In [1]: import numpy as np

In [2]: np.repeat(1 / 10**np.arange(1, 5), 2)[1:] * np.array([1., 5.]*4)[:-1]
Out[2]: array([0.1   , 0.05  , 0.01  , 0.005 , 0.001 , 0.0005, 0.0001])

Generalizing for any "pattern":

def rates(smallest_magnitude, pattern):
    n = len(pattern)
    pows = np.repeat(1 / 10**np.arange(1, smallest_magnitude), n)[(n-1):]
    mults = np.array(pattern * (smallest_magnitude - 1))[:-(n-1)]
    return np.round(pows * mults, smallest_magnitude)

Demo:

In [4]: print(*rates(5, [1, 5]))  # Your original 'pattern'
0.1 0.05 0.01 0.005 0.001 0.0005 0.0001

In [5]: print(*rates(5, [2, 4, 8]))
0.2 0.04 0.08 0.02 0.004 0.008 0.002 0.0004 0.0008 0.0002

In [6]: print(*rates(5, [3, 5, 7, 9]))
0.3 0.05 0.07 0.09 0.03 0.005 0.007 0.009 0.003 0.0005 0.0007 0.0009 0.0003

You can generate random numbers with a uniform distribution, and then sort the numbers into ascending order in the first part, and into descending order in the second part.

import numpy as np
np.random.seed(0)


def gen_rnd_sensor_data(low: float,
                        high: float,
                        n_incr: int,
                        n_decr: int) -> np.ndarray:
    incr = np.random.uniform(low=low, high=high, size=n_incr)
    incr.sort()
    decr = np.random.uniform(low=low, high=high, size=n_decr)
    decr[::-1].sort()
    return np.concatenate((incr, decr))

Then you can call this function with:

print(gen_rnd_sensor_data(0, 1, 5, 3))

This generates data within 0. and 1., the first 5 values are increasing, the last 3 are decreasing. Within the program, every time you call the function, you get different results, but if you rerun your program, you get the same results, so you can debug your program.