Note
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Time Series Plot¶
What it's for: A Time Series plot visualizes how one or more variables change over a continuous temporal interval.
When to use: Use this for monitoring data, model output at a specific location, or area-averaged values over time. It is the primary tool for identifying trends, diurnal cycles, seasonal patterns, and episodic events.
How to read: * X-axis: Represents Time (UTC or local). * Y-axis: Represents the value of the variable. * Interpretation: Look for temporal trends, variability, and the timing of maximum/minimum values. Multiple lines can be used to compare models against observations or different model scenarios.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from monet_plots.plots.timeseries import TimeSeriesPlot
dates = pd.date_range("2023-01-01", periods=100, freq="D")
values = np.cumsum(np.random.normal(0, 1, 100)) + 50
df = pd.DataFrame({"time": dates, "values": values})
plot = TimeSeriesPlot(df=df, figsize=(12, 6))
plot.plot(x="time", y="values", title="Daily Time Series", ylabel="Temperature (°C)")
plt.show()
Total running time of the script: ( 0 minutes 0.417 seconds)
Download Python source code: plot_timeseries.py