Feature/over time

[blooop]

Summary by Sourcery

Add sample_over_time to the list of benchmark parameters and plot both distance and sample_noise in the sweep.

New Features:

• Added sample_over_time as a benchmark parameter.

Tests:

• Updated the test to include sample_over_time and plot both distance and sample_noise.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request modifies the example meta to include sample_over_time as a sweepable parameter and updates the result variables to be strings instead of references.

Class diagram showing updated BenchMeta configuration

classDiagram
    class BenchMeta {
        +plot_sweep()
        +sample_over_time
        +float_vars
        +categorical_vars
        +sample_with_repeats
    }
    class ResultVars {
        +distance: string
        +sample_noise: string
    }
    BenchMeta -- ResultVars
    note for ResultVars "Changed from object references to string identifiers"

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Flow diagram showing the updated parameter handling

flowchart LR
    A[Input Parameters] --> B{Parameter Type}
    B -->|Sweep Parameters| C[sample_over_time]
    B -->|Result Variables| D[String Identifiers]
    D --> E[distance]
    D --> F[sample_noise]
    C --> G[Plot Generation]
    E --> G
    F --> G

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File-Level Changes

Change	Details	Files
Updated the result variables to be strings instead of references.	Changed result_vars from [BenchableObject.param.distance] to ["distance", "sample_noise"]	bencher/example/meta/generate_meta.py
Added sample_over_time as a sweepable parameter.	Uncommented sample_over_time in the sweep_vars list.	bencher/example/meta/generate_meta.py

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[sourcery-ai]

Hey @blooop - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider keeping the strongly-typed parameter references (BenchableObject.param.distance) instead of string literals to maintain type safety and IDE support

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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