Note
Click here to download the full example code
Xarray Integration¶
What it's for:
This example demonstrates how MONET Plots integrates directly with xarray data
structures.
When to use:
Use this whenever your data is already stored in xarray DataArray or Dataset
formats. MONET Plots will automatically leverage the coordinates and attributes
(like units, long_name, and standard_name) to automate plot labeling and
formatting.
How to read: * Axes/Labels: Notice that the axes labels and plot title are automatically populated from the xarray metadata. * Interpretation: The plot is interpreted according to its specific visualization type (in this case, a Time Series), but with significantly less manual configuration required.
import numpy as np
import pandas as pd
import xarray as xr
from monet_plots.plots.timeseries import TimeSeriesPlot
# Create sample xarray data
dates = pd.date_range("2023-01-01", periods=100, freq="h")
temperature = (
15 + 5 * np.sin(np.arange(100) * 2 * np.pi / 24) + np.random.normal(0, 1, 100)
)
da = xr.DataArray(
temperature,
dims=["time"],
coords={"time": dates},
name="temperature",
attrs={"units": "°C", "long_name": "Air Temperature"},
)
# Create and plot
plot = TimeSeriesPlot(
da,
x="time",
y="temperature",
title="Temperature Time Series",
ylabel="Temperature (°C)",
)
ax = plot.plot()
plot.save("temperature_timeseries.png")
plot.close()
Total running time of the script: ( 0 minutes 0.241 seconds)
Download Python source code: plot_xarray_integration.py