activitymodel

Occupant activity calendars for building energy simulation, built from the French time-use survey (INSEE Enquête Emploi du temps 2009-2010, PROGEDO-ADISP doi:10.13144/lil-0695).

activitymodel is a pure library: it never resolves a data path at import time and never fetches data. It is used by three packages at three moments:

It replaces the two prototypes activity_clustering and activity-occupant-association; doc/design.md records what was wrong with them and why each part looks the way it does now.

Quick start

import polars as pl
import activitymodel as am

# 1. (pipeline) build the library from the survey files, once
from activitymodel.pipeline import LibraryBuildOptions, build_library
library = build_library("/data/edt2010", LibraryBuildOptions())
library.write("/data/buildingdata_pipeline/processed/activity_library")

# 2. (anywhere) load it back - buildingdata will serve the four tables
library = am.DiaryLibrary.read("/data/buildingdata_pipeline/processed/activity_library")

# 3. (simulation) harmonise census occupants and generate a year
occupants = am.harmonise_census_individuals(pl.read_parquet("indcvi_slice.parquet"))
occupants = occupants.with_row_index("occupant_id")
calendar = am.generate_calendar(library, occupants, year=2024, seed=42)

calendar.day_index.shape        # (n_occupants, 366)  int32 pointers into the day pool
codes = calendar.codes(0, 500)  # (500, 52704) uint8, detailed activities, Europe/Paris clock
long = calendar.to_long(0, 500) # occupant-major polars frame with an Enum activity column

python -m activitymodel.pipeline /data/edt2010 out/ --weekday 16 --saturday 10 --sunday 10

What is in the box

activitymodel/
  taxonomy.py      the 24 detailed activities, their ACTPR codes, the coarser levels
                   (presence = building_eload's, macro, mid)
  vocabulary.py    shared socio-demographic categories, age bands, matching ladder
  harmonise.py     survey individuals / census individuals -> shared vocabulary
  survey/          read the ADISP files, code the diaries (location and day-kind aware)
  clustering.py    one-hot + PCA + Ward (or weighted k-means), k-selection curves
  library.py       DiaryLibrary: the published dataset, its schema and validation
  matching.py      occupants -> (cluster_weekday, cluster_saturday, cluster_sunday)
  calendar.py      day sampling, French public holidays, AnnualCalendar
  diagnostics/     activity profiles, matrix distances, association scores
  pipeline.py      build_library() and the command line
  tests/           synthetic fixtures; `-m integration` runs on the real files
doc/
  design.md            gap analysis of the prototypes and the decisions taken
  dataset_contract.md  the four tables, their columns, dtypes and invariants
  integration.md       what changes in buildingdata, buildingmodel,
                       buildingcalibration and building_eload, in order
  integration/         reference snippets for those changes

Measured on the real data (Paris, RP 2021, library 16/10/10 clusters)

Install

uv venv --python 3.12 .venv
uv pip install -e ".[dev,survey,fast]"
.venv/bin/python -m pytest -q

Extras: survey (pandas, to read the Stata delivery), fast (fastcluster), analysis (scikit-learn, matplotlib), dev.

Licence and data

Code: MIT. The survey is available on request from PROGEDO-ADISP and is not redistributable; the diary library holds no survey row (days are detached from people, people are aggregated into cells of at least five respondents). See doc/dataset_contract.md.