[Python] How can i set the ticks of ax and ax2 to the same position but I have different...

I have this python code snippet that sets the ticks of the two y-axes. But I can't figure out how I can synchronize the axe's scales.

if y_data_left:
all_left = pd.concat([y for _, y, _, _ in y_data_left])
ymin_left, ymax_left = all_left.min(), all_left.max()
if ymin_left > 0:
ymin_left = 0
if ymin_left == ymax_left or ymax_left - ymin_left <= 0.01:
ymax_left += 1
margin_left = ymax_left * 1.1
ax.set_ylim(ymin_left, margin_left)
major_y_left = get_nice_tick_interval(ymin_left, ymax_left, 5) / tick_factor
ax.yaxis.set_major_locator(ticker.MultipleLocator(major_y_left))
ax.yaxis.set_minor_locator(ticker.MultipleLocator(major_y_left / 5))

fig.canvas.draw()

# Y-Achse rechts unabhängig skalieren, aber gleiche Positionen übernehmen
if ax2 and y_data_right:
all_right = pd.concat([y for _, y, _, _ in y_data_right])

ymin_right = 0 if ymin_right > 0 else all_right.min()
ymax_right = all_right.max()

ax2.set_ylim(ymin_right, ymax_right)

major_left = ax.get_yticks()
n_ticks = len(major_left)

major_right_interval = get_nice_tick_interval(ymin_right, ymax_right, n_ticks - 1)
yt_right = np.arange(ymin_right, ymax_right + major_right_interval, major_right_interval)
ax2.set_yticks(yt_right)

# Set Minor-Ticks
if n_ticks > 1:
minor_interval = major_right_interval / 5
ax2.yaxis.set_minor_locator(MultipleLocator(minor_interval))

ax2.tick_params(axis="y", labelright=True)


Here is the get_nice_tick_interval function:

def get_nice_tick_interval(vmin, vmax, target_steps=5, factor=1.0):
target_steps *= factor
if vmin == vmax:
vmin -= 1
vmax += 1
raw_interval = (vmax - vmin) / target_steps
exponent = np.floor(np.log10(raw_interval))
fraction = raw_interval / 10**exponent
if fraction <= 1:
nice_fraction = 1
elif fraction <= 2:
nice_fraction = 2
elif fraction <= 5:
nice_fraction = 5
else:
nice_fraction = 10
return nice_fraction * 10**exponent


I have tried to get the pixels of the plot like right_ticks = ax2.transData.inverted().transform(np.column_stack([np.zeros_like(left_pixels), left_pixels]))[:, 1] but that didn't work. I hoped that the ticks would be across from each other but that hasn't been the case.

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