Quickstart documentation in README
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Domain
- documentation
Research direction
Start with the README and the example notebook referenced in the issue, then trace the shown TileSet, Mozaic, populate_tiles, curate_mozaics, and mozaic_plot usage. Done means the README covers Colab and Docker installation and troubleshooting, explains Tiles, TileSets, and Mozaics with their forecast relationships, and provides applicable default-scenario examples or utilities.
Written by the indexing model from the issue text.
Description
Mostly would like to see:
- Installation instructions and troubleshooting (Colab and Docker versions)
- Conceptual explanation/diagram of the types of objects -- Tiles, TileSets, and Mozaics -- and how they relate to general top-line "forecast" nomenclature
- Example code snippets and/or built-in utility functions for default scenario runbook
For (3), here is an example of reference code I got in the notebook Brad shared with me. It does appear that some parts of it were not applicable to the case I was investigating (e.g., mobile usage was not a factor). But it would be helpful to better understand what these different objects are that are being created in a loop, how they relate to each other, and why certain function calls take the different objects as inputs (as opposed to being methods called on those objects by virtue of their class).
FORECAST_START_DATE = "2025-09-01"
forecast_end_date = "2026-06-30"
date_range = [FORECAST_START_DATE, FORECAST_START_DATE]
t_d = {}
metric_mozaics_d = {}
country_mozaics_d = {}
population_mozaics_d = {}
for i in pd.date_range(*date_range):
first_forecast_date = str(i.date())
print(first_forecast_date)
# initialize collections to store relevant desktop forecasting components
t_d[first_forecast_date] = TileSet() # all desktop tiles
metric_mozaics_d[first_forecast_date] = {} # all desktop metric mozaics
country_mozaics_d[first_forecast_date] = defaultdict(lambda: defaultdict(Mozaic)) # all desktop country mozaics
population_mozaics_d[first_forecast_date] = defaultdict(lambda: defaultdict(Mozaic)) # all desktop population mozaics
print("Populating tiles and generating tile-level forecasts:")
populate_tiles(
datasets["desktop"],
t_d[first_forecast_date],
desktop_forecast_model,
first_forecast_date,
forecast_end_date
)
print("\n\nCurating mozaics and reconciling tile-level forecasts:\n")
# note: holidays that do not appear in historical data but to appear in forecasted dates will generate a warning message
curate_mozaics(
datasets["desktop"],
t_d[first_forecast_date],
desktop_forecast_model,
metric_mozaics_d[first_forecast_date],
country_mozaics_d[first_forecast_date],
population_mozaics_d[first_forecast_date],
)
for metric, data in metric_mozaics_d[first_forecast_date].items():
mozaic_plot(data, use_moving_average=True, show_detrended=False, title=f"{metric}, Global")
- Dominant language
- Python
- Stars
- 3
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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