{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# useful to autoreload the module without restarting the kernel\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from mppi import InputFiles as I, Calculators as C, Utilities as U\n", "import matplotlib.pyplot as plt\n", "import os\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "omp = 1\n", "mpi = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of the band structure with QuantumESPRESSO" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "run_dir = 'Pw_bands'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We compute the band structure of Silicon and Gallium arsenide using the tools for QuantumESPRESSO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Band structure of GaAs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step consists in a scf computation " ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "scf_prefix = 'gaas_scf'\n", "nscf_prefix = 'gaas_nscf'\n", "bands_prefix = 'gaas_bands'" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "inp = I.PwInput(file='IO_files/gaas_scf.in')\n", "inp.set_prefix(scf_prefix)\n", "inp.set_energy_cutoff(60)\n", "inp.set_kpoints(points=[6,6,6])\n", "#inp" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a parallel QuantumESPRESSO calculator with scheduler direct\n" ] } ], "source": [ "code = C.QeCalculator(omp=omp,mpi=mpi,skip=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "run 0 command: cd Pw_bands; mpirun -np 4 pw.x -inp gaas_scf.in > gaas_scf.log\n", "run0_is_running: True \n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/gaas_scf.save/data-file-schema.xml']}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[scf_prefix])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before performing the bands computation we make an nscf computation on a regular kpath, this will be useful as input\n", "for other tutorials of the package." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "inp.set_nscf(8,force_symmorphic=True) #use force_symmorphic = True since this computation is used as input of a Yambo computation later\n", "inp.set_prefix(nscf_prefix)\n", "inp.set_kpoints(points=[8,8,8])\n", "#inp" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Copy source_dir Pw_bands/gaas_scf.save in the Pw_bands/gaas_nscf.save\n", "run 0 command: cd Pw_bands; mpirun -np 4 pw.x -inp gaas_nscf.in > gaas_nscf.log\n", "run0_is_running: True \n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/gaas_nscf.save/data-file-schema.xml']}" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[nscf_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we perform the bands computation specifying the kpoints on a path. \n", "\n", "To define the path we write the coordinates of the high symmetry points (using the tpiba_b type of\n", "pw) and we make usage of the function build_kpath" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "G = [0.,0.,0.]\n", "X = [0.,0.,1.]\n", "L = [0.5,0.5,0.5]\n", "W = [1.0,0.5,0.]\n", "K = [0.,1.,1.]\n", "\n", "# useful to label the high-sym point on the path\n", "high_sym = {'X':X,'L':L,'G':G,'K':K,'W':W} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The high symmetry points for some lattice structure are also written in the Constants module of the Utilities, for instance" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'G': [0.0, 0.0, 0.0],\n", " 'X': [0.0, 0.0, 1.0],\n", " 'L': [0.5, 0.5, 0.5],\n", " 'K': [0.0, 1.0, 1.0],\n", " 'W': [1.0, 0.5, 0.0]}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U.high_sym_fcc" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.5, 0.5, 0.5, 30],\n", " [0.0, 0.0, 0.0, 30],\n", " [0.0, 0.0, 1.0, 30],\n", " [0.0, 1.0, 1.0, 30],\n", " [0.0, 0.0, 0.0, 0]]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "klist = U.build_kpath(L,G,X,K,G,numstep=30)\n", "klist" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "inp.set_bands(8)\n", "inp.set_prefix(bands_prefix)\n", "inp.set_kpoints(type='tpiba_b',klist=klist)\n", "#inp" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/gaas_bands.save already exsists. Source folder Pw_bands/gaas_scf.save not copied\n", "Skip the run of gaas_bands\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/gaas_bands.save/data-file-schema.xml']}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_gaas = code.run(inputs=[inp],run_dir=run_dir,names=[bands_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')\n", "results_gaas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once that the computation is over we can create an instance of BandStructure. The gap can be set when we init the class" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apply a scissor of 1.1945698886779335 eV\n" ] } ], "source": [ "bands_gaas = U.BandStructure.from_Pw(results_gaas['output'][0],high_sym,set_gap=1.42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class contains some methods that return the bands, the kpath or the position of the high symmetry points on the path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It contains also a plot method that show the band structure" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.title('GaAs bands')\n", "plt.ylim(-8,5)\n", "bands_gaas.plot(plt,selection=[1,2,3,4,5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Band structure of GaAs with spin-orbit coupling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We repeat the computation of the band structure of GaAs including the spin-orbit. To do so we use a full relativistic pseudopotential\n", "that is able to include the spin-orbit. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step consists in a scf computation " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "scf_prefix = 'gaas_scf_so'\n", "nscf_prefix = 'gaas_nscf_so'\n", "bands_prefix = 'gaas_bands_so'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'control': {'verbosity': \"'high'\",\n", " 'pseudo_dir': \"'../pseudos'\",\n", " 'calculation': \"'scf'\",\n", " 'prefix': \"'gaas_scf_so'\"},\n", " 'system': {'occupations': \"'fixed'\",\n", " 'ibrav': '2',\n", " 'celldm(1)': '10.677',\n", " 'ntyp': '2',\n", " 'nat': '2',\n", " 'ecutwfc': 80,\n", " 'lspinorb': '.true.',\n", " 'noncolin': '.true.'},\n", " 'electrons': {'conv_thr': '1e-08'},\n", " 'ions': {},\n", " 'cell': {},\n", " 'atomic_species': {'Ga': ['1.0', 'Ga_rel.pz-rrkj3.UPF'],\n", " 'As': ['1.0', 'As_rel.pz-rrkj3.UPF']},\n", " 'atomic_positions': {'type': 'alat',\n", " 'values': [['Ga', [0.0, 0.0, 0.0]], ['As', [0.25, 0.25, 0.25]]]},\n", " 'kpoints': {'type': 'automatic',\n", " 'values': ([6.0, 6.0, 6.0], [0.0, 0.0, 0.0])},\n", " 'cell_parameters': {},\n", " 'file': 'IO_files/gaas_scf.in'}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inp = I.PwInput('IO_files/gaas_scf.in')\n", "inp.set_prefix(scf_prefix)\n", "inp.set_energy_cutoff(80)\n", "inp.set_spinorbit()\n", "inp.set_kpoints(points = [6.,6.,6.])\n", "inp.add_atom('Ga','Ga_rel.pz-rrkj3.UPF')\n", "inp.add_atom('As','As_rel.pz-rrkj3.UPF')\n", "inp" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a parallel QuantumESPRESSO calculator with scheduler direct\n" ] } ], "source": [ "code = C.QeCalculator(omp=omp,mpi=mpi,skip=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skip the run of gaas_scf_so\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/gaas_scf_so.save/data-file-schema.xml']}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[scf_prefix])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before performing the bands computation we make an nscf computation on a regular kpath, this will be useful for the bands analysis \n", "using the ypp tools. \n", "\n", "We compute 12 bands since in this case the spin degeneracy is equal to 1 and there 8 occupied bands" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "inp.set_nscf(12,force_symmorphic=True) #use force_symmorphic = True since this computation is used as input of a Yambo computation later\n", "inp.set_prefix(nscf_prefix)\n", "#inp" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/gaas_nscf_so.save already exsists. Source folder Pw_bands/gaas_scf_so.save not copied\n", "Skip the run of gaas_nscf_so\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/gaas_nscf_so.save/data-file-schema.xml']}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[nscf_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we perform the bands computation specifying the kpoints on a path. \n", "\n", "To define the path we write the coordinates of the high symmetry points (using the tpiba_b type of\n", "pw) and we make usage of the function build_kpath" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "hsp = U.high_sym_fcc" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.5, 0.5, 0.5, 30],\n", " [0.0, 0.0, 0.0, 30],\n", " [0.0, 0.0, 1.0, 30],\n", " [0.0, 1.0, 1.0, 30],\n", " [0.0, 0.0, 0.0, 0]]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "klist = U.build_kpath(hsp['L'],hsp['G'],hsp['X'],hsp['K'],hsp['G'],numstep=30)\n", "klist" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "inp.set_bands(12)\n", "inp.set_prefix(bands_prefix)\n", "inp.set_kpoints(type='tpiba_b',klist=klist)\n", "#inp" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/gaas_bands_so.save already exsists. Source folder Pw_bands/gaas_scf_so.save not copied\n", "Skip the run of gaas_bands_so\n", "Job completed\n" ] } ], "source": [ "results_gaas_so = code.run(inputs=[inp],run_dir=run_dir,names=[bands_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once that the computation is over we can create an instance of BandStructure" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apply a scissor of 1.0019927808099969 eV\n" ] } ], "source": [ "bands_gaas_so = U.BandStructure.from_Pw(results_gaas_so['output'][0],hsp,set_gap=1.42)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.title('GaAs bands with spin-orbit')\n", "plt.ylim(-8,4)\n", "bands_gaas_so.plot(plt,selection=[2,3,4,5,6,7,8,9,10,11])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in this case we need to display twice the bands respect to the previous example since each band accomodate only one electron in\n", "a specific spin state. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Band structure of Silicon" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step consists in a scf computation " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "scf_prefix = 'si_scf'\n", "bands_prefix = 'si_bands'" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "inp = I.PwInput(file='IO_files/si_scf.in')\n", "inp.set_prefix(scf_prefix)\n", "inp.set_energy_cutoff(60)\n", "inp.set_kpoints(points=[6,6,6])\n", "#inp" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a parallel QuantumESPRESSO calculator with scheduler direct\n" ] } ], "source": [ "code = C.QeCalculator(omp=omp,mpi=mpi,skip=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skip the run of si_scf\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/si_scf.save/data-file-schema.xml']}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[scf_prefix])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we perform the bands computation specifying the kpoints on a path. \n", "\n", "To define the path we write the coordinates of the high symmetry points (using the tpiba_b type of\n", "pw) and we make usage of the function build_kpath" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "hsp = U.high_sym_fcc" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.5, 0.5, 0.5, 30],\n", " [0.0, 0.0, 0.0, 30],\n", " [0.0, 0.0, 1.0, 30],\n", " [0.0, 1.0, 1.0, 30],\n", " [0.0, 0.0, 0.0, 0]]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "klist = U.build_kpath(hsp['L'],hsp['G'],hsp['X'],hsp['K'],hsp['G'],numstep=30)\n", "klist" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "inp.set_bands(8)\n", "inp.set_prefix(bands_prefix)\n", "inp.set_kpoints(type='tpiba_b',klist=klist)\n", "#inp" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/si_bands.save already exsists. Source folder Pw_bands/si_scf.save not copied\n", "Skip the run of si_bands\n", "Job completed\n" ] } ], "source": [ "results_si = code.run(inputs=[inp],run_dir=run_dir,names=[bands_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once that the computation is over we can create an instance of PwBands" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apply a scissor of 0.6044443799174577 eV\n" ] } ], "source": [ "bands_si = U.BandStructure.from_Pw(results_si['output'][0],hsp,set_gap=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It contains also a plot method that show the band structure" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.title('Si bands')\n", "plt.ylim(-5,4)\n", "bands_si.plot(plt,selection=[1,2,3,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Band structure of Graphene" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "scf_prefix = 'graphene_scf'\n", "nscf_prefix = 'graphene_nscf'\n", "bands_prefix = 'graphene_bands'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lattice is oriented with the zig-zag along the y axis. The basis vector are chosen as:\n", "\n", "$$\n", "a_1 = \\frac{a}{2}(3,\\sqrt{3},0) \\, ,\\quad a_2 = \\frac{a}{2}(3,-\\sqrt{3},0) \\, ,\\quad\n", "a_3 = (0,0,\\delta_z)\n", "$$\n", "\n", "Here $a$ represents the CC nn distance. The position of the atoms in the lattice is given by:\n", "\n", "$$\n", "A = (0,0,0) \\, \\qquad B = \\frac{a}{2}(1,\\sqrt{3},0)\n", "$$\n", "\n", "The lattice constant is given by $a_{lat} = \\sqrt{3}a$.\n", "\n", "The position of the high symmetry points of the reciprocal lattice (in units of $2\\pi/a_{alat}$) are given by:\n", "\n", "$$\n", "M = (\\frac{1}{\\sqrt{3}},0) \\, \\quad\n", "K = (\\frac{1}{\\sqrt{3}},\\frac{1}{3}) \n", "$$" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "pseudo_pbe = 'C_pbe-20082014.UPF' #pbe\n", "delta = 15 # Angstrom\n", "a0 = 1.42495 # PBE cc equilibrium distance among nearest C atoms\n", "engCutoff = 60 # Ry\n", "numKpoints = 12\n", "\n", "import numpy as np\n", "a1 = [a0/2.*3.,a0/2.*np.sqrt(3.),0.]\n", "a2 = [a0/2.*3.,-a0/2.*np.sqrt(3.),0.]\n", "a3 = [0.,0.,delta]\n", "A = [0.,0.,0.]\n", "B = [a0/2.,a0/2.*np.sqrt(3.),0.]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "inp = I.PwInput()\n", "inp.set_scf()\n", "inp.set_prefix(scf_prefix)\n", "inp.set_pseudo_dir()\n", "inp.add_atom(atom='C',pseudo_name=pseudo_pbe,mass=12.011)\n", "inp.set_atoms_number(2)\n", "inp.set_energy_cutoff(engCutoff)\n", "inp.set_atomic_positions([['C',A],['C',B]],type='angstrom')\n", "inp.set_lattice(ibrav=0,cell_vectors=[a1,a2,a3],cell_units='angstrom')\n", "inp.set_occupations(occupations='smearing',degauss=50.)\n", "inp.set_kpoints(points = [numKpoints,numKpoints,1])\n", "#inp" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a parallel QuantumESPRESSO calculator with scheduler direct\n" ] } ], "source": [ "code = C.QeCalculator(omp=omp,mpi=mpi,skip=True)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skip the run of graphene_scf\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/graphene_scf.save/data-file-schema.xml']}" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[scf_prefix])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We perfrom also a nscf computation on a regular grid of kpoint. Results of this computation are used by the\n", "other tutorials of the package" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'control': {'calculation': \"'nscf'\",\n", " 'verbosity': \"'high'\",\n", " 'prefix': \"'graphene_nscf'\",\n", " 'pseudo_dir': \"'../pseudos'\"},\n", " 'system': {'force_symmorphic': '.false.',\n", " 'ntyp': '1',\n", " 'nat': '2',\n", " 'ecutwfc': 60,\n", " 'ibrav': 0,\n", " 'occupations': \"'smearing'\",\n", " 'smearing': \"'fermi-dirac'\",\n", " 'degauss': 0.00367493225078649,\n", " 'nbnd': 8},\n", " 'electrons': {'conv_thr': 1e-08, 'diago_full_acc': '.false.'},\n", " 'ions': {},\n", " 'cell': {},\n", " 'atomic_species': {'C': [12.011, 'C_pbe-20082014.UPF']},\n", " 'atomic_positions': {'type': 'angstrom',\n", " 'values': [['C', [0.0, 0.0, 0.0]],\n", " ['C', [0.712475, 1.2340428991226358, 0.0]]]},\n", " 'kpoints': {'type': 'automatic', 'values': ([12, 12, 1], [0.0, 0.0, 0.0])},\n", " 'cell_parameters': {'type': 'angstrom',\n", " 'values': [[2.137425, 1.2340428991226358, 0.0],\n", " [2.137425, -1.2340428991226358, 0.0],\n", " [0.0, 0.0, 15]]}}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inp.set_nscf(8)\n", "inp.set_prefix(nscf_prefix)\n", "inp" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/graphene_nscf.save already exsists. Source folder Pw_bands/graphene_scf.save not copied\n", "Skip the run of graphene_nscf\n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': ['Pw_bands/graphene_nscf.save/data-file-schema.xml']}" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "code.run(inputs=[inp],run_dir=run_dir,names=[nscf_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we perform the bands computation specifying the kpoints on a path. \n", "\n", "To define the path we write the coordinates of the high symmetry points (using the tpiba_b type of\n", "pw) and we make usage of the function build_kpath" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "G = [0.,0.,0.]\n", "M = [1./np.sqrt(3.),0.,0.]\n", "K = [1./np.sqrt(3.),1./3.,0.]\n", "\n", "# useful to label the high-sym point on the path\n", "high_sym = {'G':G,'K':K,'M':M}" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "klist = U.build_kpath(G,M,K,G,numstep=30)\n", "#klist" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "inp.set_bands(8)\n", "inp.set_prefix(bands_prefix)\n", "inp.set_kpoints(type='tpiba_b',klist=klist)\n", "#inp" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The folder Pw_bands/graphene_bands.save already exsists. Source folder Pw_bands/graphene_scf.save not copied\n", "Skip the run of graphene_bands\n", "Job completed\n" ] } ], "source": [ "results_gra = code.run(inputs=[inp],run_dir=run_dir,names=[bands_prefix],source_dir=os.path.join(run_dir,scf_prefix)+'.save')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once that the computation is over we can create an instance of PwBands" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "bands_gra = U.BandStructure.from_Pw(results_gra['output'][0],high_sym)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It contains also a plot method that show the band structure" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(['G', 'G', 'K', 'M'],\n", " [0.0, 1.5773502657018026, 0.9106835999999987, 0.57735027])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bands_gra.get_high_sym_positions()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAENCAYAAAAMmd6uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXd4XMd16H+zvaEueu8gAIK9iUUkRVKkJIqiZHVZsWTHsuMkjvPi58RxnDjNJXFe7NhxtyU3FUumKFGNpEixSKTYxE4Qhei9LbCL7bt33h8LQuwVjcD9fd/9tty5M2f2zp575syZGSGlREVFRUVl4qMZawFUVFRUVEYHVeGrqKioTBJUha+ioqIySVAVvoqKisokQVX4KioqKpMEVeGrqKioTBJUha8yoRBC7BBC/HCs5bgUQohlQggphEgYo/JPCCG+MRZlq4wPVIWvMmwIIZKFEP8thKgWQviEEJ1CiD1CiL8UQtjGWj4VlcmObqwFUJkYCCFygA8AJ/B14BjgBcqAPwV6gOcvc61BShkYFUFVVCYxqoWvMlz8GFCAOVLKF6WUp6SUdVLKN6SU64EXziYcdGv8uRBigxDCDXxTCKEVQvxSCFEnhPAO9hK+IoTQnHPdc0KIN4QQ/yCE6BBCDAghnhVCmC+QRSOE+KYQonuwl/HdC/IxCCG+I4RoFkJ4hBAHhBCrzzl/1vWyQgixbzDNQSHErHMLEUIsFELsHDzfIoT4sRAi+hp+qwVCiCODvaBDQojZ5+RpF0K8MCibVwhxUgjx9AXl7hBC/OgqdUwSQrw2mEeDEOLTFwohhPicEKJqUI5uIcRmIYRqBE5gVIWvctMIIezAauB/pZTuS6WRF6/h8U/AW0A58L9E2mIL8DBQAnwN+Hvg6QuuWwpMB1YAnwDuBL5zQZongBCwEPgL4EvAI+ecf3Ywn8eBqcCvgU1CiOkX5PMt4O+AWUR6KL8XQojBOpcDW4DXB+V5AJgB/OpS9b+A7wJ/C8wBaoE3hBCWwXMm4CNgLZHe0feBnwohVlxnHZ8DCoCVwHrgT4CcsyeFEHOI/O7/DBQT+T3fuQbZVW5lpJTqoR43dQDzAQncf8H3zcDA4PGTc76XwA+uId9vA++e8/k5oA+wnfPdJwE/YB38vAPYe0E+W4FfDL7PJ9ITybogzUbgR4Pvlw3KuPqc84sGv8sY/Pwb4JcX5DFjME3SZepzNt8nzvnONlinP73C7/DiWfmvsY5Fg+UsOud8NhAGvjH4+QGgH4ga6/ajHqN3qN03lZFkCaAFfkbEcj2XgxcmFkJ8noi/PxswA3qg4YJkx6SUA+d83gsYiCjyY2fTXHBNK5A0+H4WIIBTg8b6WYzA9gvLuiAPBvNpBmYDBUKIc63qsxnmA50X1u8CmQGQUg4IIY4DpQBCCC2RXsUjQPqgXAYiSv5ysp2V72wdS4g81PafU06DEKL1nPRbify2dUKIzUR6KxuklK4ryK1yi6MqfJXhoIaIRTnl3C+llHUAQgjPJa45z/UzqDi/B3wZ2ENk8PfPgftvQJ7gBZ8lH7svNYOf514infcK+Zx1SZ2bzy+A/75E+S3XI+wFfBn4G+CvgONEekff5GNlfinZzsp3oYv2skvhSildg2MStwOrgK8SGUuZK6Vsvdx1Krc2qsJXuWmklD1CiC3AXwghfnCBBX6tLAb2SSmHYuiFEPmXSFcuhLDKj8cKFgAB4Mw1lnOYiCWeIqV87wbkPMtHQJmUsuYGrl1AxHePEMJKZBzhN4PnFgObpJS/HTwviLho+q4j/9NElP88Ig9PhBBZQNq5iaSUISK9mu1CiH8i0itZS6RHpjIBUQdtVYaLLxBpT4eEEI8JIUqFEEVCiMeIDGqGr3J9FTBLCHGXEKJQCPF1IgOrF6IDfiWEKBNCrCLi5/+5vMxg8YVIKauA3wPPCSEeFELkCSHmCCG+LIR44BrrCpGB4nlCiJ8IIWYKIQqEEGuFED+9hmv/QQixSghRRmSQN8DHIatVwAohxGIhxBTgh0DudciFlLKSyADsT4UQtwkhZhAZ/xjqwQzK+leDsmcTGcCOAiqupyyVWwvVwlcZFqSUtUKImURcA/8KZBJxO1QAPyKiuK7ET4kMej5PxAL/I/BfwIXhhDuBk8B7gGUw3VeuU9yniUQB/QeQAfQS8Xdfs8UvpTwmhLgd+LdBmbRErPZXr+HyvyNSt2IidVl7zgPr34go+LeJKOjniDygSq9VtkGeAn5OxILvJhKNc65bqI9I9M4/EvkdzxAZON59neWo3EIIKdUdr1RuDYQQzwEJUsq1Yy2LisqtiOrSUVFRUZkkqApfRUVFZZKgunRUVFRUJgmqha+ioqIySRhXUToJCQkyJyfnhq6trKwEoLi4eBglUhkNJvu9m+z1V7n5NnDo0KFuKWXi1dKNK4Wfk5PDwYMXzbi/JpYtWwbAjh07hk8glVFhst+7yV5/lZtvA0KIC5cguSSqS0dFRUVlkjCuLPyb4R/+4R/GWgSVG2Sy37vJXn+V0WsD4ypKZ86cOfJGXToqKioqkxUhxCEp5ZyrpZswLp0jR45w5MiRsRZD5QaY7PdustdfZfTawISx8NWBr1uXyX7vJnv9VYZl0HZyWfgqKioqKldGVfgqKioqk4QJE6WjojIRCAQCOJ1O+vv78fl8+P3+oUNKiRACIQQajQar1Upubi5xcXFjLbbKLYKq8FVURoFwOExLSwstLS20t7fj6O5lwDlAKBCkpbEZgP/45rfxBHzXnXdcXBx5eXkUFhZSXFzMBXv1qqgMMWEU/je/+c2xFkHlBpmo9y4YDFJdXc3xI8eoravFHwwAYMZAjGIhRhrQYuThO9ajIMnyxGPDTFxyPPaiNGLLUzBZzBiNRgwGA1qtFkVRkFIipcThcFBbW0ttbS3Hjx/n0KFDTJ06lfvuuw+9Xj/GtVe5HkbrPzBhonRUVMYLnW0d7Nu+hxO1FfjDAczSQFY4gQxpJ8OeSkxWAoYMG/o0G/pkKxqjFiklwZYBPMe78R7rIuzwo0s0E7u+AFN+7FXLDIfD7Nmzh23btpGens6jjz5KVFTUKNRWZTxwrVE6E0bh79mzB4CFCxcOp0gqo8Ctfu8GBgaor6+n7kwttRU1OHxONFKQSzJlSQXkFRdgyonFkBmFxqi96PoL6y+lxFfpoO/1M4R7fVhmJRFzdy5am+GqslRUVLBhwwbMZjOPPvooaWlpV71GZey52f/ApFP4aizzrcutdu/OKvizR3d3NwB6tKSEY8lOzWLm4tnEl6YhdFcPhLtc/ZVAGNd7Tbh2NSMMWmLX5WOdmXRxBhfQ3t7O888/j8fj4f7776esrOy666gyuoxWHP6E8eGrqIwUfX19NDQ00NjYSH19PT09PQAYDAayMjIpFukkNBtISU3Bfn8RhszhcaVoDFpiVudgmZGIY0MNjpcq8df0EXtfPhrDxT2Fs6SkpPDMM8/w4osv8vLLL9PZ2cnSpUvRaNQo7MmOqvBVVM4hFArR0dFBc3MzjY2NNDU14XQ6ATAajWRlZTFz5kxycnJIkNE4Xqgk3OcjekUWUcszEdrhV6r6ZCuJz0zDub0R1/ZGAo1O7E+UoE+xXvYam83GU089xaZNm9i5cyednZ3cf//9GAxXdwupTFxUha8yJpyNKR8NFEXB4XDQ0dFBe3s7HR0dhMNhTEYTRp0Bk8FEIBygrbOdtrY2wuEwANHR0WRmZpKVlUVWVhbJyclDVrJ7fzvdrx9DazWQ+PnpGLOiR7QOQiuIWZWNMTea3pcq6fjhEWLX5WGdm3LZ31Gn07F+/XqSk5PZunUrv/zlL3nssceIjb36ILDKxERV+CqjSiAQ4M033+TMmTMsWbKEOXOu6na8JqSUuN1uHA4HDoeDnp4eenp66O7upru7m1AoBIAQgjhDNNqwoDMYIEAQP0G0aEjUxTItvoisvGxyZxUSm2y/uJywQt+mWtwftmEsjCX+0SloraMXAmkqiCP5i7Po/UMlfRtqCDS4iFufj9Bf2sUjhGDhwoUkJibyyiuv8LOf/YyHHnqI3NzcUZNZZfwwYQZtz640N2PGjOEUSWUY6e3t5aWXXqKjo4OUlBTa29ux2+2kp6eTm5vLzJkzgYhFHgqFCAaDBINB/H4/Pp8Pn8+H1+vF7Xbj8XhwuVy4XK6hmalnlfpZovRW4rASHTQTH7QQp9iIk1YMFiP6FCv6NBvaWCPCoCHsDhKocxKo70cGFBCgT7dhKozDVBiJsPFVOXDuaCbY5MJ2ewYxa3IQmpvvpdxI25WKxPluA67tTejTbdifKEEXb7riNd3d3bz44ov09PRw5513smDBAnWS1jjhZvXXpIvSURnfVFdX88c//hGAT3ziExQUFFBdXc2WLVvo7u5Gq41YqGcnFl0NrdBg0ZqwYsKiGLAGDVgVE9HSTJQ0EYUZY7QZXcLgkWRBn2SJxL1H6S+r6GRYIdDkwl/Th6+6j0CTExRAABKkRaBfFEfy8hLEOBgE9Z7qofcPlQiNIP7RKZiKrrzMgs/nY+PGjZw+fZry8nLuvfde1a8/AZh0Cv/dd98FYOXKlcMpkspNoigKu3btYseOHSQnJ/PII48QHx8/dN5b38eP/uW/cfrdzMgtQyDQSg0aBDq06NBikDr0aDFKPUZ0mIQRo9mILsqIJkqPNsqANsaINtaILtaINs6ELs6E0N+8QlZ8IZq2H6b2nb10uOpp9VQjkZijY8gsmUp6SRnpxaUkZuei0V4+cuZK3GzbDXV76fndKYLtHqxzU4hek3NFN5OiKLz//vts376dlJQUHn744fPuicroc7NtYNIp/Fstlnsy4PF42LBhAzU1NUybNo21a9cOWZNSkbjea8K5rYGHX/wS2hgDb/3wlYiLRCsQWg1CHzk0Ri3CqENjjhzCpB01V8TRrW+z7Vc/xp6Rxex71hOblEJfZztNJ47SfPokzq5OAHRGI8m5+STl5pOUk09CRhbxGZkYTOarljEcbVcJhHFuaWBgTwsak47o1TmRAd0ruJyqqqrYsGEDUkruv/9+pkyZcsPlq9wcahy+yi1NS0sLf/jDHxgYGOCee+5hzpw5Q0o61Oej98VKAvVOzDMSMbxvA40galH6GEv9MYoSZtfvfsWhN18jd+Yc7vniVzBaLABklE5l6rKIJebs7qS16jRt1ZW01VRyfPsWQn7/UD7W2DhiklOJsidgi7djiY7BHBWN0WJBbzShMxjwe9yAoPHEUcKhUOQIBvB7PPg9bvxuN37PAL6BAXzuAXwDLnwDLgJeL8rZ9KEQGq2WwuL5TNHORXm1BvfBDqKWpGMqikNjuvivXlRUxOc+9zlefvllXnzxRRYuXMiKFSuG3GsqEw9V4asMK1JKDhw4wObNm7HZbDz99NNkZGQMnfed6aP3+QpkSBL/SDGWmUnwk/E1cBjweXnzf/6T2kP7mXXXOpb+yWfQaC6tBKMTkohOSGLKwtuByIOir72NnuZGepqb6Otoo7+znY4z1Zw58CGhwQXUzqWzvhaAl//1a5csQwgNRosFky0Kk82GyRZFbHIqBpMZrV6PRqdDq9MR8HpoOHaYirZdZFtLmSFXEHzeBVqBMT8Wc0k85qkJaKM+9tnHxcXx6U9/ms2bN7Nnzx6am5t58MEHiY4e2TBTlbFhWBS+EOJXwFqgU0o5dfC7eOAlIAeoBx6WUjqGozyV8YnP52PTpk2cPHmSwsJC1q9fj9UamRwkpWTg/Vb6365Fl2DG/mQp+kTLGEt8Mc7uTjZ+51/obmpkxaf/jBmr77mu6zUaLfFpGcSnZVA47/xzUkqCfh8+lwu/10PQ5yMUCPBy7acBePifvoVWp0er06HV6zFarBitVvRG03W5sBxtLdR+dJA9u95EtgaYVX4XCT1G+l47Q9+btVjnphC1NBNdrBGIxOvfc889ZGZmsmnTJn784x+zfv16iouLr6vuKuOf4bLwnwN+CPzmnO/+Dtgmpfy2EOLvBj//7TCVpzLOaGtr4+WXX8bhcLBy5UoWLlw4NElJCYTp21CN50gXpjI78Q8XoTGOv85la9VpXvvuvxEKBHjgq98gZ/qsYc1fCIHBZL7Ir2+y2gDILC0flnLiUtOZfU86M1bfzY7f/IItm39OenEZd3/mS4SPDeDe1457fzvW2clELcscCuecNm0aaWlpvPLKK7zwwgvMnz+fVatWodONv3ulcmMM26CtECIHeOMcC78SWCalbBNCpAI7pJRXNBluZtC2srISQLVKRhkpJfv27WPr1q1YLBYefPBBsrOzh85HIkgqCHa4iV6ZHVl+4IKBxPFw707u3MbWn/0Amz2B+7/yT9gzMket7JGuf8Xu99jysx9itFhY+6W/JSW1ANeOZtwH20GCbVE60XdkDvn5g8EgW7duZf/+/aSkpPDggw+SkJAwIrKpRLjZNjDqUTqXUPh9UsrYwfcCcJz9fMF1zwDPAGRlZc1uaGgYFnlURh63281rr71GVVUVRUVF3HfffUMuHPg4RhwhsD9ajKl4/IX+KeEwu55/jkNvvEpm2TTu/eu/wxw18fzXXY31vP5f/05/ZwdLHvsUc+59AMUVoH9LA56DHWii9MSsycUyM2nogXz69Glee+01QqEQq1evZvbs2epErXHKuFL4g58dUsorzgq5GQt/06ZNANx77703dL3K9VFbW8urr76Kx+Nh1apVzJ8/f0gZyPDgLND3rm0W6FjdO6/LyRvf/w8ajx9h+p33sPxTn0U7Bu6L0aq/3+Nm80++T/W+PeTNnsddX/g/mGw2Ak0uHK+fIdjkwpAVRez6AgxpETeT0+lk48aN1NbWUlRUxLp167DZbCMq52TkZtvAeFD4o+rSUePwR4dQKMS2bdvYu3cvdrudBx98kNTU1KHzYWeAnhdOE6jrxzInmbj7Cq46AWos7l1HbQ2v/79v4Xb0sOJPv0D58jtHrewLGc36Syk5/M4b7PztL7HF27n3S39LSkERUpF4Puqk/506FE8w4uZZmY3GGNlWcf/+/WzduhWj0ci6devUmP1hZiLE4b8OfAr49uDrayNYlsoo0NHRwYYNG+jo6GDu3LmsWrXqvGn5vmoHvS9VIv1h4h4qwjo7eQylvTRSSo5v38L2Z3+CJTqWR77xHVILJ8+4jxCCWXfdS2pBEZu+921e+MevcPsTTzPr7nVY5yRjLo2n/516Bna34D3eTex9+ZhL7CxYsIDc3Fw2bNjAiy++yPTp01mzZg1m89UnlqmMH4YrLPMFYBmQIIRoBv6JiKL/gxDiM0AD8PBwlKUy+iiKwt69e9m+fTsmk4nHH3+coqKiofMyLHFui7hwdIkW7J8tR598+bXax4qA18PWn/8vpz/YSVb5DO754v/FEh0z1mKNCamFxTz5nf9hy0++z47f/JzGE0dY/WdfwhIdQ9wDhVhmJeHYUEPPr09hnmondl0+ycnJfPazn2XXrl3s3r2bM2fOcO+996qBErcQw6LwpZSPXebUiuHIX2Xs6OnpYePGjTQ1NTFlyhTWrl17ng835BicNdvgxDIridj1BVfcjWmsaD9TzVs/+E/62ttZ9PAnmXf/Q5edTDVZMNuiWPc3X+PI5oiL57df+Uvu/uL/JbO0HGNODMlfnIlrdzPObU34qg8RsyYH6/xU7rjjDqZMmcLGjRt54YUXmD59OqtXr8ZiGX/zKlTORw2wVbkkiqJw4MABtm7dik6n4/7772fatGnnRWl4jnXh2FANEuIfLcYy4+r7rY42ihLmwGt/ZM/Lv8cSG8dD//jvwxbvPhEQQjBzzb2kTynjje99h5f/5WvMW/8Qtz34GFqdjujlWVjKE3FsrKHvtTN4DncS90AhaWlpPPPMM0PWfnV1NWvWrKG8vFyN5BnHTJjF05qamgDIzBy9+OmJSldXF6+//jpNTU0UFBSwbt2686baK74QfZtq8RzqwJAZRfyjxejsN+7LHal719PSxOYffY+2mkqKb1vCyj/9c0zjMMJkvLTdgM/L9md/yskd75KSX8jdf/ll4lIj6xtJKfEc7qT/zVoUbxjbknSiV2ShMWhpb29n06ZNtLS0kJ+fz9q1a4mLu/IyzSrnc7NtYNKtlqly84TDYfbs2cOOHTvQ6/WsWbOG6dOnn2ex+Wv76f1DJeF+P1HLMolemTUi+7jeDKFgkIObNvDhhhfRG03c8enPM2Xh7arleY1U7fuArT/9AaFQkOWf+izld6we+u3C7iD9b9fhOdiBNtZI7Lp8zKX2oR7htm3bkFKydOlSFixYoM7SHSUmncJ/6aWXAHjkkUeGU6RJQ1NTE5s2baKzs5PS0lLuvvvu83z1MqjQv7WBgd3NaONNxD9cjDF7eCYoDee9azh2hO3P/oTe1maKFizmjqc/hzV2fFub47Htunq7eed//5vGE0fJnzOfVZ/9i/N+R39dP45Xawh1ejCV2oldl4cu1kR/fz9vvfUWlZWV2O127r77bvLz88ewJrcGN9sGJp3CV+Pwbwyv18u2bds4ePAg0dHR3HXXXZSUlJyXxl/fj+OVakLdXqzzUoi5Jw+NcfgGPIfj3vW0NLHr989Se2g/MckprHj68+TOHJ79ckea8dp2paLw0duvs/uFX6M3mVn5mT+j+LYlH58PKbjeb8G1rRGAqOWZRN2egdBpqKqq4u2338bhcFBaWsrq1auJiZmcEVHXwkSIw1cZx0gpOXbsGFu2bMHj8bBgwQKWL1+O0WgcSqP4Q/S/U497bxvaOCMJn5mKqXB8Wct97W18uOFFTu16D73JyJLHn2LWXevQqdv23TRCo2H2PevJmTGbd37037zxve9QtW8PKz79eSzRMQidhuhlmVimJ9L/Zi3OLQ14DnUQsy6fouIicnNz2bNnD7t376aqqopFixaxaNEidUvFMURV+GOElBKHw0FDQwP19fU0NzeTnJzM0qVLSU4e2QlLbW1tvPXWWzQ1NZGens4TTzxBWlra0HnFE2RgbxsDe1ojsy4XphG9OmdYrfqbQUpJW3Ulh97cSPW+PWh1Ombdcx/z1n0CS8xFyzWp3CT29Ewe+5f/5MDrf2TPy8/TfOo4dzz9eYoWLEIIgS7OhP2TpfiqHPS9foaeZ09iKokn5u5cli5dyrRp03j33XfZuXMnhw4dYsWKFUyfPn1oNdUbxe1209zcTFNTE83NzbS2tpKYmEh5eTllZWVERUUN0y8wcVBdOqOIlJKWlhYqKiqoqKigt7cXAIvFQnp6Oo2Njfj9fkpLS29a8Xu9Xurr66mrq6Ovrw+NRoNGCPy9Hmo7GjEbjCydtohpU6YiZMRHL0MKgUYX7v1tyICCqTiOqBVZGLNGdjGxa713A709VO37gBPbt9DVWI/RYmXaqruYddc6bHHjb2G2a+VWaLtn6WqoY/NPvk9HbQ0Fcxew4tN/hi3ePnRehhQGPmjBub0JGVKw3ZYWieYx62hsbGTz5s20tLSQmprKypUrycvLu+7B9GAwyLZt2/jwww8B0Gg0pKSkkJqaSktLC+3t7QghyM3NpaysjClTppy3qN94ZLRcOqrCH2FCoRB1dXVUVVVRWVmJ0+lEo9GQm5tLcXExOTk5JCQkoNFo8Hg87N27l3379hEIBLjtttu444470OsvvyH1uYTDYY4dO8bBgwdpbW1FSoler8cebyfsCRIa8KOEFTIUO7NCuRi5RL4aMJUnoJ1hxat1Ew6F0BuM6IxG9EYj+sH13LV6/bBFvVzu3gV9Pjpqa2g8eYyGY4dprT4NUpKcV0D5HaspWbwUg/nWn+wzXtvu5VDCYQ69uZE9f/g9Wr2epU9+hqnLV53XHsKuAP2b6/Ec6kBj0RG9Khvr3BSkgBMnTrBt2zb6+/vJzc1lxYoV5+2KdiVaW1vZsGED3d3dzJ49m2nTppGamnqem6izs5MTJ05w/PhxHA4HQgiysrIoKSlhypQpxMaOv16gqvCvk+7uboBxsW63w+HgzJkz1NTUcObMGYLBIHq9nry8PEpKSiguLr7iGiQej4ft27dz8OBBkpKSeOCBB0hJSbls+rOKfteuXTgcDpKSkigpKSE3Jxd7nxH3thZC3V70GTZi7szBkGFD8YZQfGEUXwihFXg9To7v3Er1sQ9xONrgKu1Co9WiN5nQG03oTebIw2Bwj1adwRh51RuGdm/S6vWRXoZWi9BoI6+DCqJ/YAAlFMJqMuIbcOHq6aavvQ1He2tEDiFIzs0nf858iuYvwp6RdWM3Zpwyntru9eBoa2HLT39Ac8UJMkqmsvJPv3DRvQm0DND3Ri2Bun50CWZi1uRgKrMTDoc5ePAgu3btwuPxUFJSwrJlyy7bqw2Hw+zevZtdu3ZhtVq57777KCgouKJ8Ukra29uHetRdXV0AJCYmUlhYSEFBAVlZWeMidPRm28CkU/hjycDAAPX19dTX11NbWzvkqomOjqaoqIiiosgA1rVa6mepqqritddew+v1snTpUubMmXNe1zQUCnH06FF2795NX18fqampLFu2jMLCQvyVDpybGwi2u9GnWIhelYOpNP4iq9zd52D/xpc5+u7bSEWhcN5C4tIyiLLbscXb0emNhAJ+gn4/Qb+PoM9LwOslMPga9PsI+v2Ezr4G/IT8fkLBAKFAkHBo8AiGUMJhlHDosvXVaLUYLVai7IlEJyaRmJ1LUm4+GVPKxuWEKZVIJM/x97ay+/nnCHg9zLn3ARY88Ah648fLYUsp8VX00v9OHaFOL4asKGLuzsWYE4Pf72fv3r3s2bOHQCBwkTuzv7+fw4cP89FHH+F0OikvL+fuu+++oUXburu7qaqqorq6moaGBhRFwWAwkJ2dTU5ODjk5OaSkpNySm7hPOoX/3HPPAfDUU08Nn0CXQFEUurq6aG5uHhowOvt0NhgMZGVlUVBQQH5+PgkJCTft9nC73bzxxhtUVFSg0WgoKipixowZ9Pf388EHH+B0OklLS2Pp0qURRV/dh2t7E4EGJ1q7iZhV2ZinJV60y1TQ52P/63/k4KYNhENBypauZMEDjxCTNPIrXEpFQVHCkU6ElPz6179GaDR8+jOfGfGyxyOj1XZHEo+zn12/e5aTO98lOjGZ5U89Q/7seee1fxmWeA510L+1AcXq67xiAAAgAElEQVQVwFQcR/SqbAwZURe5M0tKSgiFQtTU1CClJD8/n/nz55+3aN/N4Pf7qauro6amhvr6+qH/sNFoJDMzk4yMDDIyMkhPTx+VFUFvtg1MOoU/En5Qv99Pd3c3HR0dtLe309bWRkdHB4FAAACz2UxGRsaQhZCamjpi1kF7eztHjx7l2LFjuN1uALKysrj99tvJy87Fe7QL1+4WQh0etDEGou7Iwjon+aJZsFJRqPhgJ7uff46B3h6Kb1vC4kf/hNiU1EsVOyrcaj7s4WYi1b/51Ane/eWP6GluJHvaTJZ/6pmLtotUAmHce1tx7WxG8YQwldmJXpmNLt6Ed8DN3gP7OPDRQfR6PTNnzWTWrFkjvlSDy+Ua6qU3NTXR2dk5dM5ut5OSkkJycjIpKSmkpKQQFRU1rDO31Tj8UUBKidfrxel04nA46Ovrw+Fw0NPTQ1dXF06ncyitwWAgOTmZGTNmkJ6eTkZGBvHxF7tIRoJQjxdrTZi5zlzKjQk0OJswhLQkV8VCbTutdEBIQZ9iJe7hIizTEhG6i0PeWk6fYsdvf0F7TRXJeYWs/au/JX1K6YjLrzJ5yCidypPf+R+ObnmTPS8/z2++8hfMXLOW2x58HKMl4o7UGLRELc3EOj+VgfdbcO1uofNkz1AeUzBRxEIQAsNHZsLNrfQn92HIjMI0xY7QDv9/LioqivLycsrLIwvr+Xw+Wltbh8I9W1paOHny5FB6g8GA3W7HbreTkJBAXFwcMTExxMTEEB0dPW7dQhNC4Xu9XrxeL1JKKioqUBSFcDhMIBAgEAgQDAbx+Xx4vV48Hg9erxeXy4XL5SIcDp+Xl9FoJD4+nuzsbBISEkhMTCQ5OZm4uLibjhu+HqSU+Gv6GNjTiu90L0jQ2PToU62U5E9DY9UPhVISlpiK4jAWxl7yAdTX3sau55+let8ebHHxrPnCX1O6ZDliFOujMnnQ6nTMuvs+pixayvsv/oZDb73OqV3vcdtDjzNtxZqhbSQ1Jh3RK7Ox3paG90hnpC1rNBGFrhGEXQFCnR6CnR58VQ4IS7QxBqwL0rDOS0Frvb4xsevBZDKRl5dHXl7e0Hc+n4+Ojg46Ojro7u6mp6eH5uZmTpw4cdH1NpsNi8WC1WrFarVisVgwGAwYjcahV61WGwlkGIzQG43B4wmh8Ht7e+no6AA+XpPiQvR6PWazGbPZjMViITMzk6ioqKEjLi6OuLg4zGbzmC6ypXiCeA53MrCvnVCnB41VT9TyTKxzU9DFRQbC+js7CIdDxKZkXFFWj7OffRte4siWt9DqdCx86AnmrL0fveny+8uqqAwXlphY7vzcF5m+6m52/vaXbP/VTzj89iaWPPEUBXMWfNx2jaAUG+hqqKOjtoaOuhq6GuqIS0ll9tr7yX1sNkjwVToY2NOKc3M9zm0NWKYnYZ2fgiFzeN0rl8NkMpGdnU12dvZ53weDQZxOJ319ffT399Pf34/L5cLtduPxeGhtbcXj8eD3+7mcC72zs3NU5gpMCIWfkJBAamoqQgg+//nPo9Fo0Gq16PV6DAYDer1+3HaxIGLNBxqcuPe34znWHXHPZNiIe2jQPaPX4BsY4OTWtzm1azutVRVA5A+VXlxK+pRSErNziU/LwBoXT9Dn5eAbGzn4xquE/H6m3rGKhQ89cUtPTlK5dUnOK+Chf/wmtR8dYNfvfsXr3/134lLTkYqCx9lPwOv5OLEQxKemk1Y4hZbKU7z67X/GnpHF7LXrKVm8nMRSO8EONwN7WvF81InnUAf6FAvWuSlYZiahsYyc1X859Hr9kHvnSkgpCYVC+P1+AoEA4XAYRVFQFIXNmzePigdhwgzaejyRRnMr7boTcvgijfajDkI9PoRRi2VGItZ5qRjSbUgpaa44wdGtb1Ozfw/hUAh7Rhalt9+ByWqj5fRJmk+fwtnVMZSnwWxGCA1+j5vC+QtZ9MiT2NPH9x4Bt+K9G04mU/2VcJjj27dw5tA+jBYr5uhozFHRWKJjsGdmk5STh8EUiYoJh4JU7n2fg5s20NVQhyUmlmkrVjNt5V1E2RNQfCE8R7tw728n2DIAOg3mMjuWmUmYCmPH3bLdV+Jm28Cki9K5VQgPBPAe78ZzrItAXWRQ2JgXg2VWMubyBDRGLV6Xk4rd73F069v0tjZjtFopXXIHZUtXkJSbf1H3dcDRS09zI72tzThaW/B73MxYfQ+pBepeoyrjDykl7j4/Aw5/5LXPj98dJG9mIgkZF69/I6Wk8fhRPnrndWo/OoAQgoK5C5hx51oyyyI7bAVaBnAfaMd7rAvFE0Jj1WOZnoh5RuKouXzGkkmn8H/0ox8B8IUvfGE4RRoWwk4/3lO9eE904z/TBxJ0SRYs0xOxzEpCF2dCKgqNJ45x/L0tQ9Z8akEx01bdRfFti8+byHKWoD/Myd0tnNjVQjiooDNo0Rk0WKKNLPxEPva0W2Oy0ni+d6PBZKq/xxngvd+dpv5Y9yXP50xLYO49OSRdZq+F/s52jmx5ixPvbcU34CIuNY2yZasoW7oCW1w8MqTgq3LgOdyJt6IHQpGBXvPUBMxTEzBkR180J2U8cLNtYNIp/PEUyywVSbDNja+qF++pXoJNLgB0dhPmaYlYpieiT4kM0PS2NnNq13tUvP8ezq5OTFYbJUuWM3X5KpJy8i6Zv88d5PiOZo5tb8bnDpJWGEtMoplQIEwwoNBR10/QF2bJo0WULEwd99bNeLp3Y8FkqX/d0S7e+91pAt4ws+/KJjErCmusEVusEaERHN/RzNFtTfg9IbLK4pm9JpvUgktHngUDfqr2vs/x7VtoOX0SodGQN2suZbevIHfmHHQGA4ovhLeiF+/xbnxVvRCSaGx6TMXxmKbEYSqMQ2MaH8OYahz+LUbY6cd/ph9ftQNflQNlIAiAPsNG9OpszKV2dEkWhBC4ero59ua7VLy/g47aaoTQkD1tBosf+xSFc2+77Fruzm4vR7c3ceqDNkL+MDnldmbflUNK3vkbS7j7/Wz91Sne++1pWqocLH2sGMM4adgqk4+AL8QHL1dz6oM2EjJtrP/rMuLTLo5ImXtPLtPvyOTErhaOvNvIq/91mKScaGauyiJvZiKacyxzvcFI2dIVlC1dQW9rCyd2bOXUzm2cORgZGyicv5CSxcvImD4V68wkFH8I32kH3lM9eE/24DnUARqBMScaY2EsxvxYDOlRIxLjP54YcS0ghKgHXEAYCF3LU2i8I6Uk3OvD3+giUN+P/0w/oW4vABqLDmNhHKaiiAWhjY4ob2d3JzXvbKNy7/u0Vp4CIDEnj6VPfoYpi5ZeNoJGSklHnZMj7zZRe7hz0H+ZxMxVWZf0dwJYY4ys+6sZHHq7ngNv1NFZ72L1Z6eSkHFruHhUJg5tNX28+9wpnD0+Zq3JZt7aXLSXmBR4FoNZx6zV2ZQvz6BybxtH3m1i889PEJ1gYtodmZTclorBfL7aik9L5/bHn2LxI0/SeOIoFe/voHLv+5x4byvW2DgK5i6gYN5CMsvKsUxPRIYlgUYnvspefKcja05BA8KgxZgbjSE3BmNWNPoMGxrD+I3uuxFGy+xbLqW8tNNunCOlJOwMEGwZINg6QKBlgECTa8iCF0YtxtwYrPNSMObFoE+zITQCqSh01tdyZvM+zhzcT2f9GQASs3JY9MiTFC1YTHxa+mXLDQXCVB3o4MTOFroaXRjMOmasymLa8gxscVePo9doBHPvySWtIJYtvzrJK985yJKHCyldnDbuXTwqtz7hkML+TXUc3tJAlN3E/f9nFmmF174ssd6gZerSDEqXpFN/tJvDWxt4/w/V7HutluL5KUxdln7RGJVGqyVn+ixyps9i5Wf91B7aT9WHH3BqVyQAwmS1kTd7Hrkz55AzbRYxubnErMklPBDAX9uP/0xfpJde6RjMUKBPs2LIjMKQZkOfakWfYr3kLPZbBbWfP4gMKYT6/IR7fYS6IrP7gp0eQh0eFM/gCo8CdAlmTEVxGLKiMWRFRRrAYFfT3eeg5oMd1B/9iIZjh/H094EQpBWVcPsTT5M/Z8EVlTxAT+sAFe+3cXpfG353iPg0K0sfL6ZoXvINuWXSi+N45GvzePfZk+z4fSUtVX0se0J18aiMHD0tA2x99hQ9zQOULEpl8UOFN9zeNBpB3sxE8mYm0lHv5MSOZir2tHFiVwvpRbGULk4jb2YiOv35lrjeYKT4tiUU37aEYMBPw7Ej1Ozfw5lD+zm1aztCaEgrnkLujDlklU8nuawAy7REIBJJF2h0DR5OPIc6ce9tGxRIoEs0o0+yRF4TI6+6eBPCrBv3xtSID9oKIeoAByCBn0opf3bB+WeAZwCysrJmNzQ03FR5UpGgSGRYIgNhZCCMElCQ3hCKJ4jiCRH2BFFcAcLOwaPPT9jpj0h4Vi6zDn2yBX2SBX2KFX2aFX2q7bxt/ly93bScPkXzqRM0nTpOb0sTAOaoaLKnzSRn+ixyZ8y+6rZ7AW+Imo86qfiglfZaJxqtIHd6IuVL00kruvSg1Y38LofeaWD/plqiE82s/uxUEjPVLeBUhg9FkRzZ2si+TbUYzTqWP1lC7rThX+PfOxCg4oM2Tu5uwdntw2jRUTQvhZJFqVdt04oSpr2mirrDB6k9fJDOukjP22C2kFFSRmZpORklU0nMyUWri0zikkrEhRtoGyDY6ibY5ibU5SHU6ztfZxi0aGON6OKMaGOMaKx6NDY9WpsejUWPxqRDGLUIoxaNUTu0hAQacdP/8XETpSOESJdStgghkoCtwF9KKXddKu2NRukEmlx0/uQoKPK8G3BFuQxatDEGtNEGtNFGtPEmdPEmdHYTOrsZje38HZ187gE662rprKuhrbqS1ppKBnoiXiq9yUzGlFIySsvJmjqd5Nz8q65TEw4rNJ3qpXJfO3VHuwkHFeJSLJQuTqN4fgrmqJHZ6LmlysHWX57E5w6x6MECpi5NH/dWicr4p6/Dw7Zfn6K91knezESWPV48Ym34LFKRNFc5qPigjdrDXYRDCvZ0G0Xzkimcm0xU/NVdn+4+B02njtN08hhNJ4/jaGsBQKc3kJRXQGphMakFRSTl5BGbnHre/1qGFEI9XkJdXkIOH+E+PyGHn3Cfj7ArgOIOgnKNldGAZVoi8Y9OuZGfYvwo/PMKE+IbwICU8ruXOn+jCj/U7+fbf/PPIOCLj3wu8sTUahAGDcKgRRg0aEy6yFPWMvh6mQ25A14PjrZWelqa6G1poqe5ka6GOvo7P57NGp2YRGrhFNIKi0ktmkJybgGaa1i6QQkrtFT1UXOok9rDXfjcQYxWHYVzkimen0JybvSoKF+vK8C7z1XQeLKH/JmJLH9yCsYxmJJ+lu9+N9IcvvzlL4+ZDGPJrVx/qUiO72xh74YatHoNSx4pomhe8qgbET53kOoDHVTtb6e9NjKhMbUghsI5yeTNTMQaY7ymfAZ6e2itqqC16jRt1ZV01NUQDg5G3BlNJGTnkJSdS3x6JvGp6cSlZRCdkHhJA08qMrKznDuIMhBECYSR/hCKP4z0h5FhCWGJVCQ/+MNP0UQZ+Pv/940bqv+4UPhCCCugkVK6Bt9vBf5FSvnOpdKPZBx+OBTCN+DC63Lic7kY6OvF7ehlwNGLq7uL/q4O+jva8bo+XhJZo9USm5xKQnYuSTl5Q4c19trX5g4GwjSd6qXuWDf1x7rxDQTRG7XkTEugYHYS2VPtV4xaGCmkIjn8biP7NtZijTWy6jNlpObHXP3CEWCyxKFfjlu1/s5uL9t/U0FLVR9ZZfEs/2QJtrhrU6wjSX+Xd0j5O9o9ICAlNyYyFjAjkZjEa9/QJBQM0tPUQGdDLV31dXQ1RA6/xz2URqc3EJ2YRFRCItEJiUQnJGGLt2OJicUcHY0lOvKqN5ou+yCcKHH4ycCrg5XUAc9fTtnfDD3NjXTW14KU/P7v/xolrBAOBT/els/vI+T3X/Jand6ALd5OTHIKhfMWEpOcQmxKKvb0LGJTUob8eNdDX6eHplO9NJ7soem0g3BQwWDWkT3VTsGsJLLK4tGNcbiX0Ahm3ZlNWmEsW395klf/6yPmrc1l1prs8+KdVVQuRCqSE7ta2PPqGYSAZU8Uj6vor5hEM3PuzmH2Xdk42jzUHunkzOEu9vyxhj1/rCE22UL2VDvZZXbSCmPR6i9vcOn0epLzCkjO+3j/XCklnv4+HK0t9LY109vagrOrA1d3F2ca6iLBGpdACA16kwmD2YzBZEar0yG0WrRaHZ31tZisIx82PaIKX0pZC0wfyTIAEIODHkJgjopGaDRodfrIxtomEzqjCaPFgtkWjSkqCpMtCltsHLb4BIxW681vQ9jnp6XaQUtVH80VvTi7fQBEJ5opW5xG7vQEUgtj0Y7DxZxScmN4+Gvz2Pl8Jfter6X5dC8rniq9Jv+nyuTD2e1l+28raKnsI7MkjuVPlozbtiKEID7NSnxaLnPuzsXZ7aXuaDeNJ3s4sbOFo9ua0Bk0pBXEklYUS3pxHElZUWiu8j8VQmCNjcMaG0dG6dSLzgcDfjx9Djz9/Xic/XicfXidzov2gw6HQkgljBIOj9rDckLE5tnTM0nMzgXgga/+84iWpYQVetvctNc66ajrp+1MP/2dkUlXBpOWtKI4ZqzMIqssnpjEW2P1Q6NZx6pPl5JZEs/ul6p46d/2s/TxYgrnjPz+tiq3BlKRHNvRzIcbzyA0YtxZ9ddCdIKZ6Ssymb4ik2AgTEulg8aTvbRUOfhwYy0AeqOWlPwYknOjScmNvJquc6MVvcFITFIKMUkp13zND7ZcMo5l2JkQCh8YkY2GA94QjnYP3c0uupsH6G6KvIYCkaF3c5Se5NwYpt6eTlphLAmZUbesO0QIQcnCVNIKY9j6q1Ns+cVJGo73sOTRIozmkW0mo7FJ9HhmvNff0e5m+29O017bT1ZZPMuemDJurfprRW/QklOeQE55JGzU4wzQWt1HS6WDttp+Dr1Vz9nhzZhEMwmZNhIybCRkRGHPsGGLMw7rw2602sCEWTztRpBS4nMHGej14+zx4uz24ez20t/pobfNg7vvY7+/3qQlIcNGYmYUybnRJOfGEJ1w+UGYWxklrHDw7QYOvlWPNdbAHX9SQuYUdfOUyUY4rHBkayMH3qhHZ9Cw5OFCiuanTMg2fyEBX4iuBhftdf10NkQMPWeXd+i8zqAhNtkSOZIsRCeYiYo3Yos3ERVnuuK4wEgwLqJ0rpcbVfg+d5C2M/3IsERRImFO4bBCyB8m4A9HXr1hfO4gPk8Q30AQT38At9OPEjq//kaLjphEM3EpVmJTLMSnWLFnWIm2m8flsqojSXttP9t+XUFfh4ept6dz2wP56gzdSUJ7XT87fneanhY3+TMTWfJo0TWHNk5UAr4QPc0D9LQM4Ojw0Nfhpa/DjavHx4Vq1Bylx2QzYBl8Ndv06E1aDCYteqMOvUmLVqdBoxFotAKhEUTFm0jMurHJkJNK4XfUO/nMQ38FwF2zn7xkGr1Ri8mqx2TTY7TosMYYscYasMQYscYYiUk0E2U3Xbe/bqITCoT58LVajm5vItpuYvknp5AxzNb+v/7rvwLw9a9/fVjzvVUYT/UP+ELse62WYzuascYYuf3RIvJmJI61WOOacFBhoM+Hq9fPQK8PV68Pd38Ar+vsETEyA/7QRQbmWd4+9Fvs6VZ+vfGHNyTDpFL4QX+YZUuXgYDXX3kbjTby1NQZtOiNWnQG7S3rWx8vtFb3se03FTi7vExZkMLCBwsw24ZnJuWtGoc+XIyX+tce6WL3S1UM9Pkpvz2dBevzL1qZUuXmCIcUgr7wkPI/65FY9+BdCI3g/Q9ubPB2vMThjwp6oxa9KRLXbk9XlwAeCdIKY3ns6/M4+FY9h7c0Un+ih8UPFkwan+5ExtnjZfdL1dQf6yY+zconPjv1oj0WVIYHrU6D1qbBZDvfk6C/zMz/4WZCKHyV0UFn0LJgfT6Fc5N573enefe5Ck7samXxw4Uk51x6SzqV8Us4rHB0WxMH3qgDYOEDBUxbkTEu54uoDA+qwle5buzpNh74v7M5vbeND1+r5ZVvH6R4QQoL7ssfF1PrVa5Oc6WDXS9W4WhzkzMtgdsfLbrlQy1Vrs6EUfh2u32sRZhUaDSC0kVpFMxK4tA7DRzZ1kjNoU7KFqcx887s61L8k/3ejWb9Bxw+PnilhppDnUQnmLj7z8rJna4Oyo41o9UGJsSgrcrY4+z2cvDteir3toMGShelMWNl5i0z23iiEwqGObqtiYNvNyAVyazV2cy6M2vM13RSGR4mVZSOyvjB2e3l0OYGTu9pQwlL0otiKVmURv7MRFW5jAFSSmqPRBYOc3b7yJmWwJKHC4lOGN+ze1Wuj0mn8L/61a8C8K1vfWs4RVK5Qdx9fir2tlGxpw1nl3donaH0oljSi+KwZ9iGQmUn+70bqfp3N7t4/w/VtFT1EZ9mZfGDhWSWqjOmxyM32wYmVVgmwN69e8daBJVzsMYamXNXDrNXZ9Na3UfVgQ5aKh3UH4vsEqbVaTBH67FEGXjjla2RBbnyngABAiIroGpAIyKzEMXgjESNVqDVatDoRCTETadBq9eg02uG5l2cDdM1GHUYLToMZh06g2bcho8Od9sdcPjZt6mW03vbMFn0LH2siNLFaVddBVJl7Bgt/TVhFL7K+ERoBOnFcaQXRzaNGXD4aKnqo7t5YGgmYjgkQSr0tAwgZcQNISUgz05MGdw9SJEoIQUlLAmHlIums18JjU5gsuox2wyYo/SYowxYYwxYY43Y4kzY4o1E282Yo/Tj9sFwNQK+EIe3NHJkayOKlMxYkcnsu3LU2eMqQ6gKX2VUscWZKJ6fQvH8j7/7rz9G1g95/BsLriuvyEY3knBQIRQME/SHCQUUgv4wAV+IoC+M3xvC7wni94TwuYN4XUG8rgDO7n7c/QHCwfM3HdUbtcQkmYlNshCXYiEu1UpCho2YJMu4na0dDimc3N3Kwbfq8LqCFM5NZsF9eaqfXuUiVIWvcsui0WrQaM/OUrx+K1ZKid8TYsDhx9UbWSk1slqql84GJzUfdcJgL0Jn0JCQEUVSdmS11JS8GKLsY7taqqJIqva3s39THa4eH2mFsdzz5wXqJDiVyzJhFH5GRsZYi6Byg4zVvRMi4uYxWfUkZFy8JEcoEMbR7qGnZYCuJhddjS5OfdDKsfeagcg4RfrgTkmZJfE3PHHpeusvFUnt0S72b6qjt9VNYlYUy54oJrMk/pZ1R012Rus/MGGidFRURgMlrNDT6qb9TH9kw4wqB15XEIC4FAvZ5QnklNtJzY8Z9kFSKSV1R7rZ/2YdPc0DxCZbmL8uj/xZiaqin+RMurBMFZWxQEpJb6ubpopeGk700FrdhxKWmGx6cqcnUDA7iYziuJtS/lKR1B3r5sCbdXQ3DRCTZGbuPbkUzklSI29UgEmo8L/0pS8B8L3vfW84RVIZBSbSvQv4QjSe7KX2SBf1x7sJ+sKYo/QUzEmmeH4KSdlRF1njl6u/ElaoPtjJR5sb6G11E5NoZs49ORTNTVYV/QTjZv8D4yYOXwixBvg+oAV+IaX89kiUc+TIkZHIVmUUmEj3zmDSUTA7iYLZSYSCYRpP9lJ9oINTu1s5/l4zcalWShelUjw/BXNUZD+BC+sfCoY5vbedw1sacHb7iE+zsvLpUtWin8CM1n9gRBW+EEIL/C+wCmgGDgghXpdSnhrJclVUxgM6vZa8GYnkzUjE7wlSc6iTij1tfPBKDXs3niF/ZhJTl6YPpfe6Ahzf2cKJnc14XUGScqJZ9GAhudMSJt32miojw0hb+POAGillLYAQ4kXgPkBV+CqTCqNFT9mSdMqWpNPTOsCp3a2c/rCd6gMddNb3o9NIfv33ewgHFbLL7cxYmUV6Uaw6GKsyrIy0wk8Hms753AzMPzeBEOIZ4BmArKysERZHRWXssafZWPxgPlMTWjnx0gEUrw+fVk9K5wGmLUwg+5Oz0caoO06pDD9jHocvpfz/7Z13fFRV2se/dyYzSSa9J6RXeu8dpVcVFFzRFVAQ6+7qurusu+/6rlveVVddO6iIihQVBBtIkSogvScEAukJIZn0ZDLtvH/ciGXpSWYmmfP9fO4nydwyz8k593fPfc5znrMIWATqoO2NXictLa3ZbJI4FneqO6vRSOXq1ZSvWIklP5/IsDD6pYagCQikl08GtW/s4vR7rxJ0x+0Ez5qFLirK2SZLHICj7oEWjdJRFGUg8LQQYmzj3wsAhBCXTAknwzIlbRFht1P33XdUfPwJVRs3gsWCoU8fgmbehd+oUSi6H2YJmzIyKFu8mKqv1oGiEHjbbYTMvR99bKwTSyBxdVwiLFNRFA8gExgJFAD7gLuEECcudbwUfElbwpxfQOWaNVSuWYMlPx+Nvz8Bt9xC0IzpeKakXPFcS0EBpW+/TeUnqxB2OwGTJhEy/wE8ExMdZL2kNeESgt9oyATgJdSwzMVCiL9f7timCP68efMAWLRo0Q2dL3EebanubJWVVG3YQNXaz6jbvx8UBUP//gROm4bf6FFovP47/cKVym85X4Jx8WLKV65EmM0ETJlC6MMPyR5/G6Op94DLxOELIb4Cvmrp78nMzGzpr5C0EK297mw1tdRs2ULVunXU7NgBFgv6hATCfvUYAVOmoIuOvuL5Vyq/LiKciAV/IGTu/ZS9/Q7ly5dT+cUXBN52K6Hz51/12pLWgaPuAacP2kokrRFbRQXVW7dSvXETtTt2IMxmPCIiCJ45E/+JE/Dq0qVZQyo9QkOJ+MPvCZ49m7K33qJi5Uoq1qwlaMYMQuc/gEdoaLN9l6TtIgVfIrkGhBCYz2VTs20bNVu2UHfgANhseERGEjhjBv7jx+HdoweKpmVnwuoiwon801OE3DeH0tffoHz5cipWryb4l/cQMmcOWn+ZGllyeaTgSySXwVZVRd3evdR8+y21O5Wq3sYAACAASURBVL/FkqdOKfFMTSHk/vvxG3kzXl27OmVylC4qiqhn/krwnNmUvvIKZW8upHz5CkLnzSPo7ploPD0dbpPE9Wkzgt+jRw9nmyC5QVyl7mzV1dQfPEjdvn3U7t2H6fhxsNtRDAZ8+vcnZM5sfIYOQx/TvH7zppTfMzGR6BdeIOT++yl58SVKnnsO49KlhD36KAG3TEHRapvRUklL4ah7oM1ky5RIrgdhs2HOzqb+6DHqjxym/tBhGjIzQQjQ6fDu2hWfAf0xDBiAoUcPFL3e2SZfE7V7vqPk3//GdOwYnqkphD3+OL4jRsgUDW0clwnLvB6k4EtaAntdHQ1nztCQmYkp4xSmjHQaTqZjr6sDQOPri3e3bnj36oWhdy+8e/RA491614MVQlD99QYuvPgi5pwcDH36EP7kb/Hu3t3ZpklaCLcT/LvvvhuApUuXNqdJEgfQHHVnq6nFWlSIOT8fS14+5pwczNnZNJw7i7Ww6OJxisGAV1oaXp0749WlC95du6BPSmrxwdYr0VJtV1gslH/8MaWvvY6trAy/sWMJ/82v0SckNOv3SJpOU9uAy8ThO4r8/HxnmyC5AYQQ5OfkIITAWlaGsNoQFgvC3IAwmbCbTNhra7FXV2OrqsJWWYXNaMRabsRWWor1wgUsxeexV1f/5LoaHx/08fEYevfB845EPFNT8UxJQRcX51RxvxQt1XYVnY7gu+4iYMotGN99l7J336V682aCpk8n9OGH8AgJaZHvlVw/jtKvNiP4EtfGajRiOn6c+uPHMZ04iTk7G1tlJbbKSuqyzgBwevCQa7qWYjDgERSER2go+oQEDH374REViS6qHfqYaHSxsWiD5YLe36P19SHs0UcIunMGF15/nfKVK6lcs4bg++YQMmsWGh8fZ5socRBS8CUtghCChvR0qr/ZQs2WLZhO/JA+SZ+QgGdqKtqgILQBAegWLUTRaIj4859RNFoUvR5Fp0Pj7YXi5Y3W1weNjw8a/wC0Af6XTE8guToeYWFE/eUvBN/zSy68+CKlr7xK+YoVhD38MIHTpv0kiZukbSIFX9KsmHNzGxOGrcVSWAiKgnePHoT95jd49+yBV6dOaH19f3KO7vPPAAi+6y5nmOx2eCYlEvPKy9QdPETJ889T/PT/YlzyHmG/+Q1+Y0bLN6M2TJsR/IEDBzrbBLfFbjZTvX49FR99fDFhmM/gwYQ+8gi+w4dd1Vfs7nXnrPIbevUk/sOl1GzZSskL/6bgV7/Cq3s3wh9/Ap/+/Zxik7viqDbQJqJ0ck7s4cTTT4JOh0avQ9Hp0Xp54x0chl9IFIHhsQRFxqNr1w5dVBRaP78WsN79sBQXU75yJRUffYytrAx9fDwBU6cScMsUdJGRzjZPch0Iq5XKtWu58MqrWIuL8RkyhPDHf4NXp07ONq1VI2w2rOfPYykqouFCCaVFZzEWZ1NbWoStvh67uQG7uQFhtqDp2pFxT71xQ9/jVlE6pvpqvMpqUKw2NFY7GpsdvVngYwKtgFrU7XvsBi884xPwTkpGn5SIZ1ISnh06oI+Pd7kIDlek/sQJjO8uoWrdOrDb8R0xgqC7Z+IzaJB0B7RSFA8PAqdNw3/iRMqXLads4ULOTZ2G/4QJhP3qMfTx8c420aURZjMNWVmYMk7RkJFOw9mz1OWcw1ZYjMZq+8mx/oCHHsw6sHlosHko2D201MUYW9zONtHDB5g2bRoAq1atAsBsM1NQXUBhyRnOF52hMPckF86lYy0uJqTSTrtyhYQKHf7lDSiN/wLFYMCrfXu8OnfGu3t3vHt0RxcTI0UMdRC2dscOyha/S92ePWh8fAi8/XaC7p7Z5NzsP687d8MVy2+rrqZs8WKMS95DmM0ETp1K6EMPyiUXG7EUFVF38CD1Bw5Sd+gQDWfOgMUCgFWvpTBEoSDAxvlAqA33Iyg+leB2yUS0SyUutjNxocn46fwuaktT24Bb9fABysrKfvK3XqsnMTCRxMBESBt98fM6Sx1HS4+yM38nywq2U1B6lugyGFIfw8DqCNoVNlCxejXljRMgtCEhGHr1xNC3L4a+ffFMS3Or/CTCaqVq3XrK3n6bhlOn8IiIIPzJJwmcfkezucZ+XnfuhiuWX+vnR/ivfkXwzJmULlxExYoVVK5ZQ+Av7iR03jy3S8dsq6ykdvduanbupG7XbjUgAVC8valvH0vGzbF861NIZqgFU2Qgg2OH0ieiDyMj+xDnF3fVTqOj2kCbEfxrxaAzMCBqAAOiBvDbvr8lryqPjbkb+Tzrcz6oOIiuh46Rc0dwl+dQ4nNNmA4foe7AAao3bgJA4++PoV9ffAYMxGfQQPSJiW3yDcBuMlGxejXGdxZjKShAn5JM1D//ScDECa0mr4yk6XiEhhL51B8JmT1LjeH/cBkVH39C8My7CL7vPjyCgpxtYovRcPYs1Rs2UrN1K/VHj4LdjsbPD58BA7DPmMyW4EKWmXdhtJ4lxCuEUfHTuDt+FH0i+uChcU1pdU2rHEisfyxzusxhdufZZBgz+CzrM9ZmrWW9eSOdQjoxc95Mxic8AyWl1O3fT93evdTu2k3Nps0AeERE4DN4ML5DBuMzaBDawEAnl6hp2KqqKF++AuP772MrK8O7Rw8innoK3xHD5fiGG6Nr1452f/sbIffdR+nrb1D2zmKMy5YTfPfdBM+e1SaE//u5I1Vfb6B640bMZ88C4NW1K6HzH8Br8CB2BZawLHMlB0vewcPkwU2xN3F76u30j+qPVuP6b/5txoc/YsQIALZu3dpkO+osdXxx9gs+TP+Qs5VniTBEMKfLHKalTcNTq+YZN+flUbt7N7Xf7qJ2927sVVWg0ahZFocNxXfYcLw6d2o1ImkpKaH8gw8oX74Ce00NPsOGEjp3Lt59+rT4G0xz1l1rpDWWvyEri9LXXqdq3To0BgNBM2e2WuE35+VR9cUXVH7+hSryWi2Gvn3xGz0Kv1GjMAf78unpT1mavpSCmgLi/OK4I+0OJidPJsS7edJTNLUNuJ0Pf+TIkc12LYPOwPT207kj7Q52Fe5i0dFF/HPvP3nr2FvM6jyLGe1n4BUbiz42lqDp0xFWK6bjx6nZsZOanTsoffU1Sl95FW1ICL5DhuA7Yjg+gwe75GpE5pwcyt5ZTOWnnyJsNvzHjSVk7ly8OnZ0mA3NWXetkdZYfs/kZKJf+Dch8x+g9I03KHvrLYwffEDQL35ByOxZeISFOdvEK2KrqaVq3VdUrv6U+kOHADD06UPwvffiN2Y0HkFBVJgqeOPk+6zYvIJqSzW9wnvxZN8nGREzotl7845qA22mh9+SCCHYf34/bx55k73Fewk3hPNIj0eYkjzlkhVvNRqp/fZbarZuo3bnTmyVlWqvoWdPfIYPw3f4cDxTU53q+68/doyyt9+hesMGFJ2OgKm3ETJnDvq4OKfZJGm9NGRlUfrmQqq+/BJFpyPw9tsJmTPbpRZZF0JQf+gwFas+oWrdekRdHfqUZAJuuYWAiRPRtWsHgNFk5L0T77EiYwX11npGxY9iVudZdAvr5uQSXB63S4/sKPYV7+PFAy9yrPQYyQHJ/Lr3rxkeM/yy4i2sVuqPHqVm23Zqtm2jISMDUH2iPsOH4TtsGD4DBjgk/7qw29XQyncWU7d3Lxo/P4LuvJPgX97j8j0ySevAnJ1N6aK3qPzsMxCCgEkTCbn/fjxTU51mk62mlqovPqd82XIaMjPRGAz4T5xA4LRpeHXvfvHerTBVsPjEYlZkrMBkNTEucRzzus4jJSjFabZfK04XfEVRngbmAhcaP/qjEOKrK53TFMEfP348AOvWrbuh868HIQSbcjfx8sGXya7KZmDUQH7f7/ckByZf9VxLcfFF8a/dswdRV4ei12Po3x/fYcPwHTqk2fOV200mKj/7DON772POysIjMpLgWfcSePsdaH2dnynRkXXnirTF8luKijAuWUL5Rx8j6uvxvekmQubMdsiY0Pc0ZGVRvmw5lWvWYK+txbNTR4J+8QsCJkz4SYbQOksdH5z8gCUnllBrqWVC0gTmdZtHUkCSQ+yEprcBVxH8GiHE89d6jqsM2l4rFruFj059xGuHX6POUsedHe7kwe4PEuAZcE3n281mdf3U7dup2boNc04OALq4OHyHDsVnyGB8+vW74fS11gsXKF++gvIVK7AZjXh26kjI7Nn4jxvnUpkRW+OgZXPSlstvLS+nfOmHlH/4IbaKCry6dCF49iz8x45F8Wj+IUQhBLU7v8X4/vvU7tiBotPhP2E8QXfdhVe3bj952FhsFj7K/IhFRxdhNBm5KfYmHu35KKlBjn8bkYO2rQCdRsfMjjMZnzieVw+9yrL0Zaw7t47f9vktk5ImXbUno9Hr8R08GN/Bg4lYsABzbi4123dQs2M7FatWUf7hh6DTYejVSxX/QYPw6tjxqpE/9ceOY/zgfarWrQeLBd/hwwmePRtD/35tcs6AxHXxCAoi7NFHCLn/PirXrsX47hIKn/gtJf/+N8F33UXg7bc3Syiz3WSicu1nGN9X32K1YaGE/eoxAmfMwCM4+CfHCiHYkLOB/xz8D3nVefSN7MvLvV6me1jbXwKypXv4s4AqYD/whBCi/BLHzQPmAcTFxfXOaezlXi+u0EtKL0vnb3v+xtHSo/SL7MdTA5664ddCe0MD9QcOULPzW2p37lQX2Aa0gYEYBgzAZ8AAfAb0Rxcfj6Io2E0mqr5aR/mKFZiOHkVjMBAwbRrBM+9y+SXtXKHunIk7lV/Y7dRs2YLxvfep27sXxcuLgMmTCbr7brzap1339azl5ZQvX0750g+xGY14depE8Kx71bfYS0wQPFxymOf3P8+RC0dICUzh8d6PMyR6iNM7Qo7q4TdJ8BVF2QRcKi3iU8AeoBQQwDNAlBBizpWu19pcOpfCLux8kvkJLx18iXprPXO7zuX+rvej1zZtdqqlpIS6PXuo3bWb2l27sJaUAKCEh1IaH0jgyQK0tfXok5MImnEnAVNv+6+8866Kq9Sds3DX8ptOnaJ86VIqP/sc0dCAd5/eBM2Ygd+YMWg8Pa94rjm/AOOSJVSsWoWor8dn+DBC5tyHoV/fS4p3fnU+Lxx4gY05GwnzDuORno9wS/ItLjNZqlW4dIQQo67RmLeAL5ryXVdj0qRJLXn5a0ajaJjefjo3x93Mc/ue440jb7AhewP/O/h/m/TKqAsPJ2DKFAKmTKHB2sCO3Ss5uWElPsfPkZJZyp44hQ29tFxIq6VnxCF65Ah6hPegY3BHdFrX8ddfClepO2fhruX3at+eqGeeIezxx6lcvZrylR9R+OTv0P79HwRMnYrf6FF4dez4kxXOGk6fpuztt6n84kvQaAiYOJHgObPxSrv020GNuYa3jr3FByc/wEPjwUM9HuLeTvdi0BkcVcxrwlFtoCVdOlFCiKLG338D9BdC3Hmlc1pDWOb1sj1/O3/d/VdK6kqY2XEmj/Z89IYamxCCdGM6a8+s5ctzX1LZUEk7n3ZMS5vGlOQpVJmrOHT+EAdLDnKo5BBFtUUAeGo96RTSiS6hXegS0oWuoV2J8ZMZQCWuh7Dbqduzh/IVK6n+5huwWsHDA8/UVLy7dMFqNFKzeTOKtzdB0+8gePbsy667YLPbWHNmDS8fehmjyciU5Ck81vMxInwiHFwqx+AKUTofAD1QXTrZwAPfPwAuR1sUfFB7GS8dfImVp1YS6xfLM4OfoXdE72s693ztedadW8fnZz8nszwTvUbPzXE3c2vKrQxsNxCNcukB3JK6Eo5cOMKhkkMcu3CMdGM6DbYGAPz0fnQI7kCH4A50DO5IWlAaiQGJTXY7SSTNhbWsjPpDh6g7dozSA7uxpmciENinjaXLA79DH3L5bJ37i/fzr33/IsOYQc/wnvyu7+/oEtrFgdY7HqcL/o3QFnz4V2Jf8T7+/O2fKawpZGbHmTzW6zG8Pf57wlVlQyXf5H7Dl2e/ZG/xXgSCLiFduCXlFsYnjr/msM8fY7FbyKrI4ljpMdLL0skwZpBZnnnxIaBVtMT7x5McmExSQBIJAQkkBiQS7xePr75lxwJaQ921JO5e/p8jhOC74u9Yf249W/O2UmYqwwMtnlo9tbZ6Qr1DGZcwjjEJYwjwDECn6NBqtNRZ6njz6Jt8nf01kT6RPNH7CcYmjG0Vb7OtwocvuT76RvZl9ZTVvHjgRZamL+WzrM/oFdGLPhF96B7WnXOV59iQs4E9RXuw2q3E+cUxv/t8JiROICEgoUnfrdPoLvbqv8dqt5Jdmc2ZijNklmdypuIMGcYMNuduxi7sF48L8gwi1i+WaL9o2vm0I8oniijfKCIMEYQZwgj0DLzsm4ZEcq0IIdiev503j7zJ8bLj+Oh8GBo9lBGxIxgaMxRPrSfb8rbx5dkvWXlqJUvTl/7XNby0XjzU/SFmdZl1yc6UuyMF38EYdAaeGvAUYxPG8lnWZxw4f4CteVsv7o/2jeaejvcwJmEMnUM6t2jvxEPjQUpQCilBKYxLHHfxc7PNTF51Hucqz5FbnUtedR551XkcvXCUjdkbsQrrT66jVbSEeIUQ4h2Cv6c/gZ6BBHoG4qf3w0fnc3Hz1HripfVCr9Wj1+rRKloURaHWUouiKJytOItBZyDIK+hiVlJJ28cu7GzJ3cLCowtJN6YT7RvNXwb+hSnJU/7LzTgmYQxjEsZQ2VDJ/vP7MVtMWCuysZacRJSdYWBgB6L8O4Iipe1SyP+KI7FZIX8fnN5AnzMb6VNTAoHxXAjszRFvA5FpE+mcMsHpr6B6rZ7kwORLpoqw2W2Umcooqi2iuLaYsvoySutLKa0vxWgyUtFQwanaU1Q0VFBjrvmvh8OlOFum5h2/Ze0tFz/z0/sR4xtDvH887YPb0yG4A93CuuGvd72Mo5Ibwy7sbMzZyMKjCzldfpo4vzieGfwME5MmotNcObIsoDyPkftXQtY3UNuYvSU4CbIPw/4l4BkAKSMhbRwk3wS+4S1foFaAFPyWRAgoy4JzW+HsVji3HUyVoGghbiBEdoeKHMLy9jOqMh8OfgKjnob+88FF8+hrNVrCDeGEG8KvGmYqhMBsN1NjrqHWUkuDrQGzzYzJZsJsMyOEwCZsPPrmowgh+L9h/0eNpYZyUzkldSXkV+dzrPQY67PXA6CgkBaUxsB2AxkSPYReEb2uKgwS18Nmt7EhZwMLjywkqzKLxIBE/jHkH4xPHH/1laLqjPDN3+DAu+DpB6ljVUFPGgH+7cBcq95rp9ZB5no4sVo9L7IrJI9Uj43pB3rXCst0FG1G8KdPn+5sE1SBv3AKcndBTuNWVaDuC4iFjpMhdYzaOL1+NvBacwE+fwy+XqA21FvfgADXSS17IyiKgqfWE09vzysuFDHvnnkAjE8cf8n9VeYq0svSOVhykAPFB1iavpQlJ5bgr/fn5ribGZ8wvtWsOHQpXKLtOgCr3cr67PUsOrqIc5XnSAlM4blhzzE6fvTV685mhf2LYcvfoaEa+s6FEX8Aw0/TJqD3gQ4T1c1uh+Ij6ltA1hbY/Rp8+xJodBDdGxKGQMJgiO4DXs59c3RUG2gzUTpOoaYECg9DwX7I3w8FB8BUoe7zjYT4gZAwVBX44CS4mqtGCDj4HqxfAFo9TH4JOt/W0qVoddRZ6thTtIdNOZvYkreFGksN4d7hTE6ezO1ptxPjF+NsEyU/wmq38uXZL3nr2FvkVOWQGpTKA90eYHT86Gsb7D+7Vb0nSk5C4nAY938Q0en6DWmohpzdkLMTsr+FwkMgbIAC4Z0gpg/E9IV2PSGsPbj4hMUf43ZhmXV1dQAYDC3wqmYxQdkZuJABJelQfBSKjkJNsbpf0UBYR4jprb4uxg+6NoG/HGVZsHqu+gDpMRPG/0t9fW2jNKXuzDYz2/K3sfbMWnYU7EAIweDowdzT6R4GRg10+njItdCibdeJWGwWPj/7OW8fe5u86jw6BHdgfrf53BR307UJvfEcbPgTZHwBgfEw5m/qW3Jz1WlDNeTtVcfVvt9Mleo+rSeEd4SobhDZDULTIKyDOhbQAm2qqW3A7QS/SXGsQqi+wap8qMyHilwwnv1hK8+G78MUFa369I/sqjaEqO5qj8CzmWPVbRbY9izseB4C42Dq2xDbt3m/w0Vorjj04tpiVp1excenPqbMVEZKYApzusxhXOI4l/b1t7U4fLPNzJoza3jn2DsU1hbSKaQT87vNZ0TsiGt7ADdUw44XYPerqvtl6OMw8BHQeV393KZgt4MxS31rLz6iduqKj0L9j3I+egVAaHsIToSghB82/2jwiwSPG4suk3H414O5Th2sQai9YrtNFUxrPVgat4Yq9eltqoT6CqgthdoS1S1TU6Ie+2P0fhCSpIp6l9shvIP6hA9JueFKvS60Orj5KUi+GVbPg8VjYcQCtfG3Ul91SxPpE8nDPR5mbte5rDu3jiUnlvDHnX/k1UOvcl/X+7g15VY5m7gFMVlNrDq9inePv8v5uvN0C+vGnwb86dqzUdrtcGQZbP4r1JyHbjPUIAb/di1tuopGA6Gp6tbtDvUzIaC6SB2bK8384WfOLjj6EWoigR9hCAG/KPAJU8cXvIMbfwap4wt6H9D5qIPGWr3agdRoVf262oB1M9A2BL8kXfXHAbx185WP1erBKxB8QtXXs9j+6k//aAiIUQdKA+LU/a7gDogfCPN3wJePw5a/qQNQUxdBYKyzLXNZ9Fo9t6TcwpTkKWzP386iY4t4Zs8zvHXsLeZ2ncttqbe5dI+/tVFnqWPlqZW8d+I9ykxl9I7ozV8H//X6XGo5u2H976HoiOpHv3OZ6lN3NoqiPnD826kRPj/GaobKPCg/B1WFUF3c+LNIDRUtz4Z64w9uoitRWKtqTgvTNgQ/JEn1twHc9fwPT029D+i8wcNbdbl4Bbb8a2FL4B0I096BlNHw1W/hzcEw6SXoMtXZlrk0iqIwPHY4w2KGsbtwN68feZ1n9jzDu8ff5eGeDzMhcYKcIdwEKhsqWZ6xnKXpS6lsqGRg1EDmdZtHn8jrEGrjOdj0Fzi5Vu10TX0but7uGp2tq+Ghh5BkdbsSNqvqYTDXqpulVvVK2K2qN8Juha+eVDujLW1yi3+DI/AOUl+lANLGOteWlkJRoMcvIK4/rJoLn8yGrM0w7l/NP37QxlAUhUHRgxjYbiA7Cnbw8sGXWbBjAUuOL+Hx3o8zKHqQs01sVZTWl/L+yfdZmbGSOmsdw2OGM7fb3OtL/22qhO3Pw3dvqq6MEQtg0KNqJ62tofVQ3To/DyH9MYb/c4gpbUPwgVmzZjnbBMcQnARz1sPWf6oDW7l71N5/ux7OtuyGcVTdKYrCsJhhDIkewrpz63jl0Cs8sOkBBrUbxOO9H6d9cHuH2PFzWkvbzavO470T7/Hp6U+xCitjE8ZyX5f7ru//ZrPAgSVq+60zQo+74OY/Oc5P76I47B5oK1E6bsm5HeqAbu0FGPk/aiSDi87QdUXMNjMrMlaw8OhCqs3VTE2dyiM9HyHUu+V9qa2JDGMGi48t5uucr9EqWqYkT2F2l9nE+8df+0WEgFNfwcb/UUOc44fA2L+36o6KK+F2YZmlpaUAhIa62c1aZ4TPHlVjlZNugtveVMPDWhHOrrvKhkoWHl3I8vTl6LV65nabyz2d7nFYAjdnl/9SCCHYU7SHJSeWsKtwFz46H6anTefuTncTbrjOvDQFB2DDnyHnWwhJhTHPqDluWoOf3kE0tQ24neC3tVjm60II9TV5/QI13OvWN1rVWIar1F1OVQ7P73+erXlbifaN5sk+T3Jz3M0tPnnLVcoP6mSp9dnrWXJiCZnlmYR6hzKz40ymt59+/YnryrLUEMuTa8AQCjctgF73tqoZrI5CxuFLrh1FgT6z1Rm+n9wHy6ZDvwdg9F9bZ1SSk4j3j+eVm19hd+Funt33LL/e+mv6R/bnyb5POs2/7ygqTBV8cvoTlmcsp6SuhOSAZP466K9MTJp4/XMXakpg27/UTojWE4b/XnU3OjlfjUQKftsirD3M3Qyb/hf2vAbZO9QB3RvJO+LGDGw3kI8nf8wnmZ/w6uFXmf7FdO5Iu4OHezxMkFeQs81rVs5WnGVp+lI+z/ock81E/6j+PD3w6WufLPVj6itg1yuw5w2wNUDv2TD8dzI1sQshBb+t4eEJ4/6hztBdMx/euknNQdL3fukzvQ48NB7c2eFOxieO5/XDr7Py1Eq+OvcVD3Z/kDvb34muFbslrHYr2/K3sSJjBXuK9qDX6JmcPJm7Ot5FWlDa9V/QXAd7F8LOl9TkgV2mwU1PXT0+XeJwpOC3VVJHwYO7YM1D6mStM5tgyqvgG+Zsy1oVAZ4BLOi/gDvS7uC5/c/x7L5n+ejURzze+/Frzw3jIpTWl7L69Go+zvyY4tpiIgwRPNrzUW5Pu51gryvEiF8OawMceA92/FtNJJg6Bm7+s5pwTOKStBnBf/DBB51tguvhGw4zP4a9i9QoiTcGqQO6qaOcbdlPaA11lxKUwpuj3mRHwQ6e3/88j215jL6RfXmi9xN0Du3cpGu3ZPltdhu7i3azKnMVW/O2YhVWBkQN4A/9/sDwmOFXX3DkUljNcHipOnGqqgDiBsEd76pjSJIbwlH3QJOidBRFuQN4GugI9BNC7P/RvgXAfYANeEwI8fXVrifj8FuQ8ydg1f1qTvH+82HU/8oB3RvEYrfwSeYnvHH4DcobyhmfMJ5Hej5CnH+cs027SH51Pp+f/ZxPT39KUW0RQZ5BTEmewrS0aSQGJN7YRW0WOLICtj+rZpSN6au6bpJGSHehk3FIWKaiKB0BO7AQ+O33gq8oSidgOdAPaAdsAtKEELYrXa8pgp+XlwdAbKxMKnZZLPWw8S+qvzW8E0x9CyK7ONuqVlt3NeYaFh9fzNL0pZhtZm5NuZW53eYS7Xt9K5U1V/mrzFVsyN7A51mfc7DkIAADogYwLW0aN8feXrwOiwAAG9FJREFUfOOZQq1mOLJcdd1U5EBUD1XoU0dLoW8mmtoGHBqHryjKVn4q+AsAhBD/bPz7a+BpIcTuK11HxuE7iNObYM2D6gDbyL/AgIecOkO3tdddaX0pi44u4pPMTxBCMDl5MrM6zyIpMOmazm9K+SsbKtmWv42N2RvZVbgLs91MUkASk5MnMzFxIlG+Udd9zYtYTHD4Q3UwtjIX2vVSlxVMHSOFvplp7XH40cCeH/2d3/jZf6EoyjxgHkBcnOu8ErdpUkfBQ7vVGbobnoLTG9rEGrrOItQ7lD/2/yNzuszh3ePvsur0Kj498ylDo4cyo/0MBkcPvjFf+WXIq8pjV+EutuRv4bvC77AKK5E+kUxvP51JSZPoFNKpaYPJDTVqDP2uV9TB2Og+MOkFSBklhb6Vc9VWqCjKJuBSc/WfEkKsbaoBQohFwCJQe/hNvZ7kGvEJVXOOH3wP1v8R3hgIE19QU9NKbohIn0gW9F/AA90fYOWplazMWMkj3zxCuHc4YxLGMDJuJD3Ce1yX+AshyK/O54TxBPuK9rGrcBf5NfkAxPjGcE+nexgdP5ouoV2aHjFUZ4S9b8F3b6irPCUOg6kL1XVkpdC3Ca7a8oQQNxLSUQD82BkV0/iZxJVQFOg9S11offU8WHUfnFoHE59XU05Lbohgr2Ae7P4g93e9n+1521lzZg0fnfqIpelL8fbwpltoNzqGdCTWL5ZwQzjV5moANudspryhnIqGCsrqyzhVfoqMsgyqLep+g4eBflH9+GXnXzKo3SDi/OKaJyy0PAf2vA4H3wdLHaSNh6FPtNklNd2ZlnLpfAYsUxTlBdRB21Rgbwt9l6SphCTDnK/VQbntz6rLt93yKqSMdLZlrRqdRsfI+JGMjB9JraWWbwu+Zf/5/RwuOcyy9GWY7WYAzhrPAvDrrb++eK63hzepgalMSJpAx+COdAzpSGpQavOu1FV4WF039vhq9eHfdbqak17OzG6zNEnwFUW5DXgFCAO+VBTlsBBirBDihKIoHwEnASvw8NUidJrKE0880ZKXb/toPWDE79XIi0/nw9Kp6uzc0X9t8UUp3KHufHQ+jEkYw5iEMQDYhZ2SuhJK60vZoGzAJmxMmjSJIK8gAjwD8PbwbhlD7DbIXA+7X1OzV+p9YcCD6sC9HMNxGo66B9pMtkxJM2Kph83PqPl4gpPUAd24Ac62StIUTFVweJm6wlT5OQiIVedj9LoHvAKcbZ2kiTg7SsfhnDp1CoD27dt2VkOHoPNW8/G0HwdrH4bF42Dgw+rKRLrm73m6e921aPkvnFJnWh9ZAeYaiOkHo/4CHSarb3USl8BR90Cb6eG39lhul6WhWk3LcOBdCE1Te/sx17FI9TXg7nXX7OW3WSDjS9j/DpzbrqYo7jIN+s2F6F7N8x2SZqW1x+FL2gqefjD5Jeg4WY3bf2e06u9tod6+pAmU56hhtgc/gNoS1W0z8n/URUd8XGc1LYnzkIIvuTZSRsJDe9Q1SXe/qoZv3vIaxA90tmXujbVBXd7y0FLI2qJG26SOVRfESRkFGq2zLZS4EFLwJdeOl7/a2+98q9rbf3cc9LkPRj0tVzNyJEJA8VF1EPboSnWSVECsurJUr3sgIMbZFkpcFCn4kusnaQQ8uBu2/F2N+vh+slaHic62rG1TVQhHP1JFvuQkaPXQYZIq8onDZW9eclXajOD/6U9/crYJ7oWnL4z7J3S5Xe3tr7hL9fOP+7/r7mG6e91dsfz15ZD+ORz7RB2ARaiRNhNfgM63geEGFi6RuByOugfaTJSOxInYLLDrZdj2HCgaNaPigAehFS8DeMM0VKuLeJsq1Nh3mwWETe2N633VFcf8o9WlKC+HqQoyv4bjq9SVyuwWdT5E1zug2wy5dKDkv3BoeuTmoimCf/jwYQB69OjRnCZJrofyHFj3e8hcB2EdYcKzagKuq9Cq6s5qhvJsKDsDxrPqJKbybKjIg8p8sNRe23UC4yGqO8T243B9OzCE0sMrH9I/g6xvwGZWHwydb1MT2kX1kAnM2jBNvQfcTvDdPZbbpcj4ShX+ylw1EdeYZyA09bKHu1zdCaH20stOQ+lpVdxLT6t/l2eDsP9wrFcABCVAYBz4x4B/FPiEq8nnPP3UnryiUQW8oQZqzkNlHpSkYy04hEdlDiOWqA+JrbN8sPhGo+tyK3SaorpunLhOgcRxyDh8SeulwwRIvgn2vAE7XoDXB0CfOTDkN+DfztnWqXwv6uXnwHhO7a0bs6CscWvMYAmAhxeEpEBkN3UCU0iq+ndw4nX50K02O4fzKthSUsLmwkFknK8mkjIuaP6Ml8bKrebfc7g0iT7ngrk9uB23RAq8b3CRKonkUkjBl7QMOm8Y+jj0vAe2/hP2vQP731X90IMegYimLfx9RYRQ0whUn4fqQqgqUnvVVQWq66UiV92s9T+co2jUXnpwEsT2UwU9JEV9M/GPueGedkFFPTtPX2B7Zik7z5RSWW9Bq1HoHR/EHyd0YGTHCO7bsxiAhX+Yx6eHCvh4fx5/WH2MZ78+xb0DE/jlwHiCfKTyS5qOFHxJy+Ibpq6WNPgxtcd/8H04sgziB6vCGtUDrCZ1UNNiUt0ldgvYrKobxNagJnOz1IG5Dsy10FAFpkp1YLS+casrg7pStddee0E9/ud4B6miHpqqZgUNjFfdMcFJ6uceTRfVspoG9p4z8m1WKbvOlHG2VHXXRPh7MqZTBMPbhzE0NYwA7/8e0I7w92L+8GQeGJbEvuxyFm7L4sVNmby5LYs7+8Uyb1gSUQFydrPkxpGCL3EMQQkw/l/q5KD978DJz9Ql9OxWyG8c6Px7xPVf18NbFXJDiOpeie0HPmHgGwF+kaoLya+d+lNvaNYigdqD359tZO85I/uyjWSerwHAR6+lb2Iwd/WPY2hqGGkRvte8WImiKPRLDKZfYjCniqtZuC2LD3bn8OGeXO7qH8dDI5IJ9/dq9rJI2j5tZtB2165dAAwaNKg5TZK0JNYGOH+CXes/hoZqBnVNVF0rGp0a0qnVq4OeHl6qi0jvq4q2Z4A6IOoVADrHCV+d2cqJwioO51ZwOK+Cg7nlFFWaAPD19KBXfBD9E4MZkBRMt5hAdNprcwNdS9vNM9bx6jdn+ORgPjqtwj0D4pk/PJkQ3yuEd0paDU3VL7eL0pFImpMqk4X0wipOFlVxvKCKE4WVZJ6vxt54u0QHetMzLpA+8UH0SQimQ6QfHtco8E0hu7SWlzefZs3hArx1Wu4fmsT9QxPx83LDOQ+Si7id4MsefuvFmXVXWW/h7IUasi7UcrqkmtPnazhVXE1BxQ8DuqG+ejq3C6BbTADdYgLpHhtAuF/zvVncSPnPlNTwwsZTfHWsmGAfPY/clMLMAXF4esj0Cq0R2cO/TlwulltyzbRk3Vlsds5XmSgor6egop48Yz25xjpyymrJLqultMZ88Vi9VkNiqA/tI/1oH+lHpyh/Okb5E+Hv2TyLhV+GppT/SF4Fz36dwbdnyogO9Obx0Wnc2jMarUZO0mpNyDh8ieQSWGx2akxWqk1WKustVNZbMNaZKa81U1bTwIUaMxeqGyipNnG+ykRJdQM/79NE+HsSH+LDyA4RJIb5kBzmS1KYD/HBBoe4ZZqT7rGBfHj/AHaeLuVf6zN44uMjvLXjLL8f14ER7cNa9EElaX1IwZc4HCEEhZUm0gurSC+q4vT5Gix2O3cu2o1dqBOUrHaB2WrHbLNjMtuot9ioNdswW+2Xva6iQIiPnlBfT8L9vWgf4UdUoDdRAV5EB3oTHeRNdKA3Xrq25/YYkhrKoOTBfHW8iOe+PsXsJfvolxjMH8Z3oFdckLPNk7gIUvAlDqOmwcqaQwV8+F0u6UVVFz+vNVvRaTXY7apoG/QeaDUKeg8Neg8N3jotXjoNPp4eGHQe+HmpW6BBT4C3jiCDjmAfPYEGvVu7MjQahUnd2jG2cyQr9ubyn81nmPr6LsZ0iuDJse1JjfBztokSJyMFX9LinL1Qw9s7z7H2UAG1Zhudovz5n0md6B4bQPtIfybteQ6Aj+bL1bOaA51Wwz0DE5jaK4bFO8+xcPtZxr60nWm9Yvj16DSiA+XkLXelSYO2iqLcATwNdAT6CSH2N36eAKQDpxoP3SOEmH+168lsmW2L9KIqXttyhq+OFaHTapjcvR0z+8fRIzbwJ75ld6+7li6/sdbM61vO8P6eHBAwc0AcD9+UQqiM4XcZWkW2TEVROgJ2YCHw258J/hdCiC7Xcz0Zh982OJZfyX82n2ZT+nl89FruGZjAfUMSCfOTAuNMCirqeWXzaT4+kI+nh4Y5gxOZOyzpkmkeJK0Lh0TpCCHSG7+sKZdpFjZt2gTAqFGjnGyJ+3I0v4L/bDrN5owSArx1/GZUGrMGJRBguLKguHvdOar80YHe/N+0bswdlsSLGzN5dcsZ3t+dzbxhScwanIivp/TwOgtHtYFmicNXFGUr/93DPwFkAlXAn4QQOy5z7jxgHkBcXFzvnJycG7JBxuE7jyN5Ffxn82m+aRT6uUMTuXdQwjXP/nT3unNW+Y8XVPLixkw2Z5QQ7KPngWFJ/HJgAt76thfF5Oq4TBy+oiibgMhL7HpKCLH2MqcVAXFCiDJFUXoDaxRF6SyEqPr5gUKIRcAiUF06V7NH4joczqvgP5sy2XLqAoEGHb8dk3ZdQi9xLl2iA3hnVl8O5Zbz4qbT/HNdBm/tOMf84UnM7B8vhb8NclXBF0Jc9zuGEKIBaGj8/YCiKFlAGiAd9G2A/dlGXv7mDNszLxBk0PHk2PbcOyhBugRaKT3jgnh/Tj/2ZRt5cWMmf/synTe3ZTFvWBJ3D4jHoJf12lZokZpUFCUMMAohbIqiJAGpwNmW+C6JYxBCsDurjJe/Oc2es0ZCfPT8flwH7hkYL4W+jdA3IZhlcwewL9vIfzad5h9fZbBw21nuG5rIPQPi5ZtbG6BJd6qiKLcBrwBhwJeKohwWQowFhgF/VRTFghrFM18IYWyytRKHY7cLNmeU8PrWMxzKrSDcz5M/TezIXf3jZM+vjdI3IZil9/fnQI6R/2w+w7PrT/Hm1izuHZTA7MGJBMvVt1otbSZ52qlTash/+/btm9Mkt8Vis/PF0ULe2JpF5vkaYoK8eWB4Mnf0jmn21ATuXneuXv5j+ZW8tuUM608U463T8ot+cdw3NFFO4GpGmtoG3C5bpqR5qGmwsmJvLot3nqOw0kRahC8PjUhhUreoVpdYTNK8nD5fzRtbs1h7pBAFmNy9HfOGJdExyt/Zprk9bif4n3/+OQCTJ09uTpPchqLKet7blcOy73KoMlnpnxjMA8OTGJEWjqaF89O4e921tvIXVNSzeOc5lu/Npc5sY1haGPcPSWRoaqhLzMlpjTS1Dbid4Lt7LPeNciSvgnd2nuOrY0XYhWBcl0jmDUumR2ygw2xw97prreWvrLOw9LscluzK5kJ1A6nhvswZkshtPaPbZEbSlsRl4vAlbQ+z1c6640Us2ZXNodwK/Dw9mDUogXsHJRAb3PwLfUvaJgEGHQ/flMLcoUl8cbSQd3aeY8HqYzy7PoMZfeOY2T9OticXQwq+G1FYUc+Kvbks25tHaU0DiaE+/GVyJ27vHSND7iQ3jN5Dw9ReMdzWM5p92eUs3nmORduzWLQ9i5s7hHPPwASGpoS2uGtQcnWk4LdxbHbB9swLfPhdDt9klCCAm9qHc+8geRNKmhdFUeiXGEy/xGAKK+pZ9l0uy/fmsil9L/EhBu7sG8ftvWNkEj0nIgW/jZJnrOPjA/l8sj+PwkoTob6ePDgimTv7ytdsScvTLtCb345tz6MjU1h3rJhle3P51/oM/r3hFKM7RXBnvziGpIS69YI1zqDNDNrm5eUBEBsb25wmtSrqzTY2nCzm4/35fJtVCsDQ1DBm9IlldKcI9B6uGVbp7nXnLuU/U1LDyn25fHIgn/I6C5H+XkztFc203jEkh/k62zyn0tQ24HZROu6KzS747lwZnx4s4KtjRdSabUQHenNHnxju6BMrJ8dIXI4Gq41NJ0tYdTCfradKsAvoGRfI1J7RTOgaRYhcmOW6cTvBX7lyJQAzZsxoTpNcEiEER/Ir+fxIIV8cLeR8VQO+nh5M6BrJ1F4x9EsIblW+eXequ0vhzuUvqTKx5nABqw4UcOp8NVqNwtDUUG7p0Y7RnSLdJk9TU9uA2wl+a41lvla+F/l1x4pYd7yYXGMdeq2G4e3DmNK9HaM6RrTadLZtve6uhruX/3syiqtYe7iQzw4XUlBRj95Dw/C0MCZ0jWRkxwj823AkmYzDl2Cx2dl3zsiGk+fZePI8BRX1eGgUBqeE8shNKYztEimXp5O0GTpE+tNhnD9PjmnPgdxyvjxaxPrjxWw8eR69VsPglBBGd4pkVMdwwv29nG1uq0QKvothrDWzPfMCW06VsCWjhCqTFU8PDUNTQ/nN6DRGd4y46pKBEklrRqNR6JsQTN+EYP5nUicO5VXw1bEiNpwsZsunx/jjp9A9NpBRHcK5qUM4naL8W5UL05lIwXcyFpudo/kV7DhdytZTFziSX4EQEOKjZ0znSEZ3imBoaqhMRSxxSzQahd7xQfSOD+JPEzuSeb6GjSeL2Zhewr83ZvLvjZmE+noyLC2U4WlhDE4JJVQO+l4WqSIOxmYXpBdVsedsWeNmpKbBiqJAt5hAfjUylZvah9M1OkD2WiSSH6EoCu0j/Wgf6ccjN6dSUm1iR2Yp2zIv8E1GCasPFgDQIdKPgckhDEoOpV9isHR7/og2M2hbWqrGnYeGhjanSU2mzmzlSF4lB3PL2Z9tZH92OdUNVgDiQwwMSQllSEooA5NDCDS458ISrlp3jsLdy98c2OyCYwWVfHumlN1ZZezLNtJgtaMo0D7Cjz4JQfRNCKZ3fBDRgd4ul9WzqW3A7aJ0XAGLzc6p4mqOFVRyNL+So/kVZBRXY7Or/+PkMB/6JYYwIEmdfh4VIGPkJZKWoMFq41BuBXvPGdmXbeRQbgU1jR2tUF9PuscE0D02kO6xgXRu59/q3UBuF6WzZMkSAGbNmtXi3yWEoLDSxJmSGk6fr+ZkURXpRdWcKanGYlPF3d/Lg64xATw4PJne8UH0jAt02x781XBk3bki7l7+lsDTQ8uApBAGJIUA6htARnEVB3LKOZJXyZH8Cr45VcL3/d1wP086RvnTqZ0/7SP8SAn3JTnM12Ghzo5qA22mh9/cscwWm53iShOFFfXkGuvIKasju6yWnLI6si7UUGe2XTw2wt+TDpH+FxtM95gA4oINLvfa6Kq4exy6u5ffWVSZLBzPr+RkUdUlO20AMUHeJIX5Eh9sID7EQFywgfgQH6ICvZp1XoCMw29mzFY7NQ1WakxWqhssVNZZMNaZKa+zUF5rpqTaRElVAyXVDZyvMnG+yoT9R89CrUYhOtCb+BAD0/vEkhrhS0qYL6kRfnJRZ4mkFeLvpWNQSiiDUn7wm5utdnLKajldUsOZxu1saQ2HcsupNll/cr6vpweRAV5EBXgR5udJmK8nIb56Qnw8CfbR4+/tgb+XDn9vHb6eHnjrtE4PxGgTgp9RXMWR/AoQMPy5LdjsAptd0GC102Cx0WC1Y7Vf+U0myKAjzM+TcD8vkpJDiAn0pl3jFhdsIDrIG51c01UiadPoPTSkRviRGuH3k8+FEFTUWcg11pFrrKOosp7CChPFlSaKKus5e6GWCzUNmK32q17fy0ODl06LTqtBowGtonAkr8IhLt8mCb6iKM8BkwEzkAXMFkJUNO5bANwH2IDHhBBfN9HWy+LlocWg04Ki0CM2EK2ioNUoeOm0eHpo8NRp8NZp8fNSn7Q+nh4EGnQEGfQE+egI9Na7bCZJiUTifBRFIchHT5CPnu6XWf5TCEFNg5XSGjMVdWaqTFaq6i1UmSxUm6yYLDbqLTYaLHbqzTasdoFdqJ3T054eDtGgpvbwNwILhBBWRVH+BSwAfq8oSifgTqAz0A7YpChKmhDCdoVr3TAJoT4Xn8j/ubNnS3yFRCKRXBFFUfDz0jWuHudzXeceXeiY9NDNNmirKMptwO1CiJmNvXuEEP9s3Pc18LQQYveVrtGUQdu6ujoADAa5uEdrw93rzt3LL2l6G3DGoO0cYGXj79HAnh/ty2/87L9QFGUeMA8gLi7uhr9c3iytF3evO3cvv8RxbeCqgq8oyiYg8hK7nhJCrG085inACnx4vQYIIRYBi0Dt4V/v+d/z+uuvA/DQQw/d6CUkTsLd687dyy9xXBu4quALIUZdab+iKLOAScBI8YN/qAD48VpdMY2ftRgfffQRIG+a1oi71527l1/iuDbQpGFhRVHGAb8Dpggh6n606zPgTkVRPBVFSQRSgb1N+S6JRCKRNI2m+vBfBTyBjY2zSvcIIeYLIU4oivIRcBLV1fNwS0XoSCQSieTaaJLgCyFSrrDv78Dfm3J9iUQikTQfcraRRCKRuAkulTxNUZQLQE4TLhEKlDaTORLH4u515+7llzStDcQLIcKudpBLCX5TURRl/7VMPpC4Hu5ed+5efolj2oB06UgkEombIAVfIpFI3IS2JviLnG2A5IZx97pz9/JLHNAG2pQPXyKRSCSXp6318CUSiURyGaTgSyQSiZsgBV8ikUjchFYv+IqiRCiK8qKiKKcVRTEpilKiKMouRVEeVRTFMcvISK4LRVGWKIoiFEV55xL7/tW47wtn2OZIGv8PX/zss0mKotQpivI3Z9klcRyO1q9WvYi5oigJwLdAFfBn4ChQj7q04v1AGbDMSeZJrkweMF1RlMeEELUAiqJ4AL8Ecp1qmZNQFOUe4G3gd0KI/zjbHknL4gz9atWCD7wB2IE+34tGI+eAL5TGFJ4Sl+Qo6nrH04F3Gz+bCJiA7UCIk+xyCoqi/Br4F3CfEGKps+2ROASH61erdekoihICjAVe+9k/6yJCxpy6Ou+gLo35PXNQxd+t6q3RffMP4DYp9u6Bs/Sr1Qo+kAIowKkff6goSr6iKDWN25vOMU1yjSwD+iiKkqooSiQwDljiXJMczmjgKeAOIcRXzjZG4jCcol+t3aVzKYYCWtRZa15OtkVyBYQQ5YqifIras68Atgohct3ME3ccCAD+oijKt0KICmcbJHEqLapfrbmHfwb11b/Djz8UQpwTQpwB6i55lsTVWIw6UDun8Xd3owgYjir6mxRFCXKyPRLH4BT9arWCL4QoAzYAj8jwy1bNZsCMmgt8jZNtcQpCiAJgBOADbG7070raMM7Sr1Yr+I08hFqGA4qi/EJRlE6KoqQpivILoDsg19F1cRoHproBiUKIBmfb4yyEEEWooq8HvlEUJdS5FkkcgMP1q1X78IUQZxVF6QksAJ4BYgELkA68jrrIusTFEUJUO9sGV0AIcV5RlJuATcAWRVFGCiFKnG2XpGVwhn7JbJkSiUTiJrR2l45EIpFIrhEp+BKJROImSMGXSCQSN0EKvkQikbgJUvAlEonETZCCL5FIJG6CFHyJRCJxE6TgSyQSiZvw/6xKvBxDzi51AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.title('Graphene bands',size=14)\n", "#plt.ylim(-6,8)\n", "bands_gra.plot(plt) #,selection=[0,1,2,3,4,5,6,7]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of the band structure with ypp" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "run_dir = 'Ypp_bands'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we analyze the band structure using the post processing tools of ypp. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Band structure of GaAs with spin-orbit coupling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we consider the GaAs band structure with spin-orbit coupling. \n", "\n", "We perform a yy -s b computation starting from the .save of an nscf pw computation on a regular grid" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executing command: cd Pw_bands/gaas_nscf_so.save; p2y -a 2\n", "Executing command: ln -s /home/marco/Applications/MPPI/sphinx_source/tutorials/Pw_bands/gaas_nscf_so.save/SAVE /home/marco/Applications/MPPI/sphinx_source/tutorials/Ypp_bands\n", "Executing command: cd Ypp_bands;OMP_NUM_THREADS=1 yambo\n" ] } ], "source": [ "U.build_SAVE('Pw_bands/gaas_nscf_so.save',run_dir)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "inp = I.YamboInput(args='ypp -s b',folder=run_dir,filename='ypp.in') \n", "inp['variables']['OutputAlat'] = [10.677,''] #used to fix an error in Yambo, can be removed\n", "#inp" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a parallel Yambo calculator with scheduler direct\n" ] } ], "source": [ "code = C.YamboCalculator(mpi=1,executable='ypp',skip=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the input parameter to perform the band computation along a path" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "# set the coordinate of the high-sym points \n", "G = [0.,0.,0.]\n", "X = [0.,0.,1.]\n", "L = [0.5,0.5,0.5]\n", "W = [1.0,0.5,0.]\n", "K = [0.,1.,1.]\n", "\n", "# useful to label the high-sym point on the path\n", "high_sym = {'X':X,'L':L,'G':G,'K':K,'W':W} \n", "\n", "# set the path (use the same path of the pw computation)\n", "path = [L,G,X,K,G]\n", "# set the number of intermediate points between two high-sym ones\n", "bands_step = 30" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'args': 'ypp -s b',\n", " 'folder': 'Ypp_bands',\n", " 'filename': 'ypp.in',\n", " 'arguments': [],\n", " 'variables': {'OutputAlat': [10.677, ''],\n", " 'INTERP_Shell_Fac': [20.0, ''],\n", " 'BANDS_steps': [30, ''],\n", " 'INTERP_mode': 'NN',\n", " 'cooIn': 'alat',\n", " 'cooOut': 'alat',\n", " 'CIRCUIT_E_DB_path': 'none',\n", " 'BANDS_bands': [[1, 12], ''],\n", " 'INTERP_Grid': [['-1', '-1', '-1'], ''],\n", " 'BANDS_kpts': [[[0.5, 0.5, 0.5],\n", " [0.0, 0.0, 0.0],\n", " [0.0, 0.0, 1.0],\n", " [0.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0]],\n", " '']}}" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# scissor\n", "# inp['variables']['GfnQP_E'] = [1.0,1.0,1.0]\n", "\n", "# band structure\n", "inp['variables']['BANDS_steps'] = [bands_step,'']\n", "inp['variables']['BANDS_kpts'] = [path,'']\n", "inp['variables']['cooIn'] = 'alat'\n", "inp['variables']['cooOut'] = 'alat'\n", "inp" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delete folder: Ypp_bands/gaas_bands_so\n", "Executing command: cd Ypp_bands; mpirun -np 1 ypp -F gaas_bands_so.in -J gaas_bands_so -C gaas_bands_so\n", "run0_is_running:True \n", "Job completed\n" ] }, { "data": { "text/plain": [ "{'output': [['Ypp_bands/gaas_bands_so/o-gaas_bands_so.magnetization_y',\n", " 'Ypp_bands/gaas_bands_so/o-gaas_bands_so.magnetization_x',\n", " 'Ypp_bands/gaas_bands_so/o-gaas_bands_so.bands_interpolated',\n", " 'Ypp_bands/gaas_bands_so/o-gaas_bands_so.magnetization_z',\n", " 'Ypp_bands/gaas_bands_so/o-gaas_bands_so.spin_factors_DN',\n", " 'Ypp_bands/gaas_bands_so/o-gaas_bands_so.spin_factors_UP']],\n", " 'dbs': ['Ypp_bands/gaas_bands_so']}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_ypp = code.run(run_dir=run_dir,inputs=[inp],names=['gaas_bands_so'])\n", "results_ypp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once that the computation is over we can create an instance of PwBands" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mU\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBandStructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_Ypp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh_sym_points\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bands_interpolated'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Initialize the BandStructure class from the result of a Ypp postprocessing.\n", "The class make usage of the YamboParser of this package.\n", "\n", "Args:\n", " results (:py:class:`list`) : list that contiains the o- file provided as the output of a\n", " Ypp computation. results is the key ['output'][irun] of the run method of YamboCalculator\n", " high_sym_points(:py:class:`dict`) : dictionary with name and coordinates of the\n", " high_sym_points of the path\n", " suffix (string) : specifies the suffix of the o- file use to build the bands\n", "\u001b[0;31mFile:\u001b[0m ~/Applications/MPPI/mppi/Utilities/BandStructure.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "U.BandStructure.from_Ypp?" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "bands_ypp = U.BandStructure.from_Ypp(results_ypp['output'][0],high_sym)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.title('GaAs band from Ypp with spin-orbit',size=14)\n", "plt.ylim(-8,3)\n", "bands_ypp.plot(plt,selection=[2,3,4,5,6,7,8,9,10,11])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The desing of the plot method of the class allows us to plot bands coming from different calculations in the same plot.\n", "\n", "For instance, we can show the effect of the spin-orbit on the valence band of GaAs" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bands_gaas.plot(plt,selection= [1,3],label='no_so',show_vertical_lines=False)\n", "bands_gaas_so.plot(plt,selection = [2,6],ls='--',label='so',show_vertical_lines=False)\n", "plt.ylim(-4,0.5)\n", "plt.xlim(0,2)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The shift of the split-off band is visible." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }