Kratky Plot Coordinate Calculator

Enter q and intensity data

Data (two columns)

Accepts comma, tab, or spaces. Lines starting with # are ignored.

Delimiter Auto-detect Comma Tab Space

Rounding (digits)

Data options

Skip header row

Sort by q

Drop q≤0 or I≤0

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Dimensionless scaling

Compute x* and y* using Rg and I(0)

Useful when comparing curves across samples.

Rg (same length units as q⁻¹)

I(0) (forward intensity)

Calculate Load example Clear

Example data table

Formula used

A Kratky plot is commonly expressed as a transformed intensity: y = q² · I(q) plotted against x = q. Here, q is the scattering vector magnitude and I(q) is measured intensity.

For a dimensionless comparison across samples, the calculator can also compute: x* = q · Rg and y* = (q · Rg)² · I(q) / I(0), where Rg is radius of gyration and I(0) is forward intensity.

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How to use this calculator

1. Paste your two-column dataset as q and I(q).
2. Select a delimiter or keep auto-detect for mixed formats.
3. Enable sorting, header skipping, or filtering for nonpositive values.
4. Optional: turn on dimensionless scaling and enter Rg and I(0).
5. Click Calculate to generate the Kratky coordinates and plot.
6. Use Download CSV or Download PDF for reporting.

Article

1) Why Kratky coordinates matter

A Kratky transformation shows how intensity changes with increasing q. Plotting q²·I(q) versus q makes curves easier to compare when raw intensity spans large ranges. The transformed trace is often used as a quick structural “fingerprint” before deeper modeling. It also helps standardize visual checks during routine quality control.

2) Typical SAXS and SANS q ranges

Many SAXS datasets use q from roughly 0.01 to 0.5 Å⁻¹, while SANS may extend to lower q depending on geometry. Because the transformation multiplies by q², higher q points gain weight, so background subtraction matters. If you stitch detector distances, confirm the overlap region is consistent.

3) Reading shapes in the plot

Compact objects often show a bell-shaped peak that rises then falls toward higher q. Flexible chains can show a plateau or a slow increase across a wide region. Aggregation or interparticle effects can create a low-q upturn, so review the raw curve as well. When comparing conditions, focus on shifts in peak position and peak breadth.

4) Why sorting and filtering help

Data files may include headers, comments, repeated points, or mixed delimiters. Sorting by q produces a monotonic x-axis for cleaner plots and exports. Dropping nonpositive q or I(q) prevents invalid products and reduces misleading spikes.

5) Dimensionless scaling for comparisons

For cross-sample comparisons, dimensionless coordinates are common: x* = q·Rg and y* = (q·Rg)²·I(q)/I(0). When Rg and I(0) are reliable, curves from similar structural classes often cluster. If they are uncertain, keep scaling off and compare standard coordinates.

6) Data quality checks before plotting

At low q, beam-stop shadowing and subtraction artifacts can dominate. At high q, counting statistics and detector noise can flatten features. Confirm that corrected I(q) stays positive and remove obvious outliers before interpretation. If you have uncertainties, keep a separate error column for later fitting.

7) Practical interpretation workflow

Begin with the raw intensity, then review the transformed coordinates and plotted trace. Compare peak position, peak height, and plateau length across replicates and conditions. Keep rounding and unit conventions consistent when creating report tables and figures. For multi-sample studies, apply the same q-range window to all curves.

8) Reporting and reproducibility

Export tables that include q, I(q), and computed coordinates for transparency. Record metadata such as temperature, buffer, exposure time, and instrument configuration. Consistent exports reduce transcription errors and simplify future re-analysis. Save the input dataset alongside the exported coordinate file for traceability.

FAQs

1) What are the Kratky plot coordinates?

They are x = q and y = q²·I(q). The transformation emphasizes mid-to-high q features and can reveal differences in compactness or flexibility more clearly than raw intensity.

2) Do I need units for q and I(q)?

Use consistent units. q is commonly in Å⁻¹ or nm⁻¹. The computed q²·I(q) inherits combined units, but shape comparisons remain meaningful when inputs are consistent.

3) Why does the calculator offer sorting by q?

Sorting ensures the x-axis increases monotonically, which helps produce a smooth plotted curve and a tidy export. It also makes it easier to compare multiple datasets point-by-point.

4) What does “Drop q≤0 or I≤0” do?

It removes nonphysical or invalid rows that can occur from parsing errors, placeholders, or background-subtraction artifacts. This prevents negative or zero values from distorting the product q²·I(q).

5) When should I use dimensionless scaling?

Use it when comparing different sizes or concentrations and you have trustworthy Rg and I(0). Dimensionless curves can align similar structures, making deviations easier to interpret.

6) Can I paste tab- or space-separated data?

Yes. Auto-detect supports commas, tabs, and whitespace. You can also force a delimiter using the dropdown. Comment lines starting with # are ignored.

7) Does the plot replace model fitting?

No. It is a diagnostic visualization that complements fitting and other analyses. Always interpret features alongside instrument settings, background subtraction, and appropriate physical models.