In a domain where the phrase “a picture is worth a thousand words” rings particularly true, the journey of data visualization has witnessed a metamorphosis from simplistic graphs to today’s highly interactive and dynamic plots. Amidst this evolution, the quaint charm and professional allure of old-school styling inherent in figures from erstwhile technical papers have garnered a unique reverence. It’s this vintage aesthetic that
smplotlib, a Python library, strives to emulate, providing a bridge to the venerable SuperMongo (SM) aesthetics in the modern realm of Python-based data plotting.
pip install smplotlib
Let’s delve into the utilization of
smplotlib with a hands-on example. Though it’s important to note that
smplotlib appears to be a personalized or auxiliary library, the following example demonstrates how one might structure a complex plot using
matplotlib, and it’s here where
smplotlib could potentially be integrated to imbue the plot with a vintage aesthetic, assuming
smplotlib provides such styling functionalities.
import matplotlib.pyplot as plt import numpy as np import smplotlib # Assuming smplotlib provides styling functionalities # Generate some data x = np.linspace(0, 10, 100) y1 = np.sin(x) y2 = np.cos(x) y3 = np.tan(x) # Create the plot fig, ax = plt.subplots() # Plot the functions ax.plot(x, y1, label='sin(x)') ax.plot(x, y2, label='cos(x)') ax.plot(x, y3, label='tan(x)') # Add labels and legend ax.set_xlabel('X-axis Label') ax.set_ylabel('Y-axis Label') ax.set_title('Trigonometric Functions') ax.legend() # Set axis limits for better visualization ax.set_ylim([-10, 10]) # Assuming smplotlib provides a function called style_plot (this is a made-up function, as smplotlib's actual usage is not clear) # smplotlib.style_plot(ax) # Show the plot plt.show()
In the code snippet above:
- We utilize
numpyto generate a series of
xvalues and calculate
yvalues for sine, cosine, and tangent functions.
- We employ
plt.subplots()to create a figure and axis for plotting.
ax.plot()method is used to plot each function, specifying a label for each that will be used in the legend.
- Labels for the axes, a title for the plot, and a legend indicating which line corresponds to which function are added using
ax.set_ylim()is used to limit the Y-axis to a range that makes the graph readable, as the tangent function has vertical asymptotes that would otherwise “zoom out” the graph.
- A hypothetical
smplotlib.style_plot(ax)function is invoked to style the plot, assuming such a function exists in
This piece of code illustrates how one can create a more complex plot and incorporate labels using Matplotlib. It’s a placeholder where
smplotlib could potentially be utilized to apply vintage styling, aligning with the aesthetic of old-school technical papers.
smplotlib encapsulates more than just a stylistic add-on; it’s a homage to the timeless essence of clarity and precision that were the hallmarks of figures in bygone eras. The ability to recreate such an aesthetic in today’s fast-evolving visualization landscape not only pays homage to the past but also provides a stylistic choice that stands out amidst modernist designs.
This library is an open-source initiative, hosted on GitHub, welcoming contributions from anyone resonating with the cause of preserving the classical styling in contemporary data storytelling.
In an academic and professional world that’s continually chasing the ‘new’,
smplotlib offers a quaint respite, taking one back to the roots where simplicity and elegance were the narrators of data tales.