chore: RATIO IS IMPORTANT

[nabobalis]

Summary by Sourcery

Adjust lightcurve plot scaling and formatting to better match the legacy visuals and page layout.

Enhancements:

• Limit AIA lightcurve y-axis ticks to min and max values and format labels with compact numeric notation for clearer per-channel ranges.
• Update default timeseries figure aspect ratio to 4:5 to align with the legacy 1024x1280 layout while preserving width.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Adjusts AIA lightcurve Y-axis tick strategy to show only min/max with formatted labels and updates default timeseries figure aspect ratio to 4:5 to match legacy layout.

File-Level Changes

Change	Details	Files
Simplified AIA lightcurve Y-axis ticks to min/max with custom numeric formatting.	Collects y-data from all plotted lines on the AIA lightcurve axis Sets Y-axis ticks to the global min and max of the plotted data when available Applies a FuncFormatter to render tick labels using a compact four-significant-figure format	src/suntoday/lightcurve.py
Updated timeseries figure dimensions to a 4:5 aspect ratio consistent with the legacy 1024x1280 plot.	Documents the rationale for the chosen aspect ratio in a code comment Reduces the configured timeseries figure height from 2400 to 1500 pixels while keeping width at 1200 pixels	src/suntoday/config.py

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[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• When computing min/max for plotted, consider using NaN-safe operations (e.g., np.nanmin/np.nanmax) to avoid ending up with NaN ticks when any line contains NaNs.
• If all plotted values are identical, min and max will be equal and produce a degenerate tick set; consider adding a small epsilon or falling back to the default locator in that case to avoid a flat axis.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- When computing `min`/`max` for `plotted`, consider using NaN-safe operations (e.g., `np.nanmin`/`np.nanmax`) to avoid ending up with `NaN` ticks when any line contains NaNs.
- If all plotted values are identical, `min` and `max` will be equal and produce a degenerate tick set; consider adding a small epsilon or falling back to the default locator in that case to avoid a flat axis.

## Individual Comments

### Comment 1
<location path="src/suntoday/lightcurve.py" line_range="113-116" />
<code_context>
         ax.set_ylabel(r"Data Mean (DN)", size=LABEL_FONTSIZE)
+    # The per-channel ranges are narrow, so a full tick ladder is noise;
+    # min/max-only ticks show the range directly (matching the legacy plot).
+    plotted = [line.get_ydata() for line in ax.lines if len(line.get_ydata())]
+    if plotted:
+        ax.set_yticks([min(data.min() for data in plotted), max(data.max() for data in plotted)])
+        ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda value, _pos: f"{value:.4g}"))


</code_context>
<issue_to_address>
**issue:** NaN values in line data will propagate into tick limits and formatter.

If any `line.get_ydata()` contains NaNs, `data.min()`/`data.max()` will evaluate to NaN and produce invalid y-axis ticks. Consider using `np.nanmin`/`np.nanmax` or filtering out NaNs before computing the tick limits so they stay valid with partially missing data.
</issue_to_address>

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[sourcery-ai]

issue: NaN values in line data will propagate into tick limits and formatter.

If any line.get_ydata() contains NaNs, data.min()/data.max() will evaluate to NaN and produce invalid y-axis ticks. Consider using np.nanmin/np.nanmax or filtering out NaNs before computing the tick limits so they stay valid with partially missing data.