diff --git a/Activity01/Activity01.ipynb b/Activity01/Activity01.ipynb
index d226ee5..47eff3a 100644
--- a/Activity01/Activity01.ipynb
+++ b/Activity01/Activity01.ipynb
@@ -22,34 +22,112 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"id": "07a04c8c-a268-481a-8f1b-14878ed771b5",
"metadata": {},
"outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1dc57d2ee20b4192b4bd4866b23d1fd1",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": []
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "4b8abba5c2984e43a644c4d448a4ea26",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ " \n",
+ " ASE atomic visualization\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
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+ "\n",
+ "\n",
+ "\n",
+ " \n",
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+ "\n"
+ ],
"text/plain": [
- "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'O', 'H'), value='All'…"
+ ""
]
},
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -63,7 +141,7 @@
"# Only for jupyter\n",
"import nglview\n",
"from functools import partial\n",
- "view = partial(view, viewer='ngl')\n",
+ "view = partial(view, viewer='x3d')\n",
"\n",
"# Create a water molecule\n",
"water = molecule('H2O')\n",
@@ -96,8 +174,7 @@
"metadata": {
"collapsed": true,
"jupyter": {
- "outputs_hidden": true,
- "source_hidden": true
+ "outputs_hidden": true
},
"scrolled": true
},
@@ -460,6 +537,12 @@
"> **Note**:\n",
"> As the cluster will contain fewer atoms, the emitter index will be different\n",
"\n",
+ "```{note}\n",
+ "This is a note\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
"What do you conclude ?"
]
},
@@ -470,8 +553,7 @@
"metadata": {
"collapsed": true,
"jupyter": {
- "outputs_hidden": true,
- "source_hidden": true
+ "outputs_hidden": true
}
},
"outputs": [
@@ -606,7 +688,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.3"
+ "version": "3.11.13"
}
},
"nbformat": 4,
diff --git a/Activity03/PhysRevB.26.3181.pdf b/Activity03b/PhysRevB.26.3181.pdf
similarity index 100%
rename from Activity03/PhysRevB.26.3181.pdf
rename to Activity03b/PhysRevB.26.3181.pdf
diff --git a/Activity03/adsorbate.py b/Activity03b/adsorbate.py
similarity index 100%
rename from Activity03/adsorbate.py
rename to Activity03b/adsorbate.py
diff --git a/msspecbook/Activity01/Activity01.ipynb b/msspecbook/Activity01/Activity01.ipynb
new file mode 100644
index 0000000..0763203
--- /dev/null
+++ b/msspecbook/Activity01/Activity01.ipynb
@@ -0,0 +1,922 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7e31b322-df55-44ed-9f29-6f6efa71eafe",
+ "metadata": {},
+ "source": [
+ "# Activity 1: Getting started\n",
+ "\n",
+ "MsSpec is a Fortran code with two components: Phagen (Written by R. Natoli) and Spec (written by D. Sébilleau). Phagen computes the phase shifts of the electronic wave propagating in the matter on a spherical harmonics basis. Spec uses those phase shifts to compute the multiple scattering process and simulate the intensity of different electronic spectroscopies.\n",
+ "\n",
+ "In the most recent version of MsSpec, the program is interfaced with python (https://msspec.cnrs.fr/), allowing for much more flexibility and interplay with other simulation techniques.\n",
+ "\n",
+ "## Building atomic systems\n",
+ "\n",
+ "MsSpec works in the *cluster* approach. Building such a cluster for a calculation is a fundamental step.\n",
+ "We use the [python Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/) for this.\n",
+ "\n",
+ "ASE is a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations.\n",
+ "Building atomic systems, structures... is pretty straightforward:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "07a04c8c-a268-481a-8f1b-14878ed771b5",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " ASE atomic visualization\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# To build a molecule with ASE\n",
+ "from ase.build import molecule\n",
+ "# To view\n",
+ "from ase.visualize import view\n",
+ "\n",
+ "# Only for jupyter\n",
+ "from functools import partial\n",
+ "view = partial(view, viewer='x3d')\n",
+ "\n",
+ "# Create a water molecule\n",
+ "water = molecule('H2O')\n",
+ "# Display it\n",
+ "view(water)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "94b690a2-52f0-43e4-953d-6e7519ac4e9c",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## Barebone script for MsSpec\n",
+ "\n",
+ "MsSpec can simulate different electronic spectroscopies like PED, AED, LEED, EXAFS, APECS and more will be included in the forthcoming version. However, it is really well-suited for PhotoElectron Diffraction simulation, and the python interface is only fully available for it at the moment. Since PED covers all the MsSpec features and concepts, we will focus on this technique.\n",
+ "\n",
+ "There are typically 3 steps to follow to get a result with MsSpec:\n",
+ "\n",
+ "1. Create a *cluster*\n",
+ "2. Create an ASE *calculator*\n",
+ "3. Run the simulation\n",
+ "\n",
+ "### PED polar scan for Cu(001)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6ddd72a9-8f32-484f-9a3e-9ab3a85945a4",
+ "metadata": {
+ "editable": true,
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-output"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " _________________________________________________________________\n",
+ "\n",
+ " PHAGEN\n",
+ " _________________________________________________________________\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " parameters for this xpd calculation:\n",
+ " -----------------------------------------------------------------\n",
+ " edge= l3\n",
+ " potype= hedin norman= stdcrm absorber= 1\n",
+ " coor= angs emin= 23.25 Ry emax= 23.25 Ry\n",
+ " delta= 0.300 Ry gamma= 0.00 Ry eftri= 0.000 Ry\n",
+ " cip= 0.00 Ry lmaxt= 19 charelx: ex\n",
+ " ionization state : neutral\n",
+ " relativistic corrections of type: nr\n",
+ " final state potential generated internally\n",
+ "\n",
+ "\n",
+ " Computes the T-matrix and radial matrix elements \n",
+ "\n",
+ "\n",
+ " coordinates in angstroms Radii\n",
+ " -----------------------------------------------------------------\n",
+ " Cu 29 7.2000 7.2000 2.7000 0.0000 0.0000\n",
+ " Cu 29 3.6000 7.2000 2.7000 0.0000 0.0000\n",
+ " Cu 29 3.6000 7.2000 6.3000 0.0000 0.0000\n",
+ " Cu 29 5.4000 5.4000 2.7000 0.0000 0.0000\n",
+ " Cu 29 7.2000 3.6000 2.7000 0.0000 0.0000\n",
+ " Cu 29 7.2000 5.4000 0.9000 0.0000 0.0000\n",
+ " Cu 29 5.4000 5.4000 6.3000 0.0000 0.0000\n",
+ " Cu 29 7.2000 3.6000 6.3000 0.0000 0.0000\n",
+ " Cu 29 7.2000 5.4000 4.5000 0.0000 0.0000\n",
+ " Cu 29 5.4000 7.2000 0.9000 0.0000 0.0000\n",
+ " Cu 29 5.4000 9.0000 2.7000 0.0000 0.0000\n",
+ " Cu 29 7.2000 9.0000 0.9000 0.0000 0.0000\n",
+ " Cu 29 5.4000 7.2000 4.5000 0.0000 0.0000\n",
+ " Cu 29 5.4000 9.0000 6.3000 0.0000 0.0000\n",
+ " Cu 29 7.2000 7.2000 6.3000 0.0000 0.0000\n",
+ " Cu 29 7.2000 9.0000 4.5000 0.0000 0.0000\n",
+ " Cu 29 7.2000 10.8000 2.7000 0.0000 0.0000\n",
+ " Cu 29 7.2000 10.8000 6.3000 0.0000 0.0000\n",
+ " Cu 29 9.0000 5.4000 2.7000 0.0000 0.0000\n",
+ " Cu 29 9.0000 5.4000 6.3000 0.0000 0.0000\n",
+ " Cu 29 9.0000 7.2000 0.9000 0.0000 0.0000\n",
+ " Cu 29 9.0000 9.0000 2.7000 0.0000 0.0000\n",
+ " Cu 29 10.8000 7.2000 2.7000 0.0000 0.0000\n",
+ " Cu 29 9.0000 7.2000 4.5000 0.0000 0.0000\n",
+ " Cu 29 9.0000 9.0000 6.3000 0.0000 0.0000\n",
+ " Cu 29 10.8000 7.2000 6.3000 0.0000 0.0000\n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ "\n",
+ " ** enter calphas **\n",
+ " ---\n",
+ " total energy for atom in ground state \n",
+ " total energy for atom with a hole in l3 edge\n",
+ " calculated ionization energy for edge l3 = 946.40898981930718 eV\n",
+ " energy distance between edges l2 and l3 = 20.447898479699866 eV\n",
+ " ---\n",
+ " calculated ionization potential (ryd) = 69.588894358327252 \n",
+ " ---\n",
+ " \n",
+ " \n",
+ " symmetrizing coordinates... \n",
+ "\n",
+ "\n",
+ " symmetrized atomic coordinates of cluster \n",
+ "\n",
+ " position\n",
+ " atom no. x y z eq\n",
+ "\n",
+ " 1 osph 0 0.0000 0.0000 0.0000 0\n",
+ " 2 Cu 29 0.0000 0.0000 -2.3549 0\n",
+ " 3 Cu 29 -6.8030 0.0000 -2.3549 0\n",
+ " 4 Cu 29 -6.8030 0.0000 4.4481 0\n",
+ " 5 Cu 29 -3.4015 -3.4015 -2.3549 0\n",
+ " 6 Cu 29 0.0000 -6.8030 -2.3549 0\n",
+ " 7 Cu 29 0.0000 -3.4015 -5.7564 0\n",
+ " 8 Cu 29 -3.4015 -3.4015 4.4481 0\n",
+ " 9 Cu 29 0.0000 -6.8030 4.4481 0\n",
+ " 10 Cu 29 0.0000 -3.4015 1.0466 0\n",
+ " 11 Cu 29 -3.4015 0.0000 -5.7564 0\n",
+ " 12 Cu 29 -3.4015 3.4015 -2.3549 0\n",
+ " 13 Cu 29 0.0000 3.4015 -5.7564 0\n",
+ " 14 Cu 29 -3.4015 0.0000 1.0466 0\n",
+ " 15 Cu 29 -3.4015 3.4015 4.4481 0\n",
+ " 16 Cu 29 0.0000 0.0000 4.4481 0\n",
+ " 17 Cu 29 0.0000 3.4015 1.0466 0\n",
+ " 18 Cu 29 0.0000 6.8030 -2.3549 0\n",
+ " 19 Cu 29 0.0000 6.8030 4.4481 0\n",
+ " 20 Cu 29 3.4015 -3.4015 -2.3549 0\n",
+ " 21 Cu 29 3.4015 -3.4015 4.4481 0\n",
+ " 22 Cu 29 3.4015 0.0000 -5.7564 0\n",
+ " 23 Cu 29 3.4015 3.4015 -2.3549 0\n",
+ " 24 Cu 29 6.8030 0.0000 -2.3549 0\n",
+ " 25 Cu 29 3.4015 0.0000 1.0466 0\n",
+ " 26 Cu 29 3.4015 3.4015 4.4481 0\n",
+ " 27 Cu 29 6.8030 0.0000 4.4481 0\n",
+ "\n",
+ " computing muffin tin potential and phase shifts\n",
+ " generating core state wavefunction \n",
+ " generating final potential (complex hedin-lundqvist exchange) \n",
+ " MT radii for Hydrogen atoms determined by stdcrm unless other options are specified\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " i rs(i) i=1,natoms \n",
+ " 1 10.61 2 2.39 3 2.48 4 2.48 5 2.33 6 2.48 7 2.42 8 2.33\n",
+ " 9 2.48 10 2.34 11 2.42 12 2.43 13 2.41 14 2.34 15 2.33 16 2.33\n",
+ " 17 2.41 18 2.48 19 2.48 20 2.43 21 2.33 22 2.41 23 2.43 24 2.48\n",
+ " 25 2.41 26 2.48 27 2.48\n",
+ " N.B.: Order of atoms as reshuffled by symmetry routines \n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ " input value for coulomb interst. potential = -0.69999999999999996 \n",
+ " and interstitial rs = 3.0000000000000000 \n",
+ " lower bound for coulomb interst. potential = -0.42096854155426744 \n",
+ " and for interst. rs = 2.2601548755994800 \n",
+ "\n",
+ " lmax assignment based on l_max = r_mt * k_e + 2\n",
+ " where e is the running energy\n",
+ " optimal lmax chosen according to the running energy e for each atom\n",
+ "\n",
+ "\n",
+ " number of centers= 26\n",
+ "\n",
+ " starting potentials and/or charge densities written to file 13\n",
+ " symmetry information generated internally\n",
+ " symmetry information written to file 14\n",
+ "\n",
+ "\n",
+ " core initial state of type: 2p3/2\n",
+ "\n",
+ " fermi level = -0.18577\n",
+ "\n",
+ "\n",
+ " generating t_l (for030) and atomic cross section (for050)\n",
+ " corewf: fnisx = 0.99897282652190389 \n",
+ " writing atomic orbital energies\n",
+ " orbital energy (Ryd eV) 1s -665.31473820425299 -9051.6067087119445 \n",
+ " orbital energy (Ryd eV) 2s -83.344250979299673 -1133.8984964214017 \n",
+ " orbital energy (Ryd eV) 2p1/2 -72.640262391310984 -988.27073658171264 \n",
+ " orbital energy (Ryd eV) 2p3/2 -71.111924241975288 -967.47769675961820 \n",
+ " orbital energy (Ryd eV) 3s -10.313080620773222 -140.30945712466604 \n",
+ " orbital energy (Ryd eV) 3p1/2 -6.8622342038346620 -93.360693201889049 \n",
+ " orbital energy (Ryd eV) 3p3/2 -6.6575123914750813 -90.575453038451158 \n",
+ " orbital energy (Ryd eV) 3d3/2 -0.97575422779613052 -13.275135822501518 \n",
+ " orbital energy (Ryd eV) 3d5/2 -0.94859035000087122 -12.905571277531651 \n",
+ " orbital energy (Ryd eV) 4s -0.48955130473863673 -6.6603452768703502 \n",
+ "\n",
+ " using overlapped potential to search for core states of photoabsorber\n",
+ "\n",
+ " calculating non relativistic core states\n",
+ " ------------------------------\n",
+ " energy of core state = -647.40974057864071 for orbital =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -79.554273906250828 for orbital =2s \n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -70.720428814458870 for orbital =2p1/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -70.720428814476662 for orbital =2p3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -8.8984913351029302 for orbital =3s \n",
+ " n. of zeros found: 2 expected: 2\n",
+ " ------------------------------\n",
+ " energy of core state = -6.0089160418603047 for orbital =3p1/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -6.0089159791922135 for orbital =3p3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ "\n",
+ " calculating relativistic core states\n",
+ " energy of core state = -656.39378974400631 for orb =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -81.632060147320317 for orb =2s \n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -72.635764622230667 for orb =2p1/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -71.074395040994631 for orb =2p3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -9.2498851019868340 for orb =3s \n",
+ " n. of zeros found: 2 expected: 2\n",
+ " energy of core state = -6.2923189144601208 for orb =3p1/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -6.0867535547385900 for orb =3p3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " -------------------------------\n",
+ " density of the valence charge (au^{-3} 5.7214295960466316E-002\n",
+ " rs_v corresponding to valence density (au) 1.6099125416630322 \n",
+ " valence plasmon energy (in eV) 20.414212418262935 \n",
+ "\n",
+ " gamma = 0.000000 rsint = 3.578556\n",
+ "\n",
+ " check in subroutine cont\n",
+ " order of neighb. -- symb. -- dist. from absorber\n",
+ " \n",
+ " 22 Cu 4.8104573684277954 \n",
+ " 11 Cu 4.8104573684277971 \n",
+ " 12 Cu 4.8104573684277971 \n",
+ " 16 Cu 4.8104573684277971 \n",
+ " 19 Cu 4.8104573684277971 \n",
+ " 21 Cu 4.8104573684277971 \n",
+ " 24 Cu 4.8104573684277971 \n",
+ " 9 Cu 4.8104573684277989 \n",
+ " 13 Cu 4.8104573684277989 \n",
+ " 4 Cu 4.8104573684277998 \n",
+ " 10 Cu 4.8104573684277998 \n",
+ " 6 Cu 4.8104573684277998 \n",
+ " 17 Cu 6.8030140516481801 \n",
+ " 23 Cu 6.8030140516481801 \n",
+ " 5 Cu 6.8030140516481818 \n",
+ " 2 Cu 6.8030140516481818 \n",
+ " 15 Cu 6.8030140516481818 \n",
+ " 20 Cu 8.3319565697610241 \n",
+ " 14 Cu 8.3319565697610258 \n",
+ " 25 Cu 8.3319565697610241 \n",
+ " 7 Cu 8.3319565697610258 \n",
+ " 18 Cu 9.6209147368555961 \n",
+ " 26 Cu 9.6209147368555961 \n",
+ " 3 Cu 9.6209147368555978 \n",
+ " 8 Cu 9.6209147368555978 \n",
+ " -----------------------------------------------------------------\n",
+ " 1 Cu 0.000000\n",
+ " 4 Cu 4.810457\n",
+ " 6 Cu 4.810457\n",
+ " 9 Cu 4.810457\n",
+ " 10 Cu 4.810457\n",
+ " 11 Cu 4.810457\n",
+ " 12 Cu 4.810457\n",
+ " 13 Cu 4.810457\n",
+ " 16 Cu 4.810457\n",
+ " 19 Cu 4.810457\n",
+ " 21 Cu 4.810457\n",
+ " 22 Cu 4.810457\n",
+ " 24 Cu 4.810457\n",
+ " 2 Cu 6.803014\n",
+ " 5 Cu 6.803014\n",
+ " 15 Cu 6.803014\n",
+ " 17 Cu 6.803014\n",
+ " 23 Cu 6.803014\n",
+ " 7 Cu 8.331957\n",
+ " 14 Cu 8.331957\n",
+ " 20 Cu 8.331957\n",
+ " 25 Cu 8.331957\n",
+ " 3 Cu 9.620915\n",
+ " 8 Cu 9.620915\n",
+ " 18 Cu 9.620915\n",
+ " 26 Cu 9.620915\n",
+ " 1 Cu 0.000000\n",
+ " 24 Cu 4.810457\n",
+ " 23 Cu 6.803014\n",
+ " 25 Cu 8.331957\n",
+ " 26 Cu 9.620915\n",
+ " \n",
+ " irho = 2 entering vxc to calculate energy dependent exchange\n",
+ " energy dependent vcon = (-0.19406005312524771,0.14609730856378345) at energy 23.254999999999999 \n",
+ " check ionization potential: 69.588894358327252 \n",
+ " \n",
+ " \n",
+ " value of the mean free path:\n",
+ " -----------------------------------------------------------------\n",
+ " average mean free path in the cluster : mfp = 11.19104 angstrom at energy 23.25500\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " \n",
+ " calculating atomic t-matrix elements atm(n)\n",
+ " check orthogonality between core and continuum state\n",
+ " scalar product between core and continuum state = (-0.56419733810139194,0.62474878945367607)\n",
+ " --- sqrt(xe/pi) = (1.2415322757386615,-1.93379098468138516E-003)\n",
+ "\n",
+ "\n",
+ " ** phagen terminated normally ** \n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from ase.io import read\n",
+ "from msspec.calculator import MSSPEC\n",
+ "\n",
+ "\n",
+ "cluster = read('copper.cif')\n",
+ "# view the cluster\n",
+ "cluster.edit()\n",
+ "\n",
+ "# The \"emitter\" atom is located in the middle of the 3rd plane\n",
+ "cluster.emitter = 10\n",
+ "\n",
+ "# Create a \"calculator\"\n",
+ "calc = MSSPEC(spectroscopy='PED', algorithm='inversion', txt='msspec.log')\n",
+ "calc.set_atoms(cluster)\n",
+ "\n",
+ "data = calc.get_theta_scan(level='2p3/2')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "385f2556-852c-415c-8350-6fe1cfc897c0",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Plot the result with the interactive GUI\n",
+ "data.view()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ef1be6d9-091e-4be1-90b6-d57e58a3950a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot using matplotlib directly\n",
+ "data[0].views[0].plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85c7dcac-9f0e-4a56-8077-c96f89865dcb",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## Shaping a cluster\n",
+ "\n",
+ "Based on the previous *.cif file, create a new cluster without the deepest plane and run the same calculation for the same emitter\n",
+ "\n",
+ "```{note}\n",
+ "As the cluster will contain fewer atoms, the emitter index will be different\n",
+ "```\n",
+ "\n",
+ "What do you conclude ? (Unfold to see the answer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "554dc426-be4a-45d6-8071-f17701c7bf0c",
+ "metadata": {
+ "editable": true,
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "hide-cell"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Edit the previous cluster\n",
+ "# cluster = read('copper.cif')\n",
+ "# cluster = write('copper_3planes.cif')\n",
+ "# cluster.edit()\n",
+ "# Emitter index is now n°8\n",
+ "\n",
+ "cluster = read('copper_3planes.cif')\n",
+ "cluster.emitter = 8\n",
+ "\n",
+ "calc = MSSPEC(spectroscopy='PED', algorithm='inversion', txt='msspec.log')\n",
+ "calc.set_atoms(cluster)\n",
+ "\n",
+ "data = calc.get_theta_scan(level='2p3/2')\n",
+ "data[0].views[0].plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "102bfad0-f8ed-44ab-990f-9fb2eb5662b0",
+ "metadata": {
+ "editable": true,
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "source": [
+ "The number of atoms used for the calculation greatly impact the calculation time and memory. Most of the time, a cluster is shaped as an hemisphere to minimize the number of atoms to take into account"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f7ecc1d0-097d-4199-822a-c564e9e94337",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " ASE atomic visualization\n",
+ " \n",
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from msspec.utils import hemispherical_cluster, get_atom_index\n",
+ "from ase.build import bulk\n",
+ "\n",
+ "copper = bulk('Cu', cubic=True)\n",
+ "cluster = hemispherical_cluster(copper, planes=3, emitter_plane=2)\n",
+ "cluster.emitter = get_atom_index(cluster, 0,0,0)\n",
+ "\n",
+ "view(cluster)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1f83a3b5-1248-482d-9686-488acbc70021",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Lw4033oghQ4bgrbfecrlOv3792izr378/TCZTm5TvzMxMTJ06FfPmzcP69etRX1+P6667rtU4vvvuOwDA9OnTXR7Pbrfjtttuw5EjR/DJJ5+gR48e7c7t4rgnVxw+fBg33ngjIiMjERERgfj4ePz2t78FAJfXhyfodDpMnToV1157LZ566imsWrUKd999N9avX+/xvphQ02q1uOWWW8TlKpUKc+fOxblz51BQUNDu9mfOnMGLL76Iv/3tb52670JCQlzG/jU1NbUaiytOnDgBAJg/fz7i4+Nb/XvrrbdgNptbndc77rgDgCB6ACHu7ccff8Rtt93WKjuRUDYUA0QomnHjxnUaU+HOA+Ni3nvvPdx1112YPXs2Hn30USQkJECtVmPZsmWtApEZ7vr0Q0JCsG3bNmzZsgXffPMNNm7ciHXr1uHKK6/Epk2b3L45/vjjj7j++utx+eWX41//+heSk5Oh1Wqxdu1a8abbkvb225lYkILMzEwAQlDwxbFUrmjv+7vYEhMTEwOO4zoUdYWFhZg+fToiIyOxYcMGr1uhAOCWW27B73//exw/fhwDBgwAIFjSLrnkEkRGRrrc5t5778X69evx/vvvu7RIMaqqqlzGLLWkuroakydPRkREBJ555hn06dMHBoMB+/btw2OPPdau5bGrTJo0CcnJyXj//fdx7bXXerRtTEwMDAYDoqKi2lyjCQkJAIQ5uxLwgJD2npKSgilTpojWFlbWoKysDGfOnEHPnj2hUqmQnJzsMq2e1YJqT3QCEM/Ziy++2O4121KAjR49GpmZmfjwww/xxBNP4MMPPwTP86IwIvwDEkCE35Keng6Hw4ETJ06IFgsAKCkpQXV1NdLT09vd9pNPPkHv3r3x2WeftXoAL1mypNvjUqlUuOqqq3DVVVdhxYoVeP755/Hkk09iy5Ytoqvql19+gdVqhVardbmPTz/9FAaDAd99910rs/7F9WO6S3p6Og4cOACHw9HKCsTcKB2dw/a45pproFar8d5777kVCB0dHe0yq+pi65NGo0GfPn3EDMCLqaiowPTp02E2m5GTk4Pk5OR2j8ne+Fty/PhxGI3GTl2dzJXCLAI8z2Pjxo34y1/+4nL9Rx99FGvXrsXKlStbuRpdkZ+fj+HDh3e4ztatW1FRUYHPPvsMl19+eattL6YrLweuaGpq6pJlSaVSYcSIEdi9ezcsFovodgaA8+fPA2g/mB4ACgoKcPLkSfTu3bvNZ3/6058ACAIqKioKI0aMwJYtW1BbW9sqEPqXX34BgA7FOHMfR0REiNl4nXHHHXfgqaeewoEDB/DBBx+gX79+GDt2rFvbEsqAXGCE3zJz5kwAaFPobcWKFQDQYTwGexttaSH55ZdfsGPHjm6NqbKyss0yduNl5vmbb74Z5eXleO2119qsy8ajVqvBcVwrK8iZM2fwxRdfdGt8FzNz5kwUFxdj3bp14jKbzYZXX30VYWFhmDx5ssf7TEtLw7333otNmzbh1VdfbfO5w+HASy+9JBZC7NOnD2pqanDgwAFxnQsXLrjM4ps4caLLKtINDQ2YOXMmioqKsGHDBpcurpbs2LGjVSxVYWEhvvzyS0yfPl28NlylOVutVvz3v/9FSEgIBg0aBADYvXs3SktLXV5vL774IpYvX44nnngCDz74YIdjqqmpwalTpzBp0qQO13N17VosFvzrX/9qs25oaKjbwqWhocFlkcFPP/0UVVVVnVpi22Pu3Lmw2+2tSis0NTXh/fffx6BBg1pZZvLy8lq5xJ577jl8/vnnrf49++yzAID/+7//w+eff47Q0FAAgmXObre3KlJpNpuxdu1ajB8/vsMMsNGjR6NPnz5Yvny5y3gnV5WwmbVn8eLFyM3NJeuPH0IWIMJvGT58OObPn4833nhDdAvs2rUL77zzDmbPno0rrrii3W2vvfZafPbZZ7jxxhsxa9Ys5OfnY/Xq1Rg0aFCXAz4BoXL1tm3bMGvWLKSnp6O0tBT/+te/kJqaiksvvRQAMG/ePPz3v/9FVlYWdu3ahcsuuwwNDQ34/vvv8ac//Qk33HADZs2ahRUrVuDqq6/Gb37zG5SWlmLVqlXo27dvK6HQXe677z78+9//xl133YW9e/ciIyMDn3zyCX7++WesXLmyyy6kl156CadOncIDDzyAzz77DNdeey2io6NRUFCAjz/+GHl5eWIri9tuuw2PPfYYbrzxRjzwwAMwmUx4/fXX0b9//zYB3zfccAPeffddHD9+HP379xeX33HHHdi1axd+97vf4ejRo61q/4SFhWH27Nmt9jNkyBDMmDGjVRo8ACxdulRc5/e//z1qa2tx+eWXIyUlBcXFxXj//feRl5eHl156SXSJfPPNN8jIyBAFEePzzz/H//3f/6Ffv34YOHAg3nvvvVafT5s2DYmJieLf33//PXiexw033NDhuZ00aRKio6Mxf/58PPDAA+A4Du+++65Ld+fo0aOxbt06ZGVlYezYsQgLC8N1113ncr8nTpzA1KlTMXfuXGRmZkKlUmHPnj147733kJGR0UbAvfvuuzh79qwomrZt2yYGNd95552i9fD3v/893nrrLdx///04fvw4evbsKW57cVuRgQMHYvLkydi6dSsAiL+ZlrBEg7Fjx7b6XsePH485c+Zg0aJFKC0tRd++ffHOO+/gzJkz+M9//tPhOVWpVHjrrbdwzTXXYPDgwViwYAFSUlJQVFSELVu2ICIios1Ye/XqhUmTJol1m0gA+SHyJJ8RRMe0Vwn6YqxWK7906VK+V69evFar5dPS0vhFixa1SgXm+bZp8A6Hg3/++ef59PR0Xq/X8yNHjuTXr1/fJiWbpcG3rC7bETk5OfwNN9zA9+jRg9fpdHyPHj3422+/nT9+/Hir9UwmE//kk0+K405KSuJvueWWVlWA//Of//D9+vXj9Xo9n5mZya9du7ZNajjPC2nd999/f5uxpKend5qWzfM8X1JSwi9YsICPi4vjdTodP3ToUH7t2rUu9+duJWieF9KF33rrLf6yyy7jIyMjea1Wy6enp/MLFixokyK/adMmfsiQIbxOp+MHDBjAv/feey7najab+bi4OP7ZZ59tMza0SN1u+e/iNHB2vt577z3x/I4cOZLfsmVLq/U+/PBDfurUqXxiYiKv0Wj46OhofurUqfyXX37Zar0xY8bwf/rTn9rMn42/vX8XH2/u3Ln8pZde2vmJ5Xn+559/5idMmMCHhITwPXr04P/v//6P/+6779rst76+nv/Nb37DR0VFuTwXLSkrK+Pvu+8+PjMzkw8NDeV1Oh3fr18//qGHHnJZ1mHy5Mluz62kpISfP38+HxMTw+v1en78+PH8xo0b2+wTQKcV29tLg+d5ofLzX/7yFz4pKYnX6/X82LFjXR6nPfbv38/fdNNNfGxsLK/X6/n09HT+1ltvbVNKgbFq1SoeAD9u3Di3j0EoB47nZYiSJAiC6CLPPvss1q5dixMnTnQp44bjONx///0uXZCeUlJSguTkZKxfv150yXaF4uJi9OrVCx999FGnFiCCILwDxQARBOFXPPzww6ivr8dHH30k91BQU1ODxYsXd+hudYeVK1di6NChJH4IwoeQBYggiKDCmxYggiD8F7IAEQRBEAQRdFAWGEEQQQUZvQmCAMgCRBAEQRBEEEICiCAIgiCIoINcYC5wOBw4f/48wsPDvVZKniAIgiAIaeF5HnV1dejRo0ebJs8XQwLIBefPn++wbDpBEARBEMqlsLAQqampHa5DAsgFrPx/YWFhq6Z6gYDVasWmTZswffr0dhtxBjLBPn+AzkGwzx+gc0DzD9z519bWIi0tza02PiSAXMDcXhEREQEpgIxGIyIiIgLuwneHYJ8/QOcg2OcP0Dmg+Qf+/N0JX5E9CHrVqlXIyMiAwWDA+PHjsWvXrnbXPXz4MG6++WZkZGSA47g2XcAZRUVF+O1vf4vY2FiEhIRg6NChLjtIEwRBEAQRnMgqgFiX4iVLlmDfvn0YPnw4ZsyYgdLSUpfrm0wm9O7dGy+88AKSkpJcrlNVVYVLLrkEWq0W3377LY4cOYKXXnoJ0dHRUk6FIAiCIAg/QlYX2IoVK3DvvfdiwYIFAIDVq1fjm2++wZo1a/D444+3WX/s2LEYO3YsALj8HAD+/ve/Iy0tDWvXrhWX9erVS4LREwRBEAThr8hmAbJYLNi7dy+mTp3aPBiVClOnTsWOHTu6vN+vvvoKY8aMwZw5c5CQkICRI0fizTff9MaQCYIgCIIIEGSzAJWXl8NutyMxMbHV8sTEROTl5XV5v6dPn8brr7+OrKwsPPHEE9i9ezceeOAB6HQ6zJ8/3+U2ZrMZZrNZ/Lu2thaAEChmtVq7PBYlwuYTaPNyl2CfP0DnINjnD9A5oPkH7vw9mVPAZYE5HA6MGTMGzz//PABg5MiROHToEFavXt2uAFq2bBmWLl3aZvmmTZtgNBolHa9cZGdnyz0EWQn2+QN0DoJ9/gCdA5p/4M3fZDK5va5sAiguLg5qtRolJSWtlpeUlLQb4OwOycnJGDRoUKtlAwcOxKefftruNosWLUJWVpb4N6sjMH369IBMg8/Ozsa0adMCNv2xI4J9/gCdg2CfP0DngOYfuPNnHhx3kE0A6XQ6jB49Gjk5OZg9ezYAwXqTk5ODhQsXdnm/l1xyCY4dO9Zq2fHjx5Gent7uNnq9Hnq9vs1yrVYbcBcHI5Dn5g7BPn+AzkGwzx+gc0DzD7z5ezIfWV1gWVlZmD9/PsaMGYNx48Zh5cqVaGhoELPC5s2bh5SUFCxbtgyAEDh95MgR8f+LioqQm5uLsLAw9O3bFwDw8MMPY9KkSXj++edx6623YteuXXjjjTfwxhtvyDNJgiAIgiAUh6wCaO7cuSgrK8PixYtRXFyMESNGYOPGjWJgdEFBQatmZufPn8fIkSPFv5cvX47ly5dj8uTJ2Lp1KwAhVf7zzz/HokWL8Mwzz6BXr15YuXIl7rjjDp/OjSAIgiAI5SJ7EPTChQvbdXkxUcPIyMgAz/Od7vPaa6/Ftdde643hEQRBEAQRgMjeCoMgCIIgCMLXkAAi/Aqe59Foscs9DIIgCMLPIQFE+A0nS+sx65WfMOrZbBRWul/rgSAIgiAuRvYYIILoDJ7n8cnec1j85WE0WgXrz47TFUiLCcwilQRBEIT0kAAiFM/TXx3GOzvOAgCMOjVMFjuOXnC/2BVBEARBXAy5wAhFU1ZnFsXPozMG4OnrBgMA8i7UyTksgiAIws8hAUQomhOlgtBJjzXi/iv6YlAPoTXJ0eJat0oiEARBEIQrSAARiuZUaT0AoG98mPDfhDCoVRyqTVaU1JrlHBpBEAThx5AAIhTNSSaAEgQBZNCq0TsuFAAoDoggCILoMiSACEVzskwQQH2cAggABiY3u8EIgiAIoiuQACIUzcUWIADITA4HABylQGiCIAiii5AAIhRLbVNznE9fFxagPHKBEQRBEF2EBBChWFgAdEK4HhEGrbh8YJIggE6XN6DJSm0xCIIgCM8hAUQoFlfuLwBIjNAj2qiF3cGL6xAEQRCEJ5AAIhQLC4C+WABxHIdMpxXoCLnBCIIgiC5AAohQLKfasQABLeOAKBCaIAiC8BwSQIRiOXlREcSWNGeCkQWIIAiC8BwSQIQiabLaUVBpAuDaAjSIWYCoJQZBEATRBUgAEYrkTEUDHDwQbtAgPlzf5vO+CWFQcUAVtcQgCMLHfPXrebz9c77cwyC6iUbuARCEK1pmgHEc1+Zzg1aN3vFhOFlaj6PFtUiKNPh6iARBBCH7Cqrw4Ef7wfPAxD5xGJAULveQiC5CFiBCkXQU/8NggdDHiikQmiAI6bHaHVj06UEwr/ues5XyDojoFiSACEXSXg2glqREhQAAimuafDImgiCCmze2ncaxkuYXrn1nq+UbDNFtSAARisQdAZTgjA0qq6cYIIIgpOVMeQNeyTkBALhhRA8AwP6CKjmHRHQTEkCE4rA7eJwubwDQsQBiwdFldSSACIKQDp7n8eQXB2G2OXBZvzg8fd1gAEI7nsoGi8yjI7oKCSBCcRRVNcJic0CnUSE12tjueiSACILwBcdK6vDzyQroNCo8N3sIokN16BMfCoCsQP4MCSBCceRXCNafXrGhUKvaZoAxEkgAEQThA46XCC75YSmRSI8VhM+ontEAgL1nSQD5KySACMVR4gxqTo7qOLWdWYDqzTaYLDbJx0UQRHCSX+Z8KYsLFZeNShcE0D6yAPktJIAIxVFcKwigxPCOBVCYXoMQrRoAWYEIgpCO0+WCBah3i7Ico50C6NfCGtjsDlnGRXQPEkCE4ihhAqiT4oYcx1EcEEEQkpNf3tYC1Dc+DOEGDRqtduRRLTK/hAQQoThYa4vEiLYtMC6GCaBSEkAEQUgAz/M47XSBscBnAFCpOIzsSW4wf4YEEKE4Stx0gQEUCE0QhLSU1ZlRb7ZBxQE9Y1tnpY7qGQUA2EeB0H4JCSBCcYgCKKJzAUQuMIIgpITVJEuNNkKvUbf6TMwEIwuQX0ICiFAUNrsD5c7KzomRnbvAEkQXGLXDIAjC+zD3V+8W7i/GiJ5R4DigsLKRXsL8EBJAhKKoaLDAwQNqFYfYUPdjgOjmQxCEFOSzDLC4tlXpIwxa9E8QusHnFlb7cliEFyABRCgK1tg0PkzfYRFEBgVBEwQhJcwC1MuFBQgA+iQIy4uqTD4bE+EdSAARisLdFHhGgjNQmixABEFIAYsB6hPnWgCxe1AJ3YP8DkUIoFWrViEjIwMGgwHjx4/Hrl272l338OHDuPnmm5GRkQGO47By5coO9/3CCy+A4zg89NBD3h00IQnsJpIY3rn7C2i2AFU0WGB38JKNiyCI4MNqd6CgUrDstGcBSnCW62Avb4T/ILsAWrduHbKysrBkyRLs27cPw4cPx4wZM1BaWupyfZPJhN69e+OFF15AUlJSh/vevXs3/v3vf2PYsGFSDJ2QANYGw50MMACIDdWB44QO8tSVmSAIb1JQaYLdwcOoUyOpnXtSIlmh/RbZBdCKFStw7733YsGCBRg0aBBWr14No9GINWvWuFx/7NixePHFF3HbbbdBr2/fSlBfX4877rgDb775JqKjo6UaPuFlmlPg3bMAadQqxIbqANANiCAI73K6RQ8wjnMdk8he1sgC5H9o5Dy4xWLB3r17sWjRInGZSqXC1KlTsWPHjm7t+/7778esWbMwdepUPPfccx2uazabYTY3Pzxra2sBAFarFVartVvjUBpsPkqdV3FNIwAgLlTr9hjjwvQor7fgQnUD+sWHdLiu0ufvC4L9HAT7/AE6B+7O/2SJ8CzIiDG2u25MiFAbqKS2yW/OZyB//57MSVYBVF5eDrvdjsTExFbLExMTkZeX1+X9fvTRR9i3bx92797t1vrLli3D0qVL2yzftGkTjEajiy38n+zsbLmH4JKT59UAOJzNO4ANxb+6t1GjCoAKOT/vRv0J9+KAlDp/XxLs5yDY5w/QOehs/ttOCfcWW/V5bNhwzuU6JhsAaFDTaMOX6zdAK7tfxX0C8fs3mdzPxpNVAElBYWEhHnzwQWRnZ8NgcC+OZNGiRcjKyhL/rq2tRVpaGqZPn46IiAiphioLVqsV2dnZmDZtGrRardzDacOS3C0ArLj2qkvRPzHcrW22Nh1C3v7z6NE7EzMv79Xhukqfvy8I9nMQ7PMH6By4O//3/rMbQBWmTxiOmcOTXa7D8zye3p8Ds82BUZdMQVq08l+aA/n7Zx4cd5BVAMXFxUGtVqOkpKTV8pKSkk4DnNtj7969KC0txahRo8Rldrsd27Ztw2uvvQaz2Qy1unU5c71e7zKeSKvVBtzFwVDi3JqsdlQ3CubL1Jhwt8eXFCm4vcobrG5vo8T5+5pgPwfBPn+AzkFn888vF6wJ/ZIiOlwvMcKAgkoTKk129E7wn/MZiN+/J/OR1Vin0+kwevRo5OTkiMscDgdycnIwceLELu3zqquuwsGDB5Gbmyv+GzNmDO644w7k5ua2ET+EcmBBzHqNChEh7mvz+DBnNeh6CoImCMI71DZZxbY8vdqpAcRIoIKsfonsLrCsrCzMnz8fY8aMwbhx47By5Uo0NDRgwYIFAIB58+YhJSUFy5YtAyAETh85ckT8/6KiIuTm5iIsLAx9+/ZFeHg4hgwZ0uoYoaGhiI2NbbOcUBbFLZqgtpdx4QpWh6Oslm4+BEF4h3xnBlh8uB7hho6tCpQJ5p/ILoDmzp2LsrIyLF68GMXFxRgxYgQ2btwoBkYXFBRApWo2VJ0/fx4jR44U/16+fDmWL1+OyZMnY+vWrb4ePuFFPE2BZ5AFiCAIb5PvrADduxPrD9CyGCLdg/wJ2QUQACxcuBALFy50+dnFoiYjIwM871nFXxJG/gG7ebhbBJFBDVEJgvA2RdVCSY5UN4KaWTuMUrIA+RV+lLBHBDoltZ5VgWYkONevN9tgsti8Pi7Cv2gw2/D5/nN4eF0uthxzXVGeIDqDiZmkyM4t0sxqTTFA/oUiLEAEAXTdBRaqUyNEq0aj1Y6yOjPSY+myDkYq6s14dv0RfHe4BI1WOwDgxxPl+PnxK6DXUPID4RnFHryQUQyQf0IWIEIxdNUCxHFccyA0vYEFLU9/fQRf5J5Ho9WOXnGhiAnVobzejK9yz8s9NMIPKfbAJZ9IDVH9EhJAhGIo7WIMENAcCE0m6OCktK4JGw9dAACsvWssNj8yGfdd3hsA8J+f8j2OGyQI0QXmxv0o3hkDVNtkQ5PT+kgoHxJAhCLged4jk/PFUCB0cPO/3YWw2nmM7BmFKzITwHEcbh/bE0adGnnFddh+qkLuIRJ+hN3Biy9T7tyPIgwaGJw9MEopE8xvIAFEKAIhgFl4c/I0BghoWYiMTNDBht3B44NfCgAAd05IF5dHGrWYMzoVAPDWj6dlGRvhn1Q0mGF38FBxQFyYrtP1OY5rjgOie5DfQAKIUAQsBT7coIFR53kQM1mAgpfNeaU4X9OEaKMWM4e27te04JJe4Dhgy7EynCytl2mEhL9RUiPcR+LC9NCo3XtMspcwigPyH0gAEYqgqwHQDBJAwct7O88CAG4dkwaDtnW2V0ZcKKYOFIqqrvk53+djI/yTEjEF3v37ESvHQS4w/4EEEKEIupoCz4gJFbarNFm9NiZC+ZytaMAPx8vAccBvxvd0uc7dl/YCAHy+rwhWu8OXwyP8FBaPyAocukNiOLnA/A0SQIQiEKtAe3DDaUlMqNCrp6rB4rUxEcqHxf5c3i8e6bGuWxaMy4hBZIgWjVY78i7U+XJ4hJ/iSRFEhlgMkSxAfgMJIEIRMNdVfHctQCSAggae57H+gJD6fvs419YfAFCpOIxIiwIA7Cuo8sXQCD+n2IMUeEZCBCVi+BskgAhFUO5sZMrq+XhKjFHI1Kg322C2UR2OYOBMhQlF1Y3QqVW4vH9ch+uO6hkNANhPAohwA1YEMcEDASS6wMgC5DeQACIUARNAcV0UQOEGDdQqDgBQTXFAQcFPJ8oAAKPSozrNHBzZMwoAsK+gWuJREYGAJ0UQGQnUDsPvIAFEKILuCiCVikO00wpUUU9usGDgxxPlAIDL+sV3uu6InlHgOKCg0oSKenpDJzqmK0VZWQxQXZMNjRayQvsDJIAIRVDuFC1x4Z0XHWsPMRDaRAIo0LHZHdjhrO58ad+O3V8AEGHQom98GAAg91yNpGMj/Jsmq120IntiAQrTaxDiLMNAcUD+AQkgQnZsdocoWrpqAQIgWoAoEDrw+fVcNerMNkSGaDEkJdKtbZgbLLeQBBDRPiyLy6BVISLE/aKsQjVoVgyRrIz+AAkgQnYqGyzgeUDFNYuYrhAbRgIoWGDur0v7xomxX53BAqFzC6ulGhYRALA6PokRBnCce9cWg+KA/AsSQITslDljMmJC9W4/zFxBFqDgQRRA/Tp3fzFGOgXQgaJaOKg5PNEOxTVdr0rf3JOQLED+AAkgQnbE+B83mg52REyosD3FAAU2tU1W0YrjTvwPo29CGML0GpgsdlwwSTQ4wu/pTlueRLEdBlmA/AESQITslLMiiOFdj/8BmgVQBVmAApqdpypgd/DIiDUiLcbo9nbqFgURz9R33dJIBDZiH7AuFGVlbni6B/kHJIAI2eluCjxDtADRzSeg6Yr7i8ECoc/UkQAiXMOKIHbFAsTc8NVkhfYLSAARstMsgLrnAqMYoODgp5Pu1/+5GBYITQKIaI/uuMCiQlgpDirG6g+QACJkpzkGyDsWIBJAgUtpXRPyyxvAccCE3rEeb89cYKVNHFUMJ1wiusAiuyCAjBSH6E+QACJkx+suMJMFPE9pPoHIfmcri/4J4Yh0vm17QnSoDj1jQgAAhy/UenNoRADA83yzBSi8Cy4wZzHWGhLXfgEJIEJ2WCf4OC8FQVvtPOrMtm6Pi1AerJs7i+XpCplJ4QCA4yX13hgSEUDUNtrQZHUAaO7u7gliDFCjlV7C/AASQITseCsGyKBVw6gTStFTIHRgwixALJanKwxIFFpiHCup88aQiACCFUGMMmphcLa18IQoo2ABsjt41DbRS5jSIQFEyIrdwYsxO/HddIEBFAgdyFjtDhw4Vw2gexag/omCBehYMVmAiNawIoie9ABriV7T/BJGmWDKhwQQISuVDRY4eIDjml1Y3YECoQOXY8V1aLI6EGHQoI+zsWlXyEwStj1RWg87lYQmWsC6wCd0UQABzS9hlAmmfEgAEbLC3F/RRh006u5fjiSAAhcW/zOiZzRU3WiZkhZthFbFw2xz4GxFg7eGRwQApd0ogsiIFFPh6R6kdEgAEbLirfgfBrXDCFxY/M9IZyp7V1GrOCQLiWDIK6Y4IKKZkm4UQWSwTDBygSkfEkCErHgrBZ7BzM9Uij7wYBagUeldD4BmJBsF1xcJIKIlpXXdd4FFidWgyQWmdEgAEbJSXuedIogM1ouHssACi4p6M85WCB1MR6RGdXt/PUKdAohqAREtYCU5upOQEW2katD+AgkgQlaksgBVNtDNJ5Bg7q++CWGINHpeAPFiejh7qFIqPNGSUi80ZqZ+YP4DCSBCVsqYAAr3VgyQ8HCsbDB7ZX+EMthf6CyA2M34H0YPpwvsbIUJDVQ0k4BQBZpZgBK6IYCiKAvMbyABRMiKt/qAMWJChf3QzSew2He2GoB34n8AIEwLxDvdpcfJCkQAqDPbYLYJVaC7ZwGiIGh/gQQQISvlXvC5t6TZAkQ3n0DB7uDxq7MAYncqQF9Mc0FEEkBEc/xPuEHTpSrQjCgjpcH7C4oQQKtWrUJGRgYMBgPGjx+PXbt2tbvu4cOHcfPNNyMjIwMcx2HlypVt1lm2bBnGjh2L8PBwJCQkYPbs2Th27JiEMyC6CosB6s4bV0uY/72m0Qqr3eGVfRLycrykDiaLHWF6DfomdL0A4sWwlhiUCUYALQKgu3kvEl1gFIeoeGQXQOvWrUNWVhaWLFmCffv2Yfjw4ZgxYwZKS0tdrm8ymdC7d2+88MILSEpKcrnODz/8gPvvvx87d+5EdnY2rFYrpk+fjoYGKnqmJBwOXkxX95YLLMqoA+eskUdpqIHBoaIaAMCQlAiou1EA8WIGJDEBRJlgRIsA6G7eiygI2n+QXQCtWLEC9957LxYsWIBBgwZh9erVMBqNWLNmjcv1x44dixdffBG33XYb9HrXF+rGjRtx1113YfDgwRg+fDjefvttFBQUYO/evVJOhfCQ6kar2Iog1kuFENUqDlFUiTWgOHxeECiDkiO9ut8BThdYXnEdde4mvGYBYjFADRY7LDayQisZjZwHt1gs2Lt3LxYtWiQuU6lUmDp1Knbs2OG149TUCG+QMTExLj83m80wm5uzhmprhRuu1WqF1RpYVgQ2HyXMq7hKsMhFhWgBhx1Wh90r+402alFlsqK0xoReMa0Lmilp/nLhb+fg8Hnh95uZGOqVMbN99IzSQcUJlsKiyvpuVf/1N/ztGvA2ruZfUiPUmYoL1XbrvISoARUHOHigvNbkNfe+Nwnk79+TOckqgMrLy2G325GYmNhqeWJiIvLy8rxyDIfDgYceegiXXHIJhgwZ4nKdZcuWYenSpW2Wb9q0CUaj0SvjUBrZ2dlyDwHHazgAauhhwYYNG7y3Y7MaAIecn35BxVHXb/ZKmL/c+MM5cPDAwULh+6w4mYsNF3K9tu8ft25GvEGNkkYO7329BQOjg88K5A/XgJS0nH/uSRUAFSqK8rFhw+lu7TdErUaDjcNX3+UgWcGPkED8/k0mk9vryiqAfMH999+PQ4cO4aeffmp3nUWLFiErK0v8u7a2FmlpaZg+fToiIiJ8MUyfYbVakZ2djWnTpkGr7X5Bue5gP3ABOHIQGUkxmDlzrNf2u746F6ePliJjwBDMHJfW6jMlzV8u/OkcnK00oWnnT9BpVJh/09XQeqFhbsv5f1d3BBsOlSAsLRMzL+/lhRH7B/50DUiBq/l/8s5eoKwCl44ZhpkjU7q1/5XHf0J+hQlDx0zAuAzXngc5CeTvn3lw3EFWARQXFwe1Wo2SkpJWy0tKStoNcPaEhQsXYv369di2bRtSU1PbXU+v17uMJ9JqtQF3cTCUMLeqRsHlFR9u8OpYYp1BjDVN9nb3q4T5y40/nIPjpcLb3IDEcBgN3nUlaLVaDE6JwoZDJcgrqVf8uZACf7gGpKTl/MucNcmSokK7fU6iQnVAhQl1Zl7R5zcQv39P5iNrELROp8Po0aORk5MjLnM4HMjJycHEiRO7vF+e57Fw4UJ8/vnn2Lx5M3r1Cp43O3/C220wGKwjPNUC8n9Y/M/gHtJYYoekCIHVR85TJliwI5bk8ML9iDLB/APZXWBZWVmYP38+xowZg3HjxmHlypVoaGjAggULAADz5s1DSkoKli1bBkAInD5y5Ij4/0VFRcjNzUVYWBj69u0LQHB7ffDBB/jyyy8RHh6O4uJiAEBkZCRCQkJkmCXhinIvZV1cDBNAlAXm/zBhIpUAYvvNr2hAg9mGUL3st0RCBmx2h1iSIyGi+/ejKGqI6hfI/mufO3cuysrKsHjxYhQXF2PEiBHYuHGjGBhdUFAAlarZUHX+/HmMHDlS/Hv58uVYvnw5Jk+ejK1btwIAXn/9dQDAlClTWh1r7dq1uOuuuySdD+E+zRYg76TAM5obopIA8nfEFHiJBFBcmB6JEXqU1Jpx9EItxigwXoOQnsoGC3heKKPB7h/dQbQANdI9SMnILoAAIVZn4cKFLj9jooaRkZHRac0OqunhH3i7DxgjJowEUCBQVmdGaZ0ZHAdkJkmXjDC4RyRKaktxqKiGBFCQwoogxobqvFJsU+wHRtWgFY3shRCJ4EWyGCCxFD0JIH+Gxf/0iguV1DU1xGldOkxxQEGLt4ogMpo7wtM9SMmQACJkged5VDALkEQxQBUNFrIG+jFHLrD4H+9WgL6YQc79kwAKXrwtgJqDoMkCpGRIABGyUNtog8XZrDQ21LsxQEwAmW0ONFq9U12a8D3NLTCkrcXFAqFPlNbBbKPrJRgp82IGGEAd4f0FEkCELLAbTrhBA4NW7dV9G3Vq6DTCpU1xQP6L1BlgjNToEESGaGG18zhRUi/psQhlwixA3sgAAygLzF8gAUTIgjdrblwMx3FiHBAJIP+k3mxDfrnQK05qAcRxnHgMFndEBBdlXuoEz2hZB4jc8MqFBBAhC+yG4+0AaEY0FUP0a/Kc8T9JEQaxsreUDKZA6KCmtK4JgFCV3hswAWRz8Kg327yyT8L7kAAiZEHMAAv3bvwPI5aKIfo1LABaqvo/FzOYAqGDGm8HQYfo1NA73fAUCK1cSAARsiBVCjyDWYBYphnhX+QV1wEAMpPCfXI8ZgE6cr4Wdge5LIINbwsggDLB/AESQIQslNdJUwSREUNZGH7NiRJBAA3wkQDqHR8Gg1aFRqtdjD0igoMGsw0NFtaY2Xv3I8oEUz4kgAhZkNoCFBMq7LeSKrH6HTzP45jTAtQvwTcCSK3iMDCZAqGDEXYvMurUCPNiwU0SQMqHBBAhC2IWmJeLIDJiQoWbT2WDWZL9E9JRUmtGbZMNahWH3vGhPjsuBUIHJ6USNWUmF5jyIQFEyEJzHzBpgqBZDFAVWYD8juNO91dGrNHrNaI6YnhqFADgl9MVPjsmIT/eToFnUDsM5UMCiPA5PM+LhRClc4E50+Dp5uN3MAHUP9E37i/G5f3jAQAHimqofEIQIUUANNCiISpZgBQLCSDC59SZbbDYhDYY0rnAqA6Qv8Lif3wtgBIjDMhMCgfPAz+eKPPpsQn5kE4ANRdDJJQJCSDC55Q7bzhheu+3wWDEtLj5UFqzf3G8VGhH4asMsJZMHiBYgX44TgIoWGBFEBO8LICoHYbyIQFE+Byp43+A5hggBw/UNtINyF9wOHgxBb5/YpjPjz+5nyCAth0vh4OEc1BAFqDghQQQ4XOkToEHAK1ahXCDkNJaQW4wv6GouhEmix06tQrpsb7LAGOMzoiGUadGeb1ZrEZNBDZlEmWkRrIYIHoBUywkgAif4wsBBDTHAVEWhv/AAqB7x4dCq/b97UmvUWNSn1gAwDaKAwoKSmtZFph3+oAxokIEAVRDAkixkAAifA6LAZKqDxiDAqH9j2M+rgDtisnObLAfjpEACnTsDl58IUuI8LIFqIUAIneqMiEBRPicsnpp22AwWCA0CSD/4bhMGWAtYenwe89Woa6J3t4DmcoGCxw8wHHNDZS9RYRTAPG8kPlKKA8SQITP8ZULLJosQH7H8RIhA0xOAZQeG4qMWCNsDh7bT1FRxECGVYGODdVD42WXq0GrhkEr7JMSMZQJCSDC5/hKAMWK1aBJAPkDNrsDJ8ucKfAyCiCg2Q22jdLhAxoWAO3tFHhGVAi1w1AyJIAIn9PcB0zaGCCyAPkXZytNsNgcCNGqkRodIutYWD2gbw5eoOsngGEp8N6O/2FEUiC0oiEBRPgUnufFm47PYoAoC8wvYPV/+iWGQaXiZB3LZf3ikZkUjmqTFc+tPyLrWAjpKK0T7g1SWYCaU+HpHqRESAARPqXBYkeTVWiD4bM0eHqD9wuOFcsf/8PQqlV44eZh4Djgs/1FVBk6QBEtQOHeTYFnkAVI2ZAAInwKS4EP0aoRqtdIeizmAqNCiP7BcRkrQLtiRFoUFkzqBQB44rODaKBMnoCjVGIXGKsFRDFAyoQEEOFTxABoieN/ALIA+RtydYHviEem90dKVAiKqhux5KvDyCuuFRv5Ev6P1EHQzAJEWWDKRNpXcIK4CF9lgAHNAkhwu9kla7xKdB+zzY788gYA8hZBvJhQvQZ/u3EI7lq7G5/sPYdP9p6DVs2hT3wYBiZHIDMpHENTIzGxdyw4Tt64JcJzysU+YNK4wFhDVLIAKRMSQIRPYUUQ430ggCIMGqhVHOwOHlUmC5Ij5c0sItonv7wBNgePcL0GSRHSPIy6ypQBCVh201B8vq8IR4trUddkQ15xHfKcRRsB4N7LeuHJWYNkHCXhKTwPlNZLHARNMUCKhgQQ4VOa22BIL4A4jkO0UYfyejMqG0gAKRmxAGJSuCItKbeP64nbx/UEz/Moqm5E3oU65BXX4vD5Wnx7qBhv/piPSX3jcMWABLmHSrhJox2iO9PbjVAZkawjPGWBKRKKASJ8ii9dYEDLYoj0BqZklNACwx04jkNqtBFTByVi4ZX98PpvR+OuSRkAgEc//lXMKiKUT61Tk0QYNJK5x5stQBRAr0RIABE+RSyCGCZ9EDQARIcKN6CKBnowKRmxCapCMsA84fFrMpGZFI7yegv+8vGv1PjST6ixCpbGBAldrmJHeKpFpkhIABE+pdxHjVAZlAnmH5xQYAaYuxi0arxy+0joNSr8cLwM7+w4I/eQCDdgFiCp4n8AigFSOiSACJ/SnAbvWwFUSVkYiqXRYsfZShMAIQbIH+mfGI4nZg4EALyx7TRZgfwAXwgglgXWYLHDaqfyCUqjSwKooKAAP/74I7777jvs27cPZjO5Fwj3KPdRGwyG2A6DXGCK5WRpPXheiNfy1XUhBXPHpiFcr8GFmibsOVsl93CITqj1gQss3KAV/5+sQMrDbQF05swZPPbYY0hPT0evXr0wefJkXHPNNRgzZgwiIyMxbdo0fPzxx3A4SOUSrmm02NFgsQMA4nwWA0RB0ErneIseYP6MQavG9MFJAICvfi2SeTREZ/jCAqRWcYgwCMnWVAtIebglgB544AEMHz4c+fn5eO6553DkyBHU1NTAYrGguLgYGzZswKWXXorFixdj2LBh2L17t0eDWLVqFTIyMmAwGDB+/Hjs2rWr3XUPHz6Mm2++GRkZGeA4DitXruz2PgnfwNxfeo0KYRK3wWDEUEd4xXNcDID2T/dXS64f0QMAsOFgMbk8FA6zAEmVAs9gDVHJAqQ83BJAoaGhOH36NP73v//hzjvvxIABAxAeHg6NRoOEhARceeWVWLJkCY4ePYrly5ejsLDQ7QGsW7cOWVlZWLJkCfbt24fhw4djxowZKC0tdbm+yWRC79698cILLyApKckr+yR8Q1mLFHhf1XohAaR8WAaYv8b/tOSSPrGIDdWhssGCn0+Wyz0cogOaLUDSFt6MChHuQTVUC0hxuCWAli1bhtjYWLd2ePXVV+Omm25yewArVqzAvffeiwULFmDQoEFYvXo1jEYj1qxZ43L9sWPH4sUXX8Rtt90Gvd61cvd0n4Rv8GURREY0iwGiNFTFcqJEOV3gu4tGrcLMockAgK9+PS/zaIiOqHEaZKRqhMqgTDDlImslaIvFgr1792LRokXiMpVKhalTp2LHjh0+26fZbG4VyF1bWwsAsFqtsFoD66Jl85FjXiU1jQCAWKPWZ8eP0Asav6rBAovFAptNKEgWaN+rJ8h5DVxMXZMNRdXCddErxuCTMUk9/1lDEvDuzrP47nAx6kxNiuxBp6RrQA5qGppgtgtW6GiDWtLzEGEQvv+KuibFnO9A/v49mZPXBNATTzyB4uJij6ws5eXlsNvtSExMbLU8MTEReXl5XRpHV/a5bNkyLF26tM3yTZs2wWg0dmkcSic7O9vnx9xxjgOgRmNVCTZs2OCTYwox1xrYHDw+/fpbGJ1XvBzzVxpKOAf5dQCgQaSOx89bfDseqebv4IFonRpVZjtWfLQJI2KVmxKvhGtADsoaAUADnYrHtpxNkNIjX12mAqDCngNHEF91WLoDdYFA/P5NJpPb63pNABUVFXkU+6MkFi1ahKysLPHv2tpapKWlYfr06YiIiJBxZN7HarUiOzsb06ZNg1ar7XwDL7Lr66NAYSFGDOyDmVP7+ey4S3JzYLLYMeaSyUiJ0Mk2f6Ug5zVwMf/bcw44dARDe8Zh5szRPjmmL+Z/RHMcb/50Buc1yXhi5ghJjtEdlHQNyMGOk2VA7n4kRhoxa9Zlkh7raPYJbC/JR0JqL8ycmSnpsdwlkL9/5sFxB68JoHfeecfjbeLi4qBWq1FSUtJqeUlJSbsBzlLsU6/Xu4wn0mq1AXdxMOSYGytGmBgR4tNjx4XpUVBpQnWTAxmxwnED+bt1FyWcg5PlwttaZlKEz8ci5fxnj0rFmz+dwdbj5bBDpUg3GKCMa0AOqhqFchyJEXrJ5x8dKjxb6s12xZ3rQPz+PZmPrJWgdTodRo8ejZycHHGZw+FATk4OJk6cqJh9Et7B11WgGazmUDk1qlQcxwMoA6wlg5IjkBxpgNnmwC/5lXIPh7iIUrEnofT3IlYNupqCoBWHxxagZ555psPPFy9e7NH+srKyMH/+fIwZMwbjxo3DypUr0dDQgAULFgAA5s2bh5SUFCxbtgyAEOR85MgR8f+LioqQm5uLsLAw9O3b1619EvLg6z5gDHY8JsAI5XCsOHAywFrCcRwu7xePdXsK8cOxMkzuHy/3kIgWlDlfhqSuAQRQFpiS8VgAff75563+tlqtyM/Ph0ajQZ8+fTwWQHPnzkVZWRkWL16M4uJijBgxAhs3bhSDmAsKCqBSNRuqzp8/j5EjR4p/L1++HMuXL8fkyZOxdetWt/ZJyIOv22AwmMWprJ5S4ZVEZYNFFKX9Evy7CrQrJg8QBNC2E2VyD4W4CCaApKwCzYh01gGqplIcisNjAbR///42y2pra3HXXXfhxhtv7NIgFi5ciIULF7r8jIkaRkZGBni+86yKjvZJ+J4mqx11ZiEF3Rdm55aQBUiZMPdXWkwIQn1UGdyXXNInDipO6HVWVN2IlKgQuYdEOGEvQ/Hh0rfkabYA2SQ/FuEZXokBioiIwNKlS/HUU095Y3dEAMLEh06tQkSIbx928RQDpEgCqQWGKyKNWozsGQ0A2HacrEBKwpcusCixFYbFrZd3wnd4LQi6pqYGNTU13todEWA0x//ofNYGg0EWIGXS3AQ1MAUQAFzeT4j9IQGkLErrfBcEzSxAVjuPRqtd8uMR7uPxq/grr7zS6m+e53HhwgW8++67uOaaa7w2MCKwkKMNBoMds5xigBTFcWcAdKBagADg8v5x+Of3x/HTyXLY7A5o1LIm3hIALDYHqpwlOXxhATLq1NCqOVjtPKpNVhh1gefu9Vc8/ib++c9/tvpbpVIhPj4e8+fPb9V+giBaUl4vTwB0y2OSBUg58Dzf3AQ1gAXQsNQoRBm1qDZZkVtYjTEZMXIPKehh9wEVxyM6RPoaOBzHITJEi/J6C2oarehBsWCKwWMBlJ+fL8U4iACnWQBJH3R4MeyYJosdJgsFIiqBsjozahqtUHFA7/hQuYcjGWoVh0v7xmH9gQvYdryMBJACYO6vCC2gUvnGHc8EULWJUuGVBNljCZ8gVw0gAAjTa6DXqFqNg5AXZv3JiAtVbJVkb3G5swbQDxQHpAhKa5sACALIV1AtIGVCAojwCWUyusA4jhOPW0ECSBEcKw7sDLCWsCKIB4pqUNlA15/ciBYgne8ysqKMghW6ppG+fyVBAojwCXIGQQPNwY5kAVIGJ0qEAOhAzgBjJEYYkJkUDp6nbDAl0CyAfHdMsgApExJAhE+QMwZIOK5TADVQILQSOBbgNYAu5orMBADAlmOlMo+EKKtjLjDfWYCYAKIYIGVBAojwCczy4usq0AxW8ZUsQPLD8zxOMAGUFHgtMFxxxQBBAP1wvAx2BxXDk5PSWuElKJIsQEGP1wTQhQsXUFBQ4K3dEQGExeYQf/hyxAC1PC7FAMlPUXUjGix2aNUc0mMDNwOsJaN6RiHCoBHT4Qn5aJkF5iuoI7wy8ZoAuvLKK9GrVy9v7Y4IICqcbieNihPfhHwNE0BlVAtIdlgF6D7xYdAGSWFAjVolZoNtySM3mJyUMheYD4Og2X2vlgSQovDa3ee///0vNm/e7K3dEQEE67sTG6bzWd2NiyELkHI45qwAHcgFEF3B3GAUByQfdgcvusFlsQBRDJCi8FpN7rFjx3prV0SAIWcVaAYLvqYYIPk5IVaADo74H8bkAfHgOODw+VqU1DYhMcIg95CCjsoGC+wOHhwH+KARvAjFACmT4LA/E7JSXidfEUSG2A+MssBkJxhaYLgiLkyPYalRAICtZAWSBeb+ijHqoPahMbo5C4xewJSExwLIbrdj+fLlGDduHJKSkhATE9PqH0FcjJxFEBns2A1mOyzUkFk27A4eJ0udTVCTgksAAcAVA1gcENUDkgOxC7yP65FFhgjmpjqzjbIAFYTHAmjp0qVYsWIF5s6di5qaGmRlZeGmm26CSqXC008/LcEQCX9HdIH50uZ8EREGDXTOgNs6skLLRmGlCWabA3qNCmnRRrmH43OudNYD+ulkOSw2h8yjCT7KnCnwCT6+FzELEM9TILSS8FgAvf/++3jzzTfxyCOPQKPR4Pbbb8dbb72FxYsXY+fOnVKMkfBz5K4BBLB2GM63MLr/yMbpcsH60ysuVLaAeDkZ0iMScWE61Jtt2HOmUu7hBB3MBeZrC5BOo0K4Xgi5rSI3mGLwWAAVFxdj6NChAICwsDDU1NQAAK699lp888033h0dERCUy2R2vhgWB1RnDb4Hr1I4XdYAQEiBD0ZUKg6T+1M2mFwwF1iCDC9jUaGCFYgEkHLwWAClpqbiwoULAIA+ffpg06ZNAIDdu3dDr5f3AUcoEyVkgQHNFiiyAMnHKacA6h0fHAUQXXGl2BaD4oB8TZmML2PRzoaoVQ10A1IKHgugG2+8ETk5OQCAP//5z3jqqafQr18/zJs3D7/73e+8PkDC/1GKAIojASQ7p8sEF1gwC6BL+8VBreJwsrQehZUmuYcTVMgVBA00d4QnC5By8LgO0AsvvCD+/9y5c5Geno7t27ejX79+uO6667w6OML/sdodqDKxNhjyBUEDzUHY5AKTj9PlTgtQXHC6wAAhIHZ0ejR25Vdiy7FSzJuYIfeQggYxBihMhxIfHzuaiiEqjm4XQpwwYQImTJjgjbEQAUhlg/C2o1ZxoglYLsgCJC+1TVbRBRHMFiBAcIPtyq/EljwSQL6C53mxEWp8uF4GAUQWIKXhlgvMk+wuk8mEw4cPd3lARGDBHngxofK1wWCIAshCFiA5YAHQ8eF6hBvk6QmnFFhbjO2nKtBkpcJUvqC2yQazs/RAgpwxQGQBUgxuCaA777wTM2bMwMcff4yGhgaX6xw5cgRPPPEE+vTpg71793p1kIT/wgSQHDeciyELkLyI8T9xwW39AYQ2ID0iDTDbHNhxqkLu4QQFZU73V7hBA4NW7fPjR7MssAayACkFtwTQkSNHMGvWLPz1r39FVFQUBg8ejGnTpuG6667DpZdeiri4OIwaNQr5+fnYtGkT5s2bJ/W4CT9BzqyLi4kPpzpAcnJazAAL3vgfBsdxuCKT0uF9SWmtvC9jFAStPNwSQFqtFg888ACOHTuGHTt24N5778WQIUOQkpKCKVOm4N///jfOnz+PDz/8UKwRRBBAcxsMOYsgMpgFqNHOiaZwwnewIoh9gjz+h8HcYJvzSsHz1B5BasQaQOHyNKGlIGjl4XEQ9JgxYzBmzBgpxkIEIEqyAEWGaKFVc7DaeVTUmxEWIv+YgonTVAOoFZP6xkKnUeFcVSNOldWjb0Lw9UbzJSwDLCFCnt89BUErD+oGT0iKkmKAOI5DTKhwE2LtOQjf4HDwyKcU+FYYdRpM6B0LQLACEdIivwus2QJEFj9lQAKIkJTm3jvymJ0vhtUiKqdARJ9SVN0Is80BrZpDanSI3MNRDJP7C93hd56mvmBSI78LTLj3WOwOmCyU+acESAARkqIkFxjQHAfE+pMRvoEVQMyIDYVGTbcdxqieUQCA3MJqsgpIjNwuMKNODZ1GuPbJDaYM6E5ESIrSBBALxiYXmG+hFhiuGdQjAjq1CpUNFhRWNso9nIBGzjYYgOCCZ4HQ1A9MGZAAIiSjwWxDg9PUqxwBxGKAyALkSygF3jV6jRqDekQAAPYXVsk8msCmrFZeFxhAgdBKw60ssFdeecXtHT7wwANdHgwRWDDrj1GnRpi+211XvAITYqXkAvMpLAWeiiC2ZURaFHILq7G/oBo3jEiRezgBSaPFjjqzDYB8LjCgORCaBJAycOup9M9//tOtnXEcRwKIEBFrACnE+gO0CIImF5hPOVVKFqD2GNkzCm9vB/YXVss9lICFxf8YtCqE6zWw2WyyjINZgKgWkDJwywWWn5/v1r/Tp093aRCrVq1CRkYGDAYDxo8fj127dnW4/scff4zMzEwYDAYMHToUGzZsaPV5fX09Fi5ciNTUVISEhGDQoEFYvXp1l8ZGdB0x/kcBRRAZTIyVkQvMZzSYbSiuFR5AVASxLSPTogEAR8/Xwmyj7CApaJkBxnHy9QKkatDKQvYYoHXr1iErKwtLlizBvn37MHz4cMyYMQOlpa7rYmzfvh2333477r77buzfvx+zZ8/G7NmzcejQIXGdrKwsbNy4Ee+99x6OHj2Khx56CAsXLsRXX33lq2kRUF4ANEBB0HLA6v/EhOrEBwDRTFpMCGJDdbDYHTh8vlbu4QQkSrkXUTVoZdGlwIxz587hq6++QkFBASyW1g+SFStWeLSvFStW4N5778WCBQsAAKtXr8Y333yDNWvW4PHHH2+z/ssvv4yrr74ajz76KADg2WefRXZ2Nl577TXRyrN9+3bMnz8fU6ZMAQDcd999+Pe//41du3bh+uuv93S6RBcR004VJICYC8xksaPebFNMbFIgwwRQL4r/cQnHcRiRFoWcvFLkFlRjVM9ouYcUcCilICsrxFpJdcgUgccWoJycHAwYMACvv/46XnrpJWzZsgVr167FmjVrkJub69G+LBYL9u7di6lTpzYPSKXC1KlTsWPHDpfb7Nixo9X6ADBjxoxW60+aNAlfffUVioqKwPM8tmzZguPHj2P69OkejY/oHkp562pJqF4DvUqot1JGgdA+4VyVkN6dRgUQ22Wksx4QxQFJg1LuReQCUxYev/4uWrQIf/nLX7B06VKEh4fj008/RUJCAu644w5cffXVHu2rvLwcdrsdiYmJrZYnJiYiLy/P5TbFxcUu1y8uLhb/fvXVV3HfffchNTUVGo0GKpUKb775Ji6//HKX+zSbzTCbmx+GtbWCGdpqtcJqDSxTJZuPL+ZV4oz7iDFqFHMerVYrwnWAuQm4UNWA1Mjgc8n48hoAgIIKIQMsOVKviOvA1/N3hyE9hD5g+wuqfDIuJZ4DKSmpFUR4jFHb6r7u6/mH652FEBsssp77QP7+PZmTxwLo6NGj+PDDD4WNNRo0NjYiLCwMzzzzDG644Qb88Y9/9HSXXufVV1/Fzp078dVXXyE9PR3btm3D/fffjx49erSxHgHAsmXLsHTp0jbLN23aBKPR6Ish+5zs7GzJj3GqSA2Aw5m8g9hQckDy47lLhFaN8iZg0487UXYkeKvv+uIaAIDcEyoAKlQVnsSGDSd8ckx38NX83aHRBnBQ41xVI9Z9uQHhWt8cV0nnQEoOnxKuweL8Y9hgan659vX88+sAQIPzFTVtknfkIBC/f5PJ5Pa6Hgug0NBQMe4nOTkZp06dwuDBgwEIFh1PiIuLg1qtRklJSavlJSUlSEpKcrlNUlJSh+s3NjbiiSeewOeff45Zs2YBAIYNG4bc3FwsX77cpQBatGgRsrKyxL9ra2uRlpaG6dOnIyIiwqM5KR2r1Yrs7GxMmzYNWq20d9nnD/0AwIyZV1yCwT2UcR6tVivWHs8BwCG17yDMnJgu95B8ji+vAQB45eTPABpw9eXjcEmfWMmP1xm+nr+7vHXmZ5wsa0DcgDG4KjNB0mMp9RxIxZtndwLVtbhi0hhcOSBetvnnlzdg5aGfYYYGM2fO8NlxLyaQv3/mwXEHjwXQhAkT8NNPP2HgwIGYOXMmHnnkERw8eBCfffYZJkyY4NG+dDodRo8ejZycHMyePRsA4HA4kJOTg4ULF7rcZuLEicjJycFDDz0kLsvOzsbEiRMBNLutVKrW4U1qtRoOh8PlPvV6PfT6tr5hrVYbcBcHQ+q52R282HA0OTpUUecxwjmUSpNNUePyNb64vnmeR1G14H7IiAtX1PlW2u97VHo0TpY14ND5elw91DcFEZV2DqSCZX32iGp9L/L1/OMjBI9Cg9kOnmvuDSYXgfj9ezIfjwXQihUrUF8v+PSXLl2K+vp6rFu3Dv369fM4AwwQUtbnz5+PMWPGYNy4cVi5ciUaGhrErLB58+YhJSUFy5YtAwA8+OCDmDx5Ml566SXMmjULH330Efbs2YM33ngDABAREYHJkyfj0UcfRUhICNLT0/HDDz/gv//9b5fGR3SNKpMFdgcPjmvOfFAK4VoKgvYVFQ0WNFkd4DggOUq+FgT+wIi0aPxvzzlqieFlHA5ebH0jdxB0RIgWHAfwPFBtsiAhgn4TcuKxAOrdu7f4/6Ghod0uMDh37lyUlZVh8eLFKC4uxogRI7Bx40Yx0LmgoKCVNWfSpEn44IMP8Ne//hVPPPEE+vXrhy+++AJDhgwR1/noo4+waNEi3HHHHaisrER6ejr+9re/4Q9/+EO3xkq4DxMXMUYdtArr/h3h1GNUDFF6WAZYYrgBeo1a5tEom6EpkQCAY8V1Mo8ksKgyWWBzCC89sWHyvoypVRyiQrSoMllRZbKSAJKZLhdBsVgsKC0tbeNW6tmzp8f7WrhwYbsur61bt7ZZNmfOHMyZM6fd/SUlJWHt2rUej4PwHkpJO3UFc4GV1pIAkppzVUJAYiqlwHdKL2eV7PJ6C+qarAg3BJZrQi7Yi05MqDJexqKNOqcAolR4ufFYAB0/fhx33303tm/f3mo5z/PgOA52O5VyJ5pLzytSAOmcLjCyAEkOswCRAOqcML0GcWF6lNebcabchKGpkXIPKSBQWkueKLEaNAkgufFYAC1YsAAajQbr169HcnKyrH1VCOWiZAsQSzGuqDfD7uChVtE1LBVFTgGUQgLILXrFGVFeb0Z+RQMJIC+htHtRtFgMMfBq8PgbHgug3Nxc7N27F5mZmVKMhwgQlHbTaUmYFuA4wMELJemVOMZAodkFFpj1tLxNRmwodp+pQn5Zg9xDCRiUdi+iatDKwWOH6KBBgzyu90MEH8y9pBSzc0vUHBDrzExj/coIaSAXmGdkOPulnakgAeQtlCaAqCGqcvBYAP3973/H//3f/2Hr1q2oqKhAbW1tq38EAQClzjYYSs1yiHMKM0qFlw6e51sIILIAuQNrGMsayBLdR2kvY9HUEFUxeOwCY5WUr7rqqlbLKQiaaInSbjoXEx+mQx5IAElJZYMFjVbhftCDagC5RUYsWYC8jdgJPkIZ9yIWA0RB0PLjsQDasmWLFOMgAgylmZ0vJs45LsoEkw6xBlCEnmoAuUlGnGApqzZZUW2yiPEiRNcpVVgWGHOBURC0/HgsgCZPnizFOIgAoslqR12TDYByBVCC82ZItYCkg9xfnmPUaZAYoUdJrRn55Q0Y2ZMEUHdR2ssYBUErB48F0IEDrrt6cxwHg8GAnj17uuyrRQQP7Iaj06gQYehyrU1JiQsXbkJkAZKOomoqgtgVesWFoqTWjDMVDRjZM1ru4fg1ZpsdNY2CpUUpAig6lIKglYLHT6cRI0Z0WPtHq9Vi7ty5+Pe//w2Dgfz+wQgzOSeE6xVbJyqegqAlhzLAukavuFDsPF2J/HKT3EPxe1gTVK2aQ2SIMiprt4wBcjh4qKgOmWx4nAX2+eefo1+/fnjjjTeQm5uL3NxcvPHGGxgwYAA++OAD/Oc//8HmzZvx17/+VYrxEn6A0kzOroh3WoDKSQBJBhNAKVHkAvMEMRCaMsG6Tcsq0Ep5GWOVoB08UNtEViA58dgC9Le//Q0vv/wyZsyYIS4bOnQoUlNT8dRTT2HXrl0IDQ3FI488guXLl3t1sIR/oPQMMKB5bKUkgCSD+oB1jQxKhfcaSnwZ02vUCNWp0WCxo8pkpUB3GfHYAnTw4EGkp6e3WZ6eno6DBw8CENxkFy5c6P7oCL9EiTedi2F1gOrNNpgsNplHE3i0rgFEAsgTWC2gM+UN4Hle5tH4N0q9FzHRQ7WA5MVjAZSZmYkXXngBFkvzF2e1WvHCCy+I7TGKioqQmJjovVESfkWZs7pyQrhyY8DC9GoYtMLlX15HNyFvU2WywmRhNYBIAHlCzxgjOA6oM9tQQQ/IbqFUAcTKcFRQEoaseOwCW7VqFa6//nqkpqZi2LBhAASrkN1ux/r16wEAp0+fxp/+9CfvjpTwG5R602kJx3FICDegoNKEsvom9IylOBVvwtxfCeF6GLRUA8gTDFo1ekSGoKi6EWfKG0RrJeE5rNVNvMJexsQkDBJAsuKxAJo0aRLy8/Px/vvv4/jx4wCAOXPm4De/+Q3Cw8MBAHfeead3R0n4Ff4ggABhfAWVJqoFJAHk/uoeGXFGFFU3Ir+8AWMyYuQejt+i1HsRGw/de+SlS0VawsPD8Yc//MHbYyECBKXedC6G3sKko4iKIHaLjNhQ/HyyglpidBOlJmTEUyV6ReCWAPrqq69wzTXXQKvV4quvvupw3euvv94rAyP8E57nm286ShdA4VQLSCqKqgUBRPE/XaM5EJpqAXUHpb6MJdC9RxG4JYBmz56N4uJiJCQkYPbs2e2uR81QiWqTFVa7kLkSF6bs9M4EMkNLRkmtEHuRpJAGlP4GdYXvPjzPNzdCVZgAEl1gJIBkxS0B5HA4XP4/QVwMs/5EGbWKb4BJZmjpEAVQpLKCT/0FVgvoTIWQCq+UIn7+RJ3ZBrNNeF4pLZCc3XuoEKu8eJwGTxAdUaawzssdQS4w6ShxWtUSIkgAdYW0aCEV3mSxi+0cCM9gv+twvQYhOmW9jLV0gVGtJ/lwWwDt2LFDTHNn/Pe//0WvXr2QkJCA++67D2YzPUiCHaX63F1BAkgaHA5eTD9OJAHUJXQalfgSUVzTJPNo/BMl34uYRcpid6C2kQqxyoXbAuiZZ57B4cOHxb8PHjyIu+++G1OnTsXjjz+Or7/+GsuWLZNkkIT/UCoWQVTeTediWKHG8nozHA56C/MWVSaLGAfmD9eBUkl2ug8v1DTKPBL/RMkCyKBVI8IgRKCweybhe9wWQLm5ubjqqqvEvz/66COMHz8eb775JrKysvDKK6/gf//7nySDJPwHJd90LibWGaRtc/CoMpGbwVsUO+N/4sJ00KrJy95VkiOFDLoLZAHqEqUKvxeRBVp+3L47VVVVtWpv8cMPP+Caa64R/x47diwKCwu9OzrC7/AnAaRVqxATKoggpQVCny6rx4aDF9Bk9b+sSpZVR+6v7pEkWoBIAHUFpd+LmAVaafeeYMJtAZSYmIj8/HwAgMViwb59+zBhwgTx87q6Omi1Wu+PkPAr/KUGEEMshqigt7DKBgtu/fcO/On9fZj0wmasyD6Ocj+6SbIMMBJA3YNcYN1D6QKILEDy47YAmjlzJh5//HH8+OOPWLRoEYxGIy677DLx8wMHDqBPnz6SDJLwH9jbf3yYfzz8EiKUVwvoma8Po7zeAo4TxNArOSdw6d83Y+/ZKrmH5hbFogBS5oPHX0iOIhdYd1BqFWgG1QKSH7cF0LPPPguNRoPJkyfjzTffxJtvvgmdrrnQ3Zo1azB9+nRJBkn4D+ymk+AnDz+ltcPIOVqCL3LPQ8UBn/xhElb9ZhQGJkegyerA61tPyj08tyghF5hXYBYgygLrGkq3AFE1aPlxuxdYXFwctm3bhpqaGoSFhUGtbl1X4eOPP0ZYWJjXB0j4D2abHdUmKwDlvnVdjJLM0LVNVjz5+SEAwD2X9cbo9GgAwICkMExdsQ1bjpWhtK5JjB1QKuQC8w4tBZDDwUOlomKInqB0AaSke0+w4nGKRmRkZBvxAwAxMTGtLEJE8FHhLNimVXOIDPGPeDAl3YSWbTiK4tomZMQa8fDU/uLyvgnhGJEWBbuDx5f7z8s4QvcoIReYV0gIN4DjhFoxlZSl6BF2B4/KBv8QQJQGLx+Uo0p4DSYi4sL0fvO2qpSbUGGlCR/uErIo/37zsDaVa+eMSQUAfLy3UPGVY8kF5h10GpVYMI/cYJ5R0WCGgwdUHBAbqkwBJGaBKeDlK1ghAUR4jVKFNh7sCKVYgL7MLQIAXNI3FuN7x7b5/NphPaDXqHC8pB4HztX4enhuY7U7UNFAAshb9HC6wc5XKycTrLbJime+PoJfTlfIPZR2Yb/n2DA91Ap9GWP3niqTFRYb9diUAxJAhNdQus/dFUoIROR5Hl/kCq6tG0akuFwnMkSLGYOTAACf7D3ns7F5itDbSHCDxhjJJd5dWC0gllmnBF7bfBJrfs7HvDW7sP1UudzDcUmpH/QkjArRQuMUZ+ylgfAtJIAIr+GPAoil69c22WQrOnj4fC1OltZDp1Hh6iFJ7a7H3GBf5hYptkAii/9JCDf4jRtUybBq0OerlSGAKhsseG/nWQCA2ebAPe/swd6zlTKPqi3+cC9SqTjRxamkMhzBBAkgwmuU1Qs3aSW/dV1MRIgGOo3wM5DLCsTcX1MHJiDC0H7w+KQ+cUiONKC2yYbsIyW+Gp5HiAKIAqC9QnMmmDJcYP/56TRMFjsG94jAZf3iYLLYcdea3ThUpCy3rD8IIKD5dyK3Cz5YIQFEeA2xCKLCbzot4ThO1lpAdgePr34V3F/XD3ft/mKoVRxuHiVYgb45cEHysXUFFgCdRPE/XkFJ7TBqTFa8s12w/vz5yn54484xGJcRgzqzDX96f5+igvP9RQAprQ5ZsKEIAbRq1SpkZGTAYDBg/Pjx2LVrV4frf/zxx8jMzITBYMDQoUOxYcOGNuscPXoU119/PSIjIxEaGoqxY8eioKBAqikQaNkGw78efnIGQv+SX4GSWjMiDBpckRnf6fpTBgjr7DlbpagHDoNqAHkXJTVEfXv7GdSbbRiQGI7pgxIRolPjrbvGQKdRoaDShPzyBrmHKKL0KtAMpSRhBCuyC6B169YhKysLS5Yswb59+zB8+HDMmDEDpaWlLtffvn07br/9dtx9993Yv38/Zs+ejdmzZ+PQoUPiOqdOncKll16KzMxMbN26FQcOHMBTTz0Fg4FuylLiL29dFyPnTYjV9Zk5NBl6Tdv6WhczJCUSWjWH8nozCiuV4RZpSTG5wLxKy2KIcgreuiYr1vws9IJceGVfMb4rwqDFqJ5RAICdp5UTC+Qv96IEhZThCFZkF0ArVqzAvffeiwULFmDQoEFYvXo1jEYj1qxZ43L9l19+GVdffTUeffRRDBw4EM8++yxGjRqF1157TVznySefxMyZM/GPf/wDI0eORJ8+fXD99dcjISHBV9MKOnieF286/pQGD7S8CflWADVZ7dhwSHBltZf9dTEGrRpDUiIBAHsLlPPAYZSSC8yrJEY0F0OsaJCvGOJHuwpR02hF7/hQzBya3OqzCc6yDTsVlBZf7icCiCxA8iKrALJYLNi7dy+mTp0qLlOpVJg6dSp27NjhcpsdO3a0Wh8AZsyYIa7vcDjwzTffoH///pgxYwYSEhIwfvx4fPHFF5LNgxCyqMzOWhZKv+lcjFw3oZ9OlKOuyYakCAPG94pxe7vRPYUWGXvOKK85ajG5wLyKUooh/nC8DAAwb0J6m7o6LQWQUtyy/mIBIgEkL273ApOC8vJy2O12JCYmtlqemJiIvLw8l9sUFxe7XL+4uBgAUFpaivr6erzwwgt47rnn8Pe//x0bN27ETTfdhC1btmDy5Mlt9mk2m2E2N1+AtbW1AACr1Qqr1dqtOSoNNh9vz+tCleD/DzdooIYDVqsyC3u5mn+MUfgZlNY2+vT7/uGYkMl1ZWYc7HYb7G5mto9IjQAA7D1T2aXxSnUNAM0xQDEhasX+dqScvxQkRehRVmfGuYp6DEgwemWfnpwDu4PH/kJBbI9Mi2izzZCkUOg0KpTWmXGiuAa94kK9Msau0mixo85sAwBEG1xfh0q5BqJD2L2nyadjUcr8pcCTOckqgKTA4RAevDfccAMefvhhAMCIESOwfft2rF692qUAWrZsGZYuXdpm+aZNm2A0eueGozSys7O9ur8TNRwANUJgdRmUrjRazr+gUhj7yXOlPh37d7+qAXAIqTmLDRvOuL1djQUANDhWUofPvtoAQxd/xd6+Bsx2oK5JGMyBndtwXOF3F2/PXzJMKgAqfL9jL8z53rWwuHMOzpuABrMGOhWPU/t+whkX5Z16GlU4WavCf77ehkmJ8lqBypsAQAOtise2nE3gOihHJfc1UOEca0lNI775ZkOHY5UCuecvBSaTye11Zb1FxcXFQa1Wo6SkdU2TkpISJCW5LgiXlJTU4fpxcXHQaDQYNGhQq3UGDhyIn376yeU+Fy1ahKysLPHv2tpapKWlYfr06YiIiPB4XkrGarUiOzsb06ZNg1brvYal6w9cAI4cREZSDGbOHOu1/XobV/NPOVeDt479Aos6BDNnXu6TcZyrakTpjh+hVnG4/5apCO+g/o8r3jj9I85VNSJh0Hhc2rdt64yOkOoaOFPRAOz6GUadGjdeNw2cr+/mbiLV/KViD5+HgzsLEJvaFzOn9/PKPj05B+v2nAN+PYJR6TG4bpbr3/ZJw0m8uuU0GkJTMHPmMK+MsavsK6gG9u9CYqQRs2Zd5nIdpVwDjRY7ntmfAyvP4fKrpnl8H+gqSpm/FDAPjjvIKoB0Oh1Gjx6NnJwczJ49G4BgwcnJycHChQtdbjNx4kTk5OTgoYceEpdlZ2dj4sSJ4j7Hjh2LY8eOtdru+PHjSE9Pd7lPvV4Pvb6tr1ir1QbcxcHw9twqGwX/TUKEwS/OWcv5J0cLJvvyejM0Go1PHtw7zwjBzyPTohAT7rmVcUx6NM5VNeLXolpcMbD96tEd4e1roMIkXANJEQbodMpvg+Evv++UaOH6KKkze3287pyDA0XCA2VUeky7607qm4BXt5zGrjNVPvsNtUdVo+D+SgjXdzo3ua8BrVaLcL0GdWYbqpociAn37Vjknr8UeDIf2bPAsrKy8Oabb+Kdd97B0aNH8cc//hENDQ1YsGABAGDevHlYtGiRuP6DDz6IjRs34qWXXkJeXh6efvpp7Nmzp5VgevTRR7Fu3Tq8+eabOHnyJF577TV8/fXX+NOf/uTz+QULLI1T6UGHrogLEx7WVjuPmkbf+MR/PCEElV7Wr/PaP64YnS4EQu89q5xAaKoCLQ3JMhdD3FdQDQAY5Qy+d8XInlFiHJDc9YD8JQCaER9B7TDkQnYBNHfuXCxfvhyLFy/GiBEjkJubi40bN4qBzgUFBbhwobnq7aRJk/DBBx/gjTfewPDhw/HJJ5/giy++wJAhQ8R1brzxRqxevRr/+Mc/MHToULz11lv49NNPcemll/p8fsFCmR9WgWboNWpEGYW3Bl9kY9gdPH46ITSRvKx/XJf2McopgHILqmF3KCPzhoogSgMrhihHQ9QakxUnS+sBCCKnPQxaNUamCZ/LXQ/I7wQQVYOWDUWEKS5cuLBdl9fWrVvbLJszZw7mzJnT4T5/97vf4Xe/+503hke4QYnTAuSv9V/iw/SoNllRWmdGv8RwSY914Fw1aptsiDBoMMxZ08dTBiSGI1SnRp3ZhhOldchMkj9WjdpgSENLCxDP8z51L+WeqwYApMcaEdtJVeUJvWPxS34ldp6uwG/G9/TB6FzTXAXaP65DJtRKZRC4wY7sFiAiMPD3h58v63H86LT+XNI3Dhp1136CGrUKI5xv5EpxgzVXgfbPa0CpMJeixeZApY+LIe5zXlsdub8YSqkH5G8WoJRowcJ3rkp5ld0DHRJAhFcoqfHvh59vBVD34n8YrCCiUgRQqegC848Hj7+g16jFYoi+jgPaX1gNoGP3F6NlHFBBpfupyN7G3yrSp8cISRhnK5TTSy1YIAFEdJsGs00sPOavDz9f9eSpa7KKQaWX9eta/A+DxQHtU4gA8ncroJKRIxDa4eCxv8B9C5BBq0a/hDAAQF5xnaRj64hSP7MApccKWX5nZRSNwQoJIKLbsBtOqE7tszoW3sZXFqAdpypgd/DoFReKtJjuFdkclhoFADhTYUJdk7wVXXmepzYYEsLOaYkP40ROldWjrskGg1aFzCT34uL6O+PnTpTII4AcDh7l9f4lgHo67wPnKhsVk9AQLJAAIroN61Hkzw8+UQBJnInBMmQu8bB4oStiQnWixe2YjG/cAFDTaIXFT3vB+QPse/alANrvtFQOS41yO1atX6JgATpeUi/VsDqkptEKq10QEbFhyq9FBQjWPY2Kg8Xu8On3S5AAIrwAcxv5c/0XljEidS2OXGdPJVbHp7sMTBayv47KLICY+yvaqIVBq5Z1LIFIkgwWoH0euL8Y/RMEC9BxmSxA7AUmyqiFXuMf16FGrUKqMxD6bAW5wXwJCSCi2wRC/RdmtSiX0AJktTtw6LxQVXe4033VXUQBdMH98u9SQO4vaUl0xgAV+7BYXq4HAdAM5gI7XdYAm933DZHFDLBOUvaVRs9YIRC6oJICoX0JCSCi2wRC8CsTQFWmZleOtzlWXAeLzYEIgwYZsd7pmK0UARQIIljJiDFAPgqCttkdOF0mPIwHJbtfYyo1OgQhWjUsdocsQb3+lgLPSHfGAZEFyLeQACK6TSDUf4kK0UKjEgrMVTRI85bN3qiHp0VBpfJOMbtBycIb97HiOjhkDKCkFHhpEV1gEmcpMs5WmmCxOxCiVSMlKsTt7VQqDn2dmWByBEL7qwBigdBylg8IRkgAEd0mEB5+KhUn1lqRKhPsV6cAGuFsGeANMmJDodeoYLLYZU2jJReYtLDfVrXJiiarXfLjMfHSNyHMY7EuZyB0cxVo/7oX9YwlASQHJICIbhMILjBA+lT4X51tBbwV/wMIAZQDnCnKcrrB2DVAAkgaIkO00GuE27UvmmaecIoXJmY8gcUByREILRZB9LOXMbEWELnAfAoJIKJb8DwfMPEfUgqguiYrTjibSg73ogUIAAYmyR8HFCjXgFLhOA5JYiC09G6w485rtX8X+uL1T2QuMN9bgFhGapy/WYCcLrCaRitqTPLW9AomSAAR3aKm0QpzgNR/YWZzKTLBDhbVgOeBlKgQr5+ngclKsAD5vxtU6SSG+y4VnrnAWGVnT+jnTIU/XV4Pq48zwZh1LCHcv4S4UacRRdtZygTzGSSAiG4RSPVfpLQA/VpYA8C78T+M5kwweWqv2B28eM783Q2qZFgqvNQCqGUGWFcsQClRITDq1LDaeZ/3tyr1UxcY0OwGozgg30ECiOgWgeT6kLIa9K9iBlik1/ed6RRARdWNspjPy+vNcPCAWsUh1s9cD/5EovP6LJY4Fb6rGWAMlYoTLUe+DIRustpR0yhc//7SCLUllArve0gAEd0iEFLgGZJagCQIgGZEhmjFB9XRYt+7wZgIjg/TQ+2l9H6iLSwGqETifnXdyQBj9JMhEJr9bnUaFSJD/K8noZgJRgLIZ5AAIrqFmALvh29cFyOVACqpbcKFmiaoOGBIivctQIC8BRGbM8D8/xpQMr4qhni8GxlgDDkCocWWPOF6cJz/CfHmrvAUA+QrSAAR3UJMgY/0fwuQVHWAWAHE/onhCNVrvLpvhpyB0IFkBVQyiT4qhniiGxlgDDksQM0B0P4pxFkmWGFlo8wjCR5IABHdoiSAHn7MAtRgsaPBbPPafqUogHgxcgZCMysgBUBLCzu/xTVN4Hnpqn53JwOMwcRTfnmDZK1lLkYMgPazDDBGzxihPc75mkaYbdIXuyRIABHdpCSAXGChOjVCnJls3kyFP1gkZIANkyD+h8EE0LGSOp83oaQUeN/AMpvMNgdqG70n0FvS3QwwRo9IA8L0GtgcPM74KBNMdIH56XUYF6aDUacGzwPnqsgK5AtIABHdIpBcYBzHSRIHxNwAzE0lBekxRhh1alhsDp89cBisQ3kgWAGVjEGrRpRRCO6VqhjimYruZYAxOK65J5iv3GD+7gLjOK65JxgFQvsEEkBEl7E7eDFlPBDS4AHvB0LXNFpFkdi3Gy6FzlCpuBZxF76twEsuMN8husEkEkAnS7ufAcZgLrRTpb6yAPm3Cwygpqi+hgQQ0WUq6s2wO3ioOCA2VCf3cLwCqwbtrVpAJ50BpcmRBoQbpE3NHeDMvMkr9m0cEDVC9R3MyiZVMURvZIAx+jABVOYbQc4EULyfusAA6gnma0gAEV2GWTbiwvTQqAPjUmIWoHIvWYBavlFLjdiE0ocCqMlqR7Wz+CLFAElPkvMcS5UKz9xV3Yn/YfSOE4J6fSWAylqkwfsrGc5zdtJH5yzYCYynFiEL7C00EOJ/GN6uBs3qoPhCAGU6m6LKUXxO76fF5/wNqVPhmcWyOxlgDGYBOl3WAIdDuqw1QAjermiwAPBvF9iQHkKdsIPnqiXN9CMESAARXaZEfOPy3xvOxXg7BuiE+ECRLgCa0T9JeOCcqWhAk9U3abQt3V/+WHzO30gUU+G9Xw3aWxlgjJ4xRmhUHBqtdsk72JfXW8Czdix+7I7PTA6HVs2hymSlTDAfQAKI6DLMDB9Iro94LxdDFN+ovRBT0RnxYXpEG7Vw8M3HlZoSCoD2KUkSxgAVVjXCYnfAoFV1KwOMoVWrxPYOUrvBWAp8XJiu28HbcqLXqMWSFqyAKiEdJICILiOmwAfQw8+bFqAGsw1F1cJbXN946QUQx3Him/sxH8UBlYgp8IEjgpVMooQC6JRTNPeO634GGKNPfLMbTEqaU+D9/140LFVwgx1w9g8kpIMEENFlmAsskLJ/WsYAddcHz95648L0iPaRWX5Akm9bEJRQBphPSYx0BunXm71e8JJdr328GK/GBJD0FqDA6UfHGib/eq5G3oEEASSAiC5TXOPflVddERsmCBWrnUdNo7Vb+2IB0N4IKHUXJoCO+VgABZIVUMnEhuqhVnFw8ELcizcRBVB8qNf22TveN5lgzAUWHwAWoOHOljmHimpglzh4PNghAUR0mQtOAeSNeAGloNeoxWym7rrBTvgw/ocxwMep8M294AJHBCsZtYoT07y9HVh8yumm6uNFd62vXGAlfl4FuiV94sNg1Klhsth9FssXrJAAIrpEvdkmWkiSA0gAAd6LA2I1gHxpAWLVoM/XNKG2qXsWLHdgDx5ygfkOKeKAeJ4XH7beFUCCBehCTRPqvdhg+GLK/LwPWEvUKg5DU4Q4oF8pDkhSSAARXeKCM7g3wqBBmF4j82i8i7eqQTMLUF8fpMAzIkO0SHbWZZLaCsTzPMUAyQCLc/GmAKpssKCm0QqOA3rFec8FFmXUiWnp+RJagQKhDUZLmBuMAqGlhQQQ0SVYdlOPALP+AN6xADVZ7WI/H1+6wIDmGi5SxwHVNFphsgj1hpIDqBim0kmOFH5zF7xYDZq5v1KiQhCiU3ttv0ALN1i5dO4cf2+EejEsE+zXQgqElhISQH5GUXWjT1wbnXG+OvDifxjeqAZ9qqwePA9EG7U+L8yWmeSbOCAmguPCdDBovfvQJNqnR5QgNou8WCivOQDa+2K9T4IzEFqieBaHg0d5fWCVY2CZYHnFtTDbfFPUNBghAeRH7C+owpQXt2Di8zn4+8Y8VDZ4NwvEEy7UCDff5KjAe/P3hgXoZGlzCwxfV0j2lQWIPYAD0QqoZFKihOKCTIB6g1MSxP8wesexVHhpXGCVJgtsDh4cJ5ScCARSo0MQE6qD1c7j6AXfNjcOJhQhgFatWoWMjAwYDAaMHz8eu3bt6nD9jz/+GJmZmTAYDBg6dCg2bNjQ7rp/+MMfwHEcVq5c6eVR+xae5/HCt3mw2nk0WOx4fespXPr3zXhz22lZxhPQLjAvVINu7gHmu/gfhpgKX1wnaT+h885rIBCtgEomJVo435JYgBK8F//DEC1AEqXCM/dXjFEHbYA0ZeY4roUbrFrewUB4/nx78AJWbDqGOgV4ILyF7FfLunXrkJWVhSVLlmDfvn0YPnw4ZsyYgdLSUpfrb9++Hbfffjvuvvtu7N+/H7Nnz8bs2bNx6NChNut+/vnn2LlzJ3r06CH1NCRn24ly/JJfCZ1GhX/cMgxDUiJgstjxtw1HfdZtuSWB/PDzhgXohAwZYIy+CWFQcUCVyeq1pq6uCGQRrGTYb66krgkWm3eKIUqRAs9g+8wvl6YpanMNoMCw/jCGiQURq2Udx87TFZj9r+344/v78Mrmk7jzP7sUEYbhDWQXQCtWrMC9996LBQsWYNCgQVi9ejWMRiPWrFnjcv2XX34ZV199NR599FEMHDgQzz77LEaNGoXXXnut1XpFRUX485//jPfffx9arX93qXY4eLz4XR4A4M4J6bh1TBq+XngpLusXBwDYeKjY52NiMUCB+PBjN9LybogHOWoAMQxaNTJihbfu48XSieNAjgNTMnFhOug1KvB8syu6OzRZ7SisEgL2pRBAqdFG6NQqmG0Or7rtGGIGWIBlIg53WoDk7An2+KcHcNsbO/FrYTWMOjUiDBrkFlbjzrd+6XahWCUga/6yxWLB3r17sWjRInGZSqXC1KlTsWPHDpfb7NixA1lZWa2WzZgxA1988YX4t8PhwJ133olHH30UgwcP7nQcZrMZZnPzw662thYAYLVaYbXK/yV/e6gYh4pqEapT495L08UxXT0oAT+eKMeGg+dx36Xpbu2LbdudeTkcvHjjjQ/VKOIcuYs78482CO8FFQ0WmJrMHpvVrXYHzlYID5T0aIMs56dvQihOlzfgyPlqjM+IbD0+L1wDAFBYJVgNEsO1AXcNKJ2UKANOl5twtrwOPSI8D7JveQ5OVzSB54WSFpF6TpLzkh4bghOlDTheXIOkcO++kBZXC7+1uFD3r0N/uAaG9QiHihOKSJ4tq/Xqy6Y7899fWI2PdhdCxQG3jU3Fwil9UF5vwfy39+DXczW4482dePuu0WLhWKXgyXcqqwAqLy+H3W5HYmJiq+WJiYnIy8tzuU1xcbHL9YuLm60gf//736HRaPDAAw+4NY5ly5Zh6dKlbZZv2rQJRqPRrX1IhZ0HXshVA+BwWYIVv/zwffOHVoCDGofP1+HdzzYg1oMXoOzs7C6PqdYCWO0acOCx76ct+FV2O6LndDR/Bw+oOTXsPId1X21EjIeW9dJGwO7QQKfise+nzfBxDDQAgKtVAVBh896jSKw+7HKd7lwDAHCmRLgu8w/txYYz3dqVLHR3/nKiswrf77fbdqEqr+tupezsbOyv4ACoEaOx4ttvv/XaGFtitAnjXb9tN+pPeNcNtjtf2Hdt6Tls2FDg0bZKvwbSw9TIr+Pw2mdbcWmS992HHc3/nePCeR0T58B49Rns/vEMAOC+vsCqI2ocOl+L+9/MwW/6ercnXXcxmUxurxtYFewA7N27Fy+//DL27dvndvbNokWLWlmVamtrkZaWhunTpyMiIkKqobrFV79eQOnOg4g2avH8/MsQbmj9lX1dsRs786tgThiEmZdmdLo/q9WK7OxsTJs2rcuuwV/P1QB7f0FihAHXXTu5S/uQC3fn/9KxH3GuqhEDR03E6PRoj46x+VgZkLsfvRMiMGvWxO4OuUtwh4rx3boDaNRFY+bM8a0+88Y1YLba8eCOHADAnFlTEePjVP/u4I35y81262Hk7SlCXM9+mHllX4+3b3kOTv9UABw/hVH9UjBz5hAJRgvk6U7g1x/yoYtLx8yZg7y67w0f5gLFpZg0YhBmTujp1jb+cg0UhJ7GS9+fRLkuETNnjvLafjub/4WaJjzyy48AeDxxyyQM7tH6OTiqoBpz39yFPRVqPHfHZaLLXQkwD447yCqA4uLioFarUVJS0mp5SUkJkpKSXG6TlJTU4fo//vgjSktL0bNn8w/BbrfjkUcewcqVK3HmzJk2+9Tr9dDr277ma7Va2X8c3+eVAQDunJiBmPC2JtBZw3pgZ34VNh0txR+v6Of2frszt9J6wcSYEm2U/fx0lc7mnxIVgnNVjSipt3o8x8IqITamT3yYbOdnUEoUACEWSa3WQKVq+zLQnWugqEYowRCiVSMh0ujzVH9voITfd1dJi3G2mKi1dGsOWq0WZyoFd3a/xAjJzkdmsuCGPVXW4PVjlDewljye34+Ufg1cNSgZL31/EjtOV8IOldfrbbU3/4/2nILNwWNcrxiMSI9t8/n4PvG4MjMBm/NK8foPZ7Bi7givjqs7ePJ9yuq80Ol0GD16NHJycsRlDocDOTk5mDjR9ZvzxIkTW60PCGY8tv6dd96JAwcOIDc3V/zXo0cPPProo/juu++km4wEWO0O/HiiHAAwdWCCy3VmDE4CxwH7C6q9EhDpDueDIPuHza0r1Xbzy4XYGG+2FPCUjNhQ6NQqmCx2SQJPmzPADH4pfvyd1GjBNX+uyn1zf3tI0QX+YsTSDCXeL81QGkB9wC5mYHI4kiMNaLI6sON0hU+O2Wix48Ndgivxd5f0ane9h6f2BwB8kVvkt01bZY/eyMrKwptvvol33nkHR48exR//+Ec0NDRgwYIFAIB58+a1CpJ+8MEHsXHjRrz00kvIy8vD008/jT179mDhwoUAgNjYWAwZMqTVP61Wi6SkJAwYMECWOXaVPWeqUG+2IS5MhyE9Il2ukxBhwOiegovGV9lgYgZYALc/YK0dLnRBPChBAGnUKvRxpuAfk6AiNKXAy4tYC6ib4tbh4HGq1JkCL2HJht5xYdCoONQ12bzaxd7h4Ft0gg+8+xHHcZgyQHj53ZLnujSMt/kitwhVJitSo0MwbVBiu+sNTY3EtEGJcPDAyzknfDI2byO7AJo7dy6WL1+OxYsXY8SIEcjNzcXGjRvFQOeCggJcuHBBXH/SpEn44IMP8MYbb2D48OH45JNP8MUXX2DIEGl813Ky9ZhwwV/eP96lC4Nx9RDB/fetzwRQ4D/82NyKqrthAZLwjdodBjhT8KWoCM2ugdTowL0GlAwrPXChugn2btTWKakzo9Fqh0bFoWeMdAkfOo1KfCHI86IgL683w2JzQMUBSQH6QnZlpiCANueVSlrYFBAKHq79OR8AcNekDKg7eO4AzVag9QfOS/KiJTWyCyAAWLhwIc6ePQuz2YxffvkF48c3B21u3boVb7/9dqv158yZg2PHjsFsNuPQoUOYOXNmh/s/c+YMHnroIQlGLi1bnALoigGu3V+Ma4YmAwB2n6nsVvE+dzlfEwwCyGkB8tCt2Gixi26zXjIHBvZnPcEkEEBiG4zIwL0GlExihAEaFQebgxddQF3hpNP9lR5rlLyKcn8JetSdcwrx5MiQgKkCfTGT+sRCp1bhXFWj5EVv9xdW43hJPYw6NW4dm9bp+oN6RGDm0CTwPPDKZv+zAgXmFRMAFFU34nhJPVQccHm/+A7XTYkKwZCUCPA8sP1UueRjO98i/iNQYR23z3voYjhTIVh/ooxaRMucGTUgsbklhrdhIjiFLECyoFZxosWjOy0xWMsWFqMjJZkS9Kg7VxW4FekZoXoNxveOASBYgaTkh2NC0s0VAxIQYXAvmPjPVwrJN98dKkapF92bvoAEkEJh7q/R6dGINHZ+IY5ME+KADhXVSDquJqsd5fVCBlAgv/0z61aVyYpGi/vdmJUQ/8NgD7VTZfWw2r1bq4MaocoPcz+e64YAOuYUQKyBrpRIYZFkQeCB7opt6QaTkh+OCwLo8v5xbm8zMDkCY9KjYXPw+N+eQqmGJgkkgBTKFmf6+5RO3F+MoSlCkPRBiQVQsdO9E6JVI8oNYeavRBg0CNUJKafnPXCDKUkApUSFIFSnhtXO40y59zpxOxw8ztdQGwy58UZX+ONOAZTpAwsQs0ieKKnvVtxSS5gQD3QBxMIg9pypkqwFRbXJggPOvmOX9+/Y63AxdzjrL324q9Br360vIAGkQMw2O34+Kbiypgxw70Ic6uwbc7ioVpKGg4zzQZL+zHEcklsEmrrLaWdTyd4KEEAcx4lv3d50O5Q3BH7gqT+Q0k0LkINvjgHyhQUoLcYIg1boCXa2wjuCXHSBBbgAyogLRd+EMNgcPDbnlXS+QRf46WQ5HLzQwDnZQ+v+NUOSEWXUoqi6ET8c9022mjcgAaRAduVXotFqR0K4HoOS3atE3S8hDHqNCnVmG85Wdr82SHsEU/ozm6NnFiDhgZKhAAEENL91ezPwlJVBSIwwBGzgqT+QGtW9VPjyJqDJ6oBeo0K6DwL21SoO/RK86wZrdoHJ27LIF8x0ZvtuOChNtu82p/trsofWH0BowHzLqFQAwPs7PWtHIid091IgzP11xYAEt60sGrUKA51iiZkxpaC5BlAQCCCndcOTQGglucCA5jd7b6YeU/yPMhBrAXWxGOIFk3Bv6ZcY1mm6s7cQCyIWdz+bied5UfwFgyuWZfv+cLwM9WabV/fN8zy2HRe8Dp66vxi3jxfcYJuPlXqlQKcvIAGkQFgml6cXIosDkjIQ+kIQpMAzenjoAqs2WVBlEvzzSumNkylB4On5IHroKJnUFsUQu1If5oLzGTUg0Xf9DkWLpBeux4oGC5qsDnAckBzAGamMzKRwZMQaYbE5vF4U8URpPYprm6DXqDCuV0yX9tEnPgyT+sSC54F1u/0jGJoEkMKoarCIb+ss9dFdfBEIXRQEKfAMVg3aXRcYs/4kRRgQqldGn2EWA3S20uRRNltHBJMbVMkkR4aA4wQ3VkWDxePtmQXIFwHQDHY95hW737CyPVj8T2K4AXqNd3tkKRGO40Qr0LeHLnSytmcw99f43rHd6jd2x/h0AMBHuwth83LmqRSQAFIYv+RXAgD6J4YhLsyz3ja+CIQOprd/MQbITReY0txfABAXpkdsqA48D6/162l2OwS+CFYyOo0KCeHCPaIrtYAuNAoCqL8PBRCzAJ2pMKHJ2j1BXhQkAdAtmTlEEEBb8sq89kIDtEh/7+d++rsrpg1KREyoDmV1Zvx4UvqadN2FBJDC2OlseDe+V9sOvJ0hdSA0z/PNMUBBJIAu1DS55WJQSguMi+nv5QJ0wfjgUSopXQyENlvtKHNu4ksLUGKEHhEGDewOXsyY7CrBUgOoJUNSIpAaHYJGq91r2VZNVjt2OV+8uxIA3RKdRoXrh/cAAHy+r6jbY5MaEkAKgwmgCb09F0BSB0JXNljQaLWDC5L0Z+YCM1nsbtXeOM0EkELifxgs8DTvQvfdDkBwtELxF1Kc2U+eWoBOljXAAQ6RIRrRiuQLOI5DZpJwj+puHNC5IKkB1BKO4zDT6QbzVjbYztMVMNscSI40oK8XGuLeODIFAPDd4WLUNUlTs8hbkABSEN2J/2FIGQjN2jz0iAzplp/YXzBo1YhxtrM470Yg9BkFusAAYHAPpyj2wjXRYLah2hnoHQxuUKWT2sWu8CdKm+v/+LqeV/8k4SHb3czEZlds4KfAt4Q1v845WtJtNyIAsebcZf3ivHItDEuNRJ/4UJhtDp816O4qJIAUxK4zghmyX4Ln8T8MKQOhz5QLJuf02OC54bBg787igHieV6wLbFhqFADgcFFNt6u0sodOuEGDcDd7BRHSwURooYcub7EFhhfe+D3FW5lgwegCA4ARqVFIjjSgwWLHjye6H2fz80nB63BJ3+7F/zA4jsNNzppAn+0755V9SgUJIAXRHfcXQ8pAaGYBUkqRP1/AKqJ21hW+tM4Mk8UOtYpDmsKKsvVNCEOIVo0Gix2nu9lN+pTTcqCUNP9gp0+8IGA8je86IfYAk0EAOV1gh893/SWN5/mgjUVTqTjRCtTdbLDKBguOOF3jk/p4RwABwGynG2zn6UpF1wQiAaQgdp4WLEBddX8B0gZCn6kQ9pcRTBYgMRW+YxcY67ieHmOETqOsn5VaxWFICosN655lkD1ofdE9nOgcVin+XFWjRz2i2PcohwAakhIBtYpDSa250xeL9qg2WdHgzIIKRlcsiwPKPlICi63r6eYs63hAYjjivRgLlhIVggnO59iXuee9tl9vo6w7dRBTbbKItTG6kgHGkDIQmsW4BNPbv7up8IfPC9/doB6+KyrnCcwN1t1rggk9X2YOEe0TadSKLqCjbga515isKK41A5DHBWbUaUQ32P6C6i7tgwVAx4frgyIe8WJG94xGQrgedU02/Hyq626wn08JAmhS364/c9qDucE+3XeuS4U6fQEJIIXwS34leF5wV3RXibM4IPZQ9gY8zwenC8zNatDMnK9cASRcE90NhG62HJAAUgrMCuTu7/14qfAdRul4RITIE8c1smcUACC3sLpL2xdVC9boYLT+ABe5wQ523Q22wxl2cYkX3V+Ma4YkwaBV4XRZg6TFebsDCSCF0Bz/03X3F4NZgLzZ/6mywYK6Jhs4DugZE3wusM6ybI44Hz6De0RKPqauwCxAR87XwtrFCq1NVrtoBSQLkHJgovuImwKI3ReSjfK9lY9IiwIA7C+o6tL2wZgCfzFMAG06UtKl33SlGSiobIRaxXUr7KI9wg1aTB2YCAD4+ldlusFIACkEFv/TnQBoRnPDQe9ZgFj8T3KEIahMzswFVlLb1G4GVYPZhnyndWywQi1A6TFGhBs0MNscYgq0p5wsrYeDB6KMWq/GCxDdg4lud4OK950VREeajIbckT2jAQjZql15eJ8L0gDolozLiEFsqA7VJit+cT4/POF4jZDyPiw1UrKMzuucRRHXH7ggWXeC7kACSAGU15tF/3134n8YTACV1JpR2YUeQa4Q43+CyP0FAAnheqg4wObgUV5vdrlOXnEteF6octvV8gVSo1JxohvsYFHXhDGL/xkgQ+0Yon2YBehkaT3Mto7rwvA8j1+c1uY+EfI9kHrHhSLcoEGT1SFeV57QbAEKHmv0xWjUKkwfLFiBNnQhG4wJICncX4zJ/eMRrtfgQk0T9nbR2iclJIAUACtENTA5witv1mF6jeim8kbTQQA467RwpAdRADQg3GSSIgQ3WHvpnGIAdLIyrT+MoSlRALougI5TBpgi6RFpQGSIFjYHL6a3t8e5qkacr2mCRsWhV7h8Akil4prdYF2IAwrWGkAXM3Oo0w12uNijGl88z+OEUwBJEQDNMGjVokhTohuMBJAC+OlEcyVOb9HsBvNOHFC+0wXWKy743rjYufy10LWL4XCRsuN/GMNTu1ckkwKglQnHcaLrtbM4IBZrODQlAnqZPdkjnQIotwuZYKwGUGqQBkEzJvSORZRRi/J6C3afcd8NdrK0AbVWDnqNCqOc7kipuG44a91xQXEd4kkAyQzP8/jJaQG61EuVOAFgoNj/yTsCKFgtQAAwJkMIENxz1vUNhhUSU2r8D4MVyTxeUg9rF+5DlAKvXJj18UgnqfCs7su4DO8HvXrKCDETzDPXSE2jFXVmG4DgjgECAK1ahWnOQOOvPLCwbHcK4THp0ZLHdF7SNw7RTpG2swuxSlJCAkhmTpU14EJNE3QaFcb18t5NKVPMBOu+C6xVm4cgiwECgLHOh8XuM1Vt6llY7c0xDEq3AKVEhSA2VAebg0eRh424axqtuOAsBtmPLECKw91MsF/yhQffuF7SvvW7w4g0YQynyhpQY3K/iCOrZh4XpoNRp5FkbP4Eaz76de55NFrc6w225Zjw0j1Rguyvi9GqVbjGWbhRaW4wEkAy89OJMgDA2AzvKnH2ln68pL7b/Z9YCjwQXCnwjGGpkdCqOZTVmVFY2Tod/mRpPSx2B8L1GsXHI3AcJ1qBChs8C2Jm8T8s3oRQFkx8H7nQfguc89WNKHSmPY9yWl/kJCZUJ/YV/NWDAp2smvmQFGW/cPiKCb1j0TPGiDqzDRvcqAlUUtsk1v+5ekii1MMDAFw3TMgG+/bQhU4D9X0JCSCZaXZ/xXt1v+mxoTBoVWi02lHQzZYYLAW+R2RwpcAzDFq1WFzyYj87C4Ae2CMCKpXyM6NYPaCCes/Gyqxc/cn9pUh6x4dCp1Gh3mxDYTvB+sz6M6RHBML0yrCcjBTrAVW7vQ0TS+xaDnZUKg63jhGqLq/bU9jp+l/mFsHBA73CeaT76IV2XK8YJITrUdtkw4/Hu9/A1VuQAJIRq90h+kS9GQANCP2fWLBqnpsl8tuDpcAHY/wPY2w7cUDNBRCVHf/DGJEmCLlTtZxH5enFFHgSQIpEq1aJ7SXac4PtPMV6DUqX9eMpLBPMkzigX51ZY+xaJoBbRqdBxQG78is7bXj82b4iAMDYeN8FJKtVHK51WoG+PqAcNxgJIBnJLaxGvdmGmFCdJCnUzA3W3YrQZ4OwBcbFjE4X4hX2nGl9oxZbYCg8BZ4xvlcstGoOFWYOp8vdtwyKTVAp/kexMBHeXksMZgHyRrV5bzHCmYGUW1jtliCvbbLitPOFjCxAzSRFGjC5v+BF+N+ec+2ud+R8LfKK66BVcxgZ69syCCwbLPtIiduxSlJDAkhGfnSmv0/qEyuJ+2RAkncCofODsAv8xTABdKK0HlXO4pI8z7fIAPOPt9FQvUbMAPrheJlb2/A8TzWA/AAxENqFxbektglnKkxQcc1ZjUpgUHIEjDo1qkxWHHKjPtWhczXgeSGgX6lFR+Vi7tg0AELz0fbSzT/fL4ijKwbEw+hjL+iItCikRofAZLFjc16pbw/eDiSAZIQFQHvb/cUYSBYgrxEbpkfveGH+e52tBAorG1HXZINOrUK/RN931e4qUwYI19tWN33xpXVmVJusUHFAn3j/mWewwSxAvxZWtwk0ZfV/BvWIQIREbQ+6gk6jwuX9BMtF9pHiTtf/1RkAPZzcX224MjMRcWE6lNWZseVY25cbu4PHl7mC++nGET18PTxwHCe2xlBKNhgJIJmoabSKP+ZL+3k3AJrB3tYLKk1ocNbN8JSWKfAZQRwDBABj01kckCCAWAB7/6QwaNX+81O6wmkq332mCnVNnacfs/ifjLjQoAyC9xcG94hEfLgeFQ0WvLezoNVn25xi1xutdrzNtEFCJtKmIyWdrnvAGQA9nNxfbdBpVLhplBAM/ca2U22sQD+fLEdpnRnRRi0ul+iluzNYNtjmY6Vu3Xukxn/u2gEGK13ePzEMKRJVM40N0yM+XA+eb05j9pQqk1VMgU8PYhcYAIzJYHFAldh7thJLvz4MQHjz8ifSY42IN/CwOXixCnlHMME30E/inIIVg1aNrGn9AQCvbj6BmkbhAfPL6Qp85nR9MLGhJK7MTIBaxSGvuA6FnWSssgBoiv9xzW/Hp8OoU2P3mSo8vyFPXO5w8Hj/l7MAgGuH9YBOI8+jf2ByOPrEh8JicyDbDcErNSSAZGL9AaFeA4uMl4ruBkLnlwsZBclBmgLfEhY7ceBcDe5+Zw/MNgeuykzAA1f2lXlknjMoWgiA7MwXz/M81juzNqYOTJB8XET3mDM6Ff0SwlBtsuJfW06i3mzDIx//Cp4H5o5JwwQFZYAxokN1GOOMsevooVha14TzNU3guOaq5kRresYaseLW4QCANT/n4+M9hahrsuL37+3Fd4eFczvHmTIvB0pzg5EAkoHKBovoPrl2WLKkx2Jv7V3tCcZSaqn/kxAEHhemg8XuQLXJihFpUXj1NyOh8SP3F2NwlCCAth4va7dwHgAcvVCH02UN0GtUmDpQedYDojUatQqLZmYCANZuP4OH1+XiXFUjUqND8NdrB8o8uvZhlqmOBNABZy++vvFhiqljpESuHpKMB6/qBwB48vNDuO7Vn5B9pAQ6tQr/uHmY7NYz9tL/44lyMaFELvzvzh0AbDwkuL8G94hAb4mDSpkFiKVrewrLzBhKVVfBcZxYD6hXXCj+M3+M35bi7xPBw6hTo6zO3G7aNNBcs+OKAQkIV1DwLNE+VwxIwMTesaKbgeOA5XOGK/r7mz5I6Bi+60wlqk2uH4pi/I+zdhDRPg9e1Q/TByXCYnfgTIUJyZEG/O8PE3GrM1NMTvomhGFQcgRsDh4bD3ce+C4lJIBkgJn+mClQSpjaP1RU26VOvKxz+JAUiv8AgIen9cddkzLw7t3jEOvHabgaFXBJH8Ed0p4brKX769rh0loqCe/BcRyemNls7bnn0l6KdH21pGesEQMSw2F38NhyzPX1mMsywMj91SkqFYcVc0fgqswEXD04CV//+VKx6KQSUIobTBECaNWqVcjIyIDBYMD48eOxa9euDtf/+OOPkZmZCYPBgKFDh2LDhg3iZ1arFY899hiGDh2K0NBQ9OjRA/PmzcP58/L7GwGgtLYJO50FyWYNlf6h0jsuFOEGDRqtdrGYnbs0We1i8DT13RHonxiOp68fjNRo/w8In9JfyATZ3M4D59dzNSisbIRRp8aVmRT/408MTY3EX2cNxO3j0vDI9AFyD8ctOnKD8TxPFiAPCdNr8J+7xmL1naMVVzPpuuHJ+P3lvVsJdTmQXQCtW7cOWVlZWLJkCfbt24fhw4djxowZKC11fVPevn07br/9dtx9993Yv38/Zs+ejdmzZ+PQoUMAAJPJhH379uGpp57Cvn378Nlnn+HYsWO4/vrrfTmtdtlw8AJ4HhjZMwppPujDolJxYsror4WeucGOFdfB5uARbdRKlqlGyMfk/nHgOCGz5ueTbbPB1jvfzq4amOi3rr5g5p7LemPZTcP8JnmBCaAfjpW1qWNUUGlCtckKnVqFzCSyRvs7qdFGLJo5UPYXa9nvaitWrMC9996LBQsWAABWr16Nb775BmvWrMHjjz/eZv2XX34ZV199NR599FEAwLPPPovs7Gy89tprWL16NSIjI5Gdnd1qm9deew3jxo1DQUEBevbsKf2kOuBrH2V/tWR4WiR+OlmO3MIqzBnlvtXp0Pnmrsscp/xGn4RnJEYYMG9COt7ZcRZPfH4Q3z10ufiwdDj4FpmK5P4ipGdoSiSSIgworm3Cf7efxb2X9xY/+84ZKzKwR4TXUrg5joPZbIbdroy2DL7EarVCo9GgqanJ7+avVquh0Wi88kySVQBZLBbs3bsXixYtEpepVCpMnToVO3bscLnNjh07kJWV1WrZjBkz8MUXX7R7nJqaGnAch6ioKJefm81mmM1m8e/aWiEo1Gq1wmr1XrGm89WN2Hu2ChwHTB8Y59V9d8TQZCEQOregWjymO8c+4GxQODg53GdjlRpP5h+otDwHD17ZBxsPF+NshQkrs4/hkWlC9sies1Uorm1CmF6DS3pFBdT5omtAuefgT1N6YfFXR/GP7/IwLiMSg5IjcKioFi9+dwwAcOPwJK+Muba2FomJiTh79mxQvtzxPI+kpCQUFBT45fxDQkKQmJgIrbZtYL8n14esAqi8vBx2ux2Jia3TaxMTE5GXl+dym+LiYpfrFxe7jiZvamrCY489httvvx0REa5Np8uWLcPSpUvbLN+0aROMRu+5qTaf5wCo0TuMx76fNnttv51RawEADU6U1uHrjdkwqNHGSuaKn4+qAXAwF5/Ehg0npB6mT3Fn/oEOOwfXJXN4q1aNN348jfDqEyhp5PBtoQoAh4ERFuRkfyfvQCWCrgHlnYMIHhgarcLBKhXuW7sD9w+y4+VDaljtHIZGOxBVfggbNhzq1jE4jkNiYiJiYmIQERHhlwIgmLHZbKisrMSBAwdQUtI2Xsxkcr/Js+wuMCmxWq249dZbwfM8Xn/99XbXW7RoUSurUm1tLdLS0jB9+vR2RVNXmGiyYOyRUsSF6XCVj4NKXz+5DedrmpCYOQY1J/Zg2rRpLtUzw2xz4C+7cgDwuPPayUgLgKBfQLgmsrOzO51/IHPxOZgJoODDXGw6Uop/HtLC5qwLFBmiwZJbxwZcA1S6BpR9DiZOseC6VTtQUmfGiiMhqDZbkRplwNo/TkRkSPfHajabcfbsWURERCAuLi4oBRDP86irq0N4eLhfzj8iIgIFBQUYMmQI9PrWAd7Mg+MOsgqguLg4qNXqNiqupKQESUlJLrdJSkpya30mfs6ePYvNmzd3KGT0en2bkwgAWq3WqzeHhEgtfjuxl9f25wkjekbh/MFiHLrQgDR0PrdjpTWw2nlEhmjRKz7w3pK8/d36Iy3PwTM3DMWOUz+gzmxDZIgW91zaC/MvyVBU40xvQ9eAMs9BYpQWL906HHf+ZxeqG63QqjmsumM04iK88xJmt9vBcZz4T6WSPRfI5zgcQkkUf50/iwHSaDRtrl9PrmdZZ67T6TB69Gjk5OSIyxwOB3JycjBx4kSX20ycOLHV+oBgxm25PhM/J06cwPfff4/YWGXXwPAFrAYEa8DaGaz+z1AKgA4KkiINeO+e8Xj+xqH46bEr8Oer+gW0+CGUzWX94vHnK/uC44Al1w2m1HdCEmR3gWVlZWH+/PkYM2YMxo0bh5UrV6KhoUHMCps3bx5SUlKwbNkyAMCDDz6IyZMn46WXXsKsWbPw0UcfYc+ePXjjjTcACOLnlltuwb59+7B+/XrY7XYxPigmJgY6nU6eicrMiDSh186BohpcG9X5+kwADaYCiEHD8LQoetAQiuGR6QNw3+W9FV3BmvBvZBdAc+fORVlZGRYvXozi4mKMGDECGzduFAOdCwoKWpnoJk2ahA8++AB//etf8cQTT6Bfv3744osvMGTIEABAUVERvvrqKwDAiBEjWh1ry5YtmDJlik/mpTSGpERAreJQUmtGtbnz9Q+1sAARBEHIAYkfQkpkF0AAsHDhQixcuNDlZ1u3bm2zbM6cOZgzZ47L9TMyMsDz7Td3DFaMOg36J4bj6IVanK3v2KVltTuQd0GoAE0CiCAIQpkMHDgQjzzyCO655x65h+KX+F/0E9FlRqQJYqYzAXS8pA4WuwMRBg16+qBaNUEQBOEZjY2NOHHiBIYPHy75sTxtV9XVbRoaGnDbbbchOTkZt99+u0cp7V2BBFAQwQKhC+o7Xu/gOaoATRAEoWQOHToEnufF8A+p8LRdVVe3AYCVK1ciLCwMmzZtQkhICFauXOnl2bSGBFAQwQKhz9ZzqDfb2l1vo7Ps/Jj0aJ+MiyAIgnCP3NxcXHnllbj00kvhcDjQs2dPSYVCy3ZVgwYNwurVq2E0GrFmzRqvbgMAVVVV6N+/P4YOHYrMzExUV1d7eTatUUQMEOEb+ieGoVesEfkVJnx7qAS/mZDRZp3S2iZsO14GAJg9MsXHIyQIgvAtPM+j0SpPP6wQrdojK/upU6cwefJkPProo4iNjYXD4cDYsWPx8MMPY8qUKW0SfwDg+eefx/PPP9/hfo8cOeKyT2ZX2lV1ZRvGwoULcdVVV+HJJ59E37598f3333e4fnchARREcByHm0elYHn2CXyyr8ilAPoy9zwcPDCqZxR6x4f5fpAEQRA+pNFqx6DF8rR7OfLMDBh17j+G//CHP+Cmm27CX//6V4wfPx5z587FQw89hGXLluHHH390KYD+8Ic/4NZbb221zOFwoL6+HmFhYVCpVOjRw3Vz7q60q+rKNoyMjAycOHECpaWlSExMlDwEgwRQkHHjyB5YkX0c+wqqcbK0Hn0TmkUOz/P4dN85AMBNo1LlGiJBEARxEcXFxdi8eTO2b98Ou92OgwcPYtmyZVCpVFCr1e3WuIuJiUFMTEyrZQ6HA7W1tYiIiFBcJWiVStVuJwhvQwIoyEgI12NQNI9DVRw+3lOIRTMHip8dPl+LvOI66DQqXDfM9RsBQRBEIBGiVePIMzNkO7a77Ny5Ew6HAyNGjMCxY8fQ2NiIESNG4MyZM6iqqsKkSZNcbtcdF1hX2lV1ZRu5IAEUhExI4HGoCvh03zn8ZcYAaNXCGwCz/kwbmIhIIxUgIwgi8OE4ziM3lFxYLBYAQFNTE/bv34/09HTExMTgH//4B4YMGYKhQ4e63K47LrCW7apmz54tbpuTk9Nu7b6ubCMXyv/WCa8zKIpHXJgO5fUWbMkrxfTBSbDaHfgq9zwA4ObRFPxMEAShJCZOnAiNRoNnnnkG9fX16N27N1577TW8+uqr2LZtW7vbddcF1lm7KgB47bXX8Pnnn4t9Ot3ZRgmQAApC1Cpg9ogeeOunM3jvlwL0iArBL/mVqGiwIC5Mj8v7xcs9RIIgCKIFaWlpWLNmDR577DFcuHABGo0GJpMJGzduxOjRoyU7bmftqgAh8PnUqVMebaMESAAFKbeMSsFbP53BtuNlYto7IAgjjVpZQXEEQRAEcOedd+LOO+9ETEwM3n77bVx//fU+OW5H7aoA4Omnn8bTTz/t0TZKgARQkNInPhRzRqdi46FihOo1CNWrkRRpwN2X9ZJ7aARBEEQ7nDt3DlVVVZJXgA4GSAAFMS/O+f/27j0oqvKNA/h3EVgWlYvichMVL4kGJmp4/XlJvFKp4zjpSKMzhmmYl0jNSdOxMRhTc0YLtRAxTbw0XqHQdPI2KHlBBRMx7xdgDFhQLgvu8/uj4dQGKiXsxp7vZ2b/OO/7nrPP867rPpzznt1X8Pm4+v8dGSIiqhuXLl1C48aN4e/PP1ZfFAsgIiKiBmLEiBF49Og5P+hItcLFHkRERKQ6LICIiIhIdVgAERERkeqwACIiIiLVYQFEREREDYaI1MlxWAAREZFqNGr0xw+QVlZWWjkS+rdKSkoAAA4OL/ablbwNnoiIVMPe3h46nQ75+flwcXGBvb36PgZNJhOMRiPKysqe+1tg/yUigpKSEuTl5cHNzU0pZv8t9b3yRESkWhqNBp6enrh48SJu374NjUZj7ZAsTkRQWloKnU7XIPN3c3ODl5fXCx+HBRAREamKg4MDcnNzERgYqMozQBUVFTh27Bj69+//wpeRLM3BweGFz/xUUd8rT0REBECr1Ta4AqAuNGrUCJWVlXByclJl/lUazsU/IiIiojrCAoiIiIhUhwUQERERqQ7XANWg6kuWioqKrBxJ3auoqEBJSQmKiopUee1X7fkDnAO15w9wDpi/7eZf9bldmy9LZAFUg+LiYgCAn5+flSMhIiKif6q4uBiurq7PHKORuvpOaRtiMplw//59NG3atEF+R8KzFBUVwc/PD3fu3IGLi4u1w7E4tecPcA7Unj/AOWD+tpu/iKC4uBg+Pj7P/ZJHngGqgZ2dHVq2bGntMOqVi4uLzf3D/yfUnj/AOVB7/gDngPnbZv7PO/NThYugiYiISHVYABEREZHqsABSGa1Wi8WLF0Or1Vo7FKtQe/4A50Dt+QOcA+av7vyrcBE0ERERqQ7PABEREZHqsAAiIiIi1WEBRERERKrDAoiIiIhUhwWQyiQlJaFnz57Q6XRwd3fH6NGjzfpv376NsLAwODs7Q6/XY+7cuaisrLROsPWkvLwcXbt2hUajQXp6ulnfxYsX8b///Q9OTk7w8/PD8uXLrRNkPbh58yamTJkCf39/6HQ6tGvXDosXL4bRaDQbZ8tzAABffvkl2rRpAycnJ/Ts2RNpaWnWDqleREdH49VXX0XTpk2h1+sxevRoZGVlmY0pKytDZGQkmjdvjiZNmmDs2LHIzc21UsT1KyYmBhqNBrNnz1ba1JD/vXv3EB4ejubNm0On0yEoKAhnzpxR+kUEn3zyCby9vaHT6RAaGors7GwrRmxBQqqxa9cucXd3l9jYWMnKypLMzEzZvn270l9ZWSmBgYESGhoq58+fl+TkZPHw8JAFCxZYMeq6N3PmTBkxYoQAkPPnzyvtBoNBPD09ZeLEiZKRkSHbtm0TnU4n69evt16wdeiHH36QyZMnS0pKivz222+yd+9e0ev1EhUVpYyx9TlITEwUR0dH2bhxo2RmZkpERIS4ublJbm6utUOrc8OGDZP4+HjJyMiQ9PR0GTlypLRq1UoePXqkjJk2bZr4+fnJ4cOH5cyZM9KrVy/p06ePFaOuH2lpadKmTRvp0qWLzJo1S2m39fzz8/OldevWMnnyZDl9+rRcv35dUlJS5Nq1a8qYmJgYcXV1lT179siFCxfkzTffFH9/fyktLbVi5JbBAkglKioqxNfXV7755punjklOThY7OzvJyclR2mJjY8XFxUXKy8stEWa9S05OloCAAMnMzKxWAH311Vfi7u5uluv8+fOlY8eOVojUMpYvXy7+/v7Ktq3PQUhIiERGRirbT548ER8fH4mOjrZiVJaRl5cnAOTo0aMiIlJYWCgODg6yc+dOZcyvv/4qACQ1NdVaYda54uJi6dChgxw6dEgGDBigFEBqyH/+/PnSr1+/p/abTCbx8vKSzz//XGkrLCwUrVYr27Zts0SIVsVLYCpx7tw53Lt3D3Z2dggODoa3tzdGjBiBjIwMZUxqaiqCgoLg6emptA0bNgxFRUXIzMy0Rth1Kjc3FxEREfj222/h7OxcrT81NRX9+/eHo6Oj0jZs2DBkZWWhoKDAkqFajMFgQLNmzZRtW54Do9GIs2fPIjQ0VGmzs7NDaGgoUlNTrRiZZRgMBgBQXu+zZ8+ioqLCbD4CAgLQqlUrm5qPyMhIhIWFmeUJqCP/ffv2oUePHhg3bhz0ej2Cg4Px9ddfK/03btxATk6O2Ry4urqiZ8+eNjMHz8ICSCWuX78OAFiyZAkWLlyIAwcOwN3dHQMHDkR+fj4AICcnx6z4AaBs5+TkWDbgOiYimDx5MqZNm4YePXrUOMaW86/JtWvXsGbNGrz77rtKmy3PwcOHD/HkyZMa82vouT2PyWTC7Nmz0bdvXwQGBgL44/V0dHSEm5ub2Vhbmo/ExEScO3cO0dHR1frUkP/169cRGxuLDh06ICUlBdOnT8fMmTORkJAA4M/3tBrfEwALoAbvo48+gkajeebjypUrMJlMAICPP/4YY8eORffu3REfHw+NRoOdO3daOYt/r7b5r1mzBsXFxViwYIG1Q65ztZ2Dv7p37x6GDx+OcePGISIiwkqRk6VERkYiIyMDiYmJ1g7FYu7cuYNZs2Zh69atcHJysnY4VmEymdCtWzd89tlnCA4OxtSpUxEREYF169ZZO7T/BHtrB0AvJioqCpMnT37mmLZt2+LBgwcAgM6dOyvtWq0Wbdu2xe3btwEAXl5e1e6IqbojwsvLqw6jrju1zf/IkSNITU2t9ts3PXr0wMSJE5GQkAAvL69qd4D81/MHaj8HVe7fv49BgwahT58+2LBhg9m4hjoHteHh4YFGjRrVmF9Dz+1ZZsyYgQMHDuDYsWNo2bKl0u7l5QWj0YjCwkKzsyC2Mh9nz55FXl4eunXrprQ9efIEx44dw9q1a5GSkmLT+QOAt7e32f/5ANCpUyd8//33AP58T+fm5sLb21sZk5ubi65du1osTqux9iIksgyDwSBardZsEbTRaBS9Xq/c4VO1CPqvd8SsX79eXFxcpKyszOIx16Vbt27JpUuXlEdKSooAkF27dsmdO3dE5M8FwEajUdlvwYIFNrMAWETk7t270qFDBxk/frxUVlZW67f1OQgJCZEZM2Yo20+ePBFfX1+bXARtMpkkMjJSfHx85OrVq9X6qxYB79q1S2m7cuWKzSwCLioqMnvPX7p0SXr06CHh4eFy6dIlm89fRGTChAnVFkHPnj1bevfuLSJ/LoJesWKF0l/1WaGGRdAsgFRk1qxZ4uvrKykpKXLlyhWZMmWK6PV6yc/PF5E/b4MfOnSopKeny48//igtWrSwudvgRURu3LhR7S6wwsJC8fT0lLffflsyMjIkMTFRnJ2dbeYW8Lt370r79u1l8ODBcvfuXXnw4IHyqGLrc5CYmCharVY2bdokly9flqlTp4qbm5vZnY+2Yvr06eLq6io///yz2WtdUlKijJk2bZq0atVKjhw5ImfOnJHevXsrH4626K93gYnYfv5paWlib28vy5Ytk+zsbNm6das4OzvLli1blDExMTHi5uYme/fulYsXL8qoUaN4GzzZHqPRKFFRUaLX66Vp06YSGhoqGRkZZmNu3rwpI0aMEJ1OJx4eHhIVFSUVFRVWirj+1FQAiYhcuHBB+vXrJ1qtVnx9fSUmJsY6AdaD+Ph4AVDj469seQ5ERNasWSOtWrUSR0dHCQkJkVOnTlk7pHrxtNc6Pj5eGVNaWirvvfeeuLu7i7Ozs4wZM8asILY1fy+A1JD//v37JTAwULRarQQEBMiGDRvM+k0mkyxatEg8PT1Fq9XK4MGDJSsry0rRWpZGRMTi192IiIiIrIh3gREREZHqsAAiIiIi1WEBRERERKrDAoiIiIhUhwUQERERqQ4LICIiIlIdFkBERESkOiyAiIiISHVYABEREZHqsAAiIps1cOBAzJ49+4WOISJYtWoV/P394ezsjNGjR8NgMJiN+f3336HX63Hz5s3nHm/8+PFYuXLlC8VERC+OBRARWUVqaioaNWqEsLAwa4fyTHPnzkVsbCwSEhJw/PhxnD17FkuWLDEbs2zZMowaNQpt2rQBADx+/Bjjx4+Ht7c3JkyYgJKSEmXswoULsWzZsmpFFBFZFgsgIrKKuLg4vP/++zh27Bju379v7XBqdPr0aaxatQrbt29H//790b17d0RERCA5OVkZU1JSgri4OEyZMkVpW716NZo0aYKDBw9Cp9Nh9erVSl9gYCDatWuHLVu2WDIVIvobFkBEZHGPHj3C9u3bMX36dISFhWHTpk1m/QMHDsTMmTMxb948NGvWDF5eXtXOuhQXF2PixIlo3LgxvL298cUXXzzzkpfJZEJ0dDT8/f2h0+nwyiuvYNeuXc+Mc8WKFRg8eDC6deumtHl6euLhw4fKdnJyMrRaLXr16qW0FRQU4KWXXkJQUBACAgJQWFhodtw33ngDiYmJz3xuIqpfLICIyOJ27NiBgIAAdOzYEeHh4di4cSNExGxMQkICGjdujNOnT2P58uVYunQpDh06pPR/8MEHOHnyJPbt24dDhw7h+PHjOHfu3FOfMzo6Gps3b8a6deuQmZmJOXPmIDw8HEePHq1xfHl5OZKSkjBmzBiz9rKyMri6uirbx48fR/fu3c3GzJgxA+vXr4eDgwPi4+Mxa9Yss/6QkBCkpaWhvLz82RNFRPXG3toBEJH6xMXFITw8HAAwfPhwGAwGHD16FAMHDlTGdOnSBYsXLwYAdOjQAWvXrsXhw4cxZMgQFBcXIyEhAd999x0GDx4MAIiPj4ePj0+Nz1deXo7PPvsMP/30E3r37g0AaNu2LU6cOIH169djwIAB1fY5d+4cSktLERUVhXnz5intFRUVGDRokLJ969atas/bpk0bZGdnIy8vD56entBoNGb9Pj4+MBqNyMnJQevWrWs7bURUh1gAEZFFZWVlIS0tDbt37wYA2Nvb46233kJcXFy1AuivvL29kZeXBwC4fv06KioqEBISovS7urqiY8eONT7ntWvXUFJSgiFDhpi1G41GBAcH17jP1atX0bhxY6Snp5u1h4WFoW/fvsp2aWkpnJycqu1vZ2cHLy+vGo+t0+kAwGxxNBFZFgsgIrKouLg4VFZWmp01ERFotVqsXbtWubzk4OBgtp9Go4HJZPpXz/no0SMAQFJSEnx9fc36tFptjfsUFRXBw8MD7du3V9pu3bqF7OxsjB07Vmnz8PBAQUHBP4onPz8fANCiRYt/tB8R1R2uASIii6msrMTmzZuxcuVKpKenK48LFy7Ax8cH27Ztq9Vx2rZtCwcHB/zyyy9Km8FgwNWrV2sc37lzZ2i1Wty+fRvt27c3e/j5+dW4j4eHBwwGg9napGXLlmHkyJHo3Lmz0hYcHIzLly/XKu4qGRkZaNmyJTw8PP7RfkRUd3gGiIgs5sCBAygoKMCUKVPMFhIDwNixYxEXF4dp06Y99zhNmzbFpEmTMHfuXDRr1gx6vR6LFy+GnZ1dtfU2VeM//PBDzJkzByaTCf369YPBYMDJkyfh4uKCSZMmVdvntddeQ1lZGWJiYjB+/Hhs3boV+/fvR1pamtm4YcOGYcGCBSgoKIC7u3ut5uH48eMYOnRorcYSUf3gGSAispi4uDiEhoZWK36APwqgM2fO4OLFi7U61qpVq9C7d2+8/vrrCA0NRd++fdGpU6ca1+MAwKeffopFixYhOjoanTp1wvDhw5GUlAR/f/8ax3t6emLTpk2IjY3Fyy+/jFOnTuHEiRPVzhgFBQWhW7du2LFjR63iLisrw549exAREVGr8URUPzTy93tPiYgaoMePH8PX1xcrV640+1JCS0hKSsLcuXORkZEBO7tn/10ZGxuL3bt34+DBgxaKjohqwktgRNQgnT9/HleuXEFISAgMBgOWLl0KABg1apTFYwkLC0N2djbu3bv31DVFVRwcHLBmzRoLRUZET8MzQETUIJ0/fx7vvPMOsrKy4OjoiO7du2PVqlUICgqydmhE1ACwACIiIiLV4SJoIiIiUh0WQERERKQ6LICIiIhIdVgAERERkeqwACIiIiLVYQFEREREqsMCiIiIiFSHBRARERGpDgsgIiIiUh0WQERERKQ6LICIiIhIdf4P1Iaq9vk/EKEAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "calc = MSSPEC(spectroscopy='PED', algorithm='inversion', txt='msspec.log')\n",
+ "calc.set_atoms(cluster)\n",
+ "\n",
+ "data = calc.get_theta_scan(level='2p3/2');\n",
+ "data[0].views[0].plot();"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/msspecbook/Activity01/copper.cif b/msspecbook/Activity01/copper.cif
new file mode 100644
index 0000000..0490639
--- /dev/null
+++ b/msspecbook/Activity01/copper.cif
@@ -0,0 +1,51 @@
+data_image0
+_chemical_formula_structural Cu26
+_chemical_formula_sum "Cu26"
+_cell_length_a 14.4
+_cell_length_b 14.4
+_cell_length_c 7.2
+_cell_angle_alpha 90.0
+_cell_angle_beta 90.0
+_cell_angle_gamma 90.0
+
+_space_group_name_H-M_alt "P 1"
+_space_group_IT_number 1
+
+loop_
+ _space_group_symop_operation_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_type_symbol
+ _atom_site_label
+ _atom_site_symmetry_multiplicity
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_occupancy
+ Cu Cu1 1.0 0.25 0.5 0.37500000000000017 1.0000
+ Cu Cu2 1.0 0.25 0.5 0.8750000000000001 1.0000
+ Cu Cu3 1.0 0.3750000000000001 0.37500000000000017 0.37500000000000017 1.0000
+ Cu Cu4 1.0 0.5 0.24999999999999992 0.37500000000000017 1.0000
+ Cu Cu5 1.0 0.5 0.37500000000000017 0.12499999999999996 1.0000
+ Cu Cu6 1.0 0.3750000000000001 0.37500000000000017 0.8750000000000001 1.0000
+ Cu Cu7 1.0 0.5 0.24999999999999992 0.8750000000000001 1.0000
+ Cu Cu8 1.0 0.5 0.37500000000000017 0.625 1.0000
+ Cu Cu9 1.0 0.3750000000000001 0.5 0.12499999999999996 1.0000
+ Cu Cu10 1.0 0.3750000000000001 0.6249999999999999 0.37500000000000017 1.0000
+ Cu Cu11 1.0 0.5 0.5 0.37500000000000017 1.0000
+ Cu Cu12 1.0 0.5 0.6249999999999999 0.12499999999999996 1.0000
+ Cu Cu13 1.0 0.3750000000000001 0.5 0.625 1.0000
+ Cu Cu14 1.0 0.3750000000000001 0.6249999999999999 0.8750000000000001 1.0000
+ Cu Cu15 1.0 0.5 0.5 0.8750000000000001 1.0000
+ Cu Cu16 1.0 0.5 0.6249999999999999 0.625 1.0000
+ Cu Cu17 1.0 0.5 0.7500000000000003 0.37500000000000017 1.0000
+ Cu Cu18 1.0 0.5 0.7500000000000003 0.8750000000000001 1.0000
+ Cu Cu19 1.0 0.6249999999999999 0.37500000000000017 0.37500000000000017 1.0000
+ Cu Cu20 1.0 0.6249999999999999 0.37500000000000017 0.8750000000000001 1.0000
+ Cu Cu21 1.0 0.6249999999999999 0.5 0.12499999999999996 1.0000
+ Cu Cu22 1.0 0.6249999999999999 0.6249999999999999 0.37500000000000017 1.0000
+ Cu Cu23 1.0 0.7500000000000003 0.5 0.37500000000000017 1.0000
+ Cu Cu24 1.0 0.6249999999999999 0.5 0.625 1.0000
+ Cu Cu25 1.0 0.6249999999999999 0.6249999999999999 0.8750000000000001 1.0000
+ Cu Cu26 1.0 0.7500000000000003 0.5 0.8750000000000001 1.0000
diff --git a/msspecbook/Activity01/copper_3planes.cif b/msspecbook/Activity01/copper_3planes.cif
new file mode 100644
index 0000000..6dfa7ae
--- /dev/null
+++ b/msspecbook/Activity01/copper_3planes.cif
@@ -0,0 +1,47 @@
+data_image0
+_chemical_formula_structural Cu22
+_chemical_formula_sum "Cu22"
+_cell_length_a 14.4
+_cell_length_b 14.4
+_cell_length_c 7.2
+_cell_angle_alpha 90.0
+_cell_angle_beta 90.0
+_cell_angle_gamma 90.0
+
+_space_group_name_H-M_alt "P 1"
+_space_group_IT_number 1
+
+loop_
+ _space_group_symop_operation_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_type_symbol
+ _atom_site_label
+ _atom_site_symmetry_multiplicity
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_occupancy
+ Cu Cu1 1.0 0.25 0.5 0.37500000000000017 1.0000
+ Cu Cu2 1.0 0.25 0.5 0.8750000000000001 1.0000
+ Cu Cu3 1.0 0.37500000000000017 0.37500000000000017 0.37500000000000017 1.0000
+ Cu Cu4 1.0 0.5 0.24999999999999992 0.37500000000000017 1.0000
+ Cu Cu5 1.0 0.37500000000000017 0.37500000000000017 0.8750000000000001 1.0000
+ Cu Cu6 1.0 0.5 0.24999999999999992 0.8750000000000001 1.0000
+ Cu Cu7 1.0 0.5 0.37500000000000017 0.625 1.0000
+ Cu Cu8 1.0 0.37500000000000017 0.6249999999999999 0.37500000000000017 1.0000
+ Cu Cu9 1.0 0.5 0.5 0.37500000000000017 1.0000
+ Cu Cu10 1.0 0.37500000000000017 0.5 0.625 1.0000
+ Cu Cu11 1.0 0.37500000000000017 0.6249999999999999 0.8750000000000001 1.0000
+ Cu Cu12 1.0 0.5 0.5 0.8750000000000001 1.0000
+ Cu Cu13 1.0 0.5 0.6249999999999999 0.625 1.0000
+ Cu Cu14 1.0 0.5 0.7500000000000003 0.37500000000000017 1.0000
+ Cu Cu15 1.0 0.5 0.7500000000000003 0.8750000000000001 1.0000
+ Cu Cu16 1.0 0.6249999999999999 0.37500000000000017 0.37500000000000017 1.0000
+ Cu Cu17 1.0 0.6249999999999999 0.37500000000000017 0.8750000000000001 1.0000
+ Cu Cu18 1.0 0.6249999999999999 0.6249999999999999 0.37500000000000017 1.0000
+ Cu Cu19 1.0 0.7500000000000003 0.5 0.37500000000000017 1.0000
+ Cu Cu20 1.0 0.6249999999999999 0.5 0.625 1.0000
+ Cu Cu21 1.0 0.6249999999999999 0.6249999999999999 0.8750000000000001 1.0000
+ Cu Cu22 1.0 0.7500000000000003 0.5 0.8750000000000001 1.0000
diff --git a/msspecbook/Activity02/Activity02.ipynb b/msspecbook/Activity02/Activity02.ipynb
new file mode 100644
index 0000000..b417dd3
--- /dev/null
+++ b/msspecbook/Activity02/Activity02.ipynb
@@ -0,0 +1,1955 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "88b65284-bdd1-4140-af28-526e77b9b4b6",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# Activity 2: Setting up the \"experiment\"\n",
+ "\n",
+ "To model a spectroscopy experiment, some parameters need to be correctly defined. For example the source direction with respect to the sample surface is important even if the light is not polarized. You can access those parameters in the \"source_parameters\" attribute of your calculator object.\n",
+ "\n",
+ "Other parameters are material specific. For example the inner potential. This potential will add to the photoelectron kinetic energy inside the material. When the photoelectron escapes the sample, this internal potential is missing and this will create an energy step that will act as a refraction for the photoelectron intensity. The effect will be significant for large polar angles and for small kinetic energy of the photoelectron.\n",
+ "\n",
+ "## Sb-induced smooth growth of Ag on Ag(111) example\n",
+ "Let's look at the effect of those parameters on the following example. This example is based on ().\n",
+ "The idea is to use low energy photoelectron diffraction to see the substitution of Ag by Sb atoms on the surface plane.\n",
+ "\n",
+ "Let's start by building the cluster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0b0a420f-7074-443b-8cb4-1f609f5b123e",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " ASE atomic visualization\n",
+ " \n",
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+ " \n",
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+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from ase.build import bulk\n",
+ "from ase.visualize import view\n",
+ "\n",
+ "from msspec.calculator import MSSPEC\n",
+ "from msspec.utils import hemispherical_cluster, get_atom_index, cut_plane\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Only for jupyter\n",
+ "from functools import partial\n",
+ "view = partial(view, viewer='x3d')\n",
+ "\n",
+ "# Create the silver cell\n",
+ "Ag = bulk('Ag', cubic=True)\n",
+ "# Orientate the cell in the [111] direction\n",
+ "Ag.rotate((1,1,1), (0,0,1), rotate_cell=True)\n",
+ "# Align the azimuth to match experimental reference\n",
+ "Ag.rotate(15, 'z', rotate_cell=True)\n",
+ "\n",
+ "# Create a cluster\n",
+ "cluster = hemispherical_cluster(Ag, diameter=20, emitter_plane=0)\n",
+ "cluster = cut_plane(cluster, z=-4.8)\n",
+ "cluster.emitter = get_atom_index(cluster, 0,0,0)\n",
+ "cluster[cluster.emitter].symbol = 'Sb'\n",
+ "\n",
+ "view(cluster)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae74eedc-5ea4-4782-a49a-dcc2feac6c7c",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Now create a calculator and configure experimental parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "242638d5-aea4-46cb-8d26-061c695cbb3f",
+ "metadata": {
+ "collapsed": true,
+ "editable": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-output"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " _________________________________________________________________\n",
+ "\n",
+ " PHAGEN\n",
+ " _________________________________________________________________\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " parameters for this xpd calculation:\n",
+ " -----------------------------------------------------------------\n",
+ " edge= n4\n",
+ " potype= hedin norman= stdcrm absorber= 1\n",
+ " coor= angs emin= 3.31 Ry emax= 3.31 Ry\n",
+ " delta= 0.300 Ry gamma= 0.00 Ry eftri= 0.000 Ry\n",
+ " cip= 0.00 Ry lmaxt= 19 charelx: ex\n",
+ " ionization state : neutral\n",
+ " relativistic corrections of type: nr\n",
+ " final state potential generated internally\n",
+ "\n",
+ "\n",
+ " Computes the T-matrix and radial matrix elements \n",
+ "\n",
+ "\n",
+ " coordinates in angstroms Radii\n",
+ " -----------------------------------------------------------------\n",
+ " Sb 51 0.0000 0.0000 0.0000 0.0000 0.0000\n",
+ " Ag 47 -6.6789 -2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -6.6789 0.0000 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -6.6789 2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -8.3487 -2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -8.3487 0.0000 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -8.3487 2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -4.1743 -7.2302 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -1.6697 -5.7841 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -3.3395 -8.6762 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -4.1743 -1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -1.6697 -2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -1.6697 -0.0000 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -4.1743 -4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -5.8441 -4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 -5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 -2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -5.8441 -7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -5.0092 -5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 -4.1743 4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -1.6697 2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -1.6697 5.7841 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -4.1743 1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -5.8441 1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 0.0000 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -5.8441 -1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -7.5138 -1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 -5.0092 -2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 -5.0092 0.0000 0.0000 0.0000 0.0000\n",
+ " Ag 47 -7.5138 -4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 -4.1743 7.2302 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -5.8441 7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -3.3395 8.6762 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -5.8441 4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -7.5138 4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 -5.0092 2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 -5.0092 5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 -7.5138 1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.8349 -4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 3.3395 -5.7841 -4.7227 0.0000 0.0000\n",
+ " Ag 47 3.3395 -2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 0.8349 -7.2302 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -0.8349 -7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 -8.6762 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 -5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 0.0000 -8.6762 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.8349 1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 3.3395 -0.0000 -4.7227 0.0000 0.0000\n",
+ " Ag 47 3.3395 2.8921 -4.7227 0.0000 0.0000\n",
+ " Ag 47 0.8349 -1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -0.8349 -1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 -2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 -0.0000 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -0.8349 -4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -2.5046 -4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.0000 -5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.0000 -2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 -2.5046 -7.2302 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.8349 7.2302 -4.7227 0.0000 0.0000\n",
+ " Ag 47 3.3395 5.7841 -4.7227 0.0000 0.0000\n",
+ " Ag 47 0.8349 4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 -0.8349 4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 1.6697 5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -0.8349 1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -2.5046 1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.0000 2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 -2.5046 -1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 1.6697 8.6762 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -0.8349 7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 -2.5046 7.2302 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.0000 5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 0.0000 8.6762 0.0000 0.0000 0.0000\n",
+ " Ag 47 -2.5046 4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.8441 -1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 8.3487 -0.0000 -4.7227 0.0000 0.0000\n",
+ " Ag 47 5.8441 -4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 4.1743 -4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 6.6789 -5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 6.6789 -2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 4.1743 -7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 2.5046 -7.2302 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.0092 -5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.8441 4.3381 -4.7227 0.0000 0.0000\n",
+ " Ag 47 5.8441 1.4460 -4.7227 0.0000 0.0000\n",
+ " Ag 47 4.1743 1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 6.6789 -0.0000 -2.3613 0.0000 0.0000\n",
+ " Ag 47 6.6789 2.8921 -2.3613 0.0000 0.0000\n",
+ " Ag 47 4.1743 -1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 2.5046 -1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.0092 -2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.0092 -0.0000 0.0000 0.0000 0.0000\n",
+ " Ag 47 2.5046 -4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 4.1743 7.2302 -2.3613 0.0000 0.0000\n",
+ " Ag 47 6.6789 5.7841 -2.3613 0.0000 0.0000\n",
+ " Ag 47 4.1743 4.3381 -2.3613 0.0000 0.0000\n",
+ " Ag 47 2.5046 4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.0092 2.8921 0.0000 0.0000 0.0000\n",
+ " Ag 47 5.0092 5.7841 0.0000 0.0000 0.0000\n",
+ " Ag 47 2.5046 1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 2.5046 7.2302 0.0000 0.0000 0.0000\n",
+ " Ag 47 9.1835 -1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 7.5138 -4.3381 0.0000 0.0000 0.0000\n",
+ " Ag 47 9.1835 1.4460 -2.3613 0.0000 0.0000\n",
+ " Ag 47 7.5138 1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 7.5138 -1.4460 0.0000 0.0000 0.0000\n",
+ " Ag 47 7.5138 4.3381 0.0000 0.0000 0.0000\n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ "\n",
+ " ** enter calphas **\n",
+ " ---\n",
+ " total energy for atom in ground state \n",
+ " total energy for atom with a hole in n4 edge\n",
+ " calculated ionization energy for edge n4 = 40.434138053368777 eV\n",
+ " ---\n",
+ " calculated ionization potential (ryd) = 2.9730983028839546 \n",
+ " ---\n",
+ " \n",
+ " \n",
+ " symmetrizing coordinates... \n",
+ "\n",
+ "\n",
+ " symmetrized atomic coordinates of cluster \n",
+ "\n",
+ " position\n",
+ " atom no. x y z eq\n",
+ "\n",
+ " 1 osph 0 0.0000 0.0000 0.0000 0\n",
+ " 2 Sb 51 0.0000 0.0000 4.1725 0\n",
+ " 3 Ag 47 -12.6214 -5.4652 -4.7522 0\n",
+ " 4 Ag 47 -12.6214 0.0000 -4.7522 0\n",
+ " 5 Ag 47 -12.6214 5.4652 -4.7522 0\n",
+ " 6 Ag 47 -15.7767 -5.4652 -0.2898 0\n",
+ " 7 Ag 47 -15.7767 0.0000 -0.2898 0\n",
+ " 8 Ag 47 -15.7767 5.4652 -0.2898 0\n",
+ " 9 Ag 47 -7.8884 -13.6630 -4.7522 0\n",
+ " 10 Ag 47 -3.1553 -10.9304 -4.7522 0\n",
+ " 11 Ag 47 -6.3107 -16.3956 -0.2898 0\n",
+ " 12 Ag 47 -7.8884 -2.7326 -4.7522 0\n",
+ " 13 Ag 47 -3.1553 -5.4652 -4.7522 0\n",
+ " 14 Ag 47 -3.1553 0.0000 -4.7522 0\n",
+ " 15 Ag 47 -7.8884 -8.1978 -4.7522 0\n",
+ " 16 Ag 47 -11.0437 -8.1978 -0.2898 0\n",
+ " 17 Ag 47 -6.3107 -10.9304 -0.2898 0\n",
+ " 18 Ag 47 -6.3107 -5.4652 -0.2898 0\n",
+ " 19 Ag 47 -11.0437 -13.6630 -0.2898 0\n",
+ " 20 Ag 47 -9.4660 -10.9304 4.1725 0\n",
+ " 21 Ag 47 -7.8884 8.1978 -4.7522 0\n",
+ " 22 Ag 47 -3.1553 5.4652 -4.7522 0\n",
+ " 23 Ag 47 -3.1553 10.9304 -4.7522 0\n",
+ " 24 Ag 47 -7.8884 2.7326 -4.7522 0\n",
+ " 25 Ag 47 -11.0437 2.7326 -0.2898 0\n",
+ " 26 Ag 47 -6.3107 0.0000 -0.2898 0\n",
+ " 27 Ag 47 -6.3107 5.4652 -0.2898 0\n",
+ " 28 Ag 47 -11.0437 -2.7326 -0.2898 0\n",
+ " 29 Ag 47 -14.1990 -2.7326 4.1725 0\n",
+ " 30 Ag 47 -9.4660 -5.4652 4.1725 0\n",
+ " 31 Ag 47 -9.4660 0.0000 4.1725 0\n",
+ " 32 Ag 47 -14.1990 -8.1978 4.1725 0\n",
+ " 33 Ag 47 -7.8884 13.6630 -4.7522 0\n",
+ " 34 Ag 47 -11.0437 13.6630 -0.2898 0\n",
+ " 35 Ag 47 -6.3107 10.9304 -0.2898 0\n",
+ " 36 Ag 47 -6.3107 16.3956 -0.2898 0\n",
+ " 37 Ag 47 -11.0437 8.1978 -0.2898 0\n",
+ " 38 Ag 47 -14.1990 8.1978 4.1725 0\n",
+ " 39 Ag 47 -9.4660 5.4652 4.1725 0\n",
+ " 40 Ag 47 -9.4660 10.9304 4.1725 0\n",
+ " 41 Ag 47 -14.1990 2.7326 4.1725 0\n",
+ " 42 Ag 47 1.5777 -8.1978 -4.7522 0\n",
+ " 43 Ag 47 6.3107 -10.9304 -4.7522 0\n",
+ " 44 Ag 47 6.3107 -5.4652 -4.7522 0\n",
+ " 45 Ag 47 1.5777 -13.6630 -4.7522 0\n",
+ " 46 Ag 47 -1.5777 -13.6630 -0.2898 0\n",
+ " 47 Ag 47 3.1553 -16.3956 -0.2898 0\n",
+ " 48 Ag 47 3.1553 -10.9304 -0.2898 0\n",
+ " 49 Ag 47 0.0000 -16.3956 4.1725 0\n",
+ " 50 Ag 47 1.5777 2.7326 -4.7522 0\n",
+ " 51 Ag 47 6.3107 0.0000 -4.7522 0\n",
+ " 52 Ag 47 6.3107 5.4652 -4.7522 0\n",
+ " 53 Ag 47 1.5777 -2.7326 -4.7522 0\n",
+ " 54 Ag 47 -1.5777 -2.7326 -0.2898 0\n",
+ " 55 Ag 47 3.1553 -5.4652 -0.2898 0\n",
+ " 56 Ag 47 3.1553 0.0000 -0.2898 0\n",
+ " 57 Ag 47 -1.5777 -8.1978 -0.2898 0\n",
+ " 58 Ag 47 -4.7330 -8.1978 4.1725 0\n",
+ " 59 Ag 47 0.0000 -10.9304 4.1725 0\n",
+ " 60 Ag 47 0.0000 -5.4652 4.1725 0\n",
+ " 61 Ag 47 -4.7330 -13.6630 4.1725 0\n",
+ " 62 Ag 47 1.5777 13.6630 -4.7522 0\n",
+ " 63 Ag 47 6.3107 10.9304 -4.7522 0\n",
+ " 64 Ag 47 1.5777 8.1978 -4.7522 0\n",
+ " 65 Ag 47 -1.5777 8.1978 -0.2898 0\n",
+ " 66 Ag 47 3.1553 5.4652 -0.2898 0\n",
+ " 67 Ag 47 3.1553 10.9304 -0.2898 0\n",
+ " 68 Ag 47 -1.5777 2.7326 -0.2898 0\n",
+ " 69 Ag 47 -4.7330 2.7326 4.1725 0\n",
+ " 70 Ag 47 0.0000 5.4652 4.1725 0\n",
+ " 71 Ag 47 -4.7330 -2.7326 4.1725 0\n",
+ " 72 Ag 47 3.1553 16.3956 -0.2898 0\n",
+ " 73 Ag 47 -1.5777 13.6630 -0.2898 0\n",
+ " 74 Ag 47 -4.7330 13.6630 4.1725 0\n",
+ " 75 Ag 47 0.0000 10.9304 4.1725 0\n",
+ " 76 Ag 47 0.0000 16.3956 4.1725 0\n",
+ " 77 Ag 47 -4.7330 8.1978 4.1725 0\n",
+ " 78 Ag 47 11.0437 -2.7326 -4.7522 0\n",
+ " 79 Ag 47 15.7767 0.0000 -4.7522 0\n",
+ " 80 Ag 47 11.0437 -8.1978 -4.7522 0\n",
+ " 81 Ag 47 7.8884 -8.1978 -0.2898 0\n",
+ " 82 Ag 47 12.6214 -10.9304 -0.2898 0\n",
+ " 83 Ag 47 12.6214 -5.4652 -0.2898 0\n",
+ " 84 Ag 47 7.8884 -13.6630 -0.2898 0\n",
+ " 85 Ag 47 4.7330 -13.6630 4.1725 0\n",
+ " 86 Ag 47 9.4660 -10.9304 4.1725 0\n",
+ " 87 Ag 47 11.0437 8.1978 -4.7522 0\n",
+ " 88 Ag 47 11.0437 2.7326 -4.7522 0\n",
+ " 89 Ag 47 7.8884 2.7326 -0.2898 0\n",
+ " 90 Ag 47 12.6214 0.0000 -0.2898 0\n",
+ " 91 Ag 47 12.6214 5.4652 -0.2898 0\n",
+ " 92 Ag 47 7.8884 -2.7326 -0.2898 0\n",
+ " 93 Ag 47 4.7330 -2.7326 4.1725 0\n",
+ " 94 Ag 47 9.4660 -5.4652 4.1725 0\n",
+ " 95 Ag 47 9.4660 0.0000 4.1725 0\n",
+ " 96 Ag 47 4.7330 -8.1978 4.1725 0\n",
+ " 97 Ag 47 7.8884 13.6630 -0.2898 0\n",
+ " 98 Ag 47 12.6214 10.9304 -0.2898 0\n",
+ " 99 Ag 47 7.8884 8.1978 -0.2898 0\n",
+ " 100 Ag 47 4.7330 8.1978 4.1725 0\n",
+ " 101 Ag 47 9.4660 5.4652 4.1725 0\n",
+ " 102 Ag 47 9.4660 10.9304 4.1725 0\n",
+ " 103 Ag 47 4.7330 2.7326 4.1725 0\n",
+ " 104 Ag 47 4.7330 13.6630 4.1725 0\n",
+ " 105 Ag 47 17.3544 -2.7326 -0.2898 0\n",
+ " 106 Ag 47 14.1990 -8.1978 4.1725 0\n",
+ " 107 Ag 47 17.3544 2.7326 -0.2898 0\n",
+ " 108 Ag 47 14.1990 2.7326 4.1725 0\n",
+ " 109 Ag 47 14.1990 -2.7326 4.1725 0\n",
+ " 110 Ag 47 14.1990 8.1978 4.1725 0\n",
+ "\n",
+ " computing muffin tin potential and phase shifts\n",
+ " generating core state wavefunction \n",
+ " generating final potential (complex hedin-lundqvist exchange) \n",
+ " MT radii for Hydrogen atoms determined by stdcrm unless other options are specified\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " i rs(i) i=1,natoms \n",
+ " 1 20.39 2 2.87 3 2.77 4 2.77 5 2.77 6 2.70 7 2.73 8 2.70\n",
+ " 9 2.73 10 2.75 11 2.73 12 2.73 13 2.71 14 2.73 15 2.73 16 2.75\n",
+ " 17 2.75 18 2.71 19 2.73 20 2.77 21 2.75 22 2.71 23 2.75 24 2.73\n",
+ " 25 2.73 26 2.71 27 2.71 28 2.73 29 2.73 30 2.70 31 2.70 32 2.78\n",
+ " 33 2.73 34 2.73 35 2.75 36 2.73 37 2.77 38 2.82 39 2.70 40 2.77\n",
+ " 41 2.73 42 2.70 43 2.77 44 2.70 45 2.75 46 2.63 47 2.75 48 2.75\n",
+ " 49 2.78 50 2.73 51 2.73 52 2.70 53 2.73 54 2.73 55 2.71 56 2.73\n",
+ " 57 2.73 58 2.73 59 2.75 60 2.71 61 2.68 62 2.75 63 2.77 64 2.73\n",
+ " 65 2.73 66 2.71 67 2.75 68 2.73 69 2.73 70 2.71 71 2.73 72 2.75\n",
+ " 73 2.63 74 2.68 75 2.75 76 2.78 77 2.75 78 2.73 79 2.73 80 2.75\n",
+ " 81 2.73 82 2.80 83 2.66 84 2.72 85 2.68 86 2.77 87 2.79 88 2.73\n",
+ " 89 2.73 90 2.61 91 2.66 92 2.73 93 2.73 94 2.70 95 2.70 96 2.73\n",
+ " 97 2.72 98 2.80 99 2.75 100 2.75 101 2.70 102 2.77 103 2.73 104 2.68\n",
+ " 105 2.73 106 2.78 107 2.73 108 2.73 109 2.73 110 2.82\n",
+ " N.B.: Order of atoms as reshuffled by symmetry routines \n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ " input value for coulomb interst. potential = -0.69999999999999996 \n",
+ " and interstitial rs = 3.0000000000000000 \n",
+ " lower bound for coulomb interst. potential = -0.29797334251710700 \n",
+ " and for interst. rs = 2.2852455800656419 \n",
+ "\n",
+ " lmax assignment based on l_max = r_mt * k_e + 2\n",
+ " where e is the running energy\n",
+ " optimal lmax chosen according to the running energy e for each atom\n",
+ "\n",
+ "\n",
+ " number of centers= 109\n",
+ "\n",
+ " starting potentials and/or charge densities written to file 13\n",
+ " symmetry information generated internally\n",
+ " symmetry information written to file 14\n",
+ "\n",
+ "\n",
+ " core initial state of type: 4d3/2\n",
+ "\n",
+ " fermi level = -0.14600\n",
+ "\n",
+ "\n",
+ " generating t_l (for030) and atomic cross section (for050)\n",
+ " corewf: fnisx = 0.99905554957172005 \n",
+ " writing atomic orbital energies\n",
+ " orbital energy (Ryd eV) 1s -2254.2197062325686 -30668.658071394206 \n",
+ " orbital energy (Ryd eV) 2s -349.92047655757182 -4760.6679233848827 \n",
+ " orbital energy (Ryd eV) 2p1/2 -326.37645981722244 -4440.3515864100245 \n",
+ " orbital energy (Ryd eV) 2p3/2 -307.78562205238723 -4187.4232471296518 \n",
+ " orbital energy (Ryd eV) 3s -71.595511274322263 -974.05689811333025 \n",
+ " orbital energy (Ryd eV) 3p1/2 -61.894576493992993 -842.07568486768605 \n",
+ " orbital energy (Ryd eV) 3p3/2 -58.360215845149064 -793.99070985806634 \n",
+ " orbital energy (Ryd eV) 3d3/2 -41.434762489197446 -563.71992469820225 \n",
+ " orbital energy (Ryd eV) 3d5/2 -40.710746773190543 -553.86969121335642 \n",
+ " orbital energy (Ryd eV) 4s -12.963487953434317 -176.36824767226003 \n",
+ " orbital energy (Ryd eV) 4p1/2 -9.5316564414463976 -129.67818152263220 \n",
+ " orbital energy (Ryd eV) 4p3/2 -8.8589466857080446 -120.52596560375399 \n",
+ " orbital energy (Ryd eV) 4d3/2 -3.2983212651741982 -44.873659302843315 \n",
+ " orbital energy (Ryd eV) 4d5/2 -3.1928163113809269 -43.438264454782193 \n",
+ " orbital energy (Ryd eV) 5s -0.58290532088190472 -7.9304266237654337 \n",
+ " orbital energy (Ryd eV) 5p1/2 0.0000000000000000 0.0000000000000000 \n",
+ " orbital energy (Ryd eV) 5p3/2 0.0000000000000000 0.0000000000000000 \n",
+ "\n",
+ " using overlapped potential to search for core states of photoabsorber\n",
+ "\n",
+ " calculating non relativistic core states\n",
+ " ------------------------------\n",
+ " energy of core state = -2132.7379177559310 for orbital =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -316.37322848237210 for orbital =2s \n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -295.91355359150020 for orbital =2p1/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -295.91355359150549 for orbital =2p3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -62.246228456906671 for orbital =3s \n",
+ " n. of zeros found: 2 expected: 2\n",
+ " ------------------------------\n",
+ " energy of core state = -53.894828119631313 for orbital =3p1/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -53.894828119626986 for orbital =3p3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -38.507180608827134 for orbital =3d3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -38.507180601808614 for orbital =3d5/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " ------------------------------\n",
+ " energy of core state = -10.610924864564280 for orbital =4s \n",
+ " n. of zeros found: 3 expected: 3\n",
+ " ------------------------------\n",
+ " energy of core state = -7.7908292169069098 for orbital =4p1/2\n",
+ " n. of zeros found: 2 expected: 2\n",
+ " ------------------------------\n",
+ " energy of core state = -7.7908292168456912 for orbital =4p3/2\n",
+ " n. of zeros found: 2 expected: 2\n",
+ " ------------------------------\n",
+ " energy of core state = -3.0855168829233235 for orbital =4d3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " ------------------------------\n",
+ " energy of core state = -3.0855168747608768 for orbital =4d5/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ "\n",
+ " calculating relativistic core states\n",
+ " energy of core state = -2224.6367927623405 for orb =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -339.92375057240076 for orb =2s \n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -318.38509246577496 for orb =2p1/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -299.86683894777667 for orb =2p3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -67.479228418569178 for orb =3s \n",
+ " n. of zeros found: 2 expected: 2\n",
+ " energy of core state = -58.635821707366865 for orb =3p1/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -55.209939757056809 for orb =3p3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -39.528958911149360 for orb =3d3/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -38.813899524275961 for orb =3d5/2\n",
+ " n. of zeros found: 0 expected: 0\n",
+ " energy of core state = -11.751593844336783 for orb =4s \n",
+ " n. of zeros found: 3 expected: 3\n",
+ " energy of core state = -8.7275126249514674 for orb =4p1/2\n",
+ " n. of zeros found: 2 expected: 2\n",
+ " energy of core state = -8.0726908604143066 for orb =4p3/2\n",
+ " n. of zeros found: 2 expected: 2\n",
+ " energy of core state = -3.2433775835804393 for orb =4d3/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " energy of core state = -3.1447901276942480 for orb =4d5/2\n",
+ " n. of zeros found: 1 expected: 1\n",
+ " -------------------------------\n",
+ " density of the valence charge (au^{-3} 3.3597891455322992E-002\n",
+ " rs_v corresponding to valence density (au) 1.9225005134566180 \n",
+ " valence plasmon energy (in eV) 15.643587593594503 \n",
+ "\n",
+ " gamma = 0.000000 rsint = 4.361497\n",
+ "\n",
+ " check in subroutine cont\n",
+ " order of neighb. -- symb. -- dist. from absorber\n",
+ " \n",
+ " 68 Ag 5.4652132897655008 \n",
+ " 70 Ag 5.4652132897655008 \n",
+ " 92 Ag 5.4652132897655008 \n",
+ " 102 Ag 5.4652132897655008 \n",
+ " 53 Ag 5.4652144683942456 \n",
+ " 67 Ag 5.4652144683942456 \n",
+ " 59 Ag 5.4652145664744447 \n",
+ " 69 Ag 5.4652145664744447 \n",
+ " 55 Ag 5.4652152182501545 \n",
+ " 25 Ag 7.7289802637821712 \n",
+ " 54 Ag 7.7289810220621922 \n",
+ " 65 Ag 7.7289810220621922 \n",
+ " 57 Ag 9.4660280207740559 \n",
+ " 76 Ag 9.4660280207740559 \n",
+ " 95 Ag 9.4660280207740559 \n",
+ " 99 Ag 9.4660280207740559 \n",
+ " 56 Ag 9.4660287012556612 \n",
+ " 64 Ag 9.4660287012556612 \n",
+ " 88 Ag 9.4660289533695803 \n",
+ " 91 Ag 9.4660289533695803 \n",
+ " 26 Ag 9.4660290605691042 \n",
+ " 17 Ag 9.4660290605691042 \n",
+ " 30 Ag 9.4660293357262315 \n",
+ " 94 Ag 9.4660293357262315 \n",
+ " 49 Ag 9.4660297349968943 \n",
+ " 52 Ag 9.4660297349968943 \n",
+ " 13 Ag 9.4660301679263963 \n",
+ " 58 Ag 10.930427243222763 \n",
+ " 74 Ag 10.930427243222763 \n",
+ " 38 Ag 10.930429160944874 \n",
+ " 29 Ag 10.930429160944874 \n",
+ " 93 Ag 10.930429160944874 \n",
+ " 100 Ag 10.930429160944874 \n",
+ " 50 Ag 10.930429345466440 \n",
+ " 12 Ag 10.930429881651367 \n",
+ " 21 Ag 10.930429881651367 \n",
+ " 27 Ag 12.220589808007702 \n",
+ " 24 Ag 12.220589808007702 \n",
+ " 47 Ag 12.220589883519512 \n",
+ " 66 Ag 12.220589883519512 \n",
+ " 80 Ag 12.220590588571460 \n",
+ " 98 Ag 12.220590588571460 \n",
+ " 63 Ag 12.220591194017395 \n",
+ " 41 Ag 12.220591194017395 \n",
+ " 11 Ag 12.220591389303989 \n",
+ " 23 Ag 12.220591389303989 \n",
+ " 51 Ag 12.220591472340377 \n",
+ " 43 Ag 12.220591472340377 \n",
+ " 16 Ag 13.386985308026624 \n",
+ " 34 Ag 13.386985308026624 \n",
+ " 89 Ag 13.386985990634251 \n",
+ " 36 Ag 14.459597001404703 \n",
+ " 15 Ag 14.459597001404703 \n",
+ " 19 Ag 14.459597197163411 \n",
+ " 39 Ag 14.459597197163411 \n",
+ " 85 Ag 14.459597197163411 \n",
+ " 101 Ag 14.459597197163411 \n",
+ " 60 Ag 14.459597407699105 \n",
+ " 73 Ag 14.459597407699105 \n",
+ " 84 Ag 14.459597407699105 \n",
+ " 103 Ag 14.459597407699105 \n",
+ " 28 Ag 14.459597545716329 \n",
+ " 40 Ag 14.459597545716329 \n",
+ " 107 Ag 14.459597545716329 \n",
+ " 108 Ag 14.459597545716329 \n",
+ " 82 Ag 14.459597649002623 \n",
+ " 90 Ag 14.459597649002623 \n",
+ " 77 Ag 14.459597678147249 \n",
+ " 87 Ag 14.459597678147249 \n",
+ " 9 Ag 14.459597741966373 \n",
+ " 22 Ag 14.459597741966373 \n",
+ " 72 Ag 14.459597853178870 \n",
+ " 45 Ag 14.459597853178870 \n",
+ " 14 Ag 14.459598337843998 \n",
+ " 20 Ag 14.459598337843998 \n",
+ " 42 Ag 15.457959936408823 \n",
+ " 62 Ag 15.457959936408823 \n",
+ " 3 Ag 15.457960527564342 \n",
+ " 75 Ag 16.395641809697207 \n",
+ " 48 Ag 16.395641809697207 \n",
+ " 31 Ag 16.395642450710369 \n",
+ " 37 Ag 16.395642450710369 \n",
+ " 105 Ag 16.395642450710369 \n",
+ " 109 Ag 16.395642450710369 \n",
+ " 86 Ag 16.395642567503458 \n",
+ " 79 Ag 16.395642567503458 \n",
+ " 83 Ag 16.395642867424883 \n",
+ " 96 Ag 16.395642867424883 \n",
+ " 2 Ag 16.395643138631122 \n",
+ " 4 Ag 16.395643138631122 \n",
+ " 6 Ag 16.395643239096621 \n",
+ " 61 Ag 16.395643318697640 \n",
+ " 44 Ag 16.395643318697640 \n",
+ " 97 Ag 17.282523936995549 \n",
+ " 81 Ag 17.282523936995549 \n",
+ " 46 Ag 17.282524344946953 \n",
+ " 71 Ag 17.282524344946953 \n",
+ " 7 Ag 17.282525494884688 \n",
+ " 5 Ag 17.282525494884688 \n",
+ " 104 Ag 18.126064004248224 \n",
+ " 106 Ag 18.126064004248224 \n",
+ " 10 Ag 18.126064279645082 \n",
+ " 35 Ag 18.126064279645082 \n",
+ " 33 Ag 18.126064552066754 \n",
+ " 18 Ag 18.126064552066754 \n",
+ " 32 Ag 18.126065618176494 \n",
+ " 8 Ag 18.126065618176494 \n",
+ " 78 Ag 18.126065954366222 \n",
+ " -----------------------------------------------------------------\n",
+ " 1 Sb 0.000000\n",
+ " 53 Ag 5.465214\n",
+ " 55 Ag 5.465215\n",
+ " 59 Ag 5.465215\n",
+ " 67 Ag 5.465214\n",
+ " 68 Ag 5.465213\n",
+ " 69 Ag 5.465215\n",
+ " 70 Ag 5.465213\n",
+ " 92 Ag 5.465213\n",
+ " 102 Ag 5.465213\n",
+ " 25 Ag 7.728980\n",
+ " 54 Ag 7.728981\n",
+ " 65 Ag 7.728981\n",
+ " 13 Ag 9.466030\n",
+ " 17 Ag 9.466029\n",
+ " 26 Ag 9.466029\n",
+ " 30 Ag 9.466029\n",
+ " 49 Ag 9.466030\n",
+ " 52 Ag 9.466030\n",
+ " 56 Ag 9.466029\n",
+ " 57 Ag 9.466028\n",
+ " 64 Ag 9.466029\n",
+ " 76 Ag 9.466028\n",
+ " 88 Ag 9.466029\n",
+ " 91 Ag 9.466029\n",
+ " 94 Ag 9.466029\n",
+ " 95 Ag 9.466028\n",
+ " 99 Ag 9.466028\n",
+ " 12 Ag 10.930430\n",
+ " 21 Ag 10.930430\n",
+ " 29 Ag 10.930429\n",
+ " 38 Ag 10.930429\n",
+ " 50 Ag 10.930429\n",
+ " 58 Ag 10.930427\n",
+ " 74 Ag 10.930427\n",
+ " 93 Ag 10.930429\n",
+ " 100 Ag 10.930429\n",
+ " 11 Ag 12.220591\n",
+ " 23 Ag 12.220591\n",
+ " 24 Ag 12.220590\n",
+ " 27 Ag 12.220590\n",
+ " 41 Ag 12.220591\n",
+ " 43 Ag 12.220591\n",
+ " 47 Ag 12.220590\n",
+ " 51 Ag 12.220591\n",
+ " 63 Ag 12.220591\n",
+ " 66 Ag 12.220590\n",
+ " 80 Ag 12.220591\n",
+ " 98 Ag 12.220591\n",
+ " 16 Ag 13.386985\n",
+ " 34 Ag 13.386985\n",
+ " 89 Ag 13.386986\n",
+ " 9 Ag 14.459598\n",
+ " 14 Ag 14.459598\n",
+ " 15 Ag 14.459597\n",
+ " 19 Ag 14.459597\n",
+ " 20 Ag 14.459598\n",
+ " 22 Ag 14.459598\n",
+ " 28 Ag 14.459598\n",
+ " 36 Ag 14.459597\n",
+ " 39 Ag 14.459597\n",
+ " 40 Ag 14.459598\n",
+ " 45 Ag 14.459598\n",
+ " 60 Ag 14.459597\n",
+ " 72 Ag 14.459598\n",
+ " 73 Ag 14.459597\n",
+ " 77 Ag 14.459598\n",
+ " 82 Ag 14.459598\n",
+ " 84 Ag 14.459597\n",
+ " 85 Ag 14.459597\n",
+ " 87 Ag 14.459598\n",
+ " 90 Ag 14.459598\n",
+ " 101 Ag 14.459597\n",
+ " 103 Ag 14.459597\n",
+ " 107 Ag 14.459598\n",
+ " 108 Ag 14.459598\n",
+ " 3 Ag 15.457961\n",
+ " 42 Ag 15.457960\n",
+ " 62 Ag 15.457960\n",
+ " 2 Ag 16.395643\n",
+ " 4 Ag 16.395643\n",
+ " 6 Ag 16.395643\n",
+ " 31 Ag 16.395642\n",
+ " 37 Ag 16.395642\n",
+ " 44 Ag 16.395643\n",
+ " 48 Ag 16.395642\n",
+ " 61 Ag 16.395643\n",
+ " 75 Ag 16.395642\n",
+ " 79 Ag 16.395643\n",
+ " 83 Ag 16.395643\n",
+ " 86 Ag 16.395643\n",
+ " 96 Ag 16.395643\n",
+ " 105 Ag 16.395642\n",
+ " 109 Ag 16.395642\n",
+ " 5 Ag 17.282525\n",
+ " 7 Ag 17.282525\n",
+ " 46 Ag 17.282524\n",
+ " 71 Ag 17.282524\n",
+ " 81 Ag 17.282524\n",
+ " 97 Ag 17.282524\n",
+ " 8 Ag 18.126066\n",
+ " 10 Ag 18.126064\n",
+ " 18 Ag 18.126065\n",
+ " 32 Ag 18.126066\n",
+ " 33 Ag 18.126065\n",
+ " 35 Ag 18.126064\n",
+ " 78 Ag 18.126066\n",
+ " 104 Ag 18.126064\n",
+ " 106 Ag 18.126064\n",
+ " 1 Sb 0.000000\n",
+ " 102 Ag 5.465213\n",
+ " 65 Ag 7.728981\n",
+ " 99 Ag 9.466028\n",
+ " 100 Ag 10.930429\n",
+ " 98 Ag 12.220591\n",
+ " 89 Ag 13.386986\n",
+ " 108 Ag 14.459598\n",
+ " 62 Ag 15.457960\n",
+ " 109 Ag 16.395642\n",
+ " 97 Ag 17.282524\n",
+ " 106 Ag 18.126064\n",
+ " \n",
+ " irho = 2 entering vxc to calculate energy dependent exchange\n",
+ " energy dependent vcon = (-0.22067541407541907,0.19269106166255393) at energy 3.3073999999999999 \n",
+ " check ionization potential: 2.9730983028839546 \n",
+ " \n",
+ " \n",
+ " value of the mean free path:\n",
+ " -----------------------------------------------------------------\n",
+ " average mean free path in the cluster : mfp = 5.52069 angstrom at energy 3.30740\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " \n",
+ " calculating atomic t-matrix elements atm(n)\n",
+ " check orthogonality between core and continuum state\n",
+ " scalar product between core and continuum state = (-1.8684401029562587,8.82813425284941500E-002)\n",
+ " --- sqrt(xe/pi) = (0.77344747963235860,-1.05509171642046064E-002)\n",
+ "\n",
+ "\n",
+ " ** phagen terminated normally ** \n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "calc = MSSPEC(spectroscopy='PED', algorithm='inversion', txt='msspec.log')\n",
+ "calc.set_atoms(cluster)\n",
+ "\n",
+ "calc.source_parameters.theta = 0\n",
+ "calc.source_parameters.phi = 0\n",
+ "\n",
+ "calc.detector_parameters.angular_acceptance = 1\n",
+ "calc.detector_parameters.average_sampling = 'low'\n",
+ "\n",
+ "calc.muffintin_parameters.interstitial_potential = 0\n",
+ "data = calc.get_phi_scan(level='4d', theta=40, phi=np.linspace(0,240,121), kinetic_energy=45)\n",
+ "\n",
+ "# normalize data between [0,1]\n",
+ "dset = data[0]\n",
+ "dset.cross_section -= dset.cross_section.min()\n",
+ "dset.cross_section /= dset.cross_section.max()\n",
+ "\n",
+ "# Add experimental data points in the dataset\n",
+ "x, y = np.loadtxt('data.txt').T\n",
+ "dset.add_columns(experiment=y)\n",
+ "\n",
+ "# Add points to view\n",
+ "view = dset.views[0]\n",
+ "view.select('phi', 'experiment', legend='Exp. data')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "bfedff51-12cc-43c0-b6ab-8e17038d271e",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data[0].views[0].plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0332a072-90b0-4d3e-8ee9-06cd16bb2608",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Try to change the source direction and the inner potential of Ag to better match the experiment...\n",
+ "\n",
+ "```{note}\n",
+ "The cluster is smaller than it should for size convergence, but the calculation would take too much memory for this example\n",
+ "```\n",
+ "\n",
+ "Unfold to see the answer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9509b7a3-d7e5-4748-9d30-250bb9e87ab2",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n"
+ ]
+ }
+ ],
+ "source": [
+ "calc = MSSPEC(spectroscopy='PED', algorithm='inversion', txt='msspec.log')\n",
+ "calc.set_atoms(cluster)\n",
+ "\n",
+ "calc.source_parameters.theta = 22.5\n",
+ "calc.source_parameters.phi = 0\n",
+ "\n",
+ "calc.detector_parameters.angular_acceptance = 1\n",
+ "calc.detector_parameters.average_sampling = 'low'\n",
+ "\n",
+ "calc.muffintin_parameters.interstitial_potential = 10.2\n",
+ "data = calc.get_phi_scan(level='4d', theta=40, phi=np.linspace(0,240,121), kinetic_energy=45)\n",
+ "\n",
+ "# normalize data between [0,1]\n",
+ "dset = data[0]\n",
+ "dset.cross_section -= dset.cross_section.min()\n",
+ "dset.cross_section /= dset.cross_section.max()\n",
+ "\n",
+ "# Add experimental data points in the dataset\n",
+ "x, y = np.loadtxt('data.txt').T\n",
+ "dset.add_columns(experiment=y)\n",
+ "\n",
+ "# Add points to view\n",
+ "view = dset.views[0]\n",
+ "view.select('phi', 'experiment', legend='Exp. data')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "9dc38bd9-8788-40fc-b0d3-448b341a0d51",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "hide-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Unfold to see the answer\n",
+ "\n",
+ "# value hidden in refernce [] of the paper...\n",
+ "# The former SA73 Beamline in LURE\n",
+ "calc.source_parameters.theta = 22.5\n",
+ "\n",
+ "# Inner potentials are usually between 10 and 20 V. For Ag\n",
+ "calc.muffintin_parameters.interstitial_potential = 10.2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "009278b2-8b87-4579-8ab6-7475074acfd7",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data[0].views[0].plot();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9402f3e-bea3-4a05-9547-7187b8663867",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/msspecbook/Activity02/PhysRevB.55.R16061.pdf b/msspecbook/Activity02/PhysRevB.55.R16061.pdf
new file mode 100644
index 0000000..9625e08
Binary files /dev/null and b/msspecbook/Activity02/PhysRevB.55.R16061.pdf differ
diff --git a/msspecbook/Activity02/SbAg.py b/msspecbook/Activity02/SbAg.py
new file mode 100644
index 0000000..ed144fc
--- /dev/null
+++ b/msspecbook/Activity02/SbAg.py
@@ -0,0 +1,47 @@
+from ase.build import bulk
+from msspec.calculator import MSSPEC
+from msspec.utils import hemispherical_cluster, get_atom_index, cut_plane
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+Ag = bulk('Ag', cubic=True)
+Ag.rotate((1,1,1), (0,0,1), rotate_cell=True)
+Ag.rotate(15, 'z', rotate_cell=True)
+
+cluster = hemispherical_cluster(Ag, diameter=20, emitter_plane=0)
+
+cluster = cut_plane(cluster, z=-4.8)
+cluster.emitter = get_atom_index(cluster, 0,0,0)
+cluster[cluster.emitter].symbol = 'Sb'
+
+cluster.edit()
+
+calc = MSSPEC(spectroscopy='PED', algorithm='inversion')
+calc.set_atoms(cluster)
+
+calc.source_parameters.theta = 0
+calc.source_parameters.phi = 0
+
+calc.detector_parameters.angular_acceptance = 1
+calc.detector_parameters.average_sampling = 'low'
+
+calc.muffintin_parameters.interstitial_potential = 0
+data = calc.get_phi_scan(level='4d', theta=40, phi=np.linspace(0,240,121), kinetic_energy=45)
+
+# normalize data between [0,1]
+dset = data[0]
+dset.cross_section -= dset.cross_section.min()
+dset.cross_section /= dset.cross_section.max()
+
+# Add experimental data points in the dataset
+x, y = np.loadtxt('data.txt').T
+dset.add_columns(experiment=y)
+
+# Add points to view
+view = dset.views[0]
+view.select('phi', 'experiment', legend='Exp. data')
+
+data.view()
+
+calc.shutdown()
diff --git a/msspecbook/Activity02/data.txt b/msspecbook/Activity02/data.txt
new file mode 100644
index 0000000..19f61b8
--- /dev/null
+++ b/msspecbook/Activity02/data.txt
@@ -0,0 +1,121 @@
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diff --git a/msspecbook/Activity03/Activity03.ipynb b/msspecbook/Activity03/Activity03.ipynb
new file mode 100644
index 0000000..7abadfc
--- /dev/null
+++ b/msspecbook/Activity03/Activity03.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2aebc88d-0bb4-4d56-b7f6-977a66814229",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# Activity 3: Adsorbates and the single scattering approach\n",
+ "\n",
+ "Photoelectron diffraction is widely used to study the adsorption of atoms or molecules on a crystalline surface. Photoelectrons from adsorbates are scattered by the underlying surface, carrying information about the adsorption site, bond length and molecule orientation…. Thanks to a simulation, such information becomes quantitative with a high degree of accuracy.\n",
+ "\n",
+ "Calculations of the multiple scattering using matrix inversion have the great advantage of being exact, including all scattering paths. On the other hand, memory consumption soon becomes a problem as the kinetic energy and number of atoms to be considered increase. As an approximation, it is possible to only consider a single scattering from the emitter to any atom in the cluster. This approximation is extremely computationally fast and gives satisfactory results for adsorbates. We’ll see later that this approach is rather too simplistic for most cases.\n",
+ "\n",
+ "## Oxygen on Rh(001)\n",
+ "In a paper published in 1998, T. Gerber et al. used the quite high backscattering factor of Rhodium atoms to probe the distance of Oxygen atoms adsorbed on a Rhodium surface. Some electrons coming from Oxygen atoms are ejected toward the Rhodium surface. They are then backscattered and interfere with the direct signal comming from Oxygen atoms (see the figure below). They demonstrated both experimentally and numerically with a sinle scattering computation that this lead to a very accurate probe of adsorbed species that can be sensitive to bond length changes of the order of {math}`\\pm 0.02 \\mathring{A}`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4cd32cd-b480-44b5-af38-b38e5979ce00",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ ":::{figure-md} RhO-fig\n",
+ "![\"RhO\"](\"RhO_fig0.jpg\")
\n",
+ "\n",
+ "Interferences produced by the backscattering effect\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f6bcc08-a54c-424a-a20c-cbdd0ce13ae2",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "By using the Atoms class of the ase package, try to build a O-Rh chain where atoms are 0.4 nm apart\n",
+ "\n",
+ "*unfold to see the answer*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "55929980-7394-4a60-b554-376adce57dbf",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "hide-cell"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " ASE atomic visualization\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
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+ " \n",
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+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from ase import Atoms\n",
+ "from ase.visualize import view\n",
+ "\n",
+ "# Create an atomic chain O-Rh\n",
+ "cluster = Atoms(['O', 'Rh'], positions = [(0,0,0), (0,0,4.)])\n",
+ "view(cluster, viewer='x3d')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "736d6d08-930e-4b4f-a3fd-599ef8463035",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "As previously, we create a calculator, we attach our 2 atoms cluster to this calculator and we define the first atom in the chain as the emitter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b1f01f60-7d46-49f3-9653-57e6f8f2be02",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-output"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "'NoneType' object has no attribute 'upper'\n",
+ "'integrals' is ignored since the 'spinpol' global parameter is set to False. Enable spin polarization in the constructor of your Calculator if you want to use this option.\n",
+ "The sample temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "The sample Debye temperature was set, but will be ignored since 'use_debye_model' parameter is False.\n",
+ "You must define the absorber before setting the atoms to thecalculator.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from msspec.calculator import MSSPEC\n",
+ "\n",
+ "calc = MSSPEC(spectroscopy='PED')\n",
+ "calc.set_atoms(cluster)\n",
+ "cluster.emitter = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21763c63-9bd1-4e80-944a-9cf313894b89",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "We use the *get_scattering_factors* method to compute the scattering factors at 723 eV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "7706c659-5b62-427b-94ca-d3e890582e46",
+ "metadata": {
+ "collapsed": true,
+ "editable": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "remove-output"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " _________________________________________________________________\n",
+ "\n",
+ " PHAGEN\n",
+ " _________________________________________________________________\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " parameters for this xpd calculation:\n",
+ " -----------------------------------------------------------------\n",
+ " edge= k \n",
+ " potype= hedin norman= stdcrm absorber= 1\n",
+ " coor= angs emin= 53.14 Ry emax= 53.14 Ry\n",
+ " delta= 0.300 Ry gamma= 0.00 Ry eftri= 0.000 Ry\n",
+ " cip= 0.00 Ry lmaxt= 19 charelx: ex\n",
+ " ionization state : neutral\n",
+ " relativistic corrections of type: nr\n",
+ " final state potential generated internally\n",
+ "\n",
+ "\n",
+ " Computes the T-matrix and radial matrix elements \n",
+ "\n",
+ "\n",
+ " coordinates in angstroms Radii\n",
+ " -----------------------------------------------------------------\n",
+ " O 8 0.0000 0.0000 0.0000 0.0000 0.0000\n",
+ " Rh 45 0.0000 0.0000 4.0000 0.0000 0.0000\n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ "\n",
+ " ** enter calphas **\n",
+ " ---\n",
+ " total energy for atom in ground state \n",
+ " total energy for atom with a hole in k edge\n",
+ " calculated ionization energy for edge k = 545.41802450847172 eV\n",
+ " ---\n",
+ " calculated ionization potential (ryd) = 40.104265383081511 \n",
+ " ---\n",
+ " \n",
+ " \n",
+ " symmetrizing coordinates... \n",
+ "\n",
+ "\n",
+ " symmetrized atomic coordinates of cluster \n",
+ "\n",
+ " position\n",
+ " atom no. x y z eq\n",
+ "\n",
+ " 1 osph 0 0.0000 0.0000 0.0000 0\n",
+ " 2 O 8 0.0000 0.0000 -6.4179 0\n",
+ " 3 Rh 45 0.0000 0.0000 1.1410 0\n",
+ "\n",
+ " computing muffin tin potential and phase shifts\n",
+ " generating core state wavefunction \n",
+ " generating final potential (complex hedin-lundqvist exchange) \n",
+ " MT radii for Hydrogen atoms determined by stdcrm unless other options are specified\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " i rs(i) i=1,natoms \n",
+ " 1 10.94 2 3.04 3 4.52\n",
+ " N.B.: Order of atoms as reshuffled by symmetry routines \n",
+ " -----------------------------------------------------------------\n",
+ "\n",
+ " input value for coulomb interst. potential = -0.69999999999999996 \n",
+ " and interstitial rs = 3.0000000000000000 \n",
+ " lower bound for coulomb interst. potential = -9.0265383897774280E-003\n",
+ " and for interst. rs = 7.1645607045202233 \n",
+ "\n",
+ " lmax assignment based on l_max = r_mt * k_e + 2\n",
+ " where e is the running energy\n",
+ " optimal lmax chosen according to the running energy e for each atom\n",
+ "\n",
+ "\n",
+ " number of centers= 2\n",
+ "\n",
+ " starting potentials and/or charge densities written to file 13\n",
+ " symmetry information generated internally\n",
+ " symmetry information written to file 14\n",
+ "\n",
+ "\n",
+ " core initial state of type: 1s1/2\n",
+ "\n",
+ " fermi level = -0.06986\n",
+ "\n",
+ "\n",
+ " generating t_l (for030) and atomic cross section (for050)\n",
+ " corewf: fnisx = 0.99960409001060369 \n",
+ " writing atomic orbital energies\n",
+ " orbital energy (Ryd eV) 1s -41.398732282531711 -563.22973375300819 \n",
+ " orbital energy (Ryd eV) 2s -2.5058958613249085 -34.092712046217287 \n",
+ " orbital energy (Ryd eV) 2p1/2 -1.2046159289785867 -16.388799162324261 \n",
+ " orbital energy (Ryd eV) 2p3/2 -1.2524515821366835 -17.039603201642745 \n",
+ "\n",
+ " using overlapped potential to search for core states of photoabsorber\n",
+ "\n",
+ " calculating non relativistic core states\n",
+ " ------------------------------\n",
+ " energy of core state = -42.185368888826872 for orbital =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ "\n",
+ " calculating relativistic core states\n",
+ " energy of core state = -42.238351573130871 for orb =1s \n",
+ " n. of zeros found: 0 expected: 0\n",
+ " -------------------------------\n",
+ " density of the valence charge (au^{-3} 2.7365838297823178E-003\n",
+ " rs_v corresponding to valence density (au) 4.4350742743021581 \n",
+ " valence plasmon energy (in eV) 4.4646221434201410 \n",
+ "\n",
+ " gamma = 0.000000 rsint = 15.696380\n",
+ "\n",
+ " check in subroutine cont\n",
+ " order of neighb. -- symb. -- dist. from absorber\n",
+ " \n",
+ " 2 Rh 7.5589045018313126 \n",
+ " -----------------------------------------------------------------\n",
+ " 1 O 0.000000\n",
+ " 2 Rh 7.558905\n",
+ " 1 O 0.000000\n",
+ " 2 Rh 7.558905\n",
+ " \n",
+ " irho = 2 entering vxc to calculate energy dependent exchange\n",
+ " energy dependent vcon = (-6.82152172101615703E-003,1.67511733431198982E-002) at energy 53.139499999999998 \n",
+ " check ionization potential: 40.104265383081511 \n",
+ " \n",
+ " \n",
+ " value of the mean free path:\n",
+ " -----------------------------------------------------------------\n",
+ " average mean free path in the cluster : mfp = 30.25827 angstrom at energy 53.13950\n",
+ "\n",
+ " -----------------------------------------------------------------\n",
+ " \n",
+ " calculating atomic t-matrix elements atm(n)\n",
+ " check orthogonality between core and continuum state\n",
+ " scalar product between core and continuum state = (-4.41068611043292647E-002,-8.76975637885601326E-004)\n",
+ " --- sqrt(xe/pi) = (1.5233278454390002,-1.20034309405237769E-004)\n",
+ "\n",
+ "\n",
+ " ** phagen terminated normally ** \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "**********************************************************************************\n",
+ "********************* *********************\n",
+ "********************* *********************\n",
+ "********************* spec input file *********************\n",
+ "********************* *********************\n",
+ "********************* *********************\n",
+ "**********************************************************************************\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " CRYSTAL STRUCTURE : \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " 1.000 0 A,IBAS\n",
+ "\n",
+ "\n",
+ "\n",
+ " TYPE OF CALCULATION : POLAR PHOTOELECTRON DIFFRACTION\n",
+ "\n",
+ "\n",
+ "\n",
+ " TYPE OF CALCULATION : SCATTERING FACTOR\n",
+ "\n",
+ "\n",
+ "\n",
+ " PhD EXPERIMENTAL PARAMETERS : \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " 0 0 0 ISPIN,IDICHR,IPOL\n",
+ " 1s 2 0 LI,S-O,INITL,I_SO\n",
+ " 0 1 0 1 IPHI,ITHETA,IE,IFTHET\n",
+ " 1 577 1 577 NPHI,NTHETA,NE,NFTHET\n",
+ " 0.00 -360.00 723.00 0.500 PHI0,THETA0,E0,R0\n",
+ " 0.00 0.00 723.00 -1.000 PHI1,THETA1,EFIN,R1\n",
+ " -55.00 0.00 1253.60 THLUM,PHILUM,ELUM\n",
+ " 1 0 0.00 0 IMOD,IMOY,ACCEPT,ICHKDIR\n",
+ "\n",
+ "\n",
+ "\n",
+ " CALCULATION PARAMETERS : \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " 0 3 0 0 NO,NDIF,ISPHER,I_GR\n",
+ " 0 1 1.000 0.000 I_REN,N_REN,REN_R,REN_I\n",
+ " 0 0 0 0 ISFLIP,IR_DIA,ITRTL,I_TEST\n",
+ " 1 1 NEMET,IEMET(NEMET)\n",
+ " 0 1 100 0.00 ISOM,NONVOL,NPATH,VINT\n",
+ " 0 1 0 0 IFWD,NTHOUT,I_NO,I_RA\n",
+ " 0 20.00 0 20.00 N_RA(NAT),THFWD(NAT),IBWD(NAT),THBWD(NAT)\n",
+ " 0 20.00 0 20.00 N_RA(NAT),THFWD(NAT),IBWD(NAT),THBWD(NAT)\n",
+ " 0 2 0.0100 1 IPW,NCUT,PCTINT,IPP\n",
+ " 0 10.00 ANG ILENGTH,RLENGTH,UNLENGTH\n",
+ " 0 1 1 1 IDWSPH,ISPEED,IATT,IPRINT\n",
+ " 0 420.000 293.000 1.20 IDCM,TD,T,RSJ\n",
+ " 2 15.00 ILPM,XLPM0\n",
+ " 0.00000 0.00000 UJ2(NAT) : SUBSTRATE\n",
+ "\n",
+ "\n",
+ "\n",
+ " <<<<<<<<<< AS THE CALCULATION HAS MORE THAN 250 POINTS, SOME OUTPUTS HAVE BEEN SUPRESSED >>>>>>>>>>\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " MAXIMAL VALUES OF L FOR THE 2 PROTOTYPICAL ATOMS : \n",
+ "\n",
+ "\n",
+ " 24 35\n",
+ "\n",
+ "\n",
+ " ---> CHECK FOR ZEROS IN THE TL FILE TO REDUCE THE AMOUNT OF COMPUTING :\n",
+ "\n",
+ "\n",
+ " (ONLY THE MATRIX ELEMENTS NON ZERO TO THE FIRST 9 DECIMAL DIGITS ARE KEPT)\n",
+ "\n",
+ "\n",
+ " ENERGY POINT No 1\n",
+ "\n",
+ " PROTOTYPICAL ATOM No 1 INITIAL LMAX = 24 FINAL LMAX = 24\n",
+ " PROTOTYPICAL ATOM No 2 INITIAL LMAX = 35 FINAL LMAX = 35\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " CALCULATION OF THE SCATTERING FACTOR DONE\n"
+ ]
+ }
+ ],
+ "source": [
+ "# compute the scattering factor\n",
+ "data = calc.get_scattering_factors(level='1s', kinetic_energy=723)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14d39e21-eee0-411b-8347-25e932075c83",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "How large is the backscattering factor of Rhodium with respect to that of Oxygen ?\n",
+ "\n",
+ "```{toggle}\n",
+ "\n",
+ ":::{figure-md} SF-fig\n",
+ "![\"Scattering](\"RhO_fig1.jpg\")
\n",
+ "\n",
+ "Polar representation of the scattering factor\n",
+ ":::\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d18ecc3-27a7-4f4d-98ae-7ff1c309bbfb",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ff0c14f-b2f6-47c6-91f4-9edc7ee7260d",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "```{toggle}\n",
+ "\n",
+ ":::{figure-md} stereo-fig\n",
+ "![\"Scattering](\"RhO_fig2b.gif\")
\n",
+ "\n",
+ "Stereographic projections of O(1s) emission at {math}`E_0` = 723 eV for an oxygen atom \n",
+ "on top of a fcc site of 3 Rh atoms at various altitudes {math}`z_0`\n",
+ ":::\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e0b2cc9-bfd6-4abf-81b0-629c4d122141",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/msspecbook/Activity03/RhO_fig0.jpg b/msspecbook/Activity03/RhO_fig0.jpg
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diff --git a/msspecbook/Activity03/RhO_fig1.jpg b/msspecbook/Activity03/RhO_fig1.jpg
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diff --git a/msspecbook/Activity03/RhO_fig2a.jpg b/msspecbook/Activity03/RhO_fig2a.jpg
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diff --git a/msspecbook/_config.yml b/msspecbook/_config.yml
new file mode 100644
index 0000000..96686b9
--- /dev/null
+++ b/msspecbook/_config.yml
@@ -0,0 +1,32 @@
+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: My sample book
+author: The Jupyter Book Community
+logo: logo.png
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+ execute_notebooks: force
+
+# Define the name of the latex output file for PDF builds
+latex:
+ latex_documents:
+ targetname: book.tex
+
+# Add a bibtex file so that we can create citations
+bibtex_bibfiles:
+ - references.bib
+
+# Information about where the book exists on the web
+repository:
+ url: https://github.com/executablebooks/jupyter-book # Online location of your book
+ path_to_book: docs # Optional path to your book, relative to the repository root
+ branch: master # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+ use_issues_button: False
+ use_repository_button: False
diff --git a/msspecbook/_toc.yml b/msspecbook/_toc.yml
new file mode 100644
index 0000000..846eb4e
--- /dev/null
+++ b/msspecbook/_toc.yml
@@ -0,0 +1,9 @@
+# Table of contents
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: intro
+chapters:
+- file: Activity01/Activity01
+- file: Activity02/Activity02
+- file: Activity03/Activity03
diff --git a/msspecbook/intro.md b/msspecbook/intro.md
new file mode 100644
index 0000000..f8cdc73
--- /dev/null
+++ b/msspecbook/intro.md
@@ -0,0 +1,11 @@
+# Welcome to your Jupyter Book
+
+This is a small sample book to give you a feel for how book content is
+structured.
+It shows off a few of the major file types, as well as some sample content.
+It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information.
+
+Check out the content pages bundled with this sample book to see more.
+
+```{tableofcontents}
+```
diff --git a/msspecbook/logo.png b/msspecbook/logo.png
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diff --git a/msspecbook/requirements.txt b/msspecbook/requirements.txt
new file mode 100644
index 0000000..7e821e4
--- /dev/null
+++ b/msspecbook/requirements.txt
@@ -0,0 +1,3 @@
+jupyter-book
+matplotlib
+numpy