importdatetimeimportmatplotlib.pyplotaspltimportnumpyasnpimportmatplotlib.datesasmdatesfromzoneinfoimportZoneInfobase=datetime.datetime(2005,2,1)dates= [base+datetime.timedelta(hours=(2*i))foriinrange(732)]N=len(dates)np.random.seed(19680801)y=np.cumsum(np.random.randn(N))fig,axs=plt.subplots(3,1,layout='constrained',figsize=(6,6))lims= [(np.datetime64('2024-10-27'),np.datetime64('2024-11-10')),        (np.datetime64('2024-11-11'),np.datetime64('2024-11-22')),        (np.datetime64('2005-02-03 11:00'),np.datetime64('2005-02-04 13:20'))        ]fig,axs=plt.subplots(3,1,layout='constrained',figsize=(6,6))fornn,axinenumerate(axs):# locator = mdates.AutoDateLocator()locator=mdates.AutoDateLocator(tz=ZoneInfo("America/Los_Angeles"))formatter=mdates.ConciseDateFormatter(locator)formatter.formats= ['%y',# ticks are mostly years'%b',# ticks are mostly months'%d',# ticks are mostly days'%H:%M',# hrs'%H:%M',# min'%S.%f', ]# secs# these are mostly just the level above...formatter.zero_formats= ['']+formatter.formats[:-1]# ...except for ticks that are mostly hours, then it is nice to have# month-day:formatter.zero_formats[3]='%d-%b'formatter.offset_formats= ['','%Y','%b %Y','%d %b %Y','%d %b %Y','%d %b %Y %H:%M', ]ax.xaxis.set_major_locator(locator)ax.xaxis.set_major_formatter(formatter)ax.plot(dates,y)ax.set_xlim(lims[nn])axs[0].set_title('Concise Date Formatter')plt.show()