Note
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Scatter Plot with Regression¶
What it's for: A Scatter Plot visualizes the relationship between two continuous variables. This implementation often includes a linear regression line to summarize the trend.
When to use: Use this to assess the correlation between model output and observations, or to explore the relationship between two different physical variables (e.g., temperature vs. ozone concentration).
How to read: * X-axis: Typically the predictor or independent variable (e.g., Observations). * Y-axis: Typically the response or dependent variable (e.g., Model). * Regression Line: Shows the best-fit linear relationship. The slope and intercept provide information about systematic bias and scaling. * Interpretation: The tightness of the point cluster around the line indicates the strength of the correlation. Points on the 1:1 diagonal would represent a perfect match.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from monet_plots.plots.scatter import ScatterPlot
# 1. Prepare sample data
np.random.seed(42) # for reproducibility
n_samples = 100
# Simulate two correlated variables
x_data = np.random.normal(loc=10, scale=2, size=n_samples)
y_data = 0.7 * x_data + np.random.normal(loc=5, scale=1.5, size=n_samples)
df = pd.DataFrame({"predictor": x_data, "response": y_data})
# 2. Initialize and create the plot
plot = ScatterPlot(
df=df,
x="predictor",
y="response",
title="Scatter Plot of Response vs. Predictor",
figsize=(9, 7),
)
plot.plot(
scatter_kws={"alpha": 0.7, "s": 60, "edgecolor": "w"}, # kwargs for scatter points
line_kws={"color": "red", "linewidth": 2}, # kwargs for regression line
)
# 3. Add titles and labels
plot.ax.set_xlabel("Predictor Variable")
plot.ax.set_ylabel("Response Variable")
plot.ax.grid(True, linestyle="--", alpha=0.6)
plt.tight_layout()
plt.show()
Total running time of the script: ( 0 minutes 0.331 seconds)
Download Python source code: plot_scatter.py