A swimmer plot displays individual patient timelines as horizontal bars, commonly used in clinical oncology to visualize treatment duration, response events, and disease progression across a study cohort. Each bar represents one patient, typically sorted by treatment duration, with symbols or color changes marking key clinical events such as partial response, complete response, or progressive disease. This plot is standard in clinical trial publications and regulatory submissions for conveying patient-level longitudinal outcomes at a glance.
""" anyplot.ai
swimmer-clinical-timeline: Swimmer Plot for Clinical Trial Timelines
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 88/100 | Created: 2026-06-08
"""
import os
import sys
# Prevent implementations/python/matplotlib.py from shadowing the installed
# matplotlib package when the script is run from its own directory.
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if p not in ("", _here)]
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.lines import Line2D
# Theme tokens — Imprint palette + adaptive chrome
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
ANYPLOT_AMBER = "#DDCC77"
ARM_COLORS = {
"Arm A (Standard)": IMPRINT_PALETTE[0], # brand green — first series
"Arm B (Experimental)": IMPRINT_PALETTE[1], # lavender
}
EVENT_STYLES = {
"partial_response": {"marker": "^", "color": IMPRINT_PALETTE[3], "size": 90, "label": "Partial Response"},
"complete_response": {"marker": "*", "color": IMPRINT_PALETTE[2], "size": 150, "label": "Complete Response"},
"progressive_disease": {"marker": "D", "color": IMPRINT_PALETTE[4], "size": 75, "label": "Progression"},
"adverse_event": {"marker": "X", "color": ANYPLOT_AMBER, "size": 75, "label": "Adverse Event"},
}
# Data — Phase II oncology trial, 25 patients across two treatment arms
np.random.seed(42)
n_patients = 25
arms = ["Arm A (Standard)"] * 13 + ["Arm B (Experimental)"] * 12
durations = np.concatenate([np.random.uniform(4, 22, 13), np.random.uniform(3, 18, 12)])
ongoing_mask = np.random.random(n_patients) < 0.28
patient_ids = [f"PT-{i + 1:03d}" for i in range(n_patients)]
# Sort longest bar at top (highest y index)
order = np.argsort(durations)[::-1]
sorted_ids = [patient_ids[i] for i in order]
sorted_dur = durations[order]
sorted_arms = [arms[i] for i in order]
sorted_ongoing = ongoing_mask[order]
# Generate clinical events for each patient
events = []
event_types = list(EVENT_STYLES.keys())
event_probs = [0.50, 0.30, 0.35, 0.25]
for idx, (dur, _arm, _ongoing) in enumerate(zip(sorted_dur, sorted_arms, sorted_ongoing, strict=False)):
for etype, prob in zip(event_types, event_probs, strict=False):
if np.random.random() < prob:
t = np.random.uniform(1.0, dur * 0.85)
events.append({"patient_idx": idx, "time": t, "event_type": etype})
events_df = pd.DataFrame(events) if events else pd.DataFrame(columns=["patient_idx", "time", "event_type"])
# Apply seaborn theme
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.15,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Plot — landscape 3200×1800 px (8 in × 4.5 in @ 400 dpi)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Horizontal bars
bar_height = 0.55
for idx, (dur, arm, ongoing) in enumerate(zip(sorted_dur, sorted_arms, sorted_ongoing, strict=False)):
color = ARM_COLORS[arm]
ax.barh(idx, dur, height=bar_height, color=color, alpha=0.82, left=0, zorder=2)
if ongoing:
ax.annotate(
"",
xy=(dur + 0.95, idx),
xytext=(dur + 0.08, idx),
arrowprops={"arrowstyle": "-|>", "color": color, "lw": 1.6, "mutation_scale": 11},
zorder=3,
)
# Event markers — sns.scatterplot for seaborn idiomatic usage
for etype, style in EVENT_STYLES.items():
mask = events_df["event_type"] == etype
if mask.sum() > 0:
sns.scatterplot(
data=events_df[mask],
x="time",
y="patient_idx",
ax=ax,
marker=style["marker"],
color=style["color"],
s=style["size"],
zorder=5,
edgecolor=PAGE_BG,
linewidth=0.5,
legend=False,
)
# Axes styling
ax.set_yticks(range(n_patients))
ax.set_yticklabels(sorted_ids)
ax.set_xlabel("Time on Study (months)", fontsize=10, color=INK)
ax.set_ylabel("")
ax.set_xlim(0, sorted_dur.max() + 2.8)
ax.set_ylim(-0.65, n_patients - 0.35)
ax.tick_params(axis="x", which="both", length=0, labelsize=8, colors=INK_SOFT)
ax.tick_params(axis="y", which="both", length=0, labelsize=7.5, colors=INK_SOFT)
# Title
title = "swimmer-clinical-timeline · python · seaborn · anyplot.ai"
ax.set_title(title, fontsize=12, fontweight="medium", color=INK, pad=8)
# Spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)
# Vertical grid only (x-axis), behind bars
ax.xaxis.grid(True, alpha=0.15, linewidth=0.6, color=INK, zorder=0)
ax.set_axisbelow(True)
# Storytelling annotation — median duration comparison between arms
arm_a_med = np.median([sorted_dur[i] for i, arm in enumerate(sorted_arms) if "Arm A" in arm])
arm_b_med = np.median([sorted_dur[i] for i, arm in enumerate(sorted_arms) if "Arm B" in arm])
ax.text(
0.98,
0.97,
f"Median duration — Arm A: {arm_a_med:.1f} mo | Arm B: {arm_b_med:.1f} mo",
transform=ax.transAxes,
fontsize=7,
color=INK_MUTED,
ha="right",
va="top",
style="italic",
)
# Legend — treatment arms + event types + ongoing indicator
arm_handles = [mpatches.Patch(color=ARM_COLORS[arm], alpha=0.82, label=arm) for arm in ARM_COLORS]
event_handles = [
Line2D(
[0],
[0],
marker=style["marker"],
color="none",
markerfacecolor=style["color"],
markersize=7,
markeredgewidth=0.5,
markeredgecolor=PAGE_BG,
label=style["label"],
)
for style in EVENT_STYLES.values()
]
ongoing_handle = Line2D(
[0],
[0],
color=INK_SOFT,
marker=">",
markerfacecolor=INK_SOFT,
markersize=5,
linewidth=1.3,
label="Still on Treatment",
)
legend = ax.legend(
handles=arm_handles + event_handles + [ongoing_handle],
fontsize=7,
loc="lower right",
ncol=2,
framealpha=0.92,
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
)
legend.get_frame().set_linewidth(0.5)
# Layout — control padding without bbox_inches="tight"
fig.subplots_adjust(left=0.11, right=0.97, top=0.93, bottom=0.10)
# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
plt.close(fig)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/swimmer-clinical-timeline/seaborn/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "swimmer-clinical-timeline",
"language": "python",
"library": "seaborn",
"page": "https://anyplot.ai/swimmer-clinical-timeline/python/seaborn",
"hub": "https://anyplot.ai/swimmer-clinical-timeline",
"code_json": "https://api.anyplot.ai/specs/swimmer-clinical-timeline/seaborn/code",
"spec_json": "https://api.anyplot.ai/specs/swimmer-clinical-timeline",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/swimmer-clinical-timeline/python/seaborn/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/swimmer-clinical-timeline/python/seaborn/plot-dark.png",
"quality_score": 88.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Swimmer Plot for Clinical Trial Timelines on anyplot.ai.