plotreset 0.5.3

A wrapper for matplotlib library to make academic plots with ease.

plotreset

PlotReset is a Python package that provides a simple way to reset and customize matplotlib plot styles. Comes with a sensible set of defaults for academic use. You can also extend the styles by adding more templates. Save and load templates in a JSON format to reuse.

Installation

pip install plotreset

Usage

from plotreset import Styles
style=Styles('reset')

Then create a style object. Note that when you create the object with a specific style template name(reset, academic etc,.) it sets the style for you plots. All the plots you make after this will have the style applied.

style=Styles('academic')

Where academic is a plotreset style(you can write your own or modify academic defaults) where latex font and settings are preloaded.

To revert back to matplotlib default template simply create the object without any arguments

style=Styles()

Example:

Example.1 using the reset style template:

import matplotlib.pyplot as plt
import numpy as np
from plotreset import Styles

style = Styles("reset")

plt.figure()
x = np.linspace(-5, 5, 200)
y = 1 / (np.sqrt(2 * np.pi)) * np.exp(-(x**2) / 2)
y2 = 1 / (np.sqrt(2 * np.pi * 2)) * np.exp(-(x**2) / (2 * 0.5))
y3 = 1 / (np.sqrt(2 * np.pi * 2)) * np.exp(-(x**2) / (2 * 1.5))
plt.plot(x, y, label="Gaussian Distribution")
plt.plot(x, y2, label="Gaussian Distribution")
plt.plot(x, y3, label="Gaussian Distribution")
plt.savefig("examples/simple.svg")
plt.show()

You can update the style settings:

style.font.size = 18
style.font.family = "serif"
style.axes.grid = True
style.grid.alpha: 0.7

plt.plot(x, y, label="Gaussian Distribution")

Example.2 using the academic style template and cycles:

plotreset also comes with a predefined set of defaults that can be used for example:

to cycle through colors, linestyles, markers, etc.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from cycler import cycler

from plotreset import Styles, defaults

style = Styles("reset")
c1 = cycler(color=defaults.COLORS[:4])
c2 = cycler(linestyle=defaults.LINE_STYLES[:4])
c3 = cycler(marker=defaults.MARKERS[:4])


x = np.linspace(0, 2 * np.pi, 50)
offsets = np.linspace(0, 2 * np.pi, 8, endpoint=False)
yy = np.transpose([np.sin(x + phi) for phi in offsets])

fig = plt.figure(figsize=(10, 4))
with mpl.rc_context({"axes.prop_cycle": c1}):
    ax1 = fig.add_subplot(3, 1, 1)
    ax1.plot(yy)
    ax1.set_title("changing_colors")

with mpl.rc_context({"axes.prop_cycle": c1 + c2}):
    ax2 = fig.add_subplot(3, 1, 2)
    ax2.plot(yy)
    ax2.set_title("changing linestyle and color")

with mpl.rc_context({"axes.prop_cycle": c1 + c2 + c3}):
    ax3 = fig.add_subplot(3, 1, 3)
    ax3.plot(yy)
    ax3.set_title("changing linestyle, color and marker")

plt.show()

You can create your own templates and cycles and use them in the same way. (check the Add more styles section below for more details)

Example.3 script using the rc_context:

Changing the style using dot notation(style.font.size=10) will change the plot settings in thje global scope but if you would like to change the settings for a specific plot you can use the rc_context method.

import matplotlib as mpl
from plotreset import Styles

style = Styles("academic")

# Create example data
n = 30 * 75
p = 1 / 900
x = np.array(range(0, 15))

def res(n, p, x):
    return math.comb(n, x) * p**x * (1 - p) ** (n - x)

p_x = np.array([res(n, p, i) for i in x])

color = ["tab:blue"] * len(x)
color[0] = "tab:orange"

# The default behavior of academic style is not to draw a grid on the axes.
# We can change this behavior by using the `rc_context` method ...
# ...so that this setting only affect this plot
rc_context = {"axes.grid": True, "axes.axisbelow": True}

with mpl.rc_context(rc_context):
    plt.bar(x, p_x, color=color)
    plt.xticks(x)
    plt.ylabel("$\\mathrm{P(X)}$")
    plt.xlabel("$\\mathrm{X}$")
    plt.annotate(
        "$\\mathrm{P(X=0) = 0.082}$",
        xy=(x.max() / 2.0, p_x.max() / 2),
        ha="left",
        va="center",
    )
    plt.annotate(
        "$\\mathrm{P(X\\geq1) = 0.918}$",
        xy=(x.max() / 2.0, p_x.max() / 2 - 0.03),
        ha="left",
        va="center",
    )
    plt.annotate(
        "$\\mathrm{E(X) = 2.49}$",
        xy=(x.max() / 2.0, p_x.max() / 2 - 0.06),
        ha="left",
        va="center",
    )
plt.show()

Add more styles

You can add more styles in your scrit by creating a dictionary of style settings and call the register_template method to register the style.

import plotreset
my_template = {
    "axes.facecolor": "lightgray",
    "font.size": 14,
    # ... other style settings
}
plotreset.register_template("my_custom_style", my_template)

# Use custom template
styles = Styles("my_custom_style")

Save and Load templates from a file

You can load custom templates from a JSON file and save the current template to a JSON file. When initializing the Styles object, you can specify the path to the JSON.

The JSON file for custom settings should have the following structure:

{
  "templates": {
    "style_name1": {
      // all the rc parameters as key-value pairs Ex: "text.usetex": true
    }
    "style_name2": {
      // all the rc parameters as key-value pairs Ex: "font.size": 16,
    }
  }
}

You can give your style any name you want. But the top level JSON key should be templates.

from plotreset import Styles

# Load a custom style(style_name1) from a JSON file(custom_style_templates.json)
style = Styles("style_name1", "path/to/custom_style_templates.json")

You can also save the current style to a JSON:

style.save_current_template("my_new_style_name", "path/to/custom_style_templates.json")

If you sepecify overwrite=True in the save_current_template method and the same template name exist in the JSON file it will overwrite the template with updated style.

Here is an example JSON file with custom templates(this is the same as academic style):

{
  "templates": {
    "academic": {
      "text.usetex": true,
      "mathtext.fontset": "cm",
      "mathtext.fallback": "cm",
      "mathtext.default": "regular",
      "font.size": 16,
      "font.family": "cmr10",
      "axes.axisbelow": "line",
      "axes.unicode_minus": false,
      "axes.formatter.use_mathtext": true,
      "axes.prop_cycle": {
        "color": [
          "tab:red",
          "tab:blue",
          "tab:green",
          "tab:orange",
          "tab:purple",
          "tab:brown",
          "tab:pink",
          "tab:gray",
          "tab:olive",
          "tab:cyan",
          "k"
        ]
      },
      "axes.grid": false,
      "grid.linewidth": 0.6,
      "grid.alpha": 0.5,
      "grid.linestyle": "--",
      "xtick.top": false,
      "xtick.direction": "in",
      "xtick.minor.visible": false,
      "xtick.major.size": 6.0,
      "xtick.minor.size": 4.0,
      "ytick.right": false,
      "ytick.direction": "in",
      "ytick.minor.visible": false,
      "ytick.major.size": 6.0,
      "ytick.minor.size": 4.0,
      "figure.constrained_layout.use": true
    }
  }
}