A radial chart that maps innovations, technologies, or trends onto concentric rings representing time horizons and angular sectors representing thematic categories. Inner rings represent near-term items (e.g., "Now", "Next 6 months") while outer rings represent longer-term or emerging trends (e.g., "2-5 years", "Future"). Each item is placed as a labeled point within its sector and ring, with distinct markers or colors encoding categories. Inspired by ThoughtWorks Technology Radar and similar strategic planning visualizations.
""" anyplot.ai
radar-innovation-timeline: Innovation Radar with Time-Horizon Rings
Library: letsplot 4.10.1 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-29
"""
import math
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_label,
geom_path,
geom_point,
geom_polygon,
geom_segment,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
layer_tooltips,
scale_color_manual,
scale_x_continuous,
scale_y_continuous,
theme,
theme_void,
)
LetsPlot.setup_html()
# 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 — 8 hues, hybrid-v3 sort
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
np.random.seed(42)
# --- Data ---
rings = ["Adopt", "Trial", "Assess", "Hold"]
# Sector order chosen to separate heavy-label sectors (AI & ML, Infrastructure) across the arc
sectors = ["AI & ML", "Biotech", "Sustainability", "Infrastructure"]
# Sector colors: Imprint palette positions 1–4 in canonical order
sector_colors = {
"AI & ML": IMPRINT_PALETTE[0], # #009E73 brand green
"Biotech": IMPRINT_PALETTE[1], # #C475FD lavender
"Sustainability": IMPRINT_PALETTE[2], # #4467A3 blue
"Infrastructure": IMPRINT_PALETTE[3], # #BD8233 ochre
}
# Ring fills: semantic green→amber→ochre→red gradient communicates adoption readiness
if THEME == "light":
ring_fills = {"Adopt": "#DCEDC8", "Trial": "#FFF9C4", "Assess": "#FFE0B2", "Hold": "#FFCDD2"}
ring_alpha = 0.55
else:
# Imprint-derived tints at low alpha for dark background
ring_fills = {"Adopt": "#009E73", "Trial": "#DDCC77", "Assess": "#BD8233", "Hold": "#AE3030"}
ring_alpha = 0.14
ring_inner = {"Adopt": 0.5, "Trial": 1.5, "Assess": 2.5, "Hold": 3.5}
ring_outer = {"Adopt": 1.5, "Trial": 2.5, "Assess": 3.5, "Hold": 4.5}
# Angular nudge for Hold ring items to prevent overlap with sector header labels
ring_nudge = {"Adopt": 0.0, "Trial": 0.0, "Assess": 0.0, "Hold": 0.20}
# 270-degree arc (gap at top for ring labels)
ARC_START = math.pi * 3 / 4
ARC_SPAN = math.pi * 3 / 2
ARC_END = ARC_START + ARC_SPAN
SECTOR_SPAN = ARC_SPAN / len(sectors)
innovations = [
{"name": "LLM Agents", "ring": "Adopt", "sector": "AI & ML"},
{"name": "RAG Pipelines", "ring": "Adopt", "sector": "AI & ML"},
{"name": "Vision Models", "ring": "Trial", "sector": "AI & ML"},
{"name": "AI Code Review", "ring": "Trial", "sector": "AI & ML"},
{"name": "Neuro-symbolic AI", "ring": "Assess", "sector": "AI & ML"},
{"name": "Quantum ML", "ring": "Hold", "sector": "AI & ML"},
{"name": "mRNA Platforms", "ring": "Adopt", "sector": "Biotech"},
{"name": "Gene Editing", "ring": "Trial", "sector": "Biotech"},
{"name": "Synthetic Biology", "ring": "Trial", "sector": "Biotech"},
{"name": "Digital Twins (Bio)", "ring": "Assess", "sector": "Biotech"},
{"name": "Organ-on-Chip", "ring": "Assess", "sector": "Biotech"},
{"name": "Nanomedicine", "ring": "Hold", "sector": "Biotech"},
{"name": "Carbon Tracking", "ring": "Adopt", "sector": "Sustainability"},
{"name": "Green Cloud", "ring": "Trial", "sector": "Sustainability"},
{"name": "Circular Design", "ring": "Trial", "sector": "Sustainability"},
{"name": "Biodegradable PCBs", "ring": "Assess", "sector": "Sustainability"},
{"name": "Ocean Cleanup AI", "ring": "Assess", "sector": "Sustainability"},
{"name": "Fusion Energy", "ring": "Hold", "sector": "Sustainability"},
{"name": "Edge Computing", "ring": "Adopt", "sector": "Infrastructure"},
{"name": "WebAssembly", "ring": "Adopt", "sector": "Infrastructure"},
{"name": "Service Mesh", "ring": "Trial", "sector": "Infrastructure"},
{"name": "Satellite Internet", "ring": "Trial", "sector": "Infrastructure"},
{"name": "Confidential Compute", "ring": "Assess", "sector": "Infrastructure"},
{"name": "6G Research", "ring": "Hold", "sector": "Infrastructure"},
]
# --- Compute positions ---
df = pd.DataFrame(innovations)
sector_idx_map = {s: i for i, s in enumerate(sectors)}
ring_mid_map = {r: (ring_inner[r] + ring_outer[r]) / 2 for r in rings}
# Vectorized angular placement per sector/ring group
angles = np.zeros(len(df))
for (sector, ring), group in df.groupby(["sector", "ring"], sort=False):
center = ARC_START + sector_idx_map[sector] * SECTOR_SPAN + SECTOR_SPAN / 2
n = len(group)
spread = SECTOR_SPAN * 0.72
offsets = np.linspace(-spread / 2, spread / 2, n) if n > 1 else np.array([0.0])
angles[group.index] = center + offsets + ring_nudge[ring]
df["angle"] = angles
df["radius"] = df["ring"].map(ring_mid_map) + np.random.uniform(-0.15, 0.15, len(df))
df["x"] = df["radius"] * np.cos(df["angle"])
df["y"] = df["radius"] * np.sin(df["angle"])
# Label positions: pushed radially outward
label_offsets = {"Adopt": 0.72, "Trial": 0.62, "Assess": 0.52, "Hold": 0.42}
df["label_r"] = df["radius"] + df["ring"].map(label_offsets)
df["lx"] = df["label_r"] * np.cos(df["angle"])
df["ly"] = df["label_r"] * np.sin(df["angle"])
df["side"] = np.where(df["lx"] < 0, "left", "right")
# Label repulsion: push overlapping labels apart vertically on each side
MIN_Y_SEP = 0.75
for _ in range(35):
for side in ["left", "right"]:
side_idx = df.loc[df["side"] == side].sort_values("ly").index.tolist()
for k in range(len(side_idx) - 1):
i, j = side_idx[k], side_idx[k + 1]
if abs(df.loc[j, "lx"] - df.loc[i, "lx"]) < 2.5:
dy = df.loc[j, "ly"] - df.loc[i, "ly"]
if dy < MIN_Y_SEP:
push = (MIN_Y_SEP - dy) / 2
df.loc[j, "ly"] += push
df.loc[i, "ly"] -= push
# --- Structural geometry ---
arc_pts = np.linspace(ARC_START, ARC_END, 120)
# Ring background polygons (annular sectors)
ring_bg_rows = []
for rname in rings:
r_in, r_out = ring_inner[rname], ring_outer[rname]
xs = np.concatenate([r_out * np.cos(arc_pts), r_in * np.cos(arc_pts[::-1])])
ys = np.concatenate([r_out * np.sin(arc_pts), r_in * np.sin(arc_pts[::-1])])
for px, py in zip(np.append(xs, xs[0]), np.append(ys, ys[0]), strict=True):
ring_bg_rows.append({"x": px, "y": py, "ring": rname})
ring_bg_df = pd.DataFrame(ring_bg_rows)
# Ring boundary arcs
bnd_rows = []
for r in [0.5, 1.5, 2.5, 3.5, 4.5]:
for idx, a in enumerate(arc_pts):
bnd_rows.append({"x": r * math.cos(a), "y": r * math.sin(a), "g": f"r{r}", "o": idx})
bnd_df = pd.DataFrame(bnd_rows).sort_values(["g", "o"])
# Sector divider spokes
spoke_angles = [ARC_START + i * SECTOR_SPAN for i in range(len(sectors) + 1)]
spoke_df = pd.DataFrame(
[
{"x": 0.5 * math.cos(a), "y": 0.5 * math.sin(a), "xend": 4.5 * math.cos(a), "yend": 4.5 * math.sin(a)}
for a in spoke_angles
]
)
# Sector header labels along outer edge
sector_label_df = pd.DataFrame(
[
{
"label": s,
"x": 5.5 * math.cos(ARC_START + (i + 0.5) * SECTOR_SPAN),
"y": 5.5 * math.sin(ARC_START + (i + 0.5) * SECTOR_SPAN),
}
for i, s in enumerate(sectors)
]
)
# Ring name labels in arc gap (90 degrees = top center)
gap_angle = math.pi / 2
ring_label_df = pd.DataFrame(
[
{"label": r, "x": ring_mid_map[r] * math.cos(gap_angle), "y": ring_mid_map[r] * math.sin(gap_angle)}
for r in rings
]
)
# --- Build plot ---
plot = ggplot()
# Ring background fills with semantic color gradient (green=safe → pink=risky)
for rname in rings:
rdata = ring_bg_df[ring_bg_df["ring"] == rname]
plot += geom_polygon(aes("x", "y"), data=rdata, fill=ring_fills[rname], alpha=ring_alpha)
# Structural lines: ring boundaries and sector spokes
plot += geom_path(aes("x", "y", group="g"), data=bnd_df, color=INK_SOFT, size=0.3, alpha=0.5)
plot += geom_segment(aes(x="x", y="y", xend="xend", yend="yend"), data=spoke_df, color=INK_SOFT, size=0.3, alpha=0.5)
# Sector header labels
plot += geom_text(aes("x", "y", label="label"), data=sector_label_df, size=5, color=INK, fontface="bold")
# Ring labels with background box (geom_label for visual clarity over ring fills)
plot += geom_label(
aes("x", "y", label="label"),
data=ring_label_df,
size=4,
color=INK_SOFT,
fontface="bold",
fill=ELEVATED_BG,
alpha=0.9,
)
# Thin connector lines from points to labels (aids readability after repulsion)
plot += geom_segment(aes(x="x", y="y", xend="lx", yend="ly"), data=df, size=0.3, alpha=0.25, color=INK_MUTED)
# Innovation points with interactive tooltips (letsplot-specific for HTML export)
plot += geom_point(
aes("x", "y", color="sector"),
data=df,
size=4,
alpha=0.9,
tooltips=layer_tooltips().line("@name").line("Ring: @ring").line("Sector: @sector"),
)
# Innovation labels split by side for outward text alignment
for side, hj in [("left", 1), ("right", 0)]:
side_df = df[df["side"] == side]
plot += geom_text(aes("lx", "ly", label="name", color="sector"), data=side_df, size=4.5, hjust=hj)
title = "radar-innovation-timeline · python · letsplot · anyplot.ai"
# Styling — theme_void() removes all axis/grid defaults; custom theme restores chrome
plot += (
theme_void()
+ scale_color_manual(values=sector_colors)
+ scale_x_continuous(limits=(-7.0, 7.0))
+ scale_y_continuous(limits=(-6.2, 5.5))
+ coord_fixed()
+ labs(title=title, color="Sector")
+ ggsize(600, 600)
+ theme(
plot_title=element_text(size=16, color=INK, face="bold"),
legend_title=element_text(size=12, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_position="bottom",
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid=element_blank(),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
)
)
# Save PNG (scale=4 → 2400×2400 px) and interactive HTML
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Innovation Radar with Time-Horizon Rings on anyplot.ai.