def _showMollweide(self, param=None):
""" This plot script is based on two scripts by their respective authors:
- PlotOD.py from cryoEF package
- https://github.com/PirateFernandez/python3_rln_scripts/blob/main/rln_star_2_mollweide_any_star.py
"""
import numpy as np
from matplotlib import spines
from scipy.stats import gaussian_kde
views = []
xplotter = EmPlotter(
windowTitle="Mollweide projection plot of orientation distribution"
)
fn = np.genfromtxt(self.protocol._getFileName('anglesFn'),
delimiter=' ')
phi = fn[:, 0]
theta = fn[:, 1]
# Convert degrees to radians and obey angular range conventions
x = phi / 180 * np.pi # x is the phi angle (longitude)
y = theta / 180 * np.pi # y is the theta angle (latitude)
y = -1 * y + np.pi / 2 # The convention in RELION is [0, 180] for theta,
# whereas for the projection function it is [90, -90], so this conversion is required.
vertical_rad = np.vstack([y, x])
m = gaussian_kde(vertical_rad)(vertical_rad)
ax = xplotter.createSubPlot('', 'phi', 'theta', projection="mollweide")
# Plot your points on the projection
#ax.plot(x, y, ',', alpha=0.5, color='#64B5F6') # alpha - transparency (from 0 to 1), color - specify hex code
a = ax.scatter(x, y, cmap='plasma', c=m, s=2, alpha=0.4)
# Draw the horizontal and the vertical grid lines. Can add more grid lines if required.
major_ticks_x = [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]
major_ticks_y = [-np.pi / 2, -np.pi / 4, 0, np.pi / 4, np.pi / 2]
ax.set_xticks(major_ticks_x)
ax.set_yticks(major_ticks_y)
ax.set_xticklabels([
'-180$^\circ$', '-90$^\circ$', '0$^\circ$', '90$^\circ$',
'180$^\circ$'
],
color='grey')
ax.set_yticklabels([
'-90$^\circ$', '-45$^\circ$', '0$^\circ$', '45$^\circ$',
'90$^\circ$'
],
color='grey')
# Set the color and the thickness of the grid lines
ax.grid(which='both', linestyle='--', linewidth=1, color='#555F61')
# Set the color and the thickness of the outlines
for child in ax.get_children():
if isinstance(child, spines.Spine):
child.set_color('#555F61')
xplotter.getColorBar(a)
xplotter.tightLayout()
xplotter.show()
return views.append(xplotter)
def _showHistogram(self, param=None):
fn = self.protocol._getFileName('output_hist')
with open(fn) as f:
views = []
numberOfBins = 10
plotter = EmPlotter()
plotter.createSubPlot("PSF Resolution histogram", "Resolution (A)",
"Ang (str)")
resolution = [float(line.strip()) for line in f]
plotter.plotHist(resolution, nbins=numberOfBins)
plotter.show()
return views.append(plotter)
def visualize(self, obj, **kwargs):
self.prot = obj
# Experimental
x = np.asarray([float(xi.strip()) for xi in self.prot.x.get().split(',')])
p = np.asarray([float(xi.strip()) for xi in self.prot.p.get().split(',')])
if self.prot.descending:
x=np.flip(x,0)
p=np.flip(p,0)
logx = np.log(x)
logx = np.insert(logx, 0, np.log(np.min(x)/2))
p=p/np.sum(p)
barx = []
bary = p
widths = []
locx = []
labels = []
for i in range (0,p.shape[0]):
barx.append(0.5*(logx[i]+logx[i+1]))
widths.append(logx[i+1]-logx[i])
locx.append(np.log(x[i]))
labels.append("log(%4.2f)"%x[i])
plotter =EmPlotter(style='seaborn-whitegrid')
ax = plotter.createSubPlot("Particle size distribution", "log(Particle size)", "Fraction")
ax.bar(barx, bary, width=widths, linewidth=1, label="Experimental", edgecolor="black")
plt.xticks(locx, labels)
# Theoretical
fhSummary = open(self.prot._getPath("summary.txt"))
lineno=0
for line in fhSummary.readlines():
if lineno==0:
mu = float((line.split()[2]).split('=')[1])
elif lineno==1:
sigma = float((line.split()[2]).split('=')[1])
lineno+=1
fhSummary.close()
logx = np.arange(np.min(logx),np.max(logx),(np.max(logx)-np.min(logx))/100)
theox = norm.pdf(logx,mu,sigma)
ax.plot(logx,theox/np.max(theox)*np.max(bary), color='red', label='Theoretical (log-normal)')
# General
ax.legend()
ax.grid(True)
plotter.show()
def createCtfPlot(ctfSet, ctfId):
""" Create EmPlotter instance. """
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
xplotter = EmPlotter(windowTitle='CTFFind results')
plot_title = getPlotSubtitle(ctfModel)
a = xplotter.createSubPlot(plot_title, 'Spacial frequency (1/A)',
'Amplitude (or cross-correlation)')
legendName = ['Amplitude spectrum', 'CTF Fit', 'Quality of fit']
_plotCurves(a, fn)
xplotter.showLegend(legendName, loc='upper right')
a.set_ylim([-0.1, 1.1])
a.grid(True)
xplotter.show()
def show(self, form, *args):
prot = form.protocol
defocusGroups = prot.createDefocusGroups()
print(defocusGroups)
plotter = EmPlotter(windowTitle='%d Defocus Groups' %
len(defocusGroups),
figsize=(8, 6))
ax = plotter.createSubPlot("", "defocus (A)", "count", 1, 1)
for group in defocusGroups:
ax.bar(group.minDefocus,
group.count,
group.maxDefocus - group.minDefocus,
align='edge')
plotter.show()
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.log"
xplotter = EmPlotter(windowTitle='CTF Fitting')
plot_title = getPlotSubtitle(ctfModel)
a = xplotter.createSubPlot(plot_title, 'Resolution (Angstroms)', 'CTF')
a.invert_xaxis()
version = Plugin.getActiveVersion()
curves = [1, 4, 5] if version == '1.18' else [1, 3, 4]
for i in curves:
_plotCurve(a, i, fn)
xplotter.showLegend([
'simulated CTF',
# 'equiphase avg.',
# 'bg', # only for v1.18
'equiphase avg. - bg',
'cross correlation'
])
a.grid(True)
xplotter.show()
