From 47ac7f4488da62cffc4d8efe824bf673f305bc13 Mon Sep 17 00:00:00 2001 From: Dario Izzo Date: Mon, 3 Feb 2025 12:24:44 +0100 Subject: [PATCH 1/4] surrogate primer --- doc/notebooks/primer_vector.ipynb | 33 +- doc/notebooks/surrogate_primer_vector.ipynb | 401 ++++++++++++++++++++ doc/notebooks/udp_pl2pl.ipynb | 2 +- pykep/trajopt/CMakeLists.txt | 3 +- pykep/trajopt/__init__.py | 3 +- pykep/trajopt/_min_Bu_bu.py | 186 +++++++++ pykep/trajopt/_pl2pl_N_impulses.py | 28 +- pykep/trajopt/_primer_vector.py | 13 +- 8 files changed, 626 insertions(+), 43 deletions(-) create mode 100644 doc/notebooks/surrogate_primer_vector.ipynb create mode 100644 pykep/trajopt/_min_Bu_bu.py diff --git a/doc/notebooks/primer_vector.ipynb b/doc/notebooks/primer_vector.ipynb index d18db47f..a9972607 100644 --- a/doc/notebooks/primer_vector.ipynb +++ b/doc/notebooks/primer_vector.ipynb @@ -9,7 +9,7 @@ "In this notebook we revisit the primer vector theory from Lawden from the lens of first order variations. We then construct manually, for a specific test case, the state transition matrices needed to call {func}`pykep.trajopt.primer_vector` and show\n", "its use. The same plot can also be obtained, without having to go through the math intensive developments, using the {func}`pykep.trajopt.pl2pl_N_impulses.plot_primer_vector` method of the Multiple Impulse Transfer UDP `pykep.trajopt.pl2pl_N_impulses`\n", "\n", - "Classically, the result of the primer vector is derived using Pontryagin maximum principle, here we present an original derivation building on the work from Bauregard et al. {cite:p}`beauregard`, which allows also to extend the primer vector to new, previously untreated, cases (see the notebook [A primer vector surrogate](<./primer_vector_surrogate.ipynb>)).\n", + "Classically, the result of the primer vector is derived using Pontryagin maximum principle, here we present an original derivation building on the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`, which allows also to extend the primer vector to new, previously untreated, cases (see the notebook [A primer vector surrogate](<./primer_vector_surrogate.ipynb>)).\n", "\n", ":::{note}\n", " The developments are here shown in details as they can be extended to more generic cases that use different dynamics and number of impulses. The user must in that case provide the state ransition matrices and DVs on a time grid as constructed below.\n", @@ -275,37 +275,6 @@ "#posvels[-1][1] = [a + b for a, b in zip(posvels[-1][1], DV3)]\n" ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.12807793e+01, 1.80447033e+01, 6.65907703e-03,\n", - " -9.08505426e+07, 5.95064147e+07, 1.92133860e+06],\n", - " [-1.28990875e+00, -1.31576055e+00, -3.66387114e-02,\n", - " 1.15887081e+07, -5.09339048e+06, 2.07784246e+05],\n", - " [-3.36261520e-01, -3.88616536e-01, 6.55461044e-01,\n", - " 2.34553875e+06, -9.85626328e+05, 1.43519303e+06],\n", - " [ 3.35679528e-07, 8.01570626e-07, -1.32855560e-08,\n", - " -2.43465805e+00, 2.52926261e+00, 2.17349840e-01],\n", - " [ 3.32734693e-06, 5.70909432e-06, -6.71155865e-09,\n", - " -2.83015440e+01, 1.97666678e+01, 5.07564715e-01],\n", - " [ 2.55788135e-08, 2.62452949e-08, -1.13916259e-07,\n", - " -2.42108745e-01, 9.65279820e-02, 1.26130568e+00]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stms[-1]" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/doc/notebooks/surrogate_primer_vector.ipynb b/doc/notebooks/surrogate_primer_vector.ipynb new file mode 100644 index 00000000..e533c16e --- /dev/null +++ b/doc/notebooks/surrogate_primer_vector.ipynb @@ -0,0 +1,401 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Surrogate Primer Vector \n", + "\n", + "In this notebook we extend the theory outlined in the notebook on the [primer vector](<./primer_vector.ipynb>) to the case where we are adding simultaneously two impulses\n", + "to a trajectory where we can control one single existing impulse (as opposed to the original case where one adds only one impulse in a trajectory where two can be controlled).\n", + "\n", + "While the developments are similar, please note that the index $k$ was before reserved to indicate the only node (of three, ijk) where no finite impulse was given. In this developments it indicates, instead, the only node (of three, ijk) where a finite impulse is given.\n", + "\n", + "The developments as well as the test case used are taken from the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Theory and notation\n", + "\n", + " ------------\n", + "\n", + " a) Consider the following definition of the State Transition Matrix, $\\mathbf M_{fs}$:\n", + "\n", + " $$\n", + " \\mathbf M_{fs} := \\frac{\\partial \\mathbf x_{f}}{\\partial \\mathbf x_{s}}\n", + " $$\n", + "\n", + " Note how this definition does not depend on the dynamics. The STM allows to describe variations of the (final) state at $f$ as:\n", + "\n", + " $$\n", + " \\delta\\mathbf x_f = \\mathbf M_{fs} \\delta \\mathbf x_s\n", + " $$\n", + "\n", + " We also make use of the following definitions for the various blocks of the STM:\n", + "\n", + " $$\n", + " \\mathbf M = \\left[ \n", + " \\begin{array}{c|c} \n", + " \\mathbf M^{rr} & \\mathbf M^{rv} \\\\ \n", + " \\hline \n", + " \\mathbf M^{vr} & \\mathbf M^{vv} \n", + " \\end{array} \n", + " \\right] \n", + " = \\left[ \n", + " \\begin{array}{c|c} \n", + " \\mathbf M^{xr} & \\mathbf M^{xv} \n", + " \\end{array} \n", + " \\right] \n", + " = \\left[ \n", + " \\begin{array}{c} \n", + " \\mathbf M^{rx} \\\\\n", + " \\hline \n", + " \\mathbf M^{vx} \n", + " \\end{array} \n", + " \\right] \n", + " $$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------\n", + "\n", + "b) Assume now to have a grid of $N$ points along a multiple impulse trajectory and pick three indexes $i,j,k$. \n", + "\n", + "We ask the following question: what happens when we add three small $\\delta\\Delta V$ at the selected nodes?\n", + "\n", + "To answer this question, we compute the variation of the final state due to intermidiate variations at the nodes using the STMs:\n", + "\n", + "$$\n", + "\\delta \\mathbf x_f = \\mathbf M_{fi}\\delta\\mathbf x_i + \\mathbf M_{fj}\\delta\\mathbf x_j + \\mathbf M_{fk}\\delta\\mathbf x_k\n", + "$$\n", + "\n", + "which we set to zero, as we do not want the trajectory boundary cnstraints to change, only find a better $\\Delta V$:\n", + "\n", + "$$\n", + "\\mathbf 0 = \\mathbf M_{fi}\\delta\\mathbf x_i + \\mathbf M_{fj}\\delta\\mathbf x_j + \\mathbf M_{fk}\\delta\\mathbf x_k\n", + "$$\n", + "\n", + "which becomes (multiplying by $\\mathbf M_{kf}$):\n", + "\n", + "$$\n", + "\\mathbf M_{ki}\\delta\\mathbf x_i + \\mathbf M_{kj}\\delta\\mathbf x_j + \\delta\\mathbf x_k = \\mathbf 0\n", + "$$\n", + "\n", + "Note that, with respect to the derivations for the primer vector, we have now changed the reference node to $k$ for convenience." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------\n", + "\n", + "c) The state variations $\\delta\\mathbf x$ are, in our case, consequence of three $\\delta\\Delta \\mathbf V$, so that the previous equations becomes:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\mathbf M_i^{rv} \\delta\\Delta \\mathbf V_i + \\mathbf M_j^{rv} \\delta\\Delta \\mathbf V_j = \\mathbf 0\\\\\n", + "\\mathbf M_i^{vv} \\delta\\Delta \\mathbf V_i + \\mathbf M_j^{vv} \\delta\\Delta \\mathbf V_j + \\delta\\Delta \\mathbf V_k = \\mathbf 0\\\\\n", + "\\end{align}\n", + "$$\n", + "\n", + "solving for $\\delta\\Delta \\mathbf V_i$ and $\\delta\\Delta \\mathbf V_k$ (as a function of $j$ where one of the added impulses is present):\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\delta\\Delta \\mathbf V_i &=& - (\\mathbf M_i^{rv})^{-1}\\mathbf M_j^{rv} \\delta\\Delta \\mathbf V_j = \\mathbf A_{ij}\\delta\\Delta \\mathbf V_j\\\\\n", + "\\delta\\Delta \\mathbf V_k &=& - \\big(\\mathbf M_i^{vv}\\mathbf A_{ij} + \\mathbf M_j^{vv} \\big) \\delta\\Delta \\mathbf V_j = \\mathbf A_{kj}\\delta\\Delta \\mathbf V_j\\\\\n", + "\\end{align}\n", + "$$\n", + "\n", + "The matrices $\\mathbf A$ are telling us how the three variations of impulsive velocity changes applied in ($i$, $j$, $k$) must be related for the overall trajectory to not change its boundary conditions (i.e. $\\mathbf x_f = \\mathbf 0$).\n", + "\n", + "Note here that we chose to use the index $j$ as reference as we relate all others $\\delta\\Delta V$ to $\\delta\\Delta V_j$. It convenient to keep the chosen index as the one where no finite impulse is present.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_____________\n", + "\n", + "d) So far the three indexes we picked (i.e. $i,j,k$) were equivalent, now we assume that in $i,j$ no finite $\\Delta V$ is present. In $k$, instead, an additional $\\Delta \\mathbf V$ will exist.\n", + "\n", + "The total magnitude of the $\\Delta \\mathbf V$ can then be expressed by:\n", + "\n", + "$$\n", + "J = \\Delta V_{tot} = \\underbrace{|\\mathbf V_k + \\delta\\Delta \\mathbf V_k |}_{\\text{finite impulse}} + \\underbrace{|\\delta\\Delta \\mathbf V_i| + |\\delta\\Delta \\mathbf V_j|}_{\\text{infinitesimal}}\n", + "$$\n", + "\n", + "and its first order variation:\n", + "\n", + "$$\n", + "\\delta J = \\frac{\\Delta\\mathbf V_k}{|\\Delta \\mathbf V_k|}\\cdot \\delta\\Delta \\mathbf V_k + |\\delta\\Delta \\mathbf V_i| + |\\delta\\Delta \\mathbf V_j|\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------\n", + "\n", + "e) The surrogate primer vector\n", + "\n", + "We introduce $\\hat{\\mathbf u} = \\frac{\\delta \\Delta \\mathbf V_j}{|\\delta \\Delta \\mathbf V_j|}$ as the unit vector along the direction\n", + "of the infinitesimal $\\delta \\Delta V$ added in our reference node $j$. \n", + "\n", + "Substituting and regrouping we have:\n", + "\n", + "$$\n", + "\\delta J = |\\delta\\Delta \\mathbf V_j| \\left(1 +\\mathbf A_{kj}^T\\frac{\\Delta\\mathbf V_k}{|\\Delta \\mathbf V_k|} \\cdot \\hat {\\mathbf u}\n", + " + |\\mathbf A_{ij}\\hat {\\mathbf u}| \\right)\n", + "$$\n", + "\n", + "which we rewrite and rearrange as:\n", + "\n", + "$$\n", + "\\delta J = |\\delta\\Delta \\mathbf V_j| \\left(1 - (\\mathbf b \\cdot \\hat {\\mathbf u} - |\\mathbf B\\hat {\\mathbf u}| \\right)\n", + "$$\n", + "\n", + "where $\\mathbf B:=\\mathbf A_{ij}$, $\\mathbf b = -\\mathbf A_{kj}^T\\frac{\\Delta\\mathbf V_k}{|\\Delta \\mathbf V_k|}$.\n", + "\n", + "We now introduce the vectors:\n", + "$$\n", + "\\boxed{\\hat {\\mathbf u}^* = \\argmax_{|\\hat {\\mathbf u}|=1}\\left(\\mathbf b\\cdot \\hat {\\mathbf u} - |\\mathbf B\\hat {\\mathbf u}|\\right)}\n", + "$$\n", + "and,\n", + "$$\n", + "\\boxed{\\tilde{\\mathbf p} = (\\mathbf b \\cdot \\hat {\\mathbf u}^* - |\\mathbf B\\hat {\\mathbf u}^*| ) \\hat {\\mathbf u}^*}\n", + "$$\n", + "\n", + "which we call **surrogate primer vector**, since $\\delta J = (1-|\\tilde{\\mathbf p}|) |\\delta\\Delta \\mathbf V_j|$ as in the \n", + "classical case where the **primer vector** $\\mathbf p$ is introduced.\n", + "\n", + "From the expression of $\\delta J$ it appears clear that if we want to be able to decrease the overall $\\Delta V$ adding in $i,j$ two\n", + "impulses, it is necessary for the norm of the surrogate primer vector (computed in $i,j$) to be larger than 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough of that, let us start coding now ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pykep as pk\n", + "import numpy as np\n", + "\n", + "np.set_printoptions(legacy=\"1.25\")\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us define a toy problem. In the Keplerian dynamics, we define the following trajectory " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Problem data\n", + "n_points = 50\n", + "r0 = np.array([1., 0., 0.])\n", + "v0 = np.array([0., 1., 0.])\n", + "DVk = np.array([0.6, -0.2, 0.])\n", + "t_grid = np.linspace(0, 4*np.pi, n_points)\n", + "\n", + "# DV is applied at the end\n", + "idx_k=n_points-1\n", + "\n", + "# We first work out the surrogate primer vector for the following j,k\n", + "idx_i = 10\n", + "idx_j = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the grid is uniform, and the DV is aplied at the end, we can compute all STMs in a single propagation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "retval = pk.propagate_lagrangian_grid([r0, v0], t_grid, mu=1., stm=True)\n", + "#retval1 = pk.propagate_lagrangian_grid(retval[0][0], np.flip(t_grid), mu=1., stm=True)\n", + "#retval1.reverse()\n", + "stm_n0 = [it[1] for it in retval] # Mn0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may now loop over all possible positions for the indexes $i,j$ and compute the *surrogate primer vector* at each point." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "norm_surrogate_p = np.ones((n_points, n_points))\n", + "p = np.ones((n_points,n_points))\n", + "for idx_i in range(n_points):\n", + " Mk0 = stm_n0[idx_k]\n", + " if idx_i==idx_k:\n", + " continue\n", + " Mki = Mk0@np.linalg.inv(stm_n0[idx_i]) # Mki = Mk0*M0i\n", + " for idx_j in range(idx_i+1,n_points):\n", + " if idx_j==idx_k:\n", + " continue\n", + " Mkj = Mk0@np.linalg.inv(stm_n0[idx_j]) # Mkj = Mk0*M0j\n", + " norm_surrogate_p[idx_j, idx_i] = np.linalg.norm(pk.trajopt.primer_vector_surrogate(DVk, Mki, Mkj)[0])\n", + " norm_surrogate_p[idx_i, idx_j] = -np.inf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets see what is its maximum value:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The maximum value of the surrogate primer is attained when infinitesimal impulses are added at:\n", + "t1: 7.6937 and t2: 4.8727\n", + "Its norm is: 2.7366\n", + "Larger then one! -> Trajectory can be improved\n" + ] + } + ], + "source": [ + "idx1, idx2 = np.unravel_index(norm_surrogate_p.argmax(), norm_surrogate_p.shape)\n", + "print(\n", + " f\"The maximum value of the surrogate primer is attained when infinitesimal impulses are added at:\")\n", + "print(f\"t1: {t_grid[idx1]:.4f} and t2: {t_grid[idx2]:.4f}\")\n", + "print(f\"Its norm is: {np.max(norm_surrogate_p):.4f}\")\n", + "print(f\"Larger then one! -> Trajectory can be improved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us visualize the computed surrogate vector:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(5,5))\n", + "X,Y = np.meshgrid(t_grid, t_grid)\n", + "plt.contourf(X,Y,norm_surrogate_p)\n", + "plt.contour(X,Y,norm_surrogate_p, [1], colors='r', linestyles='solid')\n", + "plt.savefig(\"surrogate_primer_halo.png\", dpi=600)\n", + "plt.plot(t_grid,t_grid, 'k--', label=\"$t_1=t_2$\")\n", + "plt.scatter(t_grid[idx2], t_grid[idx1], label=\"Highest primer\", marker=\"*\")\n", + "plt.plot([],[], 'r', label=\"|p| = 1\")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Revealing all areas where adding two impulses would improve the trajectory leaving all boundary conditions unchanged." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# But is it true?\n", + "Where we check that the same trajectory can be improved using three impulses.\n", + "In here we optimize a three impulse fixed time trajectory reaching identical conditions as the ones of our original trajectory. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "kep3_devel", + "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": "3.13.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/notebooks/udp_pl2pl.ipynb b/doc/notebooks/udp_pl2pl.ipynb index 43db72e6..078bdb8f 100644 --- a/doc/notebooks/udp_pl2pl.ipynb +++ b/doc/notebooks/udp_pl2pl.ipynb @@ -481,7 +481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.13.1" }, "orig_nbformat": 4 }, diff --git a/pykep/trajopt/CMakeLists.txt b/pykep/trajopt/CMakeLists.txt index ee5600c6..f2112113 100644 --- a/pykep/trajopt/CMakeLists.txt +++ b/pykep/trajopt/CMakeLists.txt @@ -2,7 +2,8 @@ set(PYKEP_TRAJOPT_PYTHON_FILES __init__.py _direct_point2point.py _direct_pl2pl.py - _mga.py _mga_1dsm.py + _mga.py _mga_1dsm.py + _min_Bu_bu.py _launchers.py _pl2pl_N_impulses.py _primer_vector.py) diff --git a/pykep/trajopt/__init__.py b/pykep/trajopt/__init__.py index 5246408d..2e3b69db 100644 --- a/pykep/trajopt/__init__.py +++ b/pykep/trajopt/__init__.py @@ -19,7 +19,8 @@ from ._pl2pl_N_impulses import pl2pl_N_impulses # MIT (multiple Impulse Trajectories) -from ._primer_vector import primer_vector +from ._primer_vector import primer_vector, primer_vector_surrogate +from ._min_Bu_bu import minBu_bu_p, minBu_bu # The launchers models from ._launchers import _launchers diff --git a/pykep/trajopt/_min_Bu_bu.py b/pykep/trajopt/_min_Bu_bu.py new file mode 100644 index 00000000..f9361e16 --- /dev/null +++ b/pykep/trajopt/_min_Bu_bu.py @@ -0,0 +1,186 @@ +import numpy as np + +# from numba import njit +import scipy +import pygmo as pg + + +# @njit(cache=True) +def _stereo2cartesian(x, eij, ek): + """Transforms the stereographic projected point back + into the unit sphere. + + Args: + x (array (2,)): The stereographic coordinates + eij (array (2,3)): The basis unit vectors completing ek + ek (array (3,)): The origin for the stereographic projection (also a unit vector) + + Returns: + _type_: _description_ + """ + # Stereographic parametrization + denom = 1 + x[0] * x[0] + x[1] * x[1] + X = 2 * x[0] / denom + Y = 2 * x[1] / denom + Z = (1 - x[0] * x[0] - x[1] * x[1]) / denom + return X * eij[0] + Y * eij[1] + Z * ek + + +# @njit(cache=True) +def _fitness_and_gradient(x, B, b, eij, ek): + """Computes the fitness |Bu|- b u and its gradient with respect to + the stereographic parametrization + + Args: + x (array (2,)): The stereographic coordinates + B (array (3,3)): The matrix in Bu + b (array(3,1)): the vector in |BU| - b u + eij (array (2,3)): The basis unit vectors completing ek + ek (array (3,)): The origin for the stereographic projection (also a unit vector) + + Returns: + float, array (2,): fitness and gradient + """ + + # Stereographic parametrization + u = _stereo2cartesian(x, eij, ek) + + # Derivative of stereographic parametrization + denom2 = (1 + x[0] * x[0] + x[1] * x[1]) ** 2 + dx1 = 2 * (1 - x[0] * x[0] + x[1] * x[1]) / denom2 + dy1 = -4 * x[0] * x[1] / denom2 + dz1 = -4 * x[0] / denom2 + du1 = dx1 * eij[0] + dy1 * eij[1] + dz1 * ek + dx2 = -4 * x[0] * x[1] / denom2 + dy2 = 2 * (1 + x[0] * x[0] - x[1] * x[1]) / denom2 + dz2 = -4 * x[1] / denom2 + du2 = dx2 * eij[0] + dy2 * eij[1] + dz2 * ek + + du = np.vstack((du1, du2)).transpose() + + # Objective function and derivative + Bu_norm = np.linalg.norm(B @ u) + 1e-18 + obj = Bu_norm - b @ u + dobj = (B @ u) @ (B @ du) / Bu_norm - b @ du + return obj, dobj + + +class _primer_vector_surrogate_udp: + """min (|Bu| - bu)""" + + def __init__(self, B, b): + self.B = B + self.b = b + + def fitness(self, x): + x = (np.array(x) / np.linalg.norm(x)).reshape(3, 1) + f = np.linalg.norm(self.B @ x) - self.b @ x + return [f[0]] + + def gradient(self, x): + u = np.array(x).reshape(3, 1) + norm_u = np.linalg.norm(u) + uhat = u / norm_u + grad_uhat_u = np.eye(3) / norm_u - u @ u.T / norm_u**3 + grad_f_uhat = 1.0 / (np.linalg.norm(self.B @ uhat)) * uhat.T @ self.B.T @ self.B + grad_f_uhat -= self.b.reshape(1, 3) + return (grad_f_uhat @ grad_uhat_u)[0] + + def get_bounds(self): + lb = [-1, -1, -1] + ub = [1, 1, 1] + return (lb, ub) + + +# min_u (|Bu| - b u), where u is a unit vector +def minBu_bu(B, b): + b_norm = np.linalg.norm(b) + # Easy way out + if b_norm < 1: + return b_norm, np.array([0, 0, 0]) + + # Compute the singular value decomposition (used to bound |Bu| as well as to provide one IG) + svd = np.linalg.svd(B) + # Second easy way out: smallest singular value + sv = svd[1][-1] + if b_norm < 1 + sv: + return b_norm - sv, np.array([0, 0, 0]) + # Corresponding singular value vector + u_svd = svd[2][-1, :] + + # We must construct an orthonormal basis defining the stereographic projection. + # (we know at this point that norm b is not vanishing if we activate bound=True) + ek = b / b_norm + eij = scipy.linalg.null_space(ek.reshape((1, 3))).transpose() + + # The singular value vector direction is determined ... + # ... by forcing it in the plane where b u is positive + # This way the IG is a good one. + if b.T @ u_svd < 0: + u_svd = -u_svd + + # First one is with initial guess = bhat -> projected is always [0,0] + optim1 = scipy.optimize.minimize( + lambda x: _fitness_and_gradient(x, B, b, eij, ek), + [0.0, 0.0], + method="L-BFGS-B", + jac=True, + ) + + if sv > 1e-8: + # Second one is with svd initial guess + xy = eij @ u_svd + z = b @ u_svd / b_norm + p_svd = xy / (1 + z) + optim2 = scipy.optimize.minimize( + lambda x: _fitness_and_gradient(x, B, b, eij, ek), + p_svd, + method="L-BFGS-B", + jac=True, + ) + + else: + optim2 = optim1 + if optim1.fun < optim2.fun: + return - optim1.fun, _stereo2cartesian(optim1.x, eij, ek) + else: + return - optim2.fun, _stereo2cartesian(optim2.x, eij, ek) + + +# min_u (|Bu| - b u), where u is a unit vector +def minBu_bu_p(B, b): + b_norm = np.linalg.norm(b) + # Easy way out + if b_norm < 1: + return -b_norm, np.array([0, 0, 0]) + + # Compute the singular value decomposition (used to bound |Bu| as well as to provide one IG) + svd = np.linalg.svd(B) + # Second easy way out: smallest singular value + sv = svd[1][-1] + if b_norm < 1 + sv: + return -b_norm + sv, np.array([0, 0, 0]) + # Corresponding singular value vector + u_svd = svd[2][-1, :] + + # The singular value vector direction is determined ... + # ... by forcing it in the plane where b u is positive + # This way the IG is a good one. + if b.T @ u_svd < 0: + u_svd = -u_svd + + # First one is with initial guess = bhat -> projected is always [0,0] + prob = pg.problem(_primer_vector_surrogate_udp(B, b)) + algo = pg.algorithm(pg.nlopt(solver="slsqp")) + pop1 = pg.population(prob) + pop1.push_back(u_svd / np.linalg.norm(u_svd)) + pop1 = algo.evolve(pop1) + + pop2 = pg.population(prob) + pop2.push_back(b / b_norm) + pop2 = algo.evolve(pop2) + + if pop1.champion_f[0] < pop2.champion_f[0]: + return - pop1.champion_f[0], pop1.champion_x / np.linalg.norm(pop1.champion_x) + else: + return - pop2.champion_f[0], pop2.champion_x / np.linalg.norm(pop2.champion_x) diff --git a/pykep/trajopt/_pl2pl_N_impulses.py b/pykep/trajopt/_pl2pl_N_impulses.py index 5eed0f09..5a7a431e 100644 --- a/pykep/trajopt/_pl2pl_N_impulses.py +++ b/pykep/trajopt/_pl2pl_N_impulses.py @@ -61,6 +61,11 @@ def __init__( """ # Sanity checks + # 0) This is not working for only two impulses + if N_max <= 2: + raise ValueError( + "This UDP is not wsuitable for only two impulse trajectories. Lambert multiple revolutions should be allowed for that (to be implemented)." + ) # 1) all planets need to have the same mu_central_body if start.mu_central_body != target.mu_central_body: raise ValueError( @@ -272,35 +277,44 @@ def pretty(self, x): def plot_primer_vector(self, x, N=200, ax=None): """Plots the primer vector magnitude along the trajectory encoded in *x*. - + Args: *x* (:class:`list`): The decision vector in the correct tof encoding. *N* (:class:`int`, optional): The number of points to use when plotting the primer vector. Defaults to 200. *ax* (:class:`matplotlib.axes.Axes`, optional): The axis to plot on. Defaults to None. - + Returns: - :class:`matplotlib.axes.Axes`: The axis where the primer vector was plotted. + :class:`matplotlib.axes.Axes`: The axis where the primer vector was plotted. :class:`tuple`: A tuple containing the grid and the primer vector magnitude. """ # We start by decoding the chromosome into the structure [[r,v], DV, DT] decoded = self.decode(x) - + # We explicitly extract the encoded information dts = [it[2] * _pk.DAY2SEC for it in decoded] DVs = [it[1] for it in decoded] posvels = [it[0] for it in decoded] + if min(DVs) < 1e-3: + raise ValueError( + "Impulse magnitude too small, primer vector computation is not possible. Decrease the number of impulses." + ) + # We create one grid er segment (e.g. part of the trajectory between two impulses) # (this is not guaranteed to have the requested size N, nor has uniform spacing, since all impulses # must belong to the grid points) - N = N + len(DVs) # heuristic to make sure we are close to the requested number of points + N = N + len( + DVs + ) # heuristic to make sure we are close to the requested number of points tgrids = [ _np.linspace( sum(dts[:i]), sum(dts[: i + 1]), - max(int(dts[i] // (sum(dts) / (N - 1))), 5), # we force a minimum 5 points per segment + max( + int(dts[i] // (sum(dts) / (N - 1))), 5 + ), # we force a minimum 5 points per segment ) for i in range(len(dts) - 1) ] @@ -333,7 +347,7 @@ def plot_primer_vector(self, x, N=200, ax=None): M = stms[-1] res = [] - # When computing the primer vector we must choose which impulses to use. + # When computing the primer vector we must choose which impulses to use. # We choose the first and last impulse. But we could choose any pair of impulses, # and if the trajectory is optimal (locally) the primer vector would not change. idx_i = idxs[0] diff --git a/pykep/trajopt/_primer_vector.py b/pykep/trajopt/_primer_vector.py index f8320f5b..0e20c19f 100644 --- a/pykep/trajopt/_primer_vector.py +++ b/pykep/trajopt/_primer_vector.py @@ -1,4 +1,5 @@ import numpy as _np +import pykep as _pk def primer_vector(DVi, DVj, Mji, Mjk): """This function computes the primer vector in a point k, relative @@ -24,4 +25,14 @@ def primer_vector(DVi, DVj, Mji, Mjk): Aik = -(_np.linalg.inv(Mji[:3,3:]))@Mjk[:3,3:] Ajk = -(Mji[3:,3:]@Aik + Mjk[3:,3:]) p = - Aik.T@DVi/_np.linalg.norm(DVi) - Ajk.T@DVj/_np.linalg.norm(DVj) - return p, Aik, Ajk \ No newline at end of file + return p, Aik, Ajk + +def primer_vector_surrogate(DVk, Mki, Mkj): + Aij = -(_np.linalg.inv(Mki[:3, 3:]) @ Mkj[:3, 3:]) + Akj = -(Mki[3:, 3:] @ Aij + Mkj[3:, 3:]) + B = Aij + b = - Akj.T@DVk / _np.linalg.norm(DVk) + p_surrogate_norm, u_star = _pk.trajopt.minBu_bu_p(B, b) + p_surrogate = p_surrogate_norm * u_star + return p_surrogate, Aij, Akj + From d27413b1c343a765bb201e67288e5a2e037c38b4 Mon Sep 17 00:00:00 2001 From: Dario Izzo Date: Mon, 3 Feb 2025 17:15:31 +0100 Subject: [PATCH 2/4] surrogate notebook finalized --- doc/api.rst | 2 +- doc/notebooks/surrogate_primer_vector.ipynb | 194 +++++++++++++++++--- doc/trajopt.rst | 2 + doc/tut_trajopt.rst | 3 +- doc/utils.rst | 22 +++ pykep/plot/CMakeLists.txt | 9 +- pykep/plot/__init__.py | 1 + pykep/plot/_mit.py | 68 +++++++ pykep/trajopt/_min_Bu_bu.py | 2 +- pykep/trajopt/_primer_vector.py | 21 ++- 10 files changed, 297 insertions(+), 27 deletions(-) create mode 100644 doc/utils.rst create mode 100644 pykep/plot/_mit.py diff --git a/doc/api.rst b/doc/api.rst index 5afc3455..ba7df2ff 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -3,7 +3,7 @@ API ####### -pykep API is designed to maximize its usability. Let us know what you think about it! +pykep API is striving to maximize its usability. Let us know what you think about it! .. toctree:: :maxdepth: 2 diff --git a/doc/notebooks/surrogate_primer_vector.ipynb b/doc/notebooks/surrogate_primer_vector.ipynb index e533c16e..11e11a11 100644 --- a/doc/notebooks/surrogate_primer_vector.ipynb +++ b/doc/notebooks/surrogate_primer_vector.ipynb @@ -196,6 +196,7 @@ "outputs": [], "source": [ "import pykep as pk\n", + "import pygmo as pg\n", "import numpy as np\n", "\n", "np.set_printoptions(legacy=\"1.25\")\n", @@ -217,13 +218,14 @@ "source": [ "# Problem data\n", "n_points = 50\n", - "r0 = np.array([1., 0., 0.])\n", - "v0 = np.array([0., 1., 0.])\n", - "DVk = np.array([0.6, -0.2, 0.])\n", - "t_grid = np.linspace(0, 4*np.pi, n_points)\n", + "r0 = np.array([1.0, 0.0, 0.0])\n", + "v0 = np.array([0.0, 1.0, 0.0])\n", + "DVk = np.array([0.6, -0.2, 0.0])\n", + "DVk_norm = np.linalg.norm(DVk)\n", + "t_grid = np.linspace(0, 4 * np.pi, n_points)\n", "\n", "# DV is applied at the end\n", - "idx_k=n_points-1\n", + "idx_k = n_points - 1\n", "\n", "# We first work out the surrogate primer vector for the following j,k\n", "idx_i = 10\n", @@ -263,17 +265,19 @@ "outputs": [], "source": [ "norm_surrogate_p = np.ones((n_points, n_points))\n", - "p = np.ones((n_points,n_points))\n", + "p = np.ones((n_points, n_points))\n", "for idx_i in range(n_points):\n", " Mk0 = stm_n0[idx_k]\n", - " if idx_i==idx_k:\n", + " if idx_i == idx_k:\n", + " continue\n", + " Mki = Mk0 @ np.linalg.inv(stm_n0[idx_i]) # Mki = Mk0*M0i\n", + " for idx_j in range(idx_i + 1, n_points):\n", + " if idx_j == idx_k:\n", " continue\n", - " Mki = Mk0@np.linalg.inv(stm_n0[idx_i]) # Mki = Mk0*M0i\n", - " for idx_j in range(idx_i+1,n_points):\n", - " if idx_j==idx_k:\n", - " continue\n", - " Mkj = Mk0@np.linalg.inv(stm_n0[idx_j]) # Mkj = Mk0*M0j\n", - " norm_surrogate_p[idx_j, idx_i] = np.linalg.norm(pk.trajopt.primer_vector_surrogate(DVk, Mki, Mkj)[0])\n", + " Mkj = Mk0 @ np.linalg.inv(stm_n0[idx_j]) # Mkj = Mk0*M0j\n", + " norm_surrogate_p[idx_j, idx_i] = np.linalg.norm(\n", + " pk.trajopt.primer_vector_surrogate(DVk, Mki, Mkj)[0]\n", + " )\n", " norm_surrogate_p[idx_i, idx_j] = -np.inf" ] }, @@ -281,7 +285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Lets see what is its maximum value:" + "Lets see what is its maximum value across all $i,j$:" ] }, { @@ -294,7 +298,7 @@ "output_type": "stream", "text": [ "The maximum value of the surrogate primer is attained when infinitesimal impulses are added at:\n", - "t1: 7.6937 and t2: 4.8727\n", + "t1: 4.8727 and t2: 7.6937\n", "Its norm is: 2.7366\n", "Larger then one! -> Trajectory can be improved\n" ] @@ -304,7 +308,7 @@ "idx1, idx2 = np.unravel_index(norm_surrogate_p.argmax(), norm_surrogate_p.shape)\n", "print(\n", " f\"The maximum value of the surrogate primer is attained when infinitesimal impulses are added at:\")\n", - "print(f\"t1: {t_grid[idx1]:.4f} and t2: {t_grid[idx2]:.4f}\")\n", + "print(f\"t1: {t_grid[idx2]:.4f} and t2: {t_grid[idx1]:.4f}\")\n", "print(f\"Its norm is: {np.max(norm_surrogate_p):.4f}\")\n", "print(f\"Larger then one! -> Trajectory can be improved\")" ] @@ -318,16 +322,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -366,14 +370,160 @@ "metadata": {}, "source": [ "# But is it true?\n", - "Where we check that the same trajectory can be improved using three impulses.\n", - "In here we optimize a three impulse fixed time trajectory reaching identical conditions as the ones of our original trajectory. " + "Where we check that the transfer can indeed be improved using three impulses, and reaching identical conditions (initial-final) as the ones of our original trajectory (and in the same transfer time)\n", + "\n", + "We thus build [on the fly] an optimization problem aimed at placing three impulses optimally between the initial and the end state. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "rf, vf = retval[-1][0]\n", + "vfd = [a + b for a, b in zip(vf, DVk)]\n", + "\n", + "class my_udp:\n", + " def __init__(self, posvel0):\n", + " self.posvel0 = posvel0\n", + " # x = [u1, v1, V1, u2, v2, V2, a1, a2, a3, a4]\n", + " def decode(self, x):\n", + " tofs = pk.alpha2direct(x[6:10], 4 * np.pi)\n", + " # Up to the first impulse\n", + " r1, v1 = pk.propagate_lagrangian(self.posvel0, tofs[0], 1.0, stm=False)\n", + " # Add the first impulse\n", + " DV1 = pk.uvV2cartesian(x[:3])\n", + " v1d = [a + b for a, b in zip(v1, DV1)]\n", + " # Up to the second impulse\n", + " r2, v2 = pk.propagate_lagrangian([r1, v1d], tofs[1], 1.0, stm=False)\n", + " # Add the second impulse\n", + " DV2 = pk.uvV2cartesian(x[3:6])\n", + " v2d = [a + b for a, b in zip(v2, DV2)]\n", + " # Up to the third impulse\n", + " r3, v3 = pk.propagate_lagrangian([r2, v2d], tofs[2], 1.0, stm=False)\n", + " # Close with Lambert\n", + " l = pk.lambert_problem(r3, rf, tofs[3], 1.0)\n", + " DV3 = [a - b for a, b in zip(l.v0[0], v3)]\n", + " DV4 = [a - b for a, b in zip(vfd, l.v1[0])]\n", + " # Using the Multiple Impulsive Trajectory format (mit format)\n", + " mit = [\n", + " [self.posvel0, [0., 0., 0.], tofs[0]],\n", + " [[r1, v1], DV1, tofs[1]],\n", + " [[r2, v2], DV2, tofs[2]],\n", + " [[r3, v3], DV3, tofs[3]],\n", + " [[rf, vf], DV4, 0],\n", + " ]\n", + " return mit\n", + "\n", + " def fitness(self, x):\n", + " mit = self.decode(x)\n", + " norm_DVs = [np.linalg.norm(it[1]) for it in mit]\n", + " return [sum(norm_DVs)]\n", + "\n", + " def get_bounds(self):\n", + " lb = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 1e-9, 1e-9, 1e-9]\n", + " ub = [1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1 - 1e-9, 1 - 1e-9, 1 - 1e-9, 1 - 1e-9]\n", + " return (lb, ub)" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "... and we solve it in multistart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-start:\n", + "0 0.3105387938073315\n", + "The best solution found has a DV of 3.10539e-01\n" + ] + } + ], + "source": [ + "def solve(udp, N=20):\n", + " # We use CMA-ES \n", + " uda = pg.cmaes(4500, force_bounds=True, sigma0=0.5, ftol=1e-5)\n", + " # But if you prefer a self adaptive version of differential evolution thats also OK.\n", + " #uda = pg.sade(2500, ftol=1e-4, xtol=1e-4)\n", + " algo = pg.algorithm(uda)\n", + " print(\"Multi-start:\")\n", + "\n", + " res = list()\n", + " for i in range(N):\n", + " pop = pg.population(udp, 20)\n", + " pop = algo.evolve(pop)\n", + " res.append([pop.champion_f, pop.champion_x])\n", + " print(i, pop.champion_f[0], end= '\\r')\n", + " \n", + " best_x = sorted(res, key = lambda x: x[0][0])[0][1]\n", + " best_f = udp.fitness(best_x)[0]\n", + "\n", + " print(f\"\\nThe best solution found has a DV of {best_f:.5e}\")\n", + " return best_x, best_f\n", + "\n", + "udp = my_udp([r0,v0])\n", + "best_x, best_f = solve(udp, 1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the trajectory found as a Multiple Impulsive Trajectory (mit)." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the axis\n", + "ax = pk.plot.make_3Daxis()\n", + "# Adding the central body\n", + "pk.plot.add_sun(ax, label = \"central body\")\n", + "# Plotting the original trajectory (mono-impulsive)\n", + "ax.plot([it[0][0][0] for it in retval], [it[0][0][1] for it in retval], [it[0][0][2] for it in retval], 'gray', label=f'$\\\\Delta V = {DVk_norm:.3f}$ (1 impulse)')\n", + "# Plotting the mit\n", + "pk.plot.add_mit(ax, udp.decode(best_x), mu = 1., units = 1., c_segments=['coral'],linestyle=\"-.\")\n", + "\n", + "# Makin the plot prettier\n", + "ax.view_init(90,-90)\n", + "ax.set_axis_off()\n", + "from matplotlib.lines import Line2D\n", + "# access legend objects automatically created from data\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "line = Line2D([0], [0], label=f'$\\\\Delta V = {best_f:.3f}$ (3 impulses)', color='coral', linestyle=\"-.\")\n", + "plt.legend(handles=handles+[line], loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **surrogate primer vector** correctly had us try to add impulses and, in this case, halved the needed $\\Delta V$!" + ] } ], "metadata": { diff --git a/doc/trajopt.rst b/doc/trajopt.rst index d69611e8..54745033 100644 --- a/doc/trajopt.rst +++ b/doc/trajopt.rst @@ -59,5 +59,7 @@ In order to facilitate the use of the classes in this module, some utilities are .. autofunction:: primer_vector +.. autofunction:: primer_vector_surrogate + .. autoclass:: _launchers :members: atlas501, atlas551, soyuzf, ariane5 \ No newline at end of file diff --git a/doc/tut_trajopt.rst b/doc/tut_trajopt.rst index b5950924..a7a6b3ba 100644 --- a/doc/tut_trajopt.rst +++ b/doc/tut_trajopt.rst @@ -31,4 +31,5 @@ Trajectory Optimization notebooks/udp_mga notebooks/udp_mga_1dsm notebooks/udp_pl2pl_N_impulses - notebooks/primer_vector \ No newline at end of file + notebooks/primer_vector + notebooks/surrogate_primer_vector \ No newline at end of file diff --git a/doc/utils.rst b/doc/utils.rst new file mode 100644 index 00000000..08774103 --- /dev/null +++ b/doc/utils.rst @@ -0,0 +1,22 @@ +.. _utils: + +.. currentmodule:: pykep.utils + +Spice +############################################################################### + +.. autofunction:: spice_version + +.. autofunction:: load_spice_kernels + +.. autofunction:: unload_spice_kernels + +.. autofunction:: inspect_spice_kernel + +.. autofunction:: name2naifid + +.. autofunction:: framename2naifid + +.. autofunction:: rotation_matrix + +.. Autofunction:: naifid2name \ No newline at end of file diff --git a/pykep/plot/CMakeLists.txt b/pykep/plot/CMakeLists.txt index 0ab47260..a8d8c1c7 100644 --- a/pykep/plot/CMakeLists.txt +++ b/pykep/plot/CMakeLists.txt @@ -1,2 +1,9 @@ -set(PYKEP_PLOT_PYTHON_FILES __init__.py _planet.py _lambert.py _ballistic.py _sf_leg.py) +set(PYKEP_PLOT_PYTHON_FILES + __init__.py + _planet.py + _lambert.py + _ballistic.py + _sf_leg.py + _mit.py) + install(FILES ${PYKEP_PLOT_PYTHON_FILES} DESTINATION ${_PYKEP_INSTALL_DIR}/plot) diff --git a/pykep/plot/__init__.py b/pykep/plot/__init__.py index 002549bb..06dbb018 100644 --- a/pykep/plot/__init__.py +++ b/pykep/plot/__init__.py @@ -9,6 +9,7 @@ from ._ballistic import add_ballistic_arc from ._sf_leg import add_sf_leg from ._sf_leg import add_sf_hf_leg +from ._mit import add_mit def make_3Daxis(**kwargs): """Constructs and returns a 3D axis. All kwargs are forwarded to the call to `figure()` in matplotlib. diff --git a/pykep/plot/_mit.py b/pykep/plot/_mit.py new file mode 100644 index 00000000..547aa354 --- /dev/null +++ b/pykep/plot/_mit.py @@ -0,0 +1,68 @@ +import pykep as _pk +import numpy as _np + +def add_mit(ax, + mit, + mu, + units=_pk.AU, + N=60, + c_segments=["royalblue", "indianred"], + figsize=(5, 5), + **kwargs +): + """ + Plot a Multiple Impulse Trajectory (mit) stored in the mit format: + + mit = [ [[r,v], DV, DT], ... ] + + + Args: + *mit* (:class:`list`): The decision vector in the correct tof encoding. + + *mu* (:class:`float`): The gravitational parameter + + *ax* (:class:`mpl_toolkits.mplot3d.axes3d.Axes3D`, optional): The 3D axis to plot on. Defaults to None. + + *units* (:class:`float`, optional): The unit scale for the plot. Defaults to pk.AU. + + *N* (:class:`int`, optional): The number of points to use when plotting the trajectory. Defaults to 60. + + *c_segments* (:class:`list`, optional): The colors to alternate the various trajectory segments (inbetween DSMs). Defaults to ["royalblue", "indianred"]. + + *figsize* (:class:`tuple`): The figure size (only used if *ax* is None and axis have to be created.), Defaults to (5, 5). + + *\\*\\*kwargs*: Additional keyword arguments to pass to the trajectory plot (common to Lambert arcs and ballistic arcs) + + Returns: + :class:`mpl_toolkits.mplot3d.axes3d.Axes3D`: The 3D axis where the trajectory was plotted. + """ + if ax is None: + ax = _pk.plot.make_3Daxis(figsize=figsize) + + DVs = [_np.linalg.norm(node[1]) for node in mit] + maxDV = max(DVs) + DVs = [s / maxDV * 30 for s in DVs] + + # 3 - We loop across grid nodes + for i, node in enumerate(mit): + ax.scatter( + node[0][0][0] / units, + node[0][0][1] / units, + node[0][0][2] / units, + color="k", + s=DVs[i], + ) + + r_after_dsm = node[0][0] + v_after_dsm = [a + b for a, b in zip(node[0][1], node[1])] + _pk.plot.add_ballistic_arc( + ax, + [r_after_dsm, v_after_dsm], + node[2], + mu, + N=N, + units=units, + c=c_segments[i % len(c_segments)], + **kwargs + ) + return ax diff --git a/pykep/trajopt/_min_Bu_bu.py b/pykep/trajopt/_min_Bu_bu.py index f9361e16..7d7eda0e 100644 --- a/pykep/trajopt/_min_Bu_bu.py +++ b/pykep/trajopt/_min_Bu_bu.py @@ -152,7 +152,7 @@ def minBu_bu_p(B, b): b_norm = np.linalg.norm(b) # Easy way out if b_norm < 1: - return -b_norm, np.array([0, 0, 0]) + return -b_norm, np.array([np.nan]*3) # Compute the singular value decomposition (used to bound |Bu| as well as to provide one IG) svd = np.linalg.svd(B) diff --git a/pykep/trajopt/_primer_vector.py b/pykep/trajopt/_primer_vector.py index 0e20c19f..b0c09adc 100644 --- a/pykep/trajopt/_primer_vector.py +++ b/pykep/trajopt/_primer_vector.py @@ -3,7 +3,7 @@ def primer_vector(DVi, DVj, Mji, Mjk): """This function computes the primer vector in a point k, relative - to impulses given at i and j. + to finite impulses in i and j. Args: *DVi* (:class:`ndarray` - (3,)): the impulse at point i. @@ -28,6 +28,25 @@ def primer_vector(DVi, DVj, Mji, Mjk): return p, Aik, Ajk def primer_vector_surrogate(DVk, Mki, Mkj): + """This function computes the surrogate primer vector at two points i and j, + corresponding to a single finite impulse at point k. + + Args: + *DVk* (:class:`ndarray` - (3,)): the impulse at point k. + + *Mki* (:class:`ndarray` - (6,6)): the state transition matrix from i to k (dxk = Mki dxi). + + *Mkj* (:class:`ndarray` - (6,6)): the state transition matrix from j to k (dxk = Mkj dxj). + + Returns: + :class:`tuple`: The surrogate primer vector, the Aij matrix, the Akj matrix. + + Note: + The impulse transfer matrix Anm is defined as that matrix that allows to compute + (in this surrogate case) the variation of the impulse at point n given the + variation of the impulse at point m. In formal terms, dDVn = Anm dDVm. + All variations are such that the terminal state is kept fixed. + """ Aij = -(_np.linalg.inv(Mki[:3, 3:]) @ Mkj[:3, 3:]) Akj = -(Mki[3:, 3:] @ Aij + Mkj[3:, 3:]) B = Aij From 3102a5aec45fdda8bb4bfe4fca7b597d1e5aa4b3 Mon Sep 17 00:00:00 2001 From: Dario Izzo Date: Mon, 3 Feb 2025 18:09:59 +0100 Subject: [PATCH 3/4] sanitizing all and finalizing --- doc/api.rst | 1 + doc/notebooks/primer_vector.ipynb | 2 +- doc/notebooks/surrogate_primer_vector.ipynb | 64 +++++++-------------- doc/notebooks/udp_pl2pl_N_impulses.ipynb | 41 +++++++------ doc/utils.rst | 31 +++++++++- pykep/plot/__init__.py | 1 - pykep/trajopt/_min_Bu_bu.py | 2 +- pykep/trajopt/_pl2pl_N_impulses.py | 55 ++++++------------ pykep/utils/_planet_to_keplerian.py | 2 + 9 files changed, 93 insertions(+), 106 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index ba7df2ff..b52a3ab1 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -23,4 +23,5 @@ pykep API is striving to maximize its usability. Let us know what you think abou trajopt gym plot + utils diff --git a/doc/notebooks/primer_vector.ipynb b/doc/notebooks/primer_vector.ipynb index a9972607..ddf0af61 100644 --- a/doc/notebooks/primer_vector.ipynb +++ b/doc/notebooks/primer_vector.ipynb @@ -9,7 +9,7 @@ "In this notebook we revisit the primer vector theory from Lawden from the lens of first order variations. We then construct manually, for a specific test case, the state transition matrices needed to call {func}`pykep.trajopt.primer_vector` and show\n", "its use. The same plot can also be obtained, without having to go through the math intensive developments, using the {func}`pykep.trajopt.pl2pl_N_impulses.plot_primer_vector` method of the Multiple Impulse Transfer UDP `pykep.trajopt.pl2pl_N_impulses`\n", "\n", - "Classically, the result of the primer vector is derived using Pontryagin maximum principle, here we present an original derivation building on the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`, which allows also to extend the primer vector to new, previously untreated, cases (see the notebook [A primer vector surrogate](<./primer_vector_surrogate.ipynb>)).\n", + "Classically, the result of the primer vector is derived using Pontryagin maximum principle, here we present an original derivation building on the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`, which allows also to extend the primer vector to new, previously untreated, cases (see the notebook [A primer vector surrogate](<./surrogate_primer_vector.ipynb>)).\n", "\n", ":::{note}\n", " The developments are here shown in details as they can be extended to more generic cases that use different dynamics and number of impulses. The user must in that case provide the state ransition matrices and DVs on a time grid as constructed below.\n", diff --git a/doc/notebooks/surrogate_primer_vector.ipynb b/doc/notebooks/surrogate_primer_vector.ipynb index 11e11a11..13618e2d 100644 --- a/doc/notebooks/surrogate_primer_vector.ipynb +++ b/doc/notebooks/surrogate_primer_vector.ipynb @@ -9,18 +9,18 @@ "In this notebook we extend the theory outlined in the notebook on the [primer vector](<./primer_vector.ipynb>) to the case where we are adding simultaneously two impulses\n", "to a trajectory where we can control one single existing impulse (as opposed to the original case where one adds only one impulse in a trajectory where two can be controlled).\n", "\n", - "While the developments are similar, please note that the index $k$ was before reserved to indicate the only node (of three, ijk) where no finite impulse was given. In this developments it indicates, instead, the only node (of three, ijk) where a finite impulse is given.\n", - "\n", - "The developments as well as the test case used are taken from the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`." + "While the developments are similar, please note that the index $k$ was before reserved to indicate the only node (of three, ijk) where no finite impulse was given. In this developments it indicates, instead, the only node (of three, ijk) where a finite impulse is given." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " ## Theory and notation\n", + ":::{note}\n", + " The developments as well as the test case used are taken from the work from Bauregard, Acciarini and Izzo {cite:p}`beauregard`.\n", + " The developments are shown in details as they can be extended to more generic cases that use different dynamics and number of impulses. The user must in that case provide the state ransition matrices and DVs on a time grid as constructed below.\n", "\n", - " ------------\n", + " ## Theory and notation\n", "\n", " a) Consider the following definition of the State Transition Matrix, $\\mathbf M_{fs}$:\n", "\n", @@ -56,14 +56,9 @@ " \\mathbf M^{vx} \n", " \\end{array} \n", " \\right] \n", - " $$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---------------\n", + " $$\n", + "\n", + " ---------------\n", "\n", "b) Assume now to have a grid of $N$ points along a multiple impulse trajectory and pick three indexes $i,j,k$. \n", "\n", @@ -87,13 +82,8 @@ "\\mathbf M_{ki}\\delta\\mathbf x_i + \\mathbf M_{kj}\\delta\\mathbf x_j + \\delta\\mathbf x_k = \\mathbf 0\n", "$$\n", "\n", - "Note that, with respect to the derivations for the primer vector, we have now changed the reference node to $k$ for convenience." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "Note that, with respect to the derivations for the primer vector, we have now changed the reference node to $k$ for convenience.\n", + "\n", "-----------\n", "\n", "c) The state variations $\\delta\\mathbf x$ are, in our case, consequence of three $\\delta\\Delta \\mathbf V$, so that the previous equations becomes:\n", @@ -116,15 +106,9 @@ "\n", "The matrices $\\mathbf A$ are telling us how the three variations of impulsive velocity changes applied in ($i$, $j$, $k$) must be related for the overall trajectory to not change its boundary conditions (i.e. $\\mathbf x_f = \\mathbf 0$).\n", "\n", - "Note here that we chose to use the index $j$ as reference as we relate all others $\\delta\\Delta V$ to $\\delta\\Delta V_j$. It convenient to keep the chosen index as the one where no finite impulse is present.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_____________\n", + "Note here that we chose to use the index $j$ as reference as we relate all others $\\delta\\Delta V$ to $\\delta\\Delta V_j$. It convenient to keep the chosen index as the one where no finite impulse is present.\n", "\n", + "-----------\n", "d) So far the three indexes we picked (i.e. $i,j,k$) were equivalent, now we assume that in $i,j$ no finite $\\Delta V$ is present. In $k$, instead, an additional $\\Delta \\mathbf V$ will exist.\n", "\n", "The total magnitude of the $\\Delta \\mathbf V$ can then be expressed by:\n", @@ -137,14 +121,9 @@ "\n", "$$\n", "\\delta J = \\frac{\\Delta\\mathbf V_k}{|\\Delta \\mathbf V_k|}\\cdot \\delta\\Delta \\mathbf V_k + |\\delta\\Delta \\mathbf V_i| + |\\delta\\Delta \\mathbf V_j|\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---------\n", + "$$\n", + "\n", + "-----------\n", "\n", "e) The surrogate primer vector\n", "\n", @@ -328,7 +307,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -351,7 +330,6 @@ "X,Y = np.meshgrid(t_grid, t_grid)\n", "plt.contourf(X,Y,norm_surrogate_p)\n", "plt.contour(X,Y,norm_surrogate_p, [1], colors='r', linestyles='solid')\n", - "plt.savefig(\"surrogate_primer_halo.png\", dpi=600)\n", "plt.plot(t_grid,t_grid, 'k--', label=\"$t_1=t_2$\")\n", "plt.scatter(t_grid[idx2], t_grid[idx1], label=\"Highest primer\", marker=\"*\")\n", "plt.plot([],[], 'r', label=\"|p| = 1\")\n", @@ -436,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -444,8 +422,8 @@ "output_type": "stream", "text": [ "Multi-start:\n", - "0 0.3105387938073315\n", - "The best solution found has a DV of 3.10539e-01\n" + "0 0.3105382900601983\n", + "The best solution found has a DV of 3.10538e-01\n" ] } ], @@ -484,12 +462,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/doc/notebooks/udp_pl2pl_N_impulses.ipynb b/doc/notebooks/udp_pl2pl_N_impulses.ipynb index f21b1423..e519cd50 100644 --- a/doc/notebooks/udp_pl2pl_N_impulses.ipynb +++ b/doc/notebooks/udp_pl2pl_N_impulses.ipynb @@ -76,7 +76,7 @@ "output_type": "stream", "text": [ "Multi-start:\n", - "19 6312.5821298412195\n", + "19 5986.389457561951\n", "The best solution found has a DV of 5.98639e+00 km/s\n" ] } @@ -121,7 +121,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -137,28 +137,31 @@ "\n", " # Create the figure and define the grid\n", " fig = plt.figure(figsize=(10, 10))\n", - " gs = fig.add_gridspec(2, 2, width_ratios=[2, 1], height_ratios=[1, 1], wspace=0.01, hspace=0.01)\n", + " gs = fig.add_gridspec(\n", + " 2, 2, width_ratios=[2, 1], height_ratios=[1, 1], wspace=0.01, hspace=0.01\n", + " )\n", "\n", " # Top view (spans rows and columns)\n", - " ax1 = fig.add_subplot(gs[:, 0], projection='3d')\n", + " ax1 = fig.add_subplot(gs[:, 0], projection=\"3d\")\n", " udp.plot(best_x, ax=ax1)\n", " ax1.view_init(90, 0)\n", " ax1.axis(\"off\")\n", "\n", " # Ecliptic view 1\n", - " ax1 = fig.add_subplot(gs[0, 1], projection='3d')\n", + " ax1 = fig.add_subplot(gs[0, 1], projection=\"3d\")\n", " udp.plot(best_x, ax=ax1)\n", " ax1.view_init(0, 0)\n", " ax1.axis(\"off\")\n", "\n", " # Ecliptic view 2\n", - " ax1 = fig.add_subplot(gs[1,1], projection='3d')\n", + " ax1 = fig.add_subplot(gs[1, 1], projection=\"3d\")\n", " udp.plot(best_x, ax=ax1)\n", " ax1.view_init(0, 90)\n", " ax1.axis(\"off\")\n", " return fig\n", "\n", - "visualise(udp3, best_x_3);\n" + "\n", + "visualise(udp3, best_x_3);" ] }, { @@ -177,10 +180,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total DV (m/s): 5986.389457396789\n", - "Dvs (m/s): [219.5201622093484, 2899.3388627999943, 2867.5304323874466]\n", - "Total DT (m/s): 360.0\n", - "Tofs (days): [205.9435135734575, 154.0564864265425]\n" + "Total DV (m/s): 5986.389457561951\n", + "Dvs (m/s): [219.52486107466606, 2899.304957414648, 2867.559639072637]\n", + "Total DT (m/s): 31104000.000000004\n", + "Tofs (days): [17793484.53088946, 13310515.469110545]\n" ] } ], @@ -203,7 +206,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -251,7 +254,7 @@ "output_type": "stream", "text": [ "Multi-start:\n", - "19 5986.389456287089\n", + "19 5986.3894562564655\n", "The best solution found has a DV of 5.93793e+00 km/s\n" ] } @@ -267,7 +270,7 @@ "outputs": [ { "data": { - "image/png": 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73U5xcTEFBQVn9LERKSl0/dWvsEZG4t6+nYNHD1iUIJUNERERkRC2cOFC9u/fT2RkJHfccccPtrjdne/hnr8UsmxDLQ47PDA1kT/em0pS3Mm3wpVvuVwuevToAcC2bdvO+OMjMzLofN99ABxesIDyNWuaNF84U9kQERERCVHFxcV89NFHAFxzzTUkJCQcc/uitTU88GgRBSU+OiTbePKR9vxoXOxpHe4nx+rcuTMQPMPkbMQPGEC7yy8H4MBLL2n9xlEqGyIiIiIhKBAI8MYbb+D1eunZsycjRoxovM0wDF6bV8F/v1hCvdfg/N4unvlNe7p3jDAxcXjLysoCzr5sAHS49lpcGRn4qqo4NGtWU0ULayobIiIiIiFo2bJl7Ny5k4iICG666abG0Qq/3+Bvr5fxr48qALh2fCx/ui+VuGhNmzoXDWWjsLAQj8dzVo9htdvpePvtAJQsWkTtORSX1kJlQ0RERCTEuN1u5syZA8DkyZNJSUkBwOM1+M8XSvhkRTVWC/z0hkTuuzZRW9o2gfj4eGJjYwkEAhw6dOisHyemRw8Szj8fDCN40ngbp7IhIiIiEmI+/fRT6urqyMrKYuzYsQDUeQL82z+LWb6hlgiHhf+ansKUC2PNDdqKWCyWxtGN/Pz8c3qsDldfDUD5mjXUFxWdc7ZwprIhIiIiEkJKS0v56quvAJgyZQpWqzVYNJ4+zNrt9UQ6Lfz1/lRG9de2tk0tMTERgKpzXNwdmZVFXL9+YBiULFrUBMnCl8qGiIiISAj58MMP8fl89OjRg169euHxGvzuuRLW7awnymXhfx9Mo393l9kxW6WYmBjg3MsGQMq4cQCULVuGEQic8+OFK5UNERERkRCRn5/P119/DcBVV11FwIA/vVzCmq11uJwW/nJ/Gn26OE1O2Xo1lA23233OjxXXvz/WyEi8R45Qs2fPOT9euFLZEBEREQkRH3zwAYZhMGjQIDp16sQzs8v5Kjd4WN//3J1K3xwVjebUlGXD6nAQ17cvAJUbN57z44UrlQ0RERGREFBQUMCmTZuwWCxceeWVzP2qive+DE7n+fUtyQzuqalTza0pywZAbO/ewcfbsaNJHi8cqWyIiIiIhIBFRxcSn3feeRRWxvPkzCMATJscz/gh0SYmazuczuDI0dmes/F9UV26AFC7fz+GYTTJY4YblQ0RERERk9XU1LBq1SoABg0dx3/PKCEQgPFDorhxYpzJ6doOn88HgN1ub5LHi8zICD5uVRX+6uomecxwo7IhIiIiYrJly5bh8XhIT89g5tIEjlQG6Jzu4JGfJDWeHC7Nr6nLhtXpxB4bPAvFU1raJI8ZblQ2REREREzk9/tZvHgxAM4OV7B2Wz1Oh4Xf3ZmCK0JP1VpSU5cNAHtccGTK30TrQMKNvoJFRERETLR161bKysqwRabzxYbgoXL3XpNApw4Ok5O1PQ1lw2azNdljWiMiAAh4vU32mOFEZUNERETERGvXrsUAjjin4PHC4J4urrwgxuxYbVJD2XA4mrDoNUyD0wJxEREREWlJXq+X9evXU0VPDpXHE+Gw8PBNWqdhloqKCuDbLXDPRW1tLY8++iiX/N//MfKttxg0ZQqPPvootbW15/zY4aTpJqSJiIiIyBnZtm0b1bUeSiwTwICbJsaRnqKnZ2YpPbqIOyUl5Zwep7a2losvvpgVK1YQCAQA2JOXx29+8xvmzp3L/PnziYyMPOe84UAjGyIiIiImyc3NpZxBeIxYUhNsXH9RrNmRTpvP52bfvv9h+fIsFi2ysXx5Fvv2/Q8+X/guhC4pKQEgOTn5nB7nqaeeOqZoNAgEAqxYsYKnn376nB4/nKhsiIiIiJjA5/ORu24TpQwH4JbL43GGye5TPp+bdevGsG/f7/F48oEAHk8++/b9nnXrxoRt4SgrKwPOvWw899xzPygaDQKBAM8999w5PX440TidiIiIyEkUFRXx1VdfnfJ+/oCFGo+D8moLeXkFDO0bd8xC4++fIF1eXk5hXTf8RBPjrMd98DPefffcFhGf6pTqploLEhn5LlFRuVgs3//7AlRV5fLZZ9Oorb22Sf6ulmIYBocPHwZg1apVbNiw4RT3hxqPA3ddBJV1TiprnVTUujhS7WLvvvyTfmx+/slvb01UNkREREROoK6ujv/6r/9q/HMAGx6SqScFD0l4STh6icNHLAG/hzWzrqD6yA6SkpK49tprT7iNqgEc4S4Aouq/YvGib1riU2oSV1zx0XGKRgMDi+UjFi5MatFMTcEAAjhZvGIrfqLwEd34NniJOeYCx/+/dUa3p7Zy/wn/nszMzOb5BEKQyoaIiIjICTidTnJ6jmDlNgfVdKaONE70BBOg5shOqo/sAIJTco4cOXLCxca1ZOEhGSv1xLOxOeI3m8jImhPeZrGc/PbmZgAGdgI48eM6+tZJANd3/hyJH9fRt8e+f7L/3x8KYKcKB5U4KCeCciIoo2/vrny98vhlw2q1cvfddzfFpxoWLMapxttERERE2qgDRV7u/lMh9d5vny7FRlnJ7uAgq72d9JTgpX2ynbREO5ERXoYOPZ9NmzbRt29f1qxZg9Pp/MHjBgIBrvvpZ5T5+3Bhf/j93R1b8tM6Z8uXZx1dq3F8ERGZjByZd1qP5fcb1HmOXuoD1HkMausN6jyB4Nt6g9r6ADX1wdtr6gxq6gPU1hnU1H375+ra4PvVtQH8x18ucdpcTgsJMVYSYm0kxtpIjLWSGGcjOd5GUpyN1ITg+8nxNmy2H05NO95uVBAsGiNGjGhTu1FpZENERETkBCqq/McUDYAIhwWv36C8KgCGj6rqAIcO+4iOtBIdaeX/XvqKooM76d69J3nFFiIivETYLUTYLdTXV/PM00/wwvPPUFhUjDMqjR533IXb/asmOdvhVAzDIBAAfwB8fgN/IPhk3+c38Pkb3ufonw28vuB13qPve30GXp9BwH8bdv6EhR8+qzcMK3sO/4Qv/1VCvcfA4zWoP3rxeAzqvAb1HoN6T4B6b/Bxm4PVAlEuCzFRwf+X2EgrMVHBS2yUlbjob9/GRdt4d+bLHC7cyx23TGXE8CHn9HdHRkYyf/58nn76aZ577jny8/PJzMzk7rvv5v77728zRQM0siEiIiJyUovX1vDJCjfb9nmorD77l8x93mpy378Od+kWML7zOBYrcam9GTl1FhGuGKzW4BNlLGA9uqC7YV13w2voDU/eGp7FGYaBYQT/HDAgEAj+2W9AIBD8cyAQvK0pOOzV3Dr2OtonbsFCAIsl+HcbWCk80ptXFs3E64s+o8e0WMAVYcHltOCKsBLptBB59H2X00KU00KUq+F6K1Gu4J+DFwvRkcH3oxuvt5z2gvj6+np+8YtfEAgE+MMf/kBSUvitNwlVGtkQEREROYkxg6IYMygKwzCocAcoKvNx+Iifsko/FdUBKquDU3gapvHU1gen/9R7g9N+PEdfvd/3zYs/LBoARoDKw1vYvvoFsof81JTP0WoBu92C3QZ2mwWbDRw2C3a7BYcNbDYLjqOjM3Y7OOwudhz+AA/P0yHuFSJsRXgD7an03Io34j5uvSwWh8NChMOCK8KC8+j7zggrzobrjl7vjAiWB4e96XbLOlP79+8nEAiQkJCgotHEVDZEREREToPFYiEh1kZCrI0enc7847OyZv6waDQwAtTmv8PLH/wF/9Hhh4ap/oHG0Yvv5zn69uj7314sjaMjVqsFqwVs1uD7wbdHC4U1WCIa7nd2/nz0Et62b98OQJcuXUxO0vqobIiIiIi0gEOHDp309uKiAjq2d5z0PtL0DMNg7dq1APTr18/kNK1PeBxTKSIiIhLm0tPTz+l2aR4FBQUUFRVht9s577zzzI7T6qhsiIiIiLSA6dOnY7Ue/6mX1Wpl+vTpLZxIAHJzcwHo1atXm9olqqWobIiIiIi0gIcffpgBAwb8oHBYrVYGDBjAww8/bFKytq2hbAwcONDkJK2TyoaIiIhIC4iJiWHx4sXce++9REdHY7FYyMzM5Pe//z2LFy9ukXM25FiFhYUcOnQIm82m9RrNRAvERURERFpITEwMd9xxB4FAgK5du/Lzn//c7Eht2vLlywHo2bMnUVFRJqdpnTSyISIiItKCPB4PABERESYnadvq6+tZtmwZABdccIHJaVovlQ0RERGRFtRQNhwObXNrplWrVlFbW0tqaip9+/Y1O06rpbIhIiIi0oK8Xi+gkQ0zGYbBokWLABgzZswJdwmTc6d/WREREZEWFAic4BRxaTFbt26lsLAQp9PJiBEjzI7TqqlsiIiIiLSghoXItbW1JidpuxYuXAjAiBEjdLZGM1PZEBEREWlBDWWjpqbG5CRt065du9iyZQtWq5WxY8eaHafVU9kQERERaUEqG+YxDIM5c+YAMHLkSNLS0kxO1PqpbIiIiIi0IJUN86xbt469e/cSERHB5ZdfbnacNkFlQ0RERKQFRUdHA1BdXa3F4i3I7/czd+5cACZMmEB8fLzJidoGlQ0RERGRFhQfH4/dbsfv91NWVmZ2nDZj+fLlFBcXExMTw0UXXWR2nDZDZUNERESkBdlsNjp06ABAfn6+yWnahqqqKj788EMAJk2apB2oWpDKhoiIiEgLy8jIAODgwYMmJ2kbZs2ahdvtJj09nQsuuMDsOG2KyoaIiIhIC1PZaDnr1q1jzZo1WK1Wbr75Zux2u9mR2hSVDREREZEWprLRMqqrq3n77bcBuOiii+jUqZPJidoelQ0RERGRFtaxY0csFguHDx+mtLTU7Dit1qxZs6isrKR9+/ba6tYkKhsiIiIiLSwqKoqcnBwANm7caHKa1ik3N5fVq1djsVi4+eabcTgcZkdqk1Q2RERERExw3nnnAbBhwwaTk7Q+hYWFvPrqq0DwTI3OnTubnKjtUtkQERERMUG/fv0A2LlzJ7W1tSanaT1qa2t57rnnqK+vp1u3bkyZMsXsSG2ayoaIiIiICdq1a0e7du3w+/1s3rzZ7DitQiAQ4NVXX6WoqIiEhASmTZuGzWYzO1abprIhIiIiYpIBAwYAsGzZMnODtBKff/4569evx263M336dGJjY82O1OapbIiIiIiY5IILLsBqtbJ9+3by8vLMjhPW1q1b13hK+PXXX092dra5gQRQ2RARERExTVJSEoMGDQJg4cKFJqcJX1u3buWll17CMAxGjx7NqFGjzI4kR6lsiIiIiJho/PjxAKxZs4aKigqT04Sf3bt389xzz+Hz+Rg4cCDXX3+92ZHkO1Q2REREREyUnZ1Nly5d8Pv9LFiwwOw4YSUvL49//vOfeDweevfuzW233aYF4SFGZUNERETEZJdeeikAX375JYcOHTI5TXgoLCzkySefpLa2lq5duzJ9+nQd3BeCVDZERERETNa3b1/69etHIBDgnXfewTAMsyOFtH379vHYY4/hdrvp2LEj9957LxEREWbHkuNQ2RAREREJAVOnTsXhcLBz506+/vprs+OErM2bN/PEE0/gdrvJysrigQceIDIy0uxYcgIqGyIiIiIhIDk5mUmTJgHw3nvv4Xa7TU4UelasWMEzzzyDx+OhV69ePPzww8TExJgdS05CZUNEREQkREyYMIF27dpRWVnJjBkz8Pv9ZkcKCYZhMG/ePF577TUCgQBDhw7l3nvvxeVymR1NTkFlQ0RERCREOBwO7rrrLpxOJ9u3b2fOnDlmRzJdXV0dL7/8Mh999BEAEydO5NZbb8Vut5ucTE6HxdAKJBEREZGQkpubywsvvADArbfeyrBhw0xOZI68vDxmzJhBcXExVquVqVOnMmbMGLNjyRlQ2RAREREJQR9++CGffPIJdrudBx54gO7du5sdqcUYhsGSJUt499138fl8JCQkcOedd5KTk2N2NDlDKhsiclKGYeDxeKitraWuro7a2trG9wOBAE6nE6fTicvlOuZtREQEVqtmaoqInK1AIMBzzz3Hxo0bcTgcTJ8+nT59+pgdq9nV1NTw5ptvsnbtWiC4LfAtt9yiheBhSmVDpI0xDIOKigoKCgooKCigqqqqsUA0lIiGUtHwNhAInNXf9f0i8t33o6KiSEtLo3379rRr146kpCSVExGR7/F4PLz44ots2rQJm83G7bffzqBBg8yO1SwMw+Drr7/mvffeo7KyEpvNxlVXXcX48eOxWCxmx5OzpLIh0ko1lIpDhw5RWFjYWC4KCgqora0948ezWCxERkbicrmIjIwkMjISq9VKfX09dXV11NfXN75/Nj9WHA4H7dq1aywfDW/btWunE2FFpE3z+/28/PLLfPPNN1gsFm666SZGjRpldqwmVVBQwDvvvMOOHTsAaNeuHbfeeivZ2dnmBpNzprIhEuYMw6C8vPyYMlFQUEBhYeEJS4XFYiE1NZUOHTqQmJjYWB6+WyS+++eG0YjTeWXJMAy8Xm9j8fh+EWl463a7KSoqoqioiOLiYnw+3wmzJicnH1NC2rdvT8eOHVVCRKTNCAQCvPXWWyxbtgyAYcOGcd1114X9YXYej4dPPvmEBQsW4Pf7cTgcTJo0iQkTJuhnfCuhsiEShioqKti6dStbtmxh27ZtJzz4yWq1NpaKhkvDk/ZQ+iHu9/spLS2lsLCQoqIiCgsLGy8nKkwRERF069aNnj170qtXLzp06KBhdhFp1RrOmpg3bx6GYZCcnMztt99Oly5dzI52xjweD0uXLmX+/PlUVFQAwbUZ1113HSkpKSank6aksiESBrxeL3v27GHLli1s2bKFgwcPHnO71WolLS3tmEKRnp5OampqSJWKM2UYBlVVVY3Fo6GI5OfnU1VVdcx94+Pj6dmzZ+MlPj7epNQiIs1r9+7dvPzyy5SWlmK1Wpk4cSKXXHIJTqfT7GinVFdXx5IlS1iwYEHjz/GkpCSmTp1K//79TU4nzUFlQyQEGYbB4cOH2bx5M1u3bmXHjh14PJ5j7tOxY0d69epF7969yc7ODutScaYMw+DgwYNs27aNbdu2sXPnTrxe7zH3ycjIaBz16Nq1KxERESalFRFperW1tbzzzjusXr0agLi4OCZOnMjo0aND8vdBVVUVS5cuZeHChVRXVwOQnJzMxIkTGTZsWEhmlqahsiESImpra9m+fXvj9KjS0tJjbo+Li2ssFz179iQ2NtakpKHH6/Wye/dutm3bxtatW8nLyzvmdrvdTk5OTuO/X0ZGhqZciUirsHbtWt5//31KSkqA4CjBZZddxrBhw7DZbKZm83q9bNq0iZUrV7J58+bGnQ3T0tKYOHEiQ4cONT2jND+VDRET+f1+Nm/ezNKlS9myZcsxW8zabDZycnLo3bs3vXr1IiMjQ1vDnqaqqqrG4rZ161bKy8uPuT0zM5ORI0cydOhQoqKizAkpItJEfD4fy5cv55NPPmlc/xATE8PgwYMZOnQo2dnZLfYCi9/vZ9++fXz99desWbOGmpqaxts6derE+PHjGTx4sH6ftSEqGyImKC0tZdmyZaxYsaLxFwNAamoqvXv3pnfv3nTr1g2Xy2ViytbBMAyKiooai8e2bdsad76y2+0MHDiQUaNG0a1bN412iEhY83g8LFmyhPnz51NZWdl4fVpaGueffz49evSgY8eOTTqtNBAIcOjQIbZv38727dvZtWsXdXV1jbfHx8czbNgwhg0bRocOHZrs75XwobIh0kL8fj8bNmxg6dKlbNu2rfEsipiYGIYPH87IkSNp3769ySlbv+rqalavXs2yZcs4dOhQ4/WpqamMHDmS4cOHa3G5iIQ1v9/Ptm3bWL16NevXrz9mzZ/VaiUzM5POnTuTnZ1NcnIysbGxxMXF4XK5TviiS8OugcXFxcdc8vLyGtdgNIiKiqJPnz4MGzaMnj17ahSjjVPZEGlmxcXFLF++nBUrVhyzg1LPnj0ZPXo0/fr1w263m5iwbTIMg/3797N8+XLWrFnT+Eqc1Wqlb9++jBw5kj59+mg+sYiEtbq6OtavX8/69evZu3fvMaPp32e324mLi8PhcOD1evH5fMe8PRGn00nXrl3p0aMHPXr00LRfOYbKhkgz8Hq9rF+/nmXLlrF9+/bG6+Pi4hgxYgQjR44kNTXVxITyXfX19axdu5bly5eze/fuxuvj4+MbR530/yUi4c4wDI4cOcK+ffvYu3cv+/fvp6KigqqqqmOmPp2Iw+EgLS2NtLQ0UlNTGw9a7dixo16YkRNS2RBpQiUlJSxevJiVK1c2DitbLBZ69erF6NGjOe+88/QDOcQVFBSwfPlyVq1adcxhiT169GDChAn06dNHaztEpNXxeDxUVVVRWVmJ1+slIiICu92Ow+FovERHR2vEQs6YyoZIEygvL2fevHksX768cUephISExlGM5ORkkxPKmfL5fGzcuJFly5axdevWxjU2OTk5XHnllXTv3t3khCIiIqFPZUPkHLjdbj7//HMWL17cOJ+1V69ejBkzRvP9W5GysjIWLVp0zP9zz549mTx5MtnZ2eaGExERCWEqGyJnoa6ujoULF7JgwYLGea45OTlMmTKFrl27mpxOmkt5eTmffvopy5Ytw+/3A9CvXz+uvPJKMjIyTE4nIiISelQ2RM6A1+vlq6++4rPPPmucz5+ZmcmUKVPo3bu35vK3ESUlJcybN49Vq1ZhGAYWi4XBgwdzxRVXkJaWZnY8ERGRkKGyIXIa/H4/K1eu5OOPP248jTotLY0rrriCQYMGacFcG1VQUMDHH3/M2rVrgeC2uSNGjGDSpEkkJSWZnE5ERMR8KhsiJxEIBMjNzeXDDz+kuLgYCC78vvzyyxk+fLjWZDQD4+gCeyyWsBkpysvL48MPP2TTpk1AcK/60aNHc+mllxIXF2dyOhEREfOobIicwObNm/nggw/Iy8sDgid9X3rppVxwwQU4HA6T04U+f20t3iNH8JSVNb71VVTgr6nB53bjr67GV11NoK6OgNeL4fMR8Hrh6FqIRlYrFosFq9MZvLhc2FwurC4X9thY7LGxOOLisMfF4UhIICIlhYiUFGzR0S1eVnbv3s0HH3zAzp07AYiIiGDcuHFMmjSJiIiIFs0iIiISClQ2RL7H7Xbzzjvv8M033wDgcrm46KKLGD9+PC6Xy+R0ocUIBKgvKqI2L4/6wkLqCgupP3rxfee0dDNYXS4iUlNxpqXhysjAlZ5OZEYGzvbtsTXj/6NhGGzbto0PPviA/fv3A5CamsrNN9+szQNERKTNUdkQ+Y5169bx1ltvUVVVhdVqZdy4cUycOJGYmBizo5nOCASozcujZvduag4coHb/fmoPHCDg8ZzwY2xRUTgSE3EkJRGRmIg9Ph57dDS2mJjg2+hobC4XlogIrHY7FocDq8MBFgsYRnBKlWFg+P0EPB4C9fX4a2sb3/oqK4OXqiq8lZXBEZTSUnwVFSf+RCwWnO3bE5WdTVTnzsFLdnaTFxDDMFi/fj0zZ86kvLwci8XCuHHjmDx5skY5RESkzVDZECE4mjFz5kzWrFkDQIcOHbjlllvo1KmTycnME/B4qN65E/eOHbh37qR61y4CtbU/uJ81IgJXZiauDh1wtm/f+NaZloYtMtKE5BCor8dTWkp9SQn1BQXUHTpE3cGD1B06dPwRF4uFqOxsYnr0aLzYY2ObJEtNTQ2zZ89mxYoVgEY5RESkbVHZkDZv/fr1vPXWW1RWVmKxWLjkkku47LLL2ty6jJKSUhZ/9Bl9I21Y9+/HvW0bxtED7BpYXS6ic3KI6tyZyI4dierUCWf79ljCaDcub0UFNfv3U7NnDzX79lGzZw/eI0d+cL/IrCzi+vUj7rzziO7ePTjicg42b97MG2+80TjKMXbsWKZMmaJRDhERadVUNqTNqq6uZubMmXz99ddAcDTj5ptvblMnQnu8Bt9sq+Pjuev4+5+voq6mmNTISGZecQVxTieOxERievYkpls3ort3JzIrK6yKxenylJXh3r4d97ZtuLdvp+7gwWNutzqdxPbpQ8LgwcQPHHjWox61tbW8++67GuUQEZE2Q2VD2qQNGzbw5ptvNo5mXHzxxVx++eVtYjTD4zX4eksti9bWsGJjLTV1BmX5S1n/4Y8b7zPrz3/m8ptvxpWeHjbbzzYlb2UlVZs2UblxI5UbNx67BsRqJbZnTxKGDCFh6FAc8fFn/Pga5RARkbZCZUPalJqaGmbNmsWqVasAaNeuHbfccgudO3c2OVnzMgyDjbvr+XxlNYtza6iu/fbbPiXBxoDsGh7/t9GUlBaTnp7O5s2bSUhIMC9wCDECAWoPHKAiN5fyNWuoPXDg2xutVuL69iVp1CgSBg/G6nSe9uNqlENERNoClQ1pMzZu3Mibb75JRUUFFouFCRMmcOWVV7bq0YzSCj+frnDzyYpqDh32NV6fkmBj7KAoxg6OomenCKxWC0eOHGHt2rUMHjxYReMk6ouKKF+zhiOrV1OzZ0/j9VaXi8ShQ0kZN46onJzTHhH6/ijHmDFjmDJlCs4zKC4iIiKhSmVDWj2fz8esWbNYsmQJAGlpadxyyy106dLF5GTNwzAMcnfU88FXVSxdX0vDgdyRTgtjBkVxybBo+nV1YrW2velRTa2uoICy5cspW7YMz+HDjddHZmWRMn48SaNGndaOXLW1tcyePZvly5cDwa/Re++9l3bt2jVbdhERkZagsiGtmtvt5oUXXmDnzp1YLBbGjx/PlVde2Srnxtd7AsxfXcPsL6vYX/DtLlJ9ukRw+agYxgyKItLZ+hZ3hwLDMKjesYOSRYs4smpV4y5eVqeT5DFjSJs4EWda2ikfZ8uWLbz++uuUl5cTGRnJtGnT6NWrV3PHFxERaTYqG9JqFRQU8Mwzz1BSUoLL5eLOO++kT58+ZsdqchVuPx985WbOoirK3cFhjEinhUuGRTP5whg6p7e+YhXKfG43ZcuWUbJwIXWHDgWvtFhIGDKEtEsvJaZ795N+fFVVFc899xx79uzBarUydepUxowZ0wLJRUREmp7KhrRKmzdvZsaMGdTV1ZGcnMy9995Lenq62bGaVFmFn5lfVPLBEjd19cFv43ZJNn40LpZJI2OIidQohpkMw6Bq0yaKP/mEyo0bG6+P6dWLDlddRUyvXidc1+H1ennzzTcbNzK48MILmTp1KjabrUWyi4iINBWVDWlVDMPgyy+/ZPbs2RiGQdeuXZk+fToxMTFmR2syR6r8vP15JXO/cuPxBr99u2Y6uOGSOMYMjMJm01qMUFObl0fxp59StmwZht8PQHT37nS46ipi+/Y9bukwDIP58+czd+5cDMOgR48e3HXXXURFRbV0fBERkbOmsiGtht/v55133mHp0qUAjBgxghtvvBG73W5ysqbhrg0wc0El735RRZ0n+G3bu3MEP5kUz7A+rjZ5Hka48ZSWUvTRR5QsXty4riOmRw8ybriB6BNsebt+/Xpefvll6uvrtXBcRETCjsqGtAput5sXX3yRHTt2YLFYuPrqq5kwYUKreALu9Rl8uMTNq/MqqKwOrsno0SmCO66MZ0gvlYxw5DlyhOKPP+bwwoWNpSN+8GDSp04lMiPjB/fPz8/n2WefpaysjMjISO666y569uzZ0rFFRETOmMqGhL3CwkKeeeYZDh8+jNPp5I477uC8884zO9Y5MwyDFRtrefa9cvKLg2dkdGpv587JCYzqH9nsJSMQCFBTU0NlZSWVlZVUVVU1vr927VpKS0sB6Nu3Lzab7ZQXl8tFcnIyKSkppKSkEB0d3eaLkqe0lIL33qN0yRIwDLBYSJ0wgQ7XXIP9e1P/Kisree6559i7dy9Wq5XrrruOCy+80KTkIiIip0dlQ8Lali1bmDFjBrW1tSQnJ3PPPfeQcZxXhsNNXpGXp2cdYfWWOgASY63cfmUCk0ZEN+maDMMwKC0tZf/+/ezfv5+CgoJjykWg4ZCOZuByuUhJSTmmgDRckpKSWvVhi99Xe/Agh2bOpGLtWgBsMTGkX3MNKePGYfnOonCv18sbb7zB6tWrARgzZgzXXnutFo6LiEjIUtmQsLVo0SLeffddAoEAOTk5TJ8+ndjYWLNjnZN6T4A3P6vk7fmVeH3gsMM14+P4yaVxRLnOfXep8vLyxmKxf/9+Dhw4QHV19Uk/Jjo6mtjYWOLi4hovXq+XkpISEhMT6dy5M36//5SXmpoaSktLKSkpoaKi4qR/p8ViITMzk27dutGtWze6du1KdHT0OX/+oa5q82byXn+duvx8IHg4YMc77jhmPYdhGHz++efMnTsXgJ49ezJt2jQtHBcRkZCksiFh6aOPPmLevHkADB8+nBtvvDHsXwlfu72Ox98s4+Dh4JSpob1dPHBdIplpZ/d5BQIBdu3axa5duxqLxfGe5NvtdjIyMujYsSNZWVnEx8c3lorY2NhmWWDv8XgoKyujpKSEkpKSxhLScKmvrz/m/haLhfT09Mby0a1bt1a1w9h3GX4/JQsXcmj2bPzV1cGpVRddRPrUqcecRr5u3TpefvllPB4PHTp04OGHH261/yYiIhK+VDYk7Hz66ad88MEHAEyePJmJEyeG9dx/d22AZ987wrxlwRGGlAQbD0xN5IIBZ74uw+/3s337dnJzc1m/fj1ut/uY261WKx06dKBjx4506tSJTp06kZ6eHlJFzTAMKioq2LVrFzt37mTnzp0UFhb+4H7fLR89e/Zsda/s+6qqyH/zTcqO7q7mSEwk67bbSBg0qPE+eXl5/POf/6SiooLMzEx+9rOftbp/BxERCW8qGxJWvvjiC2bPng3AVVddxSWXXGJyonOzZmstj75WxuHy4NkLUy6MYdqUBKLP4EA+r9fL1q1byc3NZcOGDdTW1jbeFhUVRe/evcnOzqZTp05kZWURERF+J4pXVlaya9cuduzYwc6dOykoKDjmdrvdTv/+/Rk2bBi9evVqVWsYKjdvJu+ll6gvLgYgafRoMn/yE+xHp5UVFhby+OOPU1VVRXZ2Ng8++CCR3xkBERERMZPKhoSNxYsX88477wBwxRVXcNlll5mc6OzVewI8/345cxYFRx7SU+388idJ9O/mOq2Pr6urY8uWLeTm5rJp06Zjph3FxsbSv39/Bg4cSPfu3VvVE+8GVVVVjSMfW7dupaioqPG2uLg4zj//fIYNG0ZmZqaJKZtOwOOh4L33KJo3DwwDR1ISne66i7i+fQE4ePAgTzzxBNXV1eTk5PDAAw/gdDpNTi0iIqKyIWFi+fLlvP766wBMnDiRyZMnh+3Uqd35Hv7wr1L2FwTPV5gyJobpVyUQ6Tz1aEZeXh5ffPEFubm5eI+ezwCQkJDAgAEDGDhwIDk5OVit576YPFwYhkFeXh4rV65kzZo1x0wdy8zMZPjw4Zx//vlhv3kAgHvHDvY/91zjKEfqxIlkXH89VoeDAwcO8I9//IPa2lp69OjBfffdF1LT40REpG1S2ZCQt3r1al555RUMw2D8+PFcc801YVk0DMPgg6/c/HP2Ebw+SIqz8utbkjm/98mnvBiGwZYtW1iwYAHbt29vvD4lJaWxYHTq1KlNFYwT8fl8bN68mVWrVrFx40b8/uD0NKvVSu/evRk+fDj9+/cP69Eef10dB99+m5IvvgAgMjubzvffj6t9e/bs2cOTTz5JfX09ffv2Zfr06c2ywF9EROR0qWxISFu7di0zZszAMAwuuOACbrjhhrAsGtW1Af7+RhmL1tYAMLyvi1/dnExC7Imf9Hq9Xr7++mu++OKLxjUKVquVQYMGMX78eDp16hSW/xYtxe12880337Bq1Sr27dvXeH1ycjKTJk1i2LBhYV06KnJz2ff88/jdbqwuFx1vv52kkSPZsWMHTz/9NF6vlwEDBnDnnXeG9ecpIiLhTWVDQtaGDRt4/vnnCQQCjBgxgh//+Mdh+er9/gIvv3v+MHlFPmxWmH51AteOjz1hUXC73SxZsoTFixdTWVkJgNPpZPTo0YwdO5bk5OSWjN8qFBQUsHr1apYvX05VVRUQHBmaNGkSQ4cODdsn456yMvb985+4j454pV58MRk33cS2HTt49tln8fl8DBkyhNtuuy0sv3dERCT8qWxISNqyZUvjk6Xzzz+fW2+9NSyfLC1ZV8NfXimltt4gJcHGf05LoU+X4y/cPXz4MAsXLmTFihV4PB4guBZj3LhxjBo1SluaNgGPx8NXX33F559/3ri2IzU1lUsvvTRsS4cRCFAwZw6F778PQHT37nR58EG2HjjQKsq6iIiEN5UNCTnbt2/nn//8J16vl4EDB3LHHXeE3ZNAwzB4/ZNK/vVR8BC9Ad2c/L9pKSQeZ9pUbW0tH3/8MYsWLSIQCADBhc0XXXQRgwcPDrvPPRzU19fz1VdfMX/+/GNKx6RJkzj//PPD8t+8fO1a9j37LIHaWhwJCXT56U/ZXlnZOA3xwgsv5Prrr9fUOxERaVEqGxJSdu/ezZNPPonH4+G8887jrrvuCrsFrh6vwV9fK+XLNcH1GT8aG8O91yRisx37JC8QCLB69Wref//9xulSvXr14uKLL6ZHjx56UtgC6uvrWbx4MQsWLGgsHWlpaUyaNIkhQ4aEXemoKyhgzz/+Qd3Bg1gcDrLvvpudFkvjBgsTJkzgRz/6kb62RESkxahsSMgoKSnhz3/+M7W1tfTq1Yt77rkn7LburHD7+Y9nD7N5jwebFX52YxKXj4r5wf0OHDjAO++8w969e4HgE9ypU6fSp0+flo4sBM8taRjpqK4OnuSemZnJT37yEzp27GhyujPjr61l7z//SeW6dQCkT53K7qQk3njzTQAmT57MpZdeamJCERFpS1Q2JCR4PB7+9re/kZ+fT3Z2Nj/72c/C7qTrQyU+fvNUMfnFPmIiLfzX9FQG9jj2kD63280HH3zAsmXLMAwDp9PJpEmTGDduXNgVq9aorq6OxYsXM3/+fGpqarBarVx00UVcdtllYfX1aAQCHHzzTYo/+wyA5AsvZE+XLsyaPRuLxcIDDzxAr169TE4pIiJtgcqGmM4wDF599VVWrVpFTEwMv/3tb0lMTDQ71hnZlefh108Xc6QyQLskG3+5P41OHb4tD4FAgCVLlvDhhx9SUxOcXnX++edz9dVXk5CQYFJqOZGqqipmzZrFmjVrgODI009+8hO6du1qcrIzc3jBAvJeew0CAeIGDGB1aipLV64kOjqa3/72tyQlJZkdUUREWjmVDTHdV199xdtvv43FYuGhhx6iR48eZkc6Ixt21fHv/zxMdZ1BTqaDv9yfRnL8t3P99+zZw9tvv01+fj4AGRkZXH/99WH3xLUt2rBhA2+99RYVFcGF/mPGjGHKlCm4XK5TfGToKF+7lr1PPYXh9RLVtSsf2mzsPXSI7OxsHn74YY2oiYhIs1LZEFPt3buXxx57DL/fz9VXX83FF19sdqQz8vWWWn73XAn1XoPzujr5472pxEQGtxcNBAJ8/vnnfPjhhxiGQWRkJJMnT2b06NFht/C4LaupqWHOnDksW7YMgKSkJG666SZ69+5tcrLT596+nd2PPYa/pgZHhw684/VSVl/PhRdeyA033GB2PBERacVUNsQ0VVVV/PnPf6a8vJyBAwcybdq0sNolZ8XGWn7/wmG8PhjWx8Xv70rBGREsGlVVVbz88sts3boVCE6Zuvbaa4mNjTUzspyDbdu28cYbb1BaWgrA8OHDueaaa4iOjjY52empzctj1//+L97yckhO5s36emosFm699VaGDRtmdjwREWmlVDbEFH6/nyeffJIdO3bQrl07fv3rX4fV1JQVG2v5z+cP4/PDBQMi+Y87UnDYg0Vp586dvPTSS1RUVOBwOLjhhhsYPnx4WBUpOb76+no+/PBDvvzySwzDIC4ujttuu42ePXuaHe201BUVsfNPf8JbVoYvNpaZgQBep5Nf/epXZGRkmB1PRERaIZUNMcV7773HggULcB59otOhQwezI522lZtq+d1zwaIxZlAU/3F7MjabhUAgwGeffcZHH32EYRi0a9eOu+66i/T0dLMjSxPbs2cPr7/+OoWFhVitVq655hrGjh0bFoWyvriYnX/+M56SEmqdTubY7cS0b8+vf/1rIiMjzY4nIiKtjMqGtLjc3FxeeOEFAKZNm8agQYNMTnT61m6v47dPF+P1HVs0vj9tatiwYVx//fVhNVojZ8br9fLmm2+yatUqAEaNGsX1118fFodQ1peUBAtHcTGVDgcfRETQY+BApk+fHhaFSUREwofKhrSowsJC/vrXv1JfX89FF13Ej370I7Mjnbate+v5xf8VU1dvMKpfJP95Vwp2m+W406ZGjBhhdlxpAYZh8MUXXzBnzhwMwyAnJ4fp06eHxdqc+sOH2fGHP+AtK6PUZuPjyEgu/9GPwm6TBhERCW0qG9Ji6urq+Otf/0pRURHdunXjoYceCptdmfYVePnZY0VUVgcY3NPFH+9NxWGH+fPnM3fuXAzDoH379kybNk3TptqgzZs3M2PGDOrq6khKSuKee+4hMzPT7FinVFdQwI4//hFfRQVFViufREfzwM9+Rvfu3c2OJiIirYTKhrQIwzB48cUXyc3NJSEhgd/85jfExcWZHeu0HC738cD/FnG43E/vzhE8+mAazgiYPXs2X375JRDcmej666/H6XSanFbMUlhYyLPPPktxcTERERHceuutDBw40OxYp1Sbl8eOP/4Rf3U1+202VqWl8Zt/+zcdNikiIk3CanYAaRtWr15Nbm4uNpuNadOmhU3RcNcG+O1Thzlc7qdjOzt/vDeVCIfB66+/3lg0pk6dyi233KKi0ca1b9+eX/3qV/Ts2ROPx8MLL7zAxx9/TCAQMDvaSUVmZZHz859jcTjo5PfTr6SEGS++GPK5RUQkPKhsSLNzu93Mnj0bgMsvv5wuXbqYnOj0+P0G//1iCXsOeUmKs/KXB9KIcgZ48cUXWblyJVarlVtuuYVx48aZHVVCRFRUFPfffz/jx48H4OOPP2bGjBl4PB6Tk51cTPfudL7vPrBY6On1Ert1K0uWLDE7loiItAIqG9Ls3n//fdxuN+np6WG1+PSpWUdYs7UOV4SFP92XRkK0j2eeeYb169djt9uZNm0aw4cPNzumhBibzca1117LT37yE2w2G7m5uTz//PN4vV6zo51UwpAhZN1yCwBDPB5yZ86koqLC5FQiIhLuVDakWe3cuZPly5cDcOONN4bNgvAPvqpi7lduLBb499uTyUzx8eSTT7Jt2zacTif33XcfAwYMMDumhLCRI0fy4IMPEhERwZYtW3jxxRfx+Xxmxzqp1IsuIvXSSwEYWVnJvJdfNjeQiIiEPZUNaTYN5xAAjB49mpycHJMTnZ71O+t4cuYRAO6cHE/fbC+PP/44e/fuJSoqioceeihsTowWc3Xv3p17770Xh8PBxo0b+de//oXf7zc71kll3ngjju7dsQOZ33zD1q+/NjuSiIiEMZUNaTYLFiygqKiIuLg4rrrqKrPjnJbD5T7++8US/AEYPySKSwZ7eeyxxzh48CBxcXE8/PDDdO7c2eyYEkZ69OjB3Xffjd1uJzc3l1deeSWkF19brFZ6/+IXeGJiiDYM8p9/Hk9trdmxREQkTKlsSLPYs/cAr776Kn6/n2uvvZaoqCizI52S12fwXy+UcKQqQJcMB3deZuGxxx7j8OHDJCUl8fOf/5yMjAyzY0oY6t27N9OmTcNqtbJmzRpef/31kC4ctqgoev3mN3gtFpLr6lj197+bHUlERMKUztmQJldypIbOXfvjLttFhw4d2LNnDy6Xy+xYp/TM7CPM+qKK6EgLT/wskbdeeYJDhw7Rrl07HnroIRITE82OKGEuNzeXGTNmEAgEGDVqFDfeeCNWa+i+5rPmnXewfvQRAEm33EJ2GG3wICIioSF0f8tJ2PqP//0Ud9kuAAoKCti+fbvJiU5txcZaZn1RBcAjP07kk7kvc+jQIeLi4njwwQdVNKRJDBw4kNtuuw2LxcKyZcuYNWsWofx6z+DrriOvfXsADr/+OrUFBSYnEhGRcKOyIU1q1/4Ktpf1JjqpBwB9+/YN+cXUJeU+/vpqKQBXj43h0PYP2bJlCxEREdx3330kJSWZnFBakyFDhnDzzTdjsVhYvHgxs2fPDtnCYbFYGP7zn1Not2MLBNjy978TCPEdtUREJLSobEiT+tMLu7DYorjopudZs2YNa9asCemTtQMBg7++WkZldYCumQ66J6xjyZIlWCwWbr/9djp27Gh2RGmFhg8fzk033QTAwoULG0+jD0XtOnTActll1AGWoiLy3n7b7EgiIhJGVDakycz7chf7ylKBAA//OIPBgweHdNEAeH+xm2+21eF0WLhm5GE+mBs86fxHP/oR/fv3NzmdtGajRo3immuuAeC9995j165dJic6sYuuuooN7doBUPrZZ1Ru3mxyIhERCRcqG9IkPB4vz86pBKBn+yLGDA/9MzUOFHl5/v1yAKaO9TNvzgwMw+DCCy9k/Pjx5oaTNmH8+PEMGTKEQCDAjBkzQvbEbofDwdhp09jqcACw55ln8Gs7XBEROQ0qG9Iknn9nE25fClY8/PvdfcyOc0r+gMH/vlqKx2vQL8fK9pXP4vV66dOnD1OnTsVisZgdUdoAi8XCTTfdRIcOHaioqGDGjBkhe+hfz5498Y4YQaXFQqCigrw33jA7koiIhAGVDTln7hovH60Kbm07pm8lGe1iTE50anMWVbFlr4dIp4XoqneoqqokIyODO++8E5vNZnY8aUNcLhfTp0/H5XKxa9cu3n//fbMjndDV113Hyrg4AMoWL6ZywwaTE4mISKhT2ZBz9uTr2/EEoomwVPLwraE/qlFY6uOlD4LTVfq138KR4t3Ex8dz3333hcV5INL6tGvXjptvvhmAL774grVr15qc6Pji4+Ppe+mlbDo6nWr/jBn46+pMTiUiIqFMZUPOibvGx5frIwCYOMhNTHRoLwg3DIN/vF1GncegS3sP5fvnYrVamT59us7SEFMNHDiQiy66CIDXXnuNwsJCkxMd39ixY1kfG0ulxYK3rIyC994zO5KIiIQwlQ05J8+8vQOf4cJpOcL0G0N/96Yl62pZtbkOuw0iK2ZiASZOnEjnzp3NjibClClT6NatG/X19Tz//PPUheCoQUxMDCMuuIDlR0cBiz/9lJp9+8wNJSIiIUtlQ85ada2PBWuD6xvG9asmOirS5EQnV1sf4J+zjwDQNXEH/tp8srKymDRpksnJRIJsNht33nkn8fHxFBYW8sYbb4TkgX8TJkygwOlkj90OhsGBf/0LIxAwO5aIiIQglQ05ay/O2ok3EEmEpYJ7bgr9UY23Pq+kuMxPQrSPQMkH2O12br31Vux2u9nRRBrFxcUxbdo0rFYr33zzDStXrjQ70g8kJiYyfPhwVjid+Gw2avbsofSrr8yOJSIiIUhlQ85KvSfAJ6uD28Ne0LuCuNhokxOdXGGpj5kLqgCIr/8EKz4mT55Menq6yclEfignJ4fJkycDwQP/3G63yYl+6OKLL6bOZuObo2X90KxZOntDRER+QGVDzsqM2TvxBKJwUMl9Pw79UY0X5pbj8RokRxbj8m2ma9euOrhPQtqECRNIT0+nuro6JLfDTUtLY9CgQWx2OKiLjMRXWUnh3LlmxxIRkRCjsiFnzOMN8NHy4PzskT2OkJgQa3Kik9u2r54v19QABrG1H+FyOrnllluwWvXlL6HLZrNx4403ArB8+XJ2795tcqIfuuSSSwhYLDRMoCr+7DPqi4tNzSQiIqFFz7bkjL350W7q/NHYcXPfj88zO85JGYbB8++XA5Bg2YKLYq699lpSUlLMDSZyGnJychgxYgQAb731VsidLp6VlUWfPn04YLPhTk7G8Pm0Fa6IiBxDZUPO2NyvqgEY1LmM1JQEc8Ocwtrt9azbUY8FP8nGYvr27cvIkSPNjiVy2q6++mqio6M5dOgQCxcuNDvOD0ycOBEsFr70eAAoW76cmgMHTE4lIiKhQmVDzsj8JbuoqE/Cgo97buxldpyTMgyDlz4oByCBXGKc9dx0001YLBZzg4mcgZiYGK6++moA5s2bR1lZmcmJjtW1a1dycnIoAtwZGWAYHJo1y+xYIiISIlQ25Iy8+UkRADmpZWRnJpuc5uRWb6lj6z4PVnwks5wJEyaQkJBgdiyRMzZ8+HBycnKor69nVgg+kb/00ksBWFBTA1YrlevWUb1rl8mpREQkFKhsyGnLO3SEA+XtAbhlcobJaU7OMAxe/bgCgAS+ISHGyoQJE0xOJXJ2rFYrN9xwA1arlfXr17Nx40azIx2jd+/eZGZmUuLzUdOpEwAF2plKRERQ2ZAz8Mr7ezBwEBdxhFGD2psd56Ryd9SzdZ8HCz6SWM2kSZOIjAztE85FTiYjI6Nxu+aZM2fiObpGIhRYLBYuueQSABbV1IDFQuW6ddTs22duMBERMZ3KhpyWQMBgxVYXAGP6+0J+3cNbn1UCEM962iW7uOCCC0xOJHLuLrvsMhITEyktLeWrEDuxe8CAAURFRXGopgZbnz6ARjdERERlQ07Tp0sPUuuPxUodt0zpbnack9qZ5+GbbXVAgCRWM3nyZOxHTzkWCWcul4vLLrsMgC+++AKv12tyom/Z7XYGDhwIwI6ja6MqvvmGuoICE1OJiIjZVDbktMz+IrgDTteUApKTQvsQv3e/CI5qxLGVnKw4Bg8ebHIikaYzdOhQEhISqKioYPXq1WbHOcaQIUMAWLVzJ7EDBoBhUPzZZ+aGEhERU6lsyClVuH3sOxwsGJPHJJqc5uRKyn0sXFMDQCJfc9VVV+mkcGlVHA5H42YH8+fPJxAImJzoW926dSM+Pp6amhqqe/QAoHTJEnxVVSYnExERs+hZmJzSu5/txcCGy1LCxReG9tkaHy114w9AJHkM6BFPr16hnVfkbIwaNYro6GiKi4vJzc01O04jq9XKoEGDAFhbVERkdjaGx8PhEDyMUEREWobKhpzSwq+DIwV9O7pxOBwmpzkxr8/gg6+CU6gS+YarrrrK3EAizcTlcjFmzBgAPvvsMwzDMDnRtxqmUm3YuJGkoyMwJV9+iRFCIzAiItJyVDbkpApLaimojAfg6gnpJqc5ueUbail3gw035/e00unofv8irdHYsWOJiIggPz+frVu3mh2nUXZ2NikpKXg8HvJcLmwxMXhLS6lYt87saCIiYgKVDTmptz/eDViJtRcxfHCO2XFO6qOlwVGNBDYw5sLRJqcRaV4xMTGMHh38Ov8shBZhWyyWxtGNNevWkXzhhQCUfPGFmbFERMQkKhtyUks3+AAY3C20z9YoLPWxdls9AFmx+zjvvPNMTiTS/CZMmIDNZmPnzp3s2bPH7DiNGsrG5s2biRkxAoDKjRupP3zYzFgiImIClQ05oe17j1BWmwQEuOGyLmbHOakFq6sxsBDFfiZceB42m83sSCLNLjExkaFDhwKhNbqRnp5Oeno6fr+fzQcPEtu7NxgGZcuWmR1NRERamMqGnNDMT/YCkOwqpntOO5PTnJhhGHyyrAKABMtmRo0aZXIikZZzySWXYLFY2LhxI0VFRWbHadQ4lWrNGpIuuACAsqVLQ2oxu4iIND+VDTmh3J3B3WOG9Q7t07d3HPBQUAYWvAzvYyfh6OnFIm1Bu3btGrd4/uabb0xO862GsrFjxw6s3btjdTqpLyqieudOk5OJiEhLUtmQ4yo6XEl5fQoAUy7qbHKak5u/KnhgWAw7GT92pMlpRFpewxP7r7/+OmRGDlJSUujcuTOGYZC7aRMJ558PQNny5SYnExGRlqSyIcf12dI8wEqkrYJu2aF7arhhGHyxOrgLVWbsQXr27GlyIpGW179/f+x2O0VFRRw8eNDsOI0aStA333xD4tGF4uVr1ujMDRGRNkRlQ45r1aZaAHLaVZuc5OS27fdQUWPHSj2Xj+2I1aovaWl7IiMj6dOnDxBaU6kGDBgAwL59+4jo0gVbdDS+igrc27ebG0xERFqMnpnJDxiGwZ7iaACGnRdlcpqTm7ckuCA2xrKHC0YPNzmNiHm+uyA7VKZSJSYmkpycjGEY7MvLI2HwYACOrFplcjIREWkpKhvyA1v3VFLvj8aCl0tGh/Yp3Cs31gHQO6uO2NhYk9OImKdv3744nU5KS0vZv3+/2XEadekS3DZ79+7dJBzdprfim29CphCJiEjzUtmQH/h8WQEACRFFpCbHm5zmxA4Weyl1uwA/44ammB1HxFROp7PxMMs1a9aYnOZbOTk5QLBsxPbujdXpxFteTm0IFSIREWk+KhvyA2u3ewHomekxOcnJLckNnq0RRT6DB/QyOY2I+b67IDsQIouwG0Y29u3bh2G1Enu0EFXk5poZS0REWojKhhzD5zc4VB5crzGyf+iOagB89c0RADrElpCUlGRyGhHz9erVi8jISCoqKti9e7fZcYDgaeIul4v6+noOHjxI/NFF4xXr1pmaS0REWobKhhxjy+4qAoYdK3WMHBK652t4fQa7DgUPGzy/T6TJaURCg8PhaNwBKlSmUlmt1mPWbcT37w9Azd69+KqqzIwmIiItQGVDjrFkzSEA4iNKSExMMDfMSWzZU4cvYMNGNWOGdTE7jkjIGHx0x6cNGzaEzCLshrKxZ88eHAkJuDIywDCo2rrV5GQiItLcVDbkGBt31gCQ3S405nufyOKvg1vexloP0rVrjslpREJH165dsVqtVFRUUFZWZnYc4NhF4gCxR88Eqdq82bRMIiLSMlQ25Bh5pREADOoVZ3KSk1u7LXjYYNd0HzabzeQ0IqEjIiKCzMxMAPbu3WtymqDs7GysVivl5eWUlZV9Wza2bDE5mYiINDeVDWlUcNhNrS9YMsYMyzI5zYn5/AYHy1wAjBigheEi3/fdaUuhwOl0Nhag3bt3E9OjB1gs1BcW4i0vNzeciIg0K5UNabRo5QEAIm3lZHZINDnNiW3cUY7fcGCljjHDu5kdRyTkhFrZgG+nUu3Zswd7dDSuo+WjetcuM2OJiEgzU9mQRuu3uwHITK43OcnJLV979NBBZxlJSaFbikTM0lA28vPzqa8Pje/n76/biOnaFQD3zp2mZRIRkeansiGNDhwObiXbNcNicpKT27YveNhgVorX5CQioSkpKYmEhAQCgQD7Q+Sk7oYCdPDgQWpqaojuFhyVrFbZEBFp1VQ2pFF5TfC8iu6dokxOcnL5pcFS1L2jw+QkIqEr1KZSJSQkAGAYBnPnziX66MhGzf79GCFy2rmIiDQ9lQ0BoKbWT50/eHJ4766hOzXJ4zWorIsBoH/P0M0pYrZQKxvflZubi7NdO6xOJ4bHQ11BgdmRRESkmahsCAA79lcCYKOa7KwUk9Oc2M4D1RhYsVFDn+7tzY4jErIaysbevXtD5nC/8847D4ChQ4disVqJ7NgRgNp9+0xMJSIizUllQwDYvOsIANH2ShyO0J2etH5bCQCR1iPEx8ebnEYkdGVmZuJwOKiurqa4uNjsOAD07NkToPGwwahOnYDgVCoREWmdVDYEgD15tQAkRteZnOTktu8NHuaXEhcaO+yIhCq73U7HoyMHBw4cMDlNUHJyMvBt2YjMCp7nU3fwoGmZRESkealsCAD5h/0AtE8KjekWJ5Jf7AMgM1WnhoucSkpKcEpkw5N7syUmBtdZNeRxZWQAUHfokGmZRESkealsCACHK4I7PHVqH2FykpM7XBksGTlZob1jlkgoaHhyf+TIEZOTBDWMbLjdbjweD84OHQDwlJYSCJHzQEREpGmpbAj+gEFFbXDb226dYkxOc2KBgEG1J1gyenVNNjmNSOj7/kiC2SIjI3E6nUCwANljY7HFxIBhUFdYaHI6ERFpDiobQlmFHwMb4Kd7lySz4xyX2+3mkV/9B8teHcWXz3bmR1eM5n/+539wu91mRxMJWQ1lo7y83NwgR1ksFpKSgj9jSktLsVgsONu1A6A+RBaxi4hI01LZEPKLgouu7VSTlppqcpofcrvdjBkzhice+wv11YVgBDh48CC///3vGTNmjAqHyAmE2jQqoLFsNGRyHl1X4ikpMS2TiIg0H5UNYV/e0V/6trrGKQ6h5PHHH2fdunUYxrGnDAcCAdatW8fjjz9uUjKR0NZQNqqrq/F4PCanCWooGw1TuyJUNkREWjWVDeFgUXBkIMbpMznJ8T3//PMEAoHj3hYIBHj++edbOJFIeIiMjMTlcgGhM7qhsiEi0raobAiFJcEzNuJDdG34oVNsi3mq20XaKovFEnKLxL9fNhxH/+wNkTIkIiJNS2VDKKsIjhokx4fm2RXp6enndLtIWxZq6zZ+UDbi4wHwVlaalklERJqPyoZQXR88YyM+2uQgJzB9+nSs1uN/qVqtVqZPn97CiUTCR6iVje/mCQQC2I+WDV9FBYYR2oeKiojImVPZEGo8DiB0p1E9/PDDDBgwAIvFcsz1VquVAQMG8PDDD5uUTCT0JSQkAKGz/W1CQgJWq5VAIEBlZSWOuDgADJ8Pf02NyelERKSpqWwItd7gqeEJMZZT3NMcMTExLF68mMsvv5zo6GisViuZmZn8/ve/Z/HixcTEhGhLEgkBDkfwxQS/329ykiCr1Ur80dGM8vJyrBERWCOCP4P81dVmRhMRkWZgNzuAmM/rD34ZxESGbveMiYlhzJgxZGVlcddddzFw4ECzI4mEhe+PCIYCuz34M6ehANmiogh4PPhra82MJSIizSB0n11Ki/EbwS+DSFdod8+ao1MsoqKiTE4iEn5OtH20GRoKUMMaDevR72lNoxIRaX1UNtq4QMAgYARLRnRUeJSN6OgQXckuEoJCcWSjYcOHhgJki4wE0MiGiEgrpLLRxnl83+7+EuVymJjk1Hy+4KGDNltobtErEoq+P4oQCr5fNhrWbARC5JRzERFpOqH9UrY0u3rPt09AoiNDu2w0LHT1er0mJ5EGPp+b/PzHOXToeTyeQ0REpJOePp3MzIex27VwPxSEctlonEZ1dA2HcfQFBRERaT1UNtq4hrJhwYfL5TI5zclFHH31U2UjNPh8btatG4PbvQ4IvkLt8eSzb9/vKSl5nwEDFqtwhJBQKhsNBahhZMOisiEi0mppGlUbV+/9tmw07BATqjSyEVry8x8/pmh8K4DbvY78/MdNSCXfF4ojGyobIiJth8pGG9cwsmHF2zhyEKoa8nk0rzskHDr0PD8sGg0CR28Xs4XyAvFQKkAiItI8VDbauHpPw0FfgcaRg1DVkE9lIzR4PIfO6XZpGaE4svH9BeKEUDYREWlaKhttndEwbcES8mVDazZCS0RE+jndLi0rlMrG9wtQYzKrfiWJiLQ2+sne1hnBkQ0DW8iXDa3ZCC3p6dM58Y8Q69HbxWwN59NEHj3LIhT8YGSjYe1GCE75EhGRc6Oy0cZZrcFf8gahf3aFykZoycx8mJiYAfzwx4iVmJgBZGY+bEIq+b7y8nIAEhISTM3xXd9fsxE4+j1tCfEXPERE5MypbLRx0VHB7W4NrNTV1Zmc5uS0QDy02O0xDBiwmOzs3xMRkQlYiYjIJDv799r2NoRUVFQAEB8fb3KSb31/N6pAfT0AVqfTtEwiItI8QnuvU2l2UZHBJ/AGNmpqakJqqsX3aYF46LHbY8jO/n9kZ/8/s6PICTSUjVAc2WgsG0e/p60hviOeiIicOY1stHEOe8McaRtV7mpTs5xKbGws8O2TJxE5tYZpVKE4stE4japhZENlQ0Sk1VHZaOPstm8XZFZV1ZiY5NRSU1MBOHz4sMlJRMJDIBCgsrISCK2yUX+0XDRMjfRXB1/osEVHm5ZJRESah8pGG2f/zrrwyhAvG2lpaYDKhsjpqqqqwjAMLBYLcXFxZsdp9N11JIZh4DtaNuwxWucjItLaqGy0cREOC5ajp0Afqag1Oc3JNYxsVFZWUlsb2llFQkHDk/q4uLjGdRKhoGG0JS4ujkBdHfiDW3CrbIiItD6h89tHTGGxWHA5gttOllaE9sLryMhIYo4+GSkpKTE5jUjoC8X1GnV1dY3TqOLj4/FVVQHBbW+1ZkNEpPVR2RCinMFXFcsqfKe4p/m0bkPk9IXiGRsNoy0ulwun04n3yBEAHCGUUUREmo7KhhDtCu4IU+EOmJzk1BrKRnFxsclJREJfKJ6x8d2pXQCehrKRmGhaJhERaT4qG0JcVPBtZWivDwe0SFzkTOTn5wPQrl07k5N86/u7YzWMbESobIiItEoqG0J8TPDLwB0Ga641jUrk9BiGwf79+wHIzs42N8x3fH+0xVtWBoAjKcm0TCIi0nxUNoTE2OD+tzX1tlPc03yaRiVyesrKyqisrMRqtZKVlWV2nEbfn0ZVf/R72Xn0e1tERFoXlQ0hKSG4A0ytx25yklNrmEZVWVlJTU0YzPsSMUnDqEZmZiYOh8PkNN/6/jSq+qIiACJCaKqXiIg0HZUNISXBCUCd32VyklOLiopqLBy7du0yOY1I6Nq7dy8AnTp1MjnJsb47smEYBvVHp0Q6j35fi4hI66KyIXTODJ5d4THicbvdJqc5te7duwOwc+dOk5OIhK6GkY3OnTubnORY3x3Z8JaVYXg8YLUSkZJicjIREWkOKhtCp/RIAPxEs3vvIZPTnFq3bt0A2LFjh8lJREKT3+/nwIEDQOiObMTHx1N78CAArvbtsdpDfxqniIicOZUNISbSitMWPNF3887Q3+WpoWzk5+dr3YbIcRQUFODxeHC5XCG17a3X6238no2Pj6fu6Na8rsxMM2OJiEgzUtkQAJJig6eH7z4Q+tOoEhISSEtLwzAMdu/ebXYckZCzb98+IDiqYbWGzo/5hilUdrudyMhIao+WjUiVDRGRVit0fguJqdJTgtve5hd7TU5yejSVSuTEGhaHh9L5GgAlJSUAxMTE8Le//Y0xv/kNI996i1EPPcSjjz5KbW0YHPYjIiJnRGVDAOicGTxGvKTSRiAQMDnNqWmRuMiJ7dmzBwi99Rr79+/H5/Mxc+ZMfvOb33DgyBHq/X725ufzm9/8hosvvliFQ0SklVHZEAC6Zwf3vK8LxFF29ETfUNYwspGXl6d1GyLfUVhYSFFREVartbGUh4r9+/ezceNG9uzZ84MXNQKBACtWrODpp582KZ2IiDQHlQ0BoGP74FkbHpI4eHSHmFCmdRsix5ebmwtAz549iYqKMjnNsfbv38+WLVswDOO4twcCAZ577rkWTiUiIs1JZUMAyEyzAwZ+otm5t8jsOKdF6zZEfqihbAwcONDkJMeqqqqirKyM6urqk94v/+iicRERaR1UNgSAKJeV5BgPAJt3h8e0pF69egHBJ1fhsM5EpLkdPnyY/Px8rFYr/fv3NzvOMRp2yIqPjz/p/TK1M5WISKuisiGNumQEvxz2FZoc5DT17dsXl8tFWVmZplKJ8O2oRvfu3YmJiTE5zbEaTjS/+OKLT7gdr9Vq5e67727JWCIi0sxUNqTReV2DT05Kq+PwekN/C9yIiIjGqSKrV682OY2I+RrKxoABA8wNchwNZeOOO+6g34BhfP/Xj9VqZcSIEdx///0mpBMRkeaisiGN+vc4uiMV7SgsDI/hjWHDhgGwdu3asChIIs2ltLSU/fv3Y7FYQq5sGIbRWDa6d+/OI3+YQ86I35CY2hmXy0XXrl3561//yvz584mMjDQ5rYiINCWVDWnUrWMEYOAjlq07C8yOc1q6du1KUlIStbW1bNy40ew4IqZZt24dEPyeiIuLMzfM95SWluJ2u7HZbGRkZLC/0EbHAXfzj1dyqa2tZefOnTzyyCMqGiIirZDKhjRyRVhJig4uDt+wo8rkNKfHarVy/vnnA7Bq1SqT04iYJ1R3oYJvp1BlZGTgcDjYtj+4GUWPjhFmxhIRkRagsiHH6NzeAsDWfXUmJzl9Q4cOBWDz5s1UVYVHSRJpSuXl5Y2nhofaFCr4tmx06tQJd22A/GIfAD06qWyIiLR2KhtyjMF9kwEoqoyhoqLC5DSnp0OHDnTs2JFAIMA333xjdhyRFrd06VIAcnJySEhIMDfMcTRse5udnc32o6Ma7ZJsxMfYTEwlIiItQWVDjjG8XwIAtWSyYdNWc8OcgYbRDe1KJW2Nx+Phq6++AmDcuHEmp/mhQCBAXl4eEBzZWLstOGrav5vLzFgiItJCVDbkGJ3a24l2ejFwsHj1IbPjnLbzzz8fq9XKvn37wmYnLZGmsGrVKtxuN8nJySF3kB9AYWEh9fX1OJ1O2rdvzzdHy8agniobIiJtgcqGHMNisdAvJzi1YfNeA7/fb3Ki0xMbG0ufPn0A+OKLL0xOI9IyAoFA49f7uHHjsNlCb1rSrl27AOjYsSNVNQY784LTqAarbIiItAkqG/IDFwwOrtuo8GU0zrUOB5dccgkAK1eupKyszOQ0Is1v06ZNFBcXExkZyciRI82Oc1wNW/L26dOH3B31GAZkd3CQHB96xUhERJqeyob8wOBewb3u6+jAmtzwWbeRk5ND9+7d8fv9zJ8/3+w4Is1uwYIFAIwePRqXK/RGCtxuNzt27ACCu2R9s7UWgMG9Qi+riIg0D5UN+YHUBDtp8V7Awor15WbHOSOTJk0CYNmyZWGzm5bI2di/fz+7du3CarUyduxYs+Mc18aNGwkEAmRkZJCWlta4OFxTqERE2g6VDTmuoX1jADhQGj5b4AJ0796dzp074/P5tHZDWrWGr+8hQ4aQmJhocprj++5BgwcPeyko9WO3Qf+uTpOTiYhIS1HZkOMafl4sANV0ZvPmLSanOX0Wi6VxdGPJkiW43W6TE4k0vbKyMtauXQvAhAkTTE5zfLW1tWzbtg1omEIVHNXo3dlJpEu/ekRE2gr9xJfjGtjdhc0awEsiy9bsNzvOGenTpw9ZWVnU19ezcOFCs+OINLmFCxcSCATo0aMHWVlZZsc5rk2bNuHz+WjXrh0dOnRg2Ybgeo3ze2sKlYhIW6KyIccV6bLSP8cCwNpd9rDZAheOHd1YtGgRNTU1JicSaTolJSWNh/hddNFFJqc5sYYpVAMGDKCqJtC4XuPCgVFmxhIRkRamsiEnNHFkcAvcI74c9uzZa3KaM9OvXz86dOhAXV0dixcvNjuOSJOZPXs2Pp+PHj160Lt3b7PjHJfH42Hz5s1AcL3GsvW1+AOQk+kgq53D5HQiItKSVDbkhEb1j8Jq8eMhma9W7TY7zhmxWq1ceumlQHAhrdZuSGuwdetW1q9fj9VqZerUqVgsFrMjHdfmzZvxer0kJyeTlZXForXB0cUxGtUQEWlzVDbkhKJcVnpmegFYsq6WQCBgcqIzM3jwYDIyMqipqeG9994zO47IOfH7/cyaNQuACy+8kPT0dJMTnVjDQX4DBgygsvrbKVRjBqlsiIi0NSobclKXXZAGQGFNx8bDucKF1WrlxhtvxGKxsHLlyrDLL/JdixcvprCwkOjoaK644gqz45yQ1+tl48aNgKZQiYiIyoacwoWD47BaAnhI4aPP15od54x16dKFUaNGAfD222/j8/lMTiRy5qqqqvj4448BmDJlClFRoTtCsG3bNurq6oiPjyc7O5vFuZpCJSLSlqlsyEnFRFrplxP8Mlm9zRJWB/w1uOqqq4iNjaWwsJD58+ebHUfkjH3wwQfU1taSlZXFyJEjzY5zUt+dQlVVY2gKlYhIG6eyIad0+QXBXanK6cuy5StMTnPmoqKiuOaaawD49NNPOXz4sMmJRE7fgQMHWL58OQBTp07Fag3dH9t+v58NGzYAwSlUX35ToylUIiJtXOj+1pKQccGAKCIjAviIZ96i/WG3UBzg/PPPp2fPnni9Xt555x0MwzA7ksgpGYbBrFmzMAyDIUOG0LVrV7MjndTatWuprq4mLi6OLl268NGS4C5wk0bEmJxMRETMorIhpxThsHDp0ScLB6py2Lp1q8mJzpzFYuGGG27AbrezZcsW1q4Nv/Un0vYsWbKE3bt3ExERwdVXX212nJMyDIMvvvgCCO6Wtf2Anz2HvEQ4LFw8LNrkdCIiYhaVDTktV14YD4Cbrny+cLXJac5OWloaEydOBGDWrFnU1taanEjkxPLz83n33XcBmDx5MomJiSYnOrldu3Zx4MABHA4HF1xwAR8tDY5qjBscRWyUftWIiLRV+g0gpyW7g4MeWRbAysptDo4cOWJ2pLNyySWXkJaWRmVlZeP0FJFQU19fz4wZM/D5fPTt25dx48aZHemUGkY1hg0bhsUWzaJvgrtQXTFaU6hERNoylQ05bVePC76yWm70Y9my5SanOTsOh4Mf//jHjWdvNCy8FQklM2fOpKioiPj4eG6++eaQPSm8QVFRUePZGuPHj+fzVdXUew26pDvo3TnC5HQiImImlQ05bWMGBReKe0lg3uL9+P1+syOdlW7dujF58mQA3nnnHQ4cOGByIpFvff3116xYsQKLxcLtt99ObGys2ZFOaeHChRiGQd++fWnXrl3jFKorLogJ+aIkIiLNS2VDTpszwsolw4NTIvKru7N582aTE529iy++mPPOOw+fz8cLL7xAdXW12ZFEKC4u5q233gJg0qRJdO/e3eREp+Z2u1m5ciUAEyZMYPMeD/sKvLgiLFw0VAvDRUTaOpUNOSNTxjQsFO/GvAXhu6OT1Wrl1ltvJSUlhdLSUl555ZWw3NJXWg+fz8dLL71EXV0dXbt2ZdKkSWZHOi1LlizB6/WSlZVF9+7dmf1lFRBcGB4TqV8xIiJtnX4TyBnJ7uBgcHcrYGH1rmRKS0vNjnTWoqKiuOuuu3A4HGzatInPP//c7EjShs2dO5cDBw4QHR3Nbbfdhs1mMzvSKXm9XhYvXgwE12rkF/v4Kje4MPzaCaE//UtERJqfyoacsVuvTAWCJ4q/98GXJqc5N1lZWVx//fUAfPjhh2zbts3kRNIWbdy4sXE3p5tvvpmkpCSTE52eNWvWUFlZSUJCAkOGDOGd+ZUYBow4L5LO6VoYLiIiKhtyFvrmOOmaEQBsfP51cJ55OBs5ciQjR47EMAxeeumlsN3WV8LT/v37eemllwAYO3Ys/fr1MznR6fnuIX5jx46lrNLg81XBtU83TYwzM5qIiIQQlQ05K3dMbgfAEfoz+/1PTU5z7q677jqysrJwu928+OKLeDwesyNJG1BcXMzTTz9NfX09PXr0CPlTwr9r27ZtHDp0CKfTyejRo5n5RRU+Pwzo5qRPF6fZ8UREJESobMhZGdbXRWYqBHDy5ToLeXl5Zkc6JxEREdx1111ERkayd+9enn/+ebxer9mxpBUrLy/nySefxO12k5WVxd13343D4TA71mlrGNUYMWIE3oCTj49ud3ujRjVEROQ7VDbkrFgsFm6+LBmAIwzhvTkfmpzo3KWkpHDvvfficDjYsmUL//rXv8L2LBEJbTU1NTz11FOUlpaSmprKAw88gMvlMjvWaTt06BBbtmzBYrEwfvx45iyqos5j0C3LwZBe4fN5iIhI81PZkLM2bkgUqQkW/ESzYpuLHTt2mB3pnHXt2pV77rkHu93OunXreO2117QlrjQpj8fDs88+y6FDh4iLi+PBBx8Mi4P7vmvOnDkADBgwgKiYJOYsCo5q3DQxXof4iYjIMVQ25KzZbRZuviwRgFJG8u6ceRiGYXKqc9erVy/uvPNOrFYrq1ev5p133mkVn5eYz+/389JLL7Fr1y5cLhcPPPAAKSkpZsc6Ixs3bmTz5s3YbDYmT57MzAWVVNUEyGpnZ/SASLPjiYhIiFHZkHMyaUQ0GalW/ESRu789GzZsMDtSk+jfvz+33norFouFJUuWMGfOHBUOOSeGYfDWW2+xYcMG7HY79957L5mZmWbHOiM+n4/Zs2cDMG7cOOyuFGZ+ETzE744rE7BZNaohIiLHUtmQc2KzWZh+dfBMgDLOZ9acBa1m2tH555/PTTfdBMCCBQv45JNPTE4k4cowDObOncvy5cuxWCzccccddOvWzexYZ+zLL7+kuLiYuLg4Jk2axCsfV1BXb9C7cwQXDtSohoiI/JDKhpyz0f0j6ZVtx8DB5uJurF692uxITWbUqFFce+21AHz00UeNO/CInK5AIMDbb7/deEL9TTfdxIABA8wNdRYqKiqYN28eAFOmTKGo3M68ZcG1Gvf8KFFrNURE5LhUNuScWSwW7r0muDNVBf2YOXdpq9o2dvz48VxxxRUAzJ49m08//VRTquS0eDweXnjhBZYsWYLFYuG6665j1KhRZsc6K3PnzqW+vp7s7GyGDRvGC++XEzCCLzb0zdG5GiIicnwqG9Ik+uY4GdHXCVjZVTGAJUuWmB2pSU2aNImJEycC8MEHH/D666/j8/lMTiWhzO12849//IP169djt9uZNm0aY8eONTvWWdm7dy8rV64EggdgbtzlYcXGWqxWmHZVgrnhREQkpKlsSJOZfnUSFouBm+7M+ng9dXV1ZkdqMhaLhSlTpnD99ddjsVhYsWIFTz31FDU1NWZHkxBUWlrK3//+d/bu3UtkZCQPPfQQAwcONDvWWQkEAsycOROA4cOH07FjJ559rxyAK0fH0LFd+BxEKCIiLU9lQ5pMpw4OJo2IBmBf7QXMeT/8D/r7vjFjxnDvvffidDrZsWMHjz76KCUlJWbHkhCSl5fHo48+SlFREYmJiTzyyCN07drV7FhnbdWqVezfvx+Xy8WUKVP44usath/wEOWycMvl8WbHExGREKeyIU3qrqsSiXIa1JPG+19Vt4qD/r6vb9++/OIXvyAhIYGioiL+93//lz179pgdS0LA1q1befzxx6msrCQ9PZ1f/vKXdOjQwexYZ622tpa5c+cCwamEFnsMz8w+AsBNl8SRGGszM56IiIQBlQ1pUvExNu6fGlwsXsJoXnh5TquaTtUgMzOTX/3qV2RlZeF2u3niiSdYs2aN2bHERKtWreLpp5+mrq6O7t27NxbScDZv3jwqKytJS0tj3LhxPD+nnHJ3gE4dHEy9KM7seCIiEgZUNqTJXToimj5dHBhEsL18CO+9N8fsSM0iISGBn//85/Tr1w+fz8dLL73EJ598op2q2pj6+nreeOMNXnnlFQKBAEOGDOH+++8nMjK8z50oLCzkyy+/BGDq1Kls3uNj3vJqAH5xUxIOu7a6FRGRU1PZkCZnsVj4xY9TsFkN3HTjk6VFbN261exYzcLpdDJ9+nTGjx8PwIcffsgzzzxDRUWFycmkJRw4cIA///nPLFu2DIvFwqWXXsptt92GwxHei6YNw+Ddd98lEAjQt29funXvzWNvlQHBReHa6lZERE6XyoY0i+wODm64JLh4tIiLeOW1d6itrTU5VfOwWq1ce+213HDDDdjtdjZt2sQf//hH1q1bZ3Y0aSaBQIAvvviCRx99lOLiYhISEnjooYeYPHkyVmv4/1hdtWoVW7ZswWazce211/LmZxXkFflIirNyl7a6FRGRM2AxNOdDmkm9J8AdfyigoMRPIl9z1Ygabr75ZrNjNauDBw/yyiuvkJ+fDwS3Cp06dWrYT6mRb1VUVPDqq682jtb179+fH//4x8TExJicrGkUFhbyl7/8BY/Hw5VXXknvARdx158K8Pnhd9NSGDsoyuyIIiISRsL/JTgJWc4IKz+7IQmAIwzhixV5bNy40eRUzSsjI4Nf/vKXXHLJJVgsFlauXMkf//jHVrkrV1u0ceNG/vjHP7J161YcDgc33ngj06dPbzVFw+Px8OKLL+LxeOjRowcXX3wJj71Zhs8Pw/u6GDNQpVlERM6MRjak2f3va6V8uqIaOxX0j3uX//rdr4mKav2vju7atYtXXnmF0tJSLBYLEyZM4Morrwz7+fxtkdfrZc6cOSxatAgIlso77rgjrLe1PZ633nqLJUuWEBsby7/927/xySoLz79fjstp4aX/6ED7ZLvZEUVEJMyobEizq6kLcNefgtOp4tjEVUOLue2228yO1SLq6up49913Wb58OQDp6encdtttZGZmmpxMTodhGGzYsIH33nuPw4cPAzB+/HimTJnS6krj2rVrefHFF7FYLDzwwANYo7rwwKNF+APwix8ncfmo1jF6IyIiLUtlQ1rElr31PPS3QgKGhXTm8sj0CxgwYIDZsVrMhg0beP3113G73VitVkaOHMkVV1xBXJzOKghVBw8e5N1332X79u0AxMXF8ZOf/IS+ffuanKzplZSU8Kc//Ym6ujomTpzIJROv5O6/FJJf7OPCgZH857QULBZtdSsiImdOZUNazCsfV/DKxxVYqaNv9Ez+8J8/azVz3U9HVVUVb7/9Nrm5uUBw29yLL76YCRMm4HRqK9FQUVVVxUcffcTSpUsxDAO73c6ECROYOHEiLpfL7HhNzufz8dhjj7Fv3z66dOnCww8/zGNvlvPJimpSE2y88O/tiYvWSeEiInJ2VDakxfj9Bg/9vZCt+7xEcoDLz9vEPXdPbxVbhZ6JnTt38t5777F//34A4uPjueKKKxgxYkSb+7cIJT6fj8WLFzNv3rzGbZoHDhzI1VdfTUpKisnpms/s2bP54osviIqK4t/+7d/YsM/Ff79YgsUCj/00jf7dW1/BEhGRlqOyIS3qUImPaX84RJ0HUlnETRPjmTJlitmxWlwgEGDt2rXMnTuX0tJSILie4+qrr6Z3796astKCDMNg06ZNzJ49m+LiYgAyMzO59tpr6d69u8npmtfGjRt55plnAJg+fTodOvblrj8W4K41+PGlcdw5OcHcgCIiEvZUNqTFfbrCzf++VgYEyOJt7rt1HMOGDTM7lim8Xi+LFy/mk08+aXw1vWfPnlx99dVkZWWZnK51CwQCbNmyhQULFjRuTRwbG8vkyZPbxChTeXk5f/zjH6murmbs2LFcc81Ufv6PYjbuqqdXdgT/+EU77DaVXhEROTcqG9LiDMPgL6+UMn91DTZqyLG9xi9/dgc5OTlmRzNNdXU1n376KYsWLcLv9wPB0jF27Fj69u3b6p/4tiSPx8OqVatYuHAhRUVFANjtdsaNG8ell17aJg5gDAQC/OMf/2Dnzp1kZWXxyCOP8MJcN+8urCLSaeH5f2tPRmrr2m1LRETMobIhpqj3BHjwb0XsyvfipJA+0XP47W8eITk52exopiopKWHu3LmsXbuWhm/N5ORkxowZw8iRI9vE+STNpaKigsWLF7NkyRKqq6sBcLlcjBo1irFjx7apr72PPvqIefPm4XQ6+e1vf0vunij++moZAL+/K4ULB+rrTEREmobKhpimsNTHPX8poLLaII5NDO6wlkce+UWbeGX5VEpKSvjqq69YtmxZ4/Qqh8PBsGHDGDNmDBkZGSYnDB95eXksXLiQNWvWNI4aJScnM27cOEaMGNHmvt42btzIs88+i2EY3HbbbcSm9udnjxfh9cHNk+K4/coEsyOKiEgrorIhpsrdXscvnywmEIA05nNh33ruueceTRs6yuPxsHr1ahYtWsShQ4car+/WrRtjx46lX79+2GzalvT7amtr2bRpE8uWLWtcjwGQk5PD+PHj6d+/f5v8Gtu1axdPPvkkXq+XUaNGMfHy67n3r0WUVvgZ2S+S/56egtWqdRoiItJ0VDbEdLO+qOSZ2eU0LBi/cnxXrr32WrNjhRTDMNi5cyeLFy9m/fr1BAIBAGJiYjjvvPPo378/PXv2JCIiwuSk5qmurmbDhg3k5uaybds2fD4fAFarlUGDBjF+/Hiys7PNDWmi/Px8Hn/8cWpra+nbty+33zGdR/6vhK37PHTq4OCpR9oRHdn2CpiIiDQvlQ0xnWEY/PnlUhZ8HVww3olXuO2myxk9erTZ0UJSWVkZS5YsYdmyZbjd7sbrIyIi6NWrF/3796dv375t4sDEyspK1q9fT25uLjt27GgsYQDt2rVj0KBBjBo1iqSkJBNTmq+kpIS//e1vVFZWkpOTwwMPPMAT77j5bGU1MZEWnvl1ezLStCBcRESansqGhIQ6T4CH/l7ErjwvDsrobHmDhx+aRo8ePcyOFrL8fj87d+5k/fr1bNiwgSNHjjTeZrFYyMnJoX///vTr14/U1FQTkzadQCDA4cOH2bJlC7m5uezevZvv/gjLyMhg4MCBDBgwgA4dOui8EoIL4//+979TUlJCRkYGDz/8MB+v8PHM7HKsFvjz/amc37ttrVsREZGWo7IhIaOk3McDjxZRfMSPi0P0iJzLb3/9MGlpaWZHC3mGYZCXl8eGDRvYsGED+fn5x9yemppKp06d6NixIx07diQrKyssFkZXVVWxb9++Yy4NC+YbdOrUqbFg6GvlWDU1NTzxxBPk5+eTkpLCL37xC1Zts/OXV4IHSd57TQJTJ8SZnFJERFozlQ0JKQcKvTz4t0Kqagyi2c2gtCX86pe/IDo62uxoYaW0tLRxxGPXrl3HTC9qkJaW9oMC4nK5TEgb5PF4yMvLY9++fezfv5+9e/c2nq7+XQ6Hg+zsbPr378+AAQPa/BSpE/F4PDz11FPs2rWLuLg4fvGLX7C7MIb/eO4wgQD8aFws91+boNEfERFpViobEnK27K3n508U4fFCHBs5P2M9Dz30ILGxsWZHC0s1NTXs27ePAwcONF7Kysp+cD+LxUJKSgoJCQnEx8cTFxdHXFxc4/sNb6Ojo8/4CWpdXR3l5eWUl5dz5MiR477/3fUn39WuXTs6d+5MdnY22dnZZGRkaAeuU/D7/Tz//PNs3LiRyMhIHn74YcrrU/nl/xVT7zW4aGgUv7klWTtPiYhIs1PZkJC0cmMt//HsYQIGJLGSfu238dOf/pT4+Hizo7UKVVVVx5SPAwcOHLPm42RsNhtxcXHY7XaAxlETwzCOe/F6vdTV1Z3WY8fGxjaWis6dO9OpU6ewmO4VSgKBAK+99hqrVq3C4XDw4IMPYo3syM8eK8JdazCsj4v/uScVu01FQ0REmp/KhoSsT1a4efS14CvwaXxBj9R9/PSnP9W0mWZSVVVFQUEBlZWVVFRUHPdtw8nbZ8PlcpGYmEhCQgIJCQnHvN9wOZtRE/mWYRjMnj2bhQsXYrVaufvuu0np0IuH/h48S6NPlwgefSgNV4S2uBURkZahsiEh7c1PK3jxgwoA2vE5XZP389Of/pSUlBSTk7VNXq+XqqoqKisr8fv9WCyWxgvQeFDed6+32+3ExcVphKIFfPrpp3zwwQcA3HrrrXTrNYSf/r2Ig4d9dE538MTP2xEbpaIhIiItR2VDQpphGDw3p5yZC6oASGMBXRJ28dOf/pR27dqZnE4kdCxevJh33nkHgGuvvZbzBl3IL/9RzIEiH+2TbfzfL9qRkmA3OaWIiLQ1KhsS8gzD4IW5Fbz9eSUQnFKVHbedhx56iPT0dJPTiZgrEAjw4Ycf8tlnnwEwceJEho2+nEf+r5hDh32kJdr4+0/TdGifiIiYQmVDwoJhGLz0YQVvfBosHKl8SaeYzTz44INkZWWZnE7EHF6vl9dff52vv/4agMsuu4yBQyfyi/8rprjMT4cUO3//aRrtkzWiISIi5lDZkLBhGAavfFzBq/OChSOFxWRFruPBBx8kOzvb3HAiLaympobnnnuOnTt3YrVa+fGPf0xG5yH84h/FlFb4yUyz8/efpZGqqVMiImIilQ0JO6/Nq+BfHwUXjaewhAznN9x//3107drV5GQiLaO0tJR//vOfFBQU4HK5uOuuu3DG5fDL/yvmSFWA7A4O/vZQGknxOo9ERETMpbIhYemNTyuYcXSXqgS+IcvxFffeezc9e/Y0OZlI8zpw4AD//Oc/qaysJCEhgfvuu4+aQBq/erKYyuoAXTMdPPpQGvExKhoiImI+lQ0JW+8urOSZ2eUYBsSwk462edx04zWMHDnS7GgizWLz5s28+OKL1NfXk56ezv3338/WPCd/+FcpdfUGPbMj+OsDadreVkREQobKhoS1xWtr+NPLJXh94OIQmbzLmFEDue6663A4tPuOtB5Lly7l7bffJhAI0KNHD6ZPn84nK338890jBAwY3NPF7+9KITpSRUNEREKHyoaEvU276/mPZw9TWR3AQTmZzKRbxxjuuusukpOTzY4nck4Mw+DDDz/k008/BWD48OFcf8ONPP9+FXMWuQG4bFQ0P7shCbtNp6+LiEhoUdmQViGvyMtvnj5MQYkPG3Vk8C6p0Ue4/fbb6d27t9nxRM6Kz+fj9ddfZ/Xq1UBwa9vxEybxh3+VsnJTHQDTr0rg+otjG09xFxERCSUqG9JqHKny8x/PHGbrPg8W/LTnExIsm7niiiuYOHEiVquml0j4qKqqYsaMGezYsQOr1cpNN91Et15D+fdnDrMr30uEw8K/3ZbMhQOjzI4qIiJyQiob0qr8//buPDyqOs/3+LvWVCoVkpANQwCRsAZUUHYRZFFQsUVAAiK4YETtdrrv0nPvTM+0c+/0dPczt3taRREUZBNbUeyWKLaY1qgYkMgii4CsIWzZl8pS2zn3j2AEWQyQIguf1/PUk6pT5/zqe4ok1Cfnt9T5DX7zagnrt9UCEMtmksjmhn59mD17Nm63PphJy7d9+3ZWrFhBVVUVERERPPbYY5iubjzzcjElFSHioq38+9xEeneNaO5SRURELkhhQ9qckGGy/P3vF/+L5DgpvENKYv2HttTU1GauUOTcfD4fq1ev5rPPPgMgJSWFhx9+mC92R7PwnXJCBnS5xsFvn0zUquAiItIqKGxIm7VhRy2/XVJCVY2B3VLHNeZfiHUcZcaMGQwePLi5yxM5w6FDh1iyZAmFhYUAjBkzhtFj7+aPf65suFJ3201u/tuM9ppxSkREWg2FDWnTjhcHeeblIr49EgBMEviMeL5g1MiRTJ48Gbtdfx2W5hUKhfjwww957733MAyD2NhYZs2ahdV9Hf/2chHHS0I47PDk5DjuudWjgeAiItKqKGxIm+cPmDz/Zinvra8GIIp9XMN7dOuSxAMPPKBuVdJsiouLWbJkCQcOHABgwIABZGRkkL3Z5MW3yggEoUO8jV/PSaBnF43PEBGR1kdhQ64aa3O9PPvnMvwBEzs1JPM+MdYDjB49mrvuuouICH2YkyvDNE02bNjAm2++ic/nw+VyMW3aNHr3vZln/1zGx1/VADD8+kh+OSteK4KLiEirpbAhV5V9R/z8x5ISDh0PABDD1ySRTWL7KDIyMujbt28zVyhtndfr5fXXX2fLli0ApKWlMXv2bPYec/PHlaWUVISwWSFzUixTRmv9DBERad0UNuSq4w+YvLqmnDezqzBNcFqrSDayiOIw/fv3Z+rUqcTGxjZ3mdIG7dq1i+XLl1NRUYHNZmPixIkMHjaa+W9X8OHG+m5+qUl2/tfsePpoWlsREWkDFDbkqvX1vjp+v6yU48VBANqTRwKf4HbZueeee7j11lu1EKA0iYqKCrKysli/fj0AHTp04OGHH+ZoeQJ/OHU1w2KBqWOiefjuGCKc+r4TEZG2QWFDrmq1dQYvrS5nzedeANz2SuKDWUSRT5cuXZgxYwadOnVq5iqltfL5fGRnZ7Nu3Tp8Ph8Ao0aNYuzt9/Dyu9X8bcP3VzP+cVY86dfpaoaIiLQtChsiwJc7a/nPFfV/YQaIs+6mvfEREdYabrvtNu666y5cLlczVymthWEY5ObmkpWVRUVFBQDXXnstkybdR0HFNcxfXU5xef3VjCmjo3lkoq5miIhI26SwIXJKVY3B4nfLefczL6YJdmuQ9kYOcXxF+7gYpk2bxvXXX9/cZUoLt2vXLlavXs2xY8cAiI+P59577yUmqS/zVpXz9b76KxypSXZ++WA8fbvpaoaIiLRdChsiP7A338+zfy7lm0N+ANy2UhJCa3FzhN69e3PnnXfSrVu3Zq5SWpqCggJWr17N7t27AXC73UyYMIEbb7qF5Wuryfrci2FChMPCjDvacf/YaF3NEBGRNk9hQ+QcDMPkg9xqFv6lnMpqA4AYdpLAxzjw0r17d+6880569OihqUmvcmVlZaxZs4aNGzdimiZ2u52RI0cybtwdfLzFZPGaCqpq6r+HRg1w8/h9sSS318r1IiJydVDYELmAyuoQi9+tYM3n9V2rbNYQseZXxJm52Kmla9euTJgwgfT0dIWOq0xtbS3r1q0jOzubQKB+3ZabbrqJe+65h/ySaOa/XcaBo/Xbr+vo4GdT47ihh8b9iIjI1UVhQ6QR9hz28cJb5ezYX9/f3mELEmduJMbYiA0/nTp1YsKECVx//fWaLreN8/l85Obm8v777+P11s9i1q1bN+677z4qAtfwalYF20+Ny2gXZeXhiTHcPdyDzaYwKiIiVx+FDZFGMk2TL3fVsejdcvYdqf+LtcsRIM74gujQJqwESUlJYfz48QwYMECho40pLCzk008/JTc3l9raWgCSkpK49957sUT1YmlWBVu/PRVG7TBxRDQPTmhHjMfWnGWLiIg0K4UNkYtkGCafbq3l1TXlHDlZvyBgVISfuNBnuIObsRIiKSmJ8ePHM3DgQGw2fdhsrQzDYOfOneTk5LBr166G7YmJiYwePZq4lIEsX+vlq911QH3IuHO4hxl3tCMxVuMyREREFDZELlEoZLLuy2qWvFdBYWn9+hxuZ5AYM4+owEbs1BIfH8+4ceMYOHAgkZGRzVyxNJbX6yU3N5dPP/2UkpISACwWC+np6dx660h8tq68me1l0676kGG3wYRhHh64ox1JGvwtIiLSQGFD5DL5AybvrffyxkeVDaHDbjWIs+3CE/iCCEpxOBxcf/31DBo0iD59+uhqRwuVn59PTk4OeXl5DYO+3W43w4YNY8jQEWw9EMnqj6s4cKz+OasVxg+NYub4GDrEK2SIiIj8kMKGSBMJhUxyttSw6qMq9uT7G7bHO/OJ8n9GJEewAB6Ph5tuuonBgwfTpUsXzWLVzAKBAFu2bCEnJ4eDBw82bE9NTWXUqFFcm9aftbk+1nzubZgG2eW0cPvgKKaOjaZjoqO5ShcREWnxFDZEmphpmny9z8ebH1WRu722YXuCpxp34CsifJuxUd/9JikpiUGDBjFo0CASEhKaq+SrjmEY5Ofns3XrVnJzc6mqqgLAZrMxYMAARoy4Fb+1I6s/8ZKzuYZQfcYgub2Ne0dGc+dwD9FuTQAgIiLyYxQ2RMIo/2SAt7Or+NvGavyB+h81u9WkY0wh1qr1OIPfYqF+e7du3Rg4cCADBgzA4/E0Z9ltks/n45tvvmHHjh3s2LGDysrKhudiYmIYMWIEPdOHsnG3lXVf1nD4eKDh+evTIrjvtmiGXx+pKWxFREQugsKGyBVQ4Q2RvamGtble9hd8/yE2xh0iOXIfoZIcHJQC9X9d79u3LwMHDqRXr1643e7mKrvVKy0tZceOHWzfvp09e/YQDAYbnnO5XPTu3Zv0fjdTYXTjo021bN3r47vfiE6HhdtucnPfbdF07+RspjMQERFp3RQ2RK6wb4/4WfuFl+xNNVTVGA3bO7avxR3aRahiE07KgfoZkFJTU0lLS6N79+6kpaXpqscFGIbB4cOHGwJGQUHBGc/Hx8fTr18/evfpR7XZib/n1fHZ1lp8ge9/Dd7QPYLbB0cxor8bT6S6SomIiFwOhQ2RZuIPmHzxdQ0f5Faz6Zs6Tv9JjPfUEmXswVqzhQhOcnrHnZSUFLp3794QPtq1a3fFa29Jqqur+fbbb9m+fTs7d+48o3uUxWLhuuuuo1+/fnS5Lp3DxTHk7qgjb1ct1XXfv+GpSXZuHxzF2EFRmlVKRESkCSlsiLQAReVBPt9ay/ptNWz91ofx/QUPYqNCdIw5ib1uB7WlX2MleMaxycnJDeGje/fuxMbGXtnirxDDMCgpKaGgoOCMW1lZ2Rn7uVwu+vTpQ3p6X2ISe7F1v4UNO+rYdcCHcdpvu7hoK7cOcHP74Ch6dXFqVjAREZEwUNgQaWEqq0Ns3FHH59tq2LSrjjr/9z+idht0TgwQF3ESs/obqou/xkLgjOMTExO59tprSUxMJCEhgYSEBBITE2nXrl2r+UDt9/s5fvz4GaHi6NGj1NXVnXP/pKQk+vRJp0Pn6/GGrmHXwQCb99RxoiR0xn7dUh0M7RfJ0L6R9OzixGptHe+HiIhIa6WwIdKC+fwGX+2u4/NtteR9U0dx+Zkfnu026JwUpH1EIWbNN1QVbsfKuT+QOxyOhuBxeghJSEigffv2OBxXbr2IQCCA1+vF6/VSXV2N1+ultLSUo0ePUlBQwIkTJzjXrya73U5KSgqpqalcc01HcHWmyBvL7nyDHft9lFYaZ+zvsEP/nq6GgKHVvUVERK4shQ2RVsI0TY4VBdn6rY9te+vY+q3vrPAB0D7aIN5TjcdeiiN0nFDNQarL84Hz/6hbLBbi4uJISEggJiYGu92O3W7H4XA03G/MY9M0G8LDd1/Pdd/n8/3o+Xo8HlJTU0lNTSUhqROGI4VKXzSHjgc5dCzAnnw/tb4zz8lhh55dIujbLYJ+3SK4sUcEkREa5C0iItJcFDZEWqkfho/t+32cLD07fAB4Ii10TrYQ7/ERYa3CZpQS8hVSV3mEitKjBAL+cx4XTlarFY/Hg8fjISoqiihPDO3adyHCkwKOeAorHBw6FuDg8QCF5zmvqEgLfa+rDxZ90yLo1SUCp0Ndo0RE5OKcOHGC7du3c+DAATweD7feeiudOnVq7rLaBIUNkTakqsZgf4GffQV+9hcE2Ffg59CxQMMK2OficlpIjLMQGxUkylGLzVKH0xbEYfXjsPmxW/zYLHXY8GGjDtPwEQwECIWCBIPf3wKB+rEjHo8Hd5QHV2QM9oho7M5o7I4osLmx2FyYFhe+oJOyKguF5SGKykIUlQcpq7xAkUBirI2uKY6GW1onJ9de49C4CxERuWQHDx7kgw8+YPv27Wdsd7vd/PrXvyY6OrqZKms7FDZE2jh/wCT/RH3wOFoU5GRJkBOlIY4XBympOPcVg8ayWsBiAau1viuW1QKmyRnrVlwMp8NCYqyNxDgbXTo4TgsXTjxudYcSEZGmUV5ezltvvcXmzZuB+v/DevfuTc+ePXnnnXcAmDVrFkOGDGnOMtsEjZYUaeOcDgtpnZyknWMVbH/A5GRpkBMlQU6WhigsC1JVbVBVY+Ctqf96+v0fXiExTMDk1PZzDOi2gdtlxe2y4I6wEumyEOWy0i7KSmKcncS4+mCRFGcnKc5Guyhrq5kxS0REWh/DMPj444/JysrC5/NhsVgYPHgwd9xxB8nJyQANYWP79u0KG01AYUPkKuZ0WOiU7KBT8o/PRGWaJnU+E3/QxDTrg0b9VxNOe2yaEBlhwe2yavyEiIi0GMXFxSxdupT9+/cD0LVrVzIyMs4Ym1FUVNRw/9Zbb73iNbZFChsi0igWi4VIl4XI5i5ERETkIpimSW5uLqtWrcLn8+FyubjvvvsYNmwYVuuZXXQ3btwIQM+ePenZs2dzlNvmKGyIiIiISJtUW1vLihUr2LJlCwBpaWnMmjWLhISEs/atq6vjk08+AeCWW265kmW2aQobIiIiItLmFBQU8PLLL1NUVITNZmPixImMHTv2rKsZ3/n000+pqakhOTmZ/v37X+Fq2y6FDRERERFpM0zT5IsvvuDNN98kEAjQvn17Hn30Ubp27XreY6qqqvjb3/4GwB133HHeQCIXT2FDRERERNqEQCDAG2+8wRdffAFAeno6s2fPxuPxXPC4NWvWUFtbS6dOnRg0aNCVKPWqobAhIiIiIq1eRUUFCxcu5ODBg1gsFiZOnMjtt9/+o1cpjhw5wvr16wGYOnWqrmo0MYUNEREREWnVDh06xMKFCykvLycyMpJHH32UPn36/OhxoVCIFStWYJomN998M2lpaVeg2quLwoaIiIiItFobN27ktddeIxgM0qFDB+bOnUtSUlKjjs3OzubIkSNERkYyefLk8+5nhkLUHDpEzcGDBMrKsHk8tB82DEdMTFOdRpulsCEiIiIirY5pmmRlZbF27VoA+vXrx0MPPURkZONWhDp58iTvvfceAFOmTCHmB8HBDIWo3LmTstxcKrZsIVRdfcbzRR99RJ/f/x6rXR+nL0TvjoiIiIi0KoFAgOXLl5OXlwfA7bffzj333NPo8RahUIjly5cTCATo3bs3Q4YMaXjOX1pK8ccfU5KTQ6CsrGG7ze0mqnt3nAkJFGdn4y8sxF9UhOuaa5r25NoYhQ0RERERaTWqqqpYsGABBw4cwGq1MmPGDIYNG3ZRbXz44YccOHAAl8vFjBkzsFgsVO/bx8m1aynPywPDAKjvLjVkCLGDB+Pp0QOL1UrpF19QnJ2Nze3GeY7FAeVMChsiIiIi0iqcPHmSF154geLiYiIjI8nMzKRnz54X1cbhw4cbuk9Nu/9+7EeOsOfFF6neu7dhH0+vXiSOHUvMTTed0U3KX1pKwYoVACRNmIDV4WiCs2rbFDZEREREpMXbv38/L730EtXV1SQkJPDkk0/SoUOHi2rD7/ezZMkSjFCI0Z07027NGvbn5wNgsdmIGzaMpPHjcXfufNaxBfnH+ds//SPd/HUk9+hB8l13Ncl5tXUW0zTN5i5CREREROR8tm3bxuLFiwkEAlx77bU88cQTREdHX3Q7r61YwbGcHG4OBokJBgGwulwkjB5N0vjxOOPiznlccWER13brQ7W3mAR3FNs3f0WHi7yicrXSlQ0RERERabE+/fRT3njjDUzTpG/fvsyZMwen03lRbZimSd6bb5K0bh29vxuP4XaTePvtJN1xB/YfWWH83/+4jmpvMQDFNdXsLChQ2GgkhQ0RERERaXFM02TNmjV88MEHAAwfPpyMjAxsNttFtVO1cyeHVq7Elp9PPGA4HKTcfXd9yIiK+tHjP9zg5auifjjdyfhrTpKSksJNN910Kad0VVI3KhERERFpUUKhECtXriQ3NxeAu+++mwkTJmCxWBrdRs2hQxx94w2qduwAIAAcT07mzn/9V5zt2jWqjS931vLP84sIGXDPMIN+1+znpptuIjY29mJP6aqlKxsiIiIi0mIEAgEWL17Mtm3bsFgszJgxg+HDhzf6eF9hIcfeeouyU0HFtFjYZbezNy6O//6rXzU6aOzY7+PXC4sJGTBmoJunZ8RjtV57Kad0VVPYEBEREZEWoa6ujgULFrBnzx7sdjuPPPIIN954Y6OODVZXc+Kvf6Vo3TrMU4O/Qz168NbRo3htNp569NFGX5HYfcjH/36hEF/AZFAfF798MB6rtfFXVeR7ChsiIiIi0uyqqqp44YUXyM/Px+Vy8fjjjzdqDQ0zGKQoO5vjf/kLIa8XgOj0dGy33cazK1cSsFq568476dOnT6Pq2Jvv55fPF1JdZ3J9WgTPZCbgsCtoXCqFDRERERFpVqWlpTz//POcPHkSj8fDT3/6UzqfY62L05mmSeXWrRSsXInvxAkAXB070nH6dOxpafzud78jEAiQnp7OhAkTGlXH7kM+fvl8Id5ak77dIviPJxNxOa2XfX5XM4UNEREREWk2hYWFPPvss5SVlREXF8fTTz9NcnLyBY+pPXKEgpUrGwZ/29u145rJk0kYORLTYmH+/PmUlJQQHx/PQw89hNX644Fh+746/veLRdTU1QeN3z2ViNuloHG5FDZEREREpFkcO3aM5557jsrKSpKTk/nZz35G+/btz7t/0Ovl+OrVFGVng2FgsdtJGj+eDvfcgy0yEoD3srLYuXMnDoeDzMxMohoxve2mXbX8emExdX6TG7pH8B9PJBKpoNEkFDZERERE5IrLz8/n+eefp7q6mo4dO/L000+fd1Vw0zAo/vvfOfb22w3jMmJuvpnU6dOJSEpq2G/Lli28//77AEyfPp1OnTr9aB05m2v4zavFBEMwqI+LZzIT1HWqCSlsiIiIiMgVtX//fl544QXq6uq49tpreeqpp857BcK7Zw9Hli+n9vBhAFypqaTOnEm79PQz9jt69CjLli0DYPTo0QwZMuRH63j30yqefaMM04SRA9z800PxGgzexBQ2REREROSK2b17Ny+99BJ+v5+0tDSeeOIJIk91gTpdoLyco3/+M6Xr1wNgc7u5ZvJkEseMwfKDVcS9Xi8vvfQSPp+Pnj17MmnSpAvWYJomS7IqWL62EoCJt3h4OiMOm6a3bXIKGyIiIiJyRWzfvp2XX36ZYDBInz59yMzMxOl0nrGPGQpRtG4dx95+G6OuDiwW4keOpOP992M/RzerUCjEokWLKCkpISEhgUcffRTbD8LI6YIhk/9aWcra3GoAZt3Zjtl3xVzU6uTSeAobIiIiIhJ227Zt45VXXiEUCnHDDTfwyCOP4HA4ztjHu2cP+UuXUnfkCADu666j0+zZRF133Xnbfeutt9izZw8RERHMnTsXj8dz3n2raw3+7ZVi8r6pw2qBn09vz923nH9/uXwKGyIiIiISVlu2bGHRokUYhsGAAQN4+OGHz7j6EKis5Ojrr1P6+ecA2DweOk6bRvytt2K5wLS1OTk55OTkYLFYmD17NikpKefdt7A0yD+9WMSBYwFcTgv/8mgCQ/ud3X1LmpbChoiIiIiEzVdffcWrr76KYRgMHDiQWbNmNQQN0zAo+eQTjr75JqHq+m5N8aNGnbfL1Ol27drFqlWrAPjJT37CjTfeeN59dx/y8auXiiitNGjfzspvnkikZ5eIpjlBuSCFDREREREJi02bNrFkyRJM02Tw4ME8+OCDDQvs1Rw+zJElS6jetw+AyC5d6PzQQ0Slpf1ou8ePH+eVV17BMAyGDBnCuHHjzrtvzuYafre0BF/A5LoUB795MpHk9voIfKXonRYRERGRJrdhwwaWL1+OaZoMHTqUBx54AKvVSqiujuOrV1P4t7+BYWB1uUiZMoXEsWPPmmXqXLxeL/Pnz6euro5u3boxffr0cw7uNk2TFWsreTWrAoDB6S5+9UgCUZFaQ+NKUtgQERERkSZ1etC45ZZbyMjIwGq1UrF1K0eWLMFfUgJA7MCBpM6cifMCq4afLhAIsHDhQoqLi4mPjyczM/OsQeYAtT6D/1xeyiebawCYPDqauffFamrbZqCwISIiIiJNZuPGjQ1BY8SIEUybNo1QZSWHli2jfNMmAJwJCXSaPZuYC4yz+CHTNFmxYgX79u3D5XLxxBNPnHPF8RMlQf51YRH7jgSw2+DnGe25c7hmnGouChsiIiIi0iQ2bdrEsmXLGoLG/VOnUvLJJxx74w1CNTVgtZI8YQId7r0Xm8t1UW1nZWWxadMmrFYrmZmZ55x5atveOv7tlWLKvQaxHivPZCZwfdrFvY40LYUNEREREblseXl5DYPBb7nlFn4yYgT7fvtbqvfuBerXzOj8yCO4u3S56LZzc3NZu3YtAA888AC9evU643nTNHnnEy8vvl2GYUBaJwf/93ENBG8J9C8gIiIiIpclLy+PV199FdM0GT50KKMiI9nzL/+CGQxijYggZepUEseNu+CaGeeze/duXnvtNQDGjx/P0KFDz3i+zm/wXytLWfdl/fiMMQPd/PcH2uNyaiB4S6CwISIiIiKXbPPmzQ1XNEb37k2fnTs5cfQoAO1uuIHODz2EMyHhkto+duwYCxcubFijY+LEiWc8f7w4yK8XFrGvIIDVCo9PimXK6Ohzzk4lzUNhQ0REREQuybZt21i8eDHWUIh72rcnYdMm6kwTe3Q0qQ8+SNyQIZf8wb+srIx58+ZRV1dHWloaM2fOPKOtDTtq+e2SEqpq6sdn/MujCfTvqfEZLY3ChoiIiIhctF27drFo0SI6+P2MMU0i8vMBaD98OKkPPPCjK4BfSE1NDS+88ALl5eV06NCBxx9/vGGK25Bhsuy9CpavrQSg17VOnpmTQJLGZ7RI+lcRERERkYuyZ88eFs+fz5DqanoFAgA44uPp/PDDxNxww2W1HQgEWLBgAceOHSMmJoannnqKqKgoAMqrQvzm1RK+2l0HwD0jPDw5JQ6nQ92mWiqFDRERERFptP379/PXZ5/lHq8Xj2kCkDBmDB2nTcMWGXlZbRuGwbJly/j2229xuVw89dRTxMfHA7Bjv4//s6iY4vIQLqeFX0xvz7jBUZd9PhJeChsiIiIi0igHvvmGzX/4A2N9PgCcSUl0mTOH6N69m6T9d955h6+++gqbzUZmZiapqakYhsmb2VW88tdyDAM6J9v59WMJdE1xNslrSngpbIiIiIjIj9q/bh3HV6ygm2FgAgm3306n++/HGhHRJO1/9NFHZGdnA/Dggw/Sq1cvKrwhfr+shA076rtNjRno5r9Nb0+kS9PathYKGyIiIiJyXsHqar59+WVqv/oKN1AdEUH6z39O+759m+w1NmzYwOrVqwGYNGkSgwYNYvu+Ov59cQlF5SEcdvjZ/e25a3iUprVtZRQ2RERERK5ytbW1zJs3jwULFnD06FFSU1PJzMzkwREjOLliBaGKCgzgcHw8dzzzDJ7Y2CZ77e3bt7NixQoAxowZw+gxY1mxtoIlWRUYJqQm2fn1nAS6parbVGtkMc1TI3tERERE5KpTW1vLuHHjyM3NxTCMhu1Wi4V+CQm8OGYMdQ4HO1JSePif/5noy5jS9of279/Pc889RyAQYNCgQdx5zwP8fnkpW/bUjwkZO8jNzzPa41a3qVZLVzZERERErmLz5s07K2gAGKbJ10VFPLdvHz3GjeMX//N/nhU0gkEvBQX/xbFjC/H7j+F0ppCSkklq6i+w2z0XfN1jx44xf/58AoEA6enp9Og/lcd/d5IKr4HLaeHpaXGMH3rhNqTl05UNERERkTbENE18ARNvjYG31qS61sBba9R/rTGoqTu1rc6gptbgP385kLKiQ+dtLzY2lj179pCUlHTG9mDQy9atI/F6twKnBxUrHs+N3HhjznkDR0lJCX/4wx8oLy+nc5duuDrP4t3PagFIS3Xwq0cT6JzsuLw3QloEhQ0RERGRH3Hy5ElWrVp11l//w8U0IWjY8YecBAwHgZCj4b4/5CQQcpy1/fRtJo3vdpSzsAdGyHfe5+12O3/84x/P2p6U9AFJSWuxWM7+KGmaFgoLJ1BYOP6s54LBIPv378cwDHwkUOycSpU/BoDr4vaTnrgTm7Vx77NpQsi0nfGe1PrgZOFJenVx4nFdmX+vH3PvvffSuXPn5i6jWagblYiIiMgFmKbJn/70JyoqKi7teMDASQg3IdwEcRMi8tTt9Pun31yA7TIrN7Diw4YPK3WnvvpO21Z/c0e1w1tZdN5W3G4333zzzVnbu3XLOWfQ+O6so6NzyMnpcp5noYwBFHEbpt+BjRriWU+grJjNZRGEiMA4dau/7/rBNlfDV04LVkbIR96qu6ku20ts+2Tun/ITbLbLfR8v3+9+9zv++Mc/4nK5mruUK05hQ0REROQCLBYLt99+O6tWrTpjuwmEiCJANEGiCeI5dYsiiOdUsIgihBuTS+sSZCGIjdpTYaH+dq779QHizPsWAjRmkti+fbqxYcP5w0Z6evpp52zBwImBA1dkzfnrtoArsobjTDi1/3e3+vAQxMPpYSqEm0LGNeYtOY8QNuqoK/ua6rK9AJSXnqSsrIyEhITLaLdpjBgxgogmWo+ktVE3KhEREZELCARN9hz2szffT/6JAEeLghwvCVJYGiQYanw7LqeFGI+VWI+NmGgrMVFWYjw22kVZ62+n7secehwdZcXlvLhZmEKGid9fP2bDFzDx+U3q/Cb+gEmtz2h4XH+rf1xZWcP/+9dJHNj7ZX2/pO9YrCR1uolbM1YSNF3U+urb+c4/3D2YGPeJc9ZhmlBW4WHGTA++mkIi3Emk9JlB6g1zsDuizto/KtJClMtKVKQVt8tCVGT9fY/LWv/cqcfR7lPbI6143FY8kRY8bisRDgsWiwWfz8fNN9/Mjh076Nu3L3l5eVfth/yWQmFDRERE5DyOFwd5+g8nKan48VQR5bKQEGsjLtpGXDsbsdFW2kXZiPHUf0h22C0YhkkwVB8KgiEIBk2CIZPAaff9wfqAEwiYBIIm/uCpr4H6my/w/f3vHn+3LRC8tPMMBes4umMpx3a+hq/6BBFRHUhJf4COfWdjs5/d9cdmhdv6PceQHv+F1XL2uAjDgCVLLCxffnp4seCJT6f/vW/SNTWGf5gWR69rI3A5LVitTbdQn8/nY/fu3fTq1UtBowVQ2BARERE5j8276/gfzxU2dxmXxGEHl9OK02EhwmnBddotwmnFFWEh0mmhvKyQPd9sw0KAfuk96H9DOpERlvrnI6xEOk+7H2HB7aoPTuebjco0LXz7rck//APU1f2gKIuVidP+kbeW/QanQyuBXw0UNkREREQuYMP2Wr7e76OkPIi3tr47kj9gEgpB0DAxDQh992nKNMFiwQJYrWC11I9fsNssWK1gs1qw28Bms2C31d+32yw47BbsdnDYLThOPXbYwWm34HBY6r/a60OD03HqdupxhOP7ba7Tnrc14mrBzp07mT9/PoZhMGrUKKZOnYrF0vgQcK51NlaurGTRosqzg8YpqampHDlypNGvIa2bwoaIiIjIVejQoUP86U9/wu/3M3DgQGbPno3VevkrddtstgtOEWy1WgmFLmKwi7RqWvtdRERE5CpTWFjIiy++iN/vp3fv3jz44INNEjQAUlJSLut5aVsUNkRERESuIpWVlcybNw+v10unTp147LHHsNubbjWEsWPHnrcrltVqJTMzs8leS1o+hQ0RERGRq0RdXR0vvvgixcXFxMfH8+STTzbpQnOffvopdrud+Pj4swKH1Wrlxhtv5Be/+EWTvZ60fAobIiIiIleBUCjEokWLyM/PJyoqip/+9KfExMQ0Wfvr16/nz3/+Mw6Hg+eee45nnnmG1NRUrFYrqampPPPMM+Tk5ODxeJrsNaXl0wBxERERkTbONE1WrFhBbm4uDoeDn//853Tt2rXJ2t+wYQPLly/HNE1Gjx7N5MmTL2pWK2m7dGVDREREpI1bu3Ytubm5WCwW5syZ06RB48svv2wIGiNHjlTQkDMobIiIiIi0YRs3biQrKwuAjIwM+vXr12Rt5+XlsXTpUkzT5JZbbuH+++9X0JAzKGyIiIiItFF79+5lxYoVAIwbN44RI0Y0WdtbtmxhyZIlmKbJ0KFDycjIUNCQsyhsiIiIiLRBJ06cYMGCBYRCIQYMGMBPfvKTJmt7y5YtLFq0CMMwGDRoEA888ECTrdMhbYu+K0RERETamKqqKl544QVqa2vp2rUrs2bNarIwcHrQGDhwYJO2LW2PvjNERERE2hC/38/8+fMpKSkhISGBuXPn4nQ6m6TtHwaN2bNnK2jIBTXdcpEiIiIi0qy+m+L20KFDuN1unnrqKaKjo5uk7c2bN7N48eKGrlO6oiGNobAhIiIi0ka8//775OXlYbVayczMJDk5uUnaPT1oDB48mAcffFBBQxpFYUNERESkDcjLy+O9994DYPr06fTo0aNJ2t20aRNLly7FMAyGDBnCzJkzFTSk0RQ2RERERFq5Q4cOsXz5cgDGjBnD8OHDm6Td01cGV9CQS6GwISIiItKKlZaW8tJLLxEIBOjbty+TJk1qknY///xzXn/99YYF+zIyMhQ05KIpbIiIiIi0Un6/nwULFlBZWUlKSgqPPPJIkwSCjz/+mFWrVgEwatQopk6dqgX75JIobIiIiIi0QqZp8tprr3HkyBE8Hg9PPPEELpfrsttdt24d77zzDgBjx45l0qRJChpyyRQ2RERERFqhjz76iE2bNmG1WpkzZw7x8fGX1Z5pmqxdu5asrCwAJkyYwN13362gIZdFYUNERESkldm1axd/+ctfAJgyZcplzzxlmiarV68mOzsbgIkTJzJhwoTLLVNEYUNERESkNSksLGTRokWYpsnQoUMZOXLkZbVnGAavv/4669evB+rDy+jRo5uiVBGFDREREZHWwufzsWDBAmpra+natSsZGRmX1c0pFAqxdOlS8vLysFgszJw5k6FDhzZhxXK1U9gQERERaQW+GxB+/Phx2rVrx2OPPYbD4bjk9vx+P6+88go7duzAZrPx8MMPM2DAgCasWERhQ0RERKRVyMnJIS8vr2FAeGxs7CW3VVtby/z589m3bx8Oh4PMzEzS09ObrliRUxQ2RERERFq4AwcO8PbbbwMwadIk0tLSLrmtyspK5s2bR0FBAS6Xi7lz5172AHOR81HYEBEREWnBqqqqeOWVVwiFQgwYMOCyBm8XFxfz/PPPU1RURLt27Xjqqafo1KlTE1YrciaFDREREZEWyjAMXn31VcrLy0lOTmbmzJmXPCC8oKCAefPmUVlZSXx8PE8//TSJiYlNXLHImRQ2RERERFqoDz/8kN27d+N0OsnMzLzkFcL37t3bMItVx44d+elPf0pMTEwTVytyNoUNERERkRZo//79Dat5T5s2jWuuueaS2snLy2PZsmUEg0HS0tKYO3cubre7KUsVOS+FDREREZEWprq6msWLF2MYBgMHDmTIkCEX3YZpmmRnZ7N69WoA+vfvz0MPPXRZ0+WKXCyFDREREZEWxDRNVqxYQVlZGYmJiUyfPv2ix2kYhsHbb7/Nxx9/DMCoUaOYMmUKVqs1HCWLnJfChoiIiEgLkpuby7Zt27DZbDz66KMXPU7D7/ezdOlStmzZAsB9993HmDFjLmulcZFLpbAhIiIi0kIUFxezatUqACZOnEjnzp0v6viKigpeeuklDh8+jM1mY/bs2dx8883hKFWkURQ2RERERFoAwzBYtmwZPp+PtLQ0xo4de1HHFxQUMH/+fMrKyoiKiiIzM5Pu3buHqVqRxlHYEBEREWkB/v73v7Nv3z4iIiKYNWvWRY2v2LFjB4sWLcLn85GUlMSTTz5JUlJSGKsVaRyFDREREZFmVlRUxJo1awCYPHkyCQkJjTruuxmn3nnnHUzTpEePHjz22GNERUWFs1yRRlPYEBEREWlGpmny+uuvEwgE6NmzJ8OHD2/UcX6/n5UrV/Lll18CMGzYMDIyMrDb9fFOWg59N4qIiIg0oy+//JLdu3fjcDgaPc1taWkpCxcuJD8/H6vVypQpUxg5cqRmnJIWR2FDREREpJnU1tby9ttvAzBhwoRGjbPYu3cvixYtoqqqiqioKObMmUPPnj3DXarIJVHYEBEREWkmH374IV6vl6SkpB+dfcowDD788EPWrFmDaZp07NiRuXPnEh8ff4WqFbl4ChsiIiIizaC0tJS///3vAEyaNOmCYy28Xi9Lly5l586dAAwZMoSMjAycTucVqVXkUilsiIiIiDSDDz74gEAgQFpaGtdff/1599uzZw9Lly6lvLwch8PBtGnTGDZs2BWsVOTSKWyIiIiIXGFer5eNGzcCcPfdd59zYHcgEODdd98lOzsbgKSkJObMmUNqauoVrVXkcihsiIiIiFxh69evJxAI0KlTp3Ou8n3w4EFee+01jh07BsAtt9zC5MmTiYiIuNKlilwWhQ0RERGRK+zrr78GYMSIEWdc1aitreWvf/0rn332GaZp4vF4mDlz5gW7WYm0ZAobIiIiIleQ3+/n8OHDAPTu3RsAn89HTk4O69ato7q6GqgfBD5p0iSio6ObrVaRy6WwISIiInIFWSwWDMMAYOPGjZSVlbF169aGkJGcnExGRobWzpA2QWFDRERE5ApyOBx07dqVgwcPkpWV1bA9ISGBO++8k4EDB2Kz2ZqxQpGmYzFN02zuIkRERESuJlVVVWRlZVFeXk5sbCz9+/ene/fuChnS5ihsiIiIiIhIWFibuwAREREREWmbFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQsFDZERERERCQs/j98nZfRDfhQNwAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -289,10 +292,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total DV (m/s): 5937.927390088956\n", - "Dvs (m/s): [197.65765144324607, 2732.3258592198454, 231.02221674962183, 2776.9216626762422]\n", - "Total DT (m/s): 360.0\n", - "Tofs (days): [209.66400763089803, 102.52543608353949, 47.810556285562484]\n" + "Total DV (m/s): 5937.927393778519\n", + "Dvs (m/s): [197.65819561032322, 2732.3138722882486, 230.93079262863296, 2777.024533251314]\n", + "Total DT (m/s): 31103999.999999996\n", + "Tofs (days): [18114971.603645075, 8855996.979107073, 4133031.4172478495]\n" ] } ], @@ -318,7 +321,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/doc/utils.rst b/doc/utils.rst index 08774103..c606b3fa 100644 --- a/doc/utils.rst +++ b/doc/utils.rst @@ -1,8 +1,11 @@ .. _utils: +Utils +############################################################################### + .. currentmodule:: pykep.utils -Spice +Spice Utils ############################################################################### .. autofunction:: spice_version @@ -19,4 +22,28 @@ Spice .. autofunction:: rotation_matrix -.. Autofunction:: naifid2name \ No newline at end of file +.. Autofunction:: naifid2name + +.. currentmodule:: pykep + +Encoding Utils +############################################################################### + +.. autofunction:: alpha2direct + +.. autofunction:: direct2alpha + +.. autofunction:: eta2direct + +.. autofunction:: direct2eta + +.. currentmodule:: pykep.utils + +.. autofunction:: uvV2cartesian + +.. autofunction:: cartesian2uvV + +Miscellanea +############################################################################### + +.. autofunction:: planet_to_keplerian \ No newline at end of file diff --git a/pykep/plot/__init__.py b/pykep/plot/__init__.py index 06dbb018..7cb8ae24 100644 --- a/pykep/plot/__init__.py +++ b/pykep/plot/__init__.py @@ -32,7 +32,6 @@ def add_sun(ax, **kwargs): """ kwargs.setdefault("c", "y") kwargs.setdefault("s", 30) - kwargs.setdefault("label", "Sun") ax.scatter(0, 0, 0, **kwargs) return ax diff --git a/pykep/trajopt/_min_Bu_bu.py b/pykep/trajopt/_min_Bu_bu.py index 7d7eda0e..f9361e16 100644 --- a/pykep/trajopt/_min_Bu_bu.py +++ b/pykep/trajopt/_min_Bu_bu.py @@ -152,7 +152,7 @@ def minBu_bu_p(B, b): b_norm = np.linalg.norm(b) # Easy way out if b_norm < 1: - return -b_norm, np.array([np.nan]*3) + return -b_norm, np.array([0, 0, 0]) # Compute the singular value decomposition (used to bound |Bu| as well as to provide one IG) svd = np.linalg.svd(B) diff --git a/pykep/trajopt/_pl2pl_N_impulses.py b/pykep/trajopt/_pl2pl_N_impulses.py index 5a7a431e..742de654 100644 --- a/pykep/trajopt/_pl2pl_N_impulses.py +++ b/pykep/trajopt/_pl2pl_N_impulses.py @@ -94,7 +94,7 @@ def __init__( self.multi_objective = multi_objective self.DV_max = [s * 1000 for s in DV_max_bounds] - self.__common_mu = start.mu_central_body + self._common_mu = start.mu_central_body # And we compute the bounds if phase_free: @@ -149,24 +149,24 @@ def decode(self, x): vsc = v_start for i, time in enumerate(T[:-1]): DV = _pk.utils.uvV2cartesian(x[3 + 4 * i : 6 + 4 * i]) - retval.append([[rsc, vsc], DV, T[i]]) + retval.append([[rsc, vsc], DV, T[i] * _pk.DAY2SEC]) # We apply the (i+1)-th impulse vsc = [a + b for a, b in zip(vsc, DV)] rsc, vsc = _pk.propagate_lagrangian( - [rsc, vsc], T[i] * _pk.DAY2SEC, self.__common_mu + [rsc, vsc], T[i] * _pk.DAY2SEC, self._common_mu ) cw = _pk.ic2par([rsc, vsc], self.start.mu_central_body)[2] > pi / 2 # We now compute the remaining two final impulses # Lambert arc to reach seq[1] dt = T[-1] * _pk.DAY2SEC - l = _pk.lambert_problem(rsc, r_target, dt, self.__common_mu, cw, False) + l = _pk.lambert_problem(rsc, r_target, dt, self._common_mu, cw, False) v_beg_l = l.v0[0] v_end_l = l.v1[0] DV1 = [a - b for a, b in zip(v_beg_l, vsc)] - retval.append([[rsc, vsc], DV1, T[-1]]) + retval.append([[rsc, vsc], DV1, T[-1] * _pk.DAY2SEC]) DV2 = [a - b for a, b in zip(v_target, v_end_l)] retval.append([[r_target, v_end_l], DV2, 0]) @@ -210,8 +210,6 @@ def plot( *figsize* (:class:`tuple`): The figure size (only used if a*ax* is None and axis have to be created.), Defaults to (5, 5). - *leg_ids* (:class:`list`): selects the legs to plot. Optional, defaults to all legs. - *\\*\\*kwargs*: Additional keyword arguments to pass to the trajectory plot (common to Lambert arcs and ballistic arcs) Returns: @@ -221,44 +219,21 @@ def plot( ax = _pk.plot.make_3Daxis(figsize=figsize) # Adding the main central body (Sun-like) - _pk.plot.add_sun(ax=ax) + ax = _pk.plot.add_sun(ax=ax) - _pk.plot.add_planet_orbit( + ax = _pk.plot.add_planet_orbit( pla=self.start, ax=ax, units=units, N=N, c=c_orbit, label=self.start.name ) - _pk.plot.add_planet_orbit( + ax = _pk.plot.add_planet_orbit( pla=self.target, ax=ax, units=units, N=N, c=c_orbit, label=self.target.name ) # We decode the chromosome mit = self.decode(x) # [[r,v], DV, DT] - DVs = [norm(node[1]) for node in mit] - maxDV = max(DVs) - DVs = [s / maxDV * 30 for s in DVs] - - # 3 - We loop across grid nodes - for i, node in enumerate(mit): - ax.scatter( - node[0][0][0] / units, - node[0][0][1] / units, - node[0][0][2] / units, - color="k", - s=DVs[i], - ) - - r_after_dsm = node[0][0] - v_after_dsm = [a + b for a, b in zip(node[0][1], node[1])] - _pk.plot.add_ballistic_arc( - ax, - [r_after_dsm, v_after_dsm], - node[2] * _pk.DAY2SEC, - self.__common_mu, - N=N, - units=units, - c=c_segments[i % len(c_segments)], - **kwargs - ) + ax = _pk.plot.add_mit( + ax, mit, self._common_mu, units=units, c_segments=c_segments, N=N, **kwargs + ) return ax def pretty(self, x): @@ -293,16 +268,18 @@ def plot_primer_vector(self, x, N=200, ax=None): decoded = self.decode(x) # We explicitly extract the encoded information - dts = [it[2] * _pk.DAY2SEC for it in decoded] + dts = [it[2] for it in decoded] DVs = [it[1] for it in decoded] + norm_DVs = [norm(it) for it in DVs] + posvels = [it[0] for it in decoded] - if min(DVs) < 1e-3: + if min(norm_DVs) < 1e-3: raise ValueError( "Impulse magnitude too small, primer vector computation is not possible. Decrease the number of impulses." ) - # We create one grid er segment (e.g. part of the trajectory between two impulses) + # We create one grid per segment (e.g. part of the trajectory between two impulses) # (this is not guaranteed to have the requested size N, nor has uniform spacing, since all impulses # must belong to the grid points) N = N + len( diff --git a/pykep/utils/_planet_to_keplerian.py b/pykep/utils/_planet_to_keplerian.py index fdddb514..34f742dc 100644 --- a/pykep/utils/_planet_to_keplerian.py +++ b/pykep/utils/_planet_to_keplerian.py @@ -12,7 +12,9 @@ def planet_to_keplerian(pla, when: _pk.epoch, mu=None): Args: *pla* (:class:`~pykep.planet`): the input planet. + *when* (:class:`~pykep.epoch`): the epoch to match the osculating elements. + *mu* (:class:`float`, optional): the central body parameter. Defaults to the one computed from the input :class:`~pykep.planet`. Returns: From efd900004014316dbcefbfac9045ba5505d283fd Mon Sep 17 00:00:00 2001 From: Dario Izzo Date: Mon, 3 Feb 2025 18:17:02 +0100 Subject: [PATCH 4/4] add mga1dsm to docs of evo encodings --- doc/trajopt.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/trajopt.rst b/doc/trajopt.rst index 54745033..37171bb7 100644 --- a/doc/trajopt.rst +++ b/doc/trajopt.rst @@ -51,6 +51,12 @@ amenable to evolutionary techniques. .. autoclass:: mga :members: pretty, plot, to_planet +.. autoclass:: mga_1dsm + :members: pretty, plot, to_planet + +.. autoclass:: pl2pl_N_impulses + :members: pretty, plot, plot_primer_vector + Utilities ********* In order to facilitate the use of the classes in this module, some utilities are provided.