diff --git a/TEM.md b/TEM.md index b0e56b2..b7b8fef 100644 --- a/TEM.md +++ b/TEM.md @@ -12,33 +12,49 @@ numbering: ### Wavefunctions After building an atomic potential as described in the [](#algorithms_page), the first step in a TEM simulation is to choose the wavefunction ({math}`\Psi`) for the simulation. The simplest case for the incident beam is to set {math}`\Psi` to unity everywhere in the plane, which means perfectly even illumination across the sample. However, it is possible to introduce more complications, such as a slightly [titled plane wave](https://abtem.readthedocs.io/en/main/user_guide/walkthrough/multislice.html#small-angle-beam-tilt). The sampling is set by the gridpoint and extent as described in seciton [](#sim_inputs_page). -In [](#fig_potential_wave_image) we show an interactive visualization of the potential of the STO/LTO interface we created in [](#manipulating-atoms), the corresponding exit wave function, and the resulting image with a reasonable [contrast transfer function](#CTF_page) applied, as a function of the slices through the specimen. +### Imaging -```{figure} #app:tem_potential_wave_image -:name: fig_potential_wave_image -:placeholder: ./static/potential_wave_image.png +A TEM image is simulated by propagating the wavefunction through the specimen potential using the multislice algorithm, which calculates how the wave evolves due to scattering by the specimen atoms and the propagation through it. The resulting exit-wave is complex, but can be visualized via its intensity. For a more realistic image, a [contrast transfer function](#CTF_page) can be applied to model the optics of the microscope; note that this is done after the computationally time-consuming multislice run which described the physics of hte interaction. + +In [](#fig_tem_Au_potential_wave_image) we show an interactive visualization of the potential of gold with a lattice constant of 4.08 Å in the <100> zone axis, the corresponding exit wave function, and the resulting image as a function of depth through the specimen. + +```{figure} #app:tem_Au_potential_wave_image +:name: fig_tem_Au_potential_wave_image +:placeholder: ./static/tem_Au_potential_wave_image.png **Visualization of slicing through the specimen for the potential, the exit wave, and the image with a CTF applied.** ``` -### Imaging +%#### Imaging example +% +%```{figure} #app:tem_imaging +%:name: fig_tem_phase +%:placeholder: ./static/tem_imaging.png +%**TEM imaging of SrTiO$_3$ grains**: +%``` -```{figure} #app:tem_imaging -:name: fig_tem_phase -:placeholder: ./static/tem_imaging.png -**TEM imaging of SrTiO$_3$ grains**: -``` +### Electron diffraction patterns +Instead of an image, we can instead simulate a selected area diffraction (SAD) experiment by using the `DiffractionPatterns` method. We use `block_direct=True` to block the direct beam: it typically has a much higher intensity than the scattered beams, and thus it is typically not possible to show it on the same scale. +You may wonder; why do the diffraction spots look like squares? This is because the incoming wave function is a periodic and infinite plane wave, hence the intersection with the Ewald sphere is pointlike. However, since we are discretizing the wave function on a square grid (i.e. pixels), the spots can only be as small as single pixels. In real SAD experiments, the spot size is broadened due to the finite extent of the crystal as well instrumental effects. -### Diffraction +We can use the `index_diffraction_spots` method to create a represention of SAD patterns as a mapping of Miller indices to the intensity of the corresponding reflections. The *conventional* unit cell have to be provided in order to index the pattern, we can provide this as the unit cell of the gold crystal we created earlier, we cannot use the the repeated cell. -```{figure} #app:tem_diffraction -:name: fig_tem_diffraction -:placeholder: ./static/tem_diffraction.png -**TEM diffraction of STO as a function of thickness**: -``` +In [](#fig_tem_Au_diffraction) we show an interactive visualization of the diffraction intensities and indexed diffraction spots as function of the depth through the specimen. We see that the {100} reflections are extinguished, as is expected from the selection rules of an F-centered crystal. We can also observe that the <220> spots end up with significantly higher intensity than the <200> spots; this is due to dynamical scattering — which is accounted for by the multislice algorithm. +```{figure} #app:tem_Au_potential_wave_image +:name: fig_tem_Au_diffraction +:placeholder: ./static/tem_Au_diffraction.png +**Visualization of redistribution of diffraction intensity as function of depth through an Au <100> specimen, and the Miller indexing of the resulting diffraction spots.** +``` +%#### Diffraction example +% +%```{figure} #app:tem_diffraction +%:name: fig_tem_diffraction +%:placeholder: ./static/tem_diffraction.png +%**TEM diffraction of STO as a function of thickness**: +%``` ### Contrast transfer and phase contrast STEM Thus far we have been considering how to form images and diffraction patterns with perfect incident illumination. However, often we're interested in seeing how aberrations or other beam modifications impacts imaging conditions. There are a variety of aberration functions ({math}`\chi(\bm{k})`) we may be interested in including as described in [](#CTF_page). diff --git a/notebooks/08_simple_TEM_imaging.ipynb b/notebooks/08_simple_TEM_imaging.ipynb index 04edb46..493c491 100644 --- a/notebooks/08_simple_TEM_imaging.ipynb +++ b/notebooks/08_simple_TEM_imaging.ipynb @@ -56,17 +56,49 @@ "id": "8fe640f4-264a-4ed4-9a70-eec6e9d39a44", "metadata": {}, "source": [ - "To start running image simulations, we need an atomic model. Creating an atomic model was covered in the previous tutorial, so if you do not have the file \"sto_lto.cif\", please run that notebook first." + "To start running image simulations, we need an atomic model. Below we create a Potential representing gold with a lattice constant of 4.08 Å in the <100> zone axis and use a PlaneWave as the initial wave function." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 133, "id": "8426333c-d25d-4fec-89cd-3b7d8832186c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c967b1fa7de2435d8f362c8931fe32bc", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "atoms = ase.io.read(\"data/sto_lto.cif\")" + "unit_cell = ase.build.bulk(\"Au\", cubic=True)\n", + "\n", + "atoms = unit_cell * (1, 1, 30)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n", + "abtem.show_atoms(atoms, ax=ax1, title=\"Beam view\")\n", + "abtem.show_atoms(atoms, ax=ax2, plane=\"xz\", title=\"Side view\", linewidth=0);" ] }, { @@ -101,22 +133,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 135, "id": "b949df4c-c2cf-40a3-b770-822ade89b3b2", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[########################################] | 100% Completed | 2.00 ss\n" - ] - } - ], + "outputs": [], "source": [ - "potential = abtem.Potential(atoms, sampling=0.05, slice_thickness=2).build().compute()" + "potential = abtem.Potential(atoms, slice_thickness=4.08 / 2, sampling=0.04)" ] }, { @@ -140,17 +164,17 @@ "id": "fffcc67c-2aa4-45cf-b5ed-a2acacea4763", "metadata": {}, "source": [ - "To describe a parallel illumination TEM experiment, we create a `PlaneWave` with an energy of $150 \\ \\mathrm{keV}$." + "To describe a parallel illumination TEM experiment, we create a `PlaneWave` with an energy of $200 \\ \\mathrm{keV}$." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 136, "id": "f9087b95-1328-44a2-b5a9-398dd521e338", "metadata": {}, "outputs": [], "source": [ - "plane_wave = abtem.PlaneWave(energy=150e3)" + "plane_wave = abtem.PlaneWave(energy=200e3)" ] }, { @@ -163,12 +187,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 138, "id": "498ebc71-7f53-4853-8b94-ea93f503d7f0", "metadata": {}, "outputs": [], "source": [ - "potential = abtem.Potential(atoms, sampling=0.05, slice_thickness=2, exit_planes=1)" + "potential = abtem.Potential(atoms, slice_thickness=4.08 / 2, sampling=0.04, exit_planes=2)" ] }, { @@ -183,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 140, "id": "e9b886c5-a78f-46b0-8e3a-0f5caf199195", "metadata": {}, "outputs": [ @@ -191,7 +215,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[########################################] | 100% Completed | 1.97 ss\n" + "[########################################] | 100% Completed | 207.78 ms\n" ] } ], @@ -219,14 +243,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 141, "id": "cad4d93a-782e-49c0-a4be-186d979b5cb6", "metadata": {}, "outputs": [], "source": [ "Cs = -20e-6 * 1e10\n", "\n", - "ctf = abtem.CTF(Cs=Cs, energy=150e3)\n", + "ctf = abtem.CTF(Cs=Cs, energy=200e3)\n", "\n", "ctf.scherzer_defocus\n", "\n", @@ -246,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 142, "id": "0278e8c0-1e7c-49d5-bef2-99fb5eff2eec", "metadata": {}, "outputs": [], @@ -254,17 +278,9 @@ "image = exit_wave.apply_ctf(ctf).intensity().compute()" ] }, - { - "cell_type": "markdown", - "id": "8c3ea076-b346-4e49-9122-36de7b62b4b6", - "metadata": {}, - "source": [ - "#### " - ] - }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 148, "id": "fd12f232-8f93-4cbf-b1c6-b983e0210dbf", "metadata": {}, "outputs": [ @@ -272,12 +288,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[########################################] | 100% Completed | 1.04 ss\n" + "[########################################] | 100% Completed | 105.14 ms\n" ] } ], "source": [ - "%matplotlib widget\n", + "%matplotlib ipympl\n", "\n", "with plt.ioff():\n", " dpi = 72\n", @@ -287,7 +303,6 @@ "wavs = exit_wave[1:][0].show(ax=axs[1])\n", "imgs = image[1:][0].show(ax=axs[2])\n", "\n", - "### Tried to use pure imshows in case that was the issue\n", "#pots = axs[0].imshow(potential[0].array[0])\n", "#wavs = axs[1].imshow(exit_wave[1:][0].intensity().array)\n", "#imgs = axs[2].imshow(image[1:][0].array)\n", @@ -313,7 +328,6 @@ " wavs._artists[0,0].set_data(exit_wave[1:][plane].array)\n", " imgs._artists[0,0].set_data(image[1:][plane].array)\n", "\n", - " ### This syntax should work but I couldn't get a widget that updates\n", " #pots.set_data(potential[plane].array[0])\n", " #wavs.set_data(exit_wave[1:][plane].intensity().array)\n", " #imgs.set_data(image[1:][plane].array)\n", @@ -329,7 +343,7 @@ "\n", "plane = widgets.IntSlider(\n", " value = 0, max = len(potential),\n", - " step = 1,\n", + " step = 2,\n", " description = \"Slice\",\n", " style = style,\n", " layout = layout,\n", @@ -362,14 +376,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 149, "id": "e23e1caa-d087-492c-b58c-2c0cd05cbf53", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19aa84d8487641178aad01a3be95e73c", + "model_id": "87ae39502dc04ba3ab072e7dcd2311ef", "version_major": 2, "version_minor": 0 }, @@ -382,16 +396,206 @@ } ], "source": [ - "#| label: app:tem_potential_wave_image\n", + "#| label: app:tem_Au_potential_wave_image\n", "\n", "display(widget);" ] }, + { + "cell_type": "markdown", + "id": "b831d64f-553a-45a1-8b6a-f7b4e2f24ee6", + "metadata": {}, + "source": [ + "Equally, we can calculate the diffraction pattern as a function of depth through the specimen, showing the redistribution of intensity to different diffraction orders." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 152, + "id": "1b16cc31-966a-47ba-b982-c49c60ec4b61", + "metadata": {}, + "outputs": [], + "source": [ + "diffraction = exit_wave.diffraction_patterns().compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 206, "id": "f2f7f1cd-20dd-4081-93d3-bad9ef653fca", "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "74684b559b9249699553e550ca1b6171", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ImageGUI(children=(VBox(children=(SelectionSlider(description='z [Å]', options=('4.080', '8.160', '12.240', '1…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diff = diffraction[1:].block_direct().show(cbar=True, interact=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "a1fdbb47-99d9-46bb-8775-49f48aa15b84", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "809093c602ca4c75a2c2443f31914522", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ScatterGUI(children=(VBox(children=(SelectionSlider(description='z [Å]', options=('4.080', '8.160', '12.240', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indexed_spots = diffraction[1:].crop(120).index_diffraction_spots(cell=unit_cell)\n", + "\n", + "visualization = (\n", + " indexed_spots.block_direct()\n", + " .crop(120)\n", + " .show(\n", + " scale=0.2,\n", + " cbar=True,\n", + " annotation_kwargs={\"threshold\": 0.001, \"fontsize\": 7},\n", + " figsize=(8, 8),\n", + " interact=True\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "05cdef6e-74dd-442b-816e-b801bcad3745", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib ipympl\n", + "\n", + "with plt.ioff():\n", + " dpi = 72\n", + " fig, axs = plt.subplots(1,2, figsize=(675/dpi, 275/dpi), dpi=dpi)\n", + "\n", + "diff = diffraction[1:].block_direct().crop(50).show(ax=axs[0])#, cbar=True)\n", + "spts = indexed_spots[1:].block_direct().crop(50).show(ax=axs[1],\n", + " scale=0.2,\n", + " cbar=True,\n", + " annotation_kwargs={\"threshold\": 0.001, \"fontsize\": 7},\n", + " figsize=(8, 8),\n", + " )\n", + "\n", + "\n", + "#pots = axs[0].imshow(potential[0].array[0])\n", + "#wavs = axs[1].imshow(exit_wave[1:][0].intensity().array)\n", + "\n", + "axs[0].set_title('Diffraction pattern')\n", + "axs[1].set_title('Indexed reflections')\n", + "\n", + "#axs[1].set_yticks([])\n", + "axs[1].set_ylabel(\"\")\n", + "\n", + "plt.tight_layout() \n", + "\n", + "# interact\n", + "def update_images(plane): \n", + " axs[0].cla()\n", + " axs[1].cla()\n", + "\n", + " diff = diffraction[1:].block_direct().crop(35).show(ax=axs[0], cbar=True)\n", + " spts = indexed_spots.block_direct().crop(120).show(ax=axs[1], cbar=True)\n", + "\n", + " fig.canvas.draw_idle()\n", + " return None\n", + "\n", + "style = {\n", + " 'description_width': 'initial',\n", + "}\n", + "\n", + "layout = Layout(width='250px',height='30px')\n", + "\n", + "plane = widgets.IntSlider(\n", + " value = 0, max = len(potential),\n", + " step = 1,\n", + " description = \"Slice\",\n", + " style = style,\n", + " layout = layout,\n", + ")\n", + "\n", + "widgets.interactive_output(\n", + " update_images, \n", + " {\n", + " 'Slice':plane,\n", + " },\n", + ")\n", + "\n", + "fig.canvas.resizable = False\n", + "fig.canvas.header_visible = False\n", + "fig.canvas.footer_visible = False\n", + "fig.canvas.toolbar_visible = True\n", + "fig.canvas.layout.width = '675px'\n", + "fig.canvas.layout.height = '275px'\n", + "fig.canvas.toolbar_position = 'bottom'\n", + "\n", + "widget2 = widgets.VBox(\n", + " [\n", + " fig.canvas,\n", + " HBox(\n", + " [plane]\n", + " )\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "62e6feec-626d-4c95-b2f4-ab4403d8318d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "38bc0611947f42a9affb8cf0dea1a453", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='275px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:tem_Au_diffraction\n", + "\n", + "display(widget2);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14dd96d7-25e0-4ffd-9fda-77dbf81b0de6", + "metadata": {}, "outputs": [], "source": [] } diff --git a/static/tem_Au_diffraction.png b/static/tem_Au_diffraction.png new file mode 100644 index 0000000..13df286 Binary files /dev/null and b/static/tem_Au_diffraction.png differ diff --git a/static/potential_wave_image.png b/static/tem_Au_potential_wave_image.png similarity index 100% rename from static/potential_wave_image.png rename to static/tem_Au_potential_wave_image.png