ │ └── radar.json │ │ │ │ ├── Winter │ │ │ │ └── radar.json │ │ │ │ └── contour.csv │ │ ├── newyork │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── WSD │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── WinterWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ └── WWT │ │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ └── radar.json │ │ │ │ ├── Summer │ │ │ │ └── radar.json │ │ │ │ ├── Winter │ │ │ │ └── radar.json │ │ │ │ └── contour.csv │ │ ├── sanjose │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Summer │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Summer │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── WSD │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Summer │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ └── WWT │ │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ └── radar.json │ │ │ │ ├── Summer │ │ │ │ └── radar.json │ │ │ │ ├── Winter │ │ │ │ └── radar.json │ │ │ │ └── contour.csv │ │ ├── santabarbara │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ ├── WSD │ │ │ │ ├── Annualized │ │ │ │ │ └── radar.json │ │ │ │ ├── SummerWeekday │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── SummerWeekend │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ ├── Winter │ │ │ │ │ ├── radar.json │ │ │ │ │ └── timeseries.csv │ │ │ │ └── contour.csv │ │ │ └── WWT │ │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ │ ├── Spring │ │ │ │ └── radar.json │ │ │ │ ├── Summer │ │ │ │ └── radar.json │ │ │ │ ├── Winter │ │ │ │ └── radar.json │ │ │ │ └── contour.csv │ │ └── tampa │ │ │ ├── AWT_curtailed │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ ├── SummerWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── Weekend │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── WinterWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ └── contour.csv │ │ │ ├── AWT_nominal │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ ├── SummerWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── Weekend │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── WinterWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ └── contour.csv │ │ │ ├── WSD │ │ │ ├── Annualized │ │ │ │ └── radar.json │ │ │ ├── SummerWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── Weekend │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ ├── WinterWeekday │ │ │ │ ├── radar.json │ │ │ │ └── timeseries.csv │ │ │ └── contour.csv │ │ │ └── WWT │ │ │ ├── Annualized │ │ │ └── radar.json │ │ │ ├── Spring │ │ │ └── radar.json │ │ │ ├── Summer │ │ │ └── radar.json │ │ │ ├── Winter │ │ │ └── radar.json │ │ │ └── contour.csv │ ├── costing │ │ ├── houston │ │ │ ├── AWT_curtailed.json │ │ │ ├── AWT_nominal.json │ │ │ ├── WSD.json │ │ │ └── WWT.json │ │ ├── newyork │ │ │ ├── AWT_curtailed.json │ │ │ ├── AWT_nominal.json │ │ │ ├── WSD.json │ │ │ └── WWT.json │ │ ├── sanjose │ │ │ ├── AWT_curtailed.json │ │ │ ├── AWT_nominal.json │ │ │ ├── WSD.json │ │ │ └── WWT.json │ │ ├── santabarbara │ │ │ ├── AWT_curtailed.json │ │ │ ├── AWT_nominal.json │ │ │ ├── WSD.json │ │ │ └── WWT.json │ │ └── tampa │ │ │ ├── AWT_curtailed.json │ │ │ ├── AWT_nominal.json │ │ │ ├── WSD.json │ │ │ └── WWT.json │ ├── init.py │ ├── lvof.py │ ├── radarplots │ │ ├── houston │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized.json │ │ │ │ └── Yearround.json │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized.json │ │ │ │ ├── Winter.json │ │ │ │ └── Yearround.json │ │ │ ├── WSD │ │ │ │ ├── Annualized.json │ │ │ │ └── Yearround.json │ │ │ └── WWT │ │ │ │ ├── Annualized.json │ │ │ │ ├── Spring.json │ │ │ │ ├── Summer.json │ │ │ │ └── Winter.json │ │ ├── newyork │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ ├── WinterWeekday.json │ │ │ │ └── WinterWeekend.json │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ ├── WinterWeekday.json │ │ │ │ └── WinterWeekend.json │ │ │ ├── WSD │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ ├── WinterWeekday.json │ │ │ │ └── WinterWeekend.json │ │ │ └── WWT │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ ├── WinterWeekday.json │ │ │ │ └── WinterWeekend.json │ │ ├── sanjose │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized.json │ │ │ │ ├── Spring.json │ │ │ │ ├── Summer.json │ │ │ │ └── Winter.json │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized.json │ │ │ │ ├── Spring.json │ │ │ │ ├── Summer.json │ │ │ │ └── Winter.json │ │ │ ├── WSD │ │ │ │ ├── Annualized.json │ │ │ │ ├── Spring.json │ │ │ │ ├── Summer.json │ │ │ │ └── Winter.json │ │ │ └── WWT │ │ │ │ ├── Annualized.json │ │ │ │ ├── Spring.json │ │ │ │ ├── Summer.json │ │ │ │ └── Winter.json │ │ ├── santabarbara │ │ │ ├── AWT_curtailed │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ └── Winter.json │ │ │ ├── AWT_nominal │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ └── Winter.json │ │ │ ├── WSD │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ └── Winter.json │ │ │ └── WWT │ │ │ │ ├── Annualized.json │ │ │ │ ├── SummerWeekday.json │ │ │ │ ├── SummerWeekend.json │ │ │ │ └── Winter.json │ │ └── tampa │ │ │ ├── AWT_curtailed │ │ │ ├── Annualized.json │ │ │ ├── SummerWeekday.json │ │ │ ├── Weekend.json │ │ │ └── WinterWeekday.json │ │ │ ├── AWT_nominal │ │ │ ├── Annualized.json │ │ │ ├── SummerWeekday.json │ │ │ ├── Weekend.json │ │ │ └── WinterWeekday.json │ │ │ ├── WSD │ │ │ ├── Annualized.json │ │ │ ├── SummerWeekday.json │ │ │ ├── Weekend.json │ │ │ └── WinterWeekday.json │ │ │ └── WWT │ │ │ ├── Annualized.json │ │ │ ├── SummerWeekday.json │ │ │ ├── Weekend.json │ │ │ └── WinterWeekday.json │ └── timeseries │ │ ├── .DS_Store │ │ ├── AWT_curtailed │ │ ├── .DS_Store │ │ ├── houston │ │ │ └── Yearround.csv │ │ ├── newyork │ │ │ ├── SummerWeekday.csv │ │ │ ├── SummerWeekend.csv │ │ │ ├── WinterWeekday.csv │ │ │ └── WinterWeekend.csv │ │ ├── sanjose │ │ │ ├── Spring.csv │ │ │ ├── Summer.csv │ │ │ └── Winter.csv │ │ ├── santabarbara │ │ │ ├── SummerWeekday.csv │ │ │ ├── SummerWeekend.csv │ │ │ └── Winter.csv │ │ └── tampa │ │ │ ├── SummerWeekday.csv │ │ │ ├── Weekend.csv │ │ │ └── WinterWeekday.csv │ │ ├── AWT_nominal │ │ ├── .DS_Store │ │ ├── houston │ │ │ └── Yearround.csv │ │ ├── newyork │ │ │ ├── SummerWeekday_nominal.csv │ │ │ ├── SummerWeekend_nominal.csv │ │ │ ├── WinterWeekday_nominal.csv │ │ │ └── WinterWeekend_nominal.csv │ │ ├── sanjose │ │ │ ├── Spring.csv │ │ │ ├── Summer.csv │ │ │ └── Winter.csv │ │ ├── santabarbara │ │ │ ├── SummerWeekday.csv │ │ │ ├── SummerWeekend.csv │ │ │ └── Winter.csv │ │ └── tampa │ │ │ ├── SummerWeekday.csv │ │ │ ├── Weekend.csv │ │ │ └── WinterWeekday.csv │ │ ├── WSD │ │ ├── houston │ │ │ └── Yearround.csv │ │ ├── newyork │ │ │ ├── SummerWeekday.csv │ │ │ ├── SummerWeekend.csv │ │ │ ├── WinterWeekday.csv │ │ │ └── WinterWeekend.csv │ │ ├── sanjose │ │ │ ├── Spring.csv │ │ │ ├── Summer.csv │ │ │ └── Winter.csv │ │ ├── santabarbara │ │ │ ├── SummerWeekday.csv │ │ │ ├── SummerWeekend.csv │ │ │ └── Winter.csv │ │ └── tampa │ │ │ ├── SummerWeekday.csv │ │ │ ├── Weekend.csv │ │ │ └── WinterWeekday.csv │ │ └── WWT │ │ ├── houston │ │ ├── Spring.csv │ │ ├── Summer.csv │ │ └── Winter.csv │ │ ├── newyork │ │ ├── Spring.csv │ │ ├── Summer.csv │ │ └── Winter.csv │ │ ├── sanjose │ │ ├── Spring.csv │ │ ├── Summer.csv │ │ └── Winter.csv │ │ ├── santabarbara │ │ ├── Spring.csv │ │ ├── Summer.csv │ │ └── Winter.csv │ │ ├── tampa │ │ ├── Spring.csv │ │ ├── Summer.csv │ │ └── Winter.csv │ │ └── test copy.ipynb ├── paperdata │ ├── ratesdata │ │ ├── aes_ohio_billing.csv │ │ ├── baltimore_billing.csv │ │ ├── coned_billing.csv │ │ ├── cpe_billing.csv │ │ ├── pge_billing.csv │ │ ├── sbce_billing.csv │ │ ├── sce_billing.csv │ │ └── teco_billing.csv │ └── ratesfigures │ │ ├── demandCharge.pdf │ │ ├── demandCharge.png │ │ ├── demandCharge.svg │ │ ├── energyCharge.pdf │ │ ├── energyCharge.png │ │ ├── energyCharge.svg │ │ └── lvof_rateanalysis.ipynb └── tariffs │ ├── ratesdata │ ├── aes_ohio_billing.csv │ ├── baltimore_billing.csv │ ├── coned_billing.csv │ ├── cpe_billing.csv │ ├── pge_billing.csv │ ├── sbce_billing.csv │ ├── sce_billing.csv │ └── teco_billing.csv │ └── ratesfigures │ ├── demandCharge.pdf │ ├── demandCharge.png │ ├── demandCharge.svg │ ├── energyCharge.pdf │ ├── energyCharge.png │ ├── energyCharge.svg │ └── lvof_rateanalysis.ipynb ├── 010-Mustjaab ├── Analysis-of-wait-times.py ├── Article_Summarizer.py ├── C__Users_mustj_Fact_Checking_Model.py ├── Canada_Risk_Management.py ├── Environmental_Protection_Analytics.py ├── Exploring_Perplexity.py ├── Job_Market_Word_Clouds.py ├── Monitoring Flow of GHG Emissions.py ├── Periodic_App.py ├── Portfolio.py ├── Post Approval Study Recommender.py ├── Punnett Square App.py ├── README.md ├── Warning Letter Classification Model.py ├── action-potential-simulator.py ├── assets │ ├── Incidents.csv │ ├── Post_Approval_Studies.csv │ └── Risk_Arrangement.csv └── environmental-violation-charge-model.py ├── 011-Vincent ├── README.md ├── interactive-matrices.py └── seam_carving.py ├── 012-eugene └── README.md ├── 013-paul-karayan ├── README.md ├── secrets_manager.py └── slackzen.py ├── 014-ryan-parker ├── README.md └── polars_intro.py ├── 015-cradle └── README.md ├── 016-sam-minot └── README.md ├── 017-arthur └── README.md ├── 018-Jove └── README.md ├── 019-smohspace ├── README.md └── akatsuki-tutorial.py ├── 020-yoann-mocquin ├── README.md └── geometric-mtf.py ├── 021-jan-aerts └── README.md ├── 022-julian-hofer └── README.md ├── 023-weights-biases └── README.md ├── LICENSE ├── README.md └── assets ├── 001-anywidget.gif ├── 002-stem-probes.png ├── 003-bennet-meyers.png ├── 004-xdsl.png ├── 005-cvxpy-nasa.png ├── 006-vrtnis.png ├── 007-haleshot.png ├── 008-marimo-tutorials.png ├── 009-WE3-Lab.png ├── 010-Mustjaab.png ├── 011-Vincent.gif ├── 012-eugene.png ├── 013-paul-karayan.png ├── 014-ryan-parker.png ├── 015-cradle.png ├── 016-sam-minot.png ├── 017-arthur.png ├── 018-Jove.png ├── 019-smohspace.gif ├── 020-yoann-mocquin.gif ├── 021-jan-aerts.gif ├── 022-julian-hofer.png └── 023-weights-biases.gif /001-anywidget/reactive_quak.py: -------------------------------------------------------------------------------- 1 | # /// script 2 | # requires-python = ">=3.12" 3 | # dependencies = [ 4 | # "altair", 5 | # "polars", 6 | # "marimo", 7 | # "quak", 8 | # "pandas", 9 | # ] 10 | # /// 11 | 12 | import marimo 13 | 14 | __generated_with = "0.8.2" 15 | app = marimo.App(width="medium") 16 | 17 | 18 | @app.cell 19 | def __(): 20 | import marimo as mo 21 | import polars as pl 22 | import quak 23 | return mo, pl, quak 24 | 25 | 26 | @app.cell 27 | def __(mo, pl, quak): 28 | df = pl.read_parquet("https://github.com/uwdata/mosaic/raw/main/data/athletes.parquet") 29 | widget = mo.ui.anywidget(quak.Widget(df)) 30 | widget 31 | return df, widget 32 | 33 | 34 | @app.cell 35 | def __(grouped_selection, mo): 36 | import altair as alt 37 | 38 | 39 | mo.ui.altair_chart( 40 | alt.Chart(grouped_selection) 41 | .mark_bar() 42 | .encode(y=alt.Y("nationality:N").sort("-x"), x="count:Q") 43 | .transform_window( 44 | rank="rank(count)", sort=[alt.SortField("count", order="descending")] 45 | ) 46 | .transform_filter(alt.datum.rank < 10) 47 | .properties(height=400) 48 | ) 49 | return alt, 50 | 51 | 52 | @app.cell 53 | def __(widget): 54 | selection = widget.data().df() 55 | return selection, 56 | 57 | 58 | @app.cell 59 | def __(selection): 60 | selection["count"] = 1 61 | grouped_selection = selection[["nationality", "count"]].groupby(["nationality"]).agg("count").reset_index() 62 | return grouped_selection, 63 | 64 | 65 | if __name__ == "__main__": 66 | app.run() 67 | -------------------------------------------------------------------------------- /001-anywidget/tldraw_colorpicker.py: -------------------------------------------------------------------------------- 1 | # /// script 2 | # requires-python = ">=3.12" 3 | # dependencies = [ 4 | # "marimo", 5 | # "matplotlib", 6 | # "numpy", 7 | # "tldraw", 8 | # ] 9 | # /// 10 | 11 | import marimo 12 | 13 | __generated_with = "0.8.9" 14 | app = marimo.App(width="medium") 15 | 16 | 17 | @app.cell 18 | def __(): 19 | import marimo as mo 20 | import matplotlib.pyplot as plt 21 | import numpy as np 22 | plt.style.use('_mpl-gallery') 23 | return mo, np, plt 24 | 25 | 26 | @app.cell 27 | def __(mo): 28 | from tldraw import ReactiveColorPicker 29 | 30 | widget = mo.ui.anywidget(ReactiveColorPicker()) 31 | return ReactiveColorPicker, widget 32 | 33 | 34 | @app.cell(hide_code=True) 35 | def __(mo): 36 | mo.md( 37 | """ 38 | This example by [Jan-Hendrik Muller](https://x.com/kolibril13?lang=en) uses [anywidget](https://anywidget.dev) to make 39 | a reactive colorpicker! Drag the arrow across the boxes, and watch the scatterplot change colors. 40 | 41 | anywidget is a Python library that makes it easy to make portable widgets; marimo has standardized on anywidget 42 | for its plugin API. Learn more at [our docs](https://docs.marimo.io/guides/integrating_with_marimo/custom_ui_plugins.html#custom-widget). 43 | """ 44 | ) 45 | return 46 | 47 | 48 | @app.cell 49 | def __(np): 50 | # make the data 51 | np.random.seed(3) 52 | x = 4 + np.random.normal(0, 2, 24) 53 | y = 4 + np.random.normal(0, 2, len(x)) 54 | # size and color: 55 | sizes = np.random.uniform(15, 80, len(x)) 56 | opacity = np.random.uniform(0, 1, len(x)) 57 | return opacity, sizes, x, y 58 | 59 | 60 | @app.cell 61 | def __(mo, np, opacity, plt, sizes, widget, x, y): 62 | fig, ax = plt.subplots() 63 | fig.set_size_inches(3, 3) 64 | ax.set(xlim=(0, 8), xticks=np.arange(1, 8), ylim=(0, 8), yticks=np.arange(1, 8)) 65 | ax.scatter(x, y, s=sizes*5, color=widget.color or None, alpha=opacity) 66 | mo.hstack([widget, plt.gca()], justify="start", widths=[1, 1]) 67 | return ax, fig 68 | 69 | 70 | if __name__ == "__main__": 71 | app.run() 72 | -------------------------------------------------------------------------------- /002-stem-probes/README.md: -------------------------------------------------------------------------------- 1 | # Georgios Varnavides: UC Berkeley Postdoctoral Miller Research Fellow 2 | 3 | _August 15, 2024_ 4 | 5 | [Georgios Varnavides](https://georgiosvarnavides.com/) is a postdoctoral Miller research fellow at UC Berkeley, where he works on material science. He's a marimo power user who cares deeply about digital science communication. 👨‍🔬🎙️ 6 | 7 | Georgios has been using [marimo's WebAssembly features](https://docs.marimo.io/guides/wasm.html) to create interactive science content that runs directly in the browser. His work spans from notebooks to apps and even interactive slides. One of his notable contributions is an interactive presentation on STEM probes, which you can explore here: 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/p/@gvarnavides/stem-probes) 10 | 11 | ## Innovative Combinations 12 | 13 | Georgios has taken his work a step further by finding a way to combine Observable Framework and marimo, utilizing an advanced marimo feature called [marimo islands](https://docs.marimo.io/guides/exporting.html#embed-marimo-outputs-in-html-using-islands) 🏝️. This innovative approach showcases the flexibility and power of marimo for creating interactive scientific content. 14 | 15 | ## Local Exploration 16 | 17 | Run Georgios' notebook as an app with: 18 | 19 | ```shell 20 | uvx marimo run --sandbox stem-probes.py 21 | ``` 22 | 23 | if you have `uv` installed, or 24 | 25 | ```shell 26 | marimo run --sandbox 002-stem-probes/stem-probes.py 27 | ``` 28 | 29 | if you don't have uv installed (you'll need to manually install its dependencies). 30 | 31 | To edit the notebook source code, replace `run` with `edit` in the above commands. 32 | 33 | > [!NOTE] 34 | > Georgios' work is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 35 | 36 | We're thrilled to have Georgios as an active and innovative member of the marimo community. His contributions demonstrate the potential of marimo for creating engaging, interactive scientific content that can run directly in web browsers. 37 | 38 | ## Spotlight Promotion 39 | 40 | This spotlight has been featured on our social media platforms. Join the conversation: 41 | 42 | - Twitter Post: [Link](https://x.com/marimo_io/status/1824130227238527005) 43 | - Discord Discussion: [Discord message](https://discord.com/channels/1059888774789730424/1268639867898695761/1273692028177481829) 44 | 45 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 46 | -------------------------------------------------------------------------------- /002-stem-probes/layouts/stem-probes.slides.json: -------------------------------------------------------------------------------- 1 | { 2 | "type": "slides", 3 | "data": {} 4 | } -------------------------------------------------------------------------------- /003-bennet-meyers/README.md: -------------------------------------------------------------------------------- 1 | # Bennet Meyers: SLAC Staff Scientist 2 | 3 | _August 22, 2024_ 4 | 5 | [Bennet Meyers](https://gismo.slac.stanford.edu/person/bennet-meyers) is a 6 | staff scientist at [SLAC](https://slac.stanford.edu), in the [GISMo 7 | lab](https://gismo.slac.stanford.edu/). He's been using marimo for over two 8 | years — well before it was open source. Bennet has played a huge role in 9 | shaping and supporting marimo; in fact, marimo probably wouldn't exist without 10 | him. 11 | 12 | Bennet was the first person to ever deploy a marimo notebook, which teaches 13 | readers how to use signal decomposition without math. It was one a motivating 14 | example for many features that are now part of the marimo library. Check it out 15 | here: [https://signal-decomp-tutorial.org/](https://signal-decomp-tutorial.org/]). 16 | 17 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/@public/signal-decomposition) 18 | 19 | You can also edit this notebook locally with 20 | 21 | ```shell 22 | uvx marimo run --sandbox notebook.py 23 | ``` 24 | 25 | if you have `uv` installed, or 26 | 27 | ```shell 28 | marimo run notebook.py 29 | ``` 30 | 31 | if you don't have uv installed (you'll need to manually install its dependencies). 32 | 33 | To edit the notebook source code, replace `run` with `edit` in the above commands. 34 | 35 | > [!NOTE] 36 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 37 | 38 | We're thrilled to have Bennet Meyers as an active and innovative member of the marimo community! 39 | 40 | ## Spotlight Promotion 41 | 42 | This spotlight has been featured on our social media platforms. Join the conversation: 43 | 44 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_its-thursday-and-that-means-its-time-activity-7232518819422842881-DG4k?utm_source=share&utm_medium=member_desktop) 45 | - Twitter Post: [Link](https://x.com/marimo_io/status/1826731440987668501) 46 | - Discord Discussion: [Discord message](https://discord.com/channels/1059888774789730424/1268639867898695761/1276291256821088410) 47 | 48 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 49 | -------------------------------------------------------------------------------- /003-bennet-meyers/assets/solar_power_soiling_reference.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/003-bennet-meyers/assets/solar_power_soiling_reference.png -------------------------------------------------------------------------------- /003-bennet-meyers/layouts/notebook.slides.json: -------------------------------------------------------------------------------- 1 | { 2 | "type": "slides", 3 | "data": {} 4 | } -------------------------------------------------------------------------------- /003-bennet-meyers/modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/003-bennet-meyers/modules/__init__.py -------------------------------------------------------------------------------- /003-bennet-meyers/modules/layout.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import io 3 | 4 | import marimo as mo 5 | from PIL import Image 6 | 7 | 8 | def _pillow_image_to_base64_string(img): 9 | buffered = io.BytesIO() 10 | img.save(buffered, format="PNG") 11 | return base64.b64encode(buffered.getvalue()).decode("utf-8") 12 | 13 | 14 | def image(path: str) -> str: 15 | img = Image.open(path) 16 | data_url = "data:image/png;base64," + _pillow_image_to_base64_string(img) 17 | return mo.image(src=data_url) 18 | -------------------------------------------------------------------------------- /005-cvxpy-nasa/README.md: -------------------------------------------------------------------------------- 1 | # CVXPY: NASA Convex Optimization Course 2 | 3 | *September 05, 2024* 4 | 5 | This summer, the [CVXPY](https://www.cvxpy.org/) team taught a course on convex optimization to scientists at NASA, powered by marimo notebooks! From designing aircraft to landing rockets, marimo brought their lessons to life. 6 | 7 | ## Course Details 8 | 9 | The CVXPY team used marimo's interactive capabilities to create engaging and hands-on learning experiences for NASA scientists. This collaboration showcases the power of marimo in educational settings, particularly for complex technical subjects like convex optimization. 10 | 11 | - Course website: [https://cvxgrp.org/nasa/](https://cvxgrp.org/nasa/) 12 | - Notebooks: [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/c/@convex-optimization-nasa/convex-optimization-short-course-nasa) 13 | 14 | ## Local Exploration 15 | 16 | To explore the CVXPY NASA course notebooks locally, you can use the following commands: 17 | 18 | ```shell 19 | uvx marimo run --sandbox .py 20 | ``` 21 | 22 | if you have `uv` installed, or 23 | 24 | ```shell 25 | marimo run .py 26 | ``` 27 | 28 | if you don't have uv installed (you'll need to manually install its dependencies). 29 | 30 | To edit the notebook source code, replace `run` with `edit` in the above commands. 31 | 32 | > [!NOTE] 33 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 34 | 35 | ## Spotlight Promotion 36 | 37 | This spotlight has been featured on our social media platforms. Join the conversation: 38 | 39 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-cvxpy-this-summer-cvxpy-activity-7237511868083290112-DEWZ?utm_source=share&utm_medium=member_desktop) 40 | - Twitter Post: [Link](https://x.com/marimo_io/status/1831745827523706992) 41 | - Discord Discussion: [Discord message](https://discord.com/channels/1059888774789730424/1268639867898695761/1281306215984660490) 42 | 43 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 44 | -------------------------------------------------------------------------------- /005-cvxpy-nasa/diet-problem.py: -------------------------------------------------------------------------------- 1 | # /// script 2 | # requires-python = ">=3.12" 3 | # dependencies = [ 4 | # "marimo", 5 | # "cvxpy", 6 | # "numpy", 7 | # ] 8 | # /// 9 | import marimo 10 | 11 | __generated_with = "0.7.21-dev18" 12 | app = marimo.App(width="medium") 13 | 14 | 15 | @app.cell 16 | def __(): 17 | import cvxpy as cp 18 | import numpy as np 19 | import marimo as mo 20 | return cp, mo, np 21 | 22 | 23 | @app.cell(hide_code=True) 24 | def __(mo): 25 | mo.md(r"""_This notebook accompanies [Lecture 2, Disciplined Convex Programming](https://www.cvxgrp.org/nasa/pdf/lecture2.pdf), of the Convex Optimization Short Course, which was held at NASA in the summer of 2024._""") 26 | return 27 | 28 | 29 | @app.cell(hide_code=True) 30 | def __(mo): 31 | mo.md( 32 | r""" 33 | **Diet problem.** We are going to solve a simple diet problem. We are choosing nonnegative amounts 34 | $x_1, \ldots, x_n$ of $n$ different foods. One unit of food 35 | $j$ has cost 36 | $c_j$ and contains 37 | $A_{ij}$ 38 | units of nutrient 39 | $i$. We want to minimize the total cost of the food, while ensuring that we get at least 40 | $b_i$ units of nutrient $i$. 41 | 42 | Write down the optimization problem for the diet problem and solve it using the data in the notebook 43 | """ 44 | ) 45 | return 46 | 47 | 48 | @app.cell 49 | def __(np): 50 | # cost vector (dollars per kg of food) 51 | c = np.array([6, 5, 2, 15]) 52 | 53 | # Nutrient content matrix 54 | # Rows corresponding to grams of protein, carbohydrates, and fat per kg of food. 55 | # The food types in the columns are lentils, broccoli, rice, and almonds. 56 | A = np.array([[250, 28, 20, 210], [600, 60, 780, 200], [10, 3, 10, 500]]) 57 | 58 | # Minimum required nutrients vector (grams per day). 59 | b = np.array([50, 300, 70]) 60 | return A, b, c 61 | 62 | 63 | @app.cell 64 | def __(): 65 | x = ... 66 | return x, 67 | 68 | 69 | if __name__ == "__main__": 70 | app.run() 71 | -------------------------------------------------------------------------------- /005-cvxpy-nasa/fuel-use.py: -------------------------------------------------------------------------------- 1 | import marimo 2 | 3 | __generated_with = "0.7.13" 4 | app = marimo.App(width="medium") 5 | 6 | 7 | @app.cell 8 | def __(): 9 | import marimo as mo 10 | return mo, 11 | 12 | 13 | @app.cell 14 | def __(mo): 15 | mo.md( 16 | r"""_This notebook accompanies [Lecture 1, Introduction to Convex Optimization](https://www.cvxgrp.org/nasa/pdf/lecture1.pdf), of the Convex Optimization Short Course, which was held at NASA in the summer of 2024._""" 17 | ) 18 | return 19 | 20 | 21 | @app.cell(hide_code=True) 22 | def __(mo): 23 | mo.md( 24 | r""" 25 | **Fuel use as function of distance and speed.** A vehicle uses fuel at a rate 26 | $f(s)$, which is a function of the vehicle speed $s$. 27 | We assume that $f : \mathbb{R} \to \mathbb{R}$ is a positive increasing convex function, with dom 28 | $f = \mathbb{R}_+$. he physical units of $s$ 29 | are m/s (meters per second), and the physical units of $f(s)$ 30 | are kg/s (kilograms per second). Let 31 | $g(d,t)$ be the total fuel used (in kg) when the vehicle moves a distance 32 | $d \geq 0$ (in meters) in time 33 | $t > 0$ (in seconds) at a constant speed. Write $g$ in DCP form. Hint: Check out the “perspective” atom. 34 | """ 35 | ) 36 | return 37 | 38 | 39 | @app.cell(hide_code=True) 40 | def __(mo): 41 | mo.accordion( 42 | { 43 | "Solution": mo.md( 44 | """ 45 | Since $g$ is the total fuel in kg when moving a distance $d$ in time $t$, we have $g(d,t) = t f(d/t)$. 46 | The function is known as the perspective function of $f$, and is convex when $f$ is convex (see Boyd and Vandenberghe, §3.2.6). 47 | """ 48 | ) 49 | } 50 | ) 51 | return 52 | 53 | 54 | if __name__ == "__main__": 55 | app.run() 56 | -------------------------------------------------------------------------------- /006-vrtnis/README.md: -------------------------------------------------------------------------------- 1 | # vrtnis: Community Contributor 2 | 3 | _September 12, 2024_ 4 | 5 | [vrtnis](https://x.com/virajtipnis) is a prolific contributor to the marimo community, creating numerous interactive notebooks that showcase the power and versatility of [marimo](https://marimo.io). Their contributions range from data visualization tools to AI-powered applications, demonstrating the broad applicability of marimo in various domains. 6 | 7 | ## Notable Contributions 8 | 9 | 1. **K-d Tree Visualizer**: An interactive tool for visualizing k-d trees; KDTree for fast generalized N-point problems. 10 | 11 | 2. **LMSYS Win Rate Predictor**: A notebook that predicts win rates using Elo Ratings for LMSYS LLMs. 12 | 13 | 3. **Pong Game**: A recreation of the classic Pong game using marimo; with fun visualization. 14 | 15 | 4. **AI Docs Bot**: vrtnis developed the AI-powered documentation bot that powers the #ask-docs-ai channel in the [marimo community Discord](https://discord.gg/JE7nhX6mD8), enhancing the support experience for marimo users. 16 | 17 | 5. **Marimo Cheatsheet**: vrtnis created a comprehensive cheatsheet for marimo, available at [https://github.com/vrtnis/marimo-cheat-sheet](https://github.com/vrtnis/marimo-cheat-sheet). This resource is invaluable for both new and experienced marimo users. 18 | 19 | You can explore vrtnis's notebooks and contributions in the ⁠#share-your-notebook channel of the marimo community Discord. 20 | 21 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/p/@spotlights/006-vrtnis) 22 | 23 | To run this notebook locally with its dependencies automatically installed in an isolated virtual environment, use: 24 | 25 | ```shell 26 | uvx marimo run --sandbox .py 27 | ``` 28 | 29 | If you don't have `uv` installed, you can use: 30 | 31 | ```shell 32 | marimo run .py 33 | ``` 34 | 35 | Note: You may need to manually install dependencies if not using `uv`. 36 | 37 | To edit the notebook source code, replace `run` with `edit` in the above commands. 38 | 39 | > [!NOTE] 40 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 41 | 42 | We're excited to have vrtnis as an active and innovative member of the marimo community, consistently pushing the boundaries of what's possible with marimo! 43 | 44 | ## Spotlight Promotion 45 | 46 | This spotlight has been featured on our social media platforms. Join the conversation: 47 | 48 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-viraj-tipnis-viraj-was-activity-7240041724792688641-INOR?utm_source=share&utm_medium=member_desktop) 49 | - Twitter Post: [Link](https://x.com/marimo_io/status/1834274394237182279) 50 | - Discord Discussion: [Discord message](https://discord.com/channels/1059888774789730424/1268639867898695761/1283835370773155973) 51 | 52 | We encourage you to engage with these posts, share your thoughts, and help us celebrate these amazing contributions to the Marimo community! 53 | -------------------------------------------------------------------------------- /006-vrtnis/lmsys.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/006-vrtnis/lmsys.pdf -------------------------------------------------------------------------------- /007-haleshot/README.md: -------------------------------------------------------------------------------- 1 | # Haleshot: marimo Ambassador and Community Contributor 2 | 3 | _September 20, 2024_ 4 | 5 | [Haleshot](https://haleshot.github.io/) is an undergraduate student pursuing a B.Tech in AI and an open-source enthusiast. He has contributed so much to the marimo community — PRs, feedback, example notebooks and tutorials, keeping ⁠marimo-blog up-to-date, and more! As a contributor and newly appointed [marimo ambassador](https://marimo.io/ambassadors), he helps to drive developer relations and collaboration. 6 | 7 | ## Featured Work: Goodreads Dataset EDA 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/p/@haleshot/goodreads-dataset-eda) 10 | 11 | Key aspects of this analysis: 12 | - Exploration of user rating patterns 13 | - Investigation of book characteristics 14 | - Identification of potential relationships between dataset features 15 | - Insights that can be used for building recommendation systems 16 | 17 | You can find this notebook and more in the [marimo-tutorials collection](https://marimo.io/c/@haleshot/marimo-tutorials) on marimo cloud. 18 | 19 | ## Marimo tutorials Repository 20 | 21 | Haleshot is a core contributor of the [marimo-tutorials](https://github.com/Haleshot/marimo-tutorials) repository which helps in showcasing innovative projects within the marimo community. This repository aims to serve as a reference point for all marimo-related notebook implementations (spanning various domains), complete with links to open them in the community cloud. 22 | 23 | We encourage you to check out the [repository]((https://github.com/Haleshot/marimo-tutorials)) and show your appreciation by giving it a ⭐! 24 | 25 | > [!NOTE] 26 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 27 | 28 | We're thrilled to have Haleshot as an active and innovative member of the marimo community! 29 | 30 | ## Spotlight Promotion 31 | 32 | This spotlight has been featured on our social media platforms. Join the conversation: 33 | 34 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-haleshot-srihari-thyagarajan-activity-7242680588460007424-hgmg?utm_source=share&utm_medium=member_desktop) 35 | - Twitter Post: [Link](https://x.com/marimo_io/status/1836913075431297278) 36 | - Discord message: [Link](https://discord.com/channels/1059888774789730424/1268639867898695761/1286474542843166821) 37 | 38 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 39 | 40 | ## Running Haleshot's Notebooks 41 | 42 | To run Haleshot's notebooks locally, you can use the following command if you have `uv` installed: 43 | 44 | ```shell 45 | uvx marimo run --sandbox notebook.py 46 | ``` 47 | 48 | If you don't have `uv` installed, you can use: 49 | 50 | ```shell 51 | marimo run notebook.py 52 | ``` 53 | 54 | Note that you may need to manually install dependencies if not using `uv`. 55 | 56 | To edit the notebook source code, replace `run` with `edit` in the above commands. 57 | -------------------------------------------------------------------------------- /009-WE3-Lab/README.md: -------------------------------------------------------------------------------- 1 | # Valuing Energy Flexibility from Water Systems: Stanford WE3 Lab 2 | 3 | _October 4, 2024_ 4 | 5 | This week's spotlight 🌟 features a [paper](https://www.nature.com/articles/s44221-024-00316-4) by [Akshay Rao](https://x.com/raodoesresearch), [Fletch](https://github.com/fletchapin), and others from Stanford's [WE3 Lab](https://x.com/WE3Lab). Their paper, "[Valuing Energy Flexibility from Water Systems](https://www.nature.com/articles/s44221-024-00316-4)," is accompanied by a marimo notebook that helps visualize their methods. In this research, they study the efficient operation of water systems in a decarbonizing grid. 6 | 7 | ## Deployed Notebook 8 | 9 | You can explore the interactive notebook here: [![Open with marimo](https://marimo.io/shield.svg)](https://lvof.we3lab.tech/) 10 | 11 | ## Local Usage 12 | 13 | To run this notebook locally, use the following command if you have `uv` installed: 14 | 15 | ```shell 16 | cd marimonotebook 17 | uvx marimo run --sandbox lvof.py 18 | ``` 19 | 20 | If you don't have `uv` installed, you can use: 21 | 22 | ```shell 23 | cd marimonotebook 24 | marimo run lvof.py 25 | ``` 26 | 27 | Note: You may need to manually install the notebook's dependencies if you're not using `uv`. 28 | 29 | To edit the notebook source code, replace `run` with `edit` in the above commands. 30 | 31 | > [!NOTE] 32 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 33 | 34 | We're excited to showcase the innovative work of the Stanford WE3 Lab team and their contribution to understanding energy flexibility in water systems! 35 | 36 | ## Spotlight Promotion 37 | 38 | This spotlight has been featured on our social media platforms. Join the conversation: 39 | 40 | - Twitter Thread: [Link](https://x.com/marimo_io/status/1841948653386399828) 41 | - Discord: [Link](https://discord.com/channels/1059888774789730424/1268639867898695761/1291508226868772864) 42 | 43 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 44 | -------------------------------------------------------------------------------- /009-WE3-Lab/figures/pdf/figure1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/figures/pdf/figure1.pdf -------------------------------------------------------------------------------- /009-WE3-Lab/figures/pdf/figure2.pdf: -------------------------------------------------------------------------------- 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"ed_normalized":1.3546111529497383e-08, 4 | "p_normalized": 1.3546111529497388e-08 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WSD/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.9841781884591358, 3 | "ed_normalized" : 0.10304485933450605, 4 | "p_normalized" : 0.14304944963350935 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WSD/Annualized/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,80.20707084811225,0.03822,2.33454 3 | 2021-07-02 01:00:00,107.7734358828182,74.3671239631592,0.03822,2.33454 4 | 2021-07-02 02:00:00,107.06760216385662,81.90344284905727,0.03822,2.33454 5 | 2021-07-02 03:00:00,90.2567446794392,74.28093969827563,0.03822,2.33454 6 | 2021-07-02 04:00:00,80.33058542087886,80.37898456755329,0.03822,2.33454 7 | 2021-07-02 05:00:00,58.35880953089501,58.53609889607591,0.03822,2.33454 8 | 2021-07-02 06:00:00,80.38349295507551,74.53737881032902,0.03822,2.33454 9 | 2021-07-02 07:00:00,58.47992203814479,90.30621366198147,0.03822,2.33454 10 | 2021-07-02 08:00:00,74.43755748436648,74.43580317866302,0.03822,2.33454 11 | 2021-07-02 09:00:00,47.94964753868955,81.99360554429413,0.03822,2.33454 12 | 2021-07-02 10:00:00,74.59111426865474,74.56465644815111,0.03822,2.33454 13 | 2021-07-02 11:00:00,74.7335966451448,74.70516634650384,0.03822,2.33454 14 | 2021-07-02 12:00:00,74.86328890734512,74.82886188964275,0.03822,2.33454 15 | 2021-07-02 13:00:00,74.87462845750997,58.88769603785278,0.03822,2.33454 16 | 2021-07-02 14:00:00,59.00216247500031,74.87794750814747,0.03822,2.33454 17 | 2021-07-02 15:00:00,74.78038350921005,74.74660882436555,0.03822,2.33454 18 | 2021-07-02 16:00:00,58.5513628668479,90.30621366304976,0.03822,2.33454 19 | 2021-07-02 17:00:00,47.79421623461083,81.5873847643536,0.03822,2.33454 20 | 2021-07-02 18:00:00,112.5345810979245,73.93238885044269,0.03822,2.33454 21 | 2021-07-02 19:00:00,47.806768881180105,86.38767202766022,0.03822,2.33454 22 | 2021-07-02 20:00:00,90.41869415218726,90.30621363257804,0.03822,2.33454 23 | 2021-07-02 21:00:00,79.90418158575795,90.30621366219933,0.03822,2.33454 24 | 2021-07-02 22:00:00,113.93646622995728,81.36385378590606,0.03822,2.33454 25 | 2021-07-02 23:00:00,87.63063881843247,88.27011958288665,0.03822,2.33454 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WWT/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.947621998449655, 3 | "ed_normalized" : 0.02015959053514673, 4 | "p_normalized" : 0.03371205088917483 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WWT/Spring/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.9333813487572047, 3 | "ed_normalized" : 0.02142103705843751, 4 | "p_normalized" : 0.03697095867003202 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WWT/Summer/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.9220804218745845, 3 | "ed_normalized" : 0.0171652597460162, 4 | "p_normalized" : 0.028146024402673206 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/houston/WWT/Winter/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.9220804218745845, 3 | "ed_normalized" : 0.0171652597460162, 4 | "p_normalized" : 0.028146024402673206 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_curtailed/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : -0.05, 3 | "ed_normalized" : 0.20873611806637807, 4 | "p_normalized" : 0.25519260960283896 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_curtailed/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : -0.05, 3 | "ed_normalized" : 0.17925542311975018, 4 | "p_normalized" : 0.19118068762850474 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_curtailed/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : -0.05, 3 | "ed_normalized" : 0.1792554231197549, 4 | "p_normalized" : 0.19118068762870208 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_curtailed/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : -0.05, 3 | "ed_normalized" : 0.24145457719810323, 4 | "p_normalized" : 0.3262347426610504 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_curtailed/WinterWeekend/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : -0.05, 3 | "ed_normalized" : 0.1792554231192349, 4 | "p_normalized" : 0.19118068762842885 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_nominal/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.936788540820831, 3 | "ed_normalized" : 0.03942708147670803, 4 | "p_normalized" : 0.08492152581527934 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_nominal/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 1.0, 3 | "ed_normalized" : 2.170456230276379e-07, 4 | "p_normalized" : 4.20541784452272e-07 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_nominal/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 1.0, 3 | "ed_normalized" : 1.3546342419335627e-08, 4 | "p_normalized" : 1.354634241933563e-08 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_nominal/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.8666347826566667, 3 | "ed_normalized" : 0.08318418747792372, 4 | "p_normalized" : 0.1791694734860884 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/AWT_nominal/WinterWeekend/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 1.0, 3 | "ed_normalized" : 1.3546091973495438e-08, 4 | "p_normalized" : 1.3546091973495443e-08 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9639103249690224, "ed_normalized": 0.10564280536119175, "p_normalized": 0.14856294170543524} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9507607872646964, "ed_normalized": 0.10994755690592038, "p_normalized": 0.15533569478507886} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/SummerWeekday/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2,electricity_demand3 2 | 2021-07-02 00:00:00,106.43863113896096,108.05617600844124,0.037473333,0.0,0.0,16.66 3 | 2021-07-02 01:00:00,107.7734358828182,108.44601162187756,0.037473333,0.0,0.0,16.66 4 | 2021-07-02 02:00:00,107.06760216385662,108.4157730665372,0.037473333,0.0,0.0,16.66 5 | 2021-07-02 03:00:00,90.2567446794392,107.44400094003174,0.037473333,0.0,0.0,16.66 6 | 2021-07-02 04:00:00,80.33058542087886,106.45055852607456,0.037473333,0.0,0.0,16.66 7 | 2021-07-02 05:00:00,58.35880953089501,112.35659626349988,0.037473333,0.0,0.0,16.66 8 | 2021-07-02 06:00:00,80.38349295507551,112.31280491958316,0.037473333,0.0,0.0,16.66 9 | 2021-07-02 07:00:00,58.47992203814479,112.286743989319,0.037473333,0.0,0.0,16.66 10 | 2021-07-02 08:00:00,74.43755748436648,57.92535237133784,0.049901949,18.44,9.15,16.66 11 | 2021-07-02 09:00:00,47.94964753868955,57.90678061495498,0.049901949,18.44,9.15,16.66 12 | 2021-07-02 10:00:00,74.59111426865474,57.986095409444175,0.049901949,18.44,9.15,16.66 13 | 2021-07-02 11:00:00,74.7335966451448,47.61372017956579,0.049901949,18.44,9.15,16.66 14 | 2021-07-02 12:00:00,74.86328890734512,47.65215192732765,0.049901949,18.44,9.15,16.66 15 | 2021-07-02 13:00:00,74.87462845750997,74.25636276170376,0.049901949,18.44,9.15,16.66 16 | 2021-07-02 14:00:00,59.00216247500031,58.1957205461398,0.049901949,18.44,9.15,16.66 17 | 2021-07-02 15:00:00,74.78038350921005,74.19966538694409,0.049901949,18.44,9.15,16.66 18 | 2021-07-02 16:00:00,58.5513628668479,57.76912775022096,0.049901949,18.44,9.15,16.66 19 | 2021-07-02 17:00:00,47.79421623461083,73.69660403867725,0.049901949,18.44,9.15,16.66 20 | 2021-07-02 18:00:00,112.5345810979245,46.96605935492515,0.049901949,18.44,0.0,16.66 21 | 2021-07-02 19:00:00,47.806768881180105,57.61548544539344,0.049901949,18.44,0.0,16.66 22 | 2021-07-02 20:00:00,90.41869415218726,47.014509382382,0.049901949,18.44,0.0,16.66 23 | 2021-07-02 21:00:00,79.90418158575795,46.90502034681235,0.049901949,18.44,0.0,16.66 24 | 2021-07-02 22:00:00,113.93646622995728,114.111260914622,0.037473333,0.0,0.0,16.66 25 | 2021-07-02 23:00:00,87.63063881843247,98.77349460688882,0.037473333,0.0,0.0,16.66 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9766789531204425, "ed_normalized": 0.10315730820754053, "p_normalized": 0.14375972567038978} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/SummerWeekend/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,106.459601389173,0.037473333,16.66 3 | 2021-07-02 01:00:00,107.7734358828182,107.79565385948352,0.037473333,16.66 4 | 2021-07-02 02:00:00,107.06760216385662,106.79616144018236,0.037473333,16.66 5 | 2021-07-02 03:00:00,90.2567446794392,90.3241939146805,0.037473333,16.66 6 | 2021-07-02 04:00:00,80.33058542087886,74.5154231287439,0.037473333,16.66 7 | 2021-07-02 05:00:00,58.35880953089501,80.38945645476791,0.037473333,16.66 8 | 2021-07-02 06:00:00,80.38349295507551,58.50919137624919,0.037473333,16.66 9 | 2021-07-02 07:00:00,58.47992203814479,80.40985173063125,0.037473333,16.66 10 | 2021-07-02 08:00:00,74.43755748436648,53.921882237395934,0.037473333,16.66 11 | 2021-07-02 09:00:00,47.94964753868955,74.44136054135205,0.037473333,16.66 12 | 2021-07-02 10:00:00,74.59111426865474,58.5091187825021,0.037473333,16.66 13 | 2021-07-02 11:00:00,74.7335966451448,74.75324972037247,0.037473333,16.66 14 | 2021-07-02 12:00:00,74.86328890734512,74.8807079069702,0.037473333,16.66 15 | 2021-07-02 13:00:00,74.87462845750997,74.88897450919895,0.037473333,16.66 16 | 2021-07-02 14:00:00,59.00216247500031,59.002343359764325,0.037473333,16.66 17 | 2021-07-02 15:00:00,74.78038350921005,74.79036029662835,0.037473333,16.66 18 | 2021-07-02 16:00:00,58.5513628668479,58.552780177866815,0.037473333,16.66 19 | 2021-07-02 17:00:00,47.79421623461083,63.76939490588663,0.037473333,16.66 20 | 2021-07-02 18:00:00,112.5345810979245,90.40203152813734,0.037473333,16.66 21 | 2021-07-02 19:00:00,47.806768881180105,47.78449704317156,0.037473333,16.66 22 | 2021-07-02 20:00:00,90.41869415218726,90.40568485668553,0.037473333,16.66 23 | 2021-07-02 21:00:00,79.90418158575795,102.10590063998636,0.037473333,16.66 24 | 2021-07-02 22:00:00,113.93646622995728,97.91826285601223,0.037473333,16.66 25 | 2021-07-02 23:00:00,87.63063881843247,81.47594600720171,0.037473333,16.66 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9529782778862761, "ed_normalized": 0.12332463190899674, "p_normalized": 0.1740272127171205} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/WinterWeekday/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,106.603185973999,0.0446782,0.0,4.21 3 | 2021-07-02 01:00:00,107.7734358828182,106.60318597399902,0.0446782,0.0,4.21 4 | 2021-07-02 02:00:00,107.06760216385662,92.3985457057262,0.0446782,0.0,4.21 5 | 2021-07-02 03:00:00,90.2567446794392,106.603185973999,0.0446782,0.0,4.21 6 | 2021-07-02 04:00:00,80.33058542087886,98.11951311272506,0.0446782,0.0,4.21 7 | 2021-07-02 05:00:00,58.35880953089501,106.60318597399902,0.0446782,0.0,4.21 8 | 2021-07-02 06:00:00,80.38349295507551,106.6031141292752,0.0446782,0.0,4.21 9 | 2021-07-02 07:00:00,58.47992203814479,106.60318597433832,0.0446782,0.0,4.21 10 | 2021-07-02 08:00:00,74.43755748436648,63.50980242183965,0.044253925,13.96,4.21 11 | 2021-07-02 09:00:00,47.94964753868955,58.04639847454092,0.044253925,13.96,4.21 12 | 2021-07-02 10:00:00,74.59111426865474,58.12995562296664,0.044253925,13.96,4.21 13 | 2021-07-02 11:00:00,74.7335966451448,63.693006841938185,0.044253925,13.96,4.21 14 | 2021-07-02 12:00:00,74.86328890734512,58.38675765051698,0.044253925,13.96,4.21 15 | 2021-07-02 13:00:00,74.87462845750997,63.69300684193817,0.044253925,13.96,4.21 16 | 2021-07-02 14:00:00,59.00216247500031,58.42722176383543,0.044253925,13.96,4.21 17 | 2021-07-02 15:00:00,74.78038350921005,63.693006841938185,0.044253925,13.96,4.21 18 | 2021-07-02 16:00:00,58.5513628668479,57.98288927302019,0.044253925,13.96,4.21 19 | 2021-07-02 17:00:00,47.79421623461083,63.6930068419402,0.044253925,13.96,4.21 20 | 2021-07-02 18:00:00,112.5345810979245,63.11675050454857,0.044253925,13.96,4.21 21 | 2021-07-02 19:00:00,47.806768881180105,63.69300684192157,0.044253925,13.96,4.21 22 | 2021-07-02 20:00:00,90.41869415218726,63.69300684193124,0.044253925,13.96,4.21 23 | 2021-07-02 21:00:00,79.90418158575795,58.88942738308876,0.044253925,13.96,4.21 24 | 2021-07-02 22:00:00,113.93646622995728,98.23240065723392,0.0446782,0.0,4.21 25 | 2021-07-02 23:00:00,87.63063881843247,106.603185973999,0.0446782,0.0,4.21 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/WinterWeekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 1.0008867812589046, "ed_normalized": 0.057835991407136626, "p_normalized": 0.07961385825223594} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WSD/WinterWeekend/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,74.35881944100991,0.0446782,4.21 3 | 2021-07-02 01:00:00,107.7734358828182,82.361373898271,0.0446782,4.21 4 | 2021-07-02 02:00:00,107.06760216385662,74.28999840934489,0.0446782,4.21 5 | 2021-07-02 03:00:00,90.2567446794392,74.27056182978274,0.0446782,4.21 6 | 2021-07-02 04:00:00,80.33058542087886,74.54960716953636,0.0446782,4.21 7 | 2021-07-02 05:00:00,58.35880953089501,88.04767802775234,0.0446782,4.21 8 | 2021-07-02 06:00:00,80.38349295507551,58.46339735713278,0.0446782,4.21 9 | 2021-07-02 07:00:00,58.47992203814479,74.47622202153056,0.0446782,4.21 10 | 2021-07-02 08:00:00,74.43755748436648,90.36980167977616,0.0446782,4.21 11 | 2021-07-02 09:00:00,47.94964753868955,74.42144320662197,0.0446782,4.21 12 | 2021-07-02 10:00:00,74.59111426865474,74.5548369385069,0.0446782,4.21 13 | 2021-07-02 11:00:00,74.7335966451448,82.53793492026878,0.0446782,4.21 14 | 2021-07-02 12:00:00,74.86328890734512,74.78147535211943,0.0446782,4.21 15 | 2021-07-02 13:00:00,74.87462845750997,74.79071625670261,0.0446782,4.21 16 | 2021-07-02 14:00:00,59.00216247500031,58.85842156488295,0.0446782,4.21 17 | 2021-07-02 15:00:00,74.78038350921005,74.70864916289007,0.0446782,4.21 18 | 2021-07-02 16:00:00,58.5513628668479,90.37033471896684,0.0446782,4.21 19 | 2021-07-02 17:00:00,47.79421623461083,74.12397565017265,0.0446782,4.21 20 | 2021-07-02 18:00:00,112.5345810979245,86.51967460528851,0.0446782,4.21 21 | 2021-07-02 19:00:00,47.806768881180105,90.37033472008022,0.0446782,4.21 22 | 2021-07-02 20:00:00,90.41869415218726,80.1392024091518,0.0446782,4.21 23 | 2021-07-02 21:00:00,79.90418158575795,81.50848712927424,0.0446782,4.21 24 | 2021-07-02 22:00:00,113.93646622995728,90.3703347189662,0.0446782,4.21 25 | 2021-07-02 23:00:00,87.63063881843247,88.29315042212852,0.0446782,4.21 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WWT/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.8225419496003059, 3 | "ed_normalized" : 0.06030860395251517, 4 | "p_normalized" : 0.10886336077480954 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WWT/Spring/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.8076363737836469, 3 | "ed_normalized" : 0.09079429007835481, 4 | "p_normalized" : 0.16932284309939694 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WWT/Summer/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.824799260015348, 3 | "ed_normalized" : 0.06650898702926933, 4 | "p_normalized" : 0.11574735965815715 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/newyork/WWT/Winter/radar.json: -------------------------------------------------------------------------------- 1 | { 2 | "rte" : 0.8297997052592295, 3 | "ed_normalized" : 0.03672496250257308, 4 | "p_normalized" : 0.0664652135057337 5 | } -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_curtailed/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.21395593053809323, "p_normalized": 0.27129929234720895} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_curtailed/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.2682215680077553, "p_normalized": 0.3991654564520543} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_curtailed/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.17925542312136047, "p_normalized": 0.19118068762957427} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_curtailed/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.20892959449592421, "p_normalized": 0.25812566769757755} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_nominal/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8350888879379181, "ed_normalized": 0.07413037281307946, "p_normalized": 0.16559608803535897} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_nominal/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.7325087492433433, "ed_normalized": 0.11238083518692564, "p_normalized": 0.2657999381027766} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_nominal/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8811959321808365, "ed_normalized": 0.04830500581205423, "p_normalized": 0.1031225600562121} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/AWT_nominal/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8603359962972877, "ed_normalized": 0.07169098364573662, "p_normalized": 0.15502003642776616} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9574493652338969, "ed_normalized": 0.11623860305196687, "p_normalized": 0.163802169612635} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9624289584885413, "ed_normalized": 0.14281608200402987, "p_normalized": 0.20051889449713137} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Spring/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,74.28320769255231,0.09822,20.7,0.0 3 | 2021-07-02 01:00:00,107.7734358828182,74.29763614710241,0.09822,20.7,0.0 4 | 2021-07-02 02:00:00,107.06760216385662,90.3147928740674,0.09822,20.7,0.0 5 | 2021-07-02 03:00:00,90.2567446794392,74.20559326344468,0.09822,20.7,0.0 6 | 2021-07-02 04:00:00,80.33058542087886,74.48501512728605,0.09822,20.7,0.0 7 | 2021-07-02 05:00:00,58.35880953089501,58.44009411063346,0.09822,20.7,0.0 8 | 2021-07-02 06:00:00,80.38349295507551,74.44994715284935,0.09822,20.7,0.0 9 | 2021-07-02 07:00:00,58.47992203814479,58.43340847383683,0.09822,20.7,0.0 10 | 2021-07-02 08:00:00,74.43755748436648,58.34105368160731,0.09822,20.7,0.0 11 | 2021-07-02 09:00:00,47.94964753868955,114.29850165697304,0.0569,20.7,0.0 12 | 2021-07-02 10:00:00,74.59111426865474,114.4506297281371,0.0569,20.7,0.0 13 | 2021-07-02 11:00:00,74.7335966451448,114.65620136439732,0.0569,20.7,0.0 14 | 2021-07-02 12:00:00,74.86328890734512,116.05010792368432,0.0569,20.7,0.0 15 | 2021-07-02 13:00:00,74.87462845750997,116.27320791870504,0.0569,20.7,0.0 16 | 2021-07-02 14:00:00,59.00216247500031,80.12490857445108,0.09822,20.7,0.0 17 | 2021-07-02 15:00:00,74.78038350921005,74.63371965211101,0.09822,20.7,0.0 18 | 2021-07-02 16:00:00,58.5513628668479,47.99048292486864,0.12734,20.7,1.78 19 | 2021-07-02 17:00:00,47.79421623461083,47.6835217177166,0.12734,20.7,1.78 20 | 2021-07-02 18:00:00,112.5345810979245,57.90688256862547,0.12734,20.7,1.78 21 | 2021-07-02 19:00:00,47.806768881180105,47.63286475264286,0.12734,20.7,1.78 22 | 2021-07-02 20:00:00,90.41869415218726,47.4699625820568,0.12734,20.7,1.78 23 | 2021-07-02 21:00:00,79.90418158575795,90.4112881151387,0.09822,20.7,0.0 24 | 2021-07-02 22:00:00,113.93646622995728,79.95416769211707,0.09822,20.7,0.0 25 | 2021-07-02 23:00:00,87.63063881843247,106.60888243491476,0.09822,20.7,0.0 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9838251483236677, "ed_normalized": 0.09477299680401659, "p_normalized": 0.13159003742682493} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Summer/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2,electricity_demand3 2 | 2021-07-02 00:00:00,106.43863113896096,107.9759341706549,0.09816,0.0,0.0,20.7 3 | 2021-07-02 01:00:00,107.7734358828182,108.35972616652272,0.09816,0.0,0.0,20.7 4 | 2021-07-02 02:00:00,107.06760216385662,108.2275667715106,0.09816,0.0,0.0,20.7 5 | 2021-07-02 03:00:00,90.2567446794392,91.94566868943171,0.09816,0.0,0.0,20.7 6 | 2021-07-02 04:00:00,80.33058542087886,92.03502339926756,0.09816,0.0,0.0,20.7 7 | 2021-07-02 05:00:00,58.35880953089501,91.8323915904778,0.09816,0.0,0.0,20.7 8 | 2021-07-02 06:00:00,80.38349295507551,113.9179730387172,0.09816,0.0,0.0,20.7 9 | 2021-07-02 07:00:00,58.47992203814479,113.6732290661564,0.09816,0.0,0.0,20.7 10 | 2021-07-02 08:00:00,74.43755748436648,112.49082839013188,0.09816,0.0,0.0,20.7 11 | 2021-07-02 09:00:00,47.94964753868955,79.85824237615176,0.09816,0.0,0.0,20.7 12 | 2021-07-02 10:00:00,74.59111426865474,74.02018327186966,0.09816,0.0,0.0,20.7 13 | 2021-07-02 11:00:00,74.7335966451448,74.16341853800289,0.09816,0.0,0.0,20.7 14 | 2021-07-02 12:00:00,74.86328890734512,74.29305952414465,0.09816,0.0,0.0,20.7 15 | 2021-07-02 13:00:00,74.87462845750997,75.55618134034651,0.09816,0.0,0.0,20.7 16 | 2021-07-02 14:00:00,59.00216247500031,58.14056838679343,0.11735,0.0,5.32,20.7 17 | 2021-07-02 15:00:00,74.78038350921005,58.14056838675414,0.11735,0.0,5.32,20.7 18 | 2021-07-02 16:00:00,58.5513628668479,46.90284521928675,0.14484,26.8,0.0,20.7 19 | 2021-07-02 17:00:00,47.79421623461083,46.90284521928675,0.14484,26.8,0.0,20.7 20 | 2021-07-02 18:00:00,112.5345810979245,46.88882254330687,0.14484,26.8,0.0,20.7 21 | 2021-07-02 19:00:00,47.806768881180105,46.902845219273104,0.14484,26.8,0.0,20.7 22 | 2021-07-02 20:00:00,90.41869415218726,46.9028452192868,0.14484,26.8,0.0,20.7 23 | 2021-07-02 21:00:00,79.90418158575795,57.33441008318731,0.11735,0.0,5.32,20.7 24 | 2021-07-02 22:00:00,113.93646622995728,57.29325617325841,0.11735,0.0,5.32,20.7 25 | 2021-07-02 23:00:00,87.63063881843247,114.98591750003442,0.09816,0.0,0.0,20.7 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9331052055227756, "ed_normalized": 0.11738877456627242, "p_normalized": 0.16745741091922547} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WSD/Winter/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,108.53167558959628,0.09822,20.7,0.0 3 | 2021-07-02 01:00:00,107.7734358828182,108.7272346457094,0.09822,20.7,0.0 4 | 2021-07-02 02:00:00,107.06760216385662,107.32278424276238,0.09822,20.7,0.0 5 | 2021-07-02 03:00:00,90.2567446794392,90.56177377280284,0.09822,20.7,0.0 6 | 2021-07-02 04:00:00,80.33058542087886,74.47776365927587,0.09822,20.7,0.0 7 | 2021-07-02 05:00:00,58.35880953089501,74.44744617120043,0.09822,20.7,0.0 8 | 2021-07-02 06:00:00,80.38349295507551,58.43845078977549,0.09822,20.7,0.0 9 | 2021-07-02 07:00:00,58.47992203814479,96.30854291498616,0.09822,20.7,0.0 10 | 2021-07-02 08:00:00,74.43755748436648,112.40985119252444,0.09822,20.7,0.0 11 | 2021-07-02 09:00:00,47.94964753868955,80.32693315723313,0.09822,20.7,0.0 12 | 2021-07-02 10:00:00,74.59111426865474,74.51814817467164,0.09822,20.7,0.0 13 | 2021-07-02 11:00:00,74.7335966451448,74.6579124897559,0.09822,20.7,0.0 14 | 2021-07-02 12:00:00,74.86328890734512,74.78514605402037,0.09822,20.7,0.0 15 | 2021-07-02 13:00:00,74.87462845750997,58.88451493255891,0.09822,20.7,0.0 16 | 2021-07-02 14:00:00,59.00216247500031,74.83331004993684,0.09822,20.7,0.0 17 | 2021-07-02 15:00:00,74.78038350921005,74.70003818227161,0.09822,20.7,0.0 18 | 2021-07-02 16:00:00,58.5513628668479,48.05521475646577,0.12734,20.7,1.78 19 | 2021-07-02 17:00:00,47.79421623461083,47.74189610492547,0.12734,20.7,1.78 20 | 2021-07-02 18:00:00,112.5345810979245,57.95614456174042,0.12734,20.7,1.78 21 | 2021-07-02 19:00:00,47.806768881180105,47.69048943230587,0.12734,20.7,1.78 22 | 2021-07-02 20:00:00,90.41869415218726,47.5272985052616,0.12734,20.7,1.78 23 | 2021-07-02 21:00:00,79.90418158575795,106.44546059937436,0.09822,20.7,0.0 24 | 2021-07-02 22:00:00,113.93646622995728,87.7182272321786,0.09822,20.7,0.0 25 | 2021-07-02 23:00:00,87.63063881843247,98.76607878044196,0.09822,20.7,0.0 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WWT/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9067434621855667, "ed_normalized": 0.06809340847018905, "p_normalized": 0.11948065967602799} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WWT/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8156458571784098, "ed_normalized": 0.13388235922818045, "p_normalized": 0.2483217032423047} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WWT/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9313988370005477, "ed_normalized": 0.060109852675433374, "p_normalized": 0.09804960850743794} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/sanjose/WWT/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9423264021407372, "ed_normalized": 0.03446036434585116, "p_normalized": 0.0582966347387467} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_curtailed/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.2363674720672011, "p_normalized": 0.3228277359972323} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_curtailed/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.1883963013973017, "p_normalized": 0.21470970766094793} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_curtailed/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.1883963013973017, "p_normalized": 0.2147097076609488} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_curtailed/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.2604517635146403, "p_normalized": 0.3771092152442556} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_nominal/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8181376992393384, "ed_normalized": 0.0925517957347139, "p_normalized": 0.20701819097553825} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_nominal/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8620735726181609, "ed_normalized": 0.05578735881971861, "p_normalized": 0.12050034920903031} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_nominal/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8620735727459498, "ed_normalized": 0.055787358770058346, "p_normalized": 0.12050034909217178} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/AWT_nominal/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.7960793594998776, "ed_normalized": 0.11100966118890125, "p_normalized": 0.25045513212617204} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.940940208125853, "ed_normalized": 0.15873578082704395, "p_normalized": 0.2251828732061309} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9154938089214454, "ed_normalized": 0.10041379576617743, "p_normalized": 0.1446677244738654} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/SummerWeekday/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,74.01864626391368,0.12955,0.0,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,108.58077074910004,0.12955,0.0,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,92.3762152541442,0.12955,0.0,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,108.38708984406114,0.12955,0.0,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,92.28981855336318,0.12955,0.0,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,74.18771471779607,0.12955,0.0,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,92.1371318020179,0.12955,0.0,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,114.1877999119361,0.12955,0.0,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,97.92604515917418,0.12955,0.0,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,74.08786243640603,0.12955,0.0,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,80.07794010138554,0.12955,0.0,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,58.29218728602989,0.12955,0.0,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,74.49980155627617,0.12955,0.0,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,74.51411877598585,0.12955,0.0,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,74.56200149337805,0.12955,0.0,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,74.44408921252413,0.12955,0.0,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,46.52994179536191,0.52939,2.55,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,46.52994179536191,0.52939,2.55,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,46.52994179536191,0.52939,2.55,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,46.52994179536191,0.52939,2.55,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,46.52994179536191,0.52939,2.55,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,90.36648334316374,0.12955,0.0,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,114.03956033894968,0.12955,0.0,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,98.72585702623589,0.12955,0.0,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/SummerWeekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9564701169470126, "ed_normalized": 0.0839302797726202, "p_normalized": 0.11821522385743476} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/SummerWeekend/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,108.35050196145755,0.12955,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,108.73711689614544,0.12955,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,108.60512810819272,0.12955,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,92.3322792497028,0.12955,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,74.30493826965704,0.12955,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,92.27354171727752,0.12955,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,114.08979428530448,0.12955,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,80.16442564313043,0.12955,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,80.16756236339353,0.12955,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,74.22832604523292,0.12955,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,74.35528941082597,0.12955,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,74.4952866963829,0.12955,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,74.62433598849343,0.12955,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,58.75743601679002,0.12955,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,74.67326898428654,0.12955,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,74.55219940655283,0.12955,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,47.92680773310164,0.19809,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,47.626083647323455,0.19809,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,47.36963772241717,0.19809,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,47.54340452113871,0.19809,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,47.380950666602,0.19809,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,90.42468892626424,0.12955,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,98.24711761574048,0.12955,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,98.86058979621002,0.12955,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9478138292042187, "ed_normalized": 0.19039094641223003, "p_normalized": 0.26941614755556076} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WSD/Winter/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,73.87167371721492,0.1332,0.0,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,57.831077324461646,0.1332,0.0,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,57.74073080480673,0.1332,0.0,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,73.80711293036916,0.1332,0.0,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,58.09174303977653,0.1332,0.0,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,74.16497275482541,0.1332,0.0,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,58.08787724582553,0.1332,0.0,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,90.4007735457164,0.1332,0.0,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,113.70359824482328,0.09081,0.0,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,97.54179511719514,0.09081,0.0,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,113.90269036456286,0.09081,0.0,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,108.66428940655771,0.09081,0.0,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,114.44050811012744,0.09081,0.0,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,114.41124545391948,0.09081,0.0,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,109.4263341502522,0.09081,0.0,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,113.63984497226002,0.09081,0.0,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,47.1844428372537,0.20081,1.24,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,47.1844428372537,0.20081,1.24,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,46.94065731187432,0.20081,1.24,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,47.11391210187792,0.20081,1.24,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,46.953571471743786,0.20081,1.24,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,57.37394662313476,0.1332,0.0,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,106.62197019197508,0.1332,0.0,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,73.53741378917489,0.1332,0.0,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WWT/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.947221414404508, "ed_normalized": 0.05996458409434799, "p_normalized": 0.09995698279792789} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WWT/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.834322026374265, "ed_normalized": 0.07051645496876327, "p_normalized": 0.12917966568542455} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WWT/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.999212, "ed_normalized": 0.07512410424075344, "p_normalized": 0.11663595276535546} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/santabarbara/WWT/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9740021578226029, "ed_normalized": 0.04128754052939652, "p_normalized": 0.06867671020404804} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_curtailed/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.2571733881620547, "p_normalized": 0.4034428892279579} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_curtailed/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.3346503538087467, "p_normalized": 0.6317848085386698} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_curtailed/Weekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.17925542312021925, "p_normalized": 0.19118068762893237} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_curtailed/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": -0.05, "ed_normalized": 0.2238853099314578, "p_normalized": 0.28843880990082343} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_nominal/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8830995416518479, "ed_normalized": 0.0667330539878182, "p_normalized": 0.14657508834563876} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_nominal/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8343573216400559, "ed_normalized": 0.09682571098838032, "p_normalized": 0.21287396570741934} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_nominal/Weekend/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 1.0, "ed_normalized": 1.3546571095543096e-08, "p_normalized": 1.35465710955431e-08} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/AWT_nominal/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8380464797558891, "ed_normalized": 0.08925977049009137, "p_normalized": 0.19576910218741145} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.967554325061081, "ed_normalized": 0.11076774354678288, "p_normalized": 0.15522730212008629} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/SummerWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9460290859586252, "ed_normalized": 0.11372159791887268, "p_normalized": 0.1610796645527776} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/SummerWeekday/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,107.91736898039008,0.05413,0.0,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,108.30240999991392,0.05413,0.0,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,91.93720766444736,0.05413,0.0,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,107.9997216066628,0.05413,0.0,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,107.23702605585795,0.05413,0.0,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,106.46070587895588,0.05413,0.0,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,106.51208514814806,0.05413,0.0,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,73.89336549307014,0.05413,0.0,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,79.87489734193457,0.05413,0.0,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,79.8839167703217,0.05413,0.0,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,79.88622041113848,0.05413,0.0,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.18693315224388,0.05413,0.0,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,47.8837845023485,0.07793,7.14,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,58.25939024731351,0.07793,7.14,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,47.80217885012334,0.07793,7.14,5.33 17 | 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0.10050749004610586, "p_normalized": 0.13849808761983723} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/Weekend/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,74.3884063810103,0.05413,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,107.73839678857097,0.05413,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,90.30800789216428,0.05413,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,106.58642196898867,0.05413,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,64.32315935068168,0.05413,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,80.37396960340418,0.05413,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,58.48770818798525,0.05413,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,74.50283363071414,0.05413,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,47.90919437730879,0.05413,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,74.36287277792941,0.05413,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,74.5431976401566,0.05413,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.7501790152152,0.05413,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,58.9239291753303,0.05413,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,74.87986133178171,0.05413,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,74.92484684795389,0.05413,5.33 17 | 2021-07-02 15:00:00,74.78038350921005,58.85479826211532,0.05413,5.33 18 | 2021-07-02 16:00:00,58.5513628668479,74.48440622597803,0.05413,5.33 19 | 2021-07-02 17:00:00,47.79421623461083,47.8352802091992,0.05413,5.33 20 | 2021-07-02 18:00:00,112.5345810979245,90.6812248446771,0.05413,5.33 21 | 2021-07-02 19:00:00,47.806768881180105,102.3310846883725,0.05413,5.33 22 | 2021-07-02 20:00:00,90.41869415218726,90.40269014873692,0.05413,5.33 23 | 2021-07-02 21:00:00,79.90418158575795,86.08029454408258,0.05413,5.33 24 | 2021-07-02 22:00:00,113.93646622995728,91.74971661305304,0.05413,5.33 25 | 2021-07-02 23:00:00,87.63063881843247,103.70480975085836,0.05413,5.33 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/WinterWeekday/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.9674785925866565, "ed_normalized": 0.11658571348209247, "p_normalized": 0.16325515797894805} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WSD/WinterWeekday/timeseries.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,114.37831363353436,0.05413,0.0,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,108.90639070508966,0.05413,0.0,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,108.77010362479524,0.05413,0.0,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,106.90974569875156,0.05413,0.0,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,74.46728795047215,0.05413,0.0,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,80.31128461924939,0.05413,0.0,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,48.03137973602671,0.07793,7.14,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,57.39401351520056,0.07793,7.14,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,57.39401351520054,0.07793,7.14,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,47.84271244870301,0.07793,7.14,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,108.74895934474785,0.05413,0.0,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.63980081794627,0.05413,0.0,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,74.76785514350507,0.05413,0.0,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,58.79589554994258,0.05413,0.0,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,74.81737282308518,0.05413,0.0,5.33 17 | 2021-07-02 15:00:00,74.78038350921005,74.69266217417862,0.05413,0.0,5.33 18 | 2021-07-02 16:00:00,58.5513628668479,92.55407610390093,0.05413,0.0,5.33 19 | 2021-07-02 17:00:00,47.79421623461083,114.06644747473108,0.05413,0.0,5.33 20 | 2021-07-02 18:00:00,112.5345810979245,57.39384003932423,0.07793,7.14,5.33 21 | 2021-07-02 19:00:00,47.806768881180105,47.66191338576069,0.07793,7.14,5.33 22 | 2021-07-02 20:00:00,90.41869415218726,47.49886563456506,0.07793,7.14,5.33 23 | 2021-07-02 21:00:00,79.90418158575795,47.37460244922785,0.07793,7.14,5.33 24 | 2021-07-02 22:00:00,113.93646622995728,97.9598859951696,0.05413,0.0,5.33 25 | 2021-07-02 23:00:00,87.63063881843247,114.9001365379158,0.05413,0.0,5.33 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WWT/Annualized/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8715931135430783, "ed_normalized": 0.05706722422669655, "p_normalized": 0.10051993410605739} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WWT/Spring/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8319631386152233, "ed_normalized": 0.09243259053117535, "p_normalized": 0.16958974390053294} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WWT/Summer/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8664250559217342, "ed_normalized": 0.05784887971565742, "p_normalized": 0.09802535687797438} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/casestudies/tampa/WWT/Winter/radar.json: -------------------------------------------------------------------------------- 1 | {"rte": 0.8999140454845792, "ed_normalized": 0.034888577407721215, "p_normalized": 0.06045315212416581} -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/costing/houston/AWT_curtailed.json: -------------------------------------------------------------------------------- 1 | { 2 | "casename": "houston", 3 | "system_type": "AWT_curtailed", 4 | "daily_rtes": [ 5 | -0.05 6 | ], 7 | "daily_ed": [ 8 | 7502.99944575324 9 | ], 10 | "daily_ed_normalized": [ 11 | 0.17925542302626452 12 | ], 13 | "daily_p": [ 14 | 333.4228719897978 15 | ], 16 | "daily_p_normalized": [ 17 | 0.19118068734175592 18 | ], 19 | "daily_benefit": [ 20 | -89706.40064899379 21 | ], 22 | "daily_baseline_electricity_cost": [ 23 | 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DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2,electricity_demand3 2 | 2021-07-02 00:00:00,106.43863113896096,108.05617600844124,0.037473333,0.0,0.0,16.66 3 | 2021-07-02 01:00:00,107.7734358828182,108.44601162187756,0.037473333,0.0,0.0,16.66 4 | 2021-07-02 02:00:00,107.06760216385662,108.4157730665372,0.037473333,0.0,0.0,16.66 5 | 2021-07-02 03:00:00,90.2567446794392,107.44400094003174,0.037473333,0.0,0.0,16.66 6 | 2021-07-02 04:00:00,80.33058542087886,106.45055852607456,0.037473333,0.0,0.0,16.66 7 | 2021-07-02 05:00:00,58.35880953089501,112.35659626349988,0.037473333,0.0,0.0,16.66 8 | 2021-07-02 06:00:00,80.38349295507551,112.31280491958316,0.037473333,0.0,0.0,16.66 9 | 2021-07-02 07:00:00,58.47992203814479,112.286743989319,0.037473333,0.0,0.0,16.66 10 | 2021-07-02 08:00:00,74.43755748436648,57.92535237133784,0.049901949,18.44,9.15,16.66 11 | 2021-07-02 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00:00:00,106.43863113896096,106.603185973999,0.0446782,0.0,4.21 3 | 2021-07-02 01:00:00,107.7734358828182,106.60318597399902,0.0446782,0.0,4.21 4 | 2021-07-02 02:00:00,107.06760216385662,92.3985457057262,0.0446782,0.0,4.21 5 | 2021-07-02 03:00:00,90.2567446794392,106.603185973999,0.0446782,0.0,4.21 6 | 2021-07-02 04:00:00,80.33058542087886,98.11951311272506,0.0446782,0.0,4.21 7 | 2021-07-02 05:00:00,58.35880953089501,106.60318597399902,0.0446782,0.0,4.21 8 | 2021-07-02 06:00:00,80.38349295507551,106.6031141292752,0.0446782,0.0,4.21 9 | 2021-07-02 07:00:00,58.47992203814479,106.60318597433832,0.0446782,0.0,4.21 10 | 2021-07-02 08:00:00,74.43755748436648,63.50980242183965,0.044253925,13.96,4.21 11 | 2021-07-02 09:00:00,47.94964753868955,58.04639847454092,0.044253925,13.96,4.21 12 | 2021-07-02 10:00:00,74.59111426865474,58.12995562296664,0.044253925,13.96,4.21 13 | 2021-07-02 11:00:00,74.7335966451448,63.693006841938185,0.044253925,13.96,4.21 14 | 2021-07-02 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-------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/newyork/WinterWeekend.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,74.35881944100991,0.0446782,4.21 3 | 2021-07-02 01:00:00,107.7734358828182,82.361373898271,0.0446782,4.21 4 | 2021-07-02 02:00:00,107.06760216385662,74.28999840934489,0.0446782,4.21 5 | 2021-07-02 03:00:00,90.2567446794392,74.27056182978274,0.0446782,4.21 6 | 2021-07-02 04:00:00,80.33058542087886,74.54960716953636,0.0446782,4.21 7 | 2021-07-02 05:00:00,58.35880953089501,88.04767802775234,0.0446782,4.21 8 | 2021-07-02 06:00:00,80.38349295507551,58.46339735713278,0.0446782,4.21 9 | 2021-07-02 07:00:00,58.47992203814479,74.47622202153056,0.0446782,4.21 10 | 2021-07-02 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2021-07-02 18:00:00,112.5345810979245,57.90688256862547,0.12734,20.7,1.78 21 | 2021-07-02 19:00:00,47.806768881180105,47.63286475264286,0.12734,20.7,1.78 22 | 2021-07-02 20:00:00,90.41869415218726,47.4699625820568,0.12734,20.7,1.78 23 | 2021-07-02 21:00:00,79.90418158575795,90.4112881151387,0.09822,20.7,0.0 24 | 2021-07-02 22:00:00,113.93646622995728,79.95416769211707,0.09822,20.7,0.0 25 | 2021-07-02 23:00:00,87.63063881843247,106.60888243491476,0.09822,20.7,0.0 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/sanjose/Summer.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2,electricity_demand3 2 | 2021-07-02 00:00:00,106.43863113896096,107.9759341706549,0.09816,0.0,0.0,20.7 3 | 2021-07-02 01:00:00,107.7734358828182,108.35972616652272,0.09816,0.0,0.0,20.7 4 | 2021-07-02 02:00:00,107.06760216385662,108.2275667715106,0.09816,0.0,0.0,20.7 5 | 2021-07-02 03:00:00,90.2567446794392,91.94566868943171,0.09816,0.0,0.0,20.7 6 | 2021-07-02 04:00:00,80.33058542087886,92.03502339926756,0.09816,0.0,0.0,20.7 7 | 2021-07-02 05:00:00,58.35880953089501,91.8323915904778,0.09816,0.0,0.0,20.7 8 | 2021-07-02 06:00:00,80.38349295507551,113.9179730387172,0.09816,0.0,0.0,20.7 9 | 2021-07-02 07:00:00,58.47992203814479,113.6732290661564,0.09816,0.0,0.0,20.7 10 | 2021-07-02 08:00:00,74.43755748436648,112.49082839013188,0.09816,0.0,0.0,20.7 11 | 2021-07-02 09:00:00,47.94964753868955,79.85824237615176,0.09816,0.0,0.0,20.7 12 | 2021-07-02 10:00:00,74.59111426865474,74.02018327186966,0.09816,0.0,0.0,20.7 13 | 2021-07-02 11:00:00,74.7335966451448,74.16341853800289,0.09816,0.0,0.0,20.7 14 | 2021-07-02 12:00:00,74.86328890734512,74.29305952414465,0.09816,0.0,0.0,20.7 15 | 2021-07-02 13:00:00,74.87462845750997,75.55618134034651,0.09816,0.0,0.0,20.7 16 | 2021-07-02 14:00:00,59.00216247500031,58.14056838679343,0.11735,0.0,5.32,20.7 17 | 2021-07-02 15:00:00,74.78038350921005,58.14056838675414,0.11735,0.0,5.32,20.7 18 | 2021-07-02 16:00:00,58.5513628668479,46.90284521928675,0.14484,26.8,0.0,20.7 19 | 2021-07-02 17:00:00,47.79421623461083,46.90284521928675,0.14484,26.8,0.0,20.7 20 | 2021-07-02 18:00:00,112.5345810979245,46.88882254330687,0.14484,26.8,0.0,20.7 21 | 2021-07-02 19:00:00,47.806768881180105,46.902845219273104,0.14484,26.8,0.0,20.7 22 | 2021-07-02 20:00:00,90.41869415218726,46.9028452192868,0.14484,26.8,0.0,20.7 23 | 2021-07-02 21:00:00,79.90418158575795,57.33441008318731,0.11735,0.0,5.32,20.7 24 | 2021-07-02 22:00:00,113.93646622995728,57.29325617325841,0.11735,0.0,5.32,20.7 25 | 2021-07-02 23:00:00,87.63063881843247,114.98591750003442,0.09816,0.0,0.0,20.7 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/sanjose/Winter.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,108.53167558959628,0.09822,20.7,0.0 3 | 2021-07-02 01:00:00,107.7734358828182,108.7272346457094,0.09822,20.7,0.0 4 | 2021-07-02 02:00:00,107.06760216385662,107.32278424276238,0.09822,20.7,0.0 5 | 2021-07-02 03:00:00,90.2567446794392,90.56177377280284,0.09822,20.7,0.0 6 | 2021-07-02 04:00:00,80.33058542087886,74.47776365927587,0.09822,20.7,0.0 7 | 2021-07-02 05:00:00,58.35880953089501,74.44744617120043,0.09822,20.7,0.0 8 | 2021-07-02 06:00:00,80.38349295507551,58.43845078977549,0.09822,20.7,0.0 9 | 2021-07-02 07:00:00,58.47992203814479,96.30854291498616,0.09822,20.7,0.0 10 | 2021-07-02 08:00:00,74.43755748436648,112.40985119252444,0.09822,20.7,0.0 11 | 2021-07-02 09:00:00,47.94964753868955,80.32693315723313,0.09822,20.7,0.0 12 | 2021-07-02 10:00:00,74.59111426865474,74.51814817467164,0.09822,20.7,0.0 13 | 2021-07-02 11:00:00,74.7335966451448,74.6579124897559,0.09822,20.7,0.0 14 | 2021-07-02 12:00:00,74.86328890734512,74.78514605402037,0.09822,20.7,0.0 15 | 2021-07-02 13:00:00,74.87462845750997,58.88451493255891,0.09822,20.7,0.0 16 | 2021-07-02 14:00:00,59.00216247500031,74.83331004993684,0.09822,20.7,0.0 17 | 2021-07-02 15:00:00,74.78038350921005,74.70003818227161,0.09822,20.7,0.0 18 | 2021-07-02 16:00:00,58.5513628668479,48.05521475646577,0.12734,20.7,1.78 19 | 2021-07-02 17:00:00,47.79421623461083,47.74189610492547,0.12734,20.7,1.78 20 | 2021-07-02 18:00:00,112.5345810979245,57.95614456174042,0.12734,20.7,1.78 21 | 2021-07-02 19:00:00,47.806768881180105,47.69048943230587,0.12734,20.7,1.78 22 | 2021-07-02 20:00:00,90.41869415218726,47.5272985052616,0.12734,20.7,1.78 23 | 2021-07-02 21:00:00,79.90418158575795,106.44546059937436,0.09822,20.7,0.0 24 | 2021-07-02 22:00:00,113.93646622995728,87.7182272321786,0.09822,20.7,0.0 25 | 2021-07-02 23:00:00,87.63063881843247,98.76607878044196,0.09822,20.7,0.0 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/santabarbara/SummerWeekday.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,74.01864626391368,0.12955,0.0,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,108.58077074910004,0.12955,0.0,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,92.3762152541442,0.12955,0.0,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,108.38708984406114,0.12955,0.0,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,92.28981855336318,0.12955,0.0,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,74.18771471779607,0.12955,0.0,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,92.1371318020179,0.12955,0.0,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,114.1877999119361,0.12955,0.0,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,97.92604515917418,0.12955,0.0,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,74.08786243640603,0.12955,0.0,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,80.07794010138554,0.12955,0.0,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,58.29218728602989,0.12955,0.0,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,74.49980155627617,0.12955,0.0,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,74.51411877598585,0.12955,0.0,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,74.56200149337805,0.12955,0.0,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,74.44408921252413,0.12955,0.0,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,46.52994179536191,0.52939,2.55,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,46.52994179536191,0.52939,2.55,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,46.52994179536191,0.52939,2.55,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,46.52994179536191,0.52939,2.55,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,46.52994179536191,0.52939,2.55,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,90.36648334316374,0.12955,0.0,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,114.03956033894968,0.12955,0.0,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,98.72585702623589,0.12955,0.0,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/santabarbara/SummerWeekend.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,108.35050196145755,0.12955,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,108.73711689614544,0.12955,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,108.60512810819272,0.12955,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,92.3322792497028,0.12955,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,74.30493826965704,0.12955,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,92.27354171727752,0.12955,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,114.08979428530448,0.12955,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,80.16442564313043,0.12955,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,80.16756236339353,0.12955,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,74.22832604523292,0.12955,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,74.35528941082597,0.12955,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,74.4952866963829,0.12955,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,74.62433598849343,0.12955,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,58.75743601679002,0.12955,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,74.67326898428654,0.12955,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,74.55219940655283,0.12955,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,47.92680773310164,0.19809,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,47.626083647323455,0.19809,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,47.36963772241717,0.19809,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,47.54340452113871,0.19809,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,47.380950666602,0.19809,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,90.42468892626424,0.12955,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,98.24711761574048,0.12955,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,98.86058979621002,0.12955,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/santabarbara/Winter.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,73.87167371721492,0.1332,0.0,8.03 3 | 2021-07-02 01:00:00,107.7734358828182,57.831077324461646,0.1332,0.0,8.03 4 | 2021-07-02 02:00:00,107.06760216385662,57.74073080480673,0.1332,0.0,8.03 5 | 2021-07-02 03:00:00,90.2567446794392,73.80711293036916,0.1332,0.0,8.03 6 | 2021-07-02 04:00:00,80.33058542087886,58.09174303977653,0.1332,0.0,8.03 7 | 2021-07-02 05:00:00,58.35880953089501,74.16497275482541,0.1332,0.0,8.03 8 | 2021-07-02 06:00:00,80.38349295507551,58.08787724582553,0.1332,0.0,8.03 9 | 2021-07-02 07:00:00,58.47992203814479,90.4007735457164,0.1332,0.0,8.03 10 | 2021-07-02 08:00:00,74.43755748436648,113.70359824482328,0.09081,0.0,8.03 11 | 2021-07-02 09:00:00,47.94964753868955,97.54179511719514,0.09081,0.0,8.03 12 | 2021-07-02 10:00:00,74.59111426865474,113.90269036456286,0.09081,0.0,8.03 13 | 2021-07-02 11:00:00,74.7335966451448,108.66428940655771,0.09081,0.0,8.03 14 | 2021-07-02 12:00:00,74.86328890734512,114.44050811012744,0.09081,0.0,8.03 15 | 2021-07-02 13:00:00,74.87462845750997,114.41124545391948,0.09081,0.0,8.03 16 | 2021-07-02 14:00:00,59.00216247500031,109.4263341502522,0.09081,0.0,8.03 17 | 2021-07-02 15:00:00,74.78038350921005,113.63984497226002,0.09081,0.0,8.03 18 | 2021-07-02 16:00:00,58.5513628668479,47.1844428372537,0.20081,1.24,8.03 19 | 2021-07-02 17:00:00,47.79421623461083,47.1844428372537,0.20081,1.24,8.03 20 | 2021-07-02 18:00:00,112.5345810979245,46.94065731187432,0.20081,1.24,8.03 21 | 2021-07-02 19:00:00,47.806768881180105,47.11391210187792,0.20081,1.24,8.03 22 | 2021-07-02 20:00:00,90.41869415218726,46.953571471743786,0.20081,1.24,8.03 23 | 2021-07-02 21:00:00,79.90418158575795,57.37394662313476,0.1332,0.0,8.03 24 | 2021-07-02 22:00:00,113.93646622995728,106.62197019197508,0.1332,0.0,8.03 25 | 2021-07-02 23:00:00,87.63063881843247,73.53741378917489,0.1332,0.0,8.03 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/tampa/SummerWeekday.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,107.91736898039008,0.05413,0.0,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,108.30240999991392,0.05413,0.0,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,91.93720766444736,0.05413,0.0,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,107.9997216066628,0.05413,0.0,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,107.23702605585795,0.05413,0.0,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,106.46070587895588,0.05413,0.0,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,106.51208514814806,0.05413,0.0,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,73.89336549307014,0.05413,0.0,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,79.87489734193457,0.05413,0.0,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,79.8839167703217,0.05413,0.0,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,79.88622041113848,0.05413,0.0,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.18693315224388,0.05413,0.0,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,47.8837845023485,0.07793,7.14,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,58.25939024731351,0.07793,7.14,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,47.80217885012334,0.07793,7.14,5.33 17 | 2021-07-02 15:00:00,74.78038350921005,58.08006076822416,0.07793,7.14,5.33 18 | 2021-07-02 16:00:00,58.5513628668479,57.72133387796064,0.07793,7.14,5.33 19 | 2021-07-02 17:00:00,47.79421623461083,57.48811304333813,0.07793,7.14,5.33 20 | 2021-07-02 18:00:00,112.5345810979245,46.85072478369659,0.07793,7.14,5.33 21 | 2021-07-02 19:00:00,47.806768881180105,47.00104710972703,0.07793,7.14,5.33 22 | 2021-07-02 20:00:00,90.41869415218726,46.86447773438225,0.07793,7.14,5.33 23 | 2021-07-02 21:00:00,79.90418158575795,112.7965780745003,0.05413,0.0,5.33 24 | 2021-07-02 22:00:00,113.93646622995728,97.8561690824878,0.05413,0.0,5.33 25 | 2021-07-02 23:00:00,87.63063881843247,92.41868741489306,0.05413,0.0,5.33 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/tampa/Weekend.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1 2 | 2021-07-02 00:00:00,106.43863113896096,74.3884063810103,0.05413,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,107.73839678857097,0.05413,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,90.30800789216428,0.05413,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,106.58642196898867,0.05413,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,64.32315935068168,0.05413,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,80.37396960340418,0.05413,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,58.48770818798525,0.05413,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,74.50283363071414,0.05413,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,47.90919437730879,0.05413,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,74.36287277792941,0.05413,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,74.5431976401566,0.05413,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.7501790152152,0.05413,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,58.9239291753303,0.05413,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,74.87986133178171,0.05413,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,74.92484684795389,0.05413,5.33 17 | 2021-07-02 15:00:00,74.78038350921005,58.85479826211532,0.05413,5.33 18 | 2021-07-02 16:00:00,58.5513628668479,74.48440622597803,0.05413,5.33 19 | 2021-07-02 17:00:00,47.79421623461083,47.8352802091992,0.05413,5.33 20 | 2021-07-02 18:00:00,112.5345810979245,90.6812248446771,0.05413,5.33 21 | 2021-07-02 19:00:00,47.806768881180105,102.3310846883725,0.05413,5.33 22 | 2021-07-02 20:00:00,90.41869415218726,90.40269014873692,0.05413,5.33 23 | 2021-07-02 21:00:00,79.90418158575795,86.08029454408258,0.05413,5.33 24 | 2021-07-02 22:00:00,113.93646622995728,91.74971661305304,0.05413,5.33 25 | 2021-07-02 23:00:00,87.63063881843247,103.70480975085836,0.05413,5.33 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WSD/tampa/WinterWeekday.csv: -------------------------------------------------------------------------------- 1 | DateTime,baseline_grid_to_plant_kW,flexible_grid_to_plant_kW,electricity_TOU,electricity_demand1,electricity_demand2 2 | 2021-07-02 00:00:00,106.43863113896096,114.37831363353436,0.05413,0.0,5.33 3 | 2021-07-02 01:00:00,107.7734358828182,108.90639070508966,0.05413,0.0,5.33 4 | 2021-07-02 02:00:00,107.06760216385662,108.77010362479524,0.05413,0.0,5.33 5 | 2021-07-02 03:00:00,90.2567446794392,106.90974569875156,0.05413,0.0,5.33 6 | 2021-07-02 04:00:00,80.33058542087886,74.46728795047215,0.05413,0.0,5.33 7 | 2021-07-02 05:00:00,58.35880953089501,80.31128461924939,0.05413,0.0,5.33 8 | 2021-07-02 06:00:00,80.38349295507551,48.03137973602671,0.07793,7.14,5.33 9 | 2021-07-02 07:00:00,58.47992203814479,57.39401351520056,0.07793,7.14,5.33 10 | 2021-07-02 08:00:00,74.43755748436648,57.39401351520054,0.07793,7.14,5.33 11 | 2021-07-02 09:00:00,47.94964753868955,47.84271244870301,0.07793,7.14,5.33 12 | 2021-07-02 10:00:00,74.59111426865474,108.74895934474785,0.05413,0.0,5.33 13 | 2021-07-02 11:00:00,74.7335966451448,74.63980081794627,0.05413,0.0,5.33 14 | 2021-07-02 12:00:00,74.86328890734512,74.76785514350507,0.05413,0.0,5.33 15 | 2021-07-02 13:00:00,74.87462845750997,58.79589554994258,0.05413,0.0,5.33 16 | 2021-07-02 14:00:00,59.00216247500031,74.81737282308518,0.05413,0.0,5.33 17 | 2021-07-02 15:00:00,74.78038350921005,74.69266217417862,0.05413,0.0,5.33 18 | 2021-07-02 16:00:00,58.5513628668479,92.55407610390093,0.05413,0.0,5.33 19 | 2021-07-02 17:00:00,47.79421623461083,114.06644747473108,0.05413,0.0,5.33 20 | 2021-07-02 18:00:00,112.5345810979245,57.39384003932423,0.07793,7.14,5.33 21 | 2021-07-02 19:00:00,47.806768881180105,47.66191338576069,0.07793,7.14,5.33 22 | 2021-07-02 20:00:00,90.41869415218726,47.49886563456506,0.07793,7.14,5.33 23 | 2021-07-02 21:00:00,79.90418158575795,47.37460244922785,0.07793,7.14,5.33 24 | 2021-07-02 22:00:00,113.93646622995728,97.9598859951696,0.05413,0.0,5.33 25 | 2021-07-02 23:00:00,87.63063881843247,114.9001365379158,0.05413,0.0,5.33 26 | -------------------------------------------------------------------------------- /009-WE3-Lab/marimonotebook/timeseries/WWT/test copy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import os\n", 10 | "import glob\n", 11 | "import json\n", 12 | "import numpy as np\n", 13 | "import pandas as pd" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "newyork/Spring.csv\n", 26 | "newyork/Summer.csv\n", 27 | "newyork/Winter.csv\n", 28 | "sanjose/Spring.csv\n", 29 | "sanjose/Summer.csv\n", 30 | "sanjose/Winter.csv\n", 31 | "tampa/Spring.csv\n", 32 | "tampa/Summer.csv\n", 33 | "tampa/Winter.csv\n", 34 | "santabarbara/Spring.csv\n", 35 | "santabarbara/Summer.csv\n", 36 | "santabarbara/Winter.csv\n", 37 | "houston/Spring.csv\n", 38 | "houston/Summer.csv\n", 39 | "houston/Winter.csv\n" 40 | ] 41 | } 42 | ], 43 | "source": [ 44 | "paths = glob.glob('**', recursive=True)\n", 45 | "for p in paths:\n", 46 | " if os.path.isfile(p) and p.endswith('.csv'):\n", 47 | " # read the csv\n", 48 | " df = pd.read_csv(p)\n", 49 | " print(p)\n", 50 | " # clip it to one day \n", 51 | " df = df.iloc[:96]\n", 52 | "\n", 53 | " # save it as a new csv under the same filename\n", 54 | " df.to_csv(p, index=False)" 55 | ] 56 | } 57 | ], 58 | "metadata": { 59 | "kernelspec": { 60 | "display_name": "watertap-dev", 61 | "language": "python", 62 | "name": "python3" 63 | }, 64 | "language_info": { 65 | "codemirror_mode": { 66 | "name": "ipython", 67 | "version": 3 68 | }, 69 | "file_extension": ".py", 70 | "mimetype": "text/x-python", 71 | "name": "python", 72 | "nbconvert_exporter": "python", 73 | "pygments_lexer": "ipython3", 74 | "version": "3.12.1" 75 | } 76 | }, 77 | "nbformat": 4, 78 | "nbformat_minor": 2 79 | } 80 | -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/aes_ohio_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,242.12,$/month,Dayton Power & Light (AES Ohio) Primary Service under Tariff Sheet No. D20 (2021 rates) electric,energy,,0,1,12,0,24,0,6,0.0468153,$/kWh,Energy charge based on Standard Service Tariff Sheet No. G10 electric,demand,maximum,0,1,12,0,8,0,4,1.5243825,$/kW,"75% of off-peak or 100% of on-peak, whichever is higher" electric,demand,maximum,0,1,12,8,20,0,4,2.03251,$/kW,"Represented both under ""maximum"" period" electric,demand,maximum,0,1,12,20,24,0,4,1.5243825,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,1.5243825,$/kW, gas,customer,,,,,,,,,92.13,$/month, gas,energy,,0,1,12,0,24,0,6,0.175544841,$/therm,Converted from ccf to therms -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/baltimore_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,660,$/month,Baltimore Gas and Electric Schedule P (2021 rates) electric,energy,,0,1,5,0,7,0,4,0.05727,$/kWh,Flat delivery charge of 0.00592 plus generation charges electric,energy,,0,1,5,7,11,0,4,0.08667,$/kWh, electric,energy,,0,1,5,11,17,0,4,0.07888,$/kWh, electric,energy,,0,1,5,17,21,0,4,0.08667,$/kWh, electric,energy,,0,1,5,21,24,0,4,0.05727,$/kWh, electric,energy,,0,1,5,0,24,5,6,0.05727,$/kWh, electric,energy,,0,6,9,0,7,0,4,0.05727,$/kWh, electric,energy,,0,6,9,7,10,0,4,0.07888,$/kWh, electric,energy,,0,6,9,10,20,0,4,0.08667,$/kWh, electric,energy,,0,6,9,20,23,0,4,0.07888,$/kWh, electric,energy,,0,6,9,23,24,0,4,0.05727,$/kWh, electric,energy,,0,6,9,0,24,5,6,0.05727,$/kWh, electric,energy,,0,10,12,0,7,0,4,0.05727,$/kWh, electric,energy,,0,10,12,7,11,0,4,0.07888,$/kWh, electric,energy,,0,10,12,11,17,0,4,0.08667,$/kWh, electric,energy,,0,10,12,17,21,0,4,0.07888,$/kWh, electric,energy,,0,10,12,21,24,0,4,0.05727,$/kWh, electric,energy,,0,10,12,0,24,5,6,0.05727,$/kWh, electric,demand,annual,0,1,12,0,24,0,6,7.04,$/kW,Delivery charge plus transmission charge -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/cpe_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,customer,,,,,,,,,,289.29,289.29,$/month,CenterPoint Energy Primary Service (2021 rates) 3 | electric,energy,,0,0,1,12,0,24,0,6,0.03822,0.03822,$/kWh,Assumed 2-50 kV (primary voltage) 4 | electric,demand,maximum,0,0,1,12,0,24,0,6,2.33454,2.33454,$/kW,"Includes distribution, transmission, and generation charges" -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/pge_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,demand,peak-summer,0,0,6,9,16,21,0,6,26.8,26.8,$/kW,Pacific Gas & Electric Industrial Rate B-20 (2021 rates) 3 | electric,demand,part-peak-summer,0,0,6,9,14,16,0,6,5.32,5.32,$/kW,Assumed 2-50 kV (primary voltage) 4 | electric,demand,part-peak-summer,0,0,6,9,21,23,0,6,5.32,5.32,$/kW,"Includes distribution, transmission, and generation charges" 5 | electric,demand,maximum,0,0,1,12,0,24,0,6,20.7,20.7,$/kW, 6 | electric,demand,peak-winter1,0,0,1,5,16,21,0,6,1.78,1.78,$/kW, 7 | electric,demand,peak-winter2,0,0,10,12,16,21,0,6,1.78,1.78,$/kW, 8 | electric,customer,,,,,,,,,,1514.6622,1514.6622,$/month,Estimated from multiplying cost of 1 meter for 1 day by 30 days 9 | electric,energy,,0,0,6,9,0,14,0,6,0.09816,0.09816,$/kWh, 10 | electric,energy,,0,0,6,9,14,16,0,6,0.11735,0.11735,$/kWh, 11 | electric,energy,,0,0,6,9,16,21,0,6,0.14484,0.14484,$/kWh, 12 | electric,energy,,0,0,6,9,21,23,0,6,0.11735,0.11735,$/kWh, 13 | electric,energy,,0,0,6,9,23,24,0,6,0.09816,0.09816,$/kWh, 14 | electric,energy,,0,0,1,2,0,16,0,6,0.09822,0.09822,$/kWh, 15 | electric,energy,,0,0,1,2,21,24,0,6,0.09822,0.09822,$/kWh, 16 | electric,energy,,0,0,1,5,16,21,0,6,0.12734,0.12734,$/kWh, 17 | electric,energy,,0,0,3,5,0,9,0,6,0.09822,0.09822,$/kWh, 18 | electric,energy,,0,0,3,5,9,14,0,6,0.0569,0.0569,$/kWh, 19 | electric,energy,,0,0,3,5,14,16,0,6,0.09822,0.09822,$/kWh, 20 | electric,energy,,0,0,3,5,21,24,0,6,0.09822,0.09822,$/kWh, 21 | electric,energy,,0,0,10,12,0,16,0,6,0.09822,0.09822,$/kWh, 22 | electric,energy,,0,0,10,12,21,24,0,6,0.09822,0.09822,$/kWh, 23 | electric,energy,,0,0,10,12,16,21,0,6,0.12734,0.12734,$/kWh, 24 | gas,customer,,,,,,,,,,148.6554,148.6554,$/month,Estimated from multiplying cost for 1 day by 30 days 25 | gas,energy,,0,0,1,3,0,24,0,6,1.68736,0.59589,$/therm or $/m3, 26 | gas,energy,,0,0,4,10,0,24,0,6,1.54031,0.54396,$/therm or $/m3, 27 | gas,energy,,0,0,11,12,0,24,0,6,1.68736,0.59589,$/therm or $/m3, 28 | gas,energy,,4000,11326.72,1,3,0,24,0,6,1.3126,0.46354,$/therm or $/m3, 29 | gas,energy,,4000,11326.72,4,10,0,24,0,6,1.22418,0.43232,$/therm or $/m3, 30 | gas,energy,,4000,11326.72,11,12,0,24,0,6,1.3126,0.46354,$/therm or $/m3, -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/sbce_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,3649.76,$/month,Santa Barbara Clean Energy TOU-SUB-8-E (2023 rates) electric,demand,peak-summer,0,6,9,16,21,0,4,2.55,$/kW,Assumed > 50 kV (transmission voltage) electric,demand,part-peak-winter1,0,1,5,16,21,0,4,1.24,$/kW,"Includes distribution, transmission, and generation charges" electric,demand,part-peak-winter2,0,10,12,16,21,0,4,1.24,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,8.03,$/kW, electric,energy,,0,6,9,16,21,0,4,0.52939,$/kWh, electric,energy,,0,6,9,0,16,0,6,0.12955,$/kWh, electric,energy,,0,6,9,21,24,0,6,0.12955,$/kWh, electric,energy,,0,6,9,16,21,5,6,0.19809,$/kWh, electric,energy,,0,1,5,16,21,0,6,0.20081,$/kWh, electric,energy,,0,1,5,0,8,0,6,0.1332,$/kWh, electric,energy,,0,1,5,21,24,0,6,0.1332,$/kWh, electric,energy,,0,1,5,8,16,0,6,0.09081,$/kWh, electric,energy,,0,10,12,16,21,0,6,0.20081,$/kWh, electric,energy,,0,10,12,0,8,0,6,0.1332,$/kWh, electric,energy,,0,10,12,21,24,0,6,0.1332,$/kWh, electric,energy,,0,10,12,8,16,0,6,0.09081,$/kWh, -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/sce_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,373.12,$/month,Southern California Edison - TOU 8 E (2021 rates) electric,demand,peak-summer,0,6,9,16,21,0,4,4.13,$/kW,Assumed 2 to 50 kV (typical primary voltage) electric,demand,part-peak-winter1,0,1,5,16,21,0,4,1.03,$/kW,"Includes distribution, transmission, and generation charges" electric,demand,part-peak-winter2,0,10,12,16,21,0,4,1.03,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,11.26,$/kW, electric,energy,,0,6,9,16,21,0,4,0.50403,$/kWh, electric,energy,,0,6,9,0,16,0,6,0.12291,$/kWh, electric,energy,,0,6,9,21,24,0,6,0.12291,$/kWh, electric,energy,,0,6,9,16,21,5,6,0.185,$/kWh, electric,energy,,0,1,5,16,21,0,6,0.15716,$/kWh, electric,energy,,0,1,5,0,8,0,6,0.09174,$/kWh, electric,energy,,0,1,5,21,24,0,6,0.09174,$/kWh, electric,energy,,0,1,5,8,16,0,6,0.08717,$/kWh, electric,energy,,0,10,12,16,21,0,6,0.15716,$/kWh, electric,energy,,0,10,12,0,8,0,6,0.09174,$/kWh, electric,energy,,0,10,12,21,24,0,6,0.09174,$/kWh, electric,energy,,0,10,12,8,16,0,6,0.08717,$/kWh, -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesdata/teco_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,customer,,,,,,,,,,130.44,130.44,$/month,Tampa Electric Company GSDT (2021 rates) 3 | electric,energy,,0,0,1,3,0,6,0,4,0.05413,0.05413,$/kWh,Assumed 2-50 kV (primary voltage) 4 | electric,energy,,0,0,1,3,6,10,0,4,0.07793,0.07793,$/kWh,"Includes distribution, transmission, and generation charges" 5 | electric,energy,,0,0,1,3,10,18,0,4,0.05413,0.05413,$/kWh, 6 | electric,energy,,0,0,1,3,18,22,0,4,0.07793,0.07793,$/kWh, 7 | electric,energy,,0,0,1,3,22,24,0,4,0.05413,0.05413,$/kWh, 8 | electric,energy,,0,0,4,10,0,12,0,4,0.05413,0.05413,$/kWh, 9 | electric,energy,,0,0,4,10,12,21,0,4,0.07793,0.07793,$/kWh, 10 | electric,energy,,0,0,4,10,21,24,0,4,0.05413,0.05413,$/kWh, 11 | electric,energy,,0,0,11,12,0,6,0,4,0.05413,0.05413,$/kWh, 12 | electric,energy,,0,0,11,12,6,10,0,4,0.07793,0.07793,$/kWh, 13 | electric,energy,,0,0,11,12,10,18,0,4,0.05413,0.05413,$/kWh, 14 | electric,energy,,0,0,11,12,18,22,0,4,0.07793,0.07793,$/kWh, 15 | electric,energy,,0,0,11,12,22,24,0,4,0.05413,0.05413,$/kWh, 16 | electric,energy,,0,0,1,12,0,24,5,6,0.05413,0.05413,$/kWh, 17 | electric,demand,winter-peak1,0,0,1,3,6,10,0,4,7.14,7.14,$/kW, 18 | electric,demand,winter-peak1,0,0,1,3,18,22,0,4,7.14,7.14,$/kW, 19 | electric,demand,summer-peak,0,0,4,10,12,21,0,4,7.14,7.14,$/kW, 20 | electric,demand,winter-peak2,0,0,11,12,6,10,0,4,7.14,7.14,$/kW, 21 | electric,demand,winter-peak2,0,0,11,12,18,22,0,4,7.14,7.14,$/kW, 22 | electric,demand,off-peak,0,0,1,12,0,24,0,6,5.33,5.33,$/kW, 23 | gas,customer,,,,,,,,,,420,420,$/month, 24 | gas,energy,,0,0,1,12,0,24,0,6,0.21781,0.076919,$/therm or $/m3, -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesfigures/demandCharge.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/paperdata/ratesfigures/demandCharge.pdf -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesfigures/demandCharge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/paperdata/ratesfigures/demandCharge.png -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesfigures/energyCharge.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/paperdata/ratesfigures/energyCharge.pdf -------------------------------------------------------------------------------- /009-WE3-Lab/paperdata/ratesfigures/energyCharge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/paperdata/ratesfigures/energyCharge.png -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/aes_ohio_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,242.12,$/month,Dayton Power & Light (AES Ohio) Primary Service under Tariff Sheet No. D20 (2021 rates) electric,energy,,0,1,12,0,24,0,6,0.0468153,$/kWh,Energy charge based on Standard Service Tariff Sheet No. G10 electric,demand,maximum,0,1,12,0,8,0,4,1.5243825,$/kW,"75% of off-peak or 100% of on-peak, whichever is higher" electric,demand,maximum,0,1,12,8,20,0,4,2.03251,$/kW,"Represented both under ""maximum"" period" electric,demand,maximum,0,1,12,20,24,0,4,1.5243825,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,1.5243825,$/kW, gas,customer,,,,,,,,,92.13,$/month, gas,energy,,0,1,12,0,24,0,6,0.175544841,$/therm,Converted from ccf to therms -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/baltimore_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,660,$/month,Baltimore Gas and Electric Schedule P (2021 rates) electric,energy,,0,1,5,0,7,0,4,0.05727,$/kWh,Flat delivery charge of 0.00592 plus generation charges electric,energy,,0,1,5,7,11,0,4,0.08667,$/kWh, electric,energy,,0,1,5,11,17,0,4,0.07888,$/kWh, electric,energy,,0,1,5,17,21,0,4,0.08667,$/kWh, electric,energy,,0,1,5,21,24,0,4,0.05727,$/kWh, electric,energy,,0,1,5,0,24,5,6,0.05727,$/kWh, electric,energy,,0,6,9,0,7,0,4,0.05727,$/kWh, electric,energy,,0,6,9,7,10,0,4,0.07888,$/kWh, electric,energy,,0,6,9,10,20,0,4,0.08667,$/kWh, electric,energy,,0,6,9,20,23,0,4,0.07888,$/kWh, electric,energy,,0,6,9,23,24,0,4,0.05727,$/kWh, electric,energy,,0,6,9,0,24,5,6,0.05727,$/kWh, electric,energy,,0,10,12,0,7,0,4,0.05727,$/kWh, electric,energy,,0,10,12,7,11,0,4,0.07888,$/kWh, electric,energy,,0,10,12,11,17,0,4,0.08667,$/kWh, electric,energy,,0,10,12,17,21,0,4,0.07888,$/kWh, electric,energy,,0,10,12,21,24,0,4,0.05727,$/kWh, electric,energy,,0,10,12,0,24,5,6,0.05727,$/kWh, electric,demand,annual,0,1,12,0,24,0,6,7.04,$/kW,Delivery charge plus transmission charge -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/cpe_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,customer,,,,,,,,,,289.29,289.29,$/month,CenterPoint Energy Primary Service (2021 rates) 3 | electric,energy,,0,0,1,12,0,24,0,6,0.03822,0.03822,$/kWh,Assumed 2-50 kV (primary voltage) 4 | electric,demand,maximum,0,0,1,12,0,24,0,6,2.33454,2.33454,$/kW,"Includes distribution, transmission, and generation charges" -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/pge_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,demand,peak-summer,0,0,6,9,16,21,0,6,26.8,26.8,$/kW,Pacific Gas & Electric Industrial Rate B-20 (2021 rates) 3 | electric,demand,part-peak-summer,0,0,6,9,14,16,0,6,5.32,5.32,$/kW,Assumed 2-50 kV (primary voltage) 4 | electric,demand,part-peak-summer,0,0,6,9,21,23,0,6,5.32,5.32,$/kW,"Includes distribution, transmission, and generation charges" 5 | electric,demand,maximum,0,0,1,12,0,24,0,6,20.7,20.7,$/kW, 6 | electric,demand,peak-winter1,0,0,1,5,16,21,0,6,1.78,1.78,$/kW, 7 | electric,demand,peak-winter2,0,0,10,12,16,21,0,6,1.78,1.78,$/kW, 8 | electric,customer,,,,,,,,,,1514.6622,1514.6622,$/month,Estimated from multiplying cost of 1 meter for 1 day by 30 days 9 | electric,energy,,0,0,6,9,0,14,0,6,0.09816,0.09816,$/kWh, 10 | electric,energy,,0,0,6,9,14,16,0,6,0.11735,0.11735,$/kWh, 11 | electric,energy,,0,0,6,9,16,21,0,6,0.14484,0.14484,$/kWh, 12 | electric,energy,,0,0,6,9,21,23,0,6,0.11735,0.11735,$/kWh, 13 | electric,energy,,0,0,6,9,23,24,0,6,0.09816,0.09816,$/kWh, 14 | electric,energy,,0,0,1,2,0,16,0,6,0.09822,0.09822,$/kWh, 15 | electric,energy,,0,0,1,2,21,24,0,6,0.09822,0.09822,$/kWh, 16 | electric,energy,,0,0,1,5,16,21,0,6,0.12734,0.12734,$/kWh, 17 | electric,energy,,0,0,3,5,0,9,0,6,0.09822,0.09822,$/kWh, 18 | electric,energy,,0,0,3,5,9,14,0,6,0.0569,0.0569,$/kWh, 19 | electric,energy,,0,0,3,5,14,16,0,6,0.09822,0.09822,$/kWh, 20 | electric,energy,,0,0,3,5,21,24,0,6,0.09822,0.09822,$/kWh, 21 | electric,energy,,0,0,10,12,0,16,0,6,0.09822,0.09822,$/kWh, 22 | electric,energy,,0,0,10,12,21,24,0,6,0.09822,0.09822,$/kWh, 23 | electric,energy,,0,0,10,12,16,21,0,6,0.12734,0.12734,$/kWh, 24 | gas,customer,,,,,,,,,,148.6554,148.6554,$/month,Estimated from multiplying cost for 1 day by 30 days 25 | gas,energy,,0,0,1,3,0,24,0,6,1.68736,0.59589,$/therm or $/m3, 26 | gas,energy,,0,0,4,10,0,24,0,6,1.54031,0.54396,$/therm or $/m3, 27 | gas,energy,,0,0,11,12,0,24,0,6,1.68736,0.59589,$/therm or $/m3, 28 | gas,energy,,4000,11326.72,1,3,0,24,0,6,1.3126,0.46354,$/therm or $/m3, 29 | gas,energy,,4000,11326.72,4,10,0,24,0,6,1.22418,0.43232,$/therm or $/m3, 30 | gas,energy,,4000,11326.72,11,12,0,24,0,6,1.3126,0.46354,$/therm or $/m3, -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/sbce_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,3649.76,$/month,Santa Barbara Clean Energy TOU-SUB-8-E (2023 rates) electric,demand,peak-summer,0,6,9,16,21,0,4,2.55,$/kW,Assumed > 50 kV (transmission voltage) electric,demand,part-peak-winter1,0,1,5,16,21,0,4,1.24,$/kW,"Includes distribution, transmission, and generation charges" electric,demand,part-peak-winter2,0,10,12,16,21,0,4,1.24,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,8.03,$/kW, electric,energy,,0,6,9,16,21,0,4,0.52939,$/kWh, electric,energy,,0,6,9,0,16,0,6,0.12955,$/kWh, electric,energy,,0,6,9,21,24,0,6,0.12955,$/kWh, electric,energy,,0,6,9,16,21,5,6,0.19809,$/kWh, electric,energy,,0,1,5,16,21,0,6,0.20081,$/kWh, electric,energy,,0,1,5,0,8,0,6,0.1332,$/kWh, electric,energy,,0,1,5,21,24,0,6,0.1332,$/kWh, electric,energy,,0,1,5,8,16,0,6,0.09081,$/kWh, electric,energy,,0,10,12,16,21,0,6,0.20081,$/kWh, electric,energy,,0,10,12,0,8,0,6,0.1332,$/kWh, electric,energy,,0,10,12,21,24,0,6,0.1332,$/kWh, electric,energy,,0,10,12,8,16,0,6,0.09081,$/kWh, -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/sce_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit,month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge,units,Notes electric,customer,,,,,,,,,373.12,$/month,Southern California Edison - TOU 8 E (2021 rates) electric,demand,peak-summer,0,6,9,16,21,0,4,4.13,$/kW,Assumed 2 to 50 kV (typical primary voltage) electric,demand,part-peak-winter1,0,1,5,16,21,0,4,1.03,$/kW,"Includes distribution, transmission, and generation charges" electric,demand,part-peak-winter2,0,10,12,16,21,0,4,1.03,$/kW, electric,demand,maximum,0,1,12,0,24,0,6,11.26,$/kW, electric,energy,,0,6,9,16,21,0,4,0.50403,$/kWh, electric,energy,,0,6,9,0,16,0,6,0.12291,$/kWh, electric,energy,,0,6,9,21,24,0,6,0.12291,$/kWh, electric,energy,,0,6,9,16,21,5,6,0.185,$/kWh, electric,energy,,0,1,5,16,21,0,6,0.15716,$/kWh, electric,energy,,0,1,5,0,8,0,6,0.09174,$/kWh, electric,energy,,0,1,5,21,24,0,6,0.09174,$/kWh, electric,energy,,0,1,5,8,16,0,6,0.08717,$/kWh, electric,energy,,0,10,12,16,21,0,6,0.15716,$/kWh, electric,energy,,0,10,12,0,8,0,6,0.09174,$/kWh, electric,energy,,0,10,12,21,24,0,6,0.09174,$/kWh, electric,energy,,0,10,12,8,16,0,6,0.08717,$/kWh, -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesdata/teco_billing.csv: -------------------------------------------------------------------------------- 1 | utility,type,period,basic_charge_limit (imperial),basic_charge_limit (metric),month_start,month_end,hour_start,hour_end,weekday_start,weekday_end,charge (imperial),charge (metric),units,Notes 2 | electric,customer,,,,,,,,,,130.44,130.44,$/month,Tampa Electric Company GSDT (2021 rates) 3 | electric,energy,,0,0,1,3,0,6,0,4,0.05413,0.05413,$/kWh,Assumed 2-50 kV (primary voltage) 4 | electric,energy,,0,0,1,3,6,10,0,4,0.07793,0.07793,$/kWh,"Includes distribution, transmission, and generation charges" 5 | electric,energy,,0,0,1,3,10,18,0,4,0.05413,0.05413,$/kWh, 6 | electric,energy,,0,0,1,3,18,22,0,4,0.07793,0.07793,$/kWh, 7 | electric,energy,,0,0,1,3,22,24,0,4,0.05413,0.05413,$/kWh, 8 | electric,energy,,0,0,4,10,0,12,0,4,0.05413,0.05413,$/kWh, 9 | electric,energy,,0,0,4,10,12,21,0,4,0.07793,0.07793,$/kWh, 10 | electric,energy,,0,0,4,10,21,24,0,4,0.05413,0.05413,$/kWh, 11 | electric,energy,,0,0,11,12,0,6,0,4,0.05413,0.05413,$/kWh, 12 | electric,energy,,0,0,11,12,6,10,0,4,0.07793,0.07793,$/kWh, 13 | electric,energy,,0,0,11,12,10,18,0,4,0.05413,0.05413,$/kWh, 14 | electric,energy,,0,0,11,12,18,22,0,4,0.07793,0.07793,$/kWh, 15 | electric,energy,,0,0,11,12,22,24,0,4,0.05413,0.05413,$/kWh, 16 | electric,energy,,0,0,1,12,0,24,5,6,0.05413,0.05413,$/kWh, 17 | electric,demand,winter-peak1,0,0,1,3,6,10,0,4,7.14,7.14,$/kW, 18 | electric,demand,winter-peak1,0,0,1,3,18,22,0,4,7.14,7.14,$/kW, 19 | electric,demand,summer-peak,0,0,4,10,12,21,0,4,7.14,7.14,$/kW, 20 | electric,demand,winter-peak2,0,0,11,12,6,10,0,4,7.14,7.14,$/kW, 21 | electric,demand,winter-peak2,0,0,11,12,18,22,0,4,7.14,7.14,$/kW, 22 | electric,demand,off-peak,0,0,1,12,0,24,0,6,5.33,5.33,$/kW, 23 | gas,customer,,,,,,,,,,420,420,$/month, 24 | gas,energy,,0,0,1,12,0,24,0,6,0.21781,0.076919,$/therm or $/m3, -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesfigures/demandCharge.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/tariffs/ratesfigures/demandCharge.pdf -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesfigures/demandCharge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/tariffs/ratesfigures/demandCharge.png -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesfigures/energyCharge.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/tariffs/ratesfigures/energyCharge.pdf -------------------------------------------------------------------------------- /009-WE3-Lab/tariffs/ratesfigures/energyCharge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/009-WE3-Lab/tariffs/ratesfigures/energyCharge.png -------------------------------------------------------------------------------- /010-Mustjaab/Periodic_App.py: -------------------------------------------------------------------------------- 1 | # /// script 2 | # requires-python = ">=3.12" 3 | # dependencies = [ 4 | # "marimo", 5 | # "pandas==2.2.3", 6 | # "periodictable==1.7.1", 7 | # ] 8 | # /// 9 | 10 | import marimo 11 | 12 | __generated_with = "0.9.10" 13 | app = marimo.App() 14 | 15 | 16 | @app.cell 17 | def __(mo): 18 | mo.md("""

Periodic Table App

""") 19 | return 20 | 21 | 22 | @app.cell 23 | def __(): 24 | import marimo as mo 25 | # pip install periodictable first before importing library 26 | import periodictable 27 | import pandas as pd 28 | return mo, pd, periodictable 29 | 30 | 31 | @app.cell 32 | def __(mo): 33 | Form = mo.ui.text(label="Atomic Number:").form() 34 | Form 35 | return (Form,) 36 | 37 | 38 | @app.cell 39 | def __(Form): 40 | Element = Form.value 41 | E = int(Element) 42 | return E, Element 43 | 44 | 45 | @app.cell 46 | def __(E, periodictable): 47 | element = periodictable.elements[E] 48 | 49 | Property_Table = { 50 | 'Property': [ 51 | 'Name', 52 | 'Symbol', 53 | 'Mass' 54 | ], 55 | 56 | 'Value': [ 57 | element.name, 58 | element.symbol, 59 | element.mass 60 | ] 61 | } 62 | return Property_Table, element 63 | 64 | 65 | @app.cell 66 | def __(Property_Table, mo, pd): 67 | Dynamic_Table = pd.DataFrame(Property_Table) 68 | mo.ui.table(Dynamic_Table) 69 | return (Dynamic_Table,) 70 | 71 | 72 | if __name__ == "__main__": 73 | app.run() 74 | -------------------------------------------------------------------------------- /010-Mustjaab/README.md: -------------------------------------------------------------------------------- 1 | # Mustjaab: Diverse Analysis and Interactive Notebooks 2 | 3 | _October 10, 2024_ 4 | 5 | This week's spotlight 🌟 shines on [@Mustjaab](https://www.linkedin.com/in/muhammad-mustjaab-8038a3236/), an enthusiastic contributor to the marimo community! 6 | 7 | Mustjaab has shared numerous fascinating notebooks in the #⁠share-your-notebook channel, showcasing a wide range of analyses and interactive features. The contributions include: 8 | 9 | 1. Analyses of greenhouse gas emissions 10 | 2. Exploration of Perplexity using [`mo.ui.chat`](https://docs.marimo.io/api/inputs/chat.html#marimo.ui.chat) 11 | 3. Various other insightful and interactive notebooks 12 | 13 | Beyond creating content, Mustjaab has been instrumental in improving marimo by regularly providing feedback on how to enhance the platform. Their enthusiasm and dedication to the community have been invaluable. 14 | 15 | ## Featured Notebook 16 | 17 | You can explore one of Mustjaab's notebooks here: 18 | 19 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/p/@muhammad-mustjaab/analysis-of-wait-times-in-canadian-hospitals-critical-procedures) 20 | 21 | To run this notebook locally, use the following command if you have `uv` installed: 22 | 23 | ```shell 24 | uvx marimo run --sandbox notebook.py 25 | ``` 26 | 27 | If you don't have `uv` installed, you can use: 28 | 29 | ```shell 30 | marimo run notebook.py 31 | ``` 32 | 33 | Note: You may need to manually install the notebook's dependencies if you're not using `uv`. 34 | 35 | To edit the notebook source code, replace `run` with `edit` in the above commands. 36 | 37 | > [!NOTE] 38 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 39 | 40 | We're incredibly grateful for all of Mustjaab's contributions and enthusiasm! ♥🙏 41 | 42 | ## Spotlight Promotion 43 | 44 | This spotlight has been featured on our social media platforms. Join the conversation: 45 | 46 | - Twitter Post: [Link](https://x.com/marimo_io/status/1844428113683550356) 47 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_join-the-marimo-discord-server-activity-7250193061899554816-u_ct?utm_source=share&utm_medium=member_desktop) 48 | - Discord Discussion: [Join our Discord](https://discord.gg/JE7nhX6mD8) 49 | 50 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the marimo community! 51 | -------------------------------------------------------------------------------- /011-Vincent/README.md: -------------------------------------------------------------------------------- 1 | # Vincent Warmerdam: Creative Notebook Developer 2 | *November 14, 2024* 3 | 4 | [Vincent Warmerdam](https://twitter.com/fishnets88) is a creative developer (and ambassador!) known for crafting innovative and educational notebooks that make complex concepts accessible and engaging. His contributions to the marimo community have consistently pushed the boundaries of what's possible with interactive notebooks. 5 | 6 | Vincent has created some of the most creative notebooks we've seen in the marimo community, combining technical expertise with an intuitive understanding of how to make complex concepts accessible through interactive experiences. 7 | 8 | One of his standout contributions is an interactive exploration of Principal Component Analysis (PCA), which transforms what could be a dry mathematical concept into an engaging hands-on experience. Another notable creation is his implementation of seam carving, inspired by 3Blue1Brown, which demonstrates how complex algorithms can be explained through interactive visualization. 9 | 10 | Check out these remarkable notebooks: 11 | - Interactive Matrices: [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.io/p/@marimo/interactive-matrices) 12 | 13 | - Seam Carving Implementation: [https://huggingface.co/spaces/marimo-team/seam-carving](https://huggingface.co/spaces/marimo-team/seam-carving) 14 | 15 | You can run these notebooks locally using: 16 | ```shell 17 | uvx marimo run --sandbox .py 18 | ``` 19 | if you have `uv` installed, or 20 | ```shell 21 | marimo run notebook.py 22 | ``` 23 | if you don't have uv installed (you'll need to manually install its dependencies). 24 | 25 | To edit the notebook source code, replace `run` with `edit` in the above commands. 26 | 27 | > [!NOTE] 28 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 29 | 30 | We're grateful to have Vincent Warmerdam as part of the marimo community, consistently inspiring and delighting us with his creative approaches to teaching and visualization! 31 | 32 | ## Spotlight Promotion 33 | This spotlight has been featured on our social media platforms. Join the conversation: 34 | - Twitter Post: [Link](https://x.com/marimo_io/status/1857319552339828949) 35 | 36 | We encourage you to engage with these posts, share your thoughts, and help us celebrate these amazing contributions to the marimo community! 37 | -------------------------------------------------------------------------------- /012-eugene/README.md: -------------------------------------------------------------------------------- 1 | # Eugene (metaboulie): marimo Community Contributor 2 | 3 | _December 7, 2024_ 4 | 5 | Eugene ([@metaboulie](https://github.com/metaboulie)) has made significant contributions to the marimo ecosystem through both core improvements and extension libraries. Their work has enhanced marimo's functionality in several key areas, particularly the table viewer and formatting support. 6 | 7 | Eugene has created two notable extension libraries that expand marimo's capabilities: 8 | 9 | 1. [marimo-themes](https://github.com/metaboulie/marimo-themes): A theming management tool that brings custom theming capabilities to marimo notebooks, allowing users to personalize their development environment. 10 | 11 | 2. [mowidget](https://github.com/metaboulie/marimo-widgets): A comprehensive collection of custom widgets including array viewers, color matrices, and productivity tools that enhance the interactive capabilities of marimo notebooks. 12 | 13 | You can try Eugene's extensions locally with: 14 | 15 | ```shell 16 | # Install theme management tool 17 | pip install motheme 18 | 19 | # Install widget collection 20 | pip install mowidget 21 | ``` 22 | 23 | To explore the themes: 24 | ```shell 25 | motheme themes 26 | ``` 27 | 28 | > [!NOTE] 29 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 30 | 31 | We're grateful to have Eugene as an active contributor who continues to push the boundaries of what's possible with marimo! 32 | 33 | ## Spotlight Promotion 34 | 35 | This spotlight has been featured on our social media platforms. Join the conversation: 36 | 37 | - Twitter Post: [Link](https://x.com/marimo_io/status/1869794968996589664) 38 | 39 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 40 | -------------------------------------------------------------------------------- /013-paul-karayan/README.md: -------------------------------------------------------------------------------- 1 | # Paul's Blogs: Showcasing marimo's Reusability 2 | 3 | Paul's blogs highlight one of marimo's biggest strengths — marimo programs are reusable in 3 ways: as interactive notebooks, as web apps, and as Python scripts. 4 | 5 | ## Featured Content 6 | 7 | Check out Paul's example notebooks, including one that prunes your Slack and gives you zen: [![Open with marimo](https://marimo.io/shield.svg)](https://paulkarayan.com/blog/marimo-cli-gui-notebook-and-microapps/) 8 | 9 | > [!NOTE] 10 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 11 | 12 | ## Spotlight Promotion 13 | This spotlight has been featured on our social media platforms. Join the conversation: 14 | - Twitter Post: [Link](https://x.com/marimo_io/status/1877499203485626495) 15 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-paul-pereyda-karayan-paul-activity-7283265534060572672-FXLv?utm_source=share&utm_medium=member_desktop) 16 | 17 | We encourage you to engage with these posts, share your thoughts, and help us celebrate these amazing contributions to the marimo community! 18 | -------------------------------------------------------------------------------- /013-paul-karayan/secrets_manager.py: -------------------------------------------------------------------------------- 1 | # /// script 2 | # requires-python = ">=3.12" 3 | # dependencies = [ 4 | # "marimo", 5 | # ] 6 | # /// 7 | 8 | import marimo 9 | 10 | __generated_with = "0.10.10" 11 | app = marimo.App(width="medium") 12 | 13 | 14 | @app.cell 15 | def _(mo): 16 | mo.md( 17 | r""" 18 | ## AWS Secrets Manager Utils 19 | 20 | Wow - we can write a secret to multiple envs! But that's not really the point. 21 | 22 | This example shows that with Marimo that you can quickly have: 23 | ``` 24 | - a CLI 25 | - a GUI "micro app" 26 | - a notebook 27 | ``` 28 | 29 | with little change to the base code. 30 | """ 31 | ) 32 | return 33 | 34 | 35 | @app.cell 36 | def _(): 37 | import subprocess 38 | import marimo as mo 39 | 40 | def bash(command): 41 | result = subprocess.run(command, shell=True, capture_output=True, text=True) 42 | return result.stdout.strip() 43 | return bash, mo, subprocess 44 | 45 | 46 | @app.cell 47 | def _(profiles): 48 | def generate_aws_secrets_commands(secret_name, secret_string): 49 | environments = ['dev', 'stage', 'prod'] 50 | 51 | region = 'us-west-2' 52 | 53 | 54 | output = ["Now youre good:\n\n"] 55 | print(str(output)) 56 | for env in environments: 57 | profile = profiles[env] 58 | aws_command = (f"""aws secretsmanager create-secret --region {region} --name '/{secret_name}' --secret-string '{secret_string}'""" 59 | ) 60 | print(aws_command) 61 | output.append(aws_command+"\n") 62 | 63 | return output 64 | return (generate_aws_secrets_commands,) 65 | 66 | 67 | @app.cell 68 | def _(mo): 69 | # create the gui 70 | form = ( 71 | mo.md(''' 72 | **GUI** 73 | 74 | {secret_name} 75 | {secret_string} 76 | ''') 77 | .batch( 78 | secret_name=mo.ui.text(label="Secret Name - \n", placeholder="/NAME"), 79 | secret_string=mo.ui.text(label="Secret (actual string)"), 80 | ) 81 | .form() 82 | ) 83 | return (form,) 84 | 85 | 86 | @app.cell 87 | def _(form, mo): 88 | mo.vstack([form, mo.md(f"Has value: {form.value}")]) 89 | return 90 | 91 | 92 | @app.cell 93 | def _(form, generate_aws_secrets_commands, mo): 94 | if mo.cli_args(): 95 | output = "" 96 | generate_aws_secrets_commands( 97 | mo.cli_args().get("secret_name"), mo.cli_args().get("secret_string") 98 | ) 99 | 100 | elif form.value: 101 | form_value = form.value 102 | output = generate_aws_secrets_commands( 103 | form_value.get("secret_name"), form_value.get("secret_string") 104 | ) 105 | 106 | else: 107 | output = "" 108 | 109 | output 110 | return form_value, output 111 | 112 | 113 | @app.cell 114 | def _(mo, output): 115 | mo.md("\n".join(output)) 116 | return 117 | 118 | 119 | @app.cell 120 | def _(): 121 | return 122 | 123 | 124 | if __name__ == "__main__": 125 | app.run() 126 | -------------------------------------------------------------------------------- /014-ryan-parker/README.md: -------------------------------------------------------------------------------- 1 | # Ryan Parker: Interactive Polars Tutorial 2 | 3 | Ryan Parker has created an engaging interactive tutorial that showcases marimo's powerful integration with Polars, a high-performance DataFrame library written in Rust. 4 | 5 | ## Featured Content 6 | 7 | Experience an interactive Polars tutorial that leverages marimo's native support for Polars DataFrames: [![Open with marimo](https://marimo.io/shield.svg)](https://rparkr.github.io/polars-intro/) 8 | 9 | The tutorial demonstrates marimo's unique capabilities: 10 | - Interactive table rendering for Polars DataFrames 11 | - Real-time search and filtering functionality 12 | - Efficient pagination through large datasets 13 | 14 | > [!NOTE] 15 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 16 | 17 | ## Spotlight Promotion 18 | This spotlight has been featured on our social media platforms. Join the conversation: 19 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_learn-polars-with-an-interactive-tutorial-activity-7288634541802409985-RfBa?utm_source=share&utm_medium=member_desktop) 20 | - Twitter Post: [Link](https://x.com/marimo_io/status/1882858625284383187) 21 | 22 | We encourage you to engage with these posts, share your thoughts, and help us celebrate these amazing contributions to the marimo community! 23 | -------------------------------------------------------------------------------- /015-cradle/README.md: -------------------------------------------------------------------------------- 1 | # cradle: GitHub Project Creator for Quant Work and Data Science 2 | 3 | _February 7, 2025_ 4 | 5 | [cradle](https://github.com/tschm/cradle) is a GitHub project creator similar to [cookiecutter](https://github.com/cookiecutter/cookiecutter) but with a specific emphasis on quantitative work and data science. Developed by [Thomas Schmelzer (@Thomster78)](https://github.com/tschm), cradle leverages marimo notebooks for experimentation, taking advantage of marimo's reproducibility and reusability features. 6 | 7 | cradle is an excellent tool for quantitative researchers and data scientists who need to quickly set up project environments that support both exploration and production-ready code. 8 | 9 | We're thrilled to have Thomas Schmelzer as an active and innovative member of the marimo community! His work on cradle demonstrates how marimo can be integrated into development workflows for more reproducible and efficient data science pipelines. 10 | 11 | ## Local Exploration 12 | 13 | You can explore cradle with: 14 | ```shell 15 | git clone https://github.com/tschm/cradle.git 16 | uvx qcradle 17 | ``` 18 | 19 | > [!NOTE] 20 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 21 | 22 | ## Spotlight Promotion 23 | 24 | This spotlight has been featured on our social media platforms. Join the conversation: 25 | 26 | - Twitter Post: [Link](https://x.com/marimo_io/status/1887711508114862233) 27 | 28 | We encourage you to engage with these posts, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 29 | -------------------------------------------------------------------------------- /016-sam-minot/README.md: -------------------------------------------------------------------------------- 1 | # Sam Minot: Interactive Data Stories with marimo 2 | 3 | _February 20, 2025_ 4 | 5 | [Sam Minot](https://github.com/FredHutch/marimo-publication) demonstrates how to create engaging data stories with interactive visualizations using marimo. His comprehensive tutorial published in Towards Data Science showcases how to leverage marimo's capabilities to create and publish interactive data visualizations for free. 6 | 7 | The tutorial provides a practical guide for data scientists and researchers looking to share their findings in an engaging, interactive format. Sam's work exemplifies how marimo can transform static data analysis into dynamic, interactive experiences that readers can explore and learn from. [![Open with marimo](https://marimo.io/shield.svg)](https://fredhutch.github.io/marimo-publication/) 8 | 9 | ## Key Highlights 10 | 11 | - Creating interactive visualizations with Python and marimo 12 | - Publishing data stories that engage readers 13 | - Free hosting and sharing of interactive notebooks 14 | 15 | We're thrilled to have Sam Minot share his expertise with the marimo community! The tutorial demonstrates how marimo can be used to create engaging, interactive data stories that are both informative and accessible. 16 | 17 | ## Local Exploration 18 | 19 | You can explore the tutorial locally with: 20 | ```shell 21 | git clone https://github.com/FredHutch/marimo-publication.git 22 | pip install -r requirements.txt (ideally in a virtual environment) 23 | uvx marimo edit --sandbox app.py 24 | ``` 25 | 26 | > [!NOTE] 27 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 28 | 29 | ## Spotlight Promotion 30 | 31 | This spotlight has been featured on our social media platforms. Join the conversation: 32 | 33 | - Article Link: [Towards Data Science](https://towardsdatascience.com/publish-interactive-data-visualizations-for-free-with-python-and-marimo/) 34 | - GitHub Repository: [marimo-publication](https://github.com/FredHutch/marimo-publication) 35 | - Website hosted with marimo: [marimo-publication](https://fredhutch.github.io/marimo-publication/) 36 | - Twitter Post: [Link](https://x.com/marimo_io/status/1892618863558459466) 37 | 38 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this amazing contribution to the Marimo community! 39 | -------------------------------------------------------------------------------- /017-arthur/README.md: -------------------------------------------------------------------------------- 1 | # Arthur S.: Prompting Application for Attorneys 2 | 3 | _March 20, 2025_ 4 | 5 | [Arthur S.](https://www.linkedin.com/in/arthrod/) has created an interactive application using marimo that showcases advanced prompting techniques for legal professionals working with Large Language Models (LLMs). The application demonstrates how attorneys can effectively use AI tools in their legal practice, with a focus on structured prompting methodologies. 6 | 7 | The project is free and open-source, making it accessible to legal professionals interested in incorporating AI tools into their workflow. [![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/arthrod/prompting) 8 | 9 | Arthur has also developed a [marimo DevContainer](https://github.com/arthrod/marimo_dev) setup that simplifies the dev environment for marimo applications, making it easier for devs to get started with marimo development. 10 | 11 | ## Local Exploration 12 | 13 | You can explore the prompting application locally with: 14 | ```shell 15 | git clone https://github.com/arthrod/prompting.git 16 | cd prompting 17 | uvx marimo edit --sandbox app.py 18 | ``` 19 | 20 | For the DevContainer setup: 21 | ```shell 22 | git clone https://github.com/arthrod/marimo_dev.git 23 | # Open in VS Code with the Remote - Containers extension 24 | # or 25 | # Open in GitHub Codespaces 26 | ``` 27 | 28 | > [!NOTE] 29 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 30 | 31 | ## Spotlight Promotion 32 | 33 | This spotlight has been featured on our social media platforms. Join the conversation: 34 | 35 | - LinkedIn link: [Post](https://www.linkedin.com/posts/marimo-io_github-arthrodprompting-prompting-techniques-activity-7308521775921803265--3lZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 36 | 37 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this amazing contribution to the marimo community! -------------------------------------------------------------------------------- /018-Jove/README.md: -------------------------------------------------------------------------------- 1 | # Jove: Query Kafka with SQL (Timeplus & marimo) 2 | 3 | _April 10, 2025_ 4 | 5 | [Jove](https://x.com/jove) recently [contributed support](https://github.com/marimo-team/marimo/pull/4376) for [Timeplus](https://timeplus.com/), a new real-time data engine, to marimo, making it accessible from marimo's SQL cells. This integration enables users to easily query Kafka with SQL directly from marimo notebooks. 6 | 7 | The integration [showcases](https://www.linkedin.com/pulse/tutorial-query-kafka-sql-timeplusmarimo-jove-zhong-emmwc) how marimo's reactive notebook environment pairs perfectly with Timeplus's real-time data processing capabilities. 8 | 9 | ## Key Highlights 10 | 11 | - Query Kafka streams with SQL directly in marimo notebooks 12 | - No JVM, no Docker, minimal CPU/memory/disk usage 13 | - Reactive updates and visualization of real-time data 14 | - SQL-native approach that simplifies complex data operations 15 | 16 | ## Why It Matters 17 | 18 | As Jove explains in his blog: 19 | 20 | > "Honestly, I built a similar app with Streamlit 2 years ago—it worked, but every piece had to be hard-coded in Python, then debug with a browser. With marimo + Timeplus, you can use the notebook interface to write the minimal Python code, also use SQL to handle large amount of data, without worrying about memory or complex JOIN issue (yes, I mean pandas or polars users)." 21 | 22 | ## Local Exploration 23 | 24 | You can explore the notebook locally with: 25 | 26 | ```shell 27 | git clone https://github.com/timeplus-io/proton.git 28 | cd proton/examples/marimo 29 | uvx marimo edit --sandbox github.py 30 | ``` 31 | 32 | > [!NOTE] 33 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 34 | 35 | ## Spotlight Promotion 36 | 37 | This spotlight has been featured on our social media platforms. Join the conversation: 38 | 39 | - Twitter Post: [Link](https://x.com/marimo_io/status/1910442291539697708) 40 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_kafka-sql-activity-7316208137596780544-YuJi?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 41 | - Blog Post: [Tutorial: Query Kafka with SQL (Timeplus & marimo)](https://www.linkedin.com/pulse/tutorial-query-kafka-sql-timeplusmarimo-jove-zhong-emmwc) 42 | 43 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this amazing contribution to the marimo community! 44 | -------------------------------------------------------------------------------- /019-smohspace/README.md: -------------------------------------------------------------------------------- 1 | # Semyeong: Exploring Venus Cloud Imagery from Akatsuki Mission 2 | 3 | _April 24, 2025_ 4 | 5 | [Semyeong](https://github.com/smoh) has created an interactive marimo notebook that explores Venus cloud imagery from the Akatsuki mission. The notebook leverages xarray for analyzing atmospheric data and includes interactive components that allow users to examine various properties of the images, serving as an educational tool for planetary science visualization. This is significant because planetary science data is often complex and multidimensional and the notebook demonstrates how marimo can transform this data into interactive, accessible tools, making it more approachable for both researchers and the public interested in space exploration. 6 | 7 | This project demonstrates the power of marimo for scientific visualization and data exploration in planetary science. [![Open with marimo](https://marimo.io/shield.svg)](https://smoh.github.io/akatsuki-tutorial/live) 8 | 9 | ## Local Exploration 10 | 11 | You can explore the notebook locally by cloning the repository: 12 | ```shell 13 | git clone https://github.com/smoh/akatsuki-tutorial.git 14 | uvx marimo edit --sandbox akatsuki-tutorial.py 15 | ``` 16 | 17 | > [!NOTE] 18 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 19 | 20 | ## Spotlight Promotion 21 | 22 | This spotlight has been featured on our social media platforms. Join the conversation: 23 | 24 | - GitHub: [smoh/akatsuki-tutorial repository](https://github.com/smoh/akatsuki-tutorial) 25 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-semyeong-o-even-the-sky-activity-7321201442403295232-Sc9v?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 26 | - Twitter Post: [Link](https://x.com/marimo_io/status/1915435693717008437) 27 | - BlueSky Post: [Link](https://bsky.app/profile/marimo.io/post/3lnl3da3huh2z) 28 | 29 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this amazing contribution to the marimo community! -------------------------------------------------------------------------------- /020-yoann-mocquin/README.md: -------------------------------------------------------------------------------- 1 | # Yoann Mocquin: Interactive Geometric MTF Notebook 2 | 3 | _April 30, 2025_ 4 | 5 | [Yoann Mocquin](https://medium.com/@yoann.mocquin) presents an interactive marimo notebook that demonstrates geometric Modulation Transfer Function (MTF) concepts. The notebook uses ray optics and interactive sliders to show how signal, blur, and diffraction affect image quality, visualized live across line-spread and MTF curves. 6 | 7 | Yoann is a physics engineer by training and a Python enthusiast. His notebook provides a hands-on way to explore the interplay between optical parameters and image formation, making it a valuable educational tool for anyone interested in optics or imaging science. 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://marimo.app/?slug=e6baok) 10 | 11 | You can also edit this notebook locally with 12 | 13 | ```shell 14 | uvx marimo run --sandbox geometric-mtf.py 15 | ``` 16 | 17 | if you have `uv` installed, or 18 | 19 | ```shell 20 | marimo run notebook.py 21 | ``` 22 | 23 | > [!NOTE] 24 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 25 | 26 | ## Spotlight Promotion 27 | 28 | This spotlight has been featured on our social media platforms. Join the conversation: 29 | 30 | - Author: [Yoann Mocquin on Medium](https://medium.com/@yoann.mocquin) 31 | - Twitter Post: [Link](https://x.com/marimo_io/status/1917630680751825077) 32 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-yoann-mocquin-an-interactive-activity-7323396401025974272-lkKx?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 33 | - BlueSky Post: [Link](https://bsky.app/profile/marimo.io/post/3lo2coyy3262e) 34 | - Mastodon Post: [Link](https://mastodon.social/@marimo_io/114428069038363659) 35 | 36 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this contribution to the marimo community! 37 | -------------------------------------------------------------------------------- /021-jan-aerts/README.md: -------------------------------------------------------------------------------- 1 | # Jan Aerts: Python SVG Data Visualization Notebooks 2 | 3 | _May 8, 2025_ 4 | 5 | [Jan Aerts](http://vda-lab.io) (KU Leuven) presents a marimo notebook series for the Data Viz course at KU Leuven, teaching Python-based SVG graphics and interactive visualization techniques. The notebooks enable students and practitioners to build custom SVG visuals and interactivity in Python, without needing JavaScript. 6 | 7 | [Jan](https://orcid.org/0000-0002-6416-2717) is a Full Professor at KU Leuven, leading the [Visual Data Analysis Lab](https://vda-lab.github.io/people). His research focuses on visual analytics, topological data analysis, and digital agriculture. He has a background in omics, life sciences, and large-scale data visualization projects. 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://g0r72a-tutorial-pythonsvg-466697.pages.gitlab.kuleuven.be/) 10 | 11 | ## Local Exploration 12 | 13 | You can try the notebook series live at: 14 | - [KU Leuven Python SVG Data Viz](https://g0r72a-tutorial-pythonsvg-466697.pages.gitlab.kuleuven.be/) 15 | 16 | > [!NOTE] 17 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 18 | 19 | ## Spotlight Promotion 20 | 21 | This spotlight has been featured on our social media platforms. Join the conversation: 22 | 23 | - Author: [Jan Aerts at VDA-Lab](http://vda-lab.io) 24 | - Twitter Post: [Link](https://x.com/marimo_io/status/1920673949136154813) 25 | - BlueSky Post: [Link](https://bsky.app/profile/marimo.io/post/3looluxixb423) 26 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-jan-aerts-python-meets-activity-7326320109050908673-EbPQ?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 27 | - Mastodon Post: [Link](https://mastodon.social/@marimo_io/114475630240753585) 28 | 29 | 30 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this contribution to the marimo community! 31 | -------------------------------------------------------------------------------- /022-julian-hofer/README.md: -------------------------------------------------------------------------------- 1 | # Julian Hofer: marimo + pixi Starter Template 2 | 3 | _May 15, 2025_ 4 | 5 | [Julian Hofer](https://julianhofer.eu) presents a starter template that pairs marimo with pixi for instant environment setup, built-in testing, linting & CI/CD. This template enables developers to dive straight into creating interactive notebooks without worrying about environment configuration. 6 | 7 | Julian enjoys dealing with computers, languages and physics — especially in combination. He also likes to travel, including the part where you get to listen to audiobooks on long train rides. 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://github.com/Hofer-Julian/marimo-pixi-starter-template) 10 | 11 | ## Local Exploration 12 | 13 | You can try the template by cloning the repository: 14 | ```bash 15 | git clone https://github.com/Hofer-Julian/marimo-pixi-starter-template 16 | ``` 17 | 18 | > [!NOTE] 19 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 20 | 21 | ## Spotlight Promotion 22 | 23 | This spotlight has been featured on our social media platforms. Join the conversation: 24 | 25 | - Twitter Post: [Link](https://x.com/marimo_io/status/1923045765016060149) 26 | - BlueSky Post: [Link](https://bsky.app/profile/marimo.io/post/3lp7v3jbdz323) 27 | - Mastodon Post: [Link](https://mastodon.social/@marimo_io/114512679179846230) 28 | 29 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this contribution to the marimo community! 30 | -------------------------------------------------------------------------------- /023-weights-biases/README.md: -------------------------------------------------------------------------------- 1 | # Weights & Biases: Weave with marimo Chat UI 2 | 3 | _May 22, 2025_ 4 | 5 | [Weights & Biases](https://wandb.ai/site/) presents a [Weave](https://weave.wandb.ai/) demo that embeds marimo's chat UI for The Weight Room Equipment Co. The demo shows how to get help with gear or escalations, with every interaction logged in W&B's Weave platform for tracking and evaluation. 6 | 7 | Weave is a lightweight toolkit for tracking and evaluating LLM applications. You can use it to log and debug language model inputs, outputs, and traces, build evaluations for language model use cases and organize information across the LLM workflow from experimentation to production. 8 | 9 | [![Open with marimo](https://marimo.io/shield.svg)](https://runweave.ai) 10 | 11 | > [!NOTE] 12 | > This project is part of our [Community Spotlights](https://marimo.io/c/@spotlights/community-spotlights) collection, where we feature outstanding projects and contributions from the marimo community. 13 | 14 | ## Spotlight Promotion 15 | 16 | This spotlight has been featured on our social media platforms. Join the conversation: 17 | 18 | - Twitter Post: [Link](https://x.com/marimo_io/status/1925597601837273399) 19 | - BlueSky Post: [Link](https://bsky.app/profile/marimo.io/post/3lprlpgyjnj2k) 20 | - Mastodon Post: [Link](https://mastodon.social/@marimo_io/114552661736041572) 21 | - LinkedIn Post: [Link](https://www.linkedin.com/posts/marimo-io_spotlight-on-weights-biasess-weave-activity-7331363397118181377-swi-?utm_source=share&utm_medium=member_desktop&rcm=ACoAADSJzvgBkjBd85IWDyUWA6ttzq8B-NDq-Hs) 22 | 23 | We encourage you to engage with these resources, share your thoughts, and help us celebrate this contribution to the marimo community! 24 | -------------------------------------------------------------------------------- /assets/001-anywidget.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marimo-team/spotlights/130839f6eed19de6573797e10e833a7d842ffc35/assets/001-anywidget.gif 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