Code for outset Rain Example

example-rain.py

from datetime import datetime, timedelta
import itertools as it
import typing

from matplotlib import pyplot as plt
from matplotlib import text as mpl_text
import numpy as np
import outset as otst
import opytional as opyt
import pandas as pd
import seaborn as sns

# ----- prepare data -----
nwls = "NW Lysimeter\n(35.18817624°N, -102.09791°W)"
swls = "SW Lysimeter\n(35.18613985°N, -102.0979187°W)"
df = pd.read_csv("https://osf.io/6mx3e/download")
df[nwls] = df["NW precip in mm"]
df[swls] = df["SW precip in mm"]
df["Max precip"] = np.maximum(df["SW precip in mm"], df["NW precip in mm"])

march_df = df[  # filter down to just data from March 2019
    (df["Decimal DOY"] >= 59) & (df["Decimal DOY"] <= 90)
].reset_index()

# ----- setup axes grid -----
grid = otst.OutsetGrid(  # initialize axes grid manager
    data=[
        (71.6, 0, 72.2, 2),  # (x0, y0, x1, y1) region to outset
        (59, 0, 90, 0.2),
        (81.3, 0, 82.2, 16),
    ],
    x="Time",
    y="Max precip",
    marqueeplot_kws={"frame_outer_pad": 0, "mark_glyph_kws": {"zorder": 11}},
    marqueeplot_source_kws={"zorder": 10, "frame_face_kws": {"zorder": 10}},
    aspect=2,
    col_wrap=2,
)

# ----- plot content -----
grid.broadcast(  # apply white underlay for lineplot to all axes
    plt.stackplot,
    march_df["Decimal DOY"].to_numpy(),
    march_df["Max precip"].to_numpy(),
    colors=["white"],
    lw=20,
    edgecolor="white",
    zorder=10,
)
# draw semi-transparent filled lineplot on all axes for each lysimeter
for y, color in zip([nwls, swls], ["fuchsia", "aquamarine"]):
    grid.broadcast(
        plt.stackplot,
        march_df["Decimal DOY"].to_numpy(),
        march_df[y].to_numpy(),
        colors=[color],
        labels=[y],
        lw=2,
        edgecolor=color,
        alpha=0.4,
        zorder=10,
    )

# dispatch marquee render
grid.marqueeplot(equalize_aspect=False)  # allow axes aspect ratios to vary


# ----- replace numeric axes tick labels with datetimes -----
def to_dt(day_of_year: float, year: int = 2019) -> datetime:
    """Convert decimal day of the year to a datetime object."""
    return datetime(year=year, month=1, day=1) + timedelta(days=day_of_year)


def format_tick_value(
    prev_value: typing.Optional[mpl_text.Text],
    value: mpl_text.Text,
) -> str:
    decimal_doy = float(value.get_text())
    prev_decimal_doy = opyt.apply_if(prev_value, lambda x: float(x.get_text()))
    if int(decimal_doy) == opyt.apply_if(prev_decimal_doy, int):
        return to_dt(decimal_doy).strftime("%H:%M")
    elif decimal_doy % 1.0 < 1 / 24:
        return to_dt(decimal_doy).strftime("%b %-d")
    else:
        return to_dt(decimal_doy).strftime("%H:%M\n%b %-d")


for ax in grid.axes.flat:
    ax.set_xticks(
        # keep only x ticks that are within axes limits
        [val for val in ax.get_xticks() if np.clip(val, *ax.get_xlim()) == val]
    )
    ax.set_xticklabels(
        list(
            it.starmap(
                format_tick_value,
                it.pairwise((None, *ax.get_xticklabels())),
            ),
        ),
    )

# ----- finalize plot -----
grid.axes.flat[0].legend(
    loc="upper left",
    bbox_to_anchor=(0.08, 0.95),
    bbox_transform=grid.figure.transFigure,
    frameon=True,
)
grid.set_ylabels("Precipitation (mm)")

plt.show()