Quick Start Guide#

This guide covers the shortest path from installing plotez to creating line and scatter plots. Every example corresponds to a runnable script in the examples/ directory.

Minimal Example#

The absolute minimum code to produce a labeled plot. Pass x_label, y_label, and plot_title for axis and title labels.

import matplotlib.pyplot as plt
import numpy as np

from plotez import plot_xy

x = np.linspace(0, 10, 100)
y = np.sin(x)
plot_xy(x, y, data_label="X vs Y")
_images/RTD_E1_simple.png

Custom Labels#

Replace auto-generated labels with meaningful scientific ones. data_label appears in the legend; all label strings support LaTeX notation (e.g. r'$\sin(x)$').

import matplotlib.pyplot as plt
import numpy as np

from plotez import plot_xy

x = np.linspace(0, 10, 100)
y = np.sin(x)

plot_xy(
    x_data=x,
    y_data=y,
    x_label="Time (s)",
    y_label="Amplitude (V)",
    data_label="Channel A",
    plot_title="Sinusoidal Signal",
)
_images/RTD_E2_custom_labels.png

Scatter Plot#

Pass is_scatter=True to switch from a line to a scatter plot: same function, same parameters, one flag.

import matplotlib.pyplot as plt
import numpy as np

from plotez import plot_xy

# set a default generator for reproducibility
rng = np.random.default_rng(1234)

x = rng.random(100)
y = 2 * x + 1 + rng.random(x.shape) * 0.2
_images/RTD_E3_scatter_plot.png

Continue Learning#

  • Error Visualization covers error bars, asymmetric errors, and error bands.

  • Multi-Panel Layouts covers two-panel figures, arbitrary grids, shared axes, layout management, and return shapes.

  • Configuration and Validation covers reusable config objects, shorthand helpers, exceptions, validation, and label collections.

  • Data Workflows covers histograms, density plots, two-column files, and integration with existing matplotlib axes.

  • API Reference contains complete function signatures and parameter details.