O0000-code/SSCI-Plots

O0000-code-Profil

Letzte erfasste Quellaktivität: 17. Mai 2026 um 17:54

SkillsMP-Katalog aktualisiert: 17. Oktober 2026 um 00:09

gesammelte Skills: 1

GitHub-Stars: 6
GitHub-Forks: 0

README des Repositorys

README-Quelldatei: README.md

README-Quelldatei lesen

Vollständige README anzeigen

<div align="center"> # SSCI-Plots **Publication-ready matplotlib styles for SSCI / SCI journal submissions.** ![License: MIT](https://img.shields.io/github/license/O0000-code/SSCI-Plots?style=flat-square&cacheSeconds=3600) ![Python](https://img.shields.io/badge/python-3.8%2B-blue?style=flat-square&logo=python&logoColor=white) ![matplotlib](https://img.shields.io/badge/matplotlib-3.5%2B-11557c?style=flat-square) ![Journals](https://img.shields.io/badge/journals-12-success?style=flat-square) ![Stars](https://img.shields.io/github/stars/O0000-code/SSCI-Plots?style=flat-square&logo=github&cacheSeconds=3600) [简体中文 README](README.zh-CN.md) <img src="examples/01-default-vs-apa.png" alt="Same data, two styles: default matplotlib on the left, apply_style(journal="apa") on the right" width="100%"> <a href="#gallery">Gallery</a> · <a href="#install">Install</a> · <a href="#whats-included">What's included</a> · <a href="#use-as-a-python-library-optional">Python library</a> </div> **One call, publication-ready figure.** - 🎯 **12 journal presets** — `apply_style(journal="lancet")` configures matplotlib to match a journal's house style. APA, Elsevier, Wiley, SAGE, Psychological Science, OUP / ASA, Nature, Science, NEJM, Lancet, BMJ, JAMA. - 🎨 **21 color-blind-safe palettes** — drawn from Tol, Okabe-Ito, ggsci, MetBrewer, Economist, BBC. Fonts are embedded, Latin statistics italicized. - 🧰 **27 helpers** — significance brackets, inline statistics, reference lines, and multi-panel composition (`compose_grid`, `small_multiples`, shared colorbar / legend, inset zoom). - 📄 **APA 7 output** — image file plus a separate Markdown caption. Figure number, title, and Note stay out of the image itself. - 🌐 **Cross-disciplinary** — psychology, economics, public health, sociology, political science, geography, education, methodology. ## Gallery Eighteen worked examples grouped by discipline. Switch presets by changing one argument to `apply_style()`. To regenerate locally: `python examples/generate_examples.py`. ### 🧠 Psychology classics Grouped bar with significance brackets, mediation diagram, correlation heatmap, interaction plot, and raincloud. <table> <tr> <td align="center" width="50%"> <img src="examples/02-apa-style.png" width="100%" alt="APA preset: grouped bar chart with significance brackets and pairwise Cohen's d"> <code>apa</code> · Bar chart with significance brackets Anxiety scores across three intervention arms, with pairwise effect sizes and an omnibus F test. </td> <td align="center" width="50%"> <img src="examples/03-elsevier-style.png" width="100%" alt="Elsevier preset: mediation diagram X to M to Y with a, b, and c-prime paths"> <code>elsevier</code> · Mediation diagram (X → M → Y) Self-esteem to GPA via academic self-efficacy. Reports the a, b, and c' paths plus the indirect effect with bootstrap 95% CI. </td> </tr> <tr> <td align="center" width="50%"> <img src="examples/04-wiley-style.png" width="100%" alt="Wiley preset: lower-triangle correlation heatmap with internalizing and positive subscales"> <code>wiley</code> · Lower-triangle correlation heatmap Six variables grouped into an internalizing subscale and a positive subscale. Pearson r in each cell with significance asterisks. </td> <td align="center" width="50%"> <img src="examples/06-oup-style.png" width="100%" alt="OUP preset: 2 by 2 interaction plot of stress and social support on wellbeing"> <code>asa</code> · Stress × support interaction How perceived stress predicts wellbeing differently under high vs. low social support, with simple-slope estimates for each level. </td> </tr> <tr> <td align="center" colspan="2"> <img src="examples/07-apa-raincloud.png" width="49%" alt="APA preset: raincloud plot of cognitive performance across three SES groups"> <code>apa</code> · Raincloud plot (three SES groups) Cognitive performance by SES band, showing the density, the box summary, and the raw observations on a single axis (Allen et al., 2019). </td> </tr> </table> ### 📊 Meta-analysis & methodology Forest plot, multi-panel composite, and specification curve. <table> <tr> <td align="center" width="50%"> <img src="examples/05-sage-style.png" width="100%" alt="Psychological Science preset: forest plot with three subgroups and random-effects diamonds"> <code>psych_science</code> · Forest plot with subgroups Ten mindfulness studies pooled with random effects, broken out by age band. Reports subgroup diamonds, overall effect, 95% prediction interval, and I2. </td> <td align="center" width="50%"> <img src="examples/08-multipanel.png" width="100%" alt="APA preset: 1x3 multi-panel composite with scatter, boxplot, and time-series"> <code>apa</code> · Multi-panel composite (A / B / C) Three related views of one study: scatter with CI, cross-group boxplot, and a treatment-vs-control time series, with consistent styling across panels. </td> </tr> <tr> <td align="center" colspan="2"> <img src="examples/16-specification-curve.png" width="49%" alt="APA preset: specification curve with 64 specifications and binary descriptor matrix"> <code>apa</code> · Specification curve Effect of X on Y across 64 analytic specifications, with the matrix below showing which analytic choices produced each estimate. </td> </tr> </table> ### 💰 Causal inference & economics Event-study DID, regression discontinuity, mobility transition matrix, and a multi-model coefficient plot. <table> <tr> <td align="center" width="50%"> <img src="examples/09-event-study-did.png" width="100%" alt="Economics preset: event-study DID plot showing pre- and post-policy coefficients with a confidence band"> <code>economics</code> · Event-study DID Treatment effects of a minimum-wage policy on teen employment, plotted over event time with a confidence band and a pre-trend placebo test. </td> <td align="center" width="50%"> <img src="examples/10-regression-discontinuity.png" width="100%" alt="Economics preset: regression discontinuity plot with fits on either side of a cutoff and a McCrary density inset"> <code>economics</code> · Regression discontinuity College enrolment around a scholarship test-score cutoff, with the RD estimate, bias-corrected CI, and a McCrary density check on the running variable. </td> </tr> <tr> <td align="center" width="50%"> <img src="examples/12-mobility-matrix.png" width="100%" alt="Intergenerational mobility transition matrix with viridis colormap and diagonal annotations"> <code>apa</code> · Intergenerational mobility matrix Probability of a child's education level given a parent's, with marginal totals and the lowest-mobility cell highlighted. </td> <td align="center" width="50%"> <img src="examples/13-coefficient-plot.png" width="100%" alt="APA preset: multi-model coefficient plot across three model specifications"> <code>apa</code> · Multi-model coefficient plot Same covariates estimated under three nested specifications side by side, with covariate groups banded and model fit reported per panel. </td> </tr> </table> ### 🌍 Clinical, political, geography & education Survival curve, ideological scaling, geographic distribution, and growth trajectories. <table> <tr> <td align="center" width="50%"> <img src="examples/11-kaplan-meier-nejm.png" width="100%" alt="NEJM preset: Kaplan-Meier survival curve with at-risk table and zoom inset"> <code>nejm</code> · Kaplan-Meier survival curve Five-year survival in a two-arm cancer trial, with hazard ratio, log-rank p, an at-risk table, and a zoom on the early follow-up window. </td> <td align="center" width="50%"> <img src="examples/14-ideological-scaling.png" width="100%" alt="APA preset: DW-NOMINATE ideological scaling KDE for senators and donors by party"> <code>apa</code> · Ideological scaling DW-NOMINATE distributions for senators and their donors, with named legislators annotated and a rug strip showing individual scores. </td> </tr> <tr> <td align="center" width="50%"> <img src="examples/15-choropleth-mobility.png" width="100%" alt="APA preset: US state-level mobility map with a validation scatter inset"> <code>apa</code> · US state mobility map Income mobility across US states, with regional patterns annotated and a validation scatter against administrative records. </td> <td align="center" width="50%"> <img src="examples/17-growth-curve.png" width="100%" alt="APA preset: 2x2 SES-faceted growth curves with individual trajectories and group mean"> <code>apa</code> · Growth curves by SES quartile Student literacy trajectories from Grades 3–8, faceted by SES quartile. Individual paths sit behind the group mean with a 95% CI band. </td> </tr> </table> ### 🎯 Capstone: research dashboard <table> <tr> <td align="center" colspan="2"> <img src="examples/18-research-dashboard.png" width="60%" alt="APA preset: six-panel heterogeneous research dashboard with mixed chart types"> <code>apa</code> · Six-panel research dashboard One mindfulness trial summarised across six panels: study design, baseline correlation, measure intercorrelations, survival, subgroup effects, and robustness across specifications. </td> </tr> </table> ## Why this exists I used to spend hours on every submission tuning matplotlib by hand: font size, spines, palette, column width. Reviewers still flagged the figures. So I sat down with the author guidelines for APA, Elsevier, Wiley, SAGE, OUP, Nature, Science, NEJM, Lancet, BMJ, and JAMA, read the color-vision-deficiency papers from Tol, Wong, and Okabe & Ito, and worked through how ggsci, MetBrewer, The Economist, BBC News, and the UK Government Analysis Function set up their charts. Everything I learned went into a single style file. SSCI-Plots is that file plus the helpers I kept reaching for. Coverage now extends past psychology into economics, public health, sociology, political science, education, business and management, geography, and methodology. ## Install Clone the repo into Claude Code's skills folder: ```bash git clone https://github.com/O0000-code/SSCI-Plots.git ~/.claude/skills/ssci-plots ``` Claude Code picks the skill up automatically. When you ask for a publication-ready figure, the agent calls `apply_style(...)`, `get_palette(...)`, `compose_grid(...)`, and so on. `SKILL.md` at the repo root tells the model when to use which helper. **Dependencies**: `matplotlib >= 3.5`, `numpy`, `pillow`. The agent installs anything missing. > Want to call the library directly without Claude Code? See [Use as a Python library](#use-as-a-python-library-optional) below. ## What's included - **12 journal presets** with matching palettes — APA, Elsevier, Wiley, SAGE, Psychological Science, OUP / ASA, Nature, Science, NEJM, Lancet, BMJ, JAMA - **21 color-blind-safe categorical palettes** — Tol, Okabe-Ito, IBM, ggsci, MetBrewer, Economist, BBC. Another 17 mood palettes ship for non-publication use, behind an explicit opt-in. - **3 diverging and 8 sequential colormaps** — BuRd, PRGn, the viridis family, and ColorBrewer; all registered with matplotlib - **70+ matplotlib rcParams** — APA 7 italics, custom mathtext, TrueType embedding, journal-specific panel labels - **27 helpers and a 55-chart catalog** — see the API list below and `references/` docs - **30 named figure sizes** plus `mode='slides'` for posters and talks <details> <summary>27 helper functions — style · palettes · APA formatting · statistical annotations · multi-panel composition</summary> **Style entry points (3)** - `apply_style(journal=None, *, mode='paper')` -- one call sets 70+ rcParams, registers colormaps, embeds TrueType fonts, and loads the matching palette when a journal is given - `fig_size(width_cm, height_cm)` -- converts cm to inches against journal column width - `save_figure(fig, name)` -- writes PDF, PNG, or both at the default DPI defined in `ssci_style.py` **Palette access (7)** - `get_palette(n, name='tol_bright', *, category=None, return_emphasis=False)` -- n color-blind-safe categorical colors - `get_grayscale(n)` -- grayscale fallback for print - `get_diverging_cmap(name='BuRd')` -- diverging colormap for heatmaps - `get_sequential_cmap(name='viridis')` -- perceptually uniform sequential colormap (viridis family, ColorBrewer, Tailwind monochromatic) - `list_palettes(category=None, publication_grade=None)` -- list available palettes with metadata - `get_emphasis_pair(focal=None, *, reference=...)` -- one saturated color plus a neutral gray for treatment-vs-control comparisons (reference defaults to the gray set in `ssci_style.py`) - `register_aesthetic_palette(name, *, acknowledge_non_publication_grade=True)` -- opt-in switch for the 17 mood palettes **APA p-value and figure-text formatting (3)** - `format_p_value(p, style='apa')` -- APA-formatted *p* (no leading zero, three decimals; `'stars'` and `'exact'` modes too) - `p_to_stars(p)` -- `***` / `**` / `*` / `n.s.` - `format_apa_figure_text(figure_number, title, note_text=None, *, error_bar_type=None, model_fit=None)` -- APA figure number / title / Note as Markdown, with optional structured note for error bars and SEM model fit **Statistical annotations (4)** - `add_significance_bracket(ax, x1, x2, y, p_value)` -- bracket with automatic p-to-stars conversion - `annotate_effect_size(ax, d, ci=None)` -- effect size with optional CI - `add_inline_stats(ax, items, position='top_left')` -- inline block of test statistics, the kind seen in replication packages from major journals - `add_reference_line(ax, value, orientation='horizontal')` -- horizontal or vertical reference line with optional label **Axes, legend, panel labels (3)** - `remove_spines(ax)` -- removes the top and right spines - `setup_legend(ax, **kw)` -- frameless legend in Title Case - `add_panel_labels(axes, *, journal=None, prefix='', suffix='', overlay_bg=None)` -- A / B / C panel labels in the case and size each journal expects, with per-journal overrides **Multi-panel composition (4)** - `compose_grid(layout, figsize='full_width', *, share=False, panel_labels='auto', ...)` -- rectangular mosaic layouts (`"ABC"`, `"AB;CD"`, `"AAB\nCDB"`); raises `SSCIPlotsMosaicError` for non-rectangular shapes - `small_multiples(n_rows, n_cols, *, share='all', panel_labels='auto', ...)` -- N x M facet grid with shared axes by default - `add_inset(ax, bbox=(0.55, 0.55, 0.4, 0.4), *, xlim=None, ylim=None, zoom_indicator=False)` -- inset axes with an optional zoom rectangle and connector lines - `compose_subfigures(n_rows, n_cols, *, height_ratios=None)` -- stacked sub-figure regions for cases where each region needs its own colorbar or legend **Cross-panel helpers (3)** - `add_marginal_hist(ax, x, y, *, kind='hist'|'kde', size='20%')` -- marginal histograms or KDEs along a scatter - `add_shared_colorbar(fig, mappable, axes, *, location='right', shrink=0.7)` -- one colorbar shared across several axes - `add_shared_legend(fig, handles_or_axes, *, loc='lower center', ncol=None, region=None)` -- deduplicated legend attached to the figure rather than to a single axes </details> Reference docs (loaded by Claude Code on demand): - [`references/chart-type-guide.md`](references/chart-type-guide.md) -- index to the 55-chart catalog across psychology, economics, public health, sociology, political science, geography, and methodology - [`references/chart-types-core.md`](references/chart-types-core.md) -- bar, scatter, line with CI, box, violin, raincloud, and correlation heatmap - [`references/chart-types-models.md`](references/chart-types-models.md) -- forest, path / SEM, mediation, interaction, simple slopes, IV diagram, scree, profile / LPA, growth curve, specification curve, causal DAG - [`references/chart-types-causal-econ.md`](references/chart-types-causal-econ.md) -- event-study, DID parallel trends, regression discontinuity, binscatter, and the multi-model coefficient plot - [`references/chart-types-applied.md`](references/chart-types-applied.md) -- Kaplan-Meier, ridgeline, choropleth, ROC and calibration, Bland-Altman, mobility transition matrix, marginal effects - [`references/chart-types-quick-reference.md`](references/chart-types-quick-reference.md) -- 11 additional chart types (histogram, KDE, swarm, factor loading, Johnson-Neyman, CONSORT, lollipop, slope / bump, dumbbell, population pyramid, tornado) - [`references/multi-panel.md`](references/multi-panel.md) -- how to choose between 1xN, 2xN, mosaic, and asymmetric layouts; shared colorbar and legend; inset zoom; marginal histograms - [`references/color-system.md`](references/color-system.md) -- choosing a palette, evaluating color-blind safety across all 21 palettes, and using saturated-plus-gray pairs for emphasis - [`references/complexity-elements-catalog.md`](references/complexity-elements-catalog.md) -- 30 small annotation elements (inline stats, reference lines, at-risk tables, and so on) with discipline defaults and a back-reference to the 18 example figures - [`references/apa-figure-standards.md`](references/apa-figure-standards.md) -- APA 7 figure rules, including conventions for mediation diagrams, raincloud Note requirements, in-figure annotations, and a cross-walk between APA 7 and journal house styles - [`references/journal-presets.md`](references/journal-presets.md) -- per-publisher specs for all 12 presets - [`references/statistical-annotations.md`](references/statistical-annotations.md) -- significance brackets, effect sizes, and inline statistics conventions ## Use as a Python library (optional) The skill layer above only handles discovery for Claude Code. To call SSCI-Plots directly from a Jupyter notebook, script, or another tool: ```bash git clone https://github.com/O0000-code/SSCI-Plots.git cd SSCI-Plots pip install matplotlib numpy pillow ``` ```python from scripts.ssci_style import apply_style, get_palette, compose_grid, save_figure apply_style(journal="lancet") # configures matplotlib + loads the Lancet palette fig, axes = compose_grid("AB;CD", figsize="full_width") # ... plot into axes['A'] / axes['B'] / axes['C'] / axes['D'] save_figure(fig, "fig_2") # writes fig_2.pdf and fig_2.png ``` See `SKILL.md` for the full helper API. A `pip install` package is on the roadmap. A companion skill, `deep-literature-search`, is in progress and should land around the same time. ## Contributing This is a hobby project. Issue response is typically a week or two; PR review can be slower. Bug reports with a minimal reproduction are the most useful. See [`CONTRIBUTING.md`](CONTRIBUTING.md). ## License MIT. See [`LICENSE`](LICENSE).

README des Repositorys wird geladen…

Skills in diesem Repository

Datenwissenschaftler

1 Berufskategorien · 100% klassifiziert

Skill

Beruf

Beschreibung

Aktualisiert

ssci-plots

Datenwissenschaftler

Publication-ready statistical figures for SSCI/SCI journals across psychology, economics, public health, sociology, political science, geography, methodology. Validated matplotlib styling, CVD-safe palettes (Tol, Okabe-Ito, ggsci, aesthetic sets), APA 7…

Quellsprache: Englisch

2026-05-17

Es werden 1 von 1 gesammelten Skills angezeigt.

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/O0000-code/SSCI-Plots.