"""The plt module provides convenience functions for creating matplotlib plots,
plus applying Lorentzian distributions about signals.
The plt module provides the following functions:
* add_lorentzians: Creates lineshape data from a provided linspace (array of x
coordinates) and peaklist).
* mplplot: Creates a lineshape plot from a peaklist and returns the x, y plot
data.
* mplplot_stick: Creates a "stick" (matplotlib "stem" plot) plot from a
peaklist and returns the x, y plot data.
* mplplot_lineshape: Creates a lineshape plot from provided x, y lineshape data
and returns the x, y plot data.
"""
import numpy as np
from nmrsim.math import add_lorentzians
from nmrsim._utils import low_high
# Pyplot assumes a TkAgg backend as a default. This can cause problems in
# environments where Tkinter is not available (e.g. some Unix systems;
# BeeWare's Briefcase packaging for Windows).
import matplotlib
try:
import tkinter as tk # noqa: F401
except ImportError:
matplotlib.use('Agg')
print('WARNING: Tkinter not found--plots will not display on screen!')
import matplotlib.pyplot as plt
# TODO: especially considering the possible swap to the 'Agg' backend,
# makes more intuitive sense for plot functions to return a plot object
# and not the coordinates.
# TODO: possibly refactor plot routines to avoid repetitive code
[docs]def mplplot(peaklist, w=1, y_min=-0.01, y_max=1, points=800, limits=None):
"""
A matplotlib plot of the simulated lineshape for a peaklist.
Parameters
----------
peaklist : [(float, float)...]
A list of (frequency, intensity) tuples.
w : float
Peak width at half height
y_min : float or int
Minimum intensity for the plot.
y_max : float or int
Maximum intensity for the plot.
points : int
Number of data points.
limits : (float, float)
Frequency limits for the plot.
Returns
-------
x, y : numpy.array
Arrays for frequency (x) and intensity (y) for the simulated lineshape.
"""
peaklist.sort()
if limits:
l_limit, r_limit = low_high(limits)
else:
l_limit = peaklist[0][0] - 50
r_limit = peaklist[-1][0] + 50
x = np.linspace(l_limit, r_limit, points)
plt.ylim(y_min, y_max)
plt.gca().invert_xaxis() # reverses the x axis
y = add_lorentzians(x, peaklist, w)
# noinspection PyTypeChecker
lines = plt.plot(x, y)
print(lines)
plt.show()
return x, y
[docs]def mplplot_stick(peaklist, y_min=-0.01, y_max=1, limits=None):
"""A matplotlib plot of a spectrum in "stick" (stem) style.
Parameters
----------
peaklist : [(float, float)...]
A list of (frequency, intensity) tuples.
y_min : float or int
Minimum intensity for the plot.
y_max : float or int
Maximum intensity for the plot.
limits : (float, float)
Frequency limits for the plot.
Returns
-------
numpy.array, numpy.array
The arrays of x and y coordinates used for the plot.
"""
fig, ax = plt.subplots()
if limits:
l_limit, r_limit = low_high(limits)
else:
l_limit = sorted(peaklist)[0][0] - 50
r_limit = sorted(peaklist)[-1][0] + 50
x, y = zip(*peaklist)
# If the next two lines are omitted, there is no baseline from the outer
# peaks to the left/right limits. Until a matplotlib solution is found,
# using this hack of adding two outer miniscule peaks to extend the
# baseline.
x = np.append(x, [l_limit, r_limit])
y = np.append(y, [0.001, 0.001])
plt.xlim(r_limit, l_limit)
plt.ylim(y_min, y_max)
ax.stem(x, y, markerfmt=' ', basefmt='C0-',
use_line_collection=True) # suppress warning until mpl 3.3
plt.show()
return x, y
# TODO: or return plt object? Decide behavior.
[docs]def mplplot_lineshape(x, y, y_min=None, y_max=None, limits=None):
"""
A matplotlib plot that accepts arrays of x and y coordinates.
Parameters
----------
x : array-like
The list of x coordinates.
y : array-like
The list of y coordinates.
y_min : float or int
Minimum intensity for the plot. Default is -10% max y.
y_max : float or int
Maximum intensity for the plot. Default is 110% max y.
limits : (float, float)
Frequency limits for the plot.
Returns
-------
x, y : The original x, y arguments.
"""
if limits:
l_limit, r_limit = low_high(limits)
else:
l_limit = min(x) # x[0] # assumes x already sorted low->high
r_limit = max(x) # x[-1]
if y_min is None or y_max is None: # must test vs None so that 0 = True
margin = max(y) * 0.1
if y_min is None:
y_min = min(y) - margin
if y_max is None:
y_max = max(y) + margin
plt.xlim(r_limit, l_limit) # should invert x axis
plt.ylim(y_min, y_max)
plt.plot(x, y)
plt.show()
return x, y