This can be solved by shapely:
%matplotlib inline
import pylab as pl
from shapely.geometry import MultiLineString, LineString
import numpy as np
from matplotlib.collections import LineCollection
x0, y0, x1, y1 = -10, -10, 10, 10
n = 11
lines = []
for x in np.linspace(x0, x1, n):
lines.append(((x, y0), (x, y1)))
for y in np.linspace(y0, y1, n):
lines.append(((x0, y), (x1, y)))
grid = MultiLineString(lines)
x = np.linspace(-9, 9, 200)
y = np.sin(x)*x
line = LineString(np.c_[x, y])
fig, ax = pl.subplots()
for i, segment in enumerate(line.difference(grid)):
x, y = segment.xy
pl.plot(x, y)
pl.text(np.mean(x), np.mean(y), str(i))
lc = LineCollection(lines, color="gray", lw=1, alpha=0.5)
ax.add_collection(lc);
The result:
To not use shapely, and do it yourself:
import pylab as pl
import numpy as np
from matplotlib.collections import LineCollection
x0, y0, x1, y1 = -10, -10, 10, 10
n = 11
xgrid = np.linspace(x0, x1, n)
ygrid = np.linspace(y0, y1, n)
x = np.linspace(-9, 9, 200)
y = np.sin(x)*x
t = np.arange(len(x))
idx_grid, idx_t = np.where((xgrid[:, None] - x[None, :-1]) * (xgrid[:, None] - x[None, 1:]) <= 0)
tx = idx_t + (xgrid[idx_grid] - x[idx_t]) / (x[idx_t+1] - x[idx_t])
idx_grid, idx_t = np.where((ygrid[:, None] - y[None, :-1]) * (ygrid[:, None] - y[None, 1:]) <= 0)
ty = idx_t + (ygrid[idx_grid] - y[idx_t]) / (y[idx_t+1] - y[idx_t])
t2 = np.sort(np.r_[t, tx, tx, ty, ty])
x2 = np.interp(t2, t, x)
y2 = np.interp(t2, t, y)
loc = np.where(np.diff(t2) == 0)[0] + 1
xlist = np.split(x2, loc)
ylist = np.split(y2, loc)
fig, ax = pl.subplots()
for i, (xp, yp) in enumerate(zip(xlist, ylist)):
pl.plot(xp, yp)
pl.text(np.mean(xp), np.mean(yp), str(i))
lines = []
for x in np.linspace(x0, x1, n):
lines.append(((x, y0), (x, y1)))
for y in np.linspace(y0, y1, n):
lines.append(((x0, y), (x1, y)))
lc = LineCollection(lines, color="gray", lw=1, alpha=0.5)
ax.add_collection(lc);