Master Publication-Quality Plots in Python with Matplotlib, SciencePlots, and Proplot

Python‑Matplotlib

import numpy as np
import matplotlib.pyplot as plt
# Build data

def model(x, p):
    return x ** (2 * p + 1) / (1 + x ** (2 * p))

x = np.linspace(0.75, 1.25, 201)
fig, ax = plt.subplots(figsize=(4,3), dpi=200)
for p in [10, 15, 20, 30, 50, 100]:
    ax.plot(x, model(x, p), label=p)

Set Global Chart Property Variables

For projects that generate many figures, you can change common attributes such as font, font size, line width, and tick size before plotting:

plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['font.size'] = 18
plt.rcParams['axes.linewidth'] = 2

Remove Axis Spines

Some publication styles require hiding the top and right spines:

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

Tick Parameters

Adjust tick length, width, direction, and add minor ticks:

# Add minor ticks
from matplotlib.pyplot import MultipleLocator
fig, ax = plt.subplots(figsize=(4,3), dpi=200)

yminorLocator = MultipleLocator(.25/2)  # y‑axis minor tick interval
xminorLocator = MultipleLocator(.25/2)  # x‑axis minor tick interval
ax.yaxis.set_minor_locator(yminorLocator)
ax.xaxis.set_minor_locator(xminorLocator)

# Major tick settings
ax.tick_params(which='major', length=5, width=1.5, direction='in', top='on', right='on')
# Minor tick settings
ax.tick_params(which='minor', length=3, width=1, direction='in', top='on', right='on')

Axis Labels

Add descriptive axis labels with custom font size and padding:

ax.set_xlabel('Voltage (mV)', fontsize=13, labelpad=5)
ax.set_ylabel('Current ($\mu$A)', fontsize=13, labelpad=5)

Summary – Apply All Settings

Combine the global settings, spine removal, tick configuration, labels, grid, legend, title, and annotation into a single script:

plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['font.size'] = 12
plt.rcParams['axes.linewidth'] = 1
plt.rcParams['legend.title_fontsize'] = 9

fig, ax = plt.subplots(figsize=(4,3), dpi=200)
colors = ["#0073C2", "#EFC000", "#868686", "#CD534C", "#7AA6DC", "#003C67"]
for p, c in zip([10, 15, 20, 30, 50, 100], colors):
    ax.plot(x, model(x, p), color=c, label=p)

# Minor ticks
yminorLocator = MultipleLocator(.25/2)
xminorLocator = MultipleLocator(.25/2)
ax.yaxis.set_minor_locator(yminorLocator)
ax.xaxis.set_minor_locator(xminorLocator)

# Tick parameters
ax.tick_params(which='major', length=5, width=1.5, direction='in', top='on', right='on')
ax.tick_params(which='minor', length=3, width=1, direction='in', top='on', right='on')

# Axis labels
ax.set_xlabel('Voltage (mV)', fontsize=13, labelpad=5)
ax.set_ylabel('Current ($\mu$A)', fontsize=13, labelpad=5)

# Grid, legend, title, annotation
ax.grid(which='major', ls='--', alpha=.8, lw=.8)
ax.legend(fontsize=8, loc='upper left', title='Order')
ax.set_title('Default Plot Style Of Matplotlib', fontsize=14, pad=10)
ax.text(.87, .06, '
Visualization by DataCharm', transform=ax.transAxes,
        ha='center', va='center', fontsize=5)

SciencePlots Library

SciencePlots is a Python package for scientific figure styling.

Installation: pip install SciencePlots The package provides three main types of configuration files:

Theme files (e.g., "science", "ieee", "scatter") that control figure size, resolution, font choices, line styles, and marker sizes.

Color palettes (e.g., "bright", "high-vis") that change curve colors.

Grid styles that add predefined grid settings.

Two typical usage patterns:

Apply a style globally in the current script:

import matplotlib.pyplot as plt
plt.style.use('science')  # use the Science theme

Note: Using the global style may sometimes cause unexpected figure size ratios.

Apply styles locally with a context manager:

# Second method
with plt.style.context(['ieee', 'grid']):
    x = np.linspace(0.0, 2.0)
    y = np.sin(x)
    plt.plot(x, y, label="Line1")
    plt.plot(x, 2*y, label="Line2")
    plt.plot(x, 3*y, label="Line3")
    plt.plot(x, 4*y, label="Line4")
    plt.plot(x, 2.2*y, label="Line5")
    plt.autoscale(tight=True)
    plt.legend(title='Order', edgecolor='k')
    plt.show()

Proplot

Installation:

pip install proplot
conda install -c conda-forge proplot

Example of creating a styled figure with Proplot:

import proplot as plot
fig, ax = plot.subplots(figsize=(4,3.5), dpi=100)
for p in [10, 15, 20, 30, 50, 100]:
    ax.plot(x, model(x, p), label=p)
ax.format(
    title='Example Of Proplot Plot Style',
    abc=True, abcloc='ur', abcstyle='(A)',
    xlabel='Voltage (mV)', ylabel='Current ($\mu$A)',
    xtickdir='in', ytickdir='in',
    xtickloc='both', ytickloc='both',
    xgridminor=False, ygridminor=False)
ax.legend(ncols=1)

References

https://blog.csdn.net/Z987421/article/details/122057801