Contents
py4DSTEM Parallax Upsampling 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¶
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',
plot_aligned_bf=False,
)
Subpixel Alignment¶
parallax.subpixel_alignment(
kde_upsample_factor=4,
plot_upsampled_BF_comparison=False,
plot_upsampled_FFT_comparison=False,
);
dpi=72
with plt.ioff():
fig, axs = plt.subplots(2,2,height_ratios=[1,2],figsize=(675/dpi,500/dpi),dpi=dpi)
cropped_object = parallax._crop_padded_object(parallax._recon_BF)
cropped_object_reference_aligned = parallax._crop_padded_object(
parallax._recon_BF_subpixel_aligned, upsampled=True
)
extent = [
0,
parallax._scan_sampling[1] * cropped_object.shape[1],
parallax._scan_sampling[0] * cropped_object.shape[0],
0,
]
axs[0,0].imshow(
cropped_object,
extent=extent,
cmap='gray',
)
axs[0,0].set_title("Aligned Bright Field")
im = axs[0,1].imshow(
cropped_object_reference_aligned,
extent=extent,
cmap='gray',
)
axs[0,1].set_title("Upsampled Bright Field")
for ax in axs[0]:
ax.set_ylabel("x [A]")
ax.set_xlabel("y [A]")
reciprocal_extent = [
-0.5 / (parallax._scan_sampling[1] / parallax._kde_upsample_factor),
0.5 / (parallax._scan_sampling[1] / parallax._kde_upsample_factor),
0.5 / (parallax._scan_sampling[0] / parallax._kde_upsample_factor),
-0.5 / (parallax._scan_sampling[0] / parallax._kde_upsample_factor),
]
xp = parallax._xp
asnumpy = parallax._asnumpy
nx, ny = parallax._recon_BF_subpixel_aligned.shape
kx = xp.fft.fftfreq(nx, d=1)
ky = xp.fft.fftfreq(ny, d=1)
k = xp.fft.fftshift(xp.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2))
recon_fft = xp.fft.fftshift(
xp.abs(xp.fft.fft2(parallax._recon_BF)) / np.prod(parallax._recon_BF.shape)
)
sx, sy = recon_fft.shape
pad_x_post = (nx - sx) // 2
pad_x_pre = nx - sx - pad_x_post
pad_y_post = (ny - sy) // 2
pad_y_pre = ny - sy - pad_y_post
pad_recon_fft = asnumpy(
xp.pad(
recon_fft, ((pad_x_pre, pad_x_post), (pad_y_pre, pad_y_post))
)
* k
)
upsampled_fft_reference = asnumpy(
xp.fft.fftshift(
xp.abs(xp.fft.fft2(parallax._recon_BF_subpixel_aligned))
/ (nx * ny)
)
* k
)
_, vmin, vmax = py4DSTEM.visualize.return_scaled_histogram_ordering(
upsampled_fft_reference
)
im_fft_aligned = axs[1,0].imshow(
pad_recon_fft,
extent=reciprocal_extent,
vmin=vmin,
vmax=vmax,
cmap="gray",
)
axs[1,0].set_title("Aligned Bright Field FFT")
im_fft = axs[1,1].imshow(
upsampled_fft_reference,
extent=reciprocal_extent,
vmin=vmin,
vmax=vmax,
cmap="gray",
)
axs[1,1].set_title("Upsampled Bright Field FFT")
for ax in axs[1]:
ax.set_ylabel(r"$k_x$ [$A^{-1}$]")
ax.set_xlabel(r"$k_y$ [$A^{-1}$]")
fig.canvas.resizable = False
fig.canvas.header_visible = False
fig.canvas.footer_visible = False
fig.canvas.toolbar_visible = True
fig.canvas.layout.width = '675px'
fig.canvas.layout.height = '525px'
fig.canvas.toolbar_position = 'bottom'
fig.tight_layout()
def update_upsampled_image(
upsample_factor=4
):
""" """
parallax.subpixel_alignment(
kde_upsample_factor=upsample_factor,
plot_upsampled_BF_comparison=False,
plot_upsampled_FFT_comparison=False,
);
cropped_object = parallax._crop_padded_object(
parallax._recon_BF_subpixel_aligned, upsampled=True
)
_cropped_object, _vmin, _vmax = py4DSTEM.visualize.return_scaled_histogram_ordering(cropped_object)
im.set_data(_cropped_object)
im.set_clim(vmin=_vmin, vmax=_vmax)
xp = parallax._xp
asnumpy = parallax._asnumpy
nx, ny = parallax._recon_BF_subpixel_aligned.shape
kx = xp.fft.fftfreq(nx, d=1)
ky = xp.fft.fftfreq(ny, d=1)
k = xp.fft.fftshift(xp.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2))
upsampled_fft = asnumpy(
xp.fft.fftshift(
xp.abs(xp.fft.fft2(parallax._recon_BF_subpixel_aligned))
/ (nx * ny)
)
* k
)
_upsampled_fft, _vmin, _vmax = py4DSTEM.visualize.return_scaled_histogram_ordering(upsampled_fft)
im_fft.set_data(_upsampled_fft)
im_fft.set_clim(vmin=_vmin, vmax=_vmax)
pad_x_post = (nx - sx) // 2
pad_x_pre = nx - sx - pad_x_post
pad_y_post = (ny - sy) // 2
pad_y_pre = ny - sy - pad_y_post
pad_recon_fft = asnumpy(
xp.pad(
recon_fft, ((pad_x_pre, pad_x_post), (pad_y_pre, pad_y_post))
)
* k
)
im_fft_aligned.set_data(pad_recon_fft)
im_fft_aligned.set_clim(vmin=_vmin, vmax=_vmax)
fig.canvas.draw_idle()
return None
upsample_factor_slider = ipywidgets.FloatSlider(min=1.5, max=6, step=0.25, value=4, style=style, description="upsampling factor")
ipywidgets.interactive_output(
update_upsampled_image,
{
'upsample_factor': upsample_factor_slider,
}
)
None
display(
ipywidgets.VBox(
[
upsample_factor_slider,
fig.canvas
],
layout=ipywidgets.Layout(align_items='center',width='675px'))
)
Loading...
Aberration Correction¶
parallax.aberration_fit();
Rotation of Q w.r.t. R = -12.790 deg
Astigmatism (A1x,A1y) = (560,-1010) Ang
Aberration C1 = -14202 Ang
Defocus dF = 14202 Ang
Transpose = False
with plt.ioff():
parallax = parallax.aberration_correct(
figsize=(6,3),
cmap='gray'
)
fig_correction = plt.gcf()
fig_correction