generate_gestrue.py

import math
import matplotlib.pyplot as plt

timestamps = []
xs = []
ys = []

sample_rate = 4  # 10ms
x_begin = x_end = 0x285

#
# y_begin = 0x900
# y_end = 0x60
#

y_begin = 0x1C9
y_end = y_begin + (0x900 - 0x60)

flag = 2
ts = 0
total_ts = int(400 / sample_rate) + 1
amplitude = 50  # adjust this to change the "width" of the snake movement

n = 0  # number of last values that should be the same

ss_begin = 2  # initial skip step
m = 30  # last m percent where ss decreases to 0

skip_counter = 0  # counter for skip step

for ts in range(0, total_ts):  # 300ms
    if ts >= total_ts - n:
        y = y_end
    else:
        # Calculate y as a cubic easing out function of ts, then scale and shift it to the range [y_begin, y_end]
        t = ts / (total_ts - n)  # normalize t to the range [0, 1]
        y = int((y_end - y_begin) * ((t - 1) ** 3 + 1) + y_begin)

    # Calculate x as a cosine function of ts, then scale and shift it to the range [x_begin - amplitude, x_begin + amplitude]
    x = int(x_begin + amplitude * (math.cos(2 * math.pi * ts / total_ts) - 1))

    if ts == 0:
        flag = 0
    elif ts == total_ts - 1:
        flag = 1
    else:
        flag = 2

    # Calculate ss as a linear interpolation from ss_begin to 0 over the last m percent of total_ts
    if ts >= total_ts * (1 - m / 100):
        ss = int(
            ss_begin * (1 - (ts - total_ts * (1 - m / 100)) / (total_ts * m / 100))
        )
    else:
        ss = ss_begin

    # Ensure ss is never zero
    if ss == 0:
        ss = 1

    # Only print and append data if skip_counter is 0
    if skip_counter == 0:
        print(
            f"{hex(ts*sample_rate)[2:].upper()} {flag} 1 0 {hex(x)[2:].upper()} {hex(y)[2:].upper()}"
        )

        timestamps.append(ts * sample_rate)
        xs.append(x)
        ys.append(y)

    # Increment skip_counter and reset it to 0 if it reaches ss
    skip_counter = (skip_counter + 1) % ss

print()

# Plot the data
plt.plot(timestamps, xs, label="X values")
plt.plot(timestamps, ys, label="Y values")
plt.xlabel("Timestamps")
plt.ylabel("Values")
plt.title("Generated Data")
plt.legend()
plt.show()