Examples

Simple Line Plot

import numpy as np
import gleplot as glp

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

fig = glp.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.plot(x, y, label='sin(x)')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Sine Wave')
ax.legend()
fig.savefig('sine.pdf')

Multiple Series

import numpy as np
import gleplot as glp

x = np.linspace(0, 2*np.pi, 100)

fig = glp.figure(figsize=(10, 6))
ax = fig.add_subplot(111)

ax.plot(x, np.sin(x), label='sin(x)', color='blue')
ax.plot(x, np.cos(x), label='cos(x)', color='red', linestyle='--')
ax.plot(x, np.tan(x), label='tan(x)', color='green', linestyle=':')

ax.set_xlabel('Angle (radians)')
ax.set_ylabel('Value')
ax.set_title('Trigonometric Functions')
ax.set_ylim(-3, 3)
ax.legend(loc='upper right')
ax.grid(True)

fig.savefig('trig_functions.pdf')

Subplots

Using add_subplot(rows, cols, index):

import numpy as np
import gleplot as glp

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

fig = glp.figure(figsize=(12, 8))

# Plot 1: Line plot
ax1 = fig.add_subplot(2, 2, 1)
ax1.plot(x, np.sin(x))
ax1.set_title('sin(x)')

# Plot 2: Scatter plot
ax2 = fig.add_subplot(2, 2, 2)
ax2.scatter(x[::5], np.cos(x[::5]), color='red')
ax2.set_title('cos(x)')

# Plot 3: Bar plot
ax3 = fig.add_subplot(2, 2, 3)
categories = ['A', 'B', 'C', 'D']
values = [10, 24, 36, 18]
ax3.bar(categories, values, color='green')
ax3.set_title('Bar Chart')

# Plot 4: Multiple lines
ax4 = fig.add_subplot(2, 2, 4)
ax4.plot(x, np.sin(x), label='sin')
ax4.plot(x, np.cos(x), label='cos')
ax4.set_title('Overlaid Functions')
ax4.legend()

fig.savefig('subplots.pdf')

Using the subplots() convenience function:

import numpy as np
import gleplot as glp

fig, axes = glp.subplots(1, 3, figsize=(18, 5))

x = np.linspace(0, 2*np.pi, 80)
axes[0].plot(x, np.sin(x), color='blue')
axes[0].set_title('sin(x)')

axes[1].plot(x, np.cos(x), color='red')
axes[1].set_title('cos(x)')

axes[2].plot(x, np.tan(x), color='green')
axes[2].set_title('tan(x)')
axes[2].set_ylim(-5, 5)

fig.savefig('trig_panels.pdf')

Each subplot generates its own begin graph / end graph block in the GLE script, positioned via amove with computed coordinates.

Shared Axes Layouts

Stacked plots with a shared x-axis:

import numpy as np
import gleplot as glp

x = np.linspace(0, 10, 200)
fig, axes = glp.subplots(3, 1, sharex=True, figsize=(8, 10))

axes[0].plot(x, np.sin(x), color='blue')
axes[0].set_title('Signal A')

axes[1].plot(x, np.cos(x), color='red')
axes[1].set_title('Signal B')

axes[2].plot(x, np.sin(x) + np.cos(x), color='green')
axes[2].set_title('Combined')
axes[2].set_xlabel('Time')

fig.savefig('shared_x_stack.gle')

Side-by-side plots with a shared y-axis:

fig, axes = glp.subplots(1, 3, sharey=True, figsize=(16, 5))
axes[0].scatter(x[::8], np.sin(x[::8]))
axes[1].scatter(x[::8], np.cos(x[::8]))
axes[2].scatter(x[::8], np.sin(x[::8]) + np.cos(x[::8]))
axes[0].set_ylabel('Amplitude')
fig.savefig('shared_y_panels.gle')

Custom Data File Prefix

Use data_prefix to control sidecar .dat names written next to your GLE file.

import numpy as np
import gleplot as glp

x = np.linspace(0, 1, 50)

fig = glp.figure(figsize=(8, 5), data_prefix='calibration')
ax = fig.add_subplot(111)
ax.plot(x, x, label='linear')
ax.plot(x, x**2, label='quadratic')
ax.legend()

fig.savefig('calibration_curves.gle')

This writes files such as calibration_0.dat and calibration_1.dat.

Scatter with Different Sizes

import numpy as np
import gleplot as glp

np.random.seed(42)
x = np.random.rand(50)
y = np.random.rand(50)
sizes = np.random.rand(50) * 100

fig = glp.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.scatter(x, y, s=sizes, alpha=0.6, color='blue')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Scatter Plot with Variable Sizes')
fig.savefig('scatter_sizes.pdf')

Filled Area

import numpy as np
import gleplot as glp

x = np.linspace(0, 4*np.pi, 100)
y1 = np.sin(x)
y2 = np.sin(x) * 0.5

fig = glp.figure(figsize=(10, 6))
ax = fig.add_subplot(111)
ax.fill_between(x, y1, y2, alpha=0.3, color='blue', label='Filled area')
ax.plot(x, y1, color='darkblue', label='Upper bound')
ax.plot(x, y2, color='lightblue', label='Lower bound')
ax.set_title('Filled Between Plot')
ax.legend()
fig.savefig('filled_area.pdf')

Logarithmic Scale

import numpy as np
import gleplot as glp

x = np.logspace(0, 3, 100)
y = x**2

fig = glp.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('X (log scale)')
ax.set_ylabel('Y (log scale)')
ax.set_title('Power Law on Log-Log Plot')
ax.grid(True, which='both')
fig.savefig('loglog.pdf')

Error Bars

Symmetric vertical error bars with a constant value:

import numpy as np
import gleplot as glp

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([2.1, 3.9, 6.2, 7.8, 10.1, 12.3, 13.8, 16.2])

fig = glp.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.errorbar(x, y, yerr=0.5, marker='o', fmt='-', color='blue',
           label='Measurement')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Distance (m)')
ax.set_title('Symmetric Error Bars')
ax.legend()
fig.savefig('errorbars.pdf')

Asymmetric error bars (different up/down magnitudes):

ax.errorbar(x, y, yerr=([2, 3, 4, 5, 3], [5, 4, 6, 3, 7]),
           marker='s', fmt='none', color='red', capsize=0.15)

Both vertical and horizontal error bars:

ax.errorbar(x, y, yerr=yerr_array, xerr=xerr_array,
           marker='o', fmt='none', color='blue', capsize=0.1)

Additional Advanced Example Scripts

The repository includes additional runnable scripts under examples/advanced for focused workflows:

text_annotations.py - annotation alignment and boxed labels
per_element_styling.py - different style choices per element
batch_figures.py - loop-based generation of many figures
line_from_file.py - model overlays from existing data files
data_prefix.py - deterministic sidecar file naming patterns

Run the full suite from the repository root:

cd examples
python run_all.py