Writing your own figure style file

In GraphingLib, all objects have default values for most parameters. For example, a curve object has a default line width, and a figure object has a default background color. These defaults are governed by the style you choose to give your figure when creating it:

fig = gl.Figure(figure_style='plain')
fig2 = gl.Figure(figure_style='dark')

GraphingLib has a number of built-in styles which are showcased at the bottom of this page. The parameters controlled by styles are called “defaults” for a reason; they can always be overridden by explicitely specifying a parameter when creating an object:

fig = gl.Figure(figure_style="dark") # uses the "dark" style

fig2 = gl.Figure(figure_style="dark")
fig2.set_visual_params(axes_edge_color="red") # "dark" style, but axes color is overridden

If no style is specified, the user’s default style is used. This default style can be set by the user using the set_default_style function or by using GraphingLib’s Style Editor. Once set, the default style will be saved and used for all figures created without a specified style. The default style can also be retrieved using the get_default_style function.

print(gl.get_default_style()) # prints "plain"
gl.set_default_style("dark")
print(gl.get_default_style()) # prints "dark"

When you install GraphingLib for the first time, the default style is the “plain” style. You can also create your own styles or modify existing ones. To do this, you can use GraphingLib’s Style Editor as described below.

GraphingLib’s Style Editor

If you have GraphingLib installed, you can run the following command in the terminal:

glse

You will be greeted with a GUI which allows you to create, edit, and delete styles. The Style Editor interface consists of a left panel and a right panel, which let you customize and previewing styles respectively. In the left panel, you can adjust all the style settings for the currently opened style. These changes are immediately reflected in the right panel, which displays a figure with the applied customizations. You can see how the style appears in different contexts by browsing through the example figures provided within the interface. If Auto Switch is checked, the example figure will automatically switch to the most appropriate example depending on what customization tab you are currently on. Additionally, there is an option to upload a custom Python script that creates a GraphingLib figure, allowing you to apply and preview the style on their own figures.

To learn more about the Style Editor, you can check out the Style Editor documentation here:

Go to Style Editor Documentation

GraphingLib Styles Showcase

Here are the currently available built-in styles in GraphingLib:

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

Here is the code used to generate the above figures if you want to try it out with your own styles:

import numpy as np
import graphing_lib as gl

style = "plain" # Change this to the style you want to showcase
colors = gl.get_colors(style)

######################
# Figure 1: Show main colors
curves = gl.Hlines(
    [i / 8 + 0.1 for i in range(8)], 0, 1, line_widths=9, colors=colors[0:8]
)
fig1 = gl.Figure(y_lim=(-0.1, 1.1))
fig1.add_elements(curves)
#######################
# Figure 2: Histogram
data = np.random.normal(0, 1, 1000)
hist = gl.Histogram(data, 20, normalize=True)
hist._label = None
hist.add_pdf()
fig2 = gl.Figure(y_lim=(0, 0.5))
fig2.add_elements(hist)
#######################
# Figure 3: Curve and filled area
curve = gl.Curve.from_function(
    lambda x: (-np.sin(x * 2 * np.pi) + x) * x, 0, 1, "A curve", colors[1]
)
curve.get_area_between(0, 1, fill_between=True, fill_color=colors[1])
fig3 = gl.Figure()
fig3.add_elements(curve)
#######################
# Figure 4: Stream plot
field = gl.Stream.from_function(
    lambda x, y: (np.sin(x) + np.cos(y), (y + x) ** 2),
    (-2, 2),
    (-2, 2),
    color=colors[2],
)
fig4 = gl.Figure()
fig4.add_elements(field)
#######################
# Figure 5: Curve fit
x_data = np.linspace(0, 2 * np.pi, 100)
y_data = np.sin(x_data) + np.random.normal(0, 0.1, 100)
scatter = gl.Scatter(x_data, y_data, face_color=colors[3], edge_color=colors[3])
fit = gl.FitFromSine(scatter, color=colors[5])
fit.label = None
fig5 = gl.Figure()
fig5.add_elements(scatter, fit)
#######################
# Create MultiFigure from all figures
canvas = gl.MultiFigure(
    5, 4, reference_labels=False, title=style, size=(8, 8), figure_style=style
)

canvas.add_figure(fig1, 0, 0, 1, 4)
canvas.add_figure(fig2, 1, 0, 2, 2)
canvas.add_figure(fig3, 1, 2, 2, 2)
canvas.add_figure(fig4, 3, 0, 2, 2)
canvas.add_figure(fig5, 3, 2, 2, 2)
canvas.set_visual_params()
canvas.show()