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- Add Sample_macroabm-Canada_provincial_run.ipynb: end-to-end workflow (data pickle creation → simulation → HDF5 results → plots) for all 10 Canadian provinces with TFP/technical-coefficient growth enabled - Add Sample_macroabm-CANADA_run_time_iteration.ipynb: two-scenario comparison (baseline vs exogenous pricing) with per-province GDP, emissions, and firm price difference plots; timestep set to quarterly (3 months, data_config.time_unit=3) Note: Sample_* notebooks are force-added (-f); *.ipynb is in .gitignore Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
IO table updates and sectoral mapping fixes needed for feeding it into the model are added to the main branch.
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Please ignore! Meant to just make this PR our fork |
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This PR improves onboarding and discoverability for the Canada MacroABM adaptation by adding a dedicated Canada section to the MkDocs site and expanding the documentation under docs/canada/. The docs index and mkdocs.yml now link three guides: the MacroABM-CA Onboarding Guide, Canada Provincial IO Development, and a new Raw Data Reference. Together they give new collaborators a single entry point for running the provincial model, understanding how data flows from raw inputs into simulation outputs, and locating Canada-specific datasets outside the public repository.
In the onboarding guide, the new Section 11 covers team conventions: keeping run scripts in scenarios/, naming them descriptively, and syncing framework updates from the upstream INET repository (inet-complexity/macro-main) without overwriting Canada-only folders.
Supporting changes round out the runnable path and reference material. scenarios/run_canada_provincial.py provides a command-line version of the basic provincial end-to-end workflow (build pickle → simulate → plot GDP/production) as another option on top of the jupyter notebooks. The Raw Data Reference catalogues each Canada-specific raw-data folder (e.g. cims_prices/, emission_factors/, provincial ICIO, policy inputs) with what it contains, where it is read in code, and how it affects the simulation. Minor housekeeping includes refreshed dependency lockfile entries and a small VS Code Python environment setting.