"""
Datetime axes, categorical axes, and declarative tick specs
=============================================================

Three complementary ways to feed an axis something other than plain numbers.
A datetime-like ``x``/``y`` (``numpy.datetime64``, ``datetime.date``, or a
list of either) plots proportionally to real elapsed time -- irregular gaps
between timestamps stay irregular, the same way matplotlib's own date axes
work. A plain list of strings plots as a categorical axis instead: each
distinct value gets an integer position in the order it's first seen, shared
across every plotting call on that axis. And ``set_xlocator()``/
``set_xformat()`` take a small, JSON-serializable spec -- not a
matplotlib-style ``Locator``/``Formatter`` object -- naming a tick-placement
or label-formatting rule, so the exact same rule replays correctly when an
interactive figure is panned or zoomed in the browser.
"""
import numpy as np
import plotpress

fig, axes = plotpress.subplots(2, 2, figsize=(11.0, 8.0))

# -- Datetime axis: irregular timestamps, spaced by real elapsed time -------
ax = axes[0, 0]
dates = np.array(
    ["2024-01-05", "2024-01-20", "2024-03-01", "2024-03-10", "2024-06-15"],
    dtype="datetime64[D]",
)
temps = [4.2, 5.1, 9.8, 10.5, 18.3]
ax.plot(dates, temps, marker="o", color="#d62728")
# The Jan 5 -> Jan 20 gap (15 days) is visibly narrower than Mar 10 -> Jun 15
# (97 days) -- a categorical axis would have space these five points evenly.
ax.set_title("Datetime axis (time-proportional spacing)")
ax.set_ylabel("Temperature (C)")

# -- Categorical axis: strings shared across two series on one axis ---------
ax = axes[0, 1]
ax.bar(["Mon", "Tue", "Wed", "Thu", "Fri"], [12, 19, 8, 15, 21],
      color="#1f77b4", label="This week", alpha=0.6, zorder=1)
# A second call naming an overlapping category set lands on the same
# positions instead of appending duplicate slots.
ax.plot(["Mon", "Wed", "Fri"], [14, 10, 18], color="#ff7f0e",
        marker="s", label="Last week", linewidth=2, zorder=2)
ax.legend()
ax.set_title("Categorical axis (shared positions across calls)")

# -- Declarative locator: ticks at every multiple of pi/2 --------------------
ax = axes[1, 0]
x = np.linspace(0, 3 * np.pi, 300)
ax.plot(x, np.sin(x), color="#2ca02c")
ax.set_xlim(0, 3 * np.pi)
ax.set_xlocator({"kind": "multiple", "base": np.pi / 2})
ax.set_xformat("pi")   # 0, pi/2, pi, 3pi/2, ...
ax.set_title("Declarative locator + formatter (multiples of pi/2)")

# -- Declarative formatter: comma-separated thousands on the y-axis ---------
ax = axes[1, 1]
ax.bar(["Q1", "Q2", "Q3", "Q4"], [1_250_000, 2_480_000, 1_960_000, 3_120_000],
      color="#9467bd")
ax.set_yformat("comma")           # "1,250,000" instead of "1.25e6"
ax.set_title("Declarative formatter (comma-separated thousands)")
ax.set_ylabel("Revenue")

fig.suptitle("Datetime axes, categorical axes, and declarative tick specs")
fig.tight_layout()