Finding intersection of two graphs with different numpy sizes

Question

I would like to find the intersection of two graphs. It took me 674 points to plot the first graph and only 14 points to plot the second graph.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv("test1.csv",,skiprows=range(9),names=['A', 'B', 'C','D'])

df2 = pd.read_csv("test2.csv",skiprows=range(1),names=['X','Y'])

x1 = df['A'].tolist()
x1 = np.array(x1)
y1 = df['D'].tolist()
y1 = np.array(y1)
x2 = df2['X'].tolist()
x2 = np.array(x2)
y2 = df2['Y'].tolist()
y2 = np.array(y2)

idx = np.argwhere(np.diff(np.sign(y1 - y2))).flatten()

fig, ax = plt.subplots()

ax.plot(x1, y1, 'blue')
ax.plot(x2, y2, 'red')

plt.show()

However, I am getting this error from the code above due to the different sizes of numpy. Any ways I can solve this?

operands could not be broadcast together with shapes (674,) (14,)

Answer 1

You should compute interpolations of both curves with scipy.interpolate.interp1d, then you can calculate indeces of intersection points with the method you used.
I assume you have two curves with x1, x2, y1 and y2 coordinates, with different lengths and x axis limits:

x1 = np.linspace(-2, 12, 674)
x2 = np.linspace(0, 8, 14)
y1 = np.sin(x1)
y2 = np.cos(x2) + 1

So, you have to compute interpolation functions:

f1 = interp1d(x1, y1, kind = 'linear')
f2 = interp1d(x2, y2, kind = 'linear')

Then, you need to evaluate new curves on a common x axis, so new curves will have the same length:

xx = np.linspace(max(x1[0], x2[0]), min(x1[-1], x2[-1]), 1000)

y1_interp = f1(xx)
y2_interp = f2(xx)

Finally, you can compute indices of interpolation points as you already did:

idx = np.argwhere(np.diff(np.sign(y1_interp - y2_interp))).flatten()

Complete Code

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d


x1 = np.linspace(-2, 12, 674)
x2 = np.linspace(0, 8, 14)
y1 = np.sin(x1)
y2 = np.cos(x2) + 1

f1 = interp1d(x1, y1, kind = 'linear')
f2 = interp1d(x2, y2, kind = 'linear')

xx = np.linspace(max(x1[0], x2[0]), min(x1[-1], x2[-1]), 1000)

y1_interp = f1(xx)
y2_interp = f2(xx)

idx = np.argwhere(np.diff(np.sign(y1_interp - y2_interp))).flatten()


fig, ax = plt.subplots()

ax.plot(x1, y1, 'blue', label = 'y1')
ax.plot(x2, y2, 'red', label = 'y2')
for index in idx:
    ax.plot(xx[index], y1_interp[index], marker = 'o', markerfacecolor = 'black', markeredgecolor = 'black')

plt.show()

Plot