originplot

originplot Skill

0. Skill Identity

Skill name: originplot
Alias: originpro-external-plotting
Chinese name: OriginPro 外部 Python 自动绘图 Skill
Primary target: Anyone who wants to use external Python to control Origin / OriginPro for scientific, engineering, experimental, or publication-quality plotting.
Core idea: Treat OriginPro as a publication-quality rendering engine controlled by Python, not as the main numerical-computation engine.

Typical pipeline:

Numerical computation / experiment
    -> standardized data file
    -> Python data adapter
    -> originpro automation layer
    -> Origin workbook / worksheet / matrix
    -> Origin graph template
    -> exported figure and saved OPJU project

1. When to Use This Skill

Use this skill when the user wants to:

Use originpro from an external Python environment.
Control Origin / OriginPro programmatically from Python.
Import CSV, DAT, TXT, Excel, or NumPy data into Origin automatically.
Batch-generate Origin figures from many data files.
Apply Origin graph templates automatically.
Export PNG, TIFF, PDF, EPS, SVG, or other publication figures from Origin.
Save Origin projects .opju automatically.
Convert a manual Origin plotting workflow into a reusable script.
Debug external Python + OriginPro automation issues.
Design a reusable plotting pipeline around OriginPro.
Produce Nature-style or high-impact-journal scientific figures through OriginPro.
Design a universal “any figure” control architecture around GraphPage / GraphLayer / Plot.
Drive plotting from a declarative config such as FigureSpec.

2. Scope and Non-Scope

This Skill Does

Design an external Python + OriginPro plotting architecture.
Provide reusable Python code skeletons for Origin automation.
Help choose between Worksheet, Matrix, GraphPage, GraphLayer, and template-based plotting.
Generate line plots, scatter plots, grouped plots, heatmaps, matrix plots, contour plots, and batch plots.
Use Origin templates as the preferred styling mechanism.
Require the embedded palette/style rules for Nature-style, paper-ready, or high-impact-journal figures.
Help export figures and save .opju projects.
Help users keep computation, data preparation, plotting, and export cleanly separated.
Help debug common originpro automation errors.
Define a universal hierarchy-control model for arbitrary figures.
Define a declarative FigureSpec / LayerSpec / PlotSpec protocol so agents can generate figures consistently.

This Skill Does Not

Use Origin as the main engine for large-scale numerical simulation.
Replace Fortran / C++ / Python / MATLAB / HPC workflows for heavy computation.
Encourage WSL/Linux to directly call originpro when the official external package requires Windows and local Origin installation.
Hard-code every visual style into Python when an Origin template is a better abstraction.
Treat originpro as a matplotlib replacement. It is an automation bridge to Origin.

3. External Facts to Remember

Stable principles:

originpro is a high-level Python API for interacting with Origin software.
External originpro controls Origin through Origin Automation Server / COM.
It can read, write, and modify Origin data objects, create graphs, and export graphs.
External originpro is intended for Windows Python controlling a local Origin installation.
Origin should be treated as the rendering and template engine; Python should be the orchestration layer.

Official reference links to include when useful:

https://www.originlab.com/doc/ExternalPython
https://www.originlab.com/doc/ExternalPython/External-Python-Code-Samples
https://www.originlab.com/doc/python
https://www.originlab.com/doc/python/Examples/Graphing
https://www.originlab.com/doc/python/Examples/Origin-Python-Data-Exchange
https://pypi.org/project/originpro/

API constraint: Do not guess unknown originpro APIs. If an API is unclear, check the official documentation or inspect the installed package source code before writing code; otherwise state the uncertainty and use a template-based workaround.

4. Top-Level Architecture

Design every OriginPro automation workflow using the following layers:

1. Computation / acquisition layer
   Fortran, C++, Python, MATLAB, lab instruments, simulation, experiment

2. File/data layer
   CSV, DAT, TXT, XLSX, NPY, NPZ, HDF5, raw instrument output

3. Python data adapter layer
   pandas, numpy, unit conversion, column naming, reshaping, validation

4. Origin data object layer
   Workbook, Worksheet, Matrixbook, Matrixsheet

5. Origin graph object layer
   GraphPage, GraphLayer, Plot, Axis, Legend, Colormap

6. Template and styling layer
   .otpu, .otp, custom graph themes, journal styles

7. Export and archive layer
   PNG, TIFF, PDF, EPS, SVG, OPJU, logs

Architectural principle:

Computation belongs outside Origin.
Data normalization belongs in Python.
Visual style belongs mostly in Origin templates.
Automation and batching belong in Python.
Final figure quality belongs to Origin.

5. Universal Figure Hierarchy Control Model

This section is mandatory for arbitrary-figure control. Any figure should be decomposed into the following control layers:

L0 Runtime / Session
L1 Data
L2 Origin Data Object
L3 GraphPage
L4 GraphLayer
L5 Plot / Primitive
L6 Axis / Scale / Tick
L7 Legend / Label / Annotation
L8 Theme / Template / Style
L9 Export / Archive / QA

L0 Runtime / Session

Controls:

Whether Origin is launched.
Visible or hidden mode.
New project or attach to existing project.
Exception-safe shutdown.
Output, template, and log directories.
Windows Python vs WSL-to-Windows bridge.

L1 Data

Controls:

Data source, schema, units, and semantics.
Missing/invalid values.
Sorting, grouping, normalization, reshaping, pivoting.
Semantic roles such as x, y, group, size, error, z.

L2 Origin Data Object

Controls:

Worksheet for XY, multi-Y, grouped tables, bar/scatter data, raw XYZ.
Matrix for regular-grid heatmaps, contour, and image-like data.
Worksheet-XYZ when data are irregular or need triangulation-style contouring.

Decision rule:

Regular 2D field -> Matrix preferred
Tabular XY / grouped data -> Worksheet preferred
Irregular XYZ -> Worksheet XYZ

L3 GraphPage

Controls:

Whole figure canvas.
Single-panel vs multi-panel layout.
Page size, margins, inter-panel spacing.
Shared legend or shared colorbar strategy.

L4 GraphLayer

Controls:

Each panel / coordinate system.
Layer position and size.
Which plots live inside the layer.
Layer-specific axis titles, ranges, panel tag, and shared-axis behavior.

L5 Plot / Primitive

Controls:

Line, scatter, bar, error bar, heatmap, contour, area, surface, box, violin, bubble.
Mapping from data fields to visual channels:

x, y, z, color, size, group, error, text

L6 Axis / Scale / Tick

Controls:

Axis titles and units.
Linear / log scale.
Range, padding, ticks, minor ticks, tick label formatting.
Shared axes.
Top/right frame visibility.

L7 Legend / Label / Annotation

Controls:

Legends.
Panel labels (a), (b), (c).
Callouts, arrows, text notes, fit equations, statistical markers.
Colorbar labels.

L8 Theme / Template / Style

Controls:

Global typography and line widths.
Semantic palette.
Bar fill, marker shapes, colormap, frame style.
Journal style such as Nature or PRL.
Prefer Origin templates for final styling.

L9 Export / Archive / QA

Controls:

PNG/PDF/SVG/TIFF/EPS export.
OPJU save.
Clean-data archiving.
Visual QA and file-existence checks.

Universal Control Order

For any figure, use this order:

Data -> Object -> Layout -> Plot -> Axis -> Annotation -> Style -> Export

Or expanded:

1. Define data semantics.
2. Choose the Origin data object.
3. Decide page layout.
4. Decide each layer's role.
5. Map data to plot primitives.
6. Configure axes.
7. Add legend/labels/annotations.
8. Apply theme/template.
9. Export and run QA.

6. Control Planes for Any Figure

When users ask how to control arbitrary figures, answer using these four planes:

6.1 Structure Control

Questions answered:

How many panels?
What page layout?
Which Origin objects?
Single page or multiple pages?

Maps to: L2 + L3 + L4

6.2 Semantic Control

Questions answered:

What do columns mean?
What maps to x/y/color/size/error?
Which variable is the comparison dimension?

Maps to: L1 + L5

6.3 Visual Control

Questions answered:

Fonts, line width, colors, borders, legend style, color maps.

Maps to: L6 + L7 + L8

6.4 Delivery Control

Questions answered:

What gets exported?
Is OPJU saved?
Is clean data archived?
Is QA performed?

Maps to: L9

7. FigureSpec Protocol Overview

To make arbitrary-figure generation agent-friendly, use a declarative config protocol.

FigureSpec
    |- RuntimeSpec
    |- DataSpec[]
    |- PageSpec
    |- LayerSpec[]
    |- PlotSpec[]
    |- StyleSpec
    |- ExportSpec

Design principle:

FigureSpec describes WHAT to build.
Python/originpro executor decides HOW to build it.

See the companion document FIGURESPEC_PROTOCOL.md for the full protocol.

8. Recommended Project Structure

originplot_project/
├── data/
│   ├── raw/
│   ├── processed/
│   └── README.md
├── specs/
│   ├── figure_2x2_nature.yaml
│   └── figure_single_heatmap.yaml
├── templates/
│   ├── line_basic.otpu
│   ├── line_multi_component.otpu
│   ├── scatter_basic.otpu
│   ├── heatmap_diverging.otpu
│   ├── contour_basic.otpu
│   ├── journal_style.otpu
│   └── nature_style.otpu
├── scripts/
│   ├── origin_session.py
│   ├── data_loader.py
│   ├── data_validator.py
│   ├── style_nature.py
│   ├── plot_line.py
│   ├── plot_scatter.py
│   ├── plot_heatmap.py
│   ├── plot_multipanel.py
│   ├── figurespec_executor.py
│   ├── batch_export.py
│   └── config.py
├── output/
│   ├── figures/
│   ├── opju/
│   ├── data/
│   └── logs/
└── README.md

9. Data-Type Decision Tree

9.1 XY or Multi-Y Curves

Use Worksheet.

9.2 XYZ Data

If regular grid, convert to Matrix; otherwise use Worksheet XYZ.

9.3 Matrix Data

Use Matrixsheet.

9.4 Batch Plotting

Use file loops + one common template + one OPJU archive per run.

10. Template Strategy

Preferred final workflow:

1. Manually create one ideal graph in Origin.
2. Save it as .otpu.
3. Let Python inject data and export.

Keep this separation:

Origin template controls:
    fonts, line widths, colors, legends, axis style, page size
Python controls:
    data, mapping, graph names, variable labels, output paths, batching

11. Mandatory Nature-Style Rules

Use the following semantic palette for Nature-style outputs unless the user explicitly overrides it:

NATURE_PALETTE = {
    "blue_main":      "#0F4D92",
    "blue_secondary": "#3775BA",
    "green_1": "#DDF3DE",
    "green_2": "#AADCA9",
    "green_3": "#8BCF8B",
    "red_1":   "#F6CFCB",
    "red_2":   "#E9A6A1",
    "red_strong": "#B64342",
    "neutral_light": "#CFCECE",
    "neutral_mid":   "#767676",
    "neutral_dark":  "#4D4D4D",
    "neutral_black": "#272727",
    "gold":   "#FFD700",
    "teal":   "#42949E",
    "violet": "#9A4D8E",
    "magenta":"#EA84DD",
}

Core rules:

Prefer Arial or Helvetica.
Keep panel borders visible.
Use frameless legends.
Use semantic colors consistently.
Use sparse ticks and no grid by default.
Use data-aware axis limits.
Save OPJU plus at least one vector export.
Add (a), (b), (c) style panel labels only after layout is finalized.

12. Installation Guidance

pip install originpro pandas numpy pyyaml

External mode must run in Windows Python.

13. Standard Session Wrapper

import sys
import originpro as op


def setup_origin(show=True, new_project=True):
    def origin_shutdown_exception_hook(exctype, value, traceback):
        try:
            op.exit()
        finally:
            sys.__excepthook__(exctype, value, traceback)

    if op and getattr(op, "oext", False):
        sys.excepthook = origin_shutdown_exception_hook
        op.set_show(show)

    if new_project:
        op.new()
    return op


def close_origin(save_path=None):
    if save_path:
        op.save(save_path)
    if getattr(op, "oext", False):
        op.exit()

14. Response Pattern for This Skill

When answering any plotting request, structure the response as:

1. Data semantics
2. Origin data object choice
3. GraphPage / GraphLayer structure
4. Plot mappings
5. Axis strategy
6. Annotation strategy
7. Style / template strategy
8. Export / OPJU / QA strategy
9. Minimal working code or FigureSpec

15. Checklist Before Providing Code

Determine:

1. Windows Python or WSL/Linux bridge?
2. Local Origin installed?
3. Data format?
4. Data type: XY, multi-Y, XYZ, matrix, batch?
5. Desired output: PNG, PDF, SVG, TIFF, OPJU?
6. Built-in template or custom .otpu?
7. Single figure or batch?
8. Needs Nature style or not?
9. For arbitrary figures: what are page, layer, plot, and annotation responsibilities?
10. Would FigureSpec be more reusable than hard-coded Python?

16. Final Design Principle

Use Python to automate.
Use OriginPro to render.
Use templates to standardize.
Use FigureSpec to formalize arbitrary figures.
Use OPJU to preserve editability.

About this skill

0. Skill Identity

1. When to Use This Skill

2. Scope and Non-Scope

This Skill Does

This Skill Does Not

3. External Facts to Remember

4. Top-Level Architecture

5. Universal Figure Hierarchy Control Model

L0 Runtime / Session

L1 Data

L2 Origin Data Object

L3 GraphPage

L4 GraphLayer

L5 Plot / Primitive

L6 Axis / Scale / Tick

L7 Legend / Label / Annotation

L8 Theme / Template / Style

L9 Export / Archive / QA

Universal Control Order

6. Control Planes for Any Figure

6.1 Structure Control

6.2 Semantic Control

6.3 Visual Control

6.4 Delivery Control

7. FigureSpec Protocol Overview

8. Recommended Project Structure

9. Data-Type Decision Tree

9.1 XY or Multi-Y Curves

9.2 XYZ Data

9.3 Matrix Data

9.4 Batch Plotting

10. Template Strategy

11. Mandatory Nature-Style Rules

12. Installation Guidance

13. Standard Session Wrapper

14. Response Pattern for This Skill

15. Checklist Before Providing Code

16. Final Design Principle