I feel you need to upsample / interpolate the
vector with fewer samples to get more samples and
downsample / decimate the
vector with higher samples to get fewer samples (
In essence matching the sampling rate of both the
vectors ).

I used `scipy.signal.resample`

to do the up / down sampling.

I tried to simulate your situation using two
random vectors of unequal sample sizes.

See if this helps you out :

```
import numpy as np
from scipy import signal
# scipy.signal module contains a interpolator /
decimator
import matplotlib.pyplot as plt
# Creating random vectors for a and b
vector_a = np.sin(2*3.14*100*np.arange(130))
# Sine signal with 100Hz freq and 130 time samples
vector_b = np.cos(2*3.14*100*np.arange(80))
# Cosine signal with 100Hz freq and 80 time
samples
# To avoid bias towards any one vector length take
the
# mean of the two sample lengths as the common
sample length
common_no_of_samples = (vector_a.shape[0] +
vector_b.shape[0]) // 2
# 105 Samples
# Upsample vector_a to have common_no_of_samples
vector_a = signal.resample(vector_a,
common_no_of_samples)
# Downsample vector_b to have common_no_of_samples
vector_b = signal.resample(vector_b,
common_no_of_samples)
fig, ax = plt.subplots()
ax.plot(np.arange(common_no_of_samples), vector_a,
'k-')
ax.plot(np.arange(common_no_of_samples), vector_b,
'c--')
# Where np.arange(common_no_of_samples) refers to
the common time axis
# vector_a and vector_b are the resampled vectors.
```

If you want as points in you could do
:

```
time_axis =
np.arange(common_no_of_samples)
vector_a = np.dstack((vector_a, time_axis))
```

This will generate points of the form :

```
array([[[ 2.23656191e-02,
0.00000000e+00],
[ -3.96584073e-01, 1.00000000e+00],
[ -7.01262520e-01, 2.00000000e+00],
[ -9.31867589e-01, 3.00000000e+00],
[ -9.95165113e-01, 4.00000000e+00],
[ -9.24625413e-01, 5.00000000e+00],
[ -6.96587056e-01, 6.00000000e+00],
[ -3.74795767e-01, 7.00000000e+00],
[ 1.59956385e-02, 8.00000000e+00],
[ 3.94192306e-01, 9.00000000e+00],
[ 7.20969109e-01, 1.00000000e+01],
[ 9.28803144e-01, 1.10000000e+01],
[ 1.00160878e+00, 1.20000000e+01],
[ 9.13659002e-01, 1.30000000e+01],
[ 6.91934367e-01, 1.40000000e+01],
[ 3.57910455e-01, 1.50000000e+01],
```