import datetime

import matplotlib.pyplot as plt
import numpy as np

import matplotlib.dates as mdates
from zoneinfo import ZoneInfo

base = datetime.datetime(2005, 2, 1)
dates = [base + datetime.timedelta(hours=(2 * i)) for i in range(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))

for nn, ax in enumerate(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()