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feat(plotly): implement chernoff-basic (#6830)
## Implementation: `chernoff-basic` - python/plotly Implements the **python/plotly** version of `chernoff-basic`. **File:** `plots/chernoff-basic/implementations/python/plotly.py` **Parent Issue:** #3003 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25925129018)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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‎plots/chernoff-basic/implementations/python/plotly.py‎

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@@ -1,14 +1,27 @@
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""" pyplots.ai
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""" anyplot.ai
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chernoff-basic: Chernoff Faces for Multivariate Data
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Library: plotly 6.5.0 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-31
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Library: plotly 6.7.0 | Python 3.13.13
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Quality: 92/100 | Updated: 2026-05-15
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"""
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import os
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import numpy as np
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import plotly.graph_objects as go
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from sklearn.datasets import load_iris
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# Theme tokens
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THEME = os.getenv("ANYPLOT_THEME", "light")
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PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
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ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
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INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
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INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
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GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
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# Okabe-Ito palette (first series is always #009E73)
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OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
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# Data - use Iris dataset with 4 measurements per flower
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np.random.seed(42)
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iris = load_iris()
@@ -18,7 +31,6 @@
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target_names = iris.target_names
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# Select subset for clear visualization (5 samples per species = 15 faces)
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# Choose samples with maximum variation within species by selecting spread across range
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indices = []
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for species in range(3):
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species_mask = y == species
@@ -44,9 +56,6 @@
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# Normalize data to 0-1 range
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X_norm = (X_subset - X_subset.min(axis=0)) / (X_subset.max(axis=0) - X_subset.min(axis=0))
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# Colors for each species
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colors = ["#306998", "#FFD43B", "#2CA02C"] # Python Blue, Python Yellow, Green
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# Create figure
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fig = go.Figure()
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@@ -66,7 +75,7 @@
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cx = col * spacing + spacing / 2
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cy = (n_rows - 1 - row) * spacing + spacing / 2
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color = colors[species]
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color = OKABE_ITO[species]
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# Feature mapping with increased variation:
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# - sepal_length (data[0]) -> face width
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type="path",
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path="M " + " L ".join([f"{x},{y}" for x, y in zip(face_x, face_y)]) + " Z",
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fillcolor=color,
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line=dict(color="#333333", width=2),
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line=dict(color=INK_SOFT, width=2),
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opacity=0.35,
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)
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)
@@ -108,8 +117,8 @@
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dict(
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type="path",
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path="M " + " L ".join([f"{x},{y}" for x, y in zip(left_eye_x, left_eye_y)]) + " Z",
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fillcolor="white",
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line=dict(color="#333333", width=2),
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fillcolor=ELEVATED_BG,
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line=dict(color=INK_SOFT, width=2),
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)
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)
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@@ -121,8 +130,8 @@
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dict(
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type="path",
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path="M " + " L ".join([f"{x},{y}" for x, y in zip(pupil_x, pupil_y)]) + " Z",
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fillcolor="#333333",
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line=dict(color="#333333", width=1),
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fillcolor=INK,
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line=dict(color=INK, width=1),
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)
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)
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@@ -133,8 +142,8 @@
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dict(
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type="path",
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path="M " + " L ".join([f"{x},{y}" for x, y in zip(right_eye_x, right_eye_y)]) + " Z",
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fillcolor="white",
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line=dict(color="#333333", width=2),
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fillcolor=ELEVATED_BG,
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line=dict(color=INK_SOFT, width=2),
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)
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)
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@@ -145,8 +154,8 @@
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dict(
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type="path",
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path="M " + " L ".join([f"{x},{y}" for x, y in zip(pupil_x, pupil_y)]) + " Z",
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fillcolor="#333333",
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line=dict(color="#333333", width=1),
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fillcolor=INK,
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line=dict(color=INK, width=1),
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)
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)
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@@ -155,9 +164,7 @@
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nose_w = face_w * 0.1
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nose_y_center = cy
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nose_path = f"M {cx},{nose_y_center + nose_h * 0.5} L {cx - nose_w},{nose_y_center - nose_h * 0.5} L {cx + nose_w},{nose_y_center - nose_h * 0.5} Z"
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all_shapes.append(
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dict(type="path", path=nose_path, fillcolor="#333333", line=dict(color="#333333", width=1), opacity=0.5)
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)
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all_shapes.append(dict(type="path", path=nose_path, fillcolor=INK, line=dict(color=INK, width=1), opacity=0.5))
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# Mouth (curved line with much more variation)
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mouth_y_base = cy - face_h * 0.35
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# Parabolic curve: positive = smile, negative = frown
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mouth_y_vals = mouth_y_base + mouth_curve * (1 - ((mouth_x_vals - cx) / mouth_width) ** 2) * radius * 0.5
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mouth_path = "M " + " L ".join([f"{x},{y}" for x, y in zip(mouth_x_vals, mouth_y_vals)])
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all_shapes.append(dict(type="path", path=mouth_path, line=dict(color="#333333", width=3)))
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all_shapes.append(dict(type="path", path=mouth_path, line=dict(color=INK, width=3)))
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# Eyebrows (angled based on face width feature)
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brow_offset_y = eye_offset_y + eye_r + face_h * 0.1
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y0=cy + brow_offset_y - brow_angle * radius,
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x1=cx - eye_offset_x + brow_width,
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y1=cy + brow_offset_y + brow_angle * radius,
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line=dict(color="#333333", width=3),
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line=dict(color=INK, width=3),
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)
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)
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@@ -194,21 +201,21 @@
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y0=cy + brow_offset_y + brow_angle * radius,
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x1=cx + eye_offset_x + brow_width,
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y1=cy + brow_offset_y - brow_angle * radius,
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line=dict(color="#333333", width=3),
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line=dict(color=INK, width=3),
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)
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)
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# Add invisible scatter for axis setup
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fig.add_trace(go.Scatter(x=[0], y=[0], mode="markers", marker=dict(opacity=0), showlegend=False))
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# Add legend entries for species
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for i, (name, color) in enumerate(zip(target_names, colors)):
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for i, (name, color) in enumerate(zip(target_names, OKABE_ITO)):
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fig.add_trace(
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go.Scatter(
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x=[None],
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y=[None],
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mode="markers",
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marker=dict(size=20, color=color, opacity=0.5, line=dict(color="#333333", width=2)),
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marker=dict(size=20, color=color, opacity=0.5, line=dict(color=INK_SOFT, width=2)),
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name=name.capitalize(),
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showlegend=True,
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)
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y=row_y,
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text=f"<b>{name.capitalize()}</b>",
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showarrow=False,
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font=dict(size=18, color="#333333"),
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font=dict(size=18, color=INK),
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xanchor="right",
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)
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# Column labels (sample numbers)
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for col in range(n_cols):
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col_x = col * spacing + spacing / 2
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fig.add_annotation(
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x=col_x,
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y=n_rows * spacing + 0.2,
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text=f"Sample {col + 1}",
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showarrow=False,
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font=dict(size=16, color="#666666"),
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x=col_x, y=n_rows * spacing + 0.2, text=f"Sample {col + 1}", showarrow=False, font=dict(size=16, color=INK_SOFT)
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)
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# Feature mapping legend
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y=n_rows * spacing / 2,
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text=mapping_text,
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showarrow=False,
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font=dict(size=14, color="#333333"),
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font=dict(size=14, color=INK_SOFT),
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align="left",
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xanchor="left",
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bgcolor="rgba(255,255,255,0.9)",
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bordercolor="#cccccc",
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bgcolor=ELEVATED_BG,
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bordercolor=INK_SOFT,
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borderwidth=1,
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borderpad=10,
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)
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# Update layout - optimized for better space utilization
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fig.update_layout(
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title=dict(
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text="chernoff-basic · plotly · pyplots.ai", font=dict(size=28, color="#333333"), x=0.5, xanchor="center"
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),
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title=dict(text="chernoff-basic · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"),
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shapes=all_shapes,
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xaxis=dict(range=[-1.2, n_cols * spacing + 3.0], showgrid=False, zeroline=False, showticklabels=False, title=""),
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yaxis=dict(
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scaleanchor="x",
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scaleratio=1,
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),
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template="plotly_white",
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paper_bgcolor=PAGE_BG,
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plot_bgcolor=PAGE_BG,
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font=dict(color=INK),
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legend=dict(
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title=dict(text="<b>Species</b>", font=dict(size=18)),
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font=dict(size=16),
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title=dict(text="<b>Species</b>", font=dict(size=18, color=INK)),
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font=dict(size=16, color=INK_SOFT),
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x=1.02,
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y=0.98,
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xanchor="left",
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bgcolor="rgba(255,255,255,0.9)",
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bordercolor="#cccccc",
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bgcolor=ELEVATED_BG,
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bordercolor=INK_SOFT,
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borderwidth=1,
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),
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margin=dict(l=100, r=180, t=80, b=40),
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plot_bgcolor="white",
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)
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# Save as PNG
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fig.write_image("plot.png", width=1600, height=900, scale=3)
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fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
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# Save interactive HTML
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fig.write_html("plot.html", include_plotlyjs=True, full_html=True)
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fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")

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