"""
@author : Zhaoqing Liu
@email : Zhaoqing.Liu-1@student.uts.edu.au
"""
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings("always")
COLOUR = ["b", "r", "g", "c", "y", "k", "m"]
# The colours above are equivalent to the colours below:
# COLOUR = ["blue", "red", "green", "cyan", "yellow", "black", "magenta"]
[docs]def plot_multi_lines(coordinates, title=None, x_label=None, y_label=None, x_limit=None, y_limit=None, x_ticks=None, y_ticks=None, legends=None, fig_name=None, enable_max_annot=False, enable_min_annot=False):
"""
Plot multiple lines in a figure.
Parameters
----------
coordinates : array-like
Where, the values (i.e. coordinates[:, 0]) corresponding to the
X-axis must be numeric and in ascending order.
title : str, default=None
x_label : str, default=None
y_label : str, default=None
x_limit : tuple, default=None
y_limit : tuple, default=None
legends : array-like, default=None
fig_name : str, default=None
fig_name is either a text or byte string giving the name (and the
path if the file isn't in the current working directory) of the
file to be opened or an integer file descriptor of the file to be
wrapped.
Returns
-------
"""
# 1st step: Create a figure as a canvas.
plt.figure()
# 2nd step: Plot on the figure.
# Plot all curves iteratively.
for i in range(1, coordinates.shape[1], 1):
x = coordinates[:, 0]
y = coordinates[:, i]
assert (len(x) > 1 and len(y) > 1), "Each line should have the coordinates of at least two points"
plt.plot(x, y, linewidth=1.0, linestyle="-", label=legends[int(i - 1)])
if enable_min_annot:
# Plot the minimum point and annotate it.
y_min = np.amin(y)
x_corr_min = x[int(np.argmin(y))]
plt.scatter(x_corr_min, y_min, s=10) # color="black"
plt.plot([x_corr_min, x_corr_min], [0, y_min], linestyle="--", lw=1.5) # color="black"
plt.annotate(r"$Minimum$", xy=(x_corr_min, y_min), xycoords="data", xytext=(+10, -30),
textcoords="offset points",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3, rad=0.3"))
if enable_max_annot:
# Plot the maximum point and annotate it.
y_max = np.amax(y)
x_corr_max = x[int(np.argmax(y))]
plt.scatter(x_corr_max, y_max, s=10) # color="magenta"
plt.plot([x_corr_max, x_corr_max], [0, y_max], linestyle="--", lw=1.5) # color="magenta"
plt.annotate(r"$Maximum$", xy=(x_corr_max, y_max), xycoords="data", xytext=(-70, -30),
textcoords="offset points",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3, rad=0.3"))
plt.grid(True, linestyle="--", linewidth=1, alpha=0.3)
plt.legend()
plt.title(title)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.xlim(x_limit)
plt.ylim(y_limit)
plt.xticks(x_ticks)
plt.yticks(y_ticks)
# # Set axes.
# ax = plt.gca()
# ax.spines["top"].set_color("none")
# ax.spines["right"].set_color("none")
# ax.xaxis.set_ticks_position("bottom")
# ax.yaxis.set_ticks_position("left")
# ax.spines["bottom"].set_position(("data", 0))
# ax.spines["left"].set_position(("data", 0))
# Save the figure into a file.
if fig_name is not None:
plt.savefig(fname=fig_name)
# 3rd step: Show the figure.
plt.show()