Dragon Curve#
Download this notebook from GitHub (right-click to download).
Dragon curve example from the L-systems topic notebook in examples/topics/geometry
.
Most examples work across multiple plotting backends, this example is also available for:
import holoviews as hv
from holoviews import opts
import numpy as np
hv.extension('bokeh')
L-system definition#
The following class is a simplified version of the approach used in the L-systems notebook, made specifically for plotting the Dragon Curve.
class DragonCurve(object):
"L-system agent that follows rules to generate the Dragon Curve"
initial ='FX'
productions = {'X':'X+YF+', 'Y':'-FX-Y'}
dragon_rules = {'F': lambda t,d,a: t.forward(d),
'B': lambda t,d,a: t.back(d),
'+': lambda t,d,a: t.rotate(-a),
'-': lambda t,d,a: t.rotate(a),
'X':lambda t,d,a: None,
'Y':lambda t,d,a: None }
def __init__(self, x=0,y=0, iterations=1):
self.heading = 0
self.distance = 5
self.angle = 90
self.x, self.y = x,y
self.trace = [(self.x, self.y)]
self.process(self.expand(iterations), self.distance, self.angle)
def process(self, instructions, distance, angle):
for i in instructions:
self.dragon_rules[i](self, distance, angle)
def expand(self, iterations):
"Expand an initial symbol with the given production rules"
expansion = self.initial
for i in range(iterations):
intermediate = ""
for ch in expansion:
intermediate = intermediate + self.productions.get(ch,ch)
expansion = intermediate
return expansion
def forward(self, distance):
self.x += np.cos(2*np.pi * self.heading/360.0)
self.y += np.sin(2*np.pi * self.heading/360.0)
self.trace.append((self.x,self.y))
def rotate(self, angle):
self.heading += angle
def back(self, distance):
self.heading += 180
self.forward(distance)
self.heading += 180
@property
def path(self):
return hv.Path([self.trace])
Plot#
hmap = hv.HoloMap(kdims='Iteration')
for i in range(7,17):
hmap[i] = DragonCurve(-200, 0, i).path
hmap.opts(
opts.Path(axiswise=False, color='black', line_width=1,
title='', xaxis=None, yaxis=None, framewise=True))
This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.
Download this notebook from GitHub (right-click to download).