feat: add scatter-color-groups implementation (9 libraries)

plots/altair/point/scatter-color-groups/default.py

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+"""
+scatter-color-groups: Scatter Plot with Color Groups
+Library: altair
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - create iris-like dataset with three species groups
+np.random.seed(42)
+
+# Generate data for three groups with different cluster centers
+n_per_group = 50
+
+# Setosa: smaller sepal length, larger sepal width
+setosa_x = np.random.normal(5.0, 0.4, n_per_group)
+setosa_y = np.random.normal(3.4, 0.4, n_per_group)
+
+# Versicolor: medium values
+versicolor_x = np.random.normal(6.0, 0.5, n_per_group)
+versicolor_y = np.random.normal(2.8, 0.3, n_per_group)
+
+# Virginica: larger sepal length, medium sepal width
+virginica_x = np.random.normal(6.6, 0.6, n_per_group)
+virginica_y = np.random.normal(3.0, 0.35, n_per_group)
+
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate([setosa_x, versicolor_x, virginica_x]),
+        "sepal_width": np.concatenate([setosa_y, versicolor_y, virginica_y]),
+        "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+    }
+)
+
+# Define custom color palette (colorblind-safe)
+color_scale = alt.Scale(domain=["setosa", "versicolor", "virginica"], range=["#306998", "#FFD43B", "#059669"])
+
+# Create scatter plot with color groups
+chart = (
+    alt.Chart(data)
+    .mark_point(size=100, opacity=0.7)
+    .encode(
+        x=alt.X("sepal_length:Q", title="Sepal Length (cm)"),
+        y=alt.Y("sepal_width:Q", title="Sepal Width (cm)"),
+        color=alt.Color("species:N", title="Species", scale=color_scale),
+        tooltip=["species:N", "sepal_length:Q", "sepal_width:Q"],
+    )
+    .properties(width=1600, height=900, title="Scatter Plot with Color Groups")
+)
+
+# Save as PNG (1600 × 900 × 3 = 4800 × 2700 px)
+chart.save("plot.png", scale_factor=3.0)

plots/bokeh/scatter/scatter-color-groups/default.py

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+"""
+scatter-color-groups: Scatter Plot with Color Groups
+Library: bokeh
+"""
+
+import numpy as np
+import pandas as pd
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+# Data - Iris-like dataset
+np.random.seed(42)
+n_per_group = 50
+
+data = pd.DataFrame({
+    "sepal_length": np.concatenate([
+        np.random.normal(5.0, 0.35, n_per_group),
+        np.random.normal(5.9, 0.50, n_per_group),
+        np.random.normal(6.6, 0.60, n_per_group),
+    ]),
+    "sepal_width": np.concatenate([
+        np.random.normal(3.4, 0.38, n_per_group),
+        np.random.normal(2.8, 0.30, n_per_group),
+        np.random.normal(3.0, 0.30, n_per_group),
+    ]),
+    "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+})
+
+# Color palette (from style guide)
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]
+species_list = data["species"].unique().tolist()
+color_map = {species: colors[i] for i, species in enumerate(species_list)}
+data["color"] = data["species"].map(color_map)
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="Scatter Plot with Color Groups",
+    x_axis_label="Sepal Length (cm)",
+    y_axis_label="Sepal Width (cm)",
+)
+
+# Plot each group separately for legend
+for species in species_list:
+    species_data = data[data["species"] == species]
+    source = ColumnDataSource(data={"x": species_data["sepal_length"], "y": species_data["sepal_width"]})
+    p.scatter(
+        x="x", y="y", source=source, size=12, alpha=0.7, color=color_map[species], legend_label=species.capitalize()
+    )
+
+# Styling
+p.title.text_font_size = "20pt"
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+p.legend.label_text_font_size = "16pt"
+p.legend.location = "top_right"
+p.grid.grid_line_alpha = 0.3
+
+# Save
+export_png(p, filename="plot.png")

plots/highcharts/scatter/scatter-color-groups/default.py

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+"""
+scatter-color-groups: Scatter Plot with Color Groups
+Library: highcharts
+
+Note: Highcharts requires a license for commercial use.
+"""
+
+import json
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+from highcharts_core.chart import Chart
+from highcharts_core.options import HighchartsOptions
+from highcharts_core.options.series.scatter import ScatterSeries
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
+
+# Color palette from style guide
+COLORS = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]
+
+# Data - Iris dataset (sepal_length, sepal_width) by species
+# fmt: off
+iris_data = {
+    "setosa": [
+        (5.1, 3.5), (4.9, 3.0), (4.7, 3.2), (4.6, 3.1), (5.0, 3.6), (5.4, 3.9), (4.6, 3.4), (5.0, 3.4),
+        (4.4, 2.9), (4.9, 3.1), (5.4, 3.7), (4.8, 3.4), (4.8, 3.0), (4.3, 3.0), (5.8, 4.0), (5.7, 4.4),
+        (5.4, 3.9), (5.1, 3.5), (5.7, 3.8), (5.1, 3.8), (5.4, 3.4), (5.1, 3.7), (4.6, 3.6), (5.1, 3.3),
+        (4.8, 3.4), (5.0, 3.0), (5.0, 3.4), (5.2, 3.5), (5.2, 3.4), (4.7, 3.2), (4.8, 3.1), (5.4, 3.4),
+        (5.2, 4.1), (5.5, 4.2), (4.9, 3.1), (5.0, 3.2), (5.5, 3.5), (4.9, 3.6), (4.4, 3.0), (5.1, 3.4),
+        (5.0, 3.5), (4.5, 2.3), (4.4, 3.2), (5.0, 3.5), (5.1, 3.8), (4.8, 3.0), (5.1, 3.8), (4.6, 3.2),
+        (5.3, 3.7), (5.0, 3.3),
+    ],
+    "versicolor": [
+        (7.0, 3.2), (6.4, 3.2), (6.9, 3.1), (5.5, 2.3), (6.5, 2.8), (5.7, 2.8), (6.3, 3.3), (4.9, 2.4),
+        (6.6, 2.9), (5.2, 2.7), (5.0, 2.0), (5.9, 3.0), (6.0, 2.2), (6.1, 2.9), (5.6, 2.9), (6.7, 3.1),
+        (5.6, 3.0), (5.8, 2.7), (6.2, 2.2), (5.6, 2.5), (5.9, 3.2), (6.1, 2.8), (6.3, 2.5), (6.1, 2.8),
+        (6.4, 2.9), (6.6, 3.0), (6.8, 2.8), (6.7, 3.0), (6.0, 2.9), (5.7, 2.6), (5.5, 2.4), (5.5, 2.4),
+        (5.8, 2.7), (6.0, 2.7), (5.4, 3.0), (6.0, 3.4), (6.7, 3.1), (6.3, 2.3), (5.6, 3.0), (5.5, 2.5),
+        (5.5, 2.6), (6.1, 3.0), (5.8, 2.6), (5.0, 2.3), (5.6, 2.7), (5.7, 3.0), (5.7, 2.9), (6.2, 2.9),
+        (5.1, 2.5), (5.7, 2.8),
+    ],
+    "virginica": [
+        (6.3, 3.3), (5.8, 2.7), (7.1, 3.0), (6.3, 2.9), (6.5, 3.0), (7.6, 3.0), (4.9, 2.5), (7.3, 2.9),
+        (6.7, 2.5), (7.2, 3.6), (6.5, 3.2), (6.4, 2.7), (6.8, 3.0), (5.7, 2.5), (5.8, 2.8), (6.4, 3.2),
+        (6.5, 3.0), (7.7, 3.8), (7.7, 2.6), (6.0, 2.2), (6.9, 3.2), (5.6, 2.8), (7.7, 2.8), (6.3, 2.7),
+        (6.7, 3.3), (7.2, 3.2), (6.2, 2.8), (6.1, 3.0), (6.4, 2.8), (7.2, 3.0), (7.4, 2.8), (7.9, 3.8),
+        (6.4, 2.8), (6.3, 2.8), (6.1, 2.6), (7.7, 3.0), (6.3, 3.4), (6.4, 3.1), (6.0, 3.0), (6.9, 3.1),
+        (6.7, 3.1), (6.9, 3.1), (5.8, 2.7), (6.8, 3.2), (6.7, 3.3), (6.7, 3.0), (6.3, 2.5), (6.5, 3.0),
+        (6.2, 3.4), (5.9, 3.0),
+    ],
+}
+# fmt: on
+groups = list(iris_data.keys())
+
+# Create chart with container ID for rendering
+chart = Chart(container="container")
+chart.options = HighchartsOptions()
+
+# Chart configuration - 4800 x 2700 px per style guide
+chart.options.chart = {"type": "scatter", "width": 4800, "height": 2700, "backgroundColor": "#ffffff"}
+
+# Title
+chart.options.title = {
+    "text": "Iris Dataset: Sepal Dimensions by Species",
+    "style": {"fontSize": "48px", "fontWeight": "bold"},
+}
+
+# X-axis configuration
+chart.options.x_axis = {
+    "title": {"text": "Sepal Length (cm)", "style": {"fontSize": "36px"}},
+    "labels": {"style": {"fontSize": "28px"}},
+    "gridLineWidth": 1,
+    "gridLineDashStyle": "Dot",
+    "gridLineColor": "rgba(0, 0, 0, 0.15)",
+}
+
+# Y-axis configuration
+chart.options.y_axis = {
+    "title": {"text": "Sepal Width (cm)", "style": {"fontSize": "36px"}},
+    "labels": {"style": {"fontSize": "28px"}},
+    "gridLineWidth": 1,
+    "gridLineDashStyle": "Dot",
+    "gridLineColor": "rgba(0, 0, 0, 0.15)",
+}
+
+# Plot options for scatter
+chart.options.plot_options = {
+    "scatter": {
+        "marker": {"radius": 12, "states": {"hover": {"enabled": True, "lineColor": "rgb(100,100,100)"}}},
+        "states": {"hover": {"marker": {"enabled": False}}},
+    }
+}
+
+# Add a series for each group with distinct colors
+for i, group in enumerate(groups):
+    series = ScatterSeries()
+    series.name = group.capitalize()
+    series.data = iris_data[group]
+    series.color = COLORS[i % len(COLORS)]
+    chart.add_series(series)
+
+# Legend configuration
+chart.options.legend = {
+    "enabled": True,
+    "align": "right",
+    "verticalAlign": "middle",
+    "layout": "vertical",
+    "itemStyle": {"fontSize": "28px"},
+}
+
+# Tooltip configuration
+chart.options.tooltip = {
+    "headerFormat": "<b>{series.name}</b><br>",
+    "pointFormat": "Sepal Length: {point.x} cm<br>Sepal Width: {point.y} cm",
+    "style": {"fontSize": "24px"},
+}
+
+# Disable credits
+chart.options.credits = {"enabled": False}
+
+# Export to PNG via Selenium screenshot
+# Download Highcharts JS (required for headless Chrome which can't load CDN)
+highcharts_url = "https://code.highcharts.com/highcharts.js"
+with urllib.request.urlopen(highcharts_url, timeout=30) as response:
+    highcharts_js = response.read().decode("utf-8")
+
+# Get chart options as JSON
+opts_json = json.dumps(chart.options.to_dict())
+
+html_content = f"""<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="utf-8">
+    <script>{highcharts_js}</script>
+</head>
+<body style="margin:0; padding:0; overflow:hidden;">
+    <div id="container" style="width: 4800px; height: 2700px;"></div>
+    <script>
+        Highcharts.chart('container', {opts_json});
+    </script>
+</body>
+</html>"""
+
+# Write temp HTML and take screenshot
+with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
+    f.write(html_content)
+    temp_path = f.name
+
+chrome_options = Options()
+chrome_options.add_argument("--headless")
+chrome_options.add_argument("--no-sandbox")
+chrome_options.add_argument("--disable-dev-shm-usage")
+chrome_options.add_argument("--disable-gpu")
+chrome_options.add_argument("--window-size=4800,2800")
+
+driver = webdriver.Chrome(options=chrome_options)
+driver.get(f"file://{temp_path}")
+time.sleep(5)  # Wait for chart to render
+driver.save_screenshot("plot.png")
+driver.quit()
+
+Path(temp_path).unlink()  # Clean up temp file
+print("Plot saved to plot.png")

plots/letsplot/point/scatter-color-groups/default.py

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+"""
+scatter-color-groups: Scatter Plot with Color Groups
+Library: letsplot
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_text,
+    geom_point,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_color_manual,
+    theme,
+    theme_minimal,
+)
+
+
+LetsPlot.setup_html()
+
+# Data - Generate iris-like dataset
+np.random.seed(42)
+n_per_species = 50
+
+# Setosa: smaller sepals
+setosa_length = np.random.normal(5.0, 0.35, n_per_species)
+setosa_width = np.random.normal(3.4, 0.38, n_per_species)
+
+# Versicolor: medium sepals
+versicolor_length = np.random.normal(5.9, 0.52, n_per_species)
+versicolor_width = np.random.normal(2.8, 0.31, n_per_species)
+
+# Virginica: larger sepals
+virginica_length = np.random.normal(6.6, 0.64, n_per_species)
+virginica_width = np.random.normal(3.0, 0.32, n_per_species)
+
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate([setosa_length, versicolor_length, virginica_length]),
+        "sepal_width": np.concatenate([setosa_width, versicolor_width, virginica_width]),
+        "species": ["Setosa"] * n_per_species + ["Versicolor"] * n_per_species + ["Virginica"] * n_per_species,
+    }
+)
+
+# Custom color palette (colorblind-safe)
+colors = ["#306998", "#FFD43B", "#059669"]
+
+# Plot
+plot = (
+    ggplot(data, aes(x="sepal_length", y="sepal_width", color="species"))
+    + geom_point(size=4, alpha=0.7)
+    + scale_color_manual(values=colors)
+    + labs(x="Sepal Length (cm)", y="Sepal Width (cm)", title="Iris Sepal Dimensions by Species", color="Species")
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=20),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=16),
+        legend_text=element_text(size=16),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save (scale 3x to get 4800 x 2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)