From c641134478abd2d3aa0b1634e16d4faa349c05b1 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Tue, 18 Jul 2023 10:54:27 -0400 Subject: [PATCH 01/47] Included the sympyfy function alongside its dependencies --- SymPyfy.ipynb | 211 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 211 insertions(+) create mode 100644 SymPyfy.ipynb diff --git a/SymPyfy.ipynb b/SymPyfy.ipynb new file mode 100644 index 0000000..44c2a31 --- /dev/null +++ b/SymPyfy.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-07-18 10:31:16.305745\n" + ] + } + ], + "source": [ + "# importing necessary dependencies\n", + "from itertools import product as itp\n", + "from pythtb import *\n", + "from triqs.lattice.tight_binding import TBLattice # TRIQS needs to be installed for this dependency to work\n", + "import sympy as sp\n", + "from sympy import *\n", + "import warnings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def sympyfy(tb_lat_obj, analytical = True):\n", + " r\"\"\"\n", + " returns the analytical form of the momentum space hamiltonian of the tight-binding model, \n", + " from a tight-binding lattice object, by utilizing Fourier series\n", + " \n", + " Parameters\n", + " ----------\n", + " tb_lat_obj: triqs TBLattice object\n", + " triqs tight binding object\n", + " analytical: boolean, default = True\n", + " a boolean which will cause the function will return an analytical Hamiltonian, when true, and \n", + " an numerical Hamiltonian otherwise\n", + " \n", + " Returns\n", + " -------\n", + " Hk_numerical: NumPy Array\n", + " the hamiltonian of the tight-binding model in momentum space in numerical form\n", + " Hk: NumPy Array\n", + " the hamiltonian of the tight-binding model in momentum space in reduced analytical form\n", + " \"\"\"\n", + " # imaginary number\n", + " I = sp.I\n", + "\n", + " # matrix from the axis directions in momentum space\n", + " kx, ky, kz = sp.symbols(\"kx ky kz\", real = True)\n", + " k_space_matrix = sp.Matrix([kx, ky, kz])\n", + "\n", + " # symbolic dot product representation between the lattice unit vectors\n", + " # and the momentum space matrix\n", + " a1k, a2k, a3k = sp.symbols(\"a1k a2k a3k\", real = True)\n", + " lattice = sp.Matrix([a1k, a2k, a3k])\n", + "\n", + " # the number of orbitals involved in the electron hoppings\n", + " num_orb = tb_lat_obj.n_orbitals\n", + "\n", + " # dictionary containing details about the hopping of the electrons\n", + " TB_lat_obj_hops = tb_lat_obj.hoppings \n", + "\n", + " # maximum hopping distances of electrons in each of the axial directions\n", + " max_x, max_y, max_z = list(np.max(np.array(list(TB_lat_obj_hops.keys())), axis = 0))\n", + "\n", + " # number of cells involved in the hopping of electrons in each of the axial directions\n", + " num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]]\n", + " \n", + " # basis of the 5D tensor real-space Hamiltonian\n", + " Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp)\n", + "\n", + " # looping through the hopping parameters of the electrons involved in the inter-orbital hoppings\n", + " # key represents the cell coordinates of where the electrons hop to relative to the home unit cell\n", + " # hopping represents the matrix with the embedded hopping amplitudes\n", + " for key, hopping in TB_lat_obj_hops.items():\n", + " rx, ry, rz = key\n", + " # reduce floating point precision of hopping parameters to 3 decimal places\n", + " hopping = np.around(hopping, decimals = 3)\n", + " Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping\n", + "\n", + " # basis of the exponential term in the calculation of Hk\n", + " Hexp = np.empty_like(Hrij, dtype = sp.exp)\n", + "\n", + " # perform the Fourier transform\n", + " for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)):\n", + " coefficients = np.array([xi - max_x, yi - max_y, zi - max_z])\n", + " r = lattice.dot(coefficients)\n", + " eikr = sp.exp(-I * r)\n", + " Hexp[xi, yi, zi, :, :] = eikr\n", + "\n", + " # summation over all real space axes\n", + " Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2))\n", + " \n", + " # rewriting the exponential terms in the analytical expression in terms of \n", + " for i, j in itp(range(num_orb), repeat = 2):\n", + " Hk[i, j] = Hk[i, j].rewrite(sp.cos)\n", + "\n", + " # dealing with the numerical Hamiltonian\n", + " # we convert it to a SymPy matrix to use the substitutions method available in SymPy\n", + " Hk_numerical = sp.Matrix(Hk)\n", + " TB_lat_obj_units = tb_lat_obj.units\n", + " TB_lat_obj_units_transpose = np.transpose(TB_lat_obj_units)\n", + " \n", + " # obtaining unit vectors\n", + " # reduce floating point precision to 3 decimal places\n", + " a1 = np.around(TB_lat_obj_units_transpose[0], decimals = 3)\n", + " a2 = np.around(TB_lat_obj_units_transpose[1], decimals = 3)\n", + " a3 = np.around(TB_lat_obj_units_transpose[2], decimals = 3)\n", + "\n", + " # numerical dot products between the unit vectors\n", + " # and the momentum space matrix\n", + " a1k_numerical = a1.dot(k_space_matrix)[0]\n", + " a2k_numerical = a2.dot(k_space_matrix)[0]\n", + " a3k_numerical = a3.dot(k_space_matrix)[0]\n", + " \n", + " # performing the numerical dot product substitutions\n", + " Hk_numerical = Hk_numerical.subs(a1k, a1k_numerical)\n", + " Hk_numerical = Hk_numerical.subs(a2k, a2k_numerical)\n", + " Hk_numerical = Hk_numerical.subs(a3k, a3k_numerical)\n", + "\n", + " # converting the numerical Hamiltonian to a NumPy array from a SymPy matrix\n", + " Hk_numerical = np.array(Hk_numerical)\n", + "\n", + " def has_complex_exponential(matrix):\n", + " \"\"\"\n", + " Checks if a NumPy array containing SymPy elements has a complex exponential element.\n", + "\n", + " Args:\n", + " matrix (NumPy array): The input NumPy array containing SymPy elements\n", + " \n", + " Returns:\n", + " bool: True if the matrix array contains a complex exponential element, False otherwise.\n", + " \"\"\"\n", + " for sublist in matrix:\n", + " for element in sublist:\n", + " if element.is_complex and element.has(exp):\n", + " return True\n", + " return False\n", + " \n", + " def is_hermitian(matrix):\n", + " \"\"\"\n", + " Checks if a NumPy array containing SymPy elements is hermitian\n", + "\n", + " Args:\n", + " matrix (NumPy array): The input NumPy array containing SymPy elements\n", + " \n", + " Returns:\n", + " bool: True if the matrix is a hermitian, False otherwise\n", + " \"\"\"\n", + " n = matrix.shape[0]\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if matrix[i,j] != matrix[j,i].conjugate():\n", + " return False\n", + " return True\n", + " \n", + " # warning indicating when the output Hamiltonian is not hermitian\n", + " if is_hermitian(Hk) == False or is_hermitian(Hk_numerical) == False:\n", + " return warnings.warn(\"The resulting Hamiltonian is not hermitian.\")\n", + "\n", + " # warning indicating when the Hamiltonian contains a complex exponential element\n", + " if has_complex_exponential(Hk_numerical) or has_complex_exponential(Hk):\n", + " return warnings.warn(\"\"\"Your expression has a complex exponential. \n", + " Choosing a different unit cell could make \n", + " your Hamiltonian expression real.\"\"\")\n", + " \n", + " # returning the analytical or numerical form of the Hamiltonian\n", + " # depending on the user's preference\n", + " if analytical:\n", + " return Hk\n", + " return Hk_numerical" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "TRIQS", + "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": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b4a2f5410ae7c75de0dd3d84ee269bfea5335d14 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 19 Jul 2023 09:31:15 -0400 Subject: [PATCH 02/47] Create .DS_store --- .DS_Store | Bin 0 -> 6148 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 Date: Wed, 19 Jul 2023 19:57:22 -0400 Subject: [PATCH 03/47] Deleted the notebook containing the sympyfy function --- SymPyfy.ipynb | 211 -------------------------------------------------- 1 file changed, 211 deletions(-) delete mode 100644 SymPyfy.ipynb diff --git a/SymPyfy.ipynb b/SymPyfy.ipynb deleted file mode 100644 index 44c2a31..0000000 --- a/SymPyfy.ipynb +++ /dev/null @@ -1,211 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: could not identify MPI environment!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Starting serial run at: 2023-07-18 10:31:16.305745\n" - ] - } - ], - "source": [ - "# importing necessary dependencies\n", - "from itertools import product as itp\n", - "from pythtb import *\n", - "from triqs.lattice.tight_binding import TBLattice # TRIQS needs to be installed for this dependency to work\n", - "import sympy as sp\n", - "from sympy import *\n", - "import warnings" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def sympyfy(tb_lat_obj, analytical = True):\n", - " r\"\"\"\n", - " returns the analytical form of the momentum space hamiltonian of the tight-binding model, \n", - " from a tight-binding lattice object, by utilizing Fourier series\n", - " \n", - " Parameters\n", - " ----------\n", - " tb_lat_obj: triqs TBLattice object\n", - " triqs tight binding object\n", - " analytical: boolean, default = True\n", - " a boolean which will cause the function will return an analytical Hamiltonian, when true, and \n", - " an numerical Hamiltonian otherwise\n", - " \n", - " Returns\n", - " -------\n", - " Hk_numerical: NumPy Array\n", - " the hamiltonian of the tight-binding model in momentum space in numerical form\n", - " Hk: NumPy Array\n", - " the hamiltonian of the tight-binding model in momentum space in reduced analytical form\n", - " \"\"\"\n", - " # imaginary number\n", - " I = sp.I\n", - "\n", - " # matrix from the axis directions in momentum space\n", - " kx, ky, kz = sp.symbols(\"kx ky kz\", real = True)\n", - " k_space_matrix = sp.Matrix([kx, ky, kz])\n", - "\n", - " # symbolic dot product representation between the lattice unit vectors\n", - " # and the momentum space matrix\n", - " a1k, a2k, a3k = sp.symbols(\"a1k a2k a3k\", real = True)\n", - " lattice = sp.Matrix([a1k, a2k, a3k])\n", - "\n", - " # the number of orbitals involved in the electron hoppings\n", - " num_orb = tb_lat_obj.n_orbitals\n", - "\n", - " # dictionary containing details about the hopping of the electrons\n", - " TB_lat_obj_hops = tb_lat_obj.hoppings \n", - "\n", - " # maximum hopping distances of electrons in each of the axial directions\n", - " max_x, max_y, max_z = list(np.max(np.array(list(TB_lat_obj_hops.keys())), axis = 0))\n", - "\n", - " # number of cells involved in the hopping of electrons in each of the axial directions\n", - " num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]]\n", - " \n", - " # basis of the 5D tensor real-space Hamiltonian\n", - " Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp)\n", - "\n", - " # looping through the hopping parameters of the electrons involved in the inter-orbital hoppings\n", - " # key represents the cell coordinates of where the electrons hop to relative to the home unit cell\n", - " # hopping represents the matrix with the embedded hopping amplitudes\n", - " for key, hopping in TB_lat_obj_hops.items():\n", - " rx, ry, rz = key\n", - " # reduce floating point precision of hopping parameters to 3 decimal places\n", - " hopping = np.around(hopping, decimals = 3)\n", - " Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping\n", - "\n", - " # basis of the exponential term in the calculation of Hk\n", - " Hexp = np.empty_like(Hrij, dtype = sp.exp)\n", - "\n", - " # perform the Fourier transform\n", - " for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)):\n", - " coefficients = np.array([xi - max_x, yi - max_y, zi - max_z])\n", - " r = lattice.dot(coefficients)\n", - " eikr = sp.exp(-I * r)\n", - " Hexp[xi, yi, zi, :, :] = eikr\n", - "\n", - " # summation over all real space axes\n", - " Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2))\n", - " \n", - " # rewriting the exponential terms in the analytical expression in terms of \n", - " for i, j in itp(range(num_orb), repeat = 2):\n", - " Hk[i, j] = Hk[i, j].rewrite(sp.cos)\n", - "\n", - " # dealing with the numerical Hamiltonian\n", - " # we convert it to a SymPy matrix to use the substitutions method available in SymPy\n", - " Hk_numerical = sp.Matrix(Hk)\n", - " TB_lat_obj_units = tb_lat_obj.units\n", - " TB_lat_obj_units_transpose = np.transpose(TB_lat_obj_units)\n", - " \n", - " # obtaining unit vectors\n", - " # reduce floating point precision to 3 decimal places\n", - " a1 = np.around(TB_lat_obj_units_transpose[0], decimals = 3)\n", - " a2 = np.around(TB_lat_obj_units_transpose[1], decimals = 3)\n", - " a3 = np.around(TB_lat_obj_units_transpose[2], decimals = 3)\n", - "\n", - " # numerical dot products between the unit vectors\n", - " # and the momentum space matrix\n", - " a1k_numerical = a1.dot(k_space_matrix)[0]\n", - " a2k_numerical = a2.dot(k_space_matrix)[0]\n", - " a3k_numerical = a3.dot(k_space_matrix)[0]\n", - " \n", - " # performing the numerical dot product substitutions\n", - " Hk_numerical = Hk_numerical.subs(a1k, a1k_numerical)\n", - " Hk_numerical = Hk_numerical.subs(a2k, a2k_numerical)\n", - " Hk_numerical = Hk_numerical.subs(a3k, a3k_numerical)\n", - "\n", - " # converting the numerical Hamiltonian to a NumPy array from a SymPy matrix\n", - " Hk_numerical = np.array(Hk_numerical)\n", - "\n", - " def has_complex_exponential(matrix):\n", - " \"\"\"\n", - " Checks if a NumPy array containing SymPy elements has a complex exponential element.\n", - "\n", - " Args:\n", - " matrix (NumPy array): The input NumPy array containing SymPy elements\n", - " \n", - " Returns:\n", - " bool: True if the matrix array contains a complex exponential element, False otherwise.\n", - " \"\"\"\n", - " for sublist in matrix:\n", - " for element in sublist:\n", - " if element.is_complex and element.has(exp):\n", - " return True\n", - " return False\n", - " \n", - " def is_hermitian(matrix):\n", - " \"\"\"\n", - " Checks if a NumPy array containing SymPy elements is hermitian\n", - "\n", - " Args:\n", - " matrix (NumPy array): The input NumPy array containing SymPy elements\n", - " \n", - " Returns:\n", - " bool: True if the matrix is a hermitian, False otherwise\n", - " \"\"\"\n", - " n = matrix.shape[0]\n", - " for i in range(n):\n", - " for j in range(n):\n", - " if matrix[i,j] != matrix[j,i].conjugate():\n", - " return False\n", - " return True\n", - " \n", - " # warning indicating when the output Hamiltonian is not hermitian\n", - " if is_hermitian(Hk) == False or is_hermitian(Hk_numerical) == False:\n", - " return warnings.warn(\"The resulting Hamiltonian is not hermitian.\")\n", - "\n", - " # warning indicating when the Hamiltonian contains a complex exponential element\n", - " if has_complex_exponential(Hk_numerical) or has_complex_exponential(Hk):\n", - " return warnings.warn(\"\"\"Your expression has a complex exponential. \n", - " Choosing a different unit cell could make \n", - " your Hamiltonian expression real.\"\"\")\n", - " \n", - " # returning the analytical or numerical form of the Hamiltonian\n", - " # depending on the user's preference\n", - " if analytical:\n", - " return Hk\n", - " return Hk_numerical" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "TRIQS", - "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": "3.11.4" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From c7a98b49f18d07a6a674d6e312ea3258a14d734a Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 19 Jul 2023 19:57:54 -0400 Subject: [PATCH 04/47] Started working on the tutorial --- SymPyfy Tutorial.ipynb | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 SymPyfy Tutorial.ipynb diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb new file mode 100644 index 0000000..eeae8a3 --- /dev/null +++ b/SymPyfy Tutorial.ipynb @@ -0,0 +1,26 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding ```sympyfy```: An Analytical Momentum-Space Hamiltonian Expresser." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we shall learn the intricacies that go behind the ```sympyfy``` function." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1fb10773d3f88bff4c82b0c21c4a40861c8791c6 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 19 Jul 2023 19:58:21 -0400 Subject: [PATCH 05/47] Added DS Store (folder metadata) to gitignore --- .DS_Store | Bin 6148 -> 6148 bytes .gitignore | 2 ++ 2 files changed, 2 insertions(+) diff --git a/.DS_Store b/.DS_Store index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..796d2cfaada08fa4d1a58834034fb3987be1137c 100644 GIT binary patch literal 6148 zcmeHKJxjzu5Pf@I5wW>SEVr;04g?FEc*I_aKS1N*2P7tlS6r7L;NS8m+52W^IPZuS zhahsyz|7n1&TKMoVJ8D1%og)0FaeNJ1xNcd--xV>4rF9AV`4~qEOCz|BCfF*c>BbE zWI)dD5G7Bo!4>V-`MsjW3!ZR`8YjdK&s?wShiQ4&Hr-k>Tg5kRQB-wXF+O|yI6gl+ zeZScD8=7|h>pi;1K^|)<`Oq0~2AqMNW`H$YBs)-a>kK#p&cKQRxgR1`FgNTK)73$f zS^%Q_Mke7{dI^b%hPh#{$Q4S^P@;wgYcYa`b3U41ZrCeoID$1F!QLFKLka2a+&?CF zgj~_BGvEwtGO(wQ6RH1~KllHeLH^_nI0HMyKo}QgF=I+rTL;NWt&P+hs)+dYimMO~ hQYogdl;R_563#~%5Oc#`kuwzk5lA$+aR&aBfluz5LU8~9 delta 70 zcmZoMXfc=|#>AjHu~2NHo+1YW5HK<@2y7N){>HLdfq56xW_AvK4xj>{$am(+{342+ UKzW7)kiy9(Jj$D6L{=~Z01@sEU;qFB diff --git a/.gitignore b/.gitignore index e0a2d13..8ccc10d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ *.pyc .ipynb_checkpoints *.h5 +.DS_Store +.DS_Store From 20a52df3c532e6483a34c0249cb02d944a25aea0 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 20 Jul 2023 13:58:55 -0400 Subject: [PATCH 06/47] Wrote the heading and first text cell --- SymPyfy Tutorial.ipynb | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index eeae8a3..6f4a1bf 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -11,8 +11,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we shall learn the intricacies that go behind the ```sympyfy``` function." + "In this notebook, we shall learn the intricacies that go behind the ```sympyfy``` function. But first, let us dive into the motivation behind writing such a function.\n", + "\n", + "The ```sympyfy``` function makes great use of the tight-binding (TB) model. In a nutshell, the TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. In particular, we care about how electrons move and interact withing the crystal lattice site framework by considering how they interact with other electrons within nearby lattices. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Luckily, there exist a set of localized wavefunctions that can help us do so - Wannier functions. The scope of Wannier functions will be limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap which will lead to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and is the main phenomenon that is captured by ```sympyfy```.\n", + "\n", + "The TB model is useful when applied to materials whose electrons are weakly correlated. Like many other models, the TB model has its own Hamiltonian equation. This equation has 2 significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights on the electronic properties of materials.\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { From a5c67a5a347e25d2b77a505abdc2623e1b17f638 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 20 Jul 2023 15:26:21 -0400 Subject: [PATCH 07/47] Wrote about sympy's parameters and TB Hamiltonian --- SymPyfy Tutorial.ipynb | 53 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 6f4a1bf..4220d01 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -18,6 +18,59 @@ "The TB model is useful when applied to materials whose electrons are weakly correlated. Like many other models, the TB model has its own Hamiltonian equation. This equation has 2 significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights on the electronic properties of materials.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Hamiltonian of the TB model looks like this:\n", + "$$\n", + "\\hat{H} = \\sum_{\\vec{R}\\vec{\\delta}}| \\vec{R}> \\cal{t} <\\vec{R} + \\vec{\\delta}| + \\sum_{\\vec{R}} |\\vec{R}>U<\\vec{R}|\n", + "$$\n", + "\n", + "where $\\vec{\\delta}$ is a set of vectors pointing from $\\vec{R}$ to its nearest neighbors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned before, the TB model is particularly useful in figuring out the band structure properties of different kinds of materials. Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fits with what the theory dictates. Moreover, the TB model is easily tractable, mathematically, which allows for an easy-to-view analytical (Hamiltonian) solution for whatever weakly-correlated system one is analyzing. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", + "There is one issue however, though the analytical solution to the TB model is useful, it can end up being verbose and drawn-out which is counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a length (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Therefore, there is a need to develop a function that can provide a reduced analytical Hamiltonian expression of whatever element that is analyzed. This is what ```sympyfy``` does! Let us look at the function definition:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```def sympyfy(tb_lat_obj, analytical = True, precision = 6):```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ```tb_lat_obj``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```tb_lat_obj```, TRIQS fortunately provides functions from which you can provide your model to a TRIQS TBLattice object. These functions are:\n", + "1. ```TB_from_pythTB``` which converts your model from a pythTB model to a TRIQS TBLattice Object.\n", + "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and convert it to a TBLattice object.\n", + "\n", + "This makes the ```tb_lat_obj``` to be the only parameter that interfaces with other TRIQS package functions.\n", + "At this time, it is recommended that one use the ```TB_from_pythTB``` as you can easily initiate cutoffs to your pythTB model before converting it to a TBLattice object. This allows one to be more in control of their model and look at the behavior of the output at different cutoffs. Such cutoff parameters, possible with the pythTB includes the:\n", + "1. ```zero_energy```: sets the zero of the energy in the band structure, which is almost always set at the Fermi level.\n", + "2. ```min_hopping_norm```: the hopping terms read from Wannier90. Hopping amplitudes (measured in electron-volts) less than the set ```min_hopping_norm``` will not be included in the resulting calculations.\n", + "3. ```max_distance```: Any hopping distance greater than the set ```max_distance``` will be ignored.\n", + "\n", + "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting numerical Hamiltonian. The numerical form of the Hamiltonian only depends on the k-space vector components, i.e., the lattice constants and lattice vectors are expressed numerically.\n", + "\n", + "Finally, the ```precision``` parameter is there if the user wants a more compact Hamiltonian form. By default, the floating point values from Wannier90 are in the order of $10^-6$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + ] + }, { "cell_type": "markdown", "metadata": {}, From 6f088164eabff32ef703c34f8ca0895f55288477 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 20 Jul 2023 18:49:38 -0400 Subject: [PATCH 08/47] Finalized first draft --- SymPyfy Tutorial.ipynb | 128 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 126 insertions(+), 2 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 4220d01..8be6114 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -68,7 +68,117 @@ "\n", "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting numerical Hamiltonian. The numerical form of the Hamiltonian only depends on the k-space vector components, i.e., the lattice constants and lattice vectors are expressed numerically.\n", "\n", - "Finally, the ```precision``` parameter is there if the user wants a more compact Hamiltonian form. By default, the floating point values from Wannier90 are in the order of $10^-6$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + "Finally, the ```precision``` parameter is there if the user wants a more compact Hamiltonian form. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling the function ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is an example on how the function can be called on the model of a Strontium compound - $SrCaCu_{2}O_4$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'w90' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtriqs\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlattice\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m TB_from_pythTB\n\u001b[0;32m----> 2\u001b[0m w90_input \u001b[39m=\u001b[39m w90(\u001b[39m'\u001b[39m\u001b[39mSrCaCu2O4 Files\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msccuo2\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m fermi_ev \u001b[39m=\u001b[39m \u001b[39m9.6082\u001b[39m\n\u001b[1;32m 4\u001b[0m w90_model \u001b[39m=\u001b[39m w90_input\u001b[39m.\u001b[39mmodel(zero_energy \u001b[39m=\u001b[39m fermi_ev, min_hopping_norm \u001b[39m=\u001b[39m \u001b[39m0.05\u001b[39m, max_distance \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'w90' is not defined" + ] + } + ], + "source": [ + "from triqs.lattice.utils import TB_from_pythTB\n", + "w90_input = w90('SrCaCu2O4 Files', 'sccuo2')\n", + "fermi_ev = 9.6082\n", + "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", + "w90_triqs = TB_from_pythTB(w90_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling ```sympyfy``` in different scenarios ###" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'sympyfy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# calling the functions in different scenarios\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe analytical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs))\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m 4\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe more precise numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs, \u001b[39mFalse\u001b[39;00m, \u001b[39m3\u001b[39m))\n", + "\u001b[0;31mNameError\u001b[0m: name 'sympyfy' is not defined" + ] + } + ], + "source": [ + "# calling the functions in different scenarios\n", + "print(\"The analytical expression \\n\", sympyfy(w90_triqs))\n", + "print(\"The numerical expression \\n\", sympyfy(w90_triqs, False))\n", + "print(\"The more precise numerical expression \\n\", sympyfy(w90_triqs, False, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results of the scenarios above:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The analytical expression \n", + " $$$[\n", + " [-1.002632*cos(a3k) - 0.124416*cos(2*a3k) + 0.018272\n", + " -0.056436*I*sin(a1k) - 0.056434*I*sin(a1k - a3k) + 0.056436*cos(a1k) + 0.056434*cos(a1k - a3k)]\n", + " \n", + " [0.056436*I*sin(a1k) + 0.056434*I*sin(a1k - a3k) + 0.056436*cos(a1k) + 0.056434*cos(a1k - a3k)\n", + " -0.904748*cos(a3k) - 0.128046*cos(2*a3k) - 0.130246]\n", + " ]$$$\n", + "\n", + "\n", + "The numerical expression \n", + " $$$[\n", + " [-1.002632*cos(3.939405*kz) - 0.124416*cos(7.87881*kz) + 0.018272\n", + " -0.056436*I*sin(1.76207*kx + 1.76207*ky) - 0.056434*I*sin(1.76207*kx + 1.76207*ky - 3.939405*kz) + 0.056436*cos(1.76207*kx + 1.76207*ky) + 0.056434*cos(1.76207*kx + 1.76207*ky - 3.939405*kz)]\n", + " \n", + " [0.056436*I*sin(1.76207*kx + 1.76207*ky) + 0.056434*I*sin(1.76207*kx + 1.76207*ky - 3.939405*kz) + 0.056436*cos(1.76207*kx + 1.76207*ky) + 0.056434*cos(1.76207*kx + 1.76207*ky - 3.939405*kz)\n", + " -0.904748*cos(3.939405*kz) - 0.128046*cos(7.87881*kz) - 0.130246]\n", + " ]$$$\n", + "\n", + "\n", + "The more precise numerical expression \n", + " $$$[\n", + " [-1.002*cos(3.939*kz) - 0.124*cos(7.878*kz) + 0.018\n", + " -0.056*I*sin(1.762*kx + 1.762*ky) - 0.056*I*sin(1.762*kx + 1.762*ky - 3.939*kz) + 0.056*cos(1.762*kx + 1.762*ky) + 0.056*cos(1.762*kx + 1.762*ky - 3.939*kz)]\n", + " [0.056*I*sin(1.762*kx + 1.762*ky) + 0.056*I*sin(1.762*kx + 1.762*ky - 3.939*kz) + 0.056*cos(1.762*kx + 1.762*ky) + 0.056*cos(1.762*kx + 1.762*ky - 3.939*kz)\n", + " -0.904*cos(3.939*kz) - 0.128*cos(7.878*kz) - 0.13]\n", + " ]$$$" ] }, { @@ -78,8 +188,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "TRIQS", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" }, "orig_nbformat": 4 }, From c6e6e90b9ee457361680a85d094c0a9182d41103 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 21 Jul 2023 08:38:26 -0400 Subject: [PATCH 09/47] Finalized first draft of tutorial. Added example output and conclusion. --- SymPyfy Tutorial.ipynb | 123 +++++++++++++++++++---------------------- 1 file changed, 58 insertions(+), 65 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 8be6114..0669249 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -11,11 +11,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we shall learn the intricacies that go behind the ```sympyfy``` function. But first, let us dive into the motivation behind writing such a function.\n", + "In this notebook, we shall learn the intricacies behind the ```sympyfy``` function. But first, let us dive into the motivation behind writing such a function.\n", "\n", - "The ```sympyfy``` function makes great use of the tight-binding (TB) model. In a nutshell, the TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. In particular, we care about how electrons move and interact withing the crystal lattice site framework by considering how they interact with other electrons within nearby lattices. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Luckily, there exist a set of localized wavefunctions that can help us do so - Wannier functions. The scope of Wannier functions will be limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap which will lead to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and is the main phenomenon that is captured by ```sympyfy```.\n", + "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", "\n", - "The TB model is useful when applied to materials whose electrons are weakly correlated. Like many other models, the TB model has its own Hamiltonian equation. This equation has 2 significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights on the electronic properties of materials.\n" + "In particular, we care about how electrons move and interact within local or nearby crystal lattice sites. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Fortunately, a complete set of localized wavefunctions can help us do so - Wannier functions. The scope of Wannier functions is limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap leading to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", + "\n", + "The TB model is useful when applied to materials whose electrons are weakly correlated. Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights into the electronic properties of materials.\n" ] }, { @@ -34,8 +36,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As mentioned before, the TB model is particularly useful in figuring out the band structure properties of different kinds of materials. Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fits with what the theory dictates. Moreover, the TB model is easily tractable, mathematically, which allows for an easy-to-view analytical (Hamiltonian) solution for whatever weakly-correlated system one is analyzing. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", - "There is one issue however, though the analytical solution to the TB model is useful, it can end up being verbose and drawn-out which is counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a length (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties." + "The TB model is useful in figuring out the band structure properties of different kinds of materials. Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", + "\n", + "Moreover, the TB model is easily tractable, allowing for an easy-to-view analytical (Hamiltonian) solution for whatever weakly-correlated system one analyzes. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", + "\n", + "There is one issue, though the analytical solution to the TB model is helpful, it can end up being verbose and drawn out, counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a length (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens, such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties.\n" ] }, { @@ -49,7 +54,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```def sympyfy(tb_lat_obj, analytical = True, precision = 6):```" + "```python\n", + "def sympyfy(tb_lat_obj, analytical = True, precision = 6):" ] }, { @@ -86,23 +92,10 @@ ] }, { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'w90' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtriqs\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlattice\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m TB_from_pythTB\n\u001b[0;32m----> 2\u001b[0m w90_input \u001b[39m=\u001b[39m w90(\u001b[39m'\u001b[39m\u001b[39mSrCaCu2O4 Files\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msccuo2\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m fermi_ev \u001b[39m=\u001b[39m \u001b[39m9.6082\u001b[39m\n\u001b[1;32m 4\u001b[0m w90_model \u001b[39m=\u001b[39m w90_input\u001b[39m.\u001b[39mmodel(zero_energy \u001b[39m=\u001b[39m fermi_ev, min_hopping_norm \u001b[39m=\u001b[39m \u001b[39m0.05\u001b[39m, max_distance \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'w90' is not defined" - ] - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ + "```python\n", "from triqs.lattice.utils import TB_from_pythTB\n", "w90_input = w90('SrCaCu2O4 Files', 'sccuo2')\n", "fermi_ev = 9.6082\n", @@ -119,66 +112,66 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'sympyfy' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# calling the functions in different scenarios\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe analytical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs))\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m 4\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe more precise numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, sympyfy(w90_triqs, \u001b[39mFalse\u001b[39;00m, \u001b[39m3\u001b[39m))\n", - "\u001b[0;31mNameError\u001b[0m: name 'sympyfy' is not defined" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# calling the functions in different scenarios\n", - "print(\"The analytical expression \\n\", sympyfy(w90_triqs))\n", - "print(\"The numerical expression \\n\", sympyfy(w90_triqs, False))\n", - "print(\"The more precise numerical expression \\n\", sympyfy(w90_triqs, False, 3))" + "from IPython.core.display import display, HTML\n", + "display(HTML(\"\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here are the results of the scenarios above:" + "```python\n", + "print(\"The analytical expression \\n\", sympyfy(w90_triqs))\n", + "print(\"The numerical expression \\n\", sympyfy(w90_triqs, False))\n", + "print(\"The more precise numerical expression \\n\", sympyfy(w90_triqs, False, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The analytical expression \n", - " $$$[\n", - " [-1.002632*cos(a3k) - 0.124416*cos(2*a3k) + 0.018272\n", - " -0.056436*I*sin(a1k) - 0.056434*I*sin(a1k - a3k) + 0.056436*cos(a1k) + 0.056434*cos(a1k - a3k)]\n", - " \n", - " [0.056436*I*sin(a1k) + 0.056434*I*sin(a1k - a3k) + 0.056436*cos(a1k) + 0.056434*cos(a1k - a3k)\n", - " -0.904748*cos(a3k) - 0.128046*cos(2*a3k) - 0.130246]\n", - " ]$$$\n", + "Depending on what the user inputs for the parameters, here are the results for the 3 scenarios above:\n", "\n", + "The analytical expression is:\n", "\n", - "The numerical expression \n", - " $$$[\n", - " [-1.002632*cos(3.939405*kz) - 0.124416*cos(7.87881*kz) + 0.018272\n", - " -0.056436*I*sin(1.76207*kx + 1.76207*ky) - 0.056434*I*sin(1.76207*kx + 1.76207*ky - 3.939405*kz) + 0.056436*cos(1.76207*kx + 1.76207*ky) + 0.056434*cos(1.76207*kx + 1.76207*ky - 3.939405*kz)]\n", - " \n", - " [0.056436*I*sin(1.76207*kx + 1.76207*ky) + 0.056434*I*sin(1.76207*kx + 1.76207*ky - 3.939405*kz) + 0.056436*cos(1.76207*kx + 1.76207*ky) + 0.056434*cos(1.76207*kx + 1.76207*ky - 3.939405*kz)\n", - " -0.904748*cos(3.939405*kz) - 0.128046*cos(7.87881*kz) - 0.130246]\n", - " ]$$$\n", + "$$\n", + "\\begin{bmatrix}\n", + "-1.002632\\cos(a3k) - 0.124416\\cos(2a3k) + 0.018272 & -0.056436i\\sin(a1k) - 0.056434i\\sin(a1k - a3k) + 0.056436\\cos(a1k) + 0.056434\\cos(a1k - a3k) \\\\\n", + "0.056436i\\sin(a1k) + 0.056434i\\sin(a1k - a3k) + 0.056436\\cos(a1k) + 0.056434\\cos(a1k - a3k) & -0.904748\\cos(a3k) - 0.128046\\cos(2a3k) - 0.130246\n", + "\\end{bmatrix}\n", + "$$\n", "\n", + "The numerical expression is:\n", "\n", - "The more precise numerical expression \n", - " $$$[\n", - " [-1.002*cos(3.939*kz) - 0.124*cos(7.878*kz) + 0.018\n", - " -0.056*I*sin(1.762*kx + 1.762*ky) - 0.056*I*sin(1.762*kx + 1.762*ky - 3.939*kz) + 0.056*cos(1.762*kx + 1.762*ky) + 0.056*cos(1.762*kx + 1.762*ky - 3.939*kz)]\n", - " [0.056*I*sin(1.762*kx + 1.762*ky) + 0.056*I*sin(1.762*kx + 1.762*ky - 3.939*kz) + 0.056*cos(1.762*kx + 1.762*ky) + 0.056*cos(1.762*kx + 1.762*ky - 3.939*kz)\n", - " -0.904*cos(3.939*kz) - 0.128*cos(7.878*kz) - 0.13]\n", - " ]$$$" + "$$\n", + "\\begin{bmatrix}\n", + "-1.002632\\cos(3.939405kz) - 0.124416\\cos(7.87881kz) + 0.018272 & -0.056436i\\sin(1.76207kx + 1.76207ky) \\\\\n", + "-0.056434i\\sin(1.76207kx + 1.76207ky - 3.939405kz) + 0.056436\\cos(1.76207kx + 1.76207ky) + 0.056434\\cos(1.76207kx + 1.76207ky - 3.939405kz) \\\\\n", + "0.056436i\\sin(1.76207kx + 1.76207ky) + 0.056434i\\sin(1.76207kx + 1.76207ky - 3.939405kz) + 0.056436\\cos(1.76207kx + 1.76207ky) + 0.056434\\cos(1.76207kx + 1.76207ky - 3.939405kz) & -0.904748\\cos(3.939405kz) - 0.128046\\cos(7.87881kz) - 0.130246\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "The more precise numerical expression is:\n", + "\n", + "$$\n", + "\\begin{bmatrix}\n", + "-1.002\\cos(3.939kz) - 0.124\\cos(7.878kz) + 0.018 & -0.056i\\sin(1.762kx + 1.762ky) \\\\\n", + "-0.056i\\sin(1.762kx + 1.762ky - 3.939kz) + 0.056\\cos(1.762kx + 1.762ky) + 0.056\\cos(1.762kx + 1.762ky - 3.939kz) \\\\\n", + "0.056i\\sin(1.762kx + 1.762ky) + 0.056i\\sin(1.762kx + 1.762ky - 3.939kz) + 0.056\\cos(1.762kx + 1.762ky) + 0.056\\cos(1.762kx + 1.762ky - 3.939kz) & -0.904\\cos(3.939kz) - 0.128\\cos(7.878kz) - 0.13\n", + "\\end{bmatrix}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion ###\n", + "```sympyfy``` is a user-friendly tool that provides a clear and comprehensible representation using the tight-binding model. This bridges the divide between experimental findings and theoretical methods, enabling researchers to swiftly confirm if their results match tight-binding predictions." ] }, { From e5b1e79d555420ef303731b29243272faf7395ed Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 21 Jul 2023 10:56:56 -0400 Subject: [PATCH 10/47] Amended the statement that the TB model is only suitable for weakly-correlated systems. --- SymPyfy Tutorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 0669249..c49e5bf 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -17,7 +17,7 @@ "\n", "In particular, we care about how electrons move and interact within local or nearby crystal lattice sites. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Fortunately, a complete set of localized wavefunctions can help us do so - Wannier functions. The scope of Wannier functions is limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap leading to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", "\n", - "The TB model is useful when applied to materials whose electrons are weakly correlated. Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights into the electronic properties of materials.\n" + "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights into the electronic properties of materials. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case.\n" ] }, { From a0af9aef4f83bfe2613f3c6f8179372b557f8dd6 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 21 Jul 2023 11:02:41 -0400 Subject: [PATCH 11/47] deleted the HTML import --- SymPyfy Tutorial.ipynb | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index c49e5bf..028840a 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -7,6 +7,13 @@ "# Understanding ```sympyfy```: An Analytical Momentum-Space Hamiltonian Expresser." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction ##" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -17,7 +24,7 @@ "\n", "In particular, we care about how electrons move and interact within local or nearby crystal lattice sites. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Fortunately, a complete set of localized wavefunctions can help us do so - Wannier functions. The scope of Wannier functions is limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap leading to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", "\n", - "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. The TB model captures valuable insights into the electronic properties of materials. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case.\n" + "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials.\n" ] }, { @@ -36,7 +43,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The TB model is useful in figuring out the band structure properties of different kinds of materials. Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", + "Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", "\n", "Moreover, the TB model is easily tractable, allowing for an easy-to-view analytical (Hamiltonian) solution for whatever weakly-correlated system one analyzes. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", "\n", @@ -96,6 +103,7 @@ "metadata": {}, "source": [ "```python\n", + "\n", "from triqs.lattice.utils import TB_from_pythTB\n", "w90_input = w90('SrCaCu2O4 Files', 'sccuo2')\n", "fermi_ev = 9.6082\n", @@ -110,16 +118,6 @@ "### Calling ```sympyfy``` in different scenarios ###" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.core.display import display, HTML\n", - "display(HTML(\"\"))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -173,11 +171,6 @@ "### Conclusion ###\n", "```sympyfy``` is a user-friendly tool that provides a clear and comprehensible representation using the tight-binding model. This bridges the divide between experimental findings and theoretical methods, enabling researchers to swiftly confirm if their results match tight-binding predictions." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From ca26c3892825f1c967b9392fd8bbf1fa25397421 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Sat, 22 Jul 2023 12:25:23 -0400 Subject: [PATCH 12/47] Cut out the section on calling the function on the Strontium compound. --- SymPyfy Tutorial.ipynb | 86 +++++------------------------------------- 1 file changed, 9 insertions(+), 77 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 028840a..e7ca95c 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -22,7 +22,7 @@ "\n", "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", "\n", - "In particular, we care about how electrons move and interact within local or nearby crystal lattice sites. To analyze these interactions, the wavefunctions of the involved electrons should capture their locality. Therefore, we need to choose a set of wavefunctions that can help us characterize the behavior of electrons within the local landscape while neglecting far-off electron interactions. Fortunately, a complete set of localized wavefunctions can help us do so - Wannier functions. The scope of Wannier functions is limited in this tutorial. Using these wavefunctions, we can characterize the behavior of electrons across nearby lattice sites as the wave functions of the electrons involved will overlap leading to the hopping of electrons from one orbital to another (inter-atomic orbital hopping). These orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", + "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", "\n", "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials.\n" ] @@ -43,11 +43,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in quite different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", + "Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", "\n", - "Moreover, the TB model is easily tractable, allowing for an easy-to-view analytical (Hamiltonian) solution for whatever weakly-correlated system one analyzes. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", + "Moreover, the TB model is easily tractable, allowing for an easy-to-view analytical (Hamiltonian) solution for whatever system one analyzes. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", "\n", - "There is one issue, though the analytical solution to the TB model is helpful, it can end up being verbose and drawn out, counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a length (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens, such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties.\n" + "There is one issue, though the analytical solution to the TB model is helpful, it can end up being verbose and drawn out, counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a lengthy (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens, such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties." ] }, { @@ -69,11 +69,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ```tb_lat_obj``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```tb_lat_obj```, TRIQS fortunately provides functions from which you can provide your model to a TRIQS TBLattice object. These functions are:\n", + "The ```w90_triqs``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```w90_triqs```, TRIQS fortunately provides functions from which you can provide your model to a TRIQS TBLattice object. These functions are:\n", "1. ```TB_from_pythTB``` which converts your model from a pythTB model to a TRIQS TBLattice Object.\n", "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and convert it to a TBLattice object.\n", "\n", - "This makes the ```tb_lat_obj``` to be the only parameter that interfaces with other TRIQS package functions.\n", + "This makes the ```w90_triqs``` to be the only parameter that interfaces with other TRIQS package functions.\n", "At this time, it is recommended that one use the ```TB_from_pythTB``` as you can easily initiate cutoffs to your pythTB model before converting it to a TBLattice object. This allows one to be more in control of their model and look at the behavior of the output at different cutoffs. Such cutoff parameters, possible with the pythTB includes the:\n", "1. ```zero_energy```: sets the zero of the energy in the band structure, which is almost always set at the Fermi level.\n", "2. ```min_hopping_norm```: the hopping terms read from Wannier90. Hopping amplitudes (measured in electron-volts) less than the set ```min_hopping_norm``` will not be included in the resulting calculations.\n", @@ -81,88 +81,20 @@ "\n", "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting numerical Hamiltonian. The numerical form of the Hamiltonian only depends on the k-space vector components, i.e., the lattice constants and lattice vectors are expressed numerically.\n", "\n", - "Finally, the ```precision``` parameter is there if the user wants a more compact Hamiltonian form. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + "Finally, the ```precision``` parameter is there for the user to specify however number of decimal places they want the Hamiltonian's floating point values to be. A smaller precision value is going to result in a more compact Hamiltonian form but with less precision. Thus, there is a tradeoff to be negotiated there. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Calling the function ##" + "## Calling the Function ##" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "Here is an example on how the function can be called on the model of a Strontium compound - $SrCaCu_{2}O_4$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "\n", - "from triqs.lattice.utils import TB_from_pythTB\n", - "w90_input = w90('SrCaCu2O4 Files', 'sccuo2')\n", - "fermi_ev = 9.6082\n", - "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", - "w90_triqs = TB_from_pythTB(w90_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calling ```sympyfy``` in different scenarios ###" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "print(\"The analytical expression \\n\", sympyfy(w90_triqs))\n", - "print(\"The numerical expression \\n\", sympyfy(w90_triqs, False))\n", - "print(\"The more precise numerical expression \\n\", sympyfy(w90_triqs, False, 3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Depending on what the user inputs for the parameters, here are the results for the 3 scenarios above:\n", - "\n", - "The analytical expression is:\n", - "\n", - "$$\n", - "\\begin{bmatrix}\n", - "-1.002632\\cos(a3k) - 0.124416\\cos(2a3k) + 0.018272 & -0.056436i\\sin(a1k) - 0.056434i\\sin(a1k - a3k) + 0.056436\\cos(a1k) + 0.056434\\cos(a1k - a3k) \\\\\n", - "0.056436i\\sin(a1k) + 0.056434i\\sin(a1k - a3k) + 0.056436\\cos(a1k) + 0.056434\\cos(a1k - a3k) & -0.904748\\cos(a3k) - 0.128046\\cos(2a3k) - 0.130246\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "The numerical expression is:\n", - "\n", - "$$\n", - "\\begin{bmatrix}\n", - "-1.002632\\cos(3.939405kz) - 0.124416\\cos(7.87881kz) + 0.018272 & -0.056436i\\sin(1.76207kx + 1.76207ky) \\\\\n", - "-0.056434i\\sin(1.76207kx + 1.76207ky - 3.939405kz) + 0.056436\\cos(1.76207kx + 1.76207ky) + 0.056434\\cos(1.76207kx + 1.76207ky - 3.939405kz) \\\\\n", - "0.056436i\\sin(1.76207kx + 1.76207ky) + 0.056434i\\sin(1.76207kx + 1.76207ky - 3.939405kz) + 0.056436\\cos(1.76207kx + 1.76207ky) + 0.056434\\cos(1.76207kx + 1.76207ky - 3.939405kz) & -0.904748\\cos(3.939405kz) - 0.128046\\cos(7.87881kz) - 0.130246\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "The more precise numerical expression is:\n", - "\n", - "$$\n", - "\\begin{bmatrix}\n", - "-1.002\\cos(3.939kz) - 0.124\\cos(7.878kz) + 0.018 & -0.056i\\sin(1.762kx + 1.762ky) \\\\\n", - "-0.056i\\sin(1.762kx + 1.762ky - 3.939kz) + 0.056\\cos(1.762kx + 1.762ky) + 0.056\\cos(1.762kx + 1.762ky - 3.939kz) \\\\\n", - "0.056i\\sin(1.762kx + 1.762ky) + 0.056i\\sin(1.762kx + 1.762ky - 3.939kz) + 0.056\\cos(1.762kx + 1.762ky) + 0.056\\cos(1.762kx + 1.762ky - 3.939kz) & -0.904\\cos(3.939kz) - 0.128\\cos(7.878kz) - 0.13\n", - "\\end{bmatrix}\n", - "$$\n" - ] + "source": [] }, { "cell_type": "markdown", From 6bbb522339e74592d68fdbe567aab4861af41271 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Mon, 24 Jul 2023 10:59:39 -0400 Subject: [PATCH 13/47] Added the _centres.xyz file for La2CuO4 --- AbinitioDMFT/data/mlwf/lco_centres.xyz | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 AbinitioDMFT/data/mlwf/lco_centres.xyz diff --git a/AbinitioDMFT/data/mlwf/lco_centres.xyz b/AbinitioDMFT/data/mlwf/lco_centres.xyz new file mode 100644 index 0000000..cdd243e --- /dev/null +++ b/AbinitioDMFT/data/mlwf/lco_centres.xyz @@ -0,0 +1,10 @@ + 8 + Wannier centres, written by Wannier90 on24Jul2023 at 10:48:54 +X -0.00000000 -0.00000000 0.00000000 +La -0.00000000 -0.00000000 4.77028391 +La 1.90914470 1.90914470 1.83281415 +Cu -0.00000000 -0.00000000 -0.00000000 +O -0.00000000 1.90914470 0.00000000 +O 1.90914470 -0.00000000 0.00000000 +O -0.00000000 -0.00000000 2.45222396 +O 1.90914470 1.90914470 4.15087411 From 5fdca750992a20a43f825f4451b095d322fd4d41 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Mon, 24 Jul 2023 13:03:39 -0400 Subject: [PATCH 14/47] Heavily changed the tutorial according to Olivier's recs. Check the description: - Introduced an example where I call sympyfy on a simple-structured Lathanum Cuprate material under different scenarios. - Changed the introduction to include the fact that w90 includes all hopping parameters but that the user has the option of using PythTB to include cutoffs on the hopping amplitude and hopping distance. - Made sure to highlight the fact that the numerical expression isn't wholly numerical but depends on the k-space vector variables. --- .DS_Store | Bin 6148 -> 6148 bytes .gitignore | 1 + SymPyfy Tutorial.ipynb | 60 +++++++++++++++++++++++++++++++++++++---- 3 files changed, 56 insertions(+), 5 deletions(-) diff --git a/.DS_Store b/.DS_Store index 796d2cfaada08fa4d1a58834034fb3987be1137c..2a26e9d43fef63c22a0f0949393db65978108789 100644 GIT binary patch delta 122 zcmZoMXfc=|#>CJzu~2NHo}wrt0|NsP3otO`Ft{-IGPp5>lm$;LR96N`Z~_JL8B!Qh zktK`ra`N-iLE1J8GJj*)tiYViw3(fQp95$pP~SEVr;04g?FEc*I_aKS1N*2P7tlS6r7L;NS8m+52W^IPZuS zhahsyz|7n1&TKMoVJ8D1%og)0FaeNJ1xNcd--xV>4rF9AV`4~qEOCz|BCfF*c>BbE zWI)dD5G7Bo!4>V-`MsjW3!ZR`8YjdK&s?wShiQ4&Hr-k>Tg5kRQB-wXF+O|yI6gl+ zeZScD8=7|h>pi;1K^|)<`Oq0~2AqMNW`H$YBs)-a>kK#p&cKQRxgR1`FgNTK)73$f zS^%Q_Mke7{dI^b%hPh#{$Q4S^P@;wgYcYa`b3U41ZrCeoID$1F!QLFKLka2a+&?CF zgj~_BGvEwtGO(wQ6RH1~KllHeLH^_nI0HMyKo}QgF=I+rTL;NWt&P+hs)+dYimMO~ hQYogdl;R_563#~%5Oc#`kuwzk5lA$+aR&aBfluz5LU8~9 diff --git a/.gitignore b/.gitignore index 8ccc10d..87539c1 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ *.h5 .DS_Store .DS_Store +.DS_Store diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index e7ca95c..a6943a7 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -22,7 +22,7 @@ "\n", "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", "\n", - "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells and are the main phenomenon captured by ```sympyfy```.\n", + "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells.\n", "\n", "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials.\n" ] @@ -69,17 +69,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ```w90_triqs``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```w90_triqs```, TRIQS fortunately provides functions from which you can provide your model to a TRIQS TBLattice object. These functions are:\n", + "The ```w90_triqs``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```w90_triqs```, TRIQS fortunately provides functions from which you can convert your model to a TRIQS TBLattice object. These functions are:\n", "1. ```TB_from_pythTB``` which converts your model from a pythTB model to a TRIQS TBLattice Object.\n", "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and convert it to a TBLattice object.\n", "\n", "This makes the ```w90_triqs``` to be the only parameter that interfaces with other TRIQS package functions.\n", - "At this time, it is recommended that one use the ```TB_from_pythTB``` as you can easily initiate cutoffs to your pythTB model before converting it to a TBLattice object. This allows one to be more in control of their model and look at the behavior of the output at different cutoffs. Such cutoff parameters, possible with the pythTB includes the:\n", + "At this time, it is recommended that one use the ```TB_from_pythTB``` as you can easily initiate cutoffs to your pythTB model before converting it to a TBLattice object, especially for complex systems. This allows one to be more in control of their model and look at the behavior of the output at different cutoffs. Such cutoff parameters, possible with the pythTB includes the:\n", "1. ```zero_energy```: sets the zero of the energy in the band structure, which is almost always set at the Fermi level.\n", "2. ```min_hopping_norm```: the hopping terms read from Wannier90. Hopping amplitudes (measured in electron-volts) less than the set ```min_hopping_norm``` will not be included in the resulting calculations.\n", "3. ```max_distance```: Any hopping distance greater than the set ```max_distance``` will be ignored.\n", "\n", - "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting numerical Hamiltonian. The numerical form of the Hamiltonian only depends on the k-space vector components, i.e., the lattice constants and lattice vectors are expressed numerically.\n", + "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting \"numerical\" Hamiltonian. The use of quotation marks around the word numerical is deliberate because this expression, while less analytical than the default analytical expression, isn't wholly numerical and still depends on the parameters of the k-space vectors, ```kx```, ```ky```, and ```kz``` (the lattice constants and lattice vectors are expressed numerically). However, for the purpose of this tutorial, I shall continue to term it as numerical hereon, without the quotation marks around it since the user can easily provide values for ```kx```, ```ky```, and ```kz``` to make it entirely numerical.\n", "\n", "Finally, the ```precision``` parameter is there for the user to specify however number of decimal places they want the Hamiltonian's floating point values to be. A smaller precision value is going to result in a more compact Hamiltonian form but with less precision. Thus, there is a tradeoff to be negotiated there. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." ] @@ -94,7 +94,57 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "We shall use $La_2CuO_4$, Lanthanum Copper Oxide, a high-temperature superconductor, as an example on how the function works. Considering the scenarios laid out above, we can imagine calling ```sympyfy``` different cases.\n", + "One of the cases would be to output the reduced analytical Hamiltonian.\n", + "```python\n", + "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = True, precision = 6))\n", + "``` \n", + "By the way, in practice, you can get away by just typing\n", + "```python\n", + "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4))\n", + "```\n", + "but the first way is clearer and is the style we're going to adopt in this tutorial. The result is:\n", + "\n", + "The analytical expression:\n", + "$$\n", + "\\begin{bmatrix}\n", + "[-0.880642\\cos(a_1k + a_3k) - 0.880642\\cos(a_2k + a_3k) + 0.158922]\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "Another of the cases would be similar to the above but setting the ```analytical``` flag to ```False``` to output the numerical form of the Hamiltonian.\n", + "```python\n", + "print(\"The numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 6))\n", + "``` \n", + "The result is:\n", + "\n", + "The numerical expression:\n", + "$$\n", + "\\begin{bmatrix}\n", + "[-0.880642\\cos(4.693953k_x + 8.512243k_y - 4.693953k_z) - 0.880642\\cos(8.512243k_x + 4.693953k_y - 4.693953k_z) + 0.158922]\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "Finally, another case would be to alter the ```precision``` parameter's value from its default of 6 to a lesser value, say 3. Here it is:\n", + "```python\n", + "print(\"The less precise numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 3))\n", + "```\n", + "The result is:\n", + "$$\n", + "\\begin{bmatrix}\n", + "-0.88\\cos(4.694k_x + 8.512k_y - 4.694k_z) - 0.88\\cos(8.512k_x + 4.694k_y - 4.694k_z) + 0.159\n", + "\\end{bmatrix}\n", + "$$\n", + "As you can see, while this presentation is less precise, it is more compact and succinct. It is worth noting that the precision tradeoffs you need to consider heavily depends on the compound one is working with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The major difference between the analytical and numerical expressions is that the numerical expression depend on ```kx```, ```ky```, and ```kz``` while the analytical expression additionally depends on ```a1```, ```a2```, and ```a3```. ```a1```, ```a2```, and ```a3``` are the lattice vectors which define the orientation (symmetry) and shape of the unit cell of the compound's crystal unit cell. ```a1```, ```a2```, and ```a3``` can easily be replaced by numerical values simply by called the ```units``` function, present in the TRIQS, on your TRIQS TB lattice object." + ] }, { "cell_type": "markdown", From 33ebd9acb0aa81f16c324de3370e6e39219453d8 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Mon, 24 Jul 2023 14:10:09 -0400 Subject: [PATCH 15/47] Added the reason why the Hamiltonian is 1-by-1 --- SymPyfy Tutorial.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index a6943a7..5fc8c20 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -18,9 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we shall learn the intricacies behind the ```sympyfy``` function. But first, let us dive into the motivation behind writing such a function.\n", - "\n", - "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", + "Let us first dive into the motivation behind writing such a function. The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", "\n", "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells.\n", "\n", @@ -112,6 +110,7 @@ "[-0.880642\\cos(a_1k + a_3k) - 0.880642\\cos(a_2k + a_3k) + 0.158922]\n", "\\end{bmatrix}\n", "$$\n", + "The reason the matrix is 1-by-1 is that this is a one orbital system with one site per unit cell.\n", "\n", "Another of the cases would be similar to the above but setting the ```analytical``` flag to ```False``` to output the numerical form of the Hamiltonian.\n", "```python\n", From 9e9643f77ea8354fe45d3d276a4ff76fd1a00649 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Tue, 25 Jul 2023 09:37:56 -0400 Subject: [PATCH 16/47] Made some changes to the introduction and introduced a band structure of the full vs cutoff model of the Lanthanum cuprate compound --- SymPyfy Tutorial.ipynb | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 5fc8c20..fe331d6 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -18,11 +18,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us first dive into the motivation behind writing such a function. The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", + "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", "\n", "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells.\n", "\n", - "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials.\n" + "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials." ] }, { @@ -121,6 +121,7 @@ "The numerical expression:\n", "$$\n", "\\begin{bmatrix}\n", + "\n", "[-0.880642\\cos(4.693953k_x + 8.512243k_y - 4.693953k_z) - 0.880642\\cos(8.512243k_x + 4.693953k_y - 4.693953k_z) + 0.158922]\n", "\\end{bmatrix}\n", "$$\n", @@ -132,10 +133,10 @@ "The result is:\n", "$$\n", "\\begin{bmatrix}\n", - "-0.88\\cos(4.694k_x + 8.512k_y - 4.694k_z) - 0.88\\cos(8.512k_x + 4.694k_y - 4.694k_z) + 0.159\n", + "[-0.88\\cos(4.694k_x + 8.512k_y - 4.694k_z) - 0.88\\cos(8.512k_x + 4.694k_y - 4.694k_z) + 0.159]\n", "\\end{bmatrix}\n", "$$\n", - "As you can see, while this presentation is less precise, it is more compact and succinct. It is worth noting that the precision tradeoffs you need to consider heavily depends on the compound one is working with." + "While this presentation is less precise, it is more compact and succinct. It is worth noting that the precision tradeoffs you need to consider heavily depends on the compound one is working with." ] }, { @@ -145,6 +146,25 @@ "The major difference between the analytical and numerical expressions is that the numerical expression depend on ```kx```, ```ky```, and ```kz``` while the analytical expression additionally depends on ```a1```, ```a2```, and ```a3```. ```a1```, ```a2```, and ```a3``` are the lattice vectors which define the orientation (symmetry) and shape of the unit cell of the compound's crystal unit cell. ```a1```, ```a2```, and ```a3``` can easily be replaced by numerical values simply by called the ```units``` function, present in the TRIQS, on your TRIQS TB lattice object." ] }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Band Structure ##\n", + "Depending on what values you set for the ```min_hopping_norm``` and ```max_distance```, you might end up with a band structure that is way off, or nearly exactly similar, from the full model present in the full already-calculated Wannier90 model. It is up to the user to specify what parameter values they need to make their cutoff model closely resemble the full Wannier90 model while also allowing for a clear succinct analytical Hamiltonian expression\n", + "\n", + "\n", + " Let us see a visual example of this below. The image shows a graph of the band energy versus the points along the k-path using a ```min_hopping_norm``` or 0.05 eV and without a set maximum distance.\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, From c788b0c2ace77ec327f2981ab1a836ca60cc3e68 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Tue, 25 Jul 2023 09:39:14 -0400 Subject: [PATCH 17/47] Added La2CuO4 band structure comparing full W90 model vs cutoff model --- Full vs Cutoff Model Band Structure.png | Bin 0 -> 62022 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Full vs Cutoff Model Band Structure.png diff --git a/Full vs Cutoff Model Band Structure.png b/Full vs Cutoff Model Band Structure.png new file mode 100644 index 0000000000000000000000000000000000000000..39fc53a042187f0e7238cacc7481a311a3f4c663 GIT binary patch literal 62022 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2023 14:28:40 -0400 Subject: [PATCH 19/47] Showed how I call the model and included additional band structure figure (at 0.01 eV min_hopping_norm) --- SymPyfy Tutorial.ipynb | 57 ++++++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 30 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index fe331d6..44894b0 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -79,7 +79,7 @@ "\n", "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting \"numerical\" Hamiltonian. The use of quotation marks around the word numerical is deliberate because this expression, while less analytical than the default analytical expression, isn't wholly numerical and still depends on the parameters of the k-space vectors, ```kx```, ```ky```, and ```kz``` (the lattice constants and lattice vectors are expressed numerically). However, for the purpose of this tutorial, I shall continue to term it as numerical hereon, without the quotation marks around it since the user can easily provide values for ```kx```, ```ky```, and ```kz``` to make it entirely numerical.\n", "\n", - "Finally, the ```precision``` parameter is there for the user to specify however number of decimal places they want the Hamiltonian's floating point values to be. A smaller precision value is going to result in a more compact Hamiltonian form but with less precision. Thus, there is a tradeoff to be negotiated there. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + "Finally, the ```precision``` parameter is there for the user to control the number of digits in the hoppings and lattice parameters. A smaller precision value is going to result in a more compact Hamiltonian form but with less precision. Thus, there is a tradeoff to be negotiated there. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." ] }, { @@ -93,63 +93,58 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We shall use $La_2CuO_4$, Lanthanum Copper Oxide, a high-temperature superconductor, as an example on how the function works. Considering the scenarios laid out above, we can imagine calling ```sympyfy``` different cases.\n", + "Let us first load the model on $La_2CuO_4$, Lanthanum Copper Oxide, a high-temperature superconductor:\n", + "```python\n", + "from triqs.lattice.utils import TB_from_pythTB\n", + "w90_input = w90('La2CuO4 Files', 'lco')\n", + "fermi_ev = 12.7367\n", + "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", + "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)\n", + "```\n", + "\n", + "Considering the scenarios laid out above, we can imagine calling ```sympyfy``` different cases.\n", "One of the cases would be to output the reduced analytical Hamiltonian.\n", "```python\n", - "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = True, precision = 6))\n", + "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = True, precision = 3))\n", "``` \n", - "By the way, in practice, you can get away by just typing\n", - "```python\n", - "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4))\n", - "```\n", - "but the first way is clearer and is the style we're going to adopt in this tutorial. The result is:\n", + "The result is:\n", "\n", "The analytical expression:\n", "$$\n", "\\begin{bmatrix}\n", - "[-0.880642\\cos(a_1k + a_3k) - 0.880642\\cos(a_2k + a_3k) + 0.158922]\n", + "[-0.88\\cos(a_1k + a_3k) - 0.88\\cos(a_2k + a_3k) + 0.159]\n", "\\end{bmatrix}\n", "$$\n", "The reason the matrix is 1-by-1 is that this is a one orbital system with one site per unit cell.\n", "\n", "Another of the cases would be similar to the above but setting the ```analytical``` flag to ```False``` to output the numerical form of the Hamiltonian.\n", "```python\n", - "print(\"The numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 6))\n", + "print(\"The numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 3))\n", "``` \n", "The result is:\n", "\n", "The numerical expression:\n", "$$\n", "\\begin{bmatrix}\n", - "\n", - "[-0.880642\\cos(4.693953k_x + 8.512243k_y - 4.693953k_z) - 0.880642\\cos(8.512243k_x + 4.693953k_y - 4.693953k_z) + 0.158922]\n", + "[-0.88\\cos(3.818k_x) - 0.88\\cos(3.818k_y) + 0.159]\n", "\\end{bmatrix}\n", - "$$\n", - "\n", - "Finally, another case would be to alter the ```precision``` parameter's value from its default of 6 to a lesser value, say 3. Here it is:\n", - "```python\n", - "print(\"The less precise numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 3))\n", - "```\n", - "The result is:\n", - "$$\n", - "\\begin{bmatrix}\n", - "[-0.88\\cos(4.694k_x + 8.512k_y - 4.694k_z) - 0.88\\cos(8.512k_x + 4.694k_y - 4.694k_z) + 0.159]\n", - "\\end{bmatrix}\n", - "$$\n", - "While this presentation is less precise, it is more compact and succinct. It is worth noting that the precision tradeoffs you need to consider heavily depends on the compound one is working with." + "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The major difference between the analytical and numerical expressions is that the numerical expression depend on ```kx```, ```ky```, and ```kz``` while the analytical expression additionally depends on ```a1```, ```a2```, and ```a3```. ```a1```, ```a2```, and ```a3``` are the lattice vectors which define the orientation (symmetry) and shape of the unit cell of the compound's crystal unit cell. ```a1```, ```a2```, and ```a3``` can easily be replaced by numerical values simply by called the ```units``` function, present in the TRIQS, on your TRIQS TB lattice object." + "The major difference between the analytical and numerical expressions is that the numerical expression depend on ```kx```, ```ky```, and ```kz``` while the analytical expression additionally depends on ```a1```, ```a2```, and ```a3```. ```a1```, ```a2```, and ```a3``` are the lattice displacement vectors which define the orientation (symmetry) and shape of the unit cell of the compound's crystal unit cell. ```a1```, ```a2```, and ```a3``` can easily be replaced by numerical values simply by called the ```units``` function, present in the TRIQS, on your TRIQS TB lattice object." ] }, { "attachments": { + "image-2.png": { + "image/png": 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" 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" + "image/png": 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" } }, "cell_type": "markdown", @@ -158,11 +153,13 @@ "## Band Structure ##\n", "Depending on what values you set for the ```min_hopping_norm``` and ```max_distance```, you might end up with a band structure that is way off, or nearly exactly similar, from the full model present in the full already-calculated Wannier90 model. It is up to the user to specify what parameter values they need to make their cutoff model closely resemble the full Wannier90 model while also allowing for a clear succinct analytical Hamiltonian expression\n", "\n", - "\n", - " Let us see a visual example of this below. The image shows a graph of the band energy versus the points along the k-path using a ```min_hopping_norm``` or 0.05 eV and without a set maximum distance.\n", + "Let us see a visual example of this below. The image shows a graph of the band energy versus the points along the k-path using a ```min_hopping_norm``` or 0.05 eV and without a set maximum distance.\n", "\n", "![image.png](attachment:image.png)\n", - "\n" + "\n", + "Note that if we decrease the ```min_hopping_norm``` value, to say 0.01 eV, additional terms appear in our Hamiltonian values which results in a cutoff model that closely resembles the full Wannier90 model, that is, the accuracy improves. The figure below shows the band structure when the ```min_hopping_norm``` is 0.01 eV and still without a set ```max_distance``` cutoff.\n", + "\n", + "![image-2.png](attachment:image-2.png)\n" ] }, { From b02a66ab36d80d69faeb1be5ed5cd3f9fa4f2e81 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 13:03:02 -0400 Subject: [PATCH 20/47] Changed the form of the Hamiltonian equation and implemented reductions to the text in the introduction --- SymPyfy Tutorial.ipynb | 49 ++++++++++++++++-------------------------- 1 file changed, 18 insertions(+), 31 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 44894b0..3cb3f5b 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Understanding ```sympyfy```: An Analytical Momentum-Space Hamiltonian Expresser." + "# Understanding ```TB_to_sympy```: An Analytical Momentum-Space Hamiltonian Expresser." ] }, { @@ -18,41 +18,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ```sympyfy``` function utilizes the tight-binding (TB) model. The TB model is used in Condensed Matter Physics to study the properties of the band structures of different kinds of conductors, semiconductors, and insulators. \n", - "\n", - "Wannier90 gives us a broad range of hopping terms. Some are more useful than others. For instance, an electron with a hopping amplitude of 0.00005 eV will not contribute as much to the final Hamiltonian output as an electron with a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. Luckily, using PythTB's ```model``` function, we can initiate a cutoff for both, should we choose. This helps us to keep hopping terms that are going to have the greatest effect on the resulting Hamiltonian, thereby introducing a locality effect where we only consider next-nearest neighbor terms. It is worth noting that these orbital hoppings can be within the same unit cell or across different unit cells.\n", - "\n", - "Like many other models, the TB model has its Hamiltonian equation. This equation has two significant terms, one of which is the hopping term which describes the hopping of electrons between local neighboring sites. This is the term that ```sympyfy``` captures. Although we do not include an interaction term in ```sympyfy```, it is a great starting point in terms of perturbation theory where we look for solutions close to the non-interacting case. Nevertheless, we can capture valuable insights into the electronic properties of materials." + "Experimental physicists can interface with the TB model when performing electronic band structure measurements such as angle-resolved photoemission spectroscopy (ARPES). They may need to know whether their analyses of their experimental data fit with what the theory dictates. The TB model is easily tractable, allowing for an clear analytical solution. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense. Therefore, there is a need to develop a function that can provide a reduced analytical Hamiltonian expression of whatever element that is analyzed. This is what ```TB_to_sympy``` does!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The Hamiltonian of the TB model looks like this:\n", - "$$\n", - "\\hat{H} = \\sum_{\\vec{R}\\vec{\\delta}}| \\vec{R}> \\cal{t} <\\vec{R} + \\vec{\\delta}| + \\sum_{\\vec{R}} |\\vec{R}>U<\\vec{R}|\n", - "$$\n", + "The ```TB_to_sympy``` function takes hopping amplitudes to generate a tight-binding (TB) model. The TB model is used in condensed matter physics to model the properties of different systems such as conductors, semiconductors, and insulators. \n", "\n", - "where $\\vec{\\delta}$ is a set of vectors pointing from $\\vec{R}$ to its nearest neighbors." + "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more important it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes larger than the cutoff or smaller than a cutoff distance. This restricts the hopping processes to be closer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Condensed matter experimental physicists, as well as theoretical physicists, interface with the TB model in different ways. Experimental physicists can interface with the TB model when performing electronic band structure measurements such as Angle-Resolved Photoemission Spectroscopy (ARPES). They need to know whether their analyses of their experimental data fit with what the theory dictates.\n", + "In second quantization, the TB Hamiltonian is given by:\n", "\n", - "Moreover, the TB model is easily tractable, allowing for an easy-to-view analytical (Hamiltonian) solution for whatever system one analyzes. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense.\n", + "$$\n", + "H = -\\sum_{i j l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", + "$$\n", "\n", - "There is one issue, though the analytical solution to the TB model is helpful, it can end up being verbose and drawn out, counter to what an experimentalist would like. To drive the point home, an experimentalist would like a succinct, compact analytical expression of the compound they are working with. Therefore, it does them no good to look at a lengthy (Hamiltonian) expression. On top of this, condensed matter physicists are often interested in looking at systems at or near the Fermi level as this is where interesting physics happens, such as when electrons cross from the valence band to the conduction band which is interesting when we look at (super)conductivity and electrical properties." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Therefore, there is a need to develop a function that can provide a reduced analytical Hamiltonian expression of whatever element that is analyzed. This is what ```sympyfy``` does! Let us look at the function definition:" + "where $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$, $\\sigma$ is the spin and $c^{\\dagger}$ and $c$ are creation and annihilation operators.\n", + "\n", + "In this tutorial, we will consider a system with one orbital and one site per unit cell. We can perform a Fourier transform of the Hamiltonian above from real space to momentum space to get $H(k)$ which is what ```sympyfy``` does.\n", + "\n", + "$$\n", + "H(\\bold{k}) = \\frac{1}{(2\\pi)^d} \\sum_{\\bold{R}} e^{i\\bold{k} \\cdot \\bold{R}} H_{\\bold{R}}\n", + "$$\n", + "\n" ] }, { @@ -60,7 +55,7 @@ "metadata": {}, "source": [ "```python\n", - "def sympyfy(tb_lat_obj, analytical = True, precision = 6):" + "def sympyfy(w90_triqs, analytical = True, precision = 6):" ] }, { @@ -93,7 +88,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us first load the model on $La_2CuO_4$, Lanthanum Copper Oxide, a high-temperature superconductor:\n", + "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:\n", "```python\n", "from triqs.lattice.utils import TB_from_pythTB\n", "w90_input = w90('La2CuO4 Files', 'lco')\n", @@ -161,14 +156,6 @@ "\n", "![image-2.png](attachment:image-2.png)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion ###\n", - "```sympyfy``` is a user-friendly tool that provides a clear and comprehensible representation using the tight-binding model. This bridges the divide between experimental findings and theoretical methods, enabling researchers to swiftly confirm if their results match tight-binding predictions." - ] } ], "metadata": { From 8fc05580c6287e35a9557856bb6e300e25479f98 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 14:54:30 -0400 Subject: [PATCH 21/47] Created a python file containing the TB_to_sympy function So that I could import it into my tutorial. --- TB_to_sympy.py | 162 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 162 insertions(+) create mode 100644 TB_to_sympy.py diff --git a/TB_to_sympy.py b/TB_to_sympy.py new file mode 100644 index 0000000..afe381d --- /dev/null +++ b/TB_to_sympy.py @@ -0,0 +1,162 @@ +def TB_to_sympy(w90_triqs, analytical = True, precision = 6): + r""" + returns the analytical form of the momentum space hamiltonian of the tight-binding model + from a tight-binding lattice object, by utilizing Fourier series + + Parameters + ---------- + w90_triqs: triqs TBLattice object + triqs tight binding object + analytical: boolean, default = True + a boolean which will cause the function will return an analytical Hamiltonian, when true, and + an numerical Hamiltonian otherwise + precision: integer, default = 6 + An integer that specifies the decimal point precision of the floating point hopping amplitudes of + the electrons involved in the hoppings + Wannier90's default precision is 10^-6 which is why the default precision is 6 + The user might want to decrease the default precision to ensure that similar hopping amplitudes + especially those tied to symmetrical hoppings across the crysal lattice are easily recognizable + + Returns + ------- + Hk: NumPy array + The hamiltonian of the tight-binding model in momentum space. This can be returned either in + numerical form (Hk_numerical) or reduced analytical form (Hk) depending the user's preference. + The default output, which depends on the optional analytical parameter, is the reduced analytical form + The numerical form only depends on the k-space vector components, i.e., the lattice constants and + lattice vectors are expressed numerically + The analytical form depends on the k-space vector components alongside the lattice vectors too + + """ + + # imaginary number + I = sp.I + + # matrix from the axis directions in momentum space + kx, ky, kz = sp.symbols("kx ky kz", real = True) + k_space_matrix = sp.Matrix([kx, ky, kz]) + + # symbolic dot product representation between the lattice unit vectors + # and the momentum space matrix + a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) + lattice = sp.Matrix([a1k, a2k, a3k]) + + # the number of orbitals involved in the electron hoppings + num_orb = w90_triqs.n_orbitals + + # dictionary containing details about the hopping of the electrons + TB_lat_obj_hops = w90_triqs.hoppings + + # maximum hopping distances of electrons in each of the axial directions + max_x, max_y, max_z = list(np.max(np.array(list(TB_lat_obj_hops.keys())), axis = 0)) + + # number of cells involved in the hopping of electrons in each of the axial directions + num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] + + # basis of the 5D tensor real-space Hamiltonian + Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) + + # looping through the hopping parameters of the electrons involved in the inter-orbital hoppings + # key represents the cell coordinates of where the electrons hop to relative to the home unit cell + # hopping represents the matrix with the embedded hopping amplitudes + for key, hopping in TB_lat_obj_hops.items(): + rx, ry, rz = key + # reduce floating point precision of hopping parameters to 3 decimal places + hopping = np.around(hopping, precision) + Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping + + # basis of the exponential term in the calculation of Hk + Hexp = np.empty_like(Hrij, dtype = sp.exp) + + # perform the Fourier transform + for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)): + coefficients = np.array([xi - max_x, yi - max_y, zi - max_z]) + r = lattice.dot(coefficients) + eikr = sp.exp(-I * r) + Hexp[xi, yi, zi, :, :] = eikr + + # summation over all real space axes + Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2)) + + # rewriting the exponential terms in the analytical expression in terms of + for i, j in itp(range(num_orb), repeat = 2): + Hk[i, j] = Hk[i, j].rewrite(sp.cos) + + # dealing with the numerical Hamiltonian + # we convert it to a SymPy matrix to use the substitutions method available in SymPy + Hk_numerical = sp.Matrix(Hk) + + # matrix containing the displacement vectors + TB_lat_obj_units = w90_triqs.units + + # obtaining the individual displacement vectors + a1 = np.around(TB_lat_obj_units[0], precision) + a2 = np.around(TB_lat_obj_units[1], precision) + a3 = np.around(TB_lat_obj_units[2], precision) + + # numerical dot products between the unit vectors + # and the momentum space matrix + a1k_numerical = a1.dot(k_space_matrix)[0] + a2k_numerical = a2.dot(k_space_matrix)[0] + a3k_numerical = a3.dot(k_space_matrix)[0] + + # performing the numerical dot product substitutions + Hk_numerical = Hk_numerical.subs(a1k, a1k_numerical) + Hk_numerical = Hk_numerical.subs(a2k, a2k_numerical) + Hk_numerical = Hk_numerical.subs(a3k, a3k_numerical) + + # converting the numerical Hamiltonian to a NumPy array from a SymPy matrix + Hk_numerical = np.array(Hk_numerical) + + def _has_complex_exponential_sympy(matrix): + """ + Checks if a NumPy array containing SymPy elements has a complex exponential element. + + Args: + matrix (NumPy array): The input NumPy array containing SymPy elements + + Returns: + bool: True if the matrix array contains a complex exponential element, False otherwise. + + """ + + for sublist in matrix: + for element in sublist: + if element.is_complex and element.has(sp.exp): + return True + return False + + def _is_hermitian_sympy(matrix): + """ + Checks if a NumPy array containing SymPy elements is hermitian + + Args: + matrix (NumPy array): The input NumPy array containing SymPy elements + + Returns: + bool: True if the matrix is a hermitian, False otherwise + + """ + + n = matrix.shape[0] + for i in range(n): + for j in range(n): + if matrix[i, j] != matrix[j, i].conjugate(): + return False + return True + + # warning indicating when the output Hamiltonian is not hermitian + if _is_hermitian_sympy(Hk) == False or _is_hermitian_sympy(Hk_numerical) == False: + return warnings.warn("The resulting Hamiltonian is not hermitian.") + + # warning indicating when the Hamiltonian contains a complex exponential element + if _has_complex_exponential_sympy(Hk_numerical) or _has_complex_exponential_sympy(Hk): + return warnings.warn("""Your expression has a complex exponential. + Choosing a different unit cell could make + your Hamiltonian expression real.""") + + # returning the analytical or numerical form of the Hamiltonian + # depending on the user's preference + if analytical: + return Hk + return Hk_numerical \ No newline at end of file From f78fa826f8177327c85746bccb91a6a0b7a110b7 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 14:56:16 -0400 Subject: [PATCH 22/47] included the necessary dependencies --- TB_to_sympy.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/TB_to_sympy.py b/TB_to_sympy.py index afe381d..a1cf588 100644 --- a/TB_to_sympy.py +++ b/TB_to_sympy.py @@ -1,3 +1,12 @@ +# importing the dependencies +from itertools import product as itp +from pythtb import * +from triqs.lattice.tight_binding import TBLattice +import sympy as sp +import warnings +import numpy as np +import matplotlib.pyplot as plt + def TB_to_sympy(w90_triqs, analytical = True, precision = 6): r""" returns the analytical form of the momentum space hamiltonian of the tight-binding model From af4cb423961c495013891abf264e442d3c6170dd Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 16:57:16 -0400 Subject: [PATCH 23/47] Made a figure showing band structure of lanthanum cuprate at 2 different energy cutoffs --- ...f lanthanum cuprate at different cutoffs.png | Bin 0 -> 78179 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 The band structure of lanthanum cuprate at different cutoffs.png diff --git a/The band structure of lanthanum cuprate at different cutoffs.png b/The band structure of lanthanum cuprate at different cutoffs.png new file mode 100644 index 0000000000000000000000000000000000000000..5b2981765f8b712b33180eeec8e563309ee2cdb6 GIT binary patch literal 78179 zcmc$`Wmr~k_bm!GDxjE%n1q0c0t$kJg@SY{sUY2rk`{^zDk;+4T}n3s(hbrm(j{GI 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a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Understanding ```TB_to_sympy```: An Analytical Momentum-Space Hamiltonian Expresser." + "# ```TB_to_sympy```: An Analytical Momentum-Space Hamiltonian Expresser." ] }, { @@ -18,16 +18,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Experimental physicists can interface with the TB model when performing electronic band structure measurements such as angle-resolved photoemission spectroscopy (ARPES). They may need to know whether their analyses of their experimental data fit with what the theory dictates. The TB model is easily tractable, allowing for an clear analytical solution. This tractability proves efficient to experimentalists as it can save time for them to quickly check whether their experimental data makes sense. Therefore, there is a need to develop a function that can provide a reduced analytical Hamiltonian expression of whatever element that is analyzed. This is what ```TB_to_sympy``` does!" + "Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The ```TB_to_sympy``` function takes hopping amplitudes to generate a tight-binding (TB) model. The TB model is used in condensed matter physics to model the properties of different systems such as conductors, semiconductors, and insulators. \n", + "The ```TB_to_sympy``` function takes hopping amplitudes to generate a tight-binding (TB) model. The TB model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. \n", "\n", - "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more important it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff, either in the hopping distances or in the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes larger than the cutoff or smaller than a cutoff distance. This restricts the hopping processes to be closer." + "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." ] }, { @@ -40,9 +40,12 @@ "H = -\\sum_{i j l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", "$$\n", "\n", - "where $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$, $\\sigma$ is the spin and $c^{\\dagger}$ and $c$ are creation and annihilation operators.\n", + "where:\n", + "- $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$.\n", + "- $\\sigma$ is the spin and $c^{\\dagger}$\n", + "- $c$ are creation and annihilation operators.\n", "\n", - "In this tutorial, we will consider a system with one orbital and one site per unit cell. We can perform a Fourier transform of the Hamiltonian above from real space to momentum space to get $H(k)$ which is what ```sympyfy``` does.\n", + "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(\\bold{k})$, which is achieved using the ```TB_to_sympy``` function.\n", "\n", "$$\n", "H(\\bold{k}) = \\frac{1}{(2\\pi)^d} \\sum_{\\bold{R}} e^{i\\bold{k} \\cdot \\bold{R}} H_{\\bold{R}}\n", @@ -54,27 +57,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Function Definition and its Parameters. ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```TB_to_sympy``` is defined as:\n", "```python\n", - "def sympyfy(w90_triqs, analytical = True, precision = 6):" + "def TB_to_sympy(w90_triqs, analytical = True, precision = 6):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The ```w90_triqs``` is a TRIQS TBLattice Object (Tight-Binding Lattice Object) which is the only mandatory parameter in our function. To obtain the ```w90_triqs```, TRIQS fortunately provides functions from which you can convert your model to a TRIQS TBLattice object. These functions are:\n", - "1. ```TB_from_pythTB``` which converts your model from a pythTB model to a TRIQS TBLattice Object.\n", - "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and convert it to a TBLattice object.\n", + "The only required parameter for our function is ```w90_triqs```, a TRIQS TBLattice Object (Tight-Binding Lattice Object). To obtain ```w90_triqs```, you can use TRIQS-provided functions that allow conversion of your model to a TRIQS TBLattice object. These functions are:\n", + "1. ```TB_from_pythTB``` that converts your model from a pythTB model to a TRIQS TBLattice Object.\n", + "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and converts it to a TBLattice object.\n", "\n", - "This makes the ```w90_triqs``` to be the only parameter that interfaces with other TRIQS package functions.\n", - "At this time, it is recommended that one use the ```TB_from_pythTB``` as you can easily initiate cutoffs to your pythTB model before converting it to a TBLattice object, especially for complex systems. This allows one to be more in control of their model and look at the behavior of the output at different cutoffs. Such cutoff parameters, possible with the pythTB includes the:\n", - "1. ```zero_energy```: sets the zero of the energy in the band structure, which is almost always set at the Fermi level.\n", - "2. ```min_hopping_norm```: the hopping terms read from Wannier90. Hopping amplitudes (measured in electron-volts) less than the set ```min_hopping_norm``` will not be included in the resulting calculations.\n", - "3. ```max_distance```: Any hopping distance greater than the set ```max_distance``` will be ignored.\n", + "Currently, it is advisable to utilize ```TB_from_pythTB``` since it enables straightforward initiation of cutoffs to your pythTB model before converting it to a TBLattice object, especially for complex systems, granting greater control over your model and allows examination of the output's behavior at different cutoffs. The possible cutoff parameters in pythTB include:\n", + "1. ```zero_energy```: This parameter establishes the energy zero point in the band structure, typically aligned with the Fermi level.\n", + "2. ```min_hopping_norm```: The hopping terms obtained from Wannier90 are filtered based on their hopping amplitudes (measured in electron volts). Terms with amplitudes less than ```min_hopping_norm``` are excluded from the calculations.\n", + "3. ```max_distance```: Hopping distances exceeding ```max_distance``` are disregarded during the calculations.\n", "\n", - "The ```analytical``` flag ensures that the default Hamiltonian that is going to be returned once ```sympyfy``` is run is going to be analytical. The user can change the ```analytical``` flag to ```False``` to see the resulting \"numerical\" Hamiltonian. The use of quotation marks around the word numerical is deliberate because this expression, while less analytical than the default analytical expression, isn't wholly numerical and still depends on the parameters of the k-space vectors, ```kx```, ```ky```, and ```kz``` (the lattice constants and lattice vectors are expressed numerically). However, for the purpose of this tutorial, I shall continue to term it as numerical hereon, without the quotation marks around it since the user can easily provide values for ```kx```, ```ky```, and ```kz``` to make it entirely numerical.\n", + "The ```analytical``` flag ensures that the default Hamiltonian returned by ```sympyfy``` is in an analytical form. If the user sets the ```analytical``` flag to ```False```, the resulting Hamiltonian will be in a \"numerical\" form. Although termed \"numerical,\" this expression still depends on the parameters of the k-space vectors (```kx```, ```ky```, and ```kz```) as the lattice constants and vectors are expressed numerically. For simplicity, we will continue to refer to it as numerical in this tutorial, assuming the user can provide numerical values for ```kx```, ```ky```, and ```kz```.\n", "\n", - "Finally, the ```precision``` parameter is there for the user to control the number of digits in the hoppings and lattice parameters. A smaller precision value is going to result in a more compact Hamiltonian form but with less precision. Thus, there is a tradeoff to be negotiated there. By default, the floating point values from Wannier90 are in the order of $10^{-6}$. While this is quite precise, it can be difficult to notice similar hopping amplitude values that may arise due to symmetrical hops in opposite directions." + "Lastly, the ```precision``` parameter allows the user to control the number of digits in the hopping amplitudes and lattice parameters." ] }, { @@ -88,73 +98,115 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:\n", - "```python\n", + "Let us first import the necessary dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# importing the dependencies\n", + "from itertools import product as itp\n", + "from pythtb import *\n", + "from triqs.lattice.tight_binding import TBLattice\n", + "import sympy as sp\n", + "import warnings\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# importing the file containing the TB_to_sympy function\n", + "import TB_to_sympy\n", + "from TB_to_sympy import TB_to_sympy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ "from triqs.lattice.utils import TB_from_pythTB\n", - "w90_input = w90('La2CuO4 Files', 'lco')\n", + "# accessing the necessary Wannier90 output files\n", + "w90_input = w90('AbinitioDMFT/data/mlwf/', 'lco')\n", "fermi_ev = 12.7367\n", "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", - "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)\n", - "```\n", - "\n", - "Considering the scenarios laid out above, we can imagine calling ```sympyfy``` different cases.\n", - "One of the cases would be to output the reduced analytical Hamiltonian.\n", - "```python\n", - "print(\"The analytical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = True, precision = 3))\n", - "``` \n", - "The result is:\n", - "\n", - "The analytical expression:\n", - "$$\n", - "\\begin{bmatrix}\n", - "[-0.88\\cos(a_1k + a_3k) - 0.88\\cos(a_2k + a_3k) + 0.159]\n", - "\\end{bmatrix}\n", - "$$\n", - "The reason the matrix is 1-by-1 is that this is a one orbital system with one site per unit cell.\n", - "\n", - "Another of the cases would be similar to the above but setting the ```analytical``` flag to ```False``` to output the numerical form of the Hamiltonian.\n", - "```python\n", - "print(\"The numerical expression: \\n\", sympyfy(w90_triqs_La2CuO4, analytical = False, precision = 3))\n", - "``` \n", - "The result is:\n", - "\n", - "The numerical expression:\n", - "$$\n", - "\\begin{bmatrix}\n", - "[-0.88\\cos(3.818k_x) - 0.88\\cos(3.818k_y) + 0.159]\n", - "\\end{bmatrix}\n", - "$$" + "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call ```TB_from_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical expression \n", + " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The analytical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = True, precision = 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The numerical expression \n", + " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The numerical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = False, precision = 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The major difference between the analytical and numerical expressions is that the numerical expression depend on ```kx```, ```ky```, and ```kz``` while the analytical expression additionally depends on ```a1```, ```a2```, and ```a3```. ```a1```, ```a2```, and ```a3``` are the lattice displacement vectors which define the orientation (symmetry) and shape of the unit cell of the compound's crystal unit cell. ```a1```, ```a2```, and ```a3``` can easily be replaced by numerical values simply by called the ```units``` function, present in the TRIQS, on your TRIQS TB lattice object." + "The main distinction between the analytical and numerical expressions is that the numerical expression depends on ```kx```, ```ky```, and ```kz```, while the analytical expression additionally depends on the lattice vectors, ```a1```, ```a2```, and ```a3```. The lattice vectors define the orientation and shape of the crystal unit cell and can be replaced with numerical values using the ```units``` function from the TRIQS TB lattice object." ] }, { "attachments": { - "image-2.png": { - "image/png": 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" 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" + "The band structure of lanthanum cuprate at different cutoffs.png": { + "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## Band Structure ##\n", - "Depending on what values you set for the ```min_hopping_norm``` and ```max_distance```, you might end up with a band structure that is way off, or nearly exactly similar, from the full model present in the full already-calculated Wannier90 model. It is up to the user to specify what parameter values they need to make their cutoff model closely resemble the full Wannier90 model while also allowing for a clear succinct analytical Hamiltonian expression\n", - "\n", - "Let us see a visual example of this below. The image shows a graph of the band energy versus the points along the k-path using a ```min_hopping_norm``` or 0.05 eV and without a set maximum distance.\n", - "\n", - "![image.png](attachment:image.png)\n", + "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this. The graph displays the band energy plotted against the points along the k-path using a ```min_hopping_norm``` of 0.05 eV and 0.01 eV with no set maximum distance.\n", "\n", - "Note that if we decrease the ```min_hopping_norm``` value, to say 0.01 eV, additional terms appear in our Hamiltonian values which results in a cutoff model that closely resembles the full Wannier90 model, that is, the accuracy improves. The figure below shows the band structure when the ```min_hopping_norm``` is 0.01 eV and still without a set ```max_distance``` cutoff.\n", + "![The band structure of lanthanum cuprate at different cutoffs.png]()\n", "\n", - "![image-2.png](attachment:image-2.png)\n" + "With a lower cutoff of 0.01 eV, more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." ] } ], From 04a632af756e815ecb6964e9ff8ec3e54d7dcf8a Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 17:19:43 -0400 Subject: [PATCH 25/47] chopping down unnecessary comments and words in docstring --- TB_to_sympy.py | 75 +++++++++++++++++++++++--------------------------- 1 file changed, 35 insertions(+), 40 deletions(-) diff --git a/TB_to_sympy.py b/TB_to_sympy.py index a1cf588..e4158f5 100644 --- a/TB_to_sympy.py +++ b/TB_to_sympy.py @@ -10,7 +10,7 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): r""" returns the analytical form of the momentum space hamiltonian of the tight-binding model - from a tight-binding lattice object, by utilizing Fourier series + from a tight-binding lattice object by utilizing Fourier series Parameters ---------- @@ -20,64 +20,59 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): a boolean which will cause the function will return an analytical Hamiltonian, when true, and an numerical Hamiltonian otherwise precision: integer, default = 6 - An integer that specifies the decimal point precision of the floating point hopping amplitudes of - the electrons involved in the hoppings - Wannier90's default precision is 10^-6 which is why the default precision is 6 - The user might want to decrease the default precision to ensure that similar hopping amplitudes - especially those tied to symmetrical hoppings across the crysal lattice are easily recognizable + specifies the precision of the floating point amplitudes. The default precision is 6 but the user + can decrease this parameter for better recognition of similar hopping amplitudes, particularly + for symmetrical hoppings across the crystal lattice Returns ------- Hk: NumPy array - The hamiltonian of the tight-binding model in momentum space. This can be returned either in - numerical form (Hk_numerical) or reduced analytical form (Hk) depending the user's preference. - The default output, which depends on the optional analytical parameter, is the reduced analytical form - The numerical form only depends on the k-space vector components, i.e., the lattice constants and - lattice vectors are expressed numerically - The analytical form depends on the k-space vector components alongside the lattice vectors too + the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical + form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output + is the reduced analytical form. The numerical form depends solely on the k-space vector components + while the analytical form takes into account both the k-space vector components and the lattice + vectors """ # imaginary number I = sp.I - # matrix from the axis directions in momentum space + # matrix from axis directions in momentum space kx, ky, kz = sp.symbols("kx ky kz", real = True) k_space_matrix = sp.Matrix([kx, ky, kz]) - # symbolic dot product representation between the lattice unit vectors - # and the momentum space matrix + # symbolic dot product representation between lattice unit vectors + # and momentum space matrix a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) lattice = sp.Matrix([a1k, a2k, a3k]) - # the number of orbitals involved in the electron hoppings + # number of orbitals involved in the unit cell num_orb = w90_triqs.n_orbitals - # dictionary containing details about the hopping of the electrons - TB_lat_obj_hops = w90_triqs.hoppings + # dictionary containing details about hopping of electrons such as + # orbital and hopping amplitude info + TB_lat_obj_hops = w90_triqs.hoppings - # maximum hopping distances of electrons in each of the axial directions + # maximum hopping distances of electrons in each direction max_x, max_y, max_z = list(np.max(np.array(list(TB_lat_obj_hops.keys())), axis = 0)) - # number of cells involved in the hopping of electrons in each of the axial directions + # number of cells involved in the hopping of electrons in each direction num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] - # basis of the 5D tensor real-space Hamiltonian + # real-space Hamiltonian Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) - # looping through the hopping parameters of the electrons involved in the inter-orbital hoppings - # key represents the cell coordinates of where the electrons hop to relative to the home unit cell - # hopping represents the matrix with the embedded hopping amplitudes + # looping through hopping parameters of electrons involved in inter-orbital hoppings for key, hopping in TB_lat_obj_hops.items(): rx, ry, rz = key - # reduce floating point precision of hopping parameters to 3 decimal places hopping = np.around(hopping, precision) Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping - # basis of the exponential term in the calculation of Hk + # basis of the exponential term in calculation of Hk Hexp = np.empty_like(Hrij, dtype = sp.exp) - # perform the Fourier transform + # perform Fourier transform for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)): coefficients = np.array([xi - max_x, yi - max_y, zi - max_z]) r = lattice.dot(coefficients) @@ -87,34 +82,35 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): # summation over all real space axes Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2)) - # rewriting the exponential terms in the analytical expression in terms of + # rewriting exponential terms in Hamiltonian expression in terms of cosine for i, j in itp(range(num_orb), repeat = 2): Hk[i, j] = Hk[i, j].rewrite(sp.cos) # dealing with the numerical Hamiltonian - # we convert it to a SymPy matrix to use the substitutions method available in SymPy + + # convert to SymPy matrix to use substitutions method available in SymPy Hk_numerical = sp.Matrix(Hk) - # matrix containing the displacement vectors + # matrix containing displacement vectors TB_lat_obj_units = w90_triqs.units - # obtaining the individual displacement vectors + # obtaining individual displacement vectors a1 = np.around(TB_lat_obj_units[0], precision) a2 = np.around(TB_lat_obj_units[1], precision) a3 = np.around(TB_lat_obj_units[2], precision) - # numerical dot products between the unit vectors - # and the momentum space matrix + # numerical dot products between unit vectors + # and momentum space matrix a1k_numerical = a1.dot(k_space_matrix)[0] a2k_numerical = a2.dot(k_space_matrix)[0] a3k_numerical = a3.dot(k_space_matrix)[0] - # performing the numerical dot product substitutions + # performing numerical dot product substitutions Hk_numerical = Hk_numerical.subs(a1k, a1k_numerical) Hk_numerical = Hk_numerical.subs(a2k, a2k_numerical) Hk_numerical = Hk_numerical.subs(a3k, a3k_numerical) - # converting the numerical Hamiltonian to a NumPy array from a SymPy matrix + # converting numerical Hamiltonian to NumPy array Hk_numerical = np.array(Hk_numerical) def _has_complex_exponential_sympy(matrix): @@ -154,18 +150,17 @@ def _is_hermitian_sympy(matrix): return False return True - # warning indicating when the output Hamiltonian is not hermitian + # warning indicating when Hamiltonian is not hermitian if _is_hermitian_sympy(Hk) == False or _is_hermitian_sympy(Hk_numerical) == False: return warnings.warn("The resulting Hamiltonian is not hermitian.") - # warning indicating when the Hamiltonian contains a complex exponential element + # warning indicating when Hamiltonian contains complex exponential element if _has_complex_exponential_sympy(Hk_numerical) or _has_complex_exponential_sympy(Hk): return warnings.warn("""Your expression has a complex exponential. Choosing a different unit cell could make your Hamiltonian expression real.""") - # returning the analytical or numerical form of the Hamiltonian - # depending on the user's preference + # returning analytical or numerical form of Hamiltonian if analytical: return Hk - return Hk_numerical \ No newline at end of file + return Hk_numerical From fe86a428892e8be46acbca23503fda1e4c80d622 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Wed, 26 Jul 2023 17:24:12 -0400 Subject: [PATCH 26/47] changed the location of where I import sympy to inside the function --- TB_to_sympy.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/TB_to_sympy.py b/TB_to_sympy.py index e4158f5..a9bf6d5 100644 --- a/TB_to_sympy.py +++ b/TB_to_sympy.py @@ -2,7 +2,6 @@ from itertools import product as itp from pythtb import * from triqs.lattice.tight_binding import TBLattice -import sympy as sp import warnings import numpy as np import matplotlib.pyplot as plt @@ -35,6 +34,8 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): """ + import sympy as sp + # imaginary number I = sp.I From 043c92f29a4a1e45b33646cd8ca587573edd4eab Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 27 Jul 2023 19:52:31 -0400 Subject: [PATCH 27/47] changed the order for one of the sentences to promote better flow in the tutorial --- SymPyfy Tutorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 7c2e697..1f138d8 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -25,7 +25,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ```TB_to_sympy``` function takes hopping amplitudes to generate a tight-binding (TB) model. The TB model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. \n", + "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. The ```TB_to_sympy``` function takes hopping amplitudes to generate a TB model. \n", "\n", "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." ] From ebf0f54cdb51504d9a47f67b921ef683fd26eafc Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Sat, 29 Jul 2023 13:23:57 -0400 Subject: [PATCH 28/47] Succinctly phrased the title and some parts of the introduction to clearly convey the message --- SymPyfy Tutorial.ipynb | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 1f138d8..6cc4a46 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -4,30 +4,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ```TB_to_sympy```: An Analytical Momentum-Space Hamiltonian Expresser." + "# ```TB_to_sympy```: Simplifying Hamiltonian Expressions for Condensed Matter Systems Within a Tight-Binding Model Framework." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Introduction ##" + "## Introduction. ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." + "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. The ```TB_to_sympy``` function takes hopping amplitudes to generate a TB model. \n", - "\n", - "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." + "The ```TB_to_sympy``` function takes hopping amplitudes to generate a TB model. The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." ] }, { From f08f1c37244e24055449e5bb000806ff6ffbd903 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Sat, 29 Jul 2023 13:24:30 -0400 Subject: [PATCH 29/47] changed the name of the first parameter and changed some variable names accordingly --- TB_to_sympy.py | 37 ++++++++++++++++++++++--------------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/TB_to_sympy.py b/TB_to_sympy.py index a9bf6d5..d8a85fa 100644 --- a/TB_to_sympy.py +++ b/TB_to_sympy.py @@ -6,14 +6,14 @@ import numpy as np import matplotlib.pyplot as plt -def TB_to_sympy(w90_triqs, analytical = True, precision = 6): +def TB_to_sympy(TBL, analytical = True, precision = 6): r""" returns the analytical form of the momentum space hamiltonian of the tight-binding model from a tight-binding lattice object by utilizing Fourier series Parameters ---------- - w90_triqs: triqs TBLattice object + TBL: triqs TBLattice object triqs tight binding object analytical: boolean, default = True a boolean which will cause the function will return an analytical Hamiltonian, when true, and @@ -48,15 +48,25 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) lattice = sp.Matrix([a1k, a2k, a3k]) + # units contains the displacement vectors + # hops contains details about hopping of electrons such as orbital + # and hopping amplitude + if TBL.units.shape == (2, 2): + TBL_units = np.eye(3) + TBL_units[:2, :2] = TBL.units + TBL_hops = {key + (0,): val for key, val in TBL.hoppings.items()} + elif TBL.units.shape == (3,3): + TBL_units = TBL.units + TBL_hops = TBL.hoppings + # raises error for when the dimensions of the tb object is neither 2D nor 3D + else: + raise ValueError("This format of the tight-binding model is not implemented for this function.") + # number of orbitals involved in the unit cell - num_orb = w90_triqs.n_orbitals - - # dictionary containing details about hopping of electrons such as - # orbital and hopping amplitude info - TB_lat_obj_hops = w90_triqs.hoppings + num_orb = TBL.n_orbitals # maximum hopping distances of electrons in each direction - max_x, max_y, max_z = list(np.max(np.array(list(TB_lat_obj_hops.keys())), axis = 0)) + max_x, max_y, max_z = list(np.max(np.array(list(TBL_hops.keys())), axis = 0)) # number of cells involved in the hopping of electrons in each direction num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] @@ -65,7 +75,7 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) # looping through hopping parameters of electrons involved in inter-orbital hoppings - for key, hopping in TB_lat_obj_hops.items(): + for key, hopping in TBL_hops.items(): rx, ry, rz = key hopping = np.around(hopping, precision) Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping @@ -92,13 +102,10 @@ def TB_to_sympy(w90_triqs, analytical = True, precision = 6): # convert to SymPy matrix to use substitutions method available in SymPy Hk_numerical = sp.Matrix(Hk) - # matrix containing displacement vectors - TB_lat_obj_units = w90_triqs.units - # obtaining individual displacement vectors - a1 = np.around(TB_lat_obj_units[0], precision) - a2 = np.around(TB_lat_obj_units[1], precision) - a3 = np.around(TB_lat_obj_units[2], precision) + a1 = np.around(TBL_units[0], precision) + a2 = np.around(TBL_units[1], precision) + a3 = np.around(TBL_units[2], precision) # numerical dot products between unit vectors # and momentum space matrix From 3c18db5a231d33ed697968b526e9de022e06d21d Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 3 Aug 2023 15:14:45 -0400 Subject: [PATCH 30/47] Added figure that compares minimal models of La2CuO4 --- Comparing Minimal Models.png | Bin 0 -> 1518844 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Comparing Minimal Models.png diff --git a/Comparing Minimal Models.png b/Comparing Minimal Models.png new file mode 100644 index 0000000000000000000000000000000000000000..2e8ce347e9842054d30745e00b89025814262581 GIT binary patch literal 1518844 zcmeFad0fqD`v;ye_}T~AlCo#5Nuf>~V@cFO+0sU#v`U?d)?tjL4I$AHqLdbi_QleI zDCx9mRY)goTDAV(*Qd<*PUHJ}p1*#t-|NTgc_vfmbnef6U(5S?U+?RlQ=8e!qelph z5D*X;t+IZNx`4pqn*ste-~Z20d`G6+>Mp*>TCd${tzog>+IG*OeFAEGtPh%6SeqWu zpJB7_kktVTb1Cr^;*yJJ7+G5%v|>p}nEmwvaf?HS5=A5a=LCMq@Pq3$tpo;1ou~f~ zx;NHiyucs<0hKkYw%YlA%3L+tMc|jgzg}|AIx)m4G5_gn z>2)D0a{tZC@6N0JZ(jcJ^8La8&Ff8mEdG1j^<(G1w=)6)Kg{O4d-KC=zPmL)%;x)B z^TTYuzc)Xk&3E_ahuM60YkowV?{Ccyv-$qs{D?N+-J2g~^WCla5pBM|H9ySe`+M^v z+I)9!ewfX7x8_H*`To}YFq`l1&5vmF-M#r?Hs9TvAJOLfTl2$gzP~p=qRn^r=7-sQ zcWZt`o9}PU53~9H-u#F*-`$%ZX7k;x`4Mftzcv4#H=BkC+p4$Q@@#v0%ZAU`aOdQ% ziJMoR-L^(>oc88l9^IOMV_Wg7YdSn}o3!fEg?To{T7g`@+WWh-WQ8VcOICQyIy!%g z;EqxEeil5l?WpPSLMP`Jmn_UeXFcwI_SsH5KlXLnnf#`o*JsAoTgiNX69@c6TKY@S zf2Gk7y4=NsVC6smGg{4w|DV5FK)~hCc}krB<_#-Xd-<{cn>Sp${%q6q|K<&^UcC$a zuVghwMxQnQ8`+M)k7M``&g1{ZV-PyJKqFv{uCDF}?^VZ!-bx~iHvL_&ZEDl!&+2++ z*Tyw;j}*AplkxtLih1NjLBX5!ij@iv4!(Unr%i*!;h*ma9z6PCL4<*qgQt+3jVb@m zw+6Lx*8d;9@W9o7x~)q`cyRE-i%Ya7ZS)p*pS$s5%IjMj4c zaud7DkBz-?Dxg;h4bVtT)41BQ*-K-?;fo4Rs$fa#Bi$M6$3%VzYWe*XNAJHzwh!pWkO_1e!(U-r14P z&%IOm-dnFHus14Q#D0yZh(g7g)niVr9;+bT(ONrA;Yjnz{Vy-ioj>INc=T%##sg2J z)}%7cy|LITD^)5>enL**CQolYC)vC(ohvMpc+CgK+f=VCUlO2pWqVr^*XGTFj^^qK z%F{VpM+wXP>cQgQE^ursnXvt*|Kn3%y;?RP8b6HMrmHKW5y*Cj+ojM4OC+`HZ{5B< zAajFpPJmitS5sDcQFM4yfk>| zGRX4raIutAVppe}m~OL^zozdY%dIXg-M3+sh`bCZt?G4>W9QEMMq8v!@)mPOOtq`x z+Uh5Fx4K@we3`evZ{vXj2dw$L1soEY3aG6Y_jZ*uNV}QYu03?rlwY!xdL8(qmM%4W zb6@7eB?EOs2lEFH9t2JJ$6oT?2PR}w?poJLsSomXrwtxD>SUBzQCjL`J>jwP4J(-^ z+I380+qQ{Wik+ripA3~;ow_^Lo)|N|;&XQ- z?E8yazJ9b;mwCo%Zd13YtDj>Xb9;9vmN)z#i!wDB7|;q1?c2A{GDmGiYDBuow9eXS zy{?bSN~bF-DsG2{s&VsfC%|_syIb

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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# calling the function\n", + "compare_minimal_models(w90_input, 12.7367, cutoff_1 = 0.038, cutoff_2 = 0.05, cutoff_3 = 0.45)" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this.\n", + "\n", + "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." + ] } ], "metadata": { From c4ce0a73f95931cf92ebaa25dfa5076491cd91df Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 11:09:32 -0400 Subject: [PATCH 33/47] No longer needed --- TB_to_sympy.py | 174 ------------------------------------------------- 1 file changed, 174 deletions(-) delete mode 100644 TB_to_sympy.py diff --git a/TB_to_sympy.py b/TB_to_sympy.py deleted file mode 100644 index d8a85fa..0000000 --- a/TB_to_sympy.py +++ /dev/null @@ -1,174 +0,0 @@ -# importing the dependencies -from itertools import product as itp -from pythtb import * -from triqs.lattice.tight_binding import TBLattice -import warnings -import numpy as np -import matplotlib.pyplot as plt - -def TB_to_sympy(TBL, analytical = True, precision = 6): - r""" - returns the analytical form of the momentum space hamiltonian of the tight-binding model - from a tight-binding lattice object by utilizing Fourier series - - Parameters - ---------- - TBL: triqs TBLattice object - triqs tight binding object - analytical: boolean, default = True - a boolean which will cause the function will return an analytical Hamiltonian, when true, and - an numerical Hamiltonian otherwise - precision: integer, default = 6 - specifies the precision of the floating point amplitudes. The default precision is 6 but the user - can decrease this parameter for better recognition of similar hopping amplitudes, particularly - for symmetrical hoppings across the crystal lattice - - Returns - ------- - Hk: NumPy array - the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical - form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output - is the reduced analytical form. The numerical form depends solely on the k-space vector components - while the analytical form takes into account both the k-space vector components and the lattice - vectors - - """ - - import sympy as sp - - # imaginary number - I = sp.I - - # matrix from axis directions in momentum space - kx, ky, kz = sp.symbols("kx ky kz", real = True) - k_space_matrix = sp.Matrix([kx, ky, kz]) - - # symbolic dot product representation between lattice unit vectors - # and momentum space matrix - a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) - lattice = sp.Matrix([a1k, a2k, a3k]) - - # units contains the displacement vectors - # hops contains details about hopping of electrons such as orbital - # and hopping amplitude - if TBL.units.shape == (2, 2): - TBL_units = np.eye(3) - TBL_units[:2, :2] = TBL.units - TBL_hops = {key + (0,): val for key, val in TBL.hoppings.items()} - elif TBL.units.shape == (3,3): - TBL_units = TBL.units - TBL_hops = TBL.hoppings - # raises error for when the dimensions of the tb object is neither 2D nor 3D - else: - raise ValueError("This format of the tight-binding model is not implemented for this function.") - - # number of orbitals involved in the unit cell - num_orb = TBL.n_orbitals - - # maximum hopping distances of electrons in each direction - max_x, max_y, max_z = list(np.max(np.array(list(TBL_hops.keys())), axis = 0)) - - # number of cells involved in the hopping of electrons in each direction - num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] - - # real-space Hamiltonian - Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) - - # looping through hopping parameters of electrons involved in inter-orbital hoppings - for key, hopping in TBL_hops.items(): - rx, ry, rz = key - hopping = np.around(hopping, precision) - Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping - - # basis of the exponential term in calculation of Hk - Hexp = np.empty_like(Hrij, dtype = sp.exp) - - # perform Fourier transform - for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)): - coefficients = np.array([xi - max_x, yi - max_y, zi - max_z]) - r = lattice.dot(coefficients) - eikr = sp.exp(-I * r) - Hexp[xi, yi, zi, :, :] = eikr - - # summation over all real space axes - Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2)) - - # rewriting exponential terms in Hamiltonian expression in terms of cosine - for i, j in itp(range(num_orb), repeat = 2): - Hk[i, j] = Hk[i, j].rewrite(sp.cos) - - # dealing with the numerical Hamiltonian - - # convert to SymPy matrix to use substitutions method available in SymPy - Hk_numerical = sp.Matrix(Hk) - - # obtaining individual displacement vectors - a1 = np.around(TBL_units[0], precision) - a2 = np.around(TBL_units[1], precision) - a3 = np.around(TBL_units[2], precision) - - # numerical dot products between unit vectors - # and momentum space matrix - a1k_numerical = a1.dot(k_space_matrix)[0] - a2k_numerical = a2.dot(k_space_matrix)[0] - a3k_numerical = a3.dot(k_space_matrix)[0] - - # performing numerical dot product substitutions - Hk_numerical = Hk_numerical.subs(a1k, a1k_numerical) - Hk_numerical = Hk_numerical.subs(a2k, a2k_numerical) - Hk_numerical = Hk_numerical.subs(a3k, a3k_numerical) - - # converting numerical Hamiltonian to NumPy array - Hk_numerical = np.array(Hk_numerical) - - def _has_complex_exponential_sympy(matrix): - """ - Checks if a NumPy array containing SymPy elements has a complex exponential element. - - Args: - matrix (NumPy array): The input NumPy array containing SymPy elements - - Returns: - bool: True if the matrix array contains a complex exponential element, False otherwise. - - """ - - for sublist in matrix: - for element in sublist: - if element.is_complex and element.has(sp.exp): - return True - return False - - def _is_hermitian_sympy(matrix): - """ - Checks if a NumPy array containing SymPy elements is hermitian - - Args: - matrix (NumPy array): The input NumPy array containing SymPy elements - - Returns: - bool: True if the matrix is a hermitian, False otherwise - - """ - - n = matrix.shape[0] - for i in range(n): - for j in range(n): - if matrix[i, j] != matrix[j, i].conjugate(): - return False - return True - - # warning indicating when Hamiltonian is not hermitian - if _is_hermitian_sympy(Hk) == False or _is_hermitian_sympy(Hk_numerical) == False: - return warnings.warn("The resulting Hamiltonian is not hermitian.") - - # warning indicating when Hamiltonian contains complex exponential element - if _has_complex_exponential_sympy(Hk_numerical) or _has_complex_exponential_sympy(Hk): - return warnings.warn("""Your expression has a complex exponential. - Choosing a different unit cell could make - your Hamiltonian expression real.""") - - # returning analytical or numerical form of Hamiltonian - if analytical: - return Hk - return Hk_numerical From 948bc677caca3eb7e006035a19aa0e40e68dc432 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 12:12:23 -0400 Subject: [PATCH 34/47] Rewrote H(k) --- SymPyfy Tutorial.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index b0e7415..4e48820 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -55,10 +55,9 @@ "\n", "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(\\bold{k})$, which is achieved using the ```TB_to_sympy``` function.\n", "\n", - "$$\n", - "H(\\bold{k}) = \\frac{1}{(2\\pi)^d} \\sum_{\\bold{R}} e^{i\\bold{k} \\cdot \\bold{R}} H_{\\bold{R}}\n", - "$$\n", - "\n" + "$\n", + "H(\\bold{k}) = \\frac{1}{(2\\pi)^d} \\sum_{\\bold{R}} e^{i \\bold{k} \\cdot \\bold{R}} H_{\\bold{R}}\n", + "$" ] }, { From ed70076c44418e4f0d44babf4a0f17c95a9091a3 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 12:43:54 -0400 Subject: [PATCH 35/47] No longer needed --- Comparing Minimal Models.png | Bin 1518844 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Comparing Minimal Models.png diff --git a/Comparing Minimal Models.png b/Comparing Minimal Models.png deleted file mode 100644 index 2e8ce347e9842054d30745e00b89025814262581..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1518844 zcmeFad0fqD`v;ye_}T~AlCo#5Nuf>~V@cFO+0sU#v`U?d)?tjL4I$AHqLdbi_QleI zDCx9mRY)goTDAV(*Qd<*PUHJ}p1*#t-|NTgc_vfmbnef6U(5S?U+?RlQ=8e!qelph z5D*X;t+IZNx`4pqn*ste-~Z20d`G6+>Mp*>TCd${tzog>+IG*OeFAEGtPh%6SeqWu zpJB7_kktVTb1Cr^;*yJJ7+G5%v|>p}nEmwvaf?HS5=A5a=LCMq@Pq3$tpo;1ou~f~ zx;NHiyucs<0hKkYw%YlA%3L+tMc|jgzg}|AIx)m4G5_gn z>2)D0a{tZC@6N0JZ(jcJ^8La8&Ff8mEdG1j^<(G1w=)6)Kg{O4d-KC=zPmL)%;x)B z^TTYuzc)Xk&3E_ahuM60YkowV?{Ccyv-$qs{D?N+-J2g~^WCla5pBM|H9ySe`+M^v z+I)9!ewfX7x8_H*`To}YFq`l1&5vmF-M#r?Hs9TvAJOLfTl2$gzP~p=qRn^r=7-sQ zcWZt`o9}PU53~9H-u#F*-`$%ZX7k;x`4Mftzcv4#H=BkC+p4$Q@@#v0%ZAU`aOdQ% ziJMoR-L^(>oc88l9^IOMV_Wg7YdSn}o3!fEg?To{T7g`@+WWh-WQ8VcOICQyIy!%g z;EqxEeil5l?WpPSLMP`Jmn_UeXFcwI_SsH5KlXLnnf#`o*JsAoTgiNX69@c6TKY@S zf2Gk7y4=NsVC6smGg{4w|DV5FK)~hCc}krB<_#-Xd-<{cn>Sp${%q6q|K<&^UcC$a zuVghwMxQnQ8`+M)k7M``&g1{ZV-PyJKqFv{uCDF}?^VZ!-bx~iHvL_&ZEDl!&+2++ z*Tyw;j}*AplkxtLih1NjLBX5!ij@iv4!(Unr%i*!;h*ma9z6PCL4<*qgQt+3jVb@m zw+6Lx*8d;9@W9o7x~)q`cyRE-i%Ya7ZS)p*pS$s5%IjMj4c zaud7DkBz-?Dxg;h4bVtT)41BQ*-K-?;fo4Rs$fa#Bi$M6$3%VzYWe*XNAJHzwh!pWkO_1e!(U-r14P z&%IOm-dnFHus14Q#D0yZh(g7g)niVr9;+bT(ONrA;Yjnz{Vy-ioj>INc=T%##sg2J z)}%7cy|LITD^)5>enL**CQolYC)vC(ohvMpc+CgK+f=VCUlO2pWqVr^*XGTFj^^qK z%F{VpM+wXP>cQgQE^ursnXvt*|Kn3%y;?RP8b6HMrmHKW5y*Cj+ojM4OC+`HZ{5B< zAajFpPJmitS5sDcQFM4yfk>| zGRX4raIutAVppe}m~OL^zozdY%dIXg-M3+sh`bCZt?G4>W9QEMMq8v!@)mPOOtq`x z+Uh5Fx4K@we3`evZ{vXj2dw$L1soEY3aG6Y_jZ*uNV}QYu03?rlwY!xdL8(qmM%4W zb6@7eB?EOs2lEFH9t2JJ$6oT?2PR}w?poJLsSomXrwtxD>SUBzQCjL`J>jwP4J(-^ z+I380+qQ{Wik+ripA3~;ow_^Lo)|N|;&XQ- z?E8yazJ9b;mwCo%Zd13YtDj>Xb9;9vmN)z#i!wDB7|;q1?c2A{GDmGiYDBuow9eXS zy{?bSN~bF-DsG2{s&VsfC%|_syIb

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" ] @@ -341,8 +273,61 @@ } ], "source": [ - "# calling the function\n", - "compare_minimal_models(w90_input, 12.7367, cutoff_1 = 0.038, cutoff_2 = 0.05, cutoff_3 = 0.45)" + "# setting up the plots\n", + "fig, ax = plt.subplots(dpi = 200)\n", + "\n", + "# plotting the full Wannier90 model\n", + "(w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "for i in range(w90_evals.shape[0]):\n", + " x_axis = list(range(w90_evals.shape[1]))\n", + " y_axis = w90_evals[i] - fermi_ev\n", + " ax.plot(x_axis, y_axis, \"k-\", zorder = -100, label = \"Full model\")\n", + "\n", + "# plotting the minimal models\n", + "for cutoff in [0.038, 0.05, 0.45]:\n", + " int_evals = minimal_model(w90_input, fermi_ev, cutoff)\n", + " for i in range(int_evals.shape[0]):\n", + " ax.plot(list(range(int_evals.shape[1])), int_evals[i], zorder = -50, label = \"Cutoff at %.4f eV\" % cutoff)\n", + " \n", + "# plotting the horizontal line that passes through the y-axis\n", + "ax.axhline(y = 0, color = 'm', linestyle = '--')\n", + "ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", + "\n", + "# increasing the width of the curves\n", + "lines = ax.lines\n", + "for line in lines:\n", + " line.set_linewidth(2.5)\n", + "\n", + "# coordinates of the high symmetry points\n", + "G = np.array([0.00, 0.00, 0.00])\n", + "N = np.array([0.25, -0.25, 0.25])\n", + "X = np.array([0.00, 0.00, 0.50])\n", + "M = np.array([0.25, 0.25, -0.25])\n", + "\n", + "# x-axis labels of the high-symmetry points\n", + "kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", + "idx_kpt = {}\n", + "\n", + "# checking k-point coordinates equal to the high-symmetry point coordinates\n", + "for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", + " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", + " \n", + "# setting the x-axis ticks\n", + "kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", + "kpath_xticks.append(idx_kpt['G'][1])\n", + "kpath_xticks = sorted(kpath_xticks)\n", + "ax.set_xticks(kpath_xticks)\n", + "ax.set_xticklabels(kpath_labels)\n", + "\n", + "# setting the vertical lines at the high-symmetry points\n", + "for n in range(len(kpath_xticks)):\n", + " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", + "\n", + "# plot properties\n", + "ax.set_xlim(0, int_evals.shape[1] - 1)\n", + "ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", + "ax.set_title(\"Comparing Minimal Models\")\n", + "ax.legend()" ] }, { From e04c69d0b8a69322f3296171a3671ee8b234e029 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 12:51:16 -0400 Subject: [PATCH 38/47] Go rid of remaining \bolds --- SymPyfy Tutorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 88df19f..ee8364c 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -53,7 +53,7 @@ "- $\\sigma$ is the spin and $c^{\\dagger}$\n", "- $c$ are creation and annihilation operators.\n", "\n", - "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(\\bold{k})$, which is achieved using the ```TB_to_sympy``` function.\n", + "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(k)$, which is achieved using the ```TB_to_sympy``` function.\n", "\n", "$$\n", "H(k) = \\frac{1}{(2\\pi)^d} \\sum_{R} e^{i k \\cdot R} H_{R}\n", From 8888f363495803e85a0033cb8964e06485fc8f6f Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 10 Aug 2023 18:05:39 -0400 Subject: [PATCH 39/47] Updating the calling of TB_to_sympy --- SymPyfy Tutorial.ipynb | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index ee8364c..76e45e8 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -123,9 +123,8 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "# importing the file containing the TB_to_sympy function\n", - "import TB_to_sympy\n", - "from TB_to_sympy import TB_to_sympy" + "# import all files from lattice in TRIQS\n", + "import triqs.lattice " ] }, { @@ -338,6 +337,13 @@ "\n", "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 94bc592b04b519242750f2b3c12e866551c32151 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 10 Aug 2023 20:13:31 -0400 Subject: [PATCH 40/47] Update Hamiltonian expression & importations --- SymPyfy Tutorial.ipynb | 59 +++++++++++++++++++++++++++++------------- 1 file changed, 41 insertions(+), 18 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 76e45e8..2505e60 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -45,7 +45,7 @@ "In second quantization, the TB Hamiltonian is given by:\n", "\n", "$$\n", - "H = -\\sum_{i j l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", + "H = -\\sum_{ l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", "$$\n", "\n", "where:\n", @@ -110,9 +110,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'TB_to_sympy' from 'triqs.lattice.utils' (/Users/munenekariuki/opt/anaconda3/envs/TRIQS/lib/python3.11/site-packages/triqs/lattice/utils.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39m# import all files from lattice in TRIQS\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtriqs\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlattice\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m TB_to_sympy\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'TB_to_sympy' from 'triqs.lattice.utils' (/Users/munenekariuki/opt/anaconda3/envs/TRIQS/lib/python3.11/site-packages/triqs/lattice/utils.py)" + ] + } + ], "source": [ "# importing the dependencies\n", "from itertools import product as itp\n", @@ -124,7 +136,12 @@ "import matplotlib.pyplot as plt\n", "\n", "# import all files from lattice in TRIQS\n", - "import triqs.lattice " + "\n", + "# # importing it from TRIQS repo\n", + "# from triqs.lattice.utils import TB_to_sympy\n", + "\n", + "# importing it from this locala tutorials repo\n", + "from TB_to_sympy import TB_to_sympy" ] }, { @@ -136,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -152,20 +169,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can call ```TB_from_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" + "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The analytical expression \n", - " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" + "ename": "NameError", + "evalue": "name 'TB_to_sympy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe analytical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, TB_to_sympy(w90_triqs_La2CuO4, analytical \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m, precision \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m))\n", + "\u001b[0;31mNameError\u001b[0m: name 'TB_to_sympy' is not defined" ] } ], @@ -175,15 +195,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The numerical expression \n", - " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" + "ename": "NameError", + "evalue": "name 'TB_to_sympy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, TB_to_sympy(w90_triqs_La2CuO4, analytical \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m, precision \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m))\n", + "\u001b[0;31mNameError\u001b[0m: name 'TB_to_sympy' is not defined" ] } ], From 996aece5edb22f3b765d28d1afab4a57a329c0d5 Mon Sep 17 00:00:00 2001 From: Alexander Hampel Date: Tue, 3 Oct 2023 17:47:17 -0400 Subject: [PATCH 41/47] remove TB_to_sympy.py file and use triqs function --- TB_to_sympy.py | 161 ------------------------------------------------- 1 file changed, 161 deletions(-) delete mode 100644 TB_to_sympy.py diff --git a/TB_to_sympy.py b/TB_to_sympy.py deleted file mode 100644 index 195e9b6..0000000 --- a/TB_to_sympy.py +++ /dev/null @@ -1,161 +0,0 @@ -# importing the dependencies -from itertools import product as itp -from pythtb import * -from triqs.lattice.tight_binding import TBLattice -import warnings -import numpy as np -import matplotlib.pyplot as plt - -def TB_to_sympy(TBL, analytical = True, precision = 6): - r""" - returns the analytical form of the momentum space hamiltonian of the tight-binding model - from a tight-binding lattice object by utilizing Fourier series - - Parameters - ---------- - TBL: triqs TBLattice object - triqs tight binding object - analytical: boolean, default = True - whether to return the Hamiltonian in analytical (true) or numerical (false) form. - precision: integer, default = 6 - specifies the number of digits in the floating point amplitudes. The default value is 6 but the user - can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings - across the crystal lattice - - Returns - ------- - Hk: NumPy array - the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical - form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output - is the reduced analytical form. The numerical form depends solely on the k-space vector components - while the analytical form takes into account both the k-space vector components and the lattice - vectors - - """ - - import sympy as sp - - # imaginary number - I = sp.I - - # symbolic dot product representation between lattice unit vectors - # and momentum space matrix - a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) - lattice = sp.Matrix([a1k, a2k, a3k]) - - # units contains the displacement vectors - # hops contains details about hopping of electrons such as orbital - # and hopping amplitude - if TBL.units.shape == (2, 2): - TBL_units = np.eye(3) - TBL_units[:2, :2] = TBL.units - TBL_hops = {key + (0,): val for key, val in TBL.hoppings.items()} - elif TBL.units.shape == (3,3): - TBL_units = TBL.units - TBL_hops = TBL.hoppings - # raises error for when the dimensions of the tb object is neither 2D nor 3D - else: - raise ValueError("This format of the tight-binding model is not implemented for this function.") - - # number of orbitals involved in the unit cell - num_orb = TBL.n_orbitals - - # maximum hopping distances of electrons in each direction - max_x, max_y, max_z = list(np.max(np.array(list(TBL_hops.keys())), axis = 0)) - - # number of cells involved in the hopping of electrons in each direction - num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] - - # real-space Hamiltonian - Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) - - # looping through hopping parameters of electrons involved in inter-orbital hoppings - for key, hopping in TBL_hops.items(): - rx, ry, rz = key - hopping = np.around(hopping, precision) - Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping - - # basis of the exponential term in calculation of Hk - Hexp = np.empty_like(Hrij, dtype = sp.exp) - - # perform Fourier transform - for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)): - coefficients = np.array([xi - max_x, yi - max_y, zi - max_z]) - r = lattice.dot(coefficients) - eikr = sp.exp(-I * r) - Hexp[xi, yi, zi, :, :] = eikr - - # summation over all real space axes - Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2)) - - # rewriting exponential terms in Hamiltonian expression in terms of cosine - for i, j in itp(range(num_orb), repeat = 2): - Hk[i, j] = Hk[i, j].rewrite(sp.cos) - - def _has_complex_exponential_sympy(matrix): - """ - Checks if a NumPy array containing SymPy elements has a complex exponential element. - - Args: - matrix (NumPy array): The input NumPy array containing SymPy elements - - Returns: - bool: True if the matrix array contains a complex exponential element, False otherwise. - - """ - - for sublist in matrix: - for element in sublist: - if element.is_complex and element.has(sp.exp): - return True - return False - - def _is_hermitian_sympy(matrix): - """ - Checks if a NumPy array containing SymPy elements is hermitian - - Args: - matrix (NumPy array): The input NumPy array containing SymPy elements - - Returns: - bool: True if the matrix is a hermitian, False otherwise - - """ - - n = matrix.shape[0] - for i in range(n): - for j in range(n): - if matrix[i, j] != matrix[j, i].conjugate(): - return False - return True - - # performing the check on the analytical Hamiltonian - if not _is_hermitian_sympy(Hk): warnings.warn("The resulting Hamiltonian is not hermitian.") - if _has_complex_exponential_sympy(Hk): warnings.warn("""Your expression has a complex exponential. - Choosing a different unit cell could make - your Hamiltonian expression real.""") - - if analytical: return Hk - - # dealing with the numerical Hamiltonian - - # convert to SymPy matrix to use substitutions method available in SymPy - Hk_numerical = sp.Matrix(Hk) - - # obtaining individual displacement vectors - TBL_units_prec = np.around(TBL_units, precision) - - # dot product between unit vectors and momentum vector - k_vec = sp.symbols("kx ky kz", real = True) - a1k_n, a2k_n, a3k_n = TBL_units_prec.dot(k_vec) - - # substitute numerical unit vectors into H_k - Hk_numerical = Hk_numerical.subs([(a1k, a1k_n), (a2k, a2k_n), (a3k, a3k_n)]) - - Hk_numerical = np.array(Hk_numerical) - - if not _is_hermitian_sympy(Hk_numerical): warnings.warn("The resulting Hamiltonian is not hermitian.") - if _has_complex_exponential_sympy(Hk_numerical): warnings.warn("""Your expression has a complex exponential. - Choosing a different unit cell could make - your Hamiltonian expression real.""") - return Hk_numerical \ No newline at end of file From 06fe73d148c17246dabc330a859cb9f8ede6ee33 Mon Sep 17 00:00:00 2001 From: Alexander Hampel Date: Tue, 3 Oct 2023 17:47:44 -0400 Subject: [PATCH 42/47] update tutorial with triqs module --- SymPyfy Tutorial.ipynb | 72 ++++++++++++++++++++---------------------- 1 file changed, 34 insertions(+), 38 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 2505e60..2953879 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -110,18 +110,21 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { - "ename": "ImportError", - "evalue": "cannot import name 'TB_to_sympy' from 'triqs.lattice.utils' (/Users/munenekariuki/opt/anaconda3/envs/TRIQS/lib/python3.11/site-packages/triqs/lattice/utils.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39m# import all files from lattice in TRIQS\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtriqs\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlattice\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m TB_to_sympy\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'TB_to_sympy' from 'triqs.lattice.utils' (/Users/munenekariuki/opt/anaconda3/envs/TRIQS/lib/python3.11/site-packages/triqs/lattice/utils.py)" + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-10-03 17:46:40.157688\n" ] } ], @@ -153,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -174,18 +177,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'TB_to_sympy' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe analytical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, TB_to_sympy(w90_triqs_La2CuO4, analytical \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m, precision \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m))\n", - "\u001b[0;31mNameError\u001b[0m: name 'TB_to_sympy' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical expression \n", + " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" ] } ], @@ -195,18 +195,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'TB_to_sympy' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mThe numerical expression \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, TB_to_sympy(w90_triqs_La2CuO4, analytical \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m, precision \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m))\n", - "\u001b[0;31mNameError\u001b[0m: name 'TB_to_sympy' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "The numerical expression \n", + " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" ] } ], @@ -244,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -270,22 +267,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -371,7 +368,7 @@ ], "metadata": { "kernelspec": { - "display_name": "TRIQS", + "display_name": "triqs-dev", "language": "python", "name": "python3" }, @@ -385,10 +382,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "orig_nbformat": 4 + "version": "3.11.5" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 308aa6b0f59325ba59c7041e8e4c5efa0721dd3b Mon Sep 17 00:00:00 2001 From: Alexander Hampel Date: Tue, 3 Oct 2023 17:48:04 -0400 Subject: [PATCH 43/47] remove gitignore changes --- .gitignore | 3 --- 1 file changed, 3 deletions(-) diff --git a/.gitignore b/.gitignore index 87539c1..e0a2d13 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,3 @@ *.pyc .ipynb_checkpoints *.h5 -.DS_Store -.DS_Store -.DS_Store From 717648e5f7ed4b9374ed7b11b7eff8677eb7426b Mon Sep 17 00:00:00 2001 From: Alexander Hampel Date: Tue, 3 Oct 2023 17:48:52 -0400 Subject: [PATCH 44/47] remove unneccesary files --- 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corrections for tutorial --- .../TB_to_sympy.ipynb | 90 ++++++++++++------- 1 file changed, 58 insertions(+), 32 deletions(-) rename SymPyfy Tutorial.ipynb => Advanced/TB_to_sympy.ipynb (98%) diff --git a/SymPyfy Tutorial.ipynb b/Advanced/TB_to_sympy.ipynb similarity index 98% rename from SymPyfy Tutorial.ipynb rename to Advanced/TB_to_sympy.ipynb index 2953879..85a01ed 100644 --- a/SymPyfy Tutorial.ipynb +++ b/Advanced/TB_to_sympy.ipynb @@ -71,16 +71,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```TB_to_sympy``` is defined as:\n", - "```python\n", - "def TB_to_sympy(w90_triqs, analytical = True, precision = 6):" + "```TB_to_sympy``` is defined as:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " returns the analytical form of the momentum space hamiltonian of the tight-binding model \n", + " from a tight-binding lattice object by utilizing Fourier series\n", + " \n", + " Parameters\n", + " ----------\n", + " TBL: triqs TBLattice object\n", + " triqs tight binding object\n", + " analytical: boolean, default = True\n", + " whether to return the Hamiltonian in analytical (true) or numerical (false) form.\n", + " precision: integer, default = 6\n", + " specifies the number of digits in the floating point amplitudes. The default value is 6 but the user\n", + " can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings\n", + " across the crystal lattice\n", + " \n", + " Returns\n", + " -------\n", + " Hk: NumPy array\n", + " the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical\n", + " form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output\n", + " is the reduced analytical form. The numerical form depends solely on the k-space vector components\n", + " while the analytical form takes into account both the k-space vector components and the lattice\n", + " vectors\n", + "\n", + " \n" + ] + } + ], + "source": [ + "from triqs.lattice.utils import TB_to_sympy\n", + "\n", + "print(TB_to_sympy.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The only required parameter for our function is ```w90_triqs```, a TRIQS TBLattice Object (Tight-Binding Lattice Object). To obtain ```w90_triqs```, you can use TRIQS-provided functions that allow conversion of your model to a TRIQS TBLattice object. These functions are:\n", + "The only required parameter for our function is ```TBL```, a TRIQS TBLattice Object (Tight-Binding Lattice Object). To obtain ```TBL```, you can use TRIQS-provided functions that allow conversion of your model to a TRIQS TBLattice object. These functions are:\n", "1. ```TB_from_pythTB``` that converts your model from a pythTB model to a TRIQS TBLattice Object.\n", "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and converts it to a TBLattice object.\n", "\n", @@ -110,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -124,7 +165,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-10-03 17:46:40.157688\n" + "Starting serial run at: 2023-10-03 19:38:51.236899\n" ] } ], @@ -136,15 +177,7 @@ "import sympy as sp\n", "import warnings\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# import all files from lattice in TRIQS\n", - "\n", - "# # importing it from TRIQS repo\n", - "# from triqs.lattice.utils import TB_to_sympy\n", - "\n", - "# importing it from this locala tutorials repo\n", - "from TB_to_sympy import TB_to_sympy" + "import matplotlib.pyplot as plt" ] }, { @@ -156,13 +189,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from triqs.lattice.utils import TB_from_pythTB\n", "# accessing the necessary Wannier90 output files\n", - "w90_input = w90('AbinitioDMFT/data/mlwf/', 'lco')\n", + "w90_input = w90('../AbinitioDMFT/data/mlwf/', 'lco')\n", "fermi_ev = 12.7367\n", "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)" @@ -172,12 +205,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" + "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here are two cases:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -195,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -241,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -262,21 +295,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, let us set up the plot:" + "Next, let us set up the comparison plot:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -357,13 +390,6 @@ "\n", "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 5e9f0a2206d280b96e31bd5e1b5e9cdf0b3a4035 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 22 Dec 2023 11:31:41 -0800 Subject: [PATCH 46/47] Implemented Wentzell's proposed changes. --- .vscode/settings.json | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..cad7657 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "cmake.configureOnOpen": false +} \ No newline at end of file From f2f82adf165a8c565f7da2bc01ab4be90577183d Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 22 Dec 2023 12:04:11 -0800 Subject: [PATCH 47/47] Added the tutorial after implementing changes This was done after the whole rebasing fiasco. Hopefully, it didn't alter anything major. I had to delete the tutorial notebook first as I was rebasing which is why I am adding the tutorial wholly now. --- SymPyfy Tutorial.ipynb | 456 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 456 insertions(+) create mode 100644 SymPyfy Tutorial.ipynb diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb new file mode 100644 index 0000000..72863e0 --- /dev/null +++ b/SymPyfy Tutorial.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ```TB_to_sympy```: Simplifying Hamiltonian Expressions for Condensed Matter Systems Within a Tight-Binding Model Framework." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction. ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you model electrons as tightly bound to atoms, you get the TB model which assumes that main electronic behavior arises from interactions between neighboring atoms, where electrons hop between atomic orbitals through tunneling or wavefunction overlap.\n", + "The TB model is a minimal model that serves as a foundation for studying electron behavior in metals, including the origins of magnetism and superconductivity:\n", + "- For theorists, it reduces the number of parameters, from which they can study the effects of the parameters separately.\n", + "- For experimentalists, it’s useful for a one-body description of the electronic structure. Therefore, if they can understand their data by using a simple representation like a TB model, their data can be understood simply. An analytical TB model with a restricted number of parameters can help experimentalists fit their data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ```TB_to_sympy``` function is designed to process a pre-constructed Tight-Binding (TB) model, transforming it into an analytic expression. This function does not generate a TB model on its own. Instead, it takes an existing TB model, which could either be derived from a Wannier90 calculation or represent a model Hamiltonian. The primary output of this function is the analytic representation of the input TB model, effectively translating its hopping amplitudes and other parameters into a symbolic form suitable for further analysis and manipulation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A generic form of a TB Hamiltonian reads:\n", + "\n", + "$$\n", + "H = -\\sum_{ l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", + "$$\n", + "\n", + "where:\n", + "- $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$.\n", + "- $\\sigma$ is the spin and $c^{\\dagger}$\n", + "- $c$ are creation and annihilation operators.\n", + "\n", + "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(k)$.\n", + "\n", + "$$\n", + "H(k) = \\frac{1}{(2\\pi)^d} \\sum_{R} e^{i k \\cdot R} H_{R}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function Definition and its Parameters. ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first import the necessary dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# importing the dependencies\n", + "from itertools import product as itp\n", + "from pythtb import *\n", + "from triqs.lattice.tight_binding import TBLattice\n", + "import sympy as sp\n", + "import warnings\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# import all files from lattice in TRIQS\n", + "\n", + "# # importing it from TRIQS repo\n", + "# from triqs.lattice.utils import TB_to_sympy\n", + "\n", + "# importing it from this locala tutorials repo\n", + "from TB_to_sympy import TB_to_sympy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To view the documentation for the `TB_to_sympy` function, run the following cell. The documentation will appear in a pop-up window or an inline frame, showing the detailed description, parameters, and return values of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function TB_to_sympy in module TB_to_sympy:\n", + "\n", + "TB_to_sympy(TBL, analytical=True, precision=6)\n", + " returns the analytical form of the momentum space hamiltonian of the tight-binding model \n", + " from a tight-binding lattice object by utilizing Fourier series\n", + " \n", + " Parameters\n", + " ----------\n", + " TBL: triqs TBLattice object\n", + " triqs tight binding object\n", + " analytical: boolean, default = True\n", + " whether to return the Hamiltonian in analytical (true) or numerical (false) form.\n", + " precision: integer, default = 6\n", + " specifies the number of digits in the floating point amplitudes. The default value is 6 but the user\n", + " can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings\n", + " across the crystal lattice\n", + " \n", + " Returns\n", + " -------\n", + " Hk: NumPy array\n", + " the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical\n", + " form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output\n", + " is the reduced analytical form. The numerical form depends solely on the k-space vector components\n", + " while the analytical form takes into account both the k-space vector components and the lattice\n", + " vectors\n", + "\n" + ] + } + ], + "source": [ + "help(TB_to_sympy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further explanation of the parameters:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary parameter required for our function is ```w90_triqs```, which is a TRIQS TBLattice Object (Tight-Binding Lattice Object). This object can be obtained in multiple ways. While it can be generated through conversion functions like ```TB_from_pythTB``` (converting a pythTB model to TRIQS TBLattice) or ```TB_from_wannier90``` (converting a Wannier90 model to TRIQS TBLattice), it's important to note that ```w90_triqs``` can also be directly defined without these conversion steps. This flexibility allows for a broader range of applications, accommodating both models derived from tools like pythTB or Wannier90 and those created explicitly as TRIQS TBLattice objects.\n", + "\n", + "When using conversion functions, ```TB_from_pythTB``` is particularly useful for initiating cutoffs in your pythTB model before converting it to a TBLattice object. This is especially beneficial for complex systems, as it provides greater control over the model and allows for the examination of the output's behavior at different cutoffs. The available cutoff parameters in pythTB include:\n", + "\n", + "1. ```zero_energy```: Sets the energy zero point in the band structure, typically aligned with the Fermi level.\n", + "2. ```min_hopping_norm```: Filters hopping terms based on their amplitudes, measured in electron volts. Terms with amplitudes below this threshold are excluded.\n", + "3. ```max_distance```: Excludes hopping distances beyond this specified maximum.\n", + "\n", + "The ```analytical``` flag in the function determines the form of the default Hamiltonian returned by ```TB_to_sympy```. If set to ```False```, the Hamiltonian is in a 'numerical' form, which, despite the name, still depends on parameters like ```kx```, ```ky```, and ```kz```. For the purpose of this tutorial, we will refer to it as numerical, assuming the user can provide these k-space vector values.\n", + "\n", + "Lastly, the ```precision``` parameter allows users to control the number of digits in the hopping amplitudes and lattice parameters, providing further customization and control over the modeling process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling the Function ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from triqs.lattice.utils import TB_from_pythTB\n", + "# accessing the necessary Wannier90 output files\n", + "w90_input = w90('AbinitioDMFT/data/mlwf/', 'lco')\n", + "fermi_ev = 12.7367\n", + "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", + "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical expression \n", + " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The analytical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = True, precision = 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The numerical expression \n", + " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The numerical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = False, precision = 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key distinction between the analytical and numerical expressions lies in their respective use of basis vectors. The numerical expression utilizes Euclidean unit vectors and depends on ```kx```, ```ky```, and ```kz```. These unit vectors are standard Cartesian coordinates that represent points in the Euclidean space.\n", + "\n", + "On the other hand, the analytical expression employs reciprocal basis vectors ```a1```, ```a2```, and ```a3```. These vectors define the orientation and shape of the crystal's unit cell in reciprocal space. The reciprocal lattice vectors are crucial in representing periodic structures and are used to express properties and behaviors of the crystal in terms of wave vectors.\n", + "\n", + "It's important to understand this distinction because it reflects the different frameworks within which each expression operates. While the numerical expression is grounded in the familiar Euclidean space, the analytical expression is more closely aligned with the intrinsic properties of the crystal structure as represented in reciprocal space. This difference is not just mathematical but also conceptual, offering varied perspectives and tools for analyzing the behavior of the crystal.\n", + "\n", + "Users can replace these lattice vectors with numerical values using the ```units``` function from the TRIQS TB lattice object. This conversion is particularly useful when translating the abstract concepts of lattice vectors into concrete numerical values for practical computations and analyses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Minimal Models ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a figure to compare three minimal models, each with different `min_hopping_norm` cutoffs, against the full model from Wannier90. In the snippet below we compare 3 minimal models. You are free to choose the cutoffs you wish." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let us create a function that deals with making the minimal models:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def minimal_model(w90_input, fermi_ev, cutoff):\n", + " \"\"\"\n", + " Constructs a minimal tight-binding model based on the input TRIQS TBLattice object.\n", + "\n", + " This function takes a pre-constructed TBLattice object, which represents the tight-binding \n", + " model of a crystal lattice, and applies additional parameters to refine the model.\n", + "\n", + " Parameters:\n", + " w90_input (TBLattice): A TRIQS TBLattice object representing the tight-binding model. \n", + " This object contains information about the lattice structure and \n", + " hopping parameters.\n", + " fermi_ev (float): The Fermi energy level in electron volts. This parameter sets the \n", + " energy zero point in the band structure.\n", + " cutoff (float): A threshold value to determine the significance of hopping terms in the \n", + " model. Hopping terms with amplitudes below this cutoff are excluded from \n", + " the calculations.\n", + "\n", + " Returns:\n", + " A modified TBLattice object: This object represents the refined tight-binding model with \n", + " applied cutoffs and adjusted Fermi level, suitable for further analysis and computation.\n", + " \"\"\"\n", + "\n", + " # get band structure from Wannier90 in the form of\n", + " # list of k-points in reduced coordinates\n", + " # energies interpolated by Wannier90\n", + " (w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "\n", + " # obtain simplified model based on various cutoffs\n", + " w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff, max_distance = None)\n", + "\n", + " # solve simplified model on the same k-path as in Wannier90\n", + " return w90_model.solve_all(w90_kpt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let us set up the plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1zwj5Qr3Hjx/Xap+JEydm2dSnGTNmwMzMTOX27t27i7cvX76stM2aNWvE2x06dEDVqlVV9te4ceNsW/mtZs2a4rSgHTt2iEtlA0B8fDx27NgBAChevDhq1KiR5fHMnTtXXPHpWxKJBN26dRPvq7rWGdG3b1+UKlVK6TYdHR20bt1a4bHp06fDyMhIaXv516UgCLh69ep3x5eR10RWyarnzL1793D27Fnx/uLFi2FgYKCy/bRp08TXfHR0NDZv3pymzcqVK8Xbbdu2Re3atVX2p2l7qtWrVyMqKgqA7Pfi5eWltn2pUqXQpUsXALJizkeOHNF4jG9FR0eLtzNS7JqI6EfDZA8REan1bTJD3Qf2jJCvB9SoUSMUKlRIbXsHBwdxNRsAOHXqlNr2xsbGaNSokdo27u7u4m0vLy+1HwSMjIxQtGhRALIPoakrbakjv9yyKu3atROTSJGRkbh9+7ba9nFxcTh58iTmz5+P8ePHY8iQIRg4cKD4s2XLFrHtjRs3NMYokUjQtm1bje0ywsjICE2bNlXbpmLFiuJtVdf0zJkz4m1tlqHu0KGDdgFmgk6dOgEAPn/+jH379omP79u3T/xQm9omKxUpUgSVK1dW20aba50RAQEBarfLv87S+7pMTQirk9mviayWFc8Z+ffD8uXLo1KlSmrbm5qaon379kr3T3X69Ok0MavTuXNnjW3k63K1a9dOY3sA+OWXX8TbylZQ1MTZ2Vm8vWvXLrx58ybdfRAR/UhYoJmIiNTKly+fwv3UYqiZ5fr16+JtbQs/16pVC/v37wcgK+qrjpubmzgSSBX5IsulS5fWeHz59vLfFqvi4eGhsY2ZmRnc3d3FEQzXr19HhQoV0rT7+PEjJk6ciPXr12s1qgiQJY80cXV1hbW1tVb9pVeJEiXUji4AoFDU9fPnz2m2h4eHK5yHulE96WmTWTp16oTJkydDEASsX79eTJytX78egCyZlh3JnrJly2pso+laZ1SZMmXUbpd/3bi5uWlMgGr7Osuq10RWy4rnTEbfTxcuXAgg7fvpq1ev8P79e/F+9erVNfanzfvdhQsXxNvr16/H3r17Ne4THh4u3g4LC9PY/lvVq1eHi4sLXrx4gbCwMJQpUwZdunRBs2bN4OHhoXL0JxHRj4rJHiIiUsvc3Bx6enritKLUb5wzi/wHCRcXF632cXV1FW9r+tBmYWGhsT/5D53pbZ+UlKSxvfw3yuo4OTmJyR7565LqxYsX8PLyEldU0pY2H4CzclqDNtdUPiGnbAqb/O/ZxMREq8SUg4ODlhF+v8KFC8PT0xNBQUE4evSouGrR0aNHAQC1a9dWeN5mlfRea22ev5l17Kx4nWXlayKrZcVzJrPfT+X7MzEx0WqlLScnJ7XbY2JiFK5/6qpj6fHp06d076Ovr4+NGzeiSZMmiI6OxsePHzF37lzMnTsXBgYGqFSpEry8vNCkSRPUrl37u1cgJCLKaZzGRUREGsl/aLh7926m9i0/UsjU1FSrfeTbafrQlt4/2LPiD3wTExOt2mk6rw4dOogfas3NzfHbb78hMDAQz549Q0xMDKRSKQTZ4gsK0zFSUlI0Hjsrv9XOjGsq/zzJyPXMDqk1RZKTk7F582Zs3rxZTFylbstqOfkBNT3Hzqw4s/I1kR0y+zmT2e+nWfG6y4zRZBmtaebp6YmbN2+ie/fuCnEmJiYiJCQEs2bNgre3N0qWLIk9e/Z8d5xERDmJI3uIiEgjT09PPHnyBABw8eLFTO1bvgZQbGysVvvIt/t2mlluFBcXp1U7ded1/vx5BAcHi9suXryIkiVLquwrN4xcyEzyH8wycj2zQ+vWrTFo0CB8/foV69evhyAIAGSJtFatWmVrLD+DvPCayOznTGa/n8r3l1mvu2+TQVFRUVqN9Mosrq6uWLVqFRYtWoTz58/j7NmzCAoKQkhICL5+/QoAePjwIfz9/TFnzhwMHz4822IjIspMHNlDREQayRfGfPHiBc6fP59pfctPH9J2KsaLFy/E21m1elRm0va85Nt9e14nTpwQb3ft2lXth1pA8RrlBfLXIy4uTqtpHK9evcrKkNIwNzdHixYtAMgKAN+8eRMA0LJlS5ibm2drLD+DvPCayOznTGa/n8r3FxcXhw8fPmjsT1M9HUtLS7EYPQA8evRIqzgzm7GxMerWrYs//vgDJ0+exIcPH7Br1y6UL19ebDNmzJhsfx8hIsosTPYQEZFGrVu3VvgQ8M8//2Ra3/IrA2mbREr9Nh+AxtVmcgP5YqSqxMTEKKzA9e15RUREiLc1FcIFoLD8cl7g5OSkUC/k0qVLGvfJzGXFtaVsJSJtViei9Msrr4nMfM5k9vupg4ODQsInJCREY3/atKlWrZp4OzAwUJsws5yxsTH8/f1x6tQpFCxYEIBselduiY+IKL2Y7CEiIo2MjY0xePBg8f6uXbuwa9eudPcTGxub5gOI/KihQ4cO4d27d2r7ePPmDY4cOaJ0/9xqy5YtGmtMbNmyBYmJiQBk367LLzsNADo6//svW9N0ioiICPz3338ZjDb38vb2Fm9v3rxZY/tNmzZlZThKNWjQQPygCACFChVC/fr1sz2On0FeeU1k5nNG/v3w+vXr4kghVb5+/YqtW7cq3T9VnTp1xNsbN27UGMO6des0tmnatKl4+99//0V8fLzGfbKLlZUVatasKd5PLZxNRPSjYbKHiIi0MmrUKIVvfTt16iQuf66N0NBQVK9eXVxpJlWDBg1QuHBhAEBCQgKGDh2qsg9BEDB48GAxKVK0aFHUq1cvHWeRM54+fYo5c+ao3B4ZGYlJkyaJ97t27ZpmWeoiRYqIt/ft26eyL6lUit69eyMhIeE7Is6dunXrJt7etGmT2pE7hw4dwrFjx7IjLAW6uroICgrC5cuXcfnyZZw9exa6urrZHsfPIK+8JjLzOVOyZEl4eXmJ9wcNGqR2xbUJEyaICXZzc3P8+uuvadr06NFDvL1t2zYEBQWp7G/btm04d+6cxjj79OkDS0tLALIl1fv37y/WK9IkMjISUqlUq7bytJmClkp+ClxWrlRIRJSVmOwhIiKtGBoaYseOHcifPz8A2TfCLVu2ROfOnXHv3j2l+wiCgMuXL6NLly6oUKEC7ty5k6aNjo4OZsyYId7fsmULevXqpbAKDCArrtqjRw/s2LFDfGzmzJkK3+7nVgYGBhg7dizmzJmTZhWghw8fon79+nj9+jUAwMbGBiNHjkzTR5MmTcQVjM6cOYMRI0aIxURTvXnzBgEBATh48GC2r0SVHRo3bowaNWoAkH2Ab9q0KU6fPp2m3b59+9CuXTuFuiDZqVixYqhSpQqqVKmCYsWK5UgMP4O89JrIzOfMjBkzxGRRUFAQAgIC0oyYTExMxLhx4xSS0JMmTVIoyJyqQYMG4qg6QRDQsmXLNEl7ANi6dSu6desGAwMDjTFaWFhg7ty54v01a9agWbNmuH//vtL2giDgwoULGDhwIFxcXNL8nrWxcOFCVKhQAUuWLBHfb7/15csX/P7777hy5QoAWSLO19c33cciIsoNuBoXERFprUiRIrh48SKaNWuG27dvIyUlBRs2bMCGDRvg6uqKcuXKwdbWFlKpFG/evMGNGzfSDIFXtnpWmzZtcPbsWSxevBgAsHLlSmzbtg0+Pj4oUKAA3r17h5MnTyqspjN06FAEBARk7QlnklmzZmHo0KEYMWIE/vnnH3h5ecHMzAyPHj3CuXPnxG+pdXV1sWLFCjGhJq9kyZLo1KkT1q9fDwCYM2cONm/ejKpVqyJ//vx4/vw5zp49i8TEROTLlw9///03+vbtm63nmdV0dHSwevVq1KpVCx8/fsS7d+/g4+ODSpUqoVy5ckhJScGVK1dw9+5dAMCCBQvE6Yc5uSQ5ZQ2+JpSrUaMGZsyYISaN9+/fD2dnZ/j4+MDJyQmfPn3C6dOnERkZKe7j5+eHYcOGKe1PIpFg1apVqFGjBt6/f4+PHz/C19cXFSpUQIUKFSCVSnHp0iU8ePAAADBv3jy1IzRTde3aFU+fPsXUqVMBAAcPHsShQ4fg7u4Od3d3mJubIzY2Fq9evcL169cRFRX1fRcGwM2bNzFgwAAMHDgQRYsWhbu7O2xtbZGUlISIiAicP39eYTWx0aNHw8nJ6buPS0SUE5jsISKidHF1dcWFCxcwd+5c/PPPP+If4M+fP8fz589V7le+fHlMmjQJfn5+SrcvWrQIBQsWxJ9//omEhAR8+fJFaY0NIyMjTJw4EWPGjMmM08kWLVq0gLGxMQYNGoSIiAiFGhmpzM3NsWLFCpXXBwCWLl2KN2/eiN+qv379Os01cnR0xNatW9VO3fiRlSxZEkePHoW/v7841eLatWu4du2a2EYikWD8+PHo27evmOxRlmSkHx9fE8qNGDECVlZWGD58OKKjo5GQkKBQ6yyVrq4uBg4ciDlz5qhNiBYtWhTHjx+Hn58fnj59CkC2etiNGzfENhKJBGPGjMGQIUO0SvYAwJQpU+Du7o5hw4YhIiICgiAgNDQUoaGhKvepVq0a9PX1tepfnvx7gCAIePz4MR4/fqy0rYGBAcaNG4eJEyem+zhERLkFkz1ERJRuZmZmmDBhAgYPHoyDBw/i2LFjuHr1qvitr4GBAaytrVGyZElUr14dLVu21GrVrPHjx6NTp05YuXIlAgMD8ezZM0RFRcHS0hJFihSBr68vevbsCWdn52w4y8zVu3dveHp64t9//8Xx48cRHh4OQRDg4uKCpk2bYtCgQXBwcFDbh4mJCQ4fPozNmzdj3bp1uH79OqKjo2Fra4siRYogICAAXbt2hZWVldLpTXlF5cqVcefOHSxZsgQ7d+7Eo0ePkJCQAAcHB3h6eqJv376oXr26wqiy1PoglLfwNaFajx490KJFC6xYsQKHDx/Gw4cP8fHjR+TLlw9OTk6oV68eunfvjtKlS2vVX7ly5RAaGoqlS5di+/btePjwIRISEmBvb49atWqhT58+CoWNtdWmTRu0aNECW7duRWBgIC5fvoz3798jJiYGpqamcHBwQKlSpVC7dm00btwYbm5u6T4GAPz2228ICAjAsWPHcP78eYSGhuL58+eIjo6Gjo4OLC0tUapUKfzyyy/o3LkzXFxcMnQcIqLcQiJoWw2NiIiItObq6ooXL14AAJ49ewZXV9ecDegndOzYMTRo0AAA4Ovrq3RkAxEREVFelPurWhIRERFlwPbt28XbVatWzcFIiIiIiLIXkz1ERESU51y5cgXr1q0T77dr1y4HoyEiIiLKXkz2EBER0Q/F19cXR48eFVcxk5eSkoItW7agfv36YkHexo0bo0yZMtkdJhEREVGOYc0eIiKiLMCaPVknddUga2trVK5cGY6OjtDX18e7d+9w4cIFhcLMBQoUwNWrVzUWvyYiIiLKS7gaFxEREf2QPn78iGPHjqncXqFCBezatYuJHiIiIvrpMNlDREREP5Tbt29j7969CA4OxosXLxAZGYlPnz7BxMQE+fPnh4eHB1q2bAk/Pz9xFBARERHRz4TTuIiIiIiIiIiI8hAWaCYiIiIiIiIiykOY7CEiIiIiIiIiykOY7CEiIiIiIiIiykOY7CEiIiIiIiIiykOY7CEiIiIiIiIiykOY7CEiIiIiIiIiykP0cjoAynrx8fEIDQ0FANjZ2UFPj792IiIiIiIiosyWnJyM9+/fAwDKli0LIyOjHImDn/p/AqGhoahWrVpOh0FERERERET007h06RKqVq2aI8fmNC4iIiIiIiIiojyEI3t+AnZ2dgq3r127loPRENGPoGrVqnjz5g0KFiyIy5cv53Q4RJSL8f2CiChv4/t8+rx+/VqcWSP/WTy7MdnzE5Cv0aOrqwtHR8ccjIaIfgS6urriv3zPICJ1+H5BRJS38X0+43KyXi6ncRERERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SFM9hARERERERER5SF6OR0A/ThSUlIQExOD6OhoJCYmQiqV5nRIRJRFJkyYgISEBBgaGuLRo0c5HQ5lkK6uLkxMTGBpaQkjI6OcDoeIiIiIsgmTPaSVL1++4NWrVxAEIadDIaJsUKRIEUilUujq6iI5OTmnw6EMSk5ORkJCAj59+gQLCwsUKlQIEokkp8MiIiIioizGZA9ppCzRI5FIoKurm4NREVFWsrGxQUpKCnR0dKCnx/8qflTyibrPnz/DwMAAtra2ORgREREREWUH/gVPaqWkpCgkeszMzGBtbQ0TExN+O0yUh928eRMpKSnQ1dVF8eLFczocyiCpVIqoqCi8e/cOAPD+/XuYm5vDwMAghyMjIiIioqzEAs2kVkxMjEKix9HREaampkz0EBH9AHR1dWFjYwMbGxvxsZiYmByMiIiIiIiyA5M9pFZ0dLR429ramkkeIqIfkLm5uXg7NjY2ByMhIiIiouzAZA+plZiYCEBWo8fExCSHoyEioowwNDQUk/Wp7+tERERElHcx2UNqpS6vrqury1E9REQ/KPmi+ikpKTkcDRERERFlNSZ7iIiIiIiIiIjyECZ7iIiIiIiIiIjyECZ7iIiIiIiIiIjyECZ7iIiIiIiIiIjyECZ7iIiIiIiIiIjyECZ7iEh0+vRpSCQSSCQSnD59Os32yZMni9tJkaurKyQSCbp27Zol/T9//ly89mvXrs2SYxARERERUd7AZA9RLiKfbNHmhx/6iYiIiIiI6FtM9hARERERERER5SF6OR0AESnXr18/9O/fX20bR0fHbIqGiIiIiIiIfhRM9hDlUvnz54e7u3tOh0FERARBEPDq1SuEhYXhzZs3ePv2LWJiYhAfH4/o6GgAwJcvX7BgwQLY2dmhYMGCsLe3R+HChWFgYJDD0RMREf18mOwhIiIiIpFUKsXt27cREhKCixcv4ubNm3j48CFiYmLU7hcdHY0hQ4YoPKarq4vChQujdOnSqFatGqpVq4bq1avD3Nw8K0+BiIjop8eaPUR5QHpWasrqVaM0SY1z8uTJAIBTp06hZcuWsLe3h7GxMUqVKoWpU6ciNjZWYb9Dhw6hcePGYrvSpUtj+vTpSExM1HjM58+fY9iwYShTpgzy5csHExMTFC9eHH369EFoaKhWcR86dAiNGjWCnZ0dTExM4ObmhuHDhyMiIiJd5//gwQMMHjwYZcqUgYWFBYyNjVGkSBF069YN165dS1dfRESZ5cOHD1i3bh3at2+P/Pnzo0KFCujbty/WrFmDa9euaUz0qCKVSvH48WP8999/GD9+PBo0aABra2t4enpiypQpuHbtGgRByOSzISIiIo7syQbXrl3DkSNHEBQUhNu3b+Pdu3fQ19eHvb09atasiR49eqB27do5HSZRtpsxYwbGjh2r8If+/fv3MXHiRBw5cgSBgYEwNTXFsGHDMH/+fIV97927h7Fjx+Ls2bM4cOAAdHV1lR5j/fr16N27NxISEhQef/z4MR4/foxVq1Zh6tSpGDNmjMo4hw4dmub4jx49wty5c7Fp0yYcOnRIq/OdOnUqpkyZguTkZIXHnz17hmfPnmHdunWYMGEC/vjjD636IyL6HrGxsdi5cye2bt2K48ePp3lvyipSqRTBwcEIDg7GpEmTUKRIEbRu3RqdOnVCmTJlsiUGIiKivI4je7KYt7c3KleujHHjxuHIkSMIDw9HYmIiYmNj8ejRI6xbtw5eXl7o3LmzViMUiPKKw4cPY8yYMfDw8MDmzZtx5coVHDlyBI0aNQIAnD9/HjNmzMDcuXMxf/58NGrUCLt27cLVq1exb98+eHh4AACOHDmCFStWKD3GwYMH0bVrVyQkJMDMzAyTJk1CUFAQLly4gDlz5sDW1hZSqRRjx47F0qVLlfYxZ84cMdFjb2+PhQsX4uLFizhz5gxGjRqFqKgotGrVCnFxcWrPd+LEiZg4cSKSk5NRs2ZNrFy5EhcuXMCVK1ewadMm1KhRA4IgYMqUKVi4cGFGLysRkUahoaEYMGAA7O3t0bVrVxw5ciTbEj3KPH36FDNnzoS7uztq1qyJ1atX4+vXrzkWDxERUV7AkT1Z7NWrVwBkHxJbt26N2rVrw9nZGVKpVPzA+erVK2zYsAHJycnYvHlzDkecfvny5fspE1UGBgb48uVLlvX/7t073L59W+X2/PnzI3/+/Fl2/Kx26dIlBAQEYNu2bQqjcurVqwdPT0+EhIRgwYIFSEpKwtChQzF37lyxTaVKlVCvXj2ULl0aL168wNKlS9G3b1+F/pOSktCnTx8IggAzMzMEBQWhQoUK4nYPDw8EBASgRo0aeP36NUaMGIHWrVvD1tZWbPP27VtMnDgRAODi4oKQkBAULFhQ3O7l5QVfX1/4+vqq/aB0+fJl/PXXXwCA8ePHY+rUqQrbK1eujHbt2qFLly7YuHEjxo0bh06dOsHS0lL7C0pEpIYgCDhz5gxmzpyJI0eO5HQ4Kl24cAEXLlzA77//jv79+2PAgAE/9P91REREOYUje7JYyZIlsW3bNrx8+RLz5s1DQEAAqlatCg8PDwwbNgw3btyAm5sbAGDLli0ICgrK4YjTLzEx8af9yUpLly5F2bJlVf4sWbIkS4+f1UxMTLB8+fI00690dXXRp08fALKVXezs7DBr1iyl+3fp0gUAcOvWLXz+/Flh+549e8Rk67hx4xQSPalcXFzw999/AwDi4uKwZs0ahe3r1q0TR+zMmTNHIdGT6pdffkGvXr3UnuvMmTORkpKCypUrY8qUKUrb6OjoYOHChTA0NMSXL1+wc+dOtX0SEWnr9OnT8PT0hI+PT65O9MiLjIzElClT4Orqit9++w3v3r3L6ZCIiIh+KEz2ZLEDBw6gTZs2KuuJ2NraYs6cOeJ9fsCjn0X9+vVhbW2tdFu5cuXE2/7+/tDX11farnz58uLtZ8+eKWw7fvw4AFlB6O7du6uMo3Xr1rCwsFDY59s+rKys0KJFC5V9qOs/KSkJhw8fBgC0atUKEolEZVtLS0uULVsWgOzbbSKi73Hjxg34+vrCx8cH58+f/+7+bGxsULNmTbRp0waDBw/G5MmTMXPmTPE91NzcHCNHjkTnzp1Rv359FC5cWO17nja+fv2Kf/75B4ULF8aYMWPEZd6JiIhIPU7jygXq1Kkj3n7y5EnOBUK5yqRJk8QVq/Ki1BFtyshPX9K23bdT6lKnwLm6uqqdAmBgYICKFSvi9OnTaabNpa7UVbFiRejpqX67rFChAgwMDJSO9rp79644OmjMmDFqC0HLe/PmjVbtiIi+9eHDB4wfPx7Lli3L8EpXRkZGqF27Njw9PeHh4YFKlSopTHOVt2DBAnz+/Bn58uVLMxIzPj4ed+7cweXLl3HhwgWcOHFCHHWZHnFxcZgxY4ZYVL9Hjx5q35eJiIh+dvxfMheQ/4Coo8PBVvRzMDExUblN/nWgbTupVKqw7ePHjwCAAgUKaIwldXpW6j6pPn36BAAa60Xo6enB2tpaaYImo1MPNBV8JiL6liAIWLNmDUaMGCG+f6WHtbU1/P39ERAQAG9vbxgbG393TEZGRqhcuTIqV66Mvn37QhAE3L9/H/v378eOHTtw5cqVdPX3/v179O3bF0uXLsWyZctQvXr1746RiIgoL2KyJxc4c+aMeLtkyZLp3j88PFzt9tevX6e7T6K8QpspBJq++f6ePuSTUH///TcaNmyosS8AMDU11aodEREgW9Gqd+/eOHHiRLr2k0gkaNy4MXr37o1GjRqpnDabWSQSCUqVKoVSpUph1KhRePLkCdauXYvVq1cjIiJC635u3ryJGjVqoG/fvpg+fbo4lYyIiIhkmOzJYSkpKZgxY4Z4v02bNunuw8nJKTNDSjcDA4McPX5OyU3nLT/CJSUlRW3b2NjYrA4nV0itB6TNdKi3b98q7JPKysoKb968EberkpycrPJbdBsbG/F2UlIS3N3dNcZDRKQtQRCwatUqDBkyJF0jAs3MzNC7d28MHjwYLi4uWRihekWLFsXUqVMxadIk/Pfff5gzZ47W9YUEQcDSpUtx4MABrFq1CvXr18/iaImIiH4cTPbksLlz5+LSpUsAAD8/P1SpUiWHI0q/rFx+nLSTL18+8ba6ofsfPnxAZGRkdoSU49zd3XHhwgU8f/4c7969UzkVKykpCdevXxf3kVe2bFm8efMGN27cQHJyssr6EDdv3lS5OluZMmXEej5Hjx7VumYPEZEmHz58QK9evbBnzx6t98mXLx+GDx+OIUOGwMrKKgujSx89PT34+/vD398fFy5cwB9//IHAwECt9g0LC0ODBg3Qr18/zJ49W+30XyIiop8FC8TkoDNnzmD06NEAZDVBli5dmqF+wsLC1P6kJpMo77KyshKLFaurf7Bly5Zsiijn1atXD4Dsm9/Vq1erbLdz505x2fbUfb7t4+PHj9i/f7/KPtT1b2Jigrp16wKQLX/M1yMRZYYLFy6gfPnyWid69PX1MXz4cDx9+hSTJ0/OVYmeb9WoUQNHjhxBcHAwvLy8tN5v6dKlqFKlCm7dupWF0REREf0YmOzJIXfu3IGfnx+Sk5NhaGiI7du3a1VIVhlHR0e1P4UKFcrk6Ck3Sv2DeN++fUpXdbt37x4mTpyY3WHlGD8/P9jb2wMApk2bhps3b6ZpExYWhhEjRgCQJWW6deumsL1Lly5igdLhw4crnc515swZLF++XG0s48aNE+v+tGvXTu2qe1KpFJs3b9ZYi4uIfk6CIGDhwoXw8vLSelWrJk2a4M6dO5gzZ47KFbVyo5o1a+L06dPYvXs3ihYtqtU+9+7dQ7Vq1bBkyZIMr0RGRESUFzDZkwOePXuGBg0a4NOnT9DV1cWWLVvg7e2d02HRD65///4AgK9fv6JOnTpYtWoVrl27hrNnz2LixInw8PCAjY0N7OzscjjS7KGvr4/ly5dDIpHgy5cv8PT0xJQpUxAcHIyLFy9i7ty5qFKlilgQdPbs2Wk+BBUoUABTp04FADx//hyVK1fG4sWLcfnyZQQFBWHMmDHw9fWFg4OD2utaq1YtMdH27NkzVKhQAUOHDsWhQ4dw/fp1hISEYOvWrRgyZAicnZ3RoUMHREVFZc2FIaIfVnx8PLp06YLBgwcjOTlZY3t7e3vs3bsXBw4cQPHixbMhwswnkUjg5+eHO3fuYMqUKTA0NNS4T0JCAgYMGIDOnTtzZUMiIvppsWZPNouIiEC9evUQEREBiUSC1atXw8/PL6fDojzA19cXgwcPxoIFCxAeHo6ePXsqbHdycsK+ffvQuHHjHIow+zVp0gRr1qxBnz59EBMTg0mTJmHSpEkKbXR1dTF16lT069dPaR+//fYbXr58iQULFuDVq1cYOHCgwnZbW1vs3LkTrVq1UhvL5MmTYWlpidGjRyMmJgbz58/H/PnzlbY1MDCAkZFROs6UiPK6t2/fomXLlggJCdGqfa9evfD333/nmVWqDA0NMWHCBLRr1w59+vTBqVOnNO6zceNG3Lp1K10jg4iIiPIKjuzJRpGRkahfvz6ePn0KAFi4cCE6d+6cw1FRXjJ//nxs3rwZXl5eMDc3h7GxMUqUKIHRo0fj+vXrKF26dE6HmO26dOmC+/fvY8iQIShVqhRMTU1hbGyMokWLolevXrh+/brGosnz58/HwYMH4evrC2traxgZGaFYsWIYPHgwrl+/rnVh9aFDh+LJkyeYMGECPDw8YGtrCz09PZiamsLNzQ0BAQH4999/8erVKxQrViwzTp+I8oDQ0FBUrVpVq0RP/vz5ceDAASxfvjzPJHrkFS9eHMePH8eiRYu0KsR869YtVK9eHcHBwdkQHRERUe4hETihOVt8/vwZv/zyC65duwYAmDFjBn7//fdsOXZ4eLi4PHvBggXx+vVrrfd99OiRuArRjzoEnIjS7+bNm0hKSoK+vj7Kly+f0+FQJuD7+Y/pzJkzaNGihVhIXh1fX1+sW7cuwzUAM8rR0RGvXr2Cg4NDttYbe/z4MTp06KBV4XtDQ0OsW7cObdu2zYbIiIjylpx6n/9RyX/+DgsLg6OjY47EwZE92SAuLg5NmjQREz3jxo3LtkQPERER/Zh27doFX19fjYkeiUSCKVOm4NChQ9me6MlJxYoVQ1BQEEaNGqWxbUJCAtq1a4dp06axcDMREf0UmOzJYomJifDz8xOHDw8ZMgR//vlnDkdFREREudmqVavQunVrJCQkqG1nZWWFI0eOYMKECdDR+fn+rDMwMMDMmTNx+PBhrZaTHzduHHr16oWkpKRsiI6IiCjnsEBzFmvfvj2OHj0KAPjll1/Qo0cP3L59W2V7AwMDuLm5ZVd4RERElMssWLAAQ4YM0diuZMmS2L9/P2t8AWjYsCGuXr2KgIAAXL9+XW3bVatW4cWLF9i9ezfy5cuXTRESERFlLyZ7stju3bvF2ydPnkS5cuXUtndxccHz58+zOCoiIiLKjWbNmqXVVO/69etj+/btsLS0zPqgEr4AH58BH58CX94AcZFAbCSQFAckJwBfP8raff0E7BsImFgDJraAWQHAujBgVRgwtQUkkiwNs3DhwggODkbXrl2xfft2tW2PHz8OX19fHDp0KHuuIRERUTZjsoeIiIgoF/j777+1SvR07twZK1euhL6+fuYHkRgLhF2S/by+KfuJ1lCMMyn+///9ClzfoLyNkSVQqBxQsBzgWAVw8QTM7DI1dAAwNjbGli1bULRoUUyfPl1t2wsXLqBu3bo4evQobGxsMj0WIiKinMRkTxZjEUAiIiLSZN68eVoVGh49ejSmTZsGSWaNkhEE4N1d4MEh4NEx4NVVICU5c/qWFx8FPDsr+0llVxIoWhco0QhwrgHoZs6fpTo6Opg2bRqKFCmCvn37QiqVqmx77do1+Pj44NixYz9VcWsiIsr7mOwhIiIiykH//vsvhg0bprHdnDlzMHz48Mw56Lv7QOh2IHQnEPUic/pMr/f3ZT8hi2Ujf0o3B8q1kyV+MqHYdM+ePeHi4oJWrVohOjpaZbvQ0FB4e3vjxIkTcHBw+O7jEhER5QY/37INRERERLnEzp070b9/f43tlixZ8v2JnsRY4OpaYJkXsKQ6EDQn5xI934qPAq6tB9Y2BuaXB87OBmLefXe39evXR3BwMBwdHdW2e/DgAby8vPDiRS65HkRERN+JyR4iIiKiHHDmzBl06NBB7ZRviUSCNWvWoF+/fhk/0Odw4MhYYE5JYP8QWR2e3OzzS+DkVOCf0sCunsCb0O/qzt3dHUFBQShcuLDadk+fPsUvv/yC169ff9fxiIiIcgMme4iIiIiyWWhoKFq0aIHExES17VatWoWuXbtm7CCRj4A9fWUjZUIWAwmqpzLlSilJQOgO4F9PYGMr4GVIhrtydXXF2bNn4ebmprbd06dPUb9+fXz48CHDxyIiIsoNWLOHiIiIKBu9fPkSDRs2xOfPn9W2W7ZsGbp165b+A3x6DpyZBdzcAggpGQtSno4+YFMMsC4CWLkC+QrIllY3sgD0DIFlvwJfImVLrvsvlS3NHvMOiHopW6498hGQ+OX7Ynh8TPZTvAHwy3igUPl0d+Ho6IgzZ86gXr16uHPnjsp2d+7cQaNGjXDixAnky5fve6ImIiLKMUz2EBEREWWTDx8+wNfXFxEREWrbzZs3D717905f518/AWf+Bi4tl42Kyah89oBrLcClJuBQGbArBegZqG6vayj7V88IKNc67faUFODTM+D1DeDFBeD5OeD9vYzF9uio7KdsG6DeZMAifQWVCxYsiNOnT6NBgwa4fv26ynaXL19G8+bNcejQIRgbG2csViIiohzEZA8RERFRNoiPj0fz5s1x//59te1+//13DBkyRPuOU6TA1TXAyb+Arx8zFpx9JaBUU8CtEZC/FJBZS7sDspW1bIrKftwDZI9FRwAPjwD3DwFPT6V/uffQ7cD9A4DnMKDmYEDfSOtdbW1tceLECTRs2BCXLl1S2e706dNo06YNdu/eDX19/fTFR0RElMNYs4eIiIgoiwmCgD59+uD8+fNq23Xu3BnTp0/XvuOIG8DKusDB39Kf6LFwArxHA4OuAb1PAbV/AwqUztxEjyrm9kCV7kDHncBvD4DGs9M/NSspDjj1F/BvLeBZULp2tbKywpEjR1C+vPpjHjhwAF26dEFKSiZMhyMiIspGTPYQERERZbH58+dj/fr1atv4+vpi5cqVkGiTbEmKBwLHASt8gAjV05GUKloXaL8NGHIT8BkjG3GTk0xtgWq9gN5ngJ4ngXLtZHWCtPXhMbCuKbBvABCvvg6SPCsrKwQGBmos2rxlyxaMGTNG+3iIiIhyASZ7iIiIiLLQ8ePHMWLECLVtqlSpgp07d2o3XehNKLC8DnBhkfYFmCW6sjo3fYOBTruBEg0BHV3t9s0uEgngWBnwXyZLRNUYCOibaL//9Y3AkprA09Na71KgQAEcO3YMTk5OatvNmjULy5cv1z4WIiKiHMZkDxEREVEWefr0Kdq2bQupVKqyTbFixXDw4EGYmZmp7yxFCpybByz3SUeBYwlQtjUw4BIQsAIo6K517DnKwgHw/QsYcgvw6A/oqikQLS86HFjfAjj8u2z0kxacnZ1x/Phx5M+fX227/v3748iRI9rFQURElMOY7CEiIiLKAjExMWjRogU+flRdS8fS0hKHDh3SmGjApxfAumbA8Unar7RVpA7Q9xwQsBKwLaZ94LmJmR3QcLqsrlBZJSt9qXLxX2BVPSDysVbN3dzccPToUVhaWqpsI5VK0aZNG9y6dUv7OIiIiHIIkz1EREREmSwlJQVdu3bF7du3VbbR0dHBtm3bULx4cfWd3f0P+NcTeBGs3cGtCstq8nTa++OM5NHE0kmWtOoeqH0h5zehwDIvIHSnVs3Lly+PgwcPwshI9cpeX758QZMmTRAREaFdDERERDmEyR4iypWCg4Ph5+eHggULQk9PDxKJBBKJBFFRUQrtNmzYAC8vL1hZWUFHRwcSiQQVKlTIkZiJiFLNnDkTu3bt0timQYMGqhukSIETU4DtnYCEaM0H1dEHvEYC/S/IavJkx6pa2c3ZQ1bE2XeadvV8kmKBXT2A45Nl11ODmjVraiykHR4ejqZNmyImJkbLoImIiLIfkz1EuVhSUhK2bt2KLl26oFSpUrCxsYG+vj5sbW1RuXJl9OvXD8ePH89zS8Lu378f3t7e2Lt3L96+fauy1sWoUaPQuXNnBAUFISoqCoIgZHOkWe/OnTvo27cvihUrBmNjY9jZ2cHLywvLli1DcnJyph0nOTkZy5Ytg5eXF+zs7FC9enX4+fnhzz//xN27d9XuGx0dja1bt+K3336Dt7c3ihUrBgsLCxgYGCB//vyoU6cOZs2ahQ8fPmgVy4sXLzB69GhUrlwZlpaW0NfXh7W1NWrWrImpU6fi/fv3WvUTFxeHv//+G9WqVYO1tTXMzMxQqlQpjBgxAi9fvtSqj6zi6+srJjCfPn2arn1XrVol7jt79uwsipC+x7lz5zB+/Hi1bTp06IDffvtNdYOvn4DNbYCgOdodtFB5oG8Q8Mt4QN84HdH+gHT1gBoDgAEXgcLe2u1zbi6wpb1Wq3W1bt0aM2bMUNvm+vXr6Ny5c577/5eIiPIQgfK8sLAwAYAAQChYsGC69n348KFw9+5d4eHDh1kUHamyd+9eoUiRIuLvTt2Pm5ubcODAgSyJY82aNeJxnj17liXH+Fbx4sUFAIK9vb2wfv164erVq0JoaKgQGhoqSKVSQRAE4eXLl4Kurq4AQPDw8BAOHDgg3Lx5UwgNDRUeP36cLXGmOnXqlHiNTp06lWn9rly5UjA0NFT5e/fw8BAiIyO/+ziRkZFC9erVVR7H0NBQWLVqlcr9jx07ptXz1NbWVjhy5IjaWDZt2iSYmJio7cfGxkY4ceKE2n4eP34slChRQmUfFhYWwsGDBzN0vTLDxo0bxVj++OOPdO1bp04dAYCgo6MjvHr1Suv9+H6ePSIjIwVHR0e1z+HKlSsLcXFxqjt5c0cQ5pUXhEnmmn8mWwrC8SmCkJSQbef4LQcHBwGA4ODgkP0Hl0oFIWSZIEwtoN31WlhFECI1/x+RkpIi9OrVS+P72p9//pkNJ0lElLNy9H3+ByT/+TssLCzH4uDIHqJcaPr06fDz8xO/8a9Xrx4WLlyIEydO4OrVqzh27BgWLVoEX19f6Ojo4OHDhxg3blwOR505Xr58iUePHgEAxo4di06dOqFSpUpwd3eHu7s7dHRkb1unTp0SR/ysXLkSTZo0Qbly5eDu7o6iRYvmWPyZJTAwEL1790ZCQgIKFCiABQsW4OLFizh8+DD8/f0BACEhIfD39/+ub5alUin8/f1x8eJFAIC/vz8OHz6MDRs2YMSIEbC2tkZCQgJ69+6NwMBAlf04OTmhc+fOmD9/Pnbv3o0LFy4gODgY27ZtQ+vWraGrq4vIyEg0b95cZXHTCxcuoHPnzoiLi4OOjg66deuGvXv34tKlS9i5cyeaNWsGAPjw4QOaN2+O58+fK+0nJiYGTZs2xYMHDwAAvXr1wokTJ3D+/Hn89ddfMDMzw+fPn9G6descK7Tq5+eHfPnyAZBNRdTWy5cvcebMGQCy9wV7e/ssiY8yRhAEdO3aFeHh4Srb5M+fH3v27IGxsYrRN3f3ASvrAZ+eaT6glSvQ7TBQdwKgp+VqVXmNjg5QvTfQ5yxQQIv6RJEPgRU+wOMTaptJJBIsXrxY/TQ7ABMmTMChQ4fSEzEREVH2yLE0E2Ubjuz5saxfv178fdnZ2QknT55U2/7WrVvCL7/8IpQvXz5L4snukT3nz58Xj3f06FGV7aZNmya2S0xMzPK41MnskT1JSUlCsWLFBACCubm50pFK/fv3F4+5bt26DB9L/vfbv39/8fEbN24Ily9fFv777z/B3NxcACAUL15cSEpKStNHcnKyxuPs2bNHPI6/v7/SNk2bNhXbLF68WGmb4cOHi20GDRqktM2kSZPENrNmzUqz/fz584Kenp4AQPDx8dEYe1bp1q2bGOeFCxe02kf+eb9x48Z0HY/v51lv7ty5akeB6OrqCmfPnlXdwfnF2o1OmWQuCHv7C0J8dPadnBq55hvfxK+CcGC49iOirml+DX3+/FkoW7as2t+rhYUFX1dElKflmvf5HwRH9hBRGhEREejXrx8AwMTEBKdPn4aPj4/afcqWLYtjx45hxIgR2RFilktISBBv6+vrf3e7H9GePXvw+LFsueAxY8YoHan0999/w8rKSrydUan7WllZKe3H2dkZY8aMAQA8evQI+/btS9NGV1dX43FatmyJkiVLAgDOnj2rtE1wsGylIRsbG/Tv319pm4kTJ4q3z58/n2Z7UlIS5s+fDwAoVaqU0pooNWrUQI8ePQDIRohdvXpVY/xZoXPnzuJtbUf3pLbLly8f/Pz8siQuypgrV65g1KhRatv89ddfqF27dtoNKSnA0fFA4BjNB9I1BFosAVosBgzzZTDaPErfCGgyB/BbLrtO6ggpwL7+wPmFapuZm5vj4MGDKFCggMo2nz9/RsuWLfHly5eMRE1ERJQlmOwhykXmzp2L2NhYAMAff/yB0qVLa7Wfjo4OOnbsqPDY6dOnxSKup0+fVrt/arvJkyen2b9bt27iY4ULFxbbqus7JiYGM2bMQI0aNWBtbQ1DQ0M4OjqiVatWOHDggNIYunbtColEopDc8vHxUTjW2rVr4erqColEgj/++CNN/Kk/qqb3qPL06VPMmTMHzZo1g6urK4yNjWFsbAwXFxe0bdsWR44cUbrf8+fPNcacGnd67N27V7zdtWtXpW1MTEzQpk0bAMDt27fFqW/p8ejRI7H4ctu2bWFionxlG/kYdu/ene7jpDI1NQUAxMfHK92emJgIQPY8U8XCwgK2trYAFBN+qU6fPi2u2NalSxdx2t+3MuucANnUnZ07dyIgIABOTk4wMjKClZUVqlWrhqlTp6ZZQS6Vt7c3XFxcAADbtm1DUlKS2uNcvXoV9+7dAwAEBASo/H1R9ouOjka7du3U/g59fX0xcuTItBuSE4E9vTUmHQAA5g5A98NAxQ7fEe1PoHxb2fS2fIU0tz06XrZSl5oC/05OTtixYwf09PRUtrl79y66du2aJxcKICKiHxOTPUS5hCAIWLduHQDZh+LevXvncEQZc/36dZQoUQJjxoxBSEgIPn36hMTERLx69Qq7du1Cs2bNEBAQoPIDf3Z79uwZihYtihEjRuDAgQN48eIF4uPjER8fj5cvX2L79u1o1KgROnXqlKmrX6kTFBQEAChRogQKFiyosp239/9WoTl37lyGj/NtX98qWLAg3NzcMnwcALh37x5u3LgBAOIIn2+lHuPZM9W1SqKjoxEZGanQXp6251SlShUx+ZTRcwKA9+/fo3bt2mjdujV2796N8PBwJCQkICoqCpcvX8bEiRNRsmRJsSaSPIlEIiZpP3z4oLHuh/zoH/lRQZSzBEFAnz598OTJE5VtChYsiPXr16dNPsZHA5taAaE7NB/IpRbQ+zTgUPn7Av5ZOFYGep3S7nqdmwvsH6J2afbatWtj3rx5arvZvXs3pk+fns5AiYiIsgaTPUS5xN27d8UlpWvXrg1zc/Mcjadq1aoIDQ3Fn3/+KT4WGBiI0NBQhZ+qVauK21+9eoW6desiIiJCHBUUGBiIK1euYP369ShfvjwA2R/EXbp0UTjeX3/9hdDQUKxevVp8bPXq1QrHatmyJY4ePYrQ0FBxuhuANDE5ODhofZ5SqRQGBgZo1qwZFixYgOPHj+PatWs4fvw4lixZgjJlygAANm7ciKlTpyrs6+DgoDHm1Li1FRMTIxZ3VZUUSSW/PXXER3rI76PtscLCwsTRZ5rExcXh0aNH+Oeff+Dj4yMW1B4yZIjS9n369AEgS3z8+++/StvI/w5S28vT9pz09PTE6XEZuXYAEBsbC29vbwQHB8PAwAB9+vTBvn37cO3aNQQFBeGvv/6CjY0N3r59i0aNGuHFixdp+tB2KldycjK2bNkCQDa1rk6dOhmKmTLf1q1bsXXrVpXbJRIJNm7ciPz58ytuiHkHrG0MPDuj+SDV+gCd9wFm+TW3pf8xLwR0PQSUb6+57bV1wI6uQHLaEYOp+vfvrzDaVZnx48fj1KlT6QyUiIgo86kej0qkpTITjyBJ+vMNW9bXleDOlIaZ1t/NmzfF25UqVcq0fjPK1NQU7u7uuHLliviYm5sbXF1dVe4zdOhQfPr0CQCwYsUKsS4KAFSuXBlt2rRBo0aNcOrUKWzfvh1du3ZFo0aNAMgSJw4ODuKoDUA2ncfdXXF1FUtLSwBQ+OD0bZv0KFSoEJ4/f45ChdIO969bty769u2L7t27Y+3atZgzZw6GDx8OCwsLALJaQe7u7hpjTo/w8HBxGoCjo6Patk5OTuLtsLCwdB9Lfh9tjyUIAsLDw1GiRAml7dauXav2w9CIESPQoYPyKSg9e/ZEUFAQNm3ahAEDBuDq1ato3rw5ChUqhJcvX2Ljxo3Ys2cPAOD3339XukpO6jmZmpqKzxV153Tr1i28f/8eCQkJMDTUUOPjG6NHj8a9e/dgYWGB48ePo0qVKgrbPT090aFDB9SoUQOvX7/G+PHj0yR03Nzc4OHhgZCQEBw4cABRUVFK4z569CjevXsHAOjYsSMkEkm6YqWsERERgQEDBqhtM378eNStW1fxwS9vgXXNgMgHGo4gARr/DVTr9X2B/sz0jYCWSwGbYsDJqerb3vsP2NoBaLcJ0Ev7fiCRSLBkyRLcvn0bly9fVtqFIAjo0KEDbty4kTbBR0RElI2Y7KHvliQVkCjN+NLPP67MHRgnnzBQVwgyt3r9+rX4QdzX11ch0ZPK0NAQq1evRvHixZGcnIxFixaJyZ6cYmpqKk7nUUYikWDOnDnYsGEDYmNjcfz4cQQEBGRZPPIFPs3MzNS2lY87JiYmVx+rQoUK+Pfff1G9enWVbXR1dbFx40Y0b94cM2bMwMqVK7Fy5UqFNj4+Phg9erTK5ZBTz0nT+QBpzyk9yZ7IyEgxtilTpqRJ9KRycXHBhAkT0L9/f2zbtg3Lli1LU2unc+fOCAkJQUJCArZv3650CiencOU+giCgV69eYoJbmdq1aysUFQcARL+WJXo+aKizpWsIBKwESjfPhGgzJj5JirfR8Xj9OV7891NcIhKSUvA1UYr4ZCnik6T4FCertxUVl4SRO27CSF8XRvo6MDPURyELIxSU+8lnqJf9yUqJBPAaAZjaAgeGyYozq/L4GLC9C9BmvdLl7I2MjLB7925UrlxZTMB+6/Xr1+jUqRMOHz6ssm4YERFRVmOyhyiXkP/grS75kFudOnVKnKajLNGTytXVFfXr18fhw4dx+vRpSKVSrVZzyi5JSUl4+/Ytvnz5Ip4PIFsh6t27d7h582aWJnvkaxkZGKT9oCFPPjnx9evXXHGsli1biomPr1+/4smTJ9i+fTv27NmDDh06YN68eWjatKnK/e/fv4/NmzcjNDRU6fYLFy5g/fr1KFu2rNLRWKnnpOl80nNOygQGBorHSi2UrYqXlxcA2XPr6tWraVZjateuHYYNG4aEhARs2LAhTbLny5cv4ipo1apVUzmqirLX6tWr1dZZsra2xubNmxWL+kZHAGubAh9V1/cBABhZAO23AS41Mila9eKTpHj49gvuv/6Cu6+jcf9NNB69jcGH2ESt9k9IShH72XE1XG1bUwNdFM1vhpIF86FUIXOULGiO0oXMYWGSDasqVu4KGFsBu3oCUjXn9vAwsLMb0HotoJs2LkdHR+zYsQN169ZVWcvt6NGjmDFjBsaOHZs5sRMREaUTkz1EuUS+fP9bQlfbmii5ye3bt8Xb6kZvpG4/fPgw4uLi8PTpUxQvXjyrw1MrKSkJy5cvx4YNG3D9+nVxVShl5EdgZQUjIyPxtro4AMXVqIyNjb/7WPL3M3osS0tLhWlIVatWRbt27bBhwwZ06dIFLVq0wKpVq5SuMhYUFITmzZsjKioKLi4u+PPPP1G/fn1YW1vj7du3+O+//zB+/Hhs2rQJZ86cwdGjR1GqVCml56Tp2qXnnJSRn96oLOmkyps3b9I8ZmVlhaZNm2LXrl0IDg7Gs2fPFFYk27lzp5iM6tSpU7ripKzx/PlzDB06VG2bf//9V3F65OdXwLqmwMen6js3dwQ67gLyq6+j9T0+xyUh5NkHXHjyASFPP+Dh2y9IyabZ2LGJUtwK/4xb4Z8VHnewNEb1ItaoUcQGNYrawNEqi1abK90CMLIEtv4KJKoZpXj/ALC7N+C/AtBN++eyl5cXZs6cid9++01lFxMmTEDt2rXTJHiJiIiyA5M9RLlE6nLSAPD27dscjCRjPn78KN7WNA1NfoUp+f1ywsePH9GgQQNcvXpVq/YZGUGTHvJJP03TpeSTgtpMW9J0LHXJnu89VqdOnXDgwAFs374dAwcORIsWLWBlZSVuT0hIQPv27REVFYWCBQsiJCRE4Xni6OiI/v37w9vbG1WqVEF4eDg6d+6cpm5G6jlpM9Xse85J1fQNTeLi4pQ+3rlzZ+zatQuCIGDjxo2YMGGCuC11Cpe+vj7at9ei0CxlqZSUFHTr1k3tc6xdu3Zo3br1/x6ICpMlej49V9+5XSmg027A3D5zgv1/0hQBV55/xLG7b3Hh6QfcfR2tbqXxHPEq6it2X3uF3ddeAQCcrI1Ro4gNfErkR50S+WFskIkjQIt4A132y1ZCi/ugut2d3bKRPS2XAjppjz9s2DCcPn0a+/fvV7p7SkoK2rdvjxs3bij8H09ERJQdmOwhyiVSV6oCgGvXruVgJFlPyEWfMoYMGSImelq2bInu3bujXLlyyJ8/P4yMjMTaEs7OzggLC8vy2OVHAqSuyqWKfIFl+WLNGT2Wug8jqceSSCQaizmr0qJFC2zfvh2xsbE4fPgwfv31V3HbkSNH8OqV7EPeoEGDVC45X6ZMGXTs2BErV67ElStXcPPmTYXXjqOjIy5evIjY2FiVxY6/PSc7O7t0F2dOneJnYGCgdaIwNT5lGjVqBDs7O7x//x4bNmwQkz1hYWE4c0a2WlPjxo1hY2OTrjgp8y1atAinT59Wub1gwYJYtGjR/x748hZY31xzoqeAu2zFLdPMSQokS1Nw8dlHHAp9jcA7bxAZo92UrNwi7ONXhH0Mx/Yr4TDW14VPSTs0ci8En5L5YWaYCX++OlQCuh4E1jZRn/C5tQ3Q0QeaLwS+qb8jkUiwdu1aVKhQQWWR/FevXqFLly7Yv38/6/cQEVG2YrKHvpu+rgSZXaz4RyA778xTunRp2NraIjIyEkFBQYiOjv6u5dfl/6hMSVFdjDKzpoxZW1uLt9++fQtnZ2eVbeVHLsnvl92io6Oxbds2AMCvv/6KTZs2qWyrrghrZjIzM4OTkxPCwsJw//59tW3lt387nUkbpUuXVuirQoUKGo/l5OSU4ZpSdnZ24u1vlyGXX/5c02p0lStXFosj379/XyHZU7p0aezatUvc5uHhobSP5ORkPHkiq5uSkWuXmnRJTEyEjY1NuqZyKZM6amfBggV49OgRLl68iOrVq2PTpk3i65eFmXPew4cPMXr0aLVtVqxY8b+k3NcoYKO/5qlbBcsCnfYBpt+fzLv96jO2XHqJQ6Gv8Sku6bv7yw2+JklxKPQNDoW+gYGeDuq42aFtVSfUKZEfujrf8X9x/lKyBNu6ZsBXNe/xNzbKknD1/0izydraGlu3boWXl5dCnTd5hw4dwvz58zFs2LCMx0pERJROTPbQd8vM5cd/ZhKJBF27dsXs2bMRGxuLlStXYvjw4RnuT36KjrpExYMH6pf+1XbVFPnlxi9evKg22XPp0iUAgImJiUJtkuz26NEjJCXJPgy1a9dOZbsHDx6onbKR2SvLeHp6YsuWLXjw4AHevHmjcpRL6ogPAKhVq1aGjiPfl6pr8ObNGzx8+DDDx0mVOnIHSDttSr6IraqCp6lSf2ff7gekPSdVyZ4rV66Iic6MnFPFihXF20ePHkWXLl3S3ce3OnfujAULFgCQTd2qXr06Nm7cCED2gVJdYWvKeikpKejVq5faqZzdu3f/3+8pMRbY3AZ4e1tlewBAofJAp72AScYT37EJyfjvZgS2XHqZphZOXpOYnIKjd9/i6N23sLcwQtuqzmhb1QkFLVRPQ1WrYFnZ9V/XHEhQc+2C58mm11Xvk2ZTzZo1MW3aNPz+++8qdx8zZgzq16+v8H8lERFRVvr5hmMQ5WJDhw4Vl2WeOHGixpEdqVJSUsQPhankkyjyxWS/tXnzZrV9y9dxkS9o+606deqIq2qtWrVKZbuXL1/i2LFj4j7ffljPTvJJBVW1VABZoVV1tL1G2mrZsqV4e+3atUrbxMXFYfv27QBko1nc3NzSfRw3NzdxVMv27dtVXgP5GPz8/NJ9nFQ7duwQb5ctW1Zhm/zzNSgoSG0/8kmub5OFderUgYWFBQBg3bp1Kqfdfe85NWrUCPr6slV65s6dqzFBpY3KlSujTJkyAIBt27YhJCQEd+7cAQC0bdtWqxXGKOusWbMGZ8+eVbnd2dkZc+fOld1JTgS2dwbCLqrv1L6ibGRJBhM9j9/FYOyeUFT76zjG7A7N9ESPkb4OCtuaokYRG/hXdED/OkUxuVlpzGpVDgvaV8TyTpWxoUc1WJvJnptWpgZY/GslzGldHlNbumNYPTe0r+aEOiXsULJgPpgbZe77fcTneMw9/hA1Z5xAz3VXcO5RZMam2tpXkNVKMsinvt3h34G7+5RuGjFiBBo1aqRy14SEBHTo0CFT/o8gIiLSBpM9RLmIg4ODWOshNjYW3t7eCh9slbl79y58fX0xe/ZshcctLS1Rrlw5ALIPKcoKIZ89e1YcSaCK/PSU1Gkvytjb24sfmgMDA7F69eo0bRITE9G9e3dxZMbAgQPVHjurFStWTByVs379eqVtDhw4gIULF6rtR9trpC0/Pz8ULVoUADB9+nSlfY4cOVIcsTVy5Eil/UyePBkSiUSsK6HMiBEjAMgKVY8aNSrN9rCwMEyfPh0AULRoUaWJkbVr1yos467M3LlzxWWqXV1dFUbgAEDdunXFROfSpUtVLr1++PBh7NmzB4Ds9fLt1DMDAwMMHjwYgGxq2LevC0C2fHtqQtLb2xtVq1ZVG7syDg4O6NatGwDg5s2b6NOnj9qEz7t378SpZ+qkTtWKjIxEz5490zxOOePt27fia0WVNWvWyKbepkiBPX2Ax8fVd2pfSTaixNhKfTslQsM/o9/Gq6g/9ww2X3yJ2ETl04fSw9naBA3LFMTQesWxrFNlnB3pg7t/NMSpEXWwpbcH/mlbAaMalkTXWoXRpooTmpe3R4MyBVG7uB0MdGV/Thrq6aBJuUIIqOyITh4uGFKvOKb7l8PabtVwZKgXbk32xeVx9bChRzWMa1wK/pUcUKqQ+fdNxQKQIgDH771Fx1UX0WJxMI7cfoOU9C4v5lgF6LgT0Fc3TVUAdvUCXpxPs0VHRwfr1q2Dvb3q4tq3bt1SKL5ORESUlTiNiyiX6datG8LDwzFx4kS8e/cOderUQYMGDdCiRQuUKlUKlpaW+PjxIx4+fIiDBw/iyJEjkEqlCnVLUvXv3x99+/bF27dvUbt2bUyYMAElSpTAx48fceDAASxduhRVqlTBhQsXVMZTsWJFGBkZIT4+HhMmTICenh5cXV3FmkAODg7istVz587FiRMn8OnTJ/Ts2RPBwcFo164drK2tcf/+fcyePRs3btwAALRp00btt6DZwcbGBo0bN8bBgwdx6NAhNGzYEH369IGzszPevXuHXbt2Ye3atShSpAiioqLw/v17pf04OzvD0dER4eHhmD17NhwcHFCiRAlx1FKBAgUUptVpoq+vjwULFqBZs2aIjo5GrVq1MH78eFSrVg2fPn3CihUrxLo0np6e37Ucd5cuXbB69WoEBwdj8eLFePPmDXr16oX379/j1q1bWLVqFaKjo6Gjo4OFCxcqHYk1efJk/PbbbwgICICnpyeKFi0KMzMzfPnyBaGhodi0aROCg4MByJIxK1asSNOPpaUlRo8ejYkTJ+LLly+oWbMmBg0ahPr168PKygpv377Fvn37sGLFCrGGzYwZM5QWPB05ciS2bduGhw8fYtSoUXj8+DHatWsHY2NjnDp1CtOmTUNycjKMjY0xb968DF+7OXPm4Pz587h9+zZWr16NkJAQ9O7dG5UrV4aZmRmioqJw584dHD9+HIcOHULZsmUVEjjKdOzYEWPGjEFKSoo4qsfNzU3ldDTKHsOGDUNUVJTK7QMGDMAvv/wCCAJwaIRsFSd18peWLa9ubKl1DIIg4NKzj1h8+gnOPlT+XpQerjYmqFHUBh5FZD8FzDM4DSqd7PIZwi6fHWoX/18Nr5iEZFx+/hEhTz7gwtMPuP3qc4aXgr8V/hl9N15Fsfxm6F+nKJqVt4e+rpbfbTp7AL9uk9VZkqooaC1NALa0A7ofBfKXVDw3Ozts2rQJv/zyi8oRRrNnz0aTJk3g7e2dntMiIiJKN4mQm5bFoSwRHh4urtRTsGBBvH79Wut9Hz16hOTkZOjp6aF48eJZFSIpsXv3bvz22294/vy5xrZlypTBP//8gwYNGig8npKSgoCAAOzdu1fpfu7u7jh69Kj4TeSkSZMwefLkNO1+//13zJo1S2kfp06dQp06dcT7169fR9OmTREREaEyXn9/f2zatEnpUt+nT5+Gj4+P0r7lTZ48GX/8ISuW+T1vY2FhYfD09MTLly+Vbnd2dsbhw4fRuHFjvHjxAl26dFE6Smbp0qXo37+/0j7WrFmDrl27pju2FStWYODAgUhMVP6ho1q1ajh48KDKVbTkr5G6GCIjI9G4ceM0y5inMjAwwKJFi9CrVy+l211dXdMUXFbG0dERq1evRv369ZVuFwQBw4cPx/z589X+TvX19TFt2jS1Iy0eP36Mxo0b49GjR0q3m5ubY9OmTd9dB+fjx4/o0KEDjhw5orGtj48PTp48qbFdgwYNxKmOADB16lSMHz/+u+IE+H6eUamvf1UcHR1x9+5dWUL37Gzg5FT1HVq5At0DgXzKa3Epc+3lJ8w4dB+XnqcdoaktHQngUcQGjcoWQt2S+WFvaZzhvr7l6OiIV69ewcHBQeMqgtr4/DUJ5x5F4vDt1zh5/x3ivmPkkqOVMX5r4IYW5R2go+0Iojt7gB3dAKj5v8XcEeh5TFbH5xsjR45UOqowlbOzM27duiVOOSUiyu0y+30+r5P//B0WFpbhlWy/F0f2EOVS/v7+aNq0KXbu3InDhw/j8uXLePfuHb58+QJzc3O4urrCw8MDAQEB8PHxUVokWEdHBzt37sSyZcuwdu1a3L17F4BsOk7btm0VagSpM2PGDBQvXhzr16/HnTt38PnzZ5WrjlSsWBEPHjzAokWLsHfvXjx48ABxcXGwtbWFh4cHunbtimbNmn3fxclETk5OuHbtGmbOnIl9+/bhxYsXMDIygqurK1q2bIkhQ4bAykrzNIt+/fqhQIECWLZsGW7cuIGPHz9+dx2XXr16oUaNGliwYAFOnDiBiIgImJqaolSpUujQoQN69uyZKTWPbG1tcf78eaxYsQKbN2/GvXv3EBMTA1tbW1SvXh1TpkwRa8koc+LECRw/fhynTp3CvXv38PbtW3z48AFGRkYoUKAAKlSogKZNm6JNmzZqn28SiQRz584Vl1Y/d+4cXrx4gbi4OJiZmaFYsWLw9vZGnz59NNYoKlasGK5fv47Fixdjx44dePz4MRITE+Hk5ITGjRtjyJAhcHFxyfA1S2VtbY3Dhw/j5MmT2LhxI86dO4fXr18jPj4e5ubmKFq0KKpVq4YmTZqkScaq0qVLFzHZI5FI0LFjx++OkzImNjYW/fr1U9tmyZIlskRP6E7NiR6zgrKpW1omep5HxmJW4H0cCn2jZcSKdHUkqFXMFo3dC6J+6QKwMTPMUD/ZzcJYH03KFUKTcoUQnyTFmYfvcTj0NY7dfZvuKWvhn75i2LabWBn0DGMbl0KtYlosbV/GD/jyFjiiuuAyosOBrb8C3Q4D+oqJsz///BNHjx7FrVu3lO768uVLDBw4EBs2bEjPqRAREaULR/b8BDiyh4jS6+bNm0hKSoK+vr7SKYL04+H7efqNGDECc+bMUbm9VatWssLjL0NkqzlJ1RTfNbKUJQYKlNZ43E+xiVhw8hE2hrxAkjT9f6Y5WBqjXVUntKnqlC3Ts7LrG9/YhGTs//9Vx25msBh1nRJ2GNOoFEoU1GJq7dEJwHn1de1QtjXgvwL45guX0NBQVKlSReXITEBWiL1NmzbahE1ElKM4sid9OLKHiIiIKJe6du3a/1bXUsLc3Bzz588HPjwBtrRXn+jRNwE67NSY6JGmCNgY8gKzjz7Al/j0jQzUkQC/lCyADtWd4eVm991Fj3MjU0M9tKvmjHbVnHH71WdsvvQS+66/Stdon9MP3uPsw/doV80Zv/uWhIWJvurG9f4AvrwGQneobhO6AyjgDngOVXi4bNmymD59On777TeVu/br1w/e3t4oUKCA1vETERFpi6txEREREcmRSqXo06ePWAxcmZkzZ8Le0gjY1Br4qqaWjo4e0HYj4KR+1bc7EZ/hv/Q8Jv13J12JHgNdHfxa3RmnRtTByi5V4FMyf55M9HzL3cEC0/zK4vyYuhjpWwI2pgZa75siAJsvvkTdf85g341XqmuE6egALZYAhTUUUz4+GXgYmObhoUOHygp3q/Dx48ccX5WSiIjyLiZ7iIiIiOSsXr0aV65cUbm9Zs2a6N29C7C1A/DxifrOms4FitVVuTkuMRnTDt1D80XBuBkWpXWMpga66O1VBEG/+2CaX1m42KhbMjzvsjDWxwCfYjj3+y+Y3Kw07C20n7YWGZOAIVtvoPPqS3jxIVZ5Iz0DWbLOrpSangRgV0/g/QOFR3V0dLB27VpYWlqq3HPnzp3YuXOn1jETERFpi8keIiIiov/38eNHjBkzRuV2fX19LF+2DDoHhwMvz6vvzHM4UKmzys2nH7xD/X/OYvnZp5Bquda4kb4OBv1SDMGjf8HYxqWybcn03M7YQBddaxXG6ZE+mBVQDoXSkfQJehSJBnPPYvGpx0iWKhnNZWQOtN8MGKsp1p8QLZvO9/WTwsNOTk5YsmSJ2uP3798fkZGRWsdLRESkDSZ7iIiIiP7fxIkT8eHDB5Xbf//9d5SJOQfc3Ky+ozL+wC8TlG76mijF+L2h6LrmMl5FfdUqLokEaFPFEadH+OC3BiVgaaL9tKWfiYGeDtpUdcKpEXUwqmEJ5DPUrjxlQnIK/g58gFb/XsCzSCWjfKyLAK3XAhJd1Z18fALs7AGkKNYQateuHfz9/VXu9v79ewwePFirOImIiLTFZA8RERERZKvQLV26VOX2woULY3wHLyBwrPqOnKoDLZfKar58IzT8M5osDMLGkJdax+XtZofDQ2pjVqvyKJiOESs/MyN9XfSvUwynR9ZB15qu0NOyjtGNsCg0nh+EzRdfpq3lU6QO0HCG+g6enADOzlZ4SCKRYMmSJbC2tla525YtW7Bv3z6tYiQiItIGkz1ERET00xMEAQMHDlRblPnfGWNhuK83IKhZ/cnKFWi3GdBXTMpIUwQsPvUYfkuC8fS9ivow33CxMcG67tWwrns1lCxortU+pMjGzBCTm5fBseHeqF3cVqt9viZJMXZPKHqtv4LImG9WWavWS+3UPADA6enAk1MKDxUoUAALFqhfxr1v37749OmT2jZERETaYrKHiIiIfnqbN2/GuXPnVG5v0bg+6n9cq37lLSML2RLrpopJhYior2i/PAR/Bz5Asha1efR0JBjgUxSBQ73g7Wan7SmQGoVtTbG+ezUsaF8RtmaGWu1z/N47NJx3FqcevPvfgxIJ0HgO4FxDzZ7/X7A5+rXCo7/++iuaNWumcq83b95g2LBhWsVGRESkCZM9RERE9FP78uULRo4cqXK7vr4+1reygORNqOpOJDpAq9WAbXGFh4MfR6LpwnO49FxNkkhOZRcrHBxcGyN9S8JIX019GEo3iUSC5uXtcWK4N36t7qzVPpExiei25jL+Ofrgf0W09QyANhsAc0fVO8ZFAju7A9JkheMvXboUFhYWKndbt24djh49qlVsRERE6jDZQ0RERD+1qVOn4vXr1yq37x3pA/MXGj6A150EFKsn3hUEAUtPP0GnVRfxMTZRYwwmBrqY2tIdO/rUQImC+bSOndLPwkQf0/zKYle/Gihip92S9QtOPkbXNZf+97s0swParAN09FXv9PI8cHKqwkMODg6YO3eu2mP169cPX79qV7ibiIhIFSZ7iIiI6Kf18OFDzJs3T+X2gMr50cjgkvpOyvgDtYaId7/EJ6HvxquYeeQ+tFlRvbyTJQ4Oro1OHi7Q0bKQMH2/yi7WODhIdt21EfQoEs0WnsPNsCjZA45VgAZT1e6D4HnAgyMKD3Xt2hW+vr4qd3n69Cn+/PNPrWIiIiJShckeIiIi+mmNGjUKSUlJSrcVMJVgQwsDSATVRZtRoCzQYpGslguAh2+/oMWiYATeeavx2DoSYEjd4tjZtwYK22o3woQyl/H/j6ha07WqVrV8XkV9Ret/L2BjyAvZA9X7AqWaq99pTx8g6n+rr0kkEqxYsQL58qkewTVr1izcvn1bq3MgIiJShskeIiIi+imdOXNG5XLXOhLgcI9CMJZGq+7A2BpotwkwkCVqTj94B/8l5/E0UvNqW87WJtjRtyaG1XeDvi7/HMtpPiXzI3BobTQoXUBj20RpCsbvvY1J+27LCm63WARYF1G9Q3wUsLsPkPK/VdycnJwwbdo0lbskJyejT58+aleHIyIiUod/XRAREdFPJyUlBcOHD1e5/Q8fI1S0ilHdgUQXaL0WsJJNAdoY8gI91l1BTEKy6n3+X5NyhXBoSG1UdrFKb9iUhWzMDLGsU2X82dIdBlok4NZdeIGe66/gC0yA1usAXTUjg16eB84p1urp168fqlWrpnKX8+fPY+XKlVrHT0REJI/JHiIiIvrpbNq0CdeuXVO6zbeoLsbXNlDfQb3JQBFvpKQI+OvgXYzfe/t/qzWpoKsjwfgmpbCofUWYGeplMHLKShKJBB09XLC9bw3YWxhpbH/6wXu0/vcCXhkXBxrP0tB4OvDqqnhXV1cXy5Ytg66u6lXXfv/9d7x580br+ImIiFIx2UNEREQ/lbi4OIwdO1bpNkdzCTYFmKjvoEQToOYgfE2Uot+mq1gR9EzjMW3NDLG5Z3X0rF0EEgmLMOd2FZwscWBwbdQubqux7f03X9BycTBu2rUAyrVV3TAlGdjVC0j434ixChUqYNiwYSp3iYqKUjsCjYiISBUme4iIiOinMnfuXISHh6d5XE8H2NbKGDbGapIxli5Ay8WIjE1Eu+UXtCrEXNnFCgcHe6J6EZvvCZuymbWpAdZ2q4bBvxTT2Pb9lwS0XRGCE0VGAVauqht+fAIEKiYaJ0+eDGdnZ5W7bNmyBYGBgdqGTUREBIDJHiIiIvqJvHnzBjNmzFC6baqPIWo6qZlepWsAtF6LiAQjtPn3Am6Gf9Z4vPbVnLGllwcKmGueEkS5j66OBMMblMCKzlVgYqB6uhUAxCeloNe2BzhZ+i9ZTSdVrq0D7u0X75qammLx4sVq+x44cCASEhLSFTsREf3cmOwholwpODgYfn5+KFiwIPT09CCRSCCRSBAVFaXQbsOGDfDy8oKVlRV0dHQgkUhQoUKFHImZiHK/SZMmISYmbeHlXwrrYlQtDXV6fKfhqYEbWv97QasVt8Y2Lolpfu4w0OOfWz+6+qULYHufGiioIWmXIgDdT0hwrXAv9R3+NxiIfi3ebdq0KVq1aqWy+ePHj/HPP/+kK2YiIvq58a8PolwsKSkJW7duRZcuXVCqVCnY2NhAX18ftra2qFy5Mvr164fjx4/nuaVZ9+/fD29vb+zduxdv376FVCpV2m7UqFHo3LkzgoKCEBUVBUFQXxz1R3Tnzh307dsXxYoVg7GxMezs7ODl5YVly5YhOVnzqj/aSk5OxrJly+Dl5QU7OztUr14dfn5++PPPP3H37l2N+6cm4zT91KlTR6t4jhw5An9/fzg6OsLQ0BCOjo7w9/fHkSNHtD6nuLg4/P3336hWrRqsra1hZmaGUqVKYcSIEXj58qXW/WQFX19f8Zo8ffo0XfuuWrVK3Hf27NlZFGHedPv2baWrG9kYS7DBzxg66mrpuAfgrkMbtFl2Aa+ivqo9jqGeDpZ2qITeXkVZnycPcXewwN4BtVDG3lxj29Z3ayEiX1nVDb5+BPYNAOT+35o/fz7MzVX3/eeffyIsLCxdMRMR0c+LyR6iXGrfvn0oWbIk2rdvj/Xr1+P+/fv4+PEjkpOT8eHDB1y7dg3//vsv6tevj1KlSuHgwYNZEsfatWvFD5bPnz/PkmN867fffoNUKoW9vT3Wr1+Pq1evIjQ0FKGhoeIfwmFhYeK3nB4eHjhw4ABu3ryJ0NBQ7Nq1K1viTHX69GnxGp0+fTrT+l21ahUqV66MZcuW4cmTJ4iPj0dkZCSCgoLQt29f1K5dGx8+fPju43z48AGenp7o27cvgoKCEBkZiYSEBISHh2Pnzp2oVKkSVq9enQlnpJkgCOjTpw8aNWqEPXv24NWrV0hMTMSrV6+wZ88eNGrUCH369NGY2Hvy5AkqVaqEUaNG4fLly/j06RNiY2Nx//59zJkzB+XKlcOhQ4ey5ZyU6dy5s3h748aN6do3tb2Ojg5+/fXXTI0rrxs9erTS5PjqFkawz6fmTyKb4rheYTLarQhBZEyi2mPYmhlga28PNCpb6HvDpVyooIURtvepgXqlCqhtJ4Uu2kZ2R7yOmmLfT04A1zeId+3t7fHXX3+pbB4XF4fffvst3TETEdHPickeolxo+vTp8PPzE7/xr1evHhYuXIgTJ07g6tWrOHbsGBYtWgRfX1/o6Ojg4cOHGDduXA5HnTlevnyJR48eAQDGjh2LTp06oVKlSnB3d4e7uzt0dGRvW6dOnRJH/KxcuRJNmjRBuXLl4O7ujqJFi+ZY/JklMDAQvXv3RkJCAgoUKIAFCxbg4sWLOHz4MPz9/QEAISEh8Pf3/66RXVKpFP7+/rh48SIAwN/fH4cPH8aGDRswYsQIWFtbIyEhAb1799aqQGi/fv3ExJyynzVr1qjdf/z48Vi+fDkAoGLFitiyZQsuXbqELVu2oGLFigCA5cuXY8KECSr7iImJQdOmTfHgwQMAQK9evXDixAmcP38ef/31F8zMzPD582e0bt0at27d0uo6ZTY/Pz/ky5cPgGwqorZevnyJM2fOAJC9L9jb22dJfHnRuXPnlCbF+1XRR/MS+qp31DXEterz8Ou6u4iOVz+arqidKfb0r4WKzlbfGy7lYqaGeljWqTK61yqstl2YUABj4zurbYPAccDn/xUL79u3L8qVK6ey+Y4dO3DixIl0xUtERD8nNVUIiSgnbNiwQVwS2M7ODtu2bYOPj0+advXq1cOAAQMQGhqKoUOHZsoIj9zg1atX4m03N7fvbvcjSk5OxsCBA5GSkgJzc3MEBwcrJLAaNmyIAQMGYMmSJTh79iw2btyoMFIkPTZs2ICzZ88CAPr37y8WCb158yZKliwJLy8vdOzYEdHR0Rg0aBDu3r0LPT3V/3Xkz58f7u7uGYrl8ePHmDVrFgCgSpUqOHv2LIyNjQEAVatWRfPmzeHt7Y0rV65g5syZ6Natm9LE3uzZs3H//n0AwKxZszBy5EhxW40aNeDj4wMvLy/ExcVh6NChOHnyZIbi/R4mJiZo1aoV1qxZg8ePHyMkJAQeHh4a99u0aZM4qimjv/OfkSAIGD16dJrHy9jpYE4D9TVYnlT8He3/+4KEZPVJ1XKOFljXrRqsTDXU/aE8QVdHgonNSsMunyFmHrmvst3ulNr4RXoDTXVDlDdIiJbV7+m4C5BIoKenh8WLF6N27doq+xw0aBBu3LgBAwM+14iISDWO7CHKRSIiItCvXz8Asg+Dp0+fVprokVe2bFkcO3YMI0aMyI4Qs5z8aiP6+qq/bde23Y9oz549ePz4MQBgzJgxShMaf//9N6ysrMTbGZW6r5WVldJ+nJ2dMWbMGADAo0ePsG/fvgwfS5O5c+eKdYgWLlwoJnpSmZiYYOHChQBkCbF58+al6SMpKQnz588HAJQqVUrplIcaNWqgR48eAGQjxK5evZqZp6E1+WSNtqN7Utvly5cPfn5+WRJXXnTo0CEEBwcrPGakB2wJMIaxvuqaOh8dfNDkYimNiZ7qha2xqWd1Jnp+Qv3qFMU0v7JQXZpJgrFJ3fFWsFTdyTfTuTw9PdGxY0eVze/duye+FxIREanCZA9RLjJ37lzExspWePnjjz9QunRprfbT0dFJ84dheurIpLabPHlymv27desmPla4cOE0BXeV9R0TE4MZM2agRo0asLa2FgvstmrVCgcOHFAaQ9euXSGRSBSSWz4+PgrHWrt2LVxdXSGRSPDHH3+kiT+jtYWePn2KOXPmoFmzZnB1dYWxsTGMjY3h4uKCtm3bqiwK/Pz5c40xp8adHnv37hVvd+3aVWkbExMTtGnTBoCs6Gzq1Lf0ePTokVh8uW3btjAxUV5bQj6G3bt3p/s42hAEQUwklSxZUuUoFw8PD5QoUQKA7Dp9W7vn9OnT4optXbp0Eaf9fSszz0kQBOzcuRMBAQFwcnKCkZERrKysUK1aNUydOjXNCnKpvL294eLiAgDYtm0bkpKS1B7n6tWruHfvHgAgICBA5e+LFKWkpIijJeXNqm+EsgVUL4+daGyHpi9/RXyS+vpQdUvmx7ru1ZDPKG8lnUl7v1Z3xoJ2FaGvqzzjEw0zjEnqqb6Tb6ZzzZo1S5zqqczkyZMRERGRoXiJiOjnwGQPUS4hCALWrVsHADA1NUXv3r1zOKKMuX79OkqUKIExY8YgJCQEnz59Egvs7tq1C82aNUNAQADi4+NzOlQAwLNnz1C0aFGMGDECBw4cwIsXLxAfH4/4+Hi8fPkS27dvR6NGjdCpU6dMXf1KnaCgIABAiRIlULBgQZXtvL29xdvnzp3L8HG+7etbBQsWFKfKZeQ42nj27Jk4NU9dLPLbw8PD0yT2tD2nKlWqwNTUFMD3ndP79+9Ru3ZttG7dGrt370Z4eDgSEhIQFRWFy5cvY+LEiShZsqRYE0meRCIRk7QfPnzQWDBafvQPp3Bpb+vWrWlqM9UvootB1dSPwukf2wsRSaZq2zQvb49/O1WGkb7qpBH9HJqVt8eKzlVgpK/8T+uTKZWwS6p6apY4nev/E9iFChVS+ALmWzExMRg1atT3hExERHkckz1EucTdu3fx/v17AEDt2rXVLr+aHapWrYrQ0FD8+eef4mOBgYFpCu5WrVpV3P7q1SvUrVsXERER4qigwMBAXLlyBevXr0f58uUByEZSdOnSReF4f/31F0JDQxVWfVq9erXCsVq2bImjR48iNDRUnO4GIE1MDg4OWp+nVCqFgYEBmjVrhgULFuD48eO4du0ajh8/jiVLlqBMmTIAZCsgTZ06VWFfBwcHjTGnxq2tmJgYhIfLvt0tWbKk2rby21NHfKSH/D7aHissLEwcfabMjh07UKJECRgbGyNfvnwoXrw4unTpglOnTmV6LN/ul55+9PT0xOlxGbl2ABAbGwtvb28EBwfDwMAAffr0wb59+3Dt2jUEBQXhr7/+go2NDd6+fYtGjRrhxYsXafrQdipXcnIytmzZAkA2tU7bJex/domJiWmKeVsaAWtaGKvYQ2a10AzHE9XXnmpfzRlz21aAvi7/lCKZOiXyY2OP6shnqLyu2R9JndI1nWvQoEFqR/hu2rQJFy5cyGi4RESUx7FAM32/aQ6AVP1StHmSrgEw9pXmdlq6efOmeLtSpUqZ1m9GmZqawt3dHVeuXBEfc3Nzg6urq8p9hg4dik+fPgEAVqxYIdZFAYDKlSujTZs2aNSoEU6dOoXt27eja9euaNSoEQBZ4sTBwQGRkZHiPoULF05T7NfS0hKArBBwqowWBAZk354+f/4chQqlXSa5bt266Nu3L7p37461a9dizpw5GD58OCwsLADIagW5u7trjDk9wsPDxalJjo6Oats6OTmJt8PCwtJ9LPl9tD2WIAgIDw8Xp1J9K3VaWKrHjx/j8ePHWL9+PVq2bIm1a9eK1+97Y/l2P/n7pqam4nNFXT+3bt3C+/fvkZCQAENDQ7XtvzV69Gjcu3cPFhYWOH78OKpUqaKw3dPTEx06dECNGjXw+vVrjB8/Pk1Cx83NDR4eHggJCcGBAwcQFRWlNO6jR4/i3bt3AICOHTtCorpACMlZtWqVuKphqkWNjOFgrjpBc1cojOkJrdX2+2t1Z/zZwh06Ovw9kKIqrtZY16MaOq+6hJgExdGgqdO5VhvMVt1B4DigaF3AwgH6+vpYuHAh6tatq7L5sGHDcP78eZVTVomI6OfF/xno+0kTf96fTCSfMChQoECm9p0dXr9+jT179gAAfH19FRI9qQwNDbF69WpxNadFixZla4zKmJqaKk30pJJIJJgzZw50dXURGxuL48ePZ2k8X758EW+bmZmpbZs6DQmQjQjKyWOZmJigXbt2WLFiBYKCgnD9+nUcPXoU48aNg42NDQBZjZ0WLVoorU2TWbGk9qOpD23OSZ3IyEisXLkSADBlypQ0iZ5ULi4u4siSbdu2IS4uLk2b1NE9CQkJ2L59u9J+OIUr/WJjYzFlyhSFx1qX1kOHcqpr68TDAAMTByBJzXdh7ao6MdFDalVytsK67tVgapB2ep9W07kO/2961i+//CLWZ1Pm4sWL4qg/IiIieUz2EOUS8h925T+E/ihOnToFqVQKAEoTPalcXV1Rv359ALJiuqn75BZJSUkIDw/HvXv3cPv2bdy+fRsRERFiwkJ+BFZWkK9lpGlZXfmRKF+/fs3RY7169QpbtmxBz5494enpiQoVKqB+/fr4888/cefOHVSsWBEAcObMGSxdujTLYkntR5slib/n+gUGBorHUvdBDAC8vLwAyJ5bylb+ateunRiLsqlcX758EYtXV6tWTeWoKlK0YMECvHnzRrxfyEyCpU3UL7P+Z1IHPBXsVW5vVdkR0/zKMtFDGlV2kSV8TJQkfDRO57p/ALj3v8UMZs+enWZ1QnmjR49WmkgmIqKfG5M9RLmE/Kob6mqi5Fa3b98Wb1evXl1t29TtcXFxaaZY5ISkpCQsXrwYHh4eMDMzg5OTE0qXLo2yZcuKP6lTaORHYGUFI6P/fRhNTFQ/ekx++Xl1HwSy41jqpkwVKFAAO3fuFBMwypYMzqxYUvvR1IemfjSRn95YqFChNCuwyf/IT+uTTz6ksrKyQtOmTQEAwcHBePbsmcL2nTt3ismoTp06pSvOn1VUVBRmzZql8NjK5kawMVH9Z88ZaTlslNZTud2/ogNmBpRjooe0VsXVGmu7pU34aLU616GRQHw0ANmUU3XFmMPDwzF7tpqpYURE9FNisocol7C1tRVvv337NgcjyZiPHz+KtzVNQ5NfYUp+v5zw8eNH1KhRAwMHDsTFixc1JgkyMoImPeSTfpqmFsknBbWZtpSTxypSpIg4ouvx48dplgzOrFhS+9FmWtb3nFNq8i+9VH37njo1SxAEbNy4UWFb6mgffX19tG/fPkPH/dnMnTtXYcn7XpX00bi46ulbnwUTjErqDUB5IqdFBXv83bo8dJnooXSqVtgaa7pWhfE3K7adTKmEPdJaqnf8EgGc/N8CCSNHjoS9vepRZzNnzhRXNCQiIgKY7CHKNVJXqgKAa9eu5WAkWS+1AHFuMGTIEHFqTcuWLfHff//h+fPniIuLQ0pKCgRBgCAICgWKs5J8ceLUVblUkS9OLF+0OCuPJZFINBZQVkV+VZlvP5Rk1nmn9hMbG6vwYV9dP3Z2dukuzpw6/dDAwCDN6mvqflq0aKG0v0aNGsHOzg6A4lSusLAwnDlzBgDQuHFjcTohqfbx40fMnTtXvF/YUoJ/fNVP35qQ1B1vYa10W71SBTCHiR76DtWL2GB558rQ11V8Dv2Z1BGfBDWJ5kvLgXDZ/0+mpqaYMWOGyqZxcXEYO3ZspsRLRER5A5M99P10DX7en0xUunRpcXRPUFAQoqOjv6s/+ZU5UlJSVLbLrClj1tb/+6CkaWSS/Hb5/bJbdHQ0tm3bBgD49ddfsWfPHjRr1gwuLi4wNjZWWPEodZWxrJY6jQwA7t+/r7at/PZSpUql+1jyyRdtj+Xk5JThmlLqEmUZiQVIe97a9pOcnIwnT54o7UMbqUmXxMRE2NjYwN3dXasfVdPd5EftPHr0CBcvXgQgW1o59fXLwszamTNnjlgDTQJgVXNjmBmoTtTsl3rgv5SaSrdVc7XGol8rQo/Lq9N3ql3cDvPaVoT8QnofYIFpyb+q2UsA9g8GpLKi9h06dEDVqlVVtl6/fj0uX76cSRETEdGPjkuv0/fLxOXHf2YSiQRdu3bF7NmzERsbi5UrV2L48OEZ7k9+Woy6RMWDBw80xqUN+bokFy9ehLOzs8q2ly5dAiBbwalw4cJa9Z8VHj16JK4M1a5dO5XtHjx4oHZaUGYvg+3p6YktW7bgwYMHePPmjcK0N3mpIz4AoFYtNdMB1BxHvi9V1+DNmzd4+PBhho+TSn5Z9m+nIxQuXBj29vaIiIhQOC9lzp49CwBwcHCAq6urwrZvz8nDw0NpH1euXBETnRk5p9SC04BsWfQuXbqku49vde7cGQsWLAAgG91TvXp1cUqXtbW1WNeHVIuMjBSvIQD0qaIPn8Kq/9R5K1hiQlI3pdtKFTLHii5VYKSftsAuUUY0KVcIUV/dMW7P/2rc7ZB6w1/nHGro3lW+09vbwIXFgOdQ6OjoYN68eWrfs4YNG4agoKBM/3+JiIh+PPyqiigXGTp0KExMTAAAEydO1DjCIVVKSkqaOh/ySRT5YrLf2rx5s9q+5Qvnyhe0/VadOnWgqyv7ULRq1SqV7V6+fIljx46J+6Quw54TkpOTxdvqVjL5999/1faj7TXSVsuWLcXba9euVdomLi5OXKa7dOnScHNzS/dx3NzcxFEt27dvV3kN5GPw8/NL93EA4OnTp+LvvUiRInBwcFDYLpFIxClO9+/fR0hIiNJ+QkJCxNdFixYt0nygqVOnDiwsLAAA69atUzma6HvPqVGjRtDXl9WAmTt3rsJzKaMqV66MMmXKAJAt0x4SEoI7d+4AANq2bavVCmM/u9mzZ4uJWRcLCWbVUz996/ek3ohCvjSPO1ubYF33qrAwVl3nhygjOlR3wUhf+RX1JBiX3B0Jgpr/C0/PAD7KCrfXrFkTbdu2Vdk0ODgYO3fuzKRoiYjoR8ZkD1Eu4uDggEWLFgGQTa/y9vbWOMrh7t278PX1TbMSh6WlJcqVKwcAWLNmjdJCyGfPnlX4FlyZQoUKibdTp70oY29vL35oDgwMxOrVq9O0SUxMRPfu3cXRNAMHDlR77KxWrFgxMVmwfv16pW0OHDigdPUoedpeI235+fmhaNGiAIDp06cr7XPkyJHiiK2RI0cq7Wfy5MniilCqkkYjRowAIKtzomy1l7CwMEyfPh0AULRoUaWJkf3796tNdrx9+xatWrUSf+8DBgxQ2m7o0KFi8m/QoEFpimF//foVgwYNAgDo6elh6NChafowMDDA4MGDAQD37t1TukLNhQsXxISkt7e32mkRqjg4OKBbN9mIkJs3b6JPnz5qr8G7d++wcuVKjf2mTtWKjIxEz5490zxOqr17907htbqimTHyGaoe3bA52QenUyqkedzWzBAbelRD/nzqE0VEGdW/TlH08PzfFzJPBXssTm6peofkr8Dh38W7M2fOVFtnbOzYsVqtSEhERHkbkz1EuUy3bt0wZcoUALIPL3Xq1IGvry+WLFmCU6dO4fr16zhx4gSWLl2Kpk2boly5cjh+/LjSvvr37w9A9mG7du3a2Lp1q7j/sGHD0KBBA1SpUkVtPBUrVhRHrkyYMAFHjx7Fw4cP8fjxYzx+/FjhA/ncuXNhZWUFAOjZsyd69OiBY8eO4erVq9i0aROqV6+OEydOAADatGmDRo0afd/F+k42NjZo3LgxAODQoUNo2LAh9uzZg6tXr+Lw4cPo2bMnWrZsiSJFiojFc5VxdnYWCwPPnj0b+/btw/3798VrlFo/RFv6+vpYsGABdHR0EB0djVq1amHRokW4dOkSAgMD0apVKyxZsgSAbNrS9yzH3aVLF3FKwOLFi9GqVSsEBgYiNDQU27dvR5cuXRAdHQ0dHR0sXLhQ6UisQYMGwcXFBYMHD8aWLVtw4cIF3LhxA8ePH8f48eNRpkwZXL9+XYxXVbLHzc1NTD5duXIFtWrVwrZt23DlyhVs27YNtWrVEkepjRw5EsWLF1faz8iRI8WRTqNGjUKfPn1w6tQphISEYPr06WjQoAGSk5NhbGyMefPmZfjazZkzR5y+uHr1apQvXx7z58/HuXPncOPGDZw+fRqLFy+Gn58fnJycNI4QA4COHTuK9bZSR/W4ubmpnI5G/zNr1ixxdFrPSvqoX1T1SIlXgg2mJXdI87iZoR7WdqsKF5uM1aUi0oZEIsG4xqXgX/F/Ixz/lTbDoxQH1Ts9CgQeHAYAuLi4iO+Vyjx+/BjLly/PtHiJiOgHJVCeFxYWJgAQAAgFCxZM174PHz4U7t69Kzx8+DCLoiNVdu3aJbi6uoq/O3U/ZcqUEQIDA9P0IZVKhZYtW6rcz93dXYiIiBDvT5o0SWkso0aNUtnHqVOnFNpeu3ZNsLe3Vxuvv7+/8PXrV6XHOnXqlMq+5U2aNEls9z1evnwpODs7q4zV2dlZuHPnjuDi4iIAELp06aK0nyVLlqjsY82aNRmKbfny5YKBgYHKfqtVqya8f/9e5f7y10hdDO/fvxeqVq2q8jgGBgbC8uXLVe6fem00/QQEBAifPn1Se85SqVTo3r272n569OghSKVStf08evRIKF68uMo+zM3Nhf3796vtQxsfPnwQGjZsqNX5+/j4aNVn/fr1FfabOnXqd8cpCHn7/fz169eCsbGxAEBwMpcIn0fnE4RJ5ip/Oo35S3D5/YDCT5ExB4WzD9/l9Kn8kBwcHAQAgoODQ06H8kNJSJIKbZedF5+DAaNnq33eCnPdBSExThAEQfjy5YtQsGBBle83tra2wufPn3P4DIkor+D7fPrIf/4OCwvLsTg4socol/L398eDBw+wadMmdOzYESVKlICVlRX09PRgbW2NSpUqoX///jhx4gRCQ0PRoEGDNH3o6Ohg586dWLx4MapWrQpTU1OYmpqiXLly+Ouvv3Dx4kWFKUiqzJgxAytWrEDt2rVhbW0t1uZRpmLFinjw4AGmT5+O6tWrw9LSEgYGBrC3t4e/vz/+++8/7Nq1S6HOTU5ycnLCtWvXxNEghoaGsLCwQPny5TFp0iTcuHFDYYUnVfr164ddu3ahQYMGyJ8/f6bUIurVqxeuXr2KXr16oUiRIjAyMoKNjQ08PT2xdOlSBAcHiyu4fQ9bW1ucP38eS5YsgaenJ2xsbGBoaAgHBwf4+/vj2rVr6NWrl8r9161bhz/++AMNGzaEm5sbrK2toaenB0tLS5QtWxZ9+vTB+fPnsXPnTpWrUaXS0dHBqlWrcPDgQbRo0QL29vbi86dFixY4dOgQVq5cqbDanDLFihXD9evXMXPmTFSpUgWWlpYwMTFBiRIlMGzYMNy6dStTCh5bW1vj8OHDOHHiBLp164bixYvDzMxMfJ1WrVoVAwYMwKFDh8SaRZrIF3uWSCTo2LHjd8eZ182cOVMcZbi8mTHM1Uzf2pZcB2dTyqd5fGoLd9QurnoEH1FmM9DTwbKOVVDETjaS7IpQEpuTfVTvEPUSODcPgGzlxj/++ENl08jISPz999+ZGS4REf1gJIKgZi1cyhPCw8PFpZwLFiyI169fa73vo0ePkJycDD09PZVTJogo77l58yaSkpKgr6+P8uXTfjCmH09efT+PiIhAkSJFkJCQgK4V9LGmhbHKtm8EKzRImIVoKE7T6lW7MMY10ZzUJeUcHR3x6tUrODg4IDw8PKfD+eG8/BAHvyXB+BCbCEt8wSnD32AlUbECpK4hMOAiYF0YycnJKFu2rMrFHIyNjfH48eM0qx8SEaUX3+fTR/7zd1hYmFjuIbtxZA8RERH9sGbNmoWEhATkN5VgTgP1IwbHJPVMk+hpULoARjcqlZUhEqnlbGOC5Z2rwEBPB1HIh7+TVa+2BWkCcGQMAFmh+hkzZqhs+vXrV0yaNCmzwyUioh8Ekz1ERET0Q3r79i2WLVsGAJjrawRrY9XTt3ZJa+NUSkWFx8o6WGBeuwrQ1VG9H1F2qOxihX/ayEZRbpX64GZKEdWNHx4GHhwBADRv3hyenp4qm65evRp3797N1FiJiOjHwGQPERER/ZDmzJmD+Ph4NCymh1/L6qts906wxJQkxVXr7C2MsKpLFZgYfH99LaLM0LScPUY1LIEU6GBiUlekCGqSkEd+B5LiIZFIMGvWLJXNUlJSMHr06CyIloiIcjsme4iIiOiHExkZiSVLlsBUH1jaRP30rfFJ3fAZZuJ9I30dLO9cBfnNc0eheKJU/byLonl5e9wUimGbtI7qhp+eA8HzAQA1atRAQECAyqb79+/H2bNnMzdQIiLK9ZjsISIioh/O/PnzERsbiz98DOFqqfrPmcPSqjiaUlXhsZkB5eDuYJHVIRKlm0QiwcyAcihjb45ZyW0RJZiqbJsS9A8QFQYAmDZtmtqVMkeNGgWuyUJE9HNhsoeIiIh+KFFRUViwYAEqF9LB0OoGKttFC8aYlNRV4bHeXkXQooJDFkdIlHHGBrpY1qkyJKa2aos160jjkXxsMgDAzc0Nffr0Udn24sWL2L9/f2aHSkREuRiTPURERPRDWbhwIWK/RGNFM2O1xZVnJrfHO1iJ92sXt8Uo3xLZESLRd3G0MsHiXythu1AXoSmuKtvp3dkJhF0CAEycOBFmZmYq244bNw4pKSmZHSoREeVSTPYQERHRD+PLly+YN28ehnoYoGIh1dNWrqS4YbP0F/G+k7UxFravCD1d/ulDP4YaRW0wrkkZTEzqprbdh13DgZQUFChQACNGjFDZ7vbt29iyZUtmh0lERLkU/+IhIiKiH8bSpUthkfIJU3wMVbZJFHQxOqknhP//M8dYXxfLO1WBpYnqKV9EuVGXmq4oWskHu6Wql1e3iQpFxLn1AIDhw4fD1tZWZduJEyciKSkp0+MkIqLch8keIiIi+iHExcVhzpzZWNrEGCb6qqdvLZW2wGPBUbz/d+tyKFXIPDtCJMpUEokEf7Z0x382vfBVUJ2s1D81BTFfPiNfvnwYO3asynZPnz7FqlWrsiJUIiLKZZjsISIioh/C8uXLUa/AJ/gW01PZ5klKISxJbi7e71bLFU3L2WdHeERZwkhfF1M6NcBqSQuVbeyEDwhaOwGCIKBfv35wdHRU2XbKlCn4+vVrVoRKRES5CJM9RERElOslJCRg1cJZmOdrpLbdmKSeSIBsBER5J0uMaVQqO8IjylLONiZw8xuHCMFaZZs6kVuw98wlGBkZYdKkSSrbvX79GosWLcqKMImIKBdhsoeIiIhyvU2bNmG4+yfYmar+02Vzsg8uCbLkjoWxPhb/WhEGevxTh/KG+uUL41KRwSq3G0sSoXNyCu5GRKNLly4oXry4yrYzZszA58+fsyJMIiLKJfgXEBEREeVqUqkUx1f/iW4VVdcseS9YYEZye/H+P23Kw9HKJDvCI8o2TToMwkO9Eiq3t/g/9u47vMmyi+P4N+mkpaWUXfbeeyPIUJaCCCiIsrcMRRDcqK9bEUGGspc4EERkyRKQIXvvKVD2Ht1NnvePSqWSJ22hSUv5fa6r79vmnOfOaalNcnIP61q++e5HbFj53//+Z5p35coVhg8f7ooSRUQkjVCzR0RERNK0336dwxtlLzjNeTemEzfICEDvuoV5rGQOd5Qm4lZenp5kbu28SdP+1iQ+WrCPNm3aUL58edO84cOHc/HixZQuUURE0gg1e0RERCTNMgyDE7PeomwOD9Oc5baKLLJXB6BagWBebVTMXeWJuF22krU5l7+5aby69QBnN8/ljwMX+eijj0zzbt26xbBhw1xRooiIpAFq9oiIiEia9deS2XQrdN40HmF4825MZ8BCsL83X7eriKeHnt5I+paz1afEWH1M4697/sAbs7dTpXYDHnnkEdO80aNHc/68+X9fIiLy4NKzIREREUmzYhcMIcDHYhr/OrYVp8kGwLBny5Ezk/PTukTShUx5sNbsZxouYj3D41FLeXX2Lj50MrsnPDyczz//3BUViohIKlOzR0RERNKkw7+P49GsV0zjR+whTLQ9AUDnWgVoUEL79MjDw6POAGy+5kexv+I5h62HQzlizcvjjz9umjd27FjOnj3rihJFRCQVqdkjImnSunXraNmyJTlz5sTT0xOLxYLFYuHatWsJ8mbMmMGjjz5K5syZsVqtWCwWKlSokCo1i0gKiokk45/vOU15J7YLMXhSPEcArzct4ZayRNIM30A86r9hGs5uuUZ3j0V8/vtBugx8xzQvMjKSzz77zBUViohIKlKzRyQNi4mJ4ccff6RTp06ULFmSLFmy4OXlRdasWalcuTIvvvgiy5cvx263p3apKWr+/PnUrVuXX3/9lfPnz2Oz2RzmDRkyhI4dO7JmzRquXbuGYRhurtT19u7dS+/evSlSpAgZMmQgW7ZsPProo4wbN47Y2FiX3e+hQ4eoUaMGFSpUwGKx0LlzZ6f5t5txiX3Uq1cvSff/+++/06pVK/LkyYOPjw958uShVatW/P7770n+HsLDw/niiy+oVq0awcHBZMyYkZIlS/Lqq69y8uTJJI/jCo0bN47/mRw7dixZ106aNCn+2vS8uerl+e+SyzvcND7X9gh/2Uvj42ll1PMV8fUy38BZJN2q3BkjuJBpuJfnfAJtV5l2ABo2aWqa9+2333L69GlXVCgiIqlEzR6RNGrevHmUKFGCdu3aMX36dA4cOMCVK1eIjY3l8uXLbNu2jW+//ZaGDRtSsmRJFi5c6JI6pk6dGv/C8u+//3bJffzXoEGDsNlshISEMH36dLZu3cru3bvZvXs3gYGBAJw6dYrhw+OOn61RowYLFixg586d7N69mzlz5rilzttWrVoV/zNatWpVio07adIkKleuzLhx4zh69CiRkZFcunSJNWvW0Lt3b+rUqcPly5dT7P5us9vtfPDBB6ZNNlcyDINevXrRtGlT5s6dy+nTp4mOjub06dPMnTuXpk2b0qtXr0Qbe0ePHqVSpUoMGTKEzZs3c/XqVcLCwjhw4ABffvkl5cqVY9GiRW76ru7WsWPH+M+/++67ZF17O99qtfL888+naF1pxpVjBO4cbxq+YWTg45gXAHi7WSmK5QhwV2UiaYunN5bH3jUN+1uieNlzDgfP36TUs6+a5kVFRfHJJ5+4okIREUklavaIpEGffPIJLVu2jH/H//HHH2fUqFGsWLGCrVu3smzZMkaPHk3jxo2xWq0cOnSIt956K5WrThknT57k8OHDALz55pt06NCBSpUqUaZMGcqUKYPVGvdna+XKlfHNiIkTJ/Lkk09Srlw5ypQpQ+HChVOt/pSyZMkSevbsSVRUFDly5ODrr79m48aNLF68mFatWgGwYcMGWrVqleIzu0aPHs3u3bsJDjbfC8LMiy++GN+Yc/QxZcoUp9e//fbbjB8f9yK/YsWK/PDDD2zatIkffviBihUrAjB+/Hjeecd8ScKtW7do1qwZBw8eBKBHjx6sWLGC9evX89FHH5ExY0auX7/Os88+y65du5L9PaaEli1bEhAQ16CYMWNGkq87efIkq1evBuL+LoSEhLikvlRlGET+OgAvi/nv9ZexbbhIEI+XzEH76vncWJxIGlSqBeSuYhpu5/EHhS2n+e1wBPWf6WKaN2HCBE6dOuWKCkVEJBWo2SOSxsyYMYM333wTwzDIli0bf/zxB8uWLaNfv340aNCASpUq8fjjj9O3b19+//13duzYQYMGDVK77BRz5zTyYsWK3Xfegyg2NpZ+/fpht9sJDAxk3bp19O/fn2rVqtGkSRPmzJlDnz59APjzzz+TPTPEmdDQUN5++20sFgsvvfRSsq/Pnj17fGPO0UfBggVNrz1y5Ej8qTBVqlRh3bp1PPfcc1StWpXnnnuOtWvXUqVK3Auazz77jKNHjzocZ9iwYRw4cACAzz//nPHjx9OgQQNq1qzJm2++ydKlS/H09CQ8PJwBAwYk+3tMCX5+fjzzzDNA3Pe9YcOGJF03c+bM+FlNd84OSlcOLMT35GrT8B57Ab6zPU72AB8+f6YcFov5SV0iDwWLBRp9YBr2tNgZ7DkLuwGRFdpg8fR2mBcdHc3HH3/sqipFRMTN1OwRSUPOnDnDiy++CMS9GFy1ahX169d3ek3ZsmVZtmwZr75qPj37QRIVFRX/uZeX133nPYjmzp3LkSNHAHjjjTcczlT64osvyJw5c/znKaVv377cvHmTp556Kn4mjbt89dVX8fsQjRo1igwZMiSI+/n5MWrUKCCuITZixIi7xoiJiWHkyJEAlCxZkkGDBt2VU7NmTbp16wbEzRDbunVrSn4bSXZnsyaps3tu5wUEBNCyZUuX1JWqosOwLxpsGrYbFt6O6YoND4Y9W55gf8cvWkUeOvlrQfEnTcNNPDZT3nKEMzdtVO7yvmnepEmT3LZkW0REXEvNHpE05KuvviIsLAyA999/n1KlSiXpOqvVSvv27RPclpx9ZG7nvffee3dd36XLv1O+CxYseNeGu47GvnXrFp9++ik1a9YkODg4foPdZ555hgULFjisoXPnzlgslgTNrfr16ye4r6lTp1KgQAEsFgvvv//vk9X/1pTcJ6rHjh3jyy+/pHnz5hQoUIAMGTKQIUMG8ufPT9u2bU03Bf77778Trfl23cnx66+/xn9utjGyn58fbdq0AWDPnj3xS9/ux+zZs/ntt9/IkiWL22e8GIbBvHnzAChRogQ1atRwmFejRg2KFy8OxP2c/rt3z6pVq+JPbOvUqVP8sr//uvPn+ssvv9x37bNnz6Z169bkzZsXX19fMmfOTLVq1fjggw/uOkHutrp165I/f34AfvrpJ2JiYpzez9atW9m/fz8ArVu3xs/P777qTpNWf4715hnT8I+2+uwwitC+Rj4eLZbNjYWJPAAefw8s5huVv+o5C4BLwWXxyVPaYU5MTIz27hERSSfU7BFJIwzDYNq0aQD4+/vTs2fPVK7o3mzfvp3ixYvzxhtvsGHDBq5evRq/we6cOXNo3rw5rVu3JjIyMrVLBeD48eMULlyYV199lQULFnDixAkiIyOJjIzk5MmTzJo1i6ZNm9KhQweXnn51pzVr1gBQvHhxcubMaZpXt27d+M/Xrl17X/d5/fr1+GVbn3/+efysIXc5fvx4/NK8O78vR27HQ0ND72rs3f7ZJTZOlSpV8Pf3B+7vZ3fx4kXq1KnDs88+yy+//EJoaChRUVFcu3aNzZs3M3ToUEqUKMHGjRvvutZiscQ3aS9fvpzohtF3zv5Jl0u4Lh3B+GuMafiyEcDnsW3Jn8WPN58o6cbCRB4Q2YpBpQ6m4Toee6hp3YsB5GvzNhYvX4d5U6ZM0d49IiLpgJo9ImnEvn37uHjxIgB16tSJP3UqtVStWpXdu3fz4Ycfxt+2ZMmSuzbcrVq1anz89OnTPPbYY5w5cyZ+VtCSJUvYsmUL06dPp3z58kDcTIpOnToluL+PPvqI3bt3M3ny5PjbJk+enOC+nn76aZYuXcru3bvjl7sBd9WUO3fuJH+fNpsNb29vmjdvztdff83y5cvZtm0by5cvZ+zYsZQuHffu53fffccHHyTcEyF37tyJ1ny77qS6desWoaGhQNwMF2fujN+e8XGvXnvtNc6ePUvt2rUTzOZKrp9//pnixYuTIUMGAgICKFq0KJ06dWLlypVOr7uz/vv5vpM6jqenZ/zyuHv92YWFhVG3bl3WrVuHt7c3vXr1Yt68eWzbto01a9bw0UcfkSVLFs6fP0/Tpk05ceLEXWMkdSlXbGwsP/zwAwD58uVL8hH2DwzDgN9fx2I3n930aWw7rhPA8Dbl8fP2dGNxIg+Quq+Bp+MmDsAQz58Ag2ivADLX7+owJyYmJn7/NBEReXDp2ZLct+ozqxPj5Al6euVl9WLjC3e/W3+vdu7cGf95pUqVUmzce+Xv70+ZMmXYsmVL/G3FihWjQIECptcMGDCAq1evAnGnetzeFwWgcuXKtGnThqZNm7Jy5UpmzZpF586dadq0KRDXOMmdOzeXLl2Kv6ZgwYKUKVMmwX0EBQUBcRsB3/bfnOTIlSsXf//9N7ly5bor9thjj9G7d2+6du3K1KlT+fLLLxk4cCCZMmUC4vYKKlOmTKI1J0doaGj80qQ8efI4zc2bN2/85/fzLuy6desYP348Xl5efPvtt/e14e2+ffsSfH3kyBGOHDnC9OnTefrpp5k6dWr8z+9Od9Z/P9/37a/9/f3jf1ecjbNr1y4uXrxIVFQUPj4+TvP/6/XXX2f//v1kypSJ5cuXx28efVvt2rV54YUXqFmzJmfPnuXtt9++q6FTrFgxatSowYYNG1iwYAHXrl1zWPfSpUu5cOECAO3bt09/mxIfWgJHlpmGN9uLMdv2KL3rFqJy/uSfEify0AgMgWo9Yf3XDsMVrUdoaN3KMnsVAio+Qdj+P4k6teeuvAkTJvDmm286fGwUEZEHg2b2yH2Lscc8tB8p6c6GQY4cOVJ0bHc4e/Ysc+fOBaBx48YJGj23+fj4MHnyZDw94/rMo0ePdmuNjvj7+zt9MmuxWPjyyy/x8PAgLCyM5cuXu7Semzdvxn+eMWNGp7m3lyFB3IygexEdHU3Pnj0xDIOBAwfGz2RKLj8/P5577jkmTJjAmjVr2L59O0uXLuWtt94iS5YsQNweOy1atHC4N01Kfd+3x0lsjMTGScylS5eYOHEiAP/73//uavTclj9//vhj4n/66SfCw8Pvyrk9uycqKopZs2Y5HCddL+GKiYTfXzcN2wwLQ2O6kDMDDGiYvk7eE3GJ2q+Aj/ns4EGeP2PFDkCWJv0dns4VFRXFsGHDXFaiiIi4npo9ImnEnS9273wR+qBYuXIlNpsNwGGj57YCBQrQsGFDIG4z3dvXpBUxMTGEhoayf/9+9uzZw549ezhz5kx8w+LOGViucOdeRt7ezk8aunMmSkRExD3d36effsq+ffvInz8/Q4cOvacxIG4J3w8//ED37t2pXbs2FSpUoGHDhnz44Yfs3bs3/mSv1atX880339x1fUp937fHSWyMxMZJzJIlS+Lv6/ZG2WYeffRRIO53y9HJX88991x8LY6Wct28eTN+8+pq1arFb1Cdbvw1Gq4eNw3PtD3OfltuJnSrjY+n+eazIvIPv2Co1d80XMJ6iqes6wHwCs5NplrtHOZ9++238cvLRUTkwaNmj0gaERAQEP/57RO5HiR79vw7Dbx69epOc2/Hw8PDOXbsmEvrSoqYmBjGjBlDjRo1yJgxI3nz5qVUqVKULVs2/uP2Epo7Z2C5gq/vv3stREdHO8298/j5/x5TnhQHDx7k448/BuJmWd3P6U7OlkzlyJGD2bNnxzdgbh+ffqeU+r5vj5PYGImNk5g7lzfmypXrrhPY7vy4c1nfuXPn7horc+bMNGvWDIhbUnf8eMLGx+zZs+ObUR06mG+++kC6fhpjzZem4atGRobHPkPdLGGUyePeTcNFHmg1XgS/rKbhgZ4/40XcoQOB1Vvhlb3gXTnh4eEMHz7cZSWKiIhrqdkjkkZkzfrvk7Lz58+nYiX35sqVK/GfJ7YM7c4Tpu68LjVcuXKFmjVr0q9fPzZu3Jhok+BeZ9Ak1Z1Nv8SWFt3ZFEzKsqU7GYZBr169iIqKomXLlvHNBlcpVKhQ/IyuI0eOcOZMwuO1U+r7vj1OUpZl3c/P73bzL7kcLeOCf5dmGYbBd999lyB2e7aPl5cX7do5fgf+gbXsHSwxjn8mAMNi23D54iVG92vhxqJE0gGfAKgzyDScz3qRth5xG+dbrB5kafoyWO5+WTB69OhUf5wWEZF7ow2aRdKI2ydVAWzbti0VK3G92xsQpwUvv/xy/NKap59+mq5du1KuXDmyZ8+Or69v/Ea4+fLl49SpUy6v/c7NiW+fymXmzs2J79y0OCk2bNjA6tWrAahVqxY//vhjgviJEycSzGI6fvx4fE6ZMmXuaRPqUqVKsXDhQiBu2VdISEh8LKW+7zx58rBx40bCwsJMNzv+7zjZsmVL9ubMt5cfent7O1yaZcZs8+mmTZuSLVs2Ll68yIwZM+L3+Tl16lT8v9MTTzwRv5wwXfh7HeyZYxrea8/P99F1aJXnCAEP4NJWkVRXpSv8NQZuOP6b+pLnXGbbHiUSH3xyFiGw6tPc2PRLgpxbt24xcuRI3n//fXdULCIiKUjNHrlvXlav1C4hVaT0912qVCmyZs3KpUuXWLNmDTdu3Liv49et1n/fobPb7aZ5KbVkLDj43xNyzp8/T758+Uxz75y5dOd17nbjxg1++uknAJ5//nlmzpxpmnv7lDFXu72M7NSpUxw4cMBp7p3xkiVLJut+7lzCNHjw4ETz//zzT/78808A3n333Xtq9jhrlJUqVSr+8/v5vkuVKsWcOXPi82rUqOFwjNjYWI4ePepwjKS43XSJjo4mS5Ys931ize1ZO19//TWHDx9m48aNVK9enZkzZ8b/95uuNma2xWIsGoyzM8XejenEzU1zeXu2lpGI3BMvX6j3GvzmeP+e7JZrvOCxnEm2JwHIVPsFwg/9Rey1swnyRo4cmeAkShEReTCo2SP3LSWPH3+YWSwWOnfuzLBhwwgLC2PixIkMHDjwnse7c1mMs0bFwYMHE60rKe588b9x40anzZ5NmzYBcSc4FSx49z4B7nL48OH4k6Gee+4507yDBw86XRaU0sdg165dmx9++IGDBw9y7ty5BMve7nR7xgfAI488kqI1uMKdx7LfOasH4o6sDwkJ4cyZMwm+L0duN51y585NgQIFEsRq164d//nq1atNmz1btmyJb3Tey8/u9obTEHcseqdOnZI9xn917NiRr7+OOy55xowZVK9ePX5JV3BwsMuX2rnV1ilYLuw1Dc+1PcL68z60KpmRbNmyubEwkXSm/POwbiRcPuIw/KLnfL63PUYEvli9fAhu3I8LP72VIOf69euMHj2at956y+EYIiKSNmnPHpE0ZMCAAfGb5A4dOjTRGQ632e32u/b5uLOJcudmsv/1/fffOx37zo1z75wN8l/16tXDwyPupJxJkyaZ5p08eZJly5bFX3P7GPbUEBsbG/+52V4qEHciiTNJ/Rkl1dNPPx3/+dSpUx3mhIeHxx/TXapUKYoVS96R1PXq1cMwDNOPHTt2xJ8ABdCpU6f42HvvvZfcb4ljx47F/7sXKlSI3LlzJ4hbLBZatIjbl+XAgQNs2LDB4TgbNmyI/++iRYsWdzXa6tWrF//u87Rp00xnE935c23ZsmWyv5+mTZvi5RU3u++rr75K8Lt0rypXrkzp0qWBuGPaN2zYwN69cQ2Rtm3bJumEsQdC2GVsKz40Dxs+fBLdhsuLRzLolQHuq0skPfLwhPrmTZqslht08FgW/3WGAuXxL/PYXXlfffVVkvZCExGRtEPNHpE0JHfu3IwePRqIW15Vt27dRGc57Nu3j8aNGzNs2LAEtwcFBVGuXDkApkyZ4nCDxT///DN+JoGZO5en3F724khISEj8i+YlS5YwefLku3Kio6Pp2rVr/Gyafv36Ob1vVytSpEh8s2D69OkOcxYsWODw9Kg7JfVnlFQtW7akcOHCAHzyyScOxxw8eHD8jC2zZVjvvfde/IlQZk2jlDB//nynzY7z58/zzDPPxP+79+3b12HegAED4pt//fv3v2sz7IiICPr3j1uO4OnpyYABA+4aw9vbm5deegmA/fv33/XfBcBff/0V35CsW7cuVatWTeQ7vFvu3Lnp0qULADt37qRXr15OfwYXLlxg4sSJiY57e6nWpUuX6N69+123pwfGHx/gEXXNND4qtiWHN6ykSbVS6e+YeZHUUOppyF7aNNzbcz7+/Pv3NnP9rlh9AxLkXL58mW+++cZVFYqIiAuo2SOSxnTp0oX//e9/QNwLxHr16tG4cWPGjh3LypUr2b59OytWrOCbb76hWbNmlCtXjuXLlzscq0+fPkDci+06derw448/xl//yiuv0KhRI6pUqeK0nooVK8bPXHnnnXdYunQphw4d4siRIxw5ciTBC/KvvvqKzJnjjkfu3r073bp1Y9myZWzdupWZM2dSvXp1VqxYAUCbNm1o2rTp/f2w7lOWLFl44oknAFi0aBFNmjRh7ty5bN26lcWLF9O9e3eefvppChUq5HQpSb58+eI33h02bBjz5s3jwIED8T+jmzdvJqsuLy8vvv76a6xWKzdu3OCRRx5h9OjRbNq0iSVLlvDMM88wduxYIG7ZUmofx92/f3/y58/PSy+9xA8//MBff/3Fjh07WL58OW+//TalS5dm+/bt8fWaNXuKFSvGq6++CsTNRnvkkUf46aef2LJlCz/99BOPPPJI/Cy1wYMHU7RoUYfjDB48OH6m05AhQ+jVqxcrV65kw4YNfPLJJzRq1IjY2FgyZMjAiBEj7vn7/vLLL+OXL06ePJny5cszcuRI1q5dy44dO1i1ahVjxoyhZcuW5M2bN9EZYgDt27eP32/r9qyeYsWKmS5He+Cc3Qlbp5qGj9lzMu5CWa6t+yH+d0FE7pPVCvXfMA0HW27RyWNp/NcefpkIqnv30tRhw4Y5nQUrIiJpjCHp3qlTpwzAAIycOXMm69pDhw4Z+/btMw4dOuSi6sTMnDlzjAIFCsT/2zn7KF26tLFkyZK7xrDZbMbTTz9tel2ZMmWMM2fOxH/97rvvOqxlyJAhpmOsXLkyQe62bduMkJAQp/W2atXKiIiIcHhfK1euNB37Tu+++2583v04efKkkS9fPtNa8+XLZ+zdu9fInz+/ARidOnVyOM7YsWNNx5gyZco91TZ+/HjD29vbdNxq1aoZFy9eNL3+zp9RcmvYsWOHMW/evPjrzb5vwzDifzaJfbRu3dq4evWq0/u12WxG165dnY7TrVs3w2azOR3n8OHDRtGiRU3HCAwMNObPn5+sn4kjly9fNpo0aZKk779+/fpJGrNhw4YJrvvggw/uu07DSAN/z+12I3r844bxbqDpR6fX/2d4h5Qwqlatatjt9tSpU5Itd+7cBmDkzp07tUsRM3a7YXxT2/S/vatDcxmlX5tl5H9tQfyHd0iJu/6OjRgxIrW/ExFJBfo7nzx3vv4+depUqtWhmT0iaVSrVq04ePAgM2fOpH379hQvXpzMmTPj6elJcHAwlSpVok+fPqxYsYLdu3fTqFGju8awWq3Mnj2bMWPGULVqVfz9/fH396dcuXJ89NFHbNy4MUmnCH366adMmDCBOnXqEBwcHL83jyMVK1bk4MGDfPLJJ1SvXp2goCC8vb0JCQmhVatW/Pbbb8yZMyfBPjepKW/evGzbti1+NoiPjw+ZMmWifPnyvPvuu+zYsSPBSVFmXnzxRebMmUOjRo3Inj17iuxF1KNHD7Zu3UqPHj0oVKgQvr6+ZMmShdq1a/PNN9+wbt06smbNet/3c7+mTZvG+++/T5MmTShWrBjBwcF4enoSFBRE2bJl6dWrF+vXr2f27NlOj0KHuN/ZSZMmsXDhQlq0aEFISEj870+LFi1YtGgREydOTHDanCNFihRh+/btfPbZZ1SpUoWgoCD8/PwoXrw4r7zyCrt27UqRDY+Dg4NZvHgxK1asoEuXLhQtWpSMGTPG/3datWpV+vbty6JFi+L3LErMnZs9WywW2rdvf991pgm7ZuF1epNpeLmtIr9tOU30mQO8+uqrKb7xuchDzWKB+m+ahoMsYXT2WJLgtiyN+oAl4d/azz//nMjISJeUKCIiKctiGE7OwpV0ITQ0lLx58wKQM2dOzp49m8gV/zp8+DCxsbF4enqaLpkQkfRn586dxMTE4OXlRfny5VO7HEkBqfr3POomUSMq4RNxwXHY8OSxK2+wfvxQ8ofk4PDhw6m6ebskT548eTh9+jS5c+cmNDQ0tcsRM4YBE+rDme0Ow9cNP+pEjeQG/vG3XVkxgZtb5iXIGzt2LC+++KJLSxWRtEV/55Pnztffp06dit/uwd00s0dERERcKmbl56aNHoCJtifY9vtsjOiIBBt1i0gKslignvnsnkyWcLp6Lk5wW1DtF/AIyJLgti+++CJFTiAUERHXUrNHREREXOfSEawbx5qGzxrBDNufk4gjGwkKCqJr165uLE7kIVO0IeQ2P5ihq8diMvHvEetWHz8yN+iRIOf48ePMmjXLZSWKiEjKULNHREREXObmb0PwMMxnAXwU0ZpTS6cA0KtXLwICAkxzReQ+JbJ3T6Algm6eixLc5l+iNr4FKyW47dNPP0U7QYiIpG1q9oiIiIhL2A8tJ+DkCtP4RnsJpi3fj+3WZby8vOjfv78bqxN5SBVuAHlrmIY7eywhkLAEtwU/3hs8/l1euXv3bhYvXvzfS0VEJA1Rs0dERERSni2Wm78NMQ8bFt48/Si3dvwOQLt27cidO7e7qhN5eFksUP8N03CgJeKuk7m8gkMIrNwiwW2ffvqpS8oTEZGUoWaPiIiIpLjwv8aT6dZR0/j3sfVZ89vPYNgBGDRokLtKE5GCdSFfLdNwV8/FZCQ8wW2ZarXFI2Nw/Ndr1qxh3bp1LitRRETuj5o9IiIikrLCr8DKT0zDNww/3tvkS8ylEwA0bNiQcuXKuas6EbFYoK75zLsgSxgdPZYluM3q40dQ3c4JbtPsHhGRtEvNHhEREUlRFxf8Dz/bDdP48FuNOL56bvzXAwcOdEdZInKnQvUgT1XTcDfPRfgRmeC2jGUa4B1SIv7rBQsWsHv3bldVKCIi90HNHhEREUkxtvMHCN43zTR+1J6LrxYdxIiJAqBkyZI0btzYXeWJyG0WC9R9zTScxXKTFzyW33V78OO9AEv815999pkrqhMRkfukZo+IiIikmHM/D8QDu2l8aGh1bhzYGP/1gAEDsFgspvki4kJFHoeQiqbhnp4L8CUqwW0+uYqSsdzj8V//+OOPHD9+3GUliojIvVGzR0RERFLEjV0LyX3JfMPWVbFlmDV3afzXwcHBtG/f3h2liYgjFgs8ar53TzbLDdp5/HHX7UGPdsLi4w+AzWbjyy+/dFmJIiJyb9TsERERkftniyFyweum4VjDymtbshB77Vz8bb1798bPz88d1YmImeJNIUdZ03Bvz/n4EJ3gNg//IIIeaRf/9aRJkzh//rzLShQRkeRTs0ec8vDwACA2NhabzZbK1YiIyL2w2+3xf8Nv/11PaaeWfE326JOm8enhtdi8Ykn8156envTt29cltYhIMlgsUHewaTiH5RrPeqy+6/aAys3xDM4DQGRkJF9//bXLShQRkeRTs0ecuvMd12vXrqVeISIics9u3bqFYRgAZMiQIcXHj715kaDN5ss4rhn+DF14DiP239kBbdu2JSQkJMVrEZF7UKI5ZCtpGn7R8ze8iE1wm8XqQeZ6XeK/HjNmDDdumJ/CJyIi7qVmjzgVFBQU//mFCxe4cOECkZGR8S8aREQk7bLb7dy4cYNz5/5dOhUQEJDi93P4pzcJMMJM45+drcLp/dsS3PbKK6+keB0ico+sVnj0VdNwbstlWnjcvR+XX9Hq+OYvD8D169cZN26cy0oUEZHk8UztAiRt8/X1JVOmTFy/fh2Ay5cvc/nyZSwWi8uWAohI6ru95Mdms3H48OFUrkbulc1mS9Ccz5AhA/7+/il6H5ePbafYqZ/vPIk5gcO2XHz184YEt9WuXZvKlSunaB0icp9Kt4RVn8Jlx3/ze3vMZ46tDsZ/3ivOXL8bZ6cNAMPO8OHD6d+/P76+vm4oWEREnFGzRxKVK1cuvL29uXjxYvxthmEQGxvr5CoReZBdvnwZm82Gh4eHS5b9iPtlyJCBfPnypewx54bB5TmvksViPtvztR0hRF07mOA2zeoRSYOsHlBnEPza22G4iPUMjaxbWGKvluB27xyF8C/TgLDdyzl37hzTpk2jV69e7qhYREScULNHEmWxWMiaNSuBgYHcunWLsLAwoqOjsdvtqV2aiLjIsWPHiIqKwsfHhwIFCqR2OXKPbjfrAgIC8Pf3T9lGD3B03WyKhW0xja+ILMHcxWsS3FagQAFatGiRonWISAop+wys/BiuO95svY/nbyyJrsp/p/IF1elA+IG1GDGRfPnll3Tv3l0zwEVEUpmaPZJk3t7eBAcHExwcnNqliIiL1a9fn9OnT5M7d269QysO2WOi8F051DQeY3jwyuIwsCWcBfrSSy/pRaBIWuXhBbX6w2LHp3OVtx7jEese1tkTHtXuGZCFwGqtuL7uew4fPsz8+fN5+umn3VCwiIiY0QbNIiIikmx75n5BbtsZ0/ikS2XYvWtvgtsCAgLo1q2bq0sTkftRsT34ZTUN9/H4zeHtgdVb4ZExCwBffPGFS0oTEZGkU7NHREREkuXW1XMU2jfGNH7FnpG3f95/1+3dunUjMDDQlaWJyP3y9oMaL5qGH/HYS3nLkbtut3r5EvRoBwDWr1/P+vXrXVaiiIgkTs0eERERSZYjP7xORsJN4x8dKsDli5cS3GaxWOjfv7+rSxORlFC1O3gHmIb7eDqe3eNfpgHeOQoDmt0jIpLa1OwRERGRJDt7cAtlz/9qGj8Qm4uv52696/ann36aQoUKubAyEUkxGYKgqvmSy8YeWyhiCb3rdovFSlC9zgDMmzePQ4cOuahAERFJjJo9IiIikjSGwfW5g/BwctT64HV+xEZH3XX7gAEDXFiYiKS4mn3B09c0/KLnfIe3ZyhQEd8CFTAMg+HDh7uqOhERSYSaPSIiIpIk+1b+QInIHabxJWFFWbBq+123V6pUiTp16riwMhFJcRmzx23WbKKFdR15LBcdxoLqdgYsTJ06lQsXLrimPhERcUrNHhEREUlUbFQEmda+bxqPNjwY+Nslh7FXXnkFi8XiqtJExFVqvQQWD4chT4udHh4LHMZ8chbBr2QdoqKiGDPGfDN3ERFxHTV7REREJFE7Zn9Gbvs50/j4c8XZd+jEXbfnypWLNm3auLI0EXGVzPmh7DOm4baeq8jKdYexoDodwOrJmDFjCA8339BdRERcQ80eERERcerahVBKHP7WNH7JHsA7s+4+ah2gb9++eHt7u6o0EXG12q+YhnyJoYvnYocxr8y5CKjQhMuXLzNlyhRXVSciIibU7BERERGnDv/0BhmJMI1/sDeEa9fufnff19eXXr16ubI0EXG17CWh+BOm4U6eywnA8cydTLXaYvHOwPDhw7HZbK6qUEREHFCzxw0uXLjAggULGDp0KE2bNiVr1qxYLBYsFgudO3dO7fJERERMHd/zF5UvOT51B2BfTC5Gz7t7U2aADh06kDVrVleVJiLuUnugaSgj4bT3WO4w5uGfmcCqT3Ps2DHmzp3rqupERMQBz9Qu4GGQI0eO1C5BREQk2Qy7nYj5r2F1ctT6wFUe2G2xDmM6bl0knchbFQrUgb/XOAz39F7M5IgmRHH3ks3Aqi25uX0xX3zxBa1bt9Zm7SIibqKZPW6WN29eGjVqlNpliIiIJGrPHz9QKmqnaXxJWDGWrN/nMNaoUSNKlSrlqtJExN2c7N2T2bjO0x7rHMasPn5kqtWWTZs2sWaN42aRiIikPDV73GDo0KHMnz+fc+fOcfLkScaNG5faJYmIiDgVExVB8Lr/mcajDQ9ennPaNK5ZPSLpTOEGkKu8abi/7+9YsDuMBVRogkdgdoYNG+aq6kRE5D/U7HGD999/n2bNmmk5l4iIPDB2zfmM3Ib5Uevf36jIweNnHcaKFy9O48aNXVWaiKQGi8Xp7J48tlM0sDrev8vi4UXQI+2YP38++/c7PrlPRERSlpo9IiIiksDNy6cpfsj8qPXLRiCvTN5oGn/55ZexWvUUQyTdKfkUBOU3DQ8JWGYa8y/TAM/g3Hz55ZeuqExERP5Dz8REREQkgSM/Oj9qfUZYLa7dCHMYCwoKomPHjq4qTURSk9UDavYzDReP2kUF6xGHMYvVg6BHnue7777jwoULrqpQRET+oWZPOhAaGur04+xZx9PsRURE/uvcwU2Uv/CbafyIJT9vfrPANN6zZ0/8/f1dUZqIpAUVX4AMmU3DQ7P8YRrzL1UXe2AuvvnmG1dUJiIid9DR6+lA3rx5U7sEERFJDwyDG78OJqeTo9bnGQ2ICN/tMObh4UHfvn1dVZ2IpAXe/lC1O/z5hcNwxVt/kt/SghOG470qg+q0Z8yYMbz22mv4+vq6slIRkYeaZvaIiIgIAEfW/ESxiB2m8c0+Nfly/CzTeOvWrcmXL58LKhORNKVaT/DwcRiyGHY+ymV+xLpf0Rpc98zM999/76rqREQENXvShVOnTjn92LRpU2qXKCIiaZwRE4n/6vdM49GGB9szP+F0abCOWxd5SGTMDuWfMw3XurGYbB63TONBddozfPhwDMN8FqGIiNwfNXvSgTx58jj9yJUrV2qXKCIiadz+X78gl828kbMu67NMmDTDNF6tWjVq1KjhitJEJC2q1d80ZI2N4NN8m03jGQpW4sgNC8uWmZ/eJSIi90fNHhERkYdc5NWz5N87xjR+2QgksmhLtmzZYpozYMAALBaLK8oTkbQoa1Eo/oRpuN71uQR4xprGg+p04Mvhw11RmYiIoGaPiIjIQ+/YrDfwd3LU+rYifZk8aZJpPCQkhGeeecYVpYlIWlbrJdOQR/glPiuyzzTum7c0fx66yJ49e1xRmYjIQ0/NHhERkYfYtWNbKHHmV9P4YfKTp3prfvnlF9Ocfv364eXl5YLqRCRNy1cDclcxDTe+MRt/b/MZf0GPPM9XI0a4oDAREVGzR0RE5GFlGFz95VWsTo5aP119KJMnjMdutzuM+/r60rNnT1dVKCJpmcXidO8ejytH+KjUadO4T+4S/LxmDxcuXHBFdSIiDzU1e0RERB5S5zfPoeCt7abx9V41qPhIYyZMmGCa06FDB7JkyeKK8kTkQVCyOWQuYBpudms2Gb3NX3L4V3uWsWO/cUFhIiIPNzV7REREHkaxUViWvWMajjY88HniI76bMZ3r16+b5r388suuqE5EHhRWD6jZzzTsGbqBoRXN9wTzyV2Cb+etJjIy0hXViYg8tDxTu4CHwdq1azly5Ej815cuXYr//MiRI0ydOjVBfufOnd1UmYiIPKxCl3xFnpgzpvE/MrWiUflKtGv7vGlOw4YNKV26tCvKE5EHSYUXYOXHEHHFYbhl5C+87/U8YTGOl4xayjzJd9/NpHv3bq6sUkTkoZJqzZ5Lly5x+vRpLl68yOXLl8mQIQPZsmUjW7ZsFCpUCKs1/Uw6mjhxItOmTXMYW7duHevWrUtwm5o9IiLiSsbN8wRvGWkav2QEUvSZ91m8eDGHDx82zRswYIALqhORB463H1TtDn9+7jDsdXABg6t347214Q7jPrlL8MV3E+nWrSsWi/mGziIiknRua/bcvHmTefPmsWrVKtasWZNgpst/+fv7U6NGDerUqcOTTz5JpUqV3FWmiIhIund67tvkMRy/6AL4M3dPWuXLTe9unU1zihUrRpMmTVxQnYg8kKr1hHUjwRblIGjQjsV85vEoETbHzZwbeWuzZMkS/V0REUkhLp8+s3XrVjp27EjOnDnp1KkTU6ZM4fDhwxiGYfpx69YtVqxYwXvvvUfVqlUpVaoUY8aMISwszNXlusTUqVOdfr///RAREXGV2NM7CTn2s2n8gJGPR9oMZO/evSxfvtw07+WXX05Xs3BF5D5lzAYV2pmGfXZ9T//auczjuUvw4cQ5rqhMROSh5LKZPVu3buXtt99m6dKlAPFNjFy5clG1alUqV65M9uzZCQ4OJnPmzERERHDlyhWuXr3KoUOH2Lx5M7t27SImJoYDBw7w0ksv8d577zF48GBefvllfHx8XFW6iIhI+mQYXJ4ziByYv7Gws/RrtA3y550h5su8goKC6NixoysqFJEHWY2+sHWq41j0TbpmXM9IS36iDA+HKUczlGD37t2ULVvWdTWKiDwkXNLs6dKlCzNmzMButwNQqVIlXnjhBVq3bk2+fPmSPE50dDR//vkn33//PXPnzuXy5cu88cYbfPPNN8yYMYPatWu7onwREZF0KWrPfHJc2WwaX0lVmj7VlkuXLjFjxgzTvB49epAxY0ZXlCgiD7JsxaBIQziyzGHYd+sEetSeweg1oQ7jPrlLMHTsD8z9Rs0eEZH75ZL519OmTcPT05MePXpw4MABtmzZwiuvvJKsRg+At7c3jz/+OJMnT+bcuXNMnz6d4sWLc+LECf744w9XlC4iIpI+xUQSufAN03C04cGVWm8T6OvF+PHjTY9B9vDwoF8/82OWReQhV+NF89i1E/TJcwwvI8Y0ZWNYFs6dO+eCwkREHi4uafa8+OKLHDlyhHHjxlGsWLEUGdPHx4f27duzd+9efvzxR4oWLZoi44qIiDwMwv78mkyRjt9NB5jj1ZzmDR4lOjqaMWPGmOa1atUq2W/eiMhDpHADyFbSNOy3dRwdquU2jXuHlODdMTNdUZmIyEPFJc2eMWPGkCdPHlcMjcVioU2bNrRrZ74BnIiIiNzhxhm81g03DV8yAsnU5E28Pa3Mnj2bM2fOmObquHURccpicT675+RfDKxk4OHw1K44i07YiIiIcEFxIiIPDx2jISIiks7dWvg23nbzF04/ZexIk0rFMAyDr776yjSvatWq1KxZ0xUlikh6Uq4N+GUxDWfcNoFWZTKbxj1CSvPZpFmuqExE5KHhsmbPSy+9xJYtW1w1vIiIiCTFyY1kPGh+nPE+e34qPv0SVquFv/76y+lj94ABA7BYLK6oUkTSE68MUKWreXzvL7zdpBCWWPPZPTO2XIg/zVdERJLPZc2e0aNHU716dUqVKsWnn37KqVOnXHVXIiIi4ojdRvhvg5ym/JrrJWoVzQHAiBEjTPNCQkJ45plnUrI6EUnPqnYHq5fjmD2WTLunUi+P4yPYAWw5SzFh9u8uKk5EJP1z6TIuwzA4ePAgb731FgULFuSxxx5j+vTphIWFufJuRUREBDC2f4ffpd2m8fm2GrRq2QaAEydOMGeO+QygPn364O3tneI1ikg6FZATyjppEG+dwqftH4HYaNOUUSsOuaAwEZGHg8uaPcuXL6dz585kzJgRwzCw2+2sWrWKLl26kDNnTjp27MiyZcs0PVNERMQVIq4Rs/Q987DhzY6SgyiRMxCIO1zBbrc7zPX19aVnz56uqFJE0jNnGzVHXCXH379RITDcNOVGpkIs3bDLBYWJiKR/Lmv2NGjQgMmTJ3P+/Hm+//57mjZtioeHB4ZhEBYWxsyZM2nSpAl58+bltddeY8+ePa4qRURE5KFjX/Up3lFXTOPj7S3o9kQdAG7dusWECRNMc9u3b0+2bNlSvEYRSedylYf8tc3jG77hi64NMWyxDsMWi5X3Z/3louJERNI3l5/G5evry3PPPcfChQsJDQ1l+PDhVKpUCcMwMAyDM2fOMGzYMMqXL0+lSpUYOXIkFy5ccHVZIiIi6dfFg7DJvHlzyp6NmBr9CAnKAMD06dO5du2aaf7LL7+c0hWKyMOiZh/z2KVDFI3YRUHOm6aEeuZi17GzLihMRCR9c+vR69mzZ2fAgAFs2bKFvXv38tprr5E3b974xs/OnTsZOHAgefLkoVmzZsyaNYuoKPNd+kVEROQ/DAPb4tewGo7fKQf4yqMTPRqUBsButzNy5EjT3Mcff5wyZcqkeJki8pAo1gQyFzSPbxjDhy88imG3OQxbrB68Pm2Fi4oTEUm/3NrsuVPJkiX55JNPOHHiBH/88QddunQhICAAwzCIjY1l8eLFtGvXjpw5c9KrVy/Wrl2bWqWKiIg8OA4uwuPYStPwWltpStV/nkwZ4k7J+f333zl0yHwT1AEDBqR0hSLyMLF6QPXe5vGjf1A7xCD45jHTlL3hAYReueWC4kRE0q9Ua/bcqV69ekyaNIlz587dtb/P9evXmTBhAvXq1UvtMkVERNK2mEhsi980DccaVr7N0JMOtQrE3+bsuPWiRYvStGnTFCxQRB5KFV8An0Dz+IaxDGlWHsNwvEm8xcOTt6Ytd1FxIiLpU5po9tx25/4+27dvp3Tp0lgsFgCd2iUiIpKYv0bjcf1v0/AMW0OeadoQH08PAHbt2sWyZctM819++WWs1jT1VEFEHkQ+AVCpo3l85088V68s3uf2mqb8edrO9YgYFxQnIpI+palncFFRUfz888889dRTVKpUiX379qV2SSIiIg+G66ex/znMNHzFyMjvWbvwVPmQ+Nu++uor0/xMmTLRqVOnFC1RRB5i1XuBxeSlhy0Ky7bpdK2Zx/Ryw9OHz+asd1FxIiLpT5po9vz555/06NGDnDlzxs/siYmJwTAMMmXKRI8ePVizZk1qlykiIpJ2LX8Xa2yEaXhYbFv6PVkFqzVuxuzZs2eZOXOmaX6PHj3ImDFjipcpIg+poHxQsrl5fPMkXun4NLbTe0xTft55kcgYxxs5i4hIQp6pdccHDx5kxowZzJw5k5MnTwL/LtXy9PSkUaNGdOrUiaeeegofH5/UKlNERCTtO7kBdv9sGt5rz09owWeoUzRb/G1jxowhJsbxkggPDw9eeumlFC9TRB5yNfrAvnmOYzdC8f17BU0LeLLUZLVWjEcGJv+xlz6Ny7muRhGRdMKtzZ5Lly7xww8/MGPGDLZu3Qok3IunfPnydOrUieeff57s2bO7szQREZEHk90Gi4c4TXk/thPvPlE6/uvw8HC++eYb0/w2bdqQN2/eFCtRRASAvNUhZzk4t8txfOM43u8ziQVvzMI7ZxGHKd+sPESvhmXx+GeWooiIOObyZk9UVBTz5s1jxowZLF26lNjYWODfJk+uXLl4/vnn6dSpE2XKlHF1OSIiIunL9hlwdqdpeJ6tFrnLP0bpkEzxt02bNo0rV66YXjNw4MAULVFEBACLJe4Y9nl9HMdPrCOX5RLlvc+zH8fNnptkYP72kzxdOb8LCxURefC5rNmzevVqZsyYwezZs7l58ybwb4MnQ4YMtGjRgo4dO9KoUSOd9CEiInIvwq9gLH8fs/e3ww0fhtlf4IdGxeJvs9vtTjdmfvTRR6lSpUoKFyoi8o8yrWHZOxB+2XF84zje79mZluO34RWc22HKZ79tp0WlfPGn9oqIyN1c1uypX78+FoslvsFjsVioU6cOHTt2pE2bNgQEBLjqrkVERB4OK/6HJcJ8hs6Y2BY0qVWJPJn94m9buHAhhw8fNr1m0KBBKVqiiEgCXr5QuTOs+dJxfPfPVGv4P7JfmcpVk2bP2Sgv1h25RO079iETEZGEXDqlxjAMChcuzPvvv8/Ro0dZvXo13bp1U6NHRETkfp3eirF1qmn4pD0bP3k9Rd/6CZdCfPmlyQssoGjRojRr1iylKhQRcaxKN7B4OI7FRsK2abzWtj62W1dNh/jk1y0uKk5EJH1wWbOnZ8+erF27lsOHD/POO+9QoEABV92ViIjIw8Vug4WDsGCYpnwY254e9UsR5Ocdf9vWrVtZvXq16TWvvPKKllaLiOtlyg2lnjKPb5rIMy2exOOo+d+rvZft7Dl93QXFiYikDy57Rvftt99Sq1YtVw0vIiLy8No2Hc5sNw3/YavAnoy16VSrQILbhw8fbnpNcHAwHTt2TKkKRUScq9bLPHYjFM8jS+hRvyT2qHDTtGELzTenFxF52KXa23d2u50//viDjz76iP79+9OtWzfOnj2bICc6Oprw8HCioqJSqUoREZE0JuwyrHjfNBxlePFebCcGNi6Br9e/yyROnTrFrFmzTK/r3bs3/v7+KVqqiIipfDXijmE3s3E8fXp0IXLvctOUVUdvcOJymAuKExF58KVKs2fhwoUULVqUhg0bMnToUMaOHcvUqVO5ejXhutxJkyYREBBA9uzZCQvTH3IRERFWvA8R5vtYfGtrjl+OIrSsmHBj09GjRxMbG+vwGi8vL/r165eiZYqIOHX7GHYzJ9aSOSqU5sX8MWJjTMcYvfyAa+oTEXnAub3ZM3HiRJ566imOHz+OYRhkyZIl/sSu/+rWrRtBQUHcunWLuXPnurlSERGRNCZ0S9wSLhOn7NkYG/sUrzctgYf13yOJb968ybhx40yve/7558mVK1eKlioikqgyrcEvi3l84ziG9O/Frb1/mKb8sv0MF29qFYCIyH+5tdlz5MgR+vbtC0CDBg3Yt28fFy5cMM339vamdevWGIbB0qVL3VWmiIhI2vPPpsw42ZT5vdiOVC6ci7rFEh5HPGXKFK5fN9/IdODAgSlVpYhI0t0+ht3M7p8pnjcbFX0vYxh2hyk2rExee8w19YmIPMDc2uwZMWIEMTExlC5dmkWLFlGiRIlEr6lTpw4AO3bscHF1IiIiadjWKXB2h2l4ma0SK+yVeaNpSSyWf2f12Gw2RowYYXrd448/TrlyTvbNEBFxpSQcwz7kxU5EHNpgOsTUtUe5FeV4maqIyMPKrc2eFStWYLFYGDBgAN7e3olfABQuXBiAkydPurI0ERGRtCvsEqz4n2k40vDi/diOPFU+hLJ5MiWI/frrrxw/ftz0Ws3qEZFUldgx7Jsn0fCx+mQ+v8U0JcJmYfaWUy4oTkTkweXWZs+pU3F/hCtUqJDka26fDBIebn7sooiISLq2/F2INF+GNTa2BeetORjcuHiC2w3DYNiwYabXlSpViiZNmqRYmSIi98TZMezXT2E5uJiBnVoReXK3adq3Kw9is5svcxURedi4tdlze1q52YbMjly8eBGAwMBAl9QkIiKSpp3aBNu/Mw2fsGdnnK0Z7WvkJ2+wX4LYunXr2LDBfOnDK6+8kmDJl4hIqkj0GPZxdOjQAfveJaYp527ZWLbvnAuKExF5MLm12RMSEgLAoUOHknzN6tWrAShQoIArShIREUm7bDGwwPkyq/diO+Ht40f/BkXvin3++eem12XLlo327dvfd4kiIvctCcew+908TtemNYi+ZL61wzd/JP01hohIeufWZs+jjz6KYRh8//33Scq/dOkS48aNw2Kx0KBBAxdXJyIiksZs+AbOmy9bWGqrzEp7RXrXK0ywf8K98Pbt28f8+fNNr+3Xrx++vr4pVqqIyH1J7Bj2zRPp27cPYVt/M03ZeeYW209edUFxIiIPHrc2e3r27AnAokWLmDJlitPc0NBQnnjiCS5duoSHh0f8tSIiIg+Fqydg1Sem4UjDi//FdiRnoC9dHyl4V9zZXj1+fn707ds3RcoUEUkRiR3DvvMn8mYLpHGJYGzh5nuYTfjzaMrXJiLyAHJrs6dq1ar07t0bwzDo3r07zz77LLNmzYqP79q1i59++olu3bpRvHhxtm7disViYdCgQRQpUsSdpYqIiKQew4BFr0KM+eEEo2OfJtTIxsCGxcjgnfDY4tOnT/Pdd+b7/HTv3p0sWZy8gy4ikhoqdwGLycuTmDDY+SOvvNSPm9sXmg7x+55zhF7VwS4iIp7uvsNRo0YRFhbGjBkz+OWXX/jll1/iN4d84YUX4vNub+LcuXNnPv74Y3eXKSIiknr2zYPDS03DR+whjLc1o1iOjLSunOeu+MiRI4mJiXF4rYeHB6+88kqKlSoikmKC8kLxJ+DAAsfxzROp0WcjRYy3uBgbjcXT+64UOxamrv+bt58s5eJiRUTSNrfO7IG4J5nTpk3j559/pmLFihiG4fCjVKlSfP/990yePFknhYiIyMMj8josfs1pypsx3YjGizealsTDmvAx8vr163z77bem17Zt21aHHohI2lW1u3ns0iEsf//JoL49uLV3pWnazL+OczPSccNbRORh4faZPbe1bt2a1q1bc+bMGbZs2cKFCxew2WxkyZKFihUrUrhw4dQqTUREJPWs+ABumR8f/FNsPTYZJalRKJh6xbPdFR83bhw3b940vX7w4MEpUqaIiEsUqgdZisLlw47jmybwzDNTefWDL6F8Y4cpEbEwa0so3WrfvZ+ZiMjDItWaPbeFhITw1FNPpXYZIiIiqS90K2yeaBq+bATwSWw7AN5oWvKuma9RUVGMGDHC9PpGjRpRoUKFlKhURMQ1LJa42T2/m8xwPLgIr/Dz9G3fipE7tpKhUGWHaRNWH6ZTzfx4erh9IYOISJqgv34iIiJpgS0W5r8MGKYpH8a05xoBNC8fQvm8QXfFZ86cydmzZ02vHzJkSAoUKiLiYhXagZe/45hhhy1T6NmzJ5E7F5kOce5mDEv3nXdRgSIiaZ+aPSIiImnBxm/g/G7T8Dpbaebaa+PlYWFwo+J3xe12O1988YXp9ZUqVaJBgwYpUqqIiEv5ZoJybczj26aRNSiAZx8tS/TFE6Zp41cfcUFxIiIPBpc0e37++WdXDBsvNDSU9evXu/Q+RERE3ObaSVhpfvJklOHF27FdAQvta+QnXxa/u3IWLFjAgQMHTMcYMmSIDjwQkQeHs42awy7Cvt8Y8PLL3Nj8q2najtAbbD1xNeVrExF5ALik2dO2bVvKli2b4k2fkydP8uKLL1KkSBGWL1+eomOLiIikCsOAha9CTLhpyujYFhw3chHg40n/BkUd5nz++eem1xcsWJDWrVvfd6kiIm6Tswzkq2ke3zyBMmXKUC2HBVuYeUNn0ppjLihORCTtc0mzp2jRouzdu5fnnnuOAgUK8Oabb7J37957GissLIzvvvuOpk2bUqRIEcaPH4/NZqNIkSIpXLWIiEgq2DcPDi8xDR+xhzDO1hyAF+sXJtjf+66cdevWsW7dOtMxBg0ahKdnqp/JICKSPM5m95zaCGd3MfDl/tzcttA0bfGec5y6Yt5MFxFJr1zyzG/v3r18/fXXfPrpp5w8eZLPPvuMzz77jKJFi1KjRg2qVq1KxYoVyZ49O5kzZyZz5sxERERw5coVrl69yqFDh9i8eTObNm1i06ZNREZGYhhxG1Y2bdqUzz77jDJlyriidBEREfeJuAqLnW+a/GZMN6LxImegL10fcXyMsLO9erJkyUKXLl3uq0wRkVRR8inwzw5hFxzHN0/giWYjyfra28TERmPxvLsZbgBT1v3N0OalXFuriEga45Jmj6enJwMHDqRXr16MHTuWMWPGcPLkSQ4dOsThw4eZMWNGksa53eDx8PCgRYsWDB48mOrVq7uiZBEREfdb8jbcMj8t5qfYemwySgIwsFExfL087srZv38/8+bNMx2jf//++PndvcePiEia5+kNlTvDnybLVHf9jLXh/3ipV1feX/QHARWaOEz7YePfDGhYlEBfL9fVKiKSxrj0NC5/f38GDx7MsWPHWLx4MV26dCF//vwYhpHoh6+vL3Xr1uXzzz/nxIkTzJ49W40eERFJP47+ATu+Mw1fNgL4JLYdAMVzBNC6Uh6HecOGDTMdI0OGDPTt2/f+6hQRSU1VuoDl7kY3ALERsOMHunTpgv3ACtMhImINZm8JdVGBIiJpk1sW8FutVho3bkzjxo0BOH36NOvXryc0NJSLFy9y5coVfH19yZYtG9myZaNs2bJUqVIFLy9130VEJB2KugW/vew05cOY9lwjAIDXnyiBh/Xuk7ROnTrldLZst27dyJo16/3VKiKSmgJDoMSTsP83x/HNEwmo3pvOLRvz/dEtZChcxWHalLXH6FyrAFYHf0tFRNKjVNmtMXfu3Dz77LOpcdciIiKp748P4PpJ0/AqW3nm2msDULNQFuoVy+Ywb9iwYcTExDiMWa1WBg4ceP+1ioiktmo9zJs9V47CsZX079+fcY89Y9rsOXUtklWHLtCgRA4XFioikna4dBmXiIiI/MfJDbBxnGn4luHLWzFdgbh3n994ogQWy93vRF+4cIEJEyaYjtOmTRsKFnS8obOIyAOlQB3IWtw8vnkihQoVomH5fERfMm+kT1573AXFiYikTWr2iIiIuEtMJPzWn7jzYRz7LPY5ThM3k+ep8iGUyxPkMG/kyJFERESYjvPGG2/cT6UiImmHxeL8GPZDv8O1kwx4+WVubp1vmrb2yGWOXLjpggJFRNIeNXtERETc5c8v4NIh0/Ame3G+sz0OgJeHhcGNHb+Tfe3aNUaPHm06TrNmzShXrtz91SoikpaUfw68MzqOGXbYMpl69epR0HIBe+Qt02GmrT/hogJFRNIWNXtERETc4cx2WPuVaTjK8OL1mB4Y/zw0d6hRgLzBjo9MHzt2LDdu3DAd680337y/WkVE0hrfQCjX1jy+bTqW2CgG9OvDzV1LTdN+3nKS6xGO9zoTEUlP1OwRERFxtdgomPsiGDbTlBGxrTlmhAAQ6OvJS48VcZgXFhbGV1+ZN43q169PzZo1769eEZG0qFoP81j4Zdj3K88//zxex9djGHaHaZGxBj9vOeWiAkVE0g41e0RERFxt1Sdwcb9peLe9AONtT8Z//dJjRQny83aYO3HiRC5dumQ6lmb1iEi6lb0k5K9tHt80AV9fX3q1f4aIwxtN06asPYrNbr53mohIeqBmj4iIiCuFboF1I03DsYaV12J6YsMDgPxZ/OhYs4DD3OjoaL744gvTsapWrcpjjz12X+WKiKRp1Zxs1Hx6C5zZzosvvkjYjkXmadejWXnggguKExFJO9TsERERcZWYCJjbO27zUBNjbE+zzygQ//XrTUrg7en44Xn69OmcPn3adKy33nrL4THtIiLpRolmEJDLPL5pIiEhIbSoWZLoi+abMU9ee8wFxYmIpB1q9oiIiLjKHx/C5cOm4b32/IyOfTr+66oFMtOkTE6HubGxsXz22WemY5UuXZrmzZvfc6kiIg8EDy+o3Nk8vmcORFz95xj230zT1h+7wuHzOoZdRNKvNNHsWbRoEdeuXUvtMkRERFLOib/grzGm4WjDg0ExLxKDZ/xtbz1ZynRmzk8//cSRI0dMx3vjjTewWtPEw7qIiGtV6gRWT8ex2AjY+SPVqlWjdMYIbBHmDZ2p6/92TX0iImlAmnhW2Lx5c0aNGpXaZYiIiKSM6DCY1wcw3wB0ZGxrDhj54r9uUSGECnmDHObabDY++OAD07EKFSpE27ZOjiQWEUlPAnPFLecys2UyGAYDX+rLrcSOYQ/XMewikj6liWaPYRgYRsInxL/++iuffvppKlUkIiJyH5a+A1fM94PYaS/Et7Z/l1x5e1oZ3Li4af6sWbM4ePCgaXzIkCF4epq8yy0ikh5V7WYeu3QI/l5Ly5YtyXh2G4bd5jAt2gazdAy7iKRTaaLZ48jOnTt56623UrsMERGR5Dm0BLZMMg1HGV4Miukdf/oWQLfaBcmT2c9hvt1udzqrJ0+ePHTu3PmeyxUReSAVqANZiprHt0zGy8uLvp2fc3oM+6Q1R3QMu4ikS2m22SMiIvLAuXUR5vV1mvJl7DMcMfLEf53F35s+9Qqb5s+ePZv9+/ebxt944w18fHySX6uIyIPMYoEqXc3j++fDrQv06NGDqD1LTNPO3Yxhxf7zLihQRCR1qdkjIiKSEgwDfusHYRdNU7bZizDR9mSC215pWIwAXy+H+Xa7nf/973+m44WEhNC1q5MXOyIi6Vn558DT13HMHgPbZ5AlSxbaNqhM9IXjpsNMWnPURQWKiKQeNXtERERSwtYpcOh303CY4cMrMX2w3/HQWzR7Rp6rmtf0mrlz57J3717T+Ouvv46vr8kLHRGR9M4vGMq0No9vmQp2Gy+/9BI3t843Tdv49zUOntMx7CKSvqSZZs8333xD27Zt+eKLL1i5ciU3btxI7ZJERESS5tJh+P1Npynvx3bkhJEzwW1vPlESTw/HD8WJzerJlSsXPXr0SH6tIiLpibOlXNdPwpEVlC5dmuo5PbBFmL++mPHX3ylfm4hIKkoTzZ4yZcpw+fJlfv75Z1577TUef/xxRowYAUD79u354osvWLZsGRcvmk+NFxERSRW2GJjTHWIjTFOW2Kowy1YvwW21i2SlXvFsptfMmzePXbt2mcZfe+01zeoREcldGXKWM4//s2H+Ky/15dZO58ew34qKTenqRERSTZpo9uzatYubN2+yYcMGxo4dS9euXalQoQKenp58//33vPbaazRp0oScOXOSO3dunnzySd5+++3ULltERARWfQJnd5iGLxhBvB7THbDE32a1wFtPlsRisTi8xjAMp7N6cuTIQc+ePe+1YhGR9COxjZoPLYFrJ2natClZr+7DMOwO06JsMHdbqIuKFBFxvzTR7AHw8fGhWrVq9O7dmwkTJrB161Zu3brF5s2bGTduHD169KBy5cpcuXKFxYsX88knn6R2ySIi8rA7uhLWDHeaMjimF1cJTHDbC9XzUzJXoMkVMH/+fHbs2GEaHzJkCBkyZEhWqSIi6VbZZ8E7wCRowNZpWK1WXu7RgYgjm02Hmbj6EIahY9hFJH1IM80eR7y8vKhcuTI9evTg22+/ZdOmTdy8eZPt27czYcKE1C5PREQeZrcuwC89AfMXBlNjG7HaXj7BbZkyeDGwYTHTa+x2O0OHDjWNZ8+end69eye7XBGRdMsnI5Rvax7fNh1sMXTq1AnbwZWmaSeuxbDx+BUXFCgi4n4ua/ZMnjyZ3bt3p/i4np6elC9fXkfNiohI6rHb4xo9YRdMUw7bc/NJ7PN33T6wYTEy+3ubXjdnzhx27txpGh88eDB+fn7Jq1dEJL1ztpQr7AIcWEBAQACdGlcj5uoZ09TJfx52QXEiIu7nsmZP9+7dqVixIvv27XPVXYiIiKSOdV/BMfN3h6MMTwbE9CWKhE2d4jkCeKF6PtPrbDab01k9WbNm1aweERFHcpSGvDXM41smA9C/Xz/Cdvxumrb8wCUu3IhM6epERNzOpcu4nK15vXLlCnPmzOHEiROuLEFERCRlndwAf3zkNOWT2OfZaxS46/ahzUuZHrUOMHPmTA4cOGAaf+ONN8iYMWOSSxUReag4m91z/E+4dJiCBQtSJ48X9pgoh2l2LMzc8Ldr6hMRcaNU27Pn7NmzPPvssxQuXNhpXmRkJKdPn3ZTVSIiIk6EX4HZ3cCwmaYstVVmqq3xXbc3Lp2DR4pkNb0uJiaG9957zzSeK1cuXnzxxWSVKyLyUCnVAjIEm8e3TAHg1ZdeJHz/n6ZpU9YcIcbm+NQuEZEHRapv0JzYjvdHjx4lb968hISEuKkiERERBwwDfusPN8yP5j1tZGFwTC/uPGYdwNvTyltPlHI6/JQpUzh+/Lhp/O2339YJXCIiznj5QsUXzOM7ZkJMBI8++ig5bx0yTbsRY2H5vvMuKFBExH1SvdmTVOfP6w+uiIikovVfw4EFpuFYw0r/6P5c5+5lVj3qFCRfFvNNlSMjI/nggw9M4/nz56dbt27Jq1dE5GFUuYt5LPIa7J2LxWJhUJdniTpz0DR17LI9KV+biIgbPTDNHhERkVRzbDUsf89pyvDYZ9lm3H2keo5AH/rUK+L02vHjxxMaaj5jaOjQofj4+CSpVBGRh1qWwlCovnn8n42a27VrB0fMl3LtvhDNkQs3U7o6ERG3UbNHRETEmeunYXZXMMz3b/jTVpZvbM0dxt5oWhJ/H0/Ta8PCwvj4449N40WKFKFjx45Jr1dE5GFX1clMyNDNcHYXvr6+dH2sPLaIG6ap4/7Y74LiRETcQ80eERERM7FRMKsjhF8yTbloZGJgTB8MBw+plfIF0aKC8z3nxowZ43Sp8vvvv4+np3mzSERE/qNYUwjIZR7/Z3ZPvz69idizwjTt1x1nCYuKTenqRETcQs0eERERM0vehNNbTMOxWOkf059LZLorZrHAe0+VxmKxOLgyztWrV/n0009N46VLl6Zt27bJq1lE5GHn4QmVOpnHd82CyBvkypWLenk9MUxmbsbgyezNJ1xUpIiIa7m82ePsSa6IiEiateMH2DzRacqnMe3YYHd8ytazlfNQLk+Q0+s/++wzrl69ahr/3//+h4eHR6KliojIf1TqCBaTv58xYbB7FgCv9+tOxLGtpsOMXbYn0dODRUTSIpc3e+rXr0+jRo0YOHAgU6ZMYfPmzYSHh7v6bkVERO7d2Z2wYIDTlN/t1Zloe8JhLMDXk8GNSzi9PjQ0lJEjR5rGK1asSMuWLRMtVUREHMiUG4o3NY9vngyGQZUqVcgbedw07XyUJ5v/vuKCAkVEXMulzR7DMLhw4QIrVqxg5MiRdO/enRo1ahAYGEijRo3i81auXMnFixddWYqIiEjS3DwHP7SD2EjTlDNe+RgU3RNwPHt1cOPiZAtwfnrWu+++S2Sk+X189NFHmh0rInI/qjg5hv3CXji1CYAhHZoRc+2caerw38yX84qIpFUu2/Fx7Nix7Nixgx07drBnz54Es3kMw+Ds2bPxT2Iff/xxALJnz065cuUSfNhsNleVKCIiklBMJPz4Atw4bZoS6+lHh1v9CSODw3iZ3IG8UD2/07vZu3cvU6dONY3Xr1+fJk2aJKlkERExUagBZC4AV/92HN8yGfJVp3Wrlgye1AsqtnKYtvFMFBdvRiXaxBcRSUtc1uzp3bt3/OeGYXDw4MH45s+OHTvYuXPnXaePnD9/nuXLl7N8+fL42/SupoiIuIVhwG/9nW7IDPCupS9Hjdym8Q9alMHD6vyx680338RuNz/K/bPPPtPjn4jI/bJaoXIXWP6u4/jeudDkEzz9gulWvxSTL0Vj8fS+K82weDB+2S7ealXVxQWLiKQct5zGZbFYKFGiBM899xyffvopv//+O2fPnuXs2bMsWrSIjz/+mDZt2lCsWDEsFguGYcR/OHsyLCIikmLWDo/fsNPM5lzPM/NmRdP4c1XzUjFfZud3s3Ytv/32m2m8TZs2VK2qFxQiIimiYnvwuLuBA4AtCnbMBKB/zy5EHl5vOszMjSew27VRs4g8OFw2sycpcuTIQZMmTRJMVY+IiGDnzp0JZgHt2bOHiIiIVKxURETStf0LYMX/nKaE5a1Hh2OON2QGCPLzYkgT55syG4bBkCFDTOOenp58+OGHzmsVEZGk888KpVrA7p8dx7dMhhp9CQ4Opn4eDzaYDBNuycDS3adoUj6fy0oVEUlJqdrscSRDhgzUqFGDGjVqxN9mGAaHDh1KxapERCTdOrcbfunpNMXIWow+Uf2JtEWZ5rzWpATB/ibvHv9j3rx5/PXXX6bxnj17UrRoUef1iohI8lTpat7suXIMjq+GwvV5p08HGg5bgXeOwg5Th/26Uc0eEXlguGUZ1/2yWCwUL148tcsQEZH05topmPksxISZ52TIzMLSX7H6pHmjp0LeINpWyev0rmJjY3njjTdM4/7+/gwdOjTRkkVEJJny1YRsJc3jWyYBULp0afJGnTBNOxLmw+mrTh4vRETSkAei2SMiIpLiwq/Ad63h5lnzHKsnV5tN5M3V5k/urRb48OkyWBPZlHn8+PEcOHDANP7qq6+SI0eORMsWEZFksljiZveYObAIbsQ9Frz2XAPsUeGO86wefPzjqpSvT0TEBdTsERGRh09MBPzQDi4ddJ73xBcM3ZWFG5GxpimdahWgTO5MToe5evWq01k72bNnZ9CgQc5rERGRe1e+LXj5OY4ZNtg+A4CnmzXF88wO02GWHL5FrE0HyIhI2qdmj4iIPFzsNpjTHU6ZbcP5j2q9WBnQjPk7z5imhGTyZVCjxJcZf/DBB1y+fNk0PnToUAICAhIdR0RE7pFvJij7jHl86zSw27BarbSvUcA0LdY7I9OXb035+kREUpiaPSIi8vAwDFg8BA4scJ5X5HHCG/yPt+fucZr2vxZlyOjj/KyDQ4cOMWrUKNN4sWLF6NGjh/N6RETk/jlbynUjFA4vA2BIj3bEnj9imjp26e6UrkxEJMWp2SMiIg+PNV/C5onOc0IqwrPT+GrFMU5fizBNe6JsTh4vlfgeO6+++iqxsebLwL788ku8vZ2f4iUiIikgpGLch5ktkwHImDEjNbLGmKZd9MzGnuPmsz5FRNICNXtEROThsOFb+OMD5zmZC8LzP7Pnko1Ja4+bpgX4evJe89KJ3uWyZcuYP3++abxhw4Y8+eSTiY4jIiIpxNnsnsNL4dpJAD7s2Qp75C2HaRaLlaHTlrqiOhGRFKNmj4iIpH9bpsDvrznP8csKHX4h2jcLg2fvwm6Yp77etATZA32dDhcbG8vAgQNN41arleHDh2OxOD/FS0REUlCZ1uATaBI0YNt0AEoULUSOSPNj2Lde9SYiKtoFBYqIpAw1e0REJH3b8T0seMV5jpc/vPAzBBdi7Koj7D97wzS1Sv7MtKuaL9G7nThxInv2mO/506tXL8qUKZPoOCIikoK8/aFcW/P4thlgi1vC9UrzqqZplgyZ+HhaIvu/iYikIjV7REQk/do9G+b1BZxM07F4QJtpkLsS+87cYPQf5ptyenlY+LhVWaxW57Nxrl27xjvvvGMaz5QpE++//35i1YuIiCtU6WIeu3UODi4GoN0T9fC8+rdp6qyt2rdHRNKuNNHsOXPmDF27dqVbt26pXYqIiKQX++fDLz3BsDvPazEaijYkxmbn1Z93Eutk/daLdQtTLEfiR6S/9957XLp0yTQ+dOhQsmXLlug4IiLiAjlKQ97q5vF/Nmq2WCy0KJ3FNC0qc0Hmr9yQ0tWJiKSINNHsuXr1KlOnTmXq1KmpXYqIiKQH++bBz13AsDnPe3I4VHgegLErj7LPyfKt4jkC6NegaKJ3vXPnTqdHrRctWpR+/folOo6IiLiQs42aj62EK8cAeLfrUxiRN01TP529NqUrExFJEWmi2SMiIpJits+EnzuD3fzYXAAafwJV42aU7jtzg1F/HDZN9bBa+OLZcnh7On/YtNvt9OnTB7vdfDaRjloXEUkDSrUA3yDz+NapAARm9KO0v+NTuQBOeubmxKnTKVubiEgKULNHRETSjw3fwrw+iS/devw9qNkHgBibncGznS/f6l23EOXyBCV699OmTWP9+vXmd/v44zRr1izRcURExMW8MkCFF8zj22dCbBQA77V/3DTNI0Mgb307O6WrExG5b2r2iIjIg88wYPXniR+vDlDvDaj97+lc3646yt4z5su3imbPyEuPJb5868qVKwwZMsQ07uXlxahRo3TUuohIWlG5s3ks/FLc3m9AtZIFCIo6b5q6+rSNyMjIFC5OROT+qNkjIiIPNsOApW/Dyo8Sz609EOr+2xDaFXqNkSvMl29ZLTDs2fL4eHokOvRbb73ldFPmQYMGUaJEicRrFBER98hWDArUMY9vmRL/abe6xU3TPHIW5+ups1KyMhGR+6Zmj4iIPLhio2Bub/hrdOK5tQfCY0Phn5k14dGxDPhxh9PlW73qFqZ83qBEh968eTPjxo0zjefNm5e333478RpFRMS9nB3DfmItXDwEQK9mNbFGh5mmTlp1AMMwfzwREXG3NNHsyZw5Mx07dqRjx46pXYqIiDwowi7D9Baw68fEcx9/Hx5/N77RA/Dxov0cu2T+xL1I9oy8nITlWzabjT59+jh9kj9ixAj8/f0Tr1NERNyrRHPwy2oe3xo3u8fb00q9fOab64fnKMvylatTujoRkXuWJpo9ISEhTJ06lSlTpiSeLCIicvEgTGwAJ/9KJNESd7x67QEJbl2x/zzfbThpepXVAl88Uw5fr8SXb02YMIEtW7aYxps0aULLli0THUdERFKBpzdUbG8e3/E9xEQA8M7zDUzTPDIE8uHU+SldnYjIPUsTzR4REZEkO/oHTGwIV/92nmfxgFYT4o9Xv+3izSiGzN7l9NL+DYpSMV/mREs5c+YMr7/+umncx8dHmzKLiKR1lTuZxyKvwd5fASiYPZA8XubHsB+MycKxY8dStjYRkXukZo+IiDwYDAM2jofvnoGo685zPXzguZlQ7tn/DGHw+pxdXA6LNr20Qt4g+jcokoRyDPr06cP16+a1vPbaaxQpkvhYIiKSioILQWHzWTtsmRz/af+mlUzTfPOX59PRk1KyMhGRe6Zmj4iIpH2R1+HnTrB4MBg257m+maD9HCje9K7QzI0nWXHggumlft4ejGhbAU+PxB8eZ8+ezbx580zjBQsWdDrrR0RE0pAqXc1joZvg3B4AWlYrjLc9yjT1tz2XuHnzZkpXJyKSbGr2iIhI2nZmO4x7FPaZN1biBReC7n9AwbuP0j18/iYfLtzn9PJ3m5eiQNbEN1K+cuUK/fr1c5ozatQoMmTIkOhYIiKSBhRrAhlzmsfv2Ki5ednspmlexeoweeq0lK5ORCTZ1OwREZG06fayrUmNEt+fByB/bei+ArLevWwqLCqWF2duIzLGbnp549I5aFMlb5JKGzhwIBcumM8QateuHU8++WSSxhIRkTTAwwsqOTkZeOdPEBW3X0+fJhXMh8mYmVFzVmK3mz/eiIi4g5o9IiKS9oRdglkd4pZt2cz314lXoT10mAt+wXeFDMPgnV/3cOSC+aaa2QN8+KRVuSRtpLxkyRKmTTN/1zZLliyMHDky8ZpFRCRtqdQRLCYvj6Jvwp45ABTOlpGimcyHuZm9HIsXL3ZBgSIiSadmj4iIpC375sGY6rA/iUfYPv4etBgdd3yuAz9tPsUv2087HWLYs+UJ9nd8/Z1u3bpFr169nOZ8/fXXZMuWLdGxREQkjQnKC0Ubmcfv2Kj5xUblTNN8C1Zi2DeTTeMiIu6gZo+IiKQNN87CrI5xH+GXEs/3yxq3EXPtV8BkRs7eM9cZ+ttep8N0q12QR4slrTnz1ltvceLECdP4k08+Sbt27ZI0loiIpEHONmo+uwNObwPgiXIh+FgdL9WyWKxsv+7L3r3OH39ERFxJzR4REUldtljYOA5GV03aJswQtz9P77VQ5HHTlJuRMfSduY3oWPN9EyrkDeK1JiWSdJd//vkno0aNMo0HBATwzTffJGkpmIiIpFFFHodMTvZv+2ejZl8vD56pbJ6XsWxDRn79dUpXJyKSZG5t9nz66aecPXvWnXcpIiJp2ZEV8O0jsHhI3H4IibLAo4Oh4zwIzGWaZRgGr8/Zzd+Xw01zMmXwYvTzFfH2TPyh8Pr163To0AHDMExzPvvsM/LmTdoGzyIikkZZPaBSJ/P47jkQeR2ADo8UMk3zDMzGrDV7uXz5ckpXKCKSJG5t9rz55pvkz5+f5s2b8+uvvxIbG+vOuxcRkbTi9DaY0Qq+awUXDyTtGr+s0OEXaPA2eHg6TZ287m8W7nb+5sLwNuXJk9kvSXf90ksvcfLkSdN4nTp1Et3LR0REHhCVOoDFw3EsJgx2zQKgRM5ASmXPYDqMd4l6TJgwwRUViogkyu3LuGJjY1m0aBGtW7cmd+7cvPrqq1rPKiLysDizHX58ASbUh6Mrkn5d8SfhxfVQuEGiqX8eushHC/c5zelVtxCPlcyRpLuePXs206dPN437+PgwceJErFatjBYRSRcCckKJJ8zjW6bAPzM9O9UpYpqWoUg1xkyaQUxMTEpXKCKSKLc+M929ezcDBgwga9asGIbBxYsX+eqrryhXrhw1atRgwoQJ3LyZlGn8IiLywLDb4fAymNYcxteDAwuSfq1vJmg5Hp6bCQGJN2eOXbxFv++3YTdfbUXVApl5tVHxJN39mTNnEp2x88knn1CsWLEkjSciIg8IZxs1X9gLoZsBaFYuBF+TSUAWqwc3spZm7ty5LihQRMQ5tzZ7SpcuzfDhwzl9+jS//PILzZs3x8PDA8Mw2Lx5M7179yZXrlx06tSJ1atXu7M0ERFJaTfPw9oRMKoizHwGjv+ZvOvLPgt9N0P5tqanbd3pekQM3adv4Uak+RLhYH9vRrWrhJdH4g9/hmHQtWtXrly5YprToEEDXn755UTHEhGRB0zBepC5gHn8n2PY/X08aelso+byjflqxIiUrExEJElSZc65p6cnTz/9NPPmzSM0NJTPP/+ckiVLYhgG4eHhfPfddzRo0IAiRYrw8ccfc/r06dQoU0REkivsEmyfCdOfhuElYPm7cPXv5I0RXBg6zIXWE5M0mwfAZjd46YftHLsYZppjtcCIthXImck3SWOOHTuWJUuWmMYzZcrE1KlTtXxLRCQ9slqhchfz+N65EB73ZkC7avlM07yCcrLjTDibNm1K6QpFRJxK9Weo2bNn59VXX2XPnj1s2LCBnj17EhgYiGEYHDt2jHfeeYcCBQrwxBNPMGfOHK15FRFJS2Ii4e91sOozmNQYhhWFeX3g2EowzI88d8g3EzT+BPpsSNLePHf6dPF+Vh+66DTnjaYlebRYtiSNd+DAAQYPHuw0Z+zYsTp9S0QkPavYHqxejmOxkbDzRwDK5s5EyZwZTYfJWL4xI0eOdEWFIiKmUr3Zc6dq1arx7bffcvbsWaZPn07OnDkxDAObzcaSJUto06YNuXPn5vXXX9cR7iIi7mKLhVsX4NweOLAQ1o+CeX3h2zrwSR6Y+gSs+hhObUh+gwfAwwdq9oP+26FmH/D0TtblP285xYQ1x53mtK6Uh+51CiZpvPDwcJ599lkiIiJMc5577jmef/75ZNUpIiIPGP+sUOop8/iWyWAYWCwWnq+e3zTNr1hNZs//nTNnzrigSBERx5yfXZsK/v77b6ZNm8a0adM4f/48FosFwzAw/tnx/tKlS3zxxReMGjWKDz74gIEDB6ZyxSIiDxDDgJtn4eJBuHocrhyHm+cg/BKEX4bocLBFwa3zcfk3z8IHWQEnOx7fKw/vuHdNHx0MgSH3NMTKgxd445fdTnMq5gvio5ZlsCRh3x+Afv36sWfPHtN47ty5GTNmTLLqFBGRB1SVrrBnjuPY5cNwYh0UqE2Lirn5cOF+omLvftPD4uGFT4m6jB07lg8//NDFBYuIxLEYt7soqSgiIoI5c+YwZcoUVq9enaC5U7JkSbp160aHDh3YvXs3kyZNYvbs2cTExGCxWJg2bRrt27dP5e8gbQsNDY1fauCZyZOyo8qmckUi4laG8c+MGyPJM2/2DDxIzNVYvDJ7UmZ40k6uShaLB1hNji9JIsOAGLvdaR/KYiFJmzHfZrfZiYl1vlzYy8tL+/SI3GHPy3uIuRqDV2Yvyowsk9rliKQ8WwymDzYWK1jj3j+PtRvYTY+DNMBuw9s7ebNXRdIC/Z1Pnugr0ewdsBeAU6dOkSdPnlSpI1Vn9vz1119MmTKFWbNmxR+5bhgGfn5+tGnThu7du1OrVq34/AYNGtCgQQM++ugjWrduzfbt2/nqq6/U7EkGA4MYu/Y9EnloJXF2i3HH/8ck8ZrksccdyX6fLBYgkfJi7LZkDAhWL+eNHBs2bMkZUySdM/75i6HnGJJuWeL/xwED7vi9tzh7CLFa9N+IPJD0dz55Yu3mJ8O6k9ubPbf345kyZQqHDx8GiJ/FU7lyZbp3787zzz9PQECA6RgFChTg008/pXHjxhw6dMgtdYuIiIiIiIiIPAjc2ux58sknWbp0KXa7Pb7BExQUxPPPP0+PHj0oX758kscqWDBuo83w8HCX1CoiIiIiIiIi8iBya7Nn8eLF8Z8/+uijdO/enWeeeQZfX99kj+Xn58ejjz6a5A03RUREREREREQeBm5t9mTPnp1OnTrRvXt3ihYtel9jhYSEsGrVqpQpTEREREREREQknXBrsyc0NBRPzzR32vtDxYIFL6tXapchIsll2MFuI+WOQLf8u7vxnbsc/zNb0sK//58W/mbE2gzsiRweabVY8PRI+mxPwzCIiYnB2aGUVqsVL6/U//5F0rK09vdCxGUM2z+PxSY84k7aMgyIsZkfQmDYY/GwWvW6SB4Y+jufPIY11Q88B9LI0eviWncevZ4zZ07Onj2byhWJSJIdXwNL3oBzu+99DN8gKFQPCtSG/I9AtuKJHnueJ08eTp8+Te7cuQkNDb33+75PYVGxdJ+2hb+OXXaaVypXID/2qkGgb9KegNy6dYtatWqxe7f5zzU4OJjt27eTL1++ZNUs8rBJK38vRFwu8gZ8WQJiwhzH678NdQdjtxvU/3IVJy473lv05vZFRK+fTmhoKJkyZXJhwSIpQ3/nk+fO19+pefS68/NlRUQkdVw7BT+1h2nN7q3RExACNftB50Uw+Ci0mQbVekCOUok2etKK6xExdJi0MdFGT97gDEztWjXJjR673U779u2dNnoApk+frkaPiIj8yzcQyrY2j2+dCnYbVquFtlXzmqb5l6pLWGQMU6ZMSfkaRUT+4da5gydPnkz2NRaLBV9fXzJlyoS3t7cLqhIRSUNssbDxG1j5ifk7h2Y8vKHU01CxfdwsngekqePIxZtRdJ6yib1nbjjNyxnoy3fdqpM9IOkb/b/zzjvMmzfPac57773Hk08+meQxRUTkIVGlK2yb7jh2IxSOLIdijXmmUh6+XHoIm/3uRRRWH3/8itfi66+/pn///nh4PLiP1yKSdrm12XP7uPR7lSdPHmrUqEHnzp1p2rRpClUlIpJGXNgPv/SEc7uSd13GnFC9F1TqCP5ZXVObGx27eIvOUzZz8orj6e+3ZQ/w4YeeNcifxT/JY3///fd8/PHHTnOeffZZ3nnnnSSPKSIiD5GQipCrApzd4Ti+ZTIUa0z2QF/qFcvGigMXHKZlLNuQ4z++yYIFC2jRooXLyhWRh5dbl3EZhnFfH6dOnWL27Nk0a9aMxo0bc/XqVXeWLyLiGnY7/DUWxtVNXqMnc0F4ahQM2AV1BqaLRs/2k1d55tu/Em30ZPun0VMwa9IbPatWraJr165OcypVqsTUqVOxWrXKWURETFRx8lhyeGncUmygjZOlXL75y+EZlJMRI0akcHEiInHcOrPn9rrUb7/9lo0bN+Lr60vjxo2pUqUK2bJlA+DixYts2bKFJUuWEBUVRbVq1ejZsyc3btxgz549zJs3j0uXLrF8+XKefvppVq9e7c5vQUQkZV0/Db++CMeT8bcsMA/UHQIVngeP9HMiwq/bTzNkzi6iY81PMAHIlcmX77pXp3C2jEkee/v27Tz11FNERUWZ5uTMmZN58+bh5+eX5HFFROQhVKY1LH0bohwsNTbsccu8GrxFgxLZyZrRh0u3HD/2ZCzbkFWrZrBz507Kly/v4qJF5GHj1mZPp06d6N27N5s2baJFixaMGzeO7NmzO8y9cOECPXv2ZP78+ZQuXZqJEycCMGrUKHr37s306dNZu3YtP/30E23btnXntyEikjJ2z4aFAyHyetLyvTNCnUFQow94JX2PmrTOZjf4YslBvl19NNHcfMF+zOxenbzBSW/IHDlyhCZNmnDz5k3THB8fH3799ddUOy1BREQeID4ZoVwb2DzRcXzbdKg7BC8PL1pXys24P485TPMv+zjX1s5k5MiRTJ482YUFi8jDyK3z1H/99VfGjx9PzZo1+eWXX0wbPQDZs2dn7ty51KhRgylTpjBr1iwAfH19mTx5MpUqVQLgxx9/dEvtIiIpJjYKfusPc7olvdFT/nnovy1uuVY6avRcuhW3EXNSGj1Fs2fk5941k9XoOXv2LI0bN+bCBcd7Jtw2ceJEqlevnuRxRUTkIVe5i3ns1jk49DsAz1YxX8rlGZCFDAUrMXPmzEQfp0REksutzZ6xY8disVh4+eWXsVgsieZbLBYGDBiAYRiMHz8+/nar1UqPHj0wDIMtW7a4smQRkZR14wxMecL8JI//yloMOi2Alt9AQA7X1uZmm45f4cmv17Dm8KVEc6sVCGZ271rkCEx6o+v69es0bdqUY8ccv6N62+uvv0779u2TPK6IiAg5y0CeaubxLXEzdYpkz0jl/JlN0/zLNSQ6Oppx48aldIUi8pBza7Nn1664jUeLFCmS5Gtu5+7evTvB7eXKlQPg8uXLKVSdiIiLnVgftwnz6aQ0qS1Qqz/0WgMF67i8NHeKjrXzxZIDPDf+L87fMN9D57YnyuZkerdqZPJL+v5EERERPPXUU+zcudNpXufOnRM9nUtERMQhZxs1H/0DrhwHoK2T2T1+Rapj9cvE2LFjiY6OTukKReQh5tZmz40bcZuYXbx4McnX3M79714LGTJkAMDLK/1sTioi6ZRhwKYJMK05hCVhmnZgHug0Hxp9mK6WbAHsO3ODFmPWMWblUexG4vm96hZidLtK+Hp5JPk+wsLCaNasGX/++afTvGbNmjFhwoQkzTQVERG5S+mnwTfIPL51KgBPlsuFn7fjxzGLhyf+pRtw7ty5+G0rRERSglubPXnzxnW1Z86cmeRrZsyYAUC+fPkS3H57XevtU7xERNIkWwz81g8WvQr22MTzy7WFF9elu9k8YVGxfLRwH81Hr2X/WQenl/yHt6eVEW0r8EbTklitSW/G3Lp1iyeeeII//vjDaV7t2rX56aef8PR06zkFIiKSnnhliDsZ08z27yA2Gn8fT5qVy2WalrFcQwC++uorDCMJ74SIiCSBW5s9Tz31FIZh8N133zFixIhE87/66itmzpyJxWLhqaeeShDbuHEjAPnz53dFqSIi9y/qFnzfNu7JXmJ8AuGZydBqPGQIcnlp7mK3G/yyLZTHh69mwprj2JIwnSd3UAZ+7lWTpyvmTtZ93bhxgyZNmiQ6o6ds2bL89ttvOmJdRETun7ONmsMvwYH5ALStar6UyztrPrxDirNt2zbWrVuX0hWKyEPKrc2e119/naxZswIwaNAgqlatyqhRo1i/fj1HjhzhyJEjrF+/nq+//pqqVavy6quvAnGzd15//fUEY/34449YLBYaNmzozm/hvp08eZJXX32VkiVL4u/vT3BwMNWqVWPYsGGEh4endnkiklJuXYCpT8LRFYnnZi0GPf6AMq1dX5ebGIbBqoMXaDZqLQNn7eTs9cgkXfdYiewsfKk25fMGJev+rl+/TuPGjRN9klygQAF+//13Mmc23yxTREQkybIVg/y1zeNbpgBQKV9mCmfzN03LWK4RACNHjkzR8kTk4eXW+etZsmRh2bJlNGnShPPnz7Nt2za2bdtmmm8YBjlz5uT3338nODg4/vZjx45RrVo1qlWrRuvWD86Lo4ULF/LCCy9w/fq/Ry2Hh4ezefNmNm/ezMSJE1m0aBGFChVKxSpF5L5dPgrftYarxxPPLdEMnv4GfANdX5cbxNrsLN13nrGrjrDndOLLtW7z8rDwaqPi9KhTKFnLtgCuXr1K48aN2bx5s9O8HDlysHTpUkJCQpI1voiIiFNVusCJtY5jf6+Bi4ewZCtG26p5+XjRAYdp/iXqcHXFBH755RdOnDih1Qsict/cOrMHoHz58uzfv5/+/fsTGBiIYRgOPwIDA+nfvz979+6NP3nrtkKFCjFlyhSmTJlC0aJF3f0t3JOdO3fSpk0brl+/TsaMGfnoo49Yv349K1asoEePHgAcPHiQJ598klu3bqVytSJyz05vg0mNktDosUCDt6HNjHTR6Dl9LYIRyw9R+7OV9Jm5LVmNnhI5A5jXtza96hZOdqMnNDSUOnXqJNroCQkJYfXq1Q/MY4aIiDxASjYHv6zm8X82am5ZMQ+eJo9zVh8//ErUxm63M2bMGBcUKSIPm1TZmTIoKIiRZ9iIWQAAljdJREFUI0fy+eefs3XrVvbs2cPVq1cByJw5M6VLl6ZKlSr4+PikRnkuMWDAAMLDw/H09GTp0qXUrFkzPtagQQOKFi3KkCFDOHDgAMOHD2fo0KGpWK2I3JNjq+CH5yEmzHmeTyC0ngjFGrulLFcwDIPDF27x56GLLNx9lu0nryV7DE+rhZ6PFuLlx4vi45n007Zu27t3L02aNCE0NNRpXp48eVi5ciVFihRJ9n2IiIgkytMHKr4A60yWYO2YCY+9Q7aADDQokZ2l+847TMtYriFhu5czYcIEhg4dSsaMGV1YtIikd25t9kyfPh2A4sWLU716dXx8fKhVqxa1atVyZxlut3nzZlatWgVAt27dEjR6bhs0aBBTpkxh//79jBgxgjfeeEPHyos8SI6sgB+fh9hE9qYJyAXt50CO0u6pKwVcDYvm7PVIQq+Gc+DcTfaducGWE1e4dCv6nsesWiAzH7UsS7EcAfd0/R9//EHr1q25du2a07x8+fKxcuVKLY8VERHXqtzZvNkTeQ32zYPyz9G2al7TZo9vntJ4Bufm2pXTTJ8+nT59+risXBFJ/9za7OncuTMWi4UffviB6tWru/OuU9Wvv/4a/3mXLo537LdarXTs2JE33niDq1evsmrVqgdu82mRh9bh5XGNHluU87ysxeMaPUHmJ3K4is1ucPpqBGevR3DuRiRnr0dy7p+P6xExRMbaiIi2ERVrJzLGxoWbcd/LueuRVPxgWYrVEZLJl0GNitOyYu5kL9m6bdy4cfTt2xebzeY0r2DBgqxcuVL7HoiIiOsFF4JC9eHYSsfxLZOh/HPULZaN7AE+8Y+z/5WxbEOurZ7K119/Te/evbFa3b7rhoikE25t9mTKlIkbN248dHsmrFmzBgB/f38qV65smle3bt34z9euXatmj8iD4NBS+OkFsCUyyyVvDWj3A/gFO89LATcjY9h35gb7z97gwLmb7D93k0PnbhIR47w5cid7Eo5IT45AX09erFeELo8UwNcr+Uu2AKKjoxk0aBCjR49ONLdw4cKsXLmSvHnd31gTEZGHVJWu5s2eUxvh/F48c5SmdeU8fLPqqMO0jGUe49qaGRw8eJAlS5bQtGlTFxYsIumZW5s9BQsWZOfOnfH78zws9u/fD0CRIkXw9DT/kZcoUeKua1Ja1LkoVvusdsnYt1XaUImAiklbmrGn1R4uL7zs0noA6kbVTTzpHxsKbSDqdCIzNO5TxgoZqbzRvPF3p+iL0fyV5y+X1gOQs0tOin9bPEm5V5ZeYXfz3S6uCIqMKELuF3MnKffk5yc5/k4STr+6TxVWVyBTjUxxXxxcDD91AHuMw9x9cwZxcX9NsFjB6gl9XPMzq3ixJlvPXmPD0cv8dewye05fx6xX89HEDGS/lviMmudtFi4BmW9ZmDDML1n1nM1iZ2iXuOVsWTP60L1OQV6ono8A37uXpsbeiGVdNufHpd8WExNDc6M5zWnuNM9qseJ1yotjRY5xjGMAZG+XnZJTSybpfq6tvsbORjuTlHs/Cn9emDwv50lSbujIUI4OcfzCICWVX1qeoLpBScrd33k/F3644NqCgEcuPoJnYNKermyusJnw/eEurSdD4QxU21ctSbn2KDtrAte4tB6AbK2yUeqHUknKvb7hOjvq7kjR+4+KjnvMjDr973OMgh8UJN+QfEm6/vQ3pzky4EiK1uRI2fllCW6UtIb7wd4HOTflnIsrgpqhNfHO5p2k3K3Vt3Jrh2sP8fDJ7UONYzWSnO/q55QAWZ7MQplfyiQp9+b2m2yrYX7Sb0rJ/3Z+CrxT4O5A8aaQMSfc+vd35+z2xzi08MW4Lz46B9ZLVAcq2cweW/3A/jsGdqxPWlntlbSfcek5pcnazMkm0Xc4/PJhznx7Jkm596P60er45vFNUu72R7dzY2PSD3e4F15Zvah1Ounbh6wJWIM92u7CiiBzw8yUW1Au8UQgbF8YWypucWk9AHkH56XQh0lb/n7++/Mc6OL4hLmUVOr7UmRrnS1JuUcHHyX0a+f7OaaEqnur4lckec+R3c2tzZ6WLVuyY8cO5s+fT4MGDdx516kmMjKSS5cuAXGbhDqTOXNm/P39CQsL49SpU0m+j8Q2Jz179myCr43olH3H/i7JGN6INVxfTzLZo+0ur8mISd747vgZGbHJ+YdzT00k4/HVsLnpd+n2XRxYCLM6mTZ6AAy7B4btdoPDdbVV/XA5Udakje9pAy9b8pZPJTffw2ahQt4gXqiej+blQxKdyZPUfzfPpD5kOfj9TM7vt2G453fJsCWjJjf9fhtGMmpKg3+/jRjX15TcJ/4P29/v22Mn5/cbu5seUx7w5yf6/U5KsptqMvv99vCCSh3gzy/+zTWsdzwXgNu/iF44e2z1jE9N8veTnOdMD+nvd3LHT3OvCdz1/DvpE8Ex7G56fpKMGedue03wAHBrs+fll19m8uTJfPPNNzRv3vyhaPjcvHkz/vOk7Kh/u9mTnOPXtUxBxM2O/gE/d3ba6AEgc0G3lGOzG5CGlvTny+LHr32T/s6wiIhIulGpE6z5EgzXzggREUmMW18eBAYGsmzZMkqUKEHjxo3p2bMnq1at4sqVK8l6N/FBEhn578k83t6JT9O9fdx8RESEy2oSkftwfj/82D7xPXqq9YJcSZuWm974eKahzpOIiIg7BeWFItp3U0RSn1tn9nh4/DuV3zAMJk2axKRJk5J0rcViITY21lWluYyv77/rVKOjEz+mOCoqbt17hgwZknwfiS35Onv2LNWqJW1/ARFJxJI3IUeY85wafaDxxzBln3tqEhERkbSjSlc4vCS1qxCRh5xbmz3/nb2TXmfz3Ckg4N+NipOyNCssLO5FZFKWfN2W2F5AIpKCohP577hmP2j0IVju7VhxV8ri742vl2bdiIiIuFTRhhCYB264fpNYEREzbm32vPvuu+68uzTB19eXrFmzcunSpUQ3Ur569Wp8s8eV+/BYvF38IjQZw1s8La6vJ5ms3laX12TxSt747vgZWTyT8w/nnpqSs9DU4uHi3yVbDGCAxUmTulZ/aPhBfKMnOb/fNsPAbjdSbBvnfMF+VM6fmZK5AiiRM5CSuQLJFuDDph82EXE9CctE75iImNyf6738fsfGxmKzJ31HQKvVipfn3ad7md5HMn6/LRb3/F2yeCSjJlf/ft++n2Q0KdPi32+Ll+trsnonr2H6UPz9dvD3Ijm/31jd9JjygD8/0e93UpLdVFNiv99WD6jcCVZ+hMVix+LhYI8/j7jtHWJsBqaP/oYd45/HRi9PL6xWJ/8+yXnO9JD+fid3fKu3FXtydr6+B8l6zuSu59/Oz9VIwGJ10/MTa9p7zvQgsBgPw/SaVPboo4+yZs0a/P39uXbtmunx63/99Re1asUdBzh06FDef//9FLn/0NDQ+OZRzpw57zqdS0SciLgGU56AC3ud51XuAs2+StaMnqth0Uxd/zfT/vqba+GJbPaciNxBGahRKAs1C2ehRqFg8mS+v6Mg8+TJw+nTp8mdO3eijep7FR0dzZgxY/jggw+4evVqkq7x9vbmiy++oH///slqTIiI67jj74XIA+fGWfiqNBgmb2Q8+SVU7c7ENcf4cOF+hyn26EhCx3TAiI6gVq1arFu3zoUFi5jT3/nkufP196lTp1JtJY5bZ/Y8rGrXrs2aNWsICwtj69atVK9e3WHe6tWr4z9/5JFH3FWeiJiJjYZZHRJv9JRuFfekLYnNh4hoG5PWHuPb1ce4FXXve5EVy5GRpmVy8UTZXBTLkfGBaX7ExMQwbdo0PvroI/7+++8kX1eiRAl++OEHKlSo4LLaREREUkRgLijeFA4scBzfPBmqdKNVpTx89vsBYhwc52719sW/5KPc2rmE9evXs2nTJu3DKSJJps0b3ODpp5+O/3zKlCkOc+x2O9OnTwcgKCiI+vXru6M0ETFjGLDgFTj+p/O8Io9Dy3FxU7YTYbMb/LzlFPWHrWLY0kP31OgpnM2fVxsVY/nAuix9pS6vNCxG8ZwBD0SjJzw8nLFjx1K8eHF69OiR5EaPxWLhlVdeYevWrWr0iIjIg6NKV/PYhb0Quplgf28alsphmpax3L8ne40cOTIlqxORdC7VZvbY7XZWrVrFX3/9xblz5wgPD+fDDz8kV65c8TnR0dHExsbi4eERfyT5g6hatWrUqVOHNWvWMGnSJDp16kTNmjUT5Hz55Zfs3x83hfPll1/Gyyvpe1GIiAusGQY7vnOek7c6tJkOnt6JDvfnoYt8vGg/B87dTHYp3p5WniiTk3bV8lGtYPAD0di507Fjx5g4cSLjxo3jypUrybq2SJEiTJkyhdq1a7uoOhERERcpVB8yF4CrfzuOb5kCeavRpkpeFu0+5zDFJ6QEXlnzEXPpJLNmzeKLL74gJCTEZSWLSPqRKs2ehQsX8tJLL931ru6gQYMSNHsmTZpEv379yJgxI2fOnMHf39/NlaackSNH8sgjjxAREUGjRo148803qV+/PhEREfz444+MHz8egGLFijFo0KBUrlbkIbd7NvzxofOcHGXg+Z/A2/nfpTPXIhg6by/L959Pdhn5s/jRoUZ+WlfKQ2b/xBtKacmNGzeYN28eM2bMYNmyZcm+3tvbm9dff53XX3+dDBkyuKBCERERF7NaoXJnWP6e4/jeX6DJx9Qpmo1cmXw5ez3SYVrGsg25unISsbGxjB07lg8/TOQ5iogIqbCMa+LEiTz11FMcP34cwzDIkiWL6RHs3bp1IygoiFu3bjF37lw3V5qyKlasyE8//URgYCC3bt3izTffpGbNmjRo0CBBo2fhwoUJjmsXETc78Rf8+qLznEz5oP0vkCGzaYrNbjBp7XEeH7462Y2ekrkCGdWuIn8Mqkf3OoUemEbPmTNnmDhxIi1btiR79ux07Njxnho9jRs3Zvfu3bz//vtq9IiIyIOtQnuwmszYj42EnT/iYbXwTGXzDVz9yzQAa9x79OPGjSMiIgmnaorIQ8+tzZ4jR47Qt29fABo0aMC+ffu4cOGCab63tzetW7fGMAyWLl3qrjJdpnnz5uzatYtXXnmFYsWK4efnR1BQEFWqVOGzzz5j+/btFClSJLXLFHl4XT4KPz4PtmjzHJ9M8MIsCDBfX7879DpPj1nHBwv2ER6d9OPEK+fPzJTOVVn0Um2alw/BIxnHTLpbdHQ0O3bsYOrUqfTs2ZOSJUuSO3duevTowa+//kpUVFSyxyxdujSLFy/m999/p1ixYi6oWkRExM0yZoOSzc3jWyaDYfBs5bymKR5+mfArErcx86VLl/j+++9TukoRSYfcuoxrxIgRxMTEUKZMGRYtWoS3d+LvVtepU4eJEyeyY8cO1xfoBvnz52f48OEMHz48tUsRkTtFXofv20KEkz1lrJ7QdjpkL+l4iBgbw5YcZPK649gdT1h0qEzuQN5oWpJahbOk+n48hmFw4MABIiPjppKHh4czatQoLl68yMWLF/n77785duwYx48fJybm/o6Lv61QoUK88847tG/fHk9PHRIpIiLpTJWucUu2HLl0CE6sI1+B2tQqnIX1Ry87TPMv15DwQ+uBuNdUXbt2TfXnDCKStrn1WfWKFSuwWCwMGDAgSY0egMKFCwNw8uRJV5YmIg8zux1+6QWXDzvPazYCCtVzGNp75joDftzB4Qu3kny3uYMyMKRJcZqXC8GairN4Tpw4wYoVK1ixYgV//PEH5879u0nk1atXeemll1xyv8WKFeO1116jQ4cO2pReRETSrwK1IUtR8+cZW6ZAgdq0qZLXtNmToWAlPDJmwXbrMnv27GHlypU0aNDAhUWLyIPOrc2eU6dOASTr6NzbmzKHh4e7oiQREVj9KRxa7DynziCo1OGum+12g4lrj/HFkoPE2JI2nSfQ15N+DYrQsWYBfL0SP7I9pcXGxrJmzRrmzZvHwoULOXLkiFvvv27dugwaNIgnn3wSq9XtW8eJiIi4l8UCVbrAkjcdx/fNg7DPaFImJwHzPLkZGXv3EFYP/Ms04MaGn4G42T1q9oiIM25t9tyeami2IbMjFy9eBCAwMNAlNYnIQ27/fFj9mfOcMq2h/tt33XzmWgSDZu3kr2OO34VzpHWlPLz1ZEmC3bzp8s2bN/n999+ZN28eixYt4urVq269/+DgYDp16kSPHj0oWdLxMjgREZF0q3w7WP4+2BzsaWePgR0z8X3kZVpUCOG7DY5XNGQs2zC+2bNgwQKOHDmi/T5FxJRb31INCQkB4NChQ0m+ZvXq1QAUKFDAFSWJyMPswn6Y29t5Tp6q0GJs3PGpd/h9zzmajPgzyY2egln9+b5Hdb5sU95tjZ6YmBgWLFhAu3btyJEjB23atGHmzJlua/QEBATQvn17FixYwNmzZxk+fLgaPSIi8nDyC4YyrczjW6aA3U6bKuYbNXsFh+CTpzQQ9+b5qFGjUrpKEUlH3NrsefT/7N13dFTl1sfx38ykJ9TQIXQIHekiIEhHpCjFgihFrIjSVFQUVKTYQKUrKIgUaQIiIEhHpRcpAorSe0uAlJk57x95iTdmziTAzKR9P2tlyTx7n3P2XVfGzJ6n3HuvDMNI9Q7y58+f18SJE2WxWJimCMCzblySZj4qxbnZYyesgPTwN5J/UOKQ3eHUiB8P6Nlvtumqi2nW/+Vvs6hP49L68aUGuqdUHk9U7pZhGPr111/Vu3dvFSpUSG3atNGsWbN8dkxrxYoV1bdvX61cuVLnzp3T9OnT1bp161Tv0wYAQKZVo7t57NIR6cgaVS6cQ+UKZDNNC6vSNPHPU6dO1dWrVz1ZIYBMxKfNnqefflqStHTpUk2dOtVt7vHjx3X//ffr/PnzstlsidcCwB1zOqR5TyX8YmXGFpDQ6MlWIHHofHSsnpiyWRPW/pmqx1QqnF1L+zRQv+aRXt+b5+rVqxo7dqyqVKmiunXrauzYsTp//rxXn1m0aFG1adNGb775phYvXqwLFxI2jfz444/VpEkTBQYGevX5AABkKBG1pXwVzeNbp8pisbid3RMS2UCWgGBJCUu0p0yZ4ukqAWQSPt2zp1atWnr22Wc1YcIEPfXUU1q6dKk6deqUGN+9e7f27NmjFStWaNasWYqJiZHFYlH//v1ZjwrAc9Z9IB1e6T6n9cdSRK3ElzuOXtLzM7br1JWYFG9vsUjPNyqll5qUVYCfd3vqO3fu1Pjx4zVjxgxdu3bNI/f09/eXxWJRXFycQkND9fzzzytPnjzKkyeP8ufPr5IlS6p48eIKDg72yPMAAMgSbm7UvHSA6/iBH6So02pfrbCG/7jf5cEP1oAghZarr+jdP0mSPv30U7344ouy2Xx/4AOA9M2nzR5J+uyzz3Tt2jVNnz5d8+fP1/z58xM3bu7SpUti3s1NnLt166b333/f12UCyKz+XC2tGeE+p1avJCdvffvbUQ1ZtFdxDmeKty+cM1ifPHyXapfIfaeVmnI6nVq6dKlGjhypDRs2eOSe1atXV5MmTdSkSRPVr19fkZGROnHihHLmzKlRo0Z55BkAAGR5VTpLP70lxbs4adhwSDumK/e9A9WsQn4t3XPa5S1CqzRPbPYcOXJES5YsUbt27bxZNYAMyOdn3tpsNn399df67rvvVK1aNRmG4fKnQoUK+vbbbzVlypTEZhAA3JGrJxOWb8nNiYDF6kkth0uSHE5D7y7Zp9cX7ElVo+ehaoX148sNvNboiY+P17Rp01SlShW1adPmjho9gYGBuv/++zVx4kSdPHlS27Zt06hRo9SiRQuFhoZ6sGoAAJAoKIdUuaN5fNvXktOhTm6WcgUVLi+/8CKJr0ePHu3BAgFkFj6f2XNThw4d1KFDB508eVJbt27V2bNn5XA4FB4ermrVqqlUqVJpVRqAzMhhl+b2kK672ccme2Gp09eSzV/X4+x6adZO/bTvTIq3DvCz6r32ldyusb8TN27c0OTJk/XRRx/p6FHXx7GmRnBwsNq1a6dOnTqpefPmCgsL82CVAAAgVWp0l7ZPcx27ckw6vFL3lmmuAtmDdPqq6+XjYZWb6fKahD1Q16xZo127dqlq1areqhhABpRmzZ6bChUqpLZt26Z1GQAyu5/flY7+Yh63+kudp0lheXX2aox6fr1Ve05cSfG2RXIFa8LjNVSpcA4PFpsgPj5eX375pd59912dPHnytu5htVrVtGlTPf7442rfvr2yZTM/4QMAAPhA4epSwbukUztdx7dOla1sC3WoUVhjV7s+FCKsYmNdXjct4dAJSWPGjGGzZgBJ+HwZFwD43B/LpI2j3ec0f08qUlMHTl9V+7EbU9XoaVg2r5a8WN/jjR6Hw6FvvvlG5cqV03PPPXdbjZ5SpUpp1KhROn78uJYvX66uXbvS6AEAIL2o6eYY9kPLpSvH1amG+YxhW1guBZeskfh6xowZOnv2rCcrBJDB0ewBkLldPioteMZ9Tvm2Up1ntOnweXUc/4tOpuLErT5NymhKt1rKGRLgoUITNqZfvHixqlatqq5du+qvv/66peutVqvat2+v5cuX6+DBgxo4cKAKFizosfoAAICHVOooBZh8CWM4pe3TVDxPqOq42QcwrHKzxD/HxcVp4sSJnq4SQAaWpsu4oqKidOTIEUVFRcnhcKSYf++99/qgKgCZhsMuze0pxVw2z8ldUmr3uVbsO6Pe3+5IcSPmIH+rRj9cTS0rFfBoqfv27dPLL7+sn3766ZavzZ07t55//nk988wzKlKkSMoXAACAtBUYJlV9WNryhev4tq+leweqc80I/XbkosuU4NK1ZQ3NKee1y5KkcePG6dVXX1VAgOe+iAKQcaVJs2fy5MkaN26cdu/eneprLBaL7Ha7F6sCkOmsHSkd32wetwVKnb7W/H1RGjh3txxON6d0ScoTFqgvn6ypqhE5PVbipUuXNGTIEI0dOzZVTe//FRERof79+6tnz55stgwAQEZTo7t5syf6tHRwmVpVbqW3F+1VdGzyz0EWq01hFe/T1c0LJEmnT5/WnDlz9Pjjj3uzagAZhE+XcTkcDrVv317PPvusdu/ebXrsutkPAKTa3xul9R+6z7l/lKb9nV395uxKsdFTNn+YFr5wj8caPQ6HQxMnTlSZMmX06aef3lKjp2LFivr666/1559/6qWXXqLRAwBARlSgklSktnl861SFBPipTVXzJdlhVZonef3JJ5/wuQmAJB/P7JkwYYIWLVokScqfP7+6d++uGjVqKHfu3LJa2T4IgIfcuCTNfzphzbsJo8rDGnelnj5YsTfF2zUok0dju1RX9iB/j5T3+++/q1evXvr1119v6boyZcro3XffVadOnXjPBAAgM6jZ3XwW8p+rpItH1KlmhGZuPuYyxT88QgGFyinu5AFJ0vbt27Vp0ybVq1fPWxUDyCB82uyZNm2aJKlChQpav369cuXK5cvHA8gKDENa1Ee6etw8JXcpfRTwjD5fcTDF23WuWUTDHqwsf9udN1diYmI0bNgwjRw5UvHx8am+LiIiQkOGDNETTzwhP7803WoNAAB4UsUHpWWvSTEmp4Bu/1rVmryt0vnCdPhstMuUsCrNdPH/mz2S9Nlnn9HsAeDbZVz79++XxWLR4MGDafQA8I7t06T9i0zDhtVfXxUarM83nE7xVk/fW1IjO1TxSKNn3bp1uuuuu/Tee++lutETHh6u0aNH69ChQ+rRoweNHgAAMhv/YOmuLubx7dNlccSrc03zAxhCyzWQxT8w8fXcuXN1/Lj5l14AsoY0WQcQGRmZFo8FkNmdO5jw7ZgbayOe1dCtKZ9SMbBFpAa1KieLxXJHJV27dk0vvPCCGjZsqD/++CNV19hsNvXp00eHDh3SSy+9pMDAwJQvAgAAGVONbuax6+elA4v1YLUi8rO6/p3EGhiikMh/Z/I4HA5NmDDBw0UCyGh82uwpU6aMJOniRdfHBwLAbbPHSfN6SvHXTVP+yVlH3f+ok+Kt3mlXUS/cV/qOGz2//vqrqlWrpnHjxqX6mqZNm2rXrl0aM2YMMyABAMgK8kZKxeqbx7dOVd5sgWpcLp9pSljlZkleT5w4UTExMZ6qEEAG5NNmzyOPPCLDMLRkyRJfPhZAVrB2pHR6t2n4hl9OdTz9hAw3b3s2q0Ufd66qJ+oWv6NS4uPj9dZbb6levXo6dOhQqq4pXry4Fi5cqBUrVqhixYp39HwAAJDB1OxuHvt7vXT+kDrXjDBNCSpaWX65CiW+Pn/+vGbPnu3JCgFkMD5t9vTp00dVqlTR+PHjtX79el8+GkBmdmyLtOFjtynPX++lczKfKeNvs2jsY9X1UHXzNfGpsX//ftWtW1fvvvuunE7z08BuslqtGjBggH7//Xe1a9fujmcTAQCADKh8Gykk3Dy+7Ss1isyrvNnMl3aHVWqS5PWYMWM4hh3Iwnza7AkMDNSKFStUo0YNNWvWTK+88op27tzJFEMAty/umrTgGbfHrE+1t9BqZzXTuJ/Vos8era6WlQrcdhmGYeiLL75Q9erVtW3btlRdU61aNW3ZskUffPCBQkNDb/vZAAAgg/MLlKo9bh7fOUN+zlg9VL2waUpo5aaS5d+Pdzt27NCmTZs8WSWADMSnzR6bzaaCBQvql19+UVxcnD766CPVqFFDoaGhstlsbn84hQaASz+9LV380zT8h7OIRtgfNY3brBZ9+mi1O2r0REVFqUuXLurVq1eqmtfBwcH64IMPtHnzZlWvXv22nwsAADKR6k+ax25ckvZ9r041zJdy+WULV1CJpL9XfPrpp56qDkAG49Nmj2EYiT//fZ2aHwBI4vAqactk03CcYVPf+OcVK9enb1kt0uiH79L9lQvedgk7d+5UjRo1NHPmzFTl161bV7t379aAAQNoYgMAgH+Fl5JK3mce3zpVpfOFqUYx82XpYVWSbtQ8b948jmEHsiifftJ4++23ffk4AJnZjUvS973dpoy2d9A+o7jLmNUiffLwXWpTtZDLeEoMw9CECRPUt29fxcbGppjv5+enoUOH6pVXXqHJAwAAXKvZXfprtevYsV+lM/vUuWYRbfvnksuUkNK1ZQ3OLueNq5L+PYb9vffe81bFANIpmj0AMqalA6Wok6bhbc4ymuho4zJmsUgfdqqqdneZr3t359q1a+rVq1eqZ/NUqFBB06dPZ8kWAABwL/J+KSy/FH3GdXzbVLVuMlxDF+/T9ThHsrDF5q/Qio0UtXVR4tjEiRP15ptvKigoyFtVA0iHfLqMCwA8Yt/30p7vTMPXjUD1j39WDtlcxt9pV+m2T93666+/dM8996S60fPyyy9r27ZtNHoAAEDKbP5S9SfM47tmKcwSq9ZulqCHVU66lOv8+fOaNWuWpyoEkEHQ7AGQsVy7IP3Q323K+/bH9Lfh+pegl5uWUde7i93Wo1esWKGaNWtq9+7dKebmzp1bixcv1ieffMI3aQAAIPWqPyHJ4joWe1X6fZ461zLfqDkgXwkFFCidZOzTTz9lD1Qgi/Fas6dfv37q16+fzp496zLucDh09OhRHT161O19/vrrL1WvXl01atTwRpkAMpofX5GunTMNr3VU0TeOpi5jXe8uppealLnlRxqGoREjRqhly5a6dMn1Gvn/Va9ePe3cuVMPPPDALT8LAABkcTmLSmWam8e3TlXNYrlUIk+oacp/Z/dwDDuQ9Xit2TN69GiNGTNG58+fdxk/cOCAihcvrpIlS7q9z40bN7Rz507t3LnTC1UCyFD2L5F+n2savmyE6pX4p+Xq27DWVQpqSNuKslhMvikzce3aNXXu3FmDBg1K1Tdir732mlavXq2ICPNv3AAAANyq2d08dnK7LKd2qVNN8yXpoRUayuKX9DRSjmEHspY0X8bFdEIAqXL9orSkr9uUt+Of1BnlTjZev3Qefdy5qmzWW2v0nDhxQvfee6/mzjVvMN2UO3du/fjjjxo+fLj8/f1v6TkAAABJlGkuZXezv+C2qepQvYjMfrWxBoUppOw9ScY4hh3IWtK82QMAqbJskHTN9bJQSfrJUUPfO+slG69cOIcmdK2hQD/XmzWb2b59u2rXrq3t27enmHvXXXdp27Ztatmy5S09AwAAwCWrTarxpHl893fKHxCnRpH5TFNC/7OUy+FwaPz48Z6qEEA6R7MHQPr3xzJpt/kpEleMEL0e30P/Xb5VKEeQvnyypsIC/W7pcfPnz1f9+vV18qT50e43Pf7449q4caOKFy9+S88AAABwq1pXyWLyZVX8NWnPHHV2s5QruHhV+eXIn2Rs0qRJiomJ8WSVANIpmj0A0rcbl6UlL7tNeSf+CZ1TriRjYYF+mtK9lvJlT/1JWIZhaPjw4erQoYNu3LjhNtdms2nMmDGaNm2aQkJCUv0MAACAVMleUIpsZR7fMkWNI/Mpd2iAaUpo5SZJXnMMO5B10OwBkL6teEOKOmUaXu2oqnnOBknGbFaLPn+smsoVyJ7qx8THx6tXr156/fXXU8zNmzevVq1apT59+tzyhs8AAACp5m6j5rN7FXDyNz1YrbBpSljlppIl6Uc+jmEHsgaaPQDSryPrpB3fmIavGsEaFP+U/rt8a0jbim7XsP9XdHS02rZtqy+//DLF3EqVKmnz5s1q2LBhqu8PAABwW0o2lnIVN49vnqzONc1PAPXLnk9BxaokGduxY4c2btzooQIBpFc0ewCkT/E3pMUvu015z/64Tis8yVjP+iXU9e5iqX7M6dOn1bBhQy1btizF3NatW7M/DwAA8B2rVarZwzy+f5EiQ6JVNSKnaUrYfzZqljiGHcgKaPYASJ/WfShd/NM87KisOY5GScaals+n1+8vn+pH/PHHH6pbt26qTtzq27evvv/+e2XPnvqlYQAAAHesWlfJz2QPQqdd2vaV242aQ8rWlTUoLMnY/PnzOYYdyORu7Yia2zBu3Djly5d8OcXZs/8eofzOO++YXv+/eQCyiDP7pI2jTcPXjMBky7fK5AvT6EeqyWZN3R46GzduVNu2bXXx4kW3eX5+fho7dqyefvrpVN0XAADAo0JyS5U6SjtNlrZvm6o2z/XRO4utirU7k4UtfgEKKd9Q0Tt+SBy7eQz7sGHDvFU1gDTm9WbP+PHjTWM3NzYdOnSot8sAkFE4HdLiPgnfVJn4yN5ZJ5Q38XW2ID9NeiL1R6wvXbpUHTt2TPHErWzZsmn+/Plq2rRp6moHAADwhtq9zJs90WeU/cgy3V+5lBbsOOEyJaxKsyTNHkmaOHGi3nzzTQUHB3u6WgDpgFeXcRmG4ZEfAFnI1inS8S2m4V3OkvrK0SLxtcUiffpoNZXIE5qq28+aNUvt2rVLsdFTuHBhbdiwgUYPAABIe4XukorUNo9vnqxObpZyBRYoLf98JZOMXbhwgWPYgUzMazN7Vq9e7a1bA8isrpyQVprP9LMbVg2Kf0rO/+lTD2wRqftSefLWhAkT9Pzzz6fYRK5YsaJ+/PFHRUSYn24BAADgU7V7Scc3u44d/UV3B59URO5gHbvo+gutsMpNdWnVpCRjn332mbp165a44gJA5uG1Zg/HEgO4JYYhLR0oxUWZpnzhaK19RvHE160rF9RzDUul4taGRowYoddffz3F3EaNGmnBggXKmTNnaqoGAADwjQrtpOWvS9fOuQxbt36hTjVe1Mc/HXQZD614ny6tmSo54hPHbh7DXr9+fa+UDCDtcBoXgPRh/2Lpjx9Mw0edeTXa/lDi63IFsmlUxyopfhNlGIZeffXVVDV6HnnkES1btoxGDwAASH/8AqUa3czje75Tp4phMvvVyBacTSFl6iQb5xh2IHOi2QMg7cVcSZjV48br9qcUo0BJCRsyT+xaQ6EpbMjscDjUq1cvffDBBymW8OKLL2rGjBkKDAxMfd0AAAC+VKO7ZLG5jsVfV8Ej89WgTF7XcUlhlZslG5s/f76OHTvmqQoBpBM0ewCkvZVDpOjTpuF5jvra4Kyc+PqjTlVVLNz9hsyxsbF6+OGH9eWXX6b4+LfffltjxoyR1cpbIgAASMdyFJbKtTaPb56szjUKmYaDSlSTLVueJGM3j2EHkLnwyQZA2vrnl4QTuExcNMI0LP7xxNe9GpRQ84oF3N7y+vXratu2rebNm5fi40ePHq0hQ4awMSEAAMgYavcyj106ouZBe5UzxN9l2GKxKqxSk2TjkydPVkxMjKcqBJAO0OwBkHYc8dKSvm5T3ot/XBeVXZJUvWhOvdKynNv8a9eu6YEHHtCKFSvc5tlsNn311Vd66aWXbq1mAACAtFS8gZTX/PehgK1fqv1dhU3joVWaSUr6Jdf58+f13XffeapCAOkAzR4AaefXcdK5/abh9Y5Kmu9sIEnKFeKvzx+rLn+b+dtWdHS07r//fq1evdrtYwMCAjR37lw9+eSTt1c3AABAWrFY3M/uObRCj5V1mIb9cxZQYNFKycbHjh3rieoApBM0ewCkjSvHpTUjTMMxhr/esPeUZJHFIn3y8F0qlDPYNP/q1atq2bKl1q1b5/axoaGh+vHHH9W+ffvbLBwAACCNVXlYCshmEjRU9ugcVSyU3fRyVxs1//bbb9q6dauHCgSQ1mj2AEgby16T4q+bhsfYO+iokV+S1Pu+0moUmc8098qVK2rRooU2btzo9pG5c+fWzz//rMaNG99ezQAAAOlBYDbprsfM49un69FqeUzDIZH1ZAkISTbO7B4g86DZA8D3Dv0k7V9sGj7oLKzJjvslSbVL5NbLTcua5l66dElNmzbVr7/+6vaR+fPn17p161S7du3bqxkAACA9qfWUeSzmsh7y/1UBfq4/7ln9AxVa/t5k4zNnztT58+c9VSGANESzB4Bvxd+Qlg5wm/JmfA/Z5accwf4a/fBdslldn5R14cIFNWnSJMUpx4UKFdLatWtVsWLF2y4bAAAgXclbVirZyDQcsnOKWlTIbxoPq5J8KVdsbKy+/PJLT1QHII3R7AHgWxtGS5f+Ng3PczTQZqO8JGnEQ5VN9+k5d+6cGjdurB07drh9XJEiRbR27VpFRkbebsUAAADpU+2nzWOnd6tn8XOm4cBCkfLPUyzZ+Pjx4+VwmG/wDCBjoNkDwHcu/Clt+MQ0fMUI0fD4hPXnj9aOUKvKBV3m3Wz07N692+3jihYtqrVr16p06dK3XzMAAEB6VballCPCNFzl5Hcq7OaAi7DKTZON/fPPP/rhhx88Uh6AtOPnjZsePXrUG7dV0aJFvXJfAD5gGNLSgZIj1jTlA/vDOq8cKpU3VIMfqOAy5+LFi2rWrJl+//13t48rUaKEfv75ZxUvXvxOqgYAAEi/rDapZg9p1VDX4X0L1bVGT41Yf8NlPLTifbq09mvJaU8y/vnnn6tt27YeLxeA73il2VOiRAmP39Nischut6ecCCB92ve99Ocq0/AuZ0l962iiAJtVYx6pppCA5G9Ply9fVvPmzbVr1y63jypVqpRWr16tiAjzb7oAAAAyhepPSGtGuP5CzRmvR20/a4TucnmpLTSngkvX0o2DvyQZ/+mnn/THH3+wDB7IwLyyjMswDK/8AMigYqOkZYNMw07Dojfje8gpq15pGalKhXMky7l69apatWqlbdu2uX1U2bJltXbtWho9AAAgawjNI1V6yDScY+83ql8yp2k8rHLyjZolady4cXdaGYA05JWZPVOnTnUbHzdunLZs2SJ/f381b95ctWvXVv78+WUYhs6ePastW7ZoxYoVio+PV61atfTcc895o0wAvrJmhBR10jT8jaOp9hgl1aBMHvWol3xmYHR0tFq3bp3i8erly5fXqlWrVLCg671+AAAAMqXavaRdM13Hok6qT7k/tOEv1ydzBZesIVtYbjmiLyYZ/+qrrzRs2DCFhYV5uloAPuCVZs+TTz5pGnvqqae0detWNW/eXF9++aUKFy7sMu/EiRPq1auXli9frsqVK2vy5MneKBWAt53ZK/063jR8zsiuD+2dlCPYXx92qirrf45Zv379utq0aaMNGza4fUy5cuW0evVq5c9vfsQoAABAplS4RsLPCdczoGue+U7Zgl5WVEzybTEsVptCKzbW1d/mJhm/evWqvvnmGz377LNeKRmAd/n0NK65c+dqypQpqlmzpn744QfTRo8kFS5cWIsXL1aNGjU0ZcoUzZkzx4eVAvAIw5B+GCAZ5sd3Do9/TFcVpvfaV1L+7EFJYjExMWrfvr3WrFnj9jFlypTRzz//TKMHAABkXbV6mYasRzfpmbLXTONhVVwv5fr888/ZTgPIoHza7Jk4caIsFov69esnm82WYr7NZlP//v1lGIYmTZrkgwoBeNSeudLRTabh35zlNN/ZQA9UKag2VQslicXGxqpDhw766aef3D6iZMmS+vnnn1m6BQAAsraKD0oheUzDjxlLTWP+uQsrsHDyk1D37t2rdevWeaQ8AL7l02bP7t27JSVsoJpaN3P37NnjlZoAeElstPTTYNNwvGHT4PjuypctSO+2q5Q0Fh+vRx55REuXmv9SIklFixbVzz//rCJFinikZAAAgAzLP0iq2d00nOuv71Unn/lsa3ezewBkPD5t9kRFRUmSzp49m+prbubevBZABrH+QynqlGl4iqOlDhoRGtmhinKFBiSOO51Ode/eXQsXLnR7+8KFC+vnn39WsWLFPFUxAABAxlazp2R1vS2rxRGnV/KYH3YRUq6+LAHBycYXLFig48ePe6xEAL7h02bPzQ9l06ZNS/U1N3OLFi3qlZoAeMGFP2X8MtY0fNrIpTH2Dnq0dlHdVy5f4rhhGHrxxRc1Y8YMt7cvUKCAfv75Z5UqVcpjJQMAAGR42QtKFdqbhqudmatgm+vZPdaAYIWWq59s3OFwsKUGkAH5tNnTrl07GYahWbNmadSoUSnmf/jhh5o5c6YsFosefPBBH1QIwCOWDZLFEWcaHh7/qMJz59IbrcsnGX/zzTc1btw4t7fOmzevVq1adUvLQQEAALKMOuanZ1mvndGAIgdM46GVXS/lmjhxomJjY++4NAC+49Nmz2uvvaYCBQpIkgYNGqRq1app9OjR2rhxow4dOqTDhw9r48aNGj16tGrUqKFXX31VUsK3+Df/DCCdO7hcOrTcNLzZGalFRj191OkuhQX+O8141KhRev/9993eOjw8XKtWrVKFCsk3EAQAAICkiFoJx7Cb6GhfYhoLKlJBfrmT74V49uxZzZs3zyPlAfAN1ws6vSRnzpxauXKlWrRooRMnTmj37t3q37+/ab5hGCpSpIiWLVumnDlz+q5QALfHHitj2WuymIQdhkVD4p/Uk3VLqHaJ3InjkyZNSrGhmyNHDq1YsUKVK1f2YMEAAACZUJ1npfmuj2LPcWGXmoQd1apo19tkhFVuqstrv0o2PnbsWD322GOerBKAF/l0Zo8kVahQQXv37lXfvn2VM2dOGYbh8idnzpzq16+ffv/9d77FBzKKX8bKcvEv0/BMR2NF5SqvV1pGJo7NmjVLzz5rPt1YkoKDg/XDDz+oevXqHisVAAAg06rQXgorYBrun3O1aSysUhPJaks2vmnTJm3fvt0T1QHwAZ/O7Lkpe/bs+uijjzR8+HBt27ZNe/bs0aVLl2QYhnLnzq3KlSurRo0aCggISPlmANKHqyflXPeBaQf5shGqD+2dNfahKgoJSHjr+eGHH9S1a1cZhmF6W39/fy1YsED16tXzQtEAAACZkF+AVKuntHqYy3D5i6uUT211VrmSxWxhuRRcooZu/Lk5WWzs2LH68ssvPV4uAM9Lk2bPTQEBAapbt67q1q2blmUA8ADjp7dkjb9uGv/Q3lmtaldUvdJ5JElr165Vx44dZbfbTa+xWq369ttv1aJFC4/XCwAAkKnV6Cat+0BycWiGxWnXK3k2asD5B1xeGlalmctmz7fffqsPPvhAuXPndnEVgPTE58u4AGRC//wiy57vTMP7nMW0OvR+Dbq/nCRpx44datOmjWJiYtzedvLkyerYsaNHSwUAAMgSwvJJlcx/j3ogbpkC5fr01OBStWQNyZlsPCYmRlOmTPFUhQC8KE1n9kRFRenIkSOKioqSw+FIMf/ee+/1QVUAbonTIfsPA9y+mQyJf0LvPXaXsgf568iRI2rVqpWioqLc3vbjjz9Wjx49PFsrAABAVlLnGWnXty5DQXEX1SFgs76Nq58sZrH5KbTifYrasiBZbNy4cerbt69stuT7+gBIP9Kk2TN58mSNGzdOu3fvTvU1FovF7XIPAGlk21fyO/u7afh7xz0qUq2p7iuXT+fOnVOLFi105swZt7d866231LdvX09XCgAAkLUUuksqWlc6+ovL8AshP+nbuHqSi7NUw6o0c9nsOXLkiJYtW6bWrVt7uFgAnuTTZVwOh0Pt27fXs88+q927d5uexGX2AyCduX5R8T+9Yx42AjUxoJveeqCCoqOj1bp1ax06dMjtLfv06aMhQ4Z4uFAAAIAsqs4zpqHCMYdU0/KHy1hAnqIKKBTpMvb55597pDQA3uPTmT0TJkzQokWLJEn58+dX9+7dVaNGDeXOnVtWK9sHARmNfdV78o+7bBr/3N5ez3dooFB/i9q27aQtW7a4vd+TTz6pTz75RBZL8m+XAAAAcBvKtZGyF5GuHncZfjF0pZ6MLucyFla5mS6eTN4MWrZsmQ4fPqzSpUt7tFQAnuPTZs+0adMkSRUqVND69euVK1fyo/4AZBCn98i6bapp+G9nfh0u/aQGVCqg7t27a9myZW5v98ADD+iLL76g8QsAAOBJNj+p9lPSyiEuww0cv6mQHtFJ5UkWC6vQUJd+niwjPjZZbNy4cfr44489XS0AD/Hpp6r9+/fLYrFo8ODBNHqAjMwwdOP7/rLKaZoy0nhSb7WvpjfeeCOx0Wvm7rvv1uzZs+Xnl6Z7xgMAAGRO1Z+U/IJdhqyGQ0/6/+QyZgkIVkjZei5jU6ZM0bVr1zxWIgDPSpOv0CMjXa/9BJAxOPfMVfCp30zjqx1VVaPZI5o//QuNGDHC7b0iIyO1ZMkShYSEeLpMAAAASFJIbqlKZ9Pw4/6rFaIYl7GwKs1cjl+5ckWzZs3ySHkAPM+nzZ4yZcpIki5evOjLxwLwpLjrurH0DfOwYdOs8BcUcnKrXn75Zbe3KliwoJYvX67w8HAPFwkAAIAk3GzUHOqMVkfbWpexoKKV5ZezoMvYuHHjOEgHSKd82ux55JFHZBiGlixZ4svHAvCg62s+UWiM+dHpUx33q16pfOr+5BNu/+OfPXt2LVu2TMWKFfNGmQAAAPhf+StKJRuZhnv5Lzddoh9WuanL8e3bt6d4AAeAtOHTZk+fPn1UpUoVjR8/XuvXr/flowF4wpXj8vtljGn4jJFT/5R5Ui8/2UFxcXGmeQEBAfr+++9VpUoVb1QJAAAAV+5+wTQUodNqat3mMpb9rhaSxfVHx3HjxnmkNACe5dNmT2BgoFasWKEaNWqoWbNmeuWVV7Rz507FxLheHwogfbnw/esKMJKfxnDTBL+umjWsjy5dumSaY7FYNGPGDDVq1MgLFQIAAMBU6aZSnrKm4Z5+P7oct4TkVHCpWi5js2fP1oULFzxSHgDP8Wmzx2azqWDBgvrll18UFxenjz76SDVq1FBoaKhsNpvbH07pAdKW/Z9fFf7X96bxnc5SWrT5qI4cOuD2Pp9++qk6duzo6fIAAACQEqtVuvt503Ad6wFVsfzpMpa9WiuX4zExMfrqq688UR0AD/Jps8cwjMSf/75OzQ+ANOJ06tK8fm5TPo5qoe1LprvNGTRokHr37u3JygAAAHArqj4iBec2DZvN7gkqUV22bK4P1ZgwYYKcTtf7/QBIGz6dLvP222/78nEAPOTqlhnKe3Wvafx7R119O2WK23s89thjGjZsmKdLAwAAwK3wD5ZqPSWtG+Uy3Nr2q0bEP6pT+k9jx2JVWOVmurIp+XHrhw8f1sqVK9W8eXNvVAzgNtDsAeBebLSMn4aYhm8YAXp9Q6AcV8+a5jRo0EBTpkyRxWLxQoEAAAC4JbWekjaOlhzJD9Twk1NP+i3XCPtjyWK5aj6gK7/MkYzks3jGjRtHswdIR3y6jAtAxnNiyXDlsJ83jY+Laqh965eaxsuUKaMFCxYoMDDQG+UBAADgVmXLL1XubBp+zPazQnUj2bgRnFNBJaq5vGbx4sU6duyYx0oEcGdo9gAwFX/hb+XZM8k0fsII17BFhyWH3WU8PDxcS5cuVXi46/XdAAAASCN1zTdqzm65rk62tS5jOau3djnudDo1aZL5740AfItmDwBTR2cPVKCST++9aeg/1XXpz10uYwEBAfr+++9VunRpb5UHAACA25W/olTyPtNwD9uPsir5cq2gkjVlDc3p8povvvhCcXHmvzsC8J00O8/cMAzt3LlTu3bt0vnz53Xjxo0UT9x66623fFQdgAv71qrU2RWm8a32Upoyf41p/Ouvv1a9evW8UBkAAAA8om5v6a/VLkNFrefUzLpVy521k4wbFqvCKjXV1d/mJrvm9OnTWrhwoTp3Nl8iBsA30qTZ8/XXX2vo0KH6559/buk6mj2Ajzidiv5+wH/PYEhi4OZwOaJ2uIwNGzZMjzzyiHdqAwAAgGeUbiLlLSedO+Ay3NPvRy2Pq51sPE+ddrr62zxJyb+sHz9+PM0eIB3w+TKuN954Qz169NDff/8twzDc/khK9hqA9x1cMUnFYg+axudcq641q9a4jPXo0UODBg3yUmUAAADwGItFutt8757a1j9U1XI42bgjOJeCilVxec2aNWu0b98+j5UI4Pb4tNnz22+/afjw4ZKkZs2aaefOndq+fbskyWKxyOFw6Pz581q2bJnatWsnwzBUv359nTp1Sk5n8vWiADwv/voVhf82wjR+zQjUq4vOSM7kmzI3adJEEyZM4Ih1AACAjKJKZykkj2m4p9+PLsfDa7c1vWb8+PF3XBaAO+PTZs/Nv/TFihXTDz/8oCpVqsjf3z8xbrFYlDt3bjVv3lwLFizQ2LFjtWHDBrVs2ZKNvgAf2T9niMKNS6bxMScr6++D+5ONly1bVnPnzk3ydxoAAADpnH+wVKunafh+628qpPPJxgNK1pI1OLvLa6ZNm6bo6GiPlQjg1vm02bNp0yZZLBb16dNHfn4pbxf03HPPqUOHDtq9e7fGjRvngwqBrO3yiUMq9/c00/gxZ7je/257svEcOXJo0aJFypkzpxerAwAAgFfUekqyBbgM+Vmc6u63LNm4U1aFVWrs8pqrV6/q22+/9WiJAG6NT5s9p06dkiRVrFjx3wKs/5YQHx+f7JquXbvKMAzNnj3b+wUCWdzxOQMUoOTLs256c2cBXbtyOcmY1WrVzJkzFRkZ6eXqAAAA4BVh+RKWc5l41PazsutasvEC93QwvWb8+PHsuwqkIZ82e242c/Lly5c4FhYWlvjnc+fOJbsmIiJCknT4cPKNwQB4zt9bl6vSlTWm8V9jS2jaD5uTjY8aNUqtWrXyYmUAAADwurtfMA2FWWLUxbYq2XhsUC4FFq7g8pqdO3fq119/9Vh5AG6NT5s9efPmlZQwre+m/Pnzy2azSZL270++D8jN2UBRUVE+qBDImgyHXVr2mmncaVjU76d4yelIMt61a1f169fP2+UBAADA2/JXkEo1MQ1391umQCXfR7VAPfezewCkDZ82e24u3zpw4EDiWEBAQOK4q6VaM2bMkCQVKlTIBxUCWdPvP4xVcftfpvFZl8vrl21Jj9CsXbu2Jk2axMlbAAAAmUW9l0xD+SyX1d62Mdm4rUQtWQNDXV4ze/ZsnT+ffHNnAN7n02ZPgwYNZBiGVq9enWT84YcflmEYmjJlit566y3t3btXW7ZsUe/evTVz5kxZLBaWiQBeEhN1SUW2f2gajzaC9Mq8v5OMFSpUSAsWLFBQUJCXqwMAAIDPlLhXKniXafhp2xJZ5Ewy5pBV2as0dZkfFxenKVOmeLJCAKnk02ZP+/btJUlLlixJspTrpZdeUvHixeV0OjVs2DBVqVJFd999d+K0v1y5cmnQoEG+LBXIMvbOHqxcumoa/+hIaZ04cTrxdWBgoBYuXMhsOwAAgMzGYnE7u6eU9ZSaWpOfzFqwfkfTayZMmCCn02kaB+AdPl/GtXr1ai1YsEB2+78n/oSEhGj16tWqV6+eDMNI8lOpUiWtWrVKRYoU8WWpQJZw5u/9qnzM/FjMfxzhGjF3R5KxL7/8UrVq1fJ2aQAAAEgL5dtKuYqbhp/xW5Js7HpALgUULOsy/8iRI1q+fLmnqgOQSn6+fmDDhg1djhcrVkzr16/XH3/8ob1798put6tMmTKqVq2ajysEso4zcwcov8VhGh+0JbdibhxJfD1w4EB16dLFF6UBAAAgLdj8pLq9paUDXIZrWg+qhuUPbTMik4wXve9RHf52qMtrxo8fz7YcgI/5vNmTksjISEVGRqacCOCO/PHLElWJ3mAa33SjuGYu/3eabtOmTTV8+HBflAYAAIC0dFcXac1w6foFl+Fn/ZaoV/x/PrMVrSFLQLCMuBvJ8pcsWaK///5bxYsX90KxAFzx6TIuAOmD025XwMo3zOOGRS//eE2SIUkqWrSoZs6cKZvN5qMKAQAAkGYCQqTaz5iGm9m2qZTlRJKxeMOq8GrNXeYbhqFJkyZ5tEQA7qW7Zs/ly5d17tw5GYaR1qUAmdb27z9TCcffpvFvzkdqy54/JUkBAQGaN2+e8uTJ46PqAAAAkOZq95L8Q0zDT9t+SDZWqEEn0/wvv/xSsbGxHikNQMp80uxxOBz6/ffftXXrVp09ezZZPDY2VkOGDFFERITCw8NVoEABhYWFqUOHDtq7d68vSgSyjOgrF1Vyz8em8SgjSK/NO5z4euzYsapZs6YvSgMAAEB6EZJbqtbVNPygbb3y6VKSsSu2nPLPV9Jl/tmzZzV//nyPlgjAnFebPYZhaOjQocqTJ4+qVq2qOnXqqGDBgqpbt65+++03SVJ8fLxatWqld999VydPnkw8hevGjRtauHChateurVWrVnmzTCBL2T97sHK7OWr9g0NFderMRUlSz5499dRTT/mqNAAAAKQndV+QLK6X8QdYHOrhtyzZeNlW3U1vN378eI+VBsA9rzZ7evXqpXfeeUdXr15Ncpz6b7/9platWungwYN6/fXXtWbNGhmGoVy5cqlOnTqqUaOGgoODE5s+Xbp00ZUrV7xZKpAlnPl7n6qemGka/9serlHzd0uSatSooc8//9xXpQEAACC9yVVMqtjeNPyYbaWy6XqSsbhCd8kSEOwyf/369dqzZ48nKwRgwmvNnrVr12rKlCmSpKCgIHXs2FEDBgxQx44dFRwcrCtXrmj06NGaMmWK/P39NWnSJJ07d06//PKLtmzZovPnz6t///6SpHPnzumrr77yVqlAlnFm3isKcHPU+mu/hCo2Nk65c+fWvHnzFBQU5MPqAAAAkO7c08c0lN1yQ4/akq7CiHNaVPDuNqbXTJgwwWOlATDntWbP1KlTJUn58uXT1q1bNWfOHI0aNUpz5szRtm3blD9/fk2ePFmXL19W37599dRTT8lisSReHxwcrA8++EAtWrSQYRj64YfkG4ABSL0/Ny9Vlaj1pvGN14tp9s+/S5KmTZumYsWK+ao0AAAApFeF7pJKNjIN9/BbpgDFJxnLV7eDaf706dMVHR3toeIAmPFas+eXX36RxWJR3759Vb58+SSxcuXKqW/fvnI4EmYYdO1qvvHXk08+KUls1AzcAcNhl21FSketJ/xH95VXXlHr1q19VRoAAADSu3ovmYYKWC6pnW1jkrFLClVQ4fIu86OiojRjxgyPlgcgOa81e06ePClJqlu3rsv4/46XLl3a9D5lypSRJF28eNGD1QFZy94fxqm4/S/T+Izzkdr6+xHdc889eu+993xYGQAAANK9kvdJBSqbhp+1LZZVziRj5R4wP+Rj/PjxMgzDY+UBSM5rzZ5r165JknLnzu0ynjNnzsQ/BwYGmt7n5p4hcXFxnisOyELioi+p8PYPTeNRRpBenf+XwsPDNWvWLPn7+/uwOgAAAKR7FotU72XTcCnrKbW0bk4yFp07UtagMJf5u3bt0q+//urJCgH8h1dP45KUZB+e1IwD8Kz93w1RLpmfZvfhX6V06vR5TZ8+XRERET6sDAAAABlGhfZSzqKm4d5+30v6d7ZOvFMq2rCzaT7HsAPe5fVmD4C0c/XEHyr/9zem8X8ceTRy3k699tpratWqlQ8rAwAAQIZi83N7MlcF6z+6z7ozyVj26ub7QM6ZM0cXLlzwVHUA/oNmD5CJnZgzQAEWu2n89a3hqlm9ut59910fVgUAAIAMqVpXKSy/abi330L97+yeK85AhZWs7jI3NjY28QRnAJ7n5+0HjBs3Tvny5Us2fvbs2cQ/v/POO6bX/28egNQ7uWO5yl9ZZxrfGFNSS387rF07d8rPz+tvBQAAAMjo/IOkur2lnwa7DNewHtLd1v361Vkhcaxs657a/tl2l/kTJkxQv379ZLUyBwHwNK9/wnO3FvPmvj1Dhw71dhlA1uJ0KHbxQPOwYVG/n+I1edIkFS1qvvYaAAAASKJmD2nDx9KNSy7DL9gWJmn2XA4tJmtoTjmvXU6W++eff2rlypVq3ry5t6oFsiyvtlANw/DID4Bbs2/xGJVw/mMa//ZyJVWudrc6duzow6oAAACQ4QWGSXWeMw03sP2uqpbDia8dhlSq6eOm+WzUDHiH12b2rF692lu3BuCG4/pl5dv2sWkrN8oI0udbnFr10xjfFgYAAIDMoXYvadOnUly0y3Bvv+/VK75/4uugik2lReMkw5ksd9GiRTp+/LiKFCnitXKBrMhrzZ6GDRt669YA3Fj/+TNqZI0yjX98rKLGff6hQkNDfVgVAAAAMo2Q3FKtntJG118eNrNtU6T9qP4wErYLuOrwU87ydXV538ZkuU6nU5MnT2ZrD8DD2AkLyESO/b5Rda/9ZBr/x5FX/uXuV/Xqrk9FAAAAAFKlbm/JL8g0/LzfoiSvS7fsbpo7efJkxcfHe6w0ADR7gEzDMAwd+uoFBVocpjmfnaup1wb082FVAAAAyJTC8knVnzANP2D9RcUspxNfn/cvIFv2vC5zT506pUWLFrmMAbg9NHuATGLuJwPVOOyIafyX+NJ6/rUPONoSAAAAnnFPH8nqemcQm8XQc7Z/GziGpHL39zC9FRs1A57Fpz4gEzj0xwGVPznHNO40LPo1f1eVLB7hw6oAAACQqeWMkKo8Yhp+yLZeBXXh34FS9SSrzWXuqlWrdPDgQU9XCGRZNHuADM5ut2v2Ww+rUtgV05x58ffoxRf6+LAqAAAAZAn1+0oW1x8rAywOPe23JPF1tN2qfNWamt5q4sSJHi8PyKpo9gAZ3Ccj3tHTpU+bxqOMYIXdP0R+Nv66AwAAwMPylJYqtDcNP2r7WeH690vJiCaPm+ZOnTpVN27c8GR1QJbFpz8gA9u9e7esWyYqn3+Mac5s//ZqWa+WD6sCAABAltKgv2koyBKvp/yWJr4+b8kl/zzFXOZeunRJc+aYb00AIPVo9gAZlN1u1+Dej6t3VfNjKv9x5lPNLkNksVh8WBkAAACylAKVpLKtTMNP2FYol64mvi7f9hnTXDZqBjyDZg+QQX3wwQd6qtQZBVrNj1pfWuBZ3VWigA+rAgAAQJbkZnZPqCU2yeyeG/kryxIY6jL3t99+044dOzxeHpDV0OwBMqD9+/dr49fvqE0x8+VbvzrLq2Un829NAAAAAI+JqCWVaGgafvJ/ZvfEOS0qXO8h01xm9wB3jmYPkME4HA493bO7Pro/m2mO07Boe/lXVCJvmA8rAwAAQJbW8FXTUJglJsnsnly120lyvdXAjBkzdOWK+UmzAFJGswfIYMaMGaNa2qnI7LGmOXPVWJ3btPZhVQAAAMjyiteTStxrGn7StkI5FSVJumoEKaRkdZd5169f1/Tp071SIpBV0OwBMpBDhw5pzLA39PZ9IaY5V4wQXb77NeUJC/RhZQAAAICkhq+Zhv47u6d0q56muePHj5dhGB4tDchKaPYAGYTT6VTPnj319r1+yuFvvinzZNsj6tLY9bckAAAAgFcVrycVb2Aa7mZb/u/snrCi8suR32Xevn37tH79eq+UCGQFNHuADGLChAm68edGdati/tf2D2cRFWr2gkID/XxYGQAAAPA/Grmf3dPT70dJkiEponEX01w2agZuH80eIAM4efKkXh80SJ+1DpPV9T52kqRJoU+rc+2SvisMAAAA+K/i9VOc3ZND0ZKkgMiGsvgFuMybN2+ezpw545USgcyOZg+QAfTp00dtit/Q3YXMc3501FKz1g/Lz8ZfawAAAKQxNydzZbPcUM//37snxrApR5UmLvPi4+M1ZcoUr5QHZHZ8KgTSuUWLFmn54nka1dx8U+ZYw1+L8j+vFhVdr3kGAAAAfKpEA7eze7r/z949hRs9Zpo3ceJEORzm+1UCcI1mD5CORUVF6YUXXtAbDQJVMNT8NIIJjgf0VJtGsljcrPECAAAAfCmF2T3P+i2RJEX751JAoXIu8/755x8tW7bMK+UBmRnNHiAdGzx4sIKun1S/uubHqJ80cutQ6Z6qUSy3DysDAAAAUlCigVSsvmn4Sdty5dVlSVKxpl1N89ioGbh1NHuAdGrr1q367LPP9HHzQAXYzPNGOB7Xy/dX811hAAAAQGrdN8g0FGyJ0/N+30uS7AWryBqS02Xe0qVL9ffff3uhOCDzotkDpEN2u129evVS85JWtYn0N837zVlOYdU7qXS+MB9WBwAAAKRS8fpSyUam4cdsq1RQF+SURXlqt3WZYxiGJk2a5KUCgcyJZg+QDo0ZM0Z7d+/U6BZBpjkOw6IRRne93LSsDysDAAAAbtF9b5qGAi12vei3QJKUq1Zbyep6SvuXX36puLg4r5QHZEY0e3wgOjpa69at04cffqjOnTurRIkSslgsslgsKl68eFqXh3Tm+PHjevvtt9WnToAi85j/FZ3paKwGDe5TvuzmDSEAAAAgzUXUksq2NA13sq1VUcsZxViDFFLmbpc5Z8+e1fz5871VIZDp0OzxgTZt2qhhw4YaOHCgvvvuO9abwq3+/fsrTNf1VkPzTZkvG6GaGtBFTzcs5cPKAAAAgNt03+umIX+LQy/5zZMkFWnMRs2AJ9Ds8QHD+PfI7Fy5cqlZs2YKC2OPFSS3cuVKzZkzR8ObBCp7oPkx6h/bO+rJZjUUFujnw+oAAACA21SwqlShnWn4QetGlbYcV2z2IgooUNplzrp167R3715vVQhkKjR7fOCxxx7TjBkzdOjQIV28eFErVqxQeHh4WpeFdCY2NlYvvPCC6haxqXu1ANO8A84IbczRVo/WLurD6gAAAIA71Oh1Sa6/0LRaDL38/7N78tbrbHqLCRMmeKMyINOh2eMDTz/9tB577DGVLu26Qw1I0scff6w/Dx3UuNbu9+AZan9C/VtVlL+Nv74AAADIQPKVk6qYN3IesP2mipa/FVD6btlCc7nMmTZtmq5du+atCoFMg0+LQDpw9OhRvfvuu3qulr/uKuD6BAJJ+sFRWzcK11OrSgV8WB0AAADgIQ1flSzmv+8O9Jstp6zKVr21y/jVq1c1c+ZMb1UHZBo0e4B0oG/fvspmjdF795nP6rlhBOj9+C4a1KqcLBbz/XwAAACAdCu8lFSti2m4kW2X6lr3KnfttpLN32XO+PHjk+yLCiA5mj1AGlu2bJnmz5+vUU0DlSPIvInzmb29ypWrqDol2e8JAAAAGdi9r0g28z0qX/WbJYdfsEIrNHQZ3759u7Zs2eKt6oBMgaN8MoHjx4+7jZ86dcpHleBWxcbG6sUXX1T9ojY9eZf5f/D+chbQFGdrLWpVzofVAQAAAF6QM0Kq9ZT06ziX4busf6qldYuW1e2oa3tWuswZP368ateu7c0qgQyNZk8mEBERkdYl4DaNHj1aR/48rHnPhLrNG2J/Uu1qlFTZ/Nl8VBkAAADgRQ0GSNunS3FRLsMD/Wbrp1yjFBhRWbHH9iSLz5o1Sx999JFy587t7UqBDIllXEAaOXXqlN577z29UDtAVfKbb1K31FFbm23V1LdZWR9WBwAAAHhRaLhUr49puJT1lDrZ1ipH7fYu4zExMfr666+9VByQ8dHs+X92u10Wi+WOf7766iuf137s2DG3P5s3b/Z5TUjZoEGDFKZreqdRoGnOdSNQ78U/rp71S6hADvdHsgMAAAAZyt3PS6H5TMN9/eYqV6mq8svp+iTaCRMmsFEzYIJmTyZQpEgRtz8FCxZM6xLxH5s3b9bXX3+dik2ZH9SNkIJ6pmEpH1YHAAAA+EBgmNTwFdNwfstldfdboWw12riMHzx4UD///LO3qgMyNPbs+X9+fn7av3//Hd+HxgpS4nQ61adPHzUoalPXquabMv/pLKgvHPdrUOMyyh7k+thJAAAAIEOr0U36Zax06YjL8HN+i/TtXSN1ecO3MmKvJYuPHz9eTZo08XKRQMZDs+d/lCvHSUfwvhkzZmjblt+0/Wn3mzK/Ze+mArmzqcvdRX1UGQAAAOBjNn+p8ZvSvJ4uw9kt1/V80DK9UrWFrm6enyy+cOFCnTx5UoUKFfJ2pUCGwjIuwIeio6P16quv6sXaAarsZlPmJY67tdFZWQNblFOgn3keAAAAkOFVfEgqUMU03M22XBVr15OsyX8vdjgc+uKLL7xZHZAh0ewBfGj48OGyXTutoW42Zb5mBOq9+C6qXDiHHqjMskAAAABkclar1GyoaTjQYtegnMsVWq6By/ikSZNkt9u9VR2QIdHsAXzkr7/+0kcffaRPWwYpW6D5psxj7A/ptMI1qFU5Wa3meQAAAECmUaqxjBINTcNtbL+qYf0aLmMnTpzQkiVLvFUZkCGxZ48PHD58WBs2bEgyFh0dnfjP/x7X3rJlSxUo4Pp4QWRcr776qloWd+jB8uazeg45C2uqo5UaRebVPaXz+LA6AAAAIG1Zmr8rY2JDWeT6OPV38q/Rz0UrK+bonmSx8ePHq3379l6uEMg4aPb4wIYNG9S9e3eXsQsXLiSLrV69mmZPJrNx40YtWzRX+54Pc5v3lr2b7BY/vdaKzcIBAACQxRSsKqPKI7LsnukyXMN6SI81ba0pU5I3e1asWKHDhw+rdOnS3q4SyBBYxgV4mWEY6t+/v965L1AROcz/ys1zNNAvzorqUL2IyhXI7sMKAQAAgPTB2vQtxVuDTONvF96skLyFXcYmTpzorbKADIdmjw9069ZNhmGk+qdRo0ZpXTI8aPbs2Yo/ukV9ageY5lwywjQsvosC/azq16ysD6sDAAAA0pHsheS4+wXTcFHrOfVtHekyNnXqVMXExHirMiBDodkDeFFMTIzeGPSqJj0QLJubzZbftz+mi8quHvVLqFDOYB9WCAAAAKQvQQ37Kcov3DQ+sOg+5cmdI9n4hQsX9N1333mzNCDDoNkDeNGnn36qB/KfVo1CNtOc35zl9J2joXKF+Ou5RqV8WB0AAACQDgWGKf7eQabhHJbreqddcZex8ePHe6koIGOh2QN4yblz5/TVmGF67z7z07fiDJtej+8pyaLejcsoe5C/7woEAAAA0qnc9XvoREBJ0/hTEf+oXP7ke/v88ssv2rVrlzdLAzIEmj2AlwwdOlTD6scrW6D58q3xjnb60yisiNzBevzuoj6sDgAAAEjHrDbF3DfUNOxvceqzh/K7jDG7B6DZA3jFgQMHdPLnyXqwvPlMnb+cBTTO3laSNLBFOQX6mS/1AgAAALKaUnXbakdATdN403yX9EBk8kNQZsyYoaioKG+WBqR7NHsAL3j7tX4a08L89C1JesPeU7EKUOXCOfRA5YI+qgwAAADIOG40elt2w/xj6+gHcijgP9+ZRkdH65tvvvFyZUD6RrMH8LD169erbszPishh/tdrnqOBfnFWlCQNur+crG5O6gIAAACyqrvvbqAlAa1M46XCYtX37uRfso4fP16GYXizNCBdo9kDeJBhGJr2/ovqU8d8Vs8lI0zD4rtIku6LzKt7SuXxVXkAAABAhmK1WqT7XtdFI8w0582GwSqULemXp3v27NGmTZu8XR6QbtHsATxo8YK5ernEYVkt5jN13rc/povKLqtFeq1VeR9WBwAAAGQ899euoC/8u5jGw/wNjWya/ARcNmpGVkazB/AQu92u4zP7qmI+842Wf3OW03eOhpKkDtWLKLJANl+VBwAAAGRIAX5Whd/bS/ucxUxzHq8SoHsikv4e/t133+ncuXPeLg9Il2j2AB6yaPJw9Sp31TQea/jp9fiekiwK9LOqX/OyvisOAAAAyMAeqVNCo6w93eZ82jJI/7sVZlxcnKZOnerlyoD0iWYP4AE3rkWp5O8fy99mvnxrjP0h/WkUliT1rF9CBXME+6o8AAAAIEMLDfRTlXtaapGjrmlOjUI29arun2Rs4sSJcjqd3i4PSHdo9gAe8NvoJ3RXXvP/iOx1FtMkxwOSpFwh/nq2USlflQYAAABkCk/eU1wfq4tuGOaHoQxvEqR8of9+AfvXX39pxYoVvigPSFdo9gB36Mpf21Xnxs+mcbth1SvxT8suP0nSi43LKHuQv2k+AAAAgOTCwwLVsGY1jbW3M83JFWzRB82SbtbMRs3Iimj2AHfC6dT5qY8p2M88ZZLjAe01SkiSiuYO0eN3m28sBwAAAMDcUw1K6kvjAR115jXNeaJqgBoW+3ez5iVLlujo0aO+KA9IN2j2AHfg0k8fqJTtlGn8T2dBjbE/lPh6QItIBfjx1w4AAAC4HRG5Q9SiSjG9be/mNm9c6yD5//+v3U6nU5MnT/Z+cUA6wqdO4HZd+FMhG0e6TXk1vpdilbCm+K6InGpTpaAvKgMAAAAyrWcaltJqZzUtc9QyzamQ16b+9/y7t88XX3yh+Ph4X5QHpAs0e4Db4XTo+szuCrQ6TFO+tjfTVqNc4uvBD5SXxWJ+WhcAAACAlJUvmF1NyuXT0PgndM0INM0bfG+giudM+P379OnTWrhwoY8qBNIezR7gdvw6XiHnd5mGjxt5NMr+SOLr1pULqkax3L6oDAAAAMj0ejcurVMK12h7B9OcEH+LPm0ZlPiajZqRldDsAW7VuYNyrhziNuXV+F66pmBJUoDNqldblnObDwAAACD1qhXNpQZl8miqo6UOOCNM89pE+qt9uYTTVFavXq0DBw74qkQgTdHsAW6Fwy5j4bOyOs3X+063N9VGZ+XE193qFVfR8BBfVAcAAABkGS82LiO7/PRGfA+3eZ+3ClL2/1/tNWHCBB9UBqQ9mj3Ardj0qSwntpmGjzrzarj9scTXuUL89cJ9pX1RGQAAAJCl1C6RW7VL5NY2I1Kz7I1M8wpnt2pUs4TlXF9//bWuX7/uowqBtEOzB0itM/tkrBnuNmVg/LO6rn/XBb/UpIxyBPt7uzIAAAAgS3qxccIXqyPsj+qiEWaa90yNADUqbtPly5c1a9YsX5UHpBmaPUBq2GOl+U/L4ogzTZlib6nfjPKJr0vmCVWXu4v5ojoAAAAgS6pfOo/uisipy8qWZIa9K5PbBCvYj42akTXQ7AFS4+d3pTN7TMN/OQtolP3hJGOD7i8vfxt/xQAAAABvsVgsibN7vnM01AZHRdPc0rmtGtIoUFu3btXWrVt9VSKQJvgkCqTkr7XSps9Nww7DogHxzypGgYljd5fMrabl8/miOgAAACBLa1wunyoUzC7JokH2p3TdCDTN7V83QDUKWpndg0yPZg/gzo1L0sLnJBmmKZMdrbXdKJv42mKR3mxdQRaLxQcFAgAAAFnb/87uOWbk14f2zqa5NqtFU9oFa+7smbp06ZKvSgR8jmYPYMYwpCX9pKsnTFP2O4vqY3unJGMPVSuiSoVzeLs6AAAAAP+vRcUCKpMvYYPmrxwttMNpfiJulfw2vVjdoalTp/qqPMDnaPYAZvZ8J+2dbxqONfz1UvwLitO/p20F+Vs1sEWkL6oDAAAA8P+sVov6NCkjSXLKqlfjeynOsJnmv9UwUCu/GS2Hw+GrEgGfotkDuHL5qPRDf7cpI+yP6KARkWTs6QYlVSBHkMkVAAAAALyldeWCKps/YXbPQSNCY+3tTXMDbBYNr31Ry5Z876PqAN+i2QP8l8MuzX9Gir1qmrLOUVlfOVokGSuQPUjPNirl7eoAAAAAuGC1WtS36b97aY5ztNMBZ4RpftUCNl1d9LovSgN8jmYP8F9rR0pHN5mGLxlhGhD/rIz//PV5rVU5hQT4ebs6AAAAACZaVCzw/ydzSfHy06vxveQwzA9O6Vz4pI6sm+Wr8gCfodkD/K+/1krrPnCb8np8T51VriRjNYrlUru7CnmzMgAAAAApsFot6tvs39k9u4zSmuhoY5pvs1oUuqK/FBvti/IAn6HZA9wUfVaa30vujln/zn6vfnTWSTJmsUhvt+GodQAAACA9aFo+n6oU+fd03NH2DtrvLGqan88vWjFLXvVFaYDP0OwBJMnplBY8I0WfMU3525lfQ+1PJBvvVKOIqhTJ6cXiAAAAAKSWxZJ0dk+c/NU3/nm3p3MF7flGOrzSF+UBPkGzB5CkTWOkP382DccZNvWOf1HRCkkyHhbop4Etynm7OgAAAAC3oFHZvKpWNGfi6wNGUX1i7+T2GuP73tL1i16uDPANmj3Asc3Sqnfdpgy3P6bfjZLJxvs0Ka282QK9VRkAAACA22CxWNS/WWSSsYmOB7TVWdbkCskSdUpa9KJkmG/rAGQUNHuQtV2/KM3tIRkO05SfHDU01dEy2XjJPKHqdk8Jb1YHAAAA4DbVKx2u2sVzJ752yqr+8c/quuHmy9oDS6QtX/igOsC7aPYg63I6pHlPSVeOmaacNHJrYPzTkpJvvjz4gQoK8OOvEAAAAJAe/XfvHkn6xyigYfYu7i9c/oZ0eo8XKwO8j0+qyLrWjpT+XGUathtW9YnrrcvKlizWKDKv7iuXz5vVAQAAALhDdUuF696yeZOMzXA00RpHVfOLHLHSd92luGterg7wHpo9yJr+WJbQ7HHjY3tHbTWSb77sZ7Vo8AMVvFUZAAAAAA96pUXkf0YsGhj/jM4Z2c0vunBI+vEVr9YFeBPNHmQ9F/+SFjztNmW9o5LGO9q6jHWvV1yl8oZ5ozIAAAAAHlapcA61u6tQkrFzyql+8c+7v3DHN9Lu77xYGeA9NHuQtcRdl2Y/IcVcMU056cytl+J7y3Dx1yNPWIBebFLGmxUCAAAA8LD+zSLlb0u6D+d6ZxWNt7dxf+GSvtKFP71YGeAdNHuQdRhGwpv1GfPN1mKdNj0X/7IuyvWUzoEtIpU9yN9bFQIAAADwgqLhIXqsdtFk4x/ZO2mHs7T5hXFR0ndPJnxpDGQgNHuQdWyeLO2e5TZlqKObdhmu3+yrRuRUpxoR3qgMAAAAgJe92KSMQgNsScbs8tOL8b111Qg2v/D0noQvjQ3DyxUCnkOzB1nDnz9Ly15zmzI7po6+dTR2GbNYpPfaVZLVmvwIdgAAAADpX56wQD3VoGSy8eNGPr0W38v9xbtnSVu+8FJlgOfR7EHmd/6QNKebZDhMU3ZH5dBbek6S62bO43WKqXKRHN6pDwAAAIBP9Lq3pMJDA5KNL3XerW/t97m/eNlr0tFfvVQZ4Fk0e5C5Xb8offuwFGu+IfPFGIuetw1WrJK/6UtSeGiABjT/73GNAAAAADKasEA/9W7setuGd+xPaJ+zmPnFTrs050kp6rSXqgM8h2YPMi9HvPRdN+mi+e75TsNQ7/OddNxayDRn0P3llSOETZkBAACAzOCxOkUVkTv5Hj0xCtQz8S/rshFqfnH06YSGjz3OixUCd45mDzKvZa9JR9a6TXlzTxFtytPeNF6reC51qF7Yw4UBAAAASCuBfja91rK8y9gxI79eiu8tp7u9mI/9Kq14wzvFAR5CsweZ0+bJKW6g9tUeaXoR8zdpm9Wid9tXksXCpswAAABAZnJ/5QKqVTyXy9haZ1V9bO/k/gabJyV85gDSKZo9yHz2L5F+fMVtyu7LIXo99knZQnKa5nS7p7jKFcju4eIAAAAApDWLxaLBD1QwjY91tNOyayns2/njq9KhlR6uDPAMmj3IXI7+Ks3rKRlO05QbgXn1wLI8CqjUwjQnX7ZAvdy0jDcqBAAAAJAOVCmSUx2qF3EZM2TVQNtAHYoKNL+B4UjYI/TMXu8UCNwBmj3IPM79kXDylj3GNMXwD1XPn3PI0bCP21sNbVtR2YLYlBkAAADIzF5pGalgf5vLWJRC9Gx8X0XHudnAJy4q4TNI1BkvVQjcHpo9yByunpK+6SDFXHaTZNGWok/rx9iK8s9lfvpW0/L51LJSAY+XCAAAACB9yZ89SM82LGUa/zOkip7ZX0tOw03D58oxadajUtx1L1QI3B6aPcj4Yq5IMzomvMm64Wz+nnpO/EXZ63QwzQkJsOqddmzKDAAAAGQVT99bUgVzBJnG15d8Qa+tT2HW/4lt0oJnJKf5dhKAL9HsQcYWHyPN6iKd+d19Xt3e+u54fp0v0VwWm59p2sAW5VQoZ7CHiwQAAACQXgUH2PRqy3Kmcat/oL4K763JO+zub7R/UcJBMe5mAQE+QrMHGZc9TprTVfp7vfu8Sh3kaDJEb361TIGFzd/EKxXMpifqFvdsjQAAAADSvbZVC+muiJym8ZBStdX/QGUddka4v9GWydLqYZ4tDrgNNHuQMTnipbndpUMr3OcVbyC1H6/Pps5SbLlWpmkWGRrZqapsVpZvAQAAAFmN1WrR223Mj2KXpOyNn1GTL8/JEV7W/c3WfSBt+tyD1QG3jmYPMh6nQ5r/tHRgifu8/JWkR2Yoxi6N3nRO1gDz5Vk97immioVyeLhQAAAAABlFtaK59Ght85k7fjny6WrplvoqrpUUksf9zVa8IW2f7uEKgdSj2YOMxemUvn9B2jvffV6OCKnLXCkoh14cM1vKH2meaovXgJbuu/gAAAAAMr9XWpRTrhDzzZiz135QQ6YsVXyn6ZJfCnt9Lu4j7fvewxUCqUOzBxmHYUhLXpZ2zXSfF5pXeny+lL2gDp28oBVnQ92mf9KljoIDbJ6rEwAAAECGlCs0QK+1Mt/n02LzV1zlh/TtusPSI99IVjendBlOad5T0qGVXqgUcI9mDzIGp0Na1Fva/rX7vOBc0hPfS3nLyjAMdR+/ShZ/82MUq+e4ocYVCnq4WAAAAAAZVacaEapeNKdpPLh4Vb0/Y7mcJRtLHSZLcrPvpyNOmvWo9MePHq8TcIdmD9I/e5w0t4e04xv3eYE5pK4LpfwVJUlT1x3U8Xg3s3quX9aXL7b2XJ0AAAAAMjyr1aJ321eSReZHqF8r21LzFv8oVXxQajPa/Q0dcdLsx6V9izxbKOAGzR6kb3HXpVmPSfsWus8LCJO6zpcK3SVJOn7put7/8Q+3l/SsHKRcYSmsswUAAACQ5VQslENd7y5mGvcLy60hC3clvKjRTWr2jvsbOu3Sd92kPXM9ViPgDs0epF8xV6VvOkiHf3Kf5x8idflOKlJTkuR0Guo/a7vsMt+HJ+D4Vr3R80FPVgsAAAAgExnQspxCrHbT+LX8VTV50bqEF/Vekur3dX9DwyHN7yXtTGEPUsADaPYgfbp2Qfq6jXR0k/s8vyDp0ZlSsXsSh77+5W/99s8V00vsUef1Xscaslr51x8AAACAa9mD/PV220pucz5Yc0LXYv+/IdTkban20+5vajilhc9JW6d4qErANT7tIv05f0j6sql0aqf7vICwhBk9JRslDh08E6XhS/e7vSzv3z+pUzv26gEAAADgXuc6JVXQYv5FclxAdr0+69eEFxaL1GqUVLd3Cnc1pCV9pVXvJJw4DHgBzR6kL3+tlb5oIl38y31eUE7piUVSiXsTh2LtDr00a6fiHOZvmNF7ftJH/XvIYnGzYz4AAAAASLJYLBrXo4GccTdMc77fd1lb/7548wKp+XtS/X4p33z9RwnLuuyxHqoW+BfNHqQf276WvnlIijHvnEuSwvJL3X+UitRIMvzRioPaf+qq6WX2qPOqFH9QjRs39kS1AAAAALKAamUiVMnp5stoi0X9Zm1XTLwj8bWavCU1GpTyzfd8J01/ULp+0TPFAv+PZg/SntMhrXhTWtwnYZd6d3IUTWj05K+QZHjT4fOavN79bKALP3yiEe+8dafVAgAAAMhiPn2xg2KP7zONH70cq09XHfp3wGKRGr2WsI9PSv7ZKH3ZXLp4xAOVAglo9iBtXb+YcLT6ps9Szs1TVuqxTAovlWT4yvV49f9ul9vlrlc2z1ezKkV1991332HBAAAAALKa0qVKqbYOybDHmeZMXPun9hz/zyqFBv2kFu+n/IALhxK2szi86g4rBRLQ7EHaObZZmnivdHBZyrlF60rdl0k5CicZNgxDry/co1NXYkwvjTt7RJfXTdO77757pxUDAAAAyKLeGfiCLm+YYRp3GNJLs3foRpwjaaDuC1KHLyVbgPsHXL8gfdNBWj08YfUDcAdo9sD3nE5p46fS1FbSlWMp51d5RHrieyk0PFnou63H9cPuU6aXGvY4nV/8oR7u2EFVq1a9k6oBAAAAZGFVq1bVvfniFXvqkGnOX+eu6b0fXCz3qtwx4TNNUM4UnmJIa0ckNH2unb+jepG10eyBb12/KM18RPppcMr780hS48HSgxMkv8Bkof2nrmrw97+7vfzSmq/kuHhMQ4cOvd2KAQAAAECSNPjNN3Thx09lOMw/y8z47ahW7juTPFDsHumplVKu4ik/6K/V0oQG0tHfbr9YZGk0e+A7f/4sTagvHVqecq5fkNTpK+neAQmbm/1HdKxdL8zYrli70/QWN45sV9S2xXryyScVGRl5B4UDAAAAgFS7dm3dV62Mrvwyx23eK/N262yUi60m8pSReq6UCtdM+WFRJ6Wv7pfWjJQc8bdZMbIqmj3wvtgoafHLCUcKXj2Rcn5YfqnbUqnigy7DhmFo0Pw9+uv8NdNbOK5f0YWloxUYGKAhQ4bcXt0AAAAA8B+DBw/WlV9mK/bkAdOci9fiNPC73TJcnSITlld6crFU8aGUH+a0S2veTzit69zBO6gaWQ3NHniPYUj7F0tj60jbpqbumpL3Sc9ukIrUME355rejWrzrpNvbXPhxjBzRF/Xiiy+qaNGit1I1AAAAAJiqX7++Gjaor/OLP5Qz9rpp3tqD5/T1pr9dBwNCpI5TpFYfSFb/lB96crs0oV7CLB977O0VjiyFZg+84+Jf0sxHpdmPp242j8UqNX5Teny+FJbPNG3P8St6d7GLDc/+x5Xf5unG4c3KkSOHBg0adKuVAwAAAIBbgwcPlv3yaV1cNclt3vs/HtDBM1GugxaLVOdpqccyKUdEyg91xCXM8pnQQPprza0XjSyFZg88K+aq9NNbCbN5Dv6YumuyFZSeXCLdO1Cymv8reeVGvF74drviHOb79MQc36fL66ZJkl577TXlzp37lsoHAAAAgJQ0btxYd999t67tWalrBzaY5sXZnXphxnZdi3VzOE2RmtIz66TSzVL38PN/SNPaSbO6JHzJDrhAsweeEXc94Tj1MVWljWMSus6pUapJwrKt4vXcpjmchvrN3qmjF82nSTquX9H5RSMlp0OFChVSnz59buV/AQAAAACkisVi0eDBgyVJF5d/LnuU+THph85G69V5Jvv33BSSW3psTsJqB4stdUUcWCJ9Xlv6YYAUdfpWykcWQLMHdybmakKT59O7Eo5Tv3Exddf5h0qtP5IenyeF5kkx/YPlf2jVgbNuc84v+UiOqAuSpCFDhigkJCR1tQAAAADALWrVqpWqV68uZ0y0Liz5WIZhvgJhye5Tmrrxb/c3tFoTVjs89ZOUp2zqinDGS1smS2Pukpa9Ll1JxRYayBJo9uD2XPpH+ult6ZNKCU2e6DOpv7ZYfem5jVKtp1weq/5fC3Yc14S1f7rNubJplmKObJckRUZGqnv37qmvBwAAAABukcVi0ZtvvilJijm6W1c3L3Cb//7S/dp8JBVfjheukbCs6+4XJKX8eUmSZL8h/To2YaXFwuelkztTdx0yLZo9SD17bMLpWjM6//9yrdFS7JXUXx+cW2o3Vuq2RMpdIlWX7Dx2Wa/O2+M2J+af3bq84dvE1++//778/PxSXxcAAAAA3IZ27dqpatWqkqTL66Yr9uQfprl2p6EXvt2us1djUr6xf7DU8n3pqVVS/sqpL8gZL+2cIU1qKE1uIm2fJsXcwmc2ZBo0e+Be3HXpjx+l73tLH5ZJOF3r0HJJbtabJmORqnWVem+Vqj2eqtk8knT6SoyenrZVcXbz6ZD2qAs6v/gD6f+nTNapU0cPPvjgLdQGAAAAALfHarXq3XffTXjhtOvcwuFyXDdvrpyLilXvb3co3s2hM0kUqSE9vVpq9q4UkO3WijuxVVr0ovRBGWnOE9KeudKNy7d2D2RYTH9AUnHXpdO7pX82Sn9vTPinPRWdZzPF6id0pAtWvaXLYuIdemb6Vp2NijXNccbH6tz89+S4dilxbOTIkbKkspkEAAAAAHfqgQceUO3atbV582Y5os7r/KJRytf5HVmsrjda3vz3RQ1fekBvtamQugfY/KV6faSqj0g/vyttn65b+vLdESvt+z7hx+onFaktFa+fcEhOoWpSUI7U3wsZBs2erMbpkM79kbCx8vXzUvRZ6dLf0qUj0tn90vmDibNk7kiBytJ9b0hlW6Z6Jk9iiU5Dr8zdrV3H3U83vPDjp4o7fSjxddu2bdWwYcPbKhcAAAAAbofFYtGwYcPUrFnC0ekx/+zS5fXfKFfDJ02vmbLxiMoVyKbOtSJS/6CwfFLbz6Q6z0o/D5P++OHWi3XapaObEn7W/f9YrhJS/ooJW23kKiGF5ZdC80pB2ROaQ87/PzbecEhXT93y57ssJ9r9wUK+QrMnq7l2Thpb23v3z19Zure/VL5dwm7yt8gwDA1bul+Ldp10m3fll+90ff/axNd+fn4aNWrULT8PAAAAAO5UkyZN1LBhQ61dm/AZ5eqvcxVYsKxCytY1veb1BXsUkTtEdUuF39rD8leUHv1WOrZFWjtCOrzyTkpP+OL/0hHzeHRUwj+jzkgfl7uzZ2UFVz0wecID2LMHnlG8gdRlnvTseqnig7fV6JGkSev+0pcb3LzRSLp+6DddXjctydjzzz+vyMjI23omAAAAANwJi8Wi9957739GDJ3/4RPFXzT/EtvuNPTcjG06cv7a7T00opb0+Dzp2Q1SpQ6SxfWyMWRNNHtw+wKzS7Wflp7/NeGErTJN72hK39xtxzX8xwNuc+LO/aPzSz7U/65RzZkzp956663bfi4AAAAA3Kn69eurZcuWia+NuOs6t2CYnPHme6Bevh6vblM363y0+V6lKSpQWeo4Rer7u9RokJSt4O3fC5kGzR7cGqtfwj48Hb6U+v8h3f+BlK/8Hd/25wNn9Oq83W5zHDeu6tz8d2XE3UgyPnjwYIWH3+LURwAAAADwsMSTuf5f/Pl/dGHpaLfX/HPhunp8tUXXYu139vDshaRGr0kv/54w46fKw5J/6J3dExkWe/YgZUE5pNJNpbKtEmbvBOfy6O23H72k52dsl8NpvqO8Mz5W5+a9J/vl00nGS5UqpRdeeMGj9QAAAADA7ahZs6YefPBBLViwIHHs+oENupz7G+Vs8LjpdbuPX9HzM7briydryt92h3MybH4Jn99KN004bfnIWumPH6WDy6Xo0ylfj0yBZg+Sy11SKlBFiqiTcCRf/oqSybGBd2r/qavq8dUWxcSbb2JlOB06v2ikYk/sSxYbOXKkAgMDvVIbAAAAANyqoUOHauHChTKMf7/MvrJplvxzF1FoxUam1609eE795uzS6Ifvks3qoROvAkKkyFYJP4YhXTgs/b1eOvqrdGq3dP4Pz5zGjHSHZk9WY/OXClWT/EOk0DxSSJ6ENZ03j9nLUybhiD0f+ON0lLp88ZsuX493m3dx+ee6cXhzsvH69evroYce8lZ5AAAAAHDLKleurEcffVTffvttkvHzP46RLUdeBRWpaHrt4l0nFeJv0/CHKsvqqYbPTRZLwue9PGWkmj0SxuKuS+cPJpzGdfGvhKPVr5+Xrp2X4m8kHLtu+1VSXMKMofDSnq0pM7LGSfo9raug2ZPlhIRLT69J6yp08EyUHpv8qy5ei3Obd2nt14re/ZPL2McffyzLHWwIDQAAAADeMGzYMM2bN0+xsf+z8bIjXufmvav8XUYpIE9R02tnbz2m4ACb3m5TwfufdwJCpEJ3JfyYebeIdPmEFJpPenGbd+vJDI4fl96OSOsq2KAZvvf7iSt6ZNKvupBCo+fq1kW6+ut3LmNdu3ZVrVq1vFEeAAAAANyR4sWL66WXXko27oyJ1tk5b8sedcHt9V9t+ltvL9qbZCkYcCto9sCntv1zSY+mYkZP7KFNurRqsstYtmzZNHLkSG+UBwAAAAAe8frrrytPnjzJxh1R53T2u7dlxF5ze/20X/7RGwt/l9PNQTaAGZo98Jl1B8+p65e/KSrG/ZGCuW+c0OmFoyS5flMbMmSIChYs6IUKAQAAAMAzcuTIoSFDhriMxZ/7W6fnvCU/w+H2Ht/+dlQDvtuleAebKOPW0OyBT8zbdlw9vtqi63Hu38yq5vXTjrEvJGwE5kL58uX14osveqNEAAAAAPCop59+WpGRkS5jcSf/0Jm5QxRgc78vz/wdJ/TM9G26kcJnKeB/0eyBVxmGoc9/PqT+3+2SPYXphw3KhOvIN69LDvOZP59//rn8/f09XSYAAAAAeJy/v79GjRplGo/+a4dKnlylAD/3H81/PnBWXb74VReiY93mATfR7IHXxMQ71Hf2Tn244mCKuU3L51eN69u0Z+d205zOnTurcePGniwRAAAAALyqTZs2atSokWl8+Vcfq0+1wBQbPtuPXtaD4zbp8NkoD1eIzIhmD7zi9JUYPTzxFy3ceTLF3LZVC+nNRvn11huDTHNCQkL04YcferJEAAAAAPA6i8Wijz76yO0x6pOGvqzJj1dXkL/7j+hHL17Xg+M2ac0fZz1dJjIZmj3wuPWHzqn1p+u16/iVFHMfrV1UH3euqj4vvqCoKPMO9ZtvvqmIiAhPlgkAAAAAPlG9enV17drVNL5nzx7tWjFbX3evrWyBfm7vFRVjV/evtmjMykOc1AVTNHvgMXaHUx//dFBPTNmsCykcrS5J/ZqV1fsPVtL8eXO1ePFi07wyZcqoX79+niwVAAAAAHxq2LBhCg0NNY0PHjxYxUPtmv1MXeXNFuj2XoYhfbLyoLp/tUXn2ccHLtDsgUf8eS5aHSf8ok9XHZKRQnPZZrVoVMcq6tOkjC5evJji6VqffvqpAgPdv9kBAAAAQHpWpEgRDR482DR+9epVDRgwQBUKZde8Z+9RiTzmjaGb1h48p5aj12nV/jOeLBWZAM0e3BG7w6kvNxxR60/Xa+exyynm5wj21/QetdW5ZsKSrP79++vsWfP1pl26dFHLli09VS4AAAAApJm+ffuqbNmypvFvvvlGK1euVNHwEM1/7h7dXTJ3ivc8Hx2nnl9v1aD5u3XlRrwny0UGRrMHt23nsctqN3aj3l2yTzHxzhTzS+cL06Le9XRP6TySpBUrVujrr782zc+TJ49Gjx7tqXIBAAAAIE0FBATos88+c5vz7LPP6saNG8oVGqBpPero4Zqp27t05uZjavrxWi3ZfVJGSsstkOnR7MEtO3n5hgZ8t0sPjtuovSevpuqaVpUKaMHz96hYeMJUxOjoaD3zzDNurxkzZozy5Mlzx/UCAAAAQHrRvHlzPfTQQ6bxP//8U8OGDZMkBfhZNaJDZb3TrqL8beaned10LipWvb/doccm/6bfT6R8YA4yL5o9SLWzUTEavnS/7vtwjeZuO57i3jyS5G+zaEibChrXpbqyBfknjg8ePFh///236XX333+/Hn30UQ9UDQAAAADpy+jRoxUWFmYaHzlypPbs2SMp4ej2J+oW1+xn6qpgjqBU3f+Xvy7ogc826KVZO3T4rPmpx8i8aPYgRX+ei9bghb+r/sjVmrjuL8XaU16yJUkl84Rq7rP3qFu9ErJY/u1Cb9iwQWPGjDG9LiwsTOPHj09yDQAAAABkFhEREXrvvfdM43a7Xd26dVN8/L978FQvmktLXqyvZhXyp/o53+88qWafrNMLM7Zr+9FLLO/KQmj2wKXoWLu+33lCj076VU0+Wqvpv/6juFQ2eSSp2z3F9UOfBqoakTPJeFRUlJ544gm3bzIjRoxQ0aJFb7d0AAAAAEj3evfurZo1a5rGt2/frg8++CDJWHhYoCZ1raGRHSorJMCWqucYhvTDnlN6aNwmtf50g6b/8rcucFx7pueX1gUgfTAMQ/9cuK5Nf17Qmj/Oas3Bc7fU3LmpRJ5QDXuwku4p5Xqvnb59++rIkSOm19erV0/PPffcLT8XAAAAADISm82mSZMmqVatWnI4HC5zhgwZorZt26pSpUqJYxaLRQ/XKqq6JfPojYV7tP7Q+VQ/c9+pqxr8/V4NWbxPdUuG675y+VSvdLjK5ssmq5WVFZkJzZ4sxu40tPv4ZV2IjtOpKzE6evG69p+6qn2nrupc1O13dwP8rHqhUWk907Ckgvxdd5gXLVqkL7/80vweAQH64osvZLUy4QwAAABA5letWjX1799fo0aNchmPj49Xt27dtGnTJgUEBCSJFQ0P0bQetbVo10m9s3ifLlyLS/VzHU5DGw6f14bDCY2inCH+qlgou8oXyK5i4SEqmCNYucMC5G+1yu40Eq85dvG62G3DvVOXb6R1CZJo9mQ5F6Nj1fbzjR69Z5uqhTSweaSKhoeY5pw4cUI9evRwe5/33ntP5cqV82htAAAAAJCeDR06VIsWLdKBAwdcxrdt26YhQ4bo/fffTxazWCxqd1dhNYrMp3FrDmvqxr9va4XG5evx2nj4gjYevpAsdv7/JwWci4pVg1Grb/neWY39aupnWnkTUyhw2+qXzqPvX6inzx6t5rbR43A41KVLF124kPyN46Z7771X/fr180aZAAAAAJBuBQUFaerUqW5XOIwYMULr1q0zjecI9tegVuW1ql9DPVStsGwsycryaPbgljWvkF8LX6inb56qk2wDZleGDRumtWvXmsazZcumr7/+WjZb6jYYAwAAAIDM5O6771b//v1N44ZhqGvXrrp8+bLb+0TkDtHHD9+l1f0b6dHaRRVg4yN/VsX/80iV8NAAPdOwpNYMaKRJT9TUXalo8kjSmjVrNHToULc5Y8aMUfHixe+8SAAAAADIoIYOHaoKFSqYxo8ePapevXql6vj0ouEhGv5QZW0a1FivtiyniNzBniwVGQB79sBUWKCfmpbPp/srF1TDyLwK9Lu1mTfHjx/Xww8/LKfTfM3oww8/rG7dut1hpQAAAACQsQUHB+vbb79V7dq1FRfnerPluXPn6rPPPlOfPn1Sdc88YYF6rlEpPXNvSf165IKW7jmlZb+f1vno1G/mjIyJZg8SBfhZVbFQdtUrlUf3lApX9WK5TE/WSklsbKw6deqks2fPmuaUKFFCEydOlIXt3AEAAABAVatW1fvvv68BAwaY5gwYMEB16tRRnTp1Un1fq9Wie0rl0T2l8mhIm4rafeKKNh0+r42HL2j38cu6Fuf66HdkXDR7shh/P6vqlMitsEA/FcgRpII5ghSRO0TlC2ZXyTyh8vPQms6+ffvq119/NY37+flp1qxZypEjh0eeBwAAAACZQd++ffXjjz9q1apVLuPx8fHq1KmTtm3bprx5897y/f1sVlUvmkvVi+ZS78Zl5HQaOnrxuvaduqp/LlzXqSs3dOpKjK7H2WV3GDrjZ5Xj/6+LzJ/tDv/XZX4xgTd0Iq2LEM2eLCdXSIBmP1PXq88YN26cxo8f7zZn+PDhql27tlfrAAAAAICMxmq1atq0aapWrZrpSoljx46pQ4cOWrlypQICAu7weRYVzxOq4nlCXcaLvBugE5elPGEBWt733jt6VlZw/PhxRbyT1lWwQTM8bPny5SmuH+3QoYPbneYBAAAAICsrVKiQvv32W7dbXqxfv169e/dO1YbNyHpo9sBj9u7dq86dO8vhMF/vWa5cOU2dOpV9egAAAADAjSZNmmjIkCFucyZPnqzPPvvMNwUhQ6HZA4/4559/1KJFC129etU0JywsTAsWLFC2bKzzBAAAAICUvPnmm2rRooXbnJdffllz5871UUXIKGj24I6dO3dOzZs314kT5ttQWSwWffPNNypXrpwPKwMAAACAjMtqterbb79V6dKlTXMMw1CXLl20du1aH1aG9I5mD+7IlStX1KpVKx08eNBt3qhRo9SuXTsfVQUAAAAAmUPu3Lm1aNEitysk4uLi1K5dO+3YscOHlSE9o9mD23bp0iU1a9ZM27Ztc5v31FNPsSEzAAAAANym8uXLa+bMmW73Pr1y5YqaNm2qnTt3+q4wpFs0e3BbLl68qKZNm2rLli1u85o2baqxY8eyITMAAAAA3IHWrVtr9OjRbnMuXryoJk2a0PABzR7cumPHjqlhw4bavn2727zatWtrwYIFCggI8FFlAAAAAJB59enTR6+++qrbnIsXL+q+++7T+vXrfVQV0iOaPbglu3fvVt26dfX777+7zStXrpx++OEHhYWF+agyAAAAAMj8hg8frieeeMJtzuXLl9WsWTPNnz/fR1UhvaHZg1RbvHix6tev7/bULUkqUaKEVqxYoTx58vioMgAAAADIGiwWi7744gu1b9/ebV5sbKw6duyoUaNGyTAM3xSHdINmD1Jkt9s1aNAgtW3bVlFRUW5zS5UqpbVr1yoiIsJH1QEAAABA1uLv76/Zs2eneOKxYRh69dVX1bFjR129etVH1SE9oNkDtw4fPqzGjRtrxIgRKeaWKVOGRg8AAAAA+EBAQIDmzJmjtm3bppg7f/581axZU5s2bfJBZUgPaPbAJbvdrk8++URVqlRJ1cZe1apV09q1a1W4cGEfVAcAAAAACAgI0HfffacuXbqkmHvo0CHVr19f/fv317Vr13xQHdISzR4kYRiGFi5cqMqVK6tfv366ceNGite0bNlSa9euVcGCBX1QIQAAAADgpoCAAE2bNk0DBw5MMdcwDH388ccqU6aMJk+eLLvd7oMKkRZo9kBSwuZd33zzjerUqaMHH3xQBw4cSNV1Tz31lBYtWqRs2bJ5uUIAAAAAgCtWq1WjRo3Sp59+KpvNlmL+qVOn9PTTT6tChQr6/PPPU9ybFRkPzZ4s7MqVK1q0aJF69eqliIgIde3aVVu2bEnVtYGBgZo8ebImTZokf39/L1cKAAAAAEjJiy++qJUrVypfvnypyj906JBefPFFFS5cWI899phmzZqlc+fOeblK+IJfWhcA37p8+bJatmypAwcO6J9//rmte5QoUUJz585V9erVPVwdAAAAAOBONGrUSNu3b1fnzp1TvSFzVFSUZs6cqZkzZ0qSChYsqAoVKig8PFyXL1+WlPBZ8oUXXvBW2T47Ht7bz4mOjvbq/VOLZk8WExMTo+XLl9/29c8//7xGjBjBsi0AAAAASKcKFy6stWvX6qOPPtJbb72luLi4W7r+1KlTOnXqVJKxa9euady4cZ4sE17EMi6kSunSpbVmzRqNHTuWRg8AAAAApHN+fn569dVXtWPHDtWtWzety4GP0eyBW3nz5tVnn32mvXv3qmHDhmldDgAAAADgFlSoUEEbN27U7NmzVbJkybQuBz5CswcuFSpUSO+9954OHz6s3r17KyAgIK1LAgAAAADcBovFos6dO2v//v0aN26cIiMj07okeBnNHiSy2Wxq0qSJZs+erb///ltvvPGGsmfPntZlAQAAAAA8ICAgQM8995z27dunZcuWqX379goKCkrrsuAFbNCchfn5+al8+fKqVq2aWrVqpZYtWypnzpxpXRYAAAAAwIusVqtatGihFi1a6Nq1a1q5cqWWLl2qbdu26ffff1dsbGxal4g7RLMni8mZM6cWLlyo8PBwlSlTRoGBgWldEgAAAAAgjYSGhqpdu3Zq166dJMlut+vPP//UuXPndPnyZT3xxBO6dOmScuXKpU8//TSNq03OYrGkdQlJXLhwQS+99FJal0GzJ6sJCgpio2UAAAAAgEt+fn6KjIxM3NcnJCREly5dUkhIiB5//PE0ri79O378eLpo9rBnDwAAAAAAQCZCswcAAAAAACATodnjA0ePHtX48eP18MMPKzIyUqGhoQoKClKRIkXUrl07zZw5U3a7Pa3LBAAAAAAAmQB79njZW2+9pffee0+GYSSLnThxQidOnNCiRYv08ccfa968eSpatGgaVAkAAAAAADILZvZ42cmTJ2UYhkJDQ/X4449r6tSp2rBhg7Zu3arp06erVq1akqStW7eqadOmio6OTuOKAQAAAABARkazx8vCw8M1cuRInTp1StOnT1e3bt1Ur1491ahRQ48//rh++eUXde7cWZJ06NAhffLJJ2lcMQAAAAAAyMho9njZyJEj9corryhbtmwu4zabTePGjVNAQIAkae7cub4sDwAAAAAAZDI0e9KB8PBwValSRZL0559/pnE1AAAAAAAgI6PZk07ExsZKkqxW/i8BAAAAAAC3j85COnD27Fnt379fklSuXLk0rgYAAAAAAGRkHL2eDnzwwQey2+2SlLhZ8604fvy42/ipU6duqy4AAAAAAJDx0OxJY7/99ptGjx4tSSpSpIief/75W75HRESEh6sCAAAAAAAZFcu40tCZM2fUsWNH2e12WSwWff311woJCUnrsgAAAAAAQAbGzJ7/Z7fb5e/vf8f3mTp1qrp165ZiXlRUlFq3bp24BOv9999X48aNb+uZx44dcxs/deqUateufVv3BgAAAAAAGQvNnjQQExOjdu3aadu2bZKkfv366bXXXrvt+xUpUsRTpQEAAAAAgAyOZs//8/PzSzwR604ULFjQbdxut6tz585avXq1JOmpp57SRx99dMfPBQAAAAAAkGj2JOHtY8+dTqe6du2qxYsXS5IefvhhTZw40avPBAAAAAAAWQsbNPvQM888o1mzZkmSHnjgAU2fPl1WK/8XAAAAAAAAz6HT4CP9+vXTF198IUlq0qSJ5s6d65ENoQEAAAAAAP4XzR4fGDJkiD755BNJ0j333KPvv/9egYGBaVwVAAAAAADIjNizx8s+++wzDR06VJJUuHBhjRo1SkeOHHF7TWRkJLN+AAAAAADAbaHZ42Xz5s1L/POJEydUv379FK85cuSIihcv7sWqAAAAAABAZsUyLgAAAAAAgEyEmT1etmbNmrQuAQAAAAAAZCHM7AEAAAAAAMhEaPYAAAAAAABkIjR7AAAAAAAAMhH27MkC7HZ74p8dDoeOHz+ehtUAyAgcDkfiP3nPAOAO7xcAkLnxPn9rTp06lfjn//0s7msWwzCMNHs6fGLLli2qXbt2WpcBAAAAAECWsXnzZtWqVStNns0yrizgzJkzaV0CAAAAAABZSlp+FmcZVxZQrly5xD9v2rRJERERaVgNgPTu1KlTibMBN2/erIIFC6ZxRQDSK94vACBz433+1h07dkz33HOPpKSfxX2NZk8WEBQUlPjniIgIFSlSJA2rAZCRFCxYkPcMAKnC+wUAZG68z9+6//0s7mss4wIAAAAAAMhEaPYAAAAAAABkIjR7AAAAAAAAMhGaPQAAAAAAAJkIzR4AAAAAAIBMhGYPAAAAAABAJkKzBwAAAAAAIBOxGIZhpHURAAAAAAAA8Axm9gAAAAAAAGQiNHsAAAAAAAAyEZo9AAAAAAAAmQjNHgAAAAAAgEyEZg8AAAAAAEAmQrMHAAAAAAAgE6HZAwAAAAAAkInQ7AEAAAAAAMhEaPYAAAAAAABkIjR7MjGn06mFCxfqueeeU9WqVZU/f34FBAQoe/bsKlmypNq1a6cRI0bo4MGDaV0qgDSwZs0aWSyWxJ9HHnkkxWu6deuWmA8g64iOjlaxYsVksViUJ08enTt3LsVrBg4cmPh+MWHCBB9UCQDwBD5Hpuy/v0dny5ZN169fT/G6GzduKEeOHEmuXbNmjVdqpNmTSS1dulQVK1bUgw8+qAkTJmj37t06e/as4uPjFRUVpSNHjmjRokUaNGiQIiMj1ahRI23atCmtywaQhubMmaM9e/akdRkA0qGwsLDEhs2FCxf00ksvuc3funWrPvnkE0nSvffeq2eeecbrNQIA7hyfI29PdHS0Fi5cmGLe999/r6tXr3q/INHsyZRGjhypBx54QAcOHJAk1atXTyNGjNDy5cu1detWbdiwQfPmzVO/fv0UGRkpSVq7dq3eeeedtCwbQBozDENvv/12WpcBIJ1q1aqVunTpIkmaOXOmfvjhB5d58fHx6tmzpxwOh4KCgjR58mRmAwJABsDnyNsTFBQkSZo+fXqKuTdzbl7jTX5efwJ8atq0aXrttdckSXny5NGMGTPUvHlzl7kPPfSQPvzwQy1evFiDBg3yZZkA0pk8efLo/PnzWrBggbZv367q1aundUkA0qHRo0drxYoVOnfunJ577jnt3btX2bJlS5IzYsQI7d69W5L09ttvq2zZsmlRKgDgFvA58va1bdtWc+bM0U8//aTTp0+rQIECLvPOnj2rFStWSJLatWun2bNne7UuZvZkIidOnNCzzz4rSQoNDdW6detM/4LeZLFY1LZtW23btk09e/b0RZkA0qGXXnpJgYGBksTsHgCm8uTJo9GjR0uSjh07lvjB4Kb9+/dr2LBhkqRq1appwIABvi4RAHCL+Bx5Z5o3b64CBQrI4XBo5syZpnkzZ86U3W5X/vz51axZM6/XRbMnE/n4449148YNSdJ7772n8uXLp/raoKAgderUyVulAUjnihQpoqefflqStGTJEv32229pXBGA9Oqxxx7T/fffL0kaP368NmzYIClhQ8+ePXsqNjZWfn5++vLLL+XnxyRyAEjv+Bx5Z2w2mx599FFJ7pdyTZs2TVLCf0dtNpvX66LZk0kYhpH4L09YWFiW764CuHWDBg1ScHCwJOmtt95K42oApGcTJkxQtmzZZBiGevXqpdjYWH322Wf65ZdfJEkDBgxQtWrV0rhKAEBK+BzpGV27dpUk7dixQ3v37k0W37dvn7Zv354k19to9mQSe/fu1fnz5yVJDRo0SLZ+HgBSUrBgQT333HOSpBUrViR+Ww8A/xUREaHhw4dLkg4cOKDnnntOb7zxhiSpbNmyLAcFgAyCz5GeUa1aNVWqVEmS69k9N8cqVqzosy9DaPZkEjc3QpTExqoAbttrr72m0NBQSczuAeDe888/r3r16kmSpk6dqmvXrslisWjy5Mk+OWUEAHDn+BzpOTdn7MyYMUNOpzNx3DAMzZgxI0mOL7CQOpO42Y2VpLx587rN3bt3rwzDcBkrUaJE4gc9AFlP3rx51bt3b40cOVKrV6/W6tWrdd9996V1WQDSoZuNnYoVKyb+XvHMM8/o3nvvTePKAACpxedIz+nSpYsGDRqk48ePa+3atYm/Q69Zs0bHjh2T1WpVly5dfFYPzZ5MIioqKvHPYWFhbnOrVq0qh8PhMrZ69Wo1atTIk6UByGAGDhyocePGKSoqSoMHD2Y5FwBTGzduTPKLf6FChdKwGgDAreJzpOcULlxY9913n1atWqXp06cnNntuLuFq1KiRihQp4rN6WMaVSfzv2spr166lYSUAMrrw8HC9/PLLkhI+yC1fvjxtCwKQLp06dUoDBw5MMjZs2DAdPHgwjSoCANwqPkd61hNPPCFJmjt3rm7cuKEbN25o3rx5kny7hEui2ZNphIeHJ/753LlzbnPtdrsMw0j8YRNFAP/Vr18/5cyZUxJ79wBw7fnnn9fly5dlsVj0wQcfyGq1KjY2Vk8//bTpNH8AQPrC50jPeuihhxQSEqKoqCh9//33Wrhwoa5evarg4GB16NDBp7XQ7Mkkqlatmvjnm0e6AcDtypkzp/r16ydJ2rx5s5YsWZLGFQFIT+bOnauFCxdKStinZ8CAAerdu7ckae3atfryyy/TsDoAQGrxOdKzwsLC9OCDD0pKWL51cwlX+/btfX7SGc2eTKJixYqJXdn169czBQ/AHXv55ZcT31feeustvqkHIEm6dOmSXnzxRUkJ+xOMHDlSUsISrqJFi0qSXnnlFZ05cybNagQApA6fIz3v5lKuFStW6KeffpLk+yVcEs2eTMNisST+SxUVFaWvvvoqbQsCkOFly5YtcT+OHTt2JH6LDyBr69evn06fPi1JGjt2rLJnzy4p4dvMcePGSUpoCPXp0yfNagQApA6fIz2vSZMmKliwoOx2u+x2u/Lnz6/mzZv7vA6aPZlIv379FBwcLEl6/fXXdfjw4TSuCEBG17t3b+XLl0+S9PbbbzO7B8jiVq5cmfhBoGPHjmrXrl2SeOvWrfXII49IkubMmcMSUADIAPgc6Vk2m01du3ZVYGCgAgMD9fjjj8tms/m8Dpo9mUiRIkU0duxYSdLVq1fVoEEDrVmzJsXrLl265OXKAGRUoaGhevXVVyVJe/bs0dKlS9O4IgBp5dq1a3r66aclSbly5dJnn33mMm/MmDHKnTu3pIRNnKOjo31WIwDg1vE50vNGjhypmJgYxcTE6MMPP0yTGvzS5Knwmu7du+vEiRN66623dPr0ad13332699571bZtW1WpUkXh4eEyDENnz57Vrl27tGDBAm3evDnx+psdXQC46bnnntOHH36oU6dO6fz582ldDoA08uabb+rIkSOSpA8//FAFChRwmZcvXz59+OGH6tGjh44dO6Y33nhDY8aM8WWpAIBbxOfIzIdmTyb05ptvqmrVqurfv78OHTqkdevWad26dW6vqVevnkaOHKk6der4qEoAGUVwcLBef/31xA1ZAWQ9mzdv1qeffipJaty4sXr06OE2v3v37poxY4ZWrVqlzz//XF26dFHt2rV9USqA/2vnjm0UBoIwjM5JJJu6DBApRSA5pALkZtyBKYOIDoiogoTMoQMk7lo4XXCgX+/lK0248wUDf2SPzCL2hOr7vvb7fZ3P57pcLnW9XuvxeNQ8z9Vaq67rarPZ1G63q8PhUOv1+t0jAx9sGIYax7Hu9/u7RwH+2fP5rOPxWK/Xq1prdTqdfvVumqbabre1LEsNw1C3261WK19PgE9mj8zx9e3aJgAAAEAMB5oBAAAAgog9AAAAAEHEHgAAAIAgYg8AAABAELEHAAAAIIjYAwAAABBE7AEAAAAIIvYAAAAABBF7AAAAAIKIPQAAAABBxB4AAACAIGIPAAAAQBCxBwAAACCI2AMAAAAQROwBAAAACCL2AAAAAAQRewAAAACCiD0AAAAAQcQeAAAAgCBiDwAAAEAQsQcAAAAgiNgDAAAAEETsAQAAAAgi9gAAAAAEEXsAAAAAgvwAqLSh1xNk9VwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setting up the plots\n", + "fig, ax = plt.subplots(dpi = 200)\n", + "\n", + "# plotting the full Wannier90 model\n", + "(w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "for i in range(w90_evals.shape[0]):\n", + " x_axis = list(range(w90_evals.shape[1]))\n", + " y_axis = w90_evals[i] - fermi_ev\n", + " ax.plot(x_axis, y_axis, \"k-\", zorder = -100, label = \"Full model\")\n", + "\n", + "# plotting the minimal models\n", + "for cutoff in [0.038, 0.05, 0.45]:\n", + " int_evals = minimal_model(w90_input, fermi_ev, cutoff)\n", + " for i in range(int_evals.shape[0]):\n", + " ax.plot(list(range(int_evals.shape[1])), int_evals[i], zorder = -50, label = \"Cutoff at %.4f eV\" % cutoff)\n", + " \n", + "# plotting the horizontal line that passes through the y-axis\n", + "ax.axhline(y = 0, color = 'm', linestyle = '--')\n", + "ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", + "\n", + "# increasing the width of the curves\n", + "lines = ax.lines\n", + "for line in lines:\n", + " line.set_linewidth(2.5)\n", + "\n", + "# coordinates of the high symmetry points\n", + "G = np.array([0.00, 0.00, 0.00])\n", + "N = np.array([0.25, -0.25, 0.25])\n", + "X = np.array([0.00, 0.00, 0.50])\n", + "M = np.array([0.25, 0.25, -0.25])\n", + "\n", + "# x-axis labels of the high-symmetry points\n", + "kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", + "idx_kpt = {}\n", + "\n", + "# checking k-point coordinates equal to the high-symmetry point coordinates\n", + "for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", + " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", + " \n", + "# setting the x-axis ticks\n", + "kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", + "kpath_xticks.append(idx_kpt['G'][1])\n", + "kpath_xticks = sorted(kpath_xticks)\n", + "ax.set_xticks(kpath_xticks)\n", + "ax.set_xticklabels(kpath_labels)\n", + "\n", + "# setting the vertical lines at the high-symmetry points\n", + "for n in range(len(kpath_xticks)):\n", + " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", + "\n", + "# plot properties\n", + "ax.set_xlim(0, int_evals.shape[1] - 1)\n", + "ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", + "ax.set_title(\"Comparing Minimal Models\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this.\n", + "\n", + "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "TRIQS", + "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": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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zy#2dQ5qkT+bo{haggUn39p7|{&^tCp^5aeslI+C0zIKYxyLP7i=ba+-HyhLmvHaZz zb&Am6?NFx{`kNi<6rsP_piV9HcN^3xLVveI|G#M=x7LT1{rBLh2Pyo};%zhIpV##M zZVR^fZvMMp5&!?=d09-3{kJbSO{4!Jhb4Wt0yBYHBK^UC{;qTJ@7$lxoqy-nD0GU= zJNKqjY~HywonrIut?3k-ckfN7wt45?bc)S8x299uynAap#pd06)2VIVxi_6+^Ukg5 z)Hd(lnohBK_uh1Bn|JO_r`Wu6YdW>fySJuOY~H;$o!aJ|d*i10ueG0_)?4uMgDw0W O{ps_kU6``^hyMf1N=f7Z From 435c8eb270d3791c105bd7955efdbbd8061ff2f7 Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 12:45:35 -0400 Subject: [PATCH 36/47] Created a Python file from which we can call our main function from the tutorial notebook --- TB_to_sympy.py | 161 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 TB_to_sympy.py diff --git a/TB_to_sympy.py b/TB_to_sympy.py new file mode 100644 index 0000000..195e9b6 --- /dev/null +++ b/TB_to_sympy.py @@ -0,0 +1,161 @@ +# importing the dependencies +from itertools import product as itp +from pythtb import * +from triqs.lattice.tight_binding import TBLattice +import warnings +import numpy as np +import matplotlib.pyplot as plt + +def TB_to_sympy(TBL, analytical = True, precision = 6): + r""" + returns the analytical form of the momentum space hamiltonian of the tight-binding model + from a tight-binding lattice object by utilizing Fourier series + + Parameters + ---------- + TBL: triqs TBLattice object + triqs tight binding object + analytical: boolean, default = True + whether to return the Hamiltonian in analytical (true) or numerical (false) form. + precision: integer, default = 6 + specifies the number of digits in the floating point amplitudes. The default value is 6 but the user + can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings + across the crystal lattice + + Returns + ------- + Hk: NumPy array + the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical + form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output + is the reduced analytical form. The numerical form depends solely on the k-space vector components + while the analytical form takes into account both the k-space vector components and the lattice + vectors + + """ + + import sympy as sp + + # imaginary number + I = sp.I + + # symbolic dot product representation between lattice unit vectors + # and momentum space matrix + a1k, a2k, a3k = sp.symbols("a1k a2k a3k", real = True) + lattice = sp.Matrix([a1k, a2k, a3k]) + + # units contains the displacement vectors + # hops contains details about hopping of electrons such as orbital + # and hopping amplitude + if TBL.units.shape == (2, 2): + TBL_units = np.eye(3) + TBL_units[:2, :2] = TBL.units + TBL_hops = {key + (0,): val for key, val in TBL.hoppings.items()} + elif TBL.units.shape == (3,3): + TBL_units = TBL.units + TBL_hops = TBL.hoppings + # raises error for when the dimensions of the tb object is neither 2D nor 3D + else: + raise ValueError("This format of the tight-binding model is not implemented for this function.") + + # number of orbitals involved in the unit cell + num_orb = TBL.n_orbitals + + # maximum hopping distances of electrons in each direction + max_x, max_y, max_z = list(np.max(np.array(list(TBL_hops.keys())), axis = 0)) + + # number of cells involved in the hopping of electrons in each direction + num_cells_x, num_cells_y, num_cells_z = [2 * max_coord + 1 for max_coord in [max_x, max_y, max_z]] + + # real-space Hamiltonian + Hrij = np.zeros((num_cells_x, num_cells_y, num_cells_z, num_orb, num_orb), dtype = sp.exp) + + # looping through hopping parameters of electrons involved in inter-orbital hoppings + for key, hopping in TBL_hops.items(): + rx, ry, rz = key + hopping = np.around(hopping, precision) + Hrij[rx + max_x, ry + max_y, rz + max_z] = hopping + + # basis of the exponential term in calculation of Hk + Hexp = np.empty_like(Hrij, dtype = sp.exp) + + # perform Fourier transform + for xi, yi, zi in itp(range(num_cells_x), range(num_cells_y), range(num_cells_z)): + coefficients = np.array([xi - max_x, yi - max_y, zi - max_z]) + r = lattice.dot(coefficients) + eikr = sp.exp(-I * r) + Hexp[xi, yi, zi, :, :] = eikr + + # summation over all real space axes + Hk = np.sum(Hrij * Hexp, axis = (0, 1, 2)) + + # rewriting exponential terms in Hamiltonian expression in terms of cosine + for i, j in itp(range(num_orb), repeat = 2): + Hk[i, j] = Hk[i, j].rewrite(sp.cos) + + def _has_complex_exponential_sympy(matrix): + """ + Checks if a NumPy array containing SymPy elements has a complex exponential element. + + Args: + matrix (NumPy array): The input NumPy array containing SymPy elements + + Returns: + bool: True if the matrix array contains a complex exponential element, False otherwise. + + """ + + for sublist in matrix: + for element in sublist: + if element.is_complex and element.has(sp.exp): + return True + return False + + def _is_hermitian_sympy(matrix): + """ + Checks if a NumPy array containing SymPy elements is hermitian + + Args: + matrix (NumPy array): The input NumPy array containing SymPy elements + + Returns: + bool: True if the matrix is a hermitian, False otherwise + + """ + + n = matrix.shape[0] + for i in range(n): + for j in range(n): + if matrix[i, j] != matrix[j, i].conjugate(): + return False + return True + + # performing the check on the analytical Hamiltonian + if not _is_hermitian_sympy(Hk): warnings.warn("The resulting Hamiltonian is not hermitian.") + if _has_complex_exponential_sympy(Hk): warnings.warn("""Your expression has a complex exponential. + Choosing a different unit cell could make + your Hamiltonian expression real.""") + + if analytical: return Hk + + # dealing with the numerical Hamiltonian + + # convert to SymPy matrix to use substitutions method available in SymPy + Hk_numerical = sp.Matrix(Hk) + + # obtaining individual displacement vectors + TBL_units_prec = np.around(TBL_units, precision) + + # dot product between unit vectors and momentum vector + k_vec = sp.symbols("kx ky kz", real = True) + a1k_n, a2k_n, a3k_n = TBL_units_prec.dot(k_vec) + + # substitute numerical unit vectors into H_k + Hk_numerical = Hk_numerical.subs([(a1k, a1k_n), (a2k, a2k_n), (a3k, a3k_n)]) + + Hk_numerical = np.array(Hk_numerical) + + if not _is_hermitian_sympy(Hk_numerical): warnings.warn("The resulting Hamiltonian is not hermitian.") + if _has_complex_exponential_sympy(Hk_numerical): warnings.warn("""Your expression has a complex exponential. + Choosing a different unit cell could make + your Hamiltonian expression real.""") + return Hk_numerical \ No newline at end of file From ab2718a305ecd83ddfe3dfea0bc21156e7c6eeea Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 12:46:35 -0400 Subject: [PATCH 37/47] Improved how we get the plot by getting rid of the inefficient function by replacing it with a for loop that basically does the same plotting work for us --- SymPyfy Tutorial.ipynb | 205 +++++++++++++++++++---------------------- 1 file changed, 95 insertions(+), 110 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 4e48820..88df19f 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -55,9 +55,9 @@ "\n", "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(\\bold{k})$, which is achieved using the ```TB_to_sympy``` function.\n", "\n", - "$\n", - "H(\\bold{k}) = \\frac{1}{(2\\pi)^d} \\sum_{\\bold{R}} e^{i \\bold{k} \\cdot \\bold{R}} H_{\\bold{R}}\n", - "$" + "$$\n", + "H(k) = \\frac{1}{(2\\pi)^d} \\sum_{R} e^{i k \\cdot R} H_{R}\n", + "$$" ] }, { @@ -110,24 +110,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: could not identify MPI environment!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Starting serial run at: 2023-08-04 11:00:45.708992\n" - ] - } - ], + "outputs": [], "source": [ "# importing the dependencies\n", "from itertools import product as itp\n", @@ -152,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -191,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -228,110 +213,57 @@ "Let's create a figure to compare three minimal models, each with different `min_hopping_norm` cutoffs, against the full model from Wannier90. In the snippet below we compare 3 minimal models. You are free to choose the cutoffs you wish." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let us create a function that deals with making the minimal models:" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "def compare_minimal_models(w90_input, fermi_ev, cutoff_1 = 0.038, cutoff_2 = 0.05, cutoff_3 = 0.45):\n", - " r\"\"\"\n", - " returns a figure that compares 3 minimal tight-binding models against the full Wannier90 model\n", - " \n", - " Parameters\n", - " ----------\n", - " w90_input: pythtb.w90\n", - " a reading of the output files from Wannier90\n", - " fermi_ev: float\n", - " the energy, in eV, at the fermi level. Can be gotten from the density-functional code\n", - " cutoff_1: float, default = 0.038\n", - " the first hopping norm/amplitude cutoff in eV\n", - " cutoff_2: float, default = 0.05\n", - " the second hopping norm/amplitude cutoff in eV\n", - " cutoff_3: float, default = 0.45\n", - " the third hopping norm/amplitude cutoff in eV\n", - "\n", - " \"\"\"\n", - "\n", + "def minimal_model(w90_input, fermi_ev, cutoff):\n", " # get band structure from Wannier90 in the form of\n", " # list of k-points in reduced coordinates\n", " # energies interpolated by Wannier90\n", " (w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", "\n", - " # obtain simplified models based on various cutoffs\n", - " w90_model_1 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_1, max_distance = None)\n", - " w90_model_2 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_2, max_distance = None)\n", - " w90_model_3 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_3, max_distance = None)\n", - "\n", - " # solve simplified models on the same k-path as in Wannier90\n", - " int_evals_1 = w90_model_1.solve_all(w90_kpt)\n", - " int_evals_2 = w90_model_2.solve_all(w90_kpt)\n", - " int_evals_3 = w90_model_3.solve_all(w90_kpt)\n", - "\n", - " # setting up the plots\n", - " fig, ax = plt.subplots(dpi = 200)\n", + " # obtain simplified model based on various cutoffs\n", + " w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff, max_distance = None)\n", "\n", - " # plotting the full Wannier90 model\n", - " for i in range(w90_evals.shape[0]):\n", - " x_axis = list(range(w90_evals.shape[1]))\n", - " y_axis = w90_evals[i] - fermi_ev\n", - " ax.plot(x_axis, y_axis, \"k-\", zorder = -100)\n", - "\n", - " # plotting the minimal models\n", - " for i in range(int_evals_1.shape[0]):\n", - " ax.plot(list(range(int_evals_1.shape[1])), int_evals_1[i], \"r-\", zorder = -50)\n", - " ax.plot(list(range(int_evals_2.shape[1])), int_evals_2[i], \"g-\", zorder = -50)\n", - " ax.plot(list(range(int_evals_3.shape[1])), int_evals_3[i], \"c-\", zorder = -50)\n", - " \n", - " # plotting the horizontal line that passes through the y-axis\n", - " ax.axhline(y = 0, color = 'm', linestyle = '--')\n", - " ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", - "\n", - " # increasing the width of the curves\n", - " lines = ax.lines\n", - " for line in lines:\n", - " line.set_linewidth(2.5)\n", - " \n", - " # coordinates of the high symmetry points\n", - " G = np.array([0.00, 0.00, 0.00])\n", - " N = np.array([0.25, -0.25, 0.25])\n", - " X = np.array([0.00, 0.00, 0.50])\n", - " M = np.array([0.25, 0.25, -0.25])\n", - "\n", - " # x-axis labels of the high-symmetry points\n", - " kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", - " idx_kpt = {}\n", - "\n", - " # checking k-point coordinates equal to the high-symmetry point coordinates\n", - " for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", - " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", - " \n", - " # setting the x-axis ticks\n", - " kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", - " kpath_xticks.append(idx_kpt['G'][1])\n", - " kpath_xticks = sorted(kpath_xticks)\n", - " ax.set_xticks(kpath_xticks)\n", - " ax.set_xticklabels(kpath_labels)\n", - " \n", - " # setting the vertical lines at the high-symmetry points\n", - " for n in range(len(kpath_xticks)):\n", - " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", - "\n", - " # plot properties\n", - " ax.set_xlim(0, int_evals_1.shape[1] - 1)\n", - " ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", - " ax.set_title(\"Comparing Minimal Models\")\n", - " ax.legend([\"Full model\", \"Cutoff at \" + str(cutoff_1) + \" eV\", \"Cutoff at \" + str(cutoff_2) + \" eV\", \"Cutoff at \" + str(cutoff_3) + \" eV\"])" + " # solve simplified model on the same k-path as in Wannier90\n", + " return w90_model.solve_all(w90_kpt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let us set up the plot:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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zy#2dQ5qkT+bo{haggUn39p7|{&^tCp^5aeslI+C0zIKYxyLP7i=ba+-HyhLmvHaZz zb&Am6?NFx{`kNi<6rsP_piV9HcN^3xLVveI|G#M=x7LT1{rBLh2Pyo};%zhIpV##M zZVR^fZvMMp5&!?=d09-3{kJbSO{4!Jhb4Wt0yBYHBK^UC{;qTJ@7$lxoqy-nD0GU= zJNKqjY~HywonrIut?3k-ckfN7wt45?bc)S8x299uynAap#pd06)2VIVxi_6+^Ukg5 z)Hd(lnohBK_uh1Bn|JO_r`Wu6YdW>fySJuOY~H;$o!aJ|d*i10ueG0_)?4uMgDw0W O{ps_kU6``^hyMf1N=f7Z literal 0 HcmV?d00001 From ff4c19da00f92961d70563c51efd0f540dcb9c5d Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Thu, 3 Aug 2023 15:15:24 -0400 Subject: [PATCH 31/47] Added notes to intro (from poster) and replaced outdated minimal model figure --- SymPyfy Tutorial.ipynb | 61 +++++++++++++++++++++++++++++++++++------- 1 file changed, 52 insertions(+), 9 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index 6cc4a46..b53b1bf 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -21,6 +21,16 @@ "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you model electrons as tightly bound to atoms, you get the TB model which assumes that main electronic behavior arises from interactions between neighboring atoms, where electrons hop between atomic orbitals through tunneling or wavefunction overlap.\n", + "The TB model is a minimal model that serves as a foundation for studying electron behavior in metals, including the origins of magnetism and superconductivity:\n", + "- For theorists, it reduces the number of parameters, from which they can study the effects of the parameters separately.\n", + "- For experimentalists, it’s useful for a one-body description of the electronic structure. Therefore, if they can understand their data by using a simple representation like a TB model, their data can be understood simply. An analytical TB model with a restricted number of parameters can help experimentalists fit their data." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -101,9 +111,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-08-03 14:28:46.701982\n" + ] + } + ], "source": [ "# importing the dependencies\n", "from itertools import product as itp\n", @@ -128,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -167,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -190,22 +215,40 @@ "The main distinction between the analytical and numerical expressions is that the numerical expression depends on ```kx```, ```ky```, and ```kz```, while the analytical expression additionally depends on the lattice vectors, ```a1```, ```a2```, and ```a3```. The lattice vectors define the orientation and shape of the crystal unit cell and can be replaced with numerical values using the ```units``` function from the TRIQS TB lattice object." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Band Structure ##" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "attachments": { - "The band structure of lanthanum cuprate at different cutoffs.png": { - "image/png": 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" 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} }, "cell_type": "markdown", "metadata": {}, "source": [ - "## Band Structure ##\n", "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this. The graph displays the band energy plotted against the points along the k-path using a ```min_hopping_norm``` of 0.05 eV and 0.01 eV with no set maximum distance.\n", "\n", - "![The band structure of lanthanum cuprate at different cutoffs.png]()\n", + "![Comparing Minimal Models.png]()\n", "\n", "With a lower cutoff of 0.01 eV, more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { From 30c8b8b2cc2e279412de57b81b4a62dfb6f1f62f Mon Sep 17 00:00:00 2001 From: Collins Kariuki Date: Fri, 4 Aug 2023 11:04:49 -0400 Subject: [PATCH 32/47] Finished first draft of the section on comparing minimal models --- SymPyfy Tutorial.ipynb | 145 +++++++++++++++++++++++++++++++++++------ 1 file changed, 125 insertions(+), 20 deletions(-) diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb index b53b1bf..b0e7415 100644 --- a/SymPyfy Tutorial.ipynb +++ b/SymPyfy Tutorial.ipynb @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -125,7 +125,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-08-03 14:28:46.701982\n" + "Starting serial run at: 2023-08-04 11:00:45.708992\n" ] } ], @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -219,36 +219,141 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Band Structure ##" + "## Comparing Minimal Models ##" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Let's create a figure to compare three minimal models, each with different `min_hopping_norm` cutoffs, against the full model from Wannier90. In the snippet below we compare 3 minimal models. You are free to choose the cutoffs you wish." + ] }, { - "attachments": { - "Comparing Minimal Models.png": { - "image/png": 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- } - }, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, "metadata": {}, + "outputs": [], "source": [ - "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this. The graph displays the band energy plotted against the points along the k-path using a ```min_hopping_norm``` of 0.05 eV and 0.01 eV with no set maximum distance.\n", + "def compare_minimal_models(w90_input, fermi_ev, cutoff_1 = 0.038, cutoff_2 = 0.05, cutoff_3 = 0.45):\n", + " r\"\"\"\n", + " returns a figure that compares 3 minimal tight-binding models against the full Wannier90 model\n", + " \n", + " Parameters\n", + " ----------\n", + " w90_input: pythtb.w90\n", + " a reading of the output files from Wannier90\n", + " fermi_ev: float\n", + " the energy, in eV, at the fermi level. Can be gotten from the density-functional code\n", + " cutoff_1: float, default = 0.038\n", + " the first hopping norm/amplitude cutoff in eV\n", + " cutoff_2: float, default = 0.05\n", + " the second hopping norm/amplitude cutoff in eV\n", + " cutoff_3: float, default = 0.45\n", + " the third hopping norm/amplitude cutoff in eV\n", + "\n", + " \"\"\"\n", + "\n", + " # get band structure from Wannier90 in the form of\n", + " # list of k-points in reduced coordinates\n", + " # energies interpolated by Wannier90\n", + " (w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "\n", + " # obtain simplified models based on various cutoffs\n", + " w90_model_1 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_1, max_distance = None)\n", + " w90_model_2 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_2, max_distance = None)\n", + " w90_model_3 = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff_3, max_distance = None)\n", + "\n", + " # solve simplified models on the same k-path as in Wannier90\n", + " int_evals_1 = w90_model_1.solve_all(w90_kpt)\n", + " int_evals_2 = w90_model_2.solve_all(w90_kpt)\n", + " int_evals_3 = w90_model_3.solve_all(w90_kpt)\n", "\n", - "![Comparing Minimal Models.png]()\n", + " # setting up the plots\n", + " fig, ax = plt.subplots(dpi = 200)\n", "\n", - "With a lower cutoff of 0.01 eV, more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." + " # plotting the full Wannier90 model\n", + " for i in range(w90_evals.shape[0]):\n", + " x_axis = list(range(w90_evals.shape[1]))\n", + " y_axis = w90_evals[i] - fermi_ev\n", + " ax.plot(x_axis, y_axis, \"k-\", zorder = -100)\n", + "\n", + " # plotting the minimal models\n", + " for i in range(int_evals_1.shape[0]):\n", + " ax.plot(list(range(int_evals_1.shape[1])), int_evals_1[i], \"r-\", zorder = -50)\n", + " ax.plot(list(range(int_evals_2.shape[1])), int_evals_2[i], \"g-\", zorder = -50)\n", + " ax.plot(list(range(int_evals_3.shape[1])), int_evals_3[i], \"c-\", zorder = -50)\n", + " \n", + " # plotting the horizontal line that passes through the y-axis\n", + " ax.axhline(y = 0, color = 'm', linestyle = '--')\n", + " ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", + "\n", + " # increasing the width of the curves\n", + " lines = ax.lines\n", + " for line in lines:\n", + " line.set_linewidth(2.5)\n", + " \n", + " # coordinates of the high symmetry points\n", + " G = np.array([0.00, 0.00, 0.00])\n", + " N = np.array([0.25, -0.25, 0.25])\n", + " X = np.array([0.00, 0.00, 0.50])\n", + " M = np.array([0.25, 0.25, -0.25])\n", + "\n", + " # x-axis labels of the high-symmetry points\n", + " kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", + " idx_kpt = {}\n", + "\n", + " # checking k-point coordinates equal to the high-symmetry point coordinates\n", + " for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", + " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", + " \n", + " # setting the x-axis ticks\n", + " kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", + " kpath_xticks.append(idx_kpt['G'][1])\n", + " kpath_xticks = sorted(kpath_xticks)\n", + " ax.set_xticks(kpath_xticks)\n", + " ax.set_xticklabels(kpath_labels)\n", + " \n", + " # setting the vertical lines at the high-symmetry points\n", + " for n in range(len(kpath_xticks)):\n", + " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", + "\n", + " # plot properties\n", + " ax.set_xlim(0, int_evals_1.shape[1] - 1)\n", + " ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", + " ax.set_title(\"Comparing Minimal Models\")\n", + " ax.legend([\"Full model\", \"Cutoff at \" + str(cutoff_1) + \" eV\", \"Cutoff at \" + str(cutoff_2) + \" eV\", \"Cutoff at \" + str(cutoff_3) + \" eV\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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