Hugin vs Matplotlib

Side-by-side examples comparing Hugin (OCaml) with Matplotlib (Python). Hugin uses a declarative, pipeline-oriented API while Matplotlib uses an imperative, object-oriented approach.

Key Differences

Line Plot

Hugin:

open Hugin

let () =
  let x = Nx.linspace Nx.float32 0. (2. *. Float.pi) 100 in
  layers [
    line ~x ~y:(Nx.sin x) ~label:"sin(x)" ~color:Color.blue ();
    line ~x ~y:(Nx.cos x) ~label:"cos(x)" ~color:Color.vermillion
      ~line_style:`Dashed ();
  ]
  |> title "Trigonometric Functions"
  |> xlabel "Angle (radians)"
  |> ylabel "Value"
  |> ylim (-1.2) 1.2
  |> grid_lines true
  |> legend
  |> render_png "trig.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)

plt.figure()
plt.plot(x, np.sin(x), label="sin(x)", color="blue")
plt.plot(x, np.cos(x), label="cos(x)", color="red", linestyle="--")
plt.title("Trigonometric Functions")
plt.xlabel("Angle (radians)")
plt.ylabel("Value")
plt.ylim(-1.2, 1.2)
plt.grid(True)
plt.legend()
plt.savefig("trig.png")

Scatter Plot

Hugin:

open Hugin

let () =
  let x = Nx.rand Nx.float32 [| 200 |] in
  let y = Nx.rand Nx.float32 [| 200 |] in
  let c = Nx.add x y in
  point ~x ~y ~color_by:c ~size:8. ~marker:Circle ()
  |> title "Random Scatter"
  |> render_png "scatter.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

x = np.random.rand(200)
y = np.random.rand(200)
c = x + y

plt.figure()
plt.scatter(x, y, c=c, s=64, marker="o")
plt.title("Random Scatter")
plt.colorbar()
plt.savefig("scatter.png")

Bar Chart

Hugin:

open Hugin

let () =
  let x = Nx.create Nx.float32 [| 4 |] [| 1.; 2.; 3.; 4. |] in
  let h = Nx.create Nx.float32 [| 4 |] [| 3.; 7.; 2.; 5. |] in
  bar ~x ~height:h ~color:Color.orange ()
  |> title "Quarterly Revenue"
  |> xticks [ (1., "Q1"); (2., "Q2"); (3., "Q3"); (4., "Q4") ]
  |> ylabel "Revenue ($M)"
  |> render_png "bar.png"

Matplotlib:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
h = [3, 7, 2, 5]

plt.figure()
plt.bar(x, h, color="orange")
plt.title("Quarterly Revenue")
plt.xticks(x, ["Q1", "Q2", "Q3", "Q4"])
plt.ylabel("Revenue ($M)")
plt.savefig("bar.png")

Histogram

Hugin:

open Hugin

let () =
  let data = Nx.randn Nx.float32 [| 1000 |] in
  hist ~x:data ~bins:(`Num 30) ~density:true ~color:Color.sky_blue ()
  |> title "Normal Distribution"
  |> xlabel "Value"
  |> ylabel "Density"
  |> render_png "hist.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

data = np.random.randn(1000)

plt.figure()
plt.hist(data, bins=30, density=True, color="skyblue")
plt.title("Normal Distribution")
plt.xlabel("Value")
plt.ylabel("Density")
plt.savefig("hist.png")

Multi-Panel Layout

Hugin:

open Hugin

let () =
  let x = Nx.linspace Nx.float32 0. (2. *. Float.pi) 100 in
  let p1 = line ~x ~y:(Nx.sin x) () |> title "sin" in
  let p2 = line ~x ~y:(Nx.cos x) () |> title "cos" in
  let p3 = line ~x ~y:(Nx.tan x) () |> title "tan" |> ylim (-5.) 5. in
  let p4 = hist ~x:(Nx.rand Nx.float32 [| 500 |]) () |> title "random" in
  Layout.grid [ [ p1; p2 ]; [ p3; p4 ] ]
  |> render_png "grid.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)

fig, axes = plt.subplots(2, 2)
axes[0, 0].plot(x, np.sin(x)); axes[0, 0].set_title("sin")
axes[0, 1].plot(x, np.cos(x)); axes[0, 1].set_title("cos")
axes[1, 0].plot(x, np.tan(x)); axes[1, 0].set_title("tan")
axes[1, 0].set_ylim(-5, 5)
axes[1, 1].hist(np.random.rand(500)); axes[1, 1].set_title("random")
plt.tight_layout()
plt.savefig("grid.png")

Heatmap

Hugin:

open Hugin

let () =
  let data = Nx.init Nx.float32 [| 8; 10 |] (fun idx ->
    let i = Float.of_int idx.(0) and j = Float.of_int idx.(1) in
    Float.sin (i *. 0.5) *. Float.cos (j *. 0.4))
  in
  heatmap ~data ~annotate:true ~cmap:Cmap.viridis ()
  |> title "Heatmap"
  |> render_png "heatmap.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

data = np.fromfunction(
    lambda i, j: np.sin(i * 0.5) * np.cos(j * 0.4), (8, 10)
)

fig, ax = plt.subplots()
im = ax.imshow(data, cmap="viridis")
for i in range(8):
    for j in range(10):
        ax.text(j, i, f"{data[i, j]:.2g}", ha="center", va="center")
ax.set_title("Heatmap")
plt.colorbar(im)
plt.savefig("heatmap.png")

Styling

Hugin:

open Hugin

let () =
  let x = Nx.linspace Nx.float32 0. (2. *. Float.pi) 50 in
  line ~x ~y:(Nx.sin x)
    ~color:Color.vermillion
    ~line_style:`Dashed
    ~line_width:2.5
    ~marker:Triangle
    ~alpha:0.7
    ()
  |> render_png "styled.png"

Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 50)

plt.figure()
plt.plot(x, np.sin(x), color="red", linestyle="--",
         linewidth=2.5, marker="^", alpha=0.7)
plt.savefig("styled.png")

Themes

Hugin provides built-in themes with context scaling. Matplotlib uses style sheets.

Hugin:

(* Dark theme scaled for a presentation *)
let theme = Theme.dark |> Theme.talk in
line ~x ~y () |> with_theme theme |> render_png "slide.png"

Matplotlib:

plt.style.use("dark_background")
plt.rcParams.update({"font.size": 14})
plt.plot(x, y)
plt.savefig("slide.png")

Save and Export

Hugin:

let spec = line ~x ~y () |> title "My Plot" in
spec |> render_png "plot.png";
spec |> render_svg "plot.svg";
spec |> render_pdf "plot.pdf";
spec |> show  (* interactive SDL window *)

Matplotlib:

plt.plot(x, y)
plt.title("My Plot")
plt.savefig("plot.png")
plt.savefig("plot.svg")
plt.savefig("plot.pdf")
plt.show()

In Hugin, the spec is an immutable value. You can render the same spec to multiple formats without rebuilding it. In Matplotlib, the figure is mutable state that savefig and show consume.

Interactive Display

Hugin:

show ~width:1600. ~height:1200. spec

The SDL window is resizable. The plot re-renders at the new dimensions. Press Escape or Q to close.

Matplotlib:

plt.show()