multiple input modes

[safabouguezzi]

Summary by CodeRabbit

• New Features

  • Automatic detection of input data layout supporting multiple file organization formats.
  • Support for location/catchment-based identification in combined data files.

• Documentation

  • Completely updated README with tool overview, input data specifications, and output format details.

[coderabbitai]

Caution

Review failed

Pull request was closed or merged during review

📝 Walkthrough

Walkthrough

The pull request introduces automated input-mode detection at container startup, replacing manual configuration. It adds detect_input.py to auto-generate /in/input.json by inspecting file structure (supporting three input layouts), updates data loading to handle location-based grouping, documents the new functionality in the README, and modifies tool specifications and example inputs accordingly.

Changes

Cohort / File(s)	Summary
Container Startup Dockerfile, src/detect_input.py	Restructured container CMD to run detect_input.py before run.py, ensuring input auto-detection occurs at startup. New script inspects /in directory structure and auto-generates /in/input.json with column names, supporting three input modes (single combined file, per-location obs/sim directories, or two combined files with location column).
Input Configuration in/input.json, in/discharge_5694.csv.metadata.json	Updated example input to reference CAMELS_simulations.csv and CAMELS_observations.csv, added location_column: catchment_id parameter, and removed example metadata file.
Data Loading & Processing src/evaluation.py, src/run.py	Extended load_data() signature to accept optional location_column and restructured logic to support three auto-detected input modes: per-location files, combined files with location matching, and single combined file. Updated run script to extract and pass location_column from configuration.
Tool Definition & Documentation src/tool.yml, README.md	Added optional location_column parameter to tool schema. Replaced README with product-oriented documentation describing auto-detection, three input modes with matching rules, column-name defaults, metric outputs (NSE, KGE, R², MSE, RMSE), and references to CAMELS-DE and Hy2DL model.

Sequence Diagram(s)

sequenceDiagram
    participant Container
    participant detect_input.py
    participant FileSystem as File System
    participant run.py
    participant load_data as evaluation.load_data()
    
    Container->>detect_input.py: Execute (startup)
    detect_input.py->>FileSystem: Inspect /in for structure
    FileSystem-->>detect_input.py: File list & structure
    
    alt Mode 1: obs/ & sim/ subdirs
        detect_input.py->>FileSystem: Read obs & sim files
        FileSystem-->>detect_input.py: Column names
    else Mode 2: Two combined files
        detect_input.py->>FileSystem: Read first two files
        FileSystem-->>detect_input.py: Column names & content
    else Mode 0: Single combined file
        detect_input.py->>FileSystem: Read single file
        FileSystem-->>detect_input.py: Column names
    end
    
    detect_input.py->>FileSystem: Write /in/input.json
    detect_input.py->>Container: Print JSON
    
    Container->>run.py: Execute (after detect_input.py)
    run.py->>FileSystem: Read input.json config
    FileSystem-->>run.py: Parameters & paths
    
    run.py->>load_data: Call with detected config
    load_data->>FileSystem: Load observation/simulation data
    FileSystem-->>load_data: DataFrames
    
    alt Mode 1/2: Per-location or matched files
        load_data->>load_data: Group & merge by location
    else Mode 0: Single combined file
        load_data->>load_data: Extract obs/sim columns
    end
    
    load_data-->>run.py: Dict[location, DataFrame]
    run.py->>run.py: Compute metrics & generate report

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Poem

🐰 Files hop into place with auto-detect flair,
Three input modes handled with algorithmic care,
Location columns merge where data flows,
From startup to metrics—the detection grows! ✨

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title 'multiple input modes' directly and clearly summarizes the main objective of the changeset, which introduces support for three auto-detected input modes for loading simulation and observation data.

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}

✨ Finishing Touches

📝 Generate docstrings

• Create stacked PR
• Commit on current branch

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch feature/multiple-input-modes

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