"""Plot a single parameter."""
import itertools
import numpy as np
from matplotlib import pyplot as plt
from bioplottemplates import log
from bioplottemplates.libs import libmsg, libutil
from bioplottemplates.logger import S, T
[docs]def plot(
x_data,
y_data,
*,
labels="No label provided",
title=None,
xlabel=None,
ylabel=None,
colors=('b', 'g', 'r', 'c', 'm', 'y', 'k'),
alpha=0.7,
xmax=None,
xmin=None,
ymax=None,
ymin=None,
grid=True,
grid_color="lightgrey",
grid_ls="-",
grid_lw=1,
grid_alpha=0.5,
legend=True,
legend_fs=6,
legend_loc=4,
vert_lines=None,
figsize=(8, 6),
filename='plot_param.pdf',
**kwargs
):
"""
Plot a single plot with the combined RMSD.
Bellow parameters concern data representation and are considered
of highest importance because their incorrect use can mislead
data analysis and consequent conclusions.
Plot style parameters concernning only plot style, i.e., colors,
shapes, fonts, etc... and which do not distort the actual data,
are not listed in the paremeter list bellow. We hope these
parameter names are self-explanatory and are listed in the function
definition.
Parameters
----------
x_data : interable of numbers
Container of the X axis data. Should be accepted
by matplotlib.
y_data : np.ndarray, shape=(M,)
Container of the Y axis data.
Where M is the RMSDs data for the combined chains.
labels : str, optional
The label to represent in plot legend.
If a list of series is provided, a list of labels
can be provided as well.
Defauts to: "no labels provided".
filename : str, optional
The file name with which the plot figure will be saved
in disk. Defaults to rmsd_individual_chains_one_subplot.pdf.
You can change the file type by specifying its extention in
the file name.
fig_size : tuple of float or int
The size ratio of the subplot in the figure.
"""
log.info(T("Plotting parameter:"))
# prepares data in
y_data = np.array(y_data)
if y_data.ndim == 1:
y_data = y_data[np.newaxis, :]
plot_labels = libutil.make_list(labels)
plot_colors = libutil.make_list(colors)
plot_colors = itertools.cycle(plot_colors)
assert y_data.shape[0] == len(plot_labels), (
'{} vs {}'.format(y_data.shape[0], len(plot_labels)))
# done data preparation
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
plt.tight_layout(rect=[0.05, 0.02, 0.995, 0.985])
fig.suptitle(
title,
x=0.5,
y=0.990,
va="top",
ha="center",
)
for yy, ll in zip(y_data, plot_labels):
ax.plot(
x_data,
yy,
label=ll,
color=next(plot_colors),
alpha=alpha,
)
ax.set_xlabel(xlabel, weight='bold')
ax.set_ylabel(ylabel, weight='bold')
# setting axes scales
xmin = xmin or x_data[0]
xmax = xmax or x_data[-1]
ymin = ymin or np.array(y_data).min()
ymax = ymax or np.array(y_data).max()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
if grid:
ax.grid(
color=grid_color,
linestyle=grid_ls,
linewidth=grid_lw,
alpha=grid_alpha,
)
if isinstance(vert_lines, (list, tuple)):
for line in vert_lines:
ax.axvline(x=float(line), color='k')
if legend:
ax.legend(
fontsize=legend_fs,
loc=legend_loc,
)
fig.savefig(filename)
log.info(S(libmsg.fig_saved.format(filename)))
plt.close("all")
return
