Contents
py4DSTEM Parallax Reconstruction Notebook
Imports¶
%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import py4DSTEM
import ipywidgets
from IPython.display import display
style = {'description_width': 'initial'}
Load Data¶
file_path = 'data/'
file_data_01 = file_path + 'parallax_apoferritin_simulation_100eA2_01.h5'
file_data_02 = file_path + 'parallax_apoferritin_simulation_100eA2_02.h5'
dataset_01 = py4DSTEM.read(file_data_01)
dataset_02 = py4DSTEM.read(file_data_02)
dataset = py4DSTEM.DataCube(np.hstack((dataset_01.data,dataset_02.data)),calibration=dataset_01.calibration)
dataset
DataCube( A 4-dimensional array of shape (24, 48, 128, 128) called 'datacube',
with dimensions:
Rx = [0.0,10.666666666666666,21.333333333333332,...] A
Ry = [0.0,10.666666666666666,21.333333333333332,...] A
Qx = [0.0,0.1538085070134974,0.3076170140269948,...] mrad
Qy = [0.0,0.1538085070134974,0.3076170140269948,...] mrad
)
py4DSTEM Reconstruct¶
with plt.ioff():
parallax = py4DSTEM.process.phase.Parallax(
datacube=dataset,
energy = 300e3,
object_padding_px=(8,8),
).preprocess(
edge_blend=4,
plot_average_bf=False,
).reconstruct(
alignment_bin_values=[32,32,16,16,8,8],
progress_bar=False,
figsize=(10,4.5),
cmap='gray',
)
fig_reconstruct = plt.gcf()
fig_reconstruct
Measured Cross Corellation Shifts¶
def return_plot_ind(
gpts=parallax._region_of_interest_shape,
dp_mask=parallax._dp_mask,
plot_arrow_freq=1
):
""" """
dp_mask_ind = np.nonzero(dp_mask)
xx, yy = np.meshgrid(np.arange(gpts[0]),np.arange(gpts[1]),indexing='ij')
freq_mask = np.logical_and(xx % plot_arrow_freq == 0, yy % plot_arrow_freq == 0)
masked_ind = np.logical_and(freq_mask, dp_mask)
plot_ind = masked_ind[dp_mask_ind]
return plot_ind
def return_static_quiver_plot(kxy, uv, plot_ind, ax, color="red", title="measured bright field shifts"):
""" """
quiver = ax.quiver(
kxy[plot_ind, 1],
kxy[plot_ind, 0],
uv[plot_ind,1],
uv[plot_ind,0],
angles="xy",
scale_units="xy",
scale=1,
color=color
)
kr_max = np.linalg.norm(kxy,axis=1).max()
ax.set_xlim([-1.2 * kr_max, 1.2 * kr_max])
ax.set_ylim([-1.2 * kr_max, 1.2 * kr_max])
ax.set_title(title)
ax.set_ylabel(r"$k_x$ [$A^{-1}$]")
ax.set_xlabel(r"$k_y$ [$A^{-1}$]")
ax.set_aspect("equal")
return quiver
kxy = parallax._kxy
measured_shifts = parallax._xy_shifts * np.array(parallax._reciprocal_sampling)[None]
plot_ind = return_plot_ind(plot_arrow_freq=4)
dpi=72
with plt.ioff():
dpi = dpi
measured_quiver_fig, measured_quiver_ax = plt.subplots(figsize=(350/dpi,350/dpi), dpi=dpi)
measured_quiver = return_static_quiver_plot(
kxy,
measured_shifts,
plot_ind,
measured_quiver_ax,
)
measured_quiver_fig.canvas.resizable = False
measured_quiver_fig.canvas.header_visible = False
measured_quiver_fig.canvas.footer_visible = False
measured_quiver_fig.canvas.toolbar_visible = True
measured_quiver_fig.canvas.layout.width = '350px'
measured_quiver_fig.canvas.layout.height = '380px'
measured_quiver_fig.canvas.toolbar_position = 'bottom'
measured_quiver_fig.tight_layout()
def update_measured_quiver(change):
plot_arrow_freq = change["new"]
plot_ind = return_plot_ind(plot_arrow_freq=plot_arrow_freq)
new_shifts = measured_shifts[plot_ind]
new_kxy = kxy[plot_ind]
measured_quiver.N = new_kxy.shape[0]
measured_quiver.XY = np.fliplr(new_kxy)
measured_quiver.set_offsets(np.fliplr(new_kxy))
measured_quiver.set_UVC(new_shifts[:,1],new_shifts[:,0])
measured_quiver_fig.canvas.draw_idle()
return None
arrow_freq_slider_measured = ipywidgets.IntSlider(min=1, max=16, step=1, value=4, style=style, description="plot arrow frequency")
arrow_freq_slider_measured.observe(update_measured_quiver,names='value')
display(
ipywidgets.VBox(
[
arrow_freq_slider_measured,
measured_quiver_fig.canvas
],
layout=ipywidgets.Layout(align_items='center',width='400px'))
)
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