#!/usr/bin/env python
##############################################################################
#
# diffpy.morph by DANSE Diffraction group
# Simon J. L. Billinge
# (c) 2008 Trustees of the Columbia University
# in the City of New York. All rights reserved.
#
# File coded by: Chris Farrow
#
# See AUTHORS.txt for a list of people who contributed.
# See LICENSE.txt for license information.
#
##############################################################################
"""Collection of plotting functions (originally specifically) for
PDFs."""
import matplotlib.pyplot as plt
import numpy
from bg_mpl_stylesheets.styles import all_styles
plt.style.use(all_styles["bg-style"])
# FIXME - make this return the figure object in the future, so several views
# can be composed.
[docs]
def plot_funcs(pairlist, labels=None, offset="auto", xmin=None, xmax=None):
"""Plots several functions f(x) on top of one another.
Parameters
----------
pairlist
Iterable of (x, fx) pairs to plot.
labels
Iterable of names for the pairs. If this is not the same length as
the pairlist, a legend will not be shown (default []).
offset
Offset to place between plots. Functions will be sequentially shifted
in the y-direction by the offset. If offset is 'auto' (default), the
optimal offset will be determined automatically.
xmin
The minimum r-value to plot. If this is None (default), the lower
bound of the function is not altered.
xmax
The maximum r-value to plot. If this is None (default), the upper
bound of the function is not altered.
"""
if labels is None:
labels = []
if offset == "auto":
offset = _find_offset(pairlist)
gap = len(pairlist) - len(labels)
labels = list(labels)
labels.extend([""] * gap)
for idx, pair in enumerate(pairlist):
x, fx = pair
plt.plot(x, fx + idx * offset, label=labels[idx])
plt.xlim(xmin, xmax)
if gap == 0:
plt.legend(loc=0)
plt.legend()
# plt.xlabel(r"$r (\mathrm{\AA})$")
# plt.ylabel(r"$G (\mathrm{\AA}^{-1})$")
plt.show()
return
[docs]
def compare_funcs(
pairlist,
labels=None,
xmin=None,
xmax=None,
show=True,
maglim=None,
mag=5,
rw=None,
legend=True,
l_width=1.5,
):
"""Plot two functions g(r) on top of each other and difference
curve.
The second function will be shown as blue circles below and the first as
a red line. The difference curve will be in green and offset for clarity.
Parameters
----------
pairlist
Iterable of (r, gr) pairs to plot
labels
Iterable of names for the pairs. If this is not the same length as
the pairlist, a legend will not be shown (default []).
xmin
The minimum x-value to plot. If this is None (default), the lower
bound of the function is not altered.
xmax
The maximum x-value to plot. If this is None (default), the upper
bound of the function is not altered.
show
Show the plot (default True)
maglim
Point after which to magnify the signal by mag. If None (default), no
magnification will take place.
mag
Magnification factor (default 5)
rw
Rw value to display on the plot, if any.
legend
Display the legend (default True).
"""
if labels is None:
labels = [2]
labeldata = None
labelfit = None
else:
labeldata = labels[1]
labelfit = labels[0]
xfit, fxfit = pairlist[0]
xdat, fxdat = pairlist[1]
# View min and max
xvmin = max(xfit[0], xdat[0])
xvmin = xmin or xvmin
xvmax = min(xfit[-1], xdat[-1])
xvmax = xmax or xvmax
gap = 2 - len(labels)
labels = list(labels)
labels.extend([""] * gap)
# Put fx1 on the same grid as xdat
ftemp = numpy.interp(xdat, xfit, fxfit)
# Calculate the difference
diff = fxdat - ftemp
# Put rw in the label
labeldiff = "difference" if len(labels) < 3 else labels[2]
if rw is not None:
labeldiff += " (Rw = %.3f)" % rw
# Magnify if necessary
if maglim is not None:
fxfit = fxfit.copy()
fxfit[xfit > maglim] *= mag
sel = xdat > maglim
fxdat = fxdat.copy()
fxdat[sel] *= mag
diff[sel] *= mag
ftemp[sel] *= mag
# Determine the offset for the difference curve.
sel = numpy.logical_and(xdat <= xvmax, xdat >= xvmin)
ymin = min(min(fxdat[sel]), min(ftemp[sel]))
ymax = max(diff[sel])
offset = -1.1 * (ymax - ymin)
# Scale the x-limit based on the r-extent of the signal. This gives a nice
# density of function peaks.
xlim = xvmax - xvmin
scale = xlim / 25.0
# Set a reasonable minimum of .8 and maximum of 1
scale = min(1, max(scale, 0.8))
figsize = [13.5, 4.5]
figsize[0] *= scale
fig = plt.figure(1, figsize=figsize)
# Get the margins based on the figure size
lm = 0.12 / scale
bm = 0.20 / scale
rm = 0.02 / scale
tm = 0.15 / scale
axes = plt.Axes(fig, [lm, bm, 1 - lm - rm, 1 - bm - tm])
fig.add_axes(axes)
plt.minorticks_on()
plt.plot(xdat, fxdat, linewidth=l_width, label=labeldata)
plt.plot(xfit, fxfit, linewidth=l_width, label=labelfit)
plt.plot(xdat, offset * numpy.ones_like(diff), linewidth=3, color="black")
diff += offset
plt.plot(xdat, diff, linewidth=l_width, label=labeldiff)
if maglim is not None:
# Add a line for the magnification cutoff
plt.axvline(
maglim,
0,
1,
linestyle="--",
color="black",
linewidth=1.5,
dashes=(14, 7),
)
# FIXME - look for a place to put the maglim
xpos = (xvmax * 0.85 + maglim) / 2 / (xvmax - xvmin)
if xpos <= 0.9:
plt.figtext(xpos, 0.7, "x%.1f" % mag, backgroundcolor="w")
# Get a tight view
plt.xlim(xvmin, xvmax)
ymin = min(diff[sel])
ymax = max(max(fxdat[sel]), max(ftemp[sel]))
yspan = ymax - ymin
# Give a small border to the plot
gap = 0.05 * yspan
ymin -= gap
ymax += gap
plt.ylim(ymin, ymax)
# Make labels and legends
# plt.xlabel(r"r ($\mathrm{\AA})$")
# plt.ylabel(r"G $(\mathrm{\AA}^{-1})$")
if legend:
plt.legend(
bbox_to_anchor=(0.005, 1.02, 0.99, 0.10),
loc=3,
ncol=3,
mode="expand",
borderaxespad=0,
)
if show:
plt.show()
return
[docs]
def plot_param(target_labels, param_list, param_name=None, field=None):
"""Plot Rw values for multiple morphs.
Parameters
----------
target_labels: list
Names (or field if --sort-by given) of each file acting as target for
the morph.
param_list: list
Contains the values of some parameter corresponding to each file.
param_name: str
Name of the parameter.
field: list or None
When not None and entries in field are numerical, a line chart of Rw
versus field is made.
When None (default) or values are non-numerical, it plots a bar chart
of Rw values per file.
"""
# ensure all entries in target_labels are distinct for plotting
unique_labels = set()
for idx in range(len(target_labels)):
item = target_labels[idx]
# if repeat found, add additional label
if item in unique_labels:
counter = 1
new_name = f"{item} ({counter})"
while new_name in unique_labels:
counter += 1
new_name = f"{item} ({counter})"
item = new_name
target_labels[idx] = item
unique_labels.update({item})
# Check if numerical field
numerical = True
if field is None:
numerical = False
else:
for item in target_labels:
if type(item) is not float:
numerical = False
if numerical:
# Plot the parameter against a numerical field
plt.plot(target_labels, param_list, linestyle="-", marker="o")
if param_name is not None:
plt.ylabel(rf"{param_name}")
plt.xlabel(rf"{field}")
plt.minorticks_on()
# Create bar chart for each file
else:
# Ensure file names do not crowd
bar_size = 1 # FIXME: depends on resolution
max_len = bar_size
for item in target_labels:
max_len = max(max_len, len(item))
angle = numpy.arccos(bar_size / max_len)
angle *= 180 / numpy.pi # Convert to degrees
plt.xticks(rotation=angle)
# Plot Rw for each file
plt.bar(target_labels, param_list)
if param_name is not None:
plt.ylabel(rf"{param_name}")
if field is None:
plt.xlabel(r"Target File")
else:
plt.xlabel(rf"{field}")
# Show plot
plt.tight_layout()
plt.show()
return
[docs]
def truncate_func(x, fx, xmin=None, xmax=None):
"""Truncate a function f(x) to specified bounds.
Parameters
----------
x
The x-values of the function f(x).
fx
Function f(x) values at each x-value.
xmin
The minimum x-value. If this is None (default), the lower bound of
the function is not altered.
xmax
The maximum x-value. If this is None (default), the upper bound of
the function is not altered.
Returns
-------
x, fx
Returns the truncated x, fx.
"""
if xmin is not None:
sel = x >= xmin
fx = fx[sel]
x = x[sel]
if xmax is not None:
sel = x <= xmax
fx = fx[sel]
x = x[sel]
return x, fx
def _find_offset(pairlist):
"""Find an optimal offset between functions."""
maxlist = [max(p[1]) for p in pairlist]
minlist = [min(p[1]) for p in pairlist]
difflist = numpy.subtract(maxlist[:-1], minlist[1:])
offset = 1.1 * max(difflist)
return offset