Ashby chart
Materials Science — density vs. Young's modulus, log-log
Example from the compendium of canonical charts
Python Code
"""Materials Science — Ashby chart (density vs. Young's modulus, log-log)."""
import numpy as np
import plotly.graph_objects as go
URL = "https://raw.githubusercontent.com/mrealpe/OpenMaterialsSelector/master/datos.csv"
# Fixed color map for common material families
FAMILY_COLORS = {
"Metal": "#845EEE", # VIOLET
"Metals": "#845EEE",
"Polymer": "#55B685", # GREEN
"Polymers": "#55B685",
"Ceramic": "#52B3D0", # TEAL
"Ceramics": "#52B3D0",
"Composite": "#DA5597", # PINK
"Composites": "#DA5597",
"Foam": "#52B3D0", # TEAL
"Foams": "#52B3D0",
"Natural": "#a07fd6",
"Glass": "#c87c22",
"Wood": "#3d9c6e",
"Rubber": "#e06060",
"Other": "#60646c", # MUTED
}
def get_family_color(family_str):
for key, color in FAMILY_COLORS.items():
if key.lower() in str(family_str).lower():
return color
return FAMILY_COLORS["Other"]
def load_data():
try:
df = fetch_csv(URL, encoding="utf-8-sig")
print(f" columns: {list(df.columns)}")
print(f" shape: {df.shape}")
return df
except Exception as e:
print(f" primary load failed ({e}), trying latin-1")
try:
import io, requests
r = requests.get(URL, verify=False, timeout=60)
r.raise_for_status()
import pandas as pd
df = pd.read_csv(io.StringIO(r.content.decode("latin-1")))
print(f" columns (latin-1): {list(df.columns)}")
return df
except Exception as e2:
print(f" latin-1 also failed: {e2}")
return None
def find_col(df, candidates):
"""Find first column name matching any candidate (case-insensitive, partial match)."""
for cand in candidates:
for col in df.columns:
# Strip BOM and whitespace
clean = col.strip().lstrip("").lower()
if cand.lower() in clean:
return col
return None
def generate():
import pandas as pd
df = load_data()
density_col = None
modulus_col = None
category_col = None
name_col = None
if df is not None:
# Strip BOM from all column names
df.columns = [c.strip().lstrip("") for c in df.columns]
print(f" cleaned columns: {list(df.columns)}")
density_col = find_col(df, ["density", "densidad", "rho"])
modulus_col = find_col(df, ["young", "elastic", "modulus", "modulo", "e (gpa)", "young's"])
category_col = find_col(df, ["category", "familia", "family", "class", "type"])
name_col = find_col(df, ["name", "nombre", "material"])
print(f" density_col={density_col}, modulus_col={modulus_col}, "
f"category_col={category_col}, name_col={name_col}")
if df is None or density_col is None or modulus_col is None:
print(" falling back to synthetic Ashby data")
# Representative data for major material families
data = {
"Name": [
"Mild steel", "Aluminium alloy", "Titanium alloy", "Copper",
"Cast iron", "Stainless steel",
"HDPE", "Polypropylene", "Polycarbonate", "Nylon 66", "PTFE",
"Alumina (Al2O3)", "Silicon carbide", "Borosilicate glass",
"CFRP (UD)", "GFRP (UD)", "Kevlar composite",
"Rigid foam (PU)", "Metal foam (Al)",
"Oak (wood)", "Bamboo",
],
"Category": [
"Metal", "Metal", "Metal", "Metal", "Metal", "Metal",
"Polymer", "Polymer", "Polymer", "Polymer", "Polymer",
"Ceramic", "Ceramic", "Ceramic",
"Composite", "Composite", "Composite",
"Foam", "Foam",
"Natural", "Natural",
],
"Density": [
7.85, 2.7, 4.5, 8.9, 7.2, 7.9,
0.95, 0.91, 1.2, 1.14, 2.15,
3.9, 3.2, 2.23,
1.6, 2.0, 1.35,
0.06, 0.3,
0.6, 0.7,
],
"Modulus": [
210, 70, 115, 120, 175, 200,
0.8, 1.5, 2.4, 3.0, 0.5,
380, 410, 70,
150, 40, 80,
0.03, 5,
12, 20,
],
}
df = pd.DataFrame(data)
density_col = "Density"
modulus_col = "Modulus"
category_col = "Category"
name_col = "Name"
# Clean numeric columns
df[density_col] = pd.to_numeric(df[density_col], errors="coerce")
df[modulus_col] = pd.to_numeric(df[modulus_col], errors="coerce")
df = df.dropna(subset=[density_col, modulus_col])
df = df[(df[density_col] > 0) & (df[modulus_col] > 0)]
print(f" valid rows: {len(df)}")
# Get category label (first word)
if category_col:
df["_family"] = df[category_col].astype(str).str.split().str[0]
else:
df["_family"] = "Other"
def hex_to_rgba(hex_color, alpha):
h = hex_color.lstrip("#")
r, g, b = int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
return f"rgba({r},{g},{b},{alpha})"
fig = go.Figure()
families = df["_family"].unique()
for fam in sorted(families):
sub = df[df["_family"] == fam]
color = get_family_color(fam)
if len(sub) >= 2:
# Draw family region as a filled ellipse in log-space
log_x = np.log10(np.clip(sub[density_col].values, 1e-9, None))
log_y = np.log10(np.clip(sub[modulus_col].values, 1e-9, None))
cx, cy = log_x.mean(), log_y.mean()
rx = max(log_x.std() * 1.8, 0.18)
ry = max(log_y.std() * 1.8, 0.25)
t = np.linspace(0, 2 * np.pi, 80)
ell_x = 10 ** (cx + rx * np.cos(t))
ell_y = 10 ** (cy + ry * np.sin(t))
fig.add_trace(go.Scatter(
x=ell_x, y=ell_y,
mode="lines",
fill="toself",
fillcolor=hex_to_rgba(color, 0.15),
line=dict(color=color, width=1.5),
showlegend=False,
hoverinfo="skip",
legendgroup=fam,
))
hover = (
sub[name_col].astype(str)
if name_col
else sub.index.astype(str)
)
fig.add_trace(go.Scatter(
x=sub[density_col],
y=sub[modulus_col],
mode="markers",
name=fam,
legendgroup=fam,
marker=dict(color=color, size=8, opacity=0.85,
line=dict(color="white", width=0.5)),
text=hover,
hovertemplate=(
"<b>%{text}</b><br>"
"Density: %{x:.2f} g/cm³<br>"
"Modulus: %{y:.1f} GPa<extra></extra>"
),
))
fig.update_layout(
xaxis=dict(
title="Density (g/cm³)",
type="log",
dtick=1,
showgrid=True,
),
yaxis=dict(
title="Young's Modulus (GPa)",
type="log",
dtick=1,
showgrid=True,
),
legend=dict(
x=1.02, y=1.0,
xanchor="left",
font=dict(size=10),
),
margin=dict(t=20, b=60, l=70, r=140),
)
return fig
if __name__ == "__main__":
generate()Made with Plotly