# How to add the drop-shadow effect to contour plots in Julia with Plots.jl (or Makie.jl)

**URL:** https://discourse.julialang.org/t/how-to-add-the-drop-shadow-effect-to-contour-plots-in-julia-with-plots-jl-or-makie-jl/105604
**Category:** Visualization
**Tags:** plotting
**Created:** [October 31, 2023, 1:04am UTC](https://discourse.julialang.org/t/how-to-add-the-drop-shadow-effect-to-contour-plots-in-julia-with-plots-jl-or-makie-jl/105604 "2023-10-31T01:04:27Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![jondoe297](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jondoe297/32/202835_2.png) [@jondoe297](https://discourse.julialang.org/u/jondoe297)
#### Post date: [October 31, 2023, 1:04am UTC](https://discourse.julialang.org/t/how-to-add-the-drop-shadow-effect-to-contour-plots-in-julia-with-plots-jl-or-makie-jl/105604/1 "2023-10-31T01:04:27Z")

</div>

I am very new to Julia, coming from python. I am trying to make a contour plot with drop-shadow effect. (From [Scientific Visualization Book](https://github.com/rougier/scientific-visualization-book))

```python
import scipy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvas
from matplotlib.patheffects import Stroke, Normal
from scipy.ndimage import gaussian_filter

# Some data
def f(x, y):
    return np.exp(-x **2 - y** 2) - np.exp(-(x - 1)**2 - (y - 1)**2)

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = 2.0 * f(X, Y)

n = 11 # Number of levels
dz = (Z.max() - Z.min()) / n
levels = np.linspace(Z.min(), Z.max(), n, endpoint=True)
cmap = plt.get_cmap("winter")

def drop_shadow(Z, l0, l1, sigma=5, alpha=0.5):
    """Compute the drop shadow for a contour between l0 and l1.

    This works by first:
    1. render the contour in black using an offline image
    2. blue the contour using a Guassian filter
    3. set the alpha channel accordingly
    """
    fig = Figure(figsize=(5, 5))
    canvas = FigureCanvas(fig)
    ax = fig.add_axes([0, 0, 1, 1], frameon=False)
    ax.contourf(
        Z,
        vmin=Z.min(),
        vmax=Z.max(),
        levels=[l0, l1],
        origin="lower",
        colors="black",
        extent=[-1, 1, -1, 1],
    )
    ax.set_xlim(-1, 1), ax.set_ylim(-1, 1)
    canvas.draw()
    A = np.array(canvas.renderer.buffer_rgba())[:, :, 0]
    del fig
    A = gaussian_filter(A, sigma)
    A = (A - A.min()) / (A.max() - A.min())
    I = np.zeros((A.shape[0], A.shape[1], 4))
    I[:, :, 3] = (1 - A) * alpha
    return I

fig = plt.figure(figsize=(8, 8), dpi=100)
ax = fig.add_axes([0, 0, 1, 1], frameon=False)

zorder = -100
for i in range(len(levels) - 1):
    l0, l1 = levels[i], levels[i + 1]
    I = drop_shadow(Z, l0, l1)
    ax.imshow(I, extent=[-3, 3, -3, 3], origin="upper", zorder=zorder)
    zorder += 1
    ax.contourf(
        Z,
        vmin=Z.min(),
        vmax=Z.max(),
        levels=[l0, l1],
        origin="lower",
        cmap=cmap,
        extent=[-3, 3, -3, 3],
        zorder=zorder,
    )
    zorder += 1
ax.set_xlim(-2.9, 2.9)
ax.set_ylim(-2.9, 2.9)
plt.show()

```

this python code would produce an image like

 ![contour-dropshadow](https://global.discourse-cdn.com/julialang/original/3X/f/3/f34c17b3c4d5722b6b92459f757c6fb29b9234e1.png)

I would like to make a similar plot with Julia, preferably with Plots.jl