Enhance 1D Plotting With Ratio Plots

In the realm of data analysis and visualization, the ability to generate insightful plots is paramount. Often, we move beyond simple 1D plots to understand relationships, identify trends, and compare different datasets. This is where ratio plots come into play, offering a powerful way to visualize the relationship between two datasets or components of a dataset. This article delves into the implementation and benefits of integrating ratio plots directly into a 1D plotting routine, as proposed for the [[analysis.plot]] functionality. We'll explore how this feature can streamline your workflow and provide deeper insights into your data.

Understanding the Need for Ratio Plots in Data Analysis

When you're diving deep into your data, especially when comparing multiple experimental runs, simulation results, or different configurations, simply overlaying individual plots can sometimes be insufficient. You might want to see how one dataset behaves relative to another. This is precisely the problem that ratio plots solve. They allow you to visualize the quotient, difference, or other comparative metrics between two datasets, often in a dedicated panel below the main plot. This separation helps in clearly highlighting deviations, agreements, or systematic differences that might be obscured in a standard overlay. For instance, if you're comparing the output of a new model against a baseline, a ratio plot can immediately show you where the new model is over- or under-performing, and by how much. Similarly, in scientific experiments, comparing a measured quantity against a theoretical prediction using a ratio plot can quickly reveal the accuracy of the prediction and the nature of any discrepancies. The ability to configure these plots directly within the analysis framework, using a declarative TOML configuration, significantly enhances user-friendliness and reproducibility. Instead of manually scripting these comparative plots after the initial data loading, the framework handles it automatically, reducing the potential for errors and saving valuable time. This approach promotes a more iterative and efficient data exploration process, allowing analysts to focus on interpreting the results rather than on the mechanics of plot generation. The flexibility in defining various types of ratios – from simple division to more complex metrics like pull or asymmetry – further empowers users to tailor their visualizations to the specific analytical questions they are trying to answer. This means that whether you're examining statistical distributions, physical quantities, or performance metrics, the ratio plot feature can adapt to provide the most relevant comparative view.

Introducing the ratio Configuration in TOML

The proposed [[analysis.plot]] configuration introduces a new ratio parameter, designed to be highly flexible and intuitive. This parameter can be a single dictionary defining one ratio plot, or a list of dictionaries to generate multiple ratio plots simultaneously. Let's break down the structure and options available within this ratio configuration. At its core, the ratio parameter accepts a dictionary where you specify the type of ratio you want to compute and display. The available types are:

• ratio: This is the most straightforward option, representing the direct quotient of two datasets (numerator divided by denominator). This is excellent for understanding proportional relationships.
• split_ratio: This likely refers to a scenario where the ratio is calculated for binned data, showing the ratio of counts or values within each bin.
• pull: Commonly used in physics and statistics, a pull plot shows the difference between the observed data and a model, scaled by the uncertainty of the data. It's a powerful tool for assessing the goodness-of-fit.
• difference: This option calculates the absolute difference between the numerator and denominator datasets. This is useful for understanding additive discrepancies.
• relative_difference: Similar to difference, but expressed as a percentage of one of the datasets (usually the denominator). This normalizes the difference, making it easier to compare across different scales.
• efficiency: Often used in contexts like particle physics or machine learning, this shows the fraction of true positives that are correctly identified. It's a specific type of ratio often plotted against some discriminating variable.
• asymmetry: This type of ratio is used to quantify differences in distributions that are symmetric around a central value, often used in contexts where a process might have slight biases. For example, it could be (R-L)/(R+L) where R and L are counts on the right and left.

Beyond the type, you can customize the appearance and behavior of your ratio plot:

• ylabel: This allows you to specify a custom label for the y-axis of the ratio plot. This is crucial for clarity, as the default label might not fully capture the nature of the ratio being displayed.
• compare: This is a critical parameter that takes a list of dataset names (strings). The first name in the list is typically treated as the numerator, and the second as the denominator. This is where you explicitly define which datasets are involved in the ratio calculation. The configuration supports recalling datasets by their name key, as managed by the load_all_datasets function which stores datasets in a dictionary.
• color: You can specify the color of the ratio plot. If not provided, a default color (like black, denoted by k) is used. This allows for easy differentiation if you are plotting multiple ratio plots or aligning them with the main plot's colors.

When you need to compare more than one ratio, the ratio parameter can be provided as a list of these configuration dictionaries. Each dictionary in the list will then generate a separate ratio plot, stacked or arranged appropriately within the plotting area. This extensibility ensures that even complex comparative analyses can be managed through a single, clean configuration file. The underlying load_all_datasets function, as shown in the provided Python snippet, plays a vital role by efficiently loading and preparing all necessary datasets. It uses the name key to identify and retrieve datasets, and the merge_on parameter can be used to ensure that only common data points across all datasets are considered, which is essential for accurate ratio calculations. This robust data loading mechanism, combined with the flexible ratio plot configuration, creates a powerful and user-friendly tool for data visualization and analysis.

Technical Implementation: RatioConfig and Plotting Routines

To bring the proposed ratio configuration to life within the [[analysis.plot]] functionality, a thoughtful technical implementation is required. This involves defining new data structures and extending existing plotting routines. The core of this enhancement would be the introduction of a RatioConfig dataclass. This dataclass would mirror the structure defined in the TOML configuration, providing a Pythonic way to represent and access the ratio plot settings. It would likely include fields for type (which could be an enum or a string to hold the various ratio types), ylabel, compare (a list of strings for dataset names), and color. Handling the type field, especially when it's a tuple like `(