Python and NumPy
Recover the greenhouse columns from a logger stream
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Group consecutive readings using the supplied number of greenhouses per day.
Botie's flat logger output contains complete days in sequence. We need one row per greenhouse for a comparison over time.
Inspect the component calculation
Group a sequence into rows( Flat sequence: Flat logger sequence Entries per row: Greenhouses per day )
Examples and results
- Flat logger sequence
- 010
- 120
- 230
- 314
- 422
- 536
- Greenhouses per day
3
- Required result
10 20 30 14 22 36
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
The logger order and row width are supplied. The construction cannot infer missing labels or repair an incomplete day. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
Your last tested pieces can start the full construction. An existing draft there keeps its own pieces.
One greenhouse over four days
This check uses the pieces from “Turn day rows into greenhouse histories”.
BOTIE
- Flat logger sequence
- 04
- 17
- 210
- 313
- Greenhouses per day
1
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
Which fact about the logger makes this reshape valid, and what would go wrong if the width were guessed?
Reshaping relies on known order. It cannot infer missing observation labels.
The logger order and row width are supplied. The construction cannot infer missing labels or repair an incomplete day.
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