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: altair 6.2.1 | Python 3.13.13
Quality: 92/100 | Updated: 2026-06-08
"""
import os
import sys
# Remove this script's directory from sys.path so 'import altair' finds the installed package
_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _dir]
import altair as alt
import numpy as np
import pandas as pd
from PIL import Image
# Theme tokens — Imprint palette + theme-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 categorical palette — positions 1 & 2 for treatment arms
ARM_COLORS = ["#009E73", "#C475FD"] # Arm A (Combo), Arm B (Mono)
# Event colors with semantic Imprint palette matches
event_color_map = {
"Partial Response": "#BD8233", # Imprint pos 4 — ochre, partial positive
"Complete Response": "#2ABCCD", # Imprint pos 6 — cyan, strong positive
"Progressive Disease": "#AE3030", # Imprint pos 5 — matte red, negative outcome
"Ongoing": INK_MUTED, # theme-adaptive neutral
}
event_shape_map = {
"Partial Response": "triangle-up",
"Complete Response": "cross",
"Progressive Disease": "diamond",
"Ongoing": "triangle-right",
}
# Data — simulated Phase II oncology trial, 25 patients, two treatment arms
np.random.seed(42)
n_patients = 25
patient_ids = [f"PT-{i + 1:03d}" for i in range(n_patients)]
arms = np.random.choice(["Arm A (Combo)", "Arm B (Mono)"], n_patients, p=[0.52, 0.48])
durations = np.round(np.random.uniform(4, 48, n_patients), 1)
durations = np.sort(durations)[::-1]
ongoing_mask = np.random.choice([True, False], n_patients, p=[0.3, 0.7])
events_list = []
for i, pid in enumerate(patient_ids):
dur = durations[i]
if dur > 8:
pr_time = np.round(np.random.uniform(4, min(dur * 0.5, 16)), 1)
events_list.append({"patient_id": pid, "time": pr_time, "event_type": "Partial Response"})
if dur > 20 and np.random.random() > 0.5:
cr_time = np.round(np.random.uniform(12, min(dur * 0.7, 30)), 1)
events_list.append({"patient_id": pid, "time": cr_time, "event_type": "Complete Response"})
if not ongoing_mask[i] and dur > 6:
pd_time = np.round(dur - np.random.uniform(0, 3), 1)
events_list.append({"patient_id": pid, "time": pd_time, "event_type": "Progressive Disease"})
if ongoing_mask[i]:
events_list.append({"patient_id": pid, "time": dur, "event_type": "Ongoing"})
bars_df = pd.DataFrame({"patient_id": patient_ids, "duration": durations, "arm": arms, "ongoing": ongoing_mask})
sort_order = bars_df.sort_values("duration", ascending=True)["patient_id"].tolist()
events_df = pd.DataFrame(events_list)
events_df = events_df.merge(bars_df[["patient_id", "arm"]], on="patient_id")
# Interactive arm highlight — click treatment arm in legend to focus
arm_selection = alt.selection_point(fields=["arm"], bind="legend")
# Bars — use fill (not color) so the arm legend stays separate from event color legend
bars = (
alt.Chart(bars_df)
.mark_bar(height=12, cornerRadiusEnd=3)
.encode(
x=alt.X(
"duration:Q",
title="Time on Study (Weeks)",
axis=alt.Axis(titleFontSize=12, labelFontSize=10, tickSize=0, grid=True, gridOpacity=0.15, gridColor=INK),
),
y=alt.Y("patient_id:N", title=None, sort=sort_order, axis=alt.Axis(labelFontSize=10, tickSize=0)),
fill=alt.Fill(
"arm:N",
title="Treatment Arm",
scale=alt.Scale(domain=["Arm A (Combo)", "Arm B (Mono)"], range=ARM_COLORS),
legend=alt.Legend(orient="right", symbolSize=120, symbolStrokeWidth=0),
),
opacity=alt.condition(arm_selection, alt.value(1.0), alt.value(0.25)),
tooltip=["patient_id:N", "arm:N", alt.Tooltip("duration:Q", title="Weeks on Study")],
)
.add_params(arm_selection)
)
# Median reference line for population context
median_dur = float(np.median(durations))
rule_df = pd.DataFrame({"median": [median_dur]})
median_rule = alt.Chart(rule_df).mark_rule(strokeDash=[6, 4], strokeWidth=1.2, color=INK_SOFT).encode(x="median:Q")
median_label = (
alt.Chart(rule_df)
.mark_text(align="left", dx=4, dy=-8, fontSize=9, color=INK_MUTED, fontStyle="italic")
.encode(x="median:Q", y=alt.value(0), text=alt.value(f"Median: {median_dur:.0f} wk"))
)
# Event markers — shape + color both encode event_type; Vega-Lite merges into one legend
markers = (
alt.Chart(events_df)
.mark_point(filled=True, size=180, stroke=PAGE_BG, strokeWidth=1.0)
.encode(
x=alt.X("time:Q"),
y=alt.Y("patient_id:N", sort=sort_order),
shape=alt.Shape(
"event_type:N",
title="Clinical Event",
scale=alt.Scale(domain=list(event_shape_map.keys()), range=list(event_shape_map.values())),
legend=alt.Legend(orient="right", symbolSize=120, symbolStrokeWidth=0),
),
color=alt.Color(
"event_type:N",
title="Clinical Event",
scale=alt.Scale(domain=list(event_color_map.keys()), range=list(event_color_map.values())),
legend=alt.Legend(orient="right", symbolSize=120, symbolStrokeWidth=0),
),
tooltip=["patient_id:N", "event_type:N", alt.Tooltip("time:Q", title="Week")],
opacity=alt.condition(arm_selection, alt.value(1.0), alt.value(0.25)),
)
)
# Title — 56 chars, below 67-char baseline, fontsize=16
title_text = "swimmer-clinical-timeline · python · altair · anyplot.ai"
chart = (
(bars + median_rule + median_label + markers)
.properties(
width=480,
height=320,
background=PAGE_BG,
padding={"left": 0, "right": 0, "top": 0, "bottom": 0},
title=alt.Title(title_text, fontSize=16, fontWeight="normal", color=INK, anchor="start", offset=12),
)
.configure_view(fill=PAGE_BG, stroke=None, strokeWidth=0, continuousWidth=480, continuousHeight=320)
.configure_axis(domainColor=INK_SOFT, tickColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
.configure_legend(
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
labelFontSize=10,
titleFontSize=10,
padding=6,
cornerRadius=3,
)
.configure_title(color=INK)
)
# Save PNG with scale_factor=4.0, then pad to exact 3200×1800
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
TW, TH = 3200, 1800
_img = Image.open(f"plot-{THEME}.png").convert("RGB")
_w, _h = _img.size
if _w > TW or _h > TH:
raise SystemExit(
f"altair vl-convert produced {_w}×{_h}, exceeds target {TW}×{TH}. "
f"Shrink chart .properties(width=, height=) values and re-render."
)
if _w < TW or _h < TH:
_canvas = Image.new("RGB", (TW, TH), PAGE_BG)
_canvas.paste(_img, ((TW - _w) // 2, (TH - _h) // 2))
_canvas.save(f"plot-{THEME}.png")
chart.save(f"plot-{THEME}.html")
Part of Swimmer Plot for Clinical Trial Timelines on anyplot.ai.