cSAXS/cdtools
0
0
Fork 0
mirror of https://github.com/cdtools-developers/cdtools.git synced 2026-09-09 13:02:41 +02:00
Code Issues Packages Projects Releases Wiki Activity

.

Browse Source
This commit is contained in:
Anastasiia Kutakh
2021-08-27 12:35:06 -04:00
parent d7697433ba
commit d48624bf23
1 changed files with 13 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
CDTools/models/polarized_fancy_ptycho.py
+13 -12
View File
@@ -3,6 +3,7 @@ from CDTools.models import CDIModel, FancyPtycho
from CDTools.datasets import Ptycho2DDataset
from CDTools import tools
from CDTools.tools import plotting as p
# from CDTools.tools import polarized_plotting as pp
from CDTools.tools import analysis
from matplotlib import pyplot as plt
from datetime import datetime
@@ -490,30 +491,30 @@ class PolarizedFancyPtycho(FancyPtycho):
plot_list = [
('',
lambda self, fig, dataset: self.plot_wavefront_variation(dataset,fig=fig,mode='root_sum_intensity',image_title='Root Summed Probe Intensities',image_colorbar_title='Square Root of Intensity'),
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='root_sum_intensity', image_title='Root Summed Probe Intensities', image_colorbar_title='Square Root of Intensity'),
lambda self: len(self.weights.shape) >= 2),
('',
lambda self, fig, dataset: self.plot_wavefront_variation(dataset,fig=fig,mode='amplitude',image_title='Probe Amplitudes (scroll to view modes)',image_colorbar_title='Probe Amplitude'),
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='amplitude', image_title='Probe Amplitudes (scroll to view modes)', image_colorbar_title='Probe Amplitude'),
lambda self: len(self.weights.shape) >= 2),
('',
lambda self, fig, dataset: self.plot_wavefront_variation(dataset,fig=fig,mode='phase',image_title='Probe Phases (scroll to view modes)',image_colorbar_title='Probe Phase'),
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='phase', image_title='Probe Phases (scroll to view modes)', image_colorbar_title='Probe Phase'),
lambda self: len(self.weights.shape) >= 2),
('Basis Probe Amplitudes (scroll to view modes)',
lambda self, fig: p.plot_amplitude(self.probe, fig=fig, basis=self.probe_basis,units=self.units)),
lambda self, fig: p.plot_amplitude(self.probe, fig=fig, basis=self.probe_basis, units=self.units)),
('Basis Probe Phases (scroll to view modes)',
lambda self, fig: p.plot_phase(self.probe, fig=fig, basis=self.probe_basis,units=self.units)),
lambda self, fig: p.plot_phase(self.probe, fig=fig, basis=self.probe_basis, units=self.units)),
('Average Density Matrix Amplitudes',
lambda self, fig: p.plot_amplitude(np.nanmean(np.abs(self.get_rhos()),axis=0), fig=fig),
lambda self: len(self.weights.shape) >=2),
lambda self, fig: p.plot_amplitude(np.nanmean(np.abs(self.get_rhos()), axis=0), fig=fig),
lambda self: len(self.weights.shape) >= 2),
('% Power in Top Mode (only accurate after tidy_probes)',
lambda self, fig, dataset: p.plot_nanomap(self.corrected_translations(dataset), analysis.calc_top_mode_fraction(self.get_rhos()), fig=fig,units=self.units),
lambda self: len(self.weights.shape) >=2),
lambda self, fig, dataset: p.plot_nanomap(self.corrected_translations(dataset), analysis.calc_top_mode_fraction(self.get_rhos()), fig=fig, units=self.units),
lambda self: len(self.weights.shape) >= 2),
('Object Amplitude',
lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis,units=self.units)),
lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis, units=self.units)),
('Object Phase',
lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis,units=self.units)),
lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis, units=self.units)),
('Corrected Translations',
lambda self, fig, dataset: p.plot_translations(self.corrected_translations(dataset), fig=fig,units=self.units)),
lambda self, fig, dataset: p.plot_translations(self.corrected_translations(dataset), fig=fig, units=self.units)),
('Background',
lambda self, fig: plt.figure(fig.number) and plt.imshow(self.background.detach().cpu().numpy()**2))
]