Update Activity 8

msspecbook/Activity08/Activity08.ipynb

@@ -9,12 +9,85 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": null,
+   "id": "394b0c02-f28e-4074-86e5-9dfdf0447adb",
-   "id": "1df76138-c957-4da2-bcc8-ec977c209b81",
    "metadata": {},
-   "outputs": [],
+   "source": [
-   "source": []
+    "intro"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5a4036dc-d087-419f-9a44-749a8b313d5c",
+   "metadata": {},
+   "source": [
+    "(aln-paper)=\n",
+    ":::{seealso}\n",
+    "based on this paper from V. Lebedev *et al.*\n",
+    "[J. Cryst. Growth. **207(4)** p266-72 (1999)](https://doi.org/10.1016/S0022-0248(99)00375-9)\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc1cf08e-77ba-49ea-99a9-8a7bac7c98c7",
+   "metadata": {},
+   "source": [
+    ":::{figure-md} AlN-fig1\n",
+    "<img src=\"fig1.jpg\" alt=\"AlN growth\" width=\"600px\" align=\"center\">\n",
+    "\n",
+    "legend\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc180c7b-eb23-47c6-8d18-e092bc777843",
+   "metadata": {},
+   "source": [
+    ":::{figure-md} AlN-fig2\n",
+    "<img src=\"fig2.jpg\" alt=\"AlN crystal direction\" width=\"600px\" align=\"center\">\n",
+    "\n",
+    "legend\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86c20245-abca-4587-bccf-90e0fb09f73c",
+   "metadata": {},
+   "source": [
+    ":::{figure-md} AlN-fig3\n",
+    "<img src=\"fig3.jpg\" alt=\"AlN XPD\" width=\"400px\" align=\"center\">\n",
+    "\n",
+    "legend\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a3a48ef-196f-435a-b342-3a73e62160f8",
+   "metadata": {},
+   "source": [
+    ":::{figure-md} AlN-fig4\n",
+    "<img src=\"fig4.jpg\" alt=\"AlN number of clusters\" width=\"600px\" align=\"center\">\n",
+    "\n",
+    "legend\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abc64fdb-5895-4112-a987-66b3420d78eb",
+   "metadata": {},
+   "source": [
+    ":::{figure-md} AlN-fig5\n",
+    "<img src=\"fig5.jpg\" alt=\"AlN results\" width=\"600px\" align=\"center\">\n",
+    "\n",
+    "legend\n",
+    ":::"
+   ]
   }
  ],
  "metadata": {

msspecbook/Activity08/AlN.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# coding: utf8
+
+
+from ase.build import bulk
+import numpy as np
+from msspec.calculator import MSSPEC, XRaySource
+from msspec.utils import hemispherical_cluster, get_atom_index
+
+def create_clusters(nplanes=6):
+    def get_AlN_tags_planes(side, emitter):
+        AlN = # AlN is a Wurtzite crystal with a=3.11 and c=4.975 angstroms       # <= HERE
+        [atom.set('tag', i) for i, atom in enumerate(AlN)]
+        if side == 'Al':
+            AlN.rotate([0,0,1],[0,0,-1])
+            Al_planes = range(0, nplanes, 2)
+            N_planes  = range(1, nplanes, 2)
+        else:
+            N_planes  = range(0, nplanes, 2)
+            Al_planes = range(1, nplanes, 2)
+        if emitter == 'Al':
+            tags = [0, 2]
+            planes = Al_planes
+        else:
+           tags = [1, 3]
+           planes = N_planes
+        return AlN, tags, planes
+
+    clusters = []
+    for side in ('Al', 'N'):
+        for emitter in ('Al', 'N'):
+            AlN, tags, planes = get_AlN_tags_planes(side, emitter)
+            for emitter_tag in tags:
+                for emitter_plane in planes:
+                    cluster = # hemis…, construct the cluster here with           # <= HERE
+                              # 2 planes below the emitter
+                    cluster.absorber = get_atom_index(cluster, 0, 0, 0)
+                    cluster.info.update({
+                        'emitter_plane': emitter_plane,
+                        'emitter_tag'  : emitter_tag,
+                        'emitter'      : emitter,
+                        'side'         : side,
+                    })
+                    clusters.append(cluster)
+                    print("Added cluster {}-side, emitter {}(tag {:d}) in "
+                          "plane #{:d}".format(side, emitter, emitter_tag,
+                                               emitter_plane))
+    return clusters
+
+
+def compute(clusters, theta=np.arange(-20., 80., 1.), phi=0.):
+    data = None
+    for ic, cluster in enumerate(clusters):
+        # Retrieve info from cluster object
+        side    = cluster.info['side']
+        emitter = cluster.info['emitter']
+        plane   = cluster.info['emitter_plane']
+        tag     = cluster.info['emitter_tag']
+
+        # Set the level and the kinetic energy
+        if emitter == 'Al':
+            level = #####                                                         # <= HERE
+            ke    = #####                                                         # <= HERE
+        elif emitter == 'N':
+            level = #####                                                         # <= HERE
+            ke    = #####                                                         # <= HERE
+
+        calc = # Create a calculator using the RA series expansion algorithm      # <= HERE
+
+        calc.source_parameters.energy = #####                                     # <= HERE
+        calc.source_parameters.theta  = #####                                     # <= HERE
+
+        calc.detector_parameters.angular_acceptance = #####                       # <= HERE
+        calc.detector_parameters.average_sampling   = 'medium'
+
+        calc.calculation_parameters.scattering_order = max(1, min(4, plane))
+        calc.calculation_parameters.path_filtering  = 'forward_scattering'
+        calc.calculation_parameters.off_cone_events = 1
+        [a.set('forward_angle', 30.) for a in cluster]
+
+        calc.set_atoms(cluster)
+
+        data = calc.get_theta_scan(level=level, theta=theta, phi=phi,
+                                   kinetic_energy=ke, data=data)
+        dset = data[-1]
+        dset.title = "\'{}\' side - {}({}) tag #{:d}, plane #{:d}".format(
+            side, emitter, level, tag, plane)
+
+    return data
+
+
+def analysis(data):
+    tmp_data = {}
+    for dset in data:
+        info = dset.get_cluster().info
+        side = info['side']
+        emitter = info['emitter']
+        try:
+            key = '{}_{}'.format(side, emitter)
+            tmp_data[key] += dset.cross_section
+        except KeyError:
+            tmp_data[key] = dset.cross_section.copy()
+
+    tmp_data['theta']   = dset.theta.copy()
+    tmp_data['Al_side'] = tmp_data['Al_Al'] / tmp_data['Al_N']
+    tmp_data['N_side']  = tmp_data['N_Al']  / tmp_data['N_N']
+
+    # now add all columns
+    substrate_dset = data.add_dset('Total substrate signal')
+    substrate_dset.add_columns(**tmp_data)
+
+    view = substrate_dset.add_view('Ratios',
+                                   title=r'Al(2p)/N(1s) ratios on both polar '
+                                         r'sides of AlN in the (10$\bar{1}$0) '
+                                         r'azimuthal plane',
+                                   xlabel=r'$\Theta (\degree$)',
+                                   ylabel='Intenisty ratio')
+    view.select('theta', 'Al_side', legend='Al side',
+                where="theta >= 0 and theta <=70")
+    view.select('theta', 'N_side', legend='N side',
+                where="theta >= 0 and theta <=70")
+    view.set_plot_options(autoscale=True)
+
+    return data
+
+
+clusters = create_clusters()
+data     = compute(clusters)
+data     = analysis(data)
+data.view()
+

msspecbook/Activity08/AlN_completed.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+# coding: utf8
+
+from ase.build import bulk
+import numpy as np
+from msspec.calculator import MSSPEC, XRaySource
+from msspec.utils import hemispherical_cluster, get_atom_index
+
+def create_clusters(nplanes=6):
+    def get_AlN_tags_planes(side, emitter):
+        AlN = bulk('AlN', crystalstructure='wurtzite', a=3.11, c=4.975)
+        [atom.set('tag', i) for i, atom in enumerate(AlN)]
+        if side == 'Al':
+            AlN.rotate([0,0,1],[0,0,-1])
+            Al_planes = range(0, nplanes, 2)
+            N_planes  = range(1, nplanes, 2)
+        else:
+            N_planes  = range(0, nplanes, 2)
+            Al_planes = range(1, nplanes, 2)
+        if emitter == 'Al':
+            tags = [0, 2]
+            planes = Al_planes
+        else:
+           tags = [1, 3]
+           planes = N_planes
+        return AlN, tags, planes
+
+    clusters = []
+    for side in ('Al', 'N'):
+        for emitter in ('Al', 'N'):
+            AlN, tags, planes = get_AlN_tags_planes(side, emitter)
+            for emitter_tag in tags:
+                for emitter_plane in planes:
+                    cluster = hemispherical_cluster(AlN,
+                                                    emitter_tag=emitter_tag,
+                                                    emitter_plane=emitter_plane,
+                                                    planes=emitter_plane+2)
+                    cluster.absorber = get_atom_index(cluster, 0, 0, 0)
+                    cluster.info.update({
+                        'emitter_plane': emitter_plane,
+                        'emitter_tag'  : emitter_tag,
+                        'emitter'      : emitter,
+                        'side'         : side,
+                    })
+                    clusters.append(cluster)
+                    print("Added cluster {}-side, emitter {}(tag {:d}) in "
+                          "plane #{:d}".format(side, emitter, emitter_tag,
+                                               emitter_plane))
+    return clusters
+
+
+def compute(clusters, theta=np.arange(-20., 80., 1.), phi=0.):
+    data = None
+    for ic, cluster in enumerate(clusters):
+        # Retrieve info from cluster object
+        side    = cluster.info['side']
+        emitter = cluster.info['emitter']
+        plane   = cluster.info['emitter_plane']
+        tag     = cluster.info['emitter_tag']
+
+        # Set the level and the kinetic energy
+        if emitter == 'Al':
+            level = '2p'
+            ke    = 1407.
+        elif emitter == 'N':
+            level = '1s'
+            ke    = 1083.
+
+        calc = MSSPEC(spectroscopy='PED', algorithm='expansion')
+
+        calc.source_parameters.energy = XRaySource.AL_KALPHA
+        calc.source_parameters.theta  = -35
+
+        calc.detector_parameters.angular_acceptance = 4.
+        calc.detector_parameters.average_sampling   = 'medium'
+
+        calc.calculation_parameters.scattering_order = max(1, min(4, plane))
+        calc.calculation_parameters.path_filtering  = 'forward_scattering'
+        calc.calculation_parameters.off_cone_events = 1
+        [a.set('forward_angle', 30.) for a in cluster]
+
+        calc.set_atoms(cluster)
+
+        data = calc.get_theta_scan(level=level, theta=theta, phi=phi,
+                                   kinetic_energy=ke, data=data)
+        dset = data[-1]
+        dset.title = "\'{}\' side - {}({}) tag #{:d}, plane #{:d}".format(
+            side, emitter, level, tag, plane)
+
+    return data
+
+
+def analysis(data):
+    tmp_data = {}
+    for dset in data:
+        info = dset.get_cluster().info
+        side = info['side']
+        emitter = info['emitter']
+        try:
+            key = '{}_{}'.format(side, emitter)
+            tmp_data[key] += dset.cross_section
+        except KeyError:
+            tmp_data[key] = dset.cross_section.copy()
+
+    tmp_data['theta']   = dset.theta.copy()
+    tmp_data['Al_side'] = tmp_data['Al_Al'] / tmp_data['Al_N']
+    tmp_data['N_side']  = tmp_data['N_Al']  / tmp_data['N_N']
+
+    # now add all columns
+    substrate_dset = data.add_dset('Total substrate signal')
+    substrate_dset.add_columns(**tmp_data)
+
+    view = substrate_dset.add_view('Ratios',
+                                   title=r'Al(2p)/N(1s) ratios on both polar '
+                                         r'sides of AlN in the (10$\bar{1}$0) '
+                                         r'azimuthal plane',
+                                   xlabel=r'$\Theta (\degree$)',
+                                   ylabel='Intenisty ratio')
+    view.select('theta', 'Al_side', legend='Al side',
+                where="theta >= 0 and theta <=70")
+    view.select('theta', 'N_side', legend='N side',
+                where="theta >= 0 and theta <=70")
+    view.set_plot_options(autoscale=True)
+
+    return data
+
+
+clusters = create_clusters()
+data     = compute(clusters)
+data     = analysis(data)
+data.view()
+