feat(highcharts): implement survival-kaplan-meier (#2491)

## Implementation: `survival-kaplan-meier` - highcharts

Implements the **highcharts** version of `survival-kaplan-meier`.

**File:** `plots/survival-kaplan-meier/implementations/highcharts.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20584330823)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/survival-kaplan-meier/implementations/highcharts.py

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+""" pyplots.ai
+survival-kaplan-meier: Kaplan-Meier Survival Plot
+Library: highcharts unknown | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-29
+"""
+
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+import numpy as np
+from highcharts_core.chart import Chart
+from highcharts_core.options import HighchartsOptions
+from highcharts_core.options.series.area import AreaRangeSeries, LineSeries
+from highcharts_core.options.series.scatter import ScatterSeries
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
+
+# Generate synthetic clinical trial survival data
+np.random.seed(42)
+n_patients = 120
+
+# Group A: Standard treatment - Weibull distribution
+times_a = np.random.weibull(1.5, n_patients // 2) * 24
+events_a = np.random.binomial(1, 0.7, n_patients // 2)  # 70% event rate
+max_time = 36
+times_a = np.minimum(times_a, max_time)  # Censored at study end
+
+# Group B: New treatment - better survival
+times_b = np.random.weibull(1.8, n_patients // 2) * 30
+events_b = np.random.binomial(1, 0.55, n_patients // 2)  # 55% event rate
+times_b = np.minimum(times_b, max_time)
+
+# Kaplan-Meier calculation for Group A (inline, no function)
+sorted_idx_a = np.argsort(times_a)
+times_a_sorted = times_a[sorted_idx_a]
+events_a_sorted = events_a[sorted_idx_a]
+unique_times_a = np.unique(times_a_sorted)
+n_at_risk_a = len(times_a_sorted)
+survival_a = 1.0
+var_sum_a = 0.0
+km_times_a = [0.0]
+km_survival_a = [1.0]
+km_lower_a = [1.0]
+km_upper_a = [1.0]
+censored_times_a = []
+censored_survival_a = []
+
+for t in unique_times_a:
+    mask = times_a_sorted == t
+    d = events_a_sorted[mask].sum()
+    c = (~events_a_sorted[mask].astype(bool)).sum()
+    if d > 0:
+        survival_a *= 1 - d / n_at_risk_a
+        if n_at_risk_a > d:
+            var_sum_a += d / (n_at_risk_a * (n_at_risk_a - d))
+    se = survival_a * np.sqrt(var_sum_a) if var_sum_a > 0 else 0
+    lower = max(0, survival_a - 1.96 * se)
+    upper = min(1, survival_a + 1.96 * se)
+    km_times_a.append(float(t))
+    km_survival_a.append(float(survival_a))
+    km_lower_a.append(float(lower))
+    km_upper_a.append(float(upper))
+    if c > 0:
+        censored_times_a.append(float(t))
+        censored_survival_a.append(float(survival_a))
+    n_at_risk_a -= d + c
+
+# Kaplan-Meier calculation for Group B (inline, no function)
+sorted_idx_b = np.argsort(times_b)
+times_b_sorted = times_b[sorted_idx_b]
+events_b_sorted = events_b[sorted_idx_b]
+unique_times_b = np.unique(times_b_sorted)
+n_at_risk_b = len(times_b_sorted)
+survival_b = 1.0
+var_sum_b = 0.0
+km_times_b = [0.0]
+km_survival_b = [1.0]
+km_lower_b = [1.0]
+km_upper_b = [1.0]
+censored_times_b = []
+censored_survival_b = []
+
+for t in unique_times_b:
+    mask = times_b_sorted == t
+    d = events_b_sorted[mask].sum()
+    c = (~events_b_sorted[mask].astype(bool)).sum()
+    if d > 0:
+        survival_b *= 1 - d / n_at_risk_b
+        if n_at_risk_b > d:
+            var_sum_b += d / (n_at_risk_b * (n_at_risk_b - d))
+    se = survival_b * np.sqrt(var_sum_b) if var_sum_b > 0 else 0
+    lower = max(0, survival_b - 1.96 * se)
+    upper = min(1, survival_b + 1.96 * se)
+    km_times_b.append(float(t))
+    km_survival_b.append(float(survival_b))
+    km_lower_b.append(float(lower))
+    km_upper_b.append(float(upper))
+    if c > 0:
+        censored_times_b.append(float(t))
+        censored_survival_b.append(float(survival_b))
+    n_at_risk_b -= d + c
+
+# Create chart
+chart = Chart(container="container")
+chart.options = HighchartsOptions()
+
+# Chart configuration
+chart.options.chart = {
+    "width": 4800,
+    "height": 2700,
+    "backgroundColor": "#ffffff",
+    "marginBottom": 250,
+    "marginLeft": 220,
+    "marginRight": 150,
+}
+
+# Title
+chart.options.title = {
+    "text": "survival-kaplan-meier · highcharts · pyplots.ai",
+    "style": {"fontSize": "72px", "fontWeight": "bold"},
+}
+
+# Subtitle
+chart.options.subtitle = {
+    "text": "Clinical Trial: Survival Probability Over Time",
+    "style": {"fontSize": "42px", "color": "#666666"},
+}
+
+# X-axis configuration
+chart.options.x_axis = {
+    "title": {"text": "Time (Months)", "style": {"fontSize": "48px"}, "margin": 30},
+    "labels": {"style": {"fontSize": "36px"}, "y": 40},
+    "min": 0,
+    "max": 40,
+    "tickInterval": 6,
+    "gridLineWidth": 1,
+    "gridLineColor": "#e0e0e0",
+    "gridLineDashStyle": "Dash",
+    "lineWidth": 2,
+    "lineColor": "#333333",
+}
+
+# Y-axis configuration
+chart.options.y_axis = {
+    "title": {"text": "Survival Probability", "style": {"fontSize": "48px"}},
+    "labels": {"style": {"fontSize": "36px"}},
+    "min": 0,
+    "max": 1.0,
+    "tickInterval": 0.2,
+    "gridLineWidth": 1,
+    "gridLineColor": "#e0e0e0",
+    "gridLineDashStyle": "Dash",
+}
+
+# Legend configuration - positioned closer to the data (left side, middle)
+chart.options.legend = {
+    "enabled": True,
+    "align": "left",
+    "verticalAlign": "middle",
+    "layout": "vertical",
+    "x": 250,
+    "y": -200,
+    "itemStyle": {"fontSize": "36px"},
+    "itemMarginBottom": 20,
+    "backgroundColor": "rgba(255, 255, 255, 0.85)",
+    "borderRadius": 5,
+    "padding": 15,
+}
+
+# Colors
+color_a = "#306998"  # Python Blue
+color_b = "#FFD43B"  # Python Yellow
+
+# Plot options for step function
+chart.options.plot_options = {
+    "series": {"animation": False},
+    "line": {"step": "left", "lineWidth": 5, "marker": {"enabled": False}},
+    "arearange": {"step": "left", "fillOpacity": 0.2, "lineWidth": 0, "marker": {"enabled": False}},
+    "scatter": {"marker": {"symbol": "diamond", "radius": 8, "enabled": True}, "enableMouseTracking": True},
+}
+
+# --- Group A: Confidence Interval ---
+ci_data_a = [[km_times_a[i], km_lower_a[i], km_upper_a[i]] for i in range(len(km_times_a))]
+ci_series_a = AreaRangeSeries()
+ci_series_a.data = ci_data_a
+ci_series_a.name = "95% CI (Standard)"
+ci_series_a.color = color_a
+ci_series_a.show_in_legend = False
+chart.add_series(ci_series_a)
+
+# --- Group A: Survival Curve ---
+curve_data_a = [[km_times_a[i], km_survival_a[i]] for i in range(len(km_times_a))]
+curve_series_a = LineSeries()
+curve_series_a.data = curve_data_a
+curve_series_a.name = "Standard Treatment"
+curve_series_a.color = color_a
+chart.add_series(curve_series_a)
+
+# --- Group A: Censored Marks (tick marks on curve) ---
+if censored_times_a:
+    censor_data_a = [{"x": censored_times_a[i], "y": censored_survival_a[i]} for i in range(len(censored_times_a))]
+    censor_series_a = ScatterSeries()
+    censor_series_a.data = censor_data_a
+    censor_series_a.name = "Censored (Standard)"
+    censor_series_a.color = color_a
+    censor_series_a.marker = {
+        "symbol": "diamond",
+        "lineWidth": 3,
+        "lineColor": color_a,
+        "fillColor": "#ffffff",
+        "radius": 8,
+    }
+    censor_series_a.show_in_legend = False
+    chart.add_series(censor_series_a)
+
+# --- Group B: Confidence Interval ---
+ci_data_b = [[km_times_b[i], km_lower_b[i], km_upper_b[i]] for i in range(len(km_times_b))]
+ci_series_b = AreaRangeSeries()
+ci_series_b.data = ci_data_b
+ci_series_b.name = "95% CI (New)"
+ci_series_b.color = color_b
+ci_series_b.show_in_legend = False
+chart.add_series(ci_series_b)
+
+# --- Group B: Survival Curve ---
+curve_data_b = [[km_times_b[i], km_survival_b[i]] for i in range(len(km_times_b))]
+curve_series_b = LineSeries()
+curve_series_b.data = curve_data_b
+curve_series_b.name = "New Treatment"
+curve_series_b.color = color_b
+chart.add_series(curve_series_b)
+
+# --- Group B: Censored Marks (tick marks on curve) ---
+if censored_times_b:
+    censor_data_b = [{"x": censored_times_b[i], "y": censored_survival_b[i]} for i in range(len(censored_times_b))]
+    censor_series_b = ScatterSeries()
+    censor_series_b.data = censor_data_b
+    censor_series_b.name = "Censored (New)"
+    censor_series_b.color = color_b
+    censor_series_b.marker = {
+        "symbol": "diamond",
+        "lineWidth": 3,
+        "lineColor": color_b,
+        "fillColor": "#ffffff",
+        "radius": 8,
+    }
+    censor_series_b.show_in_legend = False
+    chart.add_series(censor_series_b)
+
+# Download Highcharts JS modules
+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")
+
+highcharts_more_url = "https://code.highcharts.com/highcharts-more.js"
+with urllib.request.urlopen(highcharts_more_url, timeout=30) as response:
+    highcharts_more_js = response.read().decode("utf-8")
+
+# Generate HTML with inline scripts
+html_str = chart.to_js_literal()
+html_content = f"""<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="utf-8">
+    <script>{highcharts_js}</script>
+    <script>{highcharts_more_js}</script>
+</head>
+<body style="margin:0;">
+    <div id="container" style="width: 4800px; height: 2700px;"></div>
+    <script>{html_str}</script>
+</body>
+</html>"""
+
+# Save HTML file for interactive viewing
+with open("plot.html", "w", encoding="utf-8") as f:
+    f.write(html_content)
+
+# 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
+
+# Configure Chrome for headless screenshot
+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=4800,2900")
+
+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

plots/survival-kaplan-meier/metadata/highcharts.yaml

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+library: highcharts
+specification_id: survival-kaplan-meier
+created: '2025-12-29T22:57:27Z'
+updated: '2025-12-29T23:12:17Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584330823
+issue: 0
+python_version: 3.13.11
+library_version: unknown
+preview_url: https://storage.googleapis.com/pyplots-images/plots/survival-kaplan-meier/highcharts/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/survival-kaplan-meier/highcharts/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/survival-kaplan-meier/highcharts/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Inline Kaplan-Meier calculation with Greenwood formula for CI (KISS principle
+    followed)
+  - Excellent colorblind-safe blue/yellow color scheme with good contrast
+  - 'Proper step function implementation using Highcharts step: left option'
+  - Confidence interval bands correctly implemented with AreaRangeSeries and appropriate
+    transparency
+  - Censored observation markers now visible as diamond shapes on the curves
+  - Realistic clinical trial data with meaningful difference between treatment groups
+  weaknesses:
+  - Legend positioned in middle-left area potentially overlapping with data visualization
+    region
+  - Censored markers could be slightly larger for better visibility at the high resolution