scientific-advanced-visualization

Scientific Advanced Visualization

Provides interactive visualization, 3D rendering, publication-quality figures, and animation for scientific data.

When to Use

• When drawing interactive 3D scatter plots and surface plots
• When creating dynamic visualizations with Plotly / Altair
• When visualizing multivariate data with Parallel Coordinates / Radar
• When creating publication-quality (Nature/Science style) figures for journal submission
• When creating animations of time series and simulation results
• When creating composite figures with multiple panels

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Quick Start

1. Plotly Interactive 3D

import numpy as np
import pandas as pd


def plotly_3d_scatter(df, x, y, z, color=None, size=None,
                      title="3D Scatter Plot"):
    """
    Plotly 3D 散布図。

    Parameters:
        df: pd.DataFrame — データ
        x, y, z: str — 軸カラム名
        color: str | None — 色分けカラム
        size: str | None — サイズカラム
        title: str — タイトル
    """
    import plotly.express as px

    fig = px.scatter_3d(df, x=x, y=y, z=z, color=color, size=size,
                        title=title, opacity=0.7)
    fig.update_layout(
        scene=dict(
            xaxis_title=x, yaxis_title=y, zaxis_title=z),
        width=900, height=700)

    path = "3d_scatter.html"
    fig.write_html(path)
    print(f"3D Scatter: {len(df)} points → {path}")
    return fig


def plotly_surface(X_grid, Y_grid, Z_grid, title="Surface Plot"):
    """
    Plotly 3D サーフェスプロット。

    Parameters:
        X_grid, Y_grid, Z_grid: np.ndarray — メッシュグリッド
        title: str — タイトル
    """
    import plotly.graph_objects as go

    fig = go.Figure(data=[go.Surface(x=X_grid, y=Y_grid, z=Z_grid,
                                     colorscale="Viridis")])
    fig.update_layout(
        title=title,
        scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z"),
        width=900, height=700)

    path = "surface_plot.html"
    fig.write_html(path)
    print(f"Surface: {Z_grid.shape} grid → {path}")
    return fig

2. Altair declarative visualization

def altair_faceted_chart(df, x, y, color, facet_col=None,
                         chart_type="scatter"):
    """
    Altair 宣言的ファセット付きチャート。

    Parameters:
        df: pd.DataFrame — データ
        x, y: str — 軸カラム
        color: str — 色分けカラム
        facet_col: str | None — ファセットカラム
        chart_type: str — "scatter" / "line" / "bar" / "box"
    """
    import altair as alt

    base = alt.Chart(df).encode(
        x=alt.X(x, scale=alt.Scale(zero=False)),
        y=alt.Y(y, scale=alt.Scale(zero=False)),
        color=color)

    if chart_type == "scatter":
        chart = base.mark_circle(size=60, opacity=0.7)
    elif chart_type == "line":
        chart = base.mark_line()
    elif chart_type == "bar":
        chart = base.mark_bar()
    elif chart_type == "box":
        chart = base.mark_boxplot()
    else:
        chart = base.mark_circle()

    if facet_col:
        chart = chart.facet(facet_col, columns=3)

    chart = chart.properties(width=300, height=250).interactive()

    path = "altair_chart.html"
    chart.save(path)
    print(f"Altair {chart_type}: {len(df)} rows → {path}")
    return chart

3. Multivariate visualization

def parallel_coordinates_plot(df, class_col, features=None,
                              title="Parallel Coordinates"):
    """
    Parallel Coordinates プロット。

    Parameters:
        df: pd.DataFrame — データ
        class_col: str — 分類カラム
        features: list[str] | None — 表示特徴量 (None で全数値)
        title: str — タイトル
    """
    import plotly.express as px

    if features is None:
        features = df.select_dtypes(include=[np.number]).columns.tolist()
        if class_col in features:
            features.remove(class_col)

    fig = px.parallel_coordinates(
        df, color=class_col, dimensions=features,
        title=title, color_continuous_scale=px.colors.diverging.Tealrose)

    fig.update_layout(width=1000, height=500)

    path = "parallel_coordinates.html"
    fig.write_html(path)
    print(f"Parallel Coordinates: {len(features)} dims → {path}")
    return fig


def radar_chart(categories, values_dict, title="Radar Chart"):
    """
    Radar (Spider) チャート — 複数グループ比較。

    Parameters:
        categories: list[str] — 軸ラベル
        values_dict: dict[str, list[float]] — {グループ名: 値リスト}
        title: str — タイトル
    """
    import plotly.graph_objects as go

    fig = go.Figure()

    for name, vals in values_dict.items():
        fig.add_trace(go.Scatterpolar(
            r=vals + [vals[0]],
            theta=categories + [categories[0]],
            fill="toself", name=name, opacity=0.6))

    fig.update_layout(
        polar=dict(radialaxis=dict(visible=True)),
        title=title, width=600, height=500)

    path = "radar_chart.html"
    fig.write_html(path)
    print(f"Radar: {len(values_dict)} groups × {len(categories)} axes → {path}")
    return fig

4. Publication-quality figures (Nature/Science style)

def publication_figure(plot_func, figsize=(3.5, 2.8),
                       dpi=300, style="nature",
                       output="publication_fig.pdf"):
    """
    出版品質 (Nature/Science style) 図生成。

    Parameters:
        plot_func: callable — matplotlib 描画関数 (ax を引数に取る)
        figsize: tuple — 図サイズ (インチ, Nature 1 col = 3.5in)
        dpi: int — 解像度
        style: str — "nature" / "science" / "acs"
        output: str — 出力パス (.pdf / .svg / .png)
    """
    import matplotlib.pyplot as plt
    import matplotlib as mpl

    # Nature/Science スタイル設定
    style_params = {
        "nature": {
            "font.family": "Arial",
            "font.size": 7,
            "axes.linewidth": 0.5,
            "xtick.major.width": 0.5,
            "ytick.major.width": 0.5,
            "lines.linewidth": 1.0,
            "lines.markersize": 3,
        },
        "science": {
            "font.family": "Helvetica",
            "font.size": 8,
            "axes.linewidth": 0.6,
            "xtick.major.width": 0.6,
            "ytick.major.width": 0.6,
            "lines.linewidth": 1.2,
            "lines.markersize": 4,
        },
        "acs": {
            "font.family": "Arial",
            "font.size": 9,
            "axes.linewidth": 0.5,
            "xtick.major.width": 0.5,
            "ytick.major.width": 0.5,
            "lines.linewidth": 1.0,
            "lines.markersize": 4,
        }
    }

    with mpl.rc_context(style_params.get(style, style_params["nature"])):
        fig, ax = plt.subplots(figsize=figsize)
        plot_func(ax)
        ax.spines["top"].set_visible(False)
        ax.spines["right"].set_visible(False)
        plt.tight_layout()
        fig.savefig(output, dpi=dpi, bbox_inches="tight")
        plt.close()

    print(f"Publication figure ({style}): {figsize} @ {dpi}dpi → {output}")
    return output

5. Animation

def create_animation(data_frames, x_col, y_col, time_col,
                     title="Animation", fps=10):
    """
    Plotly アニメーション。

    Parameters:
        data_frames: pd.DataFrame — 時間列を含むデータ
        x_col, y_col: str — 軸カラム
        time_col: str — 時間 / フレームカラム
        title: str — タイトル
        fps: int — フレームレート
    """
    import plotly.express as px

    fig = px.scatter(data_frames, x=x_col, y=y_col,
                     animation_frame=time_col,
                     title=title, opacity=0.7,
                     range_x=[data_frames[x_col].min() * 0.9,
                              data_frames[x_col].max() * 1.1],
                     range_y=[data_frames[y_col].min() * 0.9,
                              data_frames[y_col].max() * 1.1])

    fig.update_layout(
        width=800, height=600,
        updatemenus=[dict(type="buttons",
                          buttons=[dict(label="▶ Play",
                                        method="animate",
                                        args=[None, {"frame": {"duration": 1000 // fps}}])])])

    path = "animation.html"
    fig.write_html(path)
    print(f"Animation: {data_frames[time_col].nunique()} frames @ {fps}fps → {path}")
    return fig

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Pipeline integration

eda-correlation → advanced-visualization → presentation-design
  (探索的解析)      (高度可視化)             (プレゼンテーション)
       │                  │                       ↓
 pca-tsne ───────────────┘           interactive-dashboard
  (次元削減)                           (ダッシュボード)

Pipeline output

File	Description	Next skill
3d_scatter.html	Interactive 3D scatter plot	→ dashboard
publication_fig.pdf	Publication-quality figures	→ presentation
parallel_coordinates.html	Multivariate visualization	→ reporting
animation.html	Animation	→ presentation

ToolUniverse integration

TU Key	Tool name	Integration details
biotools	bio.tools	Advanced visualization tool search